Did an Intervention Programme Aimed at Strengthening the Maternal and Child Health Services in Nigeria Improve the Completeness of Routine Health Data Within the Health Management Information System?

Document Type : Original Article

Authors

1 Department of Community Medicine, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria

2 Nuffield Centre for International Health and Development, University of Leeds, Leeds, UK

3 Department of Health Administration and Management, College of Medicine, University of Nigeria (Enugu Campus), Nsukka, Nigeria

4 Department of Management, University of Nigeria (Enugu Campus), Nsukka, Nigeria

Abstract

Background
During 2012-2015, 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.
 
Methods
This 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 2 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 3 periods.
 
Results
Quantitative 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.
 
Conclusion
Quantitative 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.

Keywords

References

  1. Mirzoev T, Green A, Gerein N, et al. Role of evidence in maternal health policy processes in Vietnam, India and China: findings from the HEPVIC project. Evid Policy. 2013;9(4):493-511. doi:10.1332/174426413x669845
  2. Onwujekwe O, Uguru N, Russo G, et al. Role and use of evidence in policymaking: an analysis of case studies from the health sector in Nigeria. Health Res Policy Syst. 2015;13:46. doi:10.1186/s12961-015-0049-0
  3. Mirzoev T, Poudel AN, Gissing S, et al. Is evidence-informed urban health planning a myth or reality? lessons from a qualitative assessment in three Asian cities. Health Policy Plan. 2019;34(10):773-783. doi:10.1093/heapol/czz097
  4. Abubakar A, Idris S, Nguku P, Sabitu K, Sambo M. Assessment of integrated disease surveillance and response strategy implementation in selected Local Government Areas of Kaduna state. Ann Niger Med. 2013;7(1):14-19. doi:10.4103/0331-3131.119981
  5. MEASURE Evaluation. Improving Continuity of Care by Strengthening Health Information Systems. MEASURE Evaluation; 2017.
  6. Lippeveld T. Routine health information systems: the glue of a unified health system. Keynote Address at the Workshop on Issues and Innovation in Routine Health Information in Developing Countries, Potomac; March 14-16, 2001. Washington, DC: JSI; 2001.
  7. AbouZahr C, Boerma T. Health Information Systems: The Foundations of Public Health. WHO; 2011.
  8. Stevens GA, Hogan DR, Boerma T. Improving reporting of health estimates. Bull World Health Organ. 2016;94(7):483. doi:10.2471/blt.16.179259
  9. Federal Ministry of Health. National Standard Operating Procedure for the Collection and Management of Integrated Routine Health Data in Nigeria. Abuja, Nigeria: Department of Health Planning, Research & Statistics; 2015.
  10. Federal Ministry of Health. Revised Policy-Programme and Strategic Plan of Action. 2006. http://cheld.org/wp-content/uploads/2012/04/NATIONAL-HEALTH-MANAGEMENT-INFORMATION-SYSTEM.pdf.  Accessed February 28, 2020.
  11. Anifalaje AA. Decentralisation and health systems performance in developing countries: impact of “decision space” on primary health care delivery in Nigeria. International Journal of Healthcare Delivery Reform Initiatives (IJHDRI). 2009;1(1):25-47. doi:10.4018/jhdri.2009010103
  12. Health Reform Foundation of Nigeria. Nigerian Health Review. https://www.herfon.org.ng/.  Accessed August 14, 2020. Published 2007.
  13. Adejumo A. An Assessment of Data Quality in Routine Health Information Systems in Oyo State, Nigeria [Thesis]. Cape Town, South Africa: University of the Western Cape; 2017. https://etd.uwc.ac.za/handle/11394/5497.  Accessed August 10, 2020.
  14. Makinde OA, Onazi M, Adeleke O, Ohadi EM, Dieng AD, Osika JS, Abt Associates Inc. Assessment of the Routine Health Management Information System in Delta State, Federal Republic of Nigeria. Bethesda, MD: Health Systems 20/20 project, Abt Associates Inc; 2012.
