The obstetrical literature defines infertility as the inability to conceive after 1 year of unprotected intercourse in the fertile phase of the menstrual cycle [5]. Fecundability is the chance of being pregnant in a single menstrual cycle and fecundity is the probability of achieving a live birth within a single reproductive cycle [6].
Fertility is a concern to both men and women with IBD [7]. Most studies show that the rates of infertility in patients with CD are similar to those reported in the general population, although the data are conflicting [8,9]. It appears that disease location (particularly colonic) and a history of surgery for active disease are associated with a lower likelihood of conception [9].
In women with UC that have not undergone surgical treatment, fertility is not affected. On the contrary, those women who have undergone colectomy with ileal pouch anal anastomosis (IPAA), fecundability is significantly reduced [10,11]. Two meta-analyses have since demonstrated this phenomenon [12,13]. The underlying mechanism for this finding is thought to be due to adhesions created in the pelvis during the creation of the pouch, as women who undergo subtotal colectomy preserve their fertility [14,15].
Demographers use the term “fertility” to refer to actual reproductive performance and use the term “fecundity” to refer to the biological capacity. This can lead to some confusion because the medical profession tends to use the term “fertility” to refer to what demographers call “fecundity.” For example, couples who have tried unsuccessfully for at least 12 months to conceive a child are usually called “infertile” by physicians, whereas demographers would say that such a couple is “infecund.” A woman is classified as having impaired fecundity if she believes that it is impossible for her to have a baby, if a physician has told her not to become pregnant because the pregnancy would pose a health risk for her or her baby, or if she has been continuously married for at least 36 months, has not used contraception, and yet has not gotten pregnant.
Among women who are normally fecund and who regularly engage in unprotected sexual intercourse, the probability is very close to 1.0 that she will become pregnant over the course of 12 months. This varies somewhat by age, however, with the probability peaking in the early 20s and declining after that. Furthermore, women who are lactating are much less likely to conceive than nonlactating women.
The measures of fertility used by demographers attempt generally to gauge the rate at which women of reproductive age are bearing live births. Because poor health can lead to lower levels of conception and higher rates of pregnancy wastage (spontaneous abortions and stillbirths), improved health associated with declining mortality can actually increase fertility rates by increasing the likelihood that a woman who has intercourse will eventually have a live birth. Most rates are based on period data, which refer to a particular calendar year and represent a cross section of the population at one specific time. Cohort measures of fertility, on the other hand, follow the reproductive behavior of specific birth-year groups (cohorts) of women as they proceed through the childbearing years. Some calculations are based on a synthetic cohort, which treats period data as though they referred to a cohort. Thus, data for women ages 20–24 and 25–29 in the year 2000 represent the period data for two different cohorts. If it is assumed that the women who are now 20–24 will have just the same experience 5 years from now as the women who are currently 25–29, then a synthetic cohort has been constructed from the period data.
The CBR is “crude” because (1) it does not take into account which people in the population are actually at risk of having the births and (2) it ignores the age structure of the population, which can greatly affect how many live births can be expected in a given year. Thus, the CBR (which is sometimes called simply “the birth rate”) can, on the one hand, mask significant differences in the actual reproductive behavior between two populations and, on the other hand, can imply differences that do not really exist. For example, if a population of 1000 people contains 300 women who were of childbearing age and one-tenth of them (30) had a baby in a particular year, the CBR would be (30 births/1000 total people) = 30 births per 1000 population. However, in another population, one-tenth of all women may also have had a child that year. Yet, if out of 1000 people there were only 150 women of childbearing age, then only 15 babies would be born, and the CBR would be 15 per 1000. CBRs in the world at the start of the twenty-first century ranged from a low of 8 per 1000 (in Bulgaria and Latvia) to a high of 51 per 1000 in Niger. The CBR in Canada was 11, compared with 14 in the United States, and 23 in Mexico.
Despite its shortcomings, the CBR is often used because it requires only two pieces of information: the number of births in a year and the total population size. If, in addition, a distribution of the population by age and sex is available, usually obtained from a census (but also obtainable from a large survey, especially in less-developed nations), then more sophisticated rates can be calculated.
The general fertility rate (GFR) uses information about the age and sex structure of a population to be more specific about who actually has been at risk of having the births that are recorded in a given year. The GFR (which is sometimes called simply “the fertility rate”) is the total number of births in a year (b) divided by the number of women in the childbearing ages (30F15, denoting females starting at age 15 with an interval width of 30, i.e., women ages 15–44):
Smith has noted that the GFR tends to be equal to about 4.5 times the CBR. Thus, in 2000 the GFR in the United States of 67.5 was just slightly more than 4.5 times the CBR of 14.7 for that year.
