Excess Out-of-Hospital Mortality and Declining Oxygen Saturation: The Sentinel Role of Emergency Medical Services Data in the COVID-19 Crisis in Tijuana, Mexico

Socioeconomic factors and COVID-19 mortality in immune-mediated rheumatic diseases patients: regional analysis from Argentina, Mexico and Brazil

BACKGROUND

SARS-CoV-2 infection has become a major international issue, not only from a medical point of view, but also social, economic and political. Most of the available information comes from the United States, Europe, and China, where the population and the socioeconomic status are very different from Latin American countries. This study evaluates the effect of regional socioeconomic characteristics on mortality due SARS-CoV-2 infection in patients with immune-mediated rheumatic diseases (IMRD) from Argentina, Mexico and Brazil.

METHODS

Data from three national registries, SAR-COVID (Argentina), CMR-COVID (Mexico) and ReumaCoV-Brasil (Brazil), were combined. Adult IMRD patients with SARS-CoV-2 infection were recruited. National data for each province/state, including population density, number of physicians per inhabitant, income, unemployment, GINI index, Municipal Human Development Index (MHDI), stringency index, vaccination rate and most frequent viral strains per period were assessed as risk factors for mortality due to COVID-19.

RESULTS

A total of 4744 patients were included, 2534 (53.4%) from SAR-COVID, 1166 (24.6%) from CMRCOVID and 1044 (22.0%) from ReumaCoV-Brasil. Mortality due to COVID-19 was 5.4%. In the multivariable analysis, higher number of physicians per 1000 inhabitants and being infected during the vaccination period of each country were associated with lower mortality. After adjustment for socioeconomic factors, there was no association with country of residence and mortality.

CONCLUSION

These findings corroborate the complex interplay between socioeconomic factors, rheumatic disease activity, and regional disparities as determinants of death due to COVID-19 in Argentina, Brazil and Mexico. Thus, this research provides valuable insights for guiding public health policies and clinical practice in the ongoing fight against the COVID-19 pandemic.

Exploring Prehospital Data for Pandemic Preparedness: A Western Brazilian Amazon Case Study on COVID-19

BACKGROUND

The timely management of rapidly evolving epidemiological scenarios caused by disease outbreaks is crucial to prevent devastating consequences. However, delayed laboratory diagnostics can hamper swift health policy and epidemic response, especially in remote regions such as the western Brazilian Amazon. The aim of the article is to analyze the impact of the COVID-19 pandemic on the volume and characteristics of emergency medical services (EMS) in Manaus, focusing on how the pandemic affected sensitive indicators such as response time and the use of advanced life support ambulances. Additionally, the study seeks to understand how changes in prehospital EMS patterns, triggered by the pandemic, could be utilized as health surveillance tools, enabling a more rapid response in epidemic scenarios.

METHODS

This retrospective, descriptive study included data from the SAMU (Serviço de Atendimento Móvel de Urgência) medical records between January and June 2020.

RESULTS

A total of 45,581 calls resulted in mobile units being dispatched during this period. These patients were predominantly male (28,227, 61.9%), with a median age of 47 years (IQR 30-67). The median response time significantly increased during the pandemic, reaching a median of 45.9 min (IQR 30.6-67.7) (p < 0.001). EMS calls were reduced for trauma patients and increased for other medical emergencies, especially respiratory conditions, concomitantly to an escalation in the number of deaths caused by SARS and COVID-19 (p < 0.001). The employment of advanced life support ambulances was higher during the pandemic phase (p = 0.0007).

CONCLUSION

The COVID-19 pandemic resulted in a temporary disorder in the volume and reason for EMS calls in Manaus. Consequently, sensitive indicators like the response time and the employment of advanced life support ambulances were negatively affected. Sudden prehospital EMS pattern changes could play an important role in health surveillance systems, allowing for earlier establishment of countermeasures in epidemics. The impact of the COVID-19 pandemic on prehospital EMS and its role in health surveillance should be further explored.

Socioeconomic Inequalities and Ethnic Discrimination in COVID-19 Outcomes: the Case of Mexico

In Mexico, Indigenous people were hospitalised and killed by COVID-19 at a disproportionate rate compared to the non-Indigenous population. The main factors contributing to this were poor health conditions and impoverished social and economic circumstances within the country. The objective of this study is to examine the extent to which ethnic disparities are attributable to processes of structural discrimination and further explore the factors that exacerbate or mitigate them. Using administrative public data on COVID-19 and Census information, this study uses the Oaxaca-Blinder decomposition method to examine the extent to which disparities are illegitimate and signal discrimination against Indigenous people. The results show that although ethnic disparities were mainly attributable to observable differences in individual and contextual characteristics, 22.8% (p < 0.001) of the ethnic gap in hospitalisations, 17.5% in early deaths and 16.4% in overall deaths remained unexplained and could potentially indicate systemic discrimination. These findings highlight that pre-existing and longstanding illegitimate disparities against Indigenous people jeopardise the capacity of multi-ethnic countries to achieve social justice in health.

Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study

BACKGROUND

The phenomenon of Coronavirus disease 2019 (COVID-19) brought-in-dead (BID), i.e., COVID-19 deaths occurring outside hospital settings, suggests missed opportunities for life-saving care. However, much is still unknown with regards to its potential determinants. The present study aimed to examine the factors associated with COVID-19 BID by integrating new variables from multiple databases.

METHODS

This multi-database comparative cross-sectional study examined COVID-19 in-patient deaths (IPD) and COVID-19 BID (n = 244 in each group) in Selangor, Malaysia. BID cases, IPD cases, and their sociodemographic, clinical, and health behaviour factors were identified from the COVID-19 mortality investigation reports submitted to the Selangor State Health Department between 14 February 2022 and 31 March 2023. Data linkage was used to connect three open-source databases-GitHub-MOH, Socioeconomic Data and Applications Center, and OpenStreetMap-and identify health infrastructure and geospatial factors. The groups were compared using chi-square tests, independent t-tests, and logistic regression analyses to identify factors associated with COVID-19 BID.

RESULTS

The COVID-19 IPD and BID cases were comparable. After adjusting for confounders, non-Malaysian nationality (AOR: 3.765, 95% CI: 1.163, 12.190), obesity (AOR: 5.272, 95% CI: 1.131, 24.567), not seeking treatment while unwell (AOR: 5.385, 95% CI: 3.157, 9.186), and a higher percentage of COVID-19-dedicated beds occupied on the date of death (AOR: 1.165, 95% CI: 1.078, 1.259) were associated with increased odds of COVID-19 BID. On the other hand, being married (AOR: 0.396, 95% CI: 0.158, 0.997) and the interaction between the percentage of COVID-19-dedicated beds occupied and the percentage of ventilators in use (AOR: 0.996, 95% CI: 0.994, 0.999) emerged as protective factors.

CONCLUSION

These findings indicated that certain groups have higher odds of COVID-19 BID and thus, require closer monitoring. Considering that COVID-19 BID is influenced by various elements beyond clinical factors, intensifying public health initiatives and multi-organisational collaboration is necessary to address this issue.

Predictors of hospital length of stay and mortality among COVID-19 inpatients during 2020-2021 in Hormozgan Province of Iran: A retrospective cohort study

Background and Aims

About one-fifth of patients with COVID-19 need to be hospitalized. Predicting factors affecting the hospital length of stay (LOS) can be effective in prioritizing patients, planning for services, and preventing the increase in LOS and death of patients. The present study aimed to identify the factors that predict LOS and mortality in COVID-19 patients in a retrospective cohort study.

