Literature DB >> 29180552

Neighborhood Socioeconomic Status and Receipt of Opioid Medication for New Back Pain Diagnosis.

Sarah Gebauer1, Joanne Salas2, Jeffrey F Scherrer2.   

Abstract

BACKGROUND: Although treatment for new back pain is heavily guideline driven, deviations occur frequently. Neighborhood socioeconomic status (nSES) may contribute to these deviations.
OBJECTIVE: Determine whether nSES is associated with type of treatment provided for patients seeking treatment for new back pain in primary care clinics.
METHODS: This retrospective cohort was conducted in academic internal and family medicine practices. Data were examined from the Primary Care Patient Data Registry. Eligibility criteria included age ≥18 years, free of HIV and cancer, and presenting to primary care with a new diagnosis of back pain, resulting in1646 patients included. Patients' nSES was determined using ZIP code and calculating a validated index of 7 census-tract variables. Multinomial logistic regression was used to measure the association between nSES and 3 treatment outcomes compared with no pharmacologic management. Outcomes included opioid prescription, nonsteroidal anti-inflammatory (NSAID)/muscle relaxant prescription, or combined opioid/nonopioid treatment within 90 days of initial presentation. Covariates included age, sex, race, high clinic utilization (HCU), depression, anxiety, substance use, obesity, comorbidities, smoking, number of pain conditions, and physical therapy (PT) referral.
RESULTS: The cohort was 67.9% female with an average age of 55.72 years (Standard Error [SE] = 0.387). Compared with no pharmacologic treatment, individuals in the low nSES group had 63% higher odds of receiving an opioid only compared with the high nSES group (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.01 to 2.62). There was no significant association between nSES and odds of nonopioid or combined treatment compared with no pharmacotherapy (OR, 1.17; 95% CI, 0.97 to 1.50), (OR, 1.09; 95% CI, 0.67 to 1.78), respectively. Covariates associated with increased odds of opioid only included HCU, ever smoker, and increasing comorbidity index. PT referral was associated with NSAID/muscle relaxant only, and increasing age and comorbidity index were inversely associated with odds of NSAID/muscle relaxant only. Finally, covariates associated with increased odds of receiving both therapies included high clinic utilizusation, ever smoking, and PT referral.
CONCLUSIONS: These data characterize a possible association between low nSES and increased risk of receiving an opioid only when being treated for new back pain. This may be evidence that patients of low nSES are at increased risk of receiving guideline-noncompliant treatment for new back pain. © Copyright 2017 by the American Board of Family Medicine.

Entities:  

Keywords:  Back Pain; Opioid Analgesics; Social Determinants of Health; Socioeconomic Status

Mesh:

Substances:

Year:  2017        PMID: 29180552     DOI: 10.3122/jabfm.2017.06.170061

Source DB:  PubMed          Journal:  J Am Board Fam Med        ISSN: 1557-2625            Impact factor:   2.657


  13 in total

1.  Geospatial Variations and Neighborhood Deprivation in Drug-Related Admissions and Overdoses.

Authors:  Julien Cobert; Paul M Lantos; Mark M Janko; David G A Williams; Karthik Raghunathan; Vijay Krishnamoorthy; Eric A JohnBull; Atilio Barbeito; Padma Gulur
Journal:  J Urban Health       Date:  2020-12       Impact factor: 3.671

2.  Prevalence and Expenses of Outpatient Opioid Prescriptions, With Associated Sociodemographic, Economic, and Work Characteristics.

Authors:  Abay Asfaw; Toni Alterman; Brian Quay
Journal:  Int J Health Serv       Date:  2019-10-11       Impact factor: 1.663

3.  Secondary School Socioeconomic Status and Athletic Training Practice Characteristics.

Authors:  Hannah J Robison; Janet E Simon; Erik J Nelson; Sarah N Morris; Erin B Wasserman; Carrie L Docherty
Journal:  J Athl Train       Date:  2022-04-01       Impact factor: 3.824

4.  Opioid use and social disadvantage in patients with chronic musculoskeletal pain.

Authors:  Abby L Cheng; Brian K Brady; Ethan C Bradley; Ryan P Calfee; Lisa M Klesges; Graham A Colditz; Heidi Prather
Journal:  PM R       Date:  2021-05-03       Impact factor: 2.298

5.  Racial, Ethnic, and Socioeconomic Discrepancies in Opioid Prescriptions Among Older Patients With Cancer.

Authors:  Lucas K Vitzthum; Vinit Nalawade; Paul Riviere; Whitney Sumner; Tyler Nelson; Loren K Mell; Timothy Furnish; Brent Rose; María Elena Martínez; James D Murphy
Journal:  JCO Oncol Pract       Date:  2021-02-03

6.  Obesity and the Receipt of Prescription Pain Medications in the US.

Authors:  Gawon Cho; Virginia W Chang
Journal:  J Gen Intern Med       Date:  2021-02-08       Impact factor: 6.473

Review 7.  Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review.

Authors:  Elizabeth Golembiewski; Katie S Allen; Amber M Blackmon; Rachel J Hinrichs; Joshua R Vest
Journal:  JMIR Public Health Surveill       Date:  2019-10-07

8.  Chest Pain, Atherosclerotic Cardiovascular Disease Risk, and Cardiology Referral in Primary Care.

Authors:  Vishaal Buch; Hayley Ralph; Joanne Salas; Paul J Hauptman; Dawn Davis; Jeffrey F Scherrer
Journal:  J Prim Care Community Health       Date:  2018 Jan-Dec

9.  Analysis of State Insurance Coverage for Nonpharmacologic Treatment of Low Back Pain as Recommended by the American College of Physicians Guidelines.

Authors:  Robert Bonakdar; Dania Palanker; Megan M Sweeney
Journal:  Glob Adv Health Med       Date:  2019-07-29

10.  A retrospective cohort study evaluating correlates of deep tissue infections among patients enrolled in opioid agonist treatment using administrative data in Ontario, Canada.

Authors:  Kristen A Morin; Chad R Prevost; Joseph K Eibl; Michael T Franklyn; Alexander R Moise; David C Marsh
Journal:  PLoS One       Date:  2020-04-24       Impact factor: 3.240

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