Introduction

Infertility affects one in five women in the USA, according to the CDC [1]. Studies have shown that infertility impacts women in a variety of significant ways; one such study found that women with infertility had a 32% increased relative risk of mortality of any cause than women without infertility [2]. With the exception of advancing age and increasing BMI, it was found that infertility rates were comparable across various races/ethnicities, educational backgrounds, and household income brackets [3,4,5]. However, the rates at which women seek treatment for infertility correlate with socioeconomic status, higher education, and whether or not they were insured [5]. Treatments for infertility have been collectively referred to as medically assisted reproduction (MAR), encompassing assisted reproductive technology (ART), in vitro fertilization (IVF), and intrauterine insemination (IUI), among others [1]. Studies show that historically marginalized patients are less likely to seek treatment for infertility compared to their white counterparts [6]. Healthcare inequities research seeks to identify potential barriers to MAR access and is necessary to increase accessibility to successful treatment for future patients with infertility.

Healthcare inequities are defined as systematic differences in access to healthcare between populations due to specific characteristics pertaining to each group [7]. Inequities in healthcare are often associated with demographic characteristics including gender, socioeconomic status, education level, geographic location, race, and ethnicity [7]. In general, research indicates that certain social inequities majorly influence the health, well-being, and quality of life of affected patients with infertility [8]. Within MAR research, many studies focus on health factors linked to infertility [9]. In conjunction with these existing studies, further research on healthcare inequities can help elucidate barriers to accessing care.

To our knowledge, this scoping review was the first of its kind to investigate research into healthcare inequities for people seeking care for infertility. A scoping review design was chosen due to the ability of scoping reviews to compile large bodies of literature and subsequently identify knowledge gaps within that literature. This design is helpful in identifying specific research questions that may be further pursued by systematic reviews [10]. In this study, multiple databases were used to evaluate studies pertaining to MAR, specifically as it relates to healthcare inequities. The purpose of our study was to identify which areas of MAR research concerning healthcare inequities may be inadequate, so that more targeted research in those areas can be pursued in the future.

Methods

We conducted a scoping review to identify the state of inequities research related to MAR and to determine where gaps exist. We conducted our study in accordance with best practice recommendations according to the Joanna Briggs Institute (JBI) methodology for scoping reviews [11]. For standardization of reporting, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) [12]. We used a pilot-tested methodology incorporating standardized search strategies, inclusion/exclusion criteria, and data extraction materials. Our entire methodology and protocol were uploaded to Open Science Framework (OSF) to ensure transparency and reproducibility of results [13]. This study did not constitute human subject research and was not subject to Institutional Review Board oversight.

Literature Search

In July 2022, we performed a database search of MEDLINE (via PubMed) and Ovid Embase databases to identify published articles on health inequities related to MAR. According to the JBI Manual, two online databases should be used to search for articles pertaining to the review topic [11]. A 2016 study showed that use of these two specific databases together retrieved over 97% of citations included within 120 systematic reviews [14]. Published articles pertaining to inequities within the topic of MAR were obtained using PubMed’s (MeSH) and Embase’s (Emtree) list of vocabulary. Search terms were created using the National Institute of Health’s (NIH) list of health inequity populations, which encompass the following: race and ethnicity, sex, gender, LGBTQ + identity, under-resourced/rural populations, education level, income, and occupational status [15]. Our search was modeled after a recent publication within the field of laryngology following guidance and recommendations from the JBI Manual [11, 16]. This search was published a priori on OSF [13].

Research Question

Our scoping review was focused on the following research question: Among patients needing MAR, what research has been published on inequities, and how has that research changed over the past six years (2016–2021)? We set out to answer sub-questions which included (1) did studies investigating similar inequities in MAR have concordance of findings and (2) to what extent has Sexual and Gender Minorities been researched since the NIH’s 2016 addition? The purpose of our scoping review was to identify the state of research into inequities within MAR. Additionally, we sought to provide recommendations for future research.

Training

We used two different resources for training of investigators which were delivered in person and/or on video. Prior to conducting this study, all authors were trained on the purpose and methodology of conducting a scoping review according to the JBI Reviewers’ Manual 2020 [11]. The Cochrane Learning Live webinar provided supplemental information on scoping reviews as well [17].

Selection Process

After identifying citations from our systematic search, we uploaded our results into Rayyan, a platform used for conducting title and abstract screening [18, 19]. Two authors (A. H. and K. R.) first removed duplicates from the initial search and then conducted title and abstract screening according to inclusion criteria described below. The authors conducted screening in a masked, duplicate fashion. Conflicts in decisions between investigators were resolved through discussion, with a third investigator (R. B.) available for adjudication. Rationale for exclusion of studies was recorded and presented in the PRISMA flow diagram.

