Variations in societal characteristics of spatial disease clusters: examples of colon, lung and breast cancer in Japan

Gonorrhea cluster detection in Manitoba, Canada: Spatial, temporal, and spatio-temporal analysis

In Canada, Gonorrhea infection ranks as the second most prevalent sexually transmitted infection. In 2018, Manitoba reported an incidence rate three times greater than the national average. This study aims to investigate the spatial, temporal, and spatio-temporal patterns of Gonorrhea infection in Manitoba, using individual-level laboratory-confirmed administrative data provided by Manitoba Health from 2000 to 2016. Age and sex patterns indicate that females are affected by infections at younger ages compared to males. Moreover, there is an increase in repeated infections in 2016, accounting for 16% of the total infections. Spatial analysis at the 96 Manitoba regional health authority districts highlights significant positive spatial autocorrelation, demonstrating a clustered distribution of the infection. Northern districts of Manitoba and central Winnipeg were identified as significant clusters. Temporal analysis shows seasonal patterns, with higher infections in late summer and fall. Additionally, spatio-temporal analysis reveals clusters during high-risk periods, with the most likely cluster in the northern districts of Manitoba from January 2006 to June 2014, and a secondary cluster in central Winnipeg from June 2004 to November 2012. This study identifies that Gonorrhea infection transmission in Manitoba has temporal, spatial, and spatio-temporal variations. The findings provide vital insights for public health and Manitoba Health by revealing high-risk clusters and emphasizing the need for focused and localized prevention, control measures, and resource allocation.

Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand

BACKGROUND

Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs.

METHODS

To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility.

RESULTS

In the simulation study, Getis Ord [Formula: see text] and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord [Formula: see text] and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies.

CONCLUSIONS

Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.

Socioeconomic Predictors of Trends in Cancer Mortality among Municipalities in Japan, 2010-2019

Background:

A study investigating associations between various socioeconomic factors and standardized mortality ratios (SMR) of each type of cancer among municipalities in Japan has not been conducted using the data of the past decade. Herein, we investigated the predictors of a recent trend of municipal SMRs of cancer using the Vital Statistics in Japan and revealed the change in the SMRs depending on the identified predictors.

Methods:

Data on cancer mortality for each municipality in 2010 and 2019 were used. We calculated empirical Bayes SMR (EBSMR) for each municipality by type of cancer and sex and then fitted a multiple linear regression model using possible predictors in 2010 as explanatory variables and the EBSMR in 2019 as the outcome variable. We also classified municipalities into quintiles based on the values of an identified predictor in 2010, and SMRs of each type of cancer in 2010 and 2019 were calculated for each quintile.

Results:

The total population was positively associated with EMSMRs of multiple cancer types, whereas educational level was negatively associated with EMSMRs of multiple cancer types. In addition, SMRs of municipalities with the lowest educational level deteriorated from 2010 to 2019 for many cancer types among men and women, and the difference between municipalities with the highest and lowest educational level for the SMR of cancer in all sites widened in 2019 for men. On the other hand, the SMR of municipalities with the highest educational level or the largest total population tended to be higher than municipalities with lower counterparts in both 2010 and 2019 for women.

Conclusion:

There was a difference in the trend of the SMRs of multiple types of cancer depending on municipal educational level, whereas municipalities with larger population or educational level continued to have higher SMRs of cancer in all sites for women.

Socioeconomic Disparities in All-Cause and Cause-Specific Mortality Rates among Municipalities in Japan, 1999-2019

Differences in all-cause and cause-specific mortality rates depending on municipal socioeconomic status (SES) in Japan have not been revealed over the last 20 years. This study exposes the difference in 1999 and 2019 using the Vital Statistics. All of the municipalities were grouped into five quintiles based on their SES, and standardized mortality ratio (SMR) of each municipal quintile compared with all of Japan was calculated for all-cause mortality and representative cause of deaths. As a result, although SMR for all-cause mortality for women tended to be lower in low SES quintiles in 1999, the reverse phenomenon was observed in 2019. Additionally, although SMR for all-cause of mortality for men was the lowest in the highest SES quintiles already in 1999, the difference in the SMR for all-cause mortality rates between the lowest and highest SES quintiles increased in 2019. The improvement of the SMR in the highest SES quintile and the deterioration in the lowest was also observed in representative types of cancer, heart disease, stroke, pneumonia, liver disease, and renal failure for men and women. Therefore, this study indicates a disparity in mortality depending on municipal SES enlarged in the last 20 years.

Differences in Cancer Mortality Trends between Metropolitan and Non-Metropolitan Areas in Japan, 1999-2018

Background:

Although socioeconomic statuses affect cancer mortality rates, the specific difference between metropolitan and non-metropolitan areas in Japan has not been evaluated. This study analyzed differences in cancer mortality between metropolitan and non-metropolitan areas in Japan, using an age-period-cohort (APC) analysis.

Methods:

Data on cancer mortality from 1999 to 2018 for metropolitan and non-metropolitan areas in Japan were used. Here metropolitan areas were defined as government ordinance-designated municipalities in 1999 and special wards of Tokyo. In addition to general mortality data for all cancer sites, data on mortality for stomach, colorectal, liver, gallbladder, pancreatic, lung, prostate, and breast cancers were used for analysis. A Bayesian APC analysis was administered to the data for each type of cancer for area and for sex-distinguished data. Additionally, the ratios for estimated mortality rate by periods and cohorts between the two areas were calculated.

Results:

The age-standardized mortality rate for cancer in all sites in non-metropolitan areas was lower than that in metropolitan areas throughout the analyzed years for both men and women, but the mortality difference decreased during the periods for men. The rates of decrease in mortality rate in cohorts differed for some cancers between the two area types, and the mortality rate ratios of metropolitan compared with non-metropolitan areas decreased for cancer in all sites over the analyzed cohorts for men. Also, the rate of decrease in mortality rate over the cohorts was completely different between the areas for stomach cancer in men and for liver cancer for women.

