Influenza Vaccine Effectiveness in Mainland China

Review Inﬂuenza Vaccine Effectiveness in Mainland China: A Systematic Review and Meta-Analysis

Xiaokun Yang 1 , Hongting Zhao 1, Zhili Li 1, Aiqin Zhu 2 , Minrui Ren 1, Mengjie Geng 1, Yu Li 1, Ying Qin 1, Luzhao Feng 3, Zhibin Peng 1, Zhijie An 4, Jiandong Zheng 1,*, Zhongjie Li 1,* and Zijian Feng 5,*

1 Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Center for Disease Control and Prevention, Beijing 102206, China; [email protected] (X.Y.); [email protected] (H.Z.); [email protected] (Z.L.); [email protected] (M.R.); [email protected] (M.G.); [email protected] (Y.L.); [email protected] (Y.Q.); [email protected] (Z.P.) 2 Division of Infectious Disease Prevention and Disinfection Management, Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai 200136, China; [email protected] 3 School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China; [email protected] 4 National Immunization Program, Chinese Center for Disease Control and Prevention, Beijing 102206, China; [email protected] 5 Chinese Center for Disease Control and Prevention, Beijing 102206, China * Correspondence: [email protected] (J.Z.); [email protected] (Z.L.); [email protected] (Z.F.); Tel.: +86-010-5890-0541 (J.Z.); +86-010-5890-0543 (Z.L.); +86-010-5890-0309 (Z.F.)

Abstract: Influenza endangers human health but can be prevented in part by vaccination. Assessing influenza vaccine effectiveness (VE) provides scientific evidence for developing influenza vaccination policy. We conducted a systematic review and meta-analysis of studies that evaluated influenza VE in mainland China. We searched six relevant databases as of 30 August 2019 to identify studies and used Review Manager 5.3 software to analyze the included studies. The Newcastle–Ottawa scale was used to assess the risk of publication bias. We identified 1408 publications, and after removing Citation: Yang, X.; Zhao, H.; Li, Z.; duplicates and screening full texts, we included 21 studies in the analyses. Studies were conducted in Zhu, A.; Ren, M.; Geng, M.; Li, Y.; Qin, Y.; Feng, L.; Peng, Z.; et al. Influenza Beijing, Guangzhou, Suzhou, and Zhejiang province from the 2010/11 influenza season through the Vaccine Effectiveness in Mainland 2017/18 influenza season. Overall influenza VE for laboratory confirmed influenza was 36% (95% CI: China: A Systematic Review and 25–46%). In the subgroup analysis, VE was 45% (95% CI: 18–64%) for children 6–35 months who Meta-Analysis. Vaccines 2021, 9, 79. received one dose of influenza vaccine, and 57% (95% CI: 50–64%) who received two doses. VE was https://doi.org/10.3390/vaccines 47% (95% CI: 39–54%) for children 6 months to 8 years, and 18% (95% CI: 0–33%) for adults ≥60 years. 9020079 For inpatients, VE was 21% (95% CI: −11–44%). We conclude that influenza vaccines that were used in mainland China had a moderate effectiveness, with VE being higher among children than the Received: 18 December 2020 elderly. Influenza VE should be continuously monitored in mainland China to provide evidence for Accepted: 20 January 2021 policy making and improving uptake of the influenza vaccine. Published: 23 January 2021

Keywords: influenza; vaccine effectiveness; China; systematic review; meta-analysis Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Influenza is an acute respiratory infectious disease that causes a large burden of disease globally. According to WHO estimates, 5 to 10% of adults and 20 to 30% of children will suffer from seasonal influenza infection every year, resulting in 3 to 5 million Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. cases of severe illness and 290,000–650,000 deaths [1]. In each year of the 2010/2011 This article is an open access article through 2014/2015 seasons, there was an estimated 65 to 190 million people infected and distributed under the terms and 88,100 influenza-associated excess respiratory deaths in China [2]. Although there are conditions of the Creative Commons antiviral drugs for influenza, such as oseltamivir and zanamivir [3], influenza vaccination Attribution (CC BY) license (https:// is considered the most economical and effective way to prevent influenza [4]. However, creativecommons.org/licenses/by/ with the exception of a few cities where local government subsidize influenza vaccination 4.0/). programs, influenza vaccination has not been introduced in a national, government-funded

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program for people in mainland China, nor is the influenza vaccine included in health insurance. People pay out-of-pocket for the influenza vaccine, which may contribute to China’s extremely low vaccine uptake rate of 1.5 to 2.2% [5]. Health and economic analyses of seasonal influenza vaccination may help local and national governments justify influenza vaccination programs. An essential component of the health and economic evaluation of vaccines is their effectiveness. Influenza vaccine effectiveness varies year to year. Monitoring influenza VE can provide evidence for program and policy making. Methods for determining influenza VE vary significantly. Some studies have evaluated the safety and immunogenicity of the influenza vaccine [6–9] and some influenza VE studies based on influenza-like illness (ILI), which may underestimate influenza VE [10–12]. The use of laboratory diagnosed influenza for VE studies has been facilitated by widespread adoption of rapid laboratory testing for influenza virus in medical institutions at all levels of China [13–16]. We report the results of a systematic review and meta-analysis of influenza VE studies in mainland China to provide evidence for improving VE monitoring and support influenza vaccine policy making.

2. Materials and Methods 2.1. Literature Retrieval We used the key words “inﬂuenza,” “inﬂuenza vaccination”, “vaccine effectiveness”, and “China” as search terms to locate published articles. We searched the Wanfang Database, China National Knowledge Infrastructure (CNKI), China Biology Medicine (CBM), and VIP journal database for Chinese language studies; we searched PubMed and Web of Science for English language studies. We used literature reference tracing to identify additional articles and reduce the chance of omitting studies meeting the eligibility criteria.

2.2. Eligibility Criteria To be eligible for this systematic review and meta-analysis a study had to meet the following criteria: (1) the study setting was in mainland China; (2) the study design was either a test-negative design (TND) case–control study, another type of case–control study, or a cohort study; (3) patients with ILI or acute respiratory infection (ARI) had to have been tested by reverse transcription polymerase chain reaction(RT-PCR) for the influenza virus; (4) the study reported influenza VE; and (5) the study was a post-marketing influenza VE evaluation.

2.3. Data Retrieval We developed a spreadsheet to organize extracted data. Authors X.Y. and H.Z. extracted data from the included studies; Z.L., M.R., and M.G. checked the data for complete- ness and accuracy; reviewer disagreements were resolved by Z.L. Information extracted included title, first author, year of publication, study period, study site, study design, study participant description, subject inclusion criteria, samples obtained, and vaccination rate of the influenza positive group and the influenza negative group.

2.4. Literature Quality Evaluation We used the Newcastle–Ottawa Scale (NOS) [17] to evaluate the quality of the eligible studies from three aspects—population selection, comparability, and exposure evaluation— each aspect receiving up to three points for a total possible score of nine points. We considered scores of more than six points to indicate studies of high quality with low risk of bias; scores of four or ﬁve points indicated moderate quality with average risk of bias; scores of three points or fewer indicated low quality with high risk of bias.

