Portugal, a medium- to low-level endemic country (21·6 cases/100 000 population in 2012), has one of the highest European Union tuberculosis (TB) incidences. Although incidence is declining progressively, the country's heterogeneity in both regional endemics and their evolution suggests the importance of a better understanding of subnational epidemiology to customize TB control efforts. We aimed to update knowledge on municipality-years pulmonary TB incidence clustering, identify areas with different time trends, and show the potential of combining complementary clustering methods in control of infectious diseases. We used national surveillance municipality-level data (mainland Portugal, 2000–2010). Space–time clustering and spatial variation in temporal trends methods were applied. Space–time critical clusters identified (P < 0·001) were still the Lisbon and Oporto regions. The global incidence declined at a 5·81% mean annual percentage change, with high space–time heterogeneity and distinct time trend clusters (P < 0·001). Municipalities with incidences declining more rapidly belonged to critical areas. In particular, the Oporto trend cluster had a consistent −8·98% mean annual percentage change. Large space–time heterogeneities were identified, with critical incidences in the greater Lisbon and Oporto regions, but declining more rapidly in these regions. Oporto showed a consistent, steeper decrease and could represent a good example of local control strategy. Combining results from these approaches gives promise for prospects for infectious disease control and the design of more effective, focused interventions.

We investigated the incidence of cases of nosocomial pathogens and risk factors in an intensive treatment unit ward to determine if the number of cases is dependent on location of patients and the colonization/infection history of the ward. A clustering approach method was developed to investigate the patterns of spread of cases through time for five microorganisms [methicillin-resistant Staphylococcus aureus (MRSA), Acinetobacter spp., Klebsiella spp., Candida spp., and Pseudomonas aeruginosa] using hospital microbiological monitoring data and ward records of patient-bed use. Cases of colonization/infection by MRSA, Candida and Pseudomonas were clustered in beds and through time while cases of Klebsiella and Acinetobacter were not. We used structural equation modelling to analyse interacting risk factors and the potential pathways of transmission in the ward. Prior nurse contact with colonized/infected patients, mediated by the number of patient-bed movements, were important predictors for all cases, except for those of Pseudomonas. General health and invasive surgery were significant predictors of cases of Candida and Klebsiella. We suggest that isolation and bed movement as a strategy to manage MRSA infections is likely to impact upon the incidence of cases of other opportunist pathogens.