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The impact of apparent temperature on the emergency visits for traumatic fractures in Hangzhou, China

BMC Public Health volume 24, Article number: 1681 (2024) Cite this article

Abstract

Background

Traumatic fractures occur frequently worldwide. However, research remains limited on the association between short-term exposure to temperature and traumatic fractures. This study aims to explore the impact of apparent temperature (AT) on emergency visits (EVs) due to traumatic fractures.

Methods

Based on EVs data for traumatic fractures and the contemporary meteorological data, a generalized Poisson regression model along with a distributed lag nonlinear model (DLNM) were undertaken to determine the impact of AT on traumatic fracture EVs. Subgroup analysis by gender and age and sensitivity analysis were also performed.

Results

A total of 25,094 EVs for traumatic fractures were included in the study. We observed a wide “J”-shaped relationship between AT and risk of traumatic fractures, with AT above 9.5 °C positively associated with EVs due to traumatic fractures. The heat effects became significant at cumulative lag 0–11 days, and the relative risk (RR) for moderate heat (95th percentile, 35.7 °C) and extreme heat (99.5th percentile, 38.8 °C) effect was 1.311 (95% CI: 1.132–1.518) and 1.418 (95% CI: 1.191–1.688) at cumulative lag 0–14 days, respectively. The cold effects were consistently non-significant on single or cumulative lag days across 0–14 days. The heat effects were higher among male and those aged 18–65 years old. The sensitivity analysis results remained robust.

Conclusion

Higher AT is associated with cumulative and delayed higher traumatic fracture EVs. The male and those aged 18–65 years are more susceptible to higher AT.

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Introduction

Traumatic injuries are a major cause of mortality and disability globally [1]. In China, injuries are the fifth leading cause of death in the whole population and the most common cause of death in young adults, which causes a substantial disease burden [2]. Traumatic fractures are the most common consequence of traumatic injuries and severely affect quality of life of patients. Therefore, identifying the risk factors for traumatic fractures is important to help policymakers develop prevention and control strategies.

While the individual risk factors for traumatic fractures, such as older age, occupation, smoking, excess alcohol consumption, sleeping disorder, and previous fracture history have been well elucidated [3, 4], the environmental risk factors for traumatic fractures have not been sufficiently studied. Recently, some studies have explored the association between meteorological factors, especially temperature and the incidence of hip fracture and osteoporosis fracture. The majority of these studies have consistently demonstrated that lower temperatures are associated with an increased risk of fractures [5,6,7,8]. However, the fracture types in these studies are all nontraumatic fractures, which are characterized by distinct injury mechanisms compared to traumatic fractures. Nontraumatic fractures commonly arise from low-energy injuries, whereas traumatic fractures predominantly occur as a result of high-energy injuries, such as motor vehicle collisions, falls downstairs, and sports activity [9]. Several studies have found that high temperature increased the risk of road traffic injury [10, 11]. However, there is still a lack of explicit quantification regarding the relationship between temperature and traumatic fractures.

Thus, based on emergency visits (EVs) data, our study aims to investigate the exposure-response relationship between temperature and traumatic fractures using a distributed lag nonlinear model (DLNM). Researchers have suggested that raw temperature can be misrepresentative of the true thermal effects in previous studies, because temperature perception is influenced by both actual temperature and other meteorological factors, such as humidity and wind speed [12,13,14]. Therefore, we employed the apparent temperature (AT) that combines ambient temperature, relative humidity, and wind speed as the indicator of heat exposure, which could better reflect the thermal sensations perceived by the human, especially in cities where the humidity is high, such as Hangzhou, China [15]. This study added to the body of knowledge on the relationship between temperature and traumatic fractures and provided a valuable reference for fracture prevention strategies.

Methods

Study setting

Hangzhou, the capital of Zhejiang Province, is a typical southern city in China. There are four distinct seasons in its subtropical climate, with hot and humid summers and cold winters. The total area of the city is 16,850 km2 and it comprises 10 districts and three counties, with a population of 8.35 million as of 2021 [16]. The study sites selected for our study included the Third People’s Hospital of Xiaoshan District, the First People’s Hospital of Yuhang District, the Second People’s Hospital of Fuyang District, and the Second People’s Hospital of Jiande county. They are well-located in Hangzhou to serve the surrounding densely populated residential areas (Fig. 1).

