[Pollution attributable mortality]. / Mortalité attribuable à la pollution.

POLLUTION ATTRIBUTABLE MORTALITY. Pollution is estimated to be responsible for 9 million premature deaths per year in the world. For each cause of death with a risk increased by a pollutant, the number of deaths attributable to it is computed by comparison with the number of deaths expected under a reference pollution level, which is 10 µg/m3 for ambient particulate matter pollution. Only 8% of the deaths attributable to pollution occur in high income countries, because of the large effects of water and indoor air pollution (caused by traditional cooking methods) in low and middle-income countries. In France, by this method, one estimates that 13.200 deaths a year are attributable to ambient particulate matter pollution and 1.100 to ozone. Santé publique France, which has concluded that 48.000 deaths a year were attributable to air pollution in France, overvalues the risk by a factor of nearly 4 by overestimating the risks associated with air pollution and taking a utopian reference scenario.

MORTALITÉ ATTRIBUABLE À LA POLLUTION. On estime que la pollution est responsable de 9 millions de décès prématurés par an dans le monde. Pour chaque cause de décès dont le risque est augmenté par la pollution, un nombre de décès attribuable à la pollution est calculé par comparaison avec le nombre attendu pour un niveau de pollution de référence qui est de 10 µg/m3 pour la pollution particulaire de l'air extérieur. Seulement 8 % des décès attribuables à la pollution surviennent dans les pays à revenu élevé (effets importants des pollutions de l'eau et de l'air intérieur par des modes de cuisson traditionnels dans les pays à revenus bas ou moyens). En France, par cette méthode, on estime que 13 200 décès par an sont liés à la pollution particulaire de l'air extérieur et 1 100 à l'ozone. Santé publique France, qui conclut que 48 000 décès par an sont attribuables à la pollution de l'air en France, surévalue donc le risque d'un facteur proche de 4 en surestimant l'effet de la pollution et en prenant une pollution de référence utopique.

Body Roundness Index and All-Cause Mortality Among US Adults.

Importance: Obesity, especially visceral obesity, is an established risk factor associated with all-cause mortality. However, the inadequacy of conventional anthropometric measures in assessing fat distribution necessitates a more comprehensive indicator, body roundness index (BRI), to decipher its population-based characteristics and potential association with mortality risk. Objective: To evaluate the temporal trends of BRI among US noninstitutionalized civilian residents and explore its association with all-cause mortality. Design, Setting, and Participants: For this cohort study, information on a nationally representative cohort of 32 995 US adults (age ≥20 years) was extracted from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 and NHANES Linked Mortality File, with mortality ascertained through December 31, 2019. Data were analyzed between April 1 and September 30, 2023. Exposures: Biennial weighted percentage changes in BRI were calculated. Restricted cubic spline curve was used to determine optimal cutoff points for BRI. Main Outcome and Measures: The survival outcome was all-cause mortality. Mortality data were obtained from the Centers for Disease Control and Prevention website and linked to the NHANES database using the unique subject identifier. Weibull regression model was adopted to quantify the association between BRI and all-cause mortality. Results: Among 32 995 US adults, the mean (SD) age was 46.74 (16.92) years, and 16 529 (50.10%) were women. Mean BRI increased gradually from 4.80 (95% CI, 4.62-4.97) to 5.62 (95% CI, 5.37-5.86) from 1999 through 2018, with a biennial change of 0.95% (95% CI, 0.80%-1.09%; P < .001), and this increasing trend was more obvious among women, elderly individuals, and individuals who identified as Mexican American. After a median (IQR) follow-up of 9.98 (5.33-14.33) years, 3452 deaths (10.46% of participants) from all causes occurred. There was a U-shaped association between BRI and all-cause mortality, with the risk increased by 25% (hazard ratio, 1.25; 95% CI, 1.05-1.47) for adults with BRI less than 3.4 and by 49% (hazard ratio, 1.49; 95% CI, 1.31-1.70) for those with BRI of 6.9 or greater compared with the middle quintile of BRI of 4.5 to 5.5 after full adjustment. Conclusions and Relevance: This national cohort study found an increasing trend of BRI during nearly 20-year period among US adults, and importantly, a U-shaped association between BRI and all-cause mortality. These findings provide evidence for proposing BRI as a noninvasive screening tool for mortality risk estimation, an innovative concept that could be incorporated into public health practice pending consistent validation in other independent cohorts.

Time trends in the mortality of testicular cancer across the BRICS: an age-period-cohort analysis for the GBD 2019.

