Icd 10 code for chronic neck pain

Spine (Phila Pa 1976). Author manuscript; available in PMC 2017 Sep 13.

Published in final edited form as:

PMCID: PMC5596507

NIHMSID: NIHMS902790

Patricia L. Sinnott, PT, PhD, MPH,

Structured Abstract

Study design

We reviewed existing methods for identifying patients with neck and back pain in administrative data. We compared these methods using data from the Department of Veterans Affairs.

Objective

To answer the following questions: 1) what diagnosis codes should be used to identify patients with neck and back pain in administrative data; 2) because the majority of complaints are characterized as non-specific or mechanical, what diagnosis codes should be used to identify patients with non-specific or mechanical problems in administrative data; and 3) what procedure and surgical codes should be used to identify patients who have undergone a surgical procedure on the neck or back.

Summary of background data

Musculoskeletal neck and back pain are pervasive problems, associated with chronic pain, disability, and high rates of healthcare utilization. Administrative data have been widely used in formative research which has largely relied on the original work of Volinn, Cherkin, Deyo and Einstadter and the Back Pain Patient Outcomes Assessment Team first published in 1992. Significant variation in reports of incidence, prevalence, and morbidity associated with these problems may be due to non standard or conflicting methods to define study cohorts.

Methods

A literature review produced seven methods for identifying neck and back pain in administrative data. These code lists were used to search VA data for patients with back and neck problems, and to further categorize each case by spinal segment involved, as non- specific/mechanical and as surgical or not.

Results

There is considerable overlap in most algorithms. However, gaps remain.

Conclusions

Gaps are evident in existing methods and a new framework to identify patients with neck and back pain in administrative data is proposed.

Keywords: Back pain, Neck pain, Low back pain, Back Pain/epidemiology, Databases, Factual, Disease/classification, Health Services Research

Neck and back pain are highly prevalent problems and administrative data are commonly used to describe the incidence, prevalence, and geographic variation in practice for these conditions.1–11 This work has largely relied on the methods published in 1992 by Volinn, Cherkin, Deyo and Einstadter and the Back Pain Patient Outcomes Assessment Team (BOAT)12, 13, 14 that identified neck and back pain from ICD-915 codes and used hospitalization as a proxy for morbidity.12–14, 16, 17 This original work has led to a broad range of research on neck and back problems using hospital, workers’ compensation and Medicare data. 18–25 More recently, the International Society for Pharmacoeconomics and Outcomes Research26 has adopted guidelines for conducting and reviewing research using retrospective administrative databases, but this guideline, while successful in creating an international standard for doing this type research, does not address the idiosyncracies of spine data. With these new guidelines and two decades of changes in medical practice24 as well as a change in focus from analyzing events to evaluating episodes of care,27, 28 a revised methodology may be needed. The purpose of this study was to review methods used to identify patients with neck or back pain in administrative data and, if appropriate, establish a revised framework. Our objective for these analyses was to answer three questions: 1) what diagnosis codes should be used to identify patients with neck and back pain in administrative data; 2) because the majority of complaints are characterized as non-specific or mechanical, what diagnosis codes should be used to distinguish these particular patients in administrative data from patients with more complex problems; and 3) what diagnosis and procedure and surgical codes should be used to identify patients who have undergone surgical or invasive procedures on the neck or back.

Methods

We used a snowball sampling approach to identify algorithms used to identify patients with neck or back pain problems in administrative data. We started by searching the Web of Knowledge (Thomson Reuters Institute for Scientific Information) for articles that referenced Cherkin, Deyo’s 1992 publication.13 Next, we searched PubMed to identify studies that reported using diagnosis or procedure codes to identify these patients in administrative data, using key, words and MeSH terms; we focused on studies written in English. (Search logic is available in Appendix A) These searches plus additional recommendations identified 170 manuscripts for review. We excluded twelve papers based on review of the title (for example, conditions other than neck or back pain) and reviewed 158 abstracts and 48 manuscripts in depth. (APPENDIX B lists the manuscripts reviewed in depth.) Studies were excluded, for example, if they did not include a code algorithm or list 29–31, if the study included only a limited number of diagnoses or reported on a limited number of surgical procedures 32–39, or if the reports did not involve the use of previously collected administrative data. We additionally excluded studies that were not about neck or back pain, and those studies that used the original Cherkin and Deyo algorithm13, 18–23 without modification. This review yielded six papers including Cherkin and Deyo13 that listed diagnosis and procedure-based algorithms for coding neck and back pain (see Table 1).12–14, 16, 17, 40,

