Effects of different levels of non-pharmaceutical interventions on hand, foot and mouth disease in Guangzhou, China

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Abstract

Background

Non-pharmaceutical interventions (NPIs) against coronavirus disease 2019 (COVID-19) may have suppressed the transmission of other infectious diseases. This study aimed to evaluate the impact of different degrees of NPIs during the COVID-19 pandemic on hand, foot and mouth disease (HFMD) in Guangzhou, China.

Methods

Weekly reported HFMD cases and pathogens information during 2015–2021 in Guangzhou were collected from the China National Notifiable Disease Reporting System. The observed number of HFMD cases in 2020 and 2021 was compared to the average level in the same period during 2015–2019. Then, an interrupted time-series segmented regression analysis was applied to estimate the impact of NPIs on HFMD, such as social distancing, suspension of schools, community management and mask wearing. The effects across different subgroups stratified by gender, children groups and enterovirus subtype of HFMD were also examined.

Results

A total of 13,224 and 36,353 HFMD cases were reported in 2020 and 2021, which decreased by 80.80% and 15.06% respectively compared with the average number of cases in the same period during 2015–2019. A significant drop in the number of HFMD cases during time when strict NPIs were applied (relative change: 69.07% [95% confidence interval (CI): 68.84%–69.30%]). The HFMD incidence rebounded to historical levels in 2021 as the lockdown eased. The slightest reduction of HFMD cases was found among children at kindergartens or childcare centres among the three children groups (children at kindergartens or childcare centres: 55.50% [95% CI: 54.96%–56.03%]; children living at home: 72.64% [95% CI: 72.38%–72.89%]; others: 74.06% [95% CI: 73.19%–74.91%]).

Conclusions

The strong NPIs during the COVID-19 epidemic may have a significant beneficial effect on mitigating HFMD. However, the incidence of HFMD rebounded as the NPIs became less stringent. Authorities should consider applying these NPIs during HFMD outbreaks and strengthening personal hygiene in routine prevention.

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Most cited references 47

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Interrupted time series regression for the evaluation of public health interventions: a tutorial

James Lopez Bernal, Steven Cummins, Antonio Gasparrini (2016)

Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.

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Effect of non-pharmaceutical interventions to contain COVID-19 in China

Shengjie Lai, Nick Ruktanonchai, Liangcai Zhou … (2020)

Summary On March 11, 2020, the World Health Organization declared COVID-19 a pandemic 1 . The outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective 2 , but quantitative research is still needed to assess the efficacy of NPIs and their timings 3 . Using epidemiological and anonymised human movement data 4,5 , here we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimated that there were a total of 114,325 COVID-19 cases (interquartile range 76,776 -164,576) in mainland China as of February 29, 2020. Without NPIs, the COVID-19 cases would likely have shown a 67-fold increase (interquartile range 44 - 94) by February 29, 2020, with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to have prevented more infections than travel restrictions and contact reductions, but combined NPIs achieved the strongest and most rapid effect. The lifting of travel restrictions since February 17, 2020 does not appear to lead to an increase in cases across China if the social distancing interventions can be maintained, even at a limited level of 25% reduction on average through late April. Our findings contribute to an improved understanding of NPIs on COVID-19 and to inform response efforts across the World.

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Distributed lag non-linear models

A Gasparrini, Timothy Armstrong, M. Kenward (2010)

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.

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Author and article information

Contributors

Zhoubin Zhang: zhoubinzhanggz@21cn.com

Xianbo Wu: wuxb1010@smu.edu.cn

Journal

Journal ID (nlm-ta): BMC Public Health

Journal ID (iso-abbrev): BMC Public Health

Title: BMC Public Health

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2458

Publication date (Electronic): 20 December 2022

Publication date PMC-release: 20 December 2022

Publication date Collection: 2022

Volume: 22

Electronic Location Identifier: 2398

Affiliations

[1 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, Department of Epidemiology, School of Public Health, Guangdong Provincial Key Laboratory of Tropical Disease Research), , Southern Medical University, Baiyun District, ; Nos.1023–1063, Shatai South Road, Guangzhou, 510515 China

[2 ]GRID grid.508371.8, ISNI 0000 0004 1774 3337, Guangzhou Center for Disease Control and Prevention, ; Guangzhou City, 510440 Guangdong China

Article

Publisher ID: 14850

DOI: 10.1186/s12889-022-14850-x

PMC ID: 9767397

PubMed ID: 36539790

SO-VID: de390527-73c1-46f8-ab5b-9b525ab98105

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 24 May 2022

Date accepted : 9 December 2022

Custom metadata

ScienceOpen disciplines: Public health

Keywords: hfmd,covid-19,non-pharmaceutical interventions,infectious disease control,natural experiment

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ScienceOpen disciplines: Public health

Keywords: hfmd, covid-19, non-pharmaceutical interventions, infectious disease control, natural experiment

Effects of different levels of non-pharmaceutical interventions on hand, foot and mouth disease in Guangzhou, China

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Novel Coronavirus Disease COVID-19

Most cited references 47

Interrupted time series regression for the evaluation of public health interventions: a tutorial

Effect of non-pharmaceutical interventions to contain COVID-19 in China

Distributed lag non-linear models

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