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Big Data for Public Health Research and Practice

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 69504

Special Issue Editor

Dr. Quynh C. Nguyen

E-Mail Website
Guest Editor
Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA
Interests: neighborhoods; health disparities; social media; Google Street View; health technology; artificial intelligence; social epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are organizing a Special Issue on the use of big data to inform public health research and practice for the International Journal of Environmental Research and Public Health. This journal is peer-reviewed and publishes articles in the interdisciplinary area of environmental health sciences and public health.

To enable decision-making, we need timely data on the determinants of health and well-being. Big data can often be operational or “organic” data generated not for research purposes, including social media, news feeds, Google Street View images, online reviews, blogs, and billing, pharmacy, and laboratory data. These data are providing new ways of obtaining information on factors such as social norms, built environment features, health behaviors, and individual characteristics that can impact health. This Special Issue is focused on innovative ways big data are leveraged for health research. Some possible topics are listed below; however, other topics are also welcomed:

  • Use of electronic health records, billing data, and pharmacy data to understand individualized risk factors and treatment success;
  • Characterization of built environments with big data derived from various sources (e.g., Street View images and remote sensing imagery data) as well as their impacts on people’s health;
  • Using various user-generated content (e.g., GPS data, accelerometer data, users’ review data, social media data, and web search data) to study individual behaviors and social/cultural environments as well as their impacts on people’s health;
  • Development of new methods or tools (e.g., natural language processing, machine learning, database management, high performance computing, data mining, cloud computing, computer vision, visualization, geographic information systems, and spatial analysis) for big-data-based health research;
  • Use of big data in COVID-19-related research;
  • Application or development of causal inference methods for big data;
  • Investigating and addressing data quality and uncertainty issues;
  • Blending and integration of big data from different sources.

Dr. Quynh C. Nguyen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • natural language processing
  • machine learning
  • environment
  • public health

Related Special Issue

Published Papers (16 papers)

