Social Determinants of Health and Medication Adherence in Older Adults with Prevalent Health Conditions in the United States: An analysis of the National Health and Nutrition Examination Survey (NHANES) 2009-2018

Social Determinants of Health and Medication Adherence in Older Adults with Prevalent Health Conditions in the United States: An analysis of the National Health and Nutrition Examination Survey (NHANES) 2009-2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Social Determinants of Health and Medication Adherence in Older Adults with Prevalent Health Conditions in the United States: An analysis of the National Health and Nutrition Examination Survey (NHANES) 2009-2018 Omolola A. Adeoye-Olatunde, Tessa J. Hastings, Michelle L. Blakely, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3872074/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The older adult population is rapidly expanding in the United States (US), with high blood pressure, high cholesterol, and diabetes ranking among the top health conditions for older adults. Medication nonadherence, not taking medications as prescribed, is prevalent among those managing multiple chronic conditions. Despite its complexity, evidence is lacking on the social determinants of health (SDOH) influencing medication adherence among older adults with high blood pressure, high cholesterol, and/or diabetes in the US. Thus, the primary objective of this study was to identify and prioritize SDOH associated with medication adherence among a nationally representative sample of US older adults with high blood pressure, high cholesterol, and/or diabetes. Methods Using the World Health Organization Commission on Social Determinants of Health and Pharmacy Quality Alliance Medication Access Conceptual Frameworks, publicly available National Health and Nutrition Examination Survey datasets (2009–2018) were cross-sectionally analyzed among respondents aged 65 and older with study diseases. Respondents reporting taking their study disease state medication(s) were considered adherent. Data analysis included descriptive statistics, Rao-Scott Chi-Square tests, and logistic regression analyses. Highly correlated predictors were removed to address multicollinearity, and the rest were consolidated into a single variable. The study used a significance level of 0.05. Results Analyses included 5,513 respondents' data. Univariate analysis showed that several structural (gender, p = .009; ethnicity, p = .038; social class, p = .023) and intermediary (e.g., level of alcohol consumption, p = .004; disability status, p = .014; affordability of household balanced meals, p < .001) determinants of health were significantly associated with medication adherence. Multivariable analysis revealed significant differences in medication adherence for alcohol consumption (p = .034) and usual place for healthcare (p = .001). For instance, individuals who usually go to a doctor’s office or health maintenance organization have 330% higher odds of adhering to medications than those with no usual place for healthcare (p = .002). Conclusions Study findings underscore pertinent implications for public health and policy, prioritizing specific SDOH most likely to affect medication adherence in common chronic conditions among older adults in the US. Strikingly, the observed relationship between alcohol consumption trends and adherence is a distinct finding warranting further investigation. Medication Adherence Social Determinants of Health Older Adults High blood pressure High cholesterol Diabetes Figures Figure 1 Figure 2 Background Older adults are the fastest growing segment of the US population in the past decade with nearly 55.8 million people. 1 There is a high prevalence of multiple chronic diseases in this population. 2 Not taking medications as prescribed, also known as nonadherence, is a common phenomenon among older adults taking more than one medication for multiple chronic conditions. 3 , 4 Over 35% of older adults in the United States (US) take at least five prescription medications for which the estimated nonadherence rate is up to 60%. 5,6 The avoidable healthcare expenditures associated with medication nonadherence are approximately $ 528.4 billion annually. 7 Nationally, chronic cardiovascular (e.g., high blood pressure/cholesterol) and diabetes conditions are leading causes of death particularly among health-disparity populations including racial and ethnic minority groups. Poor medication adherence and disparate social determinants of health (SDOH) play critical roles. 8 – 11 SDOH are "the environmental conditions where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks. 12 National concerted efforts have been geared toward addressing SDOH, medication nonadherence, and health disparities related to these chronic conditions, but not concurrently or consistently. 12 , 13 Our previously published paper, focused exclusively on diabetes, explored the nuanced relationship between older adults’ SDOH and medication adherence. 14 However, examining older adults’ medication adherence across high blood pressure, high cholesterol, and diabetes conditions is warranted. First, in addition to the previously mentioned disparities in disease-related mortality, high blood pressure, high cholesterol, and diabetes rank in the top five most common chronic conditions for older adults in the United States (US). 15 Second, any combination of high blood pressure, high cholesterol, and diabetes frequently occurs concurrently in one individual, and optimizing medications to manage these concurrent conditions may differ from managing only one condition. 16 Thus, the primary aim of this study was to identify and prioritize SDOH associated with medication adherence among a nationally representative sample of older adults with high blood pressure, high cholesterol, and/or diabetes in the US. We hypothesized that structural and intermediary determinants of health would be associated with medication adherence. Secondary aims included characterizing SDOH, estimating medication adherence, and describing implications for health disparity populations among older adults in the US diagnosed with high blood pressure, high cholesterol, and/or diabetes. Methods Study Design This cross-sectional study utilized 2009–2018 National Health and Nutrition Examination Survey (NHANES) data to examine associations between SDOH and medication adherence in older adults with high blood pressure, high cholesterol, and/or diabetes in the US. NHANES, a public database, offers comprehensive health and nutrition data for a representative US sample. 17 The study adhered to STROBE guidelines for observational research. 18 Conceptual framework Adeoye-Olatunde et al.’s integrated conceptual framework (Fig. 1 ) guided the categorization of SDOH covariates in the NHANES dataset, minimizing selection bias. 14 First, the World Health Organization (WHO) Commission on Social Determinants of Health framework defines structural determinants as "social determinants of health inequities," and these inequities function through intermediate determinants impacting health outcomes (e.g., medication adherence). 19 Therefore, structural and intermediary determinants were operationalized as SDOH. Healthcare access and health outcomes were redefined as medication access and medication adherence respectively. Finally, unaddressed medication access barriers from the WHO framework were incorporated from the Pharmacy Quality Alliance (PQA) Medication Access framework. 10 Study population The study population for analysis included all respondents ages 65 and older from the 2009–2018 NHANES datasets, whose doctors told them to take at least one prescription for high blood pressure, cholesterol and/or were told they had diabetes. Five biannual data years (2009–2018) were downloaded from the NHANES database. Applicable datasets were combined by respondent study identification number. All other respondents' data were excluded from analyses. Due to the retrospective nature of the study, a formal power analysis was not performed. However, a post hoc power analysis produced a power greater than 99% with an alpha value of 0.05 when the difference in proportions between the groups was 4% or greater. Covariates The variables needed for analysis (as defined by the conceptual framework) were retained, while all other variables were eliminated from the combined dataset. 20 Notably, the NHANES dataset dichotomizes gender into "male" and "female" and does not define gender as a socially constructed term that may vary among cultures. Some of the selected a priori variables were altered (e.g., assigning age categories of '65–69', '70–74', and '75+') and missing data were excluded listwise. When variables had unspecific response ranges (e.g., income-to-poverty ratios greater than or equal to 5), those ranges were considered missing. The study's outcome variable, medication adherence, was dichotomized into "adherent" and "not adherent." Respondents were considered adherent if they responded that they were currently taking all prescribed oral medications for each study disease state they had (i.e., high blood pressure, high cholesterol, diabetes). Conversely, respondents not currently taking at least one of the prescribed oral medications were considered not adherent. Statistical Analysis Descriptive statistics were employed to characterize the study population and selected variables. Univariate analyses for overall medication adherence utilized logistic regression for continuous predictors and Rao-Scott Chi-Square tests for categorical predictors. Logistic regression was used for multivariable analysis of medication adherence. Predictors with p-values less than 0.20 in the univariate analyses were considered as predictors in the multivariable analysis. To decrease the effects of multicollinearity, predictors that were highly correlated (logistic regression comparisons with an OR ≥ 2.477 corresponding to a Cohen's d of 0.50) with multiple other predictors were removed from the model. The remaining predictors with multicollinearity were combined into a single predictor variable. 21 A 5% significance level was used for all tests. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC). Adjustments for the complex NHANES survey sampling design (differential clustering, stratification, and weighting) were included in each of the analyses. 22 Results A total number of 5,513 respondents met the inclusion criteria. Table 1 shows that most respondents were 75 years of age or older (46.2%), identified as female (51.7%), Non-Hispanic White (50.6%), married (54.7%), completed at least a high school education, but did not graduate from college (49.1%), and not employed (85.6%). Across the sample (N = 5,513), high blood pressure was most prevalent (78.7%), followed by high cholesterol (65.6%) and diabetes (32.8%). Most respondents (79.4%) adhered to prescribed medications for high blood pressure, cholesterol and/or diabetes (Table 1 ). Table 1 Categorical characteristics of the study sample (N = 5,513). Variable (N) Level n (%) Age Group (N = 5,513) 65–69 years 1,546 (28.0) 70–74 years 1,421 (25.8) 75 + years 2,546 (46.2) Gender (N = 5,513) Female 2,849 (51.7) Male 2,664 (48.3) Race a (N = 5,513) Other 1,593 (28.9) Non-Hispanic Black 1,128 (20.5) Non-Hispanic White 2,792 (50.6) Ethnicity b (N = 5,342) Hispanic 1,037 (19.4) Non-Hispanic 4,305 (80.6) Education (N = 5,492) = High School Graduate, but not College Graduate 2,696 (49.1) College Graduate 1,069 (19.5) Alcohol Consumption Category c (N = 3,899) Never Drinks 1,564 (40.1) Light Drinking 2,043 (52.4) Moderate Drinking 233 (6.0) Heavy Drinking 59 (1.5) Disability Status (N = 5,510) No Disability 3,862 (70.1) Has Disability 1,648 (29.9) Employment Status d (N = 5,508) Not Employed 4,717 (85.6) Employed 791 (14.4) Household Balanced Meals (N = 5,360) Could Not Afford 790 (14.7) Could Afford 4,570 (85.3) Insurance e (N = 5,502) Medicaid 669 (12.2) Medicare 4,134 (75.1) Other 543 (9.9) None 156 (2.8) Interview Language (N = 5,513) English 4,941 (89.6) Spanish 572 (10.4) Lower Social Class f (N = 4,947) Not Lower Social Class 2,945 (59.5) Lower Social Class 2,002 (40.5) Marital Status (N = 5,509) Not Married 2,494 (45.3) Married 3,015 (54.7) Smoking Status (N = 2,788) Does Not Smoke 2,279 (81.7) Smokes 509 (18.3) Usual Place for Healthcare (N = 5,513) Does Not Have Usual Place 142 (2.6) Has Usual Place 5,371 (97.4) Usual Place for Healthcare Type (N = 5,365) Clinic or Health Center 1,101 (20.5) Doctor's Office or HMO 3,940 (73.4) Hospital Emergency Room 105 (2.0) Hospital Outpatient 155 (2.9) Other 64 (1.2) Told By Doctor to Take Prescription for High blood pressure (N = 4,401) No 61 (1.4) Yes 4,340 (98.6) Told By Doctor to Take a Prescription for Cholesterol (N = 4,814) No 1,198 (24.7) Yes 3,616 (74.6) Doctor Told You Have Diabetes (N = 5,509) No 3,700 (67.1) Yes 1,809 (32.8) Overall Adherence (N = 5,513) Not Adherent 1,136 (20.6) Adherent 4,377 (79.4) Abbreviations: HMO - Health maintenance organization a The "Other" race category contains