Racial and Ethnic Disparities Among Women Undergoing a Trial of Labor After Cesarean Delivery: Performance of the VBAC Calculator with and without Patients' Race/Ethnicity

Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates

Among health care researchers, there is increasing debate over how best to assess and ensure the fairness of algorithms used for clinical decision support and population health, particularly concerning potential racial bias. Here we first distill concerns over the fairness of health care algorithms into four broad categories: (a) the explicit inclusion (or, conversely, the exclusion) of race and ethnicity in algorithms, (b) unequal algorithm decision rates across groups, (c) unequal error rates across groups, and (d) potential bias in the target variable used in prediction. With this taxonomy, we critically examine seven prominent and controversial health care algorithms. We show that popular approaches that aim to improve the fairness of health care algorithms can in fact worsen outcomes for individuals across all racial and ethnic groups. We conclude by offering an alternative, consequentialist framework for algorithm design that mitigates these harms by instead foregrounding outcomes and clarifying trade-offs in the pursuit of equitable decision-making.

Application of Predictive Analytics in Pregnancy, Birth, and Postpartum Nursing Care

ABSTRACT

Predictive analytics has emerged as a promising approach for improving reproductive health care and patient outcomes. During pregnancy and birth, the ability to accurately predict risks and complications could enable earlier interventions and reduce adverse events. However, there are challenges and ethical considerations for implementing predictive models in perinatal care settings. We introduce major concepts in predictive analytics and describe application of predictive modeling to perinatal care topics such as fertility, preeclampsia, labor onset, vaginal birth after cesarean, uterine rupture, induction outcomes, postpartum hemorrhage, and postpartum mood disorders. Although some predictive models have achieved adequate accuracy (AUC 0.7-0.9), most require additional external validation across diverse populations and practice settings. Bias, particularly racial bias, remains a key limitation of current models. Nurses and advanced practice nurses, including nurse practitioners certified registered nurse anesthetists, and nurse-midwives, play a vital role in ensuring high-quality data collection and communicating predictive model outputs to clinicians and users of the health care system. Addressing the ethical challenges and limitations of predictive analytics is imperative to equitably translate these tools to support patient-centered perinatal care.

Fairness in Low Birthweight Predictive Models: Implications of Excluding Race/Ethnicity

CONTEXT

To evaluate algorithmic fairness in low birthweight predictive models.

STUDY DESIGN

This study analyzed insurance claims (n = 9,990,990; 2013-2021) linked with birth certificates (n = 173,035; 2014-2021) from the Arkansas All Payers Claims Database (APCD).

METHODS

Low birthweight (< 2500 g) predictive models included four approaches (logistic, elastic net, linear discriminate analysis, and gradient boosting machines [GMB]) with and without racial/ethnic information. Model performance was assessed overall, among Hispanic individuals, and among non-Hispanic White, Black, Native Hawaiian/Other Pacific Islander, and Asian individuals using multiple measures of predictive performance (i.e., AUC [area under the receiver operating characteristic curve] scores, calibration, sensitivity, and specificity).

RESULTS

AUC scores were lower (underperformed) for Black and Asian individuals relative to White individuals. In the strongest performing model (i.e., GMB), the AUC scores for Black (0.718 [95% CI: 0.705-0.732]) and Asian (0.655 [95% CI: 0.582-0.728]) populations were lower than the AUC for White individuals (0.764 [95% CI: 0.754-0.775 ]). Model performance measured using AUC was comparable in models that included and excluded race/ethnicity; however, sensitivity (i.e., the percent of records correctly predicted as "low birthweight" among those who actually had low birthweight) was lower and calibration was weaker, suggesting underprediction for Black individuals when race/ethnicity were excluded.

CONCLUSIONS

This study found that racially blind models resulted in underprediction and reduced algorithmic performance, measured using sensitivity and calibration, for Black populations. Such under prediction could unfairly decrease resource allocation needed to reduce perinatal health inequities. Population health management programs should carefully consider algorithmic fairness in predictive models and associated resource allocation decisions.

Automating Racism: Is Use of the Vaginal Birth After Cesarean Calculator Associated with Inequity in Perinatal Service Delivery?

OBJECTIVE

The clinical application of race-adjusted algorithms may perpetuate health inequities. We assessed the impact of the vaginal birth after cesarean (VBAC) calculator, which was revised in 2021 to address concerns about equity. The original algorithm factored race and ethnicity and gave lower VBAC probabilities to Black and Hispanic patients.

METHODS

From 2019 to 2020, we conducted a multi-site, ethnographic study consisting of interviews and audio recordings of 14 prenatal visits. We used grounded theory to describe the social processes of racialization.

FINDINGS

Across 4 sites, 12 obstetricians, 5 midwives, and 31 pregnant/postpartum patients participated. Seventy-four percent (N = 23) of the pregnant/postpartum individuals identified as racially minoritized, and the remaining 24% (N = 8) identified as White. We identified four processes that facilitated the "automation" of racism: adhering to strict cutoffs; the routine adoption of calculators; obfuscating the calculator; and the reflexive categorization of race and ethnicity. When clinicians adhered to strict cutoffs, they steered low-scoring Black and Hispanic patients toward repeat cesareans. If clinicians obfuscated the calculator, Black and Hispanic patients had to work to decode the role of race and ethnicity in their probabilities in order to pursue a VBAC. By reflexively categorizing race and ethnicity, the use of the calculator forced patients to choose a singular identity, even if it obscured the truth about their multi-faceted race or ethnicity.

CONCLUSION

The VBAC calculator's inclusion of race and ethnicity helped to automate racism by coding race into institutional practices and care interactions. This resulted in some clinicians discouraging or prohibiting Black and Hispanic patients from attempting a VBAC.

SIGNIFICANCE

To date, no empiric study has examined whether the VBAC calculator produced inequities in access to VBAC services and reproduced racism in care. The VBAC calculator resulted in fewer VBAC attempts among racially minoritized patients, denying them the opportunity to undergo labor and a vaginal birthing experience.

The role of cell-free DNA biomarkers and patient data in the early prediction of preeclampsia: an artificial intelligence model

BACKGROUND

Accurate individualized assessment of preeclampsia risk enables the identification of patients most likely to benefit from initiation of low-dose aspirin at 12 to 16 weeks of gestation when there is evidence for its effectiveness, and enables the guidance of appropriate pregnancy care pathways and surveillance.

OBJECTIVE

The primary objective of this study was to evaluate the performance of artificial neural network models for the prediction of preterm preeclampsia (<37 weeks' gestation) using patient characteristics available at the first antenatal visit and data from prenatal cell-free DNA screening. Secondary outcomes were prediction of early-onset preeclampsia (<34 weeks' gestation) and term preeclampsia (≥37 weeks' gestation).

METHODS

This secondary analysis of a prospective, multicenter, observational prenatal cell-free DNA screening study (SMART) included singleton pregnancies with known pregnancy outcomes. Thirteen patient characteristics that are routinely collected at the first prenatal visit and 2 characteristics of cell-free DNA (total cell-free DNA and fetal fraction) were used to develop predictive models for early-onset (<34 weeks), preterm (<37 weeks), and term (≥37 weeks) preeclampsia. For the models, the "reference" classifier was a shallow logistic regression model. We also explored several feedforward (nonlinear) neural network architectures with ≥1 hidden layers, and compared their performance with the logistic regression model. We selected a simple neural network model built with 1 hidden layer and made up of 15 units.

RESULTS

Of the 17,520 participants included in the final analysis, 72 (0.4%) developed early-onset, 251 (1.4%) preterm, and 420 (2.4%) term preeclampsia. Median gestational age at cell-free DNA measurement was 12.6 weeks, and 2155 (12.3%) had their cell-free DNA measurement at ≥16 weeks' gestation. Preeclampsia was associated with higher total cell-free DNA (median, 362.3 vs 339.0 copies/mL cell-free DNA; P<.001) and lower fetal fraction (median, 7.5% vs 9.4%; P<.001). The expected, cross-validated area under the curve scores for early-onset, preterm, and term preeclampsia were 0.782, 0.801, and 0.712, respectively, for the logistic regression model, and 0.797, 0.800, and 0.713, respectively, for the neural network model. At a screen-positive rate of 15%, sensitivity for preterm preeclampsia was 58.4% (95% confidence interval, 0.569-0.599) for the logistic regression model and 59.3% (95% confidence interval, 0.578-0.608) for the neural network model. The contribution of both total cell-free DNA and fetal fraction to the prediction of term and preterm preeclampsia was negligible. For early-onset preeclampsia, removal of the total cell-free DNA and fetal fraction features from the neural network model was associated with a 6.9% decrease in sensitivity at a 15% screen-positive rate, from 54.9% (95% confidence interval, 52.9-56.9) to 48.0% (95% confidence interval, 45.0-51.0).

CONCLUSION

Routinely available patient characteristics and cell-free DNA markers can be used to predict preeclampsia with performance comparable to that of other patient characteristic models for the prediction of preterm preeclampsia. Logistic regression and neural network models showed similar performance.

The Construction and Meaning of Race Within Hypertension Guidelines: A Systematic Scoping Review

BACKGROUND

Professional society guidelines are evidence-based recommendations intended to promote standardized care and improve health outcomes. Amid increased recognition of the role racism plays in shaping inequitable healthcare delivery, many researchers and practitioners have critiqued existing guidelines, particularly those that include race-based recommendations. Critiques highlight how racism influences the evidence that guidelines are based on and its interpretation. However, few have used a systematic methodology to examine race-based recommendations. This review examines hypertension guidelines, a condition affecting nearly half of all adults in the United States (US), to understand how guidelines reference and develop recommendations related to race.

METHODS

A systematic scoping review of all professional guidelines on the management of essential hypertension published between 1977 and 2022 to examine the use and meaning of race categories.

RESULTS

Of the 37 guidelines that met the inclusion criteria, we identified a total of 990 mentions of race categories. Black and African/African American were the predominant race categories referred to in guidelines (n = 409). Guideline authors used race in five key domains: describing the prevalence or etiology of hypertension; characterizing prior hypertension studies; describing hypertension interventions; social risk and social determinants of health; the complexity of race. Guideline authors largely used race categories as biological rather than social constructions. None of the guidelines discussed racism and the role it plays in perpetuating hypertension inequities.

DISCUSSION

Hypertension guidelines largely refer to race as a distinct and natural category rather than confront the longstanding history of racism within and beyond the medical system. Normalizing race as a biological rather than social construct fails to address racism as a key determinant driving inequities in cardiovascular health. These changes are necessary to produce meaningful structural solutions that advance equity in hypertension education, research, and care delivery.

Social disparities in delivery choice among patients with history of cesarean

OBJECTIVE

Given the call to reduce rates of non-medically indicated cesarean deliveries (CDs) by encouraging trials of labor after cesarean (TOLAC), this study looks at social characteristics of patients choosing a TOLAC versus a scheduled repeat cesarean delivery (SRCD) to determine disparities regarding delivery method choice.

METHODS

This was a retrospective cohort study of patients with a history of one CD between April 29, 2015-April 29, 2020. Patients were divided based on type of delivery chosen at admission. Chi-squared tests examined proportional differences between groups and logistic regression models examined odd ratios of choosing TOLAC versus SRCD according to socially dependent categories including race/ethnicity, health insurance, pre-pregnancy body mass index, and Social Vulnerability Index (SVI).

RESULTS

1,983 patients were included. Multivariable logistic regression models revealed that patients with a high SVI (reference: low/medium SVI) (AOR 2.0, CI: 1.5, 2.5), self-identified as Black/ African American (AOR: 2.4, CI: 1.6, 3.6) or Hispanic/Latina (AOR: 2.0, CI: 1.4, 2.8) (reference: White), had public insurance (reference: private insurance) (AOR: 3.7, CI: 2.8, 5.0), and who had an obese BMI (reference: non-obese BMI) were more likely to opt for a TOLAC rather than SRCD.

CONCLUSION

These findings demonstrate differences in delivery method preferences. Specifically, more disadvantaged patients are more likely to choose TOLAC, suggesting that social and economic factors may play a role in delivery preferences. These findings have implications for improving individualized counselling and engaging in shared decision-making around mode of delivery.

Reassessing the Use of Race in Clinical Algorithms: An Interactive, Case-Based Session for Medical Students Using eGFR

Introduction

Medical curricula implicitly teach that race has a biological basis. Clinical rotations reinforce this misconception as race-based algorithms are used to guide clinical decision-making. This module aims to expose the fallacy of race in clinical algorithms, using the estimated glomerular filtration rate (eGFR) equation as an example.

Methods

We created a 60-minute module in consultation with nephrologists. The format was an interactive, case-based presentation with a didactic section. A third-year medical student facilitated the workshops to medical students. Evaluation included pre/post surveys using 5-point Likert scales to assess awareness regarding use of race as a biological construct. Higher scores indicated increased awareness.

Results

Fifty-five students participated in the module. Pre/post results indicated that students significantly improved in self-perceived knowledge of the history of racism in medicine (2.6 vs. 3.2, p < .001), awareness of race in clinical algorithms (2.7 vs. 3.7, p < .001), impact of race-based eGFR on quality of life/treatment outcomes (4.5 vs. 4.8, p = .01), differences between race and ancestry (3.7 vs. 4.3, p < .001), and implications of not removing race from the eGFR equation (2.7 vs. 4.2, p < .001). Students rated the workshops highly for quality and clarity.

Discussion

Our module expands on others' work to expose the fallacy of race-based algorithms and define its impact on health equity. Limitations include a lack of objective assessment of knowledge acquisition. We recommend integrating this module into preclinical and clinical curricula to discuss the use of race in medical literature and clinical practice.

The impact of devaluing Women of Color: stress, reproduction, and justice

This commentary is in response to the Call for Papers put forth by the Critical Midwifery Studies Collective (June 2022). We argue that due to a long and ongoing history of gendered racism, Women of Color are devalued in U.S. society. Devaluing Women of Color leads maternal healthcare practitioners to miss and even dismiss distress in Women of Color. The result is systematic underdiagnosis, undertreatment, and the delivery of poorer care to Women of Color, which negatively affects reproductive outcomes generally and birth outcomes specifically. These compounding effects exacerbate distress in Women of Color leading to greater distress. Stress physiology is ancient and intricately interwoven with healthy pregnancy physiology, and this relationship is a highly conserved reproductive strategy. Thus, where there is disproportionate or excess stress (distress), unsurprisingly, there are disproportionate and excess rates of poorer reproductive outcomes. Stress physiology and reproductive physiology collide with social injustices (i.e., racism, discrimination, and anti-Blackness), resulting in pernicious racialized maternal health disparities. Accordingly, the interplay between stress and reproduction is a key social justice issue and an important site for theoretical inquiry and birth equity efforts. Fortunately, both stress physiology and pregnancy physiology are highly plastic-responsive to the benefits of increased social support and respectful maternity care. Justice means valuing Women of Color and valuing their right to have a healthy, respected, and safe life.

Development of a prediction model of postpartum hospital use using an equity-focused approach

BACKGROUND

Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification.

OBJECTIVE

This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach.

STUDY DESIGN

We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use.

RESULTS

The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications.

CONCLUSION

The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.

Evaluating the performance of an AI-powered VBAC prediction system within a decision-aid birth choice platform for shared decision-making

Background

Vaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM).

Materials and Methods

Employing a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification.

Results

Forty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86-0.94) and an area under the curve of 0.89 (95% CI: 0.86-0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis.

Conclusion

Using AI techniques offers a more accurate assessment of VBAC risks, boosting women's confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare.

Predicting primary cesarean delivery in pregnancies complicated by gestational diabetes mellitus

BACKGROUND

Prediction models have shown promise in helping clinicians and patients engage in shared decision-making by providing quantitative estimates of individual risk of important clinical outcomes. Gestational diabetes mellitus is a common complication of pregnancy, which places patients at higher risk of primary CD. Suspected fetal macrosomia diagnosed on prenatal ultrasound is a well-known risk factor for primary CD in patients with gestational diabetes mellitus, but tools incorporating multiple risk factors to provide more accurate CD risk are lacking. Such tools could help facilitate shared decision-making and risk reduction by identifying patients with both high and low chances of intrapartum primary CD.

OBJECTIVE

This study aimed to develop and internally validate a multivariable model to estimate the risk of intrapartum primary CD in pregnancies complicated by gestational diabetes mellitus undergoing a trial of labor.

