Employment and quality of life in liver transplant recipients

Authors


Abstract

The purposes of liver transplantation (LT) include the extension of survival, improvement in quality of life, and the return of the recipient as a contributing member of society. Employment is one measure of the ability to return to society. The aim of this study is to determine the factors affecting employment/subemployment after LT. A total of 308 adult liver transplant recipients who were seen at the University of California, Los Angeles were administered the Medical Outcomes Short Form 36 (SF-36) and a questionnaire regarding work history and insurance coverage. Multivariate analysis were used to identify independent variables associated with posttransplantation employment. Interaction terms were used to examine effect modification. Of 308 transplant recipients, 218 (70.8%) worked prior to transplantation, and 78 (27%) worked posttransplantation. Pretransplant variables that were independently associated with posttransplantation employment included the following: lack of disability income (odds ratio [OR] = 1.86; 95% confidence interval [CI], 1.32-7.18; P = 0.36); health maintenance organization (HMO)/preferred provider organization (PPO) insurance (OR = 3.08; 95% CI, 1.32-7.18; P < 0.01); the number of hours worked (OR = 1.17; 95% CI, 1.08-1.28; P < 0.01); and the lack of diabetes mellitus (OR = 0.23; 95% CI, 0.70-0.73; P < 0.01). An interaction term between disability income and hours worked prior to transplantation (OR = 0.16; 95 % CI, 0.03-0.83; P = 0.03) was independently associated with posttransplantation employment. In a separate regression model of SF-36 responses, posttransplantation physical functioning (OR = 1.17; 95% CI, 1.10-1.26; P < 0.01) and role-physical (OR = 1.1; 95% CI, 1.02-1.16; P < 0.01) were independently associated with employment after transplantation. In conclusion, HMO or PPO insurance, lack of disability income coverage prior to transplant, the absence of diabetes mellitus, the number of hours worked prior to transplantation, and high physical functioning were associated with posttransplantation employment. Liver Transpl 13:1330–1338, 2007. © 2007 AASLD.

Orthotopic liver transplantation (LT) is the definitive therapy for patients with decompensated liver disease. The ultimate goals of LT are to increase life expectancy, improve quality of life, and return recipients to their daily activities. LT is associated with improved survival compared to no transplantation when Model for End-Stage Liver Disease (MELD) scores are greater than 15, and LT leads to improvement in quality of life.1, 2 However, the ability for LT to return patients to their daily activities, including employment, is controversial.

Approximately half of liver transplant recipients return to work after surgery.3–7 This statistic is strikingly low when one considers that many liver transplant recipients are receive transplantation during their most productive years. Unfortunately, this range is comparable to that seen in recipients of kidney, lung, and heart transplants.8–10 Studies in liver transplant recipients have shown that the most important factors affecting employment include age at time of transplantation, duration of disability prior to transplantation, and physical/general health performance status.2, 5–7

There are no formal guidelines established to study employment in transplant populations. Although employment is typically defined as any service for which an individual receives wages, its exact definition is variable among published studies.7, 11 For example, many studies include students and homemakers among the employed, although these groups do not earn traditional wages.11 Posttransplantation employment is a relative term that depends on the type of work (full-time, part-time, students), change in work description, and retirement. Furthermore, employment statistics in the literature do not always take into account significantly meaningful employment such as return to a previous job or similar salary level.

Our study surveyed liver transplant recipients about their work history, insurance coverage, and mental/physical health to determine the factors affecting employment after LT. We also investigated the problem of subemployment, defined as working transplant recipients who changed jobs or who experienced a decrease in salary after transplantation. By identifying risk factors, we hope to shed insight on ways to alleviate the problem of sub/unemployment in liver transplant recipients.

Abbreviations

LT, liver transplantation; CI, confidence interval; HMO, health maintenance organization; PPO, preferred provider organization; OR, odds ratio; SF-36, Medical Outcomes Short Form 36; MELD, Model for End-Stage Liver Disease.

