When thinking about how to live a healthier life, we usually hear the same advice: Eat a healthy diet, be physically active, and avoid smoking. After all, these three health risk behaviors contribute to the development of four chronic conditions (cardiovascular disease, chronic lower respiratory disease, diabetes, and cancer), which together lead to more than 50 percent of all deaths in the United States.
However, the general health advice for a population doesn’t necessarily reflect the nuances of individual health. Individuals are not aggregate statistics and, importantly, we do not live in a vacuum. Our health is a function of both our behaviors and our environments, and this context is critical for understanding health in the workplace and beyond. To this end, a recent landmark study in the Journal of the American Medical Association (JAMA) assessed the patterns of health at the state level from 1990 to 2016 using the Global Burden of Disease Study (GBD) data.
Life expectancy is often looked to as a measure of general health. Life expectancy within the United States varies greatly based on state. Hawaii tops the list with an average life expectancy at birth in 2016 of 81.3 years, while Mississippi (74.7 years) found itself on the lower end of the spectrum – a staggering difference of 6.6 years. The problem, however, is that life expectancy as a measure of health conceals the underlying risk factors and their variation by county, gender and socioeconomic status, which are all essential components of a personalized and impactful health strategy.
At a more granular level, tobacco use, nutrition and a high body mass index (BMI) remain the three most important risk factors in the United States, though there are important differences by state. Alcohol and drug use rank as the greatest risk factors for eight states, a function of the opioid crisis that is currently ravaging the United States. Importantly, the JAMA study suggests that while smoking prevalence has decreased between 1990 and 2016, BMI and fasting plasma glucose levels have both increased. In fact, the paper suggests that these unique challenges “have the potential to change the health trajectory for individuals in many states” but require not only the attention of the individuals who need to manage their weight, but also the systems that can support these shifts.
At Vitality, we understand that risk does not exist within a vacuum – our members are a function of both their behaviors and their broader environments. Just as we see differences in health risk factors across states, Vitality also sees regional differences across our clients’ employee populations. In our personalized approach to wellness, our strategic recommendations combine the macro trends across an organization with the regional variations that need to be addressed at a more grass-roots level. This approach allows us to help employers set their overall health priorities for their companies, while remaining true to Vitality’s focus on the individual.
The JAMA study serves as an important reminder that an aggregate approach to risk is not optimal – the most effective interventions will be the ones that target the specific needs of a specific population in a specific area. By pairing environmental knowledge (state-level risks) with scientifically-robust mechanisms with which to incentivize healthy eating, increased physical activity and more, Vitality seeks to empower employers, and thus communities, to help their members make better health decisions and to continue to fulfill Vitality’s mission to make communities healthier and to enhance and protect their lives.
Lianne Jacobs, Product Analyst, has a Master’s in Public Health from Yale University. She is the only indoor cycling instructor who can’t ride a bike. She enjoys traveling the world, laughing at her own jokes, and tricking her husband into eating baked goods made with hidden vegetables.
 The US Burden of Disease Collaborators. The State of US Health, 1990-2016 Burden of Diseases, Injuries, and Risk Factors Among US States. JAMA. 2018;319(14):1444–1472. doi:10.1001/jama.2018.0158. Accessed from: https://jamanetwork.com/journals/jama/fullarticle/2678018