Abstract
Malaria is a major public health concern and requires quality data management system for effective preventive measures. The District Health Management Information System (DHMIS II) has been used to routinely capture data at health facilities. However, little is known about the quality of routine malaria data captured on the DHIMS II database in Community-Based Health Planning and Service (CHPS) compounds. The study therefore determined the quality of routine malaria data captured on the DHIMS II database in CHPS compounds in the Hohoe Municipality, Ghana. A retrospective cross-sectional analysis of Out Patient Department (OPD) malaria indicators was conducted using data from January 2018 to December 2022 at CHPS compounds in the Hohoe Municipality. Data were collected from three sources: the DHIMS II, monthly morbidity report forms, and consulting room registers. The study assessed three (3) malaria indicators: suspected malaria cases, tested malaria cases, and confirmed OPD malaria cases. A data validation tool was developed to determine the quality of malaria indicators measuring availability, completeness (percentage of missing data), and accuracy. The data was analysed descriptively using Microsoft excel. Out of the four (4) health facilities, 50% (2/4) met the suggested target of (≥ 90%) in 2018 and 2020 whiles all the (4) facilities met the recommended target in 2021 and 2022 for the availability of monthly OPD morbidity reports. For the availability of monthly data returns on anti-malarial, none of the facilities met the recommended target from 2018 to 2022. All 4 facilities met the recommended target in 2021 and 2022. For completeness of source data, 25% of the facility had complete data that met the required target in specific years (2021-2022). For accuracy, 50% of the facilities showed accurate reporting with a Good (± 5%) accuracy level. The remaining 50% underreported data, resulting in a Poor (± 11-20%) accuracy level. The study finds that while half of the facilities had reliable and complete malaria data in their source registers, there are inconsistencies with the DHIMS II database regarding the standards of data quality. Most facilities faced significant issues like unavailability of data, uncompleted data and underreporting of data, making it not-advisable to rely on DHIMS II for critical health decisions. Although, half of the facilities showed evidence of good data quality, there is still a need for improvement in the capturing of routine malaria data.