Sanctions, on Rise, Are as Deadly as Armed Conflict, Study Says
Unilateral and economic sanctions imposed by the US and the European Union lead to a substantial increase in mortality that disproportionately hurts children younger than five years old, the study published in the Lancet Global Health journal found. Sanctions can hobble public health provision and keep humanitarian organizations from operating effectively, weighing on the death toll.
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Medscape
3 minutes ago
- Medscape
Can Treating Siblings Aid Azithromycin's Effect on Infants?
TOPLINE: Mass administration of azithromycin to both infants aged 1-11 months and children aged 12-59 months reduced infant mortality compared with treating infants alone. Although the interaction effect was not statistically significant, mortality appeared to be lower among infants living with treated older siblings. METHODOLOGY: In this secondary analysis of a cluster-randomized trial, researchers evaluated whether mass azithromycin administration reduced infant mortality and examined how that benefit varied when an older sibling in the same household also received azithromycin. They included 98,969 infants aged 1-11 months with a minimum weight of 3 kg and no known macrolide allergies who received biannual 20-mg/kg doses of oral azithromycin or placebo from November 2020 to July 2023 in Niger. Participants were divided into three arms: infants and children aged 1-59 months on azithromycin (child arm), infants aged 1-11 months on azithromycin and children aged 12-59 months on placebo (infant arm), and children aged 1-59 months on placebo (placebo arm). The primary outcome was the community-level rate of all-cause mortality among infants aged 1-11 months after 2 years of azithromycin distribution, stratified by the presence or absence of older siblings (12-59 months) in the household. TAKEAWAY: The mortality rate of infants per 1000 person-years at risk was lowest in the child arm at 18.5 deaths (95% CI, 16.7-20.4) compared with 22.3 deaths (95% CI, 20.0-24.7) in the infant arm and 23.9 deaths (95% CI, 21.6-26.2) in the placebo arm. In communities receiving azithromycin vs placebo, mortality among infants aged 1-11 months declined by 23% (95% CI, 11%-33%). Of this reduction, 23.5% (95% CI, 1.2%-72.7%) was attributable to treating infants directly, while 76.5% (95% CI, 27.3%-98.8%) resulted from also treating children aged 12-59 months. Among infants with older siblings, the incidence rate ratio comparing child and infant arms was 0.78 (95% CI, 0.65-0.93), while it was 0.91 (95% CI, 0.73-1.15) for those without siblings (P for interaction = .26). IN PRACTICE: 'The main trial results support implementation of azithromycin MDA [mass drug administration] to infants and children aged 1-59 months to achieve the greatest benefit. The analyses presented herein further support this conclusion and suggest that the spillover mechanism may include treating older siblings,' the authors wrote. SOURCE: This study was led by Ahmed M. Arzika, MPH, Centre de Recherche et d'Intervention en Santé Publique, Birni N'Gaoure, Niger. It was published online on July 10, 2025, in JAMA Network Open. LIMITATIONS: Limited statistical power restricted the detection of interaction effects in subgroup analyses. Even in high ‐ mortality settings, deaths remain too infrequent to reveal significant differences between subgroups. The large simple trial design prevented the evaluation of cause-specific mortality. Thus, analysis of specific causes of death likely associated with spillover effects of the mass administration of azithromycin could not be determined. DISCLOSURES: This study received support from the Bill & Melinda Gates Foundation and National Institute of Allergy and Infectious Diseases of the National Institutes of Health. Pfizer Inc donated the azithromycin and placebo used in the trial. A few authors reported receiving grants from the funding organization. This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.
