Understanding general public and healthcare provider knowledge gaps in male factor infertility

Introduction

Background

It is estimated that 1 in 6 couples will struggle with infertility, and male factors play a role in over half of these cases (1). Male infertility is a complex disease process with numerous environmental, occupational, genetic, acquired, iatrogenic, and idiopathic causes (2-4). Many common medical treatments (e.g., testosterone therapy) can cause profound suppression of spermatogenesis (2,5).

Rationale and knowledge gap

While ongoing research continues to identify new causes for decreased sperm counts and idiopathic infertility, little objective data are known regarding public knowledge and perception of male infertility. This issue is critically important, as societal norms and stigma precluding men from participating in open dialogue surrounding infertility or sharing their struggles with their support network or medical providers will continue to propagate the knowledge gap and exacerbate mental health concerns during this stressful process (6,7).

In parallel with the limited knowledge of male infertility by the general public, the knowledge and attitude of healthcare professionals (HCPs) surrounding male infertility are also suboptimal according to the studies performed to date. While education surrounding female fertility has received a breadth of research and Fertility & Infertility Treatment Knowledge Score (FIT-KS), a validated, 29-item questionnaire largely focused on female factors has been studied, no such score exists specifically for male factor infertility (8,9).

Objective

Motivated by this gap in the literature, we hypothesized that male factor infertility knowledge is poor but increases with educational status and personal experience.

To assess this hypothesis, we conducted an anonymous national survey-based study to better characterize male infertility knowledge across educational status, personal experience with infertility, socioeconomic characteristics, and income level. We assessed knowledge about infertility incidence, insurance coverage, risk factors, and outcomes with a diverse US-based adult cohort including participants without medical training, HCPs, urologists, and reproductive urologists.

We further propose a unique, objective scoring system, Knowledge of Male Fertility (KM-Fert), to quantify gaps in knowledge and assess future interventions aimed at raising awareness about this significant public health problem. We present this article in accordance with the STROBE reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-122/rc).

Methods

After obtaining approval from the institutional review board (IRB) of Cleveland Clinic Foundation (No. 22-308), a brief questionnaire was designed by a fellowship-trained reproductive urologist to assess baseline demographics, prior personal experience with infertility, perceived prevalence, cost for treatment, and insurance coverage (Table S1). Participants were also asked to identify potential risk factors that may lead to infertility. Risk factors ranged from well-established factors including testosterone therapy, alcohol consumption, obesity, and smoking to less well-established potential risk factors such as cell phone usage. Results were then aggregated and compared to the referent standard (fellowship-trained reproductive urologist responses). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). A waiver of informed consent was approved given the anonymous nature and minimal risk of the study.

Furthermore, participants were then provided an opportunity to freely respond to queries regarding male infertility definition, stigma, and barriers to care. Responses were then grouped by theme including external pressures/negative self-assessment (stigma, embarrassment, fear, shame, etc.), internal biases or attitudes (pride, masculinity, ego, bias towards an only female issue), lack of knowledge/understanding, cost or lack of access, and other/miscellaneous. If respondents gave more than one answer, the first response provided was used in analysis.

Surveys were distributed via social media posts, email, and text message. Emails and messages were sent to colleagues, medical trainees, and acquaintances. To provide a more comprehensive response pool, an Amazon Mechanical Turk (MTurk) crowd-sourcing platform was also utilized. To ensure data quality, strict qualifications were utilized to obtain MTurk responses, and only legitimate, non-redundant responses from US-based IP addresses were accepted. Survey responses were stored in an encrypted database, and statistical analysis was performed with R (Version 4.3.1, The R Foundation for Statistical Computing).

Statistical analysis

Categorical variables were compared with Pearson’s chi square or Fisher’s exact test, where applicable. Adjustments, where needed, were performed with the Monte Carlo simulation. Continuous variables were compared with the Kruskal-Wallis test. Post-hoc analysis was performed with the Dunn test and corrected with the Bonferroni method. Normality was assessed with Shapiro-Wilk, Kolmogorov-Smirnov, or quantile-quantile (Q-Q) plot, where appropriate. Simple linear regression analysis was performed between categorical-continuous variables and continuous-continuous variables.

