Accuracy of pharmacy and coded‐diagnosis information in identifying tuberculosis in patients with rheumatoid arthritis

Fiske, Christina T.; Griffin, Marie R.; Mitchel, Edward F.; Grijalva, Carlos G.

doi:10.1002/pds.3243

Cited by 11 publications

(16 citation statements)

References 9 publications

(11 reference statements)

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“…In a recently published study by Fiske et al () of RA Medicaid patients using a TB registry as the gold standard, ICD‐9 data alone grossly overestimated the number of TB cases (449 versus 10 confirmed cases in the registry); even when ICD‐9 codes were combined with pharmacy data, the false‐positive rate was still 75%. Trepka et al () also demonstrated that the sensitivity and PPV for discharge diagnosis for TB are low (47.7% and 38.3%, respectively).…”

Section: Resultsmentioning

confidence: 98%

“…Upon hand searching references, 1 additional study was identified. In total, 24 studies met our criteria for entry into the systematic review () (Figure and Table ). Seventeen studies examined bacterial infections, including 8 specifically examining the validation of pneumonia (Table ).…”

Section: Resultsmentioning

confidence: 99%

“…Opportunistic infections were examined in 9 articles (). Three studies examined a variety of opportunistic infections ().…”

Section: Resultsmentioning

confidence: 99%

“…Six studies specifically examined algorithms for identifying TB (). Calderwood et al developed algorithms for TB detection incorporating ICD‐9 codes for TB, pharmacy data, and an order for acid‐fast bacilli.…”

Section: Resultsmentioning

confidence: 99%

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Systematic Review of Validation Studies of the Use of Administrative Data to Identify Serious Infections

Barber

Lacaille

Fortin

2013

Arthritis Care & Research

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Objective. To conduct a systematic review of the literature on the validation of algorithms identifying infections in administrative data for future use in populations with rheumatic diseases. Methods. Medline and EMBase were searched using the themes "administrative data" and "infection" between 1950 and October 2012. Inclusion criteria consisted of validation studies of administrative data identifying infections in adult populations. Article quality was assessed using a validated tool. Results. A total of 5,941 articles were identified, 90 articles underwent detailed review, and 24 studies were included. The majority (17 of 24) examined bacterial infections and 9 examined opportunistic infections. Eighteen studies were from the US and all but 4 studies used International Classification of Diseases, Ninth Revision codes. Rheumatoid arthritis patients were studied in 6 of 24 articles. The studies on bacterial infections in general reported highly variable sensitivity and positive predictive value (PPV) for the diagnosis of infections using administrative data (sensitivity range 4.4 -100%, PPV range 21.7-100%). Algorithms to identify opportunistic infections similarly had a highly variable sensitivity (range 20 -100%) and PPV (range 1.3-100%). Thirteen studies compared the diagnostic accuracy of different algorithms, which revealed that strategies including a comprehensive algorithm using a greater number of diagnostic codes or codes in any position had the highest sensitivity for the diagnosis of infection. Algorithms that incorporated microbiologic or pharmacy data in combination with diagnostic codes had improved PPV for identification of tuberculosis. Conclusion. Algorithms for identifying infections using administrative data should be selected based on the purpose of the study, with careful consideration as to whether a high sensitivity or PPV is required.

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

“…Opportunistic infections were examined in 9 articles (). Three studies examined a variety of opportunistic infections ().…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Systematic Review of Validation Studies of the Use of Administrative Data to Identify Serious Infections

Barber

Lacaille

Fortin

2013

Arthritis Care & Research

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“…While there have been no specific reviews of ICD-9 code accuracy regarding FXS, SB, or MD in the United States, broader reviews of the literature reveal that unintentional coder errors caused by the limits of the clinician's knowledge and experience with the condition, misinterpreted information from the clinical record, and data entry mistakes lead to inaccuracy in ICD coding. [29][30][31] To control for miscoding, our inclusion criteria required two or more diagnoses of FXS, SB, or MD. The specification of at least two occurrences of a diagnosis was used in a Canadian study, which linked two surveillance systems; for spina bifida, they found an agreement rate of 64.1 %.…”

