Mining Google Trends data for nowcasting and forecasting colorectal cancer (CRC) prevalence.

Background: Colorectal cancer (CRC) is the third most prevalent and second most lethal form of cancer in the world. Consequently, CRC cancer prevalence projections are essential for assessing the future burden of the disease, planning resource allocation, and developing service delivery strategies, as well as for grasping the shifting environment of cancer risk factors. However, unlike cancer incidence and mortality rates, national and international agencies do not routinely issue projections for cancer prevalence. Moreover, the limited or even nonexistent cancer statistics for large portions of the world, along with the high heterogeneity among world nations, further complicate the task of producing timely and accurate CRC prevalence projections. In this situation, population interest, as shown by Internet searches, can be very important for improving cancer statistics and, in the long run, for helping cancer research. Methods: This study aims to model, nowcast and forecast the CRC prevalence at the global level using a three-step framework that incorporates three well-established univariate statistical and machine-learning models. First, data mining is performed to evaluate the relevancy of Google Trends (GT) data as a surrogate for the number of CRC survivors. The results demonstrate that population web-search interest in the term "colonoscopy" is the most reliable indicator to nowcast CRC disease prevalence. Then, various statistical and machine-learning models, including ARIMA, ETS, and FNNAR, are trained and tested using relevant GT time series. Finally, the updated monthly query series spanning 2004-2022 and the best forecasting model in terms of out-of-sample forecasting ability (i.e., the neural network autoregression) are utilized to generate point forecasts up to 2025. Results: Results show that the number of people with colorectal cancer will continue to rise over the next 24 months. This in turn emphasizes the urgency for public policies aimed at reducing the population's exposure to the principal modifiable risk factors, such as lifestyle and nutrition. In addition, given the major drop in population interest in CRC during the first wave of the COVID-19 pandemic, the findings suggest that public health authorities should implement measures to increase cancer screening rates during pandemics. This in turn would deliver positive externalities, including the mitigation of the global burden and the enhancement of the quality of official statistics.

Ozone pollution in London and Edinburgh: spatiotemporal characteristics, trends, transport and the impact of COVID-19 control measures.

Air pollution remains the most serious environmental health issue in the United Kingdom while also carrying non-trivial economic costs. The COVID-19 lockdown periods reduced anthropogenic emissions and offered unique conditions for air pollution research. This study sources fine-granularity geo-spatial air quality and meteorological data for the capital cities of two UK countries (i.e. England's capital London and Scotland's capital Edinburgh) from the UK Automatic Urban and Rural Network (AURN) spanning 2016-2022 to assess long-term trends in several criteria pollutants (PM10, PM2.5, SO2, NO2, O3, and CO) and the changes in ozone pollution during the pandemic period. Unlike other studies conducted thus far, this research integrates several tools in trend estimation, including the Mann-Kendall test, the Theil-Sen estimator with bootstrap resampling, and the generalized additive model (GAM). Moreover, several investigations, including cluster trajectory analysis, pollution rose plots, and potential source contribution function (PSCF), are also employed to identify potential origin sources for air masses carrying precursors and estimate their contributions to ozone concentrations at receptor sites and downwind areas. The main findings reveal that most of the criteria pollutants show a decreasing trend in both geographies over the seven-year period, except for O3, which presents a significant ascending trend in London and a milder ascending trend in Edinburgh. However, O3 concentrations have significantly decreased during the year 2020 in both urban areas, despite registering sharp increases during the first lockdown period. In turn, these findings indicate on one hand that the O3 generation process is in the VOC-limited regime in both UK urban areas and, on the other hand, confirm previous findings that, when stretching the analysis period, diminishing ozone levels can lead to NOx reduction even in VOC-controlled geographies. Trajectory analysis reveals that northern Europe, particularly Norway and Sweden, is a principal ozone pollution source for Edinburgh, whereas, for London, mainland Europe (i.e., the Benelux countries) is another significant source. The results have important policy implications, revealing that effective and efficient NOx abatement measures spur ozone pollution in the short-term, but the increase can be transient. Moreover, policymakers in London and Edinburgh should consider that both local and transboundary sources contribute to local ozone pollution.