METHODS

Study Subjects

This study included adult liver transplant recipients who were seen for follow-up at the University of California, Los Angeles Pfleger Liver Institute during a 12-month period between August 2005 and July 2006. Subjects who were unable to understand written or spoken English were provided with a translator in their native language.

All recipients seen in the Liver Institute were approached for study participation by the investigators during their routine clinic appointments. After a brief verbal description of the study, subjects were administered questionnaires as described below. Study participants encompassed a 20-yr time frame relative to transplant, ranging from those immediately posttransplantation to those having been transplanted 20 yr ago. Patient participation was completely voluntary and no compensation was given for participating in the study. The University of California, Los Angeles Institutional Review Board approved the study.

The medical records of all participating subjects were reviewed regarding date of LT, transplant indication, comorbidities, laboratory data, and current clinical status. Liver disease was categorized into viral hepatitis, alcoholic liver disease, cholestatic, malignant, and other. Pretransplantation liver disease severity was assessed using MELD, as previously described.12 MELD score calculation did not include exceptional adjustments for hepatocellular carcinoma and other diagnoses. Pretransplantation MELD scores were calculated from laboratory test results immediately prior to LT. Pretransplantation survey data were verified via the Pfleger Liver Institute transplant database and the University of California, Los Angeles Electronic Medical Record to ensure accuracy of participants' responses.

Employment was defined as any service for which a person received wages. Since the Fair Labor Standards Act does not define full- or part-time employment, we defined full-time employment as working 20 or more hours weekly (http://www.dol.gov/esa/regs/statutes/whd/FairLaborStandact.pdf). Part-time employment was defined as working fewer than 20 hours weekly. Subemployment was defined as a decrease in the number of hours worked weekly, or a decrease in annual salary given fixed work hours. Student status was considered separately pre- and posttransplantation, and was defined as active school matriculation at the time of transplantation (pre) or study enrollment (post).

Questionnaires

Each subject completed 2 self-administered questionnaires: a health-related quality of life questionnaire—the Medical Outcomes Short From 36 (SF-36) and an employment survey.

The SF-36 is a generic questionnaire that includes 36 items separated into 8 scales (Physical Functioning, Role-Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role-Emotional, and Mental Health).13 Two summary scores can be obtained: a Mental Component and a Physical Component. Range for total scores is 0 to 100, for Physical Component 8 to 73, and Mental Component 10 to 74. A higher score indicates a higher quality of life, while a lower score indicates a lower quality of life. Raw scores were transformed to a 0 to 100 scale.13

The employment survey consisted of 19 questions regarding pre-and posttransplantation personal and household income, full- and part-time employment type, full- and part-time student status, number of hours worked weekly, time unable to work prior to and posttransplantation, changes in salary and occupation posttransplantation, health insurance, disability insurance coverage status, and education level. The survey inquired about whether insurance coverage (disability coverage vs. traditional insurance coverage) affected the participant's decision to seek employment. The survey also questioned participants regarding date of transplant, gender, marital status, disability income, and ethnicity/race.

Only data on posttransplantation quality of life were available. Thus pretransplantation SF-36 domains were not included in the first regression model. A second regression model using the posttransplantation quality of life data was employed to explore the association between quality of life in transplant recipients and outcome of posttransplantation employment.

Statistics

Continuous variables were presented as means (± standard deviation). We used a Pearson correlation coefficient for continuous variables and a Spearman correlation coefficient for categorical variables.14, 15 Means were compared using t-tests for independent groups. Backward stepwise regression analysis was used to evaluate predictors of employment and subemployment. Interaction terms were created, and additional analyses were performed to examine all possible interactions among significant variables.

Predictors included gender, ethnicity, marital status, pre- and posttransplantation personal and household income, employment type, health insurance, disability income, school matriculation, education level, liver disease etiology, and pretransplantation disease severity (MELD). The second regression model included each domain of the SF-36 to test the association between posttransplantation quality of life data and employment. Standard model checking techniques were used. A P-value <0.05 in a 2-tailed test was considered statistically significant. Statistical analyses were performed using STATA (STATA Corporation, College Station, TX).