Section: Discussionmentioning

confidence: 99%

Use of State Administrative Data Sources to Study Adolescents and Young Adults with Rare Conditions

Royer¹,

Hardin

McDermott

et al. 2014

J GEN INTERN MED

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BACKGROUND: Effective care of young people with rare conditions requires ongoing coordinated medical treatment as well as educational and social support services. However, information on treatment is often lacking due to limited data. South Carolina has a repository of comprehensive health and human service data with which individuals may be tracked across the data systems of multiple state agencies and organizations. OBJECTIVE: To develop a method for studying health care of young persons with rare conditions using this repository. METHODS: We identified individuals aged 15 to 24 years diagnosed during 2000-2010 with Fragile X syndrome (FXS), spina bifida (SB), or muscular dystrophy (MD) using a series of algorithms. ICD-9-CM codes were used to initially identify the cohort from medical billing data. Demographics, medical care, employment, education, and socioeconomic status data were then extracted from linked administrative sources. RESULTS: We identified 1,040 individuals with these rare conditions: 125 with FXS, 695 with SB, and 220 with MD. The vast majority of the cases (95 %) were identified in the Medicaid database. Half of the cohort was male, with a higher percentage in the FXS and MD groups. Sixty-two percent of the cohort was enrolled in the last year of high school. Over half of the cohort received support services from the state's disability and special-needs agency; 16 % received food assistance. Thirty-eight percent were employed at some point during the study period. Forty-nine individuals with SB and 56 with MD died during the study period. CONCLUSIONS: We used a linked statewide data system to study rare conditions. Strengths include the diversity of information, rigorous identification strategies, and access to longitudinal data. Despite limitations inherent to administrative data, we found that linked state data systems are valuable resources for investigating important public health questions on rare conditions.

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A Population‐Based Study of Infection‐Related Hospital Mortality in Patients With Dermatomyositis/Polymyositis

Murray

Schmajuk

Trupin

et al. 2015

Arthritis Care & Research

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OBJECTIVE Dermatomyositis (DM) and polymyositis (PM) are debilitating inflammatory myopathies associated with significant mortality. We evaluated the relative contribution of infection to hospital mortality in a large population-based study of individuals with DM/PM. METHODS Data derive from the 2007 to 2011 Healthcare Cost and Utilization Project National Inpatient Samples and included all hospital discharges that met a validated administrative definition of DM/PM. The primary outcome was hospital mortality. Variables for infections and comorbidities were generated from discharge diagnoses using validated administrative definitions. Logistic regression was used to investigate the relationship between infection and mortality in individuals with DM/PM, adjusting for sociodemographics, utilization variables, and comorbidities. Relative risks were calculated to compare the overall prevalence of specific infections and associated mortality in DM/PM hospitalizations with those seen in the general hospitalized population. RESULTS 15,407 hospitalizations with DM/PM met inclusion criteria for this study and inpatient mortality was 4.5% (700 deaths). In adjusted logistic regression analyses, infection (OR 3.4, 95% CI 2.9–4.0) was the strongest predictor of hospital mortality among individuals with DM/PM. Bacterial infection (OR 3.5, 95% CI 3.0–4.1), comprised primarily of pneumonia and bacteremia, and opportunistic fungal infections (OR 2.5, 95% CI 1.5–4.0) were independently associated with hospital mortality. The overall burden of infection in hospitalizations with DM/PM was significantly increased in comparison with the general hospitalized population (RR 1.5, 95% CI 1.4–1.6). CONCLUSION Among hospitalized individuals with DM/PM, infection is the leading cause of mortality. Strategies to mitigate infection risk in both the clinic and hospital settings should be evaluated to improve disease outcomes.

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Accuracy of pharmacy and coded‐diagnosis information in identifying tuberculosis in patients with rheumatoid arthritis

Cited by 11 publications

References 9 publications

Systematic Review of Validation Studies of the Use of Administrative Data to Identify Serious Infections

Systematic Review of Validation Studies of the Use of Administrative Data to Identify Serious Infections

Use of State Administrative Data Sources to Study Adolescents and Young Adults with Rare Conditions

A Population‐Based Study of Infection‐Related Hospital Mortality in Patients With Dermatomyositis/Polymyositis

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