Analysis of death causes of residents in poverty-stricken Areas in 2020: take Liangshan Yi Autonomous Prefecture in China as an example

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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Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

Frontiers in Public Health ◽

10.3389/fpubh.2021.766707 ◽

2021 ◽

Vol 9 ◽

Author(s):

Joshua J. Levy ◽

Rebecca M. Lebeaux ◽

Anne G. Hoen ◽

Brock C. Christensen ◽

Louis J. Vaickus ◽

...

Keyword(s):

United States ◽

Deep Learning ◽

Mortality Rate ◽

Satellite Images ◽

The United States ◽

Mortality Rates ◽

Image Features ◽

County Level ◽

Crude Mortality Rate ◽

Crude Mortality

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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Mortality from endometrial cancer in female population of Belgrade

Vojnosanitetski pregled ◽

10.2298/vsp0403267p ◽

2004 ◽

Vol 61 (3) ◽

pp. 267-272

Author(s):

Vesna Pantovic ◽

Mirjana Jarebinski ◽

Tatjana Pekmezovic ◽

Anita Knezevic ◽

Darija Kisic

Keyword(s):

Endometrial Cancer ◽

Mortality Rate ◽

Malignant Tumors ◽

Age Groups ◽

Mortality Rates ◽

Mortality Decline ◽

Female Population ◽

Average Percentage ◽

Standardized Mortality Rate

Data about mortality from malignant tumors of endometrium were analyzed in the Belgrade area during the period 1975-2000. The obtained results showed that the average percentage of endometrial cancer in mortality structure from all the cancers of female population was 2.65%. During the observed 26-years period, malignant tumors of endometrium constituted 17.38% of all the tumors of gynecological localization. The standardized mortality rate in 1975 (population worldwide used as a standard) 7.06/100 000 population while in 2000 it was 1.78/100 000 population, respectively, which showed almost fourfold mortality decline during the observed period (y=4.72-0.16x). A trend of declining risk of dying from endometrial cancer was present in all the age groups. The obtained results indicated that in the observed period the average mortality rates ranged from 0.14/100 000 population in females aged up to 34 years (y=0.30-0.01x), and reached the highest value in females aged 65-74 years (14.57/100 000; y=23.43-0.66x), and 75 years of age and over (19.62/100 000; y=31.17-0.85x).

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The descriptive epidemiology of suicide in Tuscany, 1988-2002

Epidemiologia e Psichiatria Sociale ◽

10.1017/s1121189x00004450 ◽

2006 ◽

Vol 15 (3) ◽

pp. 202-210 ◽

Cited By ~ 9

Author(s):

Michele Arcangelo Martiello ◽

Francesco Cipriani ◽

Fabio Voller ◽

Eva Buiatti ◽

Mariano Giacchi

Keyword(s):

Spatial Analysis ◽

Rural Communities ◽

Mortality Rate ◽

Empirical Bayes ◽

Mining District ◽

Mine Closure ◽

Descriptive Epidemiology ◽

Bayes Estimates ◽

Crude Mortality Rate ◽

Crude Mortality

SUMMARYAims – To describe the epidemiology of Suicide in Tuscany according to the triad of time, place and person. Methods – The 4, 764 cases of suicide, defined according to categories E950-E959 of ICD-9 in Tuscany over the period 1988–2002, were obtained from the Tuscan Mortality Register. Mortality indicators were calculated and analyzed. The spatial analysis was carried out by deriving Empirical Bayes Estimates for the 287 municipalities. Results – The crude mortality rate in the 2000–2002 is 7.8 per 100000 population (male: 12.4; female: 3.5). The age-standardized rate in the 2000–2002 is 5.8 per 100, 000 population (male: 9.6; female: 2.6). The highest risk for suicide, especially in the case of males, are concentrated in the southern hinterland Tuscany, in a cluster of rural municipalities that represent the old mining district of Tuscany. The SMRs according to residential municipality (population per square kilometre), confirm a greater risk of suicide for males residing in rural communities. Conclusions – The cluster of excessive mortality from suicide in Southern Tuscany could be the consequence of social determinants, related to the urban and social crisis following agriculture decline and mine closure.Declaration of Interest: none.

