Reducing the smoking-related health burden in the USA through diversion to electronic cigarettes: a system dynamics simulation study

System dynamics modeling for cancer prevention and control: A systematic review

Cancer prevention and control requires consideration of complex interactions between multilevel factors. System dynamics modeling, which consists of diagramming and simulation approaches for understanding and managing such complexity, is being increasingly applied to cancer prevention and control, but the breadth, characteristics, and quality of these studies is not known. We searched PubMed, Scopus, APA PsycInfo, and eight peer-reviewed journals to identify cancer-related studies that used system dynamics modeling. A dual review process was used to determine eligibility. Included studies were assessed using quality criteria adapted from prior literature and mapped onto the cancer control continuum. Characteristics of studies and models were abstracted and qualitatively synthesized. 32 studies met our inclusion criteria. A mix of simulation and diagramming approaches were used to address diverse topics, including chemotherapy treatments (16%), interventions to reduce tobacco or e-cigarettes use (16%), and cancer risk from environmental contamination (13%). Models spanned all focus areas of the cancer control continuum, with treatment (44%), prevention (34%), and detection (31%) being the most common. The quality assessment of studies was low, particularly for simulation approaches. Diagramming-only studies more often used participatory approaches. Involvement of participants, description of model development processes, and proper calibration and validation of models showed the greatest room for improvement. System dynamics modeling can illustrate complex interactions and help identify potential interventions across the cancer control continuum. Prior efforts have been hampered by a lack of rigor and transparency regarding model development and testing. Supportive infrastructure for increasing awareness, accessibility, and further development of best practices of system dynamics for multidisciplinary cancer research is needed.

Predicting the long-term effects of electronic cigarette use on population health: a systematic review of modelling studies

OBJECTIVE

To systematically review and synthesise the findings of modelling studies on the population impacts of e-cigarette use and to identify potential gaps requiring future investigation.

DATA SOURCE AND STUDY SELECTION

Four databases were searched for modelling studies of e-cigarette use on population health published between 2010 and 2023. A total of 32 studies were included.

DATA EXTRACTION

Data on study characteristics, model attributes and estimates of population impacts including health outcomes and smoking prevalence were extracted from each article. The findings were synthesised narratively.

DATA SYNTHESIS

The introduction of e-cigarettes was predicted to lead to decreased smoking-related mortality, increased quality-adjusted life-years and reduced health system costs in 29 studies. Seventeen studies predicted a lower prevalence of cigarette smoking. Models that predicted negative population impacts assumed very high e-cigarette initiation rates among non-smokers and that e-cigarette use would discourage smoking cessation by a large margin. The majority of the studies were based on US population data and few studies included factors other than smoking status, such as jurisdictional tobacco control policies or social influence.

CONCLUSIONS

A population increase in e-cigarette use may result in lower smoking prevalence and reduced burden of disease in the long run, especially if their use can be restricted to assisting smoking cessation. Given the assumption-dependent nature of modelling outcomes, future modelling studies should consider incorporating different policy options in their projection exercises, using shorter time horizons and expanding their modelling to low-income and middle-income countries where smoking rates remain relatively high.

Identifying the implementation effect of technology transfer policy using system dynamics: a case study in Liaoning, China

Given that technology transfer provides an important boost for promoting national economic development, technology transfer policy (TTP) has attracted more and more attention from academia and industry. The government issued many policies. However, the implementation effect of TTP still needs to be clarified. This study is carried out from the progressive level of "text content-influence path-implementation effect.'' It aims to adopt a systematic analysis method to analyze policy tools and policy implementation stages, then builds a conceptual framework of the influence path of TTP. Then the relationship between variables in the qualitative model was clarified, and the system dynamics (SD) model was used to build a quantitative model with four feedback loops. Finally, taking Liaoning, China as an example, the system simulation and sensitivity analysis of the main parameters are implemented in Vensim PLE. Different policy tools have different roles in the TTP impact stages of research, transfer, and industrialization. Based on the data of 2013-1019, the SD model constructed in this paper can be used to predict the implementation effect of TTP during 2020-2015. Simulation and sensitivity analysis results provide practical enlightenment for government departments to improve the implementation effect of the existing TTP. This study also provides other researchers with a systematic understanding for improving the implementation effect of TTP with a "text content-influence path-implementation effect" conduction chain and provides new insights for further research on TTP.

Global, regional, and national burden of cancers attributable to tobacco smoking in 204 countries and territories, 1990-2019

BACKGROUND

Cancers are leading causes of mortality and morbidity, with smoking being recognized as a significant risk factor for many types of cancer. We aimed to report the cancer burden attributable to tobacco smoking by sex, age, socio-demographic index (SDI), and cancer type in 204 countries and territories from 1990 to 2019.

METHODS

The burden of cancers attributable to smoking was reported between 1990 and 2019, based upon the Comparative Risk Assessment approach used in the Global Burden of Disease (GBD) study 2019.

RESULTS

Globally, in 2019 there were an estimated 2.5 million cancer-related deaths (95% UI: 2.3 to 2.7) and 56.4 million DALYs (51.3 to 61.7) attributable to smoking. The global age-standardized death and DALY rates of cancers attributable to smoking per 100,000 decreased by 23.0% (-29.5 to -15.8) and 28.6% (-35.1 to -21.5), respectively, over the period 1990-2019. Central Europe (50.4 [44.4 to 57.6]) and Western Sub-Saharan Africa (6.7 [5.7 to 8.0]) had the highest and lowest age-standardized death rates, respectively, for cancers attributable to smoking. In 2019, the age-standardized DALY rate of cancers attributable to smoking was highest in Greenland (2224.0 [1804.5 to 2678.8]) and lowest in Ethiopia (72.2 [51.2 to 98.0]). Also in 2019, the global number of DALYs was highest in the 65-69 age group and there was a positive association between SDI and the age-standardized DALY rate.

CONCLUSIONS

The results of this study clearly illustrate that renewed efforts are required to increase utilization of evidence-based smoking cessation support in order to reduce the burden of smoking-related diseases.