Spatial statistical methods in health

Spatiotemporal analysis and seasonality of tuberculosis in Pudong New Area of Shanghai, China, 2014-2023

BACKGROUND

Spatiotemporal analysis is a vital method that plays an indispensable role in monitoring epidemiological changes in diseases and identifying high-risk clusters. However, there is still a blank space in the spatial and temporal distribution of tuberculosis (TB) incidence rate in Pudong New Area, Shanghai. Consequently, it is crucial to comprehend the spatiotemporal distribution of TB in this district, this will guide the prevention and control of TB in the district.

METHODS

Our research used Geographic Information System (GIS) visualization, spatial autocorrelation analysis, and space-time scan analysis to analyze the TB incidence reported in the Pudong New Area of Shanghai from 2014 to 2023, and described the spatiotemporal clustering and seasonal hot spot distribution of TB incidence.

RESULTS

From 2014 to 2023, the incidence of TB in the Pudong New Area decreased, and the mortality was at a low level. The incidence of TB in different towns/streets has declined. The spatial autocorrelation analysis revealed that the incidence of TB was spatially clustered in 2014, 2016-2018, and 2022, with the highest clusters in 2014 and 2022. The high clustering area was mainly concentrated in the northeast. The space-time scan analysis indicated that the most likely cluster was located in 12 towns/streets, with a period of 2014-2018 and a radiation radius of 15.74 km. The heat map showed that there was a correlation between TB incidence and seasonal variations.

CONCLUSIONS

From 2014 to 2023, the incidence of TB in the Pudong New Area of Shanghai declined, but there were spatiotemporal clusters and seasonal correlations in the incidence area. Local departments should formulate corresponding intervention measures, especially in high-clustering areas, to achieve accurate prevention and control of TB within the most effective time and scope.

Bayesian cluster geographically weighted regression for spatial heterogeneous data

Spatial statistical models are commonly used in geographical scenarios to ensure spatial variation is captured effectively. However, spatial models and cluster algorithms can be complicated and expensive. One of these algorithms is geographically weighted regression (GWR) which was proposed in the geography literature to allow relationships in a regression model to vary over space. In contrast to traditional linear regression models, which have constant regression coefficients over space, regression coefficients are estimated locally at spatially referenced data points with GWR. The motivation for the adaption of GWR is the idea that a set of constant regression coefficients cannot adequately capture spatially varying relationships between covariates and an outcome variable. GWR has been applied widely in diverse fields, such as ecology, forestry, epidemiology, neurology and astronomy. While frequentist GWR gives us point estimates and confidence intervals, Bayesian GWR enriches our understanding by including prior knowledge and providing probability distributions for parameters and predictions of interest. This paper pursues three main objectives. First, it introduces covariate effect clustering by integrating a Bayesian geographically weighted regression (BGWR) with a post-processing step that includes Gaussian mixture model and the Dirichlet process mixture model. Second, this paper examines situations in which a particular covariate holds significant importance in one region but not in another in the Bayesian framework. Lastly, it addresses computational challenges in existing BGWR, leading to enhancements in Markov chain Monte Carlo estimation suitable for large spatial datasets. The efficacy of the proposed method is demonstrated using simulated data and is further validated in a case study examining children's development domains in Queensland, Australia, using data provided by Children's Health Queensland and Australia's Early Development Census.

Geo-spatial high-risk clusters of Tuberculosis in the global general population: a systematic review

INTRODUCTION

The objective of this systematic review is to identify tuberculosis (TB) high-risk among the general population globally. The review was conducted using the following steps: elaboration of the research question, search for relevant publications, selection of studies found, data extraction, analysis, and evidence synthesis.

METHODS

The studies included were those published in English, from original research, presented findings relevant to tuberculosis high-risk across the globe, published between 2017 and 2023, and were based on geospatial analysis of TB. Two reviewers independently selected the articles and were blinded to each other`s comments. The resultant disagreement was resolved by a third blinded reviewer. For bibliographic search, controlled and free vocabularies that address the question to be investigated were used. The searches were carried out on PubMed, LILACS, EMBASE, Scopus, and Web of Science. and Google Scholar.

RESULTS

A total of 79 published articles with a 40-year study period between 1982 and 2022 were evaluated. Based on the 79 studies, more than 40% of all countries that have carried out geospatial analysis of TB were from Asia, followed by South America with 23%, Africa had about 15%, and others with 2% and 1%. Various maps were used in the various studies and the most used is the thematic map (32%), rate map (26%), map of temporal tendency (20%), and others like the kernel density map (6%). The characteristics of the high-risk and the factors that affect the hotspot's location are evident through studies related to poor socioeconomic conditions constituting (39%), followed by high population density (17%), climate-related clustering (15%), high-risk spread to neighbouring cities (13%), unstable and non-random cluster (11%).

CONCLUSION

There exist specific high-risk for TB which are areas that are related to low socioeconomic conditions and spectacular weather conditions, these areas when well-known will be easy targets for intervention by policymakers. We recommend that more studies making use of spatial, temporal, and spatiotemporal analysis be carried out to point out territories and populations that are vulnerable to TB.

