Current practices in spatial analysis of cancer data: data characteristics and data sources for geographic studies of cancer

A geospatial assessment of industrial releases and pediatric neuroblastic tumours at diagnosis: A retrospective case series

Environmental risk factors associated with malignancy of pediatric neuroblastic tumours are not well-known and few studies have examined the relationship between industrial emissions and neuroblastic tumour diagnosis. A retrospective case series of 310 patients was evaluated at a tertiary hospital in Toronto, Canada between January 2008, and December 2018. Data from the National Pollutant Release Inventory (NPRI) were used to estimate exposure for a dozen chemicals with known or suspected carcinogenicity or embryotoxicity. Comparative analysis and predictive logistic regression models for malignant versus benign neuroblastic tumours included variables for residential proximity, number, and type of industries, mean total emissions within 2 km, and inverse distance weighted (IDW) quantity of chemical-specific industrial emissions estimated within 10 and 50 km of cases. No significant difference was seen between malignant and benign cases with respect to the mean nearest residential distance to industry, the number or type of industry, or the mean total quantity of industrial emissions within a 2 km radius of residential location of cases. However, there were statistically significant differences in the interpolated IDW emissions of dioxins and furans released between 1993 and 2019 within 10 km. Concentrations were significantly higher in malignant neuroblastic tumours at 1.65 grams (g) toxic equivalent (TEQ) (SD 2.01 g TEQ) compared to benign neuroblastic tumours at 1.13 g TEQ (SD 0.84 g TEQ) (p = 0.05). Within 50 km 3 years prior to diagnosis, malignant cases were exposed to higher levels of aluminum, benzene, and nitrogen dioxide (p = 0.02, p = 0.04, and p = 0.02 respectively). Regression analysis of the IDW emissions within a 50 km radius revealed higher odds of exposure to benzene for malignant neuroblastic tumours (OR = 1.03, CI: 1.01-1.05, p = 0.01). These preliminary findings suggest a potential role of industrial emissions in the development of malignant pediatric neuroblastic tumours and underscore the need for further research to investigate these associations.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

BACKGROUND

Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.

METHODS

This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.

RESULTS

Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.

CONCLUSIONS

This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Enhancing Health Equity Through Cancer Health Economics Research

Cancer displays some of the largest health-equity concerns of all diseases. This paper draws attention to how health economics research can assess strategies to reduce or even eliminate health disparities and provides pivotal examples of existing research as well as areas for future contributions. The paper also highlights critical data limitations that currently restrain the impact health economics research could have. We then explore new areas of inquiry where economic research is sparse but could have an important impact on health equity, particularly in topics involving Medicare and Medicaid policies that expand reimbursement and generosity of coverage. Health economics studies are notably absent from policies and practices surrounding clinical trials, representing an opportunity for future research. We urge health economics researchers to consider experiments, interventions, and assessments through primary data collection; we further encourage the formulation of multidisciplinary teams to ensure that health economics skills are well melded with other areas of expertise. These teams are needed to maximize novelty and rigor of evidence. As policies are promulgated to address disparities in cancer, involvement of economics in a multidisciplinary context can help ensure that these policies do not have unintended impacts that may deepen inequities.

Investigation of Relationships Between the Geospatial Distribution of Cancer Incidence and Estimated Pesticide Use in the U.S. West

The objective of the study was to evaluate the potential geospatial relationship between agricultural pesticide use and two cancer metrics (pediatric cancer incidence and total cancer incidence) across each of the 11 contiguous states in the Western United States at state and county resolution. The pesticide usage data were collected from the U.S. Geological Survey Pesticide National Synthesis Project database, while cancer data for each state were compiled from the National Cancer Institute State Cancer Profiles. At the state spatial scale, this study identified a significant positive association between the total mass of fumigants and pediatric cancer incidence, and also between the mass of one fumigant in particular, metam, and total cancer incidence (P-value < 0.05). At the county scale, the relationship of all cancer incidence to pesticide usage was evaluated using a multilevel model including pesticide mass and pesticide mass tertiles. Low pediatric cancer rates in many counties precluded this type of evaluation in association with pesticide usage. At the county scale, the multilevel model using fumigant mass, fumigant mass tertiles, county, and state predicted the total cancer incidence (R-squared = 0.95, NSE = 0.91, and Sum of square of residuals [SSR] = 8.22). Moreover, this study identified significant associations between total fumigant mass, high and medium tertiles of fumigant mass, total pesticide mass, and high tertiles of pesticide mass relative to total cancer incidence across counties. Fumigant application rate was shown to be important relative to the incidence of total cancer and pediatric cancer, at both state and county scales.

