Inequalities in Health: Methodological Approaches to Spatial Differentiation (original) (raw)
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Human Resources for Health, 2021
Background An analysis of the regional distribution of health resources is one of the tools for evaluating equal geographic access to health care. The usual analytical approach to an assessment of regional differences is to evaluate each health resource separately. This is a sensible approach, because there may be systematic reasons for any differences, for example, higher salaries in urban areas. However, a separate evaluation of the regional distribution of health resource capacities may be misleading. We should evaluate all health resource capacities as a whole and consider the substitutability of resources. Objective This study aims to measure regional inequalities in the Czech Republic with the help of alternative approaches to the evaluation of regional inequalities in the case of several substitutable health resources. Methods Five alternative evaluation methods (models) are described and applied: the separate evaluation, expert model, market model, common weights model, and ...
Regional indices of socio-economic and health inequalities: a tool for public health programming
Journal of Preventive Medicine and Hygiene, 2019
Summary Objectives The aim was to provide an affordable method of computing socio-economic (SE) deprivation indices at the regional level, in order to reveal the specific aspects of the relationship between SE inequalities and health outcomes. The Umbria Region Socio-Health Index (USHI) was computed and compared with the Italian National Deprivation Index at the Umbria regional level (NDI-U). Methods The USHI was computed by applying factor analysis to census tract SE variables correlated with general mortality and validated through comparison with the NDI-U. Results Overall mortality presented linear positive trends in USHI, while trends in NDI-U proved non-linear or non-significant. Similar results were obtained with regard to specific causes of death according to deprivation groups, gender and age. Conclusions The USHI better describes a local population in terms of health-related SE status. Policy-makers could therefore adopt this method in order to obtain a better picture of SE...
International Journal for Equity in Health, 2013
Introduction: In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index.
A multi-criteria spatial deprivation index to support health inequality analyses
Deprivation indices are useful measures to analyze health inequalities. There are several methods to construct these indices, however, few studies have used Geographic Information Systems (GIS) and Multi-Criteria methods to construct a deprivation index. Therefore, this study applies Multi-Criteria Evaluation to calculate weights for the indicators that make up the deprivation index and a GIS-based fuzzy approach to create different scenarios of this index is also implemented. The Analytical Hierarchy Process (AHP) is used to obtain the weights for the indicators of the index. The Ordered Weighted Averaging (OWA) method using linguistic quantifiers is applied in order to create different deprivation scenarios. Geographically Weighted Regression (GWR) and a Moran’s I analysis are employed to explore spatial relationships between the different deprivation measures and two health factors: the distance to health services and the percentage of people that have never had a live birth. This last indicator was considered as the dependent variable in the GWR. The case study is Quito City, in Ecuador. The AHP-based deprivation index show medium and high levels of deprivation (0,511 to 1,000) in specific zones of the study area, even though most of the study area has low values of deprivation. OWA results show deprivation scenarios that can be evaluated considering the different attitudes of decision makers. GWR results indicate that the deprivation index and its OWA scenarios can be considered as local estimators for health related phenomena. Moran’s I calculations demonstrate that several deprivation scenarios, in combination with the ‘distance to health services’ factor, could be explanatory variables to predict the percentage of people that have never had a live birth. The AHP-based deprivation index and the OWA deprivation scenarios developed in this study are Multi-Criteria instruments that can support the identification of highly deprived zones and can support health inequalities analysis in combination with different health factors. The methodology described in this study can be applied in other regions of the world to develop spatial deprivation indices based on Multi-Criteria analysis
SPATIAL AND REGIONAL HEALTH INEQUALITIES IN EUROPE
Purpose – We have studied the spatial interrelations of the regional health inequalities in Europe in view of the social selection hypothesis. The primary objective of our paper is to demonstrate the effect of health status on various socioeconomic development indicators. Design/methodology/approach – According to the social selection hypothesis, an individual's health status influences both his own socioeconomic status and the economy as a whole. Accordingly, we have operationalised the regional health status and socioeconomic development and analysed their correlations. For such purpose we have used correlation analysis and explanatory spatial data analysis (ESDA): spatial autocorrelation and regional regression models. Findings – According to our results, there are synergistic effects between the two phenomena, both in view of global and local statistics. Regions with better health status are characterised with better socioeconomic conditions. The spatial regression models have also justified and confirmed the use of the social selection hypothesis for the explanation of regional differences in economic development. Research limitations/implications – It is advisable to use panel databases for the future analysis of this topic. To give correct answer on social selection hypothesis on regional level, other examinations (Markov chain model) must be done. Practical implications – Furthermore, it is advisable to extend the range of health status indicators with variables such as noncommunicable chronic diseases or other causes of death associated with socioeconomic phenomena (e.g. TBC incidence indicates poverty). Originality/Value – As far as the social selection hypothesis is concerned, our paper presents new and innovative results whose approach (method for the exploration of regional data) has not been discussed so far in the international literature.
