Revealing the Unequal Burden of COVID-19 by Income, Race/Ethnicity, and Household Crowding: US County Versus Zip Code Analyses

6594 Background: Patient reported financial hardship (FH) in cancer care is a growing challenge for patients, their caregivers and healthcare providers. As treatment costs escalate, it is imperative to develop effective strategies to proactively recognize and mitigate FH within oncology practice. Using automated processes to screen and refer patients to appropriate resources is a potential option. At Mayo Clinic, screening for FH involves using a single financial strain question ‘ How hard is it for you to pay for the very basics like food, housing, medical care, and heating?’ completed by all cancer patients annually as part of the Social Determinants of Health (SDOH) assessment. In this study, we describe the prevalence and predictors for FH (denoted by the answer ‘hard and very hard’) in our patient population. Methods: Patients receiving cancer care at the three Mayo Clinic sites (Minnesota, Arizona, and Florida) who completed the FH screen at least once were included in this study. Demographics (age, gender, race/ ethnicity, insurance, employment status, marital status, and zip code) and disease state data for included patients was extracted from the EMR and Mayo Clinic Cancer Registry. Disease state was categorized by type of cancer (hematological or solid malignancy) and cancer stage. Zip code was used to derive median income, rural/urban residence and distance from the cancer center. Multivariable logistic regression models were utilized to examine factors associated with FH. Results: The final study cohort included 31,969 patients with median age 66 years (IQR 57,73), 51% females, and 76% married. Race/ethnicity composition was 93% White, 3% Black, and 4% Hispanic. 52% of patients had Medicare and 43% had commercial insurance. Other notable factors included 48% retired, 41% working/ students, 76% married, and 72% urban residents. Median time from cancer diagnosis was 1.1 year (IQR 0.1, 3.8) and median income was $64,406 (IQR 53,067, 82,038). 31% of patients had hematological malignancies, 20% of the cancers for which staging information was available were metastatic. FH was reported by 4% (n = 1194) of the patients. A significantly higher likelihood of endorsing FH (p < 0.001 for all) was noted in Hispanic (OR 1.64), Black (OR 1.84), American Indian/Alaskan native (OR 2.02), below median income (OR 1.48), rural (OR 1.17), self-pay (OR 2.77), Medicaid (OR 2.29), Medicare (OR 1.43), unemployed/disabled (OR 2.39), single (OR 2.07), or divorced (OR 2.43) patients. Older age, being retired, and living farther from the cancer center were associated with significantly less likelihood of endorsing FH. Conclusions: Our study successfully leveraged the EMR to identify key sociodemographic groups more likely to report FH. An electronic trigger to flag such patients at high-risk of FH and proactively address FH is currently being developed.

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Trends in medical and surgical admission length of stay by race/ethnicity and socioeconomic status: a time series analysis

10.1101/2021.05.27.21257925 ◽

2021 ◽

Author(s):

Arnab K Ghosh ◽

Orysya Soroka ◽

Mark A Unruh ◽

Martin Shapiro

Keyword(s):

Socioeconomic Status ◽

Length Of Stay ◽

Median Income ◽

Adverse Health Outcomes ◽

Race Ethnicity ◽

The Impact ◽

Racial Ethnic ◽

Fourth Quartile ◽

Zip Code ◽

Surgical Admission

Length of stay, a metric of hospital efficiency, differs by race/ethnicity and socioeconomic status (SES). Longer LOS is associated with adverse health outcomes. We assessed differences in average adjusted length of stay (aALOS) over time by race/ethnicity, and SES stratified by discharge destination (home or non-home). Using the 2009-2014 State Inpatient Datasets from three states, we examined trends in aALOS differences by race/ethnicity, and SES (defined first vs fourth quartile of median income by zip code) controlling for patient, disease and hospital characteristics. For those discharged home, racial/ethnic and SES aALOS differences remained stable. Notably, for those discharged to non-home destinations, Black vs White, and low vs high SES aALOS differences increased significantly from 2009 to 2013, more sharply after Q3 2011, the introduction of the Affordable Care Act (ACA). Further research to understand the impact of the ACA on hospital efficiencies, and relationship to racial/ethnic and SES differences in LOS is warranted.

