This paper studies how universal public health insurance shapes the evolution of health inequality over the life cycle. Using linked Canadian administrative records, we develop a novel, objective, diagnosis-based measure of cumulative health shocks that captures persistent health deterioration rather than transitory illness. Applying this measure, we document large and stable income gradients in health shocks, lifetime disease burden, and mortality that persist despite universal insurance coverage. Exploiting differences in policy exposure across cohorts—most notably between Baby Boomers and Generation X, who experienced universal insurance at different stages of the life cycle—we show that these gradients do not narrow under expanded public coverage. To interpret these findings, we develop a life-cycle model in which individuals choose preventive health investment before shocks and curative care after shocks under alternative insurance regimes. Universal insurance reduces the effective price of treatment but can crowd out preventive investment, particularly among lower-income households facing liquidity constraints and lower expected returns to health investment. The model replicates the observed income–health gradient both in the absence and presence of insurance and explains why universal coverage may fail to equalize long-run health outcomes. Empirically, the implied crowding-out effect is strongest for chronic conditions that permanently scar health, while higher-income individuals partially offset it through increased prevention. Together, the results highlight a fundamental distinction between equalizing access to care and equalizing the accumulation of health capital. Policies that complement universal coverage with targeted support for preventive investment, especially earlier in the life cycle, are more likely to reduce persistent health inequality.

One of the legal commitments for executive systems in the Law of the Fourth Development Plan of Iran is to have a 2.5 percent gain in the total factor productivity. This Law expressly enforces the executive systems to take the productivity circle, which measuring productivity indexes is one of the most important parts of it. In this article, we evaluate the efficiency of manufacturing industries by data envelopment analysis and after that we use the DEA Malmquist Productivity Index to measure the efficiency change, technology change and productivity growth of Iran’s manufacturing industries, during the third developing plan. Also, we use the super efficiency technique for ranking the efficient units. The results show that, coke and refined petroleum industry and non metallic mineral industry are efficient in the all five years of the third developing plan and have the first and second rank in these years.

• Understanding how socioeconomic status and health interact throughout life is essential for improving health equity and guiding effective policy. This study develops an objective and multidimensional measure of health shocks based on the Charlson Comorbidity Index, allowing for precise identification of severe health events and their cumulative impact. By linking the Discharge Abstract Database, which contains detailed hospitalization records, with the Longitudinal Administrative Databank, a rich source of socioeconomic information, we connect individual health histories with long-run income trajectories. The results show that health shocks substantially reduce future income, even after controlling for prior income, family characteristics, age, and sex, indicating that serious illness imposes lasting economic costs. The effects differ across groups: lower-income and older individuals experience sharper income declines after a shock, while higher-income groups show greater resilience. These findings highlight how socioeconomic status influences both exposure to and recovery from adverse health events. By combining objective health and income data over two decades, this paper provides new evidence on the mechanisms through which health shocks reinforce inequality and shape intergenerational economic outcomes.

Stroke mortality remains a significant public health challenge in the United States, with persistent geographic disparities. While previous studies have documented the rural-urban gap in stroke outcomes, limited attention has been paid to the role of the "stroke belt," a region with historically high stroke mortality rates. Understanding how these disparities evolve over time and interact with regional factors provides important insights into health equity and resource allocation. This study investigates trends in age-adjusted stroke mortality, focusing on differences between rural and urban areas, and examining how these trends vary within and outside the stroke belt region.
We conducted a cross-sectional analysis using data from the Centers for Disease Control and Prevention (CDC)-WONDER database from 1999 to 2022. This dataset includes county-level mortality data across the United States. For this analysis, we extracted stroke mortality rates for individuals aged 55 and older. Stroke mortality was identified using International Classification of Diseases, Tenth Revision (ICD-10) codes. County-level socioeconomic data, including per capita income and unemployment rates, were obtained from the Area Health Resources File (AHRF). Counties were classified as rural or urban based on the U.S. Department of Agriculture Rural-Urban Continuum Codes, and further categorized as being within or outside the stroke belt region. Age-adjusted stroke mortality rates per 100,000 population were calculated using the 2000 U.S. standard population. Analytical techniques included weighted regression models with county fixed effects to examine trends and disparities, adjusted for county-level socioeconomic characteristics.

Nationally, stroke mortality rates declined substantially between 1999 and 2022. However, the rural-urban gap widened over time. In 2022, stroke mortality in the rural stroke belt remained notably higher compared to other regions, with rates approximating those of urban non-stroke belt areas in the early 2000s (around 40–45 deaths per 100,000 population). Similarly, rural non-stroke belt areas in 2014 had stroke mortality rates comparable to those observed in urban stroke belt areas nearly a decade earlier. Urban non-stroke belt counties exhibited the fastest declines, maintaining the lowest mortality rates throughout the study period. Adjusting for socioeconomic characteristics partially narrowed these disparities but significant gaps persisted, particularly in rural stroke belt areas.

While stroke mortality rates have declined nationwide, disparities remain stark, with rural stroke belt counties lagging significantly. The observed trends suggest that rural areas, particularly within the stroke belt, are on a slower trajectory of improvement, maintaining mortality rates similar to urban non-stroke belt areas from 10–15 years prior. These findings underscore the need for targeted policy interventions to address geographic inequities in stroke prevention, acute care, and rehabilitation services. Strengthening healthcare infrastructure and access in rural and stroke belt regions is crucial to achieving equitable health outcomes.