Contrasting patterns in the small-scale heterogeneity of human helminth infections in urban and rural environments in Brazil

Marked heterogeneity exists in the patterns of parasitic infection between individuals, households and communities. Analysis of parasite distributions within populations is complicated by the fact that parasite distributions are highly aggregated and few studies have explicitly incorporated this distribution when investigating small-scale spatial heterogeneities. This study aimed to quantify the small-scale (within- and between-household) heterogeneity of helminth infection in an area of Minas Gerais State, Brazil, with rural and urban sectors. Parasitological data from a cross-sectional survey of 1,249 individuals aged 0-86 years from 242 households were analysed. Within-household clustering of infection was assessed using random effect logistic regression models and between-household spatial heterogeneity was assessed using a Bayesian negative binomial spatial model. The overall prevalence of hookworm (Necator americanus) was 66.9%, the prevalence of Schistosoma mansoni was 44.9% and the prevalence of Ascaris lumbricoides was 48.8%. Statistical analysis indicated significant (within) household and (between household) spatial clustering of hookworm in both rural and urban areas and of S. mansoni in rural areas. There was no evidence of either household or spatial clustering of S. mansoni in urban areas. The spatial correlation of S. mansoni was estimated to reduce by half over a distance of 700 m in the rural area. Rural hookworm had a much smaller half-distance (28 m) and urban hookworm showed an even smaller half-distance (12 m). We suggest that such species-specific differences in patterns of infection by environment are primarily due to variation in exposure and parasite life cycle, although host genetic factors cannot be ruled out.

Rural-Urban Disparities in Access to Breast Cancer Screening: A Spatial Clustering Analysis

PURPOSE

The purpose of this study was to examine rural-urban differences in access to breast cancer screening in a predominantly rural Midwestern state in the United States.

METHODS

The study is a retrospective analysis of pooled cross-sectional data for the years 2008 to 2012. We conducted hot spot analyses of the rate of late-stage diagnosis of breast cancer at the census tract level in Nebraska for cases diagnosed between 2008 and 2012, using cancer registry data. We also conducted hot spot analyses of access to mammography facilities (distance to the nearest center) using data on mammography facilities from the US Food and Drug Administration and rates of screening using the National Private Insurance Claims data for year 2013.

RESULTS

The spatial clustering analyses found that urban areas in Nebraska had lower distances to mammography centers, higher screening rates and lower rates of late-stage diagnosis of breast cancer. Rural areas had higher distance to the mammography centers and higher rates of late-stage at diagnosis for breast cancer.

CONCLUSIONS

The evidence from this study points to geographic disparities in access to screening for breast cancer. Mitigating the access issues that rural women face would require interventions specifically targeted to rural populations.

No rise in incidence but geographical heterogeneity in the occurrence of primary biliary cirrhosis in North East England

In this study, we examined temporal changes in the incidence of primary biliary cirrhosis (PBC) and investigated associations between PBC incidence and sociodemographic factors and spatial clustering. We included 982 patients aged ≥40 years from North East England with incident PBC diagnosed during 1987–2003. Age-standardized incidence rates with 95% confidence intervals were calculated. Negative binomial regression was used to analyze incidence and socioeconomic deprivation. Clustering analysis was performed using point process methods, testing the null hypothesis that disease risk does not vary spatially and that PBC cases occur independently. The age-standardized incidence rate was 53.50 per million persons per year (95% confidence interval: 48.65, 58.35) in 1987–1994 and 45.09 per million persons per year (95% confidence interval: 41.10, 49.07) in 1995–2003. Risk of PBC increased in areas with higher levels of socioeconomic deprivation (P = 0.035). More specifically, risk increased in areas with higher levels of overcrowded homes (P = 0.040), higher levels of households without cars (P < 0.001), and higher levels of non-owner-occupied homes (P < 0.001). Overall, there was evidence of spatial clustering (P = 0.001). The findings confirm that overall incidence of PBC did not rise over time, but sociodemographic variations suggest that certain aspects of deprivation are involved in its etiology.

