scholarly journals 201 Analysis of Social Determinants of Health Affecting Patient Outcomes during the SARS-CoV-2 (COVID-19) Pandemic in Elmhurst, New York

2020 ◽  
Vol 76 (4) ◽  
pp. S78
Author(s):  
D. Goodin ◽  
L. Wong ◽  
S. Bentley ◽  
R. Lane ◽  
G. Loo
Keyword(s):  
New York ◽  
Social Determinants Of Health ◽  
Patient Outcomes ◽  
Social Determinants ◽  
Determinants Of Health

scholarly journals Primary and Secondary Health Impacts of COVID-19 among Minority Individuals in New York State

2021 ◽  
Vol 18 (2) ◽  
pp. 683
Author(s):  
Lauren A. Clay ◽  
Stephanie Rogus
Keyword(s):  
New York ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
New York State ◽  
Determinants Of Health ◽  
Health Impacts ◽  
Minority Populations ◽  
York State ◽  
The Social ◽  
Chi Square Analysis

In addition to the direct health impacts of COVID-19, the pandemic disrupted economic, educational, healthcare, and social systems in the US. This cross-sectional study examined the primary and secondary impacts of the COVID-19 pandemic among low-income and minority groups in New York State using the social determinants of health framework. New Yorkers were recruited to complete a web-based survey through Qualtrics. The survey took place in May and June 2020 and asked respondents about COVID-19 health impacts, risk factors, and concerns. Chi-square analysis examined the health effects experienced by race and ethnicity, and significant results were analyzed in a series of logistic regression models. Results showed disparities in the primary and secondary impacts of COVID-19. The majority of differences were reported between Hispanic and white respondents. The largest differences, in terms of magnitude, were reported between other or multiracial respondents and white respondents. Given the disproportionate burden of COVID-19 on minority populations, improved policies and programs to address impacts on lower-paying essential jobs and service positions could reduce exposure risks and improve safety for minority populations. Future research can identify the long-term health consequences of the pandemic on the social determinants of health among populations most at risk.

Home Care Nurses Claim Medicare Ignores Social Determinants of Health

2019 ◽  
Vol 31 (4) ◽  
pp. 224-230 ◽  
Author(s):  
William Cabin
Keyword(s):  
New York ◽  
Home Care ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Home Health ◽  
Determinants Of Health ◽  
Additional Patient ◽  
Convenience Sample ◽  
Related Services ◽  
Home Care Nurses

There is significant literature on the importance of addressing social determinants of health (SDOH) in order to improve health care outcomes. In response, the Centers for Medicare and Medicaid Services (CMS) has expanded the ability of Medicare Advantage plans to cover SDOH-related services. Medicare home health does not cover SDOH-related services. A literature review indicates no studies on the nature, significance, or impacts of the lack of SDOH coverage in Medicare home health. The current study is an initial, exploratory study to address the literature gap, based on interviews of a convenience sample of 37 home care nurses between January 2013 and May 2014 in the New York City metropolitan area. Results indicate that nurses believe the lack of SDOH coverage in Medicare home health results in exacerbation of existing patient conditions; creation of new, additional patient conditions; increased home care readmissions and re-hospitalizations; increased caregiver burden; and exacerbation of patients’ mental health and substance abuse needs.

scholarly journals HOME CARE NURSES CLAIM MEDICARE IGNORES SOCIAL DETERMINANTS OF HEALTH

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S154-S154
Author(s):  
William D Cabin
Keyword(s):  
New York ◽  
Home Care ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Home Health ◽  
Determinants Of Health ◽  
Additional Patient ◽  
Convenience Sample ◽  
Related Services ◽  
Home Care Nurses

