Medical and economic impact of

autoimmune hepatitis

Jayant A. Talwalkar, MD, MPH*, W. Ray Kim, MD, MBADivision of Gastroenterology and Hepatology Mayo Clinic 200 First Street S.W. Rochester,

MN 55905, USA

Chronic liver disease and cirrhosis are recognized as entities that require

frequent involvement by health care delivery systems worldwide. Furthermore,

emerging evidence shows that these conditions incur a significant disease burden

and increased resource use. Although autoimmune liver disease constitutes just

one aspect of this resource demand, it has a major medical and economic impact.

Despite a growing number of studies on the health care outcomes of primary

biliary cirrhosis (PBC) and primary sclerosing cholangitis (PSC), there is little

information about the health care outcome of autoimmune hepatitis (AIH). This

article proposes to review the terminology and concepts used in estimating

disease burden and economic assessment for chronic disease in general; sum-

marize the results from epidemiologic and economic investigations performed in

PBC and PSC; and outline the elements and strategies that are necessary to

develop similar information in AIH.

Epidemiologic principles of chronic disease

Measures of disease frequency

Chronic disease states can be characterized by their frequency of occurrence

and distribution within a population. The application of epidemiologic principles

allows for an estimation of magnitude within an exposure–disease relationship.

Measures of disease frequency include prevalence, incidence, and cumulative

incidence. Prevalence is defined as the proportion of individuals in a population

who have a given disease at a specific instant. This provides an estimate of

1089-3261/02/$ – see front matter D 2002, Elsevier Science (USA). All rights reserved.

PII: S1089 -3261 (02 )00030 -2

* Corresponding author.

E-mail address: talwalkar.jayant@mayo.edu (J.A. Talwalkar).

Clin Liver Dis 6 (2002) 649–667

disease risk for an individual at some point in time. Incidence is defined as the

number of new cases of disease that develop in a population at risk during a

specified time interval. Cumulative incidence is defined as the proportion of

people who develop disease during a specified period of time. Determining

cumulative incidence assumes that an entire population has been followed for the

specified length of time [1]. Alterations in disease prevalence may be from

changes in its incidence, duration, or both [2]. For example, medical therapies

that prevent or delay liver-related death, such as immunosuppressive treatment in

AIH [3,4] and ursodeoxycholic acid (UDCA) in PBC [5–11] will also increase

the prevalence rate of these conditions.

Study design and methods

Several strategies have been developed for conducting epidemiologic research

of chronic disease. Descriptive studies are important for estimating the distribution

of disease, and they serve as a basis for future investigations of disease etiology.

These investigations may involve populations or individuals reported in a case

series or cross-sectional format. Limitations with this approach include the

inability to establish a direct link between exposure and disease at an individual

level [1]. The use of analytic study designs, however, can facilitate the examina-

tion of a putative exposure’s relationship with disease. Two analytic methods have

been extensively used in the epidemiologic study of chronic disease: the case-

control and observational cohort study designs.

For rare conditions with protracted latency periods, the case-control study

design has been used to facilitate comparisons between affected individuals and

those without disease. The use of this study design established the link between

cigarette smoking and lung cancer among residents of the United Kingdom [12].

Individuals who are identified with a given disease or condition are termed cases.

The selection of cases has been primarily from hospital settings, tertiary referral

centers, or population-based catchment regions. Control subjects (individuals

without the disease or condition of interest) should ideally represent the

population of unaffected persons who would be considered cases if they

developed disease. The salient aspects of comparability between cases and

controls include strict definitions for case ascertainment, knowledge of the

baseline risk for disease development independent of exposure, and completeness

of available data [13,14]. In AIH, use of the International Autoimmune Hepatitis

Group (IAHG) scoring system would qualify as strict diagnostic criteria from

which a homogenous group of cases is assembled [15]. The use of limited

numbers of cases often requires an increasing number of controls to improve the

power for detecting relevant exposure–disease associations when present. Case

to control ratios beyond 1:4 (approximately 90% power) yield little in terms of

additional power. For larger numbers of cases (100 to 200), the optimal case to

control ratio is 1:1 [1,14,16].

The case-control study design has several advantages (Box 1) [1]. Data

collection is retrospective but rapid and inexpensive compared with prospective

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667650

investigations [17]. By identifying a large number of cases, an assessment of

multiple exposures can be performed as the illness has already occurred. This is

particularly true in rare conditions, such as PBC, PSC, and AIH. Conversely, the

case-control study design also has several disadvantages (Box 1). The primary

limitation is selection bias resulting from the differential ascertainment of both

cases and controls based on their exposure status. Although hospital-based cases

and controls are accessible and numerous, a potential concern is that control

individuals are also ill. Consequently, they may relate different histories of

exposure compared with healthy populations. Referral centers are also suscep-

tible to selection bias.

Although it will improve the level of comparability between cases and

controls, the use of population-based investigations may be affected by recall

bias. This bias reflects the discrepancy between the exposures recalled by the

controls and those recalled by the cases whose responses may be influenced by

recognition of their disease. Missing data from medical records at the time of

initial evaluation also hampers case-control studies. Therefore, the temporal

relationship between exposure and disease will be difficult to establish resulting

in exposure misclassification. This results in a bias that dilutes or underestimates

the effect of a relevant exposure when identified [1,14].

In contrast to case-control methods, the observational cohort study design

involves collecting information prospectively among individuals yet to develop a

disease or condition of interest. Individuals are classified as exposed or non-

Box 1. Strengths and limitations of the case-control study design

Strengths

Rapid and inexpensive compared with other designsApplicable for studying disease with long latency periodsOptimal for study of rare diseasesCan examine multiple exposures for a single disease

Limitations

Cannot generally evaluate rare exposuresIncidence rates of disease unknown unless subjects are fromdefined population

Temporal relationship between exposure and disease difficultto establish

Selection and recall bias

Adapted from Hennekens CH, Buring JE. Epidemiology in medi-cine. Boston: Little, Brown and Co.; 1999. p. 173; with permission

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 651

exposed while monitoring for disease over time. Changes in exposure levels can

also be accounted for before disease occurrence to accurately estimate its

magnitude of effect. With this approach, a direct measurement of disease

incidence can be performed [1].

The observational cohort study design has allowed for an improved under-

standing of the impact that multiple exposures have on the development of

coronary heart disease [18] and cancer [19–21]. Observational cohort studies

may be retrospective or prospective in nature. A general example of a retrospec-

tive design would be the use of serum samples obtained before disease

development to assess disease-related factors after the prospective study of

exposures and disease development [1].

