Lack of racial and ethnic disparities in mortality in minority patients hospitalised with COVID-19 in a mid-Atlantic healthcare system.

INTRODUCTION: In the USA, minoritised communities (racial and ethnic) have suffered disproportionately from COVID-19 compared with non-Hispanic white communities. In a large cohort of patients hospitalised for COVID-19 in a healthcare system spanning five adult hospitals, we analysed outcomes of patients based on race and ethnicity. METHODS: This was a retrospective cohort analysis of patients 18 years or older admitted to five hospitals in the mid-Atlantic area between 4 March 2020 and 27 May 2022 with confirmed COVID-19. Participants were divided into four groups based on their race/ethnicity: non-Hispanic black, non-Hispanic white, Latinx and other. Propensity score weighted generalised linear models were used to assess the association between race/ethnicity and the primary outcome of in-hospital mortality. RESULTS: Of the 9651 participants in the cohort, more than half were aged 18-64 years old (56%) and 51% of the cohort were females. Non-Hispanic white patients had higher mortality (p<0.001) and longer hospital length-of-stay (p<0.001) than Latinx and non-Hispanic black patients. DISCUSSION: In this large multihospital cohort of patients admitted with COVID-19, non-Hispanic black and Hispanic patients did not have worse outcomes than white patients. Such findings likely reflect how the complex range of factors that resulted in a life-threatening and disproportionate impact of incidence on certain vulnerable populations by COVID-19 in the community was offset through admission at well-resourced hospitals and healthcare systems. However, there continues to remain a need for efforts to address the significant pre-existing race and ethnicity inequities highlighted by the COVID-19 pandemic to be better prepared for future public health emergencies.

The Importance of Equity Value Judgments and Estimator-Estimand Alignment in Measuring Disparity and Identifying Targets to Reduce Disparity.

The choice of which covariates to adjust for (so-called allowability designation (AD)) in health disparity measurements reflects value judgments about inequitable versus equitable sources of health differences, which is paramount for making inferences about disparity. Yet, many off-the-shelf estimators used in health disparity research are not designed with equity considerations in mind, and they imply different ADs. We demonstrated the practical importance of incorporating equity concerns in disparity measurements through simulations, motivated by the example of reducing racial disparities in hypertension control via interventions on disparities in treatment intensification. Seven causal decomposition estimators, each with a particular AD (with respect to disparities in hypertension control and treatment intensification), were considered to estimate the observed outcome disparity and the reduced/residual disparity under the intervention. We explored the implications for bias of the mismatch between equity concerns and the AD in the estimator under various causal structures (through altering racial differences in covariates or the confounding mechanism). The estimator that correctly reflects equity concerns performed well under all scenarios considered, whereas the other estimators were shown to have the risk of yielding large biases in certain scenarios, depending on the interaction between their ADs and the specific causal structure.

Racial Inequities in the Control of Hypertension and the Explanatory Role of Residential Segregation: a Decomposition Analysis in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil).

The mechanisms underlying racial inequities in uncontrolled hypertension have been limited to individual factors. We investigated racial inequities in uncontrolled hypertension and the explanatory role of economic segregation in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). All 3897 baseline participants with hypertension (2008-2010) were included. Uncontrolled hypertension (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg), self-reported race (White/Brown/Black people), and neighborhood economic segregation (low/medium/high) were analyzed cross-sectionally. We used decomposition analysis, which describes how much a disparity would change (disparity reduction; explained portion) and remain (disparity residual; unexplained portion) upon removing racial differences in economic segregation (i.e., if Black people had the distribution of segregation of White people, how much we would expect uncontrolled hypertension to decrease among Black people). Age- and gender-adjusted prevalence of uncontrolled hypertension (39.0%, 52.6%, and 54.2% for White, Brown, and Black participants, respectively) remained higher for Black and Brown vs White participants, regardless of economic segregation. Uncontrolled hypertension showed a dose-response pattern with increasing segregation levels for White but not for Black and Brown participants. After adjusting for age, gender, education, and study center, unexplained portion (disparity residual) of race on uncontrolled hypertension was 18.2% (95% CI 13.4%; 22.9%) for Black vs White participants and 12.6% (8.2%; 17.1%) for Brown vs White participants. However, explained portion (disparity reduction) through economic segregation was - 2.1% (- 5.1%; 1.3%) for Black vs White and 0.5% (- 1.7%; 2.8%) for Brown vs White participants. Although uncontrolled hypertension was greater for Black and Brown vs White people, racial inequities in uncontrolled hypertension were not explained by economic segregation.

