RESUMEN
Background: The aim of the study was to investigate the potential effect of different structural interventions for preventing cardiovascular disease. Methods: Medline and EMBASE were searched for peer-reviewed simulation-based studies of structural interventions for prevention of cardiovascular disease. We performed a systematic narrative synthesis. Results: A total of 54 studies met the inclusion criteria. Diet, nutrition, tobacco and alcohol control and other programmes are among the policy simulation models explored. Food tax and subsidies, healthy food and lifestyles policies, palm oil tax, processed meat tax, reduction in ultra-processed foods, supplementary nutrition assistance programmes, stricter food policy and subsidised community-supported agriculture were among the diet and nutrition initiatives. Initiatives to reduce tobacco and alcohol use included a smoking ban, a national tobacco control initiative and a tax on alcohol. Others included the NHS Health Check, WHO 25 × 25 and air quality management policy. Future work and limitations: There is significant heterogeneity in simulation models, making comparisons of output data impossible. While policy interventions typically include a variety of strategies, none of the models considered possible interrelationships between multiple policies or potential interactions. Research that investigates dose-response interactions between numerous modifications as well as longer-term clinical outcomes can help us better understand the potential impact of policy-level interventions. Conclusions: The reviewed studies underscore the potential of structural interventions in addressing cardiovascular diseases. Notably, interventions in areas such as diet, tobacco, and alcohol control demonstrate a prospective decrease in cardiovascular incidents. However, to realize the full potential of such interventions, there is a pressing need for models that consider the interplay and cumulative impacts of multiple policies. Rigorous research into holistic and interconnected interventions will pave the way for more effective policy strategies in the future. Study registration: The study is registered as PROSPERO CRD42019154836. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme as award number 17/148/05.
This study aimed to explore the potential effects of various policy changes on the prevention of heart disease. By searching two large medical databases, we identified studies that employed computer models to estimate the impact of these policies on heart disease rates. In total, 54 studies matched our criteria. These studies considered a diverse range of policy interventions. Some delved into food and nutrition, investigating aspects like unhealthy food taxes, healthy food subsidies, stricter food regulations, and nutritional assistance programs. Others examined the impact of policies targeting tobacco and alcohol, encompassing smoking bans, nationwide tobacco control measures, and alcohol taxation. Further policies assessed included routine health checkups, global health goals, and measures to enhance air quality. One significant challenge lies in the varied approaches and models each study employed, making direct comparisons difficult. Furthermore, there's a gap in understanding how these policies might influence one another, as the studies did not consider potential interactions between them. While these policies show promise in the computer models, more comprehensive research is needed to fully appreciate their combined and long-term effects on heart health in real-world scenarios. As of now, we recognize the potential of these interventions, but further studies will determine their true impact on reducing heart disease rates.
RESUMEN
BACKGROUND: Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. The aim of the study was to guide researchers and commissioners of cardiovascular disease preventative services towards possible cost-effective interventions by reviewing published economic analyses of interventions for the primary prevention of cardiovascular disease, conducted for or within the UK NHS. METHODS: In January 2021, electronic searches of MEDLINE and Embase were carried out to find economic evaluations of cardiovascular disease preventative services. We included fully published economic evaluations (including economic models) conducted alongside randomised controlled trials of any form of intervention that was aimed at the primary prevention of cardiovascular disease, including, but not limited to, drugs, diet, physical activity and public health. Full systematic review methods were used with predetermined inclusion/exclusion criteria, data extraction and formal quality appraisal [using the Consolidated Health Economic Evaluation Reporting Standards checklist and the framework for the quality assessment of decision analytic modelling by Philips et al. (Philips Z, Ginnelly L, Sculpher M, Claxton K, Golder S, Riemsma R, et al. Review of guidelines for good practice in decision-analytic modelling in health technology assessment. Health Technol Assess 2004;8(36)]. RESULTS: Of 4351 non-duplicate citations, eight articles met the review's inclusion criteria. The eight articles focused on health promotion (n = 3), lipid-lowering medicine (n = 4) and blood pressure-lowering medication (n = 1). The majority of the populations in each study had at least one risk factor for cardiovascular disease or were at high risk of cardiovascular disease. For the primary prevention of cardiovascular disease, all strategies were cost-effective at a threshold of £25,000 per quality-adjusted life-year, except increasing motivational interviewing in addition to other behaviour change strategies. Where the cost per quality-adjusted life-year gained was reported, interventions varied from dominant (i.e. less expensive and more effective than the comparator intervention) to £55,000 per quality-adjusted life-year gained. FUTURE WORK AND LIMITATIONS: We found few health economic analyses of interventions for primary cardiovascular disease prevention conducted within the last decade. Future economic assessments should be undertaken and presented in accordance with best practices so that future reviews may make clear recommendations to improve health policy. CONCLUSIONS: It is difficult to establish direct comparisons or draw firm conclusions because of the uncertainty and heterogeneity among studies. However, interventions conducted for or within the UK NHS were likely to be cost-effective in people at increased risk of cardiovascular disease when compared with usual care or no intervention. FUNDING: This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in Health Technology Assessment. See the NIHR Journals Library website for further project information.
RESUMEN
BACKGROUND: As part of our ongoing systematic review of complex interventions for the primary prevention of cardiovascular diseases, we have developed and evaluated automated machine-learning classifiers for title and abstract screening. The aim was to develop a high-performing algorithm comparable to human screening. METHODS: We followed a three-phase process to develop and test an automated machine learning-based classifier for screening potential studies on interventions for primary prevention of cardiovascular disease. We labelled a total of 16,611 articles during the first phase of the project. In the second phase, we used the labelled articles to develop a machine learning-based classifier. After that, we examined the performance of the classifiers in correctly labelling the papers. We evaluated the performance of the five deep-learning models [i.e. parallel convolutional neural network ( CNN ), stacked CNN , parallel-stacked CNN , recurrent neural network ( RNN ) and CNN-RNN]. The models were evaluated using recall, precision and work saved over sampling at no less than 95% recall. RESULTS: We labelled a total of 16,611 articles, of which 676 (4.0%) were tagged as 'relevant' and 15,935 (96%) were tagged as 'irrelevant'. The recall ranged from 51.9% to 96.6%. The precision ranged from 64.6% to 99.1%. The work saved over sampling ranged from 8.9% to as high as 92.1%. The best-performing model was parallel CNN , yielding a 96.4% recall, as well as 99.1% precision, and a potential workload reduction of 89.9%. FUTURE WORK AND LIMITATIONS: We used words from the title and the abstract only. More work needs to be done to look into possible changes in performance, such as adding features such as full document text. The approach might also not be able to be used for other complex systematic reviews on different topics. CONCLUSION: Our study shows that machine learning has the potential to significantly aid the labour-intensive screening of abstracts in systematic reviews of complex interventions. Future research should concentrate on enhancing the classifier system and determining how it can be integrated into the systematic review workflow. FUNDING: This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in Health Technology Assessment. See the NIHR Journals Library website for further project information.
