ArticlePLoS neglected tropical diseases2020
Ruling out early trimester pregnancy when implementing community-based deworming programs.
Article in PLoS neglected tropical diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 10 citations in OpenAlex.
- Eliminating morbidity caused by neglected tropical diseases by 2030.Revista panamericana de salud publica = Pan American journal of public health · 2023Article
- Improving anthelmintic treatment for schistosomiasis and soil-transmitted helminthiases through sharing and reuse of individual participant data.Wellcome open research · 2022Article
- Achieving equity in UHC interventions: who is left behind by neglected tropical disease programmes in Cameroon?Global health action · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundLarge-scale deworming programs have, to date, mostly targeted preschool- and school-age children. As community-based deworming programs become more common, deworming will be offered to women of reproductive age. The World Health Organization recommends preventive chemotherapy be administered to pregnant women only after the first trimester. It is therefore important for deworming programs to be able to identify women in early pregnancy. Our objective was to validate a short questionnaire which could be used by deworming program managers to identify and screen out women in early pregnancy. METHODOLOGY/PRINCIPAL
findingsIn May and June 2018, interviewers administered a questionnaire, followed by a pregnancy test, to 1,203 adult women living in the Peruvian Amazon. Regression analyses were performed to identify questions with high predictive properties (using the pregnancy test as the gold standard). Test parameters were computed at different decision tree nodes (where nodes represented questions). With 106 women confirmed to be pregnant, the positive predictive value of asking the single question 'Are you pregnant?' was 100%, at a 'cost' of a false negative rate of 1.9% (i.e. 21 women were incorrectly identified as not pregnant when they were truly pregnant). Additional questions reduced the false negative rate, but increased the false positive rate. Rates were dependent on both the combination and the order of questions. CONCLUSIONS/SIGNIFICANCE: To identify women in early pregnancy when deworming programs are community-based, both the number and order of questions are important. The local context and cultural acceptability of different questions should inform this decision. When numbers are manageable and resources are available, pregnancy tests can be considered at different decision tree nodes to confirm pregnancy status. Trade-offs in terms of efficiency and misclassification rates will need to be considered to optimize deworming coverage in women of reproductive age.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.