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Mortality among Guarani Indians in Southeastern and Southern Brazil

Cadernos de Saúde Pública ◽

10.1590/s0102-311x2011001400010 ◽

2011 ◽

Vol 27 (suppl 2) ◽

pp. s222-s236 ◽

Cited By ~ 12

Author(s):

Andrey Moreira Cardoso ◽

Carlos E. A. Coimbra Jr. ◽

Carla Tatiana Garcia Barreto ◽

Guilherme Loureiro Werneck ◽

Ricardo Ventura Santos

Keyword(s):

Mortality Rate ◽

Health Inequalities ◽

Causes Of Death ◽

Infant Mortality Rate ◽

Indigenous Populations ◽

Southeastern Brazil ◽

Infant Deaths ◽

Life Expectancy At Birth ◽

Crude Mortality Rate ◽

Crude Mortality

Worldwide, indigenous peoples display a high burden of disease, expressed by profound health inequalities in comparison to non-indigenous populations. This study describes mortality patterns among the Guarani in Southern and Southeastern Brazil, with a focus on health inequalities. The Guarani population structure is indicative of high birth and death rates, low median age and low life expectancy at birth. The crude mortality rate (crude MR = 5.0/1,000) was similar to the Brazilian national rate, but the under-five MR (44.5/1,000) and the infant mortality rate (29.6/1,000) were twice the corresponding MR in the South and Southeast of Brazil. The proportion of post-neonatal infant deaths was 83.3%, 2.4 times higher than general population. The proportions of ill-defined (15.8%) and preventable causes (51.6%) were high. The principal causes of death were respiratory (40.6%) and infectious and parasitic diseases (18.8%), suggesting precarious living conditions and deficient health services. There is a need for greater investment in primary care and interventions in social determinants of health in order to reduce the health inequalities.

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Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Neighborhood Features

10.1101/2020.10.12.20211755 ◽

2020 ◽

Author(s):

Joshua J. Levy ◽

Rebecca M. Lebeaux ◽

Anne G. Hoen ◽

Brock C. Christensen ◽

Louis J. Vaickus ◽

...

Keyword(s):

United States ◽

Deep Learning ◽

Mortality Rate ◽

Satellite Images ◽

The United States ◽

Mortality Rates ◽

Image Features ◽

County Level ◽

Crude Mortality Rate ◽

Crude Mortality

AbstractWhat is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?BackgroundFollowing a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.MethodsSatellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.ResultsPredicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g. sidewalks, driveways and hiking trails) associated with lower mortality.ConclusionsThe application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

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The burden of disease among Brazilian older adults and the challenge for health policies. Results of the Global Burden of Disease Study 2017

10.21203/rs.3.rs-27806/v1 ◽

2020 ◽

Author(s):

VALÉRIA MARIA DE AZEREDO PASSOS ◽

Ana Paula Silva Champs ◽

Renato Teixeira ◽

Maria Fernanda Furtado Lima-Costa ◽

Renata Kirkwood ◽

...

Keyword(s):

Older Adults ◽

Information System ◽

Life Expectancy ◽

Burden Of Disease ◽

Health Policies ◽

Global Burden Of Disease ◽

Global Burden ◽

Years Of Life Lost ◽

Life Expectancy At Birth ◽

Life Years

Abstract Background Brazil is the world’s fifth most populous nation, and is currently experimenting a fast demographic ageing process in a context of scarce resources and social inequalities. To understand the health profile of older adults in Brazil is fundamental for planning public policies. Methods The estimates were derived from data obtained through the collaboration between Brazilian Ministry of Health with the Institute of Health Metrics and Evaluation of the University of Washington. The Brazilian Institute of Geography and Statistics provided the population estimates. Data on causes of death came from the Mortality Information System. To calculate morbidity, population-based studies on the prevalence of diseases in Brazil were comprehensively searched, in addition to information obtained from national databases such as the Hospital Information System, the Outpatient Information System, and the Injury Information System. We presented the Global Burden of Disease (GBD) 2017 estimates among Brazilian older adults (60 + years old) for life expectancy at birth (LE), healthy life expectancy (HALE), cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs), from 2000 to 2017. Results LE at birth significantly increased from 71.3 years (95%UI to 70.9–71.8) to 75.2 years (95%UI 74.7–75.7). There was a trend of increasing HALE, from 62.2 years (95%UI 59.54–64.5) to 65.5 years (95%UI 62.6–68.0). The proportion of DALYs among older adults increased from 7.3–10.3%. Chronic noncommunicable diseases are the leading cause of death among middle-aged and older adults, while Alzheimer's disease is a leading cause only among older adults. Mood disorders, musculoskeletal pain and hearing or vision losses are among the leading causes of disability. Conclusions The increase in LE and the decrease of the DALYs rates are probably results of the improvement of social conditions and health policies. However, the smaller increase of HALE than LE means that despite living more, people spend a substantial time of their old age with disability and illness. Preventable or potentially controllable diseases are responsible for most of the burden of disease among Brazilian older adults. Health investments are necessary to obtain longevity with quality of life in Brazil.