those respondents who did not identify as Non-Hispanic Black or Non-Hispanic White. The "Other" race category included Mexican American [541 (9.8%)], Other Hispanic [496 (9.0%)], Non-Hispanic Asian [385 (7.0%)], and Other Races – Including Multiracial [171 (3.1%)]. b Ethnicity categories were developed from NHANES race/Hispanic origin categories. The "Hispanic" group included respondents identified as Mexican American or other Hispanic. The "non-Hispanic" group included respondents who were classified as Non-Hispanic White, Non-Hispanic Black, or Non-Hispanic Asian. Respondents identified as Other Race – Including Multiracial – were categorized as missing. c Alcohol consumption categories were calculated using responses for the number of days alcoholic drinks were consumed annually, the number of drinks consumed on those drinking days, and guidelines from previous literature.22 d Not employed included those reporting that they were not working at a job or business, looking for work, or retired. Those who reported working at a job or business were employed. e Respondents with Medicaid as at least one source of health insurance were included in the "Medicaid" category, respondents with Medicare (but not Medicaid) as at least one source of insurance were included in the "Medicare" category, and all other respondents without Medicaid or Medicare were included in the "Other" category. The insurance types included in the "Other" category included private insurance, Medi-Gap, military health care, state-sponsored health plans, other government insurance, and single-service health plans. f Respondents with annual family incomes of $25,000 or less were classified as lower social class. After allowing for study adjustments to the Demographic Assessment for Health Literacy (DAHL), 23 scores could range from 27.2 to 91.3. The mean health literacy (DAHL) score amongst respondents was 68.4 (standard deviation (SD) = 14.4), indicating marginal to adequate health literacy, with a minimum score of 27.2 and a maximum score of 91.3. The mean household income to poverty ratio was 2.0 (SD = 1.7), indicating a family income at 200% of the poverty level, 24 with a minimum of 0.0 and a maximum of 5.0. The mean prescription medication count was 5.2 (SD = 3.1), with a minimum of one and a maximum of 22 medications. In Table 2 , univariate analysis of categorical predictors revealed significant differences in adherence to medications based on structural determinants, including ethnicity (p = .038), gender (p = .009), and lower social class status (p = .023) as well as intermediary determinants, including the level of alcohol consumption 25 (p = .004), disability status (p = .014), ability to afford household balanced meals (p < .001), insurance (p = .010), marital status (p = .020), and whether or not they had a usual place for healthcare (p < .001). Univariate analysis of continuous predictors revealed no significant differences in adherence to medications based on prescription medication count (p = .209), structural determinants, including household income to poverty ratio (p = .560), or intermediary determinants, including health literacy level (p = .607). Table 2 Univariate analysis of overall high blood pressure, cholesterol and/or diabetes medication adherence with categorical predictors (N = 5,513). Medication Adherence Adherent N = 4,377 Not Adherent N = 1,136 Determinant Type Determ-inant Study Variable Level N (%) Weighted Frequency (%) N (%) Weighted Frequency (%) p-value Structural Determinants Gender Gender Female 2,243 (78.7) 14,895,216 (78.6) 606 (21.3) 4,057,443 (21.4) 0.009 a Male 2,134 (80.1) 12,127,712 (82.1) 530 (19.9) 2,651,076 (17.9) Race/Ethnicity Race Black 877 (77.7) 2,378,365 (77.2) 251 (22.3) 703,198 (22.8) 0.194 b Other 1,259 (79.0) 3,830,912 (79.8) 334 (21.0) 970,409 (20.2) White 2,241 (80.3) 20,813,651 (80.5) 551 (19.7) 5,034,913 (19.5) Race/Ethnicity Ethnicity Hispanic 798 (77.0) 1,944,734 (77.0) 239 (23.0) 581,497 (23.0) 0.038 a Not Hispanic 3,440 (79.9) 24,197,206 (80.3) 865 (20.1) 5,922,864 (19.7) Education Education <HS Grad 1,375 (79.6) 5,564,328 (81.0) 352 (20.4) 1,304,073 (19.0) 0.124 College Grad 865 (80.9) 7,196,222 (82.3) 204 (19.1) 1,551,196 (17.7) HS Grad 2,121 (78.7) 14,200,116 (78.8) 575 (21.3) 3,825,406 (21.2) Occupation Employment Status Not Employed 3,740 (79.3) 22,521,140 (79.8) 977 (20.7) 5,706,790 (20.2) 0.357 Employed 633 (80.0) 4,468,210 (81.9) 158 (20.0) 989,108 (18.1) Social Class Lower Social Class Not Lower Social Class 2,372 (80.5) 17,860,937 (81.2) 573 (19.5) 4,131,568 (18.8) 0.023 a Lower Social Class 1,561 (78.0) 6,822,390 (78.2) 441 (22.0) 1,902,092 (21.8) Intermediary Determinants Biological Factor Age Group 65–69 1,215 (78.6) 8,433,785 (80.8) 331 (21.4) 2,002,420 (19.2) 0.343 70–74 1,157 (81.4) 7,210,978 (81.3) 264 (18.6) 1,656,536 (18.7) 75+ 2,005 (78.8) 11,378,165 (78.9) 541 (21.2) 3,049,563 (21.1) Behaviors Alcohol Consumption Category Heavy 51 (86.4) 308623 (91.5) 8 (13.6) 28799 (8.5) 0.004 a Light 1,644 (80.5) 12,001,883 (81.2) 399 (19.5) 2,775,231 (18.8) Moderate 196 (84.1) 1,687,609 (87.1) 37 (15.9) 249,753 (12.9) Never 1,209 (77.3) 6,585,634 (77.8) 355 (22.7) 1,877,114 (22.2) Medication Access – Disability Status Disability Status No Disability 3,140 (81.3) 20,436,146 (81.3) 722 (18.7) 4,707,626 (18.7) 0.014 a Has Disability 1,234 (74.9) 6,565,770 (76.6) 414 (25.1) 2,000,893 (23.4) Material Circumstance Household Balanced Meals Could Afford 3,684 (80.6) 24,233,194 (81.0) 886 (19.4) 5,692,994 (19.0) < 0.001 a Could Not Afford 574 (72.7) 2,183,622 (72.5) 216 (27.3) 829,633 (27.5) Medication Access - Insurance Insurance Medicaid 511 (76.4) 1,851,559 (77.0) 158 (23.6) 551,630 (23.0) 0.010 a Medicare 3,285 (79.5) 21,900,045 (79.7) 849 (20.5) 5,582,851 (20.3) None 122 (78.2) 420,133 (77.5) 34 (21.8) 122,155 (22.5) Other 450 (82.9) 2,798,088 (86.2) 93 (17.1) 446,237 (13.8) Medication Access - Language Interview Language English 3,922 (79.4) 25,960,391 (80.1) 1,019 (20.6) 6,443,307 (19.9) 0.962 Spanish 3,922 (79.4) 25,960,391 (80.1) 1,019 (20.6) 6,443,307 (19.9) Psychosocial Marital Status Not Married 1,926 (77.2) 10,826,419 (77.9) 568 (22.8) 3,063,461 (22.1) 0.020 a Married 2,447 (81.2) 16,181,484 (81.6) 568 (18.8) 3,645,058 (18.4) Behaviors Smoking Status Does Not Smoke 1,807 (79.3) 11,639,086 (80.2) 472 (20.7) 2,880,040 (19.8) 0.428 Smokes 401 (78.8) 2,215,476 (82.4) 108 (21.2) 472,675 (17.6) Medication Access- Provider Availability Usual Place for Healthcare Does Not Have Usual Place 82 (57.7) 436,930 (58.7) 60 (42.3) 307,623 (41.3) < 0.001 a Has Usual Place 4,295 (80.0) 26,585,997 (80.6) 1,076 (20.0) 6,400,896 (19.4) Medication Access- Provider Availability Usual Place for Healthcare Type Clinic/Health Center 865 (78.6) 4,525,839 (81.2) 236 (21.4) 1,045,314 (18.8) 0.091 b Doctor Office 3,190 (81.0) 20,956,293 (80.8) 750 (19.0) 4,974,518 (19.2) Hos ER 73 (69.5) 281,622 (66.9) 32 (30.5) 139,496 (33.1) Hos OP 116 (74.8) 520,633 (81.1) 39 (25.2) 121,431 (18.9) Other 47 (73.4) 272,574 (72.0) 17 (26.6) 106,062 (28.0) Abbreviations: HS- high school; Grad- graduate; Hos- hospital; ER- emergency room; OP- outpatient a Predictors were significant at the alpha = 0.05 level. b Predictors with p values less than 0.20 were considered in the multivariate analysis. Based on the multicollinearity criterion used in this study, lower social class, household could afford balanced meals, ethnicity, and education predictors were excluded from the multivariable analysis. The remaining variables (gender and marital status) with multicollinearity were combined into a single patient demographic variable. The multivariable analysis (Table 3 , Fig. 2 ) revealed that overall significant differences in medication adherence existed based on two intermediary determinants: alcohol consumption and usual place for healthcare. Table 3 Multivariable analysis a of overall high blood pressure, cholesterol and/or diabetes medication adherence (N = 3,448). Determinant Type Multivariable analysis Estimate Odds Ratio (95% CI) p-value Race b 0.566 Structural Determinants Race (Black vs. White) -0.036 0.902 (0.727, 1.121) 0.637 Race (Other vs. White) -0.030 0.908 (0.694, 1.187) 0.736 Combined Structural-Intermediate Determinants Gender, Marital Status 0.097 Gender, Marital Status (Female Married vs. Female Not Married) -0.001 1.257 (0.855, 1.847) 0.992 Gender, Marital Status (Male Married vs. Female Not Married) 0.061 1.337 (0.993, 1.801) 0.497 Gender, Marital Status (Male Not Married vs. Female Not Married) 0.170 1.492 (1.050, 2.118) 0.134 Intermediary Determinants Alcohol Consumption Category c 0.034 d Alcohol Consumption Category (Light vs. Never) -0.276 1.164 (0.881, 1.538) 0.080 Alcohol Consumption Category (Moderate vs. Never) 0.078 1.657 (1.085, 2.531) 0.667 Alcohol Consumption Category (Heavy vs. Never) 0.625 2.866 (1.122, 7.318) 0.078 Disability Status (Disability vs. No Disability) -0.062 0.884 (0.659, 1.185) 0.404 Insurance e 0.080 Insurance (Medicaid vs. None) -0.189 0.885 (0.423, 1.853) 0.256 Insurance (Medicare vs. None) -0.144 0.926 (0.502, 1.709) 0.168 Insurance (Other vs. None) 0.401 1.597 (0.791, 3.224) 0.018 d Usual Place for Healthcare f 0.001 d Usual Place for Healthcare (Clinic/Health Center vs. None) 0.381 3.796 (1.904, 7.569) 0.019 d Usual Place for Healthcare (Doctor's Office or HMO vs. None) 0.505 4.297 (2.274, 8.118) 0.002 d Usual Place for Healthcare (Emergency Room vs. None) -0.103 2.341 (0.937, 5.850) 0.719 Usual Place for Healthcare (Hospital Outpatient vs. None) 0.450 4.068 (1.674, 9.888) 0.133 Usual Place for Healthcare (Other vs. None) -0.278 1.964 (0.593, 6.510) 0.531 Abbreviations: HMO - Health maintenance organization; CI – Confidence interval a Multivariable analysis for medication adherence utilized logistic regression. Predictors with p-values less than 0.20 in the univariate analyses were considered as predictors in the multivariable analysis. To decrease the effects of multicollinearity, predictors that were highly correlated (logistic regression comparisons with OR ≥ 2.477 corresponding to a Cohen's d of 0.50) with multiple other predictors were removed from the model. The remaining predictors with multicollinearity were combined into a single predictor variable 18 b The "Other" race category contains those respondents who did not identify as Non-Hispanic Black or Non-Hispanic White. "Other" races include Mexican American [541 (9.8%)], Other Hispanic [496 (9.0%)], Non-Hispanic Asian [385 (7.0%)], and Other Races – Including Multiracial [171 (3.1%)]. c Alcohol consumption categories were calculated using responses for the number of days alcoholic drinks were consumed annually, the number of drinks consumed on those drinking days, and guidelines from previous literature. 22 d Predictors were significant at the alpha = 0.05 level. e Respondents with Medicaid as at least one source of health insurance were reflected in the "Medicaid" category, respondents with Medicare (but not Medicaid) as at least one source of insurance were reflected in the "Medicare" category, all other respondents without Medicaid or Medicare are reflected in the "Other" category. Insurance types reflected in the "Other" category include private insurance, Medi-Gap, military health care, state-sponsored health plans, other government insurance, and single-service health plans. f Whether the respondent had any usual place for healthcare and the specific usual place for healthcare type were combined into one predictor variable as all responses for a usual place for healthcare type had a response of "Yes" for a usual place for healthcare. The combined variable includes original responses for the usual healthcare place type variable plus the "Does Not Have Usual Place" level from the usual healthcare binary variable. Alcohol consumption was significantly associated with overall medication adherence (p=.034), with a significant increasing trend as consumption increases. However, there were no significant pairwise associations with any consumption level vs. no consumption. The odds of being adherent to prescribed medications were 330% higher for those individuals who usually go to a Doctor's Office or health maintenance organization (HMO) for health care when compared to those who do not have a usual place to go for healthcare (p =.002) and 280% higher for those individuals who usually go to a Clinic/Health Center for health care when compared to those who do not have a usual place to go (p=.019). While the overall effect of insurance was not significant, there was a significant pairwise difference between patients with "other" (e.g., private) insurance vs. patients with no insurance. Patients with "other" (e.g., private) insurance were 60% more likely to be adherent to prescribed medications than patients with no insurance (p=.018). Discussion This pioneering study examined the association between SDOH and medication adherence among adults 65 years and older with high blood pressure, high cholesterol, and/or diabetes in the US, utilizing NHANES data and an integrated SDOH and medication access framework. The findings offer a comprehensive understanding of how SDOH influence US older adults’ medication adherence, with valuable implications for public health, policy, and future research. The univariate analysis results support the hypothesis as several structural and intermediary determinants of health were significantly associated with medication adherence. Examining implications for populations experiencing health disparities is vital to public health. According to the National Institutes of Health, populations experiencing health disparities include racial and ethnic minority groups, people with lower socioeconomic status, underserved rural communities and sexual and gender minority groups. 