COVID-19 Case Fatality Rates Research Articles

Selective COVID-19 Coinfections in Diabetic Patients with Concomitant Cardiovascular Comorbidities Are Associated with Increased Mortality.

Coinfections and comorbidities add additional layers of difficulties into the challenges of COVID-19 patient management strategies. However, studies examining these clinical conditions are limited. We have independently investigated the significance of associations of specific bacterial species and different comorbidities in the outcome and case fatality rates among 129 hospitalized comorbid COVID-19 patients. For the first time, to best of our knowledge, we report on the predominance of Klebsiella pneumoniae and Acinetobacter baumannii in COVID-19 non-survival diabetic patients The two species were significantly associated to COVID-19 case fatality rates (p-value = 0.02186). Coinfection rates of Klebsiella pneumoniae and Acinetobacter baumannii in non-survivors were 93% and 73%, respectively. Based on standard definitions for antimicrobial resistance, Klebsiella pneumoniae and Acinetobacter baumannii were classified as multidrug resistant and extremely drug resistant, respectively. All patients died at ICU with similar clinical characterisitics. Of the 28 major coinfections, 24 (85.7%) were in non-survivor diabetic patients, implying aggravating and worsening the course of COVID-19. The rates of other comorbidities varied: asthma (47%), hypertension (79.4%), ischemic heart disease (71%), chronic kidney disease (35%), and chronic liver disease (32%); however, the rates were higher in K. pneumoniae and were all concomitantly associated to diabetes. Other bacterial species and comorbidities did not have significant correlation to the outcomes. These findings have highly significant clinical implications in the treatment strategies of COVID-19 patients. Future vertical genomic studies would reveal more insights into the molecular and immunological mechanisms of these frequent bacterial species. Future large cohort multicenter studies would reveal more insights into the mechanisms of infection in COVID-19.

COVID-19 Vaccination and Case Fatality Rate in India

India has experienced two severe waves of infection with COVID-19. Since there isn’t any effective drug, emerging variants of the virus, an unknown epidemiological life cycle, lockdown, and restrictions at public places slowed the spread of novel Coronavirus in the country. However, a large number of new COVID-19 cases and deaths have been reported from across the country. We carried out a study to evaluate the effectiveness of vaccination in India, randomly selected five states and a union territory to control the severity of COVID-19 (case fatality rate). During the study period, March 21st 2020 to 6th August 2021, with the initiation of vaccination, the case fatality rate was reduced to less than 1.0% in the selected states and union territory. Thus, vaccination for COVID-19 infection is found to be effective in decreasing the severity of the disease. The eligible population should get themselves inoculated at the nearest health facilities.

Virtual Cancer Care Equity in Canada: Lessons From COVID-19.

The COVID-19 pandemic radically shifted healthcare delivery to patients with cancer. Virtual cancer care, or the remote delivery of health care, has become an important resource for patients in Canada to maintain access to cancer care during the pandemic. With an increased number of people regularly accessing the internet and smartphones being ubiquitous for nearly all ages, technology in health care has grown. Virtual cancer care has been referenced as the fourth pillar of cancer care and it appears it may be here to stay. This article explores the benefits and challenges associated with virtual cancer care and outlines the importance of ensuring it is safe and equitable. Oncology nurses can identify where virtual care can be used to mitigate inequities and call attention when these tools exacerbate inequities.

Change in country-level COVID-19 lethality is associated with improved testing: no apparent role of medical care or disease-specific knowledge.

Reductions in the case fatality rate of COVID-19 in the unvaccinated have been credited to improvements in medical care. Here I test whether either of these factors predicts reductions in the case fatality rate, and whether observed reductions are better explicable by improved ascertainment of mild cases. Using weighted log-log regression, I compute the association between changes in the case fatality rate and test density between 3 July 2020 and 5 January 2021 in 162 countries; and check whether case fatality rate change is associated with either per capita medical spending (proxy for critical care access) or timing of the pandemic (proxy for COVID-specific knowledge). The median test density increased from 175 tests per thousand population to 1200, while the median case fatality rate dropped from 4.1% to 2.0%. While the case fatality rate was higher at both timepoints in Europe/North America than Africa / Asia, its association with test density was similar across countries. For each doubling in test density, the mean case fatality rate decreased by 18% (P<0.0001) with a median (interquartile rate) country-level decline of 20% (5-30) per doubling of test density. The rate of change of the case fatality rate was not associated with either medical care access or COVID-specific knowledge (all P>0.10). Declines in the case fatality rate were adequately explained by improved testing, with no effect of either medical knowledge or improvements in care. The true lethality of COVID-19 may not have changed much at the population level. Prevention should remain a priority.

