Ovarian Cancer Incidence in the U.S. and Toxic Emissions from Pulp and Paper Plants: A Geospatial Analysis.

Ovarian cancer is the fifth leading cause of female cancer mortality in the U.S. and accounts for five percent of all cancer deaths among women. No environmental risk factors for ovarian cancer have been confirmed. We previously reported that ovarian cancer incidence rates at the state level were significantly correlated with the extent of pulp and paper manufacturing. We evaluated that association using county-level data and advanced geospatial methods. Specifically, we investigated the relationship of spatial patterns of ovarian cancer incidence rates with toxic emissions from pulp and paper facilities using data from the Environmental Protection Agency's Toxic Release Inventory (TRI). Geospatial analysis identified clusters of counties with high ovarian cancer incidence rates in south-central Iowa, Wisconsin, New York, Pennsylvania, Alabama, and Georgia. A bivariate local indicator of spatial autocorrelation (LISA) analysis confirmed that counties with high ovarian cancer rates were associated with counties with large numbers of pulp and paper mills. Regression analysis of state level data indicated a positive correlation between ovarian cancer and water pollutant emissions. A similar relationship was identified from the analysis of county-level data. These data support a possible role of water-borne pollutants from pulp and paper mills in the etiology of ovarian cancer.

Families with newborns: Using a cartographic model to identify those who are at risk for fires.

We assessed whether a home fire safety intervention targeting families with newborn children in Jefferson County, Kentucky, reached those at severe risk using a cartographic model. Demographic and economic factors of 61 families were compared by census tract. Using geographic information systems (GIS), families were assigned a risk level (low, medium, high, or severe) based on the risk model. Families who participated differed from census tracts in that of being minority race (p=0.01). The median risk category of the families was medium risk. Sixty-five tracts were identified as high or severe risk and in need of future intervention. The model yielded a way to prioritize at-risk families. GIS is a useful tool for examining whether prevention interventions reached those in the severe risk category.

Geographic modeling for children at risk for home fires and burns.

This study developed a predictive model for fires and burns among parents and children in Jefferson County, Kentucky. Eight risk factors for pediatric burns with census tract level data available were identified. Risk factors were synthesized to develop a cartographic model with risk levels low, medium, high, and severe. Validation was performed with fire dispatch data. At-risk areas were concentrated in the county's northwest. Risk was correlated with fire incidence rate (ρ=0.67, p<0.001). Significant risk factors were race (ß=0.54, p<0.001), education (ß=0.38, p<0.001), and year home built (ß=-0.17, p=0.005). Cartographic modeling is a underutilized tool to identify at-risk areas.

Theory-Based Cartographic Risk Model Development and Application for Home Fire Safety.

There is a gap in the use of predictive risk models to identify areas at risk for home fires and burn injury. The purpose of this study was to describe the creation, validation, and application of such a model using a sample from an intervention study with parents of newborns in Jefferson County, KY, as an example. Performed was a literature search to identify risk factors for home fires and burn injury in the target population. Obtained from the American Community Survey at the census tract level and synthesized to create a predictive cartographic risk model was risk factor data. Model validation was performed through correlation, regression, and Moran's I with fire incidence data from open records. Independent samples t-tests were used to examine the model in relation to geocoded participant addresses. Participant risk level for fire rate was determined and proximity to fire station service areas and hospitals. The model showed high and severe risk clustering in the northwest section of the county. Strongly correlated with fire rate was modeled risk; the best predictive model for fire risk contained home value (low), race (black), and non high school graduates. Applying the model to the intervention sample, the majority of participants were at lower risk and mostly within service areas closest to a fire department and hospital. Cartographic risk models were useful in identifying areas at risk and analyzing participant risk level. The methods outlined in this study are generalizable to other public health issues.

Geographic Mapping as a Tool for Identifying Communities at High Risk for Fires.

