A deep dive into environmental factors that impact health outcomes, including proximity to industrial sites, lead exposure, and urbanization.
In health care, some of the most significant breakthroughs can often emerge not from the examination room, but from the data and patterns that reside in the everyday lives of patients. For health plans working to improve outcomes for their members, the key to unlocking these insights lies in understanding social determinants of health (SDoH)–the conditions in which people are born, grow, live, work, and age. These factors significantly affect a wide range of health and quality-of-life outcomes. In this article, we’ll examine several important environmental factors that impact health outcomes, including proximity to industrial sites, lead exposure, and urbanization.
The impact of industry on health outcomes
Recent analyses have shed light on how proximity to toxic waste and industrial sites can negatively impact cardiovascular and mental health. One such example is oil and natural gas (ONG) extraction areas, which often release toxic chemicals into nearby communities. Proximity to these ONG sites have been correlated with adverse cardiovascular impacts, such as higher blood pressure. Moreover, separate research has found that living near these sites reduces one’s lifespan by an average of 1.2 years.
These effects aren’t limited to just the physical. Another study examining the impact of environmental stressors on mental health found that residents living in areas with high levels of industrial activity reported feeling a sense of powerlessness and more symptoms of depression.
As shown, these environmental factors can lead to a range of health issues, underscoring the need for health plans to understand and address these local health influences. Being able to identify the members that live near sites that release toxic chemicals opens the door to targeted outreach that can help mitigate these risks, such as increasing access to mental health services.
Lead exposure’s long-term effects on communities
Lead exposure is another example of how environmental conditions can shape health outcomes. People who live in homes built before 1978—when the use of lead-based paint for residential use was federally banned—may be affected. Lead, a toxic metal, can be found in the deteriorating paint, making its way into the bodies of residents. The health impacts of lead exposure can be severe and long-lasting, especially in children, who can suffer from decreased IQ and behavioral problems. Studies have also found an association between exposure to lead and mental illness including phobia, depression, mania, and schizophrenia.
This underscores the imperative need to address SDoH in efforts to improve health outcomes. With access to this kind of detailed information, such as in the percentage of members living in houses built before 1980, health plans can find out if there is a correlation between this factor and the prevalence of certain conditions, like depression. Through this analysis, they’ll have the data necessary to justify an investment in mental health programs in areas at high risk of lead exposure.
The health costs of urban living
Another environmental factor that can adversely impact patient health is urbanization. In comparison to rural areas, cities tend to have higher levels of air and noise pollution, less open space, and a greater number of crimes. These characteristics of urban areas have been associated with increased risk for mental health disorders, such as anxiety or depression. Other factors, such as social isolation, discrimination, and poverty, in urban areas also can affect mental health.
Access to these data points is vital to understanding member health, especially when you consider the potential compounding impact of SDoH factors on comorbidities—cases in which a person has more than one disease or condition at the same time. For example, one study found that there is a direct correlation between the prevalence of chronic obstructive pulmonary disease (COPD) and the rate of mental health conditions. When these mental health conditions are present in COPD patients, this can, in turn, affect mortality rate, hospital length of stay, and quality of life. Following this line of thought, SDoH factors that affect conditions like COPD could have a “snowball” effect, leading to comorbidities that can, in turn, lead to even more negative patient outcomes. Given this, it becomes even more important for health plans to be able to identify members living in high-risk areas and develop preventive care strategies.
Leveraging technology for deeper insights
This is where analytics technology comes into play. A sophisticated analytics solution enables health plans to delve deeper into their data, uncovering the hidden influencers of health within their own member populations. By leveraging such technology, health plans can not only better understand the diverse needs of their members, but also tailor their services to meet these needs effectively.
For payers in the Medicaid market that provide health care coverage to individuals and families who are low-income and have disabilities, understanding SDoH factors are paramount to creating impactful programs that improve patient outcomes. Analytics can uncover demographic information on your members, including age, gender, and ZIP code. From there, experts can leverage these insights to find correlations between SDoH factors and the prevalence of specific conditions within your member population. For example, Episcource can analyze your members’ ZIP codes to uncover their proximity to polluted areas, the percentage of members who live in houses built before 1980, and their access to transportation. Plans can leverage these types of analyses to identify which member segments need additional care or services.
Take one of our health plan clients in the Medicaid market, for example. Utilizing insights from Episource Analyst, our experts identified the segment of their members with the highest risk score opportunity; in other words, the sickest patients most urgently in need of care. After reviewing our analysis, the client found that the majority of these members were located in remote areas of South Texas. To prioritize these members for visit completion, the payer adopted strategies such as flying out city-based specialists, deploying transportation services with contracted vehicles, and expanding its telemedicine programs. With the help of analytics, the client was able to uncover a SDoH factor negatively affecting a portion of their member population—access to health care services—and implement programs to counteract its influence.
The exploration of SDoH is not just about data and metrics; it's about understanding the human story behind these figures. By leveraging tools, health plans can move beyond traditional care models to a more holistic, informed approach that truly makes a difference in the lives of their members.
About the authors
Danielle Kopetman, associate solutions consultant, Episource
Kopetman collaborates closely with payers and provider groups to identify opportunities, deliver actionable insights, and support programs that focus on risk adjustment and enhancing patient care. She earned her Bachelor's degree from Tulane University.
Falak Jain, senior data solutions analyst, Episource
Jain works with payers and provider groups to deliver actionable insights and innovative solutions by analyzing their data across Episource's suite of products and services. He is passionate about using his skills to improve patient care and solve challenging problems. Jain also focuses on internal process improvement and automating manual workloads to increase efficiency. He holds an MS in business analytics and a Bachelor's degree in mechanical engineering.