Developing acute respiratory infection severity indicators for public health surveillance using primary care computerised medical records

Introduction: Acute respiratory infection (ARI) surveillance systems generate essential intelligence to help health authorities protect the public from the consequences of epidemic and pandemic-prone pathogens such as influenza. This Doctor of Philosophy thesis explores how data from primary care computerised medical records (CMRs) can be used to strengthen surveillance of ARIs. Specifically, it describes the development and evaluation of timely population-level severity indicators of ARIs derived from primary care CMRs. Methods: The thesis consists of four pieces of work: (1) defining an algorithm for the identification of episodes of ARI from the CMR; (2) a systematic review to identify possible markers of severe disease relevant to ARIs in primary care; (3) an assessment of the data quality of these severity markers in the primary care CMR; and (4) a retrospective evaluation of these severity markers to determine their suitability for use in prospective public health surveillance of ARIs. Key findings: The case detection algorithm provided a unified and flexible approach that increased sensitivity for identifying ARIs and established a suitable cohort for assessing severity. The systematic review identified 30 potential severity markers, comprising seven severe outcomes and 23 more timely predictors of severe outcomes. Severe outcomes included death, hospitalisation, intensive care admission, and complications, while predictors included symptoms, signs, investigations, treatments, and healthcare utilisation markers. The data quality of severity markers varied significantly and was heavily affected by the pandemic. Several predictors showed strong potential as timely severity indicators, with some symptoms, signs, and healthcare utilisation markers demonstrating significant associations with severe outcomes. Conclusions: This thesis demonstrates that primary care CMR data can be used to create timely severity indicators for ARIs. Future work should focus on piloting severity indicators prospectively and in near real time and improving the recording of severity markers. This could provide a pathway to the implementation of reliable and timely severity indicators for routine public health surveillance.