Spatial Heterogeneity of Enteric Fever in 2 Diverse Communities in Nepal
Bibliographic record
Abstract
BACKGROUND: Typhoid fever is endemic in the urban Kathmandu Valley of Nepal; however, there have been no population-based studies of typhoid outside of this community in the past 3 decades. Whether typhoid immunization should be prioritized in periurban and rural communities has been unclear. METHODS: We performed population-based surveillance for enteric fever in 1 urban catchment (Kathmandu) and 1 periurban and rural catchment (Kavrepalanchok) as part of the Surveillance for Enteric Fever in Asia Project (SEAP). We recruited individuals presenting to outpatient and emergency departments at 2 study hospitals with suspected enteric fever and performed blood cultures. Additionally, we conducted a household survey in each catchment area to characterize care seeking for febrile illness. We evaluated spatial heterogeneity in febrile illness, care seeking, and enteric fever incidence. RESULTS: Between September 2016 and September 2019, we enrolled 5736 participants with suspected enteric fever at 2 study hospitals. Among these, 304 (5.3%) were culture positive for Salmonella Typhi (249 [81.9%]) or Paratyphi A (55 [18.1%]). Adjusted typhoid incidence in Kathmandu was 484 per 100 000 person-years and in Kavrepalanchok was 615 per 100 000 person-years. While all geographic areas for which estimates could be made had incidence >200 per 100 000 person-years, we observed spatial heterogeneity with up to 10-fold variation in incidence between communities. CONCLUSIONS: In urban, periurban, and rural communities in and around Kathmandu, we measured a high but heterogenous incidence of typhoid. These findings provide some support for the introduction of conjugate vaccines in Nepal, including outside urban areas, alongside other measures to prevent enteric fever.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".