Temporal dynamic in the impact of COVID- 19 outbreak on cause-specific mortality in Guangzhou, China.

BACKGROUND: Studies related to the SARS-CoV-2 spikes in the past few months, while there are limited studies on the entire outbreak-suppressed cycle of COVID-19. We estimate the cause-specific excess mortality during the complete circle of COVID-19 outbreak in Guangzhou, China, stratified by sociodemographic status. METHODS: Guangzhou Center for Disease Control Prevention provided the individual data of deaths in Guangzhou from 1 January 2018 through 30 June 2020. We applied Poisson regression models to daily cause-specific mortality between 1 January 2018 and 20 January 2020, accounting for effects of population size, calendar time, holiday, ambient temperature and PM2.5. Expected mortality was estimated for the period from 21 January through 30 June 2020 assuming that the effects of factors aforementioned remained the same as described in the models. Excess mortality was defined as the difference between the observed mortality and the expected mortality. Subgroup analyses were performed by place of death, age group, sex, marital status and occupation class. RESULTS: From 21 January (the date on which the first COVID-19 case occurred in Guangzhou) through 30 June 2020, there were three stages of COVID-19: first wave, second wave, and recovery stage, starting on 21 January, 11 March, and 17 May 2020, respectively. Mortality deficits were seen from late February through early April and in most of the time in the recovery stage. Excesses in hypertension deaths occurred immediately after the starting weeks of the two waves. Overall, we estimated a deficit of 1051 (95% eCI: 580, 1558) in all-cause deaths. Particularly, comparing with the expected mortality in the absence of COVID-19 outbreak, the observed deaths from pneumonia and influenza substantially decreased by 49.2%, while deaths due to hypertension and myocardial infarction increased by 14.5 and 8.6%, respectively. In-hospital all-cause deaths dropped by 10.2%. There were discrepancies by age, marital status and occupation class in the excess mortality during the COVID-19 outbreak. CONCLUSIONS: The excess deaths during the COVID-19 outbreak varied by cause of death and changed temporally. Overall, there was a deficit in deaths during the study period. Our findings can inform preparedness measures in different stages of the outbreak.

Comorbidity and its impact on 1,590 patients with COVID-19 in China: A Nationwide Analysis

ObjectiveTo evaluate the spectrum of comorbidities and its impact on the clinical outcome in patients with coronavirus disease 2019 (COVID-19). DesignRetrospective case studies Setting575 hospitals in 31 province/autonomous regions/provincial municipalities across China Participants1,590 laboratory-confirmed hospitalized patients. Data were collected from November 21st, 2019 to January 31st, 2020. Main outcomes and measuresEpidemiological and clinical variables (in particular, comorbidities) were extracted from medical charts. The disease severity was categorized based on the American Thoracic Society guidelines for community-acquired pneumonia. The primary endpoint was the composite endpoints, which consisted of the admission to intensive care unit (ICU), or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared among patients with COVID-19 according to the presence and number of comorbidities. ResultsOf the 1,590 cases, the mean age was 48.9 years. 686 patients (42.7%) were females. 647 (40.7%) patients were managed inside Hubei province, and 1,334 (83.9%) patients had a contact history of Wuhan city. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. 269 (16.9%), 59 (3.7%), 30 (1.9%), 130 (8.2%), 28 (1.8%), 24 (1.5%), 21 (1.3%), 18 (1.1%) and 3 (0.2%) patients reported having hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, chronic obstructive pulmonary disease, chronic kidney diseases, malignancy and immunodeficiency, respectively. 130 (8.2%) patients reported having two or more comorbidities. Patients with two or more comorbidities had significantly escalated risks of reaching to the composite endpoint compared with those who had a single comorbidity, and even more so as compared with those without (all P<0.05). After adjusting for age and smoking status, patients with COPD (HR 2.681, 95%CI 1.424-5.048), diabetes (HR 1.59, 95%CI 1.03-2.45), hypertension (HR 1.58, 95%CI 1.07-2.32) and malignancy (HR 3.50, 95%CI 1.60-7.64) were more likely to reach to the composite endpoints than those without. As compared with patients without comorbidity, the HR (95%CI) was 1.79 (95%CI 1.16-2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61-4.17) among patients with two or more comorbidities. ConclusionComorbidities are present in around one fourth of patients with COVID-19 in China, and predispose to poorer clinical outcomes. HighlightsO_ST_ABSWhat is already known on this topicC_ST_ABS- Since November 2019, the rapid outbreak of coronavirus disease 2019 (COVID-19) has recently become a public health emergency of international concern. There have been 79,331 laboratory-confirmed cases and 2,595 deaths globally as of February 25th, 2020 - Previous studies have demonstrated the association between comorbidities and other severe acute respiratory diseases including SARS and MERS. - No study with a nationwide representative cohort has demonstrated the spectrum of comorbidities and the impact of comorbidities on the clinical outcomes in patients with COVID-19. What this study adds- In this nationwide study with 1,590 patients with COVID-19, comorbidities were identified in 399 patients. Comorbidities of COVID-19 mainly included hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, chronic obstructive pulmonary disease, chronic kidney diseases, malignancy and immunodeficiency. - The presence of as well as the number of comorbidities predicted the poor clinical outcomes (admission to intensive care unit, invasive ventilation, or death) of COVID-19. - Comorbidities should be taken into account when estimating the clinical outcomes of patients with COVID-19 on hospital admission.