A spatial analysis of co-circulating dengue and chikungunya virus infections during an epidemic in a region of Northeastern Brazil

The aim of this study was to describe, through spatial analysis, the cases of arboviruses (dengue and chikungunya), including deaths, during the first epidemic after the circulation of the chikungunya virus (CHIKV) in the state of Pernambuco, Northeastern Brazil. This was an ecological study in both Pernambuco and the state capital, Recife, from 2015 to 2018. The odds ratios (OR) were estimated, and the statistical significance was considered p≤0.05. For the spatial analysis, Kulldorff's space-time scan statistics method was adopted to identify spatial clusters and to provide the relative risk (RR). In order to assess the significance at a level of p < 0.01 of the model, the number of Monte Carlo replications was 999 times. To perform the scan statistics we used the Poisson probability model, with a circular scanning window; annual temporal precision and retrospective analysis. A total of 227 deaths and 158,728 survivors from arboviruses was reported during the study period, with 100 deaths from dengue and 127 from CHIKV. The proportion of deaths from dengue was 0.08% and from chikungunya was 0.35%. The proportion of all those infected (deaths plus survivors) with dengue was 77.42% and with chikungunya was 22.58%. Children aged 0 to 9 years were around 3 times more likely to die than the reference group (OR 2.84; CI95% 1.16-5.00). From the age of 40, the chances of death increased significantly: 40-49 (OR 2.52; CI95% 1.19-5.29), 50-59 (OR 5.55; CI95% 2.76-11.17) and 60 or more (OR 14.90; CI95% 7.79-28.49). Males were approximately twice as likely to die as females (OR 1.77; CI95% 1.36-2.30). White-skinned people were less likely to die compared to non-white (OR 0.60; CI95% 0.41-0.87). The space-time analysis of prevalence in the state of Pernambuco revealed the presence of four clusters in the years 2015 and 2016, highlighting the Metropolitan Macro-region with a relative risk=4 and the Agreste and Hinterland macro-regions with a relative risk=3.3. The spatial distribution of the death rate in the municipality of Recife smoothed by the local empirical Bayesian estimator enabled a special pattern to be identified in the southwest and northeast of the municipality. The spatiotemporal analysis of the death rate revealed the presence of two clusters in the year 2015. In the primary cluster, it may be noted that the aforementioned aggregate presented a RR=7.2, and the secondary cluster presented a RR=6.0. The spatiotemporal analysis with Kulldorff's space-time scan statistics method, proved viable in identifying the risk areas for the occurrence of arboviruses, and could be included in surveillance routines so as to optimize prevention strategies during future epidemics.

A geographical study on amyotrophic lateral sclerosis in Rio Grande Do Norte, Brazil, from 2005 to 2018

OBJECTIVE

amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disorder with variable incidence and prevalence worldwide. However, clinical-epidemiological studies on ALS are scarce in Brazil. Thus, this study investigated whether ALS incidence had uniform spatial distribution in population-based cluster analysis in Rio Grande do Norte state (Brazil), from 2005 to 2018.

METHODS

new cases of ALS were identified in a database of the ALS multidisciplinary care center of the Onofre Lopes University Hospital in Natal (Rio Grande do Norte, Brazil). Approaches were based on incidence (empirical Bayes estimator and Moran's I analysis) and cluster analyses (Moran scatter plot and spatial correlogram).

RESULTS

a total of 177 patients (59% males) participated in the study; the mean age of ALS onset was 57 years. Mean annual incidence of ALS was 0.3769 per 100,000 inhabitants (95% confidence interval of 0.0889), higher in males than in females (0.4516 per 100,000 vs. 0.3044 per 100,000). According to spatial statistics, patients were homogeneously distributed throughout the studied area.

CONCLUSION

a low estimate was observed compared with other populations. Results did not indicate areas of increased risk or significant spatial geographic dependence, suggesting a random ALS incidence in Rio Grande do Norte.

Geospatial Analysis of Cervical Cancer Distribution in South Sulawesi Province

Background: Cervical cancer, which is classified as a non-communicable disease, is a health problem that is of global concern at this time.1 Indonesia ranks second in the highest number of cervical cancer cases in the world with 32,469 cases per year. 1 For this reason, optimization efforts are carried out to prevent the increase in the prevalence of cervical cancer patients in the Province of South Sulawesi. Objective: The purpose of this study was to make a geospatial analysis of the distribution of cervical cancer patients. Methods:  Geospatial analysis using Global Moran's I and Local Moran's I. Result: The results of the geospatial analysis of the prevalence of cervical cancer in South Sulawesi Province show that in 2016 there were two spatial hotspot clusters (H-H), one coldspot spatial cluster (L-L), two spatial outlier clusters (H-L), and one spatial outlier cluster (L-H). In 2019, there were only two spatial hotspot clusters. Geospatial analysis of the prevalence of cervical cancer shows an increase in efforts to prevent cervical cancer from 2016 to 2019. However, there are still spatial hotspot clusters in 2019, especially in rural areas.. Conclusion: The efforts to prevent cervical cancer need to be optimized, especially in rural areas, in the future.

Spatiotemporal pattern of Covid-19 outbreak in Turkey

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context.

Cytogenetic markers and their spatial distribution in a population living in proximity to areas sprayed with pesticides

Human populations are in contact with potentially toxic substances in varying amounts, if the exposure is work-related or direct, generally the amount of toxin is usually greater than if the exposure is environmental through the drifts that occur. It was proposed to determine the existence of genotoxic damage evaluated through Chromosomal Aberrations and Micronuclei assays and their spatial distribution pattern, as well as the possible relationship between that damage and the values found in biochemical biomarkers, in groups of individuals environmental exposure (respiratory exposure) to mixtures of pesticides, in the province of Córdoba-Argentina. Biochemical and hematological determinations were made in each samples. The results reveal that the monitoring of human populations through the analysis of cytogenetic markers enabled the detection of direct damage in man caused by polluting substances and the results were obtained rapidly. The disadvantage of this type of study is the inability to estimate the degree of exposure.

