Evidence map›Paper›PMID 34588227›Full record

ArticleBMJ open2021

Impact of COVID-19 social distancing measures on future incidence of invasive pneumococcal disease in England and Wales: a mathematical modelling study.

Yoon Hong Choi, Elizabeth Miller

Abstract read
In one paragraph

Article in BMJ open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yoon Hong ChoiStatistics, Modelling and Economics Department, Public Health England, London, UK yoon.choi@phe.gov.uk.ORCID 0000-0001-5561-4366
Elizabeth MillerDepartment of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn January 2020, the UK moved to a 1+1 schedule for the 13-valent pneumococcal conjugate vaccine (PCV13) with a single priming dose at 3-month and a 12-month booster. We modelled the impact on invasive pneumococcal disease (IPD) out to 2030/2031 of reductions in PCV13 coverage and population mixing associated with restrictions on non-essential healthcare visits and social distancing measures introduced in 2020/2021 to reduce SARS-CoV-2 transmission.

designUsing an existing model of pneumococcal transmission in England and Wales, we simulated the impact of a 40% reduction in coverage and a 40% reduction in mixing between and within age groups during two lockdowns in spring 2020 and autumn/winter 2020/2021. More and less extreme reductions in coverage and mixing were explored in a sensitivity analysis.

main outcome measuresPredicted annual numbers of IPD cases under different coverage and mixing reduction scenarios with uncertainty intervals (UIs) generated from minimum and maximum values of the model predictions using 500 parameter sets.

resultsThe model predicted that any increase in IPD cases resulting from a reduction in PCV13 coverage would be more than offset by a reduction in pneumococcal transmission due to social distancing measures and that overall reductions in IPD cases will persist for a few years after resumption of normal mixing. The net reduction in cumulative IPD cases over the five epidemiological years from July 2019 was predicted to be 13 494 (UI 12 211, 14 676) all ages. Similar results were obtained in the sensitivity analysis.

conclusionCOVID-19 lockdowns are predicted to have had a profound effect on pneumococcal transmission resulting in a reduction in pneumococcal carriage prevalence and IPD incidence for up to 5 years after the end of the lockdown period. Carriage studies will be informative in confirming the predicted impact of the lockdown measures after they have been lifted.

Indexed as

COVID-19Pneumococcal InfectionsCommunicable Disease ControlEnglandHumansIncidenceInfantPhysical DistancingPneumococcal VaccinesSARS-CoV-2WalesPneumococcal VaccinesCOVID-19epidemiologyinfectious diseasespublic health

Identifiers

PMID34588227
PMCPMC8479589

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.