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Article

Demographic, Clinical, and Social Factors Associated with an Increased Risk of Death Among Older Adults Aged 75 Years and Older During Heatwaves in Milan, Between Mid-July and Mid-September 2022

by
Daria Russo
,
Sara Tunesi
and
Antonio Giampiero Russo
*
Epidemiology Unit, Agency for Health Protection of the Metropolitan City of Milan, 20123 Milan, Italy
*
Author to whom correspondence should be addressed.
Environments 2026, 13(5), 234; https://doi.org/10.3390/environments13050234
Submission received: 4 February 2026 / Revised: 18 April 2026 / Accepted: 20 April 2026 / Published: 22 April 2026
Versions Notes

Abstract

Extreme heat is a major weather-related cause of death and is expected to intensify in European cities. We quantified Milan-specific temperature–mortality relationships, defined impact-based heat thresholds around the minimum mortality temperature (MMT) and identified vulnerable subgroups using individual-level risk factors. We conducted a time-stratified case-crossover study including 2230 natural deaths among Milan residents aged ≥75 years occurring between 15 July and 15 September 2022. The MMT (29 °C) was used as the reference temperature [odds ratio (OR) = 1], and mortality risks were evaluated across high-impact (1.20 < OR ≤ 1.50, ≥35 °C) maximum temperature (Tmax) days. Compared with MMT days, mortality was higher on high-impact days (OR 1.44), with somewhat larger estimates among adults aged ≥85 years (OR 1.63) and men (OR 1.50). Disability (OR 1.51) and socioeconomic deprivation (OR 1.89) were also associated with higher vulnerability, with relatively higher estimates observed in women aged ≥85 years and in men with comorbidities or living alone. Overall, the findings suggest that extreme heat may have had a greater impact on the oldest old and on socially or clinically vulnerable groups, highlighting the possible relevance of targeted heat–health interventions and neighborhood-focused prevention strategies.

1. Introduction

During the summer of 2022, the European Region experienced record temperatures, with the mean temperature for June-August 1.3 °C above the 1991–2020 climate average, exceeding that of the previous summer by 0.4 °C. In Italy, summer 2022 was characterized by a maximum temperature anomaly of +2.3 °C compared to the climate average, ranking as the second-highest on record after 2003, according to data from ISAC-CNR [1,2]. As part of the heat–health action plans adopted across the WHO European Region to forecast health impacts of periods of extreme heat, Italy has implemented the Heat–Health Watch Warning System (HHWWS) [3] since 2004. The HHWWS estimates increases in daily mortality among older adults (≥65 years) by comparing expected and observed deaths during heatwave episodes [4]. The system [5,6] defines city-specific temperature thresholds for warning levels (0–3), indicating the expected health risk associated with forecast heat, based on the relationship between temperature and mortality. Level 1 (attention) is issued when meteorological conditions indicate a possible forthcoming heatwave, with a low expected impact on mortality (i.e., <20% excess mortality). Level 2 (alarm) is issued when meteorological conditions are associated with a high risk for the population (i.e., 20% excess mortality). Level 3 (emergency) is issued on the third consecutive day of level 2 and identifies an ongoing heatwave episode [7]. Numerous epidemiological studies have demonstrated that elevated temperatures particularly affect more vulnerable population subgroups, such as the elderly [7,8,9,10,11]. Age, pre-existing medical conditions and social deprivation are also key factors that increase the likelihood of experiencing adverse health outcomes related to heat and extreme temperatures [12,13,14,15,16,17,18,19,20,21,22]. The pathophysiological mechanisms of ill health during periods of heat generally result from impaired thermoregulatory responses, such as impaired sweating and vascular dilation, or behavioral responses [23,24]. Recent evidence indicates that the probability and intensity of heatwaves are increasing in most European metropolitan areas because of their highly urbanized settings and persistent urban heat islands [25,26,27,28,29]. At the same time, intra-urban analyses show marked social and spatial inequalities in heat-related mortality, especially in large cities [30,31].
In Milan, the local climate monitoring system is coordinated by the Epidemiology Unit of the Agency for Health Protection of the Metropolitan City of Milan (ATS-MI), which daily disseminates the HHWW bulletin to health, social, and healthcare services, including hospitals and elder-care facilities, to support preventive actions tailored to warning levels and population vulnerability [32]. However, preventive activities may mitigate, but not completely remove, the risk of death during heat waves. Moreover, evidence remains limited on the demographic, clinical and social characteristics associated with an increased risk of death [2,15,33,34] on days classified as high risk by the HHWWS during a recent extreme summer [35] in Milan. As part of heatwave-related mortality prevention activities and using routinely collected administrative data, this case-crossover study aimed to quantify the impact of maximum temperatures on mortality among adults aged ≥75 years in Milan during the exceptionally hot summer of 2022, focusing on days with level 2–3 HHWWS warnings. Specifically, we compared mortality on days at the minimum mortality temperature (MMT) with mortality on days above medium- and high-impact Tmax thresholds, with particular attention to demographic, clinical and social characteristics associated with mortality risk at the individual level risk factors. These findings may inform the implementation of timely, targeted prevention measures tailored both to warning levels (heatwaves) and to individual vulnerability.

