scholarly journals Spatiotemporal tools for emerging and endemic disease hotspots in small areas – an analysis of dengue and chikungunya in Barbados, 2013 – 2016

2019 ◽  
Author(s):  
Catherine A. Lippi ◽  
Anna M. Stewart-Ibarra ◽  
Moory Romero ◽  
Avery Q.J. Hinds ◽  
Rachel Lowe ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Early Warning Systems ◽  
Surveillance Data ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Spatial Scan Statistics ◽  
Small Areas ◽  
Health Goals ◽  
Spatial Scan

AbstractObjectiveTo detect potential hotspots of transmission of dengue and chikungunya in Barbados, and assess impact of input surveillance data and methodology on observed patterns of risk.MethodsUsing two methods of cluster detection, Moran’s I and spatial scan statistics, we analyzed the geospatial and temporal distribution of disease cases and rates across Barbados for dengue fever in 2013–2016, and a 2014 chikungunya outbreak.ResultsDuring years with high numbers of dengue cases, hotspots for cases were found with Moran’s I in south and central regions in 2013 and 2016, respectively. Using smoothed disease rates, clustering was detected every year for dengue. Hotspots were not detected via spatial scan statistics, but coldspots suggesting lower rates of disease activity were found in southwestern Barbados during high case years of dengue.ConclusionsSpatial analysis of surveillance data is useful in identifying outbreak hotspots, complementing existing early warning systems. We caution that these methods should be used in a manner appropriate to available data, and reflecting explicit public health goals – managing for overall case numbers, or targeting anomalous rates for further investigation.

scholarly journals Geographical variations and associated factors of defaulting from immunization among children aged 12 to 23 months in Ethiopia: using spatial and multilevel analysis of 2016 Ethiopian Demographic and Health Survey

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Mukemil Awol ◽  
Zewdie Aderaw Alemu ◽  
Nurilign Abebe Moges ◽  
Kemal Jemal
Keyword(s):  
Multilevel Analysis ◽  
Health Survey ◽  
Associated Factors ◽  
Demographic And Health Survey ◽  
Community Level ◽  
Scan Statistics ◽  
Moran’S I ◽  
Moran's I ◽  
Depth Analysis ◽  
Global Moran’S I

Abstract Background In Ethiopia, despite the considerable improvement in immunization coverage, the burden of defaulting from immunization among children is still high with marked variation among regions. However, the geographical variation and contextual factors of defaulting from immunization were poorly understood. Hence, this study aimed to identify the spatial pattern and associated factors of defaulting from immunization. Methods An in-depth analysis of the 2016 Ethiopian Demographic and Health Survey (EDHS 2016) data was used. A total of 1638 children nested in 552 enumeration areas (EAs) were included in the analysis. Global Moran’s I statistic and Bernoulli purely spatial scan statistics were employed to identify geographical patterns and detect spatial clusters of defaulting immunization, respectively. Multilevel logistic regression models were fitted to identify factors associated with defaulting immunization. A p value < 0.05 was used to identify significantly associated factors with defaulting of child immunization. Results A spatial heterogeneity of defaulting from immunization was observed (Global Moran’s I = 0.386379, p value < 0.001), and four significant SaTScan clusters of areas with high defaulting from immunization were detected. The most likely primary SaTScan cluster was seen in the Somali region, and secondary clusters were detected in (Afar, South Nation Nationality of people (SNNP), Oromiya, Amhara, and Gambella) regions. In the final model of the multilevel analysis, individual and community level factors accounted for 56.4% of the variance in the odds of defaulting immunization. Children from mothers who had no formal education (AOR = 4.23; 95% CI: 117, 15.78), and children living in Afar, Oromiya, Somali, SNNP, Gambella, and Harari regions had higher odds of having defaulted immunization from community level. Conclusions A clustered pattern of areas with high default of immunization was observed in Ethiopia. Both the individual and community-level characteristics were statistically significant factors of defaulting immunization. Therefore, the Federal Ethiopian Ministry of Health should prioritize the areas with defaulting of immunization and consider the identified factors for immunization interventions.

