Population Health Metrics最新文献

Background: The coronavirus disease 2019 (COVID-19) pandemic is associated with increases in morbidity and mortality worldwide. The mechanisms of how SARS-CoV-2 may cause cardiovascular (CV) complications are under investigation. The aim of the study was to assess the impact of the COVID-19 pandemic on CV risk.

Methods: These are single-centre Bialystok PLUS (Poland) population-based and case‒control studies. The survey was conducted between 2018 and 2022 on a sample of residents (n = 1507) of a large city in central Europe and patients 6-9 months post-COVID-19 infection (n = 126). The Systematic Coronary Risk Estimation 2 (SCORE2), the Systematic Coronary Risk Estimation 2-Older Persons (SCORE2-OP), the Cardiovascular Disease Framingham Heart Study and the LIFEtime-perspective model for individualizing CardioVascular Disease prevention strategies in apparently healthy people (LIFE-CVD) were used. Subsequently, the study populations were divided into CV risk classes according to the 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice.

Results: The study population consisted of 4 groups: a general population examined before (I, n = 691) and during the COVID-19 pandemic (II, n = 816); a group of 126 patients post-COVID-19 infection (III); and a control group matched subjects chosen from the pre-COVID-19 pandemic (IV). Group II was characterized by lower blood pressure, low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) values than group I. Group III differed from the control group in terms of lower LDL-c level. There was no effect on CV risk in the general population, but in the population post-COVID-19 infection, CV risk was lower using FS-lipids, FS-BMI and LIFE-CVD 10-year risk scores compared to the prepandemic population. In all subgroups analysed, no statistically significant difference was found in the frequency of CV risk classes.

Conclusions: The COVID-19 pandemic did not increase the CV risk calculated for primary prevention. Instead, it prompted people to pay attention to their health status, as evidenced by better control of some CV risk factors. As the COVID-19 pandemic has drawn people's attention to health, it is worth exploiting this opportunity to improve public health knowledge through the design of wide-ranging information campaigns.

Background: The aim is to estimate age- and sex-specific direct medical costs related to diagnosed type 1 and type 2 diabetes in Germany between 2010 and 2040.

Methods: Based on nationwide representative epidemiological routine data from 2010 from the statutory health insurance in Germany (almost 80% of the population's insurance) we projected age- and sex-specific healthcare expenses for type 1 and 2 diabetes considering future demographic, disease-specific and cost trends. We combine per capita healthcare cost data (obtained from aggregated claims data from an almost 7% random sample of all German people with statutory health insurance) together with the demographic structure of the German population (obtained from the Federal Statictical Office), diabetes prevalence, incidence and mortality. Direct per capita costs, total annual costs, cost ratios for people with versus without diabetes and attributable costs were estimated. The source code for running the analysis is publicly available in the open-access repository Zenodo.

Results: In 2010, total healthcare costs amounted to more than €1 billion for type 1 and €28 billion for type 2 diabetes. Depending on the scenario, total annual expenses were projected to rise remarkably until 2040 compared to 2010, by 1-281% for type 1 (€1 to €4 billion) and by 8-364% for type 2 diabetes (€30 to €131 billion). In a relatively probable scenario total costs amount to about €2 and €79 billion for type 1 and type 2 diabetes in 2040, respectively. Depending on annual cost growth (1% p.a. as realistic scenario vs. 5% p.a. as very extreme setting), we estimated annual per capita costs of €6,581 to €12,057 for type 1 and €5,245 to €8,999 for type 2 diabetes in 2040.

Conclusions: Diabetes imposes a large economic burden on Germany which is projected to increase substantially until 2040. Temporal trends in the incidence and cost growth are main drivers of this increase. This highlight the need for urgent action to prepare for the potential development and mitigate its consequences.

Background: According to the World Health Organization (WHO), mental health is 'a state of wellbeing in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community'. Any population metric of mental health and wellbeing should therefore not only reflect the presence or absence of mental challenges but also a person's broad mental capacity and functioning across a range of cognitive, social, emotional and physical dimensions. However, while existing metrics of mental health typically emphasize ill health, existing metrics of wellbeing typically focus on happiness or life satisfaction, indirectly infer wellbeing from a selection of social and economic factors, or do not reflect a read out of the full spectrum of mental functioning that impacts people's everyday life and that spans the continuum from distress and the inability to function, through to the ability to function to one's full potential.

