Diet-related chronic disease in the northeastern United States: a model-based clustering approach

Background: Obesity and diabetes are global public health concerns. Studies indicate a relationship between socioeconomic, demographic and environmental variables and the spatial patterns of diet-related chronic disease. In this paper, we propose a methodology using model-based clustering and variable selection to predict rates of obesity and diabetes. We test this method through an application in the northeastern United States. Methods: We use model-based clustering, an unsupervised learning approach, to find latent clusters of similar US counties based on a set of socioeconomic, demographic, and environmental variables chosen through the process of variable selection. We then use Analysis of Variance and Post-hoc Tukey comparisons to examine differences in rates of obesity and diabetes for the clusters from the resulting clustering solution. Results: We find access to supermarkets, median household income, population density and socioeconomic status to be important in clustering the counties of two northeastern states. The results of the cluster analysis can be used to identify two sets of counties with significantly lower rates of diet-related chronic disease than those observed in the other identified clusters. These relatively healthy clusters are distinguished by the large central and large fringe metropolitan areas contained in their component counties. However, the relationship of socio-demographic factors and diet-related chronic disease is more complicated than previous research would suggest. Additionally, we find evidence of low food access in two clusters of counties adjacent to large central and fringe metropolitan areas. While food access has previously been seen as a problem of inner-city or remote rural areas, this study offers preliminary evidence of declining food access in suburban areas. Conclusions: Model-based clustering with variable selection offers a new approach to the analysis of socioeconomic, demographic, and environmental data for diet-related chronic disease prediction. In a test application to two northeastern states, this method allows us to identify two sets of metropolitan counties with significantly lower diet-related chronic disease rates than those observed in most rural and suburban areas. Our method could be applied to larger geographic areas or other countries with comparable data sets, offering a promising method for researchers interested in the global increase in diet-related chronic disease

How places influence crime: The impact of surrounding areas on neighbourhood burglary rates in a British City

Burglary prevalence within neighbourhoods is well understood but the risk from bordering areas is under-theorised and under-researched. If it were possible to fix a neighbourhood’s location but substitute its surrounding areas, one might expect to see some influence on its crime rate. But by treating surrounding areas as independent observations, ecological studies assume that identical neighbourhoods with markedly different surroundings are equivalent. If not, knowing the impact of different peripheries would have significance for crime prevention, land use planning and other policy domains. This paper tests whether knowledge of the demographic makeup of surrounding areas can improve on the prediction of a neighbourhood's burglary rate based solely on its internal socio-demographics. Results identify significant between-area effects with certain types of periphery exerting stronger influences than others. The advantages and drawbacks of the Spatial Error and Predictor Lag model used in the analysis are discussed and areas for further research defined

Identifying change over time in small area socio-economic deprivation

The measurement of area level deprivation is the subject of a wide and ongoing debate regarding the appropriateness of the geographical scale of analysis, the input indicator variables and the method used to combine them into a single figure index. Whilst differences exist, there are strong correlations between schemes. Many policy-related and academic studies use deprivation scores calculated cross-sectionally to identify areas in need of regeneration and to explain variations in health outcomes. It would be useful then to identify whether small areas have changed their level of deprivation over time and thereby be able to: monitor the effect of industry closure; assess the impact of area-based planning initiatives; or determine whether a change in the level of deprivation leads to a change in health. However, the changing relationship with an outcome cannot be judged if the ‘before’ and ‘after’ situations are based on deprivation measures which use different, often time-point specific variables, methods and geographies. Here, for the whole of the UK, inputs to the Townsend index obtained from the 1991 and 2001 Censuses have been harmonised in terms of variable detail and with the 1991 data converted to the 2001 Census ward geography. Deprivation has been calculated so that the 1991 scores are directly comparable with those for 2001. Change over time can be then identified. Measured in this way, deprivation is generally shown to have eased due to downward trends in levels of lack of access to a car, non-home ownership, household overcrowding but most particularly, to reductions in levels of unemployment. Despite these trends, not all locations became less deprived with gradients of deprivation largely persisting within the UK’s constituent countries and in different area types. For England, Wales and Scotland, the calculation of Townsend scores can readily be backdated to incorporate data from the 1971 and 1981 Censuses to create a 1971–2001 set of comparable deprivation scores. The approach can also be applied to the Carstairs index. Due to differences in data availability prior to 1991, incorporating small areas in Northern Ireland would be challenging