Analysis of text from open-ended interviews has become an important research tool in numerous fields, including business, education, and health research. Coding is an essential part of such analysis, but questions of quality control in the coding process have generally received little attention. This article examines the text coding process applied to three HIV-related studies conducted with the Centers for Disease Control and Prevention considering populations in the United States and Zimbabwe. Based on experience coding data from these studies, we conclude that (1) a team of coders will initially produce very different codings, but (2) it is possible, through a process of codebook revision and recoding, to establish strong levels of intercoder reliability (e.g., most codes with kappa 0.8). Furthermore, steps can be taken to improve initially poor intercoder reliability and to reduce the number of iterations required to generate stronger intercoder reliability.

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We examined how drugs, high-risk sexual behaviors, and socio-demographic variables are associated with recent HIV infection among men who have sex with men (MSM) in a case-control study. Interviewers collected risk factor data among 111 cases with recent HIV infection, and 333 HIV-negative controls from Chicago and Los Angeles. Compared with controls, cases had more unprotected anal intercourse (UAI) with both HIV-positive and HIV-negative partners. MSM with lower income or prior sexually transmitted infections (STI) were more likely to be recently HIV infected. Substances associated with UAI included amyl nitrate ("poppers"), methamphetamine, Viagra (or similar PDE-5 inhibitors), ketamine, and gamma hydroxybutyrate (GHB). Cases more frequently used Viagra, poppers, and methamphetamine during UAI compared with controls. In multivariate analysis, income, UAI with HIV-positive partners, Viagra, and poppers remained associated with recent HIV seroconversion. Better methods are needed to prevent HIV among MSM who engage in high-risk sex with concurrent drug use.

BackgroundUnderstanding relationships between individual-level demographic, socioeconomic status (SES) and U.S. opioid fatalities can inform interventions in response to this crisis. MethodsThe Mortality Disparities in American Community Study (MDAC) links nearly 4 million 2008 American Community Survey responses to the 2008-2015 National Death Index. Univariate and multivariable models were used to estimate opioid overdose fatality hazard ratios (HR) and 95% confidence intervals (CI).