Spatial Scan Statistics Analysis Research Articles

Spatial analysis and influencing factors of pulmonary tuberculosis among students in Nanning, during 2012-2018.

BackgroundEconomically underdeveloped areas in western China are hotspots of tuberculosis, especially among students. However, the related spatial and temporal patterns and influencing factors are still unclear and there are few studies to analyze the causes of pulmonary tuberculosis in students from the perspective of space.MethodsWe collected data regarding the reported incidence of pulmonary tuberculosis (PTB) among students at township level in Nanning, from 2012 to 2018. The reported incidence of pulmonary tuberculosis among students in Nanning was analyzed using spatial autocorrelation and spatial scan statistical analysis to depict hotspots of PTB incidence and spatial and temporal clustering. Spatial panel data of the reported incidence rates and influencing factors at district and county levels in Nanning were collected from 2015 to 2018. Then, we analyzed the spatial effects of incidence and influencing factors using the spatial Durbin model to explore the mechanism of each influencing factor in areas with high disease prevalence under spatial effects.ResultsFrom 2012 to 2018, 1609 cases of PTB were reported among students in Nanning, with an average annual reported incidence rate of 14.84/100,000. Through the Joinpoint regression model, We observed a steady trend in the percentage of cases reported each year (P>0.05). There was spatial autocorrelation between the annual reported incidence and the seven-years average reported incidence from 2012 to 2018. The high-incidence area was distributed in the junction of six urban areas and spread to the periphery, with the junction at the center. The population of college students, per capita financial expenditure on health, per capita gross domestic product, and the number of health technicians per 1,000 population were all influencing factors in the reported incidence of PTB among students.ConclusionWe identified spatial clustering of the reported incidence of PTB among students in Nanning, mainly located in the urban center and its surrounding areas. The clustering gradually decreased from the urban center to the surrounding areas. Spatial effects influenced the reported incidence of PTB. The population density of college students, per capita health financial expenditure, gross domestic product (GDP) per capita, and the number of health technicians per 1,000 were all influencing factors in the reported incidence of PTB among students.

Spatial distribution of COVID-19 in Ethiopia - geospatial analysis

Background: COVID-19 was the devastating worldwide public health problem in recent years. COVID19 disease affecting large populations in different continents of the world starting on 11 March 2020. This study will be useful to give information on geographical surveillance of COVID-19 in Ethiopia, to know regions of high or low rates of COVID 19, to give evidence as baseline data for future research on COVID-19 in Ethiopia, and to predict the prevalence of COVID-19 from the untested population in different regions of Ethiopia using the art geospatial techniques. Methods: We have used secondary data collected from March 13, 2020, to November 23/ 2021 From COVID19 – Ethiopia - Ethiopian Health Data. ArcGIS 10.3 and SaTscan software were used for spatial analysis and geographical analysis respectively. Results: A high prevalence of COVID-19 was found in Addis Ababa (67.36%) and Oromia (13.85%). Spatial autocorrelation analysis indicated that the spatial distribution of COVID-19 is nonrandom (Moran’s Index: 0.134297, P-value: 0.052384). In spatial scan statistical analysis, eight clusters were identified and the higher rates of COVID-19 were observed in the Afar (RR= 1.80, p<0.001) and Addis Ababa (RR= 50.33, p<0.001) regions. Conclusion: The spatial distribution of COVID-19 cases in Ethiopia was not random. Hot spot research revealed a significant prevalence of COVID-19 patients in the Addis Ababa, Dire Dawa, Harari, and Oromia regions. For the reduction of COVID-19 infections in Ethiopia, preventative and control activities should be extended to high-risk locations.

Spatio-temporal distribution and associated factors of anaemia among children aged 6–59 months in Ethiopia: a spatial and multilevel analysis based on the EDHS 2005–2016

ObjectivesAnaemia is a global public health problem with major health and socioeconomic consequences. Though childhood anaemia is a major public health problem in Ethiopia, there is limited evidence on the...