Spatial patterns of genome-wide expression profiles reflect anatomic and fiber connectivity architecture of healthy human brain

Unraveling the relationship between molecular signatures in the brain and their functional, architectonic, and anatomic correlates is an important neuroscientific goal. It is still not well understood whether the diversity demonstrated by histological studies in the human brain is reflected in the spatial patterning of whole brain transcriptional profiles. Using genome-wide maps of transcriptional distribution of the human brain by the Allen Brain Institute, we test the hypothesis that gene expression profiles are specific to anatomically described brain regions. In this work, we demonstrate that this is indeed the case by showing that gene similarity clusters appear to respect conventional basal-cortical and caudal-rostral gradients. To fully investigate the causes of this observed spatial clustering, we test a connectionist hypothesis that states that the spatial patterning of gene expression in the brain is simply reflective of the fiber tract connectivity between brain regions. We find that although gene expression and structural connectivity are not determined by each other, they do influence each other with a high statistical significance. This implies that spatial diversity of gene expressions is a result of mainly location-specific features but is influenced by neuronal connectivity, such that like cellular species preferentially connects with like cells.

The household distribution of trachoma in a Tanzanian village: an application of GIS to the study of trachoma

The distribution of active trachoma in Kahe Mpya, Tanzania, an endemic village of approximately 1000 people, was mapped spatially and analysed for associated risk factors and evidence of clustering. An association between distance to water source and active disease was demonstrated, although this was reduced after accounting for the lack of independence between cases in the same household. Significant clustering of active trachoma within households was demonstrated, adding support to the hypothesized importance of intra-familial transmission. The spatial distribution of trachoma was analysed using the spatial scan statistic, and evidence of clustering of active trachoma cases detected. Understanding the distribution of the disease has implications for understanding the dynamics of transmission and therefore appropriate control activities. The demonstrated spatial clustering suggests inter-familial as well as intra-familial transmission of infection may be common in this setting. The association between active trachoma and geographical information system (GIS) measured distance to water may be relevant for planning control measures.

The risk of West Nile Virus infection is associated with combined sewer overflow streams in urban Atlanta, Georgia, USA

BACKGROUND

At present, the factors favoring transmission and amplification of West Nile Virus (WNV) within urban environments are poorly understood. In urban Atlanta, Georgia, the highly polluted waters of streams affected by combined sewer overflow (CSO) represent significant habitats for the WNV mosquito vector Culex quinquefasciatus. However, their contribution to the risk of WNV infection in humans and birds remains unclear.

OBJECTIVES

Our goals were to describe and quantify the spatial distribution of WNV infection in mosquitoes, humans, and corvids, such as blue jays and American crows that are particularly susceptible to WNV infection, and to assess the relationship between WNV infection and proximity to CSO-affected streams in the city of Atlanta, Georgia.

MATERIALS AND METHODS

We applied spatial statistics to human, corvid, and mosquito WNV surveillance data from 2001 through 2007. Multimodel analysis was used to estimate associations of WNV infection in Cx. quinquefasciatus, humans, and dead corvids with selected risk factors including distance to CSO streams and catch basins, land cover, median household income, and housing characteristics.

RESULTS

We found that WNV infection in mosquitoes, corvids, and humans was spatially clustered and statistically associated with CSO-affected streams. WNV infection in Cx. quinquefasciatus was significantly higher in CSO compared with non-CSO streams, and WNV infection rates among humans and corvids were significantly associated with proximity to CSO-affected streams, the extent of tree cover, and median household income.

CONCLUSIONS

Our study strongly suggests that CSO-affected streams are significant sources of Cx. quinquefasciatus mosquitoes that may facilitate WNV transmission to humans within urban environments. Our findings may have direct implications for the surveillance and control of WNV in other urban centers that continue to use CSO systems as a waste management practice.

Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions?

As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.