Abstract There is significant literature on the importance of addressing social determinants of health (SDOH) in order to improve health care outcomes. In response, the Centers for Medicare and Medicaid Services (CMS) has expanded Medicare Advantage plans ability to cover SDOH-related services. Medicare home health does not cover SDOH-related services. A literature review indicates no studies on the nature, significance, or impacts of the lack of SDOH coverage in Medicare home health. The current study is an initial, exploratory study to address the literature gap, based on interviews of a convenience sample of 37 home care nurses between January 2013 and May 2014 in the New York City metropolitan area. Results indicate nurses believe the lack of SDOH coverage in Medicare home health results in exacerbation of existing patient conditions; creation of new, additional patient conditions; increased home care readmissions and re-hospitalizations; increased caregiver burden; and exacerbation of patients’ mental health and substance abuse needs.

scholarly journals Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots

2020 ◽  
Author(s):  
Andrew Maroko ◽  
Denis Nash ◽  
Brian Pavilonis
Keyword(s):  
New York ◽  
New York City ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
York City ◽  
Hot Spots ◽  
Determinants Of Health ◽  
Cold Spots ◽  
The Impact ◽  
Zip Code

AbstractThere have been numerous reports that the impact of the ongoing COVID-19 epidemic has disproportionately impacted traditionally vulnerable communities, including well-researched social determinants of health, such as racial and ethnic minorities, migrants, and the economically challenged. The goal of this ecological cross-sectional study is to examine the demographic and economic nature of spatial hot and cold spots of SARS-CoV-2 rates in New York City and Chicago as of April 13, 2020.In both cities, cold spots (clusters of low SARS-CoV-2 rate ZIP code tabulation areas) demonstrated typical protective factors associated with the social determinants of health and the ability to social distance. These neighborhoods tended to be wealthier, have higher educational attainment, higher proportions of non-Hispanic white residents, and more workers in managerial occupations. Hot spots (clusters of high SARS-CoV-2 rate ZIP code tabulation areas) also had similarities, such as lower rates of college graduates and higher proportions of people of color. It also appears to be larger households (more people per household), rather than overall population density, that may to be a more strongly associated with hot spots.Findings suggest important differences between the cities’ hot spots as well. They can be generalized by describing the NYC hot spots as working-class and middle-income communities, perhaps indicative of service workers and other occupations (including those classified as “essential services” during the pandemic) that may not require a college degree but pay wages above poverty levels. Chicago’s hot spot neighborhoods, on the other hand, are among the city’s most vulnerable, low-income neighborhoods with extremely high rates of poverty, unemployment, and non-Hispanic Black residents.

Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators (Preprint)

2020 ◽  
Author(s):  
Andrew Deonarine ◽  
Genevieve Lyons ◽  
Chirag Lakhani ◽  
Walter De Brouwer
Keyword(s):  
New York ◽  
New York City ◽  
Built Environment ◽  
Social Determinants Of Health ◽  
Satellite Imagery ◽  
Risk Score ◽  
Social Determinants ◽  
York City ◽  
Complex Disease ◽  
Determinants Of Health

BACKGROUND Although it is well-known that older individuals with certain comorbidities are at the highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at the highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health. OBJECTIVE This study aims to develop a COVID-19 community risk score that summarizes complex disease prevalence together with age and sex, and compares the score to different social determinants of health indicators and built environment measures derived from satellite images using deep learning. METHODS We developed a robust COVID-19 community risk score (COVID-19 risk score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications such as death. We mapped the COVID-19 risk score to corresponding zip codes in New York City and associated the score with COVID-19–related death. We further modeled the variance of the COVID-19 risk score using satellite imagery and social determinants of health. RESULTS Using 2019 chronic disease data, the COVID-19 risk score described 85% of the variation in the co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28,000 census tract neighborhoods (median population size of tracts 4091). The COVID-19 risk score was associated with a 40% greater risk for COVID-19–related death across New York City (April and September 2020) for a 1 SD change in the score (risk ratio for 1 SD change in COVID-19 risk score 1.4; <i>P</i>&lt;.001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 risk score in the United States in census tracts (<i>r</i><sup>2</sup>=0.87). CONCLUSIONS The COVID-19 risk score localizes risk at the census tract level and was able to predict COVID-19–related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.