Spatial distribution and determinants of intimate partner violence among reproductive-age women in Ethiopia: Spatial and Multilevel analysis

BackgroundIntimate partner violence is a serious global public health problem particularly in low-and middle-income countries such as Ethiopia where women's empowerment is limited. Despite the high prevalence of intimate partner violence in Ethiopia, there is limited evidence on the spatial distribution and determinants of intimate partner violence among reproductive-age women. Exploring the spatial distribution of intimate partner violence is crucial to identify hotspot areas of intimate partner violence to design targeted health care interventions. Therefore, this study aimed to investigate the spatial distribution and determinants of intimate partner violence among reproductive-age women in Ethiopia.MethodsA secondary data analysis was done based on the 2016 Ethiopian Demographic and Health Survey (EDHS) data. A total weighted sample of 6090 reproductive-age women were included in the study. The spatial scan statistical analysis was done to identify the significant hotspot areas of intimate partner violence. A multilevel binary logistic regression analysis was fitted to identify significant determinants of intimate partner violence. Deviance, Intra-cluster Correlation Coefficient (ICC), Median Odds Ratio, and Proportional Change in Variance (PCV) were used for model comparison as well as for checking model fitness. Variables with a p-value less than 0.2 were considered in the multivariable analysis. In the multivariable multilevel analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were reported to declare statistical significance and strength of association between intimate partner violence and independent variables.ResultsThe spatial analysis revealed that the spatial distribution of intimate partner violence was significantly varied across the country (Moran’s I = 0.1007, p-value < 0.0001). The SaTScan analysis identified a total of 192 significant clusters, of these 181 were primary clusters located in the Benishangul-Gumuz, Gambella, northwest Amhara, and west Oromia regions. In the multivariable multilevel analysis; women aged 45–49 years (AOR = 2.79, 95% CI 1.52–5.10), women attained secondary education (AOR = 0.61, 95% CI 0.38–0.98), women in the richest household (AOR = 0.58, 95% CI 0.35–0.97), > 10 family size (AOR = 3.85, 95% CI 1.41–10.54), and high community women empowerment (AOR = 0.66, 95% CI 0.49–0.8)) were significantly associated with intimate partner violence.ConclusionsIntimate partner violence among reproductive-age women had significant spatial variation across the country. Women's age, education status, family size, community women empowerment, and wealth status were found significant determinants of intimate partner violence. Therefore, public health programs should design targeted interventions in identified hot spot areas to reduce the incidence of intimate partner violence. Besides, health programmers should scale up public health programs designed to enhance women's autonomy to reduce the incidence of intimate partner violence and its consequences.

Geographical Variation of COPD Mortality and Related Risk Factors in Jiading District, Shanghai.

Background: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in China. Although numerous studies have been conducted to determine the risk factors for COPD mortality such as ambient air pollution, the results are not fully consistent.Methods: This study included mortality analysis and a case-control design by using the data extracted from the Mortality Registration System in Jiading District, Shanghai. Traditional logistic regression, geographically weighted logistic regression (GWLR), and spatial scan statistical analysis were performed to explore the geographic variation of COPD mortality and the possible influencing factors.Results: Traditional logistic regression showed that extreme lower temperature in the month prior to death, shorter distance to highway, lower GDP level were associated with increased COPD mortality. GWRL model further demonstrated obvious geographical discrepancies for the above associations. We additionally identified a significant cluster of low COPD mortality (OR = 0.36, P = 0.002) in the southwest region of Jiading District with a radius of 3.55 km by using the Bernoulli model. The geographical variation in age-standardized mortality rate for COPD in Jiading District was explained to a certain degree by these factors.Conclusion: The risk of COPD mortality in Jiading District showed obvious geographical variation, which were partially explained by the geographical variations in effects of the extreme low temperature in the month prior to death, residential proximity to highway, and GDP level.

Spatial clusters distribution and modelling of health care autonomy among reproductive\u2010age women in Ethiopia: spatial and mixed\u2010effect logistic regression analysis

