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. 2017 Jan 28;32(3):499–504. doi: 10.1093/humrep/dew361

Is human fecundity changing? A discussion of research and data gaps precluding us from having an answer

Melissa M Smarr 1,*, Katherine J Sapra 1, Alison Gemmill 2, Linda G Kahn 3, Lauren A Wise 4, Courtney D Lynch 5, Pam Factor-Litvak 3, Sunni L Mumford 6, Niels E Skakkebaek 7, Rémy Slama 8, Danelle T Lobdell 9, Joseph B Stanford 10, Tina Kold Jensen 7, Elizabeth Heger Boyle 11, Michael L Eisenberg 12, Paul J Turek 13, Rajeshwari Sundaram 14, Marie E Thoma 15, Germaine M Buck Louis 1
PMCID: PMC5850610  PMID: 28137753

Abstract

Fecundity, the biologic capacity to reproduce, is essential for the health of individuals and is, therefore, fundamental for understanding human health at the population level. Given the absence of a population (bio)marker, fecundity is assessed indirectly by various individual-based (e.g. semen quality, ovulation) or couple-based (e.g. time-to-pregnancy) endpoints. Population monitoring of fecundity is challenging, and often defaults to relying on rates of births (fertility) or adverse outcomes such as genitourinary malformations and reproductive site cancers. In light of reported declines in semen quality and fertility rates in some global regions among other changes, the question as to whether human fecundity is changing needs investigation. We review existing data and novel methodological approaches aimed at answering this question from a transdisciplinary perspective. The existing literature is insufficient for answering this question; we provide an overview of currently available resources and novel methods suitable for delineating temporal patterns in human fecundity in future research.

Keywords: epidemiology, fecundity, fertility, gynecologic diseases, time-to-pregnancy, urologic diseases

Introduction

The question of whether human fecundity is changing elicits polarized views regarding the interpretation of available data. Because it is nearly impossible to distinguish whether fewer births reflect reduced fecundity (i.e. the biological capacity to reproduce) or changing reproductive behavior (e.g. contraception, delayed childbearing), some authors have concluded that the question is not answerable (Sallmén et al., 2005). Still, there are salient reasons to address this issue given that reduced fecundity has profound psychological and financial costs for individual couples. Furthermore, a growing body of evidence suggests that both fecundity and fertility are markers of somatic health and longevity, including higher risks of reproductive and non-reproductive cancers (Jacobsen et al., 2000; Brinton et al., 2005; Walsh et al., 2010; Eisenberg et al., 2015; Hanson et al., 2016), cardiovascular disease (Eisenberg et al., 2015) and mortality for men and women (Jensen et al., 2009; Eisenberg et al., 2014), prompting calls for their potential use in health screening (Omu, 2013; Ventimiglia et al., 2015). Moreover, such findings have implications for previously described research paradigms that assess the role of fecundity in the pathway to later onset diseases, as conceptualized in the testicular dysgenesis syndrome (Skakkebaek et al., 2001) and ovarian dysgenesis syndrome (Buck Louis et al., 2011).

On 20–21 September 2015, the Eunice Kennedy Shriver National Institute of Child Health and Human Development convened a workshop entitled: ‘Is human fecundity changing?’ at which an invited transdisciplinary group of scientists discussed the available weight of evidence. Participants represented the fields of demography, social science, population health science, epidemiology and medicine (i.e. gynecology, reproductive endocrinology, urology and family medicine). Other workshop goals were to examine research barriers and identify ways forward for human fecundity research. Further details about the workshop are found elsewhere https://www.nichd.nih.gov/about/meetings/2015/Pages/091015.aspx.

This paper discusses the data reviewed during the workshop in the context of important methodologic considerations. Possible strategies for future population-based research are presented, from the most basic to the most sophisticated. We have organized the paper as four questions and concluding remarks.

Discussion

What are the distinctions between human fecundity and fertility?

