Prevalence, Types and Contributing Factors of Female Infertility at Orotta National Referral Maternity Hospital, Eritrea: A Cross-Sectional Study.

Infertility is defined as failure to achieve pregnancy after 12 months or more of regular unprotected sexual intercourse. 1–7 The global incidence of infertility is approximately 8–12%. 8 The most common causes of female infertility are ovulation disorders, menstrual disorders, autoimmune disorders, systemic diseases, thyroid disorders, and hyperprolactinemia. 9 Primary infertility refers to couples that have never conceived, whereas secondary infertility refers to couples that have failed to conceive despite a previous pregnancy. 8 Different studies indicated that the prevalence of infertility was 8.5%–27.6%. 2 , 4 , 10–16 Previous studies conducted at Orotta National Referral Maternity Hospital (ONRMH) and across Eritrea at various times reported infertility prevalence of 10.8% 17 and 2.8%, 18 respectively. Different studies showed that primary infertility was more prevalent, with rates ranging from 51.5% to 70.9%, 6 , 19–22 whereas other studies revealed secondary infertility predominance, with prevalence ranging from 43.2% to 90.7%. 5 , 14 , 15 , 23 , 24 One study indicated that the prevalence of infertility was estimated as male (43.3%), female (32%), both (12.5%), and unexplained (13.6%). 2 Furthermore, a similar study reported that 35.5% couples had male factor, 42.8% female factor infertility, 18.4% combined male and female infertility, and in 3.4% the cause of infertility was not identified. 6 Another study indicated that female factors of 35%, a combination of male and female factors of 20%, male problems of 30% and unexplained 15%. 25 Common contributory factors of female infertility included ovarian problems, hormonal imbalances, tubal blockage, pelvic inflammatory disease, age-related factors, uterine problems, endometriosis, and advanced maternal age. 2 , 4–6 , 9 , 21 , 26–32 Polycystic ovarian syndrome was the leading cause of female infertility, 20 , 33 , 34 and pelvic inflammatory disease, tubal factors, and abortion were the most commonly identified causes of female infertility. 35 Age, abnormal BMI, rural residence, types of infertility, and marital duration were found to be significantly associated with infertility. 5 , 9 , 11 , 14 , 15 , 18 , 24 , 29 , 36–41 The prevalence of primary and secondary infertility in ONRMH over the past 9 years was 63.9% and 36.1%, respectively. 18 A study done in the same hospital reported 23% primary female infertility, 17% secondary female infertility, and 11% male infertility. 17 Another study conducted at Massawa Hospital reported that 79.5% of the study participants had a primary type of infertility. 42 A previous study conducted in ONRMH indicated that the categories of infertility according to etiological factors showed that male factor (48%), female factor (25%), both (12%), and unexplained (15%). 18 The prevalence and risk factors of female infertility are not determined at the national level in general and at this hospital level in particular. Thus, this study aims to address the gaps in existing previous research data in the country as lack of national data for infertility, differences in diagnostic criteria among the different studies, and if barriers to accessing infertility care are existing among the patients. Determining the prevalence, types, risk factors, and causes of female infertility in the Eritrean context could influence clinicians to review the existing clinical guidelines for female infertility, investigations, and treatment modalities. Furthermore, patient management related to the findings for public health policy on prevention of the risk factors, especially in terms of early intervention, education, or resource allocation as introduction of assisted reproduction technology in the country. This study has clinical significance that the results can be used to implement in management and policy strategies in low resource setting countries like Eritrea. As Eritrea is among the low-income countries, the social or economic impact of infertility will further complicate their financial crisis, as most of the treatment modalities as assisted reproductive technology are expensive. The cultural impact of infertility could also result in divorce, which further worsen their infertility and standards of living. Thus, this study aimed to determine the prevalence, types, and risk factors of female infertility in patients who visited ONRMH. As there was no previous study done to determine the risk factors of female infertility, this study was a cross-sectional and hospital-based study, as this could be a baseline for further national community-based studies.

