Background
Definition
Legal background
DRG data collected by DeStatis
Quality assurance data collected by the Institute pursuant to § 137a SGB V (eQS data)
Methods and data sources of secondary data analysis
Strengths and limitations
DeStatis DRG microdata | Statutory quality assurancea | Health insurance claims data | Voluntary registries | |
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Legal background | Statutory data collection | Statutory data collection | Statutory data collection | Voluntary participation |
Coverage | All in-hospital cases except military and psychiatric hospital | 99.1% of carotid artery procedures [16] | Patient data on insurees of the respective insurance company | Depending on participation, mostly small samples |
Original purpose of data collection | Hospital remuneration | External quality assurance | Administration and remuneration | Depending on registry, mostly quality assurance and research |
Collected data | Standard demographic data, length of stay, ICD code for main and secondary diagnoses, OPS codes for procedures in this admission, reason for discharge, others | Standard demographic data, length of stay, clinical details (e. g. degree of carotid stenosis), performed procedures, procedural details, clinical complications and others | Standard demographic data, length of stay, ICD codes for hospital stays and ambulatory treatment, drug and cure prescriptions, OPS/EBM codes for procedures, disability periods, others | Depending on registry and purpose |
Follow-up period | In-hospital stay (case perspective) | In-hospital stay (case perspective) | Longitudinal follow-up possible (patient perspective) | Longitudinal follow-up possible (patient perspective) |
Risk of … | ||||
Selection bias | Low for inpatients (outpatients not included) | Low for treated patients (non-treated or miscoded patients not included) | Intermediate (only patients of selected insurance companies included) | Intermediate to high, depending on participating centers and policy for inclusion |
Information bias | Low regarding “hard” outcomes such as in-hospital mortality, intermediate for other outcomes depending on data validation by the MDK | Intermediate, depending on internal and external data validation | Low regarding “hard” outcomes, intermediate for other outcomes depending on data validation by the MDK | Low to high, depending on data validation policies of the register |
Topics and examples of secondary data analysis
Epidemiology and time trends
Epidemiology and inpatient treatment of vascular diseases in Germany
Incidence, treatment and mortality of abdominal aortic aneurysms (AAA)
Incidence, treatment and mortality of thoracic aortic aneurysm (TAA) and thoracoabdominal aortic aneurysm (TAAA) repair
Vascular complications in diabetic patients
Treatment and outcomes of carotid artery revascularization
Comparisons and risk factors
Risk factors for AAA repair
Risk factors for TAA and TAAA repair
Risk factors for carotid revascularization
Volume-outcome relationships for AAA and carotid revascularization
Regional variations
Regional variations in AAA
Regional variations in carotid revascularization
Practical conclusion
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Real-world data can be considered an inevitable adjunct to the evidence gained by RCTs.
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Secondary data analysis of statutorily collected routine data is able to include nearly all cases treated nationwide and thus, selection bias may be considered low.
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The risk of information bias in statutorily collected data can be considered low regarding hard outcomes (e.g. mortality) but the frequency of secondary outcomes (e.g. complications) might be underestimated.
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Nationwide data are crucial for structural analysis of healthcare supply (volume-outcome associations, regional variations).