Correspondence to Dr Changping Li; [email protected] ; Dr Wenyi Zhang; [email protected]
STRENGTHS AND LIMITATIONS OF THIS STUDY
The study methodology adjusted for both environmental factors including relative humidity, wind speed, precipitation and various time trends such as seasonality and day of week.
Concentrations of pollutants were derived from monitoring stations rather than individual exposure measurements.
The small number of cases resulted in low statistical power.
Introduction
Hyperventilation syndrome (HVS) is a respiratory disorder in which breathing is too deep and/or too fast (hyperventilation) due to alveolar ventilation exceeding metabolic demand and a decrease in arterial partial pressure of carbon dioxide (PaCO2).1 2
During general medical care, rapid breathing may be incorrectly perceived by physicians as a shortage of breath due to cardio or lung abnormality due to the lack of objectively verifiable somatic symptoms of HVS.3 4 Especially when dizziness and impaired awareness coexist with physical sensations on one side, such as numbness and weakness, doctors mostly consider focal disorders.5 However, clinical experience has shown that the intensity and unpleasantness of dyspnoea are higher in patients with HVS than in the healthy population, and since HVS is often overlooked in clinical diagnosis and does not receive adequate medical attention, the physical, social and psychological well-being of patients with HVS, as well as other health-related quality of life, may be severely compromised.6–9
The onset of HVS is strongly dependent on a multitude of elements, the main causes including physical and psychological.1 10 11 Currently, there are also studies reporting additional respiratory drive at rest and during exercise in people with hyperthermia.12–14 Gaudio et al have demonstrated that hyperventilation can occur when normal individuals are exposed to high temperatures.15 A study in China has shown that the onset of HVS is related to temperature.16
The above results indicate that patients with HVS report severe suffering related to symptoms and that ambient temperature plays an important part in the onset of HVS. The diurnal temperature range (DTR), defined as the difference between the maximum and minimum temperatures within 1 day, is considered to be an important indicator of climate change and variability, as it provides more information than the mean temperature alone.17 Given the characteristics of the healthcare system, the number of emergency department visits may represent a sensible surrogate for the measurement of acute health response to fluctuations in environmental factors at the population level.18 Currently, most of the studies were individual intervention trials, such as changes in individual minute volume, heart rate and respiratory rate under high-temperature interventions or simply described the relationship between temperature and HVS onset. To our knowledge, rare studies have used quantitative data to examine the relationship between mean ambient temperature and DTR on emergency admissions for HVS at the population level, especially the lag effect.
We used daily-scale HVS emergency admission data from the largest emergency centre in Beijing and distributed lagged non-linear model (DLNM) and generalised additive model (GAM) to assess the effect of ambient temperature and DTR and to gain insight into the factors affecting HVS visits and provide more references for future prevention of HVS development.
Materials and methods
Data collection
We obtained the number of daily HVS emergency admissions from the Beijing Red Cross Emergency Centre for the period 1 January 2017 to 31 December 2018. The HVS were identified according to code F45.303 of the International Classification of Diseases, Tenth Revision. Beijing Red Cross Emergency Center sets up stations according to the area, population distribution and emergency needs of Beijing, with a short emergency radius and a high satisfaction rate of emergency calls and undertakes daily medical emergency services and emergency medical rescue work in Beijing.
Daily meteorological data were collected from the National Meteorological Information Center of China (http://data.cma.cn/). The meteorological data include daily mean temperature, daily mean relative humidity, daily mean air pressure, mean wind speed and daily precipitation. Daily air pollution data were obtained from the daily averages of 35 stations in Beijing by the National Environmental Monitoring Center of China (http://106.37.208.233:20035), including particulate matter up to 2.5 µm in size (PM2.5), particulate matter up to 10 µm in size (PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone(O3) and carbon monoxide (CO) (online supplemental figure S1).
Patient and public involvement
No patient was involved.
Statistical analysis
First, the reported HVS case data, ambient temperature and DTR data were cleaned and organised into daily-scale time-series data for descriptive analysis to evaluate their distribution and characteristics.
