The stories about racism and health: the development of a framework for racism narratives in medical literature using a computational grounded theory approach.

INTRODUCTION: The scientific study of racism as a root cause of health inequities has been hampered by the policies and practices of medical journals. Monitoring the discourse around racism and health inequities (i.e., racism narratives) in scientific publications is a critical aspect of understanding, confronting, and ultimately dismantling racism in medicine. A conceptual framework and multi-level construct is needed to evaluate the changes in the prevalence and composition of racism over time and across journals. OBJECTIVE: To develop a framework for classifying racism narratives in scientific medical journals. METHODS: We constructed an initial set of racism narratives based on an exploratory literature search. Using a computational grounded theory approach, we analyzed a targeted sample of 31 articles in four top medical journals which mentioned the word 'racism'. We compiled and evaluated 80 excerpts of text that illustrate racism narratives. Two coders grouped and ordered the excerpts, iteratively revising and refining racism narratives. RESULTS: We developed a qualitative framework of racism narratives, ordered on an anti-racism spectrum from impeding anti-racism to strong anti-racism, consisting of 4 broad categories and 12 granular modalities for classifying racism narratives. The broad narratives were "dismissal," "person-level," "societal," and "actionable." Granular modalities further specified how race-related health differences were related to racism (e.g., natural, aberrant, or structurally modifiable). We curated a "reference set" of example sentences to empirically ground each label. CONCLUSION: We demonstrated racism narratives of dismissal, person-level, societal, and actionable explanations within influential medical articles. Our framework can help clinicians, researchers, and educators gain insight into which narratives have been used to describe the causes of racial and ethnic health inequities, and to evaluate medical literature more critically. This work is a first step towards monitoring racism narratives over time, which can more clearly expose the limits of how the medical community has come to understand the root causes of health inequities. This is a fundamental aspect of medicine's long-term trajectory towards racial justice and health equity.

Examining Exposure to Messaging, Content, and Hate Speech from Partisan News Social Media Posts on Racial and Ethnic Health Disparities.

We investigated the content of liberal and conservative news media Facebook posts on race and ethnic health disparities. A total of 3,327,360 liberal and conservative news Facebook posts from the United States (US) from January 2015 to May 2022 were collected from the Crowd Tangle platform and filtered for race and health-related keywords. Qualitative content analysis was conducted on a random sample of 1750 liberal and 1750 conservative posts. Posts were analyzed for a continuum of hate speech using a newly developed method combining faceted Rasch item response theory with deep learning. Across posts referencing Asian, Black, Latinx, Middle Eastern, and immigrants/refugees, liberal news posts had lower hate scores compared to conservative posts. Liberal news posts were more likely to acknowledge and detail the existence of racial/ethnic health disparities, while conservative news posts were more likely to highlight the negative consequences of protests, immigration, and the disenfranchisement of Whites. Facebook posts from liberal and conservative news focus on different themes with fewer discussions of racial inequities in conservative news. Investigating the discourse on race and health in social media news posts may inform our understanding of the public's exposure to and knowledge of racial health disparities, and policy-level support for ameliorating these disparities.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records.

