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To raise awareness about health inequalities, a well-functioning health inequality monitoring system (HIMS) is crucial. Drawing on work conducted under the Joint Action Health Equity Europe, the aim of this paper is to illustrate the strengths and weaknesses in current health inequality monitoring based on lessons learned from 12 European countries and to discuss what can be done to strengthen their capacities. Fifty-five statements were used to collect information about the status of the capacities at different steps of the monitoring process. The results indicate that the preconditions for monitoring vary greatly between countries. The availability and quality of data are generally regarded as strong, as is the ability to disaggregate data by age and gender. Regarded as poorer is the ability to disaggregate data by socioeconomic factors, such as education and income, or by other measures of social position, such as ethnicity. Few countries have a proper health inequality monitoring strategy in place and, where in place, it is often regarded as poorly up to date with policymakers’ needs. These findings suggest that non-data-related issues might be overlooked aspects of health inequality monitoring. Structures for stakeholder involvement and communication that attracts attention from policymakers are examples of aspects that deserve more effort.

Introduces a vision for the future of health equity and explains practical policy measures for how to achieve it. Health inequity is one of the defining problems of our time. But current efforts to address the problem focus on mitigating the harms of injustice rather than confronting injustice itself. In Equal Care, Seth A. Berkowitz, MD, MPH, offers an innovative vision for the future of health equity by examining the social mechanisms that link injustice to poor health. He also presents practical policies designed to create a system of social relations that ensures equal care for everyone. As Berkowitz illustrates, the project of social democracy works to improve health by bringing relationships of equality to the sites of human cooperation: in civil society, in political processes, and in economic activities. This book synthesizes three elements necessary for such a project—normative justification, mechanistic knowledge, and technical proficiency—into a practical vision of how to create health equity. Drawing from the fields of medicine, social epidemiology, sociology, economics, political science, philosophy, and more, Berkowitz makes clear that health inequity is social failure embodied, and the only true cures are political. Equal Care is essential reading for anyone concerned with the future of health equity.

It is estimated that more than 50 million Latinos live in the United States. This is projected to more than double by 2050. InHealth Issues in Latino Malesexperts from public health, medicine, and sociology examine the issues affecting Latino men's health and recommend policies to overcome inequities and better serve this population. The book addresses sexual and reproductive health; alcohol, tobacco, and drug use; mental and physical health among those in the juvenile justice or prison systems; chronic diseases; HIV/AIDS; Alzheimer's and dementia; and health issues among war veterans. It discusses utilization, insurance coverage, and research programs, and includes an extensive appendix charting epidemiological data on Latino health.

A guide to a holistic approach to healthcare measurement aimed at improving access and outcomes Healthcare System Access is an important resource that bridges two areas of research—access modeling and healthcare system engineering. The book's mathematical modeling approach highlights fundamental approaches on measurement of and inference on healthcare access. This mathematical modeling facilitates translating data into knowledge in order to make data-driven estimates and projections about parameters, patterns, and trends in the system. The complementary engineering approach uses estimates and projections about the system to better inform efforts to design systems that will yield better outcomes. The author—a noted expert on the topic—offers an in-depth exploration of the concepts of systematic disparities, reviews measures for systematic disparities, and presents a statistical framework for making inference on disparities with application to disparities in access. The book also includes information health outcomes in the context of prevention and chronic disease management. In addition, this text: * Integrates data and knowledge from various fields to provide a framework for decision making in transforming access to healthcare * Provides in-depth material including illustrations of how to use state-of-art methodology, large data sources, and research from various fields * Includes end-of-chapter case studies for applying concepts to real-world conditions Written for health systems engineers, Healthcare System Access: Measurement, Inference, and Intervention puts the focus on approaches to measure healthcare access and addresses important enablers of such change in healthcare towards improving access and outcomes.

