Dynamic health risks of sudden temperature drops in subtropical winter time: A case study in Hong Kong

Yawen Wang; Shuang Liu; Chao Ren; Jean Woo; Ka Chun Chong; Yue Man Chiu; Pak Kan So; Ho Man Chong; Edward Ng

doi:10.1371/journal.pclm.0000980

Abstract

Despite growing concerns about heatwaves, cold environment and temperature fluctuations could also pose great health risks. Winter sudden temperature drops (STD) from extreme warmth to extreme cold are emerging as increasingly frequent and intense extreme weather events in sub-tropical regions. To investigate the dynamic STD-associated health risks in Hong Kong winter time over the last three decades, this study identified STD events based on daily minimum temperatures and evaluated their impacts on ambulance callouts, hospitalizations, and mortality risks. From 1884 to 2024, 391 winter STD events were identified. STD was significantly associated with increased risks of respiratory-related (RR: 1.11, 95% CI: 1.04–1.19) and skin-related (RR: 1.29, 95% CI: 1.03–1.63) ambulance callouts, total hospitalization (RR: 1.09, 95% CI: 1.05–1.15), and mortality (RR: 1.31, 95% CI: 1.27–1.34), particularly for events with larger drop magnitude (9.42), longer duration (≥ 4days), and faster cooling rate (2.81). Warm-initiated STDs were linked with increased skin-related ambulance demands (RR: 1.33, 95% CI: 1.07–1.66). For STDs declining to relatively cold levels, risks increased for ambulance demands (RR: 1.05, 95% CI: 1.01–1.10), hospitalizations (RR: 1.20, 95% CI: 1.13–1.27), and mortality (RR: 1.10, 95% CI: 1.07–1.14), with hospitalization (RR: 1.25, 95% CI: 1.04–1.50) and mortality (RR: 1.32, 95% CI: 1.21–1.44) risks progressively escalating as temperatures dropped to extreme cold level. Individuals under 18 years (RR: 1.20, 95% CI: 1.10–1.30) were more susceptible to STD-induced ambulance demands. Populations over 80 years and over 75 years were most vulnerable to hospitalization(RR: 1.13, 95% CI: 1.09–1.18) and mortality (RR: 1.35, 95% CI: 1.30–1.39), respectively. In summary, STD progressively contributes to dynamic health risks. Findings serve as references for local cold-adaptation strategies and guide future research on climate change and health.

Citation: Wang Y, Liu S, Ren C, Woo J, Chong KC, Chiu YM, et al. (2026) Dynamic health risks of sudden temperature drops in subtropical winter time: A case study in Hong Kong. PLOS Clim 5(7): e0000980. https://doi.org/10.1371/journal.pclm.0000980

Editor: Teodoro Georgiadis, Institute for BioEconomy CNR, ITALY

Received: March 23, 2026; Accepted: June 18, 2026; Published: July 10, 2026

Copyright: © 2026 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Daily meteorological data can be found from Hong Kong Observatory website (https://www.hko.gov.hk/en/index.html), while daily air pollution data are publicly available at the Environmental Protection Department of Hong Kong(https://www.epd.gov.hk/epd/english/top.html). Ambulance callout data was obtained from the Hong Kong Fire Service Department and is available upon request (https://www.hkfsd.gov.hk/eng/index.html, Email: hkfsda@hkfsd.gov.hk). Hospital admission data was collected from Hong Kong Hospital Authority, and the data is available upon request (https://www.ha.org.hk/visitor/ha_index.asp, Email: enquiry@ha.org.hk). Mortality data was collected from Hong Kong Census & Statistics Department and is available upon request (https://www.censtatd.gov.hk/en/page_1338.html, Email: population@censtatd.gov.hk). Tutorial R code for DLNM model is available at the package author’s personal page (http://www.ag-myresearch.com). Code for events identification and their health risk assessment are provided in the Text A-B in S1 Appendix. The dates of the identified events are not required to replicate our study. We have provided the identification code in Text A of S1 Appendix, and the necessary meteorological data can be downloaded from the Hong Kong Observatory website. This allows researchers to independently identify the events using the original data.

Funding: This work was supported by the Hong Kong Research Grants Council (Research Impact Fund 2022/23 No. R4040-22 to EN). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

While the ongoing trend of global warming has led to increasing concern about the health impacts of heatwaves, it is crucial to acknowledge that cold-related public health risks remain significant [1, 2], especially in subtropical climate regions. According to existing evidence, despite the ongoing rise in global temperatures, health risks associated with cold exposures generally surpass those related to heat exposure [3], with cold-attributed mortality risk being nine times higher than that from extreme heat [4]. In addition to extreme warm or cold events, climate change is expected to lead to more frequent and intense temperature fluctuations between warm and cold conditions in over 60% of the global area, particularly in subtropical regions, resulting in more complex impacts on human and society [5]. People in subtropical cities are particularly vulnerable to these fluctuations due to their long-term physiological adaptation to warmer climates and the limited accessibility to indoor heating systems and thermal insulation in residential buildings [6].

Previous studies have established strong relationships between health risks and temperature fluctuations in subtropical regions, commonly assessed through measures such as temperature change between neighboring days (TCN), diurnal temperature range (DTR), or temperature variability (TV) [7–9]. Several studies have examined the health impacts of extreme temperature differences over two days or significant temperature variations spanning multiple days, reporting that large temperature fluctuations are associated with increased mortality risks [7, 10]. Recently, a new concept has emerged to understand sudden temperature drop (STD), characterized by a rapid transition from extreme warmth to extreme cold over several days [5]. However, the specific effects of STD on human health have been less thoroughly evaluated, especially in subtropical regions that are expected to experience more frequent STDs in the future [5].

The well-documented U-shaped relationship between temperature and various health risks suggests that as temperature drops from extreme warmth to extreme cold, health risks may initially decrease before rising again [11–13]. However, most previous research has focused on specific health outcomes, leaving the broader, dynamic changes in health risks during STDs insufficiently explored [14]. Since the cooling phase of a STD event could last several days before reaching the coldest temperature, people may not only experience the immediate temperature drop but also be affected by the cumulative effect of sustained temperature drops over the event period. Thus, it would be necessary to investigate the consequential health risks across the whole cooling phase, check whether the severity of health impacts would change under such cold exposures, and identify the corresponding vulnerable populations to different health risks. Such understanding and findings would be crucial for supporting targeted health risk mitigation and prevention strategy development before and during STD events.

Building on these research gaps, this study selected Hong Kong as a case, as it is a representative subtropical city that potentially experiences more frequent temperature fluctuations in the future [15]. Specifically, the objectives of this study included: (1) assessing the impact of STD on various health outcomes (i.e., ambulance demands, hospitalization, and mortality); and (2), understanding the dynamic changes in health risks during STD, informing the development of targeted intervention strategies and public health responses. This study used daily total and cause-specific ambulance callouts, hospital admission, and mortality data to evaluate the impacts of STD on these health outcomes. The findings would provide a scientific basis for understanding the dynamic changes in health risks during winter STD in subtropical regions, supporting the development of specialized local weather alerts, and providing useful reference for other subtropical cities facing similar climate change challenges and health risks.

