Abstract
Purpose:
Determine the association between incremental increases in the number of social risk factors and the prevalence of any disability and disability type.
Design:
The cross-sectional analysis was conducted using 2017 Behavioral Risk Factor Surveillance System data from states whose surveys included items about social risk factors.
Setting:
Respondents from 17 US states.
Subjects:
Respondents included 136 432 adults.
Measures:
Dichotomized social risk factors included food, housing, and financial insecurity, unsafe neighborhood, and healthcare access hardship.
Analysis:
Weighted χ2 and logistic regression analyses adjusted for demographic characteristics, measures of socioeconomic position, and comorbid health conditions were used to examine differences in the prevalence of disability by social risk factor and via a social risk index created by summing the social risk factors.
Results:
Compared to those reporting 0 social risk factors, respondents reporting ≥4 had more than thrice the odds of reporting a cognition ((adjusted odds ratio [AOR]=3.37; 95%CI [2.75–4.13]), independent living (AOR=3.24 [2.52–4.15]), self-care (AOR=3.33 [2.55–4.34]), or any disability (AOR=3.90 [3.24–4.70]); more than twice the odds of reporting a vision (AOR=2.61 [1.93–3.52]) or mobility (AOR=2.72 [2.16–3.41]) disability; and more than 1.5 times the odds of reporting a hearing disability (AOR=1.59 [1.22–2.07]).
Conclusions:
Incremental increases in the number of social risk factors were independently associated with higher odds of disability. Intervention efforts should address the social context of US adults with disabilities to improve health outcomes.
Keywords: social determinants of health, social risk factors, health equity, healthcare disparities, health status disparities, outcomes, PURPOSE
Approximately 1 in 4 noninstitutionalized United States (US) adults have at least one disability.1 In 2006, disability associated healthcare expenditures totaled nearly 400 billion dollars within the US, representing 26.7% of all health care expenditures.2 Despite significant healthcare related spending, people with disabilities fare far worse across a number of health indicators compared to people without disabilities.3 Moreover, people with disabilities self-report their health to be fair or poor at four times the rate of people without disabilities.4 These data have led some to consider persons with disabilities as an unrecognized health disparities population.5 Recently, however, more attention has been given to the role of social determinants of health (SDHs) in disparities among people with disabilities.6
Social determinants of health are broadly defined as the conditions in which people are born, work, live and play and include areas such as economic stability, education, social and community context, and built environment.7 Social risk factors are individual-level adverse SDH that can be identified through screening tools (eg positive for housing insecurity), while social needs are social risks that are prioritized by patients (eg request for housing assistance).8 Disparities related to social risk factors have been documented among people with disabilities. For example, adults with disabilities are 2.5 times more likely to report delaying or skipping healthcare interventions entirely because of cost regardless of specific disability.9,10 Perceived safety hazards within neighborhoods of adults with disabilities living below the poverty line predict increased risk of mobility disability.11,12 In addition, detrimental changes in social risk factors such as employment, income, and housing are known to occur following disability leading to further declines in health.13–15
Despite the majority of published literature examining one social risk factor at a time, these factors do not routinely exist in isolation but instead occur in combinations of social risk factors.16 Shifting from a single risk factor analysis to a more comprehensive perspective that seeks to understand the complexity of co-occurring social risk factors may offer greater insight into the impact of the accumulation of influences on health.17,18 However, no studies to date have examined the association of multiple or co-occurring social risk factors with disability prevalence. Thus, the objective of the current study was to determine the association between multiple or co-occurring social risk factors and the prevalence of impairments in hearing, vision, cognition, mobility, self-care, independent living. Our goal is to explore the value of a social risk index, which is a composite metric that measures changes in a representative group of systemic social issues, or compounding measure that aggregates multiple indicators.19 Our social risk index represents the number of modifiable social risk factors present where higher scores indicating greater exposure and potential risk. This measure more comprehensively assesses social risk factors than any single factor and therefore may better describe the relationship between social risk factors and the prevalence of disability.
Methods
This study adheres to the Reporting of studies Conducted using Observational Routinely collected Data (RECORD) guidelines.20 No patients were involved in the design or conduct of this study. This study was determined to be exempt by the Institutional Review Board at Duke University (Pro00106743).
Design
This study was retrospective analysis of the Centers for Disease Control and Prevention’s (CDC) 2017 Behavioral Risk Factor Surveillance System (BRFSS) survey from 17 states that chose to administer the SDH module. The BRFSS is an annual, nationally representative computer-assisted telephone survey of health-related risk behaviors, health conditions, and use of preventative services among noninstitutionalized adults 18 years and older.
