Tembeling 2.0

Category: Public Health

  • A Simple Guide to MHIT and Medical Insurance in Malaysia

    Introduction

    Many people purchase medical insurance without fully understanding why premiums keep rising or why different insurance products can feel different when someone needs treatment. This article explains, in plain language, what MHIT is, how it compares with conventional medical insurance, and why MHIT may help reduce medical cost inflation if implemented under the right conditions. It also clarifies a common misunderstanding about “basic coverage”, especially childbirth, which is often assumed to be included but usually is not.

    What is MHIT

    MHIT stands for Medical and Health Insurance or Takaful. In Malaysia, MHIT commonly refers to the Base MHIT plan introduced under a national reform effort involving Bank Negara Malaysia and relevant ministries.

    MHIT is a standardised medical insurance plan offered by private insurers under a common framework. A key feature is that participation is voluntary, meaning people choose whether or not to enrol, and there is no legal requirement for the population to join.

    MHIT focuses on essential care, mainly inpatient private hospital care such as ward admission, surgery, investigations, and inpatient treatment. It is intended as a foundation layer, providing safe and sufficient cover for common hospital needs rather than unlimited choice or premium extras.

    An important development highlighted by the Malaysian Medical Association is the involvement of general practitioners in outpatient management of selected high-volume conditions such as dengue fever, pneumonia, bronchitis, and influenza. This approach is intended to strengthen primary care and reduce avoidable hospitalisation by managing suitable cases earlier and more efficiently in the community, rather than defaulting to hospital admission, as reported by

    CodeBlue (January 2026)
    .

    What is conventional medical insurance

    Conventional medical insurance refers to private medical cards offered by insurance companies, including plans such as AIA My Medical Plan and similar products from other insurers.

    These plans are not standardised. They vary widely in annual limits, lifetime limits, cost-sharing rules, hospital panels, and optional add-ons. Many conventional plans offer higher limits and broader coverage, including more outpatient and long-term care benefits, but they are generally more complex and can be more expensive over time.

    A key point for the public is that “basic coverage” in conventional medical insurance is often narrower than assumed. Normal childbirth is usually excluded. Caesarean section is commonly excluded unless it is medically necessary. Maternity benefits usually require a separate rider, a waiting period, and additional premium. This means conventional insurance is not automatically broader for basic care unless the policy has been upgraded with add-ons.

    Important terms explained

    1. Annual limit

    The annual limit is the maximum amount the insurer will pay in one policy year.

    Example: If the annual limit is RM100,000 and the hospital bill is RM120,000, the patient pays RM20,000.

    2. Deductible

    A deductible is the amount the patient must pay before the insurance starts paying.

    Example: A RM500 deductible means the patient pays the first RM500 of the bill.

    3. Co-payment or co-insurance

    This refers to cost sharing between the patient and the insurer.

    Example: A 10 percent co-insurance means the patient pays 10 percent of the bill while the insurer pays the remaining 90 percent.

    4. Clinical care guidelines

    Clinical care guidelines are evidence-based recommendations that guide doctors on appropriate investigations, treatments, and length of hospital stay. They aim to support safe, consistent care based on research and patient outcomes.

    Conclusion

    MHIT provides standard, evidence-based care with a design aimed at affordability, predictability, and cost discipline. For basic and common inpatient care, MHIT can be broadly comparable in practical effect to conventional medical insurance, including plans such as AIA My Medical Plan, because the core clinical management is similar and financial protection can be strong within the plan limits.

    Conventional medical insurance remains more suitable for complex, long-term, or very high-cost conditions where extended outpatient care, advanced therapies, and very high limits are required. MHIT’s role in strengthening primary care and managing common infections earlier and more efficiently supports both cost control and better system functioning, while the public healthcare system continues to remain the central safety net.

  • PrEP Must Be Implemented with Mandatory Behavioural Intervention

    From a public health perspective, harm reduction is not confined to lowering biological risk alone. It must also address the behaviours that generate that risk. While HIV pre-exposure prophylaxis, or PrEP, is effective in reducing the probability of HIV transmission, it does not modify sexual risk behaviour and cannot replace personal responsibility or behavioural change.

    From the standpoint of the Health Belief Model, behaviour is strongly influenced by an individual’s perceived susceptibility and perceived severity of harm. When PrEP is introduced without structured behavioural intervention, it may substantially reduce the perceived risk of HIV transmission. This lowered perception of harm weakens the motivation to change behaviour, resulting in the continuation, or even escalation, of high-risk sexual practices. In this context, PrEP risks undermining the behavioural drivers essential for sustainable prevention.

    When PrEP is promoted without mandatory and structured behavioural interventions, it ceases to function as genuine harm reduction and instead becomes a mechanism that enables the continuation of unhealthy and high-risk sexual behaviours under the false assurance of biomedical protection. This represents a shift from prevention to harm containment, which is inconsistent with established public health principles.

    Beyond biomedical and behavioural considerations, public health interventions must also recognise the moral, ethical, and spiritual dimensions of human behaviour. For Muslim communities in particular, health promotion should not be detached from faith. Islam emphasises accountability before God, moral conduct, self-restraint, and the pursuit of what is good and beneficial for oneself and society. Prevention strategies should therefore encourage individuals not only to avoid harm, but also to return to values grounded in belief in God and commitment to doing good.

    Crucially, HIV prevention and control cannot be addressed by the Ministry of Health alone. It requires a deliberate, coordinated, and sustained multi-agency approach. Ministries responsible for education, youth and sports, higher education, religious affairs, social welfare, women and family development, as well as law enforcement, community leaders, religious institutions, non-governmental organisations, and families must all share responsibility. Behaviour, values, and social norms are shaped far beyond the healthcare system, and ignoring this reality weakens any national response.

    The provision of PrEP must therefore be embedded within a comprehensive and structured prevention framework that is multi-sectoral by design. This includes rigorous behavioural risk assessment prior to initiation, continuous sexual health counselling, reinforcement of safer practices, adherence monitoring, and periodic reassessment of ongoing need, supported by education, moral guidance, and community engagement across multiple agencies. Without these elements, the use of PrEP risks normalising sustained high-risk behaviour and may contribute to rising rates of other sexually transmitted infections.

    A relevant comparison is nicotine replacement therapy. Nicotine patches are never offered in isolation. They are part of structured cessation programmes with counselling, monitoring, and a clear objective of stopping smoking. PrEP, in contrast, is too often framed as a long-term biomedical solution without a defined behavioural trajectory or exit strategy. This difference is significant and must be addressed in policy and implementation.

