Excess natural-cause mortality in US counties and its association with reported COVID-19 deaths.

In the United States, estimates of excess deaths attributable to the COVID-19 pandemic have consistently surpassed reported COVID-19 death counts. Excess deaths reported to non-COVID-19 natural causes may represent unrecognized COVID-19 deaths, deaths caused by pandemic health care interruptions, and/or deaths from the pandemic's socioeconomic impacts. The geographic and temporal distribution of these deaths may help to evaluate which explanation is most plausible. We developed a Bayesian hierarchical model to produce monthly estimates of excess natural-cause mortality for US counties over the first 30 mo of the pandemic. From March 2020 through August 2022, 1,194,610 excess natural-cause deaths occurred nationally [90% PI (Posterior Interval): 1,046,000 to 1,340,204]. A total of 162,886 of these excess natural-cause deaths (90% PI: 14,276 to 308,480) were not reported to COVID-19. Overall, 15.8 excess deaths were reported to non-COVID-19 natural causes for every 100 reported COVID-19 deaths. This number was greater in nonmetropolitan counties (36.0 deaths), the West (Rocky Mountain states: 31.6 deaths; Pacific states: 25.5 deaths), and the South (East South Central states: 26.0 deaths; South Atlantic states: 25.0 deaths; West South Central states: 24.2 deaths). In contrast, reported COVID-19 death counts surpassed estimates of excess natural-cause deaths in metropolitan counties in the New England and Middle Atlantic states. Increases in reported COVID-19 deaths correlated temporally with increases in excess deaths reported to non-COVID-19 natural causes in the same and/or prior month. This suggests that many excess deaths reported to non-COVID-19 natural causes during the first 30 mo of the pandemic in the United States were unrecognized COVID-19 deaths.

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021.

BACKGROUND: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING: Bill & Melinda Gates Foundation.

Rates and causes of death after release from incarceration among 1 471 526 people in eight high-income and middle-income countries: an individual participant data meta-analysis.

BACKGROUND: Formerly incarcerated people have exceptionally poor health profiles and are at increased risk of preventable mortality when compared with their general population peers. However, not enough is known about the epidemiology of mortality in this population-specifically the rates, causes, and timing of death in specific subgroups and regions-to inform the development of targeted, evidence-based responses. We aimed to document the incidence, timing, causes, and risk factors for mortality after release from incarceration. METHODS: We analysed linked administrative data from the multi-national Mortality After Release from Incarceration Consortium (MARIC) study. We examined mortality outcomes for 1 471 526 people released from incarceration in eight countries (Australia, Brazil, Canada, New Zealand, Norway, Scotland, Sweden, and the USA) from 1980 to 2018, across 10 534 441 person-years of follow-up (range 0-24 years per person). We combined data from 18 cohort studies using two-step individual participant data meta-analyses to estimate pooled all-cause and cause-specific crude mortality rates (CMRs) per 100 000 person-years, for specific time periods (first, daily from days 1-14; second, weekly from weeks 3-12; third, weeks 13-52 combined; fourth, weeks 53 and over combined; and fifth, total follow-up) after release, overall and stratified by age, sex, and region. FINDINGS: 75 427 deaths were recorded. The all-cause CMR during the first week following release (1612 [95% CI 1048-2287]) was higher than during all other time periods (incidence rate ratio [IRR] compared with week 2: 1·5 [95% CI 1·2-1·8], I2=26·0%, weeks 3-4: 2·0 [1·5-2·6], I2=53·0%, and weeks 9-12: 2·2 [1·6-3·0], I2=70·5%). The highest cause-specific mortality rates during the first week were due to alcohol and other drug poisoning (CMR 657 [95% CI 332-1076]), suicide (135 [36-277]), and cardiovascular disease (71 [16-153]). We observed considerable variation in cause-specific CMRs over time since release and across regions. Pooled all-cause CMRs were similar between males (731 [95% CI 630-839]) and females (660 [560-767]) and were higher in older age groups. INTERPRETATION: The markedly elevated rate of death in the first week post-release underscores an urgent need for investment in evidence-based, coordinated transitional healthcare, including treatment for mental illness and substance use disorders to prevent post-release deaths due to suicide and overdose. Temporal variations in rates and causes of death highlight the need for routine monitoring of post-release mortality. FUNDING: Australia's National Health and Medical Research Council.

Incidence and Prediction of Unrelated Mortality After Successful Endoscopic Eradication Therapy for Barrett's Neoplasia.