Spatial drivers of COVID-19 vulnerability in Nigeria

INTRODUCTION

the spread and diffusion of COVID-19 undoubtedly shows strong spatial connotations and alignment with the physical indices of civilization and globalization. Several spatial risk factors have possible influence on its dispersal trajectory. Understanding their influence is critical for mobilization, sensitization and managing non-pharmaceutical interventions at the appropriate spatial-administrative units.

METHODS

on 01 April 2020, we constructed a rapid spatial diagnostics and generated vulnerability map for COVID-19 infection spread at state level using 12 core spatial drivers. The risk factors used include established COVID-19 cases (as at 01 April 2020), population, proximity to the airports, inter-state road traffic, intra-state road traffic, intra city traffic, international road traffic, possible influx of elites from abroad, preponderance of high risk political elite, likelihood of religious gathering, likelihood of other social gatherings, and proximity to existing COVID-19 test centers. These were also tested as predictors of COVID-19 spread using multiple regression analysis.

RESULTS

the results show that 6 States - Lagos, Kano, Katsina, Kaduna, Oyo and Rivers - and the Federal Capital Territory have very high vulnerability, 17 states have high vulnerability and 13 states have medium vulnerability to COVID-19 transmission. Several drivers show a strong association with COVID-19 with the coefficient of correlation ranging from 0.983 - 0.995. The regression analysis indicates that between 96.6 and 99.0 percent of the total variation in the COVID-19 infections across Nigeria can be explained by the predictors.

CONCLUSION

the spatial pattern of infection across the states are substantially consistent with the predicted pattern of vulnerability.

Spatial Patterns in Hospital-Acquired Infections in Portugal (2014-2017)

BACKGROUND

Hospital-Acquired Infections (HAIs) represent the most frequent adverse event associated with healthcare delivery and result in prolonged hospital stays and deaths worldwide.

AIM

To analyze the spatial patterns of HAI incidence from 2014 to 2017 in Portugal.

METHODS

Data from the Portuguese Discharge Hospital Register were used. We selected episodes of patients with no infection on admission and with any of the following HAI diagnoses: catheter-related bloodstream infections, intestinal infections by Clostridium difficile, nosocomial pneumonia, surgical site infections, and urinary tract infections. We calculated age-standardized hospitalization rates (ASHR) by place of patient residence. We used empirical Bayes estimators to smooth the ASHR. The Moran Index and Local Index of Spatial Autocorrelation (LISA) were calculated to identify spatial clusters.

RESULTS

A total of 318,218 HAIs were registered, with men accounting for 49.8% cases. The median length of stay (LOS) was 9.0 days, and 15.7% of patients died during the hospitalization. The peak of HAIs (n = 81,690) occurred in 2015, representing 9.4% of the total hospital admissions. Substantial spatial inequalities were observed, with the center region presenting three times the ASHR of the north. A slight decrease in ASHR was observed after 2015. Pneumonia was the most frequent HAI in all age groups.

CONCLUSION

The incidence of HAI is not randomly distributed in the space; clusters of high risk in the central region were seen over the entire study period. These findings may be useful to support healthcare policymakers and to promote a revision of infection control policies, providing insights for improved implementation.

Use of a generalized additive model for a spatial analysis of bovine brucellosis risk in the state of Mato Grosso in 2002 and 2014

Diseases that affect cattle represent obstacles to the development of livestock activity. Brucellosis is a significant such disease because it is transmissible, has a chronic nature, and causes health and economic damages to the herd and rural producer. Data from surveys performed in 2002 and 2014 were compared to identify the spatial distribution of bovine brucellosis and to evaluate clusters of outbreaks and areas of greater risk to have infected cattle in the state of Mato Grosso, Brazil. The present study analyzed the data obtained in the aforementioned investigations with a statistical model based on a spatial point process called a generalized additive model (GAM). The analysis made it possible to identify the regions of highest and lowest risk in the state of Mato Grosso. Of the 1001 properties analyzed in 2002, 198 were in areas with high-odds ratio, and 121 were in a low-odds ratio area. Of the 1248 properties sampled in 2014, 119 were in a high-odds ratio area, and 162 were in a low-odds ratio area. Areas with high-odds ratio are more likely to have infected cattle and can be considered to be at higher risk for the disease. The results of the present study highlight the reduction in foci, prevalence, and its relationship with the spatial distribution of bovine brucellosis. The study results should help the official defense service of Mato Grosso direct its activities according to the profile of each region.

Spatial and temporal analysis of tuberculosis in an area of social inequality in Northeast Brazil

BACKGROUND

Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. It is a disease known worldwide for its vulnerability factors, magnitude and mortality. The objective of the study was to analyze the spatial and temporal dynamics of TB in the area of social inequality in northeast Brazil between the years 2001 and 2016.

