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Published in: BMC Medical Informatics and Decision Making 1/2018

Open Access 01-12-2018 | Database

Leveraging healthcare utilization to explore outcomes from musculoskeletal disorders: methodology for defining relevant variables from a health services data repository

Authors: Daniel I. Rhon, Derek Clewley, Jodi L. Young, Charles D. Sissel, Chad E. Cook

Published in: BMC Medical Informatics and Decision Making | Issue 1/2018

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Abstract

Background

Large healthcare databases, with their ability to collect many variables from daily medical practice, greatly enable health services research. These longitudinal databases provide large cohorts and longitudinal time frames, allowing for highly pragmatic assessment of healthcare delivery. The purpose of this paper is to discuss the methodology related to the use of the United States Military Health System Data Repository (MDR) for longitudinal assessment of musculoskeletal clinical outcomes, as well as address challenges of using this data for outcomes research.

Methods

The Military Health System manages care for approximately 10 million beneficiaries worldwide. Multiple data sources pour into the MDR from multiple levels of care (inpatient, outpatient, military or civilian facility, combat theater, etc.) at the individual patient level. To provide meaningful and descriptive coding for longitudinal analysis, specific coding for timing and type of care, procedures, medications, and provider type must be performed. Assumptions often made in clinical trials do not apply to these cohorts, requiring additional steps in data preparation to reduce risk of bias. The MDR has a robust system in place to validate the quality and accuracy of its data, reducing risk of analytic error. Details for making this data suitable for analysis of longitudinal orthopaedic outcomes are provided.

Results

Although some limitations exist, proper preparation and understanding of the data can limit bias, and allow for robust and meaningful analyses. There is the potential for strong precision, as well as the ability to collect a wide range of variables in very large groups of patients otherwise not captured in traditional clinical trials. This approach contributes to the improved understanding of the accessibility, quality, and cost of care for those with orthopaedic conditions.

