life expectancy
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2022 ◽  
Vol 4 (1) ◽  
Author(s):  
Andi Riyanto ◽  
Syarwani Canon ◽  
Ivan Rahmad Santoso

This study aims to determine the factors that influence the distribution of income in Sulawesi. The data used is data sourced from the Central Statistics Agency, for the period 2011-2020 in 6 Sulawesi Island Provinces. The method used is multiple regression analysis with the help of the eviews 9 application. The results show that per capita expenditure has a positive and significant effect on income distribution, average years of schooling has a negative and significant effect on income distribution, life expectancy has a negative but not significant effect on income distribution.


2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Xue Yin ◽  
Jaeil Ahn ◽  
Simina M. Boca

Abstract Objective Life expectancy can be estimated accurately from a cohort of individuals born in the same year and followed from birth to death. However, due to the resource-consuming nature of following a cohort prospectively, life expectancy is often assessed based upon retrospective death record reviews. This conventional approach may lead to potentially biased estimates, in particular when estimating life expectancy of rare diseases such as Morquio syndrome A. We investigated the accuracy of life expectancy estimation using death records by simulating the survival of individuals with Morquio syndrome A under four different scenarios. Results When life expectancy was constant during the entire period, using death data did not result in a biased estimate. However, when life expectancy increased over time, as is often expected to be the case in rare diseases, using only death data led to a substantial underestimation of life expectancy. We emphasize that it is therefore crucial to understand how estimates of life expectancy are obtained, to interpret them in an appropriate context, and to assess estimation methods within a sensitivity analysis framework, similar to the simulations performed herein.


2022 ◽  
Author(s):  
Patrick Heuveline

Timely, high-quality mortality data have allowed for assessments of the impact of Covid-19 on life expectancies in upper-middle- and high-income countries. Extant data, though imperfect, suggest that the bulk of the pandemic-induced mortality might have occurred elsewhere. This article reports on changes in life expectancies around the world as far as they can be estimated from the evidence available at the end of 2021. The global life expectancy appears to have declined by .92 years between 2019 and 2020 and by another .72 years between 2020 and 2021, but the decline seems to have ended during the last quarter of 2021. Uncertainty about its exact size aside, this represents the first decline in global life expectancy since 1950, the first year for which a global estimate is available from the United Nations. Annual declines in life expectancy (from a 12-month period to the next) appear to have exceeded two years at some point before the end of 2021 in at least 50 countries. Since 1950, annual declines of that magnitude had only been observed in rare occasions, such as Cambodia in the 1970s, Rwanda in the 1990s, and possibly some sub-Saharan African nations at the peak of the HIV/AIDS pandemic.


2022 ◽  
Vol 9 ◽  
Author(s):  
Cuifeng Jiang ◽  
Hsuling Chang ◽  
Imran Shahzad

The present study attempts to examine the impact of digitization and green technology on the health outcomes of BRICS countries over the period of 1993–2019. Internet users measure digitalization, and health outcome is determined by life expectancy. The study employed the ARDL estimation approach for empirical investigation of country-specific analysis. GDP per capita and current health expenditures have been incorporated as control variables. The study findings reveal that digitalization results in increasing life expectancy in the long-run in BRICS except for Brazil. While green technology tends to enhance life expectancy in the long-run in Russia and China, it produces an insignificant impact on health outcomes in the short-run. While GDP and health expenditures also improve life expectancy in mostly BRICS economies in the long-run and short-run. Our study provides some policy implications for BRICS nations.


2022 ◽  
pp. 1-11
Author(s):  
Jeff Schaffert ◽  
Christian LoBue ◽  
Linda S. Hynan ◽  
John Hart ◽  
Heidi Rossetti ◽  
...  

