The Association between Long-term PM2.5 Exposure and Risk for Pancreatic Cancer: An Application of Social Informatics.

There is a profound need to identify modifiable risk factors to screen and prevent pancreatic cancer. Air pollution, including fine particulate matter (PM2.5), is increasingly recognized as a risk factor for cancer. We conducted a case-control study using data from the electronic health record (EHR) of Duke University Health System, 15-year residential history, NASA satellite fine particulate matter (PM2.5) and neighborhood socioeconomic data. Using deterministic and probabilistic linkage algorithms, we linked residential history and EHR data to quantify long term PM2.5 exposure. Logistic regression models quantified the association between a one interquartile range (IQR) increase in PM2.5 concentration and pancreatic cancer risk. The study included 203 cases and 5027 controls (median age of 59 years, 62% female, 26% Black). Individuals with pancreatic cancer had higher average annual exposure (9.4 µg/m3) as compared to IQR increase in average annual PM2.5 was associated with greater odds of pancreatic cancer (OR=1.20; 95% CI: 1.00-1.44). These findings highlight the link between elevated PM2.5 exposure and increased pancreatic cancer risk. They may inform screening strategies for high-risk populations and guide air pollution policies to mitigate exposure.

Comparing Social Disadvantage Indices in Pediatric Populations.

BACKGROUND AND OBJECTIVES: Place-based social disadvantage indices are increasingly used to promote health equity, but vary in design. We compared associations between 3 commonly used indices (Social Vulnerability Index [SVI], Area Deprivation Index [ADI], and Child Opportunity Index [COI]) and infant well-child check (WCC) attendance and adolescent obesity. We hypothesized that the COI would have the strongest association with child health outcomes. METHODS: We conducted a cross-sectional analysis of 2014-2019 Duke University Health System electronic health record data. Eligible participants were ≤18 years old, had outpatient encounters during the study period, and resided in Durham County, North Carolina. We aggregated indices into deciles; higher deciles represented greater disadvantage. Multivariable logistic regression models quantified the association between each index and infant WCC attendance (ages 0-15 months) and adolescent obesity (11-17 years). RESULTS: There were 10 175 and 14 961 children in the WCC and obesity cohorts, respectively. All 3 indices were similarly associated with WCCs (SVI odds ratio [OR] 1.10, 95% confidence interval [CI] 1.08-1.12; ADI OR 1.10, 95% CI 1.08-1.12; COI OR 1.12, 95% CI 1.10-1.14) and obesity (SVI OR 1.06, 95% CI 1.04-1.07; ADI OR 1.08, 95% CI 1.06-1.10; COI OR 1.07, 95% CI 1.05-1.08). ORs indicate the increase in the outcome odds for every 1-decile index score increase. CONCLUSIONS: Higher disadvantage as defined by all 3 indices was similarly associated with adolescent obesity and decreased infant WCC attendance. The SVI, ADI, and COI may be equally suitable for pediatric research, but population and outcome characteristics should be considered when selecting an index.

Design and Rationale of the Outcomes Database to Prospectively Assess the Changing Therapy Landscape in Renal Cell Carcinoma Registry: A Multi-institutional, Prospective Study of Patients with Metastatic Renal Cell Carcinoma.

Introduction and hypotheses: The Outcomes Database to prospectivelY aSSEss the changing TherapY landscape in Renal Cell Carcinoma (ODYSSEY RCC) Registry is a large, nationally representative prospective registry of patients with metastatic renal cell carcinoma (mRCC) that aims to provide a real-world picture of longitudinal clinical management and patient experiences that impact clinical outcomes. The primary goal of this study is to understand the cancer management and health-related quality of life in patients with mRCC in routine real-world clinical practice in the USA. Design: This is an observational, phase 4 study with planned enrollment of up to 800 patients aged ≥19 yr with mRCC in the USA. Patients will be identified through electronic health record (EHR) data from the PCORnet network of sites for care received at collaborating sites. A unique aspect of the study is the multiple data sources that will be linked to the EHR data. These include: (1) Medicare claims data, (2) laboratory results, (3) tissue specimens, (4) radiographic images, and (5) patient-reported outcomes, physicians' treatment selection, and discontinuation surveys. Protocol overview: We created a novel data resource that can inform patient care. Investigators have the opportunity to use these to study novel research questions after submitting an ancillary proposal and upon approval of the executive committee. Limitations include the potential for selection bias, residual confounding, and missing information. Summary: The ODYSSEY Registry will provide an advanced data resource that can examine numerous clinical questions related to patient and physician choice, and support methodological research related to omics and artificial intelligence. Patient summary: Cancer medications and treatments are changing rapidly. Collecting data on real-world clinical practice and patient-answered questionnaires will help us better understand cancer management and health-related quality of life while receiving metastatic renal cell carcinoma-specific treatment.

BOLD: Blood-gas and Oximetry Linked Dataset.

Pulse oximeters measure peripheral arterial oxygen saturation (SpO2) noninvasively, while the gold standard (SaO2) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO2 and SaO2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70-100%. Minority racial and ethnic groups account for ~25% of the data - a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions.

Development of an interactive dashboard for gun violence pattern analysis and intervention design at the local level.

Introduction: Gun violence remains a concerning and persistent issue in our country. Novel dashboards may integrate and summarize important clinical and non-clinical data that can inform targeted interventions to address the underlying causes of gun violence. Methods: Data from various clinical and non-clinical sources were sourced, cleaned, and integrated into a customizable dashboard that summarizes and provides insight into the underlying factors that impact local gun violence episodes. Results: The dashboards contained data from 7786 encounters and 1152 distinct patients from our Emergency Department's Trauma Registry with various patterns noted by the team. A multidisciplinary executive team, including subject matter experts in community-based interventions, epidemiology, and social sciences, was formed to design targeted interventions based on these observations. Conclusion: Targeted interventions to reduce gun violence require a multimodal data sourcing and standardization approach, the inclusion of neighborhood-level data, and a dedicated multidisciplinary team to act on the generated insights.

Defining the Need for Causal Inference to Understand the Impact of Social Determinants of Health: A Primer on Behalf of the Consortium for the Holistic Assessment of Risk in Transplantation (CHART).

Objective: This study aims to introduce key concepts and methods that inform the design of studies that seek to quantify the causal effect of social determinants of health (SDOH) on access to and outcomes following organ transplant. Background: The causal pathways between SDOH and transplant outcomes are poorly understood. This is partially due to the unstandardized and incomplete capture of the complex interactions between patients, their neighborhood environments, the tertiary care system, and structural factors that impact access and outcomes. Designing studies to quantify the causal impact of these factors on transplant access and outcomes requires an understanding of the fundamental concepts of causal inference. Methods: We present an overview of fundamental concepts in causal inference, including the potential outcomes framework and direct acyclic graphs. We discuss how to conceptualize SDOH in a causal framework and provide applied examples to illustrate how bias is introduced. Results: There is a need for direct measures of SDOH, increased measurement of latent and mediating variables, and multi-level frameworks for research that examine health inequities across multiple health systems to generalize results. We illustrate that biases can arise due to socioeconomic status, race/ethnicity, and incongruencies in language between the patient and clinician. Conclusions: Progress towards an equitable transplant system requires establishing causal pathways between psychosocial risk factors, access, and outcomes. This is predicated on accurate and precise quantification of social risk, best facilitated by improved organization of health system data and multicenter efforts to collect and learn from it in ways relevant to specialties and service lines.

Residential Structural Racism and Prevalence of Chronic Health Conditions.