Methods

A total of 27,859 patients were admitted to 22 hospitals from February 20, 2020 to June 21, 2021. The data collected from 12,454 patients were screened according to the inclusion and exclusion criteria. The data were captured from the MCMC (Medical Care Monitoring Center) database. The study tracked patients until their hospital discharge or death. Hospital LOS and mortality were assessed as the study outcomes.

Results

As the results revealed, 50.8% of patients were male and 49.2% were female. The mean hospital LOS of the discharged patients was 4.94. Yet, 9.1% of the patients (n = 1133) died. Among the predictors of mortality and long hospital LOS were the age above 60, admission to the ICU, coughs, respiratory distress, intubation, oxygen level less than 93%, cigarette and drug abuse, and a history of chronic diseases. Masculinity, gastrointestinal symptoms, and cancer were the effective variables in mortality, and positive CT was a factor significantly affecting the hospital LOS.

Conclusion

Paying special attention to high-risk patients and modifiable risk factors such as heart disease, liver disease, and other chronic diseases can diminish the complications and mortality rate of COVID-19. Providing training, especially for those who care for patients experiencing respiratory distress such as nurses and operating room personnel can improve the qualifications and skills of medical staff. Also, ensuring the availability of sufficient supply of medical equipment is strongly recommended.

Increased Prehospital Emergency Medical Service Time Interval and Nontransport Rate of Patients With Fever Using Emergency Medical Services Before and After COVID-19 in Busan, Korea

BACKGROUND

In Korea, patients with fever have been preemptively isolated to isolation beds in the emergency department (ED) since the coronavirus disease 2019 (COVID-19) pandemic began. However, isolation beds were not always available, and transport delays or failure (nontransport), especially for infants, were reported in the media. Few studies have focused on delays and failure in transporting fever patients to the ED. Therefore, this study aimed to examine and compare the emergency medical service (EMS) time interval and nontransport rate of patients with fever using EMSs before and after COVID-19.

METHODS

This retrospective observational study analyzed the prehospital EMS time interval and nontransport rate of fever patients who contacted EMSs in Busan, South Korea, from March 1, 2019 to February 28, 2022, using emergency dispatch reports. All fever patients (≥ 37.5°C) who contacted EMSs during this study were included. The EMS time interval was defined as the time between the patient's EMS call and ED arrival time. Nontransport was defined as a case recorded as not being transported in the emergency dispatch reports. The study population of 2019 was compared to the population of 2020 and 2021 with the independent t-test, Mann-Whitney U test, and χ² test. As a subgroup, the EMS time intervals and nontransport rates of infants with fever were compared before and after COVID-19.

RESULTS

A total of 554,186 patients accessed the EMS during the study period, and 46,253 patients with fever were included. The EMS time interval (mean ± standard deviation, minutes) of fever patients was 30.9 ± 29.9 in 2019, 46.8 ± 127.8 in 2020 (P < 0.001) and 45.9 ± 34.0 in 2021 (P < 0.001). The nontransport rate (%) was 4.4 in 2019, 20.6 in 2020 (P < 0.001), and 19.5 in 2021 (P < 0.001). For infants with fever, the EMS time interval was 27.6 ± 10.8 in 2019, 35.1 ± 15.4 in 2020 (P < 0.001), and 42.3 ± 20.5 in 2021 (P < 0.001), and the nontransport rate (%) was 2.6 in 2019, 25.0 in 2020, and 19.7 in 2021.

CONCLUSION

After the emergence of COVID-19, in Busan, the EMS time interval of fever patients was delayed, and approximately 20% of fever patients were not transported. However, infants with fever had shorter EMS time intervals and higher nontransport rates than the overall study population. A comprehensive approach, including prehospital and hospital ED flow improvements, is required beyond increasing the number of isolation beds.

COVID-19 as a risk factor for long-term mortality in patients managed by the emergency medical system: A prospective, multicenter, ambulance-based cohort study

Introduction

COVID-19 has initially been studied in terms of an acute-phase disease, although recently more attention has been given to the long-term consequences. In this study, we examined COVID-19 as an independent risk factor for long-term mortality in patients with acute illness treated by EMS (emergency medical services) who have previously had the disease against those who have not had the disease.

Methods

A prospective, multicenter, ambulance-based, ongoing study was performed with adult patients with acute disease managed by EMS and transferred with high priority to the emergency department (ED) as study subjects. The study involved six advanced life support units, 38 basic life support units, and five emergency departments from Spain. Sociodemographic inputs, baseline vital signs, pre-hospital blood tests, and comorbidities, including COVID-19, were collected. The main outcome was long-term mortality, which was classified into 1-year all-cause mortality and 1-year in- and out-of-hospital mortality. To compare both the patients with COVID-19 vs. patients without COVID-19 and to compare survival vs non-survival, two main statistical analyses were performed, namely, a longitudinal analysis (Cox regression) and a logistic regression analysis.

Results

Between 12 March 2020 and 30 September 2021, a total of 3,107 patients were included in the study, with 2,594 patients without COVID-19 and 513 patients previously suffering from COVID-19. The mortality rate was higher in patients with COVID-19 than in patients without COVID-19 (31.8 vs. 17.9%). A logistic regression showed that patients previously diagnosed with COVID-19 presented higher rates of nursing home residency, a higher number of breaths per minute, and suffering from connective disease, dementia, and congestive heart failure. The longitudinal analysis showed that COVID-19 was a risk factor for mortality [hazard ratio 1.33 (1.10-1.61); p < 0.001].

Conclusion

The COVID-19 group presented an almost double mortality rate compared with the non-COVID-19 group. The final model adjusted for confusion factors suggested that COVID-19 was a risk factor for long-term mortality.

Coronavirus disease 2019 (COVID-19) excess mortality outcomes associated with pandemic effects study (COPES): A systematic review and meta-analysis

Background and aim

With the Coronavirus Disease 2019 (COVID-19) pandemic continuing to impact healthcare systems around the world, healthcare providers are attempting to balance resources devoted to COVID-19 patients while minimizing excess mortality overall (both COVID-19 and non-COVID-19 patients). To this end, we conducted a systematic review (SR) to describe the effect of the COVID-19 pandemic on all-cause excess mortality (COVID-19 and non-COVID-19) during the pandemic timeframe compared to non-pandemic times.

Methods

We searched EMBASE, Cochrane Database of SRs, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and Cochrane Controlled Trials Register (CENTRAL), from inception (1948) to December 31, 2020. We used a two-stage review process to screen/extract data. We assessed risk of bias using Newcastle-Ottawa Scale (NOS). We used Critical Appraisal and Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology.

Results

Of 11,581 citations, 194 studies met eligibility. Of these studies, 31 had mortality comparisons (n = 433,196,345 participants). Compared to pre-pandemic times, during the COVID-19 pandemic, our meta-analysis demonstrated that COVID-19 mortality had an increased risk difference (RD) of 0.06% (95% CI: 0.06-0.06% p < 0.00001). All-cause mortality also increased [relative risk (RR): 1.53, 95% confidence interval (CI): 1.38-1.70, p < 0.00001] alongside non-COVID-19 mortality (RR: 1.18, 1.07-1.30, p < 0.00001). There was "very low" certainty of evidence through GRADE assessment for all outcomes studied, demonstrating the evidence as uncertain.

Interpretation

The COVID-19 pandemic may have caused significant increases in all-cause excess mortality, greater than those accounted for by increases due to COVID-19 mortality alone, although the evidence is uncertain.

Systematic review registration

[https://www.crd.york.ac.uk/prospero/#recordDetails], identifier [CRD42020201256].