METHODS

An ecological time series study with the use of spatial analysis techniques was carried out from 2001 to 2016. The units of analysis were the 75 municipalities in the state of Sergipe. Data from the Notification of Injury Information System were used. For the construction of the maps, the cartographic base of the state of Sergipe, obtained at the Instituto Brasileiro de Geografia e Estatística, was used. Georeferenced data were analysed using TerraView 4.2.2 software (Instituto Nacional de Pesquisas Espaciais) and QGis 2.18.2 (Open Source Geospatial Foundation). Spatial analyses included the empirical Bayesian model and the global and local Moran indices. The time trend analyses were performed by the software Joinpoint Regression, Version 4.5.0.1, with the variables of sex, age, cure and abandonment.

RESULTS

There was an increasing trend of tuberculosis cases in patients under 20 years old and 20-39 years old, especially in males. Cured cases showed a decreasing trend, and cases of treatment withdrawal were stationary. A spatial dependence was observed in almost all analysed territories but with different concentrations. Significant spatial correlations with the formation of clusters in the southeast and northeast of the state were observed. The probability of illness among municipalities was determined not to occur in a random way.

CONCLUSION

The identification of risk areas and priority groups can help health planning by refining the focus of attention to tuberculosis control. Understanding the epidemiological, spatial and temporal dynamics of tuberculosis can allow for improved targeting of strategies for disease prevention and control.

Geografia e saúde coletiva no Brasil

Resumo Este artigo faz uma reflexão a respeito da importância da geografia para a pesquisa em saúde coletiva no Brasil. Por meio de uma revisão bibliográfica narrativa, o autor descreve os principais temas abordados, agrupando os artigos selecionados de três das principais revistas brasileiras da área - Revista de Saúde Pública, Cadernos de Saúde Pública e Saúde e Sociedade - por escolas do pensamento geográfico. Discute também os avanços alcançados, assim como os desafios teóricos e metodológicos da saúde coletiva com base nos conhecimentos geográficos. Observou-se a importância do geoprocessamento em saúde para estudos de distribuição espacial, principalmente de doenças infectocontagiosas e parasitárias. Da mesma forma, foi possível identificar o crescimento da produção científica em estudos com base no pensamento crítico, com destaque para as publicações recentes na revista Saúde e Sociedade. A comparação dos trabalhos publicados também proporcionou a identificação de desafios metodológicos a serem enfrentados para o estudo da saúde coletiva com embasamento ainda maior de conhecimentos geográficos, como o uso de modelos preditivos e análise de superfícies de tendências, assim como o desenvolvimento de novas ferramentas cartográficas para a compreensão da realidade social em transformação e movimento.

Application of GIS technology in public health: successes and challenges

The uptake and acceptance of Geographic Information Systems (GIS) technology has increased since the early 1990s and public health applications are rapidly expanding. In this paper, we summarize the common uses of GIS technology in the public health sector, emphasizing applications related to mapping and understanding of parasitic diseases. We also present some of the success stories, and discuss the challenges that still prevent a full scope application of GIS technology in the public health context. Geographical analysis has allowed researchers to interlink health, population and environmental data, thus enabling them to evaluate and quantify relationships between health-related variables and environmental risk factors at different geographical scales. The ability to access, share and utilize satellite and remote-sensing data has made possible even wider understanding of disease processes and of their links to the environment, an important consideration in the study of parasitic diseases. For example, disease prevention and control strategies resulting from investigations conducted in a GIS environment have been applied in many areas, particularly in Africa. However, there remain several challenges to a more widespread use of GIS technology, such as: limited access to GIS infrastructure, inadequate technical and analytical skills, and uneven data availability. Opportunities exist for international collaboration to address these limitations through knowledge sharing and governance.

Spatial and temporal epidemiology of malaria in extra-Amazonian regions of Brazil

Background

Mosquitoes, Plasmodium parasites, and humans live in sympatry in some extra-Amazonian regions of Brazil. Recent migrations of people from Amazonia and other countries to extra-Amazonian regions have led to many malaria outbreaks. Lack of relevant expertise among health professionals in non-endemic areas can lead to a neglect of the disease, which can be dangerous given its high fatality rate. Therefore, understanding the spatial and temporal epidemiology of malaria is essential for developing strategies for disease control and elimination. This study aimed to characterize imported (IMP) and autochthonous/introduced (AU/IN) cases in the extra-Amazonian regions and identify risk areas and groups.

Methods

Epidemiological data collected between 2007 and 2014 were obtained from the Notifiable Diseases Information System of the Ministry of Health (SINAN) and from the Department of the Unified Health System (DATASUS). High malaria risk areas were determined using the Local Indicator of Spatial Association. IMP and AU/IN malaria incidence rates were corrected by Local Empirical Bayesian rates.

Results

A total of 6092 malaria cases (IMP: 5416, 88.9 %; AU/IN: 676, 11.1 %) was recorded in the extra-Amazonian regions in 2007–2014. The highest numbers of IMP and AU/IN cases were registered in 2007 (n = 862) and 2010 (n = 149), respectively. IMP cases were more frequent than AU/IN cases in all states except for Espírito Santo. Piauí, Espírito Santo, and Paraná states had high incidences of AU/IN malaria. The majority of infections were by Plasmodium falciparum in northeast and southeast regions, while Plasmodium vivax was the predominant species in the south and mid-west showed cases of dual infection. AU/IN malaria cases were concentrated in the coastal region of Brazil, which contains the Atlantic Forest and hosts the Anopheles transmitters. Several malaria clusters were also associated with the Brazilian Pantanal biome and regions bordering the Amazonian biome.

