Neighborhood Disadvantage Association with Sleep Apnea and Longitudinal Cardiovascular Events in a Large Clinical Cohort

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65 The association between neighborhood socioeconomic disadvantage and poor cardiovascular 66 outcomes is well established; however, less is known about its interplay with obstructive sleep apnea. 67

68 Adult cardiovascular disease-naïve patients who underwent sleep testing at Cleveland Clinic in Ohio 69 from August of 1998 to August of 2021 were included in this cohort. The primary exposure was Area 70 Deprivation Index (ADI) calculated by national rank, i.e. 25th, 50th, and 75th percentiles; higher 71 quartiles reflecting greater deprivation (ADI-Q1-4) with Q1 as reference. Cox proportional hazard 72 models were used to determine the hazard of composite outcome of major adverse cardiovascular 73 events (MACE), i.e. including heart failure, stroke, atrial fibrillation and coronary artery disease or 74 death, adjusted for demographics, comorbidities, cardiac medications and objective OSA-related 75 measures including of Apnea Hypopnea Index (AHI) and sleep-related hypoxia (percentage of sleep 76 time spent<90%SaO2,T90). Linear models were used to examine the relationship between ADI and 77 OSA-related measures. Interaction terms were tested between ADI and OSA-related measures. 78

Of 72,443 adults age was 50.4±14.2 years, 50.5% were men, and 18.4% Black individuals. 79 The median AHI was 14.3[5.8, 33.3] with a median follow-up of 4.39 [IQR,1.76-7.92] years. The 80 relative incidence of initial MACE in the presence of competing risk of death was 17% higher 81 (HR,1.17[95%CI 1.09-1.27],p<.001) for those living in ADI-Q4. Greater levels of area deprivation were 82 associated with sleep-related hypoxia measures including higher degree of T90(p<.001); lower mean 83 SaO2(p<.001), and lower minimum SaO2(p<.001). Significant interactions between T90 and ADI 84 were observed with the risk of MACE(p=0.002) or death(p=0.005). T90 conferred a 37% increased 85 risk of MACE(HR, 1.37[95%CI:1.23-1.53]) for those living in ADI-Q1; and a 26% increased risk(HR, 86 1.26[95%CI:1.14-1.38%]) among patients living in ADI-Q4. For individuals living in ADI-Q2 and Q3, 87 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 4 T90 conferred a respective 56% and 51% increased risk of death (HR,1.56[95%CI:1.23 - 1.96]; HR, 88 1.51[95%CI:1.21-1.88]), respectively. 89

90 Neighborhood disadvantage was associated with an increased risk for MACE or death in this clinical 91 cohort and this association was modified by sleep-related hypoxia. Further research is needed to 92 identify neighborhood-specific social determinants contributing to sleep-cardiovascular health 93 disparities to develop neighborhood-specific interventions. 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 5 Clinical Perspective 114 What is new? 115  This is the largest-to-date longitudinal study using a large clinically phenotyped sample that 116 uncovers the association of neighborhood socioeconomic position and sleep apnea with major 117 cardiovascular events and mortality. 118  In this cohort, patients with increased sleep-related hypoxia living in both extremes of area 119 deprivation had an increased risk for major adverse cardiovascular events, whereas individuals 120 with increased sleep-related hypoxia living in moderate areas of deprivation had an increased 121 risk for death. 122 What Are the Clinical Implications? 123  Addressing disparities in sleep-related hypoxia may be a modifiable and targetable intervention 124 to decreased overall health disparities cardiovascular health. 125  There is a call to action to develop future studies examining neighborhood-level social 126 determinants of health that influence increased sleep-related hypoxia in patients with sleep 127 apnea to improve cardiovascular outcomes across all populations at risk for health inequities. 128 129 130 131 132 133 134 135 136 137 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 6 Non-standard Abbreviations and Acronyms: 138 ADI: Area Deprivation Index 139 AHI: Apnea Hypopnea Index 140 CPAP: Continuous Positive Pressure 141 CV: Cardiovascular 142 OSA: Obstructive Sleep Apnea 143 PSG: Polysomnogram 144 SEP: Socioeconomic Position 145 STARTLIT: Sleep Signals, Testing, and Reports Linked to Patients Traits 146 SaO2: Oxygen Saturation 147 mSaO2: Mean Oxygen Saturation 148 T90: Percentage of sleep time spent<90% of oxygen saturation 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 7

164 Despite substantial progress in reducing cardiovascular disease (CVD)-related morbidity and 165 mortality, CVD remains the leading cause of death in the United States.1,2 Over the past decade, 166 CVD prevention and treatment have markedly advanced, however, disparities in CVD prevalence, 167 risk factors, and health outcomes across different racial and ethnic and socioeconomic position (SEP) 168 groups persist.3–6 Evidence has consistently shown that socioeconomic deprivation is an important 169 and underrecognized determinant of cardiovascular health accounting for 44% of geographic 170 disparities in cardiovascular mortality.7 For instance, in ischemic heart disease, low SEP has been 171 linked with increased disease burden, less access to care, and higher mortality rates.8,9 Furthermore, 172 residence in socioeconomically disadvantaged communities has been associated with increased risk 173 of rehospitalization and death in patients with heart failure and myocardial ischemia after accounting 174 for an individual’s SEP factors 10 Therefore, understanding factors that contribute to SEP-related 175 disparities in CVD is needed to inform equitable approaches to improve cardiovascular health. 176 177 Obstructive Sleep Apnea (OSA) is a highly prevalent condition that affects 40-80% of individuals with 178 CVD and is associated with an increased prevalence of cardiovascular (CV) risk factors and CVD-179 related morbidity and mortality. As such, sleep disturbances have become recognized as a target to 180 improve CV health and a component of the American Heart Association Life’s Essential 8.11,12 181 Emerging data identify racial and ethnic disparities in OSA severity, diagnosis, and treatment.13 14 182 Moreover a few studies in the pediatric population have identified SEP to be associated with 183 increased OSA severity. 15–18 However, the influence of OSA as a potential contributor to 184 socioeconomic disparities in CV health outcomes has received minimal attention. 185 186 The area deprivation index (ADI) is a validated neighborhood marker of socioeconomic disadvantage 187 consisting of a range of socioeconomic indicators including poverty, education, housing, and 188 employment.19 Several studies have shown that living in areas with high ADI is associated with an 189 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 8 increased incidence of CVD and worse cardiovascular outcomes.7,20–22 Although the association 190 between ADI and increased CV outcomes is well-established, a critical existing knowledge gap is lack 191 of understanding of the association of area deprivation and CV outcomes in OSA and how sleep 192 disturbances may influence socioeconomic disadvantage in relation to CV outcomes. We 193 hypothesize that living in greater areas of disadvantage is associated with an increased incidence of 194 major adverse cardiovascular events (MACE) and mortality in a clinical sleep referral cohort and that 195 these associations will be modified by the degree of OSA severity. 196 197