Injury-Related Emergency Medical Service Calls, Traffic Accidents, and Crime in Mexico City Before and During the COVID-19 Pandemic

INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic had detrimental impacts across multiple sectors of the Mexican health care system. The prehospital care system, however, remains largely under-studied. The first objective of this study was to calculate the monthly per capita rates of injury-related 9-1-1 calls, traffic accidents, and crime at the state-level (Mexico City) during the early pandemic period (January 1 through June 30, 2020), while the second objective was to conduct these calculations at the borough-level for the same outcomes and time period. The third objective was to compare monthly per capita rates of injury-related 9-1-1 calls, traffic accidents, and crime at the state-level (Mexico City) during the pre-pandemic (January 1 through June 30, 2019), early pandemic (January 1 through June 30, 2020), and later pandemic periods (January 1 through June 30, 2021).

METHODS

A retrospective analysis was conducted to examine injury-related 9-1-1 calls, traffic accidents, and crime at the state-level (Mexico City) and borough-levels. Monthly per capita rates were calculated using four datasets, including Mexico City's Public Release 9-1-1 Emergency Calls, National Institute of Statistics and Geography's (INEGI) Traffic Accidents Micro-Dataset, Mexico City's Attorney General's Office Crime Dataset, and Projections of the Population of the Municipalities of Mexico, 2015 to 2030. All statistical analyses were conducted using STATA 17.0.

RESULTS

During the early pandemic period, injury-related 9-1-1 emergency calls, traffic accidents, and crime experienced similar trends in monthly per capita rates at the state-level and borough-levels. While the monthly per capita rates remained constant from January to March 2020, starting in March, there was a precipitous decrease across all three outcomes, although decline rates varied across boroughs. The monthly per capita rates across the three outcomes were higher during the pre-pandemic period compared to the early pandemic period. As the COVID-19 pandemic progressed, the monthly per capita rates during the later pandemic period increased across the three outcomes compared to the early pandemic period, although they did not reach pre-pandemic levels during the study period.

CONCLUSION

The precipitous decline in injury-related 9-1-1 calls, traffic accidents, and crime in Mexico City occurred at the same time as the issuance of the first wave of public health orders in March 2020. The largest decrease across the three outcomes occurred one to two months post-issuance of the orders.

Ambulances Required to Relieve Overcapacity Hospitals: A Novel Measure of Hospital Strain During the COVID-19 Pandemic in the United States

Study objective

Methods

Results

Conclusion

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities. The model, implemented for the region of Lombardy, Italy, showed robust performance, with an overall 80% accuracy in identifying the active spread of the disease. The further post-processing of the output was implemented to classify the territory into five risk classes, resulting in effectively anticipating the demand for interventions by EMS. This model shows state-of-art potentiality for future applications in the early detection of the burden of the impact of COVID-19, or other similar epidemics, on the healthcare system.

Non-COVID outcomes associated with the coronavirus disease-2019 (COVID-19) pandemic effects study (COPES): A systematic review and meta-analysis

BACKGROUND

As the Coronavirus Disease-2019 (COVID-19) pandemic continues, healthcare providers struggle to manage both COVID-19 and non-COVID patients while still providing high-quality care. We conducted a systematic review/meta-analysis to describe the effects of the COVID-19 pandemic on patients with non-COVID illness and on healthcare systems compared to non-pandemic epochs.

METHODS

We searched Ovid MEDLINE/EMBASE/Cochrane Database of Systematic Reviews/CENTRAL/CINAHL (inception to December 31, 2020). All study types with COVID-pandemic time period (after December 31, 2019) with comparative non-pandemic time periods (prior to December 31, 2019). Data regarding study characteristics/case-mix/interventions/comparators/ outcomes (primary: mortality; secondary: morbidity/hospitalizations/disruptions-to-care. Paired reviewers conducted screening and abstraction, with conflicts resolved by discussion. Effect sizes for specific therapies were pooled using random-effects models. Risk of bias was assessed by Newcastle-Ottawa Scale, with evidence rating using GRADE methodology.

RESULTS

Of 11,581 citations, 167 studies met eligibility. Our meta-analysis showed an increased mortality of 16% during the COVID pandemic for non-COVID illness compared with 11% mortality during the pre-pandemic period (RR 1.38, 95% CI: 1.28-1.50; absolute risk difference: 5% [95% CI: 4-6%], p<0.00001, very low certainty evidence). Twenty-eight studies (17%) reported significant changes in morbidity (where 93% reported increases), while 30 studies (18%) reported no significant change (very low certainty). Thirty-nine studies (23%) reported significant changes in hospitalizations (97% reporting decreases), while 111 studies (66%) reported no significant change (very low certainty). Sixty-two studies (37%) reported significant disruptions in standards-to-care (73% reporting increases), while 62 studies (37%) reported no significant change (very low certainty).

CONCLUSIONS

There was a significant increase in mortality during the COVID pandemic compared to pre-pandemic times for non-COVID illnesses. When significant changes were reported, there was increased morbidity, decreased hospitalizations and increased disruptions in standards-of-care.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020201256 (Sept 2, 2020).

Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques

The new pandemic caused by the COVID-19 virus has generated an overload in the quality of medical care in clinical centers around the world. Causes that originate this fact include lack of medical personnel, infrastructure, medicines, among others. The rapid and exponential increase in the number of patients infected by COVID-19 has required an efficient and speedy prediction of possible infections and their consequences with the purpose of reducing the health care quality overload. Therefore, intelligent models are developed and employed to support medical personnel, allowing them to give a more effective diagnosis about the health status of patients infected by COVID-19. This paper aims to propose an alternative algorithmic analysis for predicting the health status of patients infected with COVID-19 in Mexico. Different prediction models such as KNN, logistic regression, random forests, ANN and majority vote were evaluated and compared. The models use risk factors as variables to predict the mortality of patients from COVID-19. The most successful scheme is the proposed ANN-based model, which obtained an accuracy of 90% and an F1 score of 89.64%. Data analysis reveals that pneumonia, advanced age and intubation requirement are the risk factors with the greatest influence on death caused by virus in Mexico.

Effects of COVID-19 lockdown strategies on emergency medical services

Introduction

The aim of this study is to evaluate the change in the number of EMS cases by comparing the lockdown period, the non-lockdown period, and the pre-pandemic period.

Methods

In our study, 3 periods of EMS cases were compared to evaluate the effect of lockdown. The first period (Period A) included in the study was the lockdown period (01-12-2020 and 31–01–2021. The second period (period B) is the period between 01 and 10-2020 and 30–11–2020, where there was no lockdown despite the pandemic. The third period (period C) in the study is the period between 01 and 12-2019 and 31–01–2020 before the pandemic.

Results

A total of 120,989 cases in 3 periods were included in the study. It was determined that the highest number of patients were in period C (42,703, 35.3%), while the least was in period A (39,054, 32.2%). On the other hand, it was found that the number of calls was highest in period A (246,200, 35.1%), while the least was in period C (212,267, 30.2%). Response times were longer in the pandemic period than in the pre-pandemic period. Mean talk time were longer during the pandemic period. The most frequent diagnosis in period A (21.6%) and B (42.2%) was COVID-19. The second most frequent disease group in these two periods was cardiovascular diseases.

Conclusion

While the number of EMS cases decreased during the pandemic period, it decreased even more during the lockdown period. However, the number of calls increased significantly during the lockdown period, and the response times and talk times increased accordingly.

Cystatin C is a marker for acute kidney injury, but not for mortality among COVID-19 patients in Mexico

Background

Aim

Methods

Results

Conclusion

A cross-sectional study of factors associated with COVID-19 testing among people who inject drugs: missed opportunities for reaching those most at risk

BACKGROUND

People who inject drugs (PWID) are vulnerable to SARS-CoV-2 infection. We examined correlates of COVID-19 testing among PWID in the U.S.-Mexico border region and described encounters with services representing potential opportunities (i.e., 'touchpoints') where COVID-19 testing could have been offered.