Conclusion

Malaria is widespread outside the Amazonian region of Brazil, including in more urbanized and industrialized states. This fact is concerning because these highly populated areas retain favourable conditions for spreading of the parasites and vectors. Control measures for both IMP and AU/IN malaria are essential in these high-risk areas.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0934-6) contains supplementary material, which is available to authorized users.

How health managers can use data mining for predicting individuals' risks of contracting nosocomial pneumonia

This article explains how managers can use a new data-mining technique for solving problems related to individual risks of contracting nosocomial pneumonia. Using the genetic algorithm, a search technique provides practitioners with an optimal choice of parameters for Gini boosting type decision tree models. Thus, managers and technicians can choose better models. These new parameters are genetically controlled: number of trees, depth of trees, trimming factor, cross-validation (to avoid overfitting), proportion of the population used, and the minimum size to split a node. This technique has been satisfactorily tested on health data.

Spatial analysis and geographic variation of fatal and injury crashes in mazandaran province from 2006 to 2010

Background:

Road safety and traffic accidents change in time and space. Although, time variations have always been considered the subject being focused by researchers, the effect of spatial correlation and spatial components on the risk of accident have been less investigated. Due to its specific geographical position, Mazandaran Province is one of the highest traffic provinces. This study aims to investigate the factors influencing suburban crashes of Mazandaran province by considering the spatial correlation.

Methods:

This study is aggregated (descriptive -analytical) and the study period was 2006 to 2010. Social and environmental factors effects on the risk of accidents have been studied considering the correlation structure of the regions and regardless of this structure with Poisson regression, negative binomial and Full Bayes hierarchical models. Geographical pattern of risk distribution for the observed values of SMRs and the estimated values after smoothing have been plotted and analyzed.

Results:

Comparing the measures of models goodness of fit indicates that hierarchical Bayes model fits the data better. Plotting the geographical pattern, the north central parts of the province have been identified as the high-risk areas. Human factors were identified as the important factors for the risk of accident.

Conclusions:

The purpose of this procedure is to separate the random effect of residuals correlation. Using this method, the measure of the model goodness of fit got reduced reflecting a better model than the prototype model. The significance of the structured spatial effect shows the existence of unknown explanatory variables with correlated structure whose identification and control can reduce the risk of accidents.

Avaliação da situação de saúde por profissionais da atenção primária em saúde com base no georreferencimento dos sistemas de informação

A atenção primária em saúde (APS) pouco tem se valido dos sistemas de informação para avaliar a situação de saúde da população devido à dificuldade de compreensão dos relatórios. É usual a definição genérica das ações a partir de constatações empíricas. O objetivo desse trabalho é avaliar se a introdução de indicadores georreferenciados pode ser uma tecnologia para melhorar a identificação da situação de saúde das pessoas, o que ajudaria no planejamento das ações das equipes. Para tanto, foi aplicado um questionário nos profissionais de oito equipes em três momentos: o primeiro, antes da leitura dos relatórios do sistema de informação, o segundo após a leitura e o terceiro usando os georreferenciados. Os resultados mostraram diferença significativa na classificação da situação de saúde quando da utilização do georreferenciamento comparado aos momentos anteriores (p < 0,05). O georreferenciamento facilitou a análise da situação de saúde, propiciando melhor monitoramento dos processos de trabalho. Por fim, a utilização aponta para uma racionalização das ações e possível qualificação da atenção à saúde. Sugere-se o uso do georreferenciamento na agenda de trabalho para que se tornem uma ferramenta efetiva e norteadora das ações.

Análise espacial do risco de leptospirose canina na Vila Pantanal, Curitiba, Paraná

A leptospirose é uma grave zoonose associada às áreas de baixa renda dos centros urbanos. Embora roedores urbanos sejam considerados como principal reservatório para a leptospirose, o cão também pode desenvolver a doença e se tornar carreador assintomático. O objetivo do presente trabalho foi utilizar a metodologia estatística baseada na teoria de processos pontuais espaciais, buscando identificar a forma como se distribuem os cães sororreagentes para a leptospirose e seus determinantes de risco em uma vila na cidade de Curitiba. A análise do modelo possibilitou identificar as regiões de sobre-risco, onde o risco de soropositividade canina à leptospirose é significativamente maior. A relação significativa do efeito espacial no desenvolvimento da doença, além das variáveis estudadas, revela que não apenas um, mas a ação conjunta dos fatores relacionados ao animal, ao proprietário e ao ambiente influencia o risco maior da doença nos locais de maior efeito espacial. O resultado da análise indica claramente os territórios em maior risco na região da Vila Pantanal, possibilitando o planejamento de ações mais específicas e dirigidas a essas áreas em um contexto de vigilância da saúde.

Spatial analysis of leprosy incidence and associated socioeconomic factors

OBJECTIVE

To identify clusters of the major occurrences of leprosy and their associated socioeconomic and demographic factors.

METHODS

Cases of leprosy that occurred between 1998 and 2007 in São José do Rio Preto (southeastern Brazil) were geocodified and the incidence rates were calculated by census tract. A socioeconomic classification score was obtained using principal component analysis of socioeconomic variables. Thematic maps to visualize the spatial distribution of the incidence of leprosy with respect to socioeconomic levels and demographic density were constructed using geostatistics.

RESULTS

While the incidence rate for the entire city was 10.4 cases per 100,000 inhabitants annually between 1998 and 2007, the incidence rates of individual census tracts were heterogeneous, with values that ranged from 0 to 26.9 cases per 100,000 inhabitants per year. Areas with a high leprosy incidence were associated with lower socioeconomic levels. There were identified clusters of leprosy cases, however there was no association between disease incidence and demographic density. There was a disparity between the places where the majority of ill people lived and the location of healthcare services.