198 A retrospective cohort study was conducted using the STARLIT Registry (Sleep Signals, Testing, and 199 Reports Linked to Patient Traits) at the Cleveland Clinic. First, we investigated the association 200 between ADI and a composite incident MACE outcome and all-cause mortality in a large cohort of 201 patients evaluated for sleep disorders. Second, we examined the association of objective measures 202 of ADI and OSA. Finally, effect modification of OSA on the association of area deprivation and CV 203 outcomes was assessed. The study was approved by the Cleveland Clinic IRB as a minimal-risk 204 research study for which informed consent was waived. This study followed the Strengthening the 205 Reporting of Observational Studies in Epidemiology (STROBE)23 reporting guideline for reporting of 206 cohort studies. 207 Study Populations and Data Source 208 We identified patients from the STARLIT Registry (see Supplemental Methods for registry details) 209 without established CVD (any history of atrial fibrillation, heart failure, cerebrovascular events, and 210 coronary artery disease) at the time the sleep study was performed and who subsequently underwent 211 follow-up. Patients were included if they were > 18 years old, had a diagnostic sleep study 212 [polysomnogram (PSG)], split PSG, or type III sleep study with a minimum of >3 h diagnostic time 213 available, naïve to OSA treatment, a resident of Ohio at or before the sleep study and if they lived in 214 an identifiable census tract. Demographics, primary payor, body mass index kg/m2 (BMI), 215 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 9 comorbidities, cardiovascular medications, and objective OSA-related measures were extracted from 216 the sleep study registry and medical record (see Table S1 for cardiovascular medications and 217 comorbidities details). The latest version of the Elixhauser Comorbidity Index (V2021.1)24 was used 218 to evaluate for medical complexity and utilized due to the optimal performance in cardiac conditions.25 219 All sleep studies were conducted, and respiratory events were scored in accordance with the 220 American Academy of Sleep Medicine guidelines.26 Objective OSA-related measures of interest 221 included frequency of apneas and hypopneas (apnea hypopnea index, AHI) and sleep-related 222 hypoxemia (percentage of sleep time spent ≤90%SaO2 [T90]). The latter was investigated given the 223 association of T90 with poor CV outcomes in prior studies.27 T90 was assessed by median due to its 224 skewed nature and for interpretability; an approach used in prior studies. 28,29 Other hypoxia 225 measures were investigated including the mean oxygen saturation (mSaO2) and SaO2 nadir. Natural 226 language processing30 was used to obtain documentation of continuous positive airway pressure 227 (CPAP) prescription at the time of the first sleep study. 228 Neighborhood Socioeconomic Position 229 ADI is a census block composite measure of neighborhood disadvantage that uses 17 poverty, 230 education, housing, and employment indicators. It stratifies geographic areas based on 231 socioeconomic disadvantage and is calculated by national rank ranging from 1 to 100 and by state 232 rank from 1 to 10. Higher percentiles, reflect greater neighborhood deprivation and quartiles were 233 constructed from the 25th, 50th and 75th percentiles, ADI –Q1 through Q4.19 We identified patients’ 234 addresses within the medical record on the date of the sleep study and addresses were geocoded to 235 the census block group and linked to the University of Wisconsin Neighborhood Atlas.19,31 236 Outcomes 237 The primary outcome of interest was the incidence of MACE, a composite of cardiovascular 238 outcomes, defined as the first occurrence of cerebrovascular events, heart failure, atrial fibrillation, 239 coronary artery disease, or death. Outcomes were identified by the International Classification of 240 Diseases, Ninth Revision, and International Classification of Diseases, Tenth Revision (ICD 9 and 241 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 10 ICD 10) codes in clinical and procedural encounters (codes reported in eTable1 in Supplement). 242 Death information was extracted from Cleveland Clinic electronic health records, the Ohio Vitals Data 243 (up to 2019), and the National Death Index. We also separately analyzed individual components of 244 MACE, i.e. incident cerebrovascular events, heart failure, atrial fibrillation, coronary artery disease, 245 and death. Identification of the first event of occurrence was after the index date (sleep study date) 246 with a censoring date of the last clinical encounter up to October 18, 2021. The censoring date was 247 chosen based on the most recent upgrade and revision of the sleep study registry. 248 Statistical Analysis 249 Data are presented as mean ± standard deviation (SD) or median [25th, 75th percentiles] for 250 continuous variables and counts (percentages) for categorical variables. Comparisons of variables 251 across ADI quartiles were made using ANOVA with pairwise testing, adjusted for multiple 252 comparisons. To evaluate the association between ADI and the composite outcome of MACE and all-253 cause mortality, unadjusted and adjusted Cox proportional hazards models were used to estimate 254 hazard ratios (HR) and 95% confidence intervals (CI) for each quartile of ADI compared to the 255