Importance: Studies elucidating determinants of residential neighborhood-level health inequities are needed. Objective: To quantify associations of structural racism indicators with neighborhood prevalence of chronic kidney disease (CKD), diabetes, and hypertension. Design, Setting, and Participants: This cross-sectional study used public data (2012-2018) and deidentified electronic health records (2017-2018) to describe the burden of structural racism and the prevalence of CKD, diabetes, and hypertension in 150 residential neighborhoods in Durham County, North Carolina, from US census block groups and quantified their associations using bayesian models accounting for spatial correlations and residents' age. Data were analyzed from January 2021 to May 2023. Exposures: Global (neighborhood percentage of White residents, economic-racial segregation, and area deprivation) and discrete (neighborhood child care centers, bus stops, tree cover, reported violent crime, impervious areas, evictions, election participation, income, poverty, education, unemployment, health insurance coverage, and police shootings) indicators of structural racism. Main Outcomes and Measures: Outcomes of interest were neighborhood prevalence of CKD, diabetes, and hypertension. Results: A total of 150 neighborhoods with a median (IQR) of 1708 (1109-2489) residents; median (IQR) of 2% (0%-6%) Asian residents, 30% (16%-56%) Black residents, 10% (4%-20%) Hispanic or Latino residents, 0% (0%-1%) Indigenous residents, and 44% (18%-70%) White residents; and median (IQR) residential income of $54 531 ($37 729.25-$78 895.25) were included in analyses. In models evaluating global indicators, greater burden of structural racism was associated with greater prevalence of CKD, diabetes, and hypertension (eg, per 1-SD decrease in neighborhood White population percentage: CKD prevalence ratio [PR], 1.27; 95% highest density interval [HDI], 1.18-1.35; diabetes PR, 1.43; 95% HDI, 1.37-1.52; hypertension PR, 1.19; 95% HDI, 1.14-1.25). Similarly in models evaluating discrete indicators, greater burden of structural racism was associated with greater neighborhood prevalence of CKD, diabetes, and hypertension (eg, per 1-SD increase in reported violent crime: CKD PR, 1.15; 95% HDI, 1.07-1.23; diabetes PR, 1.20; 95% HDI, 1.13-1.28; hypertension PR, 1.08; 95% HDI, 1.02-1.14). Conclusions and Relevance: This cross-sectional study found several global and discrete structural racism indicators associated with increased prevalence of health conditions in residential neighborhoods. Although inferences from this cross-sectional and ecological study warrant caution, they may help guide the development of future community health interventions.

BOLD: Blood-gas and Oximetry Linked Dataset - Open Source Research.

Pulse oximeters measure peripheral arterial oxygen saturation (SpO 2 ) noninvasively, while the gold standard (SaO 2 ) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a new comprehensive dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO 2 and SaO 2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70-100%. Minority racial and ethnic groups account for ∼25% of the data - a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions.

Do You PROMIS (Patient Reported Outcomes Measurement Information System)? Physical Function and Pain Interference Scores After Total Knee and Hip Arthroplasty.

Background: Physical function and pain outcomes vary after arthroplasty. We investigated differences in postoperative Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) and pain interference (PI) scores for patients undergoing total knee arthroplasty (TKA) and total hip arthroplasty (THA). We aimed to identify preoperative factors that predict postoperative PROMIS scores. Methods: Patients who underwent TKA and THA from 2014-2020 were eligible. Preoperative variables including demographics, comorbidities, and pain scores were obtained from the medical record. Patients completed surveys measuring postoperative PF and PI. Descriptive statistics and separate linear regression models for each anatomical location were performed to examine factors predicting postoperative PROMIS PF and PI scores. Results: Surveys were completed by 2411 patients (19.5% response rate). Unadjusted mean PF postoperative scores were 47.2 for TKA and 48.8 for THA. Preoperative predictors of lower PF included female sex; body mass index and comorbidities for TKA and THA; and age, tobacco use, and non-White race for THA. Mean PI scores were 47.9 for THA and 49.0 for TKA. Preoperative predictors of increased PI included non-White race and increased body mass index for TKA and THA; higher preoperative pain for TKA; and female sex and increased comorbidity for THA. Conclusions: Postoperative PROMIS scores were similar for TKA and THA, with THA having slightly higher PF and lower PI scores. Regression models using preoperative variables showed similar performance for TKA compared with THA. These findings suggest areas for future development of clinical decision support tools.

