Background: Tuberculosis is an important public health concern among migrant farmworkers in the United States; providing appropriate screening and treatment is difficult due to their highly mobile existence. Purpose: To analyze the relationship between variables (susceptibility, severity, barriers, benefits, cues to action, normative beliefs, subjective norm, attitude, and intention) from the Health Belief Model (HBM) and the Theory of Reasoned Action (TRA) and participation by Mexican migrant farmworkers in a tuberculosis screening program. Method: A convenience sample of 206 migrant farmworkers were recruited after a presentation of a tuberculosis education program and were tracked during the administration and reading of the tuberculosis skin test. Participants were interviewed in Spanish by the principal investigator using the Tuberculosis Interview Instrument (TII) developed for this study. Results: Most subjects were male, aged 18-27 years, and had less than a sixth-grade education. Of the 206 subjects, 152 (73.4%) received the skin test, 149 (98%) had the skin test read, and 44 (29.5%) had positive skin tests. Based on logistic regression analysis, the model that best predicted intention included cues to action, subjective norm, susceptibility, and attitude. Participation in screening was best predicted by a model containing only two variables: intention and susceptibility. Conclusions: In this study, logistic regression analysis revealed that a more parsimonious model than the full HBM and TRA model accurately predicted both intention and behavior. The findings may be helpful in developing tuberculosis education and screening programs for Mexican migrant farmworkers.

Migrant farmworkers tuberculosis

mass screening nursing methodology human middle aged Skin Tests Aged Logistic Models ethnology Mexico United States Humans lung tuberculosis Adolescent model skin test male Tuberculosis, Pulmonary female Article adult migration patient participation statistical model Transients and Migrants Models, Nursing Texas