2. Materials and Methods

2.1. Subjects and Individual Information

The study was carried out during the warmest period of 2022 (from mid-July to mid-September), which included a major heatwave from 15 to 25 July 2022 [1,2]. The study was conducted in Milan, a large and densely populated city in Northern Italy. According to the Köppen–Geiger climate classification, Milan lies in the Cfa zone, with a humid subtropical climate characterized by hot summers and no dry season [36]. In 2022, Milan had approximately 1.4 million inhabitants, with a mean age of 45.8 years; individuals aged ≥75 years account for about 13% of the population.
Using routinely collected administrative data from the Lombardy Region Health Information System, we identified all residents of Milan aged 75 and older (75+) who were alive on 14 July 2022 and had been residing in the city since 1 January 2022. The study population was restricted to adults 75+, as this is the target group of the local heat-related prevention system and the age group for which information was required for risk stratification. In addition, 75+ are disproportionately affected by heatwaves, with more than 80% of excess heat-related deaths occurring among older adults [8,37]. The outcome was all natural causes mortality occurring in this population between mid-July and mid-September 2022 (ICD-10: A00–R99 [38]). At the individual level, we collected demographic, socioeconomic, clinical and care-related information, including: gender (female/male), age class (75–84/≥85), deprivation status (yes/no), disability status (yes/no), living alone (yes/no), pre-existing medical conditions (yes/no), and ongoing treatment (yes/no). These variables were obtained through record linkage across several ATS-MI healthcare databases, using anonymised unique individual codes. Specifically, date were linked from the New Regional Registry (NAR) used to identify residents registered with the Regional Health Service and their general practitioner; the Nominative Register of Causes of Death (ReNCaM) used to identify mortality outcome and coded according to ICD-10 [38]; Hospital discharge sheets (HDS) containing information regarding hospitalisations and coded according to the ICD 9th revision (ICD-9-CM [39]); the Pharmaceutical Claims Databases used to identified all pharmaceutical prescriptions; the Exemption Information Flow used to identify individuals with exemption for a specific disease or conditions [40]; Social and Health Flows used to identify subjects’ linked with social and healthcare services providers.
Records were considered valid if documented within the two years preceding death, to better capture conditions relevant at the time of death. Where multiple eligible records referred to the same characteristics were available within this time window, the record closest to the date of death was retained in order to capture the most up-to date information available for those conditions. By integrating the available administrative data sources, the following variables were derived:
  • Illness status (Ill): Individuals with at least one record in the HDS or the Exemption Information Flow.
  • Treatment status (Treated): Individuals with at least one outpatient prescription recorded in the Pharmaceutical Flows, retained only if treatment coverage exceeded 50%, calculated as dispensed Defined Daily Doses (DDDs) divided by treatment duration. Treatments initiated in the previous year with ≤12 boxes or initiated in the current year with a duration ≤7 days were excluded.
  • Socioeconomic deprivation (Deprived): Individuals meeting at least one of the following criteria: income-related exemption or residence in the most deprived census section (deprivation index = 5) [41].
  • Disability status (Disabled): Individuals classified as disabled according to the algorithm developed by the ATS-MI [42].
Detailed list of ICD-9-CM diagnoses, Anatomical Therapeutic Chemical (ATC) codes, Exemptions and Service Centers are reported in the Supplementary Materials (Tables S1–S4).
Ethics approval was not required because evaluation of residents’ use of health services is a statutory function of ATS-MI (L.R. 23/2015) and is a critical component of surveillance and monitoring of the National Healthcare System (NHS). Individuals’ identities were anonymised; their unique identification number (fiscal code) was converted into a string by the ATS-MI information system, which had no role in the data analysis.

