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

BACKGROUND: Racial inequities in maternal morbidity and mortality persist into the postpartum period, leading to a higher rate of postpartum hospital use among Black and Hispanic people. Delivery hospitalizations provide an opportunity to screen and identify people at high risk to prevent adverse postpartum outcomes. Current models do not adequately incorporate social and structural determinants of health, and some include race, which may result in biased risk stratification. OBJECTIVE: This study aimed to develop a risk prediction model of postpartum hospital use while incorporating social and structural determinants of health and using an equity approach. STUDY DESIGN: We conducted a retrospective cohort study using 2016-2018 linked birth certificate and hospital discharge data for live-born infants in New York City. We included deliveries from 2016 to 2017 in model development, randomly assigning 70%/30% of deliveries as training/test data. We used deliveries in 2018 for temporal model validation. We defined "Composite postpartum hospital use" as at least 1 readmission or emergency department visit within 30 days of the delivery discharge. We categorized diagnosis at first hospital use into 14 categories based on International Classification of Diseases-Tenth Revision diagnosis codes. We tested 72 candidate variables, including social determinants of health, demographics, comorbidities, obstetrical complications, and severe maternal morbidity. Structural determinants of health were the Index of Concentration at the Extremes, which is an indicator of racial-economic segregation at the zip code level, and publicly available indices of the neighborhood built/natural and social/economic environment of the Child Opportunity Index. We used 4 statistical and machine learning algorithms to predict "Composite postpartum hospital use", and an ensemble approach to predict "Cause-specific postpartum hospital use". We simulated the impact of each risk stratification method paired with an effective intervention on race-ethnic equity in postpartum hospital use. RESULTS: The overall incidence of postpartum hospital use was 5.7%; the incidences among Black, Hispanic, and White people were 8.8%, 7.4%, and 3.3%, respectively. The most common diagnoses for hospital use were general perinatal complications (17.5%), hypertension/eclampsia (12.0%), nongynecologic infections (10.7%), and wound infections (8.4%). Logistic regression with least absolute shrinkage and selection operator selection retained 22 predictor variables and achieved an area under the receiver operating curve of 0.69 in the training, 0.69 in test, and 0.69 in validation data. Other machine learning algorithms performed similarly. Selected social and structural determinants of health features included the Index of Concentration at the Extremes, insurance payor, depressive symptoms, and trimester entering prenatal care. The "Cause-specific postpartum hospital use" model selected 6 of the 14 outcome diagnoses (acute cardiovascular disease, gastrointestinal disease, hypertension/eclampsia, psychiatric disease, sepsis, and wound infection), achieving an area under the receiver operating curve of 0.75 in training, 0.77 in test, and 0.75 in validation data using a cross-validation approach. Models had slightly lower performance in Black and Hispanic subgroups. When simulating use of the risk stratification models with a postpartum intervention, identifying high-risk individuals with the "Composite postpartum hospital use" model resulted in the greatest reduction in racial-ethnic disparities in postpartum hospital use, compared with the "Cause-specific postpartum hospital use" model or a standard approach to identifying high-risk individuals with common pregnancy complications. CONCLUSION: The "Composite postpartum hospital use" prediction model incorporating social and structural determinants of health can be used at delivery discharge to identify persons at risk for postpartum hospital use.

Racial and Ethnic Inequities in Development of Type 2 Diabetes After Gestational Diabetes Mellitus.

OBJECTIVE: To estimate racial and ethnic disparities in type 2 diabetes mellitus after gestational diabetes mellitus (GDM) and to investigate baseline pregnancy clinical and social or structural characteristics as mediators. METHODS: We conducted a retrospective cohort of individuals with GDM using linked 2009-2011 New York City birth and hospital data and 2009-2017 New York City A1c Registry data. We ascertained GDM and pregnancy characteristics from birth and hospital records. We classified type 2 diabetes as two hemoglobin A 1c test results of 6.5% or higher. We grouped pregnancy characteristics into clinical (body mass index [BMI], chronic hypertension, gestational hypertension, preeclampsia, preterm delivery, caesarean, breastfeeding, macrosomia, shoulder dystocia) and social or structural (education, Medicaid insurance, prenatal care, and WIC [Special Supplemental Nutrition Program for Women, Infants, and Children] participation). We used Cox proportional hazards models to estimate associations between race and ethnicity and 8-year type 2 diabetes incidence, and we tested mediation of pregnancy characteristics, additionally adjusting for age and nativity (U.S.-born vs foreign-born). RESULTS: The analytic data set included 22,338 patients with GDM. The 8-year type 2 diabetes incidence was 11.7% overall and 18.5% in Black, 16.8% in South and Southeast Asian, 14.6% in Hispanic, 5.5% in East and Central Asian, and 5.4% in White individuals with adjusted hazard ratios of 4.0 (95% CI 2.4-3.9), 2.9 (95% CI 2.4-3.3), 3.3 (95% CI 2.7-4.2), and 1.0 (95% CI 0.9-1.4) for each group compared with White individuals. Clinical and social or structural pregnancy characteristics explained 9.3% and 23.8% of Black, 31.2% and 24.7% of Hispanic, and 7.6% and 16.3% of South and Southeast Asian compared with White disparities. Associations between education, Medicaid insurance, WIC participation, and BMI and type 2 diabetes incidence were more pronounced among White than Black, Hispanic, and South and Southeast Asian individuals. CONCLUSION: Population-based racial and ethnic inequities are substantial in type 2 diabetes after GDM. Characteristics at the time of delivery partially explain disparities, creating an opportunity to intervene on life-course cardiometabolic inequities, whereas weak associations of common social or structural measures and BMI in Black, Hispanic and South and Southeast Asian individuals demonstrate the need for greater understanding of how structural racism influences postpartum cardiometabolic risk in these groups.

