Depression is associated with poor self-reported adherence to antiretroviral therapy among people living with HIV attending an HIV clinic in the UK: results from a cross-sectional study

Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations.

Each of 10 published studies investigating the relationship between HIV infection and risk for depressive disorders concluded that HIV-positive individuals are at no greater risk for depression than comparable HIV-negative individuals. This study used meta-analytic techniques to further examine the relationship between depressive disorders and HIV infection. Meta-analytic techniques were used to aggregate and reanalyze the data from 10 studies that compared HIV-positive and HIV-negative individuals for rates of major depressive disorder (N=2,596) or dysthymic disorder (N=1,822). The frequency of major depressive disorder was nearly two times higher in HIV-positive subjects than in HIV-negative comparison subjects. On the other hand, findings were inconclusive with regard to dysthymic disorder. Rates of depression do not appear to be related to the sexual orientation or disease stage of infected individuals. Although the majority of HIV-positive individuals appear to be psychologically resilient, this meta-analysis provides strong evidence that HIV infection is associated with a greater risk for major depressive disorder. Future research should focus on identifying pathways of risk and resilience for depression within this population.