Construction of residential histories to estimate long-term environmental exposures in the California Teachers Study cohort.

Environmental epidemiologic studies using geospatial data often estimate exposure at a participant's residence upon enrollment, but mobility during the exposure period can lead to misclassification. We aimed to mitigate this issue by constructing residential histories for participants in the California Teachers Study through follow-up (1995-2018). Address records have been collected from the US Postal Service, LexisNexis, Experian, and California Cancer Registry. We identified records of the same address based on geo-coordinate distance (≤250 m) and street name similarity. We consolidated addresses, prioritizing those confirmed by participants during follow-up questionnaires, and estimating the duration lived at each address using dates associated with records (e.g., date-first-seen). During 23 years of follow-up, about half of participants moved (48%, including 14% out-of-state). We observed greater mobility among younger women, Hispanic/Latino women, and those in metropolitan and lower socioeconomic status areas. The cumulative proportion of in-state movers remaining eligible for analysis was 21%, 32%, and 41% at 5, 10, and 20 years post enrollment, respectively. Using self-reported information collected 10 years after enrollment, we correctly identified 94% of movers and 95% of non-movers as having moved or not moved from their enrollment address. This dataset provides a foundation for estimating long-term environmental exposures in diverse epidemiologic studies in this cohort. IMPACT: Our efforts in constructing residential histories for California Teachers Study participants through follow-up (1995-2018) benefit future environmental epidemiologic studies. Address availability during the exposure period can mitigate misclassification due to residential changes, especially when evaluating long-term exposures and chronic health outcomes. This can reduce differential misclassification among more mobile subgroups, including younger women and those from lower socioeconomic and urban areas. Our approach to consolidating addresses from multiple sources showed high accuracy in comparison to self-reported residential information. The residential dataset produced from this analysis provides a valuable tool for future studies, ultimately enhancing our understanding of environmental health impacts.

Invited Commentary: Standards, Inputs, and Outputs-Strategies for Improving Data-Sharing and Consortia-Based Epidemiologic Research.

Data-sharing improves epidemiologic research, but the sharing of data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study group that has shared its data and any researcher who has received shared data. In this issue of the Journal, Temprosa et al. (Am J Epidemiol. 2021;191(1):147-158) describe how the Consortium of Metabolomics Studies (COMETS) developed and deployed a flexible analytical platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are the supporting rather than the leading actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium's analytical requirements. Different specialties will have different specific research needs and will probably continue to use and develop an array of diverse analytical and technical solutions for their projects. COMETS Analytics shows how important-and enabling-the upstream attention to data standards and data consistency is to producing high-quality metabolomics, consortia-based, and large-scale epidemiology research.

Using Marketing Automation to Modernize Data Collection in the California Teachers Study Cohort.

BACKGROUND: Like other cancer epidemiologic cohorts, the California Teachers Study (CTS) has experienced declining participation to follow-up questionnaires; neither the reasons for these declines nor the steps that could be taken to mitigate these trends are fully understood. METHODS: The CTS offered their 6th study questionnaire (Q6) in the fall of 2017 using an integrated, online system. The team delivered a Web and mobile-adaptive questionnaire to 45,239 participants via e-mail using marketing automation technology. The study's integrated platform captured data on recruitment activities that may influence overall response, including the date and time invitations and reminders were e-mailed and the date and time questionnaires were started and submitted. RESULTS: The overall response rate was 43%. Participants ages 65 to 69 were 25% more likely to participate than their younger counterparts (OR = 1.25; 95% CI, 1.18-1.32) and nonwhite participants were 28% less likely to participate than non-Hispanic white cohort members (OR = 0.72; 95% CI, 0.68-0.76). Previous questionnaire participation was strongly associated with response (OR = 6.07; 95% CI, 5.50-6.70). Invitations sent after 2 pm had the highest response (OR = 1.75; 95% CI, 1.65-1.84), as did invitations sent on Saturdays (OR = 1.48; 95% CI, 1.36-1.60). CONCLUSIONS: An integrated system that captures paradata about questionnaire recruitment and response can enable studies to quantify the engagement patterns and communication desires of cohort members. IMPACT: As cohorts continue to collect scientific data, it is imperative to collect and analyze information on how participants engage with the study.See all articles in this CEBP Focus section, "Modernizing Population Science."

Insights from Adopting a Data Commons Approach for Large-scale Observational Cohort Studies: The California Teachers Study.

BACKGROUND: Large-scale cancer epidemiology cohorts (CEC) have successfully collected, analyzed, and shared patient-reported data for years. CECs increasingly need to make their data more findable, accessible, interoperable, and reusable, or FAIR. How CECs should approach this transformation is unclear. METHODS: The California Teachers Study (CTS) is an observational CEC of 133,477 participants followed since 1995-1996. In 2014, we began updating our data storage, management, analysis, and sharing strategy. With the San Diego Supercomputer Center, we deployed a new infrastructure based on a data warehouse to integrate and manage data and a secure and shared workspace with documentation, software, and analytic tools that facilitate collaboration and accelerate analyses. RESULTS: Our new CTS infrastructure includes a data warehouse and data marts, which are focused subsets from the data warehouse designed for efficiency. The secure CTS workspace utilizes a remote desktop service that operates within a Health Insurance Portability and Accountability Act (HIPAA)- and Federal Information Security Management Act (FISMA)-compliant platform. Our infrastructure offers broad access to CTS data, includes statistical analysis and data visualization software and tools, flexibly manages other key data activities (e.g., cleaning, updates, and data sharing), and will continue to evolve to advance FAIR principles. CONCLUSIONS: Our scalable infrastructure provides the security, authorization, data model, metadata, and analytic tools needed to manage, share, and analyze CTS data in ways that are consistent with the NCI's Cancer Research Data Commons Framework. IMPACT: The CTS's implementation of new infrastructure in an ongoing CEC demonstrates how population sciences can explore and embrace new cloud-based and analytics infrastructure to accelerate cancer research and translation.See all articles in this CEBP Focus section, "Modernizing Population Science."