Fig. 1
figure 1

Locations of the four hospitals selected in this study in Hangzhou, China

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Data sources

Emergency visits data

Medical records were extracted from the Hospital Information System of four hospitals, covering the period from January 2017 to December 2022. The medical records included variables such as date of EVs, age, gender, residence address, fracture cause, and the principal diagnosis. Medical records that met the following criteria were included in our study: (1) The primary diagnoses were coded according to ICD-10 as follows: S02, S12, S22, S32, S42, S52, S62, S72, S82, S92, T02, T08, T10, and T12; (2) The fracture causes included road transport accidents, falls, sports injuries, being struck by an object, hit by others, and other accidental injuries. The patients who were not permanent residents of Hangzhou were excluded from this study.

Meteorological data

Contemporary meteorological variables including daily mean temperature, maximum temperature, minimum temperature, relative humidity, precipitation, sunshine duration, and wind speed, were obtained from China Meteorological Data Service Center (http://data.cma.cn/). AT was calculated as follows [14]:

$$\text{A}\text{T}=\text{T}+0.33\text{*} \text{e}-0.70\text{*}\text{W}\text{S}-4.00$$
(1)
$$\text{e}=\frac{\text{R}\text{H}}{100}\text{*}6.105\text{*}\text{e}\text{x}\text{p}\left(17.27\text{*}\frac{\text{T}}{237.7+\text{T}}\right)$$
(2)

where T is the daily mean temperature (°C), e is water vapor pressure (hPa), and WS is the daily mean wind speed (m/s). The e is calculated in Eq. (2) with T and RH, where RH denotes daily mean relative humidity (%).

Statistical analysis

The demographic data on EVs for traumatic fractures and meteorological factors were summarized using a descriptive analysis. To visualize the trending of the key variables over time, a time series diagram was plotted.

Based on the evidence that the lagged and non-linear effect of temperature on nontraumatic fractures [7, 8], a DLNM with the generalized Possion Regression Model were applied to investigate the non-linear and delayed associations between AT and traumatic fracture EVs [17, 18]. To avoid to the possible multicollinearity among variables, Spearman’s correlation analysis was carried out (Supplementary Material Table S1, Figure S1), and wind speed, sunshine duration, and precipitation were identified as confounders. The model is described in Eq. (3):

$$\eqalign{ Log[E({Y_t})] = & \alpha + \beta *A{T_{t,l}} + ns{\rm{(}}time,df = 2*6{\rm{)}} \cr & + ns{\rm{(Pr}}{{\rm{e}}_t},df = 3{\rm{)}} + ns{\rm{(SS}}{{\rm{H}}_{\rm{t}}},df = 3{\rm{)}} \cr & + ns{\rm{(W}}{{\rm{S}}_{\rm{t}}},df = 3{\rm{)}} + \gamma *{\rm{Do}}{{\rm{w}}_{\rm{t}}} + \delta *{\rm{Holida}}{{\rm{y}}_t}\left( 3 \right) \cr}$$
(3)

where Yt denotes the number of EVs for traumatic fractures on day t; α is the intercept; ATt, l is the cross-basis generated from the DLNM, where l represents the number of lag days. The maximum lag day was set into 14 days. Furthermore, ns denotes the natural cubic spline function, df denotes the degrees of freedom, and time is a variable used to control for the long-term trend. According to previous literature7, 2 df per year for time trends was selected. Pret, SSHt and WSt denotes precipitation, sunshine duration and wind speed on day t, and 3 df for them were used. β, γ and δ are the corresponding coefficients for ATt, l, the day of the week (Dow) and holidays (Holiday), respectively.

With the model mentioned above, we established the overall exposure-response relationship between AT and EV for traumatic fractures [19]. Taking the optimum AT corresponding to the lowest risk of EVs as a reference, the relative risk (RR) and 95% confidence interval (CI) at the 0.5th (extreme cold, -4.3 °C), 5th (moderate cold, 0.8 °C), 95th (moderate heat, 35.7 °C), and 99.5th (extreme heat, 38.8 °C) percentiles of AT were estimated, respectively. The 0.5th, 5th, 95th, and 99.5th percentiles were determined based on the distribution of daily AT data over the six-year period (2017–2022). Subgroup analyses were conducted by gender(male and female) and age group (0–17 years old, 18–65 years old, and ≥ 66 years old). All statistical analyses were performed in R software (4.2.1) with the “dlnm”, “splines”, and “ggplot2” packages. Two-sided P-values ≤ 0.05 were considered statistically significant.

