The Health Economic Implications of Perioperative
Delirium in Older Orthopaedic Surgery Patients with
Fragility Hip Fractures
by
Michael George Zywiel MD
A thesis submitted in conformity with the requirements for the degree of Masters of Science
Institute of Health Policy, Management and Evaluation University of Toronto
© Copyright by Michael Zywiel 2016
ii
The Health Economic Implications of Perioperative Delirium in
Older Orthopaedic Surgery Patients with Fragility Hip Fractures
Michael George Zywiel MD
Masters of Science
Institute of Health Policy, Management and Evaluation University of Toronto
2016
Abstract
Patients who experience fragility hip fractures are at high risk for delirium. However, little is
known about changes in health resource utilization associated with this adverse event. The
objectives of this work were to quantify the difference in episode of care costs and length of stay
from the hospital perspective between patients who do and do not experience perioperative
delirium. Patient care data from a single centre were linked with micro-case costing reports.
Propensity matching and regression modeling were used to control for potential confounders.
Delirium was found to be associated with significant incremental episode of care costs and
length of stay. The difference in costs was partly, but not fully, explained by differences in
length of stay. Future work should focus on confirming the generalizability of these findings,
assessing the impact using broader economic perspectives, and implementing cost-effective
interventions to reduce the rate of perioperative delirium in this population.
iii
Acknowledgments
This work would not have been possible without the support of a number of individuals and
organizations. I would like to acknowledge Michael Mont and Ron Delanois at the Rubin
Institute for Advanced Orthopedics in Baltimore Maryland. Michael took a chance on me in a
particularly difficult period, and I am forever grateful for everything that both he and Ron taught
me about clinical research, orthopaedic surgery, and the value of mentorship. I would like to
acknowledge David Backstein, who believed that I would rise to the occasion if given the
chance. I would like acknowledge Nizar Mahomed and Rajiv Gandhi for all of their advice,
support and encouragement, as well as all members of the Arthritis Program at the University
Health Network in Toronto. I would like to acknowledge the Division of Orthopaedic Surgery at
the University of Toronto, as well as the Surgeon Scientist Program, both of which have
provided critical support to allow me to pursue my academic interests. I would like to thank my
thesis supervisor Peter Coyte, as well as my thesis committee members Raj Rampersaud,
Anthony Perruccio and Jay Wunder, for their support and most of all, patience, through the
process. I would like to acknowledge Rushil Chaudhary and Androu Waheeb for their assistance
with portions of the data collection that facilitated this work. Finally, I would like to thank all my
friends and family, especially Marisa Louridas.
iv
Table of Contents
Acknowledgments............................................................................................................................3
Table of Contents .............................................................................................................................4
List of Tables ...................................................................................................................................8
List of Figures ................................................................................................................................10
List of Appendices .........................................................................................................................12
Chapter 1: Introduction ..................................................................................................................1
Chapter 2: Background ..................................................................................................................5
2.1 Anatomy of hip fractures .....................................................................................................5
2.2 Epidemiology and pathogenesis of hip fractures .................................................................7
2.2.1 Burden ......................................................................................................................7
2.2.2 Mechanism ...............................................................................................................9
2.2.3 Risk factors ............................................................................................................10
2.3 Clinical presentation and treatment ...................................................................................11
2.3.1 Surgical versus non-surgical treatment ..................................................................11
2.3.2 Type of surgery ......................................................................................................12
2.3.3 Timing of surgery ..................................................................................................13
2.4 Adverse events associated with hip fractures ....................................................................14
2.5 Perioperative delirium ........................................................................................................15
2.5.1 Risk factors for perioperative delirium ..................................................................15
2.5.2 Clinical consequences of delirium .........................................................................19
2.5.3 Delirium prevention and treatment ........................................................................20
2.6 Hospital funding for hip fracture care ................................................................................21
2.6.1 Defining terms .......................................................................................................22
2.6.2 Cost plus funding ...................................................................................................23
v
2.6.3 Global budgets .......................................................................................................25
2.6.4 Activity based funding ...........................................................................................27
2.6.5 Bundled payments ..................................................................................................30
2.7 Health economic aspects of hip fractures and delirium .....................................................31
2.7.1 Health economic burden of hip fractures ...............................................................31
2.7.2 Determining the health resource impact of delirium in the context of hip fractures ..................................................................................................................32
2.8 Rationale for thesis ............................................................................................................37
2.9 Objectives ..........................................................................................................................38
Chapter 3: Methods ......................................................................................................................40
3.1 Design and setting ..............................................................................................................40
3.2 Patients, clinical data sources and collection .....................................................................41
3.2.1 Inclusion and exclusion criteria .............................................................................42
3.2.2 Demographic data ..................................................................................................45
3.2.3 Clinical data ...........................................................................................................45
3.2.4 Surgical data...........................................................................................................46
3.2.5 Delirium data .........................................................................................................47
3.2.6 Other adverse events data ......................................................................................48
3.3 Length of stay ....................................................................................................................49
3.3.1 Theoretical framework for determinants of length of stay ....................................49
3.3.2 Potential determinants of length of stay .................................................................50
3.3.3 Length of stay data .................................................................................................55
3.4 Defining the episode of care ..............................................................................................56
3.5 Episode of care cost ...........................................................................................................59
3.5.1 Selection of health economic perspective ..............................................................59
3.5.2 Accounting for hospital costs .................................................................................62
vi
3.5.3 Theoretical framework for determinants of episode of care costs .........................63
3.5.4 Potential determinants of episode of care costs .....................................................63
3.5.5 Episode of care cost data ........................................................................................66
3.6 Procedures ..........................................................................................................................67
3.6.1 Examination of data ...............................................................................................67
3.6.2 Association between delirium and episode of care costs and length of stay .........68
3.6.3 Alternative estimations ..........................................................................................72
3.6.4 Evaluation of degree to which differences in episode of care costs are explained by length of stay ....................................................................................77
3.7 Sample size ........................................................................................................................77
3.8 Ethical considerations ........................................................................................................78
Chapter 4: Results ........................................................................................................................79
4.1 Descriptive statistics ..........................................................................................................79
4.2 Unadjusted findings ...........................................................................................................81
4.3 Propensity matching results ...............................................................................................85
4.3.1 Sensitivity analysis.................................................................................................90
4.4 Estimation using alternative models ..................................................................................91
4.4.1 Overall length of stay .............................................................................................91
4.4.2 Post-operative length of stay ..................................................................................93
4.4.3 Episode of care costs ..............................................................................................94
4.4.4 Model diagnostics ..................................................................................................96
4.5 Assessment of the degree to which length of stay explains differences in episode of care costs associated with delirium ..................................................................................100
Chapter 5: Discussion ................................................................................................................104
5.1 Findings with respect to the objectives ............................................................................104
5.1.1 Length of stay ......................................................................................................105
vii
5.1.2 Episode of care costs ............................................................................................108
5.2 Policy implications ...........................................................................................................111
5.3 Limitations .......................................................................................................................113
5.4 Conclusion .......................................................................................................................116
5.5 Future research .................................................................................................................116
References ..................................................................................................................................121
Appendices ..................................................................................................................................141
Copyright Acknowledgements.....................................................................................................153
viii
List of Tables
Table 1: Predisposing and precipitating factors reported to be associated with delirium in
hospitalized patients (bolded items represent those identified in studies specific to patients with
hip fractures). ................................................................................................................................ 19
Table 2: List of major comorbidities collected in dataset ............................................................. 46
Table 3: Hip fracture types collected in dataset ............................................................................ 46
Table 4: American Society of Anesthesiologists physical status classification ........................... 47
Table 5: Grading of adverse events using the OrthoSAVES system ............................................ 49
Table 6: Potential determinants of length of stay for patients with hip fractures ......................... 52
Table 7: Potential determinants of episode of care costs for patients with hip fractures .............. 65
Table 8: Baseline characteristics of study population ................................................................... 80
Table 9: In hospital characteristics of study population ............................................................... 84
Table 10: Comparison of baseline characteristics of propensity matched cohort ........................ 86
Table 11: Results of propensity matched analyses for primary outcome measures ..................... 87
Table 12: Comparison of surgical factors between propensity-matched pairs ............................. 90
Table 13: Determinants of overall length of stay .......................................................................... 92
Table 14: Determinants of post-operative length of stay .............................................................. 94
ix
Table 15: Coefficients for model of episode of care costs ............................................................ 95
Table 16: Coefficients for model of episode of care costs with length of stay included as
predictor ...................................................................................................................................... 103
x
List of Figures
Figure 1: Diagram of the hip joint, including (A) major proximal femoral anatomic sructures, and
types of hip fractures stratified by (B) intracapsular and (C) extracapsular location. .................... 6
Figure 2: PRISMA flow diagram summarizing search for risk factors for delirium .................... 17
Figure 3: PRISMA diagram of literature search concerning association between delirium and
health economic outcomes ............................................................................................................ 34
Figure 4: The confusion assessment method (CAM) algorithm for diagnosing delirium ............ 48
Figure 5: PRISMA flow diagram for literature search of determinants of length of stay in patients
with hip fractures .......................................................................................................................... 51
Figure 6: Care pathway for patients experiencing an isolated fragility hip fracture .................... 57
Figure 7: PRISMA flow diagram summarizing search strategy for potential determinants of
episode of care costs ..................................................................................................................... 64
Figure 8: Comparison of propensity scores between patients who did and did not experience
delirium ......................................................................................................................................... 71
Figure 9: Histogram depicting distribution of observed values for total length of hospital stay .. 73
Figure 10: Histogram depicting distribution of observed values for total episode of care costs .. 73
Figure 11: Comparison of distribution of total length of stay between patients who did and did
not experience delirium ................................................................................................................. 82
xi
Figure 12: Comparison of distribution of length of stay following surgery between patients who
did and did not experience delirium .............................................................................................. 82
Figure 13: Comparison of distribution of episode of care costs between patients who did and did
not experience delirium ................................................................................................................. 83
Figure 14: Plot of predicted versus observed values for total length of stay ................................ 97
Figure 15: Plot of predicted versus actual values for length of stay following surgery ............... 98
Figure 16: Plot of predicted versus observed values for total episode of care costs .................... 99
Figure 17: Plot of observed versus predicted values for episode of care cost for the model that
includes length of stay as a predictor variable ............................................................................ 101
xii
List of Appendices
Appendix A: Hospital Research Ethics Board approval letter .................................................... 141
Appendix B: University Research Ethics Board approval letter ................................................ 142
Appendix C: Results of systematic literature review concerning potential determinants of length
of stay and acute care hospital costs ........................................................................................... 143
Appendix D: Results of logistic regression model used to construct propensity scores ............ 149
Appendix E: Table of correlations between potential predictor variables .................................. 150
Appendix F: Table of AIC and BIC values for alternative estimation models considered ........ 150
Appendix G: Coefficients for models of episode of care costs using negative binomial and
gamma distributions .................................................................................................................... 152
1
Chapter 1: Introduction
Fragility hip fractures, defined as those occurring following a fall from standing height or less, or
occurring in the absence of marked trauma, account for a substantial number of unplanned
orthopaedic hospital admissions. Data from the United States demonstrates over 300,000 hip
fractures annually,1 while it is estimated that approximately 30,000 hip fractures occur per year
in Canada,2 almost all of which are attributable to low energy mechanisms.3 The risk of
experiencing a hip fracture is particularly high in white postmenopausal women, who have a 1 in
6 lifetime risk of this injury.4 Hip fractures result in profound acute functional impairment,
requiring hospitalization and emergency orthopaedic surgery for either fracture repair or hip
replacement.5 Furthermore, these injuries are associated with a high incidence of in-hospital
adverse events (AEs), with perioperative delirium alone having a reported incidence as high as
61 percent.6,7
Perioperative delirium is an acute state of altered mental status with a multifactorial etiology and
with variable duration. Predisposing factors include older age, baseline cognitive impairment,
and multiple medical comorbidities, while precipitating factors include acute injury, pain, and
surgery.8 In the general hospitalized population, this AE has been reported to substantially delay
discharge from acute care,9 inhibit rehabilitation,10 cause secondary AEs,11 be associated with an
increased risk of post-discharge mortality,12 and to potentially have profound impact on
healthcare utilization. With an increasing proportion of the aging population at risk for fragility
hip fractures,13 the implications of postoperative delirium are potentially significant from both
clinical and health services perspectives.
2
Historically, the primary model of hospital funding in Canada has been the use of a global
budget.14 With this approach, each hospital receives a fixed amount of funding (ie. the global
budget) from the health care payor (in Canada, the Provincial Ministry of Health) to provide
hospital-based health care services for a defined period of time (commonly one year). This
global budget is meant to pay for all direct and indirect labour and material costs associated with
the delivery of health care services within a given hospital, with the exception of the costs of
physicians’ professional fees which are typically billed separately and directly to the relevant
health care payor. While the use of global budgets results in predictable budgeting for the health
care payor and can serve to constrain expenditure growth, it does not necessarily closely reflect
the demands for health care services placed on the receiving institution. Furthermore, global
budgeting provides limited incentives to health care providers to improve the quality or
efficiency of health care services.15
Recognizing the limited incentives for improving the quality and efficiency of care associated
with global funding models, there has been increasing interest in Canada in the use of alternate
hospital funding schemes, some of which have been well established in other countries. The
primary alternative mode of hospital payment used in many countries is activity based funding
(ABF).16,17 In this model, hospitals or providers are allocated funding based on the quantity and
type of care provided. More recently, some health care payors have begun considering the use of
bundled payment reimbursement. In contrast to ABF, which generally reimburses
administratively defined episodes of care (eg. from admission to discharge from a single
hospital), bundled payment schemes provide per-patient funding for entire clinical episodes of
care (for example, from initial consultation for a given diagnosis, to surgical treatment, acute
care and rehabilitation hospital stay, outpatient follow-up visits, and treatment of any
3
complications related to the initial procedure). Bundled payments may provide further incentives
to improve the efficiency and quality of care, especially as the cost of any readmissions,
reoperations, or other complication management would be borne by the health care provider
without additional reimbursement. As health care payors in Canada increasingly explore and
implement the use of activity-based funding and bundled payment models,18,19 perioperative
delirium may have a substantial impact on the financial viability of hip fracture surgical
programs from the acute care hospital perspective. Specifically, hospitals that are able to provide
high quality, efficient care may benefit from episode of care costs that are lower than the
reimbursements received for providing care to patients with hip fractures. In contrast, those that
fail to achieve sufficient efficiency or quality of care are liable to find that their reimbursements
are lower than the cost of providing care for this patient population.
Numerous factors can potentially contribute to differences in episode of care costs for inpatient
surgical care between facilities as well as between individual patients. With respect to patient
factors, it has been reported that adverse events are associated with significant incremental
episode of care costs from the acute care hospital perspective across a range of primary
diagnoses.20-22 Patients who undergo surgery for fragility hip fractures appear to be at
particularly high risk for perioperative adverse events, with a recent large database study
encompassing 9,640 patients and over 200 centers reporting a 30 day adverse event rate of
25%,23 and a recent pilot study of primary clinical data reporting an adverse event rate of 72%.24
Perioperative delirium has been reported to be among the more common adverse events, with
reported rates ranging from 4 to 62% in patients with hip fractures.6,25,26 Furthermore, studies to
date have suggested that delirium is associated with increased acute and post-discharge health
care costs. However, they have been few in number, and limited in scope to either general
4
medical or elective surgical patients. 27,28 Given limited knowledge to date in the orthopaedic and
health services literature, the overall goal of this thesis was to understand the health economic
implications of perioperative delirium in older orthopaedic surgery patients with fragility hip
fractures. Specifically, the objectives were to 1) quantify the difference in episode of care costs
from the hospital perspective between patients who do and do not experience perioperative
delirium; 2) quantify the difference in hospital length of stay (LOS) between patients who do and
do not experience perioperative delirium; and, 3) given an identified difference in costs, to
determine to what degree the association between delirium and episode of care costs from the
hospital perspective is explained by potential differences in length of stay.
5
Chapter 2: Background
2.1 Anatomy of hip fractures
Hip fractures refer to a group of injuries characterized by the disruption of the cortex of the
proximal femur. Specifically, these are commonly defined as fractures that occur proximal to a
point that is 5 centimeters distal to the lesser trochanter.29 This is in contrast to more distally-
located injuries that are referred to as femoral shaft or distal femur fractures, depending on
location. While the hip joint itself consists of the articulation between the most proximal portion
of the femur (ie the femoral head) and the articular concavity of the pelvis known as the
acetabulum (formed by the intersection of the ilium, ischium and pubis), the term ‘hip fracture’
refers exclusively to injuries of the proximal femur.
The hip is a synovial joint, which is characterized by the presence of lubricating synovial fluid
retained in the peri-articular milieu by a thick fibrous joint capsule. In the hip, the proximal
attachment of the capsule is just outside the margin of the acetabular rim, whereas distally it
inserts at the base of the femoral neck.30 Based on the anatomy of the joint capsule, hip fractures
can be subdivided into intra-capsular (those involving the femoral neck and/or head), and extra-
capsular (those occurring distal to the base of the femoral neck) injuries (Figure 1).29 The intra-
capsular bony regions are characterized by a relatively fragile blood supply, dependent primarily
on small intra-capsular retinacular vessels that travel along the surface of the femoral neck,
which is at risk of disruption in the case of displaced fractures.31 Furthermore, the intra-articular
periosteum is relatively acellular, with few osteoprogenitor cells as compared to both the extra-
articular femur as well as other human bones.32 Both of these factors may contribute to poorer
6
healing of intra-articular as compared to extra-articular fractures, highlighting the clinical
relevance of this subdivision.
Figure 1: Diagram of the hip joint, including (A) major proximal femoral anatomic sructures, and
types of hip fractures stratified by (B) intracapsular and (C) extracapsular location.
7
Hip fractures can be further subdivided based on anatomic location relative to the bony
landmarks of the proximal femur. Intracapsular fractures are commonly subdivided into those
that involve the femoral head, and those that involve the femoral neck only. Subgroups of the
latter include subcapital fractures (occurring at the junction of the femoral neck and head) and
basicervical fractures (occurring at the base of the femoral neck). Similarly, extra-articular
fractures can be classified into subtrochanteric fractures (those that extend distal to the lesser
trochanter), and pertrochanteric fractures (encompassing the region between the greater and
lesser trochanters), with the latter further subdivided into intertrochanteric fractures (an oblique
fracture line that starts proximally on the lateral cortex extending distally to the medial cortex),
and reverse obliquity fractures (where the fracture line extends from proximal-medial to distal-
lateral). These different fracture patterns are characterized by different displacement patterns, as
well as variable stability with a given surgical construct, and thus have important implications in
terms of treatment. Most notably, intra-capsular fractures display two distinct displacement
patterns: undisplaced fractures, where the head is impacted into the femoral neck and is
mechanically stable; and displaced fractures, where there is partial or complete loss of contact
between fracture surfaces with associated loss of mechanical stability.
