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Validation of infant immunization billing codes in administrative data.

ABSTRACT: Ontario has a single payer provincial health insurance program. Administrative data may provide a potentially robust source of information for post-marketing vaccine studies. Vaccine-specific immunization billing codes were introduced in 2011. Our objective was to validate Ontario's universal health care administrative datasets to assess infant immunization status. Electronic medical record data from the Electronic Medical Record Administrative data Linked Database (EMRALD) was used as the reference standard to calculate performance characteristics of the Ontario Health Insurance Plan (OHIP) database vaccine-specific and general immunization codes for 4 primary infant immunizations: diphtheria, tetanus, acellular pertussis, inactivated polio, Haemophilus influenzae type B (DTaP-IPV-Hib) combination vaccine, pneumococcal conjugate vaccine, measles, mumps, rubella (MMR) vaccine, and meningococcal conjugate serogroup C vaccine. OHIP billing claims had specificity ranging from 81% to 92%, sensitivity 70% to 83%, positive predictive value (PPV) 97% to 99%, and negative predictive value (NPV) 13% to 46% for identifying the various specific vaccines in administrative data. For cohorts vaccinated in the new code introduction phase, using both the vaccine-specific and general codes had higher sensitivity than the vaccine-specific codes alone. In conclusion, immunization billing claims from administrative data in Ontario had high specificity and PPV, moderate sensitivity, and low NPV. This study identifies some of the applications of utilizing administrative data for post-marketing vaccine studies. However, limitations of these data decrease their utility for measuring vaccine coverage and effectiveness. Therefore, the establishment of a comprehensive and linkable immunization registry should be a provincial priority.

SUBMITTER: Schwartz KL

PROVIDER: S-EPMC4514409 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Validation of infant immunization billing codes in administrative data.

Schwartz Kevin L KL Tu Karen K Wing Laura L Campitelli Michael A MA Crowcroft Natasha S NS Deeks Shelley L SL Wilson Sarah E SE Wilson Kumanan K Gemmill Ian I Kwong Jeffrey C JC

Human vaccines & immunotherapeutics 20150101 7

Ontario has a single payer provincial health insurance program. Administrative data may provide a potentially robust source of information for post-marketing vaccine studies. Vaccine-specific immunization billing codes were introduced in 2011. Our objective was to validate Ontario's universal health care administrative datasets to assess infant immunization status. Electronic medical record data from the Electronic Medical Record Administrative data Linked Database (EMRALD) was used as the refer ...[more]

PMID: 26075651

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Project description:Community-acquired pneumonia (CAP) remains one of the most common indications for pediatric hospitalization in the United States, and it is frequently the focus of research and quality studies. Use of administrative data is increasingly common for these purposes, although proper validation is required to ensure valid study conclusions.To validate administrative billing data for hospitalizations owing to childhood CAP.Case-control study of 4 tertiary care, freestanding children’s hospitals in the United States.A total of 998 medical records of a 25% random sample of 3646 children discharged in 2010 with at least 1 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code representing possible pneumonia were reviewed. Discharges (matched on date of admission) without a pneumonia-related discharge code were also examined to identify potential missed pneumonia cases. Two reference standards, based on provider diagnosis alone (provider confirmed) or in combination with consistent clinical and radiographic evidence of pneumonia (definite), were used to identify CAP.Twelve ICD-9-CM–based coding strategies, each using a combination of primary or secondary codes representing pneumonia or pneumonia-related complications. Six algorithms excluded children with complex chronic conditions.Sensitivity, specificity, and negative and positive predictive values (NPV and PPV, respectively) of the 12 identification strategies.For provider-confirmed CAP (n?=?680), sensitivity ranged from 60.7% to 99.7%; specificity, 75.7% to 96.4%; PPV, 67.9% to 89.6%; and NPV, 82.6% to 99.8%. For definite CAP (n?=?547), sensitivity ranged from 65.6% to 99.6%; specificity, 68.7% to 93.0%; PPV, 54.6% to 77.9%; and NPV, 87.8% to 99.8%. Unrestricted use of the pneumonia-related codes was inaccurate, although several strategies improved specificity to more than 90% with a variable effect on sensitivity. Excluding children with complex chronic conditions demonstrated the most favorable performance characteristics. Performance of the algorithms was similar across institutions.Administrative data are valuable for studying pediatric CAP hospitalizations. The strategies presented here will aid in the accurate identification of relevant and comparable patient populations for research and performance improvement studies.

Project description:BackgroundDischarge diagnoses are used to track national trends and patterns of maternal morbidity. There are few data regarding the validity of the International Classification of Diseases (ICD) codes used for this purpose. The goal of our study was to try to better understand the validity of administrative data being used to monitor and assess trends in morbidity.MethodsHospital stay billing records were queried to identify all delivery admissions at the Massachusetts General Hospital for the time period 2001 to 2011 and the University of Michigan Health System for the time period 2005 to 2011. From this, we identified patients with ICD-9-Clinical Modification (CM) diagnosis and procedure codes indicative of severe maternal morbidity. Each patient was classified with 1 of 18 different medical/obstetric categories (conditions or procedures) based on the ICD-9-CM code that was recorded. Within each category, 20 patients from each institution were selected at random, and the corresponding medical charts were reviewed to determine whether the ICD-9-CM code was assigned correctly. The percentage of correct codes for each of 18 preselected clinical categories was calculated yielding a positive predictive value (PPV) and 99% confidence interval (CI).ResultsThe overall number of correctly assigned ICD-9-CM codes, or PPV, was 218 of 255 (86%; CI, 79%-90%) and 154 of 188 (82%; CI, 74%-88%) at Massachusetts General Hospital and University of Michigan Health System, respectively (combined PPV, 372/443 [84%; CI, 79-88%]). Codes within 4 categories (Hysterectomy, Pulmonary edema, Disorders of fluid, electrolyte and acid-base balance, and Sepsis) had a 99% lower confidence limit ≥75%. Codes within 8 additional categories demonstrated a 99% lower confidence limit between 74% and 50% (Acute respiratory distress, Ventilation, Other complications of obstetric surgery, Disorders of coagulation, Cardiomonitoring, Acute renal failure, Thromboembolism, and Shock). Codes within 6 clinical categories demonstrated a 99% lower confidence limit <50% (Puerperal cerebrovascular disorders, Conversion of cardiac rhythm, Acute heart failure [includes arrest and fibrillation], Eclampsia, Neurotrauma, and Severe anesthesia complications).ConclusionsICD-9-CM codes capturing severe maternal morbidity during delivery hospitalization demonstrate a range of PPVs. The PPV was high when objective supportive evidence, such as laboratory values or procedure documentation supported the ICD-9-CM code. The PPV was low when greater judgment, interpretation, and synthesis of the clinical data (signs and symptoms) was required to support a code, such as with the category Severe anesthesia complications. As a result, these codes should be used for administrative research with more caution compared with codes primarily defined by objective data.

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Validation of infant immunization billing codes in administrative data.

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Validation of infant immunization billing codes in administrative data.

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