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Keywords:

  • transients and migrants;
  • minority groups;
  • epidemiologic methods;
  • sensitivity and specificity;
  • Germany;
  • Turkey

Migrants often face particular social, economic and health disadvantages relative to the population of the host country. In order to adapt health services to the needs of migrants, health researchers need to identify differences in risk factor and disease profiles, as well as inequalities concerning treatment and prevention. Registries of health-related events could be employed for these purposes. In Germany, however, routine data bases often hold no, or inaccurate, information on the national origin of the cases registered. We developed an algorithm based on a large data set of Turkish family and first names (n=15 000), with religion as additional criterion, to identify cases of Turkish origin in registries in a largely automatic search. We tested the performance of the algorithm in a population registry and in a cancer registry. The algorithm discriminates well against Greek and Arab names, with 1% false positive matches in our study. It achieves a specificity of > 99.9% in delimiting Turkish from German cases in the cancer registry. The sensitivity can be increased to 85%, provided the small proportion of case records with uncertain origin can be assessed manually. The name algorithm can be useful for registry-based health research among Turkish migrants in Germany. Possible applications are e.g. in cancer registries to compare survival among German and Turkish cancer patients, or in health insurance registries to compare the relative importance of work-related degenerative diseases. In specific circumstances, the algorithm may also be useful in aetiological research.