Population-weighted metrics (incidence, mortality, disability-adjusted life years (DALYs), mortality to incidence ratio (MIR) can obscure per-case severity for less prevalent but high-impact conditions. This paper introduces DALY per case, total DALYs divided by incident cases, as a standardized estimate of healthy life-years lost per new diagnosis, integrating years of life lost (YLL) and years lived with disability (YLD). Validated using cancers and applied across diverse diseases, the metric enables prevalence-independent severity comparisons. Using GBD 2021, we computed DALY per case across diseases (all ages, both sexes), validated on 34 cancers, and tested generalizability in five non-cancer conditions (type 2-diabetes, tuberculosis, HIV/AIDS, ischemic heart disease, Alzheimer's). We compared rankings with incidence, mortality, and total DALYs. A 2-Dimensional framework plotted total DALYs (population burden) vs. DALY-per-case (individual severity) with median-based quadrant thresholds. Uncertainty intervals (UIs) were propagated per GBD conventions; stability was assessed via relative UI width, band-crossing, and sensitivity analyses. Construct/convergent validity used correlations with 5-year survival Surveillance, Epidemiology, and End Results Program (SEER) and MIR; full and reduced regressions tested independence. High-severity cancers included malignant bone tumours (27.6 DALYs/case), neuroblastoma (26.3), and brain/CNS (24.9), contrasting with population-dominant burdens such as lung (46.5million DALYs; 20.4/case) and colorectal (24.4million; 11.1/case). Relative uncertainty spanned 27% (breast) to 96% (Hodgkin lymphoma); rankings were largely preserved despite wide UIs in select sites. DALY-per-case correlated inversely with 5-year survival (r=-0.72, p < 0.001) and positively with MIR (r = 0.75, p < 0.001). In regression, MIR showed the strongest effect (β = 0.52, p = 0.06); survival lost significance when MIR was included, indicating shared but non-redundant variance. DALY-per-case provides a disease-agnostic toolkit, including a 2Dimensional burden-severity framework and validation against existing indicators, to quantify per-diagnosis severity and inform policy across communicable and non-communicable diseases.

Non-communicable diseases (NCDs), constitute a major global public health challenge and represent the primary causes of mortality and morbidity worldwide. This study aims to estimate Quality-Adjusted Life Year (QALY) losses associated with six NCDs among Iranian adults. This study quantified QALY losses associated with six NCDs, including asthma, ischemic heart disease (IHD), stroke, hypertension, diabetes mellitus, and high cholesterol. Health-related quality of life (HRQoL) scores were derived from EQ-5D-3L questionnaire data collected in 2021 from a nationally representative sample of 27,576 participants. Morbidity prevalence was obtained from the same survey, while mortality data were sourced from the Global Burden of Disease (GBD) study. Total QALY loss for each condition was calculated by summing losses attributable to both morbidity and mortality. Women experienced a greater decline in HRQoL than men across all conditions. The highest disutilities were observed for stroke, IHD, and asthma in women, and for stroke, asthma, and IHD in men. The highest QALY losses were associated with hypertension (1,399,097), IHD (1,123,053), and high cholesterol (749,136). Diabetes mellitus accounted for 428,163 QALYs lost followed by Stroke (373,365) and asthma (215,498). Given the substantial health burden posed by NCDs, there is an urgent need for prevention and management strategies that are both evidence-based and gender-sensitive. Strengthening national policies aimed at reducing the NCDs burden will not only enhance population health outcomes but also generate significant economic returns.

While Poverty-Free Life Expectancy captures the average number of years individuals are expected to live above the poverty threshold, it fails to account for disparities in the distribution of these years across the population. Inspired by recent developments in the measurement of Healthy Lifespan Inequality, we propose a new indicator: Poverty-Free Lifespan Inequality. This paper introduces the formal definition of Poverty-Free Lifespan Inequality, elaborates its mathematical foundations, and discusses its policy relevance. Using Sullivan-type methods and age-specific poverty prevalence data, we derive the distribution of exit from poverty-free life and compute inequality using the Gini index. We demonstrate that Poverty-Free Lifespan Inequality provides critical insights into the heterogeneity of economic well-being over the life course.

