| Literature DB >> 33714653 |
Jiahao Wang1, Yun Lyu2, Haijun Zhang3, Rize Jing4, Xiaozhen Lai5, Huangyufei Feng6, Maria Deloria Knoll7, Hai Fang8.
Abstract
BACKGROUND: The COVID-19 pandemic has caused significant diseases and economic burdens in the world. Vaccines are often considered as a cost-effective way to prevent and control infectious diseases, and the research and development of COVID-19 vaccines have been progressing unprecedently. It is needed to understand individuals' willingness to pay (WTP) among general population, which provides information about social demand, access and financing for future COVID-19 vaccination.Entities:
Keywords: COVID-19; Financing; SARS-CoV-2; Vaccine; Willingness to pay
Mesh:
Substances:
Year: 2021 PMID: 33714653 PMCID: PMC7914003 DOI: 10.1016/j.vaccine.2021.02.060
Source DB: PubMed Journal: Vaccine ISSN: 0264-410X Impact factor: 3.641
Comparison of the regional distribution between respondents in the study and that in total population from 2020 China statistical yearbook a.
| Provincial administrative regions | Respondents in the study (N = 2058) | Total population in China (N = 1401.85 million) | ||
|---|---|---|---|---|
| N | % | N (in million) | % | |
| Beijing | 138 | 6.7 | 21.54 | 1.5 |
| Tianjin | 37 | 1.8 | 15.62 | 1.1 |
| Hebei | 96 | 4.7 | 75.92 | 5.4 |
| Shanxi | 68 | 3.3 | 37.29 | 2.7 |
| Inner Mongolia | 24 | 1.2 | 25.4 | 1.8 |
| Liaoning | 69 | 3.4 | 43.52 | 3.1 |
| Jilin | 24 | 1.2 | 26.91 | 1.9 |
| Heilongjiang | 38 | 1.8 | 37.51 | 2.7 |
| Shanghai | 146 | 7.1 | 24.28 | 1.7 |
| Jiangsu | 130 | 6.3 | 80.7 | 5.8 |
| Zhejiang | 88 | 4.3 | 58.5 | 4.2 |
| Anhui | 62 | 3.0 | 63.66 | 4.5 |
| Fujian | 62 | 3.0 | 39.73 | 2.8 |
| Jiangxi | 38 | 1.8 | 46.66 | 3.3 |
| Shandong | 129 | 6.3 | 100.7 | 7.2 |
| Henan | 134 | 6.5 | 96.4 | 6.9 |
| Hubei | 99 | 4.8 | 59.27 | 4.2 |
| Hunan | 68 | 3.3 | 69.18 | 4.9 |
| Guangdong | 295 | 14.3 | 115.21 | 8.2 |
| Guangxi | 64 | 3.1 | 49.6 | 3.5 |
| Hainan | 5 | 0.2 | 9.45 | 0.7 |
| Chongqing | 30 | 1.5 | 31.24 | 2.2 |
| Sichuan | 86 | 4.2 | 83.75 | 6.0 |
| Guizhou | 19 | 0.9 | 36.23 | 2.6 |
| Yunnan | 18 | 0.9 | 48.58 | 3.5 |
| Tibet | 1 | 0.0 | 3.51 | 0.3 |
| Shaanxi | 51 | 2.5 | 38.76 | 2.8 |
| Gansu | 17 | 0.8 | 26.47 | 1.9 |
| Qinghai | 2 | 0.1 | 6.08 | 0.4 |
| Ningxia | 7 | 0.3 | 6.95 | 0.5 |
| Xinjiang | 13 | 0.6 | 23.23 | 1.7 |
Respondent characteristics.
| Characteristics | Respondents (N = 2058) | |
|---|---|---|
| N | % | |
| Age group | ||
| 18 ~ 25 | 475 | 23.1 |
| 26 ~ 30 | 400 | 19.4 |
| 31 ~ 40 | 523 | 25.4 |
| 41 ~ 50 | 510 | 24.8 |
| 51 and above | 150 | 7.3 |
| Gender | ||
| Female | 1115 | 54.2 |
| Male | 943 | 45.8 |
| Education level | ||
| Middle school and below | 123 | 6.0 |
| High school | 663 | 32.2 |
| Associate or bachelor | 1140 | 55.4 |
| Master and above | 132 | 6.4 |
| Marriage status | ||
| Married | 1385 | 67.3 |
| Others (single, divorced or widowed) | 673 | 32.7 |
| Region | ||
| Rural | 420 | 20.4 |
| Urban | 1638 | 79.6 |
| Health status | ||
| Good and above (good, very good) | 1527 | 74.2 |
| Fair or below (fair, poor, very poor) | 531 | 25.8 |
| Having the chronic disease | ||
| Yes | 193 | 9.4 |
| No | 1865 | 90.6 |
| Annual family income in 2019 | ||
| ≤ CNY 50,000 (USD 7,246) | 277 | 13.4 |
| CNY 50,000 ~ 100,000 (USD 7,246–14,492) | 548 | 26.6 |
| CNY 100,000 ~ 150,000 (USD 14,492–21,739) | 506 | 24.6 |
| CNY 150,000 ~ 200,000 (USD 21,739–28,986) | 352 | 17.1 |
| CNY 200,000 ~ 300,000 (USD 28,986–43,478) | 239 | 11.7 |
| ≥ CNY 300,000 (USD 43,478) | 136 | 6.6 |
| Employment status | ||
| Employed | 1651 | 80.2 |
| Unemployed | 407 | 19.8 |
| Employee size in the workplace | ||
| ≤ 10 | 156 | 7.6 |
| 10 ~ 29 | 227 | 11.0 |
| 30 ~ 100 | 448 | 21.7 |
| 100 ~ 300 | 356 | 17.3 |
| ≥ 300 | 464 | 22.6 |
| Missing | 407 | 19.8 |
| Pandemic impact on income | ||
| Large or very large | 905 | 44.0 |
| Fair | 467 | 22.7 |
| Small or very small | 325 | 15.8 |
| Missing | 361 | 17.5 |
| There are confirmed or suspected cases in the county | ||
| Yes | 1538 | 74.7 |
| No or not clear | 520 | 25.3 |
| Lacking of protective equipment (e.g. masks, etc) | ||
| Yes | 1136 | 55.2 |
| No | 922 | 44.8 |
| Perceived risk of infection | ||
| High or very high | 251 | 12.2 |
| Fair | 575 | 27.9 |
| Low or very low | 1232 | 59.9 |
| The COVID-19 pandemic in China was in a declining trend | ||
| Yes | 1436 | 69.8 |
| No | 622 | 30.2 |
Financing mechanism preference for COVID-19 vaccination of respondents.
| Characteristics | Respondents (N = 2058) | |
|---|---|---|
| N | % | |
| Individuals need to pay out of pocket for COVID-19 vaccination | ||
| No | 323 | 15.7 |
| Yes, pay for a portion | 1604 | 77.9 |
| Yes, pay fully | 131 | 6.4 |
| Governments need to pay for COVID-19 vaccination | ||
| No | 188 | 9.1 |
| Yes, pay for a portion | 1,605 | 78.0 |
| Yes, pay fully | 265 | 12.9 |
| Health insurance needs to pay for COVID-19 vaccination | ||
| No | 452 | 22.0 |
| Yes, pay for a portion | 1,334 | 64.8 |
| Yes, pay fully | 272 | 13.2 |
The distribution of WTP (CNY) and self-paid proportion for COVID-19 vaccination of respondents.
