Employment outcomes after solid organ transplantation are an area of concern for transplant professionals and the general public. The economic burden of transplantation has created challenges in governmental agencies with respect to the funding of these procedures: this has been demonstrated by the withdrawal of Medicaid funding for solid organ transplantation by Oregon in 1987 and more recently by Arizona in late 2010.1, 2 A return to gainful employment should be considered an important outcome parameter after liver transplantation (LT) because it is associated with normal socialization, the achievement of personal goals, financial independence, and self-esteem.3, 4
Several investigators have studied the rates of employment after LT.5-14 Most previously published studies have been based on small cohorts and have shown considerable variations in the sample size, which has ranged from 52 recipients in the 1996 study by Hunt et al.6 to 308 recipients in the 2007 study by Saab et al.14 Even though the majority of LT procedures are performed on working-age adults during their most productive work years, approximately half of the recipients (or less) return to work after surgery; several centers have reported posttransplant rates of employment ranging from 22% to 60%.5-14 In addition, according to 2008 data from the United Network for Organ Sharing (UNOS), only 22% of adult LT recipients return to work after transplantation.15 In addition, 60% to 70% of LT patients recover without ongoing medical problems and yet are not employed after their surgery.16, 17
Several factors may be associated with unemployment after LT. Some reports have shown that unemployment after transplantation is associated with poor health, disability status, early retirement, and a fear of losing disability or Medicaid benefits.6, 11 Most literature on employment after LT has focused on patients' quality of life, societal reintegration, and working competence.
It is worthwhile to examine the rate of employment with the UNOS data set, which is a nationwide transplant registry. This data set provides a large, multi-institutional sample of adult LT recipients. The goal of this study was to mitigate the limitations of previous studies through a comprehensive assessment of return-to-work outcomes of LT recipients via an analysis of the UNOS database. This study was focused on data collected since the adoption of the acuity-based Model for End-Stage Liver Disease (MELD) scoring system (February 27, 2002). The objectives of this study were (1) to analyze the large, national UNOS database for a nearly 7-year period to determine the number of LT recipients who were employed and unemployed within 24 months after LT and (2) to examine the factors associated with employment after LT.
MATERIALS AND METHODS
This study examined employment after transplantation with data from the UNOS data set. The primary unit of analysis was the individual LT recipient. The sample for this study consisted of adult LT recipients who received a transplant between February 27, 2002 (when the MELD system was adopted) and December 31, 2008. This analysis included only recipients who received a deceased donor organ and compared those recipients who worked for income within 2 years of transplantation to recipients who did not work for income after transplant surgery.
The initial database included 93,972 LT procedures. Subjects were excluded for the following reasons:
- 1The patients were less than 18 years old (n = 11,418). This eliminated children, who were more likely to be attending school than seeking employment.
- 2The patients were 65 years old or older (n = 6805). Patients of this age group were more likely to be retired than seeking employment.
- 3The transplantation procedures involved the liver and another organ (liver and heart, n = 83; liver and intestine, n = 973; liver and kidney, n = 3437; liver and lung, n = 36; and liver and pancreas, n = 620). This limited the focus of the study to persons undergoing only LT.
- 4The patients were listed in the registry before February 1, 2002 (n = 51,152). There were no MELD scores for this period.
- 5The patients were listed after June 30, 2008 (n = 2321). There was no 24-month posttransplant observation period.
- 6The patients previously underwent LT (n = 10,544). They may have been listed for a second transplant.
- 7Living donors were involved (n = 3750). These patients were relatively healthy in comparison with patients undergoing deceased donor transplantation.
- 8The patients had a recurrent disease (n = 4139). These patients often had disabilities associated with disease recurrence.
- 9The patients had no transplant recipient follow-up (TRF) forms (n = 2481). Therefore, we had no posttransplant information.
A particular concern was the employment status reported on the transplant candidate registration (TCR) and transplant recipient registration (TRR) forms. Although information about the employment status was generally consistent on the TCR and TRR forms, there were cases in which this information was missing on 1 form. To address this, we classified these patients as employed or not employed as long as this information was available on at least one of the forms. When there was no answer to this question on either form, the cases were recorded as missing and were dropped from the analysis. At the time of listing (ie, on the TCR form), 26.5% of the patients reported being employed. This rate was 19.53% at the time of transplantation on the TRR form.