Second, a quasi-Poisson regression model with DLNM was used to examine the association between mean temperature and DTR with daily HVS visits. After controlling for the confounding factors of day of the week, seasonality and long-term trend, the quasi-Poisson regression model of mean temperature and DTR was fitted as shown below:
log function as the connecting function in the model; E(Yt) is the expected number of HVS emergency admissions on day t; α is the intercept; basis.temp denotes the cross-base matrix of daily mean temperature and DTR, which designates the exposure-lag-response correlation in both the exposure–response space as well as the lag-response space. β is the coefficient of basis.temp. We used a natural cubic spline function with df=3 to adjust the model by adding other meteorological variables without covariance. Pearson r>0.65 was considered to have covariance. Natural cubic splines with df=7/year were used to control for seasonal and long-term trends. λ is the regression coefficient, and dow is a categorical variable for day of the week.
In the cross-basis functions of this study, natural cubic spline functions were used for both the exposure and lag dimensions, with nodes set at the 25th, 50th and 75th percentiles of mean temperature and DRT and 5 df to account for the lagged effect of mean temperature and DRT (lag dimensions).19 We used a lag period of 5 days, and the results of the subsequent analysis suggest that ambient temperature has a predominantly acute effect on the HVS emergency admissions and the effect of DTR on HVS emergency admission lasted until day 3. Our choice of shorter lag days is reasonable.
We plotted the relative risks (RRs) for temperature-HVS visits association curves that appear to have a minimum emergency visit temperature (MVT) of approximately 12°C. We used the MVT as a reference to indicate the RR with 95% CI of exposure–response for the risk of HVS visits at different temperatures.20 In this study, cold and moderate cold effects were defined as the HVS risks at the 5th and 25th mean temperature percentiles, heat and moderate heat effects were defined as the HVS risks at the 95th and 75th mean temperature percentiles, compared with the MVT. For the association between DTR and HVS emergency admissions, we similarly plotted the DTR-HVS RRs and it also appears to have a minimum risk of admission at approximately 12°C, which was used as the reference value for calculating the RRs. Low DTR and high DTR were defined as the 1th and 99th percentile of the DTR, respectively, to calculate the RR of emergency admissions for HVS at different DTR compared with the reference value.
Additionally, we supplemented our analysis with temperature-HVS and DTR-HVS associations between different genders and age groups. We used a simple plotting of the relationship between age and HVS visit number using restricted cubic splines, combined with WHO age classification, to divide age into two groups: age ≤44 and age>44 (online supplemental figure S2).21
We also performed several sensitivity analyses to assess the robustness of our results. First, we introduced air pollutants from the same period of the study into the model to identify whether they affected the temperature-HVS and DTR-HVS correlation. Second, we varied the nodes of the exposure dimension as well as the lagged dimension using different df. Finally, the time trends of the different df were similarly adjusted.
The statistical analyses we performed were all two sided, and p values less than 0.05 were considered to be statistically significant. We used R software (V.4.0.3) for model operation, the ‘mgcv’ package for the GAM model, and the ‘dlnm’ package for the DLNM.
Results
Descriptive analysis
Table 1 summarises the descriptive statistics of the daily HVS admissions, meteorological variables and contamination variables. A total of 1351 patients with HVS were reported in the Beijing Red Cross Emergency Resuscitation Center during the study period. The number of cases was significantly higher in women and age ≤44 years than in men and age >44 years. The annual difference in temperature (42.9°C) and precipitation (60.33 mm) reflected the typical continental monsoon climate in Beijing. The time-series distribution and scatter plot of the number of visits versus daily temperature are shown in online supplemental figure S3.
