Systematic Review on Infertlity Among African American Women

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Peer-reviewed

Enquiry Article

National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Health Surveys

• Maya N. Mascarenhas,
• Seth R. Flaxman,
• Ties Boerma,
• Sheryl Vanderpoel,
• Gretchen A. Stevens

x

• Published: December eighteen, 2012
• https://doi.org/x.1371/journal.pmed.1001356

Figures

Abstract

Background

Global, regional, and national estimates of prevalence of and tends in infertility are needed to target prevention and treatment efforts. By applying a consistent algorithm to demographic and reproductive surveys available from developed and developing countries, we estimate infertility prevalence and trends, 1990 to 2010, by land and region.

Methods and Findings

We accessed and analyzed household survey information from 277 demographic and reproductive health surveys using a consistent algorithm to calculate infertility. We used a demographic infertility measure out with alive nascence as the outcome and a 5-y exposure catamenia based on union status, contraceptive use, and want for a child. Nosotros corrected for biases arising from the use of incomplete information on past matrimony status and contraceptive apply. Nosotros used a Bayesian hierarchical model to estimate prevalence of and trends in infertility in 190 countries and territories. In 2010, among women 20–44 y of historic period who were exposed to the risk of pregnancy, 1.9% (95% uncertainty interval 1.vii%, 2.2%) were unable to attain a live nascence (primary infertility). Out of women who had had at least ane live nativity and were exposed to the risk of pregnancy, 10.5% (9.5%, 11.seven%) were unable to have some other kid (secondary infertility). Infertility prevalence was highest in Southern asia, Sub-Saharan Africa, North Africa/Middle E, and Central/Eastern Europe and Central Asia. Levels of infertility in 2010 were similar to those in 1990 in nearly world regions, apart from declines in primary and secondary infertility in Sub-Saharan Africa and primary infertility in South Asia (posterior probability [pp] ≥0.99). Although at that place were no statistically meaning changes in the prevalence of infertility in most regions amongst women who were exposed to the hazard of pregnancy, reduced child-seeking beliefs resulted in a reduction of principal infertility among all women from 1.vi% to 1.5% (pp = 0.90) and a reduction of secondary infertility among all women from 3.9% to 3.0% (pp>0.99) from 1990 to 2010. Due to population growth, yet, the absolute number of couples affected by infertility increased from 42.0 million (39.6 million, 44.viii 1000000) in 1990 to 48.five 1000000 (45.0 meg, 52.vi million) in 2010. Limitations of the study include gaps in survey data for some countries and the use of proxies to determine exposure to pregnancy.

Conclusions

We analyzed demographic and reproductive household survey data to reveal global patterns and trends in infertility. Independent from population growth and worldwide declines in the preferred number of children, nosotros found trivial evidence of changes in infertility over two decades, apart from in the regions of Sub-Saharan Africa and South asia. Further research is needed to identify the etiological causes of these patterns and trends.

Please encounter later in the article for the Editors' Summary

Editors' Summary

Groundwork

Reproductive health is a priority global health surface area: the target for Millennium Development Goal 5B is to provide universal access to reproductive health by 2015. The indicators for monitoring progress in reaching this target are contraceptive prevalence rate, boyish birth rate, antenatal care coverage, and the unmet need for family planning. Infertility, the disability to conceive later on a prolonged menstruum (the length of fourth dimension varies in different definitions) of unprotected intercourse, is a critical but much neglected aspect of reproductive health. The inability to have children affects couples worldwide and causes emotional and psychological distress in both men and women. Many factors—including physiological, genetic, environmental, and social— contribute to infertility. According to the World Health System, infertility resulting from sexually transmitted diseases or reproductive tract infections is particularly problematic in Africa and Latin America.

Why Was This Report Washed?

The researchers used a uniform measure of infertility that incorporated live birth as the outcome of interest (as this data is more than unremarkably reported than pregnancies), a five-yr "exposure period," that is, a five-yr menstruum of being in an intimate relationship, not using contraceptives, and wanting a child (as the researchers calculated that this flow was necessary to adapt the fourth dimension it takes to become significant and have a child, and to allow for incomplete information on frequency of unprotected intercourse). The researchers used a statistical model (Bayesian hierarchical model) to generate estimates for levels and trends of infertility in 190 countries over the fourth dimension catamenia 1990 to 2010 using data collected from national demographic and reproductive health surveys. The nigh information was available for Southern asia and Sub-Saharan Africa.

What Did the Researchers Do and Notice?

The researchers found that in 2010, ane.9% of women aged 20–44 years who wanted to accept children were unable to have their first live nascence (primary infertility), and 10.5% of women with a previous live nascence were unable to accept an additional live birth (secondary infertility). The researchers found that the levels of infertility were similar in 1990 and 2010, with only a slight overall decrease in primary infertility (0.1%, merely with a more pronounced driblet in Sub-Saharan Africa and South Asia) and a modest overall increase in secondary infertility (0.4%). Historic period affected infertility rates: the prevalence of primary infertility was college among women aged 20–24 years than amidst older women. The age pattern was reversed and fifty-fifty more pronounced for secondary infertility. And principal infertility rates amidst women wanting children too varied by region, from 1.5% in Latin America and the Caribbean in 2010, to 2.6% in North Africa and the Eye Eastward. With a few exceptions, global and state patterns of secondary infertility were like to those of primary infertility.

What Do These Findings Mean?

These findings suggest that in 2010, an estimated 48.five million couples worldwide were unable to have a child after five years. However, these findings also suggest that global levels of primary and secondary infertility hardly changed between 1990 and 2010. It is of import to annotation that an infertility measure based on ability to become significant (rather than having a live birth—the outcome used in this report) may show different levels of infertility, and using an exposure menstruum shorter than the five years used in this study would produce higher rates of infertility. However, because of the lack of widespread information collection on time to pregnancy, the methods used and results shown in this study provide useful insights into global, regional, and state patterns and trends in infertility.

Commendation: Mascarenhas MN, Flaxman SR, Boerma T, Vanderpoel S, Stevens GA (2012) National, Regional, and Global Trends in Infertility Prevalence Since 1990: A Systematic Analysis of 277 Wellness Surveys. PLoS Med nine(12): e1001356. https://doi.org/10.1371/journal.pmed.1001356

Academic Editor: Nicola Low, University of Bern, Switzerland

Received: July 27, 2012; Accustomed: Nov 7, 2012; Published: December eighteen, 2012

Copyright: © 2012 Mascarenhas et al. This is an open-access article distributed under the terms of the Creative Eatables Attribution License, which permits unrestricted utilize, distribution, and reproduction in whatsoever medium, provided the original author and source are credited.

