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Resumo Introdução Estatísticas espaciais são usadas para auxiliar gestores de saúde na tomada de decisão, informando a taxa de ocorrência de agravos na população e destacando quando estas alcançam valores além do esperado. Objetivo Compreender o funcionamento e aplicabilidade das Estatísticas Espaciais Scan flexível e Scan circular, comparando seus resultados na detecção de aglomerados espaciais usando dados epidemiológicos reais do dengue no estado da Paraíba - Brasil. Método Descreveu-se o processo detalhado da aplicação das estatísticas Scan flexível e Scan circular para a detecção de áreas significativas de risco (aglomerados) do dengue na Paraíba, nos anos de 2009 a 20013, por meio dos software FLeXScan e SaTScan. Resultados Ambos os métodos revelaram o oeste do estado como a região com maior frequência de aglomerados detectados com alto risco, em todos os anos analisados, levando-se em consideração os mapas de risco de incidência do dengue na Paraíba, nos anos de 2009 a 2013. Conclusão As estatísticas Scan flexível e Scan circular são praticamente similares quanto à eficiência na detecção de aglomerados do dengue. Entretanto, verificaram-se problemas de superestimação no método Scan circular e subestimação no método Scan flexível na detecção dos aglomerados. Destacou-se ainda o auxílio destas estatísticas espaciais aos gestores de saúde quanto à localização das regiões de agravo da doença, tornando mais efetivo o direcionamento das ações de combate de forma politicamente correta.
Abstract Background Spatial statistics are used to help health managers make decisions, informing the rate of occurrence of diseases in the population and highlighting when they reach values beyond expectations. Objective To understand the functioning and applicability of Spatial Statistics Flexible Scan and Circular Scan by comparing their results in the detection of spatial clusters using real epidemiological data of dengue in the state of Paraíba - Brazil. Method The detailed process for applying the flexible scan and circular scan statistics for detecting significant dengue risk areas (clusters) in Paraíba, between 2009 and 20013, was described using the software FLeXScan and SaTScan. Results Both methods showed the highest frequency of clusters detected at high risk in the western region of the state, in all the years analyzed, considering the risk maps of dengue incidence in Paraíba, between 2009 and 2013. Conclusion The flexible scan and circular scan statistics are practically similar in terms of efficiency in detecting clusters of dengue. However, there were problems of overestimation in the circular Scan method and underestimation in the flexible Scan method in the detection of clusters. It is also worth highlighting that these spatial statistics help health managers locate the regions of disease aggravation, making it more effective to direct combat actions in a politically correct manner.
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Objective To explore the optimal combination of parameters for the maximum spatial cluster size and maximum temporal cluster size of scan statistics. Methods The daily incidence data of hand-foot-and-mouth disease (HFMD) in Jingzhou in 2016 was collected as data source. The maximum spatial cluster sizes were set to 50%, 40%, 30%, 20%, and 10% of the population at risk. The maximum temporal cluster sizes were set to 7d, 14d, 30d, and 60d. A total of 20 parameter setting schemes were formed and spatial-temporal scanning was conducted one by one. The areas where the number of towns covered by the scanning area was less than 25 were selected, and the clustered epidemic of hand-foot-mouth disease can be detected at the same time in Xiejiaping Town of Songzi City and Sanzhou Town of Jianli County. The combination of large LLR and RR values was the optimal parameter setting. Results When the spatial windows were set to 20% of the population at risk, and the temporal windows were set to 30d, a total of 6 aggregation areas were detected. The number of covered townships was less than 25, and the clustered epidemic of Xiejiaping Township and Sanzhou Town were successfully detected. The LLR and RR values of the detected aggregation area were relatively large. This combination was the optimal parameter setting. Conclusion The combination of different parameters has a significant impact on the results of spatial-temporal scan statistics. It is recommended that parameters be optimized before applying this method.
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Objective To explore the spatiotemporal distribution pattern, and identify risk cluster of esophageal cancer in Huai’an City so as to provide evidence for control and prevention of esophageal cancer. Methods Data of esophageal cancer incidence at township level in Huai’an City from 2011 to 2015 was collected. Spatial autocorrelation and local indications of spatial autocorrelation (LISA) were implemented to evaluate the spatial pattern of esophageal cancer incidence. Spatial scan statistics was used to examine spatio-temporal clustering of risk areas. Results The average incidence of esophageal cancer in Huai’an from 2011 to 2015 was 67.12/10 million, the incidence of male was significantly higher than that of female. The results of Moran’s I values implyed the spatial autocorrelation at township level. The results of LISA indicated that there were local hot spots and cold spots. The significant high-risk clusters included townships in Huai’an County, Huaiyin County and Jinhu County. The low-risk clusters were located in the main urban area and Xuyi County. Conclusions There are significant spatio-temporal aggregation for the distribution of incidence of esophageal cancer in Huai’an City and same spatiotemporal high-risk clusters between male and female. Our findings have a foundation to explore the multi-factorial etiology of esophageal cancer and have vital practical value for health services and policies implementation.
