Improved salt iodation methods for small-scale salt producers in low-resource settings in Tanzania

Background: Universal salt iodation will prevent iodine deficiency disorders (IDD). Globally, salt-iodation technologies mostly target large and medium-scale salt-producers. Since most producers in low-income countries are small-scale, we examined and improved the performance of hand and knapsack-sprayers used locally in Tanzania. Methods: We studied three salt facilities on the Bagamoyo coast, investigating procedures for preparing potassium-iodate solution, salt spraying and mixing. Different concentrations of solution were prepared and tested using different iodation methods, with the aim of attaining correct and homogeneous iodine levels under real-life conditions. Levels achieved by manual mixing were compared to those achieved by machine mixing. Results: The overall median iodation level in samples of salt iodated using previously existing methods was 10.6 ppm (range 1.1 – 110.0 ppm), with much higher levels in the top than the bottom layers of the salt bags, p < 0.0001. Experimentation using knapsack-sprayers and manual mixing led to the reliable achievement of levels (60.9 ppm ± 7.4) that fell within the recommended range of 40 – 80 ppm. The improved methods yielded homogenous iodine concentrations in all layers of salt-bags (p = 0.58) with 96% of the samples (n = 45) falling within 40 – 80 ppm compared to only 9% (n = 45) before the experiment and training (p < 0.0001). For knapsack-spraying, a machine mixer improved the iodine levels and homogeneity slightly compared to manual mixing (p = 0.05). Conclusion: Supervised, standardized salt iodation procedures adapted to local circumstances can yield homogeneous iodine levels within the required range, overcoming a major obstacle to universal salt iodation

The effects of spatial population dataset choice on estimates of population at risk of disease

Background: The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.Methods: The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.Results: The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.Conclusions: Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions. © 2011 Tatem et al; licensee BioMed Central Ltd.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Initial sequencing and analysis of the human genome

The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62798/1/409860a0.pd