Multidimensionality of Health Inequalities: A Cross-Country Identification of Health Clusters through Multivariate Classification Techniques

Abstract

Despite major efforts in scientific literature to explain and understand the social determinants of health inequalities, the complex association between social causes and health outcomes remains empirically questionable and theoretically puzzling. To date, the studies on social determinants of health has mainly been generated by research techniques and methods that were developed to answer specific questions about the causes and effects of particular indicators on specific health outcomes. The present exploratory study follows a complex system approach to capture the interdependence between socioeconomic status, lifestyles, and health in a single measure that enables international comparisons of population health. Specifically, this study is aimed to: (a) classify individuals’ state of health according the usage of multidimensional data on physical and mental health, SES, lifestyles and risk behaviors, in order to (b) compare the relative strength of the different predictors of health groups (or clusters) at the individual-level and, finally, (c) to measure the level of health inequalities between different countries. From a complex system approach, this study uses multivariate classification methods to compare health groups in a sample of 29 countries and shows that interdependence models may be useful to describe and compare between-country health inequalities that are not visible through techniques for the analysis of dependence. The present work offers two fundamental contributions. On the one hand, this study compares the relative relevance of different indicators that are susceptible to affect individual health outcomes; on the other hand, the resulting multidimensional classification of countries according health clusters provides an alternative for inter-country health comparisons.

Social Vulnerability and Mental Health Inequalities in the “Syndemic”: Call for Action

“…Due to the multifactorial complex nature of causality in health, there is a web of determinants that creates a form of non-linear, complex system ( 91 ). Clustering methods ( 92 ) may help us understand this better, especially if “person-centered” ( 68 ), in order to catch the complex interactions of these cathegories - or “equity stratifiers”–on individual health and wellbeing. However, transformative action is therefore necessary and cannot be guided by theory alone, but must address emerging needs.…”

Health profiles and socioeconomic characteristics of nonagenarians residing in Mugello, a rural area in Tuscany (Italy)

“…Cognitive function was measured according to the Mini-Mental State Examination (MMSE): the higher the score (0-30), the better the cognitive status is [35]. MMSE scores were divided into three categories to distinguish people with severe (0-17), mild (18)(19)(20)(21)(22)(23), and no cognitive impairment (24)(25)(26)(27)(28)(29)(30) [36]. Functional status was assessed according to the ability to perform five of the activities of daily living (ADLs) (eating, dressing, bathing, toileting, transferring) [37].…”

“…LCA is a subset of structural equation modeling suitable for addressing multidimensional concepts, as in the case of health, to find groups of cases with similar characteristics in multivariate categorical data. The use of LCA in population health studies is extensive, with applications that vary from younger [15] to older individuals and elderly people [12][13][14][16][17][18][19][20][21][22][23][24]. Some scholars used this approach to identify profiles of health by considering functional, cognitive and psychological indicators [12-14, 16, 17, 22], with some evaluating socioeconomic differences among the health profiles [12,13,17,22] and others predicting the health care expenditures of people belonging to different groups [14,16].…”

Socioeconomic predictors of COVID-19-related health disparities among United States workers: A structural equation modeling study

“…However, few studies have applied this approach to understanding the myriad factors contributing to systemic COVID-19 vulnerabilities [24,25]. The phenomenon of clustering of behavioral, social and economic risk factors that lead to disease has long been studied to understand population-level health disparities [26,27]. For the purposes of this study, we define vulnerability as a cluster of closely correlated factors that place specific population subgroups at greater risk for adverse COVID-19-associated outcomes.…”