The impact of patient navigation on racial and ethnic disparities: Results from the patient navigation research program.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. 6510-6510 ◽  
Author(s):  
Naomi Ko ◽  
Frederick Snyder ◽  
Peter C. Raich ◽  
Electra D. Paskett ◽  
Donald Dudley ◽  
...  
Keyword(s):  
Research Program ◽  
Patient Navigation ◽  
Ethnic Disparities ◽  
Racial And Ethnic Disparities ◽  
The Impact

scholarly journals Boston Patient Navigation Research Program: The Impact of Navigation on Time to Diagnostic Resolution after Abnormal Cancer Screening

2012 ◽  
Vol 21 (10) ◽  
pp. 1645-1654 ◽  
Author(s):  
Tracy A. Battaglia ◽  
Sharon M. Bak ◽  
Timothy Heeren ◽  
Clara A. Chen ◽  
Richard Kalish ◽  
...  
Keyword(s):  
Cancer Screening ◽  
Research Program ◽  
Patient Navigation ◽  
Diagnostic Resolution ◽  
The Impact

scholarly journals Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Kevin C Ma ◽  
Tigist F Menkir ◽  
Stephen M Kissler ◽  
Yonatan H Grad ◽  
Marc Lipsitch
Keyword(s):  
New York ◽  
Race And Ethnicity ◽  
Census Data ◽  
New York State ◽  
Herd Immunity ◽  
Ethnic Disparities ◽  
Racial And Ethnic Disparities ◽  
Epidemic Dynamics ◽  
Seroprevalence Data ◽  
The Impact

Background: The impact of variable infection risk by race and ethnicity on the dynamics of SARS CoV-2 spread is largely unknown. Methods: Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk changes across groups. Results: A simple model where interactions occur proportionally to contact rates reduced the HIT, but more realistic models of preferential mixing within groups increased the threshold toward the value observed in homogeneous populations. Across all models, the burden of infection fell disproportionately on minority populations: in a model fit to Long Island serosurvey and census data, 81% of Hispanics or Latinos were infected when the HIT was reached compared to 34% of non-Hispanic whites. Conclusions: Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in unequal distributions of SARS-CoV-2 infection. Funding: K.C.M. was supported by National Science Foundation GRFP grant DGE1745303. Y.H.G. and M.L. were funded by the Morris-Singer Foundation.

The impact of patient navigation on receipt of quality breast cancer treatment in the national patient navigation research program.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 72-72 ◽  
Author(s):  
Naomi Ko ◽  
Tracy Ann Battaglia ◽  
Julie Darnell ◽  
Elizabeth Calhoun ◽  
Frederick Snyder ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Radiation Therapy ◽  
Cancer Patients ◽  
Hormonal Therapy ◽  
Research Program ◽  
Patient Navigation ◽  
Quality Care ◽  
Recommended Care ◽  
National Patient ◽  
The Impact

72 Background: The discrepancy in breast cancer outcomes for underserved populations has been linked to lack of receipt of quality treatment. Patient navigation programs are being rapidly adopted as a model to improve cancer outcomes for these vulnerable populations, yet the effect of navigation on their quality of cancer care is unknown. Methods: We conducted a secondary analysis of the National Patient Navigation Research Program (PNRP) data to assess the impact of navigation on receipt of quality care among women diagnosed with breast cancer. Data pooled from 7 PRNP sites were used to determine the proportion of newly diagnosed cancer patients whose care met National Comprehensive Cancer Network (NCCN) quality metrics: 1) hormonal therapy for HR+ patients 2) post-lumpectomy radiation therapy; and 3) chemotherapy for hormone negative, >1cm tumors, in patients <70 years of age. Chi-square tests were performed to compare probability of receiving recommended care among navigated and control patients. Results: A total of 1,006 breast cancer patients eligible for treatment were enrolled across all sites: 491 (49%) in the intervention arm, 515 (51%) in the control arm (mean age: 56 years; 38% African American, 23% Hispanic; 13% uninsured and 38% Medicaid). Among those eligible for hormone therapy, 283/357 (79%) navigated patients received hormonal therapy compared to 237/371 (64%) of controls (p < 0.001). Among those eligible for radiation therapy post lumpectomy, 235/277 (85%) of navigated patients received radiation compared to 270/324 (83%) of controls (p=0.62). Among those eligible for chemotherapy, 79/122 (65%) of navigated patients received chemotherapy compared to 81/100 (81%) of controls (p < 0.007). Logistic regression models to determine the odds of receiving recommended care for navigated and non-navigated patients, adjusting for patient demographics, will be conducted. Conclusions: Navigation had a positive effect for receipt of hormonal therapy, but not for radiation therapy and chemotherapy. Future studies are needed to assess the role navigation may play in ensuring quality care for the most vulnerable.

