Predictors of COVID-19 testing rates: A cross-country comparison
Bibliographic record
Abstract
OBJECTIVES: Cross-country comparisons of coronavirus disease (COVID-19) have largely been applied to mortality analyses. The goal of this analysis is to explore predictors of COVID-19 testing through cross-country comparisons, to better inform international health policies. METHODS: Testing and case-based data were amassed from Our World in Data, and information regarding predictors was gathered from the World Bank. We investigate Human Development Index (HDI), health expenditure, universal health coverage (UHC), urban population, service industry workers (%), and air pollution as predictors. We explored testing data through July 31, 2020, or most recently available, using case-indexing methods, which involve synchronizing countries by date of first reported COVID-19 case as an index date and normalizing to the cumulative tests 25 days post-index date. Three multivariable linear regression models were built in a stepwise fashion to explore the association between the indexed number of COVID-19 tests and HDI scores. RESULTS: A total of 86 countries were included in the final analytical sample, excluding countries with missing data. HDI and urban population were found to be significantly associated with testing levels. CONCLUSIONS: Results suggest that social conditions and government capacity remain consistently salient in the consideration of testing rates. International efforts to assist low-HDI countries are needed to support the global COVID-19 response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".