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1.
PLoS One ; 17(4): e0266563, 2022.
Article in English | MEDLINE | ID: covidwho-1789186

ABSTRACT

BACKGROUND: Social status gradients are powerful health determinants for individuals living in poverty. We tested whether winning an Academy award (Oscar) for acting was associated with long-term survival. METHODS: We conducted a longitudinal cohort analysis of all actors and actresses nominated for an Academy award in a leading or a supporting role. For each, a control was identified based on age, sex, and co-staring in the same film. RESULTS: Overall, 2,111 individuals were analyzed with 1,122 total deaths occurring during a median follow-up of 68.8 years. Comparisons of winners to controls yielded a 4.8% relative difference average life-span (95% confidence interval: 1.6 to 7.9, p = 0.004), a 5.1 year absolute increase in life expectancy (95% confidence interval: 3.0 to 7.2, p < 0.001), and a 41% improvement in mortality hazard (95% confidence interval: 19 to 68, p < 0.001). The increased survival tended to be greater in recent years, for individuals winning at a younger age, and among those with multiple wins. The increased survival replicated in secondary analyses comparing winners to nominees and was not observed in analyses comparing nominees to controls. CONCLUSIONS: Academy award winning actors and actresses show a positive association between success and survival, suggesting the importance of behavioral, psychological, or other modifiable health factors unrelated to poverty.


Subject(s)
Awards and Prizes , Life Expectancy , Cohort Studies , Humans , Longevity , Motion Pictures
2.
JAMA Netw Open ; 5(4): e227067, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1787609

ABSTRACT

Importance: Prior studies reported that US life expectancy decreased considerably in 2020 because of the COVID-19 pandemic, with estimates suggesting that the decreases were much larger among Hispanic and non-Hispanic Black populations than non-Hispanic White populations. Studies based on provisional data suggested that other high-income countries did not experience the large decrease in life expectancy observed in the US; this study sought to confirm these findings according to official death counts and to broaden the pool of comparison countries. Objective: To calculate changes in US life expectancy between 2019 and 2020 by sex, race, and ethnicity and to compare those outcomes with changes in other high-income countries. Design, Setting, and Participants: This cross-sectional study involved a simulation of life tables based on national death and population counts for the US and 21 other high-income countries in 2019 and 2020, by sex, including an analysis of US outcomes by race and ethnicity. Data were analyzed in January 2022. Exposures: Official death counts from the US and 21 peer countries. Main Outcomes and Measures: Life expectancy at birth and credible range (CR) based on 10% uncertainty. Results: Between 2019 and 2020, US life expectancy decreased by a mean of 1.87 years (CR, 1.70-2.03 years), with much larger decreases occurring in the Hispanic (3.70 years; CR, 3.53-3.87 years) and non-Hispanic Black (3.22 years; CR, 3.03-3.40 years) populations than in the non-Hispanic White population (1.38 years; CR, 1.21-1.54 years). The mean decrease in life expectancy among peer countries was 0.58 years (CR, 0.42-0.73 year) across all 21 countries. No peer country experienced decreases as large as those seen in the US. Conclusions and Relevance: Official death counts confirm that US life expectancy decreased between 2019 and 2020 on a scale not seen in 21 peer countries, substantially widening the preexisting gap in life expectancy between the US and peer countries. The decrease in US life expectancy was experienced disproportionately by Hispanic and non-Hispanic Black populations, consistent with a larger history of racial and ethnic health inequities resulting from policies of exclusion and systemic racism. Policies to address the systemic causes of the US health disadvantage relative to peer countries and persistent racial and ethnic inequities are essential.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , Cross-Sectional Studies , Humans , Infant, Newborn , Life Expectancy , Life Tables
3.
Misganaw, Awoke, Naghavi, Mohsen, Walker, Ally, Mirkuzie, Alemnesh H.; Giref, Ababi Zergaw, Berheto, Tezera Moshago, Waktola, Ebba Abate, Kempen, John H.; Eticha, Getachew Tollera, Wolde, Tsigereda Kifle, Deguma, Dereje, Abate, Kalkidan Hassen, Abegaz, Kedir Hussein, Ahmed, Muktar Beshir, Akalu, Yonas, Aklilu, Addis, Alemu, Biresaw Wassihun, Asemahagn, Mulusew A.; Awedew, Atalel Fentahun, Balakrishnan, Senthilkumar, Bekuma, Tariku Tesfaye, Beyene, Addisu Shunu, Beyene, Misrak Getnet, Bezabih, Yihienew Mequanint, Birhanu, Biruk Tesfaye, Chichiabellu, Tesfaye Yitna, Dachew, Berihun Assefa, Dagnew, Amare Belachew, Demeke, Feleke Mekonnen, Demissie, Getu Debalkie, Derbew Molla, Meseret, Dereje, Nebiyu, Deribe, Kebede, Desta, Abebaw Alemayehu, Eshetu, Munir Kassa, Ferede, Tomas Y.; Gebreyohannes, Eyob Alemayehu, Geremew, Abraham, Gesesew, Hailay Abrha, Getacher, Lemma, Glenn, Scott D.; Hafebo, Aregash Samuel, Hashi, Abdiwahab, Hassen, Hamid Yimam, Hay, Simon I.; Hordofa, Diriba Fufa, Huluko, Dawit Hoyiso, Kasa, Ayele Semachew, Kassahun Azene, Getinet, Kebede, Ermiyas Mulu, Kebede, Hafte Kahsay, Kelkay, Bayew, Kidane, Samuel Z.; Legesse, Samson Mideksa, Manamo, Wondimu Ayele, Melaku, Yohannes Adama A.; Mengesha, Endalkachew Worku, Mengesha, Sisay Derso, Merie, Hayimro Edemealem, Mersha, Abera M.; Mersha, Amanual Getnet, Mirutse, Mizan Kiros, Mohammed, Ammas Siraj, Mohammed, Hussen, Mohammed, Salahuddin, Netsere, Henok Biresaw, Nigatu, Dabere, Obsa, Mohammed Suleiman, Odo, Daniel Bogale, Omer, Muktar, Regassa, Lemma Demissie, Sahiledengle, Biniyam, Shaka, Mohammed Feyisso, Shiferaw, Wondimeneh Shibabaw, Sidemo, Negussie Boti, Sinke, Abiy H.; Sintayehu, Yitagesu, Sorrie, Muluken Bekele, Tadesse, Birkneh Tilahun, Tadesse, Eyayou Girma, Tamir, Zemenu, Tamiru, Animut Tagele, Tareke, Amare Abera, Tefera, Yonas Getaye, Tekalegn, Yohannes, Tesema, Ayenew Kassie, Tesema, Tefera Tadele, Tesfay, Fisaha Haile, Tessema, Zemenu Tadesse, Tilahun, Tadesse, Tsegaye, Gebiyaw Wudie, Tusa, Biruk Shalmeno, Weledesemayat, Geremew Tassew, Yazie, Taklo Simeneh, Yeshitila, Yordanos Gizachew, Yirdaw, Birhanu Wubale, Zegeye, Desalegn Tegabu, Murray, Christopher J. L.; Gebremedhin, Lia Tadesse.
Lancet ; 399(10332): 1322-1335, 2022 Apr 02.
Article in English | MEDLINE | ID: covidwho-1768603

