Why fertility changes

Better functioning ovaries, with larger egg stores, produce more AMH. Levels of the hormone decline as the timeline of female fertility progresses — average levels in 30 to 35 year olds are roughly two-thirds that of younger women while levels in women aged over 45 years are a quarter of those seen in women in their 20s.

When they are born, women's ovaries already contain all the eggs they will ever produce Credit: BBC. Andrea Jurisicova, an embryologist at the Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, has spent years studying the mechanisms that underpin the decline in female fertility with age, and investigating what can be done to slow this.

Quality matters too, and is much more technically challenging to assess than egg numbers. While egg counts decline as women age, so does the quality of the chromosomes and the DNA contained within each egg.

Beyond the age of 35, the frequency of these chromosomally abnormal eggs increases by 0. Chromosomes are bundles of tightly coiled DNA that hold the genetic information needed for an organism to develop. An egg with too many or too few chromosomes, broken or damaged chromosomes will often fail to develop properly.

But most chromosomal abnormalities tend to be lethal to the extremely young embryo, resulting in the embryo failing to implant in the lining of the womb or a very early miscarriage, often between five and eight weeks of pregnancy. While the risk of chromosomal abnormalities is known to be higher in the eggs of older women, a recent European study found that the level of chromosomal abnormalities is also high in younger women too — from 13 into their early 20s. The findings suggest that female fertility timeline follows a n-shaped pattern, with peak fertility observed in the mids and lower levels of fertility both in very young and older women.

Older mothers may face greater risks during pregnancy, labour and delivery, but there are problems associated with older fathers too Credit: Getty Images. Elsewhere in the egg, faulty mitochondria — the tiny power stations that provide energy for our cells and which we all inherit from our mothers — can also be a problem in older women.

Studies have shown that up to half the eggs of women who are older than 35 carry mutations in their mitochondrial DNA , compared to a third of the eggs in younger women. Everyone expects to be a little less fertile when you are older, but the extent of that decline takes a lot of people by surprise. It would be wrong to focus only on female fertility. Some studies have shown that sperm quality also declines with age in men, starting in their 20s.

Sperm mobility — the ability of it to swim around — has been found to decline by around 0. Demographers are not, however, sure. Demographic transition was completed in most industrial countries over the period , while contemporary transitions are currently underway in many developing countries. Other factors can also influence TFR [ 9 , 14 ] and may be correlated with the factors analyzed here see Discussion.

Lutz [ 15 ] suggested a rationale for population policies based on the relation of TFR to education and health.

Increased education school years of girls and women is associated with declining fertility in many countries. Education can change family relations and childbearing decisions.

More and longer education can bring about empowerment of women, later marriage, later onset of childbearing, and smaller family size e. Nevertheless, fertility differs between more and less educated women in nearly all countries, but the precise mechanism that leads to lower TFR with longer education is not well known [ 21 ]. Reduced family size as nations and economies develop might be due to increasing income per capita, and to trade-off between quantity and quality of children [ 14 , 22 ] review in [ 23 ].

Families are therefore expected to invest in more highly educated but fewer children, and TFR declines. Based on theoretical models and data from European and other countries, Galor [ 24 ] analyzed four suggested causes of the demographic transition and declining TFR — rising income, reduced child mortality, children as old-age security, and rising demand for education. He rejected the first three but see [ 25 ] , emphasizing the role of increasing education for fertility decline.

Growing economy, industrial production and technology favored higher child quality, and hence smaller families [ 24 ]. In two studies based on countries as units, TFR was more strongly related to education than to GDP per capita [ 19 , 26 ]. Lower TFR may therefore favor economic development, rather than the other way around [ 28 , 29 ].

Faith and religious authority can influence TFR at individual and country levels. Increased faith has accompanied population growth in parts of the world [ 32 , 33 ]. Mean TFR — for the most secular countries was 2. The corresponding values — were 1. Several other studies also suggest that religiosity favors high TFR [ 35 , 36 , 37 ]. FP programs spread information, counsel couples and make contraceptives easily available, all of which may reduce TFR.