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Ensuring Confidentiality and Rich Data in Large Data Sets: Birth Rates vary between Warm and Cold Seasons in the US Population

10.31235/osf.io/xz8et ◽

2019 ◽

Author(s):

Carlos Siordia ◽

Ophra Leyser-Whalen

Keyword(s):

Birth Rate ◽

Large Data ◽

Large Data Sets ◽

Data Sets ◽

Homogeneity Assumption ◽

The Us ◽

Timing Of Births ◽

Race Ethnicity ◽

Rich Data ◽

Zip Code

Previous work argues that confidentiality is compromised by using an individual’s sex, full date of birth, and US zip code. With use of the American Community Survey we test this assumption while maintaining participant confidentiality to study how timing of births vary by season, region, race/ethnicity, origin, sex, and birth cohort. We found that region and demographic factors help explain the likelihood for giving birth in warm months, which provides evidence contrary to the birth-rate temporal-homogeneity assumption.

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From Crisis to Progress: Housing and Latino Youth since 2000

The Annals of the American Academy of Political and Social Science ◽

10.1177/00027162211041364 ◽

2021 ◽

Vol 696 (1) ◽

pp. 46-78

Author(s):

Jacob S. Rugh

Keyword(s):

Latino Youth ◽

The Novel ◽

Housing Conditions ◽

National Data ◽

Broadband Internet ◽

Nativity Status ◽

The Past ◽

Household Crowding ◽

Novel Coronavirus ◽

Zip Code

Latino youth housing conditions have transformed dramatically over the past 20 years. Rates of household crowding have plummeted, nearly all Latino children are U.S.-born citizens, and broadband Internet access is widespread. However, Latino youth remain disadvantaged and their housing conditions remain understudied as they come of age in an era of housing crises, from foreclosures, evictions, to the novel coronavirus pandemic. This article examines Latino youth housing conditions since 2000, including crowding and mixed-nativity/status households. Multivariate analyses of national data show that eviction, foreclosure, and a representative zip code sample of COVID-19 case rates are strongly linked to the housing conditions of Latino youth. The article illustrates these links by analyzing and mapping eviction rates, foreclosure rates, and zip code coronavirus cases in the census tracts of Maricopa County, Arizona. The results underscore the urgent need for policies that invest in housing Latino youth to ensure that progress of the last 20 years is lasting.

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COVID-19 and exacerbation of screening mammography inequities.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.6543 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 6543-6543

Author(s):

Gary X. Wang ◽

Jarvis Chen ◽

Leslie Lamb ◽

Christian Testa ◽

Pamela Waterman ◽

...

Keyword(s):

