Evidence map›Paper›PMID 38355478›Full record

Observational studyBMC public health2024

Spatiotemporal pattern of leprosy in southwest China from 2010 to 2020: an ecological study.

Mengyan Zhang, Longchong Qiao, Peiwen Sun, Haiqin Jiang, Ying Shi, Wenyue Zhang, Youming Mei, Meiwen Yu, Hongsheng Wang

Open access · goldAbstract readObservational Study
In one paragraph

Observational study in BMC public health, 2024. 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
3.1field-weighted citation impact, top 9% 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, 8 citations in OpenAlex.

  1. Article
  2. Observational
  3. Article
  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

9 authors at 3 institutions in 1 country.

Mengyan ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Longchong QiaoDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Peiwen SunInstitute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
Haiqin JiangDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Ying ShiInstitute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
Wenyue ZhangInstitute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
Youming MeiInstitute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China.
Meiwen YuInstitute of Dermatology, Chinese Academy of Medical Sciences & Peking Union Medical College, Nanjing, Jiangsu, China. yumeiwen@163.com.
Hongsheng WangDepartment of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China. whs33@vip.sina.com.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNJiangsu Provincial Center for Disease Control and Prevention · CNNanjing Medical University · CN

Funding

Chinese Academy of Medical Sciences Innovation Fund for Medical Science 2022-I2M-C&T-B-094Jiangsu Provincial Medical Key Laboratory, Jiangsu Province Capability Improvement Project through Science, Technology and Education ZDXYS202204National Natural Science Foundation of China 81972950, 82173431, 82103748The Nanjing Incubation Program for National Clinical Research Center 2019060001The National Key R&D Program of China 2019YFE0113300
6 · The paper itself

Abstract

backgroundDespite many efforts to control leprosy worldwide, it is still a significant public health problem in low- and middle-income regions. It has been endemic in China for thousands of years, and southwest China has the highest leprosy burden in the country.

methodsThis observational study was conducted with all newly detected leprosy cases in southwest China from 2010 to 2020. Data were extracted from the Leprosy Management Information System (LEPMIS) database in China. The Joinpoint model was used to determine the time trends in the study area. Spatial autocorrelation statistics was performed to understand spatial distribution of leprosy cases. Spatial scan statistics was applied to identify significant clusters with high rate.

resultsA total of 4801 newly detected leprosy cases were reported in southwest China over 11 years. The temporal trends declined stably. The new case detection rate (NCDR) dropped from 4.38/1,000,000 population in 2010 to 1.25/1,000,000 population in 2020, with an average decrease of 12.24% (95% CI: -14.0 to - 10.5; P < 0.001). Results of global spatial autocorrelation showed that leprosy cases presented clustering distribution in the study area. Most likely clusters were identified during the study period and were frequently located at Yunnan or the border areas between Yunnan and Guizhou Provinces. Secondary clusters were always located in the western counties, the border areas between Yunnan and Sichuan Provinces.

conclusionsGeographic regions characterized by clusters with high rates were considered as leprosy high-risk areas. The findings of this study could be used to design leprosy control measures and provide indications to strengthen the surveillance of high-risk areas. These areas should be prioritized in the allocation of resources.

Indexed as

LeprosyChinaCluster AnalysisDatabases, FactualHumansSpatial AnalysisSpatio-Temporal AnalysisChinaClusterEpidemiologyLeprosyTemporal trends

Identifiers

PMID38355478
PMCPMC10865634
OpenAlexW4391822376

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.