Following data aggregation, a novel scoring system was developed to objectively quantify overall understanding of male infertility and named KM-Fert. The scoring system includes the most salient points for total understanding of male infertility using responses provided by andrology-trained clinicians as the gold standard referent values. These responses were sought via targeted emails to known andrology-trained clinicians at various institutions. The scoring is as follows:

• Correct estimation ±10 percentage points of the accurate contribution of a male factor to infertility, approximated to be 50% of cases (e.g., estimation between 40–60%) =2 points.
• Correctly estimates that less than 50% of US male fertility treatments are covered by insurance =2 points.
• Correctly identifying testosterone therapy as a risk factor for infertility =2 points.
• Correctly identifying either tobacco use, alcohol use, recreational/illicit drug use, or obesity as a risk factor =0.5 points each.
• Correctly identifying cancer treatments or cystic fibrosis (CF) as risk factors =1 points each.

Grading based upon total points was then classified based on post-hoc quartiles:

• 8.5 to 10 (75^th percentile) = excellent knowledge.
• 7 to less than 8.5 points (50^th–75^th percentile) = good knowledge.
• 5 (25^th percentile) to less than 7 points = fair knowledge.
• 0 to less than 5 points = poor knowledge.

Results

A total of 242 respondents completed the survey. Demographics are listed in Table 1. The average age of respondents was 39.73 (±13.57) years old. One hundred thirty-one (54.1%) respondents identified as female, 108 (44.6%) as male, and 3 (0.1%) identified as non-binary. Sixty-one HCPs responded to the survey [n=53 physicians, n=8 nurse practitioners (NP) or physician assistants (PA)] of which 37 were urology trained. Of the urologists, 16 were reproductive urology subspecialists.

Race was not statistically significantly correlated with socioeconomic status (P=0.06) or educational level (P=0.26) (Table S1). Socioeconomic status appeared to be independent of education level in this cohort (n=220, P=0.28).

Qualitative thematic analysis of the free responses provided to query about barriers to pursuing infertility care and dialogue revealed that most respondents fell into the external pressures/negative self-assessment category (n=119, 49.2%) with common responses being “stigma”, “embarrassment”, “fear”, and “shame”. Other common themes included internal biases/attitudes (n=54, 22.3%), lack of knowledge (n=33, 13.6%), cost/lack of access (n=17, 7%), and other reasons (n=16, 6.6%).

Nearly all respondents regardless of healthcare knowledge identified cigarette smoking or vaping (85.1%), alcohol (82.6%), recreation/illicit drug use (88%), obesity (82.2%), cancer treatments (90.1%), sexually transmitted disease (86.4%), and testicular injury (91.7%) as risk factors for infertility. Conversely, a minority believed hot tub use (44.2%), tight undergarments (37.6%), coronavirus disease 2019 (COVID-19) infection (41.3%), and fever (34.3%) were risk factors.

Surprisingly, only 67.4% of respondents (n=163) identified testosterone therapy as a risk factor for male infertility. Education was independently associated with knowledge of testosterone therapy as a risk factor (Table 2) (P<0.0001), with PhD, PA/NP, and medical school education having the highest percentage (86.7%, 75%, 94.3%, respectively). With physicians removed, this relationship still held true (P=0.007). Similarly, socioeconomic status was significantly associated (P=0.004, n=220 following removal of undisclosed income) with knowledge of testosterone risk; however, the distribution was bimodal.

Those with a high school education reported the highest estimated out-of-pocket (OOP) cost for male fertility (median =$31,500, Table 2, P=0.008) relative to other education levels. Post-hoc analysis demonstrated this difference to be significant between the HS and medical school graduate cohort ($31,500 vs. $17,000, adjusted P=0.02) and HS and some college cohort ($31,500 vs. $17,000, adjusted P=0.03, Table 2) In contrast, socioeconomic status did not demonstrate statistically different median estimations of insurance coverage of male fertility treatments (P=0.18) or OOP cost (P=0.86). Estimated percent coverage of male fertility did significantly predict the estimated mean OOP cost on regression analysis (P=0.009); however, this relationship was weakly correlated in the negative direction (Coeff=−86.36, r²=0.027).

HCP sub-analysis

When examining reproductive urology subspecialists alone (n=16), the median KM-Fert was expectedly high (10) and higher than other HCPs {10 [interquartile range (IQR): 9.25, 10] vs. 8 (IQR: 7.5, 10), P=0.002}. Specialty was not predictive of amount of insurance coverage (P=0.76) or outpatient treatment cost (P=0.17). Similarly, knowledge grade of urologists was significantly associated with greater “Excellent” versus other HCPs (59.5% vs. 33.3%, respectively, P=0.02). Medical specialty was not significantly associated with median estimated OOP treatment cost [$18,500 (IQR: $11,875, $30,125) vs. $11,500 (IQR: $6,000, $30,000), P=0.18 for non-urologists vs. urologists, respectively].

