Smoking and health-related quality of life in English general population: Implications for economic evaluations

Copyright @ 2012 Vogl et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.Background: Little is known as to how health-related quality of life (HRQoL) when measured by generic instruments such as EQ-5D differ across smokers, ex-smokers and never-smokers in the general population; whether the overall pattern of this difference remain consistent in each domain of HRQoL; and what implications this variation, if any, would have for economic evaluations of tobacco control interventions. Methods: Using the 2006 round of Health Survey for England data (n = 13,241), this paper aims to examine the impact of smoking status on health-related quality of life in English population. Depending upon the nature of the EQ-5D data (i.e. tariff or domains), linear or logistic regression models were fitted to control for biology, clinical conditions, socio-economic background and lifestyle factors that an individual may have regardless of their smoking status. Age- and gender-specific predicted values according to smoking status are offered as the potential 'utility' values to be used in future economic evaluation models. Results: The observed difference of 0.1100 in EQ-5D scores between never-smokers (0.8839) and heavy-smokers (0.7739) reduced to 0.0516 after adjusting for biological, clinical, lifestyle and socioeconomic conditions. Heavy-smokers, when compared with never-smokers, were significantly more likely to report some/severe problems in all five domains - mobility (67%), self-care (70%), usual activity (42%), pain/discomfort (46%) and anxiety/depression (86%) -. 'Utility' values by age and gender for each category of smoking are provided to be used in the future economic evaluations. Conclusion: Smoking is significantly and negatively associated with health-related quality of life in English general population and the magnitude of this association is determined by the number of cigarettes smoked. The varying degree of this association, captured through instruments such as EQ-5D, may need to be fed into the design of future economic evaluations where the intervention being evaluated affects (e.g. tobacco control) or is affected (e.g. treatment for lung cancer) by individual's (or patients') smoking status

Estimating the potential survival gains by eliminating socioeconomic and sex inequalities in stage at diagnosis of melanoma.

BACKGROUND: Although inequalities in cancer survival are thought to reflect inequalities in stage at diagnosis, little evidence exists about the size of potential survival gains from eliminating inequalities in stage at diagnosis. METHODS: We used data on patients diagnosed with malignant melanoma in the East of England (2006-2010) to estimate the number of deaths that could be postponed by completely eliminating socioeconomic and sex differences in stage at diagnosis after fitting a flexible parametric excess mortality model. RESULTS: Stage was a strong predictor of survival. There were pronounced socioeconomic and sex inequalities in the proportion of patients diagnosed at stages III-IV (12 and 8% for least deprived men and women and 25 and 18% for most deprived men and women, respectively). For an annual cohort of 1025 incident cases in the East of England, eliminating sex and deprivation differences in stage at diagnosis would postpone approximately 24 deaths to beyond 5 years from diagnosis. Using appropriate weighting, the equivalent estimate for England would be around 215 deaths, representing 11% of all deaths observed within 5 years from diagnosis in this population. CONCLUSIONS: Reducing socioeconomic and sex inequalities in stage at diagnosis would result in substantial reductions in deaths within 5 years of a melanoma diagnosis.This article is an independent research supported by different funding bodies, beyond the authors’ own employing organisations. MJR was partially funded by a Cancer Research UK Postdoctoral Fellowship (CRUK_A13275). GL is supported by a Postdoctoral Fellowship award by the National Institute for Health Research (NIHR PDF-2011-04-047) to end of 2014 and a Cancer Research UK Clinician Scientist Fellowship award (A18180) from January 2015. The views expressed in this publication are those of the authors and not necessarily those of the National Health Service (NHS), the National Institute for Health Research, the Department of Health, Cancer Research UK, or any other organisation. We thank all staff at the National Cancer Registration Service, Public Health England, Eastern Office, who helped collect and code data used in this study. We particularly acknowledge the help of Dr Clement H Brown and Dr Brian A Rous who were responsible for staging.This is the final published version. It first appeared at http://www.nature.com/bjc/journal/v112/n1s/full/bjc201550a.html

Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10-14 and 50-54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings The global TFR decreased from 2.72 (95% uncertainty interval [UI] 2.66-2.79) in 2000 to 2.31 (2.17-2.46) in 2019. Global annual livebirths increased from 134.5 million (131.5-137.8) in 2000 to a peak of 139.6 million (133.0-146.9) in 2016. Global livebirths then declined to 135.3 million (127.2-144.1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2.1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27.1% (95% UI 26.4-27.8) of global livebirths. Global life expectancy at birth increased from 67.2 years (95% UI 66.8-67.6) in 2000 to 73.5 years (72.8-74.3) in 2019. The total number of deaths increased from 50.7 million (49.5-51.9) in 2000 to 56.5 million (53.7-59.2) in 2019. Under-5 deaths declined from 9.6 million (9.1-10.3) in 2000 to 5.0 million (4.3-6.0) in 2019. Global population increased by 25.7%, from 6.2 billion (6.0-6.3) in 2000 to 7.7 billion (7.5-8.0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58.6 years (56.1-60.8) in 2000 to 63.5 years (60.8-66.1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019. Interpretation Over the past 20 years, fertility rates have been dropping steadily and life expectancy has been increasing, with few exceptions. Much of this change follows historical patterns linking social and economic determinants, such as those captured by the GBD Socio-demographic Index, with demographic outcomes. More recently, several countries have experienced a combination of low fertility and stagnating improvement in mortality rates, pushing more populations into the late stages of the demographic transition. Tracking demographic change and the emergence of new patterns will be essential for global health monitoring. Copyright (C) 2020 The Author(s). Published by Elsevier Ltd.Peer reviewe

Five insights from the Global Burden of Disease Study 2019

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides a rules-based synthesis of the available evidence on levels and trends in health outcomes, a diverse set of risk factors, and health system responses. GBD 2019 covered 204 countries and territories, as well as first administrative level disaggregations for 22 countries, from 1990 to 2019. Because GBD is highly standardised and comprehensive, spanning both fatal and non-fatal outcomes, and uses a mutually exclusive and collectively exhaustive list of hierarchical disease and injury causes, the study provides a powerful basis for detailed and broad insights on global health trends and emerging challenges. GBD 2019 incorporates data from 281 586 sources and provides more than 3.5 billion estimates of health outcome and health system measures of interest for global, national, and subnational policy dialogue. All GBD estimates are publicly available and adhere to the Guidelines on Accurate and Transparent Health Estimate Reporting. From this vast amount of information, five key insights that are important for health, social, and economic development strategies have been distilled. These insights are subject to the many limitations outlined in each of the component GBD capstone papers.Peer reviewe