Conclusion:

Mortality rates for cancer in all sites tended to diverge between the two area types in younger cohorts for men, and people in younger cohorts in non-metropolitan areas should take more extensive preventive measures against cancer than their counterparts in metropolitan areas.

Assessment of Relative Asthma Risk in Populations Living Near Incineration Facilities in Seoul, Korea

While incineration is among the most commonly used technologies for waste disposal, there is ongoing public concern regarding the adverse health impact. The aim of this study is thus to use health statistics to assess the relative risk of asthma-related hospitalization for those living in close proximity to incineration facilities. We also examine differences in asthma risk related to age demographics. The spatial relationship between incineration facilities and asthma-related hospital admissions in Seoul is analyzed for the period of 2009–2011 using the Rapid Inquiry Facility (RIF) and SaTScan software. The relative risk of asthma-related hospitalization decreased with increasing distance from incinerators, but increased among those living within a 2-km radius. The relative risks of asthma-related hospitalization were 1.13 (95% confidence interval (CI): 1.10–1.17), 1.12 (95% CI: 1.08–1.17), and 1.18 (95% CI: 1.10–1.27) for all ages, those aged below 15 years, and those aged 65 years and older, respectively. This study is the first to observe an increased risk of asthma-related hospitalization in relation to a person’s distance from an incinerator in Seoul, Korea. It is clear that asthma should be considered an adverse health outcome during health impact assessments of incineration plants.

Application of GIS Spatial Analysis and Scanning Statistics in the Gynecological Cancer Clustering Pattern and Risk Screening: A Case Study in Northern Jiangxi Province, China

OBJECTIVE

The incidence of gynecological cancer is high in China, and the effects of related treatments and preventive measures need to be improved.

METHODS

This study uses GIS spatial analysis methods and a scanning statistical analysis to study the major gynecological cancers in northern Jiangxi Province from 2016 to 2018.

RESULTS

The incidence and spatial pattern of cervical cancer, ovarian cancer, and uterine cancer had agglomeration characteristics and changes during the study period. The gynecological cancer had a spatial autocorrelation and agglomeration in its spatial pattern. The Moran's Index of the overall gynecological cancer incidence rate was 0.289 (p = 0.005). Ripley's L(d) function showed that the agglomeration radius was between 51.40 and 52.82 km. The results of the kernel density estimation showed that the cases of gynecological cancer were concentrated in the central and northeastern areas of the study area. The overall county-level incidence of gynecological cancer varied from 0.26 to 11.14 per 100,000. The results of the gravity center analysis showed that the spatial distribution of the gravity center point of gynecological cancer had moved toward the east during the past three years. The results of a hotspot analysis showed that there were five hotspot areas that had gynecological cancers. The most likely clusters of gynecological cancer at the county level in northern Jiangxi Province were distributed in the adjacent areas of Jiujiang, Yichun, and Nanchang, with a relative risk of 1.85.

CONCLUSION

The research shows that GIS can display the distribution of cancer cases and can use spatial analysis methods and scanning statistical techniques to obtain key areas of cancer incidence. These results can provide data and key areas for the formulation of regional public health policies and provide recommendations for cancer screening and the rational allocation of health resources.

Spatial variations of women's home delivery after antenatal care visits at lay Gayint District, Northwest Ethiopia

BACKGROUND

Home delivery is the most frequent childbirth practice in Ethiopia and brings health risks for many mothers and their babies which in turn affecting the whole families. Characterizing the spatial variations and the associated factors of home deliveries after antenatal care visit is necessary to prioritize risks and facilitate geographically based interventions.

METHOD

A community-based cross-sectional study design was carried out between February and March 2016. A total of 528 women who had just given birth were interviewed face-to-face using a questionnaire. Geo-referenced data were collected using a handheld global positioning system (GPS). The Bernoulli model was applied using the SatScan ™ software to analyze the purely spatial clusters of home deliveries. ArcGIS version 10.1 was used to visualize clusters of home delivery.

RESULTS

The overall proportion of home deliveries was 278(52.7%), and home deliveries had spatial variations. A primary cluster [LLR = 14.54, p < 0.001] was detected in village of Safida Giorgis. Secondary clusters were detected in Checheho [LLR = 9.17, p < 0.05] and ZurAmba [LLR = 8.51, p < 0.05]. Predictors for home delivery included the distance between the health extension worker's and mother's house [AOR = 2.2, 95% CI: 1.1, 4.3], residence [AOR = 3.8, 95% CI: 1.3, 10.9], source of information for ANC [AOR = 0.3, 95% CI: 0.13, 0.7], ANC visits [AOR = 6.1, 95% CI:1.9, 19.3], health education [AOR = 3.4, 95% CI: 1.5, 7.4], decision on place of delivery [AOR = 0.3, 95% CI: 0.1, 0.8], and knowledge on place of delivery [AOR = 0.04, 95% CI: 0.0, 0.1].

CONCLUSION

The proportion of home delivery after ANC visit was decreasing compared to other studies conducted in the region. In addition, spatial variations of home delivery were observed in the study area. Promoting women's education and behavioral change communication at the grass root level, provision of the services both at home and health facilities and improving the quality and capacity of the health providers are some of the recommendations forwarded.

Space-Time Statistical Insights about Geographic Variation in Lung Cancer Incidence Rates: Florida, USA, 2000⁻2011

The geographic distribution of lung cancer rates tends to vary across a geographic landscape, and covariates (e.g., smoking rates, demographic factors, socio-economic indicators) commonly are employed in spatial analysis to explain the spatial heterogeneity of these cancer rates. However, such cancer risk factors often are not available, and conventional statistical models are unable to fully capture hidden spatial effects in cancer rates. Introducing random effects in the model specifications can furnish an efficient approach to account for variations that are unexplained due to omitted variables. Especially, a random effects model can be effective for a phenomenon that is static over time. The goal of this paper is to investigate geographic variation in Florida lung cancer incidence data for the time period 2000–2011 using random effects models. In doing so, a Moran eigenvector spatial filtering technique is utilized, which can allow a decomposition of random effects into spatially structured (SSRE) and spatially unstructured (SURE) components. Analysis results confirm that random effects models capture a substantial amount of variation in the cancer data. Furthermore, the results suggest that spatial pattern in the cancer data displays a mixture of positive and negative spatial autocorrelation, although the global map pattern of the random effects term may appear random.

