Many Americans don't understand how daily factors, likecommute time or community participation, act as key healthpredictors. (Photo: Shutterstock)

Many non-clinical factors can determine if someone is at agreater risk of contracting COVID-19 –something employers shouldconsider when bringing workers back on premises as states graduallyease stay-at-home orders.

Welltok contracted with Ipsos to poll 2,000 adults to determine gaps in awareness ofthe social determinants of health (SDOH)–lifestyle, environment,education, job status, relationship, financial security andcommunity safety–as a way of highlighting how SDOH data can be usedto benefit workers as they return to the workplace.

Related: COVID-19 and social determinants of health:Considerations for employers

"With SDOH being the major drivers of an individual's healthstatus, these variables can be analyzed to better target and engageemployees with personalized resources that meet their specificneeds and interests," the authors write. "SDOH data can not onlyreveal an employee's health risks today, but it can also beleveraged to anticipate future needs and risks. Additionally, itcan predict their receptivity to specific health programs andlikelihood to engage."

While most survey respondents understand that their health canbe impacted by such social factors as the type of work they do orwho they live with, more than half do not understand how dailyfactors, like length of commute or participation in communityactivities, are also key health predictors.

For example, Welltok's predictive models illustrate things suchas:

• Consumers who vote in mid- and off-term elections are lesslikely to excessively use the ER and more likely to engage inwell-being programs.
• The type of car a person has and what type of home they live inhelp predict a person's likelihood of being diagnosed with achronic illness.
• A person's relationship status and commuting length areindicators of medication adherence.

"Consumers say that providers usually ask them about theirphysical health (i.e. diet, exercise) and emotional health, but notabout SDOH factors like education or social connectedness," theauthors write. "This means important details about SDOH are notcaptured in a person's clinical or claims records. On top of this,nearly half (48 percent) of people do not even know what type ofinformation outside of their medical history they should be sharingwith their provider to get better support."

Moreover, half of the survey respondents are afraid of beingnegatively impacted if they shared too many personal details abouttheir life with their employer. Yet at the same time, 56 percent ofthe respondents say they get irrelevant support from their company,and 82 percent would increase participation in health and wellbeingprograms with personalized support.

Indeed, SDOH data can be used to predict and identify theexistence of certain struggles within employees, includingloneliness, financial insecurity and emotional distress, accordingto the report. Benefits include improved worker productivity;higher employee engagement in well-being programs; increased ROI onwell-being investments; intelligent benefit design based onpopulation-level insights; lower employee turnover.

"Machine learning and predictive models can identify which typesof programs specific employees might be most receptive to," theauthors write. "In this way, employers can optimize spend byproviding the right resources to the right employees."

Read more: 

• Employers move toward predictiveanalytics
• Americans need more awareness, guidance on socialdeterminants of health
• 9 best practices to increase engagement in wellnessprograms