Conclusion

The MDR provides a robust pool of longitudinal healthcare data at the person-level. The benefits of using the MDR database appear to outweigh the limitations.
Literature
1.
go back to reference Lohr KN, Steinwachs DM. Health services research: an evolving definition of the field. Health Serv. Res. 2002;37:7–9.PubMed Lohr KN, Steinwachs DM. Health services research: an evolving definition of the field. Health Serv. Res. 2002;37:7–9.PubMed
3.
go back to reference Carter TC, He MM. Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine. J. Healthc. Eng. 2016;2016:1–14.CrossRef Carter TC, He MM. Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine. J. Healthc. Eng. 2016;2016:1–14.CrossRef
4.
go back to reference Burghard C. Big Data and Analytics Key to Accountable Care Success. IDC Health Insights; 2012 Dec. Burghard C. Big Data and Analytics Key to Accountable Care Success. IDC Health Insights; 2012 Dec.
5.
go back to reference Thorpe KE, Zwarenstein M, Oxman AD, Treweek S, Furberg CD, Altman DG, et al. A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J. Clin. Epidemiol. 2009;62:464–75.CrossRefPubMed Thorpe KE, Zwarenstein M, Oxman AD, Treweek S, Furberg CD, Altman DG, et al. A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. J. Clin. Epidemiol. 2009;62:464–75.CrossRefPubMed
6.
go back to reference Rowbotham MC, Gilron I, Glazer C, ASC R, Smith BH, Stewart WF, et al. Can pragmatic trials help us better understand chronic pain and improve treatment. Pain. 2013;154:643–6.CrossRefPubMed Rowbotham MC, Gilron I, Glazer C, ASC R, Smith BH, Stewart WF, et al. Can pragmatic trials help us better understand chronic pain and improve treatment. Pain. 2013;154:643–6.CrossRefPubMed
9.
go back to reference Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12:e1001885.CrossRefPubMedPubMedCentral Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12:e1001885.CrossRefPubMedPubMedCentral
10.
go back to reference Perry DC, Parsons N, Costa ML. “Big data” reporting guidelines: how to answer big questions, yet avoid big problems. Bone Joint J. 2014;96-B:1575–7.CrossRefPubMed Perry DC, Parsons N, Costa ML. “Big data” reporting guidelines: how to answer big questions, yet avoid big problems. Bone Joint J. 2014;96-B:1575–7.CrossRefPubMed
11.
go back to reference Mirkes EM, Coats TJ, Levesley J, Gorban AN. Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes. Comput. Biol. Med. 2016;75:203–16.CrossRefPubMed Mirkes EM, Coats TJ, Levesley J, Gorban AN. Handling missing data in large healthcare dataset: A case study of unknown trauma outcomes. Comput. Biol. Med. 2016;75:203–16.CrossRefPubMed
13.
go back to reference Dinov ID, Heavner B, Tang M, Glusman G, Chard K, Darcy M, et al. Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations. PLoS One. 2016;11:e0157077.CrossRefPubMedPubMedCentral Dinov ID, Heavner B, Tang M, Glusman G, Chard K, Darcy M, et al. Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations. PLoS One. 2016;11:e0157077.CrossRefPubMedPubMedCentral
15.
go back to reference Viceconti M, Hunter P, Hose R. Big data, big knowledge: big data for personalized healthcare. IEEE J Biomed Health Inform. 2015;19:1209–15.CrossRefPubMed Viceconti M, Hunter P, Hose R. Big data, big knowledge: big data for personalized healthcare. IEEE J Biomed Health Inform. 2015;19:1209–15.CrossRefPubMed
16.
18.
go back to reference Little RJA. A Test of Missing Completely at Random for Multivariate Data with Missing Values. J. Am. Stat. Assoc. 1988;83:1198–202.CrossRef Little RJA. A Test of Missing Completely at Random for Multivariate Data with Missing Values. J. Am. Stat. Assoc. 1988;83:1198–202.CrossRef
19.
go back to reference Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol. Methods. 2002;7:147–77.CrossRefPubMed Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol. Methods. 2002;7:147–77.CrossRefPubMed
20.
go back to reference Henry AJ, Hevelone ND, Lipsitz S, Nguyen LL. Comparative methods for handling missing data in large databases. J. Vasc. Surg. 2013;58:1353–9.e6. Henry AJ, Hevelone ND, Lipsitz S, Nguyen LL. Comparative methods for handling missing data in large databases. J. Vasc. Surg. 2013;58:1353–9.e6.