Inclusion and Exclusion Criteria

The population, concept, and context framework provided by the JBI manual was used to develop criteria for inclusion in this scoping review [11]. Our population included literature with one of the following study designs: cohort studies, retrospective database reviews, cross-sectional analyses, literature reviews, qualitative studies, scoping reviews, systematic reviews/meta-analyses, clinical trials, and case–control studies. The concept of our scoping review consisted of research focusing on health inequities related to MAR. To increase sample validity and feasibility, our review only included literature published in English [20, 21]. The context was addressed by limiting our inequities to the NIH’s list of health disparity populations [15]. Studies included in this analysis were published between 2016 and 2021. This restriction was based on the NIH’s classification of Sexual and Gender Minorities as a health inequity population in 2016 [22]. Finally, we limited the scope of our review to publications with study populations from the United States due to inconsistent connotations regarding inequities across cultures [23].

Criteria for exclusion in our data analysis included studies which (1) were published prior to 2016 or after 2021, (2) were published in a non-English language, (3) were unrelated to MAR, (4) were related to fertility preservation (e.g., cryopreservation), (5) did not analyze one of the health inequities listed, (6) non-human studies, and (7) were published as a letter to the editor, correspondence, or commentary. These study designs were excluded as they do not consistently report original research. The PRISMA flow diagram shows the reasons for exclusion throughout the screening and data extraction process.

Data Charting

We used a pilot-tested Google Form to extract data from studies including title of the study, author name, PubMed Identifier, publication year, years analyzed, study design, number of subjects enrolled, and setting (e.g., single institution, multi-institution). Data points were based on recommendations from the JBI Reviewers’ Manual 2020 [11].

As a calibration exercise, two researchers (A. H. and K. R.) extracted data from five identical studies. Findings were then reconciled through discussion with a third author (R. B.) available to resolve disagreements as necessary. Following completion of this calibration exercise, the authors then completed extraction from the full sample obtained in the screening process. Full sample data extraction was completed in a similar fashion. Findings for each inequity were summarized in a table including the author name, publication year, setting, number of participants, conditions examined, inequities examined, and a brief summary of the findings from each study.

Use of Language

We used standard language based on the American Medical Association’s (AMA) Advancing Health Equity: A Guide to Language, Narrative and Concepts to provide equity-centered, person-first language [24]. If an included study did not adhere to this guide, efforts were made to standardize language without losing original author intent.

The following race-conscious terms adopted from the AMA were used throughout this study: Black, white, Hispanic/Latina/Latino/Latinx, Native peoples/Indigenous peoples/American Indian and Alaska Native [24]. The term “historically marginalized” was used to refer to groups who have been oppressed, excluded, and segregated [24].

The AMA defines “sex” and “gender” as separate concepts. “Sex” refers to the assignment at birth as either a male or female “Gender” refers to the emotional, psychological, or social behaviors which define someone as a man, woman, both, or neither [24]. Studies which did not delineate between sex or gender were coded under both terms. We used the acronym LGBTQ + as a general term to describe all of the sexual orientations as well as sex and/or gender identities [24].

Data Summary and Presentation

Our findings were summarized using Stata 17.0 (StataCorp, LLC, College Station, TX, USA). We described frequencies and percentages of (1) which inequities were found, (2) study designs (e.g., cross-sectional analyses, cohort studies, etc.), and (3) settings of each study (e.g., National database, regional database, etc.). The frequency of studies investigating research into Sexual and Gender Minorities since 2016 was also assessed.

Results

General Characteristics of Included Studies

Our literature search initially yielded 5318 articles. After 917 articles were identified as duplicates and removed, the titles and abstracts of 4401 unique results were screened, with 96 included studies remaining. Among these, 30 studies did not meet inclusion criteria and were therefore excluded from data extraction. Sixty-six studies were included for full-text review and extraction. The PRISMA flow diagram—designated Fig. 1—demonstrates the study selection process and provides brief reasoning for exclusions. Table 1 outlines the frequencies of the inequities examined, study designs, and study settings. The most common inequity examined within our sample was race/ethnicity (45/66; 68.2%), followed by income status (30/66; 45.5%), and education (22/66; 33.3%). The least common inequities examined included sex and/or gender (4/66; 6.1%), occupational status (5/66; 7.6%), and rural/under-resourced populations (5/66; 7.6%). Table 2 summarizes the main findings of included studies with respect to inequities examined. Figure 2 demonstrates the frequencies of the top three most commonly examined inequities.

