Study Type

These are considered complex epidemiological studies as they require bespoke modelling of interventions like public health restrictions after the calculation of population- and/or patient-level disease epidemiology estimates. These analyses will take the form of interrupted time series to analyse the impact of an intervention (e.g., a public health restriction or change/s in law) on the occurrence of an outcome at the population-level (e.g., incidence of COVID-19 before vs after imposition of public health restrictions) and/or at the patient level (e.g., characteristics of people newly diagnosed before vs after change/s in law).

Study Design

Population-level cohort/s AND Patient-level characterisation.

Participant/s

Population-level analyses will include the entire source population with at least 1 year of data visibility available before start of study period. Additional eligibility criteria could apply as in population-level disease epidemiology (see above). Stratification will be used in the case of Difference-in-difference studies to obtain a “control” (unimpacted) counterfactual for comparison.

Patient-level analyses will be restricted to newly diagnosed subjects, using a 1-year washout, and with potential additional eligibility (see above).

Exposure/s

At least one public health measure, regulatory action (e.g., banning or change in use of a medicinal product) or other intervention will have been imposed at a known date in time. In the case of Difference-in-difference analyses, these should only affect a known subpopulation, with another subpopulation not affected by the intervention acting as the ‘unimpacted’ counterfactual.

The study period will be pre-specified, and divided into pre-exposure (i.e., unimpacted time) and post-exposure (i.e., impacted) calendar time.

In Difference-in-difference studies, the observed pre- vs post-exposure changes in the ‘Exposed’ will be compared to those in the ‘Unexposed’ subpopulation.

A lag period between the intervention and the start of the post-exposure study period could be considered to allow for the action to have an impact on the study outcome/s.

Outcome/s

The following will be estimated for a pre-specified study period, including at least 1 year before and after the intervention/s of interest:

• Population-based incidence rates of a condition/group of condition/s over time
• Population-based prevalence of a condition/group of conditions over time
• For Difference-in-differences: population-based incidence and prevalence of a condition/group of condition/s over time, and in the exposed vs unexposed populations separately

At the patient level, two cohorts of newly diagnosed people will be studied, namely those diagnosed in the period before (unimpacted) vs after the intervention (impacted). The following outcome/s will be obtained and compared for both cohorts:

• Patient-level characteristics amongst the newly diagnosed before vs after intervention
• (Optional) Patient-level characteristics amongst the prevalent cases on a given date/s before vs after intervention
• Prognosis / progression to a pre-specified outcome within a pre-specified time for those diagnosed before vs after the exposure of interest
• Standard care description, including n (%) receiving each of a pre-specified list of medicine/s, common combinations among those diagnosed before vs after the exposure of interest
• (Optional) Standard care description, including n (%) receiving each of a pre-specified list of medicine/s, common combinations among prevalent cases on specified dates before vs after the exposure of interest

Follow-up

For population-level analyses, follow-up will start on a pre-specified calendar time point, at least 1 year before the proposed intervention, and will continue for at least 1 year after it.

For patient-level analyses, follow-up will go from the date of diagnosis (newly diagnosed cases) or prespecified date (prevalent cases) until the earliest of loss to follow-up, end of data availability, or death.

Analyses

Incidence and prevalence rate/s of disease over time will be estimated as detailed in section 3.1. Once these are available, segmented regression methods will be used to estimate the impact of the proposed intervention/s on pre- vs post-intervention change/s in trends of population-level prevalence and/or incidence. Coefficients for the segmented regression will be reported  to quantify the impact of the intervention/s on the incidence and prevalence of use, together with Durbin-Watson residuals as a diagnostic. In case of autocorrelation, ARIMA/X models will be fitted instead of segmented regression if data permits.

For difference-in-difference studies, parallel trends before the intervention will be identified as a requisite for this type of study. If confirmed, Difference-in-difference models will be used to subtract the difference of the unexposed group to the exposed one whilst controlling for time varying factors, thus estimating the causal effect of the intervention.

For patient-level analyses, standardised mean differences of each of the covariates for the comparison between new cases diagnosed or prevalent cases in the pre- vs post-intervention period will be obtained as a measure of the impact of the exposure on the profile of new cases.