It remains unclear if and how body mass index (BMI) levels have changed over time in HIV endemic regions. We described trends in mean BMI and prevalence of overweight between 2003-2019 in 10 countries in Africa including people living with (PLWH) and without (PLWoH) HIV. We pooled Demographic and Health Surveys (DHS) from countries where ≥2 surveys >4 years apart were available with height/weight measurements and HIV tests. HIV status was ascertained with a finger-prick dried blood spot (DBS) specimen tested in a laboratory. The DBS is taken as part of the regular DHS procedures. We summarized age and socioeconomic status standardized sex-specific mean BMI (kg/m2) and prevalence of overweight (BMI ≥25 kg/m2) by HIV status. We fitted country-level meta-regressions to ascertain if changes in ART coverage were correlated with changes in BMI. Before 2011, women LWH (22.9 [95% CI: 22.2-23.6]) and LWoH (22.6 [95% CI: 22.3-22.8]) had similar mean BMI. Over time, mean BMI increased more in women LWH (+0.8 [95% CI: 0.7-0.8] BMI units) than LWoH (+0.2 [95% CI: 0.2-0.3]). Before 2013, the mean BMI was similar between men LWH (21.1 (95% CI: 20.3-21.9)) and LWoH (20.8 (95% CI: 20.6-21.1)). Over time, mean BMI increased more in men LWoH (+0.3 [95% CI: 0.3-0.3]) than LWH (+0.1 [95% CI: 0.1-0.1]). The same profile was observed for prevalence of overweight. ART coverage was not strongly associated with BMI changes. Mean BMI and prevalence of overweight were similar in PLWH and PLWoH, yet in some cases the estimates for PWLH were on track to catch up with those for PLWoH. BMI monitoring programs are warranted in PLWH to address the rising BMI trends.

The medical literature has demonstrated that macro-variables and social factors can influence suicide rates. Additionally, social science literature has shown that women in prominent political positions (such as mayors) can influence the behavior of other women. The purpose of our work is to demonstrate that women in such positions reduce suicide rates within a group affected by gender inequality: married women.

Observational studies are critical tools in clinical research and public health response, but challenges arise in ensuring the data produced by these studies are scientifically robust and socially valuable. Resolving these challenges requires careful attention to prioritising the most valuable research questions, ensuring robust study design, strong data management practices, expansive community engagement, and access and benefit sharing of results and research materials. This paper opens with a discussion of how well-designed observational studies contribute to biomedical evidence and provides examples from across the clinical literature of how these methods generate hypotheses for future research and uncover otherwise unattainable insights by providing examples from across the clinical literature. Then, we present obstacles that remain in ensuring observational studies are optimally designed, conducted and communicated.

Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, , whenever the system is abnormal and no action is taken, or an immediate cost, , with action, where < . If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.

) is a clinically significant pathogen and a highly genetically diverse species due to its large accessory genome. The functional consequence of this diversity remains unknown mainly because, to date, functional genomic studies in have been primarily performed on reference strains. Given the growing public health threat of infections, understanding the functional genomic differences among clinical isolates can provide more insight into how its genetic diversity influences gene essentiality, clinically relevant phenotypes, and importantly, potential drug targets. To determine the functional genomic diversity among strains, we conducted transposon-sequencing (TnSeq) on 21 genetically diverse clinical isolates, including 15 . subsp. isolates and 6 . subsp. isolates, cataloging all the essential and non-essential genes in each strain. Pan-genome analysis revealed a core set of 3,845 genes and a large accessory genome of 11,507. We identified 259 core essential genes across the 21 clinical isolates and 425 differentially required genes, representing ~10% of the core genome. We also identified genes whose requirements were subspecies, lineage, and isolate-specific. Finally, by correlating TnSeq profiles, we identified 19 previously uncharacterized genetic networks in . Altogether, we find that clinical isolates are not only genetically diverse but functionally diverse as well.