Sensitivity analysis

To evaluate the robustness of the main results from the model, a series of sensitivity analyses were performed, by changing the dfs for time (1–3), AT (4–6), precipitation (3–5), sunshine duration (3–5), and the maximum lag days (7 or 21 days), respectively. In addition, two different temperature indicators were compared using the cross-basis matrix of ambient temperature instead of AT. Since the sunshine duration was positively correlated with precipitation, the models excluding sunshine duration and precipitation separately were also conducted to compared with the main result.

Results

Descriptive statistics

Table 1 showed the basic information of daily EVs for traumatic fractures and its subgroups, as well as the daily meteorological factors between 2017 and 2022. During the study period, a total of 25,094 EVs for traumatic fractures were recorded, with an average of 11 patients visiting emergency rooms per day. Of all these cases, the proportion of EVs for the male (61.62%) was more than that for the female (38.38%). Patients aged 18–65 years accounted for 72.86%, which was higher than those aged ≥ 66 years old (20.04%), while the patients aged 0–17 years old only accounted for 7.10%. For meteorological factors, the medians of AT, mean temperature, maximum temperature, minimum temperature, relative humidity, wind speed, sunshine duration, and precipitation were 18.8 °C, 19.1 °C, 24.0 °C, 15.3 °C, 76%, 2.05 m/s, 4.2 h, and 0.1 mm, respectively.

Table 1 Descriptive statistics of daily EVs for patients with traumatic fractures in Hangzhou, China during 2017–2022
Full size table

Supplementary Figure S2 showed the time trend of EVs for traumatic fractures, AT, mean temperature, precipitation, and sunshine duration. The meteorological variables showed a significant seasonal pattern, especially for AT and mean temperature. The daily EVs showed a discernible seasonal pattern and slightly increased with temperature. As shown in Supplementary Figure S1 and Table S1, daily mean AT was strongly and positively associated with daily mean temperature, maximum temperature, and minimum temperature, while sunshine duration was negatively correlated with precipitation and relative humidity.

Effects of AT on EVs for traumatic fractures

Overall effect of AT on EVs for traumatic fractures

Figure 2 showed the overall exposure-response association between AT and EVs for traumatic fractures.

Fig. 2
figure 2

Overall exposure-response relationship between AT and EVs for patients with traumatic fractures

Full size image

Generally, the overall exposure-response curve was wide “J”-shaped. Low AT (below − 3.0 °C) appeared to have a slight effect on EVs, but the effect was not significant. The lowest risk of EVs emerged at -3.0 °C and the risk effect became significant at 9.5 °C. As AT continued to rise above 9.5 °C, the risk gradually escalated, within the range of 18 °C to 30 °C, the risk remained relatively stable, while surpassing 30 °C led to a gradual resurgence in risk.

Delayed effect of specific AT on EVs for traumatic fractures

Compared with the reference of -1.8 °C, the single lag effects of the 0.5th, 5th, 95th, and 99.5th percentiles of AT were presented in Supplementary Table S2. Only a slight risk effect was found at lag 11 for extreme heat (RR = 1.026, 95% CI: 1.001–1.052). The corresponding cumulative lag effects were presented in Table 2. The cumulative effects for moderate heat and extreme heat were similar, which emerged from lag 0–11 day (RR = 1.213, 95% CI: 1.015–1.450; RR = 1.260, 95% CI: 1.027–1.546) and peaked at lag 0–14 day (RR = 1.311, 95% CI: 1.132–1.518; RR = 1.418, 95% CI: 1.191–1.688). However, no significant effects for moderate and extreme cold were identified at single and cumulative lag days.

Table 2 Cumulative heat and cold effects of specific ATs on EVs for traumatic fracture
Full size table

Subgroup analysis

The overall exposure-response relationships between AT and EVs for traumatic fractures varied in subgroups (Supplementary Figure S3–S4). Both groups of gender showed similar exposure-response curves, but the female did not show significant overall effects. In terms of age groups, the patients aged 0–17 and 18–65 years old were relatively more vulnerable to high AT.

