Taking Costs and Diagnostic Test Accuracy into Account When Designing Prevalence Studies: An Application to Childhood Tuberculosis Prevalence
Bibliographic record
Abstract
BACKGROUND: When planning a study to estimate disease prevalence to a pre-specified precision, it is of interest to minimize total testing cost. This is particularly challenging in the absence of a perfect reference test for the disease because different combinations of imperfect tests need to be considered. We illustrate the problem and a solution by designing a study to estimate the prevalence of childhood tuberculosis in a hospital setting. METHODS: All possible combinations of 3 commonly used tuberculosis tests, including chest X-ray, tuberculin skin test, and a sputum-based test, either culture or Xpert, are considered. For each of the 11 possible test combinations, 3 Bayesian sample size criteria, including average coverage criterion, average length criterion and modified worst outcome criterion, are used to determine the required sample size and total testing cost, taking into consideration prior knowledge about the accuracy of the tests. RESULTS: In some cases, the required sample sizes and total testing costs were both reduced when more tests were used, whereas, in other examples, lower costs are achieved with fewer tests. CONCLUSION: Total testing cost should be formally considered when designing a prevalence study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.444 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".