Sample
Of the states that administered the SDH module, 13 (Florida, Georgia, Iowa, Kentucky, Massachusetts, Minnesota, Mississippi, New Hampshire, Pennsylvania, Utah, West Virginia, Wisconsin, and Wyoming) administered the module to their entire sample while 4 (Colorado, Maryland, Ohio, and Oklahoma) administered the module to 1 or more splits in their samples. Split samples maximize information gathering while reducing survey fatigue by dividing the sample into equivalent probability-based samples. We merged the sample split data sets with the national BRFSS data sets using publicly available 2017 BRFSS data files from the CDC. More information about split samples methods and use is available from the CDC.21
Measures
Social Risk Factor Predictor Variables
Based on the Commission on Social Determinants of Health Final Report published by World Health Organization,7 we selected all 6 questions from the BRFSS SDH module and 1 question on health care access from the core BRFSS survey. We combined these variables into 5 social risk factors: food insecurity, housing insecurity, financial insecurity, unsafe neighborhood, and healthcare access hardship. A positive response for food insecurity was defined as a response of “often true” or “sometimes true” to the questions “The food that I bought just didn’t last, and I didn’t have money to get more” and “I couldn’t afford to eat balanced meals.” A positive response for housing insecurity was defined as a response of “yes” to the question “During the last 12 months, was there a time when you were not able to pay your mortgage, rent or utility bills?” A positive response for financial insecurity was defined as a response of “not have enough money to make ends meet” or “have just enough money to make ends meet” to the question “In general, how do your finances usually work out at the end of the month?” A positive response to unsafe neighborhood was defined as a response of “unsafe” or “extremely unsafe” to the question “How safe from crime do you consider your neighborhood to be?” A positive response for health care access hardship was defined as a response of “yes” to the question “Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?” Each response was dichotomized to indicate exposure. Positive responses to each social risk factor were summed to create a social risk index. The social risk index has potential scores ranging from 0 (no social risk factors reported) to 5 (a positive response to each social risk factor was reported). Higher scores indicate the presence of an increased number of social risk factors.
Disability Related Outcome Variables
Respondents were identified as having 1 of the 6 disability types if they answered “yes” to the relevant question. Respondents who responded “yes” to at least one of the disability questions were identified as having any disability. The questions for specific disability types are: “Are you deaf or do you have serious difficulty hearing?” (hearing); “Are you blind, or do you have serious difficulty seeing, even when wearing glasses?” (vision); “Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?” (cognition); “Do you have serious difficulty walking or climbing stairs?” (mobility); “Do you have difficulty dressing or bathing?” (self-care); and “Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctor’s office or shopping?” (independent living). These 6 questions are standardized by the US Department of Health and Human Services and are used across nationally-representative surveys including the American Community Survey, the Survey of Income and Program Participation, and the Current Population Survey.22
Covariates
We selected covariates based on their potential to influence the exposure and/or outcome.23 Demographic characteristics included age, gender, race/ethnicity, marital status, and health insurance. Measures of socioeconomic position included educational attainment, income, and home ownership. Health conditions included heart attack, coronary heart disease, stroke, asthma, skin cancer, other cancer, chronic obstructive pulmonary disease (COPD), depression, chronic kidney disease, obesity, and multimorbidity (defined as 2 or more comorbid conditions).24 We included these health conditions because people with disabilities often have co-occurring medical conditions.
Analysis
Descriptive statistics for the overall sample and by social risk index were analyzed for demographic characteristics, measures of socioeconomic position, and health conditions. Collinearity among predictor social risk variables was assessed using the Phi coefficient. We defined a correlation ≥.50 as indicative of collinearity. Prevalence and 95% confidence intervals (CIs) of each of the 5 selected social risk categories and social risk index were calculated. Weighted Wald χ2 analyses were used to calculate bivariate associations between disability prevalence and each social risk category and social risk index score. Prevalence and 95% confidence intervals (CIs) of each of the 5 social risk factors were calculated. Multivariable logistic regression models adjusted for all covariates were used to examine the association of any disability and disability type with each social risk factor and social risk index score. Multicollinearity was assessed for each model using variance inflation factors (VIFs). We defined a VIF ≥3.0 as indicative of multicollinearity. Significance was set at P < .05. Analyses were performed using R (R Foundation for Statistical Computing, Vienna, Austria; version 4.0.2) and were weighted to account for the complex sampling design and non-response. We report weighted percentages and weighted adjusted odds ratios with 95% CIs.
Results
Sample Description
Weighted estimates of demographics, measures of socioeconomic position, and health conditions for the full sample (n = 136 432) and each of the social risk index categories is presented in Table 1. All ages were well-represented. Respondents were predominantly white (70.9%), had an annual income of $50,000 or more (49.0%), and had at least a high school diploma (88.1%). As reflected by the states sampled, 8.4% of respondents resided in the West, whereas 47.4% resided in the South.
Table 1.
Demographic characteristics of individuals from 17 states in the 2017 BRFSS samplea.