A unique study design that emerges from an observational cohort is the nested

case-control study. In this format, controls from within the observational cohort

are individuals who have not developed disease but have prospective information

collected on their exposure histories in a similar fashion to cases. The problems

with selection bias associated with hospital and referral-based studies are

minimized in this format [1,14]. For autoimmune liver diseases, the choice of

a case-control study design format remains appropriate based on the rarity of this

condition and its long latency period before presentation. The selection of

appropriate controls from a nested case-control or population-based setting to

examine the numerous potential exposures, which may be associated with

autoimmune liver diseases may be difficult to perform.

Although appearing more favorable than case-control methods, several

limitations associated with observational cohort studies do exist (Box 2) [1].

These limitations include an increased time and expense requirement for

prospective investigations, the possibility of individuals who are asked and

refuse to participate (nonparticipation bias), the availability of complete data, and

the invalidity of results if significant loss to follow-up is identified. Length of

follow-up related to the latency period for disease development may also take

decades to establish.

For analytic epidemiologic study methods, two specific measures of asso-

ciation are commonly reported. The term relative risk (RR) is used to describe the

likelihood of disease among exposed individuals relative to those not exposed. In

an observational cohort study, this is the ratio of cumulative incidences between

exposed and nonexposed persons (Table 1). When the existence of disease among

individuals is already known, a rate of disease development cannot be deter-

mined. In case-control studies, use of an odds ratio (OR) for measuring disease

risk based on exposure status is performed [1,14,22]. OR is considered to be

a close estimate of the RR when magnitudes of association are less than three-

fold [23].

Estimating disease burden

Databases produced by the National Center for Health Statistics (NCHS)

and Agency for Healthcare Research and Quality (AHRQ) have been used to

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667652

develop estimates for prevalence, health care use, and costs of medical condi-

tions including chronic liver disease and cirrhosis. Types of databases include

(1) household surveys, which estimate disease prevalence and healthcare expen-

ditures [24]; (2) nonfederal hospital data on inpatients [25]; and (3) ambulatory

care in office-based, hospital, and emergency department settings [26,27]. The

primary shortcomings of these data sets are that disease occurrences are self-

reported and can result in underestimates of prevalence. Another limitation

pertains to data coding using different versions of the International Classification

of Disease (ICD) system [28]. This method presents a possibility for introducing

Table 1

Sibling relative risks (RR) of autoimmune conditions including primary biliary cirrhosis

Autoimmune condition Sibling RR

Rheumatoid arthritis 8

Primary biliary cirrhosis 10.5

Ulcerative colitis 12

Graves’ disease 15

Insulin-dependent diabetes mellitus 15

Crohn’s disease 20

Systemic lupus erythematosus 20

Adapted from Jones DE, Watt FE, Metcalf JV, et al. Familial primary biliary cirrhosis reassessed:

a geographically-based population study. J Hepatol 1999;30:402–407; with permission.

Box 2. Strengths and limitations of the observational cohort design

Strengths

Direct measurement of incidence in exposed andunexposed persons

Minimizes bias in exposure ascertainmentOptimal for study of rare exposuresCan examine multiple effects from a single exposureCan evaluate temporal relationship between exposureand disease

Limitations

Cannot generally evaluate rare exposuresExpensive and time-consuming if prospectiveRequires availability of adequate recordsValidity of results affected by losses to follow-up

Adapted from Hennekens CH, Buring JE. Epidemiology in medi-cine. Boston: Little, Brown and Co.; 1999. p. 173; with permission.

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 653

error with respect to disease identification. The use of three-digit (cirrhosis) rather

than four-digit level coding (chronic hepatitis C) may result in a loss of accuracy

in determining prevalence or costs of care [29].

Direct costs have been associated with physician services, ambulatory and

inpatient hospital care, outpatient hospital care, pharmaceutical therapy, and

equipment. Indirect costs have been estimated by lost wages from employment as

a consequence of work loss while consuming health care. Other segments of

indirect cost requiring measurement include reduced productivity while working,

lost unpaid labor, and lost leisure time. It is likely then that overall costs of

health care have a significant proportion stemming from indirect costs [29,30].

The estimated total cost of disease includes both direct and indirect costs.

Conservative estimates for both cost sectors occur for several reasons. For many

diseases, the scope of direct costs does not include long-term care. Many existing

datasets have excluded information from federal facilities (e.g., Armed Forces,

Veterans Administration). There are insufficient data for productivity losses

caused by mortality, by illness other than time lost in receiving health care, and

by family members and caretakers. The lack of health insurance coverage or access

to services is likely correlated with the under-reporting of disease as well [30].

Based on data estimates from the 1995 National Health Interview Survey

adjusted to 1998, there were more than 5.5 million prevalent cases of chronic

liver disease and cirrhosis in the United States for a rate of 2030 cases per

100,000 population (excluding chronic hepatitis C). There appears to be little

variation in prevalence by race. However, an estimated 60% of chronic liver

disease and cirrhosis patients are male, and more than 80% of patients are

between the ages of 25 and 64 years. Twenty-five thousand deaths from chronic

liver disease and cirrhosis (9.3 deaths per 100,000 population) occurred in 1998.

The total direct cost of treating chronic liver diseases and cirrhosis in 1998 was

estimated in excess of $1.4 billion, of which more than 90% arose in the hospital

inpatient setting ($1.2 billion). The remaining costs of care were from physician

office visits ($64.8 million), hospital outpatient departments ($57.1 million),

emergency departments ($7.0 million), and prescription drug costs ($16.9 million).

The estimated total work-loss was estimated in 1998 at $185.9 million in the

value of lost wages resulting from hospital stays for persons of working age. By

including the time associated with visits to physician offices, hospital emergency

departments, and outpatient departments, total indirect costs were estimated at

$221.5 million annually. Adjusting for inflation, this comes to $234.0 million in

year 2000 dollars. For the medical care of chronic hepatitis C in 1998 alone, a

total of $693 million and $51 million in direct and indirect costs was estimated,

respectively [30].

Economic evaluation of health care programs

Based on the recognition of limited resources, an increasing emphasis has

been placed on identifying strategies that are associated with optimal health care

delivery [31,32]. For economic evaluations of health care programs, a compar-

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667654

ative analysis between alternate courses of action is required to determine if

decision making at a policy level is appropriately performed. The evaluation of a

single program, which often results in a description of cost or outcome, is useful

to estimate disease burden but is not considered an economic evaluation based on

the absence of a comparator program [33].

