Abstract
Purpose:
To describe the association between geographic location of residence and use of aneuploidy screening or prenatal genetic counseling and how it is modified by maternal race and ethnicity.
Methods:
Retrospective cohort of individuals at a tertiary care center between 2017-2019. County of residence was classified as rural or metropolitan based in US Office of Management and Budget 2019 definitions. Maternal race and ethnicity were self-identified. Our composite outcome was defined as use of aneuploidy screening or genetic counseling visit. The composite outcome was compared by geographic location and ethnicity. Logistic regression was used to model the relationship between geographic location and the composite outcome.
Results:
A total of 8774 pregnancies were included. Of these, 4770 (54%) had genetic screening, and 3781 (43%) had at least 1 genetic counseling visit. Rural patients were significantly less likely to have the composite outcome compared with metropolitan peers (37.1% vs 47.2%, P < .001). In addition, we identified differences in the composite outcome between White rural patients and LatinX rural patients (37.7% vs 35.6%, P < .001) and between Asian rural patients and LatinX and Black rural patients (41.0% vs 35.6%, P < .001; 41.0% vs 36.8%, P < .001). Logistic regression demonstrated that rural patients were significantly less likely to have the composite outcome compared with metropolitan peers, after adjusting for LatinX ethnicity and gestational age at first prenatal visit (OR 0.72, [0.55, 0.95], P = .002).
Conclusion:
Rural, minority patients were significantly less likely to receive reproductive genetic services compared with metropolitan peers extending our knowledge of disparities in maternity care.
Keywords: Aneuploidy screening, Disparities, Reproductive genetics
Introduction
Advances in prenatal genetic screening and diagnosis are the most rapidly developing aspect of early pregnancy care.1 According to guidelines set forth by the American College of Obstetricians and Gynecologists, aneuploidy screening and/or diagnosis should be offered to all pregnant people. In addition, pretest counseling is recommended that accounts for a pregnant person’s values and the benefits and limitations of the suggested testing strategy.2 Likewise, the American College of Medical Genetics recommends that cell-free DNA-based screening (cfDNA) should be offered as the first line screening strategy above other traditionally accepted methods of aneuploidy screening. In addition to routine aneuploidy screening, the American College of Medical Genetics states that patients should be counseled about the availability, benefits, and limitations of additional screening options with cfDNA, such as detecting sex chromosome aneuploidy or subchromosomal variants.3 This recommendation, in particular, requires nuanced counseling of screen performance and predictive value of negative and positive results.4
Importantly, barriers in accessing pregnancy care and likely reproductive genetic care predate aneuploidy screening guidelines and may be exacerbated with ever evolving care guidelines. There are several reasons barriers to reproductive genetic care may exist. First, aneuploidy screening and diagnosis is most frequently offered by maternity care providers, who have few resources to stay up to date on the rapidly evolving landscape of prenatal genetics and have expressed discomfort in the evolving counseling regarding genetic conditions and testing strategies.5,6 Second, aneuploidy screening and diagnosis are often allotted minimal time during a prenatal visit. Ethicists have often expressed concern regarding the lack of written materials and time allotted to truly obtain informed consent for a screen that can initiate an elective diagnostic cascade.7,8 Unsurprisingly, patients have reported little understanding of cell-free DNA or not being able to recall giving consent for this particular screen.9 Third, access to reproductive genetic counselors, who are trained to provide values-centered counseling regarding prenatal screening and diagnosis, is limited. The genetics workforce is notably scarce, with providers concentrated in urban areas and academic centers; in turn, pregnant people receiving community based care may not have access to providers with genetics expertise for aneuploidy screening and diagnosis despite this being considered routine care.10,11
Most importantly, significant racial, ethnic, and socioeconomic disparities have been documented in access to reproductive genetic services. Christopher et al12 note that individuals of younger age, self-identified Black race and ethnicity, and late presentation to prenatal care are less likely to have access to reproductive genetic counseling during pregnancy after adjusting for insurance status. Likewise, for those electing for prenatal diagnosis, Swanson et al13 demonstrate that individuals self-identifying as being of Black or LatinX race and ethnicity and those that are publicly insured are less likely to undergo chromosomal microarray, even in the presence of fetal anomalies. Furthermore, a pregnant person’s location of residence has been shown to affect pregnancy care and pregnancy outcomes. Those living in rural areas are more likely to present later to care; in turn, this may affect access to prenatal screening and diagnosis. These data are some of the limited available literature assessing disparities in reproductive genetic care and allude to systemic barriers that prevent equal access.14
The objective of this study is to assess the associations between reproductive genetic services, self-identified race, and location of residence. We hypothesize that individuals of Black or LatinX race and ethnicity and individuals living in rural areas are less likely to access reproductive genetic services including aneuploidy screening or genetic counseling.
