Population Health Metrics最新文献

Background: The literature on healthy longevity has typically focused on average values (i.e., healthy life expectancy). Recent studies have started to expand this focus by investigating the whole healthy lifespan distribution, especially the standard deviation of healthy longevity, which captures inter-individual variation. Despite these advancements, research gaps remain on how distributions differ by health indicator and sex. This study aimed to compare healthy longevity distributions at age 60 between different health measures and sexes.

Methods: We used data from the Survey of Health, Ageing and Retirement in Europe and the Human Mortality Database. A Markov chain model was used to estimate the first three statistical moments of healthy longevity distributions. The maximum entropy method was then applied to derive the full distributions. The healthy lifespan outsurvival statistic and the Hellinger distance were used to compare distributions between males and females.

Results: For most health measures, the probabilities of health loss at younger ages were higher for males than for females, and females had a longer healthy life expectancy. Males had more dispersed distributions with a lower mode. For most health measures, healthy longevity distributions were negatively skewed, with a mode age (i.e., the age with the highest probability of health loss) higher than the healthy life expectancy age. The probability for a man to have a longer healthy lifespan than a female was below 50% for various health measures and was the lowest for living free of cardiovascular disease. In contrast, the probability for a man to live free of arthritis or rheumatism for longer than a female was above 50%. The most similar distributions between males and females were observed with life free of any chronic conditions and life with no more than one chronic condition.

Conclusions: This study extended the scope of healthy longevity research by complementing a focus on the statistical moments with observations on the mode of the distributions and with formal comparisons based on the healthy lifespan outsurvival statistic and the Hellinger distance, which are applied for the first time in the healthy longevity field.

Background: Bangladesh, home to 165 million people, reported its first COVID-19 case in March 2020. This prompted a range of public health measures to control the epidemic. However, limited access to COVID-19 testing and incomplete or inaccurate death registration likely obscured the pandemic's true impact. We use longitudinal data from the Matlab Health and Demographic Surveillance System (HDSS) in Bangladesh to assess excess mortality and underlying causes of death during the COVID-19 pandemic.

Methods: We analysed mortality among 299,775 individuals residing within the Matlab HDSS catchment area between January 1, 2018 and December 31, 2021. Crude mortality rates were compared between the Pre-COVID-19 (2018-2019) and COVID-19 (2020-2021) periods. Adjusted sub-distribution hazard ratios (SHR) were estimated using the Fine and Gray competing risk model. Causes of death were determined using the WHO 2016 Verbal Autopsy questionnaire with supplementary COVID-19 module. We assessed changes in the distribution of causes of death and calculated cause-specific mortality rates by period and sex.

Results: Crude mortality rate increased to from 7.4 deaths per 1000 person-years in 2018-2019 (pre-COVID-19 period) to 8.5 deaths per 1000 person-years during the COVID-19 period (2020-2021). Among individuals aged 60 years and above, the COVID-19-related mortality rate was 3.5 deaths per 1000 person-years during the COVID-19 period. Overall mortality rate increased from 44.1 (95% CI: 42.4-45.9) deaths to 50.9 (95% CI: 49.1-52.7) deaths per 1000 person-years, corresponding to an adjusted SHR of 1.19 (95% CI: 1.12-1.25). Compared with the Pre-COVID-19 period, mortality attributable to non-communicable diseases (NCDs) increased by 11% (mortality rate ratio (MRR): 1.11; 95% CI: 1.04-1.18), while mortality from respiratory diseases increased by 82% (MRR: 1.82; 95% CI: 1.24-2.73) during the COVID-19 period.

Conclusion: During the COVID-19 period, mortality increased in rural Bangladesh, with the sharpest increase observed among older adults with noncommunicable and respiratory diseases. Future pandemic preparedness efforts should prioritise these high-risk subgroups to reduce adverse health outcomes and mortality.