26 The study findings indicated that ethnicity and several indicators of lower socioeconomic status, including insurance status, lower social class, and ability to afford balanced meals, were significantly associated with medication adherence. Several studies highlight disparities in medication adherence among different health disparity groups in the US. For example, it is well-established that medication adherence is lower among racial/ethnic minorities and individuals with no insurance and lower socioeconomic status, corroborating the findings of this study related to ethnicity, insurance status and socioeconomic status. 27,28 Measures of associations with rurality or sexual and gender identity were not possible using the publicly available NHANES dataset. Future publicly available datasets should focus on including these data to facilitate the investigation of medication adherence among rural populations and sexual and gender minority groups. Some univariate analysis findings differed from those of previous literature studies, such as the nonsignificant association between medication adherence and health literacy. Post hoc analysis of a randomized clinical trial among patients aged 50 years or older with heart failure revealed that health literacy level strongly predicted medication adherence in the usual care group but not in the intervention group. 29 These inconsistencies in findings may be attributed to differences in measuring health literacy and adherence, defining "older adults," disease state focus areas, national vs. local and real-world vs. clinical trial settings. However, further research is needed to better understand the association between medication adherence and health literacy among older adults in the US. Surprisingly, the multivariable analysis results did not support our hypothesis, as only intermediary determinants of health remained significantly associated with medication adherence. One possible reason for structural determinants not being significantly associated with medication adherence is the presence of intermediary determinants in the multivariable analysis. When both the structural and intermediary determinants were included in the multivariate logistic regression model, the results for each predictor were calculated as if the remaining predictors were held constant and reported independently from associations with other determinants. 30 Structural determinants with significance in the univariate analysis but nonsignificance in the multivariable analysis imply that they are significant only due to their association with the other intermediary determinants and predictors. Thus, significant intermediary determinants were found to affect medication adherence without influencing variable associations. 30 The study findings indicate that when examined collectively, two modifiable intermediary determinants of health remained significantly associated with medication adherence: alcohol consumption and having a usual place for healthcare. The finding that alcohol consumption was significantly associated with overall medication adherence (p=.043), with a significant increasing trend as consumption increased, was unexpected as previous systematic review studies observed negative effects of alcohol consumption on medication adherence. However, evidence is inconsistent among non-HIV studies (e.g., high blood pressure, diabetes). 31,32 Future mixed-methods (using qualitative and quantitative approaches) research is warranted to better understand why these different effects of alcohol consumption on medication adherence are being observed. When examining pairwise associations, individuals with "other" types of insurance, such as private insurance, demonstrated a higher likelihood of medication adherence compared to uninsured patients. Access to healthcare through a comprehensive health insurance plan is a core component of SDOH that should be prioritized. However, ensuring accessibility remains a challenge for populations experiencing health disparities. The adoption of the Affordable Care Act in 2014 to subsidize health insurance was effective in improving medication adherence among eligible patients with hypertension and diabetes. 33 This observed improvement was attributed to the policy increasing healthcare access to populations living below the federal poverty line and older adults in the US; underscoring how health disparities can be significantly reduced through robust policies that prioritize this population. Therefore, policies that widen health insurance coverage should be enacted to address the implications of medication nonadherence, and the mechanism by which policies address SDOH should be further investigated. This study was not without limitations. First, we prioritized the wealth of publicly available SDOH factors in the NHANES database due to resource constraints. Therefore, we did not include variables such as medication costs as barriers to medication access in our analyses because they were not included in the NHANES database. Future research should utilize other databases that include these data, such as the Medical Expenditure Panel Survey (MEPS). These study findings are relevant to older adults with any combination of high blood pressure, cholesterol or diabetes. There could be differences in the association of SDOH associated with medication adherence according to specific disease state combinations and age groups, warranting additional research. The study used self-report medication adherence data, which may contain inaccuracies and responses influenced by a desire to present oneself favorably. Additionally, this approach to defining and measuring medication adherence could have inflated respondents' adherence rates, as there were no questions about the frequency or method of taking prescribed oral medications. Future research should explore these relationships using more comprehensive adherence measures. Conclusion Univariate analysis findings affirm the hypothesis that structural (e.g., ethnicity) and intermediary determinants of health (e.g., ability to afford balanced meals) influence medication adherence among older adults with high blood pressure, high cholesterol, and/or diabetes. Notably, the multivariable analysis highlights the significant relationship between two intermediary determinants of health—alcohol consumption and usual place of healthcare— and medication adherence. This underscores the theoretical framework suggesting that structural determinants impact health outcomes through intermediary determinants. These results offer valuable insights into public health interventions and policy recommendations. Further research is warranted to understand the observed increasing medication adherence trend with increased alcohol consumption. Additionally, exploring nuanced associations between social determinants of health and medication adherence across other age groups and common disease states presents an avenue for future investigation. Abbreviations CSDH: Commission on Social Determinants of Health HMO: health maintenance organization (HMO) MEPS: Medical Expenditure Panel Survey NHANES: National Health and Nutrition Examination Survey PQA: Pharmacy Quality Alliance SDOH: social determinants of health (SDOH) STROBE: Strengthening the Reporting of Observational Studies in Epidemiology US: United States WHO: World Health Organization Declarations Ethical approval and consent to participate: Not applicable. Datasets were publicly available and did not contain any personal identifiable information. Availability of data and materials All NHANES datasets supporting the conclusions of this article, interpretations, computations, and mapping of study variables to conceptual framework elements are available in the Figshare data dictionary, https://doi.org/10.6084/m9.figshare.21947018. This study's Figshare data dictionary is licensed as CC BY 4.0. It is free to share and adapt as long as the original authors are credited, and new creations are licensed. Competing interests The authors declare that they have no competing interests. Funding This publication was made possible, in part, with support from the Indiana Clinical and Translational Sciences Institute funded, in part by Grant Number UL1TR002529 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Authors' contributions OAAO: Conceptualization, Methodology, Validation, Formal analysis, Writing—original draft, Writing—review & editing, Visualization, Supervision, Project administration, Funding acquisition. TJH: Conceptualization, Methodology, Writing—review & editing, Funding acquisition. MLB: Conceptualization, Methodology, Writing—review & editing, Funding acquisition. LB: Validation, Formal analysis, Investigation, Data curation, Resources, Writing—review & editing, Visualization. ABA: Visualization, Writing—original draft, Writing—review & editing. FS: Conceptualization, Methodology, Formal analysis, Writing—review & editing, Supervision, Funding acquisition. Acknowledgments None References Caplan Z. U.S. Older Population Grew From 2010 to 2020 at Fastest Rate Since 1880 to 1890 [Internet]. Census.gov. 2023 [cited 2024 Jan 10]. Available from: https://www.census.gov/library/stories/2023/05/2020-census-united-states-older-population-grew.html Maresova P, Javanmardi E, Barakovic S, Barakovic Husic J, Tomsone S, Krejcar O, et al. Consequences of chronic diseases and other limitations associated with old age – a scoping review. BMC Public Health. 2019 Nov 1;19(1):1431. Yap AF, Thirumoorthy T, Kwan YH. Medication adherence in the elderly. J Clin Gerontol Geriatr. 2016 Jun 1;7(2):64–7. Lee S, Jiang L, Dowdy D, Hong A, Ory MG. Attitudes, Beliefs, and Cost-Related Medication Nonadherence Among Adults Aged 65 or Older With Chronic Diseases. Prev Chronic Dis. 2018;15. Ruscin MJ, Linnebur SA. Aging and Medications - Older People’s Health Issues [Internet]. Merck Manuals Consumer Version. 2022 [cited 2024 Jan 14]. Available from: https://www.merckmanuals.com/home/older-people%E2%80%99s-health-issues/aging-and-medications/aging-and-medications Mickelson RS, Holden RJ. Medication adherence: Staying within the boundaries of safety. Ergonomics. 2018 Jan 2;61(1):82–103. Available from: https://doi.org/10.1080/00140139.2017.1301574 Dan K. Medication non-adherence: a common and costly problem [Internet]. PAN Foundation. 2020 [cited 2024 Jan 8]. Available from: https://www.panfoundation.org/medication-non-adherence/ National Center for Health Statistics. Health, United States, [2019]: Table [006]. [Internet]. Hyattsville, MD; 2019 [cited 2023 Jan 16]. Available from: https://www.cdc.gov/nchs/hus/data-finder.htm NIMHD: Health disparity populations [Internet]. [cited 2023 Jan 16]. Available from: https://www.nimhd.nih.gov/about/overview/ Pharmacy Quality Alliance. Access to care: Development of a medication access framework for quality measurement [Internet]. 2019 [cited 2024 Jan 16]. Available from: https://www.pqaalliance.org/assets/Research/PQA-Access-to-Care-Report.pdf Ferdinand KC, Yadav K, Nasser SA, Clayton-Jeter HD, Lewin J, Cryer DR, et al. Disparities in hypertension and cardiovascular disease in blacks: The critical role of medication adherence. J Clin Hypertens Greenwich. 2017/09/01 ed. 2017 Oct;19(10):1015–24. Healthy people 2030: Social determinants of health [Internet]. [cited 2024 Jan 16]. Available from: https://health.gov/healthypeople/objectives-and-data/social-determinants-health Centers for Medicare and Medicaid Services. Social determinants of health state health official letter [Internet]. 2021 [cited 2024 Jan 16]. Available from: https://www.medicaid.gov/sites/default/files/2021-01/sho21001.pdf Blakely ML, Sherbeny F, Hastings TJ, Boyd L, Adeoye-Olatunde OA. Exploratory analysis of medication adherence and social determinants of health among older adults with diabetes. Explor Res Clin Soc Pharm [Internet]. 2023 Nov 15 [cited 2024 Jan 8];12:100371. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10696385/ National Council on Aging. Top 10 Chronic Conditions Affecting Older Adults [Internet]. Chronic Conditions for Older Adults. 