Forbes
an hour ago
- Forbes
How To Keep Your Child From Dying In A Hot Car This Summer
It's one of the most excruciating, painful ways to die - and each death is entirely avoidable. In the last 25 years, more than 1,010 children and infants have died of heatstroke after either being left in hot cars or due to becoming trapped after getting into unattended, unlocked cars, say experts. It's happening more often, too. In 2024, 39 children died of heatstroke in vehicles — up 35% from 2023, according to 2018 and 2019 brought a record number of such hot car deaths — 53 children died each year. That's the most in at least 25 years. 52.6% died when forgotten by a parent or caregiver, 23.8% gained access to a vehicle on their own, 21.8% were left in the vehicle on purpose, and 1.9% were 'unknown.' Here are some vital things to know about children and heatstroke in automobiles - and how to possibly save a life. A child's body temperature rises much faster than an adult's. When a child is left in a vehicle, that child's temperature can rise quickly — and the situation can quickly become dangerous. Heatstroke begins when the core body temperature reaches about 104 degrees. Death occurs at a core body temperature of 107 or above. Rolling Windows Down Or Parking In Shade Doesn't Lower The Temperature In The Car The very reason some people feel comfortable leaving their kid in a car on a hot day is because they're dashing into 7-11 or similar. But The temperature increase is highest in the first 15-30 minutes after a car is left parked in the sun. The objects in a car's interior such as seats, carpeting, and the dashboard also absorb some of that heat. A Stanford University study found that on a sunny day with temperatures ranging from 72°F to 96°F (22°C to 35°C), the temperature was an average of 40°F higher (8°C) inside the cars they tested than it was outdoors. Get in the habit of checking the back seat - train yourself After living in NYC for most of my life, I never get off a subway, leave a table in a restaurant or get out of a vehicle without looking back to see if there's a wallet, keys, phone or anything at all left behind. Make it a habit to check your entire vehicle — especially the back seat — before locking the doors and walking away. Some advocate placing a personal item like a purse or briefcase or even one shoe in the back seat as another reminder to look before you lock the car. But those are gimmicks. Take responsibility to train yourself to look in the backseat each and every time, the same as you check to see if the stove is off before leaving the house, or that you have wallet, keys and phone. Store car keys and fobs out of a child's reach and teach children that a vehicle is not a play area. The inside of a car is a fascinating area for a kid. It's forbidden, first - kids don't get to drive, so they love to sit behind the wheel, push the buttons, yank the steering wheel and more when they're in there alone. But the same as you teach them that Mommy or Daddy's laptop or the cat's tail or anything hot shouldn't be touched, teach them that the inside of a car isn't a playground. Have a special place in the house for the keys, out of reach of curious little fingers. Get in the habit of locking your vehicle, every time. Always lock your car when you aren't using it. Even if you don't have a child of your own, a child in your neighborhood could get into your unlocked vehicle. Over 230 children have died from vehicular heatstroke since 1998 because they gained access to a vehicle and became trapped. Even if you're just dashing into a convenience store, lock the car and take the keys. Don't assume a professional caregiver knows better than to leave kids in a hot car Most caregivers are responsible, but they are the same as everyone else in that they are capable of making mistakes, forgetting and being negligent. On particularly scorching days, don't be afraid to offer a gentle reminder to the person in charge of your child's welfare to refrain from leaving your kid in a hot car, even for a few minutes. Butt in when necessary If you see a child alone in a locked car, act immediately and call 911. A child in distress due to heat should be removed from the vehicle as quickly as possible and rapidly cooled. Don't hesitate to break a window if you have to. Seconds can count. Washington passed a bill earlier this year preventing prosecution or lawsuits when a civilian breaks a car window to relieve a human being or animal from suffering the effects of extreme heat, though certain criteria must apply. Other states should follow suit.
Forbes
an hour ago
- Forbes
Monetizing Generative AI In Healthcare: Two Competing Paths
Generative AI is advancing faster than any technology in modern memory. When OpenAI released ChatGPT in late 2022, few in the medical community took notice. Most doctors saw it as a novelty, perhaps useful for administrative tasks or as a basic reference tool, but too unreliable for clinical care. In just a few years, that perception has shifted. Today's GenAI tools from companies like Google, Microsoft and Nvidia outperform most physicians on national medical exams and clinical challenges. Although none are currently recommended for patient use without physician oversight, that restriction is likely to be lifted soon. GenAI capabilities continue to double annually, and OpenAI's GPT-5 model is expected to launch within days. That raises a pressing question: How will generative AI be monetized in healthcare? Two competing visions are emerging. The first follows a familiar playbook: tech companies developing new FDA-approved tools for diagnosis and treatment. The second is clinician-led and would allow patients to use readily available, inexpensive large language models to manage their chronic diseases and assess new symptoms. To understand the advantages of each approach, it's first helpful to examine how different generative AI tools are from the AI applications used in medicine today. The Current Standard: Narrow AI Healthcare's traditional AI tools rely on technologies developed more than 25 years ago. Known as 'narrow AI,' these models are trained on large datasets to solve specific, well-defined problems. These applications are developed by training AI models to compare two datasets, detect dozens of subtle differences between them and assign a probability factor to each. For reliable results, the training data must be objective, accurate and replicable. That's why nearly all narrow AI tools are applied in visual specialties like radiology, pathology and ophthalmology — not cognitive fields that depend on subjective entries in the electronic medical record. Although these tools outperform clinicians, they