Evidence map›Paper›PMID 33556102›Full record

ArticlePloS one2021

Skewed logit model for analyzing correlated infant morbidity data.

Ngugi Mwenda, Ruth Nduati, Mathew Kosgei, Gregory Kerich

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Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ngugi MwendaSchool of Science and Aerospace Studies, Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya.ORCID 0000-0001-9610-3124
Ruth NduatiDepartment of Paediatrics, University of Nairobi, Nairobi, Kenya.
Mathew KosgeiSchool of Science and Aerospace Studies, Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya.
Gregory KerichSchool of Science and Aerospace Studies, Department of Mathematics, Physics and Computing, Moi University, Eldoret, Kenya.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInfant morbidity is a topic of interest because it is used globally as an indicator of the status of health care in a country. A large body of evidence supports an association between bacterial vaginosis (BV) and infant morbidity. When estimating the relationship between the predictors and the estimated variable of morbidity severity, the latter exhibits imbalanced data, which means that violation of symmetry is expected. Two competing methods of analysis, that is, (1) probit and (2) logit techniques, can be considered in this context and have been applied to model such outcomes. However, these models may yield inconsistent results. While non-normal modeling approaches have been embraced in the recent past, the skewed logit model has been given little attention. In this study, we exemplify its usefulness in analyzing imbalanced longitudinal responses data. METHODOLOGY: While numerous non-normal methods for modeling binomial responses are well established, there is a need for comparison studies to assess their usefulness in different scenarios, especially under a longitudinal setting. This is addressed in this study. We use a dataset from Kenya about infants born to human immunodeficiency virus (HIV) positive mothers, who are also screened for BV. We aimed to investigate the effect of BV on infant morbidity across time. We derived a score for morbidity incidences depending on illnesses reported during the month of reference. By adjusting for the mother's BV status, the child's HIV status, sex, feeding status, and weight for age, we estimated the standard binary logit and skewed logit models, both using Generalized Estimating Equations.

resultsResults show that accounting for skewness in imbalanced binary data can show associations between variables in line with expectations documented by the literature. In addition, an in-depth analysis accounting for skewness has shown that, over time, maternal BV is associated with multiple health conditions in infants.

interpretationMaternal BV status was positively associated with infant morbidity incidences, which highlights the need for early intervention in cases of HIV-infected pregnant women.

Indexed as

Infant MortalityLogistic ModelsData Interpretation, StatisticalFemaleHIV InfectionsHumansIncidenceInfantKenyaLongitudinal StudiesMalePregnancyPregnancy Complications, Infectious

Identifiers

PMID33556102
PMCPMC7870004

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