The Nexus between Pollution and Obesity and the Magnifying Role of Media Consumption: International Evidence from GMM Systems Estimates.

The aim of this paper is to uncover the associations between air pollution, media consumption, and the prevalence of obesity. Based on data availability, this study draws on an unbalanced panel of 28 countries and develops and extracts relationships through robust System-General Method of Moments (Sys-GMM) estimators that account for the dynamic nature and high persistence of the variables of interest. In light of previous findings, economic development, trade openness, and government consumption are included as controls in the dynamic panel models. The estimation results consistently indicate that pollution is a strong determinant of obesity, a link that remains robust through the alternative proxies for pollution (i.e., total greenhouse gas emissions (GHG) and carbon (CO2) intensity of energy generation). However, CO2 intensity shows the strongest association with obesity. Furthermore, the findings indicate that media consumption is an independent and significant driver of obesity, whilst its inclusion among regressors further magnifies the impact and significance of the pollution factor. Moreover, the combined effect of media consumption and pollution significantly contributes to spurring obesity in all model specifications. Thus, a vicious cycle emerges between air pollution, media consumption, and obesity, with synergistic detrimental health effects. The current findings highlight the importance of continuing and consistent efforts to mitigate pollution and reach related low-carbon policy targets. Moreover, for the sustainable reduction and prevention of obesity, these efforts should be complemented by policy interventions and public campaigns aimed at "healthy" media consumption, such as encouraging regular physical exercise and healthy nutrition.

A Novel Approach to Modeling and Forecasting Cancer Incidence and Mortality Rates through Web Queries and Automated Forecasting Algorithms: Evidence from Romania.

Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of "oncotourism". This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models-the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR-are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system.

Infodemiological study on the impact of the COVID-19 pandemic on increased headache incidences at the world level.

The analysis of the public interest as reflected by Internet queries has become a highly valuable tool in many fields. The Google Trends platform, providing timely and informative data, has become increasingly popular in health and medical studies. This study explores whether Internet search frequencies for the keyword "headache" have been increasing after the COVID-19 pandemic outbreak, which could signal an increased incidence of the health problem. Weekly search volume data for 5 years spanning February 2017 to February 2022 were sourced from Google Trends. Six statistical and machine-learning methods were implemented on training and testing sets via pre-set automated forecasting algorithms. Holt-Winters has been identified as overperforming in predicting web query trends through several accuracy measures and the DM test for forecasting superiority and has been employed for producing the baseline level in the estimation of excess query level over the first pandemic wave. Findings indicate that the COVID-19 pandemic resulted in an increased global incidence of headache (as proxied by related web queries) in the first 6 months after its outbreak, with an excess occurrence of 4.53% globally. However, the study also concludes that the increasing trend in headache incidence at the world level would have continued in the absence of the pandemic, but it has been accelerated by the pandemic event. Results further show mixed correlations at the country-level between COVID-19 infection rates and population web-search behavior, suggesting that the increased headache incidence is caused by pandemic-related factors (i.e. increased stress and mental health problems), rather than a direct effect of coronavirus infections. Other noteworthy findings entail that in the Philippines, the term "headache" was the most frequently searched term in the period spanning February 2020 to February 2022, indicating that headache occurrences are a significant aspect that defines population health at the country level. High relative interest is also detected in Kenya and South Africa after the pandemic outbreak. Additionally, research findings indicate that the relative interest has decreased in some countries (i.e. US, Canada, and Australia), whereas it has increased in others (i.e. India and Pakistan) after the pandemic outbreak. We conclude that observing Internet search habits can provide timely information for policymakers on collective health trends, as opposed to ex-post statistics, and can furthermore yield valuable information for the pain management drug market key players about aggregate consumer behavior.