Pragmatism in practice: lessons learned during screening and enrollment for a randomised controlled trial in rural northern Ethiopia

Background. We use the example of the Gojjam Lymphoedema Best Practice Trial (GoLBeT), a pragmatic trial in a remote rural setting in northern Ethiopia, to extract lessons relevant to other investigators balancing the demands of practicality and community acceptability with internal and external validity in clinical trials. Methods. We explain in detail the preparation for the trial, its setting in northern Ethiopia, the identification and selection of patients (inclusion and exclusion criterion, identifying and screening of patients at home, enrollment of patients at the health centres and health posts), and randomisation. Results. We describe the challenges met, together with strategies employed to overcome them. Conclusions. Examples given in the previous section are contextualised and general principles extracted where possible. We conclude that it is possible to conduct a trial that balances approaches that support internal validity (e.g. careful design of proformas, accurate case identification, control over data quality and high retention rates) with those that favour generalisability (e.g. ‘real world’ setting and low rates of exclusion). Strategies, such as Rapid Ethical Assessment, that increase researchers’ understanding of the study setting and inclusion of hard-to-reach participants are likely to have resource and time implications, but are vital in achieving an appropriate balance

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach

Abstract Background In low-income countries it is difficult to obtain complete data that show spatial heterogeneity in the risk of tuberculosis within-and-between smaller administrative units. This may contribute to the partial effectiveness of tuberculosis control programs. The aim of this study was to estimate the spatial risk of tuberculosis distribution in Gurage Zone, Southern Ethiopia using limited spatial datasets. Methods A total of 1601 patient data that were retrieved from unit tuberculosis registers were included in the final analyses. The population and geo-location data were obtained from the Central Statistical Agency of Ethiopia. Altitude data were extracted from ASTER Global Digital Elevation Model Version 2. Aggregated datasets from sample of 169(40%), 254(60%) and 338(80%) kebeles were used to estimate the spatial risk of TB distribution in the Gurage Zone by using a geostatistical kriging approach. The best set of input parameters were decided based on the lowest prediction error criteria of the cross-validation technique. ArcGIS 10.2 was used for the spatial data analyses. Results The best semivariogram models were the Pentaspherical, Rational Quadratic, and K-Bessel for the 40, 60 and 80% spatial datasets, respectively. The predictive accuracies of the models have improved with the true anisotropy, altitude and latitude covariates, the change in detrending pattern from local to global, and the increase in size of spatial dataset. The risk of tuberculosis was estimated to be higher at western, northwest, southwest and southeast parts of the study area, and crossed between high and low at west-central parts. Conclusion This study has underlined that the geostatistical kriging approach can be applied to estimate the spatial risk of tuberculosis distribution in data limited settings. The estimation results may help local public health authorities measure burden of the disease at all locations, identify geographical areas that require more attention, and evaluate the impacts of intervention programs

Spatial and space-time clustering of tuberculosis in Gurage Zone, Southern Ethiopia

<div><p>Introduction</p><p>Spatial targeting is advocated as an effective method that contributes for achieving tuberculosis control in high-burden countries. However, there is a paucity of studies clarifying the spatial nature of the disease in these countries. This study aims to identify the location, size and risk of purely spatial and space-time clusters for high occurrence of tuberculosis in Gurage Zone, Southern Ethiopia during 2007 to 2016.</p><p>Materials and methods</p><p>A total of 15,805 patient data that were retrieved from unit TB registers were included in the final analyses. The spatial and space-time cluster analyses were performed using the global Moran’s <i>I</i>, Getis-Ord and Kulldorff’s scan statistics.</p><p>Results</p><p>Eleven purely spatial and three space-time clusters were detected (<i>P <</i>0.001).The clusters were concentrated in border areas of the Gurage Zone. There were considerable spatial variations in the risk of tuberculosis by year during the study period.</p><p>Conclusions</p><p>This study showed that tuberculosis clusters were mainly concentrated at border areas of the Gurage Zone during the study period, suggesting that there has been sustained transmission of the disease within these locations. The findings may help intensify the implementation of tuberculosis control activities in these locations. Further study is warranted to explore the roles of various ecological factors on the observed spatial distribution of tuberculosis.</p></div

Global spatial autocorrelation of TB distribution in Gurage Zone, Southern Ethiopia, 2007–2016.

<p>Global spatial autocorrelation of TB distribution in Gurage Zone, Southern Ethiopia, 2007–2016.</p

Additional file 2: of Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach

The geostatistical kriging models. (XLSX 232 kb

Spatial locations of significant hotspots of TB identified by using Getis-Ord statistic in Gurage Zone, Southern Ethiopia, 2007–2016.

<p>Spatial locations of significant hotspots of TB identified by using Getis-Ord statistic in Gurage Zone, Southern Ethiopia, 2007–2016.</p

Significant purely spatial clusters for high occurrence of TB in Gurage Zone, Southern Ethiopia, 2007–2016.

<p>Significant purely spatial clusters for high occurrence of TB in Gurage Zone, Southern Ethiopia, 2007–2016.</p

Space-time clusters for high occurrence of TB in Gurage Zone, Southern Ethiopia, 2007–2016.

<p>Space-time clusters for high occurrence of TB in Gurage Zone, Southern Ethiopia, 2007–2016.</p

Annual purely spatial clusters for high occurrence of TB identified by using SaTScan statistic in Gurage Zone, Southern Ethiopia, 2007–2016.

<p>Annual purely spatial clusters for high occurrence of TB identified by using SaTScan statistic in Gurage Zone, Southern Ethiopia, 2007–2016.</p