Another concern was that the UNOS data set categorized MELD scores for status 1 cases as missing. To address these missing values, we conducted a sensitivity analysis. One set of models included the cases with missing MELD scores as a distinct category as well as all patients with recorded scores. A second set of models reran the analysis without the status 1 cases. The odds ratios (ORs) for the 2 sets of models were similar in their magnitude and statistical significance. To minimize ambiguity about the unmeasured MELD values, we elected to report only the results obtained from the models in which the status 1 cases were omitted.
The dependent variable was dichotomous and represented whether an individual was working or not working. Employment was defined as follows: the LT recipient indicated that he or she was working full-time or part-time. This variable was measured with Scientific Registry of Transplant Recipients (SRTR) data derived from answers to questions on the TRF form. On the TRF form, this variable is captured as “working for income.” Employment was set to 1 (yes) if “yes” was recorded for this variable on the TRF form at any point within 24 months after transplantation; all patients without this observation were set to “no employment” after transplantation.
This study is based on Grossman's labor health capital model.18 In this model, an individual's participation in the labor force is determined by a series of demographic and health-related factors and external factors that influence his or her incentive or ability to work. The demographic variables within this model include age, sex, ethnicity, education, and pretransplant employment (Table 1). These attributes condition the likelihood of work. Three health-related measures were available from the UNOS and were used in this analysis: the MELD score at the time of transplantation, the liver disease etiology, and the posttransplant functional status (Table 1).
Table 1. Definitions of Independent Variables
|Demographic|| || || |
| Age||TRF||Chronological age at the time of follow-up||Continuous variable collapsed into 4 categories (18-40, 41-55, 56-62, and 63-65 years)|
| Sex||TRF||Male or female||Dichotomous variable coded as 0 for male and 1 for female|
| Race||TCR||Background with which the individual most identifies||Categorical variable with 8 options collapsed into 4 categories (white, black, Hispanic, and other)|
| Educational level||TCR||Highest level of education||Categorical variable with 8 options collapsed into 3 categories (less than a high school education, less than a college degree, and college or higher degree)|
| Pretransplant employment||TCR/TRR||Working for income before transplantation||Dichotomous variable coded as 1 for yes and 0 for no|
|Health-related|| || || |
| MELD score at the time of transplantation||TRR||MELD score quantifying the acuity of illness with respect to liver disease||Analyzed as a categorical variable (≤21, 22-30, and ≥31)|
| Etiology of liver disease||TCR||Diagnosis of liver disease||69 options collapsed into 6 categories (hepatitis, cirrhosis, alcohol, biliary disease, HCC, and other)|
| Functional status||TRF||Functional status at the time of follow-up: 80%-100% (no limitation) or 70% or less (limited mobility)||10 options collapsed into 2 categories (no limitation and limited mobility)|
The demographic and health-related independent variables that were analyzed in this study are displayed in Table 2. Sex data were obtained from the SRTR TRF form, and sex was coded as 1 for females and as 0 for males. Race was measured as a categorical variable: American white, black or African American, Hispanic/Latino, and other. American white was the reference category. Education was represented in the analysis as a categorical variable: less than a high school education, a high school education, or a college or higher degree. Less than a high school education was the reference category. The pretransplant employment status was identified from SRTR data derived from questions on the TCR and TRR forms.