  15. MEASURE Evaluation. Strengthening Health Information Systems in Nigeria—Developing a Master Facility List. MEASURE Evaluation; 2019.
  16. Federal Ministry of Health of Nigeria. Nigeria Every Newborn Action Plan: A Plan to End Preventable Newborn Deaths in Nigeria. Abuja, Nigeria: Federal Ministry of Health; 2016.
  17. Federal Ministry of Health of Nigeria. National Health Information System Strategic Plan, 2014-2018. Abuja, Nigeria: Federal Ministry of Health; 2014.
  18. United Nations. Zero Draft: Outcome Document to Adopt Post-2015 Development Agenda. Office of the Secretary-General's Envoy on Youth; 2015. https://www.un.org/youthenvoy/2015/06/zero-draft-outcome-document-adopt-post-2015-development-agenda/.  Accessed February 28, 2020. Published 2015.
  19. Federal Ministry of Health. Nigerian Health Sector Performance Report: Subsidy Reinvestment and Empowerment Programme-Maternal and Child Health Services-Implementation Manual. Abuja, Nigeria: 2012. https://wbwaterdata.org/dataset/nigeria-subsidy-reinvestment-and-empowerment-programme-maternal-and-child-health-initiative.
  20. Ebenso BE, Uzochukwu B, Manzano A, et al. De-constructing a complex programme using a logic map: realist evaluation of maternal and child health in Nigeria. In: The State of the Art of Realist Methodologies: A Two Day Conference;  November; 2015; United Kingdom, Leeds.
  21. Mirzoev T, Etiaba E, Ebenso B, et al. Study protocol: realist evaluation of effectiveness and sustainability of a community health workers programme in improving maternal and child health in Nigeria. Implement Sci. 2016;11(1):83. doi:10.1186/s13012-016-0443-1
  22. Determinants of Effectiveness of a Novel Community Health Workers Programme in Improving Maternal and Child Health in Nigeria. https://gtr.ukri.org/projects?ref=MR%2FM01472X%2F1. 
  23. Baum CF, Schaffer ME. ACTEST: Stata Module to Perform Cumby-Huizinga General Test for Autocorrelation in Time Series. Statistical Software Components. https://ideas.repec.org/c/boc/bocode/s457668.html.  Accessed February 27, 2020. Published January 2015.
  24. Ugo O, Ezinne EA, Modupe O, Nicole S, Winifred E, Kelechi O. Improving quality of care in primary health-care facilities in rural Nigeria: successes and challenges. Health Serv Res Manag Epidemiol. 2016;3:2333392816662581. doi:10.1177/2333392816662581
  25. Malaria Consortium. FINAL REPORT Support to the National Malaria Programme. https://www.malariaconsortium.org/media-downloads/687.  Accessed February 27, 2020. Published 2008.
  26. Enemuoh JC, Onwujekwe OE, Uzochukwu BSC, Oranuba J, Ilika AL. Quality of monitoring and evaluation of malaria control activities: tracking stock-outs and replenishment of supplies in Anambra state, Nigeria. Sci J Public Health. 2013;1(5):201-208. doi:10.11648/j.sjph.20130105.13
  27. ANMCBP. A state malaria control B project. Report on Training of Local Government Team on Monitoring and Evaluation Tools. Awka 9th-15th August 2010, Anambra State Ministry of Health; 2010.
  28. Mbachu CO, Uzochukwu BSC, Onwujekwe OE, Ilika AL, Oranuba J. How do health workers perceive and practice monitoring and evaluation of malaria control interventions in South-east Nigeria? BMC Health Serv Res. 2013;13(1):81. doi:10.1186/1472-6963-13-81
  29. Okoli U, Morris L, Oshin A, Pate MA, Aigbe C, Muhammad A. Conditional cash transfer schemes in Nigeria: potential gains for maternal and child health service uptake in a national pilot programme. BMC Pregnancy Childbirth. 2014;14:408. doi:10.1186/s12884-014-0408-9
  30. Nnebue CC, Onwasigwe CN, Adogu PO, Onyeonoro UU. Awareness and knowledge of disease surveillance and notification by health-care workers and availability of facility records in Anambra state, Nigeria. Niger Med J. 2012;53(4):220-225. doi:10.4103/0300-1652.107557