lowest quartile (ADI-Q1). These models were used to determine the hazard risk of the 256 composite outcome of either an initial MACE event or death, right censored to a common date, and 257 adjusted for covariates. Covariates included age, sex, BMI (kg/m2), race, cardiovascular medications, 258 smoking status (current or former vs never), individual comorbidities comprising the Elixhauser score, 259 the total Elixhauser score, and OSA-related measures including AHI ≥30 events/hour and T90 260 dichotomized at the median. AHI cut-off of ≥30 events/hour was chosen given the strong and 261 consistent association of severe OSA with CVD incidence and morbidity.32–34 The proportional odds 262 assumption was checked for all models. In addition, the statistical interactions of ADI and OSA-263 related measures were analyzed. We used Cox proportional hazard models to determine the hazard 264 of the combined outcome (initial MACE or death) for each OSA-related measure, and the proportional 265 hazard method of Fine and Gray35 (sub-distribution method) to determine the hazard for MACE (or 266 death) in the presence of death (or MACE) as a competing risk. To evaluate the linear relationship 267 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 11 between objective OSA-related measures and ADI, we used general linear models adjusted for age, 268 BMI, gender, payor, and race. Outcomes were transformed (e.g. log, natural log, square root function, 269 etc.) as needed to improve model fit. Results are presented from adjusted models. 270 Secondary analyses included sensitivity analysis excluding patients on prescribed positive airway 271 pressure (PAP) after the sleep study. Given that hypopnea scoring (3% vs 4% oxygen desaturation) 272 may differentially classify OSA severity,36,37 a stratified analysis of hypopnea definition, i.e. 3% 273 desaturation or EEG microarousal compared with 4% desaturation was conducted. Lastly analysis of 274 incidence of individual components of MACE by ADI was performed. All statistical analyses were 275 performed based on an overall significance level of 0.05, using SAS software (version 9.4, Cary, NC). 276 277

278 Our final analytic sample was comprised of 72,443 adults (Figure S1). Areas of least and greatest 279 deprivation were seen in both urban and rural areas of north east Ohio counties (Figure 1) The mean 280 age was 50.4 ± 14.2 years and included 35,875 (49.5%) women and 36,559 (50.5%) men of whom 281 52,868 (73.0%) were non-Hispanic White, 13,326 (18.4%) non-Hispanic Black and 2,443 (3.4%) were 282 Hispanic or Latino. Demographics and clinical characteristics are summarized in Table 1. Compared 283 to residents living in areas of least deprivation (ADI-Q1), those in areas of greatest deprivation (ADI-284 Q4) were more likely to be women: 7,393 (40.1%) vs 10,432 (59.9%), p<.001, younger: 52.3 ± 14.0 285 vs 48.9 ± 13.9, p<.001, of black race: 898 (4.9%) vs 8,529 (49.0%), p<.001 with a higher BMI: 31.9 ± 286 7.3 vs 37.3 ± 9.2, p<.001) respectively, and to have more comorbidities (increased Elixhauser 287 Comorbidity Index Score, 1.1 ± 1.2 vs 1.5 ± 1.4, p<.001). 288 289 Association of Neighborhood Disadvantage with MACE and Death 290 The median follow-up was 4.39 [IQR,1.76-7.92] years. At the end of the study period, 8,547 (11.8%) 291 patients experienced a primary composite endpoint (MACE or death) with a higher relative incidence 292 observed among patients living in areas of greatest deprivation (ADI-Q4) compared to patients living 293 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 12 in any other area (ADI-Q1-Q3) (p<.001) (Figure 2). After adjustment for potential confounders, the 294 primary MACE or death endpoint was 28% higher for individuals living in areas with greatest 295 deprivation (ADI-Q4) compared to areas of lowest deprivation (ADI-Q1) (HR, 1.28[95%CI, 1.20,-1.38]; 296 p<.001) (Table 2.). For individuals living in areas of highest deprivation, the risk of death (in 2.3%) 297 with a competing risk of a MACE event was 79% greater than for those living in any other quartile 298 after adjustment for covariates (adjusted-HR, 1.79 [95% CI, 1.51- 2.12]; p<.001) (Figure 2). 299 Conversely, for patients living in areas of least deprivation and most resources, the risk of MACE with 300 death as a competing risk was 17% higher compared to those living in greater deprivation (adjusted-301 HR,1.17 [95% CI,1.09, 1.27]; p<.001) (Figure 2). 302 303 Linear Relationship of Sleep Disordered Breathing Measures and Neighborhood Deprivation 304 We observed a significant linear increase between hypoxia measures with increasing area 305 deprivation (in ADI quartiles). In patients who underwent PSG studies, increased T90 (p<.001, 306 decreased mSaO2(p<.001), and decreased SaO2 nadir (p<.001) were associated with increasing 307 ADI. In patients who underwent type III sleep studies, greater ADI (higher quartiles) was associated 308 with decreased mSaO2(p<.001) and decreased SaO2 nadir (p<.001). Likewise, higher AHI was 309 significantly associated with increasing ADI (p=0.003) in patients who underwent PSG; however, no 310 linear relationship was observed between AHI and ADI quartiles among type III sleep study groups 311 (p=0.082). 312 313 Interaction of Sleep Disordered Breathing and Neighborhood Disadvantage on MACE and Death 314 There was no significant interaction between AHI with ADI on the risk of MACE in the presence of 315 competing risk of death (p=0.586) and on the risk of death in the presence of competing risk of MACE 316 (p=0.614). However, there were significant statistical interactions between T90 with ADI on the risk of 317 MACE in the presence of competing risk of death (p=0.002) and death in the presence of competing 318 risk of MACE (p=0.005). For those living in ADI-Q1 (lowest area deprivation), increased degree of 319 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 13 sleep-related hypoxia defined by T90 conferred a 37% increased risk of MACE (adjusted-HR, 320 1.37[95% CI:1.23 - 1.53]). Among individuals living in ADI-Q4, increased T90 was associated with a 321 26% increased risk of MACE: adjusted-HR: 1.26 (95% CI:1.14 - 1.38). For those individuals living in 322 ADI Q2 and Q3, T90 was associated with a respective 56% and 51% increased risk of death 323 (adjusted-HR, 1.56[95%CI:1.23-1.96]; adjusted-HR, 1.51[95%CI,1.21- 1.88]), respectively (Figure 3). 324 325 Incidence of Individual Components of MACE by Degree of Neighborhood Disadvantage 326 The cumulative incidence of cerebrovascular events, coronary artery disease, atrial fibrillation, and 327 heart failure was 18.3%, 11.6%, 35.7%, and 37.7% respectively. There was a greater relative 328 incidence seen among patients living in higher areas of deprivation compared to individuals living in 329 low areas of deprivation (p<.001) in each component of MACE except for atrial fibrillation. A higher 330 relative incidence of atrial fibrillation was observed among individuals living in areas of less 331 deprivation compared to individuals living in high areas of deprivation (p<.001) (Figure 4). 332 333 Secondary Analysis 334 After including only 45879 (63%) patients without PAP therapy after the initial sleep study, the results 335 of the analyses of ADI and the primary endpoint of MACE or death were similar to the main results 336 (Table S3). Results were also similar for the statistical interaction of T90 and ADI on the risk of MACE 337 in the presence of competing risk of death (p=0.040) and death in the presence of competing risk of 338 MACE (p=0.012) (Table S4). In those without PAP therapy, for patients living in areas of lowest 339 deprivation (ADI-Q1), sleep-related hypoxia defined by T90 conferred a 43% increased risk of MACE 340 and an 18% and 27% increased risk of MACE for those living in greatest areas of deprivation (Q3 and 341 Q4) (adjusted-HR, 1.43 [95% CI, 1.26 - 1.62]; adjusted-HR, 1.18 [95% CI:1.04 -1.34]; adjusted-HR, 342 1.27 [95% CI: 1.14-1.42]). Among those living in ADI-Q2 increased T90 was not associated with an 343 increased risk of MACE (adjusted-HR, 1.13 [95% CI, 0.998 - 1.27] When death was considered, 344

