Intestinal schistosomiasis-related mortality in Minas Gerais, Brazil, 2000-2019: Temporal trends and spatial patterns for determining priority areas.

OBJECTIVE: To identify priority areas for schistosomiasis control, we analysed the epidemiological characteristics, temporal trends and spatial patterns of schistosomiasis-related mortality in the state of Minas Gerais from 2000 to 2019. METHODS: Ecological and time-series study with spatial analysis techniques on deaths from Schistosomiasis mansoni. A log-linear regression model was used to identify changes in mortality rates. Moran's global index, local indicators of spatial association and a retrospective spatio-temporal permutation model were applied to identify the spatial and temporal distribution of mortality rates and assist in identifying priority areas for interventions. RESULTS: A total of 1290 deaths from schistosomiasis were recorded between 2000 and 2019, with an average mortality rate of 0.33 deaths/100,000. Although the overall mortality rate in the state of Minas Gerais decreased significantly over time (average annual percentage change = -9.6; 95% confidence interval = -14.4 to -4.6; p < 0.001), it increased in the mesoregions of Jequitinhonha, Mucuri Valley, and Rio Doce Valley. Spatial analysis identified the displacement and emergence of high-risk clusters from the central region of the state to the mesoregion of Rio Doce Valley. CONCLUSION: Temporal changes and shifting of high-risk areas from the central region to the mesoregion of Rio Doce Valley may indicate possible failures in early diagnosis and treatment of the schistosomiasis control program in these areas. Our research contributes to a better understanding of the spatio-temporal dynamics of death rates due to schistosomiasis infections and might help health authorities to direct resources most efficiently to avoid serious clinical outcomes in Minas Gerais.

Identifying hidden Zika hotspots in Pernambuco, Brazil: a spatial analysis.

BACKGROUND: Northeast Brazil has the world's highest rate of Zika-related microcephaly. However, Zika case counts cannot accurately describe burden because mandatory reporting was only established when the epidemic was declining in the region. METHODS: To advance the study of the Zika epidemic, we identified hotspots of Zika in Pernambuco state, Northeast Brazil, using Aedes-borne diseases (dengue, chikungunya and Zika) and microcephaly data. We used Kulldorff's Poisson purely spatial scan statistic to detect low- and high-risk clusters for Aedes-borne diseases (2014-2017) and for microcephaly (2015-2017), separately. Municipalities were classified according to a proposed gradient of Zika burden during the epidemic, based on the combination of cluster status in each analysis and considering the strength of the evidence. RESULTS: We identified 26 Aedes-borne diseases clusters (11 high-risk) and 5 microcephaly clusters (3 high-risk) in Pernambuco. According to the proposed Zika burden gradient, our results indicate that the northeast of Pernambuco and the Sertão region were hit hardest by the Zika epidemic. The first is the most populous area of Pernambuco, while the second has one of the highest rates of social and economic inequality in Brazil. CONCLUSION: We successfully identified possible hidden Zika hotspots using a simple methodology combining Aedes-borne diseases and microcephaly information.

Epidemiologia espacial e a detecção de aglomerados espaciais do dengue na Paraíba: uma comparação entre os métodos Scan flexível e Scan circular / Spatial epidemiology and the detection of spatial clusters of dengue in Paraíba: a comparison between the flexible Scan and circular Scan methods

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.

Detection of risk clusters for deaths due to tuberculosis specifically in areas of southern Brazil where the disease was supposedly a non-problem.

BACKGROUND: Tuberculosis (TB) is the infectious disease that kills the most people worldwide. The use of geoepidemiological techniques to demonstrate the dynamics of the disease in vulnerable communities is essential for its control. Thus, this study aimed to identify risk clusters for TB deaths and their variation over time. METHODS: This ecological study considered cases of TB deaths in residents of Londrina, Brazil between 2008 and 2015. We used standard, isotonic scan statistics for the detection of spatial risk clusters. The Poisson discrete model was adopted with the high and low rates option used for 10, 30 and 50% of the population at risk, with circular format windows and 999 replications considered the maximum cluster size. Getis-Ord Gi* (Gi*) statistics were used to diagnose hotspot areas for TB mortality. Kernel density was used to identify whether the clusters changed over time. RESULTS: For the standard version, spatial risk clusters for 10, 30 and 50% of the exposed population were 4.9 (95% CI 2.6-9.4), 3.2 (95% CI: 2.1-5.7) and 3.2 (95% CI: 2.1-5.7), respectively. For the isotonic spatial statistics, the risk clusters for 10, 30 and 50% of the exposed population were 2.8 (95% CI: 1.5-5.1), 2.7 (95% CI: 1.6-4.4), 2.2 (95% CI: 1.4-3.9), respectively. All risk clusters were located in the eastern and northern regions of the municipality. Additionally, through Gi*, hotspot areas were identified in the eastern and western regions. CONCLUSIONS: There were important risk areas for tuberculosis mortality in the eastern and northern regions of the municipality. Risk clusters for tuberculosis deaths were observed in areas where TB mortality was supposedly a non-problem. The isotonic and Gi* statistics were more sensitive for the detection of clusters in areas with a low number of cases; however, their applicability in public health is still restricted.

Where did I get dengue? Detecting spatial clusters of infection risk with social network data.

