Impact of Primary Health Care data quality on their use for infectious disease surveillance

Abstract

Background The surge of emerging and re-emerging infectious disease outbreaks underscores the need for robust Early Warning Systems (EWS) to inform mitigation and response measures. Administrative healthcare databases offer valuable epidemiological insights without imposing additional burdens on health services. However, administrative data are primarily collected for operational use, making data quality assessment crucial to ensure accurate interpretation of epidemiological analysis results. This study focuses on the development and implementation of a Data Quality Index for surveillance integrated into an EWS for influenza-like illness outbreaks based on a nationwide Primary Health Care (PHC) dataset. Methods We established a composite indicator measuring completeness and timeliness of PHC data from the Brazilian National Information System on Primary Health Care. Completeness was defined as the proportion of weeks within an 8-week rolling window with any register of encounters. Timeliness was assessed by calculating the interval between the date of encounter and its corresponding registry in the information system. Using the backfilled PHC dataset as a gold standard, we evaluated the impact of data quality in the EWS for influenza-like illness outbreaks using different levels of data quality of the weekly updated real-time PHC dataset across all 5,570 Brazilian municipalities from October 10, 2023, to March 10, 2024. Results In the study period, the backfilled PHC dataset registered 198,335,762 encounters due to influenza-like illness, averaging 8,623,294 encounters per week. Analysis of concordant warnings between the backfilled and the real-time dataset showed that 100% completeness and at least 80% timeliness were optimal for the highest concordance. Municipalities with at least 60% of weeks featuring a suitable Data Quality Index showed the highest concordance of warnings between the backfilled and real-time datasets. Conclusion Our study highlights the critical role of data quality in enhancing the performance of early warning systems based on PHC data. In addition, we provide a practical approach for monitoring data quality in real time. Our findings demonstrate that optimal completeness and timeliness of data significantly impact the EWS's ability to detect ILI outbreaks. Continuous monitoring and improvement of data quality should be prioritized to ensure the reliability and effectiveness of surveillance systems.