美国的医疗保健费用increaseddramatically over the past five decades, from $74 billion in 1970 to $3.8 trillion in 2019. This trend has been fueled in large part by an increase in the number of people who struggle with chronic conditions such as cancer, diabetes, hypertension, and lung disease. Stanford University School of Medicineestimates这50％的美国人患有慢性病，超过85％的美国医疗保健支出用于治疗患有慢性疾病的患者。
While the proliferation of chronic diseases in the U.S. is secondary in part to the aging of our population, behavioral risk factors such as cigarette smoking, obesity, poor nutrition, lack of physical activity and excessive alcohol and drug use remain theprimary美国人慢性病的原因。不幸的是，在过去两年中，许多这些行为（包括酒精和烟草的使用）已显示出令人担忧的上升趋势。
The underlying drivers of risky health behaviors are complex and are often associated with social determinants of health (SDoH). SDoH are the social and economic conditions that significantly affect the health of individuals, communities, and populations. Income level, education, employment status, housing stability, access to transportation, neighborhood conditions and safety, and race/ethnicity are among the major SDoH factors that influence health outcomes. An example of the link between SDoH and behaviors is the numerous studies that have shown that people with low income and limited education attainment have higher rates of抽烟比普通人群。这种与较高吸烟率的联系也适用于居住在农村社区的人们。
Researchshowsthat SDoH and behavioral risk factors account for as much as 80% of health outcomes, with clinical care accounting for only 20%. Given the trillions of dollars the U.S. spends on health care, this is both astounding and troubling.
Clearly, health care spending could be drastically reduced in the U.S. if we could reduce high-risk health behaviors by helping individuals to quit smoking, reduce their alcohol intake, avoid drugs, eat healthier, and exercise. However, changing individual behaviors can be exceedingly difficult, particularly if an individual is part of a social network that supports and enables risky health behaviors. A better approach may be to address the underlying issues and societal pressures that drive health behaviors.
Even for people who are highly motivated to change, social network barriers are formidable deterrents. This is especially so when the social network is an individual’s family or longtime circle of friends because resisting the norms of these groups is difficult, and leaving them can come at a high emotional or even financial cost.
One of the ways social networks make it challenging for individuals to change counterproductive health behaviors is by shaping the information a member receives about their health condition or the impact on their health of risky behaviors such as overeating, smoking or a sedentary lifestyle. Digital social networks such as Facebook are notorious for enabling the spread of disinformation about health issues.
Vaccinations are an excellent case in point. Americans who already are skeptical about vaccines will tend to consume posts on social media that reinforce their negative beliefs about being vaccinated. Then they might share those posts with like-minded group members, further solidifying the information illiteracy regarding vaccines throughout the group.
The critical role of SDoH data
The interplay between social determinants, behaviors, and health outcomes is extremely complex. To begin to understand and address them requires far more than having raw data delivered into a provider’s inbox without context. Furthermore, individuals with adverse social determinants and unhealthy behaviors are often rightly concerned about stigma and potential bias in their clinical care.
Emerging technologies and advanced analytics can assist health care providers and payors that are looking to change care delivery, and approaches around member engagement incorporate information describing social determinants. This work needs to be done carefully to ensure that the risks are well understood and are acceptable when compared with the potential benefits. However, for payors and providers that are invested in moving toward value-based care (VBC) models, SDoH data is essential to proactively influence health outcomes. Successful VBC programs also require a collaborative, team-based approach to care that engages with community-based organizations that have expertise in the delivery of social services such as access to food, job training, and housing assistance.
For example, one large, integrated health system in the Southeast determined health disparities among their children with asthma – particularly for ED visits and hospitalizations. This system relied on SDoH analytics to identify patients at higher risk for poor outcomes from their asthma. Then an intervention was developed using a concept called shared decision-making to help better engage with patients, provide additional information about asthma, assess levels of disease control and allow for co-ownership of the care plan for patients and their families, resulting in improved asthma outcomes.
Using team-based care is also essential when looking to address the impact of SDoH on clinical outcomes. Clinicians may see patients three or four times a year in 10- to 15-minute increments. It’s unrealistic to think those encounters provide them with a full view of a patient’s environmental and social conditions and influences, many of which directly and disproportionately affect individual health behaviors and outcomes. Instead, teams with expertise in social work, behavioral health, care planning, and community resources work with the clinical teams in tandem to develop the optimal care plans that incorporate adverse social determinants and refer patients into needed social services.
Advanced analytics can provide the insights needed to understand social determinants and help develop interventions that assist patients in overcoming some of the challenges and adverse environmental and social factors that are barriers to healthier behavior. Moreover, analytics are needed to support the team-based approach to care delivery. With advanced analytics bringing together data on clinical and social needs, as well as health behaviors, providers and payors can enable effective care coordination and successfully implement value-based care models.