Characteristics of and Public Health Emergency Responses to COVID-19 and H1N1 Outbreaks: A Case-Comparison Study

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

BACKGROUND

Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

OBJECTIVES

This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

METHODS

Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

RESULTS

Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

CONCLUSION

Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

The Impact of COVID-19 Pandemic Lockdown on Emergency Department Visits in a Tertiary Hospital

Purpose

This study aimed to highlight the impact of the COVID-19 pandemic lockdown on emergency department (ED) visits of non-COVID-19 patients in a tertiary hospital and evaluate protocol development during this period.

Patients and Methods

Clinical data of patients who visited the ED of Foshan Hospital of Traditional Chinese Medicine during the first-level response in Foshan, Guangdong province in 2020 (from January 23 to February 24) and the same period in 2019 and 2021 were collected. A retrospective cross-sectional analysis was performed to understand the characteristics of critically ill patients and compare the proportion of hospitalizations, deaths, and emergency ambulance calls (EACs).

Results

The number of patients presenting to the ED was significantly decreased, with a 37.75% reduction in 2020 (6196) compared to the same period in 2019 (9954). A rise in patient ED presentations was observed in the same period in 2021 (10,503). This decline was mostly in the 15-45 age group. In 2019, 2020, and 2021, critically ill patients treated by the ED totaled 568 (5.706%), 339 (5.495%), and 590 (5.617%), respectively. Compared to the same period in 2019 and 2021, the proportion of critically ill patients with respiratory system involvement, severe trauma, and poisoning decreased most significantly in 2020 (P<0.05). In contrast, the rates of EACs, hospitalizations, and deaths increased significantly (P<0.05).

Conclusion

The number of ED visits to hospitals was decreased during the 2020 lockdown, while the rates of EACs, hospitalizations, and deaths increased significantly though there were no documented COVID-19 cases. Optimizing emergency medical resources and ensuring the safety of healthcare providers and patients were essential to provide efficient emergency diagnosis and treatment during the lockdown.

COVID-19 and H1N1 Influenza: Are They 2 Sides of the Same Coin?

Background

Swine flu (H1N1) and Coronavirus diseases (COVID-19) have been compared in the past few months. Both pandemics sparked a worldwide major panic. Although both have some common symptoms and diagnoses, they are quite different in many aspects. The current study aimed to investigate the differences in clinical and viral behaviors between H1N1 Influenza and COVID-19 pneumonia.

Methods

This was a retrospective study of adult patients hospitalized with H1N1 influenza pneumonia between January 2019 and February 2020, and patients hospitalized with COVID-19 during the outbreak. A demographic and clinical characteristic of H1N1 influenza and COVID-19 patients were recorded. Both groups were compared-using an independent samples student t test for continuous variables and a chi-square test for categorical data-to identify significantly different parameters between the 2 diseases.

Results

A total of 78 patients were included and divided into 2 groups: 33 patients (42.3%) with H1N1 and 45 patients (57.7%) with COVID-19. The mean age of the patients was 43.3 ± 10.6 years. Bronchial asthma was significantly higher among patients with H1N1, while diabetes mellitus was significantly higher among patients with COVID-19. Right lower lobe affection was significantly present among those with H1N1 than those with COVID (100% vs 0%). The monocytic count was significantly higher among those with H1N1 than COVID-19 (11.63 ± 1.50 vs 7.76 ± 1.68; P < 0.001). Respiratory rates of more than 22 c/min significantly increased in patients with HINI than in those with COVID-19 (18.2% vs 4.4%; P = 0.05). Mortality increased in patients with HINI than in those with COVID-19 (18.2% vs 6.7%). However, the difference did not reach statistical significance (P = 0.15).

Conclusion

Clinically, it is difficult to distinguish between H1N1 and COVID-19. Thus, a polymerase chain reaction is recommended for all patients suffering from influenza-like symptoms to rule out influenza A subtype H1N1 and/or SARS-CoV2.

Variant influenza: connecting the missing dots

BACKGROUND

In June 2009, the World Health Organization declared a new pandemic, the 2009 swine influenza pandemic (swine flu). The symptoms of the swine flu pandemic causing strain were comparable to most of the symptoms noted by seasonal influenza.

AREA COVERED

Zoonotic viruses that caused the swine flu pandemic and its preventive measures.