scholarly journals The Impact of Insurance Coverage in Diminishing Racial and Ethnic Disparities in Behavioral Health Services

2012 ◽  
Vol 47 (3pt2) ◽  
pp. 1322-1344 ◽  
Author(s):  
Margarita Alegria ◽  
Julia Lin ◽  
Chih-Nan Chen ◽  
Naihua Duan ◽  
Benjamin Cook ◽  
...  
Keyword(s):  
Health Services ◽  
Behavioral Health ◽  
Insurance Coverage ◽  
Ethnic Disparities ◽  
Racial And Ethnic Disparities ◽  
Behavioral Health Services ◽  
The Impact

A Method to Measure the Impact of Primary Care Programs Targeted to Reduce Racial and Ethnic Disparities in Health Outcomes

2003 ◽  
Vol 9 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Stephen L. Luther ◽  
James Studnicki ◽  
Jeffrey Kromrey ◽  
Kathleen Lomando-Frakes ◽  
Pauline Grant ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Outcomes ◽  
Ethnic Disparities ◽  
Racial And Ethnic Disparities ◽  
The Impact

scholarly journals Racial disparities in prevalence, determinants, and impacts of COVID-19 in pregnancy: Protocol for a study using data from New Jersey hospitals

2021 ◽  
Author(s):  
Slawa Rokicki ◽  
Pauline Nguyen ◽  
Alaine Sharpe ◽  
Dyese Taylor ◽  
Suzanne Spernal ◽  
...  
Keyword(s):  
New Jersey ◽  
Pregnant Women ◽  
Ethnic Disparities ◽  
The United States ◽  
Policy Implications ◽  
Future Research ◽  
Multivariable Logistic Regression Analysis ◽  
Racial And Ethnic Disparities ◽  
Infant Birth ◽  
The Impact

Introduction Racial and ethnic disparities in COVID-19 related infections, hospitalizations, and deaths have been well-documented. However, little research has examined racial and ethnic disparities in COVID-19 prevalence, determinants, and impacts among pregnant women. Within the United States, New Jersey was an early epicenter of the pandemic and experienced high rates of disease in the fall of 2020. Methods This study uses data from two New Jersey hospitals, which implemented universal testing of COVID-19 of pregnant women admitted for labor and delivery starting in March 2020. We will estimate prevalence of COVID-19 between March 2020 and November 2020 and compare prevalence rates across race and ethnicity. We will conduct multivariable logistic regression analysis to examine the associations of COVID-19 infection with patient demographic and health status predictors. We will also use multivariable linear and logistic regressions to examine the impact of COVID-19 symptomatic and asymptomatic infection on maternal and infant birth outcomes. Discussion This study will generate important policy implications on birth equity in the time of COVID-19 and guide future research studies related to COVID-19 in pregnant women. Results of this study will help to guide interventions and policies to center safe, accessible, and equitable maternity care within the strategic response to the pandemic.

scholarly journals Modeling the impact of racial and ethnic disparities on COVID-19 epidemic dynamics

2021 ◽  
Author(s):  
Kevin C. Ma ◽  
Tigist F. Menkir ◽  
Stephen Kissler ◽  
Yonatan H. Grad ◽  
Marc Lipsitch
Keyword(s):  
New York ◽  
Race And Ethnicity ◽  
Census Data ◽  
New York State ◽  
Herd Immunity ◽  
Ethnic Disparities ◽  
Mixing Model ◽  
Racial And Ethnic Disparities ◽  
Seroprevalence Data ◽  
The Impact

AbstractThe impact of variable infection risk by race and ethnicity on the dynamics of SARS-CoV-2 spread is largely unknown. Here, we fit structured compartmental models to seroprevalence data from New York State and analyze how herd immunity thresholds (HITs), final sizes, and epidemic risk changes across racial and ethnic groups. A proportionate mixing model reduced the overall HIT, but more realistic levels of assortative mixing increased the threshold. Across all models, the burden of infection fell disproportionately on minority populations: in an assortative mixing model fit to Long Island census data, 80% of Hispanics or Latinos were infected when the HIT is reached compared to 33% of non-Hispanic whites. Our findings, which are meant to be illustrative and not best estimates, demonstrate how racial and ethnic disparities can impact epidemic trajectories and result in a dis-proportionate distribution of the burden of SARS-CoV-2 infection.

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