Spatiotemporal patterns of hemorrhagic fever with renal syndrome in Hebei Province, China, 2001-2016

Hemorrhagic fever with renal syndrome (HFRS) is highly endemic in China, where approximately 90% of the total human cases in the world are reported. The Hebei province, one of areas with the highest prevalence, has reported HFRS cases every year in the last two decades. This study describes the spatiotemporal patterns of HFRS in the Hebei province from 2001 to 2016, detects the high-risk spatiotemporal clusters of HFRS, and provides valuable information for planning and implementation of local preventive measures. For the purpose of the analysis, HFRS cases recorded during the sixteen years in the Hebei province were aggregated into three temporal periods (2001-2006, 2007-2012, and 2013-2016). Spatiotemporal analyses, including Global spatial autocorrelation analysis and Kulldorff's scan statistical analysis, were applied to analyze te spatiotemporal clusters of HFRS at the county level. The results revealed that the spatial extent of the HFRS epidemic in the Hebei province changed dynamically from 2001 to 2016, which indicated that a comprehensive preventative strategy should be implemented in the northeastern regions of the Hebei province in spring.

Detecting spatiotemporal clusters of dementia mortality in the United States, 2000-2010

Based on national death certificate data during 2000 and 2010, we employed the space-time scan statistic to identify spatiotemporal clusters of dementia mortality in the contiguous United States. Results revealed that, for both Alzheimer's disease and all-cause dementia mortality in the total population, the most likely clusters occurred in the Northeast region, with lower than average relative risk. The most likely excess mortality clusters were in the Pacific Northwest and Ohio River Valley and Carolinas. Temporal information of clusters suggested reduction in the relative risk of Alzheimer's disease and all-cause dementia mortality in most of the highly likely clusters. The results should propel public health agencies to evaluate the capacity of local health and social care to meet dementia patients' needs before death in the high-risk cluster areas. Further investigation of causal factors of these clusters is needed.

Waterborne disease outbreak detection: an integrated approach using health administrative databases

Hundreds of waterborne disease outbreaks (WBDO) of acute gastroenteritis (AGI) due to contaminated tap water are reported in developed countries each year. Such outbreaks are probably under-detected. The aim of our study was to develop an integrated approach to detect and study clusters of AGI in geographical areas with homogeneous exposure to drinking water. Data for the number of AGI cases are available at the municipality level while exposure to tap water depends on drinking water networks (DWN). These two geographical units do not systematically overlap. This study proposed to develop an algorithm which would match the most relevant grouping of municipalities with a specific DWN, in order that tap water exposure can be taken into account when investigating future disease outbreaks. A space-time detection method was applied to the grouping of municipalities. Seven hundred and fourteen new geographical areas (groupings of municipalities) were obtained compared with the 1,310 municipalities and the 1,706 DWN. Eleven potential WBDO were identified in these groupings of municipalities. For ten of them, additional environmental investigations identified at least one event that could have caused microbiological contamination of DWN in the days previous to the occurrence of a reported WBDO.

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

BACKGROUND

The provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance.

METHODS

To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM.

RESULTS

T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65-79 year olds, 80 + year olds, unemployment rate among the 55-65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations.

CONCLUSION

The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany's largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the respective person. Future allocation of GPs and current prevention strategies should therefore reflect the location-specific higher healthcare demand among the elderly and socially underprivileged populations.

A Comparison of Spatial Pattern Detection Methods for Major Cancer Mortality in Korea

Spatial pattern detection can be a useful tool for understanding the geographical distribution of cancer mortality. This study compared the results from several special analysis methods to examine the geographical variations in Korean major cancer mortality during 2010 to 2013. The spatial scan statistic and Tango's method were used to detect spatial clusters with unusually high or low rates. The local indicator of spatial association was used for cluster detection with accounting for spatial autocorrelation among adjacent areas. A nonparametric approach was used to identify local districts with significantly higher or lower rates, compared to the average national rate. The different methods revealed similar results in the overall patterns, but slightly different specific results. These techniques can provide complementary information and should be used based on the distinguishing features of each method. It may be better to use several different methods for these analyses rather than a single method.

A Bayesian Method for Cluster Detection with Application to Brain and Breast Cancer in Puget Sound

Cluster detection is an important public health endeavor, and in this article, we describe and apply a recently developed Bayesian method. Commonly used approaches are based on so-called scan statistics and suffer from a number of difficulties, which include how to choose a level of significance and how to deal with the possibility of multiple clusters. The basis of our model is to partition the study region into a set of areas that are either "null" or "non-null," the latter corresponding to clusters (excess risk) or anticlusters (reduced risk). We demonstrate the Bayesian method and compare with a popular existing approach, using data on breast, brain, lung, prostate, and colorectal cancer, in the Puget Sound region of Washington State.

Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states

BACKGROUND

Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters.

METHODS

In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared.

RESULTS

Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters.

CONCLUSIONS

Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.

Spatial analysis of PM10 and cardiovascular mortality in the Seoul metropolitan area

OBJECTIVES

Numerous studies have revealed the adverse health effects of acute and chronic exposure to particulate matter less than 10 μm in aerodynamic diameter (PM10). The aim of the present study was to examine the spatial distribution of PM10 concentrations and cardiovascular mortality and to investigate the spatial correlation between PM10 and cardiovascular mortality using spatial scan statistic (SaTScan) and a regression model.

