Public Health Implications of Google Searches for Sunscreen, Sunburn, Skin Cancer, and Melanoma in the United States

2018 ◽  
Vol 33 (4) ◽  
pp. 611-615 ◽  
Author(s):  
Zachary H. Hopkins ◽  
Aaron M. Secrest
Keyword(s):  
Public Health ◽  
United States ◽  
Skin Cancer ◽  
Search Engine ◽  
State Level ◽  
The United States ◽  
Google Trends ◽  
Search Term ◽  
Melanoma Incidence ◽  
Google Search

Purpose: Google Trends (GT) offers insights into public interests and behaviors and holds potential for guiding public health campaigns. We evaluated trends in US searches for sunscreen, sunburn, skin cancer, and melanoma and their relationships with melanoma outcomes. Design: Google Trends was queried for US search volumes from 2004 to 2017. Time-matched search term data were correlated with melanoma outcomes data from Surveillance Epidemiology and End Results Program and United States Cancer Statistics databases (2004-2014 and 2010-2014, respectively). Setting: Users of the Google search engine in the United States. Participants: Google search engine users in the United States. This represents approximately 65% of the population. Measures: Search volumes, melanoma outcomes. Analysis: Pearson correlations between search term volumes, time, and national melanoma outcomes. Spearman correlations between state-level search data and melanoma outcomes. Results: The terms “sunscreen,” “sunburn,” “skin cancer,” and “melanoma” were all highly correlated ( P < .001), with sunscreen and sunburn having the greatest correlation ( r = 0.95). Sunscreen/sunburn searches have increased over time, but skin cancer/melanoma searches have decreased ( P < .05). Nationally, sunscreen, sunburn, and skin cancer were significantly correlated with melanoma incidence. At the state level, only sunscreen and melanoma searches were significantly correlated with melanoma incidence. Conclusions: We conclude that online skin cancer prevention campaigns should focus on the search terms “sunburn” and “sunscreen,” given the decreasing online searches for skin cancer and melanoma. This is reinforced by the finding that sunscreen searches are higher in areas with higher melanoma incidence.

Abstract 17380: Can Understanding of National Search Engine Queries Inform Heart Failure Prevention? National Internet Search Engine Queries Correlates With CHF Prevalence but Not Outcomes

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
adebayo atanda ◽  
Olajide Buhari ◽  
Mohammed Alarfaj ◽  
Hassan Khalil ◽  
Alberto Batresh ◽  
...  
Keyword(s):  
United States ◽  
Heart Failure ◽  
Congestive Heart Failure ◽  
Search Engine ◽  
The United States ◽  
Mortality Rates ◽  
Google Trends ◽  
Diagnostic Tools ◽  
Internet Search ◽  
Google Search

Introduction: Congestive heart failure (CHF) remains an epidemic with rising prevalence and a contributing cause of 1 in 9 deaths in the United States. An understanding of internet search engines for congestive heart failure as informational and initial diagnostic tools may enable targeted education strategies. Hypothesis: There is a correlation between CHF google search and outcomes. Methods: We used google trends, a publicly available google tool, to identify search frequency for CHF and related terms like early signs of heart failure, congestive heart failure facts over a 2 year period from 2014-2016 across regions of the United States. We then evaluated the prevalence of hospitalization and mortality rates among Medicare beneficiaries based on Center for Disease Control (CDC) data. Utilizing Pearson correlation (R) test, we determined the association between relative search frequency (RSF) in various states versus CHF hospitalization and mortality rates. Results: Across the 50 states in United States, there were 25 searches related to the search terms. There was a moderate positive correlation (R 0.4-0.7) between CHF hospitalization (R= 0.43) and mortality (R=0.51) with relative search frequency in google trends. Conclusions: We demonstrated a correlation between internet search and CHF prevalence and hospitalization. The emergence of data analytics in CHF care may enable greater understanding of patient questions in CHF to better target education and prevention.

Can increases in Twitter posts predict increases in cumulative incidence of COVID-19 in the United States? Evidence that social media can inform epidemic surveillance. (Preprint)

2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani
Keyword(s):  
Public Health ◽  
United States ◽  
Cumulative Incidence ◽  
Hot Spots ◽  
Adult Population ◽  
State Level ◽  
The United States ◽  
Education Attainment ◽  
Public Health Systems ◽  
Per Capita

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.

scholarly journals Patterns and correlates of mis-implementation in state chronic disease public health practice in the United States

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Margaret M. Padek ◽  
Stephanie Mazzucca ◽  
Peg Allen ◽  
Emily Rodriguez Weno ◽  
Edward Tsai ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
Chronic Disease ◽  
Multinomial Logistic Regression ◽  
Public Health Practice ◽  
State Level ◽  
The United States ◽  
Organizational Level ◽  
Effective Programs

Abstract Background Much of the disease burden in the United States is preventable through application of existing knowledge. State-level public health practitioners are in ideal positions to affect programs and policies related to chronic disease, but the extent to which mis-implementation occurring with these programs is largely unknown. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Methods A 2018 comprehensive survey assessing the extent of mis-implementation and multi-level influences on mis-implementation was reported by state health departments (SHDs). Questions were developed from previous literature. Surveys were emailed to randomly selected SHD employees across the Unites States. Spearman’s correlation and multinomial logistic regression were used to assess factors in mis-implementation. Results Half (50.7%) of respondents were chronic disease program managers or unit directors. Forty nine percent reported that programs their SHD oversees sometimes, often or always continued ineffective programs. Over 50% also reported that their SHD sometimes or often ended effective programs. The data suggest the strongest correlates and predictors of mis-implementation were at the organizational level. For example, the number of organizational layers impeded decision-making was significant for both continuing ineffective programs (OR=4.70; 95% CI=2.20, 10.04) and ending effective programs (OR=3.23; 95% CI=1.61, 7.40). Conclusion The data suggest that changing certain agency practices may help in minimizing the occurrence of mis-implementation. Further research should focus on adding context to these issues and helping agencies engage in appropriate decision-making. Greater attention to mis-implementation should lead to greater use of effective interventions and more efficient expenditure of resources, ultimately to improve health outcomes.

scholarly journals Patterns and Correlates of Mis-implementation in State Chronic Disease Public Health Practice in the United States

2021 ◽  
Author(s):  
Margaret Padek ◽  
Stephanie Mazzucca ◽  
Peg Allen ◽  
Emily Rodriguez Weno ◽  
Edward Tsai ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
Decision Making ◽  
Chronic Disease ◽  
Multinomial Logistic Regression ◽  
Public Health Practice ◽  
State Level ◽  
The United States ◽  
Organizational Level ◽  
Effective Programs

Abstract Background: Much of the disease burden in the United States is preventable through application of existing knowledge. State-level public health practitioners are in ideal positions to affect programs and policies related to chronic disease, but the extent to which mis-implementation occurring with these programs is largely unknown. Mis-implementation refers to ending effective programs and policies prematurely or continuing ineffective ones. Methods: A 2018 comprehensive survey assessing the extent of mis-implementation and multi-level influences on mis-implementation was reported by state health departments (SHDs). Questions were developed from previous literature. Surveys were emailed to randomly selected SHD employees across the Unites States. Spearman’s correlation and multinomial logistic regression were used to assess factors in mis-implementation. Results: Half (50.7%) of respondents were chronic disease program managers or unit directors. Forty nine percent reported that programs their SHD oversees sometimes, often or always continued ineffective programs. Over 50% also reported that their SHD sometimes or often ended effective programs. The data suggest the strongest correlates and predictors of mis-implementation were at the organizational level. For example, the number of organizational layers impeded decision-making was significant for both continuing ineffective programs (OR=4.70; 95% CI=2.20, 10.04) and ending effective programs (OR=3.23; 95% CI=1.61, 7.40). Conclusion: The data suggest that changing certain agency practices may help in minimizing the occurrence of mis-implementation. Further research should focus on adding context to these issues and helping agencies engage in appropriate decision-making. Greater attention to mis-implementation should lead to greater use of effective interventions and more efficient expenditure of resources, ultimately to improve health outcomes.

scholarly journals Feeding the disparities: the geography and trends of breastfeeding in the United States

2018 ◽  
Author(s):  
Romain Garnier ◽  
Ana I. Bento ◽  
Pejman Rohani ◽  
Saad B. Omer ◽  
Shweta Bansal
Keyword(s):  
Public Health ◽  
United States ◽  
Breast Milk ◽  
Low Income ◽  
Temporal Trends ◽  
State Level ◽  
The United States ◽  
Poverty Level ◽  
Future Health ◽  
Breastfeeding Rates

AbstractThere is scientific consensus on the importance of breastfeeding for the present and future health of newborns, in high- and low-income settings alike. In the United States, improving breast milk access is a public health priority but analysis of secular trends are largely lacking. Here, we used data from the National Immunization Survey of the CDC, collected between 2003 and 2016, to illustrate the temporal trends and the spatial heterogeneity in breastfeeding. We also considered the effect sizes of two key determinants of breastfeeding rates. We show that, while access to breast milk both at birth and at 6 months old has steadily increased over the past decade, large spatial disparities still remain at the state level. We also find that, since 2009, the proportion of households below the poverty level has become the strongest predictor of breastfeeding rates. We argue that, because variations in breastfeeding rates are associated with socio-economic factors, public health policies advocating for breastfeeding are still needed in particular in underserved communities. This is key to reducing longer term health disparities in the U.S., and more generally in high-income countries.