If vital statistics data are not available, it is still possible to estimate fertility levels from the age and sex data in a census or large survey. The child–woman ratio (CWR) provides an index of fertility that is conceptually similar to the GFR but relies solely on census data. The CWR is measured by the ratio of young children (ages 0–4) enumerated in the census to the number of women of childbearing ages (15–44):
Notice that there is typically an older upper limit on the age of women for the CWR than for the GFR because some of the children ages 0–4 will have been born up to 5 years prior to the census date. Census 2000 counted 19,176,000 children ages 0–4 in the United States and 61,577,000 women ages 15–44; thus, the CWR was 311 children ages 0–4 per 1000 women of childbearing age. By contrast, the 2000 census in Mexico counted 10,635,000 children ages 0–4 and 23,929,000 women ages 15–49 for a CWR of 444.
The CWR can be affected by the underenumeration of infants, by infant and childhood mortality (some of the children born will have died before being counted), and by the age distribution of women within the childbearing years; researchers have devised various ways to adjust for each of these potential deficiencies. Just as the GFR is roughly 4.5 times the CBR, so it is that the CWR is approximately 4.5 times the GFR. The CWR for the United States in 2000, as previously noted, was 311, which was slightly more than 4.5 times the GFR in that year of 67.5.
As part of the Princeton European Fertility Project, a fertility index has been produced that has been useful in making historical comparisons of fertility levels. The overall index of fertility (If) is the product of the proportion of the female population that is married (Im) and the index of marital fertility (Ig). Thus:
Marital fertility (Ig) is calculated as the ratio of marital fertility (live births per 1000 married women) in a particular population to the marital fertility rates of the Hutterites in the 1930s. Because they were presumed to have had the highest overall level of “natural” fertility, any other group might come close to, but is not likely exceed, that level. Thus, the Hutterites represent a good benchmark for the upper limit of fertility. An Ig of 1.0 means that a population's marital fertility was equal to that of the Hutterites, whereas an Ig of 0.5 represents a level of childbearing only half that. Calculating marital fertility as a proportion, rather than as a rate, allows the researcher to readily estimate how much of a change in fertility over time is due to the proportion of women who are married and how much is due to a shift in reproduction within marriage.
One of the more precise ways of measuring fertility is the age-specific fertility rate (ASFR). This requires a rather complete set of data: births according to the age of the mother and a distribution of the total population by age and sex. An ASFR is the number of births (b) occurring in a year to mothers ages x to x + n (nbx) per 1000 women (pf) of that age (usually given in 5-year age groups):
For example, in the United States in 2000 there were 112 births per 1000 women ages 20–24. In 1955 in the United States, childbearing activity for women ages 20–24 was more than twice that, as reflected in the ASFR of 242. In 2000 the ASFR for women ages 25–29 was 121, compared with 191 in 1955. Thus, we can conclude that between 1955 and 2000 fertility dropped more for women ages 20–24 (a 54% decline) than for women ages 25–29 (a 37% drop).
ASFRs require that comparisons of fertility be done on an age-by-age basis. Demographers have also devised a method for combining ASFRs into a single fertility index covering all ages. This is called the total fertility rate (TFR). The TFR employs the synthetic cohort approach and approximates how many children women have had when they are all through with childbearing by using the age-specific fertility rates at a particular date to project what could happen in the future if all women went through their lives bearing children at the same rate that women of different ages were bearing them at that date. For example, as previously noted, in 2000 American women ages 25–29 were bearing children at a rate of 121 births per 1000 women per year. Thus, over a 5-year span (from ages 25 to 29), for every 1000 women we could expect 605 (= 5 × 121) births among every 1000 women if everything else remained the same. Applying that logic to all ages, we can calculate the TFR as the sum of the ASFRs over all ages:
The ASFR for each age group is multiplied by 5 only if the ages are grouped into 5-year intervals. If data by single year of age are available that adjustment is not required. The TFR can be readily compared from one population to another because it takes into account the differences in age structure and its interpretation is simple and straightforward. The TFR is an estimate of the average number of children born to each woman, assuming that current birth rates remain constant and that none of the women die before reaching the end of the childbearing ages. In 2000, the TFR in the United States was 2.13 children per woman, which was well below the 1955 figure of 3.60 children per woman. A rough estimate of the TFR (measured per 1000 women) can be obtained by multiplying the GFR by 30 or by multiplying the CBR by 4.5 and then again by 30. Thus, in the United States in 2000, the TFR of 2130 per 1000 women was slightly more than, but still close to, 30 times the GFR of 67.5.
