SUMMARY BACKGROUND HIV counseling and testing is the gateway to treatment and care and provides important preventative and personal benefits to recipients. However, in developing countries the majority of HIV infected persons have not been tested for HIV. Combining community mobilization, mobile community-based HIV testing and counseling, and post-test support may increase HIV testing rates. METHODS We randomly assigned half of 10 rural communities in Tanzania, 8 in Zimbabwe, and 14 in Thailand to receive a multiple component community-based voluntary counseling and testing (CBVCT) intervention together with access to standard clinic-based voluntary counseling and testing (SVCT). The control communities received only SVCT. The intervention was provided for approximately 3 years. The primary study endpoint is HIV incidence and is pending completion of the post-intervention assessment. This is a descriptive interim analysis examining the percentage of the total population aged 16–32 years tested for HIV across study arms, and differences in client characteristics by study arm. FINDINGS A higher percentage of 16–32 year-olds were tested in intervention communities than in control communities (37% vs. 9% in Tanzania; 51% vs. 5% in Zimbabwe; and 69% vs. 23% in Thailand). The mean difference between the percentage of the population tested in CBVCT versus SVCT communities was 40.4% across the 3 country study arm pairs, (95% CI 15.8% – 64.7%, p-value 0.019, df=2). Despite higher prevalence of HIV among those testing at SVCT venues the intervention detected 3.6 times more HIV infected clients in the CBVCT communities than in SVCT communities (952 vs. 264, p< 0.001). Over time the rate of repeat testing grew substantially across all sites to 28% of all those testing for HIV by the end of the intervention period. INTERPRETATION This multiple component, community-level intervention is effective at both increasing HIV testing rates and detecting HIV cases in rural settings in developing countries.

Case-cohort data analyses often ignore valuable information on cohort members not sampled as cases or controls. The Atherosclerosis Risk in Communities (ARIC) study investigators, for example, typically report data for just the 10%-15% of subjects sampled for substudies of their cohort of 15,972 participants. Remaining subjects contribute to stratified sampling weights only. Analysis methods implemented in the freely available R statistical system (http://cran.r-project.org/) make better use of the data through adjustment of the sampling weights via calibration or estimation. By reanalyzing data from an ARIC study of coronary heart disease and simulations based on data from the National Wilms Tumor Study, the authors demonstrate that such adjustment can dramatically improve the precision of hazard ratios estimated for baseline covariates known for all subjects. Adjustment can also improve precision for partially missing covariates, those known for substudy participants only, when their values may be imputed with reasonable accuracy for the remaining cohort members. Links are provided to software, data sets, and tutorials showing in detail the steps needed to carry out the adjusted analyses. Epidemiologists are encouraged to consider use of these methods to enhance the accuracy of results reported from case-cohort analyses.

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The case-cohort study involves two-phase sampling: simple random sampling from an infinite super-population at phase one and stratified random sampling from a finite cohort at phase two. Standard analyses of case-cohort data involve solution of inverse probability weighted (IPW) estimating equations, with weights determined by the known phase two sampling fractions. The variance of parameter estimates in (semi)parametric models, including the Cox model, is the sum of two terms: (i) the model based variance of the usual estimates that would be calculated if full data were available for the entire cohort; and (ii) the design based variance from IPW estimation of the unknown cohort total of the efficient influence function (IF) contributions. This second variance component may be reduced by adjusting the sampling weights, either by calibration to known cohort totals of auxiliary variables correlated with the IF contributions or by their estimation using these same auxiliary variables. Both adjustment methods are implemented in the R survey package. We derive the limit laws of coefficients estimated using adjusted weights. The asymptotic results suggest practical methods for construction of auxiliary variables that are evaluated by simulation of case-cohort samples from the National Wilms Tumor Study and by log-linear modeling of case-cohort data from the Atherosclerosis Risk in Communities Study. Although not semiparametric efficient, estimators based on adjusted weights may come close to achieving full efficiency within the class of augmented IPW estimators.

Background NIMH Project Accept (HPTN 043) was a cluster-randomized trial that tested whether a multicomponent, multi-level prevention strategy (community-based voluntary counselling and testing [CBVCT]) reduced HIV incidence compared to standard voluntary counselling and testing (SVCT). Methods Forty-eight communities were enrolled at five sites in South Africa, Tanzania, Zimbabwe, and Thailand. CBVCT was designed to make testing more accessible in communities, engage communities through outreach, and provide post-test support services. SVCT comprised standard VCT services established at existing facilities. Communities were randomized in matched pairs to 36 months of CBVCT or SVCT. Data were collected at baseline (n=14,567) and post-intervention (n=56,683) by cross-sectional random surveys of 18–32 year-old community residents. HIV incidence was estimated using a cross-sectional multi-assay algorithm. Thailand was excluded from incidence analyses due to low HIV prevalence. Findings The estimated incidence in the CBVCT was 1.52% vs. 1.81% in the SVCT with an estimated reduction in HIV incidence of 13·9% (relative risk [RR]=0·86; 95% confidence interval [CI]=0·725–1·023; p=0·08). Women older than 24 years had RR=0·70 (95% CI=0·54–0·90; p=0·009). CBVCT increased testing rates by 25% overall (95% CI=12%–39%; p=0·0003), by 45% among men and 15% among women. No overall effect on sexual risk behaviour was observed. However, among HIV-infected participants, CBVCT reduced the number of sexual partners by 8% (95% CI=1%–15%; p=0.03) and the proportion of multiple partnerships by 30% (95% CI=8%-46%; p=0.01). Social norms regarding HIV testing were improved in CBVCT communities. Interpretations The intervention was effective in increasing HIV testing, particularly among men, promoted positive social norms regarding testing, and reduced behavioural risk among HIV-infected participants. A modest reduction in HIV incidence was observed. This intervention focused primarily on HIV detection. Current and future studies that include strategies for HIV treatment and viral suppression should demonstrate further incidence reductions.