EXPERT OPINION

As per Centers for Disease Control and Prevention (CDC), the clinical manifestations in humans produced by the 2009 H1N1 'swine flu' virus were equivalent to the manifestations caused by related flu strains. The H1N1 vaccination was the most successful prophylactic measure since it prevented the virus from spreading and reduced the intensity and consequences of the pandemic. Despite the availability of therapeutics, the ongoing evolution and appearance of new strains have made it difficult to develop effective vaccines and therapies. Currently, the CDC recommends yearly flu immunization for those aged 6 months and above. The lessons learned from the A/2009/H1N1 pandemic in 2009 indicated that readiness of mankind toward new illnesses caused by mutant viral subtypes that leap from animals to people must be maintained.

Can Major Public Health Emergencies Affect Changes in International Oil Prices?

In order to deepen the understanding of the impact of major public health emergencies on the oil market and to enhance the risk response capability, this study analyzed the logical relationship between major public health emergencies and international oil price changes, identified the change points, and calculated the probability of abrupt changes to international oil prices. Based on monthly data during six major public health emergencies from 2009 to 2020, this study built a product partition model. The results show that only the influenza A (H1N1) and COVID-19 pandemics were significant reasons for abrupt changes in international oil prices. Furthermore, the wild poliovirus epidemic, the Ebola epidemic, the Zika epidemic, and the Ebola epidemic in the Democratic Republic of the Congo had limited effects. Overall, the outbreak of a Public Health Emergency of International Concern (PHEIC) in major global economies has a more pronounced impact on international oil prices.

Preparedness of Primary Health Care Leaders During COVID-19 Outbreak, Riyadh, Saudi Arabia: A Qualitative Study

Purpose

Our study aims to explore the preparedness of the frontline leaders of the primary health care (PHC) centres in dealing with the COVID-19 pandemic and to understand their experiences in implementing preventive interventions necessary to routinise health care service delivery.

Methods

A qualitative exploratory study was conducted using in-depth interviews with the participants in English. The interviews were transcribed verbatim, and inductive coding followed by thematic analysis was performed using NVivo version 12.

Participants

Six participants in charge of managing their respective primary health care settings were included in the study. Purposive sampling was used to identify participants until saturation was reached. After agreement, the interviews were scheduled as per availability.

Results

The results were grouped into three major themes and nine subthemes. Most leaders reported that they were trained in pandemic preparedness, but there was a lack of focused readiness to handle a massive-scale, infectious disease outbreak or pandemic. The initial lack of guidelines specific to COVID-19 was a barrier in making decisions related to staff and patient care. Services were interrupted initially and there was lack of staff since many acquired COVID-19 and were isolated. The shortage of the staff was delt by repurposing staff from other departments to the essential care services. Fears related to pandemic was one of the main concerns reported among staff and patients. Several initiatives were taken to ensure staff safety and uninterrupted service delivery to patients. The use of technology was an effective mechanism in preparing for the pandemic.

Conclusion

This qualitative study helped in understanding the experiences of primary health care leaders during the COVID-19 pandemic. The facilities lacked overall preparedness at the beginning of the pandemic; however, many initiatives were taken in course of time to ensure smooth operations and continued service delivery to the patients.

The 1918 Influenza Pandemic: Back to the Future?

BACKGROUND

It is just over a century since the 1918 flu pandemic, sometimes referred to as the "mother" of pandemics. This brief retrospective of the 1918 pandemic is taken from the viewpoint of the current SARS-CoV-2/COVID-19 pandemic and is based on a short lecture given during the 2021 Virtual Congress of the ERA-EDTA.

SUMMARY

This review summarizes and highlights some of the earlier pandemic's salient features, some parallels with today, and some potential learnings, bearing in mind that the flu pandemic occurred over 100 years ago at a time of major turmoil during the climax to WWl, and with limited medical expertise and knowledge, research facilities, or well-structured and resourced healthcare services. While there is little or no information on renal complications at the time, or an effective treatment, some observations in relation to COVID-19 and vaccination are included. Key Messages: Lessons are difficult to draw from 1918 other than the importance and value of non-pharmaceutical measures to limit viral transmission. While the economic impact of the 1918 pandemic was significant, as it is now with COVID-19, subsequent economic analysis has shown that protecting public health and preserving economic activity are not mutually exclusive. Both H1N1 and SARS-CoV-2 viruses are neurotropic and may cause chronically debilitating neurological diseases, including conditions such as encephalitis lethargica (still debated) and myalgic encephalomyelitis (chronic fatigue syndrome), respectively. Although coronavirus and influenza viral infections have some similarities, they are certainly not the same, as we are realising, and future infectious pandemics may still surprise us, but being "forewarned is forearmed."