METHODS

Between October, 2020 and September, 2021, participants aged ≥18 years from San Diego, California, USA and Tijuana, Baja California, Mexico who injected drugs within the last month completed surveys and SARS-CoV-2, HIV, and HCV serologic testing. Logistic regression identified factors associated with COVID-19 testing including potential touchpoints, comorbidities and COVID-19 related misinformation and disinformation.

RESULTS

Of 583 PWID, 30.5% previously had a COVID-19 test. Of 172 PWID who tested SARS-CoV-2 seropositive (30.1%), 50.3% encountered at least one touchpoint where COVID-19 testing could have been offered within the prior six months. Factors independently associated with at least two fold higher odds of COVID-19 testing were living in San Diego, recent incarceration, receiving substance use treatment, and experiencing ≥1 chronic health condition. Homelessness, having received ≥1 dose of COVID-19 vaccine, and having a HIV or HCV test since the COVID-19 epidemic began were also independently associated with having had a prior COVID-19 test.

CONCLUSION

We identified several factors independently associated with COVID-19 testing and multiple touchpoints where COVID-19 testing could be scaled up for PWID, such as SUD treatment programs and syringe service programs. Integrated health services are needed to improve access to rapid, free COVID-19 testing in this vulnerable population.

Prehospital COVID19-related Encounters Predict Future Hospital Utilization

Objective: Identify if prehospital patient encounters can predict SARS-CoV-2 (COVID19) related hospital utilization. Methods: EMS data from COVID19-related prehospital encounters was pulled from NEMSIS systems in Minnesota. This data was plotted against hospital general medical-surgical bed and ICU bed usage during the initial COVID19 surge and again during a second surge. A validation dataset from 2019 was also utilized. Results: There was a total of 6,460 influenza-like-illness calls, and 2,161 COVID19-specific calls during the studied timeframe. A total of 24,806 medical-surgical bed-days and 20,208 ICU bed-days were analyzed. During initial COVID surge (April-July 2020), EMS encounters best correlated with medical-surgical bed utilization 10 days in the future (r² = 0.85, N = 113, p = <0.001), with each encounter correlating with a utilization of 7.1 beds. ICU bed utilization was best predicted 16 days in the future (r² = 0.86, N = 107, p = <0.001) with each encounter correlating with the use of 4.5 ICU beds. Similarly strong and clinically significant correlations were found during the second surged during July and August. There was no significant correlation when comparing to a similar dataset using 2019 ILI calls. Conclusion: Minnesota prehospital COVID19-related prehospital encounters are shown to accurately predict hospital bed utilization 1-2 weeks in advance. This was reproducible across two COVID19 surges. Trends in EMS patient encounters could serve as a valuable data point in predicting COVID19 surges and their effects on hospital utilization.

An Ethnographic Assessment of COVID-19‒Related Changes to the Risk Environment for People Who Use Drugs in Tijuana, Mexico

Objectives. To characterize the effects of the onset of the COVID-19 pandemic on the risk environment of people who use drugs (PWUD) in Tijuana, Mexico. Methods. We used intensive participant-observation ethnography among street-based PWUD and key informants, such as frontline physicians and harm reductionists. Results. PWUD described an unprecedented cessation of police violence and extortion during the initial pandemic-related lockdown, though this quickly reversed and police violence worsened. Government-provided housing and medical treatment with methadone were temporarily provided to PWUD in a dedicated clinic, yet only for PWUD with COVID-19 symptoms. Concurrently, non‒COVID-19‒related hospital care became virtually inaccessible, and many PWUD died of untreated, chronic illnesses, such as hepatitis C, and soft-tissue infections. Border closures, decreases in social interaction, and reduced drug and sex tourism resulted in worsening food, income, and housing insecurity for many PWUD. By contrast, potent illicit drugs remained easily accessible in open-air drug markets. Conclusions. The pandemic exacerbated health risks for PWUD but also offered profound glimpses of beneficial structural changes. Efforts are needed in Tijuana and elsewhere to institutionalize positive pandemic-related shifts and ameliorate novel harms for PWUD. (Am J Public Health. 2022;112(S2):S199-S202. https://doi.org/10.2105/AJPH.2022.306796).

Factors Associated with COVID-19 Testing among People who Inject Drugs: Missed Opportunities for Reaching those Most at Risk

People who inject drugs (PWID) are vulnerable to SARS-CoV-2 infection. We examined correlates of COVID-19 testing among PWID in the U.S.-Mexico border region and described encounters with services or venues representing potential opportunities (i.e., 'touchpoints') where COVID-19 testing could have been offered. Between October, 2020 and September, 2021, participants aged ≥18 years from San Diego, California, USA and Tijuana, Baja California, Mexico who injected drugs within the last month completed surveys and SARS-CoV-2, HIV, and HCV serologic testing. Logistic regression was used to identify factors associated with COVID-19 testing prior to enrollment. Of 583 PWID, 30.5% previously had a COVID-19 test. Of 172 PWID who tested SARS-CoV-2 seropositive in our study (30.1%), 50.3% encountered at least one touchpoint within the prior six months where COVID-19 testing could have been offered. Factors independently associated with at least two fold higher odds of COVID-19 testing were living in San Diego (versus Tijuana), having recently been incarcerated or attending substance use disorder (SUD) treatment and having at least one chronic health condition. In addition, recent homelessness, having had at least one COVID-19 vaccine dose and having been tested for HIV or HCV since the pandemic began were independently associated with COVID-19 testing. We identified several factors independently associated with COVID-19 testing and multiple touchpoints where COVID-19 testing could be scaled up for PWID, such as SUD treatment programs and syringe service programs. Integrated health services are needed to improve access to rapid, free COVID-19 testing in this vulnerable population.

Predictors of Mortality Among Hospitalized COVID-19 Patients at a Tertiary Care Hospital in Ethiopia

BACKGROUND

The very unprecedented virus causing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued causing catastrophes in economy and loss of human lives. Despite countries' urgent and resilient public health actions against the COVID-19 pandemic, the disease is causing a large number of deaths. However, predictors of mortality among hospitalized COVID-19 patients have not been well investigated in Ethiopia. Therefore, this study aimed to identify the predictors of mortality among hospitalized COVID-19 patients at a tertiary care hospital in Ethiopia.

METHODS

A hospital-based retrospective cohort design study was implemented among hospitalized COVID-19 patients at a tertiary care hospital in Harar, Ethiopia from March 20 to August 20, 2021. Data of 531 admitted patients were entered using Epi-data 3.1 and exported to STATA 14.2 for analysis. Binary logistic regression was used to identify significant predictors of outcome variables with an adjusted odds ratio (AOR) with a 95% confidence interval.

RESULTS

Of the total 531 study participants, 101 deaths occurred. The mortality rate was 16.2 per 1000 person-days of observation with median survival time of 44 days with IQR [28, 74]. Smoking history [AOR=2.55, 95% CI (1.15, 5.65)], alcohol history [AOR=2.3, 95% CI (1.06, 4.97)], comorbidities [AOR=2.95, 95% CI (1.26, 6.91)], and increasing oxygen saturation [AOR=0.92, 95% CI (0.89, 0.95)], and lymphocyte count [AOR=0.90, 95% CI (0.88, 0.97)] were independent significant predictors of death from Covid-19.

CONCLUSION

The incidence of mortality among hospitalized COVID-19 patients was found to be high. Devising individual, tailored management for patients with "risk" behaviors, comorbid conditions, and poor prognostic markers such as lymphopenia and low oxygen saturation, may reduce the incidence of death among hospitalized COVID-19 patients.

