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Stat! Statistical Software for the Clustering of Health Events
... The SaTScan identifay speci c palce spatially signi cant higher or lower rate of aggregates is found. Its output presents the hotspot areas in circular windows, indicating the areas in the windows are higher than expected distributions compared to the areas outside of the cluster windows [22][23][24][25]. ...
... In this study, a total of 25,774 Ethiopian DHS participants whose age range 15 to 59 years for men and 15 to 49 years for female with HIV test result in 2016 was included in the analysis. Above half of (51.58%) were females and, according to age, 37% younger (15)(16)(17)(18)(19)(20)(21)(22)(23)(24) age, about four fth (79%) whose residence was from rural residential, about four-fth (82%) whose household head was male and about two fth (39%) of the respondent are uneducated. ...
... The reason for this result is this data is collected prevalence not incidence (show current prevalence, but doesn't show at which age the rst time respondents infected by HIV/AIDS) in the theoretical part most of the study shows incidence case. UNAIDS 2018 report indicates about 4,400 new HIV infected among adults (aged 15 and above) out of which about 32% are among young people (15)(16)(17)(18)(19)(20)(21)(22)(23)(24) [3], it indicates incidence is high at a younger age but the prevalence in older age is the cumulative effect of from younger to older age. ...
BackgroundHIV is a major public health problem, especially in developing countries including Ethiopia. Exploring the spatial pattern, distribution and associated factors of HIV Seropositivity is important to monitor, and design effective intervention programs. Therefore, this study showed associated factors and spatial variation of HIV Seropositivity in Ethiopia.Method
This secondary data analysis, sampling technique and procedures were done by Ethiopipan Central Statistical Agency. A total sample of 25,774 individuals data were extracted from the 2016 EDHS data mainly HIV biomarkers, IR, MR, and GPS. Spatial heterogeneity analysis were done using tools like Morans I, Local G*, Interpolation, and Kulldorff’s scan statistic. The spatial analysis was carried out by using open source software (QGIS, GeoDa, SaTScan). Multilevel logistic regression analysis was used to identify both individual and household level factors associated with HIV Seropositivity and the analysis was carried out by Stata 14. Finally AOR with 95% confidence interval of mixed-effect logistic regression result in the full model was used to report.ResultThe prevalence of HIV/AIDS was found 0.93% at the national level. The highest prevalence regions were Gambela, Addis Ababa, Harari, and Dire Dawa, which accounts for 4.79%, 3.36%, 2.65%, and 2.6%, respectively. Similarly, the most likely high-risk HIV Seropositivity spatial cluster was found in the Gambela region and Addis Ababa followed by Harari and Diredawa. In a multilevel analysis at the individual level being married [AOR = 2.19 95%CI: (1.11, 4.31)] and previously married [AOR = 6.45, 95%CI (3.06, 13.59)] were significantly associated with serropositivity. Regarding household level place of residence [urban: AOR = 6.13 CI: (3.12, 12.06)] were associated with HIV Seropositivity.Conclusion
The distribution of HIV cases was not random. High cluster HIV cases were found in Gambela, Addis Abeba, Harari, and Dire Dawa. At the individual level, some characters are high risk like being previously married, start sex at a younger age, female household headed, urban residence, and lower household size is more affected by HIV/AIDS. So any concerned bodywork around this risk group and area can be effective in the reduction of transmission.
... The SaTScan may distinguish particular locations were higher or lower rate of spatial aggregate. Its output presents the hotspot areas in circular windows, indicating areas of windows are higher than expected distributions compared to the areas outside of the cluster windows [21][22][23][24]. ...
... Women have been beyond half of 13,295 (51,58%). As for age 9536 (37%) younger (15)(16)(17)(18)(19)(20)(21)(22)(23)(24). The majority of rural residents 20,368 (79%), and 21,094 (82%) of male households. ...
Background: HIV is a major public health issue, especially in developing countries. It is important to track and design successful intervention programs to explore the spatial pattern, distribution, and associated factors of HIV Seropositivity. This study therefore showed the spatial variation of HIV Seropositivity and related factors in Ethiopia.
... The SaTScan may distinguish particular locations were higher or lower rate of spatial aggregate. Its output presents the hotspot areas in circular windows, indicating areas of windows are higher than expected distributions compared to the areas outside of the cluster windows [21][22][23][24]. ...
... Women have been beyond half of 13,295 (51,58%). As for age 9536 (37%) younger (15)(16)(17)(18)(19)(20)(21)(22)(23)(24). The majority of rural residents 20,368 (79%), and 21,094 (82%) of male households. ...
Background
HIV is a major public health issue, especially in developing countries. It is important to track and design successful intervention programs to explore the spatial pattern, distribution, and associated factors of HIV Seropositivity. This study therefore showed the spatial variation of HIV Seropositivity and related factors in Ethiopia.
Methods
A total sample of 25,774 individual data collected from the 2016 EDHS data were primarily HIV biomarkers, IR, MR, and GPS. Spatial heterogeneity analysis was used with methods such as Morans I, Interpolation, and Kulldorff ‘s scan statistic. Spatial analysis was conducted using open source tools (QGIS, GeoDa, SaTScan). Multilevel logistic regression analysis was performed using Stata14 to identify HIV-associated factors. Finally, the AOR with a 95% confidence interval was used to report the mixed-effect logistic regression result in the full model.
Result
The prevalence of HIV / AIDS at national level was 0.93%. The highest prevalence regions were Gambela, Addis Abeba, Harari and Diredawa, accounting for 4.79, 3.36, 2.65 and 2.6%, respectively. Higher HIV seropositive spatial clusters have been established in the Gambela and Addis Ababa regions. Multilevel analysis at the individual level being married [AOR = 2.19 95% CI: (1.11–4.31)] and previously married [AOR = 6.45, 95% CI: (3.06–13.59)], female [AOR = 1.8, 95% CI: (1.19–2.72)], first-sex at age ≤15 [AOR = 4.39, 95% CI: (1.70–11.34)], 18—19 [AOR = 2.67 95% CI: (1.05–6.8)], middle age group (25-34) [AOR = 6.53, 95% CI: (3.67–11.75)], older age group (>34) [AOR = 2.67 95% CI: (1.05–6.8)], primary school [AOR = 3.03, 95% CI: (1.92–4.79)], secondary school [AOR = 3.37, 95% CI: (1.92–5.92) were significantly associated with serropositivity. Regarding household level, place of residence [urban: AOR = 6.13 CI: (3.12, 12.06)], female-headed households (AOR = 2.24 95% CI: (1.57–3.73), media exposure [low exposure (AOR = 0.53 95% CI: (0.33–0.86), no exposure AOR = 0.39 95% CI: (0.23–0.65)] and increased household size [AOR = 0.72 95% CI: (0.65–0.8)] were associated with HIV Seropositivity.
Conclusion
High cluster HIV cases were found in Gambela, Addis Abeba, Harari, and Diredawa. Having a history of married, start sex at a younger age, female-headed household, urban residence, and lower household size is more affected by HIV/AIDS. So any concerned body work around this risk group and area can be effective in the reduction of transmission.
... According to the Ethiopian Demographic and Health Survey (EDHS) report, in Ethiopia, the prevalence of anemia among children 6-59 months old was 53% in 2005, 44% in 2011, and 57% in 2016. From the sampled children in EDHS-2011, 28.6%, 21.7%, and 49.7% were above moderate (both moderate and severe), mildly anemic, and nonanemic, respectively [12]. In all years Ethiopia has severe public health problems in anemia [1]. ...
... The SaTScan declares where spatially significant higher an lower rates of aggregates are found. Its output presents the hotspot areas in circular windows, indicating the areas in the windows are higher than expected distributions compared to the areas outside of the cluster windows [19][20][21][22]. ...
Background: Anemia is recognized as a significant public health problem in Ethiopia.
Method: This secondary analysis, sampling technique and procedures done by DHS. A total of 8482 children aged 6–59 months were included in the study from EDHS 2016. Used tools for spatial heterogeneity analysis are Morans I, Local G*, Heat map, and Kulldorff’s scan statistic, those carried out open source software (QGIS, GeoDa, SaTScan). Multilevel logistic regression analysis was used to identify both individual and household level factors associated with anemia and severe anemia, which measures between household variability using IHHC of the null model and generates 4 models PCV, AIC, and log-likelihood ratio used model selection for a report, which is carried out Stata 14.