The single and cumulative lag effects for extreme heat in subgroups were presented in Supplementary Table S3-S4, Figs. 3 and 4. In the gender subgroup, the lag-specific extreme heat effects for the male were only significant at lag 2 day (RR = 1.054, 95% CI: 1.009–1.102) and lag 3 day (RR = 1.043, 95% CI: 1.007–1.081). The lag-cumulative extreme heat effects for male became significant at lag 0–3 (RR = 1.274, 95% CI: 1.005–1.613) and reached the highest (RR = 1.605, 95% CI: 1.327–1.941) at lag 0–14 day. For the female, significant lag-specific extreme heat effects were only found at lag 11 day to lag 13 day, and the lag-cumulative extreme heat effects were non-significant.

Fig. 3
figure 3

Extreme heat effects (38.8 °C vs. -1.8 °C) for daily EVs of traumatic fracture patients across different lag days in the gender groups

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Fig. 4
figure 4

Extreme heat effects (38.8 °C vs. -1.8 °C) on daily EVs for traumatic fracture patients across different lag days in the age groups

Full size image

For the patients aged 0–17 years old, the lag-specific extreme heat effects emerged at lag 11 day (RR = 1.133, 95% CI: 1.037–1.237) and lasted until lag 14 day (RR = 1.312, 95% CI: 1.041–1.654). The lag-cumulative extreme heat effect was only observed at lag 0–14 day (RR = 1.872, 95% CI: 1.081–3.242). For the patients aged 18–65 years old, lag-specific extreme heat effects were only found at lag 2 day (RR = 1.044, 95% CI: 1.002–1.088) and lag 3 day (RR = 1.038, 95% CI: 1.005–1.072). The lag-cumulative extreme heat effects became significant at lag 0–4 (RR = 1.249, 95% CI: 1.001–1.558) and peaked at lag 0–14 day (RR = 1.530, 95% CI: 1.285–1.822). For the patients aged ≥ 66 years old, the single and cumulative extreme heat effects were not observed.

Sensitivity analysis

As the degree of freedom of the time trend (1–3 df/year), AT (4–6 df), sunshine duration (3–5 df), and precipitation (3–5 df) was altered, the results remained robust (Supplementary Figure S5–S8). When altering the maximum lag days (7 or 21 days), the tendency barely changed (Supplementary Figure S9). Moreover, a similar exposure-response relationship was observed for the two models employing AT and mean temperature as independent variables (Supplementary Figure S10). The exposure-response curves for the two models excluding sunshine duration and precipitation separately were similar with the overall curves (Supplementary Figure S11).

Discussion

To the best of our knowledge, this study is the first to quantify the association between AT and traumatic fractures. Using data on EVs for traumatic fractures and meteorological factors in Hangzhou, China, a wide “J”-shaped exposure-response relationship was observed between AT and the risk of traumatic fracture, wherein higher AT were associated with an increased cumulative and delayed risk of traumatic fractures. In addition, we discovered that the male and the patients aged 18–65 years old were more susceptible to high AT.

The association between temperature and fractures have been reported in previous studies. Different from our findings, most studies found a significant association between low temperature and fractures [7, 8, 20,21,22]. This discrepancy may be caused by the differences in fracture type. Osteoporotic fractures, i.e., non-traumatic fractures, which is common in the elderly and caused by low energy injuries such as falls, has been paid much attention in previous related studies. When the temperature is low, snow and ice tend to make roads slippery. In such weather conditions, the elderly usually wear thicker clothes, which makes them clumsier and uncoordinated [23, 24]. Therefore, they are more likely to experience fall-related osteoporotic fractures. By contrast, our study focused on traumatic fractures, which are mainly caused by high energy injuries such as road traffic injuries. Consistent with our findings, several studies showed that road traffic injuries were significantly correlated with high temperature [10, 11, 25]. We found that the male and those aged 18–65 years old were more vulnerable to high AT. The possible reason may be that these groups are more likely to be exposed to high-risk work environments, insofar as they comprise the main labor force in society, which increases the risk of kinds of traumatic fractures.

