The goals of this article are: (i) to understand how individual characteristics affect the likelihood of patients defaulting their pulmonary tuberculosis (PTB) treatment regimens; (ii) to quantify the predictive capacity of these risk factors; and (iii) to quantify and map spatial variation in the risk of defaulting. We used logistic regression models and generalized additive models with a spatial component to determine the odds of default across continental Portugal. We focused on new PTB cases, diagnosed between 2000 and 2013, and included some individual information (sex, age, residence area, alcohol abuse, intravenous drug use, homelessness, HIV, imprisonment status). We found that the global default rate was 4·88%, higher in individuals with well-known risk profiles (males, immigrants, HIV positive, homeless, prisoners, alcohol and drug users). Of specific epidemiological interest was that our geographical analysis found that Portugal's main urban areas (the two biggest cities) and one tourist region have higher default rates compared to the rest of the country, after adjusting for the previously mentioneded risk factors. The challenge of treatment defaulting, either due to other individual non-measured characteristics, healthcare system failure or patient recalcitrance requires further analysis in the spatio-temporal domain. Our findings suggest the presence of significant within-country variation in the risk of defaulting that cannot be explained by these classical individual risk factors alone. The methods we advocate are simple to implement and could easily be applied to other diseases.
Keywords: Noncompliance; pulmonary tuberculosis; risk factors; spatial pattern; treatment.