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.

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.

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.

Active surveillance of metastatic renal cell carcinoma: Results from a prospective observational study (MaRCC).

BACKGROUND: Systemic therapy (ST) can be deferred in patients who have metastatic renal cell carcinoma (mRCC) and slow-growing metastases. Currently, this subset of patients managed with active surveillance (AS) is not well described in the literature. METHODS: This was a prospective observational study of patients with mRCC across 46 US community and academic centers. The objective was to describe baseline characteristics and demographics of patients with mRCC initially managed by AS, reasons for AS, and patient outcomes. Descriptive statistics were used to characterize demographics, baseline characteristics, and patient-related outcomes. Wilcoxon 2-sample rank-sum tests and χ2 tests were used to assess differences between ST and AS cohorts in continuous and categorical variables, respectively. Kaplan-Meier survival curves were used to assess survival. RESULTS: Of 504 patients, mRCC was initially managed by AS (n = 143) or ST (n = 305); 56 patients were excluded from the analysis. Disease was present in 69% of patients who received AS, whereas the remaining 31% had no evidence of disease. At data cutoff, 72 of 143 patients (50%) in the AS cohort had not received ST. The median overall survival was not reached (95% CI, 122 months to not estimable) in patients who received AS versus 30 months (95% CI, 25-44 months) in those who received ST. Quality of life at baseline was significantly better in patients who were managed with AS versus ST. CONCLUSIONS: AS occurs frequently (32%) in real-world clinical practice and appears to be a safe and appropriate alternative to immediate ST in selected patients.

Predicting Recurrent Care Seeking of Physical Therapy for Musculoskeletal Pain Conditions.

OBJECTIVE: Musculoskeletal pain conditions are a leading cause of pain and disability internationally and a common reason to seek health care. Accurate prediction of recurrence of health care seeking due to musculoskeletal conditions could allow for better tailoring of treatment. The aim of this project was to characterize patterns of recurrent physical therapy seeking for musculoskeletal pain conditions and to develop a preliminary prediction model to identify those at increased risk of recurrent care seeking. DESIGN: Retrospective cohort. SETTING: Ambulatory care. SUBJECTS: Patients (n = 578,461) seeking outpatient physical therapy (United States). METHODS: Potential predictor variables were extracted from the electronic medical record, and patients were placed into three different recurrent care categories. Logistic regression models were used to identify individual predictors of recurrent care seeking, and the least absolute shrinkage and selection operator (LASSO) was used to develop multivariate prediction models. RESULTS: The accuracy of models for different definitions of recurrent care ranged from 0.59 to 0.64 (c-statistic), and individual predictors were identified from multivariate models. Predictors of increased risk of recurrent care included receiving workers' compensation and Medicare insurance, having comorbid arthritis, being postoperative at the time of the first episode, age range of 44-64 years, and reporting night sweats or night pain. Predictors of decreased risk of recurrent care included lumbar pain, chronic injury, neck pain, pregnancy, age range of 25-44 years, and smoking. CONCLUSION: This analysis identified a preliminary predictive model for recurrence of care seeking of physical therapy, but model accuracy needs to improve to better guide clinical decision-making.

Psychosocial determinants of cardiovascular events among black Americans with chronic kidney disease or associated risk factors in the Jackson heart study.

BACKGROUND: Individuals with chronic kidney disease (CKD), hypertension (HTN), or diabetes mellitus (DM) are at increased risk for cardiovascular disease (CVD). The extent to which psychosocial factors are associated with increased CVD risk within these individuals is unclear. Black individuals experience a high degree of psychosocial stressors due to socioeconomic factors, environment, racism, and discrimination. We examined the association between psychosocial factors and risk of CVD events among Black men and women with CKD and CKD risk factors in the Jackson Heart Study. METHODS AND RESULTS: We identified 1919 participants with prevalent CKD or CKD risk factors at baseline. We used rotated principal component analysis - a form of unsupervised machine learning that may identify constructs not intuitively identified by a person - to describe five groups of psychosocial components (including negative moods, religiosity, discrimination, negative outlooks, and negative coping resources) based on a battery of questionnaires. Multiple imputation by chained equation (MICE) was used to impute missing covariate data. Cox models were used to quantify the association between psychosocial components and incident CVD, defined as a fatal coronary heart disease event, myocardial infarction, cardiac procedure (angiography or revascularization procedure), or stroke. Of the 929 participants in the analysis, 67% were female, 28% were current/former smokers with mean age of 56 years and mean BMI of 33 kg/m2. Over a median follow-up of 8 years, 6% had an incident CVD event. In multivariable models, each standard deviation (SD) increase in the religiosity component was associated with an increased hazard for CVD event (hazard ratio [HR] = 1.52, 95% CI: 1.09-2.13). CONCLUSIONS: Religiosity was associated with CVD among participants with prevalent CKD or CKD risk factors. Studies to better understand the mechanisms of this relationship are needed.