CONCLUSIONS

The spatial analysis techniques utilized identified the poorer neighborhoods of the city as the areas with the highest risk for the disease. These data show that health departments must prioritize politico-administrative policies to minimize the effects of social inequality and improve the standards of living, hygiene, and education of the population in order to reduce the incidence of leprosy.

Spatial distribution of Schistosoma mansoni infection before and after chemotherapy with two praziquantel doses in a community of Pernambuco, Brazil

Praziquantel chemotherapy has been the focus of the Schistosomiasis Control Program in Brazil for the past two decades. Nevertheless, information on the impact of selective chemotherapy against Schistosoma mansoni infection under the conditions confronted by the health teams in endemic municipalities remains scarce. This paper compares the spatial pattern of infection before and after treatment with either a 40 mg/kg or 60 mg/kg dose of praziquantel by determining the intensity of spatial cluster among patients at 180 and 360 days after treatment. The spatial-temporal distribution of egg-positive patients was analysed in a Geographic Information System using the kernel smoothing technique. While all patients became egg-negative after 21 days, 17.9% and 30.9% reverted to an egg-positive condition after 180 and 360 days, respectively. Both the prevalence and intensity of infection after treatment were significantly lower in the 60 mg/kg than in the 40 mg/kg treatment group. The higher intensity of the kernel in the 40 mg/kg group compared to the 60 mg/kg group, at both 180 and 360 days, reflects the higher number of reverted cases in the lower dose group. Auxiliary, preventive measures to control transmission should be integrated with chemotherapy to achieve a more enduring impact.

Expressão geográfica da epidemia de Aids em Campinas, São Paulo, de 1980 a 2005

OBJETIVO: Analisar a distribuição espacial dos casos notificados de Aids em adultos e sua relação com as condições de vida no município de Campinas, SP. MÉTODOS: Dados sobre Aids em homens (n = 2.945) e mulheres (n = 1.230) acima de 13 anos de idade, moradores de Campinas e notificados no Sistema Nacional de Agravos de Notificação foram utilizados para mapear a distribuição espacial da doença e a relação de masculinidade. Foram construídos mapas para os períodos de 1980 a 1995, de 1996 a 2000 e de 2001 a 2005. As variáveis incluídas na análise foram: endereço, sexo e idade. Foi utilizado indicador composto ponderado para estudar as condições de vida e saúde no território. Os endereços de moradia dos pacientes foram geocodificados em base cartográfica, após correção e padronização na base de arruamento. Foi ajustado modelo aditivo generalizado para analisar a distribuição espacial da razão de casos homem/mulher no espaço, nos três períodos do estudo. RESULTADOS: A razão de casos homem/mulher foi maior nas regiões de melhores condições de vida (central) e no entorno do presídio (noroeste), onde se estabelecem provisoriamente famílias de detentos e ex-detentos, enquanto essa razão foi menor em bairros da periferia da cidade (sudoeste). CONCLUSÕES: As tendências de feminização e pauperização da epidemia da Aids se confirmam diante da diminuição da razão de casos homens/mulheres no período, particularmente nas populações vulneráveis e empobrecidas. Sistemas de informações geográficas e análise espacial de dados podem ser úteis às ações de vigilância e controle da epidemia de Aids.

Análise da distribuição espacial da gravidez adolescente no Município de Belo Horizonte - MG

O georreferenciamento dos eventos de saúde ganha relevância na análise e avaliação de riscos à saúde coletiva, na medida em que incorpora variáveis relacionadas com o meio ambiente e com o perfil socioeconômico da população. Partindo da premissa de que a maternidade precoce não acontece de forma aleatória no interior da sociedade, buscou-se identificar a dependência espacial da gravidez na adolescência com aspectos socioeconômicos e de vulnerabilidade social. Assumindo a existência de uma dependência espacial entre esses fatores, foram utilizadas ferramentas do Exploratory Spatial Data Analysis, isto é, técnicas para descrever e visualizar distribuições espaciais, identificar situações atípicas, descobrir padrões de associação espacial, agrupamento de valores semelhantes (clusters) e sugerir regimes espaciais ou outras formas de heterogeneidade espacial. Destaca-se a correlação, estatisticamente significativa, entre vulnerabilidade social e taxa de nascidos vivos para mulheres com idade entre 12 e 19 anos e entre 20 e 29 anos. Constatou-se a presença de conglomerados com altas proporções de mães adolescentes em associação com as piores condições socioeconômicas e uma menor proporção de mães adolescentes em setores de melhores condições. Tal estudo poderá subsidiar a organização e o planejamento das ações e estratégias na área da saúde sexual e reprodutiva para o público jovem e adolescente, sobretudo em regiões de elevada vulnerabilidade econômica e social.