    If the Ministry of Health advocates PrEP as part of the national HIV prevention strategy, it carries a responsibility to ensure that its delivery is ethically sound, behaviourally anchored, and evidence informed. Biomedical tools must support behavioural change, moral responsibility, and ethical reflection, not substitute for them, and this must be reinforced through coordinated action across agencies.

    PrEP can play a role in HIV prevention, but only as an adjunct within a structured, monitored, behaviour-focused, values-conscious, and genuinely multi-agency strategy. Sustainable HIV control will not be achieved through medication alone. Behavioural modification, ethical responsibility, moral guidance, and shared societal accountability remain central and must be treated as non-negotiable components of any effective national HIV prevention programme.

  • Planetary Health Through an Islamic Lens

    We live in the Anthropocene, an era defined by human impact on the planet. From greenhouse gases altering the climate to plastics filling our oceans, the footprint of humankind is everywhere. While this age is often spoken of with despair, Islam offers a way of looking at the world that can transform how we live in it.

    The Islamic lens shifts our gaze. Planetary health is not only about survival or managing resources. It is about recognising the Creator, honouring the trust He has placed on us, and living responsibly in balance with the rest of creation.

    Consumption and moderation

    The Anthropocene is marked by overconsumption: fast fashion, fast food, endless energy demands. Islam teaches the opposite: eat and drink, but waste not by excess (Qur’an 7:31). Imagine if Muslims, who number nearly two billion, practiced this daily. Wasting less food, eating simply, and valuing halal and tayyib (wholesome) consumption would reduce emissions from food production, cut landfill waste, and preserve resources. A prophetic tradition teaches us to use water sparingly even while standing by a flowing river. In the Anthropocene, where water stress affects billions, such guidance is transformative.

    Balance in land and resources

    Deforestation, soil degradation, and loss of biodiversity define the Anthropocene. The Qur’an describes creation as set in mīzān (balance) and warns not to disrupt it. Classical Islamic societies applied this through hima (protected zones) where grazing and logging were restricted to preserve ecosystems. Reviving this ethic today could mean Muslims leading in protecting forests, restoring landscapes, and creating green sanctuaries in cities. Restoring balance is not only ecological work but also a fulfilment of our role as khulafā’ (trustees).

    Energy and responsibility

    The burning of fossil fuels drives much of the Anthropocene’s crisis. While large systems are slow to change, Islamic ethics can shape individual and community responsibility. A mosque that runs on solar power, an institution that reduces energy waste, or families that choose public transport over private cars are all examples of acts of worship. When energy use is guided by the principle of amānah (trust), conservation becomes an expression of faith.

    Waste and plastics

    Plastic is a defining pollutant of our age, choking rivers and oceans. Islam directly prohibits wastefulness. The Prophet ﷺ taught that even a small crumb of bread should not be discarded. This mindset, if truly lived, means resisting the throwaway culture of the Anthropocene. Carrying reusable containers, supporting circular economies, and avoiding single-use plastics become not only environmental actions but also spiritual duties.

    Justice across generations

    The Anthropocene has created deep inequities. The poorest often suffer most from climate change while contributing least to its causes. Islam’s principle of justice (ʿadl) and doing good (iḥsān) requires that we think of others, including future generations. Cutting waste, living simply, and advocating for fair policies are ways Muslims can enact intergenerational justice. The Prophet ﷺ said: “If the Final Hour comes while you have a seedling in your hand, plant it.” This teaching encourages us to act responsibly today even if we may not see the results.

    A different Anthropocene

    If Muslims were to live fully by these principles of moderation, balance, justice, and responsibility, the Anthropocene would look very different. Instead of being an age defined by human exploitation, it could become an age defined by human stewardship.

    Planetary health through an Islamic lens is not only about protecting ecosystems but also about aligning our daily lives with the recognition of Allah. In doing so, we rediscover balance, reduce waste, live responsibly, and honour the trust of creation. That is how Islam, if practiced with consciousness, can truly change the world.

  • Ban Vape Now to Protect Public Health in Malaysia

    Executive Summary

    Vaping has emerged as a growing public health and security crisis in Malaysia. Once promoted as a safer alternative to smoking, vaping is now strongly linked to nicotine addiction, youth uptake, serious health harms, and even drug abuse. The enforcement of the Akta Kawalan Produk Merokok Demi Kesihatan Awam 2024 (Act 852), effective 1 October 2024, is a positive step but its implementation has proven difficult. The Ministry of Health (MOH) is now burdened with too many roles including policymaking, regulation, licensing, monitoring, and enforcement. With limited resources, MOH cannot manage this growing threat effectively. An immediate ban on all vape products is necessary. In the longer term, stronger measures must be taken to restrict cigarette use and move toward a smoke-free future.

    Key Issues

    1. Act 852 is difficult to implement

    Act 852 requires comprehensive regulation of vape products, including licensing of all retailers, monitoring of product contents and marketing, control of online and physical sales, and enforcement of advertisement bans. MOH is expected to take full responsibility for these tasks while also managing other core public health functions. This regulatory and enforcement burden is unrealistic and unsustainable.

    2. Vaping is not harm reduction

    Peer-reviewed research in BMJ Open (2023) involving ASEAN tobacco control experts confirms that nicotine vaping products are not viewed as effective cessation tools and are instead considered a public health threat. Most adult vapers in Malaysia (75%) are dual users who continue smoking cigarettes, thus undermining the notion of risk reduction.

    3. Vaping causes serious health harms

    Published studies report that Malaysian vape users commonly experience dry mouth, cough, headaches, and dizziness. More severe outcomes include EVALI (e-cigarette or vaping product use-associated lung injury), which has been documented in Malaysia. Each case costs an estimated RM150,000 to treat, with a projected national healthcare burden of RM368 million annually by 2030 if left unregulated.

    4. Estimated cost burden for Malaysia

    With over 1 million adult daily users in Malaysia (based on 5.4% prevalence), even a 0.1% complication rate requiring hospitalisation would result in 1,000 EVALI cases annually. At RM150,000 per case, this would translate to RM150 million in direct inpatient costs alone, not accounting for outpatient care, productivity loss, or future chronic disease management. Meanwhile, vape tax revenue of RM500 million per year is unlikely to cover the rising health and enforcement costs.

