Prevalence and Associated Factors of Infertility among Married Couples of Kunnathukal Panchayath in Trivandrum District.

Infertility can be defined as “failure to achieve a pregnancy after 12 months or more of regular unprotected sexual intercourse.”[ 1 ] Globally, 8-12% of couples experience infertility during their fertile period whereas, it is 8.2% in India, and 11.1% in Kerala.[ 2 3 4 5 ] Infertility has been a neglected area in maternal and child health. With the advent of technology, the scope and success rate for infertility treatment are found to be higher. Facilitating this, there is a need for evidence-based knowledge regarding the prevalence of infertility and its determinants. This study attempts to identify the prevalence of infertility and its determinants to facilitate prevention of infertility.

In this study, 1270 women and men participated as couples and their mean age was 35.23 ± 6.90 years and 39.76 ± 7.37 years, respectively. Majority (61.7%) of the couples in the study were Christians. Majority (>80%) of the couples had studied up to high school and higher secondary. Only 0.3% of the couples were illiterate. Majority (83.5%) of the female participants were homemakers. However, majority (71.7%) of the males were unskilled workers. 47.20% of the couples lived in joint family, and least (8.0%) lived in three-generation family. 42.8% of the couples belonged to middle class and only 0.3% belonged to lower socioeconomic class, as per Modified BG Prasad Scale 2019. Prevalence of infertility was found to be 152, 12% (10.2-13.7%). Among those who had a history of infertility, majority (65.8%) had primary infertility and the remaining of them had secondary infertility (34.2%). Only 56.6% of those who had a history of infertility sought treatment for infertility. Among those who sought treatment for infertility, 95.3% underwent investigations and 63.4% of them were found to have abnormalities. The commonest abnormality found was oligozoospermia followed by PCOS and fibroid. Among those found with abnormality, 88.5% had taken treatment, of which 52.2% conceived [ Table 1 ]. Distribution of couples based on treatment seeking behavior for infertility Approximately one-fifth of the infertile females (21.7%) were skilled/semiskilled laborers when compared to fertile females (11.5%). A statistically significant association was found between occupation of females and infertility ( P = <0.001). The odds of being infertile were found to be 2.2 times higher for females who were skilled/semiskilled laborers compared to the females who were unemployed/homemakers [ Table 2 ]. Association between sociodemographic characteristics and infertility ( n =1270) Among both infertile and fertile females, majority had regular menstrual cycles and no history of menorrhagia. The differences observed with these variables among infertile and fertile groups were not statistically significant. However, the history of dysmenorrhea and type of dysmenorrhea were found to have a statistically significant association between infertile and fertile females ( P < 0.001 and 0.02, respectively). The strength of association in having experienced dysmenorrhea among infertile females was twice that of females in the fertile group. Among females in infertile group, congestive type of dysmenorrhea was experienced six times more compared to their counterparts. 31.6% females who were infertile and 45.1% among fertile experienced dyspareunia and this difference was statistically significant ( P = 0.002) [ Table 3 ]. Association between menstrual and sexual characteristics and infertility ( n =1270) Of all the comorbidities studied, 3.9% and 6% of infertile females and 1.3% and 3% of fertile females had fibroid uterus and hypothyroidism, respectively. The presence of fibroid uterus and hypothyroidism among females had statistically significant association with fertility status ( P = 0.02 and 0.04, respectively). It was observed that odds of being infertile was found to be 3.2 times more for women who had a history of uterine fibroid compared to those who had no history of uterine fibroid. Strength of association in having hypothyroidism among infertile females was twice that of females in fertile group [ Table 4 ]. Association between comorbidities and infertility ( n =1270) Among the infertile females, about 5.3% had a family history of infertility which is comparable to that of fertile females (3.8%), and this difference observed was not statistically significant ( P = 0.37). Median BMI with IQR of infertile and fertile females were 22.24 (20.03, 26.23) kg/m 2 and 21.60 (19.67, 24.30) kg/m 2 , respectively. The difference observed was statistically significant between infertile and fertile females ( P = 0.02). *Reference Model summary: Nagelkerke R 2 for the model– 0.084, Sensitivity of the model-50% and specificity-88.0%. Dysmenorrhea, type of dysmenorrhea, and uterine fibroid were found to have adjusted OR significant and emerged as predictors of infertility by this model (Enter method) [ Table 5 ]. Multivariate analysis to find the predictors of infertility

