scholarly journals Distributed Counterfactual Modeling Approach for Investigating Hospital-Associated Racial Disparities in COVID-19 Mortality

Author(s):  
Mackenzie J Edmondson ◽  
Chongliang Luo ◽  
Nazmul Islam ◽  
David Asch ◽  
Jiang J Bian ◽  
...  

Several studies have found that black patients are more likely than white patients to test positive for or be hospitalized with COVID-19, but many of these same studies have found no difference in in-hospital mortality. These studies may have underestimated racial differences due to reliance on data from a single hospital system, as adequate control of patient-level characteristics requires aggregation of highly granular data from several institutions. Further, one factor thought to contribute to disparities in health outcomes by race is site of care. Several differences between black and white patient populations, such as access to care and referral patterns among clinicians, can lead to patients of different races largely attending different hospitals. We sought to develop a method that could study the potential association between attending hospital and racial disparity in mortality for COVID-19 patients without requiring patient-level data sharing among collaborating institutions. We propose a novel application of a distributed algorithm for generalized linear mixed modeling (GLMM) to perform counterfactual modeling and investigate the role of hospital in differences in COVID-19 mortality by race. Our counterfactual modeling approach uses simulation to randomly assign black patients to hospitals in the same distribution as those attended by white patients, quantifying the difference between observed mortality rates and simulated mortality risk following random hospital assignment. To illustrate our method, we perform a proof-of-concept analysis using data from four hospitals within the OneFlorida Clinical Research Consortium. Our approach can be used by investigators from several institutions to study the impact of admitting hospital on COVID-19 mortality, a critical step in addressing systemic racism in modern healthcare.

2012 ◽  
Vol 17 (6) ◽  
pp. 381-384 ◽  
Author(s):  
Kimberley A Kaseweter ◽  
Brian B Drwecki ◽  
Kenneth M Prkachin

BACKGROUND: Evidence of inadequate pain treatment as a result of patient race has been extensively documented, yet remains poorly understood. Previous research has indicated that nonwhite patients are significantly more likely to be undertreated for pain.OBJECTIVE: To determine whether previous findings of racial biases in pain treatment recommendations and empathy are generalizable to a sample of Canadian observers and, if so, to determine whether empathy biases mediate the pain treatment disparity.METHODS: Fifty Canadian undergraduate students (24 men and 26 women) watched videos of black and white patients exhibiting facial expressions of pain. Participants provided pain treatment decisions and reported their feelings of empathy for each patient.RESULTS: Participants demonstrated both a prowhite treatment bias and a prowhite empathy bias, reporting more empathy for white patients than black patients and prescribing more pain treatment for white patients than black patients. Empathy was found to mediate the effect of race on pain treatment.CONCLUSIONS: The results of the present study closely replicate those from a previous study of American observers, providing evidence that a prowhite bias is not a peculiar feature of the American population. These results also add support to the claim that empathy plays a crucial role in racial pain treatment disparity.


2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 137-137
Author(s):  
Kerin B. Adelson ◽  
Xiaoliang Wang ◽  
Mustafa Ascha ◽  
Rebecca A. Miksad ◽  
Timothy N Showalter ◽  
...  

137 Background: Prior studies indicate that Black patients with cancer are more likely to receive aggressive EOL care, including chemotherapy within 14 days (d) prior to death. However, most studies are limited to specific subgroups, and it is unclear if disparities remain in the immunotherapy era. In this study, we evaluated racial differences in systemic oncologic EOL treatment among a national all-payer cohort of patients treated in routine practice. Methods: We conducted a retrospective cohort study utilizing data from the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database. Patients with confirmed cancer diagnosis, with documented treatment on or after 1/1/2011 and who died between 2015 and 2019, were included. Patients with documented race of White or Black or African American were included. We defined our outcome measures as receipt of any systemic oncologic treatment within 30d or 14d prior to death, and also stratified by mono-chemotherapy (Chemo) and immunotherapy ± targeted therapy (ICI). We used mixed-level logistic regression models to assess the likelihood of receiving each treatment, compared to patients without any EOL treatment, between Black and White patients, adjusted for patient- and practice-level characteristics as fixed effects and a practice-specific random intercept. Race-specific adjusted rates were estimated using stratified analysis. Results: A total of 40,675 White and 5,150 Black patients were included in the analysis. Compared to White patients, Black patients were younger at diagnosis, were more likely to be female and have Medicaid coverage. Black patients were more likely to be treated at practices with higher patient-to-physician ratio (25.8% in highest quintile vs. 18.7%) and with a high proportion (> 10%) of patients with Medicaid (38.1% vs. 31.6%). Compared to White patients within the same practice, Black patients were less likely to receive any EOL treatment within 30d (adjusted odds ratio [aOR]: 0.87; 95% CI: 0.81-0.93) or 14d (aOR: 0.87; 95% CI: 0.80-0.96). Adjusted rates of any EOL treatment within 30d prior to death were 33.8% and 37.6% among Black and White patients, respectively. When stratified by treatment types, Black patients were less likely to receive ICI within 30d prior to death, compared to White patients (aOR: 0.87; 95% CI: 0.76-1.00). Conclusions: Our findings differ from prior studies of oncologic EOL care and suggest that in contemporary practice Black patients are less likely to receive anti-cancer therapy near EOL, largely driven by lower rates of ICI use. Future research should investigate the complex causal pathway underlying observed racial differences among patient and practice-level factors.