Methods: We present the Mental Health Quotient, or MHQ, a population metric of mental wellbeing that comprehensively captures mental functioning, and examine how it relates to functional productivity. We describe the 47-item assessment and the life impact rating scale on which the MHQ metric is based, as well as the rationale behind each step of the nonlinear algorithm used to construct the MHQ metric.

Results: We demonstrate a linear relationship between the MHQ metric and productive life function where movement on the scale from any point or in any direction relates to an equivalent shift in productive ability at the population level, a relationship that is not borne out using simple sum scores. We further show that this relationship is the same across all age groups. Finally, we demonstrate the potential for the types of insights arising from the MHQ metric, offering examples from the Global Mind Project, an initiative that aims to track and understand our evolving mental wellbeing, and since 2020 has collected responses from over 1 million individuals across 140 + countries.

Conclusion: The MHQ is a metric of mental wellbeing that aligns with the WHO definition and is amenable to large scale population monitoring.

Background: The gaps in healthy life expectancy (HLE) between Indigenous and non-Indigenous Australians are significant. Detailed and accurate information is required to develop strategies that will close these health disparities. This paper aims to quantify and compare the causes and their relative contributions to the life expectancy (LE) gaps between the Indigenous and non-Indigenous population in the Northern Territory (NT), Australia.

Methods: The age-cause decomposition was used to analyse the differences in HLE and unhealthy life expectancy (ULE), where LE = HLE + ULE. The data was sourced from the burden of disease and injury study in the NT between 2014 and 2018.

Results: In 2014-2018, the HLE at birth in the NT Indigenous population was estimated at 43.3 years in males and 41.4 years in females, 26.5 and 33.5 years shorter than the non-Indigenous population. This gap approximately doubled the LE gap (14.0 years in males, 16.6 years in females) at birth. In contrast to LE and HLE, ULE at birth was longer in the Indigenous than non-Indigenous population. The leading causes of the ULE gap at birth were endocrine conditions (explaining 2.9-4.4 years, 23-26%), followed by mental conditions in males and musculoskeletal conditions in females (1.92 and 1.94 years, 15% and 12% respectively), markedly different from the causes of the LE gap (cardiovascular disease, cancers and unintentional injury).

Conclusions: The ULE estimates offer valuable insights into the patterns of morbidity particularly useful in terms of primary and secondary prevention.

Background: Short birth interval (SBI) has profound implications for the health of both mothers and children, yet there remains a notable dearth of studies addressing wealth-based inequality in SBI and its associated factors in India. This study aims to address this gap by investigating wealth-based disparities in SBI and identifying the underlying factors associated with SBI in India.

Methods: We used information on 109,439 women of reproductive age (15-49 years) from the fifth round of the National Family Health Survey (2019-21). We assessed wealth-based inequality in SBI for India and its states using the Erreygers Normalised Concentration Index (ECI). Additionally, we used a multilevel binary logistic regression to assess the factors associated with SBI in India.

Results: In India, the prevalence of SBI was 47.8% [95% CI: 47.4, 48.3] during 2019-21, with significant variation across states. Bihar reported the highest prevalence of SBI at 61.2%, while Sikkim the lowest at 18.1%. SBI prevalence was higher among poorer mothers compared to richer ones (Richest: 33.8% vs. Poorest: 52.9%). This wealth-based inequality was visible in the ECI as well (ECI= -0.13, p < 0.001). However, ECI varied considerably across the states. Gujarat, Punjab, and Manipur exhibited the highest levels of wealth-based inequality (ECI= -0.28, p < 0.001), whereas Kerala showed minimal wealth-based inequality (ECI= -0.01, p = 0.643). Multilevel logistic regression analysis identified several factors associated with SBI. Mothers aged 15-24 (OR: 12.01, p < 0.001) and 25-34 (2.92, < 0.001) were more likely to experience SBI. Women who married after age 25 (3.17, < 0.001) and those belonging to Scheduled Caste (1.18, < 0.001), Scheduled Tribes (1.14, < 0.001), and Other Backward Classes (1.12, < 0.001) also had higher odds of SBI. Additionally, the odds of SBI were higher among mothers in the poorest (1.97, < 0.001), poorer (1.73, < 0.001), middle (1.62, < 0.001), and richer (1.39, < 0.001) quintiles compared to the richest quintile. Women whose last child had passed away were also significantly more likely to have SBI (2.35, < 0.001). Furthermore, mothers from communities with lower average schooling levels (1.18, < 0.001) were more likely to have SBI. Geographically, mothers from eastern (0.67, < 0.001) and northeastern (0.44, < 0.001) regions of India were less likely to have SBI.

