Horizontal Inequity in Access to Health Care in Four South American Cities

This paper analyzes and compares socioeconomic inequalities in the use of healthcare services by the elderly in four South-American cities: Buenos Aires, Santiago, Montevideo and San Pablo. We use data from SABE, a survey on Health, Well-being and Aging administered in 2000. After having accounted for socioeconomic inequalities in healthcare needs, we find socioeconomic inequities favoring the rich in the use of preventive services (mammograms, pap tests, breast examinations, and prostate exams) in all of the studied cities. We also find inequities in the likelihood of having a medical visit in Santiago and Montevideo, and in some measures of quality of access in Santiago, Sao Paulo, and Buenos Aires. Santiago depicts the highest inequities in medical visits and Uruguay the worse indicators in mammograms and pap scans tests. For all cities, inequities in preventive services at least double inequities in other services. We do not find evidence of a trade-off between levels of access and equity in access to healthcare services. The decomposition of healthcare inequalities suggests that inequities within each health system are more important than between systems.inequalities, healthcare, medical visit, preventive services

Horizontal inequity in access to health care in four South American cities

This paper analyzes and compares socioeconomic inequalities in the use of healthcare services by the elderly in four South-American cities: Buenos Aires (Argentina), Santiago (Chile), Montevideo (Uruguay) and San Pablo (Brazil). We use data from SABE, a survey on Health, Well-being and Aging administered in several Latin American cities in 2000. After having accounted for socioeconomic inequalities in healthcare needs, we find socioeconomic inequities favoring the rich in the use of preventive services (mammograms, pap tests, breast examinations, and prostate exams) in all of the studied cities. We also find inequities in the likelihood of having a medical visit in Santiago and Montevideo, and in some measures of quality of access in Santiago, Sao Paulo, and Buenos Aires. Santiago depicts the highest inequities in medical visits and Uruguay the worse indicators in mammograms and pap scans tests. For all cities, inequities in preventive services at least double inequities in other services. We do not find evidence of a trade-off between levels of access and equity in access to healthcare services. The decomposition of healthcare inequalities suggests that inequities within each health system (public or private) are more important than between systems.inequalities, healthcare, medical visit, preventive services.

LEGAL AID INEQUITIES PREDICT HEALTH DISPARITIES

LEGAL AID INEQUITIES PREDICT HEALTH DISPARITIE

Equity in healthcare financing in Italy

This Ph.D. dissertation discusses equity in healthcare financing in Italy. Each of the three chapters of this dissertation constitutes an independent research output, answering stand-alone research questions. Chapter 1 is a systematic review on equity in healthcare financing in OECD and non- OECD countries. This deals both with the methodology used and the evidence around the world on vertical equity in healthcare financing. Chapters 2 and 3 report empirical evidence from Italy. Chapter 2 analyses the progressivity of healthcare financing in the Italian system and focuses on Italian regions, performing a comparison of progressivity of the healthcare financing across regional systems. Chapter 3 provides an assessment of how differences in co-payments between the Italian regions contribute to growing inequalities in access to public health care services in Italy. A common ground among chapters is the measurement of equity and inequalities in health financing, with particular reference to differences among the Italian regions

A Global Health Analysis of Socio-Economic Determinants of Health, and Human Digital Development, Health Equity and mHealth

Access to the internet, often via mobile devices, provides individuals with the ability to access the resources and economic opportunities required to live the lives they choose. Using data drawn from the World Health Organization, United Nations Development Program, and World Bank, two levels of analysis are conducted to answer the research questions: “What is the relationship between social determinants of health and human digital development?” and, “What is the relationship between health equity and mHealth?”. First, multiple regression was utilized to test two hypotheses, and second, a k-means cluster analysis was carried out categorize the countries based on these variables. Our results suggest that there is correlation between social determinants of health and human digital development; except in the case of the homelessness variable. Of the health equity variables, only the GINI index correlates with the mobile health index. A four-cluster solution in the cluster analysis illustrates that the majority of countries demonstrate low mHealth, Human Digital Development, GINI and high Education Inequality and Life Expectancy inequality. These findings have implications for how human digital development and mobile health can address social determinants of health. Future research will need to delve deeper into these connections

Social exclusion and health inequalities in the time of COVID-19

Social exclusion as a process leads to a state of multiple relative deprivations in diverse areas of social life, like employment, education, healthcare, social ties, respect. Individuals or groups may have a worse position in several areas, particularly with other individuals or groups in society. Coronavirus pandemics disproportionately affect poorer communities and socially excluded people. Socially excluded are double victims; due to their position, they are more prone to infection by a coronavirus, further increasing their exclusion. The purpose of this contribution is to provide a conceptual framework for analyzing the relationship between social exclusion and health disparities during the COVID-19 pandemic. The goal is to comprehend the causes and consequences of unequal power relationships and offer critical assessments of current policies and measures to reduce health inequalities. Health and social inequalities are a significant constraint to economic revival and a successful fight against pandemics. The extent of the economic and health crisis caused by pandemic shock largely depends on past health and social inequality

Socioeconomic inequalities and inequities in the screening and treatment of diabetes and hypertension in Kenya

The burden of non-communicable diseases (NCDs) is on a disproportionate rise in low-and middleincome countries (LMICs). Equity in the utilisation of screening and treatment services for NCDs is important in reducing associated disease burden. For instance, the 2030 Sustainable Development Goal 3.4 that aims to reduce by one-third premature NCDs mortality, has adopted prevention and treatment as critical interventions for achieving this target. However, little is known about equity in the use of screening and treatment services for major NCDs like diabetes and hypertension in Kenya. This dissertation assesses horizontal equity (i.e. equal treatment for equal need) in the screening and treatment for diabetes and hypertension. Further, it examines factors contributing to inequality. Data from the 2015 STEPwise cross-sectional survey on NCDs risk factors were used in the analysis. Concentration curves, concentration indices and horizontal inequity index were used to assess socioeconomic inequality and inequity in the screening and treatment for diabetes and hypertension. The Wagstaff decomposition approach was used to examine factors contributing to socioeconomic inequality in screening and treatment. For a granular presentation of inequity and inequality findings, analyses were conducted across the wealth and regional divides in Kenya. Overall, the rich benefited disproportionately more in the utilisation of screening and treatment services, given their population share of need. Of note, inequalities in the use of screening and treatment interventions for diabetes and hypertension were observed in the geographic regions. In general, non-need factors such as educational attainment, area of residence, exposure to media, employment, and wealth status were the largest contributors to inequality in both screening and treatment. By contrast, need factors like sex also significantly contributed to inequality in diabetes and hypertension screening. After controlling for need, a statistically significant pro-rich inequity in the use of diabetes and hypertension screening was observed. Both the use of diabetes and hypertension treatment were pro-rich though a statistically significant result was only seen for the former. For equity in the screening and treatment for diabetes and hypertension in Kenya, demand enhancing mechanisms such as health education through the mass media and free NCD screening in the public sector should be implemented. Also, given the interplay of factors beyond the health sector that affect utilisation of healthcare services, there is a need for multi-sectoral approaches at various levels to address drivers of social inequality with a critical focus in rural and marginalised areas