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Maternal Mortality Rates Among Black Women

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Maternal Mortality Rates Among Black Women

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This compilation offers a comprehensive exploration of Black maternal health, drawing from a diverse range of sources including empirical studies, governmental reports, scholarly articles, and literature reviews. By synthesizing insights from various disciplines and perspectives, this resource aims to provide a nuanced understanding of the systemic factors contributing to disparities in maternal health outcomes among Black women. From analyzing maternal mortality rates to examining barriers in accessing prenatal care, each subsection delves into crucial aspects of Black maternal health, fostering informed discourse and guiding evidence-based interventions. Through this compilation, readers are invited to delve into the complexities of Black maternal health and engage in efforts aimed at addressing disparities and promoting equitable outcomes for all individuals.

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Centers for Disease Control and Prevention

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On May 7, 2019, this report was posted online as an MMWR Early Release.

Emily E. Petersen, MD ¹; Nicole L. Davis, PhD ¹; David Goodman, PhD ¹; Shanna Cox, MSPH ¹; Nikki Mayes ¹; Emily Johnston, MPH ¹; Carla Syverson, MSN ¹; Kristi Seed ¹; Carrie K. Shapiro-Mendoza, PhD ¹; William M. Callaghan, MD ¹; Wanda Barfield, MD ¹ ( View author affiliations)

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Summary
What is already known about this topic?
Approximately 700 women die annually in the United States from pregnancy-related complications.
What is added by this report?
Among pregnancy-related deaths for which timing was known, 31.3% deaths occurred during pregnancy, 16.9% on the day of delivery, 18.6% on days 1-6 postpartum, 21.4% on days 7-42 postpartum, and 11.7% on days 43-365 postpartum. Leading causes of death varied by timing relative to the end of pregnancy. Approximately three in five pregnancy-related deaths were preventable. Contributing factors can be categorized at the community, health facility, patient, provider, and system levels.
What are the implications for public health practice?
Most pregnancy-related deaths are preventable, demonstrating the need to identify and implement strategies to address the multiple contributing factors.

Abstract

Background: Approximately 700 women die from pregnancy-related complications in the United States every year.

Methods: Data from CDC's national Pregnancy Mortality Surveillance System (PMSS) for 2011-2015 were analyzed. Pregnancy-related mortality ratios (pregnancy-related deaths per 100,000 live births; PRMRs) were calculated overall and by sociodemographic characteristics. The distribution of pregnancy-related deaths by timing relative to the end of pregnancy and leading causes of death were calculated. Detailed data on pregnancy-related deaths during 2013-2017 from 13 state maternal mortality review committees (MMRCs) were analyzed for preventability, factors that contributed to pregnancy-related deaths, and MMRC-identified prevention strategies to address contributing factors.

Results: For 2011-2015, the national PRMR was 17.2 per 100,000 live births. Non-Hispanic black (black) women and American Indian/Alaska Native women had the highest PRMRs (42.8 and 32.5, respectively), 3.3 and 2.5 times as high, respectively, as the PRMR for non-Hispanic white (white) women (13.0). Timing of death was known for 87.7% (2,990) of pregnancy-related deaths. Among these deaths, 31.3% occurred during pregnancy, 16.9% on the day of delivery, 18.6% 1-6 days postpartum, 21.4% 7-42 days postpartum, and 11.7% 43-365 days postpartum. Leading causes of death included cardiovascular conditions, infection, and hemorrhage, and varied by timing. Approximately sixty percent of pregnancy-related deaths from state MMRCs were determined to be preventable and did not differ significantly by race/ethnicity or timing of death. MMRC data indicated that multiple factors contributed to pregnancy-related deaths. Contributing factors and prevention strategies can be categorized at the community, health facility, patient, provider, and system levels and include improving access to, and coordination and delivery of, quality care.

Conclusions: Pregnancy-related deaths occurred during pregnancy, around the time of delivery, and up to 1 year postpartum; leading causes varied by timing of death. Approximately three in five pregnancy-related deaths were preventable.

Implications for Public Health Practice: Strategies to address contributing factors to pregnancy-related deaths can be enacted at the community, health facility, patient, provider, and system levels.

Introduction

Approximately 700 women die annually in the United States from pregnancy-related complications ( 1). Significant racial/ethnic disparities in pregnancy-related mortality exist; black women have a pregnancy-related mortality ratio approximately three times as high as that of white women ( 2, 3). Better understanding is needed on the circumstances surrounding pregnancy-related deaths and strategies to prevent future deaths.

This report describes the timing and characteristics of pregnancy-related deaths in the United States using 2011-2015 national CDC Pregnancy Mortality Surveillance System (PMSS) data. Data from 13 state maternal mortality review committees (MMRCs) during 2013-2017 were used to determine the percentage of pregnancy-related deaths that were preventable and factors that contributed to the deaths. MMRC-identified strategies for prevention are reported.

Methods

PMSS was established in 1986 by CDC and the American College of Obstetricians and Gynecologists (ACOG) to evaluate the causes of death and risk factors associated with pregnancy-related deaths. PMSS methodology has been described previously ( 2); CDC's Division of Reproductive Health requests that all states, the District of Columbia, and New York City send death certificates, linked live birth or fetal death certificates, and additional data when available, on deaths that occurred during pregnancy or within 1 year after delivery. Information on individual deaths are reviewed by medically trained epidemiologists to determine the pregnancy-relatedness and cause ( 4). A death is determined to be pregnancy-related if the death was caused by a pregnancy complication, a chain of events initiated by pregnancy, or the aggravation of an unrelated condition by the physiologic effects of pregnancy. Cause of death coding includes the following 10 mutually exclusive categories: hemorrhage; infection; amniotic fluid embolism; thrombotic pulmonary or other embolism (i.e., air, septic, or fat); hypertensive disorders of pregnancy (i.e., preeclampsia or eclampsia)*; anesthesia complications; cerebrovascular accidents †; cardiomyopathy; other cardiovascular conditions (e.g., congenital heart disease, ischemic heart disease, cardiac valvular disease, hypertensive heart disease, and congestive heart failure); and other noncardiovascular medical conditions (e.g., endocrine, hematologic, immunologic, and renal).

Pregnancy-related death data from PMSS for 2011-2015 were analyzed. The pregnancy-related mortality ratio (PRMR) is the number of pregnancy-related deaths per 100,000 live births. PRMRs were calculated by race/ethnicity, age, marital status, education, and year. Birth data, used for determining the number of live births, were obtained from U.S. natality files from the National Center for Health Statistics ( 5). SAS (version 9.4; SAS Institute) was used for all analyses.

Cause and timing of pregnancy-related deaths were analyzed. Timing of death was identified as "during pregnancy" when keywords on the death certificate noted the death was during pregnancy or the pregnancy checkbox option "pregnant at the time of death" was checked. Otherwise, timing of death in relation to the end of pregnancy was determined by comparing date of death on the death certificate with date of live birth or fetal death on linked birth or fetal death certificates. The specific timing of postpartum deaths was classified as unknown if there was no linked birth or fetal death certificate.

Data shared by 13 state MMRCs for deaths that occurred during 2013-2017 § were analyzed. Using a standardized data collection system, each multidisciplinary MMRC reviewed available data sources (e.g., medical records, social service records, autopsy reports, and vital records) to determine preventability, factors that contributed to the death, and prevention strategies to address contributing factors. Deaths attributable to suicide, drug overdose, homicide, and unintentional injury were excluded from analyses. MMRCs used the following definition of preventability: "a death is considered preventable if the committee determines that there was some chance of the death being averted by one or more reasonable changes to patient, community, provider, health facility, and/or system factors" ( 6). Percentage of deaths determined by MMRCs to have been preventable were calculated, and chi-squared tests were used to assess whether preventability differed by race/ethnicity or by timing of death. Thematic analyses of MMRC-identified factors that might have contributed to deaths and strategies to prevent future deaths also were conducted.

Results

During 2011-2015, a total of 3,410 pregnancy-related deaths occurred in the United States; the overall PRMR was 17.2 pregnancy-related deaths per 100,000 live births. The highest PRMRs were in women who were black (42.8) and American Indian/Alaska Native (32.5); these PRMRs were 3.3 and 2.5 times as high, respectively, as were those in white women (13.0) ( Table 1). The PRMR was highest among women aged ≥35 years and women who were not married. The overall PRMR fluctuated by year, ranging from 15.9 (2012) to 18.0 (2014).

When combined, cardiovascular conditions were responsible for >33% of pregnancy-related deaths; these conditions include cardiomyopathy (10.8%), other cardiovascular conditions (15.1%), and cerebrovascular accidents (7.6%). Other leading causes of pregnancy-related death included other noncardiovascular medical conditions (14.3%), infection (12.5%), and obstetric hemorrhage (11.2%). The cause of death could not be determined for 6.7% of pregnancy-related deaths.

Timing of death was known for 2,990 (87.7%) pregnancy-related deaths. Among these deaths, 937 (31.3%) occurred during pregnancy, 506 (16.9%) on the day of delivery, 556 (18.6%) 1-6 days postpartum, 640 (21.4%) 7-42 days postpartum, and 351 (11.7%) 43-365 days postpartum ( Table 2). Timing of deaths did not significantly differ between black and white women for most periods; however, a greater proportion of deaths among black women (14.9%) occurred 43-365 days postpartum compared to the proportion of deaths among white women (10.2%) that occurred during the same period (p<0.01).

Distribution of timing of death varied by cause of death (Table 2). Most deaths caused by amniotic fluid embolism occurred on the day of delivery or within 6 days postpartum. Approximately 60% of deaths caused by hypertensive disorders of pregnancy occurred 0-6 days postpartum, whereas those caused by cerebrovascular accidents occurred most frequently 1-42 days postpartum. Deaths caused by cardiomyopathy most commonly occurred 43-365 days postpartum; deaths caused by other cardiovascular conditions occurred most commonly during pregnancy and within 42 days postpartum.

The leading causes of death also varied by time relative to the end of pregnancy. During pregnancy, other noncardiovascular and other cardiovascular conditions were the leading causes of death ( Figure); on the day of delivery, hemorrhage and amniotic fluid embolism were the major causes of death. Hemorrhage, hypertensive disorders of pregnancy, and infection were leading causes of death during the first 6 days postpartum. From 6 weeks postpartum (43 days) through the end of the first year (365 days), cardiomyopathy was the leading cause of death.

Among 251 pregnancy-related deaths evaluated for preventability by the 13 MMRCs, a determination was made for 232 (92.4%). Among these, 139 (60.0%) were determined to be preventable deaths. Preventability did not significantly differ between black and white women (p = 0.4), or between Hispanic and white women (p = 0.7), with 57.4% of deaths among black women, 62.7% among white women, and 58.3% among Hispanic women determined to be preventable. Preventability was also similar by timing of pregnancy-related death (59.0% during pregnancy, 53.3% during delivery, 57.1% 1-6 days postpartum, 66.7% 7-42 days postpartum, and 61.9% 43-365 days postpartum; [p = 0.8]).

MMRCs identified an average of three to four contributing factors and two to three prevention strategies per pregnancy-related death. Contributing factors were thematically coded as community factors (e.g., unstable housing and limited access to transportation); health facility factors (e.g., limited experience with obstetric emergencies and lack of appropriate personnel or services); patient factors (e.g., lack of knowledge of warning signs and nonadherence to medical regimens); provider factors (e.g., missed or delayed diagnosis and lack of continuity of care); and system-level factors (e.g., inadequate access to care and poor case coordination) ( Table 3). MMRC-identified prevention strategies addressing community factors included expanding clinical office hours and the number of providers who accept Medicaid, prioritizing pregnant and postpartum women for temporary housing programs, and improving access to transportation. Actions addressing health facility factors included implementing obstetric emergency protocols and simulation training, providing telemedicine for facilities without on-site obstetric expertise, and implementing systems to foster communication among multiple providers. Although patient-level contributing factors were commonly identified, prevention strategies to mitigate these factors are often reliant upon providers and health systems. For example, prevention strategies to address patient-level factors included improving patient education materials and providing home health and patient support services. Provider-level prevention strategies included offering provider education to reduce missed or delayed diagnoses, implementing a maternal early warning system ( 7), and improving hand-off communication between obstetricians and other providers. MMRC-identified prevention strategies addressing system-level factors included developing policies to ensure that women deliver at a health facility with an appropriate level of maternal care and extending Medicaid coverage for pregnant women to include 1 year of postpartum care.

Discussion

Pregnancy-related deaths occur not only during delivery but also during pregnancy and up to 1 year postpartum. The leading causes of pregnancy-related deaths varied by timing of death. Acute obstetric emergencies such as hemorrhage and amniotic fluid embolism most commonly occurred on the day of delivery, whereas deaths caused by hypertensive disorders of pregnancy and thrombotic pulmonary embolism most commonly occurred 0-6 days postpartum, and during pregnancy and 1-42 days postpartum, respectively. Cardiomyopathy was the most common cause of death in the late postpartum period (43-365 days postpartum). The higher proportion of pregnancy-related deaths in the late postpartum period among black women is likely attributable to higher proportion of pregnancy-related deaths due to cardiomyopathy among these women ( 8). Approximately three in five pregnancy-related deaths were determined by MMRCs to be preventable, and preventability did not differ significantly by race/ethnicity or timing of death. Recognizing the major causes of death by timing can help identify opportunities for intervention.

These data demonstrate the need to address the multiple factors that contribute to pregnancy-related deaths during pregnancy, labor and delivery, and postpartum. No single intervention is sufficient; reducing pregnancy-related deaths requires reviewing and learning from each death, improving women's health, and reducing social inequities across the life span, as well as ensuring quality care for pregnant and postpartum women ( 9). Throughout the preconception, pregnancy, and postpartum periods, providers and patients can work together to optimally manage chronic health conditions ( 10). Standardized approaches to addressing obstetric emergencies can be implemented in all hospitals that provide delivery services. The Alliance for Innovation on Maternal Health (AIM) has provided sets of bundled guidance to provide for such standardization. ¶ Implementation of this guidance is often supported by perinatal quality collaboratives, state-based initiatives that aim to improve the quality of care for mothers and infants ( 11). Ensuring that pregnant women at high risk for complications receive care in facilities prepared to provide the required level of specialized care also can improve outcomes; professional organizations have developed criteria for recommended levels of maternal care ( 12). CDC has created the Levels of Care Assessment Tool (LOCATe) for public health decision makers to evaluate risk-appropriate care ( 13). In the postpartum period, follow-up care is critical for all women, particularly those with chronic medical conditions and complications of pregnancy (e.g., hypertensive disorders of pregnancy). ACOG recommends that postpartum women have contact with obstetric providers within the first 3 weeks postpartum and recognizes postpartum care as an ongoing process tailored to each woman's individual needs ( 14).

The findings in this report are subject to at least four limitations. First, errors in reported pregnancy status on the death certificates have been described, potentially leading to overestimation or underestimation of the number of pregnancy-related deaths ( 15). Second, data for specified race or Hispanic-origin groups other than non-Hispanic white and non-Hispanic black should be interpreted with caution because of inconsistencies in reporting these data on death certificates and surveys. Third, generally the pregnancy-relatedness cannot be determined in PMSS for injury deaths such as drug overdoses, suicides, or homicides, or for cancer-related deaths, because of limited information concerning death circumstances. As such, these types of death are often not included in the PRMR. For consistency among data sources, these conditions were not investigated in MMRC data, although MMRC data have found suicides and drug overdoses to be a leading underlying cause of pregnancy-related mortality ( 6). Most (75.0%) of these deaths occur in the late postpartum period. Finally, not all preventable deaths reported by MMRCs had a prevention strategy to address contributing factors; improving quality, completeness, and timeliness of MMRC data can translate into opportunities for prevention. MMRC-identified prevention strategies are based on comprehensive case review by a multidisciplinary group of clinical and nonclinical experts and might not always be drawn from published evidence-based interventions, in part because of a lack of programmatic and policy-based evidence. MMRCs' access to comprehensive medical and social service records highlights their unique and critical role in understanding all factors contributing to pregnancy-related deaths and using those data to identify strategies to potentially prevent future deaths and contribute to the evidence base.

