J ClinEpidemiolVol. 45,No. 7,pp. 703-714,1992 Printed in Great Britain. All rights reserved
0895-4356/92 $5.00f0.00
Copyright 0 1992Pergamon Press Ltd
IDENTIFICATION OF FRACTURES FROM COMPUTERIZED MEDICARE FILES WAYNE
A. RAY,* MARIE R. GRIFFIN,RANDY L. FOUGHTand MARGARETL. ADAMS
Department
of Preventive
Medicine, Vanderbilt University TN 37232, U.S.A.
School of Medicine, Nashville,
(Received 4 July 1991; received for publication 24 February 1992)
Abstract-Study of non-hip fractures, which are a serious public health problem for persons > 65 years of age, has been hindered by the absence of an economical method for case identification. We assessed the utility of computerized Medicare inpatient, emergency room, hospital outpatient department and physician claims for identifying fractures in an elderly Tennessee Medicaid population. We used these files for 1987 to identify 3086 possible fractures and reviewed medical records for a sample of 1440. Using this sample, we developed a definition of probable fractures that excluded claims unlikely to represent newly diagnosed fractures. For all fractures, this definition had a positive predictive value of 94%, which for individual fracture sites, ranged from 79% (tibia/fibula) to 98% (hip). Of fractures in the reviewed sample, 91% were identifed as probable fractures; this upper bound for sensitivity varied between 75% (femoral shaft) and 100% (patella). These data suggest that computerized Medicare files can be used for rapid and economical fracture ascertainment among persons 265 years of age. However, further work is needed to obtain better estimates of sensitivity.
restriction or a physician visit were at sites other than the hip [6]. In a recent study of fall-related injuries in Miami Beach, Florida residents 2 65 years of age, 70% of fractures were at sites other than the hip [14]. Because these other types of fractures have been less extensively studied, there are many unresolved questions concerning their epidemiology, etiology, and consequences [ 11. Because fractures generally are acute, painful injuries that are readily diagnosed and promptly treated, it should be possible to identify them from computerized medical record databases that include both inpatient and outpatient data. Computerized Medicare files [16], which now include claims from inpatient and outpatient providers for 97% of persons > 65 years of age in the U.S. [17], may thus be a valuable resource for fracture epidemiology. We utilized a database developed for epidemiologic research that includes computerized Medicare files [18], to determine whether or not these files could be
INTRODUCTION
Skeletal fractures are a major public health problem for older persons [l, 21. Adverse pronounced for are most consequences fractures of the proximal femur (hip) [3-6], which have been relatively well studied [7-l l] because 95% or more of hip fractures are treated in the hospital [ 12, 131and computerized records of hospital discharges are readily available. However, these databases have limited utility for research concerning fractures at other sites which are more often treated on an outpatient basis [12, 141. These other fractures are also associated with substantial disability and cost [15]. Moreover, they account for the majority of fractures in persons 265 years of age. The 1977 National Health Interview Survey found that 80% of non-vertebral fractures in this age group resulting in activity *Author for correspondence. 703
WAYNE
704
A.
used to reliably identify fractures at sites other than the proximal femur.
METHODS
In this study, we developed a definition to identify probable fractures from computerized Medicare claims and estimated its positive predictive value and sensitivity for the population of Tennessee Medicaid enrollees 265 years of age. Ideally, the study population would have consisted of a random sample of Medicare enrollees. However, we chose the Medicaid population (virtually all of whom are enrolled in Medicare) because Medicare files had been obtained for this population and were organized in a format efficient for research [ 181 and we had permission from the state Medicaid program to contact providers and request access to the medical records of Medicaid enrollees.
k4Y
et al.
surgical procedures (the admission diagnosis is not currently available in Medicare inpatient files maintained by the Health Care Financing Administration). Emergency room and clinic records each have ICD-9-CM codes for up to five diagnoses and one surgical procedure. Part B files consist of claims from physicians. We obtained these files from the state Medicaid program, which regularly obtains these claims for Medicaid enrollees from the Part B Medicare carriers for Tennessee. There is a record for each physician service, which includes the physician’s number and specialty code, the patient’s Medicaid number, the service date, and a Phys ician’s Current Procedural Terminology, 4th edition (CPT4) [20] procedure code. At the time of this study, diagnoses were not required for Medicare Part B physician claims. However, diagnostic data will be available in these files in 1991. Study population
Sources of data
The study population was identified from the Medicaid enrollment file [18], which includes Medicaid identification number, the basis of eligibility for Medicaid enrollment, demographic characteristics, personal identifiers, and periods of enrollment. This file has been linked with death certificates to obtain the date and coded underlying cause of death. Medical care for the diagnosis or treatment of fractures was identified from Medicare claims files, which consist of records of claims for medical services submitted by providers and subsequently reimbursed by Medicare. Because Medicare consists of two distinct, separately administered programs [19] (Part A, which is hospital insurance, and Part B, which is supplemental medical insurance), there are two types of claims files. Part A files consist of claims from hospitalbased providers: hospitals, emergency rooms, and hospital outpatient departments (clinics). We obtained these files directly from the Part A fiscal intermediary for Tennessee and linked them with Medicaid data. There is a record for each provider encounter (hospital stay or emergency room or clinic visit), which includes the provider number, the patient’s Medicaid number and the dates of the encounter. Hospital records have International Classification of Diseases, 9th revision, Clinical Modifications (ICDg-CM) codes for up to six diagnoses (one admission and five discharge) and up to three
The study population consisted of Tennessee Medicaid enrollees who were 265 years of age on 1 January 1987 (16% of Tennesseans in this age group), were enrolled continuously in Medicaid for at least 365 days, and had no claim suggesting occurrence of a fracture in the preceding 31 days. Follow-up extended through 31 December 1987 unless terminated earlier by loss of Medicaid enrollment or death. Fracture identification Possible fracture claims. For persons in the
study population, we identified Medicare hospital, emergency room, clinic, and physician claims with diagnosis or procedure codes that could indicate a fracture (Appendix). For each person, we identified the first such claim in 1987 and all such claims in the next 7 days. From these claims, we selected the one thought to be most informative (see Appendix) as the possible fracture claim, which was used to determine fracture sites(s) and to identify medical records for review. However, the service date from the first claim in this group was used to determine the index date, which was our estimate of the date of fracture occurrence. For example, consider an emergency room visit with a diagnosis of fracture of the hip followed by a hospital admission the next day with the same diagnosis. The hospital claim would be used to assign fracture site and, if this possible fracture was selected for medical record review, the hospital chart would be sought. However, the index date
Identifying Fractures
would be the date of the emergency room visit. Other claims in this group also could be used to provide additional confirmation that a fracture had occurred or assist in determination of fracture site. An example would be an emergency room visit with a diagnosis of “Fracture of unspecified bones” (ICD-9-CM code 829) with a concurrent physician claim with a procedure of “treatment of closed humeral shaft fracture” (CPT4 code 24500). Fracture sites. A possible fracture claim usually represented occurrence of a single fracture, the site of which could be readily determined from the diagnosis. However, a single injury event (fall, motor vehicle crash) can cause multiple fractures that are treated by a single provider and thus generate only one possible fracture claim. We therefore developed an algorithm (Appendix) to determine the number of possible fractures and their sites. The algorithm generally assigned one site for each different diagnosis on the possible fracture claim. Thus, a person with an inpatient possible fracture claim having diagnoses of fracture of the hip and fracture of the radius/ulna would generate two possible fractures. The primary exceptions (Appendix) were claims that did not have fracture diagnoses, in which case procedures were used, or claims that involved sites where the ICD-9-CM codes were consistently inaccurate. For example, the ICD-9-CM rules specify that a diagnosis of “wrist fracture” be coded as a carpal fracture (code 8 14.0); however, we found this generally referred to fractures of the distal radius/ulna (codes 813.4, 813.5). Other exceptions were designed to correct errors in referring to bones with similar names (phalanges of the hand vs those of the foot) and to reduce the known problem of miscoding hip fractures as femoral shaft fractures [2 I]. Probable fractures. We used the information from medical records (see below) to develop criteria (Appendix) designed to exclude possible fractures identified by the computer that were unlikely to represent newly diagnosed fractures. Probable fractures were then defined as possible fractures that did not meet any of these exclusion criteria. The primary exclusions were clinic fracture diagnoses with no corresponding procedure for fracture treatment, hospital admission fracture diagnoses with no fracture discharge diagnosis or fracture treatment procedure, and diagnoses and procedures indicating care of an old fracture or arthroplasty for treatment of arthropathy.
705 Possible Fractures 3066/2Q?3 Not Pre.,,, 6881666
Pzq
239612306
p;;
EliQible for Sample 6261606
I
Sampled 2931279
I
Eligible for Sample 229412202
i &pled 1404/1351
I
Revibwed
Reviewed
129/l 27
1311/125Q
Fig. 1. Sampling procedure for possible fractures, by whether or not the possible fracture met the study definition of a probable fracture. The numbers are n fractures/n persons. Tennessee Medicaid Fracture Study, 1987.
Medical record review. There were 2973 persons in the study with a possible fracture claim, who had a total of 3086 possible fractures. We reviewed medical records for a sample of these possible fractures (Fig. 1) to develop the probable fracture definition and to estimate its positive predictive value and sensitivity. We used a cluster sampling technique [22] designed to obtain a 50% sample of possible fractures while minimizing the number of providers to be visited. A cluster was defined as all possible fractures from a single provider. Because we did not have access to physician records, the sample was restricted to the 2922 possible fractures where the possible fracture claim was from a hospitalbased provider. Of these, 1697 (58%) were included in the sample (Fig. 1). A trained nurse attempted to obtain the medical record for each of these and to complete a structured study form. Reviews were completed for 1440 (85%). The completion rate varied by provider type: 96% for hospitals (n = 973 possible fractures in sample), 88% for emergency rooms (n = 462), and 38% for clinics (n = 262). A primary reason for the low completion rate for clinics was the large number of referrals for radiologic examination with no radiology report or physician diagnosis kept in the clinic chart. The reviewer recorded any fracture(s) newly diagnosed in the 14 days preceding the index date and their site(s), cause, date, place of occurrence, and the patient residence on the fracture date. If no fracture had been diagnosed, the abstractor noted the reason for the provider encounter.
WAYYNE A. RAY et al.
