Positional accuracy of geocoded addresses in epidemiologic research

A new approach to geostatistical synthesis of historical records reveals capuchin spatial responses to climate and demographic change

Recent proliferation of GPS technology has transformed animal movement research. Yet, time-series data from this recent technology rarely span beyond a decade, constraining longitudinal research. Long-term field sites hold valuable historic animal location records, including hand-drawn maps and semantic descriptions. Here, we introduce a generalised workflow for converting such records into reliable location data to estimate home ranges, using 30 years of sleep-site data from 11 white-faced capuchin (Cebus imitator) groups in Costa Rica. Our findings illustrate that historic sleep locations can reliably recover home range size and geometry. We showcase the opportunity our approach presents to resolve open questions that can only be addressed with very long-term data, examining how home ranges are affected by climate cycles and demographic change. We urge researchers to translate historical records into usable movement data before this knowledge is lost; it is essential to understanding how animals are responding to our changing world.

Optimized spatial information for 1990, 2000, and 2010 U.S. census microdata

We report on the successful completion of a project to upgrade the positional accuracy of every response to the 1990, 2000, and 2010 U.S. decennial censuses. The resulting data set, called Optimized Spatial Census Information Linked Across Time (OSCILAT), resides within the restricted-access data warehouse of the Federal Statistical Research Data Center (FSRDC) system where it is available for use with approval from the U.S. Census Bureau. OSCILAT greatly improves the accuracy and completeness of spatial information for older censuses conducted prior to major quality improvements undertaken by the Bureau. Our work enables more precise spatial and longitudinal analysis of census data and supports exact tabulations of census responses for arbitrary spatial units, including tabulating responses from 1990, 2000, and 2010 within 2020 block boundaries for precise measures of change over time for small geographic areas.

Application of Bayesian Regression for the Identification of a Catchment Area for Cancer Cases in Dogs and Cats

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer (n = 27,390) and visit (n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data.

Identification of spatio-temporal clusters of lung cancer cases in Pennsylvania, USA: 2010-2017

Background

It is known that geographic location plays a role in developing lung cancer. The objectives of this study were to examine spatio-temporal patterns of lung cancer incidence in Pennsylvania, to identify geographic clusters of high incidence, and to compare demographic characteristics and general physical and mental health characteristics in those areas.

Method

We geocoded the residential addresses at the time of diagnosis for lung cancer cases in the Pennsylvania Cancer Registry diagnosed between 2010 and 2017. Relative risks over the expected case counts at the census tract level were estimated using a log-linear Poisson model that allowed for spatial and temporal effects. Spatio-temporal clusters with high incidence were identified using scan statistics. Demographics obtained from the 2011–2015 American Community Survey and health variables obtained from 2020 CDC PLACES database were compared between census tracts that were part of clusters versus those that were not.

Results

Overall, the age-adjusted incidence rates and the relative risk of lung cancer decreased from 2010 to 2017 with no statistically significant space and time interaction. The analyses detected 5 statistically significant clusters over the 8-year study period. Cluster 1, the most likely cluster, was in southeastern PA including Delaware, Montgomery, and Philadelphia Counties from 2010 to 2013 (log likelihood ratio = 136.6); Cluster 2, the cluster with the largest area was in southwestern PA in the same period including Allegheny, Fayette, Greene, Washington, and Westmoreland Counties (log likelihood ratio = 78.6). Cluster 3 was in Mifflin County from 2014 to 2016 (log likelihood ratio = 25.3), Cluster 4 was in Luzerne County from 2013 to 2016 (log likelihood ratio = 18.1), and Cluster 5 was in Dauphin, Cumberland, and York Counties limited to 2010 to 2012 (log likelihood ratio = 17.9). Census tracts that were part of the high incidence clusters tended to be densely populated, had higher percentages of African American and residents that live below poverty line, and had poorer mental health and physical health when compared to the non-clusters (all p < 0.001).

Conclusions

These high incidence areas for lung cancer warrant further monitoring for other individual and environmental risk factors and screening efforts so lung cancer cases can be identified early and more efficiently.

Quality of automatic geocoding tools: a study using addresses from hospital record files in Temuco, Chile

Abstract: Automatic geocoding methods have become popular in recent years, facilitating the study of the association between health outcomes and the place of living. However, rather few studies have evaluated geocoding quality, with most of them being performed in the US and Europe. This article aims to compare the quality of three automatic online geocoding tools against a reference method. A subsample of 300 handwritten addresses from hospital records was geocoded using Bing, Google Earth, and Google Maps. Match rates were higher (> 80%) for Google Maps and Google Earth compared with Bing. However, the accuracy of the addresses was better for Bing with a larger proportion (> 70%) of addresses with positional errors below 20m. Generally, performance did not vary for each method for different socioeconomic status. Overall, the methods showed an acceptable, but heterogeneous performance, which may be a warning against the use of automatic methods without assessing quality in other municipalities, particularly in Chile and Latin America.

Health geo-governance to assess the vulnerability of Batna, Algeria to COVID-19: the role of GIS in the fight against a pandemic

The health systems in many countries are still unable to control the evolution and persistence of the COVID-19 pandemic despite the large mobilisation of national resources. International attention has focussed on finding a cure, and preventive measures and national and international strategies to be adopted and implemented with regard to other future pandemics have been neglected despite their predictability and high probability of occurrence. This work aims to anticipate a reading on experience feedback in light of the current pandemic situation, and to identify the main spatial elements of vulnerability in Batna, Algeria, which seems to control the ability of an urban area to prevent the spread of the COVID-19 virus. We used a digital model based on a multi-criteria approach implemented in a geo-decisional GIS database to serve as a decision support tool for dealing with an epidemiological situation as a preventive or curative action. The results from the model seem to adequately reflect the reality of confirmed incidents in Batna. In addition, the results of the analysis of the spatiotemporal evolution of the virus clearly confirm that the urban sectors characterised by high vulnerability are those that have recorded an increasing number of confirmed COVID-19 incidents since the start of the epidemic until December 2020.

Neighborhood greenspace and cardiometabolic risk factors: Cross-sectional and longitudinal analysis in ELSA-Brasil participants

Greater neighborhood greenspace has been associated with better cardiometabolic risk factors, especially in high-income countries. This cross-sectional and longitudinal study assessed this association in approximately 2000 participants of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) residing in Belo Horizonte, a large Brazilian capital city. Neighborhood greenspace was studied through the Normalized Difference Vegetation Index (NDVI) and two additional types, percentages of tree cover and herbaceous cover. Multivariable logistic regression models estimated the cross-sectional and longitudinal associations of neighborhood greenspace with three metabolic factors - (i) obesity, (ii) abdominal obesity, and (iii) low HDL-cholesterol - after adjustment for individual sociodemographic factors and neighborhood average household income per capita. Cross-sectional results showed that higher neighborhood greenspace was associated with lower odds of obesity, abdominal obesity and low HDL-c. However, neighborhood greenspace was not associated with the incidence of any of these risk factors. The percentage of tree cover seemed to contribute more to the associations found with NDVI than the percentage of herbaceous cover. The results support the evidence that increased neighborhood greenspace contributes to maintain a better cardiometabolic health.