2.2 Epidemiology and pathogenesis of hip fractures
2.2.1 Burden
Hip fractures represent a considerable proportion of musculoskeletal injuries, with approximately
300,000 occurring per year in the United States,1 and approximately 30,000 per year in Canada.2
This represents over 25% of all fractures requiring hospitalization.33 The most recent estimate of
global hip fracture burden dates from 1990, at which time it was estimated that 1.61 million hip
fractures occurred per year.34 Numerous studies have consistently reported an increasing
8
incidence of hip fractures with older age. In a study of approximately 600,000 patients in a single
health region in Scotland, Singer et al. reported a relatively stable incidence of 0 to 5 cases per
10,000 persons per year between the ages of 15 and 60, followed by a marked and sustained
increase in annual incidence with age, ranging from 5/10,000 in men and 8/10,000 in women
between 60 and 64 years of age, and up to 224/10,000 in men and 385/10,000 in women between
90 and 95 years of age.35 A large variation in age-adjusted incidence of hip fractures between
countries has been reported, ranging from 2 per 100,000 in Nigeria to 574 per 100,000 in
Denmark per year.36 Variation in rates may in part be explained by differences in case definition,
inaccuracies in the reporting or coding of cases and/or the population denominator, differences in
access to medical care resulting in substantial number of cases failing to come to medical
attention, and various other health system factors.36 However, there is also evidence suggesting
differences based on race, with lower rates of hip fractures in both African-American and
African black individuals when compared to the general Western population.37,38 This may be
related to differences in bone density and rates of age-related bone loss, with Saeed et al.
reporting greater bone density in elderly African-American individuals compared to Caucasian-
Americans.39 Notwithstanding these potential reasons, globally, higher incidences are
consistently reported in North American, northern European, and northern Asian countries.36,38
Recent data from Canada shows a significant reduction in age-adjusted hip fracture rates over the
20 year period from 1985 to 2005, with a 31.8% decrease in males and 25.0% decrease in
females.2 While the reasons for this are unclear, possible explanations put forward include: a
healthier and more active aging population that is at lower risk of falls; a higher prevalence of
obesity that is associated with protective metabolic effects as well as increased mechanical
padding over the hip; and increased osteoporosis screening and preventative pharmacologic
9
treatment.40 However, despite the decrease in age-specific hip fracture rates, the combined
effects of an increasingly aging population have resulted in an overall incidence of hip fractures
in Canada that have remained unchanged over the study period, and the absolute number of hip
fractures has increased modestly.2 Trends in other developed countries including the United
States, Northern Europe and Oceania have been broadly similar, with unchanged or decreasing
adjusted incidence over the past 20 years, but unchanged or increasing overall numbers of hip
fractures per year.41-43 In contrast, there is evidence to suggest that both absolute and age-
adjusted incidence of hip fractures in a number of Asian countries continues to increase.42 Given
the large and rapidly aging populations in a number of Asian countries, the annual worldwide
burden of hip fractures has been predicted to increase almost fourfold to 6.26 million in 2050,
from 1.61 million in 1990, assuming overall stable age-adjusted incidence rates.34
2.2.2 Mechanism
Hip fractures typically occur secondary to one of two types of mechanisms. Less commonly,
they can result from high-energy mechanisms, such as motor vehicle collisions or falls from
height. These fractures are typically associated with multiple other orthopaedic or non-
orthopaedic injuries, and occur primarily among younger adult patients.44 Robinson et al.
reported a high-energy mechanism of injury in 87% of patients 40 years of age or younger with a
hip fracture,26 while Hahnhaussen et al. found this to be the case in less than 7% of patients 65
years of age or older with a hip fracture.45 Overall, only a small minority of hip fractures are
secondary to high energy mechanisms, estimated to represent between 2 and 3% of femoral neck
fractures.3 The remaining hip fractures are attributable to low energy mechanisms.
10
2.2.3 Risk factors
The large majority of hip fractures occur in older individuals, following a seemingly minor and
low-energy incident. Epidemiologic data from the United Kingdom indicate that 92% of all hip
fractures occurred in individuals 65 years of age or older.46 In up to 5% of cases, no antecedent
history of any trauma is identified.29 In either case, such fractures fall into the category of
fragility fractures, which are defined by the World Health Organization as “a fracture caused by
injury that would be insufficient to fracture a normal bone; the result of reduced compressive
and/or torsional strength of bone” 47 and by the Scientific Advisory Council of the Osteoporosis
Society of Canada as a fracture “that occurs as a result of minimal trauma, such as a fall from a
standing height or less, or no identifiable trauma.”48 While a number of congenital and acquired
conditions may weaken bone sufficiently to result in a fragility fracture, the vast majority of
cases are secondary to osteopenia (decreased bone mineral density) or osteoporosis (bone
mineral density ≤2.5 SD below the reference mean in young adults).47
A number of population-based studies indicate that decreased bone mineral density is
significantly associated with increased risk of a fragility hip fracture. Following a meta-analysis,
Marshall et al. reported an age-adjusted relative risk for a hip fracture of 2.6 (95% CI 2.0-3.5) for
every 1 SD decrease in femoral neck bone mineral density.49 The prevalence of osteoporosis is
known to increase with age, with postmenopausal women being at particularly high risk. In a
Swedish population-based study, the prevalence of osteoporosis in individuals between 50 and
54 years of age was 2.5% and 6.3% in men and women respectively, rising to 16.6% and 47.2%
in men and women between the ages of 80 and 84.50 A Canadian population-based study found
that the prevalence of osteoporosis in individuals 50 years of age or older was 6.6% and 15.8%
in men and women respectively, while the prevalence of osteopenia was 39.1% and 45.9%.51 In a
11
validation study of the Canadian fracture risk assessment tool, Leslie et al. found decreasing
femoral neck bone density and increasing age to be the strongest predictor of 10-year fracture
risk with hazard ratios of 2.186 per 1 SD decrease in bone density, and 2.230 per 10 year
increase in age.52 Remaining significant predictors included female sex, lower body mass index
(BMI), prior osteoporotic fracture, rheumatoid arthritis, corticosteroid use, smoking, and alcohol
abuse. Overall, white postmenopausal women are at particular risk, with Cummings et al.
identifying a 1 in 6 lifetime risk of hip fracture in this population using survey data from
Olmstead County, Minnesota.4
2.3 Clinical presentation and treatment
Hip fractures are serious injuries with marked implications for both short and longer-term
function. They are among the top ten diagnoses with greatest negative impact on disability-
adjusted life years in women.53 In virtually all cases, symptomatic hip fractures are marked by an
inability to weight bear on the injured leg, as well as profound pain with any movement at the
fracture site. This includes disabling pain with attempts to sit up in bed, as well as with rolling
from side to side, or with any patient nursing activities such as repositioning and toileting. While
a small subset of hip fractures are relatively stable and patients may tolerate some degree of
mobilization, most patients with hip fractures are completely bedbound until such time until the
fracture is healed or otherwise stabilized sufficiently to allow weight bearing.54,55
2.3.1 Surgical versus non-surgical treatment
The overall goal of hip fracture treatment is to return the patient to their pre-injury functional
status. From a clinical perspective, there is little controversy concerning the choice between
operative and non-operative treatment, with surgical management recommended for virtually all
patients.5,56,57 However, the management of these injuries is resource intensive. The estimated
12
cost of acute hospital care of an isolated hip fracture in Canada was estimated at $20,163 in 2010
dollars, including physician fees.58 A recent study from the United States found that the cost of
operative treatment of hip fractures was associated with 1.28 to 1.59 times greater mean medical
costs from the payor perspective in the first 6 months following injury as compared to non-
operative treatment.59 Consequently, despite the well described clinical benefits of surgical
treatment of hip fractures, the proportion of patients treated surgically may vary between health
care systems based on the availability of resources. This is supported by a 2010 report that found
that 75% of patients with hip fractures in the country of Georgia are not hospitalized for
treatment of their injury, while only 13% of patients who experience a hip fracture in Russia
undergo surgical treatment.60 These low rates of surgical treatment are attributed to a
combination of limited availability and access to the required specialized health care services, as
well as the requirement that patients pay for their own surgical implants, which are cost
prohibitive for many.36,60
Early surgical management allows for immediate post-operative weight bearing and early
mobilization. Because of the ubiquity of surgical treatment for these injuries in countries with
advanced health care systems, studies comparing operative and non-operative treatment are
limited, often do not reflect contemporary surgical practice, and are typically underpowered or
subject to a high risk of bias. Nevertheless, the studies that are available suggest that non-
operative treatment is associated with higher mortality, prolonged hospital stay, and decreased
likelihood of return to pre-injury level of function.61-63
2.3.2 Type of surgery
A range of surgical treatments are available, with the choice dependent on a combination of
factors such as: fracture pattern, patient age, pre-injury functional status, and surgeon experience.
13
In general, displaced intra-capsular fractures are treated with hip arthroplasty, because of the
high risk of fracture non-union and femoral head osteonecrosis secondary to the associated
disrupted blood supply.57 The remaining fracture types are typically addressed with open or
closed reduction and internal fixation using cannulated screws, a sliding hip screw, or an
intramedullary nail, with the goal of restoring fracture alignment and stability.56,57 Regardless of
the technique used, the construct should be sufficiently stable to allow immediate post-operative
weight bearing and mobilization, and control fracture-associated pain.
2.3.3 Timing of surgery
While there is some controversy concerning the exact timing of surgery, there is general
consensus that it should be performed on an urgent basis. Studies have demonstrated that longer
delays to surgery are associated with increased risk of pressure ulcers, longer in-hospital length
of stay and higher mortality.64,65 However, there is a paucity of studies that directly compare
outcomes based on time to surgery. While some authors advocate for surgery to be performed in
as little as 6 hours after admission,66 the current Ontario guidelines support a benchmark of
surgery within 48 hours of admission.65 As a result, opportunities for pre-operative optimization
of physiologic status and/or coexisting medical comorbidities are limited. In general, it is not
recommended to delay hip fracture surgery except in the presence of a detrimental condition that
would require treatment independently of the fracture, and where there is a reasonable likelihood
of correcting the condition within a reasonable time frame.67 Thus, while issues such as
hypocoagulable states or dehydration can be addressed pre-operatively, patients frequently
undergo surgery in the presence of one or more factors that markedly increase the risk of
perioperative complications.
14
2.4 Adverse events associated with hip fractures
Patients who experience a hip fracture have been reported to be at high risk for perioperative
adverse events (AEs). For the purposes of this work, perioperative can be defined as the period
of time spanning from arrival in hospital to discharge. The Canadian Food and Drug Regulations
define an adverse event as “any adverse occurrence in the health of a clinical trial subject who is
administered a drug, that may or may not be caused by the administration of the drug, and
includes an adverse drug reaction.”68 The United States Food and Drug Administration defines
this as “any untoward medical occurrence associated with the use of a drug in humans, whether
or not considered drug related.”69 In the context of operatively treated hip fractures, an AE can
thus be understood to be any unintended or undesirable medical or surgical occurrence
temporally related to the hip fracture, although not necessarily directly attributable to it. A
number of factors contribute to the high rate of AEs associated with hip fractures. The femur is
the largest bone in the body and has excellent vascularity. Consequently, a fracture involving the
femur can result in over a liter of blood loss,70 placing substantial strain on the cardiovascular
system. The injury is also associated with considerable pain, especially with any attempted
movement, which can also increase cardiovascular demand.71 The immobility associated with a
hip fracture is known to increase the risk of adverse events such as pressure sores,64 urinary tract
infections,72 and venous thromboembolic events.73 Furthermore, patients with hip fractures are
typically elderly and have multiple medical comorbidities, resulting in decreased physiologic
reserve to cope with pre-operative, intra-operative, and post-operative stressors.74,75 A recent
pilot study from Ireland by Doody et al. reported that 72% of hip fracture patients experienced an
AE in the post-operative period alone,24 while a large international quality improvement database
study reported that 25% of patients experienced an AE within 30 days of surgery.23 There are a
number of potential explanations for this considerable variation in reported AE rates, including
15
differences in the definitions of an AE as well as in the methodology used to identify and record
AEs. It is clear, however, that even more conservative estimates demonstrate that a considerable
proportion of patients undergoing surgery for a hip fracture experience one or more AEs.
2.5 Perioperative delirium
Perioperative delirium is one of the most commonly reported AEs in patients who are
hospitalized following a hip fracture.76 It is defined as an acute state of altered mental status with
typically waxing and waning course, distinct from any underlying chronic cognitive impairment,
and has a multifactorial etiology.8 Inouye described the development of delirium as involving
“the complex interrelationship between a vulnerable patient (one with predisposing factors) and
exposure to precipitating factors or noxious insults.”8
There is considerable variability in the reported rates of in-hospital delirium in patients with hip
fractures, ranging from 4% to 62%.6,7,25 This wide range in reported rates may be partly
explained by differences in clinical expertise and opinion in diagnosing this condition. Given the
wide range of sensitivities for different diagnostic strategies,77 and that up to two thirds of cases
have been reported to go undiagnosed in the clinical setting,78 it is likely that the true rates are
closer to the higher end of this range. Indeed, the reported rates have ranged from 40 to 53%
when assessed using the Confusion Assessment Method,79 which is a commonly used validated
instrument that has been demonstrated to have high sensitivity and specificity for delirium.80-82
2.5.1 Risk factors for perioperative delirium
To understand the factors that affect patients’ propensity to experience delirium, a systematic
search of the published literature was conducted to identify known risk factors for this condition.
16
A preliminary search identified a number of review articles, including systematic reviews and
meta-analyses, recently published on this topic.
Literature search methodology
Review articles and meta-analyses with full text versions available in the English language, and
which assessed risk factors for in-hospital delirium, were deemed eligible. Inclusion criteria were
limited to reports that used explicit systematic literature review methodology to identify studies
for inclusion. Only studies that assessed risk factors for delirium in patients with hip fractures,
mixed surgical patients, and general hospitalized patients were deemed eligible. Studies that
were limited to the assessment of risk factors for delirium in intensive care patients, or specific
non-hip fracture procedures, were excluded. An electronic search was performed of MeSH
headings in the Ovid Medline and EMBASE databases up to December 2013 using the following
search string: (delirium and risk factors). Citation records, titles, abstracts and full-text versions
underwent a process of systematic deduplication, screening, and review for eligibility consistent
with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)
statement and flow diagram.83 The initial search identified 238 records prior to de-duplication,
with 6 published works remaining in the final review as seen in Figure 2. Risk factors were
extracted from the identified studies,84-89 and categorized into predisposing and precipitating
factors according to the conceptual framework proposed by Inouye.8
17
Figure 2: PRISMA flow diagram summarizing search for risk factors for delirium
Risk factors for perioperative delirium identified in the literature
A total of 16 predisposing and 17 precipitating factors were identified in the search, summarized
in Table 1. Of these, 8 predisposing and 13 precipitating factors had been specifically identified
in studies assessing patients with hip fractures. As suggested by Bitsch, these factors can be
further subdivided into preoperative , intraoperative and postoperative categories.87 Many of the
predisposing factors are baseline preoperative factors that variably predispose individuals to a
delirium event prior to experiencing a hip fracture, such as advanced age, male gender, baseline
medical comorbidities, and American Society of Anesthesiologists (ASA) class. Both trauma and
18
acute pain have been identified as risk factors for delirium, suggesting that patients’ risk for
delirium increases from the moment that they experience their hip fracture. There are also
several reported precipitating factors that may present before patients undergo surgical treatment
of their injury. These include dehydration, poor nutrition, and increased wait time for surgery, all
of which may be present in the interval between experiencing the injury and undergoing surgery.
This is supported by reports that a substantial proportion of patients with hip fractures who
experience delirium develop symptoms preoperatively.90 Consequently, it is appropriate to
consider delirium in patients with hip fractures as a perioperative, rather than postoperative
phenomenon, with the fracture itself as the initial potential precipitating event.
The list of predisposing and precipitating factors identified is extensive, and several are typically
present in patients with hip fractures. These patients are typically older, and commonly present
with baseline cognitive impairment and multiple medical comorbidities. Additionally, by the
nature of the injury, the precipitating factors of trauma, acute pain, and surgery are commonly
present. The large majority of patients experiencing fragility hip fractures have multiple
predisposing and precipitating factors for delirium from the moment of injury. Thus, while many
different hospitalized patient populations are at risk for delirium, including those with both
surgical and non-surgical problems, patients admitted following a fragility hip fracture appear to
be at substantial risk.
19
Table 1: Predisposing and precipitating factors reported to be associated with delirium in
hospitalized patients (bolded items represent those identified in studies specific to patients with
hip fractures).
2.5.2 Clinical consequences of delirium
In the general hospitalized population, delirium has been reported to substantially delay
discharge from acute care,9 inhibit rehabilitation,10 cause secondary AEs,11 and be associated
with an increased risk of post-discharge mortality.12 Studies specific to the hip fracture
population are more limited, but consistently demonstrate associations between delirium and
unfavorable clinical consequences. These include a significantly higher risk of post-operative
inpatient falls,91 new onset dementia following discharge,92,93 and failing to regain pre-injury
function and mobility within one year of discharge.94 Several authors have reported significantly
higher incidence of mortality up to two years following discharge in patients with hip fractures
20
who experienced perioperative delirium.92,94-96 However, other studies have suggested that these
findings may be due to differences in the number and severity of underlying medical
comorbidities, rather than an independent predictive value of delirium itself.97,98
2.5.3 Delirium prevention and treatment
While authors have reported that up to 40-50% of cases of in-hospital delirium are
preventable,99,100 high quality studies evaluating the effectiveness of interventions to reduce the
occurrence and/or severity of delirium are relatively sparse overall, with only relatively few
specifically evaluating patients with hip fractures.100,101 Several authors have reported that
routine geriatric consultation provided early in the course of hospitalization provides significant
reductions in the incidence of delirium,67,102,103 as can a geriatric-orthopaedic patient co-
management model.104 Authors have also reported that the use of a femoral nerve block to
provide pre-operative pain control can have a beneficial effect in terms of reducing the incidence
of delirium in patients with hip fractures,105 as can limiting the depth of intra-operative sedation
when patients receive spinal anesthesia.106 Milisen et al. suggested that a routine nursing-led
screening and prevention intervention may have benefits in terms of reducing the duration and/or
severity of delirium, although the authors were not able to identify any impact on the overall
incidence.107 Holroyd-Leduc et al. identified potential benefits of the implementation of a
standardized computerized clinician order set in reducing the incidence of delirium, although the
effectiveness was markedly influenced by buy-in from front-line clinical personnel when
compared between centers.108 Observational studies have also indicated a potential benefit to
minimizing pre-operative waiting time,95,109 although this has not been evaluated in the context
of a comparative study and/or trial.
21
Studies in non-orthopaedic patients have also suggested that there may be some benefit in
avoiding anticholinergic medications, although the evidence is conflicting.110,111 Similarly,
conflicting evidence has been published concerning potential benefits of using hydromorphone
as the first-line parenteral opioid medication for pain control.106,112
Overall, a number of studies have suggested potential benefit of preventative interventions to
reduce the incidence of perioperative delirium. However, data for a number of them remain
conflicting, and others have been suggested based on identified risk factors but not yet evaluated.
Given these factors, and the fact that many of these interventions can be resource-intensive,
potentially requiring additional skilled personnel and/or material or operating room resources,
barriers may currently exist to their implementation in the routine clinical care environment.
2.6 Hospital funding for hip fracture care
A range of different approaches to paying the cost of in-hospital hip fracture care are available,
and these mirror the different approaches to hospital funding across health care systems
worldwide. These range from publicly-funded systems such as in Canada, to mixed payor
systems such as in many European countries, to the requirement for patients to pay many costs
out of pocket on a pay-as-you-go basis. The latter is most common in economically
disadvantaged countries that lack the health system administration and/or resources to fund
comprehensive health care services using a shared risk approach. Given the marked differences
between countries in terms of the resources and structures of health care delivery, this review
will focus on approaches to hospital funding for hip fracture care in Canada in general, and the
Province of Ontario in particular, given that the latter will be the focus of the subsequent work
for this thesis. Furthermore, some attention will be given to selected approaches to hospital
funding for hip fracture care in the United States where relevant. In North America, hospital care
22
for fragility hip fractures is typically funded by government agencies through taxation revenue.