Use of modern contraceptives is important [ 11 , 21 , 40 ], and there is experimental evidence that FP programs increase contraceptive use and reduce TFR [ 41 , 42 , 43 ]. Other factors, such as education and religiosity, also influence contraceptive use e. For our analyses, data on contraceptives and education, economy, religion were available from six regions, while data on FP programs were available from four regions. FP programs, potentially important in four high fertility regions, are analyzed separately from other factors, but all factors are treated in the Discussion.

We analyse TFR at regional and country levels. Most studies analyze single factors and groups of countries [ 11 , 14 , 21 , 47 ]. Studies that include both developing and developed countries usually deal with one or two factors but see [ 19 ]. To our knowledge, TFR and its relations to education, economy, religion, contraception and FP programs have not been analyzed together in the major global regions, our aim here. Many studies use countries as sample units in statistical analyses and tests, but countries may not be statistically independent units.

Neighboring countries can be similar culturally, economically or politically, and also distant countries can have political and economic ties [ 48 ] and similarities in health status and social norms e. Some countries may therefore form clusters of similar units, differing markedly from other clusters.

Moreover, the number of countries in an area is a partly arbitrary consequence of political events, which may divide a nation into two or more e. Countries therefore deviate from requirements of independent sample units in many probabilistic statistical methods.

Using countries as units in statistical tests may therefore lead to pseudo-replication, inflated sample size and misleading results as regards probability levels [ 50 , 51 ], a problem that deserves further attention from statisticians.

We therefore avoid statistical testing and multivariate statistical modeling, instead using simple correlation, regression and graphical analysis e. We do not analyze all countries together but group them into six global regions, forming sets of geographically or otherwise related countries that share similarities, as explained below.

Among the regions we examine the extent to which TFR is related to the five factors and how the factors correlate with each other, exploring potential differences between regions. Regions may differ systematically in unmeasured factors that affect TFR. Compared to analyzing all countries together, analyzing regions separately can then reduce the influence of unmeasured variation in the analysis, increasing the possibility of clarifying differences between the five factors studied as regards their importance for TFR.

We complement this approach by analyzing differences and similarities within regions, with countries as units. We use estimates of TFR and the factors from to see below. The results therefore concern the recent situation and help identify factors of likely importance for future causal analyses of TFR.

We included countries with available data for education, economy, religion and contraception for FP programs, see below. Russia, China and several other countries could not be included due to lack of data. We also considered shared history and degree of economic and political ties, and differ from UNESCO [ 55 ] mainly in using Eastern Europe as a separate region motivated by spatial proximity and common history of Soviet influence.

The six regions are as follows see also Table 1 :. In total 25 countries. Arab States. Sub-Saharan Africa. Countries in central, E and S Asia. This is the most diverse region, with countries that to some extent share cultures and political systems, although these vary markedly.

We did not divide Asia into smaller regions as they would contain too few countries for meaningful analyses. The data are means for — We used data for females, as theory and population policies focus on female education. Data for religion come from standardized surveys of religiosity by Gallup, Inc.

For each country a sample of respondents is drawn, and weights are assigned so the data reflect the population in terms of gender, age, education, household size, and socioeconomic status. The survey, starting in , has been repeated several times in each country. We use a compilation of pooled Gallup data for individual countries collected from to about [ 33 ]: Table 1 —6. The average number of respondents per country was [ 33 ], p. As a fourth factor in our analyses of the six regions we used contraceptive prevalence rate CPR.

No data were available for family planning FP programs in the two European regions. CPR should bear some relation to FP programs but may not reflect their strength, which we analyze separately see below. A limitation is that sexually active unmarried women and those not in unions, e.

We only included modern methods, as they are most effective and emphasized. They were included in analyses of these factors. Program-effort scores are given for four components policy, service, records and evaluation, and contraceptive availability and access and for a total of 30 measures across components 3—13 measures per component, see [ 60 ].