Background: Life expectancy (LE) following Alzheimer’s disease (AD) is highly variable. The literature to date is limited by smaller sample sizes and clinical diagnoses. Objective: No study to date has evaluated predictors of AD LE in a retrospective large autopsy-confirmed sample, which was the primary objective of this study. Methods: Participants (≥50 years old) clinically and neuropathologically diagnosed with AD were evaluated using National Alzheimer’s Coordinating Center (N = 1,401) data. Analyses focused on 21 demographic, medical, neuropsychiatric, neurological, functional, and global cognitive predictors of LE at AD dementia diagnosis. These 21 predictors were evaluated in univariate analyses. Variables found to be significant were then entered into a forward multiple regression. LE was defined as months between AD diagnosis and death. Results: Fourteen predictors were significant in univariate analyses and entered into the regression. Seven predictors explained 27% of LE variance in 764 total participants. Mini-Mental State Examination (MMSE) score was the strongest predictor of LE, followed by sex, age, race/ethnicity, neuropsychiatric symptoms, abnormal neurological exam results, and functional impairment ratings. Post-hoc analyses revealed correlations of LE were strongest with MMSE ≤12. Conclusion: Global cognitive functioning was the strongest predictor of LE following diagnosis, and AD patients with severe impairment had the shortest LE. AD patients who are older, male, white, and have more motor symptoms, functional impairment, and neuropsychiatric symptoms were also more likely have shorter LE. While this model cannot provide individual prognoses, additional studies may focus on these variables to enhance predictions of LE in patients with AD.


2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Rujun Liao ◽  
Lin Hu ◽  
Qiang Liao ◽  
Tianyu Zhu ◽  
Haiqun Yang ◽  
...  

Abstract Background Continuous surveillance of death can measure health status of the population, reflect social development of a region, thus promote health service development in the region and improve the health level of local residents. Liangshan Yi Autonomous Prefecture was a poverty-stricken region in Sichuan province, China. While at the end of 2020, as the announcement of its last seven former severely impoverished counties had shaken off poverty, Liangshan declared victory against poverty. Since it is well known that the mortality and cause of death structure will undergo some undesirable changes as the economy develops, this study aimed to reveal the distribution of deaths, as well as analyze the latest mortality and death causes distribution characteristics in Liangshan in 2020, so as to provide references for the decision-making on health policies and the distribution of health resources in global poverty-stricken areas. Methods Liangshan carried out the investigation on underreporting deaths among population in its 11 counties in 2018, and combined with the partially available data from underreporting deaths investigation data in 2020 and the field experience, we have estimated the underreporting rates of death in 2020 using capture-recapture (CRC) method. The crude mortality rate, age-standardized mortality rate, proportion and rank of the death causes, potential years of life lost (PYLL), average years of life lost (AYLL), potential years of life lost rate (PYLLR), standardized potential years of life lost (SPYLL), premature mortality from non-communicable diseases (premature NCD mortality), life expectancy and cause-eliminated life expectancy were estimated and corrected. Results In 2020, Liangshan reported a total of 16,850 deaths, with a crude mortality rate of 608.75/100,000 and an age-standardized mortality rate of 633.50/100,000. Male mortality was higher than female mortality, while 0-year-old mortality of men was lower than women’s. The former severely impoverished counties’ age-standardized mortality and 0-year-old mortality were higher than those of the non-impoverished counties. The main cause of death spectrum was noncommunicable diseases (NCDs), and the premature NCD mortality of four major NCDs were 14.26% for the overall population, 19.16% for men and 9.27% for women. In the overall population, the top five death causes were heart diseases (112.07/100,000), respiratory diseases (105.85/100,000), cerebrovascular diseases (87.03/100,000), malignant tumors (73.92/100,000) and injury (43.89/100,000). Injury (64,216.78 person years), malignant tumors (41,478.33 person years) and heart diseases (29,647.83 person years) had the greatest burden on residents in Liangshan, and at the same time, the burden of most death causes on men were greater than those on women. The life expectancy was 76.25 years for overall population, 72.92 years for men and 80.17 years for women, respectively, all higher than the global level (73.3, 70.8 and 75.9 years). Conclusions Taking Liangshan in China as an example, this study analyzed the latest death situation in poverty-stricken areas, and proposed suggestions on the formulation of health policies in other poverty-stricken areas both at home and abroad.


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