| Characteristic | 0 of 5 Social Risk Factors (n = 79 813) | 1 of 5 Social Risk Factors (n = 33 373) | 2 of 5 Social Risk Factors (n = 12 833) | 3 of 5 Social Risk Factors (n = 6863) | ≥4 of 5 Social Risk Factors (n = 3113) | All (n = 136 432) |
|---|---|---|---|---|---|---|
|
| ||||||
| Age group, y | ||||||
| 18–24 | 4367 (11.5) | 2021 (12.3) | 1054 (15.8) | 504 (12.8) | 211 (12.2) | 8191 (12.3) |
| 25–34 | 7506 (15.3) | 3495 (16.2) | 1793 (19.4) | 1096 (22.0) | 514 (21.6) | 14 499 (16.6) |
| 35–44 | 8434 (15.2) | 3830 (14.8) | 1799 (16.4) | 1154 (20.7) | 646 (22.8) | 15 944 (15.8) |
| 45–54 | 11 652 (16.0) | 5202 (16.8) | 2284 (17.3) | 1368 (17.1) | 689 (20.4) | 21 286 (16.5) |
| 55–64 | 17 001 (17.1) | 7122 (17.0) | 2879 (16.6) | 1658 (17.9) | 724 (16.9) | 29 475 (17.0) |
| ≥65 | 30 853 (24.9) | 11 703 (22.9) | 3024 (14.5) | 1083 (9.5) | 329 (6.1) | 47 037 (21.7) |
| Gender | ||||||
| Male | 37 182 (51.0) | 14 393 (47.3) | 5145 (45.5) | 2561 (41.4) | 1170 (41.3) | 60 618 (48.5) |
| Female | 42 599 (49.0) | 18 961 (52.7) | 7678 (54.5) | 4298 (58.6) | 1941 (58.7) | 75 747 (51.5) |
| Married or partnered | 49 274 (61.7) | 17 369 (52.7) | 5369 (43.4) | 2604 (41.0) | 1116 (36.1) | 75 877 (55.3) |
| Race/ethnicity | ||||||
| White | 67 246 (76.8) | 26 139 (68.8) | 9042 (59.2) | 4565 (57.7) | 2022 (55.3) | 109 267 (70.9) |
| Black | 4968 (10.0) | 2719 (13.0) | 1593 (18.7) | 963 (20.3) | 441 (22.7) | 10 770 (12.8) |
| Other | 2149 (4.6) | 1088 (4.6) | 494 (4.4) | 284 (4.1) | 135 (4.0) | 4171 (4.5) |
| Multiracial | 935 (1.0) | 517 (1.3) | 257 (1.7) | 204 (2.1) | 92 (2.6) | 2024 (1.3) |
| Hispanic | 2984 (7.6) | 2197 (12.2) | 1216 (16.0) | 719 (15.8) | 351 (15.4) | 7512 (10.5) |
| Household income, $ | ||||||
| <15 000 | 2851 (4.4) | 2724 (9.3) | 2300 (18.8) | 1622 (24.1) | 833 (29.0) | 10 490 (9.4) |
| 15 000–24,999 | 6352 (10.1) | 5774 (20.5) | 3078 (27.9) | 1968 (35.2) | 943 (34.4) | 18 267 (17.1) |
| 25 000–34,999 | 5459 (8.0) | 3660 (13.5) | 1560 (15.2) | 780 (13.7) | 380 (14.2) | 11 883 (10.8) |
| 35 000–49,999 | 8951 (13.0) | 4387 (15.4) | 1538 (14.7) | 704 (12.4) | 303 (11.2) | 15 913 (13.7) |
| ≥50 000 | 40 759 (64.5) | 10 839 (41.3) | 2350 (23.4) | 786 (14.6) | 273 (11.2) | 55 020 (49.0) |
| Owns home | 63 889 (77.4) | 23 444 (67.9) | 6909 (53.9) | 3116 (46.0) | 1241 (40.3) | 98 730 (69.1) |
| Educational attainment | ||||||
| Less than high school | 3422 (7.8) | 2779 (14.1) | 1690 (18.9) | 1010 (20.9) | 464 (21.8) | 9458 (11.9) |
| High school diploma/GED | 20 056 (27.8) | 10 440 (31.9) | 4553 (34.6) | 2368 (33.4) | 1094 (33.3) | 38 650 (30.1) |
| Some college | 21 364 (30.1) | 9671 (30.6) | 3921 (31.8) | 2300 (33.8) | 1051 (33.7) | 38 449 (30.8) |
| College degree | 34 627 (34.3) | 10 358 (23.3) | 2609 (14.7) | 1168 (11.9) | 496 (11.2) | 49 320 (27.2) |
| Employment status | ||||||
| Employed | 41 252 (60.3) | 16 135 (55.3) | 5800 (51.5) | 2990 (49.6) | 1363 (51.3) | 67 703 (57.1) |
| Unemployed | 2227 (3.5) | 1625 (5.9) | 978 (8.9) | 752 (12.7) | 406 (14.6) | 6065 (5.6) |
| Unable to work | 2844 (3.4) | 2565 (31.5) | 2190 (13.9) | 1427 (18.5) | 509 (14.1) | 10 172 (7.1) |
| Other | 34 627 (32.8) | 12 753 (7.3) | 3732 (25.7) | 1636 (19.2) | 800 (20.0) | 51 254 (30.2) |
| Census region | ||||||
| Midwest | 23 172 (24.6) | 9543 (24.0) | 3501 (23.6) | 1719 (23.4) | 666 (22.5) | 38 686 (24.2) |
| Northeast | 11 537 (21.2) | 4687 (19.5) | 1691 (18.4) | 897 (17.4) | 344 (13.7) | 19 205 (20.0) |
| South | 34 176 (45.9) | 14 143 (48.1) | 5709 (48.8) | 3323 (51.1) | 1653 (55.7) | 59 251 (47.4) |
| West | 10 928 (8.3) | 5000 (8.4) | 1932 (9.2) | 924 (8.1) | 450 (8.1) | 19 290 (8.4) |
| Has health insurance | 76 051 (93.6) | 30 084 (88.0) | 10 894 (81.4) | 5433 (76.4) | 2146 (69.7) | 124 900 (90.0) |
| Health conditions | ||||||
| Heart attack | 4049 (3.8) | 2143 (5.0) | 955 (5.7) | 589 (6.3) | 320 (7.4) | 8105 (4.6) |
| Coronary heart disease | 4138 (3.9) | 2048 (4.9) | 889 (4.7) | 533 (5.3) | 297 (6.8) | 7954 (4.4) |
| Stroke | 2804 (2.6) | 1532 (3.7) | 790 (4.9) | 512 (5.6) | 273 (6.8) | 5965 (3.4) |