Spatiotemporal patterns of reinfestation by Triatoma guasayana (Hemiptera: Reduviidae) in a rural community of northwestern Argentina

Triatoma guasayana (Wygodzinsky & Abalos) is a secondary vector of Trypanosoma cruzi (Chagas), the etiologic agent of Chagas disease, in the Chaco region of Argentina, Bolivia, and Paraguay. The spatial distribution of T. guasayana in a rural community in northwestern Argentina is described and analyzed using very high spatial resolution satellite imagery, geographic information systems, and spatial statistics. Since a 1992 residual spraying with insecticides of all houses, site-specific domestic and peridomestic reinfestations by triatomine bugs were monitored using various methods semiannually from 1993 to 2002. The reinfestation by T. guasayana started with finding of only adult bugs in a few sites. Bug abundance was significantly clustered and predominantly peridomestic in the southern and northern extremes of the community. The identified source of reinfestation in the northern cluster was a colonized wood pile, whereas no potential peridomestic source was found for the southern cluster. The spatial distribution of T. guasayana was positively associated with the abundance and spatial distribution of goats. Active dispersal from the hypothesized source and the surrounding sylvatic environment, and passive transport of bugs in wood piles seems to be the most likely mechanisms underlying the observed spatial pattern of T. guasayana. The absence of domestic colonization indicates that, to date, there is no trend toward increased local domiciliation of T. guasayana. The clustering zones can be considered "hot spots" where bug invasion from other sources is expected to be higher and where eventually, introduction of sylvatic T. cruzi to suitable hosts may occur.

Spatial Clustering of Dengue Fever Incidence and Its Association with Surrounding Greenness

With more than 58,000 cases reported by the country's Centers for Disease Control, the dengue outbreaks from 2014 to 2015 seriously impacted the southern part of Taiwan. This study aims to assess the spatial autocorrelation of the dengue fever (DF) outbreak in southern Taiwan in 2014 and 2015, and to further understand the effects of green space (such as forests, farms, grass, and parks) allocation on DF. In this study, two different greenness indexes were used. The first green metric, the normalized difference vegetation index (NDVI), was provided by the long-term NASA MODIS satellite NDVI database, which quantifies and represents the overall vegetation greenness. The latest 2013 land use survey GIS database completed by the National Land Surveying and Mapping Center was obtained to access another green metric, green land use in Taiwan. We first used Spearman's rho to find out the relationship between DF and green space, and then three spatial autocorrelation methods, including Global Moran's I, high/low clustering, and Hot Spot were employed to assess the spatial autocorrelation of DF outbreak. In considering the impact of social and environmental factors in DF, we used generalized linear mixed models (GLMM) to further clarify the relationship between different types of green land use and dengue cases. Results of spatial autocorrelation analysis showed a high aggregation of dengue epidemic in southern Taiwan, and the metropolitan areas were the main hotspots. Results of correlation analysis and GLMM showed a positive correlation between parks and dengue fever, and the other five green space metrics and land types revealed a negative association with DF. Our findings may be an important asset for improving surveillance and control interventions for dengue.

The spatial clustering analysis of COVID-19 and its associated factors in mainland China at the prefecture level

Coronavirus disease 2019 (COVID-19) has become a worldwide public health threat. Many associated factors including population movement, meteorological parameters, air quality and socioeconomic conditions can affect COVID-19 transmission. However, no study has combined these various factors in a comprehensive analysis. We collected data on COVID-19 cases and the factors of interest in 340 prefectures of mainland China from 1 December 2019 to 30 April 2020. Moran's I statistic, Getis-Ord Gi^⁎ statistic and Kulldorff's space-time scan statistics were used to identify spatial clusters of COVID-19, and the geographically weighted regression (GWR) model was applied to investigate the effects of the associated factors on COVID-19 incidence. A total of 67,449 laboratory-confirmed cases were reported during the study period. Wuhan city as well as its surrounding areas were the cluster areas, and January 25 to February 21, 2020, was the clustering time of COVID-19. The population outflow from Wuhan played a significant role in COVID-19 transmission, with the local coefficients varying from 14.87 to 15.02 in the 340 prefectures. Among the meteorological parameters, relative humidity and precipitation were positively associated with COVID-19 incidence, while the average wind speed showed a negative correlation, but the relationship of average temperature with COVID-19 incidence inconsistent between northern and southern China. NO₂ was positively associated, and O₃ was negatively associated, with COVID-19 incidence. Environment with high levels of inbound migration or travel, poor ventilation, high humidity or heavy rainfall, low temperature, and high air pollution may be favorable for the growth, reproduction and spread of SARS-CoV-2. Therefore, applying appropriate lockdown measures and travel restrictions, strengthening the ventilation of living and working environments, controlling air pollution and making sufficient preparations for a possible second wave in the relatively cold autumn and winter months may be helpful for the control and prevention of COVID-19.