Quantifying Associations Between Child Health and Neighborhood Social Vulnerability: Does the Choice of Index Matter?

Importance: Policymakers have increasingly utilized place-based social disadvantage indices to quantify the impacts of place on health and inform equitable resource allocation. Indices vary in design, content, and purpose but are often used interchangeably, potentially resulting in differential assignments of relative disadvantage depending on index choice. Objective: To compare associations between three commonly used disadvantage indices (Social Vulnerability Index (SVI), Area Deprivation Index (ADI), and Child Opportunity Index (COI)) and two epidemiologically distinct child health outcomes-infant well-child check (WCC) attendance and adolescent obesity. Design: Cross-sectional analysis of Duke University Health System electronic health record (EHR) data from January 2014 to December 2019. Participants: Children ≤18 years of age with outpatient encounters between January 2014 and December 2019, and who were Durham County residents were eligible. WCC attendance was assessed for infants ages 0-15 months; obesity was assessed for children ages 11-17 years. Exposures: 2014 Social Vulnerability Index (SVI), 2015 Area Deprivation Index (ADI), and 2015 Child Opportunity Index (COI) 2.0. Main Outcomes: 1) Infant WCC attendance: attending less than the minimum recommended six WCCs in the first 15 months of life, and 2) Adolescent obesity: BMI ≥ the 95th percentile at both the most recent encounter and an encounter within the prior 9-36 months. Results: Of 10175 patients in the WCC cohort, 20% (n = 2073) had less than six WCCs. Of 14961 patients in the obesity cohort, 20% (n = 2933) had obesity. All three indices were associated with both WCCs (OR for SVI 1.10, 95% CI 1.08-1.12; OR for ADI 1.10, 95% CI 1.08-1.12; OR for COI 1.12, 95% CI 1.10-1.14) and obesity (OR for SVI 1.05, 95% CI 1.04-1.08; OR for ADI 1.08, 95% CI 1.06-1.10; OR for COI 1.07, 95% CI 1.05-1.08). Conclusions and relevance: Higher social disadvantage as defined by all three indices was similarly associated with both adolescent obesity and decreased infant WCC attendance. While the COI incorporates a broader set of child-specific variables, the SVI and ADI may often be just as suitable for pediatric research. Users should consider population and outcome characteristics when selecting an index.

Association of Perceived Neighborhood Health With Hypertension Self-care.