The delayed impact of elevated AT on traumatic fractures could potentially be attributed to the cumulative influence of temperature on both physical and psychological states. Extreme temperatures have been found to elicit a sequence of non-immediate responses in vulnerable populations [26], resulting in an increased rate of hospitalization among individuals with pre-existing conditions such as dehydration, ischemic stroke, mental health disorders, and cardiovascular ailments following episodes of extreme heat [27, 28]. The presence of these underlying medical conditions will inevitably augment the susceptibility to falls and accidents, consequently elevating the likelihood of experiencing traumatic fractures. In line with our research findings, Lee et al. [29] observed a notable rise in fall-related injuries during periods of elevated temperatures in South Korea. Employing a DLNM, they further investigated the delayed effects of these injuries and determined that the risk of falling increased several days subsequent to surpassing a specific temperature threshold.

For the impact of high AT on traumatic fractures, there are some possible explanations. First, high temperature leads to individual negligence, fatigue, and a decline in cognitive function [30,31,32]; Secondly, alcohol consumption increases on days with higher temperatures [33, 34]; In addition, the performance of the equipment and road traffic infrastructures are inhibited by high temperature, such as lower driving performance, larger steering adjustments of the vehicle, and loss of signals [35, 36]. The above reasons all increase the probability of injuries-related fracture. It is worth noting that when the temperature is suitable, the risk of fracture also slightly increases. One possible explanation is that suitable temperatures promote people to spend more time outdoor physical activity, resulting in an increased probability of injury-related fracture [37].

There are several strengths to this study. First, to our knowledge it is the first to examine the quantitative relationship between AT and traumatic fractures. Second, the EVs as the outcome indicator could more sensitively capture the acute association between temperature and fractures than outpatient and inpatient data. Several limitations need to be acknowledged. First, the hospitals selected in our study did not include all medical institutions across Hangzhou, resulting in a limited sample representation. However, as the four study sites are evenly distributed geographically in Hangzhou, this may have reduced selection bias to some extent. Second, meteorological variables were collected based on fixed monitoring stations. The use of these data may not reflect actual personal exposure, which can lead to exposure measurement bias. Finally, other confounding factors such as individual socioeconomic status, comorbidity and fracture patterns were not taken into consideration, due to the ecological design.

Conclusion

In conclusion, our study has found that increased AT is positively associated with traumatic fracture EVs. To mitigate the occurrence of traumatic fractures, it is imperative to implement targeted public health policies encompassing fracture prevention strategies, including but not limited to curbing outdoor activities during hot weather, developing high temperature warning systems, and optimizing medical resource allocation. The future research, encompassing multiple cities and examining various subtypes of traumatic fractures while accounting for different confounding variables, is anticipated to yield a more comprehensive understanding of the impact of AT on traumatic fracture.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

References

  1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019. Lancet (London, England). Oct. 2020;17(10258):1204–22. https://doi.org/10.1016/s0140-6736(20)30925-9.

    Article  Google Scholar 

  2. Center for Health Statistics and Information the Ministry of Health of the People’s Republic of China. China health statistics annual 2022. http://www.nhc.gov.cn/mohwsbwstjxxzx/tjtjnj/202305/6ef68aac6bd14c1eb9375e01a0faa1fb.shtml (accessed May 17, 2023).

  3. Chen W, Lv H, Liu S, et al. National incidence of traumatic fractures in China: a retrospective survey of 512 187 individuals. Lancet Glob Health. 2017;5(8):e807–17. https://doi.org/10.1016/S2214-109X(17)30222-X.

    Article  PubMed  Google Scholar 

  4. Zhang X, Yu Z, Yu M, et al. Alcohol consumption and hip fracture risk. Osteoporos Int. 2015;26(2):531–42. https://doi.org/10.1007/s00198-014-2879-y.

    Article  CAS  PubMed  Google Scholar 

  5. Li C, Jiang X, Yue Q, et al. Relationship between meteorological variations, seasonal influenza, and hip fractures in the elderly: a modelling investigation using 22-year data. Sci Total Environ. 2023;862:160764. https://doi.org/10.1016/j.scitotenv.2022.160764.