Variations in Obesity Rates between US Counties: Impacts of Activity Access, Food Environments, and Settlement Patterns

There is much ongoing research about the effect of the urban environment as compared with individual behaviour on growing obesity levels, including food environment, settlement patterns (e.g., sprawl, walkability, commuting patterns), and activity access. This paper considers obesity variations between US counties, and delineates the main dimensions of geographic variation in obesity between counties: by urban-rural status, by region, by area poverty status, and by majority ethnic group. Available measures of activity access, food environment, and settlement patterns are then assessed in terms of how far they can account for geographic variation. A county level regression analysis uses a Bayesian methodology that controls for spatial correlation in unmeasured area risk factors. It is found that environmental measures do play a significant role in explaining geographic contrasts in obesity.

Understanding the spatio-temporal pattern of COVID-19 outbreak in India using GIS and India's response in managing the pandemic

Due to the outbreak of Coronavirus, humans all over the world are facing several health problems. The present study has explored the spatio-temporal pattern of Coronavirus spread in India including spatial clustering, identification of hotspot, spatial heterogeneity, and homogeneity, spatial trend, and direction of COVID-19 cases using spatial statistical analysis during the period of 30 January to 20 June 2020. Besides, the polynomial regression model has been used for predictions of COVID-19 affected population and related deaths. The study found positive spatial heterogeneity in COVID-19 cases in India. The study has also identified 17 epicentres across the country with high incidence rates. The directional distribution of ellipse polygon shows that the spread of COVID-19 now trending towards the east but the concentration of cases is mainly in the western part of the country. The country's trend of COVID-19 follows a fourth-order polynomial growth and is characterized by an increasing trend. The prediction results show that as on 14 October India will reach 14,660,400 COVID-19 cases and the death toll will cross 152,945. Therefore, a "space-specific" policy strategy would be a more suitable strategy for reducing the spatial spread of the virus in India. Moreover, the study has broadly found out seven sectors, where the Government of India lacks in terms of confronting the ongoing pandemic. The study has also recommended some appropriate policies which would be immensely useful for the administration to initiate strategic planning.

Spatial clustering of high load ocular Chlamydia trachomatis infection in trachoma: a cross-sectional population-based study

Chlamydia trachomatis (Ct) is the most common cause of bacterial sexually transmitted infection and infectious cause of blindness (trachoma) worldwide. Understanding the spatial distribution of Ct infection may enable us to identify populations at risk and improve our understanding of Ct transmission. In this study, we sought to investigate the spatial distribution of Ct infection and the clinical features associated with high Ct load in trachoma-endemic communities on the Bijagós Archipelago (Guinea Bissau). We collected 1507 conjunctival samples and corresponding detailed clinical data during a cross-sectional population-based geospatially representative trachoma survey. We used droplet digital PCR to estimate Ct load on conjunctival swabs. Geostatistical tools were used to investigate clustering of ocular Ct infections. Spatial clusters (independent of age and gender) of individuals with high Ct loads were identified using local indicators of spatial association. We did not detect clustering of individuals with low load infections. These data suggest that infections with high bacterial load may be important in Ct transmission. These geospatial tools may be useful in the study of ocular Ct transmission dynamics and as part of trachoma surveillance post-treatment, to identify clusters of infection and thresholds of Ct load that may be important foci of re-emergent infection in communities.

