Abstract:
BACKGROUND South Africa set a target to eliminate malaria by 2023, with KwaZulu-Natal (KZN) Province the malariaendemic
province closest to achieving this goal. Objective two of the National Malaria Elimination Strategic Plan
(NMESP) focused on strengthening surveillance systems to support the country’s elimination efforts. Regular evaluations
of the malaria surveillance systems against the targets of the NMESP objective are crucial in improving their performance
and impact. This study aimed to assess whether the malaria surveillance system in KwaZulu-Natal Province
meets the NMESP surveillance objective and goals.
METHODS A mixed-methods cross-sectional study design was used to evaluate the malaria surveillance system,
focusing on the District Health Information System 2 (DHIS2). The study assessed the data quality, timeliness, simplicity,
and acceptability of the system. Key personnel from KZN’s Provincial malaria control programme were interviewed
using self-administered questionnaires to evaluate their perception of the system’s simplicity and acceptability.
Malaria case data from January 2016 to December 2020 were extracted from the DHIS2 and evaluated for data quality
and timeliness.
RESULTS The survey respondents generally found the DHIS2-based surveillance system acceptable (79%, 11/14)
and easy to use (71%, 10/14), stating that they could readily find, extract, and share data (64%, 9/14). Overall data
quality was good (88.9%), although some variables needed for case classification had low completeness and data
availability. However, case notifications were not timely, with only 61% (2 622/4 329) of cases notified within 24 h
of diagnosis. During the 5-year study period, the DHIS2 captured 4 333 malaria cases. The majority of cases (81%, 3
489/4 330) were categorized as imported, and predominately in males (67%, 2 914/4 333).
CONCLUSION While the malaria surveillance system in KZN Province largely met the NMESP surveillance strategic
goals, it failed to achieve the overarching surveillance objective of 100% notification of cases within 24 h of diagnosis.
The majority of reported cases in KZN Province were classified as imported, emphasizing the importance of complete
data for accurate case classification. Engaging with healthcare professionals responsible for case notification and disseminating
aggregated data back to them is needed to encourage and improve notification timeliness.
