Post on 01-Jan-2021
transcript
Sustained socio-economic inequalities in hospital admissions for cardiovascular
events among people with diabetes in England
Running title: Socioeconomic inequalities in cardiovascular admissions in diabetes
Zainab Shather*1 MPH, Anthony A Laverty*1 MSc PhD, Alex Bottle1 MSc PhD,
Hilary Watt1 CStat MSc, Azeem Majeed MD1, Christopher Millett1 PhD, Eszter P
Vamos1 PhD
*These authors contributed equally to this work
1Public Health Policy Evaluation Unit, School of Public Health, Imperial College
London, London, UK
Word count of main text: 3,163 Abstract: 247.
Tables: 3 Figures: 1
Conflict of interests: AB reports grants from Dr Foster and Medtronic, outside the submitted work. No other authors declared conflict of interest.
Funding: The Public Health Policy Evaluation Unit at Imperial College London is supported by funding from the National Institute for Health Research’s School of Public Health Research programme.
Address for Correspondence:
Eszter P Vamos MD, MPH, FFPH, PhD
Senior Clinical Lecturer in Public Health
& Clinical Consultant in Public Health
Deputy Director of Public Health Policy Evaluation Unit
Public Health Policy Evaluation Unit, School of Public Health
Imperial College London
London W6 8RP, UK
1
Tel: 00 44 (0) 207 594 0817
Fax: 00 44 (0) 207 594 0854
Email: e.vamos@imperial.ac.uk
2
Abstract
Objective
This study aimed to determine changes in absolute and relative socio-economic
inequalities in hospital admissions for major cardiovascular events and procedures
among people with diabetes in England.
Methods
We identified all patients with diagnosed diabetes aged ≥45 years admitted to hospital
in England between 2004-2005 and 2014-2015 for acute myocardial infarction,
stroke, percutaneous coronary intervention (PCI) and coronary artery bypass graft
(CABG). Socio-economic status was measured using Index of Multiple Deprivation.
Diabetes-specific admission rates were calculated for each year by deprivation
quintile. We assessed temporal changes using negative binomial regression models.
Results
Most admissions for cardiovascular causes occurred among people aged ≥65 years
(71%) and men (63.3%), and the number of admissions increased steadily from the
least to the most deprived quintile. People with diabetes in the most deprived quintile
had 1.94-fold increased risk of acute myocardial infarction (95% CI 1.79-2.10), 1.92-
fold risk of stroke (95% CI 1.78-2.07), 1.66-fold risk of CABG (95% CI 1.50-1.74),
and 1.76-fold risk of PCI (95% CI 1.64-1.89) compared with the least deprived group.
Absolute differences in rates between the least and most deprived quintiles did not
significantly change for acute myocardial infarction (P=0.29) and were reduced for
stroke, CABG and PCI (by 17.5, 15 and 11.8 per 100,000 people with diabetes,
respectively, P≤0.01 for all).
3
Conclusions
Socio-economic inequalities persist in diabetes-related hospital admissions for major
cardiovascular events in England. Besides improved risk stratification strategies
considering socio-economically defined needs, wide-reaching population-based
policy interventions are required to reduce inequalities in diabetes outcomes.
4
Introduction
Cardiovascular disease remains a leading cause of morbidity and premature mortality
and is a substantial contributor to health inequalities globally1. The risk of morbidity
and death, particularly cardiovascular mortality, shows marked variations according
to socio-economic status, as measured by income, education, social class and area-
based deprivation indices2. Lower socio-economic status is also a powerful predictor
of higher incidence of Type 2 diabetes as well as its acute and long-term
complications3. This mirrors the socio-economic patterning of risk factors such as
poor diet, lack of exercise and smoking, psychological stress and access to and use of
evidence-based preventive and therapeutic interventions3,4. The fast-growing
prevalence of Type 2 diabetes with a disproportionate burden on disadvantaged
populations represents a serious concern for health systems and societies.
Epidemiological studies show that despite targeted interventions, inequalities in
coronary heart disease mortality in the general population have not only persisted but
widened since the 1970s in England and other developed countries2,5-7. Although
absolute inequalities in mortality narrowed over time as cardiovascular mortality has
fallen in all socio-economic groups, faster declines in more affluent groups have led
to an increase in relative socioeconomic inequalities in many countries5,7. Worryingly,
there is increasing evidence that health inequality gaps have been widening since
2010 in England alongside reductions in public service funding8.
While extensively studied in the general population, little is known about how people
with diabetes from different socioeconomic groups have benefited from reductions in
cardiovascular disease over the last decade. Few longitudinal studies have examined
5
the temporal relations between measures of socio-economic status and cardiovascular
outcomes in diabetes internationally3,9-18. Some, but not all, studies reported widening
socio-economic inequalities in people with diabetes9-11,19. Previous studies have been
limited by small sample sizes, short follow-up times or were conducted a long time
ago and most of them focused on cardiovascular mortality rather than morbidity.
The objective of this nationwide study was to describe hospital admissions for acute
myocardial infarction, stroke, percutaneous coronary intervention (PCI) and coronary
artery bypass graft (CABG) in people with diabetes by socio-economic deprivation
between 2004-2005 and 2014-2015 in England. We also assessed whether the
absolute and relative socio-economic gradient in study outcomes have changed among
people with diabetes during this 11-year period.
