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Background: Surrounding green, air pollution, and noise have been associated with cardiometabolic diseases, but most studies have assessed only one of these correlated exposures. Objectives: We aimed to evaluate associations of combined exposures to green, air pollution, and road traffic noise with cardiometabolic diseases. Methods: In this cross-sectional study, we studied associations between self-reported physician-diagnosed diabetes, hypertension, heart attack, and stroke from a Dutch national health survey of 387,195 adults and residential surrounding green, annual average air pollutant concentrations [including particulate matter with aerodynamic diameter ≤10μm (PM10), PM with aerodynamic diameter ≤2.5μm (PM2.5), nitrogen dioxide (NO2), and oxidative potential (OP) with the dithiothreitol (DTT) assay (OPDTT)] and road traffic noise. Logistic regression models were used to analyze confounding and interaction of surrounding green, air pollution, and noise exposure. Results: In single-exposure models, surrounding green was inversely associated with diabetes, while air pollutants (NO2, OPDTT) and road traffic noise were positively associated with diabetes. In two-exposure analyses, associations with green and air pollution were attenuated but remained. The association between road traffic noise and diabetes was reduced to unity when adjusted for surrounding green or air pollution. Air pollution and surrounding green, but not road traffic noise, were associated with hypertension in single-exposure models. The weak inverse association of surrounding green with hypertension attenuated and lost significance when adjusted for air pollution. Only PM2.5 was associated with stroke and heart attack. Conclusions: Studies including only one of the correlated exposures surrounding green, air pollution, and road traffic noise may overestimate the association of diabetes and hypertension attributed to the studied exposure. https://doi.org/10.1289/EHP3857

Background: Evidence is emerging that poor mental health is associated with the environmental exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single exposures with poor mental health. Objectives: To evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health. Methods: In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise exposure with poor mental health. Results: In single exposure models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single exposure models. Associations of environmental exposures with poor mental health differed somewhat by age. Conclusions: Studies including only one of these three correlated exposures may overestimate the influence of poor mental health attributed to the studied exposure, while underestimating the influence of combined environmental exposures. Keywords: NDVI, NO2, PM2.5, Joint odds ratio, Anxiolytics, Hypnotics & sedatives, Antidepressants

We examined if the assessment of the health impact of a national Dutch regeneration programme depends on using either a repeated cross-sectional or longitudinal study design. This is important as only the latter design can incorporate migration patterns. For both designs, we compared trends in medication use between target and control districts. We found differences in medication use trends to be modest under the longitudinal design, and not demonstrable under the repeated cross-sectional design. The observed differences were hardly influenced by migration patterns. We conclude that in the Netherlands migration patterns had little effect on the health impact assessment of this national urban regeneration programme, so either the cross-sectional or longitudinal evaluation study design will do.

Background Local policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered. Methods We used data on 26 binary-valued health-related indicators collected on 387,195 persons in the Netherlands. We associated the health-related indicators at the individual level with a set of 12 predictors obtained from national registry data. We formulated a structured additive regression model for small area estimation. The model captured potential non-linear relations between the predictors and the outcome through additive terms in a functional form using penalized splines and included a term that accounted for spatially correlated heterogeneity between neighbourhoods. The registry data were used to predict individual outcomes which in turn are aggregated into higher geographical levels, i.e. neighbourhoods. We validated our method by comparing the estimated prevalences with observed prevalences at the individual level and by comparing the estimated prevalences with direct estimates obtained by weighting methods at municipality level. Results We estimated the prevalence of the 26 health-related indicators for 415 municipalities, 2599 districts and 11,432 neighbourhoods in the Netherlands. We illustrate our method on overweight data and show that there are distinct geographic patterns in the overweight prevalence. Calibration plots show that the estimated prevalences agree very well with observed prevalences at the individual level. The estimated prevalences agree reasonably well with the direct estimates at the municipal level. Conclusions Structured additive regression is a useful tool to provide small area estimates in a unified framework. We are able to produce valid nationwide small area estimates of 26 health-related indicators at neighbourhood level in the Netherlands. The results can be used for local policy makers to make appropriate health policy decisions.

Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental exposures on SGH. Our aim was to evaluate associations of combined residential exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined exposures with poor SGH. In single-exposure models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-exposure models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-exposure models. Studies including only one of these correlated exposures may overestimate the risk of poor SGH attributed to the studied exposure, while underestimating the risk of combined exposures.

BackgroundLarge-scale regeneration programmes to improve the personal conditions and living circumstances in deprived areas may affect health and the lifestyle of the residents. Previous evaluations concluded that a large-scale urban regeneration programme in the Netherlands had some positive effects within 3.5 years. The aim of the current study was to evaluate the effects at the longer run.MethodsWith a quasi-experimental research design we assessed changes in the prevalence of general health, mental health, physical activity, overweight, obesity, and smoking between the pre-intervention (2003–04 –mid 2008) and intervention period (mid 2008–2013–14) in 40 deprived target districts and comparably deprived control districts. We used the Difference-in-Difference (DiD) to assess programme impact. Additionally, we stratified analyses by sex and by the intensity of the regeneration programme.ResultsChanges in health and health related behaviours from pre-intervention to the intervention period were about equally large in the target districts as in control districts. DiD impact estimates were inconsistent and not statistically significant. Sex differences in DiD estimates were not consistent or significant. Furthermore, DiD impact estimates were not consistently larger in target districts with more intensive intervention programmes.ConclusionWe found no evidence that this Dutch urban regeneration programme had an impact in the longer run on self-reported health and related behaviour at the area level.

BackgroundThis article examines risk factor and health differences between Antillean migrants in the Netherlands and Antillean and Dutch non-migrants, and relates these findings to four commonly used explanations for migrant health disparities.MethodsNationally representative data from the 2012 Dutch Public Health Monitor and the 2013 National Health Survey Curaçao was used. The weighted rates were calculated and significance assessed using the χ2 test. Logistic regression analyses were used to compare health behaviours and outcomes between Antillean migrants and the non-migrant populations.ResultsOverall, Antillean migrants had poorer physical and mental health than Antillean and Dutch non-migrants. For overweight/obesity and tobacco and alcohol use, Antillean migrants had rates in-between those of the Antillean and Dutch non-migrants. The poor health of Antillean migrants persisted in the second generation, who were born in the Netherlands.ConclusionsPatterns of differences in physical and mental health among the study populations were suggestive of a ‘stressful environment’ effect. The poorer health of Antillean migrants may be partly determined by host-country-specific stressors, such as perceived discrimination, spatial concentration in multi-ethnic neighbourhoods and reduced social mobility.

Als aanvulling op het artikel ‘Gezondheidsmonitoring van kwetsbare groepen in Nederland; weten we genoeg?’ dat in nummer 6 van TSG in 2013 is verschenen, willen wij u via deze reactie attenderen op de ontwikkeling van een landelijk model voor een monitor Sociaal Kwetsbare Groepen, waar de Academische Werkplaats Publieke Gezondheid Brabant, Tranzo, Tilburg samen met het Onderzoekscentrum Maatschappelijke zorg van het Radboudumc Nijmegen sinds een aantal jaren aan werkt.

Het doel van de Lokale en Nationale Monitor Gezondheid is een beeld te schetsen van (determinanten van) gezondheid op zowel lokaal als nationaal niveau, als input voor lokaal en nationaal gezondheidsbeleid. Om dit te bereiken wordt de manier waarop gegevens lokaal worden verzameld zo op elkaar afgestemd dat de gegevens vergelijkbaar zijn en samengevoegd kunnen worden tot landelijke cijfers. Dit is een langlopend traject waaraan nu een nieuwe impuls wordt gegeven. Het traject van afstemming wordt gecoordineerd door het RIVM.