Several methods have been developed for the economic evaluation of two or

more competing strategies. Common to these designs is the determination of a

ratio between consequences of health (outcomes) and the cost required to execute

the program or strategy. The term cost-minimization analysis is used when

outcomes of two or more alternative strategies are considered equivalent. Cost-

effectiveness analysis is used when the consequences of health are not valued by

monetary indices but by metrics, such as ‘‘per correct diagnosis’’ or ‘‘quality of

life years (QALY) gained.’’ Cost-utility analysis evaluates program outcomes

adjusted by preferences for particular health states or utilities. These three methods

have been successfully used for therapies that attempt to extend life at the expense

of side effects. Cost–benefit analysis, however, values both costs and outcomes

using monetary indices, which allows for comparisons between health care and

other commodities and services. Health outcomes in a cost–benefit analysis are

measured by an individual’s willingness to pay for a desired health outcome [34].

Several elements are required for performing economic analyses. These include

(1) a complete description of both costs and health outcomes; (2) the inclusion and

complete description of a competing alternative strategy; (3) the identification of

evidence to support previously established effectiveness; and (4) the appropriate

valuation and inclusion of relevant costs. The importance of conducting evalua-

tions with standardized methods is to allow for relevant comparisons between

different programs to determine resource allocation priorities. Differences in re-

source availability, clinical practice patterns, and availability of alternate strategies

may affect the results of economic evaluations. The prevalence of disease may

also affect the cost-effectiveness of interventions, which can vary by geographic

location and population. Areas of uncertainty regarding the natural history of a

given disease can further be addressed by mathematical models (known as

Markov state transition models). These models are valuable for applying results

of economic evaluations in different settings to determine if results remain ap-

plicable [33].

Health-related quality of life assessment

Health-related quality of life (HRQoL) is a multidimensional concept that

describes the physical, mental, and social function of an individual as it relates to

their overall health [35,36]. As an emerging outcome in the management of

chronic medical disease, numerous health status instruments or questionnaires

have been developed to facilitate HRQoL measurement [37]. To achieve psycho-

metric integrity, health status instruments require acceptable measures of reliabil-

ity and validity. Reliability is defined as the dependability of a scale or instrument

independent of its construction or assessment method. Measures of reliability

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 655

include internal consistency and test-retest reliability. Internal consistency is

defined as how well items in a questionnaire are measuring HRQoL without

excessive redundancy. Test-retest reliability is defined as the stability of an

instrument in measuring HRQoL when administered at different times. Validity

is defined as the degree of fit between a construct (HRQoL) and the features which

comprise it (items from a questionnaire). Four types of measurement validity are

generally required to determine an instrument’s acceptability. Face validity is

defined as a qualitative consensus that an instrument can measure HRQoL.

Content validity measures the completeness of an instrument’s ability to capture

important features of HRQoL. Concurrent validity is the association of a newly

developed instrument with a pre-existing tool that has been accepted as a method

for measuring HRQoL. Construct validity is defined as the extent of convergence

or discrimination of a specific instrument between the multiple domains (physical,

mental, etc.) of HRQoL. Construct validity is often the most difficult and time-

consuming to achieve [38].

Most health status instruments allow respondents to rate their perceived

HRQoL where a quantitative value can be derived for comparisons between other

groups or diseases. Changes in an individual patient’s health status can also be

measured in terms of effects (i.e., life years gained from an intervention) [39] or

the values placed on health status (preferences or utilities or willingness to pay)

[40]. Health state preferences or utilities are determined by asking respondents

about their threshold for risk in obtaining a desired outcome (e.g., the number of

days given up for a current state of health to achieve 1 day of improved or perfect

health state). The elicited threshold is equivalent to the utility or preference placed

on an individual’s current health state [40,41]. Willingness to pay (or contingent

valuation) is defined as the monetary threshold required for obtaining a product or

service (such as health care) based on varying levels of perceived need. It is used

as a measure of consequence in cost–benefit analyses. This measure allows for the

determination of how important health-related outcomes are to an individual or

population [42,43]. Thus, cost–benefit analysis is more global in scope than cost-

minimization or cost-effectiveness analyses, which assume that the goal (delivery

of health care) is already of importance [33].

Medical and economic assessment in cholestatic liver disease

PBC and PSC are chronic liver diseases that share features with AIH based on

the presence of immune system dysregulation. In contrast to AIH, several

investigations have been performed to date that examine disease burden and

the economic impact from cholestatic liver disease.

Population-based epidemiologic investigations

Several epidemiologic investigations of PBC have been reported. Because

significant variation exists, however, in the design of these studies, relevant

comparisons between them are difficult. While providing incidence and preva-

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667656

lence rates of disease, there are studies that lack appropriate criteria for case

definition and date of diagnosis, which results in variations in time periods for

determining incidence and prevalence. For example, several studies report the

point prevalence of PBC, whereas others report prevalence over a time period. The

use of traditional endpoints, such as mortality, is limited by the absence of death

certificate verification and the fact that PBC is a chronic disease that is not

uniformly fatal. The chronicity of disease allows for competing risks of mortality,

such as cancer and heart disease, to play important roles in determining survival

among these patients. Thus, death from liver disease may not always be accurately

recorded. Cases provided from undefined populations have limited the usefulness

of referral-based studies as the denominator required to characterize prevalence

rates will be uncertain. Population-based investigations, therefore, have been

performed to address this inadequacy and provided more accurate estimates from

different geographic regions. The use of multiple strategies in case finding has also

allowed for more accurate estimations of disease occurrence [44].

Fourteen population-based epidemiologic investigations involving 2207 cases

of PBC have been reported since 1980 [45–58]. Most investigations are based in

Europe and Great Britain with no information available from Asia, South

America, or the African subcontinent. Methods of case finding shared by most

investigations include physician surveys, medical records review, and screening

of laboratory data. Case definitions include the existence of antimitochondrial

antibody positivity and liver histology compatible or diagnostic of PBC. The use

of serum hepatic biochemical tests as criteria for case definition was reported in

9 of 14 (64%) studies [47,49–52,54,56,57,59]. Incidence rates from all 14 investi-

gations ranged from 2.2 to 27 cases per million population with prevalence rates

from 19 to 402 cases per million population. The vast majority of cases are of

female gender (female/male ratios between 3:1 and 22:1). Asymptomatic patients

were reported in frequencies ranging from 0% to 80%. The rising incidence (10 to

22 cases per million) and prevalence (180 to 240 cases per million) of PBC in

northern England has been partly attributed to longer survival from liver

transplantation and earlier diagnosis [51].