Geospatial Distribution of Racial Disparities in Influenza Vaccination in Nursing Homes.

OBJECTIVES: This study aimed to assess the distribution of racial disparities in influenza vaccination between White and Black short-stay and long-stay nursing home residents among states and hospital referral regions (HRRs). DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: We included short-stay and long-stay older adults residing in US nursing homes during influenza seasons between 2011 and 2018. Included residents were aged ≥65 years and enrolled in Traditional Medicare. Analyses were conducted using resident-seasons, whereby residents could contribute to one or more influenza seasons if they resided in a nursing home across multiple seasons. METHODS: Our comparison of interest was marginalized vs privileged racial group membership measured as Black vs White race. We obtained influenza vaccination documentation from resident Minimum Data Set assessments from October 1 through June 30 of a particular influenza season. Nonparametric g-formula was used to estimate age- and sex-standardized disparities in vaccination, measured as the percentage point (pp) difference in the proportions of individuals vaccinated between Black and White nursing home residents within states and HRRs. RESULTS: The study included 7,807,187 short-stay resident-seasons (89.7% White and 10.3% Black) in 14,889 nursing homes and 7,308,111 long-stay resident-seasons (86.7% White and 13.3% Black) in 14,885 nursing homes. Among states, the median age- and sex-standardized disparity between Black and White residents was 10.1 percentage points (pps) among short-stay residents and 5.3 pps among long-stay residents across seasons. Among HRRs, the median disparity was 8.6 pps among short-stay residents and 5.0 pps among long-stay residents across seasons. CONCLUSIONS AND IMPLICATIONS: Our analysis revealed that the magnitudes of vaccination disparities varied substantially across states and HRRs, from no disparity in vaccination to disparities in excess of 25 pps. Local interventions and policies should be targeted to high-disparity geographic areas to increase vaccine uptake and promote health equity.

Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.

BACKGROUND: The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers' perceptions of AI-CDS for liver transplant listing decisions. METHODS: In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. RESULTS: Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient's role in the AI-CDS. CONCLUSIONS: Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.

Geographic Variation in Racial Disparities in Receipt of High-Dose Influenza Vaccine Among US Older Adults.

BACKGROUND: Racial disparities in receipt of high-dose influenza vaccine (HDV) have been documented nationally, but whether small-area geographic variation in such disparities exists remains unknown. We assessed the distribution of disparities in HDV receipt between Black and White traditional Medicare beneficiaries vaccinated against influenza within states and hospital referral regions (HRRs). METHODS: We conducted a nationally representative retrospective cohort study of 11,768,724 community-dwelling traditional Medicare beneficiaries vaccinated against influenza during the 2015-2016 influenza season (94.3% White and 5.7% Black). Our comparison was marginalized versus privileged racial group measured as Black versus White race. Vaccination and type of vaccine were obtained from Medicare Carrier and Outpatient files. Differences in the proportions of individuals who received HDV between Black and White beneficiaries within states and HRRs were used to measure age- and sex-standardized disparities in HDV receipt. We restricted to states and HRRs with ≥ 100 beneficiaries per age-sex strata per racial group. RESULTS: We detected a national disparity in HDV receipt of 12.8 percentage points (pps). At the state level, the median standardized HDV receipt disparity was 10.7 pps (minimum, maximum: 2.9, 25.6; n = 30 states). The median standardized HDV receipt disparity among HRRs was 11.6 pps (minimum, maximum: 0.4, 24.7; n = 54 HRRs). CONCLUSION: Black beneficiaries were less likely to receive HDV compared to White beneficiaries in almost every state and HRR in our analysis. The magnitudes of disparities varied substantially across states and HRRs. Local interventions and policies are needed to target geographic areas with the largest disparities to address these inequities.