2. Methods

2.1. Study setting

Hong Kong (22°08’N–22°35’N, 113°49’E–114°31’E) is situated on the southern coast of China and experiences a humid subtropical monsoon climate with hot summers and mild winters. Hong Kong’s population is ageing rapidly. By 2050, Hong Kong is forecasted by the World Health Organization to rank fifth in the world for cities with the largest percentage of older adults, i.e., 40% of the population in Hong Kong will be aged 65 years or above, who are particularly vulnerable to ambient temperature changes due to their reduced thermoregulatory capacities [16–18]. This study focused on winter period that included typical winter months (i.e., from December to next February) and shoulder months (i.e., November and March) in Hong Kong.

2.2. Data collection

Daily ambulance callout data from 1 January 2011–31 December 2019 was obtained from the Fire and Ambulance Services Academy of the Hong Kong Fire Services Department. For each case, the age, gender, and causes of emergency were also collected. The causes of emergency were classified into 11 categories: Central nervous system (CNS), Cardiovascular system (CVS), Gastrointestinal (GI), Genitourinary (GU), Mechanism of injury (MOI), Musculoskeletal system, Obstetric and gynecological conditions (Obstetric_Gynae), Respiratory System (Respiratory), Injury Sites or Anatomical Sites (Sites); Skin and Soft Tissue (SKIN. Including rapid skin rash, skin itchiness, skin blister, urticaria, and other skin and soft tissue related emergencies); and other reasons. One patient may experience more than one cause of emergency.

Daily hospital admission data from 1 January 1999–31 December 2019 was obtained from the Hong Kong Hospital Authority, which collects patient-level information from all public hospitals in Hong Kong, accounting for 83% of overall local hospitalizations. The International Classification of Diseases (ICD-9) was used for recording the causes of hospitalization of each patient. Cause-specific hospital admissions included: x1: Blood and blood-forming organs diseases (ICD-9-CM 280–289); x2: Circulatory system diseases (ICD-9-CM 390–459); x3: Digestive system diseases (ICD-9-CM 520–579); x4: Endocrine, nutritional, and metabolic diseases (ICD-9-CM 240–279); x5: Genitourinary system diseases (ICD-9-CM 580–629); x6: Infectious and parasitic diseases (ICD-9-CM 001–139); x7: Injury and poisoning (ICD-9-CM 800–999); x8: Mental disorders (ICD-9-CM 290–319); x9: Musculoskeletal system and connective tissue diseases (ICD-9-CM 710–739); x10: Neoplasms (ICD-9-CM 140–239); x11: Nervous system and sense organs diseases (ICD-9-CM 320–389); x12: Respiratory system diseases (ICD-9-CM 460–519); x13: Skin and subcutaneous tissue diseases (ICD-9-CM 680–709); and x14: Symptoms, signs, and ill-defined conditions (ICD-9-CM 780–799). Additionally, the age of each case was gathered to enable subgroup analyses [19].

Daily mortality data for the years 1995–2021 was obtained from the Hong Kong Census & Statistics Department. The International Classification of Diseases (ICD-9, ICD-10 and ICD10-updated) was used for recording the causes of deaths. Cause-specific mortalities include total non-external deaths, non-cancer non-external deaths, cardiovascular deaths, respiratory deaths, pneumonia deaths, and deaths attributed to symptoms, signs and abnormal clinical and laboratory findings that not elsewhere classified.

Daily meteorological data including daily minimum and mean temperatures (°C), mean relative humidity (%), and windspeed (km/h) were represented by the weather station located in the city center and obtained from the Hong Kong Observatory. Daily mean concentrations of respirable suspended particulates (PM₁₀, ug/m³) from 13 general monitoring stations across the city were collected, and the average was used to represent overall exposure.

2.3. Sudden temperature drop events identification

A warm-to-cold STD event refers to a short period (within a five-day window) during which the daily minimum temperature (Tmin) suddenly shifts from warm to cold conditions [5]. Tmin was selected as it not only aligns with the triggering signal and weather metrics of Hong Kong’s official cold weather warning system, but also exhibits a higher rate of change than Tmax (Fig A in S1 Appendix). Given that daily Tmin variations have been demonstrated to strongly affect local public health, it serves as a more sensitive and representative indicator for capturing long-term STD events [20, 14]. As illustrated in Fig 1, the daily Tmin data from 1884-2024 was first detrended using a 31-day rolling window to eliminate the effects of long-term warming trends, and the mean and standard deviation of the detrended Tmin from 1991 to 2020 (reference period) were calculated. In alignment with WMO climate normals, the reference period was set as this 30-year period accounted for the recent warming winter climate, thereby ensuring a more accurate baseline for detecting warm and cold day anomalies. All warm and cold days during 1884–2024 were then identified when the detrended Tmin surpassed or dropped below the mean value by one standard deviation [5, 21], and a STD event was confirmed if a warm day was followed by a cold day within a five-day window.

Download:

Fig 1. Workflow for identifying sudden temperature drop events.

https://doi.org/10.1371/journal.pclm.0000980.g001

2.4. Statistical analysis

2.4.1. Investigation of winter sudden temperature drop events patterns.

Five characteristics of the identified winter STD events were firstly evaluated, including: (1) Event frequency: 10-year growth rate of winter events since 1884; (2) Starting temperature (°C): the Tmin of the first day that triggered the event; (3) Drop magnitude (°C): the absolute difference of Tmin between the warmest day and the coldest day; (4) Event duration (days): the time interval from the warmest day to the coldest day of an event; (5) Cooling rate (°C/day): the ratio of the drop magnitude to the duration. These characteristics, excluding event frequency, were classified into high/low groups using the 75^th percentile of their distributions among winter events during 1884–2024.

According to Hong Kong Cold Weather Warning (CWW) system, a Tmin of 12 °C is set as the threshold to define absolute cold. Based on this concept, all identified STDs were further grouped into Type_1 event, referring to the event with the coldest Tmin higher than 12 °C, and Type_2 event, which means the identified event would reach to or fall below 12 °C.

2.4.2. Health effect of winter warm-to-cold temperature drop events.

A quasi-Poisson generalized linear model combined with a distributed lag nonlinear model was applied to evaluate the impact of winter STD exposures on various health outcomes [22]. The model is expressed as:

where μ_t is the expected number of daily health outcomes on day t. β₀ represents the intercept. The cb(•) denotes a cross-basis function designed to capture the delay in the effects of the STD events. The maximum lag period was set as 28 days. The natural cubic spline function ns(•) denotes a smoothed relationship between log(μ_t) and the other factors of day t. Meteorological and air pollution factors including daily mean temperature, wind speed, relative humidity, and PM₁₀ were adjusted as potential confounders. The long-term trends, seasonality, and day of week effects were also considered. The logged annual population log(N_t) and holidays were included as offsets. The degrees of freedom was selected based on quasi Akaike information criterion.

