How useful is a name-based algorithm in health research among Turkish migrants in Germany?

Migrants often face particular social, economic and health disadvantages re lative to the population of the host country. In order to adapt health serv ices to the needs of migrants, health researchers need to identify differen ces in risk factor and disease profiles, as well as inequalities concerning treatment and prevention. Registries of health-related events could be emp loyed for these purposes. In Germany, however, routine data bases often hol d no, or inaccurate, information on the national origin of the cases regist ered. 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 id entify 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 A-ra b names, with 1% false positive matches in our study. It achieves a specifi city of > 99.9% in delimiting Turkish from German cases in the cancer regis try. The sensitivity can be increased to 85%, provided the small proportion of case records with uncertain origin can be assessed manually. The name a lgorithm can be useful for registry-based health research among Turkish mig rants in Germany. Possible applications are e.g. in cancer registries to co mpare survival among German and Turkish cancer patients, or in health insur ance registries to compare the relative importance of work-related degenera tive diseases. In specific circumstances, the algorithm may also be useful in aetiological research.