Space–time patterns ofCampylobacterspp. colonization in broiler flocks, 2002–2006

SUMMARYThis study was performed to investigate space–time patterns ofCampylobacterspp. colonization in broiler flocks in Norway. Data on theCampylobacterspp. status at the time of slaughter of 16 054 broiler flocks from 580 farms between 2002 and 2006 was included in the study. Spatial relative risk maps together with maps of space–time clustering were generated, the latter by using spatial scan statistics. These maps identified the same areas almost every year where there was a higher risk for a broiler flock to test positive forCampylobacterspp. during the summer months. A modifiedK-function analysis showed significant clustering at distances between 2·5 and 4 km within different years. The identification of geographical areas with higher risk forCampylobacterspp. colonization in broilers indicates that there are risk factors associated withCampylobacterspp. colonization in broiler flocks varying with region and time, e.g. climate, landscape or geography. These need to be further explored. The results also showed clustering at shorter distances indicating that there are risk factors forCampylobacterspp. acting in a more narrow scale as well.

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Geospatial inequality of anaemia among children in Ethiopia

Geospatial health ◽

10.4081/gh.2021.1036 ◽

2021 ◽

Vol 16 (2) ◽

Author(s):

Bilal Shikur Endris ◽

Geert-Jan Dinant ◽

Seifu H. Gebreyesus ◽

Mark Spigt

Keyword(s):

Risk Factors ◽

Multivariable Analysis ◽

Spatial Association ◽

Public Health Problem ◽

Health Interventions ◽

Scan Statistics ◽

High Fertility ◽

Spatial Scan Statistics ◽

Spatial Scan ◽

Blood Specimens

Anaemia remains a severe public health problem among children in Ethiopia and targeted approaches, based on the distribution and specific risk factors for that setting are needed to efficiently target health interventions. An analysis was performed using Ethiopia Demographic and Health Survey 2016 data. Blood specimens for anaemia testing were collected from 9268 children aged 6-59 months. A child was considered as anaemic if the bloodhaemoglobin count was less than 11.0 g/dL. We applied Kulldorf’s spatial scan statistics and used SaTScanTM to identify locations and estimate cluster sizes. In addition, we ran the local indicator of spatial association and the Getis-Ord Gi* statistics to detect and locate hotspots and multilevel multivariable analysis to identify risk factors for anaemia clustering. More than half of children aged 6-59 months (57%) were found to be anaemic in Ethiopia. We found significant geospatial inequality of anaemia among children and identified clusters (hotspots) in the eastern part of Ethiopia. The odds of anaemia among children found within the identified cluster was 1.5 times higher than children found outside the cluster. Women anaemia, stunting and high fertility were associated with anaemia clustering.

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Spatial Distribution and Factors Associated with Low birth weight in Ethiopia using data from Ethiopian Demographic and Health Survey 2016; Spatial and Multilevel analysis

10.21203/rs.3.rs-70101/v1 ◽

2020 ◽

Author(s):

Alemneh Mekuriaw Liyew ◽

Malede Mequanent Sisay ◽

Achenef Asmamaw Muche

Keyword(s):

Spatial Distribution ◽

Birth Weight ◽

Low Birth Weight ◽

Logistic Regression Model ◽

Scan Statistics ◽

Spatial Scan Statistics ◽

Factors Associated ◽

Risk Areas ◽

Multi Level ◽

Spatial Scan

Abstract Background Low birth weight (LBW) is a leading cause of neonatal mortality. In Ethiopia, it is a public health problem that contributes to the majority of newborn deaths. To date, the effect of contextual factors on LBW was largely overlooked in Ethiopia. Besides, there is also limited evidence on the geographic variation of low birth weight in Ethiopia. Therefore, this study aimed to explore spatial distribution as well as individual and community-level factors associated with low birth weight in Ethiopia. Method: Secondary data analysis was conducted using the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total of 1502 neonates were included in this study. Spatial autocorrelation analysis was conducted to assess the spatial dependency of LBW. Besides, the spatial scan statistics and ordinary kriging interpolation were done to detect the local level clusters and to assess predicted risk areas respectively. Furthermore, a multi-level logistic regression model was fitted to determine individual and community-level factors associated with low birth weight. Finally, most likely clusters with log-likelihood ratio (LLR), relative risk and p-value from spatial scan statistics, and AOR with 95% CI for multi-level logistic regression model were reported. Results Low birth weight was spatially clustered in Ethiopia. Primary (LLR = 11.57; P = 0.002) clusters were detected in the Amhara region. It showed that neonates within the spatial window had 2.66 times higher risk of being LBW baby as compared to those outside the window. Besides, secondary (LLR = 11.4; P = 0.003;LLR = 10.14,P = 0.0075) clusters were identified at Southwest Oromia, north Oromia, south Afar, and Southeast Amhara regions. Neonates who were born from severely anemic (AOR = 1.47;95%CI 1.04,2.01), and uneducated (AOR = 1.82;95%CI1.12,2.96) mothers, as well as those who were born before 37 weeks of gestation (AOR = 5.91;95%CI3.21,10.10) and females (AOR = 1.38;95%CI1.04,1.84), had significantly higher odds of being low birth weight babies. Conclusion The high-risk areas of low birth weight were detected in Afar, Amhara, and Oromia regions. Therefore, targeting the policy interventions in those risk areas by focusing on the improvement of maternal education, strengthening anemia control programs and elimination of modifiable causes of prematurity could be vital for reduce the low birth weight disparity in Ethiopia.