A community-based cross-sectional study was conducted among 1270 married couples at Kunnathukal Panchayath. It was concluded that the prevalence of infertility among married couples in Kunnathukal Panchayath was 12% (10.2-13.7%), which is almost similar to our state’s average (10.5%) according to DLHS-3. Among those who had a history of infertility (12%), the majority (65.8%) had primary infertility. Occupation of females, age at the time of menarche, dysmenorrhea, type of dysmenorrhea, dyspareunia, fibroid uterus, Hypothyroidism, and BMI were the risk factors found to have statistically significant association with fertility status. In multivariate analysis, dysmenorrhea, type of dysmenorrhea, and uterine fibroid were found to have adjusted OR significant and emerged as predictors of infertility. Infertility has emerged as a serious public health problem in India. More community-based studies should be performed to determine the infertility burden, the causative factors of infertility, and its consequences. The provision of health education as an integral part of infertility management should be made a part of reproductive healthcare programs. Facilities should be made available for infertility management at an affordable cost. Sound knowledge about various factors related to infertility can help policymakers and healthcare providers to design and implement various policies. There are no conflicts of interest.

This community-based cross-sectional study was primarily aimed at looking for the prevalence of infertility among married couples who were residing in the Kunnathukal Panchayath, Trivandrum district. Prevalence of infertility was found to be 12%. This was comparable to DLHS-3 report for Kerala, where infertility was reported as 10.5%. This prevalence of infertility was also similar with other southern states and union territories like Andhra Pradesh (11%) and Lakshadweep (12.2%); northern states like West Bengal (14.1%), Bihar (12.4%), Haryana (11.1%) and Uttar Pradesh (10.1%) according to DLHS-3.[ 4 ] The observation was also similar to the study conducted in Assam by Pranabika Mahanta, in which the infertility prevalence was 10.8%.[ 6 ] However, there were other studies conducted in West Bengal, Punjab, Tamil Nadu, Karnataka, and Uttar Pradesh; the prevalence was found in the range of 2.1-9.6%.[ 4 7 8 9 10 11 ] Though we see that infertility ranges from 2.1-14.1%, this study gives us the evidence that burden of infertility is more toward the higher end. Of those couples who had a history of infertility in our study, majority (65.5%) had primary infertility, 31.6% had secondary infertility, and 2.6% had both types of infertility. This observation was almost comparable to a hospital-based study conducted in Thiruvananthapuram by S Shamila and SL Shashikala, in which majority (75.4%) had primary infertility and 24.5% had secondary infertility.[ 12 ] In this study, majority (61.70%) of the couples were Christians, followed by Hindus (35.90%) and Muslims (2.40%). As reported by Chethana R and Shilpa, in Bangalore, most of the couples belonged to Hindu religion which was followed by Muslim and Christian.[ 13 ] The higher proportion of Christians in our study may be due to their predominant inhabitation in this region, and this variation can also be attributed to differences in customs, way of living, traditions and habits. There was no statistically significant association ( P = 0.73). A similar finding was also reported by Ashwini Katole.[ 14 ] Though infertility was more among those females who had completed high school and higher secondary education compared to less educated people, this difference was not statistically significant ( P = 0.28) between infertile and fertile females. Study conducted by Paul C Adamson et al. [ 15 ] also showed similar finding. In our study, it was observed that the odds of being infertile were found to be 2.2 times higher for females who were skilled/semiskilled laborers compared to the females who were homemakers and this association was found to be statistically significant ( P = 0.01). Similar findings were also found in study conducted by Samreen Kazmi et al .[ 12 ] In both the infertile and fertile groups, maximum proportion of couples belonged to middle socioeconomic class (40.1%, 43.1%), followed by lower middle class (31.6%, 31.0%), and upper class (8.6%, 9.3%). This difference observed was not statistically significant ( P = 0.65). In contrast to our study, in a study conducted by Samreen Kazmi et al. , 26.38% and 30.69% belonged to middle socioeconomic status among infertile and fertile couples. However, 11.1% and 5.2% belonged to upper class in infertile and fertile couples. This difference was found to be statistically significant.[ 8 ] Age at menarche was found to be significantly different between infertile and fertile females our study ( P = 0.02). A result in parallel with this was observed in a study conducted by Paul C Adamson et al. in Mysore.[ 16 ] Regularity of menstrual cycle, menstrual cycle duration and menstrual flow duration were not found to have any statistically significant association with fertility status, whereas dysmenorrhea ( P = <0.001) and type of dysmenorrhea ( P = 0.02) was found to be associated with fertility status according to present study. Among infertile females in this study, congestive dysmenorrhea was experienced six times more compared to their counterparts. There are certain studies that revealed statistically significant association between menstrual history variables and fertility status.[ 12 17 18 ] Statistically significant association ( P = 0.02) was found between fibroid uterus and infertility. Among the infertile females, 3.9% of them reported uterine fibroid in contrast to 1.3% of non-infertile females. In a systemic review conducted by Peter C Klatsky et al. ,[ 19 ] they found out that there was an association between uterine fibroid and infertility. Also, a statistically significant association was found between hypothyroidism and fertility status ( P = 0.04) in our study. According to a study conducted by Indu Verma et al. ,[ 16 ] 23.9% of the infertile females were hypothyroid. After treatment performed for hypothyroidism, 76.6% of them became pregnant within 1 year. Other comorbidities like anemia, TB, PID, and endometriosis did not have any significant association with infertility. This could be due to the very least number of study participants with those comorbidities. However, studies conducted by P singh et al .,[ 20 ] Namavar Jahromi et al .,[ 21 ] Tao X et al .,[ 22 ] found that there is an association between anemia, TB, PID, and fertility status. Among the infertile females, 5.3% had a family history of infertility, which is comparable to that of non-infertile females (3.8%). No statistically significant association was found between family history of infertility and infertility among them ( P = 0.37). A statistically significant association was found between a family history of infertility and infertility based on a study conducted by Ashwini Katole.[ 14 ] BMI was found to be significantly different between infertile and non-infertile females in our study ( P = 0.02). This result was in line with the observation by Nirmalya Manna et al .[ 10 ] Only 56.6% of the infertile couples in the present study area sought treatment for infertility. This observation was comparable to a community-based study conducted in West Bengal by Manna et al .[ 10 ] in which only 58.12% were evaluated for infertility. However, in contrast to our study finding, it was observed in DLHS-3 survey that 80% of females had sought infertility treatment at the all-India level, and in Kerala, it was found to be 85.5%.[ 23 ] The reflection of DLHS-3 survey of 85.5% couple who had sought treatment for infertility is found to be high compared to my study. This could be probably because DLHS-3 data includes both urban and rural couple, whereas my study population confined to rural area alone. Estimation of prevalence of infertility was based on a questionnaire-based interview method. Despite extensive data seeking, the current study relied on women’s response to these questionnaires and all respondents did not have medical documents to support the verbally given information. As this was a cross-sectional study, we cannot make causal inferences on factors contributing to disparities in prevalence rates and is difficult to establish a temporal relationship between the study variables. In this current study, couples who sought treatment for infertility were found to be 56.6%. Our study had a limitation in that we could not explore the reasons for not seeking treatment. Hence, there is a need to further explore this gap that is identified in this study.