Funding: This study was partially funded past the Beak & Melinda Gates Foundation, Subcontract No. 498744 of the Global Burden of Disease 2010 Study (University of Washington). The results in this paper are prepared independently of the final estimates of the Global Burden of Disease study. The funders had no role in written report design, data collection and analysis, decision to publish, or preparation of the manuscript. TB, SV, and GAS are are staff members of the World Wellness Arrangement. The authors alone are responsible for the views expressed in this publication and they do not necessarily stand for the decisions, policy, or views of the World Health Organization.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: DHS, Demographic and Health Surveys; pp, posterior probability; STI, sexually transmitted infection; WHO, World Health Organization

Introduction

The global health customs has had nifty success in improving maternal and child wellness in the past decade, partly through a focus on reproductive health [1],[2]. Infertility is a disquisitional component of reproductive health, and has frequently been neglected in these efforts [3]. The inability to have children affects men and women across the world. Infertility can lead to distress and low, besides as bigotry and ostracism [3],[4]. An accurate profile of the prevalence, distribution, and trends of infertility is an of import commencement step towards shaping evidence-based interventions and policies to reduce the burden of this neglected disability globally.

Few comparative analyses of global infertility accept been conducted, and none, to our knowledge, accept applied a consistent algorithm to demographic and reproductive health survey data from both developing and developed countries, nor used these data to approximate regional and global trends in infertility prevalence. Boivin et al. estimated global infertility by summarizing prevalence data from seven studies: five from developed countries and two from developing countries [five]. A Demographic and Health Surveys (DHS) written report likewise estimated infertility for developing countries using survey data from 47 national DHS surveys [6]. The report's estimate of infertility and assay of trends did not apply to developed countries, nor to China. Ericksen and Brunette [vii] and Larsen [8] applied consistent definitions of infertility in their analyses of household survey data, but considered only Sub-Saharan African countries.

The main challenges in generating global estimates of infertility are the scarcity of population-based studies and the inconsistent definitions used in the few loftier-quality studies available [9],[10]. In population-based studies of infertility, there has been petty consistency in how prevalence is calculated [nine],[11]. An explicit detailing of the numerator and denominator of each definition is needed to make clear what is being measured. The authors of a recent literature review concluded that information technology is non possible to synthesize infertility prevalence information in the published literature considering of the incomparable definitions used [nine].

An alternative to synthesizing data found in the literature is to employ a consistent definition to regularly collected demographic and reproductive health survey data. In this paper, we used a consistent algorithm to measure infertility using household survey information. Our measure out is a demographic definition that uses live birth as the outcome and a 5-y exposure period based on marriage status, use of contraceptives, and want for a child [6]–[8],[12]. There are challenges associated with inferring prevalence from household survey data. Few household surveys ask how long the respondent has tried to get pregnant, and none include a comprehensive medical history and clinical examination. Instead, these surveys may collect information on births, couple status, fertility preferences, and contraceptive use. In a previous analysis we performed sensitivity analyses around each of these components to identify important biases that may arise when information is incomplete [thirteen]. We institute that a 5-y exposure menstruum is needed to arrange the time information technology takes to become pregnant and requite birth, and helps forestall unreported temporary separations, periods of postpartum sexual forbearance, or lactational amenorrhea from unduly affecting the infertility measure. Births, rather than pregnancies, are the preferred outcome, as data on live births is nerveless more than often and reported more accurately: neither pregnancies in the outset trimester nor voluntary terminations are reliably reported in household surveys [14]–[16]. Lastly, we argued previously that the intent to have a child serves as a proxy for regular, unprotected sexual intercourse, and may right for underreporting of contraceptive apply [xiii],[17].

Clinical and epidemiologic infertility definitions are likewise used to monitor infertility; yet, they are not appropriate when making population-based estimates of infertility using household surveys. The clinical definition of infertility used by the Globe Wellness Organization (WHO) is "a disease of the reproductive system defined by the failure to reach a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse" [18], while the WHO'southward epidemiologic definition is "women of reproductive historic period at gamble of becoming pregnant who report unsuccessfully trying for a pregnancy for more than two years" [19]. Clinical definitions are designed for early on detection and treatment of infertility [18]–[20]. A definition and assessment of infertility based on medical histories and diagnostic tests is appropriate for clinical settings, where the aim is to empathise causes and provide treatment as soon every bit it is indicated. However, measuring patterns and trends in infertility at the population level necessitates a measure that may be elicited using a standard set of survey questions [17]. The WHO'south epidemiologic definition is more closely aligned with clinical practice than demographic definitions are, and may be measured using survey data. However, few household surveys make up one's mind whether a couple is trying to get meaning, and the majority do not collect information on past pregnancies, just on previous live births.

In this written report, we analyzed data from a range of reproductive and demographic surveys to estimate infertility prevalence. We practical consistent definitions of primary infertility (disability to have any live nativity) and secondary infertility (inability to take an additional live nascency). We developed a Bayesian hierarchical model to generate estimates for levels and trends of infertility and their uncertainties past state for the time period 1990 to 2010.

Methods

Report Blueprint

We estimated prevalence of primary and secondary infertility, their trends between 1990 and 2010, and their uncertainties, in 190 countries and territories. We used survey data consisting of interviews with the female partner. Although infertility occurs in couples and may accept a male person or a female cause, estimates are indexed on the woman in each couple. Nosotros made estimates for women anile 20–44 y, excluding infertility during the beginning (15–19 y) and end (45–49 y) of the reproductive period, when fewer couples are seeking a child and estimates of prevalence are less stable. We additionally estimated the proportion of women in each region who were exposed to the adventure of pregnancy, i.eastward., those who were in a union, were not using contraceptives, and had a kid or wished to take one, either her outset (primary infertility) or an additional (secondary infertility) child. We grouped the countries into the seven regions (High Income, Central/Eastern Europe and Central Asia, Eastward Asia/Pacific, Latin America/Caribbean area, North Africa/Middle East, Sub-Saharan Africa, and South Asia) and 21 nested subregions of the Institute for Health Metrics and Evaluation Global Burden of Disease 2010 study (Table A in Text S1).

Our analysis included four steps: (ane) identification and extraction of data, (two) adjustment of extracted data for known biases as needed, (three) application of a statistical model to gauge infertility prevalence and exposure proportion trends by land and age of the female partner, and (iv) calculation of the number of couples currently affected by infertility. We calculated the estimates' uncertainty, taking into business relationship both sampling error and doubt from each step of statistical modeling.

Information Sources

We included data from demographic and reproductive health surveys that we could obtain at the (anonymized) private level, and hence to which we could utilise a consistent definition of infertility. We identified data sources from national demographic studies in a recent systematic literature review of infertility prevalence [9], equally well as data that were known to the authors of the present study. To exist included, each survey had to collect women'southward age, electric current couple status, current contraceptive apply, time since commencement and last births, time since commencement union, and want to take a child. Data available only every bit summary statistics were excluded.