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Analysis The primary analysis was calculation of the positive predictive value and estimation of the sensitivity of the probable fracture definition for the specific fracture sites. The positive predictive value was the proportion of probable fractures where the occurrence of a fracture at the site indicated by the claims was confirmed from the medical records. Positive predictive values were computed from the sample of probable fractures for which these reviews were completed. We could not calculate the true sensitivity of the probable fracture definition because we did not prospectively ascertain fractures for the study population. However, for fractures identified through review of medical records we calculated the proportion that also were identified by the probable fracture definition. This estimated sensitivity is an upper bound for the true sensitivity and describes the penalty associated with making the probable fracture definition more stringent. Estimated sensitivity could have been calculated directly from the sample of possible fractures for which record review was completed. However, the completion rate was lowest for possible fractures that did not meet the definition of a probable fracture (Fig. 1), which was primarily due to the large number of clinic records in this category that had insufficient information to determine whether or not a fracture had occurred. Since these possible, nonprobable fractures contain the false negatives, undersampling of this category would cause sensitivity to be overstated. Thus, sensitivity estimates were adjusted to compensate for undersampling.
To assess the effects of selecting providers for the sample based upon number of possible fractures, we calculated rates adjusted for provider size. Since these did not differ materially from the unadjusted rates, only the latter are presented. In all analyses, the unit of analysis is the individual fracture rather than the person. This does not affect the site-specific analysis because each person can have at most one fracture per site in the study. However, a person may contribute more than one fracture to the all-site analyses. For these analyses, we performed alternative calculations using the person rather than the fracture as the unit of analysis. Because this did not alter any of our findings materially, only the fracture-based analyses are presented.
RESULTS
Medical record review was completed for 1311 probable fractures and 129 possible, but not probable fractures. Of the probable fractures, 1228 (94%) were confirmed by medical record review; the remainder were either not a fracture (n = 53, due to 39 instances of suspected, but not confirmed fractures, 4 visits for fracture follow-up care, 2 non-fracture injuries, and 8 visits for other reasons) or had the wrong site assigned by our algorithm (n = 30). Of the non-probable fractures, 18 (14%) were confirmed as fractures by medical record review, 103 were not fractures (54 suspected fractures, 7 visits for fracture follow-up care, 2 other injuries, and 40 visits for other reasons) and 8 had the wrong site assigned by our algorithm.
Table I. Positive predictive value and estimated sensitivity of the probable fracture definition. Tennessee Medicaid Fracture Study, 1987 Probable fracture with record review completed (n) Hip Radius/ulna Humerus Ribs/sternum Pelvis Ankle Femoral shaft Hand Tibia/fibula Skull/face Foot Clavicle/scapula Patella All
538 162 109 107 67 69 53 43 47 38 40 21 17 1311
New fracture (%) 99 97 97
84 94 97 96 86 96 92 95 100 94 96
Positive predictive value (%)
Estimated sensitivity (%) 97
95
84 93 96 :‘6 79 89 t: 82 94
E 82 89 78 75
87 87 97 90 91 100 91
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Table 2. Positive predictive value (PPV) and estimated sensitivity (SENS) of the probable fracture definition, by patient demographic characteristics. Tennessee Medicaid Fracture Study, 1987 Hip
Other sites
All sites
n
PPV
SENS
n
PPV
SENS
n
PPV
SENS
98 440
100.0 97.3
100.0 96.1
122 651
90.2 90.9
91.2 86.4
220 1092
94.5 93.5
94.5 89.9
92 236 210
97.8 98.7 96.7
95.4 97.7 96.5
252 304 217
92.9 90.1 89.4
91.3 86.6 81.1
344 541 427
94.2 93.9 93.0
92.3 91.0 87.7
420 84
98.1 96.4
97.1 96.2
575 136
90.6 91.9
87.7 88.5
996 220
93.8 93.6
91.2 91.3
Urban Suburban Rural
155 192 191
97.4 97.9 97.9
99.3 93.0 100.0
244 306 223
89.8 91.2 91.5
88.3 86.4 89.6
399 498 415
92.7 93.8 94.5
92.3 88.9 93.3
NH index date No Yes
382 156
98.2 96.8
97.7 94.0
618 155
91.1 89.7
89.1 80.3
1001 311
93.8 93.2
92.1 86.4
Sex
Male Female
Age 65-74 75-84 285 Race *
White Non-white County
*Excludes 96 persons with unknown race.
The positive predictive value of the probable fracture definition (Table 1) was 295% for the hip, radius/ulna, humerus, ankle, and foot, and was 285% for all sites except the ribs/sternum (84%), patella (82%), and the tibia/fibula (79%). The major source of error for the ribs/sternum was encounters where fractures were suspected but not conclusively diagnosed and for the tibia/fibula the primary problem was that the ankle was the fracture site indicated by medical record review (13% of probable tibia/fibula fractures). The probable fracture definition had an overall estimated sensitivity of 91% (Table 1). The estimated sensitivity was 285% for all sites except for rib/sternum (82%), where the primary source of false negatives was clinic claims with a fracture diagnosis but no confirmatory treatment procedure; ankle (78%), which was often misclassified in the computer as tibia/fibula; and femoral shaft (75%), which was often misclassified as hip. The positive predictive value and estimated sensitivity of the probable fracture definition did not vary materially across subgroups defined by sex, age, race, county of residence, and nursing home status (Table 2). For the 1228 probable fractures confirmed by medical record review, we assessed the concordance of the index date from the fracture claim with the date of the fracture from the medical record. These dates were the same for 70% of
persons, within 1 day for 85%, and within 7 days for 97%. The agreement of index and fracture dates did not vary materially by fracture site. To assess the need for non-inpatient data for identifying fractures, we calculated the proportion of all probable fractures (n = 2398) that were identified from emergency room, clinic, or physician claims (Table 3). Thirty-seven percent of all probable fractures were so identified. Hip (5%), pelvic (14%), and femoral shaft (15%) fractures were infrequently identified from outpatient claims, whereas foot (88%), hand (86%) and radius/ulna (72%) fractures usually were identified from outpatient data.
DISCUSSION
For probable fractures identified from Medicare claims that were included in the study sample, medical records indicated that a new diagnosis of fracture was made in the preceding 14 days for 96%, and that for 94% the fracture site determined from the computerized files was concordant with the medical record. The reasons for false positives were most often coding of a fracture diagnosis for a negative or inconclusive diagnostic workup or incorrect specification of fracture site. An estimated 9 1% of confirmed fractures with Medicare claims indicating fracture diagnosis or treatment
708
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Table 3. Proportion of all probable fractures identified from outpatient claims. Tennessee Medicaid Fracture Study, 1987 Site Hip Radius/ulna Humerus Ribs/sternum Pelvis Ankle Femoral shaft Hand Tibia/fibula Skull/face Foot Clavicle/scapula Patella All
Probable fractures @I) 891 325 203 255 123 118 73 94 74 76 96 42 28 2398
were identified as probable fractures (estimated sensitivity). For most sites, both the positive predictive value and estimated sensitivity exceeded 85%. Lower values occurred for sites for which specific treatment procedures such as reduction or fixation were not normally performed or for which there was misclassification between adjacent sites. For rib fractures, lack of a specific treatment procedure made it difficult to distinguish suspected fractures from true fractures using the computerized files. For fractures of the ankle and tibia/fibula, misclassifiction in the Medicare data of ankle fractures as tibia/fibula fractures reduced estimated sensitivity for the former site and the positive predictive value for the latter. Because fractures of the femoral shaft comprise only lO-12% of all femoral fractures, even a low rate of misclassification of proximal femur (hip) fractures as shaft fractures could markedly reduce positive predictive value for the shaft. For this reason, if there was any indication that a femoral fracture was a hip fracture, we assigned it to this site. We were thus able to achieve a positive predictive value of 87% for fracture of the shaft; however, the occasional misclassification that resulted decreased estimated sensitivity to 75% for this site. Estimated sensitivity was further decreased by fractures that involved both the proximal and distal femur. Although we classified these as shaft fractures in the medical record review, they often were coded as hip fractures in the computerized files. Our algorithm for identifying probable fractures was complex. We assessed the performance of a simpler definition in which each hospital and emergency room diagnosis of fracture was counted as a fracture. Its positive
Identified from outpatient claims (%) 4.7 72.3 55.2 47.5 13.8 55.1 15.1 86.2 43.2 55.3 87.5 61.9 42.9 36.7
predictive value for all sites was 83%. The positive predictive value of the simpler definition was relatively good for the hip (90”/0), humerus (89%), radius/ulna (88%) and pelvis (85%), but was poorer for the hand (51%), tibia/fibula (70%), ribs/sternum (74%), and femoral shaft (75%). The accuracy of our estimates of the positive predictive value primarily depends upon the quality of the data in the medical record (which we took as the “gold standard”). Because fractures at most skeletal sites are readily diagnosed, this should not be a major source of error. Furthermore, we used conservative record review criteria (e.g. a notation of “symptoms consistent with fracture” was not included as a fracture). The estimated sensitivities are overestimates. Reasons for incomplete ascertainment of fractures include incorrect or miscoded diagnoses and procedures, fractures that are neither diagnosed nor treated, and claims missing from the Medicare files. Inclusion of both diagnosis and procedure codes from several types of providers (e.g. hospital and physician) reduces the impact of coding errors. We excluded vertebral fractures from this study because they frequently are not diagnosed. For the sites studied, underascertainment will be most pronounced for the ribs. Because providers are reimbursed on the basis of claims submitted to Medicare and Medicaid, missing claims should be infrequent. Inspection of claims histories for persons with physician probable fracture claims but no concurrent claims from hospital-based providers suggested that on the order of 2% of hospital claims may be missing from our files. The extent of missing claims may be greater in states where some enrollees participate in capitated Medicare reim-
Identifying Fractures
bursement plans, where the fiscal incentive to submit claims is not present, or in data obtained from the Health Care Financing Administration, where claims returned to the fiscal intermediary for adjudication may be missing (this can be 5--10% of claims for some states [23]). This may partially explain the surprisingly large proportion of hip fractures in a recent study (8%) for which there were no inpatient claims [24]. Because most fractures are painful events requiring treatment and are readily diagnosed, and because there are fiscal and regulatory incentives for providers to submit complete, accurate claims, underascertainment should be low. However, further research is needed to obtain more accurate estimates of the sensitivity of claims-based identification of fractures. The generalizability of the findings from the study sample depends upon both the characteristics of the underlying population and the sampling procedure. Although the Medicaid population has atypical demographics [ 181, these did not correlate with performance of the probable fracture definition. Of the 3086 possible fractures, 47% were included in the sample and had medical record reviews completed; thus, estimates of overall positive predictive value and sensitivity were based upon a large sample size. However, the accuracy of estimates for sites with a small number of possible fractures will be poorer. We used a cluster sampling design with probability proportional to size; thus, possible fractures not sampled were more likely to originate from claims submitted by smaller providers. However, controlling for provider size did not materially alter estimates. We only sampled first fractures. It is possible that subsequent claims for fractures at the same site would have a poorer positive predictive value. It is also possible that because the same data set was used to develop the probable fracture definition and calculate its positive predictive value and estimated sensitivity, its performance would be poorer in a different sample. Although there are several potential limitations of the study definition of probable fractures, their effects should be minor for many skeletal sites of interest. The positive predictive value and sensitivity of the definition should be adequate for many types of epidemiologic investigations. It has the advantage of providing rapid and economical fracture ascertainment for a population that may be difficult to study with
709
other methods [25]. Indeed, in the U.S., there currently are approximately 30 million persons 65 or older enrolled in Medicare for whom computerized claims files are now available from the Health Care Financing Administration. Use of Medicare files to study the epidemiology of skeletal fractures in this very large, enumerated population should further our understanding of this serious class of injuries. Acknowledgemenrs-This study was supported in part by a grant (R49/CCR402307) from the Centers for Disease Control. a contract (500-84-00371 with the Health Care Financing Administration, and a cooperative agreement (FD-U-000073-05) from the Food and Drug Administration.