2.5. Statistical Analyses We used Review Manager 5.3 software to conduct meta-analyses. Heterogeneity was assessed by the I2 statistic. The value of I2 reﬂects the percent of heterogeneity in the total variation; an I2 greater than 50% is evidence of heterogeneity. In our testing for Vaccines 2021, 9, x FOR PEER REVIEW 3 of 13

2.5. Statistical Analyses

Vaccines 2021, 9, 79 We used Review Manager 5.3 software to conduct meta-analyses. Heterogeneity3 ofwas 13 assessed by the I2 statistic. The value of I2 reflects the percent of heterogeneity in the total variation; an I2 greater than 50% is evidence of heterogeneity. In our testing for heterogeneity, we used a fixed-effect model (p > 0.05 and I2 < 50%) or a random-effect model (p ≤ 2 heterogeneity,0.05 or I2 ≥ 50%) we [18]; used we a fixed-effectpresent results model as (oddsp > 0.05 ratios and (ORI <) 50%)with or95% arandom-effect confidence intervals model ≤ 2 ≥ ((pCI) and0.05 display or I results50%) [ 18in]; forest we present plots. We results conducted as odds subgroup ratios (OR analyses) with 95% with confidence delimited intervals (CI) and display results in forest plots. We conducted subgroup analyses with data. We compared changes in I2 statistic values when studies with the highest VE, the delimited data. We compared changes in I2 statistic values when studies with the highest lowest VE, and the largest sample size were excluded. We considered the results to be VE, the lowest VE, and the largest sample size were excluded. We considered the results stable if the I2 statistic change was <10%. Influenza VE was calculated as (1- odds ratio) to be stable if the I2 statistic change was <10%. Influenza VE was calculated as (1 − odds ×100%, where the odds ratio is the odds of vaccination in cases divided by the odds of ratio) ×100%, where the odds ratio is the odds of vaccination in cases divided by the odds vaccination in controls. of vaccination in controls.

3. Results 3.1. Basic Information of Eligible Studies The searchsearch termsterms identified identified 1408 1408 studies studies from from the the six six databases—282 databases—282 from from the the Wanfang Wan- database,fang database, 472 from 472 CNKI,from CN 302KI, from 302 VIPfrom journal VIP journal database, database, 282 from 282 CBM, from 130 CBM, from 130 Web from of Science,Web of Science, and 27 from and PubMed.27 from PubMed. Our eligibility Our eligibility review identified review identified 21 articles 21 for articles including for in- in thecluding analyses in the [19 analyses–39] (Figure [19–391).] The(Figure 21 eligible 1). The studies21 eligible were studies conducted were conducted between the between 2010– 2011the 201 influenza0–2011 seasoninfluenza and season the 2017–2018 and the influenza 2017–2018 season. influenza The mostseason. common The most settings common were: Beijing;settings Guangzhou,were: Beijing; Guangdong Guangzhou, province; Guangdong Suzhou, province; Jiangsu Suzhou, province; Jiangsu and Yongkang province; andand YiwuYongkang in Zhejiang and Yiwu province. in Zhejiang Study province. subjects in Study the Guangzhou subjects in the studies Guangzhou were children studies aged were 6 monthschildren toaged 8 years 6 months old; subjects to 8 years in old; Beijing subjects studies in Beijing included studies outpatients included and outpatients hospitalized and patientshospitalized of all patients ages; subjects of all ages; in Suzhousubjects studiesin Suzhou were studies 3-to-6-year-old were 3-to-6 preschool-year-old preschool children. Researchchildren. Research methods methods included included case–control case– studiescontrol andstudies TND and studies. TND studies. Descriptions Descriptions of the eligibleof the eligible studies studies are shown are shown in Table in1 .Table 1.

Figure 1. Literature screeningscreening processprocess forfor systematicsystematic reviewsreviews andand meta-analysis.meta-analysis.

Vaccines 2021, 9, 79 4 of 13

Table 1. Characteristics of 21 studies included in the meta-analysis.

Published Study Vaccination Rate Vaccination Rate (Ref.) Study Period Study Objects Study Method Year Place in Case Group in Control Group 2010/09– Guang 6 months~5 years [19] 2013 case–control study 16.9% (52/308) 36.0% (279/774) 2011/09 Zhou community children 2011/01– Guang 6 months~5 years 1:2 case–control [20] 2013 12.4% (26/209) 27.3% (114/418) 2011/06 Zhou community children study 2012/01– Guang 6 months~5 years 1:2 case–control [20] 2013 19.9% (208/1046) 30.9% (647/2092) 2012/06 Zhou community children study 2013/02– Guang 6 months~5 years 1:1 case–control [21] 2014 13.1% (164/1250) 24.7% (309/1250) 2013/06 Zhou community children study 2013/02– Guang 8 months~6 years 1:1 case–control [22] 2015 9.0% (158/1754) 19.6% (343/1754) 2013/06 Zhou community children study 2014/02– Guang 6 months~5 years 1:2 case–control [23] 2016 14.0% (123/879) 21.5% (385/1793) 2014/07 Zhou community children study 2014/02– Guang 6 months~8 years 1:1 case–control [24] 2019 12.5% (102/819) 23.0% (188/819) 2014/07 Zhou community children study 2015/03– Guang 6 months~8 years 1:1 case–control [24] 2019 12.0% (248/2080) 14.8% (308/2080) 2015/07 Zhou community children study 2016/03– Guang 6 months~8 years 1:1 case–control [24] 2019 7.9% (101/1286) 12.0% (154/1286) 2016/05 Zhou community children study 2012/12– Outpatients of all age Test-negative [25] * 2014 Bei Jing 2.0% (14/695) 4.4% (57/1303) 2013/01 groups design 2013/11– Outpatients of more Test-negative [26] 2018 Bei Jing 6.8% (9/133) 10.2% (36/354) 2014/04 than 60 years old design 2013/12– Inpatients of all age Test-negative [27] 2016 Bei Jing 11.9% (42/353) 12.6% (173/1372) 2015/05 groups design 2014/11– 6~18 years old [28] 2017 Bei Jing case–control study 43.3% (91/210) 40.8% (692/1698) 2014/12 students 2014/11– Outpatients of all age Test-negative [29] 2017 Bei Jing 4.3% (149/3434) 3.7% (215/5863) 2015/04 groups design 2015/10– Outpatients of all age Test-negative [30] * 2017 Bei Jing 13.4% (47/351) 11.8% (207/1761) 2016/05 groups design 2015/11– Outpatients of all age Test-negative [31] 2018 Bei Jing 4.2% (124/2969) 4.3% (342/8031) 2016/03 groups design 2016/11– Outpatients of all age Test-negative [32] 2018 Bei Jing 2.9% (75/2626) 3.6% (278/7816) 2017/04 groups design 2016/11– Outpatients of all age Test-negative [33] 2019 Bei Jing 5.6% (18/322) 7.2% (137/1905) 2017/04 groups design 2016/11– 6~18 years old [34] 2019 Bei Jing case–control study 17.0% (30/175) 36.5% (229/628) 2017/04 students 2016/11– Inpatients of more Test-negative [35] 2019 Bei Jing 11.0% (16/145) 13.3% (70/528) 2017/04 than 60 years old design 2017/11– Inpatients of more Test-negative [35] 2019 Bei Jing 12.8% (19/149) 17.1% (56/328) 2018/04 than 60 years old design 2016/10– Outpatients of Test-negative [36] 2019 Zhe Jiang 6.1% (17/277) 10.9% (41/375) 2017/04 6months~6 years design 2017/10– Outpatients of Test-negative [36] 2019 Zhe Jiang 4.0% (5/126) 9.9% (38/383) 2018/04 6months~6 years design 2011/10– 6 months~5 years 1:1 case–control [37] 2016 Su Zhou 4.2% (18/427) 11.5% (49/427) 2012/09 community children study 2015/10– Kindergarten children Test-negative [38] * 2018 Su Zhou 10.3%(17/165) * 11.3% (23/203) * 2016/02 aged 3–6 years design 2016/10– Kindergarten children Test-negative [39] * 2018 Su Zhou 10.6%(17/160) * 13.3% (53/398) * 2017/02 aged 3–6 years design Ref. [25] *: The diagnosis of inﬂuenza-positive patients was based on the isolation of inﬂuenza viruses from cell cultures; other studies were based on RT-PCR. Ref. [25] * and [30] *: Vaccination information was self-reported; vaccination information of other studies can be found in electronic database of vaccination. Ref. [38] * and [39] *: The original study method was cohort study, but used test-negative design method to analyze, so we showed the positive rate of tested-sample in Table1.