Materials and Methods
This study was approved by the University of North Carolina at Chapel Hill Institutional Review Board (IRB # 19-2930). We performed a retrospective chart review of individuals with singleton pregnancies who received pregnancy and delivery care at a single, tertiary care academic center in North Carolina between 2017 and 2019. Our center encompasses clinics that cover a large geographic area in the state of North Carolina and multiple specialties that provide maternity care, including general obstetrics and gynecology, midwifery, family medicine, and maternal fetal medicine. We elected to review data from 2017 to 2019 because of the potential change in practices and barriers that emerged after March 2020 due to the COVID 19 pandemic. At our institution, providers were able to order first trimester screening and quad screening. Between 2017 and 2019, some clinics independently ordered cfDNA, whereas others exclusively sent to genetic counseling for ordering all genetic screening. Genetic counseling appointments occurred on a referral basis only after a patient was referred by their obstetric provider. At this time, telehealth appointments were not available for genetic counseling. Genetic counseling visits occurred in person with a certified genetic counselor. Visits were available at 3 outpatient maternity care locations in 2 metropolitan counties in NC, which lie within a 35 mile radius.
Importantly, several changes have occurred within the practice of aneuploidy screening since 2019. First, cell-free DNA-based testing has become the dominant strategy for aneuploidy screening with evolution of American College of Obstetricians and Gynecologists/SMFM guidelines in 2020.2 However, given the lack of a statewide standard for screening in North Carolina, evolution of practice to only cell-free DNA-based testing is limited. In fact, our own clinical experience demonstrates that use of methods with lower detection rates for aneuploidy, such as quad screening, remain a standard of care in some clinics in North Carolina. Second, telehealth during the COVID-19 pandemic significantly changed the approach to obstetric care. However, a statewide assessment of telehealth for maternity care demonstrated that telehealth was less frequently used by rural clinics compared with clinics in metropolitan areas.15 Given this clinical context of maternity care in North Carolina, it is likely that issues of equity and access identified between 2017 and 2019 remain ongoing issues despite new practice patterns in 2024.
Data collected from the electronic medical record included patient age, self-identified race and ethnicity, primary language spoken by patient, insurance status, gestational age at initial prenatal visit, and county of residence. Patients were included in the analysis if they had all their prenatal and delivery care at a practice affiliated with our institution and delivered at a hospital within our health care system. Individuals that transferred in to care later in gestation, transferred to another institution, or delivered at another health care system were not included in this analysis. Pregnancy outcomes of those included in this cohort were not collected and analyzed; thus, individuals who experienced pregnancy loss, termination of pregnancy, or nonviable delivery may be included in this cohort. Individuals self-reported their race and ethnicity; however, this is input into the electronic medical record as an association with a single race or ethnicity. As such, this may not accurately reflect individuals who identify as having greater than 1 race and ethnicity and may, arbitrarily, associate with a singular group. In addition, types of reproductive genetic services were also recorded. Aneuploidy screening was defined as having received cfDNA based screen, First Trimester Serum Screen or Triple Screen (first trimester screen), or Quadruple Marker Screen (quad screen). Genetic counseling services were defined as having at least 1 visit with a certified genetic counselor. Use of diagnostic testing, such as amniocentesis, chorionic villus sampling, was not available.