Importance: Hypertension self-management is recommended for optimal blood pressure (BP) control, but self-identified residential contextual factors that hinder hypertension self-care are understudied. Objective: To quantify perceived neighborhood health and hypertension self-care and assess interactions with the area deprivation index (ADI) and healthy food availability at home. Design, Setting, and Participants: A cross-sectional study was conducted in Baltimore, Maryland, including primary care adults enrolled in the Achieving Blood Pressure Control Together trial between September 1, 2013, and June 30, 2014. Participants were Black and had at least 2 BP readings greater than or equal to 140/90 mm Hg in the 6 months before enrollment. Analyses were conducted from August 5, 2021, to January 28, 2022. Exposures: Participants' perceived neighborhood health, defined as the mean standardized score across 4 subdomains of aesthetic quality, walkability, safety, and violence, with a higher score signifying better neighborhood health. Main Outcomes and Measures: Hypertension self-care behavior and self-efficacy. Multivariable generalized linear models were fit regressing each outcome on perceived neighborhood health (higher scores on each domain signify better perceived neighborhood health), adjusted for confounders, and interaction terms between neighborhood health and potential modifiers (ADI [higher percentiles correspond to more deprivation] and healthy food availability [higher scores indicate greater availability]) of the primary association were included. Results: Among 159 participants (median [IQR] age, 57 [49-64] years; mean [SD] age, 57 (11) years; 117 women [74%]), median (IQR) hypertension self-care behavior was 50 (45-56) and self-efficacy was 64 (57-72). Better perceived neighborhood health was associated with greater hypertension self-care behavior (ß, 2.48; 95% CI, 0.63-4.33) and self-efficacy (ß, 4.42; 95% CI, 2.25-6.59); these associations persisted for all neighborhood health subdomains except aesthetic quality. There were no statistically significant interactions between perceived neighborhood health or its subdomains with ADI on self-care behavior (P = .74 for interaction) or self-efficacy (P = .85 for interaction). However, better perceived neighborhood aesthetic quality had associations with greater self-care behavior specifically at higher healthy food availability at home scores: ß at -1 SD, -0.29; 95% CI, -2.89 to 2.30 vs ß at 1 SD, 2.97; 95% CI, 0.46-5.47; P = .09 for interaction). Likewise, associations of perceived worse neighborhood violence with lower self-care behavior were attenuated at higher healthy food availability at home scores (ß for -1 SD, 3.69; 95% CI, 1.31-6.08 vs ß for 1 SD, 0.01; 95% CI, -2.53 to 2.54; P = .04 for interaction). Conclusions and Relevance: In this cross-sectional study, better perceived neighborhood health was associated with greater hypertension self-care among Black individuals with hypertension, particularly among those with greater in-home food availability. Thus, optimizing hypertension self-management may require multifaceted interventions targeting both the patients' perceived contextual neighborhood barriers to self-care and availability of healthy food resources in the home.

A comparison of deprivation indices and application to transplant populations.

The choice of deprivation index can influence conclusions drawn regarding the extent of deprivation within a community and the identification of the most deprived communities in the United States. This study aimed to determine the degree of correlation among deprivation indices commonly used to characterize transplant populations. We used a retrospective cohort consisting of adults listed for liver or kidney transplants between 2008 and 2018 to compare 4 deprivation indices: neighborhood deprivation index, social deprivation index (SDI), area deprivation index, and social vulnerability index. Pairwise correlation between deprivation indices by transplant referral regions was measured using Spearman correlations of population-weighted medians and upper quartiles. In total, 52 individual variables were used among the 4 deprivation indices with 25% overlap. For both organs, the correlation between the population-weighted 75th percentile of the deprivation indices by transplant referral region was highest between SDI and social vulnerability index (liver and kidney, 0.93) and lowest between area deprivation index and SDI (liver, 0.19 and kidney, 0.15). The choice of deprivation index affects the applicability of research findings across studies examining the relationship between social risk and clinical outcomes. Appropriate application of these measures to transplant populations requires careful index selection based on the intended use and included variable relevance.

Assessment of Population-Level Disadvantage Indices to Inform Equitable Health Policy.

