Evidence map›Paper›PMID 37605994›Full record

ArticleEpidemiology and infection2023

Determinants of measles persistence in Beijing, China: A modelling study.

Jianjiu Chen, Wenyi Zhang, Yong Wang, Wan Yang

Abstract read
In one paragraph

Article in Epidemiology and infection, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

4 authors.

Jianjiu ChenDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.ORCID 0000-0001-6460-6210
Wenyi ZhangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Yong WangChinese PLA Center for Disease Control and Prevention, Beijing, China.
Wan YangDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.ORCID 0000-0002-7555-9728

Funding

Disease Persistence and Population Dynamics: Modeling Measles under Mass VaccinationR01AI145883 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI YANG, WAN · 2019 to 2024
$2.5M
NIAID NIH HHS R01 AI145883
6 · The paper itself

Abstract

In Beijing, the capital of China, routine measles mass vaccination has been in place for decades with high coverage; and since the 2000s, catch-up vaccination programmes have been implemented for migrant workers coming to the city. However, measles epidemics in Beijing persisted. Here, we explored the contributing factors of persistent measles transmission in Beijing using an epidemic model in conjunction with a particle filter. Model inputs included data on birth, death, migration, and vaccination. We formulated a series of hypotheses covering the impact of migrant influx, early waning of maternal immunity, and increased mixing among infants; we compared the plausibility of the hypotheses based on model fit to age-grouped, weekly measles incidence data from January 2005 to December 2014, and out-of-fit prediction during 2015-2019. Our best models showed close agreement with the data, and the out-of-fit prediction generally captured the trend of measles incidence from 2015 to 2019. We found that large influx of migrants with considerably higher susceptibility likely contributed to the persistent measles transmission in Beijing. Our findings suggest that stronger catch-up vaccination programmes for migrants may help eliminate measles transmission in Beijing.

Indexed as

EpidemicsMeaslesBeijingChinaFamilyHumansInfantcatch-up vaccinationmathematical modellingmeasles (rubeola)migrationparticle filtering

Identifiers

PMID37605994
PMCPMC10540187

What OpenQuestion holds

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Registered trials

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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.