Non-Clinical Data Is Now Healthcare’s Biggest Information AssetAn analysis of geographic social determinant data and its relationship to hypertension

Khan M. Siddiqui, MD1,2; Ross Goglia, MBA2; Nikole Wiley, BS/BA2; Daniel Neems, PhD2 1Johns Hopkins University, Baltimore, MD; 2higi SH llc, Chicago, IL

Background: Two of the biggest healthcare topics in 2018 have been 1) social determinants of health (SDOH)1,2,3 and 2) the new AHA blood pressure guidelines 4,5, which places many more individuals’ blood pressure in the hypertensive range. Various studies have proven that SDOH play a much bigger role in health outcomes and expenditures than previously realized 1,2,3. Hypertension and related diseases are one focus of that conversation. Blood pressure measurements and survey responses are both collected at large scales via the higi network of 11,000+ self-service health stations (Chicago, IL – www.higi.com), making it an ideal platform for measuring the relationship between patient-reported SDOH and hypertension across communities.

Objective: The aim of this study was to assess the prevalence of SDOH factors and ascertain their respective relationships with hypertension rates across a large number of geographies in the United States. Secondary aims were to identify the most influential SDOH factors as they relate to hypertension, as well as examine the variation of influential SDOH factors across communities, and pinpoint the areas where those factors appear most intense, posing more risk to the healthcare system.

Methods:A SDOH survey was deployed on 1,125 stations across the Northeast and Midwest from 3/7/2018 through 10/31/2018 to collect patient-reported SDOH information and measure blood pressure values during the same patient session. A total of 15 val-idated SDOH questions were selected from the Health Leads SDOH Screening Toolkit (Figure 1, healthleadsusa.org) for this study. The questions were grouped into 3 sets of 5, with one of the 3 sets randomly surfacing within a given session. Due to logistical con-straints, not all questions or stations were utilized for the entire duration of the study. Some questions also received more responses by virtue of being placed earlier within their respective question sets. To ensure data quality, responses from patients outside certain age ranges were excluded for some questions: <18 for high-school degree, <21 or >65 for employment, and <21 for marriage.

Blood pressure values and survey response data were compiled by zip code. Single-variate regression analyses were run to mea-sure the correlation between hypertension rates and SDOH rates. Census data on median household income was incorporated to characterize communities for map analyses. Zip codes with less than 50 total responses for a given question were excluded from regressions and maps.

Note: Yes or No responses for each question are underlined to indicate undesired SDOH states

Figure 1: Social Determinant Survey Question Responses

Results: A total of 1,299,321 individual SDOH survey responses were analyzed, with response rates and summary data shown in Figure 1. Responses came from males 60.8% of the time, with a 49.1 average age and 50 median age. Four of the 15 undesired SDOH states had statistically significant relationships with hypertension rate for a zip code – 1) not seeing the doctor because of cost (p=0.0073), 2) Medicaid eligibility (p<0.0001), 3) unmarried status (p<0.0001), and 4) lack of daily physical activity (p<0.0001). Of those 4, all but the first showed slopes greater than 0.15. Lack of daily physical activity displayed the largest slope at nearly 0.20. When analyzed by gender, unmarried status revealed a noticeably greater effect for females (slope of 0.2975 vs. 0.0918), and lack of physical activity did the same for males (slope of 0.1383 vs. 0.0079). Lack of transportation also displayed statistical significance when analyzed for females (p=0.0164, slope=0.2881), whereas it did not for males or overall.

Map analyses showed that significant geographic variation exists for SDOH factors (Figures 3-6). Furthermore, it can be seen that many lower income areas within inner cities have higher rates of SDOH issues. Higher income areas within metropolitan areas tend to show some high rates as well, but generally show wider variation in SDOH issue instensity. For lack of daily physical activity, Westborough, MA 01581, Arlington, VA 22206, and Bridgeport, CT 06606 showed the highest rates – average of 61.1%. Balti-more, MD 21218, Swampscott, MA 01907, and Plainview, NY 11803 showed the lowest rates – average of 18.5%.

Limitations: The population using higi stations may contribute selection bias – it may not represent all of the U.S. population, de-mographically, socially, or health-wise. SDOH information presented here also constitutes patient-reported data. SDOH regression results show correlation, but not causation, of hypertension. Conclusion: SDOH questions asked in this study spanned a wide range of non-clinical issues, but financial and marital status, as well as exercise habits, appear to have the strongest relationship with hypertension across populations. This finding suggests that organizations such as health systems and clinics, insurers, employee wellness programs, and public health departments would do well to pay special attention to these SDOH risk factors in hyper-tension management, as well as the specific communities where they’re most prevalent. Locating those communities by simply analyzing standard income data may give misleading information.

Conflict of Interest: This study was funded by higi SH llc and the authors are employees of the company.

References:1. Social Determinants of Health: Know What Affects Health. Centers for Disease Control and Prevention, 2018 (https://www.cdc.gov/socialdeterminants/index.htm)2. Health and social services expenditures: associations with health outcomes. Bradley, Elizabeth H. et al. BMJ, 20113. Medicaid-Financed Services in Supportive Housing for High-Need Homeless Beneficiaries: The Business Case. Nardone, Michael et al. Centers for Healthcare Strat-

egy Inc, 20124. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pres-

sure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Whelton, Paul K. et al. Hyper-tension, 2018; 71: e13-e115

5. How did another 30% of Americans develop high blood pressure overnight? An analysis of the new AHA/ACC blood pressure guidelines and their impact in communities across the country. Siddiqui, Khan M. et al. AHA Hypertension Scientific Sessions, 2018 (https://higi.com/wp-content/uploads/2018/09/AHA-Poster-BP-Guidelines.jpg)

Note: P-values less than 0.05 are shown in bold, and slopes greater than 0.15 in red..

Figure 2: Single-Variant Regression Analysis - High Blood Pressure vs Social Determinant Rates (by Zipcode)

Figure 3: Responses by Zip Code - Do you participate in 30 minutes of physical activity on a daily or near daily basis?

Figure 4: Responses by Zip Code - Do you participate in 30 minutes of physical activity on a daily or near daily basis? (New York City)

Figure 5: Responses by Zip Code - Do you participate in 30 minutes of physical activity on a daily or near daily basis? (Washington, DC)

Figure 6: Responses by Zip Code - Do you participate in 30 minutes of physical activity on a daily or near daily basis? (Detroit)