Subgroup analysis included evaluating the impact of STD on various cause-specific hospitalization and ambulance callouts. The effects of STD exposure were also stratified by age groups (i.e., 0–18 years, 19–49 years, 50–64 years, 65–79 years, and 80 years and above). Gender differences were analyzed for ambulance demands and mortality since such information for hospitalization was not available.

2.4.3. Sensitivity analysis.

Sensitivity analysis was conducted by: (1) changing the lag period to 21 days; (2) using a seven-day interval to define the STD events and evaluating the associated health risks.

The winter STD events were identified with Python, while health impacts were assessed with R software (version 4.3.2) with “dlnm” and “splines” packages. P < 0.05 was considered as statistically significant.

3. Results

3.1. Trends and characteristics of winter sudden temperature drop events

From 1884 to 2024, we identified a total of 391 winter STD events (126 Type 1 and 265 Type 2). Throughout this 140-year period, the frequency of winter STD has remained relatively stable at a rate of 25 events per decade (Fig 2). Compared to Type_2 events, type_1 events, primarily observed in shoulder months such as early winter (November) and early spring (March), were characterized with relatively warm starting temperatures (21.14°C), smaller drop magnitudes (7.32°C), and slower cooling rates (2.21°C/day). Type_2 events mostly occurred during typical winter season, beginning with relatively low temperatures (19.33°C), and were featured with a large drop magnitude (9.42°C), a faster cooling rate (2.81°C/day), and a tendency to reach extreme cold levels. The duration of these events consistently concentrated around 3 to 4 days (Fig 3).

Download:

Fig 2. Pattern of different winter sudden temperature drop events.

https://doi.org/10.1371/journal.pclm.0000980.g002

Download:

Fig 3. Trends in the characteristics of different winter sudden temperature drop events.

https://doi.org/10.1371/journal.pclm.0000980.g003

Over the study period, the annual number of hospitalizations was the highest (1.81 million cases per year), followed by mortality and ambulance callouts. The highest incidence rate was observed in hospital admissions, both annually (25.64 per 10⁶) and during the winter period (10.80 per 10⁶), while the rates for ambulance callouts and mortality in these months were 3.77 per 10⁶ and 0.24 per 10⁶, respectively (Table 1).

Download:

Table 1. Basic characteristics of the health outcomes in Hong Kong.

https://doi.org/10.1371/journal.pclm.0000980.t001

3.2. Overall health impacts of winter sudden temperature drops

Compared to non-event, STD was associated with increased risks of overall hospitalization (RR: 1.09, 95% CI: 1.05–1.15) and mortality (RR: 1.31, 95% CI: 1.27–1.34), but not with ambulance demands (Fig 4, Fig B-D in S1 Appendix). Individuals aged 0–18 years and those over 50 years old were both vulnerable to STD in terms of hospitalization risk, while those under 18 years experienced a more pronounced impact on ambulance demand and those aged 45 years and above exhibited higher mortality risks. Most cause-specific hospitalization and mortality risks increased during STD, with notable rises in respiratory-related (RR: 1.11, 95% CI: 1.04–1.19) and skin related (RR: 1.29, 95% CI: 1.03–1.23) ambulance demands. Both genders were vulnerable to increased mortality risk during STD (Fig B-D in S1 Appendix).

Download:

Fig 4. Impact of sudden temperature drops on health outcomes.

Health risks of hospitalization and ambulance callouts under all events (overall), events that never reach 12°C (Type_1), and events that would reach to 12°C (Type_2) were evaluated. 12°C of Tmin is the trigger threshold of Hong Kong’s local cold weather warning.

https://doi.org/10.1371/journal.pclm.0000980.g004

For four factors describing the identified winter STD events, high level of drop magnitude (>10.50°C), longer duration (>5 days), and faster cooling rate (>3.20°C/ day) were linked to a more significant increase in hospitalization risk, while a warmer starting point (>21.65°C) was associated with lower hospital admissions (Fig 5). Similar patterns were also observed for ambulance callouts risks and mortality risks (Fig E and Fig F in S1 Appendix).

Download:

Fig 5. Impact of sudden temperature drop characteristics on hospitalization risk.

The starting point, dropping magnitude, event duration, and cooling rate was divided into high (H) and low (L) groups based on the 75^th percentiles. x1: Blood and blood-forming organs diseases; x2: Circulatory system diseases; x3: Digestive system diseases; x4: Endocrine, nutritional, and metabolic diseases; x5: Genitourinary system diseases; x6: Infectious and parasitic diseases; x7: Injury and poisoning; x8: Mental disorders; x9: Musculoskeletal system and connective tissue diseases; x10: Neoplasms; x11: Nervous system and sense organs diseases; x12: Respiratory system diseases; x13: Skin and subcutaneous tissue diseases; and x14: Symptoms, signs, and ill-defined conditions.

https://doi.org/10.1371/journal.pclm.0000980.g005

3.3. Dynamic changes in health risks during sudden temperature drops

Based on the statistical analysis results, we found a series of varied dynamic health outcomes during the dropping phase of such STD events from extreme warmth to extreme cold. The details are shown below:

At the beginning stage when the temperature dropped from warm to a relatively moderate level (Type_1 events), an increase in skin-related ambulance demands was observed (RR: 1.33, 95% CI: 1.07–1.66), while the emergency healthcare needs for other causes or specific populations were less affected. During this stage, changes in hospital admissions and mortality risks remained statistically non-significant.
When temperature continued to drop and triggered the local cold warning level (i.e., daily Tmin equals to 12°C, Type_2 events), more pronounced health impacts were observed. The overall ambulance demand increased (RR: 1.05, 95% CI: 1.01–1.10), particularly for respiratory-related demands (RR: 1.23, 95% CI: 1.13–1.34). Vulnerable groups included males (RR: 1.06, 95% CI: 1.01–1.11) and individuals under 18 years old (RR: 1.30, 95% CI: 1.17–1.45). Hospitalization risks for both total (RR: 1.20, 95% CI: 1.13–1.27) and nearly all cause-specific admissions increased, affecting all age groups. Mortality risks rose during these events, with overall (RR: 1.10, 95% CI: 1.07–1.14) and cause-specific mortality increasing, particularly among the elderly aged 65 years and older, who were identified as a vulnerable group.
When the temperature further dropped from the cold warning level to an even more extreme cold situation (i.e., below threshold parts of the Type_2 events), various health risks were notably elevated. For ambulance callouts, more vulnerable groups were observed at this stage compared to temperature drops that did not reach the extreme cold level (i.e., above threshold part). Specifically, a remarkable increase was seen among those aged 18 years or younger (RR: 2.45, 95% CI: 1.64–3.65) during STD episodes, along with a similar upward trend in respiratory-related emergency healthcare needs (RR: 1.58, 95% CI: 1.15–2.16). In terms of hospital admission and mortality, drops below the 12°C threshold had a more substantial impact than less severe events (Table 2, Table A in S1 Appendix). Briefly, hospital admission risks increased by 25.00% during this stage (RR: 1.25, 95% CI: 1.04–1.50), with the elderly, particularly those aged 80 years and above (RR: 1.35, 95% CI: 1.18–1.55), being more vulnerable. Consistent rising patterns were observed for certain cause-specific hospitalizations. Under cumulative exposure to these temperature drops, mortality risks rose significantly (RR: 1.32, 95% CI: 1.21–1.44), particularly among those aged 65–74 years and those who died with diagnoses related to symptoms, signs and abnormal clinical and laboratory findings (Table A in S1 Appendix).

