Knowledge, Attitudes, and Practices of the General Population Regarding Peripheral Blood Chromosomal Testing in the Premarital or Preconception Context.

Conception and design: C.H. Administrative support: C.H. Provision of study materials or patients: C.H. Collection and assembly of data: C.H. Data analysis and interpretation: C.H.; L.Z.; H.W. Manuscript writing: C.H.; L.Z.; H.W. Final approval of manuscript: C.H.; L.Z.; H.W.

The study was approved by the ethics committee of Hangzhou Linping District Maternal and Child Health Care Hospital (LLSC‐KYKT‐2023‐0057‐A).

This cross‐sectional study was conducted from October 2023 to December 2023 at authors' hospital and enrolled individuals who participated in free premarital medical examination and free prepregnancy health examination. The study was conducted in accordance with the tenets of the Declaration of Helsinki and its amendment, as well as the Good Clinical Practices. The study was approved by the ethics committee of Hangzhou Linping District Maternal and Child Health Care Hospital (LLSC‐KYKT‐2023‐0057‐A). Informed consent was obtained electronically from the participants before completing the survey. The inclusion criteria were (1) clear consciousness, normal cognitive function, ability to communicate without communication barriers, and ability to complete the survey, (2) informed consent and voluntary participation in the study, and (3) ≥ 18 years and < 50 years for women/< 60 years for men. The study was conducted at premarital health screening centers, where individuals and couples receive health screening, counseling, and guidance prior to marriage or conception. Therefore, in this context, “married” includes individuals who have completed their marriage ceremony and obtained a legal marriage certificate, are getting married, and are very close to completing the legal formalities (e.g., they have booked a wedding date and are undergoing the necessary premarital health check), or have completed one of the steps required to legally marry, but have not yet completed all the formalities (e.g., they have undergone a health check but are still waiting for final legal registration). While the term “married” is used, it is important to note that some participants may technically be “unmarried” in the strict legal sense but are still considered part of the target population for premarital health services. These individuals were included in the study because they were seeking premarital health services and planning to get married, which is consistent with the study's focus on premarital or preconception health. The following strategies were implemented to address potential selection bias in subject recruitment. (1) Inclusive recruitment: the participants were recruited from Hangzhou Linping District Maternal and Child Health Hospital, a major center for premarital and prepregnancy health checks. The hospital serves a broad and diverse population, including individuals from diverse socioeconomic backgrounds. (2) Convenience sampling with broad inclusion criteria: convenience sampling was used while ensuring that the inclusion criteria were broad and inclusive. All couples planning to marry or planning pregnancy who visited a health screening center during the data collection period were eligible to participate. (3) General timing: data collection lasted for at least 3 months to ensure the capture of a representative sample across different times and dates, thus reducing the likelihood of time‐specific bias. (4) Response rate considerations: taking into account the estimated response rate of 80%, the aim was to collect at least 480 valid questionnaires based on the power analysis (described below). The design of the questionnaire was based on the relevant guidelines and literature. After the initial design, modifications were made by incorporating feedback from one genetic counseling expert and one maternal and child health care expert, resulting in a pilot version. The questionnaire was initially administered to a small sample of 38 participants. The preexperimental feedback yielded a Cronbach's α coefficient of 0.917. The final questionnaire consisted of four dimensions: demographic information, knowledge dimension, attitude dimension, and practice dimension. The demographic information included age, gender, residence (urban or rural), education, marital status, obstetrical history, history of birth with chromosomal defects, desire for parenthood after marriage, currently trying to conceive, and monthly income. The knowledge dimension comprised 10 questions, with response options ranging from very knowledgeable (3 points) to heard about it (2 points) to completely unaware (1 