Low use of routine medical care among African Americans with high CKD risk: the Jackson Heart Study.

BACKGROUND: Use of routine medical care (RMC) is advocated to address ethnic/racial disparities in chronic kidney disease (CKD) risks, but use is less frequent among African Americans. Factors associated with low RMC use among African Americans at risk of renal outcomes have not been well studied. METHODS: We examined sociodemographic, comorbidity, healthcare access, and psychosocial (discrimination, anger, stress, trust) factors associated with low RMC use in a cross-sectional study. Low RMC use was defined as lack of a physical exam within one year among participants with CKD (estimated glomerular filtration rate < 60 mL/min/1.73m2 or urine albumin-to-creatinine ratio > 30 mg/g) or CKD risk factors (diabetes or hypertension). We used multivariable logistic regression to estimate the odds of low RMC use at baseline (2000-2004) for several risk factors. RESULTS: Among 3191 participants with CKD, diabetes, or hypertension, 2024 (63.4%) were ≥ 55 years of age, and 700 (21.9%) reported low RMC use. After multivariable adjustment, age < 55 years (OR 1.61 95% CI 1.31-1.98), male sex (OR 1.71; 1.41-2.07),

Association of functional and structural social support with chronic kidney disease among African Americans: the Jackson Heart Study.

BACKGROUND: There is limited evidence on the relationship between social support and renal outcomes in African Americans. We sought to determine the association of social support with prevalent chronic kidney disease (CKD) and kidney function decline in an African American cohort. We also examined whether age modifies the association between social support and kidney function decline. METHODS: We identified Jackson Heart Study (JHS) participants with baseline (Exam in 2000-2004) functional and structural social support data via the Interpersonal Support Evaluation List (ISEL) and social network size questions, respectively. With ISEL as our primary exposure variable, we performed multivariable regression models to evaluate the association between social support and prevalent CKD [estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2 or urine albumin-creatinine ratio (ACR) ≥30 mg/g], eGFR decline, and rapid renal function decline (RRFD) (> 30% decrease in eGFR over approximately 10 years). All models were adjusted for baseline sociodemographics, diabetes, hypertension, smoking status, and body mass index; models for eGFR decline and RRFD were additionally adjusted for eGFR and ACR. In models for eGFR decline, we assessed for interaction between age and social support. For secondary analyses, we replaced ISEL with its individual domains (appraisal, belonging, self-esteem, and tangible) and social network size in separate models as exposure variables. RESULTS: Of 5301 JHS participants, 4015 (76%) completed the ISEL at baseline. 843 (21%) had low functional social support (ISEL score < 32). Participants with low (vs. higher) functional social support were more likely to have lower income (47% vs. 28%), be current or former tobacco users (39% vs. 30%), have diabetes (25% vs. 21%) or CKD (14% vs. 12%). After multivariable adjustment, neither ISEL or social network size were independently associated with prevalent CKD, eGFR decline, or RRFD. Of the ISEL domains, only higher self-esteem was associated with lower odds of prevalent CKD [OR 0.94 (95% CI 0.89-0.99)]. The associations between social support measures and eGFR decline were not modified by age. CONCLUSIONS: In this African-American cohort, social support was not associated with prevalent CKD or kidney function decline. Further inquiry of self-esteem's role in CKD self-management and renal outcomes is warranted.

Obesity and synergistic risk factors for chronic kidney disease in African American adults: the Jackson Heart Study.

