Geospatial inequality of anaemia among children in Ethiopia

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.

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

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.

Spatial scan statistics can be dangerous

Spatial scan statistics are widely used tools for the detection of disease clusters. Especially, the circular spatial scan statistic proposed by Kulldorff along with SaTScan software has been used in a wide variety of epidemiological studies and disease surveillance. However, as it cannot detect non-circular, irregularly shaped clusters, many authors have proposed non-circular spatial scan statistics. Above all, the flexible spatial scan statistic proposed by Tango and Takahashi along with FleXScan software has also been used. However, it does not seem to be well recognized that these spatial scan statistics, especially SaTScan, tend to detect the most likely cluster, much larger than the true cluster by absorbing neighboring regions with nonelevated risk of disease occurrence. Therefore, if researchers reported the detected most likely cluster as they are, it might lead to a criticism to them due to the fact that some regions with nonelevated risk are included in the detected most likely cluster. In this paper, to avoid detecting such undesirable and misleading clusters which might cause a social concern, we shall propose the use of the restricted likelihood ratio proposed by Tango and illustrate the procedure with two kinds of mortality data in Japan.

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

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.

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

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.

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

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.

Crime Hotspot Detection

Given a set of crime locations, a statistically significant crime hotspot is an area where the concentration of crimes inside is significantly higher than outside. The motivation of crime hotspot detection is twofold: detecting crime hotspots to focus the deployment of police enforcement and predicting the potential residence of a serial criminal. Crime hotspot detection is computationally challenging due to the difficulty of enumerating all potential hotspot areas, selecting an interest measure to compare these with the overall crime intensity, and testing for statistical significance to reduce chance patterns. This chapter focuses on statistical significant crime hotspots. First, the foundations of spatial scan statistics and its applications (i.e. SaTScan) to circular hotspot detection are reviewed. Next, ring-shaped hotspot detection is introduced. Third, linear hotspot detection is described since most crimes occur along a road network. The chapter concludes with future research directions in crime hotspot detection.

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

ObjectiveTo 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.