6
Materials and Methods
Hospital Episode Statistic (HES) is a national administrative database managed by the
NHS Digital that contain records of all in-patient and day-case admissions to NHS
hospitals across England, including private patients treated in NHS facilities20.
England offers universal health coverage with care free at the point of delivery. HES
is an administrative dataset used primarily for financial reimbursements of NHS
hospitals for the care they provide based on coding data, and reporting is mandatory.
We identified all people with diagnosed diabetes aged 45 years and above, who were
admitted to hospital between the financial years 2004-2005 and 2014-2015 in England
for acute myocardial infarction, stroke, CABG and PCI. Data extracted for each
admission included age, gender, registered family practice, principal diagnosis and up
to 19 secondary diagnoses on admission using the ICD-10 (International
Classification of Diseases) classification, up to 12 procedures and in-patient deaths.
Interventions were identified using the Office of Population Censuses and Surveys 4
(OPCS4) coding.
We included patients with both Type 1 diabetes (ICD-10 E10) and Type 2 diabetes
(ICD-10 E11), recorded in any diagnostic field. Cardiovascular admissions, identified
as principal diagnosis on admissions, included acute myocardial infarction (ICD-10
I21 and I22) and stroke (ICD-10 I60-I64). Revascularisation procedures were
identified using procedure codes in any field for PCI (OPCS4 K49-K50, K79) or
CABG (OPCS4 K40-K46).
7
We used the Index of Multiple Deprivation 2010 as a measure of socio-economic
status. The Index of Multiple Deprivation is the most commonly used method of
measuring deprivation in England at the small area level (Lower-layer Super Output
Area, an area covering 1,000 to 3,000 people or 400 to 1,200 households)21. We used
deprivation quintiles defined for the whole population of England (i.e. population of
England grouped into equal fifths according to deprivation scores). We assigned each
patient a deprivation quintile (1-least deprived, 5-most deprived) based on their family
practice postcode22.
Diabetes-specific cardiovascular admission rates were calculated by gender, four age-
bands (45-54, 55-64, 65-74 and >=75 years) and deprivation quintiles using the
number of admissions as numerator and the number of people with diabetes in
particular age, sex and deprivation bands as denominator. Data on the total number of
people with diagnosed diabetes in England for each study year were obtained from the
Quality and Outcomes Framework (QOF). QOF is a national primary care pay-for-
performance scheme introduced in the UK in 2004 that holds data on number of
diabetes patients for over 99% of family practices in England 23. As the age, gender
and deprivation distribution of people with diabetes are not available from QOF, we
used Health Survey for England (HSE) data between 2006 and 2014 and estimated the
number of people with diabetes within each age, sex and deprivation stratum24. For
those study years when HSE surveys did not include data on diabetes prevalence (in
2004, 2005, 2007 and 2008), we used 2006 HSE data similar to previous studies25,26.
Admission rates were directly standardized by age and gender using the first study
year’s population structure as reference. All event rates were expressed per 100,000
people with diabetes. In-patient case-fatality rates were calculated using the number
8
of in-patients deaths as numerator and the number of admissions by age, gender, and
deprivation quintile as the denominator.
Statistical analyses
Group differences were tested using Pearson’s Chi-square test for categorical
variables. For graphical presentation of age- and sex-standardized admission rates, we
used locally weighted scatterplot smoothing for each study outcome by deprivation
quintile over study years. We used two sets of analyses to estimate changes in
admission rates. First, we fitted linear regression models to estimate how differences
in absolute admission rates between the least and most deprived groups changed
during the study period. Second, we fitted negative binomial regression models for
each study outcome to estimate rate ratios for the deprivation groups using the least
deprived group as reference. We used age, gender, deprivation quintile, study year
and a quadratic term for study year as independent variables. We then tested for an
interaction between year and deprivation to assess changes in relative inequalities
over time. We also fitted negative binomial regression models to obtain rate ratios for
in-patient case-fatality. All statistical analyses were conducted using Stata version
14.0.
9
Results
The baseline characteristics of people with diabetes over the age of 45 years admitted
to hospital for acute myocardial infarction, stroke, CABG and PCI in England
between 2004-2005 and 2014-2015 by socio-economic deprivation quintiles are
shown in Table 1. The number of admissions steadily increased across deprivation
groups from the least to the most deprived quintile for all outcomes. In 2004-2005,
approximately one-quarter of admissions occurred among patients in the most
deprived quintile, while 15% of admissions were from the most affluent groups for all
four outcomes (Table 1). We found a similar pattern when examining the proportion
of admissions further broken down by age and sex. The majority of admissions for all
cardiovascular causes occurred among people aged ≥65 years (71%) and men (63.3%)
across all quintiles. However, with increasing levels of deprivation, significantly
larger proportions of younger patients were represented among those affected by
cardiovascular disease (Table 1).
Absolute changes in admissions rates
Figure 1 shows locally weighted scatterplot smoothing curves for the age- and gender-
standardized admission rates for study outcomes between 2004-2005 and 2014-2015
in people with diabetes in England. A consistent inverse socio-economic patterning
was evident for all outcomes for all study years with increasing admission rates along
the deprivation quintiles from the least (quintile 1) to most deprived groups (quintile
5).