Predictors and Trends in First-Trimester Hemoglobin A1c Screening in New York City, 2009 to 2017.

Glycated hemoglobin is an adjunct tool in early pregnancy to assess glycemic control. We examined trends and maternal predictors for those who had A1c screening in early pregnancy using hospital discharge and vital registry data between 2009 and 2017 linked with the New York City A1C Registry (N = 798,312). First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017. The likelihood of screening became less targeted to high-risk patients over time, with a decrease in mean A1c values from 5.8% (95% confidence interval [CI]: 5.8, 5.9) to 5.3 (95% CI: 5.3, 5.4). The prevalence of gestational diabetes mellitus increased while testing became less discriminate for those with high-risk factors, including pregestational type 2 diabetes, chronic hypertension, obesity, age over 40 years, as well as Asian or Black non-Hispanic race/ethnicity. KEY POINTS: · First-trimester A1c screening increased from 2.3% in 2009 to 7.7% in 2017 in New York City.. · The likelihood of screening became less targeted to high-risk patients over time.. · The prevalence of gestational diabetes mellitus increased, while testing became less discriminate..

Influence of Gestational Diabetes Mellitus on Diabetes Risk and Glycemic Control in a Retrospective Population-Based Cohort.

OBJECTIVE: Racial/ethnic-specific estimates of the influence of gestational diabetes mellitus (GDM) on type 2 diabetes remain underexplored in large population-based cohorts. We estimated racial/ethnic differences in the influence of GDM on diabetes risk and glycemic control in a multiethnic, population-based cohort of postpartum women. RESEARCH DESIGN AND METHODS: Hospital discharge and vital registry data for New York City (NYC) births between 2009 and 2011 were linked with NYC A1C Registry data between 2009 and 2017. Women with baseline diabetes (n = 2,810) were excluded for a final birth cohort of 336,276. GDM on time to diabetes onset (two A1C tests of ≥6.5% from 12 weeks postpartum onward) or glucose control (first test of A1C <7.0% following diagnosis) was assessed using Cox regression with a time-varying exposure. Models were adjusted for sociodemographic and clinical factors and stratified by race/ethnicity. RESULTS: The cumulative incidence for diabetes was 11.8% and 0.6% among women with and without GDM, respectively. The adjusted hazard ratio (aHR) of GDM status on diabetes risk was 11.5 (95% CI 10.8, 12.3) overall, with slight differences by race/ethnicity. GDM was associated with a lower likelihood of glycemic control (aHR 0.85; 95% CI 0.79, 0.92), with the largest negative influence among Black (aHR 0.77; 95% CI 0.68, 0.88) and Hispanic (aHR 0.84; 95% CI 0.74, 0.95) women. Adjustment for screening bias and loss to follow-up modestly attenuated racial/ethnic differences in diabetes risk but had little influence on glycemic control. CONCLUSIONS: Understanding racial/ethnic differences in the influence of GDM on diabetes progression is critical to disrupt life course cardiometabolic disparities.

Left Behind: Medicaid Immigrant Exclusions and Access to Maternal Health Care Across the Reproductive-Perinatal Continuum.

Noncitizen immigrants are often excluded from accessing critical safety-net programs, such as Medicaid. Access to health care plays a central role in current policy debates on maternal health. Yet, immigrant exclusions are rarely considered in maternal health policy research. Through open-ended interviews with 31 policymakers, researchers, and program administrators, we examined state variations in approaches to providing care for pregnant, post, and intrapartum immigrant women. We found four themes: (a) a patchwork safety-net exists that provides some access to immigrants ineligible for Medicaid; (b) patchwork coverage leads to patchwork care, which can contribute to maternal health inequities; (c) immigrant Medicaid policy is assembled along a hierarchy of deservingness based on documentation status; (d) Trump-era public charge rules and political climate may have a substantial chilling effect on benefit uptake regardless of eligibility. We discuss implications for efforts to expand Medicaid postpartum and address the maternal health crisis.

Analysis of State Medicaid Expansion and Access to Timely Prenatal Care Among Women Who Were Immigrant vs US Born.

