An effect of 24-hour temperature change on outpatient and emergency and inpatient visits for cardiovascular diseases in northwest China

Time-series analysis of temperature variability and cardiovascular emergency department visits in Atlanta over a 27-year period

BACKGROUND

Short-term temperature variability, defined as the temperature range occurring within a short time span at a given location, appears to be increasing with climate change. Such variation in temperature may influence acute health outcomes, especially cardiovascular diseases (CVD). Most research on temperature variability has focused on the impact of within-day diurnal temperature range, but temperature variability over a period of a few days may also be health-relevant through its impact on thermoregulation and autonomic cardiac functioning. To address this research gap, this study utilized a database of emergency department (ED) visits for a variety of cardiovascular health outcomes over a 27-year period to investigate the influence of three-day temperature variability on CVD.

METHODS

For the period of 1993-2019, we analyzed over 12 million CVD ED visits in Atlanta using a Poisson log-linear model with overdispersion. Temperature variability was defined as the standard deviation of the minimum and maximum temperatures during the current day and the previous two days. We controlled for mean temperature, dew point temperature, long-term time trends, federal holidays, and day of week. We stratified the analysis by age group, season, and decade.

RESULTS

All cardiovascular outcomes assessed, except for hypertension, were positively associated with increasing temperature variability, with the strongest effects observed for stroke and peripheral vascular disease. In stratified analyses, adverse associations with temperature variability were consistently highest in the moderate-temperature season (October and March-May) and in the 65 + age group for all outcomes.

CONCLUSIONS

Our results suggest that CVD morbidity is impacted by short-term temperature variability, and that patients aged 65 and older are at increased risk. These effects were more pronounced in the moderate-temperature season and are likely driven by the Spring season in Atlanta. Public health practitioners and patient care providers can use this knowledge to better prepare patients during seasons with high temperature variability or ahead of large shifts in temperature.

Effects of large temperature change lasting for several days on cardiovascular disease hospital admissions among farmers in China's Western villages

Global climate change has resulted in an increase in the frequency, intensity, and duration of extreme events. However, we know very little about the associations between large temperature changes lasting for several days (LTCD) and CVD, particularly in less-developed, rural areas. We collected daily data on cardiovascular-related diseases and weather conditions from 119 villages in seven counties between 2010 and 2016. A distributed lag non-linear model was used to investigate the association between LTCD and relative risk (RR) of hospital admissions for CVD. Analyses were stratified by the duration and intensity of LTCD, age, and sex. The RR of CVD increases with the intensity and duration of LTCD. Men and young people were more vulnerable to LTCD of longer duration and higher intensity than women and old. Our findings may help local public health authorities develop adaptive preventive strategies targeting inhabitants of villages.

Temperature fluctuation and acute myocardial infarction in Beijing: an extended analysis of temperature ranges and differences

Purpose

Few studies examined the relationship between temperature fluctuation metrics and acute myocardial infarction (AMI) hospitalizations within a single cohort. We aimed to expand knowledge on two basic measures: temperature range and difference.

Methods

We conducted a time-series analysis on the correlations between temperature range (TR), daily mean temperature differences (DTDmean), and daily mean-maximum/minimum temperature differences (TDmax/min) and AMI hospitalizations, using data between 2013 and 2016 in Beijing, China. The effects of TRn and DTDmeann over n-day intervals were compared, respectively. Subgroup analysis by age and sex was performed.

Results

A total of 81,029 AMI hospitalizations were included. TR1, TDmax, and TDmin were associated with AMI in J-shaped patterns. DTDmean1 was related to AMI in a U-shaped pattern. These correlations weakened for TR and DTDmean with longer exposure intervals. Extremely low (1st percentile) and high (5°C) DTDmean1 generated cumulative relative risk (CRR) of 2.73 (95% CI: 1.56-4.79) and 2.15 (95% CI: 1.54-3.01). Extremely high TR1, TDmax, and TDmin (99th percentile) correlated with CRR of 2.00 (95% CI: 1.73-2.85), 1.71 (95% CI: 1.40-2.09), and 2.73 (95% CI: 2.04-3.66), respectively. Those aged 20-64 had higher risks with large TR1, TDmax, and TDmin, while older individuals were more affected by negative DTDmean1. DTDmean1 was associated with a higher AMI risk in females.

Conclusion

Temperature fluctuations were linked to increased AMI hospitalizations, with low-temperature extremes having a more pronounced effect. Females and the older adult were more susceptible to daily mean temperature variations, while younger individuals were more affected by larger temperature ranges.

A study of temperature variability on admissions and deaths for cardiovascular diseases in Northwestern China

OBJECTIVE

To explore the effect of temperature variability (TV) on admissions and deaths for cardiovascular diseases (CVDs).

