TY - JOUR
T1 - Regional disparities in maternal and child health indicators
T2 - Cluster analysis of districts in Bangladesh
AU - Raheem, Enayetur
AU - Khan, Jahidur Rahman
AU - Hossain, Mohammad Sorowar
PY - 2019/2/6
Y1 - 2019/2/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85061147152&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0210697
DO - 10.1371/journal.pone.0210697
M3 - Article
C2 - 30726250
AN - SCOPUS:85061147152
SN - 1932-6203
VL - 14
SP - 1
EP - 12
JO - PLoS One
JF - PLoS One
IS - 2
M1 - 0210697
ER -