BackgroundWhile millions of women in many African countries have little autonomy in health care decision-making, in most low and middle-income countries, including Ethiopia, it has been poorly studied. Hence, it is important to have evidence on the factors associated with women’s health care decision making autonomy and the spatial distribution across the country. Therefore, this study aimed to investigate the spatial clusters distribution and modelling of health care autonomy among reproductive-age women in Ethiopia.MethodsWe used the 2016 Ethiopian Demographic and Health Survey (EDHS) data for this study. The data were weighted for design and representativeness using strata, weighting variable, and primary sampling unit to get a reliable estimate. A total weighted sample of 10,223 married reproductive-age women were included in this study. For the spatial analysis, Arc-GIS version 10.6 was used to explore the spatial distribution of women health care decision making and spatial scan statistical analysis to identify hotspot areas. Considering the hierarchical nature of EDHS data, a generalized linear mixed-effect model (mixed-effect logistic regression) was fitted to identify significant determinants of women’s health care decision making autonomy. The Intra-Class Correlation (ICC) were estimated in the null model to estimate the clustering effect. For model comparison, deviance (-2LLR), Akakie Information Criteria (AIC), and Bayesian Information Criteria (BIC) parameters were used to choose the best-fitted model. Variables with a p-value < 0.2 in the bivariable analysis were considered for the multivariable analysis. In the multivariable mixed-effect logistic regression analysis, the Adjusted Odds Ratio (AOR) with 95% Confidence Interval (CI) were reported to declare the strength and significance of the association between women’s decision-making autonomy and independent variables.ResultsIn this study, about 81.6% (95% CI: 80.6%, 82.2%) of women have autonomy in making health care decisions. The spatial distribution of women’s autonomy in making health decisions in Ethiopia was non-random (global Moran’s I = 0.0675, p < 0.001). The significant hotspot areas of poor women’s autonomy in making health care decisions were found in north Somali, Afar, south Oromia, southwest Somali, Harari, and east Southern Nations Nationalities and Peoples (SNNP) regions. In the mixed-effect logistic regression analysis; being urban (AOR = 1.59, 95% CI: 1.04, 2.45), having secondary education (AOR = 1.60, 95% CI: 1.06, 2.41), having an occupation (AOR = 1.19, 95% CI: 1.01, 1.40) and being from the richest household (AOR = 2.14, 95% CI: 1.45, 3.14) were significantly associated with women autonomy in deciding for health care.ConclusionsThe spatial distribution of women’s autonomy in making the decision for health care was non-random in Ethiopia. Maternal education, residence, household wealth status, region, and maternal occupation were found to influence women’s autonomy. Public health interventions targeting the hotspot areas of poor women autonomy through enhancing maternal occupation and employment is needed to improve women empowerment in making decisions for health care.

Spatio-temporal distribution and associated factors of home delivery in Ethiopia. Further multilevel and spatial analysis of Ethiopian demographic and health surveys 2005\u20132016

BackgroundGlobally, between 2012 and 2017, 80% of live births occurred at health facilities assisted by skilled health personnel. In Ethiopia, in 2016 only 26% of live births attended by skilled health personal. This study aimed to assess the spatial patterns and associated factors of home delivery in Ethiopia using 2005, 2011, and 2016 Ethiopian Demographic and Health Surveys.MethodsA total of 33,482 women who gave live birth in the 5 years preceding each survey were included for this study. ArcGIS version 10.7 software was used to visualize the spatial distribution of home delivery. The Bernoulli model was applied using Kilduff SaTScan version 9.6 software to identify significant purely spatial clusters for home delivery in Ethiopia. A multilevel logistic regression model was fitted to identify factors associated with home delivery. A p-value < 0.05 was taken to declare statistically significant predictors.ResultsHome delivery was declined from 94.78% in 2005, 90.05% in 2011, and 73.44% in 2016 in Ethiopia. Among the three surveys, consistently high clustering of home delivery was observed in Amhara and Southern Nations Nationalities and People’s Regions (SNNPR) of Ethiopia. In spatial scan statistics analysis, a total of 128 clusters (RR = 1.04, P-value < 0.001) in 2005, and 90 clusters (RR = 1.11, P-value < 0.001) in 2011, and 55 clusters (RR = 1.29, P-value < 0.001) in 2016 significant primary clusters were identified. Educational status of women and husband, religion, distance to the health facility, mobile access, antenatal care visit, birth order, parity, wealth index, residence, and Region were statistically significant predictors of home delivery.ConclusionThe spatial distribution of home delivery among the three consecutive surveys were non-random in Ethiopia. Educational status of women and husband, religion, distance to the health facility, wealth index, residence, parity, mobile access, Region, and antenatal care visit were statistically significant predictors of home delivery in Ethiopia. Therefore, an intervention needs to improve the coverage of antenatal care visit, and health care facilities. Ministry of health and other stakeholders should give special attention to women living in Amhara and SNNPR states of Ethiopia.

Spatial Distribution and Factors Associated with Khat Chewing among Adult Males 15-59 Years in Ethiopia Using a Secondary Analysis of Ethiopian Demographic and Health Survey 2016: Spatial and Multilevel Analysis.

