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

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

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.

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review

Background

A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness.

Objective

This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods.

Methods

We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both.

Results

Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown.

Conclusions

There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.

Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study

BACKGROUND

Mental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group.

OBJECTIVE

By measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health.

METHODS

We explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification.

RESULTS

We found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time.

CONCLUSIONS

These results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

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

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.

Twitter as a promising microblogging application for psychiatric consultation - Understanding the predictors of use, satisfaction and e-health literacy

BACKGROUND

The use of social media is widespread globally. It provides a quicker and faster means of efficient exchange of communications. The use of Twitter Applications to seek mental health advice is becoming popular.

OBJECTIVES

This study aims to identify the determinants associated with Twitter use in psychiatric consultations and to assess the level of satisfaction in using the microblogging platform. In addition, the level of e-health literacy is also assessed among users.

METHODS

The target population included Twitter users seeking psychiatric consultation. A leading psychiatrist's twitter account with 4.5 million followers was selected and consent obtained. A validated Patient Satisfaction Questionnaire was adopted to assess the level of satisfaction in Twitter use and e-health literacy. The questionnaire was tagged to the chosen Twitter account and reminders were sent until the sample size was reached. Data was analysed using SPSS version 22.0. The analysis included descriptive statistics tabulation, multi-response analysis, and cross-tabulation for satisfaction variables and the chi-square test was used to measure association between different variables.

RESULTS

The study obtained 155 completed response sheets, of which 52 were Twitter users seeking psychiatric advice while the rest sought general health advice. Most of the study participants were females (71.6 %). Women, single status and income range between 4000-9000 Saudi riyal were found to be significantly associated with Twitter use for psychiatric consultation. Generally, most of the participants were satisfied with Twitter in seeking psychiatric consultation that reduced financial disbursement. Furthermore, concerns were expressed regarding the waiting period, word limitations and issues of privacy. The e-health literacy was higher among the participants.

CONCLUSION

Psychiatric consultations via Twitter is more popular among women. By addressing privacy issues and reducing response time, Twitter may be used as a major platform to deliver mental health services to the population.

Infodemiology and Infoveillance: Scoping Review

Background

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

Objective

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

Methods

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

Results

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

Conclusions

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

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

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, P<.001; ovarian cancer: r=.58, P<.001; larynx cancer: r=.82, P<.001; kidney cancer: r=.73, P<.001; squamous cell carcinoma: r=.94, P<.001; multiple myeloma: r=.84, P<.001; thyroid cancer: r=.77, P<.001; malignant skin melanoma: r=.55, P<.001; mesothelioma: r=.79, P<.001; testicular cancer: r=.57, P<.001; basal cell carcinoma: r=.83, P<.001). The monthly Baidu index was significantly correlated with cancer mortality rates for 24 of 27 cancers. In terms of the whole population, the number of women who searched for cancer-related information has slowly risen over time. People aged 30-39 years were most likely to use search engines to retrieve cancer-related knowledge. East China had the highest Web search volumes for cancer.

Conclusions

Search behaviors indeed reflect public awareness of cancer from a different angle. Research on internet search behaviors could present an innovative and timely way to monitor and estimate cancer incidence and mortality rates, especially for cancers not included in national registries.