Fig. 1
figure 1

PRISMA flowchart

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Table 1 Frequencies and percentages of inequities among included trials
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Table 2 Characteristics of included studies
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Fig. 2
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Frequency of inequities examined over time

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Race/Ethnicity

Within the included studies, 45 examined race/ethnicity. The majority of these examined negative outcomes following MAR such as in vitro fertilization, intrauterine insemination, and intracytoplasmic sperm injection, among others. Low live birth rates following MAR were found in Black [6, 34, 40, 46, 50, 51, 61, 63, 65, 69, 73, 76, 77, 88, 90], Asian [6, 40, 50, 51, 61, 63, 69, 73, 77], Hispanic [6, 34, 51, 57], and Middle Eastern/North African women [75]. Low intrauterine/clinical pregnancy rates were seen in Black [6, 51, 65, 69, 85, 88], Asian [6, 51, 65, 69, 85, 88], Hispanic [6, 51, 69], and American Indian/Alaska Native [33] women. Two studies found high spontaneous abortion rates in Black women [6, 73]. Low implantation rates were seen in both Asian [57] and Black [76] women. Humphries et al. found an association between increased risk of pregnancy loss and Hispanic, Black, and Asian race/ethnicity [51]. American Indian/Alaska Native women had low delivery rates [33]. Salem et al. found low fertilization rates after intracytoplasmic sperm injection in Middle Eastern/North African women [75]. One study found that BIPOC women undergoing ART were more likely to give birth to a low gestational age or extreme preterm infant [85]. Another study found that Black women using ART were more likely to have preterm or low birth weight infants [34]. In contrast, two studies found that race and ethnicity were not associated with blastocyst formation rate [56] or spontaneous abortion rates [88]. Six studies found that race and ethnicity were not associated with other aspects of infertility treatment, such as financial strain, duration of treatment, receiving medical evaluation, or having treatment declined [35, 39, 41, 47, 52, 72].

In addition to investigating outcomes following MAR, the included studies evaluated the likelihood of seeking infertility treatment. Four studies found that ART use was highest in white women [27, 28, 43, 73], while three studies found that Asian women had the highest rate of use [38, 84, 89]. Seven studies found that Hispanic women were less likely to seek out infertility treatments or use ART [6, 35, 38, 45, 54, 83, 84]. Some studies found Black women [6, 26, 38, 54, 83, 84], Asian women [6, 35], American Indian/Alaska Native women [38], or Pacific Islander women [38] to have lower rates of infertility treatments. Two studies found that Black women were more likely to discontinue IVF treatments [29, 76].

Sex/Gender

There were four studies that examined sex and/or gender findings in our sample. Rangel et al. found that female surgeons were more likely to use ART than male surgeons [74]. Although multiple health organizations recommend both individuals in a couple struggling with infertility be evaluated, Strasser and Dupree found that men were only evaluated a quarter of the time [80]. Private funding in the form of grants helped increase access to fertility care for single men and women [71]. In one study, participants supported limitations to IVF treatment due to age, disproportionately in favor of restricting access for women compared to men [91].

Income

Of the included studies, 23 found that income was positively correlated with access to treatment and/or decreased financial strain [6, 25, 27, 30, 36, 39,40,41,42, 45, 47, 52, 54, 58, 64, 69, 70, 72, 73, 80,81,82, 89]. Two studies reported no correlation between income and limitations to treatment [29, 78]. One study found that lower incomes were associated with lower live birth rates [46].

Rural/Under-resourced

Regarding location, three studies found that living in rural areas created a barrier to accessing treatment [48, 65, 70]. One study found that those living in a metro area were less likely to have ethical concerns about infertility treatment and one study found no correlation to MAR being associated with geographical region [32, 47].

Education

In our sample, 22 studies examined education level and its varying associations to infertility treatment. Of the included studies, 14 found that higher levels of education were positively associated with receiving MAR treatment [27, 34, 38,39,40, 45, 54, 64, 69, 72, 73, 78, 81, 84]. Five studies showed inconsistent associations between higher levels of education and various MAR outcomes [32, 42, 47, 68, 91]. Three studies found no correlation between the level of education and MAR outcomes [46, 52, 86].