Clinical characteristics of 2019 novel coronavirus infection in China

BackgroundSince December 2019, acute respiratory disease (ARD) due to 2019 novel coronavirus (2019-nCoV) emerged in Wuhan city and rapidly spread throughout China. We sought to delineate the clinical characteristics of these cases. MethodsWe extracted the data on 1,099 patients with laboratory-confirmed 2019-nCoV ARD from 552 hospitals in 31 provinces/provincial municipalities through January 29th, 2020. ResultsThe median age was 47.0 years, and 41.90% were females. Only 1.18% of patients had a direct contact with wildlife, whereas 31.30% had been to Wuhan and 71.80% had contacted with people from Wuhan. Fever (87.9%) and cough (67.7%) were the most common symptoms. Diarrhea is uncommon. The median incubation period was 3.0 days (range, 0 to 24.0 days). On admission, ground-glass opacity was the typical radiological finding on chest computed tomography (50.00%). Significantly more severe cases were diagnosed by symptoms plus reverse-transcriptase polymerase-chain-reaction without abnormal radiological findings than non-severe cases (23.87% vs. 5.20%, P<0.001). Lymphopenia was observed in 82.1% of patients. 55 patients (5.00%) were admitted to intensive care unit and 15 (1.36%) succumbed. Severe pneumonia was independently associated with either the admission to intensive care unit, mechanical ventilation, or death in multivariate competing-risk model (sub-distribution hazards ratio, 9.80; 95% confidence interval, 4.06 to 23.67). ConclusionsThe 2019-nCoV epidemic spreads rapidly by human-to-human transmission. Normal radiologic findings are present among some patients with 2019-nCoV infection. The disease severity (including oxygen saturation, respiratory rate, blood leukocyte/lymphocyte count and chest X-ray/CT manifestations) predict poor clinical outcomes.

The Impacts of Mosquito Density and Meteorological Factors on Dengue Fever Epidemics in Guangzhou, China, 2006-2014: a Time-series Analysis / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To explore the associations between the monthly number of dengue fever(DF) cases and possible risk factors in Guangzhou, a subtropical city of China.</p><p><b>METHODS</b>The monthly number of DF cases, Breteau Index (BI), and meteorological measures during 2006-2014 recorded in Guangzhou, China, were assessed. A negative binomial regression model was used to evaluate the relationships between BI, meteorological factors, and the monthly number of DF cases.</p><p><b>RESULTS</b>A total of 39,697 DF cases were detected in Guangzhou during the study period. DF incidence presented an obvious seasonal pattern, with most cases occurring from June to November. The current month's BI, average temperature (Tave), previous month's minimum temperature (Tmin), and Tave were positively associated with DF incidence. A threshold of 18.25 °C was found in the relationship between the current month's Tmin and DF incidence.</p><p><b>CONCLUSION</b>Mosquito density, Tave, and Tmin play a critical role in DF transmission in Guangzhou. These findings could be useful in the development of a DF early warning system and assist in effective control and prevention strategies in the DF epidemic.</p>

