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Incidence of infertility and risk factors of impaired fecundity among newly married couples in a Chinese population
Reproductive BioMedicine Online, 1, 30, pages 92 - 100
- Infertility rates vary with different definitions and study designs.
- Incidence of infertility was obtained from a prospective follow-up study.
- Couples who used coal as cooking fuel were more likely to have impaired fecundity.
- Higher BMI and long-term health problems in women tended to reduce the fecundity.
- Men who had married at later ages were more likely to have delayed pregnancies.
The aims of this study were to obtain the incidence of infertility, to examine the causes of infertility and to explore risk factors for impaired fecundity in a rural region of northern China using a prospective follow-up design. A total of 2151 newly married couples planning to become pregnant within the next 12 months were enrolled between 2009 and 2012 from two counties of Shanxi Province in northern China. Couples were followed up for at least 1 year or until a clinical pregnancy occurred. Information about clinical pregnancy was obtained. The 12-month and 24-month infertility rates were 13.6% (95% CI 11.9 to 15.3) and 8.5% (95% CI 6.7 to 10.3), respectively. About 63% of women became pregnant within 6 months of follow up, and 86% did so within 12 months of follow up. The main causes of female infertility were ovulation disorders, fallopian tube problems and polycystic ovary syndrome. The primary cause of male infertility was sperm quality problems. Couples who used coal as cooking fuel, women with a higher body mass index, women with long-term health problems, and men who had married at later ages were more likely to have delayed pregnancies.
Keywords: epidemiology, incidence, infertility, newly married couples, risk factors.
Infertility is a common medical problem that affects 5–8% of couples in developed countries ( World Health Organization, 1987 ) and 5.8% to 44.2% in developing nations ( Rutstein and Macro, 2004 ). Infertility rates vary greatly among, and also within, countries: Iran 3–8% (Ahmadi Asr Badr et al, 2006, Safarinejad, 2008, and Vahidi et al, 2009); UK 2–26% (Buckett, Bentick, 1997, Gunnell, Ewings, 1994, and Oakley et al, 2008); and China 1–18% (Che, Cleland, 2002, Gao, Gao, 2005, and Liu et al, 2005). These variations may partially stem from the true differences in the prevalence of infertility among populations. Differences in the definition of infertility and in study design (cross-sectional or prospective) may contribute a greater part. A population-based cohort design in which couples are followed forward in time from when they start attempting to become pregnant is considered the gold standard as a measure of infertility (Keiding et al, 2002 and Thoma et al, 2013).
Most epidemiological studies on human infertility have used a cross-sectional design (Bhattacharya et al, 2009, Gurunath et al, 2011, Keiding et al, 2002, and Thoma et al, 2013), relying solely on recall in defining the lengths of respondents' attempts to become pregnant. In addition, studies of the causes of infertility have mostly been hospital-based (Bayasgalan et al, 2004, Farhi, Ben-Haroush, 2011, and Hou, 2011). Because of a possible selection bias that may have resulted from referral, these studies are unlikely to present a complete picture of the causes of infertility and the risk factors at the population level. Under these circumstances, the aims of this study were to obtain the incidence of infertility, to examine the causes of infertility and to explore risk factors for impaired fecundity in a rural region of northern China using a prospective follow-up design.
Materials and methods
Design and study population
This study used a prospective follow-up design. The participants were those who had sought premarital health check-ups at the maternal and child health care centres of Xiyang County and Shouyang County of Shanxi Province in northern China. Enrollment began in November 2009 and ended in July 2012. Couples were invited to participate in the study at the time of the check-up. The inclusion criteria were as follows: not pregnant at the time of the check-up; planning to become pregnant in the following 12 months; planning to deliver in local hospitals; and consent to participate in the study. Women who were pregnant at the time of check-up, or did not plan to become pregnant in the following 12 months, were not eligible for inclusion. The sample size of our study was determined by the number of couples who met the inclusion criteria in the study location during the study period. The participants were followed up for over 1 or 2 years based on the definitions of infertility listed below.
Definition of infertility
Although the World Health Organization manual for the investigation and diagnosis of infertile couples uses 12 months of waiting time until pregnancy ( Rowe et al., 1993 ), many studies use 24 months of waiting time until pregnancy to define infertility (Gurunath et al, 2011 and Rowe et al, 1993). To facilitate comparison of this study with others, the following two commonly used definitions were used to calculate the infertility rate: 12-month infertility rate: a disease of the reproductive system defined by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse ( Zegers-Hochschild et al., 2009 ); and 24-month infertility rate: the percentage of women of reproductive age (15–49 years) at risk of pregnancy (not pregnant, sexually active, not using contraception and non-lactating) who report attempting pregnancy for 2 years or more ( WHO, 2001 ).