Uncertainty in geospatial health: challenges and opportunities ahead

PURPOSE

Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few.

METHODS

We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis.

RESULTS

We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health.

CONCLUSIONS

Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.

Does Increasing Access to Prenatal Care Reduce Racial Disparities in Birth Outcomes?

PURPOSE

To examine the effectiveness of early and adequate prenatal care (PNC) in reducing racial disparities in pre-term birth (PTB) among low-income women.

DESIGN AND METHODS

This retrospective study examined birth records for 14,950 low-income Black and White women. The primary outcome of interest was racial disparities in PTB. Exposures of interest were first trimester entry into, and adequacy of, PNC. Maternal residential proximity to nearest PNC provider was calculated. Bivariate analyses were performed for PTB by race. Binary logistic regression was performed, controlling for maternal age, smoking status and racial segregation. Attributable risk of PTB for no or late entry into PNC, and percent difference by race was calculated.

RESULTS

We find that early and adequate PNC significantly decreases the risk of preterm birth, however, we find no evidence that this reduces racial disparities. Low income black females in a large metropolitan county have greater geographic access to and utilization of PNC than low-income white females, yet racial disparities in preterm birth remain. Attributable risk of PTB for no or late entry into PNC was lower for Black women (32.2%) than White women (39.4%).

CONCLUSIONS

Our findings suggest that adequate PNC alone does not reduce the marked racial disparities in preterm birth.

PRACTICE IMPLICATIONS

Public health agencies and health care providers need to look beyond access to care, to achieve racial equity in birth outcomes. Expansion of evidence-based, comprehensive nursing interventions shown to reduce preterm birth, such as the Nurse Family Partnership home visiting program, could contribute to these efforts.

Recognition Method of New Address Elements in Chinese Address Matching Based on Deep Learning

Location services based on address matching play an important role in people’s daily lives. However, with the rapid development of cities, new addresses are constantly emerging. Due to the untimely updating of word segmentation dictionaries and address databases, the accuracy of address segmentation and the certainty of address matching face severe challenges. Therefore, a new address element recognition method for address matching is proposed. The method first uses the bidirectional encoder representations from transformers (BERT) model to learn the contextual information and address model features. Second, the conditional random field (CRF) is used to model the constraint relationships among the tags. Finally, a new address element is recognized according to the tag, and the new address element is put into the word segmentation dictionary. The spatial information is assigned to it, and it is put into the address database. Different sequence tagging models and different vector representations of addresses are used for comparative evaluation. The experimental results show that the method introduced in this paper achieves the maximum generalization ability, its F1 score is 0.78, and the F1 score on the testing dataset also achieves a high value (0.95).

Impact of geo-imputation on epidemiologic associations in a study of outdoor air pollution and respiratory hospitalization

Imputation of missing spatial attributes in health records may facilitate linkages to geo-referenced environmental exposures, but few studies have assessed geo-imputation impacts on epidemiologic inference. We imputed patient Census tracts in a case-crossover analysis of fine particulate matter (PM_2.5) and respiratory hospitalizations in New York State (2000-2005). We observed non-significantly higher PM_2.5 exposures, high accuracy of binary exposure assignment (89 to 99%), and marginally different hazard ratios (HRs) (-0.2 to 0.7%). HR differences were greater in urban versus rural areas. Given its efficiency and nominal influence on accuracy of exposure classification and measures of association, geo-imputation is a candidate method to address missing spatial attributes for health studies.

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

Maps are well recognized as an effective means of presenting and communicating health data, such as cancer incidence and mortality rates. These data can be linked to geographic features like counties or census tracts and their associated attributes for mapping and analysis. Such visualization and analysis provide insights regarding the geographic distribution of cancer and can be important for advancing effective cancer prevention and control programs. Applying a spatial approach allows users to identify location-based patterns and trends related to risk factors, health outcomes, and population health. Geographic information science (GIScience) is the discipline that applies Geographic Information Systems (GIS) and other spatial concepts and methods in research. This review explores the current state and evolution of GIScience in cancer research by addressing fundamental topics and issues regarding spatial data and analysis that need to be considered. GIScience, along with its health-specific application in the spatial epidemiology of cancer, incorporates multiple geographic perspectives pertaining to the individual, the health care infrastructure, and the environment. Challenges addressing these perspectives and the synergies among them can be explored through GIScience methods and associated technologies as integral parts of epidemiologic research, analysis efforts, and solutions. The authors suggest GIScience is a powerful tool for cancer research, bringing additional context to cancer data analysis and potentially informing decision-making and policy, ultimately aimed at reducing the burden of cancer.

Incorporating a location-based socioeconomic index into a de-identified i2b2 clinical data warehouse

OBJECTIVE

Clinical research data warehouses are largely populated from information extracted from electronic health records (EHRs). While these data provide information about a patient's medications, laboratory results, diagnoses, and history, her social, economic, and environmental determinants of health are also major contributing factors in readmission, morbidity, and mortality and are often absent or unstructured in the EHR. Details about a patient's socioeconomic status may be found in the U.S. census. To facilitate researching the impacts of socioeconomic status on health outcomes, clinical and socioeconomic data must be linked in a repository in a fashion that supports seamless interrogation of these diverse data elements. This study demonstrates a method for linking clinical and location-based data and querying these data in a de-identified data warehouse using Informatics for Integrating Biology and the Bedside.

MATERIALS AND METHODS

Patient data were extracted from the EHR at Nebraska Medicine. Socioeconomic variables originated from the 2011-2015 five-year block group estimates from the American Community Survey. Data querying was performed using Informatics for Integrating Biology and the Bedside. All location-based data were truncated to prevent identification of a location with a population <20 000 individuals.

RESULTS

We successfully linked location-based and clinical data in a de-identified data warehouse and demonstrated its utility with a sample use case.

DISCUSSION

With location-based data available for querying, research investigating the impact of socioeconomic context on health outcomes is possible. Efforts to improve geocoding can readily be incorporated into this model.

CONCLUSION

This study demonstrates a means for incorporating and querying census data in a de-identified clinical data warehouse.

Applications of Space Technologies to Global Health: Scoping Review

Background

Space technology has an impact on many domains of activity on earth, including in the field of global health. With the recent adoption of the United Nations’ Sustainable Development Goals that highlight the need for strengthening partnerships in different domains, it is useful to better characterize the relationship between space technology and global health.

Objective

The aim of this study was to identify the applications of space technologies to global health, the key stakeholders in the field, as well as gaps and challenges.

