scholarly journals Mental disorders and utilization of mental health services in newly diagnosed cancer patients: An analysis of German health insurance claims data

2020 ◽  
Author(s):  
Sandra Salm ◽  
Katja Blaschke ◽  
Peter Ihle ◽  
Ingrid Schubert ◽  
Antje Dresen ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Insurance ◽  
Mental Disorders ◽  
Cancer Patients ◽  
Claims Data ◽  
Newly Diagnosed ◽  
German Health ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims

scholarly journals Implementation of an algorithm for the identification of breast cancer deaths in German health insurance claims data: a validation study based on a record linkage with administrative mortality data

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e026834 ◽  
Author(s):  
Ingo Langner ◽  
Christoph Ohlmeier ◽  
Ulrike Haug ◽  
Hans Werner Hense ◽  
Jonas Czwikla ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Health Insurance ◽  
Validation Study ◽  
Record Linkage ◽  
Claims Data ◽  
Mortality Rates ◽  
German Health ◽  
Insurance Claims Data ◽  
Health Insurance Claims Data ◽  
Health Insurance Claims

ObjectiveTo adapt a Canadian algorithm for the identification of female cases of breast cancer (BC) deaths to German health insurance claims data and to test and validate the algorithm by comparing results with official cause of death (CoD) data on the individual and the population level.DesignValidation study, secondary data, medical claims.SettingClaims data of two statutory health insurance providers (SHIs) for inpatient and outpatient care, CoD added via record linkage with epidemiological cancer registry (ECR).ParticipantsAll women insured with the two SHIs and who deceased in the period 2006–2013, were residents of North Rhine Westphalia (NRW) and were linked with ECR data: n=22 413.Main outcome measuresBased on inpatient and outpatient diagnoses in the year before death, six algorithms were derived and the accordance of the algorithm-based CoD with the official CoD was evaluated calculating specificity, sensitivity, negative and positive predictive values (NPV, PPV). Furthermore, algorithm-based age-specific BC mortality rates covering several calendar years were calculated for the entire insured female population and compared with official national rates.ResultsOur final algorithm, derived from the NRW subsample, comprised codes indicating the presence of BC, metastases, a terminal illness phase and the absence of codes for other tumours. Overall, specificity, sensitivity, NPV and PPV of this algorithm were 97.4%, 91.3%, 98.9% and 81.7%, respectively. In the age range 40–80 years, sensitivity and PPV slightly decreased with increasing age. Algorithm-based age-specific BC mortality rates agreed well with official rates except for the age group 85 years and older.ConclusionsThe algorithm-based identification of BC deaths in German claims data is feasible and valid, except for higher ages. The algorithm to ascertain BC mortality rates in an epidemiological study seems applicable when information on the official CoD is not available in the original database.

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.

scholarly journals Repurposing large health insurance claims data to estimate genetic and environmental contributions in 560 phenotypes

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