A modular simulation framework for organ allocation.

BACKGROUND: We describe and validate a new simulation framework addressing important limitations of the Simulated Allocation Models (SAMs) long used to project population effects of transplant policy changes. METHODS: We developed the Computational Open-source Model for Evaluating Transplantation (COMET), an agent-based model simulating interactions of individual donors and candidates over time to project population outcomes. COMET functionality is organized into interacting modules. Donors and candidates are synthetically generated using data-driven probability models which are adaptable to account for ongoing or hypothetical donor/candidate population trends and evolving disease management. To validate the first implementation of COMET, COMET-Lung, we attempted to reproduce lung transplant outcomes for U.S. adults from 2018-2019 and in the 6 months following adoption of the Composite Allocation Score (CAS) for lung transplant. RESULTS: Simulated (median [Interquartile Range, IQR]) vs observed outcomes for 2018-2019 were: 0.162 [0.157, 0.167] vs 0.170 waitlist deaths per waitlist year; 1.25 [1.23, 1.28] vs 1.26 transplants per waitlist year; 0.115 [0.112, 0.118] vs 0.113 post-transplant deaths per patient year; 202 [102, 377] vs 165 nautical miles travel distance. The model accurately predicted the observed precipitous decrease in transplants received by type O lung candidates in the six months following CAS implementation. CONCLUSIONS: COMET-Lung closely reproduced most observed outcomes. The use of synthetic populations in the COMET framework paves the way for examining possible transplant policy and clinical practice changes in populations reflecting realistic future states. Its flexible, modular nature can accelerate development of features to address specific research or policy questions across multiple organs.

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling.

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states.

Forecasting disease trajectories in critical illness: comparison of probabilistic dynamic systems to static models to predict patient status in the intensive care unit.

OBJECTIVE: Conventional prediction models fail to integrate the constantly evolving nature of critical illness. Alternative modelling approaches to study dynamic changes in critical illness progression are needed. We compare static risk prediction models to dynamic probabilistic models in early critical illness. DESIGN: We developed models to simulate disease trajectories of critically ill COVID-19 patients across different disease states. Eighty per cent of cases were randomly assigned to a training and 20% of the cases were used as a validation cohort. Conventional risk prediction models were developed to analyse different disease states for critically ill patients for the first 7 days of intensive care unit (ICU) stay. Daily disease state transitions were modelled using a series of multivariable, multinomial logistic regression models. A probabilistic dynamic systems modelling approach was used to predict disease trajectory over the first 7 days of an ICU admission. Forecast accuracy was assessed and simulated patient clinical trajectories were developed through our algorithm. SETTING AND PARTICIPANTS: We retrospectively studied patients admitted to a Cleveland Clinic Healthcare System in Ohio, for the treatment of COVID-19 from March 2020 to December 2022. RESULTS: 5241 patients were included in the analysis. For ICU days 2-7, the static (conventional) modelling approach, the accuracy of the models steadily decreased as a function of time, with area under the curve (AUC) for each health state below 0.8. But the dynamic forecasting approach improved its ability to predict as a function of time. AUC for the dynamic forecasting approach were all above 0.90 for ICU days 4-7 for all states. CONCLUSION: We demonstrated that modelling critical care outcomes as a dynamic system improved the forecasting accuracy of the disease state. Our model accurately identified different disease conditions and trajectories, with a <10% misclassification rate over the first week of critical illness.

Review on clinician bias and its impact on racial and socioeconomic disparities in pediatric heart transplantation.

This expert review seeks to highlight implicit bias in health care, transplant medicine, and pediatric heart transplantation to focus attention on the role these biases may play in the racial/ethnic and socioeconomic disparities noted in pediatric heart transplantation. This review breaks down the transplant decision making process to highlight points at which implicit bias may affect outcomes and discuss how the science of human decision making may help understand these complex processes.

Costs of End-of-Life Hospitalizations in the United States for People With Pulmonary Diseases.

