Dataset Information

Healthcare Utilization and Direct Costs in Patients with Ankylosing Spondylitis Using a Large US Administrative Claims Database.

ABSTRACT: INTRODUCTION:In addition to the considerable patient and societal burdens, the financial burdens of ankylosing spondylitis (AS) are substantial. Understanding both all-cause and AS-specific direct costs in patients with AS is important if we are to understand the financial impact on patients with AS and payers in the United States. This study assessed both all-cause and AS-specific healthcare utilization and direct costs in US patients with AS using administrative claims data. METHODS:Adults aged ≥ 18 years enrolled in the MarketScan® Commercial and Medicare databases with ≥ 1 inpatient or ≥ 2 non-rule-out outpatient diagnoses of AS between January 1, 2013, and December 31, 2013, were included. Patients had continuous enrollment with medical and pharmacy benefits for ≥ 12 months before and after the index date (first diagnosis). Non-AS controls were matched up to 5:1 to patients with AS on age, geographic region, index calendar year, and sex. All-cause and AS-specific healthcare utilization and direct costs were measured during the follow-up period and reported as per patient per year. RESULTS:Patients with AS (N = 6679) had significantly higher rates of total all-cause inpatient admission (12% vs 6%), emergency department visits (23% vs 15%), nonhospital-based outpatient visits (100% vs 84%), hospital-based outpatient visits (68% vs 46%), other outpatient services (97% vs 81%), and medication use (97% vs 82%) compared with matched controls (N = 19,951). Patients with AS had approximately tenfold higher median total healthcare costs than matched controls ($24,978 vs $2139 per patient per year), largely driven by increased outpatient and pharmacy costs; P < 0.05 for all comparisons. The median (IQR) total AS-specific healthcare costs were $10,250 ($774, $28,824). CONCLUSION:In this analysis of claims data, increased outpatient and pharmacy costs were key contributors to higher all-cause total healthcare costs in US patients with AS. FUNDING:Novartis Pharmaceuticals Corporation, East Hanover, NJ.

SUBMITTER: Walsh JA

PROVIDER: S-EPMC6251838 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectiveTo assess the health care costs and utilization in patients with hereditary hemorrhagic telangiectasia (HHT) in the United States.Patients and methodsRetrospective analysis of patients with HHT diagnosed between 2007 and 2017 was performed using deidentified administrative claims data from the OptumLabs Data Warehouse. Adult patients with new (incident) diagnosis of HHT between January 1, 2007, and December 31, 2017, were included. Comparisons were made using the Wilcoxon rank sum test.ResultsThree thousand nine hundred seventy-seven patients with a first diagnosis of HHT between 2007 and 2017 were identified, of which 3590 were matched 1:1 to non-HHT patients with similar baseline characteristics and comorbidities. These 3590 patients with HHT were 63.1% female and 83.9% white with a mean age of 51.1 ± 18.5 years, and a mean follow-up period of 3.2 ± 2.2 years (range, 1.0-11.7 years). Compared with the control group, the cumulative 5-year median total health care cost for patients with HHT was 41.4% higher ($21,118 vs $14,929; P < .001) in those with private commercial insurance and 31.7% higher ($35,462 vs $26,925; P < .001) in those with Medicare Advantage coverage. The median annual health care costs were significantly higher in patients with HHT with commercial insurance and Medicare Advantage in the first year after diagnosis ($4,333 vs $1,804; P < .001), and ($7,322 vs $5,245; P < .001), respectively, and remained higher throughout the duration of follow-up. Further analysis showed that outpatient clinic visits, hospital admission, imaging rates, invasive procedures, iron infusions, and blood transfusions were all significantly higher in the HHT group.ConclusionPatients with HHT have significantly higher health care costs compared with a matched control group. A better understanding of the reasons underlying these cost differences will provide opportunities for patients, providers, and other stakeholders to better manage this rare condition.

Project description:The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing. The health care burden resulting from the multidisciplinary management of this complex disease is unknown. We assessed the total health care cost and resource utilization associated with a new NAFLD diagnosis, compared with controls with similar comorbidities. We used OptumLabs Data Warehouse, a large national administrative claims database with longitudinal health data of over 100 million individuals enrolled in private and Medicare Advantage health plans. We identified 152,064 adults with a first claim for NAFLD between 2010 and 2014, of which 108,420 were matched 1:1 by age, sex, metabolic comorbidities, length of follow-up, year of diagnosis, race, geographic region, and insurance type to non-NAFLD contemporary controls from the OptumLabs Data Warehouse database. Median follow-up time was 2.6 (range 1-6.5) years. The final study cohort consisted of 216,840 people with median age 55 (range 18-86) years, 53% female, 78% white. The total annual cost of care per NAFLD patient with private insurance was $7,804 (interquartile range [IQR] $3,068-$18,688) for a new diagnosis and $3,789 (IQR $1,176-$10,539) for long-term management. These costs are significantly higher than the total annual costs of $2,298 (IQR $681-$6,580) per matched control with similar metabolic comorbidities but without NAFLD. The largest increases in health care utilization that may account for the increased costs in NAFLD compared with controls are represented by liver biopsies (relative risk [RR] = 55.00, 95% confidence interval [CI] 24.48-123.59), imaging (RR = 3.95, 95% CI 3.77-4.15), and hospitalizations (RR = 1.87, 95% CI 1.73-2.02). Conclusion: The costs associated with the care for NAFLD independent of its metabolic comorbidities are very high, especially at first diagnosis. Research efforts shouldfocus on identification of underlying determinants of use, sources of excess cost, and development of cost-effective diagnostic tests.

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Project description:IntroductionAdministrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for body mass index (BMI), limits the sample sizes that can be analyzed to address certain research questions. The objective of this study was to construct models by implementing machine-learning (ML) algorithms to predict BMI classifications (≥ 30, ≥ 35, and ≥ 40 kg/m2) in administrative healthcare claims databases, and then internally and externally validate them.MethodsFive advanced ML algorithms were implemented for each BMI classification on a random sampling of BMI readings from the Optum PanTher Electronic Health Record database (2%) and the Optum Clinformatics Date of Death (20%) database, while incorporating baseline demographic and clinical characteristics. Sensitivity analyses with oversampling ratios were conducted. Model performance was validated internally and externally.ResultsModels trained on the Super Learner ML algorithm (SLA) yielded the best BMI classification predictive performance. SLA model 1 utilized sociodemographic and clinical characteristics, including baseline BMI values; the area under the receiver operating characteristic curve (ROC AUC) was approximately 88% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m2 (internal validation), while accuracy ranged from 87.9% to 92.8% and specificity ranged from 91.8% to 94.7%. SLA model 2 utilized sociodemographic information and clinical characteristics, excluding baseline BMI values; ROC AUC was approximately 73% for the prediction of BMI classifications of ≥ 30, ≥ 35, and ≥ 40 kg/m2 (internal validation), while accuracy ranged from 73.6% to 80.0% and specificity ranged from 71.6% to 85.9%. The external validation on the MarketScan Commercial Claims and Encounters database yielded relatively consistent results with slightly diminished performance.ConclusionThis study demonstrated the feasibility and validity of using ML algorithms to predict BMI classifications in administrative healthcare claims data to expand the utility for RWE generation.

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Healthcare Utilization and Direct Costs in Patients with Ankylosing Spondylitis Using a Large US Administrative Claims Database.

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