The financial impact of employment decisions for individuals with HIV

Individuals living with HIV face challenging employment decisions that have personal, financial, and health impacts. The decision to stay or to leave the work force is much more complicated for an individual with HIV because the financial choices related to potential health benefits are not clearly understood. To assist in the decision-making process for an individual with HIV, we propose to develop a decision model that compares the potential costs and benefits of staying in or leaving the work force.

A hypothetical cohort of HIV-infected individuals was simulated in our decision model. Characteristics of these individuals over a one-year period were extracted from the medical literature and publicly available national surveys. Men and women between the ages of 18 and 59 were included in our simulated cohort.

A decision tree model was created to estimate the financial impact of an individual's decision on employment. The outcomes were presented as the cost-savings associated with the following employment statuses over a one-year period: 1) staying full-time, 2) switching from full-to part-time, 3) transitioning from full-time to unemployment, and 4) staying unemployed. CD4 T cell counts and employment statuses were stratified by earned income. Employment probabilities were calculated from national databases on employment trends in the United States. Sensitivity analyses were conducted to test the robustness of the effects of the variables on the outcomes.

Overall, the decision outcome that resulted in the least financial loss for individuals with HIV was to remain at work. For an individual with CD4 T cell count > 350, the cost difference between staying employed full-time and switching from full-time to part-time status was a maximum of $2,970. For an individual with a CD4 T cell count between 200 and 350, the cost difference was as low as $126 and as great as $2,492. For an individual with a CD4 T cell count < 200, the minimum cost difference was $375 and the maximum cost difference was $2,253.

Based on our simulated model, we recommend an individual with CD4 T cell count > 350 to stay employed full-time because it resulted in the least financial loss. On the other hand, for an individual with a CD4 T cell < 350, the financial cost loss was much more variable. Our model provides an objective decision-making guide for individuals with HIV to weigh the costs and benefits of employment decisions.