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Record W4225879558 · doi:10.1080/09603123.2022.2053662

Residential area greenness and hypertension: a national assessment on urban adults in Bangladesh

2022· article· en· W4225879558 on OpenAlexaff
Jahidur Rahman Khan, Raaj Kishore Biswas, Md. Belal Hossain, Rownak Jahan Archie

Bibliographic record

VenueInternational Journal of Environmental Health Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBody mass indexMedicineMediationEnvironmental healthLogistic regressionBlood pressureOdds ratioDemographyOddsNon-communicable diseaseDiseaseInternal medicine

Abstract

fetched live from OpenAlex

This study investigated the associations between greenness and blood pressure (BP) metrics (systolic BP [SBP], diastolic BP [DBP], hypertension) among urban adults in Bangladesh and the potential mediation effects of body mass index (BMI) using 2011 Bangladesh Demographic and Health Survey data for 2360 urban adults (aged ≥35 years). The Enhanced Vegetation Index was used as a measure of residential area greenness. Associations between greenness and BP metrics were estimated using linear and binary logistic regression models. We also conducted mediation analyses. Greater area-level greenness was inversely associated with SBP (β -1.33, 95%CI: -2.46, -.20), DBP (β -.83, 95%CI: -1.64, -.02), and hypertension (adjusted odds ratio .87, 95%CI: .77, .98). BMI substantially mediated associations between greenness and BP metrics. Adopting urban greening policies could reduce the risk of hypertension, thus can contribute to reduction of non-communicable disease burden in Bangladesh. Longitudinal studies are required to further investigate the causal pathways.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.383
Teacher spread0.303 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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