Welcome to the programmatic area on fertility within MEASURE Evaluation’s Family Planning and Reproductive Health Indicators Database. Fertility is one of the subareas found in the family planning section of the database. All indicators for this area include a definition, data requirements, data source(s), purpose, issues and—if relevant—gender implications. Fertility is the natural ability to conceive offspring, and is central to population health.  High fertility has profound effects on the health and well-being of women, their families, and communities, as well as on socioeconomic development from national to local levels, on supplies of food and other resources, and on environmental sustainability (UNFPA, 2009). The core fertility indicators in this database include higher-level indicators for tracking trends in total fertility and age-specific fertility rates, as well as indicators that influence fertility and the use of family planning and related services. Key indicators to monitor and evaluate fertility can be found in the links at left.   Full Text High fertility has profound effects on the health and well-being of women, their families, and communities, as well as on socioeconomic development from national to local levels, on supplies of food and other resources, and on environmental sustainability (UNFPA, 2009). Fertility above the replacement level (about 2.1 births per woman) is a key contributing factor to world population growth, in conjunction with reduced mortality and young age structure. While the average total fertility rate has fallen from more than 6 children per woman in the 1960s to less than 3 by the early 2000s (UNFPA, 2002), according to the 2007 UN medium projection, world population will continue to grow at least until 2050, adding 3.7 billion to the 2005 population of 6.5 billion (Bruce and Bongaarts, 2009). High fertility in women is associated with early marriage, high adolescent birth rates, higher risk of complications from pregnancy and child birth, maternal and infant morbidity and mortality, lower educational attainment and reduced capacity to earn income (UN, 2011).  The ability of women and couples to achieve desired levels of fertility and to space or limit births through effective use of family planning (FP) is directly related to Millennium Development Goal (MDG) #5. improve maternal health and indirectly to #1. reduce poverty and hunger and #4. reduce child mortality.    Conversely, the incidence of infertility in a population has important demographic and health implications. However, despite infertility being a serious public health problem, it has received low priority in many developing countries – particularly in the context of policies on fertility control – because of continued donor focus on reduced family size (Inhorn and van Balen, 2002).  Because high infertility has a dampening effect on overall fertility and the rate of population growth, improvements in the ability to bear children actually can impede efforts to lower the fertility rate in areas of low contraceptive prevalence. At the individual and family level, the inability to bear children for many couples can lead to a sense of loss, failure, and exclusion. Rutstein and Shah (2004) estimate that in 2002, more than 186 million ever-married women of reproductive age (15 to 49 years) in developing countries (excluding China) were infertile because of primary or secondary infertility. This number represents more than one-fourth of the ever-married women of reproductive age in these countries.   Primary infertility (also called primary sterility) is defined as the inability to bear any children, either due to the inability to conceive or the inability to carry a pregnancy to a live birth (Rutstein and Shah, 2004). Secondary infertility is the inability to bear a child after having an earlier birth and is associated with sexually transmitted infections (STIs), unsafe abortion, postpartum infections, and female genital cutting (Ombelet et al., 2008). The burden of infertility in most societies is placed more heavily on women and childlessness may lead to divorce, separation, the man taking another wife, and the social and economic isolation of women.  Programs addressing infertility in developing countries focus primarily on prevention through reducing the incidence of and improving treatment for STIs and other causes (Ombelet et al., 2008). Assisted reproductive technology is being offered in some higher income urban settings, although the costs are prohibitive for widespread access to these services.  