Sample Size Considerations in the Design of Cluster Randomized Trials of Combination HIV Prevention

BACKGROUND: Cluster randomized trials have been utilized to evaluate the effectiveness of human immunodeficiency virus (HIV) prevention strategies on reducing incidence. Design of such studies must take into account possible correlation of outcomes within randomized units. PURPOSE: To discuss power and sample size considerations for cluster randomized trials of combination HIV prevention, using an HIV prevention study in Botswana as an illustration. METHODS: We introduce a new agent-based model to simulate the community-level impact of a combination prevention strategy and investigate how correlation structure within a community affects the coefficient of variation–an essential parameter in designing a cluster randomized trial. RESULTS: We construct collections of sexual networks and then propagate HIV on them to simulate the disease epidemic. Increasing level of sexual mixing between intervention and standard of care communities reduces the difference in cumulative incidence in the two sets of communities. Fifteen clusters per arm and 500 incidence cohort members per community provides 95% power to detect the projected difference in cumulative HIV incidence between standard of care and intervention communities (3.93% and 2.34%) at the end of the third study year, using a coefficient of variation 0.25. Although available formulas for calculating sample size for cluster randomized trials can be derived by assuming an exchangeable correlation structure within clusters, we show that deviations from this assumption do not generally affect the validity of such formulas. LIMITATIONS: We construct sexual networks based on data from Likoma Island, Malawi and base disease progression on longitudinal estimates from an incidence cohort in Botswana and in Durban as well as a household survey in Mochudi, Botswana. Network data from Botswana and larger sample sizes to estimate rates of disease progression would be useful in assessing the robustness of our model results. CONCLUSIONS: Epidemic modeling plays a critical role in planning and evaluating interventions for prevention. Simulation studies allow us to take into consideration available information on sexual network characteristics, such as mixing within and between communities as well as coverage levels for different prevention modalities in the combination prevention package

Meta-Analyses of the Associations of Respiratory Health Effectswith Dampness and Mold in Homes

The Institute of Medicine (IOM) of the National Academy of Sciences recently completed a critical review of the scientific literature pertaining to the association of indoor dampness and mold contamination with adverse health effects. In this paper, we report the results of quantitative meta-analysis of the studies reviewed in the IOM report. We developed point estimates and confidence intervals (CIs) to summarize the association of several respiratory and asthma-related health outcomes with the presence of dampness and mold in homes. The odds ratios and confidence intervals from the original studies were transformed to the log scale and random effect models were applied to the log odds ratios and their variance. Models were constructed both accounting for the correlation between multiple results within the studies analyzed and ignoring such potential correlation. Central estimates of ORs for the health outcomes ranged from 1.32 to 2.10, with most central estimates between 1.3 and 1.8. Confidence intervals (95%) excluded unity except in two of 28 instances, and in most cases the lower bound of the CI exceeded 1.2. In general, the two meta-analysis methods produced similar estimates for ORs and CIs. Based on the results of the meta-analyses, building dampness and mold are associated with approximately 30% to 80% increases in a variety of respiratory and asthma-related health outcomes. The results of these meta-analyses reinforce the IOM's recommendation that actions be taken to prevent and reduce building dampness problems

Phylogenetic Relatedness of Circulating HIV-1C Variants in Mochudi, Botswana

Background: Determining patterns of HIV transmission is increasingly important for the most efficient use of modern prevention interventions. HIV phylogeny can provide a better understanding of the mechanisms underlying HIV transmission networks in communities. Methods: To reconstruct the structure and dynamics of a local HIV/AIDS epidemic, the phylogenetic relatedness of HIV-1 subtype C env sequences obtained from 785 HIV-infected community residents in the northeastern sector of Mochudi, Botswana, during 2010–2013 was estimated. The genotyping coverage was estimated at 44%. Clusters were defined based on relatedness of HIV-1C env sequences and bootstrap support of splits. Results: The overall proportion of clustered HIV-1C env sequences was 19.1% (95% CI 17.5% to 20.8%). The proportion of clustered sequences from Mochudi was significantly higher than the proportion of non-Mochudi sequences that clustered, 27.0% vs. 14.7% (p = 5.8E-12; Fisher exact test). The majority of clustered Mochudi sequences (90.1%; 95% CI 85.1% to 93.6%) were found in the Mochudi-unique clusters. None of the sequences from Mochudi clustered with any of the 1,244 non-Botswana HIV-1C sequences. At least 83 distinct HIV-1C variants, or chains of HIV transmission, in Mochudi were enumerated, and their sequence signatures were reconstructed. Seven of 20 genotyped seroconverters were found in 7 distinct clusters. Conclusions: The study provides essential characteristics of the HIV transmission network in a community in Botswana, suggests the importance of high sampling coverage, and highlights the need for broad HIV genotyping to determine the spread of community-unique and community-mixed viral variants circulating in local epidemics. The proposed methodology of cluster analysis enumerates circulating HIV variants and can work well for surveillance of HIV transmission networks. HIV genotyping at the community level can help to optimize and balance HIV prevention strategies in trials and combined intervention packages