Table 1Descriptive data of HVS daily visits and environmental factors in Beijing, 2017–2018
Mean | SD | Min | P25 | Medium | P75 | Max | |
Demographic information | |||||||
Total | 1.85 | 1.40 | 0.00 | 1.00 | 2.00 | 3.00 | 9.00 |
Male | 0.36 | 0.61 | 0.00 | 0.00 | 0.00 | 1.00 | 3.00 |
Female | 1.49 | 1.26 | 0.00 | 1.00 | 1.00 | 2.00 | 8.00 |
Age≤44 | 1.05 | 1.06 | 0.00 | 0.00 | 1.00 | 2.00 | 6.00 |
Age>44 | 0.80 | 0.87 | 0.00 | 0.00 | 1.00 | 1.00 | 4.00 |
Meteorological factors | |||||||
Mean temperature (°C) | 12.07 | 11.81 | −12.00 | 0.48 | 13.72 | 22.97 | 30.90 |
Diurnal temperature range | 11.87 | 4.09 | 2.23 | 8.70 | 11.65 | 14.91 | 23.80 |
Air pressure (hPa) | 993.76 | 9.45 | 974.07 | 985.47 | 994.47 | 1000.83 | 1019.13 |
Precipitation (mm) | 1.61 | 6.25 | 0.00 | 0.00 | 0.00 | 0.00 | 60.33 |
Wind speed (m/s) | 1.70 | 0.63 | 0.47 | 1.27 | 1.57 | 2.00 | 5.03 |
Relative humidity (%) | 52.76 | 19.47 | 14.67 | 36.33 | 50.67 | 70.00 | 94.67 |
Air pollution factors | |||||||
NO2 (μg/m3) | 40.63 | 19.58 | 6.00 | 27.00 | 36.00 | 50.00 | 145.00 |
SO2 (μg/m3) | 5.95 | 6.45 | 1.00 | 2.00 | 4.00 | 7.00 | 81.00 |
O3 (μg/m3) | 61.60 | 37.77 | 3.00 | 33.25 | 55.00 | 84.00 | 181.00 |
CO (μg/m3) | 0.87 | 0.63 | 0.20 | 0.50 | 0.76 | 1.02 | 7.28 |
PM2.5 (μg/m3) | 52.58 | 49.00 | 3.00 | 20.00 | 40.00 | 67.75 | 430.00 |
PM10 (μg/m3) | 80.92 | 68.70 | 0.00 | 41.00 | 65.50 | 99.75 | 858.00 |
HVS, hyperventilation syndrome; Px, xth percentile.
Association between mean temperature and HVS admissions
There is a strong correlation between air pressure and temperature (online supplemental table S1). We add relative humidity, wind speed and precipitation to the model for adjustment.
Figure 1A shows the exposure–response surface of the mean temperature on HVS visits along lag days, with the temperature-HVS association at high temperatures exhibiting a peak at lag (0–1) days.
Figure 1. (A) Exposure-lag response association between mean temperature and HVS emergency admissions. (B) The overall relative risk of mean temperature on HVS emergency admissions.The overall relative risk (95% CI) of ambient temperature and HVS emergency admission on lag (0-5) days compared with the MVT. In panel B, the grey shading represents the 95% CI. The solid vertical line represents the temperature with the lowest risk of HVS emergency admission (12°C), and the dashed lines indicate the location of the fifth and 95th percentiles of the temperature distribution. HVS, hyperventilation syndrome; MVT, minimum emergency visits temperature.
Figure 1B shows the temperature–HVS relationship after we add other meteorological factors to the model for adjustment, with an MVT at approximately 12°C and higher risk at low and high temperatures compared with the MVT.
Table 2 shows the overall effect of moderate heat and extreme heat at specific lags (0–3, 0–5) on HVS admissions in the total population and different subgroups. At moderate heat, the overall lag for HVS admissions reaches its maximum at lag (0–3) days with 2.021 (95% CI 1.101 to 3.71). The effect of cold was not statistically significant at a lag (0–5) day (online supplemental table S2). At moderate heat and extreme heat, the RR of single-day lag for overall HVS disease reaches its maximum at lag0 days with 1.860 (95% CI 1.058 to 3.270) and 1.995 (95% CI 1.016 to 3.915), respectively (online supplemental table S3).
Table 2The overall relative risk (95% CI) of moderate heat and extreme heat on HVS admissions in the total population and different subgroups
Groups | Moderate heat (23°C) | Extreme heat (28°C) | ||
Lag 0–3 days | Lag 0–5 days | Lag 0–3 days | Lag 0–5 days | |
Overall HVS disease | 2.021 (1.101, 3.71)* | 1.506 (0.800, 2.835) | 1.640 (0.803, 3.347) | 1.723 (0.811, 3.661) |
Gender | ||||
Male | 1.381 (0.331, 5.752) | 1.314 (0.295, 5.847) | 0.898 (0.168, 4.813) | 1.802 (0.300, 10.836) |
Female | 2.270 (1.139, 4.523)* | 1.627 (0.795, 3.330) | 1.924 (0.856, 4.322) | 1.775 (0.757, 4.163) |
Age | ||||
≤44 | 2.372 (1.032, 5.453)* | 2.520 (1.056, 6.014)* | 2.215 (0.851, 5.767) | 3.490 (1.264, 9.638)* |
>44 | 2.089 (0.705,6.194) | 0.902 (0.295, 2.758) | 1.540 (0.416, 5.703) | 0.753 (0.193, 2.944) |
*p<0.05 was considered statistically significant and is indicated in bold font.
HVS, hyperventilation syndrome.