Policy Points The rapid uptake of disadvantage indices during the pandemic highlights investment in implementing tools that address health equity to inform policy. Existing indices differ in their design, including data elements, social determinants of health domains, and geographic unit of analysis. These differences can lead to stark discrepancies in place-based social risk scores depending on the index utilized. Disadvantage indices are useful tools for identifying geographic patterns of social risk; however, indiscriminate use of indices can have varied policy implications and unintentionally worsen equity. Implementers should consider which indices are suitable for specific communities, objectives, potential interventions, and outcomes of interest. CONTEXT: There has been unprecedented uptake of disadvantage indices such as the Centers for Disease Control and Prevention Social Vulnerability Index (SVI) to identify place-based patterns of social risk and guide equitable health policy during the COVID-19 pandemic. However, limited evidence around data elements, interoperability, and implementation leaves unanswered questions regarding the utility of indices to prioritize health equity. METHODS: We identified disadvantage indices that were (a) used three or more times from 2018 to 2021, (b) designed using national-level data, and (c) available at the census-tract or block-group level. We used a network visualization to compare social determinants of health (SDOH) domains across indices. We then used geospatial analyses to compare disadvantage profiles across indices and geographic areas. FINDINGS: We identified 14 indices. All incorporated data from public sources, with half using only American Community Survey data (n = 7) and the other half combining multiple sources (n = 7). Indices differed in geographic granularity, with county level (n = 5) and census-tract level (n = 5) being the most common. Most states used the SVI during the pandemic. The SVI, the Area Deprivation Index (ADI), the COVID-19 Community Vulnerability Index (CCVI), and the Child Opportunity Index (COI) met criteria for further analysis. Selected indices shared five indicators (income, poverty, English proficiency, no high school diploma, unemployment) but varied in other metrics and construction method. While mapping of social risk scores in Durham County, North Carolina; Cook County, Illinois; and Orleans Parish, Louisiana, showed differing patterns within the same locations depending on choice of disadvantage index, risk scores across indices showed moderate to high correlation (rs 0.7-1). However, spatial autocorrelation analyses revealed clustering, with discrepant distributions of social risk scores between different indices. CONCLUSIONS: Existing disadvantage indices use varied metrics to represent place-based social risk. Within the same geographic area, different indices can provide differences in social risk values and interpretations, potentially leading to varied public health or policy responses.

Association between Gentrification and Health and Healthcare Utilization.

There is tremendous interest in understanding how neighborhoods impact health by linking extant social and environmental drivers of health (SDOH) data with electronic health record (EHR) data. Studies quantifying such associations often use static neighborhood measures. Little research examines the impact of gentrification-a measure of neighborhood change-on the health of long-term neighborhood residents using EHR data, which may have a more generalizable population than traditional approaches. We quantified associations between gentrification and health and healthcare utilization by linking longitudinal socioeconomic data from the American Community Survey with EHR data across two health systems accessed by long-term residents of Durham County, NC, from 2007 to 2017. Census block group-level neighborhoods were eligible to be gentrified if they had low socioeconomic status relative to the county average. Gentrification was defined using socioeconomic data from 2006 to 2010 and 2011-2015, with the Steinmetz-Wood definition. Multivariable logistic and Poisson regression models estimated associations between gentrification and development of health indicators (cardiovascular disease, hypertension, diabetes, obesity, asthma, depression) or healthcare encounters (emergency department [ED], inpatient, or outpatient). Sensitivity analyses examined two alternative gentrification measures. Of the 99 block groups within the city of Durham, 28 were eligible (N = 10,807; median age = 42; 83% Black; 55% female) and 5 gentrified. Individuals in gentrifying neighborhoods had lower odds of obesity (odds ratio [OR] = 0.89; 95% confidence interval [CI]: 0.81-0.99), higher odds of an ED encounter (OR = 1.10; 95% CI: 1.01-1.20), and lower risk for outpatient encounters (incidence rate ratio = 0.93; 95% CI: 0.87-1.00) compared with non-gentrifying neighborhoods. The association between gentrification and health and healthcare utilization was sensitive to gentrification definition.

Geospatial Analysis of Organ Transplant Referral Regions.

