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The Sociobehavioral Phenotype: Applying a Precision Medicine Framework to Social Determinants of Health
Ravi B. Parikh, MD, MPP; Sachin H. Jain, MD, MBA; and Amol S. Navathe, MD, PhD
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The Sociobehavioral Phenotype: Applying a Precision Medicine Framework to Social Determinants of Health

Ravi B. Parikh, MD, MPP; Sachin H. Jain, MD, MBA; and Amol S. Navathe, MD, PhD
Sociobehavioral phenotypes are actionable risk profiles based on empirically derived social, economic, and behavioral factors that, if applied appropriately, can help healthcare organizations address social determinants of health.
After defining and validating sociobehavioral phenotypes, clinicians and delivery scientists can identify which phenotypes are actionable by developing randomized trials of interventions in specific subgroups. Unlike the situation in metastatic lung cancer a decade ago, healthcare delivery scientists have an armamentarium of community-based interventions to use among selected high-risk individuals. These include home-based and visiting nurse care, telephonic case management, wireless technologies, community health workers, patient-centered medical homes, social service programs, and direct financial assistance. By examining the effect of a social service or patient engagement intervention stratified by a sociobehavioral phenotype, policy makers and clinicians can determine which subpopulations derive benefit from each intervention, which can facilitate more efficient enrollment into these interventions (Table).

Nascent examples of sociobehavioral phenotyping are a harbinger of broader success. Since 2013, the VHA has deployed a screening for homelessness, the Homelessness Screening Clinical Reminder, for all patients who access outpatient VHA care.8 Applying machine learning models to VHA EHR data reliably identifies patient phenotypes at high risk (>90%) of reporting homelessness, a risk factor for acute care utilization.9 For individuals at high risk of homelessness, the VHA deploys interventions, including a homelessness primary care action team and temporary housing, that are associated with reduced ED utilization among those with previously high utilization.10

Identifying Heterogeneity Within Phenotypes

As in a genomics-based precision medicine framework, there will still be heterogeneity within a sociobehavioral phenotype. For example, the aforementioned examples of sociobehavioral phenotyping define phenotypes by singular socioeconomic risk factors, such as homelessness. Although homelessness could contribute to healthcare utilization, reasons for homelessness are varied. The single male who faces chronic housing insecurity due to substance use and mental illness would likely have a different set of needs than the mother of 4 children who is fleeing domestic violence. Each of these reasons demands different interventions to address underlying causes of social instability. Unsupervised clustering algorithms applied to very large data sets could help identify heterogeneity within socioeconomic risk factor groups. Such algorithms may differentiate between the constellation of homelessness, substance abuse, and poor primary care follow-up versus a constellation of homelessness, food insecurity, and diabetes. By leveraging data across a large number of patients, delivery scientists and policy makers can identify combinations of social factors that result in a particular pattern of morbidity, utilization, and cost, then prioritize different interventions or sequences of interventions for such phenotypes.

Limitations

There are several challenges in defining and implementing socio­behavioral phenotypes. Patient-level socioeconomic data are often not readily available in structured data sets. Additionally, accounting for socioeconomic risk factors in individual patient care may increase information overload among overstretched clinicians—who may have trouble managing the data to which they already have access. And certain sociobehavioral phenotypes, such as food insecurity, carry risks of stigma and further alienating patients. Patients who fall into certain sociobehavioral phenotypes will need protections to ensure that they are not excluded from appropriate care or subjected to bias. Partnerships between academia and patient and advocacy groups could aid in refining data-generated phenotypes and associated interventions to ensure maximal uptake and acceptance.

Conclusions

Targeting interventions based on their ability to improve specific sociobehavioral risk factors is more likely to be successful than broad—and often blind—application of resource-constrained interventions. Just as oncologists in the precision medicine era screen for actionable mutations to guide use of targeted therapies, delivery scientists and clinicians can study and target interventions based on sociobehavioral phenotypes. Perhaps then we may experience a revolution in addressing socioeconomic determinants of health—similar to the precision medicine revolution of today.