2023 [cited 2024 Jan 8]. Available from: https://www.ncoa.org/article/the-top-10-most-common-chronic-conditions-in-older-adults Song Y, Liu X, Zhu X, Zhao B, Hu B, Sheng X, et al. Increasing trend of diabetes combined with hypertension or hypercholesterolemia: NHANES data analysis 1999-2012. Sci Rep. 2016/11/03 ed. 2016 Nov 2;6:36093. CDC. NCHS. About the National Health and Nutrition Examination Survey [Internet]. Hyattsville, MD; Available from: https://www.cdc.gov/nchs/nhanes/about_nhanes.htm von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008 Apr;61(4):344–9. World Health Organization. A conceptual framework for action on the social determinants of health [Internet]. World Health Organization; 2010 [cited 2024 Jan 16] p. 76. Available from: https://apps.who.int/iris/handle/10665/44489 Adeoye-Olatunde O. NHANES Data Dictionary; Social Determinants of Health and Medication Adherence in Older Adults with Prevalent Health Conditions in the United States [Internet]. Figshare; 2023 [cited 2024 Jan 15]. Available from: https://doi.org/10.6084/m9.figshare.21947018.v1 Frost J. Regression analysis: An intuitive guide for using and interpreting linear models. Statisics By Jim Publishing; 2019. Akinbami LJ, Chen TC, Davy O, Ogden CL, Fink S, Clark J, et al. National health and nutrition examination survey, 2017-March 2020 prepandemic file: Sample design, estimation, and analytic guidelines. Vital Health Stat Ser 1 Programs Collect Proced [Internet]. 2023 Jun 13;(190):1–36. Available from: https://pubmed.ncbi.nlm.nih.gov/35593699/ Hanchate AD, Ash AS, Gazmararian JA, Wolf MS, Paasche-Orlow MK. The demographic assessment for health literacy (DAHL): A new tool for estimating associations between health literacy and outcomes in national surveys. J Gen Intern Med. 2008/07/12 ed. 2008 Oct;23(10):1561–6. US Census Bureau. Poverty glossary [Internet]. [cited 2023 Jan 16]. Available from: https://www.census.gov/topics/income-poverty/poverty/about/glossary.html Butler L, Popkin BM, Poti JM. Associations of alcoholic beverage consumption with dietary intake, waist circumference, and body mass index in US adults: National health and nutrition examination survey 2003-2012. J Acad Nutr Diet. 2017/12/26 ed. 2018 Mar;118(3):409-420.e3. National Institutes of Health. Minority health and health disparities: Definitions and parameters [Internet]. 2023. Available from: https://www.nimhd.nih.gov/about/strategic-plan/nih-strategic-plan-definitions-and-parameters.html McQuaid EL, Landier W. Cultural issues in medication adherence: Disparities and directions. J Gen Intern Med [Internet]. 2018 Feb 1;33(2):200–6. Available from: https://doi.org/10.1007/s11606-017-4199-3 Fernandez-Lazaro CI, Adams DP, Fernandez-Lazaro D, Garcia-González JM, Caballero-Garcia A, Miron-Canelo JA. Medication adherence and barriers among low-income, uninsured patients with multiple chronic conditions. Res Soc Adm Pharm. 2019 Jun 1;15(6):744–53. Noureldin M, Plake KS, Morrow DG, Tu W, Wu J, Murray MD. Effect of health literacy on drug adherence in patients with heart failure. Pharmacother J Hum Pharmacol Drug Ther. 2012 Sep 1;32(9):819–26. Wilder ME, Kulie P, Jensen C, Levett P, Blanchard J, Dominguez LW, et al. The impact of social determinants of health on medication adherence: A systematic review and meta-analysis. J Gen Intern Med. 2021/01/31 ed. 2021 May;36(5):1359–70. Grodensky CA, Golin CE, Ochtera RD, Turner BJ. Systematic review: Effect of alcohol intake on adherence to outpatient medication regimens for chronic diseases. J Stud Alcohol Drugs. 2012/10/06 ed. 2012 Nov;73(6):899–910. Leslie KH, McCowan C, Pell JP. Adherence to cardiovascular medication: A review of systematic reviews. J Public Health Oxf. 2018;41(1):e84–94. Datta BK, Fazlul I. Role of Subsidized Coverage Eligibility in Medication Adherence Among Patients With Hypertension and Diabetes: Evidence From the NHIS 2011–2018. AJPM Focus. 2022 Dec 1;1(2):100021. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3872074","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269127778,"identity":"5f31e23a-d080-436b-b496-bd097b656772","order_by":0,"name":"Omolola A. Adeoye-Olatunde","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFCCBDApByIkwMwDRGoxJl1LYgPRWvjbkw8+5qmwSd8ukfvwNk8FgxzfjQT8WiTOPEs25jmTlrtzRrqxNc8ZBmNJQloYbuSYSfO2Hc7dcCONDchgSNxASIv8jfxv0rz/DqcbgLX8Y6gnqMXgRg5QZcPhBIiWBgYgg4AWwzPPjA3nHEsz3NnzjNlyzjEJw5lnHuDXInc8+eGDNzU28ubsaYw3QAy+4wRsAQEmHpALIWwJwspBgPEHQssoGAWjYBSMAkwAACsqRHxG7ZzqAAAAAElFTkSuQmCC","orcid":"","institution":"Purdue University College of Pharmacy Center for Health Equity and Innovation","correspondingAuthor":true,"prefix":"","firstName":"Omolola","middleName":"A.","lastName":"Adeoye-Olatunde","suffix":""},{"id":269127779,"identity":"464c88cb-117d-4e16-908a-d91f8a95c6b2","order_by":1,"name":"Tessa J. Hastings","email":"","orcid":"","institution":"University of South Carolina College of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Tessa","middleName":"J.","lastName":"Hastings","suffix":""},{"id":269127780,"identity":"1fd313ca-75fa-4a6f-8e6a-3a07a4d8e890","order_by":2,"name":"Michelle L. Blakely","email":"","orcid":"","institution":"University of Wyoming School of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"L.","lastName":"Blakely","suffix":""},{"id":269127781,"identity":"0b55ae7f-e66b-4855-91ac-194cb02e7eed","order_by":3,"name":"LaKeisha Boyd","email":"","orcid":"","institution":"Indiana University School of Medicine and Richard M. Fairbanks School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"LaKeisha","middleName":"","lastName":"Boyd","suffix":""},{"id":269127782,"identity":"9049971e-aabb-4a02-95ae-5bfd8fe0b70c","order_by":4,"name":"Azeez B. Aina","email":"","orcid":"","institution":"Purdue University College of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Azeez","middleName":"B.","lastName":"Aina","suffix":""},{"id":269127783,"identity":"00f8a919-8f52-4697-ad83-5f364c873ffb","order_by":5,"name":"Fatimah Sherbeny","email":"","orcid":"","institution":"Florida A\u0026M University, Institute of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Fatimah","middleName":"","lastName":"Sherbeny","suffix":""}],"badges":[],"createdAt":"2024-01-17 06:44:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3872074/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3872074/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50182712,"identity":"c0178b1f-a26e-4071-b39e-02fef9d36895","added_by":"auto","created_at":"2024-01-25 18:58:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185546,"visible":true,"origin":"","legend":"\u003cp\u003eAdeoye-Olatunde et al.'s integrated conceptual framework on social determinants of health and medication adherence.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3872074/v1/9038cb59d1a90ef41aeac612.png"},{"id":50181920,"identity":"82fba745-92ee-4a3a-ba5d-e455e2c05315","added_by":"auto","created_at":"2024-01-25 18:50:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131969,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for medication adherence multivariable analysis.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI – confidence interval; HMO - health maintenance organization\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Forest plot visualizing the odds ratios, 95% confidence intervals, and p-values from the multivariable logistic regression analysis of medication adherence (Table 3). Values were calculated using applicable NHANES survey weights, strata, and clusters.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3872074/v1/50e1fd42a70b3c9b242a0ceb.png"},{"id":51527399,"identity":"b14f16df-6619-4c6a-9ac4-e2c25f2ed694","added_by":"auto","created_at":"2024-02-23 06:07:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":719461,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3872074/v1/a3d0e368-6f81-419f-9d99-a9d5dd91fdec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Determinants of Health and Medication Adherence in Older Adults with Prevalent Health Conditions in the United States: An analysis of the National Health and Nutrition Examination Survey (NHANES) 2009-2018","fulltext":[{"header":"Background","content":"\u003cp\u003eOlder adults are the fastest growing segment of the US population in the past decade with nearly 55.8\u0026nbsp;million people.\u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/sup\u003e There is a high prevalence of multiple chronic diseases in this population.\u003csup\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003c/sup\u003e Not taking medications as prescribed, also known as nonadherence, is a common phenomenon among older adults taking more than one medication for multiple chronic conditions.\u003csup\u003e \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e \u003c/sup\u003e Over 35% of older adults in the United States (US) take at least five prescription medications for which the estimated nonadherence rate is up to 60%.\u003csup\u003e5,6\u003c/sup\u003e The avoidable healthcare expenditures associated with medication nonadherence are approximately \u003cspan\u003e$\u003c/span\u003e528.4\u0026nbsp;billion annually.\u003csup\u003e \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e \u003c/sup\u003e \u003c/p\u003e \u003cp\u003eNationally, chronic cardiovascular (e.g., high blood pressure/cholesterol) and diabetes conditions are leading causes of death particularly among health-disparity populations including racial and ethnic minority groups. Poor medication adherence and disparate social determinants of health (SDOH) play critical roles.\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e SDOH are \"the environmental conditions where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e National concerted efforts have been geared toward addressing SDOH, medication nonadherence, and health disparities related to these chronic conditions, but not concurrently or consistently.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur previously published paper, focused exclusively on diabetes, explored the nuanced relationship between older adults\u0026rsquo; SDOH and medication adherence.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e However, examining older adults\u0026rsquo; medication adherence across high blood pressure, high cholesterol, and diabetes conditions is warranted. First, in addition to the previously mentioned disparities in disease-related mortality, high blood pressure, high cholesterol, and diabetes rank in the top five most common chronic conditions for older adults in the United States (US).\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Second, any combination of high blood pressure, high cholesterol, and diabetes frequently occurs concurrently in one individual, and optimizing medications to manage these concurrent conditions may differ from managing only one condition.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Thus, the primary aim of this study was to identify and prioritize SDOH associated with medication adherence among a nationally representative sample of older adults with high blood pressure, high cholesterol, and/or diabetes in the US. We hypothesized that structural and intermediary determinants of health would be associated with medication adherence. Secondary aims included characterizing SDOH, estimating medication adherence, and describing implications for health disparity populations among older adults in the US diagnosed with high blood pressure, high cholesterol, and/or diabetes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e \u003cp\u003eThis cross-sectional study utilized 2009\u0026ndash;2018 National Health and Nutrition Examination Survey (NHANES) data to examine associations between SDOH and medication adherence in older adults with high blood pressure, high cholesterol, and/or diabetes in the US. NHANES, a public database, offers comprehensive health and nutrition data for a representative US sample.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The study adhered to STROBE guidelines for observational research.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConceptual framework\u003c/p\u003e \u003cp\u003eAdeoye-Olatunde et al.\u0026rsquo;s integrated conceptual framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) guided the categorization of SDOH covariates in the NHANES dataset, minimizing selection bias.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e First, the World Health Organization (WHO) Commission on Social Determinants of Health framework defines structural determinants as \"social determinants of health inequities,\" and these inequities function through intermediate determinants impacting health outcomes (e.g., medication adherence).\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Therefore, structural and intermediary determinants were operationalized as SDOH. Healthcare access and health outcomes were redefined as medication access and medication adherence respectively. Finally, unaddressed medication access barriers from the WHO framework were incorporated from the Pharmacy Quality Alliance (PQA) Medication Access framework.