Measuring coverage of maternal and child health services using routine health facility data: a Sierra Leone case study

BMC Health Services Research

Cheikh M. Faye¹  Moussa Bagayoko¹  Safia S. Jiwani²  Agbessi Amouzou²  Abdoulaye Maïga²  Dauda Kamara³  Ibrahim B. Koroma³  Osman Sankoh⁴ 

[1]African Population and Health Research Center, Nairobi, Kenya

[2]Bloomberg School of Public Health, Department of International Health, Johns Hopkins University, 615 N Wolfe St. 21205, Baltimore, USA

[3]Ministry of Health and Sanitation, Freetown, Sierra Leone

[4]Statistics Sierra Leone, Freetown, Sierra Leone

[5]Njala University, University Secretariat, Njala, Moyamba, Sierra Leone

[6]School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

[7]Heidelberg Institute for Global Health, University of Heidelberg Medical School, Heidelberg, Germany

关键词: RHIS data;    Measuring coverage;    Data quality;    Maternal and child health;    Sierra Leone;   

DOI  :  10.1186/s12913-021-06529-7

来源: Springer

【 摘 要 】

BackgroundThere are limited existing approaches to generate estimates from Routine Health Information Systems (RHIS) data, despite the growing interest to these data. We calculated and assessed the consistency of maternal and child health service coverage estimates from RHIS data, using census-based and health service-based denominators in Sierra Leone.MethodsWe used Sierra Leone 2016 RHIS data to calculate coverage of first antenatal care contact (ANC1), institutional delivery and diphtheria-pertussis-tetanus 3 (DPT3) immunization service provision. For each indicator, national and district level coverages were calculated using denominators derived from two census-based and three health service-based methods. We compared the coverage estimates from RHIS data to estimates from MICS 2017. We considered the agreement adequate when estimates from RHIS fell within the 95% confidence interval of the survey estimate.ResultsWe found an overall poor consistency of the coverage estimates calculated from the census-based methods. ANC1 and institutional delivery coverage estimates from these methods were greater than 100% in about half of the fourteen districts, and only 3 of the 14 districts had estimates consistent with the survey data. Health service-based methods generated better estimates. For institutional delivery coverage, five districts met the agreement criteria using BCG service-based method. We found better agreement for DPT3 coverage estimates using DPT1 service-based method as national coverage was close to survey data, and estimates were consistent for 8 out of 14 districts. DPT3 estimates were consistent in almost half of the districts (6/14) using ANC1 service-based method.ConclusionThe study highlighted the challenge in determining an appropriate denominator for RHIS-based coverage estimates. Systematic and transparent data quality check and correction, as well as rigorous approaches to determining denominators are key considerations to generate accurate coverage statistics using RHIS data.

【 授权许可】

CC BY