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Research

11 pages, 4854 KiB  
Article
Health Impacts of High BMI in China: Terrible Present and Future
by Hong Xiang, Runjuan Yang, Jiaxin Tu, Xi Guan and Xufeng Tao
Int. J. Environ. Res. Public Health 2022, 19(23), 16173; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph192316173 - 02 Dec 2022
Cited by 3 | Viewed by 1482
Abstract
Temporal trends and future expectations of health effects due to high body mass index (BMI) remain uncertain in China. The trends of high-BMI-related death in China were evaluated and predicted until 2040 using data and methods from the Global Burden of Disease study. [...] Read more.
Temporal trends and future expectations of health effects due to high body mass index (BMI) remain uncertain in China. The trends of high-BMI-related death in China were evaluated and predicted until 2040 using data and methods from the Global Burden of Disease study. The absolute numbers and age-standardized rates of death and disability-adjusted life years (DALYs) were also calculated by age, gender, and cause. From 1990 to 2019, the high-BMI-related death percent, number and rate were all significantly increased in China, and death rate may exceed that globally in the next 10 years. High BMI caused more deaths and DALYs for men than for women, and the gap appeared to increase over time. In 2019, the burden of high BMI among ages 0–14 and 15–19 for children and adolescents were lower than that among adults (>20 years). The most common cause of death associated with high BMI was stroke, followed by ischemic heart disease and hypertensive heart disease. High BMI burden is a significant public health challenges in China. BMI surveillance and evaluation of evidence-based preventive strategies should be immediately initiated in Chinese residents due to the rapid increase in the burden of high BMI. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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18 pages, 2570 KiB  
Article
Using Convolutional Neural Networks to Derive Neighborhood Built Environments from Google Street View Images and Examine Their Associations with Health Outcomes
by Xiaohe Yue, Anne Antonietti, Mitra Alirezaei, Tolga Tasdizen, Dapeng Li, Leah Nguyen, Heran Mane, Abby Sun, Ming Hu, Ross T. Whitaker and Quynh C. Nguyen
Int. J. Environ. Res. Public Health 2022, 19(19), 12095; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191912095 - 24 Sep 2022
Cited by 9 | Viewed by 4841
Abstract
Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study [...] Read more.
Built environment neighborhood characteristics are difficult to measure and assess on a large scale. Consequently, there is a lack of sufficient data that can help us investigate neighborhood characteristics as structural determinants of health on a national level. The objective of this study is to utilize publicly available Google Street View images as a data source for characterizing built environments and to examine the influence of built environments on chronic diseases and health behaviors in the United States. Data were collected by processing 164 million Google Street View images from November 2019 across the United States. Convolutional Neural Networks, a class of multi-layer deep neural networks, were used to extract features of the built environment. Validation analyses found accuracies of 82% or higher across neighborhood characteristics. In regression analyses controlling for census tract sociodemographics, we find that single-lane roads (an indicator of lower urban development) were linked with chronic conditions and worse mental health. Walkability and urbanicity indicators such as crosswalks, sidewalks, and two or more cars were associated with better health, including reduction in depression, obesity, high blood pressure, and high cholesterol. Street signs and streetlights were also found to be associated with decreased chronic conditions. Chain link fence (physical disorder indicator) was generally associated with poorer mental health. Living in neighborhoods with a built environment that supports social interaction and physical activity can lead to positive health outcomes. Computer vision models using manually annotated Google Street View images as a training dataset were able to accurately identify neighborhood built environment characteristics. These methods increases the feasibility, scale, and efficiency of neighborhood studies on health. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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19 pages, 6577 KiB  
Article
Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case
by Fernando Arias, Ariel Guerra-Adames, Maytee Zambrano, Efraín Quintero-Guerra and Nathalia Tejedor-Flores
Int. J. Environ. Res. Public Health 2022, 19(16), 10328; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph191610328 - 19 Aug 2022
Cited by 5 | Viewed by 1724
Abstract
Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as [...] Read more.
Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish-language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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11 pages, 692 KiB  
Article
The #StopAsianHate Movement on Twitter: A Qualitative Descriptive Study
by Jiepin Cao, Chiyoung Lee, Wenyang Sun and Jennie C. De Gagne
Int. J. Environ. Res. Public Health 2022, 19(7), 3757; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19073757 - 22 Mar 2022
Cited by 17 | Viewed by 6336
Abstract
Evidence-based intervention and policy strategies to address the recent surge of race-motivated hate crimes and other forms of racism against Asian Americans are essential; however, such efforts have been impeded by a lack of empirical knowledge, e.g., about racism, specifically aimed at the [...] Read more.