Methods

We used a scoping review methodology, including a literature review and the involvement of stakeholders, via a brief self-administered, open-response questionnaire. A distinct search on several search engines was conducted for each of the four key technological domains that were previously identified by the UN Office for Outer Space Affairs’ Expert Group on Space and Global Health (Domain A: remote sensing; Domain B: global navigation satellite systems; Domain C: satellite communication; and Domain D: human space flight). Themes in which space technologies are of benefit to global health were extracted. Key stakeholders, as well as gaps, challenges, and perspectives were identified.

Results

A total of 222 sources were included for Domain A, 82 sources for Domain B, 144 sources for Domain C, and 31 sources for Domain D. A total of 3 questionnaires out of 16 sent were answered. Global navigation satellite systems and geographic information systems are used for the study and forecasting of communicable and noncommunicable diseases; satellite communication and global navigation satellite systems for disaster response; satellite communication for telemedicine and tele-education; and global navigation satellite systems for autonomy improvement, access to health care, as well as for safe and efficient transportation. Various health research and technologies developed for inhabited space flights have been adapted for terrestrial use.

Conclusions

Although numerous examples of space technology applications to global health exist, improved awareness, training, and collaboration of the research community is needed.

Environmental and individual attributes associated with child maltreatment resulting in hospitalization or death

Maltreatment continues to be a leading cause of death for young children. Researchers are beginning to uncover which neighborhood attributes may be associated with maltreatment outcomes. However, few studies have been able to explore these influences while controlling for individual family attributes, and none have been able to parse out the most severe outcomes-injuries resulting in hospitalization or death. This study utilizes a retrospective, case-control design on a dataset containing both individual and environmental level attributes of children who have been hospitalized or died due to maltreatment to explore the relative influence of attributes inside and outside the household walls. Binary conditional logistic regression was used to model the outcome as a function of the individual and environmental level predictors. Separate analyses also separated the outcome by manner of maltreatment: abuse or neglect. Finally, a sub-analysis included protective predictors representing access to supportive resources. Findings indicate that neighborhood attributes were similar for both cases and controls, except in the neglect only model, wherein impoverishment was associated with higher odds of serious maltreatment. Dense housing increased risk in all models except the neglect only model. In a sub-analysis, distance to Family Resource Centers was inversely related to serious maltreatment. In all models, variables representing more extreme intervention and/or removal of the victim and/or perpetrator from the home (foster care or criminal court involvement) were negatively associated with the risk of becoming a case. Medi-Cal insurance eligibility of a child was also negatively associated with becoming a case. Government interventions may be playing a critical role in child protection. More research is needed to ascertain how these interventions assert their influence.

The temporal-spatial distribution of seriously maltreated children

This descriptive study utilized Bernoulli and Poisson spatial scan statistical models in SatScan v.9.4 to examine the distribution in space and time of residence of maltreatment cases-operationalized as families with serious maltreatment (resulting in death or hospitalization) of children under 6 years-for the presence of clusters ("hot spots"). In the Poisson model, a population dataset of serious maltreatment cases were non-randomly dispersed in four major areas, with these "hot spots" moving over time and space. Most cases were outside these clusters. In the Bernoulli model, the geographic distribution of a case-control dataset of families with serious maltreatment who were previously investigated by child welfare did not differ compared to controls previously investigated by child welfare with no serious maltreatment. Findings suggest that child fatality prevention efforts such as Back to Sleep and Never Shake a Baby campaigns should continue to be universal efforts, targeted to all parents.

Impacts of geocoding uncertainty on reconstructed PFOA exposures and their epidemiological association with preeclampsia

Many epidemiology studies have investigated associations of perfluorooctanoate (PFOA) exposures with a variety of adverse health outcomes for participants in the C8 Health Project. The exposure concentrations (i.e., air and groundwater) used in these studies were determined primarily based on participant's residential locations. However, for residential addresses that could not be geocoded to the street level, the exposure concentrations were assigned based on population-weighted ZIP code centroid, which may result in exposure mischaracterization. The aim of this current study is to evaluate the potential impact of mischaracterized exposure concentrations due to geocoding uncertainty on the predicted serum PFOA concentrations and the epidemiological association between PFOA exposure and preeclampsia. For both workplace addresses and incompletely geocoded residential addresses, we used Monte Carlo (MC) simulation to assign alternate geographic locations within the reported ZIP code (instead of population-weighted ZIP code centroids) and the corresponding exposure concentrations. We found that mischaracterization of residential exposure due to population-weighted ZIP code centroid assignment had no significant impact on the serum PFOA concentration predictions and the epidemiological association of PFOA exposure with preeclampsia. In contrast, the uncertainty in workplace exposure moderately impacted the rank exposure among the participants. We observed a 41% increase in the average adjusted odds ratio of preeclampsia occurrence that may be due to differing proportions of cases (64.3%) and controls (54.5%) with workplace address geocodes during pregnancy. This finding suggests that differential exposure mischaracterization can be reduced by obtaining accurate exposure information such as street addresses and tap water consumption, for both workplaces and residences. The analysis we present is one approach for estimating the potential impacts of positional errors in a geocoding-based exposure assessment on exposure estimates and epidemiological study results.

Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining

In response to the widespread concern about the adequacy, distribution, and disparity of access to a health care workforce, the correct identification of physicians’ practice locations is critical to access public health services. In prior literature, little effort has been made to detect and resolve the uncertainty about whether the address provided by a physician in the survey is a practice address or a home address. This paper introduces how to identify the uncertainty in a physician’s practice location through spatial analytics, text mining, and visual examination. While land use and zoning code, embedded within the parcel datasets, help to differentiate resident areas from other types, spatial analytics may have certain limitations in matching and comparing physician and parcel datasets with different uncertainty issues, which may lead to unforeseen results. Handling and matching the string components between physicians’ addresses and the addresses of the parcels could identify the spatial uncertainty and instability to derive a more reasonable relationship between different datasets. Visual analytics and examination further help to clarify the undetectable patterns. This research will have a broader impact over federal and state initiatives and policies to address both insufficiency and maldistribution of a health care workforce to improve the accessibility to public health services.

Geographic Discordance Between Patient Residence and Incident Location in Emergency Medical Services Responses

STUDY OBJECTIVE

The location of a patient's residence is often used for emergency medical services (EMS) system planning. Our objective is to evaluate the association between patient residence and emergency incident zip codes for 911 calls.

METHODS

We used data from the 2013 National Emergency Medical Services Information System (NEMSIS) Public-Release Research Dataset. We studied all 911 calls with a valid complaint by dispatch, identifying zip codes for both the residence and incident locations (n=12,376,784). The primary outcomes were geographic and distance discordances between patient residence and incident zip codes. We used a multivariate logistic regression model to determine geographic discordance between residence and incident zip codes by dispatch complaint, age, and sex. We also measured distances between locations with geospatial processing.