At enrollment, a structured questionnaire was used to collect information on demographic characteristics, disease history, menstrual and pregnancy history, the use of folic acid or other nutrient supplements, and the history of exposure to various harmful substances, including cigarette smoke and passive smoking, alcohol use, indoor air pollution, exposure to emission from factories, mobile phone use and self-reported exposure to organic pollutants and heavy metals. Few couples reported nutrient supplementation (calcium: 3.7%, vitamin C: 2.3%, folic acid: 2.1%, vitamin E: 1.8%, other: <1%); used a mobile phone irregularly (2.7%); or had been exposed to organic pollutants (2.5%) or heavy metals (0.6%); therefore, these variables were not reported in this paper.
The information on clinical pregnancy was obtained through the maternal healthcare system, which collects and records information on pregnant women, or through two follow-up surveys, which were conducted by trained local healthcare workers through telephone or face-to-face interviews between August and October 2012 and between July and August 2013, respectively. The participants were visited in person by local healthcare workers if they could not be reached by telephone.
The outcome follow-up questionnaire included pregnancy information on the couples after more than 1 year of marriage, the time to pregnancy (beginning from the date when they have regular unprotected intercourse), the potential reasons for not becoming pregnant (such as using contraceptives or having irregular intercourse since enrollment, including lived apart, divorced, and unmarried), the clinic consultation, and the causes of infertility. In the last survey, all of the infertile couples were invited for a preliminary infertility work-up.
The 12-month and 24-month infertility rates were calculated on the basis of the definition of infertility: the number of infertile women represents the numerator and the number of women exposed to the risk of pregnancy represents the denominator. The 95% confidence intervals (95% CI) of infertility rates were calculated on the basis of the normal approximation approach ( Fang and Sun, 2008 ), with the use of the formula ofp ± 1.96 sqrt [p(1-p)/n], in whichprepresents infertility rate. Couples who had diagnosed causes of infertility but became pregnant within 24 months of trying were excluded in the calculation of the 24-month infertility rate because they did not meet the criteria for 24-month infertility and it was not appropriate to treat them as fecund. In the sensitivity analysis, the 24-month infertility rate was repeated among women, including those who became pregnant within 24 months of attempting regardless of the infertility diagnosis to assess the robustness of infertility rate according to varying eligibility criteria. Unadjusted and adjusted fecundity ratios were obtained from discrete-time Cox models for couples with different characteristics to analyse the risk factors for delayed pregnancy ( Cox, 1972 ). Factors for which unadjusted fecundity ratios hadPvalues less than 0.05 were included in the multivariable model. The county (Shouyang/Xiyang) from where the partipants were recruited was also included in the model as a co-variable. The fecundity ratios represent the odds of becoming pregnant during each month among the exposed relative to the unexposed, or per 1-year increase in age, or per 1 kg/m2increase in body mass index (BMI). A fecundity ratio less than 1.0 indicates impaired fecundity or longer time to pregnancy. Two-tailedP< 0.05 was considered statistically significant on all of the tests. Individuals with missing values were excluded from all of the analyses.
The study was approved by the Institutional Review Board of Peking University (reference number: IRB0001052-09075 date of approval 29 October 2009 and reference number: IRB00001052-13060 19 November 2013). All of the participants provided verbal informed consent.
A total of 2151 couples were enrolled, of whom 116 couples were lost to follow up during the 12-month follow up because of changes in address and phone number; an additional 11 couples were lost during the 24-month follow up because of changes in address and phone number (n= 3), or because they now lived apart or had divorced (n= 5), did not try to become pregnant (n= 1), refused to participate (n= 1), or were suspected of having reproductive system malformations (n= 1). In total, 2035 couples were followed up for more than 1 year. The following couples were excluded during the follow-up period: couples who were using contraceptives (n= 324); those who were having irregular sexual intercourse (n= 40); those who had divorced during follow up (n= 38) or were unmarried after the recruitment (n= 6); 1627 couples were included in the analysis (Supplementary Figure S1A ). At enrollment, the mean age was 24.0 ± 3.6 years for women and 26.1 ± 3.7 years for men. The baseline characteristics of the couples included in the study are presented in Table 1 . Seventy-two per cent of the couples lived in a village and 28% lived in a town. About 60% of the participants had completed junior high school. About 50% of women described themselves as farmers, compared with about 30% of men. Nearly 90% of the newly married couples (womenn= 1525; menn= 1434) were less than 30 years of age, and approximately 80% of the women had a BMI of 15–25 kg/m2. Fifteen per cent of the women had a history of previous pregnancy, and 9% of them had experienced previous spontaneous abortions.
