Spatial scan statistics using elliptic windows

Spatio-temporal variation of malaria hotspots in Central Senegal, 2008-2012

Background

In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation.

Methods

This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model.

Results

The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR = 0.48, IC95%: 0.33–0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown.

Conclusion

In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed.

Trial registration

The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.

Spatial variation in the frequency of knockdown resistance genotypes in Florida Aedes aegypti populations

BACKGROUND

The development of insecticide resistance in disease-vectoring mosquito species can lead to vector control failure and disease resurgence. However, insecticide applications remain an essential public health intervention. In Florida, insecticide resistance in Aedes aegypti, an anthropophilic mosquito species capable of transmitting dengue, chikungunya, and Zika virus, is a major concern. Understanding the location, scale, and driving factors of insecticide resistance can enhance the ability of vector control organizations to target populations effectively.

METHODS

We used previously collected data on frequencies of mutations that confer resistance to commonly used pyrethroid insecticides in Ae. aegypti specimens from 62 sites distributed across 18 counties in Florida. To determine the scale of clustering for the most resistant variant, we used a Ripley's K function. We also used a spatial scanning statistic technique to identify locations of clusters where higher than expected frequencies of susceptible or resistant mosquitoes occurred. We then tested for associations between landscape, demographic, and insecticide-use factors using a beta regression modelling approach and evaluated the effect of spatial lag and spatial error terms on overall explanatory power of these models.

RESULTS

The scale at which maximum clustering of the most resistant variant occurs is approximately 20 kilometers. We identified statistically significant clusters of genotypes associated with resistance in several coastal cities, although some of these clusters were near significant clusters of susceptible mosquitoes, indicating selection pressures vary at the local scale. Vegetation density, distance from roads, and pyrethroid-use by vector control districts were consistently significant predictors of knockdown resistance genotype frequency in the top-performing beta regression models, although pyrethroid use surprisingly had a negatively associated with resistance. The incorporation of spatial lags resulted in improvements to the fit and explanatory power of the models, indicating an underlying diffusion process likely explains some of the spatial patterns observed.

CONCLUSIONS

The genetic mutations that confer resistance to pyrethroids in Ae. aegypti mosquitoes in Florida exhibit spatial autocorrelation and patterns that can be partially explained by landscape and insecticide-use factors. Further work at local scales should be able to identify the mechanisms by which these variables influence selection for alleles associated with resistance.

A scan statistic for binary outcome based on hypergeometric probability model, with an application to detecting spatial clusters of Japanese encephalitis

As a useful tool for geographical cluster detection of events, the spatial scan statistic is widely applied in many fields and plays an increasingly important role. The classic version of the spatial scan statistic for the binary outcome is developed by Kulldorff, based on the Bernoulli or the Poisson probability model. In this paper, we apply the Hypergeometric probability model to construct the likelihood function under the null hypothesis. Compared with existing methods, the likelihood function under the null hypothesis is an alternative and indirect method to identify the potential cluster, and the test statistic is the extreme value of the likelihood function. Similar with Kulldorff's methods, we adopt Monte Carlo test for the test of significance. Both methods are applied for detecting spatial clusters of Japanese encephalitis in Sichuan province, China, in 2009, and the detected clusters are identical. Through a simulation to independent benchmark data, it is indicated that the test statistic based on the Hypergeometric model outweighs Kulldorff's statistics for clusters of high population density or large size; otherwise Kulldorff's statistics are superior.

Geographic distribution of autism in California: a retrospective birth cohort analysis

Prenatal environmental exposures are among the risk factors being explored for associations with autism. We applied a new procedure combining multiple scan cluster detection tests to identify geographically defined areas of increased autism incidence. This procedure can serve as a first hypothesis-generating step aimed at localized environmental exposures, but would not be useful for assessing widely distributed exposures, such as household products, nor for exposures from nonpoint sources, such as traffic. Geocoded mothers' residences on 2,453,717 California birth records, 1996-2000, were analyzed including 9,900 autism cases recorded in the California Department of Developmental Services (DDS) database through February 2006 which were matched to their corresponding birth records. We analyzed each of the 21 DDS Regional Center (RC) catchment areas separately because of the wide variation in diagnostic practices. Ten clusters of increased autism risk were identified in eight RC regions, and one Potential Cluster in each of two other RC regions.After determination of clusters, multiple mixed Poisson regression models were fit to assess differences in known demographic autism risk factors between the births within and outside areas of elevated autism incidence, independent of case status.Adjusted for other covariates, the majority of areas of autism clustering were characterized by high parental education, e.g. relative risks >4 for college-graduate vs. nonhigh-school graduate parents. This geographic association possibly occurs because RCs do not actively conduct case finding and parents with lower education are, for various reasons, less likely to successfully seek services.

Spatial Scan Statistics Adjusted for Multiple Clusters

The spatial scan statistic is one of the main epidemiological tools to test for the presence of disease clusters in a geographical region. While the statistical significance of the most likely cluster is correctly assessed using the model assumptions, secondary clusters tend to have conservatively highP-values. In this paper, we propose a sequential version of the spatial scan statistic to adjust for the presence of other clusters in the study region. The procedure removes the effect due to the more likely clusters on less significant clusters by sequential deletion of the previously detected clusters. Using the Northeastern United States geography and population in a simulation study, we calculated the type I error probability and the power of this sequential test under different alternative models concerning the locations and sizes of the true clusters. The results show that the type I error probability of our method is close to the nominalαlevel and that for secondary clusters its power is higher than the standard unadjusted scan statistic.