In Canada, this is a consequence of the universal publicly-funded single-payor system, whereas
in the US, the large majority of these patients are covered by the Medicare social insurance
program that provides universal publicly-funded coverage to patients 65 years of age or older.
2.6.1 Defining terms
There are a number of terms that are commonly used in the discussion of health systems, health
care funding, and the risks and incentives associated with different models and approaches, but
that cannot be assumed to have a universal meaning. Specifically, these are the concepts of
‘quality,’ ‘efficency’ and ‘value.’
The concept of quality can have a range of meanings and definitions.113 Conceptually, Harteloh
proposed that quality exists as an “optimal balance between possibilities realized and a
framework of norms and values.”113 In other words, the concept of quality in health care includes
both properties of the care provided (eg. safe, fast), as well as a set of attributes used as a
reference to evaluate the care provided. Donobedian proposed a conceptual framework for the
assessment of quality of care based on three domains: structural quality (characteristics of the
environment in which care takes place, including facilities, money, and personnel); process
quality (characteristics of the actions associated with giving or receiving care); and outcome
quality (characteristics of the results of care including patient or population knowledge, function,
and health status).114 Different authors have proposed important attributes of quality care within
these frameworks.115,116 Building on this work, the World Health Organization (WHO) defined
six dimensions of high quality health care: effective, efficient, accessible, acceptable/patient
centered, equitable, and safe.117 For the purposes of this thesis, the WHO concepts will be
adopted with minor modification. Specifically, increases in quality of care will be considered
23
being synonymous with improvements in one or more of the following five dimensions:
effective, accessible, acceptable/patient-centered, equitable, and safe.
Efficiency will be considered separately from the larger concept of quality. As defined by the
WHO, efficient health care is delivered in a manner that maximizes resource use (including
financial resources) and avoids waste.117 Given that resources, whether material or labour, are
inherently associated with a financial cost, and that a considerable focus of this thesis concerns
issues of health care cost, the concept of efficiency of care will be considered separately from the
concept of quality of care as described above.
2.6.2 Cost plus funding
A relatively straightforward approach to health care funding, and which reflects the actual
services provided, is a cost or cost-plus approach. With this funding model, hospitals are
reimbursed the actual costs incurred in providing care, plus an additional payment to serve as an
incentive. When implemented in 1965, the Medicare social health insurance program in the
United States, which provides comprehensive health insurance coverage to most residents 65
years of age or older, relied on a cost-plus model for reimbursement to hospitals.118 While the
United States maintains a multiple payor system that includes other government (Medicaid and
Tricare) and third-party payors, as well as self-payment by patients, in 2012 Medicare had 41.6
million enrollees 65 years of age or older,119 and paid for 26% of national health care
expenditure on hospital services in the United States,120 thus representing a substantial
proportion of reimbursement for hospital services.
Conceptually, the strength of a cost-plus approach is that it reimburses hospitals at a rate that is
directly related to the cost of health care provided, accounting for the type, complexity, and
24
volume of care. Specifically, the hospital funding received varies directly based on the overall
cost of the care provided. The theoretical benefit to health care payors is that cost-plus
reimbursement ensures a fair and predictable return on investment to the hospital, with a clearly
defined and controlled premium paid to the hospital above the actual cost of the care provided.
However, because there is no constraint on the cost of care itself, and the fact that hospitals are
reimbursed for the full cost of the care provided, the financial risks associated with increasing
costs of care are borne largely by the health care payor.118
Because there is no constraint on the cost of services provided with a cost-plus approach, the
primary incentive for hospitals with this approach is to maximize expenditures, thus increasing
reimbursements. Consequently, hospitals are rewarded for investing in expensive equipment and
technology, and for providing high cost, high-intensity care. Conversely, improvements in the
quality or efficiency of care may be disincentivized with this approach. This has been
demonstrated through the finding that cost plus reimbursement is associated with higher costs in
more competitive health care markets, as hospitals invest in expensive technology and facilities
to attract patients.121 From the perspective of the health care payor, a cost-plus approach lacks
inherent mechanisms to constrain expenditures on hospital services and in fact, may encourage
expenditure growth by incentivizing hospitals to increase spending. The limited ability of the
health care payor to constrain growth in expenditures was demonstrated when limits on Medicare
reimbursements to hospitals legislated by the United States government in 1972 failed to
meaningfully constrain the growth of health care costs.122 Between 1969 and 1981, hospital
expenditures in the United States increased by 101 percent under a cost-plus approach, as
compared to 16 percent in Ontario under a more restrictive global budgeting model.123
Ultimately, starting in 1983, Medicare underwent a transition to an activity-based hospital
25
funding model, with the limitations of the cost-plus system in controlling expenditure growth as
an important reason for this change.122 Given the limitations of cost-plus funding in controlling
health care spending or incentivizing improvements in quality of efficiency of care, and
historical transitions away from this model of hospital funding due to these weaknesses, there is
little role for cost-plus funding in the foreseeable future.
2.6.3 Global budgets
In Canada, the majority of hospital funding is provided by provincial Ministries of Health, using
a global funding model.14 In this approach, hospitals are provided a fixed amount of funding
(termed a global budget) from the health care payor (the Provincial Ministry of Health, often
through an intermediary regional health care organization). The monies received are to be used
to provide all needed hospital-based services for the funding period (commonly one or more
years) to patients who are insured under the government funded and administered Provincial
health insurance program. This includes all direct and indirect labour and material costs
associated with providing in-hospital patient care, with the exception of physician professional
fees, which are billed separately by individual doctors directly to the relevant health care payor.
While each province administers its own insurance scheme, the principles of health care
coverage are consistent across Canada, and are based on universal coverage and access to
medically necessary health care services.124 Furthermore, hospitals and health care providers are
forbidden from charging patients for services covered under the social insurance framework.
Consequently, with very few exceptions, all patients who are admitted to hospital with a fragility
hip fracture in Canada have the costs of their care absorbed by the hospital’s global budget.
Conceptually, the strength of using global budgets is that this approach provides short-term
budget predictability for both payors and hospitals.14 Global budgets are negotiated on a regular
26
basis between hospitals and the health care payor. A number of factors contribute to the amount
of funding provided to each hospital. These include consideration of historical case volumes and
spending, inflation, projected changes in demand, and capital investment requirements.
However, budgets are also influenced by political factors and negotiating tactics, which are not
necessarily reflective of the actual demand for health care services.125 From the health care payor
perspective, global budgets are advantageous in that they encourage budgetary predictability and
provide a relatively straightforward mechanism for constraining overall expenditure on hospital
services.126,127 From the hospital perspective, funding through a global budget provides short-
term revenue predictability, and may provide some measure of flexibility in covering indirect
costs.128 However, while the funding received may be carefully modeled based on historical
demand and costs, it will not necessarily accurately reflect the actual demand for health care
services for a given funding cycle. Consequently, the financial risks associating with increasing
health care costs are borne primarily by hospitals, at least in the short term. Furthermore, to
ensure that budgets are balanced, hospitals are limited primarily to making adjustments to their
expenses. When faced with increasing demand for elective hospital-based services, hospitals
may be forced to limit access to services in an attempt to limit growth in expenditures, resulting
in increasing wait times.129 Nevertheless, patients with hip fractures are admitted to hospitals on
an emergency basis, and given the clinical standards of care (admission to hospital and early
surgery), there is no substantial opportunity to defer or limit access to inpatient hip fracture care.
Consequently, any increase in expenditures on hip fracture care (for example, due to an increased
number of presenting patients) must be absorbed from other budget items that have greater
flexibility for deferral of expenses (eg. elective patient care).
27
Because global budgets are determined largely by historical activity, and because budgetary
balancing for the hospital is limited primarily to adjustments to expenditures, this funding
approach incentivizes hospitals to maintain consistency and predictability between actual and
historical expenditures. As a result, there is limited economic incentive to improving the quality
or efficiency of care.14 For example, assuming a fixed number of patients treated, improvements
in the efficiency of care that reduce length of stay would result in decreased overall bed
utilization, and might paradoxically be reflected in decreased amount of funding allocated by the
health care payor in subsequent years. Alternatively, an innovation that reduces the mean length
of stay following surgery would theoretically allow a hospital to increase the number of patients
undergoing this procedure in a given year without increasing bed utilization. However, the
necessary increased operating room utilization to perform more surgeries would increase the
hospital’s overall costs for providing care over the year, something that would generally not be
provided for in a global funding model. Furthermore, efforts to improve the quality of care (for
example, providing additional resources to decrease the risk of a surgical error) may incur
additional financial costs to the hospital, but in the absence of any change in the actual number of
patients treated, may not be reflected in future funding allocations. Thus, while global budgets
provide predictability to the health care payor, they have been criticized for emphasizing
maintenance of the status quo, and lacking incentives to improve the quality or efficiency of
patient care.15
2.6.4 Activity based funding
An alternative approach to both cost-plus and global funding that reflects the type and volume of
care provided is the use of activity-based funding (ABF).16,17 In this model, hospitals are
reimbursed on a per-patient basis for a given administrative episode of care (commonly defined
28
as the period from admission to discharge at a single health care facility). The reimbursement
amounts are common across peer hospitals, and reflect the expected cost for the treatment of a
given diagnosis and/or procedure in an efficient health care facility, with potential adjustments
based on factors that are expected to affect the cost of care. 130 The reimbursement amounts are
based on patient groupings that represent the expected cost of care, and reflect the diagnosis and
other clinical factors that may influence cost, such as comorbidities. These groupings are known
as Case Mix Groups (CMGs) in Canada, and Diagnosis Related Groups (DRGs) in the United
States.131 In the United States, activity-based funding has been the model for all hospital
reimbursement by Medicare for approximately 30 years.14 In Canada, there have been recent
pilot initiatives in various jurisdictions involving transitions to ABF. In Ontario, specific high
volume diagnoses and procedures were identified for transition from a global funding to ABF
model, collectively termed Quality Based Procedures (QBPs).18 Among others these included hip
and knee replacement, cataract surgery, and hip fracture care. With these procedures, funding is
removed from the global budget, and hospitals receive a fixed reimbursement per patient that is
intended to cover the costs of care as derived from historical administrative data spanning a
range of hospitals, but is not directly tied to the actual costs incurred at the treating hospital.
Conceptually, the strength of ABF is that it shares financial risk between the health care payor
and the hospital, while providing incentives to the hospital to improve the quality and efficiency
of care.132 Specifically, hospitals are incentivized to improve efficiency of care by the
opportunity to retain the difference between the amount reimbursed and the actual cost of the
services provided (or, at minimum, to avoid incurring costs that exceed the reimbursement).
Furthermore, ABF has been theorized to incentivize quality of care.133 For many elective
procedures that have historically been associated with substantial wait times, complete adoption
29
of ABF would likely improve access to care by eliminating the resource limitations inherent in
global funding. It may also incentivize hospitals to adopt interventions and best practices to
reduce the incidence and/or severity of adverse events and ultimately improve the safety of care.
Given the previously described associations between adverse events and both increased costs of
care as well as length of stay, ABF would provide incentives to reducing adverse events and their
associated costs as a consequence of the resulting increased difference between the cost of care
and the amount reimbursed.
However, ABF is also associated with certain risks to both health care payors and hospitals.
While the payor is able to set per-patient reimbursement amounts, ABF provides limited
influence over total health care expenditures short of imposing additional expenditure control
mechanisms.14 Additionally, there is a risk that hospitals will respond by shifting resources
toward patients and services that provide the highest return,134 potentially resulting in
improvements in quality of care for certain areas at the expense of decreased quality of care in
others. The use of adjustments to reimbursements for a given grouping (eg. to account for patient
complexity or concurrent diagnoses) risks incentivizing hospitals and providers to increase the
amount of care provided while remaining relatively invariant to differences in quality.135-137
Consequently, patient groupings must be carefully defined and reimbursement amounts managed
to achieve the optimal balance between maximizing value to the payor and ensuring sufficient
incentive to the hospital. Informing such an endeavour requires considerable data acquisition and
analysis, and not all hospitals are sufficiently resourced or equipped with the necessary
infrastructure to perform this work. Another risk is that hospitals may discharge patients earlier
to lower acuity facilities, thus reaping financial benefits associated with increased efficiency in
terms of bed utilization while deferring costs to the receiving hospital. From the hospital
30
perspective, ABF is associated with the risk that institutions’ costs of providing care will exceed
the reimbursements received, and that they may struggle to achieve the improvements in
efficiency needed to keep their costs at break-even level.
With respect to hip fracture care specifically, hospitals have little influence over the number of
patients treated over a given time period, as these patients are brought to and admitted from the
emergency room with no real opportunity to delay their care or refer to a different institution.
Consequently, transitioning to ABF provides opportunities by providing reimbursements on a
per-patient basis, rather than a global budget based on historical patient numbers that may not be
consistent from year to year. However, there is also the risk that hospitals may be reimbursed at
levels below their cost of providing care. Given the reported high prevalence of perioperative
adverse events in patients with hip fracture, and their associated costs, improvements in the
quality of care that reduce AE rates may potentially decrease costs of care from the hospital
perspective of these patients. If true, organizations that are able to provide high quality, cost-
efficient care for hip fractures may regularly be reimbursed at levels that exceed costs, allowing
the additional funds to be re-invested into improving patient care. In contrast, a failure to control
costs may result in a particular hospital department regularly being reimbursed less than the cost
of the hip fracture care provided, threatening the financial viability of this program at some
hospitals.
2.6.5 Bundled payments
Bundled payments represent an extension of the ABF funding model, where a single
reimbursement is provided for an entire clinical episode of care. In contrast to the episodes of
care with ABF that typically encompass a single hospital admission, bundled payment
reimbursements include health care services for a given diagnosis or complaint extending beyond
31
the acute care admission.138 For example, such a clinical episode of care could encompass the
initial consultation for a given diagnosis, the acute care hospitalization and surgical treatment,
the rehabilitation hospital stay, any outpatient follow-up visits, as well as the management of any
complications related to the initial procedure. Conceptually, bundled payments provide similar
incentives for improving the quality and efficiency of care as ABF, but extend these to
encompass all care associated with a given diagnosis or complaint. By including readmissions
and the treatment of any complications in the reimbursement for the care bundle, hospitals would
potentially be incentivized to improve the quality of patient care in order to minimize these
associated costs.139 The challenge in establishing reimbursement levels is to set them sufficiently
low as to maximize return in terms of health care provided to the population, while ensuring that
they are sufficiently high such that efficient health care providers are provided with financial
incentive to provide care.
The implementation of bundled payments requires integration of health care services such that
the hospital receiving the reimbursement is able to offer or at least access the full range of
inpatient and outpatient services required. To date, experience with the use of bundled payments
worldwide is limited, although there has been considerable recent interest on the part of both
payors and hospitals in the United States in this approach.140,141 In Canada in general, and
Ontario specifically, there has been limited experience with bundled payments to date.142
2.7 Health economic aspects of hip fractures and delirium
2.7.1 Health economic burden of hip fractures
Hip fractures can place a considerable burden on health care systems, and particularly so within
countries such as Canada where cases are surgically treated in emergency. It has been suggested
that hip fractures may represent up to 20% of a trauma hospital’s orthopaedic surgical case
32
volume.35 A recent study using administrative health care data from the province of Ontario
reported that annual direct costs from the Ministry of Health perspective attributable to hip
fractures in patients over the age of 65 were $1.1 billion 2010 Canadian dollars,143 representing
close to 1% of the total $135 billion public-sector health care spending in 2010.144 Acute care
hospitalization is reported to be the largest single cost component, representing approximately
40% of the total 1 year attributable direct costs.143,145 Estimates from the United States suggest
that the direct costs attributable to lower extremity injuries following falls in those 65 years of
age or older were responsible for approximately $14.8 billion USD in direct costs in 2012, of
which $12.1 billion were hospital costs.146,147 Data are limited concerning the specific drivers of
acute care hospitalization costs. In a study of 193 patients with hip fractures treated at a single
center in the United States, Kates et al. reported that expenditures directly related to the surgical
procedure itself (such as anesthesia and cardiac pre-operative assessment, operating room access,
and orthopaedic implants) represented 44% of the total variable acute care hospitalization costs
from the hospital perspective.148 Expenditures related to the inpatient stay (such as the inpatient
bed and staffing, laboratory and diagnostic testing, and medications) represented 54% of costs
over a mean 4.4 day hospital stay, or 12% of the surgical cost per day of inpatient stay.
2.7.2 Determining the health resource impact of delirium in the context of hip fractures
Given the clinical consequences of delirium as described in section 2.5.1, this AE has the
potential to adversely impact health resource requirements from the acute care hospital
perspective associated with the treatment of patients with hip fractures. Conceptually, assuming
costs for a given individual element of the health care provided (eg. a single dose of medication,
or a defined period of time of nursing care) are held constant (as can reasonably be assumed in
the case of patients cared for in a single institution over the same time period), there are two
33
dimensions that could contribute to potential differences in episode of care costs between
patients: duration of care, and intensity of care. First, should perioperative delirium delay patient
discharge from the acute care hospital setting, this would increase both the number of bed-days
required for that patient’s care, and increase the associated hoteling component of the total
episode of care cost. Second, patients who experience perioperative delirium may require greater
intensity of care, whether due to the delirium itself or associated secondary AEs, resulting in
increased episode of care costs. Assessing the health resource impact of delirium in patients with
hip fractures from the acute care hospital perspective requires the determination of: 1. whether
delirium is associated with differences in acute care hospital length of stay; 2. whether delirium
is associated with differences in episode of care costs; and 3. assuming a difference in episode of
care costs, the proportion of the difference that is attributable to length of stay versus differences
in intensity of care.
To determine the current state of knowledge regarding the potential impact of perioperative
delirium on health resource requirements with respect to the three questions above, a systematic
review of the literature was performed.
2.7.2.1 Literature Search Methodology
Original studies with full text versions available in the English language, and which assessed
associations between delirium and health economic outcomes in patients undergoing inpatient
surgery, were deemed eligible. To ensure studies with relevant information that assessed mixed
patient cohorts were not missed, the search was not limited to studies that only assessed patients
with hip fractures. For the purposes of the search, health economic outcomes were defined as
either acute care length of stay, or costs from the perspective of the hospital, health care payor, or
society. An electronic search was performed of the Ovid Medline, EMBASE and PsychINFO
34
databases up to January 2014 using the following search string: (orthopaedi* or orthopedi* or
surgery or surgical or peri-operative or perioperative or post-operative or postoperative or in-
hospital or hospitalized or hospitalised or inpatien* or in-patien*) and (delirium or delirious or
'cognitive dysfunction') and ('length of stay' or 'duration of stay' or cost or cost* or economi*).
Citation records, titles, abstracts and full-text versions underwent a process of systematic
deduplication, screening, and review for eligibility consistent with the PRISMA statement and
flow diagram.83 The initial search identified 1482 records prior to de-duplication, with 13
studies remaining in the final review (Figure 3).
