Evidence map›Paper›PMID 35355613›Full record

ArticleFrontiers in medicine2022

Changing Epidemiology of TB in Shandong, China Driven by Demographic Changes.

Qianying Lin, Sourya Shrestha, Shi Zhao, Alice P Y Chiu, Yao Liu, Chunbao Yu, Ningning Tao, Yifan Li, Yang Shao, Daihai He and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 41% of its field
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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

11 authors at 8 institutions in 3 countries.

Qianying LinDepartment of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Sourya ShresthaBloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Shi ZhaoDepartment of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Alice P Y ChiuDepartment of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Yao LiuShandong Provincial Hospital Affiliated to Shandong University, Jinan, China.
Chunbao YuShandong Chest Hospital, Jinan, China.
Ningning TaoShandong Provincial Hospital Affiliated to Shandong University, Jinan, China.
Yifan LiShandong Provincial Hospital Affiliated to Shandong University, Jinan, China.
Yang ShaoShandong Provincial Hospital Affiliated to Shandong University, Jinan, China.
Daihai HeDepartment of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Huaichen LiDepartment of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Shandong Provincial Hospital · CNHong Kong Polytechnic University · HKChinese University of Hong Kong · CNJohns Hopkins University · USShandong Chest Hospital · CNShandong First Medical University · CNShandong University · CNUniversity of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) incidence has been in steady decline in China over the last few decades. However, ongoing demographic transition, fueled by aging, and massive internal migration could have important implications for TB control in the future. We collated data on TB notification, demography, and drug resistance between 2004 and 2017 across seven cities in Shandong, the second most populous province in China. Using these data, and age-period-cohort models, we (i) quantified heterogeneities in TB incidence across cities, by age, sex, resident status, and occupation and (ii) projected future trends in TB incidence, including drug-resistant TB (DR-TB). Between 2006 and 2017, we observed (i) substantial variability in the rates of annual change in TB incidence across cities, from -4.84 to 1.52%; (ii) heterogeneities in the increments in the proportion of patients over 60 among reported TB cases differs from 2 to 13%, and from 0 to 17% for women; (iii) huge differences across cities in the annual growths in TB notification rates among migrant population between 2007 and 2017, from 2.81 cases per 100K migrants per year in Jinan to 22.11 cases per 100K migrants per year in Liaocheng, with drastically increasing burden of TB cases from farmers; and (iv) moderate and stable increase in the notification rates of DR-TB in the province. All of these trends were projected to continue over the next decade, increasing heterogeneities in TB incidence across cities and between populations. To sustain declines in TB incidence and to prevent an increase in Multiple DR-TB (MDR-TB) in the future in China, future TB control strategies may (i) need to be tailored to local demography, (ii) prioritize key populations, such as elderly and internal migrants, and (iii) enhance DR-TB surveillance.

Indexed as

age-period-cohort modelagingheterogeneityTuberculosisurbanization

Identifiers

PMID35355613
PMCPMC8959836
OpenAlexW4221000878

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.