"The Glasgow effect?"- the result of the geographical patterning of deprived areas?

Spatial characteristics of non-communicable diseases and their associations to social conditions in a large urban cohort in Germany-Results from the Hamburg City Health Study

BACKGROUND

Non-communicable diseases (NCDs) are responsible for many deaths. They are associated with several modifiable and metabolic risk factors and are therefore prone to significant regional variations on different scales. However, only few intra-urban studies examined spatial variation in NCDs and its association with social circumstances, especially in Germany. Thus, the present study aimed to identify associations of personal risk factors and local social conditions with NCDs in a large German city.

METHODS

This study is based on a population-based cohort of the Hamburg City Health Study including 10,000 probands. Six NCDs were analyzed (chronic obstructive pulmonary disease [COPD], coronary heart disease [CHD], diabetes mellitus, heart failure, depression, and hypertension) in 68 city district clusters. As risk factors, we considered socio-demographic variables (age, sex, education) and risk behaviour variables (smoking, alcohol consumption). Logistic regression analyses identified associations between the district clusters and the prevalence rates for each NCD. Regional variation was detected by Gini coefficients and spatial cluster analyses. Local social condition indexes were correlated with prevalence rates of NCDs on city district level and hot-spot analyses were performed for significant high or low values.

RESULTS

The analyses included 7,308 participants with a mean age of 63.1 years (51.5% female). The prevalence of hypertension (67.6%) was the highest. Risk factor associations were identified between smoking, alcohol consumption and education and the prevalence of NCDs (hypertension, diabetes, and COPD). Significant regional variations were detected and persisted after adjusting for personal risk factors. Correlations for prevalence rates with the local social conditions were significant for hypertension (r = 0.294, p < 0.02), diabetes (r = 0.259, p = 0.03), and COPD (r = 0.360, p < 0.01).

CONCLUSIONS

The study shows that regional differences in NCD prevalence persist even after adjusting for personal risk factors. This highlights the central role of both personal socio-economic status and behaviors such as alcohol and tobacco consumption. It also highlights the importance of other potential regional factors (e.g. the environment) in shaping NCD prevalence. This knowledge helps policy- and decision-makers to develop intervention strategies.

Investigating the association between regeneration of urban blue spaces and risk of incident chronic health conditions stratified by neighbourhood deprivation: A population-based retrospective study, 2000-2018

Chronic non-communicable diseases are leading causes of poor health and mortality worldwide, disproportionately affecting people in highly deprived areas. We undertook a population-based, retrospective study of 137,032 residents in Glasgow, Scotland, to investigate the association between proximity to urban blue spaces and incident chronic health conditions during a canal regeneration programme. Hazard ratios (HRs) were estimated using Cox proportional hazards models adjusted for age and sex, with the incidence of a given health condition as the dependent variable. The analyses were stratified by socioeconomic deprivation tertiles. We found that, in areas in the highest deprivation tertile, proximity to blue space was associated with a lower risk of incident cardiovascular disease (HR 0.85, 95% Confidence Interval (CI) 0.76-0.95), hypertension (HR 0.85, 95% CI 0.79-0.92), diabetes (HR 0.88, 95% CI 0.83-0.94), stroke (HR 0.85, 95% CI 0.77-0.94) and obesity (HR 0.90, 95% CI 0.86-0.94), but not chronic pulmonary disease, after adjusting for age and sex covariates. In middle and low deprivation tertiles, living closer to the canal was associated with a higher risk of incident chronic pulmonary disease (middle: HR 1.56, 95% CI 1.24-1.97, low: HR 1.34, 95% CI 1.05-1.73). Moreover, in the middle deprivation tertile, a higher risk of stroke (HR 1.36, 95% CI 1.02-1.81) and obesity (HR 1.14, 95% CI 1.01-1.29) was observed. We conclude that exposure to blue infrastructure could be leveraged to mitigate some of the health inequalities in cities.

Geospatial mapping and data linkage uncovers variability in outcomes of foot disease according to multiple deprivation: a population cohort study of people with diabetes

AIMS/HYPOTHESIS

Our aim was to investigate the geospatial distribution of diabetic foot ulceration (DFU), lower extremity amputation (LEA) and mortality rates in people with diabetes in small geographical areas with varying levels of multiple deprivation.

METHODS

We undertook a population cohort study to extract the health records of 112,231 people with diabetes from the Scottish Care Information - Diabetes Collaboration (SCI-Diabetes) database. We linked this to health records to identify death, LEA and DFU events. These events were geospatially mapped using multiple deprivation maps for the geographical area of National Health Service (NHS) Greater Glasgow and Clyde. Tests of spatial autocorrelation and association were conducted to evaluate geographical variation and patterning, and the association between prevalence-adjusted outcome rates and multiple deprivation by quintile.