Residential Mobility and Geospatial Disparities in Colon Cancer Survival

BACKGROUND

Identifying geospatial cancer survival disparities is critical to focus interventions and prioritize efforts with limited resources. Incorporating residential mobility into spatial models may result in different geographic patterns of survival compared with the standard approach using a single location based on the patient's residence at the time of diagnosis.

METHODS

Data on 3,949 regional-stage colon cancer cases diagnosed from 2006 to 2011 and followed until December 31, 2016, were obtained from the New Jersey State Cancer Registry. Geographic disparity based on the spatial variance and effect sizes from a Bayesian spatial model using residence at diagnosis was compared with a time-varying spatial model using residential histories [adjusted for sex, gender, substage, race/ethnicity, and census tract (CT) poverty]. Geographic estimates of risk of colon cancer death were mapped.

RESULTS

Most patients (65%) remained at the same residence, 22% changed CT, and 12% moved out of state. The time-varying model produced a wider range of adjusted risk of colon cancer death (0.85-1.20 vs. 0.94-1.11) and resulted in greater geographic disparity statewide after adjustment (25.5% vs. 14.2%) compared with the model with only the residence at diagnosis.

CONCLUSIONS

Including residential mobility may allow for more precise estimates of spatial risk of death. Results based on the traditional approach using only residence at diagnosis were not substantially different for regional stage colon cancer in New Jersey.

IMPACT

Including residential histories opens up new avenues of inquiry to better understand the complex relationships between people and places, and the effect of residential mobility on cancer outcomes.See related commentary by Williams, p. 2107.

Augmenting disease maps: a Bayesian meta-analysis approach

Analysis of spatial patterns of disease is a significant field of research. However, access to unit-level disease data can be difficult for privacy and other reasons. As a consequence, estimates of interest are often published at the small area level as disease maps. This motivates the development of methods for analysis of these ecological estimates directly. Such analyses can widen the scope of research by drawing more insights from published disease maps or atlases. The present study proposes a hierarchical Bayesian meta-analysis model that analyses the point and interval estimates from an online atlas. The proposed model is illustrated by modelling the published cancer incidence estimates available as part of the online Australian Cancer Atlas (ACA). The proposed model aims to reveal patterns of cancer incidence for the 20 cancers included in ACA in major cities, regional and remote areas. The model results are validated using the observed areal data created from unit-level data on cancer incidence in each of 2148 small areas. It is found that the meta-analysis models can generate similar patterns of cancer incidence based on urban/rural status of small areas compared with those already known or revealed by the analysis of observed data. The proposed approach can be generalized to other online disease maps and atlases.

Modeling the Spatial Distribution of Cancer and Determining the Associated Risk Factors

Background:

Cancer is the second leading cause of death globally. Despite developing countries including Ethiopia continuing to shoulder the greatest burden, insufficient research has been conducted to determine geographical and other characteristic effects. The main objective of this study was to assess the distribution and risk of cancer and determine the effects of some common clinical patient characteristics on current patient status by taking into account the spatial effect.

Methods:

The data for this study were obtained from the oncology ward of Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia. About 415 cancer patients were included in the study. Spatial mixed ordinal logistic regression model was used to explore the geographical patterns of the incidence of cancer and identify the risk factors.

Results:

The findings of this study show that only 1.45% of patients were cured and 46.02% improved, whereas the rest have shown no change and even worse status after treatment. The estimated odds of patients who received chemotherapy was 4.284 times the estimated odds of patients who received palliative care. Prognostic factor (stage of cancer tumor), complication of cancer such as anemia during diagnosis, and treatment of patients given in the hospital had significant effect on the patient status.

