Cancer Map Patterns

Kulldorff, Martin; Song, Changhong; Gregorio, David I.; Samociuk, Holly; DeChello, Laurie M

doi:10.1016/j.amepre.2005.09.009

Cited by 76 publications

(25 citation statements)

References 33 publications

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“…This methodology has become the most widely used test for clustering detection in epidemiology [10-12], and its efficiency and accuracy has been well documented [13,14]. A spatial scan statistics is a cluster detection test able to find the location of areas with higher or lower numbers of cases (for instance HIV infections) than expected under spatial randomness, and then evaluate their statistical significance by gradually scanning a circular window that spans the study region.…”

Section: Methodsmentioning

confidence: 99%

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa

Cuadros

Awad

Abu‐Raddad

2013

International Journal of Health Geographics

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BackgroundThe geographical structure of an epidemic is ultimately a consequence of the drivers of the epidemic and the population susceptible to the infection. The ‘know your epidemic’ concept recognizes this geographical feature as a key element for identifying populations at higher risk of HIV infection where prevention interventions should be targeted. In an effort to clarify specific drivers of HIV transmission and identify priority populations for HIV prevention interventions, we conducted a comprehensive mapping of the spatial distribution of HIV infection across sub-Saharan Africa (SSA).MethodsThe main source of data for our study was the Demographic and Health Survey conducted in 20 countries from SSA. We identified and compared spatial clusters with high and low numbers of HIV infections in each country using Kulldorff spatial scan test. The test locates areas with higher and lower numbers of HIV infections than expected under spatial randomness. For each identified cluster, a likelihood ratio test was computed. A P-value was determined through Monte Carlo simulations to evaluate the statistical significance of each cluster.ResultsOur results suggest stark geographic variations in HIV transmission patterns within and across countries of SSA. About 14% of the population in SSA is located in areas of intense HIV epidemics. Meanwhile, another 16% of the population is located in areas of low HIV prevalence, where some behavioral or biological protective factors appear to have slowed HIV transmission.ConclusionsOur study provides direct evidence for strong geographic clustering of HIV infection across SSA. This striking pattern of heterogeneity at the micro-geographical scale might reflect the fact that most HIV epidemics in the general population in SSA are not far from their epidemic threshold. Our findings identify priority geographic areas for HIV programming, and support the need for spatially targeted interventions in order to maximize the impact on the epidemic in SSA.

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Section: Methodsmentioning

confidence: 99%

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa

Cuadros

Awad

Abu‐Raddad

2013

International Journal of Health Geographics

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“…The geographical data obtained from GIS/GPS techniques were used to determine the potential areas with an excess of HIV infection by using the Spatial Scan Statistics (SaTScan) programme developed by Kulldorf [15]. This has become the most widely used test for clustering in recent years, both because of its efficacy in detecting single "hotspots", as well as availability of the free software package [16] for implementing the test.…”

Section: Methodsmentioning

confidence: 99%

Targeting the hotspots: investigating spatial and demographic variations in HIV infection in small communities in South Africa

Wand

Ramjee

2010

Journal of the International AIDS Society

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BackgroundIn South Africa, the severity of the HIV/AIDS epidemic varies according to geographical location; hence, localized monitoring of the epidemic would enable more effective prevention strategies. Our objectives were to assess the core areas of HIV infection in KwaZulu-Natal, South Africa, using epidemiological data among sexually active women from localized communities.MethodsA total of 5753 women from urban, peri-rural and rural communities in KwaZulu-Natal were screened from 2002 to 2005. Each participant was geocoded using a global information system, based on residence at time of screening. The Spatial Scan Statistics programme was used to identify areas with disproportionate excesses in HIV prevalence and incidence.ResultsThis study identified three hotspots with excessively high HIV prevalence rates of 56%, 51% and 39%. A total of 458 sexually active women (19% of all cases) were included in these hotspots, and had been exclusively recruited by the Botha's Hill (west of Durban) and Umkomaas (south of Durban) clinic sites. Most of these women were Christian and Zulu-speaking. They were also less likely to be married than women outside these areas (12% vs. 16%, p = 0.001) and more likely to have sex more than three times a week (27% vs. 20%, p < 0.001) and to have had more than three sexual partners (55% vs. 45%, p < 0.001). Diagnosis of genital herpes simplex virus type 2 was also more common in the hotspots. This study also identified areas of high HIV incidence, which were broadly consistent with those with high prevalence rates.ConclusionsGeographic excesses of HIV infections at rates among the highest in the world were detected in certain rural communities of Durban, South Africa. The results reinforce the inference that risk of HIV infection is associated with definable geographical areas. Localized monitoring of the epidemic is therefore essential for more effective prevention strategies - and particularly urgent in a region such as KwaZulu-Natal, where the epidemic is particularly rampant.

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“…12,28-32 What is needed is geostatistical software that can handle both individual-level and aggregated data to control adequately for behavioral and environmental factors associated with health outcomes at the various levels.…”

mentioning

confidence: 99%

Racial and Geographic Disparities in Late-Stage Prostate Cancer Diagnosis in Florida

Xiao¹,

Tan²,

Goovaerts³

2011

Journal of Health Care for the Poor and Underserved

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Disparities in prostate cancer diagnosis among racial/ethnic groups and across Florida were mapped for the period 1996–2002 and their relationship with putative factors (individual, census tract and county level) was investigated using multilevel modeling and contingency analysis. More counties had higher rates of late-stage diagnosis for Black men than for White men and the location of these racial disparities changed with time. An important finding was the substantially larger correlation between county-level rates for Black and White men in 2002 relatively to 1996, which suggests a convergence in their spatial patterns. Major significant factors for late-stage diagnosis included lack of insurance, low household income, smoking, not being married and presence of farm house. These findings should help the design of intervention programs to target counties with the greatest racial disparities in health outcomes. Additional analysis is needed to disentangle the observed racial/ethnic and geographic differences.

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Cancer Map Patterns

Abstract: When publishing cancer maps we recommend evaluating the spatial patterns observed using Tango's MEET, a global clustering test, and the spatial scan statistic, a cluster detection test.

Cited by 76 publications

References 33 publications

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa

Mapping HIV clustering: a strategy for identifying populations at high risk of HIV infection in sub-Saharan Africa

Targeting the hotspots: investigating spatial and demographic variations in HIV infection in small communities in South Africa

Racial and Geographic Disparities in Late-Stage Prostate Cancer Diagnosis in Florida

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