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Current Concepts Review

Database and Registry Research in Orthopaedic Surgery

Part I: Claims-Based Data

Pugely, Andrew J. MD1; Martin, Christopher T. MD1; Harwood, Jared MD2; Ong, Kevin L. PhD3; Bozic, Kevin J. MD, MBA4; Callaghan, John J. MD1

Author Information
The Journal of Bone and Joint Surgery: August 5, 2015 - Volume 97 - Issue 15 - p 1278-1287
doi: 10.2106/JBJS.N.01260
  • Free
  • Disclosures


Peer Review: This article was reviewed by the Editor-in-Chief and one Deputy Editor, and it underwent blinded review by two or more outside experts. The Deputy Editor reviewed each revision of the article, and it underwent a final review by the Editor-in-Chief prior to publication. Final corrections and clarifications occurred during one or more exchanges between the author(s) and copyeditors.

The orthopaedic surgery literature has been historically dominated by observational cohort studies. Although randomized controlled trials (RCTs) are the gold standard for clinical research, the high costs and resource utilization of conducting such studies have limited their practicality1,2. Costs may run several million U.S. dollars as in the SPORT (Spine Patient Outcomes Research Trial) and BrAIST (Bracing for Adolescent Idiopathic Scoliosis Trial) studies ($30 million and $7 million, respectively)3,4. As the public demands higher levels of evidence, federal funding for research in the U.S. paradoxically remains stagnant. Observational research serves an essential role in orthopaedic surgery, especially because many important clinical questions cannot ethically undergo study by RCTs1. Among the relevant retrospective cohort studies, large-scale databases have emerged as a viable epidemiological tool in orthopaedic surgery.

The use of large-scale databases in observational research within the orthopaedic literature has expanded, likely in response to priority shifts within health-care policy and improvements in information technology. Several different data sets based in the U.S. have been used and the results have been published within the most-cited orthopaedic journals (Table I). In a general sense, these data sets have been categorized as insurance, payer, or administrative claims databases or as clinical registries. These data have given researchers a powerful tool for addressing a variety of clinical questions of national interest, including those regarding orthopaedic disease and its treatment, volume, resource utilization, costs, and complications (Table II). The use of administrative claims databases has reached national importance in the U.S. as evidenced by the public reporting of surgical outcomes, with strict financial penalties for underperforming institutions5,6.

TABLE I - List of Commonly Used Databases for Health Services Research in Orthopaedics*
Database Maintained By Approximate Cost for Raw Data (US$) Web Site URL
Medicare (CMS) CMS 3000-20,000 per yr (for all files)
 Part A (Inpatient) CMS
 Part B (Physician and Carrier) CMS
 Summary CMS No charge
Other insurance
 PearlDiver Analytics company 25,000-50,000 per yr
 MarketScan Analytics company 5000-20,000 per study cohort
 BCBS Insurance company 20,000->50,000 per study
 United Healthcare Insurance company 20,000->50,000 per study
 Premier Analytics company 5000-20,000 per study cohort
Discharge databases
 NIS AHRQ HCUP 50-500 per yr
 KID AHRQ HCUP 50-500 per yr
 SID AHRQ HCUP, state-specific maintenance and distribution 50-500 per yr
*CMS = Centers for Medicare & Medicaid Services, BCBS = Blue Cross Blue Shield, NIS = National Inpatient Sample, AHRQ = Agency for Healthcare Research and Quality, HCUP = Healthcare Cost and Utilization Project, KID = Kids’ Inpatient Database, and SID = State Inpatient Databases.

TABLE II - Comparison of Database Characteristics*
Database Patients Included Payers Coding Scheme Comorbidities Laboratory Results Operative Variables Follow-up
Medicare All Medicare beneficiaries CMS ICD-9 extracted No No Continuous
 Part A 100%, or samples CMS ICD-9
 Part B 100%, or samples CMS ICD-9 and CPT
 Summary All beneficiaries CMS ICD-9 and CPT
Other insurance All beneficiaries Variable ICD-9 extracted Continuous
 PearlDiver Sample Variable ICD-9 and CPT Some yrs No
 MarketScan Sample (large companies) Variable ICD-9 and CPT Some yrs No
 United Healthcare and BCBS Sample or all Variable ICD-9 and CPT Some yrs No
 Premier Variable Variable ICD-9, CPT, and database-specific variables Some yrs Some yrs
Discharge databases Definitions
 NIS 20% sample All ICD-9 No No Inpatient
 KID 20% sample All ICD-9 No No Inpatient
 SID Variable All ICD-9 No No Variable
*All databases were based on claims data, and all databases had data collection done by coders. CMS = Centers for Medicare & Medicaid Services, ICD-9 = International Classification of Diseases, Ninth Revision, CPT = Current Procedural Terminology Code, BCBS = Blue Cross Blue Shield, NIS = Nationwide Inpatient Sample, KID = Kids’ Inpatient Database, and SID = State Inpatient Databases.

