scholarly journals Spatial Risk Factors for Pillar 1 COVID‐19 Excess Cases and Mortality in Rural Eastern England, UK

Risk Analysis ◽  
2021 ◽  
Author(s):  
Julii Brainard ◽  
Steve Rushton ◽  
Tim Winters ◽  
Paul R. Hunter
Keyword(s):  
Risk Factors ◽  
Spatial Risk

scholarly journals Spatial risk factors for Pillar 1 COVID-19 case counts and mortality in rural eastern England, UK

2020 ◽  
Author(s):  
Julii Brainard ◽  
Steve Rushton ◽  
Tim Winters ◽  
Paul R. Hunter
Keyword(s):  
Risk Factors ◽  
Air Quality ◽  
Population Density ◽  
Urban Areas ◽  
Structural Equation Models ◽  
Care Home ◽  
Age Distribution ◽  
Spatial Risk ◽  
Bed Capacity ◽  
Excess Deaths

Understanding is still developing about risk factors for COVID-19 infection or mortality. This is especially true with respect to identifying spatial risk factors and therefore identifying which geographic areas have populations who are at greatest risk of acquiring severe disease. This is a secondary analysis of patient records in a confined area of eastern England, covering persons who tested positive for SARS-CoV-2 through end May 2020, including dates of death and residence area. For each residence area (local super output area), we obtained data on air quality, deprivation levels, care home bed capacity, age distribution, rurality, access to employment centres and population density. We considered these covariates as risk factors for excess cases and excess deaths in the 28 days after confirmation of positive covid status relative to the overall case load and death recorded for the study area as a whole. We used the conditional autoregressive Besag-York-Mollie model to investigate the spatial dependency of cases and deaths allowing for a Poisson error structure. Structural equation models were also applied to clarify relationships between predictors and outcomes. Excess case counts or excess deaths were both predicted by the percentage of population age 65 years, care home bed capacity and less rurality: older population and more urban areas saw excess cases. Greater deprivation did not correlate with excess case counts but was significantly linked to higher mortality rates after infection. Neither excess cases nor excess deaths were predicted by population density, travel time to local employment centres or air quality indicators. Only 66% of mortality could be explained by locally high case counts. The results show a clear link between greater deprivation and higher COVID-19 mortality that is separate from wider community prevalence and other spatial risk factors.

scholarly journals A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping

2021 ◽  
Vol 15 (9) ◽  
pp. e0009679
Author(s):  
Gwenyth O. Lee ◽  
Luis Vasco ◽  
Sully Márquez ◽  
Julio C. Zuniga-Moya ◽  
Amanda Van Engen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Unmanned Aerial Vehicle ◽  
Data Streams ◽  
Spatial Organization ◽  
Disease Risk ◽  
Dengue Infection ◽  
Mixed Effect ◽  
Dengue Outbreak ◽  
Spatial Risk ◽  
Aerial Vehicle

Dengue is recognized as a major health issue in large urban tropical cities but is also observed in rural areas. In these environments, physical characteristics of the landscape and sociodemographic factors may influence vector populations at small geographic scales, while prior immunity to the four dengue virus serotypes affects incidence. In 2019, a rural northwestern Ecuadorian community, only accessible by river, experienced a dengue outbreak. The village is 2–3 hours by boat away from the nearest population center and comprises both Afro-Ecuadorian and Indigenous Chachi households. We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases. We mapped visible water containers seen in UAV images and calculated both the green-red vegetation index (GRVI) and household proximity to public spaces like schools and meeting areas. To identify risk factors for symptomatic dengue infection, we used mixed-effect logistic regression models to account for the clustering of symptomatic cases within households. We identified 55 dengue cases (9.5% of the population) from 37 households. Cases peaked in June and continued through October. Rural spatial organization helped to explain disease risk. Afro-Ecuadorian (versus Indigenous) households experience more symptomatic dengue (OR = 3.0, 95%CI: 1.3, 6.9). This association was explained by differences in vegetation (measured by GRVI) near the household (OR: 11.3 95% 0.38, 38.0) and proximity to the football field (OR: 13.9, 95% 4.0, 48.4). The integration of UAV mapping with other data streams adds to our understanding of these dynamics.

Gang Membership and Gang Crime in New Zealand: A National Study Identifying Spatial Risk Factors

2021 ◽  
pp. 009385482110342
Author(s):  
Gregory D. Breetzke ◽  
Sophie Curtis-Ham ◽  
Jarrod Gilbert ◽  
Che Tibby
Keyword(s):  
Risk Factors ◽  
New Zealand ◽  
Social Disorganization ◽  
Spatial Regression ◽  
The United States ◽  
Gang Membership ◽  
National Study ◽  
Cultural Perspectives ◽  
Spatial Risk ◽  
The Social

In this exploratory study, we identify the spatial risk factors associated with gang membership and gang crime in New Zealand using social disorganization as a theoretical framework. Gang membership data from the Gang Intelligence Center and gang crime data from New Zealand Police are included in spatial regression models to identify risk factors. Overall marginal support was found for the use of social disorganization constructs to explain gang membership and gang crime in New Zealand. Higher deprivation and higher diversity were both found to be associated with gang membership and gang crime, respectively. Some similarities and notable differences were observed between our results and the mainly U.S.-centric results of past spatial gang research. This study allows for a greater understanding of the generalizability of the social disorganization theory to explain gang membership and gang crime in areas with markedly different cultural perspectives and ethnocentricities to the United States.

