Inequitable Screening for Pregnancy Intention by Race,Insurance,and Site of Care in Two High-Volume Urban Obstetric Hospitals

Abstract

Background:

In the United States, public health efforts are focused on reducing unintended pregnancy. Yet, differences in rates of unintended pregnancy, abortion, and unintended births by race are driven by a combination of patient-, health system-, and provider-related factors. Despite this complicated scaffolding underpinning pregnancy intention, patients are often screened for pregnancy intention or planning when they have a positive pregnancy test. We hypothesized screening may vary by patient and health system characteristics.

Objective:

To identify associations between patient and health system characteristics and receiving screening for pregnancy intention or planning.

Study Design:

We performed a secondary analysis of a retrospective cohort study of all singleton deliveries in 2019 at two Philadelphia hospitals. Our primary outcome was the presence or absence of pregnancy intention screening by the clinical team. We used logistic regression to determine patient and hospital characteristics associated with screening for pregnancy intention.

Results:

We identified 9,672 deliveries, 48% of births were among Black individuals, 91% were non-Hispanic, and 45% had public or no insurance; of all births, 33% were screened for pregnancy intention or planning. Patients were more likely to be asked if their pregnancy was intended if they were: Black (2.38 [2.10–2.750]) or publicly insured or uninsured (2.78 [2.43–3.20]). The hospital site where the patient received care was the primary driver of whether a patient was asked about pregnancy intention (10.59 [9.35–12.0]). After accounting for the hospital sites, patients of Black race remained significantly more likely to be screened than White patients.

Conclusions:

Inequities in pregnancy intention or planning screening were driven by differences in institutional practices and patient race. These findings underscore the need for equitable screening practices that ensure all patients receive high-quality, unbiased, and patient-centered reproductive care.

Introduction

Public health and policy discourse around the decision to parent primarily focuses on the adverse consequences of unintended pregnancy.^1
–3 The impetus behind public health goals to reduce unintended pregnancy is based on associations with poor obstetric and neonatal outcomes,⁴ including inadequate or late start of prenatal care,^5,6 premature and low birth weight infants,⁷ and lower rates of breastfeeding.⁸ However, the construct of pregnancy intention is often oversimplified and falsely dichotomized into “intended” or” unintended” categories. This binary classification system does not fully capture the complexities of reproductive decision making.^9
–11 Even with an expanded measurement of pregnancy intention along a continuum that includes mistimed and unwanted pregnancies, intention alone fails to account for the broader social, emotional, and structural contexts influencing reproductive decisions, such as access to contraception, socioeconomic status, and personal aspirations. Though pregnancy intention fails to account for the nuanced experiences and contexts in which reproductive decisions are made, it is widely used in in epidemiological studies of important public health outcomes. In addition, population-based surveys often collect information about pregnancy intention or planning retrospectively,^12,13 and these constructs vary over time and are subject to recall and social desirability bias.^14

–17

In addition to these nuances, there is often confusion between the concepts of “intended” and “planned” pregnancies. An intended pregnancy typically refers to whether a person wanted a pregnancy at, or sooner than, the time the pregnancy occurred, whereas planning captures the level of preparation or deliberation that went into achieving that pregnancy.^18,19 A pregnancy may be intended but unplanned, as when someone desires to become pregnant but did not actively take steps to do so in a particular timeframe. Conversely, a pregnancy could be unintended but planned, such as when societal pressures or partner influence led to a pregnancy the individual did not personally desire. Because simplistic labels like “intended” or “planned” fail to account for the range of experiences surrounding conception and may not be equally salient to all reproductively aged people,^20,21 associations between pregnancy intention, planning, and clinical outcomes related to birth are debated.^{9

–12,14,22,23}

Difficult to measure constructs of pregnancy planning and intention are further confounded by the racial, ethnic, and socioeconomic differences in rates of pregnancy, abortion, and unintended births. Differences in these outcomes are driven by a combination of patient preferences, patient behaviors, patient circumstances, health system, and provider-related factors.^20,21,24 For example, people of minoritized identities report being subjected to race-based sexual behavior stereotypes,^25,26 feeling pressured to use contraceptives and discrimination in counseling,^27,28 nonconsensual sterilizations,^29,30 coercive family planning programs,^31

–34 barriers to contraceptive access,³⁵ and mischaracterization of pregnancy intention.³⁶ These disparities in access to and quality of health care mean pregnancy planning behaviors may overlap with other demographic and socioeconomic characteristics that influence ability to plan pregnancy, and an outsized burden of adverse maternal and birth outcomes, all of which are grounded in historical and systemic racism.^{24,37

–41} For these reasons, pregnancy intention may be confounded or mediated by other factors, and may not accurately capture family planning efforts, particularly when family planning is not seen as feasible or desirable by patients.