Population-based investigations have also examined the risk for complications

in PBC, including the development of cancer. Among 559 Swedish patients exam-

ined from 1958 to 1988 with a mean follow-up time from diagnosis of 9.0 ±

5.4 years [60], there was an overall excess risk for cancer compared to expected

rates for the entire population (standardized incidence ratio 1.6; 95% confidence

interval [CI], 1.1 to 2.2). No excess risk for breast cancer (standardized incidence

ratio, 0.9; 95% CI, 0.3 to 2.1) or hepatocellular carcinoma (standardized incidence

ratio, 2.91; 95% confidence interval, 0.4 to 10.5), however, was observed in the

PBC cohort. In contrast, an increased risk for hepatocellular carcinoma was

observed in the north of England among PBC patients compared with the general

population in conjunction with an excess frequency of cancer overall [61].

An increased prevalence of PBC among family members of affected patients

using population-based methodologies has been observed [62–64]. Prospective

interviews to estimate familial risk among 157 patients with definite or probable

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 657

PBC from northern England have identified a prevalence rate of 6.4% [63]. All

familial cases were identified among women with PBC. A prevalence of 0.72%

occurred among first-degree relatives (primarily siblings) and was predominantly

based on several mother/daughter relationships. The overall prevalence rate among

offspring of PBC patients was 1.2% (2.3% among female children). Given a

population-based prevalence rate of 0.039%, the RR for PBC among siblings was

estimated at 10.5 and similar to other autoimmune conditions (see Table 1). Other

RR for PBC that have been described include: 18.4 among first-degree relatives,

30.6 among offspring, and 58.7 among daughters of all women with PBC. Among

the six identified mother/daughter pairs, an earlier age of disease presentation for

daughters (39.2 years) versus mothers (53.6 years) was observed (P < 0.05).

The selection of appropriate controls in determining the significance of etio-

logic factors associated with PBC has been recently addressed as well [62].

Among 100 PBC cases and 223 controls identified from population-based re-

gistries in the United Kingdom and returning completed questionnaires, no signifi-

cant associations were found among various medical, surgical, and lifestyle factors

and PBC other than tobacco use (odds ratio [OR] = 2.4-3.5) and a history of pso-

riasis (OR = 4.6) or eczema (OR = 0.13). The risk of other autoimmune diseases

(rheumatoid arthritis, thyroid disease, and celiac sprue) in first-degree relatives of

PBC cases was twice as great compared with controls but not statistically sig-

nificant. Among 241 subjects with PBC residing in the United States [64], the

presence of Sjogren’s syndrome (17.4%), Raynaud’s phenomenon (12.5%), and

autoimmune thyroid disease (11.5%) among subjects with PBC were in excess of

frequencies among 261 siblings (1.2%, 4.6%, and 1.8%) and 225 friends (0%,

2.9%, and 1.5%) acting as controls. Compared with siblings, patients with PBC

were more often female (OR = 4.2; 95% CI = 2.2, 8.3). They also had one or more

autoimmune diseases (OR = 2.3; 95% CI = 1.2, 4.4), a history of shingles (OR =

2.7; 95% CI = 1.1, 6.7), history of tonsillectomy (OR = 2.5; 95% CI = 1.5, 4.1),

and previous cholecystectomy (OR = 2.3; 95% CI = 1.2, 4.6) more commonly.

Despite the large number of cases and controls from this investigation, the in-

complete verification of all PBC cases by multiple criteria and the use of controls

from undefined populations limit the ability to generalize these results.

To date, only one population-based investigation of PSC epidemiology has

been reported from Norway [65]. Using accepted criteria including cholangi-

ography for the diagnosis of PSC, 17 new cases were identified from a defined

catchment area over a 10-year period. The mean annual incidence rate for PSC was

1.3 per 100,000 population with a point prevalence of 8.5 per 100,000 persons.

Other estimates of disease burden from PSC are derived from heterogeneous

populations and probability calculations based on the epidemiologic character-

istics of ulcerative colitis.

Population-based natural history investigations

The natural history of PBC has been described most commonly among

referral-based cases using mathematical models developed from clinical informa-

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667658

tion. The Mayo natural history model for PBC is the most extensively validated

among several independent populations and is recognized as a valuable tool in

clinical practice [66,67]. Questions regarding the model’s ability to function

effectively given the spectrum of disease among community-based PBC patients

have recently been addressed. Among 46 patients with PBC identified from the

only reported population-based investigation from the United States [49],

17 deaths were recorded during a follow-up period of 378 person-years. In

comparison to the expected survival of age- and gender-matched Caucasian

individuals from Minnesota (81% at 10 years), a significant decrease was

observed among PBC cases (59% at 10 years, P < 0.01). When actual survival

among community-based PBC cases was compared with predicted survival based

on the Mayo natural history model, the 7-year survival of PBC patients was 65%

compared with the 61% predicted by the Mayo model (P = 0.69). Although a

number of survival models have been developed for PSC, there have been no

examinations to date among community-based populations with the disease.

Health-related quality of life assessment

For patients with PBC and PSC, the presence of greater impairments in health-

related quality of life has been reported using generic and liver disease-specific

health status instruments [68–71]. No assessment in a population-based setting

has been performed to date, however, among persons with chronic liver disease.

One hundred fifty-seven adult patients with PBC or PSC completed the

NIDDK-QA questionnaire before and 1 year after liver transplantation at the

Mayo Clinic (n = 95) or Baylor University Medical Center (n = 62) [72]. All

domains of HRQoL measured by the NIDDK-QA showed statistically significant

improvement at 1 year after liver transplantation. No pretransplant differences,

however, were observed between patients with PBC or PSC.

To assess the psychometric properties of the NIDDK-QA in the nontransplant

setting [70], 96 ambulatory subjects (82% female gender) with cholestatic liver

disease were assessed. Seventy-nine (82%) subjects with PBC and 17 subjects

(18%) with PSC were included. In all domains, the ambulatory patients had

improved HRQoL scores compared to subjects with end-stage liver disease

awaiting transplantation. Correlation between domain scores of the NIDDK-QA

and the Short-Form 36 (SF-36) health survey was significant for both physical

and mental function domains. Among individuals with PBC, a significant

correlation between Mayo risk score and overall well-being was observed. No

significant correlation between Mayo risk score and SF-36 physical and mental

function scores, however, was reported. Similar analyses among PSC patients

were not performed because of a reduced sample size.

