scholarly journals On the Trends of Registered Birth and Death Rates in Nigeria: Evidence from Generalized Linear Models

2019 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Emmanuel Chukwuma Anikweze
Keyword(s):  
Generalized Linear Models ◽  
Regression Models ◽  
Linear Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Natural Increase ◽  
Negative Binomial Regression Model ◽  
Annual Data ◽  
Death Rates ◽  
Binomial Regression

This study investigated the trends of registered Death and Birth in Nigeria using Generalized Linear Models. Annual data on Death and Birth was collected from National Population Commission for the period of 2004 to 2017. The Natural increase calculated revealed a positive trend in the natural increase in Nigeria from 2004 to 2017. Evidence from summary statistics revealed some level of over dispersion (variance > mean). This study explored Poisson Regression Models and Negative Binomial Regression Models using two links (identity and log). The results revealed a positive increase in registration of birth and death rates in Nigeria and among the competing the models, Negative Binomial regression model with identity link emerged as the best model for modeling birth and death rates registration in Nigeria. Data on numbers of deaths and causes of death are essential if countries are to determine priorities, formulate and monitor policies for public health care as well as other government policies that may be based on such data

scholarly journals Trend Analysis of Tuberculosis Cases and the Effect of HIV Cases on Tuberculosis Cases in Some West African Countries Using Panel Poisson and Negative Binomial Regression Models

2019 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Gbenga Solomom Akinyemi
Keyword(s):  
West Africa ◽  
Regression Model ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
West African ◽  
World Health ◽  
Negative Binomial Regression Model ◽  
African Countries ◽  
Binomial Regression

Tuberculosis cause of death worldwide and the leading cause from a single infectious agent, ranking above Human immunodeficiency virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS). The aim of this study is to ascertain the trend of tuberculosis prevalence and the effect of HIV prevalence onl Tuberculosis case in some West African countries from 2000 to 2016 using count panel data regression models. The data used annual HIV and Tuberculosis cases spanning from 2000 to 2016 extracted from online publication of World health Organization (WHO). Panel Poisson regression model and Negative binomial regression model for fixed and random effects were used to analyzed the count data, the result revealed a positive trend in TB cases while increased in HIV cases leads to increase in TB cases in West African countries. Among the competing models used in this study, Panel Negative Binomial Regression Model with fixed effect emerged the best model with log likelihood value of -1336.554. This study recommended that Government and NGOs need more strategies to fight against HIV menace in West Africa as this will in turn reduced TB cases in West Africa.

scholarly journals Count Models for Identifying Factors Associated with Child Mortality in Ethiopia

2021 ◽  
Author(s):  
Endale Alemayehu Ali ◽  
Tsigereda Tilahun
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Child Mortality ◽  
Regression Models ◽  
Negative Binomial ◽  
Average Rate ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Count Models ◽  
Binomial Regression

Abstract Introduction: The under-five mortality rate, often known by its acronym U5MR or simply as the child mortality rate, indicates the probability of dying between births exactly five years of age, expressed per 1,000 live births. In comparison, the probability of dying after the first month and before reaching age 1 was 12 per 1,000, the probability of dying after age 1 and before age 5 was 10 per 1,000, and the probability of dying after age 5 and before age 15 was 7 per 1,000. Objectives: The study was aimed to determine the major factors of child mortality in Ethiopia using different counting models. In detail the study has the objective of identifying the risk factors of child mortality in Ethiopia and also to prioritize the best counting models that fit the data well. Methods: The Ethiopian demographic and health survey of 2016 was used for this study. About 10641 women aged between 15-49 were included in the survey. To analyze the data, counting models like the Poisson regression model, negative binomial model, zero-inflated regression models, and zero-inflated negative binomial regression model were applicable. Results: The results of the study indicated that of the total 10641 women respondents, 7576 (71.2%) have not faced the problem of child mortality. Thus, this result has the clue that the count models, especially the models that can handle the dispersion may be applicable. The average rate of child mortality is less than the variance of child mortality and this indicated that there is an over-dispersion of the data. Of all the candidate models, a zero-inflated negative binomial regression model was found to be the best model since it has a minimum AIC(15517). The coefficient table of the best model indicated that of child mortality for the women from rural residence is 1.2532 greater than those from urban with a 95% confidence interval (0.0905, 0.3610). Conclusion: The model comparison technique is indicated that the zero-inflated negative binomial regression models were the best mode that fit the data well. Under this model, the residency of women, birth order, Preceding Birth Interval, Size of a child at birth (smaller than average), and number of household members are significant variables in determining the status of child mortality in Ethiopia

scholarly journals A relação entre o número de internações por doenças respiratórias e variáveis climáticas em Santa Maria – RS

2020 ◽  
Vol 42 ◽  
pp. e53
Author(s):  
Monica Cristina Bogoni Savian ◽  
Luciane Flores Jacobi ◽  
Roselaine Ruviaro Zanini
Keyword(s):  
Regression Model ◽  
Respiratory Diseases ◽  
Linear Models ◽  
Negative Binomial ◽  
Hospital Admissions ◽  
Secondary Data ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Average Wind Speed ◽  
Binomial Regression

The present study evaluated, through generalized linear models, the relationship between the number of hospital admissions for respiratory diseases and meteorological elements, in order to verify the regression model that best fits the data, as well as to predict the number of hospitalizations due to respiratory diseases. This is an ecological, descriptive study using secondary data, obtained from a public database. Data on respiratory diseases considered in the present study were obtained from the DATASUS website in the period from January 1998 to December 2014. The climate variables employed as explanatory variables for modeling the data were obtained from the INMET website, more specifically the Meteorological Database for Teaching and Research. From the realized evaluation, it was possible to conclude that the negative binomial regression model showed superiority in relation to the Poisson regression model, with the last regression model being the log linear negative binomial regression model.  The results show a positive relationship between the variables considered in the municipality. There is an expected relative increase in the number of hospitalizations for respiratory diseases if average wind speed, total sunshine, relative humidity and season are observed.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

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