Importance: Disparities exist in access to timely prenatal care between immigrant women and US-born women. Exclusions from Medicaid eligibility based on immigration status may exacerbate disparities. Objective: To examine changes in timely prenatal care by nativity after Medicaid expansion. Design, Setting, and Participants: A cross-sectional difference-in-differences (DID) and triple-difference analysis of 22 042 624 singleton births from January 1, 2011, to December 31, 2019, in 31 states was conducted using US natality data. Data analysis was performed from February 1, 2021, to August 24, 2022. Exposures: Within 16 states that expanded Medicaid in 2014, the rate of timely prenatal care by nativity in years after expansion was compared with the rate in the years before expansion. Similar comparisons were conducted in 15 states that did not expand Medicaid and tested across expansion vs nonexpansion states. Main Outcomes and Measures: Timely prenatal care was categorized as prenatal care initiated in the first trimester. Individual-level covariates included age, parity, race and ethnicity, and educational level. State-level time-varying covariates included unemployment, poverty, and Immigrant Climate Index. Results: A total of 5 390 814 women preexpansion and 6 544 992 women postexpansion were included. At baseline in expansion states, among immigrant women, 413 479 (27.3%) were Asian, 110 829 (7.3%) were Black, 752 176 (49.6%) were Hispanic, and 238 746 (15.8%) were White. Among US-born women, 96 807 (2.5%) were Asian, 470 128 (12.1%) were Black, 699 776 (18.1%) were Hispanic, and 2 608 873 (67.3%) were White. Prenatal care was timely in 75.9% of immigrant women vs 79.9% of those who were US born in expansion states at baseline. After Medicaid expansion, the immigrant vs US-born disparity in timely prenatal care was similar to the preexpansion level (DID, -0.91; 95% CI, -1.91 to 0.09). Stratifying by race and ethnicity showed an increase in the Asian vs White disparity after expansion, with 1.53 per 100 fewer immigrant women than those who were US born accessing timely prenatal care (95% CI, -2.31 to -0.75), and in the Hispanic vs White disparity (DID, -1.18 per 100; 95% CI, -2.07 to -0.30). These differences were more pronounced among women with a high school education or less (DID for Asian women, -2.98; 95% CI, -4.45 to -1.51; DID for Hispanic women, -1.47; 95% CI, -2.48 to -0.46). Compared with nonexpansion states, differences in DID estimates were found among Hispanic women with a high school education or less (triple-difference, -1.86 per 100 additional women in expansion states who would not receive timely prenatal care; 95% CI, -3.31 to -0.42). Conclusions and Relevance: The findings of this study suggest that exclusions from Medicaid eligibility based on immigration status may be associated with increased health care disparities among some immigrant groups. This finding has relevance to current policy debates regarding Medicaid coverage during and outside of pregnancy.

Communicating Effectively via Tele-oncology (Comskil TeleOnc): a Guide for Best Practices for Communication Skills in Virtual Cancer Care.

The emergence of a novel coronavirus (SARS-CoV-2, causing coronavirus disease 2019 or COVID-19) has disrupted the US medical care system. Telemedicine has rapidly emerged as a critical technology enabling health care visits to continue while supporting social distancing to reduce the risk of COVID-19 transmission among patients, families, and clinicians. This model of patient care is being utilized at major cancer centers around the USA-and tele-oncology (telemedicine in oncology) has rapidly become the primary method of providing cancer care. However, most clinicians have little experience and inadequate training in this new form of care delivery. Because many practicing oncology clinicians are not familiar with telemedicine technology and the best practices for virtual communication, we strongly believe that training in this field is essential. Utilizing best practices of communication skills training, this paper presents a brief tele-oncology communication guide (Comskil TeleOnc) to address the timely need to maximize high-quality care to patients with cancer. The goal of the Comskil TeleOnc Guide is to recognize, elicit, and effectively respond to patients' medical needs and concerns while utilizing empathic responses to communicate understanding, alleviate distress, and provide support via videoconferencing. We recommend five strategies to achieve the communication goal outlined above: (1) Establish the clinician-patient relationship/create rapport, (2) set the agenda, (3) respond empathically to emotions, (4) deliver the information, and (5) effectively end the tele-oncology visit. The guide proposed in this paper is not all-encompassing and may not be applicable to all health care institutions; however, it provides a practical, patient-centered framework to conduct a tele-oncology visit.

Acceptability and efficacy of a communication skills training for nursing students: Building empathy and discussing complex situations.

Nurses must demonstrate effective communication across complex interpersonal domains, as emphasized by numerous professional healthcare organizations. However, formal communication skills training has been only modestly integrated into baccalaureate nursing programs, and of those studied systematically, there are notable methodological concerns. The current study focused on application of a well-researched communication program (Comskil) to student nurses completing summer internships at a comprehensive cancer center as part of their clinical education. The Comskil training program for student nurses is an in-person, day-long training that includes three sections: responding empathically to patients; discussing death, dying, and end-of-life goals of care; and responding to challenging family interactions. Student nurse participants provided strongly favorable perceptions of the program, with 90% indicating that they agreed or strongly agreed with all perception items. A significant pre-to post-training improvement in self-reported confidence was observed (p < .01). Additionally, pre- and post-training observational coding of standardized patient assessments indicated significant improvements in usage of the following skill categories: total skill use, information organization, and empathic communication (p < .001). Overall, these results suggest that communication skills training for student nurses is a feasible, acceptable, and effective way of increasing confidence and skills usage in complex clinical scenarios.