Factors associated with the incidence of urban visceral leishmaniasis: an ecological study in Teresina, Piauí State, Brazil

The objective of this study was to identify socioeconomic and environmental factors associated with the incidence of visceral leishmaniasis in the city of Teresina, Piauí State, Brazil. This was an ecological study based on 1,744 cases reported from 1991 to 2000, and the city's neighborhoods served as the unit of analysis. Mean annual incidence rates were related to socioeconomic and demographic indicators and a vegetation index derived from remote sensing images by means of spatial multiple linear regression models. The neighborhoods with the highest incidence rates were mostly located in the city's peripheral areas. Multivariate analysis identified an interaction between population growth and the vegetation index, so that areas with high population growth and abundant vegetation showed the highest incidence rates. The percentage of households with piped water was inversely associated with visceral leishmaniasis incidence. Spatial distribution of visceral leishmaniasis in Teresina during the 1990s was heterogeneous, and incidence of the disease was associated with the peripheral neighborhoods with the heaviest vegetation cover, subject to rapid occupation and lack of adequate sanitation infrastructure.

Georeferenced data in epidemiologic research

This paper reviews some conceptual and practical issues regarding the application of georeferenced data in epidemiologic research. Starting with the disease mapping tradition of geographical medicine, topics such as types of georeferenced data, implications for data analysis, spatial autocorrelation and main analytical approaches are heuristically discussed, relying on examples from the epidemiologic literature, most of them concerning mapping disease distribution, detection of disease spatial clustering, evaluation of exposure in environmental health investigation and ecological correlation studies. As for concluding remarks, special topics that deserve further development, including the misuses of the concept of space in epidemiologic research, issues related to data quality and confidentiality, the role of epidemiologic designs for spatial research, sensitivity analysis and spatiotemporal modeling, are presented.

Spatial analysis of urban violence based on emergency room data

OBJECTIVE

To estimate the spatial intensity of urban violence events using wavelet-based methods and emergency room data.

METHODS

Information on victims attended at the emergency room of a public hospital in the city of São Paulo, Southeastern Brazil, from January 1, 2002 to January 11, 2003 were obtained from hospital records. The spatial distribution of 3,540 events was recorded and a uniform random procedure was used to allocate records with incomplete addresses. Point processes and wavelet analysis technique were used to estimate the spatial intensity, defined as the expected number of events by unit area.

RESULTS

Of all georeferenced points, 59% were accidents and 40% were assaults. There is a non-homogeneous spatial distribution of the events with high concentration in two districts and three large avenues in the southern area of the city of São Paulo.

CONCLUSIONS

Hospital records combined with methodological tools to estimate intensity of events are useful to study urban violence. The wavelet analysis is useful in the computation of the expected number of events and their respective confidence bands for any sub-region and, consequently, in the specification of risk estimates that could be used in decision-making processes for public policies.

Tuberculosis in intra-urban settings: a Bayesian approach

OBJECTIVE

To model the effect of socio-economic deprivation and a few transmission-related indicators of the tuberculosis (TB) incidence at small area level, to discuss the potential of each indicator in targeting places for developing preventive action.

METHODS

Ecological spatial study of TB incidence in Olinda, a city in the north-east of Brazil, during the period 1996-2000. Three socio-economic indicators (mean number of inhabitants per household; percentage of heads of household with <1 year's formal education; percentage of heads of households with monthly income lower than the minimum wage) and two transmission-related indicators (number of cases of retreatment; number of households with more than one case during the period under study), all calculated per census tract, were used. We adopted four different full hierarchical Bayesian models to estimate the relative risk of the occurrence of TB via Markov chain Monte Carlo.

RESULTS

The best specified model includes all the selected covariates and the spatially structured random effect. The gain in goodness-of-fit statistic when the spatial structure was included confirms the clustered spatial pattern of disease and poverty. In this model, the covariates within the non-zero credibility interval were the number of persons per house, the number of cases of retreatment and the number of households with more than one case (all with relative risk > or = 1.8) in each census tract.

CONCLUSIONS

The possibility to estimate in the same framework both the contribution of covariates at ecological level and the spatial pattern should be encouraged in epidemiology, and may help with establishing Epidemiological Surveillance Systems on a territorial basis, that allows rational planning of interventions and improvement of the Control Programme effectiveness.

Spatial analysis of hemorrhagic fever with renal syndrome in China

Background

Hemorrhagic fever with renal syndrome (HFRS) is endemic in many provinces with high incidence in mainland China, although integrated intervention measures including rodent control, environment management and vaccination have been implemented for over ten years. In this study, we conducted a geographic information system (GIS)-based spatial analysis on distribution of HFRS cases for the whole country with an objective to inform priority areas for public health planning and resource allocation.

Methods

Annualized average incidence at a county level was calculated using HFRS cases reported during 1994–1998 in mainland China. GIS-based spatial analyses were conducted to detect spatial autocorrelation and clusters of HFRS incidence at the county level throughout the country.

Results

Spatial distribution of HFRS cases in mainland China from 1994 to 1998 was mapped at county level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of HFRS cases was nonrandom and clustered with a Moran's I = 0.5044 (p = 0.001). Spatial cluster analyses suggested that 26 and 39 areas were at increased risks of HFRS (p < 0.01) with maximum spatial cluster sizes of ≤ 20% and ≤ 10% of the total population, respectively.

Conclusion

The application of GIS, together with spatial statistical techniques, provide a means to quantify explicit HFRS risks and to further identify environmental factors responsible for the increasing disease risks. We demonstrate a new perspective of integrating such spatial analysis tools into the epidemiologic study and risk assessment of HFRS.

Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe

BACKGROUND

Quantitative description and mapping of malaria seasonality is important for timely spatial targeting of interventions and for modelling malaria risk. There is a need for seasonality models that predict quantitative variation in transmission between months.