A total of 3696 patients visited the ONRMH Outpatient Department during the study time; out of which 427 had infertility, making a prevalence of 11.6%. Most (68.4%) of the females were aged 25 to 35 years, and 76.3% had normal BMI. The majority (80.8%) of the patients had primary infertility, and 19.2% secondary infertility ( Table 2 ). Table 2 Sociodemographic Characteristics of Female Patients with Infertility at ONRMH, 2024 (N=427) Variables Count (N) Percent (%) Age (years) 35 65 15.2 BMI 30 10 2.3 Residence Urban 182 42.6 Rural 245 57.4 Main occupation Housewife 317 74.2 Merchant/Private 33 7.7 Government employee 72 16.9 Military 5 1.2 Level of education Illiterate 62 14.5 Elementary/Junior 196 45.9 Secondary 131 30.7 Higher education 38 8.9 Ethnicity Tigrigna 286 67.0 Tigre 80 18.7 Saho 49 11.5 *Others 12 2.8 Religion Orthodox 278 65.1 Muslim 137 32.1 Catholic 12 2.8 Gravida Gravida 0 345 80.8 Gravida 1 59 13.8 Gravida > 1 23 5.4 Parity Parity 0 345 80.8 Parity 1 60 14.1 Parity > 1 22 5.2 Type of infertility Primary 345 80.8 Secondary 82 19.2 Note : *Others (Blin, Afar, Rashaida). Sociodemographic Characteristics of Female Patients with Infertility at ONRMH, 2024 (N=427) Note : *Others (Blin, Afar, Rashaida). About half (48%) of patients had attempted for five to ten years to get pregnant, and 19.9% had tried for more than ten years. Besides, 91.8% had regular menses, and 30% had a history of PID. The majority (57.4%) of patients had normal HSG, and 4.9% had bilaterally blocked tubes. Furthermore, the male factor (51.3%) and the female factor (17.8%), both problems (17.1%), and unexplained (13.8%), were confirmed via investigative modalities. From the female factor, 29.3% had ovarian factor and 7.7% had tubal factor infertility ( Table 3 ). Table 3 Gynecologic History and Factors of Infertility in Patients at ONRMH (N=427)  Variables Count (N) Percent (%) Attempting pregnancy in years 10 85 19.9 Contributing to a pregnancy with another woman Yes 22 5.2 No 405 94.8 She had a pregnancy with another man Yes 15 3.5 No 412 96.5 Pregnancy with current partner Yes 79 18.5 No 348 81.5 Treated for infertility previously Yes 252 59.0 No 175 41.0 History of previous PID Yes 128 30.0 No 299 70.0 Regular menses every month Yes 392 91.8 No 35 8.2 Treated for tuberculosis previously Yes 17 4.0 No 410 96.0 Intercourse is ever painful (Dyspareunia) Always 122 28.6 Sometimes 26 6.1 Never 279 65.3 Timing intercourse with ovulation Always 139 32.6 Never 288 67.4 HSG result Normal 245 57.4 One blocked 12 2.8 Both blocked 21 4.9 Uterine problem 2 0.5 Not indicated (Note done) 147 34.4 Final diagnosis Male factor 219 51.3 Female factor 76 17.8 Both problems 73 17.1 Unexplained 59 13.8 Factors of infertility or causes of infertility Male factor 292 68.4 Tubal factor 33 7.7 Ovarian factor 125 29.3 Unexplained 59 13.8 Gynecologic History and Factors of Infertility in Patients at ONRMH (N=427) Regarding the hormone analysis profile, 5.6% and 15% of females had high levels of follicle-stimulating hormone and luteinizing hormone, respectively, while 28.6% had low progesterone levels. ( Table 4 ). Table 4 Hormone Analysis Profile of Patients with Infertility at ONRMH, 2024 (N=427) Variables Female Hormone Results Frequency Percent Estradiol Low 16 3.7 Normal 408 95.6 High 3 0.7 Follicle-stimulating hormone Low 2 0.5 Normal 401 93.9 High 24 5.6 Luteinizing hormone Low 1 0.2 Normal 362 84.8 High 64 15.0 Progesterone Low 122 28.6 Normal 305 71.4 Prolactin Normal 425 99.5 High 