OBJECTIVE: This article describes an innovative method to identify infertility interventions from drug reimbursement data, which did not contain diagnoses. METHODS: An algorithm of usual infertility care steps was designed and information on drug prescriptions were retrieved from the Drug Reimbursement Register and linked to a register covering examinations and interventions in private health care. The data were compared to in vitro fertilization (IVF) statistics and the Birth Register. RESULTS: Despite the novelty of the idea, the complexity of the pharmacological details, the use of administrative registers not commonly used for research, and the involvement of two institutions, the identification of the exposed cohorts went well. An estimated 16 work-weeks were used. The numbers of started IVF-cycles with fresh embryos were similar to those in statistics, but our data had more transfers of frozen embryos. The linkage to the Birth Register gave further support that our algorithm worked well. Classification into different IVF-classes was not accurate. CONCLUSION: Even though diagnoses were not recorded in the Finnish drug reimbursement data, the algorithm approach allowed exposure identification.
OBJECTIVE: This article describes an innovative method to identify infertility interventions from drug reimbursement data, which did not contain diagnoses. METHODS: An algorithm of usual infertility care steps was designed and information on drug prescriptions were retrieved from the Drug Reimbursement Register and linked to a register covering examinations and interventions in private health care. The data were compared to in vitro fertilization (IVF) statistics and the Birth Register. RESULTS: Despite the novelty of the idea, the complexity of the pharmacological details, the use of administrative registers not commonly used for research, and the involvement of two institutions, the identification of the exposed cohorts went well. An estimated 16 work-weeks were used. The numbers of started IVF-cycles with fresh embryos were similar to those in statistics, but our data had more transfers of frozen embryos. The linkage to the Birth Register gave further support that our algorithm worked well. Classification into different IVF-classes was not accurate. CONCLUSION: Even though diagnoses were not recorded in the Finnish drug reimbursement data, the algorithm approach allowed exposure identification.
Authors: Anna Barbuscia; Pekka Martikainen; Mikko Myrskylä; Hanna Remes; Edgardo Somigliana; Reija Klemetti; Alice Goisis Journal: Hum Reprod Date: 2020-01-01 Impact factor: 6.918