Risk factors for death from hand–foot–mouth disease: a meta-analysis

Ni, Xin; Li, Xianchan; Xu, Chao; Xiong, Qing; Xie, Bo; Wang, L.H.; Peng, Yiru; Li, X.W.

doi:10.1017/s0950268819002279

Cited by 13 publications

(8 citation statements)

References 41 publications

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“…Various risk factors for severe HFMD were reported in the literature (18)(19)(20)61), but there is a lack of consensus. This study aimed to identify potential risk factors for severe HFMD.…”

Section: Discussionmentioning

confidence: 99%

“…Previous meta-analyses examined HFMD ( 18 , 19 , 61 ). Indeed, Sun et al ( 18 ) examined the risk factors for severe HFMD.…”

Section: Discussionmentioning

confidence: 99%

“…Fang et al ( 62 ) examined the risk factors for severe HFMD. Ni et al ( 61 ) investigated the association between risk factors and death from HFMD. Therefore, in an innovative view, the present study focused on the risk factors and the changes in disease severity (from mild to severe).…”

Section: Discussionmentioning

confidence: 99%

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Risk Factors for Severe Hand-Foot-Mouth Disease in China: A Systematic Review and Meta-Analysis

Huang

Zhu

et al. 2021

Front. Pediatr.

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Background: This study aimed to identify potential risk factors for severe hand-foot-mouth disease (HFMD).Methods: The PubMed, Embase, the Cochrane Library, Sinomed, WanFang, CNKI, and VIP databases were searched (up to August 2021).Results: Twenty-nine studies (9,241 and 927,355 patients with severe HFMD and controls, respectively; all from China) were included. EV71 was associated with higher odds of severe HFMD compared with other agents (OR = 4.44, 95%CI: 3.12–6.33, p < 0.001). Being home-raised (OR = 1.99, 95%CI: 1.59–2.50, p < 0.001), higher number of children in the family (OR = 2.09, 95%CI: 1.93–2.27, p < 0.001), poor hand hygiene (OR = 2.74, 95%CI: 1.78–4.23, p < 0.001), and no breastfeeding (OR = 2.01, 95%CI: 1.45–2.79, p < 0.001) were risk factors for severe HFMD. First consulting to a district-level or above hospital (OR = 0.34, 95%CI: 0.25–0.45, p < 0.001) and diagnosis of HFMD at baseline (OR = 0.17, 95%CI: 0.13–0.24, p < 0.001) were protective factors against severe HFMD. Fever, long fever duration, vomiting, lethargy, leukocytosis, tic, and convulsions were each associated with severe HFMD (all p < 0.05), while rash was not.Conclusions: EV71, lifestyle habits, frequent hospital visits, and symptoms are risk factors for severe HFMD in children in China, while early diagnosis and admission to higher-level hospitals are protective factors.

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Section: Discussionmentioning

confidence: 99%

“…Previous meta-analyses examined HFMD ( 18 , 19 , 61 ). Indeed, Sun et al ( 18 ) examined the risk factors for severe HFMD.…”

Section: Discussionmentioning

confidence: 99%

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Risk Factors for Severe Hand-Foot-Mouth Disease in China: A Systematic Review and Meta-Analysis

Huang

Zhu

et al. 2021

Front. Pediatr.

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“…It is mainly caused by enterovirus 71 and coxsackie A16 (2). Most patients with HFMD have mild symptoms and typically recover within 14 days without a secondary cutaneous infection (3). However, a few patients will have severe manifestations such as acute flaccid paralysis, aseptic meningitis, neurogenic pulmonary edema, and circulatory system failure, with poor prognosis (4,5).…”

Section: Introductionmentioning

confidence: 99%

Searching for Risk Factors and Establishing Predictive Models for Severe and Critical Hand-Foot-and-Mouth Disease

Dong

et al. 2021

Iran J Pediatr

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Objectives: Severe and critical Hand-Foot-and-Mouth Disease (HFMD) patients have an acute onset and poor prognosis. This study intended to establish an appropriate risk prediction model by analyzing the blood biochemical indicators of patients. Methods: A total of 3,204 patients with HFMD were enrolled in this study, including 2,131 mild patients, 962 severe patients, and 111 critical patients. We first analyzed the data of each group through multivariate statistics based on SIMCA-P and screened out the variables that had important contributions to the discrimination of each group. Furthermore, the risk factors and predictors were screened out by comparison with the results of univariate statistical analysis. Finally, binary logistic regression analysis was used to establish a suitable prediction model. Results: With the aggravation of HFMD patients' conditions, the blood content and risk warning ability of seven indicators of SP, DP, NEUT%, TP, GLB, RBP, and Glu were significantly increased. We found for the first time that the more severe the HFMD patients, the lower the levels of Chr, %MRETIC, and %HRETIC in their blood. The average prediction accuracy of the established models for Mild/Severe, Severe/Critical, and Severe/Critical was 82.89, 96.16, and 89.37%, respectively, and the AUROC was 0.8722 (95%CI, 0.8583 - 0.8861), 0.9499 (95%CI, 0.9339 - 0.9659), and 0.7913 (95% CI, 0.7471 - 0.8356), respectively. Conclusions: Multivariate statistical analysis based on SIMCA-P could be used to analyze the clinical data of HFMD patients. Besides, SP, DP, NEUT%, TP, GLB, RBP, and Glu could be used as risk factors for severe and critical HFMD patients. The abnormal changes of Chr, %MRETIC, and %HRETIC reflected the possible damage to bone marrow hematopoietic function in HFMD patients. The predictive model established by us could be used for the differential diagnosis of Mild/Severe, Mild/Critical, and Severe/Critical.

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“…A discrete SEIADR (susceptible–exposed–infectious–asymptomatic–dead–recovered) epidemic model has been also developed in [ 34 ]. To read more articles, please refer to [ 12 , 16 , 17 , 28 , 31 , 33 , 45 , 50 , 51 ].…”

Section: Introductionmentioning

confidence: 99%

A fractional system of delay differential equation with nonsingular kernels in modeling hand-foot-mouth disease

Ghanbari

2020

Adv Differ Equ

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In this article, we examine a computational model to explore the prevalence of a viral infectious disease, namely hand-foot-mouth disease, which is more common in infants and children. The structure of this model consists of six sub-populations along with two delay parameters. Besides, by taking advantage of the Atangana–Baleanu fractional derivative, the ability of the model to justify different situations for the system has been improved. Discussions about the existence of the solution and its uniqueness are also included in the article. Subsequently, an effective numerical scheme has been employed to obtain several meaningful approximate solutions in various scenarios imposed on the problem. The sensitivity analysis of some existing parameters in the model has also been investigated through several numerical simulations. One of the advantages of the fractional derivative used in the model is the use of the concept of memory in maintaining the substantial properties of the understudied phenomena from the origin of time to the desired time. It seems that the tools used in this model are very powerful and can effectively simulate the expected theoretical conditions in the problem, and can also be recommended in modeling other computational models in infectious diseases.

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Risk factors for death from hand–foot–mouth disease: a meta-analysis

Cited by 13 publications

References 41 publications

Risk Factors for Severe Hand-Foot-Mouth Disease in China: A Systematic Review and Meta-Analysis

Risk Factors for Severe Hand-Foot-Mouth Disease in China: A Systematic Review and Meta-Analysis

Searching for Risk Factors and Establishing Predictive Models for Severe and Critical Hand-Foot-and-Mouth Disease

A fractional system of delay differential equation with nonsingular kernels in modeling hand-foot-mouth disease

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