Accuracy of residential geocoding in the Agricultural Health Study

Accuracy of self-reported distance to nearest unconventional oil and gas well in Pennsylvania, Ohio, and West Virginia residents and implications for exposure assessment

Self-reported distances to industrial sources have been used in epidemiology as proxies for exposure to environmental hazards and indicators of awareness and perception of sources. Unconventional oil and gas development (UOG) emits pollutants and has been associated with adverse health outcomes. We compared self-reported distance to the nearest UOG well to the geographic information system-calculated distance for 303 Pennsylvania, Ohio, and West Virginia residents using Cohen's Weighted Kappa. Agreement was low (Kappa = 0.18), and self-reports by Ohioans (39% accuracy) were more accurate than West Virginians (22%) or Pennsylvanians (13%, both p < 0.05). Of the demographic characteristics studied, only educational attainment was related to reporting accuracy; residents with 12-16 years of education were more accurate (31.3% of group) than those with <12 or >16 years (both 16.7%). Understanding differences between objective and subjective measures of UOG proximity could inform studies of perceived exposures or risks and may also be relevant to adverse health effects. IMPACT: We compared objective and self-reported measures of distance to the nearest UOG well for 303 Appalachian Basin residents. We found that residents' self-reported distance to the nearest UOG well had limited agreement with the true calculated distance category. Our results can be used to inform the collection and contextualize the use of self-reported data in communities exposed to UOGD. Self-reported metrics can be used in conjunction with objective assessments and can be informative regarding how potentially exposed populations perceive environmental exposures or risks and could provide insights into awareness of distance-related policies, such as setbacks.

SEnDAE: A resource for expanding research into social and environmental determinants of health

BACKGROUND AND OBJECTIVE

Social and Environmental Determinants of Health (SEDoH) are of increasing interest to researchers in personal and public health. Collecting SEDoH and associating them with patient medical record can be challenging, especially for environmental variables. We announce here the release of SEnDAE, the Social and Environmental Determinants Address Enhancement toolkit, and open-source resource for ingesting a range of environmental variables and measurements from a variety of sources and associated them with arbitrary addresses.

METHODS

SEnDAE includes optional components for geocoding addresses, in case an organization does not have independent capabilities in that area, and recipes for extending the OMOP CDM and the ontology of an i2b2 instance to display and compute over the SEnDAE variables within i2b2.

RESULTS

On a set of 5000 synthetic addresses, SEnDAE was able to geocode 83%. SEnDAE geocodes addresses to the same Census tract as ESRI 98.1% of the time.

CONCLUSION

Development of SEnDAE is ongoing, but we hope that teams will find it useful to increase their usage of environmental variables and increase the field's general understanding of these important determinants of health.

Drinking water sources and water quality in a prospective agricultural cohort

We describe drinking water sources and water quality for a large agricultural cohort. We used questionnaire data from the Agricultural Health Study (N = 89,655), a cohort of licensed pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), to ascertain drinking water source at enrollment (1993-1997). For users of public water supplies (PWS), we linked participants' geocoded addresses to contaminant monitoring data [five haloacetic acids (HAA5), total trihalomethanes (TTHM), and nitrate-nitrogen (NO3-N)]. We estimated private well nitrate levels using random forest models accounting for well depth, soil characteristics, nitrogen inputs, and other predictors. We assigned drinking water source for 84% (N = 74,919) of participants. Among these, 69% of IA and 75% of NC participants used private wells; 27% in IA and 21% in NC used PWS. Median PWS nitrate concentrations (NO3-N) were higher in IA [0.9 mg/L, interquartile range (IQR): 0.4-3.1 mg/L] than NC (0.1 mg/L, IQR: 0.1-0.2 mg/L), while median HAA5 and TTHM concentrations were higher in NC (HAA5: 11.9 µg/L, IQR: 5.5-33.4 µg/L; TTHM: 37.7 µg/L, IQR: 10.7-54.7 µg/L) than IA (HAA5: 5.0 µg/L, IQR: 3.7-10.7 µg/L; TTHM: 13.0 µg/L, IQR: 4.2-32.4 µg/L). Private well nitrate concentrations in IA (1.5 mg/L, IQR: 0.8-4.9 mg/L) and NC (1.9 mg/L, IQR: 1.4-2.5 mg/L) were higher than PWS. More private wells in IA (12%) exceeded 10 mg/L NO3-N (regulatory limit for PWS) than NC (<1%). Due to the proximity of their drinking water sources to farms, agricultural communities may be exposed to elevated nitrate levels.

Ambient Air Pollution Exposure Assessments in Fertility Studies: a Systematic Review and Guide for Reproductive Epidemiologists

Purpose of Review

We reviewed the exposure assessments of ambient air pollution used in studies of fertility, fecundability, and pregnancy loss.

Recent Findings

Comprehensive literature searches were performed in the PUBMED, Web of Science, and Scopus databases. Of 168 total studies, 45 met the eligibility criteria and were included in the review. We find that 69% of fertility and pregnancy loss studies have used one-dimensional proximity models or surface monitor data, while only 35% have used the improved models, such as land-use regression models (4%), dispersion/chemical transport models (11%), or fusion models (20%). No published studies have used personal air monitors.

Summary

While air pollution exposure models have vastly improved over the past decade from a simple, one-dimensional distance or air monitor data to models that incorporate physiochemical properties leading to better predictive accuracy, precision, and increased spatiotemporal variability and resolution, the fertility literature has yet to fully incorporate these new methods. We provide descriptions of each of these air pollution exposure models and assess the strengths and limitations of each model, while summarizing the findings of the literature on ambient air pollution and fertility that apply each method.

Geocoding precision of birth records from 2008 to 2017 in Kentucky, USA

Maternal address information captured on birth records is increasingly used to estimate residential environmental exposures during pregnancy. However, there has been limited assessment of the geocoding precision of birth records, particularly since the adoption of the 2003 standard birth certificate in 2015. To address this gap, this study evaluated the geocoding precision of live and stillbirth records of Kentucky residents over ten years, from 2008 through 2017. This study summarized the demographic characteristics of imprecisely geocoded records and, using a bivariate logistic regression, identified covariates associated with poor geocoding precision among three population density designations-metro, non-metro, and rural. We found that in metro areas, after adjusting for area deprivation, education, and the race, age and education of both parents, records for Black mothers had 48% lower odds of imprecise geocoding (aOR=0.52, 95% CI: 0.48, 0.56), while Black women in rural areas had 96% higher odds of imprecise geocoding (aOr=1.96, 95% CI: 1.68, 2.28). This study also found that over the study period, rural and non-metro areas began with a high proportion of imprecisely geocoded records (38% in rural areas, 19% in non-metro), but both experienced an 8% decline in imprecisely geocoded records over the study period (aOr=0.92, 95% CI: 0.92, 0.94). This study shows that, while geocoding precision has improved in Kentucky, further work is needed to improve geocoding in rural areas and address racial and ethnic disparities.

Assessing Unconventional Oil and Gas Exposure in the Appalachian Basin: Comparison of Exposure Surrogates and Residential Drinking Water Measurements

Health studies report associations between metrics of residential proximity to unconventional oil and gas (UOG) development and adverse health endpoints. We investigated whether exposure through household groundwater is captured by existing metrics and a newly developed metric incorporating groundwater flow paths. We compared metrics with detection frequencies/concentrations of 64 organic and inorganic UOG-related chemicals/groups in residential groundwater from 255 homes (Pennsylvania n = 94 and Ohio n = 161). Twenty-seven chemicals were detected in ≥20% of water samples at concentrations generally below U.S. Environmental Protection Agency standards. In Pennsylvania, two organic chemicals/groups had reduced odds of detection with increasing distance to the nearest well: 1,2-dichloroethene and benzene (Odds Ratio [OR]: 0.46, 95% confidence interval [CI]: 0.23-0.93) and m- and p-xylene (OR: 0.28, 95% CI: 0.10-0.80); results were consistent across metrics. In Ohio, the odds of detecting toluene increased with increasing distance to the nearest well (OR: 1.48, 95% CI: 1.12-1.95), also consistent across metrics. Correlations between inorganic chemicals and metrics were limited (all |ρ| ≤ 0.28). Limited associations between metrics and chemicals may indicate that UOG-related water contamination occurs rarely/episodically, more complex metrics may be needed to capture drinking water exposure, and/or spatial metrics in health studies may better reflect exposure to other stressors.

