Evidence map›Paper›PMID 25985204›Full record

ArticlePloS one2015

Urban-hazard risk analysis: mapping of heat-related risks in the elderly in major Italian cities.

Marco Morabito, Alfonso Crisci, Beniamino Gioli, Giovanni Gualtieri, Piero Toscano, Valentina Di Stefano, Simone Orlandini, Gian Franco Gensini

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
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  3. Plants (Basel, Switzerland) · 2025
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  4. Article
  5. Article
  6. The Utilization of the WMO-1234 Guidance to Improve Citizen's Wellness and Health: An Italian Perspective.International journal of environmental research and public health · 2022
    Article
  7. Article
  8. A Raster-Based Subdividing Indicator to Map Urban Heat Vulnerability: A Case Study in Sydney, Australia.International journal of environmental research and public health · 2018
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. A Spatial Framework to Map Heat Health Risks at Multiple Scales.International journal of environmental research and public health · 2015
    Article
  16. On the Science-Policy Bridge: Do Spatial Heat Vulnerability Assessment Studies Influence Policy?International journal of environmental research and public health · 2015
    Article
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

8 authors.

Marco MorabitoInstitute of Biometeorology, National Research Council, Florence, Italy; Interdepartmental Centre of Bioclimatology, University of Florence, Florence, Italy.
Alfonso CrisciInstitute of Biometeorology, National Research Council, Florence, Italy.
Beniamino GioliInstitute of Biometeorology, National Research Council, Florence, Italy.
Giovanni GualtieriInstitute of Biometeorology, National Research Council, Florence, Italy.
Piero ToscanoInstitute of Biometeorology, National Research Council, Florence, Italy.
Valentina Di StefanoFondazione per il Clima e la Sostenibilità, Florence, Italy.
Simone OrlandiniInterdepartmental Centre of Bioclimatology, University of Florence, Florence, Italy; Fondazione per il Clima e la Sostenibilità, Florence, Italy; Department of Agrifood Production and Environmental Sciences, University of Florence, Florence, Italy.
Gian Franco GensiniInterdepartmental Centre of Bioclimatology, University of Florence, Florence, Italy; Clinica Medica e Cardiologia, University of Florence, Florence, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundShort-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks.

objectivesDevelopment of high-resolution, heat-related urban risk maps regarding the elderly population (≥ 65).

methodsA long time-series (2001-2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using "Crichton's Risk Triangle" hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI).

resultsThe GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities.

conclusionsThis study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies.

Indexed as

CitiesHot TemperatureRisk AssessmentAgedHumansItalyLinear ModelsModels, TheoreticalSeasons

Identifiers

PMID25985204
PMCPMC4436225

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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.