Background Khat chewing has become prevalent in the world due to the improvement of road and air transportation. In Ethiopia, khat chewing is more prevalent and widely practiced by men. Khat has a negative effect on social, economic, and mental health. There is variation in khat cultivation, use, and factors that associated with khat chewing in the Ethiopian regions. Therefore, this study is aimed at showing spatial distribution and factors associated with khat chewing among male adults 15-59 years in Ethiopia. Methods A total of 12,594 men were included in this study. ArcGIS version 10.7 software was used to show the spatial distribution of chewing khat among adult men in Ethiopia. The Bernoulli model was applied using Kilduff SaTScan version 9.6 software to identify significant purely spatial clusters for chewing khat in Ethiopia. A multilevel logistic regression model was fitted to identify factors associated with khat chewing. A P value < 0.05 was taken to declare statistically significant predictors. Results The EDHS 2016 survey showed that the high proportion of chewing khat was found in Dire Dawa, Harari, Southern Oromia, Somali, and Benishangul Gumuz regions. In spatial scan statistics analysis, a total of 126 clusters (LLR = 946.60, P value < 0.001) were identified. Age group 30-44 years old (AOR = 1.60, 95% CI: 1.37, 1.86) and 45-59 years old (AOR = 1.33, 95% CI: 1.09, 1.61), being single (AOR = 1.86, 95% CI: 1.64, 2.12), Muslim religion followers (AOR = 15.03, 95% CI: 11.90, 18.90), media exposed (AOR = 0.77, 95% CI: 0.68, 0.86), had work (AOR = 2.48, 95% CI: 2.08, 2.95), alcohol drinker (AOR = 3.75, 95% CI: 3.10, 4.53), and region (Afar, Amhara, Benishangul Gumuz, Gambela, Harari, Oromia, Somali, Southern Nations, Nationalities, and People's Region (SNNPR), and Tigray) and two cities (Addis Ababa and Dire Dawa) were statistically significant factors affecting chewing khat in Ethiopia. Conclusions In Ethiopia, the spatial distribution of khat chewing among adult men was nonrandom. A high proportion of khat chewing was observed in Dire Dawa, Harari, Southern Oromia, Somali, and Benishangul Gumuz regions. Older age group, being single marital status, alcohol drinker, media unexposed, had no work, and Muslim religion follower were factors affecting khat chewing. Policymakers should be given spatial attention in reducing the prevalence of chewing khat by teaching the health impact of khat chewing through media in the identified regions.

Spatial and temporal clustering of patients hospitalized with laboratory-confirmed influenza in the United States

BackgroundTiming of influenza spread across the United States is dependent on factors including local and national travel patterns and climate. Local epidemic intensity may be influenced by social, economic and demographic patterns. Data are needed to better explain how local socioeconomic factors influence both the timing and intensity of influenza seasons to result in national patterns. MethodsTo determine the spatial and temporal impacts of socioeconomics on influenza hospitalization burden and timing, we used population-based laboratory-confirmed influenza hospitalization surveillance data from the CDC-sponsored Influenza Hospitalization Surveillance Network (FluSurv-NET) at up to 14 sites from the 2009/2010 through 2013/2014 seasons (n = 35,493 hospitalizations). We used a spatial scan statistic and spatiotemporal wavelet analysis, to compare temporal patterns of influenza spread between counties and across the country. ResultsThere were 56 spatial clusters identified in the unadjusted scan statistic analysis using data from the 2010/2011 through the 2013/2014 seasons, with relative risks (RRs) ranging from 0.09 to 4.20. After adjustment for socioeconomic factors, there were five clusters identified with RRs ranging from 0.21 to 1.20. In the wavelet analysis, most sites were in phase synchrony with one another for most years, except for the H1N1 pandemic year (2009–2010), wherein most sites had differential epidemic timing from the referent site in Georgia. ConclusionsSocioeconomic factors strongly impact local influenza hospitalization burden. Influenza phase synchrony varies by year and by socioeconomics, but is less influenced by socioeconomics than is disease burden.

Spatial and seroepidemiology of canine visceral leishmaniasis in an endemic Southeast Brazilian area.