Download:

Table 2. Health impact of Type_2 sudden temperature drop events.

https://doi.org/10.1371/journal.pclm.0000980.t002

3.4. Sensitivity analysis

When setting the maximum lag period to 21 days, the evaluated hospitalization risks remained consistent. However, the impact of STD on ambulance callouts became more significant when the maximum lag was changed from 28 days to 21 days, and additional vulnerable groups were identified, highlighting that STD has a more immediate effect on emergency healthcare needs compared to other health outcomes (Table B in S1 Appendix). Using a seven-day interval to define the STD resulted in less significant impacts on health outcomes, but the overall patterns of the changes in health outcomes remained stable (Table C in S1 Appendix).

4. Discussion

As climate change contributes to a warming world and creates more warm winters in subtropical climate regions, less attention has been paid to cold weather events. STD has become a new type of extreme cold weather event during winter time in subtropical regions, leaving its health impacts unclear. This study assessed how STD progressively affects the dynamic changes in various health outcomes, namely ambulance callouts, hospitalization, and mortality, among the Hong Kong population. The findings revealed that STD was initially linked to increased ambulance demands among vulnerable populations with certain health conditions. As the temperature continued to decline, the risk of hospitalization rose concurrently with ongoing ambulance demands. When temperatures reached an extremely cold level, in addition to the increased hospitalizations, the risk of mortality elevated remarkably. The findings provided a clear overview of the dynamic changes in health risks during STD, and they could serve as a significant reference for improving local cold weather resilience actions.

4.1. Sudden temperature drops and the health impacts

Although limited previous studies have examined STD from warm to cold over several days, conclusions from extreme temperatures and temperature fluctuations may offer valuable insights for interpreting our findings.

Regarding ambulance demand, cold temperatures are related to increased emergency healthcare needs, particularly for respiratory and cardiovascular conditions, which are especially sensitive to cold during the winter months [23, 24, 25]. The effects of temperature changes, however, have yielded inconsistent findings. Some studies indicate that days with large temperature variations are linked to increased emergency ambulance dispatches, while temperature drops between consecutive days appear to have a protective effect, reducing emergency healthcare demands [16, 26]. Conversely, a study in China reported that temperature drops between neighboring days were associated with increased emergency department visits [27]. Another study highlighted that only ambulance dispatches for respiratory diseases were sensitive to cold, while dispatches related to cardiac conditions and other certain reasons were not significantly affected [23]. Our study found that STD was associated with increased ambulance demands, particularly among young people and for emergencies caused by respiratory and skin diseases. Additionally, the effects were more pronounced during events with longer durations and faster cooling rates.

Regarding the impact of inter-day temperature changes on hospitalization risks, most existing evidence is based on studies of temperature drops between consecutive days. A multi-site study in China reported an increased risk of hospital admissions when the temperature dropped by 1.90°C between neighboring days during the cold season, with individuals aged 15–64 years and patients with certain medical conditions being more sensitive to these extreme temperature drops [28]. Another study by Chen et al. supported the association between extreme temperature drops between consecutive days and an increased risk of acute aortic dissection [3]. Although reports of STD over several days are relatively rare, this study consistently found that hospital admission risks increased with STD, except for individuals aged 19–49 years. Furthermore, STD with various characteristics were linked to increases in both total and cause-specific hospital admission risks, except for events with relatively small drop magnitudes or those ending at relatively warm levels.

The impacts of temperature drops between neighboring days on mortality have also been widely evaluated, with inconsistent findings across different countries and regions. In contrast, the impact of STD has received less attention [29, 30]. One multi-country study assessed both interday and intraday temperature variations and reported an increased mortality risk with prolonged exposure to temperature variation [7]. Additionally, another study reported a positive relationship between extreme day-to-day temperature changes and increased mortality risks in Jiangsu, China, and the USA [31]. This study observed an increased mortality risk associated with STD, especially when temperatures dropped to or below relatively extreme levels.

4.2. Dynamic changes in various health risks during sudden temperature drops

Few studies have assessed how temperature drops progressively influence the severity of health outcomes. One study in Germany analyzed the impact of extreme cold temperatures on case severity in emergency care, finding that such exposure decreased the general emergency admission risk but increased the risk of fatal emergency admission and case mortality [32]. Another study in Japan evaluated the impact of cold temperature on mortality and morbidity, and reported that extreme cold was associated with a higher mortality risk than morbidity risk [33]. Specifically, mortality from circulatory diseases was more sensitive to cold than morbidity, whereas for respiratory diseases, the risk was higher for morbidity than mortality. Research conducted in southern New England, Taiwan, and Spain supports the finding that the patterns and magnitudes of temperature impacts on morbidity and mortality differed [34, 35, 36]. Although limited evidence is available for the impact of temperature drops on various health outcomes, this research consistently reported that STD has greater impacts on mortality than hospital admission, while milder temperature drops at warm levels mainly contribute to increased ambulance demands.

The observed dynamic changes in the impact of STD on various health outcomes may be attributed to several reasons. First, differences in the distribution of diseases and vulnerable populations across emergency healthcare demands, hospital admission, and mortality may contribute to the varying health risks. The ambulance demands often reflect immediate, short-term responses to sudden temperature changes, particularly among vulnerable groups and for certain disease causes. Such responses could be captured earlier when the temperature begins to decline during winter. When the temperature continuously declines to a colder level, more people who are vulnerable to a larger proportion of cold-sensitive diseases would be observed, leading to more severe conditions in hospital admission and persistent ambulance callouts. When extreme cold levels are reached, such exposure would be fatal for vulnerable populations such as the elderly with pre-existing health conditions [33]. Another reason would be the lag effect of sudden temperature drops exposure. As suggested by prior studies, it typically takes several hours to days after exposure for health risk to increase, although the risk may be slightly higher than none [37, 38]. For example, a study in China evaluated the impact of 24-h temperature drops on cardiovascular diseases caused healthcare needs, and reported that the most significant effect was observed earlier for outpatient and emergency visit than for inpatient admissions [39]. This lag effect may be partly due to cumulative exposure to cold temperatures, with longer exposure leading to more severe health outcomes. This study also found that ambulance demands were more pronounced within 21 days after exposure compared to 28 days, while the longer lag period was associated with increased hospitalization risks. Given the limited biological research on how temperature change progressively affect human physiological responses, the findings of this study would provide a scientific foundation for future investigations into the biological mechanisms underlying these effects.

4.3. Implications

The findings of this research have several practical implications. (1) Firstly, the STD events are projected to be more frequent and intense in the future in most regions globally under climate change context, including East Asia and Southeast Asia [5]. As STD is an emerging extreme cold weather event in Hong Kong with significant negative health impacts, it is essential to develop specialized weather alert systems to raise public awareness about these events. Such alerts would be able to inform communities in advance, encouraging precautionary measures to reduce health risks. (2) Secondly, based on the observed increase in ambulance demands, hospitalizations, and mortality risks during STD, targeted prevention and protection plans could be implemented, especially for vulnerable populations. These plans would be tailored to different stages of STD events to effectively safeguard at-risk groups. (3) Thirdly, the results provide the local government with a comprehensive understanding of the dynamic health outcomes associated with STD, enabling them to allocate healthcare resources more efficiently and coordinate responses effectively. This holistic approach would help improve preparedness and resilience against this emerging weather phenomenon.

4.4. Strength and limitations

This study has two strengths. First, while a large body of existing cold-health research concentrates on absolute extreme cold situations, the temperature fluctuations that occur prior to reaching extreme cold levels are less evaluated. This research evaluated the temperature drops from warm to cold, which may finally result in extremely low levels, and assessed an increased health risk during such events. The findings highlighted the significance of considering such events in cold-health research and in developing cold-health mitigation strategies in real-world settings. Second, this study assessed a range of health outcomes, including ambulance demands, hospital admissions, and mortality, which reflect varying levels of health severities. Analyzing these diverse health outcomes provided us with a more in-depth understanding of how temperature drops from warm to cold would influence health conditions, and the findings would be more comprehensive for guiding local health and climate interventions.

However, some limitations should be acknowledged. First, due to data quality and coverage in different weather stations, this study retrieved historical weather records from a single representative urban weather station, which may not fully capture intra-urban variations during such temperature drop events. Nonetheless, the selected station’s 140-year continuous weather record ensures highly representative results for capturing long-term changing climate trends. In addition, for ambulance data, the causes of the emergencies were primarily assessed by the attending paramedic at the scene based on the patient’s signs, symptoms, vital signs, and onset history. Although this assessment may be subject to reporting biases that could affect the precision of individual cause estimates, these biases were likely to be similar across both the event and non-event periods. Therefore, the ratio of risks between these periods would be less influenced by such biases. Nonetheless, we acknowledge this as a limitation and encourage further research to develop methodological approaches that can better address and mitigate these biases. Another limitation of this study is the varying time spans across the health outcomes, which may introduce minor temporal inconsistencies. While we used the maximum available data to ensure high statistical power and controlled for long-term trends using natural cubic splines, future studies with perfectly synchronized, long-term datasets are needed to further validate these comparative trends. Moreover, the health impact of STDs may also be influenced by human adaptation and social determinants, including living environment, socioeconomic status, and access to healthcare. These factors were not considered in this study due to the unavailability of relevant data. Lastly, given the relatively short duration of available health data and fewer STD events identified in this period, a more detailed analysis of event patterns, such as varying durations and their specific health impacts, was limited. However, despite these limitations, the findings of this research could still indicate the patterns of the overall increased health risks during STDs, and further comprehensive research is recommended for better understanding the health impacts of such events.

5. Conclusion

Climate change has not only led to warmer winters in subtropical climate regions but also created significant temperature fluctuations, such as STDs from warm to cold in winter time. These STD events would initially contribute to increased ambulance callouts, with more severe temperature drops subsequently resulting in increased hospitalizations and mortality. The findings emphasize the need to integrate STD into early warning systems and establish specialized weather alerts to increase public awareness. Additionally, healthcare resources should be allocated strategically to effectively address the health risks associated with STD events.

Supporting information

S1 Appendix. Appendix.

https://doi.org/10.1371/journal.pclm.0000980.s001

(DOCX)

Acknowledgments

We acknowledge the data contributions from the Hong Kong Observatory, the Hong Kong Fire Services Department, Hong Kong Hospital Authority, and the Hong Kong Census & Statistics Department.