BACKGROUND: Lung transplantation is a lifesaving intervention for people with advanced lung disease, but it is costly and resource-intensive. To investigate the cost-effectiveness of lung transplantation as a treatment option in pulmonary disease, we must understand costs attributable to end-of-life hospitalizations for end-stage lung disease. RESEARCH QUESTION: What are the costs associated with end-of-life hospitalizations for people with pulmonary disease, and how have these trends changed over time? STUDY DESIGN AND METHODS: Adults aged 18 to 74 years with hospitalization data in the Cost and Utilization Project National Inpatient Sample data from 2009 to 2019 with a pulmonary disease admission were included in this analysis. Those with a history of lung transplantation were excluded. International Classification of Diseases codes were used to identify pulmonary disease admissions, complications, and procedures and interventions. Total charges were calculated for hospitalizations and stratified by patient status at time of discharge. Trends in charges over time were assessed by demographic and hospital factors. RESULTS: One hundred nine thousand nine hundred twenty-four (4.1%) hospital admissions for pulmonary disease resulted in in-hospital mortality. Those with obstructive lung disease accounted for 94.1% of hospitalizations and 88.1% cases of in-hospital mortality. Estimated costs for end-of-life hospitalizations were $29,981 on average with wide variation in cost by diagnosis and procedure utilization. Inpatient costs were highest for younger people who received more procedures. Among the most expensive admissions, mechanical ventilation accounted for the greatest proportion of interventions. Significant increases in the use of mechanical ventilation, extracorporeal membrane oxygenation, and dialysis occurred over the time period. The rate of hospital transfers increased with a proportionately greater increase across admissions resulting in in-hospital mortality. INTERPRETATION: Costs accrued during end-of-life hospitalizations vary across people but represent a significant health care cost that can be averted for selected people who undergo lung transplantation. These costs should be considered in studies of cost-effectiveness in lung transplantation.

Incorporating Effects of Time Accrued on the Waiting List into Lung Transplantation Survival Models.

Rationale: U.S. lung transplant mortality risk models do not account for patients' disease progression as time accrues between mandated clinical parameter updates. Objectives: To investigate the effects of accrued waitlist (WL) time on mortality in lung transplant candidates and recipients beyond those expressed by worsening clinical status and to present a new framework for conceptualizing mortality risk in end-stage lung disease. Methods: Using Scientific Registry of Transplant Recipients data (2015-2020, N = 12,616), we modeled transitions among multiple clinical states over time: WL, posttransplant, and death. Using cause-specific and ordinary Cox regression to estimate trajectories of composite 1-year mortality risk as a function of time from waitlisting to transplantation, we quantified the predictive accuracy of these estimates. We compared multistate model-derived candidate rankings against composite allocation score (CAS) rankings. Measurements and Main Results: There were 11.5% of candidates whose predicted 1-year mortality risk increased by >10% by day 30 on the WL. The multistate model ascribed lower numerical rankings (i.e., higher priority) than CAS for those who died while on the WL (multistate mean; median [interquartile range] ranking at death, 227; 154 [57-334]; CAS median [interquartile range] ranking at death, 329; 162 [11-668]). Patients with interstitial lung disease were more likely to have increasing risk trajectories as a function of time accrued on the WL compared with other lung diagnoses. Conclusions: Incorporating the effects of time accrued on the WL for lung transplant candidates and recipients in donor lung allocation systems may improve the survival of patients with end-stage lung diseases on the individual and population levels.

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors.

BACKGROUND: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. METHODS: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties. RESULTS: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). CONCLUSION: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

A new method for classifying prognostic risk factors in lung transplant candidates.