22.
go back to reference Steinwachs DM, Hughes RG. Health Services Research: Scope and Significance. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2011. Steinwachs DM, Hughes RG. Health Services Research: Scope and Significance. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2011.
23.
go back to reference 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 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
24.
go back to reference Childs JD, Fritz JM, Wu SS, Flynn TW, Wainner RS, Robertson EK, et al. Implications of early and guideline adherent physical therapy for low back pain on utilization and costs. BMC Health Serv. Res. 2015;15:150.CrossRefPubMedPubMedCentral Childs JD, Fritz JM, Wu SS, Flynn TW, Wainner RS, Robertson EK, et al. Implications of early and guideline adherent physical therapy for low back pain on utilization and costs. BMC Health Serv. Res. 2015;15:150.CrossRefPubMedPubMedCentral
25.
go back to reference Fritz JM, Kim J, Dorius J. Importance of the type of provider seen to begin health care for a new episode low back pain: associations with future utilization and costs. J. Eval. Clin. Pract. 2016;22:247–52.CrossRefPubMed Fritz JM, Kim J, Dorius J. Importance of the type of provider seen to begin health care for a new episode low back pain: associations with future utilization and costs. J. Eval. Clin. Pract. 2016;22:247–52.CrossRefPubMed
26.
go back to reference Langan SM, Cook C, Benchimol EI. Improving the Reporting of Studies Using Routinely Collected Health Data in Physical Therapy. J. Orthop. Sports Phys. Ther. 2016;46:126–7.CrossRefPubMed Langan SM, Cook C, Benchimol EI. Improving the Reporting of Studies Using Routinely Collected Health Data in Physical Therapy. J. Orthop. Sports Phys. Ther. 2016;46:126–7.CrossRefPubMed
27.
go back to reference Defense Health Program. Defense Health Program Fiscal Year (FY) 2018 Budget Estimates: Operation and Maintenance Procurement Research, Development, Test, and Evaluation [Internet]. Department of Defense; May 2017. Report No.: 17-C-0531. Defense Health Program. Defense Health Program Fiscal Year (FY) 2018 Budget Estimates: Operation and Maintenance Procurement Research, Development, Test, and Evaluation [Internet]. Department of Defense; May 2017. Report No.: 17-C-0531.
28.
go back to reference Fury M, John M, Schexnayder S, Molligan H, Lee O, Krause P, et al. The Implications of Inaccuracy: Comparison of Coding in Heterotopic Ossification and Associated Trauma. Orthopedics. 2017;40:237–41.CrossRefPubMed Fury M, John M, Schexnayder S, Molligan H, Lee O, Krause P, et al. The Implications of Inaccuracy: Comparison of Coding in Heterotopic Ossification and Associated Trauma. Orthopedics. 2017;40:237–41.CrossRefPubMed
29.
go back to reference Chuang Y-C, Weng S-F, Hsu Y-W, Huang CL-C, Wu M-P. Increased risks of healthcare-seeking behaviors of anxiety, depression and insomnia among patients with bladder pain syndrome/interstitial cystitis: a nationwide population-based study. Int. Urol. Nephrol. 2015;47:275–81.CrossRefPubMed Chuang Y-C, Weng S-F, Hsu Y-W, Huang CL-C, Wu M-P. Increased risks of healthcare-seeking behaviors of anxiety, depression and insomnia among patients with bladder pain syndrome/interstitial cystitis: a nationwide population-based study. Int. Urol. Nephrol. 2015;47:275–81.CrossRefPubMed
30.
go back to reference Mikkonen P, Heikkala E, Paananen M, Remes J, Taimela S, Auvinen J, et al. Accumulation of psychosocial and lifestyle factors and risk of low back pain in adolescence: a cohort study. Eur. Spine J. 2016;25:635–42.CrossRefPubMed Mikkonen P, Heikkala E, Paananen M, Remes J, Taimela S, Auvinen J, et al. Accumulation of psychosocial and lifestyle factors and risk of low back pain in adolescence: a cohort study. Eur. Spine J. 2016;25:635–42.CrossRefPubMed
31.
go back to reference Onen SH, Alloui A, Gross A, Eschallier A, Dubray C. The effects of total sleep deprivation, selective sleep interruption and sleep recovery on pain tolerance thresholds in healthy subjects. J. Sleep Res. 2001;10:35–42.CrossRefPubMed Onen SH, Alloui A, Gross A, Eschallier A, Dubray C. The effects of total sleep deprivation, selective sleep interruption and sleep recovery on pain tolerance thresholds in healthy subjects. J. Sleep Res. 2001;10:35–42.CrossRefPubMed