Result: The highest risk of both anemia and severe anemia regions are Somalia, Afar, DireDawa, and Harari. Specifically, all zones of Somalia, Afar (zone1, and 3), DireDawa, Harari, and Oromia higher risk of anemia and some of them are high risk for severe anemia. The high concentration of the disease showed in DireDawa, Harari, Jigjiga some part of Gambela and Benishangul Afar at the boundary of Amhara and Tigray and Eastern part of Tigray for anemia. Harari, DireDawa, and Afar at the border of Djibouti had high concentrated(density) severe anemia. Children (Younger age, lower preceding birth interval, stunted, underweight); mothers (younger age and anemic); households (poor and denser family number); and children from the highest and the lowest ecological zone are high risk for anemia. Additionally, children from the younger, work less, uneducated, poor, and anemic mother were high risk of a severe anemia
Conclusion: Across the country, anemia is high, especially rift value areas highly affected by both anemia and severe anemia like Somalia regions and its neighbors. Those regions are not productive areas, lack of health facility, malaria region, backward for any access. So the concerned body must be tackled to minimize this childhood series problem. Before starting any intervention first must be prioritized according to risk, concentration, and characteristics.
... The existence of local spatio-temporal clustering of cases in space and time were then evaluated using the retrospective space-time permutation model of the spatial scan statistics [41] on SaTScan 9.1.1 software [47,48]. In this analysis, the study area is scanned for the identification of clusters of cases occurring close in both space and time, and the model determines the significance of the most likely clusters by comparing the number of observed cases occurring within each of the possible scanning windows with the expected number of cases generated in 999 Monte Carlo randomizations of date to the observed locations and dates at a maximum window size of 50% of the population at risk. ...
... Spread and propagation. The existence of a significant trend on the direction of movement of the epidemic was assessed with the directional test statistics on the ClusterSeer software (TerraSeer, Crystal Lake, IL, USA) using a relative time connection matrix and week (7 days) as the time-step unit [48]. In the relative-directional matrix, the model builds a vector line that connects each case with all other subsequent cases. ...
Background
Anthrax is a zoonotic disease primarily of herbivores, caused by Bacillus anthracis, a bacterium with diverse geographical and global distribution. Globally, livestock outbreaks have declined but in Africa significant outbreaks continue to occur with most countries still categorized as enzootic, hyper endemic or sporadic. Uganda experiences sporadic human and livestock cases. Severe large-scale outbreaks occur periodically in hippos (Hippopotamus amphibious) at Queen Elizabeth Protected Area, where in 2004/2005 and 2010 anthrax killed 437 hippos. Ecological drivers of these outbreaks and potential of hippos to maintain anthrax in the ecosystem remain unknown. This study aimed to describe spatio-temporal patterns of anthrax among hippos; examine significant trends associated with case distributions; and generate hypotheses for investigation of ecological drivers of anthrax.
Methods
Spatio-temporal patterns of 317 hippo cases in 2004/5 and 137 in 2010 were analyzed. QGIS was used to examine case distributions; Spearman’s nonparametric tests to determine correlations between cases and at-risk hippo populations; permutation models of the spatial scan statistics to examine spatio-temporal clustering of cases; directional tests to determine directionality in epidemic movements; and standard epidemic curves to determine patterns of epidemic propagation.
Key findings
Results showed hippopotamus cases extensively distributed along water shorelines with strong positive correlations (p<0.01) between cases and at-risk populations. Significant (p<0.001) spatio-temporal clustering of cases occurred throughout the epidemics, pointing towards a defined source. Significant directional epidemic spread was detected along water flow gradient (206.6°) in 2004/5 and against flow gradient (20.4°) in 2010. Temporal distributions showed clustered pulsed epidemic waves.
Conclusion
These findings suggest mixed point-source propagated pattern of epidemic spread amongst hippos and points to likelihood of indirect spread of anthrax spores between hippos mediated by their social behaviour, forces of water flow, and persistent presence of infectious carcasses amidst schools. This information sheds light on the epidemiology of anthrax in highly social wildlife, can help drive insight into disease control, wildlife conservation, and tourism management, but highlights the need for analytical and longitudinal studies aimed at clarifying the hypotheses.
... The spatiotemporal directionality test uses a relative time connection matrix to recognize the average direction of the AIS dispersal over time. The average direction of confirmed invasions over time was estimated using a vector whose direction was the average direction of lines that connect each invasion with all subsequent invasions (Jacquez, 1996). The magnitude, i.e. the angular concentration, of the average direction of the invasion was the Table 1 Spatial clusters resulted from the multivariate multinomial model of scan static test for zebra mussels (ZM) and Eurasian watermilfoil (EWM) invasions, adjusted by the human population density (p b 0.05). ...
... Den. Category include: (1) b10; (2) between 10 and 100; and (3) (Jacquez, 1996). If the confirmed AIS cases followed a consistent spatiotemporal trend towards a given direction, then the angular variance is expected to be small while the angular concentration, a value between 0 and 1, would be large. ...
Recognizing common reporting patterns of aquatic invasive Zebra mussels (Dreissena polymorpha, ZM) and Eurasian watermilfoil (Myriophyllum spicatum, EWM) helps to better understand invasions. We hypothesize that confirmed invasions may be confounded by human population density, leading to overrepresentation of invasions in highly populated areas and underrepresentation in less populated areas. Here we recognize dispersal patterns of confirmed ZM and EWM invasions in Minnesota, USA, using spatial clustering and directionality tests, while adjusting for human density. By 2015, 125 (0.68%) and 304 (1.65%) of 18,411 Minnesota waterbodies were reported to have ZM and EWM, respectively. A multivariate multinomial model of the scan test was used to identify clustering of invasions. The resulting 23 clusters included 13 with either or both ZM and EWM, and most clusters (11/13) occurred in areas with >10 people per square kilometer. Whereas, among the 10 clusters without invasion, nine were from less populated areas. The standard deviation ellipse and the spatiotemporal directionality tests indicated a northwestern trend of invasions, which is in the same direction as the I-94 interstate highway connecting urban centers. Results suggested that confirmed ZM and EWM invasions are potentially confounded by human densities, which is explained by varying human impact on either or both dispersal and reporting of invasions. Considering this impact of human density, we suggest that a combination of passive and targeted surveillance, where the magnitude of efforts are stratified by the human densities, may provide insight into the true invasion status and its progression in the Great Lakes region.
... A directional statistic was used to determine whether a systematic directional spread of outbreaks occurred during the study period (Jacquez and Oden, 1994). A chain of infection was constructed by first sequencing the outbreaks by date of occurrence (the primary outbreak first, followed by the second outbreak, and so on). ...
... The direction test (Jacquez and Oden, 1994) evaluates the null hypothesis of no association between the times at which cases occur and the directions of the vectors formed by connecting the spatial locations of these cases. Rejection of this hypothesis implies that the direction from one case to the next is similar for cases that occur during the same time period. ...
... We used a directional statistics to test the null hypothesis that no association exists between the times at which subdistricts were infected and the overall directions of the vectors formed by connecting the spatial locations of the subdistricts became infected over the outbreaks wave [19]. Briefly, a chain of the individual infected subdistricts was constructed in ascending order of the outbreak date (the earliest case first, then the second case and so on). ...
... In the directional statistics, the average direction and its significance is evaluated through a randomization procedure which holds the locations (sine and cosine matrices) constant and randomly assigns connections between pairs of cases by randomizing their times of occurrence. This procedure is also repeated to generate a distribution of the angular concentration under the null hypothesis [19]. Hence, the effect of random inaccuracy in the outbreak dates on results obtained in the directional statistics is considered negligible. ...
The number of outbreaks of HPAI-H5N1 reported by Bangladesh from 2007 through 2011 placed the country among the highest reported numbers worldwide. However, so far, the understanding of the epidemic progression, direction, intensity, persistence and risk variation of HPAI-H5N1 outbreaks over space and time in Bangladesh remains limited.
To determine the magnitude and spatial pattern of the highly pathogenic avian influenza A subtype H5N1 virus outbreaks over space and time in poultry from 2007 to 2009 in Bangladesh, we applied descriptive and analytical spatial statistics. Temporal distribution of the outbreaks revealed three independent waves of outbreaks that were clustered during winter and spring. The descriptive analyses revealed that the magnitude of the second wave was the highest as compared to the first and third waves. Exploratory mapping of the infected flocks revealed that the highest intensity and magnitude of the outbreaks was systematic and persistent in an oblique line that connects south-east to north-west through the central part of the country. The line follows the Brahmaputra-Meghna river system, the junction between Central Asian and East Asian flyways, and the major poultry trading route in Bangladesh. Moreover, several important migratory bird areas were identified along the line. Geostatistical analysis revealed significant latitudinal directions of outbreak progressions that have similarity to the detected line of intensity and magnitude.
The line of magnitude and direction indicate the necessity of mobilizing maximum resources on this line to strengthen the existing surveillance.
