Evidence map›Paper›PMID 39699703›Full record

ArticleHealth economics review2024

An analysis of factors influencing technical efficiency of health expenditures in China.

Jingjing Cheng, Xianming Kuang, Ping Zhou, Weiran Sha

Abstract read
In one paragraph

Article in Health economics review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

4 authors.

Jingjing ChengSchool of Business Administration, Northeastern University, Shenyang, 110819, China. evercjjee@126.com.ORCID http://orcid.org/0000-0002-9916-494X
Xianming KuangCenter for Economic Research, China Institute for Reform and Development, Haikou, 570311, China.
Ping ZhouDepartment of Radiotherapy, First Affiliated Hospital of Hainan Medical University, Haikou, 570102, China.
Weiran ShaDepartment of Medical Administration, First Affiliated Hospital of Hainan Medical University, Haikou, 570102, China.

Funding

China Health Economics Association CHEAZZ20222314
6 · The paper itself

Abstract

China's primary healthcare (PHC) system, together with rural healthcare services, remains the Achilles' heel in the national healthcare system. Healthcare workers, specifically village doctors, are an integral part of the healthcare system. Using the two-stage data envelopment analysis (DEA) and Tobit regression analysis, this study aims to investigate the efficiency of healthcare expenditures on medical resources and services in China, as well as determine how different types of healthcare work influence efficiency. Compared with other types of healthcare workers, village doctors exerted a prominent impact on provincial and rural efficiency at all stages and played a key role in augmenting the efficiency of healthcare expenditures on health outcomes. Besides, township health centers (THCs) and village clinics (VCs) faced administrative overstaffing, mainly involving pharmacists, other nonmedical technologists, and health administrators, which adversely affected the efficiency of healthcare expenditures. This study suggests that the higher the proportion of these non-village doctor positions (e.g., pharmacists, health administrators, and nonmedical technologists) in THCs and VCs, the lower the efficiency of China's PHC system. Overall, the priority should be enhancing the training and remuneration of village doctors and other healthcare workers in rural areas to further enhance their performance and increase the overall efficiency of China's healthcare system.

Indexed as

Data envelopment analysisPrimary healthcareRural health

Identifiers

PMID39699703
PMCPMC11660505

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.