LB744 Use of indoor tanning diagnosis codes in claims data

Abstract Background With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data. Objective In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness. Methods We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis–specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes). Results A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset. Conclusion We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

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Abstract 18020: Identifying Hospitalizations Related to Heart Failure in Dronedarone Users Who Are Supplementary Medicare Beneficiaries

Circulation ◽

10.1161/circ.130.suppl_2.18020 ◽

2014 ◽

Vol 130 (suppl_2) ◽

Author(s):

Chuntao Wu ◽

Andrew Koren ◽

Jane Thammakhoune ◽

Jasmanda Wu ◽

Hayet Kechemir ◽

...

Keyword(s):

Heart Failure ◽

Confidence Interval ◽

Claims Data ◽

Search Algorithm ◽

Medicare Beneficiaries ◽

Current Procedural Terminology ◽

Diagnosis Codes ◽

Medicare Database ◽

The Impact

Background: When using inpatient claims data to identify hospitalizations in supplemental Medicare beneficiaries, e.g., in the MarketScan database, there is a concern that the coverage of hospitalizations in such inpatient claims may be incomplete. However, whether hospitalizations are covered by inpatient claims or not, they incur professional charges that are recorded in the professional claims data in the MarketScan Medicare database. In the context of identifying hospitalizations that might be related to heart failure (HF) in dronedarone users, we compared different approaches to identify such hospitalizations. Objective: To assess the impact of using professional claims in addition to inpatient claims on identifying hospitalizations that might be related to HF. Methods: A total of 20,834 dronedarone users who were supplemental Medicare beneficiaries between July 2009 (launch date in US) and December 2012 were identified in the MarketScan database. The hospitalizations that might be related to HF within 30 days prior to initiating dronedarone were identified by searching (1) inpatient claims and (2) both inpatient and professional claims using related ICD-9-CM diagnosis codes for HF and Current Procedural Terminology codes for hospitalizations. Results: A total of 1,162 patients who had HF hospitalizations within 30 days prior to initiating dronedarone were identified by searching inpatient claims between July 2009 and December 2012. Supplementing with professional claims identified an additional 177 patients who had HF hospitalizations, increasing the total number to 1,339. Therefore, 13.2% (177/1,399) of the patients who had HF hospitalizations could only be identified in professional claims. Thus, the prevalence of hospitalizations that might be related to HF within 30 days prior to initiating dronedarone was 5.6% (1,162/20,834; 95% confidence interval (CI): 5.3 - 5.9%) when hospitalizations were identified using inpatient claims alone. Adding professional claims in the search algorithm, the prevalence of HF hospitalizations was 6.4% (1,339/20,834, 95% CI: 6.1 - 6.8%). Conclusions: Using professional claims, in addition to inpatient claims, can improve the identification of hospitalizations that might be related to HF.

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Use of Diagnosis Codes and/or Wound Culture Results for Surveillance of Surgical Site Infection after Mastectomy and Breast Reconstruction

Infection Control and Hospital Epidemiology ◽

10.1086/652155 ◽

2010 ◽

Vol 31 (05) ◽

pp. 544-547 ◽

Cited By ~ 21

Author(s):

Margaret A. Olsen ◽

Victoria J. Fraser

Keyword(s):

Surgical Site Infection ◽

Claims Data ◽

Breast Surgery ◽

International Classification Of Diseases ◽

Site Infection ◽

Wound Culture ◽

Diagnosis Codes ◽

Classification Of Diseases ◽

Major Breast

We compared surveillance of surgical site infection (SSI) after major breast surgery by using a combination of International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes and microbiology-based surveillance. The sensitivity of the coding algorithm for identification of SSI was 87.5%, and the sensitivity of wound culture for identification of SSI was 78.1%. Our results suggest that SSI surveillance can be reliably performed using claims data.

