Evidence map›Paper›PMID 34627189›Full record

ArticleBMC public health2021

Spatio-temporal variation in tuberculosis incidence and risk factors for the disease in a region of unbalanced socio-economic development.

Li Wang, Chengdong Xu, Maogui Hu, Jiajun Qiao, Wei Chen, Tao Li, Songbo Qian, Mingtao Yan

Open access · goldAbstract read
In one paragraph

Article in BMC public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 3 pooled it
3.3field-weighted citation impact, top 6% 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

26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 65 citations in OpenAlex.

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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

8 authors at 5 institutions in 2 countries.

Li Wang *College of geography and environmental science, Henan University, KaiFeng, 475001, China.
Chengdong Xu *State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China.
Maogui HuState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resource Research, Chinese Academy of Sciences, Beijing, 100101, China. humg@lreis.ac.cn.
Jiajun QiaoCollege of geography and environmental science, Henan University, KaiFeng, 475001, China. jjqiao@henu.edu.cn.
Wei ChenUniversity of Chinese Academy of Sciences, Beijing, 100049, China.
Tao LiChinese Center for Disease Control and Prevention, Beijing, 102206, China.
Songbo QianChangchun University of Science and Technology, Changchun, 130022, China.
Mingtao YanCollege of geography and environmental science, Henan University, KaiFeng, 475001, China.
Henan University · CNChinese Academy of Sciences · CNChangchun University of Science and Technology · CNChinese Center For Disease Control and Prevention · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious research pointed to a close relationship between the incidence of tuberculosis (TB) in aging populations and socio-economic conditions, however there has been lack of studies focused on a region of unbalanced socio-economic development. The aim of this paper is to explore the spatio-temporal variation in TB incidence and examine risk determinants of the disease among aging populations in a typical region.

methodsData on TB-registered cases between 2009 and 2014, in addition to social-economic factors, were collected for each district/county in Beijing, Tianjin and Hebei, a region characterized by an aging population and disparities in social-economic development. A Bayesian space-time hierarchy model (BSTHM) was used to reveal spatio-temporal variation in the incidence of TB among the elderly in this region between 2009 to 2014. GeoDetector was applied to measure the determinant power (q statistic) of risk factors for TB among the elderly.

resultsThe incidence of TB among the elderly exhibited geographical spatial heterogeneity, with a higher incidence in underdeveloped rural areas compared with that in urban areas. Hotspots of TB incidence risk among the elderly were mostly located in north-eastern and southern areas in the study region, far from metropolitan areas. Areas with low risk were distributed mainly in the Beijing-Tianjin metropolitan areas. Social-economic factors had a non-linear influence on elderly TB incidence, with the dominant factors among rural populations being income (q = 0.20) and medical conditions (q = 0.17). These factors had a non-linear interactive effect on the incidence of TB among the elderly, with medical conditions and the level of economic development having the strongest effect (q = 0.54).

conclusionsThe findings explain spatio-temporal variation in TB incidence and risk determinants of elderly TB in the presence of disparities in social-economic development. High-risk zones were located mainly in rural areas, far from metropolitan centres. Medical conditions and the economic development level were significantly associated with elderly TB incidence, and these factors had a non-linear interactive effect on elderly TB incidence. The findings can help to optimize the allocation of health resources and to control TB transmission in the aging population in this region.

Indexed as

Economic DevelopmentTuberculosisAgedBayes TheoremHumansIncidenceRisk FactorsSpatio-Temporal AnalysisAging populationBayesian space–time hierarchy modelDisease risk factorsGeoDetectorSocio-economic statusTuberculosis

Identifiers

PMID34627189
PMCPMC8501584
OpenAlexW3207767493

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.