were consistent with primary results. We also took into consideration the hypopnea definition 345 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 14 data which was available in 72,166 (99%) patients, of which 50,124 (69.5%) were scored using the 346 3% hypopnea definition and 22,042 (30.5%) used the 4% hypopneas definition. Similar to primary 347 findings, in sleep studies using the 3% hypopnea definition, results were similar to the full cohort 348 (Table S5 and S6). However, when 4% hypopnea scoring was used, even though the primary MACE 349 or death endpoint was similar to primary results, there was no statistically significant interaction 350 between T90 and ADI (Table S5 and S7). 351 352

353 Principal Findings 354 We identified that greater versus lower levels of neighborhood deprivation measured by the ADI 355 national rank were associated with increased risk for the composite of MACE and death. Similarly, the 356 degree of sleep-related hypoxia was greater among individuals living in neighborhoods of higher 357 deprivation and this association was more pronounced than the association observed with OSA 358 severity defined by AHI. Moreover, a significant statistical interaction was observed between sleep-359 related hypoxia with ADI in relation to MACE and death risk. Individuals with increased sleep-related 360 hypoxia living in both extremes of area deprivation (Q4 and Q1) had an increased risk for MACE, 361 whereas individuals with increased sleep-related hypoxia living in moderate areas of deprivation (Q2-362 Q3) had an increased risk for death. These findings are timely, particularly given the knowledge gaps 363 recently identified in a multi-institute NIH workshop report underscoring the need to better understand 364 the link between sleep health disparities and poor health outcomes to inform targetable 365 inverventions.38 366 Neighborhood Socioeconomic Disadvantage and Cardiovascular Outcomes 367 A robust body of literature suggests that neighborhood socioeconomic factors are associated with 368 poor cardiovascular outcomes and CV risk factors for the general population. However, to our 369 knowledge, this is the first study to investigate the association of neighborhood levels of deprivation 370 as it relates to major adverse cardiovascular events in adult patients from a large OSA referral 371 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 15 sample phenotyped with overnight sleep studies. Our results thereby highlight the impact of existing 372 neighborhood-related socioeconomic disparities in CVD outcomes among individuals with highly 373 prevalent comorbidities such as OSA. Previous studies have shown that markers of increased 374 neighborhood deprivation such as the social vulnerability index (SVI) and ADI are associated with 375 increased risk for CV risk factors, premature CV mortality, and cardiac readmissions. 21,39,40 376 Moreover, at the county level, United States counties with greater degree of ADI and socioeconomic 377 deprivation are associated with increased premature CV mortality.41 However, none of these studies 378 have focused on patients with OSA nor accounted for potential confounders that could have biased 379 the results such as medications, smoking, and obesity, the latter in particular representing a well-380 recognized risk factor for CVD and mortality.42–45 Moreover, SVI measures the social risk of 381 populations and considers age, race, and ethnicity as variables, confounding the overall social risk 382 assessment. On the other hand, ADI allows the ability to exclusively characterize different levels of 383 socioeconomic deprivation from specific areas without the confounding influence of race and ethnicity 384 within the measure. Additionally, and similar to other studies, we observed an increased incidence of 385 CVD among patients living in areas of greater deprivation except for AF. One potential explanation for 386 this observation is the association of age, SEP, in the incidence AF as previous research has found 387 an increased lifetime risk of AF among individuals with higher SEP.46 388 Neighborhood Socioeconomic Disadvantage and OSA 389 Our findings suggest that sleep-related hypoxia-- more so than the degree of OSA characterized by 390 frequency of apneas and hypopneas-- is associated with socioeconomic disadvantage. Furthermore, 391 the degree of sleep-related hypoxia increases with greater areas of deprivation which may be 392 explained by different determinants at multiple levels of influence. At the individual level, this 393 association may be explained by the increased comorbid cardiopulmonary conditions, smoking 394 history, or the presence of individual socioeconomic factors in OSA. At the community level, it is 395 highly possible that neighborhood-related environmental factors dictated by racial residential 396 segregation or social cohesion may represent an important factor in these differences. It is known that 397 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 16 socially disadvantaged neighborhoods are more exposed to environmental hazards, such as 398 increased air pollution as well as increased light and noise at night which can disturb sleep. Along 399 these lines, increased air pollution is associated with OSA and a reduction in oxygen saturation, 47–49 400 hence serving as a potential explanation for the increased degree of sleep-related hypoxia among 401 individuals living in greater areas of disadvantage. 402 Association of OSA and Neighborhood Socioeconomic Disadvantage as it related to MACE 403 and Death 404 We observe that greater levels of sleep-related hypoxia among individuals living in both extreme 405 levels of socioeconomic deprivation increase the risk of MACE; a finding not observed among those 406 with moderate deprivation. Although understanding factors associated with these results requires 407 further investigation, this observation may be explained by the differences in lifestyle and dietary 408 choices across quartiles, environmental exposures, and lack of concordance between individual and 409 community-level social risks. An increased prevalence of social determinants of health (SDOH) at the 410 individual level across quartiles has been reported, including in quartiles of lowest deprivation in 411 primary care patients. Therefore, differences in the type and prevalence of SDOH seen at the 412 individual level may have a different influence on health outcomes independently of neighborhood-413 related SDOH. Further studies are needed to investigate SDOH at multiple levels of influence, i.e., 414 individual and community, to better understand the mechanisms that underlie health disparities 415 across ADI quartiles. 