References

1. Seltenrich N. Between Extremes: Health Effects of Heat and Cold. Environ Health Perspect. 2015;123(11):A275-80. pmid:26523798
- View Article
- PubMed/NCBI
- Google Scholar
2. Wang L, Liu T, Hu M, Zeng W, Zhang Y, Rutherford S, et al. The impact of cold spells on mortality and effect modification by cold spell characteristics. Sci Rep. 2016;6:38380. pmid:27922084
- View Article
- PubMed/NCBI
- Google Scholar
3. Chen J, Gao Y, Jiang Y, Li H, Lv M, Duan W, et al. Low ambient temperature and temperature drop between neighbouring days and acute aortic dissection: a case-crossover study. Eur Heart J. 2022;43(3):228–35. pmid:34849712
- View Article
- PubMed/NCBI
- Google Scholar
4. Zhao Q, Guo Y, Ye T, Gasparrini A, Tong S, Overcenco A, et al. Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. Lancet Planet Health. 2021;5(7):e415–25. pmid:34245712
- View Article
- PubMed/NCBI
- Google Scholar
5. Wu S, Luo M, Lau GN-C, Zhang W, Wang L, Liu Z, et al. Rapid flips between warm and cold extremes in a warming world. Nat Commun. 2025;16(1):3543. pmid:40263258
- View Article
- PubMed/NCBI
- Google Scholar
6. Son J-Y, Gouveia N, Bravo MA, de Freitas CU, Bell ML. The impact of temperature on mortality in a subtropical city: effects of cold, heat, and heat waves in São Paulo, Brazil. Int J Biometeorol. 2016;60(1):113–21. pmid:25972308
- View Article
- PubMed/NCBI
- Google Scholar
7. Guo Y, Gasparrini A, Armstrong BG, Tawatsupa B, Tobias A, Lavigne E, et al. Temperature Variability and Mortality: A Multi-Country Study. Environ Health Perspect. 2016;124(10):1554–9. pmid:27258598
- View Article
- PubMed/NCBI
- Google Scholar
8. Liu S, Wang Y, Ren C. Cold weather patterns and health impacts across climate regions in a warming world: A systematic review at the global scale. Environmental Impact Assessment Review. 2026;118:108311.
- View Article
- Google Scholar
9. Zheng J, Yue L, Wang B, Li Y, Zhang L, Xue B, et al. Seasonal characteristics of ambient temperature variation (DTR, TCN, and TV0-t) and air pollutants on childhood asthma attack in a dry and cold city in China. Environ Res. 2023;217:114872. pmid:36435499
- View Article
- PubMed/NCBI
- Google Scholar
10. Shi L, Kloog I, Zanobetti A, Liu P, Schwartz JD. Impacts of Temperature and its Variability on Mortality in New England. Nat Clim Chang. 2015;5:988–91. pmid:26640524
- View Article
- PubMed/NCBI
- Google Scholar
11. Murage P, Hajat S, Kovats RS. Effect of night-time temperatures on cause and age-specific mortality in London. Environ Epidemiol. 2017;1(2):e005. pmid:33195962
- View Article
- PubMed/NCBI
- Google Scholar
12. Scovronick N, Sera F, Acquaotta F, Garzena D, Fratianni S, Wright CY, et al. The association between ambient temperature and mortality in South Africa: A time-series analysis. Environ Res. 2018;161:229–35. pmid:29161655
- View Article
- PubMed/NCBI
- Google Scholar
13. Song X, Wang S, Hu Y, Yue M, Zhang T, Liu Y, et al. Impact of ambient temperature on morbidity and mortality: An overview of reviews. Sci Total Environ. 2017;586:241–54. pmid:28187945
- View Article
- PubMed/NCBI
- Google Scholar
14. Wang Y, Liu S, Ren C, Woo J, Chong KC, Ng E. Increased mortality risks of winter temperature flips: A growing concern in aging society of subtropical climate regions. PLOS Clim. 2026;5(6):e0000859.
- View Article
- Google Scholar
15. Yao L, Gao K, Lau K, Liu Z, Ren C, Fung JCH, et al. Impacts of sub-daily rapid temperature drops on winter outdoor thermal comfort under climate change: A case study in Hong Kong. Urban Climate. 2026;67:102943.
- View Article
- Google Scholar
16. Cheng J, Xu Z, Zhao D, Xie M, Yang H, Wen L, et al. Impacts of temperature change on ambulance dispatches and seasonal effect modification. Int J Biometeorol. 2016;60(12):1863–71. pmid:27146142
- View Article
- PubMed/NCBI
- Google Scholar
17. Jung D, Kim H, An J, Hong T. Thermoregulatory responses of young and elderly adults under temperature ramps. Building and Environment. 2023;244:110760.
- View Article
- Google Scholar
18. Wang Z, Yu H, Jiao Y, Chu X, Luo M. Chinese older people’s subjective and physiological responses to moderate cold and warm temperature steps. Building and Environment. 2019;149:526–36.
- View Article
- Google Scholar
19. Li K, Wang Y, Jiang X, Li C, Chen J, Zeng Y, et al. Relationship between temperature variability and daily hospitalisations in Hong Kong over two decades. J Glob Health. 2023;13:04122. pmid:37824178
- View Article
- PubMed/NCBI
- Google Scholar
20. Ho JY-E, Guo Y, Chong KC, Chan PW, Ho CK, Law HF, et al. Suitable temperature indicator for adverse health impacts in sub-tropical cities: a case study in Hong Kong from 2010-2019. Int J Biometeorol. 2025;69(1):233–44. pmid:39476018
- View Article
- PubMed/NCBI
- Google Scholar
21. Park T-W, Ho C-H, Jeong S-J, Choi Y-S, Park SK, Song C-K. Different characteristics of cold day and cold surge frequency over East Asia in a global warming situation. J Geophys Res. 2011;116(D12).
- View Article
- Google Scholar
22. Gasparrini A. Distributed Lag Linear and Non-Linear Models in R: The Package dlnm. J Stat Softw. 2011;43(8):1–20. pmid:22003319
- View Article
- PubMed/NCBI
- Google Scholar
23. Sangkharat K, Mahmood MA, Thornes JE, Fisher PA, Pope FD. Impact of extreme temperatures on ambulance dispatches in London, UK. Environ Res. 2020;182:109100. pmid:31918315
- View Article
- PubMed/NCBI
- Google Scholar
24. Wang Q, He Y, Hajat S, Cheng J, Xu Z, Hu W, et al. Temperature-sensitive morbidity indicator: consequence from the increased ambulance dispatches associated with heat and cold exposure. Int J Biometeorol. 2021;65(11):1871–80. pmid:33963898
- View Article
- PubMed/NCBI
- Google Scholar
25. Xu E, Li Y, Li T, Li Q. Association between ambient temperature and ambulance dispatch: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2022;29(44):66335–47. pmid:35499723
- View Article
- PubMed/NCBI
- Google Scholar
26. Wang R, Tian J, Li L, Liu X, Leng M, Ye Z, et al. Relationship between diurnal temperature range and emergency ambulance dispatches due to stroke in Guangzhou, China. Sci Total Environ. 2022;817:153037. pmid:35031377
- View Article
- PubMed/NCBI
- Google Scholar
27. Yuan K, Lv X, Zhang Y, Liu R, Liang T, Zhang Z, et al. Exposure to hourly ambient temperature and temperature change between neighboring days and risk of emergency department visits for cause-specific cardiovascular disease. Urban Climate. 2024;58:102197.
- View Article
- Google Scholar
28. Huang Y-S, Song H-J, Cheng Y-B, Tong S, Zhang R, Yao X-Y, et al. Temperature change between neighboring days and hospital admissions in China. Advances in Climate Change Research. 2023;14(6):847–55.
- View Article
- Google Scholar
29. Vicedo-Cabrera AM, Forsberg B, Tobias A, Zanobetti A, Schwartz J, Armstrong B, et al. Associations of inter- and intraday temperature change with mortality. Am J Epidemiol. 2016;183(4):286–93.
- View Article
- Google Scholar
30. Zhan Z, Zhao Y, Pang S, Zhong X, Wu C, Ding Z. Temperature change between neighboring days and mortality in United States: A nationwide study. Sci Total Environ. 2017;584–585:1152–61. pmid:28162760
- View Article
- PubMed/NCBI
- Google Scholar
31. Liu Q, Fu C, Xu Z, Ding A. Global warming intensifies extreme day-to-day temperature changes in mid–low latitudes. Nat Clim Chang. 2025;16(1):69–76.
- View Article
- Google Scholar
32. Frasch JJ, König H-H, Konnopka C. Effects of extreme temperature on morbidity, mortality, and case severity in German emergency care. Environ Res. 2025;270:121021. pmid:39914711
- View Article
- PubMed/NCBI
- Google Scholar
33. Yuan L, Madaniyazi L, Vicedo-Cabrera AM, Honda Y, Ng CFS, Ueda K, et al. A Nationwide Comparative Analysis of Temperature-Related Mortality and Morbidity in Japan. Environ Health Perspect. 2023;131(12):127008. pmid:38060264
- View Article
- PubMed/NCBI
- Google Scholar
34. Iñiguez C, Royé D, Tobías A. Contrasting patterns of temperature related mortality and hospitalization by cardiovascular and respiratory diseases in 52 Spanish cities. Environ Res. 2021;192:110191. pmid:32980302
- View Article
- PubMed/NCBI
- Google Scholar
35. Lin Y-K, Sung F-C, Honda Y, Chen Y-J, Wang Y-C. Comparative assessments of mortality from and morbidity of circulatory diseases in association with extreme temperatures. Sci Total Environ. 2020;723:138012. pmid:32217384
- View Article
- PubMed/NCBI
- Google Scholar
36. Weinberger KR, Kirwa K, Eliot MN, Gold J, Suh HH, Wellenius GA. Projected Changes in Temperature-related Morbidity and Mortality in Southern New England. Epidemiology. 2018;29(4):473–81. pmid:29561281
- View Article
- PubMed/NCBI
- Google Scholar
37. Cui Y, Ai S, Liu Y, Qian ZM, Wang C, Sun J, et al. Hourly associations between ambient temperature and emergency ambulance calls in one central Chinese city: Call for an immediate emergency plan. Sci Total Environ. 2020;711:135046. pmid:31812379
- View Article
- PubMed/NCBI
- Google Scholar
38. Gestal Romaní S, Figueiras A, Royé D. Effect of Temperature on Emergency Ambulance Call-Outs for Cardiovascular Causes: A Scoping Review. Environ Health (Wash). 2023;1(1):6–14. pmid:39474625
- View Article
- PubMed/NCBI
- Google Scholar
39. Shi Q, Wei X, Liu Y, Meng X, Zhu W, Wang M, et al. An effect of 24-hour temperature change on outpatient and emergency and inpatient visits for cardiovascular diseases in northwest China. Environ Sci Pollut Res Int. 2021;28(33):45793–804. pmid:33877519
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Seltenrich N. Between Extremes: Health Effects of Heat and Cold. Environ Health Perspect. 2015;123(11):A275-80. pmid:26523798
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Wang L, Liu T, Hu M, Zeng W, Zhang Y, Rutherford S, et al. The impact of cold spells on mortality and effect modification by cold spell characteristics. Sci Rep. 2016;6:38380. pmid:27922084
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Chen J, Gao Y, Jiang Y, Li H, Lv M, Duan W, et al. Low ambient temperature and temperature drop between neighbouring days and acute aortic dissection: a case-crossover study. Eur Heart J. 2022;43(3):228–35. pmid:34849712
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Zhao Q, Guo Y, Ye T, Gasparrini A, Tong S, Overcenco A, et al. Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study. Lancet Planet Health. 2021;5(7):e415–25. pmid:34245712
View Article
PubMed/NCBI
Google Scholar

