Vanessa B Sheppard1, Jun He2, Arnethea Sutton1, Lee Cromwell3, Georges Adunlin4, Teresa M Salgado5, Dennis Tolsma3, Martha Trout6, Brandi E Robinson3, Megan C Edmonds7, Hayden B Bosworth8, Mahlet G Tadesse9. 1. 1 Department of Health Behavior and Policy, Virginia Commonwealth University School of Medicine and VCU Massey Cancer Center, Richmond, Virginia. 2. 2 Department of Health Behavior and Policy and Department of Biostatistics, Virginia Commonwealth University School of Medicine. 3. 5 Kaiser Permanente Center for Research and Evaluation, Atlanta, Georgia. 4. 6 Samford University, Birmingham, Alabama. 5. 3 Department of Pharmacotherapy & Outcomes Science, Virginia Commonwealth University School of Pharmacy. 6. 7 Southeast Permanente Medical Group, Atlanta, Georgia. 7. 4 Department of Health Behavior and Policy, Virginia Commonwealth University School of Medicine, Richmond, Virginia. 8. 8 Duke University School of Medicine, Durham, North Carolina. 9. 9 Department of Mathematics and Statistics, Georgetown University, Washington, DC.
Abstract
BACKGROUND: Adjuvant endocrine therapy (AET) is a critical therapy in that it improves survival in women with hormone receptor-positive (HR+) breast cancer (BC), but adherence to AET is suboptimal. The purpose of this study was to fill scientific gaps about predictors of adherence to AET among black and white women diagnosed with BC. OBJECTIVE: To assess AET adherence in black and white insured women using multiple measures, including one that uses an innovative statistical approach. METHODS: Black and white women newly diagnosed with HR+ BC were identified from 2 health maintenance organizations. Pharmacy records captured the type of oral AET prescriptions and all fill dates. Multivariable logistic regression was used to identify predictors of adherence defined in terms of proportion of days covered (PDC; ≥ 80%) and medication gap of ≤ 10 days. A zero-inflated negative binomial (ZINB) regression model was used to identify variables associated with the total number of days of medication gaps. RESULTS: 1,925 women met inclusion criteria. 80% were PDC adherent (> 80%); 44% had a medication gap of ≤ 10 days; and 24% had no medication gap days. Race and age were significant in all multivariable models. Black women were less likely to be adherent based on PDC than white women (OR = 0.72, 95% CI = 0.57-0.90, P < 0.01), and they were less likely to have a medication gap of ≤ 10 days (OR = 0.65, 95% CI = 0.54-0.79, P < 0.001). Women aged 25-49 years were less likely to be PDC adherent than women aged 65-93 years (OR = 0.65, 95% CI = 0.48-0.87, P < 0.001). In the ZINB model, women were without their medication for an average of 37 days (SD = 50.5). CONCLUSIONS: Racial disparities in adherence to AET in the study highlight a need for interventions among insured women. Using various measures of adherence may help better understand this multidimensional concept. There might be benefits from using both more common dichotomous measures (e.g., PDC) and integrating novel statistical approaches to allow tailoring adherence to patterns within a specific sample. DISCLOSURES: This research was funded by the National Institutes of Health (R01CA154848). It was also supported in part by the NIH-NCI Cancer Center Support Grant P30 CA016059, the Laboratory of Telomere Health P30 CA51008, and the TSA Award No. UL1TR002649 from the National Center for Advancing Translational Sciences. The contents of this study are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Bosworth reports grants from Sanofi, Otsuka, Johnson & Johnson, and Blue Cross/Blue Shield of NC and consulting fees from Sanofi and Otsuka. The other authors have nothing to disclose. The datasets generated during and/or analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request. The author does not own these data. Data use was granted to the author as part of a data use agreement between specific agencies and organizations.