3.2. Quality Evaluations Based on Newcastle–Ottawa Scale quality evaluation of the 21 studies (including 26 research results), ten studies received seven points, two studies received six points, and 14 studies received ﬁve points. The two most common reasons for score deductions were representativeness of the inﬂuenza positive group and absence of a reported response rate. Scores are shown in Table2. Vaccines 2021, 9, 79 5 of 13

Table 2. Quality evaluation of eligible studies: based on the Newcastle–Ottawa Scale.

Selection Comparability Exposure Comparability Same Published of Cases and Method of Total [Ref.] Is the Case Selection Deﬁnition Non- Year Representativeness Controls on the Ascertainment Ascertain- Scores Deﬁnition of of Response of the Cases Basis of the of Exposure ment for Adequate? Controls Controls Rate Design or Cases and Analysis Controls [19] 2013 1 0 1 1 2 1 1 0 7 [20] 2013 1 0 1 1 2 1 1 0 7 [20] 2013 1 0 1 1 2 1 1 0 7 [21] 2014 1 0 1 1 2 1 1 0 7 [22] 2015 1 0 1 1 2 1 1 0 7 [23] 2016 1 0 1 1 2 1 1 0 7 [24] 2019 1 0 1 1 2 1 1 0 7 [24] 2019 1 0 1 1 2 1 1 0 7 [24] 2019 1 0 1 1 2 1 1 0 7 [25] 2014 1 0 0 1 1 1 1 0 5 [26] 2018 1 0 0 1 1 1 1 0 5 [27] 2016 1 0 0 1 1 1 1 0 5 [28] 2017 1 0 1 0 2 1 1 0 6 [29] 2017 1 0 0 1 1 1 1 0 5 [30] 2017 1 0 0 1 1 1 1 0 5 [31] 2018 1 0 0 1 1 1 1 0 5 [32] 2018 1 0 0 1 1 1 1 0 5 [33] 2019 1 0 0 1 1 1 1 0 5 [34] 2019 1 0 1 1 1 1 1 0 6 [35] 2019 1 0 0 1 1 1 1 0 5 [35] 2019 1 0 0 1 1 1 1 0 5 [36] 2019 1 0 0 1 1 1 1 0 5 [36] 2019 1 0 0 1 1 1 1 0 5 [37] 2016 1 0 1 1 2 1 1 0 7 [38] 2018 1 0 0 1 1 1 1 0 5 [39] 2018 1 0 0 1 1 1 1 0 5

3.3. Meta Analysis of Inﬂuenza VE Meta-analysis was performed on the 21 included studies (26 separate research results). The heterogeneity test showed that I2 = 84%, p < 0.05, therefore, we used a random-effects model for merging data. Review Manager 5.3 determination showed the overall OR to be 0.64 (95% CI: 0.55–0.75), for a VE of 36% (95% CI: 25–45%). See Figure2.

3.4. Subgroup Analyses We conducted subgroup analyses using age of vaccinee, number of doses, and disease severity (hospitalized patients or outpatients). VE for 6 month to 35 month old children who received two doses of inﬂuenza vaccine was 57% (95% CI: 50–64%); overall VE for children 6 months to 8 years of age was 47% (95% CI: 39–54%); VE for people over 60 years old was the lowest, and VE for hospitalized patients was 21% (95% CI: −11–44%). Detailed results are shown in Table3, and meta-analysis results are shown in Figures3–8.

Table 3. Inﬂuenza VE subgroup analysis.

Heterogeneity Study Subjects I2 Model Selection OR (95%CI) VE (95%CI) Test: χ2 (P) Receipt of Two Doses of Vaccine * Children 6–35 months receiving one dose 20.8 (0.0009) 76% random-effects model 0.55 (0.36–0.82) 45% (18–64%) Children 6–35 months receiving two doses 4.76 (0.45) 0 fixed-effects model 0.43 (0.36–0.50) 57% (50–64%) Age Groups 6 months to 8 years 37.4 (0.0003) 65% random-effects model 0.53 (0.46–0.61) 47% (39–54%) More than 60 years 5.18 (0.64) 0 fixed-effects model 0.82 (0.67–1.00) 18% (0–33%) Disease Severity ** inpatients 11.74 (0.02) 66% random-effects model 0.79 (0.56–1.11) 21% (−11–44%) outpatients 10.93 (0.03) 63% random-effects model 0.87(0.68–1.10) 13% (−10–32%) * According to the influenza vaccine manufacturer prescribing information, children aged 6–35 months should receive two doses of influenza vaccine. ** Compared to outpatients, inpatients were considered more seriously ill. Vaccines 2021, 9, 79 6 of 13 Vaccines 2021, 9, x FOR PEER REVIEW 6 of 13

Coverage of Flu(+) Coverage of Flu(-) Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI Ma Chunna 2017 149 3434 215 5863 4.7% 1.19 [0.96, 1.47] Zhang Yi 2017 47 351 207 1761 4.2% 1.16 [0.83, 1.63] Zhang Li 2017 91 210 692 1698 4.4% 1.11 [0.83, 1.49] Zhang Li(A) 2018 124 2969 342 8031 4.8% 0.98 [0.79, 1.21] Qin Ying 2016 42 353 173 1372 4.1% 0.94 [0.65, 1.34] Wang Yin(B) 2018 17 165 23 203 2.7% 0.90 [0.46, 1.75] Zhang Daitao(B1) 2019 16 145 70 528 3.1% 0.81 [0.46, 1.45] Wu Shuangsheng 2018 75 2626 278 7816 4.6% 0.80 [0.62, 1.03] Fu Chuanxi(A2) 2019 248 2080 308 2080 4.9% 0.78 [0.65, 0.93] Wang Yin(A) 2018 17 160 53 398 3.0% 0.77 [0.43, 1.38] Zhang Daitao(A) 2019 18 322 137 1905 3.4% 0.76 [0.46, 1.27] Zhang Daitao(B2) 2019 19 149 56 328 3.1% 0.71 [0.41, 1.24] Zhang Li(B) 2018 9 133 36 354 2.4% 0.64 [0.30, 1.37] Fu Chuanxi(A3) 2019 101 1286 154 1286 4.5% 0.63 [0.48, 0.82] He Qing 2016 123 879 385 1793 4.7% 0.60 [0.48, 0.74] Fu Chuanxi (B2) 2013 208 1046 647 2092 4.9% 0.55 [0.46, 0.66] Luo Shuying(A1) 2019 17 277 41 375 3.0% 0.53 [0.30, 0.96] Fu Chuanxi(A1) 2019 102 819 188 819 4.5% 0.48 [0.37, 0.62] He Qing 2014 164 1250 309 1250 4.8% 0.46 [0.37, 0.57] Yang Peng 2014 14 695 57 1303 3.0% 0.45 [0.25, 0.81] Fu Chuanxi 2015 158 1754 343 1754 4.8% 0.41 [0.33, 0.50] Fu Chuanxi(B1) 2013 26 209 114 418 3.6% 0.38 [0.24, 0.60] Luo Shuying(A2) 2019 5 126 38 383 1.8% 0.38 [0.14, 0.98] Zhang Li 2019 30 175 229 628 3.8% 0.36 [0.24, 0.55] Fu Chuanxi(A) 2013 52 308 279 774 4.2% 0.36 [0.26, 0.50] Wang Yin 2016 18 427 49 427 3.1% 0.34 [0.19, 0.59]

Vaccines 2021, 9, x FORVaccines PEER 2021 REVIEW, 9, x TotalFOR (95% PEER CI) REVIEW 22348 45639 100.0% 0.64 [0.54, 0.74] 7 of 13 7 of 13

Total events 1890 5423 Heterogeneity: Tau² = 0.12; Chi² = 156.13, df = 25 (P < 0.00001); I² = 84% 0.05 0.2 1 5 20 Test for overall effect: Z = 5.63 (P < 0.00001) Vaccination Non-Vaccination * According to the* According influenza tovaccine the influenza manufacturer vaccine prescribing manufacturer information, prescribing children information, aged 6 –children35 months aged should 6–35 receivemonths should receive two doses of influenzatwo doses vaccine of influenza. ** ComparedFigureFigure vaccine 2. Meta-analysisMetato. outpatients,** -Comparedanalysis of inpatients toinfluenza inﬂuenza outpatients, were vaccine consideredinpatients effectiveness were more considered ( (VE)seriouslyVE) in mainland ill. more seriously China. ill.

