Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study.

INTRODUCTION: Timeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and makeitdifficultto identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants. METHODS: We used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019-20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation-stochastic partial differential equation (INLA-SPDE) approach. RESULTS: We found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specificdistricts in the central and eastern regions of The Gambia consistentlyexhibited the highest prevalence of delayed vaccination, while the coastal districts showed alower prevalence forallthree vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants. CONCLUSIONS: Our approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact

Spatial dependence of body mass index and exposure to night-time noise in the Geneva urban area

In this study, we calculated the night-noise mean (SonBase 2014, compatible with the EU Environmental Noise Directive) for the 5 classes obtained after computation of Local Indicators of Spatial Association (LISA; Anselin et al 1995) on the BMI of the participants in the Bus Santé study, a cohort managed by the Geneva University Hospitals (N=15’544; Guessous et al 2014). We expected the mean of dBs to be significantly higher in the group showing spatial dependence of high BMI values (high-high class). We ran an ANOVA and multiple T-tests to compare the dB means between LISA clusters. The approach was applied to the participants of the whole State Geneva cohort, and to a reduced set of individuals living in the urban environment of the municipality of Geneva only

Current practices in the spatial analysis of cancer: flies in the ointment

While many lessons have been learned from the spatial analysis of cancer, there are several caveats that apply to many, if not all such analyses. As "flies in the ointment", these can substantially detract from a spatial analysis, and if not accounted for, can lead to weakened and erroneous conclusions. This paper discusses several assumptions and limitations of spatial analysis, identifies problems of scientific inference, and concludes with potential solutions and future directions

Spatial methods for infectious disease outbreak investigations: systematic literature review

Investigations of infectious disease outbreaks are conventionally framed in terms of person, time and place. Although geographic information systems have increased the range of tools available, spatial analyses are used relatively infrequently. We conducted a systematic review of published reports of outbreak investigations worldwide to estimate the prevalence of spatial methods, describe the techniques applied and explore their utility. We identified 80 reports using spatial methods published between 1979 and 2013, ca 0.4% of the total number of published outbreaks. Environmental or waterborne infections were the most commonly investigated, and most reports were from the United Kingdom. A range of techniques were used, including simple dot maps, cluster analyses and modelling approaches. Spatial tools were usefully applied throughout investigations, from initial confirmation of the outbreak to describing and analysing cases and communicating findings. They provided valuable insights that led to public health actions, but there is scope for much wider implementation and development of new methods.</jats:p

Kriging: applying geostatistical techniques to the genetic study of complex diseases

Complex diseases often display geographic distribution patterns. Therefore, the integration of genetic and environmental factors using geographic information systems (GIS) and specific statistical analyses that consider the spatial dimension of data greatly assist in the research of their gene-environment interactions (GxE). The objectives of the present work were to assess the application of a geostatistical interpolation technique (kriging) in the study of complex diseases with a distinct heterogeneous geographic distribution and to test its performance as an alternative to conventional genetic imputation methods. Using multiple sclerosis as a case study, kriging demonstrated to be a flexible and valuable tool for integrating information from various sources and at a different spatial resolution into a model that easily allowed to visualize its heterogeneous geographic distribution in Europe and to explore the intertwined interactions between several known genetic and environmental risk factors. Even though the performance of kriging did not surpass the results obtained with current imputation techniques, this pilot study revealed a worse performance of the latter for rare variants in chromosomal regions with a low density of markers

Comparing Euclidean and River Distance Metrics in the Bayesian Maximum Entropy (BME) Estimation of E. coli Concentrations in Connecticut Rivers

Due to budget and personnel constraints, Connecticut is unable to collect data for E. coli concentration for every site every day. The Bayesian maximum entropy (BME) framework for geostatistical estimation integrates general knowledge about the space/time random field and site-specific knowledge. We developed a method to optimize the global offset function, comparing Euclidean and river distance metrics. By shrinking the kernel, we saw that as the variance decreases for the river distance approach, the spatial range holds steady. For covariance modeling, we found that river distance could estimate concentrations at a longer spatial range than could be accounted for by the tortuosity. We found areas of high concentration in the north central portion of the state and low concentrations in the east. We calculated the number of impaired river miles and we estimate that about 34% of river reaches under study had a greater than 50% chance of being impaired.Bachelor of Science in Public Healt

Combating substance abuse with the potential of geographic information system combining multivariate analysis

Substance abuse problems have been a growing concern for people from all over the world. The objective of this study is to demonstrate the usefulness of a combination between a geographic information system and a multivariate analysis in substance abuse research. However, due to the limited studies on a combination of both these methods in the substance abuse field, we review some other studies in various fields indicating the potential of this method in future substance abuse studies. As the expertise of GIS lies in spatial analysis and the multivariate analysis lies in analyzing huge data thus being capable of interpreting the results very well, we hope this method will attract researchers to applying it in their studies and can consequently and indirectly help in combating substance abuse problems.Keywords: substance abuse; geographic information system; multivariate analysis;  spatial analysis; comba

Spatial and Spatiotemporal Modeling of Epidemiological Data

This dissertation focuses on modeling approach for spatial and spatiotemporal data with epidemiological applications. Chapter one gives the general overview of spatial and spatiotemporal data and challenges in the statistical analysis of spatial and spatiotemporal data, and motivation and objectives of the study. Chapter two describes the regression models commonly used in spatial data analysis. Various types of regression methods such as OLS, GWR and MGWR were used to study the association between diabetes prevalence and socioeconomic and lifestyle factors on county level data of Midwestern United States. A new analysis workflow is purposed for regression analysis of spatial data. Chapter three describes recently developed INLA as an alternative of traditionally used MCMC in Bayesian hierarchical models. INLA method was used to identify the best regression model for the spatiotemporal regression analysis of Lyme disease count data with climatic covariates in county-level data in Minnesota. Chapter four gives the contribution of this dissertation and discusses the direction for the future research