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Record W2476182987 · doi:10.1186/s40249-016-0164-3

Why is malaria associated with poverty? Findings from a cohort study in rural Uganda

2016· article· en· W2476182987 on OpenAlexfundno aff
Lucy S. Tusting, John Rek, Emmanuel Arinaitwe, Sarah G. Staedke, Moses R. Kamya, Jorge Cano, Christian Bottomley, Deborah Johnston, Grant Dorsey, Steve W. Lindsay, Jo Lines

Bibliographic record

VenueInfectious Diseases of Poverty · 2016
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersDepartment of Medicine, University of California, San FranciscoFogarty International CenterScience and Technology DirectorateUniversity of California, San FranciscoDurham UniversityUniversity of OxfordLeverhulme TrustNational Institutes of HealthInternational Development Research CentreBill and Melinda Gates FoundationNational Institute of Allergy and Infectious DiseasesLondon School of Hygiene and Tropical MedicineMedical Research CouncilU.S. Department of Homeland Security
KeywordsMalariaMedicineOdds ratioSocioeconomic statusIncidence (geometry)DemographyEnvironmental healthRate ratioPovertyCohort studyPopulationImmunologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Malaria control and sustainable development are linked, but implementation of 'multisectoral' intervention is restricted by a limited understanding of the causal pathways between poverty and malaria. We investigated the relationships between socioeconomic position (SEP), potential determinants of SEP, and malaria in Nagongera, rural Uganda. METHODS: Socioeconomic information was collected for 318 children aged six months to 10 years living in 100 households, who were followed for up to 36 months. Mosquito density was recorded using monthly light trap collections. Parasite prevalence was measured routinely every three months and malaria incidence determined by passive case detection. First, we evaluated the association between success in smallholder agriculture (the primary livelihood source) and SEP. Second, we explored socioeconomic risk factors for human biting rate (HBR), parasite prevalence and incidence of clinical malaria, and spatial clustering of socioeconomic variables. Third, we investigated the role of selected factors in mediating the association between SEP and malaria. RESULTS: Relative agricultural success was associated with higher SEP. In turn, high SEP was associated with lower HBR (highest versus lowest wealth index tertile: Incidence Rate Ratio 0.71, 95 % confidence intervals (CI) 0.54-0.93, P = 0.01) and lower odds of malaria infection in children (highest versus lowest wealth index tertile: adjusted Odds Ratio 0.52, 95 % CI 0.35-0.78, P = 0.001), but SEP was not associated with clinical malaria incidence. Mediation analysis suggested that part of the total effect of SEP on malaria infection risk was explained by house type (24.9 %, 95 % CI 15.8-58.6 %) and food security (18.6 %, 95 % CI 11.6-48.3 %); however, the assumptions of the mediation analysis may not have been fully met. CONCLUSION: Housing improvements and agricultural development interventions to reduce poverty merit further investigation as multisectoral interventions against malaria. Further interdisplinary research is needed to understand fully the complex pathways between poverty and malaria and to develop strategies for sustainable malaria control.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.006
GPT teacher head0.246
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations101
Published2016
Admission routes1
Has abstractyes

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