Social Vulnerability and Childhood Health: Bayesian Spatial Models to Assess Risks from Multiple Stressors on Childhood Diarrhoea in Malawi

2022 ◽  
Author(s):  
Lawrence N. Kazembe
Keyword(s):  
Social Vulnerability ◽  
Multiple Stressors ◽  
Spatial Models ◽  
Childhood Health ◽  
Childhood Diarrhoea ◽  
Bayesian Spatial Models

scholarly journals Feline sporotrichosis

2021 ◽  
Vol 58 ◽  
pp. e188291
Author(s):  
Ligia Neves Scuarcialupi ◽  
Fernando Cortez Pereira ◽  
Oswaldo Santos Baquero
Keyword(s):  
Social Vulnerability ◽  
Causal Explanation ◽  
Spatial Models ◽  
Effective Prevention ◽  
Spatial Spread ◽  
Census Tracts ◽  
Priority Index ◽  
Causal Processes ◽  
Social Inequities ◽  
Bayesian Spatial Models

Over the past two decades, many Brazilian cities have been reporting an increasing incidence and spread of feline sporotrichosis. The disease is neglected, and little is known about the causal processes underlying its epidemic occurrence. This study characterized the spatiotemporal dynamics of feline sporotrichosis in Guarulhos. Moreover, we proposed and tested a causal explanation for its occurrence and zoonotic transmission, giving a key role to social vulnerability. A direct acyclic graph represented the causal explanation, while Bayesian spatial models supported its test as well as the attribution of a risk-based priority index to the census tracts of the city. Between 2011 and 2017, the disease grew exponentially and the spatial spread increased. The model findings showed a dose-response pattern between an index of social vulnerability and the incidence of feline sporotrichosis. This pattern was not strictly monotonic, so some census tracts received a higher priority index than others with higher vulnerability. According to our causal explanation, there will not be effective prevention of feline and zoonotic sporotrichosis as long as social inequities continue imposing precarious livelihoods.

Bayesian spatial models of the association between interpersonal violence, animal abuse and social vulnerability in São Paulo, Brazil

2018 ◽  
Vol 152 ◽  
pp. 48-55 ◽  
Author(s):  
Oswaldo Santos Baquero ◽  
Fernando Ferreira ◽  
Marcelo Robis ◽  
José Soares Ferreira Neto ◽  
Jason Ardila Onell
Keyword(s):  
Social Vulnerability ◽  
Interpersonal Violence ◽  
Spatial Models ◽  
Sao Paulo ◽  
São Paulo ◽  
Animal Abuse ◽  
Bayesian Spatial Models

scholarly journals Comparing Bayesian spatial models: Goodness-of-smoothing criteria for assessing under- and over-smoothing

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0233019 ◽  
Author(s):  
Earl W. Duncan ◽  
Kerrie L. Mengersen
Keyword(s):  
Spatial Models ◽  
Bayesian Spatial Models

scholarly journals Evaluating health facility access using Bayesian spatial models and location analysis methods

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0218310 ◽  
Author(s):  
Nicholas J. Tierney ◽  
Antonietta Mira ◽  
H. Jost Reinhold ◽  
Giuseppe Arbia ◽  
Samuel Clifford ◽  
...  
Keyword(s):  
Health Facility ◽  
Spatial Models ◽  
Location Analysis ◽  
Analysis Methods ◽  
Bayesian Spatial Models

scholarly journals Using Bayesian spatial models to map and to identify geographical hotspots of multidrug-resistant tuberculosis in Portugal between 2000 and 2016

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Olena Oliveira ◽  
Ana Isabel Ribeiro ◽  
Elias Teixeira Krainski ◽  
Teresa Rito ◽  
Raquel Duarte ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
High Risk ◽  
Control Strategies ◽  
Spatial Models ◽  
Multidrug Resistant ◽  
Resistant Tuberculosis ◽  
Multidrug Resistant Tuberculosis ◽  
Mdr Tb ◽  
Risk Areas ◽  
Bayesian Spatial Models

Abstract Multidrug-resistant tuberculosis (MDR-TB) is a major threat to the eradication of tuberculosis. TB control strategies need to be adapted to the necessities of different countries and adjusted in high-risk areas. In this study, we analysed the spatial distribution of the MDR- and non-MDR-TB cases across municipalities in Continental Portugal between 2000 and 2016. We used Bayesian spatial models to estimate age-standardized notification rates and standardized notification ratios in each area, and to delimitate high- and low-risk areas, those whose standardized notification ratio is significantly above or below the country’s average, respectively. The spatial distribution of MDR- and non-MDR-TB was not homogeneous across the country. Age-standardized notification rates of MDR-TB ranged from 0.08 to 1.20 and of non-MDR-TB ranged from 7.73 to 83.03 notifications per 100,000 population across the municipalities. We identified 36 high-risk areas for non-MDR-TB and 8 high-risk areas for MDR-TB, which were simultaneously high-risk areas for non-MDR-TB. We found a moderate correlation (ρ = 0.653; 95% CI 0.457–0.728) between MDR- and non-MDR-TB standardized notification ratios. We found heterogeneity in the spatial distribution of MDR-TB across municipalities and we identified priority areas for intervention against TB. We recommend including geographical criteria in the application of molecular drug resistance to provide early MDR-TB diagnosis, in high-risk areas.

scholarly journals Bayesian spatial models with a mixture neighborhood structure

2012 ◽  
Vol 109 ◽  
pp. 88-102 ◽  
Author(s):  
E.C. Rodrigues ◽  
R. Assunção
Keyword(s):  
Spatial Models ◽  
Neighborhood Structure ◽  
Bayesian Spatial Models
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