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To investigate the space-time patterns of schistosomiasis after the stage of transmission controlled in Hubei Province, so as to provide the reference for precise controlling.The data of human schistosomiasis cases in Hubei Province from 2015 to 2016 and basic information of human schistosomiasis cases and serum antibody titer of human schistosomiasis cases in 2016 were collected and analyzed. The spatial clustering of human schistosomiasis was detected by the Flexible spatial scan statistics and Kulldorff circular scan statistic, respectively.Totally 64 819 serological positive cases from 51 counties and 1 504 stool hatching positive cases from 17 endemic counties were reported in Hubei Province in 2015, and 39 365 serological positive cases were reported from 48 counties in 2016. All of them were identified as the research objects. No stool hatching positive cases were reported from the routine work in the whole province in 2016. There were 1 603 cases of the highest antibody titer (1∶80 or more), with the proportion of 4.07%. According to the results of Kulldorff spatial clustering analysis, there were eight and six spatial clustering areas in the distribution of serological positive cases and stool hatching positive cases in 2015, respectively. The numbers of spatial clustering areas in the distribution of serological positive cases and the cases of the highest antibody titer (1∶80 or more) were eight and five in 2016, respectively. According to the results of Flexible spatial clustering analysis, there were three and two spatial clustering areas in the distribution of serological positive cases and stool hatching positive cases in 2015, respectively. The numbers of spatial clustering areas in the distribution of serological positive cases and the cases of highest antibody titer (1∶80 or more) were two and one in 2016, respectively.The distribution of human schistosomiasis cases is not balanced, and there is spatial clustering in Hubei Province. So the key area for prevention and control is on the middle and lower reaches of the Yangtze River in the future.
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Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis (CL) or for malaria in Fars province, Iran in 2016.Methods: Using time-series data including 29177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software.Results: A total of 5359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied time-series models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria (Pv was set to 0.1). The results indicated two significant prospective spatial-temporal outbreaks for CL (P<0.5) including Most Likely Clusters, and one non-significant outbreak for malaria (P>0.5) in the area.Conclusions: Both CL and malaria follow a space-time trend in the area, but prospective permutation scan modeling works better for detecting CL spatial-temporal outbreaks. It is not far away from expectation since clusters are defined as accumulation of cases in specified times and places. Although this method seems to work better with finding the outbreaks of a high-frequency disease;i.e., CL, it is able to find non-significant outbreaks. This is clinically important for both high- and low-frequency infections;i.e., CL and malaria.
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Objective: To determine whether permutation scan statistics was more efficient in finding prospective spatial-temporal outbreaks for cutaneous leishmaniasis (CL) or for malaria in Fars province, Iran in 2016. Methods: Using time-series data including 29 177 CL cases recorded during 2010-2015 and 357 malaria cases recorded during 2010-2015, CL and malaria cases were predicted in 2016. Predicted cases were used to verify if they followed uniform distribution over time and space using space-time analysis. To testify the uniformity of distributions, permutation scan statistics was applied prospectively to detect statistically significant and non-significant outbreaks. Finally, the findings were compared to determine whether permutation scan statistics worked better for CL or for malaria in the area. Prospective permutation scan modeling was performed using SatScan software. Results: A total of 5 359 CL and 23 malaria cases were predicted in 2016 using time-series models. Applied time-series models were well-fitted regarding auto correlation function, partial auto correlation function sample/model, and residual analysis criteria (P
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Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis (CL) in Fars province, Iran. Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatio-temporal clusters of CL cases.Time-series data were recorded from 29 201 cases in Fars province,Iran from 2010 to 2015,which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters.Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using SaTScan software. Results: This study resulted in statistically significant spatiotemporal clusters of CL (P<0.05).The most likely cluster contained 350 cases from 1 July 2010 to 30 November 2010. Besides, 5 secondary clusters were detected in different periods of time. Finally, statistically significant sub-clusters were found within the three large clusters(P<0.05). Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province, Iran.This can have an important effect on future studies on prediction and prevention of CL.
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Objective To assess the spatiotemporal trait of cutaneous leishmaniasis (CL) in Fars province, Iran. Methods Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using SaTScan software. Results This study resulted in statistically significant spatiotemporal clusters of CL (P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November 2010. Besides, 5 secondary clusters were detected in different periods of time. Finally, statistically significant sub-clusters were found within the three large clusters (P < 0.05). Conclusions Transmission of CL followed spatiotemporal pattern in Fars province, Iran. This can have an important effect on future studies on prediction and prevention of CL.