The UK’s generational tobacco ban might not work. I’m supporting it anyway.

As the parent of two little girls, I often think about how their childhood is different from mine. The seven-year-old is learning about AI at school. The five-year-old is given internet-based homework every week. And they are both absolutely repulsed by the idea of smoking.

That was not the prevailing sentiment when I was young. My parents smoked. The customers at our family’s restaurant smoked. Cartoon characters smoked. My friends and I would buy little cigarette-box-shaped packets of sugary white sticks and pretend to smoke in the playground. Smoking was a central part of our culture.

Which is why the UK’s recent passing of a generational sales ban on tobacco products feels like such a big deal. As part of the Tobacco and Vapes Act 2026, retailers are prohibited from selling tobacco products to anyone born after January 1, 2009, in perpetuity. It doesn’t matter when those people turn 18—or 38 or 68, for that matter. It will always be illegal to sell to anyone born after that date.

This is what’s described as an “endgame” approach. While many tobacco control strategies—such as taxation or gory imagery—aim to reduce consumption, policies like the UK’s are designed to eliminate it entirely. It’s a new approach, and no one knows whether it will work.

The Maldives was the first country to implement a generational smoking ban, in November last year. It’s too soon to say how that has panned out.

Nor do we know if these laws will even last. In 2022, New Zealand passed a similar generational sales ban as part of a broader anti-smoking law. But it was never enacted—the law was repealed by a new government in February 2024.

In the UK, both major parties support the ban. But Nigel Farage, whose right-wing party has seen a recent surge in support, has promised that “the generational smoking ban will not last long if Reform gets the chance to start rebuilding our mismanaged country.”

Chris Bostic, an attorney and former policy director for the advocacy group Action on Smoking and Health, says he and his colleagues began promoting the idea of a generational ban in the United States 11 years ago. Back then, they struggled to win support, even from major health charities. “People said we were crazy … [and] that this was impossible,” he says. Opponents argued that bans would infringe on personal freedoms.

“The public health argument is: Well, what about freedom from addiction?” says Britta Matthes, a tobacco control researcher at the University of Bath in the UK. Most people who smoke began when they were teenagers, want to quit, and wish they’d never started. Tobacco is arguably the most harmful consumer product of all time. It will kill half its users who don’t quit, according to the World Health Organization.

It also kills people who don’t smoke. Of the 7 million who die from tobacco every year, 1.6 million are nonsmokers who were exposed to secondhand smoke, according to the WHO.

Generational sales bans are a long-term strategy that will only protect future smokers. Most experts agree that people who already smoke should be a main consideration for any policy, and that a multipronged approach is probably the best way to go. Janet Hoek at the University of Otago, who has explored tobacco control policies in New Zealand, believes that enforcing very low limits on nicotine levels and banning filters—an environmental scourge that does not make smoking safer, as many people believe—might be a “powerful combination,” for example.

But preventing teenagers from starting to smoke in the first place is an enticing prospect, even among the majority of people who smoke. And it’s starting to look a lot less radical.

The US has quietly been making progress on a smaller scale. Since 2021, Brookline, a town in the Boston area, has banned the sale of tobacco products to anyone born after January 1, 2000. The idea has spread. Today there are 23 towns in Massachusetts with similar bans, says Bostic. Nine towns across Minnesota, New York, and California have implemented other endgame policies.

The UK law has normalized the idea more than ever, he adds. His colleagues are already fielding calls from health agencies around the world. “People [are] saying, Wow I can’t believe the UK just did this—can we do this here?” he says.

Norms change. Like many other millennials, I vividly remember my first night out after a ban on indoor smoking took effect. My clothes didn’t stink! My hair still felt clean! And my throat wasn’t scratchy the next morning! Now that’s just normal. I hope a tobacco-free world can be the new normal for my kids.

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The relationship between mobile phone addiction and depression, anxiety among Chinese college students: the mediating role of friendship quality and the moderating effect of preference for solitude