Background: Accurate underlying cause of death (CoD) data is critical for informing public health policy, but inaccurate CoD assignment, here called garbage code (GC) deaths, compromise CoD research and monitoring. Since 1997, GCs have consistently made up over 20% of all underlying CoDs in Sweden, but the distribution of GC deaths by sociodemographic status of the deceased remains poorly understood.

Methods: We used the Swedish Cause of Death Register containing 2.50 million death records from 1997 to 2023. We mapped each record to the Global Burden of Disease (GBD) project cause list and categorized GC deaths by disease groups. We calculated the fraction of deaths that were GCs by individual age, sex, region of death, and highest educational attainment. We performed redistribution of GCs onto well-defined CoDs and assessed the odds of GC assignment with a binomial logistic regression.

Results: Since 1997, Sweden has coded at least 23% of deaths to GCs each year with 25.5% coded to GCs in 2023. The lowest educated consistently received more GC deaths, with 45.8% more GC deaths relative to non-GC deaths between ages 20 and 39 compared to the highest educated, and there were more GC deaths in (1) infections, (2) blood and endocrine diseases, (3) injuries, (4) cancers, and (5) maternal, neonatal, and congenital (MNC) diseases in 2023. GC deaths among the highest educated have continued to increase in infections, injuries, cardiovascular, digestive, and MNC diseases. After redistribution, well-defined death counts among the lowest educated increased by over 20% in 13 of the leading 20 CoDs in Sweden. Our model suggested low education increased the likelihood of having a GC by 12.8% (11.4%-14.2%) compared to the highest educated. This was second to point estimates of standardized age at death (25.2% [24.8%-25.6%]) and exceeded sex (12.1% [11.4%-12.8%] increase for males) and region (at most 7.3% [6.6%-8.1%] decrease for death outside of Stockholm).

Conclusions: We found consistent trends of high GC level in Sweden with doctors assigning more GCs to the lowest educated. Our results reveal stark sociodemographic disparities in CoD coding in Sweden and it is probable that similar disparities would be found elsewhere. This underscores the need for improving procedures and national guidelines in CoD assignment to correctly represent all social groups in research.

Introduction: Mozambique reported its first COVID-19 case in March 2020, and the pandemic exposed significant vulnerabilities in its healthcare system. Measuring mortality attributable to COVID-19 in Mozambique, has been challenging due to limitations in health information systems and incomplete death documentation outside health facilities. By mid-2023, a total of 2,234 deaths from 233,334 cases were confirmed but the figures can be much higher.  METHODS : We conducted a trend analysis of mortality using data from the Manhiça Health and Demographic Surveillance System from the periods before (2016-2019) and during the pandemic (2020-2021) to measure sex differences in mortality patterns (life expectancy and mortality rates). Excess mortality ratios during the pandemic were assessed using time series analysis with COVID-19 a generalized additive model.

Results: From 2019 to 2020, the life expectancy in males increased 5.1%, from 61.3 (95% CI: 60.3-62.2) years to 64.4 (95% CI: 63.5-65.3) years, and 6.1%, from 69.3 (95% CI: 68.5-70.2) years to 73.5 (95% CI: 72.6-74.3) years in females. However, from 2020 to 2021, a decline was observed in both males and females. In males, it dropped 3.1% while in females the life expectancy dropped 3.5%. All-age male mortality rates decreased from 15.3 to 11.2 (26.8%) deaths per 1000 person-years from 2016 to 2020, then rose to approximately 13.4 in 2021. All-age female mortality experienced a similar trend, with an increase of 9.0% from 6.7 deaths per 1000 person-years in 2020 to 7.3 in 2021. During pandemic, the male elderly population (65+ years old) experienced the highest excess mortality in July 2021, reaching a ratio of 1.57 (CI: 1.37-1.84), whereas for females, the highest excess mortality among females was observed in the age group of 05-14 years, with a ratio of 1.86 (CI: 1.44 - 2.17) in January 2021 between the observed and expected deaths.

