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Record W4416177864 · doi:10.1080/09603123.2025.2589371

Environmental health factors influencing dengue: a systematic review with thematic categorization

2025· article· en· W4416177864 on OpenAlexaboutno aff
Jitjira Chaiyarit, Kanokwun Sriwongsuk, Sutasinee Putepapas, Prat Intarasaksit

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2025
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
Fundersnot available
KeywordsDengue feverUrbanizationPublic healthPsychological interventionPopulationWater supplyEnvironmental dataWork (physics)Categorization

Abstract

fetched live from OpenAlex

mosquito breeding. This systematic review aimed to identify and synthesize environmental health factors associated with dengue risk. A comprehensive search across multiple databases yielded 64 studies conducted in urban, peri-urban, and rural settings. Data were extracted and categorized thematically, and risk of bias was assessed using the JBI Checklists, Newcastle-Ottawa Scale, and ROBINS-I tool, depending on study design. Eight key environmental health factors were identified: water storage practices, solid waste disposal, housing characteristics, drainage and standing water, vegetation and shaded areas, urbanization and population density, climate and seasonal variation, and water supply reliability. Improper water storage and unmanaged waste were consistently linked to higher mosquito entomological indices. Poor housing conditions and densely populated urban areas also correlated with increased dengue risk. Seasonal rainfall and unreliable water infrastructure intensified vulnerability, particularly in resource-limited contexts. Findings emphasize that environmental health conditions are central to dengue prevention. Effective control requires multi-sectoral strategies that integrate infrastructure upgrades, environmental management, and behavioural change. Future research should prioritize evaluating environmental interventions and developing predictive models incorporating climate, infrastructure, and human behaviour to guide public health responses.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.039
GPT teacher head0.406
Teacher spread0.367 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations2
Published2025
Admission routes1
Has abstractyes

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