2.2. Exposure Data

Exposure data were obtained as a continuous variable from open-access datasets provided by the Regional Environmental Protection Agency (ARPA) [43]. The primary exposure was the daily maximum temperature (Tmax), defined as the highest value of the hourly ambient temperature recorded each day and averaged across six urban background monitoring sites to obtain a single city-wide series.

2.3. Statistical Analysis

In order to evaluate the associations between the acute effects of short-term temperature increases on mortality, we used a time-stratified case-crossover design [44,45,46]. Within this study design, the temperature on the day of death (event-day) is compared with non-event days. Each individual serves as their own control, which inherently adjusts for all time-invariant individual characteristics (e.g., sex, chronic conditions, baseline socioeconomic status), while matching by day of the week and month controls for temporal trends and seasonality. For each “case”, determined as an event-day, at least two “control” days were selected using a bidirectional time-stratified approach, matching the day of the week and month of the case day, thereby reducing overlap bias.
Based on the city-wide daily time series of maximum temperature (Tmax) in Milan, the same daily temperature value was assigned to each individual for every day of the study period; this value varied over time (across days) but not across subjects. We then fitted conditional logistic regression models with Tmax modeled as a natural cubic spline. The number and location of internal knots were selected by minimizing the Akaike information criterion (AIC) to identify the best-fitting model (Supplementary Table S5). Based on the resulting exposure–response function, we identified the minimum mortality temperature (MMT) as the value where the risk was lowest (odds ratio = 1; Supplementary Table S6). The MMT was then used as the reference point to derive temperature thresholds for subsequent analyses.
To align the 2022 National Plan for the Prevention of Heat-Related Health Effects [1], which reports daily heat-warning levels, with Tmax data from the Regional Environmental Protection Agency (ARPA) [43], we focused on days with level 2 or 3 warnings, which trigger local prevention measures. Accordingly, the medium-impact threshold was set at temperatures below the 75th percentile of the Tmax distribution and defined as the range associated with an odds ratio (OR) between 1.0 and 1.20. Similarly, the high-impact threshold was set at temperatures above the 75th percentile of the Tmax distribution and defined as the range associated with an OR between 1.20 and 1.50.
In the second stage, we evaluated potential effect modification by time-invariant covariates at the high-impact threshold only, using an interaction model. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated from models including interaction terms between the exposure and individual-level demographic, clinical, and social characteristics (entered one at a time). For the second-stage analysis, to formally test for statistical significance of the difference between strata, effect modification was quantified using the relative effect modification (REM) index, defined as the ratio of the subgroup-specific odds ratio to the odds ratio for the reference category.
A sensitivity analysis was performed in order to examine the pattern of ORs for the medium-impact threshold, both in the whole population and across all time-invariant covariates.
All analyses were carried out using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).

