The Problem
The recent heatwave in Europe has produced thousands of excess deaths: UK about 2700 despite strong adaptation methods in an elderly population, Germany about 5500 showing how a fragmented response system between federal and state levels can cause death, and France at about 2000 also despite strong adaptation methods and also in an elderly population and overall across Europe over 10,000. What’s going wrong?
I suggest at least in terms of excess deaths from heat, the wrong problem is being solved, very well. This is how heat waves are currently managed:
| Model type | What it predicts | Operational examples | Main limitation |
|---|---|---|---|
| Meteorological heat warning | Whether forecast heat will exceed hazardous thresholds | France: Météo-France; Germany: DWD | Predicts environmental hazard, not health outcome |
| Heat–health impact warning | Expected pressure on mortality, healthcare and vulnerable populations | France’s SACS; UKHSA Heat-Health Alerts | Usually regional and categorical |
| Epidemiological mortality model | Expected excess deaths associated with temperature | European multi-country distributed-lag models | Often retrospective or research-based |
| Vulnerability mapping | Which neighbourhoods or population groups are most exposed | Age, deprivation, urban heat island, housing | Identifies groups, not individuals |
| Physiological heat-strain model | Core-temperature or heat-strain response | Occupational and military models | Designed mainly for healthy workers |
| Individual machine-learning model | Heat stress or thermal discomfort from wearable and environmental data | Small experimental and occupational studies | Rarely validated for older, frail clinical populations |
None of this tells us if a particular person is likely die from heat in the next 24 to 48 hours. The current epidemiological approach is called distributed lag non-linear modelling, DLNM [Gasparrini A, Armstrong B, Kenward MG. Distributed lag non-linear models. Statistics in Medicine. 2010 Sep 20;29(21):2224–34.] and is suitable for population risk but not for individuals. The methodology estimates:
- the non-linear relationship between temperature and mortality;
- the delay between heat exposure and death;
- the locally specific minimum-mortality temperature;
- attributable deaths above that temperature;
- variation by age, sex and location.
While this tell us whether an area will experience dangerous heat, it isn’t what we want to know (i.e. solves the wrong problem).
What we really want to know is this:
What is a specific person’s probability of heat-related deterioration, hospital admission or death during the next 24 to 48 hours?
Machine learning is now seen as the way forward [e,g, You J, Chan JH, Stouffs R, Gottkehaskamp BG, Miller C. Evaluating heat exposure and vulnerability among older adults using wearable technology to support aging in place. IOP Conf Ser: Earth Environ Sci. 2026 Feb 1;1582(1):012052. doi:10.1088/1755-1315/1582/1/012052]
In keeping with my approach to decision making in clinical settings, we need a predictor to combine environmental exposure, personalised baseline data, dynamic physiological vital signs data and heat risk increasing drugs (HRIDs). All of these exist but not as a solution: all that is needed is to put the parts together.
Let’s begin with the risk factors that affect individual risk from heat, which the current problem approach doesn’t do:
| Risk domain | Examples |
|---|---|
| Age | ≥65 years, particularly ≥75 years |
| Frailty | Frailty scales, reduced mobility, dependence for daily activities |
| Chronic disease | Heart failure, coronary disease, COPD, chronic kidney disease, diabetes, dementia |
| Medications | Diuretics, anticholinergics, antipsychotics, antidepressants, sedatives, beta-blockers, ACE inhibitors/ARBs in some circumstances |
| Functional status | Unable to obtain fluids independently, bed-bound, cognitive impairment |
| Previous history | Previous heat illness or dehydration |
| Social factors | Living alone, no air conditioning, poor housing, social isolation |
| Environmental exposure | Top-floor accommodation, urban heat island, prolonged outdoor exposure |
The Solution
In keeping with my approach of solving the right problem, we start by acknowledging that the current approach is adequate for population level heat risk, but is not suitable for assessing individual risk to avoid excess deaths from heat.
My solution brings together three important features:
1. a way for individuals and informal carers to assess personal heat risk
2. a tool for clinicians to assess heat risk in clinical and institutional settings
3. a way to capture the risk caused by medicines.
Let’s start with number 3: HRIDs: heat risk increasing drugs.
| HRID mechanism | Important examples | Potential heat interaction |
|---|---|---|
| Fluid loss or reduced circulating volume | Loop and thiazide diuretics; laxatives; SGLT2 inhibitors | Dehydration, electrolyte disturbance, hypotension |
| Renal vulnerability during dehydration | ACE inhibitors, ARBs, ARNIs, NSAIDs, diuretics | Acute kidney injury, particularly in combinations |
| Reduced sweating or impaired heat dissipation | Anticholinergics; some antihistamines; tricyclic antidepressants; some antipsychotics; topiramate, zonisamide | Reduced ability to cool through sweating |
| Central thermoregulation effects | Antipsychotics, stimulants, some antidepressants | Altered hypothalamic regulation or heat production |
| Hypotension or limited cardiovascular compensation | Antihypertensives, nitrates, beta-blockers, diuretics | Dizziness, syncope, inability to increase cardiac output appropriately |
| Reduced alertness or self-care | Benzodiazepines, hypnotics, opioids, sedating antipsychotics and antihistamines | Failure to drink, move to a cooler place or recognise deterioration |
| Narrow therapeutic index affected by dehydration | Lithium, digoxin and some anti-epileptics | Toxicity as renal clearance, fluid balance or electrolytes change |
| Glucose-management vulnerability | Insulin, sulfonylureas, SGLT2 inhibitors | Hypoglycaemia, hyperglycaemia or dehydration-related instability |
| Potentially harmful self-treatment | NSAIDs, aspirin and paracetamol often used to “treat” heatstroke | May add renal, dehydration or hepatic risk and do not treat the underlying heat emergency |
Since mortality is highest amongst the elderly, many of whom are frail and prescribed a variety of medicines (polypharmacy), this is an important feature to capture in the prediction model.
The other two are assessments for self and for others and of course they need validation. If you want to see them, email me.
To illustrate the solution, below is an HTML simulator. I did this in English, French and German, so relevant authorities and interested parties can assess it easily. Feel free to explore the functionality.
Get in touch if you think this is important and worth developing further with investment. mike_tremblay@skythunder.net