416 In our secondary analysis, while no statistically significant interaction was observed between sleep-417 related hypoxia and ADI when the cohort was stratified by 4% hypopnea scoring rule, results 418 remained the same when the 3% hypopnea rule was considered. These findings may be explained by 419 either smaller sample size of the 4% hypopnea rule subgroup or possibly point towards the known 420 inequities of using the 4% hypopnea rule in OSA severity.36,46 421 422

423 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 17 This study has limitations. As common to retrospective studies, our study is susceptible to referral 424 and selection biases. Generalizability of findings can be extrapolated to a clinical referral sample and 425 Ohio-based population. Although we attempted to address influence of underlying lung disease by 426 smoking history, we did not have granular level data on packs per day of exposure nor lung function 427 testing to more rigorously account for pulmonary-specific contributions to sleep-related hypoxia. 428 Finally, our institution is a quaternary care center and although we included only those with residence 429 in Northeast Ohio, it is possible that patients may have had follow‐up outside of our hospital system. 430 Even though this may have resulted in potential underestimation of initial MACE or death, such 431 misclassification would be expected to bias findings towards the null. 432

433 In this study, increased neighborhood socioeconomic disadvantage contributes to an increased risk of 434 MACE and death among patients with increased sleep-related hypoxia. Our findings implicate sleep- 435 related hypoxia as an important target to address disparities in OSA contributing to inequities in CV 436 outcomes. Future research should evaluate specific neighborhood-related factors i.e., noise, light and 437 air pollution, advertisements promoting tobacco and alcohol usage, lack of safe places to exercise 438 which increases the risk of obesity, which may be important determinants of sleep-related hypoxia in 439 OSA, CV health and associated disparities. 440 441 442 443 444 445 446 447 448 449 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 18 Acknowledgments 450 Author contributions: Authors were involved in the conception and design of the study, data 451 collection, data analysis and interpretation. All authors provided critical revision of the article 452 and approved the final version of the manuscript. The manuscript was reviewed and edited by 453 all the authors. All authors made the decision to submit the manuscript for publication and 454 assume responsibility for the accuracy and completeness of the analyses and for the fidelity of 455 this report to the study methods. 456 Conception and design: RM, CPO 457 Data collection, analysis, and interpretation: RM, CPO, DB 458 Drafting and revision of the manuscript for important intellectual content: All authors 459 Statistical Analysis: DB, RM, CPO 460 CPO, RM and DB had full access to all the data in the study and takes responsibility for the integrity 461 of the data and the accuracy of the data analysis and include this in the Acknowledgment section of 462 the manuscript. 463 Sources of Founding Neuroscience Transformative Research Resource Development Award and 464 the Center of Population Health Research at Cleveland Clinic. 465 Role of the sponsors: No sponsors contributed to the design, conduct, analysis of this study, nor to 466 the development and review of the manuscript. 467 468 469 470 471 472 473 474 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 19 Supplemental Material 475 Supplemental Methods. Sleep Testing, Registry, and Respiratory Event Scoring 476 Table S1. Cardiac Medications and Comorbidities 477 Table S2. Diagnosis and Procedures Codes 478 Figure S1. Identification of Eligible Patients of the Analytic Sample 479 Secondary Analysis 480 Table S3. Association of Area Deprivation Index with Primary Outcome MACE or Death Univariate 481 and 482 Multivariable Analysis without PAP Therapy after Sleep Study 483 Table S4. Interactions between Obstructive Sleep Apnea Measures with Area Deprivation Index as it 484 related to MACE in those without Positive Airway Pressure therapy 485 Table S5. Association of Area Deprivation Index with Primary outcome MACE or Death Univariate 486 and Multivariable Stratified by Hypopnea rule 3% and 4% 487 Table S6. Interactions Between Obstructive Sleep Apnea Measures with Area Deprivation Index as It 488 Relates to MACE in those with 3% Hypopnea Rule 489 Table S7. Interactions Between Obstructive Sleep Apnea Measures with Area Deprivation Index as It 490 Relates to MACE in those with 4% Hypopnea Rule 491

#26# 492 493 494 495 496 497 498 499 500 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 20

501 502 1. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart Disease 503 and Stroke Statistics-2022 Update: A Report From the American Heart Association. Circulation 504 2022;145:e153–e639. doi:10.1161/CIR.0000000000001052. 505 2. Multiple Cause of Death, 1999-2020 Request n.d. https://wonder.cdc.gov/mcd-icd10.html 506 (accessed August 20, 2022). 507 3. Glynn P, Lloyd-Jones DM, Feinstein MJ, Carnethon M, Khan SS. Disparities in Cardiovascular 508 Mortality Related to Heart Failure in the United States. J Am Coll Cardiol 2019;73:2354–2355. 509 doi:10.1016/J.JACC.2019.02.042. 510 4. Kyalwazi AN, Loccoh EC, Brewer LC, Ofili EO, Xu J, Song Y, et al. Disparities in 511 Cardiovascular Mortality Between Black and White Adults in the United States, 1999 to 2019. 512 Circulation 2022;146:211–228. doi:10.1161/CIRCULATIONAHA.122.060199. 513 5. Loccoh EC, Joynt Maddox KE, Wang Y, Kazi DS, Yeh RW, Wadhera RK. Rural-Urban 514 Disparities in Outcomes of Myocardial Infarction, Heart Failure, and Stroke in the United States. 515 J Am Coll Cardiol 2022;79:267–279. doi:10.1016/J.JACC.2021.10.045. 516 6. He J, Zhu Z, Bundy JD, Dorans KS, Chen J, Hamm LL. Trends in Cardiovascular Risk Factors 517 in US Adults by Race and Ethnicity and Socioeconomic Status, 1999-2018. JAMA 518 2021;326:1286–1298. doi:10.1001/JAMA.2021.15187. 519 7. Bevan GH, Josephson R, Al-Kindi SG. Socioeconomic Deprivation and Heart Failure Mortality 520 in the United States. J Card Fail 2020;26:1106–1107. doi:10.1016/j.cardfail.2020.07.014. 521 8. Matetic A, Bharadwaj A, Mohamed MO, Chugh Y, Chugh S, Minissian M, et al. Socioeconomic 522 Status and Differences in the Management and Outcomes of 6.6 Million US Patients With Acute 523 Myocardial Infarction. Am J Cardiol 2020;129:10–18. doi:10.1016/j.amjcard.2020.05.025. 524 9. Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, et al. Socioeconomic 525 status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study 526 and meta-analysis of 1·7 million men and women. The Lancet 2017;389:1229–1237. 527 doi:10.1016/S0140-6736(16)32380-7. 528 10. Johnson AE, Zhu J, Garrard W, Thoma FW, Mulukutla S, Kershaw KN, et al. Area deprivation 529 index and cardiac readmissions: Evaluating risk-prediction in an electronic health record. J Am 530 Heart Assoc 2021;10:20466. doi:10.1161/JAHA.120.020466. 531 11. Makarem N, Castro-Diehl C, St-Onge M-P, Redline S, Shea S, Lloyd-Jones D, et al. Redefining 532 Cardiovascular Health to Include Sleep: Prospective Associations With Cardiovascular Disease 533 in the MESA Sleep Study. Journal of the American Heart Association J Am Heart Assoc 534 2022;11:25252. doi:10.1161/JAHA.122.025252. 535 12. Lloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, et al. Life’s 536 Essential 8: Updating and Enhancing the American Heart Association’s Construct of 537 Cardiovascular Health: A Presidential Advisory From the American Heart Association. 538 Circulation 2022;146:E18–E43. doi:10.1161/CIR.0000000000001078. 539 13. Chen X, Wang R, Zee P, Lutsey PL, Javaheri S, Alcántara C, et al. Racial/ethnic differences in 540 sleep disturbances: The Multi-Ethnic Study of Atherosclerosis (MESA). Sleep 2015;38:877–541 888. doi:10.5665/sleep.4732. 542 14. Christie-Mizell CA. Neighborhood Disadvantage and Poor Health: The Consequences of Race, 543 Gender, and Age among Young Adults. Int J Environ Res Public Health 2022;19. 544 doi:10.3390/IJERPH19138107. 545 15. Spilsbury JC, Storfer-Isser A, Kirchner HL, Nelson L, Rosen CL, Drotar D, et al. Neighborhood 546 disadvantage as a risk factor for pediatric obstructive sleep apnea. J Pediatr 2006;149:342–547 347. doi:10.1016/J.JPEDS.2006.04.061. 548 16. Brouillette RT, Horwood L, Constantin E, Brown K, Ross NA. Childhood sleep apnea and 549 neighborhood disadvantage. Journal of Pediatrics 2011;158:789-795.e1. 550 doi:10.1016/j.jpeds.2010.10.036. 551 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 21 17. Wang R, Dong Y, Weng J, Kontos EZ, Chervin RD, Rosen CL, et al. Associations among 552 neighborhood, race, and sleep apnea severity in children: A six-city analysis. Ann Am Thorac 553 Soc 2017;14:76–84. doi:10.1513/ANNALSATS.201609-554 662OC/SUPPL_FILE/DISCLOSURES.PDF. 555 18. Kim J, Kou YF, Chorney SR, Mitchell RB, Johnson RF. Assessment of neighborhood‐level 556 disadvantage and pediatric obstructive sleep apnea severity. Laryngoscope Investig 557 Otolaryngol 2023;8:1114. doi:10.1002/LIO2.1098. 558 19. Kind AJH, Buckingham WR. Making Neighborhood-Disadvantage Metrics Accessible — The 559 Neighborhood Atlas. New England Journal of Medicine 2018;378:2456–2458. 560 doi:10.1056/NEJMP1802313. 561 20. Akwo EA, Kabagambe EK, Harrell FE, Blot WJ, Bachmann JM, Wang TJ, et al. Neighborhood 562 Deprivation Predicts Heart Failure Risk in a Low-Income Population of Blacks and Whites in the 563 Southeastern United States. Circ Cardiovasc Qual Outcomes 2018;11. 564 doi:10.1161/CIRCOUTCOMES.117.004052. 565 21. Johnson AE, Zhu J, Garrard W, Thoma FW, Mulukutla S, Kershaw KN, et al. Area deprivation 566 index and cardiac readmissions: Evaluating risk-prediction in an electronic health record. J Am 567 Heart Assoc 2021;10:20466. doi:10.1161/JAHA.120.020466/FORMAT/EPUB. 568 22. Bevan GH, Nasir K, Rajagopalan S, Al-Kindi S. Socioeconomic Deprivation and Premature 569 Cardiovascular Mortality in the United States. Mayo Clin Proc 2022;97:1108–1113. 570 doi:10.1016/j.mayocp.2022.01.018. 571 23. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) 572 Statement: guidelines for reporting observational studies | EQUATOR Network n.d. 573 https://www.equator-network.org/reporting-guidelines/strobe/ (accessed December 4, 2023). 574 24. Van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the elixhauser 575 comorbidity measures into a point system for hospital death using administrative data. Med 576 Care 2009;47:626–633. doi:10.1097/MLR.0B013E31819432E5. 577 25. Velu JF, Haas SD, Van Mourik MS, Koch KT, Vis MM, Henriques JP, et al. Elixhauser 578 Comorbidity Score Is the Best Risk Score in Predicting Survival After Mitraclip Implantation. 579 Structural Heart 2018;2:53–57. doi:10.1080/24748706.2017.1404172. 580 26. Berry RB, Quan S, Abreu AR, Bibbs ML, DelRosso L, Harding SM, et al. The AASM Manual for 581 the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specification. 582 Version 2.6. Darien, IL: American Academy of Sleep Medicine; 2020. 583 27. Heinzinger CM, Thompson NR, Milinovich A, Diniz Araujo ML, Orbea CP, Foldvary-Schaefer N, 584 et al. Sleep-Disordered Breathing, Hypoxia, and Pulmonary Physiologic Influences in Atrial 585 Fibrillation. J Am Heart Assoc 2023;12:e031462. doi:10.1161/JAHA.123.031462. 586 28. Smagula SF, Stone KL, Redline S, Ancoli-Israel S, Barrett-Connor E, Lane NE, et al. 587 Actigraphy and polysomnography measured sleep disturbances, inflammation, and mortality 588 among older men. Psychosom Med 2016;78:686. doi:10.1097/PSY.0000000000000312. 589 29. Punjabi NM, Caffo BS, Goodwin JL, Gottlieb DJ, Newman AB, O’Connor GT, et al. Sleep-590 Disordered Breathing and Mortality: A Prospective Cohort Study. PLoS Med 2009;6. 591 doi:10.1371/JOURNAL.PMED.1000132. 592 30. Savova GK, Masanz JJ, Ogren P V., Zheng J, Sohn S, Kipper-Schuler KC, et al. Mayo clinical 593 Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component 594 evaluation and applications. Journal of the American Medical Informatics Association 595 2010;17:507–513. doi:10.1136/jamia.2009.001560. 596 31. Neighborhood Atlas University of Wisconsin home page. 597 https://www.neighborhoodatlas.medicine.wisc.edu n.d. 598 32. Geissenberger F, Schwarz F, Probst M, Haberl S, Parkhe A, Faul C, et al. Obstructive sleep 599 apnea is associated with pulmonary artery thrombus load, disease severity, and survival in 600 acute pulmonary embolism. Clinical Research in Cardiology 2020;109:13–21. 601 doi:10.1007/S00392-019-01479-X/METRICS. 602 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 22 33. Gottlieb DJ, Yenokyan G, Newman AB, O’Connor GT, Punjabi NM, Quan SF, et al. Prospective 603 Study of Obstructive Sleep Apnea and Incident Coronary Heart Disease and Heart Failure. 604 Circulation 2010;122:352–360. doi:10.1161/CIRCULATIONAHA.109.901801. 