Dataset Information

Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework.

ABSTRACT: BACKGROUND:Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces (APIs) are now available to academics to draw a richer landscape of searches. These APIs allow users to write code in languages like Python to retrieve sample data directly from Google servers. OBJECTIVE:The purpose of this paper is to describe a novel protocol to determine the top queries, volume of queries, and the top sites reached by a population searching on the web for a specific health term. The protocol retrieves Google search data obtained from three Google APIs: Google Trends, Google Health Trends (also referred to as Flu Trends), and Google Custom Search. METHODS:Our protocol consisted of four steps: (1) developing a master list of top search queries for an initial search term using Google Trends, (2) gathering information on relative search volume using Google Health Trends, (3) determining the most popular sites using Google Custom Search, and (4) calculating estimated total search volume. We tested the protocol following key procedures at each step and verified its usefulness by examining search traffic on birth control in 2017 in the United States. Two separate programmers working independently achieved similar results with insignificant variation due to sample variability. RESULTS:We successfully tested the methodology on the initial search term birth control. We identified top search queries for birth control, of which birth control pill was the most popular and obtained the relative and estimated total search volume for the top queries: relative search volume was 0.54 for the pill, corresponding to an estimated 9.3-10.7 million searches. We used the estimates of the proportion of search activity for the top queries to arrive at a generated list of the most popular websites: for the pill, the Planned Parenthood website was the top site. CONCLUSIONS:The proposed methodological framework demonstrates how to retrieve Google query data from multiple Google APIs and provides thorough documentation required to systematically identify search queries and websites, as well as estimate relative and total search volume of queries in real time or near-real time in specific locations and time periods. Although the protocol needs further testing, it allows researchers to replicate the steps and shows promise in advancing our understanding of population-level health concerns. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):RR1-10.2196/16543.

SUBMITTER: Zepecki A

PROVIDER: S-EPMC7381000 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework.

Zepecki Anne A Guendelman Sylvia S DeNero John J Prata Ndola N

JMIR research protocols 20200706 7

<h4>Background</h4>Individuals are increasingly turning to search engines like Google to obtain health information and access resources. Analysis of Google search queries offers a novel approach, which is part of the methodological toolkit for infodemiology or infoveillance researchers, to understanding population health concerns and needs in real time or near-real time. While searches predominantly have been examined with the Google Trends website tool, newer application programming interfaces ...[more]

PMID: 32442159

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Project description:IntroductionIn Canada, most provinces have established administrative health data repositories to facilitate access to these data for research. Anecdotally, researchers have described delays and substantial inter-provincial variations in the timeliness of data access approvals and receipt of data. Currently, the reasons for these delays and variations in timeliness are not well understood. This paper provides a study protocol for (1) identifying the factors affecting access to administrative health data for research within select Canadian provinces, and (2) comparing factors across provinces to assess whether and how they contribute to inter-provincial variations in access to administrative health data for research.MethodsA qualitative, multiple-case study research design will be used. Three cases will be included, representing three different provinces. For each case, data will be collected from documents and interviews. Specifically, interviews will be carried out with (1) research stakeholders, and (2) regulatory stakeholders (10 individuals/group * 2 groups/province * 3 provinces = 60). During within-case analysis, interview data for each stakeholder group will be analyzed separately using constant comparative analysis. Document analysis will occur iteratively, and will inform interview guide adaptation, and supplement interview data. Cross-case analysis will involve systematic comparison of findings across cases.DiscussionThis study represents the first in-depth examination of access to administrative health data in Canada. The main outcome will be an overarching mid-range theory explaining inter-provincial variations in access to administrative health data in Canada. This theory will be strengthened by the inclusion of the perspectives of both researchers and those involved in the regulation of data access. The findings from this study may be used to improve equitable and timely access to administrative health data across provinces, and may be transferable to other jurisdictions where barriers to access to administrative health data have been reported.

Project description:BackgroundEnvironmental exposures are commonly estimated using spatial methods, with most epidemiological studies relying on home addresses. Passively collected smartphone location data, like Google Location History (GLH) data, may present an opportunity to integrate existing long-term time-activity data.ObjectivesWe aimed to evaluate the potential use of GLH data for capturing long-term retrospective time-activity data for environmental health research.MethodsWe included 378 individuals who participated in previous Global Positioning System (GPS) studies within the Washington State Twin Registry. GLH data consists of location information that has been routinely collected since 2010 when location sharing was enabled within android operating systems or Google apps. We created instructions for participants to download their GLH data and provide it through secure data transfer. We summarized the GLH data provided, compared it to available GPS data, and conducted an exposure assessment for nitrogen dioxide (NO2) air pollution.ResultsOf 378 individuals contacted, we received GLH data from 61 individuals (16.1%) and 53 (14.0%) indicated interest but did not have historical GLH data available. The provided GLH data spanned 2010-2021 and included 34 million locations, capturing 66,677 participant days. The median number of days with GLH data per participant was 752, capturing 442 unique locations. When we compared GLH data to 2-wk GPS data (∼1.8 million points), 95% of GPS time-activity points were within 100m of GLH locations. We observed important differences between NO2 exposures assigned at home locations compared with GLH locations, highlighting the importance of GLH data to environmental exposure assessment.DiscussionWe believe collecting GLH data is a feasible and cost-effective method for capturing retrospective time-activity patterns for large populations that presents new opportunities for environmental epidemiology. Cohort studies should consider adding GLH data collection to capture historical time-activity patterns of participants, employing a "bring-your-own-location-data" citizen science approach. Privacy remains a concern that needs to be carefully managed when using GLH data. https://doi.org/10.1289/EHP10829.

Dataset Information

Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework.

Publications

Using Application Programming Interfaces to Access Google Data for Health Research: Protocol for a Methodological Framework.

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