Population Health Metrics最新文献

Background: Financial resources beyond housing may influence survival in later life. Given China's rapid population aging and high home ownership, focusing on non-housing assets can clarify wealth-health links. We therefore examined the association between total non-housing assets and all-cause mortality among Chinese middle-aged and older adults.

Methods: A nationwide cohort of 12,670 adults (aged 45-85) was established using the harmonized CHARLS dataset (2011-2018). All-cause mortality was ascertained through 2020 by supplementing harmonized data with vital status information from the raw CHARLS 2020 wave. The main exposure was total non-housing assets. In addition, non-housing assets were combined with household consumption (median split) to create four joint groups: Group 1 (low assets/low consumption), Group 2 (low assets/high consumption), Group 3 (high assets/low consumption), and Group 4 (high assets/high consumption). All-cause mortality was tracked. Baseline characteristics and mortality were presented by asset quartile and asset consumption group. Survival curves, Cox models (adjusted for confounders), and restricted cubic splines assessed associations. Subgroup and interaction analyses, especially for marital status, were visualized using forest and stratified plots.

Results: During a 9-year follow-up, 2,418 deaths occurred. Higher total non-housing assets were associated with lower mortality: Q4 (highest) vs. Q1 (lowest), adjusted HR = 0.79 (95% CI 0.68-0.91). In fully adjusted models, we also observed a graded inverse association across asset-consumption groups (P for trend < 0.001); high-consumption categories remained protective (Group 2: HR = 0.85, 95% CI 0.74-0.97; Group 4: HR = 0.75, 95% CI 0.65-0.85). Marital status showed a significant interaction with asset level (P‑interaction < 0.001).

Conclusions: Greater non-housing assets was associated with lower mortality. Marital status has a significant interacting effect on this association. Focus should be on vulnerable elderly groups with middle-low assets, low consumption, or those who are non-married.

Background: Social capital, in its broad definitions, has been linked to improved health outcomes, yet the scarce consistency of social capital measurements and its further effects on healthcare utilization remain less clear. Particularly in Italy, where regional disparities and an aging population challenge the healthcare system, understanding these dynamics is crucial. This study proposes two population-based indicators of social capital and investigates whether they influence health itself and healthcare utilization.

Method: Italian population data from 2014 to 2023 was used to develop two social capital measurements: Social support and Social participation, applying Item Response Theory (IRT). Hence, we applied structural equation modeling (SEM) to explore the pathways between social capital, self-reported health status, and healthcare utilization. The analysis includes control variables for demographic and behavioral factors.

Results: Our main findings contribute with the current literature by identifying that population-based measures for social support and social participation may be useful for empirical research, and both direct and indirect effects of social constructs were found significantly associated with health and health utilization outcomes. Both social participation and social support were found to exert significant positive effects on self-perceived health and health utilization. The model suggests that while better social connections contribute to improved health, such increased support and participation can also lead to increased healthcare-seeking behavior.

Conclusion: Social capital plays a dual role in shaping both health outcomes and healthcare utilization in Italy. Our findings highlight the relevance of social resources as population-level determinants of health and access, suggesting that strengthening community networks and health literacy can reduce inequities and enhance the efficiency of healthcare systems.

Background: Japan has experienced widening income disparity in recent years, raising concerns about income inequality in health. This study aims to investigate trends in income-related concentration and inequality in key health outcomes between 2001 and 2022.

Methods: This study utilized repeated cross-sectional data from 500,580 individuals (238,746 men and 261,834 women) aged ≥ 6 years, obtained from eight waves of population-based national surveys conducted between 2001 and 2022. The study examined trends in the concentration index and the relative and slope indices of inequality for key health outcomes, including self-rated health, subjective symptoms, limitations in undertaking activities of daily living, and experience of stress/anxiety, as well as the number of physician visits and incidence of selected non-communicable diseases (NCDs). All measures were standardized by age and sex.

Results: Increasing concentrations of poor health status among low-income individuals and rising income-related health inequality were observed over the study period, although a greater pro-poor concentration was noted for physician visits. Additionally, income-related inequality increased for persons with hypertension, diabetes, and at least one type of NCD.

Conclusions: The results indicate persistent income-related inequalities in health within a context of universal health coverage.

Background: The Geneva Conventions form the core of International Humanitarian Law (IHL), safeguarding healthcare and protecting civilians from the brutality of war. Unfortunately, these conventions are often disregarded. Attacks on healthcare have devastating effects on healthcare systems, and it is therefore vital to document such attacks and detect possible temporal patterns. This study aims to assess temporal trends in attacks on healthcare in five conflict-affected countries: Lebanon, Myanmar, occupied Palestinian territory (oPt), Sudan and Ukraine.