RESULTS

The overall proportion of geographic discordance for all 911 calls was 27.7% (95% confidence interval [CI] 27.7% to 27.8%) and the median distance discordance was 11.5 miles (95% CI 11.5 to 11.5 miles). Lower geographic discordance rates were found among patients aged 65 to 79 years (20.2%; 95% CI 20.1% to 20.2%) and 80 years and older (14.5%; 95% CI 14.5% to 14.6%). Motor vehicle crashes (63.5%; 95% CI 63.5% to 63.6%), industrial accidents (59.3%; 95% CI 58.0% to 60.6%), and mass casualty incidents (50.6%; 95% CI 49.6% to 51.5%) were more likely to occur outside a patient's residence zip code. Median network distance between home and incident zip centroid codes ranged from 8.6 to 23.5 miles.

CONCLUSION

In NEMSIS, there was geographic discordance between patient residence zip code and call location zip code in slightly more than one quarter of EMS responses records. The geographic discordance rates between residence and incident zip codes were associated with dispatch complaints and age. Although a patient's residence might be a valid proxy for incident location for elderly patients, this relationship holds less true for other age groups and among different complaints. Our findings have important implications for EMS system planning, resource allocation, and injury surveillance.

Spatial measurement errors in the field of spatial epidemiology

Background

Spatial epidemiology has been aided by advances in geographic information systems, remote sensing, global positioning systems and the development of new statistical methodologies specifically designed for such data. Given the growing popularity of these studies, we sought to review and analyze the types of spatial measurement errors commonly encountered during spatial epidemiological analysis of spatial data.

Methods

Google Scholar, Medline, and Scopus databases were searched using a broad set of terms for papers indexed by a term indicating location (space or geography or location or position) and measurement error (measurement error or measurement inaccuracy or misclassification or uncertainty): we reviewed all papers appearing before December 20, 2014. These papers and their citations were reviewed to identify the relevance to our review.

Results

We were able to define and classify spatial measurement errors into four groups: (1) pure spatial location measurement errors, including both non-instrumental errors (multiple addresses, geocoding errors, outcome aggregations, and covariate aggregation) and instrumental errors; (2) location-based outcome measurement error (purely outcome measurement errors and missing outcome measurements); (3) location-based covariate measurement errors (address proxies); and (4) Covariate-Outcome spatial misaligned measurement errors. We propose how these four classes of errors can be unified within an integrated theoretical model and possible solutions were discussed.

Conclusion

Spatial measurement errors are ubiquitous threat to the validity of spatial epidemiological studies. We propose a systematic framework for understanding the various mechanisms which generate spatial measurement errors and present practical examples of such errors.

Completeness and Reliability of Location Data Collected on the Web: Assessing the Quality of Self-Reported Locations in an Internet Sample of Men Who Have Sex With Men

BACKGROUND

Place is critical to our understanding of human immunodeficiency virus (HIV) infections among men who have sex with men (MSM) in the United States. However, within the scientific literature, place is almost always represented by residential location, suggesting a fundamental assumption of equivalency between neighborhood of residence, place of risk, and place of prevention. However, the locations of behaviors among MSM show significant spatial variation, and theory has posited the importance of nonresidential contextual exposures. This focus on residential locations has been at least partially necessitated by the difficulties in collecting detailed geolocated data required to explore nonresidential locations.

OBJECTIVE

Using a Web-based map tool to collect locations, which may be relevant to the daily lives and health behaviors of MSM, this study examines the completeness and reliability of the collected data.

METHODS

MSM were recruited on the Web and completed a Web-based survey. Within this survey, men used a map tool embedded within a question to indicate their homes and multiple nonresidential locations, including those representing work, sex, socialization, physician, and others. We assessed data quality by examining data completeness and reliability. We used logistic regression to identify demographic, contextual, and location-specific predictors of answering all eligible map questions and answering specific map questions. We assessed data reliability by comparing selected locations with other participant-reported data.

RESULTS

Of 247 men completing the survey, 167 (67.6%) answered the entire set of eligible map questions. Most participants (>80%) answered specific map questions, with sex locations being the least reported (80.6%). Participants with no college education were less likely than those with a college education to answer all map questions (prevalence ratio, 0.4; 95% CI, 0.2-0.8). Participants who reported sex at their partner's home were less likely to indicate the location of that sex (prevalence ratio, 0.8; 95% CI, 0.7-1.0). Overall, 83% of participants placed their home's location within the boundaries of their reported residential ZIP code. Of locations having a specific text description, the median distance between the participant-selected location and the location determined using the specific text description was 0.29 miles (25th and 75th percentiles, 0.06-0.88).

CONCLUSIONS

Using this Web-based map tool, this Web-based sample of MSM was generally willing and able to provide accurate data regarding both home and nonresidential locations. This tool provides a mechanism to collect data that can be used in more nuanced studies of place and sexual risk and preventive behaviors of MSM.

Potential selection bias associated with using geocoded birth records for epidemiologic research

PURPOSE

There is an increasing use of geocoded birth registry data in environmental epidemiology research. Ungeocoded records are routinely excluded.

METHODS

We used classification and regression tree analysis and logistic regression to investigate potential selection bias associated with this exclusion among all singleton Florida births in 2009 (n = 210,285).

RESULTS

The rate of unsuccessful geocoding was 11.5% (n = 24,171). This ranged between 0% and 100% across zip codes. Living in a rural zip code was the strongest predictor of being ungeocoded. Other predictors for geocoding status varied with urbanity status. In urban areas, maternal race (adjusted odds ratio [aOR] ranging between 1.08 for Hispanic and 1.18 for black compared to white), maternal age [aOR: 1.16 (1.10-1.23) for ages 20-34 compared to <20], maternal nativity [aOR: 1.20 (1.15-1.25) for non-US versus US born], delivery at a birth center [aOR: 1.72 (1.49-2.00) compared to hospital delivery], multiparity [aOR: 0.91 (0.88-0.94)], maternal smoking [aOR: 0.82 (0.76-0.88)], and having nonprivate insurance [aOR: 1.25 (1.20-1.30) for Medicaid versus private insurance] were significantly associated with being ungeocoded. In rural areas, births delivered at birth center [aOR: 2.91 (1.80-4.73)] or home [aOR: 1.94 (1.28-2.95)] had increased odds compared to hospital births. The characteristics predictive of being ungeocoded were also significantly associated with adverse birth outcomes such as low birth weight and preterm delivery, and the association for maternal age was different when ungeocoded births were included and excluded.

CONCLUSIONS

Geocoding status is not random. Women with certain exposure-outcome characteristics may be more likely to be ungeocoded and excluded, indicating potential selection bias.

Air pollution in moderately polluted urban areas: How does the definition of "neighborhood" impact exposure assessment?

Environmental health studies commonly quantify subjects' pollution exposure in their neighborhood. How this neighborhood is defined can vary, however, leading to different approaches to quantification whose impacts on exposure levels remain unclear. We explore the relationship between neighborhood definition and exposure assessment. NO2, benzene, PM10 and PM2.5 exposure estimates were computed in the vicinity of 10,825 buildings using twelve exposure assessment techniques reflecting different definitions of "neighborhood". At the city scale, its definition does not significantly influence exposure estimates. It does impact levels at the building scale, however: at least a quarter of the buildings' exposure estimates for a 400 m buffer differ from the estimated 50 m buffer value (±1.0 μg/m(3) for NO2, PM10 and PM2.5; and ±0.05 μg/m(3) for benzene). This variation is significantly related to the definition of neighborhood. It is vitally important for investigators to understand the impact of chosen assessment techniques on exposure estimates.

Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses

We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey (DHS) clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.

Understanding the Clustering Patterns in Physician Distribution Through Affinity Propagation

The spatial distribution of physicians has a significant impact in public health research. It is critical to clarify whether the addresses provided by the physicians are the home addresses or the practice addresses, since the practice address is the key to understand relevant issues of maldistribution, accessibility and disparity. Through a pilot study as partial effort of the research project "Reducing Physician Distribution Uncertainty in Spatial Accessibility Research" sponsored by the National Institutes of Health (NIH award number 1R21CA182874-01), appropriate solutions were developed to differentiate the home addresses from practice addresses. This paper introduces how to understand the clustering patterns in physician distribution through Affinity Propagation, a relatively new clustering algorithm, to derive the potential extent of the practice locations for those physicians who provided home addresses. The physician data is derived from the 2014 American Medical Association (AMA) Physician Masterfile, while two counties (Fulton and DeKalb) in the metropolitan area of Atlanta, Georgia were selected as the study area. Both Euclidian distance and driving distance were applied in the AP algorithm, while gravity models based AP calculation were applied in comparison to the clustering of individual physicians. By justifying preference and similarity parameters in the AP calculation, hierarchical clustering patterns can be derived and perceived. Future research challenges in AP clustering are identified, while this pilot study can be extended with broader impact in public health research.

Accuracy of residential geocoding in the Agricultural Health Study

Background

Environmental exposure assessments often require a study participant’s residential location, but the positional accuracy of geocoding varies by method and the rural status of an address. We evaluated geocoding error in the Agricultural Health Study (AHS), a cohort of pesticide applicators and their spouses in Iowa and North Carolina, U.S.A.

Methods

For 5,064 AHS addresses in Iowa, we compared rooftop coordinates as a gold standard to two alternate locations: 1) E911 locations (intersection of the private and public road), and 2) geocodes generated by matching addresses to a commercial street database (NAVTEQ) or placed manually. Positional error (distance in meters (m) from the rooftop) was assessed overall and separately for addresses inside (non-rural) or outside town boundaries (rural). We estimated the sensitivity and specificity of proximity-based exposures (crops, animal feeding operations (AFOs)) and the attenuation in odds ratios (ORs) for a hypothetical nested case–control study. We also evaluated geocoding errors within two AHS subcohorts in Iowa and North Carolina by comparing them to GPS points taken at residences.

Results

Nearly two-thirds of the addresses represented rural locations. Compared to the rooftop gold standard, E911 locations were more accurate overall than address-matched geocodes (median error 39 and 90 m, respectively). Rural addresses generally had greater error than non-rural addresses, although errors were smaller for E911 locations. For highly prevalent crops within 500 m (>97% of homes), sensitivity was >95% using both data sources; however, lower specificities with address-matched geocodes (more common for rural addresses) led to substantial attenuation of ORs (e.g., corn <500 m OR_obs = 1.47 vs. OR_true = 2.0). Error in the address-matched geocodes resulted in even greater OR_obs attenuation for AFO exposures. Errors for North Carolina addresses were generally smaller than those in Iowa.

Conclusions

Geocoding error can be minimized when known coordinates are available to test alternative data and methods. Our assessment suggests that where E911 locations are available, they offer an improvement upon address-matched geocodes for rural addresses. Exposure misclassification resulting from positional error is dependent on the geographic database, geocoding method, and the prevalence of exposure.

Electronic supplementary material

The online version of this article (doi:10.1186/1476-072X-13-37) contains supplementary material, which is available to authorized users.

Geocoding large population-level administrative datasets at highly resolved spatial scales

Using geographic information systems to link administrative databases with demographic, social, and environmental data allows researchers to use spatial approaches to explore relationships between exposures and health. Traditionally, spatial analysis in public health has focused on the county, zip code, or tract level because of limitations to geocoding at highly resolved scales. Using 2005 birth and death data from North Carolina, we examine our ability to geocode population-level datasets at three spatial resolutions - zip code, street, and parcel. We achieve high geocoding rates at all three resolutions, with statewide street geocoding rates of 88.0% for births and 93.2% for deaths. We observe differences in geocoding rates across demographics and health outcomes, with lower geocoding rates in disadvantaged populations and the most dramatic differences occurring across the urban-rural spectrum. Our results suggest highly resolved spatial data architectures for population-level datasets are viable through geocoding individual street addresses. We recommend routinely geocoding administrative datasets to the highest spatial resolution feasible, allowing public health researchers to choose the spatial resolution used in analysis based on an understanding of the spatial dimensions of the health outcomes and exposures being investigated. Such research, however, must acknowledge how disparate geocoding success across subpopulations may affect findings.

Long-term exposure to traffic-related air pollution and progression of carotid artery atherosclerosis: a prospective cohort study

OBJECTIVES

Epidemiological studies have demonstrated associations between long-term exposure to traffic-related air pollution and coronary heart disease (CHD). Atherosclerosis is the principal pathological process responsible for CHD events, but effects of traffic-related air pollution on progression of atherosclerosis are not clear. This study aimed to investigate associations between long-term exposure to traffic-related air pollution and progression of carotid artery atherosclerosis.

SETTING

Healthy volunteers in metropolitan Vancouver, Canada.

PARTICIPANTS AND OUTCOME MEASURES

509 participants aged 30-65 years were recruited and followed for approximately 5 years. At baseline and end of follow-up, participants underwent carotid artery ultrasound examinations to assess atherosclerosis severity, including carotid intima-media thickness, plaque area, plaque number and total area. Annual change of each atherosclerosis marker during the follow-up period was calculated as the difference between these two measurements divided by years of follow-up. Living close to major roads was defined as ≤150 m from a highway or ≤50 m from a major road. Residential exposures to traffic-related air pollutants including black carbon, fine particles, nitrogen dioxide and nitric oxide were estimated using high-resolution land-use regression models. The data were analysed using general linear models adjusting for various covariates.

RESULTS

At baseline, there were no significant differences in any atherosclerosis markers between participants living close to and those living away from major roads. After follow-up, the differences in annual changes of these markers between these two groups were small and not statistically significant. Also, no significant associations were observed with concentrations of traffic-related air pollutants including black carbon, fine particles, nitrogen dioxide and nitric oxide.

CONCLUSIONS

This study did not find significant associations between traffic-related air pollution and progression of carotid artery atherosclerosis in a region with lower levels and smaller contrasts of ambient air pollution.