For more information on primary and secondary infertility and prevention and treatment in developing countries, see Rutstein and Shah (2004) and Ombelet et al., (2008).    In 1978, John Bongaarts (1978) developed a framework for analyzing the proximate determinants of fertility that explained the effects of four key direct determinants: (1) age-specific proportions of women married; (2) contraceptive prevalence adjusted for method effectiveness; (3) induced abortion rate; and (4) average duration of postpartum amenorrhea (See Stover [2004] for a review of the framework after twenty years of application).  In a response to critics of FP programs, Bongaarts and Sinding (2009) have laid out rationales for the success of and need for continuing these efforts, and among these are: (1) FP programs have had a major and unambiguous impact on reducing fertility rates in many countries; (2) despite this success, there are regions in which high fertility remains a challenge that imposes significant risks on health, socioeconomic development, and the environment; and (3) FP is highly cost-effective and few public health interventions are more important or less expensive than FP in reducing the morbidity and mortality of mothers, infants and young children. The authors call for renewed focus and funding for FP programs.  In keeping with the current focus on FP and fertility with the MDGs, the Global Health Initiative, and international and country-level partners working to increase integration of FP services with maternal and child health and related programs, continued systematic monitoring and evaluation of FP programs and their impacts on fertility are required. The core indicators selected for this database include the higher-level indicators for tracking trends in total fertility and age-specific fertility rates, as well as indicators that influence fertility and use of FP and related services, including wanted versus unwanted fertility, age at first birth, and infertility. __________ References: Bongaarts J., "A Framework for Analyzing the Proximate Determinants of Fertility," Population and Development Review, Vol. 4, No. 1 (Mar., 1978), pp. 105-132 Bongaarts J. and Sinding S., 2009, ‘A Response to Critics of Family Planning Programs,’ International Perspectives on Sexual and Reproductive Health. Vol 35:(1)Mar; 39-44   Bruce, J. and Bongaarts J. ‘The New Population Challenge’, From Laurie Mazur (ed.), A Pivotal Moment: Population, Justice, and the Environmental Challenge. Washington, DC: Island Press. http://www.popcouncil.org/pdfs/2009PGY_NewPopChallenge.pdf   Inhorn M. and van Balen F, Infertility around the Globe: New Thinking on Childlessness, Gender, and Reproductive Technologies (University of California Press, 2002).  Ombelet W, Cooke I, Dyer S, Serour G, Devroey P, Infertility and the provision of infertility medical services in developing countries. Human Reproductive Update. 2008 Nov–Dec; 14(6): 605–621. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2569858/ Rutstein, Shea O. and Iqbal H. Shah. 2004. Infecundity, Infertility, and Childlessness in DevelopingCountries. DHS Comparative Reports No. 9. Calverton, Maryland, USA: ORC Macro and the World Health Organization. http://www.measuredhs.com/pubs/pdf/CR9/CR9.pdf Stover, J. "Revising the Proximate Determinants of Fertility Framework: What Have We Learned in the past 20 Years?" Studies in Family Planning, Vol. 29, No. 3 (Sep., 1998), pp. 255-267 United Nations (UN), Background Release, April 11, 2011, Economic and Social Council (POP/990), New York: UN. http://www.un.org/News/Press/docs/2011/pop990.doc.htm   UNFPA, State of the World Population 2002: People, poverty, and possibilities, New York: UNFPA. http://www.unfpa.org/swp/2002/english/ch1/page2.htm  UNFPA, State of World Population 2009: Facing a changing world: women, population and climate, New York: UNFPA. http://www.unfpa.org/webdav/site/global/shared/documents/publications/2009/state_of_world_population_2009.pdf   USAID, 2004, Health and Family Planning Indicators: A Tool for Results Frameworks, Washington, DC; USAID Bureau for Africa.  World Health Organization. 2001. Reproductive health indicators for global monitoring: Report of the second interagency meeting, 2001. Geneva: World Health Organization.