Admission rates for acute myocardial infarction decreased in all deprivation quintiles
between 2004-2005 and 2009-2010, followed by the flattening out of rates until the
10
end of the study period in all groups except for quintile 5 (Figure 1). In quintile 5
(most deprived group), admission rates for acute myocardial infarction increased
between 2010-11 and 2014-2015 from 147.8 to 163.3 per 100,000 people with
diabetes. Admission rates for stroke rose between 2004-2005 and 2014-2015 in all
socio-economic groups, except for the most deprived group where admission rates
gradually declined during the study period. While CABG rates declined in all
socioeconomic groups, there was a consistent parallel increase in admission rates for
PCI.
In linear regression models, between 2004-2005 and 2014-2015, the absolute
differences in admission rates between the least (quintile 1) and most deprived groups
(quintile 5) remained unchanged for acute myocardial infarction (P=0.29) and were
reduced significantly by 17.5 per 100,000 people with diabetes for stroke (P=0.002),
by 15 per 100,000 people with diabetes for CABG (P<0.001), and by 11.8 per
100,000 people for PCI (P=0.03).
Relative differences in admission rates
Between 2004-2005 and 2014-2015, admission rate ratios for acute myocardial
infarction, stroke, CABG and PCI showed a statistically significant socioeconomic
gradient, with admission rate ratios steadily increasing with increasing levels of
deprivation (Table 2, Model 1). People with diabetes in the most deprived quintile
(quintile 5) were approximately twice as likely to be admitted to hospital for
cardiovascular as those in the least deprived quintile (quintile 1), with rate ratios of
1.94 for acute myocardial infarction, 1.92 for stroke, 1.66 for CABG, and 1.76 for
PCI admissions (P< 0.001 for all) (Table 2).
11
The interaction term between year and deprivation quintiles did not reach statistical
significance for any of the outcomes. This reflects that deprived groups experienced
similar proportional changes in study outcomes as the most affluent group over time
(Table 2, Model 2).
In-patient case-fatality
From 2004-2005 to 2014-2015, there was a reduction in in-patient case-fatality rates
for all outcomes except for PCI (Table 1). In negative binomial regression models, in-
patient case-fatality rates for acute myocardial infarction, CABG and PCI were higher
in the most deprived group (quintile 5) compared with quintile 1 (most affluent)
(Table 3, Model 1). However, only associations for PCI reached statistical
significance with the most deprived group experiencing 24% higher in-patient case-
fatality compared with the least deprived group. By contrast, for stroke admissions,
the most deprived group had significantly lower case-fatality rates compared with the
most affluent group. Trends in in-patient case-fatality did not statistically significantly
differ between the most affluent and other deprivation groups (Table 3, Model 2).
12
Discussion
In England between 2004-2005 and 2014-2015, diabetes-related hospital admission
rates for major cardiovascular causes showed a marked socio-economic gradient with
consistently higher admission rates with increasing levels of deprivation. Absolute
inequalities in admission rates remained unchanged for acute myocardial infarction
and were decreased for stroke, PCI and CABG during the study period. However,
people in different deprivation groups experienced similar proportional changes in
outcomes, leaving relative inequalities among people with diabetes unchanged for the
study period. People with diabetes in the most deprived group had 1.9-fold increased
admission rates for acute myocardial infarction and stroke, 1.8-fold increased
admission rates for PCI and stroke and 1.7-fold increased CABG admission rates
when compared with the least deprived areas. In-patient case-fatality was
approximately 6% lower for stroke and 24% higher for PCI in the most deprived
group compared with the most affluent.
Cardiovascular and related mortality have fallen substantially during the past few
decades in England and other Western countries25,27-29. Epidemiological studies have
shown that up to 75% of reductions in coronary heart disease mortality can be
explained by population-level improvements in major risk factors, predominantly
smoking, blood pressure and cholesterol, while 25-50% can be attributed to
improvements in the quality and accessibility of health services6,27,28. These advances
are, however, disproportionately spread across socio-economic groups and the slower
pace of decline in coronary heart disease mortality in the most deprived areas are
likely to mirror corresponding patterns of key risk factors30. Furthermore, adverse
trends in physical activity, obesity and Type 2 diabetes, all of which are more likely to
13
affect the socio-economically disadvantaged, offset some of the favorable changes
seen in cardiovascular outcomes30.
While widely investigated in the general population, among people with diabetes data
on recent changes in socio-economic inequalities in major cardiovascular events are
scarce internationally. Most previous studies focused on the association between
socio-economic status and all-cause or coronary heart disease mortality and few have
investigated specific cardiovascular events such as stroke19,31. This is an important
knowledge gap as people with diabetes increasingly survive cardiovascular events and
mortality itself does not provide accurate reflection on its burden15. A study from the
US reported that educational disparities in self-reported history of cardiovascular
remained uniform between 1997 and 2005 among people with diabetes, whilst
inequalities widened amongst people without diabetes9. Previous cohort studies
demonstrated a 1.5- to 3-fold increased risk of fatal and non-fatal cardiovascular
disease among lower socio-economic groups compared with more privileged groups
with Type 1 and Type 2 diabetes16,17,32.
We found that, unexpectedly, the most deprived group had lower in-patient case-
fatality related to stroke admissions compared with the most affluent group. This
finding was not consistent with findings for other study outcomes. Patients admitted
for stroke living in most deprived areas were younger compared with more affluent
groups and although our analyses were adjusted for age, they may have differed in
other risk factors that could explain this finding. It is also possible that most deprived
groups are over-represented among those with fatal stroke who do not reach hospital
and therefore, fewer patients at high risk of mortality contribute to in-patient case-
fatality rates.