Identification of optimum scopes of environmental factors for snails using spatial analysis techniques in Dongting Lake Region, China

BACKGROUND

Owing to the harmfulness and seriousness of Schistosomiasis japonica in China, the control and prevention of S. japonica transmission are imperative. As the unique intermediate host of this disease, Oncomelania hupensis plays an important role in the transmission. It has been reported that the snail population in Qiangliang Lake district, Dongting Lake Region has been naturally declining and is slowly becoming extinct. Considering the changes of environmental factors that may cause this phenomenon, we try to explore the relationship between circumstance elements and snails, and then search for the possible optimum scopes of environmental factors for snails.

METHODS

Moisture content of soil, pH, temperature of soil and elevation were collected by corresponding apparatus in the study sites. The LISA statistic and GWR model were used to analyze the association between factors and mean snail density, and the values in high-high clustered areas and low-low clustered areas were extracted to find out the possible optimum ranges of these elements for snails.

RESULTS

A total of 8,589 snail specimens were collected from 397 sampling sites in the study field. Besides the mean snail density, three environmental factors including water content, pH and temperature had high spatial autocorrelation. The spatial clustering suggested that the possible optimum scopes of moisture content, pH, temperature of the soil and elevation were 58.70 to 68.93%, 6.80 to 7.80, 22.73 to 24.23°C and 23.50 to 25.97 m, respectively. Moreover, the GWR model showed that the possible optimum ranges of these four factors were 36.58 to 61.08%, 6.541 to 6.89, 24.30 to 25.70°C and 23.50 to 29.44 m, respectively.

CONCLUSION

The results indicated the association between snails and environmental factors was not linear but U-shaped. Considering the results of two analysis methods, the possible optimum scopes of moisture content, pH, temperature of the soil and elevation were 58.70% to 68.93%, 6.6 to 7.0, 22.73°C to 24.23°C, and 23.5 m to 26.0 m, respectively. The findings in this research will help in making an effective strategy to control snails and provide a method to analyze other factors.

Geographic analysis of individual and environmental risk factors for hypospadias births

BACKGROUND

Hypospadias is a relatively common birth defect affecting the male urinary tract. We explored the etiology of hypospadias by examining its spatial distribution in North Carolina and the spatial clustering of residuals from individual and environmental risk factors.

METHODS

We used data collected by the North Carolina Birth Defects Monitoring Program from 2003 to 2005 to estimate local Moran's I statistics to identify geographic clustering of overall and severe hypospadias, using 995 overall cases and 16,013 controls. We conducted logistic regression and local Moran's I statistics on standardized residuals to consider the contribution of individual variables (maternal age, maternal race/ethnicity, maternal education, smoking, parity, and diabetes) and environmental variables (block group land cover) to this clustering.

RESULTS

Local Moran's I statistics indicated significant clustering of overall and severe hypospadias in eastern central North Carolina. Spatial clustering of hypospadias persisted when controlling for individual factors, but diminished somewhat when controlling for environmental factors. In adjusted models, maternal residence in a block group with more than 5% crop cover was associated with overall hypospadias (odds ratio = 1.22; 95% confidence interval = 1.04-1.43); that is living in a block group with greater than 5% crop cover was associated with a 22% increase in the odds of having a baby with hypospadias. Land cover was not associated with severe hypospadias.

CONCLUSION

This study illustrates the potential contribution of mapping in generating hypotheses about disease etiology. Results suggest that environmental factors including proximity to agriculture may play some role in the spatial distribution of hypospadias. Birth Defects Research (Part A) 100:887-894, 2014. © 2014 Wiley Periodicals, Inc.