INTRODUCTION: Canine visceral leishmaniasis (CVL) is a public health problem, and its prevalence is associated with the coexistence of vectors and reservoirs. CVL is a protozoonosis caused by Leishmania infantum that is endemic in the southeast region of Brazil. Thus, vector and canine reservoir control strategies are needed to reduce its burden. This study aimed to verify the CVL seroprevalence and epidemiology in a municipality in Southeast Brazil to initiate disease control strategies. METHODS: A total of 833 dogs were subjected to Dual Path Platform (DPP) testing and enzyme-linked immunosorbent assays. For seropositive dogs, epidemiological aspects were investigated using a questionnaire and a global position system. The data were submitted to simple logistic regression, kernel estimation, and Bernoulli spatial scan statistical analysis. RESULTS: The overall CVL-confirmed seroprevalence was 16.08%. The 28.93% in the DPP screening test was associated with dogs maintained in backyards with trees, shade, animal and/or bird feces, and contact with other dogs and cats, with sick dogs showing the highest chances of infection (odds ratio, 2.6; 95% confidence interval, 2.38-1.98), especially in residences with elderly people. A spatial analysis identified two hotspot regions and detected two clusters in the study area. CONCLUSIONS: Our results demonstrated that residences with elderly people and the presence of trees, shade, feces, and pet dogs and cats increased an individual’s risk of developing CVL. The major regions where preventive strategies for leishmaniasis were to be initiated in the endemic area were identified in two clusters.

Lung cancer mortality clusters in urban and rural areas of Shandong Province, China: A spatial scan statistical analysis

AbstractObjectiveTo detect the spatial distribution and high‐risk clusters of lung cancer mortality for the years 2011–2013 in Shandong Province, China. The urban–rural differences in the spatial distribution and clusters of disease mortality were also examined.MethodsLung cancer mortality data were obtained from Shandong Death Registration System during 2011–2013, and were adjusted with the underreporting rate. The purely spatial scan statistics method was carried out using a discrete Poisson model.ResultsSeven significant spatial clusters of high mortality of lung cancer were detected in Shandong Province at the county level, mainly in the eastern, northern, central, and southwestern regions. The spatial distributions were different between urban and rural populations. Population aging influenced the distribution of lung cancer clusters for the urban and rural eastern region residents.ConclusionsThis study provided evidence for the presence of clusters of lung cancer mortality in Shandong, China, and found urban–rural differences in the clusters, which might be helpful in developing effective strategies to control lung cancer in different areas.

Spatial structure of depression in South Africa: A longitudinal panel survey of a nationally representative sample of households

Wider recognition of the mental health burden of disease has increased its importance as a global public health concern. However, the spatial heterogeneity of mental disorders at large geographical scales is still not well understood. Herein, we investigate the spatial distribution of incident depression in South Africa. We assess depressive symptomatology from a large longitudinal panel survey of a nationally representative sample of households, the South African National Income Dynamics Study. We identified spatial clusters of incident depression using spatial scan statistical analysis. Logistic regression was fitted to establish the relationship between clustering of depression and socio-economic, behavioral and disease risk factors, such as tuberculosis. There was substantial geographical clustering of depression in South Africa, with the excessive numbers of new cases concentrated in the eastern part of the country. These clusters overlapped with those of self-reported tuberculosis in the same region, as well as with poorer, less educated people living in traditional rural communities. Herein, we demonstrate, for the first time, spatial structuring of depression at a national scale, with clear geographical ‘hotspots’ of concentration of individuals reporting new depressive symptoms. Such geographical clustering could reflect differences in exposure to various risk factors, including socio-economic and epidemiological factors, driving or reinforcing the spatial structure of depression. Identification of the geographical location of clusters of depression should inform policy decisions.

Disability concentration and access to rehabilitation services: a pilot spatial assessment applying geographic information system analysis

Purpose: Due to geographical disparities, many people with profound or severe disabilities experience considerable delays in rehabilitation treatment, resulting in threats to quality of life. This pilot study aims to identify areas in Greater Brisbane, Australia, with a higher concentration of people with profound or severe disabilities and to evaluate access to rehabilitation services in these areas.Methods: Data came from the 2016 Australian Census of Population and Housing and the National Health Services Directory. Four frequently used rehabilitation services by individuals with profound or severe disabilities (i.e., occupational therapy, physiotherapy, speech pathology and psychology) were the focus of the analysis. The data were analyzed using geospatial analysis methods (e.g., spatial scan statistic and network analysis).Results: A higher concentration of rehabilitation services was found in the regions with lower disability prevalence and lower potential demand for rehabilitation services. In contrast, the regions with higher disability prevalence and higher potential demand for rehabilitation services experienced poorer access to rehabilitation services.Conclusion: The findings are expected to inform policy decisions about the prioritization of rehabilitation resources and derive evidence for planning more responsive service delivery.Implications for rehabilitationThe current study has demonstrated the utilization of geographic information system methods to facilitate rehabilitation service planning.Identification of disability concentration may inform locally responsive rehabilitation service delivery.Spatial assessment of mismatch between supply and potential demand may assist policy makers and service providers in the prioritization of rehabilitation resources.The current study contributes to the World Health Organization’s call for action to ensure adequate access to rehabilitation services by people with profound or severe disabilities.