RESULTS

Within our health board region, people with diabetes had crude prevalence-adjusted rates for DFU of 4.6% and for LEA of 1.3%, and an incidence rate of mortality preceded by either a DFU or LEA of 10.5 per 10,000 per year. Spatial autocorrelation identified statistically significant hot spot (high prevalence) and cold spot (low prevalence) clusters for all outcomes. Small-area maps effectively displayed near neighbour clustering across the health board geography. Disproportionately high numbers of hot spots within the most deprived quintile for DFU (p < 0.001), LEA (p < 0.001) and mortality (p < 0.001) rates were found. Conversely, a disproportionately higher number of cold spots was found within the least deprived quintile for LEA (p < 0.001).

CONCLUSIONS/INTERPRETATION

In people with diabetes, DFU, LEA and mortality rates are associated with multiple deprivation and form geographical neighbourhood clusters.

Using population surfaces and spatial metrics to track the development of deprivation landscapes in Glasgow, Liverpool, and Manchester between 1971 and 2011

Measuring change in the spatial arrangement of deprivation over time, and making international, inter-city comparisons, is technically challenging. Meeting these challenges offers a means of furthering understanding and providing new insights into the geography of urban poverty and deprivation. In this paper, we introduce a novel approach to mapping and analysing spatio-temporal patterns of household deprivation, assessing the distribution at the landscape level. The approach we develop has advantages over existing techniques because it is applicable in situations where i) conventional approaches based on choropleth mapping are not feasible due to boundary change and/or ii) where spatial relationships at a landscape level are of interest. Through the application of surface mapping techniques to disaggregate census count data, and by applying spatial metrics commonly used in ecology, we were able to compare the development of the spatial arrangement of deprivation between 1971 and 2011 in three UK cities of particular interest: Glasgow, Manchester and Liverpool. Applying three spatial metrics - spatial extent, patch density, and mean patch size - revealed that over the 40 year period household deprivation has been more spatially dispersed in Glasgow. This novel approach has enabled an analysis of deprivation distributions over time which is less affected by boundary change and which accurately assesses and quantifies the spatial relationships between those living with differing levels of deprivation. It thereby offers a new approach for researchers working in this area.

SimAlba: A Spatial Microsimulation Approach to the Analysis of Health Inequalities

This paper presents applied geographical research based on a spatial microsimulation model, SimAlba, aimed at estimating geographically sensitive health variables in Scotland. SimAlba has been developed in order to answer a variety of "what-if" policy questions pertaining to health policy in Scotland. Using the SimAlba model, it is possible to simulate the distributions of previously unknown variables at the small area level such as smoking, alcohol consumption, mental well-being, and obesity. The SimAlba microdataset has been created by combining Scottish Health Survey and Census data using a deterministic reweighting spatial microsimulation algorithm developed for this purpose. The paper presents SimAlba outputs for Scotland's largest city, Glasgow, and examines the spatial distribution of the simulated variables for small geographical areas in Glasgow as well as the effects on individuals of different policy scenario outcomes. In simulating previously unknown spatial data, a wealth of new perspectives can be examined and explored. This paper explores a small set of those potential avenues of research and shows the power of spatial microsimulation modeling in an urban context.

Perspectives on differing health outcomes by city: accounting for Glasgow's excess mortality

Several health outcomes (including mortality) and health-related behaviors are known to be worse in Scotland than in comparable areas of Europe and the United Kingdom. Within Scotland, Greater Glasgow (in West Central Scotland) experiences disproportionately poorer outcomes independent of measurable variation in socioeconomic status and other important determinants. Many reasons for this have been proposed, particularly related to deprivation, inequalities, and variation in health behaviors. The use of models (such as the application of Bradford Hill's viewpoints on causality to the different hypotheses) has provided useful insights on potentially causal mechanisms, with health behaviors and inequalities likely to represent the strongest individual candidates. This review describes the evolution of our understanding of Glasgow's excess mortality, summarizes some of the key work in this area, and provides some suggestions for future areas of exploration. In the context of demographic change, the experience in Glasgow is an important example of the complexity that frequently lies behind observed variations in health outcomes within and between populations. A comprehensive explanation of Glasgow's excess mortality may continue to remain elusive, but is likely to lie in a complex and difficult-to-measure interplay of health determinants acting at different levels in society throughout the life course. Lessons learned from the detailed examination of different potentially causative determinants in Scotland may provide useful methodological insights that may be applied in other settings. Ongoing efforts to unravel the causal mechanisms are needed to inform public health efforts to reduce health inequalities and improve outcomes in Scotland.