An individual’s county of residence was obtained from their self-reported address and zip code in the medical record obtained at initial prenatal visit. In the state of North Carolina, there are 100 counties. Per the Core Based Statistical Areas (CBSA) of the United States, each county is categorized as metropolitan, micropolitan, or rural, with definitions set forth by the Office of Budget and Management of the US Census Bureau. The category definitions provided by CBSA are as follows: counties with (1) population center <10,000 people is defined as rural; (2) population center between 10 to 50,000 is defined as micropolitan; (3) population center >50,000 is defined as metropolitan. Using these definitions, counties were designated as metropolitan or rural: a rural county in this analysis is any county with a population center <50,000 people, and a metropolitan county is any county with a population center >50,000 people.16 There are several measurement schemes to measure rural geographic spaces. Besides CBSA, another common measurement scheme is the Rural-Urban Commuting Area (RUCA). A major difference between CBSA and RUCA coding is that CBSA is based on county designations, whereas the latter is based on census tract data. Although RUCA codes provide more granular geographic data, use of CBSA was more relevant to our analysis as perinatal health care services and resources are allocated on a county basis. For instance, a county health department serves as a major resource for early pregnancy care for many rural counties. By identifying how a county is designated, our analysis seeks to inform which county types may benefit from additional resources.
Statistical analyses were performed to determine the relationship between geographic location and access to reproductive genetic services. Reproductive genetic services were defined as a composite outcome of access to aneuploidy screening or at least 1 genetic counseling visit. We hypothesized the following: (1) use of reproductive genetic services would differ based on geographic location; (2) self-identified race and ethnicity may affect the relationship between geographic location and the primary outcome, particularly for individuals that identify as Black or LatinX because this is a pattern seen in other aspects of maternity care. For the analysis, we first assessed the demographic characteristics using descriptive statistics. We then performed a stratified analysis, evaluating the primary outcome by self-identified race and ethnicity and geographic location of residence. t test, χ2, and ANOVA were used as appropriate. We performed pairwise comparisons using Tukey’s method when performing multiple comparisons.
Finally, we performed logistic regression to model the relationship between geographic location (exposure) and reproductive genetic services (outcome). Variables included in our model were derived from the literature and from results of bivariable analyses. Variables included maternal age, gestational age at first prenatal visit, primary language other than English, and insurance status. Our full model included all variables and interaction terms. We used like-lihood ratio tests to determine the model of best fit. Variables included in the final model had P < .05, which was our level of statistical significance. All analyses were performed using STATA 15 (StataCorp).
Results
The final cohort consisted of 8774 pregnancies. Of the cohort, 3868 (44%) pregnancies had aneuploidy screening and 3781 (43%) had at least 1 genetic counseling visit. Notably, 76% of those individuals that had at least 1 genetic counseling visit elected for aneuploidy screening with cell-free DNA during the pregnancy. Demographic characteristics of the cohort are shown in Table 1.
Table 1.
Demographic characteristics
| Characteristic | N (%) |
|---|---|
| Maternal age (mean, SD) | 30.6 (5.7) |
| Gestational age at first prenatal visit (mean, SD) | 14.5 (8.7) |
| Self-identified race/ethnicity of pregnancy person | |
| White | 4326 (49) |
| Black | 1767 (20) |
| LatinX | 1666 (19) |
| Asian | 495 (6) |
| Not identified | 496 (6) |
| Non-English Primary Language | 1128 (13) |
| Rural County of Residence | 1333 (15) |
| Insured (public or private) | 8094 (92) |
| Number of OB visits (mean, SD) | 9 (4) |
SD, standard deviation.
