scholarly journals Direct and Indirect Costs of Asthma Burden in Abu Dhabi: A Retrospective Analysis of Insurance Claims Data from 2015 to 2018

2021 ◽  
Vol Volume 13 ◽  
pp. 969-980
Author(s):  
Khulood Al Mazrouei ◽  
Asma Ibrahim Almannaei ◽  
Faiza Medeni Nur ◽  
Nagham Bachnak ◽  
Ashraf Alzaabi
Keyword(s):  
Retrospective Analysis ◽  
Claims Data ◽  
Indirect Costs ◽  
Abu Dhabi ◽  
Insurance Claims ◽  
Direct And Indirect Costs ◽  
Insurance Claims Data

scholarly journals Impact of bariatric surgery on hypertensive disorders in pregnancy: retrospective analysis of insurance claims data

BMJ ◽  
2010 ◽  
Vol 340 (apr13 1) ◽  
pp. c1662-c1662 ◽  
Author(s):  
W. L. Bennett ◽  
M. M. Gilson ◽  
R. Jamshidi ◽  
A. E. Burke ◽  
J. B. Segal ◽  
...  
Keyword(s):  
Bariatric Surgery ◽  
Retrospective Analysis ◽  
Claims Data ◽  
Insurance Claims ◽  
Hypertensive Disorders ◽  
Hypertensive Disorders In Pregnancy ◽  
Insurance Claims Data ◽  
In Pregnancy

scholarly journals Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Claims Data ◽  
Health Care Spending ◽  
Insurance Claims ◽  
Diagnostic Coding ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims ◽  
Disease Specific

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.

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