At moderate heat, the overall lagged risk of HVS visits was greatest at lag (0–3) days for women at 2.270 (95% CI 1.139 to 4.523) and at lag (0–5) days for those aged ≤44 years at 2.520 (95% CI 1.056 to 6.014). At extreme heat, the overall risk of HVS visits was greatest at lag (0–5) days for age ≤44 years at 3.490 (95% CI 1.264 to 9.638). The overall risk of HVS visits in men and age >44 years was not statistically significant at lags (0–5) days (online supplemental table S4). At moderate heat, the risk of HVS visits at lag 0 days was greatest for women and age ≤44 years at 1.943 (95% CI 1.027 to 3.677) and 2.195 (95% CI 1.014 to 4.752), respectively. At extreme heat, the risk of HVS visits at lag 0 days was greatest for age ≤44 at 2.504 (95% CI 1.011 to 6.199) (online supplemental table S5).
Association between DTR and HVS admissions
The exposure–response curve for DTR and HVS admissions is described as reverse u-shaped, and we used the minimum admission DTR of 12°C as a reference value for calculating the RRs (online supplemental figure S4).22
Table 3 shows the overall risk at specific DTR (4°C, 21°C) and specific lags (0–3, 0–5) for HVS and different subgroups compared with the reference value. The overall effect of low DTR was appeared at lag (0–1) days with 0.589 (95% CI 0.395 to 0.878), lasting until lag (0–3) days with 0.535 (95% CI 0.319 to 0.897) (online supplemental table S6). The association between low DTR and HVS admission was more significant in women, aged ≤44 years at lag (0–3) days and lag (0–5) days. At high DTR, the risk of admission for male reached 0.086 (95% CI 0.014 to 0.553) at lag (0–5) days.
Table 3The overall relative risk (95% CI) of low DTR on HVS admissions in the total HVS population and different subgroups
Groups | Low DTR (4°C) | High DTR (21°C) | ||
Lag 0–3 days | Lag 0–5 days | Lag 0–3 days | Lag 0–5 days | |
Overall HVS disease | 0.535 (0.319, 0.897)* | 0.565 (0.315, 1.015) | 1.116 (0.675, 1.844) | 0.971 (0.514, 1.836) |
Gender | ||||
Male | 0.961 (0.338, 2.733) | 0.769 (0.22, 2.688) | 0.429 (0.115, 1.608) | 0.086 (0.014, 0.553)* |
Female | 0.436 (0.236, 0.805)* | 0.499 (0.251, 0.99)* | 1.337 (0.763, 2.344) | 1.452 (0.717, 2.937) |
Age | ||||
≤44 | 0.33 (0.163, 0.667)* | 0.33 (0.15, 0.728)* | 1.129 (0.573, 2.224) | 0.944 (0.399, 2.234) |
>44 | 0.7 (0.275, 1.783) | 0.829 (0.287, 2.398) | 1.214 (0.499, 2.953) | 1.008 (0.321, 3.163) |
*p<0.05 was considered statistically significant and is indicated in bold font.
DTR, diurnal temperature range; HVS, hyperventilation syndrome.
Sensitivity analysis
As a result of the sensitivity analysis between temperature and DTR and HVS visits, pollution variables (PM2.5, PM10, CO, NO2, SO2) that do not have covariance with daily mean temperature and DTR after covariance testing were introduced into the model. We varied the nodes of the exposure dimension, using lagged dimensions with df of 2–6 as well as using 4–10 df per year. Besides, holidays have been added to the model to adjust public holidays in China. These results all showed some robustness (online supplemental figure S5–S9).
Discussion
To our knowledge, this is the first study to examine the effect of ambient temperature and DTR on HVS emergency admission using a distributed lag non-linear model. The current findings support a u-type relationship between HVS visits and ambient temperature, whereby the HVS visits are higher at both low and high temperatures. However, the effect of low temperature was not shown in our study. In general, hot weather exhibited a stronger effect on HVS admissions. The effect of hot temperatures on these admissions seemed to prevail immediately for a shorter time. Studies in China have shown a strong relationship between temperature and the onset of HVS, with increased incidence at higher temperatures and decreased incidence at lower temperatures, which is consistent with our findings.16 23 In addition, the relationship between DTR and HVS emergency admissions appeared a reverse u-type, with a reduced risk of HVS visits at low DTR, which was more significant in women and in those aged≤44 years.
