Epidemiology of 2019 novel coronavirus in Jiangsu Province, China after wartime control measures: A population-level retrospective study

Wang, Kewei; Gao, Jie; Wang, Hua; Wu, Xiaolong; Yuan, Qingchen; Guo, Fei-Yu; Zhang, Zhijie; Cheng, Yang

doi:10.1016/j.tmaid.2020.101654

Cited by 38 publications

(45 citation statements)

References 16 publications

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“…Recently published observational studies have reported associations between increased mortality in COVID-19 patients with older age, male sex, and history of certain comorbidities, including cardiovascular disease, chronic respiratory diseases, hypertension, diabetes, and obesity (2)(3)(4)(5). Further, several biomarkers measured at the time of hospitalisation have been linked with greater disease severity.…”

Section: Introductionmentioning

confidence: 99%

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Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the United Kingdom

Fletcher¹,

Matcham²,

Tibúrcio³

et al. 2020

Preprint

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Background The novel coronavirus disease 2019 outbreak presents a significant threat to global health. A better understanding of patient clinical profiles is essential to drive efficient and timely health service strategies. In this study, we aimed to identify risk factors for a higher susceptibility to symptomatic presentation with COVID-19 and a transition to severe disease. Methods We analysed data on 2756 patients admitted to Chelsea & Westminster Hospital NHS Foundation Trust between 1 st January and 23rd April 2020. We compared differences in characteristics between patients designated positive for COVID-19 and patients designated negative on hospitalisation and derived a multivariable logistic regression model to identify risk factors for predicting risk of symptomatic COVID-19. For patients with COVID-19, we used univariable and multivariable logistic regression to identify risk factors associated with progression to severe disease defined by: 1) admission to the hospital's AICU, 2) the need for mechanical ventilation, 3) in-hospital mortality, and 4) at least one measurement of elevated Ddimer (≥1,000 μg/L) indicative of increased risk of venous thromboembolism. Results The patient population consisted of 1148 COVID-19 positive and 1608 COVID-19 negative patients. Age, sex, self-reported ethnicity, C-reactive protein, white blood cell count, respiratory rate, body temperature, and systolic blood pressure formed the most parsimonious model for predicting risk of symptomatic COVID-19 at hospital admission. Among 1148 patients with COVID-19, 116 (10.1%) were admitted to the AICU, 71 (6.2%) required mechanical ventilation, 368 (32.1%) had at least one record of D-dimer levels ≥1,000 μg/L, and 118 patients died. In the multivariable logistic regression, age (OR = 0.953 per 1 year, 95% CI: 0.937-0.968) C-reactive protein (OR = 1.004 per 1 mg/L, 95% CI: 1.002-1.007), and white blood cell counts (OR = 1.059 per 10 9 /L, 95% CI: 1.010-1.111) were found to be associated with admission to the AICU. Age (OR = 0.973 per 1 year, 95% CI: 0.955-0.990), C-reactive protein (OR = 1.003 per 1 mg/L, 95% CI: 1.000-1.006) and sodium (OR = 0.915 per 1 mmol/L, 0.868-0.962) were associated with mechanical ventilation. Age (OR = 1.023 per 1 year, 95% CI: 1.004-1.043), CRP (OR = 1.004 per 1 mg/L, 95% CI: 1.002-1.006), and body temperature (OR = 0.723 per 1 o C, 95% CI: 0.541-0.958) were associated with elevated D-dimer. For mortality, we observed associations with age (OR = 1.060 per 1 year, 95% CI: 1.040-1.082), female sex (OR = 0.442, 95% CI: 0.442, 95% CI: 0.245-0.777), Asian ethnic background (OR All rights reserved. No reuse allowed without permission. : medRxiv preprint = 2.237 vs White ethnic background, 95% CI: 1.111-4.510), C-reactive protein (OR = 1.004 per 1 mg/L, 95% CI: 1.001-1.006), sodium (OR = 1.038 per 1 mmol/L, 95% CI: 1.001-1.006), and respiratory rate (OR = 1.054 per 1 breath/min, 95% CI: 1.024-1.087). Conclusion Our analysis suggests there are several demographic, clinical and laboratory findings associated ...

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Section: Introductionmentioning

confidence: 99%

“…Moreover, most of these studies were conducted in countries that were first afflicted by COVID-19, principally China (2)(3)(4)(5)(8)(9)(10). Data from other populations is still therefore limited, and further research is required to corroborate all findings to better to characterise the clinical profile of patients with COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the United Kingdom

Fletcher¹,

Matcham²,

Tibúrcio³

et al. 2020

Preprint

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“…Previous studies show that the mortality in elderly patients varies from 0 to 34.5% in different regions, and is higher in severe epidemic areas [8][9][10][11]. In mild epidemic areas, the mortality of the elderly is lower and seems to be no worse than that of the young [9,10].…”

Section: Discussionmentioning

confidence: 97%

“…Owing to age related comorbidities, declined airway protection and slow immune responses, the elderly are more susceptible to COVID-19 and with higher morbidity and mortality [3,6,7]. Actually, the mortality in elderly patients varies from 0 to 34.5% in different region [8][9][10][11], and there have been less studies focusing on outcomes of elderly patients, among whom a general picture of the disease remains inadequately investigated.…”

Section: Introductionmentioning

confidence: 99%

Favorable outcomes of elderly COVID-19 patients in Guangzhou, China: a retrospective, observational study

Zhao

Zhang

et al. 2020

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Objective: To clarify the outcomes of elderly patients with COVID-19.Methods: All 265 confirmed adult patients with COVID-19 were included in this retrospective study, 43 (16.2%) of whom were 65 years and older. Electronic medical records of the subjects were reviewed to obtain information on clinical characteristics and outcomes. The allocations of medical resource were also recorded.Results: Only one death case occurred in the elderly. The mortality of elderly patients was no higher than that of young patients (2.3% vs. 0%, P = 0.126). The cure rate was 95.3% in elderly patients and 99.5% in young patients (P = 0.067), and the duration of hospitalization is 27 days in elderly patients and 18 days in young patients (P = 0.001). The elderly suffered from more comorbidities (67.4% vs. 24.8%, P < 0.001), most of which is hypertension. Significantly more severe cases occurred in elderly patients compared with young patients (37.2% vs. 16.7%, P = 0.004). The elderly were more likely to present with complications including acute respiratory distress syndrome, acute myocardial injury, septic shock and acute kidney injury (all P < 0.05), respectively. No medical staffs were infected during the treatment of COVID-19.Conclusion: The cure rate and the mortality of the elderly seemed to be no worse than that of the young, though the elderly were with longer hospitalization. Elderly patients with COVID-19 could be treatable if handled properly. More severe cases and complications in elderly patients should prompt for more complex treatment and special considerations.

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“…Without validated drugs [2], successful strategies for COVID-19 from China such as wartime control measures [3], active surveillance, contact tracing, quarantine, and social distancing [4], enhanced traffic control bundling [5], and outbreak city shutdown [6]result in dramatic reductions in number of daily newly confirmed cases and deaths. Under the condition of inter-human transmission and lockdown measures, the temporal dynamics and characteristics of the COVID-19 epidemic in a high-risk city were reported in China [7].…”

Section: Introductionmentioning

confidence: 99%

A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

Guan¹

2020

Preprint

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BACKGROUND: Currently, the globe is making efforts to curb the spreading of coronavirus disease 2019 (COVID-19). However, only a few studies have examined the associations among spreading growth, treatment efficiency, and death increase. This study aimed to examine the associations of COVID-2019 dead cases, healed cases, and recovered cases.METHODS: Data from China Data Lab in Harvard Dataverse with the China (January 22, 2020 to March 18, 2020), United States of America (USA, March 17, 2020 to April 12, 2020), and the world (January 22, 2020 to April 4, 2020) were analyzed. The main variables included in the analysis were the numbers of COVID-2019 total confirmed cases (TC), total dead cases (TD), newly dead cases (ND), newly confirmed cases (NC), newly healed cases (NH), total healed cases (TH), and newly recovered cases (NR). Pooled, dynamic, and event study analyses were conducted to reflect the associations among them.RESULTS: Descriptive analyses showed that NH/NC ratio in China are bigger than NR/NC in USA and the world. Pooled analysis showed that various roles of regions in NH and NR in a specific sample. Dynamic analysis showed significant roles of lags and NC in NH and NR. Panel event study showed that key events influence ND and NH in China significantly and NR in the world rather than NR in USA.CONCLUSION: The findings in this study indirectly confirmed the relationship between spreading growth, treatment efficiency, and death increase. China’s control strategies of COVID-19 pandemic are worth of learning by the globe.

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Epidemiology of 2019 novel coronavirus in Jiangsu Province, China after wartime control measures: A population-level retrospective study

Cited by 38 publications

References 16 publications

Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the United Kingdom

Risk factors for clinical progression in patients with COVID-19: a retrospective study of electronic health record data in the United Kingdom

Favorable outcomes of elderly COVID-19 patients in Guangzhou, China: a retrospective, observational study

A panel analysis on preventing and controlling efficiency of COVID-19 pandemic

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