Population ageing and internal migration have increased the number of older migrants, making life satisfaction (LS) an important population-level indicator of well-being in later life. LS among older migrants may be associated with changes in family and community ties after relocation. However, population-based evidence remains limited on whether and how family and community social capital are differently associated with LS across distinct later-life migration pathways. This study examines the associations between family and community social capital and LS among older migrants in urban Zhejiang, China, across three migration subgroups: quality-of-life-oriented (QLO), family-support (FS), and economically driven (ED) migrants. A cross-sectional survey was conducted among 1,216 older migrants in four major cities in Zhejiang Province, China, yielding 1,105 valid responses. Spearman’s rank correlation analysis was used to examine bivariate associations between LS and social capital indicators. Binary logistic regression models were estimated for the full sample and separately for QLO, FS, and ED migrants to assess the associations of family social capital, structural social capital (SSC), and cognitive social capital (CSC) with high LS. Pooled interaction models were further estimated to test for cross-group differences. Overall, 57.74
Global estimates are esssential for tracking international targets such as the Sustainable Development Goals (SDGs), therefore vital for planning and priority setting. Modelling and estimation of health indicators vary greatly in aspects of methodology, assumptions, data sources, data quality, and data availability. To aid with consistent reporting of global estimates, a reporting guideline; The Guidance for Accurate and Transparent Health Estimates Reporting (GATHER) was developed in 2016. However, the GATHER statement does not detail the process or statistical considerations for developing global estimates of health indicators. There is no standard statistical framework and methods for producing global estimates of health indicators, even for the same indicator such as low birthweight or preterm birth. This poses challenges for making direct comparisons and trends over time for assessing progress achieved by countries. We developed a framework for conceptual and statistical considerations in an iterative process. An informal review of the methodology used the produce global estimates of perinatal health was conducted and summarised, and expert consultative groups were convened regularly to develop methodologies for the low birthweight and preterm birth estimates (2020). The meetings occurred regularly throughout the estimation process, with methods reviewed and updated accordingly. Following the publication of the estimates, the authors critically reviewed the process and summarised the analytical framework described in the results. In this paper, we discuss key considerations for producing global estimates of health indicators to ensure a standardised approach. We focus on: conceptual framework, input data, assessment of data quality, statistical model considerations, and publication of global estimates. We define each concept and give practical examples of how each consideration could be identified and included for global estimate modelling. In addition, we provide a checklist with examples from our recent modelling of global estimates for low birthweight.
Abstract Background Between January 2020 and August 2023, India documented over 450 million confirmed cases of COVID-19 and nearly 0.5 million COVID-19 deaths. While reported mortality rates in India appeared lower than expected, the actual mortality may be higher due to underreporting and disparities in healthcare access. This study aimed to compare mortality before and during the COVID-19 pandemic and identify associated socio-demographic factors in rural Pune, Maharashtra, India using longitudinal population-based data from the Vadu Health and Demographic Surveillance System. Methods Individual and mortality data from the Vadu HDSS, India were collected from 2018 to 2021. Descriptive analyses were conducted to examine the variation in mortality by sociodemographic factors during pre-COVID-19 [2018–2019] and COVID-19 [2020–2021] periods. Cox regression with hazard ratios [HR] and 95% confidence intervals [CI] were used to measure the association between socio-demographic factors and mortality. Results There were 2039 deaths that occurred between 2018 and 2021, of which 40.6% [828/2039] were female. The overall risk of mortality increased during COVID-19 [HR1.2,95%CI:1.1–1.3], but this varied by sociodemographic characteristics. The risk of death was greater [HR1.8,95%CI:1.5–2.2] among individuals living in single-person households compared to those living in 2–5 member households, and there was a strong trend of increased mortality risk with older age. Widowed individuals had a greater risk of mortality than those who were married [HR2.1,95%CI:1.9–2.4]. Education was strongly protective against mortality; those who had any level of education had a lower risk of dying than those with no education [HR0.4,95%CI:0.3–0.5]. Conclusion Increased COVID-19 mortality in India was associated with older age, widowhood, and living alone. These findings highlight the need for targeted vaccination, early clinical care, and community outreach for vulnerable populations during future pandemics.