32.
go back to reference Schuh-Hofer S, Wodarski R, Pfau DB, Caspani O, Magerl W, Kennedy JD, et al. One night of total sleep deprivation promotes a state of generalized hyperalgesia: a surrogate pain model to study the relationship of insomnia and pain. Pain. 2013;154:1613–21.CrossRefPubMed Schuh-Hofer S, Wodarski R, Pfau DB, Caspani O, Magerl W, Kennedy JD, et al. One night of total sleep deprivation promotes a state of generalized hyperalgesia: a surrogate pain model to study the relationship of insomnia and pain. Pain. 2013;154:1613–21.CrossRefPubMed
33.
go back to reference Bassett SM, Lupis SB, Gianferante D, Rohleder N, Wolf JM. Sleep quality but not sleep quantity effects on cortisol responses to acute psychosocial stress. Stress. 2015;18:638–44.CrossRefPubMedPubMedCentral Bassett SM, Lupis SB, Gianferante D, Rohleder N, Wolf JM. Sleep quality but not sleep quantity effects on cortisol responses to acute psychosocial stress. Stress. 2015;18:638–44.CrossRefPubMedPubMedCentral
34.
go back to reference Asih S, Neblett R, Mayer TG, Brede E, Gatchel RJ. Insomnia in a chronic musculoskeletal pain with disability population is independent of pain and depression. Spine J. 2014;14:2000–7.CrossRefPubMed Asih S, Neblett R, Mayer TG, Brede E, Gatchel RJ. Insomnia in a chronic musculoskeletal pain with disability population is independent of pain and depression. Spine J. 2014;14:2000–7.CrossRefPubMed
35.
go back to reference Langley J, Davie G, Wilson S, Lilley R, Ameratunga S, Wyeth E, et al. Difficulties in functioning 1 year after injury: the role of preinjury sociodemographic and health characteristics, health care and injury-related factors. Arch. Phys. Med. Rehabil. 2013;94:1277–86.CrossRefPubMed Langley J, Davie G, Wilson S, Lilley R, Ameratunga S, Wyeth E, et al. Difficulties in functioning 1 year after injury: the role of preinjury sociodemographic and health characteristics, health care and injury-related factors. Arch. Phys. Med. Rehabil. 2013;94:1277–86.CrossRefPubMed
36.
go back to reference Mikkonen P, Leino-Arjas P, Remes J, Zitting P, Taimela S, Karppinen J. Is smoking a risk factor for low back pain in adolescents? A prospective cohort study. Spine. 2008;33:527–32.CrossRefPubMed Mikkonen P, Leino-Arjas P, Remes J, Zitting P, Taimela S, Karppinen J. Is smoking a risk factor for low back pain in adolescents? A prospective cohort study. Spine. 2008;33:527–32.CrossRefPubMed
37.
go back to reference Prasarn ML, Horodyski MB, Behrend C, Wright J, Rechtine GR. Negative effects of smoking, workers’ compensation, and litigation on pain/disability scores for spine patients. Surg. Neurol. Int. 2012;3:S366–9.CrossRefPubMedPubMedCentral Prasarn ML, Horodyski MB, Behrend C, Wright J, Rechtine GR. Negative effects of smoking, workers’ compensation, and litigation on pain/disability scores for spine patients. Surg. Neurol. Int. 2012;3:S366–9.CrossRefPubMedPubMedCentral
38.
go back to reference Santiago-Torres J, Flanigan DC, Butler RB, Bishop JY. The effect of smoking on rotator cuff and glenoid labrum surgery: a systematic review. Am. J. Sports Med. 2015;43:745–51.CrossRefPubMed Santiago-Torres J, Flanigan DC, Butler RB, Bishop JY. The effect of smoking on rotator cuff and glenoid labrum surgery: a systematic review. Am. J. Sports Med. 2015;43:745–51.CrossRefPubMed
39.
go back to reference Callréus M, McGuigan F, Akesson K. Adverse effects of smoking on peak bone mass may be attenuated by higher body mass index in young female smokers. Calcif. Tissue Int. 2013;93:517–25.CrossRefPubMed Callréus M, McGuigan F, Akesson K. Adverse effects of smoking on peak bone mass may be attenuated by higher body mass index in young female smokers. Calcif. Tissue Int. 2013;93:517–25.CrossRefPubMed
42.
