| Literature DB >> 32993819 |
X Y Huang1,2, Z Q He3, B H Wang3, K Hu4, Y Li1,2, W S Guo1.
Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is a disease with a high case-fatality rate that is caused by infection with the SFTS virus (SFTSV). Five electronic databases were systematically searched to identify relevant articles published from 1 January 2011 to 1 December 2019. The pooled rates with 95% confidence interval (CI) were calculated by a fixed-effect or random-effect model analysis. The results showed that 92 articles were included in this meta-analysis. For the confirmed SFTS cases, the case-fatality rate was 0.15 (95% CI 0.11, 0.18). Two hundred and ninety-six of 1384 SFTS patients indicated that they had been bitten by ticks and the biting rate was 0.21 (95% CI 0.16, 0.26). The overall pooled seroprevalence of SFTSV antibodies among the healthy population was 0.04 (95% CI 0.03, 0.05). For the overall seroprevalence of SFTSV in animals, the seroprevalence of SFTSV was 0.25 (95% CI 0.20, 0.29). The infection rate of SFTSV in ticks was 0.08 (95% CI 0.05, 0.11). In conclusion, ticks can serve as transmitting vectors of SFTSVs and reservoir hosts. Animals can be infected by tick bites, and as a reservoir host, SFTSV circulates continuously between animals and ticks in nature. Humans are infected by tick bites and direct contact with patient secretions.Entities:
Keywords: Meta-analysis; severe fever with thrombocytopenia syndrome virus; ticks; transmission mode
Year: 2020 PMID: 32993819 PMCID: PMC7584033 DOI: 10.1017/S0950268820002290
Source DB: PubMed Journal: Epidemiol Infect ISSN: 0950-2688 Impact factor: 2.451
Fig. 1.Flow chart of the study selection process in this meta-analysis.
Basic characteristics of SFTS patients
| First author | Publication year | Country | Province | Year of admitted patients | Age | Confirmed case | Death number | Test method | Language | Quality Rating |
|---|---|---|---|---|---|---|---|---|---|---|
| Bao | 2011 | China | JS | 2007 | 61.4 (10.5) | 7 | 1 | RT–PCR | English | Poor |
| Liu | 2012 | China | HB | 2010 | 56 (11–81) | 87 | 8 | RT–PCR | Chinese | Fair |
| Gai | 2012 | China | SD | 2008.5–2011.7 | 61 (40–83) | 59 | 11 | RT–PCR | English | Fair |
| Deng | 2013 | China | LN | 2010.6–2011.12 | 55 (17–89) | 115 | 14 | RT–PCR/ELISA | English | Good |
| Ding | 2014 | China | SD & HN | 2010–2011 | 61 (23–82) | 59 | 7 | RT–PCR | English | Fair |
| He | 2014 | China | HB | 2010.5–2012.12 | 59 (36–86) | 73 | 19 | RT–PCR | Chinese | Poor |
| Sun | 2014 | China | ZJ | 2011–2013 | 66 (31–84) | 65 | 9 | RT–PCR | English | Fair |
| Shin | 2015 | Korea | NA | 2013.3 | 69 (28–84) | 35 | 16 | RT–PCR | English | Fair |
| Wei | 2015 | China | SX | 2013.6 | 66 | 1 | 0 | RT–PCR | English | Fair |
| Xu | 2015 | China | HN | 2007.4–2011.5 | 51.4 (13.0) | 422 | 68 | RT–PCR | Chinese | Fair |
| Choi | 2016 | Korea | NA | 2013–2015 | 67.5 (57–76) | 172 | 56 | RT–PCR/IFA | English | Good |
| Kato | 2016 | Japan | NA | 2013.3–2014.9 | 78 (65–84) | 49 | 15 | RT–PCR | English | Good |
| Zhao | 2016 | China | AH & JS | 2010.10–2014.8 | 57.6 (38–78) | 40 | 7 | RT–PCR | Chinese | Fair |
| Hu | 2017 | China | JS | 2011–2013 | 56.5 (76–83) | 89 | 19 | RT–PCR/ELISA | English | Fair |
| Wang | 2017 | China | HB | 2011.1–2016.12 | 59.3 (23–87) | 521 | 44 | RT–PCR/ELISA | English | Fair |
| Hu | 2018 | China | ZJ | 2014.1–2017.4 | 57.8 (12.66) | 25 | 5 | RT–PCR/ELISA | English | Fair |
| Jia | 2018 | China | JS | 2010.10–2016.10 | 59 (51–67) | 90 | 20 | RT–PCR | English | Fair |