Data shown as n(%) unless noted otherwise.
Notably, a larger proportion of LatinX patients lived in a rural county (20.4%) and spoke a non-English language as their primary language (53%). Likewise, the mean gestational age for the first prenatal visit was higher among LatinX patients compared with Black, White, and Asian patients (18.6 vs 14.9, 12.9, and 13.7, respectively). Demographic characteristics stratified by self-reported race and ethnicity are shown in Table 2.
Table 2.
Demographic characteristics stratified by race and ethnicity
| Demographic Characteristics | Black (n = 1767) | LatinX (n = 1666) | White (n = 4326) | Asian (n = 495) |
|---|---|---|---|---|
| Maternal age (mean, SD) | 28.8 (6.1) | 30.7 (6.6) | 31.2 (5.4) | 32.4 (4.5) |
| Gestational age at first prenatal visit (mean, SD) | 14.9 (8.5) | 18.6 (9.9) | 12.9 (7.8) | 13.7 (8.0) |
| Non-English Primary Language | 15 (0.9) | 890 (53) | 30 (0.7) | 123 (24.9) |
| Rural County of Residence | 285 (16.1) | 340 (20.4) | 597 (13.8) | 39 (7.9) |
| Insured (public or private) | 1723 (97.5) | 1143 (68.6) | 4526 (98.4) | 480 (97.0) |
| Number of OB visits (mean, SD) | 8.4 (3.4) | 7.6 (3.9) | 9.1 (3.3) | 8.9 (3.0) |
Data shown as n(%) unless noted otherwise.
Access to genetic counseling services, aneuploidy screening, and the composite outcome (genetic counseling or aneuploidy screening) differed by self-reported race and ethnicity (Table 3). A significantly higher proportion of patients that identified as White or Asian had a genetic counseling visit or used cfDNA-based screening technologies compared with LatinX and Black patients. In regard to the composite outcome, significant differences were noted in pairwise comparisons between White and Black patients (56.7% vs 51.7%, P < .001), White and LatinX patients (56.7% vs 49.3%, P < .001), Asian and Black patients (63.4% vs 51.7%, P < .001), Asian and LatinX patients (63.4% vs 49.3%, P < .001), and Asian and White patients (63.4% vs 56.7%, P < .001). There was no significant difference in the composite outcome between Black and LatinX patients when using pairwise comparison.
Table 3.
Genetic counseling and aneuploidy screening stratified by race and ethnicity
| Reproductive Genetic Services | Black (n = 1767) | LatinX (n = 1666) | White (n = 4326) | Asian (n = 495) | P Value |
|---|---|---|---|---|---|
| Genetic counseling (at least 1 visit) | 680 (38.5) | 596 (35.8) | 2032 (47.0) | 263 (53.1) | .01 |
| Aneuploidy screening | |||||
| cfDNA | 149 (8.4) | 114 (6.8) | 793 (18.3) | 109 (22.0) | .02 |
| Triple screen | 387 (21.9) | 238 (14.3) | 1000 (23.1) | 142 (28.7) | .008 |
| Quad screen | 202 (11.4) | 236 (14.2) | 231 (5.3) | 32 (6.5) | .02 |
| Composite outcome | 914 (51.7) | 821 (49.3) | 2434 (56.7) | 314 (63.4) | .01 |
cfDNA, cell-free DNA-based screening.
Data shown as n(%) unless noted otherwise.
Use of genetic counseling and aneuploidy screening also significantly varied by geographic location. Individuals living in metropolitan counties were significantly more likely to have a genetic counseling visit (62.2% vs 37.8%, P < .001) and cell-free DNA or triple screen based aneuploidy screening (cfDNA 15.1% vs 7.9%, P < .001; triple screen (22.6% vs 15.2%, P < .001). By contrast, those living in rural areas were more likely to have quad screen based aneuploidy screening (12.1% vs 7.7%, P < .001) (Table 4).
