{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:04:23Z","timestamp":1770141863696,"version":"3.49.0"},"reference-count":6,"publisher":"American Society of Tropical Medicine and Hygiene","issue":"1","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,7,2]]},"abstract":"<jats:title>ABSTRACT.<\/jats:title>\n<jats:p>Aggregate malaria data reporting accuracy indicates the overall quality of reported malaria surveillance data and is calculated using a routine data quality audit (RDQA) toolkit during a health facility audit. Three example scenarios are presented that highlight the limitations of the aggregate reporting accuracy methods of three malaria RDQA toolkits. A weighted absolute percentage error-based aggregate data reporting accuracy (WADRA) approach was found to resolve these limitations by using the register values as the weighting factor, enabling the detection of low-accuracy facilities that are otherwise considered high-accuracy by the current toolkits. Accordingly, country malaria programs should consider adopting the WADRA method in their RDQAs to maximize the detection of low-accuracy facilities and enhance decisions ranging from the development and implementation of corrective action plans to the prioritization and allocation of resources for data quality improvement efforts.<\/jats:p>","DOI":"10.4269\/ajtmh.24-0804","type":"journal-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T17:39:55Z","timestamp":1745343595000},"page":"37-41","source":"Crossref","is-referenced-by-count":2,"title":["Application of a Weighted Absolute Percentage Error-Based Method for Calculating the Aggregate Accuracy of Reported Malaria Surveillance Data"],"prefix":"10.4269","volume":"113","author":[{"given":"Smita","family":"Das","sequence":"first","affiliation":[{"name":"PATH Malaria Control and Elimination Partnership (MACEPA), Seattle, Washington;"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arantxa","family":"Roca-Feltrer","sequence":"additional","affiliation":[{"name":"PATH MACEPA, Maputo, Mozambique"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Hainsworth","sequence":"additional","affiliation":[{"name":"PATH Malaria Control and Elimination Partnership (MACEPA), Seattle, Washington;"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2795","reference":[{"key":"b1","series-title":"Global Technical Strategy for Malaria 2016\u20132030.","year":"2015"},{"key":"b2","series-title":"Malaria Routine Data Quality Assessment Tool: User Manual.","year":"2020"},{"key":"b3","series-title":"Data Quality Assurance. Module 3: Data Verification and System Assessment.","year":"2022"},{"key":"b4","series-title":"PATH MACEPA Routine Data Quality Audit Toolkit.","year":"2024"},{"key":"b5","series-title":"Data Min Knowl Discov","first-page":"788","article-title":"Forecast evaluation for data scientists: Common pitfalls and best practices","volume":"37","author":"Hewamalage","year":"2022"},{"key":"b6","series-title":"Am J Trop Med Hyg","first-page":"274","article-title":"Documenting trends in malaria data reporting accuracy using routine data quality audits in Zambia, 2015\u20132022","volume":"112","author":"Das","year":"2025"}],"container-title":["The American Journal of Tropical Medicine and Hygiene"],"original-title":[],"link":[{"URL":"https:\/\/www.ajtmh.org\/view\/journals\/tpmd\/113\/1\/article-p37.xml","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.ajtmh.org\/downloadpdf\/journals\/tpmd\/113\/1\/article-p37.xml","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/www.ajtmh.org\/downloadpdf\/journals\/tpmd\/113\/1\/article-p37.xml","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T13:54:25Z","timestamp":1751464465000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ajtmh.org\/view\/journals\/tpmd\/113\/1\/article-p37.xml"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":6,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.4269\/ajtmh.24-0804","relation":{},"ISSN":["0002-9637","1476-1645"],"issn-type":[{"value":"0002-9637","type":"print"},{"value":"1476-1645","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]}}}