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The prevalence of localised scleroderma in childhood assessed in the administrative claims data from the United States

Journal of Scleroderma and Related Disorders ◽

10.1177/2397198318798588 ◽

2018 ◽

Vol 4 (1) ◽

pp. 77-78

Author(s):

Timothy Beukelman ◽

Fenglong Xie ◽

Ivan Foeldvari

Keyword(s):

United States ◽

Systemic Sclerosis ◽

Claims Data ◽

International Classification Of Diseases ◽

The United States ◽

Administrative Claims ◽

Administrative Claims Data ◽

Diagnosis Codes ◽

Classification Of Diseases

Juvenile localised scleroderma is believed an orphan autoimmune disease, which occurs 10 times more often than systemic sclerosis in childhood and is believed to have a prevalence of 1 per 100,000 children. To gain data regarding the prevalence of juvenile localised scleroderma, we assessed the administrative claims data in the United States using the International Classification of Diseases, Ninth Revision diagnosis codes. We found an estimated prevalence in each year ranging from 3.2 to 3.6 per 10,000 children. This estimate is significantly higher as found in previous studies.

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Assessing the prevalence of juvenile systemic sclerosis in childhood using administrative claims data from the United States

Journal of Scleroderma and Related Disorders ◽

10.1177/2397198318763701 ◽

2018 ◽

Vol 3 (2) ◽

pp. 189-190 ◽

Cited By ~ 4

Author(s):

Timothy Beukelman ◽

Fenglong Xie ◽

Ivan Foeldvari

Keyword(s):

United States ◽

Systemic Sclerosis ◽

Claims Data ◽

International Classification Of Diseases ◽

The United States ◽

Administrative Claims ◽

Administrative Claims Data ◽

Diagnosis Codes ◽

Classification Of Diseases ◽

Juvenile Systemic Sclerosis

Juvenile systemic sclerosis is a very rare orphan disease. To date, only one publication has estimated the prevalence of juvenile systemic sclerosis using a survey of specialized physicians. We conducted a study of administrative claims data in the United States using the International Classification of Diseases, Ninth Revision diagnosis codes and found a prevalence of approximately 3 per 1,000,000 children. This estimate will inform the planning of prospective studies.

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Incidence of Surgical Site Infection Following Mastectomy With and Without Immediate Reconstruction Using Private Insurer Claims Data

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2015.108 ◽

2015 ◽

Vol 36 (8) ◽

pp. 907-914 ◽

Cited By ~ 24

Author(s):

Margaret A. Olsen ◽

Katelin B. Nickel ◽

Ida K. Fox ◽

Julie A. Margenthaler ◽

Kelly E. Ball ◽

...

Keyword(s):

Surgical Site Infection ◽

Claims Data ◽

Diverse Population ◽

Site Infection ◽

Immediate Reconstruction ◽

National Healthcare ◽

National Healthcare Safety Network ◽

Diagnosis Codes ◽

Unilateral Mastectomy ◽

Procedure Type

OBJECTIVEThe National Healthcare Safety Network classifies breast operations as clean procedures with an expected 1%–2% surgical site infection (SSI) incidence. We assessed differences in SSI incidence following mastectomy with and without immediate reconstruction in a large, geographically diverse population.DESIGNRetrospective cohort studyPATIENTSCommercially insured women aged 18–64 years with ICD-9-CM procedure or CPT-4 codes for mastectomy from January 1, 2004 through December 31, 2011METHODSIncident SSIs within 180 days after surgery were identified by ICD-9-CM diagnosis codes. The incidences of SSI after mastectomy with and without immediate reconstruction were compared using the χ2 test.RESULTSFrom 2004 to 2011, 18,696 mastectomy procedures among 18,085 women were identified, with immediate reconstruction in 10,836 procedures (58%). The incidence of SSI within 180 days following mastectomy with or without reconstruction was 8.1% (1,520 of 18,696). In total, 49% of SSIs were identified within 30 days post-mastectomy, 24.5% were identified 31–60 days post-mastectomy, 10.5% were identified 61–90 days post-mastectomy, and 15.7% were identified 91–180 days post-mastectomy. The incidences of SSI were 5.0% (395 of 7,860) after mastectomy only, 10.3% (848 of 8,217) after mastectomy plus implant, 10.7% (207 of 1,942) after mastectomy plus flap, and 10.3% (70 of 677) after mastectomy plus flap and implant (P<.001). The SSI risk was higher after bilateral compared with unilateral mastectomy with immediate reconstruction (11.4% vs 9.4%, P=.001) than without (6.1% vs 4.7%, P=.021) immediate reconstruction.CONCLUSIONSSSI incidence was twice that after mastectomy with immediate reconstruction than after mastectomy alone. Only 49% of SSIs were coded within 30 days after operation. Our results suggest that stratification by procedure type facilitates comparison of SSI rates after breast operations between facilities.Infect Control Hosp Epidemiol 2015;36(8):907–914