RESULTS

Patients

The demographic characteristics of the study population are shown in Table 1. Three patients approached for the study declined to participate. Their reasons were not explored. The mean age (± standard deviation) at the time of study enrollment was 51.4 yr (±13.9 yr). Most patients receiving transplantation were men (59%), non-Hispanic white (42%), and had cirrhosis from hepatitis C (39%). The time distribution from transplantation to study participation is shown in Figure 1.

Table 1. Demographic Characteristics of 308 Liver Transplant Recipients
Age (yr) ± standard deviation51.4 ± 13.9
Time since transplant (yr) ± standard deviation4.4 ± 4.6
Gender (Men/women)181/127
Married (%)199 (64.6)
Ethnicity 
 Non-Hispanic White (%)129 (41.9)
 Hispanic White (%)122 (39.6)
 African American (%)14 (4.5)
 Asian (%)31 (10.1)
 Other (%)12 (3.9)
Etiology of disease 
 Hepatitis C (%)120 (38.9)
 Hepatitis B (%)34 (11.0)
 Alcohol (%)28 (9.10)
 Cholestatic (%)42 (13.6)
 Cryptogenic (%)23 (7.5)
 Other (%)39 (12.8)
Unknown (%)2 (0.7)
Education 
 No school (%)1 (0.3)
 Grammar school (%)3 (0.9)
 Junior high (%)8 (2)
 Some high school (%)62 (20.1)
 High school (%)57 (18.5)
 Some college (%)98 (31.8)
 4-year college (%)46 (14.9)
 Graduate (%)32 (10.4)
 Other (%)1 (0.3)
Figure 1.

Time from LT to study participation.

In this study, the mean (± standard deviation) pretransplant MELD score was 23.0 (±11.4). A total of 17% of the patient cohort had hepatocellular carcinoma. Mean (± standard deviation) time from liver transplant to study enrollment was 4.4 yr (± 4.6 yr) (Fig. 1). A total of 19 patients were also renal transplant recipients. A total of 10 patients were on hemodialysis. Approximately 2% were being treated for hepatitis C infection using pegylated interferon and ribavirin at the time of study enrollment.

All patients received deceased donor organs. Patients were administered solumedrol after transplantation, and started on a prednisone taper as previously described.16 The immunosuppression regimen consisted of either tacrolimus (270 patients) or cyclosporine (38 patients). Mycophenolate (184 patients), prednisone (115 patients), sirolimus (7 patients), and azathioprine (5 patients) were used in addition to tacrolimus or cyclosporine.

Mean yearly income prior to liver transplantation ranged from 40,000 to 59,000 dollars. Almost 80% of recipients had completed at least some college. Almost all patients had heath insurance (98.4%) (Table 2). Most patients were enrolled in health maintenance organization (HMO) insurance plans. A total of 76% of transplant recipients were employed full-time and 4.9% part-time prior to transplantation (Table 3). A total of 32% of transplant recipients were collecting disability income.

Table 2. Employment by Insurance Type*
Insurance typeNumber of recipients employed (%)
PretransplantationPosttransplantation
  • Abbreviations: HMO, Health Maintenance Organization, PPO, preferred provider organizations.

  • *

    Total n = 306, with 2 study participants declining to reveal their insurance status.

HMO (n = 104)80 (76.9)36 (34.6)
Medicaid (n = 39)25 (64.1)6 (15.4)
Medicare (n = 85)55 (64.7)12 (14.1)
PPO (n = 75)54 (72.0)32 (42.7)
Self pay (n = 3)2 (66.7)0 (0.0)
Table 3. Employment Information in 308 Liver Transplant Recipients
 Pretransplantation* (%)Posttransplantation (%)
  • *

    Pretransplantation (n = 268). A total of 22 recipients were students, and no information was available for 18 recipients.

