Soil-transmitted helminthiasis in four districts in Bangladesh: household cluster surveys of prevalence and intervention status
Bibliographic record
Abstract
BACKGROUND: In 2016, after 8 years of twice-annual nationwide preventive chemotherapy (PC) administration to school-age children (SAC), the Bangladesh Ministry of Health & Family Welfare (MOHFW) sought improved impact and intervention monitoring data to assess progress toward the newly adopted goal of eliminating soil-transmitted helminthiasis (STH) as a public health problem. METHODS: We surveyed four Bangladeshi districts between August and October 2017. We conducted a multi-stage, cluster-sample, household survey which produced equal-probability samples for preschool-age children (PSAC; 1-4 years), SAC (5-14 years), and adults (≥ 15 years). Standardized questionnaires were administered, using Android-based smart phones running an Open Data Kit application. Stool samples were collected and testing for STH prevalence and infection intensity used the Kato-Katz technique. RESULTS: In all, 4318 stool samples were collected from 7164 participants. Estimates of STH prevalence by risk group in three of the four surveyed districts ranged from 3.4 to 5.0%, all with upper, 1-sided 95% confidence limits < 10%. However, STH prevalence estimates in Sirajganj District ranged from 23.4 to 29.1%. Infections in that district were spatially focal; four of the 30 survey clusters had > 50% prevalence in at least one risk group. Among all tested specimens, Ascaris lumbricoides was the most common STH parasite [8.2% (n = 352)], followed by Trichuris trichiura [0.9% (n = 37)], and hookworm [0.6% (n = 27)]. In each district, PC coverage among SAC was above the 75% program target but did not exceed 45% among PSAC in any district. Improved sanitation at home, school, or work was over 90% in all districts. CONCLUSIONS: In the three low-prevalence districts, the MOHFW is considering decreasing the frequency of mass drug administration, per World Health Organization (WHO) guidelines. Also, the MOHFW will focus programmatic resources and supervisory efforts on Sirajganj District. Despite considering WHO guidance, the MOHFW will not expand PC administration to women of reproductive age partly due to the low prevalence of hookworm and T. trichiura, the STH parasites that contribute most to morbidity in that risk group. Data collected from surveys such as ours would help effectively guide future STH control efforts in Bangladesh and elsewhere.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".