A latent class assessment of healthcare access factors and disparities in breast cancer care timeliness

Abstract

Background: Delays in breast cancer diagnosis and treatment lead to worse survival and quality of life. Racial disparities in care timeliness have been reported, but few studies have examined access at multiple points along the care continuum (diagnosis, treatment initiation, treatment duration, and genomic testing).

Methods and findings: The Carolina Breast Cancer Study (CBCS) Phase 3 is a population-based, case-only cohort (n = 2,998, 50% black) of patients with invasive breast cancer diagnoses (2008 to 2013). We used latent class analysis (LCA) to group participants based on patterns of factors within 3 separate domains: socioeconomic status ("SES"), "care barriers," and "care use." These classes were evaluated in association with delayed diagnosis (approximated with stages III-IV at diagnosis), delayed treatment initiation (more than 30 days between diagnosis and first treatment), prolonged treatment duration (time between first and last treatment-by treatment modality), and receipt of OncotypeDx genomic testing (evaluated among patients with early stage, ER+ (estrogen receptor-positive), HER2- (human epidermal growth factor receptor 2-negative) disease). Associations were evaluated using adjusted linear-risk regression to estimate relative frequency differences (RFDs) with 95% confidence intervals (CIs). Delayed diagnosis models were adjusted for age; delayed and prolonged treatment models were adjusted for age and tumor size, stage, and grade at diagnosis; and OncotypeDx models were adjusted for age and tumor size and grade. Overall, 18% of CBCS participants had late stage/delayed diagnosis, 35% had delayed treatment initiation, 48% had prolonged treatment duration, and 62% were not OncotypeDx tested. Black women had higher prevalence for each outcome. We identified 3 latent classes for SES ("high SES," "moderate SES," and "low SES"), 2 classes for care barriers ("few barriers," "more barriers"), and 5 classes for care use ("short travel/high preventive care," "short travel/low preventive care," "medium travel," "variable travel," and "long travel") in which travel is defined by estimated road driving time. Low SES and more barriers to care were associated with greater frequency of delayed diagnosis (RFDadj = 5.5%, 95% CI [2.4, 8.5]; RFDadj = 6.7%, 95% CI [2.8,10.7], respectively) and prolonged treatment (RFDadj = 9.7%, 95% CI [4.8 to 14.6]; RFDadj = 7.3%, 95% CI [2.4 to 12.2], respectively). Variable travel (short travel to diagnosis but long travel to surgery) was associated with delayed treatment in the entire study population (RFDadj = 10.7%, 95% CI [2.7 to 18.8]) compared to the short travel, high use referent group. Long travel to both diagnosis and surgery was associated with delayed treatment only among black women. The main limitations of this work were inability to make inferences about causal effects of individual variables that formed the latent classes, reliance on self-reported socioeconomic and healthcare history information, and generalizability outside of North Carolina, United States of America.

Conclusions: Black patients face more frequent delays throughout the care continuum, likely stemming from different types of access barriers at key junctures. Improving breast cancer care access will require intervention on multiple aspects of SES and healthcare access.

Fig 1. Sankey flow diagram of potential delay points among patients receiving chemotherapy and/or radiation.

Care timeliness was assessed for breast cancer diagnosis (stage I/II vs. III/IV), treatment initiation (no delay/delay), and treatment duration (not prolonged/prolonged). The vertical height of each pathway indicates the relative frequency of each pathway. Color shading indicates the total number delays (minimum = 0, maximum = 3). Results are presented (A) for the overall study population, (B) for black women, and (C) for non-black women.

Fig 2. Linear-risk regression models for delayed diagnosis (N = 2,995).

Delayed diagnosis was assessed in relation to latent variables defined for SES (income, education, country of birth, job type, and marital status), care barriers (insurance, urban/rural status, job loss, self-reported financial barriers to care, self-reported transportation barriers to care), and care use (pre-diagnostic regular care, breast cancer screening, mode of initial cancer detection (mammogram vs. noticed lump), and travel (based on estimated driving time) to diagnosis and surgery). Frequency and percentage of delayed diagnosis is reported in “n(%)” columns. Contrast estimates are RFDs and 95% CIs, which compare frequency of delayed diagnosis in a given latent class with the indicated reference group. Results are presented (A) overall and (B) stratified by race; all models are age-adjusted. CI, confidence interval; RFD, relative frequency difference; SES, socioeconomic status.

Fig 3. Linear-risk regression models for delayed treatment (N = 2,947).

Delayed treatment was assessed in relation to latent class variables were defined for SES (income, education, country of birth, job type, and marital status), care barriers (insurance, urban/rural status, job loss, self-reported financial barriers to care, self-reported transportation barriers to care), and care use (pre-diagnostic regular care, breast cancer screening, mode of initial cancer detection (mammogram vs. noticed lump), and travel (based on estimated driving time) to diagnosis and surgery). Frequency and percentage of delayed diagnosis is reported in “n(%)” columns. Contrast estimates are RFDs and 95% CIs, which compare frequency of delayed treatment for a given latent class with the indicated reference group. Results are presented (A) overall and (B) race-stratified; models are adjusted for age, stage, size, and grade at diagnosis. CI, confidence interval; RFD, relative frequency difference; SES, socioeconomic status.

Fig 4. Linear-risk regression models for prolonged treatment duration with surgery-only patients excluded (N = 2,469).

Prolonged treatment was assessed in relation to latent variables Latent variables were defined for SES (income, education, country of birth, job type, and marital status), care barriers (insurance, urban/rural status, job loss, self-reported financial barriers to care, self-reported transportation barriers to care), and care use (pre-diagnostic regular care, breast cancer screening, mode of initial cancer detection (mammogram vs. noticed lump), and travel (based on estimated driving time) to diagnosis and surgery). Frequency and percentage of delayed diagnosis is reported in “n(%)” columns. Contrast estimates are RFDs and 95% CIs, which compare frequency of prolonged treatment for a given latent class with the indicated reference group. Results are presented (A) overall and (B) race-stratified; models are adjusted for age, stage, size, and grade at diagnosis. CI, confidence interval; RFD, relative frequency difference; SES, socioeconomic status.

Fig 5. Frequency of care delays according to latent class membership across all domains.

Latent variables were defined for SES (income, education, country of birth, job type, and marital status), care barriers (insurance, urban/rural status, job loss, self-reported financial barriers to care, self-reported transportation barriers to care), and care use (pre-diagnostic regular care, breast cancer screening, mode of initial cancer detection (mammogram vs. noticed lump), and travel (based on estimated driving time) to diagnosis and surgery). Frequency of care delays are shown according to SES and care barriers (Y axis labels) and care use (X axis labels). Categories containing <10 patients were not assessed; these cells are shaded white. Results are presented for (A) delayed diagnosis, (B) delayed treatment, and (C) prolonged treatment. SES, socioeconomic status.