ArticleJournal of multidisciplinary healthcare2024
The Prevalence, Characteristics, and Factors Associated with Chinese Herbal Medicine Use Among Chinese Middle-Aged and Older Adults with Chronic Lung Disease: A Cross-Sectional Study.
Article in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utilization of traditional Chinese medicine services and associated factors: a systematic review and meta-analysis.BMC health services research · 2026Pooled it
- Time to first cigarette after waking and risk of incident chronic respiratory diseases in males: A prospective national cohort study.Tobacco induced diseases · 2026Article
- The utilization, characteristics, and influencing factors of non-therapeutic traditional Chinese medicine services among middle-aged and older population in Guangzhou, China: a cross-sectional study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Identifying factors that influence Chinese herbal medicine (CHM) use in chronic lung disease (CLD) patients can inform targeted strategies to boost CHM use. However, the prevalence and factors affecting CHM use for treating CLD in middle-aged and older Chinese patients remain understudied. This study aims to examine the prevalence and influencing factors of CHM use among middle-aged Chinese patients with CLD. Patients and Methods: The data analyzed in this study were extracted from the China Health and Retirement Longitudinal Study in 2018. A cross-sectional analysis of 2784 participants aged 45 years and older taking CHM for CLD was included, and influencing factors (demographic characteristics, health status, and healthcare service use) predicting the use of CHM for CLD were identified through multivariate logistic regression model. Results: The prevalence of using CHM to treat CLD was 22.4% (624/2784). Compared to participants with CLD who did not use CHM, those with CLD who used CHM were more likely to be female, and non-smoking, non-drinking, purchased insurance (OR=2.35; 95% CI=1.09, 5.04), went to a traditional Chinese medicine hospital (OR=3.5; 95% CI=1.96, 6.25) and self-treatment (OR=1.67; 95% CI=1.33, 2.10), the health status being poor (OR =1.74; 95% Cl=1.18, 2.56), asthma (OR=1.55; 95% Cl=1.16, 2.09) and depression (OR =1.42; 95% Cl=1.14, 1.75). In the additive interaction analysis, patients who tend to self-medicate and with depression were most likely to use CHM (OR=2.52; 95% Cl=1.85, 3.42). Conclusion: A considerable middle-aged and older Chinese patient with CLD uses CHM for treatment. Multiple factors can influence CHM use, particularly self-treatment and depression.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.