Typical spatial disease surveillance systems associate a single address to each disease case reported, usually the residence address. Social network data offers a unique opportunity to obtain information on the spatial movements of individuals as well as their disease status as cases or controls. This provides information to identify visit locations with high risk of infection, even in regions where no one lives such as parks and entertainment zones. We develop two probability models to characterize the high-risk regions. We use a large Twitter dataset from Brazilian users to search for spatial clusters through analysis of the tweets' locations and textual content. We apply our models to both real-world and simulated data, demonstrating the advantage of our models as compared to the usual spatial scan statistic for this type of data.

The remarkable geographical pattern of gastric cancer mortality in Ecuador.

AIM: This study was aimed to describe the gastric cancer mortality trend, and to analyze the spatial distribution of gastric cancer mortality in Ecuador, between 2004 and 2015. METHODS: Data were collected from the National Institute of Statistics and Census (INEC) database. Crude gastric cancer mortality rates, standardized mortality ratios (SMRs) and indirect standardized mortality rates (ISMRs) were calculated per 100,000 persons. For time trend analysis, joinpoint regression was used. The annual percentage rate change (APC) and the average annual percent change (AAPC) was computed for each province. Spatial age-adjusted analysis was used to detect high risk clusters of gastric cancer mortality, from 2010 to 2015, using Kulldorff spatial scan statistics. RESULTS: In Ecuador, between 2004 and 2015, gastric cancer caused a total of 19,115 deaths: 10,679 in men and 8436 in women. When crude rates were analyzed, a significant decline was detected (AAPC: -1.8%; p<0.001). ISMR also decreased, but this change was not statistically significant (APC: -0.53%; p=0.36). From 2004 to 2007 and from 2008 to 2011 the province with the highest ISMR was Carchi; and, from 2012 to 2015, was Cotopaxi. The most likely high occurrence cluster included Bolívar, Los Ríos, Chimborazo, Tungurahua, and Cotopaxi provinces, with a relative risk of 1.34 (p<0.001). CONCLUSION: There is a substantial geographic variation in gastric cancer mortality rates among Ecuadorian provinces. The spatial analysis indicates the presence of high occurrence clusters throughout the Andes Mountains.

Spatial and temporal distribution of American cutaneous leishmaniasis in Acre state, Brazil.

BACKGROUND: Acre has reported the highest incidence of American cutaneous leishmaniasis (ACL) in Brazil in recent years. The present study seeks to identify high and low risk agglomerations of ACL in space and space-time during the period from 2007 to 2013 in Acre, and also to characterize the occurrence of the disease in time and according to sociodemographic variables. METHODS: This is an ecological study, the study population of which consisted of autochthonous ACL cases notified in the municipalities of Acre by an epidemiological surveillance system. Scan statistics of SaTScan™ software were used to identify spatial and space-time clusters. In addition, the cases were characterized by sex, age, home situation (in a rural or urban area), and temporal tendency. RESULTS: Acre reported an incidence rate of 12.4 cases per 10 000 inhabitant-years in the study period, with the rates varied greatly (standard deviation of 21.8) among their 22 municipalities. One agglomeration of high risk and three of low risk were detected in space and space-time. Four of the five micro-regions of Acre presented a stationary temporal tendency. The profile of transmission varied according to the micro-region. Generally speaking, the disease occurred more often among young people, those of male gender, and those living in rural areas. CONCLUSIONS: Acre has stood out within the Brazilian national context due to its high rates of ACL incidence in the central region of the Acre Valley. The high rates in the micro-region of Brasiléia are related to the disease's intra/peridomiciliary occurrence, and it would seem that the municipality of Sena Madureira is approaching a transmission pattern similar to that of Brasiléia. In other micro-regions, the profile of the disease's transmission is mainly related to the forest/sylvatic cycle of ACL.

20 Years Spatial-Temporal Analysis of Dengue Fever and Hemorrhagic Fever in Mexico.

BACKGROUND AND AIM: Dengue Fever (DF) is a human vector-borne disease and a major public health problem worldwide. In Mexico, DF and Dengue Hemorrhagic Fever (DHF) cases have increased in recent years. The aim of this study was to identify variations in the spatial distribution of DF and DHF cases over time using space-time statistical analysis and geographic information systems. METHODS: Official data of DF and DHF cases were obtained in 32 states from 1995-2015. Space-time scan statistics were used to determine the space-time clusters of DF and DHF cases nationwide, and a geographic information system was used to display the location of clusters. RESULTS: A total of 885,748 DF cases was registered of which 13.4% (n = 119,174) correspond to DHF in the 32 states from 1995-2015. The most likely cluster of DF (relative risk = 25.5) contained the states of Jalisco, Colima, and Nayarit, on the Pacific coast in 2009, and the most likely cluster of DHF (relative risk = 8.5) was in the states of Chiapas, Tabasco, Campeche, Oaxaca, Veracruz, Quintana Roo, Yucatán, Puebla, Morelos, and Guerrero principally on the Gulf coast over 2006-2015. CONCLUSION: The geographic distribution of DF and DHF cases has increased in recent years and cases are significantly clustered in two coastal areas (Pacific and Gulf of Mexico). This provides the basis for further investigation of risk factors as well as interventions in specific areas.