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.

Residential Proximity to Intensive Animal Agriculture and Risk of Lymphohematopoietic Cancers in the Agricultural Health Study

BACKGROUND

Although occupational exposure to animals has been associated with lymphohematopoietic malignancies, to our knowledge no studies have evaluated adult cancer risks associated with living near intensive animal agriculture.

METHODS

We linked participants in the prospective Agricultural Health Study to permitted animal feeding operations in Iowa. We created metrics reflecting the intensity of animal exposures within 2 and 5 km of participants' residences, enumerating both total and inverse distance-weighted animal units (AUs), standardized by animal size and manure production. We estimated risk of lymphohematopoietic malignancies and subtypes [hazard ratio (HR), 95% confidence interval (95% CI)], adjusting for demographic and farming-related factors, including occupational pesticide exposure. We stratified associations by animal type and animal-related work activities.

RESULTS

We observed 519 cases (1993-2015) among 32,635 pesticide applicators and 211 cases among 19,743 spouses. Among applicators, no associations were evident within 2 km, but risk of any lymphohematopoietic cancer was elevated across quintiles of weighted AUs within 5 km. Risk of non-Hodgkin lymphoma (NHL) was elevated for the second (HR = 1.5; 95% CI, 1.1, 2.1), third (HR = 1.6; 95% CI, 1.1, 2.2), and fourth (HR = 1.7; 95% CI, 1.3, 2.4) highest quintiles of weighted AUs within 5 km (Ptrend = 0.52) and increased with dairy cattle AUs (Ptrend = 0.04). We found positive trends for leukemia and some NHL subtypes with increasing numbers of both beef and dairy cattle. Risks did not vary by animal-related work (Pinteraction = 0.61). Associations were similar using the total exposure metric and inconsistent among spouses.

CONCLUSION

Residential proximity to intensive animal agriculture was positively associated with risk of NHL and leukemia, even after consideration of occupational animal and pesticide exposures.

Spatial Heterogeneity in Positional Errors: A Comparison of Two Residential Geocoding Efforts in the Agricultural Health Study

Geocoding is a powerful tool for environmental exposure assessments that rely on spatial databases. Geocoding processes, locators, and reference datasets have improved over time; however, improvements have not been well-characterized. Enrollment addresses for the Agricultural Health Study, a cohort of pesticide applicators and their spouses in Iowa (IA) and North Carolina (NC), were geocoded in 2012–2016 and then again in 2019. We calculated distances between geocodes in the two periods. For a subset, we computed positional errors using “gold standard” rooftop coordinates (IA; N = 3566) or Global Positioning Systems (GPS) (IA and NC; N = 1258) and compared errors between periods. We used linear regression to model the change in positional error between time periods (improvement) by rural status and population density, and we used spatial relative risk functions to identify areas with significant improvement. Median improvement between time periods in IA was 41 m (interquartile range, IQR: −2 to 168) and 9 m (IQR: −80 to 133) based on rooftop coordinates and GPS, respectively. Median improvement in NC was 42 m (IQR: −1 to 109 m) based on GPS. Positional error was greater in rural and low-density areas compared to in towns and more densely populated areas. Areas of significant improvement in accuracy were identified and mapped across both states. Our findings underscore the importance of evaluating determinants and spatial distributions of errors in geocodes used in environmental epidemiology studies.

