Identification of high-risk regions for schistosomiasis in the Guichi region of China: an adaptive kernel density estimation-based approach

Shrinkage estimators of the spatial relative risk function

The spatial relative risk function describes differences in the geographical distribution of two types of points, such as locations of cases and controls in an epidemiological study. It is defined as the ratio of the two underlying densities. Estimation of spatial relative risk is typically done using kernel estimates of these densities, but this procedure is often challenging in practice because of the high degree of spatial inhomogeneity in the distributions. This makes it difficult to obtain estimates of the relative risk that are stable in areas of sparse data while retaining necessary detail elsewhere, and consequently difficult to distinguish true risk hotspots from stochastic bumps in the risk function. We study shrinkage estimators of the spatial relative risk function to address these problems. In particular, we propose a new lasso-type estimator that shrinks a standard kernel estimator of the log-relative risk function towards zero. The shrinkage tuning parameter can be adjusted to help quantify the degree of evidence for the existence of risk hotspots, or selected to optimize a cross-validation criterion. The performance of the lasso estimator is encouraging both on a simulation study and on real-world examples.

Exploring the Spatial Relative Risk of COVID-19 in Berlin-Neukölln

Identifying areas with high and low infection rates can provide important etiological clues. Usually, areas with high and low infection rates are identified by aggregating epidemiological data into geographical units, such as administrative areas. This assumes that the distribution of population numbers, infection rates, and resulting risks is constant across space. This assumption is, however, often false and is commonly known as the modifiable area unit problem. This article develops a spatial relative risk surface by using kernel density estimation to identify statistically significant areas of high risk by comparing the spatial distribution of address-level COVID-19 cases and the underlying population at risk in Berlin-Neukölln. Our findings show that there are varying areas of statistically significant high and low risk that straddle administrative boundaries. The findings of this exploratory analysis further highlight topics such as, e.g., Why were mostly affluent areas affected during the first wave? What lessons can be learned from areas with low infection rates? How important are built structures as drivers of COVID-19? How large is the effect of the socio-economic situation on COVID-19 infections? We conclude that it is of great importance to provide access to and analyse fine-resolution data to be able to understand the spread of the disease and address tailored health measures in urban settings.

A field survey using LAMP assay for detection of Schistosoma mansoni in a low-transmission area of schistosomiasis in Umbuzeiro, Brazil: Assessment in human and snail samples

Background

In Brazil, schistosomiasis is a parasitic disease of public health relevance, mainly in poor areas where Schistosoma mansoni is the only human species encountered and Biomphalaria straminea is one of the intermediate host snails. A nested-PCR based on a specific mitochondrial S. mansoni minisatellite DNA region has been successfully developed and applied as a reference method in Brazil for S. mansoni detection, mainly in host snails for epidemiological studies. The amplification efficiency of LAMP is known to be higher than PCR. The present work aimed to assess the utility of our previously described SmMIT-LAMP assay for S. mansoni detection in human stool and snail samples in a low-transmission area of schistosomiasis in the municipality of Umbuzeiro, Paraíba State, Northeast Region of Brazil.

Methodology/Principal findings

A total of 427 human stool samples were collected during June-July 2016 in the municipality of Umbuzeiro and an overall prevalence of 3.04% (13/427) resulted positive by duplicate Kato-Katz thick smear. A total of 1,175 snails identified as Biomphalaria straminea were collected from 14 breeding sites along the Paraíba riverbank and distributed in 46 pools. DNA from human stool samples and pooled snails was extracted using the phenol/chloroform method. When performing the SmMIT-LAMP assay a total of 49/162 (30.24%) stool samples resulted positive, including 12/13 (92.31%) that were Kato-Katz positive and 37/149 (24.83%) previously Kato-Katz negative. By nested-PCR, only 1/46 pooled DNA snail samples was positive. By SmMIT-LAMP assay, the same sample also resulted positive and an additional one was positive from a different breeding site. Data of human and snail surveys were used to build risk maps of schistosomiasis incidence using kernel density analysis.

Conclusions/Significance

This is the first study in which a LAMP assay was evaluated in both human stool and snail samples from a low-transmission schistosomiasis-endemic area. Our SmMIT-LAMP proved to be much more efficient in detection of S. mansoni in comparison to the 'gold standard' Kato-Katz method in human stool samples and the reference molecular nested-PCR in snails. The SmMIT-LAMP has demonstrated to be a useful molecular tool to identify potential foci of transmission in order to build risk maps of schistosomiasis.