Conclusion:

Patients without anemia were more likely to be cured and improved than patients with anemia during diagnosis. Most of the patients had advanced stage (IV) of cancer tumor, which dismantles the capability of the treatment to be less effective. There was negative spatial effect on the incidence of cancer, indicating that districts with higher cancer incidence were usually surrounded by districts with lower incidence.

Spatial Autocorrelation of Breast and Prostate Cancer in Slovakia

Cancer is one of the dominant causes of death in the Slovak population. Monitoring the course of the cancer death rate in Slovakia can be considered as a relevant subject for geographical research. Relatively little is known about the geographic distribution of breast and prostate cancer incidence in Slovakia. In the submitted paper, it is hypothesized that breast and prostate cancer in the examined territory are characterized by different intensities, incidences, and spatial differences. The spatial patterns of breast and prostate cancer in Slovakia were examined by means of spatial autocorrelation analyses with the Local Moran's I and Anselin Local Moran's statistics. Data on standardized death rates of breast and prostate cancer in Slovakia between 2001 and 2018 were used. Prostate cancer in men and breast cancer in women show a positive statistically significant Global Moran's I, whose values indicate a tendency to cluster. The Anselin Local Moran's I analysis indicates significant clusters of breast cancer in the western part of Slovakia, and prostate cancer clusters mostly in the central part of Slovakia. The findings we have obtained in this study may help us investigate further hypotheses regarding the causes and identification of spatial differences in breast and prostate cancer incidence. Our findings might stimulate further research into the possible causes which underlie the clusters.

Exploring Uncertainty in Canine Cancer Data Sources Through Dasymetric Refinement

In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers.

Baby Boomers and Birth Certificates: Early-Life Socioeconomic Status and Cancer Risk in Adulthood

BACKGROUND

Early-life socioeconomic status (SES) may play a role in cancer risk in adulthood. However, measuring SES retrospectively presents challenges. Parental occupation on the birth certificate is a novel method of ascertaining early-life SES that has not been applied in cancer epidemiology.

METHODS

For a Baby-Boom cohort born from 1945-1959 in two Utah counties, individual-level Nam-Powers SES (Np-SES) was derived from parental industry/occupation reported on birth certificates. Neighborhood SES was estimated from average household income of census tract at birth. Cancer incidence was determined by linkage to Utah Cancer Registry records through the Utah Population Database. Hazard ratios (HR) for cancer risk by SES quartile were estimated using Cox proportional hazards regression.

RESULTS

Females with low Np-SES at birth had lower risk of breast cancer compared with those in the highest Np-SES group [HR_Q1/Q4 = 0.83; 95% confidence interval (CI), 0.72-0.97; HR_Q2/Q4 = 0.81; 95% CI, 0.69-0.96]. Np-SES was inversely associated with melanoma (HR_Q1/Q4 = 0.81; 95% CI, 0.67-0.98) and prostate cancer (HR_Q1/Q4 = 0.70; 95% CI, 0.56-0.88). Women born into lower SES neighborhoods had significantly increased risk for invasive cervical cancer (HR_Q1/Q4 = 1.44; 95% CI, 1.12-1.85; HR_Q2/Q4 = 1.33; 95% CI, 1.04-1.72). Neighborhood SES had similar effects for melanoma and prostate cancers, but was not associated with female breast cancer. We found no association with SES for pancreas, lung, and colon and rectal cancers.

CONCLUSIONS

Individual SES derived from parental occupation at birth was associated with altered risk for several cancer sites.

IMPACT

This novel methodology can contribute to improved understanding of the role of early-life SES on cancer risk. Cancer Epidemiol Biomarkers Prev; 26(1); 75-84. ©2016 AACR.