Using residential histories in case-control analysis of lung cancer and mountaintop removal coal mining in Central Appalachia

Population-based ecological and cross-sectional studies have observed high risk for several cancers in areas of Central Appalachia where mountaintop removal coal mines operate. Case-control studies could provide stronger evidence of such relationships, but misclassification of exposure is likely when based on current residence, since individuals could have inhabited several residences with varying environmental exposures over many years. To address this, we used residential histories for individuals enrolled in a previous case-control study of lung cancer to assess residential proximity to mountaintop removal coal mining over a 30-year period, using both survey data and proprietary data from LexisNexis, Inc. Supplementing the survey data with LexisNexis data improved precision and completeness of geographic coordinates. Final logistic regression models revealed higher odds of high exposure among cases. These findings suggest that living in close proximity to mountaintop removal coal mining sites could increase risk for lung cancer, after adjusting for other relevant factors.

Impact of residential mobility on estimated environmental exposures in a prospective cohort of older women

Longitudinal studies of environmental hazards often rely on exposure estimated at the participant's enrollment residence. This could lead to exposure misclassification if participants move over time.

Methods

We evaluated residential mobility in the Iowa Women's Health Study (age 55-69 years) over 19 years of follow-up (1986-2004). We assessed several environmental exposures of varying spatial scales at enrollment and follow-up addresses. Exposures included average nitrate concentrations in public water supplies, percent of agricultural land (row crops and pasture/hay) within 750 m, and the presence of concentrated animal feeding operations within 5 km. In comparison to gold standard duration-based exposures averaged across all residences, we evaluated the sensitivity and specificity of exposure metrics and attenuation bias for a hypothetical nested case-control study of cancer, which assumed participants did not move from their enrollment residence.

Results

Among 41,650 participants, 32% moved at least once during follow-up. Mobility was predicted by working outside the home, being a former/current smoker, having a higher education level, using a public drinking water supply, and town size of previous residence. Compared with duration-based exposures, the sensitivity and specificity of exposures at enrollment ranged from 94% to 99% and 97% to 99%, respectively. A hypothetical true odds ratio of 2.0 was attenuated 8% for nitrate, 9%-10% for agricultural land, and 6% for concentrated animal feeding operation exposures.

Conclusions

Overall, we found low rates of mobility and mobility-related exposure misclassification in the Iowa Women's Health Study. Misclassification and attenuation of hypothetical risk estimates differed by spatial variability and exposure prevalence.

Geocoding Error, Spatial Uncertainty, and Implications for Exposure Assessment and Environmental Epidemiology

Although environmental epidemiology studies often rely on geocoding procedures in the process of assigning spatial exposure estimates, geocoding methods are not commonly reported, nor are consequent errors in exposure assignment explored. Geocoding methods differ in accuracy, however, and, given the increasing refinement of available exposure models for air pollution and other exposures, geocoding error may account for an increasingly larger proportion of exposure misclassification. We used residential addresses from a reasonably large, dense dataset of asthma emergency department visits from all New York City hospitals (n = 21,183; 26.9 addresses/km²), and geocoded each using three methods (Address Point, Street Segment, Parcel Centroid). We compared missingness and spatial patterning therein, quantified distance and directional errors, and quantified impacts on pollution exposure estimates and assignment to Census areas for sociodemographic characterization. Parcel Centroids had the highest overall missingness rate (38.1%, Address Point = 9.6%, Street Segment = 6.1%), and spatial clustering in missingness was significant for all methods, though its spatial patterns differed. Street Segment geocodes had the largest mean distance error (µ = 29.2 (SD = 26.2) m; vs. µ = 15.9 (SD = 17.7) m for Parcel Centroids), and the strongest spatial patterns therein. We found substantial over- and under-estimation of pollution exposures, with greater error for higher pollutant concentrations, but minimal impact on Census area assignment. Finally, we developed surfaces of spatial patterns in errors in order to identify locations in the study area where exposures may be over-/under-estimated. Our observations provide insights towards refining geocoding methods for epidemiology, and suggest methods for quantifying and interpreting geocoding error with respect to exposure misclassification, towards understanding potential impacts on health effect estimates.