scholarly journals Evaluating Surveillance for and Estimating Administration of Rabies Postexposure Prophylaxis in the United States, 2012–2018

2021 ◽  
Vol 15 (10) ◽  
pp. e0009878
Author(s):  
Erin R. Whitehouse ◽  
Marissa K. Person ◽  
Catherine M. Brown ◽  
Sally Slavinski ◽  
Agam K. Rao ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
National Level ◽  
State Level ◽  
The United States ◽  
Surveillance Data ◽  
Human Rabies ◽  
Accurate Assessment ◽  
Postexposure Prophylaxis ◽  
Ed Visits

Background An evaluation of postexposure prophylaxis (PEP) surveillance has not been conducted in over 10 years in the United States. An accurate assessment would be important to understand current rabies trends and inform public health preparedness and response to human rabies. Methodology/Principle findings To understand PEP surveillance, we sent a survey to public health leads for rabies in 50 U.S. states, Puerto Rico, Washington DC, Philadelphia, and New York City. Of leads from 54 jurisdictions, 39 (72%) responded to the survey; 12 reported having PEP-specific surveillance, five had animal bite surveillance that included data about PEP, four had animal bite surveillance without data about PEP, and 18 (46%) had neither. Although 12 jurisdictions provided data about PEP use, poor data quality and lack of national representativeness prevented use of this data to derive a national-level PEP estimate. We used national-level and state specific data from the Healthcare Cost & Utilization Project (HCUP) to estimate the number of people who received PEP based on emergency department (ED) visits. The estimated annual average of initial ED visits for PEP administration during 2012–2017 in the United States was 46,814 (SE: 1,697), costing upwards of 165 million USD. State-level ED data for initial visits for administration of PEP for rabies exposure using HCUP data was compared to state-level surveillance data from Maryland, Vermont, and Georgia between 2012–2017. In all states, state-level surveillance data was consistently lower than estimates of initial ED visits, suggesting even states with robust PEP surveillance may not adequately capture individuals who receive PEP. Conclusions Our findings suggest that making PEP a nationally reportable condition may not be feasible. Other methods of tracking administration of PEP such as syndromic surveillance or identification of sentinel states should be considered to obtain an accurate assessment.

scholarly journals Mask adherence and rate of COVID-19 across the United States

2021 ◽  
Author(s):  
Charlie B. Fischer ◽  
Nedghie Adrien ◽  
Jeremiah J. Silguero ◽  
Julianne J. Hopper ◽  
Abir I. Chowdhury ◽  
...  
Keyword(s):  
Public Health ◽  
United States ◽  
General Public ◽  
State Level ◽  
The United States ◽  
Policy Makers ◽  
Level Data ◽  
High Adherence ◽  
Key Factor ◽  
Public Health Officials

AbstractMask wearing has been advocated by public health officials as a way to reduce the spread of COVID-19. In the United States, policies on mask wearing have varied from state to state over the course of the pandemic. Even as more and more government leaders encourage or even mandate mask wearing, many citizens still resist the notion. Our research examines mask wearing policy and adherence in association with COVID-19 case rates. We used state-level data on mask wearing policy for the general public and on proportion of residents who stated they always wear masks in public. For all 50 states and the District of Columbia (DC), these data were abstracted by month for April ⍰ September 2020 to measure their impact on COVID-19 rates in the subsequent month (May ⍰ October 2020). Monthly COVID-19 case rates (number of cases per capita over two weeks) >200 per 100,000 residents were considered high. Fourteen of the 15 states with no mask wearing policy for the general public through September reported a high COVID-19 rate. Of the 8 states with at least 75% mask adherence, none reported a high COVID-19 rate. States with the lowest levels of mask adherence were most likely to have high COVID-19 rates in the subsequent month, independent of mask policy or demographic factors. Mean COVID-19 rates for states with at least 75% mask adherence in the preceding month was 109.26 per 100,000 compared to 249.99 per 100,000 for those with less adherence. Our analysis suggests high adherence to mask wearing could be a key factor in reducing the spread of COVID-19. This association between high mask adherence and reduced COVID-19 rates should influence policy makers and public health officials to focus on ways to improve mask adherence across the population in order to mitigate the spread of COVID-19.

