Association of low muscle mass with cognitive function and mortality in USA seniors: results from NHANES 1999-2002.

BACKGROUND: Sarcopenia and cognitive impairment have been linked in prior research, and both are linked to an increased risk of mortality in the general population. Muscle mass is a key factor in the diagnosis of sarcopenia. The relationship between low muscle mass and cognitive function in the aged population, and their combined impact on the risk of death in older adults, is currently unknown. This study aimed to explore the correlation between low muscle mass and cognitive function in the older population, and the relationship between the two and mortality in older people. METHODS: Data were from the National Health and Nutrition Examination Survey 1999-2002. A total of 2540 older adults aged 60 and older with body composition measures were included. Specifically, 17-21 years of follow-up were conducted on every participant. Low muscle mass was defined using the Foundation for the National Institute of Health and the Asian Working Group for Sarcopenia definitions: appendicular lean mass (ALM) (< 19.75 kg for males; <15.02 kg for females); or ALM divided by body mass index (BMI) (ALM: BMI, < 0.789 for males; <0.512 for females); or appendicular skeletal muscle mass index (ASMI) (< 7.0 kg/m2 for males; <5.4 kg/m2 for females). Cognitive functioning was assessed by the Digit Symbol Substitution Test (DSST). The follow-up period was calculated from the NHANES interview date to the date of death or censoring (December 31, 2019). RESULTS: We identified 2540 subjects. The mean age was 70.43 years (43.3% male). Age-related declines in DSST scores were observed. People with low muscle mass showed lower DSST scores than people with normal muscle mass across all age groups, especially in the group with low muscle mass characterized by ALM: BMI (60-69 years: p < 0.001; 70-79 years: p < 0.001; 80 + years: p = 0.009). Low muscle mass was significantly associated with lower DSST scores after adjusting for covariates (ALM: 43.56 ± 18.36 vs. 47.56 ± 17.44, p < 0.001; ALM: BMI: 39.88 ± 17.51 vs. 47.70 ± 17.51, p < 0.001; ASMI: 41.07 ± 17.89 vs. 47.42 ± 17.55, p < 0.001). At a mean long-term follow-up of 157.8 months, those with low muscle mass were associated with higher all-cause mortality (ALM: OR 1.460, 95% CI 1.456-1.463; ALM: BMI: OR 1.452, 95% CI 1.448-1.457); ASMI: OR 3.075, 95% CI 3.063-3.088). In the ALM: BMI and ASMI-defined low muscle mass groups, participants with low muscle mass and lower DSST scores were more likely to incur all-cause mortality ( ALM: BMI: OR 0.972, 95% CI 0.972-0.972; ASMI: OR 0.957, 95% CI 0.956-0.957). CONCLUSIONS: Low muscle mass and cognitive function impairment are significantly correlated in the older population. Additionally, low muscle mass and low DSST score, alone or in combination, could be risk factors for mortality in older adults.

Long-term exposure to PM2.5 and mortality in a national cohort in South Korea: effect modification by community deprivation, medical infrastructure, and greenness.

BACKGROUND: Long-term exposure to PM2.5 has been linked to increased mortality risk. However, limited studies have examined the potential modifying effect of community-level characteristics on this association, particularly in Asian contexts. This study aimed to estimate the effects of long-term exposure to PM2.5 on mortality in South Korea and to examine whether community-level deprivation, medical infrastructure, and greenness modify these associations. METHODS: We conducted a nationwide cohort study using the National Health Insurance Service-National Sample Cohort. A total of 394,701 participants aged 30 years or older in 2006 were followed until 2019. Based on modelled PM2.5 concentrations, 1 to 3-year and 5-year moving averages of PM2.5 concentrations were assigned to each participant at the district level. Time-varying Cox proportional-hazards models were used to estimate the association between PM2.5 and non-accidental, circulatory, and respiratory mortality. We further conducted stratified analysis by community-level deprivation index, medical index, and normalized difference vegetation index to represent greenness. RESULTS: PM2.5 exposure, based on 5-year moving averages, was positively associated with non-accidental (Hazard ratio, HR: 1.10, 95% Confidence Interval, CI: 1.01, 1.20, per 10 µg/m3 increase) and circulatory mortality (HR: 1.22, 95% CI: 1.01, 1.47). The 1-year moving average of PM2.5 was associated with respiratory mortality (HR: 1.33, 95% CI: 1.05, 1.67). We observed higher associations between PM2.5 and mortality in communities with higher deprivation and limited medical infrastructure. Communities with higher greenness showed lower risk for circulatory mortality but higher risk for respiratory mortality in association with PM2.5. CONCLUSIONS: Our study found mortality effects of long-term PM2.5 exposure and underlined the role of community-level factors in modifying these association. These findings highlight the importance of considering socio-environmental contexts in the design of air quality policies to reduce health disparities and enhance overall public health outcomes.

