Seventeen-year national pain prevalence trends among U.S. military Veterans

Abstract: U.S. military veterans experience higher pain prevalence than nonveterans. However, it is unclear how the disparities in pain prevalence have changed over time because previous trend studies are limited to veterans using the Veterans Health Administration. This repeated cross-sectional study aimed to characterize pain prevalence trends in the overall population of U.S. veterans compared to nonveterans, using nationally-representative data. We analyzed 17 years of data from the National Health Interview Survey (2002-2018), with a mean annual unweighted sample of 29,802 U.S. adults (total unweighted n=506,639) and mean annual weighted population of 229.7 million noninstitutionalized adults. The weighted proportion of veterans ranged 11.48% in 2002 (highest) to 8.41% in 2017 (lowest). We found that veterans experience a similar or higher prevalence of pain than nonveterans across the study period, except for severe headache or migraine and facial pain. Pain prevalence among veterans increased over time, with a higher rate of increase compared to nonveterans for all pain variables. From 2002 to 2018 there was an absolute increase (95% CI) in pain prevalence among veterans (severe headache or migraine: 2.0% [1.6% to 2.4%]; facial pain: 1.9% [1.4% to 2.4%]; neck pain: 4.7% [4.1% to 5.2%]; joint pain: 11.4% [10.8% to 11.9%]; low back pain: 10.3% [9.5% to 11.1%]; any pain: 10.0% [9.6% to 10.4%]; and multiple pains: 9.9% [9.2% to 10.6%]. The continued pain prevalence increase among veterans may have implications for healthcare utilization, highlighting the need for improved pain prevention and care programs for this population with a disproportionate pain burden. PERSPECTIVE: This article uses routinely-collected cross-sectional data that are nationally-representative of U.S. adults to present changes in pain prevalence among military veterans compared to nonveterans. The findings underscore the need for improved prevention and pain care programs for veterans, who experienced a widening disproportionate pain burden from 2002 to 2018.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.

    Mental Health Peer reviewed