    Article  CAS  PubMed  Google Scholar 

  6. Nishimura H, Nawa N, Ogawa T, Fushimi K, Fujiwara T. Association of ambient temperature and sun exposure with hip fractures in Japan: a time-series analysis using nationwide inpatient database. Sci Total Environ. 2022;807(Pt 1):150774. https://doi.org/10.1016/j.scitotenv.2021.150774.

    Article  CAS  PubMed  Google Scholar 

  7. Zhang F, Zhang X, Zhou G, et al. Is Cold Apparent Temperature Associated with the hospitalizations for osteoporotic fractures in the Central areas of Wuhan? A Time-Series Study. Front Public Health. 2022;10:835286. https://doi.org/10.3389/fpubh.2022.835286.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lu S, Liu X, Niu Y, et al. Short-term effect of apparent temperature on daily hospitalizations for osteoporotic fractures in Beijing, China: a retrospective observational study. Sci Total Environ. 2023;874:162583. https://doi.org/10.1016/j.scitotenv.2023.162583.

    Article  CAS  PubMed  Google Scholar 

  9. Crandall CJ, Larson JC, LaCroix AZ, et al. Risk of subsequent fractures in Postmenopausal Women after nontraumatic vs traumatic fractures. JAMA Intern Med. 2021;181(8):1055–63. https://doi.org/10.1001/jamainternmed.2021.2617.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liang M, Min M, Guo X, et al. The relationship between ambient temperatures and road traffic injuries: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2022;29(33):50647–60. https://doi.org/10.1007/s11356-022-19437-y.

    Article  PubMed  Google Scholar 

  11. Zare Sakhvidi MJ, Yang J, Mohammadi D, et al. Extreme environmental temperatures and motorcycle crashes: a time-series analysis. Environ Sci Pollut Res Int. 2022;29(50):76251–62. https://doi.org/10.1007/s11356-022-21151-8.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Basu R, Gavin L, Pearson D, Ebisu K, Malig B. Examining the Association between Apparent temperature and Mental Health-Related Emergency Room visits in California. Am J Epidemiol. 2018;187(4):726–35. https://doi.org/10.1093/aje/kwx295.

    Article  PubMed  Google Scholar 

  13. Li N, Ma J, Liu F, et al. Associations of apparent temperature with acute cardiac events and subtypes of acute coronary syndromes in Beijing, China. Sci Rep. 2021;11(1):15229. https://doi.org/10.1038/s41598-021-94738-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Steadman R. Norms of apparent temperature in Australia. Aust Meteorol Mag. 1994;43(1):1–16.

    Google Scholar 

  15. Steadman R. The Assessment of Sultriness. Part I: a temperature-humidity index based on human physiology and Clothing Science. J Appl Meteorol. 1979;18(7):861–73.

    Article  Google Scholar 

  16. National Bureau of Statistics of China. http://www.stats.gov.cn/tjsj/ndsj/2021/indexch.htm.

  17. Gasparrini A. Modeling exposure-lag-response associations with distributed lag non-linear models. Stat Med. 2014;33(5):881–99. https://doi.org/10.1002/sim.5963.

    Article  PubMed  Google Scholar 

  18. Gasparrini A, Armstrong B, Kenward MG. Distributed lag non-linear models. Stat Med. 2010;29(21):2224–34. https://doi.org/10.1002/sim.3940.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gasparrini A, Guo Y, Hashizume M, et al. Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet (London England). 2015;386(9991):369–75. https://doi.org/10.1016/s0140-6736(14)62114-0.

    Article  PubMed  Google Scholar 

  20. Dahl C, Madsen C, Omsland TK, et al. The Association of Cold Ambient temperature with fracture risk and mortality: National Data from Norway-A Norwegian epidemiologic osteoporosis studies (NOREPOS) Study. J Bone Min Res. 2022;37(8):1527–36. https://doi.org/10.1002/jbmr.4628.

    Article  Google Scholar 

  21. Ogliari G, Ong T, Marshall L, Sahota O. Seasonality of adult fragility fractures and association with weather: 12-year experience of a UK Fracture Liaison Service. Bone. 2021;147:115916. https://doi.org/10.1016/j.bone.2021.115916.