Table 4.
Genetic counseling and aneuploidy screening by geographic location
| Reproductive Genetic Services |
Rural (n = 1333) |
Metropolitan (n = 7441) |
P Value |
|---|---|---|---|
| Genetic counseling (at least 1 visit) | 504 (37.8) | 829 (62.2) | <.001 |
| Aneuploidy screening | |||
| cfDNA | 105 (7.9) | 1126 (15.1) | <.001 |
| Triple screen | 203 (15.2) | 1679 (22.6) | <.001 |
| Quad screen | 161 (12.1) | 572 (7.7) | <.001 |
| Composite outcome | 652 (48.9) | 4089 (55.0) | <.001 |
cfDNA, cell-free DNA-based screening.
Data shown as n(%) unless noted otherwise.
Moreover, significant differences were seen when comparing the composite outcome among individuals of the same race and ethnicity but different geographic locations. Among all racial and ethnic groups, a higher proportion of individuals living in metropolitan counties had the composite outcome compared with those living in rural counties (Black metropolitan patients vs Black rural patients: 44.0% vs 36.8%, P < .001; LatinX metropolitan patients vs LatinX rural patient: 36.6% vs 35.6%, P < .001; White metropolitan patients vs White rural patients: 50.9% vs 37.7%, P < .001; Asian metropolitan patients vs Asian rural patients 60.5% vs 41.0%, P < .001). Importantly, we identified a difference in the composite outcome between White rural patients and LatinX rural patients (37.7% vs 35.6%, P < .001), and between Asian rural patients and LatinX and Black rural patients (41.0% vs 35.6%, P < .001; 41.0% vs 36.8%, P < .001, respectively).
Logistic regression analysis was performed to describe the relationship between the composite outcome (use of aneuploidy screening or at least one genetic counseling visit) and geographic location (primary exposure). We assessed how self-identified race and ethnicity, as well as other variables, adjusted this relationship. Full logistic regression model included the following additional variables as potential confounders: self-identified race/ethnicity, maternal age, non-English primary language, insurance status, and gestational age at first prenatal visit. We also included interaction terms between geographic location and the other variables to assess for effect-modification (Figure 1).
Figure 1.
Directed acyclic graph for variables considered in model.
Insurance status, maternal age, non-English primary language, self-identified Black race and ethnicity, self-identified White race and ethnicity, and self-identified Asian race and ethnicity were not significant and thus removed from the model. The final model was adjusted for self-identified LatinX race and ethnicity and gestational age at first prenatal visit. Gestational age at first prenatal visit x Geographic location was a significant interaction term that also remained in the model. Unadjusted and adjusted odds ratio (OR) are shown in Table 5. After adjusting for the latter significant variables and interaction term, the OR of receiving the composite outcome for those living in a rural county was OR 0.72 [CI 0.55, 0.95], P = .002.
Table 5.
Logistic regression model of composite outcome by geographic location
| Predictor Variable | Unadjusted OR | P Value | CI | Adjusted OR | P Value | CI |
|---|---|---|---|---|---|---|
| Rural County of Residence | 0.78 | <.001 | 0.70, 0.88 | 0.72 | .002 | 0.55, 0.95 |
OR, odds ratio.
Adjusted for self-identified LatinX ancestry and gestational age at first prenatal visit.
Discussion
This study demonstrates that individuals living in a rural county are significantly less likely to access aneuploidy screening or at least 1 genetic counseling visit compared with those that live in a metropolitan county. In addition, among those living in rural counties, Black and LatinX pregnant people were less likely to access reproductive genetic services compared with rural peers that identify as White or Asian race and ethnicity. Importantly, this analysis identifies important factors that contribute to disparities in access to reproductive genetic care, including location of residence and gestational age at first prenatal visit, and specifically identifies that these barriers are more likely to exist among certain racial and ethnic groups. In our stratified analysis, significant differences were seen between LatinX patients compared with peers of other racial and ethnic groups in the same geographic location, after accounting for gestational age at first prenatal visit. This finding may allude to the contributions of structural racism in the offering of reproductive genetic services and is an important consideration as we discuss barriers to reproductive genetic care. However, this analysis would not identify if those services were declined, which could relate to social and cultural factors that influence the use of reproductive genetic screening in this racial and ethnic group.
