On Zero-Inflated Hierarchical Poisson Models with Application to Maternal Mortality Data

Kassim Tawiah,Anani Lotsi,Samuel Iddi,Harvinder S Sidhu

doi:10.1155/2020/1407320

Abstract

Count outcomes are commonly encountered in health sector data. The occurrence of count outcomes that exhibit many zeros has necessitated the extension of the ubiquitous Poisson regression model to accommodate the zero inflation and overdispersion as a result of the extra dispersion. We explored different extensions of the Poisson model including mixed models within the generalized linear mixed model framework to account for the repeated measurement of outcomes. These models are applied to maternal mortality data from fifty-six health facilities in four regions of Ghana. The objective is to identify factors associated with maternal mortality. The best-fitting model, the zero-inflated Poisson generalized linear mixed model, revealed that maternal mortality in hospital facilities is influenced by the number of referrals (into and out) of the hospital facility, number of antenatal visits exceeding four, number of midwives, and number of medical doctors at the facility. To be able to achieve targeted results in reducing maternal mortality and achieve the Sustainable Development Goal 3, the government, together with the ministry of health, should provide adequate maternal health services, especially at the district and community level. Additionally, there is a need for increased investment in Community Health Planning Services and related healthcare infrastructure and systems within the context of the Ouagadougou Declaration, that is, improve the training of skilled birth workers (midwives and doctors) and employ them at clinics to deal with labour complications without referring them to major hospitals. Furthermore, a well-structured awareness campaign is needed with importance given to avoiding adolescent pregnancy and improving antenatal care attendance to, at least, four, the gold standard, before delivery. Also, we recommend quality assessment form an essential part of all services that are directed towards improving maternal health and that more emphasis is needed to be given on research with multiple allied partners.

Highlights

Most data in the health sector are based on counts
Affordability, and accessibility of quality maternal health services, including emergency obstetric care (EmOC), would prove pivotal in reducing maternal death. ey were of the view that, to increase the perceived seriousness of the community regarding maternal health, a well-structured awareness campaign is needed with importance given to avoiding adolescent pregnancy
Using the stepwise deletion procedure in R, nonsignificant explanatory variables of the Poisson generalized linear regression were eliminated with the significance level set at 0.05. e significant variables included year, region, number of antenatal visits exceeding four, number of referrals into the hospital facility, number of referrals out of the hospital facility, number of deliveries at the facility, number of obstetric cases with malaria, number of medical doctors, and number of midwives at the hospital facility. ese variables were used in all model extensions

Summary

Introduction

The Poisson regression model, the simplest form of the generalized linear model for count outcome and a member of the exponential family [1, 2], provides a suitable modelling approach. Very commonly, these types of count outcomes in the health sector (especially when the event is rare) have an excessive number of zeros relative to the Poisson distribution. E Poisson distribution was extended to accommodate excess zeros by mixing a discrete mass and the Poisson distribution to obtain the zero-inflated Poisson model [3]. Lee et al [5] used an independent random

Methods

Results

Conclusion