There are known inequalities in vaccine uptake and the distribution of vaccine-preventable diseases. Understanding the best ways to increase vaccine uptake among socially excluded groups is vital to reduce these inequalities. To assess the effectiveness of interventions to increase vaccine uptake among socially excluded groups. Systematic review of randomised controlled trials (RCTs) and non-randomised studies of interventions. Studies were eligible if they evaluated an intervention to increase uptake of any vaccination on the World Health Organization immunisation schedule and focused on socially excluded populations (e.g. people experiencing homelessness, people who use drugs). MEDLINE, Embase and PsycINFO were searched to January 2025. Risk of bias was assessed using Cochrane risk of bias tools. Data were analysed using random-effects meta-analyses and effect direction plots. Of 2673 records, 20 studies were eligible (18 RCTs and two non-randomised studies). Most (13 studies) were conducted among people who use drugs and investigated hepatitis B (HBV) vaccination uptake (16 studies). Various interventions were identified: accelerated HBV vaccination schedules (six studies); financial incentives (four); educational initiatives (two); motivational interviewing (two); post-natal home visits (one); enhanced outreach and on-the-spot vaccination (one), and four varying interventions delivered as part of care co-ordination or nurse-guided case management models. Nine studies were at high risk of bias, six had some concerns and five were at low risk. Meta-analyses indicated a potential beneficial effect of accelerated schedules (odds ratio (OR):1.45, 95%CI:1.10–1.91) and financial incentives (OR:5.36, 95%CI:2.61–11.01). Confidence in the evidence was judged to be ‘moderate’ for both these interventions. Evidence for the effectiveness of other types of interventions was inconclusive. We identify some promising strategies for improving uptake of vaccinations among some socially excluded groups. The conclusions that can be drawn are, however, limited by the lack of high-quality studies on the topic.

Background With the rapid development of digital technology, it is crucial to explore at the individual microlevel whether digital technology can reduce health inequality and discuss potential transmission mechanisms. Methods This study uses data from the 2020 China Health and Retirement Longitudinal Study (CHARLS 2020) and the ordinary least squares (OLS) method to estimate the impact of digital technology on health inequality. This work then discusses the potential transmission mechanisms through which digital technology influences health inequality. Finally, it analyses the heterogeneity effects of digital technology on health inequality across different groups. Results We find that digital technology has reduced both physical and mental health inequality. Strengthening family support, enhancing health investment, and improving health behaviours are the transmission paths from digital technology to health inequality. Groups with older cohorts, females, less-educated individuals, low-income individuals, and rural individuals benefit more from physical health inequality, whereas the impact of digital technology on mental health inequality does not differ across groups. Conclusion Digital technology has a significant impact on reducing both physical and mental health inequality, with particularly notable benefits for vulnerable populations. It is imperative to focus more on the targeted effects of digital technology on these marginalized groups.

Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Bill & Melinda Gates Foundation.

Peru has made remarkable progress in delivering healthcare services over the past decades, leading to large improvements in most general population health indicators. Health insurance coverage has increased steadily since the 2009 health reforms. The country is now close to achieving universal health coverage, with the Integral Health Insurance (Seguro Integral de Salud) playing a crucial role in reaching poor and underserved rural communities. However, Peru continues to grapple with a health system that remains both segmented and fragmented, with multiple public sub-systems serving different population groups. The health system still lacks the integration and co-ordination needed to ensure equitable access to high-quality care for all Peruvians. Addressing these challenges requires strengthening quality governance, investing in high-impact healthcare service improvements, and curbing inefficiencies. This review assesses the performance of Peru’s health system and provides key recommendations for achieving a more equitable, efficient, and sustainable healthcare system aligned with OECD standards.

Link and Phelan (1995) developed the theory of fundamental causes to explain why the association between socioeconomic status (SES) and mortality has persisted despite radical changes in the diseases and risk factors that are presumed to explain it. They proposed that the enduring association results because SES embodies an array of resources, such as money, knowledge, prestige, power, and beneficial social connections that protect health no matter what mechanisms are relevant at any given time. In this article, we explicate the theory, review key findings, discuss refinements and limits to the theory, and discuss implications for health policies that might reduce health inequalities. We advocate policies that encourage medical and other health-promoting advances while at the same time breaking or weakening the link between these advances and socioeconomic resources. This can be accomplished either by reducing disparities in socioeconomic resources themselves or by developing interventions that, by their nature, are more equally distributed across SES groups.

Context: Quantitative estimates of the magnitude, direction, and rate of change of health inequalities play a crucial role in creating and assessing policies aimed at eliminating the disproportionate burden of disease in disadvantaged populations. It is generally assumed that the measurement of health inequalities is a value-neutral process, providing objective data that are then interpreted using normative judgments about whether a particular distribution of health is just, fair, or socially acceptable. Methods: We discuss five examples in which normative judgments play a role in the measurement process itself, through either the selection of one measurement strategy to the exclusion of others or the selection of the type, significance, or weight assigned to the variables being measured. Findings: Overall, we find that many commonly used measures of inequality are value laden and that the normative judgments implicit in these measures have important consequences for interpreting and responding to health inequalities. Conclusions: Because values implicit in the generation of health inequality measures may lead to radically different interpretations of the same underlying data, we urge researchers to explicitly consider and transparently discuss the normative judgments underlying their measures. We also urge policymakers and other consumers of health inequalities data to pay close attention to the measures on which they base their assessments of current and future health policies.