Despite the limitations of the pregnancy intention and planning constructs in representing an ideal that improves clinical outcomes universally,¹⁰ patients are often asked whether a pregnancy was planned or intended when a pregnancy is diagnosed in the health system, or at the first prenatal visit. Little research has examined how screening for pregnancy intention or planning is implemented in clinical settings. While determining if a patient prefers to continue or interrupt a pregnancy is critical for patient-centered care, who is screened for pregnancy planning or intention and how patients are screened may vary. We therefore sought to measure variables associated with pregnancy planning or intention screening at the start of pregnancy. Given the potential for bias in how screening is conducted, we hypothesized we would observe inequitable screening practices by patient and health system characteristics. Inequitable screening practices may reinforce preexisting perceptions about certain patients, which in turn may exacerbate patient-reported discrimination in family planning counseling,^27,28,42 ascribe negative connotations to unintended or unplanned pregnancies,⁹ and stigmatize patients with unintended or unplanned pregnancies.

Increased attention to implicit bias as it relates to provider behavior informed our hypothesis that there would be differences in who received screening based on observable patient characteristics including race, ethnicity, and body mass index (BMI).^43

–46 To ascertain the degree to which observable patient characteristics associated with implicit bias^43,45,46 including race, ethnicity, age, insurance, and BMI were associated with receiving screening for pregnancy planning behavior, we assessed screening practices across the two highest volume birthing hospitals in Philadelphia. We included two hospitals to determine if the site of care and clinical norms influenced the hypothesized relationships. The purpose of this study was to provide insights into current screening practices, and how they may be adjusted to ensure equitable, patient-centered care.

Materials and Methods

Participants

We conducted a secondary analysis of a prepandemic retrospective cohort study of all singleton deliveries from January 1, 2019, through December 31, 2019, at two urban teaching hospitals within a large health system with the highest total birth volume in Philadelphia County. These two hospitals were selected because of the high volume, and because the care contexts differ slightly: each is staffed by different clinical care teams and has different practices regarding uniform screening in the form of note templates. At Hospital 2, templated notes are consistently formatted and incorporate screening for pregnancy planning behavior in any consult for a pregnant person in the emergency department and for the first prenatal visit. At the comparator site, Hospital 1, such templates are not used universally. As such, how screening, if it occurred, was operationalized across practices differed. Both sites use the same electronic medical record (EMR). Neither hospital is religiously affiliated. For each birth, EMR data were abstracted over a “look back” period of 295 days, or 42 weeks preceding the delivery event. We excluded patients who had more than one delivery in the look back period (n = 13) and who had nonsingleton births (n = 463). The cohort was originally created to describe obstetric complications, because multiple gestation is associated with obstetric complications, they were excluded from the cohort prior to data collection. This study was deemed exempt by the institutional review board of the University of Pennsylvania, as all data were routinely collected and required for health care, and thus did not require informed consent. The analytic cohort is depicted in Figure 1.

FIG. 1.

Flowchart of all deliveries at study sites and exclusion criteria. Multiples (n = 426) and pregnancies with short interpregnancy intervals (n = 13) were excluded.

Measures

The primary outcome of interest was whether a patient was screened for pregnancy planning or intention, at any point during the look back period since the most recent birth. We captured screening at the time of positive pregnancy test, first prenatal visit, or any other visit in the health system during pregnancy. Pregnancy intention is routinely documented at first prenatal visits and in gynecology visits in early pregnancy. Pregnancy planning is included in the standard note template for these visits at one hospital in the study (H2), whereas the other hospital does not standardize note templates (H1). We abstracted whether a patient was asked if a pregnancy was planned or unplanned, or intended or unintended. On a random selection of charts prior to the study period, we found providers asked, “Was this pregnancy planned?” and “Was this pregnancy intended?” and documented a binary response of yes or no, occasionally adding additional context to those answers. We classified individuals (n = 3,153) as “screened” if they were asked if the pregnancy was planned or unplanned, or intended or unintended, and those who were not (n = 6,519) as “not screened.” We additionally randomly selected 50 charts for review to determine if patients were incorrectly classified as “not screened.” We did not find any additional screening methods and therefore did not adjust our search strategy.

Individual-level covariates included maternal age, self-identified race and ethnicity, insurance status, and BMI. We included a system-level covariate, hospital site of care, to account for variation in institutional practices related to pregnancy intention screening. We determine covariates based on previous literature documenting potential influence on pregnancy planning behavior.

Race and ethnicity were obtained by patient self-report in the EMR. Race was combined into the following categories: Black, White, Asian, and other, which included patients who checked multiple categories or were missing data. Age is determined by the age at the time of birth. We categorized insurance as public (Medicaid) or private. All patients who were uninsured or had missing insurance data were grouped with public (n = 507, 5%). The cohort excluded patients transferred at the time of delivery and was comprised of persons who received prenatal care at the hospital system in which the birth occurred.

Statistical analysis

We summarized patient characteristics stratified by site of care within the hospital system with descriptive statistics, the Pearson chi-square test was used to determine statistical significance with an alpha level of 0.05. Bivariate analyses were conducted to determine the relationship between age, self-reported race, ethnicity, insurance status, BMI, and hospital system and receipt of screening for pregnancy intention. We used logistic regression to determine associations between patient characteristics, site of care, and receipt of screening.

We used a random forest machine learning model⁴⁷ to further validate the logistic regression model and determine which covariates in the model were most influential in predicting whether a patient was screened for pregnancy intention. The random forest machine learning model randomly constructs multiple decision trees to determine the hierarchy of covariates that most contribute to the outcome, in this case, screening for pregnancy intention.⁴⁷ All analyses were conducted with R Studio, version 4.3.2 (2023–10-31).

Results

Over the study period there were 9,672 births, 48% of the sample were Black, 91% were non-Hispanic, and 45% had public or no insurance. Births were split relatively evenly between the two hospitals, with 55% and 45% at Hospital 1 and Hospital 2, respectively. Of all births, 33% of patients were screened for pregnancy intention (Table 1).

In univariate comparisons within the entire cohort, we found statistically significant associations between age, race, ethnicity, insurance, BMI, hospital site, and whether a patient was screened for pregnancy intention. In multivariate logistic regression, individuals aged 19–29 had the highest odds of receiving screening for pregnancy intention. Black patients were more than twice as likely to be screened for pregnancy intention than White patients. Patients who were publicly insured or uninsured were nearly three times as likely to be screened. Patients seen at Hospital 2 were 10 times as likely to receive screening than patients at Hospital 1 (Fig. 2A). In multivariate models, neither BMI nor ethnicity was associated with receiving screening for pregnancy intention.

FIG. 2.

(A) Association of patient-level characteristics and site of care with screening likelihood among patients with a birth at two major hospitals in Philadelphia in 2019. (B) Forest plot of relative contributions of each covariate to determine the likelihood of receiving screening for pregnancy intention.

Hospital site of care was the primary driver of whether a patient was asked about pregnancy planning or intention (Fig. 2B), and we observed a highly variable screening frequency between the two hospitals. However, after accounting for hospital site, Black patients remained significantly more likely to be screened than White participants though the magnitude of the association decreased somewhat, suggesting that the site of care, that is, local norms in templated notes, and patient characteristics may both play a role in the provider’s decision to screen for pregnancy planning behavior.

Discussion

Our findings reveal significant differences in pregnancy intention screening practices between the two hospitals in our study, suggesting that inequities in screening exist at the institutional level. Hospital site was the most notable driver of the differences in screening frequency, indicating that patients receiving care at one hospital were screened more frequently for pregnancy intention than those at the other. While the primary driver of differences in screening practices by hospital may be the use of templated notes, even when controlling for hospital site Black patients were more than twice as likely to be screened for pregnancy intention than White patients, as were patients who were publicly insured or uninsured. These findings suggest a potential bias in who receives pregnancy intention screening and illustrate that protocolized screening may reduce, but does not eliminate, racially biased screening for pregnancy intention.