Critical interactions between race and the highly granular area deprivation index in liver transplant evaluation.

Neighborhood socioeconomic deprivation may have important implications on disparities in liver transplant (LT) evaluation. In this retrospective cohort study, we constructed a novel dataset by linking individual patient-level data with the highly granular Area Deprivation Index (ADI), which is advantageous over other neighborhood measures due to: specificity of Census Block-Group (versus Census Tract, Zip code), scoring, and robust variables. Our cohort included 1377 adults referred to our center for LT evaluation 8/1/2016-12/31/2019. Using modified Poisson regression, we tested for effect measure modification of the association between neighborhood socioeconomic status (nSES) and LT evaluation outcomes (listing, initiating evaluation, and death) by race and ethnicity. Compared to patients with high nSES, those with low nSES were at higher risk of not being listed (aRR = 1.14; 95%CI 1.05-1.22; p < .001), of not initiating evaluation post-referral (aRR = 1.20; 95%CI 1.01-1.42; p = .03) and of dying without initiating evaluation (aRR = 1.55; 95%CI 1.09-2.2; p = .01). While White patients with low nSES had similar rates of listing compared to White patients with high nSES (aRR = 1.06; 95%CI .96-1.17; p = .25), Underrepresented patients from neighborhoods with low nSES incurred 31% higher risk of not being listed compared to Underrepresented patients from neighborhoods with high nSES (aRR = 1.31; 95%CI 1.12-1.5; p < .001). Interventions addressing neighborhood deprivation may not only benefit patients with low nSES but may address racial and ethnic inequities.

An intersectional analysis of historical and contemporary structural racism on non-fatal shootings in Baltimore, Maryland.

Introduction Non-fatal shooting rates vary tremendously within cities in the USA. Factors related to structural racism (both historical and contemporary) could help explain differences in non-fatal shooting rates at the neighbourhood level. Most research assessing the relationship between structural racism and firearm violence only includes one dimension of structural racism. Our study uses an intersectional approach to examine how the interaction of two forms of structural racism is associated with spatial non-fatal shooting disparities in Baltimore, Maryland. Methods We present three additive interaction measures to describe the relationship between historical redlining and contemporary racialized economic segregation on neighbourhood-level non-fatal shootings. Results Our findings revealed that sustained disadvantage census tracts (tracts that experience contemporary socioeconomic disadvantage and were historically redlined) have the highest burden of non-fatal shootings. Sustained disadvantage tracts had on average 24 more non-fatal shootings a year per 10 000 residents compared with similarly populated sustained advantage tracts (tracts that experience contemporary socioeconomic advantage and were not historically redlined). Moreover, we found that between 2015 and 2019, the interaction between redlining and racialized economic segregation explained over one-third of non-fatal shootings (approximately 650 shootings) in sustained disadvantage tracts. Conclusion These findings suggest that the intersection of historical and contemporary structural racism is a fundamental cause of firearm violence inequities in Baltimore. Intersectionality can advance injury prevention research and practice by (1) serving as an analytical tool to expose inequities in injury-related outcomes and (2) informing the development and implementation of injury prevention interventions and policies that prioritise health equity and racial justice.

Recommendations for Using Causal Diagrams to Study Racial Health Disparities.

