What if I told you that tobacco use, poor diet, lack of activity and toxic agents are not the main causes of death in the United States, as conventionally accepted?
With ever-rising healthcare costs combining with often ineffective strategies to combat suffering from preventable diseases, researchers have increasingly dedicated a particular focus on identifying ways to optimize our ephemeral resources. They are finding that the true or underlying causes of death can be linked to the economic and social circumstances of the individual, such as her or his income, education and social connectedness.
The historically accepted morbidity and mortality factors are often actions and behaviors that are driven by socio-economic factors. Identifying and addressing the root causes of these tangled health webs is recognized as the most advanced methodology to create the highest impact at the lowest cost.
These critical factors are often the most difficult to address, as depicted by the well-renowned Health Impact Pyramid recognized by the National Institutes of Health.
Recent advancements in the health information technology space have enabled some dedicated healthcare organizations to advance the understanding of social determinants of health by directing their resource flow from a primary focus on the treatment of diseases to a more holistic focus on modifying the predisposing factors that inescapably propagate these health complications. Increasingly, health care organizations are asking vendors to incorporate social determinants of health risk data into patient records.
Identifying Social Determinants
Social determinants of health are considered the complex, integrated and overlapping social structures and economic systems that are responsible for health inequities, according to the Centers for Disease Control and Prevention. The six principal socio-economic factors include economic stability, neighborhood and physical environment, education, food, community and social context, and health care systems. As depicted in the accompanying chart, constructed by the Kaiser Family Foundation, each factor encompasses a variety of contributing factors, which play significant roles in determining the health of an individual.
Economic Stability: Higher economic stability precedes higher quality options for the other social determinants of health and generally lower amounts of chronic stress.
Neighborhood and Physical Environment: Contaminants in the air, water, food and soil can cause a variety of adverse health effects. In addition, the design of communities and transportation systems can significantly influence the physical and psychological health of an individual.
Education: Closely tied with an individual’s economic status, effective education for children and lifelong learning for adults are proven key contributors to optimal health.
Food: The access to and proper education around healthy food plays a large role in determining the health of an individual, as many preventable diseases are linked to their diet options.
Community and Social Context: Social support networks are important in helping solve problems, deal with adversity, and maintaining a sense of control over life circumstances. There are many studies that show the direct link between strong social relationships and the overall satisfaction and well being of an individual.
Healthcare System: The access to quality health services facilitates more options for preventive approach to care before health conditions excel to an unmanageable state.
Sourcing the data and care management
By combining these socio-economic factors with medical and pharmacy claims, labs and health risk assessments in predictive modeling, healthcare organizations can acquire more expansive views of consumers at risk for avoidable healthcare costs. There are billions of records from nearly 10,000 public information sources, from which socio-economic data can be extracted and incorporated into a health information technology system. Vendors and healthcare organizations have found that LexisNexis is a credible source for data mining.
Some of the overall types of data available from multiple sources include:
Unique name and address records
Active U.S. business entities
Business contact records
Unique cell phone numbers
Motor vehicle records
Applications for high interest loans
Access to and proper harness of this array of information enables boundless possibilities, four of which are of particular mention: risk stratification, motivational engagement prediction, stress severity projection and geo-spatial mapping systems.
Risk stratification information is used to allocate resources at a population-wide level, identify high-risk patients, alert providers and care managers about those patients, and design interventions to prevent other individuals from becoming high-risk. This also plays particular importance in readmission prevention by identifying the patients most likely to be readmitted and intervening to provide the support they need in order to avoid readmission.
In addition, an individual’s motivation – or willingness to engage in maintaining or improving their health – is just as important as the data used to determine what puts that person at risk. Understanding which individuals in the population are motivated allows health plans to wisely allocate expensive resources, like nurse care managers. A high-risk patient who is highly motivated may get as much benefit from a low-touch wellness program as she or he would from a high-touch program.
The capture and tracking of socio-economic trends among individuals enables better projection of severe stress levels. For example, increased rates of crime in a neighborhood, a house downsize, bankruptcy, or even a woman’s last name change (signaling pregnancy or divorce) are all likely indicators of increased stress severity. Stress can spur a myriad of health consequences, including high blood pressure, circulatory complications, accelerated aging, cardiovascular disease and immune defense damage, among others.
The richness of this information could furthermore spur the generation of geo-spatial mapping systems, enabling the ability to identify community trends, such as lack of access to public parks, sidewalks or close-proximity grocery stores. Even identifying trends such as high poverty rates, large quantity of amount of fast food restaurants, or high crime locations would create a more comprehensive view of the determining factors of a patient’s overall health state.
Credit bureaus, law enforcement and pay-per-click advertisers have capitalized on personally identifiable information for many years. Per their model, by harnessing this information with predictive analytics for healthcare purposes, it holds a solid promise to significantly improve healthcare administration system and save more than $300 billion in healthcare, according to McKinsey & Company, largely through reductions in expenditures.
The incorporation of social determinants of health can contribute significantly to this by improving risk prediction accuracy and revealing inconspicuous trends. Revered as the most advanced methodology, this would enable HCOs to address the true origins of health complications by identifying the economic stability, physical environment, education level, food access, social context, and health care system via a wealth of available information sources. Recognizing the obvious benefits and acknowledging the challenges, we must maintain the notion that anything worthwhile takes time and complete devotion.
Caitlin Breanne Smith is a program consultant at Wellcentive.
The post certainly reminds me of the substantial degree of uncertainty underlying healthcare. I suspect that this is the result of a substantial effort on your part to understand the dimensions of a person’s health. I wonder if you have arrived at a definition for HEALTH, or do you have a definition that you favor? I keep running into the concept of Human Capabilities as part of the definition. Are you aware of any definition for human capabilities? I have now finished 40 years as a full time Primary Physician. The opportunity to see how it “all fits together” is still a marvelous privilege.
@William Palmer – Great ideas circulating there. I foresee capabilities like this happening down the line. Hopefully sooner than later.
On a similar note, machine-learning is starting to bring a new perspective to proactive care and predictive analytics. (e.g. http://www.tagkopouloslab.ucdavis.edu/papers/2013_JAMIA_amiajnl-2013-001815.pdf)
Great post! Basically the entire “pry, poke and prod” wellness industry is totally irrelevant to a person’s health. This is the best argument I’ve seen in favor of wiping out the entire morass that is workplace wellness…
Great discussion! Thank you.
I wish we could dump all the myriad lab data–all anonymized naturally–from all the clinical and commercial and hospital labs across the country into a massive database organized geographically. We could then, for example, begin to find out about pre-symptomatic illnesses: the earliest stages of infections, of dyslipidemias, atherosclerosis, malignancies, malnutrition, collagen diseases. Eg. why is the Denver area showing so many folks with elevated sed rates and CRP’s? Are they beginning to have a resurgence of rheumatic fever?
Why throw all this data away? Or tuck it into individual EHRs and await an interoperability look-see forever from some agency?
But what if, contra Dartmouth, these same socioeconomic factors are the critical variables in predicting geographic variation in healthcare spending? See for example: http://www.federalreserve.gov/pubs/feds/2013/201304/201304pap.pdf