Search

BACKGROUND: Overweight and obesity impose a considerable individual and social burden, and the urban environments might encompass factors that contribute to obesity. Nevertheless, there is a scarcity of research that takes into account the simultaneous interaction of multiple environmental factors. OBJECTIVES: Our objective was to perform an exposome-wide association study of body mass index (BMI) in a multicohort setting of 15 studies. METHODS: Studies were affiliated with the Dutch Geoscience and Health Cohort Consortium (GECCO), had different population sizes (688-141,825), and covered the entire Netherlands. Ten studies contained general population samples, others focused on specific populations including people with diabetes or impaired hearing. BMI was calculated from self-reported or measured height and weight. Associations with 69 residential neighborhood environmental factors (air pollution, noise, temperature, neighborhood socioeconomic and demographic factors, food environment, drivability, and walkability) were explored. Random forest (RF) regression addressed potential nonlinear and nonadditive associations. In the absence of formal methods for multimodel inference for RF, a rank aggregation-based meta-analytic strategy was used to summarize the results across the studies. RESULTS: Six exposures were associated with BMI: five indicating neighborhood economic or social environments (average home values, percentage of high-income residents, average income, livability score, share of single residents) and one indicating the physical activity environment (walkability in 5-km buffer area). Living in high-income neighborhoods and neighborhoods with higher livability scores was associated with lower BMI. Nonlinear associations were observed with neighborhood home values in all studies. Lower neighborhood home values were associated with higher BMI scores but only for values up to [euro]300,000. The directions of associations were less consistent for walkability and share of single residents. DISCUSSION: Rank aggregation made it possible to flexibly combine the results from various studies, although between-study heterogeneity could not be estimated quantitatively based on RF models. Neighborhood social, economic, and physical environments had the strongest associations with BMI. https://doi.org/10.1289/EHP13393, Introduction Overweight and obesity impose a considerable individual and social burden, with global rates that have nearly tripled since 1975. (1) This striking rise could be attributed to changes in [...]

Abstract Objectives The Mental Health Inventory (MHI‐5) is frequently used as a screener for mood and anxiety disorders. However, few population‐based studies have validated it against a diagnostic instrument assessing disorders following current diagnostic criteria. Methods Within the third Netherlands Mental Health Survey and Incidence Study (NEMESIS‐3), a representative population‐based study of adults (N = 6194; age: 18–75 years), the MHI‐5 was used to measure general mental ill‐health in the past month. Presence of mood (major depressive disorder, persistent depressive disorder, or bipolar disorder) and anxiety disorders (panic disorder, agoraphobia, social phobia, or generalized anxiety disorder) in the past month was assessed with a slightly modified version of the Composite International Diagnostic Interview 3.0 per the Diagnostic and Statistical Manual of Mental disorders‐5. Results The MHI‐5 was good to excellent at distinguishing people with and without a mood disorder, an anxiety disorder, and any mood or anxiety disorder. The cut‐off value associated with the highest sensitivity and highest specificity for mood disorder was ≤68, and ≤76 for an anxiety disorder or any mood or anxiety disorder. Conclusions The MHI‐5 can identify individuals at high risk of a current mood or anxiety disorder in the general population when diagnostic interviews are too time consuming.

Abstract Objective The standard method of generating disorder‐specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities. Methods We propose an alternative, data‐driven, method of generating disorder‐specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self‐reports and uses Generalized Random Forests (GRF) to predict global (rather than disorder‐specific) disability assessed by clinician ratings or by survey respondent self‐reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder‐specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys (n = 53,645). Results Adjustments for comorbidity decreased estimates of disorder‐specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant. Conclusions The results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder‐specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity.