Bayesian Spatial Model Research Articles

Bayesian spatial modeling of moose count data: increasing estimator efficiency and exploring ecological hypotheses

AbstractMoose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model‐based analytical approach and finite‐population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well‐documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon‐Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets.

Geographic Variation in HIV Testing Among Transgender and Nonbinary Adults in the United States.

Transgender and nonbinary (TNB) populations are disproportionately affected by HIV and few local health departments or HIV surveillance systems collect/report data on TNB identities. Our objective was to estimate the prevalence of HIV testing among TNB adults by US county and state, with a focus on the Ending the HIV Epidemic (EHE) geographies. We applied a Bayesian hierarchical spatial small area estimation model to data from the 2015 US Transgender Survey, a large national cross-sectional Internet-based survey. We estimated the county- and state-level proportion of TNB adults who ever tested or tested for HIV in the last year by gender identity, race/ethnicity, and age. Our analysis included 26,100 TNB participants with valid zip codes who resided in 1688 counties (54% of all 3141 counties that cover 92% of the US population). The median county-level proportion of TNB adults who ever tested for HIV was 44% (range 10%-80%) and who tested in the last year was 17% (range 4%-44%). Within most counties, testing was highest among transgender women, black respondents, and people aged ≥25 years. HIV testing was lowest among nonbinary people and young adults aged <25 years. The proportion of TNB adults who tested within the last year was very low in most EHE counties and in all 7 rural states. HIV testing among TNB adults is likely below national recommendations in the majority of EHE geographies. Geographic variation in HIV testing patterns among TNB adults indicates that testing strategies need to be tailored to local settings.

Jointly modeling bus and taxi ridership: A Bayesian multivariate spatial analysis accounting for cross‐ridership correlation

AbstractUnderstanding the spatial patterns of transit ridership and their ascribable factors is critical for urban and transportation planning. Existing studies predominantly examine a single type of transit ridership but neglect important cross‐ridership correlation. This study aims to develop a Bayesian multivariate spatial modeling approach for jointly analyzing multiple ridership. In particular, cross‐ridership correlation is estimated using correlated spatially unstructured residuals, correlated spatial random effects, or the combination of both. The proposed approach is applied to a case study of Wuhu, China, where the correlation between bus and taxi ridership is accounted for in the modeling of transit ridership and their built environment determinants. Results show that accounting for cross‐ridership correlation significantly improves model‐fitting, but the fashion used to induce such a correlation matters. Furthermore, the role of built environment in determining transit ridership could be over‐estimated without considering cross‐ridership correlation. This study contributes to statistical modeling of transit ridership by applying a spatial multivariate approach, which can improve our understanding of the underlying processes of multiple ridership, their interactions, as well as their associations with physical environments.

Factors Affecting the Covid-19 Risk in South Sulawesi Province, Indonesia: A Bayesian Spatial Model

The transmission of Coronavirus diseases 2019 (Covid-19) grows continuously around the world. Although a number of researches of modelling Covid-19 cases have been conducted, there was limited research implementing the Bayesian Spatial Conditional Autoregressive (CAR) model. Factors affecting the Covid-19 risk especially population density and distance to the capital city have been studied, but the results are inconsistent and limited research has been done in Indonesia. This study aims to assess the most appropriate Bayesian spatial CAR Leroux models and examine factors that affect the risk of Covid-19 in South Sulawesi Province. Data on the number of Covid-19 cases (19 March 2020 - 31 January 2022), population density, and distance to the capital city were used for every 24 districts. Several criteria were used in choosing the most appropriate model. The results depict that Bayesian spatial CAR Leroux with hyperprior IG (1, 0.01) model with the inclusion of population density were preferred. It is concluded that a factor that significantly affects the number of Covid-19 cases is population density. There was a positive correlation between the population density and Covid-19 risk. Makassar city has the highest relative risk (RR) among other districts while Bone has the lowest RR of Covid-19.

Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers.

Spatial species distribution models often assume isotropy and stationarity, implying that spatial dependence is direction-invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present. Despite this, the issue of nonstationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of barriers in the distribution of Xylella fastidiosa in the demarcated area in Alicante, Spain. Occurrence data from 2018 were analyzed through spatial Bayesian hierarchical models. The stationary model, illustrating a scenario without control interventions or geographical features, was compared with three nonstationary models: a model with mountains as physical barriers, and two models with a continuous and discontinuous perimeter barrier representing hypothetical control interventions. In the stationary model, the posterior mean of the spatial range, as the distance where two observations are uncorrelated, was 4,030 m 95% credible interval (2,907 to 5,564). This distance can be used to define the buffer zone in the demarcated area. The predicted probability of X. fastidiosa presence in the area outside the barrier was 0.46 with the stationary model, whereas it was reduced to 0.29 and 0.36 with the continuous and discontinuous barrier models, respectively. Differences between the discontinuous and continuous barrier models showed that breaks, where no control interventions were implemented, resulted in a higher predicted probability of X. fastidiosa presence in the areas with low sampling intensity. These results may help authorities prioritize the areas for surveillance and disease control.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Longitudinal surface‐based spatial Bayesian GLM reveals complex trajectories of motor neurodegeneration in ALS

Longitudinal fMRI studies hold great promise for the study of neurodegenerative diseases, development and aging, but realizing their full potential depends on extracting accurate fMRI-based measures of brain function and organization in individual subjects over time. This is especially true for studies of rare, heterogeneous and/or rapidly progressing neurodegenerative diseases. These often involve small samples with heterogeneous functional features, making traditional group-difference analyses of limited utility. One such disease is amyotrophic lateral sclerosis (ALS), a severe disease resulting in extreme loss of motor function and eventual death. Here, we use an advanced individualized task fMRI analysis approach to analyze a rich longitudinal dataset containing 190 hand clench fMRI scans from 16 ALS patients (78 scans) and 22 age-matched healthy controls (112 scans). Specifically, we adopt our cortical surface-based spatial Bayesian general linear model (GLM), which has high power and precision to detect activations in individual subjects, and propose a novel longitudinal extension to leverage information shared across visits. We perform all analyses in native surface space to preserve individual anatomical and functional features. Using mixed-effects models to subsequently study the relationship between size of activation and ALS disease progression, we observe for the first time an inverted U-shaped trajectory of motor activations: at relatively mild motor disability we observe enlarging activations, while at higher levels of motor disability we observe severely diminished activation, reflecting progression toward complete loss of motor function. We further observe distinct trajectories depending on clinical progression rate, with faster progressors exhibiting more extreme changes at an earlier stage of disability. These differential trajectories suggest that initial hyper-activation is likely attributable to loss of inhibitory neurons, rather than functional compensation as earlier assumed. These findings substantially advance scientific understanding of the ALS disease process. This study also provides the first real-world example of how surface-based spatial Bayesian analysis of task fMRI can further scientific understanding of neurodegenerative disease and other phenomena. The surface-based spatial Bayesian GLM is implemented in the BayesfMRI R package

Bayesian spatial modeling of COVID-19 case-fatality rate inequalities

The ongoing outbreak of COVID-19 challenges the health systems and epidemiological responses of all countries worldwide. Although preventive measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we identify the spatial association of socioeconomic factors and the risk for dying from COVID-19 in Colombia. We confirm that from March 16 to October 04, 2020, the COVID-19 case-fatality rate and the multidimensional poverty index have a heterogeneous spatial distribution. Spatial analysis reveals that the risk of dying from COVID-19 increases in regions with a higher proportion of poor people with dwelling (RR 1.74 95%CI = 1.54–9.75), educational (RR 1.69 95%CI = 1.36–5.94), childhood/youth (RR 1.35 95%CI = 1.08–4.03), and health (RR 1.16 95%CI = 1.06–2.04) deprivations. These findings evidence the vulnerability of most disadvantaged members of society to dying in a pandemic and assist the spatial planning of preventive strategies focused on vulnerable communities.

Geographic distribution of malignant mesothelioma incidence and survival in Australia

ObjectivesTo understand the geographic distribution of and area-level factors associated with malignant mesothelioma incidence and survival in Australia. Materials and MethodsGeneralised linear models and Bayesian spatial models were fitted using population registry data. Area-level covariates were socioeconomic quintile, remoteness category and state or territory. The maximised excess events test was used to test for spatial heterogeneity. ResultsThere was strong evidence of spatial differences in standardised incidence rates for malignant mesothelioma but survival was uniformly poor. Incidence rates varied by state or territory and were lower in remote areas. Patterns in the geographic distribution of modelled incidence counts for malignant mesothelioma differed substantially from patterns of standardised incidence rates. ConclusionsGeographic variation in the modelled incidence counts of malignant mesothelioma demonstrates varying demand for diagnostic and management services. The long latency period for this cancer coupled with migration complicates any associations with patterns of exposure, however some of the geographic distribution of diagnoses can be explained by the location of historical mines and asbestos-related industries.

Intra-urban variation in tuberculosis and community socioeconomic deprivation in Lisbon metropolitan area: a Bayesian approach

BackgroundMultidrug resistant tuberculosis (MDR-TB) is a recognized threat to global efforts to TB control and remains a priority of the National Tuberculosis Programs. Additionally, social determinants and socioeconomic deprivation have since long been associated with worse health and perceived as important risk factors for TB. This study aimed to analyze the spatial distribution of non-MDR-TB and MDR-TB across parishes of the Lisbon metropolitan area of Portugal and to estimate the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.MethodsIn this study, we used hierarchical Bayesian spatial models to analyze the spatial distribution of notification of non-MDR-TB and MDR-TB cases for the period from 2000 to 2016 across 127 parishes of the seven municipalities of the Lisbon metropolitan area (Almada, Amadora, Lisboa, Loures, Odivelas, Oeiras, Sintra), using the Portuguese TB Surveillance System (SVIG-TB). In order to characterise the populations, we used the European Deprivation Index for Portugal (EDI-PT) as an indicator of poverty and estimated the association between non-MDR-TB and MDR-TB and socioeconomic deprivation.ResultsThe notification rates per 10,000 population of non-MDR TB ranged from 18.95 to 217.49 notifications and that of MDR TB ranged from 0.83 to 3.70. We identified 54 high-risk areas for non-MDR-TB and 13 high-risk areas for MDR-TB. Parishes in the third [relative risk (RR) = 1.281, 95% credible interval (CrI): 1.021–1.606], fourth (RR = 1.786, 95% CrI: 1.420–2.241) and fifth (RR = 1.935, 95% CrI: 1.536–2.438) quintile of socioeconomic deprivation presented higher non-MDR-TB notifications rates. Parishes in the fourth (RR = 2.246, 95% CrI: 1.374–3.684) and fifth (RR = 1.828, 95% CrI: 1.049–3.155) quintile of socioeconomic deprivation also presented higher MDR-TB notifications rates.ConclusionsWe demonstrated significant heterogeneity in the spatial distribution of both non-MDR-TB and MDR-TB at the parish level and we found that socioeconomically disadvantaged parishes are disproportionally affected by both non-MDR-TB and MDR-TB. Our findings suggest that the emergence of MDR-TB and transmission are specific from each location and often different from the non-MDR-TB settings. We identified priority areas for intervention for a more efficient plan of control and prevention of non-MDR-TB and MDR-TB.Graphical

Targeting the search of African swine fever-infected wild boar carcasses: A tool for early detection.

This study analyses the temporal and spatial distribution of found dead African swine fever (ASF)-positive wild boar carcasses from 2017 to January 2021 in affected European countries: Bulgaria, Estonia, Germany, Hungary, Latvia, Lithuania, Romania, Poland, Serbia and Slovakia. During this period, a total of 21,785 cases were confirmed in 19,071 unique locations. The temporal analysis of aggregated cases per month evidenced that most countries located in southern latitudes showed a higher number of cases between January and April, whereas in northern latitudes there was no clear temporal pattern. The space-time K-function evidenced a space-time clustering in the ASF-positive wild boar carcasses, which was most prominent within distances of 2 km and within 1week. A Bayesian hierarchical spatial model was calibrated to evaluate the association between the probability of finding ASF-positive wild boar carcasses and landscape factors (i.e. the presence of a path and paved road), land use and wild boar abundance. Results showed the highest likelihood of finding ASF-positive wild boar carcasses in areas of transition between woodland and shrub, green urban areas and mixed forests. The presence of a path and a higher abundance of wild boar also increased slightly the odds of finding an ASF-positive dead wild boar. In summary, this paper aims to provide recommendations to design a search strategy to find ASF-infected wild boar carcasses, which is a crucial activity in the management of the disease, not just for surveillance purposes (i.e. the early detection of an introduction and the regular monitoring to understand the epidemiology and dynamics), but also for control, namely the disposal of infected carcasses as a virus source.

A Bayesian Hierarchical Spatial Model to Correct for Misreporting in Count Data: Application to State-Level COVID-19 Data in the United States.

The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people’s lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.

Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations

An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.

Bayesian nonparametric multivariate spatial mixture mixed effects models with application to American Community Survey special tabulations

Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other sample survey data has been a topic of recent interest among data users and federal statistical agencies. One strategy is to use a multivariate spatial mixed effects model with a Gaussian observation model and latent Gaussian process model. In practice, this works well for a wide range of tabulations. Nevertheless, in situations in which the data exhibit heterogeneity within or across geographies, and/or there is sparsity in the data, the Gaussian assumptions may be problematic and lead to underperformance. To remedy these situations, we propose a multivariate hierarchical Bayesian nonparametric mixed effects spatial mixture model to increase model flexibility. The number of clusters is chosen automatically in a data-driven manner. The effectiveness of our approach is demonstrated through a simulation study and motivating application of special tabulations for American Community Survey data.

Mid-Holocene European climate revisited: New high-resolution regional climate model simulations using pollen-based land-cover

Land-cover changes have a clear impact on local climates via biophysical effects. European land cover has been affected by human activities for at least 6000 years, but possibly longer. It is thus highly probable that humans altered climate before the industrial revolution (AD1750–1850). In this study, climate and vegetation 6000 years (6 ka) ago is investigated using one global climate model, two regional climate models, one dynamical vegetation model, pollen-based reconstruction of past vegetation cover using a model of the pollen-vegetation relationship and a statistical model for spatial interpolation of the reconstructed land cover. This approach enables us to study 6 ka climate with potential natural and reconstructed land cover, and to determine how differences in land cover impact upon simulated climate. The use of two regional climate models enables us to discuss the robustness of the results. This is the first experiment with two regional climate models of simulated palaeo-climate based on regional climate models.Different estimates of 6 ka vegetation are constructed: simulated potential vegetation and reconstructed vegetation. Potential vegetation is the natural climate-induced vegetation as simulated by a dynamical vegetation model driven by climate conditions from a climate model. Bayesian spatial model interpolated point estimates of pollen-based plant abundances combined with estimates of climate-induced potential un-vegetated land cover were used for reconstructed vegetation. The simulated potential vegetation is heavily dominated by forests: evergreen coniferous forests dominate in northern and eastern Europe, while deciduous broadleaved forests dominate central and western Europe. In contrast, the reconstructed vegetation cover has a large component of open land in most of Europe.The simulated 6 ka climate using reconstructed vegetation was 0–5 °C warmer than the pre-industrial (PI) climate, depending on season and region. The largest differences are seen in north-eastern Europe in winter with about 4–6 °C, and the smallest differences (close to zero) in southwestern Europe in winter. The simulated 6 ka climate had 10–20% more precipitation than PI climate in northern Europe and 10–20% less precipitation in southern Europe in summer. The results are in reasonable agreement with proxy-based climate reconstructions and previous similar climate modelling studies. As expected, the global model and regional models indicate relatively similar climates albeit with regional differences indicating that, models response to land-cover changes differently.The results indicate that the anthropogenic land-cover changes, as given by the reconstructed vegetation, in this study are large enough to have a significant impact on climate. It is likely that anthropogenic impact on European climate via land-use change was already taking place at 6 ka. Our results suggest that anthropogenic land-cover changes at 6 ka lead to around 0.5 °C warmer in southern Europe in summer due to biogeophysical forcing.