2004 ◽  
Vol 34 (4) ◽  
pp. 705-718 ◽  
Author(s):  
NANCY L. SOHLER ◽  
EVELYN J. BROMET ◽  
JANET LAVELLE ◽  
THOMAS J. CRAIG ◽  
RAMIN MOJTABAI

Background. It is now well documented that both black and white patients with severe mental illness are likely to use different types of treatment facilities, have different lengths of hospital stays, and receive different types and dosages of psychotropic medications. It is still uncertain, however, whether these differences exist at the early stages of treatment.Method. We examined treatment patterns for a countywide sample of patients with psychotic disorders recruited at their initial psychiatric hospitalization. Illness characteristics, prior treatment histories, admission conditions, and psychotropic medication use during this hospitalization were compared for both black and white patients.Results. Black patients were less likely to have had out-patient treatment prior to their first hospitalization and were more likely to be hospitalized in public than in community psychiatric units than were white patients. Black patients were also more likely to be hospitalized primarily for a behavioral disturbance and escorted to the hospital by the emergency medical services or police, while white patients were more often hospitalized primarily for subjective suffering. These patterns were particularly significant for those with a non-schizophrenia diagnosis. However, there were few statistically significant differences between black and white patients on psychotropic medication use during the first hospitalization.Conclusions. Differences during the early stages of treatment between black and white patients with psychotic disorders appear to arise most prominently before, rather than during, their first hospitalization.


Nephron ◽  
2021 ◽  
pp. 1-11
Author(s):  
Muzamil O. Hassan ◽  
Itunu Owoyemi ◽  
Emaad M. Abdel-Rahman ◽  
Jennie Z. Ma ◽  
Rasheed A. Balogun

Introduction: Acute kidney injury (AKI) is known to be associated with increased mortality, and racial differences in hospital mortality exist in patients with AKI. However, it remains to be seen whether racial differences exist in post-hospitalization mortality among AKI patients. Methods: We analyzed data of adult AKI patients admitted to the University of Virginia Medical Center between January 1, 2001, and December 31, 2015, to compare in-hospital and post-hospitalization mortality among hospitalized black and white patients with AKI. Multivariable logistic regression analysis was used to analyze the association between race and in-hospital mortality, and 90-day post-hospitalization mortality among AKI patients that were discharged. Kaplan-Meier survival curve was used to evaluate long-term survival between black and white patients. Results: Black patients had lower in-hospital mortality than white patients after adjusting for age, sex, estimated glomerular filtration rate, hospital length of stay, severity of AKI, comorbidities, and the need for dialysis and mechanical ventilation (odds ratio: 0.82; 95% confidence interval, 0.70–0.96, p = 0.0015). Similarly, at 90-day post-hospitalization, black patients had significantly lower adjusted odds of death than white patients (odds ratio: 0.64; 95% confidence interval, 0.46–0.93; p = 0.008). The median length of follow-up was 11.9 months (0.6–46.7 months). Kaplan-Meier survival curve showed that long-term survival was significantly better in black patients compared to white patients (median duration of survival; 39.7 vs. 24.8 months; p ≤ 0.001). Conclusions: Black patients with AKI had lower in-hospital mortality, 90-day post-hospitalization mortality, and better long-term survival rates compared to white patients with AKI.


2019 ◽  
Vol 214 ◽  
pp. 46-53
Author(s):  
Lonnie T. Sullivan ◽  
Hillary Mulder ◽  
Karen Chiswell ◽  
Linda K. Shaw ◽  
Tracy Y. Wang ◽  
...  

