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Background: Empirical evidence from European countries has shown that economic conditions in early life are associated with mortality risk. This study aims to assess the effects of economic conditions in early life, as well as their interaction with parental education, on the risk of adult mortality in the U.S. Methods: To capture exogenous variation of economic conditions early in life, we use the Gross Domestic Product (GDP) cyclical deviation during a respondent's birth year. Using the linked U.S. General Social Survey and National Death Index data (1979–2008), we employed parametric frailty survival models to examine the effects of economic conditions in early life on all-cause and cause-specific mortality. Results: We found that exposure to recession in the first year of life was associated with increased all-cause mortality only among women (hazard ratio = 1.54, 95% CI 1.03–2.31). This adverse effect was also found in women's mortality from cancers (hazard ratio = 2.24, 95% CI 1.18–4.28). We also found a significant interaction between economic conditions in infancy and paternal education on women's mortality risk—higher paternal education was protective against mortality under good economic conditions in infancy; however, higher paternal education was associated with greater mortality risk under poor economic conditions in infancy. We discuss how aspiration theory may explain these results. Conclusion: Our study concludes that worse macroeconomic conditions early in life heighten the risk of mortality among women, and paternal education moderates this relationship. [ABSTRACT FROM AUTHOR]

Background: Previous studies have shown an association between living alone and cancer mortality; however, findings by sex and race/ethnicity have generally been inconsistent, and data by socioeconomic status are sparse. The association between living alone and cancer mortality by sex, race/ethnicity, and socioeconomic status in a nationally representative US cohort was examined. Methods: Pooled 1998–2019 data for adults aged 18–64 years at enrollment from the National Health Interview Survey linked to the National Death Index (N = 473,648) with up to 22 years of follow‐up were used to calculate hazard ratios (HRs) for the association between living alone and cancer mortality. Results: Compared to adults living with others, adults living alone were at a higher risk of cancer death in the age‐adjusted model (HR, 1.32; 95% CI, 1.25–1.39) and after additional adjustments for multiple sociodemographic characteristics and cancer risk factors (HR, 1.10; 95% CI, 1.04–1.16). Age‐adjusted models stratified by sex, poverty level, and educational attainment showed similar associations between living alone and cancer mortality, but the association was stronger among non‐Hispanic White adults (HR, 1.33; 95% CI, 1.25–1.42) than non‐Hispanic Black adults (HR, 1.18; 95% CI, 1.05–1.32; p value for difference <.05) and did not exist in other racial/ethnic groups. These associations were attenuated but persisted in fully adjusted models among men (HR, 1.13; 95% CI, 1.05–1.23), women (HR, 1.09; 95% CI, 1.01–1.18), non‐Hispanic White adults (HR, 1.13; 95% CI, 1.05–1.20), and adults with a college degree (HR, 1.22; 95% CI, 1.07–1.39). Conclusions: In this nationally representative study in the United States, adults living alone were at a higher risk of cancer death in several sociodemographic groups. In a nationally representative study with up to 22 years of mortality follow‐up, US working‐age adults experiencing social isolation, measured as living alone, were at a higher risk of cancer death than adults living with others. This association persisted among non‐Hispanic White adults and adults with higher education even after accounting for differences in a wide range of sociodemographic, behavioral, and health characteristics. [ABSTRACT FROM AUTHOR]

Background: The American Heart Association recently introduced a novel cardiovascular health (CVH) metric, Life's Essential 8 (LE8), for health promotion. However, the relationship between LE8 and cancer mortality risk remains uncertain., Methods: We investigated 17,076 participants from US National Health and Nutrition Examination Survey (US NHANES) and 272,727 participants from UK Biobank, all free of cancer at baseline. The CVH score, based on LE8 metrics, incorporates four health behaviors (diet, physical activity, smoking, and sleep) and four health factors (body mass index, lipid, blood glucose, and blood pressure). Self-reported questionnaires assessed health behaviors. Primary outcomes were mortality rates for total cancer and its subtypes. The association between CVH score (continuous and categorical variable) and outcomes was examined using Cox model with adjustments. Cancer subtypes-related polygenic risk score (PRS) was constructed to evaluate its interactions with CVH on cancer death risk., Results: Over 141,526 person-years in US NHANES, 424 cancer-related deaths occurred, and in UK Biobank, 8,872 cancer deaths were documented during 3,690,893 person-years. High CVH was associated with reduced overall cancer mortality compared to low CVH (HR 0.58, 95% CI 0.37-0.91 in US NHANES; 0.51, 0.46-0.57 in UK Biobank). Each one-standard deviation increase in CVH score was linked to a 19% decrease in cancer mortality (HR: 0.81; 95% CI: 0.73-0.91) in US NHANES and a 19% decrease (HR: 0.81; 95% CI: 0.79-0.83) in UK Biobank. Adhering to ideal CVH was linearly associated with decreased risks of death from lung, bladder, liver, kidney, esophageal, breast, colorectal, pancreatic, and gastric cancers in UK Biobank. Furthermore, integrating genetic data revealed individuals with low PRS and high CVH exhibited the lowest mortality from eight cancers (HRs ranged from 0.36 to 0.57) compared to those with high PRS and low CVH. No significant modification of the association between CVH and mortality risk for eight cancers by genetic predisposition was observed. Subgroup analyses showed a more pronounced protective association for overall cancer mortality among younger participants and those with lower socio-economic status., Conclusions: Maintaining optimal CVH is associated with a substantial reduction in the risk of overall cancer mortality. Adherence to ideal CVH correlates linearly with decreased mortality risk across multiple cancer subtypes. Individuals with both ideal CVH and high genetic predisposition demonstrated significant health benefits. These findings support adopting ideal CVH as an intervention strategy to mitigate cancer mortality risk and promote healthy aging., (© 2024. The Author(s).)

Objective: In this study, we examined associations between county-level measures of structural racism and county-level cancer incidence and mortality rates between race groups while accounting for factors associated with cancer rates and county-level measures of environmental burden., Methods: To explore this relationship, we conducted multiple linear regression analyses. Data for these analyses came from an index of county-level structural racism and publicly available data on 2015 to 2019 age-adjusted cancer rates from the US Cancer Statistics Data Visualization Tool, 2019 County Health Rankings and Roadmaps, the Environmental Protection Agency's 2006 to 2010 Environmental Quality Index, and 2015 to 2019 estimates from the US Census American Community Survey., Results: County-level structural racism was associated with higher county cancer incidence rates among Black (adjusted incidence rate: 17.4, 95% confidence interval [95% CI]: 9.3, 25.5) and Asian/Pacific Islander populations (adjusted incidence rate: 9.3, 95% CI: 1.8, 16.9) and higher mortality rates for American Indian/Alaskan Native (adjusted mortality rate [AMR]: 17.4, 95% CI: 4.2, 30.6), Black (AMR: 11.9, 95% CI: 8.9, 14.8), and Asian/Pacific Islander (AMR: 4.7, 95% CI: 1.3, 8.1) populations than White populations., Conclusion: Our findings highlight the detrimental impact of structural racism on cancer outcomes among minoritized populations. Strategies aiming to mitigate cancer disparities must embed processes to recognize and address systems, policies, laws, and norms that create and reproduce patterns of discrimination., Competing Interests: Conflict of Interest: No conflict of interest reported by the authors.

Background: There is evidence indicating that both lipoprotein(a) [Lp(a)] and fibrinogen (FIB) are associated with mortality, However, the impact of their combination on mortality has not been determined. Thus, the aim of this study was to examine the association between the combination of Lp(a) and FIB with all-cause and cause-specific mortality., Methods: This prospective cohort study enrolled 4,730 participants from the third National Health and Nutrition Examination Survey. The exposure variables included Lp(a), FIB and their combination, while the outcome variables consisted of all-cause, cardiovascular disease (CVD) and cancer-related mortality. Multivariate COX regression, subgroup analysis, sensitivity analysis and restricted cubic spline (RCS) were used to investigate the association between Lp(a), FIB and their combination with all-cause, CVD and cancer-related mortality., Results: Over a median follow-up period of 235 months, 2,668 individuals died, including 1,051 deaths attributed to CVD and 549 deaths due to cancer. Multivariate Cox regression analyses revealed independent associations between both Lp(a) and FIB with all-cause, CVD, and cancer-related mortality. Compared to participants in the 1st to 50th percentiles of both Lp(a) and FIB, those in the 90th to 100th percentiles exhibited multivariable adjusted HRs of 1.813 (95% CI: 1.419-2.317, P < 0.001), 2.147 (95% CI: 1.483-3.109, P < 0.001) and 2.355 (95% CI: 1.396, 3.973, P = 0.001) for all-cause, CVD and cancer-related mortality, respectively. Subgroup and sensitivity analyses did not substantially attenuate the association between the combination of high Lp(a) and high FIB with the risk of all-cause and CVD-related mortality. Additionally, the RCS analysis showed that the relationship between Lp(a) and the risk of all-cause and cancer-related mortality, as well as the relationship between FIB and the risk of cancer-related mortality, were linear (P for nonlinearity > 0.05). Conversely, the relationship between Lp(a) and the risk of CVD-related mortality, as well as the relationship between FIB and the risk of all-cause and CVD-related mortality, were nonlinear (P for nonlinearity < 0.05)., Conclusions: High levels of Lp(a) and FIB together conferred a greater risk of mortality from all-cause, CVD and cancer., (© 2024. The Author(s).)

Purpose: Understanding how stage at cancer diagnosis influences cause of death, an endpoint that is not susceptible to lead-time bias, can inform population-level outcomes of cancer screening., Methods: Using data from 17 US Surveillance, Epidemiology, and End Results registries for 1,154,515 persons aged 50-84 years at cancer diagnosis in 2006-2010, we evaluated proportional causes of death by cancer type and uniformly classified stage, following or extrapolating all patients until death through 2020., Results: Most cancer patients diagnosed at stages I-II did not go on to die from their index cancer, whereas most patients diagnosed at stage IV did. For patients diagnosed with any cancer at stages I-II, an estimated 26% of deaths were due to the index cancer, 63% due to non-cancer causes, and 12% due to a subsequent primary (non-index) cancer. In contrast, for patients diagnosed with any stage IV cancer, 85% of deaths were attributed to the index cancer, with 13% non-cancer and 2% non-index-cancer deaths. Index cancer mortality from stages I-II cancer was proportionally lowest for thyroid, melanoma, uterus, prostate, and breast, and highest for pancreas, liver, esophagus, lung, and stomach., Conclusion: Across all cancer types, the percentage of patients who went on to die from their cancer was over three times greater when the cancer was diagnosed at stage IV than stages I-II. As mortality patterns are not influenced by lead-time bias, these data suggest that earlier detection is likely to improve outcomes across cancer types, including those currently unscreened., (© 2024. The Author(s).)

Introduction: Comparing cancer mortality and associated risk factors among immigrant populations in a host country to those in their country of origin reveals disparities in cancer risk, access to care, diagnosis, and disease management. This study compares cancer mortality between the German resident population and Germany-born individuals who migrated to the US., Methods: Cancer mortality data from 2008-2018 were derived for Germans from the World Health Organization database and for Germany-born Americans resident in four states (California, Florida, Massachusetts, and New York) from respective Departments of Vital Statistics. We calculated age-standardized mortality rates (ASMRs) using the European standard population and standardized mortality ratios (SMR) compared to the German resident population along with 95% confidence intervals (CIs)., Results: Germany-born American males had lower ASMRs (253.8 per 100,000) than German resident population (325.6 per 100,000). The difference in females was modest, with ASMRs of 200.7 and 203.7 per 100,000, respectively. For all cancers, Germany-born American males had an SMR of 0.72 (95% CI: 0.70-0.74) and females 0.98 (95% CI: 0.95-1.00). Male SMRs among Germany-born Americans were significantly below one for oral cavity, stomach, colorectal, liver, lung, prostate, and kidney cancer. Among females, SMRs were below one for oral cavity, stomach, colorectal, gallbladder, breast, cervix uteri, and kidney cancer. For both sexes, SMRs were over one for bladder cancer (1.14 for males, 1.21 for females). Mortality was higher for lung cancer (SMR: 1.68), non-Hodgkin's lymphoma (1.18) and uterine cancer (1.22) among Germany-born American females compared to the German resident population., Conclusion: Germany-born American males but not females showed lower cancer mortality than German resident population. Disparities may stem from variations in risk factors (e.g., smoking and alcohol use) as well as differences in screening practices and participation, cancer treatment, besides some residual potential "healthy immigrant effect"., Competing Interests: Declaration of Competing Interest All authors have declared no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Cancer is the second leading cause of death in the United States, and the disease burden is elevated in Appalachian Kentucky, due in part to health behaviors and inequities in social determinants of health. This study's goal was to evaluate Appalachian Kentucky's cancer burden compared to non-Appalachian Kentucky, and Kentucky compared to the United States (excluding Kentucky)., Methods: The following data were analyzed: annual all-cause and all-site cancer mortality rates from 1968 to 2018; 5-year all-site and site-specific cancer incidence and mortality rates from 2014 to 2018; aggregated screening and risk factor data from 2016 to 2018 for the United States (excluding Kentucky), Kentucky, non-Appalachian Kentucky, and Appalachian Kentucky; and human papilloma virus vaccination prevalence by sex from 2018 for the United States and Kentucky., Findings: Since 1968, the United States has experienced a large decrease in all-cause and cancer mortality, but the reduction in Kentucky has been smaller and slower, driven by even smaller and slower reductions within Appalachian Kentucky. Appalachian Kentucky has higher overall cancer incidence and mortality rates and higher rates for several site-specific cancers compared to non-Appalachian Kentucky. Contributing factors include screening rate disparities and increased rates of obesity and smoking., Conclusions: Appalachian Kentucky has experienced persistent cancer disparities, including elevated all-cause and cancer mortality rates for 50+ years, widening the gap between this region and the rest of the country. In addition to addressing social determinants of health, increased efforts aimed at improving health behaviors and increased access to health care resources could help reduce this disparity., (© 2023 National Rural Health Association.)