Estimating the Impact of Pre-Exposure Prophylaxis (PrEP) on Mortality in COVID-19 Patients: A Causal Inference Approach (preprint)

Traditional machine learning (ML) approaches learn to recognize patterns in the data but fail to go beyond observing associations. Such data-driven methods can lack generalizability when the data is outside the independent and identically distributed (i.i.d) setting. Using causal inference can aid data-driven techniques to go beyond learning spurious associations and frame the data-generating process in a causal lens. We can combine domain expertise and traditional ML techniques to answer causal questions on the data. Hypothetical questions on alternate realities can also be answered with such a framework. In this paper, we estimate the causal effect of Pre-Exposure Prophylaxis (PrEP) on mortality in COVID-19 patients from an observational dataset of over 120,000 patients. With the help of medical experts, we hypothesize a causal graph that identifies the causal and non-causal associations, including the list of potential confounding variables. We use estimation techniques such as linear regression, matching, and machine learning (meta-learners) to estimate the causal effect. On average, our estimates show that taking PrEP can result in a 2.1% decrease in the death rate or a total of around 2,540 patients lives saved in the studied population.

The Effect of SARS-COV-2 Variant on Respiratory Features and Mortality Among Vaccinated and Non-Fully Vaccinated Patients (preprint)

Background SARS-CoV-2 (COVID-19) has caused over 80 million infections and 973,000 deaths in the United States, and mutations are linked to increased transmissibility. This study aimed to determine the effect of SARS-CoV-2 variants on respiratory features and mortality and to determine the effect of vaccination status. Method A retrospective review of medical records (n=63,454 unique patients) using The University of California Health COvid Research Data Set (UC CORDS) was performed to identify respiratory features, vaccination status, and mortality. Variants were identified using the CDC data tracker. Results Increased odds of death were observed among those not fully vaccinated (Delta OR: 1.64, p = 0.052; Omicron OR: 1.96, p < 0.01). Later variants (i.e., Delta and Omicron) demonstrated a reduction in the frequency of lower respiratory tract features with a concomitant increase in upper respiratory tract features. Vaccination status was associated with survival and a decrease in the frequency of many upper and lower respiratory tract features. Discussion SARS-CoV-2 variants show a reduction in lower respiratory tract features with an increase in upper respiratory tract features. Being fully vaccinated results in fewer respiratory features and higher odds of survival, supporting vaccination in preventing morbidity and mortality from COVID-19.