Figure 3: PRISMA diagram of literature search concerning association between delirium and
health economic outcomes
35
2.7.2.2 Associations between delirium on length of stay
While 12 of the 13 studies included in the final review reported associations between
perioperative delirium and acute care length of stay,27,90,94,149-157 only two of these investigated
these associations in patients undergoing treatment of a hip fracture.90,94 In a study of 921 hip
fracture patients in the United States who had a 5.1% reported incidence of delirium, Edelstein et
al. reported no significant difference in acute care hospital length of stay between groups (values
not reported), although the authors did note a significantly longer mean combined acute care and
rehabilitation stay in those patients who did experience delirium (37 vs 22 days; p<001).94 In a
review of 281 hip fracture patients in Israel who had a 31% overall reported incidence of
delirium, Adunsky et al. found no significant difference in mean length of stay between patients
who did and did not experience delirium (27.4 vs 25.5 days; p=0.3).90 However, in contrast to
most North American hospitals, patients in this study from Israel remained in the same hospital
from admission to discharge to home, without intercalary transfer to a dedicated rehabilitation
facility. Overall, evidence concerning the associations between delirium and length of stay in
patients with hip fractures is limited, with neither study providing data concerning acute care
hospital length of stay in isolation. With the exception of these two studies, no other published
data were identified concerning potential associations between perioperative delirium and acute
care hospital length of stay in patients with fragility hip fractures.
2.7.2.3 Associations between delirium and health care costs
While three studies included in the final review assessed associations between delirium and
health care costs,22,27,154 none of these reports evaluated patients with hip fractures. In 2001,
Franco et al. reported the results of a prospective study of 500 patients who were at least 50 years
of age and were admitted to a single hospital in the United States for a range of elective inpatient
36
surgical procedures.27 While the authors did not evaluate incremental costs directly, mean length
of stay and costs were reported for patients who did and did not experience delirium as measured
using the Confusion Assessment Method (CAM) tool. With an overall rate of delirium of 11.4%,
Franco et al. found an increase in mean length of stay of 1.4 days and an increase in mean costs
of $2,947 in the group of patients who experienced delirium. Zatzick et al. conducted a registry
study of 10,561 patients admitted to the general trauma surgery service at a single academic
trauma centre in the United States.154 They found an overall 0.4% rate of delirium, which was
associated with 93% greater hospital costs as compared to those patients who did not experience
delirium. Carrott et al. assessed associations between a range of adverse events and hospital costs
for 285 patients who underwent esophagectomy for cancer, 36 of whom (12.6%) experienced
peri-operative delirium.22 On univariate analysis, the authors found that delirium was associated
with a hospital cost ratio of 1.2 (p=0.002), although this association became statistically non-
significant on multivariable analysis when adjusted for a range of demographic factors,
comorbidities, and major adverse events other than delirium. In all cases, assessments of costs
were limited to the hospital perspective, without assessment of the broader health care payor or
societal perspectives.
While there have been a few studies assessing associations between delirium and hospital costs
in surgical patients, none have been assessed among patients with hip fractures. The populations
studied to date (mixed elective, trauma surgery, and esophageal cancer patients) are distinct from
patients with hip fractures in terms of reasons for admission, risk factors for delirium, and in-
hospital care trajectories, and the findings are not easily generalizable to patients with hip
fractures. There is a paucity of data concerning the impact of perioperative delirium in patients
with hip fractures.
37
2.7.2.4 Relative contributions of intensity and duration of care to episode of care costs
There were no published works that examined associations between delirium and health care
costs in patients with hip fractures, and no studies were identified that quantified the relative
contributions of length of stay versus intensity of care to potential differences in costs in this
patient population. Furthermore, none of the three studies that assessed associations between
delirium and hospital costs in other surgical populations performed such assessments.
2.8 Rationale for thesis
In the present climate of modest economic growth, rising demand for health care services, and
changing models of reimbursement, there is an important emphasis on increasing the quality and
efficiency of health care.158 As described in section 2.7.1, the treatment of patients with hip
fractures represents a considerable cost burden on the health care system. Furthermore, it is
known that patients admitted to hospital with a hip fracture experience a high rate of in-hospital
adverse events in general, and delirium specifically (sections 2.4 and 2.5.1). There is some, albeit
limited evidence that delirium in patients with hip fractures is associated with increased hospital
resource requirements in terms of length of stay (section 2.7.2.2).
Given the high rates of perioperative delirium in patients with hip fractures, the known
association between this AE and subsequent unfavorable clinical consequences, and evidence,
though limited, supporting associations between delirium and increased length of stay, it is
possible that reductions in the rate and/or severity of delirium may not only improve clinical
outcomes but may reduce the health resource impact of hip fracture care from the hospital
38
perspective. However, there is a clear gap in the literature concerning the health resource impact
of perioperative delirium in patients with hip fractures as demonstrated in section 2.7.2.
While a number of interventions to reduce the incidence and/or severity of perioperative delirium
have been evaluated, their reported clinical effectiveness is variable, with greatest benefit
believed to come from multicomponent interventions.159 The barriers to implementation of best
practices for the prevention of delirium across the whole care pathway are high however. Such
interdisciplinary interventions can be resource-intensive, typically requiring additional staff,
operating room resources, and coordination of care between multiple clinical and allied health
teams.160 Thus, the implementation of interventions to reduce the rate and/or severity of
perioperative delirium may be associated with substantial additional costs from the hospital
perspective. Cost-effectiveness analyses would be beneficial to help identify the optimal
interventions for reducing the clinical and health economic consequences of delirium in this
patient population. However, the possibility of such analyses is constrained by the current gap in
the literature around the health resource impact of perioperative delirium in patients with
fragility hip fracture.
2.9 Objectives
The overall goal of this thesis was to quantify the health economic implications of perioperative
delirium in older orthopaedic surgery patients with fragility hip fractures. Specifically, the
objectives were to 1) Quantify the difference in acute care hospital length of stay (LOS) between
patients who do and do not experience perioperative delirium; 2) Quantify the difference in
episode of care costs from the hospital perspective between patients who do and do not
experience perioperative delirium; and, 3) Given an identified difference in costs, to determine to
39
what degree the differences in episode of care costs from the hospital perspective were explained
by potential differences in length of stay.
40
Chapter 3: Methods
3.1 Design and setting
A retrospective economic evaluation was conducted using prospectively collected observational
data from one centrally located Toronto teaching hospital (Toronto Western Hospital) with a
high volume orthopaedic surgery department, whose catchment area encompasses a diverse
cross-section of individuals at risk for fragility hip fractures.
The use of a retrospective study design is associated with certain well-described risks. Selection
biases and observer biases may both be present owing to the fact that the outcome has already
occurred at the time that the study is initiated. Similarly, the risk of confounding is high with this
design. However, the use of this design is also advantageous in that it is resilient to selection and
attrition biases that are inherent in prospective designs that require patient consent for
participation. This may be a particular risk in studies of patients with hip fractures and/or
delirium, as many may not be capable of providing consent due to either baseline cognitive
deficits or altered levels of consciousness. Additionally, a retrospective design allows for the
collection and analysis of a number of variables at less cost and in a more compressed time
period as compared to prospective studies.
Some weaknesses must be acknowledged associated with limiting the study to a single teaching
hospital with a high volume orthopaedic surgery department located in the city of Toronto. There
are a number of factors that influence length of stay and cost that can vary between hospitals, for
example teaching hospital status, differences in local labour costs, and economies of scale.161
Consequently, there may be considerable variation in the outcomes of interest between hospitals
that are not captured in the present work, and it is unclear how generalizable the results will be to
41
other hospitals in the City of Toronto or the Province of Ontario. However, several benefits are
also realized as a consequence. By utilizing data collected at the hospital level, the weaknesses of
administrative data are avoided. This is particularly important in the present work as
administrative data within the province of Ontario has been reported to have a very low
sensitivity for a diagnosis of delirium, the primary predictor of interest in this work, when
compared to clinical data.162 The use of a single institution increases the consistency of the data
across all participants, as it is more likely that similar methodology was used throughout. The
inclusion of a high volume orthopaedic centre provides benefits in that the center can be
considered experienced with the treatment of the injuries of interest, and sufficient patients will
be available to allow for a meaningful analysis. Additionally, by having multiple orthopaedic
surgeons participating, the risk of performance bias is minimized. By virtue of its location, the
study hospital admits patients from a variety of cultural backgrounds, and includes patients
admitted from a mix of high rise and low rise dwellings, retirement homes, and long term care
facilities. Consequently, the study population can be expected to represent a diverse cross section
of patients who experience hip fractures.
3.2 Patients, clinical data sources and collection
A surgical care database from a single specialized academic orthopaedic center was reviewed by
principal diagnosis and procedure to identify all patients who were admitted between January 1,
2011 and December 31, 2012, and underwent surgical treatment of an acute hip fracture. This
database was assembled as part of a quality of care initiative to prospectively track adverse
events for all inpatient orthopaedic and spinal surgical procedures at the study institution.163
42
3.2.1 Inclusion and exclusion criteria
Inclusion criteria were limited to patients who were 65 years of age or older on the day of
admission. This threshold was chosen because of evidence that this age range represents the vast
majority of fragility hip fractures,45,46 and also because the age of 65 continues to be generally
accepted as the threshold for being of ‘older age’ in the developed world.164,165 As described in
section 2.2, patients with hip fractures can be stratified into two categories based on a high
energy versus low energy mechanism of injury. In the clinical care environment, these entities
are generally considered to be distinct in terms of their trajectory of care as a consequence of
different rates of associated injuries and comorbidities.44 Consequently, patients who
experienced a hip fracture as a consequence of a high-energy mechanism were excluded, and the
clinical chart was screened for all identified patients to confirm a low-energy mechanism without
any associated injury.
Patients were excluded if they had a fracture associated with neoplastic or metastatic disease,
confirmed by pathologic evaluation of intra-operative tissue. Hip fractures through neoplastic
lesions (also known as pathologic fractures) represent a small proportion of all patients with hip
fractures, and are distinct from fragility hip fractures in terms of their presentation and in-
hospital health resource requirements. Many pathologic fractures present with a period of
prodromal pain, some occur ‘spontaneously’ without an associated fall or similar event, and
some present as impending pathologic fractures where patients are admitted and undergo surgical
treatment before a complete fracture occurs.166 Furthermore, in hospital, patients with these
lesions may require additional health services such as extensive medical imaging for cancer
staging, pre-operative embolization of tumor blood supply, and oncologic co-management.166,167
Finally, post-discharge care trajectories, health resource requirements, morbidity and mortality
43
are distinct owing to the underlying diagnosis of neoplastic, and frequently metastatic, disease.168
Consequently, these patients were excluded from the present study.
In cases where a patient experienced two episodes of care during the study period for two
separate hip fractures, only the first admission was included (n=2). Several studies have assessed
second fragility hip fractures in patients who have previously injured the contralateral side.
While it is unclear whether there are any significant differences in baseline factors (apart from
having previously experienced a hip fracture), it has been established in the literature that second
hip fractures are associated with excess mortality as compared to the index injury.169,170
Consequently, experiencing a second hip fracture may be associated with differences in health
resource utilization as compared to the first injury, and may be a potential confounder if not
controlled for in any analyses. Given that there were only two admissions for second hip
fractures, it would be difficult to appropriately control for this variable given the low number of
observations, and consequently these admissions were excluded from the analysis. It is important
to recognize that assessment of whether an admission was for a first or second hip fracture were
limited to the study period only. It is possible that some of the patients included in the study had
in fact experienced a hip fracture prior to the study period. However, data from an observational
cohort of 1,229 patients with fragility hip fractures in the Netherlands suggests that this may be
relatively uncommon, with first and second year contralateral hip fracture risks of 2% and 1%,
respectively.171
A total of 242 patients were included, representing 242 unique acute care hospital episodes of
care. The study cohort included 74 men and 168 women (30.6% and 69.4%, respectively) with a
mean age of 82 years (range, 65 to 103 years). Using unique visit numbers that represent a single
interaction with the hospital, the identified episodes of care were linked to a prospectively
44
collected adverse events database, to data stored in the electronic health record, as well as to
patient-level hospital micro case costing data. Demographic, clinical, surgical, and financial data
were extracted into a single database for analysis.
No exclusions were made based on patient language or baseline cognitive status. Prospective
studies will sometimes exclude patients due to language barriers or baseline cognitive deficits,
whether explicitly or implicitly. The reasons for this may include concerns about patients’ ability
to understand the research being conducted and provide informed consent, as well as ability to
participate in data collection activities (for example, interviews or completing forms) included in
the study protocol. However, exclusion of patients based on factors such as language
comprehension risks the introduction of systemic bias into research, as supported by evidence
suggesting that non-English speaking patients in the United States require greater physician time
and health resources when compared to those that speak English.172 Since this is a retrospective,
observational study that did not require patient consent or active participation in English-
language data collection exercises, all patients were included irrespective of degree of English
language comprehension. Similarly, exclusion of patients with baseline cognitive deficits risks
introducing bias into the study findings. Specifically, cognitive impairment in general, and
dementia in particular, are very common in patients with hip fractures, with reported prevalence
of 19% and 42%, respectively.173 Furthermore, cognitive impairment in general is independently
associated with significantly increased direct medical costs.174 While dementia has been
identified as a risk factor for developing delirium,88 given that the objectives of the present work
were to assess the health economic impact of delirium in patients with hip fractures without
exclusion based on the presence or absence of other risk factors, systematic exclusion of patients
45
with cognitive impairment would potentially threaten the generalizability of the present work,
and was avoided.
3.2.2 Demographic data
The specific demographic characteristics extracted included patient age, gender, and type of
residence prior to admission (personal home, retirement home, nursing home or other skilled
care facility). Age and gender were extracted from the surgical care database and confirmed by
referencing against the electronic patient records. Information concerning the type of residence
prior to admission was extracted from the electronic patient record.
3.2.3 Clinical data
Clinical characteristics included number and type of major medical comorbidities (Table 2), type
of hip fracture (Table 3), side injured, time from initial hospital triage for possible hip fracture to
surgery in hours, time from admission to hospital for hip fracture to surgery in hours, total length
of hospital stay, length of stay following surgery in days, and discharge destination. Information
concerning the major medical comorbidities as well as the side injured was extracted from the
adverse events database, whereas the remaining clinical data were extracted from the electronic
patient record.
46
Table 2: List of major comorbidities collected in dataset
Table 3: Hip fracture types collected in dataset
3.2.4 Surgical data
Surgical data extracted included American Society of Anesthesiologists (ASA) class (Table 4),74
type of anesthesia (general vs. neuraxial), de-identified ID of the operating surgeon, the surgical
procedure performed, and duration of surgery from incision to closure in minutes. The ASA class
is a rapid comorbidity-based index of fitness for surgery that has been consistently reported to be
47
a good predictor of post-operative morbidity in surgical patients,175-177 and may be superior to
other medical comorbidity indices in this population.178
The ASA class, de-identified ID of the operating surgeon and duration of surgery were extracted
from the adverse events database. The type of anesthesia and the surgical procedure performed
were extracted from the electronic patient record.
Table 4: American Society of Anesthesiologists physical status classification
Adapted from: American Society of Anesthesiologists. New classification of physical status. Anesthesiology. 1963;24:111.74
3.2.5 Delirium data
The presence of delirium was clinically assessed prospectively using the validated Confusion
Assessment Method (CAM) tool (Figure 4) , which is a binary screening test (ie result is either
positive or negative) that has been validated against a gold standard diagnosis of delirium made
by a board-certified psychiatrist and demonstrated to be highly sensitive (94-100%), specific (90-
95%) and reliable (kappa 0.81-1.0) for the detection of delirium in the general hospitalized and
outpatient elderly population.79 The CAM assessment was performed by nursing staff at the end
of each shift and recorded on a nursing flow sheet. The presence of any positive CAM
assessment was considered indicative of the presence of perioperative delirium, consistent with
the recommendations made by the developers of this instrument.79 Additionally, we recorded
48
whether the initial and final CAM assessments were positive, which were considered indicative
of delirium on admission and discharge, respectively. These data were documented in the
electronic patient record, from which they were extracted into the study dataset.
Adapted from: Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz RI. Clarifying confusion: the confusion assessment method. Ann Intern Med. 1990;113:941-8.79
Figure 4: The confusion assessment method (CAM) algorithm for diagnosing delirium
Secondary data for delirium was obtained from the hospital’s discharge abstract database (DAD)
and recorded as a separate variable. The hospital DAD is the same data that is submitted to both
provincial and national databases managed by the Institute for Clinical Evaluative Sciences and
the Canadian Institutes for Health Information, respectively. The DAD holdings for the patient
admissions of interest were reviewed, and the presence of the ICD-10 F05 code was considered
diagnostic of delirium during the hospital stay.
3.2.6 Other adverse events data
The remaining adverse events were collected prospectively using the Orthopaedic Surgical
Adverse Events Severity System (OrthoSAVES) grading system (Table 5), a modification of the
49
validated Spine Adverse Events Severity System (SAVES),179 by trained research assistants
collaborating with the clinical care team on a daily basis to identify and track all intra and post-
operative adverse events. The OrthoSAVES system grades adverse events based on both type
and severity on a six point scale ranging from grade 1 (requiring no or minimal treatment and
resulting in no long-term sequelae), to grade 6 (fatal complication). The presence or absence of
any adverse events other than delirium was noted for each episode of care, as was the highest
grade of AE experienced during hospitalization. These data were extracted from the adverse
events database into the current study dataset.
Overall, 116 cases were marked by the presence of perioperative delirium during the hospital
stay (48%), whereas 126 were not (52%).
Table 5: Grading of adverse events using the OrthoSAVES system
3.3 Length of stay
3.3.1 Theoretical framework for determinants of length of stay
Schorr recently proposed a theoretical framework for identifying and characterizing determinants
of hospital length of stay, which can be used to inform data collection and variable selection in
50
studies assessing this outcome measure.180 This model identifies four broad categories of
determinants: 1) patient characteristics, 2) clinical caregiver characteristics, 3) social and family
environment characteristics, and 4) health system characteristics. While many variables will be
exclusive to one category, it is conceptually possible that a given variable may share
characteristics of more than one category. For the present study, the available variables primarily
represented patient characteristics, as well as clinical caregiver characteristics. There was limited
representation of variables assessing social and family environment or health system
characteristics.
3.3.2 Potential determinants of length of stay
A systematic review of the literature was performed to identify potential determinants of acute
care hospital length of stay, with the goal of informing covariate selection for analyses. Original
studies with full text versions available in the English language, and which assessed potential
determinants of acute care hospital length of stay in adult patients with hip fractures, were
deemed eligible. An electronic search was performed of the Ovid Medline and EMBASE
databases up to January 2014 using the following test search string: ((length of stay or cost) and
hip fractures). The results were combined with a second search performed using the following
MeSH headings: ((length of stay or “costs and cost analysis”) and hip fractures). Citation
records, titles, abstracts and full-text versions underwent a process of systematic deduplication,
screening, and review for eligibility consistent with the PRISMA statement and flow diagram as
presented in Figure 5.83 The initial search identified 1477 records prior to de-duplication, with
206 studies remaining in the final review. Factors reported to be significantly associated with
patient length of stay were identified and recorded. Similar factors were grouped together, the
number of studies identifying significant associations for each factor was noted, and factors were
51
stratified into the four broad categories proposed by Schorr.180 The range of potential
determinants of length of stay identified in the scoping review and their frequency of appearance
are summarized in Table 6, while the full list of studies and factors studied can be found in
Appendix C.