Bayesian spatial modeling of COVID-19 case-fatality rate inequalities

The ongoing outbreak of COVID-19 challenges the health systems and epidemiological responses of all countries worldwide. Although preventive measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we identify the spatial association of socioeconomic factors and the risk for dying from COVID-19 in Colombia. We confirm that from March 16 to October 04, 2020, the COVID-19 case-fatality rate and the multidimensional poverty index have a heterogeneous spatial distribution. Spatial analysis reveals that the risk of dying from COVID-19 increases in regions with a higher proportion of poor people with dwelling (RR 1.74 95%CI = 1.54–9.75), educational (RR 1.69 95%CI = 1.36–5.94), childhood/youth (RR 1.35 95%CI = 1.08–4.03), and health (RR 1.16 95%CI = 1.06–2.04) deprivations. These findings evidence the vulnerability of most disadvantaged members of society to dying in a pandemic and assist the spatial planning of preventive strategies focused on vulnerable communities.

Characteristics and Scoring Model of COVID-19 Severity Predictor in Obstetric and Gynecology Doctors in Indonesia

Background: Obstetrician and gynecologist are one of the contributors in patient care who are susceptible to contracting coronavirus-19 infection. Emergency measures for maternal field are high, while gold standard examination for COVID-19 takes long time with false negative rate. The case fatality rate of COVID-19 among health workers in Indonesia is reported to be high. We want to know epidemiological, clinical, and occupational characteristics of various degrees of COVID-19 severity and create scoring model to predict COVID-19 severity in ob/gyn doctors in Indonesia. Method: This is a descriptive analytic study using a cross-sectional approach. Consecutive sampling was taken from ob/gyn doctors infected with COVID-19 and analyses used were Chi-Square and Mann-Whitney U tests. A scoring model is created and using receiver operating characteristic (ROC) curves, to determine the predictive ability of the scoring model and the cutoff value to COVID-19 severity, then tested for sensitivity and specificity. Result: The mean age of the respondents was 38.2 ± 9.8 and 48.7% were male. Sixty-one percent respondents experienced mild symptoms, infected while on duty (46.1%), and 59% had nuclear family member infected with COVID-19. Age group ([Formula: see text] = 0.000), number of infection ([Formula: see text] = 0.000), vaccination status ([Formula: see text] = 0.010), comorbidities ([Formula: see text] = 0.023), and working hours ([Formula: see text] = 0.002) were significantly different on the COVID-19 severity. Area-under-curve of scoring model was 75.2% and 93.4%. The cutoff value was 10.5 points for between asymptomatic and mild degree (sensitivity 54.2% and specificity 77.8%) and 12.5 points between mild and moderate degree (sensitivity 83.3% and specificity 95.8%) respectively. Conclusion: The developed scoring model can facilitate prediction of COVID-19 severity in ob/gyn doctors, with asymptomatic (score of &lt; 10.5 points), mild degree (score of 10.5–12.5 points), and moderate degree (score of &gt; 12.5 points).

The global case fatality rate of coronavirus disease 2019by continents and national income: A meta-analysis.

The aim of this study is to provide a more accurate representation of COVID‐19's case fatality rate (CFR) by performing meta‐analyses by continents and income, and by comparing the result with pooled estimates. We used multiple worldwide data sources on COVID‐19 for every country reporting COVID‐19 cases. On the basis of data, we performed random and fixed meta‐analyses for CFR of COVID‐19 by continents and income according to each individual calendar date. CFR was estimated based on the different geographical regions and levels of income using three models: pooled estimates, fixed‐ and random‐model. In Asia, all three types of CFR initially remained approximately between 2.0% and 3.0%. In the case of pooled estimates and the fixed model results, CFR increased to 4.0%, by then gradually decreasing, while in the case of random‐model, CFR remained under 2.0%. Similarly, in Europe, initially, the two types of CFR peaked at 9.0% and 10.0%, respectively. The random‐model results showed an increase near 5.0%. In high‐income countries, pooled estimates and fixed‐model showed gradually increasing trends with a final pooled estimates and random‐model reached about 8.0% and 4.0%, respectively. In middle‐income, the pooled estimates and fixed‐model have gradually increased reaching up to 4.5%. in low‐income countries, CFRs remained similar between 1.5% and 3.0%. Our study emphasizes that COVID‐19 CFR is not a fixed or static value. Rather, it is a dynamic estimate that changes with time, population, socioeconomic factors, and the mitigatory efforts of individual countries.

