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Sallmén M, Burstyn I, Uuksulainen S, Koskinen A, Hublin C, Sainio M. Parkinson's disease and occupational exposure to organic solvents in Finland: a nationwide case-control study. Scand J Work Environ Health 2024; 50:39-48. [PMID: 37865923 PMCID: PMC10924827 DOI: 10.5271/sjweh.4125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Indexed: 10/24/2023] Open
Abstract
OBJECTIVE This study aimed to investigate the association between Parkinson's disease (PD) and occupational exposure to organic solvents generally and chlorinated hydrocarbons (CHC) in particular. METHODS We assembled a Finland-wide case-control study for birth years 1930-1950 by identifying incident PD cases from the register of Reimbursement of Medical Costs and drawing two controls per case using incidence density sampling from the Population Information System, matched on sex, birth year, and residency in Finland in 1980-2014. Occupation and socioeconomic status (SES) were identified from national censuses. We assessed cumulative occupational exposures via FINJEM job-exposure matrix. Smoking was based on occupation-specific prevalence by sex from national surveys. We estimated confounder-adjusted PD incidence rate ratios (IRR) via logistic regression and evaluated their sensitivity to errors in FINJEM through probabilistic bias analysis (PBA). RESULTS Among ever-employed, we identified 17 187 cases (16.0% potentially exposed to CHC) and 35 738 matched controls. Cases were more likely to not smoke and belong to higher SES. Cumulative exposure (CE) to CHC (per 100 ppm-years, 5-year lag) was associated with adjusted IRR 1.235 (95% confidence interval 0.986-1.547), with stronger associations among women and among persons who had more census records. Sensitivity analyses did not reveal notable associations, but stronger effects were seen in the younger birth cohort (1940-1950). PBA produced notably weaker associations, yielding a median IRR 1.097 (95% simulation interval 0.920-1.291) for CHC. CONCLUSION Our findings imply that PD is unlikely to be related to typical occupational solvent exposure in Finland, but excess risk cannot be ruled out in some highly exposed occupations.
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Affiliation(s)
| | | | | | | | | | - Markku Sainio
- Outpatient Clinic for Functional Disorders, HUS Helsinki University Hospital, Helsinki, Finland.
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Meeker JR, Burris H, Boland MR. An algorithm to identify residential mobility from electronic health-record data. Int J Epidemiol 2022; 50:2048-2057. [PMID: 34999887 DOI: 10.1093/ije/dyab064] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 03/09/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Environmental, social and economic exposures can be inferred from address information recorded in an electronic health record. However, these data often contain administrative errors and misspellings. These issues make it challenging to determine whether a patient has moved, which is integral for accurate exposure assessment. We aim to develop an algorithm to identify residential mobility events and avoid exposure misclassification. METHODS At Penn Medicine, we obtained a cohort of 12 147 pregnant patients who delivered between 2013 and 2017. From this cohort, we identified 9959 pregnant patients with address information at both time of delivery and one year prior. We developed an algorithm entitled REMAP (Relocation Event Moving Algorithm for Patients) to identify residential mobility during pregnancy and compared it to using ZIP code differences alone. We assigned an area-deprivation exposure score to each address and assessed how residential mobility changed the deprivation scores. RESULTS To assess the accuracy of our REMAP algorithm, we manually reviewed 3362 addresses and found that REMAP was 95.7% accurate. In this large urban cohort, 41% of patients moved during pregnancy. REMAP outperformed the comparison of ZIP codes alone (82.9%). If residential mobility had not been taken into account, absolute area deprivation would have misclassified 39% of the patients. When setting a threshold of one quartile for misclassification, 24.4% of patients would have been misclassified. CONCLUSIONS Our study tackles an important characterization problem for exposures that are assigned based upon residential addresses. We demonstrate that methods using ZIP code alone are not adequate. REMAP allows address information from electronic health records to be used for accurate exposure assessment and the determination of residential mobility, giving researchers and policy makers more reliable information.
