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.

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.

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.

Health effect of public sports services and public health services: empirical evidence from China.

There is no clear explanation for the extraordinary rebound in China's population mortality over the past decade. This paper utilizes panel data from 31 Chinese provinces from 2010 to 2020 to determine the distinct impacts of public sports services (PSS), public health services (PMS), and their interaction on population mortality. Empirical results show that public sports services significantly reduce mortality. Every unit increase in public sports services reduces mortality by about 2.3%. It is characterized by delayed realization. Public health services were surprisingly associated with a rebound in mortality. Further studies found strong health effect from interaction of public sports and health services. The effect was significantly strengthened in areas with fewer extreme temperatures or developed economy. The findings have important policy implications for the high-quality development of public sports and health services. It also emphasizes integration of sports and medicine and mitigates health risks associated with extreme temperatures.

Mortality trends and sociodemographic factors associated with early death in sickle cell disease patients in the state of São Paulo.

OBJECTIVE: To estimate trends in mortality rate and average age of death, and identify sociodemographic factors associated with early death in patients with sickle cell disease (SCD). METHODS: An ecological and cross-sectional study was conducted using data from the Mortality Information System. All deaths of patients residing in the state of São Paulo from 1996 to 2015 with at least one International Disease Code for SCD in any field of the death certificate were included. Simple linear regression was used to estimate trends. The Log-rank test and multiple Cox regression were used to identify factors associated with early death. RESULTS: The age-standardized mortality rate per million inhabitants increased by 0.080 per year (R2=0.761; p<0.001). When the events were stratified by age at death, the increase was 0.108 per year for those occurring at age 20 years or older, (R2=0.789; p<0.001) and 0.023 per year for those occurring before age 20 years old (R2=0.188; p=0.056). The average age at death increased by 0.617 years (7.4 months) per year (R2=0.835; p<0.001). Sociodemographic factors associated with early death identified were male gender (hazard ratio - HR=1.30), white race (HR=1.16), death occurring in the hospital (HR=1.29), and living in the Greater São Paulo (HR=1.13). CONCLUSIONS: The mortality rate and the average age of death in patients with SCD have increased over the last two decades. Sociodemographic factors such as gender, race, place of occurrence, and residence were found to be associated with early death.

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.

Adherence with statins and all-cause mortality in days with high temperature.

PURPOSE: It has been suggested that statins may exert thermo-protective effects that can reduce mortality on hot days. We aimed to examine the relationship between statin adherence and mortality in days with high temperature. METHODS: Utilizing data from a prior historical new-user cohort study, we analyzed a cohort of 229 918 individuals within a state-mandated health provider in Israel who initiated statin therapy between 1998 and 2006. Adherence to statins was assessed through the mean proportion of days covered (PDC) with statins during the follow-up period. The study's primary outcome was all-cause mortality during hot days. RESULTS: During the study follow-up period, a total of 13 165 individuals (5.7%) died. In a multivariable model, a 10% increase in PDC with statins was associated with an HR of (0.85; 95% CI: 0.72-1.00) for deaths (n = 16) in extremely hot days (≥39°C). This association was numerically stronger compared to HR = 0.94 (0.93-0.94) in cooler days and displayed a significant difference between sexes. In males, the fully-adjusted HR for a 10% increase in PDC with statins was 0.66 (0.45-0.95), while in women, it was 0.98 (0.78-1.23). In contrast, no such effect modification was observed for death in cooler days. CONCLUSIONS: These findings align with earlier research, supporting the notion that adherence with statin treatment may be associated with a reduced risk of death during extremely hot days, particularly among men.

[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.

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.

Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province.

Objectives: The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods: This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30-79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results: The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20-1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19-3.19) and 28.19 (95% CI = 14.85-53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22-15.31). Conclusion: The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions.

Deciphering mortality risk of diabetes medications in heart failure patients with diabetes mellitus under triple guideline-directed medical therapy.

BACKGROUND: Scientific evidence regarding the impact of different combinations of diabetes medications in heart failure patients with diabetes mellitus (HFwDM) remains limited. AIM: We aimed to investigate the effect of monotherapy and combination therapy for DM on all-cause mortality in HFwDM under triple guideline-directed medical therapy (GDMT). METHOD: This nationwide retrospective cohort study included adult HFwDM under triple GDMT between January 1, 2016 and December 31, 2022.We collected the data from the National Electronic Database of the Turkish Ministry of Health.We created various combination including different diabetes medications based on the current guidelines for DM.The primary endpoint was all-cause mortality. RESULTS: A total of 321,525 HFwDM under triple GDMT (female:49%, median age:68[61-75] years) were included. The highest rate of prescribed combination therapy was metformin and sulfonylureas (n = 55,266). In Cox regression analysis, insülin monotherapy had the highest risk for all-cause mortality (HR:2.25, 95CI%:2.06 - 2.45), whereas combination therapy including metformin, SGLT2i, and sulfonylureas provided the most beneficial effect on survival (HR:0.29, 95CI%:0.22-0.39) when compared to patients not receiving diabetes medication. Among patients taking diabetes medications, the inclusion of SGLT2i demonstrated a survival benefit (p < 0.05), despite concurrent use of volume-retaining medications such as insulin and thiazolidinediones. Conversely, combinations of diabetes medications without SGLT2i did not demonstrate any survival benefit compared to patients not taking diabetes medication (p > 0.05). CONCLUSION: This study underscored the use of SGLT2i as monotherapy or as a part of combination diabetes medications to improve survival among HFwDM, while also highlighting that combinations lacking SGLT2i did not confer any survival benefit.