2 0.5 Testosterone Low 14 3.3 Normal 411 96.3 High 2 0.5 Total 427 100.0 Hormone Analysis Profile of Patients with Infertility at ONRMH, 2024 (N=427) On univariable analysis, increasing maternal age, rural residence, having a pregnancy with another man, living together for > 1 year without interruption, the number of months living together in a year, and intercourse per week were associated with secondary infertility. Besides, dyspareunia, history of PID, and previous treatment also had a significant association with secondary infertility. On multivariable analysis, maternal age greater than 35 years (AOR: 7.99; 95% CI: 1.20–53.25, p<0.03) and type of previous treatment (AOR: 16.2; 95% CI: 2.88–91.12, p<0.002) had increased risk of secondary infertility ( Table 5 ). Table 5 Predictors of Type of Infertility at ONRMH, 2024 (N=427) Variables Type of Infertility N (%) COR (95% CI) P value AOR (95% CI) P value Primary Secondary Age in years 35 42(12.2) 23(28.0) 7.1(2.51–20.18) <0.001 7.99(1.20–53.25) 0.03 Residence Urban 137(39.7) 45(54.9) 1 0.01 1 0.22 Rural 208(60.3) 37(45.1) 1.85(1.14–3.00) 1.57(0.76–3.25) Had a pregnancy with another man Yes 5(1.4) 10(12.2) 1 <0.001 1 0.3 No 340(98.6) 72(87.8) 9.44(3.13–28.46) 2.1(0.52–8.53) Treated for infertility previously Yes 191(55.4) 61(74.4) 1 0.002 1 0.12 No 154(44.6) 21(25.6) 2.34(1.37–4.02) 0.45(0.16–1.24) Living together for > 1 year without interruption Yes 99(28.7) 36(43.9) 1 0.008 1 0.32 No 246(71.3) 46(56.1) 1.95(1.19–3.19) 7.98(0.14–4.45) Number of months living together in a year (months) 1 Month 191(55.4) 35(42.7) 1 1 2 - 5 58(16.8) 13(15.9) 1.22(0.61–2.47) 0.57 1.01(0.41–2.49) 0.98 6 −12 96(27.8) 34(41.5) 1.93(1.14–3.29) 0.015 0.37(0.01–22.69) 0.63 How often do you have intercourse per week Daily 124(35.9) 21(25.6) 1 1 Every other day 211(61.2) 55(67.1) 1.54(0.89–2.67) 0.12 1.3(0.60–2.89) 0.50 Weekly 10(2.9) 6(7.3) 3.54(1.16–10.78) 0.03 0.58(0.02–3.09) 0.52 Intercourse is ever painful (Dyspareunia) Always 90(26.1) 32(39.0) 0.36(0.24–0.53) <0.001 0.96(0.39–2.32) 0.20 Sometimes 22(6.4) 4(4.9) 0.18(0.06–0.53) <0.001 1.23(0.33–4.56) 0.76 Never 233(67.5) 46(56.1) 1 1 History of previous PID Yes 10(2.9) 6(7.3) 1.77(1.07–2.92) 0.03 1.27(0.53–3.01) 0.6 No 250(72.5) 49(59.8) 1 1 Type of previous treatment Medication 167 (83.9) 65(97.0) 6.23(1.45(26.74) 0.01 16.2(2.88–91.12) 0.002 Follow up without treatment 32(16.1) 2(3.0) 1 1 Predictors of Type of Infertility at ONRMH, 2024 (N=427) On univariable analysis, patients’ addresses, maternal BMI, and irregular menses showed a significant association with ovarian factor infertility. On multivariate analysis, patients from Gash Barka region (AOR: 2.55; 95% CI: 1.07–6.07; p<0.03), normal maternal BMI (AOR: 0.19; 95% CI: 0.04–0.88; p<0.03), menstrual disorder (AOR: 20.35; 95% CI: 7.35–56.39; p<0.001), medical treatment (AOR: 3.03; 95% CI 1.48–6.20; p<0.002) had increased risk of ovarian factor infertility ( Table 6 ). Table 6 Predictors of Ovarian Factor Infertility at ONRMH, 2024 (N=427) Variables Ovarian Factor Infertility COR (95% CI) P value AOR (95% CI) P value No N (%) Yes N (%) Patient Address (Zoba) Maekel 88(64.7) 48(35.3) 1 1 Debub 106(77.9) 30(22.1) 0.52(0.30–0.89) 0.02 0.95(0.49–1.87) 0.89 Northern Red Sea 48(78.7) 13(21.3) 0.50(0.25–1.01) 0.05 0.56(0.24–1.31) 0.18 Southern Red Sea 12(75.0) 4(25.0) 0.61(0.19–1.20) 0.42 0.98(0.27–3.61) 0.97 Anseba 29(67.4) 14(32.6) 0.89(0.43–1.83) 0.74 1.37(0.57–3.27) 0.48 Gash Barka 19(54.3) 16(45.7) 1.54(0.73–3.28) 0.26 