Study on the virulence evolution of SARS-CoV-2 and the trend of the epidemics of COVID-19.

This is the first attempt to investigate the effects of the factors related to non‐pharmaceutical interventions (NPIs) and the physical condition of the public on virulence evolution of SARS‐CoV‐2 and the trend of the epidemics of COVID‐19 under an adaptive dynamics framework. Qualitative agreement of the prediction on the epidemics of COVID‐19 with the actual situations convinced the rationality of the present model. The study showed that enhancing both NPIs (including public vigilance, quarantine measures, and hospitalization) and the physical condition of the public (including susceptibility and recovery speed) contributed to decreasing the prevalence of COVID‐19 but only increasing public vigilance and decreasing the susceptibility of the public could also reduce the virulence of SARS‐CoV‐2. Therefore, controlling the contact rate and infection rate was the key to control not only the epidemic scale of COVID‐19 but also the extent of its harm. On the other hand, the best way to control the epidemics was to increase the public vigilance and physical condition because both of them could reduce the prevalence and case fatality rate (CFR) of COVID‐19. In addition, the enhancement of quarantine measures and hospitalization could bring the (slight) increase in the CFR of COVID‐19.

Seasonal COVID-19 surge related hospital volumes and case fatality rates

BackgroundSeasonal and regional surges in COVID-19 have imposed substantial strain on healthcare systems. Whereas sharp inclines in hospital volume were accompanied by overt increases in case fatality rates during the very early phases of the pandemic, the relative impact during later phases of the pandemic are less clear. We sought to characterize how the 2020 winter surge in COVID-19 volumes impacted case fatality in an adequately-resourced health system.MethodsWe performed a retrospective cohort study of all adult diagnosed with COVID-19 in a large academic healthcare system between August 25, 2020 to May 8, 2021, using multivariable logistic regression to examine case fatality rates across 3 sequential time periods around the 2020 winter surge: pre-surge, surge, and post-surge. Subgroup analyses of patients admitted to the hospital and those receiving ICU-level care were also performed. Additionally, we used multivariable logistic regression to examine risk factors for mortality during the surge period.ResultsWe studied 7388 patients (aged 52.8 ± 19.6 years, 48% male) who received outpatient or inpatient care for COVID-19 during the study period. Patients treated during surge (N = 6372) compared to the pre-surge (N = 536) period had 2.64 greater odds (95% CI 1.46–5.27) of mortality after adjusting for sociodemographic and clinical factors. Adjusted mortality risk returned to pre-surge levels during the post-surge period. Notably, first-encounter patient-level measures of illness severity appeared higher during surge compared to non-surge periods.ConclusionsWe observed excess mortality risk during a recent winter COVID-19 surge that was not explained by conventional risk factors or easily measurable variables, although recovered rapidly in the setting of targeted facility resources. These findings point to how complex interrelations of population- and patient-level pandemic factors can profoundly augment health system strain and drive dynamic, if short-lived, changes in outcomes.

Novel statistics predict the COVID-19 pandemic could terminate in 2022.

Many people want to know when the COVID‐19 pandemic will end and life will return to normal. This question is highly elusive and distinct predictions have been proposed. In this study, the global mortality and case fatality rate of COVID‐19 were analyzed using nonlinear regression. The analysis showed that the COVID‐19 pandemic could terminate in 2022, but COVID‐19 could be one or two times more deadly than seasonal influenza by 2023. The prediction considered the possibility of the emergence of new variants of SARS‐CoV‐2 and was supported by the features of the Omicron variant and other facts. As the herd immunity against COVID‐19 established through natural infections and mass vaccination is distinct among countries, COVID‐19 could be more or less deadly in some countries in the coming years than the prediction. Although the future of COVID‐19 will have multiple possibilities, this statistics‐based prediction could aid to make proper decisions and establish an example on the prediction of infectious diseases.