| WTP value (CNY) | Self-paid proportion (%) | ||||||
|---|---|---|---|---|---|---|---|
| Frequency | Percent (%) | Cumulative Percent (%) | Frequency | Percent (%) | Cumulative Percent (%) | ||
| Refused a | 179 | 8.7 | 8.7 | Refused a | 179 | 8.7 | 8.7 |
| 0 | 20 | 1.0 | 9.7 | 0 | 38 | 1.9 | 10.5 |
| 1 ~ 9 | 12 | 0.6 | 10.3 | 1 ~ 9 | 25 | 1.2 | 11.8 |
| 10 | 51 | 2.5 | 12.7 | 10 | 49 | 2.4 | 14.1 |
| 11 ~ 49 | 66 | 3.2 | 15.9 | 11 ~ 19 | 53 | 2.6 | 16.7 |
| 50 | 298 | 14.5 | 30.4 | 20 | 156 | 7.6 | 24.3 |
| 51 ~ 99 | 37 | 1.8 | 32.2 | 21 ~ 29 | 127 | 6.2 | 30.5 |
| 100 | 526 | 25.6 | 57.8 | 30 | 101 | 4.9 | 35.4 |
| 101 ~ 149 | 15 | 0.7 | 58.5 | 31 ~ 39 | 78 | 3.8 | 39.2 |
| 150 | 52 | 2.5 | 61.0 | 40 | 177 | 8.6 | 47.8 |
| 151 ~ 199 | 10 | 0.5 | 61.5 | 41 ~ 49 | 177 | 8.6 | 56.4 |
| 200 | 382 | 18.6 | 80.1 | 50 | 269 | 13.1 | 69.4 |
| 201 ~ 299 | 15 | 0.7 | 80.8 | 51 ~ 59 | 130 | 6.3 | 75.8 |
| 300 | 89 | 4.3 | 85.1 | 60 | 131 | 6.4 | 82.1 |
| 301 ~ 499 | 24 | 1.2 | 86.3 | 61 ~ 69 | 117 | 5.7 | 87.8 |
| 500 | 190 | 9.2 | 95.5 | 70 | 39 | 1.9 | 89.7 |
| 501 ~ 999 | 19 | 0.9 | 96.5 | 71 ~ 79 | 47 | 2.3 | 92.0 |
| 1000 | 48 | 2.3 | 98.8 | 80 | 49 | 2.4 | 94.4 |
| 1001 ~ 2000 | 10 | 0.5 | 99.3 | 81 ~ 89 | 25 | 1.2 | 95.6 |
| 3000 | 1 | 0.1 | 99.3 | 90 | 3 | 0.2 | 95.7 |
| 5000 | 8 | 0.4 | 99.7 | 91 ~ 99 | 13 | 0.6 | 96.4 |
| 10,000 | 6 | 0.3 | 100.0 | 100 | 75 | 3.6 | 100.0 |
The distribution of WTP (CNY) in the PS format for COVID-19 vaccination of respondents.
| Frequency | Percent (%) | Cumulative Percent (%) | |
|---|---|---|---|
| Refused a | 179 | 8.7 | 8.7 |
| 0 | 115 | 5.6 | 14.3 |
| 10 | 104 | 5.1 | 19.3 |
| 50 | 419 | 20.4 | 39.7 |
| 100 | 556 | 27.0 | 66.7 |
| 200 | 385 | 18.7 | 85.4 |
| 500 | 130 | 6.3 | 91.7 |
| Willing to pay for any price | 170 | 8.3 | 100.0 |
Note: Refused a means that they refused vaccination.
Influencing factors of willingness to pay from the Tobit regression.
| Characteristics | Coefficients | SE | p-value | 95%CI |
|---|---|---|---|---|
| Age group | ||||
| 18 ~ 25 | Ref | |||
| 26 ~ 30 | −12.23 | 19.07 | 0.521 | −49.62 ~ 25.16 |
| 31 ~ 40 | −38.99 | 20.44 | 0.057 | −79.08 ~ 1.10 |
| 41 ~ 50 | −37.08 | 20.67 | 0.073 | −77.61 ~ 3.45 |
| > 51 | −25.71 | 28.25 | 0.363 | −81.11 ~ 29.70 |
| Gender | ||||
| Female | Ref | |||
| Male | −7.33 | 10.04 | 0.465 | −27.02 ~ 12.36 |
| Education level | ||||
| Middle school and below | Ref | |||
| High school | –33.27 | 23.00 | 0.148 | −78.37 ~ 11.83 |
| Bachelor | −44.28 | 23.65 | 0.061 | −90.68 ~ 2.10 |
| Master and above | −8.95 | 31.03 | 0.773 | −69.81 ~ 51.92 |
| Marriage status | ||||
| Others (single, divorced or widowed) | Ref | |||
| Married | 13.08 | 15.27 | 0.393 | −16.88 ~ 43.03 |
| Region | ||||
| Rural | Ref | |||
| Urban | 13.69 | 13.38 | 0.306 | −12.56 ~ 39.94 |
| Health status | ||||
| Fair or below (fair, poor, very poor) | Ref | |||
| Good and above (good, very good) | −10.18 | 12.02 | 0.397 | –33.76 ~ 13.40 |
| Having the chronic disease | ||||
