Assessment of Severe COVID-19 Outcomes Using Measures of Smoking Status and Smoking Intensity

Risk profiles for smoke behavior in COVID-19: a classification and regression tree analysis approach

BACKGROUND

COVID-19 pandemic emerged worldwide at the end of 2019, causing a severe global public health threat, and smoking is closely related to COVID-19. Previous studies have reported changes in smoking behavior and influencing factors during the COVID-19 period, but none of them explored the main influencing factor and high-risk populations for smoking behavior during this period.

METHODS

We conducted a nationwide survey and obtained 21,916 valid data. Logistic regression was used to examine the relationships between each potential influencing factor (sociodemographic characteristics, perceived social support, depression, anxiety, and self-efficacy) and smoking outcomes. Then, variables related to smoking behavior were included based on the results of the multiple logistic regression, and the classification and regression tree (CART) method was used to determine the high-risk population for increased smoking behavior during COVID-19 and the most profound influencing factors on smoking increase. Finally, we used accuracy to evaluated the performance of the tree.

RESULTS

The strongest predictor of smoking behavior during the COVID-19 period is acceptance degree of passive smoking. The subgroup with a high acceptation degree of passive smoking, have no smokers smoked around, and a length of smoking of ≥ 30 years is identified as the highest smoking risk (34%). The accuracy of classification and regression tree is 87%.

CONCLUSION

The main influencing factor is acceptance degree of passive smoking. More knowledge about the harm of secondhand smoke should be promoted. For high-risk population who smoke, the "mask protection" effect during the COVID-19 pandemic should be fully utilized to encourage smoking cessation.

Association between cigarette smoking patterns and severity of COVID-19: Findings from a study in 15 private Hospitals in Indonesia

INTRODUCTION

Indonesia is ranked fourth among countries with the highest smoking rates and has the highest number of male smokers globally. This study aimed to assess the association between cigarette smoking patterns and the severity of COVID-19 among patients in 15 Indonesian hospitals.

METHODS

A cross-sectional study was conducted from April to August 2020 using medical records of 490 COVID-19 patients, including the history of their smoking patterns from 15 private referral hospitals in 5 provinces. The severity was defined based on the Guidelines on the Prevention and Control of COVID-19 issued by the Indonesian Ministry of Health, which was indicated by the care provided to patients, namely outpatient, inpatient, and Intensive Care Unit (ICU) services for mild, moderate, and severe symptoms. Smoking patterns were grouped based on adult tobacco use classifications of the Centers for Disease Control and Prevention (CDC). Univariate and bivariate analyses were performed.

RESULTS

The results showed that 69.8% of respondents had not smoked cigarettes, 17.1% were active smokers, and 13.1% were former smokers. A significant difference was seen in the number of cigarettes smoked by patients in the ICU, inpatients, and outpatients, among current smokers and passive smokers (p=0.018 and p=0.005, respectively). Furthermore, there was no significant difference in the severity of COVID-19 among current smokers, former smokers, and non-smokers. The time from when smoking was stopped among former smokers was not associated with the severity of COVID-19.

CONCLUSIONS

There was no significant difference in COVID-19 severity between groups of smokers. Passive smoking and the number of cigarettes smoked by smokers daily were associated with the severity of COVID-19. Smoke-free policies should be implemented continuously to protect people from the dangers of secondhand smoke.

Comorbidities in childhood-onset and adult-onset asthma

BACKGROUND

Age of asthma onset has emerged as an important determinant of asthma phenotypes; however, the comorbidities that predominate in either childhood- or adult-onset asthma are not known.

OBJECTIVE

To identify comorbidities associated with adult-onset asthma vs childhood-onset asthma and with age of asthma diagnosis.

METHODS

We analyzed data on 27,437 adult participants in the National Health and Nutrition Examination Surveys conducted from 2001 to 2018. Logistic regression adjusted for covariates was used to identify comorbidities associated with the asthma phenotypes and age of asthma diagnosis.

RESULTS

Approximately 12.6% of participants were ever diagnosed with asthma; the prevalence of childhood-onset (before 18 years old) and adult-onset (≥ 18 years old) current asthma was 2.7% and 5.5%, respectively. After adjustment for covariates including age, adult-onset asthma was associated with higher odds of obesity (odds ratio [OR], 1.46; 95% confidence interval [CI], 1.09-1.96), hypercholesterolemia (OR, 1.67; 95% CI, 1.08-2.56), borderline high serum triglycerides (OR, 1.78; 95% CI, 1.17-2.71), and osteoarthritis (OR, 1.52; 95% CI, 1.04-2.20) than was childhood-onset asthma. Older age of asthma diagnosis (per 5-year increase) was also associated with higher odds of diabetes (OR, 1.04; 95% CI, 1.00-1.07) and hypertension (OR, 1.05; 95% CI, 1.02-1.07), whereas younger age of asthma diagnosis was associated with higher odds of chronic obstructive pulmonary disease (OR, 1.12; 95% CI, 1.04-1.19).