Social Inequities in the Impact of COVID-19 Lockdown Measures on the Mental Health of a Large Sample of the Colombian Population (PSY-COVID Study)

The identification of general population groups particularly vulnerable to the impact of COVID-19 lockdown measures on mental health and the development of healthcare policies are priority challenges in the current and future pandemics. This study aimed to identify the personal and social determinants of the impact of COVID-19 lockdown measures on mental health in a large sample of the Colombian population. In this cross-sectional study, an anonymous online survey was answered by 18,061 participants from the general population residing in Colombia during the first wave of the COVID-19 outbreak (from 20 May to 20 June 2020). The risk of depression, anxiety, and somatization disorders were measured using the Patient Health Questionnaire (PHQ-2), Generalized Anxiety Disorder Scale (GAD-2), and Somatic Symptom Questionnaire (SSQ-5), respectively. Overall, 35% of participants showed risk of depression, 29% of anxiety, and 31% of somatization. According to the analysis of social determinants of health, the most affected groups were people with low incomes, students, and young adults (18-29 years). Specifically, low-income young females were the most at-risk population group. These findings show how the lockdown measures affected the general population's mental health in Colombia and highlight some social risk factors in health.

Emergency Medical Services (EMS) Calls During COVID-19: Early Lessons Learned for Systems Planning (A Narrative Review)

BACKGROUND

Over the course of the COVID-19 progress, reports from many locations around the world indicated major increases in EMS call volume, which imposed great pressure on EMS dispatch centers (EMSDC) globally. No studies yet have been done to examine this phenomenon.

OBJECTIVE

This paper examines the interrelated effects of the unprecedented global increase of EMS call, the effect of the COVID-19 crisis on responding to non-COVID-19 emergencies, and the concurrent effects of having overwhelmed dispatch centers. It tries to explain the current evidence of the bottleneck of EMS calls during the early phase of the worldwide pandemic.

ELIGIBILITY CRITERIA

We examine the numbers of EMS calls internationally between March and June 2020, derived from published literature and news media. Only articles in English were selected, with certain keywords related to EMS calls, ambulance delay, stroke and cardiac arrest.

SOURCE OF EVIDENCE

Google Scholar was the main searching source.

RESULTS

After applying the selection criteria, a total of 29 citations were chosen, and a pattern of knowledge resulted in the emergence of five themes: EMS calls during COVID-19, Reduced EMS operator response time, Ambulance response delays, Collateral mortality and morbidity among non-COVID-19 cases, and Total ambulance call time.

CONCLUSION

Over the course of COVID-19 progress, there was a global phenomenon of exponential increases in EMS calls, which is expected to impose a great pressure on EMS dispatch centers. Several factors contributing to the bottleneck of EMS calls are identified and explained.

Risk profiles for negative and positive COVID-19 hospitalized patients

COVID-19 is a viral infection that affects people differently, where the majority of cases develop mild symptoms, some people require hospitalization, and unfortunately, a small number of patients perish. Hence, identifying risk factors is critical for physicians to make treatment decisions. The purpose of this article is to determine whether unsupervised analysis of risk factors in positive and negative COVID-19 subjects can aid in the identification of a set of reliable and clinically relevant risk profiles. Positive and negative patients hospitalized were randomly selected from the Mexican Open Registry between March and May 2020. Thirteen risk factors, three distinct outcomes, and COVID-19 test results were used to categorize registry patients. As a result, the dataset was reported via 6144 different risk profiles for each age group. The unsupervised learning method is proposed in this study to discover the most prevalent risk profiles. The data was partitioned into discovery (70%) and validation (30%) sets. The discovery set was analyzed using the partition around medoids (PAM) method, and the stable set of risk profiles was estimated using robust consensus clustering. The PAM models' reliability was validated by predicting the risk profile of subjects from the validation set and patients admitted in November 2020. In the validation set, the clinical relevance of the risk profiles was evaluated by determining the prevalence of three patient outcomes: pneumonia diagnosis, ICU admission, or death. Six positive and five negative COVID-19 risk profiles were identified, with significant statistical differences between them. As a result, PAM clustering with consensus mapping is a viable method for discovering unsupervised risk profiles in subjects with severe respiratory health problems.

Predictors of Hypoxemia and Related Adverse Outcomes in Patients Hospitalized with COVID-19: A Double-Center Retrospective Study

Hypoxemia is a hallmark of coronavirus disease 2019 (COVID-19) severity. We sought to determine predictors of hypoxemia and related adverse outcomes among patients hospitalized with COVID-19 in the two largest hospitals in Jerusalem, Israel, from 9 March through 16 July 2020. Patients were categorized as those who developed reduced (<94%) vs. preserved (≥94%) arterial oxygen saturation (SpO₂) within the first 48 h after arrival to the emergency department. Overall, 492 hospitalized patients with COVID-19 were retrospectively analyzed. Patients with reduced SpO₂ were significantly older, had more comorbidities, higher body surface area (BSA) and body mass index (BMI), lower lymphocyte counts, impaired renal function, and elevated liver enzymes, c-reactive protein (CRP), and D-dimer levels as compared to those with preserved SpO₂. In the multivariable regression analysis, older age (odds ratio (OR) 1.02 per year, p < 0.001), higher BSA (OR 1.16 per 0.10 m², p = 0.003) or BMI (OR 1.05 per 1 kg/m², p = 0.011), lower lymphocyte counts (OR 1.72 per 1 × 10³/μL decrease, p = 0.002), and elevated CRP (1.11 per 1 mg/dL increase, p < 0.001) were found to be independent predictors of low SpO₂. Severe hypoxemia requiring ventilatory support, older age, and pre-existing comorbidities, including underlying renal dysfunction and heart failure, were found to be significantly associated with in-hospital mortality. These findings suggest that assessment of predictors of hypoxemia early at the time of hospitalization with COVID-19 may be helpful in risk stratification and management.

Out-of-Hospital COVID-19 Deaths: Consequences for Quality of Medical Care and Accuracy of Cause of Death Coding

Objectives. To examine age and temporal trends in the proportion of COVID-19 deaths occurring out of hospital or in the emergency department and the proportion of all noninjury deaths assigned ill-defined causes in 2020. Methods. We analyzed newly released (March 2021) provisional COVID-19 death tabulations for the entire United States. Results. Children (younger than 18 years) were most likely (30.5%) and elders aged 64 to 74 years were least likely (10.4%) to die out of hospital or in the emergency department. In parallel, among all noninjury deaths, younger people had the highest proportions coded to symptoms, signs, and ill-defined conditions, and percentage symptoms, signs, and ill-defined conditions increased from 2019 to 2020 in all age-race/ethnicity groups. The majority of young COVID-19 decedents were racial/ethnic minorities. Conclusions. The high proportions of all noninjury deaths among children, adolescents, and young adults that were coded to ill-defined causes in 2020 suggest that some COVID-19 deaths were missed because of systemic failures in timely access to medical care for vulnerable young people. Public Health Implications. Increasing both availability of and access to the best hospital care for young people severely ill with COVID-19 will save lives and improve case fatality rates.

Racial/Ethnic, Social, and Geographic Trends in Overdose-Associated Cardiac Arrests Observed by US Emergency Medical Services During the COVID-19 Pandemic

IMPORTANCE

Provisional records from the US Centers for Disease Control and Prevention (CDC) through July 2020 indicate that overdose deaths spiked during the early months of the COVID-19 pandemic, yet more recent trends are not available, and the data are not disaggregated by month of occurrence, race/ethnicity, or other social categories. In contrast, data from emergency medical services (EMS) provide a source of information nearly in real time that may be useful for rapid and more granular surveillance of overdose mortality.