Methods: This study used two publicly available databases: the World Health Organization Surveillance System for Attacks on Health Care (WHO SSA) and the Insecurity Insight Safeguarding Health in Conflict Coalition (SHCC). Start dates and key events were determined for each conflict based on grey literature searches. From the start dates onward, data on attacks on healthcare were collected. The data collection ended on December 31, 2024. Statistical analysis entailed chi-square tests for temporal trends.

Results: The WHO SSA and SHCC database reported a total of 4,289 and 5,454 attacks, respectively, in the five investigated conflict-affected countries. For all conflict-affected countries except Lebanon, there were significant differences between the databases regarding the reported number of attacks. Temporal trend analyses revealed that, in Myanmar, oPt, Sudan and Ukraine, the highest number of attacks occurred during the 0-2 month period. In Lebanon, the highest number of attacks was observed in the 9-11-month period. All peaks in the number of attacks were associated with either the immediate or early phase of the conflict or with major conflict escalations.

Conclusions: Temporal trend analyses of five ongoing armed conflicts revealed that spikes in attacks on healthcare were either associated with the immediate or early phases of the conflict or with major conflict escalations. Although major differences exist between the WHO SSA and SHCC database, particularly regarding the reported number of attacks, the observed patterns were largely similar.

Background: Since 2015, recurrent terrorist attacks in Burkina Faso have caused large-scale displacement, impacting the psychological health of affected populations. This study explores the effects of migration forced or voluntary on depression and anxiety among adolescents and young people aged 15-24, in line with SDG 3, which aims to "ensure healthy lives and promote well-being for all at all ages".

Methods: The analysis is based on data from the baseline survey conducted by the Institut Supérieur des Sciences de la Population (ISSP) for the Sahel Resilience Building Program. A total of 1,911 adolescents and young people aged 15-24 living in four regions were interviewed. We measured mental health using two tools: the Patient Health Questionnaire-9 (PHQ-9) for depression and the Generalised Anxiety Disorder-7 (GAD-7) for anxiety. We used multinomial regressions to test the effects of migration status on depression and anxiety.

Findings: Forced migrants report higher symptoms of moderate or severe depression (11.1%) and anxiety (15.7%) compared to non-forced migrants (6.8% and 14.4%) and non-migrants (6.6% and 9.5%). Forced migrants were 2.16 times more likely (RRR = 2.16; p < 5%) than non-migrants to experience moderate or severe depression, and non-forced migrants were 2.12 times more likely (RRR = 2.12; p < 5%) than non-migrants to experience moderate or severe anxiety. Youth aged 20-24 and urban residents were also more likely to face these mental health issues.

Contributions: These findings call for more attention to the needs of both forced and non-forced migrants in terms of mental health. Psychological care mechanisms are needed in destination areas.

The aim of the study was to analyze changes and demographic inequalities in the mortality of the Lithuanian population in 2020 and 2021 compared to the period of 2015-2019, assess the major causes of death that contributed to the changes, and identify the groups of the society that suffered most.

Methods: Mortality rates for 2015-2021 from all causes, cardiovascular diseases, malignant neoplasms, external causes, diseases of the digestive system, diseases of the respiratory system, and COVID-19 in Lithuania by sex and age were calculated per 100,000 population. Mortality changes compared with the previous year and between the average of 2015-2019 years were calculated. The average annual percentage change was calculated to determine the aggregated 2015-2019 change in mortality from the leading causes of death. Coefficients of linear regression multiplied by 100 were presented as average annual changes, which were statistically significant at p < 0.05. Mortality rate differences between 2020 and 2021 years and the average of 2015-2019 years were calculated.

Results: Lithuania has recorded 9.4% higher overall mortality among males in 2020 and 18% higher mortality in 2021 compared with a period unaffected by the COVID-19 pandemic (p < 0.05). Among females - 10.7% higher mortality in 2020 and 22.6% in 2021 (p < 0.05). Male and female mortality from COVID-19 in all age groups in 2021 was higher than that in 2020, and mortality rates increased with an increase in age. Negative changes in mortality from 2015 to 2019 to 2020 among males and females of all age groups were mainly determined by COVID-19. The most significant impact of COVID-19 in 2021 on the overall mortality changes was estimated in the 55-64 and 65-74 male age groups, while female overall mortality was in the 45-54 and 65-74 age groups.