Mortality Associated with Priority Diseases in Flood-Affected Areas Using District Health Information System (DHIS2) During September-December 2022: Pakistan Experience.

OBJECTIVES: To quantify the burden of communicable diseases and characterize the most reported infections during public health emergency of floods in Pakistan. METHODS: The study's design is a descriptive trend analysis. The study utilized the disease data reported to District Health Information System (DHIS2) for the 12 most frequently reported priority diseases under the Integrated Disease Surveillance and Response (IDSR) system in Pakistan. RESULTS: In total, there were 1,532,963 suspected cases during August to December 2022 in flood-affected districts (n = 75) across Pakistan; Sindh Province reported the highest number of cases (n = 692,673) from 23 districts, followed by Khyber Pakhtunkhwa (KP) (n = 568,682) from 17 districts, Balochistan (n = 167,215) from 32 districts, and Punjab (n = 104,393) from 3 districts. High positivity was reported for malaria (79,622/201,901; 39.4%), followed by acute diarrhea (non-cholera) (23/62; 37.1%), hepatitis A and E (47/252; 18.7%), and dengue (603/3245; 18.6%). The crude mortality rate was 11.9 per 10 000 population (1824/1,532,963 [deaths/cases]). CONCLUSION: The study identified acute respiratory infection, acute diarrhea, malaria, and skin diseases as the most prevalent diseases. This suggests that preparedness efforts and interventions targeting these diseases should be prioritized in future flood response plans. The study highlights the importance of strengthening the IDSR as a Disease Early Warning System through the implementation of the DHIS2.

Research on social and economic factors influencing regional mortality patterns in China.

Regional population mortality correlates with regional socioeconomic development. This study aimed to identify the key socioeconomic factors influencing mortality patterns in Chinese provinces. Using data from the Seventh Population Census, we analyzed mortality patterns by gender and urban‒rural division in 31 provinces. Using a functional regression model, we assessed the influence of fourteen indicators on mortality patterns. Main findings: (1) China shows notable gender and urban‒rural mortality variations across age groups. Males generally have higher mortality than females, and rural areas experience elevated mortality rates compared to urban areas. Mortality in individuals younger than 40 years is influenced mainly by urban‒rural factors, with gender becoming more noticeable in the 40-84 age group. (2) The substantial marginal impact of socioeconomic factors on mortality patterns generally becomes evident after the age of 45, with less pronounced differences in their impact on early-life mortality patterns. (3) Various factors have age-specific impacts on mortality. Education has a negative effect on mortality in individuals aged 0-29, extending to those aged 30-59 and diminishing in older age groups. Urbanization positively influences the probability of death in individuals aged 45-54 years, while the impact of traffic accidents increases with age. Among elderly people, the effect of socioeconomic variables is smaller, highlighting the intricate and heterogeneous nature of these influences and acknowledging certain limitations.

[Is mortality an objective indicator for measuring the impact of an epidemic?: The example of Covid-19 in Switzerland]. / La mortalité est-elle un indicateur objectif pour mesurer l'impact d'une épidémie ?. L'exemple du Covid-19 en Suisse.

Measuring the health impact of an epidemic using appropriate indicators is necessarily complex. Mortality does not sum up all the issues, but at least it seems to be an objective indicator. There are, however, a number of different mortality indicators, which do not all convey the same message. During the Covid-19 epidemic in Switzerland, the mortality rate rose by 10.2% in 2020, while life expectancy fell by "only" 0.8%, or 8.3 months, a decline described as "modest" or "complete freefall" depending on when it was published. In reality, the population living in Switzerland in 2020 lost an average of "only" 2.4 days, as the epidemic did not last their entire lives. The use of such an indicator, in comparison with losses due to other factors, would enable us to better estimate the real impact of an epidemic.