Despite an impressive sample size, large-scale databases have notable limitations. Understanding the nuances of this emerging research arena is critical for surgeons, patients, hospitals, insurance companies, and policy makers. Researchers, through an in-depth understanding, must ask appropriate questions while maximizing study precision. In this two-part series, we describe the databases most commonly used for clinical orthopaedic research, while comparing the strengths and limitations of each. In Part 1, we explore the commonly used administrative claims databases in the U.S., and in Part 2, we focus on clinical registries.

Medicare Claims

The Centers for Medicare & Medicaid Services (CMS) runs Medicare, the national social insurance program that offers health care to four groups of U.S. citizens: those sixty-five years and older, the disabled, those with end-stage renal disease, and those with amyotrophic lateral sclerosis. In 1965, the U.S. Congress established Medicare as Title XVII of the Social Security Act, and on July 1, 1966, the program started7. In 2009, the Medicare and Medicaid budget was nearly $500 billion, or 5.3% of the gross domestic product, and was expected to increase to 10% by 2035. Medicare data are administrative claims data based on beneficiary program enrollment and reimbursement and/or payment information. Ultimately, the Medicare claims data set contains the richest and most complete source of inpatient and outpatient claims data, but is limited mainly to the elderly and disabled.

Data Collection

The Medicare program is organized into four main parts: Part A, hospital insurance; Part B, physician and outpatient services; Part C, Medicare Advantage plans; and Part D, prescription drug plans. When a patient enters the hospital, undergoes a procedure, or uses an outpatient service such as physical therapy, a claim is filed. Successfully processed claims are then recorded and available in one of several so-called files. The Medicare Provider Analysis and Reviews (MEDPAR) file contains Part-A data, i.e., hospital or inpatient claims, while the Outpatient and Carrier files contain Part-B data. Data sets may vary in sample size, typically from 100% to 5%, and in data elements. These smaller data sets often do not contain the same data elements, and may be used for different purposes. Limited data set files are typically deidentified, compliant with the Health Insurance Portability and Accountability Act (HIPAA), without Part-C HMO (health maintenance organization) information. Longitudinal data tracking is possible with either database, but certain analyses, such as national geographic variation, will be less robust with smaller sample sizes. Additionally, CMS has recently released various summary files8 on volumes, charges, payments, discharges, etc. These files do not typically contain any patient-specific data, and are publicly available. The Dartmouth Atlas of Health Care also provides up-to-date, publicly available Medicare data reports and analysis9. Overall, the complexity of the Medicare claims files necessitates experienced research personnel to ensure accurate analysis.

Study Types and Examples

Over three decades ago, Dartmouth researchers Wennberg and Cooper became interested in geographic variations in medical practice, patient outcomes, and health-care spending. In 1996, the Dartmouth Atlas was born—the first published, large-scale analysis of Medicare claims data9. During those years, prominent outcomes researchers developed modern methods for using administrative claims data for musculoskeletal research10,11.

The Medicare data have been used to analyze procedural volume and utilization, as well as demographics, complications, and hospital length of stay across geographic and temporal domains. Within the fields of spinal surgery12-14 and joint replacement surgery15,16, several studies that track geographic and temporal trends in procedural utilization have emerged. Many of these have demonstrated significant geographic variation12,14, sparking some important public discussion. Others have shown growing trends in national surgical volume over time15, which have raised questions about health-care access and costs. Medicare data have also been used to assess racial and geographic (urban versus rural) disparities in care17,18. CMS data also can be used for direct assessment of payment for services (charges and costs). Studies have examined geographic variation in Medicare payments for total knee arthroplasty19, while others have tracked and analyzed overall Medicare costs for diagnostic and procedural groups. As discussed above, the government has found these data reliable enough for risk-adjusted public reporting of complications and readmissions as well as for pay-for-performance models6.