Coverage of Flu(+) Coverage3.4.Coverage Subgroup of ofFlu(-) Flu(+) AnalysesCoverage Oddsof Flu(-) Ratio Odds Ratio Odds Ratio Odds Ratio Study or Subgroup StudyEvents or SubgroupTotal EventsEvents TotalTotalWeightEventsM-H, Random,Total Weight95% CI M-H, Random,M-H, 95% Random, CI 95% CI M-H, Random, 95% CI Fu Chuanxi (B2) 2013 Fu Chuanxi96 (B2) 2013586 195 96We 1172conducted58624.0% 195 subgroup0.981172 [0.75, 24.0%1.28] analyses0.98 [0.75,using 1.28] age of vaccinee, number of doses, and dis- Luo Shuying(A1) 2019 Luo Shuying(A1)2 2019187 ease5 severity2 387187 (hospitalized5.0% 5 0.83387 [0.16, patients 4.30]5.0% or0.83 outpatients). [0.16, 4.30] VE for 6 month to 35 month old chil- Fu Chuanxi 2015 Fu Chuanxi49 2015 794 dren81 49who793 received79421.9% 81 two0.58 793doses [0.40, 21.9%0.84] of influenza0.58 [0.40, 0.84]vaccine was 57% (95% CI: 50–64%); overall VE He Qing 2014 78 825 133 832 23.4% 0.55 [0.41, 0.74] He Qing 2014 for children78 825 6 months133 to832 8 years23.4% of age0.55 [0.41,was 0.74] 47% (95% CI: 39–54%); VE for people over 60 Fu Chuanxi(B1) 2013 Fu Chuanxi(B1)9 2013133 39 9 26513313.9% 39 0.42265 [0.20, 13.9%0.90] 0.42 [0.20, 0.90] Wang Yin 2016 Wang Yin6 2016 292 years32 6old292 was29211.8% the 32lowest0.17292 [0.07,, and 11.8%0.41] VE for0.17 hospitalized [0.07, 0.41] patients was 21% (95% CI: −11–44%). Detailed results are shown in Table 3, and meta-analysis results are shown in Figures 3– Total (95% CI) 2817 3741 100.0% 0.55 [0.36, 0.82] Total (95% CI) 8. 2817 3741 100.0% 0.55 [0.36, 0.82] Total events Total events240 485240 485 Heterogeneity: Tau² = 0.16;Heterogeneity: Chi² = 20.88, Tau²df = 5= (P0.16; = 0.0009); Chi² = 20.88, I² = 76% df = 5 (P = 0.0009); I² = 76% 0.01 0.1 1 0.01 100.1 100 1 10 100 Test for overall effect: Z = Test2.90 for(P =overall 0.004) effect: Z = 2.90 (P = 0.004) Table 3. Influenza VE subgroup analysis. Vaccination Non-VaccinationVaccination Non-Vaccination

Study Subjects Heterogeneity Test: χ2 (P) I2 Model Selection OR (95%CI) VE (95%CI) Figure 3. Subgroup analysis of children 6–35 months who received one dose of influenza vaccine. Receipt of Two FigureDosesFigure of 3.3. VaccineSubgroupSubgroup * analysisanalysis ofof children 6–356–35 months who received one dose of influenza inﬂuenza vaccine. Children 6–35 months receiving one random-effects Coverage of Flu(+)20.8 (Coverage0.0009 )of Flu(-) 76% Odds Ratio Odds0.55 Ratio (0.36–0.82) 45% (18–64%) dose Coverage of Flu(+) Coverage of Flu(-) model Odds Ratio Odds Ratio Study or Subgroup StudyEvents or SubgroupTotal EventsEvents TotalTotalWeightEventsM-H, Fixed,Total 95%Weight CI M-H, Fixed,M-H, 95% Fixed, CI 95% CI M-H, Fixed, 95% CI Children 6–35 months receiving two Wang Yin 2016 Wang Yin6 2016 2924.76 (0.456) 6 292292 1.3%0 6 1.00fixed [0.32,292- 3.14]effects1.3% model1.00 [0.32, 3.14]0.43 (0.36–0.50) 57% (50–64%) dosesLuo Shuying(A1) 2019 Luo Shuying(A1)5 2019190 14 5 398190 1.9% 14 0.74 [0.26,398 2.09]1.9% 0.74 [0.26, 2.09] Age GroupsHe Qing 2014 He Qing65 2014 825 127 65 83282525.0% 127 0.47 [0.35,832 0.65]25.0% 0.47 [0.35, 0.65] Fu Chuanxi (B2) 2013 Fu Chuanxi67 (B2) 2013586 280 67 117258635.5% 280 0.41 [0.31,random1172 0.55]35.5%-effects0.41 [0.31, 0.55] 6 monthsFu to Chuanxi(B1) 8 years 2013 14 37.4133 (0.000359 ) 265 7.6%65% 0.41 [0.22, 0.77] 0.53 (0.46–0.61) 47% (39–54%) Fu Chuanxi(B1) 2013 14 133 59 265 model7.6% 0.41 [0.22, 0.77] Fu Chuanxi 2015 Fu Chuanxi59 2015 794 144 59 79379428.7% 144 0.36 [0.26,793 0.50]28.7% 0.36 [0.26, 0.50] More than 60 years 5.18 (0.64) 0 fixed-effects model 0.82 (0.67–1.00) 18% (0–33%) Disease SeverityTotal (95% CI) ** Total (95% CI) 2820 37522820100.0% 0.43 [0.36,3752 0.50]100.0% 0.43 [0.36, 0.50] Total events Total events216 630216 630 random-effects inpatientsHeterogeneity: Chi² = 4.76,Heterogeneity: df = 5 (P = 0.45); Chi²11.74 I² = = 4.76, 0% ( 0.02df = 5 )(P = 0.45); I² = 0%66% 0.79 (0.56–1.11) 21% (−11–44%) model0.01 0.1 1 0.01 100.1 100 1 10 100 Test for overall effect: Z = Test10.05 for (P overall < 0.00001) effect: Z = 10.05 (P < 0.00001) random-effects Vaccination Non-VaccinationVaccination Non-Vaccination outpatients 10.93 (0.03) 63% 0.87(0.68–1.10) 13% (−10–32%) model FigureFigure 4.4. SubgroupFigure analysis 4. Subgroup of children analysis 6–356–35 of months children who 6– received 35 months two who doses received of influenza inﬂuenza two doses vaccine. of influenza vaccine.