Mesurer l'impact sanitaire d'une épidémie à l'aide d'indicateurs appropriés est forcément complexe. La mortalité ne résume pas tous les enjeux mais semble au moins être un indicateur objectif. Il existe cependant différents indicateurs de mortalité ne donnant pas tous le même message. Lors de l'épidémie de Covid-19 en Suisse, le taux de mortalité a augmenté de 10,2 % en 2020, alors que l'espérance de vie n'a diminué « que ¼ de 0,8 %, ou 8,3 mois, recul par ailleurs qualifié de « modeste ¼ ou de « chute libre ¼ selon quand il a été publié. En réalité, la population vivant en Suisse en 2020 n'a perdu en moyenne « que ¼ 2,4 jours car l'épidémie n'a pas duré toute sa vie. L'utilisation d'un tel indicateur, en comparaison avec les pertes dues à d'autres facteurs, permettrait une meilleure estimation de l'impact réel d'une épidémie.

Terminal trajectory of HbA1c for 10 years supports the HbA1c paradox: a longitudinal study using Health and Retirement Study data.

Objectives: We aimed to assess the potential time-varying associations between HbA1c and mortality, as well as the terminal trajectory of HbA1c in the elderly to reveal the underlying mechanisms. Design: The design is a longitudinal study using data from the Health and Retirement Study. Setting and participants: Data were from the Health and Retirement Study. A total of 10,408 participants aged ≥50 years with available HbA1c measurements at baseline (2006/2008) were included. Methods: Longitudinal HbA1c measured at 2010/2012 and 2014/2016 were collected. HbA1c values measured three times for their associations with all-cause mortality were assessed using Cox regression and restricted cubic splines. HbA1c terminal trajectories over 10 years before death were analyzed using linear mixed-effect models with a backward time scale. Results: Women constitute 59.6% of the participants with a mean age of 69 years, with 3,070 decedents during the follow-up (8.9 years). The mortality rate during follow-up was 29.5%. Increased mortality risk became insignificant for the highest quartile of HbA1c compared to the third quartile (aHR 1.148, 1.302, and 1.069 for a follow-up of 8.9, 6.5, and 3.2 years, respectively) with a shorter follow-up, while it became higher for the lowest quartile of HbA1c (aHR 0.986, 1.068, and 1.439 for a follow-up of 8.9, 6.5, and 3.2 years, respectively). Accordingly, for both decedents with and without diabetes, an initial increase in HbA1c was followed by an accelerating terminal decline starting 5-6 years before death. Conclusions and implications: The time-varying association between HbA1c and mortality mapped to the terminal trajectory in HbA1c. High and low HbA1c may have different clinical relationships with mortality. The HbA1c paradox may be partially explained by reverse causation, namely, early manifestation of death.

The association between disability and mortality: a mixed-methods study.

BACKGROUND: Globally, 1·3 billion people have a disability and are more likely to experience poor health than the general population. However, little is known about the mortality or life expectancy gaps experienced by people with disabilities. We aimed to undertake a systematic review and meta-analysis of the association between disability and mortality, compare these findings to the evidence on the association of impairment types and mortality, and model the estimated life expectancy gap experienced by people with disabilities. METHODS: We did a mixed-methods study, which included a systematic review and meta-analysis, umbrella review, and life expectancy modelling. For the systematic review and meta-analysis, we searched MEDLINE, Global Health, PsycINFO, and Embase for studies published in English between Jan 1, 2007, and June 7, 2023, investigating the association of mortality and disability. We included prospective and retrospective cohort studies and randomised controlled trials with a baseline assessment of disability and a longitudinal assessment of all-cause mortality or cause-specific mortality. Two reviewers independently assessed study eligibility, extracted the data, and assessed risk of bias. We did a random-effects meta-analysis to calculate a pooled estimate of the mortality rate ratio for people with disabilities compared with those without disabilities. We did an umbrella review of meta-analyses examining the association between different impairment types and mortality. We used life table modelling to translate the mortality rate ratio into an estimate of the life expectancy gap between people with disabilities and the general population. The systematic review and meta-analysis is registered with PROSPERO, CRD42023433374. FINDINGS: Our search identified 3731 articles, of which 42 studies were included in the systematic review. The meta-analysis included 31 studies. Pooled estimates showed that all-cause mortality was 2·24 times (95% CI 1·84-2·72) higher in people with disabilities than among people without disabilities, although heterogeneity between the studies was high (τ2=0·28, I2=100%). Modelling indicated a median gap in life expectancy of 13·8 years (95% CI 13·1-14·5) by disability status. Cause-specific mortality was also higher for people with disabilities, including for cancer, COVID-19, cardiovascular disease, and suicide. The umbrella review identified nine meta-analyses, which showed consistently elevated mortality rates among people with different impairment types. INTERPRETATION: Mortality inequities experienced by people with disabilities necessitate health system changes and efforts to address inclusion and the social determinants of health. FUNDING: National Institute for Health and Care Research, Rhodes Scholarship, Indonesia Endowment Funds for Education, Foreign, Commonwealth and Development Office (Programme for Evidence to Inform Disability Action), and the Arts and Humanities Research Council.