BACKGROUND: Predicting risk of waitlist mortality and subsequent classification of lung transplant candidates has been difficult due to inter-relatedness of risk factors, differential risk across populations, and changes in relationships over time. We developed a clinically intuitive indexing system to simplify mortality risk assessment. METHODS: Scientific Registry of Transplant Recipients data from February 19, 2015, to May 26, 2020 (n = 13,726) were used to estimate 3 constructs. Airway and oxygen function indices were estimated using confirmatory factor analysis and hierarchical clustering was used to derive respiratory support clusters. Cox proportional hazards regression was used to characterize event-free waitlist survival by constructs (3), age, sex, and diagnosis group. Model performance was compared to the Lung Allocation Score/Composite Allocation Score (LAS/CAS). RESULTS: Airway and oxygen function indices were created with substantive factor loadings forced expiratory volume (0.86), forced vital capacity (0.64), partial pressure of carbon dioxide (0.56) and PO2/fraction of inspired oxygen (0.83), partial pressure of oxygen (0.59), and mean pulmonary artery pressure (0.30), respectively. Four respiratory support clusters (C1: as needed O2, C2: continuous O2, C3: continuous O2/positive pressure ventilation (PPV), C4: PPV + extracorporeal membrane oxygenation) were identified. Constructs were used to identify patient profiles. Model area under the receiver operating characteristic curve was 0.85 [0.84, 0.87] compared to the LAS 0.92 [0.91, 0.94] at 4 weeks. Risk predictions were relatively insensitive to airway and oxygen function indices in C1 and C4 but varied across C2 and C3. CONCLUSIONS: Reducing the dimensionality of waitlist mortality risk offers an opportunity to identify clinical phenotypes that are more nuanced and thus more interpretable than current risk assessment provided by the LAS/CAS models.

Association of Neighborhood Deprivation With Cognitive and Mood Outcomes in Adults With Pharmacoresistant Temporal Lobe Epilepsy.

BACKGROUND AND OBJECTIVES: Temporal lobe epilepsy (TLE) is the most common adult form of epilepsy and is associated with a high risk of cognitive deficits and depressed mood. However, little is known about the role of environmental factors on cognition and mood in TLE. This cross-sectional study examined the relationship between neighborhood deprivation and neuropsychological function in adults with TLE. METHODS: Neuropsychological data were obtained from a clinical registry of patients with TLE and included measures of intelligence, attention, processing speed, language, executive function, visuospatial skills, verbal/visual memory, depression, and anxiety. Home addresses were used to calculate the Area Deprivation Index (ADI) for each individual, which were separated into quintiles (i.e., quintile 1 = least disadvantaged and quintile 5 = most disadvantaged). Kruskal-Wallis tests compared quintile groups on cognitive domain scores and mood and anxiety scores. Multivariable regression models, with and without ADI, were estimated for overall cognitive phenotype and for mood and anxiety scores. RESULTS: A total of 800 patients (median age 38 years; 58% female) met all inclusion criteria. Effects of disadvantage (increasing ADI) were observed across nearly all measured cognitive domains and with significant increases in symptoms of depression and anxiety. Furthermore, patients in more disadvantaged ADI quintiles had increased odds of a worse cognitive phenotype (p = 0.013). Patients who self-identified as members of minoritized groups were overrepresented in the most disadvantaged ADI quintiles and were 2.91 (95% CI 1.87-4.54) times more likely to be in a severe cognitive phenotype than non-Hispanic White individuals (p < 0.001). However, accounting for ADI attenuated this relationship, suggesting neighborhood deprivation may account for some of the relationship between race/ethnicity and cognitive phenotype (ADI-adjusted proportional odds ratio 1.82, 95% CI 1.37-2.42). DISCUSSION: These findings highlight the importance of environmental factors and regional characteristics in neuropsychological studies of epilepsy. There are many potential mechanisms by which neighborhood disadvantage can adversely affect cognition (e.g., fewer educational opportunities, limited access to health care, food insecurity/poor nutrition, and greater medical comorbidities). Future research will seek to investigate these potential mechanisms and determine whether structural and functional alterations in the brain moderate the relationship between ADI and cognition.

Association of Socioeconomic Position With Racial and Ethnic Disparities in Survival After Lung Transplant.