[14] View Article

[15] PubMed/NCBI

[16] Google Scholar

[ref5] 5. Wu S, Luo M, Lau GN-C, Zhang W, Wang L, Liu Z, et al. Rapid flips between warm and cold extremes in a warming world. Nat Commun. 2025;16(1):3543. pmid:40263258
View Article
PubMed/NCBI
Google Scholar

[18] View Article

[19] PubMed/NCBI

[20] Google Scholar

[ref6] 6. Son J-Y, Gouveia N, Bravo MA, de Freitas CU, Bell ML. The impact of temperature on mortality in a subtropical city: effects of cold, heat, and heat waves in São Paulo, Brazil. Int J Biometeorol. 2016;60(1):113–21. pmid:25972308
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref7] 7. Guo Y, Gasparrini A, Armstrong BG, Tawatsupa B, Tobias A, Lavigne E, et al. Temperature Variability and Mortality: A Multi-Country Study. Environ Health Perspect. 2016;124(10):1554–9. pmid:27258598
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref8] 8. Liu S, Wang Y, Ren C. Cold weather patterns and health impacts across climate regions in a warming world: A systematic review at the global scale. Environmental Impact Assessment Review. 2026;118:108311.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref9] 9. Zheng J, Yue L, Wang B, Li Y, Zhang L, Xue B, et al. Seasonal characteristics of ambient temperature variation (DTR, TCN, and TV0-t) and air pollutants on childhood asthma attack in a dry and cold city in China. Environ Res. 2023;217:114872. pmid:36435499
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref10] 10. Shi L, Kloog I, Zanobetti A, Liu P, Schwartz JD. Impacts of Temperature and its Variability on Mortality in New England. Nat Clim Chang. 2015;5:988–91. pmid:26640524
View Article
PubMed/NCBI
Google Scholar

[37] View Article

[38] PubMed/NCBI

[39] Google Scholar

[ref11] 11. Murage P, Hajat S, Kovats RS. Effect of night-time temperatures on cause and age-specific mortality in London. Environ Epidemiol. 2017;1(2):e005. pmid:33195962
View Article
PubMed/NCBI
Google Scholar

[41] View Article

[42] PubMed/NCBI

[43] Google Scholar

[ref12] 12. Scovronick N, Sera F, Acquaotta F, Garzena D, Fratianni S, Wright CY, et al. The association between ambient temperature and mortality in South Africa: A time-series analysis. Environ Res. 2018;161:229–35. pmid:29161655
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref13] 13. Song X, Wang S, Hu Y, Yue M, Zhang T, Liu Y, et al. Impact of ambient temperature on morbidity and mortality: An overview of reviews. Sci Total Environ. 2017;586:241–54. pmid:28187945
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref14] 14. Wang Y, Liu S, Ren C, Woo J, Chong KC, Ng E. Increased mortality risks of winter temperature flips: A growing concern in aging society of subtropical climate regions. PLOS Clim. 2026;5(6):e0000859.
View Article
Google Scholar

[53] View Article

[54] Google Scholar

[ref15] 15. Yao L, Gao K, Lau K, Liu Z, Ren C, Fung JCH, et al. Impacts of sub-daily rapid temperature drops on winter outdoor thermal comfort under climate change: A case study in Hong Kong. Urban Climate. 2026;67:102943.
View Article
Google Scholar

[56] View Article

[57] Google Scholar

[ref16] 16. Cheng J, Xu Z, Zhao D, Xie M, Yang H, Wen L, et al. Impacts of temperature change on ambulance dispatches and seasonal effect modification. Int J Biometeorol. 2016;60(12):1863–71. pmid:27146142
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref17] 17. Jung D, Kim H, An J, Hong T. Thermoregulatory responses of young and elderly adults under temperature ramps. Building and Environment. 2023;244:110760.
View Article
Google Scholar

[63] View Article

[64] Google Scholar

[ref18] 18. Wang Z, Yu H, Jiao Y, Chu X, Luo M. Chinese older people’s subjective and physiological responses to moderate cold and warm temperature steps. Building and Environment. 2019;149:526–36.
View Article
Google Scholar

