Dataset Information

Can Diagnostic Codes in Health Care Claims Data Identify Confirmed Chlamydial and Gonococcal Infections? A Retrospective Cohort Study, 2003 to 2017.

ABSTRACT:

Background

Because most sources of administrative claims data do not contain laboratory result data, researchers rely on diagnosis codes to identify cases of disease. The validity of using diagnosis codes to identify chlamydial and gonococcal infections in administrative claims data remains largely uninvestigated.

Methods

We conducted a retrospective cohort analysis using OptumLabs Data Warehouse, which includes deidentified medical (inpatient and outpatient) claims and laboratory test results. Among males and females aged 15 to 39 years during the period 2003-2017, we identified chlamydia and gonorrhea test results and corresponding diagnosis codes. Using test results as the criterion standard, we calculated the sensitivity and specificity of chlamydia and gonorrhea diagnosis codes to identify laboratory-confirmed infections.

Results

We identified 9.7 million chlamydia and gonorrhea test results among 3.1 million enrollees. Of the 176,241 positive chlamydia test results, only 11,515 had a corresponding diagnosis code, for a sensitivity of 6.5 (95% confidence interval [CI], 6.4-6.7) and a specificity of 99.5 (95% CI, 99.5-99.5). Corresponding diagnosis codes were identified for 8056 of the 31,766 positive gonorrhea test results, for a sensitivity of 25.4 (95% CI, 24.9-25.8) and a specificity of 99.7 (95% CI, 99.7-99.7).

Conclusions

Our findings indicate that using only International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modification diagnosis codes to identify chlamydial and gonococcal infections substantially underestimates the burden of these diseases and inaccurately classifies laboratory-confirmed infections.

SUBMITTER: Mauk KC

PROVIDER: S-EPMC8284366 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Purpose: Previous studies identified the study cohort or outcome of carbon monoxide poisoning (COP) by using the relevant disease diagnosis codes in secondary databases, but the validity of diagnosis codes of COP is unclear in such secondary databases. This study aimed to evaluate the accuracy of case definitions for COP using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in Taiwan's health insurance claims data. Methods: We selected a 10% simple random sample from an original cohort of patients newly undergoing carboxyhemoglobin (COHb) testing under any clinical diagnosis at four Chang Gung Memorial Hospitals in Taiwan during 2011-2020. Two clinical doctors independently ascertained the COP diagnosis by reviewing the medical records as the reference standard. We estimated the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of various case definitions (e.g., ICD-9-CM code, ICD-10-CM code and the uses of hyperbaric oxygen therapy) in the claims data. Results: We randomly selected 557 cases from the original cohort of 5,571 cases newly receiving COHb test in the study hospitals. We found 90, 35, and 9 cases were true-positive, false-positive, and false-negative episodes of COP, respectively, among 278 cases with an ICD-9-CM code of 986. A further 111, 34, and 6 cases were true-positive, false-positive, and false-negative episodes of COP, respectively, among 279 cases with an ICD-10-CM code of T58. Using ICD-9-CM codes, the sensitivity, specificity, PPV and NPV for COP were 90.9, 80.4, 72, and 94.1%, respectively. Using ICD-10-CM codes they were 94.9, 79, 76.6, and 95.5%, respectively. PPV typically increased when COP was the primary diagnosis and could reach 100% if patients with ICD-CM code 986 or T58 also received hyperbaric oxygen therapy during hospitalization. Conclusion: The COP-related ICD-CM codes alone did not accurately identify COP patients, but accuracy improved after including oxygen therapy data with the ICD-CM codes in Taiwan's claims data.

Project description:While medical records have detailed information, they are limited in reach to the availability and accessibility of those records. On the other hand, administrative data while limited in scope, have a much further reach in coverage of an entire population. However, few studies have validated the use of administrative data for identifying infections in pediatric populations. Pediatric patients from Ontario, Canada aged <18 years were randomly sampled from the Electronic Medical Record Administrative data Linked Database (EMRALD). Using physician diagnoses from the electronic medical record (EMR) as the reference standard, we determined the criterion validity of physician billing claims in administrative data for identifying infectious disease syndromes from 2012 to 2014. Diagnosis codes were assessed by infection category (respiratory, skin and soft tissue, gastrointestinal, urinary tract and otitis externa) and for all infections combined. Sensitivity analyses assessed the performance if patients had more than one reason to visit the physician. We analysed 2,139 patients and found 33.3% of all visits were for an infection, and respiratory infections accounted for 67.6% of the infections. When we combined all infection categories, sensitivity was 0.74 (95% CI 0.70-0.77), specificity was 0.95 (95% CI 0.93-0.96), positive predictive value (PPV) was 0.87 (95% CI 0.84-0.90), and negative predictive value (NPV) was 0.88 (95% CI 0.86-0.89). For respiratory infections, sensitivity was 0.77 (95% CI 0.73-0.81), specificity was 0.96 (95% CI 0.95-0.97), PPV was 0.85 (95% CI 0.81-0.88), and NPV was 0.94 (95% CI 0.92-0.95). Similar performance was observed for skin and soft tissue, gastrointestinal, urinary tract, and otitis externa infections, but with lower sensitivity. Performance measures were highest when the patient visited the physician with only one health complaint. We found when using linked EMR data as the reference standard, administrative billing codes are reasonably accurate in identifying infections in a pediatric population.