point), with a score range of 10–30 points. The attitude dimension included 10 questions using a 5‐point Likert scale, ranging from very positive (5 points) to very negative (1 point), resulting in a score range of 10–50 points. The practice dimension comprised nine questions using a 5‐point Likert scale, ranging from always (5 points) to never (1 point), with a score range of 9–45 points. The questionnaire was programmed using the Questionnaire Star app. The QR code was spread through WeChat and in the Hangzhou Linping District Maternal and Child Health Hospital marriage center consulting room. All questions were mandatory for submission. A given IP address could be used to submit only one questionnaire. Questionnaires were excluded if (1) they had logical errors (e.g., impossible age), (2) they were completed in < 90 s, (3) all knowledge items were rated “unaware,” and (4) the questionnaires had an obvious filling pattern (e.g., all last choices). The survey was designed to collect data anonymously. The participants were not required to provide any personal or identifying information, such as name, address, or contact information. Access to the raw data was limited to the principal investigator and designated research team members. All team members were trained in data protection and confidentiality practices. Once the study is completed and published, the data will be securely archived with access restricted to the corresponding author only. Ensuring the authenticity of the data is essential to the validity and reliability of the study. In order to ensure the authenticity of the entries, the following measures were taken. (1) All participants were first approached and consented in person by the research team members to explain the purpose of the study, the importance of providing accurate information, and the confidentiality of their responses. The questionnaire included clear instructions and examples to help participants understand how to accurately report their information. (2) The participants were assured that their answers would be kept confidential and that their identities would not be disclosed. This was to encourage honest and truthful answers without fear of being judged or influenced. (3) The data collectors and researchers were trained to explain to the participants the importance of providing accurate and truthful information. They were also trained to answer any questions and clarify concerns to ensure that participants understood the questions and could easily provide accurate answers. (4) During the data collection process, verification checks were implemented to identify and address any inconsistencies or outliers in the data. For example, age < 18 years questionnaire completion in < 90 s, all knowledge items were rated as “don't know,” and questionnaires with obvious filling patterns (such as all last options) were excluded. The formula n = Z 1 − α / 2 δ 2 × p × 1 − p can be used to calculate the sample size of cross‐sectional surveys. In the formula, n represents the sample size for each group, α represents the type I error (which is typically set at 0.05), Z 1− α /2  = 1.96, δ represents the allowable error (typically set at 0.05), and p is set at 0.5 (as setting it at 0.5 maximizes the value and ensures a sufficiently large sample size). Hence, the calculated sample size was 384. Considering an estimated questionnaire response rate of 80%, a minimum of 480 valid questionnaires was needed. In addition, to ensure that the study met the sample size requirements and data quality, the following data collection standards were set: (1) time frame: the data collection period would last at least 3 months, and (2) number of valid questionnaires: the goal was to collect at least 480 valid questionnaires. Hence, questionnaires were collected until both conditions were met. Because of the 3‐month criterion, 813 questionnaires were finally collected. Continuous variables were described as means ± standard deviations. Categorical data were presented as n (%). KAP scores across variable categories were analyzed using the Mann–Whitney U test or Kruskal–Wallis H test. Spearman analysis was used to analyze the correlations between two KAP dimension scores. Univariable and multivariable logistic regression analyses were performed to explore the factors associated with practice. The practice was dichotomized using the 70% cutoff point. All analyses were performed using SPSS 25.0 (IBM, Armonk, NY, USA). Two‐sided p ‐values < 0.05 were considered statistically significant. No patient involved.