[66] View Article

[67] Google Scholar

[ref19] 19. Li K, Wang Y, Jiang X, Li C, Chen J, Zeng Y, et al. Relationship between temperature variability and daily hospitalisations in Hong Kong over two decades. J Glob Health. 2023;13:04122. pmid:37824178
View Article
PubMed/NCBI
Google Scholar

[69] View Article

[70] PubMed/NCBI

[71] Google Scholar

[ref20] 20. Ho JY-E, Guo Y, Chong KC, Chan PW, Ho CK, Law HF, et al. Suitable temperature indicator for adverse health impacts in sub-tropical cities: a case study in Hong Kong from 2010-2019. Int J Biometeorol. 2025;69(1):233–44. pmid:39476018
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref21] 21. Park T-W, Ho C-H, Jeong S-J, Choi Y-S, Park SK, Song C-K. Different characteristics of cold day and cold surge frequency over East Asia in a global warming situation. J Geophys Res. 2011;116(D12).
View Article
Google Scholar

[77] View Article

[78] Google Scholar

[ref22] 22. Gasparrini A. Distributed Lag Linear and Non-Linear Models in R: The Package dlnm. J Stat Softw. 2011;43(8):1–20. pmid:22003319
View Article
PubMed/NCBI
Google Scholar

[80] View Article

[81] PubMed/NCBI

[82] Google Scholar

[ref23] 23. Sangkharat K, Mahmood MA, Thornes JE, Fisher PA, Pope FD. Impact of extreme temperatures on ambulance dispatches in London, UK. Environ Res. 2020;182:109100. pmid:31918315
View Article
PubMed/NCBI
Google Scholar

[84] View Article

[85] PubMed/NCBI

[86] Google Scholar

[ref24] 24. Wang Q, He Y, Hajat S, Cheng J, Xu Z, Hu W, et al. Temperature-sensitive morbidity indicator: consequence from the increased ambulance dispatches associated with heat and cold exposure. Int J Biometeorol. 2021;65(11):1871–80. pmid:33963898
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref25] 25. Xu E, Li Y, Li T, Li Q. Association between ambient temperature and ambulance dispatch: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2022;29(44):66335–47. pmid:35499723
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref26] 26. Wang R, Tian J, Li L, Liu X, Leng M, Ye Z, et al. Relationship between diurnal temperature range and emergency ambulance dispatches due to stroke in Guangzhou, China. Sci Total Environ. 2022;817:153037. pmid:35031377
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref27] 27. Yuan K, Lv X, Zhang Y, Liu R, Liang T, Zhang Z, et al. Exposure to hourly ambient temperature and temperature change between neighboring days and risk of emergency department visits for cause-specific cardiovascular disease. Urban Climate. 2024;58:102197.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref28] 28. Huang Y-S, Song H-J, Cheng Y-B, Tong S, Zhang R, Yao X-Y, et al. Temperature change between neighboring days and hospital admissions in China. Advances in Climate Change Research. 2023;14(6):847–55.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref29] 29. Vicedo-Cabrera AM, Forsberg B, Tobias A, Zanobetti A, Schwartz J, Armstrong B, et al. Associations of inter- and intraday temperature change with mortality. Am J Epidemiol. 2016;183(4):286–93.
View Article
Google Scholar

[106] View Article

[107] Google Scholar

[ref30] 30. Zhan Z, Zhao Y, Pang S, Zhong X, Wu C, Ding Z. Temperature change between neighboring days and mortality in United States: A nationwide study. Sci Total Environ. 2017;584–585:1152–61. pmid:28162760
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref31] 31. Liu Q, Fu C, Xu Z, Ding A. Global warming intensifies extreme day-to-day temperature changes in mid–low latitudes. Nat Clim Chang. 2025;16(1):69–76.
View Article
Google Scholar

[113] View Article

[114] Google Scholar

[ref32] 32. Frasch JJ, König H-H, Konnopka C. Effects of extreme temperature on morbidity, mortality, and case severity in German emergency care. Environ Res. 2025;270:121021. pmid:39914711
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref33] 33. Yuan L, Madaniyazi L, Vicedo-Cabrera AM, Honda Y, Ng CFS, Ueda K, et al. A Nationwide Comparative Analysis of Temperature-Related Mortality and Morbidity in Japan. Environ Health Perspect. 2023;131(12):127008. pmid:38060264
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref34] 34. Iñiguez C, Royé D, Tobías A. Contrasting patterns of temperature related mortality and hospitalization by cardiovascular and respiratory diseases in 52 Spanish cities. Environ Res. 2021;192:110191. pmid:32980302
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref35] 35. Lin Y-K, Sung F-C, Honda Y, Chen Y-J, Wang Y-C. Comparative assessments of mortality from and morbidity of circulatory diseases in association with extreme temperatures. Sci Total Environ. 2020;723:138012. pmid:32217384
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref36] 36. Weinberger KR, Kirwa K, Eliot MN, Gold J, Suh HH, Wellenius GA. Projected Changes in Temperature-related Morbidity and Mortality in Southern New England. Epidemiology. 2018;29(4):473–81. pmid:29561281
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref37] 37. Cui Y, Ai S, Liu Y, Qian ZM, Wang C, Sun J, et al. Hourly associations between ambient temperature and emergency ambulance calls in one central Chinese city: Call for an immediate emergency plan. Sci Total Environ. 2020;711:135046. pmid:31812379
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref38] 38. Gestal Romaní S, Figueiras A, Royé D. Effect of Temperature on Emergency Ambulance Call-Outs for Cardiovascular Causes: A Scoping Review. Environ Health (Wash). 2023;1(1):6–14. pmid:39474625
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref39] 39. Shi Q, Wei X, Liu Y, Meng X, Zhu W, Wang M, et al. An effect of 24-hour temperature change on outpatient and emergency and inpatient visits for cardiovascular diseases in northwest China. Environ Sci Pollut Res Int. 2021;28(33):45793–804. pmid:33877519
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

Figures

Abstract

1. Introduction

2. Methods

2.1. Study setting

2.2. Data collection

2.3. Sudden temperature drop events identification

2.4. Statistical analysis

2.4.1. Investigation of winter sudden temperature drop events patterns.

2.4.2. Health effect of winter warm-to-cold temperature drop events.

2.4.3. Sensitivity analysis.

3. Results

3.1. Trends and characteristics of winter sudden temperature drop events

3.2. Overall health impacts of winter sudden temperature drops

3.3. Dynamic changes in health risks during sudden temperature drops

3.4. Sensitivity analysis

4. Discussion

4.1. Sudden temperature drops and the health impacts

4.2. Dynamic changes in various health risks during sudden temperature drops

4.3. Implications

4.4. Strength and limitations

5. Conclusion

Supporting information

S1 Appendix. Appendix.

Acknowledgments

References