Pregnancy-related deaths occur during pregnancy, around the time of delivery, and within 1 year postpartum; leading causes of death vary by timing of death. Most pregnancy-related deaths can be prevented. Comprehensive review of pregnancy-related deaths can identify contributing factors and opportunities to implement strategies for preventing future deaths.

Acknowledgments

Offices of vital records from 50 U.S. states, New York City, and the District of Columbia; Arizona, Colorado, Delaware, Florida, Georgia, Hawaii, Illinois, Mississippi, North Carolina, Ohio, South Carolina, Tennessee, and Utah maternal mortality review committees; Building U.S. Capacity to Review and Prevent Maternal Deaths Team, CDC; CDC Foundation; Association of Maternal and Child Health Programs. CDC developed this report using information from state maternal mortality review committees and is responsible for the conclusions in the report.

Corresponding author: Emily Petersen, fmd9@cdc.gov, 404-488-4316.

¹ Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC.

References

1. CDC. Pregnancy-related deaths. Atlanta, GA: US Department of Health and Human Services, CDC; 2019. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy-relatedmortality.htm

2. CDC. Pregnancy Mortality Surveillance System. Atlanta, GA: US Department of Health and Human Services, CDC; 2019. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/pregnancy-mortality-surveillance-system.htm

3. CDC. Birth data. Atlanta, GA: US Department of Health and Human Services, CDC; 2019. https://www.cdc.gov/nchs/nvss/births.htm

4. Building U.S. Capacity to Review and Prevent Maternal Deaths. Report from nine maternal mortality review committees. Washington, DC: Review to Action; 2018. http://reviewtoaction.org/sites/default/files/national-portal-material/Report%20from%20Nine%20MMRCs%20final_0.pdfpdf icon external icon

5. Henderson ZT, Ernst K, Simpson KR, et al. The national network of state perinatal quality collaboratives: a growing movement to improve maternal and infant health. J Womens Health 2018;27:221-6. CrossRefexternal icon

6. CDC. CDC Levels of Care Assessment Tool (CDC LOCATe). Atlanta, GA: US Department of Health and Human Services, CDC; 2019. https://www.cdc.gov/reproductivehealth/maternalinfanthealth/LOCATe.html

Level

Contributing factor

Strategies to address contributing factor

Unstable housing

Prioritize pregnant and postpartum women for temporary housing programs

Lack of, or inadequate, transportation options

Strengthen or build systems to link persons to affordable transportation, or provide vouchers for transport to medical appointments

Improve availability of transportation services covered by Medicaid

Obesity and associated chronic disease complications

Improve access to healthy foods and enhance efforts to educate and promote healthy eating habits and weight management strategies

Ensure emergency department staff members ask about recent pregnancy history and consult with obstetrician on call if patient is pregnant or has recently been pregnant

Lack of appropriate personnel or services

Provide telemedicine for facilities with no obstetric provider on-site

Ensure Medicaid managed care organizations' contracts include sufficient access to specialists for patients at high risk

Lack of guiding protocols or tools to help ensure quality care provision

Ensure sepsis, hemorrhage, and massive transfusion protocols are in place and followed by staff members

Implement applicable patient safety bundles

Implement systems to foster communication among multiple providers to ensure proper case coordination

Implement protocols for using patient navigators

Implement a public education campaign to increase awareness of signs and symptoms of common complications

Nonadherence to medical regimens or advice

Standardize patient education to ensure providers relay consistent messages and implement techniques for ensuring patient understanding, such as patient "teaching back" to the provider

Make education materials available in the clinic and online

Strengthen and expand access to patient navigators, case managers, and peer support

Ensure access to interpreter services when needed

Offer home health or social work follow-up services

Offer provider education on cardiac conditions in pregnant and postpartum women

Perform thorough evaluation of patients reporting pain and shortness of breath

Inappropriate or delayed treatment

Only perform cesarean deliveries when medically indicated

Implement a maternal early warning system

Lack of continuity of care

Improve care transition communication among obstetrician-gynecologists and other primary and specialty care physicians

Enlist state perinatal quality collaboratives to identify quality improvement procedures and periodic drills/simulation training for birth facilities, including obstetric emergency drills

Design education initiatives for emergency department staff members on the care of pregnant and postpartum women

Case coordination or management

Extend expanded Medicaid coverage eligibility for pregnant women to include 1 year of postpartum care

Create quality improvement entity to manage outpatient care gaps and improve care coordination

Implement a postpartum care transition bundle for better integration of services for women at high risk

Develop procedures for all hospitals to improve documentation of abnormal test results, plan for follow-up care, and management of conditions

Develop universal health record system that allows for sharing of medical records among hospitals

Guiding policies, procedures, or standards not in place

Develop protocol for timely referrals and consults

Ensure all hospitals within a health care system follow the same protocols and policies

Suggested citation for this article: Petersen EE, Davis NL, Goodman D, et al. Vital Signs: Pregnancy-Related Deaths, United States, 2011-2015, and Strategies for Prevention, 13 States, 2013-2017. MMWR Morb Mortal Wkly Rep 2019;68:423-429. DOI: http://dx.doi.org/10.15585/mmwr.mm6818e1external icon .

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Vital Signs: Pregnancy-Related Deaths, United States ...

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Among pregnancy-related deaths for which timing was known, approximately three in five pregnancy-related deaths were preventable.

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Centers for Disease Control and Prevention

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Emily E. Petersen, MD ¹; Nicole L. Davis, PhD ¹; David Goodman, PhD ¹; Shanna Cox, MSPH ¹; Carla Syverson, MSN ¹,2; Kristi Seed ¹,2; Carrie Shapiro-Mendoza, PhD ¹; William M. Callaghan, MD ¹; Wanda Barfield, MD ¹ ( View author affiliations)

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Summary
What is already known about this topic?
Approximately 700 women die annually in the United States as a result of pregnancy or its complications; racial/ethnic disparities exist.
What is added by this report?
During 2007-2016, black and American Indian/Alaska Native women had significantly more pregnancy-related deaths per 100,000 births than did white, Hispanic, and Asian/Pacific Islander women. Disparities persisted over time and across age groups and were present even in states with the lowest pregnancy-related mortality ratios and among groups with higher levels of education. The cause-specific proportion of pregnancy-related deaths varied by race/ethnicity.
What are the implications for public health practice?
Identifying factors that drive differences in pregnancy-related deaths and implementing prevention strategies to address them could reduce racial/ethnic disparities in pregnancy-related mortality. Strategies to address racial/ethnic disparities in pregnancy-related deaths, including improving women's health and access to quality care in the preconception, pregnancy, and postpartum periods, can be implemented through coordination at the community, health facility, patient and family, health care provider, and system levels.

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Approximately 700 women die in the United States each year as a result of pregnancy or its complications, and significant racial/ethnic disparities in pregnancy-related mortality exist ( 1). Data from CDC's Pregnancy Mortality Surveillance System (PMSS) for 2007-2016 were analyzed. Pregnancy-related mortality ratios (PRMRs) (i.e., pregnancy-related deaths per 100,000 live births) were analyzed by demographic characteristics and state PRMR tertiles (i.e., states with lowest, middle, and highest PRMR); cause-specific proportionate mortality by race/ethnicity also was calculated. Over the period analyzed, the U.S. overall PRMR was 16.7 pregnancy-related deaths per 100,000 births. Non-Hispanic black (black) and non-Hispanic American Indian/Alaska Native (AI/AN) women experienced higher PRMRs (40.8 and 29.7, respectively) than did all other racial/ethnic groups. This disparity persisted over time and across age groups. The PRMR for black and AI/AN women aged ≥30 years was approximately four to five times that for their white counterparts. PRMRs for black and AI/AN women with at least some college education were higher than those for all other racial/ethnic groups with less than a high school diploma. Among state PRMR tertiles, the PRMRs for black and AI/AN women were 2.8-3.3 and 1.7-3.3 times as high, respectively, as those for non-Hispanic white (white) women. Significant differences in cause-specific proportionate mortality were observed among racial/ethnic populations. Strategies to address racial/ethnic disparities in pregnancy-related deaths, including improving women's health and access to quality care in the preconception, pregnancy, and postpartum periods, can be implemented through coordination at the community, health facility, patient, provider, and system levels.

PMSS was established in 1986 by CDC and the American College of Obstetricians and Gynecologists to better understand the causes of death and risk factors associated with pregnancy-related deaths. Methodology of PMSS has been described previously ( 2). Briefly, CDC requests that all states, the District of Columbia, and New York City identify deaths during or within 1 year of pregnancy and send corresponding death certificates, linked birth or fetal death certificates, and additional data when available. Medically trained epidemiologists review information and determine the relatedness to pregnancy and cause for each death. A death was considered pregnancy-related if it occurred during or within 1 year of pregnancy and was caused by a pregnancy complication, a chain of events initiated by pregnancy, or aggravation of an unrelated condition by the physiologic effects of pregnancy. U.S. natality files were the source of live birth data ( 3).

PRMRs were analyzed by age group, highest level of education, and calendar year for women who were non-Hispanic white, black, AI/AN, Asian or Pacific Islander (A/PI), and Hispanic. Per the PMSS assurance of confidentiality, state-specific data are not authorized to be released. States were anonymously classified by PRMR and grouped into lowest, middle, and highest tertiles by PRMR; the PRMR was calculated by race/ethnicity per state tertile. Disparity ratios (comparisons of PRMR between two racial/ethnic groups) were calculated by five 2-year intervals, demographic characteristics, and state PRMR tertiles. White decedents were the referent group because they represented the largest racial/ethnic group. Cause-specific proportionate mortality was classified in 10 mutually exclusive categories,* and differences by race/ethnicity were identified using chi-squared tests. SAS statistical software (version 9.4; SAS Institute) was used for the analyses.

During 2007-2016, a total of 6,765 pregnancy-related deaths occurred in the United States (PRMR = 16.7 per 100,000 births). PRMRs were highest among black (40.8) and AI/AN (29.7) women; these rates were 3.2 and 2.3 times the PRMR for white women (12.7) ( Table 1). From 2007-2008 to 2015-2016, the overall PRMR increased slightly from 15.0 to 17.0. The disparity ratios did not change significantly over time.

PRMR increased with maternal age; the black:white disparity was lowest among women aged <20 years (1.5) and highest among those aged 30-34 years (4.3); the AI/AN:white disparity was lowest among women aged 20-24 years (1.2) and was highest among women aged 35-39 years (5.1). Racial/ethnic disparities were present at all education levels. The PRMR among black women with a completed college education or higher was 1.6 times that of white women with less than a high school diploma. Among women with a college education or higher, the PRMR for black women was 5.2 times that of their white counterparts. The black:white disparity ratio in the PRMR for the states in the lowest, middle, and highest tertiles was 3.0, 3.3, and 2.8, respectively.

Cardiovascular conditions (including cardiomyopathy, other cardiovascular conditions, and cerebrovascular accidents), other noncardiovascular medical conditions, and infection were leading causes of pregnancy-related deaths. The proportion of pregnancy-related deaths attributed to each of 10 mutually exclusive causes varied by race/ethnicity ( Table 2). Cardiomyopathy, thrombotic pulmonary embolism, and hypertensive disorders of pregnancy contributed to a significantly higher proportion of pregnancy-related deaths among black women than among white women. Hemorrhage and hypertensive disorders of pregnancy contributed to a higher proportion of pregnancy-related deaths among AI/AN women than among white women.

Discussion

Racial/ethnic disparities in pregnancy-related mortality were evident in 2007 and continued through 2016, with significantly higher PRMRs among black and AI/AN women than among white, A/PI, and Hispanic women. The PRMR for black and AI/AN women aged ≥30 years was approximately four to five times that of their white counterparts. Even in states with the lowest PRMRs, and among groups with higher levels of education, significant disparities persisted, demonstrating that the disparity in pregnancy-related mortality for black and AI/AN women is a complex national problem.

Multiple factors contribute to pregnancy-related mortality and to racial/ethnic disparities. Previous analyses found that for each pregnancy-related death, an average of three to four contributing factors were identified at multiple levels, including community, health facility, patient/family, provider, and system ( 1). Thirteen state maternal mortality review committees reported 60% of pregnancy-related deaths were preventable, and there were no significant differences in preventability by race/ethnicity ( 1). Differences in proportionate causes of death among black and AI/AN women might reflect differences in access to care, quality of care, and prevalence of chronic diseases ( 4).

Chronic diseases associated with increased risk for pregnancy-related mortality (e.g., hypertension) are more prevalent and less well controlled in black women ( 5). Ensuring access to quality care, including specialist providers, during preconception, pregnancy, and the postpartum period is crucial for all women to identify and manage chronic medical conditions ( 4). Systemic factors (e.g., gaps in health care coverage and preventive care, lack of coordinated health care, and social services) and community factors (e.g., securing transportation for medical visits and inadequate housing) have also been identified as contributors to pregnancy-related deaths ( 1). Addressing these factors and ensuring that pregnant women at high risk for complications receive care in facilities prepared to provide the required level of specialized care can improve outcomes. †,§ In addition, innovative delivery of care models in the preconception, pregnancy, and postpartum periods might be further evaluated for their potential to reduce maternal disparities.

Quality of care likely has a role in pregnancy-related deaths and associated racial disparities. A national study of five specific pregnancy complications found a similar prevalence of complications among black and white women, but a significantly higher case-fatality rate among black women ( 6). Studies have suggested that black women are more likely than are white women to receive obstetric care in hospitals that provide lower quality of care ( 7). Hospitals and health care systems can implement standardized protocols and training in quality improvement initiatives, ensuring implementation in facilities that serve disproportionately affected communities. Quality improvement efforts, such as perinatal quality collaboratives ¶ that facilitate a change in the culture of care provision, implement standards of care,** and rapidly use data to identify opportunities for improvement, can improve the quality of care received by all pregnant and postpartum women.

Implicit racial bias has been reported in the health care system and can affect patient-provider interactions, treatment decisions, patient adherence to recommendations, and patient health outcomes ( 8). This report's findings demonstrate that black and AI/AN women have a more accelerated trajectory in age-specific PRMRs compared with white women. This might be related to the "weathering" hypothesis, which proposed that black women experience earlier deterioration of health because of the cumulative impact of exposure to psychosocial, economic, and environmental stressors ( 9). Identifying and addressing implicit bias and structural racism in health care and community settings, engaging communities in prevention efforts, and supporting community-based programs that build social support and resiliency would likely improve patient-provider interactions, health communication, and health outcomes ( 4).

Reducing disparities in pregnancy-related mortality requires addressing multifaceted contributors. Ensuring robust comprehensive data collection and analysis through state and local maternal mortality review committees, which thoroughly review pregnancy-related deaths and make actionable prevention recommendations, offer the best opportunity for identifying priority strategies to reduce disparities in pregnancy-related mortality. ††

The findings in this report are subject to at least three limitations. First, PMSS predominantly uses death certificates and linked birth or fetal death certificates to determine the pregnancy-relatedness of each death. Errors in reported pregnancy status on death certificates have been described, potentially leading to overestimation of the number of pregnancy-related deaths ( 10). Second, pregnancy-relatedness cannot generally be determined in PMSS for cancer-related deaths or injury deaths such as drug overdoses, suicides, or homicides, and thus, these are often not included in the PRMR calculated from PMSS data. Finally, small cohort sizes precluded the reporting of some factors by race/ethnicity; in addition, there might be inconsistencies in the reporting of race/ethnicity when death certificates were used for classification. §§

Most pregnancy-related deaths can be prevented, and significant racial/ethnic disparities in pregnancy-related mortality need to be addressed. Further identification and evaluation of factors contributing to racial/ethnic disparities are crucial to inform and implement prevention strategies that will effectively reduce disparities in pregnancy-related mortality, including strategies to improve women's health and access to quality care in the preconception, pregnancy, and postpartum periods. Addressing this complex national problem requires coordination and collaboration among community organizations, health facilities, patients and families, health care providers, and health systems.

