Overcrowded housing increases risk for COVID-19 mortality: an ecological study

Varshney, Karan; Glodjo, Talia; Adalbert, Jenna R

doi:10.1186/s13104-022-06015-1

Cited by 27 publications

(25 citation statements)

References 13 publications

(11 reference statements)

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“…Other studies showed that housing conditions, such as overcrowded housing, are an important risk factor for SARS-CoV-2 infection [ 1 , 26 ]. Household exposure can be a relevant risk factor for SARS-CoV-2 infection in patients who rarely leave their home.…”

Section: Discussionmentioning

confidence: 99%

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Modifiable risk factors of COVID-19 in patients with multiple sclerosis: a single-centre case–control study

et al. 2023

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Background Disease and treatment-associated immune system abnormalities may confer higher risk of Coronavirus disease 2019 (COVID-19) to people with multiple sclerosis (PwMS). We assessed modifiable risk factors associated with COVID-19 in PwMS. Methods Among patients referring to our MS Center, we retrospectively collected epidemiological, clinical and laboratory data of PwMS with confirmed COVID-19 between March 2020 and March 2021 (MS-COVID, n = 149). We pursued a 1:2 matching of a control group by collecting data of PwMS without history of previous COVID-19 (MS-NCOVID, n = 292). MS-COVID and MS-NCOVID were matched for age, expanded disability status scale (EDSS) and line of treatment. We compared neurological examination, premorbid vitamin D levels, anthropometric variables, life-style habits, working activity, and living environment between the two groups. Logistic regression and Bayesian network analyses were used to evaluate the association with COVID-19. Results MS-COVID and MS-NCOVID were similar in terms of age, sex, disease duration, EDSS, clinical phenotype and treatment. At multiple logistic regression, higher levels of vitamin D (OR 0.93, p < 0.0001) and active smoking status (OR 0.27, p < 0.0001) emerged as protective factors against COVID-19. In contrast, higher number of cohabitants (OR 1.26, p = 0.02) and works requiring direct external contact (OR 2.61, p = 0.0002) or in the healthcare sector (OR 3.73, p = 0.0019) resulted risk factors for COVID-19. Bayesian network analysis showed that patients working in the healthcare sector, and therefore exposed to increased risk of COVID-19, were usually non-smokers, possibly explaining the protective association between active smoking and COVID-19. Conclusions Higher Vitamin D levels and teleworking may prevent unnecessary risk of infection in PwMS.

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Section: Discussionmentioning

confidence: 99%

“…Demographic, ecological, and politico-economic factors together with comorbidities have influenced the severity of Coronavirus disease 2019 (COVID-19) worldwide [ 1 ]. Among others, this pandemic has also raised health concerns related to people with multiple sclerosis (PwMS) in the acute phase of the outbreak.…”

Section: Introductionmentioning

confidence: 99%

Modifiable risk factors of COVID-19 in patients with multiple sclerosis: a single-centre case–control study

et al. 2023

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“…In this scenario, the poor housing conditions, particularly for low-income and minority ethnic populations, have become vulnerable to COVID-19 spread and infection [ 150 ]. Several studies have linked poor housing conditions with worse health outcomes and infectious diseases of COVID-19 spread [ 151 , 152 ]. Ahmad et al conducted a cross-sectional analysis of county-level data in US among households with poor housing conditions, including conditions such as overcrowding, high housing cost, incomplete kitchen facilities, or incomplete plumbing facilities [ 151 ].…”

Section: Socioeconomic Factorsmentioning

confidence: 99%

“…Overcrowding and poor plumbing may lead to recurrent exposure and potentially a higher viral inoculum, which had been connected to worse COVID-19 clinical outcomes. Varshney and Adalbert conducted bivariable and multivariable analyses using COVID-19 data from cities in Los Angeles County to determine the association between household overcrowding and risk of mortality from COVID-19 [ 152 ]. The bivariate regression revealed that overcrowded households were positively associated with COVID-19 deaths (standardized β = 0.863, p < 0.001).…”

Section: Socioeconomic Factorsmentioning

confidence: 99%

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Tang

Hadi

Arsad

et al. 2022

IJERPH

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Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

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“…Conventional methods, such as stepwise selection, are usually applied for variable selection in diseases 14 – 18 . However, they have the disadvantage of overfitting 19 .…”

Section: Introductionmentioning

confidence: 99%

Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease

Liu

Yin

2022

Sci Rep

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In countries with a high incidence of tuberculosis, the typical clinical features of Crohn's disease (CD) may be covered up after tuberculosis infection, and the identification of atypical Crohn's disease and intestinal tuberculosis (ITB) is still a dilemma for clinicians. Least absolute shrinkage and selection operator (LASSO) regression has been applied to select variables in disease diagnosis. However, its value in discriminating ITB and atypical Crohn's disease remains unknown. A total of 400 patients were enrolled from January 2014 to January 2019 in second Xiangya hospital Central South University.Among them, 57 indicators including clinical manifestations, laboratory results, endoscopic findings, computed tomography enterography features were collected for further analysis. R software version 3.6.1 (glmnet package) was used to perform the LASSO logistic regression analysis. SPSS 20.0 was used to perform Pearson chi-square test and binary logistic regression analysis. In the variable selection step, LASSO regression and Pearson chi-square test were applied to select the most valuable variables as candidates for further logistic regression analysis. Secondly, variables identified from step 1 were applied to construct binary logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed on these models to assess the ability and the optimal cutoff value for diagnosis. The area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy rate, together with their 95% confidence and intervals (CIs) were calculated. MedCalc software (Version 16.8) was applied to analyze the ROC curves of models. 332 patients were eventually enrolled to build a binary logistic regression model to discriminate CD (including comprehensive CD and tuberculosis infected CD) and ITB. However, we did not get a satisfactory diagnostic value via applying the binary logistic regression model of comprehensive CD and ITB to predict tuberculosis infected CD and ITB (accuracy rate:79.2%VS 65.1%). Therefore, we further established a binary logistic regression model to discriminate atypical CD from ITB, based on Pearsonchi-square test (model1) and LASSO regression (model 2). Model 1 showed 89.9% specificity, 65.9% sensitivity, 88.5% PPV, 68.9% NPV, 76.9% diagnostic accuracy, and an AUC value of 0.811, and model 2 showed 80.6% specificity, 84.4% sensitivity, 82.3% PPV, 82.9% NPV, 82.6% diagnostic accuracy, and an AUC value of 0.887. The comparison of AUCs between model1 and model2 was statistically different (P < 0.05). Tuberculosis infection increases the difficulty of discriminating CD from ITB. LASSO regression showed a more efficient ability than Pearson chi-square test based logistic regression on differential diagnosing atypical CD and ITB.

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Overcrowded housing increases risk for COVID-19 mortality: an ecological study

Cited by 27 publications

References 13 publications

Modifiable risk factors of COVID-19 in patients with multiple sclerosis: a single-centre case–control study

Modifiable risk factors of COVID-19 in patients with multiple sclerosis: a single-centre case–control study

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease

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