Refine
Year of publication
- 2018 (4) (remove)
Document Type
- Article (4)
Language
- English (4)
Has Fulltext
- yes (4)
Is part of the Bibliography
- no (4)
Keywords
- - (3)
- GWAS (2)
- bias (2)
- causal inference (2)
- mendelian randomization (2)
- statistical methods (2)
- Parkinson's disease (1)
- comorbidity (1)
- epidemiology (1)
- healthcare (1)
Institute
- Institut für Community Medicine (4) (remove)
Publisher
- Frontiers Media S.A. (4) (remove)
Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.
Mendelian randomization (MR) is a framework for assessing causal inference using cross-sectional data in combination with genetic information. This paper summarizes statistical methods commonly applied and strait forward to use for conducting MR analyses including those taking advantage of the rich dataset of SNP-trait associations that were revealed in the last decade through large-scale genome-wide association studies. Using these data, powerful MR studies are possible. However, the causal estimate may be biased in case the assumptions of MR are violated. The source and the type of this bias are described while providing a summary of the mathematical formulas that should help estimating the magnitude and direction of the potential bias depending on the specific research setting. Finally, methods for relaxing the assumptions and for conducting sensitivity analyses are discussed. Future researches in the field of MR include the assessment of non-linear causal effects, and automatic detection of invalid instruments.
Do We Need to Rethink the Epidemiology and Healthcare Utilization of Parkinson's Disease in Germany?
(2018)
Epidemiological aspects of Parkinson's disease (PD), co-occurring diseases and medical healthcare utilization of PD patients are still largely elusive. Based on claims data of 3.7 million statutory insurance members in Germany in 2015 the prevalence and incidence of PD was determined. PD cases had at least one main hospital discharge diagnosis of PD, or one physician diagnosis confirmed by a subsequent or independent diagnosis or by PD medication in 2015. Prevalence of (co-)occurring diseases, mortality, and healthcare measures in PD cases and matched controls were compared. In 2015, 21,714 prevalent PD cases (standardized prevalence: 511.4/100,000 persons) and 3,541 incident PD cases (standardized incidence: 84.1/100,000 persons) were identified. Prevalence of several (co-)occurring diseases/complications, e.g., dementia (PD/controls: 39/13%), depression (45/22%), bladder dysfunction (46/22%), and diabetes (35/31%), as well as mortality (10.7/5.8%) differed between PD cases and controls. The annual healthcare utilization was increased in PD cases compared to controls, e.g., regarding mean ± SD physician contacts (15.2 ± 7.6/12.2 ± 7.3), hospitalizations (1.3 ± 1.8/0.7 ± 1.4), drug prescriptions (overall: 37.7 ± 24.2/21.7 ± 19.6; anti-PD medication: 7.4 ± 7.4/0.1 ± 0.7), assistive/therapeutic devices (47/30%), and therapeutic remedies (57/16%). The standardized prevalence and incidence of PD in Germany as well as mortality in PD may be substantially higher than reported previously. While frequently diagnosed with co-occurring diseases/complications, such as dementia, depression, bladder dysfunction and diabetes, the degree of healthcare utilization shows large variability between PD patients. These findings encourage a rethinking of the epidemiology and healthcare utilization in PD, at least in Germany. Longitudinal studies of insurance claims data should further investigate the individual and epidemiological progression and healthcare demands in PD.