Causal inference concepts applied to three observational studies in the context of vaccine development: from theory to practice

Hybrid multi-criteria decision-making model for assessing perceived significance of 23 potentially modifiable cancer risk factors among senior nursing officers

BACKGROUND

Potentially modifiable cancer risk factors have been increasingly recognized among the Chinese population. In this study, we aimed to investigate the perceived significance of these risk factors among senior nursing officers, who play a crucial role in providing healthcare services. We also sought to determine senior nursing officers' performance in reducing these risk factors.

METHODS

A questionnaire survey regarding 23 potentially modifiable cancer risk factors was conducted in November 2023 with 58 senior nursing officers at Taizhou Hospital in Zhejiang Province, China. The consistent fuzzy preference relation method and importance-performance analysis were used to determine the attribute weights and performance levels.

RESULTS

The senior nursing officers considered diabetes, ultraviolet radiation exposure, PM2.5 exposure, excess body weight, physical inactivity, alcohol consumption and secondhand smoking significant. However, performance in reducing secondhand smoking, physical inactivity, excess body weight, PM2.5 exposure, and ultraviolet radiation exposure required improvement.

CONCLUSIONS

The proposed hybrid multiple-criteria decision-making model enhances our understanding of the perceived significance of 23 modifiable cancer risk factors and performance in reducing them, which could facilitate improvements in health education efforts.

Risk Prediction Models for Gastric Cancer: A Scoping Review

Background

Gastric cancer is a significant contributor to the global cancer burden. Risk prediction models aim to estimate future risk based on current and past information, and can be utilized for risk stratification in population screening programs for gastric cancer. This review aims to explore the research design of existing models, as well as the methods, variables, and performance of model construction.

Methods

Six databases were searched through to November 4, 2023 to identify appropriate studies. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Data sources included PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, focusing on gastric cancer risk prediction model studies.

Results

A total of 29 articles met the inclusion criteria, from which 28 original risk prediction models were identified that met the analysis criteria. The risk prediction model is screened, and the data extracted includes research characteristics, prediction variables selection, model construction methods and evaluation indicators. The area under the curve (AUC) of the models ranged from 0.560 to 0.989, while the C-statistics varied between 0.684 and 0.940. The number of predictor variables is mainly concentrated between 5 to 11. The top 5 most frequently included variables were age, helicobacter pylori (Hp), precancerous lesion, pepsinogen (PG), sex, and smoking. Age and Hp were the most consistently included variables.

Conclusion

This review enhances understanding of current gastric cancer risk prediction research and its future directions. The findings provide a strong scientific basis and technical support for developing more accurate gastric cancer risk models. We expect that these conclusions will point the way for future research and clinical practice in this area to assist in the early prevention and treatment of gastric cancer.

Estimating the health impact of nicotine exposure by dissecting the effects of nicotine versus non-nicotine constituents of tobacco smoke: A multivariable Mendelian randomisation study

The detrimental health effects of smoking are well-known, but the impact of regular nicotine use without exposure to the other constituents of tobacco is less clear. Given the increasing daily use of alternative nicotine delivery systems, such as e-cigarettes, it is increasingly important to understand and separate the effects of nicotine use from the impact of tobacco smoke exposure. Using a multivariable Mendelian randomisation framework, we explored the direct effects of nicotine compared with the non-nicotine constituents of tobacco smoke on health outcomes (lung cancer, chronic obstructive pulmonary disease [COPD], forced expiratory volume in one second [FEV-1], forced vital capacity [FVC], coronary heart disease [CHD], and heart rate [HR]). We used Genome-Wide Association Study (GWAS) summary statistics from Buchwald and colleagues, the GWAS and Sequencing Consortium of Alcohol and Nicotine, the International Lung Cancer Consortium, and UK Biobank. Increased nicotine metabolism increased the risk of COPD, lung cancer, and lung function in the univariable analysis. However, when accounting for smoking heaviness in the multivariable analysis, we found that increased nicotine metabolite ratio (indicative of decreased nicotine exposure per cigarette smoked) decreases heart rate (b = -0.30, 95% CI -0.50 to -0.10) and lung function (b = -33.33, 95% CI -41.76 to -24.90). There was no clear evidence of an effect on the remaining outcomes. The results suggest that these smoking-related outcomes are not due to nicotine exposure but are caused by the other components of tobacco smoke; however, there are multiple potential sources of bias, and the results should be triangulated using evidence from a range of methodologies.

Biological DMARDs and disease modification in axial spondyloarthritis: a review through the lens of causal inference

Axial spondyloarthritis (axSpA) is a chronic rheumatic disease characterised by inflammation predominantly involving the spine and the sacroiliac joints. In some patients, axial inflammation leads to irreversible structural damage that in the spine is usually quantified by the modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS). Available therapeutic options include biological disease-modifying antirheumatic drugs (bDMARDs), which have been proven effective in suppressing inflammation in several randomised controlled trials (RCT), the gold standard for evaluating causal treatment effects. RCTs are, however, unfeasible for testing structural effects in axSpA mainly due to the low sensitivity to change of the mSASSS. The available literature therefore mainly includes observational research, which poses serious challenges to the determination of causality. Here, we review the studies testing the effect of bDMARDs on spinal radiographic progression, making use of the principles of causal inference. By exploring the assumptions of causality under counterfactual reasoning (exchangeability, positivity and consistency), we distinguish between studies that likely have reported confounded treatment effects and studies that, on the basis of their design, have more likely reported causal treatment effects. We conclude that bDMARDs might, indirectly, interfere with spinal radiographic progression in axSpA by their effect on inflammation. Innovations in imaging are expected, so that placebo-controlled trials can in the future become a reality. In the meantime, causal inference analysis using observational data may contribute to a better understanding of whether disease modification is possible in axSpA.