Similar observations among ambulatory patients with cholestatic liver disease

have been observed with use of the Chronic Liver Disease Questionnaire (CLDQ)

[73]. One hundred four patients with PBC (n = 75) and PSC (n = 29) underwent

HRQoL assessment using the SF-36 Health Survey and CLDQ. A mean age of

55 ± 12 years and female gender distribution of 72% characterized the study

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 659

population. For patients with PBC and PSC, the observation of greater impair-

ments in HRQoL compared with healthy populations was observed with CLDQ

and SF-36 instruments (P < 0.001 for both). Impairments in HRQoL among

PBC patients were associated with increasing disease severity measured by

Child-Pugh classification (CP) and Mayo PBC risk score. No association with

disease severity measured by CP and Mayo risk score was observed among PSC

patients. Greater impairments in mental function (measured by the SF-36) were

observed for PBC and PSC patients compared to other chronic disease states

(i.e., congestive heart failure, chronic obstructive pulmonary disease, and diabe-

tes mellitus).

Economic evaluation of health care delivery

Practice analysis

To determine the practice patterns of gastroenterologists in the United

Kingdom in managing patients with PBC [74], a questionnaire was administered

by mail and returned by 379 of 454 (83%) physicians. Ninety-one percent of

gastroenterologists reported caring for patients with PBC (median number of

patients, 10; range, 1 to 500 patients). The estimated total number of PBC patients

cared for by all physicians surveyed was 4337. Of these, only 1376 patients (32%)

were followed specifically in designated liver units. Ninety-five percent of

gastroenterologists prescribed UDCA for the medical treatment of PBC with a

wide range in dosing (median dose, 11.5 mg/kg/d; range, 1.5 to 23.1 mg/kg/d).

Notably, the prescription of alternate disease modifying therapies unproven in

previous clinical trials (including colchicine, D-penicillamine, and immunosup-

pressants) occurred among 14% of physicians. The treatment of symptoms and

complications of PBC including micronutrient deficiency, metabolic bone disease,

and pruritus was reported by only 17% of individuals. Although UDCA is being

prescribed for PBC by most practicing gastroenterologists, there continues to be

significant variations in dosing including a substantial portion of patients

receiving less than the recommended amount.

Cost-effectiveness analysis

The cost-effectiveness of UDCA in PBC compared to placebo was determined

by comparing the annual reduction in complication rates from portal hyperten-

sion, liver transplantation, and death between treatment groups using a decision

analysis model [75]. Average annual costs for each of these events were estimated

based on literature and institutional data. Approximately twice as many major

events occurred in the placebo group compared with the UDCA group. The RR

of liver transplantation (1.95; 95% CI, 1.14 to 3.68) and development of

esophageal varices (3.11; 95% CI, 1.57 to 10.65) were significantly higher in

the placebo group compared with the UDCA group. No significant increases

were noted in the RR of ascites, variceal bleeding, encephalopathy, or death

between groups. Based on the estimated annual cost of managing these events

and the price of UDCA ($2500), there was an annual cost savings per patient of

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667660

$1372, compared to the placebo group. Although a reduction in cost was

observed, the absence of information on health state preferences or utilities to

assess the impact of UDCA on quality of life was not determined in line with

current recommendations [34].

Resource utilization

To determine the optimal timing for liver transplantation in PBC, 143 patients

were followed prospectively to determine the influence of disease severity

(measured by the Mayo risk score) on survival after transplantation and overall

resource utilization [76]. Although liver transplantation was performed at an

earlier stage of disease compared to earlier studies (e.g., median risk score, 7.5 vs.

8.3; P < 0.01), patient survival probabilities at 1, 2, and 5 years were 93%, 90%,

and 88%, respectively. The risk of death after transplantation remained low until a

risk score of 7.8 was achieved. Resource utilization measured by intensive care

unit (ICU) days and intraoperative blood transfusion requirements was signifi-

cantly greater in recipients who had higher risk scores before transplantation. A

similar analysis among 436 patients with PBC or PSC who underwent orthotopic

liver transplantation identified patient age, renal failure, CP, and United Network

for Organ Sharing score as variables which predicted similar resource utilization

indices [77].

Hepatic retransplantation in cholestatic liver disease has also been associated

with a 3.8-fold increase in the risk of death compared to individuals without

retransplantation (P < 0.01) [78]. Retransplantation after 30 days from initial

transplantation was associated with a 6.7-fold increase in the risk of death

(P < 0.01). Resource utilization was also higher in patients who underwent

multiple consecutive transplantations, even after adjustment for the number of

grafts during the hospitalization.

Among 128 PSC patients with calculated Mayo PSC model and CP scores

before transplantation [79], the CP score was found to be a significantly

(P < 0.05) better predictor of death 4 months or less after liver transplantation.

Furthermore, the length of hospital stay of more than 21 days (or death before

discharge) and resource utilization of more than 200,000 U (measured by area

under the receiver operating characteristic [ROC] curve) was also more accu-

rately predicted by the CP score. Cox proportional hazards modeling identified

statistically significant (P < 0.05) associations between the CP score and clinical

and economic outcomes adjusting for the Mayo PSC risk score. Similar results,

however, were not observed for the Mayo PSC model when adjusted for

CP score.

Medical and economic assessments in AIH

Investigations of the medical and economic impact from AIH have been

primarily epidemiologic in nature to determine the distribution of disease.

Despite the recognition of two effective treatment regimens for AIH (cortico-

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 661

steroids alone or in combination with azathioprine), there has been no economic

analysis to date examining which strategy is most cost-effective among patients

eligible for either option. There have also been no investigations of the resource

utilization from complications of end-stage liver disease and liver transplantation

related to AIH.

The epidemiology of AIH has been primarily described within the spectrum

comprising patients with chronic active hepatitis (CAH). The term ‘‘idiopathic

CAH’’ has been used to recognize these patients with clinical features similar to

AIH. Initial studies in the early 1980s have provided estimates of annual inci-

dence for idiopathic CAH ranging from 1.19 to 3 cases per 100,000 population

[53,80–83]. Despite the population-based nature of these investigations, the

presence of differences in study design makes relevant comparisons difficult. The

use of multiple case-finding methods including questionnaires, histologic exam-

ination reports, and hospital discharge summaries is not observed in all studies.