Figure 5: PRISMA flow diagram for literature search of determinants of length of stay in patients
with hip fractures
52
Table 6: Potential determinants of length of stay for patients with hip fractures
53
Patient characteristics
Patient characteristics represent a broad range of factors, and can include demographic variables,
elements of the medical history, as well as the disease or complaint responsible for the hospital
admission. A number of patient characteristics were identified in the literature that were potential
determinants of length of stay, with baseline comorbidities and in-hospital adverse events being
most frequently reported. Others included age, type of hip fracture, ASA class, pre-admission
living situation and discharge destination. As indicated in previous sections, many of these
covariates were available in the hip fracture data set used, and thus were considered for inclusion
in the analyses. Other patient characteristics not identified in the literature review were also
available. Of these, gender was specifically also considered for inclusion given recommendations
from the Institute of Medicine and the orthopaedic community that this potential important
determinant be routinely included in research studies.181
Clinical caregiver characteristics
Clinical caregiver characteristics include a range of factors that vary between physicians and/or
nurses that can influence length of stay such as the specialty, professional culture, team
membership/organization, and individual treatment choices (eg. type of
procedure/medication).180 Specific to the care of patients with hip fractures, examples may
include: the type(s) of specialist(s) functioning as the most responsible physician (orthopaedic
surgeon alone versus co-management with a geriatrician, hospitalist and/or anesthetist), the use
of interdisciplinary care teams, and decisions around both surgical care (types of implants and
surgical techniques used) and ward management (for example, duration of antibiotic prophylaxis,
fluid management strategies, and transfusion thresholds). All of these factors were identified in
the review as potential determinants of length of stay. Because the study was limited to a single
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clinical group at a single hospital, many of these characteristics would be expected to be
consistent across the patient population. For example, orthopaedic surgeons served as the most
responsible physicians for all patients, and the allied health organizational structure was
consistent throughout. To control for potential differences between caregivers, a variable
representing de-identified ID of each participating surgeon was collected. Furthermore, the type
of anesthesia administered was collected as a variable. However, it must also be recognized that
the clinical caregiver characteristics in the present study may not reflect those present at other
hospitals, thus potentially limiting the generalizability of the findings.
Social and family environment characteristics
Social and family environment factors include variables such as education/working status,
engagement with family/peers, and characteristics of the political system, community, and
economic milieu.180 No potential determinants of length of stay from this category were
identified in the review, and none of the variables available in the data set were considered to
represent this group of characteristics. While important to consider in a general theoretical
framework of length of stay, this type of data was unavailable for inclusion in this work.
However, factors such as the social political and economic climate can be expected to be similar
across all patients.
Health system characteristics
A number of health system characteristics can be expected to influence length of stay, and
several such potential determinants were identified in the review. These include factors such as
the structure of health care services, insurance type, resource availability, and access to post-
acute care services (inpatient or outpatient rehab/follow up).180 For example, systems that
55
provide both acute hip fracture care and inpatient rehabilitation as part of a single admission to
one facility can reasonably be expected to report longer acute care length of stay than systems
that discharge patients from acute care to separate rehabilitation facilities.90,94 Furthermore,
coordination of care can significantly decrease lengths of stay at both acute care and
rehabilitation hospitals.182 Health systems also evolve over time, through both gradual changes in
practice patterns, resource distribution, and efficiencies, as well as occasional changes in funding
models and incentives, all of which can influence length of stay. While many of these changes
can be difficult to capture directly, the year of admission can be used as a proxy for health
system changes over time.
Given that the present work was conducted at a single hospital, any variability in health system
characteristics was assumed to be minimal. While this is beneficial in that it limited potential
confounding as a consequence of unobserved health system variables, it also increases the risk
that the results may not be generalizable to other health systems. Health system variables with
potential variability across patients and potentially associated with length of stay available in the
study dataset included year of admission, duration between admission and surgery, and discharge
disposition (new long term care versus returning to long term care versus rehabilitation).
3.3.3 Length of stay data
Length of stay was extracted from the electronic patient record. Total length of stay was
measured in days, and was defined as the difference in days between the date of admission and
date of discharge from the acute care hospital. Additionally, length of stay following surgery was
captured as a separate variable, and was defined as the difference in days between the date of
index surgery and the date of discharge from the acute care hospital.
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3.4 Defining the episode of care
An assessment of resource utilization associated with the provision of health services requires
that the episode of care be clearly defined. Solon et al. defined an episode of care as a “block of
one or more medical services received by an individual during a period of relatively continuous
contact with one or more providers of service, in relation to a particular medical problem or
situation."183 Wingert et al. re-stated this definition as a “group of health care services related to
the management of a specific illness or condition provided in a limited time period.”184 More
recently, Rosen and Mayer-Oakes described episodes of care as a “meaningful unit of analysis
for assessing the full range of primary and specialty services provided in treating a particular
health problem.”185
Conceptually, all of these definitions of episodes of care share certain characteristics, many of
which were described by Solon et al.183 The episode of care is defined by a patient’s particular
medical problem, health-related complaint or concern. Second, some constraint is needed on the
episode of care such that it has a beginning and an end. Third, a single episode of care can
encompass multiple types and quantities of health services, delivered in one or more related
encounters. Fourth, it should encompass all health services relevant to the episode of care. Fifth,
the management of a single medical issue over a patient’s lifetime can involve multiple discreet
episodes of care. Sixth, different types of episodes of care can exist simultaneously for a given
patient.
In an attempt to better understand the health care services provided to patients with fragility hip
fractures, a care map was developed to graphically depict the trajectory of care of patients who
57
experience a fragility hip fracture, from the time of injury to discharge to the place of residence.
This was developed in consultation with a multidisciplinary group of health care professionals
involved in the care of patients with hip fractures (orthopaedic surgeons, anesthetists,
geriatricians, physiatrists, nurses) at the study institution. Review of the care pathway illustrates
three well-defined environments were health services are provided following the injury and prior
to discharge to final place of residence: an acute care hospital emergency room; an acute care
hospital inpatient facility, and an inpatient rehabilitation hospital (Figure 6).
Apart from these three environments, additional health services may also be provided prior to
contact with the emergency room (paramedic services) and well as following discharge to home
(outpatient rehabilitation, outpatient follow-up). Furthermore, additional health services may be
required, or patients may return to one of the care facilities following discharge, in the case of
any adverse events potentially related to the index injury.
Figure 6: Care pathway for patients experiencing an isolated fragility hip fracture
Including all of the health services described above within a single episode of care would come
closest to encompassing all of the care of a patient with a hip fracture from the moment of the
58
injury to maximal recovery and return to final place of residence. However, such a broad
definition of an episode of care is also associated with certain disadvantages. Maximal recovery
following a major injury such as a hip fracture can be difficult to define, complicating efforts to
identify the point in time when patients reach maximal recovery. Consequently, using this as an
endpoint to the episode of care can be challenging. Alternative strategies include defining an
arbitrary time-based endpoint (for example, 30 days following discharge), or defining the
endpoint by a clearly measurable event (for example, discharge from hospital).
Including care provided at multiple facilities presents the challenge of collecting and tracking
data across multiple facilities. Particularly if relying on existing data sources, there is a risk that
data may not be comparable between sites, for example due to the use of different clinical
assessment tools or variations in data collection methodology. From the perspective of a health
care researcher, there can be substantial methodological and research ethics barriers to gaining
access to primary data from multiple independent facilities. In contrast, while administrative data
spanning multiple sites are sometimes available (for example, through the Institute for Clinical
and Evaluative Sciences in Ontario), adverse events data are limited and of variable accuracy
when compared to those collected using clinical research methods.186
In Toronto, at the time that this work was undertaken, most acute care hospitals did not have
fully integrated inpatient rehabilitation hospitals. Furthermore, even in cases where such a
relationship did exist, only a proportion of patients from the acute care facility were discharged
to the fully affiliated rehabilitation hospital, while others went to other facilities.
Given the complexity of these factors, it was elected to define the episode of care as the period
between the time of inpatient admission and time of discharge from the acute care hospital. The
59
benefit of this definition is that it allows access to comprehensive, clinical primary source data,
with consistent collection methodology across all patients and across the entire episode of care.
However, it must be recognized that this definition only encompasses a portion of the entire
pathway of the patient’s care from injury to maximal recovery.
3.5 Episode of care cost
3.5.1 Selection of health economic perspective
Considerations in perspective selection
Health economic analyses can be conducted from a range of perspectives. These can range from
narrower perspectives such as that of the patient or hospital, to broader perspectives such as the
health care payor, to the broadest societal perspective.187 It is worth noting that structural and
organizational differences in the delivery of health care between jurisdictions may result in
marked differences in the details of analyses despite adopting the same perspective. For example,
individual patients may commonly also be the dominant health care payors in countries that lack
effective social health care systems. In contrast, in Ontario, the Ministry of Health and Long
Term Care is the exclusive payor for the large majority of health care services.
While a single unifying definition of the term ‘economics’ remains elusive, a large range of
definitions proposed over the past century consistently describe economics in terms of the
quantification and distribution of resources.188 Given that resources are finite, decisions around
their allocation must account not only for the benefits associated with increased allocation for a
particular purpose, but also the opportunity costs associated with their consequent lack of
availability for other uses. Adopting different perspectives changes the pool of resources being
considered for decision-making, potentially changing the conclusions reached. A hospital
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perspective is limited to consideration of the hospital’s resources. Thus, while this may most
closely reflect incentives and factors pertinent to decision-making within the hospital
environment, it risks failing to recognize that any economic benefits gained within the hospital
may be associated with an increased resource burden on entities outside of the hospital itself.
While adopting the broader Ministry of Health perspective will consider resource requirements
within a range of health care settings, it risks failing to recognize shifting of health resource
burden from the payor to individual patients and/or unpaid caregivers. Finally, while the societal
perspective considers the broad range of resources within the whole society, and is least likely to
result in misallocation of resources from the societal perspective, it may not accurately reflect the
incentives present within individual health care delivery organizations.
A societal perspective, by accounting for both financial and non-financial resources within and
outside of the health care sector, is beneficial in considering the overall welfare of a community
or society. Similarly, within a health care system, consideration of the payor perspective
accounts for allocation of resources between organizational units (for example, hospital versus
home care services), reflecting the need to optimally distribute resources across a mix of
different health care environments. However, it must also be recognized that such broad
perspectives may not accurately reflect the factors influencing decision-making at all levels.
Since decision-making around distribution of a hospital budget necessarily concerns services
offered within the hospital, it is unlikely to include substantial consideration of analyses
conducted outside of the hospital milieu.
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Perspective adopted for the present work
With the increasing emphasis on increasing the quality and efficiency of health care services, and
the transition in Ontario toward the use of activity based funding to enhance incentives for
hospitals to achieve this goal, it is important for hospitals to identify promising targets for quality
improvement efforts. Having identified acute hospital care of patients with hip fractures as a
substantial resource burden on the health care system, it was decided to adopt the hospital
perspective for this economic analysis. Decision making around the implementation of care
pathways for patients with hip fractures are made at the hospital level, and thus the benefits of
this approach are the ability to closely reflect the economic incentives around hip fracture care as
perceived by acute care hospitals. Furthermore, as the use of activity based funding is
conceptually intended to strengthen the incentives for individual hospitals to improve the quality
and efficiency of service delivery, the use of the hospital perspective was postulated to more
accurately reflect the economic incentives experienced by these institutions.
Nevertheless, the use of the hospital perspective is associated with a number of shortcomings.
First, it does not account for costs across the whole clinical episode of care. It is likely that
delirium in the acute care setting is associated with differences in health resource requirements
following discharge that are not accounted for in analyses from the hospital perspective.
Consequently, while the results of these analyses will be relevant to decision making within
hospitals, they may incompletely inform decision making at the health care payor level.
Furthermore, delirium may be further associated with a relevant incremental health resource
burden from the societal perspective in terms of both health care needs not funded by the health
care payor, as well as ongoing demand on caregivers.
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3.5.2 Accounting for hospital costs
Costs are commonly defined as the inputs used to produce or provide a product or service. Costs
can be subdivided into direct and indirect costs. The former can be directly attributed to an
individual unit of production or service, whereas indirect costs do not have a clear relationship to
a given unit. Direct costs are typically further broken down into fixed and variable costs, with the
former remaining unchanged irrespective of the magnitude of production or service, while the
latter vary depending on the level of activity. While simple in principle, variations in the
methodology used for determining and allocating costs can result in considerable differences in
the cost determined for a given patient episode of care.
Within the context of hospital costs, different cost object units can be used. Allocating costs to
individual patient-encounters, also known as patient-level case costing, allows for precise
allocation of inputs. In contrast, the use of broader objects such as clinical departments results in
per-patient costs being determined through a process of averaging and indirect allocation,
potentially obscuring some of the variability between patients.
While the allocation of direct costs to a given cost object is generally straightforward, there are a
number of different approaches to allocating indirect costs across cost objects. Traditional
costing methods rely on the allocation of indirect costs across all patients or departments using a
single weighting variable (for example, length of stay or number of patients).189 Depending on
the weighting variable used, this approach has been termed volume-based, ratio of cost-to-
charges, or per-diem costing.190 While relatively straightforward, this approach has been noted to
suffer from the weakness that it does not account for differences in resource consumption.191 For
example, the resource requirements for a relatively healthy patient on a general medical ward are
likely to be very different to one that undergoes a complex surgical procedure and is admitted to
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intensive care, even if overall lengths of stay are similar. Consequently, this approach may result
in an inaccurate understanding in differences in total episode of care costs between patients. An
alternative approach is the use of activity-based costing, where indirect costs are assigned to the
activities that accrue those costs, and separate weighting variables are used for each activity to
assign those costs to individual cost objects (typically patients).189 The advantage of this
approach is that indirect costs can be much more accurately assigned to individual patient
encounters while accounting for differences in resource use between patients. However, it is
important to recognize that activity-based costing can be resource intensive to develop,
implement and manage. Furthermore, costs may not be comparable across organizations if
different methodologies are used to define activities and allocate costs.
3.5.3 Theoretical framework for determinants of episode of care costs
The costs of health care services provided to patients are marked by considerable variability,
both within a given institution and across different hospitals and health systems. Street et al.
proposed that differences in hospital costs can be explained by two categories of variables that
contribute to episode of care costs from the hospital perspective: 1) hospital characteristics, and
2) patient-level variables.192 Hospital characteristics are those that are consistent across all
services offered within a given institution, but may vary between hospitals. These include factors
such as hospital volume, teaching status, and geographic location. Furthermore, differences in
cost accounting methods between hospitals, described in the previous section, can result in
perceived differences in episode of care costs.
3.5.4 Potential determinants of episode of care costs
A systematic scoping review was performed to search the literature and identify potential
determinants of acute care hospital episode of care costs for patients with hip fractures. Original
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studies with full text versions available in the English language, and which assessed potential
determinants of acute care hospital care costs in adult patients with hip fractures, were deemed
eligible. The initial electronic search and record screening was performed concurrently with the
search for determinants of length of stay, described in section 3.3.2. Following systematic
screening and article review as summarized in Figure 7, 41 studies remained in the final review.
Similar factors were grouped together and stratified into the two categories proposed by Street et
al.. The range of potential determinants of episode of care costs identified in the scoping review
and their frequency of appearance are summarized in Table 7, while the full list of studies and
factors studied can be found in Appendix C.
Figure 7: PRISMA flow diagram summarizing search strategy for potential determinants of
episode of care costs
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Table 7: Potential determinants of episode of care costs for patients with hip fractures
Patient-level factors
A range of patient-level potential determinants of episode of care costs were identified. Many of
these are similar to those identified as determinants of length of stay, with in-hospital adverse
events, comorbidities, age and ASA class being among the more commonly reported
determinants for both outcomes. It is worth noting that two studies identified length of stay as a
determinant of episode of care costs.193,194 Many of the patient-level factors identified were
available in the hip fracture data set used for this thesis, thus allowing adjustment for these
potentially important covariates. Several other factors, while not available for the analysis, were
unlikely to have differed to any significant degree between patients. For example, because all
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patients were treated on a single ward at a single hospital with a consistent hip fracture care
pathway, factors such as inpatient ward type and peri-operative medical management strategies
can be expected to have been consistent throughout.
Given that the work for this thesis is based on data from a single institution, there is no
variability in hospital factors to explain potential differences in episode of care costs between
patients. Consequently, the present work was limited to the analysis of patient factors only.
However, possible variation secondary to these factors must be considered with respect to issues
of generalizability of the findings.
3.5.5 Episode of care cost data
Patient level case cost data were obtained for each episode of care from internal hospital
administrative data. These data were collected for submission as part of the province-wide
Ontario Case Costing Initiative (OCCI, http://www.occp.com), which specifies a standardized
system of collection and allocation of hospital costs. These costs include direct costs such as
labour, operating room and ward time, nursing and supplies, and diagnostic testing. It further
includes indirect costs related to facility and administrative overhead. All costs were considered
from the hospital perspective, and did not include surgeon, anesthetist or other consulting
physician billings.
The study hospital used a micro case costing approach for calculating patient level costs, with
direct tracking and costing of every material and labour input used in the treatment of a given
patient.195 This in in contradistinction to alternative estimated costing methodologies that rely on
the averaging of inputs across all hospitalized patients or patient subgroups. The sum of all direct
inputs represents the total direct cost from the hospital perspective for a given episode of care.
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Indirect costs were ascribed to individual patients using activity-based costing as described in
section 3.5.2, with different weighting variables used for each activity or feeder group in an
attempt to most accurately represent the relative resource consumption for a given patient.
Indirect costs represented 27% of the total episode of care costs for the study cohort.
Costs accrued by the hospital for patients admitted during 2011 were inflated to 2012 values
using the Health and Personal Care Consumer Price Index for the province of Ontario.196
3.6 Procedures
3.6.1 Examination of data
Using unique visit numbers, the described data were linked across data sources and extracted to a
single electronic database, which was subsequently anonymized through the use of unique study
IDs.
The available data represented a number of different types of variables. Several variables were
dichotomous, with two different nominal values possible. For each of these, a reference value
was selected and coded as 0 while the alternative value was coded as 1. Fracture type, procedure
performed and treating surgeon were all considered nominal variables with more than two
possible values, while ASA class and highest grade of AE were considered ordinal variables with
more than two possible values. For these, values were coded as sequential integers starting with
1. In cases where regression analyses were performed, dummy coding was used to recode these
values into a series of dichotomous variables suitable for entry into regression models.
Following examination of data for completeness, descriptive statistics were obtained, and
bivariate analyses were performed to assess the outcome measures of interest when stratified by
the presence or absence of delirium.
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3.6.1.1 Statistical analyses
Descriptive statistics were produced for the complete study sample. Given the size of the study
sample (n=242), the use of test statistics that assume normal sampling distribution of the mean
was felt to be appropriate, consistent with the central limit theorem. Bivariate analyses were
performed using the Student’s t test for continuous variables, the Mann-Whitney U test for
ordinal data, and the chi square test for categorical variables.
To assess the postulated benefit of using primary clinical data for the diagnosis of delirium, the
sensitivity and specificity of the DAD-derived diagnosis of delirium was calculated against
delirium as diagnosed using the CAM instrument.
3.6.2 Association between delirium and episode of care costs and length of stay
The association between delirium and both episode of care costs and length of stay was
evaluated using propensity matching techniques in an attempt to control for potentially
confounding variables. Next, a sensitivity analysis was performed in an attempt to ascertain the
susceptibility of the findings to potential hidden biases. Finally, regression techniques were used
to validate the findings through the use of alternative models.
3.6.2.1 Propensity matching
Given the observational nature of the data, and significant differences in baseline characteristics
between the delirium and non-delirium groups, a propensity matched analysis was used to assess
the impact of perioperative delirium (exposure of interest) on incremental length of stay and
episode of care costs from the hospital perspective (outcomes of interest), while controlling for
covariates that potentially predict the likelihood of developing perioperative delirium as well as
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the outcomes. The goal of propensity matching was to balance the delirium and non-delirium
groups with respect to these covariates.
