Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

DeJohn, Amber D.; Schulz, Emily English; Pearson, Amber L.; Lachmar, E. Megan

doi:10.2196/mental.9533

“…With the advancement of methodologies using “Big Data,” researchers are able to track diseases by the use of common internet search engines as a real-time tool [24]. Web search content is publicly available worldwide, providing valuable data for research, including health-related topics [25].…”

Section: Introductionmentioning

confidence: 99%

Association Between Cancer Incidence and Mortality in Web-Based Data in China: Infodemiology Study

Xu

¹

,

Wang

²

,

Yang

³

et al. 2019

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Background Cancer poses a serious threat to the health of Chinese people, resulting in a major challenge for public health work. Today, people can obtain relevant information from not only medical workers in hospitals, but also the internet in any place in real-time. Search behaviors can reflect a population’s awareness of cancer from a completely new perspective, which could be driven by the underlying cancer epidemiology. However, such Web-retrieved data are not yet well validated or understood. Objective This study aimed to explore whether a correlation exists between the incidence and mortality of cancers and normalized internet search volumes on the big data platform, Baidu. We also assessed whether the distribution of people who searched for specific types of cancer differed by gender. Finally, we determined whether there were regional disparities among people who searched the Web for cancer-related information. Methods Standard Boolean operators were used to choose search terms for each type of cancer. Spearman’s correlation analysis was used to explore correlations among monthly search index values for each cancer type and their monthly incidence and mortality rates. We conducted cointegration analysis between search index data and incidence rates to examine whether a stable equilibrium existed between them. We also conducted cointegration analysis between search index data and mortality data. Results The monthly Baidu index was significantly correlated with cancer incidence rates for 26 of 28 cancers in China (lung cancer: r =.80, P <.001; liver cancer: r =.28, P =.016; stomach cancer: r =.50, P <.001; esophageal cancer: r =.50, P <.001; colorectal cancer: r =.81, P <.001; pancreatic cancer: r =.86, P <.001; breast cancer: r =.56, P <.001; brain and nervous system cancer: r =.63, P <.001; leukemia: r =.75, P <.001; Non-Hodgkin lymphoma: r =.88, P <.001; Hodgkin lymphoma: r =.91, P <.001; cervical cancer: r =.64, P <.001; prostate cancer: r =.67, P <.001; bladder cancer: r =.62, P <.001; gallbladder and biliary tract cancer: r =.88, P <.001; lip and oral cavity cancer: r =.88, ...

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“…Hence, young adults may actively seek vegetation-covered areas and other forms of urban greenery because of their utility as sites for physical exercise and destressing [ 27 , 46 ]. Moreover, various types of neighborhood vegetation, such as trees, can lead to greater outdoor space use and increased social engagements among youth [ 47 - 49 ]. These social engagements, in turn, have the potential to promote social cohesion and mental well-being [ 13 , 27 ].…”

Section: Discussionmentioning

confidence: 99%

Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study

Abdullah

¹

,

Law

²

,

Perlman

³

et al. 2022

JMIR Public Health Surveill

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Background Despite growing evidence that reduced vegetation cover could be a putative risk factor for mental health disorders, the age- and the sex-specific association between vegetation and mental health disorder cases in urban areas is poorly understood. However, with rapid urbanization across the globe, there is an urgent need to study this association and understand the potential impact of vegetation loss on the mental well-being of urban residents. Objective This study aims to analyze the spatial association between vegetation cover and the age- and sex-stratified mental health disorder cases in the neighborhoods of Toronto, Canada. Methods We used remote sensing to detect urban vegetation and Bayesian spatial hierarchical modeling to analyze the relationship between vegetation cover and mental health disorder cases. Specifically, an Enhanced Vegetation Index was used to detect urban vegetation, and Bayesian Poisson lognormal models were implemented to study the association between vegetation and mental health disorder cases of males and females in the 0-19, 20-44, 45-64, and ≥65 years age groups, after controlling for marginalization and unmeasured (latent) spatial and nonspatial covariates at the neighborhood level. Results The results suggest that even after adjusting for marginalization, there were significant age- and sex-specific effects of vegetation on the prevalence of mental health disorders in Toronto. Mental health disorders were negatively associated with the vegetation cover for males aged 0-19 years (−7.009; 95% CI −13.130 to −0.980) and for both males (−4.544; 95% CI −8.224 to −0.895) and females (−3.513; 95% CI −6.289 to −0.681) aged 20-44 years. However, for older adults in the 45-64 and ≥65 years age groups, only the marginalization covariates were significantly associated with mental health disorder cases. In addition, a substantial influence of the unmeasured (latent) and spatially structured covariates was detected in each model (relative contributions>0.7), suggesting that the variations in area-specific relative risk were mainly spatial in nature. Conclusions As significant and negative associations between vegetation and mental health disorder cases were found for young males and females, investments in urban greenery can help reduce the future burden of mental health disorders in Canada. The findings highlight the urgent need to understand the age-sex dynamics of the interaction between surrounding vegetation and urban dwellers and its subsequent impact on mental well-being.

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“…Thus, real-time internet searches could be particularly useful. Studies have shown that tracking and monitoring search behaviors, as well as text mining on social media, can provide new ways to study public concerns about NCDs and information-seeking behaviors [ 29 - 32 ]. Researchers have realized that people can search, understand, and evaluate health information on internet resources and that they harness the information they receive to address health problems [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study

Xu¹,

Cao²,

Yang³

et al. 2020

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Background As human society enters an era of vast and easily accessible social media, a growing number of people are exploiting the internet to search and exchange medical information. Because internet search data could reflect population interest in particular health topics, they provide a new way of understanding health concerns regarding noncommunicable diseases (NCDs) and the role they play in their prevention. Objective We aimed to explore the association of internet search data for NCDs with published disease incidence and mortality rates in the United States and to grasp the health concerns toward NCDs. Methods We tracked NCDs by examining the correlations among the incidence rates, mortality rates, and internet searches in the United States from 2004 to 2017, and we established forecast models based on the relationship between the disease rates and internet searches. Results Incidence and mortality rates of 29 diseases in the United States were statistically significantly correlated with the relative search volumes (RSVs) of their search terms (P<.05). From the perspective of the goodness of fit of the multiple regression prediction models, the results were closest to 1 for diabetes mellitus, stroke, atrial fibrillation and flutter, Hodgkin lymphoma, and testicular cancer; the coefficients of determination of their linear regression models for predicting incidence were 80%, 88%, 96%, 80%, and 78%, respectively. Meanwhile, the coefficient of determination of their linear regression models for predicting mortality was 82%, 62%, 94%, 78%, and 62%, respectively. Conclusions An advanced understanding of search behaviors could augment traditional epidemiologic surveillance and could be used as a reference to aid in disease prediction and prevention.

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Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study

Cited by 12 publications

References 27 publications

Association Between Cancer Incidence and Mortality in Web-Based Data in China: Infodemiology Study

Association Between Cancer Incidence and Mortality in Web-Based Data in China: Infodemiology Study

Age- and Sex-Specific Association Between Vegetation Cover and Mental Health Disorders: Bayesian Spatial Study

Leveraging Internet Search Data to Improve the Prediction and Prevention of Noncommunicable Diseases: Retrospective Observational Study

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