Nosotros obtained data from the following survey programs: DHS, Reproductive Wellness Surveys, the World Fertility Survey, the Pan Arab Project for Family unit Health and Pan Arab Project for Child Development, the European Multicenter Report on Infertility and Subfecundity, the Fertility and Family Survey, the United states of america National Survey of Family unit Growth, and the China In-Depth Fertility Sample Surveys (Table 1; Table A and Effigy A in Text S1). We included surveys prior to 1990 to capture heterogeneity in levels of infertility in countries that did not have more contempo surveys. For each data source, we recorded data on survey population and sampling strategy. For each female survey respondent, nosotros extracted data on marriage (marriage or cohabitation), nascence history, contraceptive utilize status and history (if available), and the woman's want for a child or an additional child. We used stated want for a kid to exclude women who accept unreported actions to preclude pregnancies or births, including unreported periods of abstinence or contraceptive use, or voluntary terminations [xiii]. We included women who were undecided almost having additional children and women who declared they were unable to become pregnant in the same category as women who stated they wanted some other child, because this grouping is less likely to exist preventing pregnancies or births in ways that are not captured by other survey questions. We refer to these women as women who desire a kid. We excluded x Fertility and Family Surveys and 3 Reproductive Wellness Surveys because at least ane response was missing for more than fifteen% of respondents.

Prevalence and Exposure Definitions

Mascarenhas et al. evaluated potential bias from using standard demographic or reproductive health surveys to estimate infertility prevalence and recommended the following standard algorithms [13], which we employed (run across Figures B and C in Text S1):

1. Principal infertility is divers as the absenteeism of a live nativity for women who desire a child and have been in a union for at least v years, during which they accept not used whatsoever contraceptives. The prevalence of master infertility is calculated every bit the number of women in an infertile union divided by the number of women in both infertile and fertile unions, where women in a fertile union have successfully had at least i live birth and have been in the union for at least five years at the time of the survey.
2. Secondary infertility is defined equally the absenteeism of a live birth for women who desire a child and have been in a marriage for at to the lowest degree five years since their final alive nativity, during which they did not apply any contraceptives. The prevalence of secondary infertility is calculated as the number of women in an infertile wedlock divided by the combined number of women in infertile and fertile unions. Women in a fertile matrimony have successfully had at least ane live birth in the past five years and, at the fourth dimension of the survey, have been in a matrimony for at to the lowest degree 5 years following their commencement birth.

We likewise calculated the proportion of women of reproductive age (20–44 y) who are exposed to the take a chance of pregnancy in club to calculate the overall pct of women who are affected past unwanted infertility. Women are exposed if they are fertile, infertile, or their fertility status is not determined at the time of the survey. Specifically:

1. Exposure to primary infertility is divers as the number of women who are currently in a union, are not using whatever contraceptives, and desire a child, also as the women who are currently in a union and accept given birth to at to the lowest degree one child. The proportion exposed is calculated as the number of women exposed over the total number of women surveyed (Figure B in Text S1).
2. Exposure to secondary infertility is defined as the number of women who accept had at least one alive birth, are currently in a union, are not using any contraceptives, and desire some other child, every bit well as the women who are currently in a matrimony and have given nascence to an additional child in the last 5 y. The proportion exposed is calculated as the number of women exposed over the total number of women surveyed (Effigy C in Text S1).

A small proportion of DHS surveys in loftier-fertility countries interview only women who have been in a union. We used exposure data from these surveys for women over age thirty y, as near all women in these countries have been in a union by age xxx y.

Nosotros applied the above definitions to all of the survey data, generating four indicators for each survey: prevalence of primary and secondary infertility and exposure to main and secondary infertility. We calculated the effective sample size for each indicator to reflect the subset of survey responses used to calculate primary and secondary infertility and to account for sampling doubt (Text A and Tabular array B in Text S1). We did non calculate secondary infertility using survey data from China or make estimates of secondary infertility for Prc, considering survey-based estimates of secondary infertility are difficult to translate in a setting where government regulations strongly bear on decisions around limiting family size.

Correction of Infertility Prevalence for Incomplete Information on Contraceptive Use and Couple Condition

Many household surveys ascertain current contraceptive use, but do non collect information on by contraceptive use over a defined exposure period. Using electric current contraceptive apply as a proxy for utilise over the past five y overestimates infertility prevalence, peculiarly for secondary infertility among younger couples [13],[21]. Likewise, information on time since first matrimony are available more often than information on the length of the current marriage. Assuming that exposure is continuous from the time of first, rather than electric current, wedlock can also atomic number 82 to biases [thirteen]. Nosotros developed regressions to correct infertility estimates generated from surveys that did not provide a mensurate of continuous contraceptive use and couple status over the exposure period, using information from a subset of DHS surveys that provided complete data (Table B in Text S1). The dependent variable in these regressions was the natural log of the less-biased estimate of prevalence, and the independent variables were the biased estimate, age, and, for secondary infertility, prevalence of contraceptive use (farther details in Text B in Text S1). The dubiousness of the estimated prevalences included the statistical sampling uncertainty every bit well as the doubtfulness associated with the correction for incomplete information on contraceptive utilise and wedlock duration.

Statistical Analysis

Despite the big number of surveys used in this analysis, information were not available for many country-years of interest. In addition, some of the surveys that nosotros used were not nationally representative. As a outcome, we developed a statistical model to generate estimates for every country and year, including those for which no data were identified. We estimated four indicators: the prevalence of primary infertility, the prevalence of secondary infertility, and the proportion of couples exposed to each type of infertility (see definitions section above). We made these estimates for 190 countries, the years 1990–2010, and each age group. We used a Bayesian hierarchical model to makes estimates for each country-twelvemonth-historic period group, informed by the unit, if available, and by information from other units. Text C in Text S1 describes the model in item, the principal features of which are summarized below.

We fit a hierarchical model in which our estimates for countries were nested within subregional, regional, and global levels. Because the model is hierarchical, estimates for each country are informed by information from the country itself, if available, and by data from other countries, especially countries in the same region. A hierarchical model shares information to a greater degree when data are sparse, uncertain, or inconsistent, and to a lesser degree in information-rich countries and regions. We also modeled hierarchical linear time trends. Specifically, region-specific fourth dimension trends were nested in a global trend. We used a time-varying covariate to inform our estimates, namely, maternal instruction (average years of schooling for women of reproductive age) [22]. Subnational studies are less informative than national studies, thus nosotros included separate variance components for subnational and national information sources. These variance components were estimated as office of the model plumbing equipment procedure, allowing national data to accept greater influence on estimates than subnational data.

Age of the female partner is a major determinant of fertility. Nosotros fabricated estimates by five-y age group for the ages xx–44 y, using indicator variables for each age category. This immune us to generate a fully flexible age blueprint. While the increment in infertility with female age is biologically adamant, the historic period at which women wish to have a child is too culturally determined. Thus, we allowed for dissimilar age patterns of exposure to master fertility in the High Income region, as divers in Table A in Text S1, versus in other regions.

We estimated the post-obit sources of dubiousness (see Texts A–C in Text S1 for details): sampling incertitude in the data sources, uncertainty associated with the conversion from prevalence estimates using incomplete information on contraceptive use and couple status, uncertainty from study pattern factors for national surveys, additional doubtfulness for not-national information sources, and uncertainty from the employ of a model to estimate prevalence of primary and secondary infertility past country, year, and age group where data were not available.