Loneliness is an important public health concern, but interviewer effects in face-to-face surveys may affect population-based monitoring efforts. Prior work suggests that people may be more open about sensitive topics to female interviewers than to male interviewers. This study examines whether interviewer gender influences the reporting of loneliness. Data are from 276,073 face-to-face interviews from four rounds of the European Social Survey (ESS) and four waves of the Survey of Health, Ageing and Retirement in Europe (SHARE). Country-specific logistic regression models were estimated to assess the association between interviewer gender and reported loneliness, adjusting for respondent characteristics. Interaction effects with respondent gender were also examined. Country-specific average marginal effects were combined using random-effects meta-analyses. Sensitivity analyses used a continuous measure of loneliness (short UCLA scale). Across datasets, the effect of interviewer gender on reported loneliness was close to zero and not statistically significant (ESS: θ = −0.001, 95
The proliferation of community well-being indices (CWBI) reflects growing recognition that both individual experiences and place-based conditions shape population health and quality of life. These indices are increasingly used to inform public health policy, community planning, and population health monitoring. However, there is substantial variation across indices in how community well-being is conceptualized, measured, and reported. Despite their expanding use, few studies have systematically compared these indices as integrated measurement systems. This study aimed to characterize and compare CWBI using a measurement systems perspective. We conducted a structured, purposive review of U.S.-based CWBI to characterize approaches using five methodological components describing domain and indicator selection, statistical estimation and weighting, construction and geographic aggregation, evaluation of measurement properties, and dissemination. Evaluation of measurement properties was characterized based on reported evidence of reliability, validity, and responsiveness. The Boston University-Sharecare Community Well-being Index (BU-SC CWBI) was included as an illustrative example. Of the 315 measures identified, 11 met inclusion criteria and were characterized, along with the BU-SC CWBI. Across systems, the number of domains ranged from 2 to 10 (median 5), and the number of indicators ranged from 11 to 93 (median 43). Collectively, indices incorporated 86 distinct data sources drawn primarily from publicly available administrative and surveillance datasets; two systems also incorporated individual-level subjective measures. Although not mutually exclusive, systems were heuristically classified into three broad methodological orientations that reflect a continuum of priorities with respect to conceptual organization, aggregation approaches, and the use of empirical and model-based methods. Substantial heterogeneity was observed in statistical estimation, weighting, and evaluation of measurement properties, with limited and inconsistent reporting of reliability, validity, and related evaluation analyses. CWBI differ in both indicators used to represent well-being and methodological approaches used to construct and evaluate them. Conceptualizing these indices as measurement systems provides a structured approach for comparing approaches, clarifying trade-offs, and improving interpretability and comparability. While CWBI may be designed for different goals and applied in different contexts, a systematic approach to their characterization may facilitate transparency and more informed use in research and policy.
Hepatocellular carcinoma (HCC) is a major public health challenge in China, with regional disparities often linked to environmental factors. Gansu Province, characterized by complex topography and climatic heterogeneity, provides a unique setting to examine these determinants. This study characterized the spatiotemporal patterns of HCC incidence in Gansu (2013–2023) and quantified the contributions of terrain, climate, and air quality. This population-based ecological study analyzed county-level HCC incidence data (2013–2023) from the Gansu Provincial Cancer Registry. Age-standardized incidence rates (ASIRs) and temporal trends were assessed via direct standardization and Joinpoint regression. Spatial heterogeneity and risk regions (high/low) were identified using a Bayesian hierarchical Besag–York–Mollié (BYM) model. To evaluate environmental drivers, long-term exposures (2003–2013) were correlated with ASIR using Spearman’s rank analysis. Multicollinearity was assessed via Variance Inflation Factor (VIF). Finally, the independent attribution of environmental factors was quantified using Ridge Regression with cross-validated penalization, with coefficient stability validated through 1,000 bootstrap resamples. The HCC ASIR in Gansu peaked in 2017 (16.30 per 100,000) before declining to 7.55 by 2023, with a consistent male predominance (sex ratio 2.42:1). Bayesian analysis revealed significant spatial clustering, with high-risk regions concentrated in central and southern counties (e.g., Gannan Plateau). Quantitative attribution demonstrated that topographic features were the dominant drivers, collectively explaining 73.5