REFERENCES 1. Melton LJ III. Epidemiology of fractures. In: Riggs BL, Melton LJ III, Eds. Osteoporosis: Etiology, Diagnosis, and Management. New York: Raven Press; 1988: 133-154. 2. Cummings SR, Kelsey JL, Nevitt MC et al. Epidemiology of osteoporosis and osteoporotic fractures. Epidemiol Rev 1985; 7: 178-208. 3. Miller CW. Survival and ambulation following hip fracture. J Bone Joint Surg Am 1978; 60: 930-934. 4. Jensen JS, Tondevold E, Sorensen PH. Social rehabilitation following hip fractures. Acta Orthop Scand 1979; 50: 777-785. 5. Ray WA, Griffin MR, Baugh DK. Mortality following hip fracture before and after implementation of the prospective pay system. Arch Intern Med 1990; 150: 2109-2114. 6. Holbrook TL, Grazier KL, Kelsey JL, Stauffer RN. The Frequency of Occurrence, Impact aad Cost of Musculoskeletal Conditions in the United States. Chicago: American Academy of Orthopaedic Surgeons; 1984. 7. Ray WA, Griffin MR, West R, Strand L, Melton LJ III. Incidence of hip fracture in Saskatchewan, Canada, 1976-1985. Am J Epidemiol 1990; 131: 502-509. 8. Hedlund R, Ahlbom A, Lindgren U. Hip fracture incidence in Stockholm 1972-1981. Acta Ortbop Scand 1985; 57: 30-34. 9. Jacobsen SJ, Goldberg J, Miles TP, Brody JA, Stiers W, Rimm AA. Hip fracture incidence among the old and very old: a population-based study of 745,435 cases. Am J Public Health 1990; 80: 871-873. 10. Ray WA, Griffin MR, Schaffner W, Baugh DK, Melton LJ. Psychotropic drug use and the risk of hip fracture. N Enel J Med 1987: 316: 363-369. 11. Naessen T, PaTker R, Persson I et al. Time trends in incidence rates of first hip fracture in the Uppsala Health Care Region, Sweden, 1965-1983. Am J Epidemiol 1989; 130: 289-299. 12. Garraway WM, Stauffer RN, Kurland LT, O’Fallon WM. Limb fractures in a defined population. II. Orthopedic treatment and utilization of health care. Mayo CIin Proc 1979; 54: 708-713. 13. Evans JG, Prudham D, Wandless I. A prospective study of fractured proximal femur: incidence and outcome. Public He&It 1979; 93: 235-241. 14. Sattin RW, Huber DAL, DeVito CA, Rodriguez JG, Ros A, Bacchelli S, Stevens JA, Waxweiler RJ. The
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15.
16.
17.
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incidence of fall injury events among the elderly in a defined population. Am J Epidemiol 1990; 131: 1028-1037. Melton LJ III. Osteoporosis. In: Berg RL, Cassells JS, Eds. The Secoad Fifty Years. Promoting Health and Preventing DisablIlty. Washington, DC: National Academy Press; 1990: 76-100. Health Care Financing Administration, Bureau of Data Management and Strategy. Medicare Statistical Files Manual. HCFA Publ. No. 03272. Baltimore, MD: HCFA; 1988. Health Care Financing Administration, Bureau of Data Management and Strategy. Medicare Program Statistics: Health Care Financing Administration:Medicare Enrollment, Reimbursement and Utilization, 1983. HCFA Publ. No. 03234. Baltimore, MD: HCFA; 1987. Ray WA, Griffin MR. The use of Medicaid data for pharmacoepidemiology. Am J Epidemiol 1989; 129: 837-849.
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19. Health Care Financing Administration, Office of Research and Demonstrations. Me&are and Medicrid Data Book, 1984. HCFA Publ. No. 03210. Baltimore, MD: HCFA; 1986. 20. Fanta CM, Finkel AJ, Kirschner CG, Kosche MA, Eds. Physician’s Current Procedural Terminology, 4th edn. Chicago: American Medical Association; 1987. 21. Hedlund R, Lindgren U. Epidemiology of diaphyseal femoral fracture. Acta Ortbop Scand 1986; 57: 423-427. 22. Cochran WG. Sampling Techniques, 2nd edn. New York: John Wiley; 1963: 251-252. 23. Health Care Financing Administration, Bureau of Data Management. Unpublished data. 24. Fisher ES, Baron JA, Malenka DJ, Barrett J, Bubolz TA. Overcoming potential pitfalls in the use of Medicare data for epidemiologic research. Am J Public Health 1990; 80: 1487-1490. 25. Kelsey JL, O’Brien LA, Grisso JA, Hoffman S. Issues in carrying out epidemiologic research in the elderly. Am J Epidemiol 1989; 130: 857-866.
APPENDIX Definitions of Possible and Probable Fractures Diagnosis and procedure codes The algorithms for fracture identification utilized Medicare claims that suggested the care of patients with fractures. Such claims were identified from diagnoses, hospital procedures, physician procedures, and radiology procedures. Diagnoses. Claims from hospital-based providers (inpatient hospitals, emergency rooms, and hospital outpatient departments [clinics] include ICD-PDM diagnosis codes). The codes we used to identify possible fractures and to designate fracture sites are listed in Table Al. Diagnoses of
late effects of a fracture (905.3) and unspecified injuries (959) were excluded because a preliminary analysis suggested they had a very low positive predictive value for new fractures. The pathologic fracture diagnosis (733.1) was not used because it occurred very infrequently, and nearly always as a secondary diagnosis in conjunction with a primary diagnosis indicating the fracture site. Hospital procedures. For claims from hospital-based providers, the surgical procedure performed is coded with the ICD-9-CM system. We used codes (Table Al) for procedures indicating fracture treatment to identify possible
Table Al. Diagnosis and procedure codes used to identify possible fractures and designate fracture sites. The diagnoses and hospital procedures are coded with ICD4-CM; the physician and radiology procedures are coded with CPT4 Site
Diagnoses
Hospital procedures
Physician procedures: fracture identification
Hip
820
7855
27125-27127 27230 27232 2723627236 27238 27240 27242 27244 27246 27248
27130-27131 29015 29020 29035 29040 29046 29305 29345 29355 29365 29505 29799
29010 29025 29044 29325 29358 29520
73500 73510 73520 73530 73550
24620 24625 24635 24650 24655 24660
24580-24581 24585-24588 29075 29085 29125-29126
24583 29065 29105 29799
73070 73080 73090 73100 73110
29035 29040 29044 29046 29065 29105 29799
73020 73030 73050 73060 73070 73080
7905 7915 7925 7935 7965 8161 8162
Radius/ulna
Humerus
813
812
7853 7902 7912 7922 7932 7962
7852 7901 7911 7921 7931
24665-24666 24675 24680 25500 25505 25515 25530 25540 25545 25565 25570 25600 25605 25611 25615 25650
24670 24685 25510 25535 25560 25575 25610 25620
23600 23605 23610 23615 23620 23625 23630 23665 23670 23675 23680 24500 24505-24506 245 10 24515 24530-24531 24535-24536 24538 24540 24542 24545 24560 24565 24570 24575-2458 1 24583 24585-24588
Physician procedures: fracture confirmation
Radiology procedures
--continued
Identifying Fractures
711
Table AL-continued Site
Diagnoses
Hospital procedures
Physician procedures: fracture identification
Physician procedures: fracture contirmation
Radiology procedures 71100 71101 71110 71111 71120 71130 72010 72020 72100 72110 72114 72120 72170 72180 72190 72200 72202 72220 73500 73510 73520 73530
Rib/sternum
8070 8071 8072 8073 8074
21800 21805 21820 21820 21825
29010 29015 29020 29025 29035 29040 29044 29046 29200
Pelvis/sacrum/coccyx
8056 8057 8066 8067 808
27190-27192 27200 27202 27210-27212 27214 27220 27222 27224 27225
27120 27122 27130 27131-27132
27760 27766 27790 27810 27816 27822 27500 27506 27512
27764 27788 27808 27814 27820
29010 29025 29358 29425 29540
29015 29345 29365 29505 29799
29020 29355 29405 29515
27504 27510
29010 29025 29044 29325 29358 29520
29015 29035 29046 29345 29365 29799
29020 29040 29305 29355 29505
70328 70330 73590 73600 73620 73630 73500 73510 73520 73530 73550
27762 27786 27792 27812 27818 27823 27502 27508 27514
Ankle
824
Femoral shaft
821
Hand
814 815 816 817
7854 7903 7904 7913 7914 7923 7924 7933 7934 7963 7964
25622 25624 25626 25628 25630 25635 25640 25645 25680 25685 26600 26605 26607 26610 26615 26645 26650 26655 26660 26665 26720 26725 26727 26730 26735 26740 26742 26743-26744 26746 26750 26755-26756 26760 26765
25600 25611 25650 29085 29126 29799
25605 25615 29065 29105 29130
25610 25620 29075 29125 29131
73100 73110 73120 73130 73140
Tibia/fibula
823
7857 7906 7916 7926 7936 7966
29010 29025 29358 29425 29799
29015 29345 29365 29505
29020 29355 29405 29515
73560 73562 73564 73590 73600 73610
Skull/face
800 801 802 803 804
767
27530 27532 27534 2753627538 27540 27750 27752 27754 27756 27758 27780 2778 l-27782 27784 27800 27802 27804 27806 21300 21310 21315 21320 21325 21330 21335 21337-21340 21345-21347 21350 21355 21360 21365 21380 21385-21387 21390 21395-21400 21401 21406-21407 21420-21422 21431 21432-21433 21435 21440 21445 21450
70100 70110 70120 70130 70134 70140 70150 70160 70200 70210 70220
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Table Al-conrinued Site
Diagnoses
Hospital procedures
Skull/face [continued]
Physician procedures: fracture confirmation
21451-21455 21461 21462 21465 21470 21495
Foot
825 826
Clavicle/scapula
810 811
Patella
822
809 818-819 827 828 829 E880 E881 E882 E883 E884 E885 E886 ES87 E888 E9293
Non-specific
Physician procedures: fracture identification
28400 28405-28406 28410 28415 28420 28430 2843528436 28440 28445 28450 28455 28460 28465 28470 28475-28476 28480 28485 28490 28495-28496 28500 28505 28510 28515 28520 28525
29405 29425 29505 29515 29550 29580 29799
23500 23505 23510 23515 23570 23575 23585
29010 29025 29044 29055 29105
29015 29035 29046 29058 29240
29020 29040 29049 29065
7856
27520 27522 27524
29010 29025 29044 29355 29435
29015 29035 29046 29358 29505
29020 29040 29345 29365 29530
7850 7851 7859 7900 7909 7910 7919 7920 7929 7930 7939 7960 7969
29010 29025 29044 29055 29075 29325 29358 29425
7858 7907 7908 7917 7918 7927 7928 7937 7938 7967 7968
fractures. However, codes for procedures such as total hip replacement that usually are performed for reasons other than a fracture were not considered. Physician procedures. For physician claims, the service performed during the visit is coded with the CPT4 system. Those codes (Table Al) suggesting treatment of a fracture were used to identify possible fractures and their sites. They also were used in conjuction with other claims to confirm fracture occurrence (e.g. diagnoses from clinic claims were not accepted as a probable fracture unless there was a concurrent physician claim with a procedure indicating fracture treatment). We did not use claims from anesthesiologists because often they were inaccurate with respect to fracture occurrence or site. When claims from multiple other physicians were present, we gave priority to those from orthopedic surgeons. There were two types of physician procedure codes (Table Al). The more specific codes were used for fracture identification and fracture confirmation; the less specific were used only for fracture confirmation. For example, if a clinic claim with a diagnosis of fracture of the ankle (ICD-PCM code 824) occurred in conjunction with a physician claim for application of a short leg cast (CPT4 code 29405), we classified the claim as a probable fracture, because the physician procedure for casting indicates fracture treatment occurred and is consistent with the diagnosis
29015 29035 29046 29058 29085 29345 29365 29435
Radiology procedures 70230 70231 70250 70260 73600 73620 73630 73650 73660
