Neighborhood characteristics associated with COVID-19 burden—the modifying effect of age

Zhang, Xueying; Smith, Norah; Spear, Emily; Stroustrup, Annemarie

doi:10.1038/s41370-021-00329-1

Cited by 22 publications

(17 citation statements)

References 26 publications

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“…Such a finding could be related to the impact of the pandemic on work availability, social isolation and other daily life impacts, as shown by a survey in China which found that younger people suffered a greater psychological impact during the pandemic [ 37 ]. While age is one of the most important predictors of the severity and lethality of COVID-19, there is no consensus about its role in the persisting symptoms [ 9 , 31 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

Clinical, sociodemographic and environmental factors impact post-COVID-19 syndrome

et al. 2022

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Background Sociodemographic and environmental factors are associated with incidence, severity, and mortality of COVID-19. However, little is known about the role of such factors in persisting symptoms among recovering patients. We designed a cohort study of hospitalized COVID-19 survivors to describe persistent symptoms and identify factors associated with post-COVID-19 syndrome. Methods We included patients hospitalized between March to August 2020 who were alive six months after hospitalization. We collected individual and clinical characteristics during hospitalization and at follow-up assessed ten symptoms with standardized scales, 19 yes/no symptoms, a functional status and a quality-of-life scale and performed four clinical tests. We examined individual exposure to greenspace and air pollution and considered neighbourhood´s population density and socioeconomic conditions as contextual factors in multilevel regression analysis. Results We included 749 patients with a median follow-up of 200 (IQR = 185-235) days, and 618 (83%) had at least one of the ten symptoms measured with scales. Pain (41%), fatigue (38%) and posttraumatic stress disorder (35%) were the most frequent. COVID-19 severity, comorbidities, BMI, female sex, younger age, and low socioeconomic position were associated with different symptoms. Exposure to ambient air pollution was associated with higher dyspnoea and fatigue scores and lower functional status. Conclusions We identified a high frequency of persistent symptoms among COVID-19 survivors that were associated with clinical, sociodemographic, and environmental variables. These findings indicate that most patients recovering from COVID-19 will need post-discharge care, and an additional burden to health care systems, especially in LMICs, should be expected.

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Section: Discussionmentioning

confidence: 99%

Clinical, sociodemographic and environmental factors impact post-COVID-19 syndrome

et al. 2022

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“…LASSO regression is a popular method for variable selection in fitting high-dimension generalized linear model, which can obtain a more refined model by constructing a penalty function to reduce the variable numbers and effectively avoid overfitting. 39,40 Therefore, we applied the LASSO regression for feature selection and build prediction model in this study.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Yang¹,

Zhang²,

Xu³

et al. 2021

NSS

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Purpose To develop a risk prediction nomogram of postoperative sleep disturbance (PSD) in patients undergoing non-cardiac surgery. Patients and methods Data on 881 consecutive patients who underwent non-cardiac surgery at the Affiliated Hospital of Xuzhou Medical University between June 2020 and April 2021 were prospectively collected. Of these, we randomly divided 881 non-cardiac patients into two groups, training cohort (n = 617) and validation cohort (n = 264) at the ratio of 7:3. Characteristic variables were selected based on the data of training cohort through least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to identify the independent risk factors associated with PSD that then were incorporated into the nomogram. The predictive performance of the nomogram was measured by concordance index (C index), receiver operating characteristic (ROC) curve, and calibration with 1000 bootstrap samples to decrease the over-fit bias. Results PSD was found in 443 of 617 patients (71.8%) and 190 of 264 patients (72.0%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PSD were female sex, anxiety, dissatisfaction of ward environment, absence of combined regional nerve block, postoperative nausea and vomiting (PONV), the longer duration stayed in post anesthesia care unit (PACU), the higher dose of midazolam and sufentanil, the higher postoperative numeric rating score for pain (NRS) score. Incorporating these 9 factors, the nomogram achieved good concordance indexes of 0.82 (95% confidence interval [CI], 0.78–0.85) and 0.80 (95% CI, 0.74–0.85) in predicting PSD in the training and validation cohorts, respectively, and obtained well-fitted calibration curves. The sensitivity and specificity (95% CIs) of the nomogram were calculated, resulting in sensitivity of 74.0% (70.0–78.2%) and 75.3% (68.4–81.7%) and specificity of 79.3% (72.5–85.2%) and 70.3% (58.4–80.7%) for the training and validation cohorts, respectively. Patients who had a nomogram score of less than 262 or 262 or greater were considered to have low or high risks of PSD presence, respectively. Conclusion The proposed nomogram achieved an optimal prediction of PSD in patients undergoing non-cardiac surgery. The risks for an individual patient to harbor PSD can be determined by this model, which can lead to a reasonable preventive and treatment measures.

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“…The LASSO regression is a popular method for variable selection in fitting a high-dimension generalized linear model, which can get a more refined model by constructing a penalty function to reduce the variable numbers and effectively avoid overfitting. 36 , 37 The Lasso regression was used for our data dimension reduction and feature selection, then multivariable binary logistic regression was used to build a predictive model with regression coefficients.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study

Zhang

Shen

Wei

et al. 2022

RMHP

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The aim of this study was to explore the associated risk factors of perioperative respiratory adverse events (PRAEs) in children undergoing airway surgery and establish and validate a nomogram prediction model for PRAEs. Patients and Methods: This study involved 709 children undergoing airway surgery between November 2020 and July 2021, aged ≤18 years in the affiliated hospital of Xuzhou Medical University. They were divided into training (70%; n = 496) and validation (30%; n = 213) cohorts. The least absolute shrinkage and selection operator (LASSO) was used to develop a risk nomogram model. Concordance index values, calibration plot, decision curve analysis, and the area under the curve (AUC) were examined. Results: PRAEs were found in 226 of 496 patients (45.6%) and 88 of 213 patients (41.3%) in the training and validation cohorts, respectively. The perioperative risk factors associated with PRAEs were age, obesity, degree of upper respiratory tract infection, premedication, and passive smoking. The risk nomogram model showed good discrimination power, and the AUC generated to predict survival in the training cohort was 0.760 (95% confidence interval, 0.695-0.875). In the validation cohort, the AUC of survival predictions was 0.802 (95% confidence interval, 0.797-0.895). Calibration plots and decision curve analysis showed good model performance in both datasets. The sensitivity and specificity of the risk nomogram model were calculated, and the result showed the sensitivity of 69.5% and 64.8% and specificity of 73.3% and 81.6% for the training and validation cohorts, respectively. Conclusion:The present study showed the proposed nomogram achieved an optimal prediction of PRAEs in patients undergoing airway surgery, which can provide a certain reference value for predicting the high-risk population of perioperative respiratory adverse events and can lead to reasonable preventive and treatment measures.

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Neighborhood characteristics associated with COVID-19 burden—the modifying effect of age

Cited by 22 publications

References 26 publications

Clinical, sociodemographic and environmental factors impact post-COVID-19 syndrome

Clinical, sociodemographic and environmental factors impact post-COVID-19 syndrome

Development and Validation of Nomogram Prediction Model for Postoperative Sleep Disturbance in Patients Undergoing Non-Cardiac Surgery: A Prospective Cohort Study

Development and Validation of a Risk Nomogram Model for Perioperative Respiratory Adverse Events in Children Undergoing Airway Surgery: An Observational Prospective Cohort Study

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