Exploring spatial variations in level and predictors of unskilled birth attendant delivery in Bangladesh using spatial analysis techniques: Findings from nationally representative survey data

Md Rahman Mahfuzur; Md Arif Billah; Nicola Liebergreen; Manoj Kumer Ghosh; Md Shafiul Alam; Md Armanul Haque; Abdullah Al-Maruf

doi:10.1371/journal.pone.0275951

Exploring spatial variations in level and predictors of unskilled birth attendant delivery in Bangladesh using spatial analysis techniques: Findings from nationally representative survey data

PLoS One. 2022 Oct 25;17(10):e0275951. doi: 10.1371/journal.pone.0275951. eCollection 2022.

Authors

Md Rahman Mahfuzur¹, Md Arif Billah², Nicola Liebergreen³, Manoj Kumer Ghosh⁴, Md Shafiul Alam⁴, Md Armanul Haque⁵, Abdullah Al-Maruf⁴

Affiliations

¹ Department of Population Science and Human Resource Development, Sir Jagadish Chandra Bose Academic Building, University of Rajshahi, Rajshahi, Bangladesh.
² Department of Social Work and Counselling, Faculty of Business, Economics and Social Development, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia.
³ Bioethics Centre, University of Otago, Dunedin, New Zealand.
⁴ Department of Geography and Environmental Studies, University of Rajshahi, Rajshahi, Bangladesh.
⁵ Information Science & Library Management, University of Rajshahi, Rajshahi, Bangladesh.

Abstract

Background: Bangladesh has failed to meet the United Nations goal for reducing maternal mortality in the last decade. The high prevalence of unskilled birth attendant (UBA) delivery (47%) has resulted in negative consequences for the health of mothers and newborn babies in the country. Spatial variations in UBA delivery and its predictors are yet to be explored in Bangladesh, which could be very helpful in formulating cost-effective policies for reducing that. This study examines the spatial variations in UBA delivery and its predictors in Bangladesh.

Methods: This study analyzed the characteristics of 672 clusters extracted from the 2017/18 Bangladesh Demographic and Health Survey, and healthcare facility data from the 2017 Bangladesh Health Facility Survey. These data were analyzed using descriptive and spatial analyses (hot spot analysis, Ordinary Least Squares Regression, and Geographically Weighted Regression) techniques.

Results: Statistically significant hot spots of UBA delivery were concentrated in parts of the Mymensingh, Sylhet, Barishal, and Rangpur regions, while Khulna was the safest region. Predictive strengths of the statistically significant predictors of spatial variation in UBA delivery were observed to vary considerably across the regions. Poorest household wealth status and less than four antenatal care contacts emerged as strong predictors of UBA delivery in all the aforementioned hot spot-stricken regions, except Barisal. Additionally, primiparity and all secondary education or higher were strong predictors of lower UBA delivery rates in Mymensingh and Sylhet, while poorer household wealth status was also a strong predictor of UBA delivery in Sylhet. Multiparity was an additional strong predictor of UBA delivery in Rangpur. In Barisal, only poorer household wealth status exerted a strong positive influence on UBA delivery.

Conclusions: The remarkable spatial variations in UBA delivery and its predictors' strengths indicate that geographically-targeted interventions could be a cost-effective method for reducing the UBA delivery prevalence in Bangladesh, thereby improve maternal and child health.

MeSH terms

Bangladesh / epidemiology
Child
Female
Health Surveys
Humans
Infant
Infant, Newborn
Pregnancy
Prenatal Care*
Socioeconomic Factors
Spatial Regression*

Grants and funding

The author(s) received no specific funding for this work.