| No | Ref | |||
| Yes | −10.07 | 17.76 | 0.571 | −44.89 ~ 24.76 |
| Annual family income in 2019 | ||||
| ≤ CNY 50,000 (USD 7,246) | Ref | |||
| CNY 50,000–100,000 (USD 7,246–14,492) | 13.76 | 17.18 | 0.423 | −19.93 ~ 47.45 |
| CNY 100,000–150,000 (USD 14,492–21,739) | 34.44 | 17.93 | 0.055 | −0.73 ~ 69.61 |
| CNY 150,000–200,000 (USD 21,739–28,986) | 68.01 | 19.54 | 0.001 | 29.70 ~ 106.32 |
| CNY 200,000–300,000 (USD 28,986–43,478) | 66.35 | 22.11 | 0.003 | 22.98 ~ 109.71 |
| ≥ CNY 300,000 (USD 43,478) | 135.64 | 25.04 | less than0.001 | 86.52 ~ 184.76 |
| Employment status | ||||
| Unemployed | Ref | |||
| Employed | 25.76 | 41.71 | 0.537 | 56.05 ~ 107.57 |
| Employee size in workplace | ||||
| ≤ 10 | Ref | |||
| 10 ~ 29 | 54.30 | 23.76 | 0.022 | 7.71 ~ 100.89 |
| 30 ~ 100 | 47.00 | 21.63 | 0.032 | 4.57 ~ 89.43 |
| 100 ~ 300 | 45.68 | 22.77 | 0.045 | 1.02 ~ 90.34 |
| ≥ 300 | 66.12 | 22.14 | 0.003 | 22.70 ~ 109.53 |
| Pandemic impact on income | ||||
| Fair | Ref | |||
| Large or very large | 10.78 | 12.99 | 0.406 | −14.69 ~ 36.26 |
| Small or very small | 3.38 | 16.52 | 0.838 | −29.03 ~ 35.79 |
| There are confirmed or suspected cases in the county | ||||
| No or not clear | Ref | |||
| Yes | 3.82 | 11.91 | 0.748 | −19.54 ~ 27.18 |
| Lacking of protective equipment (e.g. masks, etc) | ||||
| No | Ref | |||
| Yes | −18.98 | 10.25 | 0.064 | −39.08 ~ 1.13 |
| Perceived risk of infection | ||||
| Fair | Ref | |||
| High or very high | 2.84 | 16.93 | 0.867 | −30.37 ~ 36.05 |
| Low or very low | 0.63 | 11.67 | 0.957 | –22.25 ~ 23.51 |
| The COVID-19 pandemic in China was in a declining trend | ||||
| No | Ref | |||
| Yes | −28.03 | 11.06 | 0.011 | −49.72 ~ -6.34 |
Key questions and items in the survey questionnaire (translated from the Chinese version used).
| Construct | Question No. and type | Items | Response Scale |
|---|---|---|---|
| Perception for financing mechanism | 37 | Do you think that individuals need to pay out of pocket for COVID-19 vaccination? | 1 = No |
| 40 | Do you think that governments need to pay for COVID-19 vaccination? | 1 = No | |
| 41 | Do you think that health insurance needs to pay for COVID-19 vaccination? | 1 = No | |
| The out of pocket WTP | 38 | If individuals are required to pay for COVID-19 vaccination to complete a full immunization, what is the self-paid proportion that you are willing to pay? | [Range:0–100%] |
| 39 | What is the maximum amount that you are willing to pay for COVID-19 vaccination (receiving all doses of the series as the vaccination schedule), if you want to get vaccinated? | 1 = Refused vaccination | |
| 50 | What is the maximum amount that you are willing to pay for COVID-19 vaccination (receiving all doses of the series as the vaccination schedule), if you want to get vaccinated? | [For respondents who choose 'Refused vaccination' in Q39, skip Q50] |
Note: […] Brackets indicate text that participants did not see, including question type and response options.