Table 2. Demographic and Health-Related Characteristics: Employment Versus Unemployment
|Demographic|| || || |
| Age [n (%)]|| || || |
| 18-40 years||647 (32.7)||1330 (67.3)||1977 (9.0)|
| 41-55 years||3233 (25.7)||9334 (74.3)||12,567 (57.3)|
| 56-62 years||1310 (21.2)||4879 (78.8)||6189 (28.2%)|
| 63-65 years||170 (14.1)||1039 (85.9)||1209 (5.5)|
| Sex [n (%)]|| || || |
| Male||4198 (27.1)||11,276 (72.9)||15,474 (70.5)|
| Female||1162 (18.0)||5306 (82.0)||6468 (29.5)|
| Race/ethnicity [n (%)]|| || || |
| White||4191 (25.8)||12,043 (74.2)||16,234 (73.9)|
| Black||450 (24.5)||1384 (75.5)||1834 (8.6)|
| Hispanic||440 (15.9)||2323 (84.1)||2763 (12.5)|
| Other||279 (25.1)||832 (74.9)||1111 (5.0)|
| Educational level [n (%)]|| || || |
| Less than a high school education||1224 (18.1)||5532 (81.9)||6756 (30.8)|
| Less than a college degree||2908 (24.7)||8873 (75.3)||11,781 (53.7)|
| College or higher degree||1228 (36.1)||2177 (63.9)||3405 (15.5)|
| Pretransplant employment [n (%)]|| || || |
| No||3161 (17.7)||14,707 (82.3)||17,868 (81.4)|
| Yes||2199 (54.0)||1875 (46.0)||4074 (18.6)|
|Health-related|| || || |
| MELD score at the time of transplantation [n (%)]|| || || |
| ≤21||2030 (25.5)||5920 (74.5)||7950 (36.2)|
| 22-30||2648 (25.3)||7824 (74.7)||10,472 (47.7)|
| ≥31||682 (19.4)||2838 (80.6)||3520 (16.0)|
| Etiology of liver disease [n (%)]|| || || |
| Alcohol||938 (20.9)||3559 (79.1)||4497 (20.5)|
| Hepatitis||190 (26.5)||528 (73.5)||718 (3.3)|
| Cirrhosis||2281 (22.9)||7682 (77.1)||9963 (45.4)|
| Biliary disease||676 (33.3)||1357 (66.7)||2033 (9.3)|
| HCC||817 (28.0)||2100 (72.0)||2917 (13.3)|
| Other||458 (25.2)||1356 (74.8)||1814 (8.3)|
| Functional status after transplantation [n (%)]|| || || |
| Missing||188 (3.7)||4861 (96.3)||5049 (23.0)|
| No limitation||5073 (37.2)||8557 (62.8)||13,630 (62.1)|
| Limited mobility||99 (3.0)||3164 (97.0)||3263 (14.9)|
The MELD scores that were available for analysis were previously calculated within the Organ Procurement and Transplantation Network (OPTN) database. They were represented in the analysis as a categorical variable. The MELD scores were grouped into 4 categories of increasing disease severity. Patients with a MELD score ≥ 31 were considered to have a high risk of mortality: according to a literature review, patients with a MELD score ≥ 31 have a particularly high risk of dying without LT. According to the MELD scores, the remaining patients were stratified into 3 general categories of disease severity and risk of dying: (1) a score less than 21 (low risk), (2) a score of 22 to 30 (medium risk), and (3) a score greater than 31 (high risk). A MELD score less than 21 was the reference category.
The etiology of chronic liver disease may influence the employment status after transplantation. The underlying disease that led to end-stage liver disease was determined from the most specific entry among all the diagnostic categories in the UNOS database. The UNOS classification scheme for the etiology of end-stage liver disease includes more than 50 codes, and many included too few patients for a disease-specific analysis. For the purpose of this analysis, the UNOS scheme was collapsed into 6 diagnostic categories:
- 1Alcoholic liver disease (the reference category).
- 2Hepatitis [liver disease caused by a hepatitis virus (A, B, C, or D) or drug-induced hepatitis].
- 3Cirrhosis (cryptogenic cirrhosis or cirrhosis due to fatty liver, autoimmune hepatitis, or hepatitis B with a positive surface antigen).
- 4Biliary disease (primary biliary cirrhosis, secondary biliary cirrhosis, cholestasis, Crohn's disease, or ulcerative colitis).
- 5Hepatocellular carcinoma (HCC; HCC or hepatoma with cirrhosis).
- 6Other liver disease (eg, Budd-Chiari syndrome, alpha-1-antitrypsin deficiency, Wilson's disease, hemochromatosis, or polycystic liver disease).
We used the diagnosis at the time of listing and assigned each patient to one of the categories. For all diagnoses, we considered the UNOS diagnosis to be accurate; it was not possible to re-evaluate the accuracy of the diagnosis codes in the UNOS data set.