... A directional statistic [11] was used to determine if there was a systematic, directional spread of outbreaks through Romania during the epidemic period. A chain of infection was constructed by first sequencing the outbreaks by date of occurrence (the primary outbreak first, followed by the second outbreak, and so on). ...
... The mean number of outbreaks per county was four. The largest number of outbreaks were reported from Brasov (29) and Prahova (28) in central Romania, Bacau (14) in northeast Romania, and Constanta (14) and Tulcea (11) in eastern Romania. The median epidemic date was 18 May (day 224 of the epidemic). ...
The aim of this study was to evaluate a range of statistical and geostatistical methods for their usefulness in providing insights into how highly pathogenic avian influenza (HPAI) subtype H5N1 might spread through a national population of village poultry. The insights gained allow the generation of disease dispersion hypotheses. The case study data set consisted of 161 outbreaks of HPAI subtype H5N1 in village poultry reported in Romania between October 2005 and June 2006. Reports of village outbreaks (%) occurred in three waves: October-December (14%), February-March (16%), and May-June (68%). Risk mapping - based on variography and kriging - was used to visualize the evolution of the epidemic. Outbreaks first appeared in eastern and southern Romania, particularly within an area that forms part of the Danube River Delta. The largest phase of the epidemic affected villages in all parts of central, southern, and eastern Romania, but outbreaks were clustered in central Romania. Outbreaks spread in an east to west direction. By using geostatistical visualisation and spatial statistics, the evolution of the epidemic could be characterised into two parts: disease introduction, local spread, and sporadic outbreaks, and long-distance disease spread with rapid epidemic propagation. This is consistent with the hypothesis that the environment and landscape (specifically the Danube River Delta) played a critical role in the introduction and initial spread of HPAI subtype H5N1 during the autumn and winter of 2005, and that the movement of poultry might have introduced the infection into central Romania during the spring and summer of 2006. Further research focusing on the spatio-temporal interface between the two parts of the epidemic might reveal how and why it progressed from a confined, local epidemic to a large, national epidemic. Such information would assist efforts to limit the global spread of HPAI subtype H5N1.
... Specifically, a purely spatial scan statistic was utilized to detect areas where inadequate MDD was higher or lower than expected. These significant areas were visually represented using circular windows [39][40][41][42]. In the final step, we examined Poisson scan statistics. ...
Background
Inadequate minimum dietary diversity (MMD) is the leading cause of malnutrition among young children in Sub-Saharan Africa (SSA). The evidence of geospatial distribution and multilevel determinants of inadequate MDD and its consequence among children is important for the Sustainable Development Goal (SDG0) 2030 agenda. Therefore, this study aimed to determine the geospatial distribution and multilevel determinants of inadequate MDD and its consequences among children in SSA.
Method
The study utilized recent Demographic and Health Surveys data including 57,912 children. Spatial and multilevel analyses were employed, and variables significantly associated with inadequate MDD and undernutrition with MDD consumption were assessed and significance was declared using a p-value threshold of <0.05. Adjusted odds ratio (AOR) with 95% confidence interval (CI) was reported.
Results
The prevalence of inadequate MDD was 80.3% with distinct spatial variation. Spatial distribution showed that; Gabon, Cameron, Ethiopia, Democratic Republic of Congo, Chad, Mali, Burkina Faso, Ivory Coast, Liberia, and Senegal had a very high burden of inadequate MDD. Factors like children’s age, maternal age, educational status, antenatal care (ANC)/ postnatal care (PNC) visits, no media exposure, wealth status, maternal stunting and wasting, and distance from health facilities were associated with inadequate MDD in SSA. The risk of anemia, stunting, and wasting were significantly associated with inadequate MDD among children in SSA.
Conclusion
The prevalence of inadequate MDD in SSA is high. Spatial distribution revealed that inadequate MDD was prevalent in most areas of the Western, Northern, Eastern, and Central parts of SSA. Maternal and children’s age, educational status, ANC/ PNC visits, no media exposure, wealth status, maternal stunting and wasting, and distance from health facilities were determinants of inadequate MDD in SSA. The spatial clustering of inadequate MDD in certain regions of SSA, suggests the need for geographically targeted interventions to address the determinants of inadequate MDD in these high-burden areas. The study revealed strategies should focus on promoting frequent ANC/ PNC visits, improving maternal nutrition, reducing poverty, and improving maternal employment status to reduce inadequate MDD among children. This study highlights a significant association between MDD and anemia, stunting, and wasting in children aged 6-–23 months. To address these critical issues, it is essential to improve MDD among children, as this intervention can play a vital role in achieving SDG target 2.2, which aims to end all forms of malnutrition by 2030.
... Furthermore, directional analysis tools may be necessary to assess systematic patterns in outbreak spread. The direction test [44] helps determine whether outbreaks exhibit a systematic directional spread. In cases where the direction of spread is not immediately apparent, the outbreak sequence is often divided into time segments (e.g., weeks or months). ...
Simple Summary
Spatial epidemiology, integrating traditional epidemiology, geography, statistics, environmental science, and ecology, provides a comprehensive framework for analyzing the spatial dimensions of health and disease. This interdisciplinary approach enhances the development of effective public health strategies and interventions. However, its multifaceted nature can bring complexities in practical application. Using the case of spatial epidemiology in swine viral diseases (SVDs), we illustrate the objectives, methodologies, and essential considerations for the application of spatial epidemiology, which we hope to offer as a comprehensive reference for researchers in this field.
Abstract
Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.
... Given a set of vectors connecting consequent disease outbreaks in their space-time chain, the test calculates the average of those individual vectors and returns its direction, with 0 • being the East direction and 90 • being the North direction. The test also returns the angular concentration, representing the variance in the angles between connected cases, where 1 indicates an absence of angular variance, and 0 indicates large angular variance, and a p-value for the angular concentration calculated through a randomization procedure, which randomly assigns connections between pairs of cases (Jacquez & Oden, 1994 ...
Novel lumpy skin disease virus (LSD) strains of recombinant origin are on the rise in South East Asia following the first emergence in 2017 and published evidence demonstrates that such genetic lineages currently dominate the circulation. Mongolia reported first LSD outbreaks in 2021 in a north-eastern region sharing borders with Russia and China. For each of 59 reported LSDV outbreaks, the number of susceptible animals ranged from 8 to 8600 with a median of 572, while the number of infected ranged from one to 355 with a median of 14. Phylogenetic inferences revealed a close relationship of LSDV Mongolia/2021 with recombinant vaccine-like LSDV strains from Russia, China, Taiwan, Thailand and Vietnam. These findings support the published data that the circulating strain of LSDV belongs to the dominant recombinant lineage recently established in the region. This article is protected by copyright. All rights reserved.
... The mean direction of LSD outbreaks spread was calculated using a space-time directionality test (Jacquez & Oden, 1994). The method calculates the average vector of individual vectors connecting consequent disease outbreaks in their space-time chain. ...
Lumpy skin disease (LSD) is an economically important transboundary disease affecting cattle, causing large economic losses such as decreased production and trade restrictions. LSD has been a historically neglected disease since it previously caused disease limited to the African continent. Currently, the epidemiology of lumpy skin disease virus is based on how the disease is transmitted in tropical and subtropical climates. The understanding of its epidemiology in hemiboreal climates is not well understood and needs urgent attention to expand the current knowledge. In this paper, the epidemiological findings on LSD in Russia over a 6‐year period are summarized and discussed. A total of 471 outbreaks were identified spanning over a 9000 km range. The outbreaks of lumpy skin disease occur primarily in small holder farms (backyard) compared to commercial farms between mid‐May through mid‐November including weather conditions with snow and freezing temperatures that preclude vector activity. Mortality and morbidity varied across the six years ranged from 1.19 to 61.8% and 0 to 50% respectively with a tendency to decline from 2015 to 2020. The geographic pattern of spread was assessed by means of directionality, indicated a northward movement from 2015 to 2016, with a consequent East turn in 2017 through Siberia to the Far East by 2020. All cases occurred along the border with Kazakhstan. Mathematical modelling showed that the disease tended to form statistically verified annual spatio‐temporal clusters in 2016 – 2018, whereas in 2019 and 2020 such segregation was not evident. The trend of spread was mainly either from south to north or from south to a north‐east direction. This article is protected by copyright. All rights reserved
... A purely spatial scan statistic was used to identify areas with higher inadequate MDD. Higher and lower aggregate concentrations were found to be spatially signi cant and were represented by circular windows [25][26][27][28]. Finally, Poisson scan statistics were analyzed, with number of observed, expected, prevalence and Relative Risk(RR) of inadequate MDD in each cluster (sercular windows) estimated [21]. ...
