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Abstract ObjectivesStudies suggest that living at high altitude decreases obesity risk, but this research is limited to single‐country analyses. We examine the relationship between altitude and body mass index (BMI) among women living in a diverse sample of low‐ and middle‐income countries. Materials and MethodsUsing Demographic and Health Survey data from 1 583 456 reproductive age women (20–49 years) in 54 countries, we fit regression models predicting BMI and obesity by altitude controlling for a range of demographic factors—age, parity, breastfeeding status, wealth, and education. ResultsA mixed‐effects model with country‐level random intercepts and slopes predicts an overall −0.162 kg/m²(95% CI −0.220, −0.104) reduction in BMI and lower odds of obesity (OR 0.90, 95% CI 0.87, 0.95) for every 200 m increase in altitude. However, countries vary dramatically in whether they exhibit a negative or positive association between altitude and BMI (34 countries negative, 20 positive). Mixed findings also arise when examining odds of obesity. DiscussionWe show that past findings of declining obesity risk with altitude are not universal. Increasing altitude predicts slightly lower BMIs at the global level, but the relationship within individual countries varies in both strength and direction. 

ABSTRACT A key challenge in conducting comparative analyses across social units, such as religions, ethnicities, or cultures, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user‐friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform's key functions and current progress, as well as long‐range goals for the platform. 

Scientists and policymakers are increasingly leveraging complex, multi-scale data from diverse, worldwide sources to understand the causes and consequences of economic development, social stratification, climate change, cultural diversity, and violent conflict. This work frequently requires integrating data across diverse datasets by complex, dynamic categories (e.g., ethnicities, languages, religions, subdistricts). However, different datasets encode corresponding categories in disparate formats and at different resolutions (e.g., Guatemala Indigenous vs. Maya vs. K’iche’). These diverse encodings must be translated across datasets before bringing them together for analysis. At global scales across thousands of categories, the combinatorial complexity creates thorny challenges for manual reconciliation and for transparent documentation and sharing of researcher decisions. There is a need to investigate direct and uncomplicated ways to support search and explore the semantics for complex and diverse datasets.We design and deploy such a tool, CatMapper, to support semantic discovery through exploration and manipulation for large, complex and diverse datasets. CatMapper enables exploring contextual information about specific categories, translating new sets of categories from existing datasets and published studies, identify and integrating novel combinations of datasets for researchers’ custom needs, including automatically generated syntax to merge datasets of interest, and publishing and sharing merging templates for public re-use and open science. CatMapper does not store observational data. Rather, it is a dynamic, interactive dictionary of keys to help users integrate observational data from diverse external datasets in disparate formats, thereby complementing and leveraging a fast-growing ecology of datasets storing observational data. We have conducted heuristic evaluation on CatMapper usability. Results shed lights on enriching semantic data discovery.