An individual's functional/health status after LT can have an effect on the likelihood of successful employment. The posttransplant functional status that was noted on the SRTR TRF form was captured as the functional status. The answers were scored from 10% to 100% (10%, moribund; 20%, very sick; 30%, severely disabled; 40%, disabled; 50%, requiring considerable assistance; 60%, requiring occasional assistance; 70%, caring for self; 80%, normal activity; 90%, able to perform normal activity; and 100%, normal with no complaints). For the purposes of this study, these categories were aggregated into 2 groups: limited mobility (10%-70%), and no limitation (80%-100%).
Data Analysis and Statistics
To accomplish the aims of this study, we obtained the OPTN data via the SRTR. A final working database was compiled in an SAS 9.0 data file to allow for the appropriate statistical measurements. The data were cleaned and the codes were labeled according to the SRTR code book provided by the OPTN (except as previously described).
Statistics were used to compare the population of LT recipients who underwent LT between 2002 and 2008 and returned to work to the population of LT recipients who did not return to work during this period. Frequencies, percentages, and measures of the central tendency were employed to summarize the characteristics of the sample and to evaluate the data.
A χ2 test was used to analyze the proportion of LT patients who were employed after transplantation.19 A univariate analysis was used to determine which of the independent variables correlated significantly with the dependent variable (ie, working for income). Those variables were then subjected to multivariate logistic regression models. These were used to ascertain whether the demographic and health-related characteristics of the LT recipients had an association with their employment status after LT.
Protection of Human Subjects
There was no contact with the subjects. In addition, this project involved a secondary analysis of publicly available data submitted to the UNOS and SRTR by individual transplant programs and organ procurement agencies. These data are managed by the SRTR through a federal contract with the Health Resources and Services Administration. The use of these data met the requirements for exempt categories of research as determined by the University of California San Francisco committee on human research.
There were 21,942 LT recipients between February 27, 2002 and December 31, 2008 who met the inclusion criteria for this study. This study analyzed the employment status of posttransplant patients within a 60-day window at the following times after transplantation: 6, 12, and 24 months. Because the posttransplant follow-up dates did not aggregate precisely into intervals of 6, 12, and 24 months, the definitions of working at these times were changed to working at any time before 6 months, working at any time before 12 months, and working at any time before 24 months.
Employment After LT
The presented results allow for working within the 24-month observation window. The analyses were conducted with the inclusion and omission of mortality cases. Because the results were similar, the findings that are shown include the mortality cases; some mortality recipients had returned to work within one of the measurement periods and may have died for reasons not connected to their transplant or liver disease. An examination of the employment status in the follow-up period showed that only 6.5% of the patients had been employed in the 0- to 6-month posttransplant follow-up period, 17.1% had been employed within 0 to 12 months after transplantation, and 24.4% had been employed within 24 months after transplantation.
Table 2 shows the demographic and health-related characteristics of the LT recipients who worked after transplantation within the 24-month follow-up period. In terms of group demographics, males composed 27.1% of the employed patients, who predominantly were white (25.8%), 18 to 40 years old (32.7%), college-educated (36.1%), and employed before transplantation (54.0%). The patients who were not actively employed predominantly were female (82%), 63 to 65 years old (85.9%), and Hispanic (84.1%), had less than a high school education (81.9%), and had no employment history before transplantation (82.3%).
As indicated in Table 2, 25.5% of the employed transplant patients had a MELD score < 21 at the time of transplantation and had no physical limitations after transplantation (37.2%). The most common etiology for transplantation in the employed group was biliary liver disease. The unemployed patients tended to have MELD scores greater than or equal to 31 and moderate to severe physical limitations before transplantation. The most common liver disease diagnosis in the unemployed group was alcoholic liver disease.
Factors Associated With Posttransplant Employment
The factors associated with employment after LT are displayed in Tables 3 and 4. In addition to these models, we conducted an analysis (not shown) involving only those cases with a TRF form indicating employment in the 12- to 24-month interval after LT so that we could determine whether the predictors of working in that period were different from the predictors when the sample included cases with information on the employment status anytime in the 0- to 24-month interval after LT. The ORs were similar in magnitude and significance in the 2 sets of models.
