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

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.

Machine Learning for Health Data Analytics

2020 ◽  
pp. 241-270
Author(s):  
Muralikrishna Iyyanki ◽  
Prisilla Jayanthi ◽  
Valli Manickam
Keyword(s):  
Machine Learning ◽  
Mortality Rate ◽  
Supervised Learning ◽  
Case Studies ◽  
Birth Rate ◽  
Statistical Parameter ◽  
Crude Birth Rate ◽  
Health Data ◽  
Crude Mortality Rate ◽  
Crude Mortality

At present, public health and population health are the key areas of major concern, and the current study highlights the significant challenges through a few case studies of application of machine learning for health data with focus on regression. Four types of machine learning methods found to be significant are supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. In light of the case studies reported as part of the literature survey and specific exercises carried out for this chapter, it is possible to say that machine learning provides new opportunities for automatic learning in expressive models. Regression models including multiple and multivariate regression are suitable for modeling air pollution and heart disease prediction. The applicability of STATA and R packages for multiple linear regression and predictive modelling for crude birth rate and crude mortality rate is well established in the study as carried out using the data from data.gov.in. Decision tree as a class of very powerful machine learning models is applied for brain tumors. In simple terms, machine learning and data mining techniques go hand-in-hand for prediction, data modelling, and decision making. The health analytics and unpredictable growth of health databases require integration of the conventional data analysis to be paired with methods for efficient computer-assisted analysis. In the second case study, confidence interval is evaluated. Here, the statistical parameter CI is used to indicate the true range of the mean of the crude birth rate and crude mortality rate computed from the observed data.

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

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.

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

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Rujun Liao ◽  
Lin Hu ◽  
Qiang Liao ◽  
Tianyu Zhu ◽  
Haiqun Yang ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Life Expectancy ◽  
Malignant Tumors ◽  
Heart Diseases ◽  
Health Policies ◽  
Years Of Life Lost ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Standardized Mortality Rate ◽  
Death Causes

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.

Birth Control in China: Local Data and Their Reliability

1981 ◽  
Vol 85 ◽  
pp. 119-137 ◽  
Author(s):  
Lucien Bianco
Keyword(s):  
Birth Control ◽  
Birth Rate ◽  
Natural Increase ◽  
Secondary Peak ◽  
Local Data ◽  
Demographic Trends ◽  
Increase Rate ◽  
The 1960S ◽  
Control Campaign

In 1979, at about the same time that the birth control campaign received renewed impetus, China released impressive data on demographic trends. If these and other more recent data are reliable, the decline of the natural increase rate has been both belated and spectacular. Contrary to what has been assumed the birth rate would seem to have reached its peak during the 1960s (43·6 per 1,000 in 1963). After a secondary peak in the late 1960s, it then declined precipitously during the 1970s, declining by almost half (46·7 per cent) over nine years (33·59 per 1,000 in 1970; 17·9 per 1,000 in 1979). The natural increase rate was, for its part, more than halved during the same period (25·95 per 1,000 in 1970; 11·7 per 1,000 in 1979).

The descriptive epidemiology of suicide in Tuscany, 1988-2002

2006 ◽  
Vol 15 (3) ◽  
pp. 202-210 ◽  
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.

scholarly journals Mortality among Guarani Indians in Southeastern and Southern Brazil

2011 ◽  
Vol 27 (suppl 2) ◽  
pp. s222-s236 ◽  
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.

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

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.

Demographic characteristics of a rural area in Kenya in 1974–80

1984 ◽  
Vol 16 (3) ◽  
pp. 411-423 ◽  
Author(s):  
J. K. van Ginneken ◽  
A. S. Muller ◽  
A. M. Voorhoeve ◽  
Omondi-Odhiambo
Keyword(s):  
Population Growth ◽  
Rural Area ◽  
Birth Rate ◽  
Crude Birth Rate ◽  
Mortality Rates ◽  
Natural Increase ◽  
Demographic Characteristics ◽  
Economic Conditions ◽  
Permanent Migration ◽  
Very High

SummaryA longitudinal, epidemiological study was carried out in a rural area of Kenya with a population of about 28,000 between 1974 and 1980. Population registration during this time showed that population growth was very high between 1974 and 1978 (4·4% per year) and much lower in 1979 and 1980 (1·1%). Natural increase was nearly as high as in Kenya as a whole (3·7%) in this period. Fertility was somewhat lower than in all Kenya (the crude birth rate was 46 per 1000) while mortality was substantially lower (7 per 1000). Evidence is presented supporting the argument that these low mortality rates are genuine. Levels of temporary and permanent migration are high and probably characteristic for many parts of Kenya. The change in population growth in 1979 and 1980 is probably due to changes in economic conditions leading in particular to less in-migration and to more out-migration.

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

2009 ◽  
Vol 3 (2) ◽  
pp. 88-96 ◽  
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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