There have been calls for race to be denounced as a biological variable and for a greater focus on racism, instead of solely race, when studying racial health disparities in the United States. These calls are grounded in extensive scholarship and the rationale that race is not a biological variable, but instead socially constructed, and that structural/institutional racism is a root cause of race-related health disparities. However, there remains a lack of clear guidance for how best to incorporate these assertions about race and racism into tools, such as causal diagrams, that are commonly used by epidemiologists to study population health. We provide clear recommendations for using causal diagrams to study racial health disparities that were informed by these calls. These recommendations consider a health disparity to be a difference in a health outcome that is related to social, environmental, or economic disadvantage. We present simplified causal diagrams to illustrate how to implement our recommendations. These diagrams can be modified based on the health outcome and hypotheses, or for other group-based differences in health also rooted in disadvantage (e.g., gender). Implementing our recommendations may lead to the publication of more rigorous and informative studies of racial health disparities.

Multicenter study of racial and ethnic inequities in liver transplantation evaluation: Understanding mechanisms and identifying solutions.

Racial and ethnic disparities persist in access to the liver transplantation (LT) waiting list; however, there is limited knowledge about underlying system-level factors that may be responsible for these disparities. Given the complex nature of LT candidate evaluation, a human factors and systems engineering approach may provide insights. We recruited participants from the LT teams (coordinators, advanced practice providers, physicians, social workers, dieticians, pharmacists, leadership) at two major LT centers. From December 2020 to July 2021, we performed ethnographic observations (participant-patient appointments, committee meetings) and semistructured interviews (N = 54 interviews, 49 observation hours). Based on findings from this multicenter, multimethod qualitative study combined with the Systems Engineering Initiative for Patient Safety 2.0 (a human factors and systems engineering model for health care), we created a conceptual framework describing how transplant work system characteristics and other external factors may improve equity in the LT evaluation process. Participant perceptions about listing disparities described external factors (e.g., structural racism, ambiguous national guidelines, national quality metrics) that permeate the LT evaluation process. Mechanisms identified included minimal transplant team diversity, implicit bias, and interpersonal racism. A lack of resources was a common theme, such as social workers, transportation assistance, non-English-language materials, and time (e.g., more time for education for patients with health literacy concerns). Because of the minimal data collection or center feedback about disparities, participants felt uncomfortable with and unadaptable to unwanted outcomes, which perpetuate disparities. We proposed transplant center-level solutions (i.e., including but not limited to training of staff on health equity) to modifiable barriers in the clinical work system that could help patient navigation, reduce disparities, and improve access to care. Our findings call for an urgent need for transplant centers, national societies, and policy makers to focus efforts on improving equity (tailored, patient-centered resources) using the science of human factors and systems engineering.

Meaningful Causal Decompositions in Health Equity Research: Definition, Identification, and Estimation Through a Weighting Framework.

Causal decomposition analyses can help build the evidence base for interventions that address health disparities (inequities). They ask how disparities in outcomes may change under hypothetical intervention. Through study design and assumptions, they can rule out alternate explanations such as confounding, selection bias, and measurement error, thereby identifying potential targets for intervention. Unfortunately, the literature on causal decomposition analysis and related methods have largely ignored equity concerns that actual interventionists would respect, limiting their relevance and practical value. This article addresses these concerns by explicitly considering what covariates the outcome disparity and hypothetical intervention adjust for (so-called allowable covariates) and the equity value judgments these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. From this discussion, we generalize decomposition estimands and formulae to incorporate allowable covariate sets (and thereby reflect equity choices) while still allowing for adjustment of non-allowable covariates needed to satisfy causal assumptions. For these general formulae, we provide weighting-based estimators based on adaptations of ratio-of-mediator-probability and inverse-odds-ratio weighting. We discuss when these estimators reduce to already used estimators under certain equity value judgments, and a novel adaptation under other judgments.

Decomposing Race and Ethnic Differences in CVD Risk Factors for Mid-life Women.

OBJECTIVE: This study decomposes race and ethnic differences in hypertension, waist circumference, obesity and allostatic load between black non-Hispanic (BNH), Mexican American (MA), and white non-Hispanic (WNH) women. DATA: This study uses 10,109 observations from The National Health and Nutrition Examination Survey from years 1999-2014 for BNH, MA women, and WNH between 40 and 75 years old. METHODOLOGY: We used the Oaxaca-Blinder decomposition to explore how demographic, socioeconomic, healthcare access, and health behavior factors are associated with race and ethnic differences in blood pressure, waist circumference, body mass index (BMI), and allostatic load score (ALS). RESULTS: We found that demographic factors, socioeconomic status, healthcare access, and health behaviors explained from 0 to 50% of the difference in CVD risk factors between BNH and WNH. However, these factors explain from 39 to 100% of the difference in CVD risk factors between MA and WNH. Differences in demographic, socioeconomic, access to care, and health behavior factor variables explained very little of the differences in CVD risk factors between NHB and MA women. CONCLUSION: The impact of the determinants on CVD risk factors varies by race and ethnicity. Efforts to address differences in CVD risk factors should promote health equity programs and acknowledge that even race and ethnic groups that have similar demographic, SES, access to care, and health behavior factors can have different outcomes.

Structural and Social Determinants of Health Factors Associated with County-Level Variation in Non-Adherence to Antihypertensive Medication Treatment.

BACKGROUND: Non-adherence to antihypertensive medication treatment (AHM) is a complex health behavior with determinants that extend beyond the individual patient. The structural and social determinants of health (SDH) that predispose populations to ill health and unhealthy behaviors could be potential barriers to long-term adherence to AHM. However, the role of SDH in AHM non-adherence has been understudied. Therefore, we aimed to define and identify the SDH factors associated with non-adherence to AHM and to quantify the variation in county-level non-adherence to AHM explained by these factors. METHODS: Two cross-sectional datasets, the Centers for Disease Control and Prevention (CDC) Atlas of Heart Disease and Stroke (2014-2016 cycle) and the 2016 County Health Rankings (CHR), were linked to create an analytic dataset. Contextual SDH variables were extracted from the CDC-CHR linked dataset. County-level prevalence of AHM non-adherence, based on Medicare fee-for-service beneficiaries' claims data, was extracted from the CDC Atlas dataset. The CDC measured AHM non-adherence as the proportion of days covered (PDC) with AHM during a 365 day period for Medicare Part D beneficiaries and aggregated these measures at the county level. We applied confirmatory factor analysis (CFA) to identify the constructs of social determinants of AHM non-adherence. AHM non-adherence variation and its social determinants were measured with structural equation models. RESULTS: Among 3000 counties in the U.S., the weighted mean prevalence of AHM non-adherence (PDC < 80%) in 2015 was 25.0%, with a standard deviation (SD) of 18.8%. AHM non-adherence was directly associated with poverty/food insecurity (ß = 0.31, P-value < 0.001) and weak social supports (ß = 0.27, P-value < 0.001), but inversely with healthy built environment (ß = -0.10, P-value = 0.02). These three constructs explained one-third (R2 = 30.0%) of the variation in county-level AHM non-adherence. CONCLUSION: AHM non-adherence varies by geographical location, one-third of which is explained by contextual SDH factors including poverty/food insecurity, weak social supports and healthy built environments.

Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal.

A society's social structure and the interactions of its members determine when key drivers of health occur, for how long they last, and how they operate. Yet, it has been unclear whether causal inference methods can help us find meaningful interventions on these fundamental social drivers of health. Galea and Hernán propose we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Am J Epidemiol. 2020;189(3):167-170). In this commentary, by way of example in health disparities research, we probe this "closer engagement of social epidemiology with formal causal inference approaches." The formidable, but not insurmountable, tensions call for causal reasoning and effect estimation in social epidemiology that should always be enveloped by a thorough understanding of how systems and the social exposome shape risk factor and health distributions. We argue that one way toward progress is a true partnership of social epidemiology and causal inference with bilateral feedback aimed at integrating social epidemiologic theory, causal identification and modeling methods, systems thinking, and improved study design and data. To produce consequential work, we must make social epidemiology more causal and causal inference more social.

