Evidence map›Paper›PMID 35387590›Full record

ArticleBMC infectious diseases2022

Infectious diseases prevention and control using an integrated health big data system in China.

Xudong Zhou, Edmund Wei Jian Lee, Xiaomin Wang, Leesa Lin, Ziming Xuan, Dan Wu, Hongbo Lin, Peng Shen

Abstract read
In one paragraph

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Use of Digital Tools in Arbovirus Surveillance: Scoping Review.Journal of medical Internet research · 2024
    Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Insects · 2023
    Article
  11. Article
  12. 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

8 authors.

Xudong ZhouThe Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China. zhouxudong@zju.edu.cn.
Edmund Wei Jian LeeWee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, WKWSCI Building, Singapore, 637718, Singapore.
Xiaomin WangInstitute of Social & Family Medicine, Zhejiang University School of Medicine, 866 Yuhangtang Road, Hangzhou, 310058, China.
Leesa LinLaboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Special Administrative Region, China.
Ziming XuanDepartment of Community Health Sciences, Boston University School of Public Health, 801 Massachusetts Ave, Boston, MA, 02118, USA.
Dan WuDepartment of Clinical Research, London School of Hygiene & Tropical Medicine, London, WC1E 7HT, UK.
Hongbo LinYinzhou Center for Disease Prevention and Control, 1221 Xueshi Road, Ningbo, 315100, Zhejiang, China. lin673160@163.com.
Peng ShenYinzhou Center for Disease Prevention and Control, 1221 Xueshi Road, Ningbo, 315100, Zhejiang, China. pengshen.yzcdc@gmail.com.

Funding

The National Social Science Fund of China 20VYJ063
6 · The paper itself

Abstract

backgroundThe Yinzhou Center for Disease Prevention and Control (CDC) in China implemented an integrated health big data platform (IHBDP) that pooled health data from healthcare providers to combat the spread of infectious diseases, such as dengue fever and pulmonary tuberculosis (TB), and to identify gaps in vaccination uptake among migrant children.

methodsIHBDP is composed of medical data from clinics, electronic health records, residents' annual medical checkup and immunization records, as well as administrative data, such as student registries. We programmed IHBDP to automatically scan for and detect dengue and TB carriers, as well as identify migrant children with incomplete immunization according to a comprehensive set of screening criteria developed by public health and medical experts. We compared the effectiveness of the big data screening with existing traditional screening methods.

resultsIHBDP successfully identified six cases of dengue out of a pool of 3972 suspected cases, whereas the traditional method only identified four cases (which were also detected by IHBDP). For TB, IHBDP identified 288 suspected cases from a total of 43,521 university students, in which three cases were eventually confirmed to be TB carriers through subsequent follow up CT or T-SPOT.TB tests. As for immunization screenings, IHBDP identified 240 migrant children with incomplete immunization, but the traditional door-to-door screening method only identified 20 ones.

conclusionsOur study has demonstrated the effectiveness of using IHBDP to detect both acute and chronic infectious disease patients and identify children with incomplete immunization as compared to traditional screening methods.

Indexed as

DengueTuberculosisBig DataChildChinaHumansMass ScreeningBig data analyticsDengueElectronic health recordsImmunizationInfectious diseasePulmonary tuberculosis

Identifiers

PMID35387590
PMCPMC8984075

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.