Gender differences in abdominal aortic aneurysms in Germany using health insurance claims data

VASA ◽  
2018 ◽  
Vol 47 (1) ◽  
pp. 36-42 ◽  
Author(s):  
Konstanze Stoberock ◽  
Henrik Christian Rieß ◽  
Eike Sebastian Debus ◽  
Thea Schwaneberg ◽  
Tilo Kölbel ◽  
...  

Abstract. Background: Endovascular aortic repair (EVAR) has emerged as standard of care for abdominal aortic aneurysm (AAA). Real-world evidence is limited to compare this technology to open repair (OAR). Major gaps exist related to short-term and long-term outcomes, particularly in respect of gender differences. Materials and methods: Health insurance claims data from Germany’s third largest insurance provider, DAK-Gesundheit, was used to investigate invasive in-hospital treatment of intact (iAAA) and ruptured AAA (rAAA). Patients operated between October 2008 and April 2015 were included in the study. Results: A total of 5,509 patients (4,966 iAAA and 543 rAAA) underwent EVAR or OAR with a median follow-up of 2.44 years. Baseline demographics, comorbidities, and clinical characteristics of DAK-G patients were assessed. In total, 84.6 % of the iAAA and 79.9 % of the rAAA were male. Concerning iAAA repair, the median age (74 vs. 73 years, p < .001) compared to men was higher in females, but their EVAR-rate (66.8 % vs. 71.1 %, p = .018) was lower. Besides higher age of female patients (80 vs. 75 years, p < .001), no further statistically significant differences were seen following rAAA repair. In-hospital mortality was slightly lower in males compared to females following iAAA (2.3 % vs. 3.1 %, p = .159) and rAAA (37.3 % vs. 43.1 %, p = .273) repair. Concerning iAAA repair, a higher rate of female patients was transferred to another hospital (3.7 % vs. 2.0 %, p = 0.008) or discharged to rehabilitation (6.0 % vs. 2.7 %, p < .001) compared to male patients. Conclusions: In this large German claims data cohort, women are generally older and more often transferred to another hospital or discharged to rehab following iAAA repair. Nonetheless, no significantly increased risk of in-hospital or late death appeared for women in multivariate analyses. Further studies are necessary to evaluate the impact of recent gender-specific treatment strategies on overall outcome under real-world settings.

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 ◽  
...  

2020 ◽  
Vol Volume 12 ◽  
pp. 1129-1138
Author(s):  
Amir Sarayani ◽  
Xi Wang ◽  
Thuy Nhu Thai ◽  
Yasser Albogami ◽  
Nakyung Jeon ◽  
...  

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