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BMC Health Services Research
Published in: BMC Health Services Research 1/2018

Open Access 01-12-2018 | Research article

Prediction of healthcare utilization following an episode of physical therapy for musculoskeletal pain

Authors: Trevor A. Lentz, Jason M. Beneciuk, Steven Z. George

Published in: BMC Health Services Research | Issue 1/2018

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Abstract

Background

In the United States, value-based purchasing has created the need for healthcare systems to prospectively identify patients at risk for high healthcare utilization beyond a physical therapy episode for musculoskeletal pain. The purpose of this study was to determine predictors of pain-related healthcare utilization subsequent to an index episode of physical therapy for musculoskeletal pain.

Methods

This study assessed data from the Optimal Screening for Prediction of Referral and Outcome (OSPRO) longitudinal cohort study that recruited individuals with a primary complaint of neck, low back, knee or shoulder pain in physical therapy (n = 440). Demographics, health-related information, review of systems, comorbidity and pain-related psychological distress measures were collected at baseline evaluation. Baseline to 4-week changes in pain intensity, disability, and pain-related psychological distress were measured as treatment response variables. At 6-months and 1-year after baseline evaluation, individuals reported use of opioids, injection, surgery, diagnostic tests or imaging, and emergency room visits for their pain condition over the follow-up period. Separate prediction models were developed for any subsequent care and service-specific utilization.

Results

Subsequent pain-related healthcare utilization was reported by 43% (n = 106) of the study sample that completed the 12-month follow-up (n = 246). Baseline disability and 4-week change in pain intensity were important global predictors of subsequent healthcare utilization. Age, insurance status, comorbidity burden, baseline pain, and 4-week changes in pain intensity, disability and pain-related psychological distress predicted specific service utilization.

