Abstract
BackgroundAs the prevalences of neglected tropical diseases reduce to low levels in some countries, policymakers require precise disease estimates to decide whether the set public health targets have been met. At low prevalence levels, traditional statistical methods produce imprecise estimates. More modern geospatial statistical methods can deliver the required level of precision for accurate decision-making.MethodsUsing spatially referenced data from 3567 cluster locations in Ethiopia in the years 2017, 2018 and 2019, we developed a geostatistical model to estimate the prevalence of trachomatous trichiasis and to calculate the probability that the trachomatous trichiasis component of the elimination of trachoma as a public health problem has already been achieved for each of 482 evaluation units. We also compared the precision of traditional and geostatistical approaches by the ratios of the lengths of their 95% predictive intervals.ResultsThe elimination threshold of trachomatous trichiasis (prevalence ≤ 0.2% in individuals aged ≥15 years) is met with a probability of 0.9 or more in 8 out of the 482 evaluation units assessed, and with a probability of ≤0.1 in 469 evaluation units. For the remaining five evaluation units, the probability of elimination is between 0.45 and 0.65. Prevalence estimates were, on average, 10 times more precise than estimates obtained using the traditional approach.ConclusionsBy accounting for and exploiting spatial correlation in the prevalence data, we achieved remarkably improved precision of prevalence estimates compared with the traditional approach. The geostatistical approach also delivers predictions for unsampled evaluation units that are geographically close to sampled evaluation units.
Highlights
By accounting for and exploiting spatial correlation in the prevalence data, we achieved remarkably improved precision of prevalence estimates compared with the traditional approach
Model-based geostatistics (MBG) borrows strength of information across different sampled locations to an extent determined by the strength of the estimated spatial correlation between locations
In our study, the MBG approach delivered trachomatous trichiasis prevalence estimates that were, on average, 10 times more precise than those given by the traditional approach to estimating trachomatous trichiasis prevalence; allowed assessment of elimination status to be made with greatly reduced uncertainty for a given sample size; and enabled estimation of disease prevalence in unsampled evaluations units sufficiently close to sampled locations
Summary
Neglected tropical diseases rank among the world’s greatest global health problems because of their substantial contribution to global morbidity, disability and mortality.[1,2,3] The highest burdens of neglected tropical diseases occur in the tropical and subtropical regions of the world, affecting the world’s poorest people and exacerbating poverty through their detrimental effects on work productivity, child development and women’s health.[1,4] In the London Declaration, 22 partners including endemic countries, nongovernmental organizations, pharmaceutical companies and donors committed to controlling, eliminating or eradicating at least 10 neglected tropical diseases by the year 2020.5 Experts in neglected tropical diseases are generally optimistic about the prospects of elimination for lymphatic filariasis, onchocerciasis and trachoma,[6] the dates by which success could be anticipated have recently been revised.[7]For selected neglected tropical diseases, elimination as a public health problem is defined as the reduction of prevalence in a given geographic area to a disease-specific level set by the World Health Organization (WHO).[8]. We used extensive prevalence survey data on trachoma in Ethiopia to demonstrate how the application of model-based geostatistical analysis can achieve very substantial gains in precision over the traditional statistical analysis methods that are currently used in this context. As the prevalences of neglected tropical diseases reduce to low levels in some countries, policymakers require precise disease estimates to decide whether the set public health targets have been met. Methods: Using spatially referenced data from 3567 cluster locations in Ethiopia in the years 2017, 2018 and 2019, we developed a geostatistical model to estimate the prevalence of trachomatous trichiasis and to calculate the probability that the trachomatous trichiasis component of the elimination of trachoma as a public health problem has already been achieved for each of 482 evaluation units. Prevalence estimates were, on average, 10 times more precise than estimates obtained using the traditional approach
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