A novel comprehensive metric to assess effectiveness of COVID-19 testing: Inter-country comparison and association with geography, government, and policy response

Core health system measures response to COVID-19 among East Asian countries

Objective

The purpose of this study is to summarize the health system response to COVID-19 in four East Asian countries, analyze the effectiveness of their health system response, and provide lessons for other countries to control the epidemic and optimize their health system response.

Methods

This study investigated and summarized COVID-19 data and health system response in four East Asian countries, China, Japan, Mongolia, and South Korea from national governments and ministries of health, WHO country offices, and official websites of international organizations, to assess the effectiveness of health system measures.

Result

As of June 30, 2022, all four countries are in a declining portion of COVID-19. China has two waves, and new cases increased slowly, with the total cases per million remaining within 4, indicating a low level. Japan has experienced six waves, with case growth at an all-time high, total cases per million of 250.994. Mongolia started the epidemic later, but also experienced four waves, with total cases per million of 632.658, the highest of the four countries. South Korea has seen an increasing number of new cases per wave, with a total case per million of 473.759.

Conclusion

In containment strategies adopted by China and Mongolia, and mitigation strategies adopted by Japan and South Korea, health systems have played important roles in COVID-19 prevention and control. While promoting vaccination, countries should pay attention to non-pharmaceutical health system measures, as evidenced by: focusing on public information campaigns to lead public minds; strengthening detection capabilities for early detection and identification; using technical ways to participate in contact tracing, and promoting precise judging isolation.

Global COVID-19 Pandemic Outcomes: A Cross-Country Comparison Study of Policy Strategies

This paper explores global COVID-19 treatment and containment strategies in 108 countries worldwide, specifically the correlation between COVID-19 deaths and the countries' vaccination rates. Comparison of data across states, provinces, territories, and countries relied upon a common method to evaluate data regarding the impact of COVID-19 policies in the last three years. Data from nine different databases were analyzed to determine if there were correlations between the percentage of countrywide COVID-19 deaths/population and countries' percent vaccinated. Secondary outcome measures include the effect of other variables on COVID-19 death rates per country population, including health expenditures and annual income per capita, COVID-19 tests per 1000 people, stringency index (a measure of each country's containment strategies), hydroxychloroquine/ivermectin scores (measure country use), hypertension, obesity, diabetes, and geographic locations. COVID-19 vaccination rates ranged from 0-99% in 108 countries. Bivariate analysis demonstrates the following independent variables to correlate with COVID-19 deaths/population (Spearman correlation coefficient, p value): countrywide COVID-19 vaccination rates (moderate relationship, r=0.39, P < .001); healthcare expenditures per capita per annum (US dollars) (moderate relationship, r=0.46, P < .001), net annual income per capita (moderate relationship, r=0.50, P < .001), COVID-19 tests per 1000 country population (moderate relationship, r=0.36, P < .003); stringency index per country (moderate relationship, r=0.28, P < .003); hydroxychloroquine index (negative relationship, r= 0.15, P = .125); and ivermectin index (negative relationship, r=0.23 P = .018). The authors found that the higher the percentage of a country's vaccination rate, stringent containment strategies, mass testing, etc., moderately correlated with higher COVID-19 death rates/population. Future studies are required to explore the findings of this study fully.

Determinants of differences in RT-PCR testing rates among Southeast Asian countries during the first six months of the COVID-19 pandemic

A positive correlation has been demonstrated between gross domestic product (GDP) per capita and COVID-19 tests per 1000 people. Although frequently used as an indicator of economic performance, GDP per capita does not directly reflect income distribution inequalities and imposed health costs. In this longitudinal ecological study, we aimed to determine if, besides GDP per capita, indicators relating to governance, public health measures enforcement, and health and research investment explain differences in RT-PCR testing rates among countries in Southeast Asia (SEA) during the first six months of the COVID-19 pandemic. Using open-access COVID-19 panel data, we estimated the effect of various indicators (GDP per capita, health expenditure per capita, number of researchers per one million population, corruption perceptions index, stringency index, regional authority index) on daily COVID-19 testing by performing fixed-effects negative binomial regression. After accounting for all indicators, the number of daily confirmed COVID-19 cases, and population density, the model provided a 2019 GDP per capita coefficient of 0.0046330 (95% CI: 0.0040171, 0.0052488; p <0.001), indicating that a rise in 2019 GDP per capita by 100 international dollars is associated with a 46.33% increase in the number of daily tests performed. Additionally, all indicators were significantly associated with the daily number of RT-PCR testing on multivariable analysis. In conclusion, we identified different country-level indicators significantly associated with differences in COVID-19 testing rates among SEA countries. Due to the study's ecological design, we caution on applying our results to the individual level given potential for systematic differences between the included countries. Additional investigation is likewise needed to understand how government expenditure on healthcare may have impacted COVID-19 testing capacity during the initial stages of the pandemic.