Coverage of Flu(+) CoverageCoverage of ofFlu(-) Flu(+) Coverage Oddsof Flu(-) Ratio Odds Ratio Odds Ratio Odds Ratio Study or Subgroup StudyEvents or SubgroupTotal EventsEvents TotalTotalWeightEventsM-H, Random,Total Weight95% CI M-H, Random,M-H, 95% Random, CI 95% CI M-H, Random, 95% CI Wang Yin(A) 2018 Wang Yin(A)17 2018165 23 17 203165 3.7% 23 0.90203 [0.46, 1.75]3.7% 0.90 [0.46, 1.75] Fu Chuanxi(A2) 2019 Fu Chuanxi(A2)248 20802019 308248 2080208010.3% 308 0.782080 [0.65, 10.3%0.93] 0.78 [0.65, 0.93] Wang Yin(B) 2018 Wang Yin(B)17 2018160 53 17 398160 4.4% 53 0.77398 [0.43, 1.38]4.4% 0.77 [0.43, 1.38] Fu Chuanxi(A3) 2019 Fu Chuanxi(A3)101 20191286 154101 12861286 8.8% 154 0.631286 [0.48, 0.82]8.8% 0.63 [0.48, 0.82] He Qing 2016 He Qing123 2016 879 385123 1793879 9.6% 385 0.601793 [0.48, 0.74]9.6% 0.60 [0.48, 0.74] Fu Chuanxi (B2) 2013 Fu Chuanxi208 (B2) 10462013 647208 2092104610.3% 647 0.552092 [0.46, 10.3%0.66] 0.55 [0.46, 0.66] Luo Shuying(A1) 2019 Luo Shuying(A1)17 2019277 41 17 375277 4.3% 41 0.53375 [0.30, 0.96]4.3% 0.53 [0.30, 0.96] Fu Chuanxi(A1) 2019 Fu Chuanxi(A1)102 2019819 188102 819819 8.8% 188 0.48819 [0.37, 0.62]8.8% 0.48 [0.37, 0.62] He Qing 2014 He Qing164 2014 1250 309164 12501250 9.8% 309 0.461250 [0.37, 0.57]9.8% 0.46 [0.37, 0.57] Fu Chuanxi 2015 Fu Chuanxi158 20151754 343158 17291754 9.9% 343 0.401729 [0.33, 0.49]9.9% 0.40 [0.33, 0.49] Fu Chuanxi(B1) 2013 Fu Chuanxi(B1)26 2013209 114 26 418209 5.7% 114 0.38418 [0.24, 0.60]5.7% 0.38 [0.24, 0.60] Luo Shuying(A2) 2019 Luo Shuying(A2)5 2019126 38 5 383126 2.1% 38 0.38383 [0.14, 0.98]2.1% 0.38 [0.14, 0.98] Fu Chuanxi(A) 2013 Fu Chuanxi(A)52 2013308 279 52 774308 7.6% 279 0.36774 [0.26, 0.50]7.6% 0.36 [0.26, 0.50] Wang Yin 2016 Wang Yin18 2016 427 49 18 427427 4.6% 49 0.34427 [0.19, 0.59]4.6% 0.34 [0.19, 0.59]

Total (95% CI) Total (95% CI)10786 1402710786100.0% 0.5214027 [0.45,100.0% 0.60] 0.52 [0.45, 0.60] Total events Total1256 events 29311256 2931 Heterogeneity: Tau² = 0.05;Heterogeneity: Chi² = 44.51, Tau²df = 13= 0.05; (P < Chi²0.0001); = 44.51, I² = 71% df = 13 (P < 0.0001); I² = 71% 0.01 0.1 1 0.01 100.1 100 1 10 100 Test for overall effect: Z = Test8.45 for(P

Figure 5. SubgroupFigure analysis 5. Subgroup of children analysis aged of6 monthschildren to aged 8 years 6 months old. to 8 years old.

Vaccines 2021, 9, x FOR PEER REVIEW 7 of 13

* According to the influenza vaccine manufacturer prescribing information, children aged 6–35 months should receive two doses of influenza vaccine. ** Compared to outpatients, inpatients were considered more seriously ill.

Coverage of Flu(+) Coverage of Flu(-) Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI Fu Chuanxi (B2) 2013 96 586 195 1172 24.0% 0.98 [0.75, 1.28] Luo Shuying(A1) 2019 2 187 5 387 5.0% 0.83 [0.16, 4.30] Fu Chuanxi 2015 49 794 81 793 21.9% 0.58 [0.40, 0.84] He Qing 2014 78 825 133 832 23.4% 0.55 [0.41, 0.74] Fu Chuanxi(B1) 2013 9 133 39 265 13.9% 0.42 [0.20, 0.90] Wang Yin 2016 6 292 32 292 11.8% 0.17 [0.07, 0.41]

Total (95% CI) 2817 3741 100.0% 0.55 [0.36, 0.82] Total events 240 485 Heterogeneity: Tau² = 0.16; Chi² = 20.88, df = 5 (P = 0.0009); I² = 76% 0.01 0.1 1 10 100 Test for overall effect: Z = 2.90 (P = 0.004) Vaccination Non-Vaccination

Figure 3. Subgroup analysis of children 6–35 months who received one dose of influenza vaccine.

Coverage of Flu(+) Coverage of Flu(-) Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Fixed, 95% CI M-H, Fixed, 95% CI Wang Yin 2016 6 292 6 292 1.3% 1.00 [0.32, 3.14] Luo Shuying(A1) 2019 5 190 14 398 1.9% 0.74 [0.26, 2.09] He Qing 2014 65 825 127 832 25.0% 0.47 [0.35, 0.65] Fu Chuanxi (B2) 2013 67 586 280 1172 35.5% 0.41 [0.31, 0.55] Fu Chuanxi(B1) 2013 14 133 59 265 7.6% 0.41 [0.22, 0.77] Fu Chuanxi 2015 59 794 144 793 28.7% 0.36 [0.26, 0.50]

Total (95% CI) 2820 3752 100.0% 0.43 [0.36, 0.50] Total events 216 630 Heterogeneity: Chi² = 4.76, df = 5 (P = 0.45); I² = 0% 0.01 0.1 1 10 100 Test for overall effect: Z = 10.05 (P < 0.00001) Vaccines 2021, 9, 79 Vaccination Non-Vaccination7 of 13

Figure 4. Subgroup analysis of children 6–35 months who received two doses of influenza vaccine.

Coverage of Flu(+) Coverage of Flu(-) Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI Wang Yin(A) 2018 17 165 23 203 3.7% 0.90 [0.46, 1.75] Fu Chuanxi(A2) 2019 248 2080 308 2080 10.3% 0.78 [0.65, 0.93] Wang Yin(B) 2018 17 160 53 398 4.4% 0.77 [0.43, 1.38] Fu Chuanxi(A3) 2019 101 1286 154 1286 8.8% 0.63 [0.48, 0.82] He Qing 2016 123 879 385 1793 9.6% 0.60 [0.48, 0.74] Fu Chuanxi (B2) 2013 208 1046 647 2092 10.3% 0.55 [0.46, 0.66] Luo Shuying(A1) 2019 17 277 41 375 4.3% 0.53 [0.30, 0.96] Fu Chuanxi(A1) 2019 102 819 188 819 8.8% 0.48 [0.37, 0.62] He Qing 2014 164 1250 309 1250 9.8% 0.46 [0.37, 0.57] Fu Chuanxi 2015 158 1754 343 1729 9.9% 0.40 [0.33, 0.49] Fu Chuanxi(B1) 2013 26 209 114 418 5.7% 0.38 [0.24, 0.60] Luo Shuying(A2) 2019 5 126 38 383 2.1% 0.38 [0.14, 0.98] Fu Chuanxi(A) 2013 52 308 279 774 7.6% 0.36 [0.26, 0.50] Wang Yin 2016 18 427 49 427 4.6% 0.34 [0.19, 0.59]

Total (95% CI) 10786 14027 100.0% 0.52 [0.45, 0.60] Total events 1256 2931 Heterogeneity: Tau² = 0.05; Chi² = 44.51, df = 13 (P < 0.0001); I² = 71% 0.01 0.1 1 10 100 Test for overall effect: Z = 8.45 (P < 0.00001) Vaccination Non-Vaccination Vaccines 2021, 9, Vaccinesx FOR PEER 2021 ,REVIEW 9, x FOR PEER REVIEW 8 of 13 8 of 13

Figure 5. Subgroup analysis of children aged 6 months to 88 yearsyears old.old.