73000 73010 73020 73030 73050 73060 73550 73560 73562 73564 73590
29020 29040 29049 29065 29305 29355 29405
of an ankle fracture. However, in the absence of a fracture diagnosis, we did not classify claims with procedures for short leg cast application as probable fractures because this procedure may be performed for other reasons. In contrast, a physician claim with the more specific procedure for treatment of a medial malleolus fracture (CPT4 code 27760) would be classified as a probable fracture. Radiology procedures. Physician claims for skeletal radiologic examinations (codes listed in Table Al) were used in some instances to help determine fracture sites. However, we did not require presence of a radiology claim for probable fractures because our data showed this did not improve the positive predictive value. Linkage of claims
A single fracture could result in claims from several providers. For example, a hip fracture could generate claims for an emergency room visit, an inpatient admission, a radiologist’s roentgenographic examination of the hip, and an orthopedic surgeon’s repair of the fracture. We attempted to link these claims together in order to be as accurate as possible insofar as determing fracture occurrence, date, and site. We began by identifying the first claim during the study period with a study diagnosis or procedure code. If this first claim was an inpatient claim, we then identified all subsequent claims with study codes from the
Identifying Fractures admission date through the day following discharge. For emergency room, clinic claims, and physician claims we identified all subsequent claims with study codes from the visit date through the following 7 days. The date of service from the first claim in this group was used to determine the index date, which was our best estimate of the date the fracture occurred (in the example above, this would be the date of the emergency room visit). The claim we judged to be the most informative, the possible fracture claim was used to obtain medical records for review and to determine the number of possible fractures and their sites (in the example above, the possible fracture claim was the inpatient claim). Selection of this claim (priority high to low) was made on the basis of diagnosis (present or absent), provider type (hospital, emergency room, clinic, physician), date (priority to earliest), or fracture site (ordering as in Table Al). Other claims in the group were used to provide additional confirmation that a fracture had occurred and to help determine fracture sites. A special case was created for physician claims with a fracture code and no other claims from hospital-based providers with study codes in the following 7 days. We hypothesized this could result from failure by another provider to code a fracture diagnosis. For example, the hospital claim for a stroke admission accompanied by a fractured radius/ulna might include a diagnosis code for the stroke but not for the fracture; however, the claim from the physician who treats the fracture should be present. In this circumstance, we identified all claims from hospital-based providers that were likely to be associated with the physician claim. For inpatient claims, this meant the physician claim service date had to fall between admission and the day following discharge; for emergency room and clinic claims, the physician date was between the visit date and the 7 following days. In this case, the possible fracture claim was the physician claim, which was used to determine the index date and fracture site. However, the highest priority claim (following the rules given above) from a hospital-based provider was selected for medical record review. Possible fractures
We defined the occurrence of a group of one or more claims with a study diagnosis or procedure code as a possible fracture event. In the simplest (and most frequent) case, this event would correspond to a single fractue, the site of which could be readily determined from the diagnosis. However, a single injury event (e.g. fall or motor vehicle crash) could result in multiple fractures. Furthermore, our review of medical records found that in some cases the diagnosis coded in the Medicare claim was not the best indicator of fracture site. Therefore, based upon the medical record review, we developed an algorithm to determine the number of possible fractures and their sites from the fracture claims. If the possible fracture claim included one or more diagnoses, then we assigned one possible fracture for each diagnosis indicating a different site. Otherwise, if the group of claims associated with the possible fracture claim included one or more physician procedures, we assigned one possible fracture for each procedure indicting a different site. If the possible fracture claim did not have diagnoses, and there were no physician procedures, then we assigned one possible fracture for each different hospital procedure on the claim. In most cases, the fracture sites were determined from diagnosis and procedure codes as described in Table Al. However, because of systematic discrepancies between the codes and fracture sites that we noted from review of medical records, there were three exceptions to this rule: (1) The diagnosis indicated the hand (codes 814-817) but there was no physician procedure for treatment of fractures of the hand. Then
713
(a) If the diagnosis was “Wrist, NOS” (not otherwise specified) (codes 814.00, 814.10), then the radius/ulna was designated as the site. (b) If the diagnosis was carpal bone fracture (codes 814.01-814.09. 814.11-814.19) but there was an associated physician treatment procedure for the distal radius/ulna, then the radius/ulna was designated as the site. (4 If neither (a) nor (b) was true, there was no physician procedure for the hand, but there was a physician procedure for treatment of a foot fracture or a radiology procedure for the foot, then the foot was designated as the site. (d) Otherwise, the hand was designated as the site. (2) The diagnosis was “tibia/fibula” (code 823) and there were no physician procedures indicating treatment of the proximal end or shaft of the tibia/fibula. If there was a physician procedure indicating the ankle, then the ankle was designated as the site; otherwise, this was the tibiaifibula. (3) The diagnosis was “femoral shaft” (code 821). If there was any physician procedure for the hip, then the hip was designated as the site. Probable fractures
Based upon review of medical records, we developed several criteria to exclude those possible fractures identified from the computerized files that were unlikely to correspond to true fractures. A probable fracture was then defined as any possible fracture that did not meet the exclusion criteria. The exclusions were: (1) Clinic diugnosis only. A clinic diagnosis with no physician procedure indicating a fracture. These usually represented negative examinations for suspected fractures or care provided for old fractures. (2) Hospital admission diagnosis only. A hospital admission diagnosis (which is separate from the 5 discharge diagnoses) with no discharge diagnosis or physician procedure indicating a fracture. These usually indicated admission for a suspected fracture that was not confirmed. (3) Care of old fractures or other bone diseases. A procedure compatible with fracture treatment where the only diagnoses present were those for a previous fracture or other bone disease: late effects of fracture (ICD-q-CM code 905.4), implant complication (codes 996.4, 996.6, 996.7, E878. I), aseptic necrosis (733.4), malunion of bone (773.8), other disorders of bone or cartilage (733.9), and fracture followup care (V540, V664, V674). These usually indicated a treatment procedure (such as hemiarthroplasty) that could be indicated for a new fracture, but which was provided for other reasons. (4) Arthroplasty for treatment of arthritis. A hospitalbased claim with a first diagnosis of osteoarthritis (code 715) and a hospital procedure for arthroplasty (codes 81.3-81.9). These possible fractures were identified on the basis of physician arthroplasty procedures that are often used to treat fractures. However, the absence of a fracture diagnosis and presence. of a diagnosis of arthritis (which also is an indication for arthroplasty) suggested a fracture was unlikely. (5) Hospi!al procedure only. This category consists of possible fractures identified on the basis of a hospital procedure, but with no other indication of a fracture. Medical record review showed that in the absence of a fracture diagnosis or a physician procedure for fracture treatment, fracture occurrence was uniikely. (6) Emergency room hip fracture rule-out. This category primarily consisted of emergency room hip fracture diagnoses but without a corresponding physician procedure. It also included emergency room hip fracture diagnoses followed within 1 day by a hospital claim that had an admission diagnosis for hip fracture but did not have a
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discharge diagnosis or physician procedure indicating a fracture. Medical record review indicated these usually represented examination for suspected hip fractures that were not confirmed. However, we found that for other sites, emergency room diagnoses, even in the absence of physician procedures, had a high positive predictive value. (7) Fracture site not indicated. Any possible fracture for which the diagnosis and procedure codes did not indicate a fracture site. This category included physician procedures for casting. This exclusion was made because these possible fractures, for which the site was not indicated by the computerized files, were by definition not concordant with the medical record. We included specific physician procedures for fracture treatment in the definition of a probable fracture, even in the absence of a concurrent claim from a hospital-based provider. We did not review medical records for this type of probable fracture claim and thus had no direct estimate
of the positive predictive value. There were 164 possible fractures (5% of all possible fractures) identified from a physician claim for which there was no concurrent claim from a hospital-based provider. Of these, 104 indicated reduction, fixation, or other fracture repair whereas 60 were for cast application. We included the former in the definition of a probable fracture because review of medical records for hospital-based provider claims (n = 42) having no fracture diagnosis or procedure but with a concurrent physician possible fracture claim of this type found a positive predictive value of 86%. We did not include the latter because casting procedures do not reliably identify the fracture site and may indicate treatment of other injuries. This exclusion will reduce sensitivity somewhat for sites such as the ankle and the radius/ulna. As Medicare physician diagnoses become available, they may be used to identify fractures that were missed by our probable fracture definition.