Wanted total fertility rate


The number of children who would be born per woman (or per 1,000 women) if she/they were to pass through the reproductive years bearing children according to a current schedule of age-specific fertility rates if only "desired" or "wanted" births occurred

For this indicator, "wanted" births are defined taking into account both desired family size and the number of surviving children. All births during a specified reference period (usually the two to five years prior to a survey) that do not exceed the respondent's stated "desired number of surviving children" are classified as wanted. Births raising the number of surviving children above the desired family size are considered unwanted.

The indicator is calculated as follows:

 WTFR = 5∑a (WBa/Ea)

WBa = the number of births to women in age group a in a given year or reference period that are "wanted;" and

Ea = the number of person-years of exposure in age group a during the reference period.

Data Requirements:

Responses to survey questions on:

Data Sources:

Population-based surveys


The WTFR is a measure of "wanted" fertility, a hypothetical measure of what the total fertility rate (TFR) would be given age-specific fertility rates for a recent past period under the condition that all women's fertility preferences were perfectly realized; that is, if only "wanted" births occurred. The measure represents an attempt to avoid the suspected bias in the wanted status of recent births indicator by defining wanted or desired status on the basis of the consistency (or lack thereof) between the reported desired family size and the number of surviving children, instead of on the basis of retrospective reports of fertility intentions at the time of becoming pregnant.

Evaluators calculate the indicator as the sum of age specific fertility rates, or the total fertility rate, after
they delete births occurring during a specified reference period that raise the number of surviving children of sample respondents above their stated desired number of children.

In the DHS, numbers of births during the specified reference period are derived from the birth history portion of the survey interview, the numbers of surviving children are derived from questions on lifetime fertility and survival status, and the information on desired family size are derived from survey questions.

The comparison of the WTFR with the TFR indicates the extent to which observed fertility exceeds desired or wanted fertility. This type of comparison provides program managers and policy-makers with some insight into the potential short- to medium-term demand for family planning services and the potential for fertility decline in the future (Westoff, 1991). In the case of Burkina Faso, for example, the comparison of the TFR (5.9) with the WTFR (5.1) suggests that a considerable share of current fertility is unwanted and that sufficient latent demand exists in this population; thus an increase in contraceptive prevalence and a decline in fertility might be reasonably expected, if family planning services are available to the population (Institut National de la Statistique et de la Demographie and Macro, 2007).


The above definition of the WTFR is based upon the work of Lightbourne (1985, 1987) and Westoff (1991) (who labels the measure the "desired total fertility rate" or DTFR). Bongaarts (1990) proposed a modified definition of the WTFR in which wanted births are defined on the basis of whether survey respondents desired additional births at the time of a survey instead of on the basis of the comparison of the desired number of children and the number of surviving children. Under this definition, births within a specified reference period are classified as wanted if the respondent reported wanting additional children at the time of a survey.

The argument for the alternative definition is that it is based upon responses to questions on preferences for additional children, an indicator of demand thought to be less affected by reporting biases than the desired family size indicators (Bongaarts, 1990). Comparison of estimates of the two versions of the WTFR for 48 DHS countries indicates that the two measures are reasonably close for most countries, with an average difference between the measures of about 9 percent-- 4.09 versus 3.76 (Bongaarts, 1990). On the basis of available evidence, either version of the WTFR is preferable
to using the wanted status of previous births in defining wanted fertility.

Total fertility rate


The number of children who would be born per woman (or per 1,000 women) if she/they were to pass through the childbearing years bearing children according to a current schedule of age-specific fertility rates.

The TFR is calculated as:

                                                     TFR = ∑ ASFR a(for single year age groups)


TFR = 5 ∑ ASFR a(for 5-year age groups)


ASFRa = age-specific fertility rate for women in age group a (expressed as a rate per woman).

Illustrative Computation

Estimate of the average annual TFR for all women aged 15-49, Egypt, 1997-2000.

TFR= 5 (.051 + .196 + .208 + .147 + .075 + .024 +.004) = 3.53
Where: the figures in parentheses are age-specific rates for the 15-19, 20-24, ... , 45-49 age categories, respectively.
Source of data: Egypt Demographic and Health Survey, 2000.


Data Requirements:

A current schedule of age-specific fertility rates (ASFRs), for one- or five-year age groups

Data Sources:

Vital statistics (numerator only), population censuses or population-based surveys


The TFR is the most widely used fertility measure in program impact evaluations for two main reasons: (1) it is unaffected by differences or changes in age-sex composition, and (2) it provides an easily understandable measure of hypothetical completed fertility.