CONCLUSION

Age- and covariates-adjusted prevalence of obesity, dyslipidemia, arthritis, diabetes, and hypertension is higher in adult-onset asthma than in childhood-onset asthma, and with older age of asthma diagnosis. Conversely, the prevalence of chronic obstructive pulmonary disease increases with younger age of asthma diagnosis.

The Impact of Smoking Status and Smoking-Related Comorbidities on Coronavirus Disease 2019 Patient Outcomes: A Causal Mediation Analysis

INTRODUCTION

Smoking history is a known risk factor for significant chronic diseases as well as pulmonary infections; however, the impact of smoking status on coronavirus disease 2019 (COVID-19) outcomes has not been conclusively characterized. This study aims to evaluate the association of smoking status on COVID-19 outcomes, and to explore the mechanism by which smoking and smoking-related comorbidities relate to COVID-19 outcomes.

AIMS AND METHODS

Patients admitted with SARS-CoV-2 infection from November 2020 through January 2021 were included in this study. Causal mediation models investigating the associations between smoking status and the outcomes of mortality, intensive care unit (ICU) admission, advanced respiratory support, mechanical ventilation, ICU length of stay, and hospital length of stay, through mediation via smoking-related comorbidities, were examined.

RESULTS

Active smokers did not experience worse COVID-19 outcomes once hospitalized. Former smokers had a higher odds of mortality (total effect OR 1.59, 95% CI 1.07 to 2.38, p = .01; indirect effect OR 1.45, 95% CI 1.09 to 1.93, p < .001), and advanced respiratory support (total effect OR 1.31, 95% CI 1.04 to 1.67, p = .02; indirect effect OR 1.26, 95% CI 1.03 to 1.54, p = .02), which were mediated by smoking-related comorbidities. While there was a nonsignificant increase in the total effect for mechanical ventilation, smoking-related comorbidities were significant mediators for their increased need (total effect OR 1.40, 95% CI 0.92 to 2.14, p = .13; indirect effect OR 1.47, 95% CI 1.10 to 1.87, p < .001).

CONCLUSIONS

Although active smokers did not experience worse COVID-19 outcomes compared to never smokers, these results should be interpreted with caution. Compared to never smokers, former smokers had greater odds of mortality, advanced respiratory support, and mechanical ventilation which was significantly mediated through smoking-related comorbidities.

IMPLICATIONS

Previous studies have linked smoking status with worse COVID-19 outcomes, and have inferred that smoking-related comorbidities may play a role in these findings. This causal mediation analysis provides statistical evidence supporting this hypothesis, clarifying the risk that smoking-related comorbidities impart on COVID-19 outcomes in those with a smoking history.

Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans

The role of smoking in the risk of SARS-CoV-2 infection is unclear. We used a retrospective cohort design to study data from veterans’ Electronic Medical Record to assess the impact of smoking on the risk of SARS-CoV-2 infection. Veterans tested for the SARS-CoV-2 virus from 02/01/2020 to 02/28/2021 were classified as: Never Smokers (NS), Former Smokers (FS), and Current Smokers (CS). We report the adjusted odds ratios (aOR) for potential confounders obtained from a cascade machine learning algorithm. We found a 19.6% positivity rate among 1,176,306 veterans tested for SARS-CoV-2 infection. The positivity proportion among NS (22.0%) was higher compared with FS (19.2%) and CS (11.5%). The adjusted odds of testing positive for CS (aOR:0.51; 95%CI: 0.50, 0.52) and FS (aOR:0.89; 95%CI:0.88, 0.90) were significantly lower compared with NS. Four pre-existing conditions, including dementia, lower respiratory infections, pneumonia, and septic shock, were associated with a higher risk of testing positive, whereas the use of the decongestant drug phenylephrine or having a history of cancer were associated with a lower risk. CS and FS compared with NS had lower risks of testing positive for SARS-CoV-2. These findings highlight our evolving understanding of the role of smoking status on the risk of SARS-CoV-2 infection.