BACKGROUND & AIMS: Follow-up (FU) strategies after endoscopic eradication therapy (EET) for Barrett's neoplasia do not consider the risk of mortality from causes other than esophageal adenocarcinoma (EAC). We aimed to evaluate this risk during long-term FU, and to assess whether the Charlson Comorbidity Index (CCI) can predict mortality. METHODS: We included all patients with successful EET from the nationwide Barrett registry in the Netherlands. Data were merged with National Statistics for accurate mortality data. We evaluated annual mortality rates (AMRs, per 1000 person-years) and standardized mortality ratio for other-cause mortality. Performance of the CCI was evaluated by discrimination and calibration. RESULTS: We included 1154 patients with a mean age of 64 years (±9). During median 59 months (p25-p75 37-91; total 6375 person-years), 154 patients (13%) died from other causes than EAC (AMR, 24.1; 95% CI, 20.5-28.2), most commonly non-EAC cancers (n = 58), cardiovascular (n = 31), or pulmonary diseases (n = 26). Four patients died from recurrent EAC (AMR, 0.5; 95% CI, 0.1-1.4). Compared with the general Dutch population, mortality was significantly increased for patients in the lowest 3 age quartiles (ie, age <71 years). Validation of CCI in our population showed good discrimination (Concordance statistic, 0.78; 95% CI, 0.72-0.84) and fair calibration. CONCLUSION: The other-cause mortality risk after successful EET was more than 40 times higher (48; 95% CI, 15-99) than the risk of EAC-related mortality. Our findings reveal that younger post-EET patients exhibit a significantly reduced life expectancy when compared with the general population. Furthermore, they emphasize the strong predictive ability of CCI for long-term mortality after EET. This straightforward scoring system can inform decisions regarding personalized FU, including appropriate cessation timing. (NL7039).

In MI with preserved LVEF, long-term ß-blocker use vs. no use did not reduce all-cause death or MI at 3.5 y.

SOURCE CITATION: Yndigegn T, Lindahl B, Mars K, et al; REDUCE-AMI Investigators. Beta-blockers after myocardial infarction and preserved ejection fraction. N Engl J Med. 2024;390:1372-1381. 38587241.

In adults with AMI who are at risk for HF, empagliflozin did not reduce a composite of first HF hospitalization or all-cause death at 18 mo.

SOURCE CITATION: Butler J, Jones WS, Udell JA, et al. Empagliflozin after acute myocardial infarction. N Engl J Med. 2024;390:1455-1466. 38587237.

Associations of Testosterone and Related Hormones With All-Cause and Cardiovascular Mortality and Incident Cardiovascular Disease in Men : Individual Participant Data Meta-analyses.

BACKGROUND: Whether circulating sex hormones modulate mortality and cardiovascular disease (CVD) risk in aging men is controversial. PURPOSE: To clarify associations of sex hormones with these outcomes. DATA SOURCES: Systematic literature review to July 2019, with bridge searches to March 2024. STUDY SELECTION: Prospective cohort studies of community-dwelling men with sex steroids measured using mass spectrometry and at least 5 years of follow-up. DATA EXTRACTION: Independent variables were testosterone, sex hormone-binding globulin (SHBG), luteinizing hormone (LH), dihydrotestosterone (DHT), and estradiol concentrations. Primary outcomes were all-cause mortality, CVD death, and incident CVD events. Covariates included age, body mass index, marital status, alcohol consumption, smoking, physical activity, hypertension, diabetes, creatinine concentration, ratio of total to high-density lipoprotein cholesterol, and lipid medication use. DATA SYNTHESIS: Nine studies provided individual participant data (IPD) (255 830 participant-years). Eleven studies provided summary estimates (n = 24 109). Two-stage random-effects IPD meta-analyses found that men with baseline testosterone concentrations below 7.4 nmol/L (<213 ng/dL), LH concentrations above 10 IU/L, or estradiol concentrations below 5.1 pmol/L had higher all-cause mortality, and those with testosterone concentrations below 5.3 nmol/L (<153 ng/dL) had higher CVD mortality risk. Lower SHBG concentration was associated with lower all-cause mortality (median for quintile 1 [Q1] vs. Q5, 20.6 vs. 68.3 nmol/L; adjusted hazard ratio [HR], 0.85 [95% CI, 0.77 to 0.95]) and lower CVD mortality (adjusted HR, 0.81 [CI, 0.65 to 1.00]). Men with lower baseline DHT concentrations had higher risk for all-cause mortality (median for Q1 vs. Q5, 0.69 vs. 2.45 nmol/L; adjusted HR, 1.19 [CI, 1.08 to 1.30]) and CVD mortality (adjusted HR, 1.29 [CI, 1.03 to 1.61]), and risk also increased with DHT concentrations above 2.45 nmol/L. Men with DHT concentrations below 0.59 nmol/L had increased risk for incident CVD events. LIMITATIONS: Observational study design, heterogeneity among studies, and imputation of missing data. CONCLUSION: Men with low testosterone, high LH, or very low estradiol concentrations had increased all-cause mortality. SHBG concentration was positively associated and DHT concentration was nonlinearly associated with all-cause and CVD mortality. PRIMARY FUNDING SOURCE: Medical Research Future Fund, Government of Western Australia, and Lawley Pharmaceuticals. (PROSPERO: CRD42019139668).

