scholarly journals Analyzing Overdispersed Antenatal Care Count Data in Bangladesh: Mixed Poisson Regression with Individual-Level Random Effects

2021 ◽  
Vol 50 (4) ◽  
pp. 78-90
Author(s):  
Zakir Hossain ◽  
Maria
Keyword(s):  
Antenatal Care ◽  
Random Effects ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Wealth Index ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
World Health ◽  
Individual Level

Poisson regression (PR) is commonly used as the base model for analyzing count data with the restrictive equidispersion property. However, overdispersed nature of count data is very common in health sciences. In such cases, PR produces misleading inferences and hence give incorrect interpretations of the results. Mixed Poisson regression with individual--level random effects (MPR_ILRE) is a further improvement for analyzing such data. We compare MPR_ILRE with PR, quasi-Poisson regression (Q_PR) and negative binomial regression (NBR) for modelling overdispersed antenatal care (ANC) count data extracted from the latest Bangladesh Demographic and Health Survey (BDHS) 2014. MPR_ILRE is found to be the best choice because of its minimum Akaike information criterion (AIC) value and the overdispersion exists in data has also been modelled very well. Study findings reveal that on average, women attended less than three ANC visits and only 6.5\% women received the World Health Organization (WHO) recommended eight or more ANC visits for the safe pregnancy and child birth. Administrative division, place of residence, birth order, exposure of media, education, wealth index and body mass index (BMI) have significant impact on adequate ANC attendance of women to reducing pregnancy complications, maternal and child deaths in Bangladesh.

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.

PEMODELAN DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat)

2021 ◽  
Vol 10 (3) ◽  
pp. 226-236
Author(s):  
Khusnul Khotimah ◽  
Itasia Dina Sulvianti ◽  
Pika Silvianti
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Kernel Weight ◽  
Negative Binomial Regression Model ◽  
West Java ◽  
Binomial Regression ◽  
Spatial Heterogenity

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

scholarly journals Determinants of Antenatal Care Visit Utilization of Child-Bearing Mothers in Kaffa, Sheka, and Bench Maji Zones of SNNPR, Southwestern Ethiopia

2019 ◽  
Vol 6 ◽  
pp. 233339281986662 ◽  
Author(s):  
Abiyot Negash Terefe ◽  
Assaye Belay Gelaw
Keyword(s):  
Health Care ◽  
Antenatal Care ◽  
Rural Areas ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Care Program ◽  
Normal Pregnancy ◽  
World Health ◽  
Child Bearing

Background: Antenatal care (ANC) is a preventive obstetric health-care program aimed at optimizing maternal fetal outcome through regular monitoring of pregnancy. Even if World Health Organization recommends a minimum of 4 ANC visits for normal pregnancy, existing evidence from developing countries including Ethiopia indicates there are few women who utilize it due to different reasons. The purpose of this article is to identify determinants significantly influencing the ANC visit utilization of child-bearing mothers in the Kaffa, Sheka, and Bench-Maji zones of Southern Nation Nationalities and Peoples Region, Ethiopia. Methods: A total of 1715 child-bearing mothers were selected. Several count models such as Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, hurdle Poisson, and hurdle negative binomial regression models were fitted to select the model which best fits the data. The parameters were estimated by maximum likelihood. Measures of goodness of fit were based on the Rootogram. Results: The data were found zeros (8.1%); the variance (3.794), which is less than its mean (3.91). Hurdle Poisson regression model was found to be better fitted with the data given. Variables are selected by backward selection method, through the analysis, zones, residence, age at first pregnancy, source of information, knowledge during danger sin, willingness, time of visit, and satisfaction, which were major predictors of ANC service utilization. The estimated odds that the number of ANC visits those child-bearing mothers made (mothers who lived in urban) are 3.52 times more likely than mothers who lived in rural keeping others variables constant and the like. Conclusion: Based on our findings, a lot of effort needs to be made by health offices to create awareness, maternal health-care programs should be expanded and intensified in rural areas, improve women’s knowledge and awareness about the risk factor of late visit, the necessary investigations and follow-up throughout the antenatal period to promote regular attendance for ANC, and fulfill the client’s satisfaction.

scholarly journals A Study of Count Regression Models for Mortality Rate

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 142-151
Author(s):  
Anwar Fitrianto
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Count Data ◽  
Bayesian Information Criterion ◽  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Poisson Regression Model ◽  
Binomial Regression