Low Income ◽

Screening Program ◽

Tertiary Care ◽

Screening Mammography ◽

Population Composition ◽

Chi Square ◽

P Values ◽

Increased Risk ◽

Race Ethnicity ◽

Zip Code

6543 Background: After state-mandated cessation of screening mammography (SM) in Spring 2020 due to COVID-19, centers were urged to resume screening, particularly of patients at increased risk. As our tertiary-care medical center’s screening program provides SM at four sites across our metropolitan area, we examined whether sites that historically served more patients from more disadvantaged areas returned slower to pre-COVID volumes. Methods: Patient records were linked by ZIP code of residence to ZIP Code Tabulation Area (ZCTA)-level area-based social metrics (ABSMs) from the 2014-2018 American Community Survey. We compared baseline pre-COVID (May-October, 2015-2019) SM population ABSMs between our four imaging sites for: % persons below poverty (≥ vs < 10%), % persons of color (POC) (quintiles: top 2 vs bottom 3), index of racialized economic segregation (quintiles: bottom 2 [more POC low-income households] vs top 3 [more white non-Hispanic (WNH) high-income households]); and race/ethnicity (% WNH vs POC). We modeled weekly SM volumes per screening day by site using Poisson regression and tested for weekly differences at each site, COVID-era (May-October 2020) vs pre-COVID; and tested for monthly differences in SM population composition by logistic regression modeling. Results: There were 89,082 pre-COVID and 16,220 COVID-era SM exams. At pre-COVID baselines the four sites differed in population composition by ABSMs and race/ethnicity (all chi-square P values <.001) (Table). The two sites that served more disadvantaged populations (A, B) returned slower to pre-COVID volumes (site-specific weekly screening volume no longer different [ P >.05] vs pre-COVID) (Table). As a result, compositions of the aggregate SM population across all sites showed a smaller proportion of patients from the most disadvantaged ZCTAs by ABSMs (all P values <.001) before returning to pre-COVID compositions three months after SM resumption. Conclusions: SM was slower to return to pre-COVID volumes at imaging sites that historically served lower-income communities of color. As a result, our COVID-era SM population skewed away from patients in disadvantaged ZCTAs. Our findings highlight the need to monitor for emergent disparities in the pandemic era. Future work will focus on understanding causes of inequitable SM engagement across our imaging sites to mitigate care disparities for our most vulnerable patients.[Table: see text]

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Association of Socioeconomic Status, Race and Insurance Status with Chronic Rhinosinusitis Patient-Reported Outcome Measures

Otolaryngology ◽

10.1177/0194599817745269 ◽

2017 ◽

Vol 158 (3) ◽

pp. 571-579 ◽

Cited By ~ 9

Author(s):

Regan W. Bergmark ◽

Lloyd P. Hoehle ◽

Darius Chyou ◽

Katie M. Phillips ◽

David S. Caradonna ◽

...

Keyword(s):

Health Status ◽

Chronic Rhinosinusitis ◽

General Health ◽

Predictor Variables ◽

Insurance Status ◽

General Health Status ◽

Patient Reported ◽

Race Ethnicity ◽

Zip Code ◽

Snot 22

Objective Disparities in health and health care access are widely prevalent. However, disparities among patients with chronic rhinosinusitis (CRS) are poorly understood. We investigated if CRS severity at presentation according to socioeconomic factors. Study Design Cross-sectional study. Setting Tertiary rhinology center. Subjects and Methods Three hundred prospectively recruited patients presenting with CRS were included. Outcome variables included CRS symptomatology, as reflected by the 22-item Sinonasal Outcome Test (SNOT-22); general health status, as reflected by the EuroQol 5-dimensional visual analog scale (EQ-5D VAS); and CRS-related antibiotic and systemic corticosteroid use. Race/ethnicity, zip code income bracket, education level, and insurance status were used as predictor variables. Regression, controlling for clinical and demographic characteristics, was used to determine associations between predictor and outcome variables. Results Mean SNOT-22 score was 33.8 (SD, 23.2), and mean EQ-5D VAS score was 74.2 (SD, 18.9). On multivariable analysis, presenting SNOT-22 and EQ-5D VAS scores were not associated with nonwhite patient race/ethnicity ( P = .634 and P = .866), education ( P = .106 and P = .586), or the percentage of households in zip code with incomes <$50,000 per year ( P = .917 and P = .979, respectively). SNOT-22 scores did not differ by insurance type, but patients receiving Medicare reported worse general health status. Use of oral antibiotics or oral steroids for CRS was not associated with predictor variables. Conclusion Patients with CRS presented to a tertiary rhinology center with similar metrics for CRS severity and pre-presentation medical management regardless of race/ethnicity, education status, or zip code income level. Patients with Medicare had worse general health status. Further research should investigate potential disparities in diagnosis of CRS, specialist referral, and treatment outcomes.