| Asthma | 8706 (11.5) | 4545 (14.3) | 2443 (18.7) | 1567 (21.4) | 839 (24.9) | 18 228 (14.0) |
| Skin cancer | 8902 (7.8) | 3243 (6.5) | 899 (4.5) | 399 (3.9) | 168 (2.9) | 13 633 (6.7) |
| Other cancer | 7886 (7.2) | 3254 (7.4) | 1117 (6.3) | 618 (6.9) | 347 (9.3) | 13 271 (7.2) |
| COPD | 5113 (5.0) | 3123 (7.4) | 1792 (10.7) | 1248 (13.0) | 654 (16.7) | 12 052 (7.1) |
| Depression | 11 579 (13.5) | 6966 (20.0) | 3990 (26.8) | 3061 (41.1) | 1632 (47.2) | 27 498 (19.4) |
| Chronic kidney disease | 2441 (2.3) | 1358 (3.1) | 712 (3.9) | 448 (5.2) | 219 (5.5) | 5217 (3.0) |
| Obesity | 47 850 (64.1) | 21 056 (67.0) | 8342 (67.9) | 4495 (68.6) | 2073 (69.8) | 84 097 (65.7) |
| Multimorbidity (≥2 comorbidities) | 29 781 (30.9) | 14 663 (38.0) | 6606 (44.0) | 4076 (52.5) | 2022 (58.7) | 57 453 (36.4) |
Abbreviations: GED, General Equivalency Diploma; COPD, chronic obstructive pulmonary disease.
Data are presented as unweighted counts and weighted percentages of study participants.
Overall, 54.3% (95% CI, 53.8%-54.8%) of the respondents reported experiencing 0 of the social risk factors of interest, 25.0% (95% CI, 24.5%-25.4%) reported 1, 11.2% (95% CI, 10.9%-11.6%) reported 2, 6.3% (95% CI, 6.0%-6.6%) reported 3, 2.8% (95% CI, 2.6%-2.9%) reported 4, and .4% (95% CI, .3%-.5%) reported 5. Mean social risk index scores were higher among women and young to middle aged adults. Respondents identifying as multiracial, black, and Hispanic had the highest mean social risk index scores.
Regarding disability, 26.6% (95% CI, 26.1%-27.0%) reported the presence of any disability while 6.9% (95% CI, 6.7%-7.1%) reported a hearing disability, 4.7% (95% CI, 4.5%-4.9%) reported a vision disability, 11.8% (95% CI, 11.4%-12.1%) reported a cognition disability, 14.7% (95% CI 14.3%-15.0%) reported a mobility disability, 4.2% (95% CI 4.0%-4.4%) reported a self-care disability, and 7.3% (95% CI 7.1%-7.6%) reported an independent living disability.
Individual Social Risk Factors and Disability Prevalence
No evidence of collinearity was found among social risk categories. The prevalence of having a hearing, vision, cognition, mobility, self-care, independent living, or any disability was higher among those reporting each social risk factors individually (Table 2). In models adjusted for demographic characteristics, socioeconomic position, and health conditions, respondents were more likely to report having a hearing, vision, cognition, mobility, self-care, or independent living disabilities (Table 3). For example, respondents were more likely report any disability (Table 3) if they had food (OR=2.01, 95% CI 1.83–2.21), housing (OR=2.06, 95% CI 1.83–2.32), financial insecurities (OR=1.68, 95% CI 1.56–1.81), unsafe neighborhood (OR=1.52, 1.30–1.79), or health care access hardship (OR=2.16, 95% CI 1.96–2.37). No evidence of multicollinearity was found.
Table 2.
Relative prevalence of any disability and disability type by individual social risk factor and social risk indexa.
| Type of Disability |
||||||||
|---|---|---|---|---|---|---|---|---|
| Total Sample | Hearing | Vision | Cognitionb | Mobility | Self-Care | Independent Living | Anyb | |
|
| ||||||||
| Social Risk Factor | ||||||||
| Food Insecurity | 22.2 | 26.3b | 44.6b | 50.9 | 37.6b | 50.2b | 50.0b | 36.8 |
| Housing Insecurity | 9.6 | 12.3b | 26.0b | 26.4 | 19.3b | 28.2b | 28.2b | 18.4 |
| Financial Insecurity | 47.4 | 55.6b | 74.3b | 75.1 | 69.3b | 79.5b | 78.0b | 65.8 |
| Unsafe Neighborhood | 5.6 | 7.5b | 11.1b | 13.0 | 9.7b | 13.9b | 13.4b | 9.4 |
| Healthcare Access Hardship | 13.0 | 16.7b | 26.3b | 28.3 | 20.9b | 28.3b | 26.8b | 21.4 |
| Social Risk Index - Total | ||||||||
| Number of Social Risk Factors | ||||||||
| 0 | 54.2 | 46.3b | 29.7b | 27.2b | 34.2b | 24.5b | 26.9b | 36.4 |
| 1 | 25.0 | 28.7b | 25.8 | 25.7b | 27.2b | 25.6b | 24.3b | 27.2 |
| 2 | 11.2 | 12.1b | 18.8 | 19.0b | 18.6b | 21.1 | 19.4 | 17.0 |
| 3 | 6.3 | 7.6 | 15.3 | 17.0b | 12.6b | 16.5 | 17.7 | 12.0 |
| ≥4 | 3.2 | 5.3 | 10.3 | 11.1b | 7.4b | 12.3 | 11.7 | 7.3 |
Data are presented as weighted percentages of study participants.