This paper discusses how overdispersed count data to be fit. Poisson regression model, Negative Binomial 1 regression model (NEGBIN 1) and Negative Binomial regression 2 (NEGBIN 2) model were proposed to fit mortality rate data. The method used is comparing the values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to find out which method suits the data the most. The results show that the data indeed display higher variability. Among the three models, the model preferred is NEGBIN 1 model.

scholarly journals Comparison among Akaike Information Criterion, Bayesian Information Criterion and Vuong's test in Model Selection: A Case Study of Violated Speed Regulation in Taiwan

2019 ◽  
Vol 3 (1) ◽  
pp. 293 ◽  
Author(s):  
Kim-Hung Pho ◽  
Sel Ly ◽  
Sal Ly ◽  
T. Martin Lukusa
Keyword(s):  
Model Selection ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Information Criterion ◽  
Future Research ◽  
Data Set ◽  
Research Issues ◽  
Creative Commons ◽  
Advantages And Disadvantages

When doing research on scientific issues, it is very significant if our research issues are closely connected to real applications. In reality, when analyzing data in practice, there are frequently several models that can appropriate to the survey data. Hence, it is necessary to have a standard criterion to choose the most ecient model. In this article, our primary interest is to compare and discuss about the criteria for selecting a model and its applications. The authors provide approaches and procedures of these methods and apply to the traffic violation data where we look for the most appropriate model among Poisson regression, Zero-inflated Poisson regression and Negative binomial regression to capture between number of violated speed regulations and some factors including distance covered, motorcycle engine and age of respondents by using AIC, BIC and Vuong's test. Based on results on the training, validation and test data set, we find that the criteria AIC and BIC are more consistent and robust performance in model selection than the Vuong's test. In the present paper, the authors also discuss about advantages and disadvantages of these methods and provide some of the suggestions with potential directions in future research. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

scholarly journals ANALISIS REGRESI COUNT DATA UNTUK PEMODELAN JUMLAH KASUS PENYAKIT TUBERKULOSIS DI KABUPATEN BANYUMAS

2021 ◽  
Vol 13 (2) ◽  
pp. 57
Author(s):  
Kristy Kristy ◽  
Jajang Jajang ◽  
Nunung Nurhayati
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Predictor Variable ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Mean ◽  
Generalized Poisson Regression

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Banyumas Regency is one of the districts with quite high Tuberculosis cases in Central Java. This study aims to analyze the factors that affect the number of tuberculosis cases in Banyumas Regency using regression analysis of count data. Poisson regression is the simplest count data regression model that has the assumption of equidispersion, that is, the mean value equal to the variance. However, in its application, these assumption is often not fulfilled, for example, there are cases of overdispersion (variance value is greater than the mean). In this study, to overcome the case of overdispersion, an approach was used using Generalized Poisson Regression (GPR) and negative binomial regression. The results showed that the data on the number of tuberculosis cases in Banyumas Regency in 2019 was overdispersion. The data modeling of the number of tuberculosis cases in Banyumas Regency with the negative binomial regression model is better than the GPR model. Meanwhile, the only predictor variable that affects the number of tuberculosis cases in Banyumas Regency is the sex ratio of productive age (15-49 years).

scholarly journals GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION UNTUK MENANGANI OVERDISPERSI PADA JUMLAH PENDUDUK MISKIN

2021 ◽  
Vol 10 (4) ◽  
pp. 532-543
Author(s):  
Nova Delvia ◽  
Mustafid Mustafid ◽  
Hasbi Yasin
Keyword(s):  
Count Data ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Weighting Function ◽  
Negative Binomial Regression ◽  
Spatial Effect ◽  
Poor People ◽  
Research Focus ◽  
Binomial Regression ◽  
Life Circumstances

Poverty is a condition that is often associated with needs, difficulties an deficiencies in various life circumstances. The number of poor people in Indonesia increase in 2020. This research focus on modelling the number of poor people in Indonesia using Geographically Weighted Negative Binomial Regression (GWNBR) method. The number of poor people is count data, so analysis used to model the count data is poisson regression.  If there is overdispersion, it can be overcome using negative binomial regression. Meanwhile to see the spatial effect, we can use the Geographically Weighted Negative Binomial Regression method. GWNBR uses a adaptive bisquare kernel for weighting function. GWNBR is better at modelling the number of poor people because it has the smallest AIC value than poisson regression and negative binomial regression. While the GWNBR method obtained 13 groups of province based on significant variables.      