Neurology ◽  
2019 ◽  
Vol 93 (18) ◽  
pp. e1664-e1674 ◽  
Author(s):  
James F. Burke ◽  
Chunyang Feng ◽  
Lesli E. Skolarus

ObjectiveTo explore racial differences in disability at the time of first postdischarge disability assessment.MethodsThis was a retrospective cohort study of all Medicare fee-for-service beneficiaries hospitalized with primary ischemic stroke (ICD-9,433.x1, 434.x1, 436) or intracerebral hemorrhage (431) diagnosed from 2011 to 2014. Racial differences in poststroke disability were measured in the initial postacute care setting (inpatient rehabilitation facility, skilled nursing facility, or home health) with the Pseudo-Functional Independence Measure. Given that assignment into postacute care setting may be nonrandom, patient location during the first year after stroke admission was explored.ResultsA total of 390,251 functional outcome assessments (white = 339,253, 87% vs black = 50,998, 13%) were included in the primary analysis. At the initial functional assessment, black patients with stroke had greater disability than white patients with stroke across all 3 postacute care settings. The difference between white and black patients with stroke was largest in skilled nursing facilities (black patients 1.8 points lower than white patients, 11% lower) compared to the other 2 settings. Conversely, 30-day mortality was greater in white patients with stroke compared to black patients with stroke (18.4% vs 12.6% [p < 0.001]) and a 3 percentage point difference in mortality persisted at 1 year. Black patients with stroke were more likely to be in each postacute care setting at 30 days, but only very small differences existed at 1 year.ConclusionsBlack patients with stroke have 30% lower 30-day mortality than white patients with stroke, but greater short-term disability. The reasons for this disconnect are uncertain, but the pattern of reduced mortality coupled with increased disability suggests that racial differences in care preferences may play a role.


2017 ◽  
Vol 59 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Min Gyung Kim ◽  
Hyunjoo Yang ◽  
Anna S. Mattila

New York City launched a restaurant sanitation letter grade system in 2010. We evaluate the impact of customer loyalty on restaurant revisit intentions after exposure to a sanitation grade alone, and after exposure to a sanitation grade plus narrative information about sanitation violations (e.g., presence of rats). We use a 2 (loyalty: high or low) × 4 (sanitation grade: A, B, C, or pending) between-subjects full factorial design to test the hypotheses using data from 547 participants recruited from Amazon MTurk who reside in the New York City area. Our study yields three findings. First, loyal customers exhibit higher intentions to revisit restaurants than non-loyal customers, regardless of sanitation letter grades. Second, the difference in revisit intentions between loyal and non-loyal customers is higher when sanitation grades are poorer. Finally, loyal customers are less sensitive to narrative information about sanitation violations.


Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Timothy B Plante ◽  
D L Long ◽  
George Howard ◽  
April P Carson ◽  
Virginia J Howard ◽  
...  

Introduction: In the US, blacks are at higher risk of hypertension than whites. The single largest contributor to this disparity is the Southern Diet pattern. Inflammation biomarkers are associated with risk of hypertension, and C-reactive protein (CRP) is higher in blacks than whites. We studied whether elevated CRP in blacks relative to whites contributes to the racial disparity in hypertension in blacks. Methods: We included 6,548 black and white men and women age ≥45 years from the REGARDS cohort without hypertension at baseline ('03-'07) and who completed visit 2 in '13-'16. Incident hypertension was defined as BP ≥140/90 mm Hg or hypertension medication use at visit 2. Using logistic regression, the black:white odds ratio (OR) for incident hypertension was calculated adjusting for age, sex, race, and baseline SBP. We assessed the percent change in the black:white OR for incident hypertension after adding CRP. The 95% CI was calculated using 1,000 bootstrapped samples. We determined the impact of known hypertension risk factors and anti-inflammatory medications on the percent mediation by CRP. Results: Hypertension developed in 46% of blacks and 33% of whites. Adjusting for demographics, the black:white OR (95% CI) was 1.51, which was reduced to 1.46, a 9.3% reduction (95% CI 5.4%, 13.2%) by CRP (Table). In models including exercise, waist circumference, BMI, and depressive symptoms, the percent mediation by CRP was 3.7% (1.0%, 6.4%). Similar patterns were seen for models incorporating socioeconomic factors and medication use. After adding Southern diet pattern and dietary Na/K ratio, CRP no longer attenuated the association (1.3% mediation; -1.5, 4.1). Conclusions: CRP significantly attenuated the black-white difference in incident hypertension; however, once dietary factors were accounted for, CRP had no impact on the black:white difference in incident hypertension. Thus, inflammation as measured by CRP, may be part of the reason that dietary factors influence the black:white disparity in incident hypertension.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 9-9
Author(s):  
Emelly Rusli ◽  
Lihong Diao ◽  
Cynthia Liu ◽  
Mona A Kelkar ◽  
Lisa Ensign ◽  
...  

