scholarly journals High-risk areas of leprosy in Brazil between 2001-2015

2020 ◽  
Vol 73 (3) ◽  
Author(s):  
Rayssa Nogueira Rodrigues ◽  
Heloisy Alves de Medeiros Leano ◽  
Isabela de Caux Bueno ◽  
Kleane Maria da Fonseca Azevedo Araújo ◽  
Francisco Carlos Félix Lana
Keyword(s):  
High Risk ◽  
Detection Rate ◽  
Ecological Study ◽  
Spatial Clustering ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Control Programs ◽  
Cluster Area ◽  
Risk Areas ◽  
Spatial Scan

ABSTRACT Objectives: to identify high-risk areas of leprosy in Brazil from 2001 to 2015. Methods: this is an ecological study of spatial analysis based on Brazilian municipalities. Spatial scan statistics were used to identify spatial clustering and measure the relative risk from the annual detection rate of new cases of leprosy. By criterion based on the Gini index, only secondary clusters were considered. Results: spatial scan statistics detected 26 clusters, in which the detection rate was 59.19 cases per 100 thousand inhabitants, while in the remainder of the country it was 11.76. Large part of the cluster area is located in the Legal Amazon. These groups included only 21.34% of the total population, but 60.40% of the new cases of the disease. Conclusions: Leprosy remains concentrated in some areas, showing the need for control programs to intensify actions in these municipalities.

scholarly journals Time Trend, Social Vulnerability, and Identification of Risk Areas for Tuberculosis in Brazil: an Ecological Study

2021 ◽  
Author(s):  
João Paulo Silva de Paiva ◽  
Mônica Avelar Figueiredo Mafra Magalhães ◽  
Thiago Cavalcanti Leal ◽  
Leonardo Feitosa da Silva ◽  
Lucas Gomes da Silva ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Social Vulnerability ◽  
Time Trend ◽  
Ecological Study ◽  
Vulnerability Index ◽  
Scan Statistics ◽  
Spatial Clusters ◽  
Social Vulnerability Index ◽  
Risk Areas

ABSTRACTINTRODUCTIONTuberculosis is one of the ten leading causes of death and the leading infectious cause worldwide. The disease represents a challenge to health systems around the world. In 2018, it is estimated that 10 million people were affected by tuberculosis, and approximately 1.5 million people died due to the disease worldwide, including 251,000 patients coinfected with HIV. In Brazil, the disease caused 4,490 deaths, with rate of 2.2 deaths per 100,000 inhabitants. The objective of this study was to analyze the time behavior, spatial distribution, and the effects of social vulnerability on the incidence of TB in Brazil during the period from 2001 to 2017.METHODSA spatial-temporal ecological study was conducted, including all new cases of tuberculosis registered in Brazil during the period from 2001 to 2017. The following variables were analyzed: incidence rate of tuberculosis, the Social Vulnerability Index, its subindices, its 16 indicators, and an additional 14 variables available on the Atlas of Social Vulnerability. The statistical treatment of the data consisted of the following three stages: a) time trend analysis with a joinpoint regression model; b) spatial analysis and identification of risk areas based on smoothing of the incidence rate by local empirical Bayesian model, application of global and local Moran statistics, and, finally, spatial-temporal scan statistics; and c) analysis of association between the incidence rate and the indicators of social vulnerability.RESULTSBrazil reduced the incidence of tuberculosis from 42.8 per 100,000 to 35.2 per 100,000 between 2001 and 2017. Only the state of Minas Gerais showed an increasing trend, whereas nine other states showed a stationary trend. A total of 326 Brazilian municipalities were classified as high priority, and 22 high-risk spatial clusters were identified. The overall Social Vulnerability Index and the subindices of Human Capital and Income and Work were associated with the incidence of tuberculosis. It was also observed that the incidence rates were greater in municipalities with greater social vulnerability.CONCLUSIONSThis study identified spatial clusters with high risk of TB in Brazil. A significant association was observed between the incidence rate of TB and the indices of social vulnerability.

scholarly journals Spatial–temporal clustering of malaria using routinely collected health facility data on the Kenyan Coast

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Alice Kamau ◽  
Grace Mtanje ◽  
Christine Mataza ◽  
Philip Bejon ◽  
Robert W. Snow
Keyword(s):  
Health Facility ◽  
Temporal Dynamics ◽  
Spatial Clustering ◽  
Control Strategies ◽  
Low Cost ◽  
Probability Model ◽  
Scan Statistics ◽  
Spatial Scan Statistics ◽  
Kenyan Coast ◽  
Spatial Scan

Abstract Background The over-distributed pattern of malaria transmission has led to attempts to define malaria “hotspots” that could be targeted for purposes of malaria control in Africa. However, few studies have investigated the use of routine health facility data in the more stable, endemic areas of Africa as a low-cost strategy to identify hotspots. Here the objective was to explore the spatial and temporal dynamics of fever positive rapid diagnostic test (RDT) malaria cases routinely collected along the Kenyan Coast. Methods Data on fever positive RDT cases between March 2018 and February 2019 were obtained from patients presenting to six out-patients health-facilities in a rural area of Kilifi County on the Kenyan Coast. To quantify spatial clustering, homestead level geocoded addresses were used as well as aggregated homesteads level data at enumeration zone. Data were sub-divided into quarterly intervals. Kulldorff’s spatial scan statistics using Bernoulli probability model was used to detect hotspots of fever positive RDTs across all ages, where cases were febrile individuals with a positive test and controls were individuals with a negative test. Results Across 12 months of surveillance, there were nine significant clusters that were identified using the spatial scan statistics among RDT positive fevers. These clusters included 52% of all fever positive RDT cases detected in 29% of the geocoded homesteads in the study area. When the resolution of the data was aggregated at enumeration zone (village) level the hotspots identified were located in the same areas. Only two of the nine hotspots were temporally stable accounting for 2.7% of the homesteads and included 10.8% of all fever positive RDT cases detected. Conclusion Taking together the temporal instability of spatial hotspots and the relatively modest fraction of the malaria cases that they account for; it would seem inadvisable to re-design the sub-county control strategies around targeting hotspots.

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

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.

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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