ABSTRACT

BACKGROUND: Previous Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) studies have reported national health estimates for Ethiopia. Substantial regional variations in socioeconomic status, population, demography, and access to health care within Ethiopia require comparable estimates at the subnational level. The GBD 2019 Ethiopia subnational analysis aimed to measure the progress and disparities in health across nine regions and two chartered cities. METHODS: We gathered 1057 distinct data sources for Ethiopia and all regions and cities that included census, demographic surveillance, household surveys, disease registry, health service use, disease notifications, and other data for this analysis. Using all available data sources, we estimated the Socio-demographic Index (SDI), total fertility rate (TFR), life expectancy, years of life lost, years lived with disability, disability-adjusted life-years, and risk-factor-attributable health loss with 95% uncertainty intervals (UIs) for Ethiopia's nine regions and two chartered cities from 1990 to 2019. Spatiotemporal Gaussian process regression, cause of death ensemble model, Bayesian meta-regression tool, DisMod-MR 2.1, and other models were used to generate fertility, mortality, cause of death, and disability rates. The risk factor attribution estimations followed the general framework established for comparative risk assessment. FINDINGS: The SDI steadily improved in all regions and cities from 1990 to 2019, yet the disparity between the highest and lowest SDI increased by 54% during that period. The TFR declined from 6·91 (95% UI 6·59-7·20) in 1990 to 4·43 (4·01-4·92) in 2019, but the magnitude of decline also varied substantially among regions and cities. In 2019, TFR ranged from 6·41 (5·96-6·86) in Somali to 1·50 (1·26-1·80) in Addis Ababa. Life expectancy improved in Ethiopia by 21·93 years (21·79-22·07), from 46·91 years (45·71-48·11) in 1990 to 68·84 years (67·51-70·18) in 2019. Addis Ababa had the highest life expectancy at 70·86 years (68·91-72·65) in 2019; Afar and Benishangul-Gumuz had the lowest at 63·74 years (61·53-66·01) for Afar and 64.28 (61.99-66.63) for Benishangul-Gumuz. The overall increases in life expectancy were driven by declines in under-5 mortality and mortality from common infectious diseases, nutritional deficiency, and war and conflict. In 2019, the age-standardised all-cause death rate was the highest in Afar at 1353·38 per 100 000 population (1195·69-1526·19). The leading causes of premature mortality for all sexes in Ethiopia in 2019 were neonatal disorders, diarrhoeal diseases, lower respiratory infections, tuberculosis, stroke, HIV/AIDS, ischaemic heart disease, cirrhosis, congenital defects, and diabetes. With high SDIs and life expectancy for all sexes, Addis Ababa, Dire Dawa, and Harari had low rates of premature mortality from the five leading causes, whereas regions with low SDIs and life expectancy for all sexes (Afar and Somali) had high rates of premature mortality from the leading causes. In 2019, child and maternal malnutrition; unsafe water, sanitation, and handwashing; air pollution; high systolic blood pressure; alcohol use; and high fasting plasma glucose were the leading risk factors for health loss across regions and cities. INTERPRETATION: There were substantial improvements in health over the past three decades across regions and chartered cities in Ethiopia. However, the progress, measured in SDI, life expectancy, TFR, premature mortality, disability, and risk factors, was not uniform. Federal and regional health policy makers should match strategies, resources, and interventions to disease burden and risk factors across regions and cities to achieve national and regional plans, Sustainable Development Goals, and universal health coverage targets. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
Global Burden of Disease , Global Health , Life Expectancy , Adult , Aged , Bayes Theorem , Cause of Death , Child , Ethiopia/epidemiology , Humans , Infant, Newborn , Quality-Adjusted Life Years , Risk Factors
4.
Ciênc. Saúde Colet ; 25(supl.1): 2403-2410, Mar. 2020. tab, graf
Article in English | WHO COVID, LILACS (Americas) | ID: covidwho-1725051

