Examining the Public's Most Frequently Asked Questions Regarding COVID-19 Vaccines Using Search Engine Analytics in the United States: Observational Study.

BACKGROUND: The emergency authorization of COVID-19 vaccines has offered the first means of long-term protection against COVID-19-related illness since the pandemic began. It is important for health care professionals to understand commonly held COVID-19 vaccine concerns and to be equipped with quality information that can be used to assist in medical decision-making. OBJECTIVE: Using Google's RankBrain machine learning algorithm, we sought to characterize the content of the most frequently asked questions (FAQs) about COVID-19 vaccines evidenced by internet searches. Secondarily, we sought to examine the information transparency and quality of sources used by Google to answer FAQs on COVID-19 vaccines. METHODS: We searched COVID-19 vaccine terms on Google and used the "People also ask" box to obtain FAQs generated by Google's machine learning algorithms. FAQs are assigned an "answer" source by Google. We extracted FAQs and answer sources related to COVID-19 vaccines. We used the Rothwell Classification of Questions to categorize questions on the basis of content. We classified answer sources as either academic, commercial, government, media outlet, or medical practice. We used the Journal of the American Medical Association's (JAMA's) benchmark criteria to assess information transparency and Brief DISCERN to assess information quality for answer sources. FAQ and answer source type frequencies were calculated. Chi-square tests were used to determine associations between information transparency by source type. One-way analysis of variance was used to assess differences in mean Brief DISCERN scores by source type. RESULTS: Our search yielded 28 unique FAQs about COVID-19 vaccines. Most COVID-19 vaccine-related FAQs were seeking factual information (22/28, 78.6%), specifically about safety and efficacy (9/22, 40.9%). The most common source type was media outlets (12/28, 42.9%), followed by government sources (11/28, 39.3%). Nineteen sources met 3 or more JAMA benchmark criteria with government sources as the majority (10/19, 52.6%). JAMA benchmark criteria performance did not significantly differ among source types (χ 2 4=7.40; P=.12). One-way analysis of variance revealed a significant difference in mean Brief DISCERN scores by source type (F 4,23=10.27; P<.001). CONCLUSIONS: The most frequently asked COVID-19 vaccine-related questions pertained to vaccine safety and efficacy. We found that government sources provided the most transparent and highest-quality web-based COVID-19 vaccine-related information. Recognizing common questions and concerns about COVID-19 vaccines may assist in improving vaccination efforts.

Public Interest in Preventive Measures of Coronavirus Disease 2019 Associated With Timely Issuance of Statewide Stay-at-Home Orders.

INTRODUCTION: One method of monitoring public preparedness is through measuring public interest in preventive measures. The objective of this study was to analyze public interest in the coronavirus disease 2019 (COVID-19) preventive measures and to identify variables associated with timely stay-at-home (SAH) orders issued by governors. METHODS: State-level search volume was collected from Google Trends. Average preventive measure interest was calculated for the query terms "hand sanitizer," "hand washing," "social distancing," and "COVID testing." We then calculated the delay in statewide SAH orders from March 1, 2020, to the date of issuance and by-state presidential voting percentage. Bivariate correlations were computed to assess the relationship between interest in preventive measures and SAH order delay. RESULTS: The correlation between average preventive measure interest and length of time before the SAH order was placed was -0.47. Average preventive measure interest was also inversely related to voting for a Republican presidential nominee in the 2016 election (R = -0.75), the latter of which was positively associated with longer delays in SAH orders (R = 0.48). CONCLUSIONS: States with greater public interest in COVID-19 preventive measures were inversely related to governor issuance of timely SAH orders. Increasing public interest in preventive measures may slow the spread of the virus that causes COVID-19, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), by improving preparedness.

Association of Public Interest in Preventive Measures and Increased COVID-19 Cases After the Expiration of Stay-at-Home Orders: A Cross-Sectional Study.

OBJECTIVE: Following stay-at-home (SAH) orders issued for coronavirus disease (COVID-19), state-level economic concerns increased and many let these orders expire. As a method to measure public preparedness, we sought to explore the association between public interest in preventive measures and the easing of SAH orders - specifically the increases in COVID-19 cases and fatalities after the orders expired. METHODS: Search volume was collected from Google Trends for "hand sanitizer," "social distancing," "COVID testing," and "contact tracing" for each state. Bivariate correlations were computed to analyze associations between public interest in preventive measures, changes in confirmed COVID-19 cases after SAH expirations, COVID-19 case-fatality rates, and by-state presidential voting percentages. RESULTS: A higher interest in preventive measures was associated with lower rates of confirmed cases after SAH orders had expired (r = -0.33), higher state-wide deaths per capita (r = 0.42), and case-fatality rates (r = 0.60). Moderate to strong negative correlations were found between states' percentage of voters supporting the Republican nominee in 2016 and proportion of queries for average preventive measures (r = -0.77). CONCLUSION: Our investigation shows that increased public interest in COVID-19 prevention was associated with longer SAH orders and less COVID-19 cases after the SAH orders' expiration; however, it was also associated with higher case-fatality rates.