Differences in Travel Time to Cancer Surgery for Colon versus Rectal Cancer in a Rural State: A New Method for Analyzing Time-to-Place Data Using Survival Analysis

PURPOSE

Rectal cancer is rarer than colon cancer and is a technically more difficult tumor for surgeons to remove, thus rectal cancer patients may travel longer for specialized treatment compared to colon cancer patients. The purpose of this study was to evaluate whether travel time for surgery was different for colon versus rectal cancer patients.

METHODS

A secondary data analysis of colorectal cancer (CRC) incidence data from the Iowa Cancer Registry data was conducted. Travel times along a street network from all residential ZIP Codes to all cancer surgery facilities were calculated using a geographic information system. A new method for analyzing "time-to-place" data using the same type of survival analysis method commonly used to analyze "time-to-event" data is introduced. Cox proportional hazard model was used to analyze travel time differences for colon versus rectal cancer patients.

RESULTS

A total of 5,844 CRC patients met inclusion criteria. Median travel time to the nearest surgical facility was 9 minutes, median travel time to the actual cancer surgery facilities was 22 minutes, and the median number of facilities bypassed was 3. Although travel times to the nearest surgery facilities were not significantly different for colon versus rectal cancer patients, rectal cancer patients on average traveled 15 minutes longer to their actual surgery facility and bypassed 2 more facilities to obtain surgery.

DISCUSSION

In general, the survival analysis method used to analyze the time-to-place data as described here could be applied to a wide variety of health services and used to compare travel patterns among different groups.

The Herbicide Glyphosate and Its Apparently Controversial Effect on Human Health: An Updated Clinical Perspective

BACKGROUND

Glyphosate (G) is the most common weed-killer in the world. Every year tons and tons of G are applied on crop fields. G was first introduced in the mid 1970s and since then its usage has gradually increased to reach a peak since 2005. Now G usage is approximately 100 -fold what it was in 1970. Its impact on human health was considered benign at the beginning. But over the years, evidence of a pervasive negative effect of this pesticide on humans has been mounting. Nonetheless, G usage is allowed by government health control agencies (both in the United States and Europe), that rely upon the evidence produced by the G producer. However, the IARC (International Agency for Research on Cancer) in 2015 has stated that G is probable carcinogenic (class 2A), the second highest class in terms of risk.

OBJECTIVE

In this review, we explore the effect of G on human health, focusing in particular on more recent knowledge.

RESULTS

We have attempted to untangle the controversy about the dangers of the product for human beings in view of a very recent development, when the so -called Monsanto Papers, consisting of Emails and memos from Monsanto came to light, revealing a coordinated strategy to manipulate the debate about the safety of glyphosate to the company's advantage.

CONCLUSION

The story of G is a recurrent one (see the tobacco story), that seriously jeopardizes the credibility of the scientific study in the modern era.

Accuracy of automatic geolocalization of smartphone location during emergency calls - A pilot study

INTRODUCTION

Widespread use of smartphones allows automatic geolocalization (i.e., transmission of location data) in countless apps. Until now, this technology has not been routinely used in connection with an emergency call in which location data play a decisive role This study evaluated a new software automatically providing emergency medical service (EMS) dispatchers with a caller's geolocation. We hypothesized that this technology will provide higher accuracy, faster dispatching of EMS and a faster beginning of thoracic compressions in a cardiac arrest scenario.

MATERIAL AND METHODS

Approval from the local Ethics Committee was obtained. 108 simulated emergency calls reporting a patient in cardiac arrest were conducted at 54 metropolitan locations, which were chosen according to a realistic pattern. At each location, a conventional emergency call, with an oral description of the location, was given first; subsequently, another call using an app with automatic geolocation was placed. Accuracy of localization, time to location, time to EMS dispatch and time to first thoracic compression were compared between both groups.

RESULTS

The conventional emergency call was always successful (n = 54). Emergency call via app worked successfully in n = 46 cases (85.2%). Automatic geolocation was provided to EMS in all these n = 46 cases (100%). Deviation from estimated position to actual position was 1173.5 ± 4343.1 m for conventional and 65.6 ± 320.5 m for automatic geolocalization (p < 0.001). In addition, time to localization was significantly shorter using automatic geolocalization (34.7 vs. 71.7 s, p < 0.001). Time to first thoracic compression was significantly faster in the geolocalization group (83.0 vs. 122.6 s; p < 0.001).