Medicare claims data can also be used to track postoperative complications such as mortality, morbidity, reoperation, and readmissions. Mortality is tracked accurately by CMS through its denominator file, and has been reported after many medical events such as hip fractures20. Shorter-term complications such as reoperation after lumbar spine surgery21 and adverse events after total joint surgery22-24 have also been reported. Longer-term outcomes, such as arthroplasty survival, have been correlated with procedure duration, hospital size, and surgeon procedure volume25,26.

Strengths and Weaknesses

The Medicare database is the largest and most complete administrative claims database for patients at least sixty-five years old, with >45 million people enrolled in Medicare as of 2012. Sheer numbers garner tremendous statistical power. One of the major advantages of the Medicare data is the ability to follow patients through phases and/or facilities of care, i.e., inpatient, outpatient, pharmaceutical use, and across time. Using claims data, it is possible to follow patients longitudinally throughout encounters, irrespective of the location of the institution delivering the health care. In addition, patient data can also be linked to internal and external data sources, including the U.S. Census, cancer registries, National Death Index, other payers (i.e., Medicaid), and provider information. Use of administrative claims also holds some inherent advantages. Coding for billable medical events, readmissions, and patient mortality is considered accurate because of their legal implications and vulnerability to government audits.

Despite widespread use, the Medicare data have some distinct disadvantages (Table III). First, the data set may not be generalizable to the younger non-Medicare population. Second, the data are expensive to access, are difficult to manipulate, and require considerable expertise. Costs for a comprehensive data set alone may exceed $200,000—resources unavailable to most clinical researchers (Table I). Third, the potential variability in medical coding is arguably the biggest disadvantage. Since coders extract information from filed claims, non-billable events, such as a resident-physician hip reduction in the emergency room, may not be included. Furthermore, comorbidities or complications that increase reimbursement may be coded at higher rates than others27. While administrative claims data often correspond well with the clinical record, studies have demonstrated poor associations for certain complications28-31, such as periprosthetic joint infection32,33, wound infections, pneumonia, urinary tract infections, and myocardial infarction28. Older data sets also lacked preexisting-condition variables. Finally, Medicare data do not contain information on implant type, surgical approach, or laterality. Ultimately, understanding the limits of the Medicare data will help readers to interpret medical studies based on these data with a critical eye.

TABLE III - Strengths and Weaknesses of National Databases*
Database Time Trends Geographic Variation Patient Comorbidities Inpatient Complications Short-Term Complications Long-Term Complications Financial Analysis Accessibility
Medicare ++ ++ + + + + + ++ – –
 Part A ++ ++ + + + + + ++ – –
 Part B ++ ++ + + + + + ++ – –
 Summary ++ + + ++ +
Other insurance
 PearlDiver + + + + + ++ +
 MarketScan + + + + + ++ +
 United Healthcare and BCBS + + + + + ++
 Premier + + + + + ++ +
Discharge databases
 NIS + + + + + ++
 KID + + + + + ++
 SID + + + + ++
*Values within the table correspond to how appropriately each database can evaluate various study questions. – – = unable to answer question (strong weakness); – = poorly suited to answer question, but can in limited fashion (minor weakness); + = able to answer question, but better database exists (minor strength); and ++ = best suited to answer question (major strength). BCBS = Blue Cross Blue Shield, NIS = National Inpatient Sample, KID = Kids’ Inpatient Database, and SID = State Inpatient Databases.

Other Insurance Databases

Claims data from the private insurance market have also been used to study orthopaedic disease. Within the last few years, insurance claims data from major U.S. insurers have become available for purchase and analysis. As hospitals and health systems continue to merge, there is considerable potential for medical research extracted from the electronic records of millions of patients. It is important to recognize the substantial heterogeneity that exists between these insurance databases, making generalizations difficult. Some of these data sets contain patient information from large private employers, CMS, private insurers, and an assortment of other payers.


PearlDiver is a health analytics company that has built and maintains one of the largest health-care databases, with >1 billion U.S. records. The company has collected private insurance claims (Humana and United Healthcare), government claims (Medicare), and other databases such as the National Inpatient Sample (NIS) in a simplified, deidentified format. PearlDiver provides a unique reorganization of data into simplified fields, called “buckets,” that allow users to track utilization and complication trends across time.