We fit the Bayesian model using Markov concatenation Monte Carlo methods to obtain 1,600 samples from the posterior distribution of the model parameters, reflecting the uncertainty from each step of the analysis; these parameter values were in turn used to calculate the posterior distribution of each indicator. Nosotros calculated trends by subtracting the judge for 1990 from the estimate for 2010 for each draw. We calculated central estimates as the mean of the draws, and uncertainty intervals as the 2.5th–97.5th percentiles of these draws. We too reported the posterior probability (pp) that an estimated increase or decrease corresponds to a truly increasing or decreasing trend. pp'south are not p-values; they are probabilities: if the pp of an increase is 0.v and so an increase and a decrease are both as probable, while a loftier pp of an increase indicates loftier certainty that an increase occurred. We considered a trend to be statistically significant if its pp was greater than 0.975. Survey analyses were carried out using Stata 10.ane, and Markov chain Monte Carlo assay was carried out in Python using the PyMC bundle [23].

We evaluated the predictive validity of our models' central estimates and their uncertainty intervals by performing cantankerous-validation. We ran each model 5 times, each time withholding data from a random sample of xx% of countries. We and so compared the model predictions to the known-but-withheld data. For each model, we calculated the root mean square mistake, median relative error, and the percent of withheld data that fell within the model's 95% uncertainty interval.

We report four results: prevalence of primary and secondary infertility among child-seeking women, i.e., among women who are exposed to the chance of pregnancy, and the percent of main and secondary infertility among all women of reproductive age, calculated as the production of the prevalence of infertility among child-seeking women and the proportion who are exposed to the gamble of pregnancy. We besides calculated the number of couples afflicted past infertility using population data from the United nations Population Division's "World Population Prospects: 2010 revision" [24]. We as well report 2 boosted indicators, percent of women exposed to the risk of primary and secondary infertility, in Figures H, I, Thou, and Due north in Text S1. All estimates were made by country and age; we calculated all-age, regional, and global estimates past weighting country- and age-specific estimates by the population of women in the relevant age group.

Results

Nosotros identified 277 demographic and reproductive health surveys, including 7 multi-state programs and two country-specific surveys, that included questions on infertility and for which we could obtain the individual-level questionnaire responses (Table 1; Table B and Effigy A in Text S1). National information were available for 101 countries, and regional data were obtained for a further three countries. At to the lowest degree two surveys were available for 69 countries. The South asia and Sub-Saharan Africa regions had the greatest information availability, with at least 1 survey available for 67% of countries and an average of more than ii surveys per country. There were fewer data available for the High Income and Fundamental/Eastern Europe and Fundamental Asia regions: we did not place whatever data for 38 of 59 countries in these regions (64%), and nosotros identified two or more than data sources for only nine of these countries.

Predictive validity statistics are shown in Table C in Text S1. Root mean foursquare prediction errors for countries for which data were left out were 1.3% for principal prevalence, 6.1% for secondary prevalence, and 9.one%–xiii.i% for exposure to primary and secondary fertility (see Figures D–I in Text S1 for graphical presentation of model fit). The models' 95% doubt intervals contained 93%–96% of left-out data points.

In 2010, 1.ix% of child-seeking women aged 20–44 y were unable to have a showtime alive birth (primary infertility; 95% doubtfulness interval 1.7%, 2.2%), and 10.5% of child-seeking women with a prior live birth were unable to have an additional live birth (secondary infertility; nine.5%, 11.7%). Levels of infertility were similar in 1990 and 2010, decreasing 0.1 (−0.1, 0.3) pct points for primary infertility (from 2.0% [1.ix%, 2.2%] in 1990; pp = 0.84) and increasing 0.iv (−0.8, 1.6) per centum points for secondary infertility (from 10.ii% [ix.3%, 11.i%] in 1990; pp of increase = 0.71).

Figure 1 presents the prevalence of primary and secondary infertility by historic period (run across Figure J in Text S1 for historic period blueprint of exposure to primary and secondary infertility). The prevalence of primary infertility was college among women anile 20–24 y (two.vii% [ii.iv%, 3.0%] in 2010) compared to women aged 25–29 y (2.0% [1.8%, 2.ii%]) and women anile 30–44 y (ranging from i.half-dozen% to ane.7% in 2010). Prevalence of secondary infertility increased sharply with age, from 2.six% (2.iii%, 3.0%) in women aged 20–24 y to 27.1% (24.7%, 29.nine%) in women aged xl–44 y. Both age patterns are less pronounced when calculated equally a percent of all women (Figure 1).

Figure 1. Global prevalence of primary and secondary infertility in 2010, by the female partner's age.

Infertility is calculated as the percentage of women who seek a kid and as the percent of all women of reproductive historic period. The solid line represents the posterior hateful, and the shaded area the 95% uncertainty interval.

https://doi.org/10.1371/periodical.pmed.1001356.g001

Patterns and Trends in Infertility amidst Kid-Seeking Women

Principal infertility prevalence among kid-seeking women varied by region in 2010, from ane.5% (ane.2%, 1.8%) in the Latin America/Caribbean region, to 2.6% (2.1%, three.1%) in the North Africa/Middle Eastward region (Figure 2; Dataset S1). Twenty-year trends in infertility prevalence were not statistically significant in most regions, with low-certainty increases in prevalence in Cardinal/Eastern Europe and Central Asia (0.iv [−0.four, ane.half-dozen] per centum points; pp = 0.79) and in the East Asia/Pacific region (0.1 percent points [−0.2, 0.iv]; pp = 0.71), and non-significant declines in the High Income, North Africa/Middle Eastward, and Latin America/Caribbean area regions (ranging from 0.0 to 0.2 percent points; pp 0.56–0.93). In South Asia, the prevalence of primary infertility declined 0.vi (0.i, 1.0) percent points (pp = 0.99); however, this decline was attenuated, declining 0.three (−0.3, 1.0) percentage points (pp = 0.88), if Earth Fertility Surveys data from 1974–1981 were excluded from the model (results not shown). The pass up in primary infertility was greatest in Sub-Saharan Africa, which experienced a substantial pass up in master infertility, from 2.vii% (2.v%, three.0%) in 1990 to i.9% (1.8%, 2.1%) in 2010, a decline of 0.8 (0.5, 1.i) percentage points over the twenty-y catamenia (pp>0.99). This resulted in a reordering of the regions by primary infertility prevalence: in 1990, Sub-Saharan Africa and Southern asia had the 2 highest prevalences of primary infertility, and in 2010, they were 4th and 2nd highest of seven regions, respectively.

Figure ii. Prevalence of primary infertility and secondary infertility, presented as the percent of women who seek a kid, and every bit the percent of all women of reproductive age, in 1990 and 2010.