Evolution of research in health geographics through the International Journal of Health Geographics (2002-2015)

Health geographics is a fast-developing research area. Subjects broached in scientific literature are most varied, ranging from vectorial diseases to access to healthcare, with a recent revival of themes such as the implication of health in the Smart City, or a predominantly individual-centered approach. Far beyond standard meta-analyses, the present study deliberately adopts the standpoint of questioning space in its foundations, through various authors of the International Journal of Health Geographics, a highly influential journal in that field. The idea is to find space as the common denominator in this specialized literature, as well as its relation to spatial analysis, without for all that trying to tend towards exhaustive approaches. 660 articles have being published in the journal since launch, but 359 articles were selected based on the presence of the word “Space” in either the title, or the abstract or the text over 13 years of the journal’s existence. From that database, a lexical analysis (tag cloud) reveals the perception of space in literature, and shows how approaches are evolving, thus underlining that the scope of health geographics is far from narrowing.

Are HPV vaccination services accessible to high-risk communities? A spatial analysis of HPV-associated cancer and Chlamydia rates and safety-net clinics

PURPOSE

While HPV vaccines can greatly benefit adolescents and young women from high-risk areas, little is known about whether safety-net immunization services are geographically accessible to communities at greatest risk for HPV-associated diseases. We explore the spatial relationship between areas with high HPV risk and proximity to safety-net clinics from an ecologic perspective.

METHODS

We used cancer registry data and Chlamydia surveillance data to identify neighborhoods within Los Angeles County with high risk for HPV-associated cancers. We examined proximity to safety-net clinics among neighborhoods with the highest risk. Proximity was measured as the shortest distance between each neighborhood center and the nearest clinic and having a clinic within 3 miles of each neighborhood center.

RESULTS

The average 5-year non-age-adjusted rates were 1,940 cases per 100,000 for Chlamydia and 60 per 100,000 for HPV-associated cancers. A large majority, 349 of 386 neighborhoods with high HPV-associated cancer rates and 532 of 537 neighborhoods with high Chlamydia rates, had a clinic within 3 miles of the neighborhood center. Clinics were more likely to be located within close proximity to high-risk neighborhoods in the inner city. High-risk neighborhoods outside of this urban core area were less likely to be near accessible clinics.

CONCLUSIONS

The majority of high-risk neighborhoods were geographically near safety-net clinics with HPV vaccination services. Due to low rates of vaccination, these findings suggest that while services are geographically accessible, additional efforts are needed to improve uptake. Programs aimed to increase awareness about the vaccine and to link underserved groups to vaccination services are warranted.

The effect of administrative boundaries and geocoding error on cancer rates in California

Geocoding is often used to produce maps of disease rates from the diagnosis addresses of incident cases to assist with disease surveillance, prevention, and control. In this process, diagnosis addresses are converted into latitude/longitude pairs which are then aggregated to produce rates at varying geographic scales such as Census tracts, neighborhoods, cities, counties, and states. The specific techniques used within geocoding systems have an impact on where the output geocode is located and can therefore have an effect on the derivation of disease rates at different geographic aggregations. This paper investigates how county-level cancer rates are affected by the choice of interpolation method when case data are geocoded to the ZIP code level. Four commonly used areal unit interpolation techniques are applied and the output of each is used to compute crude county-level five-year incidence rates of all cancers in California. We found that the rates observed for 44 out of the 58 counties in California vary based on which interpolation method is used, with rates in some counties increasing by nearly 400% between interpolation methods.

Improving the power of chronic disease surveillance by incorporating residential history

We present a global test for disease clustering with power to identify disturbances from the null population distribution which accounts for the lag time between the date of exposure and the date of diagnosis. Location at diagnosis is often used as a surrogate for the location of exposure; however, the causative exposure could have occurred at a previous address in a case's residential history. We incorporate models for the incubation distribution of a disease to weight each address into the residential history by the corresponding probability of the exposure occurring at that address. We then introduce a test statistic which uses these incubation-weighted addresses to test for a difference between the spatial distribution of the cases and the spatial distribution of the controls, or the background population. We follow the construction of the M statistic to evaluate the significance of these new distance distributions. Our results show that gains in detection power when residential history is accounted for are of such a degree that it might make the qualitative difference between the presence of spatial clustering being detected or not, thus making a strong argument for the inclusion of residential history in the analysis of such data.

