The American Journal of Managed Care October 2011
Absenteeism and Productivity Among Employees Being Treated for Hepatitis C
Objectives: To compare productivity, absence days, and absence costs for treated (HCV-Tx) and untreated (HCV-NoTx) US employees with hepatitis C virus (HCV) infection.
Study Design: Retrospective database study.
Methods: Employee records from multiple large employers in the United States with data about demographics, jobs, and healthcare use in the Human Capital Management Services database were assessed. HCV subjects were identified by International Classification of Diseases, 9th Revision codes. To test differences between cohorts, t tests and x2 tests were used. Regression modeling was used to compare absence days, costs, and objectively measured productivity, while controlling for confounding factors. For HCV-Tx employees, the index date was the date of the first treatment with interferon, peginterferon, and/ or ribavirin. For HCV-NoTx employees, the index date was the average date by company among HCV-Tx employees. Absence and productivity were measured from each employee’s index date to the last date the employee was enrolled in health benefits coverage.
Results: A total of 441 HCV-Tx and 1223 HCV-NoTx employees were evaluated. HCV-Tx workers had 0.52 more total monthly absence days and $31.31 in additional monthly absence payments per employee than untreated employees. Treated employees’ productivity was lower, with treated subjects processing 11.7% fewer units per hour and 17.4% fewer units per month than untreated employees.
Conclusions: This study quantified the substantial indirect burden of illness associated with use of current HCV treatments. New treatments are needed with improved adverse effect profiles that result in reduced absence from work and improved productivity among HCV-infected persons.
(Am J Manag Care. 2011;17(10):657-664)
United States employees with hepatitis C infection who did and did not undergo treatment were compared on productivity, absence days, and absence costs.
- Patients using existing medications for hepatitis C had increased absenteeism and higher indirect costs.
- As new agents become available, the impact of these agents needs to be considered, in terms of both direct and indirect costs.
Chronic HCV infection develops in 80% of acutely infected patients. With an incubation period ranging from 15 to 150 days, acute HCV infections commonly present with symptoms of fatigue and jaundice. However, the majority of HCV cases (60%-70%) are asymptomatic, including those that develop into chronic infections. Chronic infection results in the development of cirrhosis in between 10% and 20% of these patients, with liver cancer developing in between 1% and 5% of this population over a period of 20 to 30 years.3 HCV is estimated to cause 10,000 to 12,000 deaths annually in the United States.4 Wong and colleagues5 project as many as 16,500 deaths per year in the United States, with 27,200 total deaths from liver cancer alone between 2010 and 2019. Additionally, this model estimates 1.83 million years of life lost, with a total direct medical cost of 10.7 billion dollars and a US societal cost of premature mortality for those younger than 65 years of $54.2 billion.5 Furthermore, the prevalence of HCV is increasing in some populations.6
HCV-infected patients have been reported to experience a lower health-related quality of life compared with the general population.7-9 Symptomatic HCV infection adversely impacts psychological wellbeing, functional health status, and general health perception of those afflicted.10 Cognitive impairment or “difficulty in thinking” has been described in approximately one-third of chronic HCV patients, independent of liver function status.11 Work performance and productivity may also be diminished in these patients.12,13
The majority of HCV patients in the United States are infected with genotypes 1a or 1b and treatment guidelines are genotype specific. The current standard of care for chronic HCV infection is a combination of peginterferon plus ribavirin.4,14 Generally, genotype 1 infection requires higher dosages and more prolonged therapy than genotypes 2 or 3. Response to treatment is considered “sustained” if HCV remains undetectable for 6 months or longer after the completion of therapy.4 Relapses and the long-term sequelae associated with disease progression are rare at this point.4 It must be noted that this combination treatment is associated with significant and often dose-limiting adverse events and has been documented to negatively affect both patients’ quality of life and worker productivity using validated scales in randomized controlled clinical trials.12,13 Toxicity associated with ribavirin and a-interferon may require dose modification or discontinuation of therapy in 2% to 10% of patients.14,15
Prior research on outcomes of HCV infection is limited, especially from the employer’s perspective.12,16 Little has been reported on the impact of HCV infection on productivity and the incremental impact of treatment on productivity outside of the randomized clinical trial setting.12 Recently, Su et al16 compared controls without HCV with HCV-infected workers and found that those with HCV had lower productivity and more absence days. Reduced worker productivity due to treatment side effects during therapy has also been reported.12,13 Other prior studies comparing treated and untreated HCV populations have been based on small sample sizes,17 persons coinfected with HIV,18 specialty populations such as Veterans Affairs patients,19 or individuals with substance abuse issues.20-22
The goal of this study was to assess the impact of HCV treatment with interferon, peginterferon, and/or ribavirin on absenteeism (lost time and costs associated with workforce absenteeism) and on worker productivity using objectively captured data from an employed population.