Despite progress in reducing infant mortality in Peru, inequities persist by socioeconomic and ethnic characteristics. Documenting these disparities is essential to guide public health policy. To quantify ethnic disparities in infant mortality in Peru, examine variation by socioeconomic level and timing of death, and estimate the fraction of deaths attributable to these inequities. We conducted a repeated cross-sectional analysis of ENDES 2019–2024 birth-history data. The analytic sample included 100,807 live births to women aged 15–49 years with complete opportunity for ascertainment of death before 12 months of age. Infant mortality was defined as death before 12 months among live-born children. Maternal ethnicity (Mestizo, Afro-Peruvian, Indigenous) was based on self-identification. We estimated infant mortality rates per 1,000 live births, risk ratios (RR), and risk differences (RD) using survey-weighted Poisson regression with robust variance. We recorded 1,178 infant deaths (overall infant mortality rate: 10.6 per 1,000; 95
Abstract Background COVID-19 was initially considered to be primarily a respiratory illness. However, it soon became evident that individuals with non-communicable diseases (NCDs), particularly Diabetes Mellitus (DM) and Hypertension (HTN), faced a markedly higher risk of severe illness and death. This study assessed excess mortality during the COVID-19 pandemic among individuals living with DM and HTN in rural coastal communities in Bangladesh. Methods The study utilised data from the Chakaria Health and Demographic Surveillance System (HDSS), located in the rural sub-district of Chakaria in southeast Bangladesh on the coast of the Bay of Bengal, and operated by the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). Prior to the COVID-19 pandemic, 15,700 individuals were screened for DM and HTN, of whom 8506 aged 18 years or older were included in this study. Crude mortality rates (MR) per 1000 person-years (PYR) were estimated by age, sex, education, and household wealth quintile for individuals with DM/HTN compared to those without, both before and during the pandemic. A Cox proportional hazards model was used to examine differences in mortality risk during the pandemic between individuals with and without DM/HTN, adjusting for sociodemographic factors. Results Among the 8506 study participants, 2.7% (n = 232) had DM, 4.8% (n = 407) had HTN, less than 1% (n = 75) had both conditions and 6.6% (n = 564) had either condition. Among individuals without DM/HTN, the crude MR was 7.1 per 1000 PYR overall (6.9 before COVID-19, 7.5 during); among those with DM/HTN, the crude MR was 28.8 overall (21.2 before, 44.5 during). After adjusting for socio-demographic factors, mortality did not increase significantly during COVID-19 among individuals without DM/HTN. However, among those with DM/HTN, adjusted mortality risk during COVID-19 was nearly twice as high as in the pre-COVID-19 period (Hazard Ratio (HR) = 1.89; 95% CI: 1.22–2.91; p < 0.01). Conclusion The COVID-19 pandemic significantly increased all-cause mortality among individuals with DM or HTN in the rural sub-district of Chakaria in southeast Bangladesh on the coast of the Bay of Bengal through direct and indirect pathways.
Research, policymakers, and advocacy groups rely on valid estimates of adolescent births to inform their work. Estimates of births among very young adolescents (10–14 years old) are especially lacking. We compared adolescent fertility rates in Mexico across the census, population-based surveys, civil registration, and birth certificates. We calculate live birth rates among very young adolescents (VYA; 10–14 and 12–14) and older adolescents (15–19) in Mexico, at national and state levels, and by year (2008–2019). We compare estimates with official reconciled data from the Mexican government. For context, we also calculate all other age-specific fertility rates (ASFRs) and total fertility rates (TFRs). Overall, all national-level data sources in the period studied show declines in TFR and ASFRs for adolescents. Official government estimates for older adolescents (15–19) declined from 78·3 to 65·1 births per 1,000 adolescents 15–19 years old between 2008 and 2019. Rates for VYAs showed a decline in hospital registration (from 3·5 to 2·8 births per 1,000 adolescents aged 12 to 14 years old), and vital statistics (from 2·6 to 1·9) compared with the census. At the state level, VYA birth rates calculated using SINAC are higher than those estimated with vital statistics; the census estimates, at only three timepoints, are variable by state. Four different publicly available data sources in Mexico produce adolescent birthrate estimates that are largely consistent with official reconciled data, validating the use of each of these data sources individually. For VYA estimates, which the government does not include in population projections, hospital registration (birth certificates) may be the best source.
