International Journal of Health Geographics - Latest Articles

Evaluation of the performance of tests for spatial randomness on prostate cancer data

Virginia Hinrichsen — 2009-07-03T00:00:00Z

Background: Spatial global clustering tests can be used to evaluate the geographical distribution of health outcomes. The power of several of these tests has been evaluated and compared using simulated data, but their performance using real unadjusted data and data adjusted for individual- and area-level covariates has not been reported previously.We evaluated data on prostate cancer histologic tumor grade and stage of disease at diagnosis for incident cases of prostate cancer reported to the Maryland Cancer Registry during 1992-1997. We analyzed unadjusted data as well as expected counts from models that were adjusted for individual-level covariates (race, age and year of diagnosis) and area-level covariates (census block group median household income and a county-level socioeconomic index). We chose 3 spatial clustering tests that are commonly used to evaluate the geographic distribution of disease: Cuzick-Edwards' k-NN (k-Nearest Neighbors) test, Moran's I and Tango's MEET (Maximized Excess Events Test). Results: For both grade and stage at diagnosis, we found that Cuzick-Edwards' k-NN and Moran's I were very sensitive to the percent of population parameter selected. For stage at diagnosis, all three tests showed that the models with individual- and area-level adjustments reduced clustering the most, but did not reduce it entirely. Conclusions: Based on this specific example, results suggest that these tests provide useful tools for evaluating spatial clustering of disease characteristics, both before and after consideration of covariates.

Geographical clustering of lung cancer in the province of Lecce, Italy: 1992-2001

Massimo Bilancia — 2009-07-01T00:00:00Z

Background: The triennial mortality rates for lung cancer in the two decades 1981-2001 in the province of Lecce, Italy, are significantly higher than those for the entire region of Apulia (to which the Province of Lecce belongs) and the national reference rates. Moreover, analyzing the rates in the three-year periods 1993-95, 1996-98 and 1999-01, there is a dramatic increase in mortality for both males and females, which still remains essentially unexplained: to understand the extent of this phenomenon, it is worth noting that the standardized mortality rate for males in 1999-01 is equal to 13.92 per 10000 person-years, compared to a value of 6.96 for Italy in the 2000-2002 period.These data have generated a considerable concern in the press and public opinion, which with little scientific reasoning have sometimes identified suspected culprits of the risk excess (for example, the emission caused by a number of large industrial sites located in the provinces of Brindisi and Taranto, bordering the Province of Lecce). The objective of this paper is to study on a scientifically sound basis the spatial distribution of risk for lung cancer mortality in the province of Lecce. Our goal is to demonstrate that most of the previous explanations are not supported by data: to this end, we will follow a hybrid approach that combines both frequentist and Bayesian disease mapping methods. Furthermore, we define a new sequential algorithm based on a modified version of the Besag-York-Mollie (BYM) model, suitably modified to detect geographical clusters of disease. Results: Standardized mortality ratios (SMRs) for lung cancer in the province of Lecce: For males, the relative risk (measured by means of SMRs, i.e. the ratios between observed and expected cases in each area under internal standardization) was judged to be significantly greater than 1 in many municipal areas, the significance being evaluated under the null hypothesis of neutral risk on the ground of area-specific p-values (denoted by rho_i); in addition, it was seen that high risk areas were not randomly distributed within the province, but showed a sharp clustering. The most perceptible cluster involved a collection of municipalities around the Maglie area (Istat code: 75039), while the association among the municipalities of Otranto, Poggiardo and Santa Cesarea Terme (Istat codes: 75057, 75061, 75072) was more ambiguous. For females, it was noteworthy the significant risk excess in the city of Lecce (Istat code: 75035), where an SMR of 1.83 and rho_i<0.01 have been registered. BYM model for the province of Lecce: For males, Bayes estimates of relative risks varied around an overall mean of 1.04 with standard deviation of 0.1, with a minimum of 0.77 and a maximum of 1.25. The posterior relative risks for females, although smoothed, showed more variation than for males, ranging form 0.74 to 1.65, around a mean of 0.90 with standard deviation 0.12. For males, 95% posterior credible intervals of relative risks included unity in every area, whereas significantly elevated risk of mortality was confirmed in the Lecce area for females (95% posterior CI: 1.33 - 2.00). BYM model for the whole Apulia: For males, internally standardized maps showed several high risk areas bordering the province of Lecce, belonging to the province of Brindisi, and the presence of a large high risk region, including the southern part of the province of Brindisi and the eastern and southern part of the Salento peninsula, in which an increasing trend in the north-south direction was found.Ecological correlation study with deprivation (Cadum Index): For males, posterior mean of the ecological regression coefficient beta resulted to be 0.04 with 95% posterior credible interval equal to (-0.01, 0.08); similarly, beta was estimated as equal to -0.03 for females (95% posterior credible interval: -0.16, 0.10). Moreover, there was some indication of nonlinearly increasing relative risk with increasing deprivation for higher deprivation levels. For females, it was difficult to postulate the existence of any association between risk and deprivation.Cluster detection: cluster detection based on a modified BYM model identified two large unexplained increased risk clusters in the central-eastern and southern part of the peninsula. Other secondary clusters, which raise several complex interpretation issues, are present. Conclusions: Our results reduce the alleged role of the industrial facilities located around the province of Taranto: in particular, air pollution produced around the city of Taranto (which lies to the west of the province of Lecce) has been often identified as the main culprit of the mortality excess, a conclusion that was further supported by a recent study on the direction of prevailing winds on Salento. This hypothesis is contradicted by the finding that those municipalities that directly border on the province of Taranto (belonging to the so-called "Jonico-Salentina" band) are those that present low mortality rates (at least for males). In the same way, the responsibilities of energy production plants located in the province of Brindisi (Brindisi province lies to the north) appear to be of little relevance. For females, given the situation observed in the city of Lecce, and given the substantial increase in mortality observed in younger age classes, further investigation is required into the role played by changes in lifestyle, including greater net propensity to smoke that women have shown since the 80s onwards (a phenomenon which could be amplified in a city traditionally cultured and modern as Lecce, as the tobacco habit is a largely cultural phenomenon). For males, the presence of high levels of deprivation throughout the eastern and southern Salento is likely to play an important role: those with lower socio-economic status smoke more, and gender differences may be explained on the basis of the fact that in less developed areas women have less habit to tobacco smoking and alcohol drinking (and other harmful lifestyles), which are seen as purely masculine behaviour: research into the role of material deprivation and individual lifestyle differences between genders should be further developed.

Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya.

Mark DeVisser — 2009-06-29T00:00:00Z

Background: Tsetse flies are the primary vector for African trypanosomiasis, a disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse flies were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease were hampered by a lack of information and costs associated with the identification of infested areas. Given changing spatial and demographic factors, a model that can predict suitable tsetse fly habitat based on land cover and climate change is critical to efforts aimed at controlling the disease. In this paper we present a generalizable method, using a modified Mapcurves goodness of fit test, to evaluate the existing publicly available land cover products to determine which products perform the best at identifying suitable tsetse fly land cover. Results: For single date applications, Africover was determined to be the best land use land cover (LULC) product for tsetse modeling. However, for changing habitats, whether climatically or anthropogenically forced, the IGBP DISCover and MODIS type 1 products where determined to be most practical. Conclusion: The method can be used to differentiate between various LULC products and be applied to any such research when there is a known relationship between a species and land cover.

Spatial analysis of plague in California: Niche modeling predictions of the current distribution and potential response to climate change

Ashley Holt — 2009-06-28T00:00:00Z

Background: Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance. Results: Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras. Conclusions: Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions.

Estimation of undernutrition and mean calorie intake in Africa: methodology, findings and implications

Cornelia van Wesenbeeck — 2009-06-27T00:00:00Z

Background: As poverty and hunger are basic yardsticks of underdevelopment and destitution, the need for reliable statistics in this domain is self-evident. While the measurement of poverty through surveys is relatively well documented in the literature, for hunger, information is much scarcer, particularly for adults, and very different methodologies are applied for children and adults. Our paper seeks to improve on this practice in two ways. One is that we estimate the prevalence of undernutrition in sub-Saharan Africa (SSA) for both children and adults based on anthropometric data available at province or district level, and secondly, we estimate the mean calorie intake and implied calorie gap for SSA, also using anthropometric data on the same geographical aggregation level. Results: Our main results are, first, that we find a much lower prevalence of hunger than presented in the Millennium Development reports (17.3% against 27.8% for the continent as a whole). Secondly, we find that there is much less spread in mean calorie intake across the continent than reported by the Food and Agricultural Organization (FAO) in the State of Food and Agriculture, 2007, the only estimate that covers the whole of Africa. While FAO estimates for calorie availability vary from a low of 1760 Kcal/capita/day for Central Africa to a high of 2825 Kcal/capita/day for Southern Africa, our estimates lay in a range of 2245 Kcal/capita/day (Eastern Africa) to 2618 Kcal/capita/day for Southern Africa. Thirdly, we validate the main data sources used (the Demographic and Health Surveys) by comparing them over time and with other available data sources for various countries. Conclusions: We conclude that the picture of Africa that emerges from anthropometric data is much less negative than that usually presented. Especially for Eastern and Central Africa, the nutritional status is less critical than commonly assumed and also mean calorie intake is higher, which implies that agricultural production and hence income must also have been growing at a pace at least high enough to keep up with population growth. In terms of methodology, our estimates form a base line for 2005 for the whole continent that can be easily updated with far less information for individual countries, as we show in an example for Ethiopia.

Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand

Nakarin Chaikaew — 2009-06-24T00:00:00Z

Background: Diarrhea is a major public health problem in Thailand. The Ministry of Public Health, Thailand, has been trying to monitor and control this disease for many years. The methodology and the results from this study could be useful for public health officers to develop a system to monitor and prevent diarrhea outbreaks. Methods: The objective of this study was to analyse the epidemic outbreak patterns of diarrhea in Chiang Mai province, Northern Thailand, in terms of their geographical distributions and hotspot identification. The data of patients with diarrhea at village level and the 2001-2006 population censuses were collected to achieve the objective. Spatial analysis, using geographic information systems (GIS) and other methods, was used to uncover the hidden phenomena from the data. In the data analysis section, spatial statistics such as quadrant analysis (QA), nearest neighbour analysis (NNA), and spatial autocorrelation analysis (SAA), were used to identify the spatial patterns of diarrhea in Chiang Mai province. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect diarrhea hotspots using data at village level. Results: The hotspot maps produced by the LISA and KD techniques showed spatial trend patterns of diarrhea diffusion. Villages in the middle and northern regions revealed higher incidences. Also, the spatial patterns of diarrhea during the years 2001 and 2006 were found to represent spatially clustered patterns, both at global and local scales. Conclusion: Spatial analysis methods in GIS revealed the spatial patterns and hotspots of diarrhea in Chiang Mai province from the year 2001 to 2006. To implement specific and geographically appropriate public health risk-reduction programs, the use of such spatial analysis tools may become an integral component in the epidemiologic description, analysis, and risk assessment of diarrhea.

Geostatistical evaluation of integrated marsh management impact on mosquito vectors using before-after-control-impact (BACI) design

Ilia Rochlin — 2009-06-23T00:00:00Z

Background: In many parts of the world, salt marshes play a key ecological role as the interface between the marine and the terrestrial environments. Salt marshes are also exceedingly important for public health as larval habitat for mosquitoes that are vectors of disease and significant biting pests. Although grid ditching and pesticides have been effective in salt marsh mosquito control, marsh degradation and other environmental considerations compel a different approach. Targeted habitat modification and biological control methods known as Open Marsh Water Management (OMWM) had been proposed as a viable alternative to marsh-wide physical alterations and chemical control. However, traditional larval sampling techniques may not adequately assess the impacts of marsh management on mosquito larvae. To assess the effectiveness of integrated OMWM and marsh restoration techniques for mosquito control, we analyzed the results of a 5-year OMWM/marsh restoration project to determine changes in mosquito larval production using GIS and geostatistical methods. Methods: The following parameters were evaluated using "Before-After-Control-Impact" (BACI) design: frequency and geographic extent of larval production, intensity of larval production, changes in larval habitat, and number of larvicide applications. The analyses were performed using Moran's I, Getis-Ord, and Spatial Scan statistics on aggregated before and after data as well as data collected over time. This allowed comparison of control and treatment areas to identify changes attributable to the OMWM/marsh restoration modifications. Results: The frequency of finding mosquito larvae in the treatment areas was reduced by 70% resulting in a loss of spatial larval clusters compared to those found in the control areas. This effect was observed directly following OMWM treatment and remained significant throughout the study period. The greatly reduced frequency of finding larvae in the treatment areas led to a significant decrease (~44%) in the number of times when the larviciding threshold was reached. This reduction, in turn, resulted in a significant decrease (~74%) in the number of larvicide applications in the treatment areas post-project. The remaining larval habitat in the treatment areas had a different geographic distribution and was largely confined to the restored marsh surface (i.e. filled-in mosquito ditches); however only ~21% of the restored marsh surface supported mosquito production. Conclusion: The geostatistical analysis showed that OMWM demonstrated considerable potential for effective mosquito control and compatibility with other natural resource management goals such as restoration, wildlife habitat enhancement, and invasive species abatement. GPS and GIS tools are invaluable for large scale project design, data collection, and data analysis, with geostatistical methods serving as an alternative or a supplement to the conventional inference statistics in evaluating the project outcome.