The mean age of new AIH cases from two studies was between 51 and 56 years

of age with a third of patients older than 65 years [80,82]. In contrast, the mean

age at presentation was 32 years in another study of patients residing in a similar

geographic region [53]. The severity of hepatic disease estimated by significant

elevations in serum aminotransferase levels is also variably reported [53,82,83],

suggesting that that only severe cases of AIH were subjected to liver biopsy

assessment (selection bias). The results of immunosuppressive treatment using

corticosteroids with or without azathioprine have not been universally observed

as well. One study reported that immunosuppressive therapy could be withdrawn

in 25% to 60% of cases based on clinical and biochemical improvement only to

be reinstituted because of relapsing disease in most patients [83]. Long-term

follow-up data suggesting an increased proportion of deaths from liver-related

causes, such as portal hypertension [53], have also included patients with

identifiable etiologies of CAH such as viral hepatitis and alcoholic liver disease.

A more recent population-based epidemiologic study used strict criteria to

identify patients fulfilling diagnostic criteria for AIH [65]. Adult patients who

were admitted to four hospitals serving a defined catchment area of 130,000

population between 1986 and 1995 were examined. The exclusion of chronic

viral hepatitis was also performed. A total of 25 patients with AIH were identified

in this time frame. Furthermore, similar case ascertainment methods were applied

to identify patients with PBC (n = 21) and PSC (n = 17) for comparisons. A mean

annual incidence of 1.9 cases per 100,000 population for AIH was observed (95%

CI, 1.3 to 2.8). The point prevalence was 16.9 cases per 100,000 population (95%

CI, 8.8 to 24.9), and it was greater than that observed among patients with PBC

(14.6/100,000). AIH and PBC were diagnosed more commonly than PSC

(incidence, 1.3 cases /100,000; prevalence, 8.5 cases/100,000). Patients with

AIH were older in age at diagnosis (median age, 68 years) compared with PBC

(median age, 60 years) and PSC (median age, 37 years). Female predominance

(4:1), autoantibody positivity (antinuclear antibodies and/or smooth muscle

antibodies), and elevated serum immunoglobulin G concentrations were found

in the vast majority of cases with AIH. The median Knodell score of the

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667662

histologic features for AIH was 10.5 (range, 5 to 18), and 28% of patients with

AIH had cirrhosis at initial diagnosis. Among Norwegian patients, the incidence

and prevalence of AIH was higher than among patients from other geographic

regions including Iceland, Sweden, and Great Britain. Despite a significant

proportion of histologic cirrhosis at diagnosis, no patients were referred for liver

transplantation. The outcomes from medical therapy were not reported, and the

status of ambulatory cases was not mentioned.

Gaps in knowledge

At present, specific deficiencies exist in the knowledge base for AIH which

limit the ability to estimate disease burden and associated resource utilization.

They are as follows:

1. The absence of population-based epidemiologic investigations examining

the potential role of environmental, genetic, and reproductive/endocrino-

logic exposures which may be associated with disease development.

Adequate sample size, consistent case definitions, and appropriate control

group inclusion are required.

2. The absence of information regarding optimal clinical disease management

strategies of complications related to hepatic disease and/or treatment of

AIH (including portal hypertension and metabolic bone disease).

3. The absence of information on HRQoL, including health state preferences

among persons with AIH in the presence or absence of medical therapy or

advanced liver disease.

4. The absence of information on economic evaluations for management

strategies, including medical therapy and complications of portal hyper-

tension from end-stage liver disease.

5. The absence of prognostic model development to simulate the natural

history of AIH for improvements in predicted survival, determining optimal

timing of liver transplantation, and resource utilization.

6. The failure to recognize AIH in national and proprietary databases as a

distinct chronic liver disease. Accurate coding would allow for estimating

disease burden to facilitate health planning initiatives.

Summary

AIH is a chronic liver disease that has been associated with hepatic failure and

death in the absence of liver transplantation. As a result, AIH imparts significant

medical and economic burdens on affected patients and health care delivery

systems, respectively. The use of accepted methodologies for outcomes and

health services research has identified emerging information on the epidemiology

and natural history, HRQoL, and resource utilization for similar autoimmune

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 663

chronic liver diseases such as PBC and PSC. Similar efforts are needed in AIH,

and they are supported on the basis of existing data which suggest similar levels

of disease burden compared to PBC and PSC. As a result, the ability to plan for

disease management strategies in AIH that require the allocation of scarce

resources will be feasible.

References

[1] Hennekens CH, Buring JE, editors. Epidemiology in medicine. Boston: Little Brown and Co;

1987. p. 54–177.

[2] Freeman J, Hutchison GB. Prevalence, incidence, and duration. Am J Epidemiol 1980;112:

707–23.

[3] CzajaAJ. Drug therapy in themanagement of type 1 autoimmune hepatitis. Drugs 1999;57:49–68.

[4] Johnson PJ, McFarlane IG, Williams R. Azathioprine for long-term maintenance of remission in

autoimmune hepatitis. N Engl J Med 1995;333:958–63.

[5] Lindor KD, Dickson ER, Baldus WP, et al. Ursodeoxycholic acid in the treatment of primary

biliary cirrhosis. Gastroenterology 1994;106:1284–90.

[6] Heathcote EJ, Cauch-Dudek K, Walker V, et al. The Canadian multicenter, double-blind,

randomized controlled trial of ursodeoxycholic acid in primary biliary cirrhosis. Hepatology

1994;19:1149–56.

[7] Pares A, Caballeria L, Rodes J, et al. Long-term effects of ursodeoxycholic acid in primary

biliary cirrhosis: results of a double-blind, controlled multicentric trial: the UDCA-Cooperative

Group from the Spanish Association for the Study of the Liver. J Hepatol 2000;32:561–6.

[8] Poupon RE, Balkan B, Eschwege E, et al. A multicenter, controlled trial of Ursodiol for the

treatment of primary biliary cirrhosis. N Engl J Med 1991;324:1548–54.

[9] Poupon RE, Bonnand AM, Chretien Y, et al. Ten-year survival in ursodeoxycholic acid treated

patients in primary biliary cirrhosis. Hepatology 1999;29:1668–71.

[10] Poupon RE, Lindor KD, Cauch-Dudek K, et al. Combined analysis of randomized controlled

trials of ursodeoxycholic acid in primary biliary cirrhosis. Gastroenterology 1997;113:884–90.