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Affiliation(s)
- Jessica R Meeker
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Heather Burris
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Divsion of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Center for Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mary Regina Boland
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Excellence in Environmental Toxicology, University of Pennsylvania, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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Fecht D, Garwood K, Butters O, Henderson J, Elliott P, Hansell AL, Gulliver J. Automation of cleaning and reconstructing residential address histories to assign environmental exposures in longitudinal studies. Int J Epidemiol 2021; 49 Suppl 1:i49-i56. [PMID: 32293006 PMCID: PMC7158063 DOI: 10.1093/ije/dyz180] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 08/09/2019] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND We have developed an open-source ALgorithm for Generating Address Exposures (ALGAE) that cleans residential address records to construct address histories and assign spatially-determined exposures to cohort participants. The first application of this algorithm was to construct prenatal and early life air pollution exposure for individuals of the Avon Longitudinal Study of Parents and Children (ALSPAC) in the South West of England, using previously estimated particulate matter ≤10 µm (PM10) concentrations. METHODS ALSPAC recruited 14 541 pregnant women between 1991 and 1992. We assigned trimester-specific estimated PM10 exposures for 12 752 pregnancies, and first year of life exposures for 12 525 births, based on maternal residence and residential mobility. RESULTS Average PM10 exposure was 32.6 µg/m3 [standard deviation (S.D.) 3.0 µg/m3] during pregnancy and 31.4 µg/m3 (S.D. 2.6 µg/m3) during the first year of life; 6.7% of women changed address during pregnancy, and 18.0% moved during first year of life of their infant. Exposure differences ranged from -5.3 µg/m3 to 12.4 µg/m3 (up to 26% difference) during pregnancy and -7.22 µg/m3 to 7.64 µg/m3 (up to 27% difference) in the first year of life, when comparing estimated exposure using the address at birth and that assessed using the complete cleaned address history. For the majority of individuals exposure changed by <5%, but some relatively large changes were seen both in pregnancy and in infancy. CONCLUSIONS ALGAE provides a generic and adaptable, open-source solution to clean addresses stored in a cohort contact database and assign life stage-specific exposure estimates with the potential to reduce exposure misclassification.
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Affiliation(s)
- Daniela Fecht
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment & Health, Imperial College London, London, UK
| | - Kevin Garwood
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment & Health, Imperial College London, London, UK
| | - Oliver Butters
- Avon Longitudinal Study of Parents and Children, University of Bristol, Bristol, UK.,Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK
| | - John Henderson
- Avon Longitudinal Study of Parents and Children, University of Bristol, Bristol, UK
| | - Paul Elliott
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment & Health, Imperial College London, London, UK.,Imperial College Healthcare NHS Trust, London, UK
| | - Anna L Hansell
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment & Health, Imperial College London, London, UK.,Centre for Environmental Health and Sustainability, George Davies Centre, University of Leicester, Leicester, UK
| | - John Gulliver
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment & Health, Imperial College London, London, UK.,Centre for Environmental Health and Sustainability, George Davies Centre, University of Leicester, Leicester, UK
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Katz DSW, Batterman SA. Urban-scale variation in pollen concentrations: A single station is insufficient to characterize daily exposure. Aerobiologia (Bologna) 2020; 36:417-431. [PMID: 33456131 PMCID: PMC7810344 DOI: 10.1007/s10453-020-09641-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Accepted: 04/07/2020] [Indexed: 05/29/2023]
Abstract
Epidemiological analyses of airborne allergenic pollen often use concentration measurements from a single station to represent exposure across a city, but this approach does not account for the spatial variation of concentrations within the city. Because there are few descriptions of urban-scale variation, the resulting exposure measurement error is unknown but potentially important for epidemiological studies. This study examines urban scale variation in pollen concentrations by measuring pollen concentrations of 13 taxa over 24-hr periods twice weekly at 25 sites in two seasons in Detroit, Michigan. Spatio-temporal variation is described using cumulative distribution functions and regression models. Daily pollen concentrations across the 25 stations varied considerably, and the average quartile coefficient of dispersion was 0.63. Measurements at a single site explained 3-85% of the variation at other sites, depending on the taxon, and 95% prediction intervals of pollen concentrations generally spanned one to two orders of magnitude. These results demonstrate considerable heterogeneity of pollen levels at the urban scale, and suggest that the use of a single monitoring site will not reflect pollen exposure over an urban area and can lead to sizable measurement error in epidemiological studies, particularly when a daily time-step is used. These errors might be reduced by using predictive daily pollen levels in models that combine vegetation maps, pollen production estimates, phenology models and dispersion processes, or by using coarser time-steps in the epidemiological analysis.
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Affiliation(s)
- Daniel S W Katz
- University of Michigan - Ann Arbor, Environmental Health Sciences, 1415 Washington Heights Rd., Ann Arbor, Michigan, USA
| | - Stuart A Batterman
- University of Michigan - Ann Arbor, Environmental Health Sciences, 1415 Washington Heights Rd., Ann Arbor, Michigan, USA
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Peskoe SB, Spiegelman D, Wang M. There is no impact of exposure measurement error on latency estimation in linear models. Stat Med 2019; 38:1245-1261. [PMID: 30515870 DOI: 10.1002/sim.8038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Revised: 10/17/2018] [Accepted: 10/26/2018] [Indexed: 11/10/2022]
Abstract
Identification of the latency period for the effect of a time-varying exposure is key when assessing many environmental, nutritional, and behavioral risk factors. A pre-specified exposure metric involving an unknown latency parameter is often used in the statistical model for the exposure-disease relationship. Likelihood-based methods have been developed to estimate this latency parameter for generalized linear models but do not exist for scenarios where the exposure is measured with error, as is usually the case. Here, we explore the performance of naive estimators for both the latency parameter and the regression coefficients, which ignore exposure measurement error, assuming a linear measurement error model. We prove that, in many scenarios under this general measurement error setting, the least squares estimator for the latency parameter remains consistent, while the regression coefficient estimates are inconsistent as has previously been found in standard measurement error models where the primary disease model does not involve a latency parameter. Conditions under which this result holds are generalized to a wide class of covariance structures and mean functions. The findings are illustrated in a study of body mass index in relation to physical activity in the Health Professionals Follow-Up Study.