LGBTQ + 

Since the NIH’s classification of Sexual and Gender Minorities as a health disparity population in 2016, 15 articles within our sample investigated LGBTQ + inequities in MAR, illustrated in Fig. 3. In regards to the findings of the studies, barriers to accessing MAR were common themes. Three studies found that fertility clinic websites were lacking in educational content focused on LGBTQ + fertility issues [31, 53, 55]. One study found that over half of fertility clinics partnered with the Society for Assisted Reproductive Technology displayed LGBTQ + content on their homepage; however, these clinics tended not to be found in the Midwestern or Southern United States [87]. Other barriers discussed in our studies were difficulties with insurance approval for treatment [30], negative experiences with treatment providers [31], and lack of benefits and discrimination [62]. In contrast, one study found that access to infertility treatment was increased for LGBTQ + patients through funding from charity foundations [71]. Regarding MAR outcomes, three studies found more positive outcomes for LGBTQ + patients compared to their heterosexual and cisgender counterparts [44, 60, 66]. Downing et al. found no differences in outcome risks between same-sex couples using ART and those not using ART [37]. Similarly, Nazem et al. found that pregnancy outcomes did not differ between same-sex and heterosexual couples [67]. One study by Arocho et al. found that sexual minority women made up less than half of patients who use donor insemination [27]. Lee et al. found that sexual minorities are less likely to support age limitations on IVF use for women [91]. One study found that there was no association between sexuality and negative or positive MAR beliefs [86].

Fig. 3
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Frequency of studies investigating sexual and gender minorities inequities since 2016

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Occupational Status

Of our included studies, five examined occupational status. Rangel et al. found that through both non-assisted conception and ART, female surgeons were more likely to have major pregnancy complications than non-surgeons [74]. Greil et al. found that women who were unemployed were less likely to undergo fertility testing; meanwhile, employed women undergoing MAR were more likely to have depression symptoms [45]. One study found that white patients were more likely to report that taking time off work would be a barrier to accessing MAR [52]. Lindheim et al. found that couples undergoing ART in the USA were less likely to take on extra employment in order to afford care [62]. One study found that occupational status was not associated with financial strain due to infertility treatment [39].

Discussion

Our findings identified several limitations for patients who use or attempt to access MAR. Included studies in our sample frequently evaluated race/ethnicity, income, and education inequities and their association with MAR. However, research into MAR inequities, with respect to sex and/or gender and those in rural/under-resourced areas, are in need of ongoing investigation. We will further discuss the areas in which research is deficient and explore the significance of these gaps.

Race/ethnicity is associated with significant inequities in infertility treatment. In our review, the majority of studies evaluated race and ethnicity. The gaps within this line of research are numerous and complex, and though our study has highlighted some, there are likely more that have yet to be researched. Additional research is greatly needed in many areas; however, only a few are described here. First, our review identified gaps in access to healthcare, and additional research is needed to determine the extent and nature of inequities in infertility treatment. A usable system for race and ethnicity documentation is critically necessary for standardization, in order to facilitate the conduct of essential studies. The Society for Assisted Reproductive Technology (SART) does not require practitioners to document race/ethnicity; thus, this data is missing for a substantial proportion of patients [92]. A 2021 study estimated that information regarding racial and ethnic demographics is missing for over 47% of patients within the SART database [93]. Turner et al. conducted an investigation of race/ethnicity reporting in clinical trials which found that only 44% of studies reported any race/ethnicity data over the two-decade study period [94]. They also found a lack of cross-tabulation of race and ethnicity reporting, to better capture the diversity of these populations [94]. For example, Asian populations are difficult to properly study, owing to the complex heterogeneity of this group. Women from Asian backgrounds are often all grouped together within one category, which limits data analysis. However, placing Asian women into specific categories runs the risk of sample sizes too small for adequately powered or meaningful analyses [94]. More research is needed to evaluate the collection of patients’ race/ethnicity information. Also, a majority of studies on inequities in infertility treatment focus solely on Black and white patient differences. Studies are needed to better understand the extent, nature, and causal factors contributing to these inequities in other racial/ethnic groups.

