| Literature DB >> 33001838 |
Xiaoman Zhao1,2, Ju Fan3, Iccha Basnyat4, Baijing Hu2.
Abstract
BACKGROUND: First detected in Wuhan, China in December 2019, the COVID-19 pandemic stretched the medical system in Wuhan and posed a challenge to the state's risk communication efforts. Timely access to quality health care information during outbreaks of infectious diseases can be effective to curtail the spread of disease and feelings of anxiety. Although existing studies have extended our knowledge about online health information-seeking behavior, processes, and motivations, rarely have the findings been applied to an outbreak. Moreover, there is relatively little recent research on how people in China are using the internet for seeking health information during a pandemic.Entities:
Keywords: COVID-19; Wuhan; coronavirus; information seeking; social media
Mesh:
Year: 2020 PMID: 33001838 PMCID: PMC7572118 DOI: 10.2196/22910
Source DB: PubMed Journal: J Med Internet Res ISSN: 1438-8871 Impact factor: 5.428
Figure 1Daily numbers of #COVID-19 Patient Seeking Help hashtag entries (the orange bar plot) and daily confirmed cases in Wuhan (the blue line plot).
Age comparison between our sample and that of the WHO-China Joint Mission.a
| Sample | Participants, n | Age range | Age IQR (years) | Age median (years) |
| WHOb-China Joint Mission | 55,924 | 2 days-100 years | 39-63 | 51 |
| Our sample | 1454 | 2-99 years | 50-70 | 61 |
aAge was missing in 42 Weibo entries.
bWHO: World Health Organization.
Document frequency of names of family members.
| Word | Frequency (n=883), n (%) | Quote |
| Mother (mum) | 308 (35) | “My |
| Father (dad) | 255 (29) | “I am the son of the patient. My |
| Elder at home | 209 (24) | “The |
| Grandma | 88 (10) | “Now my |
| Parents | 80 (9) | “My |
| Grandpa | 62 (7) | “The patient is my |
| Aunt | 57 (6) | “The |
| Uncle | 52 (6) | “My |
Figure 2Number of patients seeking health information and number of fever clinic and designated hospital by district (yellow point).
Figure 3Overview of the locations of patient residential addresses (blue points), fever clinics (red triangle), and designated hospitals (yellow triangle).
Patients’ distance to the nearest fever clinic or designated hospital.
| District | <1 km distance, n | 1-2 km distance, n | 2-3 km distance, n | >3 km distance, n |
| Qiokou | 68 | 17 | 4 | 124 |
| Hongshan | 28 | 66 | 67 | 116 |
| Jiangan | 68 | 58 | 35 | 68 |
| Hanyang | 28 | 77 | 52 | 38 |
| Dongxihu | 3 | 0 | 7 | 27 |
| Jianghan | 36 | 22 | 11 | 15 |
| Wuchang | 64 | 91 | 38 | 6 |
| Huangpi | 5 | 3 | 0 | 5 |
| Zhuankou | 0 | 6 | 1 | 4 |
| Jiangxia | 0 | 0 | 2 | 4 |
| Caidian | 0 | 2 | 0 | 3 |
| Xinzhou | 0 | 0 | 0 | 1 |
| Qingshan | 39 | 39 | 15 | 1 |
| Hannan | 0 | 0 | 0 | 1 |
| All districts | 339 | 381 | 232 | 413 |
Document frequency of terms indicating patients’ underlying condition.a
| Word | Frequency (n=883), n (%) | Quote |
| Hypertension | 110 (12) | “The patient has |
| Diabetes | 82 (9) | “My father has multiple underlying diseases, including |
| Heart disease | 79 (9) | “My grandpa has a history of |
| Underlying disease | 30 (3) | “The CT scan shows ground glass opacity in both lungs. My father has serious |
aHealth condition was missing in 613 Weibo entries.
bCT: computed tomography.
Figure 4Posting times per patient.