Abstract
BackgroundSurveillance data are essential for malaria control, but quality is often poor. The aim of the study was to evaluate the effectiveness of the novel combination of training plus an innovative quality improvement method—collaborative improvement (CI)—on the quality of malaria surveillance data in Uganda.MethodsThe intervention (training plus CI, or TCI), including brief in-service training and CI, was delivered in 5 health facilities (HFs) in Kayunga District from November 2015 to August 2016. HF teams monitored data quality, conducted plan-do-study-act cycles to test changes, attended periodic learning sessions, and received CI coaching. An independent evaluation was conducted to assess data completeness, accuracy, and timeliness. Using an interrupted time series design without a separate control group, data were abstracted from 156,707 outpatient department (OPD) records, laboratory registers, and aggregated monthly reports (MR) for 4 time periods: baseline—12 months, TCI scale-up—5 months; CI implementation—9 months; post-intervention—4 months. Monthly OPD register completeness was measured as the proportion of patient records with a malaria diagnosis with: (1) all data fields completed, and (2) all clinically-relevant fields completed. Accuracy was the relative difference between: (1) number of monthly malaria patients reported in OPD register versus MR, and (2) proportion of positive malaria tests reported in the laboratory register versus MR. Data were analysed with segmented linear regression modelling.ResultsData completeness increased substantially following TCI. Compared to baseline, all-field completeness increased by 60.1%-points (95% confidence interval [CI]: 46.9–73.2%) at mid-point, and clinically-relevant completeness increased by 61.6%-points (95% CI: 56.6–66.7%). A relative − 57.4%-point (95% confidence interval: − 105.5, − 9.3%) change, indicating an improvement in accuracy of malaria test positivity reporting, but no effect on data accuracy for monthly malaria patients, were observed. Cost per additional malaria patient, for whom complete clinically-relevant data were recorded in the OPD register, was $3.53 (95% confidence interval: $3.03, $4.15).ConclusionsTCI improved malaria surveillance completeness considerably, with limited impact on accuracy. Although these results are promising, the intervention’s effectiveness should be evaluated in more HFs, with longer follow-up, ideally in a randomized trial, before recommending CI for wide-scale use.
Highlights
Surveillance data are essential for malaria control, but quality is often poor
Cost per additional malaria patient, for whom complete clinically-relevant data were recorded in the outpatient department (OPD) register, was $3.53 (95% confidence interval: $3.03, $4.15)
In most African countries, health facility (HF)-based surveillance data on malaria reported through routine health information systems are a critical source of information for disease surveillance and decision-making by national malaria control programmes
Summary
Surveillance data are essential for malaria control, but quality is often poor. The aim of the study was to evaluate the effectiveness of the novel combination of training plus an innovative quality improvement method— collaborative improvement (CI)—on the quality of malaria surveillance data in Uganda. In most African countries, health facility (HF)-based surveillance data on malaria reported through routine health information systems are a critical source of information for disease surveillance and decision-making by national malaria control programmes. As a result, modelling has been used to estimate malaria morbidity and mortality trends in many countries [15, 16] Given these limitations in HF surveillance data, periodic cross-sectional population-based surveys have been considered a superior source of information for planning and policy needs [17]. Such surveys are complex and costly, are not useful for assessing malaria prevalence trends in low-burden settings, and lack the geographic granularity of HF surveillance data. The widespread adoption of District Health Information System 2 (DHIS2) in African countries has improved the availability of HF data by integrating disease-specific surveillance systems into a single digital platform, rendering the timely use of HF surveillance data more feasible
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