  • Posttransplantation (n = 289). A total of 13 recipients were students, and no information was available for 6 recipients,

Unemployed50 (18.7)211 (73.0)
Part-time employed13 (4.9)14 (4.8)
Full-time employed205 (76.5)64 (22.1)
Hours worked (per week)  
 No work50 (18.7)211 (73.0)
 1-20 hours13 (4.9)14 (4.8)
 21-40 hours160 (59.7)52 (18.0)
 >40 hours45 (16.8)12 (4.2)

Posttransplantation Employment

After transplantation, 10% of patients reported no yearly income. A total of 22% of transplant recipients were employed full-time after transplantation and 4.8% were employed part-time. Of those that returned to work after transplantation, 42.3% were able to begin working fewer than 6 months after transplantation, 21.8% returned between 6 and 11 months, 33.3% returned after 1 yr, and the return date of 2 recipients was unknown. A total of 12 patients reported that insurance coverage (disability/medical insurance coverage) negatively affected their decision to seek employment after transplantation.

A total of 39 participants had Medicaid (Medi-Cal) as their primary insurance, and 15.4% of these individuals were employed posttransplantation (Table 2). Two individuals on Medicaid were students after transplantation.

A total of 98 transplant recipients in our cohort were collecting disability income at the time of transplantation, while 173 individuals were collecting disability income after transplantation. Of those on disability income pretransplantation, 10 discontinued collecting disability income after transplantation. Of those not on disability income pretransplantation, 85 individuals collected disability income after transplantation.

Posttransplantation Subemployment

Most recipients returned to work within 1 yr of transplantation (Table 4). Of 274 recipients, 62% experienced a reduction in income after transplantation (Table 5). Only 7.9% had an increase in salary after surgery. A total of 6% of subjects changed occupations. Of those employed prior to transplantation, most (59.7%) worked 21-40 hours weekly, and although after transplantation most of the employed worked 21-40 hours weekly, this percentage decreased to 18%. A total of 13 participants reported having been denied employment secondary to their transplantation.

Table 4. Time to Return to Work Posttransplantation*
TimeNumber (%)
  • *

    Total n = 78 (recipients employed full- and part-time posttransplantation). No data were available for 2 recipients.

<6 months33 (42.3)
6-11 months17 (21.8)
12-17 months12 (15.4)
18-24 months5 (6.4)
>24 months9 (11.5)
Table 5. Pre and Posttransplantation Salaries and Employment According to Pretransplantation Employment*
Pretransplantation salaryNumberPosttransplantation salaryNumber (%)
  • *

    Total n = 308. There were 13 recipients who were students. Salary was not known for 21 recipients.

<$20,00070<$20,0009 (13)
  $20,000-39,0001 (1)
  $40,000-59,0000 (0)
  $60,000-79,0000 (0)
  $80,000-100,0000 (0)
  >$100,0001 (1)
  Unemployed59 (84)
  Not available0 (0)
$20,000-39,00071<$20,0004 (6)
  $20,000-39,00012 (17)
  $40,000-59,0002 (3)
  $60,000-79,0001 (1)
  $80,000-100,0000 (0)
  >$100,0000 (0)
  Unemployed52 (73)
  Not available0 (0)
$40,000-59,00043<$20,0001 (2)
  $20,000-39,0001 (2)
  $40,000-59,00010 (23)
  $60,000-79,0001 (2)
  $80,000-100,0000 (0)
  >$100,0000 (0)
  Unemployed29 (67)
  Not available1 (2)
$60,000-79,00023<$20,0000 (0)
  $20,000-39,0000 (0)
  $40,000-59,0001 (4)
  $60,000-79,0007 (30)
  $80,000-100,0000 (0)
  >$100,0001 (4)
  Unemployed14 (61)
  Not available0 (0)
$80,000-100,00019<$20,0001 (5)
  $20,000-39,0000 (0)
  $40,000-59,0000 (0)
  $60,000-79,0000 (0)
  $80,000-100,0003 (16)
  >$100,0001 (5)
  Unemployed14 (74)
  Not available0 (0)
>$100,00021<$20,0000 (0)
  $20,000-39,0000 (0)
  $40,000-59,0000 (0)
  $60,000-79,0001 (5)
  $80,000-100,0000 (0)
  >$100,0007 (33)
  Unemployed12 (57)
  Not available1 (5)
No income27<$20,00011 (41)
  $20,000-39,0001 (4)
  $40,000-59,0001 (4)
  $60,000-79,0000 (0)
  $80,000-100,0000 (0)
  >$100,0000 (0)
  Unemployed14 (52)
  Not available0 (0)