P < .05.
Table 3.
Adjusted Odds Ratios (95% CIs) for any disability and disability type by social risk category and social risk indexa.
| Type of Disability |
|||||||
|---|---|---|---|---|---|---|---|
| Hearing | Vision | Cognition | Mobility | Self-Care | Independent Living | Any | |
|
| |||||||
| Social Risk Category | |||||||
| Food Insecurity | 1.40 (1.22–1.61) | 1.71 (1.47–2.00) | 2.09 (1.86–2.35) | 1.84 (1.64–2.06) | 1.84 (1.54–2.20) | 1.77 (1.55–2.02) | 2.01 (1.83–2.21) |
| Housing Insecurity | 1.34 (1.14–1.60) | 1.89 (1.55–2.29) | 1.86 (1.62–2.13) | 1.81 (1.57–2.08) | 1.69 (1.40–2.03) | 1.95 (1.66–2.28) | 2.06 (1.83–2.32) |
| Financial Insecurity | 1.27 (1.15–1.43) | 1.75 (1.50–2.04) | 1.84 (1.63–2.06) | 1.64 (1.49–1.81) | 2.12 (1.79–2.50) | 1.81 (1.59–2.07) | 1.68 (1.56–1.81) |
| Unsafe Neighborhood | 1.39 (1.10–1.76) | 1.26 (1.01–1.57) | 1.62 (1.34–1.96) | 1.32 (1.08–1.61) | 1.55 (1.21–1.97) | 1.63 (1.32–2.02) | 1.52 (1.30–1.79) |
| Healthcare Access Hardship | 1.61 (1.40–1.86) | 1.61 (1.37–1.89) | 1.94 (1.72–2.19) | 1.98 (1.76–2.23) | 1.97 (1.66–2.33) | 1.75 (1.52–2.02) | 2.16 (1.96–2.37) |
| Social Risk Index - Total | |||||||
| Number of Social Risk Factors | |||||||
| 0 | Reference | Reference | Reference | Reference | Reference | Reference | Reference |
| 1 | 1.25 (1.12–1.39) | 1.25 (1.06–1.48) | 1.46 (1.30–1.64) | 1.38 (1.26–1.52) | 1.59 (1.34–1.90) | 1.32 (1.15–1.52) | 1.46 (1.35–1.57) |
| 2 | 1.25 (1.06–1.47) | 1.65 (1.35–2.01) | 1.92 (1.66–2.21) | 2.06 (1.81–2.35) | 2.13 (1.74–2.61) | 1.64 (1.39–1.94) | 2.14 (1.92–2.39) |
| 3 | 1.45 (1.20–1.76) | 2.19 (1.78–2.69) | 2.64 (2.25–3.10) | 2.40 (2.05–2.80) | 2.62 (2.10–3.27) | 2.51 (2.09–3.02) | 2.82 (2.47–3.22) |
| ≥4 | 1.59 (1.22–2.07) | 2.61 (1.93–3.52) | 3.37 (2.75–4.13) | 2.72 (2.16–3.41) | 3.33 (2.55–4.34) | 3.24 (2.52–4.15) | 3.90 (3.24–4.70) |
Data are presented as weighted odds ratios adjusted for demographic characteristics (age, sex, race, ethnicity, marital status, and health insurance), socioeconomic position (educational attainment, income, current employment status, and home ownership), and other health conditions (heart attack, coronary heart disease, stroke, asthma, skin cancer, other cancer, chronic obstructive pulmonary disease (COPD), depression, chronic kidney disease, obesity, and multimorbidity).
Social Risk Index
The prevalence of each disability increased with the number of social risk factors reported (Table 3). The absolute prevalence of each disability type and any disability by number of social risk factors is presented in FIGURE 1. In models adjusted for demographic characteristics, socioeconomic position, and health conditions, a gradient in associations was seen where respondents were increasingly likely to report each outcome as the social risk index increased with adjusted odds ratios ranging from 1.25 (95% CI, .98–1.63) to 2.45 (95% CI, 1.04–2.94) when respondents reported ≥4 social risk categories (Table 3). For respondents reporting 4 or more risk factors they had increased risk of hearing (OR=1.59, 95% CI 1.22–2.07), vision (OR=2.61, 95% 1.93–3.52), cognition (OR=3.37, 95% CI 2.75–4.13), mobility (OR=2.72, 95% 2.16–3.41), self-care (OR=3.33, 95% CI 2.55–4.34), and independent living disability (OR=3.90, 95% 3.24–4.70) (Table 3). No evidence of multicollinearity was found.
Figure 1.
Weighted unadjusted prevalence estimates of disability by number of reported social risk factors. (A) Weighted unadjusted prevalence of hearing disability by number of social risk factors. (B) Weighted unadjusted prevalence of vision disability by number of social risk factors. (C) Weighted unadjusted prevalence of cognition disability by number of social risk factors. (D) Weighted unadjusted prevalence of mobility disability by number of social risk factors. (E) Weighted unadjusted prevalence of self-care disability by number of social risk factors. (F) Weighted unadjusted prevalence of independent living disability by number of social risk factors. (G) Weighted unadjusted prevalence of any disability by number of social risk factors.