|Factors||n (%)||Factors||n (%)|
|Education of the wife||Education of the husband|
|Primary school or less||89 (5.5)||Primary school or less||50 (3.1)|
|Junior high school||920 (56.5)||Junior high school||1013 (62.3)|
|Senior high/polytechnic school||237 (14.6)||Senior high/polytechnic school||270 (16.6)|
|College or more||381 (23.4)||College or more||294 (18.1)|
|Occupation of the wife||Occupation of the husband|
|Farmer||825 (50.7)||Farmer||540 (33.2)|
|Worker||122 (7.5)||Worker||591 (36.3)|
|Business/service personnel||186 (11.4)||Business/service personnel||120 (7.4)|
|Management/technical personnel||168 (10.3)||Management/technical personnel||142 (8.7)|
|Others||326 (20.0)||Others||234 (14.4)|
|Wife's age at marriage (years)||Husband's age at marriage (years)|
|19–25||1240 (76.2)||20–25||890 (54.7)|
|26–30||285 (17.5)||26–30||544 (33.4)|
|31–35||72 (4.4)||31–35||146 (9.0)|
|36–43||30 (1.8)||36–42||47 (2.9)|
|BMI of the wife (kg/m2)||Husband's daily smoking frequency|
|15–18.49||151 (9.3)||Never or less than 1 cigarette||775 (47.6)|
|18.5–24.99||1164 (71.5)||1–10 cigarettes||717 (44.1)|
|25–39||301 (18.5)||More than 10 cigarettes||133 (8.2)|
|Missing||11 (0.7)||Missing||2 (0.1)|
|Wife's weekly passive smoking frequency||Husband's weekly drinking frequency|
|Never||998 (61.3)||Never or less than 1 time||1556 (95.6)|
|Less than once||85 (5.2)||1–3 times||59 (3.6)|
|One to three times||222 (13.6)||4–6 times||2 (0.1)|
|Four to six times||128 (7.9)||More than 1 time per day||9 (0.6)|
|Seven times or more||193 (11.9)||Missing||1 (0.1)|
|Missing||1 (0.1)||Type of home heating system|
|Wife's long-term health problems||Stove in bedroom||540 (33.2)|
|Hepatitis B or carrier of HBV||16 (1.0)||Coal-fired home heating furnaces||865 (53.2)|
|Epilepsy||2 (0.1)||Centralized heat supply||171 (10.5)|
|Diabetes||1 (0.1)||Others||45 (2.8)|
|No||1588 (97.6)||Missing||6 (0.4)|
|Missing||20 (1.2)||Source of drinking water supply|
|Wife's previous pregnancy||Tap water||1528 (93.9)|
|Yes||240 (14.8)||Well water||77 (4.7)|
|No||1382 (84.9)||Pit water||18 (1.1)|
|Missing||5 (0.3)||Others||2 (0.1)|
|Wife's previous spontaneous abortion history||Missing||2 (0.1)|
|Yes||23 (9.4)||Living in the vicinity of factories a|
|No||192 (78.4)||Yes||381 (23.4)|
|Missing||30 (12.2)||No||1242 (76.3)|
|Xiyang||515 (31.7)||Cooking fuel|
|Shouyang||1112 (68.3)||Hard coal (Anthracite)||16 (1.0)|
|Residence||Soft coal (Bitumite)||438 (26.9)|
|Village||1176 (72.3)||Coal/natural/liquefied gas||324 (19.9)|
|Town||450 (27.6)||Others||843 (51.8)|
|Missing||1 (0.1)||Missing||6 (0.4)|
a Factories included coal mines, power station, and coking plants.
Incidence of infertility
The 12-month infertility rate was 13.6% (95% CI 11.9 to 15.3) among all of the eligible couples. This rate was similar for both primigravidas (14.0%, 95% CI 12.2 to 15.8) and multigravidas (11.2%, 95% CI 7.2 to 15.2).