0895-4356/92 $5.00f0.00
Copyright 0 1992Pergamon Press Ltd
IDENTIFICATION OF FRACTURES FROM COMPUTERIZED MEDICARE FILES WAYNE
A. RAY,* MARIE R. GRIFFIN,RANDY L. FOUGHTand MARGARETL. ADAMS
Department
of Preventive
Medicine, Vanderbilt University TN 37232, U.S.A.
School of Medicine, Nashville,
(Received 4 July 1991; received for publication 24 February 1992)
Abstract-Study of non-hip fractures, which are a serious public health problem for persons > 65 years of age, has been hindered by the absence of an economical method for case identification. We assessed the utility of computerized Medicare inpatient, emergency room, hospital outpatient department and physician claims for identifying fractures in an elderly Tennessee Medicaid population. We used these files for 1987 to identify 3086 possible fractures and reviewed medical records for a sample of 1440. Using this sample, we developed a definition of probable fractures that excluded claims unlikely to represent newly diagnosed fractures. For all fractures, this definition had a positive predictive value of 94%, which for individual fracture sites, ranged from 79% (tibia/fibula) to 98% (hip). Of fractures in the reviewed sample, 91% were identifed as probable fractures; this upper bound for sensitivity varied between 75% (femoral shaft) and 100% (patella). These data suggest that computerized Medicare files can be used for rapid and economical fracture ascertainment among persons 265 years of age. However, further work is needed to obtain better estimates of sensitivity.
restriction or a physician visit were at sites other than the hip [6]. In a recent study of fall-related injuries in Miami Beach, Florida residents 2 65 years of age, 70% of fractures were at sites other than the hip [14]. Because these other types of fractures have been less extensively studied, there are many unresolved questions concerning their epidemiology, etiology, and consequences [ 11. Because fractures generally are acute, painful injuries that are readily diagnosed and promptly treated, it should be possible to identify them from computerized medical record databases that include both inpatient and outpatient data. Computerized Medicare files [16], which now include claims from inpatient and outpatient providers for 97% of persons > 65 years of age in the U.S. [17], may thus be a valuable resource for fracture epidemiology. We utilized a database developed for epidemiologic research that includes computerized Medicare files [18], to determine whether or not these files could be
INTRODUCTION
Skeletal fractures are a major public health problem for older persons [l, 21. Adverse pronounced for are most consequences fractures of the proximal femur (hip) [3-6], which have been relatively well studied [7-l l] because 95% or more of hip fractures are treated in the hospital [ 12, 131and computerized records of hospital discharges are readily available. However, these databases have limited utility for research concerning fractures at other sites which are more often treated on an outpatient basis [12, 141. These other fractures are also associated with substantial disability and cost [15]. Moreover, they account for the majority of fractures in persons 265 years of age. The 1977 National Health Interview Survey found that 80% of non-vertebral fractures in this age group resulting in activity *Author for correspondence. 703
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used to reliably identify fractures at sites other than the proximal femur.
METHODS
In this study, we developed a definition to identify probable fractures from computerized Medicare claims and estimated its positive predictive value and sensitivity for the population of Tennessee Medicaid enrollees 265 years of age. Ideally, the study population would have consisted of a random sample of Medicare enrollees. However, we chose the Medicaid population (virtually all of whom are enrolled in Medicare) because Medicare files had been obtained for this population and were organized in a format efficient for research [ 181 and we had permission from the state Medicaid program to contact providers and request access to the medical records of Medicaid enrollees.
k4Y
et al.
surgical procedures (the admission diagnosis is not currently available in Medicare inpatient files maintained by the Health Care Financing Administration). Emergency room and clinic records each have ICD-9-CM codes for up to five diagnoses and one surgical procedure. Part B files consist of claims from physicians. We obtained these files from the state Medicaid program, which regularly obtains these claims for Medicaid enrollees from the Part B Medicare carriers for Tennessee. There is a record for each physician service, which includes the physician’s number and specialty code, the patient’s Medicaid number, the service date, and a Phys ician’s Current Procedural Terminology, 4th edition (CPT4) [20] procedure code. At the time of this study, diagnoses were not required for Medicare Part B physician claims. However, diagnostic data will be available in these files in 1991. Study population
Sources of data
The study population was identified from the Medicaid enrollment file [18], which includes Medicaid identification number, the basis of eligibility for Medicaid enrollment, demographic characteristics, personal identifiers, and periods of enrollment. This file has been linked with death certificates to obtain the date and coded underlying cause of death. Medical care for the diagnosis or treatment of fractures was identified from Medicare claims files, which consist of records of claims for medical services submitted by providers and subsequently reimbursed by Medicare. Because Medicare consists of two distinct, separately administered programs [19] (Part A, which is hospital insurance, and Part B, which is supplemental medical insurance), there are two types of claims files. Part A files consist of claims from hospitalbased providers: hospitals, emergency rooms, and hospital outpatient departments (clinics). We obtained these files directly from the Part A fiscal intermediary for Tennessee and linked them with Medicaid data. There is a record for each provider encounter (hospital stay or emergency room or clinic visit), which includes the provider number, the patient’s Medicaid number and the dates of the encounter. Hospital records have International Classification of Diseases, 9th revision, Clinical Modifications (ICDg-CM) codes for up to six diagnoses (one admission and five discharge) and up to three
The study population consisted of Tennessee Medicaid enrollees who were 265 years of age on 1 January 1987 (16% of Tennesseans in this age group), were enrolled continuously in Medicaid for at least 365 days, and had no claim suggesting occurrence of a fracture in the preceding 31 days. Follow-up extended through 31 December 1987 unless terminated earlier by loss of Medicaid enrollment or death. Fracture identification Possible fracture claims. For persons in the
study population, we identified Medicare hospital, emergency room, clinic, and physician claims with diagnosis or procedure codes that could indicate a fracture (Appendix). For each person, we identified the first such claim in 1987 and all such claims in the next 7 days. From these claims, we selected the one thought to be most informative (see Appendix) as the possible fracture claim, which was used to determine fracture sites(s) and to identify medical records for review. However, the service date from the first claim in this group was used to determine the index date, which was our estimate of the date of fracture occurrence. For example, consider an emergency room visit with a diagnosis of fracture of the hip followed by a hospital admission the next day with the same diagnosis. The hospital claim would be used to assign fracture site and, if this possible fracture was selected for medical record review, the hospital chart would be sought. However, the index date
Identifying Fractures
would be the date of the emergency room visit. Other claims in this group also could be used to provide additional confirmation that a fracture had occurred or assist in determination of fracture site. An example would be an emergency room visit with a diagnosis of “Fracture of unspecified bones” (ICD-9-CM code 829) with a concurrent physician claim with a procedure of “treatment of closed humeral shaft fracture” (CPT4 code 24500). Fracture sites. A possible fracture claim usually represented occurrence of a single fracture, the site of which could be readily determined from the diagnosis. However, a single injury event (fall, motor vehicle crash) can cause multiple fractures that are treated by a single provider and thus generate only one possible fracture claim. We therefore developed an algorithm (Appendix) to determine the number of possible fractures and their sites. The algorithm generally assigned one site for each different diagnosis on the possible fracture claim. Thus, a person with an inpatient possible fracture claim having diagnoses of fracture of the hip and fracture of the radius/ulna would generate two possible fractures. The primary exceptions (Appendix) were claims that did not have fracture diagnoses, in which case procedures were used, or claims that involved sites where the ICD-9-CM codes were consistently inaccurate. For example, the ICD-9-CM rules specify that a diagnosis of “wrist fracture” be coded as a carpal fracture (code 8 14.0); however, we found this generally referred to fractures of the distal radius/ulna (codes 813.4, 813.5). Other exceptions were designed to correct errors in referring to bones with similar names (phalanges of the hand vs those of the foot) and to reduce the known problem of miscoding hip fractures as femoral shaft fractures [2 I]. Probable fractures. We used the information from medical records (see below) to develop criteria (Appendix) designed to exclude possible fractures identified by the computer that were unlikely to represent newly diagnosed fractures. Probable fractures were then defined as possible fractures that did not meet any of these exclusion criteria. The primary exclusions were clinic fracture diagnoses with no corresponding procedure for fracture treatment, hospital admission fracture diagnoses with no fracture discharge diagnosis or fracture treatment procedure, and diagnoses and procedures indicating care of an old fracture or arthroplasty for treatment of arthropathy.
705 Possible Fractures 3066/2Q?3 Not Pre.,,, 6881666
Pzq
239612306
p;;
EliQible for Sample 6261606
I
Sampled 2931279
I
Eligible for Sample 229412202
i &pled 1404/1351
I
Revibwed
Reviewed
129/l 27
1311/125Q
Fig. 1. Sampling procedure for possible fractures, by whether or not the possible fracture met the study definition of a probable fracture. The numbers are n fractures/n persons. Tennessee Medicaid Fracture Study, 1987.
Medical record review. There were 2973 persons in the study with a possible fracture claim, who had a total of 3086 possible fractures. We reviewed medical records for a sample of these possible fractures (Fig. 1) to develop the probable fracture definition and to estimate its positive predictive value and sensitivity. We used a cluster sampling technique [22] designed to obtain a 50% sample of possible fractures while minimizing the number of providers to be visited. A cluster was defined as all possible fractures from a single provider. Because we did not have access to physician records, the sample was restricted to the 2922 possible fractures where the possible fracture claim was from a hospitalbased provider. Of these, 1697 (58%) were included in the sample (Fig. 1). A trained nurse attempted to obtain the medical record for each of these and to complete a structured study form. Reviews were completed for 1440 (85%). The completion rate varied by provider type: 96% for hospitals (n = 973 possible fractures in sample), 88% for emergency rooms (n = 462), and 38% for clinics (n = 262). A primary reason for the low completion rate for clinics was the large number of referrals for radiologic examination with no radiology report or physician diagnosis kept in the clinic chart. The reviewer recorded any fracture(s) newly diagnosed in the 14 days preceding the index date and their site(s), cause, date, place of occurrence, and the patient residence on the fracture date. If no fracture had been diagnosed, the abstractor noted the reason for the provider encounter.
WAYYNE A. RAY et al.