Although derived from the ASFR, a period fertility rate, the TFR is a measure of the anticipated level of completed fertility per woman (or per 1,000 women) if she/ they were to pass through the reproductive years bearing children according to the current schedule of ASFRs. The TFR is only a hypothetical measure of completed fertility, and thus women of reproductive age at any given point in time could have completed family sizes considerably different from that implied by a current TFR, should age-specific fertility rates rise or fall in the future.


Because the TFR is derived from a schedule of ASFRs, the comments and caveats regarding the ASFR also apply to the TFR (i.e., method of computation from different sources of data, effects of changing exposure to pregnancy, and implications of computation for currently married versus all women of reproductive age). As was also the case for the ASFR, the TFR may be computed for women who were continuously married or in union during the reference period of the measure in order to decrease the potentially confounding effects of differences in exposure to the risk of pregnancy (to the extent that differences are associated with marital status). This measure is known as the Total Marital Fertility Rate (TMFR).

Note also that whereas the standard age range for the TFR is ages 15-49, TFRs for other age ranges (e.g., 15- 34) are sometimes used for analytic purposes, for example, in order to decrease the influences of truncation when examining cohort trends from birth history data.

Age-specific fertility rates


The number of births occurring during a given year or reference period per 1,000 women of reproductive age classified in single-or five-year age groups

The ASFR is calculated as:

ASFRa = (Ba/Ea) x1000


Ba = number of births to women in age group a in a given year or reference period; and

Ea = number of person-years of exposure in age group a during the specified reference period.

Illustrative Computation

Estimates of Annual ASFRs for  all women 15-49, Egypt 1997-2000

Age Group Births (Ba)
Person- Years of Exposure (Ea) Rate/Woman Rate/1000 person years
15-19 764 14893.2 .051 51
20-24 2304 11747.2 .196 196
25-29 1994 9602.3 .208 208
30-34 1295 8805.5 .147 147
35-39 564 7549.5 .075 75
40-44 161 6643.2 .024 24
45-49 19 4498.8 .004 4

Data Requirements:

The number of births in a given year or reference period classified by age of mother and the number of women of reproductive age (i.e., 15-44 or 15-49 years), in 1-or 5-year age groups

Data Sources:

Vital statistics (numerator only), population censuses or population-based surveys


The ASFR has two primary uses: (1) as a measure of the age pattern of fertility, that is of the relative frequency of childbearing among women of different ages within the reproductive years, and (2) as an intermediate computation in the derivation of the total fertility rate (TFR).

Evaluators may derive ASFRs from several sources. When evaluators estimate ASFR from vital statistics, they use population projections or estimates of the number of women in each age group 15-49 for the denominator in the rate. When using population censuses or surveys, evaluators obtain both the numerator and denominator of the rate from the census or survey. Estimates from censuses are derived from questions on births during a specified period preceding the census (usually 12 months), while survey estimates may be derived either from questions on births within a specified prior period or from partial or complete birth histories.

A simpler, although less precise, procedure for computing the denominator of the rate is to take the average of the number of women in each age group during the reference period covered by the measure (i.e., the average of the numbers of women in each age group at the beginning and end of the reference period).

Reference periods of more than one year are frequently used to compute ASFRs from survey data, the rationale being to decrease sampling variability associated with relatively small numbers of annual births occurring to women in single or five-year age groups and the distorting effects of reference period reporting errors. Various analyses of DHS fertility data, for example, alternately use the three- or five-year period prior to the survey in calculating ASFRs (Arnold and Blanc, 1989; Lutz, 1990). When multiple years are used for computational purposes, average annual rates are normally presented.

ASFRs are sometimes presented for different groups of women; for example, ASFRs are for women currently married or in union and for all women of reproductive age in DHS country reports. In societies where fertility is largely confined to marriage, ASFRs for women currently married or in union will provide more or less complete coverage of recent fertility. Where a large share of fertility occurs outside of recognized unions, however, the restriction of the ASFR to currently married women will result in an under estimate of the level of current fertility.

