Evidence map›Paper›PMID 36268002›Full record

ArticleFrontiers in public health2022

Government drivers of breast cancer prevention: A spatiotemporal analysis based on the association between breast cancer and macro factors.

Xiaodan Bai, Xiyu Zhang, Hongping Shi, Guihong Geng, Bing Wu, Yongqiang Lai, Wenjing Xiang, Yanjie Wang, Yu Cao, Baoguo Shi and 1 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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

11 authors.

Xiaodan BaiDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Xiyu ZhangResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China.
Hongping ShiDepartment of Oncology, Heze Municipal Hospital, Heze, China.
Guihong GengDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Bing WuResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China.
Yongqiang LaiResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China.
Wenjing XiangDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Yanjie WangDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Yu CaoDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Baoguo ShiDepartment of Economics, School of Economics, Minzu University of China, Beijing, China.
Ye LiResearch Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Currently, breast cancer (BC) is ranked among the top malignant tumors in the world, and has attracted widespread attention. Compared with the traditional analysis on biological determinants of BC, this study focused on macro factors, including light at night (LAN), PM2.5, per capita consumption expenditure, economic density, population density, and number of medical beds, to provide targets for the government to implement BC interventions. Methods: A total of 182 prefecture-level cities in China from 2013 to 2016 were selected as the sample of the study. The geographically and temporally weighted regression (GTWR) model was adopted to describe the spatiotemporal correlation between the scale of BC and macro factors. Results: The results showed that the GTWR model can better reveal the spatiotemporal variation. In the temporal dimension, the fluctuations of the regression coefficients of each variable were significant. In the spatial dimension, the positive impacts of LAN, per capita consumption expenditure, population density and number of medical beds gradually increased from west to east, and the positive coefficient of PM2.5 gradually increased from north to south. The negative impact of economic density gradually increased from west to east. Conclusion: The fact that the degree of effect of each variable fluctuates over time reminds the government to pay continuous attention to BC prevention. The spatial heterogeneity features also urge the government to focus on different macro indicators in eastern and western China or southern and northern China. In other words, our research helps drive the government to center on key regions and take targeted measures to curb the rapid growth of BC.

Indexed as

NeoplasmsSpatial RegressionCitiesGovernmentParticulate MatterSpatio-Temporal AnalysisParticulate Matterbreast cancer scalegeographically and temporally weighted regression modellight at nightmacro factorstemporal and spatial heterogeneity

Identifiers

PMID36268002
PMCPMC9578696

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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.