Conclusion: The significant wealth-based inequality in SBI in India highlights the need for targeted interventions focusing on economically disadvantaged women, particularly in states with high SBI prevalence. Special attention should be given to younger mothers and those from socially disadvantaged groups to enhance maternal and child health outcomes across the country.

Background: Heterogeneity in national SARS-CoV-2 infection surveillance capabilities may compromise global enumeration and tracking of COVID-19 cases and deaths and bias analyses of the pandemic's tolls. Taking account of heterogeneity in data completeness may thus help clarify analyses of the relationship between COVID-19 outcomes and standard preparedness measures.

Methods: We examined country-level associations of pandemic preparedness capacities inventories, from the Global Health Security (GHS) Index and Joint External Evaluation (JEE), on SARS-CoV-2 infection and COVID-19 death data completion rates adjusted for income. Analyses were stratified by 100, 100-300, 300-500, and 500-700 days after the first reported case in each country. We subsequently reevaluated the relationship of pandemic preparedness on SARS-CoV-2 infection and age-standardized COVID-19 death rates adjusted for cross-country differentials in data completeness during the pre-vaccine era.

Results: Every 10% increase in the GHS Index was associated with a 14.9% (95% confidence interval 8.34-21.8%) increase in SARS-CoV-2 infection completion rate and a 10.6% (5.91-15.4%) increase in the death completion rate during the entire observation period. Disease prevention (infections: β = 1.08 [1.05-1.10], deaths: β = 1.05 [1.04-1.07]), detection (infections: β = 1.04 [1.01-1.06], deaths: β = 1.03 [1.01-1.05]), response (infections: β = 1.06 [1.00-1.13], deaths: β = 1.05 [1.00-1.10]), health system (infections: β = 1.06 [1.03-1.10], deaths: β = 1.05 [1.03-1.07]), and risk environment (infections: β = 1.27 [1.15-1.41], deaths: β = 1.15 [1.08-1.23]) were associated with both data completeness outcomes. Effect sizes of GHS Index on infection completion (Low income: β = 1.18 [1.04-1.34], Lower Middle income: β = 1.41 [1.16-1.71]) and death completion rates (Low income: β = 1.19 [1.09-1.31], Lower Middle income: β = 1.25 [1.10-1.43]) were largest in LMICs. After adjustment for cross-country differences in data completeness, each 10% increase in the GHS Index was associated with a 13.5% (4.80-21.4%) decrease in SARS-CoV-2 infection rate at 100 days and a 9.10 (1.07-16.5%) decrease at 300 days. For age-standardized COVID-19 death rates, each 10% increase in the GHS Index was with a 15.7% (5.19-25.0%) decrease at 100 days and a 10.3% (- 0.00-19.5%) decrease at 300 days.

Conclusions: Results support the pre-pandemic hypothesis that countries with greater pandemic preparedness capacities have larger SARS-CoV-2 infection and mortality data completeness rates and lower COVID-19 disease burdens. More high-quality data of COVID-19 impact based on direct measurement are needed.

Background: Both enhancing life expectancy and decreasing inequalities in lifespan between social groups are significant goals for public policy. To date, however, methodological tools to study progress in both dimensions simultaneously have been lacking. There is also a consensus that absolute and relative inequalities in lifespan must be studied together.

Methods: We introduce a novel graphical representation that combines national mortality rates with both absolute and relative measures of social inequality in mortality. To illustrate our approach, we analyze French and German data stratified by place of residence.