BackgroundThe university stage represents a critical period for the development of individual mental health. Mobile phone addiction is closely linked to depression and anxiety among college students, and both friendship quality and preference for solitude are tightly associated with college students’ mobile phone addiction and emotional health. Therefore, this study aimed to investigate the relationships and internal mechanisms among mobile phone addiction, friendship quality, preference for solitude, depression and anxiety in college students.MethodsA total of 1083 Chinese college students (58.2% female; mean age = 19.87 ± 1.692 years) were included as participants. Data were collected using the Mobile Phone Addiction Index, Friendship Quality Questionnaire, Preference for Solitude Questionnaire, and Depression Anxiety Stress Scale. Data processing and analyses were conducted using SPSS 26.0 and the PROCESS macro.Results(1) Mobile phone addiction was significantly negatively correlated with friendship quality, and significantly positively correlated with both depression and anxiety; friendship quality was significantly negatively correlated with depression and anxiety; preference for solitude was significantly positively correlated with depression and anxiety. (2) Mobile phone addiction not only directly and positively predicted depression and anxiety among college students, but also predicted depression and anxiety through the mediating role of friendship quality. (3) The direct effect of mobile phone addiction on depression and the mediating effect of friendship quality in the relationships between mobile phone addiction and depression/anxiety were both moderated by preference for solitude, whereas the moderating effect of preference for solitude on the association between mobile phone addiction and anxiety was not significant.ConclusionFriendship quality serves as an important mediating pathway between mobile phone addiction and depressive and anxiety symptoms among Chinese college students. Preference for solitude may amplify the associations of mobile phone addiction with poorer friendship quality and elevated depressive symptoms.

Jails are the frontline in fielding dangerous new type of drug withdrawal

When Lillian was booked into a rural Pennsylvania jail, she couldn’t stop vomiting. As she showered and changed into her jail uniform, “brain zaps” kept destabilizing her. “The corrections officer watching me kept having to grab me steady or I would have dropped and hit the floor,” Lillian recalled. 

She was withdrawing from fentanyl laced with medetomidine, a powerful tranquilizer that started to spread as an adulterant in the illicit opioid supply two years ago. Medetomidine causes excruciating, complicated withdrawal symptoms, often within hours of someone’s last dose, and many institutions are ill-prepared to treat them. The treatment gap is especially acute in carceral settings. 

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Applications of machine learning algorithms to detect digital addiction: a meta-analysis

Digital addiction (DA) has emerged as a significant global concern, yet traditional diagnostic methods relying on self-report questionnaires face subjective bias and threshold inconsistencies. Recent advances in machine learning (ML) offer promising alternatives for automated DA detection. This study conducted a systematic meta-analysis of 64 eligible studies (75 independent datasets; N = 165,624), employing both single-group proportion and bivariate diagnostic test accuracy (DTA) models. The pooled classification accuracy was 0.87 (95% CI [0.85, 0.90]), and the DTA framework yielded a robust AUC of 0.92, with balanced sensitivity and specificity (both 0.86). Subgroup analyses showed high accuracy across subtypes, particularly for internet (0.90) and social media addiction (0.86). Accuracy was comparable between survey-based and physiological data, though physiological markers demonstrated superior specificity (0.90). These findings underscore the potential of ML-driven tools as scalable screening instruments while emphasizing the need for representative sampling and standardized diagnostic criteria to advance digital mental health practice.

QAIAx (AIhealth4U) – AI Public Health Central: Microcity-A (re Quantum AI Agency Aka AI City Hall Project, UPSTO App Nos. 64/074,526, 64/063,557, 63/903,181, 63/729,428

Conditions: Asperger’s Disorder; Asperger Disorder; Autism Disorder; Autism; ADHD – Attention Deficit Disorder With Hyperactivity; ADHD; ASD; Alcohol Abuse/Dependence; Alcohol Addiction; Alcohol and Other Drug Use Disorders; Alcohol and Other Substance Use Prevention; Gambling Addiction; Gambling Disorder; Sex Abuse; Sex Behavior; Sex Crimes; Sex Disorder; Sex Disorders; Gender Dysphoria, Adult; Eating Behavior Disorders; Narcotic-Related Disorders; Narcotic Addiction; Narcissism; Psychiatric Disorder; Psychedelic Effects in Healthy Volunteers; Psychedelic Experiences; Psychedelic Drug Dependence; Marijuana Use Disorder; Marijuana Abuse and Dependence; Smoking (Tobacco) Addiction; Smoking Among Youth; Smoking Abstinence; Abstinence, Sex; Opiate Substitution Treatment; Opioid Abuse (Disorder); Opioid Abuse and Addiction; Cocaine Abuse; MDMA (‘Ecstasy’); Addiction Disorders; Homeless and Low Incomes People, Refugees; Homelessness; Reliability and Validity; Anger Problems; Child Abuse, Sexual

Interventions: Behavioral: AI City Hall Project (AIhealth4u – Public Health Central); Behavioral: AI City Hall Project (QAIAx Microcity A – AI Public Health Central)

Sponsors: Veterans Recovery Network Inc.; U.S. Special Operations Command; Central Virginia VA Health Care System; AI-119 Vulcan Project Research & Educational Technology Company (fka Henry Nanpei Academy Project)

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