From left-skewness to symmetry: how body-height distribution among Swiss conscripts has changed shape since the late 19th century

<div><p></p><p><i>Background</i>: It is generally accepted that height distribution in modern populations is nearly symmetrical. However, it may deviate from symmetry when nutritional status is inadequate.</p><p><i>Aim and subjects</i>: This study provides an analysis of changes in the shape of the height distributions among Swiss conscripts (<i>n</i> = 267 829) over the past 130 years based on a highly representative, standardized and unchanged data source.</p><p><i>Results</i>: The analysed distributions from the 1870s–1890s conscription years are markedly left-skewed (−0.76 to −0.82), with short and very short men significantly over-represented. Standard deviation is 7.7 cm. In particular, the left tails of the late-19th- and early-20th-century distributions are very heavy. In the first half of the 20th century the first signs of a diminution of the heavy left tail are observable, by the 1970s the phenomenon disappears and height distribution becomes symmetrical; standard deviation is now 6.5 cm.</p><p><i>Conclusion</i>: The relatively strong left-skewness during the late 19th and early 20th centuries may have been due to the interaction of a number of causes, chiefly malnutrition, a wider range in physical development at age 19 and widespread iodine deficiency.</p></div

Socioeconomic, Temporal and Regional Variation in Body Mass Index among 188,537 Swiss Male Conscripts Born between 1986 and 1992

<div><p>Background</p><p>Rising levels of overweight and obesity are important public-health concerns worldwide. The purpose of this study is to elucidate their prevalence and trends in Switzerland by analyzing variations in Body Mass Index (BMI) of Swiss conscripts.</p><p>Methods</p><p>The conscription records were provided by the Swiss Army. This study focussed on conscripts 18.5–20.5 years of age from the seven one-year birth cohorts spanning the period 1986–1992. BMI across professional status, area-based socioeconomic position (abSEP), urbanicity and regions was analyzed. Two piecewise quantile regression models with linear splines for three birth-cohort groups were used to examine the association of median BMI with explanatory variables and to determine the extent to which BMI has varied over time.</p><p>Results</p><p>The study population consisted of 188,537 individuals. Median BMI was 22.51 kg/m² (22.45–22.57 95% confidence interval (CI)). BMI was lower among conscripts of high professional status (−0.46 kg/m²; 95% CI: −0.50, −0.42, compared with low), living in areas of high abSEP (−0.11 kg/m²; 95% CI: −0.16, −0.07 compared to medium) and from urban communities (−0.07 kg/m²; 95% CI: −0.11, −0.03, compared with peri-urban). Comparing with Midland, median BMI was highest in the North-West (0.25 kg/m²; 95% CI: 0.19–0.30) and Central regions (0.11 kg/m²; 95% CI: 0.05–0.16) and lowest in the East (−0.19 kg/m²; 95% CI: −0.24, −0.14) and Lake Geneva regions (−0.15 kg/m²; 95% CI: −0.20, −0.09). Trajectories of regional BMI growth varied across birth cohorts, with median BMI remaining high in the Central and North-West regions, whereas stabilization and in some cases a decline were observed elsewhere.</p><p>Conclusions</p><p>BMI of Swiss conscripts is associated with individual and abSEP and urbanicity. Results show regional variation in the levels and temporal trajectories of BMI growth and signal their possible slowdown among recent birth cohorts.</p></div

Distribution of Body Mass Index (BMI) (mean, standard deviation (SD), median and inter-quartile range (IQR)) and frequencies of major BMI categories across year of birth and contextual variables of Swiss conscripts.

<p>Distribution of Body Mass Index (BMI) (mean, standard deviation (SD), median and inter-quartile range (IQR)) and frequencies of major BMI categories across year of birth and contextual variables of Swiss conscripts.</p

Annual change in median BMI (95% confidence intervals) estimated from the second multivariable quantile regression model of Swiss conscripts across birth cohort and region of residence.

<p>Model adjusted for professional status, tertile of median Swiss-SEP index of postcode of residence, degree of urbanicity of community of residence and linear splines for birth-year period and interaction of region with birth-year period.</p

Additional file 1: Figure S1. of Finding big shots: small-area mapping and spatial modelling of obesity among Swiss male conscripts

Distribution of postcode level estimated odds ratios (ORs) of obesity from unadjusted (top panel) and adjusted (bottom panel) models with various use of random effects. (PDF 18 kb

Presentation1_Reinfections and Cross-Protection in the 1918/19 Influenza Pandemic: Revisiting a Survey Among Male and Female Factory Workers.pdf

Objectives: The COVID-19 pandemic highlights questions regarding reinfections and immunity resulting from vaccination and/or previous illness. Studies addressing related questions for historical pandemics are limited.Methods: We revisit an unnoticed archival source on the 1918/19 influenza pandemic. We analysed individual responses to a medical survey completed by an entire factory workforce in Western Switzerland in 1919.Results: Among the total of n = 820 factory workers, 50.2% reported influenza-related illness during the pandemic, the majority of whom reported severe illness. Among male workers 47.4% reported an illness vs. 58.5% of female workers, although this might be explained by varied age distribution for each sex (median age was 31 years old for men, vs. 22 years old for females). Among those who reported illness, 15.3% reported reinfections. Reinfection rates increased across the three pandemic waves. The majority of subsequent infections were reported to be as severe as the first infection, if not more. Illness during the first wave, in the summer of 1918, was associated with a 35.9% (95%CI, 15.7–51.1) protective effect against reinfections during later waves.Conclusion: Our study draws attention to a forgotten constant between multi-wave pandemics triggered by respiratory viruses: Reinfection and cross-protection have been and continue to be a key topic for health authorities and physicians in pandemics, becoming increasingly important as the number of waves increases.</p

The checklist for early study planning and the assessment of the feasibility of studies using digital unstructured data.

The checklist for early study planning and the assessment of the feasibility of studies using digital unstructured data.</p

Search Syntax.

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.</div

Table 2. Description of Challenge Areas.

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.</div

Data Extraction Sheet.

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies.</div