Health ... Malaria
ASTMH (American Society of Tropical Medicine and Hygiene)

Title: Multi-country Routine Data Quality Assessment (RDQA) for malaria information

Burgess COMMENTARY

Peter Burgess

Presentation Abstract

Session: Poster Session C Presentations and Light Lunch

Abstract Number: 1629

Title: Multi-country Routine Data Quality Assessment (RDQA) for malaria information

Presentation Start: 10/28/2015 12:00:00 PM Presentation End: 10/28/2015 1:45:00 PM

Authors: Jeff Bernson1, Michael Hainsworth1, Marie-Reine Rutagwera2, John Miller2, Chris Lunga2, Adem Ahmed3, Melkamu Zeleke4, Girma Guesses4, Asmamaw Ayenew4, Worku Workie4, Prudence Malama5, Kalenga Kakompe5, Mercy Mwanza5, Mulakwa Kamuliwo5
1PATH, Seattle, WA, United States, 2PATH, Lusaka, Zambia, 3PATH, Addis Ababa, Ethiopia, 4PATH, Addis Ababa, Zambia, 5Ministry of Health, Lusaka, Zambia

Abstract: As National Malaria Control Programs increasingly focus on malaria elimination, real time, accurate, and actionable data are critical to target geographies and populations and to optimize the allocation of resources. The MACEPA project assessed the quality of weekly data captured through a mobile phone application built on DHIS2 in Ethiopia and Zambia over a 24-month period to understand how data quality improvement practices can be strengthened to support system needs for rapid and more focal data. Data was captured from 209 health facility catchment areas across four health zones in Amhara Region, Ethiopia, covering an estimated population of 1.6 million in Ethiopia and 220 facility catchments in Southern Province, Zambia, covering a population of nearly 2 million in Zambia. We used two methods for assessing data quality: applying data validation rules on reported data elements directly in DHIS2 to check for logical inconsistencies and produce performance indicators around reporting, completeness, timeliness, and validity; and applying a routine data quality assessment tool to describe the strengths and gaps in the data management and reporting systems, comparing source data from health facility registries with data reported through the mobile phone-based rapid reporting system. This multi-country approach has produced well documented, promising quality assessment practices and lessons learned along with an opportunity to compare the quality of data from rapid reporting systems in different contexts: varying malaria incidence profiles, different mobile technology, role of data reporters, and levels of support by partners and national health systems, providing insights into factors to strengthen and normalize data quality.