Background Social media technologies are newly emerging tools that can be used for HIV prevention and testing in low- and middle-income countries, such as Peru. This study examined the efficacy of using the Harnessing Online Peer Education (HOPE) social media intervention to increase HIV testing among men who have sex with men (MSM) in Peru. Methods In a cluster randomized controlled trial with concealed allocation, Peruvian MSM from Greater Lima/Callao (N = 556) were randomly assigned to join private intervention or control groups on Facebook for 12 weeks. In the intervention condition, forty-nine Peruvian MSM were trained and randomly assigned to be HIV prevention mentors to participants via Facebook groups over 12 weeks. Control participants received an enhanced standard of care, including standard offline HIV prevention available in Peru as well as participation in Facebook groups (without peer leaders) that provided study updates and HIV testing information. After accepting a request to join the groups, continued participation was voluntary. Participants could request a free HIV test at a local community clinic, and completed questionnaires on HIV risk behaviors and social media use at baseline and 12-week follow-up. Findings Between March 19, 2012, and June 11, 2012, and Sept 26, 2012, and Dec 19, 2012, 556 participants were randomly assigned to intervention groups (N=278) or control groups (N=278); we analyse data for 252 and 246. 43 participants (17%) in the intervention group and 16 (7%) in the control groups got tested for HIV (adjusted odds ratio 2.61, 95% CI 1.55–4.38). No adverse events were reported. Retention at 12-week follow-up was 90%. Across conditions, 7 (87.5%) of the 8 participants who tested positive were linked to care at a local clinic. Interpretation Development of peer-mentored social media communities seemed to be an effective method to increase HIV testing among high-risk populations in Peru.: Results suggest that the HOPE social media HIV intervention may improve HIV testing rates among MSM in Peru. Funding National Institute of Mental Health (NIMH MH090844)

We reviewed the use of agent-based modeling (ABM), a systems science method, in understanding noncommunicable diseases (NCDs) and their public health risk factors. We systematically reviewed studies in PubMed, ScienceDirect, and Web of Sciences published from January 2003 to July 2014. We retrieved 22 relevant articles; each had an observational or interventional design. Physical activity and diet were the most-studied outcomes. Often, single agent types were modeled, and the environment was usually irrelevant to the studied outcome. Predictive validation and sensitivity analyses were most used to validate models. Although increasingly used to study NCDs, ABM remains underutilized and, where used, is suboptimally reported in public health studies. Its use in studying NCDs will benefit from clarified best practices and improved rigor to establish its usefulness and facilitate replication, interpretation, and application.

The review found several modifiable factors that could be targeted as feasible in interventions aimed at reducing socioeconomic differences in overweight and obesity among youth.

IMPORTANCE Previous estimates suggested that 1 in 3 cases of Alzheimer disease and related dementia (ADRDs) in the US are associated with modifiable risk factors, the most prominent being physical inactivity, depression, and smoking. However, these estimates do not account for changes in risk factor prevalence over the past decade and do not consider potential differences by sex or race and ethnicity.OBJECTIVE To update estimates of the proportion of ADRDs in the US that are associated with modifiable risk factors and to assess for differences by sex and race and ethnicity. DESIGN, SETTING, AND PARTICIPANTSFor this cross-sectional study, risk factor prevalence and communality were obtained from the nationally representative US Behavioral Risk Factor Surveillance Survey data from January 2018 to December 2018, and relative risks for each risk factor were extracted from meta-analyses. Data were analyzed from December 2020 to August 2021. Respondents included 378 615 noninstitutionalized adults older than 18 years. The number before exclusion was 402 410. Approximately 23 795 (~6%) had missing values on at least 1 of the variables of interest.

BackgroundAverage treatment effects on the treated (ATT) and the untreated (ATU) are useful when there is interest in: the evaluation of the effects of treatments or interventions on those who received them, the presence of treatment heterogeneity, or the projection of potential outcomes in a target (sub-) population. In this paper we illustrate the steps for estimating ATT and ATU using g-computation implemented via Monte Carlo simulation.MethodsTo obtain marginal effect estimates for ATT and ATU we used a three-step approach: fitting a model for the outcome, generating potential outcome variables for ATT and ATU separately, and regressing each potential outcome variable on treatment intervention.ResultsThe estimates for ATT, ATU and average treatment effect (ATE) were of similar magnitude, with ATE being in between ATT and ATU as expected. In our illustrative example, the effect (risk difference [RD]) of a higher education on angina among the participants who indeed have at least a high school education (ATT) was −0.019 (95% CI: −0.040, −0.007) and that among those who have less than a high school education in India (ATU) was −0.012 (95% CI: −0.036, 0.010).ConclusionsThe g-computation algorithm is a powerful way of estimating standardized estimates like the ATT and ATU. Its use should be encouraged in modern epidemiologic teaching and practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0282-4) contains supplementary material, which is available to authorized users.

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BackgroundAlthough obesity continues to rise and remains a great public health concern in the U.S., a number of important levers such as self-perception of weight and calorie postings at point-of-purchase in restaurants are still not well-characterized in the literature, especially for low-income and minority groups in Los Angeles County (LAC). To study this gap, we examined the associations of self-perception of weight (as measured by body weight discrepancy) with food choice intentions and consumer response to calorie information among low-income adults residing in LAC during the pre-menu labeling regulation era.MethodsDescriptive and multivariable logistic regression analyses were performed to examine the aforementioned associations utilizing data from the 2007–2008 Calorie and Nutrition Information Survey (CNIS). The CNIS was a local health department study of 639 low-income adults recruited from five large, multi-purpose public health centers in LAC.ResultsSurvey participants who reported that their desired weight was less than their current weight (versus desired weight the same as current weight) had (i) higher odds of intending to select lower-calorie foods under the scenario that calorie information was available at point-of-purchase (aOR = 2.0; 95 % CI: 1.0–3.9); and (ii) had higher odds of reporting that it is “very important” to have these calorie postings on food items in grocery stores (aOR = 3.1; 95 % CI: 0.90–10.7) and in fast-food restaurants (aOR = 3.4; 95 % CI: 1.0–11.4).ConclusionsSelf-perception of weight was found to be associated with the intention to select lower-calorie foods under the scenario that calorie information was available at point-of-purchase. Future public health efforts to support menu labeling implementation should consider these and other findings to inform consumer education and communications strategies that can be tailored to assist restaurant patrons with this forthcoming federal law.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-2714-9) contains supplementary material, which is available to authorized users.

SummaryBackground: Existing evidence suggests that the prevalence of overweight and