Discussion
Identifying the cumulative risk of co-occurring social risk factors provides important information about social and structural contributions to the odds of reporting a disability. We observed an elevated prevalence of disability among individuals who met our definition associated with social risk categories. In particular we noted a gradient in the social risk index where for each additional social risk factor reported, the odds of a respondent reporting having a disability increased. These findings are especially important because the social risk categories measured have a relationship with the presence of disabilities independent of socioeconomic position and known risk factors like age, gender, employment, BMI, and race/ethnicity.25,26 These factors represent modifiable social contexts that can be influenced directly through public policy, improved access to health care, and programs.27 The mechanisms underlying these relationships might be mediated through other health behaviors such as physical activity, diet, sleep, and stress.28 Alternatively, social support or psychological disposition might be further proximal to health outcomes and present accessible opportunities to develop interventions to reduce the prevalence of disabilities.29
Our study contributes to the literature on disability and social risk in several ways. This study is, to our knowledge, the largest to examine the prevalence of disability by report of social risk categories. Moreover, we believe we are the first to examine the association of co-occurring social risk factors with disability risk which is an innovative way of conceptualizing the combined association of social risk. While many prior studies have limited their perspective on social risk to only socioeconomic factors,30,31 we defined our social risk factors as a combination of individual and neighborhood level factors and evaluated their combined influence on disability prevalence. We observed when the strongest relationship when examining any disability with the combined number of social risk factors compared to individual disabilities. These findings indicate that disability type may be an important consideration when targeting social risk factors to reduce disability risk.
Identifying the associations between co-occurring social risk factors and the presence of a disability aid in better understanding the social contribution to the prevalence of disabilities. The findings may provide additional momentum for health systems and organizations not yet addressing social risks. Because up to 80% of the factors that influence health are outside the traditional health care system, unmet social needs may limit an organizations’ ability to achieve quality benchmarks associated with value-based payment. Though some health systems are making investments to address social risk factors,32 the aggregate benefit of these investments may not exceed the cost to the primary stakeholder thus limiting the funding that addressing social risks receives.33 Spreading the cost and the financial benefit of interventions to address social risk show promise as a way to increase uptake among health care systems and managed care organizations.33
While achieving health equity for people with disabilities ultimately requires structural changes to address affordable housing, inaccessible environments, and discrimination, our findings suggest intermediate steps, which may mitigate some disparities among people with disabilities. Including screening for social risk factors in care pathways may be one way to reduce disparities seen in people with disabilities. The social risk categories examined in this study are ones which are commonly identified in screening tools.34 Operationalizing social risk factors as a count is easy to calculate and interpret which adds to the potential value of screening for these risk factors. Identifying these social risk factors can help health care systems to target interventions to address them.35 Developing care pathways to account for social risk categories should take two approaches. First, care informed by social risk factors can modify traditional medical care to account for patients’ social circumstances.36 For example, information on food insecurity may influence medication choices or dosing (eg certain medication should be taken with meals).36 Second, care targeting social risks can address the risk categories directly through linking patients to income assistance or food and housing supports.8 Prior studies have identified that screening for social risk categories can help identify unmet community needs, with patients receiving more holistic care and providers reporting lessening workloads, and improved care quality.37,38 Thus, we believe that addressing social risk factors in risk stratification models and care pathways is an important step toward improving the equity of health outcomes related to disability.
Limitations
This study is limited by its use of cross-sectional data from the BRFSS and thus causality cannot be established between social risk factors and disability. Secondarily, BRFSS data is not exhaustive regarding factors that may influence disability. Missing factors may account for the reduced effect of social risk factors found for some conditions (eg hearing and vision). Some studies have reported that social risk factors may directly contribute to the development of disabilities39 while others have noted that after the development of a disability, there is an increase in exposure to social risk factors which may inhibit rehabilitation and quality of life.13 Other social risk factors known to be associated with disability such as perceived discrimination were not available in the BRFSS. The BRFSS relies on self-reported information and retrospective reporting of social risk factors and disability, which may introduce memory and responses biases. The findings may not be representative of all adults in the US as only 17 states chose to include the SDH module in 2017 and 10 of these states have a proportion of persons in poverty below the national average percentage in 2017.40
Conclusion
Social risk factors individually and cumulatively via a social risk index are associated with greater disability prevalence. Future research aimed at determining mechanisms underlying this relationship are warranted. Results from this study inform the need for stakeholders engaged in clinical care and policy making to consider the social environment of individuals with disabilities. The findings indicate another avenue for reducing disability levels may be associated with addressing social risk factors.
So What?
What is already known on this topic?
Social risks have been previously associated with disability prevalence. Although social risks often cluster within individuals, no studies have examined associations between multiple social risks within the same individual.
What does this article add?
Incremental increases in the number of social risk categories were independently associated with higher prevalence and odds of disability.
What are the implications for health promotion practice?
Targeting individuals with multiple social risk factors may help reduce the prevalence of disability among groups experiencing disadvantage. Intervention efforts should address the social context of US adults with disabilities to improve health outcomes.