Adapting Survey Data Collection to Respond to the COVID-19 Pandemic: Experiences From a Local Health Department

2021 ◽  
Vol 111 (12) ◽  
pp. 2176-2185
Author(s):  
Amber Levanon Seligson ◽  
Karen A. Alroy ◽  
Michael Sanderson ◽  
Ariana N. Maleki ◽  
Steven Fernandez ◽  
...  
Keyword(s):  
Mental Health ◽  
New York ◽  
Social Determinants Of Health ◽  
Survey Data ◽  
Social Determinants ◽  
Health Department ◽  
Population Based ◽  
Determinants Of Health ◽  
Local Health ◽  
The Impact

The New York City (NYC) Department of Health and Mental Hygiene (“Health Department”) conducts routine surveys to describe the health of NYC residents. During the COVID-19 pandemic, the Health Department adjusted existing surveys and developed new ones to improve our understanding of the impact of the pandemic on physical health, mental health, and social determinants of health and to incorporate more explicit measures of racial inequities. The longstanding Community Health Survey was adapted in 2020 to ask questions about COVID-19 and recruit respondents for a population-based severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serosurvey. A new survey panel, Healthy NYC, was launched in June 2020 and is being used to collect data on COVID-19, mental health, and social determinants of health. In addition, 7 Health Opinion Polls were conducted from March 2020 through March 2021 to learn about COVID-19–related knowledge, attitudes, and opinions, including vaccine intentions. We describe the contributions that survey data have made to the emergency response in NYC in ways that address COVID-19 and the profound inequities of the pandemic. (Am J Public Health. 2021;111(12):2176–2185. https://doi.org/10.2105/AJPH.2021.306515 )

Integrating a Social Determinants of Health Screener at an Outpatient Pediatric Clinic in East Harlem, New York City

2021 ◽  
Vol 32 (4) ◽  
pp. 2267-2277
Author(s):  
Rebecca Rinehart ◽  
Lauren Zajac ◽  
Jennifer Acevedo ◽  
Rebecca Kann Victoria Mayer ◽  
Leora Mogilner
Keyword(s):  
New York ◽  
New York City ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
York City ◽  
Determinants Of Health ◽  
East Harlem ◽  
Pediatric Clinic

scholarly journals Clinical subphenotypes in COVID-19: derivation, validation, prediction, temporal patterns, and interaction with social determinants of health

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Chang Su ◽  
Yongkang Zhang ◽  
James H. Flory ◽  
Mark G. Weiner ◽  
Rainu Kaushal ◽  
...  
Keyword(s):  
New York ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Temporal Patterns ◽  
Determinants Of Health ◽  
Sensitivity Analyses ◽  
Medical Centers ◽  
Comorbidity Burden ◽  
Mortality Outcome ◽  
Different Populations

AbstractThe coronavirus disease 2019 (COVID-19) is heterogeneous and our understanding of the biological mechanisms of host response to the viral infection remains limited. Identification of meaningful clinical subphenotypes may benefit pathophysiological study, clinical practice, and clinical trials. Here, our aim was to derive and validate COVID-19 subphenotypes using machine learning and routinely collected clinical data, assess temporal patterns of these subphenotypes during the pandemic course, and examine their interaction with social determinants of health (SDoH). We retrospectively analyzed 14418 COVID-19 patients in five major medical centers in New York City (NYC), between March 1 and June 12, 2020. Using clustering analysis, 4 biologically distinct subphenotypes were derived in the development cohort (N = 8199). Importantly, the identified subphenotypes were highly predictive of clinical outcomes (especially 60-day mortality). Sensitivity analyses in the development cohort, and rederivation and prediction in the internal (N = 3519) and external (N = 3519) validation cohorts confirmed the reproducibility and usability of the subphenotypes. Further analyses showed varying subphenotype prevalence across the peak of the outbreak in NYC. We also found that SDoH specifically influenced mortality outcome in Subphenotype IV, which is associated with older age, worse clinical manifestation, and high comorbidity burden. Our findings may lead to a better understanding of how COVID-19 causes disease in different populations and potentially benefit clinical trial development. The temporal patterns and SDoH implications of the subphenotypes may add insights to health policy to reduce social disparity in the pandemic.

Sign in / Sign up

Export Citation Format

Share Document