Effect of vaccination on time till Long COVID, a comparison of two ways to model effect of vaccination and two outcome definitions (preprint)

Long COVID, or post-COVID syndrome, is a constellation of symptoms observed in patients at least four weeks after COVID-19 infection. We analyzed the effect of COVID-19 vaccination status on risk of either developing Long COVID symptoms or being diagnosed with Long COVID. In separate analyses we compared the effect of vaccination status at time of COVID-19 infection and the effect of vaccination status as a time-dependent covariate where vaccination could occur at any point with respect to COVID-19 infection. To address this question, we identified a subset of adult patients from Truveta Data who experienced a COVID-19 infection as indicated by a positive laboratory test between 2021-10-01 and 2022-11-31. We considered two distinct ways of modeling the effect of vaccination status (time-independent and time-dependent) and two distinct outcomes of interest (Long COVID symptoms or diagnosis with Long COVID), representing four distinct analyses. The presence of Long COVID symptoms was defined as the presence of one or more new symptoms consistent with COVID-19/Long COVID at least four weeks post COVID-19 infection. Diagnosis of Long COVID was determined by the presence of one or more ICD-10-CM or SNOMED-CT codes explicitly identifying a patient as having been diagnosed with Long COVID. Our analysis focusing on the effect of COVID-19 vaccination status at time of COVID-19 infection found that patients who had completed a primary COVID-19 vaccination sequence or had completed a primary vaccination sequence and received a booster dose at time of COVID-19 infection were on average at lower risk of either developing Long COVID symptoms or being diagnosed with Long COVID than unvaccinated patients (vaccinated versus unvaccinated HR of symptoms 0.9 [0.87-0.94], HR of diagnosis 0.86 [0.74-0.99]; vaccinated and boosted versus unvaccinated HR of symptoms 0.87 [0.83-0.91], HR of diagnosis 0.81 [0.69-0.95]). We do not find evidence that having received a booster dose in addition to having completed a primary vaccination sequence offers additional protection over having completed the primary sequence alone (vaccinated and boosted versus vaccinated HR of symptoms 0.96 [0.91-1.01], HR of diagnosis 0.94-1.13) . Our analysis of COVID-19 vaccination status modeled as a time-dependent covariate yielded similar results for patients who had completed a primary COVID-19 vaccination sequence or had completed a primary vaccination sequence and a booster dose. Both groups were on average at lower risk of developing Long COVID symptoms or being diagnosed with Long COVID than patients who where never vaccinated (vaccinated versus unvaccinated HR of symptoms 0.91 [0.88-0.95], HR of diagnosis 0.86 [0.75-0.99]; vaccinated and boosted versus unvaccinated HR of symptoms 0.88 [0.85-0.91], HR of diagnosis 0.77 [0.67-0.9]). As with the time-independent analysis, we also find that having completed a booster dose in addition to a primary COVID-19 vaccination sequence does not provide additional protection from developing Long COVID symptoms or being diagnosed with Long COVID over having completed the primary sequence alone (vaccinated and boosted versus vaccinated HR of symptoms 0.96 [0.92-1.01], HR of diagnosis 0.89 [0.76-1.06]) . We find that completing a primary vaccination sequence is associated with a decreased risk of developing Long COVID symptoms or being diagnosed with Long COVID compared with no vaccination regardless of whether vaccination status is modeled as a time-independent or time-dependent covariate. We find a similar protective effect in patients who have completed a primary vaccination sequence and a booster dose when compared to the those who are unvaccinated. However, we do not find evidence for a difference in protective effect between patients who have completed a primary vaccination sequence and a booster dose and those patients who have only completed a primary vaccination sequence. Our results support the growing evidence that having complete a primary vaccination sequence is protective against the development of Long COVID symptoms or the diagnosis of Long COVID.

COVID treatment and in-hospital length of stay inequalities between race in the US over time (preprint)

Introduction: Demonstrated health inequalities persist in the United States. SARS-CoV-2 (COVID) has been no exception, with access to treatment and hospitalization differing across race or ethnic group. Here we aim to assess differences in treatment with remdesivir and hospital length of stay across four waves of the pandemic. Methods: Using a subset of the Truveta data we examine odds ratios (OR) of in-hospital remdesivir treatment and risk ratios (RR) of in-hospital length of stay between Black or African American (Black) to white patients. We adjusted for confounding factors such as age, sex, and comorbidity status. Results: There were statically significant lower rates of remdesivir treatment and longer in-hospital lengths of stay comparing Black patients to white patients early in the pandemic (OR for treatment: 0.88, 95\% confidence interval [CI]: 0.80, 0.96; RR for length of stay: 1.17, CI: 1.06, 1.21). Rates became close to parity between groups as the pandemic progressed. Conclusions: While inpatient remdesivir treatment rates increased and length of stay decreased over the beginning course of the pandemic, there are still inequalities in patient care.

Risk of COVID-19 breakthrough infection and hospitalization in individuals with comorbidities (preprint)

Background Studies have shown that those with certain high-risk comorbidities such as diabetes, chronic kidney disease (CKD), chronic lung disease, or those with immunocompromising conditions have increased risk of hospitalization from COVID-19. Here we estimate the elevated risks of breakthrough infection and hospitalization in fully vaccinated individuals with comorbidities. Methods Using a population of fully-vaccinated patients in the de-identified Truveta Platform of electronic health records from January 1, 2019, to January 10, 2022, we used logistic regression to estimate risk of 1) a patient experiencing a breakthrough COVID-19 infection after being fully vaccinated, and 2) rate of hospitalization in those experiencing breakthrough infection. Potential confounding was adjusted with inverse probability weighting for each comorbidity by age, race, ethnicity, and sex. We present ORs and percentages of breakthrough infections by comorbidity status. Results Of 3,424,965 fully vaccinated patients, 2.79%, 2.63%, 2.38%, 1.83% with CKD, chronic lung disease, diabetes, and those in an immunocompromised state experienced breakthrough infection, respectively, compared to 1.95% in the overall population. All cormorbidities were associated with significantly increased odds of breakthrough infections and subsequent hospitalizations. Breakthrough infection hospitalizations in populations with comorbidities ranged from 26.43% for CKD to 10.23% for chronic lung disease, with corresponding ORs of 2.22 (95% CI: 1.88 - 2.63) and 1.37 (95% CI: 1.21 - 1.55), respectively. Conclusions Fully-vaccinated individuals with certain comorbidities experienced increased risk of breakthrough COVID-19 infection and subsequent hospitalizations compared to the general population. Individuals with comorbidities should remain vigilant against infection even if vaccinated.

An Open Repository of Real-Time COVID-19 Indicators (preprint)

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID- 19 activity, such as signals extracted from de-identified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data is available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.