The complexities of measuring access to parks and physical activity sites in New York City: a quantitative and qualitative approach

Andrew Maroko — 2009-06-22T00:00:00Z

Background: Proximity to parks and physical activity sites has been linked to an increase in active behaviors, and positive impacts on health outcomes such as lower rates of cardiovascular disease, diabetes, and obesity. Since populations with a low socio-economic status as well as racial and ethnic minorities tend to experience worse health outcomes in the U.S., access to parks and physical activity sites may be an environmental justice issue. Geographic Information Systems were used to conduct quantitative and qualitative analyses of park accessibility in New York City, which included kernel density estimation, ordinary least squares (global) regression, geographically weighted (local) regression, and longitudinal case studies, consisting of field work and archival research. Accessibility was measured by both density of park acreage and density of physical activity sites. Independent variables included percent non-Hispanic black, percent Hispanic, percent below poverty, percent of adults without high school diploma, percent with limited English-speaking ability, and population density. Results: The ordinary least squares linear regression found weak relationships in both the park acreage density and the physical activity site density models (Ra2 = .11 and .23, respectively; AIC = 7162 and 3529, respectively). Geographically weighted regression, however, suggested spatial non-stationarity in both models, indicating disparities in accessibility that vary over space with respect to magnitude and directionality of the relationships (AIC = 2014 and -1241, respectively). The qualitative analysis supported the findings of the local regression, confirming that although there is a geographically inequitable distribution of park space and physical activity sites, it is not globally predicted by race, ethnicity, or socio-economic status. Conclusions: The combination of quantitative and qualitative analyses demonstrated the complexity of the issues around racial and ethnic disparities in park access. They revealed trends that may not have been otherwise detectable, such as the spatially inconsistent relationship between physical activity site density and socio-demographics. In order to establish a more stable global model, a number of additional factors, variables, and methods might be used to quantify park accessibility, such as network analysis of proximity, perception of accessibility and usability, and additional park quality characteristics. Accurate measurement of park accessibility can therefore be important in showing the links between opportunities for active behavior and beneficial health outcomes.

A spatial evaluation of socio demographics surrounding National Priorities List sites in Florida using a distance-based approach

Greg Kearney — 2009-06-17T00:00:00Z

Background: Over the last two decades, various spatial techniques have been demonstrated using geographical information systems (GIS) to adequately estimate and characterize inequities of minority populations living near environmentally hazardous facilities. However, these methods have produced mixed results. In this study, we use recently developed variations of the "distance based" approach to spatially evaluate and compare demographic and socioeconomic disparities surrounding the worst hazardous waste sites in Florida. Methods: We used data from the 2000 US Census Bureau and the Florida Department of Environmental Protection to identify selected socio and economic variables within one (1) mile of 71 National Priorities List (NPL) or Superfund sites in Florida. ArcMap (ESRI, v. 9.2) was used to map the centroid locations of each of the NPL sites as well as identify and estimate the number of host and non-host tracts. The unit of analysis in this study was at the census tract level. Logistic regression (SAS v9.1.3) was used to determine if race/ethnicity and socioeconomic indicators are significant predictors of the location of NPL sites. Results: There were significant differences in race/ethnicity composition and socio-economic factors between NPL host census tracts and non-host census tracts in Florida. The percentages of Blacks (OR=5.7, p<0.001), the percentage of Hispanic/Latino (OR=5.84, p<0.001), and percent employed in blue collar occupations (OR=2.7, p<0.01) were significant predictors of location of NPL facilities. Conclusions: The recently developed distance-based method supports previous studies and suggests that race and ethnicity play substantial roles in where hazardous facilities are located in Florida. Recommendations include using distance-based methods to evaluate socio and demographic characteristics surrounding other less known environmental hazardous facilities, such as landfills, or Toxic Release Inventory (TRI) sites.

The 2005 census and mapping of slums in Bangladesh: design, select results and application

Gustavo Angeles — 2009-06-08T00:00:00Z

Background: The concentration of poverty and adverse environmental circumstances within slums, particularly those in the cities of developing countries, are an increasingly important concern for both public health policy initiatives and related programs in other sectors. However, there is a dearth of information on the population-level implications of slum life for human health. This manuscript describes the 2005 Census and Mapping of Slums (CMS), which used geographic information systems (GIS) tools and digital satellite imagery combined with more traditional fieldwork methodologies, to obtain detailed, up-to-date and new information about slum life in all slums of six major cities in Bangladesh (including Dhaka). Results: The CMS found that Bangladeshi slums are very diverse: there are wide intra- and inter-city variations in population size, density, the percent of urban populations living in slums, and sanitation conditions. Findings also show that common beliefs about slums may be outdated; of note, tenure insecurity was found to be an issue in only a small minority of slums. Conclusion: The methodology used in the 2005 Bangladesh CMS provides a useful approach to mapping slums that could be applied to urban areas in other low income societies. This methodology may become an increasingly important analytic tool to inform policy, as cities in developing countries are forecasted to continue increasing their share of total global population in the coming years, with slum populations more than doubling in size during the same period.