Testicular cancer (TCa) is a rare but impactful malignancy that primarily affects young men. Understanding the mortality rate of TCa is crucial for improving prevention and treatment strategies to reduce the risk of death among patients. We obtained TCa mortality data by place (5 countries), age (20-79 years), and year (1990-2019) from the Global Burden of Disease Study 2019. Age-period-cohort model was used to estimate the net drift, local drift, age effects, period and cohort effects. In 2019, the global mortality of TCa increased to 10842 (95% UI 9961, 11902), with an increase of 50.08% compared to 1990.The all-age mortality rate for TCa in 2019 increased from 0.17/100,000 (95% UI 0.13, 0.20) in China to 0.48/100,000 (95% UI 0.38, 0.59) in Russian Federation, whereas the age-standardized mortality rate in 2019 was highest in the South Africa 0.47/100,000 (95% UI 0.42, 0.53) and lowest in the China 0.16/100,000 (95% UI 0.13, 0.19). China's aging population shifts mortality patterns towards the elderly, while in Russian Federation, young individuals are primarily affected by the distribution of deaths. To address divergent TCa mortality advancements in BRICS countries, we propose a contextually adaptive and resource-conscious approach to prioritize TCa prevention. Tailoring strategies to contextual diversity, including policy frameworks, human resources, and financial capacities, will enhance targeted interventions and effectiveness in reducing TCa mortality.

U.S. Army mortality surveillance in active duty soldiers, 2014-2019.

Mortality surveillance is an important activity for capturing information on a population's health. This retrospective surveillance analysis utilizes administrative data sources to describe active duty U.S. Army soldiers who died from 2014 to 2019, and calculate mortality rates, assess trends by category of death, and identify leading causes of death within subpopulations. During the surveillance period, 2,530 soldier deaths were reported. The highest crude mortality rates observed during the 6-year surveillance period were for deaths by suicide, followed by accidental (i.e., unintentional injury) deaths. The crude mortality rates for natural deaths decreased significantly over the 6-year period, by an average of 6% annually. The leading causes of death were suicide by gunshot wound, motor vehicle accidents, suicide by hanging, neoplasms, and cardiovascular events. Significant differences were observed in the leading causes of death in relation to demographic characteristics, which has important implications for the development of focused educational campaigns to improve health behaviors and safe driving habits. Current public health programs to prevent suicide should be evaluated, with new approaches for firearm safety considered.

Depressive symptoms and all-cause mortality among middle-aged and older people in China and associations with chronic diseases.

Introduction: It remains unclear whether depressive symptoms are associated with increased all-cause mortality and to what extent depressive symptoms are associated with chronic disease and all-cause mortality. The study aims to explore the relationship between depressive symptoms and all-cause mortality, and how depressive symptoms may, in turn, affect all-cause mortality among Chinese middle-aged and older people through chronic diseases. Methods: Data were collected from the China Health and Retirement Longitudinal Study (CHARLS). This cohort study involved 13,855 individuals from Wave 1 (2011) to Wave 6 (2020) of the CHARLS, which is a nationally representative survey that collects information from Chinese residents ages 45 and older to explore intrinsic mechanisms between depressive symptoms and all-cause mortality. The Center for Epidemiological Studies Depression Scale (CES-D-10) was validated through the CHARLS. Covariates included socioeconomic variables, living habits, and self-reported history of chronic diseases. Kaplan-Meier curves depicted mortality rates by depressive symptom levels, with Cox proportional hazards regression models estimating the hazard ratios (HRs) of all-cause mortality. Results: Out of the total 13,855 participants included, the median (Q1, Q3) age was 58.00 (51.00, 63.00) years. Adjusted for all covariates, middle-aged and older adults with depressive symptoms had a higher all-cause mortality rate (HR = 1.20 [95% CI, 1.09-1.33]). An increased rate was observed for 55-64 years old (HR = 1.23 [95% CI, 1.03-1.47]) and more than 65 years old (HR = 1.32 [95% CI, 1.18-1.49]), agricultural Hukou (HR = 1.44, [95% CI, 1.30-1.59]), and nonagricultural workload (HR = 1.81 [95% CI, 1.61-2.03]). Depressive symptoms increased the risks of all-cause mortality among patients with hypertension (HR = 1.19 [95% CI, 1.00-1.40]), diabetes (HR = 1.41[95% CI, 1.02-1.95]), and arthritis (HR = 1.29 [95% CI, 1.09-1.51]). Conclusion: Depressive symptoms raise all-cause mortality risk, particularly in those aged 55 and above, rural household registration (agricultural Hukou), nonagricultural workers, and middle-aged and older people with hypertension, diabetes, and arthritis. Our findings through the longitudinal data collected in this study offer valuable insights for interventions targeting depression, such as early detection, integrated chronic disease care management, and healthy lifestyles; and community support for depressive symptoms may help to reduce mortality in middle-aged and older people.

Mortality attributable to PM2.5 from wildland fires in California from 2008 to 2018.

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM2.5), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM2.5 estimates. We used a concentration-response function for PM2.5, applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.