CONCLUSIONS

This pilot study showed that automatic geolocalization leads to a significantly shorter duration of the emergency call, significantly shorter times until the beginning of thoracic compressions, and a higher precision in determining the location of an emergency.

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.

A case-control study of breast cancer risk and ambient exposure to pesticides

Background:

While the estrogenic properties of certain pesticides have been established, associations between pesticide exposure and risk of breast cancer have been inconsistently observed. We investigated the relation between pesticide exposure and breast cancer risk using methods capable of objectively assessing exposure to specific pesticides occurring decades before diagnosis.

Methods:

A case–control study was conducted to evaluate the risk of postmenopausal breast cancer associated with historic pesticide exposure in California’s Central Valley, the most agriculturally productive region in the United States where pesticide drift poses a major source of nonoccupational exposure. Residential and occupational histories were linked to commercial pesticide reports and land use data to determine exposure to specific chemicals. Cases (N = 155) were recruited from a population-based cancer registry, and controls (N = 150) were obtained from tax assessor and Medicare list mailings.

Results:

There was no association between breast cancer and exposure to a selected group of organochlorine pesticides thought to have synergistic endocrine-disrupting potential; however, breast cancer was three times as likely to occur among women exposed to chlorpyrifos compared with those not exposed, after adjusting for exposure to other pesticides including organochlorines (OR = 3.22; 95% CI = 1.38, 7.53).

Conclusions:

Organophosphate pesticides, such as chlorpyrifos, have rarely been evaluated in studies of breast cancer risk. Additional research is needed to confirm these findings and to better understand the underlying mechanisms given that chlorpyrifos has been detected in local air monitoring at levels of concern for residents living in the agricultural regions where it is used.

An algorithm for quantitatively estimating non-occupational pesticide exposure intensity for spouses in the Agricultural Health Study

Residents of agricultural areas experience pesticide exposures from sources other than direct agricultural work. We developed a quantitative, active ingredient-specific algorithm for cumulative (adult, married lifetime) non-occupational pesticide exposure intensity for spouses of farmers who applied pesticides in the Agricultural Health Study (AHS). The algorithm addressed three exposure pathways: take-home, agricultural drift, and residential pesticide use. Pathway-specific equations combined (i) weights derived from previous meta-analyses of published pesticide exposure data and (ii) information from the questionnaire on frequency and duration of pesticide use by applicators, home proximity to treated fields, residential pesticide usage (e.g., termite treatments), and spouse's off-farm employment (proxy for time at home). The residential use equation also incorporated a published probability matrix that documented the likelihood active ingredients were used in home pest treatment products. We illustrate use of these equations by calculating exposure intensities for the insecticide chlorpyrifos and herbicide atrazine for 19,959 spouses. Non-zero estimates for ≥1 pathway were found for 78% and 77% of spouses for chlorpyrifos and atrazine, respectively. Variability in exposed spouses' intensity estimates was observed for both pesticides, with 75th to 25th percentile ratios ranging from 7.1 to 7.3 for take-home, 6.5 to 8.5 for drift, 2.4 to 2.8 for residential use, and 3.8 to 7.0 for the summed pathways. Take-home and drift estimates were highly correlated (≥0.98), but were not correlated with residential use (0.01‒0.02). This algorithm represents an important advancement in quantifying non-occupational pesticide relative exposure differences and will facilitate improved etiologic analyses in the AHS spouses. The algorithm could be adapted to studies with similar information.

Significant acceleration of emergency response using smartphone geolocation data and a worldwide emergency call support system

Importance

When patients are disorientated or experience language barriers, it is impossible to activate the emergency response system. In these cases, the delay for receiving appropriate help can extend to several hours.

Objectives

A worldwide emergency call support system (ECSS), including geolocation of modern smartphones (GPS, WLAN and LBS), was established referring to E911 and eCall systems. The system was tested for relevance in quickly forwarding abroad emergency calls to emergency medical services (EMS).

