Sperm counts are decreasing at an accelerated pace worldwide

Temporal trends in sperm count: a systematic review and meta-regression analysis of samples collected globally in the 20th and 21st centuries

Abstract

Introduction

In 2017, ‘Temporal Trends in Sperm Count: A Systematic Review and Meta-Regression Analysis’ was published by this journal (Levine et al., 2017). That article, which was widely discussed and highly cited, includes all eligible English-language publications in 1981–2013 that contained data on sperm count. We reported a very strong decline in sperm concentration (SC) and total sperm count (TSC) in North America, Europe, Australia/New Zealand (hereafter NEA) but too few studies have been published in South/Central America, Asia and Africa (hereafter SAA) to draw a conclusion about trends in those continents. We examined mean SC and TSC as a function of collection year, as approximated by the mid-year of the sample collection period. Because sample collection preceded the year of publication by an average of 6 years, our results were already somewhat dated by the time we published our analysis in 2017. Therefore, we conducted a new literature search in the spring of 2020 to identify eligible studies published between 1 January 2014 and 31 December 2019. Here, we report on global trends in SC and TSC in publications 1981 through 2019, which combines results of both searches and analyses. This expanded analysis addresses two important questions. With increased sample size, was a trend seen in South America, Africa and Asia? Did the trends we reported continue post-2011?

Recognition of the importance of male reproductive function, and sperm count, has increased since 2017 (Levine et al., 2018; United Nations Human Rights Office of the High Commissioner, 2019). The economic and societal burden of male infertility is now widely recognized, as is the unequal burden of male infertility which falls most heavily on low-income populations (Winters and Walsh, 2014; Hauser et al., 2015; Dupree, 2016; Skakkebaek et al., 2016). Increasingly strong evidence links reduced sperm count and concentration to increases in all-cause mortality and morbidity (Latif et al., 2017; Del Giudice et al., 2021; Ferlin et al., 2021). Links between sperm count and infertility are well-recognized (Bonde et al., 1998; Skakkebæk et al., 2022). Furthermore, the decline in sperm count is paralleled by declines in testosterone and increases in testicular cancer and male genital anomalies (Skakkebæk et al., 2022). In fact, the decline in semen quality and male reproductive health has recently been described as a crisis (De Jonge and Barratt, 2019). Relative to declines in sperm counts, these latter trends are far more difficult to document. There is currently no systematic collection of such data, making the examination of those trends difficult. Therefore, an international group of scientists, including several of the authors, has suggested the formation of a multidisciplinary monitoring system for reproductive health indicators that would provide ongoing surveillance of reproductive health outcomes (Le Moal et al., 2016).

The broad implications of a global decline in sperm count, the data gaps left unfilled by our prior analysis and the controversies surrounding these issues warrant an up-to-date meta-analysis. This meta-analysis was conducted to address these aims.

Methods

This systematic review and meta-regression analysis was conducted, and the results reported, in accordance with Meta-analysis in Observational Studies in Epidemiology (Stroup et al., 2000) (Supplementary Table SI) and Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines (Liberati et al., 2009; Moher et al., 2009). Our research team included epidemiologists, andrologists and a medical librarian. Our predefined protocol was developed following best practices (Borenstein et al., 2009; Higgins and Green 2011; National Toxicology Program 2015) and informed by our previous study. Throughout, unless otherwise noted, the methods of the current study are those employed and published in the previous study (Levine et al., 2017), including keywords and databases searched, eligibility criteria and statistical methods.

Systematic review and study selection

A comprehensive search of the PubMed/MEDLINE and EMBASE was conducted, to identify English-language publications that reported primary data on human sperm count, published in 2014–2019, i.e. from the last date included in our prior search through 2019. On 15 May 2020, we searched MEDLINE/PubMed and Embase (Excerpta Medica database) for peer-reviewed publications meeting our inclusion criteria. Following the recommendation of the Cochrane Handbook for Systematic Reviews, we searched in title and abstract for both index (MeSH) terms and keywords and filtered out animal-only studies. We used the MeSH term ‘sperm count’, which includes seven additional terms, and to increase sensitivity, we added 13 related keywords (e.g. ‘sperm density’, ‘sperm concentration’).

All English-language studies that reported primary data on human SC were considered eligible for abstract screening. We evaluated the eligibility of all subgroups within a study. For example, in a case–control study, the control group might have been eligible for inclusion even though, based on our exclusion criteria, the case group was not.

We divided eligible studies into two fertility-defined groups: (i) studies of men unselected by fertility status, hereafter ‘Unselected’ (e.g. young men unlikely to be aware of their fertility, such as young men being screened for military service or college students) and (ii) studies of men whose partners had born a child or whose partners were pregnant regardless of pregnancy outcome, hereafter ‘Fertile’. ‘Total’ refers to both groups.

A study was excluded if participants were selected based on: (i) infertility or sub-fertility; (ii) their range of semen parameters (e.g. studies selecting normospermic men); and (iii) genital abnormalities, diseases or medications. We also excluded studies limited to men with exposures that may affect fertility, such as an occupational exposure, post-clinical trial intervention or smoking. Studies of candidates for vasectomy or semen donation were included only if semen quality was not a criterion for men’s study participation. We excluded studies that used non-standard methods for sperm collection (e.g. methods other than masturbation) or counting methods other than hemocytometer and studies with fewer than 10 men.

First, the publication was either excluded or advanced to full-text screening based on the title and abstract. Any publication without an abstract was automatically referred for full-text screening. Second, we reviewed the full text and assigned it to exclusion (and categorized the reason for exclusion) or to data extraction. We then confirmed study eligibility and identified multiple publications from the same study to ensure that estimates from the same population were not used more than once.

Data extraction

For each estimate, we extracted summary statistics on SC and TSC (mean, standard deviation, minimum, maximum, median and percentiles), semen volume (mean and measured method), sample size, sample collection years, data on covariates (fertility group, country, age, ejaculation abstinence time reported and per protocol, method of semen collection, method of assessing of SC and semen volume, selection of population, study exclusion criteria and number of samples per man). The range of permissible values, both for categorical and numerical variables and information on data completeness were recorded. Data were extracted on the total population as well as on all eligible subgroups.

Quality control

The current study followed the same protocol as used in the previous study. For the new search, we used Covidence systematic review software for the process of screening the articles that were not available for our prior search. In addition, one member of the research team was replaced. Screening of this extensive systematic review was conducted by a team of eight reviewers (H.L., N.J., A.M.-A., J.M., D.W.-D., M.J., R.P., S.H.S.). As in our previous analysis, the screening protocol was piloted and reviewers were trained by screening of 50 abstracts by all reviewers followed by a comparison of results, resolution of any inconsistencies and clarification of the protocol as needed. The same quality control process was followed for full-text screening and data extraction by all reviewers. All data were entered into digital spreadsheets with explicit permissible values (no open-ended entries) to increase consistency. After data extraction, an additional round of data editing and quality control of all studies was conducted by H.L. and M.J. The process ensured that each study was evaluated by at least two different trained reviewers.

Source Link: https://academic.oup.com/humupd/advance-article/doi/10.1093/humupd/dmac035/6824414

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