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThe study population for analysis included all respondents ages 65 and older from the 2009\u0026ndash;2018 NHANES datasets, whose doctors told them to take at least one prescription for high blood pressure, cholesterol and/or were told they had diabetes. Five biannual data years (2009\u0026ndash;2018) were downloaded from the NHANES database. Applicable datasets were combined by respondent study identification number. All other respondents' data were excluded from analyses. Due to the retrospective nature of the study, a formal power analysis was not performed. However, a post hoc power analysis produced a power greater than 99% with an alpha value of 0.05 when the difference in proportions between the groups was 4% or greater.\u003c/p\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003cp\u003eThe variables needed for analysis (as defined by the conceptual framework) were retained, while all other variables were eliminated from the combined dataset.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Notably, the NHANES dataset dichotomizes gender into \"male\" and \"female\" and does not define gender as a socially constructed term that may vary among cultures.\u003c/p\u003e \u003cp\u003eSome of the selected a priori variables were altered (e.g., assigning age categories of '65\u0026ndash;69', '70\u0026ndash;74', and '75+') and missing data were excluded listwise. When variables had unspecific response ranges (e.g., income-to-poverty ratios greater than or equal to 5), those ranges were considered missing. The study's outcome variable, medication adherence, was dichotomized into \"adherent\" and \"not adherent.\" Respondents were considered adherent if they responded that they were currently taking all prescribed oral medications for each study disease state they had (i.e., high blood pressure, high cholesterol, diabetes). Conversely, respondents not currently taking at least one of the prescribed oral medications were considered not adherent.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were employed to characterize the study population and selected variables. Univariate analyses for overall medication adherence utilized logistic regression for continuous predictors and Rao-Scott Chi-Square tests for categorical predictors. Logistic regression was used for multivariable analysis of medication adherence. Predictors with p-values less than 0.20 in the univariate analyses were considered as predictors in the multivariable analysis. To decrease the effects of multicollinearity, predictors that were highly correlated (logistic regression comparisons with an OR\u0026thinsp;\u0026ge;\u0026thinsp;2.477 corresponding to a Cohen's d of 0.50) with multiple other predictors were removed from the model. The remaining predictors with multicollinearity were combined into a single predictor variable.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e A 5% significance level was used for all tests. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC). Adjustments for the complex NHANES survey sampling design (differential clustering, stratification, and weighting) were included in each of the analyses.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total number of 5,513 respondents met the inclusion criteria. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that most respondents were 75 years of age or older (46.2%), identified as female (51.7%), Non-Hispanic White (50.6%), married (54.7%), completed at least a high school education, but did not graduate from college (49.1%), and not employed (85.6%). Across the sample (N\u0026thinsp;=\u0026thinsp;5,513), high blood pressure was most prevalent (78.7%), followed by high cholesterol (65.6%) and diabetes (32.8%). Most respondents (79.4%) adhered to prescribed medications for high blood pressure, cholesterol and/or diabetes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCategorical characteristics of the study sample (N\u0026thinsp;=\u0026thinsp;5,513).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group (N\u0026thinsp;=\u0026thinsp;5,513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026ndash;69 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,546 (28.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u0026ndash;74 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,421 (25.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,546 (46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (N\u0026thinsp;=\u0026thinsp;5,513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,849 (51.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,664 (48.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003csup\u003ea\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;5,513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,593 (28.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,128 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,792 (50.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003csup\u003eb\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;5,342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,037 (19.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,305 (80.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (N\u0026thinsp;=\u0026thinsp;5,492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; High School Graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,727 (31.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;= High School Graduate, but not College Graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,696 (49.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege Graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,069 (19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption Category\u003csup\u003ec\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;3,899)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever Drinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,564 (40.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLight Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,043 (52.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233 (6.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy Drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability Status (N\u0026thinsp;=\u0026thinsp;5,510)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,862 (70.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas Disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,648 (29.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Status\u003csup\u003ed\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;5,508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,717 (85.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e791 (14.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Balanced Meals (N\u0026thinsp;=\u0026thinsp;5,360)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCould Not Afford\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e790 (14.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCould Afford\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,570 (85.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance\u003csup\u003ee\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;5,502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e669 (12.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,134 (75.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e543 (9.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156 (2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterview Language (N\u0026thinsp;=\u0026thinsp;5,513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,941 (89.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e572 (10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower Social Class\u003csup\u003ef\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;4,947)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Lower Social Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,945 (59.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower Social Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,002 (40.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status (N\u0026thinsp;=\u0026thinsp;5,509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,494 (45.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,015 (54.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status (N\u0026thinsp;=\u0026thinsp;2,788)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes Not Smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,279 (81.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmokes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e509 (18.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsual Place for Healthcare (N\u0026thinsp;=\u0026thinsp;5,513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoes Not Have Usual Place\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e142 (2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHas Usual Place\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,371 (97.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsual Place for Healthcare Type (N\u0026thinsp;=\u0026thinsp;5,365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinic or Health Center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,101 (20.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoctor's Office or HMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,940 (73.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital Emergency Room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital Outpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155 (2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTold By Doctor to Take Prescription for High blood pressure (N\u0026thinsp;=\u0026thinsp;4,401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,340 (98.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTold By Doctor to Take a Prescription for Cholesterol (N\u0026thinsp;=\u0026thinsp;4,814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,198 (24.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,616 (74.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor Told You Have Diabetes (N\u0026thinsp;=\u0026thinsp;5,509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,700 (67.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,809 (32.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Adherence (N\u0026thinsp;=\u0026thinsp;5,513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Adherent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,136 (20.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdherent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,377 (79.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: HMO - Health maintenance organization\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003eThe \"Other\" race category contains those respondents who did not identify as Non-Hispanic Black or Non-Hispanic White. The \"Other\" race category included Mexican American [541 (9.8%)], Other Hispanic [496 (9.0%)], Non-Hispanic Asian [385 (7.0%)], and Other Races \u0026ndash; Including Multiracial [171 (3.1%)].\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003eEthnicity categories were developed from NHANES race/Hispanic origin categories. The \"Hispanic\" group included respondents identified as Mexican American or other Hispanic. The \"non-Hispanic\" group included respondents who were classified as Non-Hispanic White, Non-Hispanic Black, or Non-Hispanic Asian. Respondents identified as Other Race \u0026ndash; Including Multiracial \u0026ndash; were categorized as missing.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ec\u003c/sup\u003eAlcohol consumption categories were calculated using responses for the number of days alcoholic drinks were consumed annually, the number of drinks consumed on those drinking days, and guidelines from previous literature.22\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ed\u003c/sup\u003eNot employed included those reporting that they were not working at a job or business, looking for work, or retired. Those who reported working at a job or business were employed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ee\u003c/sup\u003eRespondents with Medicaid as at least one source of health insurance were included in the \"Medicaid\" category, respondents with Medicare (but not Medicaid) as at least one source of insurance were included in the \"Medicare\" category, and all other respondents without Medicaid or Medicare were included in the \"Other\" category. The insurance types included in the \"Other\" category included private insurance, Medi-Gap, military health care, state-sponsored health plans, other government insurance, and single-service health plans.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003ef\u003c/sup\u003eRespondents with annual family incomes of $25,000 or less were classified as lower social class.