Benefits and Risks Associated With Statin Therapy for Primary Prevention in Old and Very Old Adults : Real-World Evidence From a Target Trial Emulation Study.

BACKGROUND: There is little consensus on using statins for primary prevention of cardiovascular diseases (CVDs) and all-cause mortality in adults aged 75 years or older due to the underrepresentation of this population in randomized controlled trials. OBJECTIVE: To investigate the benefits and risks of using statins for primary prevention in old (aged 75 to 84 years) and very old (aged ≥85 years) adults. DESIGN: Sequential target trial emulation comparing matched cohorts initiating versus not initiating statin therapy. SETTING: Territory-wide public electronic medical records in Hong Kong. PARTICIPANTS: Persons aged 75 years or older who met indications for statin initiation from January 2008 to December 2015 were included. Participants with preexisting diagnosed CVDs at baseline, such as coronary heart disease (CHD), were excluded from the analysis. Among 69 981 eligible persons aged 75 to 84 years and 14 555 persons aged 85 years or older, 41 884 and 9457 had history of CHD equivalents (for example, diabetes) in the respective age groups. INTERVENTION: Initiation of statin therapy. MEASUREMENTS: Incidence of major CVDs (stroke, myocardial infarction, or heart failure), all-cause mortality, and major adverse events (myopathies and liver dysfunction). RESULTS: Of 42 680 matched person-trials aged 75 to 84 years and 5390 matched person-trials aged 85 years or older (average follow-up, 5.3 years), 9676 and 1600 of them developed CVDs in each age group, respectively. Risk reduction for overall CVD incidence was found for initiating statin therapy in adults aged 75 to 84 years (5-year standardized risk reduction, 1.20% [95% CI, 0.57% to 1.82%] in the intention-to-treat [ITT] analysis; 5.00% [CI, 1.11% to 8.89%] in the per protocol [PP] analysis) and in those aged 85 years or older (ITT: 4.44% [CI, 1.40% to 7.48%]; PP: 12.50% [CI, 4.33% to 20.66%]). No significantly increased risks for myopathies and liver dysfunction were found in both age groups. LIMITATION: Unmeasured confounders, such as lifestyle factors of diet and physical activity, may exist. CONCLUSION: Reduction for CVDs after statin therapy were seen in patients aged 75 years or older without increasing risks for severe adverse effects. Of note, the benefits and safety of statin therapy were consistently found in adults aged 85 years or older. PRIMARY FUNDING SOURCE: Health Bureau, the Government of Hong Kong Special Administrative Region, China, and National Natural Science Foundation of China.

Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study.

BACKGROUND AND AIMS: Emerging evidence has raised an obesity paradox in observational studies of body mass index (BMI) and health among the oldest-old (aged ≥80 years), as an inverse relationship of BMI with mortality was reported. This study was to investigate the causal associations of BMI, waist circumference (WC), or both with mortality in the oldest-old people in China. METHODS: A total of 5306 community-based oldest-old (mean age 90.6 years) were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998 and 2018. Genetic risk scores were constructed from 58 single-nucleotide polymorphisms (SNPs) associated with BMI and 49 SNPs associated with WC to subsequently derive causal estimates for Mendelian randomization (MR) models. One-sample linear MR along with non-linear MR analyses were performed to explore the associations of genetically predicted BMI, WC, and their joint effect with all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality. RESULTS: During 24 337 person-years of follow-up, 3766 deaths were documented. In observational analyses, higher BMI and WC were both associated with decreased mortality risk [hazard ratio (HR) 0.963, 95% confidence interval (CI) 0.955-0.971 for a 1-kg/m2 increment of BMI and HR 0.971 (95% CI 0.950-0.993) for each 5 cm increase of WC]. Linear MR models indicated that each 1 kg/m2 increase in genetically predicted BMI was monotonically associated with a 4.5% decrease in all-cause mortality risk [HR 0.955 (95% CI 0.928-0.983)]. Non-linear curves showed the lowest mortality risk at the BMI of around 28.0 kg/m2, suggesting that optimal BMI for the oldest-old may be around overweight or mild obesity. Positive monotonic causal associations were observed between WC and all-cause mortality [HR 1.108 (95% CI 1.036-1.185) per 5 cm increase], CVD mortality [HR 1.193 (95% CI 1.064-1.337)], and non-CVD mortality [HR 1.110 (95% CI 1.016-1.212)]. The joint effect analyses indicated that the lowest risk was observed among those with higher BMI and lower WC. CONCLUSIONS: Among the oldest-old, opposite causal associations of BMI and WC with mortality were observed, and a body figure with higher BMI and lower WC could substantially decrease the mortality risk. Guidelines for the weight management should be cautiously designed and implemented among the oldest-old people, considering distinct roles of BMI and WC.

B-type natriuretic peptide levels predict long-term mortality in a large cohort of adults with congenital heart disease.

BACKGROUND AND AIMS: Many adult patients with congenital heart disease (ACHD) are still afflicted by premature death. Previous reports suggested natriuretic peptides may identify ACHD patients with adverse outcome. The study investigated prognostic power of B-type natriuretic peptide (BNP) across the spectrum of ACHD in a large contemporary cohort. METHODS: The cohort included 3392 consecutive ACHD patients under long-term follow-up at a tertiary ACHD centre between 2006 and 2019. The primary study endpoint was all-cause mortality. RESULTS: A total of 11 974 BNP measurements were analysed. The median BNP at baseline was 47 (24-107) ng/L. During a median follow-up of 8.6 years (29 115 patient-years), 615 (18.1%) patients died. On univariable and multivariable analysis, baseline BNP [hazard ratio (HR) 1.16, 95% confidence interval (CI) 1.15-1.18 and HR 1.13, 95% CI 1.08-1.18, respectively] and temporal changes in BNP levels (HR 1.22, 95% CI 1.19-1.26 and HR 1.19, 95% CI 1.12-1.26, respectively) were predictive of mortality (P < .001 for both) independently of congenital heart disease diagnosis, complexity, anatomic/haemodynamic features, and/or systolic systemic ventricular function. Patients within the highest quartile of baseline BNP (>107 ng/L) and those within the highest quartile of temporal BNP change (>35 ng/L) had significantly increased risk of death (HR 5.8, 95% CI 4.91-6.79, P < .001, and HR 3.6, 95% CI 2.93-4.40, P < .001, respectively). CONCLUSIONS: Baseline BNP and temporal BNP changes are both significantly associated with all-cause mortality in ACHD independent of congenital heart disease diagnosis, complexity, anatomic/haemodynamic features, and/or systolic systemic ventricular function. B-type natriuretic peptide levels represent an easy to obtain and inexpensive marker conveying prognostic information and should be used for the routine surveillance of patients with ACHD.

Mortality from leading cancers in districts of England from 2002 to 2019: a population-based, spatiotemporal study.

BACKGROUND: Cancers are the leading cause of death in England. We aimed to estimate trends in mortality from leading cancers from 2002 to 2019 for the 314 districts in England. METHODS: We did a high-resolution spatiotemporal analysis of vital registration data from the UK Office for National Statistics using data on all deaths from the ten leading cancers in England from 2002 to 2019. We used a Bayesian hierarchical model to obtain robust estimates of age-specific and cause-specific death rates. We used life table methods to calculate the primary outcome, the unconditional probability of dying between birth and age 80 years by sex, cancer cause of death, local district, and year. We reported Spearman rank correlations between the probability of dying from a cancer and district-level poverty in 2019. FINDINGS: In 2019, the probability of dying from a cancer before age 80 years ranged from 0·10 (95% credible interval [CrI] 0·10-0·11) to 0·17 (0·16-0·18) for women and from 0·12 (0·12-0·13) to 0·22 (0·21-0·23) for men. Variation in the probability of dying was largest for lung cancer among women, being 3·7 times (95% CrI 3·2-4·4) higher in the district with the highest probability than in the district with the lowest probability; and for stomach cancer for men, being 3·2 times (2·6-4·1) higher in the district with the highest probability than in the one with the lowest probability. The variation in the probability of dying was smallest across districts for lymphoma and multiple myeloma (95% CrI 1·2 times [1·1-1·4] higher in the district with the highest probability than the lowest probability for women and 1·2 times [1·0-1·4] for men), and leukaemia (1·1 times [1·0-1·4] for women and 1·2 times [1·0-1·5] for men). The Spearman rank correlation between probability of dying from a cancer and district poverty was 0·74 (95% CrI 0·72-0·76) for women and 0·79 (0·78-0·81) for men. From 2002 to 2019, the overall probability of dying from a cancer declined in all districts: the reductions ranged from 6·6% (95% CrI 0·3-13·1) to 30·1% (25·6-34·5) for women and from 12·8% (7·1-18·8) to 36·7% (32·2-41·2) for men. However, there were increases in mortality for liver cancer among men, lung cancer and corpus uteri cancer among women, and pancreatic cancer in both sexes in some or all districts with posterior probability greater than 0·80. INTERPRETATION: Cancers with modifiable risk factors and potential for screening for precancerous lesions had heterogeneous trends and the greatest geographical inequality. To reduce these inequalities, factors affecting both incidence and survival need to be addressed at the local level. FUNDING: Wellcome Trust, Imperial College London, UK Medical Research Council, and the National Institute of Health Research.

Causal association of blood lipids with all-cause and cause-specific mortality risk: a Mendelian randomization study.

Dyslipidemia has long been implicated in elevating mortality risk; yet, the precise associations between lipid traits and mortality remained undisclosed. Our study aimed to explore the causal effects of lipid traits on both all-cause and cause-specific mortality. One-sample Mendelian randomization (MR) with linear and nonlinear assumptions was conducted in a cohort of 407,951 European participants from the UK Biobank. Six lipid traits, consisting of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and lipoprotein(a), were included to investigate the causal associations with mortality. Two-sample MR was performed to replicate the association between each lipid trait and all-cause mortality. Univariable MR results showed that genetically predicted higher ApoA1 was significantly associated with a decreased all-cause mortality risk (HR[95% CI]:0.93 [0.89-0.97], P value = 0.001), which was validated by the two-sample MR analysis. Higher lipoprotein(a) was associated with an increased risk of all-cause mortality (1.03 [1.01-1.04], P value = 0.002). Multivariable MR confirmed the direct causal effects of ApoA1 and lipoprotein(a) on all-cause mortality. Meanwhile, nonlinear MR found no evidence for nonlinearity between lipids and all-cause mortality. Our examination into cause-specific mortality revealed a suggestive inverse association between ApoA1 and cancer mortality, a significant positive association between lipoprotein(a) and cardiovascular disease mortality, and a suggestive positive association between lipoprotein(a) and digestive disease mortality. High LDL-C was associated with an increased risk of cardiovascular disease mortality but a decreased risk of neurodegenerative disease mortality. The findings suggest that implementing interventions to raise ApoA1 and decrease lipoprotein(a) levels may improve overall health outcomes and mitigate cancer and digestive disease mortality.

Autoimmune comorbidity in type 1 diabetes and its association with metabolic control and mortality risk in young people: a population-based study.

AIMS/HYPOTHESIS: This register-based study aimed to describe autoimmune comorbidity in children and young adults from type 1 diabetes onset, and to investigate whether such comorbidity was associated with a difference in HbA1c or mortality risk compared with children/young adults with type 1 diabetes without autoimmune comorbidity. METHODS: A total of 15,188 individuals from the Swedish National Diabetes Register, registered with type 1 diabetes before 18 years of age between 2000 and 2019, were included. Five randomly selected control individuals from the Swedish population (Statistics Sweden) were matched to each individual with type 1 diabetes (n=74,210 [346 individuals with type 1 diabetes were not found in the Statistics Sweden register at the date of type 1 diabetes diagnosis, so could not be matched to control individuals]). The National Patient Register was used to attain ICD-10 codes on autoimmune diseases and the Cause of Death Register was used to identify deceased individuals. RESULTS: In the total type 1 diabetes cohort, mean±SD age at onset of type 1 diabetes was 9.5±4.4 years and mean disease duration at end of follow-up was 8.8±5.7 years. Of the individuals with type 1 diabetes, 19.2% were diagnosed with at least one autoimmune disease vs 4.0% of the control group. The HRs for comorbidities within 19 years from onset of type 1 diabetes were 11.6 (95% CI 10.6, 12.6) for coeliac disease, 10.6 (95% CI 9.6, 11.8) for thyroid disease, 1.3 (95% CI 1.1, 1.6) for psoriasis, 4.1 (95% CI 3.2, 5.3) for vitiligo, 1.7 (95% CI 1.4, 2.2) for rheumatic joint disease, 1.0 (95% CI 0.8, 1.3) for inflammatory bowel disease, 1.0 (95% CI 0.7, 1.2) for systemic connective tissue disorder, 1.4 (95% CI 1.1, 1.9) for uveitis, 18.3 (95% CI 8.4, 40.0) for Addison's disease, 1.8 (95% CI 0.9, 3.6) for multiple sclerosis, 3.7 (95% CI 1.6, 8.7) for inflammatory liver disease and 19.6 (95% CI 4.2, 92.3) for atrophic gastritis. Autoimmune disease in addition to type 1 diabetes had no statistically significant effect on HbA1c or mortality risk. CONCLUSIONS/INTERPRETATION: To our knowledge, this is the first comprehensive study where young individuals with type 1 diabetes were followed regarding development of a wide spectrum of autoimmune diseases, from onset of type 1 diabetes. In this nationwide and population-based study, there was already a high prevalence of autoimmune diseases in childhood, especially coeliac and thyroid disease. The presence of autoimmune comorbidity did not have a statistically significant effect on metabolic control or mortality risk.

Time Trends in Causes of Death in People With HIV: Insights From the Swiss HIV Cohort Study.

BACKGROUND: Advancements in access to antiretroviral therapy (ART) and human immunodeficiency virus (HIV) care have led to a decline in AIDS-related deaths among people with HIV (PWH) in Switzerland. However, data on the ongoing changes in causes of death among PWH over the past 15 years are scarce. METHODS: We investigated all reported deaths in the Swiss HIV Cohort Study between 2005 and 2022. Causes of death were categorized using the Coding Causes of Death in HIV protocol. The statistical analysis included demographic stratification to identify time trends and logistic regression models to determine associated factors for the underlying cause of death. RESULTS: In total, 1630 deaths were reported, with 23.7% of individuals assigned female sex at birth. These deaths included 147 (9.0%) HIV/AIDS-related deaths, 373 (22.9%) due to non-AIDS, non-hepatic cancers, 166 (10.2%) liver-related deaths, and 158 (9.7%) cardiovascular-related deaths. The median age at death (interquartile range) increased from 45.0 (40.0-53.0) years in 2005-2007 to 61.0 (56.0-69.5) years in 2020-2022. HIV/AIDS- and liver-related deaths decreased, whereas deaths from non-AIDS, non-hepatic cancers increased and cardiovascular-related deaths remained relatively stable. CONCLUSIONS: The proportionally decreasing HIV/AIDS and liver-related deaths showcase the effectiveness of ART, comprehensive HIV patient care, and interventions targeting hepatitis C virus coinfection. Future research should focus on managing cancer and cardiovascular-related conditions as the new leading causes of death among PWH. Comprehensive healthcare strategies focusing on non-AIDS-related comorbid conditions, cancer management, and sustaining liver and cardiovascular health are needed to bridge the ongoing health disparities between PWH and the general population.

Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer.

Despite the use of machine learning tools, it is challenging to properly model cause-specific deaths in colorectal cancer (CRC) patients and choose appropriate treatments. Here, we propose an interesting feature selection framework, namely union with recursive feature elimination (U-RFE), to select the union feature sets that are crucial in CRC progression-specific mortality using The Cancer Genome Atlas (TCGA) dataset. Based on the union feature sets, we compared the performance of 5 classification algorithms, including logistic regression (LR), support vector machines (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and Stacking, to identify the best model for classifying 4-category deaths. In the first stage of U-RFE, LR, SVM, and RF were used as base estimators to obtain subsets containing the same number of features but not exactly the same specific features. Union analysis of the subsets was then performed to determine the final union feature set, effectively combining the advantages of different algorithms. We found that the U-RFE framework could improve various models' performance. Stacking outperformed LR, SVM, RF, and XGBoost in most scenarios. When the target feature number of the RFE was set to 50 and the union feature set contained 298 deterministic features, the Stacking model achieved F1_weighted, Recall_weighted, Precision_weighted, Accuracy, and Matthews correlation coefficient of 0.851, 0.864, 0.854, 0.864, and 0.717, respectively. The performance of the minority categories was also significantly improved. Therefore, this recursive feature elimination-based approach of feature selection improves performances of classifying CRC deaths using clinical and omics data or those using other data with high feature redundancy and imbalance.

COVID-19 Death Determination Methods, Minnesota, USA, 2020-20221.

Accurate and timely mortality surveillance is crucial for elucidating risk factors, particularly for emerging diseases. We compared use of COVID-19 keywords on death certificates alone to identify COVID-19 deaths in Minnesota, USA, during 2020-2022, with use of a standardized mortality definition incorporating additional clinical data. For analyses, we used likelihood ratio χ2 and median 1-way tests. Death certificates alone identified 96% of COVID-19 deaths confirmed by the standardized definition and an additional 3% of deaths that had been classified as non-COVID-19 deaths by the standardized definition. Agreement between methods was >90% for most groups except children, although agreement among adults varied by demographics and location at death. Overall median time from death to filing of death certificate was 3 days; decedent characteristics and whether autopsy was performed varied. Death certificates are an efficient and timely source of COVID-19 mortality data when paired with SARS-CoV-2 testing data.

Coffee consumption is associated with a reduced risk of colorectal cancer recurrence and all-cause mortality.

Coffee consumption has been associated with a reduced risk of developing colorectal cancer (CRC). However, it is not clear whether coffee consumption is related to CRC progression. Hence, we assessed the association of coffee consumption with CRC recurrence and all-cause mortality using data from a prospective cohort study of 1719 stage I-III CRC patients in the Netherlands. Coffee consumption and other lifestyle characteristics were self-reported using questionnaires at the time of diagnosis. We retrieved recurrence and all-cause mortality data from the Netherlands Cancer Registry and the Personal Records Database, respectively. Cox proportional hazard regression models with and without restricted cubic splines were used to calculate hazard ratios (HR) and 95% confidence intervals (CI) adjusted for age, sex, education, smoking status, cancer stage and tumor location. We observed 257 recurrences during a 6.2-year median follow-up and 309 deaths during a 6.6-year median follow-up. Consuming more than 4 cups/d of coffee compared to an intake of <2 cups/d was associated with a 32% lower risk of CRC recurrence (95% CI: 0.49, 0.94,). The association between coffee consumption and all-cause mortality was U-shaped; coffee intake seemed optimal at 3-5 cups/d with the lowest risk at 4 cups/d (HR: 0.68, 95% CI: 0.53, 0.88). Our results suggest that coffee consumption may be associated with a lower risk of CRC recurrence and all-cause mortality. The association between coffee consumption and all-cause mortality appeared nonlinear. More studies are needed to understand the mechanism by which coffee consumption might improve CRC prognosis.

Changes in mortality associated with different hematologic malignancies during the pandemic in the United States.

Patients with hematologic malignancies are at increased risk of adverse COVID-19 outcomes; nonetheless, only sparse population-based data are available on mortality related to hematologic cancers during the pandemic. Number of deaths and age-standardized mortality rates for specific hematologic malignancies selected either as the underlying cause of death (UCOD), or mentioned in death certificates (multiple causes of death-MCOD) were extracted from the US National Center for Health Statistics, CDC WONDER Online Database. Joinpoint analysis was applied to identify changes in mortality trends from 1999 to 2021, and to estimate the annual percent change with 95% Confidence Intervals (CI) across time segments. Among the most common malignancies, chronic lymphocytic leukemia showed marked peaks in the monthly number of deaths attributed to COVID-19 during epidemic waves; acute myeloid leukemia showed the least variation, and non-Hodgkin lymphoma and multiple myeloma were characterized by an intermediate pattern. Age-standardized death rates relying solely on the UCOD did not show significant variations during pandemic years. By contrast, rates based on MCOD increased by 14.0% (CI, 10.2-17.9%) per year for chronic lymphocytic leukemia, by 5.1% (CI, 3.1-7.2%) for non-Hodgkin lymphoma and by 3.2% (CI, 0.3-6.1%) per year for multiple myeloma. Surveillance of mortality based on MCOD is warranted to accurately measure the impact of the COVID-19 pandemic and of other epidemics, including seasonal flu, on patients with hematologic malignancies, and to assess the effects of vaccination campaigns and other preventive measures.

Deaths with COVID-19 and from all-causes following first-ever SARS-CoV-2 infection in individuals with preexisting mental disorders: A national cohort study from Czechia.

BACKGROUND: Evidence suggests reduced survival rates following Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in people with preexisting mental disorders, especially psychotic disorders, before the broad introduction of vaccines. It remains unknown whether this elevated mortality risk persisted at later phases of the pandemic and when accounting for the confounding effect of vaccination uptake and clinically recorded physical comorbidities. METHODS AND FINDINGS: We used data from Czech national health registers to identify first-ever serologically confirmed SARS-CoV-2 infections in 5 epochs related to different phases of the pandemic: 1st March 2020 to 30th September 2020, 1st October 2020 to 26th December 2020, 27th December 2020 to 31st March 2021, 1st April 2021 to 31st October 2021, and 1st November 2021 to 29th February 2022. In these people, we ascertained cases of mental disorders using 2 approaches: (1) per the International Classification of Diseases 10th Revision (ICD-10) diagnostic codes for substance use, psychotic, affective, and anxiety disorders; and (2) per ICD-10 diagnostic codes for the above mental disorders coupled with a prescription for anxiolytics/hypnotics/sedatives, antidepressants, antipsychotics, or stimulants per the Anatomical Therapeutic Chemical (ATC) classification codes. We matched individuals with preexisting mental disorders with counterparts who had no recorded mental disorders on age, sex, month and year of infection, vaccination status, and the Charlson Comorbidity Index (CCI). We assessed deaths with Coronavirus Disease 2019 (COVID-19) and from all-causes in the time period of 28 and 60 days following the infection using stratified Cox proportional hazards models, adjusting for matching variables and additional confounders. The number of individuals in matched-cohorts ranged from 1,328 in epoch 1 to 854,079 in epoch 5. The proportion of females ranged from 34.98% in people diagnosed with substance use disorders in epoch 3 to 71.16% in individuals diagnosed and treated with anxiety disorders in epoch 5. The mean age ranged from 40.97 years (standard deviation [SD] = 15.69 years) in individuals diagnosed with substance use disorders in epoch 5 to 56.04 years (SD = 18.37 years) in people diagnosed with psychotic disorders in epoch 2. People diagnosed with or diagnosed and treated for psychotic disorders had a consistently elevated risk of dying with COVID-19 in epochs 2, 3, 4, and 5, with adjusted hazard ratios (aHRs) ranging from 1.46 [95% confidence intervals (CIs), 1.18, 1.79] to 1.93 [95% CIs, 1.12, 3.32]. This patient group demonstrated also a consistently elevated risk of all-cause mortality in epochs 2, 3, 4, and 5 (aHR from 1.43 [95% CIs, 1.23, 1.66] to 1.99 [95% CIs, 1.25, 3.16]). The models could not be reliably fit for psychotic disorders in epoch 1. People diagnosed with substance use disorders had an increased risk of all-cause mortality 28 days postinfection in epoch 3, 4, and 5 (aHR from 1.30 [95% CIs, 1.14, 1.47] to 1.59 [95% CIs, 1.19, 2.12]) and 60 days postinfection in epoch 2, 3, 4, and 5 (aHR from 1.22 [95% CIs, 1.08, 1.38] to 1.52 [95% CIs, 1.16, 1.98]). Cases ascertained based on diagnosis of substance use disorders and treatment had increased risk of all-cause mortality in epoch 2, 3, 4, and 5 (aHR from 1.22 [95% CIs, 1.03, 1.43] to 1.91 [95% CIs, 1.25, 2.91]). The models could not be reliably fit for substance use disorders in epoch 1. In contrast to these, people diagnosed with anxiety disorders had a decreased risk of death with COVID-19 in epoch 2, 3, and 5 (aHR from 0.78 [95% CIs, 0.69, 0.88] to 0.89 [95% CIs, 0.81, 0.98]) and all-cause mortality in epoch 2, 3, 4, and 5 (aHR from 0.83 [95% CIs, 0.77, 0.90] to 0.88 [95% CIs, 0.83, 0.93]). People diagnosed and treated for affective disorders had a decreased risk of both death with COVID-19 and from all-causes in epoch 3 (aHR from 0.87 [95% CIs, 0.79, 0.96] to 0.90 [95% CIs, 0.83, 0.99]), but demonstrated broadly null effects in other epochs. Given the unavailability of data on a number of potentially influential confounders, particularly body mass index, tobacco smoking status, and socioeconomic status, part of the detected associations might be due to residual confounding. CONCLUSIONS: People with preexisting psychotic, and, less robustly, substance use disorders demonstrated a persistently elevated risk of death following SARS-CoV-2 infection throughout the pandemic. While it cannot be ruled out that part of the detected associations is due to residual confounding, this excess mortality cannot be fully explained by lower vaccination uptake and more clinically recorded physical comorbidities in these patient groups.

Methodological examination of the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Score.

The objective of this study was to examine the impact of methodological changes to the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) Score on associations with risk for all-cause mortality, cancer mortality, and cancer risk jointly among older adults in the National Institutes of Health (NIH)-AARP Diet and Health Study. Weights were incorporated for each score component; a continuous point scale was developed in place of the score's fully discrete cut points; and cut-point values were changed for physical activity and red meat based on evidence-based recommendations. Exploratory aims also examined the impact of separating components with more than one subcomponent and whether all components were necessary to retain within this population utilizing a penalized scoring approach. Findings suggested weighting the original 2018 WCRF/AICR Score improved its predictive performance in association with all-cause mortality and provided more precise estimates in relation to cancer risk and mortality outcomes. The importance of healthy weight, physical activity, and plant-based foods in relation to cancer and overall mortality risk were highlighted in this population of older adults. Further studies are needed to better understand the consistency and generalizability of these findings across other populations.