Active transport between home and school assessed with GPS: a cross-sectional study among Dutch elementary school children

Background

Active transport to school is associated with higher levels of physical activity in children. Promotion of active transport has therefore gained attention as a potential target to increase children’s physical activity levels. Recent studies have recognized that the distance between home and school is an important predictor for active travel among children. These studies did not yet use the promising global positioning system (GPS) methods to objectively assess active transport. This study aims to explore active transport to school in relation to the distance between home and school among a sample of Dutch elementary school children, using GPS.

Methods

Seventy-nine children, aged 6-11 years, were recruited in six schools that were located in five cities in the Netherlands. All children were asked to wear a GPS receiver for one week. All measurements were conducted between December 2008 and April 2009. Based on GPS recordings, the distance of the trips between home and school were calculated. In addition, the mode of transport (i.e., walking, cycling, motorized transport) was determined using the average and maximum speed of the GPS tracks. Then, proportion of walking and cycling trips to school was determined in relation to the distance between home and school.

Results

Out of all school trips that were recorded (n = 812), 79.2% were classified as active transport. On average, active commuting trips were of a distance of 422 meters with an average speed of 5.2 km/hour. The proportion of walking trips declined significantly at increased school trip distance, whereas the proportion of cycling trips (β = 1.23, p < 0.01) and motorized transport (β = 3.61, p < 0.01) increased. Almost all GPS tracks less than 300 meters were actively commuted, while of the tracks above 900 meters, more than half was passively commuted.

Conclusions

In the current research setting, active transport between home and school was the most frequently used mode of travel. Increasing distance seems to be associated with higher levels of passive transport. These results are relevant for those involved in decisions on where to site schools and residences, as it may affect healthy behavior among children.

Geocoding coronial data: tools and techniques to improve data quality

Clinical, administrative and demographic health information is fundamental to understanding the nature of health and evaluating the effectiveness of efforts to reduce morbidity and mortality of the population. The demographic data item 'location' is an integral part of any injury surveillance tool or injury prevention strategy. The true value of location data can only be realised once these data have been appropriately classified and quality assured. Geocoding as a means of classifying location is increasingly used in various health fields to enable spatial analysis of data. This article reports on research carried out in Australia at the National Coroners Information System (NCIS). Trends in the use of NCIS location-based data by researchers were identified. The research also aimed to establish the factors that impacted on the quality of geocoded data and the extent of this impact. A systematic analysis of the geocoding process identified source documentation, data cleaning, and software settings as key factors impacting on data quality. Understanding and application of these processes can improve data quality and therefore inform the analysis and interpretation of these data by researchers.

An evaluation framework for comparing geocoding systems

BACKGROUND

Geocoding, the process of converting textual information describing a location into one or more digital geographic representations, is a routine task performed at large organizations and government agencies across the globe. In a health context, this task is often a fundamental first step performed prior to all operations that take place in a spatially-based health study. As such, the quality of the geocoding system used within these agencies is of paramount concern to the agency (the producer) and researchers or policy-makers who wish to use these data (consumers). However, geocoding systems are continually evolving with new products coming on the market continuously. Agencies must develop and use criteria across a number axes when faced with decisions about building, buying, or maintaining any particular geocoding systems. To date, published criteria have focused on one or more aspects of geocode quality without taking a holistic view of a geocoding system's role within a large organization. The primary purpose of this study is to develop and test an evaluation framework to assist a large organization in determining which geocoding systems will meet its operational needs.

METHODS

A geocoding platform evaluation framework is derived through an examination of prior literature on geocoding accuracy. The framework developed extends commonly used geocoding metrics to take into account the specific concerns of large organizations for which geocoding is a fundamental operational capability tightly-knit into its core mission of processing health data records. A case study is performed to evaluate the strengths and weaknesses of five geocoding platforms currently available in the Australian geospatial marketplace.

RESULTS

The evaluation framework developed in this research is proven successful in differentiating between key capabilities of geocoding systems that are important in the context of a large organization with significant investments in geocoding resources. Results from the proposed methodology highlight important differences across all axes of geocoding system comparisons including spatial data output accuracy, reference data coverage, system flexibility, the potential for tight integration, and the need for specialized staff and/or development time and funding. Such results can empower decisions-makers within large organizations as they make decisions and investments in geocoding systems.

Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function

BACKGROUND

Errors in address geocodes may affect estimates of the effects of air pollution on health.

OBJECTIVE

We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults.

METHODS

We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant's address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects.

RESULTS

Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 μg/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: -0.56, -6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: -0.14, -3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted -2.81 (95% CI: -0.26, -5.35) using NavTEQ, or 2.08 (95% CI -4.63, 0.47, p = 0.11) using Google Maps].

CONCLUSIONS

Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model.

Inaccuracy, uncertainty and the space-time permutation scan statistic

The space-time permutation scan statistic (STPSS) is designed to identify hot (and cool) spots of space-time interaction within patterns of spatio-temporal events. While the method has been adopted widely in practice, there has been little consideration of the effect inaccurate and/or incomplete input data may have on its results. Given the pervasiveness of inaccuracy, uncertainty and incompleteness within spatio-temporal datasets and the popularity of the method, this issue warrants further investigation. Here, a series of simulation experiments using both synthetic and real-world data are carried out to better understand how deficiencies in the spatial and temporal accuracy as well as the completeness of the input data may affect results of the STPSS. The findings, while specific to the parameters employed here, reveal a surprising robustness of the method's results in the face of these deficiencies. As expected, the experiments illustrate that greater degradation of input data quality leads to greater variability in the results. Additionally, they show that weaker signals of space-time interaction are those most affected by the introduced deficiencies. However, in stark contrast to previous investigations into the impact of these input data problems on global tests of space-time interaction, this local metric is revealed to be only minimally affected by the degree of inaccuracy and incompleteness introduced in these experiments.

Exposure to traffic emissions: associations with biomarkers of antioxidant status and oxidative damage

BACKGROUND

Oxidative stress has been implicated as a possible mechanism for adverse health effects associated with traffic emissions. We examined the association of an estimate of traffic emissions with blood biomarkers of antioxidant capacity (glutathione, glutathione peroxidase, trolox-equivalent antioxidant capacity) and oxidative damage (thiobarbituric acid-reactive substances (TBARS)) among 1810 healthy women, randomly selected from Erie and Niagara Counties in Western New York.

METHODS

A geographic traffic emission and meteorological dispersion model was used to estimate annual polycyclic aromatic hydrocarbon (PAH) exposure from traffic emissions for each woman based on her residence at the time of study. Associations of traffic-related PAH exposure with measures of oxidative stress and antioxidant capacity were examined in multiple regression analyses with adjustment for potential confounders.

RESULTS

Higher traffic-related PAH exposure was associated with decreased glutathione and increased glutathione peroxidase. Stronger associations between traffic-related PAH exposure and levels of glutathione and glutathione peroxidase were suggested among nonsmoking women without secondhand smoke exposure, especially among premenopausal nonsmoking women. Associations were also stronger for measurements made in warmer months.