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026gt;\u003c/p\u003e \u003cp\u003eAfter allowing for study adjustments to the Demographic Assessment for Health Literacy (DAHL),\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e scores could range from 27.2 to 91.3. The mean health literacy (DAHL) score amongst respondents was 68.4 (standard deviation (SD)\u0026thinsp;=\u0026thinsp;14.4), indicating marginal to adequate health literacy, with a minimum score of 27.2 and a maximum score of 91.3. The mean household income to poverty ratio was 2.0 (SD\u0026thinsp;=\u0026thinsp;1.7), indicating a family income at 200% of the poverty level,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e with a minimum of 0.0 and a maximum of 5.0. The mean prescription medication count was 5.2 (SD\u0026thinsp;=\u0026thinsp;3.1), with a minimum of one and a maximum of 22 medications.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, univariate analysis of categorical predictors revealed significant differences in adherence to medications based on structural determinants, including ethnicity (p\u0026thinsp;=\u0026thinsp;.038), gender (p\u0026thinsp;=\u0026thinsp;.009), and lower social class status (p\u0026thinsp;=\u0026thinsp;.023) as well as intermediary determinants, including the level of alcohol consumption\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e (p\u0026thinsp;=\u0026thinsp;.004), disability status (p\u0026thinsp;=\u0026thinsp;.014), ability to afford household balanced meals (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), insurance (p\u0026thinsp;=\u0026thinsp;.010), marital status (p\u0026thinsp;=\u0026thinsp;.020), and whether or not they had a usual place for healthcare (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Univariate analysis of continuous predictors revealed no significant differences in adherence to medications based on prescription medication count (p\u0026thinsp;=\u0026thinsp;.209), structural determinants, including household income to poverty ratio (p\u0026thinsp;=\u0026thinsp;.560), or intermediary determinants, including health literacy level (p\u0026thinsp;=\u0026thinsp;.607).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of overall high blood pressure, cholesterol and/or diabetes medication adherence with categorical predictors (N\u0026thinsp;=\u0026thinsp;5,513).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eMedication Adherence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eAdherent\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;4,377\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNot Adherent\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,136\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeterminant Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeterm-inant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWeighted Frequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eWeighted Frequency (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003e\u003cb\u003eStructural Determinants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,243 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,895,216 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e606 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,057,443 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,134 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,127,712 (82.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e530 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,651,076 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e877 (77.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,378,365 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e251 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e703,198 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.194\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,259 (79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,830,912 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e334 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e970,409 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,241 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20,813,651 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e551 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,034,913 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRace/Ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e798 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,944,734 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e239 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e581,497 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.038\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,440 (79.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,197,206 (80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e865 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,922,864 (19.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;HS Grad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,375 (79.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,564,328 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e352 (20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,304,073 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCollege Grad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e865 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,196,222 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e204 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,551,196 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHS Grad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,121 (78.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14,200,116 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e575 (21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,825,406 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEmployment Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,740 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22,521,140 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e977 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,706,790 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e633 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,468,210 (81.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e158 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e989,108 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSocial Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLower Social Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Lower Social Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,372 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,860,937 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e573 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,131,568 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.023\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower Social Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,561 (78.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,822,390 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e441 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,902,092 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"27\" rowspan=\"28\"\u003e \u003cp\u003e\u003cb\u003eIntermediary Determinants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBiological Factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,215 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8,433,785 (80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e331 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,002,420 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,157 (81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,210,978 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e264 (18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,656,536 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,005 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,378,165 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e541 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,049,563 (21.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBehaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003cp\u003eConsumption Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51 (86.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e308623 (91.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8 (13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e28799 (8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,644 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,001,883 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e399 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,775,231 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e196 (84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,687,609 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e37 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e249,753 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,209 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,585,634 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e355 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,877,114 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedication Access \u0026ndash; Disability Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisability Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,140 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20,436,146 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e722 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,707,626 (18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.014\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHas Disability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,234 (74.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,565,770 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e414 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,000,893 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaterial Circumstance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHousehold\u003c/p\u003e \u003cp\u003eBalanced Meals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCould Afford\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,684 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24,233,194 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e886 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,692,994 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCould Not Afford\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e574 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,183,622 (72.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e216 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e829,633 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMedication Access - Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eInsurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e511 (76.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,851,559 (77.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e158 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e551,630 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,285 (79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21,900,045 (79.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e849 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,582,851 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e122 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e420,133 (77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e122,155 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e450 (82.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,798,088 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e446,237 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedication Access - Language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInterview Language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,922 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25,960,391 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,019 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,443,307 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,922 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25,960,391 (80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,019 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,443,307 (19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePsychosocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,926 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10,826,419 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e568 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,063,461 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.020\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,447 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16,181,484 (81.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e568 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,645,058 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBehaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDoes Not Smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,807 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,639,086 (80.