Conclusions: Negative changes in mortality from 2015 to 2019 to 2020 among males and females of all age groups were mainly determined by COVID-19. The most significant impact of COVID-19 in 2021 on the overall mortality changes was estimated in the 55-74 male age group, while on female overall mortality in the 45-54 and 65-74 age groups.

Background: Efforts to track the mortality and public health impact of the coronavirus disease (COVID-19) in Uganda have been hampered by weak Civil registration and vital statistics (CRVS) system and suboptimal health seeking behaviors or patterns. Evaluating unexplained increases in all-cause mortality provides a complete picture of the impact of COVID-19 pandemic and guide public health policies and resource allocation to protect the most vulnerable populations.

Methods: The longitudinal population cohort data on demographic events and socioeconomic status collected from 2015 to 2021 within the Iganga Mayuge Health and Demographic Surveillance System (IMHDSS) was used. Number of deaths and person years at risk were counted for each quarter of the year from January 2015 to December 2021 and classified as "pre-pandemic" (before January 2020), and "during pandemic" (January 2020 to December 2021). Crude mortality rates were computed comparing the two periods. Time series model was used to estimate excess mortality and to locate the exact time when excess deaths occurred. Cox Proportional Hazard model was used to estimate the Hazard ratio associated with death.

Results: A total of 132,367 individuals were followed up from 2015 to 2021 and 3,424 deaths were registered. Slightly more than a half of all deaths (53%, n = 1,827) were male, and 65.4% (n = 2,238) were rural residents. Children under five years had a significantly higher CMR during COVID-19 period of 18.9, (95% CI 17.2-20.8) per 1000 person compared to 12.5 (95% CI 11.6-13.4) per 1000 person years before COVID-19. The risk of dying among children under 5 years compared to those aged between 5 and 14 years was higher during the COVID-19 pandemic period (aHR = 18.0, 95% CI 13.6-24.0) than pre-pandemic (aHR = 10.4, 95% CI 8.8-12.3).

Conclusion: The COVID-19 pandemic increased all-cause mortality in the Iganga Mayuge HDSS population cohort in Eastern Uganda, particularly among children under five, likely due to restricted healthcare access and economic disruptions. Pandemic response measures should prioritize vulnerable populations at higher risk of malnutrition and preventable diseases to mitigate future negative impacts.

Background: Cause of death (CoD) diversity indices measure the extent to which some populations die from more similar or variegated causes than others. Higher CoD diversity implies higher unpredictability of the causes of individuals dying and greater challenges for health systems. In this paper, we propose a novel method to decompose overall CoD diversity as the sum of two interpretable parts: the within- and between-group components.

Methods: The novel approach is applied to Latin America and the Caribbean (LAC) region to illustrate its usefulness. We decompose overall CoD diversity, measured by the Simpson index of diversity, into between-country and within-country components. In addition, we provide further decompositions assessing how each cause of death and each country contributes to overall CoD diversity in the region.

Results: The CoD diversity in the region followed a nonmonotonic trend. From 2000 to 2018, the CoD diversity increased from 0.81 to 0.83 for women, reaching approximately 0.84 for men. El Salvador, Peru, and Uruguay are the countries that contribute the most to explaining the differences in the mortality profile between countries, but for very different and opposing reasons. While the high diversity in El Salvador and Peru can be explained by causes of deaths related to the early stages of the epidemiological transition, such as communicable causes, respiratory causes, and external causes, Uruguay presents a high diversity because the deaths are very dispersed between chronic conditions. Cardiovascular deaths are the main contributor to both CoD diversity levels and their changes over time. As cardiovascular deaths decline, they give way to other chronic causes, which become more prominent and contribute to diversifying the corresponding mortality profiles. However, external causes also significantly contribute to forming uneven epidemiological profiles.

Conclusions: The decomposition proposed in this paper makes possible to assess whether some groups contribute more or less to the uncertainty around the causes of individuals' deaths and identify the sources of CoD diversity. In this way, this approach can contribute to a better understanding of contemporary mortality dynamics, especially in a context with large health inequalities.

The COVID-19 pandemic has disproportionately impacted vulnerable populations, such as low-income households, exacerbating existing health and economic challenges. In Cuba, the crisis exposed the effects of long-standing economic difficulties, worsened by sanctions, but the country's robust public health system and independent vaccine development enabled an effective response. This study addresses the gap in understanding how socio-economic factors and individual behaviors interact to influence disease spread. It proposes a hybrid, efficient, and parsimonious model combining ABM (Agent-Based Modeling) and ARIMAX (AutoRegressive Integrated Moving Average with eXogenous variables) time series analysis to forecast COVID-19 cases, offering valuable insights for policymakers to tailor interventions and enhance crisis management.