Identifying unmet non-COVID-19 health needs during the COVID-19 outbreak based on social media data: a proof-of-concept study in Wuhan city

BACKGROUND: The occupancy of healthcare resources by the COVID-19 outbreak had led to the unmet health needs of non-COVID-19 diseases. We aimed to explore whether the social media information could help surveil and understand the characteristics of unmet non-COVID-19 health needs during the COVID-19 outbreak in Wuhan city. METHODS: This was an observational study based on social media data. The study period was set during the 3 months of the COVID-19 outbreak. Non-COVID-19 urgent and emergent health needs in Wuhan city were derived from Sina Weibo—one of China’s largest social media platforms. Lag Spearman correlation was used to investigate the epidemiological relationship between the COVID-19 outbreak and non-COVID-19 health needs. Patient’s primary diseases and needed care were annotated and categorized according to the International Classification of Diseases 11th Revision. The delay time in seeking help was calculated and compared. RESULTS: After screening 114,795 Weibo posts, a total of 229 patients with non-COVID-19 health needs were included in our study. There were significant correlations between the daily number of COVID-19 cases at a 10-day lag, deaths at a 5-day lag, and non-COVID-19 Weibo. The actual number of non-COVID-19 patients with urgent and emergent health needs was estimated to be about 6,966. Patients with non-COVID-19 health needs were skewed to those aged 50 to 70 years. The non-COVID-19 diseases were diverse, with 46.3% as non-neoplastic diseases and 53.7% as neoplasms. The most needed cares were palliative cancer care (22.7%), chemotherapy (18.8%), and critical care (17.0%). The median delay in seeking help was 3 days [interquartile range (IQR), 1 to 15 days] for acute care, and 18.5 days (IQR, 6 to 30 days) for cancer care. CONCLUSIONS: Our preliminary findings in Wuhan city indicated that the social media data might provide a viable option to surveil and understand the unmet health needs during an outbreak. Those heterogeneous health needs derived from the social media data might inspire a more resilient healthcare system to address the unmet needs promptly.