14
Aggressive management of major cardiovascular risk factors aligned with socio-
economically defined needs are likely to reduce inequalities in cardiovascular
outcomes. Risk stratification to identify patients with diabetes for more intensive
cardiovascular prevention has assumed increasing importance recently33. Because
coronary heart disease risk in Type 2 diabetes is not similar to the risk of patients with
a previous cardiovascular event in all instances, diabetes is no longer considered a
coronary heart disease risk equivalent by many scientific groups34. Given the highly
heterogeneous risk profile of patients with diabetes, risk stratification strategies
including cardiovascular risk score estimations bear increasing importance35. By
contrast to some predictive models used in the general population, these strategies do
not include measures of socio-economic status among people with diabetes35. Whether
inclusion of deprivation improves cardiovascular risk estimations and risk
stratification for people with diabetes warrants further research.
England has a long history of concerted efforts to assess, understand and reduce
health inequalities36. The NHS provides free care at the point of use and substantial
investments have been made during the past decades to improve the quality of chronic
disease management and reduce variations in care23. Besides these efforts, important
population-wide measures were introduced to improve risk factors across the
population such as smoke-free legislation, other tobacco control policies and
measures to reduce other cardiovascular risk factors (poor diet, hypertension and
physical activity)36,37. While interventions targeting individuals at high risk of
cardiovascular disease are essential, generally, such interventions may benefit
population subgroups differently due to differences in access, uptake and compliance,
potentially resulting in widening socio-economic inequalities38. Therefore, these need
15
to be complemented by wide-reaching population-level strategies to reduce risk
factors across the whole population such as legislative and fiscal policies38.
Strengths and limitations
To our knowledge, this is the first nationwide study describing recent trends of
hospital admissions for selected cardiovascular causes in people with diabetes by
socio-economic status in England. Our data cover the entire population of England
treated in NHS hospitals and given that our study outcomes require hospital
admissions, our data are likely to provide an accurate reflection on diabetes-related
cardiovascular event rates. The diabetes denominator data was obtained from national
diabetes registers with the participation of over 99% of family practices.
This study does have limitations. We used routinely collected data and we were
unable to directly assess misclassification and miscoding. However, HES is an
administrative dataset and the reimbursement of NHS hospitals is directly determined
by coded data, and therefore, there is a strong financial incentive for providers to
accurately code clinical information. Routinely collected clinical data are nationally
audited regularly and a systematic review has evaluated its accuracy as high for both
diagnoses and procedures39. Our study only included cardiovascular outcomes that
resulted in hospital admissions and does not capture fatal cardiovascular cases, such
as those who died before reaching hospital. Furthermore, we could not stratify our
analyses by type of diabetes because national prevalence data according to types were
not available for the study years. We assigned an Index of Multiple Deprivation score
to individual patients based on their family practice postcode as individual-level
measures of socio-economic status (e.g. education, income, etc.) are not routinely
collected in UK family practice. Postcode-based deprivation scores are available for
16
each patient and are generally accepted as good proxies for individual-level
deprivation40,41. We used data from Health Survey for England to ascertain the age,
gender and deprivation distribution of diabetes in England. For 2004, 2005 and 2007
and 2008 when age-, gender- and deprivation-specific data were not available we used
data from 2006, the mid-term of this 5-year period25,26.
Conclusion
This nationwide study covering the entire population in England, where universal
health coverage provides free care for all, showed a persisting socio-economic
gradient in hospital admissions for major cardiovascular causes among people with
diabetes during the past decade. These findings highlight that social factors are still
not adequately addressed in health policy. Besides risk stratification strategies that
consider the socio-economic needs of individuals with diabetes, wide-reaching
population-based policy interventions are required that alter risk factors across the
population to reduce inequalities in diabetes outcomes.
Contributions
ZS researched the data and wrote the manuscript. AL researched the data and wrote
the manuscript. AB researched the data and wrote the manuscript. HW researched the
data and wrote the manuscript. AM reviewed/edited the manuscript and revised it for
important intellectual content. CM wrote the manuscript and revised it for important
intellectual content. EV conceived the idea, researched the data and wrote the
manuscript.
17
References
1. Roth, GA, Johnson, C, Abajobir, A, et al. Global, Regional, and National
Burden of Cardiovascular Diseases for 10 Causes, 1990 to 2015. J Am Coll
Cardiol 2017; 70(1): 1-25.
2. Jemal, A, Ward, E, Anderson, RN, Murray, T, Thun, MJ. Widening of
socioeconomic inequalities in U.S. death rates, 1993-2001. PLoS One 2008; 3(5):
e2181.
3. Agardh, E, Allebeck, P, Hallqvist, J, Moradi, T, Sidorchuk, A. Type 2
diabetes incidence and socio-economic position: a systematic review and meta-
analysis. Int J Epidemiol 2011; 40(3): 804-18.
4. Wild, S, Macleod, F, McKnight, J, et al. Impact of deprivation on
cardiovascular risk factors in people with diabetes: an observational study.
Diabet Med 2008; 25(2): 194-9.
5. Bajekal, M, Scholes, S, O'Flaherty, M, Raine, R, Norman, P, Capewell, S.
Unequal trends in coronary heart disease mortality by socioeconomic
circumstances, England 1982-2006: an analytical study. PLoS One 2013; 8(3):
e59608.