Epidemiological survey of equine influenza in Andalusia, Spain

Equine influenza is a highly contagious respiratory disease considered the most important respiratory disease in equids. Although influenza A virus (IAV) has caused outbreaks in equids worldwide, surveillance in these species in Spain has not been conducted. A cross-sectional study was carried out to determine the individual and herd prevalence of antibodies against H3N8 and H7N7 IAV in equids in Andalusia (southern Spain). Antibodies againsts IAV were measured by the single radial haemolysis assay. A spatial scan statistical analysis was carried out using a Bernoulli model. Risk factors associated with IAV infection were assessed by multivariate analysis. Antibodies to H3N8 IAV were detected in 241 out of 464 unvaccinated equids (51.9%; 95% CI: 47.4–56.5). Seropositivity against the H7N7 subtype IAV was not found in any of the analysed animals. Significantly higher seropositivity was found in geriatric (OR = 6.1, P = 0.008, 95% CI = 1.6–23.1) and adult (OR = 4.8, P < 0.001, 95% CI = 2.5–9.0) equids compared to young animals. Specific antibodies against A/equine/Shropshire/2010 (H3N8) or A/equine/Newmarket/5/2003 (H3N8) only were confirmed in 11 and 45 of the animals, respectively. The spatial analysis showed a statistically significant cluster centred in the west part of Andalusia. The results confirmed widespread H3N8 subtype IAV exposure in equine species in Andalusia. Conversely, the absence of seropositivity against H7N7 IAV obtained in the present study suggests that this subtype has not circulated in southern Spain in recent years. Because of the animal health and economic consequences of IAV in equids, further surveillance and molecular studies are required to monitor and characterize the most prevalent IAV circulating in these species in Spain.

Spatial scan analysis on tuberculosis notifications in Zhejiang province, 2005—2014

Objective To analyze the epidemic characteristics of tuberculosis (TB) cases in Zhejiang province from 2005 to 2014, and to identify the cluster spots. Methods TB notification data in Zhejiang province from 2005 to 2014 were analyzed at county and district level by using spatial scan statistic analysis method, which was showed on the digital map. Results In zhejiang province from 2005 to 2014, a total of 377 707 cases of patients with tuberculosis were reported. The average annual registration rates of the entire province was 72.61/100 000 (range: 28.85/100 000—182.90/100 000). Spatial analysis identified 14 clusters (RR=1.05—1.62) with statistical significance. Temporal analysis revealed that there were clusters between 2005 and 2009 in terms of the notification data on TB cases (RR=1.27, P=0.001). Spatio-temporal analysis identified 6 clusters (RR=1.19—3.55) with statistical significance. Counties and districts included in the first class cluster were similar by using the two different methods-spatial analysis and spatial-temporal analysis. Conclusions There is a descending trend of TB cases notification rates from western to eastern Zhejiang during the study period.The distribution of the TB case notified is not random at spatial, temporal and spatio-temporal levels. Key words: Tuberculosis; Cluster areas; Spatio-temporal analysis

Spatio-Temporal Pattern and Influencing Factors of Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China) between 2005 and 2014.

Hemorrhagic Fever with Renal Syndrome (HFRS) is considered as a globally distributed infectious disease, which results in many deaths annually in Hubei Province, China. The outbreak of HFRS is usually characterized with spatio-temporal heterogeneity and is seasonally distributed. Further, it might also be impacted by the influencing factors such as socio-economic and geographical environment. To better understand and predict the outbreak of HFRS in the Hubei Province, the spatio-temporal pattern and influencing factors were investigated in this study. Moran’s I Index value was adopted in spatial global autocorrelation analysis to identify the overall spatio-temporal pattern of HFRS outbreak. Kulldorff scan statistical analysis was performed to further identify the changing trends of the clustering patterns of HFRS outbreak. Spearman's rank correlation analysis was used to explore the possible influencing factors on HFRS epidemics such as climate and geographic. The results demonstrated that HFRS outbreak in Hubei Province decreased from 2005 to 2012 in general while increasing slightly from 2012 to 2014. The spatial and temporal scan statistical analysis indicated that HFRS epidemic was temporally clustered in summer and autumn from 2005 to 2014 except 2008 and 2011. The seasonal epidemic pattern of HFRS in Hubei Province was characterized by a bimodal pattern (March to May and September to November) while peaks often occurring in the spring time. SEOV-type HFRS was presumed to influence more on the total number of HFRS incidence than HTNV-type HFRS do. The average humidity and human population density were the main influencing factors during these years. HFRS outbreaks were more in plains than in other areas of Hubei Province. We did not find that whether the terrain of the wetland (water system) plays a significant role in the outbreak of HFRS incidence. With a better understanding of rodent infection rate, socio-economic status and ecological environment characteristics, this study may help to reduce the outbreak of HFRS disease.

