Evidence map›Paper›PMID 41230453›Full record

ReviewGlobal challenges (Hoboken, NJ)2025

State-of-Art in Studying the Public Health Effects of Heat: A Literature Review.

Lorenzo Gianquintieri, Enrico Gianluca Caiani

Abstract readReview
In one paragraph

Review in Global challenges (Hoboken, NJ), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Lorenzo GianquintieriPolitecnico di Milano Electronics, Information and Bioengineering Department Milano 20133 Italy.ORCID https://orcid.org/0000-0002-9658-5749
Enrico Gianluca CaianiPolitecnico di Milano Electronics, Information and Bioengineering Department Milano 20133 Italy.ORCID https://orcid.org/0000-0002-1770-6486

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The impact of heat and heatwaves on human health constitutes a significant hazard, disproportionately affecting various regions worldwide and continuously exacerbating due to climate change. While human pathophysiology is well understood, addressing this challenge from a public-health perspective is essential to generate population-level insights and provide policymakers with evidence-based guidance. This calls for an interdisciplinary "environmental epidemiology" approach, which embraces multiple areas including (but not limited to) health sciences, geomatics, data-science, environmental analyses, and social sciences. Since the early 2000s, scientific production on this topic is increasing exponentially. This review aims to provide a narrative synthesis of the most relevant public-health studies, offering a comprehensive overview of prevailing perspectives within the scientific community. A key challenge highlighted is the lack of standardized thresholds, measures, and confounders, further complicated by variations in measurement methods, the urban heat island effect, and local disparities. The Distributed Lag Non-linear Model (DLNM) has emerged over the past decade as the gold-An standard for modeling heat-attributable health burdens. This approach enables risk stratification based on socio-demographic factors, evaluation of confounding variables, and scenario simulations. Numerous adaptation strategies are proposed, and with the support of emerging technologies, scientific research is increasingly shifting toward data-driven methodologies, unlocking new possibilities.

Indexed as

adaptationclimate changeDLNMheatheatwaveliterature reviewrisk assessment

Identifiers

PMID41230453
PMCPMC12602464

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.