6. Bajekal, M, Scholes, S, Love, H, et al. Analysing recent socioeconomic
trends in coronary heart disease mortality in England, 2000-2007: a population
modelling study. PLoS Med 2012; 9(6): e1001237.
7. Mackenbach, JP, Kulhanova, I, Artnik, B, et al. Changes in mortality
inequalities over two decades: register based study of European countries. BMJ
2016; 353: i1732.
8. Department of Health: Annual report and accounts 2016 to 2017.
Available at https://www.gov.uk/government/publications/department-of-health-
annual-report-and-accounts-2016-to-2017 (Last accessed: 4 June 2018).
18
9. Dray-Spira, R, Gary, TL, Brancati, FL. Socioeconomic position and
cardiovascular disease in adults with and without diabetes: United States trends,
1997-2005. J Gen Intern Med 2008; 23(10): 1634-41.
10. Koskinen, SV, Martelin, TP, Valkonen, T. Socioeconomic differences in
mortality among diabetic people in Finland: five year follow up. BMJ 1996;
313(7063): 975-8.
11. Forssas, E, Keskimaki, I, Reunanen, A, Koskinen, S. Widening
socioeconomic mortality disparity among diabetic people in Finland. Eur J Public
Health 2003; 13(1): 38-43.
12. Jackson, CA, Jones, NR, Walker, JJ, et al. Area-based socioeconomic status,
type 2 diabetes and cardiovascular mortality in Scotland. Diabetologia 2012;
55(11): 2938-45.
13. Roper, NA, Bilous, RW, Kelly, WF, Unwin, NC, Connolly, VM. Excess
mortality in a population with diabetes and the impact of material deprivation:
longitudinal, population based study. BMJ 2001; 322(7299): 1389-93.
14. Chaturvedi, N, Jarrett, J, Shipley, MJ, Fuller, JH. Socioeconomic gradient in
morbidity and mortality in people with diabetes: cohort study findings from the
Whitehall Study and the WHO Multinational Study of Vascular Disease in
Diabetes. BMJ 1998; 316(7125): 100-5.
15. Newton, JN, Briggs, AD, Murray, CJ, et al. Changes in health in England,
with analysis by English regions and areas of deprivation, 1990-2013: a
systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015;
386(10010): 2257-74.
16. Secrest, AM, Costacou, T, Gutelius, B, Miller, RG, Songer, TJ, Orchard, TJ.
Associations between socioeconomic status and major complications in type 1
19
diabetes: the Pittsburgh epidemiology of diabetes complication (EDC) Study.
Ann Epidemiol 2011; 21(5): 374-81.
17. Rawshani, A, Svensson, A, Rosengren, A, Eliasson, B, Gudbjornsdottir, S.
Impact of socioeconomic status on cardiovascular disease and mortality in 24,947
individuals with type 1 diabetes. Diabetes Care 2015; 38: 1518 - 27.
18. Gnavi, R, Canova, C, Picariello, R, et al. Mortality, incidence of
cardiovascular diseases, and educational level among the diabetic and non-
diabetic populations in two large Italian cities. Diabetes Res Clin Pract 2011;
92(2): 205-12.
19. Lipscombe, LL, Austin, PC, Manuel, DG, Shah, BR, Hux, JE, Booth, GL.
Income-related differences in mortality among people with diabetes mellitus.
CMAJ 2010; 182(1): E1-E17.
20. National Health Service Digital: Hospital Episode Statistics. Available at:
http://content.digital.nhs.uk/hes (Last accessed: 4 June 2018).
21. Department for Communities and Local Government: English indices of
deprivation 2015. Available at:
https://www.gov.uk/government/statistics/english-indices-of-deprivation-2015
(Last accessed: 4 June 2018).
22. Strong, M, Maheswaran, R, Pearson, T. A comparison of methods for
calculating general practice level socioeconomic deprivation. Int J Health Geogr
2006; 5: 29.
23. National Health Service Digital: Quality and Outcomes Framework.
Available at http://content.digital.nhs.uk/qof (Last accessed: 4 June 2018).
24. UK Government Digital Service: Health Survey for England. Available at
https://data.gov.uk/dataset/health_survey_for_england. (Last accessed: 4 June
2018).
20
25. Vamos, EP, Millett, C, Parsons, C, Aylin, P, Majeed, A, Bottle, A.
Nationwide study on trends in hospital admissions for major cardiovascular
events and procedures among people with and without diabetes in England,
2004-2009. Diabetes Care 2012; 35(2): 265-72.
26. Laverty, AA, Bottle, A, Kim, SH, et al. Gender differences in hospital
admissions for major cardiovascular events and procedures in people with and
without diabetes in England: a nationwide study 2004-2014. Cardiovasc Diabetol
2017; 16(1): 100.
27. Unal, B, Critchley, JA, Capewell, S. Explaining the decline in coronary
heart disease mortality in England and Wales between 1981 and 2000.
Circulation 2004; 109(9): 1101-7.
28. Ford, ES, Ajani, UA, Croft, JB, et al. Explaining the decrease in U.S. deaths
from coronary disease, 1980-2000. N Engl J Med 2007; 356(23): 2388-98.
29. Booth, GL, Kapral, MK, Fung, K, Tu, JV. Recent trends in cardiovascular
complications among men and women with and without diabetes. Diabetes Care
2006; 29(1): 32-7.