Infertility prevalence is indexed on the female person partner; historic period-standardized prevalence among women aged 20–44 y is shown here. Horizontal lines bespeak the 95% doubt interval.

https://doi.org/10.1371/periodical.pmed.1001356.g002

The prevalence of primary infertility varied within these regions (Figure 3; Dataset S2; Figure K in Text S1). Within the Sub-Saharan Africa region, the prevalence was lowest in East Africa and Southern Africa. Republic of kenya, Zimbabwe, and Rwanda all had low prevalences of primary infertility in Sub-Saharan Africa in 2010 (1.0%–1.1%). In contrast, some countries, more often than not in central Sub-Saharan Africa, had very high prevalences: Republic of equatorial guinea, Mozambique, Angola, Gabon, Cameroon, and the Central African Republic all had prevalences of ii.five% or greater. Principal infertility prevalence also varied inside the Latin America/Caribbean area region: some Caribbean countries had prevalences of 2.5% or greater in 2010: Jamaica, Suriname, Republic of haiti, and Trinidad and Tobago. In contrast, all countries in Central Latin America and Andean Latin America had prevalences of ane.six% or less.

Figure iii. Prevalence of primary infertility among women who seek a child, in 2010.

Infertility prevalence is indexed on the female partner; historic period-standardized prevalence among women anile 20–44 y is shown here.

https://doi.org/10.1371/periodical.pmed.1001356.g003

In 2010, the lowest estimated prevalences of master infertility occurred in middle-income countries in Latin America (Republic of peru, Bolivia, Ecuador, and Republic of el salvador; 0.8%–one.0%) and in Poland, Kenya, and the Republic of korea (0.ix%–i.0%). At the other extreme, 13 countries in Eastern Europe, North Africa/Middle East, Oceania, and Sub-Saharan Africa had prevalences of iii.0% or greater.

Global and country patterns of secondary infertility were similar to those of principal infertility, with two notable exceptions: first, the prevalence of main infertility was high in some countries in the North Africa/Eye East region, notably Morocco and Republic of yemen, with prevalences greater than 3%, but prevalence of secondary infertility was depression in those same countries (Figures 2–four; Dataset S1). 2nd, the prevalence of main infertility observed in the Central/Eastern Europe and Key Asia region was depression-to-intermediate relative to that of other regions, though this region had the highest prevalence of secondary infertility.

Effigy 4. Prevalence of secondary infertility among women who have had a live birth and seek another, in 2010.

Infertility prevalence is indexed on the female partner; historic period-standardized prevalence among women anile 20–44 y is shown here.

https://doi.org/10.1371/periodical.pmed.1001356.g004

The prevalence of secondary infertility ranged from seven.2% (5.0%, 10.2%) in the High Income region and seven.2% (5.9%, 8.6%) in the N Africa/Middle Due east region to 18.0% (13.8%, 24.1%) in the Fundamental/Eastern Europe and Central Asia region. Most regions experienced non-meaning increases in the prevalence of secondary infertility betwixt 1990 and 2010 (pps = 0.64–0.81; Figure 5), with the exception of Sub-Saharan Africa, where the prevalence of secondary infertility declined from xiii.v% (12.5%, 14.5%) in 1990 to 11.6% (x.6%, 12.half dozen%; pp>0.99) in 2010.

Figure five. Absolute change in prevalence of primary and secondary infertility, measured as the percentage of women who seek a child and as the per centum of all women of reproductive age, between 1990 and 2010.

Infertility prevalence is indexed on the female person partner; alter in age-standardized prevalence amid women aged xx–44 y is shown here. Horizontal lines indicate the 95% uncertainty interval.

https://doi.org/10.1371/journal.pmed.1001356.g005

Like primary infertility, the prevalence of secondary infertility varied by country within each region, peculiarly in Sub-Saharan Africa (Figure 4; Dataset S2; Figure L in Text S1). In 2010, eight countries in 5 regions had a prevalence of secondary infertility below vi%: Rwanda, Jordan, Peru, Usa of America, Republic of bolivia, Egypt, Tunisia, and Viet Nam. At the other extreme, xix countries in Cardinal/Eastern Europe and Primal Asia and iv in Sub-Saharan Africa had prevalences greater than xvi%.

Patterns and Trends in Infertility among All Women of Reproductive Age

Mirroring worldwide declines in fertility, the proportion of women with one or more children who are at risk of pregnancy has decreased since 1990 in every world region (Figure N in Text S1). This has resulted in a decrease in the percent of women of reproductive age who are affected by secondary infertility in every world region (Figure 5; Figures O and P in Text S1). Worldwide, the age-standardized percent of women aged twenty–44 y affected by secondary infertility has decreased from 3.9% (3.6%, 4.3%) to 3.0% (2.7%, 3.three%); the pp of this decline being real is ≥0.9 in the High Income and Central/Eastern Europe and Central Asia regions, and ≥0.99 globally and in all other earth regions. The proportion of women who desire a start kid has decreased less over time, significant that the proportion of women who are affected by primary infertility has changed little, from 1.half-dozen% (1.5%, 1.7%) in 1990 to ane.5% (1.3%, ane.7%) in 2010 (pp = 0.xc).

Worldwide, 48.5 1000000 (45.0 meg, 52.half dozen one thousand thousand) couples are unable to have a kid, of which 19.2 million (17.0 million, 21.v 1000000) couples are unable to have a first child, and 29.iii one thousand thousand (26.3 one thousand thousand, 32.half-dozen million) couples are unable to have an additional child (the latter figure excludes China). xiv.four million (12.2 million, sixteen.8 million) of these couples alive in South asia, and a further 10.0 million (ix.iii meg, 10.8 one thousand thousand) live in Sub-Saharan Africa. The number of couples suffering from infertility has increased since 1990, when 42.0 million (39.half dozen million, 44.8 million) couples were unable to have a child. Though the number of infertile couples has increased globally and in almost regions, it has decreased from iv.2 million in 1990 to 3.half-dozen meg in 2010 in the High Income region, and from 4.4 million in 1990 to 3.8 1000000 in 2010 in the Cardinal/Eastern Europe and Primal Asia region. Although in that location were no significant changes in the prevalence of infertility among child-seeking women, reduced child-seeking behavior coupled with a lack of population growth resulted in a decrease in the absolute number of infertile couples in these regions.

Discussion

In 2010, an estimated 48.5 meg (45.0 million, 52.six 1000000) couples worldwide were infertile. Between 1990 and 2010, levels of primary and secondary infertility changed piddling in most world regions. The exceptions were Sub-Saharan Africa and Southern asia (for primary infertility merely), where infertility prevalence decreased during the twenty-y menstruation. Reduced child-seeking behavior (i.e., reduced exposure to pregnancy due to changing fertility preferences) means that even where infertility prevalence among those exposed to the risk of pregnancy did not change, a decreasing proportion of couples were affected by infertility because fewer attempted to have a child. Notwithstanding, the absolute number of infertile couples increased due to population growth.

Our judge of the global number of couples affected past infertility is lower than that of Boivin et al. [five] or Rutstein and Shah [6]. Boiven et al. estimated 72.4 million women were currently infertile in 2006 [5]. They used the median prevalence reported by seven published infertility studies that used a 12- or 24-mo definition of infertility; our estimates differ because we used a larger dataset and a different algorithm to summate infertility [five],[10]. Rutstein and Shah presented a diversity of infertility measures using DHS data from the late 1990s, demonstrating the importance of choices in defining infertility [6]. They estimated that 186 million ever-married women in developing countries (excluding China) were infertile in 2002; this larger number is a upshot of definitional differences: they included women who may not have been exposed to the risk of pregnancy and women aged xv–twenty y and 45–49 y, age groups that have higher prevalences of infertility than women aged 20–44 y.