Propensity matching was selected as a method for controlling for potential confounding because
of the ability to balance the two groups based on multiple covariates concurrently. This is in
contrast to strategies that match pairs for analysis based on individual covariates in series. The
latter is likely to result in many observations being discarded due to a failure to identify an
appropriate match on all matching variables. However, it is important to recognize that the
ability to balance the matched pairs using propensity matching is limited by the variables
available for matching. Consequently, the groups may remain unbalanced in terms of one or
more unobserved covariates that influence both the primary predictor variable as well as the
outcomes of interest, leaving the analysis susceptible to hidden bias.
Based on the work of Austin et al.,197 all propensity matching variables that were not influenced
by the presence of the exposure, and that could either influence the exposure and outcome, or
outcome alone, were included. The selection of these variables was informed by the literature
review concerning risk factors for delirium and the outcomes of interest as described in section
2.5.1. To produce propensity scores, a binary logistic regression model was constructed, with
perioperative delirium as the dependent variable, and the following as independent variables that
have been previously reported to predict either exposure and outcome, or outcome alone: age,
gender, number of comorbidities, ASA class, and whether patients were living in a nursing home
or other skilled care facility prior to admission.8,198-201 Additionally, year of admission was
included in the model as an indicator variable. The results of the binary logistic regression model
can be found in Appendix D. This model was used to calculate a propensity for delirium score
for each episode of care. Examination of the distribution of propensity scores confirmed the
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success of the approach as there was a trend for higher scores among patients who did develop
perioperative delirium compared to those who did not, while retaining sufficient overlap to
ensure common support for matching (Figure 8). Patients who experienced perioperative
delirium were subsequently matched based on propensity scores to those who did not experience
delirium on a one-to-one basis using Stata software and the psmatch2 routine,202 with
replacement. To maximize matching pairs while minimizing error, a maximum caliper width of
0.048 was used for matching, equivalent to one fifth of the standard deviation of the propensity
scores as recommended by Austin.203 The final analytical sample consisted of 114 cases with
perioperative delirium matched to 114 controls. Two patients with delirium were excluded
because of an inability to find a match within the caliper threshold, and 96% of pairs (110 of
114) had a caliper width of less than 0.02 (ie. difference in matched propensity scores of 0.02 of
a maximum of 1).
3.6.2.1.1 Statistical analysis
Following propensity matching, bivariate analyses were performed for the matched sample to
confirm adequate balancing of covariates between matched pairs. The paired t test was used for
continuous variables, Wilcoxon signed rank test for ordinal data, and chi square test for
categorical variables. A p value of less than 0.05 was considered as the threshold for significance
for all tests. Values for the outcome variables of interest (length of stay, episode of care costs)
were compared between matched pairs using the paired t test, and 95% confidence intervals for
the differences were obtained.
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Figure 8: Comparison of propensity scores between patients who did and did not experience
delirium
3.6.2.2 Sensitivity analysis
To examine the susceptibility of the findings to potential hidden biases, a sensitivity analysis was
performed on the propensity matched cohort by estimating the minimum magnitude of
association between a theoretical unobserved binary covariate and the likelihood of experiencing
delirium that would be necessary to explain any observed differences in the outcome variables,
as described by Rosenbaum.204 To do this, the Wilcoxon test statistic (equal to the sum of the
absolute differences for those pairs where the outcome was greater for those patients who were
positive for delirium) was calculated. This statistic, together with the number of matched pairs,
was used to evaluate the change in p value associated with a range of magnitudes of association
between the theoretical unobserved covariate and the likelihood of developing delirium.
Calculations were performed using a spreadsheet-based tool developed by Love,205 based on the
work of Rosenbaum.204 The advantage of this approach is that it allows for quantification of the
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susceptibility of the analysis to biases resulting from potentially associated variables that were
not accounted for in the development of the propensity score.
3.6.3 Alternative estimations
3.6.3.1 Model selection
Visual examination of data plots revealed both length of stay and total episode of care costs to be
right-skewed (Figure 9 and Figure 10), making them unsuitable for untransformed linear
regression using ordinary least squares estimation given the underlying assumption of normal
distribution of the dependent variable. This was confirmed on quantitative analysis, with
coefficients of skewness for total length of stay and episode of care costs of 4.1 and 2.9, and
coefficients of kurtosis for total length of stay and episode of care costs of 23.9 and 12.5,
respectively. These differed markedly from the skewness and kurtosis coefficients of zero and
three, respectively, associated with a normal distribution. Non-normal distribution was further
confirmed using the Shapiro-Wilk test of normality,206 which rejected the null hypothesis that the
samples of total length of stay and episode of care costs came from normally-distributed
populations with a high degree of certainty (p values of <0.00001 for both variables).
Given the frequent use of logarithmic transformation of skewed variables in the health economic
literature,207 the distributions of log-transformed length of stay and episode of case costs were
evaluated. However, these remained skewed and non-normal. Thus, ordinary least squares
regression methods were judged to be less appropriate for the proposed analyses when compared
to generalized linear models (GLM) that would allow direct estimation of the conditional mean.
Furthermore, the use of GLM would avoid retransformation issues associated with the use of a
logged dependent variable.207
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Figure 9: Histogram depicting distribution of observed values for total length of hospital stay
Figure 10: Histogram depicting distribution of observed values for total episode of care costs
74
Given the right-skewed distribution of the length of stay count data, both Poisson and negative
binomial regression forms were considered. On examination of variable data, over dispersion of
the observed values was seen as illustrated by the large variance (263.06) when compared to the
mean (14.67), violating the assumptions of the Poisson distribution. Given these findings, and
the fact that the values for length of stay were all positive and non-zero, a zero-truncated
negative binomial regression form was selected for this variable. Following selection of the final
model, the likelihood-ratio test revealed alpha to be significantly different from zero (0.4112;
p<0.001), confirming over dispersion.
Similar to the length of stay data, quantitative evaluation of the observed values for total episode
of care cost revealed considerable over dispersion (variance = 3.64 x108, mean = 21810.33),
violating the assumptions of the Poisson distribution. Multiple authors have reported favorable
properties of GLM using either negative binomial or gamma distributions with a logarithmic link
function for estimation of distribution of costs,208,209 and thus both of these models were
evaluated and findings from both were compared. While similar findings were obtained using
both models, marginally lower deviance was observed using the negative binomial distribution
(52.122 vs 52.124), and thus this model was preferred for the final evaluation.
3.6.3.2 Covariate selection
The selection of covariates for regression modeling was informed by both the literature review
and conceptual frameworks of contributors to the outcomes of interest, specifically length of stay
and episode of care costs from the hospital perspective. Given that regression analyses function
under the assumption that independent variables are not collinear, correlations between
covariates were assessed with findings used to further inform variable selection. Given that the
available covariates included a number of non-normally distributed continuous variables, as well
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as ordinal and binary variables, correlations between these variables were assessed using
Spearman’s rank correlation.210 This was selected over Pearson’s correlation due to its greater
robustness to non-normally distributed data, as well as ordinal data, both of which were present
in the dataset. Categorical variables were assessed using Cramer’s V. Results of correlation
testing for covariates can be found in Appendix E.
.While the strength of association between two variables increases as the value of the correlation
coefficient moves away from 0 (toward either -1 or 1), there is disagreement on what values
indicate a problematic degree of correlation. Authors commonly stratify correlation coefficient
values into three categories: weak, moderate, and strong. Malgady and Krebs suggested
reference values of 0.8, 0.6 and 0.2 for strong, moderate and weak correlations,211 while Dancey
and Reidy proposed ranges of 0.7-0.9, 0.4-0.6, and 0.1-0.3, respectively.212 While a firm
threshold for excessive correlation is elusive, groups of variables demonstrating moderate or
strong correlation were considered for exclusion to reduce the likelihood of subsequent issues
with multicollinearity or over specification of the model.
Moderate to strong correlation coefficients (0.40 or greater) were identified for fracture type and
procedure performed (Cramer’s V = 0.6378), and treating surgeon and procedure performed
(Cramer’s V = 0.4051). Additionally, as would be expected, high correlations were seen between
number of hours between triage and surgery and between admission and surgery (Spearman’s ρ
= 0.9807), as well as between length of stay and episode of care cost outcome variables
(Spearman’s ρ > 0.85 in all cases).
Multiple models were specified to avoid moderately or highly correlated covariates and
compared using the Akaike information criterion (AIC) and the Bayesian information criterion
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(BIC), which are both relative measures of model quality that consider both model fit and the
number of included covariates.213,214 Smaller values are better in this regard. As suggested by
Kass, differences in BIC or AIC of less than 2 were considered weak evidence for preference of
one model over the other,215 in which case the model with the lower number of parameters was
preferred. The final models for total length of stay and length of stay following surgery
minimized both AIC and BIC, and included procedure type over fracture type and treating
surgeon. The final model for episode of care costs minimized BIC, while AIC was within 0.10 of
AICmin. This was preferred over a strategy of minimizing AIC, which was associated with a BIC
that exceeded BICmin by 73. Time between admission and surgery was chosen over time between
triage and surgery given no substantive difference in AIC (<0.01) or BIC (0.15), and the fact that
the former was judged to be conceptually more consistent with the definition of the episode of
care adopted for the present work. AIC and BIC values for the models considered can be found
in Appendix F. Evaluation of covariates included in the final models for collinearity revealed
weak correlation in all cases, with Spearman’s rho coefficient values of less than 0.40.
3.6.3.3 Statistical methods
The associations between the dependent variables of interest (length of stay and cost) and
perioperative delirium as well as the other identified covariates were determined using the
models described above. P values of < 0.05 were considered the threshold for significance. The
obtained coefficients were exponentiated to obtain the incident rate ratios (equivalent to the
length of stay ratio and cost ratio for regressions of length of stay and cost, respectively) as well
as their 95% confidence intervals. The exponentiated values for the key predictor of interest,
combined with the mean values of the dependent variables in patients who did not experience
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perioperative delirium, were used to compute the incremental length of stay and episode of care
costs associated with delirium.
3.6.4 Evaluation of degree to which differences in episode of care costs are explained by length of stay
A subsequent regression model with episode of care cost as the dependent variable was assessed
using GLM, this time incorporating total length of stay, to assess the change in the magnitude of
the influence of perioperative delirium on episode of care costs, and to document the
independent influence of delirium on episode of care costs. Evaluation of collinearity revealed
high correlation (ρ=0.8544) between cost and length of stay following surgery. However,
correlation between independent variables did not exceed 0.35. Consequently, it was not
necessary to exclude any variables as a consequence of high multicollinearity.
3.7 Sample size
There is a paucity of available published or unpublished data concerning the potential differences
in episode of care costs on which to base a sample size calculation. Thus, the sample size
estimation is based on a minimum standardized effect size (Cohen’s d) of 0.3, which was
described by Cohen as the upper bound of a small effect size.216 Assuming a two-sided paired t-
test with alpha of 0.05 and power of 0.8, a minimum of 87 paired samples were required to
detect a small clinically important difference for the primary objectives (1 and 2) of this study
based on the standardized effect size definitions of Cohen.216 This study achieved the desired
sample size, being based on 114 paired samples.
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3.8 Ethical considerations
Research Ethics Board approval was sought and granted from the study institution, as well as
from the University of Toronto. Copies of the approvals can be found in Appendix A and
Appendix B.
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Chapter 4: Results
4.1 Descriptive statistics
A total of 242 patients were included, representing 242 unique acute care hospital episodes of
care. Two patients experienced a second admission to hospital over the study period for a
diagnosis of a contralateral hip fracture. The second admission was excluded from the analysis.
The study cohort included 74 men and 168 women (30.6% and 69.4%, respectively) with a mean
age of 82 years (range, 65 to 103 years). The gender distribution is consistent with the published
literature, which similarly demonstrates higher rates of hip fractures among women.
Demographic characteristics are presented in Table 8.
Examination of the remaining baseline demographic variables confirms wide variability in
patients’ pre admission living situation, with at least 30 patients for each of the primary stable
living situations (independent at home, home with assistance, nursing home). Two patients were
admitted from a rehabilitation facility.
There was a broad range of fracture types represented, with intertrochanteric patterns being most
common (44%), followed by femoral neck fractures (29%). While only 2 patients had a reverse
obliquity fracture (2% of all trochanteric fractures), this is a relatively uncommon pattern, with
Ozkan reporting that as few as 5% of all trochanteric hip fractures have this particular pattern.217
In contrast, basicervical fractures may be more common in the general population than the 0.8%
reported in this cohort. However, these fractures represent an intermediate stage between the
more commonly described femoral neck and intertrochanteric fractures, sharing characteristics of
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both.218 Consequently, it is likely that some fractures occurring in this location were classified in
either femoral neck or intertrochanteric categories.
Table 8: Baseline characteristics of study population
Overall, 116 (48%) patients experienced delirium during their acute care hospital stay as
measured using the CAM tool. This is consistent with previous studies that diagnosed delirium in
patients with hip fractures using this instrument, which had reported rates of between 40 and
53%. In contrast, only 33 (14%) patients were identified as having experienced delirium during
their hospital stay using the administrative hospital DAD. When compared to diagnosis of
delirium using the CAM tool, the DAD had a sensitivity of 22.4% and specificity of 99.2%. This
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is consistent with reported findings in the cardiac intensive care population at a single hospital in
Ontario that identified a sensitivity and specificity of 18.1% and 99%, respectively,162 and
confirms the benefit of electing to use primary clinical data for the diagnosis of delirium in the
present work.
There was considerable variability in both length of stay and episode of care cost values. The
mean total hospital length of stay was 14.7 days, and mean post-operative length of stay was 13
days. However, maximal values for these variables were 137 and 135 days, respectively. As can
be seen in Figure 9 length of stay values were considerably right-skewed, with the majority of
values clustered toward the lower end of the range. Similar distribution was seen for episode of
care cost data, with a mean value of $21,810, but ranging as high as $122,246. These findings are
consistent with the known behaviors of hospital length of stay and episode of care cost data,219,220
highlighting the fact that a relatively small proportion of hospital patients experience
disproportionately long lengths of stay and high care costs.
4.2 Unadjusted findings
When stratified by presence or absence of delirium, examination of histogram plots reveals a
trend toward higher total length of stay (Figure 11), length of stay following surgery (Figure 12),
and total episode of care costs (Figure 13) in those patients who experienced delirium. This was
confirmed on statistical analysis, which revealed that patients who experienced delirium had
significantly greater mean overall length of stay (18.5 vs. 11.2 days; p<0.001), mean post-
operative length of stay (16.6 vs 9.7 days; p<0.001), and total episode of care costs ($26,272 vs
$17,703; p<0.001) (Table 9).
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Figure 11: Comparison of distribution of total length of stay between patients who did and did
not experience delirium
Figure 12: Comparison of distribution of length of stay following surgery between patients who
did and did not experience delirium
83
Figure 13: Comparison of distribution of episode of care costs between patients who did and did
not experience delirium
Bivariate analyses between a range of covariates and presence or absence of delirium revealed
significant differences in a number of patient characteristics that could potentially be associated
with differences in the outcome variables of interest. These are summarized in Table 8.
Specifically, patients who experienced perioperative delirium were significantly older than those
who did not (mean age 85 years vs 80 years; p<0.001). They also had significantly higher ASA
scores (ASA III+: 92% vs 82%; p=0.019), and trended toward experiencing more severe
perioperative adverse events (p=0.052). Furthermore, there were significant differences
identified in the distribution of the pre-admission living situation (p<0.001), with a marked
difference in proportion of patients who were admitted from a nursing home or other skilled
nursing facility (28% of those who experienced delirium versus 3% of those who did not).
Overall, these findings suggest that the patients who experienced delirium were in poorer health
with a greater comorbidity burden at baseline when compared to those who did not. This is
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consistent with the risk factors for delirium described in the literature and reviewed in Section
2.5.
Table 9: In hospital characteristics of study population
Bivariate analysis of in-hospital characteristic variables demonstrated numerically greater mean
length of time between admission and surgery for those patients who experienced delirium
(difference in mean time: 7.1 hours). However, this difference was not statistically significant
(p=0.195). Given the reports in the literature suggesting associations between greater delay to
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surgery and development of perioperative delirium, this is an important potential confounder. No
significant differences were seen between other surgical care covariates available in the dataset
as summarized in Table 9, specifically no significant difference was seen in the distribution of
either type of hip fracture (p=0.358), laterality of the injury (p=0.764), type of anesthesia
(p=0.935), procedure performed (p=0.168), or duration of surgery (p=0.954).
4.3 Propensity matching results
Bivariate analysis of the propensity matched cohort revealed adequate balancing of known and
available baseline covariates between groups, as summarized in Table 10. This confirmed that
the propensity matching algorithm successfully balanced the baseline covariates that were
deemed a priori to potentially be associated with both the independent variable (delirium) and the
outcomes of interest, or the outcomes of interest only. Additionally, it is worth noting that the
matched cohort was also successfully balanced with respect to covariates that were not
specifically included in the propensity score, such as fracture type, anesthetic type, and duration
of surgery.
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Table 10: Comparison of baseline characteristics of propensity matched cohort
Length of stay
Following propensity matching to control for potential confounders, perioperative delirium was
found to be associated with significant mean incremental length of stay as presented in Table 11.
Specifically, delirium was associated with an incremental increase in mean total length of stay of
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7.4 days (95% CI 3.7 to 11.2 days; length of stay ratio: 1.65; p<0.001), and an incremental
increase in post-operative length of stay of 7.4 days (95% CI 3.8 to 11.1 days; length of stay
ratio: 1.80; p<0.001). When multiplied across the 116 patients who experienced delirium in the
study sample, this represents a total of 858 (95% CI 429 to 1300) incremental bed-days of acute
care hospital stay attributable to perioperative delirium at the study institution over the two years
encompassed by the data set, or 429 bed-days on an annualized basis.
These findings confirm that perioperative delirium is associated with substantial incremental
length of stay. The 242 patients in the study represented a total of 3,551 bed-days of acute care
hospital stay. Consequently, the incremental length of stay associated with delirium accounted
for 24% (95% CI 12% to 37%) of the total number of bed-days of acute care hospital stay
attributed to the care of patients with fragility hip fractures.
Table 11: Results of propensity matched analyses for primary outcome measures
Episode of care costs
On analysis of the propensity matched cohort, perioperative delirium was associated with
significant mean incremental episode of care costs from the hospital perspective of $8,286 (95%
CI $3,690 to $12,881; cost ratio: 1.46; p<0.001). This represents a total incremental cost of
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$961,131 (95% CI $428,040 to $1,494,196) attributable to perioperative delirium at the study
hospital, or $480,565 on an annualized basis.
The total expenditure from the hospital perspective attributable to the inpatient care of the study
patients was $5,278,099 in inflation-adjusted 2012 dollars. Thus, the incremental episode of care
costs associated with delirium represented 18% (95% CI 8% to 28%) of the portion of the
hospital budget allocated to the care of patients with fragility hip fractures.
The findings for both incremental length of stay and episode of care costs must, however, be
interpreted in the context of the relatively wide confidence intervals seen for the outcomes of
interest. There are several potential reasons for the degree of imprecision in these results. While
the analysis was conducted on a sample of 114 matched pairs, this is nevertheless a relatively
small cohort of patients, limiting the precision of the estimate for the mean incremental length of
stay. Furthermore, a wide range of lengths of stay (3 to 137 days) and episode of care costs
($5,114 to $122,246) was observed across the study cohort. Although the distributions of both
variables were right-skewed with most observations clustered in the lower end of recorded
values, the high range will have increased the standard deviations of the means and consequently
increased the imprecision of the estimates obtained. This is a well-recognized consequence of the
presence of extreme values (outliers) in datasets. Some authors have advocated strategies for
excluding these values, thereby increasing the precision of the obtained estimates. However,
excluding outliers would risk failing to account for the true variability in length of stay and
episode of care costs in the study cohort, resulting in more precise estimates but ones that would
be less generalizable to the range of patients admitted to hospital with a fragility hip fracture.