[11] Poupon RE, Poupon R, Balkau B, and the UDCA-PBC Study Group: Ursodiol for the long-term

treatment of primary biliary cirrhosis. N Engl J Med 1994;330:1342–7.

[12] Doll R, Hill AB. Smoking and carcinoma of the lung: preliminary report. Br J Med 1950;2:

739–48.

[13] Miettinen OS. The ‘‘case-control’’ study: valid selection of subjects. J Chronic Dis 1985;38:

543–8.

[14] Schlesselman JJ, editor. Case-control studies: design, conduct, analysis. New York: Oxford

University Press; 1982. p. 69–170.

[15] Alvarez F, Berg PA, Bianchi FB, et al. International Autoimmune Hepatitis Group report: review

of criteria for diagnosis of autoimmune hepatitis. J Hepatol 1999;31:929–38.

[16] Schlesselman JJ. Sample size requirements in cohort and case control studies of disease. Am J

Epidemiol 1974;99:381–4.

[17] Brittain E, Schlesselman JJ, Stadel BV. Cost of case-control studies. Am J Epidemiol 1981;

114:234–43.

[18] Sytkowski PA, Kannel WB, D’Agostino RB. Changes in risk factors and the decline in mortality

from cardiovascular disease: The Framingham Heart Study. N Engl J Med 1990;322:1635–41.

[19] Lipnick RJ, Buring JE, Hennekens CH, et al. Oral contraceptives and breast cancer. a prospective

cohort study. JAMA 1986;255:58–61.

[20] Michaud DS, Giovannucci E, Willett W, et al. Physical activity, obesity, height, and the risk of

pancreatic cancer. JAMA 2001;286:921–9.

[21] Sellers TA, Kushi LH, Cerhan JR, et al. Dietary folate intake, alcohol, and risk of breast cancer in

a prospective study of postmenopausal women. Epidemiology 2001;12:420–8.

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667664

[22] Miettinen OS. Estimability and estimation in case-referent studies. Am J Epidemiol 1976;103:

226–35.

[23] Zhang J, Yu KF. What’s the relative risk? A method of correcting the odds ratio in cohort studies

of common outcomes. JAMA 1998;280:1690–1.

[24] National Center for Health Statistics. Current estimates from the National Health Interview

Survey, 1995. Vital Health Stat 1995;10:1–136.

[25] Kozak LJ, Lawrence L. National hospital discharge survey: annual summary, 1997. Vital Health

Stat 1999;13:1–154.

[26] Schappert SM. Ambulatory care visits to physician offices, hospital outpatient departments, and

emergency departments: United States, 1997. Vital Health Stat 1999;13:1–39.

[27] Woodwell DA. National Ambulatory Medical Care Survey. 1998 Summary. Vital Health Stat

2000;315:1–26.

[28] International Classification of Diseases. 9th edition. U.S. Department of Health and Human

Services, PHS 94–1260; Washington DC. 1994. p. 1–34.

[29] Brown D, Everhart JE. Cost of digestive diseases in the United States. In: Everhart JE, editors.

Digestive diseases in the United States: epidemiology and impact. Washington DC: US Govern-

ment printing Office; 1994. p. 55–82.

[30] AGA Technical Report. The burden of gastrointestinal diseases. Bethesda (MD): American

Gastroenterological Association; 2001. p. 8–45.

[31] Warner KE, Hutton RC. Cost-benefit and cost-effectiveness analysis in health care: growth and

composition in the literature. Med Care 1980;18:1069–84.

[32] Weinstein MC. Economic assessment of medical practices and technologies. Med Dec Making

1981;1:309–30.

[33] Drummond MF, O’Brien B, Stoddardt GL, et al. Methods for the economic evaluation of health

care programmes. 2nd edition. New York: Oxford University Press; 1997. p. 6–51.

[34] Gold MR, Siegel JE, Russell LB, et al. Cost-effectiveness in health and medicine. New York:

Oxford University Press; 1996. p. 25–53.

[35] Guyatt GH, Feeny DH, Patrick DL. Measuring health-related quality of life. Ann Intern Med

1993;118:622–9.

[36] TestaMA, SimonsonDC.Assessment of quality of life outcomes.NEngl JMed1996;334:835–40.

[37] Borgoankar M, Guyatt G. Quality of life measurement in gastrointestinal and liver disorders. Gut

2000;47:444–54.

[38] Streiner DL, Norman GR. Health measurement scales: a practical guide to their development and

use. 2nd edition. Oxford: Oxford University Press; 1993. p. 4–14.

[39] Wright JC, Weinstein MC. Life years gained from a medical intervention. N Engl J Med 1998;

339:380–6.

[40] Torrance GW. Utility approach to measuring health-related quality of life. J Chron Dis 1987;

40:593–600.

[41] Torrance GW. Measurement of health-state utilities for economic appraisal: a review. J Health

Econ 1986;5:1–30.

[42] Gafni A. Using willingness-to-pay as a measure of benefits: what is the relevant question to ask

in the context of public decision making? Med Care 1991;29:1246–52.

[43] O’Brien B, Gafni A. When do the ‘‘dollars’’ make sense? Toward a conceptual framework for

contingent valuation studies in health care. Med Dec Making 1996;16:288–99.

[44] Parikh-Patel A, Gold E, Mackay IR, et al. The geoepidemiology of primary biliary cirrhosis:

contrasts and comparisons within the spectrum of autoimmune diseases. Clin Immunol 1999;

91:206–18.

[45] Danielsson A, Boqvist L, Uddenfelt P. Epidemiology of primary biliary cirrhosis in a defined

rural population in the northern part of Sweden. Hepatology 1990;11:458–64.

[46] Goudie B, MacFarlane G, Boyle P. Epidemiology of antimitochondrial antibody seropositivity

and primary biliary cirrhosis in west of Scotland. Gut 1987;28:A1346.

[47] Hamlyn AN, Macklon AF, James O. Primary biliary cirrhosis: a geographical clustering and

symptomatic onset seasonality. Gut 1983;24:980–3.

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 665

[48] Hislop W. A 20-year prospective study of cirrhosis. Br J Med 1981;282:819.

[49] Kim WR, Lindor KD, Locke III GR, et al. Epidemiology and natural history of primary biliary

cirrhosis in a U.S. community. Gastroenterology 2000;119:1631–6.

[50] Lofgren J, Jarnerot G, Danielsson D, et al. Incidence and prevalence of primary biliary cirrhosis

in a defined population of Sweden. Scand J Gastroenterol 1985;20:647–50.

