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Evaluating Alternative Designs of a Multilevel HIV Intervention in Maharashtra, India: The Impact of Stakeholder Constraints.

ABSTRACT: Background. Multilevel interventions combine individual component interventions, and their design can be informed by decision analysis. Our objective was to identify the optimal combination of interventions for alcohol-using HIV+ individuals on antiretroviral drug therapy in Maharashtra, India, explicitly considering stakeholder constraints. Methods. Using an HIV simulation, we evaluated the expected net monetary benefit (ENMB), the probability of lying on the efficiency frontier (PEF), and annual program costs of 5,836 unique combinations of 15 single-focused HIV risk-reduction interventions. We evaluated scenarios of 1) no constraints (i.e., maximize expected value), 2) short-term budget constraints (limits on annual programmatic costs of US$200,000 and $400,000), and 3) a constraint stemming from risk aversion (requiring that the strategy has >50% PEF). Results. With no constraints, the combination including long individual alcohol counseling, text-message adherence support, long group counseling for sex-risk, and long individual counseling for sex-risk (annual cost = $428,886; PEF ∼27%) maximized ENMB and would be the optimal design. With a cost constraint of $400,000, the combination including long individual alcohol counseling, text-message adherence support, brief group counseling for sex-risk, and long individual counseling for sex-risk (annual cost = $374,745; PEF ∼4%) maximized ENMB. With a cost constraint of $200,000, the combination including long individual alcohol counseling, text-message adherence support, and brief group counseling for sex-risk (annual cost = $187,335; PEF ∼54%) maximized ENMB. With the risk aversion constraint, the same configuration (long individual alcohol counseling, text-message support, and brief group counseling for sex-risk) maximized health benefit. Conclusion. Evaluating the costs, risks, and projected benefits of alternatives supports informed decision making prior to initiating study; however, stakeholder constraints should be explicitly included and discussed when using decision analyses to guide study design.

SUBMITTER: Patel AR

PROVIDER: S-EPMC6194934 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In the last few decades, the prevalence of hypertension has been drastically increased in India. The present study estimates the current prevalence of hypertension and its correlates in the state of Maharashtra. The variation in the prevalence of hypertension associated with individual-level characteristics is explained at the community and district level.Data is used from the recent round of District Level Household & Facility Survey (DLHS-4), 2012-13. The DLHS-4 has used the nationally representative sample, collected through multistage stratified sampling procedure. A similar sampling frame, used in NSSO-2007-08, has been followed. The chi-square test is used to show the significance level of the association between the estimated prevalence of hypertension and its correlates. Multilevel regression analysis is carried out to investigate the effects of individual and community level factors on the prevalence of hypertension.The overall prevalence of hypertension is 25% in Maharashtra, and a huge variation in the prevalence of hypertension is found across the districts. Dhule, Gadchiroli (with a low HDI rank), Mumbai and Satara (with higher HDI rank) are the districts with the higher (above 30%) prevalence of high blood pressure. The prevalence also significantly varies according to different correlates. The prevalence of high blood pressure is higher among elderly population (40%), among males (28%), in the urban areas (27%) and in the richest wealth quintile (28%). The prevalence is also higher among cigarette smokers (31%), alcohol consumers (30%) and people with obesity (38%) as compared to their counterparts. The results of the multilevel analysis show that the older and obese persons are at four-time higher risk of hypertension. Again, age, sex, marital status, place of residence, wealth status, unhealthy habits (i.e. smoking and alcohol consumption) and BMI are significantly associated with hypertension. The results of VPC statistics show that 14% of hypertension prevalence could be attributed to differences at the community level.The prevalence of hypertension largely varies in the districts of Maharashtra irrespective of their level of socio-economic development (i.e. HDI rank). The variation in the rate of prevalence of hypertension is higher in the community (PSU) level as compared to the variation in the prevalence rate at the district level. Hypertension is attributable to the modifiable factors like risky lifestyle practices.

Project description:Background: Advances in statistical methods and computing power have led to a renewed interest in addressing the statistical analysis challenges posed by Small-N Designs (SND). Linear mixed-effects modeling (LMEM) is a multiple regression technique that is flexible and suitable for SND and can provide standardized effect sizes and measures of statistical significance. Aims: Our primary goals are to: 1) explain LMEM at the conceptual level, situating it in the context of treatment studies, and 2) provide practical guidance for implementing LMEM in repeated measures SND. Methods & procedures: We illustrate an LMEM analysis, presenting data from a longitudinal training study of five individuals with acquired dysgraphia, analyzing both binomial (accuracy) and continuous (reaction time) repeated measurements. Outcomes & results: The LMEM analysis reveals that both spelling accuracy and reaction time improved and, for accuracy, improved significantly more quickly under a training schedule with distributed, compared to clustered, practice. We present guidance on obtaining and interpreting various effect sizes and measures of statistical significance from LMEM, and include a simulation study comparing two p-value methods for generalized LMEM. Conclusion: We provide a strong case for the application of LMEM to the analysis of training studies as a preferable alternative to visual analysis or other statistical techniques. When applied to a treatment dataset, the evidence supports that the approach holds up under the extreme conditions of small numbers of individuals, with repeated measures training data for both continuous (reaction time) and binomially distributed (accuracy) dependent measures. The approach provides standardized measures of effect sizes that are obtained through readily available and well-supported statistical packages, and provides statistically rigorous estimates of the expected average effect size of training effects, taking into account variability across both items and individuals.

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Evaluating Alternative Designs of a Multilevel HIV Intervention in Maharashtra, India: The Impact of Stakeholder Constraints.

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