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Readers Write: Six Myths Debunked: The True Significance of Social Determinants of Health

November 15, 2017 Readers Write No Comments

Six Myths Debunked: The True Significance of Social Determinants of Health
By Erin Benson

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Erin Benson is director of market planning with LexisNexis Health Care of Alpharetta, GA.

Predicting future health risks has always been important, but the ongoing move toward value-based care and the emphasis placed on health outcomes is driving the need for greater prognostic accuracy.

At least 25 cents of every healthcare dollar goes toward the treatment of diseases or disabilities that result from potentially changeable behavior. If you can identify risk factors in patients, you can potentially intervene and initiate change. That’s where social determinants of health come in, but first it’s important to separate the myths from the truth.


Myth #1: Adding socioeconomic data to the patient file causes information overload and makes it difficult for providers to zero in on what’s important and relevant.

Medical care determines only 20 percent of overall health, while social, economic, and environmental factors determine 50 percent, making them too significant to ignore. The National Quality Forum, Centers for Disease Control, and World Health Organization have all acknowledged the importance of socioeconomic data.

Incorporating the data into existing workflows and integrating it with electronic health record (EHR) systems makes risk assessment more efficient, not time-consuming. A Socioeconomic Health Score, for example, can provide an immediate picture of unforeseen and avoidable risks. It can then drive informed decisions regarding that patient’s care, as well as offer opportunities for patient-provider discussions about lifestyle.

Myth #2: Social determinants of health include everything related to a person’s lifestyle, environment, situation, and behaviors.

Only certain types of data have been clinically validated to predict health outcomes. Even when attributes are clinically validated, they may correlate to different outcomes with different accuracy strengths.

Improving predictive ability is not just a matter of adding more data. It is a science to determine which datasets enhance predictive power and how they should be weighted in drawing insights about a patient.

Myth #3: A patient’s socioeconomic attributes considered individually allow you to make accurate predictions about a patient’s overall health risk.

Any attribute examined on its own is not adequate to develop an accurate risk score understanding. A combination of relevant attributes—ranging from social and community circumstances to economic stability and education, to neighborhood and built environment—provide context and are critical to developing a complete, holistic picture of the patient.

Myth #4: Socioeconomic data comes from demographic data or must be gathered through the use of surveys.

Demographic data may be too limiting and census data tends to get outdated quickly. Survey data, too, can become outdated. Furthermore, the value of survey data depends on the accuracy of the patient supplying the information and on the staff member who manually enters the results into the system.

Research has shown that public records are a better source of socioeconomic data. Those records are vast, comprehensive, and reliable. Clinically validated information on social determinants of health can be extracted from these records to paint a picture of a patient’s social, environmental, and economic situation and predict future health outcomes.

For healthcare providers who have traditionally relied only on medical and pharmacy data, socioeconomic data can now help fill gaps in understanding the patient and provide actionable insights that can be used to improve patient care.

Myth #5: To personalize care for a patient, you can rely on aggregated data at the ZIP code or census level.

Aggregated levels of data can be useful for expanding a health system’s market share or determining resource allocation. They are not, however, suitable for predicting a patient’s individual health risk.

Within a single ZIP code can be a wide variety of income levels, crime rates, and other factors that are critical components of social determinants of health. An individual’s actual address allows for the collection of social determinants that are more accurate indicators. However, even address data alone are not effective predictive tools. They ignore the influences of education, economic stability, social context, and other important variables that impact health.

Myth #6: Socioeconomic data must be used in combination with clinical data and is not an effective risk predictor on its own.

Even in the absence of clinical data, using socioeconomic data has proven to more accurately predict risk based on total cost than traditional age/gender predictions alone. Small increases in accuracy of as little as a percent or two can have a substantial impact and should not be ignored.

Because higher-risk patients account for the majority of healthcare costs, using socioeconomic scores to more accurately identify them gives providers an opportunity to proactively address their care. The result can be a 10-20 percent savings over traditional age/gender model risk stratification alone.


Healthcare is on the brink of a significant transformation largely driven by the availability of vast amounts of socioeconomic data and advanced analytics. Now that we’ve separated fact from fiction, it should be apparent social determinants of health have great value as a reliable predictor of healthcare risk.

The truth is we’ve only scratched the surface of what can be learned and how the insights gained can be applied. What is clear now is that organizations that embrace using social determinants of health will be better able to understand and manage health risk in their patients, resulting in improved outcomes and reduced healthcare costs.

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