Coverage of Flu(+) CoverageCoverage of of Flu(+) Flu(-) Coverage ofOdds Flu(-) Ratio Odds Ratio Odds Ratio Odds Ratio Study or Subgroup Study orEvents SubgroupTotal EventsEvents Total WeightEvents M-H, TotalFixed, Weight95% CI M-H, Fixed, 95%M-H, CI Fixed, 95% CI M-H, Fixed, 95% CI Zhang Yi 2017 Zhang Yi 201725 89 13525 56889 12.0%135 1.25568 [0.76, 12.0%2.07] 1.25 [0.76, 2.07] Ma Chunna 2017 Ma Chunna 222017 308 2231 308394 11.5%31 0.90394 [0.51, 11.5%1.59] 0.90 [0.51, 1.59] Wu Shuangsheng 2018Wu Shuangsheng25 2018304 2557 304628 15.5%57 0.90628 [0.55, 15.5%1.47] 0.90 [0.55, 1.47] Zhang Daitao(B1) 2019Zhang Daitao(B1)16 2019145 1670 145528 12.2%70 0.81528 [0.46, 12.2%1.45] 0.81 [0.46, 1.45] Zhang Daitao(A) 2019 Zhang Daitao(A)16 2019152 1692 152689 13.5%92 0.76689 [0.43, 13.5%1.34] 0.76 [0.43, 1.34] Zhang Daitao(B2) 2019Zhang Daitao(B2)19 2019149 1956 149328 13.9%56 0.71328 [0.41, 13.9%1.24] 0.71 [0.41, 1.24] Zhang Li(A) 2018 Zhang Li(A) 20189 133 369 133354 8.3%36 0.64354 [0.30, 1.37]8.3% 0.64 [0.30, 1.37] Zhang Li(B) 2018 Zhang Li(B)13 2018 243 1346 243496 13.0%46 0.55496 [0.29, 13.0%1.04] 0.55 [0.29, 1.04]

Total (95% CI) Total (95% CI) 1523 15233985 100.0% 0.823985 [0.67,100.0% 1.00] 0.82 [0.67, 1.00] Total events Total events145 145523 523 Heterogeneity: Chi² = 5.18,Heterogeneity: df = 7 (P = Chi² 0.64); = 5.18,I² = 0% df = 7 (P = 0.64); I² = 0% 0.01 0.1 0.011 0.110 1001 10 100 Test for overall effect: ZTest = 1.95 for overall(P = 0.05) effect: Z = 1.95 (P = 0.05) Vaccination Non-VaccinationVaccination Non-Vaccination

Figure 6. SubgroupFigureFigure analysis 6. 6. SubgroupSubgroup of adults analysis analysis more of ofthan adults adults 60 years more more old. than than 60 60 years old.

Experimental ControlExperimental ControlOdds Ratio Odds Ratio Odds Ratio Odds Ratio Study or Subgroup StudyEvents or SubgroupTotal EventsEventsTotal TotalWeightEventsM-H, TotalRandom,Weight 95% CIM-H, Random,M-H, 95% Random,CI 95% CIM-H, Random, 95% CI Zhang Yi 2017 Zhang Yi 472017 351 207 176147 35124.0% 207 17611.16 [0.83,24.0% 1.63] 1.16 [0.83, 1.63] Qin Ying 2016 Qin Ying 422016 353 173 137242 35323.4% 173 13720.94 [0.65,23.4% 1.34] 0.94 [0.65, 1.34] Zhang Daitao(B1) 2019Zhang Daitao(B1)16 145 2019 70 16528 14516.7% 70 0.81528 [0.46,16.7% 1.45] 0.81 [0.46, 1.45] Zhang Daitao(B2) 2019Zhang Daitao(B2)19 149 2019 56 19328 14917.2% 56 0.71328 [0.41,17.2% 1.24] 0.71 [0.41, 1.24] Zhang Daitao(A) 2019 Zhang Daitao(A)18 3222019 137 109518 32218.7% 137 10950.41 [0.25,18.7% 0.69] 0.41 [0.25, 0.69]

Total (95% CI) Total (95% CI) 1320 5084 1320100.0% 50840.79 [0.56,100.0% 1.11] 0.79 [0.56, 1.11] Total events Total events142 643 142 643 Heterogeneity: Tau² = 0.10;Heterogeneity: Chi² = 11.74, Tau² df = = 0.10; 4 (P Chi²= 0.02); = 11.74, I² = 66% df = 4 (P = 0.02); I² = 66% 0.01 0.1 0.011 0.110 1001 10 100 Test for overall effect: ZTest = 1.35 for overall(P = 0.18) effect: Z = 1.35 (P = 0.18) Vaccination Non-VaccinationVaccination Non-Vaccination

Figure 7. SubgroupFigure analysis 7. SubgroupFigure of inpatients. 7.analysisSubgroup of inpatients. analysis of inpatients.

Coverage of Flu(+) CoverageCoverage ofof Flu(+)Flu(-) Coverage of Flu(-)Odds Ratio Odds Ratio Odds Ratio Odds Ratio Study or Subgroup Study orEvents SubgroupTotal EventsEvents Total WeightEvents M-H, TotalRandom,Weight 95% CIM-H, Random,M-H, 95% Random, CI 95% CIM-H, Random, 95% CI Ma Chunna 2017 Ma Chunna143 2017 3434 143215 34345863 27.8%215 58631.14 [0.92,27.8% 1.42] 1.14 [0.92, 1.42] Zhang Li(B) 2018 Zhang Li(B)124 2018 2969 124342 29698031 28.2%342 80310.98 [0.79,28.2% 1.21] 0.98 [0.79, 1.21] Wu Shuangsheng 2018Wu Shuangsheng75 20182626 27875 26267816 25.3%278 78160.80 [0.62,25.3% 1.03] 0.80 [0.62, 1.03] Zhang Li(A) 2018 Zhang Li(A) 20189 133 369 133354 7.7%36 0.64354 [0.30,7.7% 1.37] 0.64 [0.30, 1.37] Yang Peng 2014 Yang Peng 142014 659 1457 1303659 11.1%57 13030.47 [0.26,11.1% 0.86] 0.47 [0.26, 0.86]

Total (95% CI) Total (95% CI) 9821 233679821 100.0% 233670.87 [0.68,100.0% 1.10] 0.87 [0.68, 1.10] Total events Total events365 365928 928 Heterogeneity: Tau² = 0.04;Heterogeneity: Chi² = 10.93, Tau² df = = 0.04; 4 (P Chi²= 0.03); = 10.93, I² = 63% df = 4 (P = 0.03); I² = 63% 0.01 0.1 0.011 0.110 1001 10 100 Test for overall effect: ZTest = 1.18 for overall(P = 0.24) effect: Z = 1.18 (P = 0.24) Vaccination Non-VaccinationVaccination Non-Vaccination

Figure 8. SubgroupFigure analysis 8. Subgroup of outpatients. analysis of outpatients.