Nevertheless, even the lower limit of the 95% CIs represent 12% of the total number of bed-days
and 8% of the total cost attributable to the care of patients with fragility hip fractures. In both
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cases, even these low estimates represent a substantial proportion of the resources used for the
care of this patient population, confirming the marked health resource impact from the acute care
hospital perspective of this AE.
Other covariates of interest
No statistically significant differences between delirium groups were found for a number of
covariates that could be expected to directly influence either length of stay or episode of care
costs. Specifically, there were no differences in the mean time between admission and surgery,
or in the mean duration of the surgical procedure (Table 12). However, a significantly greater
proportion of patients who experienced perioperative delirium required new long-term care
and/or skilled nursing care (LTC) facility admission following the acute care hospital stay (8%
vs 0%; p=0.002). This difference is notable from both clinical and health services perspectives.
Clinically, the need for new LTC admission indicates that these patients were unlikely to regain
sufficient independence to allow them to return to their pre-injury residence. There are several
possible reasons for this. It has been reported that patients requiring a higher level of care
following this injury are more likely to have had a lower pre-admission level of independence.
Consequently, it is possible that some of these patients were close to or at the threshold of
dependence that would require transitioning to a LTC environment, even prior to their injury.
However, delirium has been reported to be associated with increased likelihood of new onset of
dementia, as well as decreased long-term functional recovery. Both of these sequelae would be
expected to increase the likelihood that a patient would be unable to return to their pre-admission
residence, and thus be more likely to require new LTC bed admission.
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Table 12: Comparison of surgical factors between propensity-matched pairs
4.3.1 Sensitivity analysis
The sensitivity analysis revealed that the findings of the propensity matched analysis were
reasonably sensitive to the effects of a theoretically unobserved covariate. The minimum
magnitudes of association between a theoretically unobserved covariate and the likelihood of
developing delirium necessary to affect our findings were: 1.64 for total length of stay, 1.91 for
length of stay following surgery, and 1.87 for episode of care cost. Thus, to attribute the
difference in total length of stay to an unobserved binary covariate unrelated to our propensity
model rather than the effect of delirium, that covariate would need to increase the odds of
experiencing delirium by a factor of at least 1.64 while being an excellent predictor of total
length of stay. Similarly, it would need to increase the odds of experiencing delirium by a factor
of at least 1.91 and be an excellent predictor of length of stay following surgery, and increase the
odds of experiencing delirium by a factor of at least 1.87 and be an excellent predictor of episode
of care cost. Interpreting the magnitudes of these associations can be challenging in the absence
of comparative values. As a comparison, in the logistic regression model used to develop the
propensity score, each additional year in patient age was associated with an odds ratio for
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developing delirium of 1.09, whereas being admitted from long term care or other skilled nursing
facility was associated with an odds ratio for developing delirium of 10.03. However, it must
also be noted that for a hidden covariate to affect the findings of the analysis as quantified in the
sensitivity analysis, it must also be a near perfect predictor of the outcome of interest. As the
strength of association between the unobserved covariate and outcome weakens, the minimum
magnitude of association with delirium must increase to continue to significantly account for the
results observed in the propensity matched analysis.
4.4 Estimation using alternative models
In order to assess whether the findings of the propensity matched cohort analysis were robust to
the use of alternative estimation techniques, regression analysis was performed in order to
evaluate the influence of perioperative delirium on the health resource outcome variables of
interest.
4.4.1 Overall length of stay
The specified negative binomial regression model for total length of stay found perioperative
delirium to be a significant predictor, being associated with a length of stay ratio of 1.72 (95% CI
1.45 to 2.06; p<0.001) as seen in Table 13 . When marginal means stratified by the presence or
absence of delirium were compared, with all other covariates held constant, the incremental
length of stay associated with perioperative delirium was 7.8 days (95% CI 4.3 to 11.3 days).
This represents a predicted total of 452 bed-days on an annualized basis attributable to
perioperative delirium in patients with fragility hip fractures at the study hospital. This is within
10% of the estimate for incremental length of stay derived from the propensity matched analysis
(7.4 days) and similar high strength of association in both cases as indicated by p values of less
than 0.001, indicating generalized agreement in findings between the two approaches.
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Table 13: Determinants of overall length of stay
Several additional covariates were found to be significant predictors of increased length of stay.
These included: increasing number of hours between admission and surgery, the presence of an
in-hospital AE other than delirium, and an increasing number of baseline comorbidities. These
are all consistent with expected relationships between these variables. Assuming similar post-
operative duration of hospitalization, one would expect patients who wait longer between
admission and surgery to have a longer overall length of stay. Similarly, patients who experience
an in-hospital adverse event, and those who are more medically complex (as evidenced by a
greater number of comorbidities), would also reasonably be expected to spend a greater period of
time in hospital.
In contrast, some covariates were found to be significant predictors of decreased length of stay.
Admission from LTC was found to be a significant independent predictor of decreased length of
stay (length of stay ratio: 0.48). Patients who reside in LTC facilities have limited independence
at baseline, and consequently have minimal rehabilitation goals in terms of increasing
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independent functioning. As a result, they are generally discharged back to their pre-admission
residence once medically stable without the need for in-hospital rehabilitation or having to await
availability of a rehabilitation or new LTC bed. This finding is interesting because although
patients from LTC are often thought of as more complex and representing a greater overall
health care burden, the resource impact of their care from the hospital perspective is significantly
lower in terms of length of stay. Additionally, admission in the second year of the study (2012 vs
2011) was found to be a significant independent predictor of decreased length of stay, which may
be explained by efficiency improvements in patient care and/or patient discharge over the study
period.
4.4.2 Post-operative length of stay
The specified model for post-operative length of stay similarly found perioperative delirium to
be a significant predictor, with an associated length of stay ratio of 1.87 (95% CI 1.53 to 2.28;
p<0.001) (Table 14). Comparison of marginal means for the model stratified by presence or
absence of delirium, with all other variables held constant, revealed an incremental post-
operative length of stay of 7.9 days (95% CI 4.4 to 11.4 days) associated with the presence of
perioperative delirium. This is within half a day (less than 10% difference) of the estimates
derived from the propensity matched analysis, indicating concordance between these analytic
approaches.
The associations between other covariates and post-operative length of stay were generally
similar to those seen in the model for overall length of stay, with positive associations seen for
the presence of adverse events other than delirium and increasing number of baseline
comorbidities. Similarly, negative associations were seen for pre-admission LTC residence, and
later year of admission. Time between admission and surgery was not a significant predictor of
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post-operative length of stay, confirming that the influence of this variable on overall length of
stay was limited to the wait for surgery.
Table 14: Determinants of post-operative length of stay
4.4.3 Episode of care costs
The GLM for episode of care costs identified perioperative delirium to be significantly
predictive, being associated with a cost ratio of 1.51 (95% CI 1.30 to 1.74; p<0.001) (Table 15).
Examination of marginal means revealed an incremental episode of care cost of $8,987 (95% CI
$5,311 to $13,100) associated with the presence of perioperative delirium when all other
variables were held constant. This is equivalent to a predicted total of $521,246 on an annualized
basis attributable to perioperative delirium in patients with fragility hip fractures at the study
hospital. The estimated incremental episode of care costs with this analysis were within 10% of
the estimate derived from the propensity matched analysis, and demonstrates a similarly high
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strength of association (p<0.001 in both cases), indicating generalized agreement between the
two analytic methods.
Table 15: Coefficients for model of episode of care costs
Additional variables found to be significant predictors of episode of care costs included: pre-
admission residence, number of comorbidities, presence of in-hospital adverse event other than
delirium, surgical procedure, ASA class and year of admission. Compared to patients who
underwent a hip hemiarthroplasty, fixation with an intramedullary nail was independently
associated with increased episode of care costs, whereas sliding hip screw fixation was
independently associated with lower episode of care costs. Potential explanations for this include
differences in pre-operative workup (for example, higher likelihood of needing cross-sectional
imaging), operating room resources (including different types of implants) required for their
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care, as well as differences in in-hospital rehabilitation trajectories. Similarly, significantly
greater episode of care costs were seen with presence of additional adverse events and some
ASA classes when compared to reference values. It is worth noting that for all three of these
categorical variables, confidence intervals were quite wide. The alternative GLM considered,
using gamma log link, yielded near-identical findings for the dependent variable in terms of both
incremental cost ratio and 95% confidence intervals (Appendix G).
4.4.4 Model diagnostics
Graphical assessment of distribution of length of stay and episode of care data confirmed the
presence of markedly right-skewed distributions for all three variables without any zero or
negative values. Additionally, length of stay data exhibited discrete counts, consistent with
poisson or negative binomial distribution. Cost data were considered to be continuous data, and
both negative binomial and gamma distributions were considered during model development.
The rationale and justifications for selecting the negative binomial distribution for all three
variables have been previously described in 3.6.3.1, while the approaches to covariate selection,
including optimizing model performance based on included covariates using the AIC and BIC
statistics, have been described in section 3.6.3.2.
Total length of stay
On assessment of the final model, the likelihood ratio chi square statistic for the final negative
binomial model as compared to the null model was 138.43 with a p value of <0.00001. Thus, the
null model (with all regression coefficients equal to zero) is rejected, indicating predictive value
for one or more of the included coefficients. While a number of R squared statistics have been
described for negative binomial models (collectively termed pseudo R squared statistics), none
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display the favorable properties of the R squared statistic in OLS in describing the proportion of
variability explained by the model, and there is considerable controversy around their use.
Consequently, R squared statistics were not used for model diagnostics. The likelihood ratio test
of the alpha dispersion parameter being equal to zero revealed a chi square value of 919.28 with
a p value of <0.0001. Thus, the null hypothesis is rejected, indicating overdispersion of the
dependent variable and confirming the desirability of the negative binomial model over the
Poisson distribution.
Figure 14: Plot of predicted versus observed values for total length of stay
Graphical evaluation of the plot of observed versus predicted total length of stay with
superimposed trend line of slope one and intercept of zero demonstrated a subset of observations
where the actual length of stay was considerably greater than the predicted value (Figure 14).
This is consistent with the heavy right-tailed distribution of the observed values, and is consistent
with distributions seen in studies assessing modeling strategies for in-hospital length of stay.221
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Length of stay following surgery
The likelihood ratio statistic comparing the final model to the null model was 129.81 with a p
value of <0.00001, thus rejecting the null model and confirming the predictive value of one or
more of the included covariates in terms of the length of stay following surgery. Similar to the
analysis of total length of stay, the null hypothesis that alpha dispersion parameter is equal to
zero was rejected based on likelihood ratio chi square statistic of 1013.87 and associated p value
of <0.0001. This confirmed overdispersion of the dependent variable, and the lower desirability
of the Poisson as compared to negative binomial distribution.
Figure 15: Plot of predicted versus actual values for length of stay following surgery
Once again, graphical comparison of the observed versus predicted values for post-operative
length of stay demonstrated a subset of observations where actual length of stay following
surgery substantially exceeded the predicted value (Figure 15), consistent with the heavy right-
skewed distribution of the observed data.
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Episode of care costs
Based on a likelihood ratio statistic with chi square of 147.01 and p value of <0.00001, the null
model was rejected in favor of the specified model. The null hypothesis that the alpha dispersion
parameter equals zero was rejected based on the likelihood ratio test chi square of 1.6x106 with p
value of <0.0001, thus supporting selection of the negative binomial distribution over a Poisson
model. Examination of the properties of the episode of care cost data further confirmed
substantial over dispersion, with a mean of 21,810 and variance of 3.63x108.
Figure 16: Plot of predicted versus observed values for total episode of care costs
Similar to the findings for length of stay data, examination of the scatterplot of observed versus
predicted episode of care costs demonstrated a subset of observations with actual values that
substantially exceeded the predicted values (Figure 16). A consequence of the heavy right-tailed
distribution of the episode of care cost data, the presence of these outliers is consistent with the
known behaviour of hospital cost data.208
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4.5 Assessment of the degree to which length of stay explains differences in episode of care costs associated with delirium
When length of stay following surgery was included in the negative binomial regression for
episode of care costs, this variable was found to be significantly predictive (cost ratio: 1.028 per
day; 95% CI 1.025 to 1.032; p<0.001) of episode of care cost as seen in Table 16. Despite a
decrease in magnitude of association, perioperative delirium remained a significant predictor of
episode of care costs independent of hospital length of stay (cost ratio: 1.104; 95% CI 1.012 to
1.204; p=0.026). When considered in the context of the model of episode of care cost that did not
include length of stay as a covariate (cost ratio: 1.51; 95% CI 1.30 to 1.74; p<0.001), this
suggests that that the influence of perioperative delirium on incremental episode of care costs is
both as a result of increased duration of acute care hospital stay, as well as due to a higher
intensity of in-hospital care independent of the duration of stay.
When compared to the model that did not include length of stay, the present model demonstrated
improved fit with lower AIC (4708.44 vs 4966.95) and BIC (4770.71 vs 5025.76) statistics,
indicating additional predictive benefit with the addition of total length of stay as a covariate.
Visual comparison of the plot of predicted versus actual episode of care costs demonstrates
tighter clustering of points around the superimposed trend line of slope 1 and intercept zero,
further suggesting improved fit of the episode of care cost model that includes length of stay as a
covariate (Figure 17).
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Figure 17: Plot of observed versus predicted values for episode of care cost for the model that
includes length of stay as a predictor variable
Several other interesting relationships are suggested when the findings with this model are
compared to the previous results. With the addition of length of stay as a covariate, both pre-
admission LTC residence and year of admission no longer demonstrate independent relationships
to episode of care costs. In terms of pre-admission residence, this finding further supports the
postulate that the association between pre-admission LTC residence and decreased episode of
care costs is a consequence of a faster discharge from hospital (thus shorter length of acute care
hospital stay) for this patient population. Similarly, the lack of independent association between
year of admission suggests that the decreased episode of care costs associated with later year of
admission are likely due to efficiencies realized in terms of earlier discharge from hospital, rather
than decreased intensity of care.
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The addition of length of stay to the model additionally revealed significant associations between
both gender and duration of surgery and episode of care costs not present in the previous model.
Duration of surgery was associated with a cost ratio of 1.20 per hour, and female gender was
associated with a cost ratio of 0.89. Erroneous findings in the context of increased significance of
one or more covariates following addition of a new independent variable have been described in
terms of addition of a suppressor variable, which is correlated with an independent variable but
not the dependent variable222. However, this is not applicable to the present model, given the
strong associations between length of stay and episode of care costs (spearman’s rho = 0.8857).
Similarly, this finding cannot be explained by multicollinearity, given the near-zero correlations
between total length of stay and both gender (Spearman’s rho = -0.0648) and length of surgery
(Spearman’s rho = 0.0151). Thus in this case, the addition of length of stay has likely unmasked
associations not previously evident, both of which are consistent with both conceptual
understanding of drivers of episode of care costs, and published literature. Operating room
resources are known to be among the more costly elements of hospital care, and it follows that
greater duration of surgery would be associated with increased episode of care costs. Similarly,
authors have reported gender disparities in hospital costs for a number of conditions. Overall, the
mean cost per acute care hospital stay in Canada has been reported to be higher for men as
compared to women. However, variability is seen depending on diagnostic grouping.223 While
mean cost per stay for primary musculoskeletal and connective tissue diagnoses were higher in
women as compared to men, it is similarly reasonable to expect that this relationship may differ
based on specific diagnosis.
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Table 16: Coefficients for model of episode of care costs with length of stay included as
predictor
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Chapter 5: Discussion
The objective of this thesis was to determine whether perioperative delirium in patients
undergoing surgery for a fragility hip fracture was associated with increased health resource
burden from the hospital perspective. Specifically, the goal was to examine associations between
perioperative delirium and both acute care hospital length of stay and episode of care costs from
the hospital perspective, as well as to determine to what degree any potential differences in
episode of care costs were determined by differences in length of stay. While it has been
previously demonstrated in the literature that perioperative delirium is a common AE in patients
who experience a fragility hip fracture, and that this is associated with a range of undesirable
clinical outcomes, there was a paucity of data concerning the health economic implications of
delirium in this population.
5.1 Findings with respect to the objectives
The overall rate of delirium in this study cohort was 48%, which was generally consistent with
the 40 to 53% range previously reported in the literature with the use of the same diagnostic
instrument for delirium in the general elderly hospitalized and outpatient population, as
described in section 2.5.1.80-82 However, there is considerable variability in the overall rates of
delirium reported in the literature for patients with fragility hip fractures, ranging from 4% to
62%.6,7,25 Some of this variability can be explained by the use of different assessment tools with
varying diagnostic criteria and sensitivity and specificity, as well as variation in the frequency
and time period of assessment. The strength of the present work is the use of the CAM tool,
which has been demonstrated to have high sensitivity, specificity and reliability, while being
quick and easy to use.79 An additional strength is that this instrument was already in use and
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documented prospectively as a routine part of clinical care by nursing staff at the study hospital.
Comparative assessment of the use of ICD-10 codes as compared to the CAM tool for the cohort
of patients in the present study was performed as described in section 4.1. This revealed poor
sensitivity of the administrative data for detecting delirium, with a value of 22.4% when
compared to diagnosis using the CAM tool, thus justifying the use of primary clinical data for
this work. Furthermore, it is consistent with previously reported findings in the intensive care
population.162
5.1.1 Length of stay
Quantifying the difference in acute care hospital length of stay (LOS) between patients who
do and do not experience perioperative delirium
The presence of perioperative delirium was found to be associated with significantly greater
mean length of acute care hospital stay in all analyses. Furthermore, the magnitude of the
association was consistent across different analytic approaches, with incremental mean total
length of stay estimates ranging from 7.4 to 7.8 days and length of stay ratios ranging from 1.66
to 1.72. High strength of association was seen in all analyses. These associations were consistent
whether analyses were conducted with respect to total acute care hospital length of stay, or were
limited to length of stay in hospital following surgery.
These findings are in contrast to some previously reported in the literature. Edelstein et al.
reported no significant difference in acute care hospital length of stay, and Adunsky et al.
reported no significant differences in total hospital length of stay, associated with perioperative
delirium in patients with fragility hip fractures.90,94 There are a number of possible explanations
for these inconsistencies. Both previous studies were conducted in health systems distinct from
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that of the present work. Edelstein et al. studied patients in New York State, while Adunsky et al.
studied patients in Israel. Health system differences between those locations and the Province of
Ontario may contribute to differences in findings. For example, Adunsky et al. noted in their
report that patients in Israel remain in the same hospital for both acute care and rehabilitation,
which is in contrast to practices in some other regions. In Ontario, patients are frequently either
discharged to a separate rehabilitation facility, or discharged to a rehabilitation bed within the
same hospital, but which may be considered administratively distinct and documented as a
separate episode of care. Additionally, delirium was identified using different methodology
across studies. Edelstein et al. reported an overall 5.1% rate of delirium, which was diagnosed
through a combination of hospital chart review and patient interview. In contrast, Adunsky et al.
reported an overall rate of delirium of 31% when assessed using the CAM tool, although the
frequency of screening with this tool was not reported. Given the waxing and waning nature of
delirium, the variable sensitivity of different diagnostic tools, and the substantially higher rate of
delirium consistently reported with the use of more sensitive tools, there is a risk that patients
who experienced delirium might have gone undetected over the study period. These findings can
be compared to an overall rate of delirium of 48% in the present work, which was assessed using
the CAM tool as a routine part of care on every nursing shift.