Intersectional decomposition analysis with differential exposure, effects, and construct.

In recent years a wide array of proposals for bringing intersectional perspectives into quantitative studies of health disparities have appeared, from studies of interaction, predictive discrimination, to mediation. Bauer and Scheim, in a companion set of articles, extend these proposals by developing new attribution-blind measures of perceived discrimination and using VanderWeele's 3-way decomposition to quantify its contribution to disparities through differential exposure and differential effects (sometimes called differential vulnerability or susceptibility). In this commentary, after providing an overview of causal inference interpretations with social characteristics, we provide a broad overview of old and new decomposition methods in the social sciences literature and contrast their strengths and weaknesses for studying intersectional inequalities. We then examine how different forms of differential effects can be expressed within these decompositions and discuss their utility for the purpose of informing interventions for reducing disparities. Last, we discuss the tension in social sciences research when prominent explanatory variables represent constructs that are only defined or exist for certain marginalized populations and may not neatly fit within the decomposition methods framework. Through these discussions, we aim to provide greater conceptual clarity for applied researchers who are interested in using decomposition methods and other approaches to advance intersectional equity.

Decomposition Analysis to Identify Intervention Targets for Reducing Disparities.

There has been considerable interest in using decomposition methods in epidemiology (mediation analysis) and economics (Oaxaca-Blinder decomposition) to understand how health disparities arise and how they might change upon intervention. It has not been clear when estimates from the Oaxaca-Blinder decomposition can be interpreted causally because its implementation does not explicitly address potential confounding of target variables. While mediation analysis does explicitly adjust for confounders of target variables, it typically does so in a way that effectively entails equalizing confounders across racial groups, which may not reflect the intended intervention. Revisiting prior analyses in the National Longitudinal Survey of Youth on disparities in wages, unemployment, incarceration, and overall health with test scores, taken as a proxy for educational attainment, as a target intervention, we propose and demonstrate a novel decomposition that controls for confounders of test scores (e.g., measures of childhood socioeconomic status [SES]) while leaving their association with race intact. We compare this decomposition with others that use standardization (to equalize childhood SES [the confounders] alone), mediation analysis (to equalize test scores within levels of childhood SES), and one that equalizes both childhood SES and test scores. We also show how these decompositions, including our novel proposals, are equivalent to implementations of the Oaxaca-Blinder decomposition but provide a more formal causal interpretation for these decompositions.

"Impact of drug-reimbursement policies on prescribing: A case-study of a newly marketed long-acting injectable antipsychotic among relapsed schizophrenia patients".

OBJECTIVE: To quantify and explain variation in use of long-acting injectable antipsychotics (LAIs) in the United States, and understand the relationship between patient characteristics, drug reimbursement policies, and LAI prescribing after relapse. METHODS: A cohort of recently relapsed patients with schizophrenia ages 18 to 64, were identified immediately after discharge from a related inpatient hospitalization, partial hospitalization, or emergency room visit, drawn from 2004 to 2006 Medicaid claims, and followed for 90 days until LAI initiation. Data on state-level Medicaid prior authorization (PA) policies for LAIs were collected. Sequential longitudinal Poisson regression models were developed to understand the relationship between patient and PA policy variables and LAI prescribing, including prior adherence to oral antipsychotics, demographics, clinical variables, and presence of PA policy for LAI. RESULTS: Among 36 282 patients, 3.1% received risperidone LAI, and 3.8% received a first-generation (FGA) LAI with wide variation across states. Prior adherence ranged from 29% to 89% but was marginally associated with initiation and did not explain variation for LAI prescribing. FGA initiation was associated with geography and race/ethnicity but not PA policy. For risperidone LAI initiation, demographics and clinical factors explained, respectively, 5.0% and 3.0% of the variation; PA policy had a large negative association with initiation (RR = 0.41; 95%CI 0.20-0.87) and explained 8.4% of the variation. CONCLUSIONS: PA policies may represent a major treatment barrier for risperidone LAI among relapsed patients. Non-adherence plays a little role in predicting which patients receive LAIs. Policy makers and health insurers will need to consider these findings when guiding the use of LAIs. KEY POINTS Among a nationwide cohort of relapsed schizophrenia patients enrolled in US Medicaid, 3.1% received Risperdal Consta, a long-acting injectable antipsychotic (LAI), and 3.8% initiated a first-generation first-generation LAI within 90 days after discharge. During 2004 to 2006, there was marked variation in 90 day post-relapse initiation of Risperdal-Consta-a newly marketed medication during this period-and also marked variation in 90 day post-relapse initiation of any first-generation LAI, which appeared to be associated with race/ethnicity and geography. Prior authorization policies were associated with substantially lower initiation of Risperdal Consta in this cohort of relapsed patients even after accounting for clinical indication (non-adherence), relapse history, demographics, adjunctive medication, and mental health service use.