The ASFR is of particular interest in countries, cities, or districts with adolescent RH interventions designed to reduce unintended pregnancy. Although the ASFR is rarely used as an outcome measure in evaluating such programs (due to the human, financial, and logistic resources needed to collect the data), it is a variable that program administrators and policy makers track over time as a macro-level indicator of program effectiveness combined with non-program influences.


Unlike the crude birth rate, the ASFR is unaffected by differences or changes in population age composition, and thus is more useful in comparing different populations or sub-groups and in measuring changes over time. The ASFR is, however, affected by differences or changes in the number or percent of women exposed to the risk of pregnancy. Thus, changes in ASFRs may provide misleading information regarding the impact of family planning programs on fertility when other factors affecting risk of pregnancy are changing (for example, for the 15-19 and 20-24 age groups when age at marriage is rising quickly).

To address this problem, evaluators may calculate ASFRs only for women who were continuously married or in union during the reference period of the measure. The resulting measure is known as the marital age-specific fertility rate (MASFR). However, to calculate this measure, evaluators require data on duration of marriage or marriage histories. In actual practice, MASFRs are more often approximated by calculating ASFRs for women married or in union at the time of a survey, although evaluators should recognize that this figure only approximates the MASFR because women who are married or in union at the time of a given survey may not have been continuously married or in union over the entire reference period of the measure (e.g., for the three to five years prior to the survey).

Age at first birth


The median age in years (which is an interpolated calculation) of women at birth of first child. Coverage includes women of all marital statuses. 

For each single age group category, evaluators determine what percent have already given birth by calculating:

(Number of women (within specific age group category) who have given birth/  Number of women (within specific age group category) of all marital statuses  ) x 100

Once the percentages have been calculated within specific age group categories, medians are calculated from accumulated single year of age percent distributions of age of woman at first birth.  The median is linearly interpolated between the age values by which 50 percent or more of the women had a first birth.

For example, if 10% of 15 year old's sampled had already given birth, 30% of 16 year old's, 41% of 17 year old's, and 62% of 18 year old's, the graph would show:

 Interpolated Calculated Median


In this illustration, the interpolated calculated median by which 50 percent or more of the women surveyed had already given birth is roughly 17.5, which when rounded up to the next completed year of age, is 18 years of age.

Data Requirements:

Woman’s age, in years, at the time of birth of her firstborn

Data Sources:

Population-based survey


Many family planning (FP) programs are interested in not just addressing unmet need for FP and promoting child spacing, but also delaying first birth because of the negative consequences of early birth on maternal and child health outcomes as well as women’s status and empowerment.  More than 40 percent of adolescent women in the developing world — the large majority of them married — will give birth before the age of 20 (Alauddin and MacLaren, 1999). When pregnancy occurs before adolescents are fully developed, they can be exposed to much higher risks of maternal morbidity and mortality.  Pregnancy is the leading cause of death for young women aged 15 to 19. This cohort is twice as likely to die during pregnancy and childbirth as those over 20, and girls under age 15 are five times more likely to die (UNFPA, 2003).  Babies born to adolescents are also at an elevated risk of poor health outcomes. The younger a woman is when she first gives birth, the longer her total child-bearing period and the more children she is likely to have which increases the risks to the life and health of both mothers and children.

For programs aiming to reduce maternal mortality, increase contraceptive use – particularly among married and unmarried adolescents – delay age at first marriage, and improve newborn health, this is a useful indicator for determining program impact.


Since the median is based on all women including those without a birth, there may not be a median for younger cohorts of women (since fewer than 50 percent of the cohort may have had a birth).  If fewer than 50 percent of the sample within a single age group category has given birth, the preferred form of the indicator is proportion of young women who had given birth by specified reference ages among respondents who are the reference age or older (e.g. the percentage of adolescents 16 years or older who had given birth by age 15).  Evaluators may compute median ages at first pregnancy or first intercourse in a similar fashion.