Additionally, the effect of ambient temperature on HVS visits was higher in women than in men at high temperatures. This is consistent with previously reported results.11 24 This difference may be related to the different emotional control of men and women. As we have mentioned before, psychological problems are one of the important factors in the development of HVS, and women have difficulties in emotion regulation and tend to show anxiety and depression.25 Depression is more prevalent in women than in men, and women are two times as likely as men to suffer from depression across countries, cultures and ethnicities. As with depression, anxiety disorders are more common in women than in men (4.6% and 2.6%, globally, respectively).26 The results showed that the risk of HVS was higher in young adults (age ≤44) than in the middle-aged and elderly populations (age >44) at high temperatures. A previous article reported that the age of HVS incidence is usually between 15 and 55 years, which is generally consistent with the results of our study.11 The latest findings suggest that extreme heat may have a greater effect on adults aged 18–64 years than older adults.27 This could reasonably explain the effect of temperature on different age groups in this study.
DTR have different effects on HVS admissions by gender and age. The protective effect of a mild temperature change is more pronounced in women. Possible reasons for this are that women have several morphological differences compared with men, including a higher surface area to volume ratio for body segments, a smaller average body size and a higher surface area to mass ratio, and these differences can affect heat balance and lead to variations in thermoregulation and thermal perception. At the same time, women have smaller muscle mass, lower basal metabolic rate and lower body heat productivity, leading to sensitive heat perception.28 29 High DTR is associated with the risk of admissions in men, possibly because men are less sensitive to temperature changes and are likely to take steps to respond to temperature changes only when the change is greater. The more significant impact of mild temperature change on HVS admissions in younger people may be due to lower sweating thresholds and higher thermoregulatory capacity to respond to temperature changes in a timely manner compared with older people.30 31
Few investigations have been performed to explore the effects of high temperatures on mammalian respiratory patterns.12 32–34 In rats under conditions of hyperthermia, the hypothalamus assumes an increase in thermoregulatory temperature and brokers an increase in pulmonary alveolar ventilation.32 This extra hyperventilation of the alveoli allows the lungs to eliminate more carbon dioxide than the tissues produce, leading to a decrease in arterial carbon dioxide in tension.35 36 The reduction in PaCO2 changes the PaCO2/HCO3 ratio, resulting in an increase in arterial pH (alkalosis). Chris et al also found that higher core temperatures during prolonged autonomic exercise can lead to a relative increase in ventilation; a decrease in PaCO2, which leads to alkalosis.37 Also, psychological factors are an important factor in the onset of HVS. Some studies have shown that the risk of suicide increases with increasing ambient temperature, but to different degrees,38 39 which is consistent with our findings. In conclusion, these studies provide some evidence to support our results. Rare studies have investigated the relationship between temperature differences and breathing patterns. However, some studies have shown that strict indoor temperatures do not guarantee human comfort and health and that mild changes in temperature are beneficial to health.40–43
The prevalence of HVS in the general adult population is estimated to be 9.5%.44 In China, patients with this disease account for 2.1% to 10.7% of medical inpatients.45 The results of long-term longitudinal follow-up data show a striking incidence of HVS in patients infected with SARS-CoV-2 more than 2 months later.46 Furthermore, as global temperatures continue to rise, extreme heat events are becoming more frequent and intense.47 These scenarios suggest that the results of this study, if confirmed, may have important public health implications. First, it is possible to warn women and other sensitive people in extreme weather by giving advance warning, create a suitable living environment by improving the housing conditions, use air conditioning and such to prevent the occurrence of HVS. Also, reminding healthcare professionals to pay more attention to patients with HVS during treatment can help them to reduce their suffering.
This study has several limitations. Our study is only an ecological study based on data from fixed weather stations in Beijing rather than individual-level exposures, thus we cannot obtain factors related to individual exposure, such as school graduation, employment, job transfer and other confounding factors, which may introduce measurement bias. However, earlier research has shown that the deviation of any survey is a random distribution and that random measurement error in the exposure variable would bias the estimate of the regression slope coefficient towards the null and, therefore, underestimate the RRs.48 In addition, we have only selected a relatively short period of 2 years for observation, and the data are only from the Beijing area, which is poorly extrapolated. The overall between mean temperature and HVS visits show a significant relationship between low temperature and HVS emergency admissions. However, in our study, no significant relationship was observed between hypothermia and HVS visits. We added the relationship between very low temperature and HVS visits compared with reference values (online supplemental table S7). Unfortunately, we still did not observe a significant relationship between hypothermia and HVS admissions, probably due to the limitation of sample size and for this reason, our subgroup analysis may have some limitations, such as coarse age stratification. Finally, decrease in partial pressure of carbon dioxide in the arteries due to hyperventilation (ventilation-to-perfusion ratio (V/Q) < 1), resulting in dyspnoea and convulsions in the body. Although the diagnosis of HVS was made by emergency physicians based on the patient’s clinical indicators, unfortunately, there were no clinical test specific indicators in our data, only clinical diagnostic results. Our future research will focus on the impact of this indicator. Overall, our study provides a reasonable discussion of the effect of temperature on the visits of HVS and has important public health implications for the prevention of HVS.