A total of 948 couples were followed up for more than 2 years. After excluding the couples who had once been diagnosed as infertile but who became pregnant within 24 months of attempting (n= 11), 937 couples were included in the analysis (Supplementary Figure S1B ). The 24-month infertility rate was 8.5% (95% CI 6.7 to 10.3) among all of the eligible couples, and this rate was similar for both primigravidas: 8.7% (95% CI 6.7 to 10.7) and multigravidas (7.9%, 95% CI 3.6 to 12.2). If we treated all couples who become pregnant within 24 months of trying as fecund regardless of the infertility diagnosis, however, the estimated infertility rate was 8.4% (95% CI 6.6 to 10.2) for all eligible women, with 8.6% (95% CI 6.6 to 10.6) for primigravidas and 7.9% (95% CI 3.6 to 12.2) for multigravidas.
Cumulative pregnancy rate
The cumulative pregnancy rates for 12-month follow up and for 24-month follow up are shown in Figure 1 . About 63% of the women in our study became pregnant within 6 months of follow up, and 86% of the women became pregnant within 12 months of follow up ( Figure 1A ). Of the women who were followed up for more than 2 years, about 64% became pregnant within 6 months of follow up, 89% within 12 months of follow up, and 91% within 24 months of follow up ( Figure 1B ).
Clinical consultation and causes of infertility
Of the infertile couples defined by the 12-month definition, 59% (131/222) of the women had had an infertility examination. About 35% (77/222) of the men had visited infertility clinics. Of the infertile couples defined by the 24-month definition, 73% (58/80) of the women had had infertility examinations. Only 46% (37/80) of the men had had an infertility examination. The causes of infertility for men and women are shown in Table 2 . Infertility was caused by a female factor in 40% of all cases in which both the wife and the husband had had infertility examinations, and in 17% of the couples, infertility was caused by a male factor; 26% of the couples had an infertility diagnosis in both partners, and 17% of the couples had no demonstrable causes of infertility in either partner.
|Causes of infertility||Married for over 1 year n (% a )||Married for over 2 years n (% a )|
|Ovulation disorders||39 (29.8)||21 (36.2)|
|Fallopian tube problems||25 (19.1)||16 (27.6)|
|Polycystic ovary syndrome (PCOS)||15 (11.5)||7 (12.1)|
|Uterine problems||11 (8.3)||7 (12.1)|
|Ovarian cysts||5 (3.8)||2 (3.4)|
|Pelvic inflammation||5 (3.8)||1 (1.7)|
|Endocrine problems||3 (2.3)||1 (1.7)|
|Abnormalities||3 (2.3)||1 (1.7)|
|Cervical problems||2 (1.5)||1 (1.7)|
|Diseases of the immune system||1 (0.8)||0|
|Nephrotic syndrome||1 (0.8)||1 (1.7)|
|Premature ovarian failure||1 (0.8)||1 (1.7)|
|Dyspareunia||1 (0.8)||1 (1.7)|
|Missing||2 (1.5)||1 (1.7)|
|Unknown b||43 (32.8)||14 (24.1)|
|Sperm quality||30 (39.0)||14 (37.8)|
|Unknown b||44 (57.1)||21 (56.8)|
|Missing||2 (2.6)||2 (5.4)|
a % equals number in each cause/total number of women or men who had had infertility examinations.
b Unknown refers to no identified cause(s).
Of the 153 couples that were never pregnant during the 1-year follow-up, 49 couples attended our preliminary infertility workup. Of these 49 couples, 12 women and 10 men had had a previous clinic consultation. The concordance rate in the causes for women was 92% (11/12) between our infertility work-up and the self-reported causes of infertility; in men, the concordance rate was 70% (7/10). Of the 104 couples who did not attend our infertility workup, 29 (28%) reported their unwillingness to attend, 55 (53%) had had a previous infertility examination (33 couples had only had a female infertility examination), three (2%) did not have time for the workup, and the reasons for 17 couples were not given (16%).
Risk factors of impaired fecundity
As shown inSupplementary Table S1 , couples in which either partner was a farmer, those who were less educated, and those who had married at later ages were more likely to have delayed pregnancies. In addition, couples who used coal as cooking fuel and used well or pit water for drinking water were also at increased odds of delayed pregnancies. Women with hepatitis B or who were hepatitis B virus carriers, who had epilepsy or diabetes, who had been exposed to passive smoke, and who were overweight were more likely to be have impaired fecundity. For men, higher smoking frequency was associated with an elevated risk of delayed pregnancy.