OBJECTIVE

To describe racial/ethnic, social, and geographic trends in EMS-observed overdose-associated cardiac arrests during the COVID-19 pandemic through December 2020 and assess the concordance with CDC-reported provisional total overdose mortality through May 2020.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included more than 11 000 EMS agencies in 49 US states that participate in the National EMS Information System and 83.7 million EMS activations in which patient contact was made.

EXPOSURES

Year and month of occurrence of overdose-associated cardiac arrest; patient race/ethnicity; census region and division; county-level urbanicity; and zip code-level racial/ethnic composition, poverty, and educational attainment.

MAIN OUTCOMES AND MEASURES

Overdose-associated cardiac arrests per 100 000 EMS activations with patient contact in 2020 were compared with a baseline of values from 2018 and 2019. Aggregate numbers of overdose-associated cardiac arrests and percentage increases were compared with provisional total mortality in CDC records from rolling 12-month windows with end months spanning January 2018 through July 2020.

RESULTS

Among 33.4 million EMS activations in 2020, 16.8 million (50.2%) involved female patients and 16.3 million (48.8%) involved non-Hispanic White individuals. Overdose-associated cardiac arrests were elevated by 42.1% nationally in 2020 (42.3 per 100 000 EMS activations at baseline vs 60.1 per 100 000 EMS activations in 2020). The highest percentage increases were seen among Latinx individuals (49.7%; 38.8 per 100 000 activations at baseline vs 58.1 per 100 000 activations in 2020) and Black or African American individuals (50.3%; 21.5 per 100 000 activations at baseline vs 32.3 per 100 000 activations in 2020), people living in more impoverished neighborhoods (46.4%; 42.0 per 100 000 activations at baseline vs 61.5 per 100 000 activations in 2020), and the Pacific states (63.8%; 33.1 per 100 000 activations at baseline vs 54.2 per 100 000 activations in 2020), despite lower rates at baseline for these groups. The EMS records were available 6 to 12 months ahead of CDC mortality figures and showed a high concordance (r = 0.98) for months in which both data sets were available. If the historical association between EMS-observed and total overdose mortality holds true, an expected total of approximately 90 632 (95% CI, 85 737-95 525) overdose deaths may eventually be reported by the CDC for 2020.

CONCLUSIONS AND RELEVANCE

In this cohort study, records from EMS agencies provided an effective manner to rapidly surveil shifts in US overdose mortality. Unprecedented overdose deaths during the pandemic necessitate investments in overdose prevention as an essential aspect of the COVID-19 response and postpandemic recovery. This is particularly urgent for more socioeconomically disadvantaged and racial/ethnic minority communities subjected to the compounded burden of disproportionate COVID-19 mortality and rising overdose deaths.

Initial prehospital Rapid Emergency Medicine Score (REMS) to predict outcomes for COVID-19 patients

OBJECTIVE

The Rapid Emergency Medicine Score (REMS) has not been widely studied for use in predicting outcomes of COVID-19 patients encountered in the prehospital setting. This study aimed to determine whether the first prehospital REMS could predict emergency department and hospital dispositions for COVID-19 patients transported by emergency medical services.

METHODS

This retrospective study used linked prehospital and hospital records from the ESO Data Collaborative for all 911-initiated transports of patients with hospital COVID-19 diagnoses from July 1 to December 31, 2020. We calculated REMS with the first recorded prehospital values for each component. We calculated area under the receiver operating curve (AUROC) for emergency department (ED) mortality, ED discharge, hospital mortality, and hospital length of stay (LOS). We determined optimal REMS cut-points using test characteristic curves.

RESULTS

Among 13,830 included COVID-19 patients, median REMS was 6 (interquartile range [IQR]: 5-9). ED mortality was <1% (n = 80). REMS ≥9 predicted ED death (AUROC 0.79). One-quarter of patients (n = 3,419) were discharged from the ED with an optimal REMS cut-point of ≤5 (AUROC 0.72). Eighteen percent (n = 1,742) of admitted patients died. REMS ≥8 optimally predicted hospital mortality (AUROC 0.72). Median hospital LOS was 8.3 days (IQR: 4.1-14.8 days). REMS ≥7 predicted hospitalizations ≥3 days (AUROC 0.62).

CONCLUSION

Initial prehospital REMS was modestly predictive of ED and hospital dispositions for patients with COVID-19. Prediction was stronger for outcomes more proximate to the first set of emergency medical services (EMS) vital signs. These findings highlight the potential value of first prehospital REMS for risk stratification of individual patients and system surveillance for resource planning related to COVID-19.

Prehospital characteristics of COVID-19 patients in Helsinki - experience of the first wave of the pandemic

Background

There is a lack of knowledge how patients with COVID-19 disease differ from patients with similar signs or symptoms (but who will have a diagnosis other than COVID-19) in the prehospital setting. The aim of this study was to compare the characteristics of these two patient groups met by the emergency medical services.

Methods

All prehospital patients after the World Health Organisation (WHO) pandemic declaration 11.3.2020 until 30.6.2020 were recruited for the study. The patients were screened using modified WHO criteria for suspected COVID-19. Data from the electronic prehospital patient reporting system were linked with hospital laboratory results to check the laboratory confirmation for COVID-19. For comparison, we divided the patients into two groups: screening- and laboratory-positive patients with a hospital diagnosis of COVID-19 and screening-positive but laboratory-negative patients who eventually received a different diagnosis in hospital.

Results

A total of 4157 prehospital patients fulfilled the criteria for suspected COVID-19 infection during the study period. Five-hundred-thirty-six (12.9%) of the suspected cases received a laboratory confirmation for COVID-19. The proportion of positive cases in relation to suspected ones peaked during the first 2 weeks after the declaration of the pandemic. In the comparison of laboratory-positive and laboratory-negative cases, there were clinically insignificant differences between the groups in age, tympanic temperature, systolic blood pressure, heart rate, on-scene time, urgency category of the call and mode of transportation. Foreign-language-speakers were overrepresented amongst the positive cases over native language speakers (26,6% vs. 7,4%, p < 0,001). The number of cases in which no signs or symptoms of COVID-19 disease were reported, but patients turned out to have a positive test result was 125 (0,3% of the whole EMS patient population and 11,9% of all verified COVID-19 patients encountered by the EMS).

Conclusions

In a sample of suspected COVID-19 patients, the laboratory-positive and laboratory-negative patients were clinically indistinguishable from each other during the prehospital assessment. Foreign-language-speakers had a high likelihood of having Covid-19. The modified WHO criteria still form the basis of screening of suspected COVID-19 patients in the prehospital setting.

Clinical Factors Associated with COVID-19 Severity in Mexican Patients: Cross-Sectional Analysis from a Multicentric Hospital Study

(1) Background: Latin America has been harshly hit by SARS-CoV-2, but reporting from this region is still incomplete. This study aimed at identifying and comparing clinical characteristics of patients with COVID-19 at different stages of disease severity. (2) Methods: Cross-sectional multicentric study. Individuals with nasopharyngeal PCR were categorized into four groups: (1) negative, (2) positive, not hospitalized, (3) positive, hospitalized with/without supplementary oxygen, and (4) positive, intubated. Clinical and laboratory data were compared, using group 1 as the reference. Multivariate multinomial logistic regression was used to compare adjusted odds ratios. (3) Results: Nine variables remained in the model, explaining 76% of the variability. Men had increased odds, from 1.90 (95%CI 0.87–4.15) in the comparison of 2 vs. 1, to 3.66 (1.12–11.9) in 4 vs. 1. Diabetes and obesity were strong predictors. For diabetes, the odds for groups 2, 3, and 4 were 1.56 (0.29–8.16), 12.8 (2.50–65.8), and 16.1 (2.87–90.2); for obesity, these were 0.79 (0.31–2.05), 3.38 (1.04–10.9), and 4.10 (1.16–14.4), respectively. Fever, myalgia/arthralgia, cough, dyspnea, and neutrophilia were associated with the more severe COVID-19 group. Anosmia/dysgeusia were more likely to occur in group 2 (25.5; 2.51–259). (4) Conclusion: The results point to relevant differences in clinical and laboratory features of COVID-19 by level of severity that can be used in medical practice.