ABSTRACT

Abstract Mortality statistics due to COVID-19 worldwide are compared, by adjusting for the size of the population and the stage of the pandemic. Data from the European Centre for Disease Control and Prevention, and Our World in Data websites were used. Analyses are based on number of deaths per one million inhabitants. In order to account for the stage of the pandemic, the baseline date was defined as the day in which the 10th death was reported. The analyses included 78 countries and territories which reported 10 or more deaths by April 9. On day 10, India had 0.06 deaths per million, Belgium had 30.46 and San Marino 618.78. On day 20, India had 0.27 deaths per million, China had 0.71 and Spain 139.62. On day 30, four Asian countries had the lowest mortality figures, whereas eight European countries had the highest ones. In Italy and Spain, mortality on day 40 was greater than 250 per million, whereas in China and South Korea, mortality was below 4 per million. Mortality on day 10 was moderately correlated with life expectancy, but not with population density. Asian countries presented much lower mortality figures as compared to European ones. Life expectancy was found to be correlated with mortality.


Resumo Neste artigo, são comparadas as estatísticas de mortalidade por COVID-19 no mundo, ajustando-se para o tamanho da população e para o estágio da pandemia em cada país. Foram utilizados dados dos websites do Centro para o Controle e Prevenção de Doenças da Europa e do Our World in Data. As análises são baseadas no número de mortes por um milhão de habitantes. Para levar em consideração o estágio da pandemia, definiu-se como linha de base a data da décima morte em cada país. As análises incluíram 78 países e territórios com 10 ou mais mortes relatadas até o dia 09 de abril. No dia 10, a Índia tinha 0,06 mortes por um milhão, a Bélgica 30,46 e San Marino 618,78. No dia 20, a Índia tinha 0,27 mortes por um milhão, a China 0,71 e a Espanha 139,62. No dia 30, quatro países da Ásia tinham as menores taxas de mortalidade, enquanto que oito países europeus tinham as maiores. Na Itália e na Espanha, a mortalidade no dia 40 era maior do que 250 por um milhão, enquanto que na China e na Coréia do Sul era abaixo de 4 por um milhão. A mortalidade no dia 10 correlacionou-se moderadamente com a expectativa de vida, mas não mostrou correlação com a densidade populacional. Os países asiáticos apresentaram taxas de mortalidade muito menores do que aquelas observadas nos europeus. A expectativa de vida correlacionou-se com a mortalidade.


Subject(s)
Humans , Pneumonia, Viral/mortality , Global Health/statistics & numerical data , Life Expectancy , Coronavirus Infections/mortality , Pandemics/statistics & numerical data , Betacoronavirus , Coronavirus Infections
5.
Age Ageing ; 51(1)2022 01 06.
Article in English | MEDLINE | ID: covidwho-1722192

ABSTRACT

Populations in Asian developed economies are rapidly ageing, such that, currently, Hong Kong and Japan have the longest life expectancy at birth for both men and women. However, extended lifespan is not necessarily accompanied by prolongation of health span, such that there is increasing prevalence of frailty and dependency, which translates into increase in complex health and social needs as well as increase in absolute numbers of older adults that require such needs. Consideration of social determinants of healthy ageing would be important in the design of equitable health and social care systems. There is a trend towards development of integrated medical social care in the community in Asian countries. Long-term care insurance and also philanthropic support play a role in the financing of such care models.


Subject(s)
Aging , Frailty , Aged , Female , Frailty/diagnosis , Frailty/epidemiology , Frailty/therapy , Hong Kong/epidemiology , Humans , Insurance, Long-Term Care , Life Expectancy , Male
6.
Lancet Glob Health ; 9(10): e1372-e1379, 2021 10.
Article in English | MEDLINE | ID: covidwho-1701046

