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Panczak, Radoslaw; Moser, André; Held, Leonhard; Jones, Philip A.; Rühli, Frank J.; Staub, Kaspar
Economics and human biology, August 2017, 2017-08-00, 20170801, Volume: 26Journal Article
•We used height data of 175,916 conscripts (18.5–20.5 years) from 2005 to 2011.•We applied Gaussian hierarchical models in Bayesian framework.•We found a strong spatial structure of height across country.•Language region, individual and area-based socioeconomic position were the main cofactors.•Spatial variation of height can be explained by individual and regional factors. Adult height reflects an individual’s socio-economic background and offers insights into the well-being of populations. Height is linked to various health outcomes such as morbidity and mortality and has consequences on the societal level. The aim of this study was to describe small-area variation of height and associated factors among young men in Switzerland. Data from 175,916 conscripts (aged between 18.50 and 20.50 years) was collected between 2005 and 2011, which represented approximately 90% of the corresponding birth cohorts. These were analysed using Gaussian hierarchical models in a Bayesian framework to investigate the spatial pattern of mean height across postcodes. The models varied both in random effects and degree of adjustment (professional status, area-based socioeconomic position, and language region). We found a strong spatial structure for mean height across postcodes. The range of height differences between mean postcode level estimates was 3.40cm according to the best fitting model, with the shorter conscripts coming from the Italian and French speaking parts of Switzerland. There were positive socioeconomic gradients in height at both individual and area-based levels. Spatial patterns for height persisted after adjustment for individual factors, but not when language region was included. Socio-economic position and cultural/natural boundaries such as language borders and mountain passes are shaping patterns of height for Swiss conscripts. Small area mapping of height contributes to the understanding of its cofactors.
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