Design

To verify that geolocation data from smartphones are exact enough to be used for emergency cases, the accuracy of GPS (global positioning system), Wi-Fi (wireless LAN network) and LBS (location based system) was tested in eleven different countries and compared to actual location. The main objective was analyzed by simulation of emergencies in different countries. The time delay in receiving help in unsuccessful emergency call cases by using the worldwide emergency call support system (ECSS) was measured.

Results

GPS is the gold standard to locate patients with an average accuracy of 2.0 ± 3.3 m. Wi-Fi can be used within buildings with an accuracy of 7.0 ± 24.1 m. Using ECSS, the emergency call leads to a successful activation of EMS in 22.8 ± 10.8 min (Median 21 min). The use of a simple app with one button to touch did never cause any delay.

Conclusions and relevance

The worldwide emergency call support system (ECSS) significantly improves the emergency response in cases of disorientated patients or language barriers. Under circumstances without ECSS, help can be delayed by 2 or more hours and might have relevant lifesaving effects. This is the first time that Wi-Fi geolocation could prove to be a useful improvement in emergencies to enhance GPS, especially within or close to buildings.

Error propagation in spatial modeling of public health data: a simulation approach using pediatric blood lead level data for Syracuse, New York

Lead poisoning produces serious health problems, which are worse when a victim is younger. The US government and society have tried to prevent lead poisoning, especially since the 1970s; however, lead exposure remains prevalent. Lead poisoning analyses frequently use georeferenced blood lead level data. Like other types of data, these spatial data may contain uncertainties, such as location and attribute measurement errors, which can propagate to analysis results. For this paper, simulation experiments are employed to investigate how selected uncertainties impact regression analyses of blood lead level data in Syracuse, New York. In these simulations, location error and attribute measurement error, as well as a combination of these two errors, are embedded into the original data, and then these data are aggregated into census block group and census tract polygons. These aggregated data are analyzed with regression techniques, and comparisons are reported between the regression coefficients and their standard errors for the error added simulation results and the original results. To account for spatial autocorrelation, the eigenvector spatial filtering method and spatial autoregressive specifications are utilized with linear and generalized linear models. Our findings confirm that location error has more of an impact on the differences than does attribute measurement error, and show that the combined error leads to the greatest deviations. Location error simulation results show that smaller administrative units experience more of a location error impact, and, interestingly, coefficients and standard errors deviate more from their true values for a variable with a low level of spatial autocorrelation. These results imply that uncertainty, especially location error, has a considerable impact on the reliability of spatial analysis results for public health data, and that the level of spatial autocorrelation in a variable also has an impact on modeling results.

Accuracy of two geocoding methods for geographic information system-based exposure assessment in epidemiological studies

BACKGROUND

Environmental exposure assessment based on Geographic Information Systems (GIS) and study participants' residential proximity to environmental exposure sources relies on the positional accuracy of subjects' residences to avoid misclassification bias. Our study compared the positional accuracy of two automatic geocoding methods to a manual reference method.

METHODS

We geocoded 4,247 address records representing the residential history (1990-2008) of 1,685 women from the French national E3N cohort living in the Rhône-Alpes region. We compared two automatic geocoding methods, a free-online geocoding service (method A) and an in-house geocoder (method B), to a reference layer created by manually relocating addresses from method A (method R). For each automatic geocoding method, positional accuracy levels were compared according to the urban/rural status of addresses and time-periods (1990-2000, 2001-2008), using Chi Square tests. Kappa statistics were performed to assess agreement of positional accuracy of both methods A and B with the reference method, overall, by time-periods and by urban/rural status of addresses.

RESULTS

Respectively 81.4% and 84.4% of addresses were geocoded to the exact address (65.1% and 61.4%) or to the street segment (16.3% and 23.0%) with methods A and B. In the reference layer, geocoding accuracy was higher in urban areas compared to rural areas (74.4% vs. 10.5% addresses geocoded to the address or interpolated address level, p < 0.0001); no difference was observed according to the period of residence. Compared to the reference method, median positional errors were 0.0 m (IQR = 0.0-37.2 m) and 26.5 m (8.0-134.8 m), with positional errors <100 m for 82.5% and 71.3% of addresses, for method A and method B respectively. Positional agreement of method A and method B with method R was 'substantial' for both methods, with kappa coefficients of 0.60 and 0.61 for methods A and B, respectively.