ABSTRACT

BACKGROUND: The tuberculosis targets for the UN Sustainable Development Goals (SDGs) call for a 90% reduction in tuberculosis deaths by 2030, compared with 2015, but meeting this target now seems highly improbable. To assess the economic impact of not meeting the target until 2045, we estimated full-income losses in 120 countries, including those due to excess deaths resulting from COVID-19-related disruptions to tuberculosis services, for the period 2020-50. METHODS: Annual mortality risk changes at each age in each year from 2020 to 2050 were estimated for 120 countries. This risk change was then converted to full-income risk by calculating a population-level mortality risk change and multiplying it by the value of a statistical life-year in each country and year. As a comparator, we assumed that current rates of tuberculosis continue to decline through the period of analysis. We calculated the full-income losses, and mean life expectancy losses per person, at birth and at age 35 years, under scenarios in which the SDG targets are met in 2030 and in 2045. We defined the cost of inaction as the difference in full-income losses and tuberculosis mortality between these two scenarios. FINDINGS: From 2020 to 2050, based on the current annual decrease in tuberculosis deaths of 2%, 31·8 million tuberculosis deaths (95% uncertainty interval 25·2 million-39·5 million) are estimated to occur, corresponding to an economic loss of US$17·5 trillion (14·9 trillion-20·4 trillion). If the SDG tuberculosis mortality target is met in 2030, 23·8 million tuberculosis deaths (18·9 million-29·5 million) and $13·1 trillion (11·2 trillion-15·3 trillion) in economic losses can be avoided. If the target is met in 2045, 18·1 million tuberculosis deaths (14·3 million-22·4 million) and $10·2 trillion (8·7 trillion-11·8 trillion) can be avoided. The cost of inaction of not meeting the SDG tuberculosis mortality target until 2045 (vs 2030) is, therefore, 5·7 million tuberculosis deaths (5·1 million-8·1 million) and $3·0 trillion (2·5 trillion-3·5 trillion) in economic losses. COVID-19-related disruptions add $290·3 billion (260·2 billion-570·1 billion) to this cost. INTERPRETATION: Failure to achieve the SDG tuberculosis mortality target by 2030 will lead to profound economic and health losses. The effects of delay will be greatest in sub-Saharan Africa. Affected countries, donor nations, and the private sector should redouble efforts to finance tuberculosis programmes and research because the economic dividend of such strategies is likely to be substantial. FUNDING: None.


Subject(s)
Life Expectancy , Tuberculosis/economics , Tuberculosis/mortality , COVID-19 , Global Burden of Disease/economics , HIV Infections/complications , Humans , Sustainable Development , Tuberculosis/prevention & control
7.
Rev. bras. estud. popul ; 39: e0182, 2022. tab
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1698772

ABSTRACT

A presente nota de pesquisa estima o impacto das mortes por Covid-19 sobre a esperança de vida no Brasil e regiões para os primeiros seis meses de 2020. Com base nos dados do Datasus e nas tábuas de vida com decremento simples, estimou-se que as mortes por Covid-19 ocorridas até 18 de agosto de 2020 tiveram impacto estatisticamente negativo na esperança de vida ao nascer, tanto masculina (-1,05 ano) quanto feminina (-0,85 ano). Em termos regionais, a maior perda em anos de vida é estimada no Norte (-1,65 ano para homens e -1,48 ano para mulheres), enquanto o Sul foi a região com menor impacto (-0,5 ano para homens e -0,36 para mulheres). Os resultados do modelo logístico para o país apontam que a mortalidade por Covid-19 tende a ser maior entre a população com mais de 65 anos, homens, pretos e de baixa instrução. As comorbidades aumentam a chance de desfecho morte, especialmente doença hepática e renal crônica. Tais análises foram ainda desagregadas por grandes regiões brasileiras.


This research note estimates the impact of deaths by Covid-19 on life expectancy in Brazil and the Regions for the first six months of 2020. Based on data from Datasus and the decreasing life tables, it was estimated that deaths by Covid-19 that occurred until August 18, 2020 had a statistically negative impact on life expectancy at birth, both male (-1.05 years) and female (-0.85 years). In regional terms, the greatest loss in years of life is estimated in the North (-1.65 years for men and -1.48 years for women), while in the South it was -0.5 year for men and -0.36 for women. The results of the logistic model for the country show that Covid-19 mortality tends to be higher among males, blacks, people with low education level and people over 65 years old. Comorbidities increase the chance of death, especially liver disease and chronic kidney disease. Such analyzes were further disaggregated by large Brazilian regions.


Esta nota de investigación estima el impacto de las muertes por Covid-19 en la esperanza de vida en Brasil y sus regiones durante los primeros seis meses de 2020. Con base en los datos de Datasus y de las tablas de vida decrecientes, se estimó que las muertes por Covid-19 que ocurrieron hasta el 18 de agosto de 2020 tuvieron un impacto estadísticamente negativo en la esperanza de vida al nacer, tanto en hombres (−1,05 años) como en mujeres (−0,85 año). En términos regionales, la mayor pérdida en años de vida se estima en el Norte (−1,65 año para los hombres y −1,48 años para las mujeres), mientras que en el Sur fue de −0,5 años para los hombres y −0,36 para las mujeres. Los resultados del modelo logístico para el país muestran que la mortalidad por Covid-19 tiende a ser mayor entre la población mayor de 65 años, hombres, afrobrasileros y de bajo nivel educativo. Las comorbilidades aumentan la probabilidad de muerte, especialmente la enfermedad hepática y la enfermedad renal crónica. Dichos análisis se desglosaron aun más por grandes regiones brasileñas.