Multi-level policy responses to tackle socioeconomic inequalities in the incidence of COVID-19 in a European urban area

BackgroundSpain has been hit hard by COVID-19 since March 2020, especially in its metropolitan areas. We share experiences from Barcelona in measuring socioeconomic inequalities in the incidence of COVID-19 in the different waves, and in implementing coordinated and equity-oriented public health policy responses.MethodsWe collected daily data on confirmed COVID-19 cases, geocoded the address of residence to assign each case to one of the 73 neighborhoods and 1068 census tracts, and calculated the cumulative incidence of COVID-19 by neighborhood and five income groups (quintiles of census tracts) by sex across four waves of the pandemic. We adjusted hierarchical Bayesian spatial models to obtain the relative risk (RR) of cumulative incidences in each quintile compared with the richest areas. A variety of public health policies implemented to tackle the pandemic and especially these inequalities in COVID-19 incidence and vaccination are selected and described.ResultsArea-level income inequalities in the incidence of COVID-19 were present at different degree in all four waves. In the second wave (10/1/2020 to 12/6/2020), RR for the poorest income quintile census tracts compared with the richest was 1.43 (95% credible interval-CI-: 1.22–1.67) for men and 1.58 (95% CI: 1.35–1.83) for women. Later, inequalities in vaccination coverage also arose. Equity-oriented policy responses included: “health hotels” or home delivery of basic products for individuals with COVID-19 and without adequate conditions for isolation; new emergency facilities for homeless people, including those with active drug use; mass screening in high incidence areas; contingency plans for nursing homes and schools; adapting community health programs for their early reactivation; digital self-appointment support points and community vaccination days.ConclusionCOVID-19 hit Barcelona neighborhoods unequally, with variations between waves. The rapid availability of geolocalized data and by socioeconomic level helped public authorities to implement targeted policies and collaborative interventions for the most vulnerable populations. Further studies would be needed to evaluate their impact.

Spatio-temporal characteristics of Tuberculosis in Ghana

Background: The number of Tuberculosis (TB) cases or deaths is declining, however, the rate of decline is not adequate to meet the World Health Organization's (WHO's) mitigation. TB remains a public health problem in Ghana with a significant economic and health burden on its citizens and health care system. Consequently, there is a need for further studies about the disease aimed at accelerating the rate of decline in cases. Methods: The spatio-temporal characteristics of TB in Ghana using Bayesian spatial and spatio-temporal regression models was analysed in this study. Data were obtained from Ghana National Tuberculosis Programme (NTP) for the 10 regions of Ghana, collected over a six-year period. The study also examines some baseline predictors of TB infections to ascertain their effects on the TB risk across the ten regions in Ghana. Results: Hot-spots of TB cases are observed in the Upper East, Upper West, Volta, Western, and Central regions and low risk in the Northern, Ashanti, Greater Accra, Brong Ahafo, Eastern and Western regions. The results indicated a clustering of risk between neighboring regions. TB cure rate, TB success rate, knowledge about TB, awareness that TB is airborne, HIV prevalence, percentage of literacy, and high income are important predictors of detection for this disease across the ten regions of Ghana. Conclusion: Most regions in Ghana have similar TB risks. A substantial reduction in TB cases requires measures that will increase detection, success and cure rates, awareness, knowledge about how this disease spreads as well adequate health facilities with easy access.

Estimating mortality from coal workers' pneumoconiosis among Medicare beneficiaries with pneumoconiosis using binary regressions for spatially sparse data.

BackgroundCoal workers' pneumoconiosis (CWP) is an occupational lung disease due to inhalation of coal dust. We estimated mortality from CWP and other pneumoconioses among Medicare beneficiaries.MethodsWe used the 5% Medicare Limited Claims Data Set, 2011–2014, to identify patients diagnosed with ICD‐9‐CM 500 (CWP) through 505 (Asbestosis, Pneumoconiosis due to other silica or silicates, Pneumoconiosis due to other inorganic dust, Pneumonopathy due to inhalation of other dust, and Pneumoconiosis, unspecified) codes. We applied binary regression models with spatial random effects to determine the association between CWP and mortality. Our inferences are based on Bayesian spatial hierarchical models, and model fitting was performed using Integrated Nested Laplace Approximation (INLA) algorithm in R/RStudio software.ResultsThe median age of the sample was 76 years. In a sample of 8531 Medicare beneficiaries, 2568 died. Medicare beneficiaries with CWP had 25% higher odds of death (adjusted OR: 1.25, 95% CI: 1.07, 1.46) than those with other types of pneumoconiosis. The number of comorbid conditions elevated the odds of death by 10% (adjusted OR: 1.10, 95% CI: 1.09, 1.10).ConclusionCWP increases the likelihood of death among Medicare beneficiaries. Healthcare professionals should make concerted efforts to monitor patients with CWP to prevent premature mortality.

