School Mask Mandates and COVID-19: The Challenge of Using Difference-in-Differences Analysis of Observational Data to Estimate the Effectiveness of a Public Health Intervention.
| Autor: | Chandra A; Department of Management, University of Toronto Scarborough, and Rotman School of Management, University of Toronto, Toronto, Ontario, Canada (A.C.)., Høeg TB; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts; Department of Emergency Medicine, University of California, San Francisco, San Francisco, California; and Department of Clinical Research, University of Southern Denmark, Odense, Denmark (T.B.H.)., Ladhani S; Paediatric Infectious Diseases Research Group, St. George's University of London, and Immunisation and Countermeasures Division, UK Health Security Agency, London, United Kingdom (S.L.)., Prasad V; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California (V.P.)., Duriseti R; Department of Emergency Medicine, Stanford School of Medicine, Palo Alto, California (R.D.). |
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| Jazyk: | angličtina |
| Zdroj: | Annals of internal medicine [Ann Intern Med] 2024 Nov; Vol. 177 (11), pp. 1566-1572. Date of Electronic Publication: 2024 Oct 08. |
| DOI: | 10.7326/M23-2907 |
| Abstrakt: | Background: There are considerable challenges when using difference-in-differences (DiD) analysis of ecological data to estimate the effectiveness of public health interventions in rapidly changing situations. Objective: To discuss the shortcomings of DiD methodology for the estimation of the effects of public health interventions using ecological data. Design: As an example, the authors consider an analysis that used DiD methodology and reported a causal reduction in COVID-19 cases due to the maintenance of school mask mandates. They did alternate analyses using various control groups to assess the robustness of the prior analysis. Setting: School districts in the greater Boston area and Massachusetts during the 2021-to-2022 academic year. Participants: Students and school staff. Measurements: Changes in COVID-19 case rates in districts that did and did not lift mask mandates. Results: Important potential confounders rendered DiD methodology inappropriate for causal inference, including prior immunity, temporal variation in rates of infection, and changes in testing practices. The racial composition and income of intervention and control groups also differed substantially. Compared with maintaining the mask requirement, dropping the requirement was associated with anywhere from an increase of 5.64 cases (95% CI, 3.00 to 8.29 cases) per 1000 persons to a decrease of 2.74 cases (CI, 0.63 to 4.85 cases) per 1000 persons, depending on choice of control group and whether students or staff were examined. Limitation: Ecological data were used; detailed data on all potential confounders were unavailable. Conclusion: Alternate analyses yielded estimates consistent with a wide range of both negative and positive associations in COVID-19 case rates after removal of mask mandates. The findings highlight the challenges of using DiD analysis of ecological data to estimate the effectiveness of interventions in divergent intervention and control groups during rapidly changing circumstances. Primary Funding Source: None. Competing Interests: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M23-2907. |
| Databáze: | MEDLINE |
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