Importance: Half of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools to identify these patients. Objective: To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems. Design, Setting, and Participants: This prognostic study assessed the 1-month and 6-month risk of suicide attempts among 1818 patients presenting to an ED between February 4, 2015, and March 13, 2017, with psychiatric problems. Data analysis was performed from May 1, 2020, to November 19, 2021. Main Outcomes and Measures: Suicide attempts 1 and 6 months after presentation to the ED were defined by combining data from electronic health records (EHRs) with patient 1-month (n = 1102) and 6-month (n = 1220) follow-up surveys. Ensemble machine learning was used to develop predictive models and a risk score for suicide. Results: A total of 1818 patients participated in this study (1016 men [55.9%]; median age, 33 years [IQR, 24-46 years]; 266 Hispanic patients [14.6%]; 1221 non-Hispanic White patients [67.2%], 142 non-Hispanic Black patients [7.8%], 64 non-Hispanic Asian patients [3.5%], and 125 non-Hispanic patients of other race and ethnicity [6.9%]). A total of 137 of 1102 patients (12.9%; weighted prevalence) attempted suicide within 1 month, and a total of 268 of 1220 patients (22.0%; weighted prevalence) attempted suicide within 6 months. Clinicians' assessment alone was little better than chance at predicting suicide attempts, with externally validated area under the receiver operating characteristic curve (AUC) of 0.67 for the 1-month model and 0.60 for the 6-month model. Prediction accuracy was slightly higher for models based on EHR data (1-month model: AUC, 0.71; 6 month model: AUC, 0.65) and was best using patient self-reports (1-month model: AUC, 0.76; 6-month model: AUC, 0.77), especially when patient self-reports were combined with EHR and/or clinician data (1-month model: AUC, 0.77; and 6 month model: AUC, 0.79). A model that used only 20 patient self-report questions and an EHR-based risk score performed similarly well (1-month model: AUC, 0.77; 6 month model: AUC, 0.78). In the best 1-month model, 30.7% (positive predicted value) of the patients classified as having highest risk (top 25% of the sample) made a suicide attempt within 1 month of their ED visit, accounting for 64.8% (sensitivity) of all 1-month attempts. In the best 6-month model, 46.0% (positive predicted value) of the patients classified at highest risk made a suicide attempt within 6 months of their ED visit, accounting for 50.2% (sensitivity) of all 6-month attempts. Conclusions and Relevance: This prognostic study suggests that the ability to identify patients at high risk of suicide attempt after an ED visit for psychiatric problems improved using a combination of patient self-reports and EHR data.

Obstetric comorbidity scores and disparities in severe maternal morbidity across marginalized groups.

BACKGROUND: A recently developed obstetrical comorbidity scoring system enables the comparison of severe maternal morbidity rates independent of health status at the time of birth hospitalization. However, the scoring system has not been evaluated in racial-ethnic and socioeconomic groups or used to assess disparities in severe maternal morbidity. OBJECTIVE: This study aimed to evaluate the performance of an obstetrical comorbidity scoring system when applied across racial-ethnic and socioeconomic groups and to determine the effect of comorbidity score risk adjustment on disparities in severe maternal morbidity. STUDY DESIGN: We analyzed a population-based cohort of live births that occurred in California during 2011 through 2017 with linked birth certificates and birth hospitalization discharge data (n=3,308,554). We updated a previously developed comorbidity scoring system to include the International Classification of Diseases, Ninth and Tenth Revisions, Clinical Modifications diagnosis codes and applied the scoring system to subpopulations (groups) defined by race-ethnicity, nativity, payment method, and educational attainment. We then calculated the risk-adjusted rates of severe maternal morbidity (including and excluding blood transfusion-only cases) for each group and estimated the disparities for these outcomes before and after adjustment for the comorbidity score using logistic regression. RESULTS: The obstetric comorbidity scores performed consistently across groups (C-statistics ranged from 0.68 to 0.76; calibration curves demonstrated overall excellent prediction of absolute risk). All non-White groups had significantly elevated rates of severe maternal morbidity before and after risk adjustment for comorbidities when compared with the White group (1.3% before, 1.3% after) (American Indian-Alaska Native: 2.1% before, 1.8% after; Asian: 1.5% before, 1.7% after; Black: 2.5% before, 2.0% after; Latinx: 1.6% before, 1.7% after; Pacific Islander: 2.2% before, 1.9% after; and multi-race groups: 1.7% before, 1.6% after). Risk adjustment also modestly increased disparities for the foreign-born group and government insurance groups. Higher educational attainment was associated with decreased severe maternal morbidity rates, which was largely unaffected by comorbidity risk adjustment. The pattern of results was the same whether or not transfusion-only cases were included as severe maternal morbidity. CONCLUSION: These results support the use of an updated comorbidity scoring system to assess disparities in severe maternal morbidity. Disparities in severe maternal morbidity decreased in magnitude for some racial-ethnic and socioeconomic groups and increased in magnitude for other groups after adjustment for the comorbidity score.

Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles.

Importance: Medically complex patients are a heterogeneous group that contribute to a substantial proportion of health care costs. Coordinated efforts to improve care and reduce costs for this patient population have had limited success to date. Objective: To define distinct patient clinical profiles among the most medically complex patients through clinical interpretation of analytically derived patient clusters. Design, Setting, and Participants: This cohort study analyzed the most medically complex patients within Kaiser Permanente Northern California, a large integrated health care delivery system, based on comorbidity score, prior emergency department admissions, and predicted likelihood of hospitalization, from July 18, 2018, to July 15, 2019. From a starting point of over 5000 clinical variables, we used both clinical judgment and analytic methods to reduce to the 97 most informative covariates. Patients were then grouped using 2 methods (latent class analysis, generalized low-rank models, with k-means clustering). Results were interpreted by a panel of clinical stakeholders to define clinically meaningful patient profiles. Main Outcomes and Measures: Complex patient profiles, 1-year health care utilization, and mortality outcomes by profile. Results: The analysis included 104 869 individuals representing 3.3% of the adult population (mean [SD] age, 70.7 [14.5] years; 52.4% women; 39% non-White race/ethnicity). Latent class analysis resulted in a 7-class solution. Stakeholders defined the following complex patient profiles (prevalence): high acuity (9.4%), older patients with cardiovascular complications (15.9%), frail elderly (12.5%), pain management (12.3%), psychiatric illness (12.0%), cancer treatment (7.6%), and less engaged (27%). Patients in these groups had significantly different 1-year mortality rates (ranging from 3.0% for psychiatric illness profile to 23.4% for frail elderly profile; risk ratio, 7.9 [95% CI, 7.1-8.8], P < .001). Repeating the analysis using k-means clustering resulted in qualitatively similar groupings. Each clinical profile suggested a distinct collaborative care strategy to optimize management. Conclusions and Relevance: The findings suggest that highly medically complex patient populations may be categorized into distinct patient profiles that are amenable to varying strategies for resource allocation and coordinated care interventions.

Lost ecosystem services as a measure of oil spill damages: a conceptual analysis of the importance of baselines.

The assessment and quantification of damages resulting from marine oil spills is typically coordinated by NOAA, and has historically utilized Habitat Equivalency Analysis (HEA) to estimate damages. Resource economists and others have called for the damage assessment process to instead estimate injuries through the valuation of lost ecosystem services. Our conceptual analysis explores ecosystem service valuation from the perspective of "baselines," which are a fundamental component of both primary and compensatory restoration activities. In practice, baselines have been defined in ecological terms, with minimal consideration of the socioeconomic side of ecosystem service provision. We argue that, for the purposes of scaling compensatory restoration, it is more appropriate to characterize baselines in value terms, thereby integrating non-market valuation approaches from the onset of the damage assessment process. Benefits and challenges of this approach are discussed, along with guidelines for practitioners to identify circumstances in which socioeconomic variables are likely to be important for baseline characterization.

Coastal ecosystem-based management with nonlinear ecological functions and values.

A common assumption is that ecosystem services respond linearly to changes in habitat size. This assumption leads frequently to an "all or none" choice of either preserving coastal habitats or converting them to human use. However, our survey of wave attenuation data from field studies of mangroves, salt marshes, seagrass beds, nearshore coral reefs, and sand dunes reveals that these relationships are rarely linear. By incorporating nonlinear wave attenuation in estimating coastal protection values of mangroves in Thailand, we show that the optimal land use option may instead be the integration of development and conservation consistent with ecosystem-based management goals. This result suggests that reconciling competing demands on coastal habitats should not always result in stark preservation-versus-conversion choices.