Census Tract Food Tweets and Chronic Disease Outcomes in the U.S., 2015⁻2018

Neighborhood Condition Prevalence Rates Correlate With COVID-19 Mortality in Milwaukee County, Wisconsin

Purpose

We sought to determine if census tract-level (ie, neighborhood) COVID-19 death rates in Milwaukee County correlated with the census tract-level condition prevalence rates (CPRs) for individual COVID-19 mortality risk.

Methods

This study used Milwaukee County-reported COVID-19 death rates per 100,000 lives for the 296 census tracts within the county to perform a linear regression with individual COVID-19 mortality risk CPR, mean age, racial composition of census tract (by percentage of non-White residents), and poverty (by percentage within census tract), followed by multiple regression with all 7 CPRs as well as the 7 CPRs combined with the additional demographic variables. CPR estimates were accessed from the Centers for Disease Control and Prevention 500 Cities Project. Demographics were accessed from the U.S. Census. The Milwaukee County Medical Examiner's office identified 898 deaths from COVID-19 in Milwaukee County from March 2020 to June 2021.

Results

Among the variables included, crude death rate demonstrated a statistically significant association with the 7 COVID-19 mortality risk CPRs (as analyzed collectively), census tract mean age, and several of the CPRs individually. The addition of census tract age, race, and poverty in multiple regression did not improve the association of the 7 CPRs with crude death rate.

Conclusions

Results from this population-level study indicated that census tracts with high COVID-19 mortality correlated with high-risk condition prevalence estimates within those census tracts, illustrating how health data collection and analysis at a census tract level could be helpful when planning pandemic-mitigating public health efforts.

Diet during the COVID-19 pandemic: An analysis of Twitter data

In this study, we measured the association between county characteristics and changes in healthy-food, fast-food, and alcohol tweets during the COVID-19 pandemic in the United States. Our analytic dataset consisted of 1,282,316 geotagged tweets that referenced food consumption posted before (63.2%) and during (36.8%) the pandemic and included all US states. We found the share of healthy-food tweets increased by 20.5% during the pandemic compared with pre-pandemic, while fast-food and alcohol tweets decreased by 9.4% and 11.4%, respectively. We also observed that time spent at home and more grocery stores per capita were associated with increased odds of healthy-food tweets and decreased odds of fast-food tweets. More liquor stores per capita was associated with increased odds of alcohol tweets. Our results highlight the potential impact of the pandemic on nutrition and alcohol consumption and the association between the built environment and health behaviors.

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We used Twitter data to quantify self-reported diet trends during the COVID-19 pandemic

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Healthy food consumption increased during the pandemic; alcohol consumption decreased

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Proximity to grocery stores and more time at home were associated with healthier diet

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Proximity to liquor stores corresponded with increased alcohol consumption

The COVID-19 pandemic upended many aspects of daily life, including how we eat and drink. Restaurant closures and retail restrictions likely impacted individuals’ consumption habits, but longitudinal surveys that monitor nutrition and/or alcohol intake are costly to administer and are prone to response bias. In this study, we use digital trace data from Twitter to track population-level patterns in nutritional intake. Linking geotagged tweets to data measuring US county characteristics and built environment, this study finds that increased time at home and access to grocery stores during the pandemic may have promoted healthy-food consumption. This study also suggests that access to alcohol retail establishments may have led to more drinking. These findings validate the importance of the built environment to health behaviors while highlighting how social media data may be used to assess the impact of public health crises.

Exploring the Linkages of Digital Food Communication and Analog Food Behavior: A Scoping Review

The linkages of digital food communication on social media platforms and analog food behavior of social media users are widely discussed in media and research, but less differentiated. Due to the interdisciplinary nature of the research field, the scientific studies are characterized by great heterogeneity in approaching the role of communication and modelling of food behavior, and thus also the conclusions on how digital food communication might be linked to analog food behavior. There is still much uncertainty regarding the relationship and underlying assumptions between digital communication and analog action. The rationale of this scoping review is to systematically summarize the findings of this heterogeneous body of knowledge. The importance and originality of this review are that it focuses explicitly on studies that provide insights into the nexus of digital food communication and analog food behavior, be it in the theoretical foundation, the results, or their interpretation. It draws on a socio-ecological model of food behavior that depicts food behavior variables in different domains and uses a differentiated categorization of food behavior (food choice, dietary intake, and eating behavior) to synthesize the results. Using the Web of Science and PubMed databases, 267 abstracts were identified and screened, of which 20 articles met the inclusion criteria and were selected for full-text analysis. The review offers some important insights on how different variables of the socio-ecological model of food behavior are related to digital food communication and different areas of analog food behavior. This review provides a more discerning understanding of which aspects of analog food behavior may be linked to social media food communication and in which ways. Implications are derived to reflect the role of communication in previous models of food behavior by adding a more nuanced and cross-cutting understanding of food communication.