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1337. Medically Attended (MA) Illness Due to Respiratory Syncytial Virus (RSV) Infection among Infants in the United States during the 2016–17, 2017–18, 2018–19, and 2019–20 RSV Seasons: The Need for All-Infant Protection

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab466.1529 ◽

2021 ◽

Vol 8 (Supplement_1) ◽

pp. S756-S756

Author(s):

Jason Gantenberg ◽

Nicole Zimmerman ◽

Andrew R Zullo ◽

Brendan Limone ◽

Clarisse Demont ◽

...

Keyword(s):

Claims Data ◽

The United States ◽

Research Support ◽

Term Infants ◽

Material Support ◽

Diagnosis Codes ◽

Icd 10 ◽

Ibm Watson ◽

Syncytial Virus ◽

Research Grant

Abstract Background RSV-associated lower respiratory tract infection (LRTI) is the leading cause of infant hospitalization. Most studies of RSV have focused on infants with underlying comorbidities, including prematurity. The purpose of this analysis is to describe the burden of RSV LRTI across all medical settings and in all infants experiencing their first RSV season. Methods Using de-identified claims data from two commercial (MarketScan Commercial, MSC; Optum Clinformatics, OC) and one public (MarketScan Medicaid, MSM) insurance database, we estimated the prevalence of MA RSV LRTI among infants born between April 1, 2016 and June 30, 2019 in their first RSV season. Estimates were made by gestational age, presence/absence of comorbidities, and setting (inpatient, emergency department and outpatient). Due to limited laboratory testing, we defined MA RSV LRTI using two sets of ICD-10-CM diagnosis codes: a specific definition (identifying RSV explicitly) and a sensitive definition that included unspecified bronchiolitis. The first specific diagnosis triggered a search for another MA RSV LRTI diagnosis (either specific or sensitive) within the next 7 days. In the sensitive analysis, the first diagnosis was allowed to meet the sensitive definition. Setting was recorded as the highest level of care attached to a MA RSV LRTI diagnosis within this 7-day period. Results Using the specific (sensitive) definitions, 4.2% (12.2%), 6.8% (16.8%), and 2.7% (7.2%) of newborns had an MA RSV LRTI diagnosis during their first respiratory season across the MSC, MSM, and OC datasets (Table 1). Term infants without comorbidities accounted for 77% (83%), 79% (86%), and 80 (81%) of all MA RSV LRTI, and 21% (10%), 19% (10%), and 21% (10%) of all infants with MA RSV LRTI had an inpatient hospital stay (Table 2). Term infants without comorbidities accounted for 69% (68%), 67% (79%), and 73% (73%) of all MA RSV LRTI inpatients (Table 2). Conclusion In commercial and public claims data, during their first RSV season, term infants without comorbidities accounted for a sizable majority of inpatient, emergency room, and outpatient encounters for RSV LRTI in the US. To address the burden of RSV LRTI, future RSV prevention efforts should target all infants. Funding Sanofi Pasteur, AstraZeneca Disclosures Jason Gantenberg, MPH, Sanofi Pasteur (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support) Nicole Zimmerman, MS, IBM Watson Health (Employee, Nicole Zimmerman is an employee of IBM, which was compensated by Sanofi to complete this work.)Sanofi (Other Financial or Material Support, Nicole Zimmerman is an employee of IBM, which was compensated by Sanofi to complete this work.) Andrew R. Zullo, PharmD, PhD, ScM, Sanofi Pasteur (Grant/Research Support, Research Grant or Support) Brendan Limone, PharmD, PharmD, Sanofi Pasteur (Other Financial or Material Support, IBM was contracted by Sanofi to perform analysis) Clarisse Demont, n/a, Sanofi Pasteur (Employee, Shareholder) Sandra S. Chaves, MD, MSc, Sanofi Pasteur (Employee) William V. La Via, MD, AstraZeneca (Shareholder)Sanofi Pasteur (Employee) Christopher Nelson, PhD, Epidemiology, Sanofi Pasteur (Employee) Christopher Rizzo, MD, Sanofi (Employee) David A. Savitz, PhD, Sanofi Pasteur (Grant/Research Support) Robertus Van Aalst, MSc, Sanofi Pasteur (Employee, Shareholder)

