Background: Are the assumptions required by the current duration (CD) approach to estimating population infertility met, and does the CD approach produce similar infertility estimates to those from the ‘gold standard’ incident prospective cohort approach within the same sample using fertility app data? The CD approach to documenting population and subgroup infertility is a promising, more feasible, and cost-effective alternative method to the incident prospective cohort design, but method assumptions have not been rigorously tested using population-based samples.
Methods: We use prospective cohort data provided by users of smartphone applications in which they record fertility and pregnancy information. The total prospective cohort sample included 167,451 people using the applications between 1 January 2015 and 28 February 2022. To generate valid comparisons, we drew six CD samples from the same data source, containing between 17,196 and 26,259 people measured in designated months. We first tested the three assumptions required by the CD approach, then estimated 12-month infertility prevalence separately using prospective and CD approaches, for all users and for subpopulations stratified by age, education, parity, and level of poverty.
Results: We demonstrate clear violation of one CD assumption, and suggestive evidence of one other assumption violation. The prospective survival function resulted in a 12-month infertility prevalence of 36.1% (95% CI 35.8–36.3) while the CD estimate adjusted for assumption violation was 37.0% (95% CI 36.2–37.7). Infertility patterns by user characteristics were similar for prospective and CD estimates, with greater levels of infertility for those 35 years old or greater, those with no children, those with less education, and those residing in areas with greater levels of poverty.
Conclusions: Researchers can potentially learn about patterns of infertility with cross-sectional data alone, provided they statistically address CD assumption violations. This finding facilitates the study of infertility in populations, subpopulations, places, and periods for which infertility estimates do not exist.