A total score is calculated from the component scores. The recommended data to use for countries is this score expressed as a percentage of the maximum score [ 60 ]. We used data from , and included only countries that we also used in analyses of the factors above. In a complementary analysis of Sub-Saharan Africa and Asia we used the full sample of countries available at track We also compared the four regions with respect to their mean program strength in All countries have equal weight in the analyses.

In graphs we present the mean regional TFR related to each factor, with the linear least square regression line and the coefficient of determination, r 2 , for the relationship e. We similarly explored relationships between TFR and the factors within the six regions, using countries as units. We refrain from statistical testing of regression slopes, as explained above. Outliers and countries at opposite ends of the line are indicated in the graphs maximum five countries.

School years, GDP per capita, religiosity and contraceptive use may be associated with each other. Their pairwise relationships are shown graphically together with the correlation coefficient r. No regression line is shown in these cases, where our purpose is mainly to identify associations among factors, and potential indirect effects on TFR.

We summarize this analysis in the Results. Detailed graphs for the six regions, with all countries, are given in Additional file 1 part 1. Our main aim here is to analyze variation in TFR. Data on variation in the five factors for all countries are shown in Additional file 1 part 2.

E Europe had the lowest TFR mean 1. Arab States had the second highest TFR 3. TFR in E Europe 1. The average number of school years for females varied from 4. Surprisingly, it increased with school years in W Europe Fig. In each region separately, we analyzed the degree to which the factors are correlated r for graphs with all countries shown, see Additional file 1 , part 1.

Religiosity was negatively correlated with the other factors, most strongly with school years and CPR Fig.

Religiosity was negatively correlated with the other factors, strongly so for school years. In the Arab states, however, GDP and religiosity were weakly positively related. In Sub-Saharan Africa the factors were generally more strongly correlated than in other regions Fig. The three factors associated with TFR decline were positively related, with highest r 0.

Asia followed the same pattern as the other regions Fig. Religiosity had strong negative correlation especially with school years, and also with GDP per capita Fig. Table 2 gives mean r values and their range for the six regions. For factors negatively associated with TFR, the highest mean positive correlation was between school years and GDP per capita.

For religiosity, the strongest mean negative correlation was between school years and religiosity Table 2. Thus, particularly the number of school years for females was correlated with two major factors: positively with GDP per capita, and negatively with religiosity.

TFR was negatively associated with FP program strength; r 2 ranged from weak 0. In three regions, r 2 is sensitive to outliers. In Arab States, r 2 changes from 0. In Asia, r 2 changes from 0.

In contrast, in Sub-Saharan Africa a weak relation becomes even weaker if Rwanda is removed r 2 changes from 0. In our complementary analysis of Sub-Saharan Africa and Asia, using all countries available at track There was high variability of TFR at low program strength, mainly due to the addition of Russia, Armenia, and Azerbaijan.

For the four regions we also re-analyzed TFR versus the other four factors, using the countries in the data set for FP program strength sample in Fig. For school years, one region had a strongly negative and another region strongly positive relation with TFR Additional file 1 , part 3.

These contrasting results indicate that the FP program dataset was not representative for the full sample of countries. There was a clearly visible gap among TFR values, and the mean values for these countries with low and high TFR were 1.

Comparing the four regions, the mean FP program strengths in based on countries in Fig. A complementary analysis with all available countries in track The broad analysis of six global regions shows associations of TFR with each of the five factors explored Figs. The similarity of results among and within regions suggests that the relationships negative or positive are real and fairly general.

Intriguing deviations occur in W and E Europe. Moreover, the factors to which TFR is related are themselves related in interesting ways, especially female education, which is positively correlated with GDP per capita and negatively correlated with religiosity. Among regions Fig. The number of school years for women increased markedly after in most regions, but increased less in Africa [ 63 ].

Many parents want to know as much about the pregnancy as possible so they can make informed decisions. If a cause for infertility is identified, the clinical care provider may suggest a specific treatment. This procedure is called intrauterine insemination IUI and causes minimal discomfort.