Subject(s)
Humans , Socioeconomic Factors , Brazil , Mortality , Life Tables , COVID-19/mortality , Statistical Analysis , Life Expectancy , Pandemics
8.
Int J Environ Res Public Health ; 19(3)2022 01 29.
Article in English | MEDLINE | ID: covidwho-1686741

ABSTRACT

Remaining life expectancy at age 60 (in short: RLE) is an important indicator of the health status of a population's elders. Until now, RLE has not been thoroughly investigated at the district level in Germany. In this study we analyzed, based on recent publicly available data (2015-2017), and for men and women separately, how large the RLE differences were in Germany across the 401 districts. Furthermore, we examined a wide range of potential social determinants in terms of their bivariate and multivariate (i.e., partial) impact on men's and women's RLE. Men's district-level RLE ranged between 19.89 and 24.32 years, women's district-level RLE between 23.67 and 27.16 years. The best single predictor both for men's and women's RLE at district level was 'proportion of employees with academic degree' with standardized partial regression coefficients of 0.42 (men) and 0.51 (women). Second and third in rank were classic economic predictors, such as 'household income' (men), 'proportion of elder with financial elder support' (women), and 'unemployment' (men and women). Indicators expressing the availability of medical services and staffing levels of nursing homes and services had at best a marginal partial impact. This study contributes to the growing body of evidence that a population's educational level is a decisive determinant of population health resp. life expectancy in contemporary industrialized societies.


Subject(s)
Life Expectancy , Social Determinants of Health , Aged , Educational Status , Female , Germany/epidemiology , Humans , Male , Middle Aged , Socioeconomic Factors
9.
PLoS Med ; 19(2): e1003904, 2022 02.
Article in English | MEDLINE | ID: covidwho-1686090

ABSTRACT

BACKGROUND: Deaths in the first year of the Coronavirus Disease 2019 (COVID-19) pandemic in England and Wales were unevenly distributed socioeconomically and geographically. However, the full scale of inequalities may have been underestimated to date, as most measures of excess mortality do not adequately account for varying age profiles of deaths between social groups. We measured years of life lost (YLL) attributable to the pandemic, directly or indirectly, comparing mortality across geographic and socioeconomic groups. METHODS AND FINDINGS: We used national mortality registers in England and Wales, from 27 December 2014 until 25 December 2020, covering 3,265,937 deaths. YLLs (main outcome) were calculated using 2019 single year sex-specific life tables for England and Wales. Interrupted time-series analyses, with panel time-series models, were used to estimate expected YLL by sex, geographical region, and deprivation quintile between 7 March 2020 and 25 December 2020 by cause: direct deaths (COVID-19 and other respiratory diseases), cardiovascular disease and diabetes, cancer, and other indirect deaths (all other causes). Excess YLL during the pandemic period were calculated by subtracting observed from expected values. Additional analyses focused on excess deaths for region and deprivation strata, by age-group. Between 7 March 2020 and 25 December 2020, there were an estimated 763,550 (95% CI: 696,826 to 830,273) excess YLL in England and Wales, equivalent to a 15% (95% CI: 14 to 16) increase in YLL compared to the equivalent time period in 2019. There was a strong deprivation gradient in all-cause excess YLL, with rates per 100,000 population ranging from 916 (95% CI: 820 to 1,012) for the least deprived quintile to 1,645 (95% CI: 1,472 to 1,819) for the most deprived. The differences in excess YLL between deprivation quintiles were greatest in younger age groups; for all-cause deaths, a mean of 9.1 years per death (95% CI: 8.2 to 10.0) were lost in the least deprived quintile, compared to 10.8 (95% CI: 10.0 to 11.6) in the most deprived; for COVID-19 and other respiratory deaths, a mean of 8.9 years per death (95% CI: 8.7 to 9.1) were lost in the least deprived quintile, compared to 11.2 (95% CI: 11.0 to 11.5) in the most deprived. For all-cause mortality, estimated deaths in the most deprived compared to the most affluent areas were much higher in younger age groups, but similar for those aged 85 or over. There was marked variability in both all-cause and direct excess YLL by region, with the highest rates in the North West. Limitations include the quasi-experimental nature of the research design and the requirement for accurate and timely recording. CONCLUSIONS: In this study, we observed strong socioeconomic and geographical health inequalities in YLL, during the first calendar year of the COVID-19 pandemic. These were in line with long-standing existing inequalities in England and Wales, with the most deprived areas reporting the largest numbers in potential YLL.


Subject(s)
COVID-19/mortality , Adult , Aged , Cardiovascular Diseases/mortality , Cause of Death , Diabetes Mellitus/mortality , England/epidemiology , Female , Health Status Disparities , Humans , Interrupted Time Series Analysis , Life Expectancy , Male , Middle Aged , Neoplasms/mortality , Residence Characteristics , Respiratory Tract Diseases/mortality , Socioeconomic Factors , Wales/epidemiology
10.
PLoS One ; 17(1): e0262846, 2022.
Article in English | MEDLINE | ID: covidwho-1662440

ABSTRACT

In many countries of the world, COVID-19 pandemic has led to exceptional changes in mortality trends. Some studies have tried to quantify the effects of Covid-19 in terms of a reduction in life expectancy at birth in 2020. However, these estimates might need to be updated now that, in most countries, the mortality data for the whole year are available. We used data from the Human Mortality Database (HMD) Short-Term Mortality Fluctuations (STMF) data series to estimate life expectancy in 2020 for several countries. The changes estimated using these data and the appropriate methodology seem to be more pessimistic than those that have been proposed so far: life expectancy dropped in the Russia by 2.16 years, 1.85 in USA, and 1.27 in England and Wales. The differences among countries are substantial: many countries (e.g. Denmark, Island, Norway, New Zealand, South Korea) saw a rather limited drop in life expectancy or have even seen an increase in life expectancy.


