Regional disparities in maternal and child health indicators: Cluster analysis of districts in Bangladesh

• Published in: PloS one 2019-02, Vol.14 (2), p.e0210697-e0210697
• Main Authors: Raheem, Enayetur, Khan, Jahidur Rahman, Hossain, Mohammad Sorowar
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Bangladesh; Biology and Life Sciences; Biomedical research; Breastfeeding & lactation; Child; Child health; Child Health - statistics & numerical data; Child Health Services - organization & administration; Child, Preschool; Children; Children & youth; Childrens health; Cluster Analysis; Clustering; Datasets as Topic; Developing Countries; Epidemiology; Female; Foreign investment; Future predictions; Health care; Health care disparities; Health care reform; Health planning; Health Policy; Health Services Needs and Demand; Health services utilization; Health Status Disparities; Health Status Indicators; Humans; Indicators; LDCs; Literacy; Male; Maternal & child health; Maternal Health - statistics & numerical data; Maternal health services; Maternal Health Services - organization & administration; Medical care utilization; Medical research; Medicine and Health Sciences; Methods; Middle Aged; Patient Acceptance of Health Care - statistics & numerical data; People and Places; Physical Sciences; Population; Public health; Regional disparities; Research and Analysis Methods; Social Sciences; Socioeconomic Factors; Standard deviation; Studies; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0210697

Efforts to mitigate public health concerns are showing encouraging results over the time but disparities across the geographic regions still exist within countries. Inadequate researches on the regional disparities of health indicators based on representative and comparable data create challenges to develop evidence-based health policies, planning and future studies in developing countries like Bangladesh. This study examined the disparities among districts on various maternal and child health indicators in Bangladesh. Cluster analysis-an unsupervised learning technique was used based on nationally representative dataset originated from Multiple Indicator Cluster Survey (MICS), 2012-13. According to our results, Bangladesh is classified into two clusters based on different health indicators with substantial variations in districts per clusters for different sets of indicators suggesting regional variation across the indicators. There is a need to differentially focus on community-level interventions aimed at increasing maternal and child health care utilization and improving the socioeconomic position of mothers, especially in disadvantaged regions. The cluster analysis approach is unique in terms of the use of health care metrics in a multivariate setup to study regional similarity and dissimilarity in the context of Bangladesh.