3.5. Sensitivity3.5. Analysis Sensitivity and PublicationAnalysis and Bias Publication Bias The highest estimatedThe highest influenza estimated VE influenza reported VEin the reported included in thestudies included was 66%studies (95% was 66% (95% CI: 41–81%) byCI: Wang 41–81%) Yin by and Wang colleagues Yin and [37]. colleagues We excluded [37]. We this excluded study and this re -studyconducted and re -conducted the meta-analysis,the meta finding-analysis, an OR finding of 0.65 an (95% OR CI:of 0.65 0.56 (95%–0.76), CI: for 0.56 a VE–0.76), of 35% for (95%a VE CI:of 35% 23– (95% CI: 23– 44%). Ma Chunna44%). andMa Chunnacolleagues and reported colleagues the lowestreported influenza the lowest VE influenza [29], which VE was [29], − 19%which was −19% (95% CI: −47–(95%4%), CI:and − 47had–4%), the studyand had with the the study largest with sample the largest size. sampleAfter excluding size. After Ma’s excluding Ma’s study, the recalculatedstudy, the ORrecalculated was 0.62 (95%OR was CI: 0.620.54 –(95%0.72), CI: for 0.54 a VE–0.72), of 38% for (95% a VE CI:of 38% 28–46 (95%%). CI: 28–46%). The differenceThe in differenceORs before in and ORs after before excluding and after the excluding studies was the less studies than was 10%, less implying than 10%, implying stability of thestability meta-analyses. of the meta Figure-analyses. 9 shows Figure a scatter 9 shows plot a(funnel scatter plot)plot (funnelthat plots plot) OR that on plots OR on the horizontalthe axis horizontal and the standardaxis and theerror standard of log(OR) error on of the log(OR) vertical on axis. the verticalThe distribution axis. The distribution of scattered pointsof scattered was approximately points was approximately symmetric, indicating symmetric, that indicating risk of publication that risk of bias publication bias was relativelywas low. relatively low.

Vaccines 2021, 9, x FOR PEER REVIEW 8 of 13

Coverage of Flu(+) Coverage of Flu(-) Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Fixed, 95% CI M-H, Fixed, 95% CI Zhang Yi 2017 25 89 135 568 12.0% 1.25 [0.76, 2.07] Ma Chunna 2017 22 308 31 394 11.5% 0.90 [0.51, 1.59] Wu Shuangsheng 2018 25 304 57 628 15.5% 0.90 [0.55, 1.47] Zhang Daitao(B1) 2019 16 145 70 528 12.2% 0.81 [0.46, 1.45] Zhang Daitao(A) 2019 16 152 92 689 13.5% 0.76 [0.43, 1.34] Zhang Daitao(B2) 2019 19 149 56 328 13.9% 0.71 [0.41, 1.24] Zhang Li(A) 2018 9 133 36 354 8.3% 0.64 [0.30, 1.37] Zhang Li(B) 2018 13 243 46 496 13.0% 0.55 [0.29, 1.04]

Total (95% CI) 1523 3985 100.0% 0.82 [0.67, 1.00] Total events 145 523 Heterogeneity: Chi² = 5.18, df = 7 (P = 0.64); I² = 0% 0.01 0.1 1 10 100 Test for overall effect: Z = 1.95 (P = 0.05) Vaccination Non-Vaccination

Figure 6. Subgroup analysis of adults more than 60 years old.

Experimental Control Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI Zhang Yi 2017 47 351 207 1761 24.0% 1.16 [0.83, 1.63] Qin Ying 2016 42 353 173 1372 23.4% 0.94 [0.65, 1.34] Zhang Daitao(B1) 2019 16 145 70 528 16.7% 0.81 [0.46, 1.45] Zhang Daitao(B2) 2019 19 149 56 328 17.2% 0.71 [0.41, 1.24] Zhang Daitao(A) 2019 18 322 137 1095 18.7% 0.41 [0.25, 0.69]

Total (95% CI) 1320 5084 100.0% 0.79 [0.56, 1.11] Total events 142 643 Heterogeneity: Tau² = 0.10; Chi² = 11.74, df = 4 (P = 0.02); I² = 66% 0.01 0.1 1 10 100 Test for overall effect: Z = 1.35 (P = 0.18) Vaccines 2021, 9, 79 Vaccination Non-Vaccination 8 of 13

Figure 7. Subgroup analysis of inpatients.

Coverage of Flu(+) Coverage of Flu(-) Odds Ratio Odds Ratio Study or Subgroup Events Total Events Total Weight M-H, Random, 95% CI M-H, Random, 95% CI Ma Chunna 2017 143 3434 215 5863 27.8% 1.14 [0.92, 1.42] Zhang Li(B) 2018 124 2969 342 8031 28.2% 0.98 [0.79, 1.21] Wu Shuangsheng 2018 75 2626 278 7816 25.3% 0.80 [0.62, 1.03] Zhang Li(A) 2018 9 133 36 354 7.7% 0.64 [0.30, 1.37] Yang Peng 2014 14 659 57 1303 11.1% 0.47 [0.26, 0.86]

Total (95% CI) 9821 23367 100.0% 0.87 [0.68, 1.10] Total events 365 928 Heterogeneity: Tau² = 0.04; Chi² = 10.93, df = 4 (P = 0.03); I² = 63% 0.01 0.1 1 10 100 Test for overall effect: Z = 1.18 (P = 0.24) Vaccination Non-Vaccination

FigureFigure 8. 8. SubgroupSubgroup analysis analysis of of outpatients. outpatients.

3.5.3.5. Sensitivity Sensitivity Analysis Analysis and and Publication Publication Bias Bias TheThe highest highest estimated influenzainfluenza VEVE reportedreported in in the the included included studies studies was was 66% 66% (95% (95% CI: CI:41–81%) 41–81%) by by Wang Wang Yin Yin and and colleagues colleagues [37 [37].]. We We excludedexcluded thisthis studystudy and re re-conducted-conducted thethe meta meta-analysis,-analysis, finding finding an an OR OR of of0.65 0.65 (95% (95% CI: CI: 0.56 0.56–0.76),–0.76), for fora VE a of VE 35% of 35%(95% (95% CI: 23 CI:– 44%23–44%). Ma). Chunna Ma Chunna and colleagues and colleagues reported reported the lowest the lowest influenza influenza VE [29], VE which [29], which was −19% was (95%−19% CI: (95% −47 CI:–4%),−47–4%), and had and the had study the with study the with largest the largestsample sample size. After size. excluding After excluding Ma’s study,Ma’s study,the recalculated the recalculated OR was OR 0.62 was (95% 0.62 CI: (95% 0.54– CI:0.72), 0.54–0.72), for a VE of for 38% a VE (95% of 38%CI: 28 (95%–46%). CI: The28–46%). difference The differencein ORs before in ORs and before after excluding and after excludingthe studies the was studies less than was 10%, less implying than 10%, stabilityimplying of stabilitythe meta of-analyses. the meta-analyses. Figure 9 shows Figure a scatter9 shows plot a scatter(funnel plotplot) (funnel that plots plot) OR that on Vaccines 2021, 9, x FOR PEER REVIEW 9 of 13 theplots horizontal OR on the axis horizontal and the standard axis and error the of standard log(OR) error on the of vertical log(OR) axis. on The the verticaldistribution axis. ofThe scattered distribution points of was scattered approximately points was symmetric, approximately indicating symmetric, that risk indicating of publication that risk bias of waspublication relatively bias low. was relatively low.

FigureFigure 9. Funnel 9. Funnel plot for plot meta for- meta-analysisanalysis of influenza of inﬂuenza VE. VE.