Evidence-based intervention and policy strategies to address the recent surge of race-motivated hate crimes and other forms of racism against Asian Americans are essential; however, such efforts have been impeded by a lack of empirical knowledge, e.g., about racism, specifically aimed at the Asian American population. Our qualitative descriptive study sought to fill this gap by using a data-mining approach to examine the contents of tweets having the hashtag #StopAsianHate. We collected tweets during a two-week time frame starting on 20 May 2021, when President Joe Biden signed the COVID-19 Hate Crimes Act. Screening of the 31,665 tweets collected revealed that a total of 904 tweets were eligible for thematic analysis. Our analysis revealed five themes: “Asian hate is not new”, “Address the harm of racism”, “Get involved in #StopAsianHate”, “Appreciate the Asian American and Pacific Islander (AAPI) community’s culture, history, and contributions” and “Increase the visibility of the AAPI community.” Lessons learned from our findings can serve as a foundation for evidence-based strategies to address racism against Asian Americans both locally and globally. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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13 pages, 1028 KiB  
Article
Effect of Obstructive Sleep Apnea on the Risk of Injuries—A Nationwide Population-Based Cohort Study
by An-Che Cheng, Gwo-Jang Wu, Chi-Hsiang Chung, Kuo-Hsiang Wu, Chien-An Sun, I-Duo Wang and Wu-Chien Chien
Int. J. Environ. Res. Public Health 2021, 18(24), 13416; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182413416 - 20 Dec 2021
Cited by 3 | Viewed by 2851
Abstract
Obstructive sleep apnea (OSA) has been reported to increase the risk of motor vehicle accidents. However, only few studies have investigated the effects of OSA on overall risk injury. The aim of study is to investigate whether OSA increases the risk of overall [...] Read more.
Obstructive sleep apnea (OSA) has been reported to increase the risk of motor vehicle accidents. However, only few studies have investigated the effects of OSA on overall risk injury. The aim of study is to investigate whether OSA increases the risk of overall injury. The data were collected during 2000–2015 from Taiwan’s National Health Insurance Research Database. A total of 8901 individuals diagnosed with OSA were inpatients, or outpatients at least three times were enrolled. Finally, 6915 participants with OSA were included as the study cohort. We matched the study cohort with a comparison cohort, at a ratio of 1:4. Cox proportional hazards regression was used to analyse the association between OSA and overall injury. Patients with OSA had 83.1% increased risk of overall injury, compared to non-OSA individuals [adjusted hazards ratio (HR) = 1.831, confidence interval (CI) = 1.674–2.020, p < 0.001]. In the stratified age group, patients aged ≧65 years had the highest risk of injury (adjusted HR= 2.014; CI = 1.842–2.222, p < 0.001). Patients with OSA were at a higher risk of falls, traffic injury, poisoning, suffocation, suicide, and abuse or homicide than non-OSA individuals, with falls and traffic injury as the leading causes of injuries. The data demonstrated that patients with OSA have a higher risk of overall injury. The study results can be a reference for developing injury prevention strategies in the future. The general population and clinicians should have more awareness regarding OSA and its negative effects on injury development. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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13 pages, 722 KiB  
Article
Google Street View-Derived Neighborhood Characteristics in California Associated with Coronary Heart Disease, Hypertension, Diabetes
by Thu T. Nguyen, Quynh C. Nguyen, Anna D. Rubinsky, Tolga Tasdizen, Amir Hossein Nazem Deligani, Pallavi Dwivedi, Ross Whitaker, Jessica D. Fields, Mindy C. DeRouen, Heran Mane, Courtney R. Lyles, Kim D. Brunisholz and Kirsten Bibbins-Domingo
Int. J. Environ. Res. Public Health 2021, 18(19), 10428; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph181910428 - 03 Oct 2021
Cited by 13 | Viewed by 8630
Abstract
Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood [...] Read more.
Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16–29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10–26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12–20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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16 pages, 2938 KiB  
Article
Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach
by Mazhar Javed Awan, Muhammad Haseeb Bilal, Awais Yasin, Haitham Nobanee, Nabeel Sabir Khan and Azlan Mohd Zain
Int. J. Environ. Res. Public Health 2021, 18(19), 10147; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph181910147 - 27 Sep 2021
Cited by 41 | Viewed by 4654
Abstract
Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals [...] Read more.
Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be challenging to detect and cure at an early stage all over the world. Patients showing symptoms of COVID-19 are resulting in hospitals becoming overcrowded, which is becoming a significant challenge. Deep learning’s contribution to big data medical research has been enormously beneficial, offering new avenues and possibilities for illness diagnosis techniques. To counteract the COVID-19 outbreak, researchers must create a classifier distinguishing between positive and negative corona-positive X-ray pictures. In this paper, the Apache Spark system has been utilized as an extensive data framework and applied a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) three architectures —InceptionV3, ResNet50, and VGG19—on COVID-19 chest X-ray images. The three models are evaluated in two classes, COVID-19 and normal X-ray images, with 100 percent accuracy. But in COVID/Normal/pneumonia, detection accuracy was 97 percent for the inceptionV3 model, 98.55 percent for the ResNet50 Model, and 98.55 percent for the VGG19 model, respectively. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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9 pages, 331 KiB  