Table 1

Comparison of Coding Algorithms

Angevine et al.17 used the most limited list of diagnoses and procedures to identify care for cervical disc disease. Martin et al.40 had the broadest list of codes to estimate costs of neck and back care using the Medical Expenditure Panel Survey (MEPS). The six studies generally used the same criteria to define non-specific/mechanical neck or back pain, excluding cases with evidence of neoplasm, trauma, inflammatory spondyloarthropathies, and infection or pregnancy. Investigators additionally limited their analyses to adults (exclusion range <15 to < 20 years of age) and clinical conditions (e.g. spinal stenosis, congenital anomalies and pathologic fracture) depending on their data sources and their research questions.

To this list of six papers, we added the AHRQ Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software42 algorithms for “Spondylosis; intervertebral disc disorders; other back problems” (category 205 ) and “Sprains and strains” (category 232). Category 232 - includes all body parts, but we included only those codes that refer specifically to the spine. Between the six papers and two HCUP categories, we had a total of eight coding algorithms. The inclusion and exclusion codes are listed in Appendix C. Because the Cherkin & Deyo paper 13 extended work by Volinn and Loeser,12, 43 we have not incorporated the Volinn list in this table.

These seven algorithms had varying and sometimes conflicting definitions of neck and back pain. Because of this and because current research and clinical practice guidelines differentiate between pain originating in the neck and pain originating in other areas of the spine,44, 45 we standardized our definitions for this project. We adopted the following definitions, consistent with international work9: “spine pain” includes conditions that originate anywhere in the spine; “neck pain” includes conditions that originate in the cervical spine; “back pain” includes conditions originating from the thoracic, lumbar, and sacral spine, including the coccyx; and “low back pain” includes conditions that arise from the lumbar and sacral spine, including the coccyx. 9 These conditions could be “associated with pain as well as causing radicular symptoms from compression or irritation of nerve roots”.13 Non-specific or mechanical spine pain was defined as “without primary neoplastic, infectious, or inflammatory cause” and excluding codes consistent with “pregnancy or major trauma”.13, 16

The algorithms shared many features and their differences were primarily definitional (See Appendix C). Most inclusion and exclusion criteria followed the Cherkin and Deyo13 algorithm, except Taylor, 16 which included thoracic diagnoses in the definition of back and neck problems, and the HCUP back category which included cervical and thoracic diagnoses and the diagnoses pertaining to the coccyx. The most significant differences were between the Cherkin and Deyo13 algorithm, HCUP category 205, and Martin et al.40 In these studies they differed in their definitions of neck vs. back and whether the report was about any neck and back problems, or limited to non-specific problems [e.g. whether to include or exclude ankylosing spondylitis and other inflammatory spondylopathies (720.0 – 720.9), curvatures of the spine, (737.0–737.9), acquired spondylolisthesis and other acquired deformity of the back or spine (738.4–738.5), nonallopathic lesions of the spine (739.1–739.4), anomalies of the spine (756.10–756.2), open or closed spinal fractures with and without mention of spinal cord injury (805 – 806), and other, multiple and ill-defined vertebral dislocations (839.0– 839.5)]. In addition, there are many codes for which the spinal segment is not defined (721.90; 721.91; 722.2; 722.6; 722.70; 722.90; 738.4; and 847.9; 996.4) and were included in both low back and neck pain algorithms. At the end of Appendix C, these non-specific codes and the exclusions due to conditions which were used by Cherkin and Deyo13 to define “non-specific or mechanical low back pain” are included.