Linear Growth Faltering Among HIV-Exposed Uninfected Children

Background: HIV-exposed uninfected (HEU) children experience increased mortality compared wit their HIV-unexposed uninfected (HUU) peers. It is unclear whether HEU children are also at increased risk for undernutrition, a modifiable risk factor for mortality. Methods: We conducted a cross-sectional, population-based survey of children under 5 years of age in five health districts in Botswana. Linear mixed-effects models were used to assess continuous outcomes while generalized estimating equations were used to estimate relative risks of stunting, wasting, and underweight between HEU (n=396) and HUU (n=1,109) children. Secondary analyses examined potential mediation by low birthweight. Results: The association between maternal HIV-exposure and child stunting varied significantly by child age (p<0.01). HEU children <1 year and ≥2 years of age had 1.85 (95% CI: 1.03-3.31; p=0.04) and 1.41 (95% CI: 1.06-1.88; p=0.02) times the risk of stunting compared with HUU children after multivariate adjustment, respectively. During the period of 1-2 years of age, when breastfeeding cessation occurred among HUU children, HUU children had increased risk of stunting compared with HEU children who were predominantly formula fed (RR: 1.56; 95% CI: 1.05-2.32; p=0.03). A mediation analysis estimated 67% of the excess risk of stunting among HEU children ≥2 years was attributable to low birthweight (p=0.02). There was no difference in risk of wasting or underweight. Conclusion: HEU children are at increased risk of stunting compared with their HUU peers; however, interventions to increase birthweight may significantly ameliorate this excess risk. Interventions to support optimal growth during weaning are needed for all breastfed children

Population uptake of HIV testing, treatment, viral suppression, and male circumcision following a community-based intervention in Botswana (Ya Tsie/BCPP): a cluster-randomised trial

BACKGROUND: In settings with high HIV prevalence and treatment coverage, such as Botswana, it is unknown whether uptake of HIV prevention and treatment interventions can be increased further. We sought to determine whether a community-based intervention to identify and rapidly treat people living with HIV, and support male circumcision could increase population levels of HIV diagnosis, treatment, viral suppression, and male circumcision in Botswana. METHODS: The Ya Tsie Botswana Combination Prevention Project study was a pair-matched cluster-randomised trial done in 30 communities across Botswana done from Oct 30, 2013, to June 30, 2018. 15 communities were randomly assigned to receive HIV prevention and treatment interventions, including enhanced HIV testing, earlier antiretroviral therapy (ART), and strengthened male circumcision services, and 15 received standard of care. The first primary endpoint of HIV incidence has already been reported. In this Article, we report findings for the second primary endpoint of population uptake of HIV prevention services, as measured by proportion of people known to be HIV-positive or tested HIV-negative in the preceding 12 months; proportion of people living with HIV diagnosed and on ART; proportion of people living with HIV on ART with viral suppression; and proportion of HIV-negative men circumcised. A longitudinal cohort of residents aged 16-64 years from a random, approximately 20% sample of households across the 15 communities was enrolled to assess baseline uptake of study outcomes; we also administered an end-of-study survey to all residents not previously enrolled in the longitudinal cohort to provide study end coverage estimates. Differences in intervention uptake over time by randomisation group were tested via paired Student's t test. The study has been completed and is registered with ClinicalTrials.gov (NCT01965470). FINDINGS: In the six communities participating in the end-of-study survey, 2625 residents (n=1304 from standard-of-care communities, n=1321 from intervention communities) were enrolled into the 20% longitudinal cohort at baseline from Oct 30, 2013, to Nov 24, 2015. In the same communities, 10 791 (86%) of 12 489 eligible enumerated residents not previously enrolled in the longitudinal cohort participated in the end-of-study survey from March 30, 2017, to Feb 25, 2018 (5896 in intervention and 4895 in standard-of-care communities). At study end, in intervention communities, 1228 people living with HIV (91% of 1353) were on ART; 1166 people living with HIV (88% of 1321 with available viral load) were virally suppressed, and 673 HIV-negative men (40% of 1673) were circumcised in intervention communities. After accounting for baseline differences, at study end the proportion of people living with HIV who were diagnosed was significantly higher in intervention communities (absolute increase of 9% to 93%) compared with standard-of-care communities (absolute increase of 2% to 88%; prevalence ratio [PR] 1·08 [95% CI 1·02-1·14], p=0·032). Population levels of ART, viral suppression, and male circumcision increased from baseline in both groups, with greater increases in intervention communities (ART PR 1·12 [95% CI 1·07-1·17], p=0·018; viral suppression 1·13 [1·09-1·17], p=0·017; male circumcision 1·26 [1·17-1·35], p=0·029). INTERPRETATION: It is possible to achieve very high population levels of HIV testing and treatment in a high-prevalence setting. Maintaining these coverage levels over the next decade could substantially reduce HIV transmission and potentially eliminate the epidemic in these areas. FUNDING: US President's Emergency Plan for AIDS Relief through the Centers for Disease Control and Prevention

Improved double-robust estimation in missing data and causal inference models

Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency properties. Unlike the recent proposals, our estimators solve outcome regression estimating equations. In a simulation study, the new estimator shows improvements in variance relative to the standard double-robust estimator that are in agreement with those suggested by asymptotic theory.Fil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella. Departamento de Economía; ArgentinaFil: Lei, Quanhong. Harvard University; Estados UnidosFil: Sued, Raquel Mariela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Robins, James M.. Harvard University; Estados Unido