Results: For all-age mortality, in France we find a steady pace of decline in both mortality and in regional inequalities in mortality over recent decades. In Germany, substantial progress was made in the 1990s, mostly driven by convergence between eastern and western Germany, followed by a period of slower progress. Age-specific analyses for Germany reveal a worrying divergence in regional trends at ages 35-74 in recent years, which is particularly pronounced among women.

Conclusion: Our novel visual approach offers a way to simultaneously examine two dimensions of progress in longevity, and facilitates meaningful comparisons between populations, even when their current mortality rates differ. The applied methods can be easily reproduced in any country for which long-term mortality series stratified by region, or any relevant socioeconomic characteristic, are available. It is useful for both scientific analysis and policy advice.

Background: There are significant geographic inequities in COVID-19 case fatality rates (CFRs), and comprehensive understanding its country-level determinants in a global perspective is necessary. This study aims to quantify the country-specific risk of COVID-19 CFR and propose tailored response strategies, including vaccination strategies, in 156 countries.

Methods: Cross-temporal and cross-country variations in COVID-19 CFR was identified using extreme gradient boosting (XGBoost) including 35 factors from seven dimensions in 156 countries from 28 January, 2020 to 31 January, 2022. SHapley Additive exPlanations (SHAP) was used to further clarify the clustering of countries by the key factors driving CFR and the effect of concurrent risk factors for each country. Increases in vaccination rates was simulated to illustrate the reduction of CFR in different classes of countries.

Findings: Overall COVID-19 CFRs varied across countries from 28 Jan 2020 to 31 Jan 31 2022, ranging from 68 to 6373 per 100,000 population. During the COVID-19 pandemic, the determinants of CFRs first changed from health conditions to universal health coverage, and then to a multifactorial mixed effect dominated by vaccination. In the Omicron period, countries were divided into five classes according to risk determinants. Low vaccination-driven class (70 countries) mainly distributed in sub-Saharan Africa and Latin America, and include the majority of low-income countries (95.7%) with many concurrent risk factors. Aging-driven class (26 countries) mainly distributed in high-income European countries. High disease burden-driven class (32 countries) mainly distributed in Asia and North America. Low GDP-driven class (14 countries) are scattered across continents. Simulating a 5% increase in vaccination rate resulted in CFR reductions of 31.2% and 15.0% for the low vaccination-driven class and the high disease burden-driven class, respectively, with greater CFR reductions for countries with high overall risk (SHAP value > 0.1), but only 3.1% for the ageing-driven class.

Conclusions: Evidence from this study suggests that geographic inequities in COVID-19 CFR is jointly determined by key and concurrent risks, and achieving a decreasing COVID-19 CFR requires more than increasing vaccination coverage, but rather targeted intervention strategies based on country-specific risks.

Background: Mortality rate estimation in small areas can be difficult due the low number of events/exposure (i.e. stochastic error). If the death records are not completed, it adds a systematic uncertainty on the mortality estimates. Previous studies in Brazil have combined demographic and statistical methods to partially overcome these issues. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010 and forecasted probabilistic mortality rates and life expectancy between 2010 and 2030.

Methods: We used a combination of the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS), Bayesian Model, Spatial Smoothing Model and an ad-hoc procedure to estimate age- and sex-specific mortality rates for all Brazilian municipalities for 2010. Then we adapted the Lee-Carter model to forecast mortality rates by age and sex in all municipalities between 2010 and 2030.

Results: The adjusted sex- and age-specific mortality rates for all Brazilian municipalities in 2010 reveal a distinct regional pattern, showcasing a decrease in life expectancy in less socioeconomically developed municipalities when compared to estimates without adjustments. The forecasted mortality rates indicate varying regional improvements, leading to a convergence in life expectancy at birth among small areas in Brazil. Consequently, a reduction in the variability of age at death across Brazil's municipalities was observed, with a persistent sex differential.

Conclusion: Mortality rates at a small-area level were successfully estimated and forecasted, with associated uncertainty estimates also generated for future life tables. Our approach could be applied across countries with data quality issues to improve public policy planning.