Geographical varation and determinant of inadequate Minimum Dietary Diversity(MDD) among young children in SSA with some consequence is becoming increasingly important for geographical targeting and policy prioritization. This study is to investigate geographic variation, factors, and consequences of inadequate MDD. Data for each nation were extrapolated from recent measures of DHS. The study consist of 57,291 children. Mapping identification and mixed effect model were applied for determining inadequate MDD. The prevalence of inadequate MDD was 79·9%. Most part of Western, Eastern and Central SSA children were more suffered. Children were suffering from anaemia, wasting, and stunting as a result of inadequate MDD. Children whose aged 6—11 months, from not media-exposed mothers, and from living a long distance of health facility were more likely to suffer inadequate MDD compared to their counterparts. The most important predictors were connected to maternal characteristics. Working with mothers is a simple way to achieve tremendous change. When adequate MDD is improved, the number of children who suffer from anemia, stunting, and wasting decreases.
... Sequences from the reported farm-level outbreaks of interest formed a genetic cluster, in which a case was defined by nucleotide identity of ≥98% between samples. Spatiotemporal description of the cases and a directional method was performed using adjacent directed time to assess the spatiotemporal interaction of the cases and calculate the average direction in which cases spread over the course of the study (11). Cases were classified into lineage/sub-lineage and RFLP patterns unless they had incorrect initial and stop codons or presence of ambiguities that would hinder a reliable classification. ...
We report an ongoing regional outbreak of an emerging porcine reproductive and respiratory syndrome virus (PRRSV2) variant within Lineage 1C affecting 154 breeding and grow-finishing sites in the Midwestern U.S. Transmission seemed to have occurred in two waves, with the first peak of weekly cases occurring between October and December 2020 and the second starting in April 2021. Most of cases occurred within a 120 km radius. Both orf5 and whole genome sequencing results suggest that this represents the emergence of a new variant within Lineage 1C distinct from what has been previously circulating. A case-control study was conducted with 50 cases (sites affected with the newly emerged variant) and 58 controls (sites affected with other PRRSV variants) between October and December 2020. Sites that had a market vehicle that was not exclusive to the production system had 0.04 times the odds of being a case than a control. A spatial cluster (81.42 km radius) with 1.68 times higher the number of cases than controls was found. The average finishing mortality within the first 4 weeks after detection was higher amongst cases (4.50%) than controls (0.01%). The transmission of a highly similar virus between different farms carrying on trough spring rises concerns for the next high transmission season of PRRS.
... A purely spatial scan statistic used to identify areas with higher number of child deaths and mothers lost their child. Spatially significant higher and lower aggregate concentrations were identified and circular windows were observed [31][32][33][34] . With the discreet Poisson model, the number of cases in each cluster (enumeration area) has been estimated 30 . ...
Child death and mothers who suffer from child death are a public health concern in Sub-Saharan Africa. The location and associated factors of child death and mothers who suffer child death were not identified. To monitor and prioritize effective interventions, it is important to identify hotspots areas and associated factors. Data from nationally representative demographic and health survey and Multiple Indicator Cluster administrated in 42 Sub-Sahara Africa countries, which comprised a total of 398,574 mothers with 1,521,312 children. Spatial heterogeneity conducted hotspot regions identified. A mixed-effect regression model was run, and the adjusted ratio with corresponding 95% confidence intervals was estimated. The prevalence of mothers who suffer child death 27% and 45–49 year of age mother 48%. In Niger, 47% of mothers were suffering child death. Women being without HIV knowledge, stunted, wasted, uneducated, not household head, poor, from rural, and from subtropical significantly increased the odds of the case (P < 0.05). The spatial analysis can support the design and prioritization of interventions. Multispectral interventions for mothers who suffer from child death are urgently needed, improve maternal health and it will reduce the future risk of cases.
... Finally, we used the location and date of new H. amphibius carcasses to assess the directional spread of anthrax-induced mortalities within the river course using the directional test statistic from the ClusterSeer2 software package (TerraSeer, Crystal Lake, Illinois, USA). The direction test uses individual case data to calculate the average direction in which cases advance during an outbreak (Jacquez 1996; see Appendix S1 for more details). ...
Globally, anthrax outbreaks pose a serious threat to people, livestock, and wildlife. Furthermore, environmental change can exacerbate these outbreak dynamics by altering the host–pathogen relationship. However, little is known about how the quantitative spatial dynamics of host movement and environmental change may affect the spread of Bacillus anthracis, the causative agent of anthrax. Here, we use real‐time observations and high‐resolution tracking data from a population of common hippopotamus (Hippopotamus amphibius) in Tanzania to explore the relationship between river hydrology, H. amphibius movement, and the spatiotemporal dynamics of an active anthrax outbreak. We found that extreme river drying, a consequence of anthropogenic disturbances to our study river, indirectly facilitated the spread of B. anthracis by modulating H. amphibius movements. Our findings reveal that anthrax spread upstream in the Great Ruaha River (~3.5 km over a 9‐day period), which followed the movement patterns of infected H. amphibius, who moved upstream as the river dried in search of remaining aquatic refugia. These upstream movements can result in large aggregations of H. amphibius. However, despite these aggregations, the density of H. amphibius in river pools did not influence the number of B. anthracis‐induced mortalities. Moreover, infection by B. anthracis did not appear to influence H. amphibius movement behaviors, which suggests that infected individuals can vector B. anthracis over large distances right up until their death. Finally, we show that contact rates between H. amphibius‐ and B. anthracis‐infected river pools are highly variable and the frequency and duration of contacts could potentially increase the probability of mortality. While difficult to obtain, the quantitative insights that we gathered during a real‐time anthrax outbreak are critical to better understand, predict, and manage future outbreaks.
... Openshaw's geographic analysis machine (GAM) (Openshaw et al. 1988), Turnbull's test (Turnbull et al. 1990), Besag -Newell's test (Besag and Newell, 1991), the Disease Mapping and Analysis Program (DMAP) (Rushton and Lolonis 1996), SaTScan (Kulldorff and Nagarwalla 1995;SaTScan 2018) and ClusterSeer (Jacquez 1996;TerraSeer, Inc. 2018). Currently, there are more than 100 cluster detection methods available (Kingsley, Schmeichel, and Rubin 2007;Ngan-Lam 2012). ...
Known by some as the ‘invisible’ people because of their precarious work and low social status, migratory and seasonal farmworkers (MSFW) are a critical and underappreciated component to the agriculture industry in the United States. Despite advances in knowledge of about the health needs of this population, identifying geographic regions of high-risk remains a challenging task for community health workers and farmworker advocacy organizations. Guided by the farmworker ecosocial model of health, this dissertation for the first time investigated the geography of farmworker health in California, Colorado, and Michigan. This study utilized two quantitative techniques. The first, spatial scan statistics, were used to measure geographic variations in farmworker disease clusters, while the second technique featured the delineation of healthcare service areas. Qualitative techniques featured interviews with key informants and farmworkers based on the theoretical foundation of social epidemiology. In the study areas, this dissertation found 209 total disease clusters (< 0.02) encompassing 259 zip codes, and 2,732 farmworkers (7% of total population) living greater than 30-minutes from community and migrant health centers (C/MHC). Patient encounters at all C/MHC’s were predominantly for diabetes and evenly distributed; however, farmworkers treated for chronic disease risk factors had the highest percentage of total encounters when comparing individual clinics. Additionally, 32 interviews conducted at C/MHC’s revealed that contextual-level barriers to healthcare are numerous in all study areas and include lack of transportation, poverty, inadequate housing, cultural practices, low educational attainment, and healthcare literacy. Farmworkers were on average young (33.9), and likely to practice circular-migration in Colorado and Michigan, while their counterparts in California resided in the area year-round. By better understanding, the health of farmworkers from multiple contextual and methodological perspectives, appropriate outreach, research, and policy strategies for migratory and seasonal farmworkers can be developed to best serve the unique geographic challenges highlighted in this dissertation.
... The estimated significance levels (P-values) of all the functional groups were adjusted for multiple hypothesis testing with Simes-Hochberg method (Jacquez, 1996). IHC was performed with cell & tissue staining kits (R&D Systems, Inc., USA) and followed by the protocol . ...
Background: Based on global gene expression profile, therapeutic effects of Qishenyiqi (QSYQ) on acute myocardial infarction (AMI) were investigated by integrated analysis at multiple levels including gene expression, pathways involved and functional group.
Methods: Sprague-Dawley (SD) rats were randomly divided into 3 groups: Sham-operated, AMI model (left anterior descending coronary artery ligation) and QSYQ-treated group. Cardiac tissues were obtained for analysing digital gene expression. Sequencing and transcriptome analyses were performed collaboratively, including analyses of differential gene expression, gene co-expression network, targeted attack on network and functional grouping. In this study, a new strategy known as keystone gene-based group significance analysis was also developed.
