IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0230751.html
   My bibliography  Save this article

The evaluating prescription opioid changes in veterans (EPOCH) study: Design, survey response, and baseline characteristics

Author

Listed:
  • Erin E Krebs
  • Barbara Clothier
  • Sean Nugent
  • Agnes C Jensen
  • Brian C Martinson
  • Elizabeth S Goldsmith
  • Melvin T Donaldson
  • Joseph W Frank
  • Indulis Rutks
  • Siamak Noorbaloochi

Abstract

In the United States (US), long-term opioid therapy has been commonly prescribed for chronic pain. Since recognition of the opioid overdose epidemic, clinical practice guidelines have recommended tapering long-term opioids to reduced doses or discontinuation. The Effects of Prescription Opioid Changes for veterans (EPOCH) study is a national population-based prospective observational study of US Veterans Health Administration primary care patients designed to assess effects of evolving opioid prescribing practice on patients treated with long-term opioids for chronic pain. A stratified random sampling design was used to identify a survey sample from the target population of patients treated with opioid analgesics for ≥ 6 months. Demographic, diagnostic, visit, and pharmacy dispensing data were extracted from existing datasets. A 2016 mixed-mode mail and telephone survey collected patient-reported data, including the main patient-reported outcomes of pain-related function (Brief Pain Inventory interference; BPI-I scores 0–10, higher scores = worse) and health-related quality of life. Data on survey participants and non-participants were analyzed to assess potential nonresponse bias. Weights were used to account for design. Linear regression models were used to assess cross-sectional associations of opioid treatment with patient-reported measures. Of 14,160 patients contacted, 9253 (65.4%) completed the survey. Participants were older than non-participants (63.9 ± 10.6 vs. 59.6 ± 13.0 years). The mean number of bothersome pain locations was 6.8 (SE 0.04). Effectiveness of pain treatment and quality of pain care were rated fair or poor by 56.1% and 45.3%, respectively. The opioid daily dosage range was 1.6 to 1038.2 mg, with mean = 50.6 mg (SE 1.1) and median = 30.9 mg (IQR 40.7). Among the 73.2% of patients who did not receive long-acting opioids, the mean daily dosage was 30.4 mg (SE 0.6) and mean BPI-I was 6.4 (SE 00.4). Among patients who received long-acting opioids, the mean daily dosage was 106.2 mg (SE 2.8) and mean BPI-I was 6.8 (SE 0.07). Higher daily dosage was associated with worse pain-related function and quality of life among patients without long-acting opioids, but not among patients with long-acting opioids. Future analyses will use follow-up data to examine effects of opioid dose reduction and discontinuation on patient outcomes.