Background Socio-economic inequalities in life expectancy and mortality have received significant academic and policy attention in many high-income countries. Most studies to date have measured area-level inequalities either using a composite socio-economic index or individual-level inequalities using a single or limited variables (e.g. education, income). However, these approaches might underestimate life expectancy inequalities compared to a composite index of individual-level socioeconomic characteristics.Methods This study fills a major gap in evidence of life expectancy inequalities by analysing 2016 Population Census data linked at the individual level to death registration data from 2016 to 19 in Australia. We used Multiple Correspondence Analysis to construct an Individual-level Socio-Economic Index (ISEI) comprising variables representing education, income, marital status and whether living alone, housing tenure, migrant status, place of residence and neighbourhood socio-economic index. The ISEI was constructed separately for each sex and 10-year age group. Life expectancy at 25 and 65 years, partial life expectancy from 25 to 65 years and age-standardised and age-specific mortality rates were calculated and compared between ISEI percentiles (approximately 85,000 population each) and percentile groups.Results Life expectancy at age 25 years ranged from 43.2 (95% confidence intervals 42.8-43.6) years in the lowest (p1) ISEI percentile to 62.9 (62.5-63.4) years in the highest (p100) for males (gap of 19.7 years) and 51.7 (51.3-52.2) to 64.3 (64.0-64.8) years for females (gap of 12.6 years). The ISEI-life expectancy relationship was strong across all percentiles, with the gradient steepest in the lowest percentiles, flatter in middle deciles and slightly steeper in the highest percentiles. Partial life expectancy from 25 to 65 years had a steep gradient in the lowest ISEI percentiles but had a narrower total gap compared with life expectancy at 65 years. Age-standardised mortality rates (25-64 years) differed between p1 and p100 by a magnitude of 17.7 times for males and 11.8 times for females, with the largest disparities at 35-54 years.Conclusions This study shows substantial and concerning inequalities in life expectancy and mortality in Australia. By analysing these indicators with greater granularity than earlier studies by using a multidimensional socio-economic index, it shows wider disparities than previously and the important contribution of a wide range of social determinants.
During the pandemic, Australia experienced relatively stringent restrictions and a distinctive life expectancy trend.This study assesses the contribution of multiple causes of death to these trends, as well as geographic differences in life expectancy, during the pandemic across Australia. Data on deaths by age, sex, cause and capital city classification in Australian death registration data from 2017 to 2023 were accessed from the Australian Bureau of Statistics. We estimated life expectancy at birth and applied decomposition techniques to quantify the contribution of multiple causes of death to changes in life expectancy, including by capital city. Multiple cause of death analysis measured whether a death involved respiratory infections and/or COVID-19, and the accompanying underlying cause of death. Over half the 0.6 years life expectancy increase from 2017-19 to 2020 was from fewer deaths involving respiratory infections such as pneumonia, especially at age 80 years and above. Following a small life expectancy decline from 2020 to 2021, most of the 0.7 years decline in life expectancy in from 2021 to 2022 resulted from large increases in COVID-19 mortality following relaxation of restrictions. Deaths with a respiratory infection but not involving COVID-19 mostly had a non-communicable disease such as cardiovascular disease as the underlying cause. All geographic regions mirrored national trends by experiencing life expectancy increases in 2020 (especially from lower respiratory infection mortality) and declines in 2022 due to rising COVID-19 deaths, with the worst trends in Melbourne. Australia experienced large fluctuations in life expectancy during the COVID-19 pandemic that were unique compared with many other high-income countries. The life expectancy increase in 2020 was mostly due to reduced circulation of respiratory infections like pneumonia during restrictions. However, upon cessation of restrictions in 2022, a large increase in COVID-19 mortality led to substantial life expectancy falls. Life expectancy trends did not vary greatly by capital city because restrictions were effective at preventing further deaths from COVID-19 after local outbreaks. Multiple cause of death data are very useful to understand the interaction of COVID-19, respiratory infections and non-communicable diseases during pandemic and the role of pandemic-related restrictions.