Results: Analysis of top keystone QSYQ-regulated genes indicated that QSYQ ameliorated ventricular remodeling (VR), which is an irreversible process in the pathophysiology of AMI. At pathway level, both well-known cardiovascular diseases and cardiac signaling pathways were enriched. The most remarkable finding was the novel therapeutic effects identified from functional group analysis. This included anti-inflammatory effects mediated via suppression of arachidonic acid lipoxygenase (LOX) pathway and elevation of nitric oxide (NO); and amelioration of dyslipidaemia mediated via fatty acid oxidation. The regulatory patterns of QSYQ on key genes were confirmed by western blot, immunohistochemistry analysis and measurement of plasma lipids, which further validated the therapeutic effects of QSYQ proposed in this study.
Conclusions: QSYQ exerts multipronged therapeutic effects on AMI, by concurrently alleviating VR progression, attenuating inflammation induced by arachidonic acid LOX pathway and NO production; and ameliorating dyslipidaemia.
... The direction method [13] in ClusterSeer was also used to evaluate the direction of disease spread. This method tests for a space-time interaction and calculates the average direction of the spread. ...
This study describes a spring 2013 outbreak of porcine epidemic diarrhea virus (PEDv), using data from 222 swine sites in 14 counties area in 4 contiguous states in the United States. During the outbreak, the premises-level incidence of PEDv was 40.5 percent (90/222 sites). One of the three companies from which data were collected had a lower incidence (19.5 percent) than the other two companies (41.1 and 47.2 percent). Sow sites had the highest incidence of PEDv during the outbreak (80.0 percent). Spatial analysis showed that PEDv was clustered rather than randomly distributed, which suggested that sites near a positive site had increased risk of acquiring PEDv infection. Meteorological data were used to investigate the hypothesis that PEDv was spread by air. If airborne dissemination played a role in this outbreak, we would expect the direction of disease spread to correlate with the predominant wind direction. Two methods were used to determine the direction of disease spread-linear direction mean analysis in ArcGIS and the direction test in ClusterSeer. The former method indicated PEDv spread was south to slightly southwest, and the latter indicated spread was to the southeast. The predominant wind direction during the month of the outbreak was toward the south, with some southeast and southwest winds; the strongest wind gusts were toward the southwest. These findings support the hypothesis that PEDv was spread by air. The results, however, should be interpreted cautiously because we did not have information on direct and indirect contacts between sites, such as movement of trucks, feed, pigs or people. These types of contacts should be evaluated before pathogen spread is attributed to airborne mechanisms. Although this study did not provide a definitive assessment of airborne spread of PEDv, we believe the findings justify additional research to investigate this potential mechanism of transmission.
... The direction test uses retrospective individual case data to calculate the average direction in which cases advanced during an outbreak [38]. Details of the direction test can be found in Additional file 1: Protocol S1. ...
Background:
Anthrax, a soil-borne zoonosis caused by the bacterium Bacillus anthracis, is enzootic in areas of North America with frequent outbreaks in west Texas. Despite a long history of study, pathogen transmission during natural outbreaks remains poorly understood. Here we combined case-level spatio-temporal analysis and high resolution genotyping to investigate anthrax transmission dynamics. Carcass locations from a single white-tailed deer, Odocoileus virginanus, outbreak were analyzed for spatial clustering using K-function analysis and directionality with trend surface analysis and the direction test.
Results:
The directionalities were compared to results of high resolution genotyping. The results of the spatial clustering analyses, combined with deer movement data, suggest anthrax transmission events occur within limited spatial areas, with carcass locations occurring within the activity space of adjacent cases. The directionality of the outbreak paralleled adjacent dry river beds. Isolates from the outbreak were represented by a single genotype based on multiple locus variable number tandem repeat analysis (MLVA); four sub-genotypes were identified using single nucleotide repeat (SNR) analysis.
Conclusions:
Areas of high transmission agreed spatially with areas of higher SNR genetic diversity; however, SNRs did not provide clear evidence of linear transmission. Overlap of case home ranges provides spatial and temporal support for localized transmission, which may include the role of necrophagous or hematophagous flies in outbreaks in this region. These results emphasize the need for active surveillance and prompt cleanup of anthrax carcasses to control anthrax both during outbreaks and between seasons.
... The direction test, conducted using the ClusterSeer software, assessed the mean direction in which cases advanced during the outbreak. The mean direction was estimated by the magnitude of the angular concentration of the vector obtained from combining the lines that connected each case with all subsequent cases, representing the inverse of the angular variance of those marks (Jacquez and Oden, 1994). If the epidemic followed a consistent trend towards a given direction, then angular variance was expected to be small, whereas the angular concentration would be large. ...
... Directionality of the epidemic was assessed using the directional test using a relative time connection matrix. Average direction of detection of the epidemic was estimated by building a vector whose direction was the average direction of the lines that connect each case with all subsequent cases and whose magnitude (angular concentration) was the angular variance of those marks (Jacquez and Oden, 1994). If the epidemic followed a consistent trend towards a given direction, then angular variance is expected to be small, whereas the angular concentration, that is restricted to the range 0-1, would be large. ...
Emergence of porcine epidemic diarrhea virus (PEDV) in the US in 2013 caused a major impact in the swine industry due to its high mortality and rapid spread through the country. Even though the role of potential sources of infection in the epidemiology of the disease at the farm level (feed, fomites) has been extensively investigated, there is a lack of knowledge about the dynamics of disease spread at the regional level. Here, we investigated the dissemination of PEDV infection in two areas located in the regions with the highest swine density in the country (Southeast and Midwest) including more than 2400 farms. Location and date of outbreaks were used to assess the spatial and temporal clustering of cases using global (Cuzick-Edwards, Knox and directional tests) and local (Bernouilli model of the spatial scan statistic) techniques in the first 10 months of the epidemic. A strong spatio-temporal pattern was detected in both areas of study, with an increased risk of disease at <2km distances of recently (<7 days) infected farms, although extent of clustering was higher in the Southeast. Results, computed for two different locations in the first months of the epidemic, suggest that local transmission from infected farms into neighboring PEDV-free sites is a likely explanation for a substantial proportion of the reported PEDV-positive farms and consistent with the rapid spread of a highly infectious disease in the absence of immunity.
... Clustering of BB-infected farms was assessed using two methods suggested for outbreak investigations [14]. First, the Cuzick-Edwards' test [15] was used to detect any overall, globally clustered distribution of cases at the farm level using the ClusterSeer 2.5.1 software [16]. The test statistic Tk, which represents the number of cases (i.e. ...
Background:
Bovine brucellosis (BB) is a zoonotic disease caused by Brucella abortus. BB is endemic in Argentina, where vaccination with Brucella abortus strain 19 is compulsory for 3-to-8 month-old heifers. The objectives of this study were to quantify the prevalence of BB and to identify factors associated with its occurrence, along with the spatial distribution of the disease, in the provinces of La Pampa and San Luis. A two-stage random sampling design was used to sample 8,965 cows (3,513 in La Pampa and 5,452 in San Luis) from 451 farms (187 in La Pampa and 264 in San Luis).
Results:
Cow and herd prevalence were 1.8 % (95 % CI: 1.3-2.2; n = 157) and 19.7 % (95 % CI: 17.0-22.4; n = 89), respectively. Both cow-level and herd-level prevalence in La Pampa (2.4 and 26.0 %, respectively) were significantly higher than in San Luis (1.4 and 15.5 %, respectively). There were not differences between the proportions of reactive cattle compared to that obtained in a survey conducted in 2005. However, herd prevalence in La Pampa was significantly (P < 0.05) higher compared to that study. Disease was found to be spatially clustered in west La Pampa. The lower the bovine density and the calf/cow ratio, the higher odds of belonging to the cluster.
Conclusions:
The increase of farm prevalence in the last five years suggests that the disease is spreading and that control measures should be applied in the region. The cluster of infected farms was located in the west region of La Pampa. There, farms have lower animal densities and smaller cow/calf indices compared to the rest of the province. Although western La Pampa has more infected herds, within-farm prevalence was not higher, which suggests that the control program has been relatively successful in controlling the disease at the farm level, and/or that low animal density inherently results in low disease prevalence. Our results provide baseline information on the epidemiology of BB and its potential pattern of transmission in Argentina, which will ultimately help to improve BB control programs in the country.
... Stat!, realizzato da Jacquez (1996), è finalizzato, analogamente al GAM, alla ricerca tramite statistiche di vario genere di cluster spaziali e, diversamente dal GAM, temporali, e di interazioni spazio-temporali. ...
... A direction test (Jacquez and Oden, 1994) was used to determine whether the human cases tend to be in a systematic, directional spread. A chain of infection is constructed by first sequencing the cases by time of occurrence. ...