Study Examples

The PearlDiver database contains information pertaining to procedure volumes, patient demographics, and charge and reimbursement information. Previous studies within orthopaedics have analyzed trends in surgical treatment, including rotator cuff disease34, hip arthroscopy35, ankle replacement36, and meniscal surgery37. Also, the private insurer records have allowed trending of pediatric orthopaedics issues such as treatment trends of adolescent clavicle fractures38.

Strengths and Weaknesses

The PearlDiver database provides users a simplified platform that an orthopaedic surgeon may learn to use. The ability to query national data cohorts from multiple payers implies the results may be more generalizable. The ability to track complications between encounters offers a more realistic look at short-term complications. As with most administrative data, reliance on claims coding serves as a limitation. Unlike the CMS MEDPAR files, PearlDiver data are deidentified, meaning that individual patients cannot easily be tracked. This limits linkage and comparative studies of procedures and/or complications. Additionally, the database contains private insurance claims (a majority from United Healthcare) that may overrepresent the southern U.S. states. The higher costs also serve as a considerable barrier to use39.


MarketScan is a group of commercially available U.S. databases maintained by Truven Health Analytics. The data sets are primarily based on claims and include millions of unique, deidentified patient records. The majority of the data comes from large private employers, but records from managed care organizations, hospitals, and CMS are also included. The data sets are primarily marketed at pharmaceutical and biotechnology clients for use in understanding volume, trends, and the value of their products.

Study Examples

The addition of variables unavailable from traditional claims data, combined with the ability to perform longitudinal follow-up, provides investigators a powerful tool to examine procedure trends, outcomes, and efficacy and to perform cost analysis. Within orthopaedics, for example, the MarketScan database has been used to examine trends in osteoporosis management following fragility fractures40. Another study, using more advanced statistical methods, has compared complications, reoperations, and health-care resource utilization for different surgical treatments for lumbar spondylolisthesis41.

Strengths and Weaknesses

Some of the database strengths include its large sample size, with data on >180 million unique patients included since 1995. The database captures patient data through complete episodes of care, from outpatient to inpatient services, allowing robust longitudinal tracking at the patient level. Payment and charge information are also available. In addition to some of the claims-based data limitations, the MarketScan data set has some unique limitations. These include the heavier representation of data from large employers (versus smaller employers), nonrandomized beneficiary sampling, limited laboratory-related data that are available only after 2006, and considerable complexity and cost associated with data use and analysis. The data sets can also be quite costly, with data user agreements that often require payment for each study performed.

United Healthcare and Blue Cross Blue Shield

Claims data from private insurers, obtained either directly from the insurer or through private analytics companies, have been used for orthopaedic research42-44. Blue Cross Blue Shield maintains a comprehensive database of its integrated U.S. medical and pharmacy claims called Blue Health Intelligence. While its analytic services are mainly targeted at the pharmaceutical and medical industry, its data have been used to perform cost analysis between procedures, such as those in the cervical spine42.

Study Examples

The database has been used to demonstrate trends in procedure volume and cost analysis. For example, one study linked data on United Healthcare patients from the United Healthcare Database to those in PearlDiver to examine the impact of the American Academy of Orthopaedic Surgeons Clinical Practice Guidelines for nonarthroplasty treatment of knee osteoarthritis43.

Strengths and Weaknesses

Strengths of these databases include the large record size, with claims data on millions of patients. The United Healthcare Database, for example, contains data on inpatient, outpatient, and physician charges and encompasses an estimated 10% of the private insurance market in the U.S. Limitations of these data sets include their extremely high costs, limited availability, and difficulty in data analytics.


Premier is a for-profit health-care company providing an array of performance solutions to hospitals and other providers. Similar to PearlDiver, they provide analytic services using administrative data from a variety of hospital-level data sources. Premier had early exposure to the quality movement with a CMS partnership to create the Premier Hospital Quality Incentive Demonstration (HQID) in 2003, which included hip and knee replacement surgery45. Sources report that the Premier database queries millions of patients discharged annually and includes data from >600 U.S. hospitals, both private and academic46. Premier has been recognized by both the National Quality Forum and CMS for their work in advancing patient safety and hospital quality.

Study Examples

Overall, the Premier data set is very similar to other administrative claims data, but the company continues to add new data from a variety of private insurers. Much of these added data contain more specific implant, pharmaceutical, and cost information. Within the orthopaedic literature, several studies have used Premier data to assess venous thromboembolism rates, costs, and prophylaxis46-48.

