Risk Factors for COVID-19 and Respiratory Tract Infections during the Coronavirus Pandemic

Abstract

(1) Background: Some individuals are more susceptible to developing respiratory tract infections (RTIs) or coronavirus disease (COVID-19) than others. The aim of this work was to identify risk factors for symptomatic RTIs including COVID-19 and symptomatic COVID-19 during the coronavirus pandemic by using infection incidence, participant baseline, and regional COVID-19 burden data. (2) Methods: Data from a prospective study of 1000 frontline healthcare workers randomized to Bacillus Calmette-Guérin vaccination or placebo, and followed for one year, was analyzed. Parametric time-to-event analysis was performed to identify the risk factors associated with (a) non-specific symptomatic respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) symptomatic RTIs confirmed as COVID-19 using a polymerase chain reaction or antigen test (COVID-19). (3) Results: Job description of doctor or nurse (median hazard ratio [HR] 1.541 and 95% confidence interval [CI] 1.299-1.822), the reported COVID-19 burden (median HR 1.361 and 95% CI 1.260-1.469 for 1.4 COVID-19 cases per 10,000 capita), or a BMI > 30 kg/m² (median HR 1.238 and 95% CI 1.132-1.336 for BMI of 35.4 kg/m²) increased the probability of RTIs+COVID-19, while positive SARS-CoV-2 serology at enrollment (median HR 0.583 and 95% CI 0.449-0.764) had the opposite effect. The reported COVID-19 burden (median HR 2.372 and 95% CI 2.116-2.662 for 1.4 COVID-19 cases per 10,000 capita) and a job description of doctor or nurse (median HR 1.679 and 95% CI 1.253-2.256) increased the probability of developing COVID-19, while smoking (median HR 0.428 and 95% CI 0.284-0.648) and positive SARS-CoV-2 serology at enrollment (median HR 0.076 and 95% CI 0.026-0.212) decreased it. (4) Conclusions: Nurses and doctors with obesity had the highest probability of developing RTIs including COVID-19. Non-smoking nurses and doctors had the highest probability of developing COVID-19 specifically. The reported COVID-19 burden increased the event probability, while positive SARS-CoV-2 IgG serology at enrollment decreased the probability of RTIs including COVID-19, and COVID-19 specifically.

Keywords: COVID-19; pharmacometrics; respiratory tract infections; risk factors; time-to-event analysis.

Figure 1

Schematic representation of the number of participants enrolled and censored due to different reasons throughout the trial. Both intention-to-treat (ITT) and per-protocol (PP) datasets are shown for both COVID-19 diagnosed with a positive PCR or antigen test (COVID-19) and non-specific respiratory tract infections including COVID-19 (RTIs+COVID-19) analyses. Participants were censored early due to withdrawal of consent/loss to follow-up/death (applicable for both ITT and PP datasets), administration of SARS-CoV-2 vaccine (PP dataset; COVID-19 and RTIs+COVID-19 endpoints) and flu vaccine (PP dataset; RTIs+COVID-19 endpoint), or when reaching the end of the trial.

Figure 2

Kaplan–Meier plots of the data (enhanced y-axis) for the time-to-first event in the intention-to-treat analysis for (a) respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) COVID-19. The shaded area around the Kaplan–Meier curve represents the standard error, obtained by the Greenwood method [39]. Vertical dashes represent censoring, while a step down represents an event. The number at risk below the figure presents the number of participants still in the trial.

Figure 3

Visual predictive checks for the final models (enhanced y-axis) for the time-to-first event in the intention-to-treat analysis for (a) respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) COVID-19. The black line in the VPC represents observed data, while the shaded area represents the 95% confidence interval based on 1000 simulations using the final parametric time-to-event model.

Figure 4

Forest plots for the statistically significant risk factor effects on cumulative probability in the intention-to-treat analysis for (a) respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) COVID-19. Risk factor effects are expressed as hazard ratios (HRs), the circle represents the median HR, while the whiskers represent a 95% confidence interval. The covariate values on the y-axis were derived from the observed data as either the unique categories of the categorical covariates or as the 10th, 50th, and 90th percentiles of the continuous covariates [40]. The numbers next to the forest plot represent the median HR and 95% confidence interval. Body mass index (BMI) < 30 kg/m² did not affect the hazard ratio while BMI > 30 kg/m² resulted in an increasing hazard ratio with an increasing covariate value.

Figure 5

The cumulative probability of having an event within one year for different combinations of statistically significant risk factors in the intention-to-treat analysis for (a) respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) COVID-19. Blue cells represent the inclusion of a significant risk factor (named above), while white cells represent the inclusion of a reference feature (absence of the named risk factor). The circle represents the median, while the whiskers represent a 95% confidence interval. The reference cumulative probability (absence of any named risk factors) is shown in a blue empty circle. One COVID-19 case per 10,000 capita—reported COVID-19 burden, where the risk factor included was set to one case per 10,000 capita (reference: zero reported COVID-19 cases per 10,000 capita). Positive serology—corresponds to positive SARS-CoV-2 IgG serology at enrollment (reference: negative SARS-CoV-2 IgG serology at enrollment). Nurse/doctor—job category classified as a nurse/doctor (reference: essential worker). BMI 35 kg/m²—body mass index (BMI) corresponding to being obese (reference: BMI < 30 kg/m²). Smoker—indicates that the participant was a smoker (reference: non-smoker). ^a The continuous time-varying risk factor of the reported COVID-19 burden was treated as a categorical time-constant risk factor for the computation of cumulative probabilities, and two categories were selected: zero COVID-19 cases per 10,000 capita and one COVID-19 case per 10,000 capita. ^b The continuous covariate BMI was categorized into two categories for the purpose of computation of cumulative probability: BMI < 30 kg/m² (indicating no impact of the risk factor on the cumulative probability) and BMI of 35 kg/m² (visualizing the impact of the risk factor on the cumulative probability).

Figure 6

Reported COVID-19 burden influence on the cumulative probability of having an event within one year for different combinations of statistically significant risk factors in the intention-to-treat analysis for (a) respiratory tract infections including COVID-19 (RTIs+COVID-19) and (b) COVID-19. The heatmap shows the median cumulative probability for each combination of the reported COVID-19 burden and other statistically significant risk factors. The blue cells represent the inclusion of a significant risk factor (named above), while the white cells represent the inclusion of a reference feature (absence of the named risk factor). The circle represents the median, while the whiskers represent a 95% confidence interval. The reference cumulative probability (absence of any named risk factors) is shown in a blue empty circle. One COVID-19 case per 10,000 capita—reported COVID-19 burden, where the risk factor included was set to one case per 10,000 capita (reference: zero reported COVID-19 cases per 10,000 capita). Positive serology—corresponds to positive SARS-CoV-2 IgG serology at enrollment (reference: negative SARS-CoV-2 IgG serology at enrollment). Nurse/doctor—job category classified as a nurse/doctor (reference: essential worker). BMI of 35 kg/m²—body mass index (BMI) corresponding to being obese (reference: BMI < 30 kg/m²). Smoker—indicates that the participant was a smoker (reference: non-smoker).^a The continuous time-varying risk factor of the reported COVID-19 burden was treated as a categorical time-constant risk factor for the computation of cumulative probabilities. ^b The continuous covariate BMI was categorized into two categories for the purpose of computation of cumulative probability: BMI < 30 kg/m² and BMI of 35 kg/m².