The LGBTQ + community experiences significant barriers to healthcare across a range of health conditions including mammography screening [95], cervical cancer screening [96], and abortion care [97]. These barriers are complicated by LGBTQ + hesitancy in seeking care due to a variety of reasons such as (1) lack of cultural awareness and sensitivity by providers and (2) fear of discrimination by providers [98, 99]. A systematic review examining culturally competent care for the LGBTQ + community seeking infertility treatment found many studies in their sample emphasized the need for patient-reported suggestions to improve competency in providers [100]. The call for patient-reported suggestions highlights the need for increased uptake of community-based participatory research to meaningfully engage historically marginalized populations while empowering them to be an active participant in the creation of research questions and outcomes to improve accessibility and adherence to care [101]. Current suggestions to improve accessibility include the use of gender-neutral language on forms and signs, the inclusion of all partners for the patients undergoing treatment regardless of their sex, and the breakdown of cis- and heteronormative expectations in healthcare settings [100]. Although some research has been done to explore ways to create more culturally sensitive care, studies examining the implementation of these suggestions and their success are pertinent to ensuring these changes are widespread.

Overall, the studies within our sample generally focused only on women with sex and/or gender inequities being sparsely investigated. Although Chandra et al. found that men and women of reproductive age have similar rates of infertility—it was found that male partners do not receive an evaluation a quarter of the time—despite multiple health organizations recommending that both members of infertile couples be evaluated [80, 102]. When infertile males were evaluated, one study noted an imbalanced diagnostic process where male infertility is usually only based on seminal analysis. However, female infertility diagnoses explored a broader variety of potential causes [103]. Furthermore, coverage for male factor infertility treatment is also largely excluded from healthcare laws in comparison to females, which disproportionately places the responsibility for reproductive treatment on female partners [104]. In order to provide more equitable access to infertility diagnosis and treatment, Coward et al. recommend further evaluation into defining the population of infertile men and the inequities in accessing treatment [105]. Future policy changes to increase insurance coverage for male infertility diagnosis and treatment, and to create guidelines to increase access to infertility evaluation, is dependent on understanding the full scope of the problem.

Rural and under-resourced populations were seldom investigated in our sample. Brodeur et al. found that almost 25 million women in the USA live in areas that do not have nearby access to an ART center and that male infertility specialists are also difficult to come by [106]. Due to a lack of fertility specialists, Chin et al. found that women in rural or under-resourced areas were likely to visit a general practitioner or an OB/GYN for fertility concerns, and were more likely to receive treatments limited to less invasive options such as oral medications [107]. To help improve treatment inequities in under-resourced areas, the American Society of Reproductive Medicine recommended improving education and awareness on the signs and symptoms of infertility and the treatment options, which can help guide patients into their options and allow them to advocate for themselves [108]. For example, the Walking Egg Project aims to bring affordable infertility care to under-resourced populations worldwide. Their project initiative is multidisciplinary in nature and emphasizes the role of education around reproductive health and proper training for providers, in order to create a decreased disease burden and greater understanding of the needs of under-resourced populations [109]. Although investigation into the use of technology to bridge the gap in access for rural and under-resourced populations has seen significant improvement, there is still a lack of research on how to increase access to ART. These gaps are in part due to the nature of the intervention, which requires a significant amount of treatment cycles, numerous phlebotomy-based tests and imaging procedures, and visits to the fertility specialist, creating logistic challenges for patients in under-resourced areas [48]. A recent pilot study—run in collaboration with the Walking Egg Project—investigated the possibility of a simplified culture system for IVF that would reduce the requirement for specialized equipment and decrease costs [110]. The multidisciplinary efforts of this research can allow for collaborative decisions to create individualized treatment plans that patients can commit to and is suitable for their lifestyle, making MAR more accessible.

Strengths and Limitations

In regard to the strengths of our paper, the protocol was written a priori and was strictly followed. To ensure reproducibility and transparency, our protocol and extracted data was uploaded to OSF [13]. All screening and data extraction were performed in a masked, duplicate fashion. Investigators were trained using materials from Cochrane and the JBI, and sample extraction was done to achieve high inter-rater reliability. Our study had several limitations. First, our sample was composed primarily of cross-sectional analyses and findings may not remain relevant over time. Furthermore, it is possible that we did not capture all available literature regarding inequities in MAR due to our inclusion criteria being limited to studies that were in English, completed in the USA, and published between 2016 and 2021. However, Bramer et al. found that searches of both EMBASE and PubMed were sufficient to retrieve 97.5% of the relevant studies [14].

Conclusion

Our study showed that race/ethnicity, income, and education were the most frequently investigated inequities regarding MAR, while sex or gender, rural/under-resourced populations, and occupational status were the least frequent. We identified research gaps in each of these areas and recommend the following for future research: (1) standardizing and diversifying race/ethnicity reporting regarding MAR, (2) increasing access to infertility care for LGBTQ + populations by providing more inclusive care, (3) increasing access to infertility care for men, and (4) increasing access to MAR for rural/under-represented populations by identifying logistic challenges.