Background: Maghrebian immigrants are a high-risk group for tuberculosis. The special features of this group (non-familial grouping [nFG], communication difficulties, illegal status) may have a negative influence on tuberculosis. Subjects and Methods: A descriptive crossover study. Maghrebian immigrants older than 14 years attended at Centro de Salud Collado-Villalba, Madrid, Spain (n = 520). Selection: a random, proportional, age and sex-stratified sample (n = 142). Individuals completed a questionnaire in arabic and were subjected to tuberculosis screening tests till they were assigned one of the definitive diagnostic classes of the American Thoracic Society. Data are shown for three cut points of Mantoux: 5, 10 and 15 mm. The univariate and multivariate analysis were based on logistic regression. Results: 80 individuals were tested. Sex ratio 4:1. The prevalence of tuberculosis infection was: Mantoux ≥ 5 mm = 76.3%, Mantoux ≥ 10 mm = 72.5%, Mantoux ≥ 15 mm = 66.3%. Three tuberculosis cases were diagnosed (annual incidence 1.9%). Whatever the cut point considered for Mantoux: masculine sex, age, nFG, years as immigrant living in nFG and, amongst married people, living without their partners in Spain, were identified as risk factors. The number of years as immigrant living in a familiar grouping and the number of family members per house were identified as protective factors. In the sex-adjusted multivariate analysis, age and living in nFG were associated to a higher incidence of infection, whereas the number of people per house was a protective factor. Conclusions: The prevalence of tuberculosis infection in Maghrebian immigrants residents in Spain is very high. The particular living conditions of this group may have a negative influence.

Control prevention tuberculosis immigrants

human middle aged Logistic Models ethnology Humans Adolescent male Spain female tuberculosis Multivariate Analysis Africa crossover procedure Cross-Over Studies prevalence Article adult Africa, Northern migration Emigration and Immigration statistical model