Evidence map›Paper›PMID 29404116›Full record

ArticleTurkish thoracic journal2016

Can a Computer-Based Prescription of Free Medication Increase Smoking Cessation Rates Efficiently?

Banu Salepci, Ali Fidan, Benan Çağlayan, Elif Torun Parmaksız, Nesrin Kıral, Sevda Şener Cömert, Gülten Aktin Güngör, Egehan Salepci

Open access · bronzeAbstract read
In one paragraph

Article in Turkish thoracic journal, 2016. 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
0.2field-weighted citation impact, top 38% of its field
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, 6 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Banu SalepciClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Ali FidanClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Benan ÇağlayanClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Elif Torun ParmaksızClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Nesrin KıralClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Sevda Şener CömertClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Gülten Aktin GüngörClinic of Chest Diseases, Dr. Lütfi Kırdar Kartal Training and Research Hospital, İstanbul, Turkey.
Egehan SalepciStudent of 6 Grade, Trakya University Faculty of Medicine, Edirne, Turkey.
Dr Lütfi Kırdar Kartal Eğitim ve Araştırma Hastanesi · TRTrakya University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesIn 2011, in the context of a research project, bupropion and varenicline were distributed to smoking cessation clinics by the Ministry of Health of Turkey to be prescribed free of charge by a computer-based system. In the present study, we compared smoking cessation rates between patients who were prescribed free medications during the period of the project and those who had to pay for their medication. MATERIAL AND

methodsSix hundred four patients who applied during the project period were given either bupropion or varenicline, which were prescribed using an algorithm-based computer system. Three hundred sixteen patients who applied after that period were prescribed medicines deemed appropriate by the attending physician but had to pay for the medication on their own. Follow-up visits were arranged for one year. Carbon monoxide (CO) levels in the expired air were used as indicators of cessation.

resultsA total of 537 patients began treatment, of which 438 (81.6%) applied during the first period (group 1) and 99 (18.4%) applied during the second period (group 2). The mean age and concomitant disease presence were higher in the second-period patients (p< 0.05). Advanced age, comorbidities, pathological findings in spirometry, and chest X-ray were also higher in those who paid for the cost of their treatment (p= 0.009, 0.001, 0.006, 0.001, respectively). Smoking cessation rates were found to be 14.8% and 27.3% after six months (p= 0.008) and 10.7% and 18.2% after one year (p= 0.059), respectively, for group 1 and group 2. Age, dependence score, cigarettes smoked (as pack-years), and percentage of patients who paid for the treatment were found to be significantly higher (p< 0.001, 0.021, 0.018, 0.001, respectively) for those who quit smoking at the end of six months. For the patients who quit smoking at the end of one year, age was found to be significantly higher (p= 0.008), and the number of males was higher, although the difference was not statistically significant (p= 0.05). When logistic regression analysis was applied, age, dependence score, and paid treatment were found to be independent variables (p= 0.002, 0.008, 0.012, respectively) for those who quit smoking at the end of six months. Only age was found to be an independent variable for those who quit smoking at the end of one year (p= 0.029).

conclusionMore smokers could receive treatment by the distribution of free drugs. However, quitting rates at the end of six months were higher when patients had to pay for their treatment. On the other hand, quitting rates at the end of one year were not affected by whether the treatment was paid for or free of charge. The most important factor increasing quitting rates at the end of six months and one year was found to be advanced age.

Indexed as

Smoking cessationsmoking cessation policiestreatment efficiency

Identifiers

PMID29404116
PMCPMC5792108
OpenAlexW2517356055

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.