CONCLUSIONS

These findings suggest that PAHs or other components of traffic emissions may impact anti-oxidative capacity among healthy women, particularly premenopausal non-smokers without secondhand smoke exposure.

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.

A research agenda: does geocoding positional error matter in health GIS studies?

Until recently, little attention has been paid to geocoding positional accuracy and its impacts on accessibility measures; estimates of disease rates; findings of disease clustering; spatial prediction and modeling of health outcomes; and estimates of individual exposures based on geographic proximity to pollutant and pathogen sources. It is now clear that positional errors can result in flawed findings and poor public health decisions. Yet the current state-of-practice is to ignore geocoding positional uncertainty, primarily because of a lack of theory, methods and tools for quantifying, modeling, and adjusting for geocoding positional errors in health analysis. This paper proposes a research agenda to address this need. It summarizes the basics of the geocoding process, its assumptions, and empirical evidence describing the magnitude of geocoding positional error. An overview of the impacts of positional error in health analysis, including accessibility, disease clustering, exposure reconstruction, and spatial weights estimation is presented. The proposed research agenda addresses five key needs: (1) a lack of standardized, open-access geocoding resources for use in health research; (2) a lack of geocoding validation datasets that will allow the evaluation of alternative geocoding engines and procedures; (3) a lack of spatially explicit geocoding positional error models; (4) a lack of resources for assessing the sensitivity of spatial analysis results to geocoding positional error; (5) a lack of demonstration studies that illustrate the sensitivity of health policy decisions to geocoding positional error.

Spatial error in geocoding physician location data from the AMA Physician Masterfile: implications for spatial accessibility analysis

The accuracy of geocoding hinges on the quality of address information that serves as input to the geocoding process; however errors associated with poor address quality are rarely studied. This paper examines spatial errors that arise due to incorrect address information with respect to physician location data in the United States. Studies of spatial accessibility to physicians in the U.S. typically rely on data from the American Medical Association's Physician Masterfile. These data are problematic because a substantial proportion of physicians only report a mailing address, which is often the physician's home (residential) location, rather than the address for the location where health care is provided. The incorrect geocoding of physicians' practice locations based on inappropriate address information results in a form of geocoding error that has not been widely analyzed. Using data for the Chicago metropolitan region, we analyze the extent and implications of geocoding error for measurement of spatial accessibility to primary care physicians. We geocode the locations of primary care physicians based on mailing addresses and office addresses. The spatial mismatch between the two is computed at the county, zip code and point location scales. Although mailing and office address locations are quite close for many physicians, they are far apart (>20 km) for a substantial minority. Kernel density estimation is used to characterize the spatial distribution of physicians based on office and mailing addresses and to identify areas of high spatial mismatch between the two. Errors are socially and geographically uneven, resulting in overestimation of physician supply in some high-income suburban communities, and underestimation in certain central city locations where health facilities are concentrated. The resulting errors affect local measures of spatial accessibility to primary care, biasing statistical analyses of the associations between spatial access to care and health outcomes.

Quantifying the magnitude of environmental exposure misclassification when using imprecise address proxies in public health research

In spatial epidemiologic and public health research it is common to use spatially aggregated units such as centroids of postal/zip codes, census tracts, dissemination areas, blocks or block groups as proxies for sample unit locations. Few studies, however, address the potential problems associated with using these units as address proxies. The purpose of this study is to quantify the magnitude of distance errors and accessibility misclassification that result from using several commonly-used address proxies in public health research. The impact of these positional discrepancies for spatial epidemiology is illustrated by examining misclassification of accessibility to several health-related facilities, including hospitals, public recreation spaces, schools, grocery stores, and junk food retailers throughout the City of London and Middlesex County, Ontario, Canada. Positional errors are quantified by multiple neighborhood types, revealing that address proxies are most problematic when used to represent residential locations in small towns and rural areas compared to suburban and urban areas. Findings indicate that the shorter the threshold distance used to measure accessibility between subject population and health-related facility, the greater the proportion of misclassified addresses. Using address proxies based on large aggregated units such as centroids of census tracts or dissemination areas can result in very large positional discrepancies (median errors up to 343 and 2088 m in urban and rural areas, respectively), and therefore should be avoided in spatial epidemiologic research. Even smaller, commonly-used, proxies for residential address such as postal code centroids can have large positional discrepancies (median errors up to 109 and 1363 m in urban and rural areas, respectively), and are prone to misrepresenting accessibility in small towns and rural Canada; therefore, postal codes should only be used with caution in spatial epidemiologic research.

Proximity of public elementary schools to major roads in Canadian urban areas

BACKGROUND

Epidemiologic studies have linked exposure to traffic-generated air and noise pollution with a wide range of adverse health effects in children. Children spend a large portion of time at school, and both air pollution and noise are elevated in close proximity to roads, so school location may be an important determinant of exposure. No studies have yet examined the proximity of schools to major roads in Canadian cities.

METHODS

Data on public elementary schools in Canada's 10 most populous cities were obtained from online databases. School addresses were geocoded and proximity to the nearest major road, defined using a standardized national road classification scheme, was calculated for each school. Based on measurements of nitrogen oxide concentrations, ultrafine particle counts, and noise levels in three Canadian cities we conservatively defined distances < 75 m from major roads as the zone of primary interest. Census data at the city and neighborhood levels were used to evaluate relationships between school proximity to major roads, urban density, and indicators of socioeconomic status.

RESULTS

Addresses were obtained for 1,556 public elementary schools, 95% of which were successfully geocoded. Across all 10 cities, 16.3% of schools were located within 75 m of a major road, with wide variability between cities. Schools in neighborhoods with higher median income were less likely to be near major roads (OR per $20,000 increase: 0.81; 95% CI: 0.65, 1.00), while schools in densely populated neighborhoods were more frequently close to major roads (OR per 1,000 dwellings/km²: 1.07; 95% CI: 1.00, 1.16). Over 22% of schools in the lowest neighborhood income quintile were close to major roads, compared to 13% of schools in the highest income quintile.

CONCLUSIONS

A substantial fraction of students at public elementary schools in Canada, particularly students attending schools in low income neighborhoods, may be exposed to elevated levels of air pollution and noise while at school. As a result, the locations of schools may negatively impact the healthy development and academic performance of a large number of Canadian children.

Development of spatial density maps based on geoprocessing web services: application to tuberculosis incidence in Barcelona, Spain

Background

Health professionals and authorities strive to cope with heterogeneous data, services, and statistical models to support decision making on public health. Sophisticated analysis and distributed processing capabilities over geocoded epidemiological data are seen as driving factors to speed up control and decision making in these health risk situations. In this context, recent Web technologies and standards-based web services deployed on geospatial information infrastructures have rapidly become an efficient way to access, share, process, and visualize geocoded health-related information.