    5. Vaping as a vehicle for drug abuse

    An increasingly alarming trend in Malaysia involves the misuse of vape devices as covert drug delivery tools. Law enforcement and the National Anti-Drugs Agency (AADK) have reported seizures of vape devices containing methamphetamine, ketamine, THC oil, and synthetic cannabinoids. These substances are often inhaled using modified pods or liquids indistinguishable from regular vape products. Students and youths are particularly vulnerable due to the discreet nature of vape use, making enforcement nearly impossible under current regulations. This trend represents both a public health emergency and a national drug control challenge.

    Policy Recommendations

    1. Ban all vape products immediately

    Enact a full ban on the manufacture, import, sale, promotion, and possession of all vape devices and liquids. Strengthen controls on online and cross-border purchases. Declare a national public health and security emergency linked to youth vaping and drug misuse.

    2. Reduce the burden on MOH

    MOH should focus on public health policy, surveillance, and prevention. Licensing, inspections, and enforcement functions should be delegated to other agencies, including municipal councils and the Ministry of Domestic Trade. MOH resources should be redirected toward cessation programmes and school-based health promotion.

    3. Begin long-term restrictions on cigarette sales

    Malaysia should adopt a structured roadmap toward a cigarette-free society. Immediate steps include increased tobacco taxation, plain packaging, limiting retail outlets, and expanding access to evidence-based cessation support. Stronger action is also needed against illicit tobacco trade.

    References

    Gravely, S., Yong, H. H., Reid, J. L., et al. (2022). The prevalence of e-cigarette use in Malaysia: Findings from the 2020 ITC Malaysia Survey. Tobacco Induced Diseases, 20(42). https://doi.org/10.18332/tid/146917 Wong, L. P., Alias, H., Aghamohammadi, N., et al. (2023). Self-reported side effects, dependence, and behaviour in e-cigarette users in Malaysia. Substance Abuse Treatment, Prevention, and Policy, 18(1). https://doi.org/10.1186/s13011-023-00558-7 Hamilton, W. L., et al. (2022). E-cigarette markets and policy responses in Southeast Asia: A scoping review. International Journal of Health Policy and Management, 11(10), 2236–2246. https://doi.org/10.34172/ijhpm.2021.104 De Guia, M. C., et al. (2023). Implications of nicotine vaping products for tobacco control in ASEAN LMICs: In-depth interviews with experts. BMJ Open, 13(9): e073106. https://doi.org/10.1136/bmjopen-2023-073106 Ibrahim, N., et al. (2023). Emerging trends in drug delivery through vaping devices. Frontiers in Public Health, 11:1198763. https://doi.org/10.3389/fpubh.2023.1198763 United Nations Office on Drugs and Crime (UNODC). (2021). Synthetic Drugs and Novel Psychoactive Substances: A Global Threat. https://www.unodc.org Ministry of Health Malaysia. (2024). Cost estimation for EVALI treatment and projections. MOH official communications reported in multiple government briefings.

  • Statistics and Machine Learning in Public Health: When to Use What

    If you’re trained in epidemiology or biostatistics, you likely think in terms of models, inference, and evidence. Now, with machine learning entering the scene, you’re probably hearing about algorithms that can “predict” disease, “detect” outbreaks, and “learn” from data. But while ML offers exciting possibilities, it’s important to understand how it differs from classical statistical approaches—especially when public health decisions depend on more than just prediction.

    Let’s explore how statistics and machine learning differ—not just in technique, but in mindset, use case, and the all-important concept of causality.

    How They Think

    Statistics and machine learning begin with different goals.

    Statistics is built to answer questions like: Does exposure X cause outcome Y? It aims to explain relationships, test hypotheses, and estimate effect sizes. It relies on assumptions—like randomness, independence, and model structure—to ensure that findings reflect the real world, not just the sample at hand.

    Machine learning, in contrast, asks: Given this data, what outcome should I predict? It doesn’t aim to explain but to perform—minimising error and maximising predictive accuracy, even if the relationships are complex or difficult to interpret.

    That’s a major shift. While statistics seeks truth about the population, ML seeks performance in unseen data.

    How They Work

    Statistical methods are grounded in probability theory and estimation. They involve fitting models with interpretable parameters: coefficients, confidence intervals, p-values. The analyst usually specifies the form of the model in advance, guided by theory and prior evidence.

    Machine learning models are trained through algorithms, often using large datasets and iterative techniques to optimise performance. Models like decision trees, support vector machines, and random forests find patterns without assuming linearity or distribution. You don’t always know what the model is “looking at”—you just know if it works.

    There are also hybrid approaches—like regularised regression, ensemble models, and causal forests—that blend the logic of both.

    What They Do Well

    Statistics excels in clarity and rigour. It tells you not just whether something matters, but how much, and with what certainty. It’s ideally suited for:

    Identifying risk factors Estimating treatment effects Designing policy interventions Publishing findings with transparent reasoning

    Machine learning is best when:

    Relationships are non-linear or unknown You have many predictors and large datasets You need fast, repeatable predictions (e.g. real-time risk scoring) The goal is performance, not explanation

    In short, statistics helps you understand, ML helps you predict.

    Where They Fall Short

    Statistics can break down when data gets messy—especially when model assumptions are violated or the number of variables overwhelms the number of observations. It also isn’t built to handle unstructured data like images or free text.

    Machine learning’s biggest limitation is often overlooked: it doesn’t care about causality. A model may predict hospitalisation risk with 95% accuracy, but it doesn’t tell you why. It might rely on variables that are associated, not causal. Worse, it might act on misleading proxies that look predictive but don’t offer actionable insight.

    This matters deeply in public health. Predicting who dies is not the same as preventing death. Models that ignore cause can lead to misguided interventions or unjust decisions.

    Another weakness of ML is interpretability. Many powerful algorithms (like gradient boosting or neural networks) are “black boxes”—hard to explain and harder to justify in policy decisions. While newer tools like SHAP can improve transparency, they still fall short of the clarity offered by traditional statistical models.

    When to Use Each

    Use statistics when:

    Your primary goal is inference or explanation You need to estimate effects or support causal conclusions You’re informing policy or making ethical decisions You want results that are interpretable and reportable

    Use machine learning when:

    Your primary goal is prediction or classification You’re handling high-dimensional or complex data You need scalable automation (e.g. early warning systems) You can validate predictions with real-world data

    Most importantly, if causality matters, don’t rely solely on ML—use statistical thinking or causal ML techniques that explicitly model counterfactuals and assumptions.

