According to the Centers for Disease Control and Prevention (CDC), in 2014, 90% of health care expenditures were for chronic physical and mental illness.4 The Centers for Medicare and Medicaid Services (CMS), in 2022, noted total health care spending totaled $4.5 trillion.5 In the 20 years between 1996 and 2016, total health care spending more than doubled. Out of pocket expenses totaled 9.1%; patients contributed $300 billion dollars to the health care system.6 Outside of health care, the cumulative inflation rate for the same period of time was 53%,7 half the rate of health care spending increases. Clearly, this is not sustainable and the outcomes do not match the economics. A key determinant of health care status is socioeconomic discrepancy. The subway map example, where life expectancy is charted by neighborhoods connected by rail stops in a large city, highlights these variations. Berwick et al study life expectancy based on socioeconomic characteristics of location in large cities. He noted that within the same cities, life expectancy varies greatly. In fact, in the borough of Manhattan, the groups which live longer are much more likely to be highly educated, not live in public housing, and be at the opposite end of the poverty scale.8,9 Berwick, paraphrasing Immanuel Kant, described the socioeconomic characteristics as "the moral determinants of health."10 Additionally, both Berwick and Marmot described goals and concepts for improving health outcomes and life quality. The point Marmot and Berwick make is that the key, initial step is identification of the causes of a problem.

A challenge for research is minimizing heterogeneity in the cohort under consideration. For example, consider these studies which have been published in credible peer-reviewed journals. Goertz et al reported the results of a prospective study in a presumably homogeneous population of military service people at three different sites. This would likely include an otherwise healthy group (to be active military) of 750 with an average age around 30. Yet, over half described lower back pain (LBP) lasting greater than 3 months, 6% were using narcotic medication while still in the treatment protocol, and 10-17% were smokers.11 In a study to evaluate “minimal interventional treatments for participants with chronic low back pain” (the MINT Study), there was a larger age group spread. Of the participants, half described a low level of education, half were not actively employed, one quarter were smokers, and the average body mass index (BMI) would be considered overweight.12 Jarvik et al, in a small Veterans Administration study found that depression was the greatest predictor for new episodes of low back pain. This was a stronger predictor than advanced degenerative changes present on a baseline MRI.13 Whang et al, in researching sacroiliac joint fusion, reported on a group ranging from 25-76 years. Back pain had been present for 5-7 years. Opioid use was approximately 60%. BMI was considered overweight; 17-20% were not working due to back pain. Current and former smokers comprised approximately 35% of the study cohort.14 These studies attempted to evaluate back pain as a homogeneous problem, yet their conclusions must be tempered by their heterogeneous sampling. Lacking from these studies are an inclusion of coincidental morbidities such as hypertension, hyperlipidemia, cardiovascular disease (CVD), Type 2 diabetes (T2D), anxiety, depression inflammatory disorders and genetic tendencies. When other coexisting health issues are not considered, back pain remains the silo. Perhaps the results of the studies would be different had they included the results of concurrent management of comorbidities (or multiple morbidities). For example, would back pain episodes be less frequent and disabling in a population where depression or obesity had been simultaneously managed?

The Institute of Medicine (IOM) has constructed a flow chart for depression. As a sequelae of depression the patient is at risk of obesity due to reduced activity and increased caloric intake. They will likely exhibit reduced physical exertion. The end result is a higher risk for T2D.15 What if, in this scenario, pain was substituted for depression? When attached to the Fear Avoidance Model, such as of Vlaeyen,16 one can observe a pattern which includes catastrophizing, hypervigilance, self-removal from tasks, depression, and a resulting loss of activity. The likely tendency is again disuse, depression, disability and a sedentary lifestyle. The result is the path described by the IOM. Park et al, examined the deleterious health effects of a sedentary lifestyle. Using data culled from the World Health Organization (WHO) they opined that “reducing sedentary behaviors and increasing physical activity are both important to promote public health.” The effects of a sedentary lifestyle in conditions such as diabetes, dyslipidemia, and osteoporosis are well described.17 Chou et al, in a study involving only males, identified a set of predictors for developing high intensity and high disability lower back pain. They concluded that the greatest risk existed in those patients with obesity (defined as, not limited to, elevated BMI and waist-hip ratio), emotional disorders such as anxiety or depression, and a lower educational level. These are patients with biological, psychological, and socioeconomic challenges.18

Defining chronicity is a challenge. NASS defines subacute back pain as an episode which lasts 6-12 weeks. Chronic back pain is an episode lasting greater than 12 weeks.19 In New York, the Workers’ Compensation Medical Treatment Guidelines, also assigns a time value to back pain, defining chronicity at three months.20 In contrast, Kuner et al disagree: “Chronic pain is not simply a temporal continuum of acute pain.”21 Their assertion is based on physiology, specifically plasticity within the nervous system. Shpaner et al describe chronic pain as a duality of both anatomical/physiological and psychological aspects.22

With many diseases, considerable evidence exists supporting synergistic benefits of collaborative care. Back pain should be no different. Aside from measuring outcomes, a set of predictors (such as summarized by Chou), and stratification, would be a significant benefit to identify patients at highest risk to develop chronic pain. These predictors could also be useful to anticipate which patients may develop or exacerbate non-spine comorbidities such as T2D and obesity. Stevans et al, using a prognostic pre-screening tool, identified those patients who had a higher risk of developing chronic pain. They identified the previously mentioned risk factors of obesity, smoking, depression, and a baseline level of disability. When they included their own definition of what they considered to be “nonconcordant care” at the level of the primary provider within the first 21 days (vis-a-vis pharmacy, diagnostic imaging, and timeliness of a specialty referral) they found the risk for transition to chronic back pain doubled.23 The solutions presented by Marmot and Berwick are more macro in process in that they are at the level of political, economic, and social policy. Within our practices we need a more applicable process. We need to emphasize moderating or eliminating risk factors such as diet, alcohol consumption, and smoking. Psychosocial concerns which result in kinesiophobia, catastrophization and then avoidant behaviors require recognition and mitigation. Educating and providing context to a patient can reduce the catastrophic effects of “scary” terms such as disc degeneration and disc bulging. It is not uncommon, as previously mentioned, for patients to gain weight from their reduced activity. Hypertension, pulmonary dysfunction, CVD, and T2D are associated with weight gain and inactivity. Adipose tissue has inflammatory potential. Increasing adipose tissue with psychosocial concerns and obesity can propagate back pain24 which is completely counterproductive to our treatment efforts. In cases of work-related back pain and pain caused by a motor vehicle accident, psychological stresses can play a strong role. Anger, grief, anxiety, and sudden transition to inactivity in the presence of pain and injury need to be immediately addressed. When the spine condition is stable, activity and getting back to normal tasks should be encouraged as soon as possible. It is not uncommon for patients to equate pain with the severity of their injury. This can result in kinesiophobia and the anxiety reactions described by Vlaeyen.16 Reassuring the patient that activity and active therapy can transiently increase their symptoms but are not worsening the injury is important for reducing fear and anxiety.

Back pain as a disease can both influence and be influenced by non-spinal comorbidities such as CVD, T2D, and depression. Additional factors including a patient’s biology, psychology, and socioeconomic conditions also influence recovery. Limiting the impacts of comorbidities is crucial to increase the value of health care. Arguably, there is a necessity for a care package encompassing both spinal and non-spinal collaboration. Collaboration is not limited solely to providers within the same institution. Continuing education, team meetings, journal clubs, participation in professional societies, and collegiality between spinal and non-spinal providers cannot be overestimated. When care of the patient is collaborative between spinal and non-spinal disciplines, hopefully the benefit of increased value can be realized.