The European Journal of Public Health, 2005
Background: The study objective was to investigate the association between health outcomes and several small-area-based socioeconomic measures and also with individual socioeconomic measures as a check on external validity. Methods: Cross-sectional design based on the analysis of the Barcelona Health Interview Survey of 1992. A representative stratified sample of the non-institutionalised population resident in Barcelona city (Spain) was obtained. The present study refers to the 4171 respondents aged over 14. We studied perceived health status, presence of chronic conditions and smoking as health outcomes. Area socioeconomic measures (1991 census) were generated at census tract level and individual socioeconomic measures were educational level and social class obtained through the survey. Results: With individual socioeconomic measures we observed that the lower the educational level or social class, the higher the probability of reporting a perceived health status of fair, poor or very poor and of presenting some chronic condition. With regard to smoking, among men this trend was similar [odds ratio (OR) ¼ 1.5; 95% confidence interval (CI) ¼ 1.2À1.9 in social classes IV-V with respect to social classes I-II], while among women it was reversed (OR ¼ 0.7; 95% CI ¼ 0.5À0.9). With the different areabased socioeconomic indicators differences were also observed in this sense, with the exception of smoking in women for which these indicators do not show any differences by socioeconomic level. Conclusions: With several census area-based socioeconomic measures similar effects on inequalities in health have been observed. In general, these inequalities were in the same sense as those obtained with individual-based measures. Small-area-based socioeconomic measures from the Spanish census could greatly enhance analysis of social inequalities in health, overcoming the absence of socioeconomic data in public health registries and in medical records.
RESEARCH ARTICLE An assessment of the geographical approach to health inequality
2011
New interest is being shown in the geographical approach to health inequality at both the research and the service provider level. The scientific and methodological basis of this approach does not take into consideration the social structure and the history of the locations/communities under investigation. The analysis of geographical differences must be verified and consideration given to possible variations in internal health inequalities between entities compared. Our approach to health inequalities is based on the theory that social health inequalities are essentially the final product of living conditions and lifestyle taking account of individual and collective history.
An assessment of the geographical approach to health inequality
Critical Public Health, 2011
New interest is being shown in the geographical approach to health inequality at both the research and the service provider level. The scientific and methodological basis of this approach does not take into consideration the social structure and the history of the locations/communities under investigation. The analysis of geographical differences must be verified and consideration given to possible variations in internal health inequalities between entities compared. Our approach to health inequalities is based on the theory that social health inequalities are essentially the final product of living conditions and lifestyle taking account of individual and collective history.
International Journal of Environmental Research and Public Health
The different geographical contexts seen in European metropolitan areas are reflected in the uneven distribution of health risk factors for the population. Accumulating evidence on multiple health determinants point to the importance of individual, social, economic, physical and built environment features, which can be shaped by the local authorities. The complexity of measuring health, which at the same time underscores the level of intra-urban inequalities, calls for integrated and multidimensional approaches. The aim of this study is to analyse inequalities in health determinants and health outcomes across and within nine metropolitan areas: Athens, Barcelona, Berlin-Brandenburg, Brussels, Lisbon, London, Prague, Stockholm and Turin. We use the EURO-HEALTHY Population Health Index (PHI), a tool that measures health in two components: Health Determinants and Health Outcomes. The application of this tool revealed important inequalities between metropolitan areas: Better scores were...
Environmental Health, 2015
Background: Reducing health inequalities involves the identification and characterization of social and exposure factors and the way they accumulate in a given area. The areas of accumulation then allow for prioritization of interventions. The present study aims to build spatial composite indicators based on the aggregation of environmental, social and health indicators and their inter-relationships. Method: Preliminary work was carried out firstly to homogenize spatial coverage, and secondly to study spatial variation of environmental (EI), socioeconomic (SI) and health (HI) indicators. The aggregation of the different indicators was performed using several methodologies for which results and decision-makers' usability were compared. Results: Four methodologies were tested: 1) A simple summation of normalized HI, EI and SI indicators (IC), 2) the sum of the normalized HI, EI and SI indicators weighted by the first principal component of a Principal Component Analysis (IC PCA), 3) the sum of normalized and weighted indicators of the first principal component of Local Principal Component Analysis (IC LPCA), and 4) the sum of normalized and weighted indicators of the first principal component of a Geographically Weighted Principal Component Analysis (IC GWPCA).