Project description:Introduction: The objective of the analysis was to determine the epidemiology of healthcare-associated infections (HAIs) in neurosurgical patients, paying special attention to two time points, 2003 and 2017, in order to evaluate the effectiveness of a surveillance program introduced in 2003 and efforts to reduce infection rates. Materials and methods: Continuous surveillance during 2003?2017 carried out using the HAI-Net methodology allowed us to detect 476 cases of HAIs among 10,332 patients staying in a 42-bed neurosurgery unit. The intervention in this before?after study (2003?2017) comprised standardized HAI surveillance with regular analysis and feedback. Results: The HAI incidence during the whole study was 4.6%. Surgical site infections (SSIs) accounted for 33% of all HAIs with an incidence rate of 1.5%. The remaining infections were pneumonia (1.1%) and bloodstream infections (0.9%). The highest SSI incidence concerned spinal fusion (FUSN, 2.2%), craniotomy (1.9%), and ventricular shunt (5.1%) while the associated total HAI incidence rates were 4.1%, 8.0%, and 18.6%, respectively. A significant reduction was found in HAI incidence between 2003 and 2017 in regard to the most common surgery types: laminectomy (4.5% vs. 0.8%); FUSN (11.8% vs. 0.8%); and craniotomy (10.1% vs. 0.4%). Significant changes were also achieved in selected elements of the unit's work: pre-hospitalization duration, hospital stay, and surgery length reductions. Simultaneously, the general condition of patients became significantly worse: there was an increase in patients' age and decreases in their general condition as expressed by ASA scores (The American Society of Anesthesiologists physical status classification system). Conclusions: HAI epidemiology changed substantially during the study period. Among the main types of HAI, SSIs were slightly predominant, but non-surgical HAIs accounted for almost two thirds of all infections; this indicates the need for surveillance of infection types other than SSIs in surgical patients. The implementation of active surveillance based on regular analysis and feedback led to a significant reduction in HAI incidence.

Project description:ObjectivesHospitalisations for serious infections are common among middle age and older adults and frequently used as study outcomes. Yet, few studies have evaluated the performance of diagnosis codes to identify serious infections in this population. We sought to determine the positive predictive value (PPV) of diagnosis codes for identifying hospitalisations due to serious infections among middle age and older adults.Setting and participantsWe identified hospitalisations for possible infection among adults >=50 years enrolled in the Tennessee Medicaid healthcare programme (2008-2012) using International Classifications of Diseases, Ninth Revision diagnosis codes for pneumonia, meningitis/encephalitis, bacteraemia/sepsis, cellulitis/soft-tissue infections, endocarditis, pyelonephritis and septic arthritis/osteomyelitis.DesignMedical records were systematically obtained from hospitals randomly selected from a stratified sampling framework based on geographical region and hospital discharge volume.MeasuresTwo trained clinical reviewers used a standardised extraction form to abstract information from medical records. Predefined algorithms served as reference to adjudicate confirmed infection-specific hospitalisations. We calculated the PPV of diagnosis codes using confirmed hospitalisations as reference. Sensitivity analyses determined the robustness of the PPV to definitions that required radiological or microbiological confirmation. We also determined inter-rater reliability between reviewers.ResultsThe PPV of diagnosis codes for hospitalisations for infection (n=716) was 90.2% (95% CI 87.8% to 92.2%). The PPV was highest for pneumonia (96.5% (95% CI 93.9% to 98.0%)) and cellulitis (91.1% (95% CI 84.7% to 94.9%)), and lowest for meningitis/encephalitis (50.0% (95% CI 23.7% to 76.3%)). The adjudication reliability was excellent (92.7% agreement; first agreement coefficient: 0.91). The overall PPV was lower when requiring microbiological confirmation (45%) and when requiring radiological confirmation for pneumonia (79%).ConclusionsDischarge diagnosis codes have a high PPV for identifying hospitalisations for common, serious infections among middle age and older adults. PPV estimates for rare infections were imprecise.