METHODS

From 2008 to 2010, the spatial distribution of PM10 in the Seoul metropolitan area was examined via kriging. In addition, a group of cardiovascular mortality cases was analyzed using SaTScan-based cluster exploration. Geographically weighted regression (GWR) was applied to investigate the correlation between PM10 concentrations and cardiovascular mortality.

RESULTS

An examination of the regional distribution of the cardiovascular mortality was higher in provincial districts (gu) belonging to Incheon and the northern part of Gyeonggido than in other regions. In a comparison of PM10 concentrations and mortality cluster (MC) regions, all those belonging to MC 1 and MC 2 were found to belong to particulate matter (PM) 1 and PM 2 with high concentrations of air pollutants. In addition, the GWR showed that PM10 has a statistically significant relation to cardiovascular mortality.

CONCLUSIONS

To investigate the relation between air pollution and health impact, spatial analyses can be utilized based on kriging, cluster exploration, and GWR for a more systematic and quantitative analysis. It has been proven that cardiovascular mortality is spatially related to the concentration of PM10.

Bowel disorders and its spatial trend in Manitoba, Canada

BACKGROUND

Bowel disorders have destructive impacts on the patients social and mental aspects of life and can cause emotional distress. The risk of developing bowel incontinence also increases with age. The rate of incidence of inflammatory bowel disease in Manitoba, Canada, has been unusually raised. Therefore, it is important to identify trends in the incidence of bowel disorders that may suggest further epidemiological studies to identify risk factors and identify any changes in important factors.

METHODS

An important part of spatial epidemiology is cluster detection as it has the potential to identify possible risk factors associated with disease, which in turn may lead to further investigations into the nature of diseases. To test for potential disease clusters many methods have been proposed. The focused detection methods including the circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), and Bayesian disease mapping (BYM) are among the most popular disease detection procedures. A frequentist approach based on maximum likelihood estimation (MLE) has been recently used to identify potential focused clusters as well. The aforementioned approaches are studied by analyzing a dataset of bowel disorders in the province of Manitoba, Canada, from 2001 to 2010.

RESULTS

The CSS method identified less regions than the FSS method in the south part of the province as potential clusters. The same regions were identified by the BYM and MLE methods as being potential clusters of bowel disorders with a slightly different order of significance. Most of these regions were also detected by the CSS or FSS methods.

CONCLUSIONS

Overall, we recommend using the methods BYM and MLE for cluster detection with the similar population and structure of regions as in Manitoba. The potential clusters of bowel disorders are generally located in the southern part of the province including the eastern part of the city of Winnipeg. These results may represent real increases in bowel disorders or they may be an indication of other covariates that were not adjusted for in the model used here. Further investigation is needed to examine these findings, and also to explore the cause of these increases.

Geospatial analyses to identify clusters of adverse antenatal factors for targeted interventions

BACKGROUND

Late antenatal care and smoking during pregnancy are two important factors that are amenable to intervention. Despite the adverse health impacts of smoking during pregnancy and the health benefits of early first antenatal visit on both the mother and the unborn child, substantial proportions of women still smoke during pregnancy or have their first antenatal visit after 10 weeks gestation. This study was undertaken to assess the usefulness of geospatial methods in identifying communities at high risk of smoking during pregnancy and timing of the first antenatal visit, for which targeted interventions may be warranted, and more importantly, feasible.

METHODS

The Perinatal Data Collection, from 1999 to 2008 for south-western Sydney, were obtained from the New South Wales Ministry of Health. Maternal addresses at the time of delivery were georeferenced. A spatial scan statistic implemented in SaTScan was then used to identify statistically significant spatial clusters of women who smoked during pregnancy or women whose first antenatal care visit occurred at or after 10 weeks of pregnancy.

RESULTS

Four spatial clusters of maternal smoking during pregnancy and four spatial clusters of first antenatal visit occurring at or after 10 weeks were identified in our analyses. In the maternal smoking during pregnancy clusters, higher proportions of mothers, were aged less than 35 years, had their first antenatal visit at or after 10 weeks and a lower proportion of mothers were primiparous. For the clusters of increased risk of late first antenatal visit at or after 10 weeks of gestation, a higher proportion of mothers lived in the most disadvantaged areas and a lower proportion of mothers were primiparous.

CONCLUSION

The application of spatial analyses provides a means to identify spatial clusters of antenatal risk factors and to investigate the associated socio-demographic characteristics of the clusters.

Spatial clustering of all-cause and HIV-related mortality in a rural South African population (2000-2006)

BACKGROUND

Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.

METHODOLOGY AND RESULTS

Kulldorff's spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (p<0.05) in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000-2006). The analysis was split pre (2000-2003) and post (2004-2006) rollout of antiretroviral therapy, respectively. Between 2000-2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related) over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000). During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001) and high all-cause mortality (RR = 1.35/1.38, p = 0.001) was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, p = 0.003/0.005) and in the first time period (2000-2003) a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified.

CONCLUSIONS

HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment and care programmes should be strengthened in easy-to-reach high density, peri-urban populations near National Roads where both HIV-related and all-cause mortality are highest.

Cell-type specificity of lung cancer associated with low-dose soil heavy metal contamination in Taiwan: an ecological study

BACKGROUND

Numerous studies have examined the association between heavy metal contamination (including arsenic [As], cadmium [Cd], chromium [Cr], copper [Cu], mercury [Hg], nickel [Ni], lead [Pb], and zinc [Zn]) and lung cancer. However, data from previous studies on pathological cell types are limited, particularly regarding exposure to low-dose soil heavy metal contamination. The purpose of this study was to explore the association between soil heavy metal contamination and lung cancer incidence by specific cell type in Taiwan.

