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The Sociobehavioral Phenotype: Applying a Precision Medicine Framework to Social Determinants of Health
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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.
Am J Manag Care. 2019;25(9):In Press
Takeaway Points
  • Social determinants of health lead to increased healthcare utilization and poor outcomes, but efforts to risk-stratify patients have largely ignored these risk factors.
  • Advanced analytics applied to electronic health records and primary survey collection can reveal distinct phenotypes of individuals with actionable sociobehavioral contributors to health.
  • Healthcare organizations can target these sociobehavioral phenotypes with evidence-based interventions.
  • We propose a research agenda to identify, validate, and apply sociobehavioral phenotypes in healthcare.
Commonly used risk algorithms in healthcare, including indices to predict hospital readmission, define “high-risk” patients by disease-based (eg, comorbidity index) or utilization-based (eg, number of emergency department [ED] visits) factors.1,2 Clinicians and systems can target interventions to such high-risk groups—so-called “precision delivery.”3 However, current programs that target high-risk individuals, including high-risk case management programs, do not uniformly reduce utilization or adverse outcomes.4

Why might such interventions be ineffective? Perhaps the problem is in our conception of risk, rather than the interventions.

An individual’s health phenotype has far-reaching connections with social, behavioral, and environmental factors, such as housing instability and food insecurity. Nevertheless, few risk models include variables associated with social determinants of health, such as income level or educational attainment, and traditional risk models often identify such individuals when it is too late to intervene upon socioeconomic risk factors.5 To complement risk-stratification models based on clinical severity, we introduce the “sociobehavioral phenotype”—actionable risk profiles based on empirically derived social, economic, and behavioral determinants of health.

Defining the Sociobehavioral Phenotype: Lessons From Precision Medicine

To build and implement sociobehavioral phenotypes, clinicians, researchers, and systems leaders can draw lessons from the field of precision medicine. Consider the analogy of metastatic non–small cell lung cancer (NSCLC): Prior to the advent of targeted and immune-based therapy, cytotoxic chemotherapy was the standard of care. Genomic sequencing of tumor cells identified pathologic mutations; researchers later targeted such mutations in drug development. Subsequent development of targeted inhibitors and evaluation in randomized trials led to many more therapeutic options becoming available for NSCLC. As new therapies targeting pathologic “driver” mutations became available, oncologists integrated screening for such mutations into the workup of NSCLC.

Just as many driver mutations exist in lung cancer, driver socioeconomic risk factors (eg, poor social support, housing instability) or behaviors (eg, medication nonadherence) underlie many individuals’ health outcomes, such as mortality or acute care utilization. Clinicians and researchers can analyze large observational data sets to classify 1 or more of these common risk factors into distinct sociobehavioral phenotypes and subsequently test interventions targeted toward those phenotypes.

The Added Value of the Sociobehavioral Phenotype

The sociobehavioral phenotype aligns well with broader calls for providers to screen for social determinants of health. In resource-constrained environments, it is of paramount importance to target interventions to the individuals most likely to derive benefit from them. However, current interventions may not serve the majority of patients with predominantly sociobehavioral contributors to poor health. Take, for example, a telephonic case management program targeted to individuals with high likelihoods of hospital readmission. Although such a program may benefit some subtypes of patients, it is unlikely that a telephone-based program would succeed for a homeless individual who does not have a reliable address or telephone number. An unknown phone number may also go unanswered due to fear or suspicion. Furthermore, telephonic or remote case management programs may fail to lower health expenditures because certain high-risk patients rely on frequent in-person contact.4 Sociobehavioral phenotyping based on electronic health record (EHR) data may identify driver risk factors to develop and refine effective interventions for such individuals.

A Coordinated Research Agenda

Learning from the precision medicine example, a coordinated research agenda with parallel and iterative data mining and experimentation could identify relevant sociobehavioral phenotypes that ameliorate social determinants of health (Figure). Mining large databases from EHRs and observational studies are necessary first steps to identify sociobehavioral phenotypes. There are examples of this: Using a large sample of clinical notes from the Veterans Health Administration (VHA), researchers have developed and validated natural language processing lexicons to identify individuals who are homeless or at high risk of being homeless.6 Additionally, neural network methods applied to data from the Health and Retirement Study have yielded potential sociobehavioral phenotypes, such as elderly patients with few children and thus little social support, that are predictive of poor health outcomes.7 Clinicians and researchers will need to supplement these EHR-derived phenotypes with primary survey data collection focused on driver social and behavioral risk factors, perhaps as part of regular screens for socioeconomic determinants of health.


 
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