{"@context":"https://schema.org","@type":"ScholarlyArticle","headline":"Abstract IA38: Patient navigation in medically underserved areas","author":[{"@type":"Person","name":"E. Calhoun"},{"@type":"Person","name":"Heather A. Pauls"},{"@type":"Person","name":"G. Vijayasiri"},{"@type":"Person","name":"J. Darnell"},{"@type":"Person","name":"Y. Molina"},{"@type":"Person","name":"Nerida M. Berrios"},{"@type":"Person","name":"R. Warnecke"},{"@type":"Person","name":"R. Campbell"}],"datePublished":"2015","isPartOf":{"@type":"Periodical","name":"Cancer Epidemiology, Biomarkers & Prevention"},"identifier":"https://doi.org/10.1158/1538-7755.DISP14-IA38","description":"Abstract\n Purpose: The Chicago Patient Navigation in Medically Underserved Areas Study, a large scale randomized trial of patient navigation, involving women who made appointments at one of three Chicago-area medical centers for either screening or diagnostic mammograms/services. The principal outcome measure is the time required to come to diagnostic resolution for women whose initial mammograms results in BIRAD values of 0,3,4 or 5. Our goal was to evaluate the effectiveness of patient navigation but also to investigate the effect of living in an officially designated Medically Underserved Area (MUA).\n Background: MUAs are designated by the Health Research Services Administration (HRSA). Areas to be considered for designation are defined as rational service areas (RSA), which in urban areas are composed of contiguous census tracts. Four elements are considered (1) the ratio of primary medical care physicians per 1,000 population, (2) the infant mortality rate, (3) the percentage of the population with income below the poverty level, and (4) the percent of the population 65 years and older. Each of the four elements is scored separately and then summed; the resulting score can range from 0 to 100. Areas with an IMU below 62 qualify for designation. Consideration for MUA status must be requested by representatives from the population of the eligible areas. Thus, designation requires that a local community organization advocates for establishment of the MUA. As a result, in Chicago, many census tracts are eligible for MUA status but are not so designated.\n Methods: All women who had an initial referral from a physician for screening or for diagnostic mammography following a clinical breast exam were randomly assigned to either the control or navigation arm of the study. Randomization was balanced by age group. Women assigned to navigation were recruited if they had a working telephone and gave informed consent. Electronic medical record (EMR) data were made available by the cooperating medical centers for all women. All women in the navigation arm and a small subsample of women in the control arm also responded to a series of questionnaires collecting background data and tracking their progress through the breast care cycle. Each woman's address was geocoded and the census tract was coded for MUA status: (1) not eligible, (2) old MUA designated before the year 2000, (3) new MUA designated in 2000 or after and (4) eligible but not designated. For this analysis, the primary outcome variable is the number of days ensuing between the date of the initial examination and the date on which a firm diagnosis of cancer or no cancer was obtained. The analysis sample consisted of all women with initial BIRAD values of 0, 3, 4 or 5. Women with BIRAD 3 are normally asked to return for re-examination in six months and in their case the number of days till resolution was adjusted to count the number of days following the suggested return date. Over the course of the study some women were observed over several exam cycles. This analysis focuses on the first cycle only. All analysis were conducted using the Cox proportional hazard model. Analyses were stratified by the initial BIRAD value.\n Results: After excluding a small number of women with missing clinical data, 5660 cases were available for analysis. Of these, 3567 cases were excluded because their initial BIRAD value was 1 or 2. For the remaining 2093 cases involving 223022 person-days at risk we obtained resolution dates for 1810 and the remainder were lost to follow up in that we did not observe a diagnostic resolution date. These cases were considered to be censored. The maximum number of days observed was 741. Variables in the Cox model were indicator variables for navigated versus control and screening versus diagnostic, a set of indicator variables for MUA status (reference group New MUA), a set of indicator variables for insurance status (private, Medicare, Medicaid or uninsured, reference group private) and marital status (single, married, divorced/separated and widowed, reference group single). Age was found to be unrelated to the rate of resolution and was not included in the final model. Women who were navigated obtained diagnostic resolution more quickly (HR 1.13; CI 1.02, 1.24; p .017) than women in the control arm. Women seen for diagnostic mammography had much faster resolution times (HR 1.88, CI 1.67, 2.09; p .<.000) than women initially seen for screening. Women who lived in areas designated as MUAs prior to 2000 experienced slower rates of diagnostic resolution than women in the reference group (HR .81; CI .67, .97;p .025). Other groups did not differ. Relative to women with private insurance, uninsured women or those on Medicaid had slower times to resolution (HR .78; CI .65, .95; p .011). None of the marital status groups differed from the reference group (single) at p <= .05 however married or divorced or separated women experienced marginally faster resolution times (p = .062 and .079 respectively).\n Conclusion: Despite years of gains in cancer screening, diagnosis, and treatment, certain populations continue to disproportionally suffer with poor outcomes and higher mortality. Patient navigation is a strategy to improve the equity in outcomes across populations. The field of navigation is maturing and solidifying the evidence for the efficacy pf patient navigation. There needs to be more efforts aimed at understanding which populations benefit the most from navigation. In addition, determining what type of navigation models works best in certain settings and with certain populations is still ripe for study.\n This study provides evidence supporting navigation as an effective tool to help women reach diagnostic resolution. Additionally analyses will be conducted to examine more fully the impact on how living in an MUA, having a medical home, and being actively engaged in your health care has on the patients' ability to navigate themselves more effectively and help provide evidence to help deploy navigation in a sustainable and cost efficient manner.\n Citation Format: Elizabeth A. Calhoun, Heather Pauls, Ganga Vijayasiri, Julie S. Darnell, Yamile Molina, Nerida Berrios, Richard Warnecke, Richard Campbell. Patient navigation in medically underserved areas. [abstract]. In: Proceedings of the Seventh AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 9-12, 2014; San Antonio, TX. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2015;24(10 Suppl):Abstract nr IA38.","url":"https://innscience.ca/es/community/literature/3152b3c0-1359-42a8-9490-1eade59d4961"}