Strengths and Weaknesses

Some notable advantages of this database include large patient numbers as well as pharmaceutical and cost information. Premier’s data also have implant charge codes that allow the identification of manufacturer and/or brand-specific devices, thereby allowing some comparative assessments between products46. The main limitations, as with Medicare, are reliance on ICD-9 codes and the risks of coding errors. Its data projects, similar to those of MarketScan, are also costly.

Encounter-Level Discharge Databases

Healthcare Cost and Utilization Project (HCUP)

The HCUP collects deidentified patient information from patient discharges at the state and national level and includes all insurance payer types. It was created in 1988, and is overseen by the U.S. federal government under the umbrella of the Agency for Healthcare Research and Quality (AHRQ)49. The purpose of the project is to gather information regarding medical outcomes, health-care utilization, and practice variations at the state and national level. Information from participating centers is organized into state-level and national-level databases, with further separation based on emergency department, ambulatory care center, or inpatient hospital stays. The HCUP family comprises several databases, of which the most commonly used include the NIS, Kids’ Inpatient Database (KID), and State Inpatient Databases (SID).

National Inpatient Sample

The NIS is the largest national all-payer database for inpatient hospital stays50 and is updated annually. With the release of the 2012 NIS data set, the Nationwide Inpatient Sample was renamed the National Inpatient Sample and its sampling approach was also altered. From 2012 alone, approximately 7.3 million discharges were captured from 4378 hospitals in forty-four states51. The purpose of the NIS is to provide national estimates of hospital discharges across the U.S. for all patient ages. Participating hospitals are stratified according to size and geographic location51, for which a randomly generated 20% sample of all discharges is evaluated. A multiplier specific to each hospital stratum is then applied in order to provide estimates of national averages.

Study Types and Examples

The NIS has been used to assess disease and procedural trends, inpatient complications, and resource utilization. Several studies have evaluated temporal trends in procedure use, such as those done after spine fusions52 or total joint arthroplasty53 or those involving rare diseases, e.g., total joint arthroplasty in patients with HIV (human immunodeficiency virus)54. Conditions or procedures of interest to orthopaedic surgeons, with an assigned ICD-9 code, have been frequently studied using the NIS. For example, in 2002, recombinant human bone morphogenetic protein-2 (rhBMP-2) was introduced (with an ICD-9 code), and to date, dozens of studies have emerged detailing national use, trends, procedural indications, and associated costs55-57. Trends in racial, economic, and geographic disparities have been investigated using the NIS.

While the NIS reports on only inpatient complications, these included data have been heavily used. Over 100 variables are collected for each inpatient stay, encompassing patient demographics, medical comorbidities, and morbidity outcomes, as well as hospital characteristics and financial information. Using relatively simple methods, baseline inpatient complication rates, including mortality, pulmonary embolism, and deep vein thrombosis58 after given procedures, have been reported. Studies have compared the effect of time of year (i.e., “July effect”)59, day of the week (i.e., weekend)60, type of hospital, and choice of procedure on these outcomes. Additionally, the NIS has been used to assess differences in outcomes and utilization over time, usually before and after an important political event such as changes to resident work hours61 or the recent economic downturn62. The NIS also includes data on inpatient charges that can be converted to costs using hospital-level CMS cost-to-charge ratios (supplied by HCUP). Several studies have used this method to report mean costs for surgeries such as total knee arthroplasty63 or the marginal effect of comorbidities on hospital resource utilization after hip fracture surgery64 or total knee arthroplasty65.

Strengths and Weaknesses

The NIS has been arguably the most frequently used database within the orthopaedic literature. Its low cost, accessibility, and relatively concise data files have led to its rapid, widespread adoption. The NIS is ideally suited for investigating epidemiologic trends in medical practice for conditions and procedures and is strengthened by a large, diverse patient cohort, inclusive of all insurance payers. The primary weakness of the NIS is its reliance on ICD-9 billing codes for diagnoses, procedures, and postoperative complications. Furthermore, the NIS is not as well suited for investigating postoperative adverse outcomes because of the poor quality of data capture and its limited recording of inpatient-only events29,30,66. Discrepancy between the NIS and the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) in reporting short-term outcomes has been recently identified after spine fusion and hip fracture procedures67,68 and highlights the need for improvements in data collection methodology and quality control processes for coding events.

