Study shows vaccines cut long COVID risk, especially for those with preexisting conditions

In a recent study published in the journal JAMA Network Open, researchers conducted a prospective cohort study to investigate at-risk populations and factors associated with extended time to recovery following COVID-19 infections. Particularly, researchers evaluated risk factors contributing to recovery exceeding 90 days (“long COVID”). Their findings from a dataset comprising 4,708 participants elucidate that women and adults with suboptimal pre-pandemic health (especially preexisting cardiovascular conditions) were more likely to suffer from long COVID. Encouragingly, vaccinations both before and during the Omicron variant wave were observed to mitigate these risks.

Epidemiologic Features of Recovery From SARS-CoV-2 InfectionStudy: Epidemiologic Features of Recovery From SARS-CoV-2 Infection. Image Credit: p.ill.i / Shutterstock

Long COVID and current knowledge on the role of vaccinations

The United States (US) Centers for Disease Control and Prevention (CDC) defines long COVID as “a chronic condition that occurs after SARS-CoV-2 infection and is present for at least three months.” It is characterized by Coronavirus disease of 2019 (COVID-19) symptoms that persist or, in some cases, develop following recovery and hospital discharge from the initial COVID-19 infection. 1 in 5 COVID-19 survivors (~20%), the frequently termed post–COVID–19 condition (PCC) is a significant public health concern given the debilitating effect it has on its patients and their families.

Unfortunately, given the relative novelty of the condition, research outcomes (particularly epidemiology) of the disease are oftentimes confounding, presumably due to substantial differences in sampling methodologies, outcome definitions, and causative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. While vaccination is now almost universally accepted to have restricted the pandemic and saved potentially millions of lives, its impacts on survivors and their risk of developing long COVID are similarly confounding. Some reports highlight its risk-dampening effect, while others show no such association, and others suggest that it may contribute to an increased risk of long-term COVID.

A notable limitation of most literature on long COVID prevalence is that they rely on electronic health records, some of which are devoid of pre-pandemic health data. This prevents these studies from accounting for preexisting health conditions that may exacerbate COVID-19 infections, potentially contributing to the subsequent development of PCC in COVID-19 survivors.

About the study

In the present report, researchers conducted a prospective cohort study comprising epidemiological data derived from 14 long-term US-based cohort studies. Unlike previous literature, which leveraged electronic health records from hospital- and laboratory-confirmed COVID-19 survivors, the present work includes data from home-diagnosed SARS-CoV-2 infections provided patients’ preexisting clinical conditions were systematically recorded. Methodologies and study outcomes were reported following recommendations of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.

Data for the study was obtained from the Collaborative Cohort of Cohorts for COVID-19 Research (C4R), a US National Institutes of Health-funded meta-cohort comprising 14 established prospective cohorts consisting of data from as early as 1971. The present study inclusion criteria consisted of COVID-19 survivors who were alive and available for follow-up as of March 1, 2020, with follow-up conducted between April 2020 and March 2023. Data was collected using two questionnaire waves administered to included participants via telephone- or in-person interviews or electronic email-delivered surveys.

Data of interest included preexisting medical conditions (particularly diabetes, hypertension, asthma, chronic obstructive pulmonary disease [COPD], and cardiovascular disease [CVD]), period of COVID-19 infection (i.e., COVID-19 pandemic wave), infection severity, and vaccination-report confirmed vaccination status. Additionally, race/ethnicity, anthropometric, and sociodemographic data were included as confounders (including smoking status, and alcohol dependence). Post-COVID-19 recovery was verified using questionnaires, with positive responses followed up by a request for the duration of COVID-19 or long COVID, as applicable.

Statistical analyses comprised Kaplan-Meier curves to estimate time-dependent recovery probability (> or <90 days) and restricted mean recovery times. The log-rank test was used to assess differences in recovery times between subcohorts (e.g., different ethnicities).

“Cox proportional hazards regression was performed to assess multivariable-adjusted associations with recovery by 90 days. All factors were included in fully adjusted models with 2 exceptions. Acute infection severity was hypothesized to operate as a mediator and, therefore, was included in sensitivity analyses only.”

Secondary analyses included the type and number of doses of mRNA vaccination and a re-computation of Cox proportional hazards models accounting for vaccination status as a variable.

Study findings and conclusions

Of the 53,143 eligible C4R participants, only 4,708 completed both follow-up surveys and were included in the present analyses. The median participant age was found to be 61.3 years, with 62.7% of the participants being women. Infection severity records indicated that 12.6% of participants (597) required hospitalization, and 3.1% (148) required critical care. 966 participants (20.5%) received vaccination before infection, but 5.9% (57) only received one dose.

“Median time to recovery was 20 days (IQR, 8-75 days) and decreased over time. Participants who were vaccinated at the time of infection had shorter median time to recovery. Probability of nonrecovery by 90 days was 22.5% (95% CI, 21.2%-23.7%) and differed for the pre-Omicron (23.3%; 95% CI, 22.0%-24.6%) vs Omicron (16.8%; 95% CI, 13.3%-20.2%) waves.”

Analyses revealed that women took longer to recover from COVID-19 infections (mean = 42.3 days) than men (mean = 31.5 days). Participants with preexisting medical conditions, especially CVD, were more likely to report non-recovery by 90 days compared to healthy individuals. Encouragingly, vaccinations before infection were associated both with a reduction in acute infection risk and a decreased risk of long COVID, even in women and individuals with preexisting CVD, mainly when more than one dose of vaccination was administered.

In summary, the present report reveals that one in five COVID-19 survivors (~20%) developed long COVID, with women and adults presenting preexisting health conditions at the highest risk of the condition. It highlights the benefits of vaccination, especially multiple doses, in attenuating this risk, especially during the Omicron wave.

“Further investigation on the longer-term prognosis and mechanisms of PCC, including comparisons of multiorgan structure and function before and after infection, is critical to inform treatment and prevention.”

Journal reference:
Hugo Francisco de Souza

Written by

Hugo Francisco de Souza

Hugo Francisco de Souza is a scientific writer based in Bangalore, Karnataka, India. His academic passions lie in biogeography, evolutionary biology, and herpetology. He is currently pursuing his Ph.D. from the Centre for Ecological Sciences, Indian Institute of Science, where he studies the origins, dispersal, and speciation of wetland-associated snakes. Hugo has received, amongst others, the DST-INSPIRE fellowship for his doctoral research and the Gold Medal from Pondicherry University for academic excellence during his Masters. His research has been published in high-impact peer-reviewed journals, including PLOS Neglected Tropical Diseases and Systematic Biology. When not working or writing, Hugo can be found consuming copious amounts of anime and manga, composing and making music with his bass guitar, shredding trails on his MTB, playing video games (he prefers the term ‘gaming’), or tinkering with all things tech.

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