Exploring the impacts of street layout on the frequency of pedestrian crashes: A micro-level study

Introduction: Pedestrian safety has become a critical issue since walking is increasingly promoted as a sustainable transport mode. However, pedestrians are vulnerable to severe injury and mortality in road crashes. Therefore, it is important to understand the factors that affect the safety of pedestrians. This paper investigates the impacts of street layout on the frequency of pedestrian crashes by examining the interactive pattern of built environment, crossing facilities, and road characteristics. Method: A surrogate exposure variable of pedestrian crashes at the road-segment level is proposed by considering the locations of crossing facilities, distribution of points of interest (POIs), road characteristics, and pedestrian activities. A network-based kernel density technique is used to identify the pedestrian crash risk at the road segment level. Bayesian spatial models based on different exposure variables are employed and compared. Results: The results suggest that models using the surrogate exposure of pedestrian crashes provide better model fit than the ones simply using the density of pedestrians. It is also found that the presence of POIs is related to a higher risk of pedestrian-vehicle crash. In addition, a significantly higher number of pedestrian crashes are found to occur on segments with more bus stops and metro stations. Results also show that the longer the distance between the crossing facilities and road segments, the more pedestrian crashes are observed. Conclusions: The proposed aggregated indicator can provide more efficient exposure and higher prediction accuracy than the density of pedestrians. Besides, the POIs, crossing facilities, and road types were all significantly related to pedestrian crashes. Practical Applications: Our results suggest that the locations of POIs and transport facilities should be planned in a way that can decrease the number of road crossed or guide pedestrians to take safe crossing path.

Spatial Co-Morbidity of Childhood Acute Respiratory Infection, Diarrhoea and Stunting in Nigeria.

In low- and middle-income countries, children aged below 5 years frequently suffer from disease co-occurrence. This study assessed whether the co-occurrence of acute respiratory infection (ARI), diarrhoea and stunting observed at the child level could also be reflected ecologically. We considered disease data on 69,579 children (0–59 months) from the 2008, 2013, and 2018 Nigeria Demographic and Health Surveys using a hierarchical Bayesian spatial shared component model to separate the state-specific risk of each disease into an underlying disease-overall spatial pattern, common to the three diseases and a disease-specific spatial pattern. We found that ARI and stunting were more concentrated in the north-eastern and southern parts of the country, while diarrhoea was much higher in the northern parts. The disease-general spatial component was greater in the north-eastern and southern parts of the country. Identifying and reducing common risk factors to the three conditions could result in improved child health, particularly in the northeast and south of Nigeria.

Spatial patterns of HPV and Tdap vaccine dose administration and the association of health department clinic access in Georgia counties

ObjectiveTo characterize counties in GA by quantifying administered doses of the HPV and Tdap vaccines collected by the state health department immunization registry and indicators of Health Department (HD) clinic access. MethodsUsing a cross sectional study design, secondary data were collected from public health data sources for the years 2016 to 2018 for 159 counties of Georgia. The study population was male and female adolescents aged 13–17. The number of administered HPV and Tdap vaccine doses were modeled in relation to number of private and public HD clinics, number of HD clinics registered in the VFC program and the availability of public transportation using Poisson regression, negative binomial regression, and Bayesian spatial analysis. ResultsChoropleth maps showed similar clustering patterns between administered doses of the HPV vaccine and Tdap vaccine and increased counts of administered vaccine doses in counties with both public and private clinics. Administered doses of HPV and Tdap vaccines were found to exhibit spatial dependence across counties. Accounting for spatial dependence, the availability of public transit had a significant positive effect on administered HPV vaccine doses, while the number of private HD clinics had a significant positive effect on administered Tdap vaccine doses. ConclusionsMaps at the county level show vaccination variability, clustering patterns and provide additional insights on the access to health care. Bayesian spatial models are needed to accurately identify and estimate factors associated with administering doses of the HPV and Tdap vaccines. Future work is needed to further examine the utilization of HPV vaccination services among urban groupings.