    What You Should Expect

    From statistics, expect:

    Clear models with interpretable outputs Transparent assumptions The ability to test hypotheses and quantify uncertainty

    From machine learning, expect:

    High performance with minimal assumptions Useful predictions even when mechanisms are unknown Some loss of interpretability (unless addressed deliberately)

    Just remember: good prediction doesn’t imply good understanding. And good models don’t always lead to good decisions—unless we interpret them wisely.

    A Path Forward for Epidemiologists and Biostatisticians

    Here’s the good news: your training in statistics and epidemiology is not a limitation—it’s your greatest asset. You already understand data, confounding, validity, and generalisability. You’re equipped to evaluate models critically and ask: Does this make sense? Is it actionable? Is it ethical?

    Start small. Try ML approaches that are extensions of what you know—like regularised logistic regression, decision trees, or ensemble methods. Explore tools like caret, tidymodels, or scikit-learn. And when you’re ready to dive deeper, look into causal ML methods like:

    • Targeted maximum likelihood estimation (TMLE)
    • Causal forests (grf)
    • Double machine learning (EconML)
    • DoWhy (for structural causal models)

    The best analysts of the future won’t just be statisticians or ML engineers—they’ll be methodologically bilingual, able to switch between explanation and prediction as the question demands.

    Your role isn’t to replace one with the other, but to integrate both—so that public health remains not just data-driven, but wisely so.

  • Good and Evil of AI in Medicine: Where Is the Boundary?

    Artificial intelligence (AI) is rapidly transforming the field of medicine, offering unprecedented opportunities to improve healthcare delivery, diagnosis, and population health management. However, with its promise comes a risk of harm, particularly when AI systems are poorly designed, implemented without appropriate safeguards, or driven by commercial interests at the expense of public good. This paper explores what constitutes good and evil in medical AI, provides examples of both, and outlines ethical boundaries and practical steps to ensure that AI serves humanity.

    AI in medicine refers to systems designed to assist with tasks such as diagnosis, prognosis, treatment recommendations, and public health surveillance. The good in medical AI lies in its capacity to enhance human well-being, reduce inequalities, and improve healthcare efficiency. AI applications can support clinical decisions, automate routine tasks, and extend healthcare reach to underserved populations (Rajkomar, Dean, & Kohane, 2019). Conversely, the potential for evil emerges when AI contributes to harm, reinforces inequities, or undermines essential human values such as compassion, accountability, and justice. This harm may arise from biased algorithms, opaque decision-making processes, or commercial exploitation that prioritises profit over patient welfare.

    The Goods

    One of the clearest demonstrations of AI’s positive contribution to medicine is in the field of early disease detection. AI systems trained on medical images have been shown to accurately detect conditions such as diabetic retinopathy and tuberculosis. A pivotal study demonstrated that an autonomous AI system could safely and effectively identify diabetic retinopathy in primary care settings, enabling earlier referrals and potentially preventing vision loss (Abràmoff, Lavin, Birch, Shah, & Folk, 2018). In tuberculosis screening, AI-based chest X-ray interpretation tools have been used in high-burden countries to prioritise patients for further diagnostic testing, particularly in settings where human expertise is limited (Codlin et al., 2025). These applications help address gaps in healthcare access and reduce delays in diagnosis and treatment.

    AI has also supported public health surveillance, particularly during emergencies such as the COVID-19 pandemic. AI models combined data from health records, mobility patterns, and social media to predict outbreaks, identify hotspots, and inform targeted interventions. This contributed to more timely and effective public health responses and resource allocation (Bullock, Luccioni, Hoffmann, & Jeni, 2020).

    The Evils

    Despite these benefits, AI has also been linked to harms that can undermine trust and exacerbate health inequities. One of the most pressing concerns is algorithmic bias. AI systems trained on data that do not represent the diversity of patient populations may produce biased outcomes. For example, machine learning tools for dermatology developed primarily using images of lighter skin tones have been found to perform less accurately on darker skin. This can lead to missed or delayed diagnoses in patients from minority groups, reinforcing existing disparities (Adamson & Smith, 2018).

    Commercial exploitation of AI is another area of concern. The rush to monetise AI in medicine has sometimes led to the deployment of systems that are insufficiently transparent or accountable. Proprietary algorithms may operate as black boxes, with their decision-making processes hidden from both clinicians and patients. This opacity undermines informed consent and shared decision-making, and can make it difficult to challenge or review AI-driven recommendations (Char, Shah, & Magnus, 2018).

    Furthermore, there is a risk that excessive reliance on AI could erode the compassionate, human-centred aspects of healthcare. While AI can assist with routine tasks and reduce administrative burdens, it must not be seen as a replacement for human empathy and professional judgement. There is concern that as AI takes on a greater role, the patient-doctor relationship could become depersonalised, weakening one of the core foundations of medical practice (Panch, Szolovits, & Atun, 2019).

    Ethical Boundaries for Responsible AI

    To ensure that AI in medicine serves the common good rather than causes harm, clear ethical boundaries are needed. Transparency is essential. AI systems must be designed in ways that make their decision-making processes understandable and open to scrutiny. This is critical to maintaining trust, supporting informed consent, and enabling clinicians to integrate AI recommendations into their decision-making with confidence.

    Fairness must also be prioritised. Developers need to ensure that AI tools are designed to promote equity rather than exacerbate disparities. This involves using diverse training datasets, actively auditing algorithms for bias, and engaging with communities to understand their needs and perspectives. Bias mitigation should be a central part of AI development and deployment, not an afterthought.

    Accountability is another key principle. Developers, healthcare providers, and regulators share responsibility for ensuring that AI systems are safe, effective, and aligned with ethical principles. Regulatory frameworks should define standards for AI in healthcare and provide mechanisms for monitoring, evaluation, and redress when harm occurs (Char et al., 2018).

    Compassion must remain central to healthcare, even as AI systems become more common. AI should be designed and used to support, rather than replace, the human connection between healthcare professionals and patients. The ultimate goal should be to free clinicians from administrative burdens and allow them to focus on what matters most: the well-being of the people they serve (Topol, 2019).

    Towards Governance and Action

    The development and use of medical AI should be guided by comprehensive national or regional governance frameworks that balance the promotion of innovation with the protection of public interest. Such frameworks need to address issues including data privacy, transparency, bias mitigation, and equitable access. They should be shaped through collaboration between governments, healthcare professionals, technologists, and civil society to ensure that they are both robust and responsive to local contexts and needs.

    Education and capacity building are also essential. Healthcare professionals, public health experts, and policymakers must be equipped with the knowledge and skills needed to engage with AI critically and effectively. Training should address not only technical competencies but also the ethical, legal, and social implications of AI.