Article
Does Exposure of Lead and Cadmium Affect the Endometriosis?
by Min-Gi Kim, Young-Sun Min and Yeon-Soon Ahn
Int. J. Environ. Res. Public Health 2021, 18(17), 9077; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18179077 - 28 Aug 2021
Cited by 6 | Viewed by 1932
Abstract
This study aimed to investigate the effects of blood lead levels (BLLs) and lead and cadmium exposure on endometriosis (EM). The study cohort consisted of female workers who underwent a lead-associated special medical examination between 1 January 2000 and 31 December 2004 ( [...] Read more.
This study aimed to investigate the effects of blood lead levels (BLLs) and lead and cadmium exposure on endometriosis (EM). The study cohort consisted of female workers who underwent a lead-associated special medical examination between 1 January 2000 and 31 December 2004 (n = 26,542). The standard admission rate (SAR) and admission odds ratio (OR) for EM were calculated using the general population and noise-exposed groups, respectively, for the same period as the reference standards. The SAR for EM was 1.24 (95% confidence interval (CI): 1.03–1.48) in lead-exposed workers and 1.44 (95% CI: 1.11–1.85) in workers with BLLs < 5 μg/dL when compared with that of the general population. Admission ORs of EM in lead-exposed workers and those with BLLs < 5 μg/dL were statistically higher than those of noise-exposed workers (OR, 1.40; 95% CI, 1.15–1.70 and OR, 1.48; 95% CI, 1.11–1.98, respectively). The relative excess risk due to interaction of lead and cadmium was 0.33. Lead exposure was associated with EM admission. EM admission in lead-exposed workers with BLLs < 5 μg/dL was statistically higher than that of the general population and noise-exposed workers. Co-exposure to lead and cadmium has a synergistic effect with EM. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
17 pages, 2396 KiB  
Article
The Comprehensive Machine Learning Analytics for Heart Failure
by Chao-Yu Guo, Min-Yang Wu and Hao-Min Cheng
Int. J. Environ. Res. Public Health 2021, 18(9), 4943; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18094943 - 06 May 2021
Cited by 10 | Viewed by 2933
Abstract
Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis [...] Read more.
Background: Early detection of heart failure is the basis for better medical treatment and prognosis. Over the last decades, both prevalence and incidence rates of heart failure have increased worldwide, resulting in a significant global public health issue. However, an early diagnosis is not an easy task because symptoms of heart failure are usually non-specific. Therefore, this study aims to develop a risk prediction model for incident heart failure through a machine learning-based predictive model. Although African Americans have a higher risk of incident heart failure among all populations, few studies have developed a heart failure risk prediction model for African Americans. Methods: This research implemented the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression, support vector machine, random forest, and Extreme Gradient Boosting (XGBoost) to establish the Jackson Heart Study’s predictive model. In the analysis of real data, missing data are problematic when building a predictive model. Here, we evaluate predictors’ inclusion with various missing rates and different missing imputation strategies to discover the optimal analytics. Results: According to hundreds of models that we examined, the best predictive model was the XGBoost that included variables with a missing rate of less than 30 percent, and we imputed missing values by non-parametric random forest imputation. The optimal XGBoost machine demonstrated an Area Under Curve (AUC) of 0.8409 to predict heart failure for the Jackson Heart Study. Conclusion: This research identifies variations of diabetes medication as the most crucial risk factor for heart failure compared to the complete cases approach that failed to discover this phenomenon. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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11 pages, 1174 KiB  
Article
The Association between Nonalcoholic Fatty Liver Disease and Stroke: Results from the Korean Genome and Epidemiology Study (KoGES)
by Yun-Jung Yang, Mi-Hyang Jung, Seok-Hoo Jeong, Yeon-Pyo Hong, Yeong In Kim and Sang Joon An
Int. J. Environ. Res. Public Health 2020, 17(24), 9568; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17249568 - 21 Dec 2020
Cited by 14 | Viewed by 2549
Abstract
(1) Background: Non-alcoholic fatty liver disease (NAFLD) is associated with various cardiometabolic diseases. However, the association between NAFLD and stroke is not well known. The purpose of our study is to reveal the relationship between NAFLD and Stroke incidence. (2) Methods: Using data [...] Read more.
