Back

Environmental, Social, and Health Burdens in relation to Sleep-Disordered Breathing among Patients of Community-Based Health Centers in the United States

Zhou, W.; Gaston, S.; Singh, R.; Konrad, K.; Buller, I.; Neo, D. T.; Jackson, W. B.; Voss, R.; Steeves-Reece, A.; Juarez, P.; Jackson, C. L.

2025-10-02 epidemiology
10.1101/2025.10.01.25337104 medRxiv
Show abstract

ImportanceSleep-disordered breathing (SDB) is preventable but underdiagnosed, with disparities among sociodemographic groups with limited material and social resources, partly driven by community-level environmental and social conditions along with healthcare-related factors. ObjectivesWe sought to investigate associations between multifactorial community-level environmental, social, and health burdens and SDB prevalence. We also determined effect modification by age, sex, race, and ethnicity. Design, Setting, and ParticipantsCross-sectional analysis of electronic health records (EHR) data collected in 2022 from 1,957,775 adult patients in the OCHIN network (>2,000 community health centers across 40 U.S. states). ExposuresPatients 2022 residential addresses were linked to a census tract-level environmental, social, and health burden (ESHB) index created by the Centers for Disease Control and Prevention and the Agency for Toxic Substances Disease Registry. The ESHB comprises 36 indicators that capture and rank community-level social (e.g., socioeconomic status), environmental (e.g., air pollution), and health (e.g., hypertension) vulnerability. Higher percentile ranks of ESHB (range: 0-1) indicated higher vulnerability. Main Outcomes and MeasuresSDB was identified via diagnostic and procedural codes, and subtypes were categorized as obstructive (OSA), central (CSA), other/unspecified (OUSA), multiple apneas, and procedure-based cases. Log-binomial regression estimated prevalence ratios (PRs) and 95% confidence intervals (CIs), adjusting for age, sex, race, and ethnicity. We assessed effect modification by testing cross-product terms. ResultsAmong 1,957,775 patients (median age was 43.0 years [IQR: 30.0-58.0]; 40.6% men), SDB prevalence was 5.5%, with CSA at 0.03%, OSA at 3.8%, OUSA at 1.4%, multiple sleep apneas at 0.03%, and procedure-based cases at 0.21%. Each 0.1-unit increase in ESHB percentile rank was associated with higher prevalence of SDB (PR=1.01 [1.01-1.01]), OUSA (PR=1.01 [1.01-1.02]), and procedure-based cases (PR=1.05 [1.03-1.06]). The ESHB-SDB association was elevated among adults aged 18-49 years, women, American Indian/Alaska Native and White, and non-Hispanic. ESHB was not associated with CSA. Conclusions and RelevanceHigher community-level environmental, social, and health vulnerabilities were associated with higher SDB prevalence (although future prospective studies are warranted). Our findings underscore the need to address community-level factors with potential tailoring of interventions across sociodemographic groups. KEY POINTSO_ST_ABSQuestionC_ST_ABSAre community-level environmental, social, and health burdens associated with the prevalence of sleep-disordered breathing (SDB) among medically underserved patients in United States (U.S.) community health centers? If so, to what extent? FindingsThis cross-sectional study included electronic health records data from 1,957,775 patients of 2,000 community-based health centers in 40 states linked to the 2022 census tract-level environmental, social, and health burden (ESHB) index. Higher ESHB was associated with a higher prevalence of SDB and its subtypes (e.g., obstructive sleep apnea [OSA] and central sleep apnea [CSA]). Elevated associations were observed among adults aged 18-49 years, women, American Indian/Alaska Native and White, and non-Hispanic. MeaningMultifactorial community adversity was associated with SDB prevalence. Our findings underscore the need for future intervention studies to address environmental, social, and health burdens at the community level to reduce SDB prevalence in medically underserved populations. Prospective studies are needed.

Matching journals

The top 14 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.