Geographical Patterns of HIV Sero-Discordancy in High HIV Prevalence Countries in Sub-Saharan Africa.

Introduction: Variation in the proportion of individuals living in a stable HIV sero-discordant partnership (SDP), and the potential drivers of such variability across sub Saharan Africa (SSA), are still not well-understood. This study aimed to examine the spatial clustering of HIV sero-discordancy, and the impact of local variation in HIV prevalence on patterns of sero-discordancy in high HIV prevalence countries in SSA. Methods: We described the spatial patterns of sero-discordancy among stable couples by analyzing Demographic and Health Survey data from Cameroon, Kenya, Lesotho, Tanzania, Malawi, Zambia, and Zimbabwe. We identified spatial clusters of SDPs in each country through a Kulldorff spatial scan statistics analysis. After a geographical cluster was identified, epidemiologic measures of sero-discordancy were calculated and analyzed. Results: Spatial clusters with significantly high numbers of SDPs were identified and characterized in Kenya, Malawi, and Tanzania, and they largely overlapped with the clusters with high HIV prevalence. There was a positive correlation between HIV prevalence and the proportion of SDPs among all stable couples across within and outside clusters. Conversely, there was a negative, but weak and not significant, correlation between HIV prevalence and the proportion of SDPs among all stable couples with at least one HIV-infected individual in the partnership. Discussion: There does not appear to be distinct spatial patterns for HIV sero-discordancy that are independent of HIV prevalence patterns. The variation of the sero-discordancy measures with HIV prevalence across clusters and outside clusters demonstrated similar patterns to those observed at the national level. The spatial variable does not appear to be a fundamental nor independent determinant of the observed patterns of sero-discordancy in high HIV prevalence countries in SSA.

Altitude as a risk factor for the development of hypospadias. Geographical cluster distribution analysis in South America

Hypospadias is the most common congenital anomaly affecting the genitals. It has been established as a multifactorial disease with increasing prevalence. Many risk factors have been identified such as prematurity, birth weight, mother's age, and exposure to endocrine disruptors. In recent decades multiple authors using surveillance systems have described an increase in prevalence of hypospadias, but most of the published literature comes from developed countries in Europe and North America and few of the published studies have involved cluster analysis. Few large-scale studies have been performed addressing the effect of altitude and other geographical aspects on the development of hypospadias. Acknowledging this limitation, we present novel results of a multinational spatial scan statistical analysis over a 30-year period in South America and an altitude analysis of hypospadias distribution on a continent level. A retrospective review was performed of the Latin American collaborative study of congenital malformations (ECLAMC). A total of 4,020,384 newborns was surveyed between 1982 and December 2011 in all participating centers. We selected all patients with hypospadias. All degrees of clinical severity were included in the analysis. Each participating center was geographically identified with its coordinates and altitude above sea level. A spatial scan statistical analysis was performed using Kulldorf's methodology and a prevalence trend analysis over time in centers below and above 2000m. During the study period we found 159 hospitals in six different countries (Colombia, Bolivia, Brazil, Argentina, Chile, and Uruguay) with 4,537 cases ofhypospadias and a global prevalence rate of11.3/10,000 newborns. Trend analysis showed that centers below 2000m had an increasing trend with an average of 10/10,000 newborns as opposed to those centers above 2000m that showed a reducing trend with an average prevalence of 7.8 (p=0.1246). We identified clusters with significant increases of prevalence in five centers along the coast at an average altitude of 219.8m above sea level (p>0.0000). Reduction in prevalence was found in clusters located in two centers on the Andes mountains. Altitude of 2,000m was associated with hypospadias (Figure), with an OR 0.59 (0.5-0.69). There are ethnic arguments to support our results supported by protective polymorphism distribution in high lands. Altitude above 2,000m is suggested to have a protective effect for hypospadias. Specific clusters have been identified with increased risk for hypospadias. Environmental risk factors in these areas need to be further studied given the association seen between altitude and the distribution of more severe cases.