After the adjustment for covariates, the associations remained between advanced age of the husband at marriage, higher BMI of the wife at marriage, using coal as fuel for cooking, and certain health conditions of the wife (hepatitis B or hepatitis B virus carriers, epilepsy and diabetes) and prolonged waiting time to pregnancy ( Table 3 ). In the adjusted model, a 1-year increase in the age of the husband at marriage and a 1 kg/m2increase in the BMI of the wife were associated with decreased odds of pregnancy by 4% and 2%, respectively. Using coal as fuel for cooking was associated with a 15% decrease in the couple's fecundity. Compared with healthy women, women with long-term health problems (having hepatitis B or being hepaptitis B virus carriers, having epilepsy or diabetes) had a 44% increased likelihood of delayed pregnancies.
|Factors||n||Adjusted fecundity ratio b (95% CI)|
|Wife's age at marriage (years)||1581||1.00 (0.97 to 1.02)|
|Husband's age at marriage (years)||1581||0.96 (0.94 to 0.98) c|
|Farmer||802||0.87 (0.74 to 1.03)|
|Worker||120||1.13 (0.89 to 1.43)|
|Farmer||529||0.95 (0.80 to 1.13)|
|Worker||570||0.92 (0.79 to 1.07)|
|Junior high school or less||984||1.00|
|Senior high school or college||597||1.04 (0.89 to 1.21)|
|Junior high school or less||1035||1.00|
|Senior high school or college||546||0.90 (0.78 to 1.04)|
|Wife's BMI (kg/m2)||1581||0.98 (0.96 to 0.99) c|
|Wife's diseases e|
|Yes||19||0.56 (0.32 to 0.99) d|
|Wife's weekly passive smoke frequency|
|Never or less than 1 time||1058||1.00|
|One to six times||334||1.10 (0.95 to 1.28)|
|Seven times or more||189||0.98 (0.80 to 1.20)|
|Husband's daily smoking frequency|
|Non smoke or less than one cigarette||759||1.00|
|One to cigarettes||691||0.95 (0.84 to 1.08)|
|More than 10 cigarettes||131||0.83 (0.65 to 1.07)|
|Coal gas, natural gas, liquefied gas or others||1138||1.00|
|Coal||443||0.85 (0.74 to 0.99) d|
|Source of drinking water supply|
|Tap water or other||1490||1.00|
|Well or pit water||91||0.88 (0.69 to 1.14)|
a 2.83% (46/1627) couples were excluded from multivariable analysis if any included variable had missing values.
b Adjusted for county (Shouyang or Xiyang) and all the variables included in the Table.
c P < 0.01.
d P < 0.05.
e Including having hepatitis B (or being an HBV carrier), epilepsy, or diabetes.
CI = confidence intervals.
In this prospective follow-up study in northern rural China, a 12-month infertility rate of 13.6% and a 24-month infertility rate of 8.5% was obtained. Infertility was a female factor in only 40% of all of the cases in which both the wife and the husband had had an infertility examination; 17% of the cases were because of a male factor; 26% of couples had an infertility diagnosis in both partners; and 17% of couples had no demonstrable cause in either partner. The main causes of female infertility were ovulation disorders, fallopian tube problems, polycystic ovary syndrome, and uterine problems, which accounted for about 70% of all cases. The main causes of infertility for men were sperm quality problems, which accounted for about 90% of all of the identified male infertility. Women with a higher BMI, those with long-term health problems, those whose husbands were older at marriage, and those who used coal as fuel for cooking were more likely to have delayed pregnancies.
The incidence of infertility
Most studies on infertility rates have been cross-sectional in design. One systematic review of the infertility surveys in China identified only two prospective follow-up studies out of 27 surveys on infertility conducted between 1980 and 2013 ( Meng et al., 2013 ), and another systematic review of infertility on a worldwide scale showed no prospective cohort or follow-up studies ( Gurunath et al., 2011 ). Although they are relatively easy to carry out, cross-sectional studies have tended to include women who were not at risk of pregnancy (had used contraception or had not been sexually active) in the denominator, or to treat women as fecund regardless of the duration of their pregnancy attempts (Li et al, 1990 and Stephen, Chandra, 2006). These methodological limitations may partly explain the wide variations in the estimation of infertility in populations.