3. Results

3.1. Exposure

Table 1 summarizes Tmax values and their distribution over the whole study period and during the heatwave. It also reports the distribution of the temperature between case and control days, highlighting that the case-crossover design is based on within-subject variation in exposure; consequently, the exposure variable reflects variability between case and control days rather than the exposure of the cases alone. The study window (mid-July to mid-September 2022) included a major heatwave from 15 to 25 July (Supplementary Figure S1). During this episode, the mean Tmax was 34.9 °C, compared with 31.2 °C in the 2011–2021 period. Tmax was also approximately 4 °C higher than in the overall study period and showed lower variability (Supplementary Figure S1). The temperature–mortality relationship was estimated using the full data series and the temperature thresholds modeled. The policy-relevant thresholds were 29 °C for the minimum mortality temperature (MMT), 30–34 °C for the medium-impact threshold, and ≥35 °C for the high-impact threshold.

3.2. Subjects

On 1 January 2022, 198538 individuals aged 75 years and older were alive and resident in Milan. By 14 July 2022, 192063 remained alive, of whom 36% were aged 85 years and older. Of the 12571 deaths recorded in 75+ during 2022, 2203 (17.6%) occurred between mid-July and mid-September. The mean number of deaths over the heatwave period was 42.73 (SD 8.7), which was substantially higher than the average observed over the entire study period, 34.97 (SD 8.72). The daily number of deaths during the heatwave ranged from 34 to 61 (Supplementary Table S7), with a peak of 61 deaths on the 24th and 26th of July. Table 2 shows results for the overall population and then for demographic, clinical and social characteristics. Relative effect modification index values are also reported. The ORs of dying on days with a Tmax ≥ 35 °C compared with MMT days were 1.44 (95% CI 1.15–1.79) among Milan residents aged ≥75 years. Higher odds were observed among individuals aged ≥85 years (OR 1.63, 95% CI 1.24–2.15; REM 1.47, 95% CI 0.92–2.36) and among men (OR 1.50, 95% CI 1.06–2.12; REM 1.07, 95% CI 0.68–1.69). Among clinical factors, disability was associated with higher heat-related mortality (OR 1.51, 95% CI 1.13–2.01; REM 1.12, 95% CI 0.71–1.77). Regarding social factors, deprivation was associated with the greatest excess risk (OR 1.89, 95% CI 1.08–3.28; REM 1.39, 95% CI 0.76–2.54). However, it should be noted that REM estimates were not statistically significant, indicating no clear evidence of differences in the heat-related mortality association across strata.
Table 3 presents sex-stratified ORs with 95% CIs, estimated from interaction models evaluating effect modification by demographic, clinical, and social risk factors. Overall, men had a higher and statistically significant risk of death on days with temperatures ≥35 °C in Milan compared with days at 29 °C. Among the demographic factors, age acted as an effect modifier: men aged 75–84 years and women aged ≥85 years showed the highest risks of death, with OR = 1.58 (95% CI: 0.92–2.72) and OR = 1.76 (95% CI: 1.24–2.50), respectively. Among the clinical factors, only men with pre-existing medical conditions (ill) showed an increased risk of death. For the social factors, men again appeared more vulnerable when exposed to days with Tmax ≥35 °C.
For the sensitivity analysis, the patterns observed in the odds ratios (ORs) for the medium-impact threshold were largely mirrored at the high-impact threshold. However, the estimated effects were generally smaller and not statistically significant, except among individuals aged ≥85 years, for whom an OR of 1.34 (95% CI: 1.05–1.71) was observed. Further details are provided in Supplementary Table S9 of the Supplementary Data.