Dr MorA further refinement of the TFR is to look at female births only (because it is only the female babies who eventually bear children). The most precise way to do this would be to calculate age-specific birth rates using only female babies; then the calculation of the TFR (Eq. 22) would represent the gross reproductive rate (GRR). Because there is not much variation by age of mothers in the proportion of babies that are female, it is simpler to use the proportion of all births that are female, and the formula is as follows:
In the United States in 2000, 48.8% of all births were girls. Because the TFR was 2.130, we multiply that figure by 0.488 (the percentage converted to a proportion) to obtain a GRR of 1.039. The GRR is generally interpreted as the number of female children that a female just born may expect to have during her lifetime, assuming that birth rates stay the same and ignoring her chances of survival through her reproductive years. A value of 1 indicates that women will just replace themselves, whereas a number less than 1 indicates that women will not quite replace themselves and a value greater than 1 indicates that the next generation of women will be more numerous than the present one.
The GRR is called “gross” because it assumes that a person will survive through all her reproductive years. Actually, some women will die before reaching the oldest age at which they might bear children. The risk of dying is taken into account by the net reproduction rate (NRR). The NRR represents the number of female children that a female child just born can expect to bear, taking into account her risk of dying before the end of her reproductive years. It is calculated as follows:
where nbfx represents the number of female children born to women between the ages of x and x + n, which is divided by the total number of women between the ages of x and x + n (npfx). This is the age-sex specific birth rate which, in this example, assumes a 5-year age grouping of women. Each age-sex specific birth rate is then multiplied by the probability that a woman will survive to the midpoint of the age interval, which is found from the life table by dividing nLx (the number of women surviving to the age interval x to x + n) by 500,000 (which is the radix multiplied by 500,000). Note that if single year of age data were used, then the denominator would be 100,000 rather than 500,000.
The NRR is always less than the GRR because some women always die before the end of their reproductive periods. How much before, of course, depends on death rates. In a low-mortality society such as the United States, the NRR is only slightly less than the GRR—the GRR of 1.039 is associated with a NRR of 1.023. Thus, in the United States the ratio of the NRR to the GRR of is 0.985. By contrast, in a high-mortality society such as Ethiopia, the difference can be substantial (the ratio of the NRR of 2.600 to the GRR of 3.700 is 0.700). As an index of generational replacement, an NRR of 1 indicates that each generation of females has the potential to just replace itself. This indicates a population that will eventually stop growing if fertility and mortality do not change. A value less than 1 indicates a potential decline in numbers, and a value greater than 1 indicates the potential for growth unless fertility and mortality change. It must be emphasized that the NRR is not equivalent to the rate of population growth in most societies. For example, in the United States the NRR in 2000 was almost exactly 1 (as I have just mentioned), yet the population was still increasing by more than 2.6 million people each year. The NRR represents the future potential for growth inherent in a population's fertility and mortality regimes. However, peculiarities in the age structure (such as a large number of women in the childbearing ages), as well as migration, affect the actual rate of growth at any point in time.
FA is “…the understanding of reproduction, fecundity, fecundability, and related individual risk factors (e.g., advanced age), sexual health factors such as STIs, and life-style factors such as smoking and obesity and nonindividual risk factors (e.g., environmental and work place factors); including the awareness of societal and cultural factors affecting options to meet reproductive family planning, as well as family building needs [46].”
FA can be tied to the use of SRH services. SRH care use has risen but disparities do exist [47,48]. For both women and men, racial and ethnic minorities are at a disadvantage. General economics also play a role in SRH care access and availability and are subject to fluctuations in national fiscal factors which can include such issues as recessions and most recently, the COVID19 pandemic [49].
In general, while reproductive aged young adults desire having children, the majority lack information about infertility risk factors. In addition, this group typically has low to moderate FA. A literature review of 71 articles, with subjects in 24 countries, found that there is greater awareness among women, more educated individuals, people reporting difficulty conceiving and those who planned their pregnancies [50]. Other studies demonstrate that knowledge about fertility is limited regardless of age, gender, or educational background [51,52].
One recent qualitative study with British adolescents (ages 16–18) and emerging adults (ages 21–24) demonstrated that these age groups wanted the knowledge but had a hard time integrating it without worrying about the implication for them in the present or future. While these young people had knowledge, its breadth and depth were not enough for them to make informed decisions. The subjects reported that they saw no point to the knowledge at a stage of life when family building was not on their minds. The researchers speculated that the content, amount, and timing of FA information needs to be tailored to the developmental stage of the learners. They conclude “Young people welcome fertility information but qualitative data illustrate the need for it to be tailored to specific age groups to maximize its benefits and ensure young people can integrate the information they need to maintain reproductive health and make informed decisions about future parenthood [53].” While the sample size was small (n=33), the findings hint at the complexity of providing useful and meaningful FA information.