    Finally, ongoing research is needed to evaluate the real-world impact of AI in healthcare. This research should assess not only clinical outcomes but also equity, patient safety, and the preservation of humanistic values. It should inform continuous improvement of AI systems and the policies that govern their use (Morley, Floridi, Kinsey, & Elhalal, 2020).

    Conclusion

    AI has the potential to greatly enhance healthcare, improving efficiency, accuracy, and access. However, without appropriate safeguards, it also carries the risk of causing harm, deepening inequities, and eroding core human values. The boundary between good and evil in medical AI lies in how these technologies are designed, implemented, and governed. By upholding principles of transparency, fairness, accountability, and compassion, and by embedding these principles in governance frameworks and professional practice, it is possible to ensure that AI serves as a tool for good in medicine.

    References

    Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digital Medicine, 1, 39.

    Adamson, A. S., & Smith, A. (2018). Machine learning and health care disparities in dermatology. JAMA Dermatology, 154(11), 1247-1248.

    Bullock, J., Luccioni, A., Hoffmann, P. H., & Jeni, L. A. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. Journal of Artificial Intelligence Research, 69, 807-845.

    Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care – Addressing ethical challenges. New England Journal of Medicine, 378, 981-983.

    Chen, I. Y., Szolovits, P., & Ghassemi, M. (2019). Can AI help reduce disparities in general medical and mental health care? AMA Journal of Ethics, 21(2), E167-E179.

    Codlin, A. J., Dao, T. P., Vo, L. N. Q., Forse, R. J., Nadol, P., & Nguyen, V. N. (2025). Comparison of different Lunit INSIGHT CXR software versions when reading chest radiographs for tuberculosis. PLOS Digital Health, 4(4), e0000813.

    Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From what to how: An overview of AI ethics tools, methods and research to translate principles into practices. AI & Society, 36, 59-71.

    Panch, T., Szolovits, P., & Atun, R. (2019). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 020303.

    Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

    Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.

  • The Evolution of Causality in Understanding Disease

    Understanding causality has always been central to the quest for knowledge about health and disease. From the philosophical inquiries of Aristotle to the precision of modern causal inference frameworks, our ideas about what causes disease and how to intervene have evolved through centuries of intellectual effort. This article traces that journey, highlighting key contributions from Aristotle, Al-Farabi, Robert Koch, Austin Bradford Hill, Ken Rothman, and Judea Pearl, and connects their ideas to modern medical practice.

    Aristotle and the origins of causal thinking

    Aristotle (384–322 BCE) introduced what is arguably the first formal framework for understanding causation. He proposed that to fully explain why something exists or happens, one must consider four types of causes: material, formal, efficient, and final causes.

    The material cause is what something is made of. In medicine, this could refer to the tissues, cells, or substances involved in disease. The formal cause is the design or pattern that gives a thing its structure, comparable to the organisation of cells or the genetic blueprint of the body. The efficient cause is the agent or force that produces change. In health, this might be an infectious agent, injury, or environmental exposure. The final cause represents the purpose or goal. For Aristotle, everything in nature had an end or purpose, and in medical terms, this could be metaphorically linked to the goal of health or survival.

    Aristotle’s framework laid the foundation for causal reasoning not only in natural science but also in ethics, politics, and medicine. His approach encouraged generations of thinkers to seek deep, structured explanations for the phenomena they observed.

    Al-Farabi and the integration of causality into Islamic philosophy

    Al-Farabi (872–950 CE), often called the Second Teacher after Aristotle, engaged deeply with Aristotle’s ideas and reinterpreted them through the lens of Islamic philosophy. Al-Farabi did not discard the Four Causes, but he gave them new meaning within a framework that aligned with tawhid, the concept of divine unity.

    Al-Farabi argued that all efficient causes ultimately trace back to the First Cause, God. He extended Aristotle’s final cause beyond natural purposes to include divine wisdom and the moral purpose of human life. In Al-Farabi’s philosophy, causality was not simply a mechanical chain of events but a reflection of divine order and purpose.

    His famous idea that human beings are madani bi al-tab‘i (social by nature) linked causality to the collective pursuit of well-being. In his vision of the Virtuous City (al-Madinah al-Fadilah), knowledge of causes guided not just individual health, but the health of the community and the moral responsibility to promote the common good.

    Robert Koch and the birth of scientific causality in medicine

    The modern scientific study of disease causation began with the work of Robert Koch (1843–1910). Koch introduced formal criteria, known as Koch’s postulates, for identifying the causal relationship between a microorganism and a disease.

    Koch’s postulates required that the microorganism be found in every case of the disease, be isolatable in pure culture, cause the disease when introduced into a healthy host, and be re-isolated from the experimentally infected host. This approach transformed causality in medicine, especially in infectious diseases, from speculative reasoning to testable science.

    Koch’s work exemplified the search for necessary causes in disease. His criteria worked well for infections like tuberculosis but less so for complex diseases that result from multiple interacting factors.

    Austin Bradford Hill and the rise of multifactorial causality

    By the mid-20th century, it had become clear that many diseases did not have single necessary causes. Chronic diseases like cancer, heart disease, and diabetes involved numerous risk factors. Austin Bradford Hill (1897–1991) addressed this complexity by proposing a set of considerations, now known as the Bradford Hill criteria, to help scientists judge whether an observed association is likely to be causal.

    The criteria include strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experimental evidence, and analogy. These considerations reflect the complexity of disease causation and guide researchers in interpreting epidemiological data.

    Hill’s approach helped move the focus from single necessary causes to component causes that contribute to sufficient causal mechanisms. This shift set the stage for modern causal models.

    Ken Rothman and the component cause model

    Ken Rothman (born 1945) further refined the understanding of disease causation by introducing the component cause model, often visualised as the causal pie model. This model describes how a disease can result from different combinations of factors, where each combination forms a sufficient cause.

    In Rothman’s model, component causes represent individual factors (such as smoking, genetic susceptibility, or environmental exposure) that combine to complete a causal mechanism. No single component cause needs to be necessary or sufficient on its own. The model illustrates why many diseases cannot be attributed to a single factor and why prevention strategies must target multiple risk factors.

    Rothman’s work has influenced generations of epidemiologists and public health professionals, providing a practical and visual tool to understand and teach multifactorial causation.

    Judea Pearl and the ladder of causation

    The most recent revolution in causality comes from Judea Pearl (born 1936), whose work has transformed causal inference into a formal, mathematical science. Pearl introduced causal diagrams, known as directed acyclic graphs (DAGs), and structural causal models to make causal relationships explicit and testable in data.

