scholarly journals Estimated Thresholds of Minimum Necessary Adherence for Effective Treatment with Direct Oral Anticoagulants – A Retrospective Cohort Study in Health Insurance Claims Data

2021 ◽  
Vol Volume 15 ◽  
pp. 2209-2220
Author(s):  
Lucas Wirbka ◽  
Walter E Haefeli ◽  
Andreas Daniel Meid
PLoS ONE ◽  
2020 ◽  
Vol 15 (3) ◽  
pp. e0230648
Author(s):  
Claudia Schulz ◽  
Gisela Büchele ◽  
Raphael Simon Peter ◽  
Dietrich Rothenbacher ◽  
Patrick Roigk ◽  
...  

2021 ◽  
pp. annrheumdis-2021-220439
Author(s):  
Ruriko Koto ◽  
Akihiro Nakajima ◽  
Hideki Horiuchi ◽  
Hisashi Yamanaka

ObjectivesIn patients with gout, treating to target serum uric acid levels (sUA) of ≤6.0 mg/dL is universally recommended to prevent gout flare. However, there is no consensus on asymptomatic hyperuricaemia. Using Japanese health insurance claims data, we explored potential benefits of sUA control for preventing gout flare in subjects with asymptomatic hyperuricaemia.MethodsThis retrospective cohort study analysed the JMDC Claims Database from April 2012 through June 2019. Subjects with sUA ≥8.0 mg/dL were identified, and disease status (prescriptions for urate-lowering therapy (ULT), occurrence of gout flare, sUA) was investigated for 1 year. Time to first onset and incidence rate of gout flare were determined by disease status subgroups for 2 years or more. The relationship between gout flare and sUA control was assessed using multivariable analysis.ResultsThe analysis population was 19 261 subjects who met eligibility criteria. We found fewer occurrences of gout flare, for both gout and asymptomatic hyperuricaemia, in patients who achieved sUA ≤6.0 mg/dL with ULT than in patients whose sUA remained >6.0 mg/dL or who were not receiving ULT. In particular, analysis by a Cox proportional-hazard model for time to first gout flare indicated that the HR was lowest, at 0.45 (95% CI 0.27 to 0.76), in subjects with asymptomatic hyperuricaemia on ULT (5.0<sUA ≤ 6.0 mg/dL), compared with untreated subjects (sUA ≥8.0 mg/dL).ConclusionsOccurrences of gout flare were reduced by controlling sUA at ≤6.0 mg/dL in subjects with asymptomatic hyperuricaemia as well as in those with gout.Trial registration numberUMIN000039985.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser

Abstract Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.


2019 ◽  
Vol 51 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Chirag M. Lakhani ◽  
Braden T. Tierney ◽  
Arjun K. Manrai ◽  
Jian Yang ◽  
Peter M. Visscher ◽  
...  

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