Conclusions
This is the first study to explore the associations between ambient temperature and DTR on HVS visits at a demographic level using quantitative data from emergency medicine. Our findings suggest that the effect of high temperature on HVS emergency admission is immediate but short-lived and the risk of HVS visits was higher in women and young people. Low DTR is associated with a reduced risk of HVS emergency admission, with the reduction being more pronounced for women and young people. This study provides evidence for the effect of ambient temperature and DTR on HVS admission. In addition, the results could lead to the development of public health intervention policies for the prevention of HVS and call for clinicians to raise the attention of patients with HVS to alleviate their suffering.
The authors thank all researchers related to the article.
Data availability statement
Data are available upon reasonable request.
Ethics statements
Patient consent for publication
Not applicable.
Ethics approval
This study involves human participants and was approved by The Ethics Committee of Tianjin Medical University (Number TMUhMEC 2021009). The study was only a retrospective study analysing the aggregate data, which did not involve personal private information; thus, the need for written informed consent was waived by the Ethics Committee of Tianjin Medical University.
JW and YZ contributed equally.
Contributors JW, YZ and WZ were the experimental designers and executors of the study, completed the data analysis and wrote the first draft of the paper; ZC, XLi and YG participated in the experimental design and analysis of the experimental results. JZ, YN, QZ and CL were the conceptualisers and leaders of the project and directed the study design, data analysis thesis writing and revision. During the revision of the manuscript, XLiu worked extensively on data supplementation, mapping and methodology. WZ and CL, as guarantors, assume full responsibility for the conduct of the work and/or research, have access to the data, and control the decision to publish. All authors read and approved the final manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer-reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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Abstract
Objectives
To assess the association between ambient temperature and diurnal temperature range (DTR) on emergency admissions for hyperventilation syndrome (HVS).
Design
Distributed lag non-linear model design was used with a lag time to 5 days.
Setting
Emergency admission data used were from the Beijing Red Cross Emergency Centre (2017–2018).
Participants and exposure
Cases were those with emergency visits to the Beijing Emergency Center during the period 2017–2018 and who were given the primary outcome indicator defined as HVS according to the International Classification of Diseases, 10th edition code F45.303. Ambient temperature and DTR were used as exposure factors with adjustments for relative humidity, wind speed, precipitation, seasonality long-term trend and day of the week.
Main outcome measure
We used the minimum emergency visits temperature as a reference to indicate the relative risk with 95% CI of exposure–response for the risk of HVS visits at different temperatures.
Results
A u-shape was described between ambient temperature and HVS visits, with a minimum risk at 12°C. Moderate heat (23°C) at lag (0–3) days, extreme heat at lag 0 days, had greatest relative risks on HVS visits, with 2.021 (95% CI 1.101 to 3.71) and 1.995 (95% CI 1.016 to 3.915), respectively. A stronger association between HVS visits and temperature was found in women and aged ≤44 years. Notably, the relationship between DTR and HVS visits appeared a reverse u-shaped. Low DTR (4°C) effect appeared at lag (0–1) days with 0.589 (95% CI 0.395 to 0.878), lasting until lag (0–3) days with 0.535 (95% CI 0.319 to 0.897) and was associated with a reduced risk of HVS visits in women and those aged ≤44 years.
Conclusions
Ambient temperature and DTR were associated with HVS visits, appearing a differentiation in gender and age groups. Timely prevention strategies during high temperatures and control mild changes in temperature might reduce the risk of HVS.
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Details

1 School of Public Health, Tianjin Medical University, Heping, Tianjin, China
2 Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, National Clinical Research Center for Respiratory Diseases, Beijing, China
3 Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; Shandong University Climate Change and Health Center, Jinan, China
4 Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
5 Chinese PLA Center for Disease Control and Prevention, Beijing, China