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Record W3033383111 · doi:10.1186/s40249-020-00675-6

Impact of a community-based intervention on Aedes aegypti and its spatial distribution in Ouagadougou, Burkina Faso

2020· article· en· W3033383111 on OpenAlex
Emmanuel Bonnet, Florence Fournet, Tarik Benmarhnia, Samiratou Ouédraogo, Roch K. Dabiré, Valéry Ridde

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInfectious Diseases of Poverty · 2020
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversité de MontréalInstitut National de Santé Publique du Québec
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionGeographyDengue feverSocioeconomicsEnvironmental healthPublic healthBaseline (sea)Distribution (mathematics)Veterinary medicineMedicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Several studies highlighted the impact of community-based interventions whose purpose was to reduce the vectors' breeding sites. These strategies are particularly interesting in low-and-middle-income countries which may find it difficult to sustainably assume the cost of insecticide-based interventions. In this case study we determine the spatial distribution of a community-based intervention for dengue vector control using different entomological indices. The objective was to evaluate locally where the intervention was most effective, using spatial analysis methods that are too often neglected in impact assessments. METHODS: Two neighbourhoods, Tampouy and Juvenat in Ouagadougou, Burkina Faso, were chosen among five after a survey was conducted, as part of an assessment related to the burden of dengue. As part of the community-based intervention conducted in Tampouy between August and early October 2016, an entomological survey was implemented in two phases. The first phase consisted of a baseline entomological characterization of potential breeding sites in the neighbourhood of Tampouy as well as in Juvenat, the control area. This phase was conducted in October 2015 at the end of the rainy season. The mosquito breeding sites were screened in randomly selected houses: 206 in Tampouy and 203 in Juvenat. A second phase took place after the intervention, in October 2016. The mosquito breeding sites were investigated in the same yards as during the baseline phase. We performed several entomological analyses to measure site productivity as well as before and after analysis using multilevel linear regression. We used Local Indicators of Spatial Association (LISAs) to analyse spatial concentrations of larvae. RESULTS: After the intervention, it is noted that LISAs at Tampouy reveal few aggregates of all types and the suppression of those existing before the intervention. The analysis therefore reveals that the intervention made it possible to reduce the number of concentration areas of high and low values of pupae. CONCLUSIONS: The contribution of spatial methods for assessing community-based intervention are relevant for monitoring at local levels as a complement to epidemiological analyses conducted within neighbourhoods. They are useful, therefore, not only for assessment but also for establishing interventions. This study shows that spatial analyses also have their place in population health intervention research.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.296
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it