Social isolation represents a critical challenge in the context of global population aging, yet its effects on body composition in older adults remain paradoxical. This study aimed to systematically examine, from a life-course perspective, the differential associations of social isolation on sarcopenia and obesity among middle-aged and older adults in China, thereby evaluating the proposed divergent associations hypothesis. Using data from 5,525 participants in the China Health and Retirement Longitudinal Study (CHARLS), we constructed life-course patterns of social isolation integrating both childhood and adulthood exposures. Outcomes were defined according to the Asian Working Group for Sarcopenia (AWGS) guidelines and the World Health Organization (WHO) criteria for Asian populations. Multivariable logistic regression models were employed to assess the primary associations, supplemented by subgroup, sensitivity, and exploratory mediation analyses. Persistent social isolation was significantly associated with increased odds of low muscle strength (OR = 1.72, 95
Abstract Background Healthy life expectancy (HALE) can be measured using different approaches, yet estimates may diverge. Understanding divergence sources could help identify which health dimensions current systems capture or miss. However, systematic subnational comparisons remain scarce. Methods We conducted a comparative ecological study across Japan’s 47 prefectures from 2001 to 2019. Three Ministry of Health, Labour and Welfare (MHLW) metrics—disability-free life expectancy based on activity limitation (DFLE-AL) and life expectancy in subjective health (LE-SH) derived from national surveys, and disability-free life expectancy based on activities of daily living (DFLE-ADL) derived from long-term care insurance records—were obtained from MHLW. Model-based HALE from the Global Burden of Disease (GBD) 2023 Study was extracted for corresponding years. We assessed cross-sectional concordance and examined associations with disease burden, risk factors, and socioeconomic indicators using fixed-effects panel regression. Results Over 2001–2019, DFLE-AL and LE-SH correlated with GBD HALE among males (r = 0.33 to 0.65) but weakly among females (r = − 0.11 to 0.32); DFLE-ADL showed concordance for both sexes (r = 0.66 to 0.96). GBD HALE exceeded MHLW estimates in females (0.53 years for DFLE-AL, 0.23 for LE-SH) but not males (− 0.18, − 0.14). In fixed-effects analysis of MHLW-minus-GBD differences, mental disorders showed negative associations with DFLE-AL and LE-SH differences among males; high BMI and musculoskeletal disorders showed positive associations with DFLE-AL differences. Among females, population ageing showed positive associations with DFLE-AL, LE-SH, and DFLE-ADL differences. Conclusions MHLW metrics and GBD HALE capture complementary health dimensions and should not be treated as interchangeable. The consistent sex difference in concordance indicates that divergence between HALE metrics reflects differences in which health dimensions are emphasised—self-reported health states, objectively assessed functional status, or disease-based disability modelling—rather than a single, uniform construct of population health.
BACKGROUND:The COVID-19 pandemic has severely affected health and well-being worldwide. While most countries have reported excess mortality associated with COVID-19, little is known about individual and social factors associated with COVID-19 and non-COVID-19 mortality in Bangladesh. This study addresses that gap by investigating the mortality rates from COVID-19 and other causes during the pandemic years (2020-2021), as well as their associated socio-demographic determinants using longitudinal population data. METHODS:From 2020 to 2021, data were collected on 573,433 individuals residing in three HDSS areas in Bangladesh: Matlab (rural), Chakaria (coastal), and the slums of Dhaka (urban). Probable causes of death were determined by medical personnel using the WHO 2016 verbal autopsy (VA) tool supplemented with a COVID-19 module. Deaths were classified as COVID-19 or non-COVID-19 using the International Classification of Diseases and Related Health Problems, tenth revision (ICD-10). Factors associated with COVID-19 and non-COVID-19 mortality were examined using Cox proportional hazards models. RESULTS:Between January 1, 2020, and December 31, 2021, a total of 6,616 deaths were recorded across the three HDSS sites, of which 5.2% were attributed to COVID-19. The COVID-19 mortality rate was highest in Matlab (58 deaths per 100,000 person-years), followed by Chakaria (15 deaths per 100,000 person-years) and the urban slums in Dhaka (11 deaths per 100,000 person-years). Household socio-economic status was significantly associated with COVID-19 mortality in the Matlab HDSS. Individuals from the lowest wealth tertile had 40% lower mortality compared to individuals from the highest wealth tertile (adjusted mortality rate ratio (aMRR): 0.60; 95% CI: 0.43-0.83). In contrast, no significant differences were observed for non-COVID-19 mortality across wealth tertiles. Age, sex, and marital status were significantly associated with both COVID-19 and non-COVID-19 deaths. CONCLUSION:Our data revealed that COVID-19 mortality was highest in the Matlab HDSS. Age, sex, and marital status were key determinants of both COVID-19 and non-COVID-19 mortality in Matlab. Notably, individuals from households in the lowest wealth tertile in Matlab had significantly lower COVID-19 mortality compared to those from households in the highest wealth tertile, while no wealth-related differences were observed for non-COVID-19 mortality.
