Benjamin Uzochukwu - Department of Community Medicine, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria
Tolib Mirzoev - Nuffield Centre for International Health and Development, University of Leeds, Leeds, UK
Chinyere Okeke - Department of Community Medicine, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria
Joseph Hicks - Nuffield Centre for International Health and Development, University of Leeds, Leeds, UK
Enyi Etiaba - Department of Health Administration and Management, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria
Uche Obi - Department of Community Medicine, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria
Tim Ensor - Nuffield Centre for International Health and Development, University of Leeds, Leeds, UK
Adaora Uzochukwu - Department of Management, University of Nigeria (Enugu Campus), Nsukka, Nigeria
Obinna Onwujekwe - Department of Health Administration and Management, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria

BackgroundDuring ۲۰۱۲-۲۰۱۵, the Federal Government of Nigeria launched the Subsidy Reinvestment and Empowerment Programme, a health system strengthening (HSS) programme with a Maternal and Child Health component (Subsidy Reinvestment and Empowerment Programme [SURE-P]/MCH), which was monitored using the Health Management Information Systems (HMIS) data reporting tools. Good quality data is essential for health policy and planning decisions yet, little is known on whether and how broad health systems strengthening programmes affect quality of data. This paper explores the effects of the SURE-P/MCH on completeness of MCH data in the National HMIS. MethodsThis mixed-methods study was undertaken in Anambra state, southeast Nigeria. A standardized proforma was used to collect facility-level data from the facility registers on MCH services to assess the completeness of data from ۲ interventions and one control clusters. The facility data was collected to cover before, during, and after the SURE-P intervention activities. Qualitative in-depth interviews were conducted with purposefully-identified health facility workers to identify their views and experiences of changes in data quality throughout the above ۳ periods. ResultsQuantitative analysis of the facility data showed that data completeness improved substantially, starting before SURE-P and continuing during SURE-P but across all clusters (ie, including the control). Also health workers felt data completeness were improved during the SURE-P, but declined with the cessation of the programme. We also found that challenges to data completeness are dependent on many variables including a high burden on providers for data collection, many variables to be filled in the data collection tools, and lack of health worker incentives. ConclusionQuantitative analysis showed improved data completeness and health workers believed the SURE-P/MCH had contributed to the improvement. The functioning of national HMIS are inevitably linked with other health systems components. While health systems strengthening programmes have a great potential for improved overall systems performance, a more granular understanding of their implications on the specific components such as the resultant quality of HMIS data, is needed.

Health Management Information, Data Completeness, Maternal and Child, Healthcare, Nigeria