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Institute
Background: We investigated height of Norwegian conscripts in view of the hypothesis of a "community effect on height" using autocorrelation analysis of district heights within a time-span of 20 years at the end of the 19th century and correlations between neighboring districts at this time. Material and methods: After digitalizing available body height data of Norwegian draftees in 1877-1878, 1880 (averaged as 1878), and 1895-1897 (averaged as 1896) we calculated the magnitude of autocorrelation of body height within the same municipality at different time points. Furthermore, we generated three different neighborhood networks, (1) based on Euclidean distances, (2) a minimum spanning tree build on those distances, (3) a network founded on real world road connections. The networks were used to determine the correlation between body height of neighboring districts depending on the number of edges required to connect two municipalities. Results: The autocorrelation value for body heights was around r = 0.5 (for all p < 0.001) in the years 1878 and 1896. The correlation between neighboring districts varied in the Euclidean distance based network between 0.47 and 0.27 approximately for both years in a sorted order, descending from nearest (0-50 km) to farthest (150-200 km, for all p < 0.001). First order neighbors in the minimum spanning tree network correlation was 0.36 in 1878 and 0.42 in 1896 (for all p < 0.001). The values of neighbor correlation in the road connection based network ranged in 1878 from 0.42 (first order neighbors) to 0.17 (forth order neighbors, for all p < 0.01) and in 1896 from 0.46 (first order neighbors) to 0.12 (forth order neighbors, for all p < 0.05). Conclusion: This initial study of Norwegian conscript height data from the 19th century showed significant medium sized effects for the within district autocorrelation between 1878 and 1896 as well as medium neighborhood correlation, slightly lower in comparison to a recent study regarding Swiss conscripts. Digitalizing more data from other years in this and later time spans as well as using older road and ship connections instead of the actual road data might stabilize and improve those findings.
Background:
Physical growth of children and adolescents depends on the interaction of genetic and environmental factors e.g. diet and living conditions. Aim: We aim to discuss the influence of socioeconomic situation, using income inequality and GDP per capita as indicators, on body height, body weight and the variability of height and weight in infants and juveniles.
Material and methods:
We re-analyzed data from 439 growth studies on height and weight published during the last 35 years. We added year-and country-matched GDP per capita (in current US$) and the Gini coefficient for each study. The data were divided into two age groups: infants (age 2) and juveniles (age 7). We used Pearson correlation and principal component analysis to investigate the data.
Results:
Gini coefficient negatively correlated with body height and body weight in infants and juveniles. GDP per capita showed a positive correlation with height and weight in both age groups. In infants the standard deviation of height increases with increasing Gini coefficient. The opposite is true for juveniles. A correlation of weight variability and socioeconomic indicators is absent in infants. In juveniles the variability of weight increases with declining Gini coefficient and increasing logGDP per capita.
Discussion:
Poverty and income inequality are generally associated with poor growth in height and weight. The analysis of the within-population height and weight variations however, shows that the associations between wealth, income, and anthropometric parameters are very complex and cannot be explained by common wisdom. They point towards an independent regulation of height and weight.