BackgroundIn many low- and middle-income countries, the registration of family-related vital events (marriages and births) remains incomplete, with consequences for women's and their children's ability to exercise fundamental rights. In humanitarian and displacement settings, the absence of documentation creates additional barriers to legal recognition, identification and protection, especially if the population is already at risk of statelessness, as in the case of the Rohingya, a minority ethnic group that has experienced genocide.Data and methodsDrawing on unique primary survey data collected among Rohingya youth (aged 15-24) in refugee camps in Bangladesh, this study examines the prevalence, timeliness, and determinants of marriage registration with relevant camp authorities, as well as associations with birth registration.ResultsWe find relatively high registration rates: approximately 65% of marriages and 58% of births are reportedly registered. Among young adults (aged 18+), few socio-demographic or locational factors are associated with marriage registration or its timeliness, but marriage-specific characteristics - including early marriage, spousal age gaps, and whether the union was arranged - emerge as important predictors. Marrying before age 18 remains linked to lower likelihood of union registration, regardless of marital duration, and to longer delays. Moreover, marriage registration strongly predicts the registration of births, with respondents in registered marriages being nearly three times as likely to register their child(ren)'s births and having about 50% higher odds of doing so in a timely manner. Registration of family events is more common and more timely among less economically disadvantaged households.ConclusionsThese findings provide initial insights into the factors influencing access to documentation among a population highly vulnerable to statistical and legal invisibility and underscore the interconnectedness of family-related vital events in contexts of forced displacement.
BackgroundSocial inequalities remain a major determinant of mortality across Europe. This study aimed first to quantify cause-specific premature mortality across urban and rural areas in mainland France during the pre-pandemic period, taking into account social inequalities, and second to assess the extent of these social inequalities using the population attributable fraction (PAF) approach.MethodsCause-specific deaths were identified from the French national mortality database and linked to municipality-level deprivation quintiles using French-European Deprivation Index (F-EDI). Residual life expectancy at age of death was defined according to the Global Burden of Disease (GBD) 2019 reference life table. Age-standardized years of life lost rates (ASYRs) were calculated by sex, deprivation quintile, and rural-urban setting. Social inequalities in mortality were assessed using absolute and relative gaps between the least and most deprived quintiles (Q1-Q5) and PAF, with Q1 as the reference.ResultsASYRs for Level 1 GBD causes increased consistently with area deprivation in both urban and rural mainland France. Non-communicable diseases accounted for most premature mortality (85-90% of total YLL) in both sexes, followed by injuries and communicable, maternal, neonatal, and nutritional causes. In urban areas, 18-31% of cause-specific premature mortality was attributed to social inequalities, compared with 8-20% in rural areas, with higher contributions among males. The absolute difference in ASYRs was slightly larger in rural than urban areas for both sexes (females: 2,854 vs. 2,463; males: 6,607 vs. 5,618). Relative inequalities were similar across both settings (females: 1.30; males: 1.39, comparing Q5 with Q1). By cause, breast cancer showed the largest inequality among females (11-12% higher in Q5), while lung cancer exhibited the highest disparity among males (75% higher in Q5 in urban areas and 43% in rural areas).ConclusionsSocial inequalities substantially contributed to cause-specific premature mortality in pre-pandemic mainland France. ASYRs increased with deprivation in both urban and rural areas and were consistently higher among males. The persistent deprivation gradient and higher PAFs highlight the particularly marked impact of social inequalities in urban areas. These findings provide a pre-pandemic baseline for evaluating post-COVID-19 trends in premature mortality and health disparities in France.
BackgroundDiabetes is associated with the development of sequelae that further reduce patients' quality of life. A valuable instrument for assessing the severity of sequelae is the disability weight. The evaluation of the risks associated with the potential deterioration of sequelae has direct implications for healthcare.ObjectivesThe objective of this study is to estimate the risk of progression from no complications to any complications in both type 2 and type 1 diabetes and to examine the progression of risk over time.MethodsSurvival analysis methods are applied. The Kaplan-Meier and the Cox proportional hazards models are employed to ascertain the probability of developing complications in the future, given that the subject is currently free of such complications. Patients were obtained from the longitudinal dataset of the Italian Association of Diabetologists, collected between 2005 and 2017.ResultsThis is the first study in Italy to estimate the risk of transition to complications in people with diabetes. The results indicate that older males with type 2 diabetes living in the central and northern regions of the country are associated with a higher risk profile. Since the patients excluded from the baseline have the same risk characteristics as the patients studied, the results can be generalized. Our findings provide evidence within a large clinical cohort and suggest potential applicability of the proposed approach in other settings beyond the Italian context.ConclusionQuantifying risk in a way that is easily understood by policymakers and the general public is a valuable tool, as diabetes complications are a significant burden for individuals and society as a whole.
