This was a descriptive and analytical cross-sectional study with data collection from December 2022 to June 2023. The study took place in the obstetrics, gynecology and reproductive medicine department of the University Teaching Hospital of Bogodogo (UTH-B).

The study involved patients followed up for hypofertility during this campaign.

Sampling technique:

This was an exhaustive sampling: All patients who consulted the department between December 19 and 30, 2022 and who reported the results of all prescribed paraclinical examinations were included in the sample.

Sample size:

Assuming a margin of error(i) of 5% and a confidence level of 95%, the z-statistic corresponding to our confidence level is 1.96. A study carried out in Chad in 2018 found a prevalence of female infertility of 30% [3] . Using Schwartz’s formula.

$n=t^2\times p\times q/m^2$

Where

t² = 1.96; m = absolute precision = 0.05; p = 0.3; q = 1-p = 0.7; n = sample size; n = (1.96)2 × 0.3 × 0.7/(0.05) 2 = 322.69.

Our minimum sample size was 323.

Couples meeting the following criteria were included in our sample:

(1) To have consulted the Gynaecology Department at UTH-B as part of the MAP campaign.

(2) To have reported morphological and biological tests.

(3) To have presented objective abnormalities during explorations.

(4) To have given verbal consent to participate in the study.

Couples whose paraclinical work-up was incomplete were not included in our sample.

Data collection took place from December 19, 2022 to June 1, 2023.

(1) Data sources were patients’ clinical records and interview.

(2) Collection instrument: We developed a collection form for the collection of study variables, which was adopted following a pre-test.

(3) Data collection procedure: The data were collected by ourselves, by filling in the data collection forms.

Data collection technique: We carried out a literature review and telephone calls to complete the information.

The following variables were collected on the data collection forms:

(1) Socio-demographic characteristics: age, profession, marital status, level of education, place of origin.

(2) Gynaeco-obstetrical, medical and surgical history.

(3) Eating habits.

(4) Clinical examination: blood pressure, Body mass index (BMI).

(5) Results of paraclinical examinations: hormone assessment (FSH, LH, estradiol, AMH, TSH prolactin…), ultrasound, hysterosalpingography (HSG), diagnostic hysteroscopy.

The manually collected data were entered, processed and analyzed on a computer, using Word, Excel version 2016 and Epi info version 7.2.5.0.

Data analysis involved 3 levels:

(1) Descriptive analysis: involved calculating percentages for qualitative variables and measures of central tendency (mean, median) and dispersion (standard deviation, minimum, maximum) for quantitative variables.

(2) Univariate analysis: the Chi-square test was used to compare percentages; when the conditions for applying the test were not met, Fisher’s exact test was used. The Student’s t test was used to compare means.

(3) Multivariate analysis using logistic regression.

(4) A p threshold of less than 0.05 was considered significant; the Odds ratio was used as a measure of association with a 95% confidence interval.

In our study, any patient in whom one of the following elements had been highlighted was considered as female infertility:

(1) Any significant anatomical abnormality of the genital tract (vagina, uterus, fallopian tubes, ovary, pelvis) that could interfere with the encounter between male gamete and female gamete.

(2) Any hormonal biological abnormality (FSH, LH, Estradiol, prolactin, TSH, AMH....) that could alter the functioning of the female genital tract.

The number of couples who returned the results of their complementary examinations was 329 out of 1200 couples who consulted during this period. The infertility of the couple was attributable to the woman alone in 108 couples, i.e. 32.8%, and the 2 partners were incriminated in 221 couples, i.e. 67.2%.

The descriptive analysis of socio-demographic and clinical characteristics have been represented in ( Table 1 ).

Patients with an age greater than 35 years represented 56.62% of the sample. They were salaried in 53.19% of cases. The duration of infertility was greater than 5 years in 64.74% of cases and the body mass index was normal in 56.23% of cases.

In our study, mainly 5 hormones (FSH, LH, estradiol, prolactin and AMH) were prescribed. AMH is the hormone that presents the most abnormalities (80.25% of cases). An overall overview of the values of these different hormones is listed in the ( Table 2 ).

Pelvic ultrasound was normal in 45.90% of cases. Myomas were the most common abnormality in 30.09% of patients. The distribution of patients according to ultrasound results is shown in Table 3 .

Hysterosalpingography (HSG) was normal in the majority of cases (51.06%). Tubal obstruction was the most common abnormality found in 33.43% of cases. The distribution of patients according to the results of hysterosalpingography is listed in Table 4 .

Hormonal disruption was the leading cause of female infertility (90.27%) followed by uterine abnormalities (39.82%). The causes of female infertility are presented in Table 5 .

In our sample, there was a strong statistical correlation between age (p = 0.0002) and BMI (0.0042) with hormonal abnormalities. The analysis of factors associated with hormonal infertility is shown in Table 6 .