METHODS

We use Zimbabwe as an example for developing an empirical map of malaria seasonality. We describe the relationship between seasonality in malaria and environmental covariates for the period 1988--1999, by fitting a spatial-temporal regression model within a Bayesian framework to provide smoothed maps of the seasonal trend. We adapt a seasonality concentration index used previously for rainfall to quantify malaria case load during the peak transmission season based on monthly values.

RESULTS

Combinations of mean monthly temperature (range 28--32 degrees C), maximum temperature (24--28 degrees C) and high rainfall provide suitable conditions for seasonal transmission. High monthly maximum and mean monthly minimum temperatures limit months of high transmission. The intensity of seasonal transmission was highest in the north western part of the country from February to May with the peak in April and lowest in the whole country from July to December. The north western lowlands had the highest concentration of malaria cases (>25%) followed by some districts in the north central and eastern part with a moderate concentration of cases (20-25%). The central highlands and south eastern part of the country had the lowest concentration of malaria cases (<20%). This pattern was closely associated to the geographic variation in the seasonality of climatic covariates particularly rainfall and temperature. Conclusions Our modelling approach quantifies the geographical variation in seasonal trend and the concentration of cases during the peak transmission season and therefore has potential application in malaria control. The use of a covariate adjusted empirical model may prove useful for predicting the seasonal risk pattern across southern Africa.

Modeling of Under-detection of Cases in Disease Surveillance

PURPOSE

Accurate epidemiological surveillance of leprosy is a matter of international public health concern. It often suffers, however, from potential problems of under-registration of reported cases, particularly in poorer and more socially deprived areas. Such problems also apply in the surveillance of many other communicable or transmissible diseases. We develop a Bayesian model for small-area disease rates that allows for censoring of case detection in suspect districts and can therefore be used to estimate under-reporting of cases in a given study region.

METHODS

Such methods are applied to leprosy incidence in a municipality of Pernambuco State in North Eastern Brazil, using a social deprivation indicator as the basis for considering data from certain districts to be censored. The time period we consider was immediately prior to an extension of the coverage and efficacy of the control program and model predictions concerning under reporting can therefore be compared with more reliable data subsequently collected from the same region.

RESULTS

The proposed method produces informative estimates of under detection of leprosy cases in the defined study region and these estimates compare well, both in size and in geographical location, with the numbers of cases subsequently detected.

CONCLUSIONS

As illustrated by the application discussed in this article, the proposed model provides a general tool that may be used in spatial epidemiological surveillance situations where the available data is suspected to contain significant under-registrations of cases in certain geographical areas.

Análise de dados espaciais em saúde pública: métodos, problemas, perspectivas

Estudos mostram que a localização espacial dos eventos em saúde e os Sistemas de Informações Geográficas (SIG), têm papel destacado e vêm se tornando mais freqüentes na literatura da área de saúde pública. Entretanto, os métodos e software necessários ao aprofundamento desta abordagem ainda apresentam limitações devido à dificuldade de uso e desconhecimento dos pesquisadores e profissionais da área. O objetivo deste trabalho é apresentar algumas aplicações exemplares de métodos voltados para a análise de padrões espaciais de eventos em saúde, discutindo vantagens, desvantagens e aplicabilidade dos modelos propostos, particularmente no campo dos estudos ecológicos e na análise do uso de serviços de saúde, além de sistematizar o estado da arte da utilização de metodologias de análise espacial na saúde pública.

Spatial distribution of schistosomiasis foci on Itamaracá Island, Pernambuco, Brazil

Acute cases of schistosomiasis have been found on the coastal area of Pernambuco, Brazil, due to environmental disturbances and disorderly occupation of the urban areas. This study identifies and spatially marks the main foci of the snail host species, Biomphalaria glabrata on Itamaracá Island. The chaotic occupation of the beach resorts has favoured the emergence of transmission foci, thus exposing residents and tourists to the risk of infection. A database covering five years of epidemiological investigation on snails infected by Schistosoma mansoni in the island was produced with information from the geographic positioning of the foci, number of snails collected, number of snails tested positive, and their infection rate. The spatial position of the foci were recorded through the Global Positioning System (GPS), and the geographical coordinates were imported by AutoCad. The software packages ArcView and Spring were used for data processing and spatial analysis. AutoCad 2000 was used to plot the pairs of coordinates obtained from GPS. Between 1998 and 2002 5009 snails, of which 12.2% were positive for S. mansoni, were collected in Forte Beach. A total of 27 foci and areas of environmental risk were identified and spatially analyzed allowing the identification of the areas exposed to varying degrees of risk.

Un estudio ecológico sobre tuberculosis en un municipio de Cuba

Se aplica un estudio ecológico para analizar la incidencia de tuberculosis en el municipio Marianao de la provincia Ciudad de La Habana de la República de Cuba en el período 1995-2000. Se realiza una caracterización de tal incidencia, se identifican patrones de distribución espacial y se determina la relación existente entre los niveles de incidencia de tuberculosis y factores del medio ambiente socioeconómico. La unidad espacial considerada son los 29 barrios del municipio Marianao. Entre otros resultados se identifica un patrón de barrios con tasas altas en la región central del municipio. Las tasas de incidencia se hallan significativamente asociadas de forma directa con el porcentaje de familias con problemas disfuncionales y con el porcentaje de población con determinados niveles de hacinamiento, y de manera inversa con la densidad poblacional, y no se encuentran significativamente asociadas ni con los niveles de educación, ni con el estado de la vivienda predominantes en el barrio.