Gastric cancer has historically been driven by long‑standing Helicobacter pylori infection. The nationwide expansion of H. pylori eradication therapy beginning in 2013 created a unique opportunity to evaluate its population‑level impact on gastric cancer mortality. However, short‑term mortality trends following eradication are difficult to interpret because they reflect overlapping influences of ageing, cohort replacement, and cumulative infection history. This study aimed to provide a model‑based, population‑level assessment of the early impact of eradication during the first decade of nationwide implementation. We applied a two‑layer analytic framework consisting of a counterfactual analysis comparing observed mortality during 2013–2021 with expected mortality had eradication uptake remained at pre‑2013 levels, combined with a structured state‑transition (Markov) model with time‑dependent parameters. To estimate annual gastric cancer deaths prevented and the proportion of mortality reduction attributable to eradication, the model integrated age‑specific biological hazard, cumulative infection history, cohort‑specific H. pylori prevalence, and annual changes in eradication uptake. Observed gastric cancer deaths declined from 48,632 in 2013 to 41,624 in 2021, whereas counterfactual gastric cancer deaths declined more modestly, from 49,779 to 49,453. The divergence between observed and counterfactual deaths steadily widened from 1,147 in 2013 to 7,829 in 2021. Model‑based estimates indicated that eradication prevented 6,461 gastric cancer deaths during 2013–2021, with annual deaths prevented increasing from 165 in 2015 to 1,604 in 2021, particularly among adults aged 60–79, who showed the most pronounced early benefit reflecting cumulative infection history and real-world uptake patterns. The early population‑level impact of H. pylori eradication is consistent with a 16
In Australia, residential aged care accounts for a significant share of government expenditure, with financial sustainability and changing population profiles emerging as critical concerns. This study utilizes a longitudinal dataset and develops a statistical simulation model to project the profiles of individuals entering residential aged care, the number of admissions, and the associated government spending. Our approach integrates ensemble learning methods, including eXtreme Gradient Boosting (XGBoost), with the Hamilton–Perry demographic projection method to provide precise predictions of residents’ length of stay. This integration enables more reliable forecasts of the future residential aged care landscape. The analysis was conducted at the state level to capture regional differences. We examined both demographic features, such as age, marital status, and gender, and medical features, including dementia status and care needs as measured by the Aged Care Funding Instrument (ACFI). We find that XGBoost outperforms previously used survival models for predicting residents’ length of stay. Our simulation model projects that both the number of residents and aggregate government expenditure in residential aged care will continue increasing until 2041. The model also provides groundwork for future research by offering micro-level insights into usage patterns and financial sustainability challenges.
BackgroundThe global burden of injury is a key indicator for assessing public health and medical needs. During the COVID-19 pandemic, this burden was impacted. This study aims to explore how the pandemic influenced the injury burden globally and regionally, and provide recommendations to relieve this burden.MethodsThe burden of injury-related data is derived from the Global Burden of Disease (GBD) 2021 Study. Autoregressive integrated moving average (ARIMA) and ARIMA-Long short-Term Memory (LSTM) models were adopted for counterfactual inference to predict the scenario without the pandemic.ResultsDuring the COVID-19 pandemic, the observed global age-standardized incidence rate (ASIR) of injury exceeded the predicted value by 107.31 per 100,000, and the observed age-standardized prevalence rate (ASPR) was higher than the predicted value by 102.81 per 100,000. Self-harm and interpersonal violence saw the largest deviations above predicted values in Europe and parts of Asia. Specifically, Armenia's ASIR was 7,829.33 per 100,000 higher than predicted, and its ASDR exceeded projections by 5,186.32 per 100,000. Besides, traffic injuries exceeded predicted levels most significantly in Southeast Asia, with Indonesia's ASIR 25.48 per 100,000 higher than projected. And the observed ASIR of unintentional injuries in China was 379.61 per 100,000 higher than the predicted value.ConclusionDuring the COVID-19 pandemic, the global burden of injuries surpassed the predicted levels for a scenario without the pandemic in 2020-2021, especially in Europe and Asia. In addressing an epidemic, prevention and emergency measures for high-burden injury types and key populations should be strengthened based on local socio-cultural contexts.