Acknowledgments
Dr Rethorn, Dr Sharpe, Dr Hastings, and Dr Allen receive support from the Center of Innovation to Accelerate Discovery and Practice Transformation (CIN-13–410) at the Durham VA Health Care System. Dr Allen is supported by a Research Career Scientist award (RCS 19–332) from the Health Services Research and Development service of the U.S. Department of Veterans Affairs (DVA). The contents do not represent the views of the DVA or the United States Government. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation or approval of manuscript. No specific funding was obtained for this study, and the authors have no conflicts of interest to disclose. No copyrighted figures, images, or survey instruments were used in this study. No data from this manuscript has been previously presented.
Footnotes
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
References
- 1.Okoro CA, Hollis ND, Cyrus AC, Griffin-Blake S. Prevalence of Disabilities and Health Care Access by Disability Status and Type Among Adults - United States, 2016. MMWR Morb Mortal Wkly Rep. 2018;67(32):882–887. doi: 10.15585/mmwr.mm6732a3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Anderson WL, Armour BS, Finkelstein EA, Wiener JM. Estimates of state-level health-care expenditures associated with disability. Publ Health Rep 2010;125(1):44–51. doi: 10.1177/003335491012500107. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.US Department of Health and Human Serices. The Surgeon General’s Call to Action to Improve the Health and Wellness of Persons with Disabilities. US Department of Health and Human Services; 2005. [Google Scholar]
- 4.Krahn GL. Drilling Deeper on the Impact of the Affordable Care Act on Disability-Related Health Care Access Disparities. Am J Publ Health 2019;109(7):956–958. doi: 10.2105/AJPH.2019.305114. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Krahn GL, Walker DK, Correa-De-Araujo R. Persons with disabilities as an unrecognized health disparity population. Am J Publ Health 2015;105 suppl 2:S198–S206. doi: 10.2105/AJPH.2014.302182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Wolbring G People With Disabilities and Social Determinants of Health Discourses. Can J Public Health/Revue Canadienne de Sante’e Publique 2011;102(4):317–319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. Final Report of the Commission on the Social Determinants of Health. 2008. https://www.who.int/social_determinants/final_report/csdh_finalreport_2008.pdf
- 8.Alderwick H, Gottlieb LM. Meanings and Misunderstandings: A Social Determinants of Health Lexicon for Health Care Systems. Milbank Q 2019;97(2):407–419. doi: 10.1111/1468-0009.12390. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Altman BM, Bernstein A. Disability and Health in the United States, 2001–2005. 2008;DHHS publication; no. (PHS) 2008–1035. [Google Scholar]
- 10.Reichard A, Stransky M, Phillips K, McClain M, Drum C. Prevalence and reasons for delaying and foregoing necessary care by the presence and type of disability among working-age adults. Disabil Health J 2017;10(1):39–47. doi: 10.1016/j.dhjo.2016.08.001. [DOI] [PubMed] [Google Scholar]
- 11.Clark CR, Kawachi I, Ryan L, Ertel K, Fay ME, Berkman LF. Perceived neighborhood safety and incident mobility disability among elders: the hazards of poverty. BMC Publ Health. 2009;9(1):162. doi: 10.1186/1471-2458-9-162. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Minkler M, Fuller-Thomson E, Guralnik JM. Gradient of disability across the socioeconomic spectrum in the United States. N Engl J Med. 2006;355(7):695–703. doi: 10.1056/NEJMsa044316. [DOI] [PubMed] [Google Scholar]
- 13.Frier A, Barnett F, Devine S, Barker R. Understanding disability and the ‘social determinants of health’: how does disability affect peoples’ social determinants of health? Disabil Rehabil 2018;40(5):538–547. doi: 10.1080/09638288.2016.1258090. [DOI] [PubMed] [Google Scholar]
- 14.Heflin CM, Altman CE, Rodriguez LL. Food insecurity and disability in the United States. Disability and Health Journal. 2019;12(2):220–226. doi: 10.1016/j.dhjo.2018.09.006. [DOI] [PubMed] [Google Scholar]
- 15.Maroto MP, David. Twenty-Five Years After the ADA: Situtating Disabiity in America’s System of Stratification. Disabil Stud Q. 2015;35(3). [Google Scholar]
- 16.Ostlin P, Schrecker T, Sadana R, et al. Priorities for research on equity and health: towards an equity-focused health research agenda. PLoS Med 2011;8(11):e1001115. doi: 10.1371/journal.pmed.1001115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Rethorn ZD, Garcia AN, Cook CE, Gottfried ON. Quantifying the collective influence of social determinants of health using conditional and cluster modeling. PLoS One. 2020;15(11):e0241868. doi: 10.1371/journal.pone.0241868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Rethorn ZD, Cook C, Reneker JC. Social Determinants of Health: If You Aren’t Measuring Them, You Aren’t Seeing the Big Picture. J Orthop Sports Phys Ther 2019;49(12):872–874. doi: 10.2519/jospt.2019.0613. [DOI] [PubMed] [Google Scholar]