The purpose of this study was to evaluate whether the sample of older adults in a home fire safety (HFS) study captured participants living in the areas at highest risk for fire occurrence. The secondary aim was to identify high risk areas to focus future HFS interventions. Geographic information systems software was used to identify census tracts where study participants resided. Census data for these tracts were compared with participant data based on seven risk factors (ie, age greater than 65 years, nonwhite race, below high school education, low socioeconomic status, rented housing, year home built, home value) previously identified in a fire risk model. The distribution of participants and census tracts among risk categories determined how well higher risk census tracts were sampled. Of the 46 census tracts where the HFS intervention was implemented, 78% (n = 36) were identified as high or severe risk according to the fire risk model. Study participants' means for median annual family income (P < .0001) and median home value (P < .0001) were significantly lower than the census tract means (n = 46), indicating participants were at higher risk of fire occurrence. Of the 92 census tracts identified as high or severe risk in the entire county, the study intervention was implemented in 39% (n = 36), indicating 56 census tracts as potential areas for future HFS interventions. The Geographic information system-based fire risk model is an underutilized but important tool for practice that allows community agencies to develop, plan, and evaluate their outreach efforts and ensure the most effective use of scarce resources.

Development of a Fire Risk Model to Identify Areas of Increased Potential for Fire Occurrences.

The purposes of this study were to use geographic information systems to create a cartographic risk model predicting areas of increased potential for fire occurrences and to validate the model. Seven literature-identified risk factors associated with burn injury were older than 65 years, non-white race, below high school education, low socioeconomic status, rented housing, year home built, and home value. Geographic information system methods were used in risk factor model development. Model validation occurred using residential county fire dispatch data and statistical analysis. Areas of high and severe risk were primarily located in the northwestern and central county regions. A strong correlation (r = .66) was found between risk model scores and fire incidence rates. Significant differences in mean fire rates by risk category (F (187,3) = 87.58, P < .0001) were found, with the exception of the low and medium risk categories. Fire incidence rates among census tracts showed positive spatial autocorrelation (Moran's I = 0.542, P < .0001) producing a map showing a significant cluster of high fire incidence in the northwestern region. The risk model has potential to lead to more targeted and effective fire prevention education programs. Such models would allow fire departments to focus limited resources in areas of highest fire risk.

A GIS-based method for household recruitment in a prospective pesticide exposure study.

BACKGROUND: Recent advances in GIS technology and remote sensing have provided new opportunities to collect ecologic data on agricultural pesticide exposure. Many pesticide studies have used historical or records-based data on crops and their associated pesticide applications to estimate exposure by measuring residential proximity to agricultural fields. Very few of these studies collected environmental and biological samples from study participants. One of the reasons for this is the cost of identifying participants who reside near study fields and analyzing samples obtained from them. In this paper, we present a cost-effective, GIS-based method for crop field selection and household recruitment in a prospective pesticide exposure study in a remote location. For the most part, our multi-phased approach was carried out in a research facility, but involved two brief episodes of fieldwork for ground truthing purposes. This method was developed for a larger study designed to examine the validity of indirect pesticide exposure estimates by comparing measured exposures in household dust, water and urine with records-based estimates that use crop location, residential proximity and pesticide application data. The study focused on the pesticide atrazine, a broadleaf herbicide used in corn production and one of the most widely-used pesticides in the U.S. RESULTS: We successfully used a combination of remotely-sensed data, GIS-based methods and fieldwork to select study fields and recruit participants in Illinois, a state with high corn production and heavy atrazine use. Our several-step process consisted of the identification of potential study fields and residential areas using aerial photography; verification of crop patterns and land use via site visits; development of a GIS-based algorithm to define recruitment areas around crop fields; acquisition of geocoded household-level data within each recruitment area from a commercial vendor; and confirmation of final participant household locations via ground truthing. The use of these procedures resulted in a sufficient sample of participants from 14 recruitment areas in seven Illinois counties. CONCLUSION: One of the challenges in pesticide research is the identification and recruitment of study participants, which is time consuming and costly, especially when the study site is in a remote location. We have demonstrated how GIS-based processes can be used to recruit participants, increase efficiency and enhance accuracy. The method that we used ultimately made it possible to collect biological samples from a specific demographic group within strictly defined exposure areas, with little advance knowledge of the location or population.