The impact of COVID-19 vaccination on case fatality rates in a city in Southern Brazil

BackgroundRecent studies have established that vaccination plays a significant role in reducing COVID-19-related deaths. Here, we investigated differences in COVID-19 case fatality rates (CFRs) among vaccinated and unvaccinated populations, and analyzed whether the age composition of confirmed cases has a significant effect on the variations in the observed CFRs across these groups.MethodsThe study considered 59,853 confirmed cases and 1,687 deaths from COVID-19, reported between January 1 to October 20, 2021, by the Health Department of Londrina, a city in Southern Brazil. We used Negative Binomial regression models to estimate CFRs according to vaccination status and age range.ResultsThere are significant differences between the CFR for fully vaccinated and unvaccinated populations (IRR = 0.596, 95% CI [0.460 - 0.772], P < .001). Vaccinated populations experience fatality rates 40.4% lower than non-vaccinated. In addition, the age composition of confirmed cases explains more than two-thirds of the variation in the CFR between these 2 groups.ConclusionsOur novel findings reinforce the importance of vaccination as an essential public health measure for reducing COVID-19 fatality rates in all age groups. The results also provide means for accurately assessing differences in CFRs across vaccinated and unvaccinated populations. Such assessment is essential to inform and determine appropriate containment and mitigation interventions in Brazil and elsewhere.

The Foegen effect: A mechanism by which facemasks contribute to the COVID-19 case fatality rate.

Extensive evidence in the literature supports the mandatory use of facemasks to reduce the infection rate of severe acute respiratory syndrome coronavirus 2, which causes the coronavirus disease (COVID-19). However, the effect of mask use on the disease course remains controversial. This study aimed to determine whether mandatory mask use influenced the case fatality rate in Kansas, USA between August 1st and October 15th 2020.This study applied secondary data on case updates, mask mandates, and demographic status related to Kansas State, USA. A parallelization analysis based on county-level data was conducted on these data. Results were controlled by performing multiple sensitivity analyses and a negative control.A parallelization analysis based on county-level data showed that in Kansas, counties with mask mandate had significantly higher case fatality rates than counties without mask mandate, with a risk ratio of 1.85 (95% confidence interval [95% CI]: 1.51–2.10) for COVID-19-related deaths. Even after adjusting for the number of “protected persons,” that is, the number of persons who were not infected in the mask-mandated group compared to the no-mask group, the risk ratio remained significantly high at 1.52 (95% CI: 1.24–1.72). By analyzing the excess mortality in Kansas, this study determines that over 95% of this effect can solely be attributed to COVID-19.These findings suggest that mask use might pose a yet unknown threat to the user instead of protecting them, making mask mandates a debatable epidemiologic intervention.The cause of this trend is explained herein using the “Foegen effect” theory; that is, deep re-inhalation of hypercondensed droplets or pure virions caught in facemasks as droplets can worsen prognosis and might be linked to long-term effects of COVID-19 infection. While the “Foegen effect” is proven in vivo in an animal model, further research is needed to fully understand it.

Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis.

This study employs a spatial autoregressive probit-log linear (SAP-Log) hurdle model to investigate the influencing factors on the probability of death and case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) at the city level in China. The results demonstrate that the probability of death from COVID-19 and the CFR level are 2 different processes with different determinants. The number of confirmed cases and the number of doctors are closely associated with the death probability and CFR, and there exist differences in the CFR and its determinants between cities within Hubei Province and outside Hubei Province. The spatial probit model also presents positive spatial autocorrelation in death probabilities. It is worth noting that the medical resource sharing among cities and enjoyment of free medical treatment services of citizens makes China different from other countries. This study contributes to the growing literature on determinants of CFR with COVID-19 and has significant practical implications.

SARS-CoV-2 infection fatality rate after the first epidemic wave in Mexico.

BackgroundEstimates of SARS-CoV-2 infection fatality rates (IFRs) in developing countries remain poorly characterized. Mexico has one of the highest reported COVID-19 case-fatality rates worldwide, although available estimates do not consider serologic assessment of prior exposure nor all SARS-CoV-2-related deaths. We aimed to estimate sex- and age-specific IFRs for SARS-CoV-2 in Mexico.MethodsThe total number of people in Mexico with evidence of prior SARS-CoV-2 infection was derived from National Survey of Health and Nutrition-COVID-19 (ENSANUT 2020 Covid-19)—a nationally representative serosurvey conducted from August to November 2020. COVID-19 mortality data matched to ENSANUT’s dates were retrieved from the death-certificate registry, which captures the majority of COVID-19 deaths in Mexico, and from the national surveillance system, which covers the subset of COVID-19 deaths that were identified by the health system and were confirmed through a positive polymerase chain reaction test. We analysed differences in IFRs by urbanization and region.ResultsThe national SARS-CoV-2 IFR was 0.47% (95% CI 0.44, 0.50) using death certificates and 0.30% (95% CI 0.28, 0.33) using surveillance-based deaths. The IFR increased with age, being close to zero at age <30 years, but increasing to 1% at ages 50–59 years in men and 60–69 years in women, and being the highest at ≥80 years for men (5.88%) and women (6.23%). Across Mexico's nine regions, Mexico City (0.99%) had the highest and the Peninsula (0.26%) the lowest certificate-based IFRs. Metropolitan areas had higher certificate-based IFR (0.63%) than rural areas (0.17%).ConclusionAfter the first wave of the COVID-19 pandemic, the overall IFR in Mexico was comparable with those of European countries. The IFR in Mexico increased with age and was higher in men than in women. The variations in IFRs across regions and places of residence within the country suggest that structural factors related to population characteristics, pandemic containment and healthcare capabilities could have influenced lethality at the local level.