Kids’ Inpatient Database

The KID, while very similar to the NIS, provides national estimates for rare pediatric conditions69 not adequately captured by other national data sets. The KID is the only national all-payer database in the U.S. and utilizes a unique sampling methodology. First published in 1997, the KID is updated every three years and, in 2009, captured data on 2 to 3 million discharges from 4121 hospitals located in forty-four states. Patients up to the age of eighteen years were included in 1997, and those up to the age of twenty years were included in subsequent iterations. The data elements collected are the same as for the NIS. Although medical researchers have used the KID commonly70-74, its adoption in the orthopaedic literature has lagged.

State Inpatient Databases

The SID represents a conglomerate of state discharge databases that are consolidated into a uniform format for comparison and incorporation into the NIS. While these databases operate under the HCUP umbrella, participation may vary by state. State inpatient databases are available for >90% of the states from HCUP. Data collection is similar to the NIS, but some states collect additional information. Differences in variables, length of follow-up, and complication data collected exist. The strengths and limitations of the SID mirror those of the other HCUP databases. In addition, some states also have separate clinical registries that run independent of the SID. Coding accuracy remains a concern for these databases. In Part II, we begin with an overview of these state registries. Many of the state registries have considerable overlap with their respective SID.

Future Directions

Over the last decade, administrative claims data have been a major source for orthopaedics-related observational studies. Clearly, these databases have taken on an expanding and influential role within the orthopaedic surgery literature (Table IV; see Appendix). Large sample sizes across many years and excellent capture of billable procedures or complications such as mortality remain the major advantages of their use. A predominant reliance on billable claims captured by coders from the medical record as well as high costs of data purchase and analysis remain major limitations. The lack of standardization, however, between these databases often makes critical study appraisal difficult. Discrepancy between databases in reporting short-term outcomes has been recently highlighted after orthopaedic surgical procedures such as spine fusion and hip fractures in several recent publications67,68. This discordance heightens the importance of understanding the context of each of these data sources, and why differences may exist.

TABLE IV - Number of Administrative Claims Studies Published in The Journal of Bone & Joint Surgery (American Volume) from January 1, 2012, through October 1, 2014*
Database No. of Studies in JBJS
Medicare (CMS) 10
Other insurance
 PearlDiver 0
 MarketScan 3
 United Healthcare and BCBS 0
 Premier 0
Discharge databases
 NIS 13
 KID 0
 SID 4
*CMS = Centers for Medicare & Medicaid Services, BCBS = Blue Cross Blue Shield, NIS = National Inpatient Sample, KID = Kids’ Inpatient Database, and SID = State Inpatient Databases.

As a final thought, with the controversial but inevitable conversion of U.S. hospitals to ICD-10 (mandated by CMS), claims-based research will gain much-needed diagnostic and procedural precision (such as laterality). ICD-10 will have a tremendous impact on health services research, as new data complexities will arise and require thoughtful solutions. Assuming the ICD-10 coding can be done accurately, these new data will allow health service researchers to answer previously unanswerable questions. In conclusion, we attempted to provide insight into the origin, common uses, and strengths and weakness of the commonly cited administrative claims databases. In Part II, we explore the use of clinical registries.


The following is a list of all database-related publications in JBJS (American Volume) from January 1, 2012, through October 1, 2014.

Government Claims

Medicare and CMS

Kelly MP, Savage JW, Bentzen SM, Hsu WK, Ellison SA, Anderson PA. Cancer risk from bone morphogenetic protein exposure in spinal arthrodesis. J Bone Joint Surg Am. 2014 Sep 3;96(17):1417-22.

Bozic KJ, Grosso LM, Lin Z, Parzynski CS, Suter LG, Krumholz HM, Lieberman JR, Berry DJ, Bucholz R, Han L, Rapp MT, Bernheim S, Drye EE. Variation in hospital-level risk-standardized complication rates following elective primary total hip and knee arthroplasty. J Bone Joint Surg Am. 2014 Apr 16;96(8):640-7.

Harris-Hayes M, Willis AW, Klein SE, Czuppon S, Crowner B, Racette BA. Relative mortality in U.S. Medicare beneficiaries with Parkinson disease and hip and pelvic fractures. J Bone Joint Surg Am. 2014 Feb 19;96(4):e27.

