Measuring the Effects of Population-level Interventions Using Healthcare Claims Data

2026 Symposium of Causal Inference in the Health Sciences
Fribourg

Aurélien Sallin, SWICA and HSG

2026-03-18

Population-level interventions with administrative data: a common but challenging setting

Need for causal analysis…

Who: public health authorities, researchers, and insurers
Why: accountability, quality improvement, and policy design

… in an imperfect world…

No control group: population-level policies affect everyone simultaneously, ruling out randomized experiments and difference-in-differences designs.
Routine data: claims data collected for billing/administrative purposes, not purpose-built for research

🎤 This talk

How we think about the problem from the perspective of SWICA
One empirical illustration (Vitamin D testing and low-value care)

A credible control group is rarely available

Problem: in many real-world settings, population-level policies affect all units simultaneously

National changes in clinical guidelines

Changes in physician compensation

Public health campaigns

Pragmatic solution: interrupted time series (ITS)

Idea: exploits the pre-intervention trend to construct a counterfactual

Challenge: strong causal assumptions (no concurrent interventions, trend continuity) and correct functional form specification*

Problem: No staggered intervention, no control group, no Diff-in-Diff. No CREDIBLE control group. No untreated units: forecast the counterfactual.

Example: Congenital syphilis (mother-to-child transmission or vertical transmission of syphilis) causes damage to the fetus if the mother’s infection is not detected and properly treated during pregnancy. October of 2016: “Syphilis No!” Project in Brazil.

Gold standard: A randomized experiment is ideal, but rarely feasible when a policy rolls out across an entire population at once.

Second best: Quasi-experimental designs such as difference-in-differences require untreated units as a comparison group, which a universal policy eliminates by construction.

Our approach: Interrupted time series exploits the pre-intervention trend to construct a counterfactual and measures the deviation from it once the policy takes effect.

Claims data support policy evaluation with no control group

Coverage: entire Swiss population enrolled under mandatory health insurance (>130 million invoices/year)
Administrative records: byproduct of the billing and reimbursement process (not purpose-built for research).
Strengths: large number of units (physicians, patients, consultations) and services billed observed over time.
Limitations: longitudinal tracking complicated by switchers; regional fragmentation of insurers; no clinical information.
⚡Key advantage for ITS: cross-sectional variation to predict each unit’s counterfactual trajectory.

ITS used with administrative data presents many advantages, but…

ITS is widely used but implemented with high risk of bias

“The risk of bias for ITS studies was high for 53.3% and very high for 19.2%.”
Hategeka et al. (2020), N = 120, 1990-2020

Concurrent changes confound the intervention.
Time series techniques misapplied.
Trends and time-dependent covariates misspecified.
…

Sound implementation guidance is needed.

Here is how we address these challenges

User’s guide: recommendations for ITS implementation, identifying assumptions, and modeling choices.
Open-access software (itscausal): tailored to wide panel data (many units, few periods), with a rolling-window ML forecasting approach.
Approach validation: simulations across different data-generating processes, benchmarking against published research.
Ongoing work: 2024 co-payment reform and generic drug substitution in Switzerland (together with ZHAW).

A visualization of a simulation study for `itscausal`

An example 💉

💉 Two policies targeted the same low-value practice

Context: Vitamin D testing rates nearly doubled between 2013 and 2020 in Switzerland.
Problem: Clinical guidelines advised against routine testing (low-value care, ~30% of healthcare in high-income countries)

Intervention 1 (April 2021): The Smarter Medicine initiative added Vitamin D testing to its list of procedures to avoid.
Intervention 2 (July 2022): The Federal Office of Public Health restricted reimbursement, shifting costs to out-of-pocket.

Results: …