For more information on assisted reproductive technologies, refer to the ASRM patient information booklet titled Assisted Reproductive Technologies. Egg donation, which involves the use of eggs donated by another woman who is typically in her 20s or early 30s, is highly successful.

The high success rate with egg donation confirms that egg quality associated with age is the primary barrier to pregnancy in older women. If you are over 40, your chance of successful pregnancy is much higher in IVF cycles using donor eggs, but many couples or single women in their early 40s will choose to accept the lower chance of become pregnant and use their own eggs. At the same time, the egg recipient is given hormone therapy to prepare her uterus to receive the fertilized eggs embryos.

Any embryos that are not transferred may be frozen cryopreserved for a future cycle. Donor-egg IVF offers a woman an opportunity to experience pregnancy, birth, and motherhood. The child, however, will not be genetically related to her but will be genetically related to the father and the egg donor.

Many programs recommend counseling so that all parties in a donor-egg agreement understand the ethical, legal, psychological, and social issues involved. Because success depends heavily upon the quality of eggs that are donated, women in their 20s with proven fertility are ideal donors. Women who wish to delay childbearing until their late 30s or early 40s may consider methods of fertility preservation such as freezing of embryos after IVF or retrieving and freezing eggs for later use.

The success of embryo freezing cryopreservation is well established, but it requires that the woman have a male partner or use donor sperm. Egg freezing for preservation of fertility is a new technology that shows promise for success. Age remains a problem faced by women interested in using elective egg freezing.

As the age of women undergoing egg freezing increases, the outcomes of assisted reproductive technology cycles utilizing their frozen eggs become less favorable. New technologies that will allow testing of embryos for chromosomal abnormalities are currently being investigated. This technology applies to embryos created during a cycle of IVF.

It may be particularly useful for older women. With preimplantation genetic diagnosis PGD , a small number of cells are removed from each embryo and genetically evaluated. The hope is that this procedure will result in improved successful pregnancy rates and avoidance of transmission of an embryo with a genetic disorder. Fertility naturally declines as women get older. However, the time decline begins and the rate at which it progresses, vary widely in women, but always begin well before menopause.

Generally, fertility begins to drop in your late 20s or early 30s and falls more rapidly after the age of Women who decide to delay pregnancy until after age 35 should obtain information on appropriate testing and treatment while remaining realistic about the chances for success with infertility therapy.

By learning about all of the options and being aware of their own needs and goals, a woman and her partner will be prepared to make the best decisions. A nonprofit, professional medical organization of more than 9, health care specialists interested in reproductive medicine. A procedure in which a small amount of amniotic fluid is removed through a needle from the fetal sac at about 16 weeks into a pregnancy. The fluid is studied for chromosomal abnormalities that may affect fetal development.

Antral follicle count. The number of fluid-filled follicles observed using ultrasound. Atresia ovarian. The natural process by which eggs age and degenerate.

The lower narrow end of the uterus that connects the uterine cavity to the vagina. Chorionic villus sampling. A procedure in which a small sample of cells is taken from the placenta early in a pregnancy for chromosomal testing. Rod-shaped structures located in the nucleus of a cell which contain hereditary genetic material. Humans have 23 pairs of chromosomes 46 total.

Two of the 46 are the sex chromosomes, which are the X and Y chromosomes. Normally, females have two X chromosomes and males have one X and one Y chromosome. Clomiphene citrate challenge test CCCT. A test of ovarian reserve in which serum FSH is checked on days 3 and 10 of the menstrual cycle and clomiphene citrate is taken on days 5 through 9. Corpus luteum. The corpus luteum secretes estrogen and large quantities of progesterone, a hormone that prepares the lining of the uterus endometrium to support a pregnancy.

Cryopreserved frozen. Sperm or embryos may be frozen and stored for future use. Donor egg. An egg from a fertile woman that is donated to an infertile woman to be used in an assisted reproductive technology procedure such as IVF. The woman receiving the egg will not be biologically related to the child but will be the birth mother on record. Donor sperm.