Spikes and Variance: Using Google Trends to Detect and Forecast Protests

2021 ◽  
pp. 1-18
Author(s):  
Joan C. Timoneda ◽  
Erik Wibbels
Keyword(s):  
Big Data ◽  
Access Point ◽  
The United States ◽  
Google Trends ◽  
Control Group ◽  
Search Term ◽  
Synthetic Control ◽  
Out Of Sample ◽  
Rare Phenomena ◽  
Google Search

Abstract Google search is ubiquitous, and Google Trends (GT) is a potentially useful access point for big data on many topics the world over. We propose a new ‘variance-in-time’ method for forecasting events using GT. By collecting multiple and overlapping samples of GT data over time, our algorithm leverages variation both in the mean and the variance of a search term in order to accommodate some idiosyncracies in the GT platform. To elucidate our approach, we use it to forecast protests in the United States. We use data from the Crowd Counting Consortium between 2017 and 2019 to build a sample of true protest events as well as a synthetic control group where no protests occurred. The model’s out-of-sample forecasts predict protests with higher accuracy than extant work using structural predictors, high frequency event data, or other sources of big data such as Twitter. Our results provide new insights into work specifically on political protests, while providing a general approach to GT that should be useful to researchers of many important, if rare, phenomena.

scholarly journals The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis

10.2196/22880 ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e22880
Author(s):  
Milad Asgari Mehrabadi ◽  
Nikil Dutt ◽  
Amir M Rahmani
Keyword(s):  
United States ◽  
Granger Causality ◽  
Public Interest ◽  
Granger Causality Test ◽  
The United States ◽  
Google Trends ◽  
Causality Test ◽  
Us States ◽  
Search Queries ◽  
Google Search

Background The COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak. Objective The objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project. Methods To test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends. Results Our results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test. Conclusions Although a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.

scholarly journals Social Media as an Early Proxy for Social Distancing Indicated by the COVID-19 Reproduction Number: Observational Study (Preprint)

2020 ◽  
Author(s):  
Joseph Younis ◽  
Harvy Freitag ◽  
Jeremy S Ruthberg ◽  
Jonathan P Romanes ◽  
Craig Nielsen ◽  
...  
Keyword(s):  
United States ◽  
Social Media ◽  
Observational Study ◽  
Social Mobility ◽  
Reproduction Number ◽  
The United States ◽  
Google Trends ◽  
Basic Reproductive Number ◽  
Social Distancing ◽  
Google Search

BACKGROUND  The magnitude and time course of the COVID-19 epidemic in the United States depends on early interventions to reduce the basic reproductive number to below 1. It is imperative, then, to develop methods to actively assess where quarantine measures such as social distancing may be deficient and suppress those potential resurgence nodes as early as possible. OBJECTIVE We ask if social media is an early indicator of public social distancing measures in the United States by investigating its correlation with the time-varying reproduction number (R<sub>t</sub>) as compared to social mobility estimates reported from Google and Apple Maps. METHODS  In this observational study, the estimated R<sub>t</sub> was obtained for the period between March 5 and April 5, 2020, using the EpiEstim package. Social media activity was assessed using queries of “social distancing” or “#socialdistancing” on Google Trends, Instagram, and Twitter, with social mobility assessed using Apple and Google Maps data. Cross-correlations were performed between R<sub>t</sub> and social media activity or mobility for the United States. We used Pearson correlations and the coefficient of determination (ρ) with significance set to <i>P</i>&lt;.05. RESULTS Negative correlations were found between Google search interest for “social distancing” and R<sub>t</sub> in the United States (<i>P</i>&lt;.001), and between search interest and state-specific R<sub>t</sub> for 9 states with the highest COVID-19 cases (<i>P</i>&lt;.001); most states experienced a delay varying between 3-8 days before reaching significance. A negative correlation was seen at a 4-day delay from the start of the Instagram hashtag “#socialdistancing” and at 6 days for Twitter (<i>P</i>&lt;.001). Significant correlations between R<sub>t</sub> and social media manifest earlier in time compared to social mobility measures from Google and Apple Maps, with peaks at –6 and –4 days. Meanwhile, changes in social mobility correlated best with R<sub>t</sub> at –2 days and +1 day for workplace and grocery/pharmacy, respectively. CONCLUSIONS Our study demonstrates the potential use of Google Trends, Instagram, and Twitter as epidemiological tools in the assessment of social distancing measures in the United States during the early course of the COVID-19 pandemic. Their correlation and earlier rise and peak in correlative strength with R<sub>t</sub> when compared to social mobility may provide proactive insight into whether social distancing efforts are sufficiently enacted. Whether this proves valuable in the creation of more accurate assessments of the early epidemic course is uncertain due to limitations. These limitations include the use of a biased sample that is internet literate with internet access, which may covary with socioeconomic status, education, geography, and age, and the use of subtotal social media mentions of social distancing. Future studies should focus on investigating how social media reactions change during the course of the epidemic, as well as the conversion of social media behavior to actual physical behavior.

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