Recognizing that there are many operational definitions of human fecundity, from a population research perspective, fecundity is defined as the biologic capacity to reproduce irrespective of pregnancy intentions, while fertility is demonstrated fecundity as measured by live births and sometimes stillbirths. Terminology may become confusing in the context of impaired fecundity, which includes women who conceive but are unable to carry a pregnancy to delivery (pregnancy loss) and couples unable to conceive within 6 months (conception delay) or 12 months (infertility) of trying. Couples’ fecundity is dynamic in that either partner could experience difficulties at any trying attempt, which resolves spontaneously, following treatment, after a change in partner, or may remain unresolved. This observation reflects the importance of behavior and environmental influences on underlying biology, as well as the couple-dependent nature of fecundity. In contrast to fertility, which is easily measured by births, fecundity cannot be directly measured at the population level and requires reliance on proxy measures. Examples of commonly utilized measures to assess fecundity in women include hormonal profiles, menstruation and ovulation, and biomarkers of follicular reserve such as anti-Müllerian hormone (Steiner, 2013). In men, fecundity can be assessed based on semen quality, clinical measures of testicular volumes and hormonal profiles (Olsen and Ramlau-Hansen, 2014). Couple fecundability is measured by the number of calendar months or menstrual cycles required to become pregnant; the underlying premise being that a shorter time-to-pregnancy (TTP) is indicative of higher fecundity.

What is the weight of evidence for temporal patterns in human fecundity?

To answer this question, it is important to empirically weigh the data on temporal patterns of male, female and couple fecundity. Fecundity research has largely focused on the male, possibly given the availability of non-invasive and quantifiable markers, via semen analysis, from the general population (Jørgensen et al., 2012) along with men from a range of population subgroups (e.g. semen donors, men attending infertility clinics and military recruits) (Swan et al., 2003; Le Moal et al., 2015). Heterogeneity in study populations and designs may explain the equivocal results aimed at answering questions on declining semen quality. However, there are other indicators of male fecundity such as worldwide increases in the incidence of testicular cancer (Znaor et al., 2014; Trabert et al., 2015) or increasing rates in genitourinary malformations (Paulozzi, 1999) that suggest fecundity is declining.

Some indication of changes in the length of time women is at risk of pregnancy can be extrapolated from data on temporal patterns in the age at menarche and onset of natural menopause. Most of the literature reports a decline in age at menarche (Parent et al., 2003) and an increase in age at natural menopause (Gold, 2011) resulting in a longer interval of presumed fecundity. Additionally, female reproductive cancers may inform our understanding of changing female fecundity, given evidence of increasing ovarian cancer incidence in European countries (Bray et al., 2005). Overall, however, there is a dearth of information focusing on temporal trends of female fecundity, and similarly so for couple fecundity. Of the few studies to assess time trends in couple fecundity, it was concluded that couple fecundity was improving, as indicated by a shorter TTP or increased odds of couple fecundity across birth cohorts for a 32-year period for the British population (Joffe, 2000), as well as the decreasing proportion of subfertile women across birth cohorts for a 20-year period in Sweden (Scheike et al., 2008).

Most data used to assess fecundity trends more broadly come from research focusing on (inter)national fertility/infertility rates and there is a general consensus that fertility is declining, as evident by total fertility rates now below two births per woman for most of Europe and North America (Skakkebaek et al., 2016). Still, limited available data sources, measurement issues (e.g. populations assessed—immigrant, etc.), research design constraints (e.g. retrospective sampling, biomarkers in population-based studies), and the inability to disentangle societal and behavioral factors (e.g. contraception use, abortion rates, pregnancy intentions) when such multilevel data are unavailable, preclude our ability to use temporal changes in fertility rates to directly answer the question of whether human fecundity is indeed changing.

What data and research methods exist to assess fecundity trends?

Adequate evaluation of temporal patterns of human fecundity requires available data and appropriate study design and statistical methods. Currently, the most commonly used data resources include registries of fertility/infertility treatment, live births, reproductive cancers and genitourinary malformations, and nationally representative surveys of reproductive health (Table I). Limitations of these sources include factors such as the lack of data on changes in diagnostic methods over time, possible surveillance bias and limited information on relevant covariates. Still, some perspective is gleaned from cross-sectional surveys that are repeated over time, like the National Survey of Family Growth (NSFG). The NSFG provides data on the prevalence of infertility, impaired fecundity and factors that may impact fertility and fecundity measures, such as pregnancy intentions in the USA (Chandra et al., 2013). Unfortunately, current data resources may not adequately identify populations at risk for inclusion in population-based assessments of fecundity and have been particularly concentrated in Europe and the USA (e.g. National ART Surveillance System (NASS), ESHRE), emphasizing the need to capture data more globally.

