Evidence map›Paper›PMID 28882022›Full record

ArticleCancer research and treatment2018

Trend Analysis for the Choice and Cost of Lung Cancer Treatment in South Korea, 2003-2013.

Dohun Kim, So Young Kim, Beomseok Suh, Jong Hyock Park

Abstract read
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Article in Cancer research and treatment, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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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

11 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

4 authors.

Dohun KimDepartment of Thoracic and Cardiovascular Surgery, College of Medicine, Chungbuk National University and Chungbuk National University Hospital, Cheongju, Korea.
So Young KimDepartment of Public Health and Preventive Medicine, Chungbuk National University Hospital, Cheongju, Korea.
Beomseok SuhDepartment of Family Medicine, Seoul National University Hospital, Seoul, Korea.
Jong Hyock ParkCollege of Medicine/Graduate School of Health Science Business Convergence, Chungbuk National University, Cheongju, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeOur study aimed to report the annual changes in lung cancer statistics and analyze trends in sociodemographic, medical, and financial factors from 2003 to 2013 in the national database from the Korean National Health Insurance (KNHI). MATERIALS AND

methodsAmong 7,489 patients with code C34 in KNHI database, only lung cancer patients newly diagnosed after 2003 were included in the study population, for a total of 4,582 patients. Descriptive statistics were used to characterize treatment patterns and medical costs according to sociodemographic factors.

resultsApproximately 70% of subjects were male, and the mean age was 67 years. Around 46% of patients were over 70 years old, and 12% were over 80 years old. The medical costs were highest for patients younger than 60 and lowest for those over 80 years old. Surgery was more common in younger patients, while "no treatment" increased greatly with age. In trend analysis, the proportions of aging (p for trend < 0.001), female (p for trend=0.003), metropolitan/urban (p for trend=0.041), and lowest or highest-income patients (p for trend=0.004) increased over time, along with the prevalence of surgery as the primary treatment (p for trend < 0.001). There was also a trend with regard to change in medical costs (p for trend < 0.001), in that those of surgery and radiotherapy increased.

conclusionSurgery as a curative treatment has increased over the past decade. However, the elderly, suburban/rural residents, and low-income patients were more likely to be untreated. Therefore, active measures are required for these increasingly vulnerable groups.

Indexed as

AdultAgedAged, 80 and overChoice BehaviorCosts and Cost AnalysisDatabases, FactualFemaleHumansLung NeoplasmsMaleMiddle AgedNational Health ProgramsPrevalenceRepublic of KoreaSocioeconomic FactorsLung neoplasmsPneumonectomyPractice patterns

Identifiers

PMID28882022
PMCPMC6056952

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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.