In Slovakia, several tens of thousands of persons live in extremely poor living conditions in segregated Roma settlements. We can hardly find populations with such a short life expectancy and a high risk of death in infancy anywhere in Europe. The inadequate infrastructure, catastrophic housing conditions, deteriorated environmental quality, high unemployment and dependence on social transfers in combination with social and geographical segregation or negative behavioral aspects markedly affect their health status and mortality rates. Despite this, only little attention has hitherto been paid to the issues of health mortality, particularly infant mortality, among individuals from this environment. Above all, there is a lack of more comprehensive research that would not only empirically express the mortality of the youngest children and identify its developmental tendencies, but also examine the internal - demographic reasons for this state. The study attempts to fill this gap through cohort-based infant mortality tables, using one- and multidimensional decomposition for the period 1993-2022 for case of marginalized communities mostly in Eastern Slovakia. Infant mortality tables corroborated not only the persistence of differences, but also the divergence of mortality compared to the non-Roma population in the country, namely classified by days, weeks and months of the infant's life. The original one- and multidimensional decomposition of interval life expectancy from birth to the first year of life confirmed a poor situation in the post-neonatal age. The above results rise from higher death rates from congenital malformations, deformations and chromosomal abnormalities, as well as from respiratory and infectious diseases. A differential analysis also proved that the level of infant mortality in the selected municipalities was closely related to birth weight. By contrast, the mother's marital status did not manifest as a differentiating factor.

As the global population ages and life expectancies rise, improving the health and equity of middle-aged and older individuals has become a universal goal, especially with the economic benefits of the demographic dividend decreasing. Health investments (HI), which are crucial for improving health outcomes (HO) and protecting human capital, play a key role in achieving these objectives. This study aims to examine the impact of HI on the health status of middle-aged and elderly individuals, analyze issues of health equity among this population, and enhance their overall health level while fostering economic growth. This study, based on Grossman's health demand theory and China Health and Retirement Longitudinal Study (CHARLS) data from 2011 to 2020 (n = 11,138), examines middle-aged and elderly individuals (aged 45 years and above) across 28 Chinese provinces. A panel data model is used to assess HI and HO, with composite indices created using the entropy method. HI includes leisure, healthcare, and living environments, whereas HO covers self-reported short- and long-term health. A high-dimensional fixed-effects model is used to analyze the impact of HI on HO. Health equity is explored using the income Gini coefficient, health investment concentration index (I-CI), and health outcome concentration index (H-CI), with decomposition performed using the Shapley method. HI positively affects HO in middle-aged and elderly individuals in China. The key factors that influence HO are gender, age, household registration (HR), and income. Income inequality is significant, with an average Gini coefficient of 0.492. The I-CI averages 0.081, indicating higher investment concentration among wealthier groups. The major factors that influence the I-CI are household registration (34.9%), income (33.1%), employment (18.8%), and education (11.7%). The H-CI averages 0.033, with better outcomes associated with higher education. The key factors influencing H-CI are age (46.7%), gender (16.7%), income (15.2%), and education (10.7%). HI significantly improves the HO and enhances the health human capital of middle-aged and elderly individuals. However, these investments tend to favor wealthier groups, whereas HO are more favorable among those with higher education. Income and education levels are the key drivers of inequity in both HI and HO.

Efficient distribution and administration of vaccines are critical to preventing unnecessary morbidity and mortality. We assess the distribution, uptake, and wastage of COVID-19 vaccine doses across the U.S., providing insights for optimizing future vaccination distribution strategies. We quantify the impact of limiting vaccine wastage and illustrate incidence and deaths averted under two targets set by the Global Alliance for Vaccines and Immunization (GAVI). We obtained COVID-19 vaccine doses administered by location and wastage data from jurisdictions, pharmacies, and federal entities from the Centers for Disease Control and Prevention through a Freedom of Information Act. From this data, along with county-level data on COVID-19 vaccine hesitancy, we conducted a retrospective analysis covering the period from December 2020 to October 2022 involving 761million vaccine doses distributed across all counties and states in the U.S. We use GAVI targets of 25% and 15% vaccine waste to serve as benchmarks for assessing the impact of potential improvements in vaccine distribution and acceptance at the county and state levels in the U.S. We estimate the proportion of vaccines wasted, and then incidence and deaths averted had adherence to GAVI waste targets occurred to inform on the quality of the national vaccination effort and identify potential regions for improvement. Among the 761million distributed COVID-19 vaccine doses, only 600million were administered, resulting in a national average of 1.8 doses per capita. Substantial regional disparities were observed, with the District of Columbia reaching 2.5 doses per capita and Alabama lagging at 1.3 doses per capita. Thirty states exceeded the GAVI 15% vaccine waste target, corresponding to 64.2million unused doses. Meeting the 15% target would have averted 36.1million incidences and 7.8 thousand deaths. Addressing the causes of county-level variations and targeting states with below-average vaccine hesitancy and above-target vaccine waste would likely maximize future vaccine distribution efforts and minimize wastage-related losses. This strategy highlights an avenue for improving future vaccine distribution policy.

