Optimizing the Delivery of Routine Malaria Data Quality Assessments: A Two-Level Logistic Regression Model to Inform an Institutionalized Approach in Mozambique.

Abstract

Mozambique has implemented routine data quality assessments (DQAs) to improve accuracy of health facility (HF) malaria reporting since 2019. However, despite this being a resource-intensive exercise, the impact of operational factors on DQAs has not yet been systematically investigated. This analysis aimed to provide insights into optimizing the operational delivery of routine DQAs. A two-level logistic regression model based on 1,354 DQAs conducted across 195 HFs (16 districts, November 2019-December 2022) was used to estimate the impact of relevant operational factors, namely number of DQAs received, baseline reporting accuracy, HF setting, workload, malaria transmission intensity, and the shift to digital reporting, on accurate reporting by HFs. A report was considered accurate if the deviation between number of confirmed malaria cases in reports and register books was less than 10%. A statistically significant interaction was observed between baseline reporting accuracy and number of DQAs. For HFs with a baseline accuracy of ≤90%, each additional DQA increased the odds of accurate reporting by 102.8% (95% CI: 71.1-140.2%). For HFs with inaccurate data at baseline, the probability of accurate reporting increased to >80% after five DQAs, whereas HFs with accurate baseline data did not improve beyond the baseline visit. Other operational factors did not significantly affect reporting accuracy. Prioritizing HFs with low baseline accuracy for more frequent DQAs (every 6 months) with at least one visit to all HFs every 3 years might optimize resource allocation in Mozambique. Similar analytic approaches can be applied in other countries to optimize resource allocations for the delivery of routine DQAs.