Evidence map›Paper›PMID 33579253›Full record

ArticleBMC public health2021

High-resolution mapping of reproductive tract infections among women of childbearing age in Bangladesh: a spatial-temporal analysis of the demographic and health survey.

Chenyang Feng, Ruixue Li, Abu Ahmed Shamim, Md Barkat Ullah, Mengjie Li, Rubee Dev, Yijing Wang, Tingting Zhao, Jing Liao, Zhicheng Du and 3 more

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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
4.0field-weighted citation impact, top 7% 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

6 citing papers in PubMed, 15 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

13 authors at 5 institutions in 5 countries.

Chenyang FengDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Ruixue LiDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Abu Ahmed ShamimJames P Grant School of public Health, BRAC University, Dhaka, Bangladesh.
Md Barkat UllahDepartment of Nutrition, University of California Davis, California, USA.
Mengjie LiDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Rubee DevDhulikhel Hospital, Kathmandu University Hospital, Kavre, Nepal.
Yijing WangDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Tingting ZhaoDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Jing LiaoDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Zhicheng DuDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Yuheng LingCNRS UMR 6240, Universite de Corse Pascal Paoli, 20250, Corti, France.
Yingsi LaiDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China. laiys3@mail.sysu.edu.cn.
Yuantao HaoDepartment of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Sun Yat-sen University · CNBRAC University · BDCentre National de la Recherche Scientifique · FRDhulikhel Hospital · NPUniversity of California, Davis · US

Funding

China Medical Board 13-133National Natural Science Foundation of China 81703320National Natural Science Foundation of China 81773543
6 · The paper itself

Abstract

backgroundReproductive tract infections (RTIs) have become major but silent public health problems devastating women's lives in Bangladesh. Accurately and precisely identifying high-risk areas of RTIs through high-resolution risk maps is meaningful for resource-limited settings.

methodsWe obtained data reported with RTI symptoms by women of childbearing age in the years 2007, 2011 and 2014 from Bangladesh Demographic and Health Survey. High-spatial Environmental, socio-economic and demographic layers were downloaded from different open-access data sources. We applied Bayesian spatial-temporal models to identify important influencing factors and to estimate the infection risk at 5 km spatial resolution across survey years in Bangladesh.

resultsWe estimated that in Bangladesh, there were approximate 11.1% (95% Bayesian credible interval, BCI: 10.5-11.7%), 13.9% (95% BCI: 13.3-14.5%) and 13.4% (95% BCI: 12.8-14.0%) of women of childbearing age reported with RTI symptoms in 2007, 2011 and 2014, respectively. The risk of most areas shows an obvious increase from 2007 to 2011, then became stable between 2011 and 2014. High risk areas were identified in the southern coastal areas, the western Rajshahi Division, the middle of Khulna Division, and the southwestern Chittagong Division in 2014. The prevalence of Rajshahi and Nawabganj District were increasing during all the survey years.

conclusionThe high-resolution risk maps of RTIs we produced can guide the control strategies targeted to priority areas cost-effectively. More than one eighth of women of childbearing age reported symptoms suggesting RTIs and the risk of RTIs varies in different geographical area, urging the government to pay more attention to the worrying situation of female RTIs in the country.

Indexed as

InfectionsReproductive Tract InfectionsBangladeshBayes TheoremFemaleHumansPrevalenceBangladeshHigh-resolution mapReproductive tract infectionsSpatial-temporal analysis

Identifiers

PMID33579253
PMCPMC7881647
OpenAlexW3127510573

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LicenceCC BY
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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.