Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies

Law, Dionne C. Gesink; Serre, Marc L.; Christakos, George; Leone, Peter A.; Miller, William C.

doi:10.1136/sti.2003.006700

Cited by 88 publications

(67 citation statements)

References 16 publications

(4 reference statements)

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“…22 These findings similarly compare to other reports indicating the dynamic interplay of individual and community-level predictive risk factors. 37,38 Thus, our study demonstrates socioeconomic and cultural characteristics of particular communities may also be important associative risk factors. Future structural interventions such as poverty alleviation, educational attainment, and employment opportunities may ameliorate the conditions within the cluster that foster greater HIV transmission.…”

Section: Discussionmentioning

confidence: 62%

“…Such behavioral phenomena have also been observed in a spatial study of sexually transmitted infections and HIV in Wake County, North Carolina. 38 Alternatively, most of the women in this study likely acquired HIV via heterosexual contact. 5 In these cases, there is a lesser probability of local partner selection given more diffuse residential patterns.…”

Section: Discussionmentioning

confidence: 92%

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Spatial Clustering of HIV Prevalence in Atlanta, Georgia and Population Characteristics Associated with Case Concentrations

et al. 2011

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We assessed prevalent HIV cases in Atlanta to examine case distribution trends and population characteristics at the census tract level that may be associated with clustering effects. We calculated cluster characteristics (area and internal HIV prevalence) via Kuldorff's spatial scan method. Subsequent logistic regression analyses were performed to analyze sociodemographics associated with inclusion in a cluster. Organizations offering voluntary HIV testing and counseling services were identified and we assessed average travel time to access these services. One large cluster centralized in downtown Atlanta was identified that contains 60% of prevalent HIV cases. The prevalence rate within the cluster was 1.34% compared to 0.32% outside the cluster. Clustered tracts were associated with higher levels of poverty (OR=1.19), lower density of multi-racial residents (OR=1.85), injection drug use (OR=1.99), men having sex with men (OR=3.01), and men having sex with men and IV drug use (OR=1.6). Forty-two percent (N=11) of identified HIV service providers in Atlanta are located in the cluster with an average travel time of 13 minutes via car to access these services (SD=9.24). The HIV epidemic in Atlanta is concentrated in one large cluster characterized by poverty, men who have sex with men (MSM), and IV drug usage. Prevention efforts targeted to the population living in this area as well as efforts to address the specific needs of these populations may be most beneficial in curtailing the epidemic within the identified cluster.

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Section: Discussionmentioning

confidence: 62%

Section: Discussionmentioning

confidence: 92%

Spatial Clustering of HIV Prevalence in Atlanta, Georgia and Population Characteristics Associated with Case Concentrations

et al. 2011

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“…Kriging is a spatial interpolation method widely used by geologists and natural scientists, though relatively infrequently used in epidemiology. [22][23][24] Kriging utilizes a weighted linear combination of the available data to obtain an exact best linear unbiased predictor. 4,7 In model 2, predictions for unsampled locations were obtained by adding together predicted values from ordinary least squares regression and predicted values obtained from ordinary kriging of the regression residuals, illustrated in simple form as: NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript j is the ordinary least squares residual predicted at un-sampled location j; n is the number of sampled (nearby) residuals ( i ) that participate in the estimation; and w i are their weights with the constraint that all weights sum to 1 (so as to be unbiased and minimize the mean square prediction error).…”

Section: Model 2 Spatial Covariance-fitted With Residual Krigingmentioning

confidence: 99%

Filling the Gaps: Spatial Interpolation of Residential Survey Data in the Estimation of Neighborhood Characteristics