4. Discussion

The relationship between temperature and mortality has attracted growing attention worldwide in the context of climate warming [47]. Recent assessments by the IPCC and major climate–health reviews highlight that extreme heat is already one of the leading weather-related causes of death, and that the frequency, intensity, and duration of heatwaves are projected to increase further in the coming decades. A recent study from Spain showed that the minimum mortality temperature has progressively increased over the last four decades (0.64 °C per decade), suggesting biological adaptation to climate change and/or the adoption of mitigation measures (e.g., use of air conditioning) [48]. This study aimed to identify vulnerable subgroups in Milan by comparing days with Tmax ≥ 35 °C with days at the minimum mortality temperature (MMT, 29 °C), focusing on individuals with impaired thermoregulation and increased baseline risk of death [25,26].
Our approach complements the Italian HHWWS by translating city-specific temperature–mortality relationships into impact-based thresholds (medium and high impact), anchored to the MMT derived from observed data. Multi-city European and Italian studies have consistently shown steep increases in mortality above city-specific thresholds, particularly among older adults and in urban areas with strong heat island effects [5,14,17,18,22,27,28,49,50,51]. Although according to the Italian definition, heatwaves are associated with an increased risk of death, our results indicate that the estimated impact of heatwaves on mortality is substantially higher when higher temperature thresholds are applied [52]. In line with Thompson et al. [21,22], who reported higher risks of all-cause and non-accidental mortality at higher temperature thresholds, we found that the risk of death increased by 44% when the temperature rose from 29 °C to ≥35 °C, slightly exceeding the 34% increase in mortality per 10 °C reported by Stafoggia et al. [17]. Sensitivity analyses using the medium-impact threshold (30–34 °C) showed smaller and mostly non-significant excess risks, except among those aged ≥85 years, suggesting that the largest relative impact in this setting is concentrated on the hottest days.

4.1. Age and Sex Patterns

Overall, men exhibited a slightly higher risk; however, ORs greater than 1.50 were largely confined to men aged 75–84 years, women aged ≥85 years, individuals with disability or deprivation (both sexes), and men with pre-existing medical conditions or living alone. Consistent with previous studies [14,17], older adults aged ≥85 years in Milan had a 63% higher risk of death during our study period; however, the difference compared with those aged 75–84 years did not reach statistical significance. Age-related reductions in sweating and thermoregulatory capacity [53,54] are likely key pathophysiological mechanisms. When stratified by sex, women aged ≥85 years were more susceptible than men of the same age, in line with evidence [17] that older women are particularly exposed to heat-related mortality. Sex differences also varied by age: Ballester et al. [2] reported higher heat-related mortality in men aged 0–64 years (+43%), whereas among women, the excess risk was +6% in those aged 65–79 years and +121% in those aged ≥80 years. Our findings are therefore consistent with the broader European literature, which shows that men tend to be more vulnerable in the younger segment of the elderly population, whereas very old women carry a disproportionate burden of heat-related mortality, possibly reflecting differences in health status, social isolation, and living arrangements.

4.2. Clinical Vulnerability and Multimorbidity

Several studies have shown that multimorbidity and disability markedly increase the risk of death during heatwaves or extreme heat events [55,56]. In line with this evidence, disability in our study was associated with a 51% higher mortality risk, suggesting a potential increase in vulnerability, although no statistically significant difference was observed versus individuals without disability.
Recent large case-crossover and cohort studies among US Medicare beneficiaries with Alzheimer’s disease and related dementias [57] and among veterans with cardiometabolic diseases [58] similarly report modest average heat effects but higher risks in older, multimorbid, and socioeconomically disadvantaged patients. These findings support the view that comorbidity and limited functional reserve lower the threshold at which heat becomes dangerous, even in a high-income setting with an established health system. In our data, men with pre-existing medical conditions showed higher risk estimates at the high-impact threshold, suggesting a possible excess risk; however, no statistically significant difference was observed compared with men without pre-existing medical conditions. The need to integrate heat warnings into chronic-disease management and medication review (particularly for drugs that impair thermoregulation or fluid balance).