Suggested Citation

  • Erin E Krebs & Barbara Clothier & Sean Nugent & Agnes C Jensen & Brian C Martinson & Elizabeth S Goldsmith & Melvin T Donaldson & Joseph W Frank & Indulis Rutks & Siamak Noorbaloochi, 2020. "The evaluating prescription opioid changes in veterans (EPOCH) study: Design, survey response, and baseline characteristics," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.
  • Handle: RePEc:plo:pone00:0230751
    DOI: 10.1371/journal.pone.0230751
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0230751
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0230751&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0230751?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Mingfeng Lin & Henry C. Lucas & Galit Shmueli, 2013. "Research Commentary ---Too Big to Fail: Large Samples and the p -Value Problem," Information Systems Research, INFORMS, vol. 24(4), pages 906-917, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hsin-Han Chen & Hui-Ling Chen & Yi-Tien Lin & Chaou-Wen Lin & Chien-Chang Ho & Hsueh-Yi Lin & Po-Fu Lee, 2020. "The Associations between Functional Fitness Test Performance and Abdominal Obesity in Healthy Elderly People: Results from the National Physical Fitness Examination Survey in Taiwan," IJERPH, MDPI, vol. 18(1), pages 1-14, December.
    2. Thomas Görzen, 2019. "Can Experience be Trusted? Investigating the Effect of Experience on Decision Biases in Crowdworking Platforms," Working Papers Dissertations 55, Paderborn University, Faculty of Business Administration and Economics.
    3. Yi Yang & Kunpeng Zhang & Yangyang Fan, 2023. "sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics," Information Systems Research, INFORMS, vol. 34(1), pages 137-156, March.
    4. Evangelista, Rui & Ramalho, Esmeralda A. & Andrade e Silva, João, 2020. "On the use of hedonic regression models to measure the effect of energy efficiency on residential property transaction prices: Evidence for Portugal and selected data issues," Energy Economics, Elsevier, vol. 86(C).
    5. Yen-Chun Chou & Howard Hao-Chun Chuang, 2018. "A predictive investigation of first-time customer retention in online reservation services," Service Business, Springer;Pan-Pacific Business Association, vol. 12(4), pages 685-699, December.
    6. Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
    7. Sujin Park & Ali Tafti & Galit Shmueli, 2024. "Transporting Causal Effects Across Populations Using Structural Causal Modeling: An Illustration to Work-from-Home Productivity," Information Systems Research, INFORMS, vol. 35(2), pages 686-705, June.
    8. Claire Teunenbroek & René Bekkers & Bianca Beersma, 2021. "They ought to do it too: Understanding effects of social information on donation behavior and mood," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 18(2), pages 229-253, June.
    9. Khalilzadeh, Jalayer & Tasci, Asli D.A., 2017. "Large sample size, significance level, and the effect size: Solutions to perils of using big data for academic research," Tourism Management, Elsevier, vol. 62(C), pages 89-96.
    10. Juan Manuel Pérez-Salamero González & Marta Regúlez-Castillo & Carlos Vidal-Meliá, 2017. "The continuous sample of working lives: improving its representativeness," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 8(1), pages 43-95, March.
    11. Thomas Niemand & Sascha Kraus & Martin Angerer & Ferdinand Thies & Alicia Mas-Tur, 2019. "More is not always better—non-linear effects in crowdfunding," International Journal of Quality Innovation, Springer, vol. 5(1), pages 1-10, December.
    12. Pei-Yu Chen & Yili Hong & Ying Liu, 2018. "The Value of Multidimensional Rating Systems: Evidence from a Natural Experiment and Randomized Experiments," Management Science, INFORMS, vol. 64(10), pages 4629-4647, October.
    13. Daniel Homocianu, 2020. "A Methodology of Discovering Comparable Models. The Case of Investing in Retirement Accounts when Considering Age, Main Residence and Education before 1989 vs. Globalization," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 67(4), pages 19-31, December.
    14. Kaizhi Yu & Yun Zhang & Hong Zou & Chenchen Wang, 2019. "Absolute Income, Income Inequality and the Subjective Well-Being of Migrant Workers in China: Toward an Understanding of the Relationship and Its Psychological Mechanisms," IJERPH, MDPI, vol. 16(14), pages 1-27, July.
    15. Irfan Kanat & Yili Hong & T. S. Raghu, 2018. "Surviving in Global Online Labor Markets for IT Services: A Geo-Economic Analysis," Information Systems Research, INFORMS, vol. 29(4), pages 893-909, December.
    16. Jae H. Kim & Kamran Ahmed & Philip Inyeob Ji, 2018. "Significance Testing in Accounting Research: A Critical Evaluation Based on Evidence," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 524-546, December.
    17. Malek Simon Grimm & Ralf Wagner, 2024. "Challenging the linearity assumption of intra-brand image confusion," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 355-374, June.
    18. Shrestha, Keshab & Subramaniam, Ravichandran & Rassiah, Puspavathy, 2017. "Pure martingale and joint normality tests for energy futures contracts," Energy Economics, Elsevier, vol. 63(C), pages 174-184.
    19. Miri Endeweld & Anat Herbst-Debby & Amit Kaplan, 2022. "Do the Privileged Always Win? Economic Consequences of Divorce by Income and Gender Groups," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(1), pages 77-100, January.
    20. Legendre, Nicolas & Nitani, Miwako & Riding, Allan, 2021. "Are franchises really more viable? Evidence from loan defaults," Journal of Business Research, Elsevier, vol. 133(C), pages 23-33.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0230751. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.