Importance: A recent National Academies of Sciences, Engineering, and Medicine study found that transplant outcomes varied greatly based on multiple factors, including race, ethnicity, and geographic location. They proposed a number of recommendations including studying opportunities to improve equity in organ allocation. Objective: To evaluate the role of donor and recipient socioeconomic position and region as a mediator of observed racial and ethnic differences in posttransplant survival. Design, Setting, and Participants: This cohort study included lung transplant donors and recipients with race and ethnicity information and a zip code tabulation area-defined area deprivation index (ADI) from September 1, 2011, to September 1, 2021, whose data were in the US transplant registry. Data were analyzed from June to December 2022. Exposures: Race, neighborhood disadvantage, and region of donors and recipients. Main Outcomes and Measures: Univariable and multivariable Cox proportional hazards regression were used to study the association of donor and recipient race with ADI on posttransplant survival. Kaplan-Meier method estimation was performed by donor and recipient ADI. Generalized linear models by race were fit, and mediation analysis was performed. Bayesian conditional autoregressive Poisson rate models (1, state-level spatial random effects; 2, model 1 with fixed effects for race and ethnicity, 3; model 2 excluding region; and 4: model 1 with fixed effects for US region) were used to characterize variation in posttransplant mortality and compared using ratios of mortality rates to the national average. Results: Overall, 19 504 lung transplant donors (median [IQR] age, 33 [23-46] years; 3117 [16.0%] Hispanic individuals, 3667 [18.8%] non-Hispanic Black individuals, and 11 935 [61.2%] non-Hispanic White individuals) and recipients (median [IQR] age, 60 [51-66] years; 1716 [8.8%] Hispanic individuals, 1861 [9.5%] non-Hispanic Black individuals, and 15 375 [78.8%] non-Hispanic White individuals) were included. ADI did not mediate the difference in posttransplant survival between non-Hispanic Black and non-Hispanic White recipients; it mediated only 4.1% of the survival difference between non-Hispanic Black and Hispanic recipients. Spatial analysis revealed the increased risk of posttransplant death among non-Hispanic Black recipients may be associated with region of residence. Conclusions and Relevance: In this cohort study of lung transplant donors and recipients, socioeconomic position and region of residence did not explain most of the difference in posttransplant outcomes among racial and ethnic groups, which may be due to the highly selected nature of the pretransplant population. Further research should evaluate other potentially mediating effects contributing to inequity in posttransplant survival.

Racial, ethnic and neighborhood socioeconomic differences in incidence of dementia: A regional retrospective cohort study.

BACKGROUND: Evidence on the effects of neighborhood socioeconomic disadvantage on dementia risk in racially and ethically diverse populations is limited. Our objective was to evaluate the relative extent to which neighborhood disadvantage accounts for racial/ethnic variation in dementia incidence rates. Secondarily, we evaluated the spatial relationship between neighborhood disadvantage and dementia risk. METHODS: In this retrospective study using electronic health records (EHR) at two regional health systems in Northeast Ohio, participants included 253,421 patients aged >60 years who had an outpatient primary care visit between January 1, 2005 and December 31, 2015. The date of the first qualifying visit served as the study baseline. Cumulative incidence of composite dementia outcome, defined as EHR-documented dementia diagnosis or dementia-related death, stratified by neighborhood socioeconomic deprivation (as measured by Area Deprivation Index) was determined by competing-risk regression analysis, with non-dementia-related death as the competing risk. Fine-Gray sub-distribution hazard ratios were determined for neighborhood socioeconomic deprivation, race/ethnicity, and clinical risk factors. The degree to which neighborhood socioeconomic position accounted for racial/ethnic disparities in the incidence of composite dementia outcome was evaluated via mediation analysis with Poisson rate models. RESULTS: Increasing neighborhood disadvantage was associated with increased risk of EHR-documented dementia diagnosis or dementia-related death (most vs. least disadvantaged ADI quintile HR = 1.76, 95% confidence interval = 1.69-1.84) after adjusting for age and sex. The effect of neighborhood disadvantage on this composite dementia outcome remained after accounting for known medical risk factors of dementia. Mediation analysis indicated that neighborhood disadvantage accounted for 34% and 29% of the elevated risk for composite dementia outcome in Hispanic and Black patients compared to White patients, respectively. CONCLUSION: Neighborhood disadvantage is related to the risk of EHR-documented dementia diagnosis or dementia-related death and accounts for a portion of racial/ethnic differences in dementia burden, even after adjustment for clinically important confounders.

Miscalibration of lung allocation models leads to inaccurate waitlist mortality predictions.