Association of peripheral artery disease and chronic limb-threatening ischemia with socioeconomic deprivation in people with diabetes: A population data-linkage and geospatial analysis

The association between the prevalence and geographical distribution of peripheral artery disease (PAD) and chronic limb-threatening ischemia (CLTI) in patients with diabetes in the context of socioeconomic deprivation is not well understood. We undertook a retrospective cohort study of 76,307 people with diabetes admitted as a hospital inpatient in a large Scottish health administrative area. Utilising linked health records, we identified diagnoses of PAD and/or CLTI and their distribution using small area cartography techniques according to multiple deprivation maps. Spatial autocorrelation techniques were applied to examine PAD and CLTI patterning. Association between crude inpatient prevalence-adjusted outcome rates and exposure to social deprivation were determined. We found crude prevalence-adjusted rates of 8.05% for PAD and 1.10% for CLTI with a five- to sevenfold difference from the least to most deprived regions. Statistically significant hot spots were found for PAD (p < 0.001) and CLTI (p < 0.001) in the most deprived areas, and cold spots for PAD (p < 0.001) but not CLTI (p = 0.72) in the least deprived areas. Major health disparities in PAD/CLTI diagnoses in people with diabetes is driven by socioeconomic deprivation.

Joint spatial survival modeling for the age at diagnosis and the vital outcome of prostate cancer

Prostate cancer (PrCA) is the most common malignancy in men and a leading cause of cancer mortality among males in the United States. Large geographical variation and racial disparities exist in both the incidence of PrCA and the survival rate after diagnosis. In this population-based study, a joint spatial survival model is constructed to investigate factors that affect the age at diagnosis of PrCA and the subsequent survival. The joint model for these two time-to-event outcomes is specified through parametric models for age at diagnosis and survival time conditional on diagnosis age. To account for possible correlation in these outcomes among men from the same geographical region, frailty terms are included in the survival model. Both spatially correlated and uncorrelated frailties are incorporated in each model considered. The deviance information criterion is used to select a best-fitting model within the Bayesian framework. The results from our final best-fitting model indicate that race, marital status at diagnosis, and cancer stage are significantly associated with both of the two time-to-event outcomes. No pattern emerged in the geographical distribution of age at PrCA diagnosis. In contrast, a spatially clustered pattern was observed in the geographic distribution of survival experience post diagnosis.

Spatial clustering patterns and regional variations for food and physical activity environments across the United States

This study examined spatial patterns of obesogenic environments for US counties. We mapped the geographic dispersion of food and physical activity (PA) environments, assessed spatial clustering, and identified food and PA environment differences across U.S. regions and rurality categories. Substantial low food score clusters were located in the South and high score clusters in the Midwest and West. Low PA score clusters were located in the South and high score clusters in the Northeast and Midwest (p < .0001). For region, the South had significantly lower food and PA environment scores. For rurality, rural counties had significantly higher food environment scores and metropolitan counties had significantly higher PA environment scores (p < .0001). This study highlights geographic clustering and disparities in food and PA access nationwide. State and region-wide environmental inequalities may be targeted using structural interventions and policy initiatives to improve food and PA access.

Regionalizing aquatic ecosystems based on the river subbasin taxonomy concept and spatial clustering techniques

Aquatic ecoregions were increasingly used as spatial units for aquatic ecosystem management at the watershed scale. In this paper, the principle of including land area, comprehensiveness and dominance, conjugation and hierarchy were selected as regionalizing principles. Elevation and drainage density were selected as the regionalizing indicators for the delineation of level I aquatic ecoregions, and percent of construction land area, percent of cultivated land area, soil type and slope for the level II. Under the support of GIS technology, the spatial distribution maps of the two indicators for level I and the four indicators for level II aquatic ecoregion delineation were generated from the raster data based on the 1,107 subwatersheds. River subbasin taxonomy concept, two-step spatial clustering analysis approach and manual-assisted method were used to regionalize aquatic ecosystems in the Taihu Lake watershed. Then the Taihu Lake watershed was divided into two level I aquatic ecoregions, including Ecoregion I1 and Ecoregion I2, and five level II aquatic subecoregions, including Subecoregion II11, Subecoregion II12, Subecoregion II21, Subecoregion II22 and Subecoregion II23. Moreover, the characteristics of the two level I aquatic ecoregions and five level II aquatic subecoregions in the Taihu Lake watershed were summarized, showing that there were significant differences in topography, socio-economic development, water quality and aquatic ecology, etc. The results of quantitative comparison of aquatic life also indicated that the dominant species of fish, benthic density, biomass, dominant species, Shannon-Wiener diversity index, Margalef species richness index, Pielou evenness index and ecological dominance showed great spatial variability between the two level I aquatic ecoregions and five level II aquatic subecoregions. It reflected the spatial heterogeneities and the uneven natures of aquatic ecosystems in the Taihu Lake watershed.