Bolognesi MP, Greiner MA, Attarian DE, Watters TS, Wellman SS, Curtis LH, Berend KR, Setoguchi S. Unicompartmental knee arthroplasty and total knee arthroplasty among Medicare beneficiaries, 2000 to 2009. J Bone Joint Surg Am. 2013 Nov 20;95(22):e174.

Chen AT, Cohen DB, Skolasky RL. Impact of nonoperative treatment, vertebroplasty, and kyphoplasty on survival and morbidity after vertebral compression fracture in the Medicare population. J Bone Joint Surg Am. 2013 Oct 2;95(19):1729-36.

Miller BJ, Lu X, Cram P. The trends in treatment of femoral neck fractures in the Medicare population from 1991 to 2008. J Bone Joint Surg Am. 2013 Sep 18;95(18):e132.

Ruiz D Jr, Koenig L, Dall TM, Gallo P, Narzikul A, Parvizi J, Tongue J. The direct and indirect costs to society of treatment for end-stage knee osteoarthritis. J Bone Joint Surg Am. 2013 Aug 21;95(16):1473-80.

Katz JN, Wright EA, Wright J, Malchau H, Mahomed NN, Stedman M, Baron JA, Losina E. Twelve-year risk of revision after primary total hip replacement in the U.S. Medicare population. J Bone Joint Surg Am. 2012 Oct 17;94(20):1825-32.

Wolf BR, Lu X, Li Y, Callaghan JJ, Cram P. Adverse outcomes in hip arthroplasty: long-term trends. J Bone Joint Surg Am. 2012 Jul 18;94(14):e103.

Bozic KJ, Lau E, Kurtz S, Ong K, Rubash H, Vail TP, Berry DJ. Patient-related risk factors for periprosthetic joint infection and postoperative mortality following total hip arthroplasty in Medicare patients. J Bone Joint Surg Am. 2012 May 2;94(9):794-800.

Private Claims


Cole T, Veeravagu A, Jiang B, Ratliff JK. Usage of recombinant human bone morphogenetic protein in cervical spine procedures: analysis of the MarketScan longitudinal database. J Bone Joint Surg Am. 2014 Sep 3;96(17):1409-16.

Balasubramanian A, Tosi LL, Lane JM, Dirschl DR, Ho PR, O’Malley CD. Declining rates of osteoporosis management following fragility fractures in the U.S., 2000 through 2009. J Bone Joint Surg Am. 2014 Apr 2;96(7):e52.

Lad SP, Babu R, Baker AA, Ugiliweneza B, Kong M, Bagley CA, Gottfried ON, Isaacs RE, Patil CG, Boakye M. Complications, reoperation rates, and health-care cost following surgical treatment of lumbar spondylolisthesis. J Bone Joint Surg Am. 2013 Nov 6;95(21):e162.

Discharge Databases


Saleh A, Small T, Chandran Pillai AL, Schiltz NK, Klika AK, Barsoum WK. Allogenic blood transfusion following total hip arthroplasty: results from the Nationwide Inpatient Sample, 2000 to 2009. J Bone Joint Surg Am. 2014 Sep 17;96(18):e155.

Yoshihara H, Yoneoka D. Trends in the incidence and in-hospital outcomes of elective major orthopaedic surgery in patients eighty years of age and older in the United States from 2000 to 2009. J Bone Joint Surg Am. 2014 Jul 16;96(14):1185-91.

Odum SM, Springer BD. In-hospital complication rates and associated factors after simultaneous bilateral versus unilateral total knee arthroplasty. J Bone Joint Surg Am. 2014 Jul 2;96(13):1058-65.

Goz V, McCarthy I, Weinreb JH, Dallas K, Bendo JA, Lafage V, Errico TJ. Venous thromboembolic events after spinal fusion: which patients are at high risk? J Bone Joint Surg Am. 2014 Jun 4;96(11):936-42.

Derman PB, Fabricant PD, David G. The role of overweight and obesity in relation to the more rapid growth of total knee arthroplasty volume compared with total hip arthroplasty volume. J Bone Joint Surg Am. 2014 Jun 4;96(11):922-8.

Jain A, Hassanzadeh H, Strike SA, Skolasky RL, Riley LH 3rd. rhBMP use in cervical spine surgery: associated factors and in-hospital complications. J Bone Joint Surg Am. 2014 Apr 16;96(8):617-23.

