Healthy nutrition in Germany: a survey analysis of social causes, obesity and socioeconomic status

Mäder, Sebastian; Rubach, Malte; Schaecke, Wolfram; Röger, Christine; Feldhoffer, Ina; Thalmeier, Eva-Magdalena

doi:10.1017/s1368980019004877

Cited by 11 publications

(7 citation statements)

References 61 publications

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“…The occupational health check-ups did not achieve those with a significant increase in BMI at the 5-year follow-up. Elsewhere, it has been suggested to target the promotion of healthy diet to boys and men and people with low education [13].…”

Section: Discussionmentioning

confidence: 99%

Occupational health check-ups and health-promoting programs and asthma

et al. 2020

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Background: The focus in occupational health checkups is in work and health, but they offer also a possibility to assess health behavior and give guidance e.g. on weight control. We wanted to study whether having occupational health checks-up, receiving physicians' advice to change health behavior or participation in health promotion programs had an effect on obesity in a five-year follow-up from 1998 to 2003 in asthmatic and non-asthmatic workers. Methods: Altogether 23,220 individuals aged 20-54 years were picked up from a randomized Finnish population sample. Univariate and multivariate logistic regression analysis was used to calculate the risk for obesity in 2003. The variables used in the modelling were gender, age, smoking, asthma, depression, and physical workload. Results: Both asthmatic and non-asthmatic workers gained weight during the follow-up. Of the asthmatics 48 and 47% of the non-asthmatics had occupational health-checkup in the last 5 years. Of the asthmatics 18 and 14% of the non-asthmatics had received physician's advice to change their health behavior (p < 0.001). Associated factors for obesity (BMI > 30) in 2003 were gender (men OR 1.19), older age (OR 1.25), smoking (OR 1.07) or depression (OR 1.44). Conclusions: Results show that having occupational health checks-up or receiving physicians' advice to change health behavior or participation in health promotion programs did not stop gain of weight during a five-year follow-up. Asthmatic workers did not differ from non-asthmatics. Male gender, older age, smoking, and depression were associated with obesity but not the physical workload.

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Section: Discussionmentioning

confidence: 99%

Occupational health check-ups and health-promoting programs and asthma

et al. 2020

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“…In Germany, for example, the prevalence of being overweight or obese among adults was 54.0% according to the GEDA (GEDA, German Health Update) study from the Robert Koch Institute (a national public health institute in Germany) in 2014/2015, with men being affected more often than women [3]. Other personal aspects affecting obesity besides gender were low education and higher age according to Mader et al [4]. In addition, several German cohort studies have shown that the average weight in middle-aged populations increased slightly during recent years [5].…”

Section: Introductionmentioning

confidence: 99%

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

Präger

Kurz²,

Holle

et al. 2023

BMC Med Res Methodol

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Background Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people’s weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE). Methods Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley’s K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic. Results We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley’s K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results. Discussion The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

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“…Furthermore, it is of note that T2DM and dyslipidaemia are more prevalent in populations with low socioeconomic status (SES) [10] and this population is usually described as having poorer diet quality [11,12]. However, current literature is limited regarding the possible associations of overall diet quality, with the lipid profile in adults at highrisk of developing T2DM, as well as in individuals with low-SES.…”

Section: Introductionmentioning

confidence: 99%