| Xia | 2018 | China | AH | 2014.4–2017.12 | 62 (10.82) | 86 | 12 | RT–PCR/ELISA | Chinese | Fair |
| Xu | 2018 | China | SD | 2014.1–2015.12 | 65.82 (11.36) | 60 | 20 | RT–PCR | English | Good |
| Song | 2018 | China | AH | 2011–2017 | 64 (24–86) | 87 | 12 | RT–PCR | Chinese | Poor |
| Li | 2018 | China | HN | 2011.4–2017.10 | 61.4 (12.20) | 2096 | 340 | RT–PCR/ELISA | English | Good |
| Kwon | 2018 | Korea | Seoul | 2015.7–2016.10 | 60 (7.00) | 11 | 1 | RT–PCR | English | Fair |
| Chen | 2019 | China | SD | 2010–2017 | NA | 2731 | 251 | RT–PCR/ELISA | English | Fair |
| He | 2019 | China | HN | 2017.8–2018.8 | 63.76 (11.88) | 74 | 0 | RT–PCR | Chinese | Fair |
| Kim | 2019 | Korea | Jeju | 2014.7–2018.11 | NA | 55 | 6 | RT–PCR/ELISA | English | Fair |
| Takahashi | 2019 | Japan | NA | 2015.11–2018.4 | 71.14 (10.35) | 7 | 1 | NA | English | Fair |
| Zong | 2019 | China | LN | 2011–2017 | NA | 438 | 19 | RT–PCR | Chinese | Fair |
Abbreviations: NA, not available; JS, Jiangsu; HB, Hubei; SD, Shandong; LN, Liaoning; HN, Henan; ZJ, Zhejiang; SX, Shaanxi; AH, Anhui; RT–PCR, reverse transcription–polymerase chain reaction; IFA, immunofluorescence assay; ELISA, enzyme-linked immunosorbent assay.
Values the mean (s.d.).
Values are listed as median (ranges).
Values are listed as median (interquartiles).
Fig. 2.(a) Geographic distribution of SFTS in mainland China. (b) Seasonal distribution of published studies on case occurrence. (c) Age distribution of asymptomatic infections. (d) The relationships between collected ticks and number of published studies. The horizontal ordinate represented the month and the ordinate represented the number of studies that meet the requirements (b and d). The horizontal ordinate represented the age group and the ordinate represents the number of asymptomatic infections (c).
Fig. 3.Forest plots of the meta-analysis on a panel of prevalence. (a) The pooled case-fatality rate of SFTS. (b) The pooled biting rate by ticks. (c) The overall seroprevalence of SFTSV among the healthy population. (d) The overall seroprevalence of total antibodies against SFTSV in animals. (e) Infection rate of SFTSV in ticks.
Basic characteristics of person-to-person transmission
| First author | Publication year | Country | Index patient | Secondary patients | Language | Test method | ||
|---|---|---|---|---|---|---|---|---|
| Age | Sex | Occupation | ||||||
| Bao | 2011 | China | 80 | Female | Farmer | Relatives | English | RT–PCR |
| Gai | 2011 | China | 77 | Male | Farmer | HCWs and relatives | English | ELISA |
| Liu | 2012 | China | 50 | Female | NA | HCWs and relatives | English | RT–PCR/IFA |
| 56 | Female | Farmer | Relatives | English | RT–PCR/IFA | |||
| Chen | 2013 | China | 63 | Male | NA | Relatives | English | RT–PCR |
| Tang | 2013 | China | 58 | Male | NA | HCWs and relatives | English | RT–PCR |
| Wang | 2014 | China | 78 | Male | NA | Relatives | English | RT–PCR |
| Gong | 2015 | China | 66 | Female | Farmer | HCWs and relatives and neighbours | English | RT–PCR |
| Jiang | 2015 | China | 66 | Female | Farmer | Relatives | English | RT–PCR/ ELISA |
| Kim | 2015 | Korea | 68 | Female | NA | HCWs | English | RT–PCR |
| Yoo | 2016 | Korea | 74 | Male | Cattle rancher | Relatives | English | RT–PCR |
| Huang | 2017 | China | 65 | Female | Farmer | HCWs and relatives and neighbours | English | RT–PCR |
| Moon | 2018 | Korea | 57 | Male | NA | HCWs | English | RT–PCR/IFA |
Abbreviations: NA, not available; RT–PCR, reverse transcription–polymerase chain reaction; IFA, immunofluorescence assay; ELISA, enzyme-linked immunosorbent assay; HCW: health care worker.