Moreover, the differential rates of screening by race regardless of the site of care suggest that preexisting biases and stereotypes about reproductive choices or circumstances, especially among those from marginalized groups, exist. Inequitable screening may further entrench harmful biases in clinical settings, including stigmatization of patients with unintended or unplanned pregnancies.⁹ This adds to the literature documenting differential counseling people of minoritized identities experience, including feeling pressured to use contraceptives and perceived discrimination in counseling.^27,28,42

From a clinical perspective, if screening is conducted without a clear purpose and fails to inform actionable care decisions, it risks reinforcing misinformed stereotypes, potentially undermining patient trust and care quality. Our findings suggest that at the institutional level, more focus should be placed on ensuring that any screening, if used, is equitable and improves clinical outcomes, rather than perpetuating negative perceptions about patients or pregnancy planning behaviors. Therefore, future work should explore if understanding pregnancy planning or intention has a meaningful impact on reproductive health care delivery and patient outcomes, with specific attention to at what point during one’s reproductive lifespan that screening occurs.

Our findings could be strengthened by an analysis of clinician characteristics, including whether differences in race-based screening disparities varied by clinician type (general obstetricians, midwifery, family medicine, and advanced practice provider), training level (resident, fellow, and attending), or clinician age, sex, and race. These data were not available for analysis. Additional patient characteristics such as gravidity, parity, and interpregnancy interval are additional covariates that would strengthen these findings. The sensitivity with which we could abstract screening questions in the absence of templated notes may have varied artificially lowering the screening rate at the nontemplated hospital. In addition, if certain patient populations were preferentially seen by H1 or H2, this may introduce selection bias into our results even when accounting for hospital sites in the multivariate model.

Conclusions

If screening for pregnancy planning or intention is undertaken, it should be done equitably and in a manner that emphasizes patient-centered pregnancy outcomes. Pregnancy intention and planning may overlap with other factors that influence the ability to plan pregnancy, maternal, and birth outcomes, all of which are grounded in historical and systemic racism. Further research is needed on how screening for pregnancy intention or planning influences patient and provider relationships, care delivery, and pregnancy outcomes.

Table 1.

Patient-Level Demographic Characteristics of Those with a Birth at Two Major Hospitals in Philadelphia in 2019 by Screening Status and Hospital Site

Screened					Not screened
Variable	H2(n = 2,443)	H1(n = 710)	Total(n = 3,153)	p value	H2(n = 1,944)	H1(n = 4,575)	Total(n = 6,519)	p value
Age (years)
19 and under	124 (5.2%)	42 (6%)	166 (2%)	<0.001	56 (3.6%)	62 (1.4%)	118 (2%)	<0.001
20–24 years	455 (19%)	186 (26%)	641 (21%)		220 (14%)	313 (7.3%)	533 (10%)
25–29 years	667 (28%)	222 (32%)	889 (29%)		444 (28%)	836 (19%)	880 (16%)
30–34 years	717 (30%)	170 (24%)	887 (29%)		482 (31%)	1,841 (43%)	2,323 (42%)
35–39 years	349 (15%)	69 (9.8%)	418 (13%)		313 (20%)	1,055 (24%)	1,368 (25%)
40–44 years	82 (3.4%)	15 (2.1%)	97 (3%)		57 (3.6%)	204 (4.7%)	261 (5%)
Race
White/Other	708 (30%)	144 (23%)	852 (29%)	<0.001	623 (34%)	3,144 (75%)	3,736 (63%)	<0.001
Black	1,615 (70%)	479 (77%)	2,094 (71%)		1,196 (66%)	1,028 (25%)	2,224 (37%)
Ethnicity
Non-Hispanic	2,299 (95%)	603 (86%)	2,290 (93%)	<0.001	1,841 (9%)	4,074 (90%)	5,915 (33%)	<0.001
Hispanic	125 (5.2%)	102 (14%)	227 (4.5%)		87 (4.5%)	457 (10%)	544 (8%)
Insurance
Private	913 (38%)	134 (19%)	1,047 (34%)	<0.001	738 (44%)	3,001 (68%)	1,651 (40%)	<0.001
Public or uninsured	1,482 (62%)	570 (81%)	2,052 (66%)		945 (56%)	1,382 (32%)	2,427 (60%)
BMI
<30	1,414 (60%)	398 (57%)	716 (66%)	0.069	716 (66%)	2,981 (76%)	3,697 (74%)	<0.001
≥30	928 (101%)	306 (43%)	373 (34%)		373 (34%)	936 (24%)	1,309 (26%)

BMI, body mass index.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

A.A. is supported by a grant from the NIH (K12-HD001265).

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