Some reproductive-aged individuals remain unvaccinated against COVID-19 due to concerns about potential adverse effects on fertility. We examined the associations of COVID-19 vaccination and SARS-CoV-2 infection with fertility among couples trying to conceive spontaneously using data from an internet-based preconception cohort study. We enrolled 2,126 self-identified females residing in the U.S. or Canada during December 2020-September 2021 and followed them through November 2021. Participants completed questionnaires every 8 weeks on sociodemographics, lifestyle, medical factors, and partner information. We fit proportional probabilities regression models to estimate associations between self-reported COVID-19 vaccination and SARS-CoV-2 infection in both partners with fecundability, the per-cycle probability of conception, adjusting for potential confounders. COVID-19 vaccination was not appreciably associated with fecundability in either partner (female FR=1.08, 95% CI: 0.95, 1.23; male FR=0.95, 95% CI: 0.83, 1.10). Female SARS-CoV-2 infection was not strongly associated with fecundability (FR=1.07, 95% CI: 0.87, 1.31). Male infection was associated with a transient reduction in fecundability (FR=0.82, 95% CI: 0.47, 1.45 for infection within 60 days; FR=1.16, 95% CI: 0.92, 1.47 for infection >60 days). These findings indicate that male SARS-CoV-2 infection may be associated with a short-term decline in fertility and that COVID-19 vaccination does not impair fertility in either partner.

Background Animal and epidemiologic studies indicate that air pollution may adversely affect fertility. Epidemiologic studies have been restricted largely to couples undergoing fertility treatment or have retrospectively ascertained time‐to‐pregnancy among pregnant women. Objectives We examined the association between residential ambient air pollution and fecundability, the per‐cycle probability of conception, in a large preconception cohort of Danish pregnancy planners. Methods During 2007–2018, we used the Internet to recruit and follow women who were trying to conceive without the use of fertility treatment. Participants completed an online baseline questionnaire eliciting socio‐demographic characteristics, lifestyle factors, and medical and reproductive histories and follow‐up questionnaires every 8 weeks to ascertain pregnancy status. We determined concentrations of ambient nitrogen oxides (NOx), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), particulate matter <2.5 µm (PM2.5) and <10 µm (PM10), and sulphur dioxide (SO2) at each participant's residential address. We calculated average exposure during the year before baseline, during each menstrual cycle over follow‐up and during the entire pregnancy attempt time. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs), adjusting for potential confounders and co‐pollutants. The analysis was restricted to the 10,183 participants who were trying to conceive for <12 cycles at study entry whose addresses could be geocoded. Results During 12 months of follow‐up, 73% of participants conceived. Higher concentrations of PM2.5 and PM10 were associated with small reductions in fecundability. For example, the FRs for a one interquartile range (IQR) increase in PM2.5 (IQR = 3.2 µg/m3) and PM10 (IQR = 5.3 µg/m3) during each menstrual cycle were 0.93 (95% CI: 0.87, 0.99) and 0.91 (95% CI: 0.84, 0.99), respectively. Other air pollutants were not appreciably associated with fecundability. Conclusions In this preconception cohort study of Danish women, residential exposures to PM2.5 and PM10 were associated with reduced fecundability.

STUDY QUESTION Can we derive adequate models to predict the probability of conception among couples actively trying to conceive? SUMMARY ANSWER Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC). WHAT IS KNOWN ALREADY Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59–64%). STUDY DESIGN, SIZE, DURATION Study participants were female, aged 21–45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013–2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry. PARTICIPANTS/MATERIALS, SETTING, METHODS On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III. MAIN RESULTS AND THE ROLE OF CHANCE Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress. LIMITATIONS, REASONS FOR CAUTION Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study. WIDER IMPLICATIONS OF THE FINDINGS Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work. STUDY FUNDING/COMPETING INTEREST(S) The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests. TRIAL REGISTRATION NUMBER N/A.

Background Semen quality assessment in population‐based epidemiologic studies presents logistical and financial challenges due to reliance on centralised laboratory semen analysis. The Trak Male Fertility Testing System is an FDA‐cleared and validated at‐home test for sperm concentration and semen volume, with a research use only sperm motility test. Here we evaluate the Trak System's overall utility among men participating in Pregnancy Study Online (PRESTO), a web‐based study of North American couples planning pregnancy. Methods US male participants aged ≥21 years with ≤6 months of pregnancy attempt time at study enrolment were invited to participate in the semen testing substudy after completing their baseline questionnaire. Consenting participants received a Trak Engine (battery‐powered centrifuge) and two test kits. Participants shared their test results via smartphone images uploaded to online questionnaires. Data were then linked with covariate data from the baseline questionnaire. Results Of the 688 men invited to participate, 373 (54%) provided consent and 271 (73%) completed at least one semen test result. The distributions of semen volume, sperm concentration, motile sperm concentration, total sperm count, and total motile sperm count were similar to 2010 World Health Organization (WHO) semen parameter data of men in the general population. The overall usability score for the Trak System was 1.4 on a 5‐point Likert scale (1 = Very Easy, 5 = Difficult), and 92% of participants believed they performed the test correctly and received an accurate result. Lastly, men with higher motile sperm count were more likely to report feeling “at ease” or “excited” following testing, while men with low motile sperm count were more likely to report feeling “concerned” or “frustrated.” Overall, 91% of men reported they would like to test again. Conclusions The Trak System provides a simple and potentially cost‐effective means of measuring important semen parameters and may be useful in population‐based epidemiologic fertility studies.

Background The accuracy of birth outcome data provided by Internet‐based cohort study participants has not been well studied. Methods We compared self‐reported data on birth characteristics in Pregnancy Study Online (PRESTO), an Internet‐based prospective cohort study of North American pregnancy planners, with birth certificate data. At enrolment, participants were aged 21‐45 years, attempting conception, and not using fertility treatment. Women completed online questionnaires during preconception, early and late pregnancy, and postpartum. We requested birth certificate data during 2014‐2019 from seven health departments in states with the most participants. After restricting to singleton births, we assessed specificity, sensitivity, and agreement comparing self‐reported data from postpartum questionnaires with birth certificate data for gestational age at delivery (GA) and birthweight (grams). Our primary measure of self‐reported GA (weeks) was calculated as [280‐(due date‐birth date)]/7. We used log‐binomial regression to assess predictors of agreement. Results We linked 85% (771/909) of women in selected states. Median age of women was 30 years (range: 21‐42), 84% had ≥ 16 years of education, nearly 96% were married, 12% had household incomes <$50 000, 32% were parous, and 85% identified as non‐Hispanic White. Median recall interval was 6 months. Among those with self‐reported data, 89% reported the same GA as the birth certificate and 98% reported GA within 1 week of the birth certificate. Self‐report of preterm birth (GA < 37 weeks) agreed with information from birth certificates for 100% of women; sensitivity was 100%, and specificity was 99%. Self‐reported low birthweight (<2500 grams) agreed with birth certificates for 93% of women; sensitivity and specificity were 93% and ≥99%, respectively. Predictors of poorer agreement included higher parity and longer pregnancy attempt time for GA, and lower education and longer recall interval for birthweight. Conclusion Self‐reported data on GA and birthweight from an Internet‐based cohort showed high accuracy compared with birth certificates.