| Literature DB >> 35687866 |
Juan Xu1, Yuquan Chen2, Mengmeng Yue1,3, Jianxing Yu1, Fuyi Han1, Li Xu1, Zhujun Shao1.
Abstract
Invasive meningococcal disease (IMD) caused by Neisseria meningitidis (Nm) continues to be a global public health concern. Understanding the prevalence of Nm serogroups in IMD is critical for developing strategies for meningococcal vaccination. We used the keywords "cerebrospinal meningitis", "meningococcal", "Neisseria meningitidis'', "meningococcal meningitis", "serogroup'' and "China'' to search five databases, including PubMed, CNKI, CBM (Chinese BioMedical Literature Database), WanFang and VIP from 2010 to 2020. The age distributions, proportions of Nm serogroups and serogroup changes in IMD were analyzed. A total of 14 studies were included according to PRISMA guidelines. In China, from 2010 to 2020, the highest proportion of Nm in IMD was NmC, with 49.7% (95% CI: 35.8%-63.5%), followed by NmB with 30.2% (95%CI:17.3%-43.0%) and NmW with 23.8% (95%CI: 7.0-40.7%). Before 2014, NmC was the major circulating serogroup, with 59.6% (95% CI: 43.8%-75.4%), followed by NmW with 24.4% (95% CI: 5.9%-42.9%). After 2015, IMD cases caused by NmB were increasing, the proportion of NmB reached to 52.4% (95% CI: 31.8%-73.1%). The age groups of children from 0 to 5 years and from 6 to 10 years represented, respectively, 29.6% (95% CI: 16.8%-42.4%) and 28.9% (95% CI: 12.1%-45.8%) of all IMD cases were reported. In China, NmB, NmC and NmW were the major serogroups causing IMD between 2010 and 2020. Since 2015, the proportion of NmB increased rapidly. The current serogroup distribution in China highlights the need of replacing the meningococcal polysaccharide vaccines that are being used in the National Immunization Program with more appropriate vaccines.Entities:
Keywords: China; Invasive meningococcal disease; meta-Analysis; serogroups; systematic review
Year: 2022 PMID: 35687866 PMCID: PMC9302495 DOI: 10.1080/21645515.2022.2071077
Source DB: PubMed Journal: Hum Vaccin Immunother ISSN: 2164-5515 Impact factor: 4.526
Figure 1.Expanded program for immunization schedule in China.
Figure 2.Flow diagram of the literature search and study selection.
Figure 3.Quality assessment of the included studies. (a) Summary plot of risk bias; (b) Traffic light plot of risk bias.
Characteristics of the included studies.
| ID | Study | Publication date | Research period | Province/Region | Median/range age (year) | Sample Type | Identification method | Number of IMD cases | Number of | Contribution To Analysis | Reference |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Lingping Chen | 2019 | 2005–2018 | Zhejiang | UN | UN | UN | 13 | 5 | NmB, NmX | [ |
| 2 | Jianhong Wu | 2013 | 2005–2011 | Zhejiang | All ages | UN | UN | 4 | 4 | NmW, Others | [ |
| 3 | Xiaoying Shan | 2018 | 2005–2018 | Shandong | UN | UN | UN | 129 | 129 | NmA, NmB, NmC, NmY, NmW, Others | [ |
| 4 | Lei Feng | 2014 | 2006–2013 | Shandong | UN | CSF, Blood | PCR | 9 | 7 | NmA, NmC, NmW | [ |
| 5 | Meng Yang | 2018 | 2005–2015 | Jiangxi | UN | UN | Antiserum | 4 | 4 | NmC | [ |
| 6 | Yinqi Sun | 2012 | 2005–2011 | Hebei | All ages | UN | UN | 12 | 7 | NmA, NmB, NmC, Others | [ |
| 7 | Baohua He | 2015 | 2012–2013 | Hebei | UN | CSF, Blood | PCR | 21 | 21 | NmB, NmC, NmW | [ |
| 8 | Feng Jiang | 2014 | 2011–2013 | Guizhou | 0-16 | UN | UN | 31 | 9 | NmA, NmC | [ |
| 9 | Junhong Li | 2015 | 2005–2010 | China | UN | UN | UN | 67 | 67 | NmA, NmB, NmC, Others | [ |
| 10 | Junhong Li | 2020 | 2015–2019 | China | All ages | UN | UN | 296 | 296 | NmA, NmB, NmC, NmY, NmW, Others | [ |
| 11 | Lili Fang | 2017 | 2014–2016 | Heilongjiang | UN | CSF, Blood | UN | 87 | 10 | NmB, NmC | [ |
| 12 | Yahui Zhan | 2018 | 2011–2016 | Jiangsu | UN | UN | Antiserum | 5 | 5 | NmB, NmW | [ |
| 13 | Shengwei Wu | 2020 | 2006–2017 | Guizhou | UN | UN | Antiserum | 4 | 4 | NmB, NmC | [ |
| 14 | Defang Dai | 2017 | 2008–2016 | Hunan | 0-14 | UN | UN | 14 | 14 | NmB, NmC, NmW | [ |
The proportion of cases of invasive meningococcal disease of different ages.