  31. Taggart J, Liaw ST, Yu H. Structured data quality reports to improve EHR data quality. Int J Med Inform. 2015;84(12):1094-1098. doi:10.1016/j.ijmedinf.2015.09.008
  32. Quantitative and Qualitative Research - Objective or Subjective? Explorable website. https://explorable.com/quantitative-and-qualitative-research.  Published 2009. Accessed February 27, 2020.
  33. Mirzoev, Das M, Ebenso BE, et al. Contextual influences on the role of evidence in health policy development: What can we learn from six policies in India and Nigeria? Evid Policy. 2015;13(1):59-79. doi:10.1332/174426415x14454407579925
  34. Gabrysch S, Campbell OM. Still too far to walk: literature review of the determinants of delivery service use. BMC Pregnancy Childbirth. 2009;9:34. doi:10.1186/1471-2393-9-34
  35. World Health Organization. Regional Office for the Western Pacific. Developing Health Management Information Systems: A Practical Guide for Developing Countries. Manila: WHO Regional Office for the Western Pacific; 2004. http://www.wpro.who.int.  Accessed February 28, 2020.
  36. World Health Organization (WHO). A Rapid Assessment of the Burden of Indicators and Reporting Requirements for Health Monitoring. WHO; 2014.
  37. Chilundo B, Aanestad M. Negotiating multiple rationalities in the process of integrating the information systems of disease-specific health programmes. Electron J Inf Syst Dev Ctries. 2004;20(1):1-28. doi:10.1002/j.1681-4835.2004.tb00129.x
  38. Mphatswe W, Mate KS, Bennett B, et al. Improving public health information: a data quality intervention in KwaZulu-Natal, South Africa. Bull World Health Organ. 2012;90(3):176-182. doi:10.2471/blt.11.092759
  39. Makombe SD, Hochgesang M, Jahn A, et al. Assessing the quality of data aggregated by antiretroviral treatment clinics in Malawi. Bull World Health Organ. 2008;86(4):310-314. doi:10.2471/blt.07.044685
  40. Gimbel S, Mwanza M, Nisingizwe MP, Michel C, Hirschhorn L. Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative. BMC Health Serv Res. 2017;17(suppl 3):828. doi:10.1186/s12913-017-2660-y
  41. Piette JD, Lun KC, Moura LA Jr, et al. Impacts of e-health on the outcomes of care in low- and middle-income countries: where do we go from here? Bull World Health Organ. 2012;90(5):365-372. doi:10.2471/blt.11.099069
  42. MEASURE Evaluation. Health Management Information System: Facilitator’s Guide for Training Hospital Staff. MEASURE Evaluation; 2013.
  43. Nwankwo B, Sambo MN. Can training of health care workers improve data management practice in health management information systems: a case study of primary health care facilities in Kaduna State, Nigeria. Pan Afr Med J. 2018;30:289. doi:10.11604/pamj.2018.30.289.15802
  44. Ahsan KZ, Tahsina T, Iqbal A, et al. Production and use of estimates for monitoring progress in the health sector: the case of Bangladesh. Glob Health Action. 2017;10:1298890. doi:10.1080/16549716.2017.1298890
  45. Nicol E, Dudley L, Bradshaw D. Assessing the quality of routine data for the prevention of mother-to-child transmission of HIV: an analytical observational study in two health districts with high HIV prevalence in South Africa. Int J Med Inform. 2016;95:60-70. doi:10.1016/j.ijmedinf.2016.09.006
  46. Nyangara FM, Hai T, Zalisk K, et al. Assessment of data quality and reporting systems for underserved populations: the case of integrated community case management programs in Nigeria. Health Policy Plan. 2018;33(4):465-473. doi:10.1093/heapol/czy003
  47. Chen H, Yu P, Hailey D, Wang N. Methods for assessing the quality of data in public health information systems: a critical review. Stud Health Technol Inform. 2014;204:13-18.
International Journal of Health Policy and Management

Articles in Press, Accepted Manuscript
Available Online from 05 December 2020

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