An eight-year snapshot of geospatial cancer research (2002-2009): clinico-epidemiological and methodological findings and trends

Geographic information systems (GIS) offer a very rich toolbox of methods and technologies, and powerful research tools that extend far beyond the mere production of maps, making it possible to cross-link and study the complex interaction of disease data and factors originating from a wide range of disparate sources. Despite their potential indispensable role in cancer prevention and control programmes, GIS are underrepresented in specialised oncology literature. The latter has provided an impetus for the current review. The review provides an eight-year snapshot of geospatial cancer research in peer-reviewed literature (2002-2009), presenting the clinico-epidemiological and methodological findings and trends in the covered corpus (93 papers). The authors concluded that understanding the relationship between location and cancer/cancer care services can play a crucial role in disease control and prevention, and in better service planning, and appropriate resource utilisation. Nevertheless, there are still barriers that hinder the wide-scale adoption of GIS and related technologies in everyday oncology practice.

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Factors associated with residential mobility in children with leukemia: implications for assigning exposures

PURPOSE

In epidemiologic studies, neighborhood characteristics are often assigned to individuals based on a single residence despite the fact that people frequently move and, for most cancer outcomes, the relevant time-window of exposure is not known. The authors evaluated residential mobility patterns for a population-based series of childhood leukemia cases enrolled in the Northern California Childhood Leukemia Study.

METHODS

Complete residential history from 1 year before birth to date of diagnosis was obtained for 380 cases diagnosed between 1995 and 2002. All residences were assigned U.S. Census block group designations using a geographic information system.

RESULTS

Overall, two-thirds (65.8%) of children had moved between birth and diagnosis, and one-third (34.5%) moved during the first year of life. Approximately 25% of the mothers had moved during the year before the child's birth. Multivariable analysis indicated greater residential mobility to be associated with older age of the child at diagnosis, younger age of the mother at child's birth, and lower household income. Among those who had moved, residential urban/rural status for birth and diagnosis residences changed for about 20% of subjects, and neighborhood socioeconomic status for 35%.

CONCLUSIONS

These results suggest that neighborhood attribute estimates in health studies should account for patterns of residential mobility. Estimates based on a single residential location at a single point in time may lead to different inferences.

Exploratory analysis of potential risk factors of a rare disease: spatial distribution of adrenocortical carcinoma in Israel as a case study

The underlying assumption of the proposed exploratory approach is that, if the geographic patterns of different diseases are compared, the cases of a 'subject' disease should occur closer to cases of a disease with similar environmental risk factors (etiology) and farther away from cases of a disease with different etiology. In the present study, the performance of proposed approach is investigated by cross-examination of the spatial patterns of three widespread cancers--lung, larynx and colorectal (CRC)--with that of a rare malignant disease--Adrenocortical Carcinoma (ACC). As the analysis indicates, the spatial distribution of ACC is more likely to be related to hereditary factors than to environmental causes, in accordance with current knowledge about this rare disease.

Methods and approaches in using secondary data sources to study race and ethnicity factors

Race and ethnicity are increasing used in cancer research to assess differences in cancer incidence and response to therapy. In this chapter, we discuss the measurement and methodologic issues that should be addressed to minimize bias and derive valid estimates when performing such assessments. These issues include 1) lack of national standards for race and ethnicity categories; 2) difficulty in comparing race and ethnic categories in longitudinal assessments; 3) broad categorization of race and ethnicity groups that do not provide adequate details for meaningful assessments; 4) inaccuracies in race and ethnicity data collection, and 5) confounding by socioeconomic and other factors. Recommendations for improving race and ethnicity data collection also are discussed.

An effective and efficient approach for manually improving geocoded data

Background

The process of geocoding produces output coordinates of varying degrees of quality. Previous studies have revealed that simply excluding records with low-quality geocodes from analysis can introduce significant bias, but depending on the number and severity of the inaccuracies, their inclusion may also lead to bias. Little quantitative research has been presented on the cost and/or effectiveness of correcting geocodes through manual interactive processes, so the most cost effective methods for improving geocoded data are unclear. The present work investigates the time and effort required to correct geocodes contained in five health-related datasets that represent examples of data commonly used in Health GIS.