METHODS

We conducted an ecological study and calculated the annual averages of eight soil heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) by using data from the Taiwan Environmental Protection Administration from1982 to 1986. The age-standardized incidence rates of lung cancer according to two major pathological types (adenocarcinoma [AC] and squamous cell carcinoma [SCC]) were obtained from the National Cancer Registry Program conducted in Taiwan from 2001 to 2005. A geographical information system was used to plot the maps of soil heavy metal concentration and lung cancer incidence rates. Poisson regression models were used to obtain the adjusted relative ratios (RR) and 95% confidence intervals (CI) for the lung cancer incidence associated with soil heavy metals.

RESULTS

For males, the trend test for lung SCC incidence caused by exposure to Cr, Cu, Hg, Ni, and Zn showed a statistically significant dose-response relationship. However, for lung AC, only Cu and Ni had a significant dose-response relationship. As for females, those achieving a statistically significant dose-response relationship for the trend test were Cr (P = 0.02), Ni (P = 0.02), and Zn (P= 0.02) for lung SCC, and Cu (P < 0.01) and Zn (P = 0.02) for lung AC.

CONCLUSION

The current study suggests that a dose-response relationship exists between low-dose soil heavy metal concentration and lung cancer occurrence by specific cell-type; however, the relevant mechanism should be explored further.

Patterns of breast cancer mortality trends in Europe

OBJECTIVES

To identify patterns of variation in breast cancer mortality in Europe (1980-2010), using a model-based approach.

METHODS

Mortality data were obtained from the World Health Organization database and mixed models were used to describe the time trends in the age-standardized mortality rates (ASMR). Model-based clustering was used to identify clusters of countries with homogeneous variation in ASMR.

RESULTS

Three patterns were identified. Patterns 1 and 2 are characterized by stable or slightly increasing trends in ASMR in the first half of the period analysed, and a clear decline is observed thereafter; in pattern 1 the median of the ASMR is higher, and the highest rates were achieved sooner. Pattern 3 is characterised by a rapid increase in mortality until 1999, declining slowly thereafter.

CONCLUSION

This study provides a general model for the description and interpretation of the variation in breast cancer mortality in Europe, based in three main patterns.

Dengue fever occurrence and vector detection by larval survey, ovitrap and MosquiTRAP: a space-time clusters analysis

The use of vector surveillance tools for preventing dengue disease requires fine assessment of risk, in order to improve vector control activities. Nevertheless, the thresholds between vector detection and dengue fever occurrence are currently not well established. In Belo Horizonte (Minas Gerais, Brazil), dengue has been endemic for several years. From January 2007 to June 2008, the dengue vector Aedes (Stegomyia) aegypti was monitored by ovitrap, the sticky-trap MosquiTRAP™ and larval surveys in an study area in Belo Horizonte. Using a space-time scan for clusters detection implemented in SaTScan software, the vector presence recorded by the different monitoring methods was evaluated. Clusters of vectors and dengue fever were detected. It was verified that ovitrap and MosquiTRAP vector detection methods predicted dengue occurrence better than larval survey, both spatially and temporally. MosquiTRAP and ovitrap presented similar results of space-time intersections to dengue fever clusters. Nevertheless ovitrap clusters presented longer duration periods than MosquiTRAP ones, less acuratelly signalizing the dengue risk areas, since the detection of vector clusters during most of the study period was not necessarily correlated to dengue fever occurrence. It was verified that ovitrap clusters occurred more than 200 days (values ranged from 97.0±35.35 to 283.0±168.4 days) before dengue fever clusters, whereas MosquiTRAP clusters preceded dengue fever clusters by approximately 80 days (values ranged from 65.5±58.7 to 94.0±14. 3 days), the former showing to be more temporally precise. Thus, in the present cluster analysis study MosquiTRAP presented superior results for signaling dengue transmission risks both geographically and temporally. Since early detection is crucial for planning and deploying effective preventions, MosquiTRAP showed to be a reliable tool and this method provides groundwork for the development of even more precise tools.

Geographic inequalities in all-cause mortality in Japan: compositional or contextual?

Background

A recent study from Japan suggested that geographic inequalities in all-cause premature adult mortality have increased since 1995 in both sexes even after adjusting for individual age and occupation in 47 prefectures. Such variations can arise from compositional effects as well as contextual effects. In this study, we sought to further examine the emerging geographic inequalities in all-cause mortality, by exploring the relative contribution of composition and context in each prefecture.

Methods

We used the 2005 vital statistics and census data among those aged 25 or older. The total number of decedents was 524,785 men and 455,863 women. We estimated gender-specific two-level logistic regression to model mortality risk as a function of age, occupation, and residence in 47 prefectures. Prefecture-level variance was used as an estimate of geographic inequalities in mortality, and prefectures were ranked by odds ratios (ORs), with the reference being the grand mean of all prefectures (value  = 1).

Results

Overall, the degree of geographic inequalities was more pronounced when we did not account for the composition (i.e., age and occupation) in each prefecture. Even after adjusting for the composition, however, substantial differences remained in mortality risk across prefectures with ORs ranging from 0.870 (Okinawa) to 1.190 (Aomori) for men and from 0.864 (Shimane) to 1.132 (Aichi) for women. In some prefectures (e.g., Aomori), adjustment for composition showed little change in ORs, while we observed substantial attenuation in ORs in other prefectures (e.g., Akita). We also observed qualitative changes in some prefectures (e.g., Tokyo). No clear associations were observed between prefecture-level socioeconomic status variables and the risk of mortality in either sex.

Conclusions

Geographic disparities in mortality across prefectures are quite substantial and cannot be fully explained by differences in population composition. The relative contribution of composition and context to health inequalities considerably vary across prefectures.

Exploring geographic variation in lung cancer incidence in Kentucky using a spatial scan statistic: elevated risk in the Appalachian coal-mining region

OBJECTIVES

We examined geographic patterns of lung cancer incidence in Kentucky. Recent research has suggested that the coal-mining industry contributes to lung cancer risk in Appalachia. We focused on the southeastern portion of the state, which has some of the highest lung cancer rates in the nation.