are 'narrow' and can be used only for the specific task they were designed to complete. Take mammography, for example. Early-stage breast cancer can mimic benign conditions such as fibrocystic disease, causing radiologists to disagree when interpreting the same image. A narrow AI model trained on 10,000 mammograms (half from patients with confirmed cancer and half without) can detect far more differences than the human eye, resulting in 10-20% greater diagnostic accuracy than doctors. To evaluate narrow AI tools, the FDA assesses both the accuracy of the training data and the consistency of the tool's performance, similar to how it approves new drugs. This approach, however, doesn't work for generative AI. The development process and resulting technology are fundamentally different. The New Opportunity: Generative AI Today's large language models (LLMs) like ChatGPT, Claude and Gemini are trained on the near-totality of internet-accessible content, including thousands of medical textbooks and academic journals. This broad, in-depth training enables LLMs to respond to virtually any medical question. But unlike narrow AI, their answers are highly dependent on how users frame their questions, prompt the model and follow up for clarification. That user dependency makes traditional FDA validation, which relies on consistent and repeatable responses, effectively impossible. However, the multimodal nature of generative AI (working across writing, audio, images and video) opens a wide range of opportunities for clinicians. For example, I recently spoke with a surgeon who used ChatGPT to interpret a post-operative chest X-ray. The AI not only provided an accurate assessment, but it did so immediately, a full 12 hours before the radiologist issued a formal report. Had there been a serious problem that the surgeon failed to recognize, the LLM would have proven lifesaving. Even without formal approval, physicians and patients increasingly consult GenAI for medical expertise. While there's still debate over whether LLMs are ready for independent clinical use by patients, the pace of progress is breathtaking. Microsoft recently reported that its Diagnostic Orchestrator tool achieved 85.5% accuracy across 304 New England Journal of Medicine case studies, compared to only 20% for physicians. How Will Generative AI Be Monetized In Medicine? As generative AI becomes more powerful each year, two distinct approaches are emerging for how it might be monetized in healthcare. Each model has trade-offs: Medical care often fails to deliver optimal outcomes at a price patients can afford. This monetization model would seek to close those gaps. Take diabetes, where fewer than half of patients achieve adequate disease control. The result: hundreds of thousands of preventable and costly heart attacks, kidney failure and limb amputations each year. In other cases, patients struggle to access timely, affordable advice for new symptoms or for managing their multiple chronic conditions One way to solve these problems would be through GenAI tools built by entrepreneurial companies. Venture funding would allow startups to apply a process called distillation to extract domain-specific knowledge from open-source foundation models like DeepSeek or Meta's LLaMA. They would then refine these models, training them on radiologist interpretations of thousands of X-rays, transcripts from patient advice centers or anonymized recordings of conversations with patients. The result: specialized generative AI tools designed to address specific gaps in care delivery. Developing these tools will be expensive, both in terms of model training and the FDA approval process. Companies would also face legal liability for adverse outcomes. Still, if successful, these tools would likely command high prices and deliver substantial profits. Although less profitable than an FDA-approved device or bot, the second path to GenAI monetization would prove easier to develop, much less expensive and far more transformative. Rather than developing new software, this approach empowers patients to use existing large language models (ChatGPT, Gemini or Claude) to access similar expertise. In this model, clinicians, educators or national specialty societies — not private companies — would take the lead and share in the benefits. They would create low-cost instructional materials for various patient-learning preferences ranging from digital guides and printed pamphlets to YouTube videos and short training courses. These resources would teach patients how to use any of the publicly available large language models safely and effectively. Using these educational tools, patients would learn how to enter blood glucose readings from home monitors or blood pressure measurements from electronic cuffs. The large language model would then assess their clinical progress and suggest whether medication adjustments might be needed. Patients could also ask questions about new symptoms, receive likely diagnoses and learn when to seek immediate medical care. In conjunction with their physician, they could use GenAI's image and video capabilities to identify signs of wound infections and alert their surgeons. Those individuals with chronic heart failure could monitor their condition more closely and catch signs of decompensation early — allowing cardiologists to intervene before hospitalization becomes necessary. A GI specialist could identify complex intestinal conditions using daily patient-reported inputs, or a neurologist could diagnose ALS by analyzing videos of a patient's gait. Unlike startup models that require tens of millions in funding and FDA approval, these educational tools could be developed and deployed quickly by doctors and other clinicians. Because they teach patients how to use existing tools rather than offer direct medical advice, they would avoid many regulatory burdens and face reduced legal liability. And with 40% of physicians already working part-time or in gig roles, there are hundreds, or likely thousands, of experts ready to contribute. And since generative AI tools can provide information in dozens of languages and literacy levels, they would offer unprecedented accessibility. Which Path Will We Take? The two models aren't mutually exclusive, and both will likely shape the future of medicine. Given the potential for massive financial return from the first approach, we can assume that dozens of entrepreneurs are already developing disease-focused generative AI tools. But rather than waiting for technology companies to introduce GenAI tools, physicians working alone or in conjunction with educational companies have the opportunity to drive the process and improve our nation's health.
