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Showing content from https://pubmed.ncbi.nlm.nih.gov/26348120/ below:

Impact of patient navigation in eliminating economic disparities in cancer care

. 2015 Nov 15;121(22):4025-34. doi: 10.1002/cncr.29612. Epub 2015 Sep 8. Impact of patient navigation in eliminating economic disparities in cancer care Susan K Parsons¹, Frederick Snyder², Melissa A Simon^{3 4 5}, Adana A M Llanos^{6 7}, Victoria Warren-Mears⁸, Donald Dudley⁹, Ji-Hyun Lee^{10 11}, Steven R Patierno^{12 13}, Talar W Markossian¹⁴, Mechelle Sanders¹⁵, Elizabeth M Whitley¹⁶, Karen M Freund¹

Affiliations

¹ Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Boston, Massachusetts.
² NOVA Research Company, Silver Spring, Maryland.
³ Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
⁴ Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
⁵ Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois.
⁶ Rutgers School of Public Health, Piscataway, New Jersey.
⁷ Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.
⁸ Northwest Tribal Epidemiology Center, Northwest Portland Area Indian Health Board, Portland, Oregon.
⁹ Department of Obstetrics and Gynecology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
¹⁰ Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
¹¹ University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico.
¹² George Washington University Cancer Institute, Washington, DC.
¹³ Duke Cancer Institute, Durham, North Carolina.
¹⁴ Public Health Sciences, Loyola University Chicago, Chicago, Illinois.
¹⁵ Department of Family Medicine, Department of Public Health Sciences, University of Rochester, Rochester, New York.
¹⁶ Colorado Department of Public Health and Environment, Denver, Colorado.

PMID: 26348120
PMCID: PMC4635033
DOI: 10.1002/cncr.29612

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Impact of patient navigation in eliminating economic disparities in cancer care

Angie Mae Rodday et al. Cancer. 2015.

. 2015 Nov 15;121(22):4025-34. doi: 10.1002/cncr.29612. Epub 2015 Sep 8. Authors Angie Mae Rodday¹, Susan K Parsons¹, Frederick Snyder², Melissa A Simon^{3 4 5}, Adana A M Llanos^{6 7}, Victoria Warren-Mears⁸, Donald Dudley⁹, Ji-Hyun Lee^{10 11}, Steven R Patierno^{12 13}, Talar W Markossian¹⁴, Mechelle Sanders¹⁵, Elizabeth M Whitley¹⁶, Karen M Freund¹ Affiliations

¹ Institute for Clinical Research and Health Policy Studies, Tufts Medical Center and Tufts University School of Medicine, Boston, Massachusetts.
² NOVA Research Company, Silver Spring, Maryland.
³ Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
⁴ Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
⁵ Robert H. Lurie Comprehensive Cancer Center, Chicago, Illinois.
⁶ Rutgers School of Public Health, Piscataway, New Jersey.
⁷ Rutgers Cancer Institute of New Jersey, New Brunswick, New Jersey.
⁸ Northwest Tribal Epidemiology Center, Northwest Portland Area Indian Health Board, Portland, Oregon.
⁹ Department of Obstetrics and Gynecology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
¹⁰ Department of Internal Medicine, University of New Mexico, Albuquerque, New Mexico.
¹¹ University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico.
¹² George Washington University Cancer Institute, Washington, DC.
¹³ Duke Cancer Institute, Durham, North Carolina.
¹⁴ Public Health Sciences, Loyola University Chicago, Chicago, Illinois.
¹⁵ Department of Family Medicine, Department of Public Health Sciences, University of Rochester, Rochester, New York.
¹⁶ Colorado Department of Public Health and Environment, Denver, Colorado.

PMID: 26348120
PMCID: PMC4635033
DOI: 10.1002/cncr.29612

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Abstract

Background: Patient navigation may reduce cancer disparities associated with socioeconomic status (SES) and household factors. This study examined whether these factors were associated with delays in diagnostic resolution among patients with cancer screening abnormalities and whether patient navigation ameliorated these delays.

Methods: This study analyzed data from 5 of 10 centers of the National Cancer Institute's Patient Navigation Research Program, which collected SES and household data on employment, income, education, housing, marital status, and household composition. The primary outcome was the time to diagnostic resolution after a cancer screening abnormality. Separate adjusted Cox proportional hazard models were fit for each SES and household factor, and an interaction between that factor and the intervention status was included.

Results: Among the 3777 participants (1968 in the control arm and 1809 in the navigation intervention arm), 91% were women, and the mean age was 44 years; 43% were Hispanic, 28% were white, and 27% were African American. Within the control arm, the unemployed experienced a longer time to resolution than those employed full-time (hazard ratio [HR], 0.85; P = .02). Renters (HR, 0.81; P = .02) and those with other (ie, unstable) housing (HR, 0.60; P < .001) had delays in comparison with homeowners. Never married (HR, 0.70; P < .001) and previously married participants (HR, 0.85; P = .03) had a longer time to care than married participants. There were no differences in the time to diagnostic resolution with any of these variables within the navigation intervention arm.

Conclusions: Delays in diagnostic resolution exist by employment, housing type, and marital status. Patient navigation eliminated these disparities in the study sample. These findings demonstrate the value of providing patient navigation to patients at high risk for delays in cancer care.

Trial registration: ClinicalTrials.gov NCT00375024 NCT00496678 NCT00613275 NCT01569672.

Keywords: breast neoplasms; colonic neoplasms; early detection of cancer; health care disparities; patient navigation; prostatic neoplasms; uterine cervical neoplasms.

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Figures
Figure 1

Kaplan-Meier curves for impact of…

Figure 1

Kaplan-Meier curves for impact of patient navigation and marital status on time to…

Figure 1

Kaplan-Meier curves for impact of patient navigation and marital status on time to diagnostic resolution following an abnormal cancer screening event.

Figure 2

Kaplan-Meier curves for impact of…

Figure 2

Kaplan-Meier curves for impact of patient navigation and housing status on time to…

Figure 2

Kaplan-Meier curves for impact of patient navigation and housing status on time to diagnostic resolution following an abnormal cancer screening event.