The Frequency and Economic Impact of Musculoskeletal Disorders for California Firefighters: Trends and Outcomes over the Past Decade.

Musculoskeletal disorders (MSDs) are the most common type of occupational injury or illness suffered by firefighters, so there is considerable interest among policymakers and stakeholders about how best to monitor, prevent, and treat firefighter MSDs. In this study, the authors update analyses from a 2010 RAND study on firefighters in California and consider the impacts of the 2013 workers' compensation reforms and the economic shocks of the late 2000s on outcomes for firefighters with MSDs. The California Department of Industrial Relations requested that the authors address a wide range of specific research questions on various aspects of firefighters' injury risk and outcomes in the workers' compensation system, from case mix and economic consequences to permanent disability rating and medical treatment patterns. The authors analyzed administrative data from the California workers' compensation system linked to data on earnings for workers injured between 2005 and 2015, with additional analyses to tailor the results to the new reforms. They compare firefighters with three groups of workers in broadly comparable occupations-police, other public-sector workers, and private-sector workers with job demands that resemble firefighting-and supplement the analysis using outside data. The authors found, among other things, that firefighters continue to face elevated risk of work-related MSDs and that earnings losses for firefighters worsened after the Great Recession of 2008-2009. Their findings will be of interest to policymakers in California and other states and to other audiences concerned with the occupational health and safety of firefighters.

Update of Markov Model on the Cost-effectiveness of Nonpharmacologic Interventions for Chronic Low Back Pain Compared to Usual Care.

STUDY DESIGN: Markov model. OBJECTIVE: Further validity test of a previously published model. SUMMARY OF BACKGROUND DATA: The previous model was built using data from ten randomized trials and examined the 1-year effectiveness and cost-effectiveness of 17 nonpharmacologic interventions for chronic low back pain (CLBP), each compared to usual care alone. This update incorporated data from five additional trials. METHODS: Based on transition probabilities that were estimated using patient-level trial data, a hypothetical cohort of CLBP patients transitioned over time among four defined health states: high-impact chronic pain with substantial activity limitations; higher (moderate-impact) and lower (low-impact) pain without activity limitations; and no pain. As patients transitioned among health states, they accumulated quality-adjusted life-years, as well as healthcare and productivity costs. Costs and effects were calculated incremental to each study's version of usual care. RESULTS: From the societal perspective and assuming a typical patient mix (25% low-impact, 35% moderate-impact, and 40% high-impact chronic pain), most interventions-including those newly added-were cost-effective (<$50,000/QALY) and demonstrated cost savings. From the payer perspective, fewer were cost-saving, but the same number were cost-effective. Results for the new studies generally mirrored others using the same interventions-for example, cognitive behavioral therapy (CBT) and physical therapy. A new acupuncture study had similar effectiveness to other acupuncture studies, but higher usual care costs, resulting in higher cost savings. Two new yoga studies' results were similar, but both differed from those of the original yoga study. Mindfulness-based stress reduction was similar to CBT for a typical patient mix but was twice as effective for those with high-impact chronic pain. CONCLUSION: Markov modeling facilitates comparisons across interventions not directly compared in trials, using consistent outcome measures after balancing the baseline mix of patients. Outcomes also differed by pain impact level, emphasizing the need to measure CLBP subgroups. LEVEL OF EVIDENCE: N/A.

Health Care Costs and Opioid Use Associated With High-impact Chronic Spinal Pain in the United States.

STUDY DESIGN: A descriptive analysis of secondary data. OBJECTIVE: The aim of this study was to estimate health care costs and opioid use for those with high-impact chronic spinal (back and neck) pain. SUMMARY OF BACKGROUND DATA: The US National Pain Strategy introduced a focus on high-impact chronic pain-that is, chronic pain associated with work, social, and self-care restrictions. Chronic neck and low-back pain are common, costly, and associated with long-term opioid use. Although chronic pain is not homogenous, most estimates of its costs are averages that ignore severity (impact). METHODS: We used 2003 to 2015 Medical Expenditures Panel Survey (MEPS) data to identify individuals with chronic spinal pain, their health care expenditures, and use of opioids. We developed prediction models to identify those with high- versus moderate- and low-impact chronic spinal pain based on the variables available in MEPS. RESULTS: We found that overall and spine-related health care costs, and the use and dosage of opioids increased significantly with chronic pain impact levels. Overall and spine-related annual per person health care costs for those with high-impact chronic pain ($14,661 SE: $814; and $5979 SE: $471, respectively) were more than double that of those with low-impact, but still clinically significant, chronic pain ($6371 SE: $557; and $2300 SE: $328). Those with high-impact chronic spinal pain also use spine-related opioids at a rate almost four times that of those with low-impact pain (48.4% vs. 12.4%), and on average use over five times the morphine equivalent daily dose (MEDD) in mg (15.3 SE: 1.4 vs. 2.7 SE: 0.6). Opioid use and dosing increased significantly across years, but the increase in inflation-adjusted health care costs was not statistically significant. CONCLUSION: Although most studies of chronic spinal pain do not differentiate participants by the impact of their chronic pain, these estimates highlight the importance of identifying chronic pain levels and focusing on those with high-impact chronic pain. LEVEL OF EVIDENCE: 3.

Exploring the prevalence and construct validity of high-impact chronic pain across chronic low-back pain study samples.

BACKGROUND CONTEXT: The US National Pain Strategy focused attention on high-impact chronic pain and its restrictions. Although many interventions have been studied for chronic low-back pain, results are typically reported for heterogeneous samples. To better understand chronic pain and target interventions to those who most need care, more granular classifications recognizing chronic pain's impact are needed. PURPOSE: To test whether chronic pain impact levels can be identified in chronic low-back pain clinical trial samples, examine the baseline patient mix across studies, and evaluate the construct validity of high-impact chronic pain. STUDY DESIGN/SETTING: Descriptive analyses using 12 large study datasets. PATIENT SAMPLES: Chronic low-back pain patients in nonsurgical, nonpharmacologic trials in the US, Canada, and UK. OUTCOME MEASURES: Preference-based health utilities from the SF-6D and EQ-5D, employment status and absenteeism. METHODS: We used two logistic regression models to predict whether patients had high-impact chronic pain and whether the remainder had low- or moderate-impact chronic pain. We developed these models using two datasets. Models with the best predictive power were used to impute impact levels for six other datasets. Stratified by these estimated chronic pain impact levels, we characterized the case mix of patients at baseline in each dataset, and summarized their health-utilities and work productivity. This study was funded by a National Center for Complementary and Integrative Medicine grant. The authors have no potential conflicts of interest. RESULTS: The logistic models had excellent predictive power to identify those with high-impact chronic pain. Although studies were all of chronic low-back pain patients, the baseline mix of patients varied widely. Across all datasets, utilities, and productivity were similar for those with high-impact chronic pain and worsened as chronic pain impact increased. CONCLUSIONS: There is a need to better categorize chronic pain patients to allow the targeting of optimal interventions for those with each level of chronic pain impact.