Evidence from internet search data shows information-seeking responses to news of local COVID-19 cases.
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ABSTRACT:
SUBMITTER: Bento AI
PROVIDER: S-EPMC7260988 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors.ObjectiveThe objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa.MethodsWe obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance.ResultsThe study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence.ConclusionsInformation-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.
| S-EPMC8120431 | biostudies-literature
Project description:BackgroundBrief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information.ObjectiveIdentification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information.MethodsWe extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried.ResultsIn our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified.ConclusionsOur results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness.
| S-EPMC6533047 | biostudies-literature
Project description:Confirmation bias in information-search contributes to the formation of polarized echo-chambers of beliefs. However, the role of valence on information source selection remains poorly understood. In Experiment 1, participants won financial rewards depending on the outcomes of a set of lotteries. They were not shown these outcomes, but instead could choose to view a prediction of each lottery outcome made by one of two sources. Before choosing their favoured source, participants were first shown a series of example predictions made by each. The sources systematically varied in the accuracy and positivity (i.e., how often they predicted a win) of their predictions. Hierarchical Bayesian modeling indicated that both source accuracy and positivity impacted participants' choices. Importantly, those that viewed more positively-biased information believed that they had won more often and had higher confidence in those beliefs. In Experiment 2, we directly assessed the effect of positivity on the perceived credibility of a source. In each trial, participants watched a single source making a series of predictions of lottery outcomes and rated the strength of their beliefs in each source. Interestingly, positively-biased sources were not seen as more credible. Together, these findings suggest that positively-biased information is sought partly due to the desirable emotional state it induces rather than having enhanced perceived credibility. Information sought on this basis nevertheless produced consequential biased beliefs about the world-state, highlighting a potentially key role for hedonic preferences in information selection and subsequent belief formation.
| S-EPMC9902457 | biostudies-literature
Project description:As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread are crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes also contain valuable information for tracking infectious disease dynamics such as influenza epidemic. In this study, we develop a statistical model using Internet search volume of relevant queries to track and predict COVID-19 pandemic in the United States. Inspired by the strong association between COVID-19 death trend and symptom-related search queries such as "loss of taste", we combine search volume information with COVID-19 time series information for US national level forecasts, while leveraging the cross-state cross-resolution spatial temporal framework, pooling information from search volume and COVID-19 reports across regions for state level predictions. Lastly, we aggregate the state-level frameworks in an ensemble fashion to produce the final state-level 4-week forecasts. Our method outperforms the baseline time-series model, while performing reasonably against other publicly available benchmark models for both national and state level forecast.
| S-EPMC9261899 | biostudies-literature
Project description:BackgroundWith online health information becoming increasingly popular among patients, concerns have been raised about the impact of patients' Internet health information-seeking behavior on their relationship with physicians. Therefore, it is pertinent to understand the influence of online health information on the patient-physician relationship.ObjectiveOur objective was to systematically review existing research on patients' Internet health information seeking and its influence on the patient-physician relationship.MethodsWe systematically searched PubMed and key medical informatics, information systems, and communication science journals covering the period of 2000 to 2015. Empirical articles that were in English were included. We analyzed the content covering themes in 2 broad categories: factors affecting patients' discussion of online findings during consultations and implications for the patient-physician relationship.ResultsWe identified 18 articles that met the inclusion criteria and the quality requirement for the review. The articles revealed barriers, facilitators, and demographic factors that influence patients' disclosure of online health information during consultations and the different mechanisms patients use to reveal these findings. Our review also showed the mechanisms in which online information could influence patients' relationship with their physicians.ConclusionsResults of this review contribute to the understanding of the patient-physician relationship of Internet-informed patients. Our main findings show that Internet health information seeking can improve the patient-physician relationship depending on whether the patient discusses the information with the physician and on their prior relationship. As patients have better access to health information through the Internet and expect to be more engaged in health decision making, traditional models of the patient-provider relationship and communication strategies must be revisited to adapt to this changing demographic.
| S-EPMC5290294 | biostudies-literature
Project description:BackgroundAccurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports.MethodsWe extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013-2016 using multiple metrics including root mean squared error (RMSE).ResultsOur method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons.ConclusionsOur method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu.
| S-EPMC5423019 | biostudies-literature
Project description:BACKGROUND:To assess where, when, and why survivors of childhood cancer seek health information. PROCEDURE:Data from the Childhood Cancer Survivor Study (CCSS) cohort (n = 1386) and Health Information National Trends Survey (n = 2385) were analyzed to determine the health information seeking strategies of childhood cancer survivors. Descriptive frequencies, ?2 analyses, t-tests, and multivariable logistic regression models were used. RESULTS:To seek health-related information for themselves, 54% (n = 742) of the childhood survivors reported using the Internet in the past 12 months, compared to 45% of the general population (adjusted OR: 2.76; 95% CI: 2.40-3.19). Childhood cancer survivors who used the Internet for health information were more likely to be female, between the ages of 18-34, have received some college education or be a college graduate, and report being in poor health. Although survivors were less likely than the general population to trust health information from the Internet (P < 0.01), they indicated that they would like a secure website that uses information from their medical records to provide individualized health-related information. CONCLUSION:The use of the Internet to access health information among the childhood cancer survivors was over 50%. Information on late effects was a high priority for most survivors, as was their interest in websites related to late effects and a website on patient information tailored to personal situations. Identification of factors associated with searching the Internet for cancer information may provide direction for development of effective cancer communication interventions for this at-risk population.
| S-EPMC5867215 | biostudies-literature
Project description:BackgroundThe knowledge about Internet search trends helps to know the information-seeking behavior of the Internet users and this would help in formulating Information Education and Communication strategy.AimThe aim of this study was to explore the global Internet search trends about vaccine-related information in English.Materials and methodsFirst, we conducted a pilot interview with 15 participants (convenience sample from a tertiary care hospital) to make a list of five common query keywords (viz., When, Where, Cost, Side effect, Schedule) for vaccine-related search. Then, we obtained the search trends from "Google trends" for 5 years with the five query keywords prefixed with vaccine (e.g. vaccine when). In the second phase, individual vaccine search was conducted with a particular vaccine name and query keyword (e.g. measles vaccine side effect).ResultsFive-year search trend showed that the highest volume of search was for "schedule" (36.79%), followed by "when" (26.57%), "cost" (21.97%), and "where" (11.99%). The "side effect" showed the lowest volume of search (2.68%) (χ2 = 10595, P < 0.0001). The search volume was increased over the years. The highest volume of the "when" and "where" were searched from the USA. The "cost" was searched in the highest volume from Australia. "Side effect" and "schedule" of the vaccine were searched with the highest volume from Philippines and Nepal, respectively.ConclusionThis study provides a glimpse of global vaccine-related search trends over a period of 5 years. Information on the "schedule" and "when" to get the vaccine should be strengthened for wider dissemination of knowledge.
| S-EPMC7113945 | biostudies-literature
Project description:Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. However, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.
| S-EPMC7451983 | biostudies-literature
Project description:Google Trends is an online tool that allows measurement of search term popularity on Google, spatially and temporally. While not an epidemiological tool for determining incidence, it can estimate the popularity of a certain disease by search volume over time.1,2 It has previously correlated well with infectious disease incidence and has demonstrated utility in disease forecasting, especially with influenza data.3 We utilized Google Trends to investigate whether search interest in common gastrointestinal (GI) symptoms would correlate with coronavirus disease 2019 (COVID-19) incidence data.
| S-EPMC7834024 | biostudies-literature