BACKGROUND: Adjuvant endocrine therapy (AET) is a critical therapy in that it improves survival in women with hormone receptor-positive (HR+) breast cancer (BC), but adherence to AET is suboptimal. The purpose of this study was to fill scientific gaps about predictors of adherence to AET among black and white women diagnosed with BC. OBJECTIVE: To assess AET adherence in black and white insured women using multiple measures, including one that uses an innovative statistical approach. METHODS: Black and white women newly diagnosed with HR+ BC were identified from 2 health maintenance organizations. Pharmacy records captured the type of oral AET prescriptions and all fill dates. Multivariable logistic regression was used to identify predictors of adherence defined in terms of proportion of days covered (PDC; ≥ 80%) and medication gap of ≤ 10 days. A zero-inflated negative binomial (ZINB) regression model was used to identify variables associated with the total number of days of medication gaps. RESULTS: 1,925 women met inclusion criteria. 80% were PDC adherent (> 80%); 44% had a medication gap of ≤ 10 days; and 24% had no medication gap days. Race and age were significant in all multivariable models. Black women were less likely to be adherent based on PDC than white women (OR = 0.72, 95% CI = 0.57-0.90, P < 0.01), and they were less likely to have a medication gap of ≤ 10 days (OR = 0.65, 95% CI = 0.54-0.79, P < 0.001). Women aged 25-49 years were less likely to be PDC adherent than women aged 65-93 years (OR = 0.65, 95% CI = 0.48-0.87, P < 0.001). In the ZINB model, women were without their medication for an average of 37 days (SD = 50.5). CONCLUSIONS: Racial disparities in adherence to AET in the study highlight a need for interventions among insured women. Using various measures of adherence may help better understand this multidimensional concept. There might be benefits from using both more common dichotomous measures (e.g., PDC) and integrating novel statistical approaches to allow tailoring adherence to patterns within a specific sample. DISCLOSURES: This research was funded by the National Institutes of Health (R01CA154848). It was also supported in part by the NIH-NCI Cancer Center Support Grant P30 CA016059, the Laboratory of Telomere Health P30 CA51008, and the TSA Award No. UL1TR002649 from the National Center for Advancing Translational Sciences. The contents of this study are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Bosworth reports grants from Sanofi, Otsuka, Johnson & Johnson, and Blue Cross/Blue Shield of NC and consulting fees from Sanofi and Otsuka. The other authors have nothing to disclose. The datasets generated during and/or analyzed during the current study are not publicly available due to privacy reasons but are available from the corresponding author on reasonable request. The author does not own these data. Data use was granted to the author as part of a data use agreement between specific agencies and organizations.
Authors: G Olyslaegers; T Zeevaert; P Pinedo; I Simon; G Pröhl; R Kowe; Q Chen; S Mobbs; U Bergström; B Hallberg; T Katona; K Eged; B Kanyar Journal: J Radiol Prot Date: 2005-12-05 Impact factor: 1.394
Authors: Robert W Carlson; Elizabeth Brown; Harold J Burstein; William J Gradishar; Clifford A Hudis; Charles Loprinzi; Eleftherios Paul Mamounas; Edith A Perez; Kathleen Pritchard; Peter Ravdin; Abram Recht; George Somlo; Richard L Theriault; Eric P Winer; Antonio C Wolff Journal: J Natl Compr Canc Netw Date: 2006-03 Impact factor: 11.908
Authors: Eric P Winer; Clifford Hudis; Harold J Burstein; Antonio C Wolff; Kathleen I Pritchard; James N Ingle; Rowan T Chlebowski; Richard Gelber; Stephan B Edge; Julie Gralow; Melody A Cobleigh; Eleftherios P Mamounas; Lori J Goldstein; Timothy J Whelan; Trevor J Powles; John Bryant; Cheryl Perkins; Judy Perotti; Susan Braun; Amy S Langer; George P Browman; Mark R Somerfield Journal: J Clin Oncol Date: 2004-11-15 Impact factor: 44.544
Authors: Cameron B Haas; Erin J Aiello Bowles; Janie M Lee; Jennifer Specht; Diana S M Buist Journal: Cancer Causes Control Date: 2022-07-07 Impact factor: 2.532
Authors: Vanessa B Sheppard; Arnethea L Sutton; Alejandra Hurtado-de-Mendoza; Jun He; Bassam Dahman; Megan C Edmonds; Mary Helen Hackney; Mahlet G Tadesse Journal: Cancer Epidemiol Biomarkers Prev Date: 2021-01-29 Impact factor: 4.254
Authors: Chiara Acquati; Tzuan A Chen; Isabel Martinez Leal; Shahnjayla K Connors; Arooba A Haq; Anastasia Rogova; Stephanie Ramirez; Lorraine R Reitzel; Lorna H McNeill Journal: Int J Environ Res Public Health Date: 2021-12-11 Impact factor: 4.614