Determining the Case Fatality Rate of COVID-19 in Italy: Novel Epidemiological Study

BackgroundCOVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR).ObjectiveThe main objective of this study was to explore the value of the discharged CFR (DCFR) to make more accurate forecasts of epidemic trends of COVID-19 in Italy.MethodsWe retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of deaths to discharged cases（including deaths and recovered cases） to calculate the total DCFR (tDCFR), monthly DCFR (mDCFR), and stage DCFR (sDCFR). Furthermore, we analyzed the trend in the mDCFR between January and December 2020 using joinpoint regression analysis, used ArcGIS version 10.7 to visualize the spatial distribution of the epidemic CFR, and assigned different colors to each province based on the CFR or tDCFR.ResultsWe calculated the numbers and obtained the new indices of the tDCFR and mDCFR for calculating the fatality rate. The results showed that the tDCFR and mDCFR fluctuated greatly from January to May. They first showed a rapid increase followed by a rapid decline after reaching the peak. The map showed that the provinces with a high tDCFR were Emilia-Romagna, Puglia, and Lombardia. The change trend of the mDCFR over time was divided into the following 2 stages: the first stage (from January to May) and the second stage (from June to December). With regard to worldwide COVID-19 statistics, among 6 selected countries, the United States had the highest tDCFR (4.26%), while the tDCFR of the remaining countries was between 0.98% and 2.72%.ConclusionsWe provide a new perspective for assessing the fatality of COVID-19 in Italy, which can use ever-changing data to calculate a more accurate CFR and scientifically predict the development trend of the epidemic.

Vaccination Coverage as a Predictor of COVID-19 Case Fatality Rates in Malaysia

Vaccination has offered the hope of restoring life to pre-COVID-19 normalcy. However, new COVID-19 cases have continued to emerge and have increased in certain regions even with increasing vaccination coverage. This study aims to explore the correlations between daily COVID-19 case fatality rates, vaccination coverage as well as daily new cases and deaths of COVID-19 in Malaysia. It aims to compare the pre- and post-vaccination COVID-19 case fatality rates and examine if any difference between the rates is statistically significant. It also aims to identify the main predictors of COVID-19 case fatality rates. This study obtained the data of daily new cases and deaths of COVID-19, as well as the daily vaccination coverage in Malaysia from official platforms and government offices for non-parametric statistical analysis. It explored the Spearman’s correlations between daily COVID-19 case fatality rates, daily new COVID-19 cases and deaths as well as the vaccination coverage in Malaysia. It examined the difference in the pre- and post-vaccination daily COVID-19 case fatality rates with Mann-Whitney U Test. It constructed a multiple regression model to identify the significant predictors of the daily case fatality rates. This study shows that daily COVID-19 case fatality rates are positively correlated with daily new COVID-19 cases and deaths, and vaccination coverage. It reveals that post-vaccination case fatality rates are lower than pre-vaccination and the difference is statistically significant. People fully vaccinated per 100 population is a significant predictor of the decline of daily case fatality rates.

Geographical distribution of trace elements (selenium, zinc, iron, copper) and case fatality rate of COVID-19: a national analysis across conterminous USA.