scholarly journals Forecasting COVID-19 Pandemic and Capital Market Efficiency in Africa

2021 ◽  
Vol 5 (1) ◽  
pp. 136-153
Author(s):  
Kolawole Lookman Subair ◽  
Ibrahim Ayoade Adekunle
Keyword(s):  
Capital Market ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Jel Classification ◽  
World Health ◽  
Policy Document ◽  
Market Capitalization ◽  
Development Indicators ◽  
Percentage Decrease

Despite a large and growing list of studies on COVID-19 across space and time and on heterogeneous social, environmental and welfare issues, the empirical relations on the consequences of COVID-19 pandemic and Africa’s market capitalization objectives remain dimly discerned. Even more worrisome is Africa, where the condition for growth and development has not been adequately fulfilled. This structural ambiguity calls for a policy document that is evidence-based to reach conclusions to aid the containment, risk analysis, structures and features of the deadly and fast-spreading disease. This study employed negative binomial and the Poisson regression to establish the contemporaneous influence of COVID-19 pandemic on market capitalization capabilities in Africa. Health data from various reports of the World Health Organization (WHO) is regressed on the all-share index from World Development Indicators (WDI) to establish a clear line of thought. It is found that the growth of confirmed cases and attributable deaths are inversely related to the growth in market capitalization in Africa. The findings from this study show that Africa market capitalization is inversely related to total growth in the number of confirmed cases of COVID-19 and attributable COVID-19 deaths. This leads to the assertion that Africa’s capital market is fast nosediving in the time of COVID-19 due to global uncertainties caused by the pandemic. With no known cure or vaccine procedure discovered yet, the global uncertainty around the novel coronavirus disease will lead to approximately 0.56 percentage decrease in market capitalization in Africa. To this end, emphases must be laid on identifying and including non-traditional sources of financing strictly tied to projects that could encourage institutional investors. It is therefore equally imperative for Africa to form a formidable and integrated capital market among themselves.  Keywords: market capitalization; COVID-19 pandemic, negative binomial Regression, poisson, Regression, Africa JEL Classification: C10, C31, G15, I12

scholarly journals Modelling Excess Zeros in Count Data with Application to Antenatal Care Utilisation

2018 ◽  
Vol 7 (3) ◽  
pp. 22 ◽  
Author(s):  
Oyindamola B Yusuf ◽  
Rotimi Felix Afolabi ◽  
Ayoola S Ayoola
Keyword(s):  
Antenatal Care ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Median Number ◽  
Information Criteria ◽  
Negative Binomial Model ◽  
Reproductive Age ◽  
Binomial Model ◽  
Excess Zeros

Poisson and negative binomial regression models have been used as a standard for modelling count outcomes; but these methods do not take into account the problems associated with excess zeros. However, zero-inflated and hurdle models have been proposed to model count data with excess zeros. The study therefore compared the performance of Zero-inflated (Zero-inflated Poisson (ZIP) and Zero-inflated negative binomial (ZINB)), and hurdle (Hurdle Poisson (HP) and Hurdle negative binomial (HNB)) models in determining the factors associated with the number of Antenatal Care (ANC) visits in Nigeria. Using the 2013 Nigeria Demographic and Health Survey dataset, a sample of 19 652 women of reproductive age who gave birth five years prior to the survey and provided information about ANC visits was utilised. Data were analysed using descriptive statistics, ZIP, ZINB, HP and HNB models, and information criteria (AIC/BIC) was used to assess model fit. Participants’ mean age was 29.5 ± 7.3 years and median number of ANC visits was 4 (range: 0 - 30). About half (54.9%) of the participants had at least 4 ANC visits while 33.9% had none. The ZINB (AIC = 83 039.4; BIC = 83 470.3) fitted the data better than the ZIP or HP; however, HNB (AIC = 83 041.4; BIC = 83 472.3) competed favorably well with it. The Zero-inflated negative binomial model provided the better fit for the data. We suggest the Zero-inflated negative binomial model for count data with excess zeros of unknown sources such as the number of ANC visits in Nigeria.

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