Corresponding author: Emily E. Petersen, fmd9@cdc.gov, 770-488-4316.

¹Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, CDC; ² DB Consulting Group, Atlanta, Georgia.

References

1. CDC. Birth data. Atlanta, GA: US Department of Health and Human Services, CDC; 2019. https://www.cdc.gov/nchs/nvss/births.htm.

2. Fryar CD, Ostchega Y, Hales CM, Zhang G, Kruszon-Moran D. Hypertension prevalence and control among adults: United States, 2015-2016. NCHS Data Brief 2017;289:1-8. PubMedexternal icon

Characteristic

Total PRMR

White PRMR

Black PRMR

Black: white disp. ratio

AI/AN PRMR

AI/AN: white disp. ratio

A/PI PRMR

A/PI: white disp. ratio

Hispanic PRMR

Hispanic: white disp. ratio

Total 16.7 12.7 40.8 3.2 29.7 2.3 13.5 1.1 11.5 0.9

Age group (yrs)

<20

10.9

10.8

16.8

1.5

19.5

1.8

- §

-

6.7

0.6

Education completed

Period

State-level PRMR tertile

Cause of death

Proportionate cause of death by race/ethnicity* No. (%) attributed to each cause

Total deaths

White

Black

AI/AN

A/PI

Hispanic

Hemorrhage

250 (9.1)

237 (9.7)

23 (19.7) †

66 (19.5) †

173 (15.8) †
752 (11.1)

Infection

418 (15.2)

235 (9.7) §

10 (8.5) §

51 (15.0)

183 (16.7)
900 (13.3)

Amniotic fluid embolism

147 (5.3)

106 (4.4)

3 (2.6)

51 (15.0) †

58 (5.3)
365 (5.4)

Thrombotic pulmonary or other embolism

246 (8.9)

265 (10.9) †

9 (7.7)

11 (3.2) §

88 (8.0)
624 (9.2)

Hypertensive disorders of pregnancy

184 (6.7)

200 (8.2) †

15 (12.8) †

21 (6.2)

106 (9.7) †
528 (7.8)

Anesthesia complications

7 (0.3)

14 (0.6)

0 (0.0)

3 (0.9)

6 (0.5)
30 (0.4)

Cerebrovascular accidents

207 (7.5)

148 (6.1) §

6 (5.1)

37 (10.9) †

92 (8.4)
490 (7.2)

Cardiomyopathy

288 (10.4)

345 (14.2) †

17 (14.5)

21 (6.2) §

75 (6.8) §
748 (11.1)

Other cardiovascular conditions

465 (16.9)

393 (16.2)

13 (11.1)

38 (11.2) §

124 (11.3) §
1,035 (15.3)

Other noncardiovascular medical conditions

384 (13.9)

343 (14.1)

16 (13.7)

26 (7.7) §

130 (11.9)
903 (13.3)

Unknown

160 (5.8)

146 (6.0)

5 (4.3)

14 (4.1)

61 (5.6)
390 (5.8)

Total 2,756 2,432 117 339 1,096 6,765¶

MMWR and Morbidity and Mortality Weekly Report are service marks of the U.S. Department of Health and Human Services.
Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services.
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Racial/Ethnic Disparities in Pregnancy-Related Deaths ...

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Emily E. Petersen, MD 1; Nicole L. Davis, PhD 1; David Goodman, PhD 1; Shanna Cox, MSPH 1; Carla Syverson, MSN 1 ,2; Kristi Seed 1 ,2; Carrie Shapiro-Mendoza, PhD 1; William M. Callaghan, MD 1; Wanda Barfield, MD 1 ( View author affiliations) View suggested citation What is already known about this topic?

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Affiliations

¹ Department of Population and Public Health Sciences, University of Southern California, Los Angeles.

² Division of Neonatology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.

³ Division of Neonatology, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, Georgia.

⁴ Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

⁵ Leonard Davis Institute, University of Pennsylvania, Philadelphia.

⁶ Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

⁷ Department of Pediatrics, New York University Langone Health, New York.

⁸ Department of Population Health, New York University Langone Health, New York.

⁹ Institute for Excellence in Health Equity, New York University Langone Health, New York.

Affiliations

¹ Department of Population and Public Health Sciences, University of Southern California, Los Angeles.

² Division of Neonatology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.

³ Division of Neonatology, Emory University School of Medicine and Children's Healthcare of Atlanta, Atlanta, Georgia.

⁴ Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

⁵ Leonard Davis Institute, University of Pennsylvania, Philadelphia.

⁶ Department of Obstetrics and Gynecology, Perelman School of Medicine, University of Pennsylvania, Philadelphia.

⁷ Department of Pediatrics, New York University Langone Health, New York.

⁸ Department of Population Health, New York University Langone Health, New York.

⁹ Institute for Excellence in Health Equity, New York University Langone Health, New York.

Abstract

Importance: Immigrant birthing people have lower rates of preterm birth compared with their US-born counterparts. This advantage and associated racial and ethnic disparities across the gestational age spectrum have not been examined nationally.

Objective: To examine associations of maternal nativity, ethnicity, and race with preterm birth.

Design, setting, and participants: This cohort study used birth certificates from the National Vital Statistics System to analyze in-hospital liveborn singleton births in the US between January 1, 2009, and December 31, 2018. Data were analyzed from January to June 2023.

Exposure: Mutually exclusive nativity, ethnicity, and race subgroups were constructed using nativity (defined as US-born or non-US-born), ethnicity (defined as Hispanic or non-Hispanic), and race (defined as American Indian or Alaska Native, Asian, Black, Native Hawaiian or Other Pacific Islander, White, or other [individuals who selected other race or more than 1 race]).

Main outcomes and measures: The primary outcome of interest was preterm birth. Modified Poisson and multinomial logistic regression models quantified relative risk (RR) of preterm birth overall (<37 weeks' gestation) and by gestational category (late preterm: 34-36 weeks' gestation; moderately preterm: 29-33 weeks' gestation; and extremely preterm: <29 weeks' gestation) for each maternal nativity, ethnicity, and race subgroup compared with the largest group, US-born non-Hispanic White (hereafter, White) birthing people. The RR of preterm birth overall and by category was also measured within each racial and ethnic group by nativity. Models were adjusted for maternal demographic and medical covariates, birth year, and birth state.

Results: A total of 34 468 901 singleton live births of birthing people were analyzed, with a mean (SD) age at delivery of 28 (6) years. All nativity, ethnicity, and race subgroups had an increased adjusted risk of preterm birth compared with US-born White birthing people except for non-US-born White (adjusted RR, 0.85; 95% CI, 0.84-0.86) and Hispanic (adjusted RR, 0.98; 95% CI, 0.97-0.98) birthing people. All racially and ethnically minoritized groups had increased adjusted risks of extremely preterm birth compared with US-born White birthing people. Non-US-born individuals had a decreased risk of preterm birth within each subgroup except non-Hispanic Native Hawaiian or Other Pacific Islander individuals, in which immigrants had significantly increased risk of overall (adjusted RR, 1.07; 95% CI, 1.01-1.14), moderately (adjusted RR, 1.10; 95% CI, 0.92-1.30), and late (adjusted RR, 1.11; 95% CI, 1.02-1.22) preterm birth than their US-born counterparts.

Conclusions and relevance: Results of this cohort study suggest heterogeneity of preterm birth across maternal nativity, ethnicity, and race and gestational age categories. Understanding these patterns could aid the design of targeted preterm birth interventions and policies, especially for birthing people typically underrepresented in research.

PubMed Disclaimer

Conflict of interest statement

Conflict of Interest Disclosures: Dr Salazar reported receiving grants from the National Institutes of Health (NIH) outside the submitted work. Dr Handley reported receiving grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) outside the submitted work. Dr Burris reported receiving research funding to the institution from Highmark Foundation, NIH, Optum-Labs, and March of Dimes outside the submitted work. Dr Ortiz reported receiving personal fees from Adverse Childhood Experiences Aware outside the submitted work. Dr Montoya-Williams reported receiving grants from the NICHD during the conduct of the study. No other disclosures were reported.

References

Osterman MJK, Hamilton BE, Martin JA, Driscoll AK, Valenzuela CP. Births: final data for 2021. Natl Vital Stat Rep. 2023;72(1):1-53. - PubMed

Allen MC, Cristofalo EA, Kim C. Outcomes of preterm infants: morbidity replaces mortality. Clin Perinatol. 2011;38(3):441-454. doi:10.1016/j.clp.2011.06.011 - DOI - PubMed

Platt MJ. Outcomes in preterm infants. Public Health. 2014;128(5):399-403. doi:10.1016/j.puhe.2014.03.010 - DOI - PubMed

Purisch SE, Gyamfi-Bannerman C. Epidemiology of preterm birth. Semin Perinatol. 2017;41(7):387-391. doi:10.1053/j.semperi.2017.07.009 - DOI - PubMed

Braveman P, Dominguez TP, Burke W, et al. . Explaining the Black-White disparity in preterm birth: a consensus statement from a multi-disciplinary scientific work group convened by the March of Dimes. Front Reprod Health. 2021;3:684207. doi:10.3389/frph.2021.684207 - DOI - PMC - PubMed

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national center for biotechnology information

# data[].attributes.attachment_link.title
Preterm Birth Risk and Maternal Nativity, Ethnicity, and Race - PubMed

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Results of this cohort study suggest heterogeneity of preterm birth across maternal nativity, ethnicity, and race and gestational age categories. Understanding these patterns could aid the design of targeted preterm birth interventions and policies, especially for birthing people typically underrepr ...

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“When I think of mental healthcare, I think of no care.” Mental Health Services as a Vital Component of Prenatal Care for Black Women - Maternal and Child Health Journal

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Purpose Black people give birth joyously despite disproportionate rates of adverse perinatal outcomes. Given that group prenatal care shows promise in mitigating these inequities, we sought to solicit the opinions of Black peripartum women on how group prenatal care could be tailored to fit their specific needs. In this study, we describe attitudes about a proposed Black group prenatal care in a single focus group of 11 Black women who receive maternal health services from Black Infant Health (BIH, a state and federal funded state-wide program for Black pregnant people with the goal to improve infant and maternal health). These data were used to design a race-conscious group prenatal care curriculum specifically for Black women at UCSF. Description This study was an analysis of focus group data generated as part of a larger project focused on community involvement in Black maternal health. English speaking pregnant or recently postpartum women age 18 or older who receive services from BIH were recruited to participated in the focus group analyzed in this study. All facilitators of the focus group were Black women in order to facilitate candid conversation about racism in prenatal care. Assessment The need for mental health care was common thread underlying all conversations about prenatal health improvements desired by our focus groups. Participants expressed the centrality of mental health access during our discussion of other themes (e.g.: ease of access, inclusion of partners, special classes for teen moms) by discussing them in terms of their relationship to mental health. Our participants’ clear expression of the centrality of mental health care to their prenatal health guided our decision to focus on mental health as a necessary pillar of any group prenatal care intervention designed to mitigate perinatal healthcare disparities in this paper. Three themes related to mental health integration into group prenatal care emerged from thematic analysis of the transcripts. Participants expressed insufficient access and advocacy, and provider distrust. Conclusion Evidence exists supporting group prenatal care as a tool for mitigation of perinatal health disparities among Black women. There is also a large body of data describing the disproportionate burden of mental health needs among Black women. The rich data we present here from Black women on their desire for the integration of these two needs fits well into the parallel conversation occurring in the literature. To our knowledge, this is the first study investigating desires of Black women regarding group prenatal care designed specifically for them. They expressed a strong desire for more access to mental health care providers who are racially conscious and aware of white supremacy, and nuanced opinions on the role of racial concordance in health equity.

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A total of 30 interviews were conducted. Table 3 depicts the characteristics of Clinical care team members. Table 4 describes their roles. Most clinical care team members were Black (57.9%), female (90.5%), and had a medical degree (42.9%). Table 5 depicts the characteristics of patients. Most patients were Black (80%), female (88.9%) and had completed high school or some college (88.9%).

The key qualitative findings are organized around the categories and themes that emerged from our data analysis process, including: 1) provider awareness of perinatal disparities, 2) trust, 3) change initiatives, 4) services and resources, 5) SDH, and 6) perinatal care practices. A summary of our findings, including themes, subthemes, and exemplar quotes can be found in Table 6. The findings across interviews elucidate current perinatal practices and their level of success as reported by providers and patients They tell a story of the need for attention to SDH, the requirement to remove barriers to accessing perinatal care, and the utmost importance of trust in a patient-provider relationship.

Perinatal disparities

Access to care

Most clinical care team members acknowledged the presence of disparities such as decreased quality of care experienced by Black individuals compared with their NHW counterparts. A clinical care team member stated, "where I feel I see a lot of disparity is not having access to insurance that really decreases the quality of care. There are a lot of restrictions in terms of where patients can go with specific types of insurance or if they don't have insurance at all." The interviews also revealed that Black persons are more likely to have fewer appointments and start care later, resulting in delays in tests or missed workups. As one clinical care team member put it, "we see a trend of missed appointments. If they can't come in their own cars, they won't come to receive care."

Patients confirmed this reality, with multiple citing insurance coverage or paperwork as one of their main challenges during pregnancy. While one patient mentioned they had to choose their site of care solely because "that's where they offered it," many patients highly valued their ability to choose where and from whom they received care, but some faced barriers to this choice. A patient told a story including a delayed appointment and an unwanted change in provider, relaying "my insurance had canceled because of my age and then she wanted me to pay out of pocket."

Many patients also found difficulties with accessing and scheduling appointments, one noting, "...and then once you get to doctor's appointments, Oh my God, Scheduling. Because it's like... today is December 7th. Well, [they] have a doctor's appointment for February the second." Another patient wished providers offered more flexibility in appointment times. Many patients we interviewed travel long distances across Chicago to receive prenatal care, often requiring babysitters for their children or other arrangements. One patient recounted, "I live out South. So I'm like an hour ride to the North side. So when you tell me, Oh, I can't be seen. And I'm going to go all the way that back south to make a new appointment to come all the way back up North, that's just a no, ...Especially when I'm on public transportation."

Long wait-times in- office also contribute to the effort it can take to reach an appointment. One patient says, "it feels like I have to carve out my whole entire day" while another reports sitting in a cold exam room for hours. While this can sometimes be the reality in clinical settings, it is important to note that it is not always easy to fit appointments into a schedule, especially among other inequities that Black birthing people deal with day-to-day. In the setting of prenatal care, issues like these can leave pregnant individuals with long gaps in care at important prenatal stages.

Resources

Given these disparities, assistance beyond the current routine prenatal care is often needed to combat challenges like the above that Black birthing people disproportionately face. While a few patients felt they didn't need additional supports, one shared, "so I was laid off, my job due to the virus... I had to continue to pay every time I had a prenatal visit, I had to pay $40" and that her clinic was able to connect her to external coverage resources. Some patients we interviewed faced challenges in finding childcare, housing, and transportation.

Clinical care team members described the need for addressing SDH, mental health, and contraception and family planning. One clinical care team member mentioned, "Particularly for low-income Black women, having the resources and support to link with social services they need, whether it's transportation, housing, nutrition support, and mental health challenges and how and when women can access those support services can be a cause for anxiety." Further examples of resources that benefit Black birthing people are discussed under the category Services and Resources.

Racism

Aside from the need for additional physical resources, attention to the experience of Black patients seeking perinatal care is of paramount importance. A theme patients returned to over and over was the importance of providers listening to their Black patients. Some patients emphasized the need for advocates like doulas to combat racial mistreatment, like a patient who acknowledged racism in hospitals and suggested "[having] someone there that can help advocate for you just based off what African-American women have been going through in hospitals." The racism she refers to may be reflected in the many instances of patients' concerns being ignored or brushed off reported in the interviews. One patient told about a time when she was "constantly telling everyone the doctors, [everybody], that something's not right and no one's listening to me." Another patient noted, "I've met some [providers] that just ... don't care. They don't listen to you. They just say, this is this and this is your only option." Still another shared that an anesthesiologist did not take her seriously and comments, "to be ignore[d] for that long, ... that's what really troubled me."

Patients who did report positive prenatal experiences talked about being treated with respect and having their voiced needs heard and taken into account. A patient who had a positive relationship with her provider details, "when I do decline something or I'm apprehensive, she still explains very well and still always lets me know that at the end of the day, it's my choice. A lot of the times with healthcare workers...you just feel powerless sometimes because... they're the doctor and you're not, but I wasn't made to feel that way." When giving advice to healthcare providers, the interviewed individuals most frequently suggested simply to listen to Black patients. The urgent need for Black birthing people to be listened to is clear with requests like, "If a woman says something is wrong, please just listen to her."

The longstanding persistence of racism (both historical and current) and discrimination are factors that contribute to these perinatal disparities. As one clinical care team member put it when referencing causes of disparities, "racism and discrimination in the healthcare system... Black women not being listened to in the context of pain or discomfort. When they make those expressions sometimes they're ignored and other times they are told it's not that big of an issue. That issue in particular is something that has been happening even in the early days of the study of obstetrics and gynecology-we can sort of track back these institutional practices of healthcare providers where Black women's pain and discomfort is ignored. It's unfortunate we continue to see those same patterns today and there have been clear examples where that has actually led to maternal death for Black women." Several other clinical care team members also mentioned how Black persons are not listened to or treated with respect, and how frequently their pain is ignored. The overall sentiment by clinical care team members was that in order to improve patient care for Black individuals, providers must address trust, respect, making sure patients feel listened to, and cultural connectivity. A clinical care team member with research in eliminating racial disparities in maternal and infant mortality shared, "the cultural humility model focuses on this partnership between patient and doctor as a way to address the racism, bias, and stereotyping and can help eliminate racial disparities in maternal mortality."

Trust

Importance

Consistent with the quoted statement from the aforementioned care team member, each patient interviewed talked about trust in some way, with most directly emphasizing its importance during their care. Two patients stated that if they did not trust their provider, they would not go to receive care from them. As one patient starkly puts it, "If I didn't trust them, I would not go... if I can't trust you, I know you're going to kill me... it may seem funny, but realistically, yeah." One patient who did not trust a group of providers shared that "I felt like they treated me like I was a number. They didn't treat me like I actually have a human being in my stomach."

As notable as the negative impact of lack of trust was the value patients placed on having a provider they could trust. One patient recounted a positive experience: "And to know that like I was able to have someone that was able to inform me, educate me, ... especially on things that I didn't know, and to answer my questions.... It was definitely uplifting." Multiple patients said they trusted providers to whom they felt they could ask any question and those who anticipated patient questions and needs.

The importance of trust was also discussed by all clinical care team members (including a majority of them acknowledging the historical context of how Black persons have been treated by the medical community). In fact, one clinical care team member mentioned, "trust is the most core fundamental value that's missing." This sentiment was shared by many other clinical care team members. Many described how trust must come before discussing care and that trust can lead to positive behavior change. Specifically, one clinical care team member stated, "trust lays the pathways and foundations for behavior change, and it creates the opportunity for there to be bi-directional change. It creates the opportunity to increase their health literacy and believe that the information that you're providing is in their best interest."

Establishing trust

For patients, trust seemed to start with building a relationship. Patients felt that interest in their lives, establishment of rapport, and a good tone went a long way in instilling trust. As the relationship progresses, several patients cited honesty, openness, and approaching patient care as a team were crucial to forming a trusting relationship with their providers. Around half the patients interviewed also mentioned some form of follow-up call or check-in as going a long way in building trust.

Most patient interviewees claimed that continuity of care and having an established relationship with a provider contributed strongly to feelings of trust. Several participants had seen their provider or group even before becoming pregnant. Statements like "I will be there with you the whole way" and "she made sure that she was present" when talking about trusted providers emphasizes the importance of longitudinal prenatal care in building trust.

Many clinical care team members discussed how forming trust is an intimate process that involves acknowledging biases with the patient and addressing distrust head on with a trust screening. One clinical care team member described, "if we talked more about trust - do you trust and feel respected by your healthcare provider - having that uncomfortable conversation can help the psychological fear of [physicians not looking out for their best interests]". This process includes providers learning to understand and not judge patients despite not agreeing with their lifestyle.

A few patient comments emphasized the need for providers to initiate these trust-building conversations. One patient shared that she would have liked her provider to address controversial topics and other "new [information] coming out" about cultural norms and practices during pregnancy that the patient didn't feel comfortable bringing up herself. Another patient stated that she doesn't bring up questions unless she has a problem. In contrast, patients who already trusted their providers expressed that they felt they could ask anything. Examples like this emphasize the need for provider proactivity in building trust in order to best care for patients' needs.

Change initiatives

Education

Patients valued education they received from providers, citing it as one of the reasons they trusted them. One patient noted, "I feel like when a doctor can talk to me, explain things to me..., then yeah. I can trust you when you tell me ... next time I see you, [I] want to have this or that." One of the most frequent comments from those with positive experiences was that the provider answered all off the patient's questions and the patient felt comfortable asking them anything. On the other hand, some patients had different experiences, reporting instances like, "they didn't even talk to me. They just gave me medicine and said this what was... wrong." Some patients valued the brochures and written information while others did not. A few patients noted that they would have liked to be more informed about what the birthing process would be like.

Many clinical care team members felt that patient education is a strength throughout pregnancy and after. Among the most commonly-mentioned change initiatives was teaching patients about important topics including breastfeeding, postpartum contraception, what to expect in postpartum, health literacy, and preeclampsia. In one clinic, a clinical care team member mentioned while discussing important topics covered about breastfeeding "...orient them and prepare them for [breastfeeding] because often people don't understand that, you know, might be really difficult. You might have a hard time, it might take a lot of work. You don't produce enough milk, you're going to feel guilty." Many clinical care team members also believed focused education of providers about social and health equity could benefit patients.

Access to care

In order for education efforts to succeed, patients need to consistently access care. As addressed previously, many Black birthing people face challenges due to disparities in accessing care. Some patients experienced this is the form of disconnection from their practices and providers. In describing a QI project meant to combat this, one clinical care team member stated: "We noticed that moms weren't coming back for postpartum care. We did a postpartum care pilot project to see what was happening. We used some appointment reminder cards and our case manager at that time to help be the glue between the clinical team and moms. We noticed they used the cards, and I felt like the small reminder did wonders for the return to postpartum care." Clinical care team members, in general, believed disparities in healthcare needed to be addressed, with foci around ensuring specific clinic staff, services, protocols, and policies that impact Black patients address their needs.

Patient advocates

Reiterating an idea brought up by a patient cited in a previous category, many clinical care team members stated the importance of establishing and expanding the use of advocates that patients can trust including, but not limited to, doulas and midwives. A clinical care team member noted, "A lot of these positive examples [initiatives] from my perspective have largely come from community based perinatal support organizations like midwifery care or community-based doulas... they are centering Black women and other women of color in healthcare practice. What I have seen that has been key in their work is that they center women of color and their experiences."

Two of nine patients did mention doulas as essential sources of support and education during their pregnancies. One patient recounted, "I feel very supported by my doula and how she's set up. Like she even came to my home to do her own prenatal visits with me. It was part of her package. So she does prenatal visits. She of course will support me during and after labor and postpartum. She's gonna still help me with breastfeeding." Another advised, "I think the best is to have a doula. Get a doula. They help, they help a lot. Cause there's a lot of stuff you're not going to understand."

Services and resources

Beneficial services

Other services and resources each clinical care team member's workplace had available for patients varied tremendously. Of note, things that worked and were viewed positively included mental health services, food accessibility services, education services, and holistic care services (midwife led services). In reference to an educational mom group that guides pregnant persons through pregnancy and breastfeeding, one clinical care team member said, "I've had patients that have done the moms group and absolutely loved it. They were really invested, motivated, they got a lot of information." Clinical care team members who did not have these and other services available (including transportation and quick and seamless connection to primary care following birth) cited them as much needed.

Several patients were happy with their clinic's ability to connect them to needed resources, including ride services, WIC, and prenatal classes. One patient noted that her clinic "provided and offered a virtual, like pregnancy plan where like, I can take pregnancy classes with other people online..." Another found it difficult to get to her appointments across the city and reported that her doctor "went above and beyond and faxed [the company] all my appointments to make it easier for me. To ensure that I had a ride there whenever my appointment was, and I didn't have to call them every time I needed a ride." Others appreciated connections to nutrition, transportation, and educational services. This highlights the essential nature of integration of these kinds of resources into prenatal care at the sites patients already receive services, as Black birthing people face barriers that can prevent receiving quality care. As an interviewee notes, during pregnancy, "everything will be needed and it's best to get it when it's... provided for you."

Addressing patient needs via navigation

Patient navigators, who are charged with connecting patients to the above resources, seemed not to be an effective solution to the problem of lower-quality care if they were not fully integrated into their system (face-to-face team interaction and shared EMR access). For these clinical care team members, "patient navigators cannot be a band aid."

Other clinical care team members described how community services were not helpful or the benefits were unknown at times due to the dearth of services available, the lack of knowledge of providers about what is available, or what the outcome of a patient using a service is. When asked about how beneficial WIC and referrals to primary care physicians were to patients, one resident physician noted, "I can't say that for any individual patient that I refer to either of these things that I'm able to see the outcome."

Social determinants of health

Effect on care

Some patients mentioned facing challenges with various SDH during their interviews, sometimes receiving additional support. One patient had "[gone] through being homeless a few times" while others had need for transportation, nutrition, and childcare resources. One patient who was connected with the Women, Infants, and Children (WIC) program during her pregnancy shared, "I was the only aware that it was provided, like after baby was born just for the baby where they, you know, informed me that no, you know, you can get it now. It's also for you too. And once I started getting it, it was a big benefit because, it provided fruits, vegetables, stuff that, that I wanted at that time."

Clinical care team members also spoke about this program and others focused on SDH. A nutritionist described that Black birthing individuals may "not realize that they can be part of WIC while they're pregnant." Our clinical care team interviewees also mentioned that there may be hesitancy to speak up in appointments to avoid being stereotyped, connecting to Black birthing patients' expressed need to trust their provider. Conversely, some patients when asked what advice they would give other patients responded, "Ask whatever you want," and directly expressed comfortability asking their provider about anything.

Clinical care team members described the screeners given to their patients and a variety of social factors that disproportionately impact Black health including, but not limited to, lack of access to care, lack of insurance, housing instability/homelessness, and lack of childcare. In reference to differences in SDH, one clinical care team member stated, "particularly for low-income Black women, having the resources and support to link with social services they need, whether it's transportation, sometimes housing can be a big issue, nutrition support, these are all issues that are related to health."

Best suited to screen

There was no consensus among clinical care team members on the particular role best suited to address these SDH (e.g. social service worker, provider, etc.). While many described that having a social worker is invaluable, responses varied from case workers to all providers to the federal government.

Trust, as discussed above, was the foundation that allowed one patient to share important information about her life with her provider and receive assistance. She said, "I was telling her my situation about me not working and everything with the job. So she recommended me to apply for County care, like a medical card... She gave me the information, I applied online and then I got the card within like two to three weeks."

Indeed, the likelihood of building and securing trust was a common underlying theme for how to identify the best suited role among clinical care team members. As one resident physician mentioned in reference to who is best suited to address SDH, "I feel like whoever the patient trusts, like whoever [healthcare worker] feels like they're building rapport with that patient and asking those questions are going to yield meaningful answers."

Frequency of screening

Many clinical care team members stated the need for screenings for SDH to occur throughout pregnancy and at every visit; but almost all of them stated they did not have time to do it themselves. As one physician described adding screenings to her workload, "So if I'm implementing a screening questionnaire, that's not a waste of my time because the information is important, but I don't think it's an optimal use of my time."

Perinatal care practices

Time devoted to patient care

Attending to these crucial patient needs can often be challenging given constraints in the clinical setting. Some clinical care team members described inefficient use of time when delivering perinatal care with patients or not having enough time with patients. One clinical care team member mentioned how there is too much time spent on "super technical aspects of health" (e.g., genetics) or the EMR and not enough on what is directly impacting their patient (i.e. SDH). Clinical care team members stated a need for extended clinic hours for patients who work late, a need also expressed by one patient, and an appointment scheduled early in the third trimester to discuss labor and what could go wrong. In discussing how to improve perinatal care, one clinical care team member said, "I think we should extend our hours every day instead of just two days a week." Lastly, a big emphasis was put on postpartum care and its importance.

Patient perceptions of postpartum care varied significantly. Many patients thought of postpartum care as support around primarily birth control and breastfeeding. One patient revealed, "first thing they talk about is the birth control." Patients also mentioned postpartum depression and C-section follow-up. Support took the form of follow-up calls, brochures, and appointments. Most who had received postpartum care were satisfied with it, but on patient remembered pressure from her provider around breastfeeding: "I just felt like I had already made up my decision that I wasn't going to do it, but then she just kept pushing it and pushing it and pushing and pushing it."

Several patients also mentioned the importance of postpartum SDH supports like WIC, childcare, and mental/emotional support. A patient noted, "But most importantly, ... they gave you the things in the tools you needed to know," emphasizing how critical postpartum care is. Two of nine patients, however, reported receiving no postpartum care or advice, and several responded to the term "postpartum" only referring to postpartum depression, indicating a need for more comprehensive postpartum education and care.

Cultural responsiveness

Part of comprehensive care includes leveraging the experiences and ideas of patients themselves to inform culturally responsive care. The majority of patients believed strongly in their own intuition and accountability in maintaining a healthy pregnancy. Many talked about drawing information from their own previous experiences, bodily feelings, research, and familial knowledge. One patient said, "So I know by now what should be going on with my body when I'm pregnant." The value of patient experience and knowledge must be taken into account to provide culturally responsive care.

Clinical care team members dedicated most of this section to describing patient education needs and cultural competency. One clinical care team member mentioned, "You have providers who are not accustomed to working with African American patients who don't always understand dialects/lingo and don't utilize language that patients can always understand, which can have a major impact on patient education and their understanding of what exactly their medical condition is." The need to focus on educating younger, new moms was described, while also realizing that multiparous persons should not be expected to know everything about pregnancy.

Patient opinions reflected the need for education and responsiveness to both populations. One patient thought it important to support first-time pregnant persons by "...be[ing] aware of the new stuff and ...each generation's change." A patient who had other pregnancies expressed the need for ongoing support and education: "even when you think you're a veteran mom, you think you know, but you don't cause everything changes over the years."

Mental health

Most patients focused on the physical aspects of prenatal care, with many citing the progress of the pregnancy and health of the baby as the most important part of prenatal care. A few patients, however, did talk about the importance of attention to mental and emotional health in the perinatal period. When asked about what prenatal care means to her, one patient responded, "prenatal care is seeing to it that I am all around taken care of physically and emotionally."

Clinical care team members reported that referrals were often available for patients to learn about and address mental health needs, but there is sometimes stigma around Black persons' mental health. As one clinical care team member put it, "Some of the cultural stigma around mental health and strong black woman syndrome... can really be a barrier in terms of acknowledging that there are challenges [with mental health] and that we should talk to someone."

Expectations for labor and delivery education

A few patients mentioned that they would have liked to be more informed about the birthing process before actually going into labor. One said, "Only thing that was shocking was that I had to get induced, so my water bag had just bust. I didn't know, like the pain was just going to come out the blue like that," and that she would have liked "more information" previously. One patient received a booklet from her provider about the process but would have liked more information. Some patients liked the printed resources while others did not use them. Many found birthing classes or doulas helpful in making expectations for labor and delivery clearer.

For many providers, it was of utmost importance to have more time to discuss the course of the pregnancy and the normal body changes that will occur. "I think a lot of expectations in labor and postpartum we should address ahead of time, globally for patients, but especially for patients that might not have as much trust in the health system... maybe around 30, 32 weeks, labor wise, here's all the things that might happen. These are things that could potentially go wrong and what might happen so if it does, you're not completely blindsided. Here are things you should feel empowered to request."

As the pregnancy progresses, more time to discuss expectations in labor and postpartum could help pregnant patients and their partners prepare for potential hardships, as the above clinical care team member described. One of the key topics to discuss during this time is postpartum contraception to ensure patients know all of their options. A clinical care team member stated, "they [postpartum women] lack the support that they need trying to secure access to contraception or family planning services." Most patients did report receiving counseling or provision of postpartum contraception, but several did not.

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Addressing racial disparities in perinatal care for African American/Black individuals in the Chicago community health setting: a qualitative study - BMC Pregnancy and Childbirth

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Background There are persistent disparities in maternal and infant perinatal outcomes experienced by Black birthing persons compared with non-Hispanic white (NHW) individuals in the US. The differences in outcomes arise from not only socioeconomic factors and individual health behaviors but also structural racism.

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Cultural Competence in Maternal Healthcare

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Cultural Competence in Maternal Healthcare

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Abstract

Studies in low-resource settings have highlighted disparities in person-centred maternity care (PCMC)-respectful and responsive care during childbirth-based on women's socioeconomic status (SES) and other characteristics. Yet few studies have explored factors that may underlie these disparities. In this study, we examined implicit and explicit SES bias in providers' perceptions of women's expectations and behaviours, as well as providers' general views regarding factors influencing differential treatment of women. We conducted a convergent mixed-methods study with 101 maternity providers in western Kenya. Implicit SES bias was measured using an adaptation of the Implicit Association Test (IAT) and explicit SES bias assessed using situationally specific vignettes. Qualitative data provided additional details on the factors contributing to disparities. Results provide evidence for the presence of both implicit and explicit bias related to SES that might influence PCMC. Differential treatment was linked to women's appearance, providers' perceptions of women's attitudes, assumptions about who is more likely to understand or be cooperative, women's ability to advocate for themselves or hold providers accountable, ability to pay for services in a timely manner, as well as situational factors related to stress and burnout. These factors interact in complex ways to produce PCMC disparities, and providing better care to certain groups does not necessarily indicate preference for those groups or a desire to provide better care to them. The findings imply the need for multilevel approaches to addressing disparities in maternity care. This should include provider training on PCMC and their biases, advocacy for women of low SES, accountability mechanisms, and structural and policy changes within health care settings.

Providers have implicit and explicit biases based on women's SES that can influence provision of person-centred maternity care (PCMC).

Providing better care for certain groups does not necessarily indicate preference for those groups.

Differential care is produced by a complex interaction of multiple factors including women's appearance, providers' perceptions of women's attitudes, assumptions about who is more likely to understand or be cooperative, women's ability to advocate for themselves or hold providers accountable, ability to pay for services in a timely manner, as well as situational factors related to stress and burnout.

Multilevel approaches are needed to address disparities in PCMC, including provider training on PCMC and their biases, advocacy for women of low SES, accountability mechanisms, and structural and policy changes within health care systems.

Introduction

Global maternal mortality remains unacceptably high. An estimated 295 000 women died from pregnancy-related causes in 2017-two-thirds in sub-Saharan Africa (SSA) alone ( WHO et al., 2019). A key factor perpetuating high maternal mortality in SSA is poor quality care, including poor PCMC ( Miller et al., 2016; Kruk et al., 2018). PCMC refers to care during childbirth that is respectful and responsive to women's preferences, needs and values. It includes dignified and respectful care, effective communication, autonomy, and social and emotional support ( Institute of Medicine, 2001; Afulani et al., 2017a). PCMC is highlighted under 'experience of care' in the World Health Organization framework for quality of maternal and newborn health care ( Tunçalp et al., 2015; Afulani et al., 2017a; WHO, 2018). Poor PCMC contributes directly and indirectly to poor maternal and neonatal outcomes ( Miller et al., 2016). In particular, many women shun giving birth in health facilities because of fear of poor PCMC manifested as mistreatment ( Bohren et al., 2014).

Despite the recognized importance of PCMC, a growing body of evidence has highlighted poor PCMC across the globe with documentation of poor communication, non-consented care, non-supportive care, and disrespectful and abusive care ( Kruk et al., 2014; Abuya et al., 2015; Bohren et al., 2015; Afulani et al., 2019b). Such poor PCMC has been attributed to facility and health system factors such as poor managerial oversight, provider demotivation, lack of necessary equipment and supplies, lack of accountability mechanisms, as well as power asymmetry, institutional structures, and social and gender norms and inequality ( Jewkes et al., 1998; Freedman et al., 2014; Sen et al., 2018). These drivers of poor PCMC imply that within the same health system and socio-cultural context, everyone would receive similar levels of PCMC. Disparities in PCMC, however, exist within the same context ( Afulani et al., 2018; Bohren et al., 2019; Montagu et al., 2019).

In particular, several studies in low-resource settings have highlighted disparities in PCMC based on women's socioeconomic status (SES). In Kenya, Ghana, and India poor women, those with low education, and those who were unemployed received the poorest PCMC ( Afulani et al., 2019a). Similarly, women report experiencing discrimination based on their SES ( Afulani et al., 2017b; Smith et al., 2020). Studies on drivers of poor PCMC suggest providers treat women differently based on women's appearance, assumptions of women's expectations and level of understanding, and women's ability to advocate for themselves ( Afulani et al., 2020a, b; Smith et al., 2020). Others have highlighted the role of women's ability to pay for services including bribes ( Warren et al., 2017; Landrian et al., 2020). However, understanding disparities was not the primary goal of these studies, and its drivers were not explored in detail. Thus, understanding drivers of disparities in PCMC remains a critical area of investigation. In this paper, we explore the potential contribution of providers' implicit and explicit SES biases to PCMC disparities in Kenya. Our focus is motivated by prior work with health care providers in this context ( Afulani et al., 2020a, b), and draws on the broader global literature on sources of disparities in quality of care.

Bias within a social context is the negative or positive evaluation of one group relative to another and can be implicit/unconscious or explicit/conscious ( Blair et al., 2011). Implicit bias operates at an unintentional level and does not require a person to endorse the belief or devote attention to its expression. Instead, it is activated quickly and unknowingly by situational cues such as a person's skin colour, accent, clothing or other factors ( Blair et al., 2011; Mendes and Koslov, 2013). In the USA, where racial bias has been extensively studied, anti-Black bias among physicians is associated with disparities in quality care, including lower likelihood of evidence-based prescribing, withholding pain medication, and lower quality interpersonal care for Black compared with White patients ( Green et al., 2007; Cooper et al., 2012; Sabin and Greenwald, 2012). Implicit SES bias in medical settings has been described to a less degree ( Haider et al., 2015a,b).

Explicit bias, on the other hand, refers to conscious attitudes and beliefs individuals hold about a group ( Daumeyer et al., 2019). Explicit bias can manifest as discrimination due to perceptions people hold about certain groups. Research shows that provider perceptions of patients based on SES and race influences treatment recommendations ( van Ryn et al., 2006). Such biases include perceiving African-Americans and low SES groups more negatively on issues such as intelligence, risk behaviour and adherence, and feeling less connected to them ( van Ryn and Burke, 2000). While studies on bias in PCMC in Africa are limited, there is evidence of discrimination in healthcare settings based on SES with some evidence of both implicit bias and explicit bias ( Andersen, 2004; Afulani et al., 2020b).

The way people are treated in healthcare settings is a reflection of broader societal norms and behaviours ( Filby et al., 2016), and disrespect thrives where it is tolerated, with individual biases reinforcing patterns of abuse ( Leape et al., 2012a, b). Thus, in societies where differential treatment based on SES is normative-and providers are typically higher on the social hierarchy than patients-providers may unconsciously treat poor women with disrespect. Providers might be more conscious of their actions when they encounter people they perceive as having higher social standing, causing them to treat such patients with greater respect ( Afulani et al., 2020b). Also, perceptions that women of low SES have lower expectations and will not hold them accountable for poor care may lead them to treat such women poorly ( Afulani et al., 2020a). Therefore, we hypothesized that providers' implicit and explicit biases based on women's SES contribute to PMC disparities.

The study aims are to examine: (1) provider implicit and explicit SES biases related to PCMC; (2) provider-level factors associated with implicit and explicit SES biases; and (3) provider general perceptions of factors that contribute to differential PCMC. We employed a convergent mixed-methods design and integrated quantitative and qualitative findings to extend understanding of potential factors underlying disparities in PCMC.

Methods

The study involved maternity providers in a rural county in western Kenya. The county is described in detail elsewhere ( Afulani et al., 2018). It has eight sub-counties, each of which has a sub-county hospital. There is also one county referral hospital and several health centres and faith-based and private health facilities. There are 32 nurses, 19 clinical officers and 4 doctors per 100 000 people in the county ( HPP, 2015). The county population is ∼1 million, with an estimated 40 000 births annually ( MCD, 2016). The estimated maternal mortality ratio is high at 673 deaths per 100 000 live births compared with 495/100 000 nationally. Based on the most recent national survey, 53% of births in the county occurred in health facilities, compared with the national average of 61% ( Kenya National Bureau of Statistics et al., 2015).

Data were collected through surveys with 101 maternity providers, followed by in-depth interviews with a subset of 31 providers from June to September 2019. Providers were purposively recruited based on position type from 30 health facilities with the highest birth volumes. Facilities included the county hospital, all sub-county hospitals and two to three other facilities in each sub-county. In each facility, the goal was to recruit one or two clinical officers (where applicable), two to five nurses and midwives (depending on number available), and one or two support staff (ward aides and cleaners). Support staff were included because they play a key role in PCMC provision in this setting ( Golub et al., 2020). The study was approved by the County Health Directorate and introduced to facility heads, who facilitated recruitment. Two bachelors-prepared female Kenyan staff (second and third authors) conducted both the surveys and interviews in English, Swahili or Luo in private locations at the facility.

A structured questionnaire was used for the survey, which lasted 40 − 60 min. The survey included measures of explicit bias and perceptions of sources of differential care ( Box 1) and provider and facility characteristics ( Table 1). Data were entered directly into the REDCap mobile application ( Harris et al., 2009). Each respondent also took a computer-based implicit bias test described in Box 2. Between three and five providers participated in the surveys in most facilities, which represented all providers available on the day of the interview and included zero to two clinical officers (except for the county referral hospital that included three clinical officers and one doctor), one to five nurses and zero to two support staff per facility.

Socioeconomic status refers to the social rank of an individual and her family, including economic status usually measured by income and/or wealth and social status typically determined by education and/or occupation ( Adler et al., 1994). In this study, we use SES to refer to descriptions related to women's education, occupation, empowerment and personal or family wealth or status, which have been found in prior studies to be predictors of PCMC ( Afulani et al., 2019b; Montagu et al., 2019).

Explicit bias was assessed using providers' perceptions of women's PCMC expectations and behaviours based on SES, preference for low and high SES women and a feeling of connection to low- and high-SES women. The two vignettes below were read in counter-balanced order to each provider, followed by ten questions.

Woman with markers of low SES: A 30-year-old poor farmer from one of the villages in the county is admitted to the ward. She dropped out of school in primary two and cannot read or write. She is not covered by insurance and attended ANC only once. She looks very unkempt and did not bring anything with her to be used for the delivery. She presented in labour with her mother-in-law and is complaining of severe abdominal pain. Thinking about this patient: How strongly do you agree/disagree with these statements?

Woman with markers of high SES: A 30-year-old woman who is the wife of a doctor in the hospital is admitted to your ward. She also works at the local bank and is covered by private health insurance. She received ANC six times during her pregnancy. She is very well dressed and has come with all the required items for her labour. She presented in labour with her mother-in-law and is complaining of severe abdominal pain. Thinking about this patient: How strongly do you agree/disagree with these statements?

The first eight questions assessed providers' perceptions of the woman in the vignette's expectations for introductions, consenting and companionship; potential to cooperate, understand explanations, exaggerate pain and to litigate; as well as provider behaviour needed to convey seriousness and gain cooperation. Response options ranged from strongly disagree to strongly agree on a 4-point scale ( Figure 1). Two final questions asked providers to what extent they would want to be a provider for the woman in the vignette and how connected they felt to her on a scale of 1 − 10. The vignettes and questions were informed by measurement of explicit bias in prior literature ( Green et al., 2007; Haider et al., 2015b) and prior research in this setting ( Afulani et al., 2020b). We incorporated feedback on the vignette and suggestions raised by various health care providers and researchers. The final version was piloted with five providers in the target population prior to the study. We conducted exploratory factor analysis to assess if the eight PCMC perceptions questions could be combined to create a composite measure ( Afifi et al., 2004).This yielded one factor with Eigenvalue >1, with factor loadings of >0.3 for all items except the question on litigation. The question on 'introductions' had low loading for the low SES vignette only and the question regarding 'sternness' had low loading for the high SES vignette only. Dropping these two questions did not significantly improve the Cronbach's alpha, so these were retained based on their conceptual importance. Cronbach's alpha for the seven items was 0.71 for the low SES Vignette and 0.69 for the high SES vignette. We generated explicit bias scores by summing responses to the seven questions for each vignette for the 98 providers who responded to all 7 questions. This yields scores ranging from 4 to 28, with higher scores indicating more explicit bias.

Providers' general perceptions of factors contributing to disparities was assessed by this question: 'Thinking of your interactions with patients, based on what attributes are you likely to treat patients differently?' A similar question was asked in the in-depth interview, followed by open-ended prompts exploring the initial response.

Implicit bias operates at an unconscious level, thus requires indirect methods to quantify it. The Implicit Association Test (IAT) is a cognitive-behavioural test that measures the strength of automatic associations between concepts in people's minds based on a sorting task ( Greenwald et al., 1998). It has been used to measure implicit bias based on race, SES, gender and other factors and shown to be valid and reliable ( Nosek et al., 2007a). The IAT has been used to examine racial and SES biases related to medical outcomes in the USA ( Green et al., 2007; Haider et al., 2015a). No study has, however, applied it to PCMC (or to health care in Africa). Thus, to assess implicit SES bias that may contribute to PCMC disparities in Kenya, we created an IAT to measure associations between women's SES characteristics and providers' perceptions of women as 'difficult' or 'good'-informed by prior work in Kenya, where providers frequently mentioned women being 'difficult' as a reason for mistreatment ( Afulani et al., 2020b). Patients who are medically or interpersonally challenging are often described as 'difficult', whereas patients who make providers' jobs easy are considered 'good' ( Adams and Murray, 1998).

Attributes of 'good' patients included likable, cooperative, respectful, intelligent and responsible; while attributes of 'difficult' patients were irresponsible, uncooperative, rude, annoying and stupid. High SES descriptors were: wealthy, well-educated, well-dressed and a Banker; low SES descriptors included poor, uneducated, old/torn clothes and a Cleaner. The programming script was based on a generic IAT template implemented in Inquisit Lab version 5. The IAT was developed with the help of a psychologist (W.M.) with experience developing IATs, and tested with various health care providers and researchers. The final version was piloted with five providers in the target population prior to the study. An individual's IAT score represents the difference in the average length of time they took to sort words during various sections of the test. It is assumed that people will more quickly sort words they associate together than those they do not. IAT scores vary between −2 and +2. In this study, a positive score indicates a stronger association between high status with good patient and between low status with difficult patient; a negative score indicates a stronger association between high status with difficult patient and low status good patient. We used dependent samples t-test to test whether the average IAT score differed significantly from zero-zero indicating no bias.

Subsequently, interviews were scheduled with providers who agreed to be re-contacted for the follow-up interview, purposively targeting one or two from each facility, including at least one clinical provider. The interviews followed an open-ended interview guide and lasted 30 − 60 min. Interviews were audio-recorded and transcribed with simultaneous translation where necessary ( Supplementary Appendix S1 -COREQ checklist). Each provider was given information about the different aspects of the study and informed written consent obtained. Participants received 300KES (∼$3) following the survey and interview.

Data analysis

Quantitative analysis

We first used descriptive statistics to characterize the measures. We then used mixed-model ANOVA to assess if responses based on the two SES vignettes differed. Associations between bias measures and provider and facility characteristics were examined using cross tabulations and linear regressions with robust standard errors. We examined conditional effects by including interaction terms for IAT score and providers' position, gender and perceived social status. We used STATA version 15.

Qualitative analysis

We followed Braun and Clarke's (2006) guidelines for thematic analysis. The first three authors coded the transcripts using deductive codes developed from the interview guide. Inductive codes generated from the data were added during coding. Few new codes were identified after coding half the transcripts. We wrote analytic and reflexive memos and discussed new codes and emerging ideas to come to consensus. After coding, we reviewed coded text and identified themes, wrote extended memos, and selected representative quotes to illustrate the range of voices. We considered both semantic and latent meanings of text and focussed on salience rather than frequency in the qualitative analysis ( Braun and Clarke, 2006). We used Dedoose version 8.3.19.

Integration

A key strength of mixed-methods is the integration of quantitative and qualitative approaches for a better understanding of issues ( Creswell, 2014). We achieved integration in the following ways. At the design stage we used a convergent mixed-methods design, with quantitative and qualitative data collection in the same period. Based on our prior work, we developed an interview guide to illustrate, expand and explain the survey findings. Integration also occured in the methods through connecting the samples (interview respondents drawn from survey respondent), and at the interpretation and reporting level through joint displays and integrating the results in the narrative where applicable ( Fetters et al., 2013).

Results

Of the 101 survey respondents, 43 worked in public hospitals, 44 in health centres and 14 in private/mission facilities. Sixty-two were nurses/midwives, 16 doctors/clinical officers and 23 support staff. Two-thirds were female with an average age of 34 years ( Table 1).

Extent of implicit and explicit SES bias

Eighty-one providers took the IAT test, as some providers (especially support staff) could not use a computer or could not read. IAT scores ranged from −0.51 to 1.44, with a mean of 0.64 (SD = 0.47; 95% CI = 0.54 − 0.74). The positive score indicates a significant bias in favour of associating positive characteristics with high SES women and negative characteristics with low SES women. The average explicit bias score was 15.8 (SD = 2.6; range 9 − 22) for the low SES woman vignette and 15.9 (SD = 2.8; range 7 − 23) for the high SES woman vignette. Scores did not differ significantly by order of vignette presentation. Mixed-model ANOVA showed no significant difference between the two summative scores ( P  = 0.79), suggesting a lack of explicit SES bias based on the summative measure.

There were some significant differences by vignette based on individual questions for expectations regarding introductions, understanding, exaggerating pain and litigation. However, the directionality of the associations was not consistent ( Figure 1). Providers were more likely to agree that the low SES woman is not likely to expect providers to introduce themselves and is not likely to understand explanations, compared with the high SES woman. On the other hand, they were more likely to agree that the high SES woman is likely exaggerating her pain and is more likely to sue them if something goes wrong compared with the low SES woman. Other differences were not statistically significant, but important to note. In both vignettes, close to half agreed that since the woman has come to the facility, it means she has consented to all examinations and treatment (45% for low SES and 46% for high SES). There was further consensus that the provider needs to be stern for women to understand the seriousness of the situation (59% for low SES and 61% for high SES), and about one-third agreed that women are likely to be uncooperative when it is time to push and will need to be physically restrained (34% for low SES and 37% for high SES).

The divergence in the results from the IAT and the summative and individual explicit bias measures can be partly explained by the qualitative data in which providers' perceptions of women based on their SES were not always consistent. Providers reported high-status women had higher expectations of care (which might explain perceptions regarding introductions), and more educated women more likely to understand information provided, and so more likely to be cooperative. But they also perceived educated women to be less trustful of information from providers, and more likely to challenge their decisions.

As in someone who is well educated tends to cooperate well ... but again it is a two-way traffic, like you can find [one] not educated, but able to cooperate. But ... the one who is more educated is more inquisitive and there are some doctors whom when asked many questions they get irritated. So in terms of knowledge and education, it is a factor that affects how you attend to a patient (CP2011).

Additionally, providers reported assumptions that high SES women were usually seeking special care (which might explain perceptions about them exaggerating pain), and these expectations made it difficult to please them. As one provider said: 'they believe that they are really rich so they have attitude and they are expecting special treatment...so as much as you try to explain that everyone is equal here they don't want to listen to you'. It was thus easier to please the poorer women who did whatever they were told and were less likely to complain about their care.

Those who are poor are easier to attend to because even if you tell them anything, they get satisfied and follow instructions, but the rich people don't appreciate. Even if you give them drugs they still complain that you did not serve them well (SS2131).

There were no significant differences in providers expressed preferences and feeling of connection to the two women in the vignettes: 69 and 61%, respectively, reported they would very much want to be a provider for the low and high SES woman and the average connection score towards both women was 7 (range of 0 − 10, where 10 is the strongest connection). This lack of preference for specific women was consistent with the qualitative findings, where providers reported everyone is important and should be treated the same. As one provider noted:

I took an oath to serve all clients in all corners of the world and Kenya and so there are no certain type of clients to be served different (CP1142).

The few who acknowledged preferring women of certain demographics did so because they held certain perceptions about their attitudes and behaviours. Providers preferred patients thought to be humble, understanding, cooperative, and compliant.

You can prefer to help particular type of a patient, someone [who comes] very humbly requesting you to help them... (SS1232).

Factors associated with explicit and implicit bias

Few provider demographic variables were significantly associated with the explicit bias scores ( Table 2). On the high SES vignette, clinical officers had a score of 13.9 compared with 15.9 for nurses/midwives and 17 for support staff, suggesting that, on average, clinical officers had less explicit bias towards the high SES woman. But this was not significant for the low SES vignette. Also, providers with higher education (college degree) and income (>10 000 KSh) had lower scores on the high SES vignette than those with lower education and income, respectively.

There were no statistically significant associations between any of the demographic variables and the IAT score and between the IAT score and vignette scores in both bivariate and multivariate regressions ( Supplementary Appendix S2). In multivariate analysis, only the difference in position was statistically significant, with clinical officers having a lower score on the high SES vignette than nurses and midwives (Coeff. = −2.46; CI: −4.60 to −0.32). The interactions between IAT score and provider position, gender and perceived social status were not significant. None of the associations were significant when the difference in the scores for the two vignettes was used as the predictor.

Sources of disparities

Despite the general lack of preference for specific types of patients, providers identified several factors that led to differential care in both the survey and interviews. The most common factor mentioned was the attitude of women or their families. Providers reported that when women were humble, cooperative and followed instructions, they were more likely to be treated well. Providers got angry and frustrated when women were rude or disrespectful to them or did not follow their instructions, which affected how they were treated.

... some of them have attitude; it is inborn and you cannot even correct it ... mostly attitude is what affect the relationship to attend to patients (CP2022).

Women's social status, level of education and economic status, were also commonly mentioned, which together make SES the most commonly mentioned source of differential care. Other sources of differential care included women's medical condition (mostly related to urgency), age, parity, ethnicity, language, religion and language barriers (joint display of quantitative and qualitive data on sources of disparities in Table 3).

Providers perceptions of women's backgrounds interacted with other factors in several ways to influence how they were treated. These factors could be considered as sources of explicit bias because providers were aware of them. However, such perceptions can become so ingrained that they may be activated unconsciously. As one provider noted: '... it can be that you have done it consciously or unconsciously but it does contribute because when you know somebody who is economically stable in the community, she can be treated differently compared with someone poor...' The factors below are thus potential sources of both implicit and explicit bias that may underlie PCMC disparities.

Attraction based on women's appearance

Providers acknowledged that when women appeared well dressed and clean, they were treated better than those who were dirty and unkempt. As one provider noted: 'Maybe the one who is dirty and carelessly dressed, you will just look at her unlike the one who is well kept, clean and well dressed ...even if you don't know her but she is well dressed, then you will treat her well. Mostly you will find yourself not treating them equally. You will see the clean one to be special than the other one'. Providers also acknowledged judging women based on their appearance, without knowing what has contributed to their situation. Even how the baby was dressed contributed to differential treatment.

... it all depends on how the patient presents themselves, for example when you go to the hospital, you will find when a mother has dressed her baby so well and the baby looks beautiful even if you are the nurse who is attending to them, you will find yourself playing with the baby, you even want to carry the baby. But when the other mother comes with her baby who is not well taken care of, you will just attend to her but nothing will attract you to the baby. So it all depends with the cleanliness of the mother (SS2061).

Assumptions about who is knowledgeable, more likely to understand and be cooperative

Providers perceived more educated women as having better understanding, hence being easier to deal with, while uneducated women from the village were assumed to lack understanding.

There are patients less likely to cooperate in the sense that some have good understanding; it depends with the environment where the client has come from. If a client is properly educated ... the understanding level is good, but the one from village, understanding level is low and [they] take long to understand so will make them not cooperate well (CP1041).

These assumptions were also held based on age, parity and religion although not always in the same direction. For example, some providers mentioned that first-time mothers are less likely to understand instructions, hence more likely to be uncooperative because of their lack of experience. Others, however, mentioned that first-time mothers are more cooperative and follow instructions, whereas women with prior births assume they know everything, thus can be difficult to deal with.

the multi behave like they know because she has gone through it. But primi does not know [and] she will ask you now what should I do? Not all primi fear because it is first time, but if you find the one that cooperate, if you tell... her to do this she will do. But multi, not most of them, but some, really disturb (CP2022).

Responsibility for understanding, compliance and cooperation was mostly placed on the woman, and not the health care provider. Some providers acknowledged the providers' role in patient understanding, such as explaining what is being done to them. But, ultimately most blamed the woman for inability to obtain cooperation. Lack of cooperation from women resulted in anger and subsequent negative behaviours such as being verbally or physically abusive. These negative responses were attributed to fear of poor outcomes for which the provider would be blamed.

Before a client becomes uncooperative, you must have explained what you will do and how you will expect them to cooperate .... So If you explain ... and they do not do what you explained, then you become angry because the mother and...baby can die...and the relatives are on your neck... these usually result because of cooperation from the mother. So when you explain and they don't cooperate, it will force you to apply some pressure to cooperate because if you become too soft, the result will be poor (CP2010).

Expectations, ability to advocate for oneself and accountability

Women of higher SES were perceived as having higher expectations for care and able to command such care by virtue of their wealth. Additionally, providers acknowledged 'doing the right thing' when they perceived that the woman was knowledgeable. More educated women were perceived as 'knowing what is right', hence treated with more caution.

...because you know that this person knows...you want to try and do what is ideal. But when handling someone who doesn't know, you seem not to care, so it does contribute (CP2172).

Also, women who knew someone who could hold providers accountable for their care were said to be more likely to receive good care. Women of higher SES were perceived as more likely to have some connection with a significant person, such as a politician, someone who works in the county or some 'big man' in the community. Additionally, having a personal connection with the woman motivated providers to treat them better.

Another thing that affects is the background of these client and their relationship with the higher people around. Maybe she is...related to MP or somebody who works at the county... [and] will always feel that she is right and whatever she said is what is important...[but] what you are saying is not important to her and if you go against her wish then she feel like she can report you to somebody of higher authority (CP1222).

Ability to provide financially for their care

Finally, providers' assumptions about women's ability to provide all that was required for their care influenced timeliness of their care. Providers acknowledged more timely care of women who were able to bring or pay for supplies such as gloves and cotton wool for their care, than those who could not. Similarly, those who were able to readily pay for needed tests and medication received more timely care.

So there are sometimes you will find that this patient is a little well off financially..., so you can tell her to bring gloves and all that we need to help deliver the baby then she brings faster. But there is this other mother whom when you tell to bring the cotton wool, gloves...[and] you find that she is dragging herself like she does not have money, so you will find yourself attending first to the one who has all the things that you require to do your work (SS1102).

Better care does not necessarily imply preference

There were complex interactions between factors that led to differential care. For example, preference for women more likely to understand was sometimes in conflict with preference for women who were cooperative, since high SES women viewed as more likely to understand, were also more likely to advocate for themselves, be less trustful, and more likely to challenge providers.

A lot of things affect how we interact with patient. One is their education background. How someone who has gone to school understands...is much better than somebody who has not gone to school in terms of communication. But again the reverse is also true like somebody who has not gone to school, I will give her my explanation even in vernacular and she will trust what I am telling her, but somebody who has gone to school ... despises the information you are giving....these people will come with their education and feel that what they know is what is right not what you are telling them (CP1221).

In such conflicting situations, providers seemed to prefer the low SES women, reflected by statements like 'there is one who doesn't know anything, you tell her this is and she does the opposite so you get irritated. But it is better to get the ignorant ones [than] the ones who are learned'. Preference for the 'ignorant ones' was because, ultimately, many providers preferred cooperation over knowledge-and obedience was highly valued. This also appeared to be an expression of the need to maintain hierarchy and control and to exercise power over women. Additionally, some providers felt they could more easily teach those without much prior knowledge, and there was more satisfaction in that.

Those who do not have knowledge, I prefer them most because I can teach them. But the one with knowledge is difficult to attend to, like you can not even give health education. How can you give health education and yet she already knows. Actually, for me I like the one without knowledge as I can teach them and tell them the importance of maintaining hygiene. But the clean one, what do you want to tell them about cleanliness and yet they are already clean? (CP1181).

While it was 'irritating' when women did not understand what they were told, providers noted it was 'hectic' to care for those who felt they know everything. Some providers particularly noted not wanting to care for their fellow providers, because they were knowledgeable and difficult to please, which made caring for them stressful. However, such patients still received good care because of their status and ability to hold them accountable.

High status is hectic, just some not all of them. But low status is normal, they are just calm, but high status pretends like they are learned and knows it all.... to deliver a fellow nurse is stress....Yes, very stressful...she will say 'I have not dilated'. 'I have not felt like am having contraction'....Yeah even me if I find myself doing that, I will just behave like a housewife so that I am attended. I won't behave like a nurse. If you act like nurse people will leave you (CP2022).

Additionally, although wealthier people were thought to be more desirable patients because they could readily provide items needed for their care, providers noted they sometimes exhibited poor attitudes towards them, which could affect how they were treated. For example, wealthier patients were sometimes perceived as looking down on the providers, which made providers want to assert authority over them. In addition, their connections with key people made them more difficult to care for.

... some may come to the facility knowing they have a lot of money than you...they feel they are superior... they feel they are young gods, gods in small letters, so they can demand and command whatsoever and they feel... (CP1161).

...some people will come with their financial background and feel they are very important back at home and so [because] you work in this health facility, some small building, you are a useless person. That one again will affect how you handle them (CP1221).

Situational factors

In addition to the factors addressed above, some providers noted that stress and burnout sometimes led to differential treatment, and this depended on how many providers were on duty or time of day. For example, women may be treated worse if they presented later in the day as providers were usually exhausted by then.

... Differential treatment...sometimes come as a result of the burn out. You know when you are alone or few in number in a given day, you tend to use more energy on clients who come first as opposed to those who come later in the day when you are exhausted and the energy level has gone down, so those clients will be treated differently according to energy level (CP1041).

Discussion

Among maternity providers in rural western Kenya, we found evidence of both implicit and explicit SES biases in providers' perceptions of women that may contribute to PCMC disparities through varied and sometimes contradictory pathways. Women's appearances evoke unconscious behaviours towards them, which reflects societal perceptions of poor/rich women. On the other, hand providers' perceptions of women's ability to understand, their propensity to be cooperative, their expectations and ability to advocate for themselves or hold providers accountable, their ability to pay for services in a timely manner, and other situational factors may differentially influence quality of care for different groups of women. These factors interact in various ways and providing better care to high SES women may not necessarily indicate a preference for such patients or a desire to provide better care to them.

The presence of implicit SES bias among providers in our study is consistent with studies reporting implicit social status and racial biases in the US health care settings ( Haider et al., 2015a,b). It is also consistent with the notion that implicit bias is prevalent in every society, although the type of biases may differ across different contexts ( Nosek et al., 2007b). The lack of explicit preference based on women's SES despite implicit bias and the non-significant association between implicit and explicit bias scores is also not surprising, and is consistent with prior studies ( Haider et al., 2011, 2015b). A probable reason is the tendency towards social desirability when people report on behaviours such as discrimination in ways that they view as more socially acceptable to others. But it might also be due to the complex interaction between the various factors that lead to differential care. Further, the association between some provider socio-demographic factors and explicit bias scores, but lack of association with implicit bias, might be because reporting on PCMC perceptions is influenced by knowledge, which is in turn influenced by socio-demographic factors. Implicit bias, on the other hand, is not within awareness so is less likely to be influenced by knowledge and social desirability. It is, however, influenced by the socio-cultural context which is similar for all study participants.

This study illuminates factors that might lead to poor PCMC for women of low SES. For example, perceptions that low SES women do not expect providers to introduce themselves may lead to providers not introducing themselves to such women, which limits the development of an initial rapport and affects overall PCMC experience ( Afulani et al., 2017b, 2020a). Similarly, the perception that low SES women are not likely to understand explanations implies information may not be provided to such patients, limiting active participation in their own care. In prior studies, involving mostly low SES women in Kenya, Ghana, and India, over 70% reported providers never introducing themselves and 60% reported providers not explaining the purpose of examinations or procedures ( Afulani et al., 2019b). The perception that high SES women are more likely to exaggerate their pain could make providers less responsive to women's experience of pain. But, this might be outweighed by the woman's ability to demand pain medications, or other perceptions about likelihood of suing providers if something goes wrong or of having connections to someone who can hold providers accountable. The complex nature of such perceptions might explain the lack of difference in explicit bias towards high and low SES women when all items are summed.

Autonomy and dignified care are key principles of PCMC. Yet close to half of providers in this study agreed that a woman coming to the facility means she has consented to all examinations and treatments and about one-third agreed on physically restraining women. Although there were no statistically significant differences by SES in these questions, the findings are nonetheless significant as they reflect surprisingly inaccurate perceptions on these issues, which may lead to overall poor PCMC-regardless of women's SES. Over one-third of women in a Kenya study reported providers never asked for consent before doing procedures on them ( Afulani et al., 2019b), which may be due to provider assumptions about consenting. Perceptions on the need to be stern to convey seriousness and physical restraint to assure cooperation might also account for reports of verbal and physical abuse of women during childbirth ( Bohren et al., 2019). Addressing these perceptions are therefore critical to improving PCMC.

We identified potentially modifiable pathways by which women's background characteristics influence differential care and the extent of PCMC they receive, which can inform healthcare interventions and policies. First, interventions to address both implicit and explicit biases based on patient appearance and assumptions on who is more likely to understand, be cooperative, and who can hold providers accountable could help reduce disparities in PCMC. An initial step will involve training to improve provider perceptions of PCMC and to help them recognize and address their biases that lead to differential treatment. Beyond training, structural interventions are needed to prevent individual provider biases from influencing care (e.g. institutional policies around introductions and consenting and removal of fee for services). Additionally, women's ability to advocate for themselves and to hold providers accountable may be the most important source of SES disparities. Empowering women to advocate for themselves, as well as training providers and companions to serve as advocates for patients and to accept women's self-advocacy, will thus be important: this will involve a shift in the thinking of what constitutes a good patient-provider encounter from the model where a passive patient is desired to embracing a model where all women are encouraged to be active participants in their care. Accountability mechanisms are also critical. It is also important to address the environmental and situational factors that lead to stress and burnout given their contribution to poor PCMC ( Afulani et al., 2020b). Furthermore, addressing stress and burnout could help address disparities given that implicit bias is more pronounced in stressful situations ( Mendes and Koslov, 2013).

Limitations and strengths

There are a number of limitations to this study: First, given that discrimination is a socially undesirable behaviour, a key limitation in studying explicit bias is social desirability bias as already noted. Second, the composite explicit bias measure used was limited because provider perceptions were not always in the same direction for different aspects of PCMC and it had borderline internal consistency. Examining the individual items and the mixed-methods design helped address this. The questions provide a foundation for additional research on how to quantitatively measure explicit biases related to PCMC. Third, although the IAT is a useful approach to measuring implicit bias, it is limited by the fact that it requires respondents to be able to read and use a computer. Some respondents therefore could not take the test, which decreased the sample size for examining implicit bias. Future research into how to study implicit bias in non-literate populations is needed. Moreover, the predictive validity of the IAT remains a disputed topic, so it is unclear whether implicit bias would translate to differences in behaviour among health care professionals ( Blanton et al., 2009; Greenwald et al., 2009; Oswald et al., 2013), and our study design did not allow examination of such relationships. The IAT developed for this study is the first of its kind for this setting and lays the groundwork for such research. Finally, there are limitations related to selection bias and generalizability, given that we used a purposive sample of providers from high-volume facilities in one county in Kenya: PCMC may be poorer in these high-volume facilities than lower volume facilities. Nonetheless, this is one of the few studies on sources of PCMC disparities in a low-resource setting, and it includes a relatively larger number of both clinical and non-clinical providers from different types of facilities than in prior studies. Furthermore, the mixed methods approach addresses a key gap in our understanding of provider assumptions that may underlie PCMC disparities.

Conclusions

Although the IAT has been used for decades to study various implicit preferences and stereotypes, to our knowledge, this is the first study to apply the IAT to PCMC and among the few to use it in African countries. Our study is also among the few to examine provider explicit biases and perceptions related to disparities in PCMC. Our findings provide evidence for the potential role of both implicit and explicit SES biases in PCMC disparities in Kenya. While women's appearances evoke unconscious behaviours towards them, providers perceptions of women's attitudes, understanding, expectations and ability to hold them accountable interact in a complex manner to produce differential care. The findings imply that multilevel approaches including interventions targeted at women and providers, as well as health system interventions and policies, are needed to improve PCMC and eliminate disparities. Further research to develop and test interventions is however needed. To achieve global goals of reducing maternal mortality and morbidity-'leaving no woman behind'-urgent action is needed to eliminate PCMC disparities. Multicomponent interventions that address provider implicit and explicit biases are essential to achieving this goal.

Supplementary data

Supplementary data are available at Health Policy and Planning online.

Funding

The research reported in this publication was supported by the Eunice Kennedy Shriver National Institute Of Child Health & Human Development of the National Institutes of Health K99 grant to PAA [K99HD093798]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Acknowledgements

We thank the County, sub-county and health facility leadership, and the providers who participated in the study. We are also grateful to Craig Cohen, Dilys Walker, Jody Steinauer and members of the UCSF Bixby Junior Investigators works in progress group, who reviewed and provided feedback on earlier versions of the paper.

Ethical approval

The study was reviewed and approved by the University of California, San Francisco Institutional Review Board (IRB number 17-22783) and the Kenya Medical Research Institute Scientific and Ethics Review Unit (SERU 3682). The Migori County health leadership also approved the study to be conducted in the county. All participants provided written informed consent prior to participation.

Conflict of interest statement. None declared.

References

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Understanding disparities in person-centred maternity care: the potential role of provider implicit and explicit bias

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Abstract. Studies in low-resource settings have highlighted disparities in person-centred maternity care (PCMC)-respectful and responsive care during child

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Frontiers | Black Perinatal Mental Health: Prioritizing Maternal Mental Health to Optimize Infant Health and Wellness

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Infant mental health is interconnected with and affected by maternal mental health. A mother or birthing person's mental health before and during pregnancy a...

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Abstract

Objective

To estimate racial and ethnic differences in rates of hospital-based care associated with postpartum depression.

Methods

This is a retrospective cohort study using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes within data from the Office of Statewide Planning and Development in California. We included primiparous women who underwent delivery hospitalization from 2008 to 2012. The primary outcome was the first postpartum hospital encounter with a ICD-9-CM code for depression over a 9-month period after delivery. We examined the cumulative incidence of hospital-based care for postpartum depression by race/ethnicity. Logistic regression was used to estimate relative risk.

Results

The study cohort consisted of 984,167 primiparous women: 314,037 (32%) were non-Hispanic White; 59,754 (6%) were non-Hispanic Black; 150,855 (15%) were non-Hispanic Asian; 448,770 (46%) were Hispanic; and 10,399 (1%) were other races. The cumulative incidence of hospital-based care for postpartum depression was highest for Black women (39; 95% CI = 34-44 per 10,000 deliveries) and lowest for Asian women (7; 95% CI = 5-8 per 10,000 deliveries). Compared with White women, hospital-based care for postpartum depression was more likely to be provided to Black women (OR = 2.3; 95% CI = 1.9-2.7), whereas care was less likely for Asians (OR = 0.4; 95% CI = 0.3-0.5) and Hispanics (OR = 0.8; 95% CI = 0.7-1.0). Similar findings were observed after excluding women with antepartum depression, adjusting for sociodemographic and clinical variables, and stratifying according to care settings.

Conclusion

Compared with White women, hospital-based care for postpartum depression more frequently impacts Black women. Identifying and improving inequities in access to and utilization of mental health care for postpartum women should be a maternal health priority.

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Author information

Authors and Affiliations

1. Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA

Avis L. Chan

2. Department of Anesthesiology, Perioperative, and Pain Medicine (MC:5640), Stanford University School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305, USA

Nan Guo & Alexander J. Butwick

3. Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA

Rita Popat

4. Department of Psychiatry, Icahn School of Medicine Mount Sinai, Manhattan, NY, USA

Thalia Robakis

5. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, USA

Yair Y. Blumenfeld & Elliott Main

6. California Maternal Quality Care Collaborative, Stanford University School of Medicine, Stanford, CA, USA

Elliott Main

7. Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA

Karen A. Scott

Corresponding author

Correspondence to Alexander J. Butwick.

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Conflict of Interest

The authors declare that they have no conflicts of interest.

Ethics Approval

The Stanford University Institutional Review Board approved this study (IRB protocol 14746).

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Chan, A.L., Guo, N., Popat, R. et al. Racial and Ethnic Disparities in Hospital-Based Care Associated with Postpartum Depression. J. Racial and Ethnic Health Disparities 8, 220-229 (2021). https://doi.org/10.1007/s40615-020-00774-y
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DOI: https://doi.org/10.1007/s40615-020-00774-y

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Keywords

# data[].attributes.attachment_link.title
Racial and Ethnic Disparities in Hospital-Based Care Associated with Postpartum Depression - Journal of Racial and Ethnic Health Disparities

# data[].attributes.attachment_link.description
Objective To estimate racial and ethnic differences in rates of hospital-based care associated with postpartum depression. Methods This is a retrospective cohort study using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes within data from the Office of Statewide Planning and Development in California.

# data[].attributes.attachment_link.readable_text
Postpartum depression is a serious and debilitating illness that affects appproximately 10%-20% of women who give birth ( 1). Available evidence suggests that rates of postpartum depression do not differ by race and ethnicity, but it does not conclusively demonstrate equal rates of illness across groups. Studies in which low-income mothers were systematically screened for clinical criteria indicate that rates of depression during the postpartum period are similar among Latinas, black women, and white women-8% for major depressive disorder and 23% for all depressive disorders in the first three months after delivery ( 2, 3). Effective screening tools and treatment strategies for combating postpartum depression have been developed ( 4, 5). However, detection and treatment rates for this condition are low ( 6- 8), particularly among high-risk women ( 9). Untreated postpartum depression can have severe impacts on the health and well-being of the affected woman and her family, including long-term consequences for the cognitive, emotional, and behavioral development of her child ( 10- 13).

Although racial and ethnic disparities in depression care are well known and well described ( 14- 16), information on treatment of postpartum depression among women from racial and ethnic minority groups is limited ( 2, 17, 18). Varying rates of postpartum depressive symptoms reported by women from racial and ethnic minority groups may reflect differences in cultural expectations and perceptions of both motherhood and mental health ( 17, 19- 21). Reasons for the complex relationship between race-ethnicity, depressive symptoms, and mental health treatment may include differences in risk factors, perceived need for care, socioeconomic status, and access to health insurance, as well as provider factors and patient-provider communication ( 19, 22- 26).

The purpose of this study was to characterize racial and ethnic differences in patterns of postpartum depression-related mental health care among low-income mothers covered by Medicaid who had new-onset depression after delivery (excluding women with bipolar disorder, schizophrenia, or a prenatal history of depression or anxiety). Our primary outcomes included racial-ethnic differences in rates of treatment initiation, follow-up, and continued care. We also examined differences in timing of initiation, as well as the use of medication versus outpatient therapy.

Methods

Data

Data for this study came from the administrative databases of New Jersey's Medicaid program and included deidentified eligibility and enrollment information, as well as medical claims for visits, procedures, and prescription medications from January 2004 through November 2008.

The institutional review board (Office of Sponsored Research) at Harvard Pilgrim Health Care Institute reviewed and waived approval of this study, because the analyses involved the use of only deidentified administrative data.

Study population

Our study population consisted of 29,601 women (13,001 white, 13,416 black, and 3,184 Latina) who gave birth between July 1, 2004, and October 31, 2007, whose deliveries were covered by New Jersey's Medicaid program, and who were continuously enrolled in Medicaid for at least six months before and one year after delivery. All measures were assessed over this period. Women who met the criteria for continuous enrollment had incomes less than 115% of the federal poverty level ($23,747 for a family of four in 2007). By focusing on a population of Medicaid beneficiaries, we were able to examine study outcomes in a group of women of similar socioeconomic status with no variation in insurance coverage-factors that have been sources of confounding in prior studies of disparities in mental health care ( 27).

We excluded 841 women with a diagnosis of bipolar disorder or schizophrenia because specific treatment requirements for these illnesses differ from recommended care for depression ( 7, 28). In addition, in order to distinguish new onset of depressive illness in the postpartum period from an ongoing episode, we excluded 1,193 women who received any antidepressant medication during pregnancy or who had any prenatal diagnosis of depression or anxiety, because anxiety is often a feature of depressive episodes and may be prone to misclassification in administrative claims data ( 7, 29).