Importance: System and center-level interventions to improve health equity in organ transplantation benefit from robust characterization of the referral population served by each transplant center. Transplant referral regions (TRRs) define geographic catchment areas for transplant centers in the US, but accurately characterizing the demographics of populations within TRRs using US Census data poses a challenge. Objective: To compare 2 methods of linking US Census data with TRRs-a geospatial intersection method and a zip code cross-reference method. Design, Setting, and Participants: This cohort study compared spatial congruence of spatial intersection and zip code cross-reference methods of characterizing TRRs at the census block level. Data included adults aged 18 years and older on the waiting list for kidney transplant from 2008 through 2018. Exposures: End-stage kidney disease. Main Outcomes and Measures: Multiple assignments, where a census tract or block group crossed the boundary between 2 hospital referral regions and was assigned to multiple different TRRs; misassigned area, the portion of census tracts or block groups assigned to a TRR using either method but fall outside of the TRR boundary. Results: In total, 102 TRRs were defined for 238 transplant centers. The zip code cross-reference method resulted in 4627 multiple-assigned census block groups (representing 18% of US land area assigned to TRRs), while the spatial intersection method eliminated this problem. Furthermore, the spatial method resulted in a mean and median reduction in misassigned area of 65% and 83% across all TRRs, respectively, compared with the zip code cross-reference method. Conclusions and Relevance: In this study, characterizing populations within TRRs with census block groups provided high spatial resolution, complete coverage of the country, and balanced population counts. A spatial intersection approach avoided errors due to duplicative and incorrect assignments, and allowed more detailed and accurate characterization of the sociodemographics of populations within TRRs; this approach can enrich transplant center knowledge of local referral populations, assist researchers in understanding how social determinants of health may factor into access to transplant, and inform interventions to improve heath equity.

Social determinants of health data in solid organ transplantation: National data sources and future directions.

Health equity research in transplantation has largely relied on national data sources, yet the availability of social determinants of health (SDOH) data varies widely among these sources. We sought to characterize the extent to which national data sources contain SDOH data applicable to end-stage organ disease (ESOD) and transplant patients. We reviewed 10 active national data sources based in the United States. For each data source, we examined patient inclusion criteria and explored strengths and limitations regarding SDOH data, using the National Institutes of Health PhenX toolkit of SDOH as a data collection instrument. Of the 28 SDOH variables reviewed, eight-core demographic variables were included in ≥80% of the data sources, and seven variables that described elements of social status ranged between 30 and 60% inclusion. Variables regarding identity, healthcare access, and social need were poorly represented (≤20%) across the data sources, and five of these variables were included in none of the data sources. The results of our review highlight the need for improved SDOH data collection systems in ESOD and transplant patients via: enhanced inter-registry collaboration, incorporation of standardized SDOH variables into existing data sources, and transplant center and consortium-based investigation and innovation.

A Prospective Multicenter Evaluation of Initial Treatment Choice in Metastatic Renal Cell Carcinoma Prior to the Immunotherapy Era: The MaRCC Registry Experience.

INTRODUCTION: The Metastatic Renal Cell Carcinoma (MaRCC) Registry provides prospective data on real-world treatment patterns and outcomes in patients with metastatic renal cell carcinoma (mRCC). METHODS AND MATERIALS: Patients with mRCC and no prior systemic therapy were enrolled at academic and community sites. End of study data collection was in March 2019. Outcomes included overall survival (OS). A survey of treating physicians assessed reasons for treatment initiations and discontinuations. RESULTS: Overall, 376 patients with mRCC initiated first-line therapy; 171 (45.5%) received pazopanib, 75 (19.9%) sunitinib, and 74 (19.7%) participated in a clinical trial. Median (95% confidence interval) OS was longest in the clinical trial group (50.3 [35.8-not reached] months) versus pazopanib (39.0 [29.7-50.9] months) and sunitinib 26.2 [19.9-61.5] months). Non-clear cell RCC (21.5% of patients) was associated with worse median OS than clear cell RCC (18.0 vs. 47.3 months). Differences in baseline characteristics, treatment starting dose, and relative dose exposure among treatment groups suggest selection bias. Survey results revealed a de-emphasis on quality of life, toxicity, and patient preference compared with efficacy in treatment selection. CONCLUSION: The MaRCC Registry gives insights into real-world first-line treatment selection, outcomes, and physician rationale regarding initial treatment selection prior to the immunotherapy era. Differences in outcomes between clinical trial and off-study patients reflect the difficulty in translating trial results to real-world patients, and emphasize the need to broaden clinical trial eligibility. Physician emphasis on efficacy over quality of life and toxicity suggests more data and education are needed regarding these endpoints.