Geographical clustering of cannabis use: results from the New Zealand Mental Health Survey 2003-2004

BACKGROUND

In epidemiology, it always has been important to study local area patterns of disease occurrence. New methods to quantify local area and household clustering of disease emerged late in the 19th century and were refined during the 20th century. Nonetheless, multi-level models to estimate local area clustering of illegal drug use did not appear until the 1990s, and to date, there is just one study with estimates of local neighbourhood clustering of cannabis use, based on a United States sample. Here, seeking the first replication of that single prior study, we estimate the degree to which cannabis use might cluster within neighbourhoods of New Zealand (NZ), and we also study higher level clustering and suspected individual-level determinants of recent cannabis use.

METHODS

A national probability community sample (n=12,992) of adults aged 16 years or more with standardized assessment of cannabis use. Alternating logistic regression produced estimates for cannabis clustering.

RESULTS

In NZ, use of cannabis was common: 41.6% had ever used it and 13.1% had used it in the past year. There was clustering within the smallest local areas (pairwise odds ratio=1.3-1.5) but not within larger government districts (PWOR=1.02). Age, male sex, ethnicity, education, and marital status were all associated with cannabis use, but did not account for observed clustering.

CONCLUSIONS

Neighborhood clustering of recent cannabis use has emerged in New Zealand, as in the US. Standard individual-level characteristics explain only some of this clustering. Other explanations must be sought, perhaps including personal networks and local supply.

Delaunay Triangulation-Based Spatial Clustering Technique for Enhanced Adjacent Boundary Detection and Segmentation of LiDAR 3D Point Clouds

In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate clusters in contact with adjacent clusters, so a new approach is required to solve the nonlinear shape, irregular density, and touching problems of adjacent clusters that are common in complex spatial data clustering, as well as to improve robustness against various types of noise in spatial clusters. Accordingly, we proposed an efficient graph-based spatial clustering technique that employs Delaunay triangulation and the mechanism of DBSCAN (density-based spatial clustering of applications with noise). In the performance evaluation using simulated synthetic data as well as real 3D point clouds, the proposed method maintained better clustering and separability of neighboring clusters compared to other clustering techniques, and is expected to be of practical use in the field of spatial data mining.

Microgeographic Heterogeneity of Border Malaria During Elimination Phase, Yunnan Province, China, 2011-2013

Malaria was concentrated in a few townships along the China–Myanmar border.

To identify township-level high-risk foci of malaria transmission in Yunnan Province, China, along the international border, we retrospectively reviewed data collected in hospitals and clinics of 58 townships in 4 counties during 2011–2013. We analyzed spatiotemporal distribution, especially hot spots of confirmed malaria, using geographic information systems and Getis-Ord G_i*(d) cluster analysis. Malaria incidence, transmission seasonality, and Plasmodium vivax:P. falciparum ratio remained almost unchanged from 2011 to 2013, but heterogeneity in distribution increased. The number of townships with confirmed malaria decreased significantly during the 3 years; incidence became increasingly concentrated within a few townships. High-/low-incidence clusters of P. falciparum shifted in location and size every year, whereas the locations of high-incidence P. vivax townships remained unchanged. All high-incidence clusters were located along the China–Myanmar border. Because of increasing heterogeneity in malaria distribution, microgeographic analysis of malaria transmission hot spots provided useful information for designing targeted malaria intervention during the elimination phase.

