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

Yusuf, Oyindamola B.; Afolabi, Rotimi Felix; Ayoola, Ayoola S

doi:10.5539/ijsp.v7n3p22

Cited by 10 publications

(13 citation statements)

References 20 publications

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“…While these zeros are important and meaningful, most researchers often treat them as missing values or delete them. In other cases, the data is either transformed into a linear model (which violates the normality assumption) or coded as a categorical dummy variable where all zeros are considered as 'absent' and those observed as 'present' (Lewsey and Thomson, 2004;Yusuf et al 2018). Under such circumstances, the analysis becomes less useful and less informative if the interest is to determine the number of occurrences (Yusuf et al, 2018).…”

Section: Zero-inflated Poisson and Negative Binomial Modelsmentioning

confidence: 99%

“…Zero-inflated model can distinguish between the two processes causing the excess zeros (Diallo et al, 2019;Yusuf et al 2018). A common feature of zero-inflated model is its ability to 1 Numbers in brackets denotes size of farmers selected in each community simultaneously produce two outcomes in count data models by: i.)…”

Section: Zero-inflated Poisson and Negative Binomial Modelsmentioning

confidence: 99%

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Determinants of Vertical Integration in Poultry Production in Ghana: Application of Count Data Models

Adams

Mensah

Etuah

et al. 2021

Preprint

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Poultry production has significant potential to reduce protein deficiency, food insecurity and poverty in Ghana. However, limited vertical integration and high cost of production in the sector have stifled growth and exposed poultry farms in the country to many risks, leading to poor business performance. This study uses cross-sectional data from 102 commercial poultry farms to assess the determinants of extent of vertical integration in the Ghanaian poultry industry by employing zero-inflated Poisson (ZIP) and Zero-inflated Binomial (ZINB) models. The results show that one in every four poultry farms in the country are vertically integrated, either partially or fully. The ZINB model, which best fits the data, reveals that the extent of vertical integration in the poultry business is significantly influenced by a set of personal (education, occupation, and farming experience) and farm level (land tenure, flock size, production cost, and farm revenue) characteristics as well as institutional factors (credit access, extension access and membership of association). The paper discusses the implications of these findings and provides appropriate recommendations for strengthening the poultry industry in Ghana.

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Section: Zero-inflated Poisson and Negative Binomial Modelsmentioning

confidence: 99%

Section: Zero-inflated Poisson and Negative Binomial Modelsmentioning

confidence: 99%

Determinants of Vertical Integration in Poultry Production in Ghana: Application of Count Data Models

Adams

Mensah

Etuah

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…A comparison of the performance of modified Poisson regression models like ZIP and Zero-inflated negative binomial (ZINB), and Hurdle Poisson(HP) and Hurdle negative binomial (HNB) 13 models in determining the factors associated with the number of ANC visits taken, have been made in such studies. A study in Nigeria conducted by Yusuf et al 14 revealed that ZINB and HNB fitted the data better than the ZIP or HP. Sekata 15 analyzed the determinants of the barriers in number of antenatal care service visits among pregnant women in rural Ethiopia.…”

Section: Iintroductionmentioning

confidence: 95%

“…A marginalized zero-inflated Poisson (ZIP) 11 model, a modified Poisson regression model (that accommodates the excess zeros) has been used for analyzing ANC counts obtained from Bangladesh Demographic and Health Survey (BDHS)data 12 . This type of modified count data model has been used widely in the literature for handling zero-inflated ANC data 12,[14][15] . A comparison of the performance of modified Poisson regression models like ZIP and Zero-inflated negative binomial (ZINB), and Hurdle Poisson(HP) and Hurdle negative binomial (HNB) 13 models in determining the factors associated with the number of ANC visits taken, have been made in such studies.…”

Section: Iintroductionmentioning

confidence: 99%

Analysis of Antenatal Care Visit Data in Bangladesh Using Zero Modified Count Regression Model

Ahmed¹,

Mallick²

2019

Dhaka Univ. J. Sci.

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In medical science, pharmaceutical studies, public health and socio-economic researches we often encounter the situation of excess of zeros in count data. This preponderance of zeros leads to overdispersion. In such cases traditional count data regression models like Poisson and negative binomial (NB) regression may not be pertinent for inference. The two most commonly used types of model that have been developed to adjust for excessivezeros in count data are Hurdle and zero-inflated models. In this study we have analyzed the antenatal care (ANC) visit data of pregnant women in Bangladesh using traditional and zero-modified count models. Based on the model selection criteria, we found that negative binomial hurdle model fits the data best. Through this analysis,we have perceived that the variables age of mother, division, birth order (order a child is born), place of residence, economic condition, media exposure of the mother, mainaccess road to village and education gap between husband and wife have significant impact on the mean number of ANC visits taken. Dhaka Univ. J. Sci. 67(2): 117-122, 2019 (July)

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“…Hence, zero-inflated regression models can be used, which include excess zeros in modelling to address the problem of overdispersion in the data set (Hu et al, 2011; Staub & Winkelmann, 2013). The zero-inflated models, that is, either the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) model, assume that data come from two different populations (Yusuf et al, 2018). In one part, the outcome is always zero counts, and in the second part, count data follow a standard Poisson process.…”

Section: Introductionmentioning

confidence: 99%

Determinants of Antenatal Care Utilisation in India: A Count Data Modelling Approach

Yadav

NAG

Jena

et al. 2021

Journal of Development Policy and Practice

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The article explores the micro-level factors (social, economic and demographic) that determine the utilisation of antenatal care (ANC) services in India using the Bayesian count data regression model. The primary purpose is to rectify the methodological loopholes in the existing literature using a count data regression model that overcomes the problems of overdispersion in the data. Using data from ‘National Family Health Survey’ (NFHS) data on women of reproductive age (15–49 years), we find that about 33% of pregnant women have not availed ANC during their pregnancy. The factors such as women’s education and partner/husband’s education, children’s birth order, household income, availability of basic amenities, like clean drinking water, media exposure, holding of bank accounts and use of mobile phones are statistically significant and positively affect ANC utilisation. Therefore, the study calls for prioritisation of and special attention to uneducated or less educated rural pregnant women. They should be incentivised adequately to utilise ANC services, which may drastically reduce inadequacy in ANC utilisation and improve mothers’ health before and after delivery. Awareness camps should be organised in every village in rural areas about pregnancy-related complications and the benefits of ANC check-ups. Massive infrastructure in the form of primary health centres or community health centres is the need of the hour in rural India.

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

Cited by 10 publications

References 20 publications

Determinants of Vertical Integration in Poultry Production in Ghana: Application of Count Data Models

Determinants of Vertical Integration in Poultry Production in Ghana: Application of Count Data Models

Analysis of Antenatal Care Visit Data in Bangladesh Using Zero Modified Count Regression Model

Determinants of Antenatal Care Utilisation in India: A Count Data Modelling Approach

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