Estimating the effect of realistic improvements of metformin adherence on COVID-19 mortality using targeted machine learning

Sky Qiu; Alan E Hubbard; Juan Pablo Gutiérrez; Ganesh Pimpale; Arturo Juárez-Flores; Rakesh Ghosh; Iván de Jesús Ascencio-Montiel; Stefano M Bertozzi

doi:10.1016/j.gloepi.2024.100142

Estimating the effect of realistic improvements of metformin adherence on COVID-19 mortality using targeted machine learning

Glob Epidemiol. 2024 Mar 30:7:100142. doi: 10.1016/j.gloepi.2024.100142. eCollection 2024 Jun.

Authors

Sky Qiu¹, Alan E Hubbard¹, Juan Pablo Gutiérrez², Ganesh Pimpale³, Arturo Juárez-Flores², Rakesh Ghosh⁴, Iván de Jesús Ascencio-Montiel⁵, Stefano M Bertozzi^{1

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Affiliations

¹ University of California, School of Public Health, Berkeley, CA, USA.
² Center for Policy, Population and Health Research, School of Medicine, Universidad Nacional Autónoma de México, Mexico City, Mexico.
³ University of California, Department of Mechanical Engineering, Berkeley, CA, USA.
⁴ Institute for Global Health Sciences, University of California, San Francisco, CA, USA.
⁵ Instituto Mexicano del Seguro Social, CDMX, Mexico.
⁶ University of Washington, School of Public Health, Seattle, WA, USA.
⁷ Instituto Nacional de Salud Pública, Cuernavaca, MOR, Mexico.

Abstract

Background: Type 2 diabetes elevates the risk of severe outcomes in COVID-19 patients, with multiple studies reporting higher case fatality rates. Metformin is a widely used medication for glycemic management. We hypothesize that improved adherence to metformin may lower COVID-19 post-infection mortality risk in this group. Utilizing data from the Mexican Social Security Institute (IMSS), we investigate the relationship between metformin adherence and mortality following COVID-19 infection in patients with chronic metformin prescriptions.

Methods: This is a retrospective cohort study consisting of 61,180 IMSS beneficiaries who received a positive polymerase chain reaction (PCR) or rapid test for SARS-CoV-2 and had at least two consecutive months of metformin prescriptions prior to the positive test. The hypothetical intervention is improved adherence to metformin, measured by proportion of days covered (PDC), with the comparison being the observed metformin adherence values. The primary outcome is all-cause mortality following COVID-19 infection. We defined the causal parameter using shift intervention, an example of modified treatment policies. We used the targeted learning framework for estimation of the target estimand.

Findings: Among COVID-19 positive patients with chronic metformin prescriptions, we found that a 5% and 10% absolute increase in metformin adherence is associated with a respective 0.26% (95% CI: -0.28%, 0.79%) and 1.26% (95% CI: 0.72%, 1.80%) absolute decrease in mortality risk.

Interpretation: Subject to the limitations of a real-world data study, our results indicate a causal association between improved metformin adherence and reduced COVID-19 post-infection mortality risk.