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Cancer Epidemiology
Published in Trevor F. Cox, Medical Statistics for Cancer Studies, 2022
We end this chapter touching on the topic of spatial epidemiology that brings together spatial statistics and epidemiology. The book edited by Lawson[35] might be of interest to the reader. We will only give one very simple example.
Inferences for Markov Chains in Continuous Time
Published in Lyle D. Broemeling, Bayesian Analysis of Infectious Diseases, 2021
Of course, spatial statistics is an active area of research and for additional details refer to Bivand, Pebesma, and Gomez-Rubio [2, pp. 249–252], and from a Bayesian perspective, to Blangiardo and Camdelli [3}. The R package “spatstat” is very useful for additional ideas about simulation of spatial processes similar to the Poisson. See the technical report from UCLA.
Spatial Models
Published in Virgilio Gómez-Rubio, Bayesian Inference with INLA, 2020
Spatial statistics is traditionally divided into three main areas depending on the type of problem and data: lattice data, geostatistics and point patterns (Cressie, 2015). Sometimes, spatial data is also measured over time and spatio-temporal models can be proposed (Cressie and Wikle, 2011). In the next sections models for the different types of spatial data will be considered. In Section 8.6 models for spatio-temporal data will be described. Blangiardo and Cameletti (2015) and Krainski et al. (2018) provide a thorough description of most of the models described in this section. Bivand et al. (2013) and Lovelace et al. (2019) provide general description on handling spatial data in R and are recommended reads.
Forty years on: a new national study of hearing in England and implications for global hearing health policy
Published in International Journal of Audiology, 2023
Dialechti Tsimpida, Maria Panagioti, Evangelos Kontopantelis
We utilised the geographical information systems (GIS) approach in public health (Wang 2020) to estimate the accuracy of the existing hearing loss data and compare it with the ELSA dataset. Spatial statistics is a distinct area of research; it focuses on examining the distributions, attributes, and relationships of features in spatial data to help gain a better understanding of the data (Scott and Janikas 2010). Spatial statistics differentiate from the traditional statistical theory by rejecting the idea of assumed independence of observations. Instead, consider that space and location influence the observations, assuming that nearby units are somehow associated (Getis 1999; Fotheringham and Rogerson 2013; Griffith 2020). The above can be summarised in Tobler’s first law of geography, which argues that “Everything is related to everything else, but near things are more related than distant things” (Tobler 1970).
Geographical patterns and effects of human and mechanical factors on road traffic crashes in Nigeria
Published in International Journal of Injury Control and Safety Promotion, 2020
Richard Adeleke, Tolulope Osayomi, Ayodeji E Iyanda
The data were analysed with the aid of spatial statistical techniques such as Global Moran’s I and spatial regression model. Other statistical techniques used include Pearson correlation and OLS regression. Spatial statistics is based on the assumption of the non-independence of observations; that is, nearby features are closely associated (Tobler, 1970). Consequently, spatial statistics are used in the analysis of spatial patterns, modelling spatial relationships and detecting spatial clusters (Osayomi, 2019). The Global Moran’s I was used to determine the nature of the geographical distribution of road traffic crashes, fatality and injury. Global Moran’s I value varies from −1 through 0 to +1. If Moran’s I value is near +1, it is an indication of a high positive spatial autocorrelation which also means that states with similar values of RTCs, fatality and injury are clustered over space. In contrast, a Moran’s I value near −1 is an indication of a high negative spatial autocorrelation, which means that states with dissimilar values of RTCs, fatality and injury are adjacent. A random pattern is depicted when the z scores are between −1.96 and +1.96 with a p value greater than 0.05. On the other hand, the pattern is clustered when the p value is less than 0.05.
Assessing Spatial Relationships between Prescription Drugs, Race, and Overdose in New York State from 2013 to 2015
Published in Journal of Psychoactive Drugs, 2019
Phillip L. Marotta, Tim Hunt, Louisa Gilbert, Elwin Wu, Dawn Goddard-Eckrich, Nabila El-Bassel
We used spatial statistics to identify clusters of counties in New York that are disproportionately impacted by the opioid epidemic. Our findings emphasize that spatial patterns differ based on the type of opioid in New York, and may require different responses to reduce opioid-related death rates. Multi-county coalitions involving the coordination of services, resource sharing, and information sharing may be promising strategies for addressing clusters of high rates of opioid-related deaths in New York State. Specifically, cluster-based spatial statistics should be conducted frequently on death rate data and information shared with local law enforcement, addiction specialists, and other medical professionals to increase awareness of spatial locations where high rates of different types of opioids are most likely to cluster together over time. Spatial analyses should coordinate with the deployment of naloxone supplies and training programs to be situated in counties with the highest rates of opioid overdose in New York State. Equipping law enforcement, emergency medical services, pharmacies, treatment providers, and laypersons with naloxone is an empirically validated strategy of reducing opioid-related deaths (Davis et al. 2015a, 2016b). These strategies should be coordinated at the county level using spatial analyses to target interventions to geographic locations with highest rates of opioid-related death rates. Mobile crisis drug treatment and emergency drug treatment services should cater to the spatial dynamics of opioid-related overdose deaths in New York State. Spatial analysis should become a greater part of centralized statewide surveillance systems conducted annually or more frequently that focus on overdose prevention though early identification of emerging clusters of hotspots of opioid-related deaths.