Estimation of cause-specific mortality in Rakai, Uganda, using verbal autopsy 1999-2019.

BACKGROUND: There are scant data on the causes of adult deaths in sub-Saharan Africa. We estimated the level and trends in adult mortality, overall and by different causes, in rural Rakai, Uganda, by age, sex, and HIV status. OBJECTIVES: To estimate and analyse adult cause-specific mortality trends in Rakai, Uganda. METHODOLOGY: Mortality information by cause, age, sex, and HIV status was recorded in the Rakai Community Cohort study using verbal autopsy interviews, HIV serosurveys, and residency data. We estimated the average number of years lived in adulthood. Using demographic decomposition methods, we estimated the contribution of each cause of death to adult mortality based on the average number of years lived in adulthood. RESULTS: Between 1999 and 2019, 63082 adults (15-60 years) were censused, with 1670 deaths registered. Of these, 1656 (99.2%) had completed cause of death data from verbal autopsy. The crude adult death rate was 5.60 (95% confidence interval (CI): 5.33-5.87) per 1000 person-years of observation (pyo). The crude death rate decreased from 11.41 (95% CI: 10.61-12.28) to 3.27 (95% CI: 2.89-3.68) per 1000 pyo between 1999-2004 and 2015-2019. The average number of years lived in adulthood increased in people living with HIV and decreased in HIV-negative individuals between 2000 and 2019. Communicable diseases, primarily HIV and Malaria, had the biggest decreases, which improved the average number of years lived by approximately extra 12 years of life in females and 6 years in males. There were increases in deaths due to non-communicable diseases and external causes, which reduced the average number of years lived in adulthood by 2.0 years and 1.5 years in females and males, respectively. CONCLUSION: There has been a significant decline in overall mortality from 1999 to 2019, with the greatest decline seen in people living with HIV since the availability of antiretroviral therapy in 2004. By 2020, the predominant causes of death among females were non-communicable diseases, with external causes of death dominating in males.

Main findings: There are significant declines in mortality in people living with HIV. However, mortality in HIV-negative people increased due to non-communicable diseases in females, and injuries and external causes of death among males.Added knowledge: In this HIV-endemic area, decreasing adult mortality has been documented over the last 20 years. This paper benchmarks the changes in cause-specific mortality in this area.Global health impact for policy action: As in many African countries, more effort is needed to reduce mortality for non-communicable diseases, injuries, and external causes of death as these seem to have been neglected.

Projected estimates of cancer in Canada in 2024.

BACKGROUND: Cancer surveillance data are essential to help understand where gaps exist and progress is being made in cancer control. We sought to summarize the expected impact of cancer in Canada in 2024, with projections of new cancer cases and deaths from cancer by sex and province or territory for all ages combined. METHODS: We obtained data on new cancer cases (i.e., incidence, 1984-2019) and deaths from cancer (i.e., mortality, 1984-2020) from the Canadian Cancer Registry and Canadian Vital Statistics Death Database, respectively. We projected cancer incidence and mortality counts and rates to 2024 for 23 types of cancer, overall, by sex, and by province or territory. We calculated age-standardized rates using data from the 2011 Canadian standard population. RESULTS: In 2024, the number of new cancer cases and deaths from cancer are expected to reach 247 100 and 88 100, respectively. The age-standardized incidence rate (ASIR) and mortality rate (ASMR) are projected to decrease slightly from previous years for both males and females, with higher rates among males (ASIR 562.2 per 100 000 and ASMR 209.6 per 100 000 among males; ASIR 495.9 per 100 000 and ASMR 152.8 per 100 000 among females). The ASIRs and ASMRs of several common cancers are projected to continue to decrease (i.e., lung, colorectal, and prostate cancer), while those of several others are projected to increase (i.e., liver and intrahepatic bile duct cancer, kidney cancer, melanoma, and non-Hodgkin lymphoma). INTERPRETATION: Although the overall incidence of cancer and associated mortality are declining, new cases and deaths in Canada are expected to increase in 2024, largely because of the growing and aging population. Efforts in prevention, screening, and treatment have reduced the impact of some cancers, but these short-term projections highlight the potential effect of cancer on people and health care systems in Canada.

Loneliness, Dementia Status, and Their Association with All-Cause Mortality Among Older US Adults.

Background: Loneliness, dementia, and mortality are interconnected. Objective: We aimed at understanding mediating pathways and interactions between loneliness and dementia in relation to mortality risk. Methods: The study tested bi-directional relationships between dementia, loneliness, and mortality, by examining both interactions and mediating effects in a large sample of older US adults participating in the nationally representative Health and Retirement Study. Out of≤6,468 older participants selected in 2010, with mean baseline age of 78.3 years and a follow-up time up to the end of 2020, 3,298 died at a rate of 64 per 1,000 person-years (P-Y). Cox proportional hazards and four-way decomposition models were used. Results: Algorithmically defined dementia status (yes versus no) was consistently linked with a more than two-fold increase in mortality risk. Dementia status and Ln(odds of dementia) were strongly related with mortality risk across tertiles of loneliness score. Loneliness z-score was also linked to an elevated risk of all-cause mortality regardless of age, sex, or race or ethnicity, and its total effect (TE) on mortality was partially mediated by Ln(odds of dementia), z-scored, (≤40% of the TE was a pure indirect effect). Conversely, a small proportion (<5%) of the TE of Ln(odds of dementia), z-scored, on mortality risk was explained by the loneliness z-score. Conclusions: In sum, dementia was positively associated with all-cause mortality risk, in similar fashion across loneliness score tertiles, while loneliness was associated with mortality risk. TE of loneliness on mortality risk was partially mediated by dementia odds in reduced models.