An App-Based Intervention to Support First Responders and Essential Workers During the COVID-19 Pandemic: Needs Assessment and Mixed Methods Implementation Study

BACKGROUND

The COVID-19 pandemic has created unprecedented challenges for first responders (eg, police, fire, and emergency medical services) and nonmedical essential workers (eg, workers in food, transportation, and other industries). Health systems may be uniquely suited to support these workers given their medical expertise, and mobile apps can reach local communities despite social distancing requirements. Formal evaluation of real-world mobile app-based interventions is lacking.

OBJECTIVE

We aimed to evaluate the adoption, acceptability, and appropriateness of an academic medical center-sponsored app-based intervention (COVID-19 Guide App) designed to support access of first responders and essential workers to COVID-19 information and testing services. We also sought to better understand the COVID-19-related needs of these workers early in the pandemic.

METHODS

To understand overall community adoption, views and download data of the COVID-19 Guide App were described. To understand the adoption, appropriateness, and acceptability of the app and the unmet needs of workers, semistructured qualitative interviews were conducted by telephone, by video, and in person with first responders and essential workers in the San Francisco Bay Area who were recruited through purposive, convenience, and snowball sampling. Interview transcripts and field notes were qualitatively analyzed and presented using an implementation outcomes framework.

RESULTS

From its launch in April 2020 to September 2020, the app received 8262 views from unique devices and 6640 downloads (80.4% conversion rate, 0.61% adoption rate across the Bay Area). App acceptability was mixed among the 17 first responders interviewed and high among the 10 essential workers interviewed. Select themes included the need for personalized and accurate information, access to testing, and securing personal safety. First responders faced additional challenges related to interprofessional coordination and a "culture of heroism" that could both protect against and exacerbate health vulnerability.

CONCLUSIONS

First responders and essential workers both reported challenges related to obtaining accurate information, testing services, and other resources. A mobile app intervention has the potential to combat these challenges through the provision of disease-specific information and access to testing services but may be most effective if delivered as part of a larger ecosystem of support. Differentiated interventions that acknowledge and address the divergent needs between first responders and non-first responder essential workers may optimize acceptance and adoption.

Serum IL-6: A potential biomarker of mortality among SARS-CoV-2 infected patients in Mexico

Background

The first case of SARS-CoV-2 in Mexico was reported in February 2020, since then, high rates of mortality due to COVID-19 have been found. Cytokine storm is linked to the severity and decreasing the survival among infected patients by COVID-19. The serum levels of Interleukin 6 (IL-6) have been correlated to mortality in COVID-19 cases and could be used as indicator of mortality in COVID-19 cases. The aim of this study was to determine levels of IL-6 and assess its usefulness as indicator of mortality among COVID-19 patients from Mexico.

Methods

A cohort study among 38 adults (28 men, 10 women) was carried out in the Regional High Specialty Hospital of the Yucatan Peninsula in Merida, Yucatan, Mexico. Demographic and clinical biochemistry data were collected. The serum levels of IL-6 were measured in each patient by specific immunoassays.

Results

High frequency of mortality (36.84%) was found in the sample. The average age of individuals that non-survive was significantly higher (59.71 ± 13.83 years) than the survival group (43.29 ± 11.80 years). Serum levels of IL-6 were significantly higher in patients that did not survive. A correlation between IL-6 levels with lymphocyte count, LDH, CRP and procaciltonin was found. The optimal cutoff value of IL-6 was 30.95 pg/mL with high sensitivity and specificity.

Conclusion

Our findings demonstrate that level of IL-6 is an indicator of mortality among hospitalized COVID-19 patients in Mexico.

Clinical effectiveness of drugs in hospitalized patients with COVID-19: a systematic review and meta-analysis

The aim was to assess the clinical effectiveness of drugs used in hospitalized patients with COVID-19 infection. We conducted a systematic review of randomized clinical trials assessing treatment with remdesivir, chloroquine, hydroxychloroquine, lopinavir, ritonavir, dexamethasone, and convalescent plasma, for hospitalized patients with a diagnosis of SARS-CoV-2 infection. The outcomes were mortality, clinical improvement, duration of ventilation, duration of oxygen support, duration of hospitalization, virological clearance, and severe adverse events. A total of 48 studies were retrieved from the databases. Eleven articles were finally included in the data extraction and qualitative synthesis of results. The meta-analysis suggests a benefit of dexamethasone versus standard care in the reduction of risk of mortality at day 28; and the clinical improvement at days 14 and 28 in patients treated with remdesivir. We can conclude that dexamethasone would have a better result in hospitalized patients, especially in low-resources settings. The analysis of the main treatments proposed for hospitalized patients is of vital importance to reduce mortality in low-income countries, since the COVID-19 pandemic had an economic impact worldwide with the loss of jobs and economic decline in countries with scarce resources.

The reviews of this paper are available via the supplemental material section.

Diagnostic accuracy of subjective dyspnoea in detecting hypoxaemia among outpatients with COVID-19: a retrospective cohort study

OBJECTIVES

The majority of patients with mild-to-moderate COVID-19 can be managed using virtual care. Dyspnoea is challenging to assess remotely, and the accuracy of subjective dyspnoea measures in capturing hypoxaemia have not been formally evaluated for COVID-19. We explored the accuracy of subjective dyspnoea in diagnosing hypoxaemia in COVID-19 patients.

METHODS

This is a retrospective cohort study of consecutive outpatients with COVID-19 who met criteria for home oxygen saturation monitoring at a university-affiliated acute care hospital in Toronto, Canada from 3 April 2020 to 13 September 2020. Dyspnoea measures were treated as diagnostic tests, and we determined their sensitivity (SN), specificity (SP), negative/positive predictive value (NPV/PPV) and positive/negative likelihood ratios (+LR/-LR) for detecting hypoxaemia. In the primary analysis, hypoxaemia was defined by oxygen saturation <95%; the diagnostic accuracy of subjective dyspnoea was also assessed across a range of oxygen saturation cutoffs from 92% to 97%.

RESULTS

During the study period, 89/501 (17.8%) of patients met criteria for home oxygen saturation monitoring, and of these 17/89 (19.1%) were diagnosed with hypoxaemia. The presence/absence of dyspnoea had limited accuracy for diagnosing hypoxaemia, with SN 47% (95% CI 24% to 72%), SP 80% (95% CI 68% to 88%), NPV 86% (95% CI 75% to 93%), PPV 36% (95% CI 18% to 59%), +LR 2.4 (95% CI 1.2 to 4.7) and -LR 0.7 (95% CI 0.4 to 1.1). The SN of dyspnoea was 50% (95% CI 19% to 81%) when a cut-off of <92% was used to define hypoxaemia. A modified Medical Research Council dyspnoea score >1 (SP 98%, 95% CI 88% to 100%), Roth maximal count <12 (SP 100%, 95% CI 75% to 100%) and Roth counting time <8 s (SP 93%, 95% CI 66% to 100%) had high SP that could be used to rule in hypoxaemia, but displayed low SN (≤50%).