KM-Fert score results

Median KM-Fert score of the reproductive urology subspecialists was a perfect score (10). This was significantly higher than other respondents [10 (IQR: 9.25, 10) vs. 7 (IQR: 5, 8), P<0.001]. Urology and/or reproductive urology training was also a significant predictor of KM-Fert (P=0.006, r²=0.12).

Mean KM-Fert score was 6.91 (2.30), corresponding to “Fair” knowledge of male infertility. The largest cohort of respondents fell into the “Good” category (n=69, 28.51%), while encouragingly the lowest percentage of respondents fell in the “Poor” knowledge category (n=43, n=17.77%).

Individuals who fell into the “Fair” or “Poor” category had modestly lower odds of having completed a college education [80.6% vs. 94.8%, P<0.001, odds ratio (OR) =0.23, 95% confidence interval (CI): 0.09, 0.55].

Education level clearly did influence total KM-Fert score (P<0.0001). Post-hoc analysis demonstrated a significantly higher median score for medical school graduates versus all other education levels except for NP or PA graduates (P=0.28); however, no other groups significantly differed (Table 3). Distribution of respondent scores are demonstrated in Figure 1.

Figure 1 Violin plot of KM-Fert Score distribution by presence of urological training (top left), SES (top right), and education level (bottom). KM-Fert, Knowledge of Male Fertility; SES, socioeconomic status.

Similarly, socioeconomic status demonstrated significant differences in median KM-Fert (P<0.001) (Figure 1). Post-hoc analysis demonstrated median KM-Fert score decreased with income (Table 3). However, this difference was limited by the removal of physicians (P=0.26). Race was not significantly associated with KM-Fert score (P=0.31). Finally, personal experience with infertility was not significantly predictive of KM-Fert score (P=0.36).

Discussion

Male fertility is a significant public health problem worldwide and may be increasing due to several factors as evidenced by emerging data suggesting ongoing decline in sperm counts (10,11). As a marker of overall health and a vital component to maintaining the human population, diagnosis and treatment of this condition is a vital but often underestimated component in maintaining the wellbeing of patients and society alike (12). Society and pop culture invoke male reproduction and sexuality as a central component in the traditional male persona or stereotype. Unfortunately, this dogma translates to men suffering from male infertility feeling marginalized, inadequate, and isolated. This suppression on a male-to-male basis then leads to more systemic suppression of the dialogue surrounding male infertility by acting as a feed-forward mechanism and further propagating the stigma surrounding this disease.

In this study, we attempted to better understand the baseline knowledge, attitudes, and perceived cost in both the general public and in HCPs in the United States. The results from this study identify a knowledge gap surrounding this sensitive topic and suggest outreach efforts to normalize the dialogue and provide high-quality information would be beneficial.

Respondents of higher socioeconomic status also tended to have better KM-Fert than other income levels, though much of this effect can be attributed to the high proportion of HCPs in this survey. Prior research has focused mostly on male identification of risk factors for infertility, although some, such as a 2012 study by Bunting et al., examine misconceptions around fertility (13-16). In general, these studies found that most respondents can identify ubiquitously understood risk factors for infertility, such as smoking, alcohol use, and obesity, although overall knowledge appears to be suboptimal (13,16,17). Interestingly, personal experience did not significantly correlate with KM-Fert score. This would suggest that reproductive health professionals should be prepared to provide comprehensive assessments and counseling regarding male infertility risk factors irrespective of a patient’s background with male infertility.

Objective measures of male fertility understanding are scarce. Bunting et al. utilized a 13-question survey mainly aimed at assessing risk factors for both males and females (14). They compared percentage correct as a marker of “Fertility Knowledge” and found employment, female gender, and collegiate education to significantly be associated with knowledge level (14). Iktidar et al. looked more closely at male fertility, specifically, via a 34-question survey and rated “Good” knowledge as greater than the mean score (18). These studies represent important work for rating and assessing male fertility knowledge among the population; however, we believe our KM-Fert score expands upon these findings in several important ways. Firstly, it provides a quantifiable measure that differentiates fertility knowledge on a scale. Secondly, it incorporates not only well-known and non-controversial risk factors (such as smoking, testosterone, and CF), but also understanding of percentage of male contribution to infertility amongst heterosexual couples as well as healthcare system support and cost of fertility. Overall, we believe this score represents a more holistic assessment of fertility beyond strictly medical risk factors and knowledge alone. That our reproductive urology cohort had a median perfect score which significantly differed from both other HCPs and the general populous adds validity to the KM-Fert assessment.