go back to reference Manassa EH, Hertl CH, Olbrisch R-R. Wound healing problems in smokers and nonsmokers after 132 abdominoplasties. Plast. Reconstr. Surg. 2003;111:2082–7. discussion 2088–9CrossRefPubMed Manassa EH, Hertl CH, Olbrisch R-R. Wound healing problems in smokers and nonsmokers after 132 abdominoplasties. Plast. Reconstr. Surg. 2003;111:2082–7. discussion 2088–9CrossRefPubMed
43.
go back to reference Morales-Espinoza EM, Kostov B, Salami DC, Perez ZH, Rosalen AP, Molina JO, et al. Complexity, comorbidity, and health care costs associated with chronic widespread pain in primary care. Pain. 2016;157:818–26.CrossRefPubMed Morales-Espinoza EM, Kostov B, Salami DC, Perez ZH, Rosalen AP, Molina JO, et al. Complexity, comorbidity, and health care costs associated with chronic widespread pain in primary care. Pain. 2016;157:818–26.CrossRefPubMed
44.
go back to reference Abate M, Vanni D, Pantalone A, Salini V. Cigarette smoking and musculoskeletal disorders. Muscles Ligaments Tendons J. 2013;3:63–9.PubMedPubMedCentral Abate M, Vanni D, Pantalone A, Salini V. Cigarette smoking and musculoskeletal disorders. Muscles Ligaments Tendons J. 2013;3:63–9.PubMedPubMedCentral
45.
go back to reference Behrend C, Prasarn M, Coyne E, Horodyski M, Wright J, Rechtine GR. Smoking Cessation Related to Improved Patient-Reported Pain Scores Following Spinal Care. J. Bone Joint Surg. Am. 2012;94:2161–6.CrossRefPubMed Behrend C, Prasarn M, Coyne E, Horodyski M, Wright J, Rechtine GR. Smoking Cessation Related to Improved Patient-Reported Pain Scores Following Spinal Care. J. Bone Joint Surg. Am. 2012;94:2161–6.CrossRefPubMed
46.
go back to reference Carbone S, Gumina S, Arceri V, Campagna V, Fagnani C, Postacchini F. The impact of preoperative smoking habit on rotator cuff tear: cigarette smoking influences rotator cuff tear sizes. J. Shoulder Elbow Surg. 2012;21:56–60.CrossRefPubMed Carbone S, Gumina S, Arceri V, Campagna V, Fagnani C, Postacchini F. The impact of preoperative smoking habit on rotator cuff tear: cigarette smoking influences rotator cuff tear sizes. J. Shoulder Elbow Surg. 2012;21:56–60.CrossRefPubMed
47.
go back to reference Bartsch RH, Weiss G, Kästenbauer T, Patocka K, Deutinger M, Krapohl BD, et al. Crucial aspects of smoking in wound healing after breast reduction surgery. J. Plast. Reconstr. Aesthet. Surg. 2007;60(9):1045.CrossRefPubMed Bartsch RH, Weiss G, Kästenbauer T, Patocka K, Deutinger M, Krapohl BD, et al. Crucial aspects of smoking in wound healing after breast reduction surgery. J. Plast. Reconstr. Aesthet. Surg. 2007;60(9):1045.CrossRefPubMed
48.
go back to reference Truntzer J, Vopat B, Feldstein M, Matityahu A. Smoking cessation and bone healing: optimal cessation timing. Eur. J. Orthop. Surg. Traumatol. 2015;25:211–5.CrossRefPubMed Truntzer J, Vopat B, Feldstein M, Matityahu A. Smoking cessation and bone healing: optimal cessation timing. Eur. J. Orthop. Surg. Traumatol. 2015;25:211–5.CrossRefPubMed
49.
go back to reference Wearing SC, Hennig EM, Byrne NM, Steele JR, Hills AP. Musculoskeletal disorders associated with obesity: a biomechanical perspective. Obes. Rev. 2006;7:239–50.CrossRefPubMed Wearing SC, Hennig EM, Byrne NM, Steele JR, Hills AP. Musculoskeletal disorders associated with obesity: a biomechanical perspective. Obes. Rev. 2006;7:239–50.CrossRefPubMed
50.
go back to reference Popkin BM, Kim S, Rusev ER, Du S, Zizza C. Measuring the full economic costs of diet, physical activity and obesity-related chronic diseases. Obes. Rev. 2006;7:271–93.CrossRefPubMed Popkin BM, Kim S, Rusev ER, Du S, Zizza C. Measuring the full economic costs of diet, physical activity and obesity-related chronic diseases. Obes. Rev. 2006;7:271–93.CrossRefPubMed
52.
go back to reference Shmagel A, Foley R, Ibrahim H. Epidemiology of Chronic Low Back Pain in US Adults: Data From the 2009-2010 National Health and Nutrition Examination Survey. Arthritis Care Res. 2016;68:1688–94.CrossRef Shmagel A, Foley R, Ibrahim H. Epidemiology of Chronic Low Back Pain in US Adults: Data From the 2009-2010 National Health and Nutrition Examination Survey. Arthritis Care Res. 2016;68:1688–94.CrossRef
53.