Characteristics and outcomes of patients with severe COVID-19 in Indonesia: Lessons from the first wave

BACKGROUND

Indonesia's national response to COVID-19 evolved rapidly throughout 2020. Understanding pandemic response and outcomes is crucial for better mitigation strategies ahead. This study describes the characteristics and outcomes of patients admitted to ICU during the early stages of the pandemic.

METHODS

This is a multi-centre prospective observational study including patients from twelve collaborating hospitals in Indonesia. All patients were clinically suspected or laboratory-confirmed COVID-19 cases admitted to ICU between January 2020 and March 2021. The primary outcome was monthly ICU mortality. Descriptive statistics of patient characteristics and treatment were generated as secondary outcomes.

RESULTS

From 559 subjects, the overall mortality was 68% and decreased over the study period, while the mortality of patients that received mechanical ventilation was 92%, consistently high over the study period. Fatal cases showed 2- and 4-day delays from symptoms onset to hospital admissions and ICU admissions, respectively. Evidence-backed approaches which could influence patient outcome, such as extracorporeal membrane oxygenation, prone positioning, renal replacement therapy, and neuromuscular blockade were scarcely administered.

CONCLUSIONS

The mortality rate of COVID-19 patients in Indonesia was extremely high during the first major outbreak of disease, particularly in those mechanically ventilated. Delayed admission and unavailability of evidence-based approaches due to high burden on health facility during COVID-19 crisis could be addressed by efficient public health measures and enhancing health infrastructure to improve the future pandemic response.

The notable global heterogeneity in the distribution of COVID-19 cases and the association with pre-existing parasitic diseases

BACKGROUND

The coronavirus Disease 2019 (COVID-19) is a respiratory disease that has caused extensive ravages worldwide since being declared a pandemic by the World Health Organization (WHO). Unlike initially predicted by WHO, the incidence and severity of COVID-19 appeared milder in many Low-to-Middle-Income Countries (LMIC). To explain this noticeable disparity between countries, many hypotheses, including socio-demographic and geographic factors, have been put forward. This study aimed to estimate the possible association of parasitic diseases with COVID-19 as either protective agents or potential risk factors.

METHODS/PRINCIPAL FINDINGS

A country-level ecological study using publicly available data of countries was conducted. We conceptualized the true number of COVID-19 infections based on a function of test positivity rate (TPR) and employed linear regression analysis to assess the association between the outcome and parasitic diseases. We considered demographic, socioeconomic, and geographic confounders previously suggested. A notable heterogeneity was observed across WHO regions. The countries in Africa (AFRO) showed the lowest rates of COVID-19 incidence, and the countries in the Americas (AMRO) presented the highest. The multivariable model results were computed using 165 countries, excluding missing values. In the models analyzed, lower COVID-19 incidence rates were consistently observed in malaria-endemic countries, even accounting for potential confounding variables, Gross Domestic Product (GDP) per capita, the population aged 65 and above, and differences in the duration of COVID-19. However, the other parasitic diseases were not significantly associated with the spread of the pandemic.

CONCLUSIONS/SIGNIFICANCE

This study suggests that malaria prevalence is an essential factor that explains variability in the observed incidence of COVID-19 cases at the national level. Potential associations of COVID-19 with schistosomiasis and soil-transmitted helminthiases (STHs) are worthy of further investigation but appeared unlikely, based on this analysis, to be critical factors of the variability in COVID-19 epidemic trends. The quality of publicly accessible data and its ecological design constrained our research, with fundamental disparities in monitoring and testing capabilities between countries. Research at the subnational or individual level should be conducted to explore hypotheses further.

