Unmasking Seasonal Cycles in Human Fertility: How holiday sex and fertility cycles shape birth seasonality

Symul, Laura; Hsieh, Peggy; Shea, Amanda; Moreno, Cláudia Roberta de Castro; Skene, Debra J.; Holmes, Susan; Martinez, Micaela E

doi:10.1101/2020.11.19.20235010

Cited by 8 publications

(12 citation statements)

References 42 publications

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“…On the other hand, Symul et al (2020) found all countries considered in their study having elevated sexual activity on weekends and decreased sexual activity on weekdays and elevated sexual activity on holidays. More importantly, for all locations, the time between Christmas and New Year had the highest level of sexual activity.…”

Section: Discussionmentioning

confidence: 77%

“…It seems that maternal employment status conditions the periods of holidays and leisure time becoming a good sociodemographic discriminator of the different birth seasonal patterns, above other sociodemographic characteristics such as age, marital status, parity, or country of birth. However, as stated in a recent study (Symul et al, 2020), holidays are unlikely to provide a complete explanation of birth seasonality, not being its primary driver. According to this study, birth seasonality is primarily driven by seasonal fertility, although increased sexual activity around holidays explains minor peaks in the birth curve.…”

Section: Discussionmentioning

confidence: 79%

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Influence of sociodemographic factors in birth seasonality in Spain

Alcaide

López

Bolúmar

2022

American J Hum Biol

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Objectives: The goal of the present research is to establish for the first time a hierarchy of sociodemographic factors according to their importance influencing birth seasonality. Methods: We used Vital Statistics data on all births registered in Spain in the period 2016-2019. Differences in the degree of seasonality between sociodemographic groups (defined by maternal age, maternal marital status, maternal education, birth order, maternal job qualification, maternal employment status, maternal location population size, and maternal country of birth) were first examined with descriptive techniques. Secondly, analysis through alternative Data Mining techniques determined the association between sociodemographic factors and birth seasonality and the factors importance rank.Results: Those factors related to maternal labor status (employment status, job qualification, and education) were found to be the most relevant influencing birth seasonality. It was found that the overall seasonal pattern in Spain was driven by lower skilled employed mothers, in contrast with not employed or high skilled employed mothers, who showed a different or weaker seasonality. Finally, we found that a change in the rhythm pattern has taken place in the last decades in Spain.Conclusions: Birth seasonality is to a large extent related to maternal employment status. Employed mothers, normally more affected by the seasonality of work calendar than the unemployed, show higher conception rates structured around holidays. This may indicate that the observed change of seasonal pattern in Spain in the last decades, as in other European countries, may be specifically driven by the progressive higher participation of women in labor market.

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Section: Discussionmentioning

confidence: 77%

Section: Discussionmentioning

confidence: 79%

Influence of sociodemographic factors in birth seasonality in Spain

Alcaide

López

Bolúmar

2022

American J Hum Biol

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“…First, hot temperatures might reduce sexual activity. Evidence suggests that seasonal variation in sexual activity is primarily driven by holidays and cultural/religious celebrations (Markey and Markey 2013;Symul et al 2020;Wellings et al 1999;Wood et al 2017). The direct connection between temperature and sexual activity was rarely examined.…”

Section: Birth Rates Conception Ratesmentioning

confidence: 99%

Temperature, Climate Change, and Fertility

Hajdu¹,

Hajdu²

2022

Handbook of Labor, Human Resources and Population Economics

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This chapter reviews the empirical literature on the impacts of temperature and climate change on human pregnancies. The focus is on the quasi-experimental studies that use panel data, apply a fixed effect approach, and exploit the random year-to-year fluctuation in temperature. The insights that emerge from the review highlight that exposure to heat in the pre-conception period has detrimental impacts on fertility. In addition, heat during pregnancy increases pregnancy losses, leads to a reduction in gestational length, and lowers birth weight. Despite the growing empirical evidence on the subject, understanding the relationship between temperature and pregnancy-related outcomes is far from perfect. Importantly, the potential impacts of climate change are rarely quantified. The chapter outlines directions for future research.

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“…This information can be used to tackle scientific challenges and address unanswered questions about the human reproductive biology. For example, this data can be used to evaluate whether seasonal and geographical variations of fertility [8] is due to changes in ovulation or loss rates or to study, at large scale, the predictability of mental health variations throughout the menstrual cycle [9], [10]. Beyond the potential of these large retrospective datasets, apps and/or connected devices also provide a scalable way to prospectively collect longitudinal data of menstrual-health related body signs and symptoms for a large population size over a long period of time without requiring in-person visits to a clinic.…”

Section: Introductionmentioning

confidence: 99%

Labeling Self-Tracked Menstrual Health Records With Hidden Semi-Markov Models

Symul

Holmes

2022

IEEE J. Biomed. Health Inform.

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Globally, millions of women track their menstrual cycle and fertility via smartphone-based health apps, generating multivariate time series with frequent missing data. To leverage this type of data for studies of fertility or studies of the effect of the menstrual cycle on symptoms and diseases, it is critical to have methods for identifying reproductive events, such as ovulation, pregnancy losses or births. Here, we present a hierarchical approach relying on hidden semi-Markov models that adapts to changes in tracking behavior, explicitly captures variable-and statedependent missingness, allows for variables of different type, and quantifies uncertainty. The accuracy on simulated data reaches 98% with no missing data and 90% with realistic missingness. On our partially labeled real-world time series, the accuracy reaches 93%. Our method also accurately predicts cycle length by learning user characteristics. Its implementation is publicly available (HiddenSemiMarkov R package) and transferable to any health time series, including self-reported symptoms and occasional tests.

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Unmasking Seasonal Cycles in Human Fertility: How holiday sex and fertility cycles shape birth seasonality

Cited by 8 publications

References 42 publications

Influence of sociodemographic factors in birth seasonality in Spain

Influence of sociodemographic factors in birth seasonality in Spain

Temperature, Climate Change, and Fertility

Labeling Self-Tracked Menstrual Health Records With Hidden Semi-Markov Models

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