Author Affiliations: Perelman School of Medicine and Leonard Davis Institute of Health Economics, University of Pennsylvania (RBP, ASN), Philadelphia, PA; Corporal Michael J. Cresencz VA Medical Center (RBP, ASN), Philadelphia, PA; CareMore Health System/Anthem Inc (SHJ), Cerritos, CA; Stanford University School of Medicine (SHJ), Palo Alto, CA.

Source of Funding: This work was funded in part by the Pennsylvania Commonwealth University Research Enhancement Program, the Leonard Davis Institute Policy Accelerator Program, and the Pennsylvania Department of Health. The department specifically disclaims responsibility for any analyses, interpretations, or conclusions.

Author Disclosures: Dr Jain is employed with CareMore Health. Dr Navathe is a noncompensated board member of Integrated Services, Inc; is a consultant or paid advisor for Navvis and Co, Agathos, and Navahealth; has received grants from Hawaii Medical Service Association, Oscar, Cigna, The Commonwealth Fund, and the Robert Wood Johnson Foundation; has received honoraria from Elsevier for his editorial role and from the National University Health System (Singapore) for his role as an advisor; has received speaker fees and travel from Cleveland Clinic; and is employed with University of Pennsylvania. Dr Parikh reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (RBP, ASN); acquisition of data (RBP); analysis and interpretation of data (RBP); drafting of the manuscript (RBP, ASN); critical revision of the manuscript for important intellectual content (RBP, SHJ, ASN); obtaining funding (RBP); administrative, technical, or logistic support (RBP, SHJ, ASN); and supervision (SHJ, ASN).

Address Correspondence to: Amol S. Navathe, MD, PhD, Division of Health Policy, University of Pennsylvania, 1108 Blockley Hall, 423 Guardian Dr, Philadelphia, PA 19146. Email: amol@wharton.upenn.edu.
REFERENCES

1. van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551-557. doi: 10.1503/cmaj.091117.

2. Billings J, Blunt I, Steventon A, Georghiou T, Lewis G, Bardsley M. Development of a predictive model to identify inpatients at risk of re-admission within 30 days of discharge (PARR-30). BMJ Open. 2012;2(4):e001667. doi: 10.1136/bmjopen-2012-001667.

3. Parikh RB, Kakad M, Bates DW. Integrating predictive analytics into high-value care: the dawn of precision delivery. JAMA. 2016;315(7):651-652. doi: 10.1001/jama.2015.19417.

4. Peikes D, Chen A, Schore J, Brown R. Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials. JAMA. 2009;301(6):603-618. doi: 10.1001/jama.2009.126.

5. Adler NE, Cutler DM, Fielding JE, et al. Addressing social determinants of health and health disparities: a vital direction for health and health care. National Academy of Medicine website. nam.edu/addressing-social-determinants-of-health-and-health-disparities-a-vital-direction-for-health-and-health-care/. Published September 19, 2016. Accessed May 1, 2019.

6. Gundlapalli AV, Redd A, Carter M, et al. Validating a strategy for psychosocial phenotyping using a large corpus of clinical text. J Am Med Inform Assoc. 2013;20(e2):e355-e364. doi: 10.1136/amiajnl-2013-001946.

7. Seligman B, Tuljapurkar S, Rehkopf D. Machine learning approaches to the social determinants of health in the Health and Retirement Study. SSM Popul Health. 2017;4:95-99. doi: 10.1016/j.ssmph.2017.11.008.

8. Byrne T, Fargo JD, Montgomery AE, Roberts CB, Culhane DP, Kane V. Screening for homelessness in the Veterans Health Administration: monitoring housing stability through repeat screening. Public Health Rep. 2015;130(6):684-692. doi: 10.1177/003335491513000618.

9. Byrne T, Montgomery AE, Fargo JD. Predictive modeling of housing instability and homelessness in the Veterans Health Administration. Health Serv Res. 2019;54(1):75-85. doi: 10.1111/1475-6773.13050.

10. Gundlapalli AV, Redd A, Bolton D, et al. Patient-aligned care team engagement to connect veterans experiencing homelessness with appropriate health care. Med Care. 2017;55(suppl 9 suppl 2):S104-S110. doi: 10.1097/MLR.0000000000000770. 
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