    Article  PubMed  Google Scholar 

  22. Turner RM, Hayen A, Dunsmuir WT, Finch CF. Air temperature and the incidence of fall-related hip fracture hospitalisations in older people. Osteoporos Int. 2011;22(4):1183–9. https://doi.org/10.1007/s00198-010-1306-2.

    Article  CAS  PubMed  Google Scholar 

  23. Mirchandani S, Aharonoff GB, Hiebert R, Capla EL, Zuckerman JD, Koval KJ. The effects of weather and seasonality on hip fracture incidence in older adults. Orthopedics. 2005;28(2):149–55. https://doi.org/10.3928/0147-7447-20050201-17.

    Article  PubMed  Google Scholar 

  24. Chow KP, Fong DYT, Wang MP, Wong JYH, Chau PH. Meteorological factors to fall: a systematic review. Int J Biometeorol. 2018;62(12):2073–88. https://doi.org/10.1007/s00484-018-1627-y.

    Article  CAS  PubMed  Google Scholar 

  25. Ma P, Wang S, Fan X, Li T. The impacts of Air Temperature on Accidental casualties in Beijing, China. Int J Environ Res Public Health. 2016;13(11). https://doi.org/10.3390/ijerph13111073.

  26. Basu R. High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008. Environ Health. 2009;8:40. https://doi.org/10.1186/1476-069x-8-40.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Sherbakov T, Malig B, Guirguis K, Gershunov A, Basu R. Ambient temperature and added heat wave effects on hospitalizations in California from 1999 to 2009. Environ Res. 2018;160:83–90. https://doi.org/10.1016/j.envres.2017.08.052.

    Article  CAS  PubMed  Google Scholar 

  28. Niu Y, Gao Y, Yang J, et al. Short-term effect of apparent temperature on daily emergency visits for mental and behavioral disorders in Beijing, China: a time-series study. Sci Total Environ. 2020;733:139040. https://doi.org/10.1016/j.scitotenv.2020.139040.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lee H, Myung W, Kim H, Lee EM, Kim H. Association between ambient temperature and injury by intentions and mechanisms: a case-crossover design with a distributed lag nonlinear model. Sci Total Environ. 2020;746:141261. https://doi.org/10.1016/j.scitotenv.2020.141261.

    Article  CAS  PubMed  Google Scholar 

  30. Lo YC, Su WP, Mei SH, Jou YY, Huang HB. Association between ambient temperature and cognitive function in a community-dwelling elderly population: a repeated measurement study. BMJ open. 2021;11(12):e049160. https://doi.org/10.1136/bmjopen-2021-049160.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Zhao Q, Wigmann C, Areal AT, Altug H, Schikowski T. Effect of non-optimum ambient temperature on cognitive function of elderly women in Germany. Environ Pollut. 2021;285:117474. https://doi.org/10.1016/j.envpol.2021.117474.

    Article  CAS  PubMed  Google Scholar 

  32. Otte im Kampe E, Kovats S, Hajat S. Impact of high ambient temperature on unintentional injuries in high-income countries: a narrative systematic literature review. BMJ open. 2016;6(2):e010399. https://doi.org/10.1136/bmjopen-2015-010399.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Silm S, Ahas R. Seasonality of alcohol-related phenomena in Estonia. Int J Biometeorol. 2005;49(4):215–23. https://doi.org/10.1007/s00484-004-0240-4.

    Article  PubMed  Google Scholar 

  34. Hagström H, Widman L, von Seth E. Association between temperature, sunlight hours and alcohol consumption. PLoS ONE. 2019;14(9):e0223312. https://doi.org/10.1371/journal.pone.0223312.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Basagaña X, Escalera-Antezana JP, Dadvand P, et al. High ambient temperatures and risk of Motor Vehicle crashes in Catalonia, Spain (2000–2011): a Time-Series Analysis. Environ Health Perspect. 2015;123(12):1309–16. https://doi.org/10.1289/ehp.1409223.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Daanen HA, van de Vliert E, Huang X. Driving performance in cold, warm, and thermoneutral environments. Appl Ergon. 2003;34(6):597–602. https://doi.org/10.1016/S0003-6870(03)00055-3.

    Article  PubMed  Google Scholar 

  37. Morency P, Voyer C, Burrows S, Goudreau S. Outdoor falls in an urban context: winter weather impacts and geographical variations. Can J Public Health. 2012;103(3):218–22. https://doi.org/10.1007/bf03403816.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This study was supported by the Key Science and Technology Program of Xiaoshan [grant no.2022215].

