Evidence map›Paper›PMID 42907776›Full record

ArticleBMC geriatrics2026

Residential proximity to major roadways and prevalent diabetes in older adults: a cross-sectional study based on CLHLS.

Yong Li, Shu Feng, Huiying Liu, Xinzhu Shi, Nan Wang

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Article in BMC geriatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yong LiCentral Hospital of Dalian University of Technology (Dalian Municipal Central Hospital), Dalian, China.
Shu FengSchool of Public Health, Shenyang Medical College, Shenyang, China.
Huiying LiuSchool of Public Health, Shenyang Medical College, Shenyang, China.
Xinzhu ShiSchool of Public Health, Shenyang Medical College, Shenyang, China.
Nan WangSchool of Public Health, Shenyang Medical College, Shenyang, China. wangnan_symc@163.com.

Funding

Independent Scientific Research Project of Liaoning Provincial Key Specialty, Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital) 2025SZ017
6 · The paper itself

Abstract

backgroundDiabetes is a major global public health concern, and China bears a growing diabetes burden that calls for targeted prevention. High-income country studies link residential proximity to roadways with diabetes, yet evidence from developing countries is limited. This population-based analysis delivers robust evidence for environmental health interventions and age-friendly urban planning for Chinese older adults.

methodsWe used nationally representative data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). After excluding those with missing values for key variables, 4,702 adults aged ≥ 65 years entered the complete-case primary analysis. Roadway proximity and diabetes status were self-reported. Logistic regression models calculated ORs and 95% CIs with sequential adjustment for demographic, lifestyle and health covariates. Stratified analyses and interaction tests assessed effect modification.

resultsThe sample included 4,702 participants (mean age 78.7 years; 55.1% men) with 13.9% diabetes prevalence. Fully adjusted ORs were 1.36 (95% CI:1.11-1.67) for residents < 100 m from major roadways and 1.31 (95% CI:1.03-1.66) for those 100-300 m away (reference group: > 300 m). A statistically significant overall trend test was obtained, yet the exposure-response relationship across distance strata was non-monotonic (P for trend < 0.05). A borderline interaction existed between roadway proximity and smoking (P for interaction = 0.044). Stratification showed a positive association among non-smokers (OR = 1.29, 95% CI:1.06-1.56) but no significant link in smokers (OR = 0.82, 95% CI:0.48-1.42). Given the marginal P-value and small smoking subgroup sample (wide CIs), this interaction needs cautious interpretation and verification in larger cohorts.

conclusionThis cross-sectional study found a significant association between proximity to major roadways and prevalent diabetes in Chinese older adults. Roadway proximity acts as a composite surrogate marker of traffic hazards, neighborhood socioeconomic deprivation and built environments, rather than merely traffic pollution or noise. These findings guide future environmental and health equity research on seniors. Longitudinal studies with objective exposure measurements are required to confirm our results and inform age-friendly urban design and geriatric health policies.

Indexed as

Diabetes MellitusLongevityNeighborhood CharacteristicsResidence CharacteristicsAgedAged, 80 and overChinaCross-Sectional StudiesFemaleHumansLongitudinal StudiesMalePrevalenceCLHLSDiabetesMajor roadwaysOlder adultsResidential proximityTraffic-related environmental exposure

Identifiers

PMID42907776
PMCPMC13685194

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.