Spatio-Temporal Distribution Characteristics and Trajectory Similarity Analysis of Tuberculosis in Beijing, China.

Tuberculosis (TB) is an infectious disease with one of the highest reported incidences in China. The detection of the spatio-temporal distribution characteristics of TB is indicative of its prevention and control conditions. Trajectory similarity analysis detects variations and loopholes in prevention and provides urban public health officials and related decision makers more information for the allocation of public health resources and the formulation of prioritized health-related policies. This study analysed the spatio-temporal distribution characteristics of TB from 2009 to 2014 by utilizing spatial statistics, spatial autocorrelation analysis, and space-time scan statistics. Spatial statistics measured the TB incidence rate (TB patients per 100,000 residents) at the district level to determine its spatio-temporal distribution and to identify characteristics of change. Spatial autocorrelation analysis was used to detect global and local spatial autocorrelations across the study area. Purely spatial, purely temporal and space-time scan statistics were used to identify purely spatial, purely temporal and spatio-temporal clusters of TB at the district level. The other objective of this study was to compare the trajectory similarities between the incidence rates of TB and new smear-positive (NSP) TB patients in the resident population (NSPRP)/new smear-positive TB patients in the TB patient population (NSPTBP)/retreated smear-positive (RSP) TB patients in the resident population (RSPRP)/retreated smear-positive TB patients in the TB patient population (RSPTBP) to detect variations and loopholes in TB prevention and control among the districts in Beijing. The incidence rates in Beijing exhibited a gradual decrease from 2009 to 2014. Although global spatial autocorrelation was not detected overall across all of the districts of Beijing, individual districts did show evidence of local spatial autocorrelation: Chaoyang and Daxing were Low-Low districts over the six-year period. The purely spatial scan statistics analysis showed significant spatial clusters of high and low incidence rates; the purely temporal scan statistics showed the temporal cluster with a three-year period from 2009 to 2011 characterized by a high incidence rate; and the space-time scan statistics analysis showed significant spatio-temporal clusters. The distribution of the mean centres (MCs) showed that the general distributions of the NSPRP MCs and NSPTBP MCs were to the east of the incidence rate MCs. Conversely, the general distributions of the RSPRP MCs and the RSPTBP MCs were to the south of the incidence rate MCs. Based on the combined analysis of MC distribution characteristics and trajectory similarities, the NSP trajectory was most similar to the incidence rate trajectory. Thus, more attention should be focused on the discovery of NSP patients in the western part of Beijing, whereas the northern part of Beijing needs intensive treatment for RSP patients.

Prevalence and geographic distribution of Besnoitia besnoiti infection in cattle herds in Portugal.

Bovine besnoitiosis, caused by the apicomplexan parasite Besnoitia besnoiti is considered an emergent disease in Europe. This study aimed to determine the prevalence and geographic distribution of B. besnoiti in cattle herds in continental Portugal and to identify potential spatial clustering of infection. A stratified two-stage cross-sectional serological survey was carried out between March 2012 and May 2013 with the five administrative NUTS II regions, Norte, Centro, Lisboa, Alentejo, and Algarve, as the stratification level. Sera from 391 herds in 220 parishes and 83 municipalities were analyzed by a serial testing strategy, with the modified agglutination test (B-MAT) as the first screening assay and the immunofluorescent antibody test (IFAT) as the confirmatory test. Within-herd prevalence of positive herds varied between 0.7 and 72.4% and was ≥10.3% in half of the infected herds. Using a Bayesian approach, the true prevalence of B. besnoiti in cattle herds was determined to be 5.1% (confidence interval (CI), 3.1-7.8%) and the mean within-herd prevalence of positive herds was 33.0% (CI, 20.3-46.0%). The sensitivity and specificity of the B-MAT were estimated to be 96.9% (CI, 93.7-98.8 %) and 99.7% (CI, 99.6-99.8%), whereas those of the IFAT were 89.6% (CI, 86.0-92.5%) and 99.7% (CI, 98.5-99.9%), respectively. Spatial scan statistics analysis identified one spatial cluster covering the majority of the Alentejo region. Seropositive herds were detected for the first time outside Alentejo, in the region Centro and in the northeast of Portugal. Further epidemiological research is needed to identify eco-biological factors, which could explain the geographic clustering of B. besnoiti in Portugal.