Table 3. Multivariate Logistic Regression Analysis of Employment Status After LT at Any Time During the 24-Month Follow-Up Period (n = 21,942)
|Age (years)|| || || |
| 18-40||1.0|| || |
|Sex|| || || |
| Male||1.0|| || |
|Educational level|| || || |
| Less than a high school education||1.0|| || |
| Less than a college degree||1.29||1.18-1.41||<0.001|
| College or higher degree||1.92||1.72-2.1||<0.001|
|Ethnicity|| || || |
| White||1.0|| || |
|Pretransplant employment|| || || |
| No||1.0|| || |
|MELD score|| || || |
| <21||1.0|| || |
|Etiology of liver disease|| || || |
| Alcohol||1.0|| || |
| Biliary disease||1.69||1.47-1.95||<0.001|
|Functional status|| || || |
| No limitation||1.0|| || |
| Limited mobility||0.05||0.04-0.06||<0.001|
Table 4. Multivariate Logistic Regression Analysis of Employment Status After LT at Any Time During the 24-Month Follow-Up Period by Sex
|Age (years)|| || || || |
|Educational level|| || || || |
| Less than a college degree||1.34||<0.001||1.18||0.05|
| College or higher degree||1.91||<0.001||1.96||<0.001|
|Ethnicity|| || || || |
|MELD score|| || || || |
|Liver disease|| || || || |
| Biliary disease||1.89||<0.001||1.51||0.003|
|Functional status|| || || || |
| Limited mobility||0.05||<0.001||0.07||<0.001|
The number of missing values for each of the study variables varied. Our methodological approach was to treat “missing” as a value and to compare the effects of this classification with respect to a reference category. This avoided the listwise or pairwise deletion of cases and did not require the imputation of values. As we discuss later for each measure, there was no systematic bias associated with missingness, and those with this classification did not have uniquely significant associations with the study outcome. In several instances (eg, educational level and functional status), the ORs for the risk factor were similar in those with missing values and subgroups for which we had measurements (eg, those with a low educational level and those with functional limitations). We conducted another analysis by re-estimating the results of Table 3, and we determined the results when the missing cases were excluded for selected items. These casewise deletions did not materially alter the relationship between the risk factor and the outcome.
As shown in the logistic regression, patients in the age reference category (18-40 years old) had a higher likelihood of being employed after LT than patients in the other examined age cohorts. Females were 0.57 times less likely than males to be employed after transplantation (P < 0.001). Separate models were run for male and female employment after transplantation (Table 4), and there were few notable differences between the models with respect to the strength of the relationship between the risk factors and the employment outcome. The most notable difference was that female Hispanics may be marginally more likely (OR = 0.62) than Hispanic males (OR = 0.56) to be employed after transplantation; however, this difference may be indicative of differences in employment options and job demands between these sex groups.
The level of education attained before transplantation was predictive of the posttransplant employment status. Patients with more than a high school education were 1.29 times more likely to be employed than patients with less than a high school education. In addition, in comparison with patients who had less than a high school education, patients with a college or higher degree were 1.92 times more likely to be in the labor force (P < 0.001).
The number of cases with missing education data was 6464 (29.5% of all sample cases). This number includes those with no reported education (n = 8), an unknown education (n = 6443), and less than a grade school education (n = 13). The missing education cases were treated in the preliminary analysis as a distinct subgroup and were compared to persons in the “less than a high school education” reference category. Those with missing education scores were not statistically significantly different from the reference group with respect to the likelihood of employment. Consequently, the cases with missing education information and those with less than a high school education were combined, and they were used as the reference category for education in Table 3. A re-estimation of the model in Table 3 that omitted the cases with missing education did not produce changes in the model coefficients within the education subgroups or for most of the other risk factors.
White was chosen as the reference category race. A comparison of white patients and patients in the other categories showed that there was no significant difference between whites and blacks or between whites and patients of other races. On the other hand, Hispanics were 0.58 times less likely than whites to work after transplantation. The possibility of an interaction between Hispanic race and the effect of education was tested, but this did not show a significant association with the outcome.
