New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Bowel cancer registry data made whole: filling in the blanks through imputation in Northern Ireland

In healthcare, cost-effectiveness analysis (CEA) compares alternative strategies based on consequences and costs to allocate healthcare resources to benefit public health. CEA modelling assembles components of costs, quality of life utilities and survival analysis. Survival analysis can project the lifetime of a simulated individual based on available data, therefore survival data is vital within CEA.Supplementary data requested from the Northern Ireland Cancer Registry (NICR) obtained outputs published in the Pathway to a Cancer Diagnosis report [1] in NI, to inform colorectal cancer (CRC) natural history contained within a larger CEA model. The proportion of individuals diagnosed with CRC was presented based on the route, stage, sex and age, with the proportions of individuals alive after 3, 6 and 12 months. Missingness existed within the data to protect the patient’s identity. If &lt; 10 individuals were diagnosed with CRC based on a specified route, age group, stage and sex, the data were omitted. Also, if &lt; 3 individuals died 3/6/12 months after diagnosis, the data were omitted. Most missing data problems are solved by Rubin’s multiple imputation methods [2]. However, this approach can be biased towards missing not-at-random data compared to missing at/completely at-random data; thus, other approaches are required.Three approaches were developed to impute the missing values. The first approach randomly generated values based on why the data was initially omitted. The second and third approaches used the NICR’s publicly available 1 and 5-year net survival rates (NSRs) for CRC, categorised by age, sex and stage, however, did not incorporate the same routes found in [1]. The second approach considered the lowest NSRs based on route, stage and age. The third approach randomly generated values within the range of possible NSRs, using both the normal and uniform distributions. The 5-year NSRs from NICR were used to estimate the proportions of individuals after 5 years, to better inform and extend survival within the CEA model. After comparing all imputation approaches with the true NICR 1-year NSRs, the most appropriate choice was the third approach, using the normal distribution. Using this approach, we can illustrate the lifetime of an individual within the CEA model and produce more plausible results.Reference:1.Bannon F, Harbinson A, Mayock M, McKenna H. Pathways to a Cancer Diagnosis: Monitoring variation in the patient journey across Northern Ireland 2012 to 2016.2.Rubin DB. Multiple imputations in sample surveys - a phenomenological Bayesian approach to nonresponse. American Statistical Association. 1978;1:20–34.<br/

An observation simulation approach to colorectal cancer in Northern Ireland

Colorectal cancer (CRC) is the third most common cancer diagnosed worldwide [1]. In 2020, approximately 1.9 million new CRC cases were reported, with over 930,000 deaths [2]. CRC develops from abnormal growths in the colon and rectum called polyps [3]. Most polyps are benign but can become malignant. In Northern Ireland (NI), there are an average of 1,216 cases per year, with the odds of developing CRC before the age of 85 is 1 in 12 for men and 1 in 18 for women [4]. Fortunately, CRC is preventable via screening. In 2010, the NI Bowel Cancer Screening Programme (BCSP) was introduced and currently invites 60-74 years olds to participate in screening using a stool-based diagnostic test, the Faecal Immunochemical Test (FIT), with a positivity threshold of 120mg/Hb [5]. The government aim to improve the programme by extending the age range to include 50-year-olds [6]. A cost-effectiveness analysis is needed, a tool used to measure the value for money and evaluate the best strategy for the programme. This presentation will focus on the model build of the natural history component of a cost-effectiveness model built using NI-specific estimates. A discrete event simulation model has been developed, divided into a natural history, no screening and screening components. The natural history component models the progression of polyps for the NI population, simulating polyp onset to the progression of clinical cancer. No screening simulates the NI population during 2010-2023 for non-screen detected CRC cases and helps validate the model. The screening component simulates the programme for 2010-2023 to replicate real-world screening incidences and models from 2024 onwards to implement the current programme along with other potential strategies under consideration. Two million individuals have been simulated to represent the NI population in 2010 using 2011 Census data taken from the Office for National Statistics [7]. Firstly, the age of non-CRC death was modelled using a Gompertz distribution and lifetables data from the NI Statistics and Research Agency [8]. Using the NI Cancer Registry [4], specifically the Colorectal Polyp Register, polyp incidence was used to simulate those at risk of developing polyps in their lifetime. Using the same data, a Poisson regression model simulated the number of polyps for each person. The proportion of malignant polyps and the polyp location in the bowel were simulated using NI literature. The Weibull distribution using polyp register data simulated the age of polyp onset. For those at risk of developing cancer, preclinical CRC stages I-IV, followed by the stage and age of CRC clinical diagnosis were calibrated using CRC data. References [1]. International Agency for Research on Cancer. Cancer Today. 2020. URL: https://gco.iarc.fr/today/home [2]. World Health Organisation (WHO). Colorectal Cancer, Key facts. 2023. URL: https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer [3]. WebMD. Understanding Colorectal Cancer- The Basics. URL: www.webmd.com/colorectal-cancer/understanding-colorectal-cancer-basics [4]. Northern Ireland Cancer Registry (NICR). Colorectal cancer report. 2023. URL: https://www.qub.ac.uk/research-centres/nicr/CancerInformation/official-statistics/BySite/Colorectalc ancer/15 [5]. NI Bowel Cancer Screening Programme (NI BCSP). 2023. URL: https://cancerscreening.hscni.net/bowel-screening/overview/ [6]. BBC Newsline. Bowel cancer: Call for earlier screening to find tumours. 2023. URL: https://www.bbc.co.uk/news/uk-northern-ireland-67099153 [7]. Office for National Statistics (ONS). National life tables: Northern Ireland. 2021. URL:https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/lifeexpecta ncies/datasets/nationallifetablesnorthernirelandreferencetables [8]. Northern Ireland Research and Statistics Agency (NISRA). “Census 2021 main statistics demography tables– age and sex”. 2022. Available: https://www.nisra.gov.uk/publications/census-2021-main-statistics-demography-tables-age-and-se