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T. Ma
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ORAL 12 - Quality of Life and Trials (ID 96)
- Event: WCLC 2015
- Type: Oral Session
- Track: Advocacy
- Presentations: 1
- Moderators:E. Bachrach Makovsky, C. Malnati
- Coordinates: 9/07/2015, 10:45 - 12:15, 708+710+712
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ORAL12.03 - The Predictors and Effects of Explicit and Implicit Attitudes Against Lung Cancer (LC) (ID 1459)
11:07 - 11:18 | Author(s): T. Ma
- Abstract
- Presentation
Background:
LC may be associated with negative societal perceptions compared to other cancers. This study measured the explicit, conscious attitudes (EAs), implicit, unconscious attitudes (IAs) and implicit stereotypes of LC relative to breast cancer (BC), explored the demographic factors associated with the explicit and implicit biases in LC, and whether these biases affect the LC drug treatment rates.
Methods:
EAs were derived from participants (Ps) [cancer patients (n = 493), caregivers (n = 1332), healthcare providers (HCPs, n = 623), and the general public (n = 1356)] ratings about how patients with LC and BC “do feel” (descriptive attitudes) or “ought to feel” (normative attitudes) about their disease. IAs and implicit stereotypes were measured with the Implicit Association Test (IAT). Analysis of covariance (ANCOVA) was used to assess the demographic factors associated with bias toward LC. Linear regressions were performed to analyze the association between the biases against LC and LC treatment rates across different states in the United States.
Results:
Females (p < 0.001), higher income (p = 0.015), and people reporting themselves with more knowledge about cancer disease (p < 0.001), caregivers (p = 0.008), and whites (p < 0.001) expressed stronger negative descriptive attitudes toward LC. Males (p = 0.007), and higher income (p = 0.010) expressed less-positive normative attitudes toward LC. Females (p < 0.001), higher education (p = 0.003), non-cancer patient participants (p = 0.019), and whites (p = 0.031) had stronger negative IAs about LC. State-level analysis showed that the lower drug treatment rates for LC patients are significantly associated with older patients population (p = 0.011) and higher percentage of government as payer (p = 0.023). State-level analysis shows no significant association between IAT scores and LC treatment rates.
Conclusion:
Explicit and implicit bias against LC compared to BC was associated with gender, education, income levels and cancer knowledge, but not treatment rates.
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