In Brazil, around 1.8 million people, mostly in the northeastern region of the country, are thought to be infected with Schistosoma mansoni. Snails of the genus Biomphalaria serve as intermediate hosts of the S. mansoni. A special program for schistosomiasis control was implemented more than 40 years ago in Brazil, decreasing prevalence, morbidity, and mortality over the past years. PCR-based diagnostic methods have been successfully applied in a few endemic areas of schistosomiasis in Brazil, although they are not still widely used due to the highly technical requirements making them unviable for routine application in field conditions. Loop-mediated isothermal amplification (LAMP) technology could be a powerful tool to apply for point-of-care testing in resource-poor settings. In previous work, a LAMP-based method to detect S. mansoni DNA, called SmMIT-LAMP, was developed by our research group to detect S. mansoni DNA testing stool samples from experimentally infected mice. Here, with the aim to apply SmMIT-LAMP as a cost-effective molecular tool for the detection of S. mansoni in field applicable conditions, we assess SmMIT-LAMP in human and snail samples collected in an endemic area of Brazil. The results obtained by Kato-Katz analysis of human stool samples and nested-PCR performed in snails were compared with the SmMIT-LAMP assay. It is the first time that a LAMP-based method has been used to identify potential transmission foci and to evaluate the epidemiological risk of acquiring schistosomiasis.

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

BACKGROUND

Spatial modelling of STH and schistosomiasis epidemiology is now commonplace. Spatial epidemiological studies help inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration; however, limited attention has been given to propagated uncertainties, their interpretation, and consequences for the mapped values. Using currently published literature on the spatial epidemiology of helminth infections we identified: (1) the main uncertainty sources, their definition and quantification and (2) how uncertainty is informative for STH programme managers and scientists working in this domain.

METHODOLOGY/PRINCIPAL FINDINGS

We performed a systematic literature search using the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol. We searched Web of Knowledge and PubMed using a combination of uncertainty, geographic and disease terms. A total of 73 papers fulfilled the inclusion criteria for the systematic review. Only 9% of the studies did not address any element of uncertainty, while 91% of studies quantified uncertainty in the predicted morbidity indicators and 23% of studies mapped it. In addition, 57% of the studies quantified uncertainty in the regression coefficients but only 7% incorporated it in the regression response variable (morbidity indicator). Fifty percent of the studies discussed uncertainty in the covariates but did not quantify it. Uncertainty was mostly defined as precision, and quantified using credible intervals by means of Bayesian approaches.

CONCLUSION/SIGNIFICANCE

None of the studies considered adequately all sources of uncertainties. We highlighted the need for uncertainty in the morbidity indicator and predictor variable to be incorporated into the modelling framework. Study design and spatial support require further attention and uncertainty associated with Earth observation data should be quantified. Finally, more attention should be given to mapping and interpreting uncertainty, since they are relevant to inform decisions regarding the number of people at risk as well as the geographic areas that need to be targeted with mass drug administration.

Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology

Background

Monitoring spatial disease risk (e.g. identifying risk areas) is of great relevance in public health research, especially in cancer epidemiology. A common strategy uses case-control studies and estimates a spatial relative risk function (sRRF) via kernel density estimation (KDE). This study was set up to evaluate the sRRF estimation methods, comparing fixed with adaptive bandwidth-based KDE, and how they were able to detect ‘risk areas’ with case data from a population-based cancer registry.

Methods

The sRRF were estimated within a defined area, using locational information on incident cancer cases and on a spatial sample of controls, drawn from a high-resolution population grid recognized as underestimating the resident population in urban centers. The spatial extensions of these areas with underestimated resident population were quantified with population reference data and used in this study as ‘true risk areas’. Sensitivity and specificity analyses were conducted by spatial overlay of the ‘true risk areas’ and the significant (α=.05) p-contour lines obtained from the sRRF.

Results

We observed that the fixed bandwidth-based sRRF was distinguished by a conservative behavior in identifying these urban ‘risk areas’, that is, a reduced sensitivity but increased specificity due to oversmoothing as compared to the adaptive risk estimator. In contrast, the latter appeared more competitive through variance stabilization, resulting in a higher sensitivity, while the specificity was equal as compared to the fixed risk estimator. Halving the originally determined bandwidths led to a simultaneous improvement of sensitivity and specificity of the adaptive sRRF, while the specificity was reduced for the fixed estimator.

Conclusion

The fixed risk estimator contrasts with an oversmoothing tendency in urban areas, while overestimating the risk in rural areas. The use of an adaptive bandwidth regime attenuated this pattern, but led in general to a higher false positive rate, because, in our study design, the majority of true risk areas were located in urban areas. However, there is a strong need for further optimizing the bandwidth selection methods, especially for the adaptive sRRF.

Electronic supplementary material

The online version of this article (doi:10.1186/s12942-015-0005-9) contains supplementary material, which is available to authorized users.

Validation of three geolocation strategies for health-facility attendees for research and public health surveillance in a rural setting in western Kenya

Understanding the spatial distribution of disease is critical for effective disease control. Where formal address networks do not exist, tracking spatial patterns of clinical disease is difficult. Geolocation strategies were tested at rural health facilities in western Kenya. Methods included geocoding residence by head of compound, participatory mapping and recording the self-reported nearest landmark. Geocoding was able to locate 72·9% [95% confidence interval (CI) 67·7–77·6] of individuals to within 250 m of the true compound location. The participatory mapping exercise was able to correctly locate 82·0% of compounds (95% CI 78·9–84·8) to a 2 × 2·5 km area with a 500 m buffer. The self-reported nearest landmark was able to locate 78·1% (95% CI 73·8–82·1) of compounds to the correct catchment area. These strategies tested provide options for quickly obtaining spatial information on individuals presenting at health facilities.