The Current Status of Vaccine Uptake and the Impact of COVID-19 on Intention to Vaccination in Patients with COPD in Beijing

Figures & data

Figure 1 Vaccination uptake and knowledge of and intention to vaccination in the questionnaire.

Table 1 Demographic and Clinical Characteristics (n = 264)

Characteristics	Mean±SD or n (%)
Age	68.4±8.8
Gender
Male	236 (89.4)
Female	28 (10.6)
Residence
Urban	247 (93.6)
Rural area	17 (6.4)
Education
Primary school or lower	48 (18.2)
Middle school	161 (61.0)
College	18 (6.8)
University or higher	37 (14.0)
Employment status
Currently working	24 (9.1)
Retired	220 (83.3)
Other	20 (7.6)
Medical insurance
No insurance	14 (5.3)
Urban Residents Medical Insurance	196 (74.2)
Free Medical Service	24 (9.1)
Medical Insurance outside Beijing	30 (11.4)
Smoking status
Current smokers	70 (26.5)
Former smokers	160 (60.6)
Never smokers	34 (12.9)
Smoking index (pack-year) ^a	35.0 ± 21.8
Modified MRC dyspnea scale
0	32 (12.1)
1	95 (36.0)
2	78 (29.6)
3	46 (17.4)
4	13 (4.9)
Frequency of hospitalization due to COPD exacerbation in 2019 (/year)	0.27 ± 0.62
Frequency of hospitalization due to COPD exacerbation in 2020 (/year)	0.17 ± 0.46
Comorbidities
Diabetes mellitus	40 (15.2)
Cardiovascular diseases	86 (32.6)
Tumors	18 (6.8)
Chronic liver or renal diseases	5 (1.9)
Autoimmune diseases	3 (1.1)
None of the above	139 (52.7)
Pharmacological treatment
ICS/LABA	121 (56.0)
LAMA	105 (48.6)
LABA/LAMA	22 (10.2)
LABA/LAMA/ICS (fixed dose triple therapy)	29 (13.4)
SABA or SAMA	13 (6.9)

Notes: Data are presented as Mean ± SD or n (%). ^aSmoking index was calculated in current and former smokers.

Abbreviations: COPD, chronic obstructive pulmonary disease; MRC, Medical Research Council; ICS, inhaled corticosteroid; LABA, long-acting β-agonist; lAMA, long-acting muscarinic antagonist; SABA, short-acting β-agonist; SAMA, short-acting muscarinic antagonist.

Table 2 The Rate of Influenza, Pneumococcal and COVID-19 Vaccination (n = 264)

Vaccines	N (%) of Vaccination
Influenza vaccine	60 (22.7)
Pneumococcal vaccine	15 (5.7)
COVID-19 vaccine	103 (39.0%)

Note: Data are presented as n (%).

Abbreviation: COVID-19, coronavirus disease 2019.

Table 3 Reasons for the Patients Who Did Not Receive Influenza or Pneumococcal Vaccination

	Not Receiving Influenza Vaccination (n = 204)	Not Receiving Pneumococcal Vaccination (n = 249)
Worry about the side effects	61 (29.9)	61 (24.5)
Do not think it necessary	54 (26.5)	60 (24.1)
Activity inconvenience	16 (7.8)	13 (5.2)
Own expense for vaccination	4 (2.0)	6 (2.4)
Others	64 (31.4)	106 (42.6)
Having no knowledge of the vaccines	50 (78.1)	87 (82.1)
Having other severe diseases	9 (14.1)	9 (8.5)
Allergic constitution	5 (7.8)	5 (4.7)
Not recommended by the general practitioners	0 (0.0)	5 (4.7)

Note: Data are presented as n (%).

Figure 2 Distribution of the reasons why the patients did not intend to get COVID-19 vaccination (n = 110).

Table 4 Factors Associated with the Change of Intention to Receive Influenza Vaccination After the COVID-19 Epidemic in Beijing (n = 204)^a

	Univariate Analysis			Multivariate Analysis
	OR	95% CI	P-value	OR	95% CI	P-value
Age ≥60 years	0.65	0.32–1.34	0.241
Education level	N/A	N/A	0.746
Current-/Ex-smoker	0.89	0.37–2.16	0.797
≥1 comorbidities	0.79	0.46–1.37	0.407
Maintaining long-term inhaled medications	1.47	0.79–2.75	0.220
Knowing the benefit of influenza vaccine	1.53	0.86–2.75	0.151
Knowing the subsidy policy of influenza vaccination in Beijing	1.78	1.03–3.13	0.039	1.45	0.77–2.71	0.251
Influenza vaccine recommended by medical staffs	1.76	0.98–3.19	0.059	1.42	0.73–2.77	0.299
COVID-19 vaccination	2.39	1.17–3.86	0.013	1.99	1.08–3.65	0.027

Notes: ^aData were analyzed in the patients who did not receive influenza vaccination in the past season (2020/2021, n = 204). Univariate analysis was performed by Chi-square test, while multivariate analysis was performed by binary logistic regression model. The change of intention to influenza vaccination in future was converted to a binary variable, which was recorded as “Yes” or “No”. Except education level, the other variables included in the analysis were also converted to binary variables. The variables with P<0.1 in the univariate analysis were included in multivariate analysis.

Abbreviations: COVID-19, coronavirus disease 2019; OR, odds ratio; CI, confidence interval.