... Ya fue lanzado un nuevo paquete, conocido por Stat! (Statistical software for the clustering of Health Events), desarrollado por BioMedware. Stat! soporta dos tipos de datos básicos: puntos, por ejemplo dirección de residencia de los casos diagnosticados, yáreas, por ejemplo tasas de morbilidad por distrito, [19]. Ninguno de estos paquetes contienen posibilidades de incluir como datos, factores de riesgo o de exposiciones supuestamente comunes a agentes químicos, ni técnicas para analizar conglomerados en estos casos. ...
Classification of an outbreak with the category of epidemics requires that some epidemiological and statistical parameters, which have to be studied simultaneously, are satisfied; mathematical theory helps epidemiologists indetection of epidemics in cases when it is not evident. At the present time, these situations ares studied with slutering techniques, with the help of specialized software inthese topics. The present work aims to study these techniques and its imporvement with including risk factors. It is also presented an application with real data.
Keywords: disease clusters, space time interaction, Scan statistics.
... Yet another option, originally proposed by Mantel (1967), is to use Monte Carlo hypothesis testing (Dwass, 1957) by permuting the times among the fixed spatial locations. This is implemented as part of the Stat! software package (Jacquez, 1994), and it has been used by, e.g., Petridou et al. (1996). Before estimating the population shift bias in Section 3, it is important t o look at any potential bias due to the distributional assumptions of the Knox test statistic.Table 1 contains bias estimates for the Poisson-and Barton-David-based approximations, respectively, when the Knox test is applied to a hypothetical child population in Sweden. ...
The Knox method, as well as other tests for space-time interaction, are biased when there are geographical population shifts, i.e., when there are different percent population growths in different regions. In this paper, the size of the population shift bias is investigated for the Knox test, and it is shown that it can be a considerable problem. A Monte Carlo method for constructing unbiased space-time interaction tests is then presented and illustrated on the Knox test as well as for a combined Knox test. Practical implications are discussed in terms of the interpretation of past results and the design of future studies.
... Yet a further improvement of this idea is the k nearest neighbour (k-NN) test for space±time interaction of Jacquez [39]. The k-NN statistic is the number of case pairs that are k nearest neighbours in both space and time, and is evaluated under the null hypothesis of independent space and time nearest neighbour relationships. ...
More and more citizens urge public health authorities to investigate reports of disease excess in their neighbourhood. These environmental concerns are legitimate and it is part of good public health practice to respond to these complaints. However, the methodological and practical problems are severe and a lot of controversy exists about the usefulness of these investigations. To clarify the possibilities and limitations in this situation, this paper proposes a typology of cluster studies. According to this framework, cluster response is distinguished from two other types of cluster studies: Cluster monitoring. screening proactively for clusters to act as an early warning system, and cluster research, scrutinizing clustering to generate and test aetiological hypotheses. To each of these three types of cluster studies corresponds a different public health context; respectively public health action, public health surveillance and public health research. Probably, part of the controversy mentioned stems from not acknowledging sufficiently the corresponding intrinsic differences in rationality and practical constraints. Cluster response is crisis management and not scientific research. In a relatively short time, an informed decision should be taken by a multidisciplinary team of experts using readily available information and knowledge. In accordance with this point of view, cluster reports should be handled stepwise and the role of statistics is to quantify a cluster exploring different points of view as an input to the decision process.
... Basically, GIS have the prerequisites for the automated implementation of autocorrelation and cluster analyses. Commercial GIS do, however, not yet include the corresponding analytical tools, so that only specific advancements permit this GISapplication model (Scholten and de Lepper, 1991;Jaquez, 1994; van den Berg and von der Ahe ¬, 1997; Zhang and Griffith, 1997;Wellie et al., 2000). The prototype of a Geographical Analysis Machine (Mark I GAM) for the identification of clusters (Openshaw and Charlton, 1987) has been cited as the first example of a spatial analysis method containing a GIS (Openshaw et al., 1990). ...
At first glance, the domain of health is no typical area to applicate Geographical Information Systems (GIS). Nevertheless, the recent development clearly shows that also within the domains of environmental health, disease ecology and public health GIS have become an indispensable tool for processing, analysing and visualising spatial data. In the field of geographical epidemiology, GIS are used for drawing up disease maps and for ecological analysis. The striking advantages of GIS for the disease mapping process are the considerably simplified generation and variation of maps as well as a broader variety in terms of determining a real units. In the frame of ecological analysis, GIS can significantly assist with the assessment of the distribution of health-relevant environmental factors via interpolation and modelling. On the other hand, the GIS-supported methods for the detection of striking spatial patterns of disease distribution need to be much improved. An important topic in this respect is the integration of the time dimension. The increasing use of remote sensing as well as the integration into internet functionalities will stimulate the application of GIS in the field of Environmental Health Sciences (EHS). In future, the integration and analysis of health-relevant data in one single data system will open up many new research opportunities.
The use of spatiotemporal analytical tools to generate risk maps and risk scores that facilitate early detection of health and environmental threats is increasingly popular in many countries and international organizations around the world. The traditional approach of spatial epidemiology focuses on mapping and conducting tests for detection of spatial aggregation of cases, referred to as “clusters”, to determine visual and geographical relational clues, and then ecologic approaches to recognize etiologic signs of disease distribution in relation to explanatory factors. The advances in spatial epidemiology are focused on the application of spatiotemporal findings to inform mitigation measures, use of big data to improve the validity and reliability of case-data based analyses, and eventually to provide risk estimates in a timely manner to support decision and policy in preventive and control measures, while supporting the improvement of existing data collection processes. This study provided a framework for choosing spatiotemporal analytical tools, summarizing the features of tools commonly used in spatial analysis, and discussing their potential use when informing decisions related to One Health scenarios. To this end, three case studies addressing endemic conditions affecting ecosystem health, animal health, and public health in Minnesota were compared. A risk score; an estimate/characterization of the disease spread, and suggestions on risk zones were introduced, using spatiotemporal analytical tools, addressing aquatic invasive species in Minnesota waters, Johne’s disease in dairy cattle, and Anthrax, affecting wildlife, livestock, and humans, respectively. The One Health concept promotes a collaborative approach, through effective communication and cooperation across disciplines and sectors, to solve complex problems that intersect animal, human and environmental health. An essential component in the process is understanding the stakeholder perspectives of the problem. Therefore, the comparison between the case studies focused on the lessons learned through the researcher-stakeholder interactions and identification of the opportunities and challenges in the process. Overall, the work presented through this dissertation, serves as precedent for establishing a protocol of “good practices” when promoting the use of spatiotemporal analytical tools to inform the implementation of scientifically driven risk management and policy solutions to One Health scenarios.
This paper describes the visualisation and the analysis of spatial medical data on different aggregation levels. We intend to give an overview of the behaviour of some methods on different generalisation levels, illustrated by some examples.
Improving human welfare is a critical global concern, but not always easy to achieve. Complications in this regard have been faced by the states of the Former Soviet Union, where socialist-style economic institutions have disappeared, and the transition to a market economy has been slow in coming. Lack of capital, ethnic conflict, and political instability have at times undermined the institutional reform that would be necessary to enable economic efficiency and development. Nowhere are such challenges more pronounced than in the new nation states of central Asia, including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Here, a severe climate limits agriculture, and industrialization has been inhibited by lack of infrastructure, low levels of human capital, and a scarcity of financial resources. These conditions are aggravated by the fact that the central Asian states are landlocked, far from centers of market demand and capital availability. Despite these daunting barriers, development potential does exist, and the goal of the paper is to consider central Asia’s pastoral economy, with a focus on Kazakhstan, which stands poised to become a regional growth pole. The article pursues its goal as follows. It first addresses the biothreat situation to central Asian livestock herds, the most significant existing impediment to realizing the full market potential of the region’s animal products. Next, it provides an outline of interventions that can reduce risk levels for key biothreats impacting central Asia, namely foot and mouth disease (FMD), which greatly impacts livestock and prohibits export, and Brucellosis, a bacterial zoonosis with high incidence in both humans and livestock in the region. Included is an important success story involving the FMD eradication programs in Brazil, which enabled an export boom in beef. After this comes a description of the epidemiological situation in Kazakhstan; here, the article considers the role of wildlife in acting as a possible disease reservoir, which presents a conservation issue for the Kazakhstani case. This is followed by a discussion of the role of science in threat reduction, particularly with respect to the potential offered by geospatial technologies to improve our epidemiological knowledge base. The article concludes with an assessment of the research that would be necessary to identify feasible pathways to develop the economic potential of central Asian livestock production as changes in policy are implemented and livestock health improves.