4. Discussion Influenza vaccination is widely considered to be the most effective and cost-beneficial way to prevent influenza [4]. Evaluating influenza VE is very important for supporting policy making. We conducted a meta-analysis of 21 published studies set in mainland China and found that the overall VE was 36% during the 2010/2011 through 2017/2018 influenza seasons, showing a moderately protective effect of vaccination. Influenza VE was higher (57%) among young children 6 to 35 months of age who received two doses influenza vaccine for their first time receiving influenza vaccine; VE was 45% among same-age children who received one dose of influenza vaccine. We found that influenza VE was higher among children than among individuals over 60 years of age. Two studies from the United States found that during the 2003–2005 season and the 2005–2007 season, VE for 6 to 59 month children who received two doses of vaccine was 57% (95% CI: 28–74%) and 56% (95% CI: 25–74%) [40,41], which is consistent with our findings. However, among children in the two U.S. studies who received a single dose, the vaccine showed no significant protective effect. This is in contrast to our meta-analysis that included six studies and found a protective effect with one dose—although lower than for two doses. This may be due in part to a larger sample size in our meta-analysis that could provide more statistical power. A retrospective study in Italy [42] found that influenza VE in children was 37.1% (95% CI: 22.2–49.2%) during the 2010/2011 to 2017/2018 influenza seasons. Our findings of higher VE may be because we did not adjust for age, gender, and epidemic season in our meta-analyses, while the study conducted in Italy did. Both studies showed better protection for children from 2010/2011 through 2017/2018 seasons than for elderly adults. Using a fixed-effect model, we found that for people over 60 years of age, VE was only 18%. Such a low protective effect is unsatisfactory. Lower VE among the elderly may be due to immunosenescence. Studies have shown that vaccine-mediated antibody titers

Vaccines 2021, 9, 79 9 of 13

4. Discussion Influenza vaccination is widely considered to be the most effective and cost-beneficial way to prevent influenza [4]. Evaluating influenza VE is very important for supporting policy making. We conducted a meta-analysis of 21 published studies set in mainland China and found that the overall VE was 36% during the 2010/2011 through 2017/2018 influenza seasons, showing a moderately protective effect of vaccination. Influenza VE was higher (57%) among young children 6 to 35 months of age who received two doses influenza vaccine for their first time receiving influenza vaccine; VE was 45% among same-age children who received one dose of influenza vaccine. We found that influenza VE was higher among children than among individuals over 60 years of age. Two studies from the United States found that during the 2003–2005 season and the 2005–2007 season, VE for 6 to 59 month children who received two doses of vaccine was 57% (95% CI: 28–74%) and 56% (95% CI: 25–74%) [40,41], which is consistent with our findings. However, among children in the two U.S. studies who received a single dose, the vaccine showed no significant protective effect. This is in contrast to our meta-analysis that included six studies and found a protective effect with one dose—although lower than for two doses. This may be due in part to a larger sample size in our meta-analysis that could provide more statistical power. A retrospective study in Italy [42] found that influenza VE in children was 37.1% (95% CI: 22.2–49.2%) during the 2010/2011 to 2017/2018 influenza seasons. Our findings of higher VE may be because we did not adjust for age, gender, and epidemic season in our meta-analyses, while the study conducted in Italy did. Both studies showed better protection for children from 2010/2011 through 2017/2018 seasons than for elderly adults. Using a fixed-effect model, we found that for people over 60 years of age, VE was only 18%. Such a low protective effect is unsatisfactory. Lower VE among the elderly may be due to immunosenescence. Studies have shown that vaccine-mediated antibody titers among the elderly are lower than those among younger individuals [43–46]. Different influenza vaccination strategies for the elderly are needed to overcome immunosenescence. The US Food and Drug Administration (FDA) approved a high-dose trivalent inactivated influenza vaccine in 2009, increasing each antigen component from the standard dose of 15 µg to 60 µg [47]. High-dose influenza vaccines demonstrate better efficacy against laboratory- confirmed influenza [48–51] and better effectiveness against confirmed influenza, influenza- related medical visits, hospitalization, and death [52–54]. The findings from our study support the need for promoting development and introduction of high-dose influenza vaccines and adjuvant vaccines that are more suitable for the elderly in mainland China. A 2016 review found that influenza VE, assessed with test-negative design studies in inpatient settings, had similar results to TND assessments in outpatient settings [55]. A study set in Spain found that influenza VE was 34% (95% CI: 6–54%) among outpatients and 32% (95% CI: 15–45%) among hospitalized patients during the 2010/2011 to 2015/2016 seasons [56]. A study from the U.S. among adults over 18 years of age conducted from 2015 to 2018 [57] showed that influenza VE was 31% (95% CI: 26–37%) for outpatients and 36% (95% CI: 27–44%) for inpatients. We used a random-effect model to evaluate VE for outpatients and inpatients and found that VE was 21% (95% CI: −11–44%) for inpatients and 13% (95% CI: −10–32%) for outpatients—lower than the findings from these other studies. It is possible that vaccine-circulating-strain mismatch is partially responsible. For example, for the 2014/2015 influenza season, the match was poor, and Ma Chunna and colleagues [29] found that influenza vaccination had no protective effect among outpatients. Zhang Yi and colleagues [30] also found no protective effect among hospitalized patients of all age groups in Beijing in the 2015/2016 influenza season. The reason for the poor VE, could also be related to the extremely low influenza vaccination rate in mainland China. Low coverage makes VE assessment more difficult, since VE study sample sizes need to be larger when vaccination rates are low. Evaluation of influenza VE will be facilitated by conducting studies in cities with higher vaccination coverage levels, which currently Vaccines 2021, 9, 79 10 of 13

tend to be cities in which local governments sponsor seasonal influenza vaccination among targeted groups, such as school children or the elderly. Our study has several limitations. First, the studies we included made some statistical adjustments in their OR calculations, but the adjusting factors were not the same in all studies. Therefore, we did not use adjustment factors in Revman 5.3 software for our meta-analysis. Second, our study only involved only two age groups—6 months to 8 years and over 60 years—limiting our ability to conduct additional subgroup analyses. Third, we did not analyze VE by influenza virus types, primarily due to lack of information about virus types in the studies. Using a standardized protocol in the future, we should be able to assess the VE of different ages and different virus types. Fourth, too few studies were based on laboratory confirmed cases of influenza in mainland China—only Beijing, Suzhou, Zhejiang and Guangzhou regularly used laboratory confirmation. The settings for our meta-analysis were in northern and southern China, which misses some variation in the epidemic seasons in mainland China. Establishing a wider area for VE studies will be useful in the future. Finally, we did not analyze VE by influenza season due to an insufficient number of VE studies. There are many challenges and opportunities for comprehensive evaluation of influenza VE in mainland China [58]. Vaccine-circulating-strain mismatch, vaccine type and technology, virus strains, vaccination season, repeated vaccination, northern vs. southern influenza seasons, among other factors can be used to understand the many influences of influenza VE. It is essential to evaluate influenza VE across a much larger scale and with systematic and strategic use of a unified research protocol.

5. Conclusions Based on published studies set in mainland China that used laboratory-confirmed influenza to evaluate influenza VE, we found that influenza vaccination has a moderate protective effect against medically-attended influenza, and that influenza VE among children is higher than among the elderly. Influenza VE evaluations should be conducted continuously, and with a unified research protocol to provide a scientific basis for formulation and adjustment evidence-based influenza vaccination policy.

Author Contributions: Conceptualization, X.Y., H.Z. and Z.L. (Zhongjie Li); methodology, X.Y., Z.L. (Zhongjie Li) and Z.F.; software, X.Y., H.Z., and Z.L. (Zhili Li); validation, X.Y., H.Z., M.R., Z.L. (Zhili Li), A.Z., and M.G.; formal analysis, X.Y., H.Z., Z.L. (Zhili Li), and Z.L. (Zhongjie Li); data curation, X.Y., and H.Z.; writing—original draft preparation, X.Y. and Z.L. (Zhongjie Li); writing—review and editing, M.G., Y.L., Y.Q., Z.P., L.F., Z.A., J.Z., Z.L. (Zhongjie Li), Z.F.; visualization, X.Y. and A.Z.; funding acquisition, Z.P., L.F., Z.L. and J.Z. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by the National Science and Technology Major Project (No. 2018ZX10713001-005), the National Natural Science Foundation of China (91846302), the China-US Collaborative Program on Emerging and Re-emerging Infectious Diseases, and Emergency Response Mechanism Operation Program, Chinese Center for Disease for Disease Control and Prevention (No.131031001000015001). The founders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments: We authors would like to thank Suizan Zhou for sharing the most recent influenza vaccination data in Guangzhou. We appreciate the English editing of the manuscript by Lance Rodewald, -the National Immunization Program of China CDC. Conflicts of Interest: The authors of this study declare that they do not have any potential conflicts of interest.

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