Conclusion

In those completing follow up measures, risk of additional pain-related healthcare utilization after physical therapy was best predicted by baseline characteristics and 4-week treatment response variables for pain intensity, disability and pain-related psychological distress. These findings suggest treatment monitoring of specific response variables could enhance identification of those at risk for future healthcare utilization in addition to baseline assessment. Further study is required to determine how specific characteristics of the clinical encounter influence future utilization.
Literature
1.
Von Korff M, Scher AI, Helmick C, Carter-Pokras O, Dodick DW, Goulet J, et al. United states national pain strategy for population research: concepts, definitions, and pilot data. J Pain Off J Am Pain Soc. 2016;17:1068–80.CrossRef
2.
Clarke JL, Skoufalos A, Scranton R. The American opioid epidemic: population health implications and potential solutions. Report from the national stakeholder panel. Popul Health Manag. 2016;19 Suppl 1:S1–10.PubMed
3.
Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain--United States, 2016. JAMA. 2016;315:1624–45.CrossRefPubMed
4.
Boyles R, Toy P, Mellon J, Hayes M, Hammer B. Effectiveness of manual physical therapy in the treatment of cervical radiculopathy: a systematic review. J Man Manip Ther. 2011;19:135–42.CrossRefPubMedPubMedCentral
5.
Bürge E, Monnin D, Berchtold A, Allet L. Cost-effectiveness of physical therapy only and of usual care for various health conditions: systematic review. Phys Ther. 2016;96:774–86.CrossRefPubMed
6.
Deyle GD, Allison SC, Matekel RL, Ryder MG, Stang JM, Gohdes DD, et al. Physical therapy treatment effectiveness for osteoarthritis of the knee: a randomized comparison of supervised clinical exercise and manual therapy procedures versus a home exercise program. Phys Ther. 2005;85:1301–17.PubMed
7.
Deyle GD, Henderson NE, Matekel RL, Ryder MG, Garber MB, Allison SC. Effectiveness of manual physical therapy and exercise in osteoarthritis of the knee. A randomized, controlled trial. Ann Intern Med. 2000;132:173–81.CrossRefPubMed
8.
Freburger JK, Carey TS, Holmes GM. Effectiveness of physical therapy for the management of chronic spine disorders: a propensity score approach. Phys Ther. 2006;86:381–94.PubMed
9.
Kuhn JE, Dunn WR, Sanders R, An Q, Baumgarten KM, Bishop JY, et al. Effectiveness of physical therapy in treating atraumatic full-thickness rotator cuff tears: a multicenter prospective cohort study. J Shoulder Elb Surg. 2013;22:1371–9.CrossRef
10.
Fritz JM, Childs JD, Wainner RS, Flynn TW. Primary care referral of patients with low back pain to physical therapy: impact on future health care utilization and costs. Spine. 2012;37:2114–21.CrossRefPubMed
11.
Fritz JM, Brennan GP, Hunter SJ, Magel JS. Initial management decisions after a new consultation for low back pain: implications of the usage of physical therapy for subsequent health care costs and utilization. Arch Phys Med Rehabil. 2013;94:808–16.CrossRefPubMed
12.
Hill JC, Dunn KM, Lewis M, Mullis R, Main CJ, Foster NE, et al. A primary care back pain screening tool: identifying patient subgroups for initial treatment. Arthritis Rheum. 2008;59:632–41.CrossRefPubMed
13.
Traeger AC, Henschke N, Hübscher M, Williams CM, Kamper SJ, Maher CG, et al. Estimating the risk of chronic pain: development and validation of a prognostic model (PICKUP) for patients with acute low back pain. PLoS Med. 2016;13:e1002019.CrossRefPubMedPubMedCentral
14.
Karran EL, McAuley JH, Traeger AC, Hillier SL, Grabherr L, Russek LN, et al. Can screening instruments accurately determine poor outcome risk in adults with recent onset low back pain? A systematic review and meta-analysis. BMC Med. 2017;15:13.CrossRefPubMedPubMedCentral
15.
Azevedo LF, Costa-Pereira A, Mendonça L, Dias CC, Castro-Lopes JM. Chronic pain and health services utilization: is there overuse of diagnostic tests and inequalities in nonpharmacologic treatment methods utilization? Med Care. 2013;51:859–69.CrossRefPubMed
16.
Langley P, Müller-Schwefe G, Nicolaou A, Liedgens H, Pergolizzi J, Varrassi G. The societal impact of pain in the European Union: health-related quality of life and healthcare resource utilization. J Med Econ. 2010;13:571–81.CrossRefPubMed
17.
Pérez C, Navarro A, Saldaña MT, Wilson K, Rejas J. Modeling the predictive value of pain intensity on costs and resources utilization in patients with peripheral neuropathic pain. Clin J Pain. 2015;31:273–9.CrossRefPubMed
18.
Hill JC, Fritz JM. Psychosocial influences on low back pain, disability, and response to treatment. Phys Ther. 2011;91:712–21.CrossRefPubMed
19.
George SZ, Beneciuk JM, Lentz TA, Wu SS. The Optimal Screening for Prediction of Referral and Outcome (OSPRO) in patients with musculoskeletal pain conditions: a longitudinal validation cohort from the USA. BMJ Open. 2017;7:e015188.CrossRefPubMedPubMedCentral
20.
George SZ, Beneciuk JM, Lentz TA, Wu SS, Dai Y, Bialosky JE, Zeppieri G Jr. Optimal Screening for Prediction of Referral and Outcome (OSPRO) for Musculoskeletal Pain Conditions: Results From the Validation Cohort. J Orthop Sports Phys Ther. 2018;48(6):460–75.
21.
Beneciuk JM, Lentz TA, He Y, Wu SS, George SZ. Prediction of persistent musculoskeletal pain at 12 months: a secondary analysis of the Optimal Screening for Prediction of Referral and Outcome (OSPRO) validation cohort study. Phys Ther. 2018;98:290–301.CrossRefPubMed
22.
Freburger JK, Holmes GM, Agans RP, Jackman AM, Darter JD, Wallace AS, et al. The rising prevalence of chronic low back pain. Arch Intern Med. 2009;169:251–8.CrossRefPubMedPubMedCentral
23.
Carey TS, Freburger JK, Holmes GM, Jackman A, Knauer S, Wallace A, et al. Race, care seeking, and utilization for chronic back and neck pain: population perspectives. J Pain Off J Am Pain Soc. 2010;11:343–50.CrossRef
24.