Characteristics of asymptomatic infected persons
| First author | Publication year | Region | Sampling time | Sample size | Number of positive | Test method | Language | Quality rating |
|---|---|---|---|---|---|---|---|---|
| Zhang | 2011 | Jiangsu, China | 2010.7–2010.11 | 1922 | 18 | D-ELISA | Chinese | Fair |
| Jiao | 2011 | Jiangsu & Anhui China | 2010 | 250 | 9 | D-ELISA | English | Fair |
| Zhao | 2012 | Shandong, China | 2011.6 | 237 | 2 | D-ELISA | English | Fair |
| Cui | 2013 | Shandong, China | 2011 | 78 | 1 | D-ELISA | English | Fair |
| Niu | 2013 | Shandong, China | 2011 | 2590 | 140 | I-ELISA | Chinese | Good |
| Wang | 2013 | Shandong, China | 2010–2011 | 315 | 4 | D-ELISA | Chinese | Poor |
| Zhan | 2013 | Hubei, China | 2010–2012 | 957 | 61 | ELISA | Chinese | Poor |
| Li | 2014 | Jiangsu, China | 2012.3–2013.1 | 2547 | 33 | D-ELISA | English | Fair |
| Liang | 2014 | Jiangsu, China | 2011 | 2510 | 10 | D-ELISA | English | Fair |
| Zhang | 2014 | Zhejiang, China | 2013.6 | 986 | 71 | ELISA | English | Fair |
| Hu | 2015 | Henan, China | 2011.7–2013.12 | 5245 | 343 | ELISA | English | Fair |
| Wei | 2015 | Shaanxi, China | 2014 | 363 | 20 | D-ELISA | English | Fair |
| Sun | 2015 | Zhejiang, China | 2013 | 1380 | 76 | ELISA | English | Fair |
| Tan | 2015 | Jiangsu, China | 2010–2011 | 866 | 2 | D-ELISA | Chinese | Fair |
| Xu | 2015 | Anhui, China | 2013.9–2013.10 | 166 | 14 | ELISA | Chinese | Fair |
| Zhou | 2015 | Shandong, China | 2011.4–2011.12 | 237 | 2 | D-ELISA | Chinese | Fair |
| Huang | 2016 | Anhui, China | 2012.6 | 270 | 17 | ELISA | English | Fair |
| Luo | 2016 | Shandong, China | 2015.11–2016.1 | 628 | 33 | ELISA | Chinese | Good |
| Xing | 2016 | Hubei, China | 2012.8–2013.5 | 419 | 35 | D-ELISA | English | Good |
| Lyu | 2016 | Anhui, China | 2014–2015 | 2126 | 99 | ELISA | English | Fair |
| Kim | 2017 | Busan, Korea | 2015.5 | 1069 | 22 | D-ELISA | English | Fair |
| Gokuden | 2018 | Kagoshima, Japan | 2015.7–2016.1 | 646 | 2 | ELISA | English | Fair |
| Kimura | 2018 | Ehime, Japan | 2015.7–2015.8 | 694 | 8 | ELISA | English | Fair |
| Shen | 2019 | Zhejiang, China | 2018.6 | 439 | 13 | D-ELISA | English | Fair |
| Du | 2019 | Henan, China | 2016.4–2016.5 | 1463 | 165 | I-ELISA | English | Fair |
Abbreviations: ELISA, enzyme-linked immunosorbent assay; D-ELISA, double-antigen sandwich enzyme-linked immunosorbent assay; I-ELISA, indirect enzyme-linked immunosorbent assay.
SFTSV seroprevalence in animals
| First author | Publication year | Country | Sampling time | Sample size | No. of infected animals | Prevalence (%) | Test method | Language | Quality rating |
|---|---|---|---|---|---|---|---|---|---|
| Zhang | 2011 | China | 2010.7–2010.11 | 931 | 103 | 11.06 | D-ELISA | Chinese | Fair |
| Jiang | 2012 | China | 2010.9 | 106 | 34 | 32.08 | D-ELISA | Chinese | Good |
| Liu | 2012 | China | 2010 | 19 | 12 | 63.16 | D-ELISA | Chinese | Fair |
| Zhao | 2012 | China | 2011.6 | 134 | 111 | 82.84 | D-ELISA | English | Fair |
| Cui | 2013 | China | 2011.6–2012.12 | 78 | 20 | 25.64 | ELISA | English | Fair |
| Ding | 2013 | China | 2011 | 641 | 268 | 41.81 | D-ELISA | English | Fair |
| Liu | 2013 | China | 2009–2011 | 103 | 20 | 19.42 | IFA | Chinese | Fair |
| Niu | 2013 | China | 2011.4–2011.12 | 3039 | 1249 | 41.10 | D-ELISA | English | Fair |
