Assessing and improving data quality from community health workers: a successful intervention in Neno,Malawi |
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Authors: | A. J. Admon J. Bazile H. Makungwa M. A. Chingoli L. R. Hirschhorn M. Peckarsky J. Rigodon M. Herce F. Chingoli P. N. Malani B. L. Hedt-Gauthier |
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Affiliation: | 1.Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA;2.Partners In Health, Boston, Massachusetts, USA;3.Abwenzi Pa Za Umoyo, Neno, Malawi;4.Department of Global Health and Social Medicine, Harvard Medical School, Cambridge, Massachusetts, USA;5.Ministry of Health, Neno District, Malawi;6.Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan, USA |
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Abstract: | Setting:A community health worker (CHW) program was established in Neno District, Malawi, in 2007 by Partners In Health in support of Ministry of Health activities. Routinely generated CHW data provide critical information for program monitoring and evaluation. Informal assessments of the CHW reports indicated poor quality, limiting the usefulness of the data.Objectives:1) To establish the quality of aggregated measures contained in CHW reports; 2) to develop interventions to address poor data quality; and 3) to evaluate changes in data quality following the intervention.Design:We developed a lot quality assurance sampling-based data quality assessment tool to identify sites with high or low reporting quality. Following the first assessment, we identified challenges and best practices and followed the interventions with two subsequent assessments.Results:At baseline, four of five areas were classified as low data quality. After 8 months, all five areas had achieved high data quality, and the reports generated from our electronic database became consistent and plausible.Conclusion:Program changes included improving the usability of the reporting forms, shifting aggregation responsibility to designated assistants and providing aggregation support tools. Local quality assessments and targeted interventions resulted in immediate improvements in data quality. |
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Keywords: | lot quality assurance sampling supervision quality improvement |
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