Initial Public-Health Emergency Response to SARS and COVID-19 Pandemics in Mainland China: A Retrospective Comparative Study

BACKGROUND

Severe acute respiratory syndrome (SARS) was reported first in China in 2003. The world is currently coping with coronavirus disease-2019 (COVID-19). We conducted a retrospective study to compare the initial public-health emergency response (PHER) to SARS and COVID-19 in mainland China.

METHODS

A qualitative comparative study was conducted to compare the PHER timelines to SARS and COVID-19 by selecting six crucial time points. Besides, we explored the speed of spread, peak time and plateau period of SARS and COVID-19, respectively, by comparing the confirmed cases in the same time interval.

RESULTS

The government of the People's Republic of China (PRC) accomplished the entire initial PHER to SARS in 127 days and for COVID-19 in 44 days. The speed of PHER for COVID-19 was 83 days faster. The peak time of SARS arose ~80 days later than that of COVID-19. Though the peak number of confirmed daily cases for COVID-19 was fivefold more than that of SARS, the onset of the stabilization period for COVID-19 was >2 months earlier than that of SARS.

CONCLUSION

Overall, the speed of the initial PHER to COVID-19 pandemic was faster than that for SARS. Compared with the speed of hospital reporting and government policymaking, the speed of pathogen identification improved the most. The COVID-19 pandemic curve entered a plateau period earlier than the SARS pandemic curve, which suggests that the pandemic was controlled more effectively because of a timely PHER. The PRC government should emphasize improving the ability of hospitals to restrain infectious diseases by enhancing the direct reporting system and cultivating crisis management to empower relevant individuals to make timely scientific decisions.

Clinico-epidemiological profile of Influenza A H1N1 cases at a tertiary care institute of Uttarakhand

Introduction:

The swine (H1N1) virus responsible for worldwide pandemics since 2009 is now causing seasonal epidemics. Since then alarming spikes of swine flu cases have been reported from Uttarakhand every year. There are limited studies conducted in this Himalayan belt to evaluate the clinical and epidemiological profile of the patients admitted in tertiary care hospitals.

Aims & Objectives:

This study aims to summarize the clinical and epidemiological attributes of swine flu and to approximate the burden of Influenza A H1N1 (Swine Flu) cases in this Himalayan belt.

Material and Methods:

Clinical and epidemiological characteristics of influenza A H1N1 cases from October 2018 to April 2019 were retrospectively and descriptively analyzed using data from the Medical Records Section and the isolation ward at Shri Guru Ram Rai Institute of Medical and Health Sciences; Shri Mahant Indiresh hospital.

Results:

A total of 1126 (51.6%) patients were tested of which 30% (338) patients were found to be H1N1 positive. Maximum cases and positivity were detected in the months of January (26.4%), February (50.3%), and March (14.8%), and the patients in the age groups of 41–50 (21.9%) and 51–60 years (19.3%) accounted for majority of the cases. The most common symptoms were fever (85.8%), cough (82.2%), sore throat (82%), and breathlessness (71.3%). A case fatality ratio of 10.9% was observed. A significant statistical association (p value < 0.00001) was reported between co-morbid conditions and death.

Conclusion:

According to the results of this study, close caution should be exercised in case of patients infected with H1N1 particularly those with co-morbidities.

Research on the Allocation of 3D Printing Emergency Supplies in Public Health Emergencies

Significant public health emergencies greatly impact the global supply chain system of production and cause severe shortages in personal protective and medical emergency supplies. Thus, rapid manufacturing, scattered distribution, high design degrees of freedom, and the advantages of the low threshold of 3D printing can play important roles in the production of emergency supplies. In order to better realize the efficient distribution of 3D printing emergency supplies, this paper studies the relationship between supply and demand of 3D printing equipment and emergency supplies produced by 3D printing technology after public health emergencies. First, we fully consider the heterogeneity of user orders, 3D printing equipment resources, and the characteristics of diverse production objectives in the context of the emergent public health environment. The multi-objective optimization model for the production of 3D printing emergency supplies, which was evaluated by multiple manufacturers and in multiple disaster sites, can maximize time and cost benefits of the 3D printing of emergency supplies. Then, an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model is developed and compared with the traditional NSGA-II algorithm analysis. It contains more than one solution in the Pareto optimal solution set. Finally, the effectiveness of 3D printing is verified by numerical simulation, and it is found that it can solve the matching problem of supply and demand of 3D printing emergency supplies in public health emergencies.