The authors received 813 questionnaires, but 29 participants did not complete the consent form on the first page of the questionnaire, 41 completed the questionnaire in < 90 s, one reported being < 18 years of age, and 112 selected “unaware” for all knowledge questions. Therefore, 630 valid questionnaires remained. The Cronbach's α coefficient for the questionnaire in all participants was 0.852 (or 0.634 for the knowledge dimension, 0.819 for the attitude dimension, and 0.686 for the practice dimension). The KMO was 0.905. Table  1 presents the characteristics of the participants. Among the participants, 17.30% were 18–25 years, 48.25% were 26–30, 23.81% were 31–35, and 10.63% were ≥ 36 (the oldest participants were 47 for women and 57 for men). Most participants were male (51.11%), living in urban areas (67.78%), had a bachelor's degree or above (44.76%), were in their first marriage (87.46%), had no history of miscarriage (self or partner) (93.81%), had no history of a baby with chromosomal abnormalities (99.21%), were desiring parenthood (93.65%), were currently trying to conceive (50.32%), and had an annual income < 10,000 (39.84%). Characteristics of the participants and KAP scores. KAP scores across variable categories were analyzed using the Mann–Whitney U test or Kruskal–Wallis H test. The mean knowledge score was 18.90 ± 4.48 (/30, 63.00%). Higher knowledge scores were observed in remarried individuals ( p  = 0.019) and those currently trying to conceive ( p  < 0.001) (Table  1 ). The level of understanding was poor for all knowledge items, with the highest percentage of “very knowledgeable” being 14.76% (K8; “Do you understand that chromosome analysis can effectively prevent the occurrence of chromosomal abnormalities in the next generation?”). Additionally, participants had a low level of understanding regarding peripheral blood chromosome testing, with only 7.62% reporting they were very familiar with it (K1). Furthermore, 10% of participants recognized that chromosomal abnormalities in number and structure are one of the primary causes of infertility (K3), while only 9.52% were aware that these issues can be addressed through third‐generation IVF technology (K10) (Table  2 ). Knowledge dimension of the participants. The mean attitude score was 40.99 ± 5.32 (/50, 81.98%). Higher attitude scores were observed in younger individuals ( p  = 0.025), females ( p  < 0.001), and higher education ( p   93.00%) were observed for all positively scored items (A1–A7). Less favorable attitudes were observed for the negatively scored items: A8 (33.33%; “8. I am not interested in in vitro fertilization (third‐generation assisted reproductive technology), so peripheral blood chromosome karyotype analysis is not meaningful for me.”), A9 (44.92%; “9. I find peripheral blood chromosome karyotype analysis too complicated, so I am unwilling to undergo the test.”), and A10 (45.87%; “10. I would be unwilling to undergo peripheral blood chromosome testing due to concerns about unnecessary anxiety and stress from potential chromosomal abnormalities.”) (Table  3 ). Attitude dimension of the participants. The mean practice score was 37.66 ± 4.43 (/45, 83.69%). Higher practice scores were observed in individuals 26–30 years of age ( p  = 0.018), females ( p  = 0.013), and higher education ( p  = 0.005) (Table  1 ). Among the participants, 63.17% had undergone peripheral blood testing, and 74.76% recommended their partner to undergo testing. The practice scores were high (> 90%) for all positively scored items. The scores were lower for the two negatively scored items: P7 (52.53%; “7. Even in the case of repeated unexplained miscarriages, I still would not undergo peripheral blood chromosome testing.”) and P8 (54.45%; “8. Even in the case of repeated unexplained miscarriages, I still would not recommend my partner to undergo peripheral blood chromosome testing.”) (Table  4 ). Practice dimension of the participants. The knowledge scores were similar between genders ( p  = 0.840). Compared with males, females had higher attitude scores (42.01 ± 5.40 vs. 40.01 ± 5.07, p  < 0.001), including in items A1, A2, A4, A6, A7, A8, A9, and A10 (all p  < 0.05) and higher practice scores (38.17 ± 4.44 vs. 37.18 ± 4.37, p  = 0.013). There were no significant differences between genders regarding items P1 and P2 (Table  5 ). The gender differences in KAP scores. As shown in Table  S1 , the knowledge scores correlated with the practice scores ( r  = 0.082, p  = 0.041), while the attitude scores correlated with the practice scores ( r  = 0.606, p  < 0.001). The knowledge scores (OR = 1.060, 95% CI: 1.018–1.103, p  = 0.004) and attitude scores (OR = 1.198, 95% CI: 1.154–1.244, p  < 0.001) were associated with the practice scores, independently from age, education level, and socioeconomic status (Table  S2 ).