30. Scholes, S, Bajekal, M, Love, H, et al. Persistent socioeconomic inequalities
in cardiovascular risk factors in England over 1994-2008: a time-trend analysis
of repeated cross-sectional data. BMC Public Health 2012; 12: 129.
31. Saydah, S, Imperatore, G, Beckles, GL. Socioeconomic status and mortality
- contribution of health care access and psychological distress among US adults
with diagnosed diabetes. Diabetes Care 2013; 36: 49-55.
32. Rawshani, A, Svensson, AM, Zethelius, B, Eliasson, B, Rosengren, A,
Gudbjornsdottir, S. Association Between Socioeconomic Status and Mortality,
Cardiovascular Disease, and Cancer in Patients With Type 2 Diabetes. JAMA
Intern Med 2016; 176(8): 1146-54.
21
33. Bertoluci, MC, Rocha, VZ. Cardiovascular risk assessment in patients with
diabetes. Diabetol Metab Syndr 2017; 9: 25.
34. American Diabetes Association: Standards of Medical Care in Diabetes
2017. Diabetes Care 2017; 40: S1 - S135.
35. Allan, GM, Nouri, F, Korownyk, C, Kolber, MR, Vandermeer, B,
McCormack, J. Agreement among cardiovascular disease risk calculators.
Circulation 2013; 127(19): 1948-56.
36. Mackenbach, JP. Can we reduce health inequalities? An analysis of the
English strategy (1997-2010). J Epidemiol Community Health 2011; 65(7): 568-75.
37. Department of Health: Reducing Health Inequalities: An Action Report.
Department of Health, 1999. Available at:
http://webarchive.nationalarchives.gov.uk/+/http://www.dh.gov.uk/en/
Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/
DH_4006054 (Last accessed: 4 June 2018).
38. Capewell, S, Graham, H. Will cardiovascular disease prevention widen
health inequalities? PLoS Med 2010; 7(8): e1000320.
39. Burns, EM, Rigby, E, Mamidanna, R, et al. Systematic review of discharge
coding accuracy. J Public Health (Oxf) 2012; 34(1): 138-48.
40. Robinson, WS. Ecological correlations and the behavior of individuals. Int
J Epidemiol 2009; 38(2): 337-41.
41. Ashworth, M, Lloyd, D, Smith, RS, Wagner, A, Rowlands, G. Social
deprivation and statin prescribing: a cross-sectional analysis using data from the
new UK general practitioner 'Quality and Outcomes Framework'. J Public
Health (Oxf) 2007; 29(1): 40-7.
22
Table 1. Characteristics of people with diabetes admitted to hospital for major cardiovascular events and procedures between 2004-2005 and 2014-2015 in England
Outcome Index of Multiple Deprivation (IMD) quintiles2004-2005
Index of Multiple Deprivation (IMD) quintiles2014-2015
Q1 Q2 Q3 Q4 Q5 P Q1 Q2 Q3 Q4 Q5 PAcute Myocardial InfarctionNumber of events (%) 1,865 (15.3) 2,205 (18.1) 2,499 (20.5) 2,740 (22.5) 2,861 (23.5) 2,556 (15.6) 2,989 (18.3) 3,265 (19.9) 3,509 (21.5) 4,039 (24.7)Age groups, years, N (%) 45-54 116 (6.2) 115 (5.2) 168 (6.7) 224 (8.2) 308 (10.8) 184 (7.2) 235 (7.9) 297 (9.1) 398 (11.3) 526 (13.0) 55-64 268 (14.4) 333 (15.1) 418 (16.7) 458 (16.7) 527 (18.4) 352 (13.8) 487 (16.3) 588 (18.0) 708 (20.2) 941 (23.3) 65-74 538 (28.8) 645 (29.2) 717 (28.7) 837 (30.5) 952 (33.3) 678 (26.5) 796 (26.6) 837 (25.6) 917 (26.1) 1,009 (25.0) ≥75 943 (50.6) 1,112 (50.4) 1,196 (47.9) 1,221 (44.6) 1,074 (37.5) <0.001* 1,342 (52.5) 1,471 (49.2) 1,543 (47.3) 1,486 (42.3) 1,563 (38.7) <0.001*Male, N (%) 1,165 (62.5) 1,372 (62.2) 1,526 (61.1) 1,613 (58.9) 1,687 (59.0) 0.02* 1,628 (63.7) 1,967 (65.8) 2,113 (64.7) 2,203 (62.8) 2,555 (63.3) 0.08*In-patient deaths, N (%) 289 (15.5) 349 (15.8) 387 (15.5) 424 (15.5) 449 (15.7) 0.99* 280 (10.9) 294 (9.8) 338 (10.3) 324 (9.2) 386 (9.6) 0.18*StrokeNumber of events (%) 1,472 (15.5) 1,781 (18.8) 1,849 (19.5) 2,071 (21.8) 2,311 (24.4) 2,774 (16.4) 3,317 (19.6) 3,510 (20.7) 3,624 (21.4) 3,714 (21.9)Age groups, years, N (%) 45-54 56 (3.8) 68 (3.8) 75 (4.1) 123 (5.9) 174 (7.5) 113 (4.1) 140 (4.2) 171 (4.9) 251 (6.9) 358 (9.6) 55-64 144 (9.8) 176 (9.9) 198 (10.7) 299 (14.4) 355 (15.4) 317 (11.4) 351 (10.6) 436 (12.4) 495 (13.7) 607 (16.3) 65-74 357 (24.2) 449 (25.2) 490 (26.5) 627 (30.3) 742 (32.1) 595 (21.4) 782 (23.6) 800 (22.8) 896 (24.7) 1,008 (27.1) ≥75 915 (62.2) 1,088 (61.1) 1,086 (58.7) 1,022 (49.4) 1,040 (45.0) <0.001* 1,749 (63.1) 2,044 (61.6) 2,103 (59.9) 1,982 (54.7) 1,741(46.9) <0.001*Male, N (%) 757 (50.6) 939 (52.7) 931 (50.3) 1,056 (51.0) 1,165 (50.4) 0.58* 1,611 (58.1) 1,825 (55.0) 1,927 (54.9) 1,984 (54.7) 2,006 (54.0) 0.062*In-patient deaths, N (%) 424 (28.8) 533 (29.9) 559 (30.2) 562 (27.1) 581 (25.1) 0.001* 500 (18.0) 573 (17.3) 601 (17.1) 599 (16.5) 547 (14.7) 0.004*CABGNumber of events (%) 696 (15.6) 810 (18.1) 886 (19.8) 952 (21.3) 1,119 (25.1) 861 (16.3) 1,035 (19.6) 1,036 (19.6) 1,176 (22.2) 1,177 (22.3)Age groups, years, N (%) 45-54 54 (7.8) 63 (7.8) 76 (8.6) 108 (11.3) 160 (14.3) 65 (7.5) 78 (7.5) 92 (8.9) 142 (12.1) 157 (13.3) 55-64 191 (27.4) 230 (28.4) 258 (29.1) 299 (31.4) 338 (30.2) 183 (21.2) 261 (25.2) 281 (27.1) 330 (28.1) 372 (31.6) 65-74 303 (43.5) 355 (43.8) 403 (45.5) 417 (43.8) 467 (41.7) 369 (42.9) 390 (37.7) 392 (37.8) 437 (37.2) 407 (34.6) ≥75 148 (21.3) 162 (20.0) 149 (16.8) 128 (13.4) 154 (13.8) <0.001* 244 (28.3) 306 (29.6) 271 (26.2) 267 (22.7) 241 (20.5) <0.001*Male, N (%) 545 (78.3) 639 (78.9) 687 (77.5) 722 (75.8) 818 (73.1) 0.02* 696 (80.8) 835 (80.7) 792 (76.4) 918 (78.1) 910 (77.3) 0.05*
23
In-patient deaths, N (%) 22 (3.1) 40 (4.9) 34 (3.8) 32 (3.4) 23 (2.1) 0.01* 13 (1.5) 16 (1.5) 27 (2.6) 21 (1.8) 18 (1.5) 0.28*
PCINumber of events (%) 1,046 (14.9) 1,281 (18.3) 1,305 (18.6) 1,526 (21.8) 1,841 (26.3) 2,668 (16.9) 2,974 (18.8) 3,248 (20.5) 3,242 (20.5) 3,682 (23.3)Age groups, years, N (%) 45-54 115 (11.0) 152 (11.9) 162 (12.4) 246 (16.1) 370 (20.1) 277 (10.4) 345 (11.6) 400 (12.3) 523 (16.1) 651 (17.7) 55-64 285 (27.2) 420 (32.8) 412 (31.6) 522 (34.2) 542 (29.4) 591 (22.1) 705 (23.7) 889 (27.4) 964 (29.7) 1,089 (29.6) 65-74 437 (41.8) 490 (38.2) 509 (39.0) 523 (34.3) 656 (35.6) 497(35.5) 1,011 (34.0) 1,058 (32.6) 982 (30.3) 1,034 (28.1) ≥75 209 (20.0) 219 (17.1) 222 (17.0) 235 (15.4) 273 (14.8) <0.001* 853 (32.0) 913 (30.7) 901 (27.7) 773 (23.8) 908 (24.7) <0.001*Male, N (%) 736 (70.4) 925 (72.2) 919 (70.4) 1,029 (67.4) 1,174 (63.7) <0.001* 1,977 (74.1) 2,160 (72.6) 2,378 (73.2) 2,308 (71.2) 2,512 (68.2) <0.001*In-patient deaths, N (%) 19 (1.8) 10 (0.8) 25(1.9) 18 (1.2) 31 (1.7) 0.08* 76 (2.8) 54 (1.8) 75(2.3) 73 (2.2) 112 (3.0) 0.01*
Note: CABG: Coronary artery bypass grafting; PCI: Percutaneous coronary intervention.*Chi-squared test
24
Table 2. Risk of admissions for cardiovascular disease causes amongst people with diabetes over the age of 45 years in England between 2004-2005 and 2014-2015 by Index of Multiple Deprivation 2010 (IMD) quintiles
Model 1* Model 2**
Outcome Quintile of IMD (Q)
IRR 95% CIIRR for interaction between year & IMD
95% CI
Acute Myocardial Infarction
Q1 least deprived Reference ReferenceQ2 1.19 1.10 – 1.29 1.01 0.99 – 1.04Q3 1.41 1.30 – 1.52 1.01 0.98 - 1.04Q4 1.64 1.52 – 1.78 1.01 0.98 – 1.03Q5 most deprived 1.94 1.79 – 2.10 1.01 0.99 – 1.04
StrokeQ1 least deprived Reference ReferenceQ2 1.20 1.12 – 1.30 1.00 0.98 – 1.02Q3 1.36 1.26 – 1.46 1.00 0.98 – 1.03Q4 1.60 1.49 – 1.73 0.99 0.97 – 1.02Q5 most deprived 1.92 1.78 – 2.07 0.99 0.96 – 1.01
CABGQ1 least deprived Reference ReferenceQ2 1.17 1.09 – 1.26 1.00 0.98 – 1.02Q3 1.25 1.16 – 1.34 1.00 0.98 – 1.02Q4 1.38 1.29 – 1.49 0.99 0.97 – 1.02Q5 most deprived 1.66 1.50 – 1.74 0.98 0.96 – 1.01