To evaluate the differences in these seven algorithms, we used administrative data from the Veterans Health Administration (VA). We identified all patients who received health care services for back or neck problems in fiscal years (FY) 2002 through 2009, analyzing the VA Patient Treatment Files for inpatient utilization and the National Patient Care Database encounter files for outpatient data, using the inclusion diagnosis codes listed in Appendix C. These databases include diagnostic and procedure information for all health care services provided by VA. We searched all available diagnoses in each encounter to identify patients with any spine-related problem. NOTE, in VA up to 20 diagnoses can be included in outpatient data.

To characterize spine conditions as non-specific, we identified the first or incident spine pain event (inpatient admission or outpatient visit or encounter) for each patient in FY2002–2009 VA data, and identified those that would be excluded based on each of the diagnoses included in the “non-specific/mechanical” exclusion list (e.g. pregnancy related or due to infection or trauma). We also identified patients who had undergone a surgical or spinal procedure by procedure code.

Results

We identified 2.77 million unique patients who received care for neck and back problems in VA in fiscal years 2002 – 2009. Tables 2 and ​3 demonstrate the results when each selection method was added to the previous algorithm for back pain and neck pain. For example, using the Cherkin and Deyo list alone, 2,129,984 unique individuals with back problems were identified in these data (Table 2). When we add the codes included in the HCUP Back category (205), we identify an additional 33,495 individuals, and when we add the HCUP sprains and strains diagnoses, we identify an additional 3,537 individuals. Finally, when we add the codes from the expanded Martin list another 3,750 individuals are identified. For the most part these differences are definitional, e.g. the Cherkin and Deyo study was about low back pain, which they defined as including thoraco-lumbar, lumbar, lumbo-sacral and sacral symptoms, but not thoracic alone and not coccygeal, while the HCUP back category and the Martin study include all spinal segments. Table 4 demonstrates the number of individuals identified using ICD-9 codes that are not segment specific.

Table 2

Count of unique patients with back pain: sequential addition of patients by reference

Table 3

Count of unique patients with neck pain: sequential addition of patients by reference

Table 4

Count of patients identified by codes that are not specific to spinal segment

Because several of the codes that were not spinal segment specific were included in both neck pain and back pain algorithms, we continued the analyses to further define whether a patient had neck pain or back pain. We selected those cases in which a code referring to a non-specific spinal segment had served to identify the case for the cohort (e.g. in the incident event in these data) and searched for additional segment-specific codes in the first and subsequent encounters. (Table 5) We found that approximately 75 per cent of all cases included diagnoses referring to the back only, and 15 per cent referred to the neck only. The remaining 10 per cent included more than one area of the spine or combinations of codes for neck, back and non specific parts of the spine. Approximately 6.5 percent of cases included only non-specific codes.

Table 5

Count of patients by spinal segment diagnoses

Next, using the Cherkin and Deyo13 definitions, to identify patients with non-specific neck or low back pain, we excluded those cases “with a primary neoplastic, infectious, or inflammatory cause” and those “associated with pregnancy or major trauma”.13, 16 We identified the number of cases to be excluded if one of these conditions appeared in the data within one year prior to the first neck or back problem, and any years after. (Table 6) We included, “any years after” in order to control for potential prodromal conditions unidentified at first onset of the spine problem. Next, we examined the neoplasm exclusions. We hypothesized that, because diagnoses of primary skin and prostate cancers are common but rarely contribute to spine pain, and are either acutely treatable or slow-growing, asymptomatic, and non-metastatic, we removed the diagnoses for primary skin cancers (ICD-9 173.–173.9) and primary prostate cancer (ICD-9-185) from the exclusion list (NOTE exclusion of secondary malignant neoplasms - e.g. ICD codes 196–239.9- served to exclude those patients with cases of skin or prostate cancer that had advanced beyond the primary site). This served to reduce the number of cases excluded from the first step by 15 percent (from 1,000,709 cases excluded to 853,615 cases excluded). Also because administrative data that reflects health care encounters to “rule out” a diagnosis might include the diagnosis to be “ruled out”, we then demonstrated this potential effect by requiring each of the exclusionary conditions to appear in the data two or more times within 12 months46. (Table 6) This second method reduced the number of cases to exclude due to pregnancy more than 83 percent, the number due to intraspinal abcess (324.1) and osteomyelitis, etc. (730.07–730.99) by approximately 45 per cent, and the number due to neoplasm (not including primary skin and prostate cancer) by one-third.