Important sex discrepancies do exist. One study found that Canadian men aged 18–50 have poor knowledge regarding male infertility [54]. About half of men (51%) correctly identified male infertility-related risk factors and 45% could identify health-related issues. Of the men studied, 58% reported wanting more information about male infertility and reproductive health and they preferred two sources for that information, medical professionals and online sources [54]. It is not a surprise that men have low knowledge about infertility or infertility risk factors; young men’s awareness and knowledge about issues such as female birth control methods is not much better—they have low awareness of highly effective female contraception methods as compared to condoms and shorter-acting female birth control methods (pills, patch) and have very poor knowledge about any form of female contraception methods other than condoms [55].
A recent United States study found that about 60% of men are in need of preconception care. In general, male preconception care focuses on conception prevention. As the authors note, preconception care requires reproductive aged persons to have “high reproductive awareness.” In other words, a plan that includes what is necessary to prevent an unintended pregnancy as well as create optimal health conditions prior to a desired pregnancy. They note that women have been the primary focus of such improvement efforts [56]. The men in that study varied on multiple factors, including age, locale, ethnicity, and poverty level, among other variables. A significant number of men in the study had health risks that could impair reproductive capacity. Poor health status, overweight or obesity, daily marijuana use, binge drinking, addictive drug use, and high STI risk were some of the factors found in this study. Significant numbers of these men had usual sources of care (71%), health insurance (76%), and a yearly physical exam (49%). At the same time, among all men in the study, healthcare provider counseling in the preceding year was reported by 11% for STIs; 10% for HIV; and 10% for contraception, highlighting the gap between observed need for care and actual delivery of the services.
Women had greater FA in 12 of the studies reviewed before. At the same time 10 studies found no difference between women’s and men’s FA. Some studies reported mixed results [50]. There are probably good reasons for women’s greater awareness that will be discussed later. One of the significant findings from the review is that while people are aware of the risk age plays in reduced fertility, they overestimate the age at which declines begin. They also overestimate the probability of becoming pregnant either spontaneously or via fertility treatment. The authors suggest that the focus on pregnancy prevention in school-based sexuality education is a contributing factor to this finding.
When it comes to men’s help seeking for infertility concerns, help seeking was quite low, especially for younger men. For example, although infertility prevalence was slightly lower for males aged 16–24 (4%) and 25–24 (9%) as compared to older aged men, only 14% of men aged 16–24 sought help as compared to 50% of men aged 24–34 and 57% of men aged 35–44 [57].
Although many conditions place children and youth at risk for infertility, including males, many youth may not be aware of conditions that occurred earlier in childhood (e.g., anorchia, testicular torsion) or conditions that may not present until adolescence (Klinefelter syndrome, Kallman syndrome) [58]. Even among males with cancer, the prevalence of help seeking for men with infertility is low. For example, the prevalence of male infertility prevalence in the Childhood Cancer Survivor Study was 46% among cancer survivors as compared to 18% in the sibling comparison group, but only 54% went for infertility care as compared to 21% of the sibling comparison group [59]. Another study that conducted a medical record review of males aged 13 with a new cancer diagnosis found that only 29% received fertility counseling and only 11% attempted sperm banking [60].
In 2006 the CDC published recommendation guidance to improve both preconception health and care with the goal to improve the health of women and couples, before conception of a first or subsequent pregnancy [61]. One of the key recommendations is that individuals take responsibility across the lifespan, and, specifically, each woman, man, and couple should be encouraged to have a reproductive life plan and use a lifespan approach to focus individual attention on reproductive health to reduce unintended pregnancies, age-related infertility, fetal exposures to teratogens, and to improve women’s health and pregnancy outcomes. In 2014 updates to the nation’s Title X guidelines by the Office of Population Affairs and the CDC incorporated these guidelines along with additional guidance to make comprehensive clinical care guidelines for women and men related to family planning and SRH care, including need for basic infertility counseling as an important component [62]. These guidelines are separate but parallel to women’s health guidance that was implemented as part of the Patient Protection and Affordable Care Act (ACA) [63] A similar emphasis on the SRH needs of men had not existed. Between 2011 and 2014 several agencies collaborated to create them for the first time for the United States. It has been noted that development of guidelines for men is critical for a variety of reasons, including the health of their partners, including men in family planning, and improving the fathering capacity of men. An additional desired outcome is the need to integrate men’s healthcare into reproductive health programs nationwide [64]. As documented elsewhere, public awareness as well as research about male infertility is substantially missing [65,66].
More recent guidelines published in 2018 by the American Academy of Pediatrics (AAP) promote counseling at-risk pediatric populations about fertility and sexual function beginning in infancy with parents or at earliest time point a patient may be affected [67]. This guidance contributes to other condition-specific guidance in 2008 by the AAP (referring patients at risk for fertility loss primarily focused on childhood cancer for fertility preservation (FP) before gonadotoxic therapy) and in 2006 by the Pediatric Endocrine Society (disclosing to youth with disorders of sex development about their condition and counseling about fertility and sexual function, using “collaborative, ongoing” approach) [68,69].
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