This retrospective study was based on longitudinal data from the Human Capital Management Services Research Reference Database (HCMS RRDb) of multiple, geographically diverse, US-based employers. The HCMS RRDb contains adjudicated health insurance and prescription drug claims, with demographics and payroll information from more than 670,000 employees over the period from 2001 to 2008. The HCMS RRDb is representative of the 2004 US Employed Civilian Labor Force (139.2 million) in terms of age and sex, and has been used in prior published research.23-25 Confidentiality and anonymity of subject-level data in this study were maintained in accordance with Health Insurance Portability and Accountability Act guidelines.26
Using medical insurance claims data, the following International Classification of Diseases, 9th Revision (ICD-9) codes were used to identify employees with HCV: 070.41 (acute hepatitis C with hepatic coma), 070.44 (chronic hepatitis C with hepatic coma), 070.51 (acute hepatitis C without mention of hepatic coma), 070.54 (chronic hepatitis C without mention of hepatic coma), or 070.7x (unspecified viral hepatitis C). Employees in the treatment group (HCV-Tx) were those with HCV and 1 or more prescriptions for interferon, peginterferon, and/or ribavirin. These employees were compared with HCVinfected employees who did not receive interferon, peginterferon, and/or ribavirin treatment (HCV-NoTx).
Analyses focused on the time after each subject’s index date. For the HCV-Tx cohort, the index date was defined based on the start of therapy. Because the nontreated cohort (HCVNoTx) did not receive therapy, the index date was defined as the average index date (by company) of the HCV-Tx cohort.
Subjects in both cohorts were required to be more than 18 years old on their index date, to have a minimum of 1 month of continuous health plan enrollment after their index date, and to be retained as employees to the last date of health plan enrollment.
Outcomes included monthly average health-related work absences (absenteeism or lost time) due to sick leave, shortand long-term disability, and workers’ compensation; payments for indirect costs for health-related work absences (due to sick leave, short- and long-term disability, and workers’ compensation); and presenteeism based on electronically measured work output data (in the form of number of units of work performed per person per day and number of hours worked per day) converted to hourly productivity (number of units of work performed per hour worked) and monthly productivity (number of units of work performed per month). Sick leave time and costs were drawn from the payroll data, short- and long-term disability time and costs data came from the individual disability insurance carriers, and workers’ compensation time and costs were obtained from workers’ compensation claims data. Because the data spanned several years, all cost variables were inflated to 2007 dollars prior to the analysis using nonseasonally adjusted Consumer Price Indices for medical services, prescription drugs, and all consumer goods.27
Differences in descriptive characteristics between the HCV-Tx and HCV-NoTx cohorts were compared using student t tests for continuous variables and x2 test for discrete variables (P ≤.05 for statistical significance).
Because of the nonnormal distributions of the data, 2-part multivariate regression models28 were used for comparisons of health benefit costs and health-related work absences between the HCV-Tx and HCV-NoTx cohorts. For example, in the cost models, logistic regression was first used to predict the likelihood of subjects having any costs (part 1). Generalized linear models were used with a gamma distribution and a log link function to model costs for subjects with more than zero costs in the second part of the model. The estimated probability of having positive cost (found from the logistic regression model) was multiplied by the estimated mean cost for persons with positive cost (found from the generalized linear model) to produce an estimated mean cost for all employees in each cohort.28 Only employees eligible for a specific work absence benefit were included in regression models for that benefit.
Productivity data for subjects used in this analysis were available only for a subset of the HCMS RRDb (those with values provided for number of units processed per hour). The analysis allowed for examination of productivity while at work (hourly productivity). The at-work productivity analyses were performed using only the second-part regression modeling described above. Subjects with hourly productivity values below the first percentile or above the 99th percentile were removed (0 employees with treatment and 1 employee without treatment) from the hourly productivity model, and subjects with monthly productivity values below the first percentile or above the 99th percentile were removed (1 employee with treatment and 1 employee without treatment) from the monthly analysis.
Separate regression models were run on the following productivity-, cost-, and absence-dependent variables: hourly productivity, monthly productivity, sick leave cost, short-term disability cost, long-term disability cost, workers’ compensation cost, sick leave days, short-term disability days, long-term disability days, and workers’ compensation days. In each case, the multivariate regression models controlled for the impact of confounding factors such as age, sex, marital status, race, exempt/nonexempt status, full-time/part-time status, salary, region, the Charlson Comorbidity Index score (a risk-adjusting score built from claims data indicators of serious comorbid conditions that are predictive of mortality29), and the number of months of eligibility after the index date. All models and statistics were generated via version 9.1 of the SAS System for Windows (SAS Institute Inc, Cary, North Carolina).
The analysis identified 1664 employees with HCV: 441 employees (26.5%) with treatment (HCV-Tx) and 1223 employees (73.5%) without treatment (HCV-NoTx). Table 1 shows descriptive statistics for both cohorts. Both groups were similar (P >.05) in average age (about 46 years); had a mean tenure (time with current employer) close to 10 years; earned about $50,000 in salary; were about one-third female; had similar regional dispersion; and had similar Charlson Comorbidity Index scores. The treated population was 7.5% more likely to be married, 11.7% more likely to be white, and 5.6% less likely to be exempt employees (those not eligible for overtime pay).