- The social determinants of health have emerged as critical factors in driving value-based care and patient wellness programs. But could initiatives addressing the SDOH have unintended, negative consequences for patients? Potentially, says a position paper published in the most recent edition of the Annals of Family Medicine.
According to lead author Laura M. Gottlieb, MD, MPH, of the Department of Family and Community Medicine at the University of California San Francisco, many of the efforts to address the SDOH could unintentionally drive health disparities and lower care quality.
Efforts to address SDOH have become more prominent in recent years. As healthcare professionals work to keep patients healthier outside of the clinic or hospital, they are taking care of social needs that can impact a patient’s ability to be healthy.
But the angle at which many organizations and policymakers have approached community health projects may not be most effective, Gottlieb explained. Several organizations implement projects to address SDOH to cut their own costs, deter hospital overutilization, and further their own value-based care goals. In making SDOH a business venture, organizations and healthcare policymakers run the risk of furthering health equity.
“As the links between our understanding of SDH and changes in health care delivery solidify, however, there is a risk that efforts to integrate medical care and social services lose touch with the aim of improving health equity,” Gottlieb wrote. “Other objectives—such as increasing revenues, reducing health care use, and controlling costs—may be given higher priority.”
SDOH projects as they relate to benefits eligibility, risk prediction modeling, and advances in precision medicine, may unintentionally drive health disparities.
Gottlieb specifically references Medicaid work requirements as one benefits eligibility issue that could negatively impact patients. Research on the SDOH certainly confirms that employment is an indicator of better health. Providing patients stable income and, in some cases, access to healthcare benefits could alleviate certain social stressors impacting health.
But actions from CMS calling on states to develop work requirements for Medicaid eligibility have taken this notion too far, Gottlieb and her co-author, Hugh Alderwick, contended.
“There are several reasons why these requirements may not actually be health promoting,” the pair wrote. “First, most people on Medicaid are already working or unable to work. Work requirements are therefore likely to have a small effect on job-seeking behaviors. Instead, the requirements are more likely to reduce access to health care services by adding substantial disincentives to enrollment, thereby exacerbating disparities rather than decreasing them.”
Additionally, the potential for health benefits from work are usually associated with “employee prestige, agency, and rewards,” Gottlieb said. Most unskilled jobs without labor regulations do not offer the health benefits some claim work requirements will promote.
“Finally, the health benefits of government assistance itself are less likely to accrue via more restrictive, means-tested programs, and more likely to accrue using entitlement programs,” Gottlieb stated. “At the very least, these new programs should be required to examine not only savings from decreased state Medicaid costs, but also the health and utilization costs to those now ineligible for care.”
Risk prediction modeling – or the practice of using data to understand a patient’s likelihood to become sick, to respond to a certain treatment, and to use a certain amount of resources – may also have unintended consequences.
Recently, commercial data analytics tools have begun offering patient social data to providers and allowing them to integrate that data into the EHR. While this gives providers more tools to address the SDOH, it could also harm patient care.
“The introduction of big social data into medical care is an important marker for translational science,” Gottlieb acknowledged. “It illustrates growing awareness that social risk factors drive health outcomes and inequities, and that these data might be used to help target prevention and intervention initiatives. It also means that big social data can be applied to health care systems’—including payers’—decisions about procedures, medications, or enrollment eligibility. Is there cause for concern?”
Maybe, the Gottlieb contended. This type of data could lead to providers or payers excluding patients from certain treatments. For example, if data suggests a patient with certain social data could potentially have higher readmission rates, the hospital or payer may refuse the patient access to a procedure.
“If the potential for bias based on social data goes unchecked, the result could be an inequitable deescalation of care for specific groups of patients,” the author explained.
At what point is it acceptable to exclude patients from certain procedures because their social circumstances skew the likelihood of recovery? What social factors are appropriate for this purpose? Which SDOHs are fueled by race, religion, or gender, and therefore are unethical to use for risk prediction modeling?
Although Gottlieb didn’t have an answer to those questions, she did emphasize the potential limitations to risk prediction modeling based on SDOH data.
Finally, Gottlieb addressed the use of SDOH data in precision medicine.
“Precision medicine also has been influenced by our growing awareness of SDH. It is well established that social deprivation, like poverty, is associated with developmental, neuroendocrine, and immunologic perturbances,” Gottlieb noted. “We can now pinpoint more specifically how social deprivation changes gene expression, which shifts biologic susceptibility to both mental and physical illness.”
There are, of course, many benefits to this trend, Gottlieb explained. It could lead to prevention efforts or even the design of certain treatments. Understanding the link between poverty and cardiomyopathy could lead to better drugs treating the condition, for example.
“However, one unintended consequence could be increased focus on individualized medical interventions rather than societal ones,” she stated. “Even when SDH are incorporated into genomics, they are unlikely to lead to sweeping changes in population health insofar as the focus is limited to individual patients.”
SDOH data in precision medicine could lead to more solutions for reducing the impacts of a social stress rather than solutions for eliminating the source of that social stress.
“If we had a pill to improve the health consequences of living in unhealthy living environments, would health care leaders and policy makers care less about changing those environments?” Gottlieb questioned. “One threat of introducing social biomarkers is sacrificing a discussion about how health inequity is part of a larger frame of social inequity.”
The focus on SDOH is certainly a positive step forward for the healthcare industry, Gottlieb acknowledged. There is an intrinsic link between these social factors and patient health, and addressing that link is important to furthering the cause of value-based care.
But at the same time, healthcare professionals must be nuanced in their approaches.
“To help guide these initiatives, there is a need for an intentional, national dialog about the potential unintended consequences of bringing knowledge and data related to SDH into a market-based health care system, and what can be done to prevent them,” the author concluded. “Though the historic push for SDH-related interventions in health care settings has been closely connected to an interest in improving health and decreasing health inequities, a more critical lens now needs to be applied when examining how current activities affect health and equity.”