Creation of synthetic homogeneous neighbourhoods using zone design algorithms to explore relationships between asthma and deprivation in Strasbourg, France

Developing Geographic Areas for Cancer Reporting Using Automated Zone Design

The reporting and analysis of population-based cancer statistics in the United States has traditionally been done for counties. However, counties are not ideal for analysis of cancer rates, due to wide variation in population size, with larger counties having considerable sociodemographic variation within their borders and sparsely populated counties having less reliable estimates of cancer rates that are often suppressed due to confidentiality concerns. There is a need and an opportunity to utilize zone design procedures in the context of cancer surveillance to generate coherent, statistically stable geographic units that are more optimal for cancer reporting and analysis than counties. To achieve this goal, we sought to create areas within each US state that are: 1) similar in population size and large enough to minimize rate suppression; 2) sociodemographically homogeneous; 3) compact; and 4) custom crafted to represent areas that are meaningful to cancer registries and stakeholders. The resulting geographic units reveal the heterogeneity of rates that are hidden when reported at the county-level while substantially reducing the need to suppress data. We believe this effort will facilitate more meaningful comparative analysis of cancer rates for small geographic areas and will advance the understanding of cancer burden in the United States.

The Use of Geographic Data to Improve Asthma Care Delivery and Population Health

The authors examine uses of geographic data to improve asthma care delivery and population health and describe potential practice changes and areas for future research.

Investigating societal determinants of oral health-Opportunities and challenges in multilevel studies

The high prevalence of oral diseases and the persistent nature of socioeconomic inequalities in oral health outcomes across societies presents a significant challenge for public health globally. A debate exists in epidemiology on the merits of investigating population variations in health and its determinants over studying individual health and its individual risk factors. The choice of analytical unit for health outcomes at the population level has policy implications and consequences for the causal understanding of population-level variations in health/disease. There is a lack of discussion in oral epidemiology on the relevance of studying population variations in oral health. Evidence on the role of societal factors in shaping variations in oral health at both the individual level and the population level is also mounting. Multilevel studies are increasingly applied in social epidemiology to address hypotheses generated at different levels of social organization, but the opportunities offered by multilevel approaches are less applied for studying determinants of oral health at the societal level. Multilevel studies are complex as they aim to examine hypotheses generated at multiple levels of social organization and require attention to a range of theoretical and methodological aspects from the stage of design to analysis and interpretation. This discussion study aimed to highlight the value in studying population variations in oral health. It discusses the opportunities provided by multilevel approaches to study societal determinants of oral health. Finally, it reviews the key methodological aspects related to operationalizing multilevel studies of societal determinants of oral health.

Developing a data-driven spatial approach to assessment of neighbourhood influences on the spatial distribution of myocardial infarction

BACKGROUND

There is a growing understanding of the role played by 'neighbourhood' in influencing health status. Various neighbourhood characteristics-such as socioeconomic environment, availability of amenities, and social cohesion, may be combined-and this could contribute to rising health inequalities. This study aims to combine a data-driven approach with clustering analysis techniques, to investigate neighbourhood characteristics that may explain the geographical distribution of the onset of myocardial infarction (MI) risk.

METHODS

All MI events in patients aged 35-74 years occurring in the Strasbourg metropolitan area (SMA), from January 1, 2000 to December 31, 2007 were obtained from the Bas-Rhin coronary heart disease register. All cases were geocoded to the census block for the residential address. Each areal unit, characterized by contextual neighbourhood profile, included socioeconomic environment, availability of amenities (including leisure centres, libraries and parks, and transport) and psychosocial environment as well as specific annual rates standardized (per 100,000 inhabitants). A spatial scan statistic implemented in SaTScan was then used to identify statistically significant spatial clusters of high and low risk of MI.

RESULT

MI incidence was non-randomly spatially distributed, with a cluster of high risk of MI in the northern part of the SMA [relative risk (RR) = 1.70, p = 0.001] and a cluster of low risk of MI located in the first and second periphery of SMA (RR 0.04, p value  =  0.001). Our findings suggest that the location of low MI risk is characterized by a high socioeconomic level and a low level of access to various amenities; conversely, the location of high MI risk is characterized by a high level of socioeconomic deprivation-despite the fact that inhabitants have good access to the local recreational and leisure infrastructure.

CONCLUSION

Our data-driven approach highlights how the different contextual dimensions were inter-combined in the SMA. Our spatial approach allowed us to identify the neighbourhood characteristics of inhabitants living within a cluster of high versus low MI risk. Therefore, spatial data-driven analyses of routinely-collected data georeferenced by various sources may serve to guide policymakers in defining and promoting targeted actions at fine spatial level.

Development of census output areas with AZTool in South Africa

Abstract The use of a single geographical unit to both collect and disseminate census data is common in many countries across the world, especially in developing countries. In South Africa this approach poses some challenges, as the design of small geographical units called enumeration areas to facilitate data collection differs considerably from the design of units that aid data analysis and interpretation. We aimed to create optimised census output areas using the Automated Zone-design Tool (AZTool) program, using the 2001 census enumeration areas as building blocks at various spatial levels, for both rural and urban settings in two South African provinces. The results were consistent and stable. The primary criterion of the confidentiality limit of 500 people was respected at all geographical levels or regions, in both urban and rural settings, for newly created optimised output areas. For the second criterion, lower intra-area correlation values at lower geographical levels for both rural and urban areas showed that higher geographical levels produced more homogeneous output areas than did lower geographical levels or regions. Our obtained intra-area correlation of 0.62 for the two provinces combined indicated that the selected homogeneity variables were good indicators of social homogeneity for creating optimised output areas in South Africa. We conclude that the AZTool software can be used to effectively and objectively create optimised output areas for South African data. Further research on the comparison of the newly created output areas with existing output areas in South Africa should be explored.