2.55(1.07–6.07) 0.03 BMI < 18.5 16(61.5) 10(38.5) 0.27(0.06–1.28) 0.10 0.43(0.07–2.56) 0.36 18.5 - 25 240(73.6) 86(26.4) 0.15(0.04–0.61) 0.008 0.19(0.04–0.88) 0.03 25 - 30 43(66.2) 22(33.8) 0.22(0.05–0.93) 0.04 0.30(0.59–1.49) 0.14 > 30 3(30.0) 7(70.0) 1 1 Residence Urban 120(65.9) 62(34.1) 1.49(0.98–2.27) 0.06 1.12(0.65–1.93) 0.70 Rural 182(74.3) 63(25.7) 1 1 Living together for > 1 year without interruption Yes 88(65.2) 47(34.8) 1.47(0.95–2.27) 0.09 0.87(0.08–9.10) 0.91 No 214(73.3) 78(26.7) 1 1 Number of months living together in a year 1 Month 170(75.2) 56(24.8) 0.6(0.38–0.96) 0.03 0.7(0.06–7.68) 0.77 2 - 5 months 48(67.6) 23(32.4) 0.88(0.47–1.62) 0.67 0.9(0.09–9.57) 0.93 6 −12 months 84(64.6) 46(35.4) 1 1 Regular menses No 6(17.1) 29(82.9) 14.9(6.01–36.97) <0.001 20.35(7.35–56.39) <0.001 Yes 296(75.5) 96(24.5) 1 1 Treatment option Medical 84(54.5) 70(45.5) 1.52(0.87–2.66) 0.14 3.03(1.48–6.20) 0.002 ART 167(86.1) 27(13.9) 0.29(0.16–0.55) <0.001 0.57(0.27–1.19) 0.14 No treatment 51(64.6) 28(35.4) 1 1 Predictors of Ovarian Factor Infertility at ONRMH, 2024 (N=427) On univariable analysis, maternal age 25 to 35 years, urban residence, history of PID, and dyspareunia revealed a significant association with tubal factor infertility. On multivariable analysis, females with urban residence (AOR: 4.1; 95% CI: 1.71–9.67; p<0.001), previous history of PID (AOR: 2.96; 95% CI: 1.03–8.46; p<0.043), history of dyspareunia (AOR: 2.9; 95% CI: 0.97–8.65; p<0.056) had increased likelihood of tubal factor infertility ( Table 7 ). Table 7 Predictors for Tubal Female Infertility at ONRMH, 2024 (N=427) Variables Tubal Factor Infertility COR (95% CI) P value AOR (95% CI) P value No N (%) Yes N (%) Age 35 years 55(84.6) 10(15.4) 1 1 Residence Urban 158(86.8) 24(13.2) 3.98(1.80–8.80) 0.001 4.1(1.71–9.67) 0.001 Rural 236(96.3) 9(3.7) 1 1 History of previous PID Yes 105(82.0) 23(18.0) 6.3(2.92–13.75) <0.001 2.96(1.03–8.46) 0.04 No 289 (96.7) 10(3.3) 1 1 Intercourse is ever painful (Dyspareunia) Always 100(82.0) 22(18.0) 6.6(2.94–14.82) <0.001 2.9(0.97–8.65) 0.06 Sometimes 24(92.3) 2(7.7) 2.5(0.51–12.24) 0.26 2.54(0.42–15.29) 0.31 Never 270(96.8) 9(3.2) 1 1 Previous treatment of infertility Yes 250(94.0) 16(6.0) 0.54(0.27–1.11) 0.09 0.47(0.21–1.08) 0.07 No 144(89.4) 17(10.6) 1 1 Predictors for Tubal Female Infertility at ONRMH, 2024 (N=427)

The prevalence of infertility was consistent with the global prevalence, with primary infertility predominance. Maternal age > 35 years was associated with secondary infertility and patients address, abnormal BMI and menstrual irregularity were associated with ovarian infertility. Furthermore, urban residence and history of PID were found as the risk factors for female tubal infertility. Community awareness particularly regarding community education about the risk factors of infertility and early diagnosis and treatment of patients with PID is highly recommended to decrease the chronic complications of this infectious process. Besides, maintaining a normal BMI and regular menses are crucial. Further investigations, such as endometrial sampling and laparoscopy, in cases with unexplained infertility are highly indicated to reach a specific diagnosis and increase the success rate of conception. Having and infrastructure needs for assisted reproduction in Eritrea and policy or clinical practice revisions is highly recommended. Further nationwide larger, population-based research to validate these hospital-based results is highly recommended.