Gender Implications:

Early age at first birth in the context of marriage may in the short-term elevate a young woman’s social status as she quickly proves her “value” by producing offspring.  But early childbearing often means decreased mobility, less education to acquire skills that may enable the young woman to better care for her and her family and earn a wage, and fewer life opportunities in general.  This in turn decrease’s the young woman’s decision-making power in areas related to her own reproductive health.  Increasing the age at first birth not only positively impacts the health status of a young mother and baby, it can dramatically impact a young woman’s future from an economic, social, and emotional perspective.


MEASURE DHS. Median Age at First Birth. DHS Statistics Live. Available online at http://www.measuredhs.com/help/Datasets/index.htm#Age_at_First_Birth_Median_Age.htm

Prevalence of infertility in women


The percentage of women of reproductive age (15-49 years) at risk of becoming pregnant (not pregnant, sexually active, not using contraception and not lactating) who report trying to become pregnant for two years or more.

This indicator is calculated as:

(Number of sexually active women age 15–49 at risk of becoming pregnant  who report trying unsuccessfully to become pregnant for two or more years / total number of women of reproductive age at risk of becoming pregnant ) x 100

Data Requirements:

Responses to a survey question asking women of reproductive age who are at risk for pregnancy  how long they have tried to get pregnant.

The question can be posed by an interviewer or asked in a written survey.  The method of questioning will depend on the literacy level of the population of women surveyed.

It is useful if data can be disaggregated by women’s age group, by “ever been pregnant” and by “length of time trying for pregnancy”.

Also, some experts prefer the “five or more years” timeframe for assessing infertility.

Data Sources:

Population-based surveys including a birth history if estimates of primary and secondary infertility are desired


This indicator provides a population-based estimate of infertility prevalence through the assessment of trying time to pregnancy (or “failure to conceive”).  While clinical studies are the principal source of data on infertility causes and treatment, a population-based estimate, though not medically verifiable, can be used to assess the social burden of the condition and the potential demand for treatment services.  The indicator can also be used as a measure of reproductive morbidity and a proxy measure of the long-term sequelae of gynecological infections. 

The prevalence of infertility as a measure of reproductive morbidity is a useful marker of progress towards improved reproductive health, defined by the ICPD as “the capability to reproduce and the freedom to decide if, when and how often to do so.”

Infertility, or the inability to produce a live birth after adequate sexual exposure without contraception, can affect both the man and the woman. To date, there has not been widespread attention on infertility, except in isolated cases or on a small scale, due to limited resources, policies aimed at reducing population growth, and the expense of modern infertility treatment (Dhont, et. al, 2010). Current estimates of infertility in developing countries are primarily based on Demographic and Health Survey (DHS) birth history data and do not include the self reported time to pregnancy question.  However, these estimates show that primary infertility, or childlessness, remains relatively rare, with rates between 1-10% in women aged 25-49. In contrast, the percentage of women experiencing secondary infertility, or an inability to produce a live birth after at least one previous birth, ranges from 9-38% (Rutstein and Shah, 2004). Prevalence is often highest in centrally located African countries, but estimated rates can vary from region to region even within the same country. 

In many parts of the developing world, where having children constitutes the main purpose of marriage, infertility is considered a curse and a tragedy for the couple, entire family and community.  There are several factors that can affect the prevalence of infertility including:

  1. Prevalence of sexually transmitted infections (STIs), e.g. gonorrhea;
  2. Incidence of postpartum and postabortion infections;
  3. Socio-cultural factors such as the practice of female genital cutting; and
  4. Age of the partners.

