scholarly journals Crude Birth Rate and Crude Mortality Rate in India: A Case of Application of Regression in Healthcare

2020 ◽  
Author(s):  
Prisilla Jayanthi ◽  
Muralikrishna Iyyanki
Keyword(s):  
Mortality Rate ◽  
Birth Rate ◽  
Crude Birth Rate ◽  
Crude Mortality Rate ◽  
Crude Mortality

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.

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.

scholarly journals The association between first and second wave COVID-19 mortality in Italy

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Marco Vinceti ◽  
Tommaso Filippini ◽  
Kenneth J. Rothman ◽  
Silvia Di Federico ◽  
Nicola Orsini
Keyword(s):  
Mortality Rate ◽  
Cubic Spline ◽  
Mortality Data ◽  
Spline Regression ◽  
Crude Mortality Rate ◽  
Crude Mortality ◽  
Time Periods ◽  
Second Wave

Abstract Background The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks. Methods We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24–June 30, 2020) and to the second wave (September 1–December 31, 2020), using cubic spline regression. Results For provinces with the lowest crude mortality rate in the first wave (February–June), i.e. < 22 cases/100,000/month, mortality in the second wave (September–December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period. Conclusions These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.

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)

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

Viruses ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 39 ◽  
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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