Evidence map›Paper›PMID 34641850›Full record

ArticleBMC health services research2021

Spatial analysis and evaluation of medical resource allocation in China based on geographic big data.

Sida Wan, Yanming Chen, Yijia Xiao, Qiqi Zhao, Manchun Li, Shuqi Wu

Abstract read
In one paragraph

Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 1 pooled it
–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

63 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Randomized controlled trial of an app for cancer pain management.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
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3 more citing papers are in PubMed but not listed here.

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

6 authors.

Sida WanJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Collaborative Innovation Center for the South Sea Studies, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 210023, People's Republic of China.
Yanming ChenSchool of Earth Sciences and Engineering, Hohai University, Nanjing, 211100, People's Republic of China. chenyanming@hhu.edu.cn.
Yijia XiaoJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Collaborative Innovation Center for the South Sea Studies, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 210023, People's Republic of China.
Qiqi ZhaoJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Collaborative Innovation Center for the South Sea Studies, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 210023, People's Republic of China.
Manchun LiJiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Collaborative Innovation Center for the South Sea Studies, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu, 210023, People's Republic of China. limanchun@nju.edu.cn.
Shuqi WuSchool of Geosciences, China university of petroleum, No.66 West Changjiang Road, Qingdao, Shandong Province, 266580, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSpatial allocation of medical resources is closely related to people's health. Thus, it is important to evaluate the abundance of medical resources regionally and explore the spatial heterogeneity of medical resource allocation.

methodsUsing medical geographic big data, this study analyzed 369 Chinese cities and constructed a medical resource evaluation model based on the grading of medical institutions using the Delphi method. It evaluated China's medical resources at three levels (economic sectors, economic zones, and provinces) and discussed their spatial clustering patterns. Geographically weighted regression was used to explore the correlations between the evaluation results and population and gross domestic product (GDP).

resultsThe spatial heterogeneity of medical resource allocation in China was significant, and the following general regularities were observed: 1) The abundance and balance of medical resources were typically better in the east than in the west, and in coastal areas compared to inland ones. 2) The average primacy ratio of medical resources in Chinese cities by province was 2.30. The spatial distribution of medical resources in the provinces was unbalanced, showing high concentrations in the primate cities. 3) The allocation of medical resources at the provincial level in China was summarized as following a single-growth pole pattern supplemented by bipolar circular allocation and balanced allocation patterns. The agglomeration patterns of medical resources in typical cities were categorized into single-center and balanced development patterns. GDP was highly correlated to the medical evaluation results, while demographic factors showed, low correlations. Large cities and their surrounding areas exhibited obvious response characteristics.

conclusionsThese findings provide policy-relevant guidance for improving the spatial imbalance of medical resources, strengthening regional public health systems, and promoting government coordination efforts for medical resource allocation at different levels to improve the overall functioning of the medical and health service system and bolster its balanced and synergistic development.

Indexed as

Big DataResource AllocationAnimalsChinaGross Domestic ProductSpatial AnalysisGeographic big dataMedical resource scoring modelResource evaluationSpatial analysis

Identifiers

PMID34641850
PMCPMC8508408

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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.