4.3. Social Vulnerability, Deprivation, and Living Arrangements

Deprivation and household composition (lives alone: yes/no) are important indicators of individual socioeconomic position, given their role as local modifiers of heat–health impacts [34,59,60]. In our data, deprivation was associated with an 89% higher risk of death when comparing days at 29 °C with days when Tmax was ≥35 °C, suggesting a potential excess risk. However, the interaction term was not statistically significant, indicating no statistically significant difference compared with non-deprived individuals. This is consistent with evidence from Turin and other European cities showing that residents of deprived neighborhoods, often characterized by denser built environments, limited green space, and higher surface temperatures, experience higher heat-attributable mortality. More recently, a spatial analysis in Milan using geocoded emergency medical services (EMS) data for cardiovascular events found that clusters with higher proportions of older and female residents and less favorable socio-urban characteristics had increased vulnerability to heat, underscoring how local built environment and social conditions interact to shape risk [61]. Living alone acted as an effect modifier in sex-stratified models, particularly among men. Because the case-crossover design estimates only relative effects, these findings should not be interpreted as indicating a protective effect, but rather as a less-than-multiplicative increase in risk. The higher relative risk observed among men living alone is consistent with studies of “social isolation” indicators in heat–health research, suggesting the importance of informal support networks, the ability to seek cooler environments, and timely access to care. Evidence from the US and other settings shows that socially vulnerable communities and those with higher deprivation indices bear a disproportionate burden of heat-related emergency responses and mortality, reinforcing the need for equity-focused prevention strategies.

4.4. Strengths and Limitations

One of the main strengths of this study is its large, population-based sample, as we included all deaths among individuals aged ≥75 years, without any selection criteria. The record linkage of individual-level data from multiple sources also enabled us to incorporate detailed personal information that is rarely available in other European settings. This allowed us to examine demographic, clinical, and social determinants of vulnerability in a single framework, similar in spirit to recent studies in veterans and EMS populations that exploit rich administrative datasets.
Deriving impact-based thresholds from the city-specific temperature–mortality curve and aligning them with the national heat-warning system may improve the translational value of our findings for local public health practice; however, these results should be interpreted cautiously, given the exceptionally hot summer under study and the limited observation period.
However, this study has some limitations. First, meteorological data were not linked to individuals’ residential addresses; instead, we used a city-wide daily mean ambient temperature for Milan, which may have led to exposure misclassification and less precise estimates of local conditions.
Another methodological limitation is that the temperature thresholds used to define the impact categories, including the MMT, were derived from the same dataset used for the main analysis. While this data-driven approach produced results broadly consistent with previous studies conducted in Milan [29,62,63], it may have introduced a degree of circularity, leading not only to overly optimistic estimates of precision but also to reduced internal validity and stability of some key estimates, including threshold parameters and REM estimates. Future studies should derive and validate these thresholds using external or historical data. They should also incorporate more spatially resolved exposure measures over a broader geographic area and may adopt a distributed lag non-linear model to better capture the delayed and non-linear effects of heat exposure on mortality. The growing availability of high-resolution land-surface temperature and urban heat island mapping, as used in Italian and international studies, could help refine exposure assessment and identify micro-areas at highest risk. Second, our analysis was restricted to summer 2022 and did not allow comparison with previous years; including multiple summers would enhance generalizability. The summer of 2022 was exceptionally hot in Italy; therefore, our findings should be interpreted as an event-specific assessment of an extremely hot summer. Accordingly, the estimated heat thresholds, including the MMT and related cut-offs, may reflect conditions at the upper end of the current climate distribution and may be less representative of more typical summers. As a consequence, the generalizability of our findings to other settings and to less extreme summers may be limited. Future analyses based on longer time series and multiple summer seasons will be needed to provide more robust and generalizable estimates.
Finally, heatwaves were defined solely based on temperature thresholds, without accounting for humidity or sustained high night-time temperatures, which may better reflect the physiological impact of heat on health. We also lacked information on indoor temperatures, use of air conditioning, and individual behaviors, which could modify the association between outdoor heat and mortality, as noted in other recent climate–health investigations.