scholarly journals Spatial distribution and determinant factors of anemia among adults aged 15–59 in Ethiopia; using mixed-effects ordinal logistic regression model

BMC Nutrition ◽  
2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Biruk Shalmeno Tusa ◽  
Sewnet Adem Kebede ◽  
Adisu Birhanu Weldesenbet
Keyword(s):  
Public Health ◽  
Spatial Distribution ◽  
Scan Statistics ◽  
Moran’S I ◽  
Demographic And Health Surveys ◽  
Targeted Prevention ◽  
Hotspot Analysis ◽  
Mixed Effect ◽  
Moran's I ◽  
Determinant Factors

Abstract Background Anemia is a global public health problem, particularly in developing countries. Assessing the geographic distributions and determinant factors is a key and crucial step in designing targeted prevention and intervention programmes to address anemia. Thus, the current study is aimed to assess the spatial distribution and determinant factors of anemia in Ethiopia among adults aged 15–59. Methods A secondary data analysis was done based on 2016 Ethiopian Demographic and Health Surveys (EDHS). Total weighted samples of 29,140 adults were included. Data processing and analysis were performed using STATA 14; ArcGIS 10.1 and SaTScan 9.6 software. Spatial autocorrelation was checked using Global Moran’s index (Moran’s I). Hotspot analysis was made using Gettis-OrdGi*statistics. Additionally, spatial scan statistics were applied to identify significant primary and secondary cluster of anemia. Mixed effect ordinal logistics were fitted to determine factors associated with the level of anemia. Result The spatial distribution of anemia in Ethiopia among adults age 15–59 was found to be clustered (Global Moran’s I = 0.81, p value <  0.0001). In the multivariable mixed-effectordinal regression analysis; Females [AOR = 1.53; 95% CI: 1.42, 1.66], Never married [AOR = 0.86; 95% CI: 0.77, 0.96], highly educated [AOR = 0.71; 95% CI: 0.60, 0.84], rural residents [AOR = 1.53; 95% CI: 1.23, 1.81], rich wealth status [AOR = 0.77; 95% CI: 0.69, 0.86] and underweight [AOR = 1.15; 1.06, 1.24] were significant predictors of anemia among adults. Conclusions A significant clustering of anemia among adults aged 15–59 were found in Ethiopia and the significant hotspot areas with high cluster anemia were identified in Somalia, Afar, Gambella, Dire Dewa and Harari regions. Besides, sex, marital status, educational level, place of residence, region, wealth index and BMI were significant predictors of anemia. Therefore, effective public health intervention and nutritional education should be designed for the identified hotspot areas and risk groups in order to decrease the incidence of anemia.

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

2009 ◽  
Vol 6 (1) ◽  
pp. 15-21 ◽  
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

Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters

2006 ◽  
Vol 15 (2) ◽  
pp. 428-442 ◽  
Author(s):  
Luiz Duczmal ◽  
Martin Kulldorff ◽  
Lan Huang
Keyword(s):  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Spatial Scan

A genetic algorithm for irregularly shaped spatial scan statistics

2007 ◽  
Vol 52 (1) ◽  
pp. 43-52 ◽  
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

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

2010 ◽  
Vol 138 (9) ◽  
pp. 1336-1345 ◽  
Author(s):  
M. E. JONSSON ◽  
M. NORSTRÖM ◽  
M. SANDBERG ◽  
A. K. ERSBØLL ◽  
M. HOFSHAGEN
Keyword(s):  
Risk Factors ◽  
Relative Risk ◽  
Function Analysis ◽  
Space Time ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Factors Associated ◽  
Time Patterns ◽  
Broiler Flock ◽  
Spatial Scan

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.

A multidimensional spatial scan statistics approach to movement pattern comparison

2018 ◽  
Vol 32 (7) ◽  
pp. 1304-1325 ◽  
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

scholarly journals 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

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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