Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers

“no clear standard has emerged on how to implement social risk screening, nor how clinicians can or should use social risk information to adjust patient care or make referrals to community resources. Moreover, some have questioned the benefit of integrating social risk screening into primary care, raising concerns about the additional burden of adding more required data collection to already busy primary care practices and the limited resources available to address identified social risk factors.

[..] relying solely on community-level data to understand the social context of an individual patient and/or to guide patient-level interventions poses a risk of ecological fallacy, or making erroneous assumptions about individuals based on aggregate information.

[..] OCHIN Inc is a nonprofit health center–controlled network that hosts a centrally managed instance of the Epic EHR (Epic Systems Corporation) for 645 CHC clinics across 21 US states. OCHIN CHCs provide care for the nation’s most vulnerable patients, the majority of whom are publicly insured or uninsured. Like most CHCs in the US, compared with the general population, patients who receive care at OCHIN CHCs are disproportionately poor, members of racial and ethnic minorities, and living with multiple chronic conditions. OCHIN hosts a research data warehouse that includes EHR data on more than 4.9 million patients, making it, to our knowledge, the largest single research-ready data source on US safety net patients. The research data warehouse also includes neighborhood and community-level data—also called community vital signs—from publicly available sources (eg, US Census, American Community Survey) that provide information about each patient’s community context. Patient addresses, collected by OCHIN network clinics, are geocoded to identify the census tract of each patient’s residence, then linked to the community vital signs data for that tract.

[..] To meet the needs of OCHIN’s diverse members, these tools include several nationally recognized screening instruments (eg, PRAPARE, Centers for Medicare and Medicaid Services Accountable Health Communities, National Academy of Medicine [formerly Institute of Medicine] recommendations) encompassing 7 social risk domains: financial resource strain, food insecurity, housing instability, relationship safety, inadequate physical activity, social connection/isolation, and stress. In May 2017, a question was added asking patients if they would like help addressing any identified social risks. The CHCs have the option to choose an entire screening questionnaire (eg, PRAPARE) or individual social risk domains.

[..] To quantify community-level social risk, all patients with a valid address in the OCHIN research data warehouse were assigned a census-tract level SDI score using information on their last available address recorded in the EHR. Originally developed by Butler et al and updated in 2015, the SDI is a composite measure of 7 demographic characteristics from the American Community Survey, including percentage living in poverty, percentage with less than 12 years of education, percentage of single-parent households, percentage living in a rented housing unit, percentage living in an overcrowded housing unit, percentage of households without a car, and percentage of nonemployed adults younger than 65 years. [..] prior studies suggest that patients living in cold spots—defined as those census tracts with an SDI score in the highest quartile nationally (≥75)—have worse health outcomes relative to those in more resource-rich tracts.

Patient-level measures of food insecurity, housing insecurity, and financial resource strain were included in our analysis. [..] First, given the lack of standardized screening recommendations, CHCs are implementing screening in a variety of ways. Instead of using an established tool (eg, PRAPARE, Accountable Health Communities), many CHCs have opted to focus on screening for specific social risk domains. To date, food insecurity, housing insecurity, and financial resource strain are among the most frequently documented social risk domains in OCHIN CHCs, in part because they are actionable (eg, through referrals to local resources). Second, relative to other patient-level measures that focus on psychosocial domains (eg, relationship safety, social connection/isolation, stress), these domains are most aligned with the socioeconomic factors integrated into the SDI. Third, despite the limited consensus on standardized SDH screening recommendations, there is emerging consensus around standardized questions to assess food insecurity, housing insecurity, and financial resource strain.

[..] Of the 36,578 patients included in the final study sample, 60.5% (n = 22,113) received public insurance, 57.9% (n = 21,181) were female, 19.2% (n = 7013) were 19 years or younger at the time of screening, and 20.3% (n = 7422) were 60 years or older.