Biosensors for the detection of disease outbreaks through wastewater-based epidemiology

Wastewater-Based Epidemiology (WBE) is a novel community-wide monitoring tool that provides comprehensive real-time data of the public and environmental health status and can contribute to public health interventions, including those related to infectious disease outbreaks (e.g., the ongoing COVID-19 pandemic). Nonetheless, municipalities without centralized laboratories are likely still not able to process WBE samples. Biosensors are a potentially cost-effective solution to monitor the development of diseases through WBE to prevent local outbreaks. This review discusses the economic and technical feasibility of eighteen recently developed biosensors for the detection and monitoring of infectious disease agents in wastewater, prospecting the prevention of future pandemics.

Public Health Management of the COVID-19 Pandemic in Australia: The Role of the Morrison Government

The Australian Commonwealth government has four health-related responsibilities during the SARS-CoV-2 pandemic: to provide national leadership; to manage external borders; to protect residents of residential aged care facilities; and to approve, procure and roll-out tests and vaccines. State governments are responsible for determining what public health measures are appropriate and implementing them-including managing the border quarantine arrangements and the testing, tracing, and isolation regime-and managing the hospital response. This paper analyses the national government's response to the pandemic and discusses why it has attracted a thesaurus of negative adjectives.

Challenges in Engendering Herd Immunity to SARS-CoV-2 infection: Possibly Impossible but Plausibility with Hope

Herd immunity scaled to the population level is what gives rise to herd immunity. When a sufficiently significant fraction of immune individuals exist in a group, it confers indirect protection from infection to vulnerable individuals. This population-level effect is frequently considered in the context of vaccination programs, which attempt to build herd immunity so that people who cannot be vaccinated, such as the very young or those with impaired immune systems, are nonetheless protected from disease. Clinical signs are a poor predictor of transmissibility for some infections, such as COVID-19, because asymptomatic hosts can be extremely infectious and contribute to the spread of the virus. COVID-19 is a quickly evolving issue that has been widely circulated throughout the world. The concept of herd immunity is frequently stated during this time, although it is readily misconstrued. The concept of herd immunity is frequently stated during this time, although it is readily misconstrued. This article elaborates on the idea and goal of herd immunity, the necessary conditions for realizing herd immunity, the restrictive requirements for applying herd immunity, and the obstacles experienced in achieving herd immunity in the context of COVID-19. This mini article explains the concept and purpose of herd immunization in the context of COVID-19.

Measuring misclassification of Covid-19 as garbage codes: Results of investigating 1,365 deaths and implications for vital statistics in Brazil

The purpose of this article is to quantify the amount of misclassification of the Coronavirus Disease-2019 (COVID-19) mortality occurring in hospitals and other health facilities in selected cities in Brazil, discuss potential factors contributing to this misclassification, and consider the implications for vital statistics. Hospital deaths assigned to causes classified as garbage code (GC) COVID-related cases (severe acute respiratory syndrome, pneumonia unspecified, sepsis, respiratory failure and ill-defined causes) were selected in three Brazilian state capitals. Data from medical charts and forensic reports were extracted from standard forms and analyzed by study physicians who re-assigned the underlying cause based on standardized criteria. Descriptive statistical analysis was performed and the potential impact in vital statistics in the country was also evaluated. Among 1,365 investigated deaths due to GC-COVID-related causes, COVID-19 was detected in 17.3% in the age group 0-59 years and 25.5% deaths in 60 years and over. These GCs rose substantially in 2020 in the country and were responsible for 211,611 registered deaths. Applying observed proportions by age, location and specific GC-COVID-related cause to national data, there would be an increase of 37,163 cases in the total of COVID-19 deaths, higher in the elderly. In conclusion, important undercount of deaths from COVID-19 among GC-COVID-related causes was detected in three selected capitals of Brazil. After extrapolating the study results for national GC-COVID-related deaths we infer that the burden of COVID-19 disease in Brazil in official vital statistics was probably under estimated by at least 18% in the country in 2020.

Underdetection in a stochastic SIR model for the analysis of the COVID-19 Italian epidemic

We propose a way to model the underdetection of infected and removed individuals in a compartmental model for estimating the COVID-19 epidemic. The proposed approach is demonstrated on a stochastic SIR model, specified as a system of stochastic differential equations, to analyse data from the Italian COVID-19 epidemic. We find that a correct assessment of the amount of underdetection is important to obtain reliable estimates of the critical model parameters. The adaptation of the model in each time interval between relevant government decrees implementing contagion mitigation measures provides short-term predictions and a continuously updated assessment of the basic reproduction number.

Estimating the case fatality ratio for COVID-19 using a time-shifted distribution analysis

Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the CFR using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the CFR. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID-19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2% to 20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.