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e472 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,880,040 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmokes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e401 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,215,476 (82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e108 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e472,675 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMedication Access- Provider Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUsual Place for Healthcare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDoes Not Have Usual Place\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e436,930 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60 (42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e307,623 (41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHas Usual Place\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,295 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26,585,997 (80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,076 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,400,896 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMedication Access- Provider Availability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eUsual Place for Healthcare Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClinic/Health Center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e865 (78.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,525,839 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e236 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,045,314 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.091\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDoctor Office\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,190 (81.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20,956,293 (80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e750 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,974,518 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHos ER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73 (69.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e281,622 (66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e139,496 (33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHos OP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e116 (74.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e520,633 (81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e121,431 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47 (73.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e272,574 (72.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e106,062 (28.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: HS- high school; Grad- graduate; Hos- hospital; ER- emergency room; OP- outpatient\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003e Predictors were significant at the alpha\u0026thinsp;=\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003e Predictors with p values less than 0.20 were considered in the multivariate analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026gt;\u003c/p\u003e \u003cp\u003eBased on the multicollinearity criterion used in this study, lower social class, household could afford balanced meals, ethnicity, and education predictors were excluded from the multivariable analysis. The remaining variables (gender and marital status) with multicollinearity were combined into a single patient demographic variable. The multivariable analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that overall significant differences in medication adherence existed based on two intermediary determinants: alcohol consumption and usual place for healthcare.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable analysis\u003csup\u003ea\u003c/sup\u003e of overall high blood pressure, cholesterol and/or diabetes medication adherence (N\u0026thinsp;=\u0026thinsp;3,448).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeterminant Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eStructural Determinants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace (Black vs. White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.902 (0.727, 1.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRace (Other vs. White)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.908 (0.694, 1.187)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eCombined\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eStructural-Intermediate Determinants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Marital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Marital Status (Female Married vs. Female Not Married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.257 (0.855, 1.847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Marital Status (Male Married vs. Female Not Married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.337 (0.993, 1.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender, Marital Status (Male Not Married vs. Female Not Married)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.492 (1.050, 2.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003e\u003cb\u003eIntermediary Determinants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol Consumption Category\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol Consumption Category (Light vs. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.164 (0.881, 1.538)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol Consumption Category (Moderate vs. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.657 (1.085, 2.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol Consumption Category (Heavy vs. Never)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.866 (1.122, 7.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDisability Status (Disability vs. No Disability)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884 (0.659, 1.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsurance \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsurance (Medicaid vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.885 (0.423, 1.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsurance (Medicare vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.926 (0.502, 1.709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInsurance (Other vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.597 (0.791, 3.224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Place for Healthcare\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Place for Healthcare (Clinic/Health Center vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.796 (1.904, 7.569)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Place for Healthcare (Doctor's Office or HMO vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.297 (2.274, 8.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Place for Healthcare (Emergency Room vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.341 (0.937, 5.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Place for Healthcare (Hospital Outpatient vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.068 (1.674, 9.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsual Place for Healthcare (Other vs. None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.964 (0.593, 6.510)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: HMO - Health maintenance organization; CI \u0026ndash; Confidence interval\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Multivariable analysis for medication adherence utilized logistic regression. Predictors with p-values less than 0.20 in the univariate analyses were considered as predictors in the multivariable analysis. To decrease the effects of multicollinearity, predictors that were highly correlated (logistic regression comparisons with OR\u0026thinsp;\u0026ge;\u0026thinsp;2.477 corresponding to a Cohen's d of 0.50) with multiple other predictors were removed from the model. The remaining predictors with multicollinearity were combined into a single predictor variable\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eThe \"Other\" race category contains those respondents who did not identify as Non-Hispanic Black or Non-Hispanic White. \"Other\" races include Mexican American [541 (9.8%)], Other Hispanic [496 (9.0%)], Non-Hispanic Asian [385 (7.0%)], and Other Races \u0026ndash; Including Multiracial [171 (3.1%)].\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ec\u003c/sup\u003eAlcohol consumption categories were calculated using responses for the number of days alcoholic drinks were consumed annually, the number of drinks consumed on those drinking days, and guidelines from previous literature.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ed\u003c/sup\u003e Predictors were significant at the alpha\u0026thinsp;=\u0026thinsp;0.05 level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ee\u003c/sup\u003eRespondents with Medicaid as at least one source of health insurance were reflected in the \"Medicaid\" category, respondents with Medicare (but not Medicaid) as at least one source of insurance were reflected in the \"Medicare\" category, all other respondents without Medicaid or Medicare are reflected in the \"Other\" category. Insurance types reflected in the \"Other\" category include private insurance, Medi-Gap, military health care, state-sponsored health plans, other government insurance, and single-service health plans.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ef\u003c/sup\u003e Whether the respondent had any usual place for healthcare and the specific usual place for healthcare type were combined into one predictor variable as all responses for a usual place for healthcare type had a response of \"Yes\" for a usual place for healthcare. The combined variable includes original responses for the usual healthcare place type variable plus the \"Does Not Have Usual Place\" level from the usual healthcare binary variable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026lt;Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026gt;\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cp\u003eAlcohol consumption was significantly associated with overall medication adherence (p=.034), with a significant increasing trend as consumption increases. However, there were no significant pairwise associations with any consumption level vs. no consumption. The odds of being adherent to prescribed medications were 330% higher for those individuals who usually go to a Doctor\u0026apos;s Office or health maintenance organization (HMO) for health care when compared to those who do not have a usual place to go for healthcare (p =.002) and 280% higher for those individuals who usually go to a Clinic/Health Center for health care when compared to those who do not have a usual place to go (p=.019). While the overall effect of insurance was not significant, there was a significant pairwise difference between patients with \u0026quot;other\u0026quot; (e.g., private) insurance vs. patients with no insurance. Patients with \u0026quot;other\u0026quot; (e.g., private) insurance were 60% more likely to be adherent to prescribed medications than patients with no insurance (p=.018).\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eThis pioneering study examined the association between SDOH and medication adherence among adults 65 years and older with high blood pressure, high cholesterol, and/or diabetes in the US, utilizing NHANES data and an integrated SDOH and medication access framework. The findings offer a comprehensive understanding of how SDOH influence US older adults\u0026rsquo; medication adherence, with valuable implications for public health, policy, and future research.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The univariate analysis results support the hypothesis as several structural and intermediary determinants of health were significantly associated with medication adherence. Examining implications for populations experiencing health disparities is vital to public health. According to the National Institutes of Health, populations experiencing health disparities include racial and ethnic minority groups, people with lower socioeconomic status, underserved rural communities and sexual and gender minority groups.\u003csup\u003e26\u003c/sup\u003e The study findings indicated that ethnicity and several indicators of lower socioeconomic status, including insurance status, lower social class, and ability to afford balanced meals, were significantly associated with medication adherence. Several studies highlight disparities in medication adherence among different health disparity groups in the US. For example, it is well-established that medication adherence is lower among racial/ethnic minorities and individuals with no insurance and lower socioeconomic status, corroborating the findings of this study related to ethnicity, insurance status and socioeconomic status.\u003csup\u003e27,28\u003c/sup\u003e Measures of associations with rurality or sexual and gender identity were not possible using the publicly available NHANES dataset. Future publicly available datasets should focus on including these data to facilitate the investigation of medication adherence among rural populations and sexual and gender minority groups. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSome univariate analysis findings differed from those of previous literature studies, such as the nonsignificant association between medication adherence and health literacy. Post hoc analysis of a randomized clinical trial among patients aged 50 years or older with heart failure revealed that health literacy level strongly predicted medication adherence in the usual care group but not in the intervention group.\u003csup\u003e29\u003c/sup\u003e These inconsistencies in findings may be attributed to differences in measuring health literacy and adherence, defining \u0026quot;older adults,\u0026quot; disease state focus areas, national vs. local and real-world vs. clinical trial settings. However, further research is needed to better understand the association between medication adherence and health literacy among older adults in the US.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Surprisingly, the multivariable analysis results did not support our hypothesis, as only intermediary determinants of health remained significantly associated with medication adherence. One possible reason for structural determinants not being significantly associated with medication adherence is the presence of intermediary determinants in the multivariable analysis. When both the structural and intermediary determinants were included in the multivariate logistic regression model, the results for each predictor were calculated as if the remaining predictors were held constant and reported independently from associations with other determinants.\u003csup\u003e30\u003c/sup\u003e Structural determinants with significance in the univariate analysis but nonsignificance in the multivariable analysis imply that they are significant only due to their association with the other intermediary determinants and predictors. Thus, significant intermediary determinants were found to affect medication adherence without influencing variable associations.\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe study findings indicate that when examined collectively, two modifiable intermediary determinants of health remained significantly associated with medication adherence: alcohol consumption and having a usual place for healthcare. The finding that alcohol consumption was significantly associated with overall medication adherence (p=.043), with a significant increasing trend as consumption increased, was unexpected as previous systematic review studies observed negative effects of alcohol consumption on medication adherence. However, evidence is inconsistent among non-HIV studies (e.g., high blood pressure, diabetes).\u003csup\u003e31,32\u003c/sup\u003e Future mixed-methods (using qualitative and quantitative approaches) research is warranted to better understand why these different effects of alcohol consumption on medication adherence are being observed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen examining pairwise associations, individuals with \u0026quot;other\u0026quot; types of insurance, such as private insurance, demonstrated a higher likelihood of medication adherence compared to uninsured patients. Access to healthcare through a comprehensive health insurance plan is a core component of SDOH that should be prioritized. However, ensuring accessibility remains a challenge for populations experiencing health disparities. The adoption of the Affordable Care Act in 2014 to subsidize health insurance was effective in improving medication adherence among eligible patients with hypertension and diabetes.\u003csup\u003e33\u003c/sup\u003e This observed improvement was attributed to the policy increasing healthcare access to populations living below the federal poverty line and older adults in the US; underscoring how health disparities can be significantly reduced through robust policies that prioritize this population. Therefore, policies that widen health insurance coverage should be enacted to address the implications of medication nonadherence, and the mechanism by which policies address SDOH should be further investigated.\u003c/p\u003e\n\u003cp\u003eThis study was not without limitations. First, we prioritized the wealth of publicly available SDOH factors in the NHANES database due to resource constraints. Therefore, we did not include variables such as medication costs as barriers to medication access in our analyses because they were not included in the NHANES database. Future research should utilize other databases that include these data, such as the Medical Expenditure Panel Survey (MEPS). These study findings are relevant to older adults with any combination of high blood pressure, cholesterol or diabetes. There could be differences in the association of SDOH associated with medication adherence according to specific disease state combinations and age groups, warranting additional research. The study used self-report medication adherence data, which may contain inaccuracies and responses influenced by a desire to present oneself favorably. Additionally, this approach to defining and measuring medication adherence could have inflated respondents\u0026apos; adherence rates, as there were no questions about the frequency or method of taking prescribed oral medications. Future research should explore these relationships using more comprehensive adherence measures. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUnivariate analysis findings affirm the hypothesis that structural (e.g., ethnicity) and intermediary determinants of health (e.g., ability to afford balanced meals) influence medication adherence among older adults with high blood pressure, high cholesterol, and/or diabetes. Notably, the multivariable analysis highlights the significant relationship between two intermediary determinants of health\u0026mdash;alcohol consumption and usual place of healthcare\u0026mdash; and medication adherence. This underscores the theoretical framework suggesting that structural determinants impact health outcomes through intermediary determinants. These results offer valuable insights into public health interventions and policy recommendations. Further research is warranted to understand the observed increasing medication adherence trend with increased alcohol consumption. Additionally, exploring nuanced associations between social determinants of health and medication adherence across other age groups and common disease states presents an avenue for future investigation.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eCSDH: Commission on Social Determinants of Health\u003c/p\u003e\n\u003cp\u003eHMO: health maintenance organization (HMO)\u003c/p\u003e\n\u003cp\u003eMEPS: Medical Expenditure Panel Survey\u003c/p\u003e\n\u003cp\u003eNHANES: National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003ePQA: Pharmacy Quality Alliance\u003c/p\u003e\n\u003cp\u003eSDOH: social determinants of health (SDOH)\u003c/p\u003e\n\u003cp\u003eSTROBE: Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003eUS: United States\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical approval and consent to participate:\u003c/p\u003e\n\u003cp\u003eNot applicable. Datasets were publicly available and did not contain any personal identifiable information.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eAll NHANES datasets supporting the conclusions of this article, interpretations, computations, and mapping of study variables to conceptual framework elements are available in the Figshare data dictionary, https://doi.org/10.6084/m9.figshare.21947018. This study\u0026apos;s Figshare data dictionary is licensed as CC BY 4.0. It is free to share and adapt as long as the original authors are credited, and new creations are licensed.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis publication was made possible, in part, with support from the Indiana Clinical and Translational Sciences Institute funded, in part by Grant Number UL1TR002529 from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eOAAO: Conceptualization, Methodology, Validation, Formal analysis, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing, Visualization, Supervision, Project administration, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eTJH: Conceptualization, Methodology, Writing\u0026mdash;review \u0026amp; editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eMLB: Conceptualization, Methodology, Writing\u0026mdash;review \u0026amp; editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eLB: Validation, Formal analysis, Investigation, Data curation, Resources, Writing\u0026mdash;review \u0026amp; editing, Visualization.\u003c/p\u003e\n\u003cp\u003eABA: Visualization, Writing\u0026mdash;original draft, Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eFS: Conceptualization, Methodology, Formal analysis, Writing\u0026mdash;review \u0026amp; editing, Supervision, Funding acquisition.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNone\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCaplan Z. 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AJPM Focus. 2022 Dec 1;1(2):100021. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medication Adherence, Social Determinants of Health, Older Adults, High blood pressure, High cholesterol, Diabetes","lastPublishedDoi":"10.21203/rs.3.rs-3872074/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3872074/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe older adult population is rapidly expanding in the United States (US), with high blood pressure, high cholesterol, and diabetes ranking among the top health conditions for older adults. Medication nonadherence, not taking medications as prescribed, is prevalent among those managing multiple chronic conditions. Despite its complexity, evidence is lacking on the social determinants of health (SDOH) influencing medication adherence among older adults with high blood pressure, high cholesterol, and/or diabetes in the US. Thus, the primary objective of this study was to identify and prioritize SDOH associated with medication adherence among a nationally representative sample of US older adults with high blood pressure, high cholesterol, and/or diabetes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing the World Health Organization Commission on Social Determinants of Health and Pharmacy Quality Alliance Medication Access Conceptual Frameworks, publicly available National Health and Nutrition Examination Survey datasets (2009\u0026ndash;2018) were cross-sectionally analyzed among respondents aged 65 and older with study diseases. Respondents reporting taking their study disease state medication(s) were considered adherent. Data analysis included descriptive statistics, Rao-Scott Chi-Square tests, and logistic regression analyses. Highly correlated predictors were removed to address multicollinearity, and the rest were consolidated into a single variable. The study used a significance level of 0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAnalyses included 5,513 respondents' data. Univariate analysis showed that several structural (gender, p\u0026thinsp;=\u0026thinsp;.009; ethnicity, p\u0026thinsp;=\u0026thinsp;.038; social class, p\u0026thinsp;=\u0026thinsp;.023) and intermediary (e.g., level of alcohol consumption, p\u0026thinsp;=\u0026thinsp;.004; disability status, p\u0026thinsp;=\u0026thinsp;.014; affordability of household balanced meals, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) determinants of health were significantly associated with medication adherence. Multivariable analysis revealed significant differences in medication adherence for alcohol consumption (p\u0026thinsp;=\u0026thinsp;.034) and usual place for healthcare (p\u0026thinsp;=\u0026thinsp;.001). For instance, individuals who usually go to a doctor\u0026rsquo;s office or health maintenance organization have 330% higher odds of adhering to medications than those with no usual place for healthcare (p\u0026thinsp;=\u0026thinsp;.002).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eStudy findings underscore pertinent implications for public health and policy, prioritizing specific SDOH most likely to affect medication adherence in common chronic conditions among older adults in the US. Strikingly, the observed relationship between alcohol consumption trends and adherence is a distinct finding warranting further investigation.\u003c/p\u003e","manuscriptTitle":"Social Determinants of Health and Medication Adherence in Older Adults with Prevalent Health Conditions in the United States: An analysis of the National Health and Nutrition Examination Survey (NHANES) 2009-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 18:50:31","doi":"10.21203/rs.3.rs-3872074/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cdab3920-54c2-4635-a522-854b5061dc05","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-23T05:59:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 18:50:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3872074","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3872074","identity":"rs-3872074","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}