Study design and study setting: A community-based cross-sectional study was conducted among married couples who were residing at Kunnathukal Panchayath in the Trivandrum district of South Kerala from January 2019 to March 2020 after getting approval from the Institutional Ethics Committee (IEC No: SMCSIMCH/EC (PHARM) 03/04/28)). Couples who had been married for a minimum of 2 years and fulfilled the operational definition of infertility, that is, women in the age group of 18-49 years at risk of becoming pregnant (not pregnant, sexually active, not using contraception, and not lactating) who ever had/have difficulty in getting pregnant for ≥2 years were included in the study. Couple whose either spouses have passed away/divorced/separated within 2 years of marriage were excluded from the study. Based on a previous study in all over the India,[ 5 ] considering the prevalence of infertility in Kerala as 11.1%, allowable error of 20% of prevalence, design effect of 1.5 and non-response rate as 10%, the sample size calculated to be 1270. Cluster sampling technique was used to select the study participants. A random direction from the center of the cluster was selected by spin the bottle approach. The index house was selected along the direction the neck of the bottle faced and then counted out. The written informed consent was taken from all the study participants. Data collection was performed by one-to-one interview method using a pretested, predesigned, semi-structured questionnaire. Privacy and confidentiality of the study subjects were maintained. Weight in kg and height in cm were recorded using bathroom scale and flexible measuring tape, respectively. All information on investigations and drugs prescribed by doctors for treating infertility were obtained based on self-reporting. Lab report and prescriptions, whenever available at the time of the interview, were used in getting reliable data, from the participants. All locked houses were revisited thrice before excluding them from the study. The collected data was entered into MS Excel Sheet 2019, and analyzed using SPSS version 20. All qualitative and quantitative variables were expressed as frequency and percentages and mean and standard deviation, respectively. Prevalence of infertility was expressed as percentage with a 95% confidence interval. The association between infertility and independent variables were assessed using Chi-square test and Fischer’s exact test for qualitative variables and Mann Whitney U test for quantitative variables. Binary logistic regression analysis was performed to find the independent predictors of infertility. The adjusted odds ratios with their 95% CI were given as final predictors in the model. P value < 0.05 or 95% CI was considered as statistical significance.