This study was aimed to determine the prevalence and risk factors of female infertility, and the findings addresses the previously identified research gaps and inform the development of clinical or preventive strategies. The prevalence of infertility at Orotta National Referral Maternity Hospital was found to be 11.6%. This was in alignment with a previous study conducted in 2010 in the same hospital,10.8%. 17 Our finding also coincides with other studies, 11.8%, 36 11.1% 2 and to the global incidence of infertility 8–12%. 8 But, this result was lower than other studies which reported 12.9%–24% 13 , 15 , 16 , 26 , 45 and a previous study conducted in ONRMH, 2.8%. 18 This variation in the prevalence of infertility among the studies could be mainly explained by the differences in the study population, methodology, and sociodemographic factors. This study indicates that primary infertility predominates (80.8%) over secondary infertility, which was consistent with previous studies conducted in Massawa Hospital, Eritrea, 79.5%, 42 and ONRMH, 63.9%. 18 Other studies from Sudan and India also presented similar findings of primary infertility dominance, 51.5–70.9%. 6 , 20 Conversely, similar studies in Cameroon and Ethiopia reported secondary infertility predominance. 14 , 15 Some regions have a high incidence of primary infertility and low secondary infertility, like North Africa and the Middle East. 2 But the reason for the difference in the type of infertility is not clearly stated. It has also been stated that there are significant differences in treatment seeking depending on the background characteristics of infertile couples. 46 This study indicated that male infertility dominance (51.3%), consistent with previous studies in Eritrea, 18 , 42 Iran, 2 and Finland. 46 But other studies indicated the female factor outweighs the male factor. 6 , 12 , 20 , 22 , 25 , 32 , 35 Differences in infertility rates (causes) between the studies may be due to the variety of diseases associated with infertility, as well as the classification of causes. 21 This variation in the cause of infertility could be associated with the study population, which may have other risk factors such as sedentary life, obesity, and anovulation for the female predominance, and environmental and social factors for the male factor. This study showed that anovulation (29.3%) and tubal factor (7.7%) predominated as the most common causes of female infertility, which was consistent with other studies. 2 , 5 , 6 , 9 , 16 , 32 , 34 , 47 The tubal factor infertility was a common cause of infertility, 20 , 21 and this could be due to infectious diseases such as Neisseria gonorrhoeae, Chlamydia trachomatis, and STIs. 6 , 13 , 35 Preventing PID, treating early during its occurrence, identifying the cause of infertility, and regulating menses are crucial for treating female infertility. This study indicated that 4.9% females had premature ovarian failure. This was lower than other studies from Sudan and Vietnam that found 9% premature ovarian failure 6 and 10% diminished ovarian reserve. 21 The unavailability of anti-mullerian hormone to diagnose poor ovarian reserve in female infertility was a challenge in this study. Maternal age greater than 35 years showed a significant association with the type of infertility, which was consistent with different studies done in Saudi Arabia, Ethiopia, China, India, and East Africa. 5 , 15 , 16 , 37 , 45 , 48 It is universally acknowledged that fertility declines with age, and researchers reported that fertility starts declining approximately at age 32 years and rapidly declines after age 37 years. 16 , 48 Advanced maternal age can lead to decreased fecundability associated with the age of the oocyte. Similarly, another study indicated that the number and quality of oocytes in the ovaries decrease naturally and progressively, resulting in a significant decrease in the fecundity of women as the age of a woman increases. 48 Other age-related risk factors, such as anovulation, obesity, STD associated tubal factor, and delayed marriage, could also contribute to and aggravate this condition. Female patients from the Gash-Barka region had a significant association with ovarian factor female infertility. Similarly, studies in Ethiopia indicated that patients from the Afar region were significantly associated with infertility, 15 and secondary infertility was higher in the Central and Northern regions of Uganda. 49 However, more research is required to understand the drivers behind the variation of infertility across regions. 