Florentino Pilar Tavares Veras, Bertoldo Junior Juracy, Barbosa George Caique Gouveia, Cerqueira-Silva Thiago, Oliveira Vinicius de Araújo, Garcia Marcio Henrique de Oliveira, Penna Gerson Oliveira, Boaventura Viviane, Ramos Pablo Ivan Pereira, Barral-Netto Manoel, Marcilio Izabel
Centro de Integração de Dados e Conhecimento em Saúde (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, R. Mundo, 121 - sala 315 - Trobogy, Salvador, 41745-715, Brazil, 55 7131762357.
Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, United Kingdom.
JMIR Public Health Surveill. 2025 Feb 21;11:e67050. doi: 10.2196/67050.
The increase in emerging and re-emerging infectious disease outbreaks underscores the need for robust early warning systems (EWSs) to guide mitigation and response measures. Administrative health care databases provide valuable epidemiological insights without imposing additional burdens on health services. However, these datasets are primarily collected for operational use, making data quality assessment essential to ensure an accurate interpretation of epidemiological analysis. This study focuses on the development and implementation of a data quality index (DQI) for surveillance integrated into an EWS for influenza-like illness (ILI) outbreaks using Brazil's a nationwide Primary Health Care (PHC) dataset.
We aimed to evaluate the impact of data completeness and timeliness on the performance of an EWS for ILI outbreaks and establish optimal thresholds for a suitable DQI, thereby improving the accuracy of outbreak detection and supporting public health surveillance.
A composite DQI was established to measure the 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 calculated as the interval between the date of encounter and its corresponding registry in the information system. The backfilled PHC dataset served as the gold standard to evaluate the impact of varying data quality levels from the weekly updated real-time PHC dataset on the EWS for ILI outbreaks across 5570 Brazilian municipalities from October 10, 2023, to March 10, 2024.
During the study period, the backfilled dataset recorded 198,335,762 ILI-related encounters, averaging 8,623,294 encounters per week. The EWS detected a median of 4 (IQR 2-5) ILI outbreak warnings per municipality using the backfilled dataset. Using the real-time dataset, 12,538 (65%) warnings were concordant with the backfilled dataset. Our analysis revealed that 100% completeness yielded 76.7% concordant warnings, while 80% timeliness resulted in at least 50% concordant warnings. These thresholds were considered optimal for a suitable DQI. Restricting the analysis to municipalities with a suitable DQI increased concordant warnings to 80.4%. A median of 71% (IQR 54%-71.9%) of municipalities met the suitable DQI threshold weekly. Municipalities with ≥60% of weeks achieving a suitable DQI demonstrated the highest concordance between backfilled and real-time datasets, with those achieving ≥80% of weeks showing 82.3% concordance.
Our findings highlight the critical role of data quality in improving the EWS' performance based on PHC data for detecting ILI outbreaks. The proposed framework for real-time DQI monitoring is a practical approach and can be adapted to other surveillance systems, providing insights for similar implementations. We demonstrate that optimal completeness and timeliness of data significantly impact the EWS' ability to detect ILI outbreaks. Continuous monitoring and improvement of data quality should remain a priority to strengthen the reliability and effectiveness of surveillance systems.
新出现和再次出现的传染病疫情不断增加，凸显了建立强大的早期预警系统（EWS）以指导缓解和应对措施的必要性。行政医疗保健数据库可提供有价值的流行病学见解，而不会给医疗服务带来额外负担。然而，这些数据集主要是为运营用途而收集的，因此数据质量评估对于确保准确解释流行病学分析至关重要。本研究重点关注使用巴西全国初级卫生保健（PHC）数据集，为纳入流感样疾病（ILI）疫情EWS的监测开发和实施数据质量指数（DQI）。
我们旨在评估数据完整性和及时性对ILI疫情EWS性能的影响，并为合适的DQI确定最佳阈值，从而提高疫情检测的准确性并支持公共卫生监测。
建立了一个综合DQI，以衡量巴西国家初级卫生保健信息系统中PHC数据的完整性和及时性。完整性定义为8周滚动窗口内有任何就诊记录的周数比例。及时性计算为就诊日期与其在信息系统中相应登记日期之间的间隔。回填的PHC数据集用作金标准，以评估2023年10月10日至2024年3月10日期间，每周更新的实时PHC数据集不同数据质量水平对巴西5570个城市ILI疫情EWS的影响。
在研究期间，回填数据集记录了198335762次与ILI相关的就诊，平均每周8623294次就诊。EWS使用回填数据集在每个城市检测到的ILI疫情警告中位数为4次（四分位间距2 - 5次）。使用实时数据集时，12538次（65%）警告与回填数据集一致。我们的分析表明，100%的完整性产生了76.7%的一致警告，而80%的及时性导致至少50%的一致警告。这些阈值被认为是合适的DQI的最佳值。将分析限制在具有合适DQI的城市，一致警告增加到80.4%。每周有71%（四分位间距54% - 71.9%）的城市达到合适的DQI阈值。在达到合适DQI周数≥60% 的城市中，回填数据集和实时数据集之间的一致性最高，达到合适DQI周数≥80% 的城市一致性为82.3%。
我们的研究结果突出了数据质量在基于PHC数据改进ILI疫情检测EWS性能方面的关键作用。所提出的实时DQI监测框架是一种实用方法，可适用于其他监测系统，为类似实施提供见解。我们证明，数据的最佳完整性和及时性对EWS检测ILI疫情的能力有显著影响。持续监测和改进数据质量应始终是加强监测系统可靠性和有效性的优先事项。
