Assessing bias associated with geocoding of historical residence in epidemiology research

Han, Daikwon; Bonner, Matthew R.; Nie, Jing; Freudenheim, Jo L.

doi:10.4081/gh.2013.93

Cited by 13 publications

(15 citation statements)

References 23 publications

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“…Environmental epidemiology requires reliable assessment of both temporal and spatial components of exposure. In response to these challenges, epidemiological studies are increasingly using residential addresses of study participants and geographic information systems (GIS) to improve characterization of environmental exposures and examine their association with human health risks for a large variety of disease conditions [ 1 ]. GIS, for instance, have been used to investigate the relationship between environmental exposures and risk of breast cancer [ 2 – 4 ], leukemia [ 5 – 7 ], Parkinson’s diseases [ 8 , 9 ], adverse birth outcomes [ 10 , 11 ], and respiratory health [ 12 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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BackgroundEnvironmental 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.MethodsWe 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.ResultsRespectively 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.ConclusionOur 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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…The accuracy of address location may have important implications on misclassification of individual exposure, depending on the spatial concentration gradient of the exposure. Although the residential addresses of the study subjects were not recorded initially to be geocoded and used for the assessment of environmental exposure, their accuracy can be considered precise enough to limit misclassification bias, in particular for urban subjects in the present study [15,33,36].…”

Section: Discussionmentioning

confidence: 99%

“…industrial facilities and traffic roads) considered as an exposure surrogate. Moreover, GIS allows integrating meteorological and topographical parameters influencing pollutant dispersion, into a GIS-based exposure metric [32][33][34][35]. The positional accuracy of subjects' residences is a key requisite to avoid exposure misclassification [36,37].…”

Section: Introductionmentioning

confidence: 99%

Development and performance evaluation of a GIS-based metric to assess exposure to airborne pollutant emissions from industrial sources

Coudon

Danjou

Faure

et al. 2018

Revue d'Épidémiologie et de Santé Publique

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Background: Dioxins are environmental and persistent organic carcinogens with endocrine disrupting properties. A positive association with several cancers, including risk of breast cancer has been suggested. Objectives: This study aimed to develop and assess performances of an exposure metric based on a Geographic Information System (GIS) through comparison with a validated dispersion model to estimate historical industrial dioxin exposure for its use in a case-control study nested within a prospective cohort. Methods: Industrial dioxin sources were inventoried over the whole French territory (n > 2500) and annual average releases were estimated between 1990 and 2008. In three selected areas (rural, urban and urban-costal), dioxin dispersion was modelled using SIRANE, an urban Gaussian model and exposure of the French E3N cohort participants was estimated. The GIS-based metric was developed, calibrated and compared to SIRANE results using a set of parameters (local meteorological data, characteristics of industrial sources, e.g. emission intensity and stack height), by calculating weighted kappa statistics (wκ) and coefficient of determination (R 2). Furthermore, as performance evaluation, the final GIS-based metric was tested to assess atmospheric exposure to cadmium. Results: The concordance between the GIS-based metric and the dispersion model for dioxin exposure estimate was strong (wκ median = 0.78 (1st quintile = 0.72, 3rd quintile =0.82) and R 2 median = 0.82 (1st quintile = 0.71, 3rd quintile = 0.87)). We observed similar performance for cadmium. Conclusions: Our study demonstrated the ability of the GIS-based metric to reliably characterize long-term environmental dioxin and cadmium exposures as well as the pertinence of using dispersion modelling to construct and calibrate the GIS-based metric.

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“…Unfortunately, Utah counties with the exception of Salt Lake and Weber were not assigned census tracts until the 1960 Decennial Census, which may or may not be representative of the conditions in the earlier years of the cohort. The measurement error could result in information bias (22) and underestimate the true effect of SES on cancer incidence. Although our study population was limited geographically, Salt Lake and Weber Counties were representative of the larger Utah population.…”

Section: Discussionmentioning

confidence: 99%

“…However, the majority of population-based studies are based on area-level measures (16–20) because measures of individual SES are not generally available in medical records or through cancer registries (18, 21). This approach may also misclassify SES for some individuals and bias results (22). …”

Section: Introductionmentioning

confidence: 99%

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

Stroup

Herget

Hanson

et al. 2017

Cancer Epidemiology, Biomarkers &Amp; Prevention

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BACKGROUND Early life socioeconomic status (SES) may play a role in cancer risk in adulthood. However, measuring SES retrospectively presents challenges. Parental occupation on the birth certificate is a novel method of ascertaining early-life SES that has not been applied in cancer epidemiology. METHODS For a Baby-Boom cohort born in 1945–1959 in two Utah counties, individual-level Nam-Powers SES (Np-SES) was derived from parental industry/occupation reported on birth certificates. Neighborhood SES was estimated from average household income of census tract at birth. Cancer incidence was determined by linkage to Utah Cancer Registry records through the Utah Population Database. Hazard ratios (HR) for cancer risk by SES quartile were estimated using Cox proportional hazards regression. RESULTS Females with low Np-SES at birth had lower risk of breast cancer compared to those in the highest Np-SES group (HRQ1/Q4=0.83 95% CI: 0.72–0.97; HRQ2/Q4=0.81 95% CI: 0.69–0.96). Np-SES was inversely associated with melanoma (HRQ1/Q4=0.81 95% CI: 0.67–0.98) and prostate cancer (HRQ1/Q4=0.70 95% CI: 0.56–0.88). Women born into lower SES neighborhoods had significantly increased risk for invasive cervical cancer (HRQ1/Q4=1.44 95% CI: 1.12–1.85; HRQ2/Q4=1.33 95% CI: 1.04–1.72). Neighborhood SES had similar effects for melanoma and prostate cancers, but was not associated with female breast cancer. We found no association with SES for pancreas, lung, and colon and rectal cancers. CONCLUSION Individual SES derived from parental occupation at birth was associated with altered risk for several cancer sites. IMPACT This novel methodology can contribute to improved understanding of the role of early-life SES in affecting cancer risk.

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Assessing bias associated with geocoding of historical residence in epidemiology research

Cited by 13 publications

References 23 publications

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

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

Development and performance evaluation of a GIS-based metric to assess exposure to airborne pollutant emissions from industrial sources

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

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