Results

Geocode correction was attempted on five health-related datasets containing a total of 22,317 records. The complete processing of these data took 11.4 weeks (427 hours), averaging 69 seconds of processing time per record. Overall, the geocodes associated with 12,280 (55%) of records were successfully improved, taking 95 seconds of processing time per corrected record on average across all five datasets. Geocode correction improved the overall match rate (the number of successful matches out of the total attempted) from 79.3 to 95%. The spatial shift between the location of original successfully matched geocodes and their corrected improved counterparts averaged 9.9 km per corrected record. After geocode correction the number of city and USPS ZIP code accuracy geocodes were reduced from 10,959 and 1,031 to 6,284 and 200, respectively, while the number of building centroid accuracy geocodes increased from 0 to 2,261.

Conclusion

The results indicate that manual geocode correction using a web-based interactive approach is a feasible and cost effective method for improving the quality of geocoded data. The level of effort required varies depending on the type of data geocoded. These results can be used to choose between data improvement options (e.g., manual intervention, pseudocoding/geo-imputation, field GPS readings).

The role of neighborhood level socioeconomic characteristics in Salmonella infections in Michigan (1997-2007): assessment using geographic information system

Background:

The majority of U.S. disease surveillance systems contain incomplete information regarding socioeconomic status (SES) indicators like household or family income and educational attainment in case reports, which reduces the usefulness of surveillance data for these parameters. We investigated the association between select SES attributes at the neighborhood level and Salmonella infections in the three most populated counties in Michigan using a geographic information system.

Methods:

We obtained data on income, education, and race from the 2000 U.S. Census, and the aggregate number of laboratory-confirmed cases of salmonellosis (1997–2006) at the block group level from the Michigan Department of Community Health. We used ArcGIS to visualize the distribution, and Poisson regression analysis to study associations between potential predictor variables and Salmonella infections.

Results:

Based on data from 3,419 block groups, our final multivariate model revealed that block groups with lower educational attainment were less commonly represented among cases than their counterparts with higher education levels (< high school degree vs. ≥ college degree: rate ratio (RR) = 0.79, 95% confidence interval (CI):0.63, 0.99; ≥ and high school degree, but no college degree vs. ≥ college degree: RR = 0.84, 95% CI: 0.76, 0.92). Levels of education also showed a dose-response relation with the outcome variable, i.e., decreasing years of education was associated with a decrease in Salmonella infections incidence at the block group level.

Conclusion:

Education plays a significant role in health-seeking behavior at the population level. It is conceivable that a reporting bias may exist due to a greater detection of Salmonella infections among high education block groups compared to low education block groups resulting from differential access to healthcare. In addition, individuals of higher education block groups who also have greater discretionary income may eat outside the home frequently and be more likely to own pets considered reservoirs of Salmonella, which increase the likelihood of contracting Salmonella infections compared to their counterparts with lower levels of education. Public health authorities should focus on improving the level of disease detection and reporting among communities with lower income and education and further evaluate the role of higher educational attainment in the predisposition for salmonellosis.

An update on cancer cluster activities at the Centers for Disease Control and Prevention

The Centers for Disease Control and Prevention (CDC) continues to be aware of the need for response to public concern as well as to state and local agency concern about cancer clusters. In 1990 the CDC published the "Guidelines for Investigating Clusters of Health Events," in which a four-stage process was presented. This document has provided a framework that most state health departments have adopted, with modifications pertaining to their specific situations, available resources, and philosophy concerning disease clusters. The purpose of this present article is not to revise the CDC guidelines; they retain their original usefulness and validity. However, in the past 15 years, multiple cluster studies as well as scientific and technologic developments have affected duster science and response (improvements in cancer registries, a federal initiative in environmental public health tracking, refinement of biomarker technology, cluster identification using geographic information systems software, and the emergence of the Internet). Thus, we offer an addendum for use with the original document. Currently, to address both the needs of state health departments as well as public concern, the CDC now a) provides a centralized, coordinated response system for cancer cluster inquiries, b) supports an electronic cancer cluster listserver, c) maintains an informative web page, and d) provides support to states, ranging from laboratory analysis to epidemiologic assistance and expertise. Response to cancer clusters is appropriate public health action, and the CDC will continue to provide assistance, facilitate communication among states, and foster the development of new approaches in duster science.