Table I.

Examples of existing data sources on human fecundity.

Data source Country of origin Type of data source Human fecundity measure(s) Website
ANZARD Australia and New Zealand Treatment registry Number of IVF cycles, pregnancies, pregnancy losses, live births, IUI with donor semen only http://npesu.unsw.edu.au/data-collection/australian-new-zealand-assisted-reproduction-database-anzard
DHS 90 LMICs Nationally and sub-nationally representative surveys Births, pregnancies, non-live-birth pregnancy terminations, use of contraceptive methods, breastfeeding, self-reported infecundity, sexual abstinence and periods living with a sexual partner (precise content varies) http://dhsprogram.com/data/
ESHRE Europe Treatment registry Number of IVF cycles, pregnancies, pregnancy losses, live births, IUI cycles www.eshre.eu/Data-collection-andresearch/Consortia/EIM.aspx
HFEA UK Treatment registry Number of women treated with IVF, number of IVF cycles, pregnancies, pregnancy losses, live births www.hfea.gov.uk
IFSS USA A single data set composed of harmonized variables across national surveys of fertility and family growth Pregnancies, reproductive health, IVF, marriage and cohabitation histories, childbearing histories, contraceptive use http://www.icpsr.umich.edu/icpsrweb/IFSS/
INDEPTH HDSS LMICs Individual event histories from selected neighborhoods Vital events collected over a series of household visits http://www.indepthishare.org/index.php/home
NASS USA Registry ART http://www.cdc.gov/art/nass/index.html
NBDPN USA Registry Genitourinary malformations www.nbdpn.org
NHANES USA Nationally representative survey Self-reported 12-month infertility (women only) in some survey rounds www.cdc.gov/nchs/nhanes/index.htm
NSFG USA Nationally representative survey Births, pregnancies, non-live-birth pregnancy terminations, use of contraceptive methods, breastfeeding, infertility construct (women and men), time attempting to conceive www.cdc.gov/nchs/nsfg/index.htm
NVSS USA Registry Live births, for some states also includes data on fertility treatment with (Utah) or without time to pregnancy www.cdc.gov/nchs/nvss/births.htm
PRAMS USA, some states Nationally representative survey of births Live births, for some states includes data on fertility treatment with or without time to pregnancy www.cdc.gov/prams/
RLA Latin America Treatment registry Number of IVF cycles, pregnancies, pregnancy losses, live births, IUI http://redlara.com/aa_ingles/default.asp
SART USA Treatment registry Number of IVF cycles, pregnancies, pregnancy losses, live births www.sart.org/research/
SEER USA Registry Reproductive cancers http://seer.cancer.gov/
SPARCS New York, USA Hospital discharge data Gynecologic/urologic disorders, infertility treatment www.health.ny.gov/statistics/sparcs/
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LMICs, low- and middle-income countries; ANZARD, Australia and New Zealand Assisted Reproduction Database; DHS, Demographic and Health Surveys; HDSS, Health and Demographic Surveillance System; HFEA, Human Fertilisation and Embryology Authority; IFSS, Integrated Fertility Survey Series; INDEPTH, International Network for the Demographic Evaluation of Populations and Their Health; NASS, National ART Surveillance System; NBDPN, National Birth Defects Prevention Network; NHANES, National Health and Nutrition Examination Survey; NSFG, National Survey of Family Growth; NVSS, National Vital Statistics System; PRAMS, Pregnancy Risk Assessment Monitoring System; RLA, Latin American Register of Assisted Reproduction; SART, Society for Assisted Reproductive Technology; SEER, Surveillance, Epidemiology and End Results Program; SPARCS, Statewide Planning and Research Cooperative System.

