The Lynx Group

Social Determinants of Health: A Compelling Clue

December 2022, Vol 13, No 6

The views and opinions expressed in this article are those solely of the author.

The 12th Annual Summit of the Association of Value-Based Cancer Care (AVBCC) in 2022 was the site of an encouraging and positive trend that is taking on more prominence in US healthcare: recognition, discussion of, and intervention around social determinants of health (SDoH). Although the concept that SDoH has an identifiable and actionable influence on health is not new, there are clear signs that thinking is moving more broadly beyond something we are considering to something we can actually use to improve healthcare.

If the goal is to treat “the right patient at the right time with the right intervention,” SDoH can be instrumental. It can even help move the “right time” into the realm of prevention. The Centers for Disease Control and Prevention defines SDoH as “the conditions in the environments where people are born, live, learn, work, play, and worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.”1 It is easy to comprehend—and appropriate to do so—that a person’s ability to access and afford food has consequences on both their physical and mental health. Likewise, opportunities to exercise, get a good education, breathe clean air, move about in the local neighborhood and be reasonably safe from crime, stay at home and be completely free from the threat of violence, and the other recognized SDoH all have a clear connection to an individual’s health.

A Good Concept

Recognizing this and developing an appetite to invest time and resources to integrate SDoH into how we manage our health has resulted in, among other things, the inclusion of SDoH in next year’s Centers of Medicare & Medicaid Services’ (CMS’) Enhancing Oncology Model (EOM). The EOM will require participants to use “screening tools to screen for, at a minimum, 3 HRSN [Health-Related Social Needs] domains: transportation, food insecurity, and housing instability.”2 Currently, the degree of commitment by CMS to incorporate SDoH into the philosophies and workflows of healthcare appears to be limited. There is no real impact to payment (or penalty) aside from a quality measure, which will likely be a participant-reported binary element (ie, Did you or did you not collect data on at least these 3 SDoH domains?). Furthermore, there is currently no mechanism by which CMS will monitor, collect, and measure participants’ deployment of these screening tools. CMS may require participants to report this information at some point during the model.3 Then again, it may not. Until there is a known and tangible outcome of how collecting SDoH data and then acting upon it will affect participants’ performance-based payment or performance-based recoupment and thus incentivize a meaningful role for it in healthcare, it will be easy for SDoH initiatives to primarily serve as a public relations move.

Nonetheless, it has become part of the conversation and there is nothing preventing providers and systems from doing more with it than is mandated, as some already are. The potential application for both payers and providers across population health initiatives is significant. Identifying at-risk populations based on SDoH and deploying preventive programs can be done as it is for traditional medical conditions. Incorporating data collection and program deployment can be rolled into clinical decision support systems, such as clinical pathways, to ensure consistent and equitable application. CMS has already established Z codes for capturing certain SDoH, although a 2019 study showed that of 33.1 million beneficiaries, only 1.59% of them had claims with Z codes.4

A Clue, Not a Conclusion

At the AVBCC conference, Barbara McAneny, MD, FASCO, MACP, Chief Executive Officer, New Mexico Oncology Hematology Consultants, Albuquerque, reminded us that zip code is the most accurate prognosticator of a patient’s health.5 A powerful and accurate insight. It is critical to note, however, that SDoH should not be viewed as a substitute for meaningful patient engagement or that the responses to SDoH screening tools provide a full picture of an individual patient. Similar to price prediction in the Oncology Care Model and the EOM, analysis and conclusion across a population can be accurate. However, the smaller the sample size becomes, the less accurate a population-based model based will be. For example, if a patient is identified as having access to exercise opportunities, it is not a given that they are exercising. If a patient lives in a low-crime zip code, there is no certainty that they are not regularly exposed to physical harm (either inside or outside the home). Living in an affluent area does not guarantee an individual’s financial security. Overextending on a mortgage, loss of a job, or other circumstances within and beyond a patient’s control can, and often do, turn what might be a statistically accurate general assumption upside down when applied specifically. These simple examples are obvious when put forth, but in bringing more attention to SDoH, the dangers of streamlining through automation and capturing data points should be considered. Identifying the right SDoH-related risks and opportunities to improve health will be done methodically and consistently, with technology as a complement to good old-fashioned conversation and patient engagement, not a substitute.

It remains to be seen whether SDoH efforts will evolve into a fundamental element of how healthcare is planned, funded, and deployed at the population and individual levels or simply become the “pet rock” of the 2020s and end up getting put in a drawer. At the moment, it is encouraging to see that they are on the table and in the spotlight more than ever.

References

  1. Office of Disease Prevention and Health Promotion. Social determinants of health. https://health.gov/healthypeople/priority-areas/social-determinants-health. Accessed October 18, 2022.
  2. Centers for Medicare & Medicaid Services. Enhancing Oncology Model: EOM overview webinar. June 30, 2022. https://innovation.cms.gov/media/document/eom-model-overview-slides. Accessed October 18, 2022.
  3. Center for Medicare & Medicaid Innovation. The Enhancing Oncology Model (EOM) request for applications. June 27, 2022. https://innovation.cms.gov/media/document/eom-rfa. Accessed October 18, 2022.
  4. Centers for Medicare & Medicaid Services Office of Minority Health. Utilization of Z codes for social determinants of health among Medicare fee-for-service beneficiaries, 2019. September 4, 2021. www.cms.gov/files/document/z-codes-data-highlight.pdf. Accessed October 18, 2022.
  5. Lawry T. Genetic code vs. zip code: the social determinants of health. June 13, 2022. www.forbes.com/sites/forbestechcouncil/2022/06/13/genetic-code-vs-zip-code-the-social-determinants-of-health/?sh=1c0a4ea3581c. Accessed October 20, 2022.

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