While our data does indeed demonstrate that HCPs have an appropriately higher baseline knowledge than non-HCPs and further stratified according to specialty training in urology and reproductive urology in particular, there is still a significant proportion of caregivers who may benefit from further education on this topic (e.g., 25% of advanced practice providers not recognizing testosterone therapy as a risk factor for infertility). While this survey was not designed to assess HCP screening patterns or referrals for male infertility, developing further strategies to assist HCPs treating reproductive-aged men with identification and referral would be of benefit. Our KM-Fert results demonstrate that, unsurprisingly, urologists have a better knowledge base surrounding male fertility than other HCPs. Given that a large percentage of patients seeking fertility care may never see a urologist, and thus may be unaware that exogenous testosterone is a risk for infertility, it is vital that urologists and andrologists proactively work to educate other healthcare colleagues around male fertility (5,19).

Fertility treatments can be highly cost-prohibitive for many couples. It is estimated that out of pocket expenses can range from hundreds to tens of thousands of dollars depending on treatment modality and/or number of attempts at successful pregnancy (20,21). Additionally, insurance coverage of treatment is consistently low (22,23). Furthermore, access to cost information may be difficult to obtain. A recent study by Larsen et al. found that only a quarter of Society for Assisted Reproductive Technology (SART) clinics provide costs of male fertility treatments and workups (24). Our data suggests that this is a genuine barrier for men seeking fertility treatment as approximately 20% of responses were categorized as due to lack of knowledge or cost/access. Interestingly, our analysis demonstrated that those with the lowest education completed estimated the highest OOP cost compared to physicians, but household income did not predict estimated OOP. This may be due to lack of transparency, lack of experience or access to male fertility treatment or poor education on outpatient costs. Regardless of etiology, our data suggests that more effective counseling and more accessible cost resources are required to increase the parity among patient understanding of fertility cost.

We believe our study provides an objective, scaled quantification of gaps in knowledge amongst the general population regarding male fertility. Secondly, andrologists having a median perfect score suggests that the scoring system has some validity. Thirdly, the score incorporates multiple areas of fertility care, including OOP cost and insurance coverage, which provides a more holistic measurement of male fertility knowledge. Our study has several important limitations. Firstly, as a cross-sectional study, conclusions about causality cannot be made. Additionally, while the methods of distributing our survey were utilized to best assess gaps between certain groups, the methodology is not a truly randomized selection of the population. Second, the KM-Fert score is arbitrary. While the scale was developed per the recommendations of a male-infertility specialist, it is not a validated scoring system like the FIT-KS for assessing fertility knowledge (8,9). Furthermore, as male fertility coverage varies by state, individual experiences may vary. However, as most states do not over comprehensive male fertility coverage, we believe that incorporating an estimation of insurance coverage into the score is reflective of the average national experience (23,25). However, unlike FIT-KS, this score focuses much more on male factors and was not intended to individually assess a person’s knowledge, thus its clinical utility differs. Rather, KM-Fert provides a scaled, post-hoc quantification of knowledge gaps among a population which can provide points of emphasis for future studies aimed at improving public understanding. Finally, our cohort was majority white race and college-educated, which may not be representative of the United States population as a whole.

Conclusions

The general population has variable knowledge surrounding male fertility, and there is an opportunity for non-urology providers treating reproductive-aged man to improve their knowledge, screening, and referrals for these patients and particularly those with low education and socioeconomic status. Reproductive urologists represent the ideal group to educate fellow HCPs in this regard, and patients may benefit from referral by other HCPs and urologists to specialists when treating male infertility. Ultimately, the societal stigma and costs associated male infertility will continue to act as a barrier to timely diagnosis and both medical and psychological treatment for men suffering from this condition.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-122/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-122/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-122/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-122/coif). S.C.V. is a consultant for Swiss Precision Diagnostics. N.P. is a consultant for Halozyme Therapeutics Inc. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the institutional review board of Cleveland Clinic Foundation (No. 22-308). A waiver of informed consent was approved given the anonymous nature and minimal risk of the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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