Quality of Life

All SF-36 domains collected posttransplantation were significantly lower in our cohort compared with the general population (P < 0.01). Two domains were significantly associated with posttransplantation employment in the second regression model: physical functioning (odds ratio [OR] = 1.17; 95% confidence interval [CI], 1.10-1.26; P < 0.01), which assesses limitations in physical activities because of health problems, and role-physical (OR = 1.1; 95% CI, 1.02-1.16; P < 0.01), which assesses limitations in usual role activities because of physical health problems. Mental health had no association with employment (OR = 0.98; 95% CI, 0.95-1.00; P = 0.09).

Variables Associated With Posttransplantation Employment

Age, gender, numbers of hours worked pretransplantation, indication for transplantation, absence of diabetes mellitus, as well as matriculation in school at the time of transplantation and lack of disability coverage prior to transplant were significantly associated with employment in univariate analysis. Ethnicity, education level, marital status, homemaker and student status, and MELD score were not predictive of employment in our cohort. Comorbidities including encephalopathy, need for hemodialysis, ascites, and systemic hypertension were also not significantly associated with employment. In a multivariate analysis before interaction terms were included, lack of disability insurance income prior to transplantation (OR = 0.50; 95% CI, 0.24-0.10; P < 0.05), HMO/preferred provider organization (PPO) insurance coverage at the time of transplantation (OR = 3.12; 95% CI, 1.51-6.47; P < 0.01), lack of diabetes mellitus (OR = 0.27; 95% CI, 0.10-0.72; P < 0.01), and the number of hours worked prior to transplantation (OR = 1.07; 95% CI, 1.03-1.11; P < 0.01) were independently significantly associated with posttransplantation employment.

Interaction Model

Pretransplant variables that were independently associated with posttransplantation employment included lack of disability income (OR = 1.86; 95% CI, 1.32-7.18; P = 0.36), HMO/PPO insurance (OR = 3.08; 95% CI, 1.32-7.18; P < 0.01), and the number of hours worked (OR = 1.17; 95% CI, 1.08-1.28; P < 0.01) and the lack of diabetes mellitus (OR = 0.23; 95% CI, 0.70-0.73; P < 0.01). A significant interaction was noted between disability income and hours worked prior to transplantation, such that receiving disability income decreased the effect of hours worked prior to transplantation (OR = 0.16; 95% CI, 0.03-0.83; P = 0.03). The coefficients of the final interaction model are shown in Table 6. The disability income variable was kept in the model as it was part of the interaction term.

Table 6. Interaction Model Coefficients
Variable*Coefficient95% CIP value
  • Abbreviations: HMO, health maintenance organization, PPO, preferred provider organization.

  • *

    Lack of disability income was kept in the model as it is part of the interaction term.

  • Interaction term: the product of hours worked pretransplantation and lack of disability income.

Lack of disability income*−0.62−0.69 to 1.94= 0.36
HMO/PPO insurance1.130.28 to 1.97<0.01
Hours worked pretransplantation0.160.07 to 0.25<0.01
Diabetes mellitus−1.49−2.67 to −0.32<0.01
Interaction term1.80−3.42 to −0.19= 0.03

In a separate regression model of SF 36 responses, post transplant physical functioning (OR = 1.17; 95% CI, 1.10-1.26; P < 0.01) and role-physical (OR = 1.1; 95% CI, 1.02-1.16; P < 0.01) were associated with posttransplantation employment.