PCIQ1 least deprived Reference ReferenceQ2 1.15 1.07 – 1.23 1.00 0.98 – 1.03Q3 1.24 1.15 – 1.33 1.01 0.98 – 1.03Q4 1.46 1.36 – 1.57 0.99 0.96 – 1.01Q5 most deprived 1.76 1.64 – 1.89 0.98 0.96 – 1.00
Note: CABG: Coronary artery bypass grafting; PCI: Percutaneous coronary intervention; IRR: Incidence Rate Ratio; CI: Confidence Interval
* Model 1: Negative binomial regression models adjusted for age, sex, study year, and quadratic term for year (year squared)
**Model 2: Negative binomial regression models adjusted for age, sex, study year, and quadratic term for year (year squared) and an interaction between year and index of multiple deprivation quintiles
25
Table 3. Rate ratios for in-patient case-fatality in people with diabetes over the age of 45 years in England between 2004-2005 and 2014-2015 among deprivation quintile groups relative to the least deprived quintile
Model 1* Model 2 **
Outcome Quintile of IMD (Q)
IRR for IMD
95% CIIRR for interaction between year & IMD
95% CI
Acute Myocardial Infarction
Q1 least deprived Reference ReferenceQ2 0.99 0.95 – 1.05 0.99 0.91 – 1.09Q3 1.01 0.96 – 1.05 0.99 0.91 – 1.08Q4 0.99 0.94 – 1.04 0.98 0.90 – 1.07Q5 most deprived 1.04 0.99 – 1.09 1.01 0.93 – 1.11
STROKEQ1 least deprived Reference ReferenceQ2 1.00 0.96 – 1.04 1.00 0.99 – 1.01Q3 1.00 0.96 – 1.04 1.00 0.99 – 1.02Q4 0.97 0.93 – 1.00 0.99 0.99 – 1.01Q5 most deprived 0.94 0.91 – 0.98 1.00 0.99 – 1.02
CABGQ1 least deprived Reference ReferenceQ2 1.07 0.90 – 1.26 0.96 0.90 – 1.01Q3 1.14 0.97 – 1.35 1.02 0.97 – 1.08Q4 1.22 1.04 – 1.43 1.01 0.96 – 1.07Q5 most deprived 1.16 0.99 – 1.37 0.99 0.93 – 1.04
PCIQ1 least deprived Reference ReferenceQ2 0.95 0.83 – 1.08 1.00 0.95 – 1.05Q3 1.07 0.94 – 1.21 0.97 0.93 – 1.02Q4 1.03 0.91 – 1.17 0.99 0.95 – 1.03Q5 most deprived 1.24 1.10 – 1.39 0.99 0.96 – 1.04
Note: CABG: Coronary artery bypass grafting; PCI: Percutaneous coronary intervention, IRR: Incidence Rate Ratio; CI: Confidence Interval
* Model 1: Negative binomial regression models adjusted for age, sex, study year, and quadratic term for year (year squared)
**Model 2: Negative binomial regression models adjusted for age, sex, study year, and quadratic term for year (year squared) and an interaction between year and index of multiple deprivation quintile
26
Figure 1. Locally weighted scatterplot smoothing curves showing age- and sex-standardised admissions rates for CVD causes in people with diabetes over the age of 45 years by deprivation quintiles between 2004-2005 and 2014-2015 in England. A) Acute myocardial infarction; B) Stroke; C) Coronary Artery Bypass Graft; D) Percutaneous Coronary Intervention. Rates are expressed as 100,000 people with diabetes over the age of 45 years.
100
120
140
160
180
Rat
e pe
r 100
,000
peo
ple
with
dia
bete
s
2004 2006 2008 2010 2012 2014Year
Quintile 1 (least deprived) Quintile 2 Quintile 3
Quintile 4 Quintile 5 (most deprived)
Acute Myocardial InfarctionA.
100
120
140
160
Rat
e pe
r 100
,000
peo
ple
with
dia
bete
s2004 2006 2008 2010 2012 2014
Year
Quintile 1 (least deprived) Quintile 2 Quintile 3
Quintile 4 Quintile 5 (most deprived)
StrokeB.
3040
5060
70R
ate
per 1
00,0
00 p
eopl
e w
ith d
iabe
tes
2004 2006 2008 2010 2012 2014Year
Quintile 1 (least deprived) Quintile 2 Quintile 3
Quintile 4 Quintile 5 (most deprived)
Coronary Artery Bypass GraftC.
6080
100
120
140
Rat
e pe
r 100
,000
peo
ple
with
dia
bete
s
2004 2006 2008 2010 2012 2014Year
Quintile 1 (least deprived) Quintile 2 Quintile 3
Quintile 4 Quintile 5 (most deprived)
Percutaneous Coronary InterventionD.
27