Table 6

Defining non-specific/mechanical back or neck pain (any occurrence by ICD-9 code, not sequential exclusion)

We next reviewed the algorithms used by the authors above 13, 14, 16, 17 to characterize hospitalizations as surgical and non-surgical, and these were straightforward. In these studies, once the spinal segment and the inclusion diagnoses had been defined, surgical and non-surgical hospitalizations were defined by the presence or not, of a limited list of ICD-9 surgery codes for spinal canal decompression, laminectomy, discectomy, fusion and refusion (ICD-9 codes 03.0, 03.02, 03.09, 03.6, 80.5, 80.50–80.52, 80.59, 81.0, 81.00–81.08, 81.3–81.39), excision of bone for graft (77.70 and 77.79) and insertion or removal of an internal fixation device or bone growth stimulator (78.50, 78.59, 78.60,78.69, 78.90,78.99). Because of changes in medical practice, and because some of these procedures might more recently be performed in an outpatient setting, we added appropriate CPT codes to this surgery list (CPT Surgery/Musculoskeletal System/Spine (Vertebral Column) CPT 22100–22865 and 62263 – 63710). We found that using the ICD codes alone we identified 36,724 patients who had undergone one of the spinal surgeries identified above, and adding the CPT codes, we identified a total of 46,615 patients, a 27% increase in the number of patients identified as having undergone a surgery or procedure. (See Appendix D for detail).

Discussion

We identified five papers published since Cherkin and Deyo13 that defined algorithms to identify patients with neck or back pain in administrative data. These methods, for the most part, are based on the algorithms developed by the Back Pain Outcomes Assessment Team (BOAT) for the study of back and neck pain.13, 14, 17 modified to address the questions of individual researchers. We found that there was overlap in the definitions of neck and back conditions and that it was necessary to clarify which spinal segments were included in each classification. In addition, we found that more than 204,000 cases in our 2002 – 2009 population were assigned a diagnosis in the first encounter that was not spinal segment specific, thus requiring additional analyses to specify the appropriate spinal segment.

We found consistency in the definitions of mechanical or non-specific spine pain (exclusion of neoplasm, infectious or inflammatory causes, pregnancy, trauma, etc.) and that requiring confirmation of an exclusionary diagnosis with a second encounter with that diagnosis reduced the number of cases excluded by 32–38 percent. We also determined that removing primary skin and prostate cancers from the neoplasm exclusions reduced the number of cases excluded by 147,094 individuals (14.7%). Finally, we determined that the addition of procedure codes to surgery algorithms had the potential to identify many more cases (in this case 275 more cases) to consider when analyzing surgery in more recent data.

Our findings suggest that, rather than code lists, the research community should adopt important technical guidelines for use in studies of neck and back pain using retrospective databases. 26 First, in order to enhance utility and comparability of results9, researchers should specify the focus of their research using anatomical references to describe neck and back pain. Second, if the researchers plan to include diagnosis codes that are not spine segment specific (e.g. ICD codes 721.90; 721.91; 722.2; 722.6; 722.70; 722.90; 738.4; and 847.9) they should confirm that these non-specific codes are associated with their segments of interest with additional exploration of the data.(For example, if the code used to select a patient is 721.9, spondylosis of unspecified site without mention of myelopathy, then the researcher should search forward in the data to determine if the preponderance of data reflected a neck or back condition.) Third, researchers should consider whether exclusionary diagnoses should be confirmed with at least two encounters and whether common conditions that rarely have impact on spine pain (for example, primary skin and prostate cancer) should be removed from exclusion lists. Fourth, researchers should use relevant diagnosis and procedure and surgery codes and both inpatient and outpatient data to identify the population of patients who receive spine related surgery and procedures. Additionally, to enhance comparability, researchers should report if they exclude any of the following conditions from their cohort: ankylosing spondylitis, etc (720.0–720.9); curvatures of the spine (737.0 – 737.9, excludes congenital); acquired spondylolisthesis and other acquired deformity of the back or spine (738.4–738.5); nonallopathic lesions of the spine (739.1–739.4); anomalies of the spine (756.10–756.2); open/closed spinal fractures (805.0–806.9); and other vertebral dislocations (839.0–839.5). Finally, the research community should determine whether the above diagnostic groups should be included or excluded in analyses and reports of non-specific neck or back problems.

