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Enrollment factors and bias of disease prevalence estimates in administrative claims data.

ABSTRACT: Considerations for using administrative claims data in research have not been well-described. To increase awareness of how enrollment factors and insurance benefit use may contribute to prevalence estimates, we evaluated how differences in operational definitions of the cohort impact observed estimates.We conducted a cross-sectional study estimating the prevalence of five gastrointestinal conditions using MarketScan claims data for 73.1 million enrollees. We extracted data obtained from 2009 to 2012 to identify cohorts meeting various enrollment, prescription drug benefit, or health care utilization characteristics. Next, we identified patients meeting the case definition for each of the diseases of interest. We compared the estimates obtained to evaluate the influence of enrollment period, drug benefit, and insurance usage.As the criteria for inclusion in the cohort became increasingly restrictive the estimated prevalence increased, as much as 45% to 77% depending on the disease condition and the definition for inclusion. Requiring use of the insurance benefit and a longer period of enrollment had the greatest influence on the estimates observed.Individuals meeting case definition were more likely to meet the more stringent definition for inclusion in the study cohort. This may be considered a form of selection bias, where overly restrictive inclusion criteria definitions may result in selection of a source population that may no longer represent the population from which cases arose.

SUBMITTER: Jensen ET

PROVIDER: S-EPMC4599703 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Enrollment factors and bias of disease prevalence estimates in administrative claims data.

Jensen Elizabeth T ET Cook Suzanne F SF Allen Jeffery K JK Logie John J Brookhart Maurice Alan MA Kappelman Michael D MD Dellon Evan S ES

Annals of epidemiology 20150321 7

<h4>Purpose</h4>Considerations for using administrative claims data in research have not been well-described. To increase awareness of how enrollment factors and insurance benefit use may contribute to prevalence estimates, we evaluated how differences in operational definitions of the cohort impact observed estimates.<h4>Methods</h4>We conducted a cross-sectional study estimating the prevalence of five gastrointestinal conditions using MarketScan claims data for 73.1 million enrollees. We extra ...[more]

PMID: 25890796

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Project description:IntroductionNontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. We aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.MethodsWe examined all patients admitted to our tertiary medical center with a primary or secondary International Classification of Disease version 9 (ICD-9) or 10 (ICD-10) code for ICH in claims from any portion of the hospitalization in 2014-2015. As a gold standard, we defined the nontraumatic ICH phenotype based on manual chart review. We tested explicit definitions based on ICD-9 and ICD-10 that had been previously published in the literature as well as four machine learning classifiers including support vector machine (SVM), logistic regression with LASSO, random forest and xgboost. We report five standard measures of model performance for each approach.ResultsA total of 1830 patients with 2145 unique ICD-10 codes were included in the initial dataset, of which 437 (24%) were true positive based on manual review. The explicit ICD-10 definition performed best (Sensitivity = 0.89 (95% CI 0.85-0.92), Specificity = 0.83 (0.81-0.85), F-score = 0.73 (0.69-0.77)) and improves on an explicit ICD-9 definition (Sensitivity = 0.87 (0.83-0.90), Specificity = 0.77 (0.74-0.79), F-score = 0.67 (0.63-0.71). Among machine learning classifiers, SVM performed best (Sensitivity = 0.78 (0.75-0.82), Specificity = 0.84 (0.81-0.87), AUC = 0.89 (0.87-0.92), F-score = 0.66 (0.62-0.69)).ConclusionsAn explicit ICD-10 definition can be used to accurately identify patients with a nontraumatic ICH phenotype with substantially better performance than ICD-9. An explicit ICD-10 based definition is easier to implement and quantitatively not appreciably improved with the additional application of machine learning classifiers. Future research utilizing large datasets should utilize this definition to address important research gaps.

Project description:BackgroundThe incidence of coronavirus disease 2019 (COVID-19) in Wuhan, China, has been estimated using imported case counts of international travellers, generally under the assumptions that all cases of the disease in travellers have been ascertained and that infection prevalence in travellers and residents is the same. However, findings indicate variation among locations in the capacity for detection of imported cases. Singapore has had very strong epidemiological surveillance and contact tracing capacity during previous infectious disease outbreaks and has consistently shown high sensitivity of case-detection during the COVID-19 outbreak.MethodsWe used a Bayesian modelling approach to estimate the relative capacity for detection of imported cases of COVID-19 for 194 locations (excluding China) compared with that for Singapore. We also built a simple mathematical model of the point prevalence of infection in visitors to an epicentre relative to that in residents.FindingsThe weighted global ability to detect Wuhan-to-location imported cases of COVID-19 was estimated to be 38% (95% highest posterior density interval [HPDI] 22-64) of Singapore's capacity. This value is equivalent to 2·8 (95% HPDI 1·5-4·4) times the current number of imported and reported cases that could have been detected if all locations had had the same detection capacity as Singapore. Using the second component of the Global Health Security index to stratify likely case-detection capacities, the ability to detect imported cases relative to Singapore was 40% (95% HPDI 22-67) among locations with high surveillance capacity, 37% (18-68) among locations with medium surveillance capacity, and 11% (0-42) among locations with low surveillance capacity. Treating all travellers as if they were residents (rather than accounting for the brief stay of some of these travellers in Wuhan) contributed modestly to underestimation of prevalence.InterpretationEstimates of case counts in Wuhan based on assumptions of 100% detection in travellers could have been underestimated by several fold. Furthermore, severity estimates will be inflated several fold since they also rely on case count estimates. Finally, our model supports evidence that underdetected cases of COVID-19 have probably spread in most locations around the world, with greatest risk in locations of low detection capacity and high connectivity to the epicentre of the outbreak.FundingUS National Institute of General Medical Sciences, and Fellowship Foundation Ramon Areces.

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Enrollment factors and bias of disease prevalence estimates in administrative claims data.

Publications

Enrollment factors and bias of disease prevalence estimates in administrative claims data.

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