Severe outcome particularly death is the largest burden of COVID-19. Clinical observations showed preliminary data that deficiency in certain trace elements, essential for the normal activity of immune system, may be associated with worse COVID-19 outcome. Relevant study of environmental epidemiology has yet to be explored. We investigated the geographical association between concentrations of Se, Zn, Fe and Cu in surface soils and case fatality rate of COVID-19 in USA. Two sets of database, including epidemiological data of COVID-19 (including case fatality rate, from the University of John Hopkinson) and geochemical concentration data of Se, Zn, Fe and Cu in surface soils (from the National Geochemical Survey), were mapped according to geographical location at the county level across conterminous USA. Characteristics of population, socio-demographics and residential environment by county were also collected. Seven cross-sectional sampling dates, with a 4-week interval between adjacent dates, constructed an observational investigation over 24 weeks from October 8, 2020, to March 25, 2021. Multivariable fractional (logit) outcome regression analyses were used to assess the association with adjustment for potential confounding factors. In USA counties with the lowest concentration of Zn, the case fatality rate of COVID-19 was the highest, after adjustment for other influencing factors. Associations of Se, Fe and Cu with case fatality rate of COVID-19 were either inconsistent over time or disappeared after adjustment for Zn. Our large study provides epidemiological evidence suggesting an association of Zn with COVID-19 severity, suggesting Zn deficiency should be avoided.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10653-022-01204-0.

Case fatality and mortality rates, socio-demographic profile, and clinical features of COVID-19 in the elderly population: A population-based registry study in Iran.

We aimed to investigate COVID‐19 case fatality rate (CFR), mortality, and screening in the older population of East Azerbaijan Province. We conducted a population‐based registry study from Death Registration System in the elderly population (N = 433 445) from the outbreak that emerged up to May 30, 2021 (before vaccination). We analyzed CFR and mortality rates due to COVID‐19 as well as the case findings and characteristics in the elderly population. Logistic regression analysis was carried out for the association between COVID‐19 mortality and effective factors. During the study, the province had 18 079 confirmed cases and 4390 deaths. The male to female CFR risk ratio was 3.2. The overall CFR and mortality rates were 24% and 1%, respectively. CFR and mortality ranged from 9.56% to 0.37% in the 60–64 age group to 70% and 2.6% in the age group ≥85 years, respectively. We found a significant trend in CFR and mortality of COVID‐19 with advanced age. Male sex, advanced age, marital status, and living alone were associated with an increased risk of COVID‐19 fatality. COVID‐19 mortality measures were higher in the older population of this province. Advanced treatment supports and interventions are needed to reduce mortality rates of COVID‐19 in the elderly population.

Predictors of COVID-19 Fatality: A Worldwide Analysis of the Pandemic over Time and in Latin America

SARS-CoV-2 has infected over one hundred million people worldwide and has affected Latin America particularly severely in terms of both cases and deaths. This study aims to determine the association between SARS-CoV-2 testing and COVID-19 fatality rate worldwide over 8 months and to examine how this relationship differs between Latin America and all other countries. This cross-sectional study used March 2021 data from 169 countries. Multivariate regressions predicted COVID-19 fatality (outcome) from the number of SARS-CoV-2 tests (exposure), while controlling for other predictors. Results for March 2021 were compared to results from June 2020. Additionally, results for Latin America were also compared to all other countries except Latin American for March 2021. SARS-CoV-2 testing was associated with a significant decrease in COVID-19 fatality rate in both June 2020 and March 2021 (RR = 0.92; 95% CI 0.87–0.96 and RR = 0.86; 95% CI 0.74–1.00, respectively). SARS-CoV-2 testing was associated with a significant decrease in COVID-19 fatality rate in Latin American countries but not in all other countries (RR = 0.45; 95% CI 0.23–0.89 and RR = 0.95; 95% CI 0.82–1.11, respectively). However, the difference between the risk ratios for June 2020 and March 2021 and between the risk ratios for Latin America and all other countries were not statistically significant. Increased SARS-CoV-2 testing may be a significant predictor of lower COVID-19 case fatality rate, specifically in Latin American countries, due to the existence of a strong association, which may have driven the worldwide results.

An Assessment of the Philippine Hospital Resources as Predictors of the Case Fatality Rate of COVID – 19

In the heightened effects of the pandemic, health resources have been in constant limbo as supplies and availability of hospital resources take a toll as COVID-19 cases surge, resulting in shortages. Thus, health systems are overwhelmed, resulting in a higher fatality rate since the capacity to provide medical attention is diminished. In this paper, hospital resources refer to mechanical ventilators, ICU, isolation, and ward beds which are the critical factors of the case fatality rate (CFR) of COVID-19 in the Philippines. Data were retrieved from the Department of Health (DOH) Case Bulletins from October 26, 2020, to June 30, 2021, with 248 total observations. This research used the Ordinary Least Squares (OLS) Multiple Regression to determine if hospital resources are the predictors of the case fatality rate of COVID-19. Furthermore, the results show a significant relationship between the hospital resources and the case fatality rate of COVID-19 in the Philippines. This study can become a framework for further research concerned about hospital resources as the predictors of case fatality rates of different diseases in a pandemic.