Evaluating Google Trends as a Tool for Integrating the ‘Smart Health’ Concept in the Smart Cities’ Governance in USA

AbstractThe aim of this paper is to introduce the methodology of using online search traffic data in order to integrate the public's online behavior in Smart Health; a concept that is currently rising concerning the health factor of Smart Cities. We use normalized data from Google Trends from January 2013 to December 2015 in the US, aiming at exploring the change in interest in various medical terms, and examine if Google Trends is a possible tool for evaluating health search queries by nowcasting the public's online interest. The results show that Google Trends’ data can be used for measuring the public's interest in health related terms, in order to assist with the evaluation of ‘Smart Health’

Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era

Background: With the internet’s penetration and use constantly expanding, this vast amount of information can be employed in order to better assess issues in the US health care system. Google Trends, a popular tool in big data analytics, has been widely used in the past to examine interest in various medical and health-related topics and has shown great potential in forecastings, predictions, and nowcastings. As empirical relationships between online queries and human behavior have been shown to exist, a new opportunity to explore the behavior toward asthma—a common respiratory disease—is present. Objective: This study aimed at forecasting the online behavior toward asthma and examined the correlations between queries and reported cases in order to explore the possibility of nowcasting asthma prevalence in the United States using online search traffic data. Methods: Applying Holt-Winters exponential smoothing to Google Trends time series from 2004 to 2015 for the term “asthma,” forecasts for online queries at state and national levels are estimated from 2016 to 2020 and validated against available Google query data from January 2016 to June 2017. Correlations among yearly Google queries and between Google queries and reported asthma cases are examined. Results: Our analysis shows that search queries exhibit seasonality within each year and the relationships between each 2 years’ queries are statistically significant (P < .05). Estimated forecasting models for a 5-year period (2016 through 2020) for Google queries are robust and validated against available data from January 2016 to June 2017. Significant correlations were found between (1) online queries and National Health Interview Survey lifetime asthma (r=–.82, P=.001) and current asthma (r=–.77, P=.004) rates from 2004 to 2015 and (2) between online queries and Behavioral Risk Factor Surveillance System lifetime (r=–.78, P=.003) and current asthma (r=–.79, P=.002) rates from 2004 to 2014. The correlations are negative, but lag analysis to identify the period of response cannot be employed until short-interval data on asthma prevalence are made available. Conclusions: Online behavior toward asthma can be accurately predicted, and significant correlations between online queries and reported cases exist. This method of forecasting Google queries can be used by health care officials to nowcast asthma prevalence by city, state, or nationally, subject to future availability of daily, weekly, or monthly data on reported cases. This method could therefore be used for improved monitoring and assessment of the needs surrounding the current population of patients with asthma

Advancing microbiome research with machine learning : key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Undervaccination and severe COVID-19 outcomes : meta-analysis of national cohort studies in England, Northern Ireland, Scotland, and Wales

Background: Undervaccination (receiving fewer than the recommended number of SARS-CoV-2 vaccine doses) could be associated with increased risk of severe COVID-19 outcomes—ie, COVID-19 hospitalisation or death—compared with full vaccination (receiving the recommended number of SARS-CoV-2 vaccine doses). We sought to determine the factors associated with undervaccination, and to investigate the risk of severe COVID-19 outcomes in people who were undervaccinated in each UK nation and across the UK. Methods: We used anonymised, harmonised electronic health record data with whole population coverage to carry out cohort studies in England, Northern Ireland, Scotland, and Wales. Participants were required to be at least 5 years of age to be included in the cohorts. We estimated adjusted odds ratios for undervaccination as of June 1, 2022. We also estimated adjusted hazard ratios (aHRs) for severe COVID-19 outcomes during the period June 1 to Sept 30, 2022, with undervaccination as a time-dependent exposure. We combined results from nation-specific analyses in a UK-wide fixed-effect meta-analysis. We estimated the reduction in severe COVID-19 outcomes associated with a counterfactual scenario in which everyone in the UK was fully vaccinated on June 1, 2022. Findings: The numbers of people undervaccinated on June 1, 2022 were 26 985 570 (45·8%) of 58 967 360 in England, 938 420 (49·8%) of 1 885 670 in Northern Ireland, 1 709 786 (34·2%) of 4 992 498 in Scotland, and 773 850 (32·8%) of 2 358 740 in Wales. People who were younger, from more deprived backgrounds, of non-White ethnicity, or had a lower number of comorbidities were less likely to be fully vaccinated. There was a total of 40 393 severe COVID-19 outcomes in the cohorts, with 14 156 of these in undervaccinated participants. We estimated the reduction in severe COVID-19 outcomes in the UK over 4 months of follow-up associated with a counterfactual scenario in which everyone was fully vaccinated on June 1, 2022 as 210 (95% CI 94–326) in the 5–15 years age group, 1544 (1399–1689) in those aged 16–74 years, and 5426 (5340–5512) in those aged 75 years or older. aHRs for severe COVID-19 outcomes in the meta-analysis for the age group of 75 years or older were 2·70 (2·61–2·78) for one dose fewer than recommended, 3·13 (2·93–3·34) for two fewer, 3·61 (3·13–4·17) for three fewer, and 3·08 (2·89–3·29) for four fewer. Interpretation: Rates of undervaccination against COVID-19 ranged from 32·8% to 49·8% across the four UK nations in summer, 2022. Undervaccination was associated with an elevated risk of severe COVID-19 outcomes. Funding: UK Research and Innovation National Core Studies: Data and Connectivity

