Background rates are critical for contextualising safety signals arising from COVID-19-related interventions in investigational or real-world settings.

To estimate background rates of medical events of interest (MEI) for which COVID-19 infection and/or COVID-19 interventions may be risk factors in two US claims databases.

This retrospective cohort study spans the pre-COVID-19 (2018–2019) and COVID-19 (2020–2021) periods. We constructed three cohorts, in each of Inovalon/HealthVerity (Inovalon/HV) and Optum databases: a COVID-19-positive adult cohort (2020–2021), a paediatric cohort (2018–2021) and a high-risk cohort (2018–2021) comprising patients at increased risk for severe COVID-19. Participants were indexed on the day they first qualified to enter each cohort during the study period. Background rates of 17 MEI were estimated per 1000 person-years (PY) with 95% CIs.

Annual incidence rates (IRs) of 17 MEI.

Overall, 758 414 (COVID-19-positive adults; 57.8% women), 12 513 664 (high-risk adults; 56.8% women) and 8 510 627 (paediatric patients; 49.1% women) patients were identified in the HV database. IRs of MEI varied substantially by year, data source, study cohort and duration of follow-up. The IRs of MEI were highest among COVID-19-positive adults and lowest among paediatric patients. For example, IR of myocarditis/pericarditis per 1000 PY was 3.0 (95% CI: 2.6 to 3.4) in the COVID-19-positive adult cohort vs 0.36 (95% CI: 0.34 to 0.37) among high-risk adults and 0.05 (95% CI: 0.05 to 0.06) among paediatric patients. In the COVID-19-positive adult cohort, we observed higher IRs during 90-day follow-up (eg, IR of acute myocardial infarction (AMI) 26.5 (95% CI: 25.3 to 27.7)) vs 365-day follow-up (eg, IR of AMI 20.0 (95% CI: 9.2 to 20.8)) and during 2020 compared with 2021. IRs were higher in the high-risk adult and paediatric populations during the pre-COVID-19 period than during the COVID-19 pandemic.

Substantial variability was observed in IRs of MEI by study cohort, year, data source and follow-up duration. When generating background rates for contextualising safety signals from COVID-19 interventions, careful consideration must be given to the indicated subpopulation of interest, COVID-19-related temporal variations and data sources.

The COVID-19 pandemic highlighted the significance of mathematical modelling in decision-making and the limited capacity in many low-income and middle-income countries (LMICs). Thus, we studied how modelling supported policy decision-making processes in LMICs during the pandemic (details in a separate paper).

We found that strong researcher–policymaker relationships and co-creation facilitated knowledge translation, while scepticism, political pressures and demand for quick outputs were barriers. We also noted that routine use of modelled evidence for decision-making requires sustained funding, capacity building for policy-facing modelling, robust data infrastructure and dedicated knowledge translation mechanisms.

These lessons helped us co-create a framework and policy roadmap for improving the routine use of modelling evidence in public health decision-making. This communication paper describes the framework components and provides an implementation approach and evidence for the recommendations. The components include (1) funding, (2) capacity building, (3) data infrastructure, (4) knowledge translation platforms and (5) a culture of evidence use.

Our framework integrates the supply (modellers) and demand (policymakers) sides and contextual factors that enable change. It is designed to be generic and disease-agnostic for any policy decision-making that modelling could support. It is not a decision-making tool but a guiding framework to help build capacity for evidence-based policy decision-making. The target audience is modellers and policymakers, but it could include other partners and implementers in public health decision-making.

The framework was created through engagements with policymakers and researchers and reflects their real-life experiences during the COVID-19 pandemic. Its purpose is to guide stakeholders, especially in lower-resourced settings, in building modelling capacity, prioritising efforts and creating an enabling environment for using models as part of the evidence base to inform public health decision-making. To validate its robustness and impact, further work is needed to implement and evaluate this framework in diverse settings.