5. Conclusions

In conclusion, raising awareness of individual heat-related health risks and implementing city-specific programs to prevent heat-related deaths in older adults should be key public health priorities in Europe. Results suggest that, in Milan, men aged 75–84 years, women ≥85 years, people with disability or deprivation, and men with pre-existing medical conditions or living alone may be more vulnerable on extremely hot days. Targeting these groups can help sharpen prevention strategies and emergency responses, reducing heat-related morbidity and mortality. Our findings suggest that heat–health action plans could benefit from incorporating individual-level vulnerability profiling alongside spatial risk assessments, such as urban heat island and EMS-based vulnerability mapping, to prioritize neighborhoods where older and socially disadvantaged residents are concentrated. This approach is aligned with emerging recommendations that health systems and local authorities develop heat preparedness plans explicitly designed to protect those most at risk in a rapidly warming climate. Additional studies covering different years and settings will be necessary to assess the stability of these estimates and the generalizability of the findings.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/environments13050234/s1. Figure S1: Daily deaths and daily ambient temperature; Table S1: Demographic, clinical and social characteristics of the study population by age; Table S2: List of diagnoses recorded in hospital discharge records; Table S3: List of the Anatomical Therapeutic Chemical (ATC) codes; Table S4: List of exemptions; Table S5: List of service centers; Table S6: Akaike information criterion (AIC) index; Table S7: Temperature thresholds derived from the temperature–mortality relationships; Table S8: Number of deaths in Milan over the entire study period and during heatwaves; Table S9: Daily maximum temperature (Tmax) and daily deaths among individuals aged ≥75 years in Milan, 10 July–15 September 2022.

Author Contributions

Conceptualization, A.G.R.; methodology, D.R. and S.T.; software, D.R.; analysis, D.R. and S.T.; data curation, S.T.; writing—original draft preparation, D.R.; writing—review and editing, D.R., S.T. and A.G.R.; visualization, D.R.; supervision, A.G.R.; funding acquisition, A.G.R. All authors have read and agreed to the published version of the manuscript.

Funding

The study was developed within the project “Health and equity co-benefits in support of climate change response plans in Italy” (CUP J55I22004450001), funded by the National Plan for Complementary Investments of the Ministry of Health.

Institutional Review Board Statement

Ethics approval was not required because evaluating residents’ use of health services and the population’s health status is a statutory function of the Agency for Health Protection (ATS) of Milan (Lombardy Regional Laws nos. 23/2015 and 22/2021) and constitutes a key component of surveillance and monitoring within the National Healthcare System (NHS).

