Accounting for autocorrelation in multi-drug resistant tuberculosis predictors using a set of parsimonious orthogonal eigenvectors aggregated in geographic space.

Spatial autocorrelation is problematic for classical hierarchical cluster detection tests commonly used in multi-drug resistant tuberculosis (MDR-TB) analyses as considerable random error can occur. Therefore, when MDRTB clusters are spatially autocorrelated the assumption that the clusters are independently random is invalid. In this research, a product moment correlation coefficient (i.e., the Moran's coefficient) was used to quantify local spatial variation in multiple clinical and environmental predictor variables sampled in San Juan de Lurigancho, Lima, Peru. Initially, QuickBird 0.61 m data, encompassing visible bands and the near infra-red bands, were selected to synthesize images of land cover attributes of the study site. Data of residential addresses of individual patients with smear-positive MDR-TB were geocoded, prevalence rates calculated and then digitally overlaid onto the satellite data within a 2 km buffer of 31 georeferenced health centers, using a 10 m2 grid-based algorithm. Geographical information system (GIS)-gridded measurements of each health center were generated based on preliminary base maps of the georeferenced data aggregated to block groups and census tracts within each buffered area. A three-dimensional model of the study site was constructed based on a digital elevation model (DEM) to determine terrain covariates associated with the sampled MDR-TB covariates. Pearson's correlation was used to evaluate the linear relationship between the DEM and the sampled MDR-TB data. A SAS/GIS(R) module was then used to calculate univariate statistics and to perform linear and non-linear regression analyses using the sampled predictor variables. The estimates generated from a global autocorrelation analyses were then spatially decomposed into empirical orthogonal bases using a negative binomial regression with a non-homogeneous mean. Results of the DEM analyses indicated a statistically non-significant, linear relationship between georeferenced health centers and the sampled covariate elevation. The data exhibited positive spatial autocorrelation and the decomposition of Moran's coefficient into uncorrelated, orthogonal map pattern components revealed global spatial heterogeneities necessary to capture latent autocorrelation in the MDR-TB model. It was thus shown that Poisson regression analyses and spatial eigenvector mapping can elucidate the mechanics of MDR-TB transmission by prioritizing clinical and environmental-sampled predictor variables for identifying high risk populations.

Comparison of mosquito control programs in seven urban sites in Africa, the Middle East, and the Americas

Mosquito control programs at seven urban sites in Kenya, Egypt, Israel, Costa Rica, and Trinidad are described and compared. Site-specific urban and disease characteristics, organizational diagrams, and strengths, weaknesses, obstacles and threats (SWOT) analysis tools are used to provide a descriptive assessment of each mosquito control program, and provide a comparison of the factors affecting mosquito abatement. The information for SWOT analysis is collected from surveys, focus-group discussions, and personal communication. SWOT analysis identified various issues affecting the efficiency and sustainability of mosquito control operations. The main outcome of our work was the description and comparison of mosquito control operations within the context of each study site's biological, social, political, management, and economic conditions. The issues identified in this study ranged from lack of inter-sector collaboration to operational issues of mosquito control efforts. A lack of sustainable funding for mosquito control was a common problem for most sites. Many unique problems were also identified, which included lack of mosquito surveillance, lack of law enforcement, and negative consequences of human behavior. Identifying common virtues and shortcomings of mosquito control operations is useful in identifying "best practices" for mosquito control operations, thus leading to better control of mosquito biting and mosquito-borne disease transmission.

Comparison of mosquito control programs in seven urban sites in Africa, the Middle East, and the Americas.

Mosquito control programs at seven urban sites in Kenya, Egypt, Israel, Costa Rica, and Trinidad are described and compared. Site-specific urban and disease characteristics, organizational diagrams, and strengths, weaknesses, obstacles and threats (SWOT) analysis tools are used to provide a descriptive assessment of each mosquito control program, and provide a comparison of the factors affecting mosquito abatement. The information for SWOT analysis is collected from surveys, focus-group discussions, and personal communication. SWOT analysis identified various issues affecting the efficiency and sustainability of mosquito control operations. The main outcome of our work was the description and comparison of mosquito control operations within the context of each study site's biological, social, political, management, and economic conditions. The issues identified in this study ranged from lack of inter-sector collaboration to operational issues of mosquito control efforts. A lack of sustainable funding for mosquito control was a common problem for most sites. Many unique problems were also identified, which included lack of mosquito surveillance, lack of law enforcement, and negative consequences of human behavior. Identifying common virtues and shortcomings of mosquito control operations is useful in identifying "best practices" for mosquito control operations, thus leading to better control of mosquito biting and mosquito-borne disease transmission.