The strengths of this study were the awarding of consistent algorithms to calculate principal and secondary infertility from 277 survey datasets, most of which were nationally representative; our utilize of a Bayesian hierarchical model to guess infertility prevalence and trends; and our systematic quantification of dubiety. We identified where survey data did not collect information on past contraceptive utilize or marital status, and corrected for biases that arose when information on contraceptive apply or matrimony was incomplete. We used definitions of primary and secondary infertility that allowed united states of america to disentangle trends in ability to have a child from trends in fertility preferences [25]. Specifically, women who were non in a union, had used any contraceptive in the previous 5 y, or did not wish to accept a kid were excluded from both the numerator and the denominator when calculating the prevalence of infertility. This allowed us to calculate trends in infertility that were contained from worldwide declines in the preferred number of children and independent of population growth in that fourth dimension catamenia.

The major limitations of our study are gaps in data for certain countries, the use of proxies to assess exposure to pregnancy, potential reporting inaccuracies, and the inability of our definition to capture all instances of infertility. Despite extensive data seeking, data gaps remained, peculiarly in high-income countries and in Central and Eastern Europe. The utilise of demographic and reproductive health surveys to infer infertility prevalence requires several assumptions. First, nosotros assume that women who are in a union, wish to take a child, and are not using contraceptives are engaged in regular, unprotected sexual intercourse. Nosotros as well rely on women'southward reported couple status, births, contraceptive use, and want for a child. These assumptions may be violated, as women may non report accurately on sensitive topics, such as past voluntary abortions [26],[27]. Women might too report not-biological children as their own. Furthermore, the reporting of the appointment of marriage and date of last birth may not exist accurate in some settings [7]. Several studies accept found that, in Cathay, reporting of births in household surveys may exist suppressed or the timing of births may be misreported because of policy considerations, which could affect our infertility estimates [28]–[30]. Finally, infertile women may state that they do not want a child, as a coping mechanism [17],[31]. Our correction of incomplete contraceptive and marriage information, use of birth as the outcome, and utilize of a five-y infertility definition reduced the susceptibility of our estimates to these biases [13]. Some types of infertility are not measured using our algorithm [32]. The algorithm cannot capture any infertile men whose female partners conceive and requite nascency to a child with some other homo, nor primary infertility in men who accept had multiple partners. Information technology is not possible to capture infertile couples trying to accept a child but using condoms intermittently for sexually transmitted infection (STI) prevention [21]. Lastly, our 5-y definition excludes from the prevalence estimation men and women who do non maintain a union for 5 y. Our prevalence gauge of infertility, however, is applied to all couples in a union, independent of the length, to calculate absolute numbers of couples affected. To the extent that infertile unions are more than probable to dissolve than fertile unions, we await our approximate to be biased downwards considering nosotros only measure infertility in unions that terminal for five y [33].

There are several of import implications of the algorithm nosotros apply to measure out infertility. We measure current infertility using a v-y exposure with nativity equally an result. An infertility measure based on ability to go pregnant may accept different patterns, trends, and levels than those presented in this paper. Infertility prevalences measured using a shorter exposure menses would have a similar geographic and temporal pattern, but would be approximately twice as high as our estimates (see Figure Q in Text S1; [13]) The shorter exposure flow identifies couples affected by temporary separations or periods of abstinence or lactational amenorrhea, infertility that resolves at betwixt 2 and 5 y, and infertile unions that dissolve after 2 y but before 5 y without a nascency. Our algorithm does not capture childlessness experienced by couples who are no longer of reproductive historic period or infertility experienced by women aged less than 20 y. Infertility that is identified and successfully treated inside a 5-y period is not captured by this definition. Finally, men and women who use contraceptives, choose to be childless, or are not in a union, may indeed be infertile. Withal, these individuals are not included in our approximate of the number of infertile unions. We aimed to summate the number of couples currently affected by infertility, and these individuals are non currently attempting to have a child, or, in the case of those not in a union, information technology is not possible to determine whether they are attempting to take a child.

Multiple factors—infectious, ecology, genetic, and even dietary in origin—can contribute to infertility [34]. These factors may affect the female person, the male, or both partners in a marriage, resulting in an disability to become pregnant or carry a child to term. Current evidence, mostly from clinical studies with few exceptions [35], indicates that differences in the incidence and prevalence of infectious diseases, leading to fallopian tube blockage in women, are the chief reason for changes over fourth dimension and differences between populations [36]–[39]. Some accept hypothesized that sperm quality is failing [forty], only the evidence is not conclusive [41].

Increasing age at childbearing could also increment the prevalence of infertility, as the ability to become pregnant and deliver a alive birth reduces with historic period in all populations. Globally, the hateful age at childbearing has remained the same (most 28 y) since the 1970s, although this masks regional and temporal heterogeneity in trends [42]. In low- and centre-income countries, age at first birth has increased, although kickoff birth still occurs at young ages: in 40 countries with one DHS survey in the 1990s and some other survey during 2000–2011, the overall median of the median age at kickoff nativity among women aged 25–49 y increased from 19.8 to 20.iii y [42]. While the historic period at starting time birth has increased, the average number of children has decreased, and thus, the mean age at childbearing has not inverse in these countries [42]. On the other manus, mean age at first birth and mean historic period at childbearing have increased in all developed countries since the 1990s [42],[43]. This does not appear to have affected primary infertility levels in those countries. However, it may have contributed to the small-scale increase in secondary infertility that nosotros estimated.

The geographic pattern of infertility prevalence we institute is consistent with previous estimates of infertility in Sub-Saharan Africa, specifically high prevalence in some Westward, Central, and Southern African countries, and depression prevalence in nearly Eastward African countries [7],[8],[44]. This pattern has mainly been attributed to the consequences of untreated reproductive tract infections, including both STIs such equally Neisseria gonorrhoeae and Chlamydia trachomatis, and, to a lesser extent, infections from unsafe abortions or obstetric practices [34],[36],[45]. The improved trends for the region as a whole may be due to reduced prevalence of STIs, possibly associated with changes in sexual behavior and STI treatment in response to the HIV epidemic. There are, however, no reliable data on regional trends in the prevalence of STIs. WHO estimated that the prevalence of C. trachomatis infections amid adult females in 2005 was 4%–half dozen% in all regions of the world, except the WHO Eastern Mediterranean and South East Asia regions, where prevalence was below 2% [46]. N. gonorrhoeae was considerably more prevalent in the WHO African region than all other regions amidst adult women and men. If the prevalence of maternal syphilis has decreased since 1990, it may have reduced the run a risk of stillbirths and therefore increased the ability to have a live nascency, which is our definition of fertility [47]–[fifty]. Infection is also associated with reduced fertility. Infertile women, especially those with primary infertility, are more probable to learn HIV infection because of greater marital instability [51], and HIV is also associated with reduced fertility in the afterwards stages of infection [52]. However, the population effect of the HIV epidemic on fertility is likely small: despite the epidemic, infertility declined in all Sub-Saharan African subregions.