METHODS

We implemented a spatial scan statistic to identify areas with lung cancer incidence rates that were higher than expected, after adjusting for age, gender, and smoking. The Kentucky Cancer Registry supplied information on cases (1995-2007). The U.S. Census (2000) and several years of Behavioral Risk Factor Surveillance System data (1996-2006) provided county-level population and smoking data. We compared the results with coal-mining data from the Mining Safety and Health Administration and public water utility data from the Kentucky Division of Water.

RESULTS

We identified three clusters of counties with higher-than-expected rates. Cluster 1 (relative risk [RR] = 1.21, p<0.01) included 12 counties in southeastern Kentucky. Cluster 2 (RR=1.17, p<0.01) included three nearby counties in the same region. Several of the 15 counties in Cluster 3 (RR=1.04, p=0.01) were part of the Louisville, Kentucky, or Cincinnati, Ohio, metropolitan areas. All of the counties in Clusters 1 and 2 produced significant amounts of coal.

CONCLUSION

Environmental exposures related to the coal-mining industry could contribute to the high incidence of lung cancer in southeastern Kentucky. Lack of evidence for this effect in western Kentucky could be due to regional differences in mining practices and access to public water utilities. Future research should collect biological specimens and environmental samples to test for the presence of trace elements and other lung carcinogens.

An examination of five spatial disease clustering methodologies for the identification of childhood cancer clusters in Alberta, Canada

Cluster detection is an important part of spatial epidemiology because it may help suggest potential factors associated with disease and thus, guide further investigation of the nature of diseases. Many different methods have been proposed to test for disease clusters. In this paper, we study five popular methods for detecting spatial clusters. These methods are Besag-Newell (BN), circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Tango's maximized excess events test (MEET), and Bayesian disease mapping (BYM). We study these five different methods by analyzing a data set of malignant cancer diagnoses in children in the province of Alberta, Canada during 1983-2004. Our results show that the potential clusters are located in the south-central part of the province. Although, all methods performed very well to detect clusters, the BN and MEET methods identified local as well as general clusters.

Clusters of spatial, temporal, and space-time distribution of hemorrhagic fever with renal syndrome in Liaoning Province, Northeastern China

BACKGROUND

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease caused by Hantavirus, with characteristics of fever, hemorrhage, kidney damage, and hypotension. HFRS is recognized as a notifiable public health problem in China, and Liaoning Province is one of the most seriously affected areas with the most cases in China. It is necessary to investigate the spatial, temporal, and space-time distribution of confirmed cases of HFRS in Liaoning Province, China for future research into risk factors.

METHODS

A cartogram map was constructed; spatial autocorrelation analysis and spatial, temporal, and space-time cluster analysis were conducted in Liaoning Province, China over the period 1988-2001.

RESULTS

When the number of permutation test was set to 999, Moran's I was 0.3854, and was significant at significance level of 0.001. Spatial cluster analysis identified one most likely cluster and four secondary likely clusters. Temporal cluster analysis identified 1998-2001 as the most likely cluster. Space-time cluster analysis identified one most likely cluster and two secondary likely clusters.

CONCLUSIONS

Spatial, temporal, and space-time scan statistics may be useful in supervising the occurrence of HFRS in Liaoning Province, China. The result of this study can not only assist health departments to develop a better prevention strategy but also potentially increase the public health intervention's effectiveness.

Covariate adjusted weighted normal spatial scan statistics with applications to study geographic clustering of obesity and lung cancer mortality in the United States

In the field of cluster detection, a weighted normal model-based scan statistic was recently developed to analyze regional continuous data and to evaluate the clustering pattern of pre-defined cells (such as state, county, tract, school, hospital) that include many individuals. The continuous measures of interest are, for example, the survival rate, mortality rate, length of physical activity, or the obesity measure, namely, body mass index, at the cell level with an uncertainty measure for each cell. In this paper, we extend the method to search for clusters of the cells after adjusting for single/multiple categorical/continuous covariates. We apply the proposed method to 1999-2003 obesity data in the United States (US) collected by CDC's Behavioral Risk Factor Surveillance System with adjustment for age and race, and to 1999-2003 lung cancer age-adjusted mortality data by gender in the United States from the Surveillance Epidemiology and End Results (SEER Program) with adjustment for smoking and income.

Gumbel based p-value approximations for spatial scan statistics

BACKGROUND

The spatial and space-time scan statistics are commonly applied for the detection of geographical disease clusters. Monte Carlo hypothesis testing is typically used to test whether the geographical clusters are statistically significant as there is no known way to calculate the null distribution analytically. In Monte Carlo hypothesis testing, simulated random data are generated multiple times under the null hypothesis, and the p-value is r/(R + 1), where R is the number of simulated random replicates of the data and r is the rank of the test statistic from the real data compared to the same test statistics calculated from each of the random data sets. A drawback to this powerful technique is that each additional digit of p-value precision requires ten times as many replicated datasets, and the additional processing can lead to excessive run times.

RESULTS

We propose a new method for obtaining more precise p-values with a given number of replicates. The collection of test statistics from the random replicates is used to estimate the true distribution of the test statistic under the null hypothesis by fitting a continuous distribution to these observations. The choice of distribution is critical, and for the spatial and space-time scan statistics, the extreme value Gumbel distribution performs very well while the gamma, normal and lognormal distributions perform poorly. From the fitted Gumbel distribution, we show that it is possible to estimate the analytical p-value with great precision even when the test statistic is far out in the tail beyond any of the test statistics observed in the simulated replicates. In addition, Gumbel-based rejection probabilities have smaller variability than Monte Carlo-based rejection probabilities, suggesting that the proposed approach may result in greater power than the true Monte Carlo hypothesis test for a given number of replicates.

