Investigating confounders of the association between survival and adjuvant radiation therapy after breast conserving surgery in a sample of elderly breast Cancer patients in Appalachia

Fabian Camacho,Roger Anderson,Gretchen Kimmick

doi:10.1186/s12885-019-6263-3

Fabian Camacho, Roger Anderson + Show 1 more

Open Access

https://doi.org/10.1186/s12885-019-6263-3

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Journal: BMC Cancer	Publication Date: Dec 1, 2019
Citations: 1	License type: open-access

Affiliation: University of Virginia, Duke University

Abstract

BackgroundTo explain the association between adjuvant radiation therapy after breast conserving surgery (BCS RT) and overall survival (OS) by quantifying bias due to confounding in a sample of elderly breast cancer beneficiaries in a multi-state region of Appalachia.MethodsWe used Medicare claims linked registry data for fee-for-service beneficiaries with AJCC stage I-III, treated with BCS, and diagnosed from 2006 to 2008 in Appalachian counties of Kentucky, Ohio, North Carolina, and Pennsylvania. Confounders of BCS RT included age, rurality, regional SES, access to radiation facilities, marital status, Charlson comorbidity, Medicaid dual status, institutionalization, tumor characteristics, and surgical facility characteristics. Adjusted percent change in expected survival by BCS RT was examined using Accelerated Failure Time (AFT) models. Confounding bias was assessed by comparing effects between adjusted and partially adjusted associations using a fully specified structural model.ResultsThe final sample had 2675 beneficiaries with mean age of 75, with 81% 5-year survival from diagnosis. Unadjusted percentage increase in expected survival was 2.75 times greater in the RT group vs. non-RT group, with 5-year survival of 85% vs 60%; fully adjusted percentage increase was 1.70 times greater, with 5-year rates of 83% vs 71%. Quantification of incremental confounding showed age accounted for 71% of the effect reduction, followed by tumor features (12%), comorbidity (10%), dual status(10%), and institutionalization (8%). Adjusting for age and tumor features only resulted in only 4% bias from fully adjusted percent change (70% change vs 66%).ConclusionQuantification of confounding aids in determining covariates to adjust for and in interpreting raw associations. Substantial confounding was present (60% of total association), with age accounting for the largest share (71%); adjusting for age plus tumor features corrected for most of the confounding (4% bias). The direct effect of BCS RT on OS accounted for 40% of the total association.

Highlights

To explain the association between adjuvant radiation therapy after breast conserving surgery (BCS Radiation therapy (RT)) and overall survival (OS) by quantifying bias due to confounding in a sample of elderly breast cancer beneficiaries in a multi-state region of Appalachia
Decreased OS was predicted by OH residence, Medicaid Dual insurance status (% Percent change (CH) = − 20), and being institutionalized (% CH = − 51)
Prior studies have hypothesized on possible mechanisms which may account for unanticipated higher mortality rates in women who forego RT after BCS, ranging from comorbidity, poverty, and lack of access [11], with particular suspicion centering on comorbidity and disability [23]

Summary

Introduction

To explain the association between adjuvant radiation therapy after breast conserving surgery (BCS RT) and overall survival (OS) by quantifying bias due to confounding in a sample of elderly breast cancer beneficiaries in a multi-state region of Appalachia. A major challenge of the latter real-word or “effectiveness “studies is to explain the magnitude of the association of a recommended therapy, such as RT, on survival apart from the influence of important confounding factors, which often include comorbidity, access to care, socio-economic status, and quality of care [10, 11]. Such a quantification of confounding may be important in determining whether a covariate needs to be adjusted for in an analysis and aid in the interpretation of unadjusted associations [13]. This geographical region has higher cancer incidence and mortality rates [14,15,16], applicable to breast cancer mortality as well [17], with heterogeneous economic diversity [18], poor health care accessibility [18, 19] significant medically underserved pockets [18], and is distinct from regions from studies using SEER (Surveillance, Epidemiology, and End Results Program)-Medicare linked databases [20,21,22]

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