DISCUSSION

The goals of LT are to increase survival, improve quality of life, and return patients to contributing members of society. The ability to work is a key element in any individual's life for both economic and psychosocial well-being. Our study identified factors significantly associated with employment after LT. Factors previously found to be associated with unemployment include gender, education level, alcohol liver disease etiology, age, pretransplantation employment, disability, and concerns about losing health insurance.2, 3, 6, 7

Although our study is a single-center experience, it is one of the largest cohorts evaluated (n = 316). It validates previous posttransplantation employment rates of approximately 30 to 40%.2, 3, 6, 7 Higher rates have been published, but these studies involved much smaller cohorts4, 5, 17 and grouped homemakers and students as employed.5, 11 Our study partitioned patients by defined full- and part-time employment, as well as full/part time students and homemakers. Some of the variability of unemployment may be explained by a lack of a uniform definition of employment in the various studies or a standardized employment questionnaire. Furthermore, to our knowledge, no published studies have focused on subemployment, whereby liver transplant recipients return to work but have either decreased the number of hours worked per week or decreased their pay scale given fixed work hours. In our study, of 77 individuals who had returned to work, 19 (25.7%) reported a decrease in household income; 5.5% changed occupations.

Our study cohort demographics were similar to all patients who underwent LT at the University of California, Los Angeles from February 1984 to December 2001 (n = 3,200). Indeed, most recipients between 1984 and 2001 were men (n = 1,406) and had cirrhosis from hepatitis C (n = 718).18 The gender and race breakdown of our study cohort was similar to all liver transplant recipients receiving transplantation in California between 1988 and 2006, matched using the United Network for Organ Sharing database.19 In California, 58.9% of those receiving transplantation were men (our cohort 58.8%), while 78% were white or Hispanic (82% in our cohort).20

In the current study, several factors were independently associated with posttransplantation employment, including HMO/PPO insurance coverage at the time of transplantation, absence of diabetes mellitus, the number of hours worked prior to transplantation, and the lack of disability income prior to transplantation. Disability income and its effect on employment is a controversial subject. Patients who have developed a disability prior to transplantation may continue to suffer from ailments that further limit their return to work after surgery. However, a secondary goal may also exist. Returning to work may compromise the ability to receive disability income and disability medical health insurance. Although this study focused on those receiving disability income, a distinction should be made between being disabled and receiving disability income since these terms are not necessarily synonymous. Patients in our study may have received disability income, but may not necessarily have been disabled. However, all SF-36 domains were lower in our cohort than in the general population. In particular, poor physical function led to significantly less employment, making it likely that our cohort had true disability (as evidenced by low SF-36 scores), leading to less employability. Adams et al.6 also found that disability prior to transplantation predicted posttransplantation unemployment, and Thomas21 found that the idea of losing health insurance and disability were associated with posttransplantation unemployment. In our study the impact of disability income mitigated the effects of pretransplantation work hours on posttransplantation employment. In other words, any predictive benefit of pretransplantation work hours were diminished if transplant recipients were on disability income prior to transplantation.

HMO/PPO medical insurance was also significantly associated with posttransplantation employment. Health insurance is closely tied to employment in the United States, as most private insurance is obtained through the workplace. Fear of insurance loss may act as an incentive for employment in an era of rising medical costs, especially for those with private insurance (HMO/PPO). On the other hand, those with government medical insurance like Medicaid may lose their medical insurance coverage if they seek gainful employment and rise above the Medicaid income qualification thresholds. While recipients having Medicaid and HMO/PPO insurance strive for the same goal of maintaining insurance coverage, the HMO group has a financial incentive to work while the Medicaid group may have an incentive to remain unemployed. Not surprisingly, government medical insurance (Medicare, Medicaid) was not significantly associated with posttransplantation employment in this study.

Another interesting finding in our study is that the probability of returning to work appears to diminish over time. Of recipients who did return to work, 42% returned to work within 6 months of transplantation. Within 2 yr, 22% of recipients were able to return to work. It is not clear if the decreasing probability of employment over time is a potential reflection of waning patient motivation and accumulation of chronic diseases/illnesses. Many patients may find it difficult to enter the workforce not only after major surgery, but also as an aging individual. With recuperation time comes advancing age, which is critical given that most transplant recipients were in their 50s at the time of transplantation. Thus, most patients have a very critical window for reentering the workforce, leading to the decreasing probability of employment over time. Our study also found that a paltry 5.5% changed occupations after surgery. Unfortunately, many patients may be unable to transfer job skills after transplantation and with increasing age.