49 Abnormal maternal BMI (underweight or overweight) was found to be a risk factor for ovarian factor female infertility. This was consistent with other studies conducted in Ethiopia, China, India, and Iran. 9 , 15 , 26 , 29 , 36 , 41 Maternal underweight was also found to be a risk factor for female ovarian infertility, which was consistent with studies in Ethiopia and China. 15 , 26 This relationship could be due to the fact that low body weight results in functional hypothalamic failure, anovulatory menstrual cycles, inadequate estrogen production, and amenorrhea. 9 , 15 , 26 Besides, this study indicated that infertility was higher among those who were overweight and obese, which was consistent with other studies in Iran, China, India, and East Africa. 9 , 26 , 37 , 48 Studies in Ethiopia and China revealed that obesity contributes to anovulation, menstrual irregularities, reduced conception rate, and reduced response to fertility treatment. 15 , 26 Patients with normal BMI had a low risk of ovarian infertility, and maintaining normal BMI is essential for fertility. Menstrual disorder was significantly associated with ovarian factor female infertility, which was consistent with other studies in Ethiopia, India, and China. 26 , 30 , 41 Besides, females with moderate menstrual flow had the lowest prevalence of infertility, while both scant and excessive menstruation led to increased infertility incidence in a study conducted in China. 26 This menstrual irregularity could have different factors, such as abnormal BMI and PCOS. Anovulation, the most common and treatable cause of female infertility, can be managed by regulating menses, and this should be advocated to alleviate ovarian factor infertility. The present study indicated that a history of PID and dyspareunia was found as the main risk factors for female tubal infertility, which was consistent with other studies in Ethiopia, India, and a systematic review. 3 , 20 , 30 Besides, there is a direct correlation or risk association between Chlamydia trachomatis infection, PID, and tubal infertility in studies conducted in Saudi Arabia and Cameroon. 5 , 13 It has been found that secondary infertility (85%) and tubal infertility were higher in developing countries (Sub-Saharan Africa) compared to a lower rate (33%) in developed countries. This could be due to higher rates of STDs in Sudan and Vietnam. 6 , 21 Patients with a previous history of PID and dyspareunia had a chronic sequel of tubal scarring with tubal blockage. Thus, community awareness of the clinical signs of PID, early detection, and timely treatment of PID is essential to prevent these complications and address infertility. Female urban residence was found as a risk factor for tubal factor infertility, like other studies in India, Ethiopia, and a systematic review. 3 , 20 , 30 On the contrary, other studies in Ethiopia, East Africa, and Sudan indicated that women residents in rural areas had a significant association with infertility. 15 , 48 , 50 Patients who have urban residence could have higher rates of sexual risk behaviors for PID compared to those residing in rural areas. This is similar to another study in India that reported urban women were more prone to experience infertility compared to rural women, indicating the presence of different environmental and lifestyle causes associated with it. 37 Besides, the higher health-seeking behavior and awareness of urban residents could also have a role in the higher tubal ligation infertility. This was consistent with a study from Finland that reported there were sociodemographic differences in the probability of seeking help for infertility, with urban women being most likely to seek help. 46 This needs further research to determine the association of urban residence with tubal female infertility. The present study used primary data with possible risk factors and complete information, and the sample size was adequate, which indicated the study had adequate power. But this study was not without limitations. The prevalence of infertility estimated in this study cannot be generalized to the nation. This study only included currently married women as the target population, but there is also a possibility of divorced and widowed women being infertile, which was not included because they did not fit the definition criteria for infertility. The unavailability of anti-mullerian hormone to diagnose poor ovarian reserve in female infertility was a challenge in this study, as some patients with unexplained infertility could have poor ovarian reserve which is completely explained by anti-mullerian hormone. Potential recall or reporting bias may exist, particularly for self-reported sociodemographic and medical histories.

