Night Shift Work Linked to Infertility in Women

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Night Shift Work Linked to Infertility in Women

Night Shift Among Women: Is It Associated With Difficulty Conceiving a First Birth?

doi.org/10.3389/fpubh.2020.595943

Background

Main article: How to Get Pregnant with Irregular Periods

In humans, the circadian rhythm which regulates sleep and wakefulness is disrupted during night shift work.

This dysregulation of circadian processes impacts various physiological systems, including the female reproductive system.

Previous studies have reported that women who work night shift have an increased risk of endometriosis, irregular menstrual cycles and longer time to conception.

However, the potential impact of this increased risk on the need for fertility treatment among night shift workers has not been investigated to date.

In Australia, fertility treatment and pharmaceutical costs has been subsidized for well over 2 decades, with no limit on age, number of cycles or existing family size.

This open access to treatment allows researchers to finally assess the risk of fertility treatment in night shift workers with minimal bias or limitations.

Aim

To investigate whether women giving birth for the first time and employed in occupations potentially involving night shift work were more likely to require fertility treatment.

Methodology

Population-wide data from the South Australian perinatal registry (January 1986 to December 2002) was utilised for this study. This includes demographic, lifestyle and health characteristics.

The perinatal registry also records each woman’s occupation prior to and or during pregnancy according to Australian Standard Classification of Occupations. To analyse this data a job-exposure matrix was developed to translate job titles into probability of exposure to ‘light at night’, and therefore night shift worker or day worker.

Infertility clinic records for any form of treatment including ovulation induction, intrauterine insemination, IVF and ICSI was then analysed and matched.

Couples where infertility was primarily attributed to male-factors were excluded from the study.

Female-factor infertility was categorized as one or more of the following: ovulatory dysfunction (including PCOS), tubal blockage/related, endometriosis, menstrual irregularity and unexplained infertility.

Results

In total 128,852 women between 1986 to 2002 were included in this study, of which 11,000 (8.5%) were employed in occupations likely to include night shift. The majority of this group, 72.7% were registered or enrolled nurses. In the day only population (117,852), not employed women (25.5%), clerks (10.1%) and sales assistants (8.0%) were most prominent.

Initial analysis found that 1.6% of first births overall required fertility treatment, however in night shift group this rate was 2.2%.

Interestingly this analysis also showed that the number of women not employed, who conceived naturally was proportionally higher than those who required fertility treatment (23.4 vs. 14.6 %), compared with other groups.

Natural conceptionFertility Treatment
Night shift workers (%)8.511.8
Day workers (%)66.073.5
Not in paid employment (%)23.414.6
Unknown occupation (%)2.10.1
Distribution of births according to employment status and method of conception.

Analysis of demographic, health and lifestyle characteristics confirmed that age, ethnicity, socioeconomic status and non-smoking was significantly linked to the use of fertility treatment. 

Extrapolating the results further, women below the age of 35 and working night shift were significantly at higher risk of needing fertility treatment;

Adjusted Odds Ratio = 1.40 (Night shift workers ≤35 VS all other women)
Adjusted Odds Ratio = 1.27 (Night shift workers ≤35 VS day workers)

Beyond the age of 35, no association was found in the data between night shift work and fertility treatment.

To rule out any bias with such a large proportion of nurses, sensitivity analyses was carried out with nurses excluded. The results of that analyses showed similar risks for women below the age of 35 and working night shift requiring fertility treatment;

Adjusted Odds Ratio = 1.34 (Night shift workers ≤35 VS all other women)
Adjusted Odds Ratio = 1.22 (Night shift workers ≤35 VS day workers)

Analysis of infertility diagnosis confirmed that endometriosis and menstrual irregularity was significantly more common, in the night shift group, as reported previously.

Night shift workersAll other womenDay workers
Endometriosis (%)31.324.825.8
Menstrual irregularity (%)31.324.824.2

Lastly, analysis of BMI (Body Mass Index) where available in women who underwent fertility treatment (n=1774) ruled out any significant differences between the 3 main groups (24.4-24.9 mean range).

SUMMARY: TRYING TO CONCEIVE (TTC) WORKING NIGHT SHIFT

In this study of 128,852 women, who gave birth to their first born, trying to conceive (TTC) whilst working night shift is more difficult, with 2.2% of workers exposed to night shifts requiring fertility treatment, compared to just 1.6% of all other women.

Limitations

  1. Lack of information (night shift exposure, working hours, diet, number of women who failed to conceive after fertility treatment, prevalence of menstrual irregularity and endometriosis among day worker group)
  2. Change in fertility treatments and accessibility over time not adjusted for in analysis

Funding

This study was funded by the Safe Work South Australia Augusta Zadow Awards program, the National Health and Medical Research Council and the Australian Research Council.

Glossary

Circadian rhythm
Internal 24 hour body clock.

n
Sample size.

Perinatal
Period of time from birth of baby up to 1 years of age.

Odds ratio
The odds that an outcome will occur. (Odds ratio = 1.5 means the outcome is 1.5 times more likely to occur)

Similar studies

Willis S K, et al. (2019). Female sleep patterns, shift work, and fecundability in a North American preconception cohort study. https://doi.org/10.1016/j.fertnstert.2019.01.037

Gaskins A J, et al. (2015). Work schedule and physical factors in relation to fecundity in nurses. https://doi.org/10.1136/oemed-2015-103026

Feveile H, et al. (2011). Industrial differences in female fertility treatment rates–a new approach to assess differences related to occupation? https://doi.org/10.1177/1403494810391525


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