Public health surveillance systems require high-quality data to represent the population. In the absence of comprehensive or random testing throughout the COVID-19 pandemic, we have developed a proxy method for synthetic random sampling to estimate the actual community-level viral incidence, based on viral testing of patients who are asymptomatic and present for elective procedures within a hospital system. The approach collects routine testing data on SARS-CoV-2 exposure among outpatients and performs statistical adjustments of sample representation using multilevel regression and poststratification (MRP), a procedure that adjusts for nonrepresentativeness of the sample and yields stable small group estimates. We extend MRP to accommodate time-varying data and granular geography. We have developed an open-source, user-friendly MRP interface for public implementation of the Bayesian analysis workflow. We illustrate the MRP interface with an application to track community-level COVID-19 viral transmission in Michigan. We present the estimated infection rate over time for the targeted population and across demographic and geographic subpopulations. The interface provides timely, substantive insights into population health trends and serves as a valuable surveillance tool for future epidemic preparedness. Beyond monitoring COVID-19, the MRP interface can analyze a wide range of health and social science data, making it broadly applicable to diverse research areas with reproducibility and scientific rigor.

Globally, cardiometabolic diseases (CMDs) are major health issues that affect the health of workforce. This study aimed to investigate the impact of employment status on transition from a healthy state to cardiometabolic multimorbidity in Chinese population. Data from China Health and Retirement Longitudinal Study (2011-2020) was utilised. Analytical sample comprised 7,681 men and women (≥ 45 years) free of CMDs at baseline. A multistate model was applied to investigate the impact of baseline employment status on the transition rates from a healthy state to cardiometabolic mono-morbidity and subsequently to multimorbidity. Inverse probability weighting was applied to account for the complex survey design. During an average follow-up time of 5.7 years, 3,324 (43.28%) participants developed one or more CMDs. After adjusting for age and sex, compared to non-agricultural employees, non-agricultural retirees had significantly higher risks and agricultural self-employed workers had only marginally higher risk of CMDs. After further adjustment for sociodemographic factors, health behaviours, and BMI, non-agricultural retirees remained significantly associated with a higher rate of transition from a healthy state to cardiometabolic mono-morbidity [HR 1.24 (95% CI 1.01-1.54)] compared to non-agricultural employees. There was no statistically significant increase in transition to multimorbidity risk in any group. Control of CMDs in Chinese older population should consider people's employment characteristics.

This study investigates territorial disparities in healthcare outcomes and service provision across Italian regions through a multidimensional analysis based on the BES (Equitable and Sustainable Well-being) framework. Two distinct but complementary sets of indicators are considered: one focusing on health outcomes (life expectancy, healthy life expectancy, and avoidable mortality), and the other on the structural availability and accessibility of healthcare services (residential beds, home care, access difficulties, and unmet needs). Using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, the study identifies spatial clusters of regions with similar profiles. Results reveal persistent North-South divides in both health and service indicators, with southern regions consistently exhibiting lower performance. While the Health dataset shows relatively homogeneous clusters, the Services dataset highlights more marked disparities. The use of DBSCAN proves effective in detecting regional groupings even in a relatively small sample, offering a valuable tool for territorial policy planning and sustainability-oriented healthcare strategies.

Indonesia is the second-highest contributor to global tuberculosis (TB) cases, accounting for 10% of the total. While previous studies have explored TB patterns in specific regions, a comprehensive nationwide analysis at a fine spatial scale is lacking. This study investigated spatiotemporal patterns of TB incidence and mortality, identified geographical hotspots, and examined their association with risk factors to inform public health policy. This retrospective study analyzed notified TB cases and deaths during treatment from Indonesia's National Tuberculosis Surveillance System across 514 districts between 2017 and 2022. Spatiotemporal Bayesian hierarchical modeling was employed to identify high-risk areas and assess associations with potential risk factors. The best-fitting model was determined by evaluating various spatial and temporal random effect structures and likelihood assumptions. TB incidence fluctuated with a trough during the COVID-19 pandemic and an overall increase, while mortality increased over time. Incidence hotspots clustered in urbanized areas, while mortality hotspots were scattered across the country. The best-fitting model to estimate risk factors for both outcomes was Poisson likelihood. This indicated that TB incidence was spatiotemporally positively linked to better healthcare access (RR: 1.016; 95% CI: 1.007-1.025) and higher municipal human development index (MHDI, RR: 1.062; 95% CI: 1.049-1.075). Mortality was associated with low treatment coverage (RR: 0.610; 95% CI: 0.552-0.674) and success rates (RR: 0.595; 95% CI: 0.491-0.721). Fluctuating TB incidence, hotspots concentrated in urbanized areas with better healthcare access and higher MHDI as well as increasing mortality linked to poor treatment outcomes underscore the need for targeted public health interventions to expand access to care, improve treatment adherence, and address the socioeconomic disparities driving TB mortality.