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

Abstract

Testing and case identification are key strategies in controlling the COVID-19 pandemic. Contact tracing and isolation are only possible if cases have been identified. The effectiveness of testing should be assessed, but a single comprehensive metric is not available to assess testing effectiveness, and no timely estimates of case detection rate are available globally, making inter-country comparisons difficult. The purpose of this paper was to propose a single, comprehensive metric, called the COVID-19 Testing Index (CovTI) scaled from 0 to 100, derived from epidemiological indicators of testing, and to identify factors associated with this outcome. The index was based on case-fatality rate, test positivity rate, active cases, and an estimate of the detection rate. It used parsimonious modeling to estimate the true total number of COVID-19 cases based on deaths, testing, health system capacity, and government transparency. Publicly reported data from 165 countries and territories that had reported at least 100 confirmed cases by June 3, 2020 were included in the index. Estimates of detection rates aligned satisfactorily with previous estimates in literature (R2 = 0.44). As of June 3, 2020, the states with the highest CovTI included Hong Kong (93.7), Australia (93.5), Iceland (91.8), Cambodia (91.3), New Zealand (90.6), Vietnam (90.2), and Taiwan (89.9). Bivariate analyses showed the mean CovTI in countries with open public testing policies (66.9, 95% CI 61.0–72.8) was significantly higher than in countries with no testing policy (29.7, 95% CI 17.6–41.9) (p<0.0001). A multiple linear regression model assessed the association of independent grouping variables with CovTI. Open public testing and extensive contact tracing were shown to significantly increase CovTI, after adjusting for extrinsic factors, including geographic isolation and centralized forms of government. The correlation of testing and contact tracing policies with improved outcomes demonstrates the validity of this model to assess testing effectiveness and also suggests these policies were effective at improving health outcomes. This tool can be combined with other databases to identify other factors or may be useful as a standalone tool to help inform policymakers.

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

“…This result is in line with Gatto et al. ( 2020 ), and is more optimistic than the estimated 4% in Kuster and Overgaard ( 2021 ) while noticeably lower than the estimated 40% in Liu et al. ( 2021 ).…”

“… 2020 ); from epidemiological and population data using deterministic models (Krantz and Rao 2020 ); by post-stratification sampling techniques (Bassi et al. 2021 ) or by also using information on testing and countries characteristics (Kuster and Overgaard 2021 ). Depending on different approaches, countries and analysis period considered, the estimated fraction of underdetected infections can vary remarkably (Noh and Danuser 2021 ).…”

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

“…Such an assumption has been used in various studies, in order to compare the effectiveness of different countries' reporting systems and to correct case numbers [4,29]. However, by assuming that the IFR is identical everywhere at all times, valuable information is lost and conclusions may be misleading.…”

“…However, it is an oversimplification to assume that this is the only relevant factor that differs between countries, since we know that demographics and health systems (among other things) can also affect survival probability. Such an assumption has been used in various studies, in order to compare the effectiveness of different countries' reporting systems and to correct case numbers [ 4 , 29 ]. However, by assuming that the IFR is identical everywhere at all times, valuable information is lost and conclusions may be misleading.…”

The majority of the variation in COVID-19 rates between nations is explained by median age, obesity rate, and island status

“…Islands are physically isolated from the rest of the world. This may be part of why islands tend to have relatively high COVID-19 testing effectiveness; Kuster & Overgaard (2021) defined a COVID-19 Testing Index (a measure of a nation's approach to testing) derived from epidemiological indicators of testing and found that island nations generally had a high testing index, with islands accounting for more than half of the top ten countries in terms of testing effectiveness. Multiple studies have suggested that island nations have a geographical advantage protecting them from COVID-19 entering the country (Baum, et al, 2021, Mayer, & Lewis 2020, and machine learning approaches have previously identified island status as a likely predictor of COVID-19 rates (Li et al, 2021).…”