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Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

ABSTRACT: School-based sampling has been used to inform targeted responses for malaria and neglected tropical diseases. Standard geostatistical methods for mapping disease prevalence use the school location to model spatial correlation, which is questionable since exposure to the disease is more likely to occur in the residential location. In this paper, we propose to overcome the limitations of standard geostatistical methods by introducing a modelling framework that accounts for the uncertainty in the location of the residence of the students. By using cost distance and cost allocation models to define spatial accessibility and in absence of any information on the travel mode of students to school, we consider three school catchment area models that assume walking only, walking and bicycling and, walking and motorized transport. We illustrate the use of this approach using two case studies of malaria in Kenya and compare it with the standard approach that uses the school locations to build geostatistical models. We argue that the proposed modelling framework presents several inferential benefits, such as the ability to combine data from multiple surveys some of which may also record the residence location, and to deal with ecological bias when estimating the effects of malaria risk factors. However, our results show that invalid assumptions on the modes of travel to school can worsen the predictive performance of geostatistical models. Future research in this area should focus on collecting information on the modes of transportation to school which can then be used to better parametrize the catchment area models.

SUBMITTER: Macharia P

PROVIDER: S-EPMC7613137 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesTo determine whether implementation of comprehensive in-school eyecare results in measurable benefits for children and young people in terms of visual status, classroom behaviours and how well their visual needs are met.DesignSchool-based observational study.Participants & methods200 pupils [mean age 10 years 9 months, 70% male, majority moderate (40%) or severe (35%) learning difficulty] of a special education school in the UK. A sector-agreed in-school eyecare framework including full eye examination and cycloplegic refraction, dispensing of spectacles (as appropriate) and written reporting of outcomes to parents/teachers was applied. Classroom behaviours were observed and recorded prior to, and after, the in-school eyecare. Surveys were employed to obtain visual histories from parents/teachers. School records and statutory documents were reviewed for diagnostic and learning disability classifications. Visual function and ocular health were profiled at baseline and significant visual deficits identified. Where such deficits were previously unrecognised, untreated or not compensated for (e.g. correction of refractive error, enlargement of educational material) they were recorded as 'unmet visual need'. At follow-up, 2-5 months after initial (baseline) measures, eye examinations, parent/teacher surveys and behaviour observations were repeated. Follow-up measures were used to determine if measurable improvements were evident in visual function, ocular health, the level of unmet need and classroom behaviour following implementation of in-school eyecare.Results199 participants completed baseline and follow-up measures. 122 (61%) participants presented with at least one significant visual or ocular health deficit and 90 (45%) participants had at least one unmet visual need. Younger pupils and those with no previous history of eyecare were more likely to demonstrate unmet visual needs at baseline (OR 1.12 95% CI 1.03 to 1.21) p = 0.012; (OR 4.44 95% CI 1.38 to 14.29 p = 0.007 respectively). On follow-up, the number of pupils with unmet visual needs dropped significantly to 36 (18%) (McNemar's test p<0.001). Visual and behavioural metrics of participants without significant visual deficits or whose visual needs were adequately addressed at baseline remained relatively unchanged between baseline and follow-up (Wilcoxon signed rank p>0.05). Where significant refractive deficits were corrected at follow-up, near visual acuity improved significantly (Wilcoxon signed rank p = 0.013), however, poor spectacle compliance was a persistent cause of unmet visual need. Off-task behaviour reduced significantly after actions to address unmet visual needs were communicated to parents and teachers (Wilcoxon signed rank p = 0.035).ConclusionsThe present study demonstrates for the first time measurable visual and behaviour benefits to children in special education settings when they receive comprehensive in-school eye examinations, on-site spectacle dispensing and jargon-free reporting of outcomes to teachers and parents.

Project description:Bayesian geostatistical regression (GR) models estimate air pollution exposure at high spatial resolution, quantify the prediction uncertainty and provide probabilistic inference on the exceedance of air quality thresholds. However, due to high computational burden, previous GR models have provided gridded ambient nitrogen dioxide (NO2) concentrations at smaller areas of investigation. Here, we applied these models to estimate yearly averaged NO2 concentrations at 1 km2 spatial resolution across 44 European countries, integrating information from in situ monitoring stations, satellites and chemical transport model (CTM) simulations. The tropospheric values of NO2 derived from the ozone monitoring instrument (OMI) onboard the National Aeronautics and Space Administration's (NASA's) Aura satellite were converted to near ground NO2 concentration proxies using simulations from the 3-D global CTM (GEOS-Chem) at 0.5° × 0.625°spatial resolution and surface-to-column NO2 ratios. Simulations from the Ensemble of regional CTMs at spatial resolution of 0.1° × 0.1°were extracted from the Copernicus atmosphere monitoring service (CAMS). The contribution of these covariates to the predictive capability of geostatistical models was for the first time evaluated here through a rigorous model selection procedure along with additional continental high-resolution satellite-derived products, including novel data from the pan-European Copernicus land monitoring service (CLMS). The results have shown that the conversion of columnar NO2 values to surface quasi-observations yielded models with slightly better predictive ability and lower uncertainty. Nonetheless, the use of higher resolution CAMS-Ensemble simulations as covariates in GR models granted the most accurate surface NO2 estimates, showing that, in 2016, 16.17 (95% C.I. 6.34-29.96) million people in Europe, representing 2.97% (95% C.I. 1.16% - 5.50%) of the total population, were exposed to levels above the EU directive and WHO air quality guidelines threshold for NO2. Our estimates are readily available to policy makers and scientists assessing the burden of disease attributable to NO2 in 2016.

Project description:BackgroundMicroarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assays. In this study applied to infectious diseases, data from a microarray designed for molecular serotyping of Streptococcus pneumoniae was used, identifying the presence of any one of 91 known pneumococcal serotypes from DNA extracts. This microarray incorporated oligonucleotide probes for all known capsular polysaccharide synthesis genes and required a statistical analysis of the microarray intensity data to determine which serotype, or combination of serotypes, were present within a sample based on the combination of genes detected.ResultsWe propose an empirical Bayesian model for calculating the probabilities of combinations of serotypes from the microarray data. The model takes into consideration the dependencies between serotypes, induced by genes they have in common, and by homologous genes which, although not identical, are similar to each other in sequence. For serotypes which are very similar in capsular gene composition, extra probes are included on the microarray, providing additional information which is integrated into the Bayesian model. For each serotype combination with high probability, a second model, a Bayesian random effects model is applied to determine the relative abundance of each serotype.ConclusionsTo assess the accuracy of the proposed analysis we applied our methods to experimental data from samples containing individual serotypes and samples containing combinations of serotypes with known levels of abundance. All but two of the known serotypes of S. pneumoniae that were tested as individual samples could be uniquely determined by the Bayesian model. The model also enabled the presence of combinations of serotypes within samples to be determined. Serotypes with very low abundance within a combination of serotypes can be detected (down to 2% abundance in this study). As well as detecting the presence of serotype combinations, an approximate measure of the percentage abundance of the serotypes within the combination can be obtained.

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Combining school-catchment area models with geostatistical models for analysing school survey data from low-resource settings: Inferential benefits and limitations

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