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Social Determinants Predict Outcomes in Data From a Multi-Ethnic Cohort of 20,899 Patients Investigated for COVID-19

Frontiers in Public Health ◽

10.3389/fpubh.2020.571364 ◽

2020 ◽

Vol 8 ◽

Author(s):

Dara J. Lundon ◽

Nihal Mohamed ◽

Anna Lantz ◽

Heather H. Goltz ◽

Brian D. Kelly ◽

...

Keyword(s):

At Risk ◽

New York ◽

Social Determinants ◽

Disease Burden ◽

Close Contact ◽

Cross Sectional ◽

History Of ◽

Race Ethnicity ◽

Sectional Analysis ◽

Zip Code

Importance: The COVID-19 pandemic exploits existing inequalities in social determinants of health (SDOH) in disease burden and access to healthcare. Few studies have examined these emerging disparities using indicators of SDOH.Objective: To evaluate predictors of COVID-19 test positivity, morbidity, and mortality and their implications for inequalities in SDOH and for future policies and health care improvements.Design, Setting, and Participants: A cross sectional analysis was performed on all patients tested for COVID-19 on the basis of symptoms with either a history of travel to at risk regions or close contact with a confirmed case, across the Mount Sinai Health System (MSHS) up until April 26th 2020.Main Outcomes and Measures: Primary outcome was death from COVID-19 and secondary outcomes were test positivity, and morbidity (e.g., hospitalization and intubation caused by COVID-19).Results: Of 20,899 tested patients, 8,928 tested positive, 1,701 were hospitalized, 684 were intubated, and 1,179 died from COVID-19. Age, sex, race/ethnicity, New York City borough (derived from first 3 digits of zip-code), and English as preferred language were significant predictors of test positivity, hospitalization, intubation and COVID-19 mortality following multivariable logistic regression analyses.Conclusions and Relevance: People residing in poorer boroughs were more likely to be burdened by and die from COVID-19. Our results highlight the importance of integrating comprehensive SDOH data into healthcare efforts with at-risk patient populations.

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Impact of Race on Care, Readmissions, and Survival for Patients with Glioblastoma: An Analysis of the National Cancer Database

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab040 ◽

2021 ◽

Author(s):

Tiffany R Hodges ◽

Collin M Labak ◽

Uma V Mahajan ◽

Christina Huang Wright ◽

James Wright ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Extent Of Resection ◽

Demographic Variables ◽

National Cancer Database ◽

Facility Type ◽

Race Ethnicity ◽

Ethnic Factors ◽

Racial Ethnic ◽

Zip Code

Abstract Background The objective of this study was to explore racial/ethnic factors that may be associated with survival in patients with glioblastoma by querying the National Cancer Database (NCDB). Methods The NCDB was queried for patients diagnosed with glioblastoma between 2004 and 2014. Patient demographic variables included age at diagnosis, sex, race, ethnicity, Charlson-Deyo score, insurance status, and rural/urban/metropolitan location of zip code. Treatment variables included surgical treatment, extent of resection, chemotherapy, radiation therapy, type of radiation, and treatment facility type. Outcomes included 30-day readmission, 30- and 90-day mortality, and overall survival. Multivariable Cox regression analyses were performed to evaluate variables associated with race and overall survival. Results A total of 103,652 glioblastoma patients were identified. There was a difference in the proportion of patients for whom surgery was performed, as well as proportion receiving radiation, when stratified by race (p<0.001). Black non-Hispanics had the highest rates of unplanned re-admission (7.6%) within 30 days (OR: 1.39 compared to White Non-Hispanics, p<0.001). Asian Non-Hispanics had the lowest 30-day (3.2%) and 90-day mortality (9.8%) when compared to other races (OR: 0.52 compared to White Non-Hispanics, p=0.031). Compared to White Non-Hispanics, we found Black Non-Hispanics (HR: 0.88, p<0.001); Asian Non-Hispanics (HR: 0.72, p<0.001), and Hispanics (HR: 0.69, p<0.001) longer overall survival. Conclusion Differences in treatment and outcomes exist between races. Further studies are needed to elucidate the etiology of these race-related disparities and to improve outcomes for all patients.