The pretransplant employment variable was constructed from baseline variables [working for income at the time of listing (the TCR form) and/or working for income at the time of transplantation (the TRR form)]. For pretransplant employment, the database had to have an affirmative statement indicating that the patient was working for income before transplantation for the variable to be coded as present. If either the TCR form or the TRR form listed “yes” for working, then the pretransplant employment variable was coded as yes. Patients who worked before transplantation were 4.8 times more likely to be employed after transplantation than those who did not work before transplantation. The number of cases with a missing pretransplant employment variable was 7832 (35.7% of all sample cases). Missing data were included in the pretransplant unemployment category.
An analysis of the pretransplant employment status was performed to determine whether patients working before transplantation were different from the balance of the transplant cases. The generation of the pre-employment model had the effect of testing the interaction of the pre-employment measure against the other measures in Table 3. The results of the pre-employment model suggested that working before transplantation somewhat lessened the protection against the employment effect of age (with respect to the main effect of age in the original model), but it did not eliminate the reduced odds of working after transplantation; this was shown by the somewhat higher OR in this model versus the data in Table 3. Furthermore, there were no significant associations with race/ethnicity (possibly a function of the small number of cases in the various race cells). The education effect was marginally higher among those with pretransplant employment. This difference between the 2 models may have been statistically significant for patients with less than a college degree but was not statistically significant for those with a college degree. For the most part, the MELD score effects were consistent with the data in Table 3, with no differences depending on the MELD score. The exception involved patients with a MELD score ≥ 31, for whom there was a reduced chance of working. This observation is likely due to the fact that the patients were too sick to be employed and their disease was too advanced for work before transplantation.
Pretransplant health-related variables were important in predicting employment after transplantation. Although the proportion of employed patients with a pretransplant MELD score < 21 was slightly higher than the proportion of patients who were not employed after transplantation, this relationship did not reach significance after adjustments for all the other study covariates (P = 0.72).
Recognizing that the MELD scores of HCC patients could be calculated on the basis of laboratory values alone or with the inclusion of exception status scores, we conducted a separate analysis to assess the sensitivity of the results to this classification choice. Using the laboratory-based MELD score increased the number of patients with a MELD score < 21 (10,231 versus 7950 in models using exception scores) and decreased the number of patients with scores ranging from 22 to 31 (8263 versus 10,472 in models using exception scores). There was little change in the number of patients with MELD scores less than 31 (3439 versus 3520). The MELD score ORs from the models predicting post-LT employment status were similar in magnitude and were nonsignificant in both sets of models. Because the results were similar and because it is our clinical practice to prioritize LT according to MELD scores including the exception status, we elected to report results from models in which MELD exception values were used.
Patients with hepatitis-related liver diseases were 63% more likely than patients with alcoholic liver disease to work after transplantation. Patients with a diagnosis of alcoholic liver disease were significantly less likely to work after transplantation than patients without alcoholic liver disease (P < 0.001). A multivariate analysis showed that patients with hepatitis-related liver diseases (OR = 1.63), biliary liver diseases (OR = 1.69), cirrhosis (OR = 1.17), HCC (OR = 1.14), or other liver diseases (OR = 1.19) were more likely than patients with alcoholic liver disease to work after transplantation.
Patients with no functional limitations after LT were more likely to be employed than those with any limitations. Patients with limited functional status were 0.05 times less likely to work after transplantation than patients with no functional limitations.
Functional status values were obtained from the last available TRF form. If there was no TRF form, the case was excluded from the study sample. The number of missing TRF values for the functional status was 2568 (11.1%) at 6 months, 2201 (1.2%) at 12 months, and 2620 (11.3%) at 24 months; these were combined into a missing category. As in the case of the previous measures, “missing” was entered into the model as a distinct subgroup category and was compared to the “no limitation” reference category. The ORs were similar for any limitations and missing information on limitations; that is, both were highly protective against employment status in comparison with no limitation.
This analysis of the large, national UNOS database showed that only one-quarter of the LT recipients (24.4%) were employed within 24 months after transplantation. LT patients who were employed were most often white (25.8%), 18 to 40 years old (32.7%), male (27.1%), college-educated (36.1%), and employed before transplantation (54.0%). The most common diagnosis for the underlying liver disease of employed recipients was biliary disease (33.3%), and most of the employed recipients had a pretransplant MELD score < 21 (25.5%) and no functional limitations (37.2%).
