Association of neighborhood deprivation and hypertension: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hypertension impacts nearly one billion individuals and is a primary health challenge. While traditional perspectives have focused on individual behavior and genetics as principal risk factors, recent research underscores the profound influence of socioeconomic factors within neighborhoods on the risk of hypertension. This systematic review and meta-analysis is aimed to elucidate the association between neighborhood deprivation and the risk of hypertension. METHODS: A comprehensive literature search was conducted across PubMed, Embase, and Web of Science from inception until December 25, 2023. Observational studies defining neighborhood deprivation and reporting hypertension incidence were included. Nested Knowledge software was used for screening and data extraction, with study quality assessed using the Newcastle-Ottawa Scale. Statistical analysis was performed with R software (V 4.3), using a random-effects model to calculate the pooled relative risk (RR). RESULTS: Twenty-six studies were included in the qualitative analysis and 22 in the meta-analysis, covering over 62 million participants. The pooled RR was 1.139 (95% CI: 1.006 - 1.290), p=0.04, indicating a higher hypertension risk in deprived neighborhoods. Subgroup analyses showed variability by country and deprivation assessment methods. RR varied from 1.00 in Japan (95% CI: 0.93-1.08) to 1.60 (95% CI: 1.07-2.39) in France and 1.57 (95% CI: 0.67-3.70) in Germany, with significant heterogeneity observed in measures of neighborhood deprivation. CONCLUSION: Our analysis confirms a significant association between neighborhood deprivation and hypertension, underscoring the importance of socioeconomic factors in public health. It highlights the need for targeted local assessments and interventions. Future research should explore the causal mechanisms and effectiveness of interventions addressing neighborhood deprivation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".