Identifying the appropriate spatial resolution for the analysis of crime patterns.

BACKGROUND: A key issue in the analysis of many spatial processes is the choice of an appropriate scale for the analysis. Smaller geographical units are generally preferable for the study of human phenomena because they are less likely to cause heterogeneous groups to be conflated. However, it can be harder to obtain data for small units and small-number problems can frustrate quantitative analysis. This research presents a new approach that can be used to estimate the most appropriate scale at which to aggregate point data to areas. DATA AND METHODS: The proposed method works by creating a number of regular grids with iteratively smaller cell sizes (increasing grid resolution) and estimating the similarity between two realisations of the point pattern at each resolution. The method is applied first to simulated point patterns and then to real publicly available crime data from the city of Vancouver, Canada. The crime types tested are residential burglary, commercial burglary, theft from vehicle and theft of bike. FINDINGS: The results provide evidence for the size of spatial unit that is the most appropriate for the different types of crime studied. Importantly, the results are dependent on both the number of events in the data and the degree of spatial clustering, so a single 'appropriate' scale is not identified. The method is nevertheless useful as a means of better estimating what spatial scale might be appropriate for a particular piece of analysis.

Cost Variation in Diabetes Care across Dutch Care Groups?

OBJECTIVE: The introduction of bundled payment for diabetes care in the Netherlands led to the origination of care groups. This study explored to what extent variation in health care costs per patient can be attributed to the performance of care groups. Furthermore, the commonly applied simple mean aggregation was compared with the more advanced generalized linear mixed model (GLMM) to benchmark health care costs per patient between care groups. DATA SOURCE: Dutch 2009 nationwide insurance claims data of diabetes type 2 patients (104,544 patients, 50 care groups). STUDY DESIGN: Both a simple mean aggregation and a GLMM approach was applied to rank care groups, using two different health care costs variables: total treatment health care costs and diabetes-specific specialist care costs per diabetes patient. PRINCIPAL FINDINGS: Care groups varied slightly in the first and mainly in the second indicator. Care group variation was not explained by composition. Although the ranking methods were correlated, some care groups' rank positions differed, with consequences on the top-10 and the low-10 positions. CONCLUSIONS: Differences between care groups exist when an appropriate indicator and a sophisticated aggregation technique is used. Currently applied benchmarking may have unfair consequences for some care groups.

Social capital and self-rated health amongst older people in Western Finland and Northern Sweden: a multi-level analysis.

BACKGROUND: Social capital can be conceptualised as an individual resource residing in relationships between individuals or as a collective resource produced through interactions in neighbourhoods, communities or societies. Previous studies suggest that social capital is, in general, good for health. However, there is a shortage of studies analysing the association between individual and collective social capital in relation to health amongst older people. PURPOSE: The purpose of this study was to assess the relationship between municipal- and individual-level social capital and self-rated health amongst older people in Western Finland and Northern Sweden. METHOD: Data were retrieved from a cross-sectional postal questionnaire survey conducted in 2010. The study included, in total, 6,838 people aged 65, 70, 75 and 80 years living in the two Bothnia regions, Västerbotten, Sweden and Pohjanmaa, Finland. The association between social capital and self-rated health was tested through multi-level logistic regression analyses with ecometric tests. Social capital was measured by two survey items: interpersonal trust and social participation. RESULTS: Individual-level social capital including social participation and trust was significantly associated with self-rated health. A negative association was found between municipal-level trust and health. However, almost all variation in self-rated health resided on the individual level. CONCLUSIONS: We conclude that contextual-level social capital on a municipal level is less important for understanding the influence of social capital on health in the Bothnia region of Finland and Sweden. On the other hand, our study shows that individual-level social participation and trust have a positive and significant association with self-rated health. We suggest that other ways of defining social capital at the collective level, such as the inclusion of neighbourhood social capital, could be one direction for future research.