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Analysis The primary analysis was calculation of the positive predictive value and estimation of the sensitivity of the probable fracture definition for the specific fracture sites. The positive predictive value was the proportion of probable fractures where the occurrence of a fracture at the site indicated by the claims was confirmed from the medical records. Positive predictive values were computed from the sample of probable fractures for which these reviews were completed. We could not calculate the true sensitivity of the probable fracture definition because we did not prospectively ascertain fractures for the study population. However, for fractures identified through review of medical records we calculated the proportion that also were identified by the probable fracture definition. This estimated sensitivity is an upper bound for the true sensitivity and describes the penalty associated with making the probable fracture definition more stringent. Estimated sensitivity could have been calculated directly from the sample of possible fractures for which record review was completed. However, the completion rate was lowest for possible fractures that did not meet the definition of a probable fracture (Fig. 1), which was primarily due to the large number of clinic records in this category that had insufficient information to determine whether or not a fracture had occurred. Since these possible, nonprobable fractures contain the false negatives, undersampling of this category would cause sensitivity to be overstated. Thus, sensitivity estimates were adjusted to compensate for undersampling.
To assess the effects of selecting providers for the sample based upon number of possible fractures, we calculated rates adjusted for provider size. Since these did not differ materially from the unadjusted rates, only the latter are presented. In all analyses, the unit of analysis is the individual fracture rather than the person. This does not affect the site-specific analysis because each person can have at most one fracture per site in the study. However, a person may contribute more than one fracture to the all-site analyses. For these analyses, we performed alternative calculations using the person rather than the fracture as the unit of analysis. Because this did not alter any of our findings materially, only the fracture-based analyses are presented.
RESULTS
Medical record review was completed for 1311 probable fractures and 129 possible, but not probable fractures. Of the probable fractures, 1228 (94%) were confirmed by medical record review; the remainder were either not a fracture (n = 53, due to 39 instances of suspected, but not confirmed fractures, 4 visits for fracture follow-up care, 2 non-fracture injuries, and 8 visits for other reasons) or had the wrong site assigned by our algorithm (n = 30). Of the non-probable fractures, 18 (14%) were confirmed as fractures by medical record review, 103 were not fractures (54 suspected fractures, 7 visits for fracture follow-up care, 2 other injuries, and 40 visits for other reasons) and 8 had the wrong site assigned by our algorithm.
Table I. Positive predictive value and estimated sensitivity of the probable fracture definition. Tennessee Medicaid Fracture Study, 1987 Probable fracture with record review completed (n) Hip Radius/ulna Humerus Ribs/sternum Pelvis Ankle Femoral shaft Hand Tibia/fibula Skull/face Foot Clavicle/scapula Patella All
538 162 109 107 67 69 53 43 47 38 40 21 17 1311
New fracture (%) 99 97 97
84 94 97 96 86 96 92 95 100 94 96
Positive predictive value (%)
Estimated sensitivity (%) 97
95
84 93 96 :‘6 79 89 t: 82 94
E 82 89 78 75
87 87 97 90 91 100 91
Identifying Fractures
707
Table 2. Positive predictive value (PPV) and estimated sensitivity (SENS) of the probable fracture definition, by patient demographic characteristics. Tennessee Medicaid Fracture Study, 1987 Hip
Other sites
All sites
n
PPV
SENS
n
PPV
SENS
n
PPV
SENS
98 440
100.0 97.3
100.0 96.1
122 651
90.2 90.9
91.2 86.4
220 1092
94.5 93.5
94.5 89.9
92 236 210
97.8 98.7 96.7
95.4 97.7 96.5
252 304 217
92.9 90.1 89.4
91.3 86.6 81.1
344 541 427
94.2 93.9 93.0
92.3 91.0 87.7
420 84
98.1 96.4
97.1 96.2
575 136
90.6 91.9
87.7 88.5
996 220
93.8 93.6
91.2 91.3
Urban Suburban Rural
155 192 191
97.4 97.9 97.9
99.3 93.0 100.0
244 306 223
89.8 91.2 91.5
88.3 86.4 89.6
399 498 415
92.7 93.8 94.5
92.3 88.9 93.3
NH index date No Yes
382 156
98.2 96.8
97.7 94.0
618 155
91.1 89.7
89.1 80.3
1001 311
93.8 93.2
92.1 86.4
Sex
Male Female
Age 65-74 75-84 285 Race *
White Non-white County
*Excludes 96 persons with unknown race.
The positive predictive value of the probable fracture definition (Table 1) was 295% for the hip, radius/ulna, humerus, ankle, and foot, and was 285% for all sites except the ribs/sternum (84%), patella (82%), and the tibia/fibula (79%). The major source of error for the ribs/sternum was encounters where fractures were suspected but not conclusively diagnosed and for the tibia/fibula the primary problem was that the ankle was the fracture site indicated by medical record review (13% of probable tibia/fibula fractures). The probable fracture definition had an overall estimated sensitivity of 91% (Table 1). The estimated sensitivity was 285% for all sites except for rib/sternum (82%), where the primary source of false negatives was clinic claims with a fracture diagnosis but no confirmatory treatment procedure; ankle (78%), which was often misclassified in the computer as tibia/fibula; and femoral shaft (75%), which was often misclassified as hip. The positive predictive value and estimated sensitivity of the probable fracture definition did not vary materially across subgroups defined by sex, age, race, county of residence, and nursing home status (Table 2). For the 1228 probable fractures confirmed by medical record review, we assessed the concordance of the index date from the fracture claim with the date of the fracture from the medical record. These dates were the same for 70% of
persons, within 1 day for 85%, and within 7 days for 97%. The agreement of index and fracture dates did not vary materially by fracture site. To assess the need for non-inpatient data for identifying fractures, we calculated the proportion of all probable fractures (n = 2398) that were identified from emergency room, clinic, or physician claims (Table 3). Thirty-seven percent of all probable fractures were so identified. Hip (5%), pelvic (14%), and femoral shaft (15%) fractures were infrequently identified from outpatient claims, whereas foot (88%), hand (86%) and radius/ulna (72%) fractures usually were identified from outpatient data.
DISCUSSION
For probable fractures identified from Medicare claims that were included in the study sample, medical records indicated that a new diagnosis of fracture was made in the preceding 14 days for 96%, and that for 94% the fracture site determined from the computerized files was concordant with the medical record. The reasons for false positives were most often coding of a fracture diagnosis for a negative or inconclusive diagnostic workup or incorrect specification of fracture site. An estimated 9 1% of confirmed fractures with Medicare claims indicating fracture diagnosis or treatment
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Table 3. Proportion of all probable fractures identified from outpatient claims. Tennessee Medicaid Fracture Study, 1987 Site Hip Radius/ulna Humerus Ribs/sternum Pelvis Ankle Femoral shaft Hand Tibia/fibula Skull/face Foot Clavicle/scapula Patella All
Probable fractures @I) 891 325 203 255 123 118 73 94 74 76 96 42 28 2398
were identified as probable fractures (estimated sensitivity). For most sites, both the positive predictive value and estimated sensitivity exceeded 85%. Lower values occurred for sites for which specific treatment procedures such as reduction or fixation were not normally performed or for which there was misclassification between adjacent sites. For rib fractures, lack of a specific treatment procedure made it difficult to distinguish suspected fractures from true fractures using the computerized files. For fractures of the ankle and tibia/fibula, misclassifiction in the Medicare data of ankle fractures as tibia/fibula fractures reduced estimated sensitivity for the former site and the positive predictive value for the latter. Because fractures of the femoral shaft comprise only lO-12% of all femoral fractures, even a low rate of misclassification of proximal femur (hip) fractures as shaft fractures could markedly reduce positive predictive value for the shaft. For this reason, if there was any indication that a femoral fracture was a hip fracture, we assigned it to this site. We were thus able to achieve a positive predictive value of 87% for fracture of the shaft; however, the occasional misclassification that resulted decreased estimated sensitivity to 75% for this site. Estimated sensitivity was further decreased by fractures that involved both the proximal and distal femur. Although we classified these as shaft fractures in the medical record review, they often were coded as hip fractures in the computerized files. Our algorithm for identifying probable fractures was complex. We assessed the performance of a simpler definition in which each hospital and emergency room diagnosis of fracture was counted as a fracture. Its positive
Identified from outpatient claims (%) 4.7 72.3 55.2 47.5 13.8 55.1 15.1 86.2 43.2 55.3 87.5 61.9 42.9 36.7
predictive value for all sites was 83%. The positive predictive value of the simpler definition was relatively good for the hip (90”/0), humerus (89%), radius/ulna (88%) and pelvis (85%), but was poorer for the hand (51%), tibia/fibula (70%), ribs/sternum (74%), and femoral shaft (75%). The accuracy of our estimates of the positive predictive value primarily depends upon the quality of the data in the medical record (which we took as the “gold standard”). Because fractures at most skeletal sites are readily diagnosed, this should not be a major source of error. Furthermore, we used conservative record review criteria (e.g. a notation of “symptoms consistent with fracture” was not included as a fracture). The estimated sensitivities are overestimates. Reasons for incomplete ascertainment of fractures include incorrect or miscoded diagnoses and procedures, fractures that are neither diagnosed nor treated, and claims missing from the Medicare files. Inclusion of both diagnosis and procedure codes from several types of providers (e.g. hospital and physician) reduces the impact of coding errors. We excluded vertebral fractures from this study because they frequently are not diagnosed. For the sites studied, underascertainment will be most pronounced for the ribs. Because providers are reimbursed on the basis of claims submitted to Medicare and Medicaid, missing claims should be infrequent. Inspection of claims histories for persons with physician probable fracture claims but no concurrent claims from hospital-based providers suggested that on the order of 2% of hospital claims may be missing from our files. The extent of missing claims may be greater in states where some enrollees participate in capitated Medicare reim-
Identifying Fractures
bursement plans, where the fiscal incentive to submit claims is not present, or in data obtained from the Health Care Financing Administration, where claims returned to the fiscal intermediary for adjudication may be missing (this can be 5--10% of claims for some states [23]). This may partially explain the surprisingly large proportion of hip fractures in a recent study (8%) for which there were no inpatient claims [24]. Because most fractures are painful events requiring treatment and are readily diagnosed, and because there are fiscal and regulatory incentives for providers to submit complete, accurate claims, underascertainment should be low. However, further research is needed to obtain more accurate estimates of the sensitivity of claims-based identification of fractures. The generalizability of the findings from the study sample depends upon both the characteristics of the underlying population and the sampling procedure. Although the Medicaid population has atypical demographics [ 181, these did not correlate with performance of the probable fracture definition. Of the 3086 possible fractures, 47% were included in the sample and had medical record reviews completed; thus, estimates of overall positive predictive value and sensitivity were based upon a large sample size. However, the accuracy of estimates for sites with a small number of possible fractures will be poorer. We used a cluster sampling design with probability proportional to size; thus, possible fractures not sampled were more likely to originate from claims submitted by smaller providers. However, controlling for provider size did not materially alter estimates. We only sampled first fractures. It is possible that subsequent claims for fractures at the same site would have a poorer positive predictive value. It is also possible that because the same data set was used to develop the probable fracture definition and calculate its positive predictive value and estimated sensitivity, its performance would be poorer in a different sample. Although there are several potential limitations of the study definition of probable fractures, their effects should be minor for many skeletal sites of interest. The positive predictive value and sensitivity of the definition should be adequate for many types of epidemiologic investigations. It has the advantage of providing rapid and economical fracture ascertainment for a population that may be difficult to study with
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other methods [25]. Indeed, in the U.S., there currently are approximately 30 million persons 65 or older enrolled in Medicare for whom computerized claims files are now available from the Health Care Financing Administration. Use of Medicare files to study the epidemiology of skeletal fractures in this very large, enumerated population should further our understanding of this serious class of injuries. Acknowledgemenrs-This study was supported in part by a grant (R49/CCR402307) from the Centers for Disease Control. a contract (500-84-00371 with the Health Care Financing Administration, and a cooperative agreement (FD-U-000073-05) from the Food and Drug Administration.
