Dataset Information

Split and combine simulation extrapolation algorithm to correct geocoding coarsening of built environment exposures.

ABSTRACT: A major challenge in studies relating built environment features to health is measurement error in exposure due to geocoding errors. Faulty geocodes in built environment data introduce errors to exposure assessments that may induce bias in the corresponding health effect estimates. In this study, we examine the distribution of the measurement error in measures constructed from point-referenced exposures, quantify the extent of bias in exposure effect estimates due to geocode coarsening, and extend the simulation extrapolation (SIMEX) method to correct the bias. The motivating example focuses on the association between children's body mass index and exposure to the junk food environment, represented by the number of junk food outlets within a buffer area near their schools. We show, algebraically and through simulation studies, that coarsening of food outlet coordinates results in exposure measurement errors that have heterogeneous variance and nonzero mean, and that the resulting bias in the health effect can be away from the null. The proposed SC-SIMEX procedure accommodates the nonstandard measurement error distribution, without requiring external data, and provides the best bias correction compared to other SIMEX approaches.

SUBMITTER: Won JY

PROVIDER: S-EPMC9018528 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Split and combine simulation extrapolation algorithm to correct geocoding coarsening of built environment exposures.

Won Jung Y JY Sanchez-Vaznaugh Emma V EV Zhai Yuqi Y Sánchez Brisa N BN

Statistics in medicine 20220131 11

A major challenge in studies relating built environment features to health is measurement error in exposure due to geocoding errors. Faulty geocodes in built environment data introduce errors to exposure assessments that may induce bias in the corresponding health effect estimates. In this study, we examine the distribution of the measurement error in measures constructed from point-referenced exposures, quantify the extent of bias in exposure effect estimates due to geocode coarsening, and exte ...[more]

PMID: 35098584

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Project description:BackgroundThe linkage of registries to Medicare claims data can help extend follow-up for patients receiving medical devices. In the present study, we tested and validated an algorithm that does not require patient identifiers to link data from a national vascular registry and Medicare claims data.MethodsWe used data from the Vascular Quality Initiative (VQI), a registry capturing data from >600 centers for several different vascular procedures, and Medicare claims from 2003 to 2018. We restricted the study to patients aged ≥65 years who had fee-for-service entitlement at their procedure. We performed an indirect linkage to combine the VQI and Medicare data at the patient level using a sequential algorithm based on the patient's date of birth, sex, zip code, procedure date, and procedure facility. We compared the indirectly linked cohort against a reference standard of a cohort directly linked using Social Security numbers. We calculated the matching rate and accuracy overall and before and after October 2015 when the International Classification of Diseases, 10th revision (ICD-10) system was adopted in the United States.ResultsA total of 144,045 VQI-Medicare-linked patients were in the reference standard cohort. Using the indirect linking algorithm, we matched 133,966 of the 144,045 VQI patients to their Medicare claims with a matching rate of 93.0%. Of the 133,966 patients, 133,104 were correctly matched (matching accuracy, 99.4%). The matching rate was higher when the indirect linkage was implemented using the ICD-10 coded data than using the ICD-9 coded data (94.0% vs 92.2%). The accuracy of the indirect linkage remained high for all procedure modules after the ICD-10 coding change (overall, 99.4%; range, 99.0%-99.7%).ConclusionsIn the present study, we successfully used indirect identifiers to link the VQI data to Medicare claims with >90% success and >99% accuracy. When direct linkage of the registry claims data using Social Security numbers is not possible because of availability or confidentiality, or both, our algorithm for indirect linkage provides a suitable alternative. The matching rate and accuracy will help ensure the accuracy of long-term follow-up and the completeness and representativeness of linked databases for relevant research and quality improvement initiatives.

Dataset Information

Split and combine simulation extrapolation algorithm to correct geocoding coarsening of built environment exposures.

Publications

Split and combine simulation extrapolation algorithm to correct geocoding coarsening of built environment exposures.

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