scholarly journals Spatial analysis of AIDS in the state of Maranhão: an ecological study 2011-2018

2022 ◽  
Vol 75 (1) ◽  
Author(s):  
Livia Cristina Sousa ◽  
Tereza Cristina Silva ◽  
Thaís Furtado Ferreira ◽  
Arlene de Jesus Mendes Caldas
Keyword(s):  
Information System ◽  
Spatial Analysis ◽  
High Risk ◽  
Spatial Autocorrelation ◽  
Ecological Study ◽  
Health Management ◽  
Temporal Distribution ◽  
Risk Areas ◽  
Spatio Temporal ◽  
Global And Local

ABSTRACT Objective: Analyze the spatio-temporal distribution of AIDS cases in Maranhão. Methods: Ecological study of AIDS cases in the Notifiable Diseases Information System, 2011-2018. Gross and adjusted incidences were calculated using the Baysean method; then, the Moran Global and Local Indices to observe the existence of spatial autocorrelation of the cases and for the delimitation of high and low risk clusters. Results: 6,349 cases were reported, which were distributed heterogeneously. There was an advance of cases to new areas and persistence in old areas, such as in the capital São Luís and its surroundings. The dissemination did not occur at random, with positive spatial autocorrelation, with evidence of the formation of clusters in the municipalities of São Luís, São José de Ribamar and Paço do Lumiar. Conclusion: High-risk areas have been identified and should be considered a priority for investment in health, management, and organization of health services.

scholarly journals Arabian Sea Tropical Cyclones: A Spatio-Temporal Analysis in Support of Natural Hazard Risk Appraisal in Oman

2021 ◽  
Author(s):  
Suad Al-Manji ◽  
Gordon Mitchell ◽  
Amna Al Ruheili
Keyword(s):  
High Risk ◽  
Tropical Cyclones ◽  
Natural Hazard ◽  
Temporal Distribution ◽  
Storm Surges ◽  
Temporal Analysis ◽  
The North ◽  
Risk Areas ◽  
Spatio Temporal ◽  
Extreme Winds

Tropical cyclones [TCs] are a common natural hazard that have significantly impacted Oman. Over the period 1881–2019, 41 TC systems made landfall in Oman, each associated with extreme winds, storm surges and significant flash floods, often resulting in loss of life and substantial damage to infrastructure. TCs affect Omani coastal areas from Muscat in the north to Salalah in the south. However, developing a better understanding of the high-risk regions is needed, and is of particular interest in disaster risk reduction institutions in Oman. This study aims to find and map TC tracks and their spatio-temporal distribution to landfall in Oman to identify the high-risk areas. The analysis uses Kernel Density Estimation [KDE] and Linear Direction Mean [LDM] methods to better identify the spatio-temporal distribution of TC tracks and their landfall in Oman. The study reveals clear seasonal and monthly patterns. This knowledge will help to improve disaster planning for the high-risk areas.

scholarly journals Spatial and spatio-temporal analysis of malaria cases in Zimbabwe

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Isaiah Gwitira ◽  
Munashe Mukonoweshuro ◽  
Grace Mapako ◽  
Munyaradzi D. Shekede ◽  
Joconiah Chirenda ◽  
...  
Keyword(s):  
High Risk ◽  
Spatial Autocorrelation ◽  
Malaria Control ◽  
Temporal Pattern ◽  
Poisson Model ◽  
Space Time ◽  
Spatial Clusters ◽  
Risk Areas ◽  
Geographic Regions ◽  
Spatio Temporal