(1) Background: Non-alcoholic fatty liver disease (NAFLD) is associated with various cardiometabolic diseases. However, the association between NAFLD and stroke is not well known. The purpose of our study is to reveal the relationship between NAFLD and Stroke incidence. (2) Methods: Using data from a Korean prospective cohort study, we excluded participants with heavy alcohol consumption and a history of stroke; hence, 7964 adults aged 40–69 years were included in this study. According to their fatty liver index (FLI), participants were divided into three groups: <30 (n = 4550, non-NAFLD), 30–59.9 (n = 2229, intermediate), and ≥60 (n = 1185, NAFLD). The incidence of stroke according to the degree of FLI was evaluated using the Cox proportional hazard model. (3) Results: During the 12-year follow-up period, 168 strokes occurred. A graded association between NAFLD and stroke incidence was observed, i.e., 1.7% (n = 76), 2.5% (n = 56), and 3.0% (n = 36) for non-NAFLD, intermediate, and NAFLD FLI groups, respectively. After adjusting for confounding variables and compared to the risk of stroke in the non-NAFLD group, the risk of stroke in the NAFLD group was the highest (hazard ratio [HR]: 1.98, 95% confidence interval [CI]: 1.17–3.34), followed by the risk of stroke in the intermediate group (HR: 1.41, 95% CI: 0.94–2.21) (p for trend < 0.001). However, the level of aspartate aminotransferase, alanine aminotransferase, or gamma-glutamyltransferase alone did not show any significant association with stroke. (4) Conclusions: This study demonstrated that the risk of stroke incidence gradually increased with the degree of FLI. Individuals with NAFLD should be properly counseled and monitored for risk for stroke. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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10 pages, 952 KiB  
Article
Association of Periodontitis with Atherosclerotic Cardiovascular Diseases: A Nationwide Population-based Retrospective Matched Cohort Study
by Min-Ji Byon, Se-Yeon Kim, Ji-Soo Kim, Han-Na Kim and Jin-Bom Kim
Int. J. Environ. Res. Public Health 2020, 17(19), 7261; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17197261 - 04 Oct 2020
Cited by 7 | Viewed by 2415
Abstract
We investigated the association between periodontitis and atherosclerotic cardiovascular disease (ACVD) development using the National Health Insurance Service—National Sample Cohort 2.0 (NHIS-NSC2) database, which contains data for approximately 1 million nationally representative random participants. We selected 52,425 participants aged 20+ years and diagnosed [...] Read more.
We investigated the association between periodontitis and atherosclerotic cardiovascular disease (ACVD) development using the National Health Insurance Service—National Sample Cohort 2.0 (NHIS-NSC2) database, which contains data for approximately 1 million nationally representative random participants. We selected 52,425 participants aged 20+ years and diagnosed with periodontitis from January to December 2003 and used propensity score matching to select an equivalent number of participants who were never diagnosed with periodontitis in the period covered by the NHIS-NSC2 database (2002–2015). The propensity scores were based on sex, age group, type of national health insurance, household income, diabetes status, and hypertension status and were used for 1:1 matching of individuals with similar propensities. A total of 104,850 participants were selected for the study. A multivariable Cox proportional hazard regression model was used to investigate the risk of ACVD development due to periodontitis from 2003 to 2015 after adjusting for sex, age, type of national health insurance, household income, hypertension status, and diabetes status. Participants with periodontitis had a higher risk of ACVD (adjusted hazard ratio: 1.09, 95% confidence interval: 1.05–1.13) than those without periodontitis. Thus, periodontitis can increase the risk of ACVD, and prevention of periodontitis may help reduce the risk of cardiovascular disease. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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13 pages, 1261 KiB  
Article
Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19
by Thu T. Nguyen, Shaniece Criss, Pallavi Dwivedi, Dina Huang, Jessica Keralis, Erica Hsu, Lynn Phan, Leah H. Nguyen, Isha Yardi, M. Maria Glymour, Amani M. Allen, David H. Chae, Gilbert C. Gee and Quynh C. Nguyen
Int. J. Environ. Res. Public Health 2020, 17(19), 7032; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17197032 - 25 Sep 2020
Cited by 113 | Viewed by 11980
Abstract
Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application [...] Read more.
Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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24 pages, 3519 KiB  
Article
Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs
by Qi Li, Cong Wei, Jianning Dang, Lei Cao and Li Liu
Int. J. Environ. Res. Public Health 2020, 17(18), 6888; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17186888 - 21 Sep 2020
Cited by 20 | Viewed by 3433
Abstract
Objective: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes of these public emotions from an [...] Read more.
Objective: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes of these public emotions from an event-driven perspective. Methods: A dataset was constructed using microblogs (n = 125,672) labeled with COVID-19-related super topics (n = 680) from 40,891 users from 1 December 2019 to 17 February 2020. Based on the skeleton and key change points of COVID-19 extracted from microblogging contents, we tracked the public’s emotional evolution modes (accumulated emotions, emotion covariances, and emotion transitions) by time phase and further extracted the details of dominant social events. Results: Public emotions showed different evolution modes during different phases of COVID-19. Events about the development of COVID-19 remained hot, but generally declined, and public attention shifted to other aspects of the epidemic (e.g., encouragement, support, and treatment). Conclusions: These findings suggest that the public’s feedback on COVID-19 predated official accounts on the microblog platform. There were clear differences in the trending events that large users (users with many fans and readings) and common users paid attention to during each phase of COVID-19. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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12 pages, 768 KiB  
