Regional variation in lifestyle patterns and BMI in young children: the GECKO Drenthe cohort.

BACKGROUND: A better understanding of lifestyle behaviours of children < 7 years and the relation with childhood overweight is needed. The aim of our prospective study was to examine how lifestyle patterns in young children are associated with the development of childhood overweight. As ecological models suggest focusing on not only the child as an individual, but also their environment, we also considered the role of socio-economic status (SES) and spatial clustering of lifestyle and body mass index (BMI). METHODS: In 1792 children (aged 3-6 years) participating in the GECKO Drenthe cohort, diet, screen time, outdoor play and sleep were assessed by questionnaires and moderate-to-vigorous physical activity and sedentary time by accelerometry (Actigraph GT3X). At 10-11 years, height and weight were measured to calculate age- and sex-specific standardized BMI z-scores (zBMI). Lifestyle patterns were identified using principal component analysis. To assess spatial clustering for the lifestyle patterns and zBMI, we calculated the Global Moran's I statistic. Linear- and logistic regression models, taking into account SES, were performed to examine the association between the lifestyle patterns and the development of overweight. For the spatial analyses, we added spatial terms for the determinants, the outcome, and the error term. RESULTS: Three lifestyle patterns were identified: (1) 'high activity', (2) 'low screen time, high sleep and healthy diet', and (3) 'high outdoor play'. No associations were observed between the 'high activity' or 'high outdoor play' patterns at young age with the development of childhood overweight (all p > 0.05). In contrast, children who adhered to the 'low screen time, high sleep and healthy diet' pattern had lower odds to become overweight and a lower zBMI at 10-11 years (odds ratio [95% CI] = 0.766 [0.65; 0.90]). These findings remained similar after taking SES into account. Regarding the spatial analyses, we found spatial clustering of zBMI, but no spatial clustering of the lifestyle patterns. CONCLUSIONS: Low screen time, high sleep duration and a healthy diet cluster into a pattern that seems favourable in the prevention of childhood overweight, independent of individual SES. The spatial analyses suggest that there are likely other neighbourhood factors that contribute to the spatial clustering of childhood overweight.

Regional variation in type 2 diabetes: evidence from 137 820 adults on the role of neighbourhood body mass index.

BACKGROUND: Body mass index (BMI) is a key covariate in the study of type 2 diabetes, but can also be theorized as a contextual effect. The purpose of this study was to explore the extent to which variation in individual risk factors and neighbourhood BMI explain the variation in type 2 diabetes prevalence across neighbourhoods and municipalities. METHODS: Cross-sectional data were collected from 137 820 adults aged ≥18 years from 3296 neighbourhoods in 296 municipalities in the Northern Netherlands. The odds of type 2 diabetes was assessed using a multilevel model. Median odds ratios were calculated and choropleth maps were created to visually assess neighbourhood variation in type 2 diabetes prevalence. RESULTS: The overall prevalence of type 2 diabetes was 4%, ranging from 0 to ≥10 and 0-7% across neighbourhoods and municipalities, respectively. Of the regional variation, 67.0 and 71.6% is explained through variation of individual risk factors at the neighbourhood and municipality level, respectively. Analysis on the smallest spatial scale, i.e. the neighbourhood, best captured the regional variance. Statistically significant interaction between individual and neighbourhood BMI was found (OR = 1.06; 95% CI = 1.03-1.08, P for interaction < 0.001), adjusted for the individual risk profile. CONCLUSION: The results suggest a more cautious interpretation of neighbourhood effects in type 2 diabetes is warranted, and reveals the need for further investigation into risk-prone groups to guide the design of community-level interventions to halt the rise in type 2 diabetes prevalence.

A spatial analysis of dietary patterns in a large representative population in the north of The Netherlands - the Lifelines cohort study.

BACKGROUND: Diet is an important modifiable risk factor for chronic diseases. In the search for effective strategies to improve dietary patterns in order to promote healthy ageing, new approaches considering contextual factors in public health medicine are warranted. The aim of this study is to examine the spatial clustering of dietary patterns in a large representative sample of adults. METHODS: Dietary patterns were defined on the basis of a 111 item Food Frequency Questionnaire among n = 117,570 adults using principal components analysis. We quantified the spatial clustering of dietary pattern scores at the neighborhood level using the Global Moran's I spatial statistic, taking into consideration individual demographic and (neighborhood) socioeconomic indicators. RESULTS: Four dietary patterns explaining 27% of the variance in dietary data were extracted in this population and named the "bread and cookies" pattern, the "snack" pattern, the "meat and alcohol" pattern and the "vegetable, fruit and fish" pattern. Significant spatial clustering of high (hot spot) and low (cold spot) dietary pattern scores was found for all four dietary patterns irrespective of age and gender differences. Educational attainment and neighborhood income explained the global clustering to some extent, although clustering at smaller regional scales persisted. CONCLUSION: The significant region-specific hot and cold spots of the four dietary patterns illustrate the existence of regional "food cultures" and underscore the need for interventions targeted at the sub-national level in order to tackle unhealthy dietary behavior and to stimulate people to make healthy dietary choices.