As such, disparate use of reproductive genetic services is likely driven by complex, multifactorial barriers. This may range from individual factors such as desire for screening and personal values, to systemic factors such as receipt of adequate counseling, provider knowledge and clinical access to genetic counseling, and gestational age at start of prenatal care. Undoubtedly, the parallel of these trends to challenges faced more broadly in prenatal care reflect larger systems issues regarding care delivery to diverse populations and those living in rural areas. As mentioned in our methods, our in person genetic counseling appointments occur at 3 maternity care locations within a 35 mile radius in 2 metropolitan counties. However, the closest surrounding rural counties range from 40 to 70 miles from our clinical sites; the most distant rural county that referred to our system is approximately 110 miles from our system. As such, availability of transportation and its associated cost may be an important barrier to care for rural populations and may be an important consideration for future research that examines the relationship between geographic location and use of reproductive genetic services.
Our findings extend existing literature about inequities in access to reproductive genetic technologies. Prior studies have documented differences in access to prenatal genetic counseling, noting that Black pregnant people and younger individuals were less likely to have a referral to genetic counseling.12 Likewise, other studies indicate that advanced maternal age is a common reason to refer to genetic counseling. In our study, referral may be less likely given the average age of our obstetric population primarily comprises women under age 35.2 Yet, in our analysis, maternal age did not meaningfully affect access to or use of genetic services. By contrast, a systematic review by Dron et al17 suggests that challenges with provider-patient communication and prior patient experiences with health care may inform use of genetic counseling services among LatinX populations. This may be an important consideration that could contextualize our findings, although our study design is limited to provide this qualitative information.
Although our study focuses on reproductive genetics, it alludes to larger disparities in maternity care that are driven by geographic location and structural racism. As demonstrated by our study, gestational age at first prenatal visit significantly modifies the relationship between geographic location and access to reproductive genetic services. In our cohort, the average gestational age at presentation was higher among Black and LatinX pregnant people compared with their White and Asian peers. Differences in gestational age of first prenatal appointment are well documented, with multiple potential etiologies, such as insurance status, access to a maternity care provider, and transportation, implicated.18 Notably, timing of initiation of prenatal care has other important implications for maternal and fetal outcomes, such as access to carrier screening, and optimizing maternal health conditions, such as hypertensive disorders and diabetes. As such, disparate access to reproductive genetic care reflects larger issues driving poor maternal and fetal outcomes and is well documented to affect some groups more than others.19,20 Thus, addressing timing of initiation of prenatal care and providing resources to access earlier care, particularly to rural locations, may be an area of interest when combatting disparities in access to reproductive genetic care.