A striking finding of this study is that approximately three-quarters of the LT recipients did not return to employment within 24 months; this is similar to the results of 2 other institutional studies.9, 14 However, these employment rates are substantially lower than those in a 2004 study, which reported that 40% of men and 25% of women returned to work within 2 years after transplantation.10 Several other studies have found employment rates ranging from 22% to 60%.5-14, 20 It is difficult to compare absolute levels of employment across these studies because many were retrospective reports that did not control for the time from LT and had various exclusion criteria. The latter studies involved smaller cohorts5, 6, 11, 12, 20 instead of the national transplant registry. Thus, the findings of these reports cannot easily be compared with the findings of the current study because of the very broad range of follow-up times (9 months to 10 years). Furthermore, most of the existing studies on returning to work after LT were conducted in single centers with small patient cohorts, whereas our study used the large UNOS data set (n = 21,942).
Age was a statistically significant predictor of employment after transplantation. Patients who were 40 years old or younger were significantly more likely to work after transplantation (P < 0.001) than patients who were older than 40 years. These findings are consistent with Grossman's health capital model,18 which suggests that the depreciation rate increases with age. These findings are also consistent with previous studies in which age was found to be a predictor of employment after transplantation.5, 7, 11 A remarkable finding of this study is that the unemployment rate for patients who were 41 to 55 years was higher than the rate for patients in the reference age group (18-40 years). It is possible that negative employer attitudes toward the hiring of older transplant recipients had an impact on employment rates; however, this study did not allow an examination of age-related work discrimination. Further studies are needed to better understand the barriers to successful employment for this age group.
In terms of sex, female transplant patients were 0.57 times less likely to work than male patients after transplantation, and this was consistent with findings from previous studies.9, 21 The low percentage of female workers may partially be a result of household workers not being included as a category in most of the studies. One study that included this category found that 59% of the sample's female transplant recipients with alcoholic liver disease were working after transplantation; 17% reported that they were household workers.22
The current study found a significant race/ethnicity difference between Hispanics and whites. Hispanics were found to be 0.58 times less likely than whites to be employed after transplantation. The cause of this difference could not be determined from the available data. Cultural issues such as language barriers and socioeconomic class were not measured in this study, and these variables may have contributed to this disparity.
The impact of education on posttransplant employment was readily apparent. Patients with more than a high school education were 1.29 times more likely to be employed than patients with less than a high school education. In addition, patients with a college degree were 1.92 times more likely to be employed after transplantation than patients who had not completed a high school education. Similar findings have been observed in other studies.9, 10, 12, 14
This study also revealed that pretransplant employment correlated with posttransplant employment, and this was consistent with previous studies.11, 12 Patients who have been out of the workforce for long periods have the greatest difficulty in returning to work. Job descriptions change, jobs disappear, and the financial and social support that patients use during the disability period may become so entrenched that they lose the motivation to reintegrate themselves into the workforce. This study did not have specific employment information for the patients in this analysis because no data on the type of employment were available in the UNOS database.
A number of health-related variables were also analyzed as potential predictors of employment. The MELD score at the time of transplantation was anticipated to be an important variable for predicting employment outcomes after LT. However, there was no correlation between the MELD score and the posttransplant employment status, and this was consistent with the results of 2 other studies.12, 14
The pretransplant liver disease diagnosis was associated with the rate of employment after LT. Patients with hepatitis-related liver diseases were 63% more likely to be employed than transplant recipients with alcoholic liver disease. Studies of the associations of alcohol and posttransplant employment have yielded conflicting results so far. A French study showed that 30% of patients with alcoholism and 60% of patients without alcoholism regained employment after transplantation.23 However, the results of this study contradict those of 2 other studies, which showed that a history of alcohol use did not influence a transplant recipient's return to work.6, 10 Patients with a biliary (cholestatic) liver disease were 69% more likely than patients with alcoholic liver disease to be employed after LT. Pruritus may be a disabling symptom for many patients with a biliary liver disease and is largely eliminated after transplantation. Thus, it was not unexpected that these patients might have greater improvements in their quality of life after transplantation, as previously shown.24 The posttransplant employment rate for patients with HCC was 14% higher than the rate for patients with alcoholic liver disease. The introduction of the MELD score shortened the waiting period for transplantation.25, 26 However, the fast-tracking of HCC patients may allow patients with heavy tumor burdens to undergo transplantation and result in an increase in the rate of HCC recurrence after transplantation27, 28; this may affect the posttransplant employment rate. In addition, many diseases recur after LT and affect patient and graft survival.3 Depending on the etiology, recurrent disease can be a major cause of allograft loss.29 For example, for patients undergoing LT for chronic hepatitis C, the recurrence of the hepatitis C virus infection is universal; it can lead to advanced fibrosis and cirrhosis in 41% of patients within 6 to 10 years, with graft failure causing up to 45% of deaths.30 Thus, recurrent disease may affect the posttransplant employment rates.