REFERENCES 1. Melton LJ III. Epidemiology of fractures. In: Riggs BL, Melton LJ III, Eds. Osteoporosis: Etiology, Diagnosis, and Management. New York: Raven Press; 1988: 133-154. 2. Cummings SR, Kelsey JL, Nevitt MC et al. Epidemiology of osteoporosis and osteoporotic fractures. Epidemiol Rev 1985; 7: 178-208. 3. Miller CW. Survival and ambulation following hip fracture. J Bone Joint Surg Am 1978; 60: 930-934. 4. Jensen JS, Tondevold E, Sorensen PH. Social rehabilitation following hip fractures. Acta Orthop Scand 1979; 50: 777-785. 5. Ray WA, Griffin MR, Baugh DK. Mortality following hip fracture before and after implementation of the prospective pay system. Arch Intern Med 1990; 150: 2109-2114. 6. Holbrook TL, Grazier KL, Kelsey JL, Stauffer RN. The Frequency of Occurrence, Impact aad Cost of Musculoskeletal Conditions in the United States. Chicago: American Academy of Orthopaedic Surgeons; 1984. 7. Ray WA, Griffin MR, West R, Strand L, Melton LJ III. Incidence of hip fracture in Saskatchewan, Canada, 1976-1985. Am J Epidemiol 1990; 131: 502-509. 8. Hedlund R, Ahlbom A, Lindgren U. Hip fracture incidence in Stockholm 1972-1981. Acta Ortbop Scand 1985; 57: 30-34. 9. Jacobsen SJ, Goldberg J, Miles TP, Brody JA, Stiers W, Rimm AA. Hip fracture incidence among the old and very old: a population-based study of 745,435 cases. Am J Public Health 1990; 80: 871-873. 10. Ray WA, Griffin MR, Schaffner W, Baugh DK, Melton LJ. Psychotropic drug use and the risk of hip fracture. N Enel J Med 1987: 316: 363-369. 11. Naessen T, PaTker R, Persson I et al. Time trends in incidence rates of first hip fracture in the Uppsala Health Care Region, Sweden, 1965-1983. Am J Epidemiol 1989; 130: 289-299. 12. Garraway WM, Stauffer RN, Kurland LT, O’Fallon WM. Limb fractures in a defined population. II. Orthopedic treatment and utilization of health care. Mayo CIin Proc 1979; 54: 708-713. 13. Evans JG, Prudham D, Wandless I. A prospective study of fractured proximal femur: incidence and outcome. Public He&It 1979; 93: 235-241. 14. Sattin RW, Huber DAL, DeVito CA, Rodriguez JG, Ros A, Bacchelli S, Stevens JA, Waxweiler RJ. The
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15.
16.
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incidence of fall injury events among the elderly in a defined population. Am J Epidemiol 1990; 131: 1028-1037. Melton LJ III. Osteoporosis. In: Berg RL, Cassells JS, Eds. The Secoad Fifty Years. Promoting Health and Preventing DisablIlty. Washington, DC: National Academy Press; 1990: 76-100. Health Care Financing Administration, Bureau of Data Management and Strategy. Medicare Statistical Files Manual. HCFA Publ. No. 03272. Baltimore, MD: HCFA; 1988. Health Care Financing Administration, Bureau of Data Management and Strategy. Medicare Program Statistics: Health Care Financing Administration:Medicare Enrollment, Reimbursement and Utilization, 1983. HCFA Publ. No. 03234. Baltimore, MD: HCFA; 1987. Ray WA, Griffin MR. The use of Medicaid data for pharmacoepidemiology. Am J Epidemiol 1989; 129: 837-849.
RAY et al.
19. Health Care Financing Administration, Office of Research and Demonstrations. Me&are and Medicrid Data Book, 1984. HCFA Publ. No. 03210. Baltimore, MD: HCFA; 1986. 20. Fanta CM, Finkel AJ, Kirschner CG, Kosche MA, Eds. Physician’s Current Procedural Terminology, 4th edn. Chicago: American Medical Association; 1987. 21. Hedlund R, Lindgren U. Epidemiology of diaphyseal femoral fracture. Acta Ortbop Scand 1986; 57: 423-427. 22. Cochran WG. Sampling Techniques, 2nd edn. New York: John Wiley; 1963: 251-252. 23. Health Care Financing Administration, Bureau of Data Management. Unpublished data. 24. Fisher ES, Baron JA, Malenka DJ, Barrett J, Bubolz TA. Overcoming potential pitfalls in the use of Medicare data for epidemiologic research. Am J Public Health 1990; 80: 1487-1490. 25. Kelsey JL, O’Brien LA, Grisso JA, Hoffman S. Issues in carrying out epidemiologic research in the elderly. Am J Epidemiol 1989; 130: 857-866.
APPENDIX Definitions of Possible and Probable Fractures Diagnosis and procedure codes The algorithms for fracture identification utilized Medicare claims that suggested the care of patients with fractures. Such claims were identified from diagnoses, hospital procedures, physician procedures, and radiology procedures. Diagnoses. Claims from hospital-based providers (inpatient hospitals, emergency rooms, and hospital outpatient departments [clinics] include ICD-PDM diagnosis codes). The codes we used to identify possible fractures and to designate fracture sites are listed in Table Al. Diagnoses of
late effects of a fracture (905.3) and unspecified injuries (959) were excluded because a preliminary analysis suggested they had a very low positive predictive value for new fractures. The pathologic fracture diagnosis (733.1) was not used because it occurred very infrequently, and nearly always as a secondary diagnosis in conjunction with a primary diagnosis indicating the fracture site. Hospital procedures. For claims from hospital-based providers, the surgical procedure performed is coded with the ICD-9-CM system. We used codes (Table Al) for procedures indicating fracture treatment to identify possible
Table Al. Diagnosis and procedure codes used to identify possible fractures and designate fracture sites. The diagnoses and hospital procedures are coded with ICD4-CM; the physician and radiology procedures are coded with CPT4 Site
Diagnoses
Hospital procedures
Physician procedures: fracture identification
Hip
820
7855
27125-27127 27230 27232 2723627236 27238 27240 27242 27244 27246 27248
27130-27131 29015 29020 29035 29040 29046 29305 29345 29355 29365 29505 29799
29010 29025 29044 29325 29358 29520
73500 73510 73520 73530 73550
24620 24625 24635 24650 24655 24660
24580-24581 24585-24588 29075 29085 29125-29126
24583 29065 29105 29799
73070 73080 73090 73100 73110
29035 29040 29044 29046 29065 29105 29799
73020 73030 73050 73060 73070 73080
7905 7915 7925 7935 7965 8161 8162
Radius/ulna
Humerus
813
812
7853 7902 7912 7922 7932 7962
7852 7901 7911 7921 7931
24665-24666 24675 24680 25500 25505 25515 25530 25540 25545 25565 25570 25600 25605 25611 25615 25650
24670 24685 25510 25535 25560 25575 25610 25620
23600 23605 23610 23615 23620 23625 23630 23665 23670 23675 23680 24500 24505-24506 245 10 24515 24530-24531 24535-24536 24538 24540 24542 24545 24560 24565 24570 24575-2458 1 24583 24585-24588
Physician procedures: fracture confirmation
Radiology procedures
--continued
Identifying Fractures
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Table AL-continued Site
Diagnoses
Hospital procedures
Physician procedures: fracture identification
Physician procedures: fracture contirmation
Radiology procedures 71100 71101 71110 71111 71120 71130 72010 72020 72100 72110 72114 72120 72170 72180 72190 72200 72202 72220 73500 73510 73520 73530
Rib/sternum
8070 8071 8072 8073 8074
21800 21805 21820 21820 21825
29010 29015 29020 29025 29035 29040 29044 29046 29200
Pelvis/sacrum/coccyx
8056 8057 8066 8067 808
27190-27192 27200 27202 27210-27212 27214 27220 27222 27224 27225
27120 27122 27130 27131-27132
27760 27766 27790 27810 27816 27822 27500 27506 27512
27764 27788 27808 27814 27820
29010 29025 29358 29425 29540
29015 29345 29365 29505 29799
29020 29355 29405 29515
27504 27510
29010 29025 29044 29325 29358 29520
29015 29035 29046 29345 29365 29799
29020 29040 29305 29355 29505
70328 70330 73590 73600 73620 73630 73500 73510 73520 73530 73550
27762 27786 27792 27812 27818 27823 27502 27508 27514
Ankle
824
Femoral shaft
821
Hand
814 815 816 817
7854 7903 7904 7913 7914 7923 7924 7933 7934 7963 7964
25622 25624 25626 25628 25630 25635 25640 25645 25680 25685 26600 26605 26607 26610 26615 26645 26650 26655 26660 26665 26720 26725 26727 26730 26735 26740 26742 26743-26744 26746 26750 26755-26756 26760 26765
25600 25611 25650 29085 29126 29799
25605 25615 29065 29105 29130
25610 25620 29075 29125 29131
73100 73110 73120 73130 73140
Tibia/fibula
823
7857 7906 7916 7926 7936 7966
29010 29025 29358 29425 29799
29015 29345 29365 29505
29020 29355 29405 29515
73560 73562 73564 73590 73600 73610
Skull/face
800 801 802 803 804
767
27530 27532 27534 2753627538 27540 27750 27752 27754 27756 27758 27780 2778 l-27782 27784 27800 27802 27804 27806 21300 21310 21315 21320 21325 21330 21335 21337-21340 21345-21347 21350 21355 21360 21365 21380 21385-21387 21390 21395-21400 21401 21406-21407 21420-21422 21431 21432-21433 21435 21440 21445 21450
70100 70110 70120 70130 70134 70140 70150 70160 70200 70210 70220
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Table Al-conrinued Site
Diagnoses
Hospital procedures
Skull/face [continued]
Physician procedures: fracture confirmation
21451-21455 21461 21462 21465 21470 21495
Foot
825 826
Clavicle/scapula
810 811
Patella
822
809 818-819 827 828 829 E880 E881 E882 E883 E884 E885 E886 ES87 E888 E9293
Non-specific
Physician procedures: fracture identification
28400 28405-28406 28410 28415 28420 28430 2843528436 28440 28445 28450 28455 28460 28465 28470 28475-28476 28480 28485 28490 28495-28496 28500 28505 28510 28515 28520 28525
29405 29425 29505 29515 29550 29580 29799
23500 23505 23510 23515 23570 23575 23585
29010 29025 29044 29055 29105
29015 29035 29046 29058 29240
29020 29040 29049 29065
7856
27520 27522 27524
29010 29025 29044 29355 29435
29015 29035 29046 29358 29505
29020 29040 29345 29365 29530
7850 7851 7859 7900 7909 7910 7919 7920 7929 7930 7939 7960 7969
29010 29025 29044 29055 29075 29325 29358 29425
7858 7907 7908 7917 7918 7927 7928 7937 7938 7967 7968
fractures. However, codes for procedures such as total hip replacement that usually are performed for reasons other than a fracture were not considered. Physician procedures. For physician claims, the service performed during the visit is coded with the CPT4 system. Those codes (Table Al) suggesting treatment of a fracture were used to identify possible fractures and their sites. They also were used in conjuction with other claims to confirm fracture occurrence (e.g. diagnoses from clinic claims were not accepted as a probable fracture unless there was a concurrent physician claim with a procedure indicating fracture treatment). We did not use claims from anesthesiologists because often they were inaccurate with respect to fracture occurrence or site. When claims from multiple other physicians were present, we gave priority to those from orthopedic surgeons. There were two types of physician procedure codes (Table Al). The more specific codes were used for fracture identification and fracture confirmation; the less specific were used only for fracture confirmation. For example, if a clinic claim with a diagnosis of fracture of the ankle (ICD-PCM code 824) occurred in conjunction with a physician claim for application of a short leg cast (CPT4 code 29405), we classified the claim as a probable fracture, because the physician procedure for casting indicates fracture treatment occurred and is consistent with the diagnosis
29015 29035 29046 29058 29085 29345 29365 29435
Radiology procedures 70230 70231 70250 70260 73600 73620 73630 73650 73660