The importance of waitlist (WL) mortality risk estimates will increase with the adoption of the US Composite Allocation Score (CAS) system. Calibration is rarely assessed in clinical prediction models, yet it is a key factor in determining access to lung transplant. We assessed the calibration of the WL-lung allocation score (LAS)/CAS models and developed alternative models to minimize miscalibration. Scientific Registry of Transplant Recipients data from 2015 to 2020 were used to assess the calibration of the WL model and for subgroups (age, sex, diagnosis, and race/ethnicity). Three recalibrated models were developed and compared: (1) simple recalibration model (SRM), (2) weighted recalibration model 1 (WRM1), and (3) weighted recalibration model 2 (WRM2). The current WL-LAS/CAS model underestimated risk for 78% of individuals (predicted mortality risk, <42%) and overpredicted risk for 22% of individuals (predicted mortality risk, ≥42%), with divergent results among subgroups. Error measures improved in SRM, WRM1, and WRM2. SRM generally preserved candidate rankings, whereas WRM1 and WRM2 led to changes in ranking by age and diagnosis. Differential miscalibration occurred in the WL-LAS/CAS model, which improved with recalibration measures. Further inquiry is needed to develop mortality models in which risk predictions approximate observed data to ensure accurate ranking and timely access to transplant. IMPACT: With changes to the lung transplant allocation system planned in 2023, evaluation of the accuracy and precision of survival models used to rank candidates for lung transplant is important. The waitlist model underpredicts risk for 78% of US transplant candidates with an unequal distribution of miscalibration across subgroups leading to inaccurate ranking of transplant candidates. This work will serve to inform future efforts to improve modeling efforts in the US lung transplant allocation system.

Neighborhood-level Social Vulnerability and Prevalence of Cardiovascular Risk Factors and Coronary Heart Disease.

Social determinants of health are implicated in the geographic variation in cardiovascular diseases (CVDs). The social vulnerability index (SVI) is an estimate of a neighborhood's potential for deleterious outcomes when faced with natural disasters or disease outbreaks. We sought to investigate the association of the SVI with cardiovascular risk factors and the prevalence of coronary heart disease (CHD) in the United States at the census tract level. We linked census tract SVI with prevalence of census tract CVD risk factors (smoking, high cholesterol, diabetes, high blood pressure, low physical activity and obesity), and prevalence of CHD obtained from the behavioral risk factor surveillance system. We evaluated the association between SVI, its sub-scales, CVD risk factors and CHD prevalence using linear regression. Among 72,173 census tracts, prevalence of all cardiovascular risk factors increased linearly with SVI. A higher SVI was associated with a higher CHD prevalence (R2 = 0.17, P < 0.0001). The relationship between SVI and CHD was stronger when accounting for census-tract median age (R2 = 0.57, P < 0.0001). A multivariable linear regression model including 4 SVI themes separately explained considerably more variation in CHD prevalence than the composite SVI alone (50.0% vs 17.3%). Socioeconomic status and household composition and disability were the SVI themes most closely associated with cardiovascular risk factors and CHD prevalence. In the United States, social vulnerability can explain significant portion of geographic variation in CHD, and its risk factors. Neighborhoods with high social vulnerability are at disproportionately increased risk of CHD and its risk factors. Social determinants of health are implicated in the geographic variation in cardiovascular diseases (CVDs). We investigated the association of social vulnerability index (SVI) with cardiovascular risk factors and the prevalence of coronary heart disease (CHD) in the United States at the census tract level. We show that cardiovascular risk factors and CHD were more common with higher SVI. A multivariable linear regression model including 4 SVI themes separately explained considerably more variation in CHD prevalence than the composite SVI alone (50.0% vs 17.3%). Socioeconomic status and household composition and/or disability were the SVI themes most closely associated with cardiovascular risk factors and CHD prevalence.

Refining the Lung Allocation Score Models Fails to Improve Discrimination Performance.