Background: African Americans are at high risk for chronic kidney disease (CKD). Obesity may increase the risk for CKD by exacerbating features of the metabolic syndrome and promoting glomerular hyperfiltration. Whether other factors also affecting these pathways may amplify or mitigate obesity-CKD associations has not been investigated. Methods: We studied interactions between obesity and these candidate factors in 2043 African Americans without baseline kidney disease enrolled in the Jackson Heart Study. We quantified obesity as body mass index (BMI), sex-normalized waist circumference and visceral adipose volume measured by abdominal computed tomography at an interim study visit. Interactions were hypothesized with (i) metabolic risk factors (dietary quality and physical activity, both quantified by concordance with American Heart Association guidelines) and (ii) factors exacerbating or mitigating hyperfiltration (dietary protein intake, APOL1 risk status and use of renin-angiotensin system blocking medications). Using multivariable regression, we evaluated associations between obesity measures and incident CKD over the follow-up period, as well as interactions with metabolic and hyperfiltration factors. Results: Assessed after a median of 8 years (range 6-11 years), baseline BMI and waist circumference were not associated with incident CKD. Higher visceral adipose volume was independently associated with incident CKD (P = 0.008) in a nonlinear fashion, but this effect was limited to those with lower dietary quality (P = 0.001; P-interaction = 0.04). In additional interaction models, higher waist circumference was associated with greater risk of incident CKD among those with the low-risk APOL1 genotype (P = 0.04) but not those with a high-risk genotype (P-interaction = 0.02). Other proposed factors did not modify obesity-CKD associations. Conclusions. Higher risks associated with metabolically active visceral adipose volume and interactions with dietary quality suggest that metabolic factors may be key determinants of obesity-associated CKD risk. Interactions between obesity and APOL1 genotype should be considered in studies of African Americans.

Cost implications of PSA screening differ by age.

BACKGROUND: Multiple guidelines seek to alter rates of prostate-specific antigen (PSA)-based prostate cancer screening. The costs borne by payers associated with PSA-based screening for men of different age groups-including the costs of screening and subsequent diagnosis, treatment, and adverse events-remain uncertain. We sought to develop a model of PSA costs that could be used by payers and health care systems to inform cost considerations under a range of different scenarios. METHODS: We determined the prevalence of PSA screening among men aged 50 and higher using 2013-2014 data from a large, multispecialty group, obtained reimbursed costs associated with screening, diagnosis, and treatment from a commercial health plan, and identified transition probabilities for biopsy, diagnosis, treatment, and complications from the literature to generate a cost model. We estimated annual total costs for groups of men ages 50-54, 55-69, and 70+ years, and varied annual prostate cancer screening prevalence in each group from 5 to 50% and tested hypothetical examples of different test characteristics (e.g., true/false positive rate). RESULTS: Under the baseline screening patterns, costs of the PSA screening represented 10.1% of the total costs; costs of biopsies and associated complications were 23.3% of total costs; and, although only 0.3% of all screen eligible patients were treated, they accounted for 66.7% of total costs. For each 5-percentage point decrease in PSA screening among men aged 70 and older for a single calendar year, total costs associated with prostate cancer screening decreased by 13.8%. For each 5-percentage point decrease in PSA screening among men 50-54 and 55-69 years old, costs were 2.3% and 7.3% lower respectively. CONCLUSIONS: With constrained financial resources and with national pressure to decrease use of clinically unnecessary PSA-based prostate cancer screening, there is an opportunity for cost savings, especially by focusing on the downstream costs disproportionately associated with screening men 70 and older.

Design and Rationale of the Metastatic Renal Cell Carcinoma (MaRCC) Registry: A Prospective Academic and Community-Based Study of Patients With Metastatic Renal Cell Cancer.

The Metastatic Renal Cell Cancer Registry, a large, nationally representative, prospective registry of patients with metastatic renal cell carcinoma (mRCC), aims to understand real-world treatment patterns and outcomes of patients with mRCC in routine clinical practice across the United States. This observational study is designed to enroll 500 patients with previously untreated mRCC from approximately 60 academic and community treatment sites; as of December 7, 2016, 500 patients have enrolled at 54 sites. Key endpoints include real-world data on reasons for treatment initiation and discontinuation; treatment regimens; disease progression; patient-reported outcomes; and healthcare resource utilization in this patient population.

Controlling for Informed Presence Bias Due to the Number of Health Encounters in an Electronic Health Record.

Electronic health records (EHRs) are an increasingly utilized resource for clinical research. While their size allows for many analytical opportunities, as with most observational data there is also the potential for bias. One of the key sources of bias in EHRs is what we term informed presence-the notion that inclusion in an EHR is not random but rather indicates that the subject is ill, making people in EHRs systematically different from those not in EHRs. In this article, we use simulated and empirical data to illustrate the conditions under which such bias can arise and how conditioning on the number of health-care encounters can be one way to remove this bias. In doing so, we also show when such an approach can impart M bias, or bias from conditioning on a collider. Finally, we explore the conditions under which number of medical encounters can serve as a proxy for general health. We apply these methods to an EHR data set from a university medical center covering the years 2007-2013.