CONCLUSION

Our study demonstrates the feasibility of geocoding residential addresses in epidemiological studies not initially recorded for environmental exposure assessment, for both recent addresses and residence locations more than 20 years ago. Accuracy of the two automatic geocoding methods was comparable. The in-house method (B) allowed a better control of the geocoding process and was less time consuming.

Agricultural crop exposure and risk of childhood cancer: new findings from a case-control study in Spain

Background

Childhood cancer is the main cause of disease-related death in children in Spain. Although little is known about the etiology, environmental factors are potential explanations for a fraction of the cases. Previous studies have shown pesticides to be associated with childhood cancer. The difficulty of collecting personal environmental exposure data is an important limitation; this lack of information about pesticides motivates the development of new methods to subrogate this exposure. We developed a crop exposure index based on geographic information to study the relationship between exposure to different types of crops and risk of childhood tumors.

Methods

We conducted a population-based case–control study of childhood cancer covering 3350 cases and 20,365 controls in two Spanish regions. We used CORINE Land Cover to obtain data about agricultural land use. We created a 1 km buffer around every child and calculated the percentage of crop surface within the buffer (Global Crop Index) for total crops and for individual types of crops. We fitted mixed multiple unconditional logistic regression models by diagnostic group.

Results

We found excess of risk among children living in the proximity of crops. For total crops our results showed excesses of risk for almost all diagnostic groups and increasing risk with increasing crop index value. Analyses by region and individual type of crop also showed excess of risk.

Conclusion

The results suggest that living in the proximity of cultivated land could be a risk factor for several types of cancer in children.

Arsenic in Drinking Water and Mortality for Cancer and Chronic Diseases in Central Italy, 1990-2010

BACKGROUND

In several volcanic areas of Italy, arsenic levels exceed European regulatory limits (10 μg/L in drinking water). There is still uncertainty about health risks from arsenic at low-medium doses (<100 μg/L).

OBJECTIVES

A large population-based study using an administrative cohort of residents in the Viterbo province (Central Italy), chronically exposed to low-medium arsenic levels via drinking water, was investigated to evaluate the effects of a lifetime exposure to arsenic on mortality from cancers and chronic diseases.

METHODS

The study population consisted of 165,609 residents of 17 municipalities, followed from 1990 until 2010. Average individual arsenic exposure at the first residence (AsI) was estimated through a space-time modeling approach using residential history and arsenic concentrations from water supply. A time-dependent Cumulative Arsenic dose Indicator (CAI) was calculated, accounting for daily water intake and exposure duration. Mortality Hazard Ratios (HR) were estimated by gender for different diseases using Cox proportional models, adjusting for individual and area-level confounders. A flexible non-parametric approach was used to investigate dose-response relationships.

RESULTS

Mean AsI exposure was 19.3 μg/L, and average exposure duration was 39.5 years. Associations of AsI and CAI indicators with several diseases were found, with greatest risks found for lung cancer in both sexes (HR = 2.61 males; HR = 2.09 females), myocardial infarction, peripheral arterial disease and COPD in males (HR = 2.94; HR = 2.44; HR = 2.54 respectively) and diabetes in females (HR = 2.56). For lung cancer and cardiovascular diseases dose-response relationship is modelled by piecewise linear functions revealing effects even for doses lower than 10 μg/L, and no threshold dose value was identified as safe for health.

CONCLUSIONS

Results provide new evidence for risk assessment of low-medium concentrations of arsenic and contribute to the ongoing debate about the threshold-dose of effect, suggesting that even concentrations below 10 μg/L carry a mortality risk. Policy actions are urgently needed in areas exposed to arsenic like in the Viterbo province, to comply with current EU regulations.