Regional Indices of Socio-Economic and Health Inequalities: a Tool for Public Health Programming
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OPEN ACCESS J PREV MED HYG 2019; 60: E300-E310 ORIGINAL ARTICLE Regional indices of socio-economic and health inequalities: a tool for public health programming R. LILLINI1, G. MASANOTTI2, F. BIANCONI2, A. GILI2, F. STRACCI2, F. LA ROSA2, M. VERCELLI3 1 Department of Health Sciences, University of Genoa. Analytical Epidemiology and Health Impact Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan; 2 Section of Public Health, Department of Experimental Medicine, University of Perugia, Italy; 3 Department of Health Sciences, University of Genova, Italy Keywords Public health • Socio-economic status • Socio-economic indices • Inequalities in health • Health care resources Summary Objectives. The aim was to provide an affordable method of com- Results. Overall mortality presented linear positive trends in puting socio-economic (SE) deprivation indices at the regional USHI, while trends in NDI-U proved non-linear or non-sig- level, in order to reveal the specific aspects of the relationship nificant. Similar results were obtained with regard to specific between SE inequalities and health outcomes. The Umbria Region causes of death according to deprivation groups, gender and Socio-Health Index (USHI) was computed and compared with the age. Italian National Deprivation Index at the Umbria regional level Conclusions. The USHI better describes a local population in (NDI-U). terms of health-related SE status. Policy-makers could therefore Methods. The USHI was computed by applying factor analysis to adopt this method in order to obtain a better picture of SE-associ- census tract SE variables correlated with general mortality and ated health conditions in regional populations and to target strat- validated through comparison with the NDI-U. egies for reducing health inequalities. Introduction of the population with respect to SE characteristics, but not to show the specific effects of SE deprivation on de- Over the last fifty years, most countries have investigat- privation-related health outcomes. ed the relationships between socio-economic (SE) sta- This methodological choice raises some critical prob- tus and inequalities in the utilisation and distribution of lems. Firstly, these indices are often not sufficiently re- healthcare resources and patient outcomes [1-6]. These lated to overall mortality, the main and most commonly studies have been carried out at the national or individual used health indicator. Worldwide, overall mortality is level, and have examined the relationships between the related to material and social differences in the popula- distribution of demographic characteristics (gender and tion and to inequalities in the distribution of public and age), SE factors (income and occupation), cultural fac- private health resources [10]. Computed according to tors (educational level), living conditions (marital sta- this “pure” definition of SE deprivation, the usual depri- tus, household composition, domestic overcrowding and vation indices do not consider whether their constituent tenure, etc.) and health outcomes in areas ranging from variables influence health status [6, 7]. They therefore the macro to the micro level [5, 6]. Indeed, an SE clas- risk neglecting to evaluate differences in the local allo- sification that takes the patient’s neighbourhood into ac- cation of resources in response to health needs. These count provides a useful starting point in describing and differences can be particularly marked in countries with improving the effectiveness of local public health inter- large differences in national and regional demographics ventions [4, 6]. and SE status, causing considerable disparities in health The definition of “neighbourhood” is debated in the lit- outcomes [5]. erature, the most common being that of the smallest of- Significant examples of such situations can be found in ficial administrative area [5-9], usually the census tract Italy, a country where population density varies from (CT), which approximates the SE and health features of region to region, ranging from 39 to 429 inhabitants the area to the resident individuals’ characteristics. per square km. Moreover, the various regions differ in This choice is justified by the aim of such studies, which terms of the rate of population ageing, proportion of the is to accurately assess the feasibility of providing pre- population that is active, birth rate, family size, and la- ventive, diagnostic and therapeutic services targeted to bour market characteristics, particularly from North to individuals who live in a specific area. South [11]. In addition, the orographic characteristics of Most of these studies have utilised indices of SE depri- the territory in the various regions impacts on the inter- vation that were computed for the whole nation [6, 7]. nal distribution of goods and wealth. In brief, the econ- Moreover, it is noteworthy that such indices were com- omy of Northern Italy is similar to, and connected with, monly constructed in order to describe the distribution that of Central Europe, while the Central and Southern E300 https://doi.org/10.15167/2421-4248/jpmh2019.60.4.1257 LOCAL DEPRIVATION INDEX FOR PUBLIC HEALTH PROGRAMMING regions of Italy are penalised by their poor connection these variables describe features of individuals (age, with the heart of Europe. marital status, educational level, employment, etc.), It is also necessary to consider how public financing is families (number of family members, single parents, av- distributed. With few exceptions, funds flow from the erage age of families, etc.) and households (ownership, central government to the single regional authorities, over-crowding, housing conditions, services available, which decide how they should be allocated and deter- etc.). mine the amount and distribution of resources devoted The NDI considered 280 of these variables, covering to socio-health policies (social support, preventive mea- five conditions which described the multidimensional sures, etc.) [11-14]. concept of social and material deprivation (persons with In countries with such characteristics, all these aspects only primary education, unemployed or searching for lead to health inequalities that are specific to each re- first employment, one-parent families and dependent gion, and particularly to sub-areas where population children living together, rented accommodation, domes- density is lower [5]. These inequalities can be accurate- tic overcrowding) [7]. The NDI was computed at the CT ly described and analysed only by means of indicators level as the sum of these five indicators in standardised that are constructed at the regional level and which can form, grouped in population quintiles at the national take local peculiarities into account [5-7]. Such indica- level [7]. In the present study, we used a regional ver- tors are computed on the basis of health-related local sion of the NDI (NDI-U), categorised in quintiles of the demographic and SE indicators [9, 12]. These indices Umbrian population. should be called Indices of SE and Health Inequalities To construct the USHI, we adopted the same method (SHI), rather than Deprivation Indices, as they describe used to compute the Liguria Socio-economic and Health the population distribution not only in terms of mere SE Inequalities Index (LSHI) [8]. Pearson’s bivariate cor- inequalities, but also according to people’s needs for relation (p < 0.05) was calculated between each of the health support. 543 basic variables and the synthetic SE indices (em- The present study aimed to describe, discuss and validate ployment/unemployment rates, ageing index, depend- the method and the technique for computing this kind of ence rate, etc.) and general mortality in Umbria in 2001- index, which could be applied in every nation affected 2004. Significantly correlated variables were picked out by marked regional differences. The Umbria region was and a tolerance test (p < 0.05) was applied to these in chosen as an example of the application of these pro- order to reduce collinearity [15]. From the nine variables cedures, which are derived from a previous successful which emerged after these steps, a principal-component attempt in another Italian region (Liguria) [8, 9, 12]. analysis extracted three factors. These defined the latent Moreover, this study assessed the ability of the Umbria structure connecting the SE variables that were able to regional index (USHI) to efficiently classify population synthetically describe the health-related SE characteris- subgroups in Umbria on the basis of a combination of tics of the population. The three factors underwent a va- health fragility and SE differences related to health out- rimax rotation, in order to render them orthogonal, and comes, in comparison with the Italian National Depriva- thus independent. These three independent factors were tion Index (NDI) computed at the level of the Umbria linearly combined into a single quantitative variable, the region (NDI-U), which distinguishes populations only values of which were re-scaled as a percentage in order on the basis of SE status [7-9, 12]. to obtain the USHI at the CT level [16] (Tab. I). Subse- quently this variable was aggregated, both for the pur- pose of its validation and to obtain a municipality index, Methods based on the CTs in each municipality (Tab. SI). This operation was necessary because the population of the The NDI is the benchmark for validating local indices Umbria region is small (825,796 inhabitants); therefore, (USHI in this study). As