Cross-sectional study designs offer another potential approach for estimating fecundity. Several authors have retrospectively queried women about the time required to become pregnant, and such data may be cautiously compared over time to estimate patterns of fecundity given that this design excludes couples who have never been pregnant, potentially excluding the most infertile couples (Joffe, 2000; Jensen et al., 2005; Scheike et al., 2008). Furthermore, the validity of this approach assumes that individuals can accurately recall such information. However, few studies have empirically evaluated recalled TTP validity, with conflicting reports of reliability irrespective of the period of recall (i.e. short and long term) (Zielhuis et al., 1992; Cooney et al., 2009; Radin et al., 2015; Jukic et al., 2016). Couple fecundity has also been estimated in cross-sectional designs using the current duration approach, which assesses that the length of time couples has been at risk for pregnancy (i.e. having intercourse but not using contraception) irrespective of pregnancy intentions (Keiding et al., 2002; Slama et al., 2006; Thoma et al., 2013). An advantage of this design is that it can include couples who have never been pregnant when estimating TTP. While, initially, less fecund couples are overrepresented in the current duration approach, this ensuing bias is corrected for in the analysis stage (Zelen, 2004).

Prospective cohort studies with preconception enrollment of couples have the ability to measure TTP and examine multiple reproductive outcomes, such as menstrual and ovulatory patterns, pregnancy loss and semen quality. To date, relatively few such studies have been conducted and it is unlikely that many new cohorts will be established and systematically maintained in large populations over time. This data gap likely reflects the challenges in establishing such cohorts, while also stimulating novel approaches such as Internet-based recruitment cohorts (Huybrechts et al., 2010; Wise et al., 2015).

While beyond the scope of this paper, a number of analytic, model-based approaches are available to incorporate timing and frequency of intercourse, ovulation proxies (e.g. hormonal profiles, changes in cervical mucus) and couples’ behaviors or lifestyles (Stanford and Dunson, 2007; Kim et al., 2010, 2012; Sundaram et al., 2012; Lum et al., 2015, 2016). Such advances in analytic strategies, including ‘big data’ approaches to pool data cohorts while handling variation stemming from study design and data collection methods (Chatterjee et al., 2016), will help to move forward fecundity research at the population level.

How can we answer the question—is human fecundity changing?

Despite the methodologic challenges, many suggestions for designing research to answer this question from a transdisciplinary perspective were offered at the workshop, as listed below. These themes are categorized as critical data gaps and suggestions for moving forward.

Existing critical data gaps:

  • Few data on the spectrum of endpoints comprising male and female fecundity over time needed for assessing temporal patterns; even fewer global data.

  • Few data on couple fecundity, reflective of the need for recruitment of couples rather than individual partners.

  • Limited fecundity research that also has data on behaviors, and social and cultural factors needed for assessing pregnancy intentions across diverse global populations.

  • Few population-based data linking fecundity and fertility with health across the lifespan.

Suggestions for moving forward:

  • Increase awareness of existing resources for addressing this question, while improving access to underutilized resources.

  • Leverage population-based surveys, health care data (e.g. electronic health records), birth registries or other surveys that query participants about reproductive health, pregnancy intentions, time at risk of pregnancy, fertility treatments used and childbearing to assess trends in fecundity and related impairments.

  • Develop novel sampling frameworks, ranging from traditional probability sampling to untargeted approaches using social media and crowdsourcing methods, for designing and conducting research.

  • Extend mobile health technologies tailored for reproductive aged populations to capture measures of fecundity, behavior and lifestyle.

  • Evaluate big data methods that combine historical data and newly conducted prospective studies to examine time trends and to evaluate individual-level risk factors.

  • Develop study design and analytic methods to correct for biases introduced by study design, pregnancy intentions, length of time already trying for pregnancy or censoring due to ART.

  • Develop/improve standardized questionnaires that reliably capture time trying and ‘at risk’ for pregnancy.

  • Develop biomarkers of male, female and couple fecundity suitable for population-based assessment.

Concluding remarks

At present, we are unable to definitively answer the question as to whether human fecundity is changing, though we recognize the question is an answerable one. An initial step is to utilize and build on existing resources that can address critical data gaps. The complexity of human fecundity, involving both biology and behavior, underscores the need for transdisciplinary collaboration in addressing these issues going forward. Should future research findings point to temporal declines in human fecundity, delineating the potential causes is critical if we are to optimize the health of populations worldwide.

Acknowledgements

We would like to acknowledge Drs Rebecca Clark, Louis De Paolo, Esther Eisenberg, Lisa Halvorson, Rosalind King and all participants for their contributions to the workshop.

Authors’ roles

All authors provided substantial contributions to the conception and design of the present work, and assisted with drafting and critical review of the paper.

Funding

This study was supported, in part, by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Conflict of interest

None declared.

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