Retrospective causal inference analysis of 4,091 COVID+ long-term care high risk patients in the USA, showing lower mortality with vitamin D, without statistical significance.
29 studies are RCTs, which show efficacy with
p=0.0000024.
Ahmed et al., 21 Nov 2021, retrospective, USA, preprint, 5 authors, dosage not specified.
Causal Inference and COVID-19 Nursing Home Patients: Identifying Factors That Reduced Mortality Risk
MD Amina Ahmed, PhD Robert Goldberg, BS Joseph Swiader, MS Zachary A P Wintrob, MS Margaret Yilmaz
doi:10.1101/2021.11.18.21266489
Less than 1% of the US population lives in long-term care facilities, yet this subset of the population accounts for 22% of total COVID-19 related deaths. 1 Because of a lack of experimental evidence to treat COVID-19, analysis of real-world data to identify causal relationships between treatments/policies to mortality and morbidity among high-risk individuals is critical. We applied causal inference (CI) analysis to longitudinal patient-level health data of 4,091 long-term care high-risk patients with COVID-19 to determine if any actions or therapies delivered from January to August of 2020 reduced COVID-19 patient mortality rates during this period. Causal inference findings determined that certain supportive care interventions caused reduced mortality rates for nursing home residents regardless of severity of disease (as measured by oxygen saturation level, presence of pneumonia and organ failure), comorbidities or social determinants of health such as race, age, and weight. 2 While we do not address the biological mechanisms associated with specific medical interventions and their impact on mortality, this analysis suggests methods to validate and optimize treatment protocols using domain knowledge and causal inference analysis of real-world data across patient populations and care settings.
References
Arabi, Gordon, Derde, Lopinavir-ritonavir and hydroxychloroquine for critically ill patients with COVID-19: REMAP-CAP randomized controlled trial, Intensive Care Med,
doi:10.1007/s00134-021-06448-5Balzer, Petersen, Van Der Laan, Search Collaboration, Targeted estimation and inference for the sample average treatment effect in trials with and without pair-matching, Stat Med,
doi:10.1002/sim.6965Hager, Tsiatis, Davidian, Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data, Biometrics,
doi:10.1111/biom.12894Kosar, White, Feifer, COVID-19 Mortality Rates Among Nursing Home Residents Declined from March to November 2020, Health Aff (Millwood),
doi:10.1377/hlthaff.2020.02191Mackenzie, Race, COVID Mortality, and Simpson's Paradox
Oprescu, Syrgkanis, Wu, Orthogonal Random Forest for Causal Inference
Pearl, On the Testability of Causal Models with Latent and Instrumental Variables
Pearl, The seven tools of causal inference, with reflections on machine learning, Communications of the ACM,
doi:10.1145/3241036Pedregosa, Scikit-learn: Machine Learning in Python, JMLR
Prosperi, Guo, Sperrin, Causal inference and counterfactual prediction in machine learning for actionable healthcare, Nat Mach Intell,
doi:10.1038/s42256-020-0197-yProsperi, Guo, Sperrin, Causal inference and counterfactual prediction in machine learning for actionable healthcare, Nat Mach Intell,
doi:10.1038/s42256-020-0197-yTarazi, Finegold, Sheingold, Samson, Zuckerman et al., COVID-19-Related Deaths And Excess Deaths Among Medicare Fee-For-Service Beneficiaries, Health Aff (Millwood),
doi:10.1377/hlthaff.2020.02521