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الزيادة الطبيعية لسكان محافظة دهوك للفترة (2007 – 2017) وتباينه مكانياً

10.31219/osf.io/592ct ◽

2021 ◽

Author(s):

Chinar Muhsin Hasan

Keyword(s):

Mortality Rate ◽

Birth Rate ◽

Crude Birth Rate ◽

Natural Increase ◽

Noticeable Increase ◽

Crude Mortality Rate ◽

Crude Mortality ◽

Increase Rate ◽

The Difference

الملخص: تهدف الدراسة المعنونة بـ ((الزيادة الطبيعية لسكان محافظة دهوك للفترة (2007 – 2017) وتباينه مكانياً)) إلى بيان معدلات الزيادة الطبيعية أي الفرق بين الولادات والوفيات في محافظة دهوك حسب الأقضية بعد أن شهدت المحافظة زيادة ملحوظة خلال السنوات الأخيرة مما استدعت إلى الوقوف عليها، وقد تكوّنَ البحث من ثلاث مباحث: المبحث الأول تطرق إلى استخراج معدل الولادات الخام، والمبحث الثاني درس معدل الوفيات الخام، والمبحث الثالث درس معدل الزيادة الطبيعية (الحيوية) خلال الفترة حسب الوحدات الإدراية، وقد توصلت الدراسة إلى أنَّ معدل الزيادة الطبيعية في محافظة دهوك انخفضت من نسبة (3.3%) في عام 2007 إلى(2.1%) في عام 2017. Abstract:The objective of the study is to increase the natural increase in the population of Duhok governorate in the period (2007 - 2017) to indicate the rates of natural increase, i.e. the difference between births and deaths in Duhok governorate by district after the governorate witnessed a noticeable increase in recent years. The research may consist of three subjects: first, crude birth rate, second crude mortality rate, and third natural increase rate during the period according to administrative units.The study found that the rate of natural increase in Duhok Governorate decreased from 3.3% in 2007 to 2.1% in 2017.

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The burden of disease among Brazilian older adults and the challenge for health policies: results of the Global Burden of Disease Study 2017

Population Health Metrics ◽

10.1186/s12963-020-00206-3 ◽

2020 ◽

Vol 18 (S1) ◽

Author(s):

Valéria Maria de Azeredo Passos ◽

Ana Paula Silva Champs ◽

Renato Teixeira ◽

Maria Fernanda Furtado Lima-Costa ◽

Renata Kirkwood ◽

...

Keyword(s):

Older Adults ◽

Information System ◽

Life Expectancy ◽

Burden Of Disease ◽

Health Policies ◽

Global Burden Of Disease ◽

Global Burden ◽

Years Of Life Lost ◽

Life Expectancy At Birth ◽

Life Years

Abstract Background Brazil is the world’s fifth most populous nation, and is currently experimenting a fast demographic aging process in a context of scarce resources and social inequalities. To understand the health profile of older adults in Brazil is fundamental for planning public policies. Methods The estimates were derived from data obtained through the collaboration between the Brazilian Ministry of Health and the Institute of Health Metrics and Evaluation of the University of Washington. The Brazilian Institute of Geography and Statistics provided the population estimates. Data on causes of death came from the Mortality Information System. To calculate morbidity, population-based studies on the prevalence of diseases in Brazil were comprehensively searched, in addition to information obtained from national databases such as the Hospital Information System, the Outpatient Information System, and the Injury Information System. We presented the Global Burden of Disease (GBD) 2017 estimates among Brazilian older adults (60+ years old) for life expectancy at birth (LE), healthy life expectancy (HALE), cause-specific mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs), from 2000 to 2017. Results LE at birth significantly increased from 71.3 years (95% UI to 70.9-71.8) to 75.2 years (95% UI 74.7-75.7). There was a trend of increasing HALE, from 62.2 years (95% UI 59.54-64.5) to 65.5 years (95% UI 62.6-68.0). The proportion of DALYs among older adults increased from 7.3 to 10.3%. Chronic noncommunicable diseases are the leading cause of death among middle aged and older adults, while Alzheimer’s disease is a leading cause only among older adults. Mood disorders, musculoskeletal pain, and hearing or vision losses are among the leading causes of disability. Conclusions The increase in LE and the decrease of the DALYs rates are probably results of the improvement of social conditions and health policies. However, the smaller increase of HALE than LE means that despite living more, people spend a substantial time of their old age with disability and illness. Preventable or potentially controllable diseases are responsible for most of the burden of disease among Brazilian older adults. Health investments are necessary to obtain longevity with quality of life in Brazil.