| Item | Age group | |||
|---|---|---|---|---|
| 0–5 years | 6–10 years | 11–18 years | >18 years | |
| Number of studies | 4 | 4 | 4 | 4 |
| Total number of cases | 202 | 149 | 175 | 215 |
| Sample size | 741 | 741 | 741 | 741 |
| 90% ( | 96% ( | 66% ( | 98% ( | |
| Model | Random | Random | Random | Random |
| Proportion (%) | 29.6 | 28.9 | 21.1 | 19.2 |
| 95% CI | 16.8 ~ 42.4 | 12.1 ~ 45.8 | 14.9 ~ 27.3 | 1.2 ~ 37.2 |
The proportion of N. meningitidis serogroups in IMD in China, 2010–2020.
| Item | NmA | NmB | NmC | NmY | NmW | Others |
|---|---|---|---|---|---|---|
| Number of studies | 6 | 10 | 11 | 2 | 7 | 5 |
| Total number of cases | 32 | 158 | 189 | 6 | 83 | 112 |
| Sample size | 515 | 558 | 568 | 425 | 476 | 503 |
| I[ | 80% ( | 90% ( | 88% ( | 0% ( | 91% ( | 91% ( |
| Random | Random | Random | Random | Random | Random | |
| Proportion (%) | 7.1 | 30.2 | 49.7 | 1.2 | 23.8 | 24.9 |
| 95% CI | 1.8–12.4 | 17.3–43.0 | 35.8–63.5 | 0.2–2.3 | 7.0–40.7 | 11.0–38.8 |
Time trend of N. meningitidis serogroups in IMD in China, 2010–2020.
| Item | NmA | NmB | NmC | NmW | NmY | Others | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2010–2014 | 2015– | 2010–2014 | 2015– | 2010–2014 | 2015– | 2010-2014 | 2015- | 2010–2014 | 2015– | 2010–2014 | 2015– | |
| Study | 5 | 1 | 7 | 5 | 10 | 3 | 5 | 3 | 1 | 1 | 4 | 2 |
| Case | 18 | 14 | 37 | 120 | 116 | 73 | 57 | 23 | 1 | 5 | 25 | 87 |
| Sample size | 206 | 296 | 229 | 322 | 246 | 317 | 159 | 312 | 116 | 296 | 194 | 309 |
| 79% | - | 80% | 69% | 81% | - | 80% | 57% | - | - | 83% | - | |
| <.01 | - | <.01 | = .01 | <.01 | >.01 | <.01 | >.01 | - | - | <.01 | >.01 | |
| Model | random | random | random | random | random | random | Random | Random | - | - | Random | Random |
| Proportion (%) | 14.0 | 4.7 | 22.3 | 52.4 | 59.6 | 22.8 | 24.4 | 16.8 | 0.9 | 1.7 | 23.1 | 28.1 |
| 95% CI | 1.1–26.9 | 2.6–7.8 | 9.8–34.8 | 31.8–73.1 | 43.8–75.4 | 18.2–27.4 | 5.9–42.9 | 0–36.9 | 0.0–4.7 | 0.6–3.9 | 6.4–39.8 | 23.1–33.1 |
Bias test for each serogroup.
| Serogroup | Method for publication bias | se.bias | Intercept | Normality test (P) | Data transformation methods* | Raw result | Calibration results | |||
|---|---|---|---|---|---|---|---|---|---|---|
| A | Peters | 4 | 2.39 | 0.665 | 0.0781 | 0.0753 | 0.6905 | PRAW | 7.1(1.8–12.4) | - |
| B | Linreg | 8 | 1.14 | 1.5249 | 0.1352 | 0.2857 | 0.5605 | PRAW | 30.2(17.3–43.0) | - |
| C | Linreg | 9 | 2.75 | 1.046 | 0.1951 | 0.0224 | 0.3891 | PRAW | 49.7(35.8–63.5) | 27.3(13.2–41.4) |
| W | Peters | 5 | 0.26 | 1.7013 | 0.2811 | 0.803 | 0.5453 | PRAW | 23.8(7.0–40.7) | - |
| Other | Peters | 3 | 1.09 | 1.4059 | 0.2392 | 0.355 | 0.6616 | PRAW | 24.9(11.0–38.8) | - |
*PRAW, untransformed proportions.