The use of adjuvant radiotherapy in elderly patients with early-stage breast cancer: changes in practice patterns after publication of Cancer and Leukemia Group B 9343.

BACKGROUND: The Cancer and Leukemia Group B (CALGB) 9343 randomized phase 3 trial established lumpectomy and adjuvant therapy with tamoxifen alone, rather than both radiotherapy and tamoxifen, as a reasonable treatment course for women aged >70 years with clinical stage I (AJCC 7th edition), estrogen receptor-positive breast cancer. An analysis of the Surveillance, Epidemiology, and End Results (SEER) registry was undertaken to assess practice patterns before and after the publication of this landmark study. METHODS: The SEER database from 2000 to 2009 was used to identify 40,583 women aged ≥70 years who were treated with breast-conserving surgery for clinical stage I, estrogen receptor-positive and/or progesterone receptor-positive breast cancer. The percentage of patients receiving radiotherapy and the type of radiotherapy delivered was assessed over time. Administration of radiotherapy was further assessed across age groups; SEER cohort; and tumor size, grade, and laterality. RESULTS: Approximately 68.6% of patients treated between 2000 and 2004 compared with 61.7% of patients who were treated between 2005 and 2009 received some form of adjuvant radiotherapy (P < .001). Coinciding with a decline in the use of external beam radiotherapy, there was an increase in the use of implant radiotherapy from 1.4% between 2000 and 2004 to 6.2% between 2005 to 2009 (P < .001). There were significant reductions in the frequency of radiotherapy delivery over time across age groups, tumor size, and tumor grade and regardless of laterality (P < .001 for all). CONCLUSIONS: Randomized phase 3 data support the omission of adjuvant radiotherapy in elderly women with early-stage breast cancer. Analysis of practice patterns before and after the publication of these data indicates a significant decline in radiotherapy use; however, nearly two-thirds of women continue to receive adjuvant radiotherapy.

Serum prostate-specific antigen (PSA) concentration is positively associated with rate of disease reclassification on subsequent active surveillance prostate biopsy in men with low PSA density.

OBJECTIVE: To investigate the association between serum prostate-specific antigen (PSA) concentration at active surveillance (AS) entry and disease reclassification on subsequent AS biopsy ('biopsy reclassification') in men with low PSA density (PSAD). To investigate whether a clinically meaningful PSA threshold for AS eligibility/ineligibility for men with low PSAD can be identified based on risk of subsequent biopsy reclassification. PATIENTS AND METHODS: We included men enrolled in the Johns Hopkins AS Study (JHAS) who had a PSAD of <0.15 ng/mL/g (640 men). We estimated the incidence rates (IRs; per 100 person years) and hazard ratios (HR) of biopsy reclassification (Gleason score ≥ 7, any Gleason pattern 4 or 5, ≥3 positive cores, or ≥50% cancer involvement/biopsy core) for categories of serum PSA concentration at the time of entry into AS. We generated predicted IRs using Poisson regression to adjust for age and prostate volume, mean percentage free PSA (ratio of free to total PSA) and maximum percentage biopsy core involvement with cancer. RESULTS: The unadjusted IRs (per 100 person years) of biopsy reclassification across serum PSA concentration at entry into JHAS showed, in general, an increase; however, the pattern was not linear with higher IRs in the group ≥ 4 to <6 ng/mL (14.2, 95% confidence interval [CI] 11.8-17.2%) when compared with ≥6 to <8 ng/mL (8.4, 95% CI 5.7-12.3%) but almost similar IRs when compared with the group ≥ 8 to <10 ng/mL (14.8, 95% CI 8.4-26.1%). The adjusted predicted IRs of reclassification showed a similar non-linear increase in IRs, whereby the rates around 4 ng/mL were similar to the rates around 10 ng/mL. CONCLUSION: Risk for biopsy reclassification increased non-linearly across PSA concentration in men with low PSAD, whereby no obvious clinically meaningful threshold could be identified. This information could be incorporated into decision-making for AS. However, longer follow-up times are needed to warrant final conclusions.

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.

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.

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.

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.