This study has several strengths that suggest that these recommendations will improve the validity and generalizability of studies which use this revised framework. First, the literature review identified 48 studies that reported using diagnosis and procedure codes to identify patients with neck and back conditions in administrative data. While the majority of these studies relied on the original algorithms developed by Cherkin and Deyo and the BOAT research group,13 consolidating the code list into a single table served to highlight the patterns of inclusion, exclusion and omission specific to each algorithm. This has provided an introduction to the scope of variation to be considered in defining a new framework. Second, we have tested these algorithms and assumptions in an extremely large administrative database. VA has been using an electronic health record for over ten years, and national compilations of longitudinal data have been used in this study. VA administrative data is comprehensive and includes inpatient, outpatient, ancillary and pharmaceutical care for a large population of Veterans (each year +/− 5 million Veterans receive health care services through the Veterans Health Administration). In addition, VA administrative data is routinely used to assess the quality and timeliness of care provided in VHA, and has been a primary resource for VA quality, safety and outcomes research.26, 47–49 As a result, testing the algorithms on such a large data set is unlikely to miss any important trends that might be present in smaller patient populations. Third, diagnosis and procedural coding activities are highly automated and professionalized in VA. This expertise is demonstrated in the frequency reports (Tables 2, ​3 and ​4) in which, as per correct coding conventions defined by the American Hospital Association, American Medical Association, the Centers for Medicare and Medicaid Services, and the National Center for Health Statistics, no patients are identified with three digit major codes, and no patients were identified with four digit subcodes if there was a five digit code available for more specificity (see Table 2, ICD-9 722.5, 722.51, 722.52). In VA, coding rules are integrated into the electronic data capture and all inpatient coding is done by credentialed experts. In practical terms, this means that diagnosis and procedure codes entered into the administrative data represent, in the most accurate way possible, the diagnoses, services and procedures received by an individual patient. In some cases, however, the professionalism in coding practice may also be a limitation, as it may not accurately reflect the errors produced in other environments where data entry is not automated or done by expert coders (for example see Stano and Smith).50 Only further research can confirm what errors occur and what methods should be used for correction.

Our review of the algorithms used to identify patients with neck and back problems in administrative data suggest that an update to the most commonly used algorithm is warranted. This new methodology would have the researcher use international standards9 to define the spinal segment(s) of interest, confirm anatomical references when including diagnosis codes that are not segment specific, confirm the presence of excluding diagnoses in more than a single encounter of care, and would define surgical patients using both surgical and procedure codes from both inpatient and outpatient events of interest. This new framework also includes the recommendation for specificity in reporting on the spinal segment of interest and the conditions to be included and excluded from the analyses. This methodology is not limited to the use of CPT and ICD9 codes but is appropriate for use in any epidemiological or health services research which uses administrative data for the study of neck and back pain conditions. With such a standardized methodology and reporting format, methodological variation in reports of the incidence, prevalence and outcomes of care can be minimized.

​

Key Points

• Methods for identifying patients with neck and back problems in administrative data have not kept pace with changes in practice and coding.

• A review of current methods suggests a new framework for identifying patients with neck and back pain in administrative data.

• An updated framework to identify patients with neck and back pain in administrative data will help capture more cases for analyses.

Acknowledgments

Funding for this study was provided by the VA Health Services Research and Development Service (HSR&D IIR 09-062) and was approved by the Stanford IRB and the VA Palo Alto Health Care Research and Development program.