Spatiotemporal Heterogeneity in the Distribution of Chikungunya and Zika Virus Case Incidences during their 2014 to 2016 Epidemics in Barranquilla, Colombia

Chikungunya virus (CHIKV) and Zika virus (ZIKV) have recently emerged as globally important infections. This study aimed to explore the spatiotemporal heterogeneity in the occurrence of CHIKV and ZIKV outbreaks throughout the major international seaport city of Barranquilla, Colombia in 2014 and 2016 and the potential for clustering. Incidence data were fitted using multiple Bayesian Poisson models based on multiple explanatory variables as potential risk factors identified from other studies and options for random effects. A best fit model was used to analyse their case incidence risks and identify any risk factors during their epidemics. Neighbourhoods in the northern region were hotspots for both CHIKV and ZIKV outbreaks. Additional hotspots occurred in the southwestern and some eastern/southeastern areas during their outbreaks containing part of, or immediately adjacent to, the major circular city road with its import/export cargo warehouses and harbour area. Multivariate conditional autoregressive models strongly identified higher socioeconomic strata and living in a neighbourhood near a major road as risk factors for ZIKV case incidences. These findings will help to appropriately focus vector control efforts but also challenge the belief that these infections are driven by social vulnerability and merit further study both in Barranquilla and throughout the world's tropical and subtropical regions.

An examination of spatial concentrations of sex exchange and sex exchange norms among drug users in Baltimore, Maryland

Baltimore, Maryland consistently ranks highest nationally in rates of sexually transmitted diseases and HIV infection. Prior studies have identified geographic areas where STI and HIV infection in the city is most prevalent. It is well established that sex exchange behavior is associated with HIV and STIs, yet it is not well understood how sex exchangers are spatially distributed within the high-risk areas. We sought to examine the spatial distribution of individuals who report sex exchange compared to those who do not exchange. Additionally we examined the spatial context of perceived norms about sex exchange. Data for the study came from a baseline sample of predominately injection drug users (n=842). Of these, 21% reported sex exchange in the prior 90 days. All valid baseline residential addresses of participants living within Baltimore city boundaries were geocoded. The Multi-Distance Spatial Cluster Analysis (Ripley's K-function) was used to separately calculate the K-functions for the addresses of participants reporting sex exchange or non-sex exchange, relative to the recruited population. Evidence of spatial clustering of sex exchangers was observed and norms aligned with these clusters. Of particular interest was the high density of sex exchangers in one specific housing complex of East Baltimore, which happens to be the oldest in Baltimore. These findings can inform targeted efforts for screening and testing for HIV and STIs and placement of both individual and structural level interventions that focus on increasing access to risk reduction materials and changing norms about risk behaviors.

Assessing the geographic coverage and spatial clustering of illicit drug users recruited through respondent-driven sampling in New York City

We assess the geographic coverage and spatial clustering of drug users recruited through respondent-driven sampling (RDS) and discuss the potential for biased RDS prevalence estimates. Illicit drug users aged 18-40 were recruited through RDS (N = 401) and targeted street outreach (TSO) (N = 210) in New York City. Using the Google Maps API™, we calculated travel distances and times using public transportation between each participant's recruitment location and the study office and between RDS recruiter-recruit pairs. We used K function analysis to evaluate and compare spatial clustering of (1) RDS vs. TSO respondents and (2) RDS seeds vs. RDS peer recruits. All participant recruitment locations clustered around the study office; however, RDS participants were significantly more likely to be recruited within walking distance of the study office than TSO participants. The TSO sample was also less spatially clustered than the RDS sample, which likely reflects (1) the van's ability to increase the sample's geographic heterogeneity and (2) that more TSO than RDS participants were enrolled on the van. Among RDS participants, individuals recruited spatially proximal peers, geographic coverage did not increase as recruitment waves progressed, and peer recruits were not less spatially clustered than seeds. Using a mobile van to recruit participants had a greater impact on the geographic coverage and spatial dependence of the TSO than the RDS sample. Future studies should consider and evaluate the impact of the recruitment approach on the geographic/spatial representativeness of the sample and how spatial biases, including the preferential recruitment of proximal peers, could impact the precision and accuracy of estimates.