Informed Consent Statement

In accordance with national legislation and institutional requirements, written informed consent for participation was not required because the study was based on routinely collected administrative data.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to data protection and privacy restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Environmental variable: distribution over the whole study period and during the heatwave.
Table 1. Environmental variable: distribution over the whole study period and during the heatwave.
Environmental VariableStudy PeriodHeatwave
from 15/07/2022 to 15/09/2022from 15/07/2022 to 25/07/2022
Temperature (°C)
Mean ± SD32.57 ± 2.9636.41 ± 0.98
Minimum2535
Maximum3838
Percentile
50th3336
75th3537
95th3738
Difference in maximum temperature between cases-day and controls-day (°C)
Mean ± SD0.15 ± 2.88NA
Minimum−8.33NA
Maximum7NA
Percentile
50th0.33NA
75th1.33NA
95th5.33NA
SD: Standard Deviation.
Table 2. Total deaths and risk of dying on days with Tmax ≥ 35 °C, compared with days at the minimum mortality temperature (MMT, 29 °C), by demographic, clinical and social characteristics.
Table 2. Total deaths and risk of dying on days with Tmax ≥ 35 °C, compared with days at the minimum mortality temperature (MMT, 29 °C), by demographic, clinical and social characteristics.
No.PercentHigh-Impact Threshold (≥35 °C)
OR (95% CI) ap-ValueREM Index b
Total deaths22031001.44 (1.15–1.79)0.0015n/a
Age
75–84 c67530.641.11 (0.76–1.62)0.60091.00
≥85152869.361.63 (1.24–2.15)0.00051.47 (0.92–2.36)
Gender
Female c128658.371.39 (1.04–1.86)0.02601.00
Male91741.631.50 (1.06–2.12)0.02271.07 (0.68–1.69)
Disabled
Yes131759.781.51(1.13–2.01)0.00561.12 (0.71–1.77)
No c88640.221.34 (0.94–1.90)0.10611.00
Ill
Yes141164.051.36 (1.03–1.79)0.02770.86 (0.53–1.38)
No c79235.951.58 (1.08–2.33)0.01941.00
Treated
Yes139463.281.43 (1.08–1.90)0.01320.99 (0.63–1.58)
No c80936.721.44 (1.00–2.06)0.04901.00
Deprived
Yes39517.931.89 (1.08–3.28)0.02511.39 (0.76–2.54)
No c180882.071.36 (1.06–1.73)0.01421.00
Lives alone
Yes96243.671.41 (1.00–1.98)0.04790.97 (0.62–1.53)
No c124156.331.45 (1.08–1.95)0.01351.00
a Odds ratios (ORs) and 95% confidence intervals (95% CI). b REM: Relative effect modification index is calculated as the ratio between the specific OR and the OR from the reference category. c Reference category for interaction.
Table 3. ORs (95% ci) for death on days with Tmax ≥ 35 °C, compared with days at the minimum mortality temperature (MMT, 29 °C), by gender, with demographic, clinical and social characteristics as effect modifiers, in Milan.
Table 3. ORs (95% ci) for death on days with Tmax ≥ 35 °C, compared with days at the minimum mortality temperature (MMT, 29 °C), by gender, with demographic, clinical and social characteristics as effect modifiers, in Milan.
MenWomenTotal
OR (95% CI) aOR (95% CI) aOR (95% CI) a
Whole Population1.50 (1.06–2.12)1.40 (1.04–1.86)1.44 (1.15–1.79)
Age
75–841.58 (0.92–2.72)0.75 (0.43–1.29)1.11 (0.76–1.62)
≥851.44 (0.92–2.26)1.76 (1.24–2.50)1.64 (1.24–2.16)
Disabled1.52 (0.96–2.41)1.50 (1.03–2.16)1.50 (1.13–2.01)
Ill1.56 (1.04–2.34)1.21 (0.83–1.75)1.36 (1.03–1.79)
Treated1.43 (0.94–2.18)1.42 (0.97–2.09)1.43 (1.08–1.90)
Deprived2.17 (0.86–5.49)1.73 (0.86–3.48)1.88 (1.08–3.28)
Lives alone1.66 (0.92–2.72)1.33 (0.89–1.97)1.41 (1.00–1.99)
a Odds ratios (ORs) and 95% confidence intervals (95% CI).
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Russo, D.; Tunesi, S.; Russo, A.G. Demographic, Clinical, and Social Factors Associated with an Increased Risk of Death Among Older Adults Aged 75 Years and Older During Heatwaves in Milan, Between Mid-July and Mid-September 2022. Environments 2026, 13, 234. https://doi.org/10.3390/environments13050234

AMA Style

Russo D, Tunesi S, Russo AG. Demographic, Clinical, and Social Factors Associated with an Increased Risk of Death Among Older Adults Aged 75 Years and Older During Heatwaves in Milan, Between Mid-July and Mid-September 2022. Environments. 2026; 13(5):234. https://doi.org/10.3390/environments13050234

Chicago/Turabian Style

Russo, Daria, Sara Tunesi, and Antonio Giampiero Russo. 2026. "Demographic, Clinical, and Social Factors Associated with an Increased Risk of Death Among Older Adults Aged 75 Years and Older During Heatwaves in Milan, Between Mid-July and Mid-September 2022" Environments 13, no. 5: 234. https://doi.org/10.3390/environments13050234

APA Style

Russo, D., Tunesi, S., & Russo, A. G. (2026). Demographic, Clinical, and Social Factors Associated with an Increased Risk of Death Among Older Adults Aged 75 Years and Older During Heatwaves in Milan, Between Mid-July and Mid-September 2022. Environments, 13(5), 234. https://doi.org/10.3390/environments13050234

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