Kurtz SM, Ong KL, Lau E, Bozic KJ. Impact of the economic downturn on total joint replacement demand in the United States: updated projections to 2021. J Bone Joint Surg Am. 2014 Apr 16;96(8):624-30.

Zahir U, Sterling RS, Pellegrini VD Jr, Forte ML. Inpatient pulmonary embolism after elective primary total hip and knee arthroplasty in the United States. J Bone Joint Surg Am. 2013 Nov 20;95(22):e175.

Odum SM, Troyer JL, Kelly MP, Dedini RD, Bozic KJ. A cost-utility analysis comparing the cost-effectiveness of simultaneous and staged bilateral total knee arthroplasty. J Bone Joint Surg Am. 2013 Aug 21;95(16):1441-9.

Jain A, Kebaish KM, Sponseller PD. Factors associated with use of bone morphogenetic protein during pediatric spinal fusion surgery: an analysis of 4817 patients. J Bone Joint Surg Am. 2013 Jul 17;95(14):1265-70.

Lin CA, Kuo AC, Takemoto S. Comorbidities and perioperative complications in HIV-positive patients undergoing primary total hip and knee arthroplasty. J Bone Joint Surg Am. 2013 Jun 5;95(11):1028-36.

Losina E, Thornhill TS, Rome BN, Wright J, Katz JN. The dramatic increase in total knee replacement utilization rates in the United States cannot be fully explained by growth in population size and the obesity epidemic. J Bone Joint Surg Am. 2012 Feb 1;94(3):201-7.

Nikkel LE, Fox EJ, Black KP, Davis C, Andersen L, Hollenbeak CS. Impact of comorbidities on hospitalization costs following hip fracture. J Bone Joint Surg Am. 2012 Jan 4;94(1):9-17.

SID (California)

Petrigliano FA, Bezrukov N, Gamradt SC, SooHoo NF. Factors predicting complication and reoperation rates following surgical fixation of proximal humeral fractures. J Bone Joint Surg Am. 2014 Sep 17;96(18):1544-51.

Meehan JP, Danielsen B, Kim SH, Jamali AA, White RH. Younger age is associated with a higher risk of early periprosthetic joint infection and aseptic mechanical failure after total knee arthroplasty. J Bone Joint Surg Am. 2014 Apr 2;96(7):529-35.

Wang Z, Chen F, Ward M, Bhattacharyya T. Compliance with surgical care improvement project measures and hospital-associated infections following hip arthroplasty. J Bone Joint Surg Am. 2012 Aug 1;94(15):1359-66.

Gay DM, Lyman S, Do H, Hotchkiss RN, Marx RG, Daluiski A. Indications and reoperation rates for total elbow arthroplasty: an analysis of trends in New York State. J Bone Joint Surg Am. 2012 Jan 18;94(2):110-7.

Other Databases

National Electronic Injury Surveillance System

Stoneback JW, Owens BD, Sykes J, Athwal GS, Pointer L, Wolf JM. Incidence of elbow dislocations in the United States population. J Bone Joint Surg Am. 2012 Feb 1;94(3):240-5.

National Survey of Ambulatory Surgery

Colvin AC, Egorova N, Harrison AK, Moskowitz A, Flatow EL. National trends in rotator cuff repair. J Bone Joint Surg Am. 2012 Feb 1;94(3):227-33.

Surveillance, Epidemiology and End Results

Miller BJ, Cram P, Lynch CF, Buckwalter JA. Risk factors for metastatic disease at presentation with osteosarcoma: an analysis of the SEER database. J Bone Joint Surg Am. 2013 Jul 3;95(13):e89.

Investigation performed at the Department of Orthopaedic Surgery and Rehabilitation, University of Iowa Hospitals and Clinics, Iowa City, Iowa

Disclosure: None of the authors received payments or services, either directly or indirectly (i.e., via his or her institution), from a third party in support of any aspect of this work. One or more of the authors, or his or her institution, has had a financial relationship, in the thirty-six months prior to submission of this work, with an entity in the biomedical arena that could be perceived to influence or have the potential to influence what is written in this work. Also, one or more of the authors has had another relationship, or has engaged in another activity, that could be perceived to influence or have the potential to influence what is written in this work. The complete Disclosures of Potential Conflicts of Interest submitted by authors are always provided with the online version of the article.


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