[..] A total of 58.0% of patients in the study sample (n = 21,197) lived in cold spot census tracts with an SDI score in the highest quartile (≥75 or Q4; most deprived), and 42.0% lived in non–cold spot census tracts (23.5% [n = 8585] lived in a Q3 census tract; 13.5% [n = 4926] in a Q2 census tract; and 5.1% [n = 1869] in a Q1 census tract). In total, 88.4% of sample patients (n = 32,337) had documented responses to questions about housing insecurity (n = 3880; 12.0% screened positive), 66.8% (n = 24,426) had documented responses to questions about food insecurity (n = 6839; 28.0% screened positive), and 39.0% (14,276) had documented responses to questions about financial resource strain (7145; 50.0% screened positive).

[..] Overall, approximately 29.7% of sample patients (n = 10,858) screened positive for housing insecurity, food insecurity, and/or financial resource strain. Of these, 60.0% (n = 6516) resided in a Q4 census tract, 22.9% in Q3 (n = 2491), 13.0% in Q2 (n = 1415), and 4.0% (n = 436) in Q1. The percentage of patients who screened positive within each quartile was relatively stable across Q2 to Q4, with 6516 of 21,197 patients (30.7%) screening positive in the highest SDI quartile (Q4 or cold spot), 2491 of 8585 (29.0%) in the third quartile, and 1415 of 4926 (28.7%) in the second quartile. Although the first-quartile SDI had the fewest number of patients screened (1869), almost a quarter (436, 23.3%) screened positive for at least 1 risk factor.

Of the patients who screened positive for 1 or more social risks (n = 10,858), 23.6% (n = 2561) were asked whether they wanted help from clinic staff to address identified risks. Of those, 35.5% (n = 908) said they wanted help. Overall, 63.3% (n = 575) of those who wanted help lived in a cold spot census tract (Q4), with 19.2% (n = 174) in Q3; 12.4% (n = 113) in Q2; and 5.1% (n = 46) in Q1. Interestingly, when looking at the percentage who wanted help within each quartile, Q1 had the highest percentage of patients who wanted help to address identified risks (n = 46; 48.0%). Conversely, Q4 had the highest percentage of patients who said they did not want help (n = 1046; 64.5%).

Of those who screened positive for at least 1 social risk factor, 60.0% (n = 6516) resided in a cold spot census tract and would have been correctly identified as having a risk using a cold-spotting approach. However, 40.0% (n = 4342) of patients reporting 1 or more social risks would not be correctly identified. Of the 25,719 patients who did not screen positive for any social risk, 57.1% (n = 14 681) resided in a cold spot and thus would be incorrectly identified as having social risk using a cold-spotting approach. Overall, the accuracy of the community-level data for identifying patients with and without social risks was 48.0% (n = 17,544).

[..] Although there was some overlap between cold spot status and the presence of patient-level social risks, with 60.0% of those who reported at least 1 social need living in a cold spot census tract, 40.0% of patients who screened positive for at least 1 social risk did not live in a cold spot. Overall, the accuracy of the community-level data for identifying patients with and without social risks was 48.0%.

[..] when asked, a larger percentage of patients said that they did not want help addressing identified risks (64.5% [n = 1653] said that they did not want help vs 35.5% [n = 908] who did). This finding is supported by a 2019 qualitative study, which found that although patients and caregivers believed social risk screening was important and acceptable, they did not expect their health care teams to address the social challenges they faced. Moreover, despite the low numbers, the counterintuitive finding that a higher percentage of patients within the most affluent quartile (Q1) said they wanted help could be indicative of a greater availability of resources in these areas, but this is unclear. Overall, these findings underscore the need for additional research to explore patient perspectives on social care screening and referrals, including whether and how health care teams should address identified risk factors. They also raise questions about whether and how the availability of resources in a patient’s neighborhood or community—either perceived or actual—might influence their desire for help.

[..] Beyond their utility in contextualizing patient care, community-level data are a vital source of information for community-level interventions (eg, advocacy, alignment) and could be used to inform the development value-based payment structures or approaches to risk adjustment. Indeed, other countries have demonstrated the value of using area-based measures of socioeconomic variation to assess community needs, inform research, adjust clinical funding, allocate community resources, and determine policy impact. More research is needed to understand how patient-level and community-level data can be used in concert to most effectively and efficiently invest limited resources.”

Full article, Cottrell EK, Hendricks M, Dambrun K et al. JAMA Network Open 2020.10.29