    One of Pearl’s key contributions is the concept of the Ladder of Causation. The ladder describes three levels of causal reasoning. The first level is association, where one observes patterns in data. The second level is intervention, where one reasons about what happens if something is changed or manipulated. The third level is counterfactuals, where one asks what would have happened under different circumstances.

    Pearl’s framework allows researchers to distinguish between mere correlation and true causation and to address complex issues such as confounding, mediation, and effect modification. His work is now central to fields ranging from epidemiology to artificial intelligence.

    Causality in modern medicine

    Understanding causality has practical implications in modern medicine. Few diseases today are thought to have single necessary and sufficient causes. Instead, most conditions arise from combinations of component causes that form sufficient causal mechanisms.

    Take lung cancer as an example. Smoking is neither a necessary cause (because lung cancer can occur in non-smokers) nor a sufficient cause (because not all smokers develop lung cancer). However, smoking is a major component cause that contributes to sufficient causal mechanisms. Interventions that reduce smoking prevalence can prevent many cases of lung cancer, even if they do not eliminate the disease entirely.

    Similarly, understanding that hypertension, high cholesterol, and physical inactivity are component causes of cardiovascular disease guides interventions that target these factors. The insights from causal reasoning help shape prevention strategies, clinical decisions, and public health policies.

    From philosophy to practice

    Tracing the journey of causality thinking from Aristotle to Pearl shows the progression from philosophical reflection to scientific precision. Aristotle’s Four Causes encouraged us to look for deeper reasons behind events. Al-Farabi integrated these ideas with a moral and social vision, reminding us that understanding causes should serve the common good. Koch’s postulates gave us tools to prove necessary causes in infectious diseases. Bradford Hill’s criteria helped navigate the complexity of chronic disease causation. Rothman’s component cause model illustrated the multifactorial nature of disease. Pearl’s ladder of causation and causal models now give us the tools to analyse and act on causal relationships in complex systems.

    Together, these frameworks have helped medicine move beyond treating symptoms to addressing root causes. They also remind us that understanding causality is not only about explaining disease but also about guiding interventions that promote health and well-being.

    References

    Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295–300.

    Koch, R. (1884). Die aetiologie der tuberkulose. Berliner Klinische Wochenschrift, 21, 221–230.

    Pearl, J. (2018). The book of why: The new science of cause and effect. Basic Books.

    Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern epidemiology (3rd ed.). Lippincott Williams & Wilkins.

    VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.

  • Why Vaping Is More Dangerous Than Cigarette Smoking And Why We Must Act Now

    Introduction

    Vaping was once promoted as a safer alternative to smoking. However, emerging evidence indicates that electronic cigarettes (e-cigarettes) may pose equal or even greater health risks compared to traditional cigarettes, particularly among youth. Unlike conventional tobacco products, vape liquids often contain a complex mix of chemicals, heavy metals, and flavouring agents that can bypass detection and regulation. Their sleek designs, enticing flavours, and the misconception of being “less harmful” have made vapes a gateway to nicotine addiction for a new generation.

    In Malaysia, the removal of nicotine from the Poisons Act in 2021 created a legal loophole that allowed unregulated vape products to proliferate. Although the Control of Smoking Products for Public Health Act 2024 was gazetted to address this issue, enforcement remains challenging. The health costs of inaction are escalating and may soon surpass the damages caused by traditional cigarettes.

    What Makes Vaping More Dangerous Than Cigarettes

    1. Rapid Uptake Among Youth and Stronger Addiction

    Vapes appeal to adolescents with thousands of flavours, sleek devices, and a strong presence on social media platforms. Many vape products contain higher nicotine concentrations than traditional cigarettes, delivered via nicotine salts that are more readily absorbed and less irritating, enabling deeper and longer inhalation (Benowitz & Fraiman, 2022).

    In Malaysia, data from the National Health and Morbidity Survey (NHMS) 2022 indicated that 14.9% of adolescents aged 13–17 were current e-cigarette users, with a higher prevalence among males (23.3%) compared to females (6.2%) (Institute for Public Health, 2022). Alarmingly, nearly half of these users initiated vaping before the age of 14.

    2. Exposure to Unregulated Chemicals and Aerosolised Toxins

    While cigarettes have known contents, vapes deliver poorly characterised chemical cocktails. Scientific studies have identified harmful substances in vape aerosols, including formaldehyde, acrolein, lead, cadmium, and nickel. These compounds can cause DNA damage, inflammation, and systemic toxicity (Olmedo et al., 2018).

    Additionally, some compounds in vape aerosols, such as vitamin E acetate and benzyl alcohol, have no history of safe inhalation use and have been implicated in severe lung injuries.

    3. Acute Lung Injuries Not Observed in Cigarette Smokers

    Traditional cigarette smoking is associated with chronic lung diseases developing over the years. In contrast, vaping has been linked to acute, life-threatening lung injuries, such as E-cigarette or Vaping Product Use-Associated Lung Injury (EVALI), occurring after weeks or months of use. Patients suffer respiratory failure requiring ventilation, and some have died (Chand et al., 2023). Such rapid-onset pulmonary toxicity is virtually unheard of with cigarette smoking.

    4. Systemic Health Effects Beyond the Respiratory System

    Vaping has demonstrated harmful effects across multiple organ systems, including the gastrointestinal tract, brain, oral cavity, kidneys, and reproductive system. Studies indicate that vaping alters brain structure and impairs memory, attention, and mood, especially in adolescents (Lopez-Ojeda & Hurley, 2024). Animal models have shown that vaping disrupts intestinal barriers and triggers inflammation (Sharma et al., 2022).

    5. Increased Risk of Smoking Initiation and Dual Use

    Rather than replacing cigarettes, vapes are creating a new generation of dual users, individuals who smoke and vape. Adolescents who vape are more than three times as likely to start smoking cigarettes (Soneji et al., 2017). This trend undermines the potential harm reduction benefits of vaping and perpetuates nicotine addiction.

    6. Unproven Efficacy as a Smoking Cessation Tool

    While some trials suggest that nicotine-containing e-cigarettes may aid smoking cessation, real-world studies show mixed results. A Cochrane review noted low certainty of evidence for sustained cessation and highlighted a high risk of relapse and dual use (Hartmann-Boyce et al., 2022). Most adult vapers do not quit smoking; instead, they continue using both products.

