Malaria burden through routing reporting: Relationship between incidence estimates
Bibliographic record
Abstract
ObjectiveTo evaluate the relationship between test positivity rate and test-confirmed malaria case rate both in time and space, to provide better understanding of the utility and representativeness of HMIS data for changing malaria burden in endemic settings.IntroductionRoutine surveillance is an important global strategy for malaria control. However, there have been few studies comparing routine indicators of burden, including test positivity rate (TPR) and test-confirmed malaria case rates (CMCR), over spatial and temporal scales.MethodsWe studied the relationship between these indicators in children under 11 years, presenting with suspected malaria at outpatient department clinics in three health facilities in locations of varying transmission across Uganda. We evaluated trends in each indicator over time (month) and space (by village) and explored the relationship between them using multivariable regression models.ResultsOverall, 65,710 participants visited the three clinics during the study period. Pairwise relationships between TPR and CMCR showed good agreement over time, particularly for TPR’s below 50% and during low transmission seasons, but the relationship was complex at the village level. Village mean annual TPR remained constant while CMCR was drastically reduced with increased distance from health facility, highlighting health care access’ importance. The forth quartile of distance from the health facility, relative to the first, was associated with reduction in CMCR with IRR of 0.40 (95% CI: 0.23-0.63; p=0.001), 0.55 (0.40-0.75; p<0.001), and 0.25 (0.12-0.51; p<0.001) for Nagongera, Kihihi, and Walukuba respectively. Regression analysis results emphasized a non-linear (cubic) relationship between TPR and CMCR, after accounting for month, village, season and demographic factors.ConclusionsAltogether, these results suggested a strong non-linear relationship between the two indicators regardless of transmission setting. However, the current malaria surveillance system in Uganda may under-represent burden from patients living furthest from sentinel health facilities.
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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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".