In our sample, there was a significant association between age, history of ectopic pregnancy and tubal anomalies (p = 0.0168 and p = 0.0001 respectively). The analysis of factors associated with infertility of tubal origin is shown in Table 7 .

In our sample, polycystic ovaries had a close association with age (p=0.0378), irregular cycle (p = 0.0001), BMI (p = 0.0381) as well as history of miscarriage (p = 0.0073). The analysis of factors associated with ovarian infertility (polycystic ovaries + premature menopause) is listed in Table 8 .

In our sample, age was a risk factor for the appearance of myomas with a correlation of 0.0022. Similarly, myomas still persist in patients who underwent myomectomy (p = 0.0483). Analysis of the factors associated with infertility of uterine origin (myoma) is shown in Table 9 .

This study not only allowed us to assess the needs for medically assisted procreation, which reflected the deep suffering of the population, but also to assess the therapeutic journey of couples. The main difficulty of our study was the cost of additional tests, which led to the exclusion of many patients from the sample. For example, the FSH, LH and prolactin assays each cost an average of US$20 and the AMH almost US$60. Out of nearly 1,200 couples who consulted, only 376 brought back usable additional tests. Another difficulty came from the fact that the additional tests were carried out in different laboratories depending on the patients’ choice. This posed enormous difficulties for comparing figures because the reference values varied from one laboratory to another. Also, our study was limited to couples who consulted at UTH-B during the campaign. This could create a bias in the full extrapolation to the entire Burkinabe population

In our study, in 32.8% of couples, infertility was of female origin alone. This figure is similar to those found in the literature. In France and Chad, the prevalence of female infertility was estimated at 30% [3] [4] . But the WHO, in a vast multinational study, determined that in 37% of infertile couples, female infertility was the cause [2] . This difference may be due to the fact that our study was carried out in a smaller population and in a limited area, and consequently the prevalence of infertility may have been underestimated.

(1) Age

The mean age was 35.18 ± 5.22 years, with extremes of 20 and 47 years. The 35-39 age group accounted for 31.31%. Moreover, 55.62% of patients were over 35. These results are similar to those of Sakandé in 2016, who reported an average age of 35.96 years, but higher than those of Tiemtoré in 2019 and Dembélé et al. who found an average age of 31.75 and 32 years respectively [8] [9] [11] . In addition, Tiagha in his study reported opposite results, i.e. 79% for under-35 s versus 22% for over-35 s [5] . This could mean that, on the one hand, recourse to parenthood takes place at a late age and, on the other hand, the diagnosis of infertility is made late, as stipulated in numerous studies [12] - [14] .

(2) Profession

53.19% of clients were female employees. In Guinea Conakry, Leno reported similar results. These data show that women with a certain degree of financial autonomy have easier access to ART [15] .

Patients were overweight or obese in 43.77% of cases. It should be noted that a BMI greater than 27 kg/m2 could lead to ovarian dysfunction by increasing estrogen production, which the body perceives as negative feedback. Furthermore, a high body fat percentage reduces the effectiveness of assisted reproduction treatment [2] [16] .

In our study, hormonal disturbance was the leading cause of female infertility (90.27%), followed by uterine anomalies (39.82%), then tubal anomalies (38.91%), ovarian anomalies (17.02%) and finally pelvic anomalies (6.08%).

Our results differ from those of the WHO meta-analysis, which reported, in order of importance, ovulatory disorders (25%), endometriosis (15%), pelvic adhesions (12%), tubal obstruction (11%), other tubal/uterine anomalies (11%) and hyperprolactinemia (7%).

In France, ovarian failure is the leading cause of infertility after the age of 35 [4] .

In African literature, the causes of female infertility have also been reported. Kalume in Goma identified the following etiological factors: the tubopelvic factor (67.6%), by far the most preponderant, was followed by ovulatory disorders (41.0%); the uterine and cervicovaginal factors were relatively less frequent [10] . As for Abebe, he found that the most frequently encountered causes of female infertility were pelvic inflammatory disease, tubal factors and abortion, with an overall prevalence of 39.38%, 39.17% and 36.41% respectively [17] .

Of note is the frequency of pelvic adhesions, which could be the consequence of a high frequency of genital infections, but which were not investigated in our context. However, African series have strongly incriminated this infectious factor [3] [15] .

Age, BMI and irregularity of the menstrual cycle were identified as factors associated with infertility in our study.

Indeed, hormonal abnormalities increase with age, notably FSH and AMH. Liu et all. showed in their 2015 study that there was a strong correlation between FSH levels and age in infertile women. This finding confirms that aging significantly increases FSH levels, which disrupts the ovarian function that causes infertility [18] . These statements are supported by similar studies revealing that aging causes infertility and leads to a significant decrease in AMH levels [2] [11] [16] . Various authors agree that age is a favourable factor in infertility [12] - [14] .

High BMI is strongly correlated with hormonal disorders. Numerous studies have demonstrated the disruption of endocrine function due to fat accumulation [2] [18] .