No previous studies examined the KAP toward blood chromosomal testing in the premarital or preconception period. Therefore, this cross‐sectional study investigated the KAP of the general population regarding peripheral blood chromosomal testing in the premarital or preconception period. The results indicate that the general population in Hangzhou displays poor knowledge but favorable attitudes and proactive practices regarding peripheral blood chromosomal testing in the premarital or preconception period. Cultivating proper knowledge and attitude should improve practice. In the management of infertility, individuals must know it is abnormal to not achieve pregnancy after 12 months of unprotected sexual intercourse, seek the proper medical attention, cooperate with the investigations, and participate in shared decision‐making with the physicians. Even though parental non‐syndromic chromosomal abnormalities are relatively rare, they appear to contribute to an important proportion of the cases of infertility (Harton and Tempest  2012 ; Vicdan et al.  2004 ; Yahaya et al.  2021 ). Hence, knowing when and why to seek chromosomal testing has public health implications. Nevertheless, previous studies reported relatively high KAP scores toward prenatal genetic testing (Abdo et al.  2018 ; Zhu et al.  2020 ), including in China (Zhu et al.  2020 ), but such studies were performed in pregnant women seeking confirmation of their fetal chromosomal status. A study in Saudi Arabia showed that infertile couples had poor knowledge and a neutral attitude toward infertility, resulting in poor practice (Abolfotouh et al.  2013 ). Similar results were observed in Fiji regarding family planning in general (Imtishal et al.  2023 ). The present study observed poor knowledge but a favorable attitude and proactive practice toward premarital or preconception peripheral blood chromosomal testing. It contrasts with a study from Oman, in which the participants had high knowledge but poor practice, with about 33% of the participants being reluctant toward testing (Al‐Farsi et al.  2014 ). Although premarital genetic testing can have profound ethical implications (Zhong et al.  2021 ), it can be justified in the context of regions with high rates of consanguineous marriages (Alswaidi et al.  2012 ). Of note is that the QR codes in the present study were distributed in a clinic offering premarital or preconception tests, possibly explaining the high attitude and practice scores. In our study, about 80% of participants were entering or had just entered their first marriage. Pre‐marriage, they lacked access to reproductive health services and awareness of relevant information, resulting in low knowledge of peripheral blood karyotype analysis. Given rising infertility rates and later marriage/childbearing ages, reproductive education should extend beyond married couples with infertility to include the broader population, particularly unmarried individuals. Tailored reproductive health guidance can enhance overall reproductive health. The present study showed that the knowledge and attitude scores were the only factors independently associated with the practice scores, implying that improving knowledge and attitude could also improve practice. Of note, all knowledge items had low scores, indicating that all aspects regarding premarital or preconception peripheral blood chromosomal testing should be targeted by educational interventions, including peripheral blood chromosomal testing itself, chromosomal abnormalities as possible causes of infertility, the impacts of chromosomal abnormalities, the indications for peripheral blood chromosomal testing, and the implications of peripheral blood chromosomal testing. A proper attitude toward the relationship between peripheral blood chromosomal testing and future infertility treatments, toward the complexity of the test results, and toward possible anxiety should be addressed. It should be emphasized that individuals seeking such tests can have counseling offered and that a physician will explain the test results. The KAP framework stipulates that knowledge is the basis for practice and that attitude is the force driving practice (Andrade et al.  2020 ; World Health Organization  2008 ). Therefore, improving knowledge through educational interventions should improve both attitudes and practice, and attitudes would further improve practice. Women generally had higher attitudes and practice scores than men, but the present study was not designed to explore the differences in KAP between men and women. Based on the literature, it could be hypothesized that seeking pregnancy and consultations for infertility could lead to higher infertility‐related stress in women (Peterson et al.  2006 ; Wischmann et al.  2009 ). The differences could also be because women appear more concerned about genetic risk factors than men (Saastamoinen et al.  2020 ). In addition, the approach to pregnancy in general and infertility is different between men and women for many reasons. The fertility window of women is more limited than that of men, the social pressure to produce children is stronger against women than men, the desire for a career is often stronger in men than in women, and the desire to bear children is often stronger in women than in men, among others (Adeleye and Feinberg  2022 ; Gullo et al.  2021 ; Inhorn and Patrizio  2015 ; Lappegård  2020 ). Still, the present study did not delve into the reasons for the disparity. Future studies could specifically examine the issue. Therefore, men should be targeted for attitude and practice improvements. This study had strengths. The participants in the study represent the