Abstract Background Although effective treatment for malaria is now available, approximately half of the global population remain at risk of the disease particularly in developing countries. To design effective malaria control strategies there is need to understand the pattern of malaria heterogeneity in an area. Therefore, the main objective of this study was to explore the spatial and spatio-temporal pattern of malaria cases in Zimbabwe based on malaria data aggregated at district level from 2011 to 2016. Methods Geographical information system (GIS) and spatial scan statistic were applied on passive malaria data collected from health facilities and aggregated at district level to detect existence of spatial clusters. The global Moran’s I test was used to infer the presence of spatial autocorrelation while the purely spatial retrospective analyses were performed to detect the spatial clusters of malaria cases with high rates based on the discrete Poisson model. Furthermore, space-time clusters with high rates were detected through the retrospective space-time analysis based on the discrete Poisson model. Results Results showed that there is significant positive spatial autocorrelation in malaria cases in the study area. In addition, malaria exhibits spatial heterogeneity as evidenced by the existence of statistically significant (P < 0.05) spatial and space-time clusters of malaria in specific geographic regions. The detected primary clusters persisted in the eastern region of the study area over the six year study period while the temporal pattern of malaria reflected the seasonality of the disease where clusters were detected within particular months of the year. Conclusions Geographic regions characterised by clusters of high rates were identified as malaria high risk areas. The results of this study could be useful in prioritizing resource allocation in high-risk areas for malaria control and elimination particularly in resource limited settings such as Zimbabwe. The results of this study are also useful to guide further investigation into the possible determinants of persistence of high clusters of malaria cases in particular geographic regions which is useful in reducing malaria burden in such areas.

scholarly journals Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Behzad Kiani ◽  
Amene Raouf Rahmati ◽  
Robert Bergquist ◽  
Soheil Hashtarkhani ◽  
Neda Firouraghi ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Policy Development ◽  
Temporal Pattern ◽  
Temporal Analysis ◽  
High Rate ◽  
Spatial Scan Statistic ◽  
Cross Sectional ◽  
Risk Areas ◽  
Spatio Temporal

Abstract Background Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

scholarly journals Spatial and temporal analysis of myocardial infarction incidence in Zanjan province, Iran

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mohsen Soleimani ◽  
Nasser Bagheri
Keyword(s):  
Myocardial Infarction ◽  
Spatial Analysis ◽  
High Risk ◽  
Incidence Rate ◽  
Temporal Analysis ◽  
Analysis Methods ◽  
Spatial And Temporal Analysis ◽  
Risk Areas ◽  
Adjusted Incidence Rate ◽  
Adjusted Incidence

Abstract Background Myocardial Infarction (MI) is a major important public health concern and has huge burden on health system across the world. This study aimed to explore the spatial and temporal analysis of the incidence of MI to identify potential clusters of the incidence of MI patterns across rural areas in Zanjan province, Iran. Materials & methods This was a retrospective and geospatial analysis study of the incidence of MI data from nine hospitals during 2014–2018. Three different spatial analysis methods (Spatial autocorrelation, hot spot analysis and cluster and outlier analysis) were used to identify potential clusters and high-risk areas of the incidence of MI at the study area. Results Three thousand eight hundred twenty patients were registered at Zanjan hospitals due to MI during 2014–2018. The overall age-adjusted incidence rate of MI was 343 cases per 100,000 person which was raised from 88 cases in 2014 to 114 cases in 2018 per 100,000 person-year (a 30% increase, P < 0.001). Golabar region had the highest age-adjusted incidence rate of MI (515 cases per 100,000 person). Five hot spots and one high-high cluster were detected using spatial analysis methods. Conclusion This study showed that there is a great deal of spatial variations in the pattern of the incidence of MI in Zanjan province. The high incidence rate of MI in the study area compared to the national average, is a warning to local health authorities to determine the possible causes of disease incidence and potential drivers of high-risk areas. The spatial cluster analysis provides new evidence for policy-makers to design tailored interventions to reduce the incidence of MI and allocate health resource to unmet need areas.

scholarly journals Spatial Distribution of Breast Cancer in Sudan 2010-2016

2019 ◽  
Author(s):  
Marwa Maweya Abdelbagi Elbasheer ◽  
Ayah Galal Abdelrahman Alkhidir ◽  
Siham Mohammed Awad Mohammed ◽  
Areej Abuelgasim Hassan Abbas ◽  
Aisha Osman Mohamed ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Spatial Distribution ◽  
Spatial Analysis ◽  
High Risk ◽  
Geographical Information ◽  
Risk Level ◽  
Histopathological Diagnosis ◽  
Cross Sectional ◽  
Distribution Maps ◽  
Risk Areas