Disparities at the intersection of marginalized groups.

PURPOSE: Mental health disparities exist across several dimensions of social inequality, including race/ethnicity, socioeconomic status and gender. Most investigations of health disparities focus on one dimension. Recent calls by researchers argue for studying persons who are marginalized in multiple ways, often from the perspective of intersectionality, a theoretical framework applied to qualitative studies in law, sociology, and psychology. Quantitative adaptations are emerging but there is little guidance as to what measures or methods are helpful. METHODS: Here, we consider the concept of a joint disparity and its composition, show that this approach can illuminate how outcomes are patterned for social groups that are marginalized across multiple axes of social inequality, and compare the insights gained with that of other measures of additive interaction. We apply these methods to a cohort of young men from the National Longitudinal Survey of Youth, examining disparities for black men with low early life SES vs. white men with high early life SES across several outcomes that predict mental health, including unemployment, wages, and incarceration. RESULTS AND CONCLUSIONS: We report striking disparities in each outcome, but show that the contribution of race, SES, and their intersection varies.

Mediators of First- Versus Second-generation Antipsychotic-related Mortality in Older Adults.

BACKGROUND: Observational studies of older adults showed higher mortality for first-generation antipsychotics than their second- generation counterparts, which led to US Food and Drug Administration warnings, but the actual mechanisms involved remain unclear. METHODS: A cohort of 9,060 initiators of first-generation antipsychotics and 17,137 of second-generation antipsychotics enrolled in New Jersey and Pennsylvania Medicare were followed for 180 days. Medical events were assessed using diagnostic and procedure codes on inpatient billing claims. For the individual and joint set of medical events (mediators), we estimated the total, direct, and indirect effects of antipsychotic type (first versus second generation) on mortality on the risk ratio scale and the proportion mediated on the risk difference scale, obtaining 95% confidence intervals through bootstrapping. We performed bias analyses for false-negative mediator misclassification in claims data, with sensitivity ranging from 0.25 to 0.75. RESULTS: There were 3,199 deaths (outcomes), 862 cardiovascular events, 675 infectious events, and 491 hip fractures (potential mediators). Mortality was higher for first- than second-generation antipsychotic initiators (adjusted risk ratio: 1.14; 95% confidence interval: 1.06, 1.22). In naïve analyses, that ignored potential misclassification, less than 4% of this difference was explained by any particular medical event. In bias analyses, the proportion mediated ranged from 6% to 16% for stroke, 3% to 9% for ventricular arrhythmia, 3% to 11% for myocardial infarction, 0% venous thromboembolism, 3% to 9% for pneumonia, 0% to 1% for other bacterial infection, and 1% to 3% for hip fracture. CONCLUSIONS: Acute cardiovascular events and pneumonia may explain part of the mortality difference between first- and second-generation antipsychotic initiators in this analysis.