The long-term changing patterns of suicide mortality in China from 1987 to 2020: continuing urban-rural disparity.

BACKGROUND: Over the past three decades, China has experienced significant changes in urban-rural, gender, and age-specific suicide mortality patterns. This study aimed to investigate the long-term trends in suicide mortality in China from 1987 to 2020. METHODS: Suicide mortality data were obtained from China's National Health Commission. Joinpoint regression analysis was used to examine changes in trends and age-period-cohort modeling to estimate age, period, and cohort effects on suicide mortality from 1987 to 2020. Net drift, local drift, longitudinal age curves, and period relative risks were also calculated. RESULTS: Crude and age-standardized suicide mortality in China showed continuing downward trends from 1987 to 2020, with a more pronounced decrease in rural areas (net drift = -7.07%, p<0.01) compared to urban areas (net drift = -3.41%, p<0.01). The decline curve of urban areas could be divided into three substages. Period and cohort effects were more prominent in rural areas. Suicide risk was highest among individuals aged 20-24 and gradually increased after age 60. Females, particularly those of childbearing age, had higher suicide risk than males, with a reversal observed after age 50. This gender reversal showed distinct patterns in urban and rural areas, with a widening gap in urban areas and a relatively stable gap in rural areas. CONCLUSIONS: Suicide mortality in China has consistently declined over the past three decades. However, disparities in age, gender, and urban-rural settings persist, with new patterns emerging. Targeted suicide prevention programs are urgently needed for high-risk groups, including females of childbearing age and the elderly, and to address the slower decrease and reversing urban-rural gender trends.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities.

BACKGROUND: Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures. METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach. RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-µg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively. CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

Measuring population health using health expectancy estimates from morbidity and mortality databases.

The progressive incorporation of quality of life indicators in health planning meets a critical need: The evaluation of the performance of health services, which are under stress by multiple causes, but in particular by an ageing population. In general, national health plans rely on health expectancies obtained using the Sullivan method. The Sullivan health expectancy index combines age-specific mortality rates and age-specific prevalence of healthy life, obtained from health surveys. The objective of this work is to investigate an equivalent estimation, using available information from morbidity and mortality datasets. Mortality and morbidity information, corresponding to years 2016 and 2017, was obtained for the population of the county of Baix Empordà (Catalonia), N = 91,130. Anonymized individual information on diagnoses, procedures and pharmacy consumption contained in the individual clinical record (ICD and ATC codes), were classified into health states. Based on the observed health transitions and mortality, life expectancies by health state were obtained from a multistate microsimulation model. Healthy life expectancies at birth and 65 years for females and males were respectively HLE0female = 39.94, HLE0male = 42.87, HLE65female = 2.43, HLE65male = 2.17. These results differed considerably from the Sullivan equivalents, e.g., 8.25 years less for HLE65female, 9.26 less for HLE65male. Point estimates for global life expectancies at birth and 65 years of age: LE0female = 85.82, LE0male = 80.58, LE65female = 22.31, LE65male = 18.86. Health indicators can be efficiently obtained from multistate models based on mortality and morbidity information, without the use of health surveys. This alternative method could be used for monitoring populations in the context of health planning. Life Expectancy results were consistent with the standard government reports. Due to the different approximation to the concept of health (data-based versus self-perception), healthy life expectancies obtained from multistate micro simulation are consistently lower than those calculated with the standard Sullivan method.

Estimation of standardized real-time fatality rate for ongoing epidemics.