605 34. Marin JM, Agusti A, Villar I, Forner M, Nieto D, Carrizo SJ, et al. Association Between Treated 606 and Untreated Obstructive Sleep Apnea and Risk of Hypertension. JAMA 2012;307:2169–607 2176. doi:10.1001/JAMA.2012.3418. 608 35. Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J 609 Am Stat Assoc 1999;94:496. doi:10.2307/2670170. 610 36. Khalid F, Ayache M, Auckley D. The differential impact of respiratory event scoring criteria on 611 CPAP eligibility in women and men. Journal of Clinical Sleep Medicine 2021;17:2409–2414. 612 doi:10.5664/JCSM.9448. 613 37. Myllymaa K, Myllymaa S, Leppänen T, Kulkas A, Kupari S, Tiihonen P, et al. Effect of oxygen 614 desaturation threshold on determination of OSA severity during weight loss n.d. 615 doi:10.1007/s11325-015-1180-4. 616 38. Jackson CL, Walker JR, Brown MK, Das R, Jones NL. A workshop report on the causes and 617 consequences of sleep health disparities. SLEEPJ n.d.;2020:1–11. doi:10.1093/sleep/zsaa037. 618 39. Jain V, Al Rifai M, Khan SU, Kalra A, Rodriguez F, Samad Z, et al. Association Between Social 619 Vulnerability Index and Cardiovascular Disease: A Behavioral Risk Factor Surveillance System 620 Study. J Am Heart Assoc 2022;11:e024414. doi:10.1161/JAHA.121.024414. 621 40. Khan SU, Javed Z, Lone AN, Dani SS, Amin Z, Al-Kindi SG, et al. Social Vulnerability and 622 Premature Cardiovascular Mortality Among US Counties, 2014 to 2018. Circulation 623 2021;144:1272–1279. doi:10.1161/CIRCULATIONAHA.121.054516/FORMAT/EPUB. 624 41. Bevan GH, Nasir K, Rajagopalan S, Al-Kindi S. Socioeconomic Deprivation and Premature 625 Cardiovascular Mortality in the United States n.d. doi:10.1016/j.mayocp.2022.01.018. 626 42. Bogers RP, Bemelmans WJE, Hoogenveen RT, Boshuizen HC, Woodward M, Knekt P, et al. 627 Association of Overweight With Increased Risk of Coronary Heart Disease Partly Independent 628 of Blood Pressure and Cholesterol Levels: A Meta-analysis of 21 Cohort Studies Including More 629 Than 300 000 Persons. Arch Intern Med 2007;167:1720–1728. 630 doi:10.1001/ARCHINTE.167.16.1720. 631 43. Wong CX, Sullivan T, Sun MT, Mahajan R, Pathak RK, Middeldorp M, et al. Obesity and the 632 Risk of Incident, Post-Operative, and Post-Ablation Atrial Fibrillation: A Meta-Analysis of 633 626,603 Individuals in 51 Studies. JACC Clin Electrophysiol 2015;1:139–152. 634 doi:10.1016/J.JACEP.2015.04.004. 635 44. S K, JC E, D L, PW W, EJ B, MG L, et al. Obesity and the risk of heart failure. N Engl J Med 636 2002;347:59–60. doi:10.1056/NEJMOA020245. 637 45. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of All-Cause Mortality With Overweight 638 and Obesity Using Standard Body Mass Index Categories: A Systematic Review and Meta-639 analysis. JAMA 2013;309:71–82. doi:10.1001/JAMA.2012.113905. 640 46. Mansukhani MP, Kolla BP, Wang Z, Morgenthaler TI. Effect of Varying Definitions of Hypopnea 641 on the Diagnosis and Clinical Outcomes of Sleep-Disordered Breathing: A Systematic Review 642 and Meta-Analysis. Journal of Clinical Sleep Medicine 2019;15:687–696. 643 doi:10.5664/JCSM.7750. 644 47. Gee GC, Payne-Sturges DC. Environmental Health Disparities: A Framework Integrating 645 Psychosocial and Environmental Concepts. Environ Health Perspect 2004;112:1645. 646 doi:10.1289/EHP.7074. 647 48. DeMeo DL, Zanobetti A, Litonjua AA, Coull BA, Schwartz J, Gold DR. Ambient Air Pollution and 648 Oxygen Saturation. Https://DoiOrg/101164/Rccm200402-244OC 2012;170:383–387. 649 doi:10.1164/RCCM.200402-244OC. 650 49. Shen YL, Liu W Te, Lee KY, Chuang HC, Chen HW, Chuang KJ. Association of PM2.5 with 651 sleep-disordered breathing from a population-based study in Northern Taiwan urban areas. 652 Environmental Pollution 2018;233:109–113. doi:10.1016/J.ENVPOL.2017.10.052. 653 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 23 Figures Legends 654 Figure 1. 655 Northeast Ohio Counties by Area Deprivation Index. 656 Abbreviations: ADI, area deprivation index. 657 Figure 2. 658 Panel A) Probability of MACE or Death end points, (ADI Q4 vs ADI Q1) HR, 1.28 (95%CI, 1.20-1.38) 659 p<.001; Panel B) Probability of MACE versus competing risk of Death, (ADI Q4 vs ADI Q1) HR, 1.17 660 (95% CI, 1.09-1.27) p<.001; Panel C) Probability of Death versus competing risk of MACE (ADI Q4 vs 661 ADI Q1) HR, 1.79 (95% CI, 1.51-2.12) p<.001). 662 Abbreviations: ADI, area deprivation index; HR, hazard ratio. 663 Figure 3. 664 A) Risk of MACE in the presence of competing risk of death: For ADI Q1 and T90, HR, 1.37 (95% CI, 665 1.23-1.53); For ADI Q4 and T90, HR, 1.26 (95% CI, 1.14-1.38); and B) Risk of Death in the presence 666 of competing risk of MACE: For ADI Q2 and T90, HR, 1.56 (95% CI, 1.23-1.96); For ADI Q3, HR, 667 1.51 (95% CI, 1.21-1.88). 668 Abbreviations: T90, percentage of sleep time spent with SaO2<90%; ADI, area deprivation index. 669 Figure 4. 670 For each of the 4 individual outcomes (except of death), any patient with previous history of each 671 individual outcomes prior sleep study date was excluded for risk assessment only for that outcome. 672 Abbreviations: ADI, area deprivation index; AF, atrial fibrillation; CVE, cerebrovascular events; CAD, 673 coronary artery disease; HF, Heart Failure. 674 675 676 677 678 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 24 Table 1. Demographic, Comorbidities, and Sleep Study elements, all study eligible patients (n=72,443) Factor Total (N=72,443) Q1 (N=18,438) Q2 (N=18,484) Q3 (N=18,103) Q4 (N=17,418) p- value Basic Demographics Age (years) 50.4 ± 14.2 52.3 ± 14.0 2,3,4 51.4 ± 14.4 1,3,4 49.1 ± 14.2 1,2 48.9 ± 13.9 1,2 <.001a Sex* <.001c Female 35,875 (49.5) 7,393 (40.1) 2,3,4 8,629 (46.7) 1,3,4 9,421 (52.0) 1,2,4 10,432 (59.9) 1,2,3 Male 36,559 (50.5) 11,042 (59.9) 9,853 (53.3) 8,679 (48.0) 6,985 (40.1) Race/Ethnicity <.001c Non-Hispanic White 52,868 (73.0) 16,116 (87.4) 15,920 (86.1) 13,873 (76.6) 6,959 (40.0) American Indian or Alaska Native 71 (0.10) 18 (0.10) 2,3,4 17 (0.09) 1,3,4 22 (0.12) 1,2,4 14 (0.08) 1,2,3 Asian/Pacific Islander 823 (1.1) 385 (2.1) 171 (0.93) 191 (1.06) 76 (0.44) Non-Hispanic Black 13,326 (18.4) 898 (4.9) 1,246 (6.7) 2,653 (14.7) 8,529 (49.0) Hispanic (any race) 2,443 (3.4) 267 (1.4) 343 (1.9) 559 (3.1) 1,274 (7.3) Multiracial 791 (1.09) 186 (1.01) 199 (1.08) 230 (1.3) 176 (1.01) Other or not stated 2,121 (2.9) 568 (3.1) 588 (3.2) 575 (3.2) 390 (2.2) Body mass index, kg/m2, mean ± SD 34.4 ± 8.5 31.9 ± 7.3 2,3,4 33.7 ± 8.0 1,3,4 35.1 ± 8.6 1,2,4 37.3 ± 9.2 1,2,3 <.001a Primary payor <.001c Private 38,669 (53.4) 11,386 (61.8) 2,3,4 10,580 (57.2) 1,3,4 9,889 (54.6) 1,2,4 6,814 (39.1) 1,2,3 Medicaid 8,031 (11.1) 641 (3.5) 1,264 (6.8) 2,055 (11.4) 4,071 (23.4) Medicare 20,926 (28.9) 5,220 (28.3) 5,488 (29.7) 4,862 (26.9) 5,356 (30.7) Self-pay 3,510 (4.8) 937 (5.1) 849 (4.6) 943 (5.2) 781 (4.5) Other/ Not reported 1,307 (1.8) 254 (1.4) 303 (1.6) 354 (2.0) 396 (2.3) ADI State