Jensen MP, Turner JA, Romano JM, Fisher LD. Comparative reliability and validity of chronic pain intensity measures. Pain. 1999;83:157–62.CrossRefPubMed
25.
Bolton JE. Accuracy of recall of usual pain intensity in back pain patients. Pain. 1999;83:533–9.CrossRefPubMed
26.
Childs JD, Piva SR, Fritz JM. Responsiveness of the numeric pain rating scale in patients with low back pain. Spine. 2005;30:1331–4.CrossRefPubMed
27.
Vernon H. The neck disability index: state-of-the-art, 1991-2008. J Manip Physiol Ther. 2008;31:491–502.CrossRef
28.
Vernon H, Mior S. The neck disability index: a study of reliability and validity. J Manip Physiol Ther. 1991;14:409–15.
29.
Hudson-Cook N, Tomes-Nicholson K, Breen A. A revised Oswestry disability questionnaire. In: Roland M, Jenner J, editors. Back pain: new approaches to rehabilitation and education. New York: Manchester University Press; 1989. p. 187–204.
30.
Fritz JM, Irrgang JJ. A comparison of a modified Oswestry low back pain disability questionnaire and the Quebec back pain disability scale. Phys Ther. 2001;81:776–88.CrossRefPubMed
31.
Beaton DE, Wright JG, Katz JN, Upper Extremity Collaborative Group. Development of the QuickDASH: comparison of three item-reduction approaches. J Bone Joint Surg Am. 2005;87:1038–46.PubMed
32.
Irrgang JJ, Anderson AF, Boland AL, Harner CD, Kurosaka M, Neyret P, et al. Development and validation of the international knee documentation committee subjective knee form. Am J Sports Med. 2001;29:600–13.CrossRefPubMed
33.
Butera KA, Lentz TA, Beneciuk JM, George SZ. Preliminary evaluation of a modified STarT back screening tool across different musculoskeletal pain conditions. Phys Ther. 2016;96:1251–61.CrossRefPubMedPubMedCentral
34.
Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–83.CrossRefPubMed
35.
Katz JN, Chang LC, Sangha O, Fossel AH, Bates DW. Can comorbidity be measured by questionnaire rather than medical record review? Med Care. 1996;34:73–84.CrossRefPubMed
36.
George SZ, Beneciuk JM, Bialosky JE, Lentz TA, Zeppieri G, Pei Q, et al. Development of a review-of-systems screening tool for orthopaedic physical therapists: results from the Optimal Screening for Prediction of Referral and Outcome (OSPRO) cohort. J Orthop Sports Phys Ther. 2015;45:512–26.CrossRefPubMed
37.
Lentz TA, Beneciuk JM, Bialosky JE, Zeppieri G, Dai Y, Wu SS, et al. Development of a yellow flag assessment tool for orthopaedic physical therapists: results from the Optimal Screening for Prediction of Referral and Outcome (OSPRO) cohort. J Orthop Sports Phys Ther. 2016;46:327–43.CrossRefPubMed
38.
Beneciuk JM, Fritz JM, George SZ. The STarT back screening tool for prediction of 6-month clinical outcomes: relevance of change patterns in outpatient physical therapy settings. J Orthop Sports Phys Ther. 2014;44:656–64.CrossRefPubMed
39.
Myers RH. Classical and modern regression with applications. 2nd ed. Pacific Grove: Duxbury Press; 2000.
40.
Weuve J, Tchetgen Tchetgen EJ, Glymour MM, Beck TL, Aggarwal NT, Wilson RS, et al. Accounting for bias due to selective attrition: the example of smoking and cognitive decline. Epidemiol Camb Mass. 2012;23:119–28.CrossRef
41.
Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiol Camb Mass. 2004;15:615–25.CrossRef
42.
43.
Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9.CrossRefPubMed
44.
Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Boston: Pearson; 2006.
45.
Green SB. How many subjects does it take to do a regression analysis. Multivar Behav Res. 1991;26:499–510.CrossRef
46.
Harris RJ. A primer of multivariate statistics. 3rd ed. Mahwah: Psychology Press; 2001.CrossRef
47.
Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006;29:725–31.CrossRefPubMed
48.
49.
Fritz JM, Cleland JA, Speckman M, Brennan GP, Hunter SJ. Physical therapy for acute low back pain: associations with subsequent healthcare costs. Spine. 2008;33:1800–5.CrossRefPubMed
50.
Lentz TA, Harman JS, Marlow NM, George SZ. Application of a value model for the prevention and management of chronic musculoskeletal pain by physical therapists. Phys Ther. 2017;97:354–64.PubMed
51.
Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393.CrossRefPubMedPubMedCentral
52.
Bishop MD, Mintken PE, Bialosky JE, Cleland JA. Patient expectations of benefit from interventions for neck pain and resulting influence on outcomes. J Orthop Sports Phys Ther. 2013;43:457–65.CrossRefPubMedPubMedCentral
53.
Bialosky JE, Bishop MD, Cleland JA. Individual expectation: an overlooked, but pertinent, factor in the treatment of individuals experiencing musculoskeletal pain. Phys Ther. 2010;90:1345–55.CrossRefPubMedPubMedCentral
54.
Hanney WJ, Masaracchio M, Liu X, Kolber MJ. The influence of physical therapy guideline adherence on healthcare utilization and costs among patients with low back pain: a systematic review of the literature. PLoS One. 2016;11:e0156799.CrossRefPubMedPubMedCentral
55.
56.
Yu S-T, Chang H-Y, Lin M-C, Lin Y-H. Agreement between self-reported and health insurance claims on utilization of health care: a population study. J Clin Epidemiol. 2009;62:1316–22.CrossRefPubMed
57.
Petrou S, Murray L, Cooper P, Davidson LL. The accuracy of self-reported healthcare resource utilization in health economic studies. Int J Technol Assess Health Care. 2002;18:705–10.CrossRefPubMed
58.
Short ME, Goetzel RZ, Pei X, Tabrizi MJ, Ozminkowski RJ, Gibson TB, et al. How accurate are self-reports? Analysis of self-reported health care utilization and absence when compared with administrative data. J Occup Environ Med. 2009;51:786–96.CrossRefPubMedPubMedCentral
Metadata
Title
Prediction of healthcare utilization following an episode of physical therapy for musculoskeletal pain
Authors
Trevor A. Lentz
Jason M. Beneciuk
Steven Z. George
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2018
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-018-3470-6

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