Conclusion: Mortality in Manhiça district declined from 2016 until 2019 but increased during COVID-19 pandemic with excess deaths in 2021, particularly among those aged 65 and older. This study highlights the value of robust health and demographic information systems in resource-limited settings for assessing public health impacts.

Background: Cigarette smoking is a major contributor to disability and premature death worldwide. Given the impact of smoking on population health, it is important to understand trends and socio-demographic patterns that can be most informative to public health planning. The objectives of this study are to establish a time series of cigarette smoking in Belgium, forecast future smoking prevalence, and examine socio-demographic patterns in smoking.

Methods: Using six waves of the Belgian Health Interview Survey (1997-2018), we modelled smoking prevalence and forecast trends to 2040 with a Bayesian generalized linear model incorporating population projections by age, sex, region, and educational attainment to capture demographic shifts over time.

Results: Based on modelled estimates anchored on BHIS data from 1997 to 2018, smoking prevalence in Belgium declined from 29.6% (95% CI: 25.0-34.6%) in 1997 to 17.2% (95% CI: 12.5-23.5%) in 2025. Model projections indicate a further decrease to 12.9% (95% CI: 7.3-22.4%) by 2040. In 2025, men are estimated to smoke at about 1.4 times the rate of women-20.2% (95% CI: 14.9-27.6%) versus 14.2% (95% CI: 10.2-19.6%)-a gap expected to narrow but persist by 2040 (14.5%, 95% CI: 8.4-25.6% vs. 11.2%, 95% CI: 6.3-19.3%). Across regions, the steepest decline is projected in Flanders (from 28.5% to 11.4%), followed by Brussels-Capital (31.1% to 13.3%) and Wallonia (31.0% to 15.5%), which is expected to remain the highest. Socioeconomic inequalities also persist: by 2040, smoking prevalence is projected to range from 19.0% (95% CI: 12.2-36.2%) among those with lower secondary education to 7.5% (95% CI: 4.7-13.1%) among those with more than secondary education.

Conclusions: Smoking prevalence in Belgium is declining and is projected to continue this downward trend. However, persistent inequalities by sex, educational attainment, and age may result in uneven health benefits across the population. Addressing these disparities through targeted tobacco control measures will be crucial to ensuring equitable health gains for all.

Background: Social isolation and loneliness (SIL) have emerged as critical population health concerns linked to various adverse health outcomes, including cardiovascular disease, stroke, dementia, depression, and premature mortality. However, the absence of a standard categorization for interventions aimed at reducing SIL has impeded consistent comparison, evaluation, and the accumulation of knowledge, affecting evidence-based policy decisions. To address this gap, we developed and empirically evaluated the ASSeTS (Access, Skills, Social engagement, Therapeutic and psychological, Systemic) classification system, a standardized approach for categorizing SIL interventions.

Methods: We conducted a systematic review to identify and evaluate existing classification systems used for SIL interventions. Seventeen databases were searched from inception to September 2023, with no language restrictions. Inclusion criteria encompassed established and widely used reviews with clear intervention categorizations and broad applicability to general population groups. Expert consultations supplemented the systematic review, providing iterative feedback and additional relevant literature missed from the literature search, to inform the development of the ASSeTS classification framework. The developed ASSeTS system was empirically tested by independent experts for clarity, applicability, and reliability, with inter-rater agreement assessed using Fleiss' kappa.

Results: The review identified 11 studies covering a range of SIL intervention categorization approaches. Based on synthesis and expert feedback, the ASSeTS system was structured into five main categories: Access, Skills, Social engagement, Therapeutic and psychological, and Systemic interventions. Empirical testing yielded moderate inter-rater reliability (κ = 0.419), indicating acceptable usability among expert raters. Higher agreement was found for categories such as therapeutic and psychological interventions, whereas systemic interventions showed lower reliability, suggesting opportunities for future refinement.

Conclusion: The ASSeTS classification system provides a much-needed standardized framework for categorizing SIL interventions, facilitating comparability, rigorous evaluation, cumulative knowledge, and evidence-based policy decisions. Future work should focus on refining less reliable categories, validating ASSeTS across various contexts, and integrating it into global policy frameworks to more effectively address the public health implications of SIL.

Background: The COVID-19 pandemic has profoundly disrupted global health systems, contributing to significant shifts in mortality patterns. Beyond the direct impact of the virus, the pandemic has exacerbated existing health inequalities and introduced new inequalities in mortality from leading causes of death in Lithuania. The aim of this study was to investigate inequalities and trends in mortality from leading causes of death based on residential location, comparing the period prior to the COVID-19 pandemic with the period during the pandemic in Lithuania.

Methods: This study analyzed mortality data from 2017 to 2023 (pre-pandemic period 2017-2019, pandemic period 2020-2023) by gender, age group (< 65/65+), and place of residence (urban/rural), using data from the State Register of Death Cases and Their Causes and the Statistics Department of Lithuania. Mortality was assessed for three major causes of death: cardiovascular diseases, cancer, and respiratory diseases. The Mann-Whitney U test was used to compare groups, and Join point regression analysis was applied to evaluate trends in mortality inequalities.

Results: Among individuals aged 65+, respiratory system-related mortality rates were significantly higher during the pandemic in both urban and rural populations compared to pre-pandemic period (p = 0.001 and p = 0.003). The mortality due to respiratory system diseases showed a notable increase, particularly among females aged 65+. In urban areas, mortality increased from 17.06 to 80.68 per 100,000, while in rural areas, it rose from 25.20 to 110.40 per 100,000 (p = 0.001). Mortality from malignant neoplasms of the rectum significantly declined in rural males (AAC = -6.71, p = 0.01). Pneumonia mortality significantly declined in urban males (AAC = -7.96, p = 0.001), while rural females exhibited a significant decline in chronic obstructive pulmonary disease mortality (AAC = -25.22, p = 0.043).

Conclusions: The study reveals urban-rural and gender differences in mortality during the COVID-19 pandemic in Lithuania, with older adults-especially rural males and urban females-experiencing increased deaths from respiratory diseases, including COVID-19. These findings may also support broader discussions on reducing health inequalities and strengthening health system resilience to better prepare for future public health challenges.

Background: 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.

Methods: 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.

Results: 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 (46.5 million DALYs; 20.4/case) and colorectal (24.4 million; 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.

Conclusions: 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.

Background: The conflict that began in Sudan in April 2023 has displaced over 6.6 million individuals, with hundreds of thousands seeking refuge in Egypt. Displaced populations face significant post-migration stressors that elevate the risk of psychological distress.

Objectives: This pilot study aimed to provide a preliminary estimate of the prevalence of probable posttraumatic stress disorder (PTSD) among Sudanese refugees in Cairo and identify associated sociodemographic risk factors.

Methods: A pilot cross-sectional survey was conducted with 397 Sudanese refugees recruited via convenience snowball sampling. We utilized the Arabic version of the PTSD Checklist for DSM-5 (PCL-5) to estimate the frequency of probable PTSD and gathered data on displacement history.

Results: The prevalence of probable PTSD in this sample was 70.8%. Multivariate analysis indicated that a prior psychiatric history and caregiving responsibilities were significantly associated with meeting the threshold for probable PTSD. Younger adults reported higher symptom scores, though age was not a significant predictor in the adjusted model. No significant differences in prevalence were observed between refugees who entered Egypt through legal versus unauthorized routes.

Conclusions: These preliminary findings reveal a high psychological burden among Sudanese refugees in Egypt, underscoring the acute nature of the crisis. There is an urgent need for the integration of culturally sensitive mental health screenings and psychosocial interventions within refugee response frameworks. Further longitudinal research is required to explore the long-term effects of displacement on this population.

Background: 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.

Methods: 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-3 L 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.

Results: 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).

Conclusions: 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.