On the use of ZIP codes and ZIP code tabulation areas (ZCTAs) for the spatial analysis of epidemiological data

BACKGROUND

While the use of spatially referenced data for the analysis of epidemiological data is growing, issues associated with selecting the appropriate geographic unit of analysis are also emerging. A particularly problematic unit is the ZIP code. Lacking standardization and highly dynamic in structure, the use of ZIP codes and ZIP code tabulation areas (ZCTA) for the spatial analysis of disease present a unique challenge to researchers. Problems associated with these units for detecting spatial patterns of disease are explored.

RESULTS

A brief review of ZIP codes and their spatial representation is conducted. Though frequently represented as polygons to facilitate analysis, ZIP codes are actually defined at a narrower spatial resolution reflecting the street addresses they serve. This research shows that their generalization as continuous regions is an imposed structure that can have serious implications in the interpretation of research results. ZIP codes areas and Census defined ZCTAs, two commonly used polygonal representations of ZIP code address ranges, are examined in an effort to identify the spatial statistical sensitivities that emerge given differences in how these representations are defined. Here, comparative analysis focuses on the detection of patterns of prostate cancer in New York State. Of particular interest for studies utilizing local, spatial statistical tests, is that differences in the topological structures of ZIP code areas and ZCTAs give rise to different spatial patterns of disease. These differences are related to the different methodologies used in the generalization of ZIP code information. Given the difficulty associated with generating ZIP code boundaries, both ZIP code areas and ZCTAs contain numerous representational errors which can have a significant impact on spatial analysis. While the use of ZIP code polygons for spatial analysis is relatively straightforward, ZCTA representations contain additional topological features (e.g. lakes and rivers) and contain fragmented polygons that can hinder spatial analysis.

CONCLUSION

Caution must be exercised when using spatially referenced data, particularly that which is attributed to ZIP codes and ZCTAs, for epidemiological analysis. Researchers should be cognizant of representational errors associated with both geographies and their resulting spatial mismatch, especially when comparing the results obtained using different topological representations. While ZCTAs can be problematic, topological corrections are easily implemented in a geographic information system to remedy erroneous aggregation effects.

Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public

BACKGROUND

To communicate population-based cancer statistics, cancer researchers have a long tradition of presenting data in a spatial representation, or map. Historically, health data were presented in printed atlases in which the map producer selected the content and format. The availability of geographic information systems (GIS) with comprehensive mapping and spatial analysis capability for desktop and Internet mapping has greatly expanded the number of producers and consumers of health maps, including policymakers and the public.Because health maps, particularly ones that show elevated cancer rates, historically have raised public concerns, it is essential that these maps be designed to be accurate, clear, and interpretable for the broad range of users who may view them. This article focuses on designing maps to communicate effectively. It is based on years of research into the use of health maps for communicating among public health researchers.

RESULTS

The basics for designing maps that communicate effectively are similar to the basics for any mode of communication. Tasks include deciding on the purpose, knowing the audience and its characteristics, choosing a media suitable for both the purpose and the audience, and finally testing the map design to ensure that it suits the purpose with the intended audience, and communicates accurately and effectively. Special considerations for health maps include ensuring confidentiality and reflecting the uncertainty of small area statistics. Statistical maps need to be based on sound practices and principles developed by the statistical and cartographic communities.

CONCLUSION

The biggest challenge is to ensure that maps of health statistics inform without misinforming. Advances in the sciences of cartography, statistics, and visualization of spatial data are constantly expanding the toolkit available to mapmakers to meet this challenge. Asking potential users to answer questions or to talk about what they see is still the best way to evaluate the effectiveness of a specific map design.

Global, local and focused geographic clustering for case-control data with residential histories

BACKGROUND

This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile.

METHODS

Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only.

RESULTS

Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study.

CONCLUSION

Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account.