Background: Past studies have indicated a potential racial disparity in Multiple Myeloma (MM) survival between black and white patients (Costa et al., 2017; Marinac et al., 2020), an issue compounded by minority underrepresentation in clinical trials (Ailawadhi et al., 2018). To better understand how racial disparities affect both MM survival and access to treatment, we performed an analysis of pooled clinical trial (CT) and Real-World EMR Data (RWD). Methods: Eligible Phase II and III open-label MM clinical trials were identified from the Medidata Enterprise Data Store, which comprises over 22,000 historical clinical trials, for de-identified aggregate analyses. De-identified Oncology RWD was sourced from the Guardian Research Network of integrated delivery systems from 2016 to 2020. Baseline characteristics were analyzed in both cohorts. Race was categorized as black, white, or other. Overall Survival (OS) was assessed using Kaplan-Meier analysis. In the RWD, therapy utilization was assessed by race. Results: The RWD contained 5871 patients, with 17.5% black, 78.3% white, and 4.2% other race. Median age in years at diagnosis was 69 for blacks, 72 for whites, and 70 for other races. The gender breakdown was 54.2% female in blacks, 46.0% in whites, 45.9% in those of other races respectively. Median number of prior regimens was 2, with no differences between racial groups. The CT data contained 851 patients, with 1.4% black, 93.5% white, and 5.1% other race. Median age in years at diagnosis was 62 for blacks, 58 for whites, and 55 for other races. The gender breakdown was 33.3% female in blacks, 43.5% in whites, and 46.7% in those of other races respectively. Median number of prior regimens was 5, with no differences between racial groups. There was no statistically significant difference in OS between racial groups in either dataset. In the CT data with median follow-up of 7.8 years, survival from date of diagnosis to last visit or date of death was 25% for blacks and 18% for whites. Currently, in the RWD, 3-year survival comparing blacks to whites is 85% to 83%. The proportion of treated RWD patients appears to be similar between black and white patient groups, with 56% of white and 53% of black patients receiving 1st line therapy, and 33% and 31% receiving 2nd line therapy, respectively. Among newer therapy modalities, white patients had a higher utilization of targeted antibody agent daratumumab (8.7% utilization among whites, 5.2% in blacks, p&lt;0.001), and although not statistically different, proteasome inhibitor carfilzomib use was also higher among whites compared to blacks (6.5% versus 5.5%). Mono daratumumab and ixazomib were used as 1st-line therapy in white patients, while these agents were administered in combination with other treatments in black patients. Adjusting for age and novel therapy use, there was also a suggestion that treatment initiation after diagnosis occurred earlier in whites than blacks (median 1.1 years vs. 1.6 years, p=0.3). Conclusions: Though there were no demonstrated differences in survival between racial groups in either dataset, the RWD suggested differences in treatment utilization, with underutilization of novel therapies like proteasome inhibitors and targeted antibody therapy and later treatment initiation in blacks. Previous studies (Fiala et al., 2017) have noted similar trends, which suggest that therapeutic advances may not be equitably available to all racial groups. This observation could not be replicated in CT data, but merits further exploration. Despite black patients making up 17.5% of patients in the RWD, a racial distribution consistent with published literature (Rosenberg et al., 2015), black patients made up only 1.3% of patients in the CT data. Furthermore, in the CT data, the median age of black patients was older than that of the white and other race groups (62 years compared to 58 and 55, respectively). This observation is magnified by evidence in both the RWD and other datasets (Fillmore et al., 2019) that shows a younger age of onset in black MM patients. Given the strong correlation between age and poorer outcomes in MM (Ludwig et al.,2008), it is possible that these clinical trials are not capturing a representative black patient population, and results may not be generalizable to other groups. Recruitment of black patients should remain a priority in clinical studies in order to effectively assess racial disparities in MM treatment access and survival. Disclosures Rusli: Acorn AI by Medidata, a Dassault Systemes Company: Current Employment, Current equity holder in publicly-traded company. Diao:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment. Liu:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment. Kelkar:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment. Ensign:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment, Current equity holder in publicly-traded company. Watson:Guardian Research Network, Inc.: Current Employment. Galaznik:Acorn AI by Medidata, a Dassault Systemes Company: Current Employment, Current equity holder in publicly-traded company.


Author(s):  
Ali Al-Ramini ◽  
Mohammad A Takallou ◽  
Daniel P Piatkowski ◽  
Fadi Alsaleem

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.


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