Effects of Mental Health Disorders on Nursing Home Residents' Nine-Month Pain Trajectories

Identifying Weekly Trajectories of Pain Severity Using Daily Data From an mHealth Study: Cluster Analysis

BACKGROUND

People with chronic pain experience variability in their trajectories of pain severity. Previous studies have explored pain trajectories by clustering sparse data; however, to understand daily pain variability, there is a need to identify clusters of weekly trajectories using daily pain data. Between-week variability can be explored by quantifying the week-to-week movement between these clusters. We propose that future work can use clusters of pain severity in a forecasting model for short-term (eg, daily fluctuations) and longer-term (eg, weekly patterns) variability. Specifically, future work can use clusters of weekly trajectories to predict between-cluster movement and within-cluster variability in pain severity.

OBJECTIVE

This study aims to understand clusters of common weekly patterns as a first stage in developing a pain-forecasting model.

METHODS

Data from a population-based mobile health study were used to compile weekly pain trajectories (n=21,919) that were then clustered using a k-medoids algorithm. Sensitivity analyses tested the impact of assumptions related to the ordinal and longitudinal structure of the data. The characteristics of people within clusters were examined, and a transition analysis was conducted to understand the movement of people between consecutive weekly clusters.

RESULTS

Four clusters were identified representing trajectories of no or low pain (1714/21,919, 7.82%), mild pain (8246/21,919, 37.62%), moderate pain (8376/21,919, 38.21%), and severe pain (3583/21,919, 16.35%). Sensitivity analyses confirmed the 4-cluster solution, and the resulting clusters were similar to those in the main analysis, with at least 85% of the trajectories belonging to the same cluster as in the main analysis. Male participants spent longer (participant mean 7.9, 95% bootstrap CI 6%-9.9%) in the no or low pain cluster than female participants (participant mean 6.5, 95% bootstrap CI 5.7%-7.3%). Younger people (aged 17-24 y) spent longer (participant mean 28.3, 95% bootstrap CI 19.3%-38.5%) in the severe pain cluster than older people (aged 65-86 y; participant mean 9.8, 95% bootstrap CI 7.7%-12.3%). People with fibromyalgia (participant mean 31.5, 95% bootstrap CI 28.5%-34.4%) and neuropathic pain (participant mean 31.1, 95% bootstrap CI 27.3%-34.9%) spent longer in the severe pain cluster than those with other conditions, and people with rheumatoid arthritis spent longer (participant mean 7.8, 95% bootstrap CI 6.1%-9.6%) in the no or low pain cluster than those with other conditions. There were 12,267 pairs of consecutive weeks that contributed to the transition analysis. The empirical percentage remaining in the same cluster across consecutive weeks was 65.96% (8091/12,267). When movement between clusters occurred, the highest percentage of movement was to an adjacent cluster.

CONCLUSIONS

The clusters of pain severity identified in this study provide a parsimonious description of the weekly experiences of people with chronic pain. These clusters could be used for future study of between-cluster movement and within-cluster variability to develop accurate and stakeholder-informed pain-forecasting tools.

Pain trajectories of nursing home residents

BACKGROUND

Understanding changes in nursing home (NH) resident pain over time would provide a more informed perspective, allowing opportunities to alter the course of illness, plan care, and set priorities. Therefore, the purpose of this analysis was to identify and characterize clinically meaningful, dynamic pain trajectories in NH residents.

METHODS

Retrospective longitudinal analysis of NH resident pain scores with a length of stay >100 days (N = 4864). Group-based trajectory modeling was applied to Minimum Data Set 3.0 assessments to identify pain trajectories. Trajectories were then characterized using unadjusted and adjusted cross-sectional associations between residents' demographic and clinical characteristics and their pain trajectory.

RESULTS

We identified four distinct trajectories: (1) consistent pain absence (48.9%), (2) decreasing-increasing pain presence (21.8%), (3) increasing-decreasing pain presence (15.3%), and (4) persistent pain presence (14.0%). Demographics of younger age and living in a rural area were associated with the persistent pain presence trajectory. Clinical variables of obesity and intact cognition were associated with being in the persistent pain presence trajectory. A smaller proportion of residents with moderately or severely impaired cognition were in any of the trajectory groups with pain.

CONCLUSIONS

We identified and characterized four pain trajectories among NH residents, including persistent pain presence which was associated with demographic characteristics (younger, female, rural) and clinical factors (obese, fracture, contracture). Moreover, residents with a diagnosis of Alzheimer's disease or dementia were less likely to be in any of the three trajectories with pain, likely representing the difficulty in evaluating pain in these residents. It is important that NH staff understand, recognize, and respond to the factors associated with the identified pain trajectories to improve mitigation of potentially persistent pain (e.g., hip fracture, contracture) or improve proxy pain assessment skills for residents at risk for under reporting of pain (e.g., Alzheimer's Disease).

Greater opioid use among nursing home residents in Ontario, Canada during the first two waves of the COVID-19 pandemic

Objectives

To examine the association between the COVID-19 pandemic and opioid use among nursing home residents followed up to March 2021, and possible variation by dementia and frailty status.

Design

Population-based cohort study with an interrupted time series analysis.

Setting and Participants

Linked health administrative databases for residents of all nursing homes (n = 630) in Ontario, Canada were examined. Residents were divided into consecutive weekly cohorts (first observation week was March 5 to 11, 2017 and last was March 21 to March 27, 2021).

Methods

The weekly proportion of residents dispensed an opioid was examined overall and by strata defined by the presence of dementia and frailty. Autoregressive Integrated Moving Average models with step and ramp intervention functions tested for immediate level and slope changes in weekly opioid use after the onset of the pandemic (March 1, 2020) and were fit on prepandemic data for projected trends.

Results

The average weekly cohort ranged from 76,834 residents (prepandemic) to 69,359 (pandemic period), with a consistent distribution by sex (69% female) and age (54% age 85 + years). There was a statistically significant increased slope change in the weekly proportion of residents dispensed opioids (parameter estimate (β) = 0.035; standard error (SE) = 0.005, P < .001). Although significant for all 4 strata, the increased slope change was more pronounced among nonfrail residents (β = 0.038; SE = 0.008, P < .001) and those without dementia (β = 0.044; SE = 0.008, P < .001). The absolute difference in observed vs predicted opioid use in the last week of the pandemic period ranged from 1.25% (frail residents) to 2.28% (residents without dementia).

Conclusions and Implications

Among Ontario nursing home residents, there was a statistically significant increase in opioid dispensations following the onset of the pandemic that persisted up to 1 year later. Investigations of the reasons for increased use, potential for long-term use and associated health consequences for residents are warranted.