While there is an extensive literature on the effectiveness of pharmaceutical and nonpharmacologic interventions for chronic low back pain (CLBP), it is challenging to synthesize the findings because of differences in the CLBP samples and in the outcome measures used. The NIH Research Task Force (RTF) on CLBP noted that these differences make it "difficult to compare epidemiologic data and studies of similar or competing interventions, replicate findings, pool data from multiple studies, resolve conflicting conclusions, develop multidisciplinary consensus, or even achieve consensus within a discipline regarding interpretation of findings." To this list we would add that these differences also prevent the use of the results to answer questions such as 'Which therapies work best? And for whom?' This project tackles two things that are needed to address these differences and allow for better cross-study comparisons: we will develop empirical links between different outcome measures to allow their expression in similar units (Aim 1), and we will refine the RTF's proposed stratification (subgrouping) of patients by the impact of their CLBP (Aim 2).
To address the first challenge, in Aim 1 this study will develop and evaluate crosswalks or links between components of the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS®) short form (PROMIS-29) and common legacy measures used for chronic pain. The purpose of these crosswalks or links is to allow researchers who measured outcomes using one instrument to estimate what the outcome would be if it had been measured using the other instrument. In particular, we will create crosswalks/links for the two most commonly used instruments used to measure outcomes for CLBP: the Roland-Morris Disability Questionnaire (RMDQ) and the Oswestry Disability Index (ODI). In addition, depending on data availability and input from our Advisory Council we will create at least two other crosswalks/links between the PROMIS-29 and other legacy measures for CLBP (e.g., the Back Pain Functional Scale) or legacy measures for other types of chronic pain (e.g., the Neck Disability Index for chronic neck pain).
To address the second challenge, in Aim 2 we will evaluate and refine the chronic pain impact stratification scheme proposed by the NIH Research Task Force on chronic low back pain. The proposed scheme uses the Impact Stratification Score (ISS) which is calculated using 9 items from the PROMIS-29. This ISS was intended to identify and categorize patients with chronic pain into groups based on the severity of their condition so that treatment can be better targeted. We will first evaluate the ISS and its properties to determine whether they are stable across different samples and determine whether they can be improved. After we have finalized the components and calculation of the ISS, we will examine its effect on the impacts of chronic pain (e.g., health-related quality of life, healthcare utilization, worker productivity) to identify meaningful cut-points to use to stratify chronic pain patients into subgroups who exhibit different levels of chronic pain impact.
Three types of data will be used in the analyses to address Aims 1 and 2: data from existing datasets built during other studies, data collected from an anonymous national convenience sample using Amazon's Mechanical Turk (MTurk) crowdsourcing platform, and data from members of the probability-based nationally representative KnowledgePanel. Aim 3 will evaluate whether MTurk is a reliable, efficient method to collect data on individuals with chronic pain. One part of this evaluation will involve comparison of the results from MTurk to what was found using KnowledgePanel.