Importantly, although our study can quantitatively evaluate structural factors that drive disparities in access to reproductive genetic care, it does not assess how personal and community viewpoints on genetic technologies in pregnancy may drive differences in use. Several studies have previously demonstrated differences in use of genetic diagnostic testing, noting complexity in maternal decision making for testing informed by personal views on disability, lived experience, prior pregnancy loss, other living children, views on termination, and community views on genetic disease informing ultimate use of genetic tests.9,21-23 Notably, Black and LatinX pregnant people are less likely to use prenatal diagnosis; beyond the aforementioned factors, other issues such as who provided counseling and how it was provided, have been shown to affect results.13 Because reproductive genetic care is under the larger maternity care umbrella, poor experiences in maternity care as a whole, not just in genetics, may inform ultimate use of reproductive technologies. Thus, considering how to provide respectful, values-consistent care to minority populations may be an important aspect that not only addresses reproductive genetic care, but also maternity care disparities overall.24
Our study has many strengths. First, this analysis utilized a large cohort from a tertiary care referral center that has a diverse racial and ethnic, and geographic catchment. Second, we utilized a longitudinal time period after use of cfDNA was well established at our institution and covered by major payers in the state of NC. Third, we utilized a time period before the COVID-19 pandemic, thus ensuring that the findings were not affected by pandemic-related changes in practice and patient/practice hardships. Although the current publication is several years after this historical cohort, practice patterns have not changed significantly in most of the state of NC, of which 70% of counties constitute as rural by the previously mentioned definitions. In addition, issues of access have been longstanding before the pandemic, and for most of obstetric care, exacerbated during COVID-19 particularly for low-resource settings.25 Finally, we had accurate and complete data sourcing, with little to no missing data in our study. Our study population specifically included individuals who received prenatal through delivery care at one of our institutional practices and affiliated hospitals and excluded those who transferred care mid-pregnancy or delivered at another institution.
However, our study is not without limitations, and our findings should be interpreted within this context. First, given that our study is from a single center, there is risk that our data are not generalizable to institutions that do not look similar to ours. We also are unable to comment on how our practices and results compare with states outside of NC, which have different patient demographics. Second, we did not perform any qualitative perspectives to determine if screening was offered but not utilized. This is an important component to inform why our numeric trends exist. Third, we utilized self-reported electronic medical record data regarding race and ethnicity. This is particularly limiting because it allowed individuals a single choice regarding their race-ethnicity and may not appropriately reflect an individuals’ diverse ancestry. In addition, because race-ethnicity was an important aspect of our analysis, this can significantly limit our results regarding how race-ethnicity affects the relationship between geographic location and use of reproductive genetic services.
Likewise, because this is a retrospective analysis using electronic medical record data, we must be cautious about data that were not available for this analysis. Although we have complete data for the variables presented, we were not able to obtain other valuable data points, such as the proportion of individuals that may have had prenatal diagnosis, such as chorionic villus sampling or amniocentesis or associated pregnancy outcomes. Prenatal diagnosis rate, in particular, may explain why maternal age was not significant in our model but could not be explored in this study. Similarly, our study found that insurance status did not significantly affect use of reproductive genetic services. However, this may be related to limited data regarding modes of payment from the electronic medical record. A very small proportion of this cohort were uninsured (8%) and data about whether they self-paid or had institution based financial assistance are not available. In addition, it is unclear if insurance type affected use of services because our analysis was limited to presence of insurance or not.
It is imperative that future studies focus on the major causes of differences in use of reproductive genetic technologies because this will inform focused areas of intervention to improve equity in reproductive genetic care. Understanding how individual and community experiences with health care inform use of prenatal screening and diagnosis may provide useful knowledge to improve the quality of maternity care, overall, for Black and LatinX minority populations. In addition, understanding unique access issue that prevent timely initiation of prenatal care is central to improving equity in reproductive genetic practices.
Acknowledgments
The authors would like to thank the North Carolina Translational and Clinical Sciences Institute for data-acquisition assistance.
Funding
Funding for this study was provided from University of North Carolina NC TRaCS Pilot Grant, and National Institutes of Health K23-HD088742 (PI: Vora).
Footnotes
Ethics Declaration
This study was approved by the University of North Carolina at Chapel Hill Institutional Review Board (IRB # 19-2930); a waiver of informed consent was obtained.
Conflict of Interest
Asha N. Talati receives research support from the National Institutes of Health, Billion to One, and The Greenwall Foundation. Divya P. Mallampati receives salary support from the Department of Health and Human Services. Neeta L. Vora receives research support from National Institutes of Health and received supplies in kind from Illumina unrelated to this research.
Data Availability
Deidentified data are available from the authors on request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Deidentified data are available from the authors on request.