go back to reference Nikolajsen L, Brandsborg B, Lucht U, Jensen TS, Kehlet H. Chronic pain following total hip arthroplasty: a nationwide questionnaire study. Acta Anaesthesiol. Scand. 2006;50:495–500.CrossRefPubMed Nikolajsen L, Brandsborg B, Lucht U, Jensen TS, Kehlet H. Chronic pain following total hip arthroplasty: a nationwide questionnaire study. Acta Anaesthesiol. Scand. 2006;50:495–500.CrossRefPubMed
54.
go back to reference Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KDJ. Patient satisfaction after total knee arthroplasty: who is satisfied and who is not? Clin. Orthop. Relat. Res. 2010;468:57–63.CrossRefPubMed Bourne RB, Chesworth BM, Davis AM, Mahomed NN, Charron KDJ. Patient satisfaction after total knee arthroplasty: who is satisfied and who is not? Clin. Orthop. Relat. Res. 2010;468:57–63.CrossRefPubMed
55.
go back to reference Brummett CM, Urquhart AG, Hassett AL, Tsodikov A, Hallstrom BR, Wood NI, et al. Characteristics of fibromyalgia independently predict poorer long-term analgesic outcomes following total knee and hip arthroplasty. Arthritis Rheumatol. 2015;67:1386–94.CrossRefPubMedPubMedCentral Brummett CM, Urquhart AG, Hassett AL, Tsodikov A, Hallstrom BR, Wood NI, et al. Characteristics of fibromyalgia independently predict poorer long-term analgesic outcomes following total knee and hip arthroplasty. Arthritis Rheumatol. 2015;67:1386–94.CrossRefPubMedPubMedCentral
56.
go back to reference Robertsson O, Stefánsdóttir A, Lidgren L, Ranstam J. Increased long-term mortality in patients less than 55 years old who have undergone knee replacement for osteoarthritis: results from the Swedish Knee Arthroplasty Register. J. Bone Joint Surg. Br. 2007;89:599–603.CrossRefPubMed Robertsson O, Stefánsdóttir A, Lidgren L, Ranstam J. Increased long-term mortality in patients less than 55 years old who have undergone knee replacement for osteoarthritis: results from the Swedish Knee Arthroplasty Register. J. Bone Joint Surg. Br. 2007;89:599–603.CrossRefPubMed
57.
go back to reference Bozic KJ, Lau E, Ong K, Chan V, Kurtz S, Vail TP, et al. Risk factors for early revision after primary total hip arthroplasty in Medicare patients. Clin. Orthop. Relat. Res. 2014;472:449–54.CrossRefPubMed Bozic KJ, Lau E, Ong K, Chan V, Kurtz S, Vail TP, et al. Risk factors for early revision after primary total hip arthroplasty in Medicare patients. Clin. Orthop. Relat. Res. 2014;472:449–54.CrossRefPubMed
58.
go back to reference Aalto TJ, Malmivaara A, Kovacs F, Herno A, Alen M, Salmi L, et al. Preoperative predictors for postoperative clinical outcome in lumbar spinal stenosis: systematic review. Spine. 2006;31:E648–63.CrossRefPubMed Aalto TJ, Malmivaara A, Kovacs F, Herno A, Alen M, Salmi L, et al. Preoperative predictors for postoperative clinical outcome in lumbar spinal stenosis: systematic review. Spine. 2006;31:E648–63.CrossRefPubMed
59.
go back to reference Kawatkar AA, Jacobsen SJ, Levy GD, Medhekar SS, Venkatasubramaniam KV, Herrinton LJ. Direct medical expenditure associated with rheumatoid arthritis in a nationally representative sample from the medical expenditure panel survey. Arthritis Care Res. 2012;64:1649–56.CrossRef Kawatkar AA, Jacobsen SJ, Levy GD, Medhekar SS, Venkatasubramaniam KV, Herrinton LJ. Direct medical expenditure associated with rheumatoid arthritis in a nationally representative sample from the medical expenditure panel survey. Arthritis Care Res. 2012;64:1649–56.CrossRef
60.
go back to reference Whittle SL, Richards BL, Husni E, Buchbinder R. Opioid therapy for treating rheumatoid arthritis pain. Cochrane Database Syst. Rev. 2011:CD003113. Whittle SL, Richards BL, Husni E, Buchbinder R. Opioid therapy for treating rheumatoid arthritis pain. Cochrane Database Syst. Rev. 2011:CD003113.
Metadata
Title
Leveraging healthcare utilization to explore outcomes from musculoskeletal disorders: methodology for defining relevant variables from a health services data repository
Authors
Daniel I. Rhon
Derek Clewley
Jodi L. Young
Charles D. Sissel
Chad E. Cook
Publication date
01-12-2018
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2018
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-018-0588-8

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