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COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020

American Journal of Public Health ◽

10.2105/ajph.2020.305913 ◽

2020 ◽

Vol 110 (12) ◽

pp. 1850-1852 ◽

Cited By ~ 1

Author(s):

Nancy Krieger ◽

Pamela D. Waterman ◽

Jarvis T. Chen

Keyword(s):

Mortality Rate ◽

Time Intervals ◽

Death Rates ◽

Economic Segregation ◽

Social Metrics ◽

Household Crowding ◽

Rate Ratios ◽

Socioeconomic Data ◽

Zip Code ◽

Ethnicity Data

Objectives. To address evidence gaps in COVID-19 mortality inequities resulting from inadequate race/ethnicity data and no socioeconomic data. Methods. We analyzed age-standardized death rates in Massachusetts by weekly time intervals, comparing rates for January 1 to May 19, 2020, with the corresponding historical average for 2015 to 2019 stratified by zip code social metrics. Results. At the surge peak (week 16, April 15–21), mortality rate ratios (comparing 2020 vs 2015–2019) were 2.2 (95% confidence interval [CI] = 1.4, 3.5) and 2.7 (95% CI = 1.4, 5.5) for the lowest and highest zip code tabulation area (ZCTA) poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. Similarly, rate ratios were significantly elevated for the highest versus lowest quintiles with respect to household crowding (1.7; 95% CI = 1.0, 2.9), racialized economic segregation (3.1; 95% CI = 1.8, 5.3), and percentage population of color (1.8; 95% CI = 1.6, 2.0). Conclusions. The COVID-19 mortality surge exhibited large inequities. Public Health Implications. Using zip code social metrics can guide equity-oriented COVID-19 prevention and mitigation efforts.

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Demographic, Lifestyle, and Dietary Factors Associated with Red Meat Consumption Among A Cross-Section of New York City Residents

International Journal of Disease Reversal and Prevention ◽

10.22230/ijdrp.2021v3n2a301 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Eric Brandt ◽

Andrew Levin ◽

Margaret Holland ◽

Leah Ferrucci

Keyword(s):

New York ◽

New York City ◽

York City ◽

Meat Consumption ◽

Red Meat ◽

Dietary Factors ◽

P Value ◽

Factors Associated ◽

Race Ethnicity ◽

Zip Code

Introduction: Red meat reduction policies have become the focus of public policy in New York City (NYC). To inform on who might be impacted the most by these policies we sought to identify factors associated with red meat consumption among NYC residents.Methods: We studied non-institutionalized adults in the cross-sectional 2013-2014 NYC Health and Nutrition Examination Survey. The outcome was self-reported weekly red meat consumption. We used multivariable linear regression to assess the association of red meat consumption with age, gender, race/ethnicity, US nativity, education, marital status, percentage of ZIP-code in poverty, physical activity, smoking, alcohol, restaurant meals, and dietary components (dark-green vegetables, other vegetables, fresh fruit, poultry, fish/shellfish, sugar-sweetened soda (SSS), and sugar-sweetened fruit drink (SSFD)).Results: Among 1,495 subjects, higher frequency of red meat consumption was associated (-coefficient; p-value) with younger age (-0.08; p=.03), male gender (0.47; p<.001), and greater weekly consumption of alcohol (0.08; p<.001), poultry (0.16; p<.001), fish/shellfish (0.15; p=.01), SSS (0.14; p<.001), and SSFD (0.06; p=.005). Red meat consumption was also associated with race/ethnicity (p=.002), wherein Asian race/ethnicity had highest consumption and ZIP-code percent in poverty (p=.003) wherein those in ZIP-codes with ≥30% in poverty consumed the least red meat.Conclusion: Demographic, lifestyle, and dietary factors were associated with red meat consumption frequency in NYC. Public health efforts in NYC should consider these associations and differences from associations in national data when designing and evaluating outcomes from programs targeting reducing red meat consumption in NYC.

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