Derek C Johnson1,2, Alicia L Nobles3,4, Theodore L Caputi4,5, Michael Liu6, Eric C Leas4,7, Steffanie A Strathdee3, Davey M Smith3, John W Ayers3,4. 1. Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, 9500 Gilman Dr, La Jolla, California, 92093, USA. dcjohnson@ucsd.edu. 2. The Center for Data Driven Health at the Qualcomm Institute, University of California San Diego, La Jolla, California, USA. dcjohnson@ucsd.edu. 3. Department of Medicine, Division of Infectious Diseases and Global Public Health, University of California San Diego, 9500 Gilman Dr, La Jolla, California, 92093, USA. 4. The Center for Data Driven Health at the Qualcomm Institute, University of California San Diego, La Jolla, California, USA. 5. Department of Health Sciences, University of York, York, UK. 6. University of Oxford, Oxford, UK. 7. Department of Family Medicine and Public Health, Division of Health Policy, University of California San Diego, La Jolla, California, USA.
Abstract
BACKGROUND: Public health is increasingly turning to non-traditional digital data to inform HIV prevention and control strategies. We demonstrate a parsimonious method using both traditional survey and internet search histories to provide new insights into HIV testing and pre-exposure prophylaxis (PrEP) information seeking that can be easily extended to other settings. METHOD: We modeled how US internet search volumes from 2019 for HIV testing and PrEP compared against expected search volumes for HIV testing and PrEP using state HIV prevalence and socioeconomic characteristics as predictors. States with search volumes outside the upper and lower bound confidence interval were labeled as either over or under performing. State performance was evaluated by (a) Centers for Disease Control and Prevention designation as a hotspot for new HIV diagnoses (b) expanding Medicaid coverage. RESULTS: Ten states over-performed in models assessing information seeking for HIV testing, while eleven states under-performed. Thirteen states over-performed in models assessing internet searches for PrEP information, while thirteen states under-performed. States that expanded Medicaid coverage were more likely to over perform in PrEP models than states that did not expand Medicaid coverage. While states that were hotspots for new HIV diagnoses were more likely to over perform on HIV testing searches. CONCLUSION: Our study derived a method of measuring HIV and PrEP information seeking that is comparable across states. Several states exhibited information seeking for PrEP and HIV testing that deviated from model assessments. Statewide search volume for PrEP information was affected by a state's decision to expand Medicaid coverage. Our research provides health officials with an innovative way to monitor statewide interest in PrEP and HIV testing using a metric for information-seeking that is comparable across states.
BACKGROUND: Public health is increasingly turning to non-traditional digital data to inform HIV prevention and control strategies. We demonstrate a parsimonious method using both traditional survey and internet search histories to provide new insights into HIV testing and pre-exposure prophylaxis (PrEP) information seeking that can be easily extended to other settings. METHOD: We modeled how US internet search volumes from 2019 for HIV testing and PrEP compared against expected search volumes for HIV testing and PrEP using state HIV prevalence and socioeconomic characteristics as predictors. States with search volumes outside the upper and lower bound confidence interval were labeled as either over or under performing. State performance was evaluated by (a) Centers for Disease Control and Prevention designation as a hotspot for new HIV diagnoses (b) expanding Medicaid coverage. RESULTS: Ten states over-performed in models assessing information seeking for HIV testing, while eleven states under-performed. Thirteen states over-performed in models assessing internet searches for PrEP information, while thirteen states under-performed. States that expanded Medicaid coverage were more likely to over perform in PrEP models than states that did not expand Medicaid coverage. While states that were hotspots for new HIV diagnoses were more likely to over perform on HIV testing searches. CONCLUSION: Our study derived a method of measuring HIV and PrEP information seeking that is comparable across states. Several states exhibited information seeking for PrEP and HIV testing that deviated from model assessments. Statewide search volume for PrEP information was affected by a state's decision to expand Medicaid coverage. Our research provides health officials with an innovative way to monitor statewide interest in PrEP and HIV testing using a metric for information-seeking that is comparable across states.
Entities:
Keywords:
Google trends; HIV; HIV testing; Internet; PrEP
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