AbstractBackgroundBreast cancer is the most prevalent cancer among females worldwide including Sudan. The aim of this study was to determine the spatial distribution of breast cancer in Sudan.Materials and methodsA facility based cross-sectional study was implemented in eighteen histopathology laboratories distributed in the three localities of Khartoum State on a sample of 4630 Breast Cancer cases diagnosed during the period 2010-2016. A master database was developed through Epi Info™ 7.1.5.2 for computerizing the data collected: the facility name, type (public or private), and its geo- location (latitude and longitude). Personal data on patients were extracted from their respective medical records (name, age, marital status, ethnic group, State, locality, administrative unit, permanent address and phone number, histopathology diagnosis). The data was summarized through SPSS to generate frequency tables for estimating prevalence and the geographical information system (ArcGIS 10.3) was used to generate the epidemiological distribution maps. ArcGIS 10.3 spatial analysis features were used to develop risk maps based on the kriging method.ResultsBreast cancer prevalence was 3.9 cases per 100,000 female populations. Of the 4423 cases of breast cancer, invasive breast carcinoma of no special type (NST) was the most frequent (79.5%, 3517/4423) histopathological diagnosis. The spatial analysis indicated as high risk areas for breast cancer in Sudan the States of Nile River, Northern, Red Sea, White Nile, Northern and Southern Kordofan.ConclusionsThe attempt to develop a predictive map of breast cancer in Sudan revealed three levels of risk areas (risk, intermediate and high risk areas); regardless the risk level, appropriate preventive and curative health interventions with full support from decision makers are urgently needed.

scholarly journals Spatial analysis of hepatobiliary abnormalities in a population at high-risk of cholangiocarcinoma in Thailand

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kavin Thinkhamrop ◽  
Apiporn T. Suwannatrai ◽  
Nittaya Chamadol ◽  
Narong Khuntikeo ◽  
Bandit Thinkhamrop ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
High Risk ◽  
Screening Program ◽  
Care Program ◽  
Population Based ◽  
Northeast Thailand ◽  
Survival Prognosis ◽  
Healthcare Facilities ◽  
History Of ◽  
Risk Areas

Abstract Cholangiocarcinoma (CCA) is a serious health challenge with low survival prognosis. The liver fluke, Opisthorchis viverrini, plays a role in the aetiology of CCA, through hepatobiliary abnormalities: liver mass (LM), bile duct dilation, and periductal fibrosis (PDF). A population-based CCA screening program, the Cholangiocarcinoma Screening and Care Program, operates in Northeast Thailand. Hepatobiliary abnormalities were identified through ultrasonography. A multivariate zero-inflated, Poisson regression model measured associations between hepatobiliary abnormalities and covariates including age, sex, distance to water resource, and history of O. viverrini infection. Geographic distribution was described using Bayesian spatial analysis methods. Hepatobiliary abnormality prevalence was 38.7%; highest in males aged > 60 years (39.8%). PDF was most prevalent (20.1% of males). The Standardized Morbidity Ratio (SMR) for hepatobiliary abnormalities was highest in the lower and upper parts of the Northeast region. Hepatobiliary abnormalities specifically associated with CCA were also more common in males and those aged over 60 years and distributed along the Chi, Mun, and Songkram Rivers. Our findings demonstrated a high risk of hepatobiliary disorders in Northeast Thailand, likely associated with infection caused by O. viverrini. Screening for CCA and improvement of healthcare facilities to provide better treatment for CCA patients should be prioritized in these high-risk areas.

Potential for arsenic contamination of rice in Bangladesh: spatial analysis and mapping of high risk areas

2006 ◽  
Vol 6 (4/5/6) ◽  
pp. 298 ◽  
Author(s):  
Zev Ross ◽  
John M. Duxbury ◽  
Stephen D. DeGloria ◽  
Debi Narayan Rudra Paul
Keyword(s):  
Spatial Analysis ◽  
High Risk ◽  
Arsenic Contamination ◽  
Risk Areas

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

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

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

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

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

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

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