This was an analytic, cross-sectional, hospital-based study, which included all patients who visited ONRMH. The total number of patients who visited the study site during the study period due to different complaints was also used to determine the prevalence of infertility in ONRMH. The study was conducted at Orotta National Referral Maternity Hospital, which is fairly equipped with modern investigative modalities and human resources. It provides outpatient and inpatient services for those who come from throughout the country and have different gynecologic and obstetric complaints. A census (nonprobability) sampling method was used, and all patients with a complaint of infertility who visited the ONRMH Outpatient Department were enrolled consecutively until the required sample size was obtained. This study was done in single referral hospital which accepts patients from all the country, but the results cannot generalize to national or other facility level, as sampling were taken only from patients visiting the hospital. The sample size was calculated based on various aspects, including the overall infertility, the desired precision level, and confidence level. Hence, to estimate the overall infertility as well as subgroup infertility, the following parameters were used: overall infertile proportion (P) of 50% (since there was no previous information on the infertile proportion, it was assumed that 50% of the visiting patients were infertile), a confidence level (Z) of 95% and a precision level (d) of 5%. The sample size was obtained using the following formula: n 1 = Z 2 p*q/d. 2 Thus, with the assumption of the estimates mentioned above, the initial sample size was 385. Considering a 10% non-response rate(r), the sample size (n) became 427. Therefore, a total of 427 females were enrolled in the study. All patients who visited the ONRMH Outpatient Department with the complaint of infertility were included in the study. Patients who did not fit the definition of infertility, if one or both couples did not consent for the study, had psychiatric/mental illness, or absence of partner during evaluation (divorced), were excluded from the research. To prevent the repetition of data from repeated visits, patients were given a special code and checked in their clinical cards. Data was collected by experienced residents from August to December 2024, using an interviewer-administered questionnaire, which was partially adopted from a previous questionnaire 43 and modified to the local context and objectives of the study. The questionnaire includes sociodemographic characteristics (age, education, income, marital status, etc)., as well as a personal history of social, sexual, surgical, and medical conditions. Patients were also investigated with hormone analysis and hysterosalpingography (HSG). Blood for hormone analysis in females with regular menses was withdrawn on 21 days of their last menstrual period, and if they have irregular menses, a blood sample was taken at the time of contact with the physician. Hormone level below or above the reference value was interpreted as abnormal, as presented below ( Table 1 ). Table 1 Normal Range Hormone Reference Values for Females 44 No. Hormones Females 1. Estradiol 33-196 pg/mL 2. Follicle-stimulating hormone 1.5–9.1 mIU/mL 3. Luteinizing hormone 0.5–16.9 IU/L 4. Progesterone 3.3–25.6 ng/mL 5. Prolactin 2.8–29 ng/mL 6. Testosterone 8 - 60 ng/dL Normal Range Hormone Reference Values for Females 44 HSG was performed under sterile conditions during the follicular phase, from the 6 th to 10 th day of the menstrual cycle. Depending on the spill of contrast through the tubes and configuration of the uterine cavity, the results were documented as normal, tubal, or uterine factors. The completeness of the questionnaire was checked on a daily basis by the data collector and further rechecked by the supervisor. Questionnaires with missing information were completed by arranging further contact with the patients. Besides, quality assurance measures as inter-rater reliability checks for questionnaire administration was done by the investigators and laboratory quality control were also implemented at different stages of the laboratory analysis. POF is traditionally defined as hypergonadotropic hypogonadism and amenorrhea arising before the age of 40. Body weight in kilograms divided by height square in meters (W/H 2 ). The results were categorized based on the classification of the Centers for Disease Control and Prevention (CDC): <18.5 (underweight), 18.5 to <25 (normal), 25 to <30 (overweight), and 30 or higher (obesity). Patients who had a history of vaginal discharge, dyspareunia, lower abdominal pain, and previous treatment for PID. Data were entered into CSPro version 7.7 with double entry. The data were exported to SPSS version 26, and descriptive analysis was employed; the results were presented as frequencies and percentages. Logistic regression was used to conduct bivariable and multivariable analysis to evaluate the association between the dependent and independent variables. Odds ratio with 95% CI was determined, and p-value < 0.05 was considered significant. Ethical approval was obtained from the Ministry of Health, Health Research Proposal and Ethical Committee of Eritrea, Orotta College of Medicine and Health Science and National Higher Education Research Institute (Letter of reference: 28/08/2024). Written informed consent was obtained from each patient, and the confidentiality of the patient’s medical records was strictly maintained, and the study complies with the Declaration of Helsinki. Personal identifiers were coded and analyzed in aggregates to ensure privacy. Any patient had the right to withdraw at any stage of the interview if she thought it was confidential, and no patient was harmed by participating in this study.