Appendix A: Pubmed Search Logic

Back and International Classification of Disease (ICD) and epidemiology; Neck and ICD and epidemiology; Back and ICD and surgery; Neck and ICD and surgery; Back and Common Procedure Terminology (CPT) and epidemiology; Neck and CPT and epidemiology; Back and CPT and surgery; and Neck and CPT and surgery. In addition we performed a search using a consolidation of the previous logic: ((“back pain”[MeSH Terms] OR (“back”[All Fields] AND “pain”[All Fields]) OR “back pain”[All Fields]) OR (“low back pain”[MeSH Terms] OR (“low”[All Fields] AND “back”[All Fields] AND “pain”[All Fields]) OR “low back pain”[All Fields]) OR (“neck pain”[MeSH Terms] OR (“neck”[All Fields] AND “pain”[All Fields]) OR “neck pain”[All Fields])) AND (cpt[All Fields] OR (common[All Fields] AND procedural[All Fields] AND terminology[All Fields]) OR icd[All Fields] OR icd9[All Fields] OR (international classification of diseases[All Fields] OR international classification of diseases/classification[All Fields] OR international classification of diseases/economics[All Fields] OR international classification of diseases/history[All Fields] OR international classification of diseases/instrumentation[All Fields] OR international classification of diseases/standards[All Fields] OR international classification of diseases/trends[All Fields] OR international classification of diseases/utilization[All Fields])) AND ((“surgery”[Subheading] OR “surgery”[All Fields] OR “surgical procedures, operative”[MeSH Terms] OR (“surgical”[All Fields] AND “procedures”[All Fields] AND “operative”[All Fields]) OR “operative surgical procedures”[All Fields] OR “surgery”[All Fields] OR “general surgery”[MeSH Terms] OR (“general”[All Fields] AND “surgery”[All Fields]) OR “general surgery”[All Fields]) OR (“epidemiology”[Subheading] OR “epidemiology”[All Fields] OR “prevalence”[All Fields] OR “prevalence”[MeSH Terms])

APPENDIX B

Manuscripts reviewed in depth:

1. Cherkin DC, Deyo RA, Volinn E, Loeser JD. Use of the International Classification of Diseases (ICD-9-CM) to identify hospitalizations for mechanical low back problems in administrative databases. Spine. 1992;17(7):817–825.

2. Dasinger LK, Krause N, Deegan LJ, Brand RJ, Rudolph L. Duration of work disability after low back injury: a comparison of administrative and self-reported outcomes. Am J Ind Med. 1999;35(6):619–631.

3. Dasinger LK, Krause N, Deegan LJ, Brand RJ, Rudolph L. Physical workplace factors and return to work after compensated low back injury: a disability phase-specific analysis. J Occup Environ Med. 2000;42(3):323–333.

4. Dasinger LK, Krause N, Thompson PJ, Brand RJ, Rudolph L. Doctor proactive communication, return-to-work recommendation, and duration of disability after a workers’ compensation low back injury. J Occup Environ Med. Jun 2001;43(6):515–525.

5. Deyo RA, Mirza SK, Martin BI, Kreuter W, Goodman DC, Jarvik JG. Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA. Apr 7 2010;303(13):1259–1265.

6. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. Jun 1992;45(6):613–619.

7. Einstadter D, Kent DL, Fihn SD, Deyo RA. Variation in the rate of cervical spine surgery in Washington State. Med Care. Aug 1993;31(8):711–718.

8. Elam K, Taylor V, Ciol MA, Franklin GM, Deyo RA. Impact of a worker’s compensation practice guideline on lumbar spine fusion in Washington State. Med Care. May 1997;35(5):417–424.

9. Faciszewski T, Jensen R, Berg RL. Procedural coding of spinal surgeries (CPT-4 versus ICD-9-CM) and decisions regarding standards: a multicenter study. Spine (Phila Pa 1976). Mar 1 2003;28(5):502–507.

10. Fishman P, Von Korff M, Lozano P, Hecht J. Chronic care costs in managed care. Health Aff (Millwood). May-Jun 1997;16(3):239–247.

11. Krause N, Dasinger LK, Deegan LJ, Brand RJ, Rudolph L. Alternative approaches for measuring duration of work disability after low back injury based on administrative workers’ compensation data. Am J Ind Med. 1999;35(6):604–618.

