Review Article
Limitations and opportunities in male fertility databases
Abstract
Over the last several years, the male component of reproduction has begun to gain clinical momentum. The medical literature has traditionally focused on infertility from the female perspective, but recent publications have demonstrated that male infertility is an important marker of overall health for infertile men as well as their family members. In order to perform large-scale, quality research related to male infertility, comprehensive databases are necessary. Currently, research in male infertility is limited by the fact that there is not a centralized, comprehensive database specifically designed to collect patient information related to male fertility. A database of this nature exists for female infertility research in the form of the Society for Assisted Reproductive Technology (SART) clinical summary report and the National ART Surveillance System (NASS) published by the Centers for Disease Control (CDC). This review outlines the strengths and weaknesses of several male fertility data sources, including the National Survey of Family Growth, the Reproductive Medicine Network, the Andrology Research Consortium (ARC), the Truven Health MarketScan® databases, the Utah Population Database, and data available from the Ober Lab related to the Hutterites. While each of these sources has been instrumental in the creation of meaningful research within the field of male fertility, a need remains for the creation of a centralized database for use in future male fertility research. The ideal database would consist of vast amounts of patient data which link individuals and couples to biologic specimens as well as data from family members, designed with parameters specifically purposed for male fertility research. The use of electronic medical records (EMR) systems such as Epic may play a role in the development of such a database going forward. At present, although some information is available through current databases, researchers must utilize suboptimal data sources to perform studies.