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Spatial Scan Statistics to Assess Sampling Strategy of Antimicrobial Resistance Monitoring Program

Foodborne Pathogens and Disease ◽

10.1089/fpd.2008.0132 ◽

2009 ◽

Vol 6 (1) ◽

pp. 15-21 ◽

Cited By ~ 4

Author(s):

A.R. Vieira ◽

H. Houe ◽

H.C. Wegener ◽

D.M.A. Lo Fo Wong ◽

R. Bødker ◽

...

Keyword(s):

Antimicrobial Resistance ◽

Monitoring Program ◽

Sampling Strategy ◽

Scan Statistics ◽

Spatial Scan Statistics ◽

Resistance Monitoring ◽

Spatial Scan

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Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters

Journal of Computational and Graphical Statistics ◽

10.1198/106186006x112396 ◽

2006 ◽

Vol 15 (2) ◽

pp. 428-442 ◽

Cited By ~ 75

Author(s):

Luiz Duczmal ◽

Martin Kulldorff ◽

Lan Huang

Keyword(s):

Scan Statistics ◽

Spatial Scan Statistics ◽

Spatial Scan

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A genetic algorithm for irregularly shaped spatial scan statistics

Computational Statistics & Data Analysis ◽

10.1016/j.csda.2007.01.016 ◽

2007 ◽

Vol 52 (1) ◽

pp. 43-52 ◽

Cited By ~ 61

Author(s):

Luiz Duczmal ◽

André L.F. Cançado ◽

Ricardo H.C. Takahashi ◽

Lupércio F. Bessegato

Keyword(s):

Genetic Algorithm ◽

Scan Statistics ◽

Spatial Scan Statistics ◽

Spatial Scan

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A multidimensional spatial scan statistics approach to movement pattern comparison

International Journal of Geographical Information Science ◽

10.1080/13658816.2018.1426859 ◽

2018 ◽

Vol 32 (7) ◽

pp. 1304-1325 ◽

Cited By ~ 13

Author(s):

Yizhao Gao ◽

Ting Li ◽

Shaowen Wang ◽

Myeong-Hun Jeong ◽

Kiumars Soltani

Keyword(s):

Movement Pattern ◽

Scan Statistics ◽

Spatial Scan Statistics ◽

Pattern Comparison ◽

Spatial Scan

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An exploratory investigation of geographic disparities of stroke prevalence in Florida using circular and flexible spatial scan statistics

PLoS ONE ◽

10.1371/journal.pone.0218708 ◽

2019 ◽

Vol 14 (8) ◽

pp. e0218708

Author(s):

Shamarial Roberson ◽

Rahel Dawit ◽

Jaleesa Moore ◽

Agricola Odoi

Keyword(s):

Scan Statistics ◽

Spatial Scan Statistics ◽

Geographic Disparities ◽

Stroke Prevalence ◽

Exploratory Investigation ◽

Spatial Scan

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Spatial distribution of iron rich foods consumption and its associated factors among children aged 6–23 months in Ethiopia: spatial and multilevel analysis of 2016 Ethiopian demographic and health survey

Nutrition Journal ◽

10.1186/s12937-020-00635-8 ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Sofonyas Abebaw Tiruneh ◽

Belete Achamyelew Ayele ◽

Getachew Yideg Yitbarek ◽

Desalegn Tesfa Asnakew ◽

Melaku Tadege Engidaw ◽

...

Keyword(s):

Spatial Distribution ◽

Health Survey ◽

Associated Factors ◽

Household Wealth ◽

Demographic And Health Survey ◽

Scan Statistics ◽

Nutritional Deficiencies ◽

Spatial Scan Statistics ◽

Developmental Problems ◽

Spatial Scan

Abstract Background Micronutrient deficiencies are the most prevalent nutritional deficiencies that cause serious developmental problems in the globe. The aim of this study was to assess the spatial distribution of iron rich foods consumption and its associated factors among children aged 6–23 months in Ethiopia. Methods The data retrieved from the standard Ethiopian Demographic and Health Survey 2016 dataset with a total sample size of 3055 children aged 6–23 months. Spatial scan statistics done using Kuldorff’s SaTScan version 9.6 software. ArcGIS version 10.7 software used to visualize spatial distribution for poor consumption of iron rich foods. Multilevel mixed-effects logistic regression analysis employed to identify the associated factors for good consumption of iron-rich foods. Level of statistical significance was declared at a two-sided P-value < 0.05. Results Overall, 21.41% (95% CI: 19.9–22.9) of children aged 6–23 months had good consumption of iron rich foods in Ethiopia. Poor consumption of iron rich foods highly clustered at Southern Afar, Southeastern Amhara and Tigray, and the Northern part of Somali Regional States of Ethiopia. In spatial scan statistics, children aged 6–23 months living in the most likely cluster were 21% more likely vulnerable to poor consumption of iron rich foods than those living outside the window (RR = 1.21, P-value < 0.001). Child aged 12–17 months (AOR = 1.90, 95% CI: 1.45–2.49) and 18–23 months (AOR = 2.05, 95% CI: 1.55–2.73), primary (AOR = 1.42, 95% CI:1.06–1.87) and secondary and above (AOR = 2.26, 95% CI: 1.47–3.46) mother’s education level, rich (AOR = 1.49, 95% CI: 1.04–2.13) and middle (AOR = 1.83, 95% CI: 1.31–2.57) household wealth status, Amhara (AOR = 0.24, 95% CI: 0.09–0.60), Afar (AOR = 0.38, 95% CI: 0.17–0.84), and Harari (AOR = 2.11, 95% CI: 1.02–4.39) regional states of Ethiopia were statistically significant factors for good consumption of iron rich foods. Conclusion Overall, the consumption of iron rich foods was low and spatially non-random in Ethiopia. Federal Ministry of Health and other stakeholders should give prior attention to the identified hot spot areas to enhance the consumption of iron rich foods among children aged 6–23 months.

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