- 19.Cheng ER, Poehlmann-Tynan J, Mullahy J, Witt WP. Cumulative social risk exposure, infant birth weight, and cognitive delay in infancy. Acad Pediatr 2014;14(6):581–588. doi: 10.1016/j.acap.2014.03.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Benchimol EI, Smeeth L, Guttmann A, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Centers for Disease Control and Prevention. BRFSS Overall Version Data Weighted with _LLCPWT. Updated October 11, 2018. 2020. Accessed September 3, 2020. https://www.cdc.gov/brfss/annual_data/2017/llcp_multiq.html
- 22.US Census Bureau. How Disability Data Are Collected from The American Community Survey. Updated October 17, 2017. Accessed February 15, 2021. 2021. https://www.census.gov/topics/health/disability/guidance/data-collection-acs.html
- 23.VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol 2019;34(3):211–219. doi: 10.1007/s10654-019-00494-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Boyd CM, Fortin M. Future of multimorbidity research: how should understanding of multimorbidity inform health system design? Publ Health Rev. 2010;32:451–474. doi: 10.1007/BF03391611. [DOI] [Google Scholar]
- 25.Mehta NK, Patel SA, Ali MK, Narayan KV. Preventing Disability: The Influence Of Modifiable Risk Factors On State And National Disability Prevalence. Health Aff. 2017;36(4):626–635. doi: 10.1377/hlthaff.2016.1281. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Courtney-Long EA, Romano SD, Carroll DD, Fox MH. Socioeconomic Factors at the Intersection of Race and Ethnicity Influencing Health Risks for People with Disabilities. J Racial Ethn Health Disparities. 2017;4(2):213–222. doi: 10.1007/s40615-016-0220-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Daniel H, Bornstein SS, Kane GC, Public Policy Committee of the American College of Physicians. Addressing Social Determinants to Improve Patient Care and Promote Health Equity: An American College of Physicians Position Paper. Ann Intern Med 2018;168(8):577–578. doi: 10.7326/M17-2441. [DOI] [PubMed] [Google Scholar]
- 28.Aitken Z, Simpson JA, Bentley R, Kavanagh AM. Disability acquisition and mental health: effect modification by demographic and socioeconomic characteristics using data from an Australian longitudinal study. BMJ Open. 2017;7(9):e016953. doi: 10.1136/bmjopen-2017-016953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Aitken Z, Krnjacki L, Kavanagh AM, LaMontagne AD, Milner A. Does social support modify the effect of disability acquisition on mental health? A longitudinal study of Australian adults. Soc Psychiatr Psychiatr Epidemiol. 2017;52(10):1247–1255. doi: 10.1007/s00127-017-1418-5. [DOI] [PubMed] [Google Scholar]
- 30.Bowen ME, Gonzalez HM. Childhood socioeconomic position and disability in later life: results of the health and retirement study. Am J Publ Health. 2010;100 suppl 1:S197–S203. doi: 10.2105/AJPH.2009.160986. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Kavanagh AM, Krnjacki L, Aitken Z, et al. Intersections between disability, type of impairment, gender and socioeconomic disadvantage in a nationally representative sample of 33,101 working-aged Australians. Disabil Health J 2015;8(2):191–199. doi: 10.1016/j.dhjo.2014.08.008. [DOI] [PubMed] [Google Scholar]
- 32.Horwitz LI, Chang C, Arcilla HN, Knickman JR. Quantifying Health Systems’ Investment In Social Determinants Of Health, By Sector, 2017–19. Health Aff 2020;39(2):192–198. doi: 10.1377/hlthaff.2019.01246. [DOI] [PubMed] [Google Scholar]
- 33.Nichols LM, Taylor LA. Social Determinants As Public Goods: A New Approach To Financing Key Investments In Healthy Communities. Health Aff 2018;37(8):1223–1230. doi: 10.1377/hlthaff.2018.0039. [DOI] [PubMed] [Google Scholar]
- 34.Social Interventions ResearchEvaluation Network. Social need screening tools comparison table. 2021. Accessed August 10, 2021. https://sirenetwork.ucsf.edu/tools-resources/resources/screening-tools-comparison [Google Scholar]
- 35.Beck AF, Klein MD, Schaffzin JK, Tallent V, Gillam M, Kahn RS. Identifying and treating a substandard housing cluster using a medical-legal partnership. Pediatrics 2012;130(5):831–838. doi: 10.1542/peds.2012-0769. [DOI] [PubMed] [Google Scholar]
- 36.Gottlieb L, Fichtenberg C, Alderwick H, Adler N. Social Determinants of Health: What’s a Healthcare System to Do? J Healthc Manag. 2019;64(4):243–257. doi: 10.1097/JHM-D-18-00160. [DOI] [PubMed] [Google Scholar]
- 37.LaForge K, Gold R, Cottrell E, et al. How 6 Organizations Developed Tools and Processes for Social Determinants of Health Screening in Primary Care: An Overview. J Ambul Care Manag 2018;41(1):2–14. doi: 10.1097/JAC.0000000000000221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Naz A, Rosenberg E, Andersson N, Labonte R, Andermann A, Collaboration C. Health workers who ask about social determinants of health are more likely to report helping patients: Mixed-methods study. Can Fam Physician 2016;62(11):e684–e693. [PMC free article] [PubMed] [Google Scholar]
- 39.Fiorati RC, Elui VM. Social determinants of health, inequality and social inclusion among people with disabilities. Rev Lat Am Enfermagem 2015;23(2):329–336. doi: 10.1590/0104-1169.0187.2559. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.US Census Bureau. Poverty Status in the past 12 months. 2020. Accessed September 7, 2020. https://data.census.gov/cedsci/ [Google Scholar]