One US study estimated the prevalence of infertility by using the current duration approach based on estimated time to preganancy, and obtained a much higher infertility rate (15%) than had previously been reported (7%) using the same data from the National Survey of Family Growth ( Thoma et al., 2013 ). Considering the methodological problems in the previous cross-sectional studies, the alternative current duration approach, which included women at risk of pregnancy at the time of interview, and attempting to obtain infertility data based on time to pregancy, could be better choices for infertility estimation. Compared with the prevalence rates reported by traditional cross-sectional studies, the incidence of infertility obtained in our study is comparable to the rate obtained by the current duration approach. The 12-month rate reported in the USA (15.5%) ( Thoma et al., 2013 ) was similar to that of the present study, but a study from France ( Slama et al., 2012 ) reported higher 12-month and 24-month rates (24% and 11%, respectively).
The current duration approach, however, lacks prospective follow up to distinguish between pregnancy attempts that ended in a pregnancy and those that ended because of other reasons (i.e. the couple stopped trying or sought infertility treatment) ( Thoma et al., 2013 ). Moreover, the current duration method includes only those at risk of pregnancy at the time of the cross-sectional survey, which may cause selection bias because of the inclusion of more couples who take longer to become pregnant. The prospective follow up of our study had the advantage of overcoming these limitations, and is therefore preferable for obtaining the true incidence of infertility.A prospective study among newly married couples who planned to become pregnant in agricultural villages in Palestine found that 13.4% did not conceive during 12 months of exposure to the risk of conception ( Issa et al., 2010 ). Another prospective cohort design – the Longitudinal Investigation of Fertility and the Environment Study in the USA also found a similar rate to that of the present study; about 13% of couples did not achieve pregnancy within 12 months of trying ( Buck Louis et al., 2012 ). The infertility rates obtained by our study were much higher than those obtained by the two previous prospective follow-up studies in China (12-month and 24-month infertility rates were 5.2% and 1.7% in 1985 ( Gao et al., 1989 ); 9% and 5% in 1987 ( Che and Cleland, 2002 )).
About 35% of couples reported that they had sought a medical consultation for infertility after 1 year of marriage. The consulting rate was 25% higher for women than for men; about 59% of infertile women and 35% of infertile men had sought a medical consultation. For couples who were married for more than 2 years, about 46% had sought a medical examination for infertility. This proportion was slightly lower than that of Shanghai, China (57%) ( Che and Cleland, 2002 ), and of Australia (72%) ( Herbert et al., 2009 ); the consultation rate of women was also higher than the figures from other countries such as Denmark (the city of Copenhagen: 47.4%) ( Schmidt et al., 1995 ), and the USA (42%) ( Stephen and Chandra, 2000 ).
The traditional rationale in rural China considers an inability to become pregnant to be the woman's problem. This explains the higher medical consultation rates for women than for men among infertile couples. Not having a child is a stigma for a family in most areas of China, and infertile couples in China were more likely to seek medical counselling than are couples in some Western countries.
Causes of infertility
Studies in most other countries have reported a male factor as the most common diagnosis of infertility (Bhattacharya et al, 2009 and Farhi, Ben-Haroush, 2011); however, our study observed a considerably lower rate of male contributions, which may have resulted partly from the low medical consultation rates for men. The distribution of infertility causes obtained in our study was different from that of a hospital-based study in China, which found that the main causes of infertility were tube or pelvic diseases and ovulation disorders in women, and azoospermia in men ( Zhong, 2007 ). Another hospital-based study found that the causes of infertility for women were tube defects, cervical factors, ovulation problems, and uterine factors; semen quality problems accounted for 87.2% of male infertility ( Hou, 2011 ). A British population-based survey in Bristol found similar results for the main causes of female infertility, which were failure of ovulation (21%) and tubal damage (14%); sperm defects or dysfunction were the most commonly defined causes of male infertility (92%), which was similar to our results for male infertility ( Hull et al., 1985 ).
