A call for better understanding of social media in surveillance and management of noncommunicable diseases

Lui, Chi‐Wai; Wang, Zaimin; Wang, Ning; Milinovich, Gabriel; Ding, Hang; Mengersen, Kerrie; Bambrick, Hilary

doi:10.1186/s12961-021-00683-4

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…Bayesian spatiotemporal methods simultaneously considering the spatial and time correlation of the data are highly recommended to produce more comprehensive information for regional high-risk factors and give evidence to generate an early warning of diabetes mortality increase [ 42 ]. In addition, mobile health (mHealth) applications with artificial intelligence chatbots have been called for chronic disease surveillance and management with the increasing popularity of social media in daily life [ 43 , 44 ]. Studies indicate that applying mHealth can track blood glucose and elicit beneficial behavior to enhance the control of diabetes for individuals [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Age- and Gender-Specific Differences in the Seasonal Distribution of Diabetes Mortality in Shandong, China: A Spatial Analysis

Zheng

Cheng

Ren

et al. 2022

IJERPH

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Diabetes mortality in Shandong is higher than the national average in China. This study first explored diabetes mortality variation spatially at the county/district level among adults aged over 30 years in terms of age and gender, specifically by season. Daily diabetes mortality data were collected from 31 mortality surveillance points across Shandong Province in 2014. A geographic information system, spatial kriging interpolation and a spatial clustering method were used to examine the spatial patterns of diabetes mortality at the county/district level by season. Sensitivity analysis was conducted using diabetes mortality data from 10 mortality surveillance points from 2011 to 2020. As a result, the total diabetes mortality in eastern counties/districts was the highest (relative risk (RR) of cluster: 1.58, p = 0.00) across the whole province. For subgroups, women had higher mortality (16.84/100,000) than men (12.15/100,000), people aged over 75 years were the most vulnerable (93.91/100,000) and the highest-risk season was winter. However, the mortality differences between winter and summer were smaller in eastern and coastal regions than in other regions for all gender- and age-specific groups. The findings provide further evidence for early warning and precision preventative strategies for diabetes mortality in different regions of Shandong Province. Future research is required to identify the risk factors for diabetes and understand the differences in the social and environmental contexts.

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Section: Discussionmentioning

confidence: 99%

Age- and Gender-Specific Differences in the Seasonal Distribution of Diabetes Mortality in Shandong, China: A Spatial Analysis

Zheng

Cheng

Ren

et al. 2022

IJERPH

Self Cite

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“…Furthermore, within the realm of disease surveillance, the real-time analytical capabilities offered by big data analytics for monitoring and managing disease outbreaks represent a significant breakthrough in the field [6]. Existing studies have focused on individual aspects of big data analytics or specific data sources, such as social media [24]. Similarly, the benefit of electronic health records in disease surveillance was highlighted by [41], which revealed that the use of EHRs offers a longitudinal collection of patient data and analysis.…”

Section: Literature Review (Choose 1)mentioning

confidence: 99%

“…This is crucial because longitudinal patient data offers a comprehensive picture of a person's interactions with many facets of healthcare. Geographical information systems are another source of big data that was studied by [24]; these studies alluded that GIS technology is effective for disease monitoring. This is through its ability to aggregate data, data visualization, spatial tracking of confirmed cases, forecasting of regional transmission, and spatial segmentation of the disease risk.…”

Section: Literature Review (Choose 1)mentioning

confidence: 99%

Ethical Framework for Multi-Source Big Data Analytics Disease Surveillance in Public Healthcare

Ramafi,

Motjolopane,

Gorejena

2023

iCARTi

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Big data analytics can revolutionize disease surveillance by integrating multiple data sources and analyzing those diverse data sources, allowing for early monitoring, detection, and response to disease outbreaks. Since the growth in technology, together with the advent of big data, there is growing interest in exploring the potential of multiple data sources for disease surveillance. However, there are still ethical concerns when it comes to big data usage for disease surveillance. Hence, this study uses of scoping review with the assistance of socio-technical systems theory, together with social contact theory to examine the ethical dimensions of employing big data analytics from diverse sources for disease surveillance, while emphasizing the importance of maintaining ethical standards in public healthcare practices. The study discovered that informed consent, privacy, and confidentiality are still major challenges when it comes to the integration of big data sources in disease surveillance. The study then recommends a unified ethical framework for enhanced disease surveillance in public healthcare through Multi-Data Source big data Analytics.

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Current status and perspectives in environmental oncology

Liu,

Gan,

et al. 2024

Chronic Diseases and Translational Medicine

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Cancer stands as a leading global cause of death, with its etiology characterized by complexity and multifaceted factors. Growing research indicates a strong correlation between environmental factors and cancer incidence, underscoring the critical importance of intervening in environmental risk factors to mitigate cancer occurrence. Despite this, specialized research institutions focusing on the intersection of environment and cancer remain scarce, with global investment in cancer prevention significantly trailing behind efforts in diagnosis and treatment. Against the backdrop of rapid global climate change, industrialization, urbanization, aging populations, and the globalization of lifestyles, we proposed the concept of Environmental Oncology (EO) to address these challenges. We discussed the rationale and necessity of developing EO and presented a comprehensive research framework focusing on cancer prevention and treatment. Future EO research will aim to identify cancer causes and implement early prevention strategies using advanced scientific technologies and methods. By emphasizing multidisciplinary collaboration and integrating molecular biology at the micro level, EO will explore the relationship between external and internal environments and cancer. EO will identify potential therapeutic targets by studying the pathways through which environmental exposures lead to carcinogenesis. EO will develop early warning systems and disseminate research findings by collecting big data, employing robust statistical models, and establishing research centers.

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A call for better understanding of social media in surveillance and management of noncommunicable diseases

Cited by 8 publications

References 26 publications

Age- and Gender-Specific Differences in the Seasonal Distribution of Diabetes Mortality in Shandong, China: A Spatial Analysis

Age- and Gender-Specific Differences in the Seasonal Distribution of Diabetes Mortality in Shandong, China: A Spatial Analysis

Ethical Framework for Multi-Source Big Data Analytics Disease Surveillance in Public Healthcare

Current status and perspectives in environmental oncology

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