[51] Metcalf JV, Bhopal RS, Gray J, et al. Incidence and prevalence of primary biliary cirrhosis in the

city of Newcastle-upon-Tyne. England. Int J Epidemiol 1997;26:830–6.

[52] Myszor M, James OFW. The epidemiology of primary biliary cirrhosis in northeast England:

an increasingly common disease? QJM 1990;75:377–85.

[53] Opedal I, Ritland S, Elgjo K. Chronic active hepatitis. Experience from a Norwegian reference

hospital during a decade. Scand J Gastroenterol 1985;107:52–7.

[54] Remmel T, Remmel H, Uibo R, et al. Primary biliary cirrhosis in Estonia with special reference

to incidence, prevalence, and outcome. Scand J Gastroenterol 1995;30:367–71.

[55] Triger DR, Berg PA, Rodes J. Epidemiology of primary biliary cirrhosis. Liver 1984;4:195–200.

[56] Triger DR. Primary biliary cirrhosis: an epidemiological study. Br J Med 1980;281:772–5.

[57] Watson RGP, Angus PW, Dewar M, et al. Low prevalence of primary biliary cirrhosis in Victoria.

Australia. Gut 1995;36:927–30.

[58] Witt-Sullivan H, Heathcote EJ, Cauch K, et al. The demography of primary biliary cirrhosis in

Ontario. Canada. Hepatology 1990;12:98–105.

[59] Eriksson S, Lindgren S. The prevalence and clinical spectrum of primary biliary cirrhosis in a

defined population. Scand J Gastroenterol 1984;19:971–6.

[60] Loof L, Adami HO, Sparen P, et al. Cancer risk in primary biliary cirrhosis: a population-based

study from Sweden. Hepatology 1994;20:101–4.

[61] Howel D, Metcalf JV, Gray J, et al. Cancer risk in primary biliary cirrhosis: a study in northern

England. Gut 1999;45:756–60.

[62] Howel D, Fischbacher CM, Bhopal RS, et al. An exploratory population-based case-control

study of primary biliary cirrhosis. Hepatology 2000;31:1055–60.

[63] Jones DE, Watt FE, Metcalf JV, et al. Familial primary biliary cirrhosis reassessed: a geograph-

ically-based population study. J Hepatol 1999;30:402–7.

[64] Parikh-Patel A, Gold EB, Worman H, et al. Risk factors for primary biliary cirrhosis in a cohort

of patients from the United States. Hepatology 2001;33:16–21.

[65] Boberg KM, Aadland E, Jahnsen J, et al. Incidence and prevalence of primary biliary cirrhosis,

primary sclerosing cholangitis, and autoimmune hepatitis in a Norwegian population. Scand J

Gastroenterol 1998;33:99–103.

[66] Dickson ER, Grambsch PM, Fleming TR, et al. Prognosis in primary biliary cirrhosis: model for

decision making. Hepatology 1989;10:1–7.

[67] Kim WR, Therneau TM, Wiesner RH, et al. A revised natural history model for primary

sclerosing cholangitis. Mayo Clin Proc 2000;75:688–94.

[68] Huet PM, Deslauriers J, Tran A, et al. Impact of fatigue on the quality of life of patients with

primary biliary cirrhosis. Am J Gastroenterol 2000;95:760–7.

[69] Kim WR, Lindor KD, Malinchoc M, et al. Reliability and validity of the NIDDK-QA instrument

in the assessment of quality of life in ambulatory patients with cholestatic liver disease. Hep-

atology 2000;32:924–9.

[70] Prince MI, James OF, Holland NP, et al. Validation of a fatigue impact score in primary biliary

cirrhosis: towards a standard for clinical and trial use. J Hepatol 2000;32:368–73.

[71] Younossi ZM, Guyatt G, Kiwi M, et al. Development of a disease-specific questionnaire to mea-

sure health related quality of life in patients with chronic liver disease. Gut 1999;45: 295–300.

[72] Gross CR, Malinchoc M, Kim WR, et al. Quality of life before and after liver transplantation for

cholestatic liver disease. Hepatology 1999;29:356–64.

[73] Younossi ZM, Kiwi M, Boparai N, et al. Cholestatic liver disease and health-related quality of

life. Am J Gastroenterol 2000;95:497–502.

[74] Verma A, Jazrawi RP, Ahmed HA, et al. Prescribing habits in primary biliary cirrhosis: a national

survey. Eur J Gastroenterol Hepatol 1999;11:817–20.

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667666

[75] Pasha T, Heathcote J, Gabriel S, et al. Cost-effectiveness of ursodeoxycholic acid therapy in

primary biliary cirrhosis. Hepatology 1999;29:21–6.

[76] Kim WR, Wiesner RH, Therneau TM, et al. Optimal timing of liver transplantation for primary

biliary cirrhosis. Hepatology 1998;28:33–8.

[77] Ricci P, Therneau TM, Malinchoc M, et al. A prognostic model for the outcome of liver trans-

plantation in patients with cholestatic liver disease. Hepatology 1997;25:672–7.

[78] Kim WR, Wiesner RH, Poterucha JJ, et al. Hepatic retransplantation in cholestatic liver disease:

impact of the interval to retransplantation on survival and resource utilization. Hepatology 1999;

30:395–400.

[79] Talwalkar JA, Seaberg E, Kim WR, et al. Predicting clinical and economic outcomes after liver

transplantation using the Mayo primary sclerosing cholangitis model and Child-Pugh score.

National Institutes of Diabetes and Digestive and Kidney Diseases Liver Transplantation Data-

base Group. Liver Transplantation 2000;6:753–8.

[80] Hodges JR, Millward-Sadler GH, Wright R. Chronic active hepatitis: the spectrum of disease.

Lancet 1982;1:550–2.

[81] Olsson R, Lindberg J, Weiland O, et al. Chronic active hepatitis in Sweden. The etiologic

spectrum, clinical presentation, and laboratory profile. Scand J Gastroenterol 1988;23:463–70.

[82] Ritland S. The incidence of chronic active hepatitis in Norway. A retrospective study. Scand J

Gastroenterol 1985;107:58–60.

[83] Tanner AR, Dellipiani AW. Chronic active hepatitis: a sixteen year survey at a district general

hospital. Postgrad Med J 1989;65:725–8.

J.A. Talwalkar, W.R. Kim / Clin Liver Dis 6 (2002) 649–667 667