BACKGROUND: As broader geographic sharing is implemented in lung transplant allocation through the Composite Allocation Score (CAS) system, models predicting waitlist and posttransplant (PT) survival will become more important in determining access to organs. RESEARCH QUESTION: How well do CAS survival models perform, and can discrimination performance be improved with alternative statistical models or machine learning approaches? STUDY DESIGN AND METHODS: Scientific Registry for Transplant Recipients (SRTR) data from 2015-2020 were used to build seven waitlist (WL) and data from 2010-2020 to build similar PT models. These included the (I) current lung allocation score (LAS)/CAS model; (II) re-estimated WL-LAS/CAS model; (III) model II incorporating nonlinear relationships; (IV) random survival forests model; (V) logistic model; (VI) linear discriminant analysis; and (VII) gradient-boosted tree model. Discrimination performance was evaluated at 1, 3, and 6 months on the waiting list and 1, 3, and 5 years PT. Area under the curve (AUC) values were estimated across subgroups. RESULTS: WL model performance was similar across models with the greatest discrimination in the baseline cohort (AUC 0.93) and declined to 0.87-0.89 for 3-month and 0.84-0.85 for 6-month predictions and further diminished for residual cohorts. Discrimination performance for PT models ranged from AUC 0.58-0.61 and remained stable with increasing forecasting times but was slightly worse for residual cohorts. WL and PT variability in AUC was greatest for individuals with Medicaid insurance. INTERPRETATION: Use of alternative modeling strategies and contemporary cohorts did not improve performance of models determining access to lung transplant.

Evaluating and Modeling Neighborhood Diversity and Health Using Electronic Health Records.

BACKGROUND: Electronic health records (EHRs) provide researchers with abundant sample sizes, detailed clinical data, and other advantages for performing high-quality observational health research on diverse populations. We review and demonstrate strategies for the design and analysis of cohort studies on neighborhood diversity and health, including evaluation of the effects of race, ethnicity, and neighborhood socioeconomic position on disease prevalence and health outcomes, using localized EHR data. METHODS: Design strategies include integrating and harmonizing EHR data across multiple local health systems and defining the population(s) of interest and cohort extraction procedures for a given analysis based on the goal(s) of the study. Analysis strategies address inferential goals, including the mechanistic study of social risks, statistical adjustment for differences in distributions of social and neighborhood-level characteristics between available EHR data and the underlying local population, and inference on individual neighborhoods. We provide analyses of local variation in mortality rates within Cuyahoga County, Ohio. RESULTS: When the goal of the analysis is to adjust EHR samples to be more representative of local populations, sampling and weighting are effective. Causal mediation analysis can inform effects of racism (through racial residential segregation) on health outcomes. Spatial analysis is appealing for large-scale EHR data as a means for studying heterogeneity among neighborhoods even at a given level of overall neighborhood disadvantage. CONCLUSIONS: The methods described are a starting point for robust EHR-derived cohort analysis of diverse populations. The methods offer opportunities for researchers to pursue detailed analyses of current and historical underlying circumstances of social policy and inequality. Investigators can employ combinations of these methods to achieve greater robustness of results. HIGHLIGHTS: EHR data are an abundant resource for studying neighborhood diversity and health.When using EHR data for these studies, careful consideration of the goals of the study should be considered in determining cohort specifications and analytic approaches.Causal mediation analysis, stratification, and spatial analysis are effective methods for characterizing social mechanisms and heterogeneity across localized populations.

"A rising tide floats all boats": The role of neighborhood collective efficacy in responding to child maltreatment.

BACKGROUND: Nearly one-quarter of the approximately 400,000 reports to child protective services originating from non-mandated reporters come from neighbors. Understanding factors leading non-mandated reporters to contact authorities is important because if modifiable, they might serve as intervention targets to promote reporting of suspected maltreatment. OBJECTIVE: Investigate associations between neighbors' reported responses to scenarios involving children in need, child/teen misbehavior, and suspected maltreatment with individual and neighborhood characteristics, including neighborhood collective efficacy, fear of victimization, and fear of retaliation. HYPOTHESIS: Increased collective efficacy would be associated with increased likelihood of neighbors taking action in response to the situation. PARTICIPANTS & SETTING: 400 caregivers of minors in Cleveland, OH, USA living in 20 census tracts. METHODS: Generalized linear mixed-effects modeling. RESULTS: Analyses adjusted for covariates confirmed our primary hypothesis: a 1-unit increase in the collective efficacy measure was associated with a 64% increase in the odds of neighbors taking action compared to doing nothing (odds ratio = 1.64, 95th percentile confidence interval 1.41-1.92). Also, participants with less than a high-school education had 36% greater odds of reporting their neighbors taking action compared to more educated participants. An interaction effect between participants' fear of victimization in their neighborhood, but not fear of retaliation, was also observed: the effect of collective efficacy on the odds of neighbors taking action was substantially greater among residents expressing moderate and high fear of victimization. CONCLUSION: Enhancing collective efficacy may be an effective strategy for fostering community response to suspected child maltreatment and other situations of a child in need because it may catalyze a variety of positive responses to these situations.

Neighborhood Socioeconomic Disadvantage Associated With Increased 90-Day Mortality Following Radical Cystectomy.

OBJECTIVE: To examine relationships between neighborhood socioeconomic disadvantage and outcomes following radical cystectomy (RC). MATERIALS AND METHODS: A retrospective single institution study of consecutive RCs performed for bladder cancer between 2011 and 2019. Major complications, mortality and survival outcomes were compared using Cochran-Armitage or Kruskal-Wallis tests. Cox proportional hazards models were used for time-to-event analyses. RESULTS: A total of 906 patients were included in analysis. Overall 90-day mortality was 2.98% (27/906). Ninety-day mortality rates observed in the least (first) and most (fourth) disadvantaged ADI quartiles were 0% (0/115) and 6.5% (12/185), respectively. Patients from the fourth quartile demonstrated worse overall survival and recurrence free survival than those in the first quartile. ADI quartile was positively associated with muscle invasive (P = .0006) and node positive (P = .042) disease. ADI percentile was an independent predictor for 90-day mortality (adjusted OR: 1.022, CI: 1.004-1.04, P = .015). CONCLUSION: Higher rates of mortality and worse oncologic outcomes were observed for patients residing in the most disadvantaged quartile. ADI was associated with higher likelihood of 90-day mortality and may therefore be useful in patient counseling, risk stratification, and post-discharge management.

Relationship of Neighborhood Deprivation and Outcomes of a Comprehensive ST-Segment-Elevation Myocardial Infarction Protocol.

Background We evaluated whether a comprehensive ST-segment-elevation myocardial infarction protocol (CSP) focusing on guideline-directed medical therapy, transradial percutaneous coronary intervention, and rapid door-to-balloon time improves process and outcome metrics in patients with moderate or high socioeconomic deprivation. Methods and Results A total of 1761 patients with ST-segment-elevation myocardial infarction treated with percutaneous coronary intervention at a single hospital before (January 1, 2011-July 14, 2014) and after (July 15, 2014- July 15, 2019) CSP implementation were included in an observational cohort study. Neighborhood deprivation was assessed by the Area Deprivation Index and was categorized as low (≤50th percentile; 29.0%), moderate (51st -90th percentile; 40.8%), and high (>90th percentile; 30.2%). The primary process outcome was door-to-balloon time. Achievement of guideline-recommend door-to-balloon time goals improved in all deprivation groups after CSP implementation (low, 67.8% before CSP versus 88.5% after CSP; moderate, 50.7% before CSP versus 77.6% after CSP; high, 65.5% before CSP versus 85.6% after CSP; all P<0.001). Median door-to-balloon time among emergency department/in-hospital patients was significantly noninferior in higher versus lower deprivation groups after CSP (noninferiority limit=5 minutes; Pnoninferiority high versus moderate = 0.002, high versus low <0.001, moderate versus low = 0.02). In-hospital mortality, the primary clinical outcome, was significantly lower after CSP in patients with moderate/high deprivation in unadjusted (before CSP 7.0% versus after CSP 3.1%; odds ratio [OR], 0.42 [95% CI, 0.25-0.72]; P=0.002) and risk-adjusted (OR, 0.42 [95% CI, 0.23-0.77]; P=0.005) models. Conclusions A CSP was associated with improved ST-segment-elevation myocardial infarction care across all deprivation groups and reduced mortality in those from moderate or high deprivation neighborhoods. Standardized initiatives to reduce care variability may mitigate social determinants of health in time-sensitive conditions such as ST-segment-elevation myocardial infarction.