The political ecology of lead poisoning in eastern North Carolina.

In the United States, childhood blood lead levels have dropped substantially since 1991, when the Centers for Disease Control and Prevention (CDC) implemented new screening guidelines. Many states, including North Carolina, have established successful screening and intervention programs. Still, pockets of higher lead poisoning rates continue to be a problem in some geographic areas. One of these areas consists of several counties in eastern North Carolina. This cluster of higher rates cannot be explained by poverty and housing characteristics alone. Instead, the explanation requires an understanding of place that encompasses a range of historical, social, political, and economic processes. This paper utilizes a political ecology approach to provide a deeper understanding of how these processes can contribute to ill health.

UV, latitude, and spatial trends in prostate cancer mortality: all sunlight is not the same (United States).

OBJECTIVE: We showed previously that Caucasian mortality rates from prostate cancer for 1970-1979 are significantly inversely correlated with ultraviolet (UV) radiation. We now present the analysis of prostate cancer mortality data over a 45-year period (1950-1994) in order to examine the persistence of this pattern. Furthermore, because vitamin D synthesis does not occur during winter months at latitudes higher than 40 degrees N, we examined this relationship above and below 40 degrees N latitude. METHODS: We used trend surface and linear regression analyses to characterize the relationship between prostate cancer mortality and UV radiation for U.S. counties at northern and southern latitudes. RESULTS: For U.S. Caucasians, prostate cancer mortality rates at the county and SEA levels followed a significant north-south spatial trend that is the inverse of UV radiation. We found significant inverse correlations between UV radiation and prostate cancer mortality at all time points over this 45-year period. These correlations were significantly more pronounced at locations north of 40 degrees N latitude. CONCLUSIONS: Our analyses confirm and extend our findings that the geographic distribution of prostate cancer mortality is the inverse of that of UV radiation. This effect is strongest in counties north of 40 degrees N latitude, where vitamin D synthesis is limited to non-winter months. These findings add additional support for the hypothesis that vitamin D insufficiency increases risk for prostate cancer.

A national, geographic database of CDC-funded HIV prevention services: development challenges and potential applications.

BACKGROUND: From 2000-2002, the Centers for Disease Control and Prevention (CDC) funded a study that was designed to improve the information available to program planners about the geographic distribution of CDC-funded HIV prevention services provided by community-based organizations (CBOs). Program managers at CDC recognized the potential of a geographic information system (GIS) to organize and analyze information about HIV prevention services and they made GIS a critical component of the study design. The primary objective of this study was to construct a national, geographically-referenced database of HIV prevention services provided by CDC-funded CBOs. We designed a survey instrument to collect information about the geographic service areas where CBOs provided HIV prevention services, then collected data from CBOs that received CDC funding for these services during fiscal year 2000. We developed a GIS database to link questionnaire responses with GIS map layers in a manner that would incorporate overlapping geographies, risk populations and prevention services. We collected geographic service area data in two formats: 1) geopolitical boundaries and 2) geographic distance. RESULTS: The survey response rate was 70.3%, i.e. 1,020 of 1,450 community-based organizations responded. The number of HIV prevention programs administered by each CBO ranged from 1 to 23. The survey provided information about 3,028 prevention programs, including descriptions of intervention types, risk populations, race and ethnicity, CBO location and geographic service area. We incorporated this information into a large GIS database, the HIV Prevention Services Database. The use of geopolitical boundaries provided more accurate results than geographic distance. The use of a reference map with the questionnaire improved completeness, accuracy and precision of service area data. CONCLUSION: The survey instrument design and database development procedures that we used for this study successfully met our objective. The development of the HIV Prevention Services Database for CDC is an important step toward the implementation of a spatial decision support system. Due to the costs involved in a nationwide survey such as this, we recommend that future data collection efforts use Web-based survey methodologies that incorporate interactive maps.