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Update on Mortality in the Democratic Republic of Congo: Results From a Third Nationwide Survey

Disaster Medicine and Public Health Preparedness ◽

10.1097/dmp.0b013e3181a6e952 ◽

2009 ◽

Vol 3 (2) ◽

pp. 88-96 ◽

Cited By ~ 36

Author(s):

Benjamin Coghlan ◽

Pascal Ngoy ◽

Flavien Mulumba ◽

Colleen Hardy ◽

Valerie Nkamgang Bemo ◽

...

Keyword(s):

Mortality Rate ◽

Rate Ratio ◽

Democratic Republic ◽

Democratic Republic Of Congo ◽

Nationwide Survey ◽

Cluster Sampling ◽

Republic Of Congo ◽

Crude Mortality Rate ◽

Crude Mortality ◽

Political Security

ABSTRACTBackground: The humanitarian crisis in the Democratic Republic of Congo (DRC) has been among the world’s deadliest in recent decades. We conducted our third nationwide survey to examine trends in mortality rates during a period of changing political, security, and humanitarian conditions.Methods: We used a 3-stage, household-based cluster sampling technique to compare east and west DRC. Sixteen east health zones and 15 west zones were selected with a probability proportional to population size. Four east zones were purposely selected to allow historical comparisons. The 20 smallest population units were sampled in each zone, 20 households in each unit. The number and distribution of households determined whether they were selected using systematic random or random walk sampling. Respondents were asked about deaths of household members during the recall period: January 2006–April 2007.Findings: In all, 14,000 households were visited. The national crude mortality rate of 2.2 deaths per 1000 population per month (95% confidence interval [CI] 2.1–2.3) is almost 70% higher than that documented for DRC in the 1984 census (1.3) and is unchanged since 2004. A small but significant decrease in mortality since 2004 in the insecure east (rate ratio: 0.96, P = .026) was offset by increases in the western provinces and a transition area in the center of the country. Nonetheless, the crude mortality rate in the insecure east (2.6) remains significantly higher than in the other regions (2.0 and 2.1, respectively). Deaths from violence have declined since 2004 (rate ratio 0.7, P = .02).Conclusions: More than 4 years after the official end of war, the crude mortality rate remains elevated across DRC. Slight but significant improvements in mortality in the insecure east coincided temporally with recent progress on security, humanitarian, and political fronts. (Disaster Med Public Health Preparedness. 2009;3:88–96)

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Complement Receptor C5aR1 Inhibition Reduces Pyroptosis in hDPP4-Transgenic Mice Infected with MERS-CoV

Viruses ◽

10.3390/v11010039 ◽

2019 ◽

Vol 11 (1) ◽

pp. 39 ◽

Cited By ~ 22

Author(s):

Yuting Jiang ◽

Junfeng Li ◽

Yue Teng ◽

Hong Sun ◽

Guang Tian ◽

...

Keyword(s):

Mortality Rate ◽

Transgenic Mice ◽

Complement Receptor ◽

Highly Pathogenic ◽

Human Macrophages ◽

Caspase 1 ◽

Pathogenic Virus ◽

Crude Mortality Rate ◽

Crude Mortality

Middle East respiratory syndrome coronavirus (MERS-CoV) is a highly pathogenic virus with a crude mortality rate of ~35%. Previously, we established a human DPP4 transgenic (hDPP4-Tg) mouse model in which we studied complement overactivation-induced immunopathogenesis. Here, to better understand the pathogenesis of MERS-CoV, we studied the role of pyroptosis in THP-1 cells and hDPP4 Tg mice with MERS-CoV infection. We found that MERS-CoV infection induced pyroptosis and over-activation of complement in human macrophages. The hDPP4-Tg mice infected with MERS-CoV overexpressed caspase-1 in the spleen and showed high IL-1β levels in serum, suggesting that pyroptosis occurred after infection. However, when the C5a-C5aR1 axis was blocked by an anti-C5aR1 antibody (Ab), expression of caspase-1 and IL-1β fell. These data indicate that MERS-CoV infection induces overactivation of complement, which may contribute to pyroptosis and inflammation. Pyroptosis and inflammation were suppressed by inhibiting C5aR1. These results will further our understanding of the pathogenesis of MERS-CoV infection.

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