Surprising Mediator Links Poverty and Knee Pain

Associations found in previous studies between osteoarthritis (OA) knee pain severity and neighborhood disadvantage may be explained, not by poverty itself, but by worsened sleep that comes with living under such conditions, researchers said.

While Area Deprivation Index (ADI) scores did not correlate directly with pain severity, they did correlate significantly with sleep efficiency, which in turn correlated significantly with pain scores, according to Felicitas Huber, PhD, of Washington University in St. Louis, and colleagues.

Neighborhood features such as crime (and fear of it), noise, light pollution, and poor walkability likely underlie impaired sleep that previous studies have linked with chronic pain severity, the group observed in Arthritis Care & Research. “Future research should explore which environmental factors (e.g., safety, noise) are most salient for sleep and pain outcomes,” they wrote.

Another surprise was that, against the researchers’ expectation from earlier findings, pain catastrophizing — emotional perceptions of pain that exceed patient’s scoring of pain severity — wasn’t significantly associated with either ADI scores or self-assessed pain level.

Countless studies have tied poverty, both at the individual and the community level, to all manner of worsened health states. Chronic pain is among them. Knee OA and back pain are perhaps the most common sources of chronic pain, and previous research has found that neighborhood disadvantage predicts both increased knee OA prevalence and worsened pain from it. Neighborhood disadvantage has also been linked repeatedly with poor sleep. Thus, Huber and colleagues felt it made sense to explore connections among these factors in an ongoing cohort study of 140 largely urban dwellers with OA knee pain in two Southern regions.

Participants were recruited at the University of Florida in Gainesville and the University of Alabama at Birmingham. Mean age was 58 and 62% were women. The cohort was almost equally divided between white and non-Hispanic Black people. Almost 60% lived in neighborhoods with high or very high ADI scores (61-80 and 81-100, respectively). None were from areas of substantial affluence (ADI ≤20). ADI scores are calculated at the census block level and include 17 socioeconomic parameters covering housing quality, household income, educational attainment, and employment.

Cohort members were tested in various ways at three visits over 3-5 weeks. Participants were also given wrist-worn actigraphy devices to wear constantly for 5-15 days prior to the second clinic visit. They were asked to push a button when they went to bed and again when arising. The device recorded nighttime movements indicating sleeplessness; sleep efficiency was calculated by dividing the time without such movements by total time in bed. This averaged 79.7% across the entire cohort.

Pain was evaluated with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) system, focusing exclusively on the pain subscale. The mean value was 7.68 out of 20. Pain catastrophizing was evaluated with a questionnaire addressing coping strategies for pain, in which catastrophizing could be scored from 0 to 6; the cohort average was 1.26.

Associations were developed using so-called serial mediation analysis, in which multiple possible causative pathways are tested. In the current study, Huber and colleagues looked at the following:

• Neighborhood>sleep>catastrophizing>pain
• Neighborhood>sleep>pain
• Neighborhood>catastrophizing>pain

As noted earlier, catastrophizing turned out not to be a particularly significant factor connecting neighborhood disadvantage to pain severity or to sleep quality. The researchers did find, however, that ADI scores correlated with sleep efficiency with an effect size of -0.08, and sleep efficiency with pain at -0.09. In a 10,000-iteration “bootstrap” analysis, the three factors were connected with a statistically significant value of 0.01.

Huber and colleagues suggested that catastrophizing appeared unimportant because it wasn’t especially strong in this cohort in the first place. They urged that this be examined further in additional studies because previous research had shown strong links with sleep parameters.

The current study didn’t look at particular neighborhood factors that might affect sleep quality. Nevertheless, Huber’s group noted that poor neighborhoods are usually characterized by high crime; nighttime noise from sirens, vehicular traffic, and aircraft; and bright lighting. “Neighborhood walkability is another salient factor to consider, given that greater physical activity is associated with improved sleep quality,” they added. And low-grade housing may have issues with temperature control, water leaks, vermin, etc., that can disrupt sleep.

Limitations to the study included, most notably, its relatively small sample and its cross-sectional design based on one-time assessments of sleep, pain, and clinical measures. Also, homeless people had to be excluded because ADI scoring requires a residential address.

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