Modern medicine has made great advances in the cure of numerous diseases, but still its more remarkable results lie in the field of prevention. The early detection of an epidemic’s outbreak is of an outmost importance in order to avoid the epidemic’s spread. Since we do not possess a single detection method that would perfectly apply to all cases it is fundamental to know in advance how precise our opted method’s results will be. In this paper a validation of two temporal disease clusters tests with the simple epidemic model is presented. Additionally, the behavior of both methods is analyzed when risk factors are added in.
The new century brings with it growing interest in crime places. This interest spans theory from the perspective of understanding the etiol- ogy of crime, and practice from the perspective of developing effec- tive criminal justice interventions to reduce crime. We do not attempt a comprehensive treatment of the substantial body of theoretical and empirical research on place and crime but focus instead on method- ological issues in spatial statistical analyses of crime data. Special attention is given to some practical and accessible methods of exploratory data analysis that arguably should be the starting place of any empirical analyses of the relationship of place to crime. Many of the capabilities to support computerized mapping and spatial statisti- cal analyses emerged only recently during the 1990s. The promise of using spatial data and analyses for crime control still remains to be demonstrated and depends on the nature of the relationship between crime and place. If spatial features serve as actuating factors for MEASUREMENT AND ANALYSIS OF CRIME AND JUSTICE
Analysis of disease data that has an implicit spatio-temporal component (such as disease outbreaks, data generated by surveillance systems and specific hypothesis-based veterinary field research) is a foundation of veterinary epidemiology and preventive medicine. Components of this process include exploratory spatial data analysis (finding interesting patterns), visualisation (showing interesting patterns) and spatial modelling (explaining interesting patterns). Spatio-temporal statistics and tests are valuable when adding precision to qualitative verbal descriptions, facilitating the comparison of distributions and drawing attention to characteristics unlikely to be noticed by visual inspection. Quantifying spatio-temporal patterns is important for understanding how disease phenomena behave. The application of a range of spatio-temporal statistics is illustrated by exploratory spatial data analysis and visualisation of the 2002 outbreak of West Nile virus encephalomyelitis in Texas equines. This large outbreak (1 698 reported cases) consisted of both point (latitude, longitude) and polygon (Texas counties) spatial data with a time component (reported date of onset of clinical disease) and case series and attack rate data. This example highlights the need to use a range of techniques to fully understand the spatio-temporal nature of disease occurrence. With knowledge of how disease occurs in time and space, appropriate and effective disease control, prevention and surveillance programmes can be implemented.
The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.
The quality of environmental studies is often compromised by the use of statistics, such as correlation and regression for example, which presuppose a statistical model, linear or otherwise, between two variables. When investigating hypotheses about relationships among geographically distributed variables, an alternative approach is to measure the amount of boundary overlap. Boundaries are geographic zones of rapid change in the intensity of a variable, and are often of scientific interest in their own right. Examples of boundaries include ecotones, genetic hybrid zones, pollution plumes, and the front of the wave of advance of an epidemic. Boundary overlap describes zones where boundaries from two or more variables coincide, and are useful for evaluating epidemiologic hypotheses relating health to environmental exposures. This paper proposes four statistics of boundary overlap, and explores their performance using simulation models and real data describing ozone concentrations and hospital admissions for respiratory conditions. The statistics are found sensitive to different aspects of boundary overlap, and provide an additional diagnostic tool in the analysis of geographically distributed variables. Overlap statistics are expected to come into increasing use as the installed base of geographic information systems increases.
Public health professionals often are asked to investigate apparent clusters of human health events or "disease clusters." A cluster is an excess of cases in space (a geographic cluster), in time (a temporal cluster), or in both space and time. This is the second part of an introductory-level review of the analysis of disease clusters for physicians and health professionals concerned with infection surveillance in hospitals. It reviews the status of the field with the hope of expanding the use of cluster analysis methods for the routine surveillance of infectious diseases in the hospital environment.
This paper describes a k nearest neighbour statistic sensitive to the pattern of cases expected of space-time clusters of health events. The Knox and Mantel tests are frequently used for space-time clustering but have two disadvantages. First, the selection of critical space-time distances for the Knox test and of a data transformation for the Mantel test is subjective. Second, the Mantel statistic is the sum of the products of space and time distances, is linear in form, and is not sensitive to non-linear associations between small space and time distances expected of contagious processes. The k nearest neighbour statistic is the number of case pairs that are k nearest neighbours in both space and time, and is evaluated under the null hypothesis of independent space and time nearest neighbour relationships. The test was applied to simulated and real data and compared to the Knox and Mantel tests using statistical power comparisons. The k nearest neighbour test proved sensitive to the space-time interaction pattern expected of disease clusters, does not require parameters (such as critical distances) to be estimated from the data, and may be used to test hypotheses about the spatial and temporal scale of the cluster process. The method addresses significant weaknesses in existing space-time cluster tests and should prove useful in the quantification and evaluation of clusters of human health events. Additional research is needed to further document the power of the test under different cluster processes.
To assess spatial clustering of childhood leukaemias and lymphomas in New Zealand, using a national dataset from a country with no nuclear installations.
New Zealand Map Grid coordinates, derived from the birth addresses of cases and controls were used in clustering analyses that applied Cuzick and Edwards' method.
The whole of New Zealand.
The cases were ascertained from the New Zealand Cancer Registry. They were diagnosed with leukaemia or lymphoma at ages 0-14 years during the period 1976 to 1987. For Hodgkin's disease, the age range was extended to include those aged from 0-24 years. The cancer registrations were linked with national birth records, to obtain the birth addresses of the cases. The controls were selected at random from birth records, with matching to cases (1:1) on age and sex. The analyses included 600 cases and 600 controls.
There was no statistically significant spatial clustering for any tumour group overall, including acute lymphoblastic leukaemia, acute nonlymphoblastic leukaemia, other leukaemias, non-Hodgkin's lymphomas, Hodgkin's disease, and all these combined. Significant clustering was found in a sub-analysis for one of three age specific subgroups of acute lymphoblastic leukaemia (ages 10-14 years, p = 0.003).
The subgroup finding may have been real or a chance association, as several comparisons were made. This study found little evidence for spatial clustering of leukaemias or lymphomas in a population with no nuclear installations.
This study was conducted to determine if the biology of certain ticks associated with horses regulates the spatial and temporal distribution of equine granulocytic ehrlichiosis (EGE) in California north of Monterey County. We compared the spatial and temporal distribution of EGE cases with the seasons of activity and life histories of ticks that infest horses. Spatially, cases collected from equine veterinarians clustered around each other in a manner different from the way in which control cities of practice were distributed, with foci limited to the Sierra Nevada and coastal foothills. Cases also clustered seasonally: most were diagnosed between November and April. The spatial and temporal pattern of EGE cases closely parallels the well-characterized life history and distribution of Ixodes pacificus Cooley & Kohls, but not other ticks commonly associated with horses. Building on previous studies, there is compelling evidence that this tick has the vectorial capacity to transmit Ehrlichia equi to horses. Based on the life history and distribution of I. pacificus in relation to EGE cases, we reason that this tick is the only biologically plausible vector of E. equi in California, and provide evidence for a tightly linked association between I. pacificus and the epidemiology of EGE.
Person, place, time: these are the basic elements of outbreak investigations and epidemiology. Historically, however, the focus in epidemiologic research has been on person and time, with little regard for the implications
Our aim was to investigate the geographic and time distributions of some biologically similar neoplasms in dogs and humans living in Michigan, USA, between 1964 and 1994. Our objective was to describe and compare the patterns of cancer in the two species while assessing the strength and dependence of those patterns. In this retrospective, registry-based study, histologically confirmed incident human and canine cancer cases were mapped, and second-order (K function) spatial analysis and one-dimensional nearest neighbor temporal analysis were performed on residence addresses and dates of hospital discharge/diagnosis. Included in the study were all 528 incident cases of canine lymphosarcoma, mammary adenocarcinoma, melanoma and spindle-cell sarcomas diagnosed at a veterinary teaching hospital between 1964 and 1994 having residence addresses in Ingham, Oakland, and Wayne Counties; and a stratified random sample of 913 incident human cases of comparable cancers diagnosed during the same time period from the same counties. Results suggest that processes determining spatial aggregation of cases in dogs and humans were not independent of each other, did not act uniformly over different geographic areas, operated at spatial scales <2000 m regardless of species, and tend to act upon dogs more strongly at shorter distances than on humans. Little evidence of interspecies concurrence of temporal clustering was found.
