BackgroundDiet 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.MethodsDietary 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.ResultsFour 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.ConclusionThe 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.Electronic supplementary materialThe online version of this article (10.1186/s12966-017-0622-8) contains supplementary material, which is available to authorized users.

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The disequilibrium and equilibrium models of migration disagree on how local amenities and labor market dynamics influence regional in-migration. Research into migration motives and decision-making show that migration for some individuals is mainly driven by proximity to the labor market, while migration for others is mainly amenity driven. As this is an ongoing process, it should result in a spatial sorting based on migration motives. This means that global models explaining in-migration underestimate the influence of both factors through averaging out of the coefficients across these diverse regions. In this article, we compare a local and a global model explaining in-migration through residential quality and labor market proximity. We find significant differences in the influence of the explanatory variables between regions. Demonstrating this spatial heterogeneity shows that the impacts of factors underpinning migration vary across regions. This result highlights the importance of the regional context in anticipating and designing regional policy concerning population dynamics.

Background While differences in population health across neighborhoods with different socioeconomic characteristics are well documented, health disparities across neighborhoods with similar socioeconomic characteristics are less well understood. We aimed to estimate population health inequalities, both within and between neighborhoods with similar socioeconomic status, and assessed the association of neighborhood characteristics and socioeconomic spillover effects from adjacent neighborhoods. Methods Based on Dutch whole-population data we determined the percentage of inhabitants with good or very good self-assessed health (SAH) and the percentage of inhabitants with at least one chronic disease (CD) in 11,504 neighborhoods. Neighborhoods were classified by quintiles of a composite neighborhoods socioeconomic status score (NSES). A set of spatial models was estimated accounting for spatial effects in the dependent, independent, and error components of the model. Results Substantial population health disparities in SAH and CD both within and between neighborhoods NSES quintiles were observed, with the largest SAH variance in the lowest NSES group. Neighborhoods adjacent to higher SES neighborhoods showed a higher SAH and a lower prevalence of CD. Projected impacts from the spatial regressions indicate how modest changes in NSES among the lowest socioeconomic neighborhoods can contribute to population health in both low- and high-SES neighborhoods. Conclusion Population health differs substantially among neighborhoods with similar socioeconomic characteristics, which can partially be explained by a spatial socio-economic spillover effect.

Many western countries struggle with the realization of an inclusive labor market: a labor market in which everyone can participate and disabled or otherwise vulnerable, disadvantaged and low productive people can participate in the real labor market up to the best of their possibilities. Following countries like Germany and Austria, the latest Dutch policy proposal is to introduce a mandatory quota for employers with more than 25 employees to create job openings for disabled for 5 percent of their workforce. A first calculation of possible job openings shows that from a national perspective a mandatory quota seems promising. However due to differences in the regional economic structure the arrangement will not be sufficient to solve spatial inequalities in regional exclusion of disabled at the level of municipalities and also not for larger regions at the NUTS-1 level like the North of the Netherlands. We conclude more attention should be paid to the spatial variation in impact when the national government decides to decentralize the implementation of national policy measures to municipalities.