Obesity prevalence in a cohort of women in early pregnancy from a neighbourhood perspective

<p>Abstract</p> <p>Background</p> <p>The evidence of an association between neighbourhood deprivation and overweight is established for different populations. However no previous studies on neighbourhood variations in obesity in pregnant women were found. In this study we aimed to determine whether obesity during early pregnancy varied by neighbourhood economic status.</p> <p>Methods</p> <p>A register based study on 94,323 primiparous pregnant women in 586 Swedish neighbourhoods during the years 19922001. Multilevel technique was used to regress obesity prevalence on socioeconomic individual-level variables and the neighbourhood economic status. Five hundred and eighty-six neighbourhoods in the three major cities of Sweden, Stockholm, Göteborg and Malmö, during 19922001, were included. The majority of neighbourhoods had a population of 4 00010 000 inhabitants.</p> <p>Results</p> <p>Seven per cent of the variation in obesity prevalence was at the neighbourhood level and the odds of being obese were almost doubled in poor areas.</p> <p>Conclusion</p> <p>Our findings supports a community approach in the prevention of obesity in general and thus also in pregnant women.</p

Area deprivation and its association with health in a cross-sectional study: are the results biased by recent migration?

<p>Abstract</p> <p>Background</p> <p>The association between area deprivation and health has mostly been examined in cross-sectional studies or prospective studies with short follow-up. These studies have rarely taken migration into account. This is a possible source of misclassification of exposure, i.e. an unknown number of study participants are attributed an exposure of area deprivation that they may have experienced too short for it to have any influence. The aim of this article was to examine to what extent associations between area deprivation and health outcomes were biased by recent migration.</p> <p>Methods</p> <p>Based on data from the Oslo Health Study, a cross-sectional study conducted in 2000 in Oslo, Norway, we used six health outcomes (self rated health, mental health, coronary heart disease, chronic obstructive pulmonary disease, smoking and exercise) and considered migration nine years prior to the study conduct. Migration into Oslo, between the areas of Oslo, and the changes in area deprivation during the period were taken into account. Associations were investigated by multilevel logistic regression analyses.</p> <p>Results</p> <p>After adjustment for individual socio-demographic variables we found significant associations between area deprivation and all health outcomes. Accounting for migration into Oslo and between areas of Oslo did not change these associations much. However, the people who migrated into Oslo were younger and had lower prevalences of unfavourable health outcomes than those who were already living in Oslo. But since they were evenly distributed across the area deprivation quintiles, they had little influence on the associations between area deprivation and health. Evidence of selective migration within Oslo was weak, as both moving up and down in the deprivation hierarchy was associated with significantly worse health than not moving.</p> <p>Conclusion</p> <p>We have documented significant associations between area deprivation and health outcomes in Oslo after adjustment for socio-demographic variables in a cross-sectional study. These associations were weakly biased by recent migration. From our results it still appears that migration prior to study conduct may be relevant to investigate even within a relatively short period of time, whereas changes in area deprivation during such a period is of limited interest.</p

Evaluation of a weighting approach for performing sensitivity analysis after multiple imputation

Abstract Background Multiple imputation (MI) is a well-recognised statistical technique for handling missing data. As usually implemented in standard statistical software, MI assumes that data are ‘Missing at random’ (MAR); an assumption that in many settings is implausible. It is not possible to distinguish whether data are MAR or ‘Missing not at random’ (MNAR) using the observed data, so it is desirable to discover the impact of departures from the MAR assumption on the MI results by conducting sensitivity analyses. A weighting approach based on a selection model has been proposed for performing MNAR analyses to assess the robustness of results obtained under standard MI to departures from MAR. Methods In this article, we use simulation to evaluate the weighting approach as a method for exploring possible departures from MAR, with missingness in a single variable, where the parameters of interest are the marginal mean (and probability) of a partially observed outcome variable and a measure of association between the outcome and a fully observed exposure. The simulation studies compare the weighting-based MNAR estimates for various numbers of imputations in small and large samples, for moderate to large magnitudes of departure from MAR, where the degree of departure from MAR was assumed known. Further, we evaluated a proposed graphical method, which uses the dataset with missing data, for obtaining a plausible range of values for the parameter that quantifies the magnitude of departure from MAR. Results Our simulation studies confirm that the weighting approach outperformed the MAR approach, but it still suffered from bias. In particular, our findings demonstrate that the weighting approach provides biased parameter estimates, even when a large number of imputations is performed. In the examples presented, the graphical approach for selecting a range of values for the possible departures from MAR did not capture the true parameter value of departure used in generating the data. Conclusions Overall, the weighting approach is not recommended for sensitivity analyses following MI, and further research is required to develop more appropriate methods to perform such sensitivity analyses

How Much Variance in Offending, Self-Control and Morality can be Explained by Neighbourhoods and Schools? An Exploratory Cross-Classified Multi-Level Analysis

Criminological studies of contextual effects on adolescent offending have focused either on residential areas (considering effects of characteristics like disadvantage and collective efficacy) or on school characteristics (studying effects of organisation and social climate, for example). However, adolescents are simultaneously exposed to multiple contexts, and the influence of these contexts on their lives should be studied simultaneously rather than separately. The principal subject of this contribution lies in analysing to which extent there is unique neighbourhood level variation and unique school level variation in adolescent offending, and in two major and stable correlates of adolescent offending, morality and low self-control. Data are used from the Study of Peers, Activities and Neighbourhoods (SPAN), with 612 adolescents in various schools and neighbourhoods in the Netherlands. The results show that there is no unique neighbourhood level variance anymore after controlling for unique school level variance, while some variation at the school level still remains with regard to self-control and morality