| He | 2014 | China | 2010.5–2012.12 | 31 | 12 | 38.71 | D-ELISA | Chinese | Poor |
| Li | 2014 | China | 2012.3–2013.2 | 2741 | 335 | 12.22 | D-ELISA | English | Fair |
| Liu | 2014 | China | 2013.1–2013.8 | 775 | 9 | 1.19 | D-ELISA | English | Fair |
| Du | 2014 | China | 2012.7–2012.10 | 312 | 141 | 45.19 | ELISA | Chinese | Fair |
| Xu | 2014 | China | 2010–2011 | 452 | 9 | 1.99 | D-ELISA | Chinese | Fair |
| Tan | 2015 | China | 2010–2011 | 215 | 6 | 2.79 | D-ELISA | Chinese | Fair |
| Xu | 2015 | China | 2013.9–2013.10 | 205 | 97 | 47.37 | ELISA | Chinese | Fair |
| Li | 2016 | China | 2013–2014 | 823 | 47 | 5.71 | ELISA | English | Fair |
| Oh | 2016 | Korea | 2013.5–2013.8 | 91 | 6 | 6.59 | IFA | English | Fair |
| Xing | 2016 | China | 2012.8–2013.5 | 50 | 27 | 54 | ELISA | English | Good |
| Tabara | 2016 | Japan | 2014.6–2015.3 | 510 | 11 | 2.16 | ELISA | English | Fair |
| Hayasaka | 2016 | Japan | 2006–2012 | 190 | 35 | 18.42 | ELISA | English | Fair |
| Sun | 2017 | China | 2014、2016 | 14 | 9 | 64 | ELISA | English | Fair |
| Wang | 2017 | China | 2015–2016 | 178 | 15 | 8.43 | D-ELISA | English | Fair |
| Zhu | 2017 | China | 2012–2014 | 354 | 0 | 0 | ELISA | Chinese | Poor |
| Lee | 2018 | Korea | 2016.3–2016.11 | 426 | 59 | 13.85 | IFA | English | Good |
| Kimura | 2018 | Japan | 2013.12–2014.2 | 107 | 20 | 18.69 | ELISA | English | Fair |
| Kang | 2018 | Korea | 2014–2015 | 737 | 43 | 6.89 (43/624) | D-ELISA | English | Fair |
| Yu | 2018 | Korea | 2017.3–2017.8 | 207 | 30 | 14.49 | ELISA | English | Fair |
| Huang | 2019 | China | 2016.5–2018.4 | 615 | 275 | 44,72 | ELISA | English | Fair |
| Yu | 2019 | Korea | 2015–2017 | 215 | 40 | 18.60 | I-ELISA | English | Fair |
| Yang | 2019 | China | 2016.5–2016.6 | 1097 | 521 | 47.49 | I-IFA | English | Fair |
Abbreviations: ELISA, enzyme-linked immunosorbent assay; D-ELISA, double-antigen sandwich enzyme-linked immunosorbent assay; I-ELISA, indirect enzyme-linked immunosorbent assay; IFA, immunofluorescence assay; I-IFA, indirect immunofluorescence assay.
SFTSV tick infections rates and vertical transmission characteristics
| First author | Publication year | Country | Sampling time | No. of ticks | No. of infected pools | No. of tick pools | Language | Quality rating |
|---|---|---|---|---|---|---|---|---|
| Zhang | 2011 | China | 2010.7–2010.10 | 3498 | 18 | 365 | English | Fair |
| Yun | 2014 | Korea | 2013.5–2013.10 | 212 | 12 | 148 | English | Fair |
| Hayasaka | 2015 | Japan | 2013.5–2013.8 | 1709 | 0 | 57 | English | Fair |
| Luo | 2015 | China | 2013.6–2013.7 | 3300 | 25 | 73 | English | Fair |
| Wang | 2015 | China | 2011 | 3048 | 122 | 1952 | English | Fair |
| Li | 2016 | China | 2013–2014 | 8520 | 45 | 722 | English | Fair |
| Oh | 2016 | Korea | 2013.5–2013.8 | 667 | 27 | 293 | English | Fair |
| Yun | 2016 | Korea | 2014.3–2014.10 | 17 570 | 5 | 23 | English | Good |
| Tian | 2017 | China | 2013.7–2013.9 | 11 | 0 | 2 | English | Fair |
| Zhu | 2017 | China | 2012–2014 | 113 | 0 | 64 | Chinese | Poor |
| Zhuang | 2018 | China | 2011 | 4910 | 89 | 202 | English | Good |
| Jung | 2018 | Korea | 2015–2017 | 3880 | 0 | 281 | English | Fair |
| Yang | 2019 | China | 2016.5–2016.6 | 4595 | 3 | 416 | English | Fair |
Fig. 4.Phylogenetic analysis of the S segment of 445 SFTSV complete sequences obtained from GenBank.
Fig. 5.Transmission models of SFTSV among ticks, animals and humans.