Evidence map›Paper›PMID 32295021›Full record

ArticleHealthcare (Basel, Switzerland)2020

Characteristics of Frequent Attenders Compared with Non-Frequent Attenders in Primary Care: Study of Remote Communities in Taiwan.

Shih-Chao Kang, Chun-Chi Lin, Chia-Chen Tsai, Yin-Han Lu, Chun-Feng Huang, Yu-Chun Chen

Abstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Pooled it
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  3. Article
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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

6 authors.

Shih-Chao KangDivision of Family Medicine, National Yang-Ming University Hospital, Yilan 260, Taiwan.
Chun-Chi LinDivision of Occupational Medicine, Taipei Veterans Hospital, Taipei 112, Taiwan.
Chia-Chen TsaiDepartment of Internal Medicine, National Yang-Ming University Hospital, Yilan 260, Taiwan.
Yin-Han LuDivision of Family Medicine, National Yang-Ming University Hospital, Yilan 260, Taiwan.
Chun-Feng HuangDivision of Family Medicine, National Yang-Ming University Hospital, Yilan 260, Taiwan.
Yu-Chun ChenFaculty of Medicine, School of Medicine, National Yang-Ming University, Taipei 112, Taiwan.ORCID 0000-0002-4124-1949

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Frequent attenders (FAs) have an impact on the medical resources and the quality of care. In Taiwan, the characteristics of FAs remain unclear in primary care. Outpatient data were screened from a central clinic within six surrounding fishing villages in northeastern Taiwan in 2017. FAs were defined as those who made >18 visits in one year, and those who made ≤18 visits were defined as non-frequent attenders (NFAs). Data of FAs and NFAs were collected and compared. The major diagnoses were reported using International Classifications of Diseases, Tenth Edition (ICD-10) codes. A total of 1586 subjects and 9077 visits were enrolled, including 119 FAs and 1467 NFAs. FAs had a more advanced age compared to NFAs. Both FAs and NFAs had more visits in summer. FAs had consumed high prescriptions and related costs. FAs also had higher therapeutic and first visit costs than NFAs. Comparing with age- sex-matched NFAs, FAs were positively associated with musculoskeletal diseases (M00-M99), hematological diseases (D50-D89), endocrine diseases (E00-E90), and mental disorders (F00-F99). Large-scale local datasets and suitable definitions of FAs for Taiwanese subjects are needed.

Indexed as

community medicinefamily medicinefrequent attendanceICD-10 codeprimary careTaiwan

Identifiers

PMID32295021
PMCPMC7349067

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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.