12. Lavis JN, Malter A, Anderson GM, et al. Trends in hospital use for mechanical neck and back problems in Ontario and the United States: discretionary care in different health care systems. CMAJ. Jan 13 1998;158(1):29–36.

13. Lipscomb HJ, Cameron W, Silverstein B. Back injuries among union carpenters in Washington State, 1989–2003. Am J Ind Med. Jun 2008;51(6):463–474.

14. Martin BI, Deyo RA, Mirza SK, et al. Expenditures and health status among adults with back and neck problems. JAMA. Feb 13 2008;299(6):656–664.

15. Martin BI, Turner JA, Mirza SK, Lee MJ, Comstock BA, Deyo RA. Trends in health care expenditures, utilization, and health status among US adults with spine problems, 1997–2006. Spine (Phila Pa 1976). Sep 1 2009;34(19):2077–2084.

16. Nilasena DS, Vaughn RJ, Mori M, Lyon JL. Surgical trends in the treatment of diseases of the lumbar spine in Utah’s Medicare population, 1984 to 1990. Med Care. Jun 1995;33(6):585–597.

17. Nimgade A, McNeely E, Milton D, Celona J. Increased expenditures for other health conditions after an incident of low back pain. Spine (Phila Pa 1976). Apr 1 2010;35(7):769–777.

18. Phibbs CS, Bhandari A, Yu W, Barnett PG. Estimating the costs of VA ambulatory care. Med Care Res Rev. Sep 2003;60(3 Suppl):54S–73S.

19. Ray GT, Collin F, Lieu T, et al. The cost of health conditions in a health maintenance organization. Med Care Res Rev. Mar 2000;57(1):92–109.

20. Romano PS, Campa DR, Rainwater JA. Elective cervical discectomy in California: postoperative in-hospital complications and their risk factors. Spine (Phila Pa 1976). Nov 15 1997;22(22):2677–2692.

21. Stano M, Smith M. Chiropractic and medical costs of low back care. Med Care. Mar 1996;34(3):191–204.

22. Steenstra IA, Verbeek JH, Prinsze FJ, Knol DL. Changes in the incidence of occupational disability as a result of back and neck pain in the Netherlands. BMC Public Health. 2006;6:190.

23. Taylor VM, Anderson GM, McNeney B, et al. Hospitalizations for back and neck problems: a comparison between the Province of Ontario and Washington State. Health Serv Res. Oct 1998;33(4 Pt 1):929–945.

24. Van Eerd D, Cote P, Beaton D, Hogg-Johnson S, Vidmar M, Kristman V. Capturing cases in workers’ compensation databases: the example of neck pain. Am J Ind Med. Jul 2006;49(7):557–568.

25. Vogt MT, Kwoh CK, Cope DK, Osial TA, Culyba M, Starz TW. Analgesic usage for low back pain: impact on health care costs and service use. Spine (Phila Pa 1976). May 1 2005;30(9):1075–1081.

26. Volinn E, Mayer J, Diehr P, Van Koevering D, Connell FA, Loeser JD. Small area analysis of surgery for low-back pain. Spine (Phila Pa 1976). May 1992;17(5):575–581.

27. Wang MC, Chan L, Maiman DJ, Kreuter W, Deyo RA. Complications and mortality associated with cervical spine surgery for degenerative disease in the United States. Spine (Phila Pa 1976). Feb 1 2007;32(3):342–347.

28. Wang MC, Laud PW, Macias M, Nattinger AB. Utility of a Combined CPT and ICD9-CM Code Algorithm in Classifying Cervical Spine Surgery for Degenerative Changes. Spine (Phila Pa 1976). Jan 17 2011.

29. Wang MC, Kreuter W, Wolfla CE, Maiman DJ, Deyo RA. Trends and variations in cervical spine surgery in the United States: Medicare beneficiaries, 1992 to 2005. Spine (Phila Pa 1976). Apr 20 2009;34(9):955–961; discussion 962–953.

30. Wang MC, Laud PW, Macias M, Nattinger AB. Strengths and limitations of International Classification of Disease Ninth Revision Clinical Modification codes in defining cervical spine surgery. Spine (Phila Pa 1976). Jan 1 2011;36(1):E38–44.