    7. Environmental and Secondhand Exposure Risks

    E-cigarette waste, including cartridges, pods, and lithium batteries, contributes to environmental pollution. Aerosol residues accumulate on indoor surfaces, exposing non-users, especially children and pregnant women, to passive vaping. The World Health Organisation has declared secondhand exposure to e-cigarette aerosol unsafe (Jankowski et al., 2019).

    Conclusion

    Scientific evidence confirms that vaping poses serious health risks across the respiratory, cardiovascular, neurological, and gastrointestinal systems. Youth are disproportionately affected, and the claimed benefits of vaping, especially for smoking cessation, are not supported by strong data. Enforcement difficulties undermine regulatory measures, and the mounting health and environmental consequences are a concern. A comprehensive ban on vape products is a necessary and urgent public health action.

    References
    • Benowitz, N. L., & Fraiman, J. B. (2022). Clinical pharmacology of electronic nicotine delivery systems (ENDS): Implications for benefits and risks in the promotion of smoking cessation. Journal of Clinical Pharmacology, 62(1), 1–14. https://doi.org/10.1002/jcph.1982
    • Chand, H. S., Muthumalage, T., Maziak, W., & Rahman, I. (2023). Pulmonary toxicity and the pathophysiology of electronic cigarette, or vaping product, use associated lung injury. Annals of the American Thoracic Society, 20(2), 177–185. https://doi.org/10.1513/AnnalsATS.202209-796ST
    • Hartmann-Boyce, J., McRobbie, H., Lindson, N., Bullen, C., Begh, R., Theodoulou, A., Notley, C., Rigotti, N. A., Turner, T., Butler, A. R., & Hajek, P. (2022). Electronic cigarettes for smoking cessation. Cochrane Database of Systematic Reviews, (11). https://doi.org/10.1002/14651858.CD010216.pub7
    • Institute for Public Health. (2022). National Health and Morbidity Survey (NHMS) 2022: Adolescent Health Survey Highlights. https://iku.gov.my/images/nhms-2022/Report_Malaysia_nhms_ahs_2022.pdf
    • Jankowski, M., Brożek, G., Lawson, J., Skoczyński, S., & Zejda, J. E. (2019). E-cigarettes are more addictive than traditional cigarettes—A study in highly educated young people. International Journal of Environmental Research and Public Health, 16(13), 2279. https://doi.org/10.3390/ijerph16132279
    • Lopez-Ojeda, W., & Hurley, R. A. (2024). Vaping and the brain: Effects of electronic cigarettes and e-liquid substances. The Journal of Neuropsychiatry and Clinical Neurosciences, 36(1), A41–A45. https://doi.org/10.1176/appi.neuropsych.20230184
    • Olmedo, P., Goessler, W., Tanda, S., Grau-Perez, M., Jarmul, S., Aherrera, A., Chen, R., Hilpert, M., Cohen, J. E., Navas-Acien, A., & Rule, A. M. (2018). Metal concentrations in e-cigarette liquid and aerosol samples: The contribution of metallic coils. Environmental Health Perspectives, 126(2), 027010. https://doi.org/10.1289/EHP2175
    • Sharma, A., Lee, J. S., & Dela Cruz, C. S. (2022). E-cigarettes compromise the gut barrier and trigger inflammation. iScience, 25(2), 103818. https://doi.org/10.1016/j.isci.2021.103818
    • Soneji, S., Barrington-Trimis, J. L., Wills, T. A., Leventhal, A. M., Unger, J. B., Gibson, L. A., Yang, J., Primack, B. A., Andrews, J. A., Miech, R. A., Spindle, T. R., Dick, D. M., Eissenberg, T., Hornik, R. C., Dang, R., & Sargent, J. D. (2017). Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: A systematic review and meta-analysis. JAMA Pediatrics, 171(8), 788–797. https://doi.org/10.1001/jamapediatrics.2017.1488

  • Separating Prescriptions and Medicines: Can It Reduce Healthcare Costs?

    Healthcare costs in Malaysia are rising, and one of the main reasons is the high price of medicines. A policy that is often discussed as a possible solution is dispensing separation (DS). This means doctors will only diagnose and prescribe medicines, while pharmacists will be the ones to supply the medicines.

    Right now in Malaysia, especially in private clinics, doctors can still give medicines directly to patients after a consultation. But many believe this system can lead to doctors prescribing more than necessary, as they also profit from selling medicines. In many developed countries, DS has already been in place for years to make healthcare more transparent and safe for patients.

    What Happened in Korea and Taiwan?

    South Korea introduced DS in the year 2000 to reduce overprescribing and the rising cost of medicines. After the policy was introduced, the cost of medicines per visit went down. However, other charges such as consultation fees and dispensing costs increased. In the end, the total cost of healthcare for patients did not go down.

    Taiwan started DS around 1997. Studies showed that the number of medicines prescribed and the cost per visit went down, especially in clinics that did not have their own pharmacy. But again, the total cost of healthcare remained mostly the same. These cases show that while DS can reduce the cost of medicines, it does not automatically reduce the total cost of care unless other changes are made.

    What About Malaysia?

    If Malaysia wants to introduce DS, there are some important points to consider. Right now, many patients only pay one fee at private clinics. This includes both the consultation and the medicine. With DS, patients may have to pay twice – once to see the doctor and again to get their medicine at a pharmacy. Without price control or proper insurance coverage, this can make treatment more expensive for patients.

    Small clinics, especially in rural areas, depend on income from selling medicines. If DS is introduced without financial support, some clinics may not survive. This will make it harder for people in remote areas to access healthcare.

    Another issue is that some pharmacies now offer health screening and even give medicines without proper prescriptions. If this continues without control, it can lead to wrong treatments and higher long-term costs due to complications. So, there must be stronger enforcement to ensure only qualified doctors make medical diagnoses and that more medicines can only be given with a proper prescription.

    A Policy Made in Desperation?

    There is growing concern that the push for DS may be driven less by long-term healthcare planning and more by political pressure. When the cost of living goes up, people expect the government to act. In such times, introducing a policy like DS may be seen as a quick way to show that something is being done to help reduce costs, even if the real effect on total healthcare spending is small. While the intention may be good, hasty implementation without a full understanding of the consequences can make things worse.

    What Can Be Done?

    DS is not the only way to control healthcare costs, but it can help if introduced correctly. It must come with other changes such as:
    • Clear and fair consultation fees,
    • Strong rules to ensure only doctors can diagnose,
    • A longer list of prescription-only medicines,
    • Better healthcare financing, like insurance or subsidies,
    • And more pharmacies in both cities and rural areas.

