Scientific Concept of the National Cohort (status ... - Nationale Kohorte

� CHD endpoint committee
� Cancer endpoint committee
� Stroke endpoint committee
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A.3 Study design
As the cohort is very large, the workload of the endpoint committees will become too large
if every event is reviewed by the committees. Based on experience from the Heinz Nixdorf
Recall Study 746 , events can be distinguished in:
a) Clearly documented, unambiguous events
b) Documented events that cannot be clearly classified by the local study centers
Only events of category b) will be reviewed by the endpoint committees. In addition, a
random sample of events from category a) will be submitted to the endpoint committees in
order to control the quality of categorization. The committees meet 1–2 times per year for
reviewing ambiguous endpoints.
For all other endpoints dedicated expert panels will be established that assess the validity
of the diagnoses based on a random sample.
A.3.8 use of secondary data sources
A.3.8.1 rationale
In addition to the primary data obtained by means of the various methods described in this
application, we will also make use of secondary data as complementary information concerning
exposure and disease status of the participants (i.e., for etiological questions).
In addition, such data may provide valuable information about health care utilization of the
study subjects (i.e., testing of hypotheses in the fields of health service research, healthrelated
quality of life research, and health economy). Secondary data will be primarily
obtained from statutory health insurances and statutory pension insurances (i.e., social
security data) and from environmental surveillance systems (i.e., environmental data).
A.3.8.2 Social security data
Social security data may provide valuable information concerning health care services.
The utilization, quality, and perception of health services may be important predictors for
morbidity. Amount, manner, and specific parameters of the use of medical services for patients
with chronic diseases or with multimorbidity can be seen as a risk factor if utilization
of medical services (health care overuse, misuse, and underuse) or if access or availability,
of care is suboptimal. In addition, social security data on occupational history can contribute
in multivariate models analyzing risk factors for certain diseases. Depending upon
the specific research question, secondary data can thus reflect either a determinant or an
endpoint of interest.
About 85% of the population in Gemany is insured by the statutory health insurance system.
Apart from civil servants and self-employed individuals, most employees are also subject to
the statutory social insurance contributions to unemployment, to long-term care insurance,
and to the statutory pension insurance scheme. Personal data related to the use of health
services (outpatient and hospital) and the occupational history (job codes) are routinely collected
and stored over long time periods on a legal basis for a majority of the population in
Germany.
A.3