This study has confirmed the conclusion of the health capital model that decreasing involvement in the workforce is associated with the health status of patients and their capacity in other functional spheres.18 Patients with no functional limitations after transplantation were more likely to work than patients with limited functional status. Our findings correlate with other studies that have found physical functioning in everyday physical activities to be significantly associated with posttransplant employment.6, 14
Our analysis of the UNOS data set found a high unemployment rate of approximately 75%, which may be related to health problems that LT recipients experience. An important contributor to poor health for many transplant recipients is nonadherence to their immunosuppressive treatment, which was not measured in this study but may contribute to organ failure and associated symptoms.31, 32 In addition, the current study could not evaluate the effects of immunosuppressive medications on health-related quality of life and employment outcomes. Diabetes mellitus, nephrotoxicity, cardiovascular disease, and osteoporosis are some of the most commonly encountered adverse effects of immunosuppression.33-35 Moreover, nephrotoxicity caused by immunosuppression is a major complication, with approximately 25% of nonrenal solid organ recipients developing chronic renal failure and requiring dialysis.36-38
A number of limitations of this study should be considered in the interpretation of its results. First, this study included only patients who underwent transplantation after the adoption of the MELD scoring system, so the results may differ from findings for earlier periods when there was more variability in disease severity at the time of transplantation. Second, the study measures were limited to those included in the UNOS data set, which offers only limited information on the socioeconomic status (income) and health status after transplantation. These measures would likely strengthen our ability to understand underlying factors that affect employment status beyond the risk factors present at the time of transplantation. Factors affecting the employment rate, such as employer attitudes and the prevailing unemployment rate at the time of transplantation, were not available. Furthermore, the UNOS data set does not include information about postoperative patients' perceived physical, social, and emotional health problems. This information might strengthen our understanding of the low employment rate beyond the functional status alone. In addition, like other nationwide administrative clinical registries, the UNOS database relies on accurate information coding. The UNOS data are not necessarily entered by individuals with clinical expertise. The data elements are self-reported by either an organ procurement agency or a transplant program. Although these data are UNOS-verified, they are dependent on systems and mechanisms of collection that could be problematic across time and/or across centers. The variability of the data is reflected by both missing values (for which we performed separate analyses and statistical adjustments) and measurement errors, which are unknown to us. All of this leads us to caution that our analysis is a preliminary multicenter window into the outcomes experienced by the LT population. Our findings and conclusions may be appropriate for more in-depth analysis and replication at individual centers.
Future studies of employment status should attempt to consider additional attributes, such as the type of employment, the income history, and the number of hours worked before transplantation. Studying these factors may provide a better understanding of the overall low employment rates of LT recipients in general and the relatively high rates of employment among those previously working and those with a college education. It is possible, for example, that patients with white-collar job are more likely to return to work than blue-collar workers because of the physical demands of blue-collar work. In addition, whether employment itself can be responsible for poor health has not been considered, nor has the possible loss of Medicaid and disability income due to re-employment. These issues may also have an effect on posttransplant employment rates.
The results of this study have potential political and financial implications in a health care delivery climate that is constantly changing. Economic pressures are increasing the expectation that patients who undergo successful LT will return to work. The transplant team needs to have a better understanding of posttransplant work outcomes for this vulnerable population, and greater attention must be paid to the full social rehabilitation of transplant recipients. Specific interventions for LT recipients should be designed to evaluate and change their health perceptions and encourage their return to work. Possible options include early occupational counseling and job referrals for posttransplant patients. The transplant teams who care for recipients must increase their re-employment rates and document these improvements to better support the rationale for LT funding.