Subject(s)
COVID-19/mortality , Life Expectancy , SARS-CoV-2/pathogenicity , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/virology , Child , Child, Preschool , Databases, Factual , Developed Countries , England/epidemiology , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Mortality , New Zealand/epidemiology , Norway/epidemiology , Republic of Korea/epidemiology , Russia/epidemiology , United States/epidemiology , Wales/epidemiology , Young Adult
11.
Ann Intern Med ; 174(12): 1700-1709, 2021 12.
Article in English | MEDLINE | ID: covidwho-1614239

ABSTRACT

BACKGROUND: Fully assessing the mortality burden of the COVID-19 pandemic requires measuring years of life lost (YLLs) and accounting for quality-of-life differences. OBJECTIVE: To measure YLLs and quality-adjusted life-years (QALYs) lost from the COVID-19 pandemic, by age, sex, race/ethnicity, and comorbidity. DESIGN: State-transition microsimulation model. DATA SOURCES: Health and Retirement Study, Panel Study of Income Dynamics, data on excess deaths from the Centers for Disease Control and Prevention, and nursing home death counts from the Centers for Medicare & Medicaid Services. TARGET POPULATION: U.S. population aged 25 years and older. TIME HORIZON: Lifetime. PERSPECTIVE: Individual. INTERVENTION: COVID-19 pandemic through 13 March 2021. OUTCOME MEASURES: YLLs and QALYs lost per 10 000 persons in the population. The estimates account for the age, sex, and race/ethnicity of decedents, along with obesity, smoking behavior, lung disease, heart disease, diabetes, cancer, stroke, hypertension, dementia, and nursing home residence. RESULTS OF BASE-CASE ANALYSIS: The COVID-19 pandemic resulted in 6.62 million QALYs lost (9.08 million YLLs) through 13 March 2021, with 3.6 million (54%) lost by those aged 25 to 64 years. The greatest toll was on Black and Hispanic communities, especially among men aged 65 years or older, who lost 1138 and 1371 QALYs, respectively, per 10 000 persons. Absent the pandemic, 38% of decedents would have had average or above-average life expectancies for their subgroup defined by age, sex, and race/ethnicity. RESULTS OF SENSITIVITY ANALYSIS: Accounting for uncertainty in risk factors for death from COVID-19 yielded similar results. LIMITATION: Estimates may vary depending on assumptions about mortality and quality-of-life projections. CONCLUSION: Beyond excess deaths alone, the COVID-19 pandemic imposed a greater life expectancy burden on persons aged 25 to 64 years, including those with average or above-average life expectancies, and a disproportionate burden on Black and Hispanic communities. PRIMARY FUNDING SOURCE: National Institute on Aging.


Subject(s)
COVID-19/mortality , Pandemics , Adult , Age Distribution , Aged , COVID-19/ethnology , COVID-19/prevention & control , COVID-19 Vaccines , Comorbidity , Cost of Illness , Health Status Disparities , Humans , Life Expectancy , Middle Aged , Quality-Adjusted Life Years , Risk Factors , SARS-CoV-2 , Sex Distribution , United States/epidemiology
12.
Public Health ; 203: 91-96, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1561429

ABSTRACT

OBJECTIVES: The aim of the study is to calculate the years of life lost (YLL) and years of potential life lost (YPLL) due to COVID-19, according to age groups in Turkey in the first year of the pandemic and the cost of this burden. STUDY DESIGN: This is an observational study with quantitative analyses. METHODS: YLL due to premature deaths was calculated for men and women by interpolating the number of deaths and the expected life expectancy. YPLL was calculated according to the age 65 years. Productivity loss is an estimation of the cost of time lost at work-related activities-in a scenario analysis-using predetermined wage rates with the human capital theory. RESULTS: Men lost 205,177 (67.57%) years of life, whereas women lost 125,330 (32.43%) years of life. The YLL average age in men was 63.66 ± 14.66 years, and the YLL average age in women was 66.07 ± 15.46 years. The average YLL age in men was younger than in women (P < 0.001). Men lost 65,180 (70.16%) YPLL, whereas women lost 27,723 (29.84%) YPLL. The average YPLL age in women was younger than in men (P < 0.001). During one year of the pandemic, premature death cost Turkey 227,396,694 USD, the cost for one premature death was 14,187 USD, and the cost of any year of life lost was 1261 USD. CONCLUSION: YLL and YPLLs are very closely associated with COVID-19 deaths in the country. The economic dimensions of the pandemic with human losses are quite high.


Subject(s)
COVID-19 , Aged , Aged, 80 and over , Efficiency , Female , Humans , Life Expectancy , Male , Middle Aged , SARS-CoV-2 , Turkey/epidemiology
13.
Annu Rev Public Health ; 42: 381-403, 2021 04 01.
Article in English | MEDLINE | ID: covidwho-1554162

ABSTRACT

In recent years, life expectancy in the United States has stagnated, followed by three consecutive years of decline. The decline is small in absolute terms but is unprecedented and has generated considerable research interest and theorizing about potential causes. Recent trends show that the decline has affected nearly all race/ethnic and gender groups, and the proximate causes of the decline are increases in opioid overdose deaths, suicide, homicide, and Alzheimer's disease. A slowdown in the long-term decline in mortality from cardiovascular diseases has also prevented life expectancy from improving further. Although a popular explanation for the decline is the cumulative decline in living standards across generations, recent trends suggest that distinct mechanisms for specific causes of death are more plausible explanations. Interventions to stem the increase in overdose deaths, reduce access to mechanisms that contribute to violent deaths, and decrease cardiovascular risk over the life course are urgently needed to improve mortality in the United States.