The results of the multivariable analysis highlight the importance of other patient factors in
driving differences in length of stay. The presence of additional in-hospital adverse events apart
from delirium itself was found to be a significant driver of length of stay, as were an increasing
number of comorbidities. These findings are consistent with the findings of the literature review
described in section 3.3.2, where in-hospital adverse events and comorbidities were the most
commonly reported determinants of increased hospital length of stay. Furthermore, this effect
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was seen in the published literature across a wide range of conditions. Some reports studied
individual AEs for example acute kidney injury,224 venous thromboembolic events,225 urinary
tract infections,226 and surgical site infections,227 finding significant associations with increased
length of stay. However, others identified significant associations across a broad range of
adverse events.228,229 Furthermore, no studies were identified that investigated one or more
adverse events and failed to identify an association with increased length of stay. Similarly,
associations with increased length of stay have been reported in the literature for individual
comorbidities such as cardiovascular disease,230 diabetes mellitus,231 and malnutrition,232 as well
as for generally increased comorbidity burden.233,234 Taken together, these data support the
finding in the present work that experiencing any in-hospital adverse event, or having a greater
comorbidity burden, is a significant predictor of increased acute care hospital length of stay
following hip fracture.
Pre-admission long term care/skilled nursing facility residence was found to predict significantly
decreased length of stay, with a length of stay ratio of 0.48. This is consistent with previously
reported finding in the state of Michigan that pre-injury nursing home residence is associated
with decreased hospital length of stay following hip fracture,235 as well as a province-wide
analysis of care of patients with hip fractures performed by Health Quality Ontario that identified
a similar relationship.236 This is likely explained by the ability to discharge these patients back to
their pre-admission residence, with no need to wait for availability of a bed at the discharge
destination, and the availability of comprehensive supports at the discharge destination.235
The findings also highlight important hospital and health system factors that can influence length
of stay. Increased time between admission and surgery was found to significantly increase total
length of stay, while later year of admission was associated with decreased time in hospital, both
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of which were identified as potential determinants in the literature review. The latter suggests
that incremental efficiencies may have been realized in shortening patients’ length of stay,
independent of factors such as decreasing time to surgery or the rate of adverse events. While the
specific reasons for this cannot be conclusively ascertained from the present work, potential
reasons include improved coordination of clinical care, increased availability of beds at the
discharge destination (most commonly, rehabilitation hospitals) allowing for earlier patient
transfer, and changes to the medical or mobilization goals that needed to be achieved with
patients before being cleared for discharge.
While the role of several potential determinants of length of stay remained consistent across both
total acute care hospital stay and post-operative stay, some important differences were identified.
The length of stay ratio attributable to delirium was greater for post-operative length of stay
when compared to total length of stay, suggesting that delirium primarily increases length of stay
following surgery, rather than increasing time between admission and surgery. Additionally, the
multivariable analyses revealed that impact of time between admission and surgery was found to
be limited to the pre-operative period, with no significant influence on post-operative length of
stay.
5.1.2 Episode of care costs
Quantify the difference in episode of care costs from the hospital perspective between
patients who do and do not experience perioperative delirium
Perioperative delirium was found to be associated with increased episode of care costs from the
hospital perspective in all analyses. Furthermore, the direction, magnitude, and strength of these
associations were consistent across all analyses, with cost ratios of between 1.47 and 1.51. These
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findings confirm the postulated association between perioperative delirium and episode of care
costs from the hospital perspective in patients with fragility hip fractures that was hypothesized
based on the literature review. Nevertheless, this is a novel finding, given the lack of any
published evidence to date specifically concerning these associations in this patient population.
Several additional cost drivers were identified on multivariable analysis. Similar to the analyses
of length of stay data, the strongest associations for increased costs were seen for higher
comorbidity burden, and the presence of an in-hospital adverse event other than delirium. This
was consistent with previously published studies that reported significantly increased hospital
costs for the care of patients with hip fractures as a consequence of higher comorbidity burden
and in-hospital adverse events.194,225,227,233,237-242
Several other determinants of increased episode of care costs were identified, including surgical
procedure performed, earlier year of admission, and higher ASA class. All of these were
consistent with previously published literature as summarized in section 3.5.4. Pre-admission
long term care residence was also found to be significantly predictive of episode of care costs.
While no previously published data were identified concerning this association specifically, this
follows from findings both in the present work and previously published literature suggesting
that pre-admission long term care residence is associated with decreased hospital length of stay,
and consequently decreased hospital costs.235,236
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Given an identified difference in costs, to determine to what degree the association between
delirium and episode of care costs from the hospital perspective was explained by potential
differences in length of stay.
With the introduction of length of stay into the multivariable model, the magnitude of the
association between perioperative delirium and episode of care costs decreased, yet remained
significant with a cost ratio of 1.10. This indicates that differences in length of stay do not fully
explain the association between delirium and episode of care costs. A portion of incremental
episode of care costs associated with delirium are attributable to increased intensity of care in the
acute care hospital setting for this group. Other important cost drivers in patients with hip
fractures such as comorbidities, other in-hospital adverse events, and type and duration of
surgery remained independently predictive of costs.
The addition of length of stay into the model unmasked two covariates as significant drivers of
episode of care cost, with male gender and longer duration of surgery both associated with
increased episode of care costs. The former is consistent with previously published evidence
identifying male gender as predictive of higher hip fracture care costs.194 While the role of
duration of surgery has not been investigated as a driver of hip fracture care costs specifically, it
is reasonable to expect that operating room resources are considerably more costly for a given
time interval as compared to general ward care. For example, while operative time for a typical
hip fracture rarely exceeds two hours of a multi-day hospital stay, operative costs have been
reported to account for around 37-40% of the total acute care cost.243,244 Consequently, the
identification of duration of surgery as a driver of episode of care cost is reasonable.
Conversely, year of admission was no longer predictive of costs after the addition of length of
stay to the model, suggesting that the differences episode of care costs between years identified
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in the earlier analysis were attributable to reductions in length of stay made over time. It is
unclear from the available data to what extent these reductions are explained by improvements in
the efficiency of care within the acute care setting or improved access to post-acute care settings
such as rehabilitation hospitals, long term care facilities, or home care services. Some authors
have also identified trends in reduced length of stay attributed to changes in funding
models,245,246 although no such changes were made during the study period. Nevertheless, the
trend of reduced length of stay over time in the study cohort is consistent with multiple
previously published studies that have demonstrated these changes across a range of different
health care systems.247-250
5.2 Policy implications
The funding of in-hospital care of patients with fragility hip fractures has recently transitioned
from a global budget model to activity-based reimbursement.236 While providing some
variability in funding using case weighting based on baseline characteristics, this new
reimbursement model provided hospitals with fixed per-patient payments irrespective of the
actual costs accrued for their care. The reimbursement amounts are based on historic province-
wide average costs for the full episode of care. Consequently, hospitals that provide cost-
efficient care may be reimbursed in excess of their costs, while those that are not efficient may
find that their costs of care exceed the payment received.
Given this change in funding models, there is a renewed emphasis on increasing the efficiency of
health care delivery for patients with hip fractures. The reduction of delirium in older patients
has been previously recognized as an important goal for improving patient outcomes.251 The
findings in the present work of substantial health economic impact of delirium further supports
the reduction of delirium as an important goal for potentially reducing the health resource
112
requirements from the hospital perspective for the treatment of patients with hip fractures. When
considered in the context of a substantial nationwide hip fracture burden, as well as the ageing
population, the overall budget impact of reducing the health economic impact of delirium in this
patient population may be substantial.
The findings of this work may have important implications in informing policy and clinical
practice around the prevention and treatment of delirium. A wide range of interventions have
been proposed to reduce the rate of delirium in general hospitalized populations, as described in
section 2.5.3. These have been reported to be most effective in populations with high baseline
rates of delirium such as those admitted to hospital for treatment of a fragility hip fracture.100
Many of these interventions can potentially be resource intensive, with the associated increased
expenditures from the hospital perspective possibly functioning as a barrier to their
implementation. However, the marked incremental episode of care costs associated with delirium
identified in the present work suggest that net reductions in health resource requirements from
the hospital perspective could be realized with modest reductions in the rate of perioperative
delirium, even when considering some need for additional intervention-related expenditures.
Consequently, the findings can empower clinical care providers to advocate for the resources
needed to implement interventions to prevent and treat delirium.
While it has been previously recognized that delirium is associated with delayed discharge from
hospital, the present findings also illuminate that it is associated with increased in-hospital
intensity of care, suggesting that preventative measures may have value over and above those
intended to accelerate discharge of patients with hip fractures from acute care hospitals. Thus,
hospital administrators and policy makers should recognize the importance of prioritizing the
development, implementation and evaluation of strategies to prevent delirium in patients with
113
hip fractures, including assessment of the cost effectiveness and cost benefit of these strategies
when comparing their initial cost against potential clinical and economic benefits.
5.3 Limitations
Several limitations to the present work must be acknowledged.
The assessment of perioperative delirium was limited to a binary measure of whether or not it
was present at any point during hospitalization. However, delirium can have a waxing and
waning course, with variable duration and severity.8 Furthermore, delirium is the result of a
complex interplay of a range of predisposing and precipitating factors, and the specific
combination of the number and timing of triggers likely varied between patients. Additionally, it
is possible that some patients experienced their fall and hip fracture as a consequence of delirium
triggered by factors that preceded their orthopaedic injury. As such, there may be variations in
the economic impact of delirium when further stratified by time of onset, duration, and severity
that were not captured in our study.
No attempt was made to quantify any discrepancies in either preventative or therapeutic
interventions that may have been implemented for any of the study patients. However, all of the
patients included in the study were treated by the same clinical team on one of two specialized
orthopaedic wards at a single institution, and thus any variability in interventions is likely to have
been minimal.
The present work was conducted using data collected from a single specialized center. As a
result, the patient characteristics, clinical care environment, and hospital and health system
characteristics may not be similar to those seen at other hospitals. Consequently, the findings
may not be reflective of those that would be seen at other facilities either in Ontario or elsewhere
114
in the world. Most patients with hip fractures at the study institution were admitted directly from
the surrounding general community rather than through subspecialty referral pathways, and the
prevalence of delirium in the present study was similar to that reported in other studies that
assessed delirium using the CAM tool.80-82 This suggests that the patient characteristics may be
broadly reflective of those that could be expected at other hospitals within the same health care
system, although it is not possible to assess the generalizability of the findings without
comparative data from other hospitals or health care systems.
While baseline mental health status is a known risk factor for delirium,8 no detailed assessment
of pre-injury mental status was available to allow an independent adjustment for this variable in
the analyses. It has been reported that over 50% of nursing home residents have a formal
diagnosis of dementia recorded at the time of admission,252 and the true prevalence of dementia
in this population is likely even higher given the substantial rate of undiagnosed dementia.253
While the inclusion of pre-admission nursing home residency likely provided some level of
adjustment in the findings for baseline dementia, this could only be considered a crude proxy.
The analyses did not independently control for the phenomenon of ‘bed blockers,’ namely
patients whose discharge from acute care hospital is markedly delayed because of lack of
availability of an appropriate discharge destination.254 Most commonly, these are patients who
require new admission to a nursing home or other long-term care facility, resources that are
limited in the study jurisdiction. It was found that all patients in the study cohort that required
new long-term care admission (n=9) had experienced in-hospital delirium, but it is not clear what
proportion of their increased length of stay is attributable to a need for lengthier and/or more
intensive acute hospital care, as compared to additional inpatient days dedicated solely to waiting
for long-term care bed availability. As a result, it was not possible to determine to what degree
115
the incremental economic impact of delirium from the hospital perspective could be reduced or
eliminated by addressing systemic resource barriers preventing timely discharge to long-term
care.
The micro case cost data used in the analysis included both direct and indirect costs from the
hospital perspective, with the latter representing 27% of the total episode of care costs for the
study cohort. Consequently, the incremental costs identified in the analysis included variable
direct costs (which in aggregate would be highly sensitive to reductions in the rate of delirium),
and indirect costs that may be less sensitive to reductions in the rate of delirium. The analyses
performed did not discriminate between the proportion of the incremental cost associated with
perioperative delirium attributable to direct versus indirect costs. Consequently, the budget
impact to the hospital associated with reductions in the rate of delirium may be overestimated by
the present analyses if the incremental indirect costs attributable to delirium would be re-
allocated to other episodes of care.
The work was limited to the length of stay and costs during acute care hospitalization, from the
hospital perspective. The analyses did not account for any potential differences in costs
associated with differences in professional fees, transfer to a rehabilitation hospital, subsequent
home care or outpatient care. Nor were any potential differences in costs from a societal
perspective evaluated, such as increased disability, caregiver burden, and long-term assisted care.
Authors have reported that perioperative delirium in the general hospitalized population is
associated with increased duration of post-discharge nursing home stay, higher daily nursing
home care costs, and higher total medical costs per day survived following discharge in the first
year.28,255
116
5.4 Conclusion
In this work, approximately half of patients who underwent surgical treatment of a fragility hip
fracture experienced perioperative delirium, and this was associated with significant incremental
acute care hospital length of stay as well as episode of care costs from the hospital perspective.
While the difference in episode of care costs was substantially explained by differences in length
of stay, the presence of delirium was nevertheless found to independently predict increased
episode of care costs, apart from length of stay, suggesting that hip fracture patients that
experience delirium require not only an increased duration of acute care hospital stay, but also an
increased intensity of care while hospitalized. Delirium was found to have a substantial budget
impact at the study hospital, with the associated incremental length of stay and episode of care
costs representing a notable proportion of total bed utilization and expenditures used for the care
of patients with fragility hip fractures. These findings suggest that perioperative delirium in
patients undergoing surgical treatment of hip fractures is a promising target for reducing costs
from the hospital perspective.
5.5 Future research
A number of opportunities are available to build on the present work to potentially strengthen the
findings, assess their generalizability, explore the use of more accessible alternative data sources,
extend the assessment to other clinical conditions, and explore opportunities for reducing the
clinical and economic impact of delirium.
Assessment of additional delirium risk factors and temporal trends
The present work adjusted for a number of predisposing and precipitating factors for delirium,
which was defined as a binary condition either present or absent at any point during
117
hospitalization. However, over 30 risk factors have been reported in the literature, many of
which were not available in the data set used. Pre-existing cognitive impairment has been
identified as an important risk factor for delirium in a number of previous studies.256-258
Additionally, some authors have noted that delirium is marked by considerable variability in time
of onset and duration.8 Consequently, future work should endeavor to confirm whether the
present findings are robust to the inclusion of other important risk factors such as pre-existing
cognitive impairment, as well as to differences in the timing of onset or duration of symptoms.
Verification of findings across hospitals and health care systems
As discussed in section 5.3, the present work was limited to data from a single institution, and
thus it is not clear whether the findings would be generalizable to other hospitals or health care
systems. At minimum, it would be beneficial to focus future work on confirming the present
findings across a cross-section of different hospitals within the Province of Ontario. Hospital
teaching status,259 hospital volume,260 and community size/location have all been reported to
influence costs.261 Consequently, findings from an analysis of data from selected academic and
community hospitals in a range of urban, suburban, rural and northern communities would be
beneficial in assessing the generalizability of the present findings across the Province, and
evaluating for potential hospital and health system factors that may influence the impact of
delirium on health resource utilization. Further work confirming these findings in other health
care systems both within and outside of Canada could subsequently be performed.
Exploring the use of administrative data sources
Large administrative health care datasets have become increasingly available to researchers,
providing ready access to large, multi-center patient cohorts that maximize analytic power and
118
generalizability of findings. Recently, several studies have been published using administrative
data sources with ICD-9 or 10 coded delirium as either a predictor or outcome of interest.262-264
However, these datasets may not capture many relevant clinical variables, and the accuracy of
coded administrative data when compared to primary clinical data is variable. Data submitted to
Canadian administrative data holdings for the patients in the present work was found to have
very poor sensitivity for delirium, mirroring findings previously reported for a cohort of cardiac
intensive care patients.162 Nevertheless, administrative data sources would be helpful in future
work to validate the findings across different hospitals and health care systems. Consequently,
future work to ascertain the quality of estimates of the health economic impact of delirium using
administrative data sources would be helpful. Should relevant differences be identified in the
estimates obtained from administrative as compared to clinical data sources, additional work to
develop reliable methods of adjusting for the low sensitivity of the former would be beneficial.
Economic evaluations of interventions to prevent and treat delirium
The results of the present work, combined with existing knowledge concerning the adverse
clinical consequences of delirium, suggest that there might be both clinical and economic
benefits to decreasing the frequency and severity of delirium in patients with hip fractures. A
number of interventions have been studied in terms of their effectiveness in reducing the
incidence of perioperative delirium in a range of patient populations, many of which involve
considerable up-front investment in additional health care personnel and hospital resources.100-
103,105,265 Given the high rate of delirium reported in patients with hip fractures, there is a need for
the identification and implementation of effective interventions to reduce this adverse event. To
date, however, there has been a paucity of evidence concerning the health economic impact of
delirium in this patient population. Future work should include cost-effectiveness analyses of
119
interventions to prevent and treat delirium in patients with hip fractures, and hospitalized patients
in general. The availability of such findings would help inform decision making by clinicians
and hospital administrators around the implementation of such interventions, with the goal of
improving clinical outcomes while reducing costs.
Evaluations using broader health economic perspectives
Further work is needed to evaluate the implications of perioperative delirium in patients with
fragility hip fractures from both the Ministry of Health and societal perspectives. This is needed
to both confirm and quantify the health economic impact of delirium beyond the acute care
setting. It has been reported that delirium in hospitalized general medical patients is associated
with incremental one year post-discharge health care costs of between 33% and 121% compared
to non-delirious patients.28 Consequently, it is very possible that delirium in patients with hip
fractures may also be associated with substantial incremental health resource requirements
following discharge from the acute care hospital, both from the Ministry of Health and societal
perspectives. If so, such findings would be important in informing decision making around
interventions and strategies to prevent and treat delirium in patients at the time of acute care
admission.
Assessment of the health economic impact of delirium in other clinical diagnoses
While a substantial proportion of patients with hip fractures were found to experience delirium,
other patient populations have been reported to experience high rates of this adverse event,
including those admitted to an intensive care unit,266 and those living in a long term care
facility.267 However, there is evidence to suggest that delirium is under recognized across a wide
range of hospitalized patients.268 Consequently, future work to evaluate the health economic
120
impact of delirium in other acute care and non-hospitalized patient populations would be
valuable to inform decision making around strategies to detect, prevent and treat delirium across
different patient populations and health care settings.
121
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Appendices
Appendix A: Hospital Research Ethics Board approval letter
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Appendix B: University Research Ethics Board approval letter
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Appendix C: Results of systematic literature review concerning potential determinants of
length of stay and acute care hospital costs
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Appendix D: Results of logistic regression model used to construct propensity scores
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Appendix E: Table of correlations between potential predictor variables
Appendix D
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Appendix F: Table of AIC and BIC values for alternative estimation models considered
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Appendix G: Coefficients for models of episode of care costs using negative binomial and
gamma distributions
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Copyright Acknowledgements
Table 4 was adapted from: American Society of Anesthesiologists. New classification of
physical status. Anesthesiology. 1963;24:111.
Figure 4 was adapted from: Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP, Horwitz
RI. Clarifying confusion: the confusion assessment method. A new method for detection of
delirium. Ann Intern Med. 1990;113:941-8.
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