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Affiliation(s)
- S B Peskoe
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - D Spiegelman
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Global Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, Connecticut
| | - M Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Channing Division of Network Medicine, Department of Medicine, Harvard Medical School and Brigham and Women's Hospital, Boston, Massachusetts
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Kioumourtzoglou MA, Spiegelman D, Szpiro AA, Sheppard L, Kaufman JD, Yanosky JD, Williams R, Laden F, Hong B, Suh H. Exposure measurement error in PM2.5 health effects studies: a pooled analysis of eight personal exposure validation studies. Environ Health 2014; 13:2. [PMID: 24410940 PMCID: PMC3922798 DOI: 10.1186/1476-069x-13-2] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2013] [Accepted: 01/06/2014] [Indexed: 05/19/2023]
Abstract
BACKGROUND Exposure measurement error is a concern in long-term PM2.5 health studies using ambient concentrations as exposures. We assessed error magnitude by estimating calibration coefficients as the association between personal PM2.5 exposures from validation studies and typically available surrogate exposures. METHODS Daily personal and ambient PM2.5, and when available sulfate, measurements were compiled from nine cities, over 2 to 12 days. True exposure was defined as personal exposure to PM2.5 of ambient origin. Since PM2.5 of ambient origin could only be determined for five cities, personal exposure to total PM2.5 was also considered. Surrogate exposures were estimated as ambient PM2.5 at the nearest monitor or predicted outside subjects' homes. We estimated calibration coefficients by regressing true on surrogate exposures in random effects models. RESULTS When monthly-averaged personal PM2.5 of ambient origin was used as the true exposure, calibration coefficients equaled 0.31 (95% CI:0.14, 0.47) for nearest monitor and 0.54 (95% CI:0.42, 0.65) for outdoor home predictions. Between-city heterogeneity was not found for outdoor home PM2.5 for either true exposure. Heterogeneity was significant for nearest monitor PM2.5, for both true exposures, but not after adjusting for city-average motor vehicle number for total personal PM2.5. CONCLUSIONS Calibration coefficients were <1, consistent with previously reported chronic health risks using nearest monitor exposures being under-estimated when ambient concentrations are the exposure of interest. Calibration coefficients were closer to 1 for outdoor home predictions, likely reflecting less spatial error. Further research is needed to determine how our findings can be incorporated in future health studies.
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Affiliation(s)
| | - Donna Spiegelman
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Lianne Sheppard
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Jeff D Yanosky
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, Pennsylvania
| | - Ronald Williams
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Francine Laden
- Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Biling Hong
- Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Helen Suh
- Department of Health Sciences, Northeastern University, Boston, Massachusetts, USA
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Baxter LK, Wright RJ, Paciorek CJ, Laden F, Suh HH, Levy JI. Effects of exposure measurement error in the analysis of health effects from traffic-related air pollution. J Expo Sci Environ Epidemiol 2010; 20:101-111. [PMID: 19223939 PMCID: PMC3139251 DOI: 10.1038/jes.2009.5] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2008] [Accepted: 12/08/2008] [Indexed: 05/27/2023]
Abstract
In large epidemiological studies, many researchers use surrogates of air pollution exposure such as geographic information system (GIS)-based characterizations of traffic or simple housing characteristics. It is important to evaluate quantitatively these surrogates against measured pollutant concentrations to determine how their use affects the interpretation of epidemiological study results. In this study, we quantified the implications of using exposure models derived from validation studies, and other alternative surrogate models with varying amounts of measurement error on epidemiological study findings. We compared previously developed multiple regression models characterizing residential indoor nitrogen dioxide (NO(2)), fine particulate matter (PM(2.5)), and elemental carbon (EC) concentrations to models with less explanatory power that may be applied in the absence of validation studies. We constructed a hypothetical epidemiological study, under a range of odds ratios, and determined the bias and uncertainty caused by the use of various exposure models predicting residential indoor exposure levels. Our simulations illustrated that exposure models with fairly modest R(2) (0.3 to 0.4 for the previously developed multiple regression models for PM(2.5) and NO(2)) yielded substantial improvements in epidemiological study performance, relative to the application of regression models created in the absence of validation studies or poorer-performing validation study models (e.g., EC). In many studies, models based on validation data may not be possible, so it may be necessary to use a surrogate model with more measurement error. This analysis provides a technique to quantify the implications of applying various exposure models with different degrees of measurement error in epidemiological research.
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Affiliation(s)
- Lisa K Baxter
- US EPA, National Exposure Research Laboratory, Research Triangle Park, NC 27711,, USA.
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