Auchincloss¹,

Roux²,

Brown³

et al. 2007

Epidemiology

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The measurement of area-level attributes remains a major challenge in studies of neighborhood health effects. Even when neighborhood survey data are collected, they necessarily have incomplete spatial coverage. We investigated whether interpolation of neighborhood survey data was aided by information on spatial dependencies and supplementary data. Neighborhood "availability of healthy foods" was measured in a population-based survey of 5186 persons in Baltimore, New York, and Forsyth County (North Carolina). The following supplementary data were compiled from Census 2000 and InfoUSA, Inc.: distance to supermarkets, density of supermarkets and fruit and vegetable stores, housing density, distance to a high-income area, and percent of households that do not own a vehicle. We compared 4 interpolation models (ordinary least squares, residual kriging, spatial error regression, and thin-plate splines) using error statistics and Pearson correlation coefficients (r) from repeated replications of cross-validations. There was positive spatial autocorrelation in neighborhood availability of healthy foods (by site, Moran coefficient range = 0.10-0.28; all P < 0.0001). Prediction performances were generally similar for the evaluated models (r ≈ 0.35 for Baltimore and Forsyth; r ≈ 0.54 for New York). Supplementary data accounted for much of the spatial autocorrelation and, thus, spatial modeling was only advantageous when spatial correlation was at least moderate. A variety of interpolation techniques will likely need to be utilized in order to increase the data available for examining health effects of residential environments. The most appropriate method will vary depending on the construct of interest, availability of relevant supplementary data, and types of observed spatial patterns.Growing evidence supports the concept that residential neighborhoods play a role in determining individual behaviors that are linked to health outcomes. [1][2][3] However, there is a scarcity of relevant data for characterizing neighborhoods that continues to challenge researchers. One option for obtaining information on neighborhoods is to conduct surveys of residents who can provide information about their residential environment. However, survey data necessarily have incomplete spatial coverage. Routinely collected data such as census AHA designed the study, conducted all analyses, and drafted the manuscript. ADR provided survey data used in this study, assisted with planning analyses, and critically edited the manuscript. DGB assisted in designing the study, interpreted findings, and critically reviewed the manuscript. TER assisted with formulating statistical analyses and critically reviewed the manuscript. CAE critically reviewed the manuscript. Geologists and other natural scientists have used geostatistical methods to interpolate when there is incomplete spatial sampling 4-7 and have used supplementary data at colocated sites to try to improve predictions at the unsampled locations. 8,9 While spatial modeling in itself may pre...

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“…Syphilis also has implications for other STDs such as human immunodeficiency virus (HIV), which in turn can facilitate reinfection with syphilis (Liu, 2000;Chen and Zhang, 2001). One study found that STDs such as syphilis showed spatial variability (Law et al, 2004). This study was initiated to discover the geographical variation of syphilis in Jiangsu province, seeking clusters and hotspots.…”

Section: Introductionmentioning

confidence: 99%

Geographical information systems-based spatial analysis and implications for syphilis interventions in Jiangsu province, People’s Republic of China

et al. 2012

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Abstract. Spatial distribution rules and risk factors for syphilis were studied in Jiangsu province, People's Republic of China during 2005 and 2009. Trend surface analysis, spatial autocorrelation analysis and spatio-temporal clustering were applied with the incidence rates of the various counties in the province to determine spatial distribution rules and risk factors. Syphilis was found to be most severe in the southern region of the province where many counties could be shown to be hotspots with positive autocorrelation. Clusters were detected in the south-western region of Jiangsu with the county-level city of Yixing as the centre. Temperature, distance from railways and highways, and the normalised difference vegetation index were determined as supporting variables with regard to the transmission of the disease by both univariate and multivariate spatial correlation analyses. Interventions, including health education and awareness campaigns, should be strengthened throughout the province targeting the south-western areas, especially the clusters and hotspots detected in order to improve the situation.

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Spatial analysis and mapping of sexually transmitted diseases to optimise intervention and prevention strategies

Cited by 88 publications

References 16 publications

Spatial Clustering of HIV Prevalence in Atlanta, Georgia and Population Characteristics Associated with Case Concentrations

Spatial Clustering of HIV Prevalence in Atlanta, Georgia and Population Characteristics Associated with Case Concentrations

Filling the Gaps: Spatial Interpolation of Residential Survey Data in the Estimation of Neighborhood Characteristics

Geographical information systems-based spatial analysis and implications for syphilis interventions in Jiangsu province, People’s Republic of China

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