Author information

Authors and Affiliations

  1. Department of Orthopedics, The Third People’s Hospital of Xiaoshan Hangzhou, Hangzhou, 311251, China

    Feng Li, Yipin Zhao, Jun Tian, Lingyun Li & Yinmiao Qiu

  2. Department of Medical Record Management and Statistics, Beijing Jishuitan Hospital, Capital Medical Univisity, Beijing, 100035, China

    Xuejiao Liu

  3. Beijing Center for Disease Prevention and Control, Institute for Nutrition and Food Hygiene, Beijing, 100035, China

    Yanlin Niu

  4. Institute for Hospital Management of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, 450000, China

    Jinghong Gao

  5. Department of Orthopedics, Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China

    Maoqiang Li, Cheng Ji, Wangxiang Yao & Liulong Zhu

  6. Department of Orthopedics, Third People’s Hospital of Hangzhou, Hangzhou, 310009, China

    Guobiao Pan

  7. Department of orthopedics, The First People’s Hospital of Yuhang District, Hangzhou, Hangzhou, 311199, China

    Mingxing Zhao & Boliang Wu

  8. Department of Orthopedics, The Second People’s Hospital of Jiande, Hangzhou, 311600, China

    Xiaoxiang Tang & Shiyu Yan

  9. Department of Orthopedics, The Second People’s Hospital of Fuyang District, Hangzhou, Hangzhou, 311400, China

    Gang Wu & Jianwei Chen

  10. Department of orthopedics, Affiliated Hospital of Jining Medical University, Jining, 272007, China

    Jianlu Tan

  11. Nanjing University of Chinese Medicine, Nangjing, 210023, China

    Yunqing Li

  12. Department of orthopedics, Beijing Shunyi Hospital, Beijing, 101300, China

    Wentao Zhao

Authors
  1. Feng Li
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  2. Xuejiao Liu
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  3. Yanlin Niu
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  4. Jinghong Gao
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  5. Maoqiang Li
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  6. Yipin Zhao
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  7. Cheng Ji
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  8. Guobiao Pan
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  9. Mingxing Zhao
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  10. Boliang Wu
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  11. Xiaoxiang Tang
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  12. Gang Wu
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  13. Jun Tian
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  14. Jianwei Chen
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  15. Shiyu Yan
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  16. Jianlu Tan
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  17. Yunqing Li
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  18. Wentao Zhao
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  19. Lingyun Li
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  20. Yinmiao Qiu
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  21. Wangxiang Yao
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  22. Liulong Zhu
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Contributions

Feng Li: Conceptualization, Methodology, Supervision, Writing Original draft preparation. Xuejiao Liu, Yanlin Niu, Jinghong Gao: Software, Validation, Formal analysis, Visualization. Maoqiang Li, Yipin Zhao, Cheng Ji, Guobiao Pan, Mingxing Zhao, Boliang Wu, Xiaoxiang Tang: Data Curation. Gang Wu, Jun Tian, Jianwei Chen, Shiyu Yan, Jianlu Tan, Yunqing Li, Wentao Zhao, Lingyun Li, Yinmiao Qiu, Wangxiang Yao: Writing- Reviewing and Editing. Liulong Zhu: Supervision, Funding acquisition, Project administration.All authors read and approved the final manuscript.

Corresponding author

Correspondence to Liulong Zhu.

Ethics declarations

Ethics approval and consent to participate

Ethical approval for the study was obtained from the Third People’s Hospital of Xiao Shan Hangzhou Ethical Review Committee (2022-005) prior to data collection. The need for informed consent was waived by the Third People’s Hospital of Xiao Shan Hangzhou Ethical Review Committee (2022-005).All data analyzed were anonymized and under protection by a confidentiality agreement. The study was performed in accordance with the Declaration of Helsinki.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Li, F., Liu, X., Niu, Y. et al. The impact of apparent temperature on the emergency visits for traumatic fractures in Hangzhou, China. BMC Public Health 24, 1681 (2024). https://doi.org/10.1186/s12889-024-19119-z

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Keywords

BMC Public Health

ISSN: 1471-2458

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