Food Acquisition during the COVID-19 Lockdown and Its Associations with the Physical-Digital Integrated Community Food Environment: A Case Study of Nanjing, China

The COVID-19 pandemic and measures such as lockdowns affect food access, dietary choices, and food security. We conducted an online survey among 517 respondents during early 2020 in Nanjing, China to explore respondents' food acquisition behaviors before and during the pandemic and associations with the community food environment. Using geographic analysis and binary logistic models, we revealed that despite inconvenience regarding food acquisition, no food security issues occurred during lockdown in Nanjing. The pandemic changed the access and frequency of obtaining food; meanwhile, pre-pandemic habits had a strong impact on food acquisition behavior. Online and in-store food acquisition showed a substitution relationship, with online food access playing a crucial role in food acquisition. Physical and digit food outlets are highly integrated in Chinese urban communities, and both objectively measured and perceived accessibility of these food outlets had a significant association with the food acquisition methods and transportation mode chosen by people during this public health crisis.

COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.

Mapping the digital food environment: A systematic scoping review

Food environments are directly linked to diets and health outcomes such as overweight, obesity, and noncommunicable diseases. The digitalization of food environments is becoming a central issue in public health, yet little is known about this emerging field. We performed a systematic scoping review to map the research on the digital food environment and investigate how the eight dimensions of the food environment, according to an established framework (availability, prices, vendor and product properties, marketing and regulation, accessibility, affordability, convenience, and desirability), might be shifting in the context of a digital society. We searched MEDLINE, EMBASE, Scopus, and Web of Science for studies published between 2000 and 2019, using search terms covering digital technology and food environment, which yielded 13,580 unique records. Our analysis of 357 studies shows that digitalization is taking place in all dimensions of the food environment, and enabling the emergence of new forms of buying and selling food, such as online grocery shopping and online food delivery, which may be changing availability of foods and affecting the physical distance to shops and time allocated for shopping. Systematic reviews identified indicated that digital food marketing and social media can influence food choices, preferences and consumption. Our findings suggest that digital and physical food environments are interconnected and influencing one another, but the impact of the digital on health and nutrition is yet unclear.

Health, Psychosocial, and Social Issues Emanating From the COVID-19 Pandemic Based on Social Media Comments: Text Mining and Thematic Analysis Approach

Background

The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioral change and policy initiatives such as physical distancing have been implemented to control the spread of COVID-19. Social media data can reveal public perceptions toward how governments and health agencies worldwide are handling the pandemic, and the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally.

Objective

This paper aims to investigate the impact of the COVID-19 pandemic on people worldwide using social media data.

Methods

We applied natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collected over 47 million COVID-19–related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we performed data preprocessing, which involved applying NLP techniques to clean and prepare the data for automated key phrase extraction. Third, we applied the NLP approach to extract meaningful key phrases from over 1 million randomly selected comments and computed sentiment score for each key phrase and assigned sentiment polarity (ie, positive, negative, or neutral) based on the score using a lexicon-based technique. Fourth, we grouped related negative and positive key phrases into categories or broad themes.

Results

A total of 34 negative themes emerged, out of which 15 were health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues were increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues were frustrations due to life disruptions, panic shopping, and expression of fear. Social issues were harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes were public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research.

Conclusions

We uncovered various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommended interventions that can help address the health, psychosocial, and social issues based on the positive themes and other research evidence. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, and in reacting to any future pandemics.

Utilization of text mining as a big data analysis tool for food science and nutrition

Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.

The Story of Goldilocks and Three Twitter's APIs: A Pilot Study on Twitter Data Sources and Disclosure

Public health and social science increasingly use Twitter for behavioral and marketing surveillance. However, few studies provide sufficient detail about Twitter data collection to allow either direct comparisons between studies or to support replication. The three primary application programming interfaces (API) of Twitter data sources are Streaming, Search, and Firehose. To date, no clear guidance exists about the advantages and limitations of each API, or about the comparability of the amount, content, and user accounts of retrieved tweets from each API. Such information is crucial to the validity, interpretation, and replicability of research findings. This study examines whether tweets collected using the same search filters over the same time period, but calling different APIs, would retrieve comparable datasets. We collected tweets about anti-smoking, e-cigarettes, and tobacco using the aforementioned APIs. The retrieved tweets largely overlapped between three APIs, but each also retrieved unique tweets, and the extent of overlap varied over time and by topic, resulting in different trends and potentially supporting diverging inferences. Researchers need to understand how different data sources can influence both the amount, content, and user accounts of data they retrieve from social media, in order to assess the implications of their choice of data source.

Implementation of an Interdisciplinary Approach to Promote Workers Global Health Status in the Oil Industry, Brazil (2006-2015)

This study intends to analyse the behaviour of epidemiological variables of workers in anoilindustry of Bahia, Brazil, before and after implementation of interdisciplinary practices in occupational health assessments between 2006 and 2015. This is a retrospective longitudinal study carried out in two time periods. Data were collected from the workers electronic medical record and time trends were analysed before (2006-2010) and after (2011-2015) the implementation of the interdisciplinary practices focusing on health promotion. The data were complementarily compared to a control group from the same industry.A statistically significant reduction for data on the number of smokers, periodontal disease and of days away from work was obtained. A significant increase in the number of physically active subjects wasalso observed. Whilenot statistically significant, a reduction in the number of workers with obesity and overweight, with caries and altered glycemia, was identified. Coronary risk and high blood pressure indicators have shown aggravation. It can be concluded that an interdisciplinary health approach during the annual occupational assessments, with action directed to the population needs, can be associated with the improvement of the health indicators assessed, contributing to increased worker productivity in the oil industry.