31. Weinstein JN, Lurie JD, Olson PR, Bronner KK, Fisher ES. United States’ trends and regional variations in lumbar spine surgery: 1992–2003. Spine. Nov 1 2006;31(23):2707–2714.

32. Baaj AA, Uribe JS, Nichols TA, et al. Health care burden of cervical spine fractures in the United States: analysis of a nationwide database over a 10-year period. J Neurosurg Spine. Jul 2010;13(1):61–66.

33. Bener A, Rahman YS, Mitra B. Incidence and severity of head and neck injuries in victims of road traffic crashes: In an economically developed country. Int Emerg Nurs. Jan 2009;17(1):52–59.

34. Besman A, Kaban J, Jacobs L, Jacobs LM. False-negative plain cervical spine x-rays in blunt trauma. Am Surg. Nov 2003;69(11):1010–1014.

35. Bourbeau R, Desjardins D, Maag U, Laberge-Nadeau C. Neck injuries among belted and unbelted occupants of the front seat of cars. J Trauma. Nov 1993;35(5):794–799.

36. Brolin K, von Holst H. Cervical injuries in Sweden, a national survey of patient data from 1987 to 1999. Inj Control Saf Promot. Mar 2002;9(1):40–52.

37. Brown CV, Antevil JL, Sise MJ, Sack DI. Spiral computed tomography for the diagnosis of cervical, thoracic, and lumbar spine fractures: its time has come. J Trauma. May 2005;58(5):890–895; discussion 895–896.

38. Campbell PG, Malone J, Yadla S, et al. Comparison of ICD-9-based, retrospective, and prospective assessments of perioperative complications: assessment of accuracy in reporting. J Neurosurg Spine. Jan 2010;14(1):16–22.

39. Cimmino MA, Ugolini D, Cauli A, et al. Frequency of musculoskeletal conditions among patients referred to Italian tertiary rheumatological centers. Clin Exp Rheumatol. Nov–Dec 2006;24(6):670–676.

40. Daniels AH, Arthur M, Hart RA. Variability in rates of arthrodesis for patients with thoracolumbar spine fractures with and without associated neurologic injury. Spine (Phila Pa 1976). Oct 1 2007;32(21):2334–2338.

41. Dunning KK, Davis KG, Cook C, et al. Costs by industry and diagnosis among musculoskeletal claims in a state workers compensation system: 1999–2004. Am J Ind Med. Mar 2009;53(3):276–284.

42. Hu RW, Jaglal S, Axcell T, Anderson G. A population-based study of reoperations after back surgery. Spine (Phila Pa 1976). Oct 1 1997;22(19):2265–2270; discussion 2271.

43. Mulligan RP, Friedman JA, Mahabir RC. A nationwide review of the associations among cervical spine injuries, head injuries, and facial fractures. J Trauma. Mar 2009;68(3):587–592.

44. Mulligan RP, Mahabir RC. The prevalence of cervical spine injury, head injury, or both with isolated and multiple craniomaxillofacial fractures. Plast Reconstr Surg. Nov 2010;126(5):1647–1651.

45. Patel AA, Spiker WR, Daubs M, Brodke D, Cannon-Albright LA. Evidence for an inherited predisposition to lumbar disc disease. J Bone Joint Surg Am. Feb 2 2011;93(3):225–229.

46. Patil PG, Turner DA, Pietrobon R. National trends in surgical procedures for degenerative cervical spine disease: 1990–2000. Neurosurgery. Oct 2005;57(4):753–758; discussion 753–758.

47. Peterson L, Junge A, Chomiak J, Graf-Baumann T, Dvorak J. Incidence of football injuries and complaints in different age groups and skill-level groups. Am J Sports Med. 2000;28(5 Suppl):S51–57.

48. Sebert SL, Brick JE, Anderson K. Acute low back pain findings and management in an academic medical center. W V Med J. Jul–Aug 1998;94(4):202–204.

APPENDIX C Inclusion and Exclusion lists consolidated

APPENDIX D Spine surgeries and procedures by year and code

APPENDIX D Surgeries and Procedures by year and code

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