CONCLUSIONS

For large data sets, it is often advantageous to replace computer intensive Monte Carlo hypothesis testing with this new method of fitting a Gumbel distribution to random data sets generated under the null, in order to reduce computation time and obtain much more precise p-values and slightly higher statistical power.

An eight-year snapshot of geospatial cancer research (2002-2009): clinico-epidemiological and methodological findings and trends

Geographic information systems (GIS) offer a very rich toolbox of methods and technologies, and powerful research tools that extend far beyond the mere production of maps, making it possible to cross-link and study the complex interaction of disease data and factors originating from a wide range of disparate sources. Despite their potential indispensable role in cancer prevention and control programmes, GIS are underrepresented in specialised oncology literature. The latter has provided an impetus for the current review. The review provides an eight-year snapshot of geospatial cancer research in peer-reviewed literature (2002-2009), presenting the clinico-epidemiological and methodological findings and trends in the covered corpus (93 papers). The authors concluded that understanding the relationship between location and cancer/cancer care services can play a crucial role in disease control and prevention, and in better service planning, and appropriate resource utilisation. Nevertheless, there are still barriers that hinder the wide-scale adoption of GIS and related technologies in everyday oncology practice.

Cancer incidence pattern in Cordoba, Argentina

Cancer is the second cause of death in Argentina; nevertheless the distribution of the cancer incidence rates throughout the country is unknown. This study was conducted to describe cancer incidence patterns in Córdoba Province. Incidence data were supplied by the Government Córdoba Cancer Registry. Demographic information (age, sex, and place of residence) and diagnosis, certified by a pathologist, about all incident cases from June 2003 to May 2005 by type and 5-year age groups were obtained. Comparison of the incidence rate of cancer in various counties was performed by using standardized incidence rates (SIR) per 100,000 inhabitants using the world standard population. Estimated SIRs were used to build up incidence maps. Two indicators were created: sex ratio and site-specific ratio. Mixed Poisson models were fitted. Taken as a whole for all counties, SIR was 121.42 and 141.57 for men and women, respectively. The most common sites in men were prostate (13.62), lung (10.12), colon (7.53), and bladder (7.03); in women were breast (22.51) and colon (3.31). The highest and lowest rates were in urban and rural areas, respectively. Cancer registry has a pivotal role in cancer control. Such information is the primary resource of information not only for epidemiological research on cancer determinants but also for planning and evaluating health services for the policies of prevention, diagnosis and treatment of the disease.

GIS-based spatial, temporal, and space–time analysis of haemorrhagic fever with renal syndrome

We obtained a list of all reported cases of haemorrhagic fever with renal syndrome (HFRS) in Shenyang, China, during 1990–2003, and used GIS-based scan statistics to determine the distribution of HFRS cases and to identify key areas and periods for future risk-factor research. Spatial cluster analysis suggested three areas were at increased risk for HFRS. Temporal cluster analysis suggested one period was at increased risk for HFRS. Space–time cluster analysis suggested six areas from 1995 to 1996 and four areas from 1998 to 2003 were at increased risk for HFRS. We also discussed the likely reasons for these clusters. We conclude that GIS-based scan statistics may provide an opportunity to classify the epidemic situation of HFRS, and we can pursue future investigations to study the likely factors responsible for the increased disease risk based on the classification.

Evaluating spatial methods for investigating global clustering and cluster detection of cancer cases

There have been articles on comparing methods for global clustering evaluation and cluster detection in disease surveillance, but power and sample size (SS) requirements have not been explored for spatially correlated data in this area. We are developing such requirements for tests of spatial clustering and cluster detection for regional cancer cases. We compared global clustering methods including Moran's I, Tango's and Besag-Newell's R statistics, and cluster detection methods including circular and elliptic spatial scan statistics (SaTScan), flexibly shaped spatial scan statistics, Turnbull's cluster evaluation permutation procedure, local indicators of spatial association, and upper-level set scan statistics. We identified eight geographic patterns that are representative of patterns of mortality due to various types of cancer in the U.S. from 1998 to 2002. We then evaluated the selected spatial methods based on state- and county-level data simulated from these different spatial patterns in terms of geographic locations and relative risks, and varying SSs using the 2000 population in each county. The comparison provides insight into the performance of the spatial methods when applied to varying cancer count data in terms of power and precision of cluster detection.

Spatial dependency of V. cholera prevalence on open space refuse dumps in Kumasi, Ghana: a spatial statistical modelling

BACKGROUND

Cholera has persisted in Ghana since its introduction in the early 70's. From 1999 to 2005, the Ghana Ministry of Health officially reported a total of 26,924 cases and 620 deaths to the WHO. Etiological studies suggest that the natural habitat of V. cholera is the aquatic environment. Its ability to survive within and outside the aquatic environment makes cholera a complex health problem to manage. Once the disease is introduced in a population, several environmental factors may lead to prolonged transmission and secondary cases. An important environmental factor that predisposes individuals to cholera infection is sanitation. In this study, we exploit the importance of two main spatial measures of sanitation in cholera transmission in an urban city, Kumasi. These are proximity and density of refuse dumps within a community.

RESULTS

A spatial statistical modelling carried out to determine the spatial dependency of cholera prevalence on refuse dumps show that, there is a direct spatial relationship between cholera prevalence and density of refuse dumps, and an inverse spatial relationship between cholera prevalence and distance to refuse dumps. A spatial scan statistics also identified four significant spatial clusters of cholera; a primary cluster with greater than expected cholera prevalence, and three secondary clusters with lower than expected cholera prevalence. A GIS based buffer analysis also showed that the minimum distance within which refuse dumps should not be sited within community centres is 500 m.

CONCLUSION

The results suggest that proximity and density of open space refuse dumps play a contributory role in cholera infection in Kumasi.

Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality

BACKGROUND

Kulldorff's spatial scan statistic and its software implementation - SaTScan - are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S.

RESULTS

We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results.

