LAUSR

Abstract Interest in using internet search data, such as that from the Google Health Trends Application Programming Interface (GHT-API), to measure epidemiologically relevant exposures or health outcomes is growing due to their accessibility and timeliness. Researchers input search term(s), geography and time period, and the GHT-API returns a scaled probability of that search term, given all searches within the specified geo-time period. In this study, we detail a method for using these data to measure a construct of interest in five iterative steps: first, identify phrases the target population may use to search for the construct of interest; second, refine candidate search phrases with incognito Google searches to improve sensitivity and specificity; third, craft the GHT-API search term(s) by combining the refined phrases; fourth, test search volume and choose geographic and temporal scales; and fifth, retrieve and average multiple samples to stabilize estimates and address missingness. An optional sixth step involves accounting for changes in total search volume by normalizing. We present a case study examining weekly state-level child abuse searches in the United States during the COVID-19 pandemic (January 2018-August 2020) as an application of this method and describe limitations.