An analysis of coronavirus disease 2019 with spline regression at province level during first-level response to major public health emergency out of Hubei, China

This study aims to locate the knots of cumulative coronavirus disease 2019 (COVID-19) case number during the first-level response to public health emergency in the provinces of China except Hubei. The provinces were grouped into three regions, namely eastern, central and western provinces, and the trends between adjacent knots were compared among the three regions. COVID-19 case number, migration scale index, Baidu index, demographic, economic and public health resource data were collected from 22 Chinese provinces from 19 January 2020 to 12 March 2020. Spline regression was applied to the data of all included, eastern, central and western provinces. The research period was divided into three stages by two knots. The first stage (from 19 January to around 25 January) was similar among three regions. However, in the second stage, growth of COVID-19 case number was flatter and lasted longer in western provinces (from 25 January to 18 February) than in eastern and central provinces (from 26 February to around 11 February). In the third stage, the growth of COVID-19 case number slowed down in all the three regions. Included covariates were different among the three regions. Overall, spline regression with covariates showed the different change patterns in eastern, central and western provinces, which provided a better insight into regional characteristics of COVID-19 pandemic.

Trust, risk perception, and COVID-19 infections: Evidence from multilevel analyses of combined original dataset in China

Previous studies have revealed medical, democratic, and political factors altering responses to unexpected infectious diseases. However, few studies have attempted to explore the factors affecting disease infection from a social perspective. Here, we argue that trust, which plays an important role in shaping people' s risk perception toward hazards, can also affect risk perception toward infections from a social perspective. Drawing on the indication that risk perception of diseases helps prevent people from being infected by promoting responsible behaviors, it can be further asserted that trust may alter the infection rate of diseases as a result of risk perception toward infectious diseases. This is an essential point for preventing the spread of infectious diseases and should be demonstrated. To empirically test this prediction, this study uses the COVID-19 outbreak in China as an example and applies an original dataset combining real-time big data, official data, and social survey data from 317 cities in 31 Chinese provinces to demonstrate whether trust influences the infection rate of diseases. Multilevel regression analyses reveal three main results: (1) trust in local government and media helps to reduce the infection rate of diseases; (2) generalized trust promotes a higher rather than lower infection rate; and (3) the effects of different types of trust are either completely or partly mediated by risk perception toward diseases. The theoretical and practical implications of this study provide suggestions for improving the public health system in response to possible infectious diseases.

Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses

Background

Given the current pandemic, differentiation between pneumonia induced by COVID-19 or influenza viruses is of utmost clinical significance in the patients' management. For this purpose, this study was conducted to develop sensitive artificial intelligence (AI) models to assist radiologists to decisively differentiate pneumonia due to COVID-19 versus influenza viruses.

Methods

Cross sectional chest CT images (N=12744) from well-evaluated cases of pneumonias induced by COVID-19 or H1N1 Influenza viruses, and normal individuals were collected. We examined the computer tomographic (CT) chest images from 137 individuals. Various pre-trained convolutional neural network models, such as ResNet-50, InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19 were fine-tuned on our datasets. The datasets were used for training (60%), validation (20%), and testing (20%) of the final models. Also, the predictive power and means of precision and recall were determined for each model.

Results

Fine-tuned ResNet-50 model differentiated the pneumonia due to COVID-19 or H1N1 influenza virus with accuracies of 96.7% and 92%, respectively This model outperformed all others, i.e., InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19.

Conclusion

Fine-tuned and pre-trained image classifying models of AI enable radiologists to reliably differentiate the pneumonia induced by COVID-19 versus H1N1 influenza virus. For this purpose, ResNet-50 followed by InceptionV3 models proved more promising than other AI models. Also in the supplements, we share the source codes and our fine-tuned models for use by researchers and clinicians globally toward the critical task of image differentiation of patients infected with COVID-19 versus H1N1 Influenza viruses.