Background: Prognostic risk stratification in older adults with type 2 diabetes (T2D) is important for guiding decisions concerning advance care planning. Materials and methods: A retrospective longitudinal study was conducted in a real-world sample of older diabetic patients afferent to the outpatient facilities of the Diabetology Unit of the IRCCS INRCA Hospital of Ancona (Italy). A total of 1,001 T2D patients aged more than 70 years were consecutively evaluated by a multidimensional geriatric assessment, including physical performance evaluated using the Short Physical Performance Battery (SPPB). The mortality was assessed during a 5-year follow-up. We used the automatic machine-learning (AutoML) JADBio platform to identify parsimonious mathematical models for risk stratification. Results: Of 977 subjects included in the T2D cohort, the mean age was 76.5 (SD: 4.5) years and 454 (46.5%) were men. The mean follow-up time was 53.3 (SD:15.8) months, and 209 (21.4%) patients died by the end of the follow-up. The JADBio AutoML final model included age, sex, SPPB, chronic kidney disease, myocardial ischemia, peripheral artery disease, neuropathy, and myocardial infarction. The bootstrap-corrected concordance index (c-index) for the final model was 0.726 (95% CI: 0.687-0.763) with SPPB ranked as the most important predictor. Based on the penalized Cox regression model, the risk of death per unit of time for a subject with an SPPB score lower than five points was 3.35 times that for a subject with a score higher than eight points (P-value <0.001). Conclusion: Assessment of physical performance needs to be implemented in clinical practice for risk stratification of T2D older patients.

[Tuberculosis mortality trends and the correlation with the socio-demographic index in Brazil between 2010 and 2019]. / Tendência da mortalidade por tuberculose e relação com o índice sóciodemográfico no Brasil entre 2005-2019.

The scope of this article is to analyze the trend of the standardized mortality rate (SMR) for tuberculosis and its correlation with the developmental status in Brazil. An ecological time series study was conducted to analyze data of deaths from tuberculosis reported between 2005 and 2019 in all states. Data were extracted from the Mortality Information System, the Brazilian Institute of Geography and Statistics, and the Global Burden of Disease study. The temporal trend was analyzed using Prais-Winsten regression. Spearman's correlation analysis between SMR and Socio-Demographic Index (SDI) was also performed. From 2005 to 2019, 68,879 deaths from tuberculosis were recorded in Brazil. The average mortality rate was 2.3 deaths per 100,000 inhabitants. The decreasing trend of SMR due to tuberculosis was observed in Brazil and in all regions. There was a significant negative correlation between SDI and TMP. TMP due to tuberculosis revealed a decreasing trend in Brazil and in all regions. Most states showed a decreasing trend and none of them had an increasing trend. An inverse relationship was found between developmental status and mortality due to tuberculosis.

O objetivo do artigo é analisar a tendência da taxa de mortalidade padronizada (TMP) por tuberculose e sua correlação com o status de desenvolvimento no Brasil. Estudo ecológico de séries temporais que analisou dados de óbitos por tuberculose notificados entre 2005 e 2019 de todos os estados. Os dados foram extraídos do Sistema de Informação sobre Mortalidade, do Instituto Brasileiro de Geografia e Estatística e do estudo da Carga Global de Doenças. A tendência temporal foi analisada pela regressão de Prais-Winsten. A análise da correlação de Spearman entre a TMP e o índice sociodemográfico (socio-demographic index - SDI) também foi realizada. De 2005 a 2019, foram registrados 68.879 óbitos por tuberculose no Brasil. A taxa média de mortalidade foi de 2,3 óbitos por 100.000 habitantes. A tendência decrescente da TMP por tuberculose foi observada no Brasil e em todas as regiões. Verificou-se correlação negativa significativa entre o SDI e a TMP. A maioria dos estados apresentou tendência decrescente e nenhum deles teve tendência crescente. Uma relação inversa foi verificada entre o SDI e a mortalidade por tuberculose.

Age at death during the Covid-19 lockdown in French metropolitan regions: a non parametric quantile regression approach.

BACKGROUND: Lockdowns have been implemented to limit the number of hospitalisations and deaths during the first wave of 2019 coronavirus disease. These measures may have affected differently death characteristics, such age and sex. France was one of the hardest hit countries in Europe with a decreasing east-west gradient in excess mortality. This study aimed at describing the evolution of age at death quantiles during the lockdown in spring 2020 (17 March-11 May 2020) in the French metropolitan regions focusing on 3 representatives of the epidemic variations in the country: Bretagne, Ile-de-France (IDF) and Bourgogne-Franche-Comté (BFC). METHODS: Data were extracted from the French public mortality database from 1 January 2011 to 31 August 2020. The age distribution of mortality observed during the lockdown period (based on each decile, plus quantiles 1, 5, 95 and 99) was compared with the expected one using Bayesian non-parametric quantile regression. RESULTS: During the lockdown, 5457, 5917 and 22 346 deaths were reported in Bretagne, BFC and IDF, respectively. An excess mortality from + 3% in Bretagne to + 102% in IDF was observed during lockdown compared to the 3 previous years. Lockdown led to an important increase in the first quantiles of age at death, irrespective of the region, while the increase was more gradual for older age groups. It corresponded to fewer young people, mainly males, dying during the lockdown, with an increase in the age at death in the first quantile of about 7 years across regions. In females, a less significant shift in the first quantiles and a greater heterogeneity between regions were shown. A greater shift was observed in eastern region and IDF, which may also represent excess mortality among the elderly. CONCLUSIONS: This study focused on the innovative outcome of the age distribution at death. It shows the first quantiles of age at death increased differentially according to sex during the lockdown period, overall shift seems to depend on prior epidemic intensity before lockdown and complements studies on excess mortality during lockdowns.

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.

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.

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.

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.

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.