Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2017 Dec 21;17(Suppl 3):828.
doi: 10.1186/s12913-017-2660-y.

Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative

Collaborators, Affiliations

Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative

Sarah Gimbel et al. BMC Health Serv Res. .

Abstract

Background: High-quality data are critical to inform, monitor and manage health programs. Over the seven-year African Health Initiative of the Doris Duke Charitable Foundation, three of the five Population Health Implementation and Training (PHIT) partnership projects in Mozambique, Rwanda, and Zambia introduced strategies to improve the quality and evaluation of routinely-collected data at the primary health care level, and stimulate its use in evidence-based decision-making. Using the Consolidated Framework for Implementation Research (CFIR) as a guide, this paper: 1) describes and categorizes data quality assessment and improvement activities of the projects, and 2) identifies core intervention components and implementation strategy adaptations introduced to improve data quality in each setting.

Methods: The CFIR was adapted through a qualitative theme reduction process involving discussions with key informants from each project, who identified two domains and ten constructs most relevant to the study aim of describing and comparing each country's data quality assessment approach and implementation process. Data were collected on each project's data quality improvement strategies, activities implemented, and results via a semi-structured questionnaire with closed and open-ended items administered to health management information systems leads in each country, with complementary data abstraction from project reports.

Results: Across the three projects, intervention components that aligned with user priorities and government systems were perceived to be relatively advantageous, and more readily adapted and adopted. Activities that both assessed and improved data quality (including data quality assessments, mentorship and supportive supervision, establishment and/or strengthening of electronic medical record systems), received higher ranking scores from respondents.

Conclusion: Our findings suggest that, at a minimum, successful data quality improvement efforts should include routine audits linked to ongoing, on-the-job mentoring at the point of service. This pairing of interventions engages health workers in data collection, cleaning, and analysis of real-world data, and thus provides important skills building with on-site mentoring. The effect of these core components is strengthened by performance review meetings that unify multiple health system levels (provincial, district, facility, and community) to assess data quality, highlight areas of weakness, and plan improvements.

Keywords: Data quality assessment; Decision making; Health systems research; Health systems strengthening; Maternal and child health; Mozambique; Quality improvement; Rwanda; Zambia.

PubMed Disclaimer

Conflict of interest statement

Authors’ information

Sarah Gimbel, PhD, MPH, RN; Moses Mwanza, BA; Marie Paul Nisingizwe, MSc; Cathy Michel, MPH; Lisa Hirschhorn, MD, MPH.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Core activities ranked by importance across sites
Fig. 2
Fig. 2
Categorization of data quality activities

References

    1. Abouzahr C, Boerma T. Health information systems: the foundations of public health. Bull World Health Organ. 2005;83:578–583. - PMC - PubMed
    1. Handley K, Boerma T, Victora C, Evans TG. An inflection point for country health data. Lancet Glob Health. 2015;3(8):e437-e8. doi.10.1016/S2214-109X(15)00067-4. - PMC - PubMed
    1. Sherr K, Cuembelo F, Michel C, Gimbel S, Micek M, Kariaganis M, Pio A, Manuel JL, Pfeiffer J, Gloyd S. Strengthening integrated primary health care in Sofala, Mozambique. BMC Health Serv Res. 2013;13(Suppl 2):S4. doi: 10.1186/1472-6963-13-S2-S4. - DOI - PMC - PubMed
    1. Micek MA, Gimbel-Sherr K, Baptista AJ, Matediana E, Montoya P, Pfeiffer J, Melo A, Gimbel-Sherr S, Johnson W, Gloyd S. Loss to follow-up of adults in public HIV care systems in central Mozambique: identifying obstacles to treatment. J Acquir Immune Defic Syndr. 2009;52:397–405. doi: 10.1097/QAI.0b013e3181ab73e2. - DOI - PMC - PubMed
    1. Mavimbe JC, Braa J, Bjune G. Assessing immunization data quality from routine reports in Mozambique. BMC Public Health. 2005;5:108. doi: 10.1186/1471-2458-5-108. - DOI - PMC - PubMed

Publication types

LinkOut - more resources