Article
Risk of Acquired Cholesteatoma and External Auditory Canal Stenosis in Traumatic Brain Injury: A Nationwide Population-Based Cohort Study
by Hung-Che Lin, Cheng-Ping Shih, Hsin-Chien Chen, Chun-An Cheng, Yuahn-Sieh Huang, Chen-Shien Lin, Chi-Hsian Chung, Bor-Rong Huang, Jih-Chin Lee, Wei-Chuan Shangkuan, Wu-Chien Chien and Chi-Ming Chu
Int. J. Environ. Res. Public Health 2020, 17(18), 6624; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17186624 - 11 Sep 2020
Cited by 3 | Viewed by 2125
Abstract
The aim of study is to investigate the risk of developing acquired cholesteatoma and external auditory canal (EAC) stenosis after traumatic brain injury (TBI) from the Taiwan National Health Insurance Research Database (NHIRD). Each subject was individually traced from their index date to [...] Read more.
The aim of study is to investigate the risk of developing acquired cholesteatoma and external auditory canal (EAC) stenosis after traumatic brain injury (TBI) from the Taiwan National Health Insurance Research Database (NHIRD). Each subject was individually traced from their index date to identify those who received a diagnosis of acquired cholesteatoma and EAC stenosis. Cox regression analyses were applied to determine the risk of TBI-related acquired cholesteatoma and EAC stenosis. The follow-up data collected over 10 years were obtained from the TBI and comparison cohorts, of 455,834 and 911,668 patients, respectively. Multivariate analysis demonstrated that TBI significantly increased the risk of cholesteatoma (adjusted hazard ratio (HR), 1.777; 95% confidence interval (CI), 1.494−2.114, p < 0.001) and EAC stenosis (adjusted (HR), 3.549; 95% (CI), 2.713−4.644, p < 0.001). In our subgroup injury analysis, falls had the highest associated risk (4.308 times), followed by traffic injuries (66.73%; 3.718 times that of the control group). Otolaryngologists should not neglect the clinical importance and carefully investigate the possibility of subsequent cholesteatoma and EAC stenosis, which leads to hearing impairment in patients with TBI. Our research also shows the important role in preventing TBI, especially as a result of traffic injuries and falls. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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13 pages, 574 KiB  
Article
Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases
by Quynh C. Nguyen, Yuru Huang, Abhinav Kumar, Haoshu Duan, Jessica M. Keralis, Pallavi Dwivedi, Hsien-Wen Meng, Kimberly D. Brunisholz, Jonathan Jay, Mehran Javanmardi and Tolga Tasdizen
Int. J. Environ. Res. Public Health 2020, 17(17), 6359; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17176359 - 01 Sep 2020
Cited by 63 | Viewed by 7553
Abstract
The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the [...] Read more.
The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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10 pages, 1620 KiB  
Article
Turbinate Submucosal Reduction Operation Reduced Migraine Admission among Patients with Chronic Hypertrophic Rhinitis
by Chun-An Cheng, Yin-Han Chang, Chun-Gu Cheng, Hung-Che Lin, Chi-Hsiang Chung and Wu-Chien Chien
Int. J. Environ. Res. Public Health 2020, 17(15), 5455; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph17155455 - 29 Jul 2020
Viewed by 1809
Abstract
Rhinitis increases migraine risk. Chronic hypertrophic rhinitis can be treated with turbinate submucosal reduction operation. The relationship between migraine and chronic hypertrophic rhinitis after turbinate submucosal reduction operation is still unclear. The goal of this study was to evaluate the correlation between turbinate [...] Read more.
Rhinitis increases migraine risk. Chronic hypertrophic rhinitis can be treated with turbinate submucosal reduction operation. The relationship between migraine and chronic hypertrophic rhinitis after turbinate submucosal reduction operation is still unclear. The goal of this study was to evaluate the correlation between turbinate submucosal reduction operation and subsequent migraine admission in Asian chronic hypertrophic rhinitis patients. We identified patients suffering from chronic hypertrophic rhinitis and receiving turbinate submucosal reduction operation. The control group was selected from patients with chronic hypertrophic rhinitis without operation. The event was migraine admission. The risk factors of migraine admission were established using multivariate Cox proportional hazard regression. The risk of migraine admission after turbinate submucosal reduction operation is represented by a hazard ratio (HR) of 0.858 (95% confidence interval (CI): 0.633–0.962). The higher risk of migraine included depression with HR 4.348 (95% CI: 2.826–6.69), anxiety with HR 3.75 (95% CI: 2.267–6.203), fibromyalgia with HR of 7.326 (95% CI: 3.427–15.661), and asthma with HR 1.969 (95% CI: 1.11–3.491). Our study revealed that turbinate submucosal reduction operation led to a 14.2% reduction in migraine admission. Clinicians should understand the benefit of turbinate submucosal reduction operation and provide suitable treatments for comorbid conditions. Further prospective studies are required to confirm our findings. Full article
(This article belongs to the Special Issue Big Data for Public Health Research and Practice)
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