4.1 Article

Assessing Cancer Health Literacy among Spanish-Speaking Latinos

Journal

JOURNAL OF CANCER EDUCATION
Volume 33, Issue 6, Pages 1333-1340

Publisher

SPRINGER
DOI: 10.1007/s13187-017-1255-y

Keywords

Cancer health literacy; Latinos; Hispanics; Assessment; Psychometric evaluation; Spanish; Assessment tools; Cancer literacy levels

Funding

  1. Research Centers in Minority Institutions Program (RCMI) of the National Institute on Minority Health and Health Disparities (NIMHD) [2G12MD007595]
  2. NIMHD [5S21MD0000100]
  3. National Institute of General Medical Sciences of the National Institutes of Health [1U54GM104940]
  4. Resource Centers for Minority Aging Research program of the National Institute on Aging [P30 AG15272]
  5. National Cancer Institute [1U54CA153511]
  6. NATIONAL CANCER INSTITUTE [U54CA153511] Funding Source: NIH RePORTER
  7. NATIONAL CENTER ON MINORITY HEALTH AND HEALTH DISPARITIES [S21MD000100] Funding Source: NIH RePORTER
  8. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [U54GM104940] Funding Source: NIH RePORTER
  9. NATIONAL INSTITUTE ON AGING [P30AG015272] Funding Source: NIH RePORTER
  10. National Institute on Minority Health and Health Disparities [G12MD007595, ZIAMD000014] Funding Source: NIH RePORTER

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Health literacy is a dynamic construct that changes with specific health conditions; thus, new disease-specific health literacy tools are needed. Since cancer is the leading cause of death among Latinos, the largest and fastest-growing minority population in the nation, there is a need to develop tools to assess cancer health literacy (CHL) among the Spanish-speaking population. The Cancer Health Literacy Test, Spanish version (CHLT-30-DKspa) was applied to identify Spanish-speaking individuals with low CHL and ascertain which items in the tool best discriminate between CHL level groups. Cross-sectional field test of the CHLT-30-DKspa among Spanish-speaking Latinos. Latent class analysis (LCA) identified participants with varying CHL levels. Probability of correct answers, odds ratios, and standardized errors were used to identify the items that allow the classification of individuals among the latent classes. LCA resulted in a three-latent-class model predicting 39.4% of participants to be in the HIGH class, 43.3% in the MEDIUM class, and 17.3% in the LOW class. Eleven items (the CHLT-11-DKspa) meet the criteria to clearly separate participants with HIGH and LOW classes of CHL. Although the best model fit was a three-class solution, results showed a clear separation of individuals from HIGH versus LOW levels of CHL, but separation of those in the MEDIUM level was not as clear. The CHLT-11-DKspa is a shorter measure that may be relatively easy to use in a clinical encounter to identify Spanish-speaking patients with the poorest levels of CHL who may require additional support to understand medical instructions and care plans.

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