Dataset Information

Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults.

ABSTRACT:

Objectives

Identifying factors associated with opioid use in middle-aged and older adults is a fundamental step in the mitigation of potentially unnecessary opioid consumption and opioid-related harms.

Methods

Using longitudinal data on a community-based cohort of adults aged 50-90 years residing in Johnston County, North Carolina, we examined sociodemographic and clinical factors in non-opioid users (n = 786) at baseline (2006-2010) as predictors of opioid use at follow-up (2013-2015). Variables included age, sex, race, obesity, educational attainment, employment status, household poverty rate, marital status, depressive symptoms, social support, pain catastrophizing, pain sensitivity, insurance status, polypharmacy, and smoking status.

Results

At follow-up, 13% of participants were using prescription opioids. In the multivariable model, high pain catastrophizing (adjusted odds ratio; 95% confidence interval = 2.14; 1.33-3.46), polypharmacy (2.08; 1.23-3.53), and history of depressive symptoms (2.00; 1.19-3.38) were independent markers of opioid use.

Discussion

Findings support the assessment of these modifiable factors during clinical encounters in patients ≥ 50 years old with chronic pain.

SUBMITTER: Shiue KY

PROVIDER: S-EPMC8854450 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults.

Shiue Kristin Y KY Dasgupta Nabarun N Naumann Rebecca B RB Nelson Amanda E AE Golightly Yvonne M YM

Journal of aging and health 20210818 2

<h4>Objectives</h4>Identifying factors associated with opioid use in middle-aged and older adults is a fundamental step in the mitigation of potentially unnecessary opioid consumption and opioid-related harms.<h4>Methods</h4>Using longitudinal data on a community-based cohort of adults aged 50-90 years residing in Johnston County, North Carolina, we examined sociodemographic and clinical factors in non-opioid users (<i>n</i> = 786) at baseline (2006-2010) as predictors of opioid use at follow-up ...[more]

PMID: 34404244

Similar Datasets

Project description:BackgroundMultimorbidity (≥ 2 chronic diseases) is associated with greater disability and higher treatment burden, as well as difficulty coordinating self-management tasks for adults with complex multimorbidity patterns. Comparatively little work has focused on assessing multimorbidity patterns among patients seeking care in community health centers (CHCs).ObjectiveTo identify and characterize prevalent multimorbidity patterns in a multi-state network of CHCs over a 5-year period.DesignA cohort study of the 2014-2019 ADVANCE multi-state CHC clinical data network. We identified the most prevalent multimorbidity combination patterns and assessed the frequency of patterns throughout a 5-year period as well as the demographic characteristics of patient panels by prevalent patterns.ParticipantsThe study included data from 838,642 patients aged ≥ 45 years who were seen in 337 CHCs across 22 states between 2014 and 2019.Main measuresPrevalent multimorbidity patterns of somatic, mental health, and mental-somatic combinations of 22 chronic diseases based on the U.S. Department of Health and Human Services Multiple Chronic Conditions framework: anxiety, arthritis, asthma, autism, cancer, cardiac arrhythmia, chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), congestive heart failure, coronary artery disease, dementia, depression, diabetes, hepatitis, human immunodeficiency virus (HIV), hyperlipidemia, hypertension, osteoporosis, post-traumatic stress disorder (PTSD), schizophrenia, substance use disorder, and stroke.Key resultsMultimorbidity is common among middle-aged and older patients seen in CHCs: 40% have somatic, 6% have mental health, and 24% have mental-somatic multimorbidity patterns. The most frequently occurring pattern across all years is hyperlipidemia-hypertension. The three most frequent patterns are various iterations of hyperlipidemia, hypertension, and diabetes and are consistent in rank of occurrence across all years. CKD-hyperlipidemia-hypertension and anxiety-depression are both more frequent in later study years.ConclusionsCHCs are increasingly seeing more complex multimorbidity patterns over time; these most often involve mental health morbidity and advanced cardiometabolic-renal morbidity.

Project description:BackgroundThe effects of physical activity (PA) across different domains and intensities on depressive symptoms remain inconclusive. Incorporating the community-built environment (CBE) into longitudinal analyses of PA's impact on depressive symptoms is crucial.ObjectiveThis study aims to examine the effects of PA at different intensities-low-intensity PA (eg, walking activities) and moderate-to-vigorous-intensity PA (eg, activities requiring substantial effort and causing faster breathing or shortness of breath)-across leisure-time and occupational domains on depressive symptom trajectories among middle-aged and older adults. Additionally, it investigated how CBEs influence depressive symptoms and PA trajectories.MethodsThis longitudinal study included 6865 middle-aged and older adults from the China Health and Retirement Longitudinal Survey. A CBE variable system was developed using a community questionnaire to assess attributes of the physical built environment. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale. Latent growth curve modeling was applied to analyze 3 waves of the cohort data (2015, 2018, and 2020) to explore the differential effects of PA on depressive symptoms and the role of the CBE.ResultsIn the 2015 and 2018 waves, higher low-intensity leisure-time physical activity (LTPA) was associated with lower depressive symptoms (β=-.025, P=.01 and β=-.027, P=.005, respectively). Across all waves, moderate-to-vigorous-intensity LTPA showed no significant predictive effects (P=.21 in 2015, P=.57 in 2018, and P=.85 in 2020, respectively). However, higher occupational physical activity (OPA), particularly at moderate-to-vigorous intensities, was consistently associated with higher depressive symptoms. Parallel process latent growth curve modeling revealed that the initial level of total LTPA negatively predicted the initial level of depressive symptoms (β=-.076, P=.01). OPA exhibited dual effects, positively predicting the initial level of depressive symptoms (β=.108, P<.001) but negatively predicting their upward trajectory (β=-.136, P=.009). Among CBE variables, better infrastructure conditions (β=-.082, P<.001) and greater accessibility to public facilities (β=-.036, P=.045) negatively predicted the initial level of depressive symptoms. However, greater accessibility to public facilities positively predicted the upward trajectory of depressive symptoms (β=.083, P=.04). Better infrastructure conditions (β=.100, P=.002) and greater accessibility to public transport (β=.060, P=.01) positively predicted the initial level of total LTPA. Meanwhile, better infrastructure conditions (β=-.281, P<.001) and greater accessibility to public facilities (β=-.073, P<.001) negatively predicted the initial level of total OPA. Better infrastructure conditions positively predicted the declining trajectory of total OPA (β=.100, P=.004).ConclusionsThis study underscores the importance of considering the differential effects of PA across domains and intensities on depressive symptoms in public policies and guidelines. Given the influence of the environment on PA and depressive symptoms, targeted community measures should be implemented.

Dataset Information

Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults.

Objectives

Methods

Results

Discussion

Publications

Sociodemographic and Clinical Predictors of Prescription Opioid Use in a Longitudinal Community-Based Cohort Study of Middle-Aged and Older Adults.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets