Bayesian mapping of pulmonary tuberculosis in Antananarivo, Madagascar

<p>Abstract</p> <p>Background</p> <p>Tuberculosis (TB), an infectious disease caused by the <it>Mycobacterium tuberculosis </it>is endemic in Madagascar. The capital, Antananarivo is the most seriously affected area. TB had a non-random spatial distribution in this setting, with clustering in the poorer areas. The aim of this study was to explore this pattern further by a Bayesian approach, and to measure the associations between the spatial variation of TB risk and national control program indicators for all neighbourhoods.</p> <p>Methods</p> <p>Combination of a Bayesian approach and a generalized linear mixed model (GLMM) was developed to produce smooth risk maps of TB and to model relationships between TB new cases and national TB control program indicators. The TB new cases were collected from records of the 16 Tuberculosis Diagnostic and Treatment Centres (DTC) of the city from 2004 to 2006. And five TB indicators were considered in the analysis: number of cases undergoing retreatment, number of patients with treatment failure and those suffering relapse after the completion of treatment, number of households with more than one case, number of patients lost to follow-up, and proximity to a DTC.</p> <p>Results</p> <p>In Antananarivo, 43.23% of the neighbourhoods had a standardized incidence ratio (SIR) above 1, of which 19.28% with a TB risk significantly higher than the average. Identified high TB risk areas were clustered and the distribution of TB was found to be associated mainly with the number of patients lost to follow-up (SIR: 1.10, CI 95%: 1.02-1.19) and the number of households with more than one case (SIR: 1.13, CI 95%: 1.03-1.24).</p> <p>Conclusion</p> <p>The spatial pattern of TB in Antananarivo and the contribution of national control program indicators to this pattern highlight the importance of the data recorded in the TB registry and the use of spatial approaches for assessing the epidemiological situation for TB. Including these variables into the model increases the reproducibility, as these data are already available for individual DTCs. These findings may also be useful for guiding decisions related to disease control strategies.</p

Patterns of malaria-related hospital admissions and mortality among Malawian children: an example of spatial modelling of hospital register data

BACKGROUND: Malaria is a leading cause of hospitalization and in-hospital mortality among children in Africa, yet, few studies have described the spatial distribution of the two outcomes. Here spatial regression models were applied, aimed at quantifying spatial variation and risk factors associated with malaria hospitalization and in-hospital mortality. METHODS: Paediatric ward register data from Zomba district, Malawi, between 2002 and 2003 were used, as a case study. Two spatial models were developed. The first was a Poisson model applied to analyse hospitalization and minimum mortality rates, with age and sex as covariates. The second was a logistic model applied to individual level data to analyse case-fatality rate, adjusting for individual covariates. RESULTS AND CONCLUSION: Rates of malaria hospitalization and in-hospital mortality decreased with age. Case fatality rate was associated with distance, age, wet season and increased if the patient was referred to the hospital. Furthermore, death rate was high on first day, followed by relatively low rate as length of hospital stay increased. Both outcomes showed substantial spatial heterogeneity, which may be attributed to the varying determinants of malaria risk, health services availability and accessibility, and health seeking behaviour. The increased risk of mortality of children referred from primary health facilities may imply inadequate care being available at the referring facility, or the referring facility are referring the more severe cases which are expected to have a higher case fatality rate. Improved prognosis as the length of hospital stay increased suggest that appropriate care when available can save lives. Reducing malaria burden may require integrated strategies encompassing availability of adequate care at primary facilities, introducing home or community case management as well as encouraging early referral, and reinforcing interventions to interrupt malaria transmission

Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes

Mpumalanga Province, South Africa is a low malaria transmission area that is subject to malaria epidemics. SaTScan methodology was used by the malaria control programme to detect local malaria clusters to assist disease control planning. The third season for case cluster identification overlapped with the first season of implementing an outbreak identification and response system in the area. SaTScan™ software using the Kulldorf method of retrospective space-time permutation and the Bernoulli purely spatial model was used to identify malaria clusters using definitively confirmed individual cases in seven towns over three malaria seasons. Following passive case reporting at health facilities during the 2002 to 2005 seasons, active case detection was carried out in the communities, this assisted with determining the probable source of infection. The distribution and statistical significance of the clusters were explored by means of Monte Carlo replication of data sets under the null hypothesis with replications greater than 999 to ensure adequate power for defining clusters. SaTScan detected five space-clusters and two space-time clusters during the study period. There was strong concordance between recognized local clustering of cases and outbreak declaration in specific towns. Both Albertsnek and Thambokulu reported malaria outbreaks in the same season as space-time clusters. This synergy may allow mutual validation of the two systems in confirming outbreaks demanding additional resources and cluster identification at local level to better target resources. Exploring the clustering of cases assisted with the planning of public health activities, including mobilizing health workers and resources. Where appropriate additional indoor residual spraying, focal larviciding and health promotion activities, were all also carried out

NADH oxidation drives respiratory Na(+) transport in mitochondria from Yarrowia lipolytica

It is generally assumed that respiratory complexes exclusively use protons to energize the inner mitochondrial membrane. Here we show that oxidation of NADH by submitochondrial particles (SMPs) from the yeast Yarrowia lipolytica is coupled to protonophore-resistant Na(+) uptake, indicating that a redox-driven, primary Na(+) pump is operative in the inner mitochondrial membrane. By purification and reconstitution into proteoliposomes, a respiratory NADH dehydrogenase was identified which coupled NADH-dependent reduction of ubiquinone (1.4 mumol min(-1) mg(-1)) to Na(+) translocation (2.0 mumol min(-1) mg(-1)). NADH-driven Na(+) transport was sensitive towards rotenone, a specific inhibitor of complex I. We conclude that mitochondria from Y. lipolytica contain a NADH-driven Na(+) pump and propose that it represents the complex I of the respiratory chain. Our study indicates that energy conversion by mitochondria does not exclusively rely on the proton motive force but may benefit from the electrochemical Na(+) gradient established by complex I