    Conclusion

    Separating prescriptions and medicine supply is not an easy step, but it is worth thinking about for a better, safer, and fairer healthcare system. We can learn from countries like Korea and Taiwan, but we must adjust the plan to fit our local needs. If done carefully and supported by proper policies, DS can bring long-term benefits for patients and help improve the whole healthcare system in Malaysia. But it must be done for the right reasons, not just as a quick response to public pressure.

  • The Evolution of Research on Vape

    The increasing prevalence of vaping, particularly among adolescents and young adults, has sparked significant research interest in its potential health implications, especially regarding mental health and addiction. This synthesis aims to chronologically highlight the progression of research on the dangers of vaping by organizing studies according to emerging questions and findings.

    In early studies, concerns were primarily centered on nicotine dependence and the health risks associated with e-cigarettes. (Foulds et al., 2014) conducted a foundational study emphasizing the need for systematic data collection to understand e-cigarette use patterns and their health impacts. This study initiated a series of research questions related to user characteristics and product safety, culminating in a growing recognition of the unique health risks posed by e-cigarettes, especially among young populations (Foulds et al., 2014).

    Midway through the 2010s, findings increasingly linked vaping with psychological disorders. (Becker & Rice, 2021) highlighted how vaping among adolescents correlates with mental health issues, suggesting that physical and behavioral health risks emerged alongside the rising tide of e-cigarette popularity (Becker & Rice, 2021). Furthermore, (Javed et al., 2022) underscored the connection between vaping culture and adverse mental health outcomes, specifically noting the appeal of flavored e-cigarettes to school-aged youths (Javed et al., 2022).

    This period raised critical questions regarding whether e-cigarette use functioned as both a gateway to traditional smoking and a contributor to existing mental health struggles.

    As research continued to evolve, the impact of vaping on both mental health and substance use behaviors became clearer. Studies like those by (Morean et al., 2015; and Becker et al., 2020) explored how e-cigarettes were used by high school students for both nicotine and cannabis, raising alarms over polysubstance use and its potential to exacerbate cognitive deficits and other mental health issues (Morean et al., 2015; Becker et al., 2020). The growth of such usage patterns provoked inquiries regarding the adequacy of current health policies and intervention strategies aimed at youth tobacco control.

    By 2020 and beyond, researchers began to focus on dual vaping behaviors, assessing the interplay between nicotine and cannabis use among adolescents. (Lanza et al., 2020) reported that the prevalence of dual-use further complicated health outcomes, attributing risks such as cognitive impairment and increased substance dependence to this behavior (Lanza et al., 2020). This segment of research established critical precursors to understanding the holistic ramifications of vaping on adolescent health, emphasizing the need for nuanced public health messaging.

    Current research emphasizes the role of psychological factors as significant predictors of vaping uptake and continuation. Studies by (Jongenelis et al., 2024; and Oliver et al., 2023) have demonstrated that perceptions of harm and existing mental health symptoms significantly influence both vaping intentions and behaviors among youths (Jongenelis et al., 2024; Oliver et al., 2023).

    These findings have led to increased urgency in addressing vaping from a preventive health perspective, raising questions regarding the effectiveness of educational interventions and health promotion strategies within school systems (Thomas et al., 2024).

    The evolution of research on vaping highlights a complex interplay between substance use, mental health, and public health implications. As vaping continues to rise among youth, ongoing studies will need to address the changing landscape of both products and user behaviors, ensuring that health initiatives effectively mitigate the risks associated with e-cigarette use. This synthesis underscores the dangers of vaping as evidenced by existing literature and encourages further exploration into tailored interventions that address the unique challenges posed by this rapidly evolving public health issue.

    References

    Becker, T. and Rice, T. (2021). Youth vaping: a review and update on global epidemiology, physical and behavioral health risks, and clinical considerations. European Journal of Pediatrics, 181(2), 453-462. https://doi.org/10.1007/s00431-021-04220-x

    Becker, T., Arnold, M., Ro, V., Martin, L., & Rice, T. (2020). Systematic review of electronic cigarette use (vaping) and mental health comorbidity among adolescents and young adults. Nicotine & Tobacco Research, 23(3), 415-425. https://doi.org/10.1093/ntr/ntaa171

    Foulds, J., Veldheer, S., Yingst, J., Hrabovsky, S., Wilson, S., Nichols, T., … & Eissenberg, T. (2014). Development of a questionnaire for assessing dependence on electronic cigarettes among a large sample of ex-smoking e-cigarette users. Nicotine & Tobacco Research, 17(2), 186-192. https://doi.org/10.1093/ntr/ntu204

    Javed, S., Usmani, S., Sarfraz, Z., Sarfraz, A., Hanif, A., Firoz, A., … & Ahmed, S. (2022). A scoping review of vaping, e-cigarettes and mental health impact: depression and suicidality. Journal of Community Hospital Internal Medicine Perspectives, 12(3), 33-39. https://doi.org/10.55729/2000-9666.1053

    Jongenelis, M., Gill, M., Lawrence, N., & Wakefield, C. (2024). Quitting intentions and behaviours among young australian e‐cigarette users. Addiction, 119(9), 1608-1615. https://doi.org/10.1111/add.16530

    Lanza, H., Barrington‐Trimis, J., McConnell, R., Cho, J., Braymiller, J., Krueger, E., … & Leventhal, A. (2020). Trajectories of nicotine and cannabis vaping and polyuse from adolescence to young adulthood. Jama Network Open, 3(10), e2019181. https://doi.org/10.1001/jamanetworkopen.2020.19181

    Morean, M., Kong, G., Camenga, D., Cavallo, D., & Krishnan‐Sarin, S. (2015). High school students’ use of electronic cigarettes to vaporize cannabis. Pediatrics, 136(4), 611-616. https://doi.org/10.1542/peds.2015-1727

    Oliver, A., Kossowsky, J., Minegishi, M., Levy, S., & Weitzman, E. (2023). The association of vaping with social/emotional health and attitudes toward covid-19 mitigation measures in adolescent and young adult cohorts during the covid-19 pandemic. Substance Abuse, 44(1-2), 73-85. https://doi.org/10.1177/08897077231165860

    Thomas, L., McCausland, K., Leaversuch, F., Freeman, B., Wolf, K., Leaver, T., … & Jancey, J. (2024). The school community’s role in addressing vaping: findings from qualitative research to inform pedagogy, practice and policy. Health Promotion Journal of Australia, 36(1). https://doi.org/10.1002/hpja.895