Loneliness and household air pollution from solid fuel combustion have each been linked to unhealthy ageing, yet their joint relevance for cognitive and functional outcomes remains unclear. We conducted a cross-sectional analysis of the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), including 13,879 community-dwelling adults aged ≥ 45 years. Loneliness and primary household cooking fuel were treated as binary exposures. Outcomes were total cognitive score and functional dependency. Multivariable linear regression and logistic regression were fitted, incorporating a product term to assess multiplicative interaction. Additive interaction for functional dependency was evaluated using the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S). Overall, 24.0
Reliable and timely data are essential for addressing maternal and child health challenges in Nigeria, where substantial regional disparities persist. Routine health information systems (RHIS) data offer a potential source of frequent subnational coverage and prevalence estimates, but their use depends on the quality of reported events (numerators) and appropriate estimation of the population at risk (denominators). This study evaluated a methodology to improve RHIS-derived numerators and denominators for generating timely subnational coverage and prevalence estimates in Nigeria. RHIS data from three Nigerian states (2017–2022) were used to estimate four or more antenatal care (ANC4) coverage and infant fever prevalence. Unadjusted and adjusted estimates were calculated following a comprehensive data quality assessment and imputation process. Adjustments accounted for private sector service usage, incomplete reporting, non-use of services, and stillbirths and pregnancy loss for ANC4 estimates. Service-based and population-based denominators were used. Estimates were triangulated with population-based survey (PBS) data. ANC4 reporting completeness increased from 25.9
Abstract Background Despite the comprehensive death reporting system in South Africa, significant gaps persist in our understanding of the pandemic’s impact in rural areas. Methods We computed monthly death counts stratified by demographic factors from a population-based demographic surveillance cohort in rural KwaZulu-Natal. We fit seasonally adjusted negative binomial regression models to estimate excess mortality during the COVID-19 pandemic period (5 March 2020 to 31 December 2021) compared to the pre-pandemic period (1 January 2015 to 4 March 2020). We then assessed the relative risk of death by health and sociodemographic factors and considering apotential interaction with the COVID-19 pandemic. Finally, we calculated changes in life expectancy at the age of 15 and conducted a decomposition analysis to identify life expectancy changes by age group. Results We observed two major periods of excess mortality during the beta and delta waves of the COVID-19 pandemic, respectively. During the pre-pandemic period, people with HIV (PWH) had slightly higher mortality rates (RR = 1.8; 95%CI 1.3 - 2.4) than their HIV-negative counterparts. This excess risk reduced during the COVID-19 period (HIV RR = 1.5; 95%CI 1.2 - 2.0). Similar patterns were noted for individuals with diabetes (pre-pandemic RR = 1.6; 95%CI 1.2 - 2.2 vs during COVID-19 RR = 1.4; 95%CI 1.1 - 1.9). In contrast, individuals with hypertension experienced a 15% relative increase death rate during the pandemic compared to pre-COVID period (p-interaction = 0.003). Similarly, hypertensive PWH had significantly elevated mortality risks during COVID-19 (RR = 2.18; 95% CI: 1.45–3.26, p < 0.001). Throughout the study, women consistently had higher life expectancy at age 15 than men. However, women contributed more to pandemic-related life expectancy reductions, along with specific age and health groups. Conclusion We found contrasting impacts of co-morbidities on mortality during the COVID-19 pandemic in rural South Africa, and that reductions in life expectancy at age 15 were greater in women than men. These findings emphasize the critical need to consider specific needs of rural populations, and tailor public health strategies that prioritize vulnerable populations and address health inequities to mitigate the effects of pandemics in low-resource settings.
Population longevity represents a central dimension of sustainable development. Although national income and life expectancy are closely associated, recent development initiatives increasingly emphasize health-system capacity, public health interventions, environmental quality, and demographic change. The extent to which these factors are associated with longevity conditional on income, and the potential of flexible predictive models to generate forward-looking longevity scenarios, remain uncertain. This study examines life expectancy at birth as a function of six drivers, using data from 28 countries over the period 2003–2022 and employing a range of Machine Learning (ML) techniques. The results reveal nonlinear associations, including a concave relationship between income and longevity that emerges without imposing a functional form a priori. The preferred ML model is then used to project life expectancy over 2023–2032 under baseline, accelerated health-expenditure, and integrated health-system and prevention scenarios. Mean life expectancy in the sample increases from 78.10 years in 2022 to 78.21 years in 2032 under baseline trends, 78.34 years under accelerated health expenditure, and 78.98 years under the integrated health-system and prevention scenario, with larger gains concentrated in countries that have not yet closed key coverage gaps. These projections are predictive scenarios rather than causal counterfactuals, but they illustrate how ML models can support forward-looking population-health planning, provide decision-makers with interpretable estimates of alternative longevity trajectories, and offer a basis for extending the analysis to broader country coverage.
Measuring health-related quality of life using Quality-Adjusted Life Years (QALYs) is central for health technology assessment. While Brazil has a validated EQ-5D-3 L value set, no nationally representative EQ-5D-5 L value set is yet available. Mapping algorithms such as the nonparametric crosswalk developed by van Hout et al. (2012) address this gap by using empirical response-transition probabilities between 5 L and 3 L items; however, these probabilities were derived from European populations and may not reflect Brazilian response patterns. To derive locally based transition probabilities from a Brazilian sample and use them within the nonparametric crosswalk framework to generate EQ-5D-5 L utility estimates, and to compare these with estimates obtained using the original European-derived transition probabilities. A cross-sectional study was conducted with 413 Brazilian participants (310 with myasthenia gravis; 103 from a social intervention program [1]). Both EQ-5D-3 L and EQ-5D-5 L were administered during the same assessment session, with the 3 L preceding the 5 L. Empirical conditional response probabilities of EQ-5D-3 L levels given each EQ-5D-5 L level were derived per dimension and used to estimate utilities for all 3,125 EQ-5D-5 L health states, weighted by the validated Brazilian EQ-5D-3 L tariff. Uncertainty was quantified via non-parametric bootstrap resampling of participants. Results were compared with estimates from the van Hout algorithm using European transition probabilities applied with the same Brazilian tariff. The locally derived model produced a mean utility of 0.375 (SD 0.162) across 3,125 EQ-5D-5 L states, compared with 0.385 (SD 0.147) for the European-based model. The Brazilian model classified more states as worse than death (1.73