Post-ballgame complications are also an important factor contributing to infertility. The gamble is higher for unsafe practices than for safe abortion procedures. The relatively high levels of secondary infertility in the Central/Eastern Europe and Central Asia region may be associated with the higher incidence of abortion. In these regions, the abortion rate declined between 1995 and 2003, but stayed at levels higher than the global average [16]. Both induced abortions and higher levels of STIs/HIV may play a role in explaining the elevated levels of secondary infertility in the Caribbean. Declines in dangerous abortion rates in Sub-Saharan Africa between 1995 and 2003 may take contributed to declines in infertility rates [16].

Amid women who have had a pregnancy or birth, pregnancy complications may crusade infections of the reproductive tract that result in infertility. Maternal mortality ratios—an indicator of obstetric risk—are estimated to have declined slightly in Sub-Saharan Africa and more essentially South asia since 1990, and it is possible that injuries/infections caused or aggravated by childbirth declined together with decreases in maternal mortality [2].

Including questions on how long women have tried to become pregnant in national or international survey programs would allow for the apply of a definition that is more than closely aligned with clinical do than the algorithm used in this written report. This may lead to more than reliable estimation of levels and trends in infertility than current methods, which in plough would inform policy and programme requirements to address this neglected area of reproductive wellness. However, in the absence of widespread data collection on time to pregnancy, the methods used and results presented here provide valuable insights into global, regional, and land patterns and trends in infertility.

Supporting Information

Acknowledgments

We thank Ulla Larsen for advice on report methods, Alison Gemmill for thoughtful comments on the manuscript, Colin Mathers for advice and support during the written report, and Jessica Ho for aid with figures. We also thank Mohamed Ali and Jørn Olsen for assistance with data sources.

Author Contributions

Wrote the outset typhoon of the manuscript: GAS. Contributed to the writing of the manuscript: MNM SRF TB SV GAS. ICMJE criteria for authorship read and met: MNM SRF TB SV GAS. Hold with manuscript results and conclusions: MNM SRF TB SV GAS. MNM and GAS accessed and analyzed health survey data. SRF developed the Bayesian statistical model with input from MNM and GAS. All authors contributed to the study design, analysis, and writing of the report. GAS oversaw the inquiry.