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1200. Healthcare Claims-Based Lyme Disease Case-Finding Algorithms in the United States: A Systematic Literature Review

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab466.1392 ◽

2021 ◽

Vol 8 (Supplement_1) ◽

pp. S691-S692

Author(s):

Young Hee Nam ◽

Sarah J Willis ◽

Aaron Mendelsohn ◽

Susan Forrow ◽

Jeffrey Brown ◽

...

Keyword(s):

United States ◽

Lyme Disease ◽

Literature Review ◽

Systematic Literature Review ◽

Claims Data ◽

The United States ◽

Case Finding ◽

Diagnosis Codes ◽

Disease Case ◽

The Us

Abstract Background Lyme disease (LD) is the fifth most common notifiable disease in the US with 30,000-40,000 LD cases reported annually via public health surveillance. Recent healthcare claims-based studies utilizing case-finding algorithms estimate national LD cases are >10-fold higher than reported by surveillance. The reliability of claims-based data depends on the accuracy of the case-finding algorithms using the information available in the claims primarily generated for the administrative purposes. To assess the true burden of LD, it is imperative to use validated well-performing LD case-finding algorithms (“LD algorithms”). We conducted a systematic literature review to identify LD algorithms based upon healthcare claims data in the US and their respective performance. Methods We searched PubMed and Embase for articles published in English from January 1, 2000 through the most recent date as of February 20, 2021. We selected articles including all of the following search terms: (1) “Lyme disease”; (2) “claim*” or “administrative* data”; and (3) “United States” or “the US*”. We then reviewed the titles, abstracts, and full texts to identify articles describing LD algorithms developed for claims data. Figure 1 shows the flow diagram following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Results We found 15 articles meeting the inclusion criteria. Of these, 7 study algorithms used only LD diagnosis codes (ICD-9, 088.81; ICD-10, A69.2 or A69.2x), 4 studies additionally used antibiotic dispensing records, and 4 studies additionally used serologic test order codes (CPT 86617, 86618). Three studies used different algorithms for inpatient and outpatient settings. Only one study (in Tennessee, a low-incidence state for LD) provided validation results for their algorithm, which only used a LD diagnosis code (ICD-9, 088.81), with reported sensitivity=50% and positive predictive value=5%. Conclusion Validation data on the LD algorithms developed for healthcare claims data are limited, and suggest algorithms using only LD diagnosis codes may not perform well. Further validation of high-performance claims-based LD algorithms is critical to inform the true burden of LD overall and within subgroups. Disclosures Bradford D. Gessner, MD, MPH, Pfizer Inc. (Employee) James Stark, PhD, Pfizer Inc. (Employee) Sarah Pugh, PhD, Pfizer Inc. (Employee)

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Relative and Absolute Rankings of Diagnosis Codes in Claims Data

Korean Journal of Family Practice ◽

10.21215/kjfp.2016.6.4.331 ◽

2016 ◽

Vol 6 (4) ◽

pp. 331-338

Author(s):

Jin Hee Kim ◽

Jae Moon Yun ◽

Eui Heon Chung ◽

Mi So Kang

Keyword(s):

Claims Data ◽

Diagnosis Codes

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