Subject(s)
Life Expectancy/trends , Humans , United States/epidemiology
14.
Int J Health Plann Manage ; 37(2): 1131-1156, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1549200

ABSTRACT

The present study was conducted in Indian states to examine the effect of monetary and non-monetary factors on Infant Mortality Rate (IMR) and Life Expectancy at Birth (LEB) by using the panel regression model. In addition, an attempt was also made to analysis the unequal pattern of health infrastructure and services across states over time with the help of a composite index on health infrastructure and services. It was found that the index value of the best performing state Chhattisgarh is more than fourth six times that of the worst performing state. The study also showed that, despite the higher level of average per capita public health expenditure and moderately better health infrastructure, the COVID 19 induced death rate was high in Punjab, Sikkim, Delhi and Goa. The panel regression results revealed that, an average increase of 1% in the monetary factor, public health expenditure to Gross State Domestic Product ratio (PHEGSDPR), would decrease the average of IMR by about 10%. Moreover, the elasticity of IMR with respect to non-monetary factor, health infrastructure and services per 0.1 million population (HISPLP), was negative and significant. Likewise, the explanatory variables, HISPLP and PHEGSDPR have a positive and significant effect on the LEB.


Subject(s)
COVID-19 , Humans , Infant , Infant Mortality , Infant, Newborn , Life Expectancy , Outcome Assessment, Health Care , SARS-CoV-2
15.
Psychol Aging ; 37(2): 260-271, 2022 Mar.
Article in English | MEDLINE | ID: covidwho-1541134

ABSTRACT

The coronavirus pandemic threatens the health, future, and life of individuals and might hence accentuate perceptions of the fragility and finitude of life. We investigated how different perceptions of the pandemic (regarding the virus as a health threat and perceiving social and financial restrictions due to the pandemic) relate to different perceptions of life's finitude (i.e., future time perspective, death anxiety, and ideal life expectancy). Using longitudinal data from 1,042 adults (68% women; aged 18-95 years) gathered within the first and within the second peak of the pandemic in Germany, we expected decreases in future time perspective and ideal life expectancy, as well as increases in death anxiety in response to threatening perceptions of the pandemic. The results indicated decreasing future time perspectives, an accentuation of death anxiety right at the beginning of the pandemic, as well as stable ideal life expectancies. There was a tendency for more pronounced change among older adults. Initial levels and changes in the perceptions of finitude could partly be explained by initial and changing perceptions of the pandemic. Next to perceptions targeting the threat of the virus itself, perceptions of strong social and financial restrictions during the pandemic contributed to an altered stance toward the finitude of life. Concluding, we discuss stability and variation in perceptions of the finitude of life during a time of major societal change and a potentially life-threatening pandemic. (PsycInfo Database Record (c) 2022 APA, all rights reserved).


Subject(s)
COVID-19 , Aged , Aged, 80 and over , Aging , Anxiety/epidemiology , Female , Humans , Life Expectancy , Male , SARS-CoV-2
16.
JAMA Netw Open ; 4(11): e2131455, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1520138

ABSTRACT

Importance: This randomized clinical trial examines the feasibility and acceptability of a decision-making tool for increasing patient interest in individualized recommendations for preventive care services. Objective: To pilot a tool to help patients compare life expectancy gains from evidence-based preventive services. Design, Setting, and Participants: This randomized clinical trial examined patient and physician responses to a pilot decision tool incorporating personalized risk factors at 3 US primary care clinics between 2017 and 2020. Eligible patients were between ages 45 to 70 years with 2 or more high-risk factors. Patients were followed-up after 1 year. Interventions: The gain in life expectancy associated with guideline adherence to each recommended preventive service was estimated. Personalized estimates incorporating risk factors in electronic health records were displayed in a physician-distributed visual aid. During development, physicians discussed individualized results with patients using shared decision-making (SDM). During the trial, patients were randomized to receive individualized recommendations or usual care (nonmasked, parallel, 1:1 ratio). Main Outcomes and Measures: Primary outcome was patient interest in individualized recommendations, assessed by survey. Secondary outcomes were use of SDM, decisional comfort, readiness to change, and preventive services received within 1 year. Results: The study enrolled 104 patients (31 development, 39 intervention, 34 control), of whom 101 were included in analysis (mean [SD] age, 56.5 [5.3] years; 73 [72.3%] women; 80 [79.2%] Black patients) and 20 physicians. Intervention patients found the tool helpful and wanted to use it again, rating it a median 9 of 10 (IQR, 8-10) and 10 of 10 (8-10), respectively. Compared with the control group, intervention patients more often correctly identified the service least likely (18 [46%] vs 0; P = .03) to improve their life expectancy. A greater number of patients also identified the service most likely to improve their life expectancy (26 [69%] vs 10 [30%]; P = .07), although this result was not statistically significant. Intervention patients reported greater mean [SD] improvement in SDM (4.7 [6.9] points) and near-term readiness to change (13.8 points for top-3-ranked recommendations). Point estimates indicated that patients in the intervention group experienced greater, although non-statistically significant, reductions in percentage of body weight (-2.96%; 95% CI, -8.18% to 2.28%), systolic blood pressure (-6.42 mm Hg; 95% CI, -16.12 to 3.27 mm Hg), hemoglobin A1c (-0.68%; 95% CI, -1.82% to 0.45%), 10-year atherosclerotic cardiovascular disease risk score (-1.20%; 95% CI, -3.65% to 1.26%), and low-density lipoprotein cholesterol (-8.46 mg/dL; 95% CI, -26.63 to 9.70 mg/dL) than the control group. Nineteen of 20 physicians wanted to continue using the decision tool in the future. Conclusions and Relevance: In this clinical trial, an individualized preventive care decision support tool improved patient understanding of primary prevention and demonstrated promise for improved shared decision-making and preventive care utilization. Trial Registration: ClinicalTrials.gov Identifier: NCT03023813.