73000 73010 73020 73030 73050 73060 73550 73560 73562 73564 73590
29020 29040 29049 29065 29305 29355 29405
of an ankle fracture. However, in the absence of a fracture diagnosis, we did not classify claims with procedures for short leg cast application as probable fractures because this procedure may be performed for other reasons. In contrast, a physician claim with the more specific procedure for treatment of a medial malleolus fracture (CPT4 code 27760) would be classified as a probable fracture. Radiology procedures. Physician claims for skeletal radiologic examinations (codes listed in Table Al) were used in some instances to help determine fracture sites. However, we did not require presence of a radiology claim for probable fractures because our data showed this did not improve the positive predictive value. Linkage of claims
A single fracture could result in claims from several providers. For example, a hip fracture could generate claims for an emergency room visit, an inpatient admission, a radiologist’s roentgenographic examination of the hip, and an orthopedic surgeon’s repair of the fracture. We attempted to link these claims together in order to be as accurate as possible insofar as determing fracture occurrence, date, and site. We began by identifying the first claim during the study period with a study diagnosis or procedure code. If this first claim was an inpatient claim, we then identified all subsequent claims with study codes from the
Identifying Fractures admission date through the day following discharge. For emergency room, clinic claims, and physician claims we identified all subsequent claims with study codes from the visit date through the following 7 days. The date of service from the first claim in this group was used to determine the index date, which was our best estimate of the date the fracture occurred (in the example above, this would be the date of the emergency room visit). The claim we judged to be the most informative, the possible fracture claim was used to obtain medical records for review and to determine the number of possible fractures and their sites (in the example above, the possible fracture claim was the inpatient claim). Selection of this claim (priority high to low) was made on the basis of diagnosis (present or absent), provider type (hospital, emergency room, clinic, physician), date (priority to earliest), or fracture site (ordering as in Table Al). Other claims in the group were used to provide additional confirmation that a fracture had occurred and to help determine fracture sites. A special case was created for physician claims with a fracture code and no other claims from hospital-based providers with study codes in the following 7 days. We hypothesized this could result from failure by another provider to code a fracture diagnosis. For example, the hospital claim for a stroke admission accompanied by a fractured radius/ulna might include a diagnosis code for the stroke but not for the fracture; however, the claim from the physician who treats the fracture should be present. In this circumstance, we identified all claims from hospital-based providers that were likely to be associated with the physician claim. For inpatient claims, this meant the physician claim service date had to fall between admission and the day following discharge; for emergency room and clinic claims, the physician date was between the visit date and the 7 following days. In this case, the possible fracture claim was the physician claim, which was used to determine the index date and fracture site. However, the highest priority claim (following the rules given above) from a hospital-based provider was selected for medical record review. Possible fractures
We defined the occurrence of a group of one or more claims with a study diagnosis or procedure code as a possible fracture event. In the simplest (and most frequent) case, this event would correspond to a single fractue, the site of which could be readily determined from the diagnosis. However, a single injury event (e.g. fall or motor vehicle crash) could result in multiple fractures. Furthermore, our review of medical records found that in some cases the diagnosis coded in the Medicare claim was not the best indicator of fracture site. Therefore, based upon the medical record review, we developed an algorithm to determine the number of possible fractures and their sites from the fracture claims. If the possible fracture claim included one or more diagnoses, then we assigned one possible fracture for each diagnosis indicating a different site. Otherwise, if the group of claims associated with the possible fracture claim included one or more physician procedures, we assigned one possible fracture for each procedure indicting a different site. If the possible fracture claim did not have diagnoses, and there were no physician procedures, then we assigned one possible fracture for each different hospital procedure on the claim. In most cases, the fracture sites were determined from diagnosis and procedure codes as described in Table Al. However, because of systematic discrepancies between the codes and fracture sites that we noted from review of medical records, there were three exceptions to this rule: (1) The diagnosis indicated the hand (codes 814-817) but there was no physician procedure for treatment of fractures of the hand. Then
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(a) If the diagnosis was “Wrist, NOS” (not otherwise specified) (codes 814.00, 814.10), then the radius/ulna was designated as the site. (b) If the diagnosis was carpal bone fracture (codes 814.01-814.09. 814.11-814.19) but there was an associated physician treatment procedure for the distal radius/ulna, then the radius/ulna was designated as the site. (4 If neither (a) nor (b) was true, there was no physician procedure for the hand, but there was a physician procedure for treatment of a foot fracture or a radiology procedure for the foot, then the foot was designated as the site. (d) Otherwise, the hand was designated as the site. (2) The diagnosis was “tibia/fibula” (code 823) and there were no physician procedures indicating treatment of the proximal end or shaft of the tibia/fibula. If there was a physician procedure indicating the ankle, then the ankle was designated as the site; otherwise, this was the tibiaifibula. (3) The diagnosis was “femoral shaft” (code 821). If there was any physician procedure for the hip, then the hip was designated as the site. Probable fractures
Based upon review of medical records, we developed several criteria to exclude those possible fractures identified from the computerized files that were unlikely to correspond to true fractures. A probable fracture was then defined as any possible fracture that did not meet the exclusion criteria. The exclusions were: (1) Clinic diugnosis only. A clinic diagnosis with no physician procedure indicating a fracture. These usually represented negative examinations for suspected fractures or care provided for old fractures. (2) Hospital admission diagnosis only. A hospital admission diagnosis (which is separate from the 5 discharge diagnoses) with no discharge diagnosis or physician procedure indicating a fracture. These usually indicated admission for a suspected fracture that was not confirmed. (3) Care of old fractures or other bone diseases. A procedure compatible with fracture treatment where the only diagnoses present were those for a previous fracture or other bone disease: late effects of fracture (ICD-q-CM code 905.4), implant complication (codes 996.4, 996.6, 996.7, E878. I), aseptic necrosis (733.4), malunion of bone (773.8), other disorders of bone or cartilage (733.9), and fracture followup care (V540, V664, V674). These usually indicated a treatment procedure (such as hemiarthroplasty) that could be indicated for a new fracture, but which was provided for other reasons. (4) Arthroplasty for treatment of arthritis. A hospitalbased claim with a first diagnosis of osteoarthritis (code 715) and a hospital procedure for arthroplasty (codes 81.3-81.9). These possible fractures were identified on the basis of physician arthroplasty procedures that are often used to treat fractures. However, the absence of a fracture diagnosis and presence. of a diagnosis of arthritis (which also is an indication for arthroplasty) suggested a fracture was unlikely. (5) Hospi!al procedure only. This category consists of possible fractures identified on the basis of a hospital procedure, but with no other indication of a fracture. Medical record review showed that in the absence of a fracture diagnosis or a physician procedure for fracture treatment, fracture occurrence was uniikely. (6) Emergency room hip fracture rule-out. This category primarily consisted of emergency room hip fracture diagnoses but without a corresponding physician procedure. It also included emergency room hip fracture diagnoses followed within 1 day by a hospital claim that had an admission diagnosis for hip fracture but did not have a
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discharge diagnosis or physician procedure indicating a fracture. Medical record review indicated these usually represented examination for suspected hip fractures that were not confirmed. However, we found that for other sites, emergency room diagnoses, even in the absence of physician procedures, had a high positive predictive value. (7) Fracture site not indicated. Any possible fracture for which the diagnosis and procedure codes did not indicate a fracture site. This category included physician procedures for casting. This exclusion was made because these possible fractures, for which the site was not indicated by the computerized files, were by definition not concordant with the medical record. We included specific physician procedures for fracture treatment in the definition of a probable fracture, even in the absence of a concurrent claim from a hospital-based provider. We did not review medical records for this type of probable fracture claim and thus had no direct estimate
of the positive predictive value. There were 164 possible fractures (5% of all possible fractures) identified from a physician claim for which there was no concurrent claim from a hospital-based provider. Of these, 104 indicated reduction, fixation, or other fracture repair whereas 60 were for cast application. We included the former in the definition of a probable fracture because review of medical records for hospital-based provider claims (n = 42) having no fracture diagnosis or procedure but with a concurrent physician possible fracture claim of this type found a positive predictive value of 86%. We did not include the latter because casting procedures do not reliably identify the fracture site and may indicate treatment of other injuries. This exclusion will reduce sensitivity somewhat for sites such as the ankle and the radius/ulna. As Medicare physician diagnoses become available, they may be used to identify fractures that were missed by our probable fracture definition.
