Causal Inference and COVID-19 Nursing Home Patients: Identifying Factors That Reduced Mortality Risk
Ahmed et al.,
Causal Inference and COVID-19 Nursing Home Patients: Identifying Factors That Reduced Mortality Risk,
medRxiv, doi:10.1101/2021.11.18.21266489 (Preprint)
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.
Ahmed et al., 21 Nov 2021, retrospective, USA, preprint, 5 authors, dosage not specified.
Abstract: medRxiv preprint doi: https://doi.org/10.1101/2021.11.18.21266489; this version posted November 21, 2021. The copyright holder for this
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
Causal Inference and COVID-19 Nursing Home Patients: Identifying Factors That Reduced Mortality Risk
Amina Ahmed MD1, Robert Goldberg PhD2, Joseph Swiader BS2, Zachary A.P. Wintrob MS MA2,
Margaret Yilmaz MS2
Abstract
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.
1
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
medRxiv preprint doi: https://doi.org/10.1101/2021.11.18.21266489; this version posted November 21, 2021. The copyright holder for this
preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
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