Rank, mean 4.5 ± 3.0 1.2 ± 0.38 2,3,4 2.7 ± 0.71 1,3,4 5.4 ± 0.99 1,2,4 8.8 ± 0.99 1,2,3 <.001a ADI State Rank, median 4.0 [2.0, 7.0] 1.00 [1.00, 1.00] 2,3,4 3.0 [2.0, 3.0] 1,3,4 5.0 [5.0, 6.0] 1,2,4 9.0 [8.0, 10.0] 1,2,3 <.001b Epworth Sleepiness Scale* 9.3 ± 5.2 8.8 ± 4.9 2,3,4 9.2 ± 5.1 1,3,4 9.5 ± 5.2 1,2,4 9.9 ± 5.5 1,2,3 <.001a Comorbidities Obesity 19,304 (26.6) 3,551 (19.3) 2,3,4 4,476 (24.2) 1,3,4 5,199 (28.7) 1,2,4 6,078 (34.9) 1,2,3 <.001c Diabetes 9,557 (13.2) 1,763 (9.6) 2,3,4 2,253 (12.2) 1,3,4 2,425 (13.4) 1,2,4 3,116 (17.9) 1,2,3 <.001c Hypertension 21,989 (30.4) 5,180 (28.1) 4 5,417 (29.3) 4 5,266 (29.1) 4 6,126 (35.2) 1,2,3 <.001c Heart Failure 317 (0.44) 83 (0.45) 78 (0.42) 92 (0.51) 64 (0.37) 0.24c Any cancers 4,346 (6.0) 1,257 (6.8) 3,4 1,218 (6.6) 3,4 985 (5.4) 1,2 886 (5.1) 1,2 <.001c Asthma 14,031 (19.4) 2,769 (15.0) 2,3,4 3,093 (16.7) 1,3,4 3,596 (19.9) 1,2,4 4,573 (26.3) 1,2,3 <.001c COPD 4,764 (6.6) 746 (4.0) 2,3,4 1,034 (5.6) 1,3,4 1,263 (7.0) 1,2,4 1,721 (9.9) 1,2,3 <.001c Smoking Status <.001c Never 37,725 (52.1) 10,890 (59.1) 2,3,4 9,761 (52.8) 1,3,4 9,067 (50.1) 1,2,4 8,007 (46.0) 1,2,3 Former 22,308 (30.8) 5,485 (29.7) 5,885 (31.8) 5,708 (31.5) 5,230 (30.0) Passive smoker 512 (0.71) 123 (0.67) 127 (0.69) 146 (0.81) 116 (0.67) Current 8,768 (12.1) 1,235 (6.7) 1,940 (10.5) 2,390 (13.2) 3,203 (18.4) Unknown 3,130 (4.3) 705 (3.8) 771 (4.2) 792 (4.4) 862 (4.9) Total Elixhauser Commodity Index 1.3 ± 1.4 1.1 ± 1.2 2,3,4 1.2 ± 1.3 1,3,4 1.3 ± 1.4 1,2,4 1.5 ± 1.4 1,2,3 <.001a All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 25 679 680 681 Total Elixhauser Commodity Index 1.0 [0.0, 2.0] 1.0 [0.0, 2.0] 2,3,4 1.0 [0.0, 2.0] 1,3,4 1.0 [0.0, 2.0] 1,2,4 1.0 [0.0, 2.0] 1,2,3 <.001 e Total Elixhauser comorbidities 5 535 (0.74) 78 (0.42) 120 (0.65) 133 (0.73) 204 (1.2) Medications Anti-Arrhythmics 279 (0.39) 73 (0.40) 81 (0.44) 77 (0.43) 48 (0.28) 0.054c Beta Blockers 11,124 (15.4) 2,565 (13.9) 2,3,4 2,936 (15.9) 1 2,724 (15.0) 1,4 2,899 (16.6) 1,3 <.001c ARBs 6,532 (9.0) 1,653 (9.0) 1,663 (9.0) 1,547 (8.5) 4 1,669 (9.6) 3 0.008c ACE-I 11,187 (15.4) 2,639 (14.3) 2,4 2,837 (15.3) 1,4 2,744 (15.2) 4 2,967 (17.0) 1,2,3 <.001c Aspirin 7,031 (9.7) 1,928 (10.5) 2,3 1,707 (9.2) 1,3,4 1,512 (8.4) 1,2,4 1,884 (10.8) 2,3 <.001c Digoxin 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) Statins 17,582 (24.3) 4,944 (26.8) 2,3,4 4,629 (25.0) 1,3,4 3,952 (21.8) 1,2,4 4,057 (23.3) 1,2,3 <.001c Nitrates 1,444 (2.0) 300 (1.6) 2,4 381 (2.1) 1 333 (1.8) 4 430 (2.5) 1,3 <.001c P2Y12 Receptor Blocker 1,102 (1.5) 260 (1.4) 313 (1.7) 3 232 (1.3) 2,4 297 (1.7) 3 0.001c Sleep Measures Sleep study type <.001c PSG and Split 52,874 (73.0) 12,294 (66.7) 2,3,4 12,913 (69.9) 1,3,4 13,354 (73.8) 1,2,4 14,313 (82.2) 1,2,3 Type III 19,569 (27.0) 6,144 (33.3) 5,571 (30.1) 4,749 (26.2) 3,105 (17.8) Hypopnea scoring rule <.001c 3% 50,124 (69.2) 13,942 (75.6) 13,265 (71.8) 12,579 (69.5) 10,338 (59.4) 4% 22,042 (30.4) 4,422 (24.0) 5,145 (27.8) 5,457 (30.1) 7,018 (40.3) Not reported 277 (0.38) 74 (0.40) 2,3,4 74 (0.40) 1,3,4 67 (0.37) 1,2,4 62 (0.36) 1,2,3 AHI, median [IQR] 14.3 [5.8, 33.3] 15.1 [6.2, 33.1] 3,4 14.6 [6.0, 33.0] 4 13.8 [5.6, 33.3] 1 13.5 [5.4, 34.1] 1,2 <.001b % Sleep Time with SaO2<90% (T90)* 3.8 [0.40, 23.6] 4.4 [0.50, 25.6] 3,4 4.4 [0.50, 25.9] 3,4 3.6 [0.40, 23.1] 1,2,4 2.9 [0.40, 19.0] 1,2,3 <.001b Mean O2 saturation (%)* 92.9 ± 2.9 92.8 ± 2.7 2,4 92.7 ± 2.7 1,3,4 92.8 ± 2.9 2,4 93.1 ± 3.2 1,2,3 <.001a Minimum O2 saturation (%)* 83.1 ± 7.9 83.5 ± 7.4 3,4 83.3 ± 7.6 4 83.1 ± 8.0 1,4 82.5 ± 8.6 1,2,3 <.001a *Data not available for all 72,443 subjects. Missing values: ADI Quartiles = 597; BMI = 516; Percent sleep time SaO2 <90% (T90)= 9,059, Gender Description = 9; Mean O2 saturation(%) = 1,918; Minimum O2 saturation= 1,959; Epworth Sleepiness Scale = 1,456. Abbreviations: BMI; Body mass index; Q: Quartiles; SD: Standard Deviation; ADI: Area of Deprivation Index; COPD: Chronic Obstructive Pulmonary Disease; ARBs: Angiotensin Receptor Blockers; ACE-I: Angiotensin Converting Enzyme Inhibitors (ACEI); AHI: Apnea Hypopnea Index; PSG: polysomnogram; T90 (percentage of sleep time spent with SaO2<90%); IQR: Interquartile Range. Statistics presented as Median [P25, P75], N (column %). p-values: b=Kruskal-Wallis test, c=Pearson's chi-square test, d=Fisher's Exact test, e. Friedman non-parameric ANOVA 1: Significantly different from 1st quartile 2: Significantly different from 2nd quartile 3: Significantly different from 3rd quartile 4: Significantly different from 4th quartile Post-hoc pairwise comparisons were done using Bonferroni adjustment. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 26 Table 2. Association of ADI with Primary Outcome MACE or Death in Univariate and 682 Multivariable Analysis using Cox Proportional Hazard Regression (n=72,443) 683 Factor Univariate Analysis Multivariable Regression HR 95% CI p value HR 95% CI p value Area Deprivation Index, National Rank quartiles (ref: 1st quartile) Overall <.001 Overall <.001 - 2nd quartile 1.07 (1.01, 1.14) 0.026 1.02 (0.96, 1.09) 0.518 - 3rd quartile 1.02 (0.96, 1.08) 0.582 1.02 (0.96, 1.10) 0.498 - 4th quartile 1.40 (1.32, 1.48) <.001 1.28 (1.20, 1.38) <.001 684 Abbreviations: ADI, Area Deprivation Index; MACE, Major Adverse Cardiovascular Events; HR: 685 Hazards ratio; CI: Confidence Interval. Multivariable Analysis was adjusted for age; sex; race; body 686 mass index; T90 above median; AHI > 30 events/hour; cardiac medications including: anti-arrhythmic, 687 beta blockers, aspirin, digoxin, statins, nitrates, P2Y12 receptor blockers, angiotensin receptor 688 blocker, angiotensin converting enzyme inhibitors blockers; smoking status, comorbidities part of the 689 Elixhauser score and Elixhauser score. 690 691 692 693 694 695 696 697 698 699 700 701 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 27 702 Figure 1. Areas of Deprivation Index Distribution in Quartiles Across Northeast Ohio Counties 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 28 728 Figure 2. Cumulative Incidence Function Plots of Outcomes in the Presence of Competing 729 Risks 730 731 732 733 734 735 736 737 738 739 740 741 742 743 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 29 Figure 3. Effects of ADI and T90 (Interaction) on Adjusted Hazard Ratios and 95% Confidence 744 Limits for MACE and Death Outcomes in the Presence of Competing Risks, T90 and ADI 745 Quartiles 746 747 748 749 750 751 752 753 754 755 756 757 758 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint 30 759 760 761 Figure 4. Cumulative Incidence Function Plots for Individual End Points of MACE by ADI 762 Quartiles 763 764 765 All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprintthis version posted February 3, 2024. ; https://doi.org/10.1101/2024.01.31.24302108doi: medRxiv preprint