There is conclusive evidence that much of the infertility in Africa is attributable to infections that produce irreversible reproductive tract damage in men and women, suggesting a need for public health programs to reduce these causes, including STI control and education programs to raise awareness about the link between high-risk sexual behavior and infertility (Okonofua, 2003).  Curative treatment of infertility is inaccessible for most couples in developing countries due to its very high cost and low success rate (Dhont, 2010).  However, if a country-wide program implements a prevention strategy involving the effective control of STIs, appropriate postpartum care, and safe abortion techniques, this indicator may be one way of measuring the long-term impact of such initiatives.

In regions where infertility is high, there will be more demand for treatment services, both in the traditional and formal health sectors.  Until effective fertility treatments become more affordable and accessible, health authorities can in the meantime determine the extent of the problem and invest in improving information, education and counseling on causes and treatments of infertility, which have proven to reduce the stigmatization and suffering of infertile clients (Dhont, 2010).


Information for this indicator comes from a single, self-reported question that is easy to calculate and interpret.  It is assumed that information about risk of pregnancy (exposure to sexual intercourse and status of contraceptive use, pregnancy and lactation) is also collected.  Use of this indicator avoids potential biases associated with the use of birth history intervals to calculate childlessness and infertility, including misclassification due to incomplete contraceptive and marital histories.  

The indicator measures the extent of difficulty or failure to become pregnant, rather than inability to produce a live birth.  As a consequence, the indicator may fail to capture infertile women who have achieved conception but experienced one or more spontaneous abortions. In addition, it should be noted that some women reporting a waiting time until pregnancy of two or more years may in fact become pregnant in the future without intervention. 

This indicator does not differentiate between primary and secondary infertility.  However, if birth histories are also collected in the survey, this distinction can be made.  The differentiation can be important as often secondary infertility is more prevalent in a population and suggests poor access or quality of health care during the previous pregnancy, delivery, or postpartum period, and/or ineffective treatment of gynecological infections, including STIs.

A significant issue is that this indicator addresses only a woman’s failure to conceive. Nevertheless, this failure of conception is used as a measure of a couple’s infertility, which comprises inability to conceive by both the male and the female partner. The cause of the couple’s infertility could be female, male or both. Using this indicator, and therefore failing to address the male factor in infertility, may contribute to a further stigmatization of women (WHO, 2006).

Gender Implications:

Infertility in many parts of the world has damaging consequences for men’s and women's health. Due to the high cultural premium placed on childbearing in many countries, infertility often poses serious social problems for couples (Okonofua, 2003). However, women are often more severely affected than men, even when the infertility is due to a male factor, often leading to divorce, financial difficulties, self-blame, social ostracisation and sometimes physical abuse of women (Okonofua, et. al, 2007). In certain areas where motherhood defines an individual woman’s social status, self-worth, and treatment in the community, the inability to produce offspring means a woman is not regarded as a proper woman.  Consequently, there is now a growing body of scientific opinion that suggests that addressing infertility could be one way to empower women and improve their sexual and reproductive health (Okonofua, 2002).


Dhont, N., S. Luchters, W. Ombelet, J. Vyankandondera, A. Gasarabwe, J. van de Wijgert, and M. Temmerman. 2010. “Gender Differences and Factors Associated with Treatment-seeking Behavior for Infertility in Rwanda.” Human Reproduction 25 (8): 2024-2030.

Rutstein, S.O., Shah, I.H. 2004. Infecundity, Infertility, and Childlessness in Developing Countries.  DHS Comparative Reports No. 9. Calverton, MD, USA: ORC Macro and the World Health Organization.

Okonofua, F.E.  2003.  “New Reproductive Technologies and Infertility Treatment in Africa.” African Journal of Reproductive Health 7: 7-8.

“Reproductive Health Indicators: Guidelines for their generation, interpretation and analysis for global monitoring”.  WHO, 2006.

Okonofua, F.E., D. Harris, A. Zerai, A. Odebiyi, and R.C. Snow. 1997. “The Social Meaning of Infertility in Southwest Nigeria.” Health Trans Rev 7: 205–220.

Okonofua, F.E. 2002. “What about us? Bringing infertility into reproductive health care.” Quality/Calidad/Qualite 13: 1–2.