Effective HIV treatment and the employment of HIV+ adults

Health Services Research, Dec, 2004 by Dana P. Goldman, Yuhua Bao

We collected data on state-specific policies in 1997 that affect the generosity of coverage. These include a dichotomous variable indicating a limit of three prescriptions per month by the state Medicaid programs (Medicaid HIV Policy Project 1998), and whether the state ADAP covers a non-nucleoside reverse transcriptase inhibitor--the newest class of antiretroviral drugs (Doyle, Jefferys, and Kelly 1997). (4) In 1997, of the 35 states with individuals represented in HCSUS, three states (Texas, South Carolina, and Nevada) had a limit of three prescriptions per month for their Medicaid enrollees. Fourteen states had an ADAP that did not cover any non-nucleoside reverse transcriptase inhibitors (NNRTIs) (Arizona, Colorado, Connecticut, Georgia, Idaho, Louisiana, Maine, Mississippi, Nevada, Oregon, Pennsylvania, South Carolina, Tennessee, and Virginia).

ECONOMETRIC APPROACH

It takes an unknown amount of time for the employment benefits of treatment to be realized. In the absence of data about the duration and timing of labor market spells, we take a simple dynamic approach to estimate how treatment affects outcomes. That is, in most of our analyses we ask how employment outcomes change between baseline and the third wave as a function of HAART treatment during the intervening period) We consider three types of outcomes. First, we examine the probability that a patient "returns to work," conditional on individuals not working at baseline. Second, we look at whether individuals "remain employed," that is, conditional on working at baseline, the probability of still working at the second follow-up. Third, we study the effect of HAART on hours of work per week among those who were working at the second follow-up ("hours of work"). We do not look at changes in hours because--unlike work status--there is substantial measurement error in this variable, and first differencing two noisy measures yields very imprecise results.

Because the conditioning sample is different in each case, we model these outcomes separately. For the continuous outcome of hours of work, we use two-stage least squares. In the first stage, the probability of having HAART is modeled as a linear function of personal characteristics, baseline HIV severity, and our instruments (state policies); in the second stage, hours of work at the second follow-up is modeled as a linear function of the predicted probability of having HAART (derived from the first-stage) and the same individual-level information. (6) For each of the two dichotomous outcomes of "return to work" and "remain employed," we specify a joint, nonlinear model--a bivariate probit--of employment and treatment of HAART.

Bivariate Probit Model

For returning to work and remaining employed, we denote the dichotomous outcome as "Employment" and the dichotomous treatment as "Haart." We make the assumption that both employment and HAART are determined by an underlying continuous index ("Employment*" and "Haart*"). When Employment* or Haart* exceed zero, the corresponding outcome takes the value 1 and 0 otherwise. We denote the instrumental variables for HAART (state policies that affect HAART but not employment) as Z and the other personal characteristics (baseline HIV status and sociodemographics) as X. The bivariate probit model then becomes: