Explore the vast range of titles available.

Background Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. Results Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality ( n  = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios. Conclusions DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.

In a trial comparing chemotherapy and preoperative chemoradiotherapy with perioperative chemotherapy in patients with resectable gastric or gastroesophageal junction cancer, no significant difference in overall survival was noted.

There are large and persistent disparities in life expectancy among racial–ethnic groups in the USA, but the extent to which these patterns vary geographically on a local scale is not well understood. This analysis estimated life expectancy for five racial–ethnic groups, in 3110 US counties over 20 years, to describe spatial–temporal variations in life expectancy and disparities between racial–ethnic groups. We applied novel small-area estimation models to death registration data from the US National Vital Statistics System and population data from the US National Center for Health Statistics to estimate annual sex-specific and age-specific mortality rates stratified by county and racial–ethnic group (non-Latino and non-Hispanic White [White], non-Latino and non-Hispanic Black [Black], non-Latino and non-Hispanic American Indian or Alaska Native [AIAN], non-Latino and non-Hispanic Asian or Pacific Islander [API], and Latino or Hispanic [Latino]) from 2000 to 2019. We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and then constructed abridged life tables to estimate life expectancy at birth. Between 2000 and 2019, trends in life expectancy differed among racial–ethnic groups and among counties. Nationally, there was an increase in life expectancy for people who were Black (change 3·9 years [95% uncertainty interval 3·8 to 4·0]; life expectancy in 2019 75·3 years [75·2 to 75·4]), API (2·9 years [2·7 to 3·0]; 85·7 years [85·3 to 86·0]), Latino (2·7 years [2·6 to 2·8]; 82·2 years [82·0 to 82·5]), and White (1·7 years [1·6 to 1·7]; 78·9 years [78·9 to 79·0]), but remained the same for the AIAN population (0·0 years [–0·3 to 0·4]; 73·1 years [71·5 to 74·8]). At the national level, the negative difference in life expectancy for the Black population compared with the White population decreased during this period, whereas the negative difference for the AIAN population compared with the White population increased; in both cases, these patterns were widespread among counties. The positive difference in life expectancy for the API and Latino populations compared with the White population increased at the national level from 2000 to 2019; however, this difference declined in a sizeable minority of counties (615 [42·0%] of 1465 counties) for the Latino population and in most counties (401 [60·2%] of 666 counties) for the API population. For all racial–ethnic groups, improvements in life expectancy were more widespread across counties and larger from 2000 to 2010 than from 2010 to 2019. Disparities in life expectancy among racial–ethnic groups are widespread and enduring. Local-level data are crucial to address the root causes of poor health and early death among disadvantaged groups in the USA, eliminate health disparities, and increase longevity for all. National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Science Research, US National Institutes of Health.

Large disparities in mortality exist across racial–ethnic groups and by location in the USA, but the extent to which racial–ethnic disparities vary by location, or how these patterns vary by cause of death, is not well understood. We aimed to estimate age-standardised mortality by racial–ethnic group, county, and cause of death and describe the intersection between racial–ethnic and place-based disparities in mortality in the USA, comparing patterns across health conditions. We applied small-area estimation models to death certificate data from the US National Vital Statistics system and population data from the US National Center for Health Statistics to estimate mortality by age, sex, county, and racial–ethnic group annually from 2000 to 2019 for 19 broad causes of death. Race and ethnicity were categorised as non-Latino and non-Hispanic American Indian or Alaska Native (AIAN), non-Latino and non-Hispanic Asian or Pacific Islander (Asian), non-Latino and non-Hispanic Black (Black), Latino or Hispanic (Latino), and non-Latino and non-Hispanic White (White). We adjusted these mortality rates to correct for misreporting of race and ethnicity on death certificates and generated age-standardised results using direct standardisation to the 2010 US census population. From 2000 to 2019, across 3110 US counties, racial–ethnic disparities in age-standardised mortality were noted for all causes of death considered. Mortality was substantially higher in the AIAN population (all-cause mortality 1028·2 [95% uncertainty interval 922·2–1142·3] per 100 000 population in 2019) and Black population (953·5 [947·5–958·8] per 100 000) than in the White population (802·5 [800·3–804·7] per 100 000), but substantially lower in the Asian population (442·3 [429·3–455·0] per 100 000) and Latino population (595·6 [583·7–606·8] per 100 000), and this pattern was found for most causes of death. However, there were exceptions to this pattern, and the exact order among racial–ethnic groups, magnitude of the disparity in both absolute and relative terms, and change over time in this magnitude varied considerably by cause of death. Similarly, substantial geographical variation in mortality was observed for all causes of death, both overall and within each racial–ethnic group. Racial–ethnic disparities observed at the national level reflect widespread disparities at the county level, although the magnitude of these disparities varied widely among counties. Certain patterns of disparity were nearly universal among counties; for example, in 2019, mortality was higher among the AIAN population than the White population in at least 95% of counties for skin and subcutaneous diseases (455 [97·8%] of 465 counties with unmasked estimates) and HIV/AIDS and sexually transmitted infections (458 [98·5%] counties), and mortality was higher among the Black population than the White population in nearly all counties for skin and subcutaneous diseases (1436 [96·6%] of 1486 counties), diabetes and kidney diseases (1473 [99·1%]), maternal and neonatal disorders (1486 [100·0%] counties), and HIV/AIDS and sexually transmitted infections (1486 [100·0%] counties). Disparities in mortality among racial–ethnic groups are ubiquitous, occurring across locations in the USA and for a wide range of health conditions. There is an urgent need to address the shared structural factors driving these widespread disparities. National Institute on Minority Health and Health Disparities; National Heart, Lung, and Blood Institute; National Cancer Institute; National Institute on Aging; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Office of Disease Prevention; and Office of Behavioral and Social Sciences Research, US National Institutes of Health.

Objective To estimate the global burden of low back pain (LBP). Methods LBP was defined as pain in the area on the posterior aspect of the body from the lower margin of the twelfth ribs to the lower glutaeal folds with or without pain referred into one or both lower limbs that lasts for at least one day. Systematic reviews were performed of the prevalence, incidence, remission, duration, and mortality risk of LBP. Four levels of severity were identified for LBP with and without leg pain, each with their own disability weights. The disability weights were applied to prevalence values to derive the overall disability of LBP expressed as years lived with disability (YLDs). As there is no mortality from LBP, YLDs are the same as disability-adjusted life years (DALYs). Results Out of all 291 conditions studied in the Global Burden of Disease 2010 Study, LBP ranked highest in terms of disability (YLDs), and sixth in terms of overall burden (DALYs). The global point prevalence of LBP was 9.4% (95% CI 9.0 to 9.8). DALYs increased from 58.2 million (M) (95% CI 39.9M to 78.1M) in 1990 to 83.0M (95% CI 56.6M to 111.9M) in 2010. Prevalence and burden increased with age. Conclusions LBP causes more global disability than any other condition. With the ageing population, there is an urgent need for further research to better understand LBP across different settings.

Our recently published M-CRIB atlas comprises 100 neonatal brain regions including 68 compatible with the widely-used Desikan-Killiany adult cortical atlas. A successor to the Desikan-Killiany atlas is the Desikan-Killiany-Tourville atlas, in which some regions with unclear boundaries were removed, and many existing boundaries were revised to conform to clearer landmarks in sulcal fundi. Our first aim here was to modify cortical M-CRIB regions to comply with the Desikan-Killiany-Tourville protocol, in order to offer: (a) compatibility with this adult cortical atlas, (b) greater labeling accuracy due to clearer landmarks, and (c) optimisation of cortical regions for integration with surface-based infant parcellation pipelines. Secondly, we aimed to update subcortical regions in order to offer greater compatibility with subcortical segmentations produced in FreeSurfer. Data utilized were the T2-weighted MRI scans in our M-CRIB atlas, for 10 healthy neonates (post-menstrual age at MRI 40-43 weeks, four female), and corresponding parcellated images. Edits were performed on the parcellated images in volume space using ITK-SNAP. Cortical updates included deletion of frontal and temporal poles and 'Banks STS,' and modification of boundaries of many other regions. Changes to subcortical regions included the addition of 'ventral diencephalon,' and deletion of 'subcortical matter' labels. A detailed updated parcellation protocol was produced. The resulting whole-brain M-CRIB 2.0 atlas comprises 94 regions altogether. This atlas provides comparability with adult Desikan-Killiany-Tourville-labeled cortical data and FreeSurfer-labeed subcortical data, and is more readily adaptable for incorporation into surface-based neonatal parcellation pipelines. As such, it offers the ability to help facilitate a broad range of investigations into brain structure and function both at the neonatal time point and developmentally across the lifespan.

Background Supported self-management has been recommended by asthma guidelines for three decades; improving current suboptimal implementation will require commitment from professionals, patients and healthcare organisations. The Practical Systematic Review of Self-Management Support (PRISMS) meta-review and Reducing Care Utilisation through Self-management Interventions (RECURSIVE) health economic review were commissioned to provide a systematic overview of supported self-management to inform implementation. We sought to investigate if supported asthma self-management reduces use of healthcare resources and improves asthma control; for which target groups it works; and which components and contextual factors contribute to effectiveness. Finally, we investigated the costs to healthcare services of providing supported self-management. Methods We undertook a meta-review (systematic overview) of systematic reviews updated with randomised controlled trials (RCTs) published since the review search dates, and health economic meta-analysis of RCTs. Twelve electronic databases were searched in 2012 (updated in 2015; pre-publication update January 2017) for systematic reviews reporting RCTs (and update RCTs) evaluating supported asthma self-management. We assessed the quality of included studies and undertook a meta-analysis and narrative synthesis. Results A total of 27 systematic reviews ( n  = 244 RCTs) and 13 update RCTs revealed that supported self-management can reduce hospitalisations, accident and emergency attendances and unscheduled consultations, and improve markers of control and quality of life for people with asthma across a range of cultural, demographic and healthcare settings. Core components are patient education, provision of an action plan and regular professional review. Self-management is most effective when delivered in the context of proactive long-term condition management. The total cost ( n  = 24 RCTs) of providing self-management support is offset by a reduction in hospitalisations and accident and emergency visits (standard mean difference 0.13, 95% confidence interval −0.09 to 0.34). Conclusions Evidence from a total of 270 RCTs confirms that supported self-management for asthma can reduce unscheduled care and improve asthma control, can be delivered effectively for diverse demographic and cultural groups, is applicable in a broad range of clinical settings, and does not significantly increase total healthcare costs. Informed by this comprehensive synthesis of the literature, clinicians, patient-interest groups, policy-makers and providers of healthcare services should prioritise provision of supported self-management for people with asthma as a core component of routine care. Systematic review registration RECURSIVE: PROSPERO CRD42012002694 ; PRISMS: PROSPERO does not register meta-reviews

Nearly two decades ago, the Eight Americas study offered a novel lens for examining health inequities in the USA by partitioning the US population into eight groups based on geography, race, urbanicity, income per capita, and homicide rate. That study found gaps of 12·8 years for females and 15·4 years for males in life expectancy in 2001 across these eight groups. In this study, we aimed to update and expand the original Eight Americas study, examining trends in life expectancy from 2000 to 2021 for ten Americas (analogues to the original eight, plus two additional groups comprising the US Latino population), by year, sex, and age group. In this systematic analysis, we defined ten mutually exclusive and collectively exhaustive Americas comprising the entire US population, starting with all combinations of county and race and ethnicity, and assigning each to one of the ten Americas based on race and ethnicity and a variable combination of geographical location, metropolitan status, income, and Black–White residential segregation. We adjusted deaths from the National Vital Statistics System to account for misreporting of race and ethnicity on death certificates. We then tabulated deaths from the National Vital Statistics System and population estimates from the US Census Bureau and the National Center for Health Statistics from Jan 1, 2000, to Dec 31, 2021, by America, year, sex, and age, and calculated age-specific mortality rates in each of these strata. Finally, we constructed abridged life tables for each America, year, and sex, and extracted life expectancy at birth, partial life expectancy within five age groups (0–4, 5–24, 25–44, 45–64, and 65–84 years), and remaining life expectancy at age 85 years. We defined the ten Americas as: America 1—Asian individuals; America 2—Latino individuals in other counties; America 3—White (majority), Asian, and American Indian or Alaska Native (AIAN) individuals in other counties; America 4—White individuals in non-metropolitan and low-income Northlands; America 5—Latino individuals in the Southwest; America 6—Black individuals in other counties; America 7—Black individuals in highly segregated metropolitan areas; America 8—White individuals in low-income Appalachia and Lower Mississippi Valley; America 9—Black individuals in the non-metropolitan and low-income South; and America 10—AIAN individuals in the West. Large disparities in life expectancy between the Americas were apparent throughout the study period but grew more substantial over time, particularly during the first 2 years of the COVID-19 pandemic. In 2000, life expectancy ranged 12·6 years (95% uncertainty interval 12·2–13·1), from 70·5 years (70·3–70·7) for America 9 to 83·1 years (82·7–83·5) for America 1. The gap between Americas with the lowest and highest life expectancies increased to 13·9 years (12·6–15·2) in 2010, 15·8 years (14·4–17·1) in 2019, 18·9 years (17·7–20·2) in 2020, and 20·4 years (19·0–21·8) in 2021. The trends over time in life expectancy varied by America, leading to changes in the ordering of the Americas over this time period. America 10 was the only America to experience substantial declines in life expectancy from 2000 to 2019, and experienced the largest declines from 2019 to 2021. The three Black Americas (Americas 6, 7, and 9) all experienced relatively large increases in life expectancy before 2020, and thus all three had higher life expectancy than America 10 by 2006, despite starting at a lower level in 2000. By 2010, the increase in America 6 was sufficient to also overtake America 8, which had a relatively flat trend from 2000 to 2019. America 5 had relatively similar life expectancy to Americas 3 and 4 in 2000, but a faster rate of increase in life expectancy from 2000 to 2019, and thus higher life expectancy in 2019; however, America 5 experienced a much larger decline in 2020, reversing this advantage. In some cases, these trends varied substantially by sex and age group. There were also large differences in income and educational attainment among the ten Americas, but the patterns in these variables differed from each other and from the patterns in life expectancy in some notable ways. For example, America 3 had the highest income in most years, and the highest proportion of high-school graduates in all years, but was ranked fourth or fifth in life expectancy before 2020. Our analysis confirms the continued existence of different Americas within the USA. One's life expectancy varies dramatically depending on where one lives, the economic conditions in that location, and one's racial and ethnic identity. This gulf was large at the beginning of the century, only grew larger over the first two decades, and was dramatically exacerbated by the COVID-19 pandemic. These results underscore the vital need to reduce the massive inequity in longevity in the USA, as well as the benefits of detailed analyses of the interacting drivers of health disparities to fully understand the nature of the problem. Such analyses make targeted action possible—local planning and national prioritisation and resource allocation—to address the root causes of poor health for those most disadvantaged so that all Americans can live long, healthy lives, regardless of where they live and their race, ethnicity, or income. State of Washington, Bloomberg Philanthropies, Bill & Melinda Gates Foundation.

Background Preclinical studies have demonstrated that propranolol inhibits several pathways involved in breast cancer progression and metastasis. We investigated whether breast cancer patients who used propranolol, or other non-selective beta-blockers, had reduced breast cancer-specific or all-cause mortality in eight European cohorts. Methods Incident breast cancer patients were identified from eight cancer registries and compiled through the European Cancer Pharmacoepidemiology Network. Propranolol and non-selective beta-blocker use was ascertained for each patient. Breast cancer-specific and all-cause mortality were available for five and eight cohorts, respectively. Cox regression models were used to calculate hazard ratios (HR) and 95% confidence intervals (CIs) for cancer-specific and all-cause mortality by propranolol and non-selective beta-blocker use. HRs were pooled across cohorts using meta-analysis techniques. Dose–response analyses by number of prescriptions were also performed. Analyses were repeated investigating propranolol use before cancer diagnosis. Results The combined study population included 55,252 and 133,251 breast cancer patients in the analysis of breast cancer-specific and all-cause mortality respectively. Overall, there was no association between propranolol use after diagnosis of breast cancer and breast cancer-specific or all-cause mortality (fully adjusted HR = 0.94, 95% CI, 0.77, 1.16 and HR = 1.09, 95% CI, 0.93, 1.28, respectively). There was little evidence of a dose–response relationship. There was also no association between propranolol use before breast cancer diagnosis and breast cancer-specific or all-cause mortality (fully adjusted HR = 1.03, 95% CI, 0.86, 1.22 and HR = 1.02, 95% CI, 0.94, 1.10, respectively). Similar null associations were observed for non-selective beta-blockers. Conclusions In this large pooled analysis of breast cancer patients, use of propranolol or non-selective beta-blockers was not associated with improved survival.

Coral reefs are among the most diverse and iconic ecosystems on Earth, but a range of anthropogenic pressures are threatening their persistence. Owing to their remoteness, broad spatial coverage and cross‐jurisdictional locations, there are no high‐resolution remotely sensed maps available at the global scale. Here we present a framework that is capable of mapping coral reef habitats from individual reefs (~200 km2) to entire barrier reef systems (200 000 km2) and across vast ocean extents (>6 000 000 km2). This is the first time this has been demonstrated using a consistent and transparent remote sensing mapping framework. The ten maps that we present achieved good accuracy (78% mean overall accuracy) from multiple input image datasets and training data sources, and our framework was shown to be adaptable to either benthic or geomorphic reef features and across diverse coral reef environments. These new generation high‐resolution map data will be useful for supporting ecosystem risk assessments, detecting change in ecosystem dynamics and targeting efforts to monitor local‐scale changes in coral cover and reef health. Here we present a mapping framework for coral reefs from the scale of individual reefs to the entire reef systems across millions of square kilometers of ocean. The framework can utilize a wide range of input covariate and training data sources, and outputs both geomorphic and benthic map types. The maps presented are the largest ever coral reefs maps produced from a consistent and transparent remote sensing approach.