general population of couples who received pre‐pregnancy counseling in Hangzhou for the following reasons. (1) Population diversity: the participants included a wide range of ages, education levels, and socioeconomic status, reflecting the diversity of people seeking pre‐pregnancy services in Hangzhou. (2) Geographical coverage: Linping District Maternal and Child Health Hospital serves a large geographical area within Hangzhou, ensuring that the sample includes individuals from different communities. (3) Healthcare visits: The hospital is the main point of visit for premarital and prepregnancy health services, making it a key location for recruiting a representative sample of the target population. (4) Comparison with population data: The demographic characteristics of the study sample were compared with the available population data of Hangzhou, suggesting similar age, education level, and socioeconomic status, suggesting that the participants may be representative of a broader population. By addressing gaps in the literature, the present study provides a fundamental understanding of KAP related to peripheral blood karyotyping in individuals seeking marriage or fertility but also provides actionable insights for future research and practice. The findings highlight the need for targeted education and counseling interventions to enhance knowledge, foster positive attitudes, and promote proactive practices. Future studies could build on this work to explore the long‐term effects of such interventions and refine strategies to improve preconception health and genetic screening. Healthcare providers should receive continuing education about karyotype analysis in the context of fertility. Patient‐friendly handouts and brochures could be designed to explain the purpose, process, and potential outcomes of karyotyping. Web‐based interactive or mobile applications could be built to enable the patients to explore karyotypic outcomes and their implications. A referral network could be established to ensure that patients have access to genetic counselors for in‐depth discussions. Finally, patient feedback should be collected to identify areas of communication that need improvement. This study had limitations. The participants were from a single hospital and, hence, from a limited geographical area, limiting the sample size and the generalizability of the results. The study could have favored a more socioeconomically favored population seeking fertility services. Randomized selection of participants from the community should be implemented in future studies. The study was performed at the participant level, but how many couples actually participated is unknown. The study was cross‐sectional, preventing the analysis of causality. In addition, the present study is only a snapshot of the situation at a precise moment. Nevertheless, it could serve as a historical baseline for future studies. The questionnaire was self‐designed, possibly leading to biases due to local practices, culture, and policies, limiting the exportability of the tool and the generalizability of the results. All KAP studies are at risk of social desirability bias, in which the participants can be tempted to answer what they know they should do instead of what they are doing (Bergen and Labonte  2020 ; Latkin et al.  2017 ). Finally, the participants had a higher education than the general population in China, suggesting a social bias. Even though everyone could participate in the study, the QR codes were distributed at free premarital medical and prepregnancy health examinations, and the results might not represent the true general population.

Informed consent was obtained electronically from the participants before completing the survey. I confirm that all methods were performed in accordance with the relevant guidelines. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Infertility is the inability to conceive after a minimum of 12 months of regular unprotected sexual intercourse and includes female and male factors (Fields et al.  2013 ; Kamel  2010 ; Lindsay and Vitrikas  2015 ; Practice Committee of the American Society for Reproductive Medicine  2023 ). The lifetime prevalence of infertility is 17.8% in high‐income countries and 16.5% in low‐ and middle‐income countries (World Health Organization  2023 ). Infertility occurs in about 17% of reproductive‐aged couples worldwide, with 10.7% in the Eastern Mediterranean Region, 13.1% in the African Region, 16.5% in the European Region, 20.0% in the Region of the Americas, and 23.2% in the Western Pacific Region and is more common in developed countries (Kamel  2010 ; Elhussein et al.  2019 ; World Health Organization  2023 ). Infertility is a global public health concern and is defined as a priority by the World Health Organization ( 2023 , 2024 ). Infertility causes psychological problems, such as frustration, depression, anxiety, hopelessness, and guilt (Hasanpoor‐Azghdy et al.  2014 ), and social problems, such as reduced work efficiency and job loss, culminating in reduced income to cater to the family (Nahar and Richters  2011 ). Infertility treatments may incur huge financial costs and result in economic distress, particularly in developing nations where treatment costs are often paid by the patients (Dyer and Patel  2012 ). The cause of infertility in couples may be multifactorial and may include combined male and female factors (about 40%), male factor infertility alone (about 26%–30%), ovulation disorders (about 21%–25%), tubal factors (about 14%–20%), cervical, uterine, or peritoneal disorders (about 10%–13%), and idiopathic about (25%–28%) (Fields et al.  2013 ; Kamel  2010 ; Lindsay and