Outcome variable definitions

Primary outcomes included postpartum mental health treatment initiation, follow-up, and receipt of continued care. We defined initiation of treatment as having filled a prescription for an antidepressant medication or having had an outpatient mental health visit in the six months after delivery. New Jersey's Medicaid program does not require a diagnosis code for claims payment, and depression diagnoses are generally underreported in claims data ( 30, 31). Therefore, a depression diagnosis was not required to indicate treatment initiation.

We identified pharmacy claims for antidepressants by using national drug codes (NDC) and data from First DataBank (San Bruno, California) and the American Hospital Formulary Service (American Society of Health-System Pharmacists). Outpatient mental health visits were defined with codes from the International Classification of Diseases, Ninth Revision ( ICD-9), Healthcare Common Procedure Coding System, and Current Procedural Terminology. All individual, family, and group outpatient psychotherapy visits were considered outpatient mental health visits unless they were coded as treatment for substance abuse. Other office visits were considered outpatient mental health visits if the claim included a diagnosis of depressive disorder, dysthymic disorder, or depressive adjustment disorder ( ICD-9 codes 296.2, 296.3, 300.4, 301.12, 309.1, and 311).

Among women who initiated mental health treatment in the postpartum period, we defined an acute treatment phase beginning on the date of treatment initiation and extending for 120 days ( 32). We examined two measures of continuity of care within the acute treatment phase. First, we measured follow-up, which was defined as either filling an antidepressant prescription for the second time (refill) or receiving a second outpatient mental health visit. Second, we examined rates of continued care, defined as having filled a prescription for an antidepressant medication at least three times or having had at least three outpatient mental health visits during the acute treatment phase. This measure was constructed as a minimum threshold of clinically adequate depression care based on randomized controlled trial evidence on psychotherapeutic treatment of postpartum depression ( 33), Healthcare Effectiveness Data and Information Set measures for antidepressant use ( 34), and recommendations for the care of women with postpartum depression ( 4).

Among those initiating care, secondary outcomes included timing of treatment initiation and the use of different treatment modalities (medication versus psychotherapeutic care). Time from delivery to treatment initiation (measured in days) was assessed among all women who initiated care, because earlier initiation may mitigate the potential negative maternal and child impacts of untreated illness ( 35, 36). Given evidence of racial-ethnic differences in depression treatment patterns among pregnant Medicaid enrollees ( 18), we also examined racial-ethnic differences in whether women received at least one antidepressant prescription or one outpatient mental health visit in the acute treatment phase as well as the odds of receiving a medication refill or second visit among women initiating each treatment modality.

Race-ethnicity

We used each woman's race and ethnicity as self-reported on enrollment in Medicaid, which were categorized as black, white, or Latina. We excluded women in the six other race-ethnicity categories from this analysis because of small numbers (N=1,354). Studies using administrative data support the validity of self-reported race and ethnicity variables for white, black, and Latina categories ( 37- 39). Consistent with prior research, our statistical analyses used white women as a comparison group for both black women and Latinas.

Covariates

Other covariates included age at delivery, drug dependency, diabetes, high-risk pregnancy, preterm birth, and cesarean delivery. Age was self-reported at enrollment, and other covariates were defined according to diagnosis and revenue codes. We defined drug dependency by diagnosis of substance use disorder ( ICD-9 codes 303, 304, or 305 and excluding 305.1 and 305.8). Diabetes was defined as having a diagnosis of diabetes mellitus or gestational diabetes ( ICD-9 codes starting with 250 or 648.0) or filling a prescription for a diabetes medication (that is, American Hospital Formulary Service designation as an antidiabetic agent). We identified high-risk pregnancy by ICD-9 diagnosis ( ICD-9 codes starting with V23). ICD-9 codes 644.20 and 644.21 indicated preterm birth before 37 weeks gestation, and diagnosis-related group codes (370, 371, 650, and 651) indicated cesarean delivery.

Statistical analysis

We used patient-level logistic regression models and Cox proportional hazards models in our analyses of racial-ethnic differences in study outcomes. All regression models controlled for the covariates mentioned above. Logistic regression models including potential interactions were evaluated with likelihood ratio tests, which indicated that such models did not provide additional information. Therefore, results from models excluding interaction terms are presented. County of delivery was not included in final regression models, per recommendations for disparities research ( 27, 40). We examined this variable in sensitivity analyses, however, and the results were consistent with the findings reported below. On the basis of final logistic regression models and mean covariate values, we also present results as predicted probabilities for an "average" white, black, or Latina woman: age 25-29 at delivery, without diabetes, high-risk pregnancy, or drug dependency and no evidence of a preterm or cesarean delivery. This method has been used in mental health disparities research to highlight racial-ethnic differences in predicted health care utilization and expenditures among patients with the same clinical characteristics ( 27, 41).

We used Cox proportional hazards models to examine racial differences in time from delivery to treatment initiation among women who received at least one antidepressant prescription or outpatient mental health visit. In these models, we evaluated and could not reject the proportional hazards assumption. All analyses were conducted with SAS Statistical Software, version 9.1.

Results

Descriptive statistics for women in the study population are presented in Table 1. Overall, white, black, and Latina women had comparable demographic and clinical profiles. However, white women tended to have slightly lower rates of pregnancy- and delivery-related complications, compared with black women and Latinas.

Table 2 presents mean values for study outcomes as well as adjusted odds ratios (AORs) and 95% confidence intervals (CIs) from patient-level logistic regression models for the primary study outcomes, enabling comparison of black women and Latinas with white women. A higher percentage (9%) of white women initiated antidepressant treatment or outpatient mental health services in the six months after delivery, compared with black women (4%) and Latinas (5%). Among those who initiated treatment, there were differential rates of follow-up and receipt of continued care by race-ethnicity. Also, there were markedly higher rates of follow-up on initial antidepressant use among whites (44%) than among blacks (23%) and Latinas (27%).

After analyses controlled for relevant demographic and clinical factors, the odds of initiating treatment remained significantly (p<.001) lower for blacks (AOR=.43) and Latinas (AOR=.59), compared with whites. Other predictors of treatment initiation included pregnancy- and delivery-related complications (results not shown). As for care received by those who initiated treatment, blacks and Latinas were less likely than whites to have any follow-up treatment (blacks, AOR=.66, p<.001; Latinas, AOR=.67, p<.05) or continued care (blacks, AOR=.81, p=.069; Latinas, AOR=.67, p<.05).

Focusing on differences in treatment modality, we found that most women who initiated treatment received at least one antidepressant prescription and at least one outpatient mental health visit. However, compared with white women, black women receiving treatment had higher odds of having an outpatient mental health visit (AOR=2.19). Also, black women and Latinas who filled an initial antidepressant prescription had significantly (p<.001) lower odds than whites of refilling a prescription within the 120-day acute treatment phase (black women, AOR=.37; Latinas, AOR=.43).

Table 3 presents predicted probabilities of experiencing the main study outcomes by race-ethnicity for an "average woman," with mean covariate values (see Table 1). The predicted probability of treatment initiation differed by race: 3% for a black woman, 4% for a Latina, and 7% for a white woman. Racial-ethnic differences in the predicted probabilities of follow-up and continued care persisted among women who initiated care, with an average white woman being more likely to receive follow-up (64%, compared to 54% and 55% for a black woman or Latina, respectively) and continued care (28% versus 24% and 21%, respectively). Racial-ethnic variations in predicted probabilities were similar for black women and Latinas who had pregnancy-related conditions or delivery complications compared with white women who had the same clinical profile.

Results of a survival analysis indicated that among those who initiated care, time from delivery to treatment initiation was longer for blacks (hazard ratio=.89, CI=.81-.99) and Latinas (hazard ratio=.82, CI=.69-.96) than for whites.

Discussion

We found low levels of postpartum depression treatment among all low-income women in our study, with particularly low treatment initiation rates for black women (4%) and Latinas (5%). Although underlying rates of illness could not be identified in this study, prior research indicates a diagnosed prevalence of approximately 23% among low-income women (with half characterized by postpartum onset of illness) ( 3) and similar rates of illness across racial-ethnic groups ( 2). The differences in initiation and continuation of care uncovered in this study imply that a disproportionate number of black women and Latinas who suffer from postpartum depression do not receive needed services. These differences represent stark racial-ethnic disparities potentially related to outreach, detection, service provision, quality, and processes of postpartum mental health care. Although suboptimal detection and treatment rates are not uncommon for this condition or in this population ( 7, 42, 43), these results emphasize that postpartum depression remains an underrecognized and undertreated condition for all low-income women, especially for those from racial and ethnic minority groups.

Whereas extensive prior literature has detailed racial and ethnic disparities in mental health care ( 14- 16, 44), this study presents the first evidence of racial-ethnic differences in the utilization patterns of postpartum depression-related mental health services. We found that blacks and Latinas were less likely than whites to initiate mental health care after delivery, with whites having approximately twice the odds of starting treatment, in spite of evidence of similar rates of underlying illness ( 2, 3). In the context of postpartum depression, time from delivery to treatment initiation is of particular importance, in that early detection and treatment can minimize the potential negative impacts of the illness ( 35, 36). In our study population, black women and Latinas were less likely to initiate care, and among those who initiated care the time from delivery to treatment initiation was significantly longer for blacks and Latinas than for whites.

We also found that racial and ethnic differences in care persisted among those who began treatment. Even though they initiated care at lower rates than whites, black women and Latinas who started treatment had lower chances of receiving follow-up or continued care compared with white women who initiated treatment. This study also revealed racial-ethnic differences in type of care (antidepressant medication versus outpatient visits) among those initiating treatment. Black women had higher odds of initiating care in an outpatient mental health setting, but blacks and Latinas who initiated antidepressant use had much lower chances than white women of refilling a prescription. Prior research has revealed that preferences for depression treatment modality differ by race, with blacks and Latinas generally preferring psychotherapy over antidepressant medications ( 15, 45- 47) As such, this finding may provide information about patient desire or willingness to refill a prescription or clinician response to patient preferences.

By using data for continuously eligible Medicaid recipients, we were able to measure racial-ethnic differences in care among women with similar income levels and health insurance coverage. However, Medicaid administrative claims data present a number of limitations. The data in our study lack personal variables (such as information on treatment preferences and perceived need) as well as clinical variables (such as prior history of depression or severity of symptoms), which may have enhanced our analysis and interpretation. Services not reimbursed by Medicaid are not captured in claims data, and thus we were not able to measure sources of community-based support for postpartum depression. We used the gold standard of self-reported race-ethnicity data in this study. However, because women in the study were allowed to identify themselves using only one racial or ethnic category upon enrollment in Medicaid, there is a potential for misclassification, particularly for multiracial or multiethnic women.

A limitation of this study is that we could not use depression diagnoses to define women who suffered postpartum depression, because depression is often undercoded in administrative data ( 30) and New Jersey's Medicaid program does not require a diagnosis for payment of utilization claims. Therefore, it is possible that we included in our study women with a mental health condition that was not related to depression or women with some treatment claims for non-depression-related mental health services. However, we addressed this limitation through several means. First, we excluded patients with a diagnosis of schizophrenia or bipolar disorder, conditions that are generally more reliably coded than depression in claims ( 31). Also, in New Jersey's Medicaid program, providers receive higher reimbursement for diagnoses such as bipolar disorder and schizophrenia, thus improving the likelihood that these diagnoses will be coded in the claims (and that such patients would be excluded from our study sample). Further, 95% of women in our study sample who initiated care received at least one antidepressant prescription, indicating that for nearly all of these women, the treating clinician's diagnostic impression was of a postpartum clinical syndrome in the depression-anxiety spectrum.

Conclusions

Our study presents evidence of low rates of postpartum depression treatment initiation and continuation, indicating barriers to care among low-income mothers; racial-ethnic disparities imply additional challenges for black women and Latinas. The presence of such disparities points to the need for clinical and institutional policies and programs to address the particular barriers to mental health care faced by black women and Latinas in the months after delivery.

The prenatal and postpartum periods represent a time of unique vulnerability to mental illness as well as complex emotions and expectations about motherhood ( 20, 29). Regular contact with the health care system during this time provides a feasible opportunity to address potential underdetection and undertreatment of postpartum depression in the clinical context, and alternate models of and innovations in postpartum health care delivery (such as home health visits after childbirth) may allow for more flexible and responsive care for new mothers. Specific barriers to care among low-income black women or Latinas may include stigma, communication problems, and logistical issues (including insurance coverage, time constraints, child care, and transportation) ( 48, 49). Prior literature suggests that addressing logistical challenges, cultural beliefs, and patient-provider communication may be helpful in meeting mental health treatment needs ( 50), and such efforts should be incorporated into the provision of postpartum health care, both in primary care and specialty mental health settings. However, there is a need for further research on barriers to postpartum depression care, on interventions aimed at reducing barriers, and on racial-ethnic differences in patient perceptions and experiences of both mental health and motherhood among low-income women.

References

14 Harman J , Edlund M , Fortney J : Disparities in the adequacy of depression treatment in the United States. 2004 , Link Psychiatric Services 55:1379-1385, Google Scholar

22 Kimerling R , Baumrind N : Access to specialty mental health services among women in California. 2005 , Link Psychiatric Services 56:729-734, Google Scholar

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32 American Psychiatric Association: Practice guideline for the treatment of patients with major depressive disorder (revision). 2000 American Journal of Psychiatry 157(Apr suppl):1-45, Google Scholar

37 Waldo DR : Accuracy and bias of race/ethnicity codes in the Medicare enrollment database. 2005 Health Care Finance and Review 26:61-72, Google Scholar

Figures and Tables

Characteristics of low-income white, black, and Latina mothers covered by Medicaid

Outcomes for low-income white, black, and Latina women who initiated treatment for postpartum depression within six months of delivery

Model-based predicted probabilities for main treatment outcomes for the "average" low-income white, black, or Latina mother with no maternity complications

# data[].attributes.attachment_link.title
Racial and Ethnic Disparities in Postpartum Depression Care Among Low-Income Women

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Objective: The goal of this study was to characterize racial-ethnic differences in mental health care utilization associated with postpartum depression in a multiethnic cohort of Medicaid recipients. Methods: In a retrospective cohort study, administrative claims data from New Jersey's Medicaid program were obtained for 29,601 women (13,001 whites, 13,416 blacks, and 3,184 Latinas) who delivered babies between July 2004 and October 2007.

# data[].attributes.attachment_link.title
Reducing Postpartum Depressive Symptoms Among Black and... : Obstetrics & Gynecology

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vioral educational intervention or enhanced usual care. Those in the intervention arm received a two-step behavioral educational intervention that prepares and educates mothers about modifiable factors associated with symptoms of postpartum depression (physical symptoms, low social support, low self-efficacy, and infant factors), bolsters social support, enhances management skills, and increases participants’ access to resources. Enhanced usual care participants received a list of community resources and received a 2-week control call. Participants were surveyed before randomization and, 3 weeks, 3 months, and 6 months later to assess depressive symptoms. The primary outcome, depression, was assessed using the Edinburgh Postnatal Depression Scale (score of 10 or greater). RESULTS: Positive depression screens were less common among intervention compared with enhanced usual care posthospitalization: 3 weeks (8.8% compared with 15.3%, P=.03), 3 months (8.4% compared with 13.24%, P=.09), and 6 months (8.9% compared with 13.7%, P=.11). An intention-to-treat repeated-measures analysis for up to 6 months of follow-up demonstrated that mothers in the intervention group were less likely to screen positive for depression compared with enhanced usual care (odds ratio 0.67, 95% confidence interval [CI] 0.47–0.97; number needed to treat 16, 95% CI 9–112). CONCLUSION: An action-oriented behavioral educational intervention reduced positive depression screens among black and Latina postpartum mothers. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov, www.clinicaltrials.gov, NCT01312883. LEVEL OF EVIDENCE: I…