STUDY DESIGN

This study identified a cohort of patients with gestational diabetes mellitus derived from a large, National Institutes of Health-funded medical record abstraction study who delivered singleton live-born infants at ≥34 weeks of gestation at a large tertiary care center between January 2002 and March 2013. The exclusion criteria included previous CD, contraindications to vaginal delivery, scheduled primary CD, and known fetal anomalies. Candidate predictors were clinical variables routinely available to a practitioner in the third trimester of pregnancy found to be associated with an increased risk of CD in gestational diabetes mellitus. Stepwise backward elimination was used to build the logistic regression model. The Hosmer-Lemeshow test was used to demonstrate goodness of fit. Model discrimination was evaluated via the concordance index and displayed as the area under the receiver operating characteristic curve. Internal model validation was performed with bootstrapping of the original dataset. Random resampling with replacement was performed for 1000 replications to assess predictive ability. An additional analysis was performed in which the population was stratified by parity to evaluate the model's predictive ability among nulliparous and multiparous individuals.

RESULTS

Of the 3570 pregnancies meeting the study criteria, 987 (28%) had a primary CD. Of note, 8 variables were included in the final model, all significantly associated with CD. They included large for gestational age, polyhydramnios, older maternal age, early pregnancy body mass index, first hemoglobin A1C recorded in pregnancy, nulliparity, insulin treatment, and preeclampsia. Model calibration and discrimination were satisfactory with the Hosmer-Lemeshow test (P=.862) and an area under the receiver operating characteristic curve of 0.75 (95% confidence interval, 0.74-0.77). Internal validation demonstrated similar discriminatory ability. Stratification by parity demonstrated that the model worked well among both nulliparous and multiparous patients.

CONCLUSION

Using information routinely available in the third trimester of pregnancy, a clinically pragmatic model can predict intrapartum primary CD risk with reasonable reliability in pregnancies complicated by gestational diabetes mellitus and may provide quantitative data to guide patients in understanding their individual primary CD risk based on preexisting and acquired risk factors.

Mitigating Racial And Ethnic Bias And Advancing Health Equity In Clinical Algorithms: A Scoping Review

In August 2022 the Department of Health and Human Services (HHS) issued a notice of proposed rulemaking prohibiting covered entities, which include health care providers and health plans, from discriminating against individuals when using clinical algorithms in decision making. However, HHS did not provide specific guidelines on how covered entities should prevent discrimination. We conducted a scoping review of literature published during the period 2011-22 to identify health care applications, frameworks, reviews and perspectives, and assessment tools that identify and mitigate bias in clinical algorithms, with a specific focus on racial and ethnic bias. Our scoping review encompassed 109 articles comprising 45 empirical health care applications that included tools tested in health care settings, 16 frameworks, and 48 reviews and perspectives. We identified a wide range of technical, operational, and systemwide bias mitigation strategies for clinical algorithms, but there was no consensus in the literature on a single best practice that covered entities could employ to meet the HHS requirements. Future research should identify optimal bias mitigation methods for various scenarios, depending on factors such as patient population, clinical setting, algorithm design, and types of bias to be addressed.

Why Causation Matters: Rethinking "Race" as a Risk Factor

Although it is tempting to construe the correlation between Black "race" and higher rates of preterm birth as causal, this logic is flawed. Worse, the continued use of Black "race" as a risk factor for preterm birth is actively harmful. Using Black "race" as a risk factor suggests a causal relationship that does not exist and, critically, obscures what actually causes Black patients to be more vulnerable to poorer maternal and infant outcomes: anti-Black racism. Failing to name anti-Black racism as the root cause of Black patients' vulnerability conceals key pathways and tempts us to construe Black "race" as immutably related to higher rates of preterm birth. The result is that we overlook two highly treatable pathways-chronic stress and implicit bias-through which anti-Black racism negatively contributes to birth. Thus, clinicians may underuse important tools to reduce stress from racism and discrimination while missing opportunities to address implicit bias within their practices and institutions. Fortunately, researchers, physicians, clinicians, and medical staff can positively affect Black maternal and infant health by shifting our causal paradigm. By eliminating the use of Black "race" as a risk factor and naming anti-Black racism as the root cause of Black patients' vulnerability, we can practice anti-racist maternity care and take a critical step toward achieving birth equity.

Systemic Oppression, the Impact on Obstetric Care, and Interventions to Achieve Ideal Obstetric Outcomes

Systemic oppression contributes to adverse obstetric outcomes. It is possible to interrupt these adverse outcomes and achieve ideal patient outcomes by learning about our participation in oppression.