The impact of relative humidity and atmospheric pressure on mortality in Guangzhou, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>Although many studies have examined the effects of ambient temperatures on mortality, little evidence is on health impacts of atmospheric pressure and relative humidity. This study aimed to assess the impacts of atmospheric pressure and relative humidity on mortality in Guangzhou, China.</p><p><b>METHODS</b>This study included 213,737 registered deaths during 2003-2011 in Guangzhou, China. A quasi-Poisson regression with a distributed lag non-linear model was used to assess the effects of atmospheric pressure/relative humidity.</p><p><b>RESULTS</b>We found significant effect of low atmospheric pressure/relative humidity on mortality. There was a 1.79% (95% confidence interval: 0.38%-3.22%) increase in non-accidental mortality and a 2.27% (0.07%-4.51%) increase in cardiovascular mortality comparing the 5th and 25th percentile of atmospheric pressure. A 3.97% (0.67%-7.39%) increase in cardiovascular mortality was also observed comparing the 5th and 25th percentile of relative humidity. Women were more vulnerable to decrease in atmospheric pressure and relative humidity than men. Age and education attainment were also potential effect modifiers. Furthermore, low atmospheric pressure and relative humidity increased temperature-related mortality.</p><p><b>CONCLUSION</b>Both low atmospheric pressure and relative humidity are important risk factors of mortality. Our findings would be helpful to develop health risk assessment and climate policy interventions that would better protect vulnerable subgroups of the population.</p>

Impact of heat wave in 2005 on mortality in Guangzhou, China / 生物医学与环境科学（英文）

<p><b>OBJECTIVE</b>To assess the impact of the heat wave in 2005 on mortality among the residents in Guangzhou and to identify susceptible subpopulations in Guangzhou, China.</p><p><b>METHODS</b>The data of daily number of deaths and meteorological measures from 2003 to 2006 in Guangzhou were used in this study. Heat wave was defined as ⋝7 consecutive days with daily maximum temperature above 35.0 °C and daily mean temperature above the 97th percentile during the study period. The excess deaths and rate ratio (RR) of mortality in the case period compared with the reference period in the same summer were calculated.</p><p><b>RESULTS</b>During the study period, only one heat wave in 2005 was identified and the total number of excess deaths was 145 with an average of 12 deaths per day. The effect of the heat wave on non-accidental mortality (RR=1.23, 95% CI: 1.11-1.37) was found with statistically significant difference. Also, greater effects were observed for cardiovascular mortality (RR=1.34, 95% CI: 1.13-1.59) and respiratory mortality (RR=1.31, 95% CI: 1.02-1.69). Females, the elderly and people with lower socioeconomic status were at significantly higher risk of heat wave-associated mortality.</p><p><b>CONCLUSION</b>The 2005 heat wave had a substantial impact on mortality among the residents in Guangzhou, particularly among some susceptible subpopulations. The findings from the present study may provide scientific evidences to develop relevant public health policies and prevention measures aimed at reduction of preventable mortality from heat waves.</p>

Effect of influenza-like diseases on the number of outpatient visits / 南方医科大学学报

<p><b>OBJECTIVE</b>To estimate the effect of influenza-like illness (ILI) on outpatient visits and assess its impact on public health.</p><p><b>METHODS</b>We analyzed the data of weekly number of ILI and outpatient visits in Departments of Internal Medicine, Pediatrics and Emergency at two influenza surveillance hospitals during a period of 137 weeks in Guangzhou. Spectral analysis and time-series analysis were performed to evaluate the variation of outpatient visits over time. The predictive model was fitted with weekly outpatient visits as the dependent variable and weekly number of ILI as the independent variable. The optimal model was established according to the coefficient of determination, Akaike-information criterion and residual analysis. The validity of the model was assessed prospectively using the 31-week data that were not used for the model establishment.</p><p><b>RESULTS</b>The outpatient visits increased significantly over time and showed significant seasonality (P<0.001). A significant correlation was found between the weekly number of ILI and outpatient visits (r=0.568, P<0.001). The residuals of the fitted autoregression model were white-noise series and the coefficient of determination was 75% for the data used to establish the model and 56% for the subsequent 31-week data.</p><p><b>CONCLUSIONS</b>The autoregression model can be used to estimate the effect of weekly number of outpatient visits based on the weekly number of ILI and thus assess the effects of influenza on public health.</p>