ArticleBMJ open2024
Spatiotemporal distribution characteristics and impact factors of hepatitis C in Chongqing, China, 2014-2020.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Spatial and temporal analysis of viral hepatitis in mainland China from 2007 to 2023: implications for targeted public health interventions.Frontiers in public health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to explore the spatial and temporal distribution of hepatitis C and its influencing factors in Chongqing, providing a scientific basis for the relevant departments to formulate targeted preventive measures for the high prevalence of hepatitis C in the region and population.
designWe collected data on hepatitis C cases in Chongqing (located in the southwest of China) from 2014 to 2020, and analysed the spatiotemporal heterogeneity of hepatitis C incidence in different populations and identified factors that might influence the incidence of hepatitis C by constructing a Bayesian spatiotemporal model. SETTINGS: The study subjects included clinically diagnosed cases and confirmed cases of hepatitis C with current address in Chongqing and onset date between 1 January 2014 and 31 December 2020.
participantsThe study used aggregated data, including 33 900 clinically diagnosed cases and confirmed cases of hepatitis C.
resultsFrom 2014 to 2020, the high-risk areas of hepatitis C were primarily concentrated in the main and new urban areas of Chongqing. In contrast, the low-risk areas were mainly found in southeast and northeast Chongqing. There was also an increasing trend in the risk of incidence in the low-risk areas. Analysis of different populations revealed that men aged 45-59 years had a higher risk of developing hepatitis C in the main urban area compared with other age groups. Additionally, the risk for this population group showed an increasing trend in the southeast and northeast of Chongqing as well as the main urban area. Among women, the rising trend of hepatitis C risk was stronger for those aged 30-44 years in southeast Chongqing and for those aged 45-59 years in northeast Chongqing compared with other age groups. The analysis of influencing factors found that gross domestic product per capita, population density and the proportion of tertiary industry were associated with an increased risk of hepatitis C.
conclusionsHigh-risk areas for hepatitis C virus were mainly located in the main and new urban areas of Chongqing, where the male prime-age population was the focus of prevention and treatment. In the future, the relevant authorities should concentrate on high-risk areas and at the same time strengthen screening and serological surveys for hepatitis C in low-risk areas and various populations, and raise public awareness of prevention, so as to reduce the incidence of hepatitis C.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.