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Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

ABSTRACT: Objective: We developed and internally validated an algorithm for disability status in multiple sclerosis (MS) using administrative data. Methods: We linked administrative data from Manitoba, Canada to a clinical dataset with Expanded Disability Status Scale (EDSS) scores for people with MS. Clinical EDSS scores constituted the reference standard. We created candidate indicators using the administrative data. These included indicators based on use of particular health care services (home care, long-term care, rehabilitation admission), use of specific diagnostic codes (such as spasticity, quadriplegia), and codes based on use of Employment and Income Insurance. We developed algorithms to predict severe disability (EDSS ≥6.0), and to predict disability as a continuous measure. We manually developed algorithms, and also employed regression approaches. After we selected our preferred algorithms for disability, we tested their association with health care use due to any cause and infection after potential confounders. Results: We linked clinical and administrative data for 1,767 persons with MS, most of whom were women living in urban areas. All individual indicators tested had specificities >90% for severe disability, and all but a diagnosis of visual disturbance had positive predictive values (PPV) >70%. The combination of home care or long-term care use or rehabilitation admission had a sensitivity of 61.9%, specificity of 90.76%, PPV of 70.06% and negative predictive of 87.21%. Based on regression modeling, the best-performing algorithm for predicting the EDSS as a continuous variable included age, home care use, long-term care admission, admission for rehabilitation, visual disturbance, other paralytic syndromes and spasticity. The mean difference between observed and predicted values of the EDSS was −0.0644 (95%CI −0.1632, 0.0304). Greater disability, whether measured using the clinical EDSS or either of the administrative data algorithms was similarly associated with increased hospitalization rates due to any cause and infection. Conclusion: We developed and internally validated an algorithm for disability in MS using administrative data that may support population-based studies that wish to account for disability status but do not have access to clinical data sources with this information. We also found that more severe disability is associated with increased health care use, including due to infection.

SUBMITTER: Marrie R

PROVIDER: S-EPMC8592934 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesAs there were only regional studies in Hungary about the prevalence of multiple sclerosis (MS), we aimed to estimate its epidemiological features using data of Hungary's single-payer health insurance system.MethodsPseudonymized database of claims reported by hospitals and outpatient services between 2004-2016 was analyzed and linked with an independent database of outpatient pharmacy refills between 2010-2016. We established an administrative case definition of MS and validated it on medical records of 309 consecutive patients. A subject was defined as MS-patient if received MS diagnosis (International Classification of Diseases, 10th edition, code G35) on three or more occasions at least in 2 calendar years and at least once documented by a neurologist. Patients were counted as incident cases in the year of the first submitted claim for MS. We allowed a 6-year-long run-in period, so only data between 2010-2015 are discussed.ResultsSensitivity of the administrative case definition turned out to be 99%, while specificity was >99%. Crude prevalence of MS has increased from 109.3/100,000 in 2010 to 130.8/100,000 in 2015 (p-value = 0.000003). Crude incidence declined from 7.1/100,000 (2010) to 5.4/100,000 (2015) (p-value = 0.018). Direct standardization - based on European standard population and results of nationwide Hungarian census of 2011 - revealed that age standardized prevalence was 105.2/100,000 (2010), which has grown to 127.2/100,000 (2015) (p-value = 0.000001). Age standardized incidence rate declined from 6.7/100,000 (2010) to 5.1/100,000 (2015) (p-value = 0.016). The ratio of MS-patients receiving ≥1 prescription for disease modifying treatment increased from 0.19 (2010) to 0.29 (2015) (p-value = 0.0051). The female/male ratio of prevalent cases remained 2.6.DiscussionThe prevalence of MS in Hungary is higher than previously reported, the incidence rate is moderate. The prevalence is rising, the incidence rate shows decline. The proportion of patients receiving disease modifying treatment grows but was still around 30% in 2015.

Project description:BackgroundThe Expanded Disability Status Scale (EDSS) quantifies disability and measures disease progression in multiple sclerosis (MS), however is not available in administrative claims databases.ObjectivesTo develop a claims-based algorithm for deriving EDSS and validate it against a clinical dataset capturing true EDSS values from medical records.MethodsWe built a unique linked dataset combining claims data from the German AOK PLUS sickness fund and medical records from the Multiple Sclerosis Management System 3D (MSDS3D). Data were deterministically linked based on insurance numbers. We used 69 MS-related diagnostic indicators recorded with ICD-10-GM codes within 3 months before and after recorded true EDSS measures to estimate a claims-based EDSS proxy (pEDSS). Predictive performance of the pEDSS was assessed as an eight-fold (EDSS 1.0-7.0, ≥8.0), three-fold (EDSS 1.0-3.0, 4.0-5.0, ≥6.0), and binary classifier (EDSS <6.0, ≥6.0). For each classifier, predictive performance measures were determined, and overall performance was summarized using a macro F1-score. Finally, we implemented the algorithm to determine pEDSS among an overall cohort of patients with MS in AOK PLUS, who were alive and insured 12 months prior to and after index diagnosis.ResultsWe recruited 100 people with MS insured by AOK PLUS who had ≥1 EDSS measure in MSDS3D between 01/10/2015 and 30/06/2019 (620 measurements overall). Patients had a mean rescaled EDSS of 3.2 and pEDSS of 3.0. The pEDSS deviated from the true EDSS by 1.2 points, resulting in a mean squared error of prediction of 2.6. For the eight-fold classifier, the macro F1-score of 0.25 indicated low overall predictive performance. Broader severity groupings were better performing, with the three-fold and binary classifiers for severe disability achieving a F1-score of 0.68 and 0.84, respectively. In the overall AOK PLUS cohort (3,756 patients, 71.9% female, mean 51.9 years), older patients, patients with progressive forms of MS and those with higher comorbidity burden showed higher pEDSS.ConclusionGenerally, EDSS was underestimated by the algorithm as mild-to-moderate symptoms were poorly captured in claims across all functional systems. While the proxy-based approach using claims data may not allow for granular description of MS disability, broader severity groupings show good predictive performance.

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Development and Internal Validation of a Disability Algorithm for Multiple Sclerosis in Administrative Data

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