Associations of Noise and Socioeconomic and -Demographic Status on Cardiovascular and Respiratory Diseases on Borough Level in a Large German City State

Spatial Aspects of Health-Developing a Conceptual Framework

Numerous studies and models address the determinants of health. However, in existing models, the spatial aspects of the determinants are not or only marginally taken into account and a theoretical discussion of the association between space and the determinants of health is missing. The aim of this paper is to generate a framework that can be used to place the determinants of health in a spatial context. A screening of the current first serves to identify the relevant determinants and describes the current state of knowledge. In addition, spatial scales that are important for the spatial consideration of health were developed and discussed. Based on these two steps, the conceptual framework on the spatial determinants of health was derived and subsequently discussed. The results show a variety of determinants that are associated with health from a spatial point of view. The overarching categories are global driving forces, policy and governance, living and physical environment, socio-demographic and economic conditions, healthcare services and cultural and working conditions. Three spatial scales (macro, meso and micro) are further subdivided into six levels, such as global (e.g., continents), regional (e.g., council areas) or neighbourhood (e.g., communities). The combination of the determinants and spatial scales are presented within a conceptual framework as a result of this work. Operating mechanisms and pathways between the spatial levels were added schematically. This is the first conceptual framework that links the determinants of health with the spatial perspective. It can form the working basis for future analyses in which spatial aspects of health are taken into account.

Associations between a Subjective Living Environment and Quality of Life among People with Arterial Hypertension-Results from the Hamburg City Health Study

Hypertension is a global public health concern and an important contributor to cardiovascular disease. It remains disputed how important life circumstances are for the etiology of hypertension. Thus, the aim of this study is to assess the spatial variation of hypertension within an urban population and to investigate the association with the quality of life of city dwellers and their subjective evaluation of their residential district, as well as their home environment, using the example of Hamburg, Germany. In this cross-sectional study, the first 10,000 participants from the Hamburg City Health Study (HCHS) were analysed. Only participants who had resided at the current address for a minimum of five years were considered. In the descriptive analysis, participants with and without arterial hypertension were compared considering various parameters. The subjective quality of the living environment was obtained using an appropriate subjective living environment index. Quality of life was mapped using the EuroQol Group quality of life questionnaire (EQ-5D) score and the two (mental and physical health) scores of the Short Form Health Questionnaire SF-8. The Gini-coefficient was used to quantify the regional economic variation within Hamburg. Linear and logistic regression analyses were performed. Regional levels were 68 city district clusters in Hamburg. The analysis included n = 8192 participants living at least five years in Hamburg at the time of participation in the HCHS. There was a spatial variation in the prevalence of arterial hypertension within Hamburg. Prevalence rates between city district clusters ranged from 50.0% to 88.5%. The results showed that city district clusters with a worse subjective perception of the living environment were partly associated with an increased prevalence of arterial hypertension. Furthermore, a negative association was observed between arterial hypertension prevalence and the sociodemographic status of participants in the city district clusters. Thus, participants with a high level of education suffered less frequently from arterial hypertension than participants with a rather low level of education. The subjective living environment index and quality of life were significantly related to the occurrence of arterial hypertension; however, more extensive and detailed studies are necessary to derive possible clinical implications.

Spatial Analytics Based on Confidential Data for Strategic Planning in Urban Health Departments

Spatial data analytics can detect patterns of clustering of events in small geographies across an urban region. This study presents and demonstrates a robust research design to study the longitudinal stability of spatial clustering with small case numbers per census tract and assess the clustering changes over time across the urban environment to better inform public health policy making at the community level. We argue this analysis enables the greater efficiency of public health departments, while leveraging existing data and preserving citizen personal privacy. Analysis at the census tract level is conducted in Mecklenburg County, North Carolina, on hypertension during pregnancy compiled from 2011–2014 birth certificates. Data were derived from per year and per multi-year moving counts by aggregating spatially to census tracts and then assessed for clustering using global Moran’s I. With evidence of clustering, local indicators of spatial association are calculated to pinpoint hot spots, while time series data identified hot spot changes. Knowledge regarding the geographical distribution of diseases is essential in public health to define strategies that improve the health of populations and quality of life. Our findings support that spatial aggregation at the census tract level contributes to identifying the location of at-risk “hot spot” communities to refine health programs, while temporal windowing reduces random noise effects on spatial clustering patterns. With tight state budgets limiting health departments’ funds, using geographic analytics provides for a targeted and efficient approach to health resource planning.