Vitrikas  2015 ; Practice Committee of the American Society for Reproductive Medicine  2023 ). Infertility is a complex trait influenced by genetics, lifestyle habits, the microbiota, and the environment (D'Argenio et al.  2021 ). Some genetic variants may increase an individual's susceptibility to the influence of specific environmental factors on fertility or directly lead to reduced fertility, while healthy lifestyle choices can partly mitigate the negative effects of environmental exposures (Vanderhout et al.  2021 ; Gallo  2022 ). The disorders causing fertility issues can be congenital, genetic, or triggered by environmental or lifestyle factors. The genetic causes of fertility can include chromosomal translocations and other structural or numerical chromosome changes in one of the partners, as well as point mutations (D'Argenio et al.  2021 ; Khamees and Al‐Ouqaili  2022 ; Kanaan et al.  2022 , 2023 ). Although emphasis is often made on the genetic and environmental factors influencing female fertility, genetics and the environment also influence male fertility (Vanderhout et al.  2021 ; Al‐Ouqaili et al.  2022 ; Al‐Qaisi et al.  2022 ). Particularly, about 15%–30% of male infertility appears to be of genetic origin, either chromosomal or single‐gene anomalies (Okutman et al.  2018 ). Chromosomal anomalies would account for 2%–14% of male infertility and about 10% of female infertility (Harton and Tempest  2012 ; Vicdan et al.  2004 ). Furthermore, polycystic ovarian syndrome (PCOS) and endometriosis are also complex disorders with a probable genetic component, and the two conditions are causes of infertility in women (Zorrilla and Yatsenko  2013 ; Abdul‐Lateef et al.  2024 ). The prevalence of endometriosis in China is estimated at 12% (Dai et al.  2019 ), while the prevalence of PCOS is 8.6% (Shen et al.  2024 ). Other causes of infertility in women include premature ovarian failure, leiomyoma, Turner syndrome, Noonan syndrome, sickle cell disease, galactosemia, fragile X‐associated primary ovarian insufficiency, Kallman syndrome, and congenital adrenal hyperplasia (Zorrilla and Yatsenko  2013 ). In men, genetic causes of infertility can include Klinefelter syndrome, azoospermia, oligozoospermia, double Y syndrome, Y chromosome microdeletions, testicular disorder of sex development, testicular dysgenesis disorders, Kallman syndrome, congenital bilateral aplasia of the vas deferens, androgen insensitivity syndrome, sickle cell disease, Kartagener Syndrome, myotonic dystrophy, Fanconi anemia, β‐thalassemia, and cystic fibrosis (Zorrilla and Yatsenko  2013 ). Therefore, the possible genetic component of infertility warrants a genetic evaluation. Hence, couples with apparently idiopathic infertility can be proposed chromosomal examinations to seek the presence of such abnormalities that could explain infertility and suggest the proper course of action (Yahaya et al.  2021 ). Preconception karyotype analysis can help prevent congenital abnormalities, especially in areas with high frequencies of genetic predisposition or idiopathic infertility (Kanaan et al.  2023 ; Cariati et al.  2019 ); future parents living in such areas could benefit from preconception karyotype analysis since it can help identify the causes of their infertility and help guide infertility management. Nevertheless, determining the causes of infertility is associated with costs, a lack of knowledge about the possibility of genetic causes of infertility, cultural differences, and the availability/accessibility of genetic testing in different parts of the world, constituting barriers to genetic testing (Ombelet  2011 ; Ethics Committee of the American Society for Reproductive Medicine  2021 ; Njagi et al.  2023 ). Cultural and regional differences can significantly impact the acceptance of genetic testing due to factors like historical mistrust of medical systems, varying levels of health literacy, beliefs about fate and personal responsibility, privacy concerns, and differing cultural understandings of genetic information, particularly within minority groups who may have experienced historical exploitation related to genetics, like the eugenics movement (Suther and Kiros  2009 ; Marcus and Cetin  2023 ; Ormond  2008 ; Likhanov et al.  2023 ). The management of infertility requires the full cooperation of the patients. Indeed, individuals must know when to consult, cooperate with the investigations, and participate in shared decision‐making. It requires proper knowledge and attitude. Knowledge, attitude, and practice (KAP) studies are tools that evaluate the gaps, misunderstandings, and misconceptions toward a specific subject (Andrade et al.  2020 ; World Health Organization  2008 ). KAP studies can provide data for designing educational interventions on a specific health‐related subject in a given population (Andrade et al.  2020 ; World Health Organization  2008 ). The KAP of women toward prenatal genetic testing has been reported to be relatively good in Jordan (Abdo et al.  2018 ) and China (Zhu et al.  2020 ), but these studies examined the prenatal genetic testing of the fetus, not necessarily the genetic testing of the parents. Several studies examined the KAP toward infertility among infertile couples (Abolfotouh et al.  2013 ; Imtishal et al.  2023 ), but they did not examine genetic testing. Therefore, this study aimed to investigate the KAP of the general population regarding peripheral blood chromosomal testing in the premarital or preconception period. The results could help design educational interventions and improve the management of patients with infertility.

The authors declare no conflicts of interest.

Data S1. Data S2.