Methods

Data used on this study is based on Tuberculosis (TB) cases registered in Barcelona city during 2009. Residential addresses are geocoded and loaded into a spatial database that acts as a backend database. The web-based application architecture and geoprocessing web services are designed according to the Representational State Transfer (REST) principles. These web processing services produce spatial density maps against the backend database.

Results

The results are focused on the use of the proposed web-based application to the analysis of TB cases in Barcelona. The application produces spatial density maps to ease the monitoring and decision making process by health professionals. We also include a discussion of how spatial density maps may be useful for health practitioners in such contexts.

Conclusions

In this paper, we developed web-based client application and a set of geoprocessing web services to support specific health-spatial requirements. Spatial density maps of TB incidence were generated to help health professionals in analysis and decision-making tasks. The combined use of geographic information tools, map viewers, and geoprocessing services leads to interesting possibilities in handling health data in a spatial manner. In particular, the use of spatial density maps has been effective to identify the most affected areas and its spatial impact. This study is an attempt to demonstrate how web processing services together with web-based mapping capabilities suit the needs of health practitioners in epidemiological analysis scenarios.

Prostate cancer and ambient pesticide exposure in agriculturally intensive areas in California

In a population-based case-control study in California's intensely agricultural Central Valley (2005-2006), the authors investigated relations between environmental pesticide/fungicide exposure and prostate cancer. Cases (n = 173) were obtained from a population-based cancer registry, and controls (n = 162) were obtained from Medicare listings and tax assessor mailings. Past ambient exposures to pesticides/fungicides were derived from residential history and independently recorded pesticide and land-use data, using a novel geographic information systems approach. In comparison with unexposed persons, increased risks of prostate cancer were observed among persons exposed to compounds which may have prostate-specific biologic effects (methyl bromide (odds ratio = 1.62, 95% confidence interval: 1.02, 2.59) and a group of organochlorines (odds ratio = 1.64, 95% confidence interval: 1.02, 2.63)) but not among those exposed to other compounds that were included as controls (simazine, maneb, and paraquat dichloride). The authors assessed the possibility of selection bias due to less-than-100% enrollment of eligible cases and controls (a critical methodological concern in studies of this kind) and determined that there was little evidence of bias affecting the estimated effect size. This study provides evidence of an association between prostate cancer and ambient pesticide exposures in and around homes in intensely agricultural areas. The associations appear specific to compounds with a plausible biologic role in prostate carcinogenesis.

Accuracy of commercially available residential histories for epidemiologic studies

A key problem facing epidemiologists who wish to account for residential mobility in their analyses is the cost and difficulty of obtaining residential histories. Commercial residential history data of acceptable accuracy, cost, and coverage would be of great value. The present research evaluated the accuracy of residential histories from LexisNexis, Inc. The authors chose LexisNexis because the Michigan Cancer Registry has considered using their data, they have excellent procedures for privacy protection, and they make available residential histories at 25 cents per person. Only first and last name and address at last-known residence are required to access the residential history. The authors compared lifetime residential histories collected through the use of written surveys in a case-control study of bladder cancer in Michigan to the 3 residential addresses routinely available in the address history from LexisNexis. The LexisNexis address matches, as a whole, accounted for 71.5% of participants' lifetime addresses. These results provided a level of accuracy that indicates routine use of residential histories from commercial vendors is feasible. More detailed residential histories are available at a higher cost but were not analyzed in this study. Although higher accuracy is desirable, LexisNexis data are a vast improvement over the assumption of immobile individuals currently used in many spatial and spatiotemporal studies.

Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach

PURPOSE

This paper examines the effect of spatial aggregation error on statistical estimates of the association between spatial access to health care and late-stage cancer.

METHODS

Monte Carlo simulation was used to disaggregate cancer cases for two Illinois counties from zip code to census block in proportion to the age-race composition of the block population. After the disaggregation, a hierarchical logistic model was estimated examining the relationship between late-stage breast cancer and risk factors including travel distance to mammography, at both the zip code and census block levels. Model coefficients were compared between the two levels to assess the impact of spatial aggregation error.

RESULTS

We found that spatial aggregation error influences the coefficients of regression-type models at the zip code level, and this impact is highly dependent on the study area. In one study area (Kane County), block-level coefficients were very similar to those estimated on the basis of zip code data; whereas in the other study area (Peoria County), the two sets of coefficients differed substantially raising the possibility of drawing inaccurate inferences about the association between distance to mammography and late-stage cancer risk.

CONCLUSIONS

Spatial aggregation error can significantly affect the coefficient values and inferences drawn from statistical models of the association between cancer outcomes and spatial and non-spatial variables. Relying on data at the zip code level may lead to inaccurate findings on health risk factors.

Residential address errors in public health surveillance data: a description and analysis of the impact on geocoding

The residential addresses of persons with reportable communicable diseases are used increasingly for spatial monitoring and cluster detection, and public health may direct interventions based upon the results of routine spatial surveillance. There has been little assessment, however, of the quality of address data in reportable disease notifications and of the corresponding impact of these errors on geocoding and routine public health practices. The objectives of this study were to examine address errors for a selected reportable disease in a large urban center in Canada and to assess the impact of identified errors on geocoding and the estimated spatial distribution of the disease. We extracted data for all notifications of campylobacteriosis from the Montreal public health department from 1995 to 2008 and used an address verification algorithm to determine the validity of the residential address for each case and to suggest corrections for invalid addresses. We assessed the types of address errors as well as the resulting positional errors, calculating the distance between the original address and the correct address as well as changes in disease density. Address errors and missing addresses were prevalent in the public health records (10% and 5%, respectively) and they influenced the observed distribution of campylobacteriosis in Montreal, with address correction changing case location by a median of 1.1 km. Further examination of the extent of address errors in public health data is essential, as is the investigation of how these errors impact routine public health functions.

The effects of local street network characteristics on the positional accuracy of automated geocoding for geographic health studies

Background

Automated geocoding of patient addresses for the purpose of conducting spatial epidemiologic studies results in positional errors. It is well documented that errors tend to be larger in rural areas than in cities, but possible effects of local characteristics of the street network, such as street intersection density and street length, on errors have not yet been documented. Our study quantifies effects of these local street network characteristics on the means and the entire probability distributions of positional errors, using regression methods and tolerance intervals/regions, for more than 6000 geocoded patient addresses from an Iowa county.

Results

Positional errors were determined for 6376 addresses in Carroll County, Iowa, as the vector difference between each 100%-matched automated geocode and its ground-truthed location. Mean positional error magnitude was inversely related to proximate street intersection density. This effect was statistically significant for both rural and municipal addresses, but more so for the former. Also, the effect of street segment length on geocoding accuracy was statistically significant for municipal, but not rural, addresses; for municipal addresses mean error magnitude increased with length.

Conclusion

Local street network characteristics may have statistically significant effects on geocoding accuracy in some places, but not others. Even in those locales where their effects are statistically significant, street network characteristics may explain a relatively small portion of the variability among geocoding errors. It appears that additional factors besides rurality and local street network characteristics affect accuracy in general.