Epidemiologic determinants of 46 cases of aural abscessation in free-living eastern box turtles (Terrapene carolina) admitted to the Wildlife Center of Virginia (Virginia, USA) from 1991 to 2000 were evaluated. County human population density, year and season of admission, weight, and sex did not affect the risk for box turtles to develop aural abscessation. Counties with cases of aural abscessation were not randomly distributed, but rather were clustered into two multi-county regions. Geographic location was the only risk factor associated with aural abscessation in box turtles found in this study. Possible etiologies could include chronic infectious disease, malnutrition, or chronic exposure to environmental contamination with organochlorine compounds.
This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile.
Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only.
Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study.
Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account.
The Centers for Disease Control and Prevention (CDC) continues to be aware of the need for response to public concern as well as to state and local agency concern about cancer clusters. In 1990 the CDC published the "Guidelines for Investigating Clusters of Health Events," in which a four-stage process was presented. This document has provided a framework that most state health departments have adopted, with modifications pertaining to their specific situations, available resources, and philosophy concerning disease clusters. The purpose of this present article is not to revise the CDC guidelines; they retain their original usefulness and validity. However, in the past 15 years, multiple cluster studies as well as scientific and technologic developments have affected duster science and response (improvements in cancer registries, a federal initiative in environmental public health tracking, refinement of biomarker technology, cluster identification using geographic information systems software, and the emergence of the Internet). Thus, we offer an addendum for use with the original document. Currently, to address both the needs of state health departments as well as public concern, the CDC now a) provides a centralized, coordinated response system for cancer cluster inquiries, b) supports an electronic cancer cluster listserver, c) maintains an informative web page, and d) provides support to states, ranging from laboratory analysis to epidemiologic assistance and expertise. Response to cancer clusters is appropriate public health action, and the CDC will continue to provide assistance, facilitate communication among states, and foster the development of new approaches in duster science.
To determine whether West Nile virus (WNV) disease hyperendemic foci (hot spots) exist within the horse population in Texas and, if detected, to identify the locations.
Reports of 1,907 horses with WNV disease in Texas from 2002 to 2004. Procedures: Case data with spatial information from WNV epidemics occurring in 2002 (1,377 horses), 2003 (396 horses), and 2004 (134 horses) were analyzed by use of the spatial scan statistic (Poisson model) and kriging of empirical Bayes smoothed county attack rates to determine locations of horses with WNV disease in which affected horses were consistently (in each of the 3 study years) clustered (hyperendemic foci, or hot spots).
2 WNV hot spots in Texas, an area in northwestern Texas and an area in eastern Texas, were identified with the scan statistic. Risk maps of the WNV epidemics were qualitatively consistent with the hot spots identified. Conclusions and
WNV hot spots existed within the horse population in Texas (2002 to 2004). Knowledge of disease hot spots allows disease control and prevention programs to be made more efficient through targeted surveillance and education.
Cuzick and Edwards (JR Stat Soc [B] 1990;52:73-104) have proposed a case-control test to detect spatial clustering. The test statistic is the sum, over all cases, of the number of each case's k nearest neighbors that also are cases. Their approach is attractive in that it accounts for geographic variation in population density and because it allows one to account for confounders, both known and unknown, through the judicious selection of controls. However, the test assumes case locations are known exactly, when, in practice, case locations are usually approximated by the centers of areas such as census tracts and zip code zones. In such situations, "ties" arise when cases and controls are assigned to the same area, and the loss of information precludes calculation of the test statistic. The author's approach enumerates the ways in which the ties may be resolved to obtain upper and lower bounds on the exact, unobserved, test statistic. The null hypothesis of no clustering is rejected when the upper and lower bounds are significant, and it is accepted when they are not significant. Judgment is withheld when the upper bound is significant but the lower bound is not significant. This approach allows Cuzick and Edwards' test to be used with inexact locations typical of most cluster investigations.
This paper describes a k nearest neighbour statistic sensitive to the pattern of cases expected of space-time clusters of health events. The Knox and Mantel tests are frequently used for space-time clustering but have two disadvantages. First, the selection of critical space-time distances for the Knox test and of a data transformation for the Mantel test is subjective. Second, the Mantel statistic is the sum of the products of space and time distances, is linear in form, and is not sensitive to non-linear associations between small space and time distances expected of contagious processes. The k nearest neighbour statistic is the number of case pairs that are k nearest neighbours in both space and time, and is evaluated under the null hypothesis of independent space and time nearest neighbour relationships. The test was applied to simulated and real data and compared to the Knox and Mantel tests using statistical power comparisons. The k nearest neighbour test proved sensitive to the space-time interaction pattern expected of disease clusters, does not require parameters (such as critical distances) to be estimated from the data, and may be used to test hypotheses about the spatial and temporal scale of the cluster process. The method addresses significant weaknesses in existing space-time cluster tests and should prove useful in the quantification and evaluation of clusters of human health events. Additional research is needed to further document the power of the test under different cluster processes.
N points are independently drawn from the uniform distribution on (0, 1). Denote by E(n|N; p), the event: There exists a subinterval of (0, 1) of length p that contains at least n out of the N points. We find the probability, P(n|N; p), of E(n|N; p) for n>N/2 in terms of simple tabulated quantities.
A new method for detecting spatial clustering of events in populations with non-uniform density is proposed. The method is based on selecting controls from the population at risk and computing interpoint distances for the combined sample. Nonparametric tests are developed which are based on the number of cases among the k nearest neighbours of each case and the number of cases nearer than the k nearest control. The performance of these tests is evaluated analytically and by simulation and the method is applied to a data set on the locations of cases of childhood leukaemia and lymphoma in a defined geographical area. In particular the impact on power of the choice of k and of the ratio of cases to controls is examined. Modifications of the procedure to study distances from predefined objects, to match for known risk factors which would produce unwanted clustering and issues related to estimation are also discussed.
The scan statistic evaluates whether an apparent cluster of disease in time is due to chance. The statistic employs a ‘moving window’ of length w and finds the maximum number of cases revealed through the window as it scans or slides over the entire time period T. Computation of the probability of observing a certain size cluster, under the hypothesis of a uniform distribution, is infeasible when N, the total number of events, is large, and w is of moderate or small size relative to T. We give an approximation that is an asymptotic upper bound, easy to compute, and, for the purposes of hypothesis testing, more accurate than other approximations presented in the literature. The approximation applies both when N is fixed, and when N has a Poisson distribution. We illustrate the procedure on a data set of trisomic spontaneous abortions observed in a two year period in New York City.
I derive two new statistics, Ipop and I*pop, that adjust Moran's I to study clustering of disease cases in areas (for example, counties) with different, known population densities. A simulation of Lyme disease in Georgia suggests that these new statistics can be more powerful than those currently in use. This is because they consider both spatial pattern and non-binomial variance in rates as evidence supporting disease clusters.
The scan statistic is used in many areas of science to test the null hypothesis of uniformity against a clustering alternative. In this article product-type approxiamations and Bonferroni-type upper bounds are derived for the tail probabilities of the scan statistic. These new approximations appear to be remarkably accurate and are utilized to compute approximations for the expected size and the standard deviation of the scan statistic. Moreover, accurate approximations are obtained for the distribution and the moments of the smallest interval containing m ordered observations from a uniform distribution in (0,1]. A simulation study is carried out to evaluate the approximations derived in this article.
Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 1982. Includes bibliographical references (leaves [93]-96).
A test for nonrandom patterns in populations of "cells" of binary attributes is formulated for applications in settings where it is impossible or impractical to completely specify the adjacency matrix, as in circumstances involving enormous numbers of cells and/or censored data. Applications are made to problems concerning spatial distributions of specially labeled cells in nervous system research.
A combinatorial test for measuring unimodal clustering has been developed. The null distribution of this statistic is derived as well as the power function for a class of alternatives representing exponential contagion. The statistic is asymptotically normal, with prior results indicating the convergence to be fairly rapid. The mean and variance are derived to facilitate the construction of large sample tests. Results of the method applied to cases of acute leukemia among residents of Metropolitan Atlanta, Georgia, over a 14 year period, showed a tendency for cases to cluster in the 0-14 and 50+ age groups.
This paper presents combinatorial (exact) methods that are useful in the analysis of disease cluster data obtained from small environments, such as buildings and neighbourhoods. Maxwell-Boltzmann and Fermi-Dirac occupancy models are compared in terms of appropriateness of representation of disease incidence patterns (space and/or time) in these environments. The methods are illustrated by a statistical analysis of the incidence pattern of bone fractures in a setting wherein fracture clustering was alleged to be occurring. One of the methodological results derived in this paper is the exact distribution of the maximum cell frequency in occupancy models.
The detection of disease clustering and a generalized regression approach Centers for Disease Control. 'Guidelines for investigating clusters of health events
- N Mantel
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Assessment of risk trends and patterns
- R C Grimson
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Conference on Records and Statistics, NCHS, DHHS Publ. number (PHS) 90-1214 pp. 327-333, 1989.