References

1. 1. Cousens S, Blencowe H, Stanton C, Chou D, Ahmed Due south, et al. (2011) National, regional, and worldwide estimates of stillbirth rates in 2009 with trends since 1995: a systematic analysis. Lancet 377: 1319–1330.
   • View Commodity
   • Google Scholar
2. 2. World Health Organization, United nations Children's Fund, United Nations Population Fund, The World Bank (2012) Trends in maternal bloodshed: 1990 to 2010. Geneva: Earth Wellness Organization.
3. 3. Cui Westward (2010) Mother or nothing: the desperation of infertility. Bull World Health Organ 88: 881–882 .
   • View Commodity
   • Google Scholar
4. 4. Chachamovich JR, Chachamovich East, Ezer H, Fleck MP, Knauth D, et al. (2010) Investigating quality of life and health-related quality of life in infertility: a systematic review. J Psychosom Obstet Gynaecol 31: 101–110 .
   • View Commodity
   • Google Scholar
5. v. Boivin J, Bunting Fifty, Collins JA, Nygren KG (2007) International estimates of infertility prevalence and treatment-seeking: potential need and need for infertility medical care. Hum Reprod 22: 1506–1512 .
   • View Article
   • Google Scholar
6. 6. Rutstein SO, Shah IH (2004) Infecundity, infertility, and childlessness in developing countries. Calverton (Maryland): ORC Macro. 57 p.
7. 7. Ericksen K, Brunette T (1996) Patterns and predictors of infertility amid African women: a cross-national survey of 20-seven nations. Soc Sci Med 42: 209–220 .
   • View Article
   • Google Scholar
8. viii. Larsen U (2000) Primary and secondary infertility in sub-Saharan Africa. Int J Epidemiol 29: 285–291 .
   • View Article
   • Google Scholar
9. 9. Gurunath Due south, Pandian Z, Anderson RA, Bhattacharya S (2011) Defining infertility—a systematic review of prevalence studies. Hum Reprod Update 17: 575–588 .
   • View Article
   • Google Scholar
10. 10. Dyer SJ (2009) International estimates on infertility prevalence and treatment seeking: potential need and need for medical care. Hum Reprod 24: 2379–2380 .
    • View Commodity
    • Google Scholar
11. xi. Marchbanks PA, Peterson HB, Rubin GL, Wingo PA (1989) Research on infertility: definition makes a divergence. The Cancer and Steroid Hormone Study Group. Am J Epidemiol 130: 259–267.
    • View Commodity
    • Google Scholar
12. 12. Larsen U, Menken J (1989) Measuring sterility from incomplete birth histories. Demography 26: 185–201.
    • View Article
    • Google Scholar
13. 13. Mascarenhas MN, Cheung H, Mathers CD, Stevens GA (2012) Measuring infertility in populations: constructing a standard definition for utilize with demographic and reproductive health surveys. Popul Health Metr 10: 17.
    • View Article
    • Google Scholar
14. fourteen. Houzard S, Bajos N, Warszwawski J, de Guibert-Lantoine C, Kaminski Yard, et al. (2000) Analysis of the underestimation of induced abortions in a survey of the general population in France. Eur J Contracept Reprod Health Care five: 52–60.
    • View Article
    • Google Scholar
15. fifteen. Jones RK, Kost K (2007) Underreporting of induced and spontaneous abortion in the United states of america: an assay of the 2002 National Survey of Family Growth. Stud Fam Plann 38: 187–197 .
    • View Article
    • Google Scholar
16. 16. Sedgh One thousand, Singh S, Shah IH, Ahman E, Henshaw SK, et al. (2012) Induced abortion: incidence and trends worldwide from 1995 to 2008. Lancet 379: 625–632 .
    • View Commodity
    • Google Scholar
17. 17. Larsen U (2005) Inquiry on infertility: which definition should we use? Fertil Steril 83: 846–852 .
    • View Commodity
    • Google Scholar
18. 18. Zegers-Hochschild F, Adamson GD, de Mouzon J, Ishihara O, Mansour R, et al. (2009) The International Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organisation (WHO) revised glossary on Fine art terminology, 2009. Hum Reprod 24: 2683–2687 .
    • View Article
    • Google Scholar
19. xix. World Health Organization (2006) Reproductive health indicators for global monitoring: guidelines for their generation, interpretation and analysis for global monitoring. Geneva: World Wellness Organization. 63 p.
20. 20. Rowe PJ, Comhaire FH, Hargreave TB, Mellows HJ, Press CU (1993) WHO manual for the standardized investigation and diagnosis of the infertile couple. Cambridge: Cambridge University Printing.
21. 21. Ali M, Cleland J, Shah I (2012) Causes and consequences of contraceptive discontinuation: testify from sixty Demographic and Wellness Surveys. Geneva: World Health Organization.
22. 22. Gakidou E, Cowling 1000, Lozano R, Murray CJ (2010) Increased educational attainment and its consequence on child bloodshed in 175 countries between 1970 and 2009: a systematic analysis. Lancet 376: 959–974 .
    • View Article
    • Google Scholar
23. 23. Patil A, Huard D, Fonnesbeck CJ (2010) PyMC: Bayesian stochastic modelling in Python. J Stat Softw 35: e1000301.
    • View Article
    • Google Scholar
24. 24. United nations Population Division (2011) Earth population prospects: 2010 revision. New York: Un Population Division.
25. 25. Stöbel-Richter Y, Beutel ME, Finck C, Brähler Due east (2005) The "wish to have a child", childlessness and infertility in Germany. Hum Reprod 20: 2850–2857 .
    • View Article
    • Google Scholar
26. 26. Ahmed G, Schellstede WP, Williamson NE (1987) Underreporting of contraceptive utilise in People's republic of bangladesh. Int Fam Plann Perspect 13: 136–140.
    • View Commodity
    • Google Scholar
27. 27. Fu H, Darroch J (1999) Contraceptive failure rates: new estimates from the 1995 National Survey of Family Growth. Fam Plann Perspect 31: 56–63.
    • View Commodity
    • Google Scholar
28. 28. Ding QJ, Hesketh T (2006) Family size, fertility preferences, and sex ratio in China in the era of the ane child family policy: results from national family planning and reproductive health survey. BMJ 333: 371–373 .
    • View Commodity
    • Google Scholar
29. 29. Merli M (2000) Are births underreported in rural China? Manipulation of statistical records in response to Prc'south population policies. Demography 37: 109–126.
    • View Commodity
    • Google Scholar
30. 30. Goodkind DM (2004) China's missing children: the 2000 census underreporting surprise. Popul Stud (Camb) 58: 281–295 .
    • View Article
    • Google Scholar
31. 31. Saloman J, Murray CJL (2002) A conceptual framework for understanding adaptation, coping and aligning in health state valuations. Summary measures of population health. Geneva: World Health Organization.
32. 32. Greil AL, McQuillan J, Johnson K, Slauson-Blevins Thou, Shreffler KM (2010) The hidden infertile: infertile women without pregnancy intent in the U.s.. Fertil Steril 93: 2080–2083.
    • View Commodity
    • Google Scholar
33. 33. Larsen U (2003) Infertility in central Africa. Trop Med Int Health eight: 354–367.
    • View Article
    • Google Scholar
34. 34. Cates West, Farley TM, Rowe PJ (1985) Worldwide patterns of infertility: is Africa unlike? Lancet 2: 596–598.
    • View Article
    • Google Scholar
35. 35. Larsen U, Masenga G, Mlay J (2006) Infertility in a community and clinic-based sample of couples in Moshi, Northern Tanzania. East Afr Med J 83: 10–17.
    • View Commodity
    • Google Scholar
36. 36. Mayaud P (2001) The role of reproductive tract infections. In: Boerma JT, Mgalla Z, editors. Women and infertility in sub-Saharan Africa: a multi-disciplinary perspective. Amsterdam: Royal Tropical Found Press. pp. 71–108.
37. 37. Collins JA, Van Steirteghem A (2004) Overall prognosis with current handling of infertility. Hum Reprod Update ten: 309–316.
    • View Article
    • Google Scholar
38. 38. Cates West Jr, Rolfs RT Jr, Aral And then (1990) Sexually transmitted diseases, pelvic inflammatory disease, and infertility: an epidemiologic update. Epidemiol Rev 12: 199–220.
    • View Article
    • Google Scholar
39. 39. Globe Wellness Organization (1987) Infections, pregnancies, and infertility: perspectives on prevention. Fertil Steril 47: 964–968.
    • View Article
    • Google Scholar
40. forty. Carlsen E, Giwercman A, Keiding N, Skakkebaek NE (1992) Evidence for decreasing quality of semen during by 50 years. BMJ 305: 609–613.
    • View Commodity
    • Google Scholar
41. 41. te Velde E, Burdorf A, Nieschlag E, Eijkemans R, Kremer JAM, et al. (2010) Is human fecundity declining in Western countries? Hum Reprod 25: 1348–1353 .
    • View Article
    • Google Scholar
42. 42. United nations Population Segmentation (2011) Earth fertility report 2009. New York: United Nations Population Segmentation.
43. 43. Organisation for Economic Co-operation and Development (2011) OECD family database. Available: http://www.oecd.org/social/socialpoliciesanddata/oecdfamilydatabase.htm. Accessed 25 July 2012.
44. 44. Larsen U (1994) Sterility in sub-Saharan Africa. Popul Stud 48: 459–474.
    • View Article
    • Google Scholar
45. 45. Collet M, Reniers J, Frost Due east, Gass R, Yvert F, et al. (1988) Infertility in Primal Africa: infection is the cause. Int J Gynaecol Obstet 26: 423–428.
    • View Article
    • Google Scholar
46. 46. World Health Organization (2011) Prevalence and incidence of selected sexually transmitted infections: methods and results used by WHO to generate 2005 estimates. Geneva: World Health Organization.
47. 47. Bhutta ZA, Yakoob MY, Lawn JE, Rizvi A, Friberg IK, et al. (2011) Stillbirths: what departure can we make and at what cost? Lancet 377: 1523–1538 .
    • View Article
    • Google Scholar
48. 48. Blencowe H, Cousens S, Kamb M, Berman S, Lawn JE (2011) Lives Saved Tool supplement detection and treatment of syphilis in pregnancy to reduce syphilis related stillbirths and neonatal mortality. BMC Public Wellness 11(Suppl 3): S9 .
    • View Article
    • Google Scholar
49. 49. Gloyd S, Chai S, Mercer MA (2001) Antenatal syphilis in sub-Saharan Africa: missed opportunities for mortality reduction. Wellness Policy Plan 16: 29–34.
    • View Article
    • Google Scholar
50. 50. McClure EM, Saleem South, Pasha O, Goldenberg RL (2009) Stillbirth in developing countries: a review of causes, gamble factors and prevention strategies. J Matern Fetal Neonatal Med 22: 183–190 .
    • View Commodity
    • Google Scholar
51. 51. Favot I, Ngalula J, Mgalla Z, Klokke AH, Gumodoka B, et al. (1997) HIV infection and sexual behaviour amongst women with infertility in Tanzania: a hospital-based study. Int J Epidemiol 26: 414–419.
    • View Article
    • Google Scholar
52. 52. Lewis JJC, Ronsmans C, Ezeh A, Gregson S (2004) The population impact of HIV on fertility in sub-Saharan Africa. AIDS 18(Suppl 2): S35–S43.
    • View Article
    • Google Scholar

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