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

Undervaccination and severe COVID-19 outcomes: meta-analysis of national cohort studies in England, Northern Ireland, Scotland, and Wales

Background Under-vaccination (receiving fewer than the recommended number of SARS-CoV-2 vaccine doses) may be associated with increased risk of severe COVID-19 outcomes (i.e., COVID-19 hospitalisation or death) compared to full-vaccination (receiving the recommended number of SARS-CoV-2 vaccine doses). We sought to determine the factors associated with under-vaccination, and to investigate the risk of severe COVID-19 outcomes in people who were under-vaccinated in each UK nation, and across the UK. Methods We used anonymised, harmonised electronic health record (EHR) data with whole population coverage to carry out cohort studies in England, Northern Ireland, Scotland and Wales. We estimated adjusted odds ratios (aORs) for under-vaccination as of June 1, 2022. We also estimated adjusted hazard ratios (aHRs) for severe COVID-19 outcomes over the period June 1 to September 30, 2022, with under-vaccination as a time-dependent exposure. We combined results from nation-specific analyses in a UK-wide fixed-effect meta-analysis. We estimated the reduction in severe COVID-19 outcomes associated with a counterfactual scenario where everyone in the UK was fully-vaccinated on June 1, 2022. Findings The percentages of people under-vaccinated (number of individuals in cohort) on June 1, 2022 were 45.7% (58.9 million), 49.8% (1.8 million), 34.2% (5.0 million) and 32.8% (3.5 million) in England, Northern Ireland, Scotland and Wales, respectively. People who were younger, from more deprived backgrounds, of non-white ethnicity or had lower number of comorbidities were less likely to be fully-vaccinated There was a total of 40,411 severe COVID-19 outcomes in the cohorts, with 14,165 in the under-vaccinated. We estimated the reduction in severe COVID-19 outcomes by the end of follow-up associated with full vaccination on June 1, 2022 (95% confidence intervals (CIs)) as 210 (94, 326), 1,544 (1,399, 1,689) and 5,426 (5,340, 5,512) in the 5-15, 16-74 and ≥75 year age groups respectively in the UK. Adjusted hazard ratios for severe COVID-19 outcomes in the meta-analysis for the ≥75 year age group (95% CIs) were 2.70 (2.61, 2.78), 3.13 (2.93, 3.34), 3.61 (3.13, 4.17) and 3.08 (2.89, 3.28) for 1, 2, 3 and 4 doses fewer than recommended. Interpretation Rates of under-vaccination against COVID-19 ranged from 32.8% to 49.8% across the four UK nations. in summer 2022. Under-vaccination was associated with an elevated risk of severe COVID-19 outcomes

Quantifying the UK Online Interest in Substances of the EU Watchlist for Water Monitoring: Diclofenac, Estradiol, and the Macrolide Antibiotics

Due to the increased interest in micropollutants, this paper aims at quantifying and analyzing the UK online interest in Diclofenac, Estradiol, Azithromycin, Clarithromycin, and Erythromycin, substances included in the EU watchlist for monitoring, in order to examine if the public’s online behavior and the use of these substances, in terms of issued prescriptions, are correlated. Using time series data from Google Trends from January 2004 to December 2014, an analysis of these substances in the UK, and in each UK region, i.e., England, Wales, Scotland, and Northern Ireland, is at first performed, followed by an analysis of interest by substance. The results show high interest in Diclofenac with a slight decline, while the Macrolides are significantly less popular though increasing. For Estradiol, the interest is low and declining throughout the examined period, in contrast to the scientific community, where Estradiol is the most studied substance. Prescription items and Google hits are highly correlated in the UK for Diclofenac, Azithromycin, and Clarithromycin, while no correlation is observed for Estradiol. Results from this study indicated that online search traffic data can be valuable in examining the public’s online behavior towards the monitored micropollutants, and could assist with the evaluation and forecasting of their concentrations in the waste, surface, and ground water in the UK