Risk factors for impaired fecundity
In our study population, using coal as fuel for cooking was an important risk factor for the impaired fecundity of newly married couples, suggesting that exposure to emissions from indoor coal combustion may have other reproductive health effects in addition to birth defects ( Li et al., 2011 ). Woman with higher BMI was more likely to have a delayed pregnancy or to be infertile, which is consistent with the findings from a number of other studies (Bhattacharya et al, 2009, Safarinejad, 2008, and Wise et al, 2013). Men's age might have an effect on fertility, but the woman's age showed no effect on infertility in this study. This finding differs from the results of other studies that indicated that advanced age of women was related to infertility or longer waiting times to pregnancy (Axmon et al, 2006, Esmaeilzadeh et al, 2012, and Li, 1991). These studies, however, did not consider the ages of the husband, which would make these findings not comparable with our study. Another possibility could explain the differences concerning the ages of women in our study and the other studies. Most of the women in our study had married before the age of 30 years. The narrow age range of distribution made it less likely for our study to find an effect of women's age on infertility. We also found that women who had certain health conditions, including hepatitis B (or being hepatitis B virus carriers), epilepsy or diabetes, were also more likely to have impaired fecundity in the present study. This may suggest that immunological, inflammatory, or neurological status may interfere with reproductive functions or processes.
In the study population, all young men and women who plan to register for marriage are advised to seek premarital medical check-ups, which ensured an appropriate sample of newly married couples who planned to have a family. Because some couples had already had a sex life before they registered for marriage, the beginning date of the time to pregnancy was not the actual marriage certificate date but the date when they had regular unprotected intercourse, so the accuracy of time to pregnancy can be ensured. The prospective design of our study allowed for a direct and accurate estimation of the incidence of infertility, and the relatively low mobility of the study population ensured a low drop-out rate during the follow up. The two infertility definitions in the present study facilitated comparisons with other studies. In addition, the population-based design provided more representative information than a hospital-based study would have provided.
Although all young men and women who plan to register for marriage are advised to seek premarital medical check-ups, some women may have already become pregnant or have had live births at the time that they registered for marriage. Because women who had already given birth were unlikely to have pregnancies in the near future because of the one-child policy in China, these women were more likely to be excluded from recruiment. The exclusion of these couples could have resulted in an over-estimation of the infertility rate. The phenomenon was much more obvious in Xiyang County, which had a higher incidence of infertility than did Shouyang County. Another limitation of this study was that information about the frequency of sexual intercourse during the follow-up period was not collected. This made it impossible for us to consider this factor as covariate in multivariate analysis. In addition, reproductive medicine is less developed in rural areas than in urban cities in China. A proportion of the subfertile couples did not receive a thorough infertility workup, resulting in unknown causes of infertility being found in the analysis. In the present study, information on men's previous reproductive history was not collected (i.e. possible pregnancies with other partners and infertility counselling). This limited our further analysis on the causes of male infertility. According to the premarital health check-up records, 8.7% of men had been previously married; therefore, this limitation should have affect overall results of the present study significantly. Finally, as only couples who came to seek premarital health check-ups were enrolled, the results may not generalizable to rural population all over China.
The two counties in which this study was conducted had medium levels of socioeconomic development. The findings obtained from this study might only be generalizable to other rural counties with similar socioeconomic development but not to cities, where socioeconomic development is relatively advanced.
In conclusion, a 12-month incidence infertility rate of 13.6% and a 24-month incidence infertility rate of 8.5% was observed from a population of newly married Chinese rural couples. Higher BMI and long-term health problems of the wife, advanced age of the husband at marriage, and using coal as fuel for cooking were independent risk factors for impaired fecundity in this population.
This study was supported by grants from the National Key Technology R&D Program (Grant No. 2013BAI12B02) and the National Basic Research Program, Ministry of Science and Technology of the People's Republic of China (Grant No. 2007CB511901). The funding agency had no role in the design, analysis, or writing of this article. The authors thank the local maternal and child health care workers in the two counties of Shanxi Province for their assistance with the subject recruitment and data collection.
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Meng Qinqin is a candidate PhD in the department of epidemiology and biostatistics, School of public health, Peking University. Her research field is reproductive epidemiology.
a Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 College Rd, Haidian District, Beijing, China
b Institute of Reproductive and Child Health/Ministry of Health Key Laboratory of Reproductive Health, School of Public Health, Peking University Health Science Center, 38 College Rd, Haidian District, Beijing, China
c Center of Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, 49 North Huayuan Rd, Haidian District, Beijing, China
d Department of Andrology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, 17 Qihelou St, Dongcheng District, Beijing, China
* Corresponding author.
© 2014 Reproductive Healthcare Ltd., Published by Elsevier B.V.