Sandhya et al. [83] found vitamin D (VD) deficiency was seen in 141 (60 %) SS patients, whereas 60 (25.5 %) and 34 (14.5 %) had VD insufficiency and sufficiency, respectively. Serum VD level was found to be lower in SS than controls [84]. VD deficiency is relatively frequent in patients with SS, suggesting a possible role in the pathogenesis of the disease [85]. In the past two decades, a growing number of evidence has shown optimal physiological levels of VD in serum, follicular fluid, and oocyte are of high relevance for female reproduction. VD appears to not only have stimulatory effects on folliculogenesis, but also a negative impact on oocyte maturation, with appropriate concentration and supplementation [86]. The lack of VD has been associated with the decrease of live birth rates of women undergoing in vitro fertilization [87].
In a recent systematic review and meta-analysis, scholars demonstrated the risk of hypothyroidism was found to be higher in patients with SS than in controls (OR = 3.81; 95 % CI = 1.86–7.83, I2 = 59.0 %) [88]. Thyroid hormone level seem to play a positive role for ovulation and folliculogenesis [89]. Although the available evidence is limited, it seems to support a role of thyroid hormone in fertility and early pregnancy [89]. Further and future studies should focus on thyroid hormone disturbances and their clinical and pathophysiological effects. The presence of thyroid peroxidase autoantibodies (TPO-Ab) negatively influences folliculogenesis, embryo quality and pregnancy rates, but no data are available on the potential mechanisms [89]. TPO-Ab was detected in patients with SS combined with normal thyroid function [90].
Female SS patients seemed to have experienced more stressful life events, particularly negative stressful events, prior to the disease onset [91,92]. Gaskin et al. [93] demonstrated, in a nurse population, that working longer hours (more than 40 h per week) was associated with increased time to conceive, revealing a relation of tiredness or stress with reduced fecundity.
Finally, it was reported the incidence of endometriosis was higher in SS patients (8.5 % vs.2.1 %; P = 0.03). 6.3 % of patients have had surgical intervention due to endometriosis, while the control group is 0.7 % (P = 0.009) [12]. Recent studies have found patients with a history of endometriosis have an increased risk of SS (HR = 1.45, 95 % CI = 1.27–1.65), especially in the 20–39 age group (HR = 1.53, 95 % CI = 1.25–1.88) and within the first five years after the diagnosis of endometriosis (HR = 1.57, 95 % CI = 1.32–1.87) [94]. 10 %-15 % of women of reproductive age suffer from endometriosis [95], of which 20 %-30 % suffer from infertility [96]. The risk of infertility caused by endometriosis is significantly increased (OR = 3.3, 95 % CI = 3.1–3.5) [97]. There may be some relationship between SS and endometriosis, which may affect fertility in a way.
“Natural fertility” broadly refers to the level of fertility reached in the absence of birth control. The Hutterites and the Amish traditionally do not practice birth control, and they usually have very large families (10). In contrast, in much of the modern world fertility is lower than natural fertility levels because of a lengthening of the intervals between menarche and first birth and between successive births, or because of stopping childbearing once the desired family size is attained.
The idea behind the concept, developed by historical demographer Louis Henry (10, 11), was to create a physiologic benchmark against which researchers could judge the fertility patterns when contraception is used (12). At first, “fertility control” was operationalized to assess the stopping behavior, but later the concept was extended by demographers Coale and Trussell to include spacing (13). In alternative formulations, fertility control also includes traditional means of pregnancy avoidance such as coitus interruptus, sexual abstinence, celibacy, or delayed age at marriage. Fertility control has additionally been demonstrated as a response to economic stress in pre-transitional Europe (14).
Although many researchers have called into question the validity of a dichotomy opposing natural to controlled fertility (12), the analysis of populations in which childbearing is in principle unaffected by conscious choice has proved to be useful in the study of the influence of fertility on longevity. To be sure, fertility is never fully natural nor is it ever fully controlled, but deliberate control, when generalized, has the potential to introduce almost insurmountable problems of statistical modeling. For example, perceived health status may influence fertility decisions—a process that is usually not observable—and this may in turn lead to biased estimates of the effect of fertility on mortality if the less healthy individuals choose to limit their family size precisely because they assess their health to be poor (15). In contrast, in a natural fertility context, perceived health should not influence the decision to have another child, although poor health may itself be decisive if it affects fecundity (i.e., the biological capacity to reproduce).
Given natural fertility, life tables show that most married women bear children until age 35–39 years, with a drastic fall of fertility after these ages. Only a tiny minority conceive naturally and are able to bring a pregnancy to term successfully after the age of 45 years. Because there are no parity-specific checks to reproduction, late-fertile women usually also have the largest family sizes. These highly select women, who have been extensively written about, are shown to have long lives in many of the studies reviewed below. They represent an important puzzle for evolutionary theories of aging (16–18).
The definition of fertility is different from that of fecundity, so as infertility and infecundity. Fertility is defined as ability to conceive after 1 year of unprotected intercourse. In contrast, fecundity refers to reproductive behavior which allows for pregnancy to occur. The reported prevalence of infertility in the general population ranged from 2% to 8%, whereas the reported infertility rate in patients with inactive UC were similar [38,39]. Fertility can be affected by RPC and IPAA as well as proctocolectomy with Brooke end ileostomy [40,41]. The reported infertility rate in patients with RPC and IPAA ranged from 20% to 90%. [13,41] A majority of earlier studies, however, involved in open surgery while the impact of laparoscopic IPAA surgery on fertility has been shown to carry advantages, including female fertility.
The impact of RPC and IPAA on female fertility has been extensively studied. A Swedish retrospective study with interview, gynecological examination, and hysterosalpingography reported an infertility rate of 0% in UC patients without colectomy vs. 93% for UC women with RPC and IPAA [13]. A Lahey Clinic study of 110 patients reported an infertility rate of 5% in UC patients before IPAA and 16% after IPAA [16]. An additional study of 300 women reported an infertility rate of 38% in patients before IPAA and 56% in patients after IPAA [41]. Infertility in IPAA may improve over time. For example, a study reported that fertility reduced to 53% of patients with IPAA in a short term but the number increased to 76% after 6 years [42].
Fecundability may be a better marker than fertility for the measurement of ability to become pregnant. For example, a Scandinavian study of fertility in 237 patients with IPAA showed a reduction in births to 35% from the expected [43]. A second report from the same group, however, assessed fecundity and found an 80% reduction in fertility rate after IPAA [44].
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.
Eliran Mor MDThe 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.
A 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.