Subject(s)
Decision Making , Decision Support Techniques , Physician-Patient Relations , Preventive Medicine/methods , Aged , Attitude of Health Personnel , Evidence-Based Medicine , Female , Guideline Adherence , Humans , Life Expectancy , Male , Middle Aged , Physicians/psychology , Pilot Projects
18.
Lancet ; 398(10313): 1837-1850, 2021 11 13.
Article in English | MEDLINE | ID: covidwho-1510434

ABSTRACT

Type 1 diabetes is on the rise globally; however, the burden of mortality remains disproportionate in low-income and middle-income countries (LMICs). As 2021 marks 100 years since the discovery of insulin, we revisit progress, global burden of type 1 diabetes trends, and understanding of the pathogenesis and management practices related to the disease. Despite much progress, inequities in access and availability of insulin formulations persist and are reflected in differences in survival and morbidity patterns related to the disease. Some of these inequities have also been exacerbated by health-system challenges during the COVID-19 pandemic. There is a clear opportunity to improve access to insulin and related essential technologies for improved management of type 1 diabetes in LMICs, especially as a part of universal health coverage. These improvements will require concerted action and investments in human resources, community engagement, and education for the timely diagnosis and management of type 1 diabetes, as well as adequate health-care financing. Further research in LMICs, especially those in Africa, is needed to improve our understanding of the burden, risk factors, and implementation strategies for managing type 1 diabetes.


Subject(s)
Developing Countries , Diabetes Mellitus, Type 1/epidemiology , Diabetes Mellitus, Type 1/pathology , Diabetes Mellitus, Type 1/therapy , Global Burden of Disease/trends , Hypoglycemic Agents/therapeutic use , Insulin/therapeutic use , Adolescent , Child , Child, Preschool , Disease Management , History, 20th Century , History, 21st Century , Humans , Hypoglycemic Agents/economics , Hypoglycemic Agents/history , Insulin/economics , Insulin/history , Life Expectancy , Universal Health Insurance
20.
Front Public Health ; 9: 751197, 2021.
Article in English | MEDLINE | ID: covidwho-1505682

ABSTRACT

Background: More than 1 year after the beginning of the international spread of coronavirus 2019 (COVID-19), the reasons explaining its apparently lower reported burden in Africa are still to be fully elucidated. Few studies previously investigated the potential reasons explaining this epidemiological observation using data at the level of a few African countries. However, an updated analysis considering the various epidemiological waves and variables across an array of categories, with a focus on African countries might help to better understand the COVID-19 pandemic on the continent. Thus, we investigated the potential reasons for the persistently lower transmission and mortality rates of COVID-19 in Africa. Methods: Data were collected from publicly available and well-known online sources. The cumulative numbers of COVID-19 cases and deaths per 1 million population reported by the African countries up to February 2021 were used to estimate the transmission and mortality rates of COVID-19, respectively. The covariates were collected across several data sources: clinical/diseases data, health system performance, demographic parameters, economic indicators, climatic, pollution, and radiation variables, and use of social media. The collinearities were corrected using variance inflation factor (VIF) and selected variables were fitted to a multiple regression model using the R statistical package. Results: Our model (adjusted R-squared: 0.7) found that the number of COVID-19 tests per 1 million population, GINI index, global health security (GHS) index, and mean body mass index (BMI) were significantly associated (P < 0.05) with COVID-19 cases per 1 million population. No association was found between the median life expectancy, the proportion of the rural population, and Bacillus Calmette-Guérin (BCG) coverage rate. On the other hand, diabetes prevalence, number of nurses, and GHS index were found to be significantly associated with COVID-19 deaths per 1 million population (adjusted R-squared of 0.5). Moreover, the median life expectancy and lower respiratory infections rate showed a trend towards significance. No association was found with the BCG coverage or communicable disease burden. Conclusions: Low health system capacity, together with some clinical and socio-economic factors were the predictors of the reported burden of COVID-19 in Africa. Our results emphasize the need for Africa to strengthen its overall health system capacity to efficiently detect and respond to public health crises.


Subject(s)
COVID-19 , Pandemics , Africa/epidemiology , Humans , Life Expectancy , SARS-CoV-2
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