BACKGROUND: The fatality rate is a crucial metric for guiding public health policies during an ongoing epidemic. For COVID-19, the age structure of the confirmed cases changes over time, bringing a substantial impact on the real-time estimation of fatality. A 'spurious decrease' in fatality rate can be caused by a shift in confirmed cases towards younger ages even if the fatalities remain unchanged across different ages. METHODS: To address this issue, we propose a standardized real-time fatality rate estimator. A simulation study is conducted to evaluate the performance of the estimator. The proposed method is applied for real-time fatality rate estimation of COVID-19 in Germany from March 2020 to May 2022. FINDINGS: The simulation results suggest that the proposed estimator can provide an accurate trend of disease fatality in all cases, while the existing estimator may convey a misleading signal of the actual situation when the changes in temporal age distribution take place. The application to Germany data shows that there was an increment in the fatality rate at the implementation of the 'live with COVID' strategy. CONCLUSIONS: As many countries have chosen to coexist with the coronavirus, frequent examination of the fatality rate is of paramount importance.

Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index χ and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all χ > 0.1 except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all χ ≤ 0.001 except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both χ = 0.3182 for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.

Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021).

Since its emergence in December 2019, the COVID-19 pandemic has resulted in a significant increase in deaths worldwide. This article presents a detailed analysis of the mortality burden of the COVID-19 pandemic across 569 regions in 25 European countries. We produce age and sex-specific excess mortality and present our results using Age-Standardised Years of Life Lost in 2020 and 2021, as well as the cumulative impact over the two pandemic years. Employing a forecasting approach based on CP-splines that considers regional diversity and provides confidence intervals, we find notable losses in 362 regions in 2020 (440 regions in 2021). Conversely, only seven regions experienced gains in 2020 (four regions in 2021). We also estimate that eight regions suffered losses exceeding 20 years of life per 1000 population in 2020, whereas this number increased to 75 regions in 2021. The contiguity of the regions investigated in our study also reveals the changing geographical patterns of the pandemic. While the highest excess mortality values were concentrated in the early COVID-19 outbreak areas during the initial pandemic year, a clear East-West gradient appeared in 2021, with regions of Slovakia, Hungary, and Latvia experiencing the highest losses. This research underscores the importance of regional analyses for a nuanced comprehension of the pandemic's impact.

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.

BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. METHODS: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. RESULTS: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856-0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. CONCLUSION: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care.

Association between the stress-hyperglycemia ratio and all-cause mortality in community-dwelling populations: An analysis of the National Health and Nutrition Examination Survey (NHANES) 1999-2014.

BACKGROUND: Reportedly, the stress-hyperglycemia ratio (SHR) is closely associated with poor prognosis in patients with severe acute disease. However, the community-dwelling may also be in a state of stress due to environmental exposure. Our study aimed to explore the association between SHR and all-cause mortality in the community-dwelling population. METHODS: A total of 18 480 participants were included out of 82 091 from the NHANES 1999-2014 survey. The Kaplan-Meier survival analyses were used to assess the disparities in survival rates based on SHR, and the log-rank test was employed to investigate the distinctions between groups. The multivariate Cox regression analysis and restricted cubic spline (RCS) analysis were performed to assess the association of SHR with all-cause mortality. A subgroup analysis was also conducted. RESULTS: A total of 3188 deaths occurred during a median follow-up period of 11.0 (7.7; 15.4) years. The highest risk for all-cause mortality was observed when SHR≤ 0.843 or SHR ≥0.986 (log-rank p < .001). After adjusting for the confounding factors, compared with subjects in the second SHR quartile (Q2), participants in the highest (Q4, adjusted hazard ratio [HR] 1.49, 95% confidence interval [CI] 1.28-1.73) and lowest quartiles (Q1, adjusted HR 1.37, 95% CI 1.16-1.60) have a higher probability of all-cause death. The RCS observed a dose-response U-shaped association between SHR and all-cause mortality. The U-shaped association between SHR and all-cause mortality was similar across subgroup analysis. CONCLUSIONS: The SHR was significantly associated with all-cause mortality in the community-dwelling population, and the relationship was U-shaped.

Burden of disease scenarios for 204 countries and territories, 2022-2050: a forecasting analysis for the Global Burden of Disease Study 2021.

BACKGROUND: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. METHODS: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. FINDINGS: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8-63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0-45·0] in 2050) and south Asia (31·7% [29·2-34·1] to 15·5% [13·7-17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4-40·3) to 41·1% (33·9-48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6-25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5-43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5-17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7-11·3) in the high-income super-region to 23·9% (20·7-27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5-6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2-26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [-0·6 to 3·6]). INTERPRETATION: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. FUNDING: Bill & Melinda Gates Foundation.