scholarly journals Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community survey

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
William J Moss ◽  
Harry Hamapumbu ◽  
Tamaki Kobayashi ◽  
Timothy Shields ◽  
Aniset Kamanga ◽  
...  
Keyword(s):  
Risk Factors ◽  
Remote Sensing ◽  
Community Survey ◽  
Cross Sectional ◽  
Spatial Risk

scholarly journals Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

2021 ◽  
Vol 18 (13) ◽  
pp. 6832
Author(s):  
Han Yue ◽  
Tao Hu
Keyword(s):  
United States ◽  
Risk Factors ◽  
Mortality Rate ◽  
Population Density ◽  
Potential Risk ◽  
Interactive Effects ◽  
Geographical Detector ◽  
Spatial Risk ◽  
Potential Risk Factors ◽  
The Government

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.

scholarly journals Current characteristics of animal rabies cases in Thailand and relevant risk factors identified by a spatial modeling approach

2021 ◽  
Vol 15 (12) ◽  
pp. e0009980
Author(s):  
Weerapong Thanapongtharm ◽  
Sarin Suwanpakdee ◽  
Arun Chumkaeo ◽  
Marius Gilbert ◽  
Anuwat Wiratsudakul
Keyword(s):  
Risk Factors ◽  
Generalized Additive Models ◽  
Spatial Models ◽  
Additive Models ◽  
Human Rabies ◽  
Spatial Risk ◽  
Risk Areas ◽  
Spatio Temporal ◽  
Current Characteristics ◽  
And Control

The situation of human rabies in Thailand has gradually declined over the past four decades. However, the number of animal rabies cases has slightly increased in the last ten years. This study thus aimed to describe the characteristics of animal rabies between 2017 and 2018 in Thailand in which the prevalence was fairly high and to quantify the association between monthly rabies occurrences and explainable variables using the generalized additive models (GAMs) to predict the spatial risk areas for rabies spread. Our results indicate that the majority of animals affected by rabies in Thailand are dogs. Most of the affected dogs were owned, free or semi-free roaming, and unvaccinated. Clusters of rabies were highly distributed in the northeast, followed by the central and the south of the country. Temporally, the number of cases gradually increased after June and reached a peak in January. Based on our spatial models, human and cattle population density as well as the spatio-temporal history of rabies occurrences, and the distances from the cases to the secondary roads and country borders are identified as the risk factors. Our predictive maps are applicable for strengthening the surveillance system in high-risk areas. Nevertheless, the identified risk factors should be rigorously considered and integrated into the strategic plans for the prevention and control of animal rabies in Thailand.

scholarly journals The Nexus between Bovine Tuberculosis and Fasciolosis Infections in Cattle of the Kafue Basin Ecosystem in Zambia: Implications on Abattoir Surveillance

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Musso Munyeme ◽  
Hetron Mweemba Munang’andu ◽  
Andrew Nambota ◽  
John Bwalya Muma ◽  
Andrew Malata Phiri ◽  
...  
Keyword(s):  
Risk Factors ◽  
Bovine Tuberculosis ◽  
Odds Ratio ◽  
Strong Predictor ◽  
Noncommunicable Diseases ◽  
Neglected Diseases ◽  
Chronic Infections ◽  
Spatial Risk ◽  
High Prevalence ◽  
Simple Logistic

Bovine tuberculosis (bTB) and fasciolosis are important but neglected diseases that result in chronic infections in cattle. However, in Zambia, these diseases are mainly diagnosed at abattoirs during routine meat inspection. Albeit the coinfection status, these diseases have been reported as nothing more than normal separate findings without an explanatory phenomena. Forthwith, we formulated this study to assess the possible association of the two diseases in a known high prevalence area on the Kafue basin ecosystem. Of the 1,680 animals screened, 600 (35.7%; 95% CI 33.4%–38%) and 124 (7.4%; 95% CI 6.1%–8.6%) had fasciolosis and tuberculous lesions; respectively, whilst 72 had both fasciola and tuberculous lesions representing 12% (95% CI 9.4%–14.6%) and 58.1% (95% CI; 49.3%–66.7%) of the total positives for fasciola and tuberculosis, respectively. Jaundice was seen in 304 animals, 18.1% (95% CI; 16.3%–19.9%) and was significantly correlated to fasciolosis (r=0.59,P<0.0001). A significant association (χ2=76.2,df=1, andP<0.0001) was found between fasciolosis and tuberculous lesions. Simple logistic regression intimated fasciolosis as a strong predictor for tuberculous lesions with animals that had fasciola being five times more likely to have tuberculous lesions (odds ratio = 4.8, 95% CI: 3.3–7.0). This study indicates that transmission and spatial risk factors of communicable and noncommunicable diseases such as bTB and fasciolosis can be correlated in an ecosystem such as the Kafue flats.

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