Health-related quality of life in our participants was significantly lower than the general population, similar to that seen in Sahota et al.7 and Rongey et al.5 Depressed quality of life has been reported in heart, lung, and pancreas transplantation.22–24 Our study results were consistent with current published conclusions that poor physical health is a factor in unemployment posttransplantation.6 Physical functioning, but not mental health measures, was significantly associated with employment posttransplantation in our study. This finding is not surprising, as patients are extensively screened prior to transplantation by psychiatrists but undergo no formal physical therapy evaluation.

The presence of diabetes was associated with a decreased rate of employment. For instance, a transplant recipient with diabetes is only 0.23 times as likely to be employed as he/she is to be unemployed. Indeed, a cross-sectional study analysis of the Health and Retirement Study found that diabetes was a significant predictor of lost of employment productivity, including increased number of sick days.24 Others have similarly found a negative impact of diabetes on employment.25–27

Our report has several potential limitations. First, the results may be affected by selection bias. Patients may be more likely to come to the clinic if they are experiencing health-related complications or other issues that may interfere with employability. Our study cohort enrollment was limited to patients followed in our clinic. This may be a source of bias resulting in an underestimation of employability. However, the mean time from transplantation to study enrollment was greater than 4 yr, with a range of approximately 20 yr. One expects that most immediate complications from transplantation would have been addressed. Furthermore, recipients are followed at the Pfleger Liver Institute at a minimum of once yearly, giving ample opportunity to address complications. Moreover, since our cohort is one of the largest examined, it not only increases generalizability but also minimizes potential selection biases.

Since the length of time from transplantation to study participation is large, a respondent's recollection may not be completely accurate. Yet transplantation is a vivid time in an individual's life, and participants may be more likely to remember details surrounding such an event. Psychiatric studies support the idea that stressful events may promote certain memory recall.28–30 However, these recollections cannot be absolutely accurate, and thus prospective studies, which survey patients prior to and after transplantation, may prove helpful.29 It should also be noted that participation in any study does not ensure honesty.

Another potential limitation is patients may not have disclosed their employability or earnings for fear of compromising any disability income and health-care related insurance. Indeed, the idea of losing health insurance and disability may affect patient's interest in returning to work. In a recent study by Rongey et al.5 regarding employment in liver transplant recipients, 18% of patients surveyed reported lost and/or denied health insurance since their surgery. In addition, a number of patients reported being denied or terminated from their employment because of their transplantation. Thus, we strove to make the interview as comfortable for the patient as possible. Patients were asked about study participation in private examination rooms, and were assured that their responses would be used for research purposes only. Only 3 patients declined to participate.

Our study did not collect data on the duration of disability. The duration of disability could bias the results since patients who remained on disability for a greater time may be less likely to return to work. A recent study noted that patients who received Social Security Insurance for more than 6 months were less likely to return to work after transplantation.7 Future surveys should study the impact of time on disability on posttransplantation employment.

Future studies should focus on work-related discrimination. Employers may be resistant to hire a transplant recipient because of concerns about physical functioning, infection risks, need for ongoing office visits, and higher insurance costs. In our study, 13 patients reported having been denied employment due to their transplant. Company size, type of work, and pay scale may impact employers' views.

In summary, unemployment is common after LT. The probability of unemployment appears to increase over time after surgery. Liver transplant recipients are more likely to be employed if they have HMO/PPO insurance, did not receive disability income prior to transplantation, do not have diabetes mellitus, worked more hours prior to transplantation, and have high physical functioning.

Acknowledgements

We thank Ivon Brito, Rocio Albarran, Ehsan Taqavi, and Sepideh Adhami for their administrative assistance

Ancillary