CONCLUSION

The geovisual analytics approach described in this manuscript facilitates the interpretation of spatial cluster detection methods by providing cartographic representation of SaTScan results and by providing visualization methods and tools that support selection of SaTScan parameters. Our methods distinguish between heterogeneous and homogeneous clusters and assess the stability of clusters across analytic scales.

METHOD

We analyzed the cervical cancer mortality data for the United States aggregated by county between 2000 and 2004. We ran SaTScan on the dataset fifty times with different parameter choices. Our geovisual analytics approach couples SaTScan with our visual analytic platform, allowing users to interactively explore and compare SaTScan results produced by different parameter choices. The Standardized Mortality Ratio and reliability scores are visualized for all the counties to identify stable, homogeneous clusters. We evaluated our analysis result by comparing it to that produced by other independent techniques including the Empirical Bayes Smoothing and Kafadar spatial smoother methods. The geovisual analytics approach introduced here is developed and implemented in our Java-based Visual Inquiry Toolkit.

Analysis of geographical clustering of birth defects in Heshun county, Shanxi province

Birth defects remain the leading cause of stillbirth and infant mortality in China, especially in rural areas. The objective of this research is to seek evidence of clustering of cases of birth defects within a highly endemic area and to identify the environmental and landscape characteristics associated with increased risk for birth defects in Heshun county. This study uses four years of data, 1998-2001, to identify clusters of birth defects in Heshun county. A spatial scan statistic was employed to examine the spatial and spatio-temporal clustering of birth defects in the study area. Statistically significant clusters with high relative risk (RR = 2.981, p = 0.008) were identified using a spatial scan statistic. The spatial cluster located in southeast Heshun county has a radius of 7.52 km and includes 27 villages and 27 cases, accounting for 21.25% of the total cases during the study period. The methodology applied in this study was useful for evaluating the spatial distribution of birth defects in Heshun county from 1998-2001. The identified areas may be critical to control birth defects and may provide important direction for further study and targeted interventions.

Development and management of a geographic information system for health research in a developing-country setting: a case study from Bangladesh

In the last decade, geographic information systems (GIS) have become accessible to researchers in developing countries, yet guidance remains sparse for developing a GIS. Drawing on experience in developing a GIS for a large community trial in rural Bangladesh, six stages for constructing, maintaining, and using a GIS for health research purposes were outlined. The system contains 0.25 million landmarks, including 150,000 houses, in an area of 435 sq km with over 650,000 people. Assuming access to reasonably accurate paper boundary maps of the intended working area and the absence of pre-existing digital local-area maps, the six stages are: to (a) digitize and update existing paper maps, (b) join the digitized maps into a large-area map, (c) reference this large-area map to a geographic coordinate system, (d) insert location landmarks of interest, (e) maintain the GIS, and (f) link it to other research databases. These basic steps can produce a household-level, updated, scaleable GIS that can both enhance field efficiency and support epidemiologic analyses of demographic patterns, diseases, and health outcomes.

Analysis of the geographic distribution of HFRS in Liaoning Province between 2000 and 2005

Background

Hemorrhagic fever with renal syndrome (HFRS) is endemic in Liaoning Province, China, and this province was the most serious area affected by HFRS during 2004 to 2005. In this study, we conducted a spatial analysis of HFRS cases with the objective to determine the distribution of HFRS cases and to identify key areas for future public health planning and resource allocation in Liaoning Province.

Methods

The annual average incidence at the county level was calculated using HFRS cases reported between 2000 and 2005 in Liaoning Province. GIS-based spatial analyses were conducted to detect spatial distribution and clustering of HFRS incidence at the county level, and the difference of relative humidity and forestation between the cluster areas and non-cluster areas was analyzed.

Results

Spatial distribution of HFRS cases in Liaoning Province from 2000 to 2005 was mapped at the county level to show crude incidence, excess hazard, and spatial smoothed incidence. Spatial cluster analysis suggested 16 and 41 counties were at increased risk for HFRS (p < 0.01) with the maximum spatial cluster sizes at ≤ 50% and ≤ 30% of the total population, respectively, and the analysis showed relative humidity and forestation in the cluster areas were significantly higher than in other areas.

Conclusion

Some clustering of HFRS cases in Liaoning Province may be etiologically linked. There was strong evidence some HFRS cases in Liaoning Province formed clusters, but the mechanism underlying it remains unknown. In this study we found the clustering was consistent with the relative humidity and amount of forestation, and showed data indicating there may be some significant relationships.

A simulation study of three methods for detecting disease clusters

Background

Cluster detection is an important part of spatial epidemiology because it can help identifying environmental factors associated with disease and thus guide investigation of the aetiology of diseases. In this article we study three methods suitable for detecting local spatial clusters: (1) a spatial scan statistic (SaTScan), (2) generalized additive models (GAM) and (3) Bayesian disease mapping (BYM). We conducted a simulation study to compare the methods. Seven geographic clusters with different shapes were initially chosen as high-risk areas. Different scenarios for the magnitude of the relative risk of these areas as compared to the normal risk areas were considered. For each scenario the performance of the methods were assessed in terms of the sensitivity, specificity, and percentage correctly classified for each cluster.

Results

The performance depends on the relative risk, but all methods are in general suitable for identifying clusters with a relative risk larger than 1.5. However, it is difficult to detect clusters with lower relative risks. The GAM approach had the highest sensitivity, but relatively low specificity leading to an overestimation of the cluster area. Both the BYM and the SaTScan methods work well. Clusters with irregular shapes are more difficult to detect than more circular clusters.

Conclusion

Based on our simulations we conclude that the methods differ in their ability to detect spatial clusters. Different aspects should be considered for appropriate choice of method such as size and shape of the assumed spatial clusters and the relative importance of sensitivity and specificity. In general, the BYM method seems preferable for local cluster detection with relatively high relative risks whereas the SaTScan method appears preferable for lower relative risks. The GAM method needs to be tuned (using cross-validation) to get satisfactory results.