CONCLUSIONS

Subjective dyspnoea measures have inadequate accuracy for ruling out hypoxaemia in high-risk patients with COVID-19. Safe home management of patients with COVID-19 should incorporate home oxygenation saturation monitoring.

Silent hypoxaemia in COVID-19 patients

The clinical presentation of COVID-19 due to infection with SARS-CoV-2 is highly variable with the majority of patients having mild symptoms while others develop severe respiratory failure. The reason for this variability is unclear but is in critical need of investigation. Some COVID-19 patients have been labelled with 'happy hypoxia', in which patient complaints of dyspnoea and observable signs of respiratory distress are reported to be absent. Based on ongoing debate, we highlight key respiratory and neurological components that could underlie variation in the presentation of silent hypoxaemia and define priorities for subsequent investigation.

Silent hypoxaemia in COVID-19 patients

The clinical presentation of COVID-19 due to infection with SARS-CoV-2 is highly variable with the majority of patients having mild symptoms while others develop severe respiratory failure. The reason for this variability is unclear but is in critical need of investigation. Some COVID-19 patients have been labelled with 'happy hypoxia', in which patient complaints of dyspnoea and observable signs of respiratory distress are reported to be absent. Based on ongoing debate, we highlight key respiratory and neurological components that could underlie variation in the presentation of silent hypoxaemia and define priorities for subsequent investigation.

Effect of COVID19 on prehospital pronouncements and ED visits for stroke and myocardial infarction

OBJECTIVE

The Novel Coronavirus19 (COVID19) arrived in northern New Jersey (NJ) in early March 2020, peaked at the beginning of April, and then declined. Starting in March, some patients who called 911 and required advanced life support (ALS) may have decompensated more rapidly than would have been expected, possibly because of concomitant COVID19 infection and/or delays in seeking medical care because of fear of exposure to the virus, and social isolation. In this study, our goal was to determine if there was an increase in prehospital ALS pronouncements and a decrease in ED visits for potentially serious conditions such as MI and stroke during the peak of the COVID-19 pandemic in northern NJ.

METHODS STUDY DESIGN

Retrospective cohort of prehospital patients pronounced dead by paramedics and patients with MI and stroke in the EDs of receiving hospitals of these paramedics. Study Setting and Population: Ten ground ALS units in northern NJ and nine receiving hospital EDs. Each ALS unit is staffed by two NJ-certified mobile intensive care paramedics and respond with a paramedic flycar in a two-tiered dispatch system.

DATA ANALYSIS

We identified prehospital pronouncements using the EMSCharts electronic record (Zoll Medical, Chelmsford, Massachusetts). We tabulated the number of pronouncements by week from January 1 to June 30 in 2019 and 2020. We tabulated the combined total number of pronouncements and ED visits by month along with visits for MI and stroke and calculated the changes during the same timeframe. We used Chi-square to test for statistical significance for the monthly changes from 2019 to 2020.

RESULTS

For January through June in 2019 and 2020, there were 12,210 and 13,200 ALS dispatches, and 366 and 555 prehospital pronouncements, respectively. In 2020, pronouncements rose from a weekly baseline of 13 in early March, reached a peak of 45 at the beginning of April, then returned to the baseline level by the end of May. April 2020, the month with the most pronouncements, had 183% more pronouncements than April 2019 but total ED visits and visits for MI and stroke were 49%, 46% and 42% less, respectively (p < 0.0001 for each of these changes).

CONCLUSION

Following the arrival of the COVID-19 pandemic in northern NJ, we found pre-hospital ALS death pronouncements increased and ED visits for MI and stroke decreased. Although we have speculated about the reasons for these findings, further studies are needed to determine what the actual causes were.

The emergence of COVID-19 as a global pandemic: Understanding the epidemiology, immune response and potential therapeutic targets of SARS-CoV-2

An acute respiratory disease caused by a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that surfaced in China in late 2019, continues to spread rapidly across the globe causing serious concerns. The coronavirus disease 2019 (COVID-19) is declared as a public health emergency worldwide by the World Health Organization (WHO). Increasing evidences have demonstrated human-to-human transmission that primarily affects the upper respiratory tract followed by lower respiratory tract damage leading to severe pneumonia. Based on the current status, the elderly population and people with prior co-morbidities are highly susceptible to serious health effects including cytokine up-regulation and acute respiratory distress syndrome (ARDS). Currently, COVID-19 research is still in the preliminary stage necessitating rigorous studies. There is no specific drug or vaccine targeting SARS-CoV-2 currently and only symptomatic treatment is being administered, but several antivirals are under active investigation. In this review, we have summarized the epidemiology, entry mechanism, immune response, and therapeutic implications, possible drug targets, their ongoing clinical trials, and put forward vital questions to offer new directions to the COVID-19 research.

Excess deaths from COVID-19 correlate with age and socio-economic status. A database study in the Stockholm region

BACKGROUND

The COVID-19 pandemic has affected the entire health care system, internationally as well as in Sweden. We aimed to study excess deaths (all death causes, but also COVID-19-related deaths) during the COVID-19 pandemic regarding age, socio-economic status, the situation in nursing homes, and place of death for nursing home residents.

DESIGN

We performed a descriptive regional registry data study using VAL, the Stockholm Regional Council's central data warehouse, which covers almost all health care use in the county of Stockholm. T tests and chi-square tests were used for comparisons.

RESULTS

Compared with 2016-2019, there were excess deaths in March-May 2020 (p < 0.0001), mainly explained by COVID-19, but in April there were also unexplained excess deaths. Individuals dying from COVID-19 were older than patients dying from other causes (p < 0.0001). There were more patient deaths among people residing in less advantaged socio-economic areas (p < 0.0001). Nursing home residents dying from COVID-19 were more often admitted to acute hospitals than residents dying from other causes (p < 0.0001). Also, the proportion of admissions of nursing home residents dying from other causes increased from April to May 2020 (p < 0.0001).

CONCLUSIONS

Dying from COVID-19 mainly affects the elderly, nursing home residents, and persons from less advantaged socio-economic groups. The pandemic has resulted in an increase in acute admissions of dying nursing home residents to acute hospitals.

The Neutrophil-to-Monocyte Ratio and Lymphocyte-to-Neutrophil Ratio at Admission Predict In-Hospital Mortality in Mexican Patients with Severe SARS-CoV-2 Infection (Covid-19)

There is a deep need for mortality predictors that allow clinicians to quickly triage patients with severe coronavirus disease 2019 (Covid-19) into intensive care units at the time of hospital admission. Thus, we examined the efficacy of the lymphocyte-to-neutrophil ratio (LNR) and neutrophil-to-monocyte ratio (NMR) as predictors of in-hospital death at admission in patients with severe Covid-19. A total of 54 Mexican adult patients with Covid-19 that met hospitalization criteria were retrospectively enrolled, followed-up daily until hospital discharge or death, and then assigned to survival or non-survival groups. Clinical, demographic, and laboratory parameters were recorded at admission. A total of 20 patients with severe Covid-19 died, and 75% of them were men older than 62.90 ± 14.18 years on average. Type 2 diabetes, hypertension, and coronary heart disease were more prevalent in non-survivors. As compared to survivors, LNR was significantly fourfold decreased while NMR was twofold increased. LNR ≤ 0.088 predicted in-hospital mortality with a sensitivity of 85.00% and a specificity of 74.19%. NMR ≥ 17.75 was a better independent risk factor for mortality with a sensitivity of 89.47% and a specificity of 80.00%. This study demonstrates for the first time that NMR and LNR are accurate predictors of in-hospital mortality at admission in patients with severe Covid-19.