SummaryTuberculosis (TB) has reappeared as a serious public health problem. Non-compliance to antituberculous drug treatment is cited as one of the major obstacles to the containment of the epidemic. Compliance may be optimized by Directly Observed Treatment (DOT) and short-course treatment regimens. Since 1986, Tanzanian TB patients have received daily DOT at health facilities for the first 2 months of the treatment course. However, adherence and cure rates have been falling as the number of TB cases continues to increase and the burden on already stretched health facilities threatens to become unmanageable. We used an open cluster randomized controlled trial to compare community-based DOT (CBDOT) using a short-course drug regimen with institutional-based DOT (IBDOT). A total of 522 (301 IBDOT and 221 CBDOT) patients with sputum-positive TB were recruited. Overall, there was no significant difference in conversion and cure rates between the two strategies [M-H pooled odds ratio (OR) 0.62; 95% confidence interval (CI) 0.23, 1.71 and OR ¼ 1.58; 95% CI 0.32, 7.88, respectively] suggesting that CBDOT may be a viable alternative to IBDOT. CBDOT may be particularly useful in parts of the country where people live far from health facilities.

BackgroundMalaria transmission is measured using entomological inoculation rate (EIR), number of infective mosquito bites/person/unit time. Understanding heterogeneity of malaria transmission has been difficult due to a lack of appropriate data. A comprehensive entomological database compiled by the Malaria Transmission Intensity and Mortality Burden across Africa (MTIMBA) project (2001–2004) at several sites is the most suitable dataset for studying malaria transmission–mortality relations. The data are sparse and large, with small-scale spatial–temporal variation.ObjectiveThis work demonstrates a rigorous approach for analysing large and highly variable entomological data for the study of malaria transmission heterogeneity, measured by EIR, within the Rufiji Demographic Surveillance System (DSS), MTIMBA project site in Tanzania.DesignBayesian geostatistical binomial and negative binomial models with zero inflation were fitted for sporozoite rates (SRs) and mosquito density, respectively. The spatial process was approximated from a subset of locations. The models were adjusted for environmental effects, seasonality and temporal correlations and assessed based on their predictive ability. EIR was calculated using model-based predictions of SR and density.ResultsMalaria transmission was mostly influenced by rain and temperature, which significantly reduces the probability of observing zero mosquitoes. High transmission was observed at the onset of heavy rains. Transmission intensity reduced significantly during Year 2 and 3, contrary to the Year 1, pronouncing high seasonality and spatial variability. The southern part of the DSS showed high transmission throughout the years. A spatial shift of transmission intensity was observed where an increase in households with very low transmission intensity and significant reduction of locations with high transmission were observed over time. Over 68 and 85% of the locations selected for validation for SR and density, respectively, were correctly predicted within 95% credible interval indicating good performance of the models.ConclusionMethodology introduced here has the potential for efficient assessment of the contribution of malaria transmission in mortality and monitoring performance of control and intervention strategies.