METHOD

The admissions data of CVDs were collected in 4 general hospitals in Jinchang City, Gansu Province from 2013 to 2016. The monitoring data of death for CVDs from 2013 to 2017 were collected through the Jinchang City Center for Disease Control and Prevention. Distributed lag nonlinear model (DLNM) was combined to analyze the effects of TV (daily temperature variability (DTV) and hourly temperature variability (HTV)) on the admissions and deaths for CVDs after adjusting confounding effects. Stratified analysis was conducted by age and gender. Then the attribution risk of TV was evaluated.

RESULTS

There was a broadly linear correlation between TV and the admissions and deaths for CVDs, but only the association between TV and outpatient and emergency room (O&ER) visits for CVDs have statistically significant. DTV and HTV have similar lag effect. Every 1 ℃ increase in DTV and HTV was associated with a 3.61% (95% CI: 1.19% ~ 6.08%), 3.03% (95% CI: 0.27% ~ 5.86%) increase in O&ER visits for CVDs, respectively. There were 22.75% and 14.15% O&ER visits for CVDs can attribute to DTV and HTV exposure during 2013-2016. Males and the elderly may be more sensitive to the changes of TV. Greater effect of TV was observed in non-heating season than in heating season.

CONCLUSION

TV was an independent risk factor for the increase of O&ER visits for CVDs, suggesting effective guidance such as strengthening the timely prevention for vulnerable groups before or after exposure, which has important implications for risk management of CVDs.

The effect of ambient temperature and risk of cardiovascular disease hospitalization in China: a meta-analysis

The effect of ambient temperature on relative risk (RR) of cardiovascular disease (CVD) is different in China than in other countries due to the different geographical environment, climate the different inter- and intra-individual characteristics of the population within China. It is therefore important to integrate information to evaluate the impact of temperature on RR of CVD in China. We performed a meta-analysis to evaluate the effect of temperature on RR of CVD. The Web of Science, Google Scholar, and China National Knowledge Infrastructure databases were searched back to 2022 and nine studies were included in the study. The Cochran Q test and I2 statistics were used to assess heterogeneity, while Egger's test was used to assess publication bias. The pooled estimated size of the relationship between ambient temperature and CVD hospitalization in the random effect model was 1.2044 (95%CI: 1.0610-1.3671) for the cold effect and 1.1982 (95%CI: 1.0166-1.4122) for the heat effect. The Egger's test showed a potential publication bias for the cold effect, whereas there was no apparent publication bias for the heat effect. There is a significant effect of ambient temperature on RR of CVD for both the cold effect and heat effect. The effect of socioeconomic factors should be considered more thoroughly in future studies.

Hospital admission tendencies caused by day-to-day temperature changes during summer: a case study for the city of Novi Sad (Serbia)

Increased temperature risk in cities threatens the health and well-being of urban population and is fueled by climate change and intensive urbanization. Consequently, further steps must be taken for assessing temperature conditions in cities and their association with public health, in order to improve public health prevention at local or regional level. This study contributes to solving the problems by analyzing the connection between extreme temperatures and the tendencies of all-cause hospital admissions. The analyses used (a) 1-h air temperature data, and (b) daily data of all-cause hospital admissions. The datasets include the summer period (June, July, August) for the years 2016 and 2017. We tested the effects of two temperature indices, day-to-day change in maximum temperature - T_max,c and daily temperature range - T_r, with all-cause hospital admission subgroups, such as all-cause cases - H_a, hospital admissions in the population below 65 - H_a<65, and hospital admissions in the population aged 65 and over - H_a≥65. The results show the highest values of H_a when T_max,c is between 6 and 10 °C. Therefore, more intensive hospital admissions can be expected when T_max increases from day-to-day (positive values of T_max,c), and it is more visible for H_a and H_a<65 (1 °C = 1% increase in hospital admissions). Also, T_r values between 10 °C and 14 °C cause an increase in the number of hospital admissions, and it is more noticeable for H_a≥65.

Health Risks of Temperature Variability on Hospital Admissions in Cape Town, 2011-2016

Epidemiological studies have provided compelling evidence of associations between temperature variability (TV) and health outcomes. However, such studies are limited in developing countries. This study aimed to investigate the relationship between TV and hospital admissions for cause-specific diseases in South Africa. Hospital admission data for cardiovascular diseases (CVD) and respiratory diseases (RD) were obtained from seven private hospitals in Cape Town from 1 January 2011 to 31 October 2016. Meteorological data were obtained from the South African Weather Service (SAWS). A quasi-Poisson regression model was used to investigate the association between TV and health outcomes after controlling for potential effect modifiers. A positive and statistically significant association between TV and hospital admissions for both diseases was observed, even after controlling for the non-linear and delayed effects of daily mean temperature and relative humidity. TV showed the greatest effect on the entire study group when using short lags, 0-2 days for CVD and 0-1 days for RD hospitalisations. However, the elderly were more sensitive to RD hospitalisation and the 15-64 year age group was more sensitive to CVD hospitalisations. Men were more susceptible to hospitalisation than females. The results indicate that more attention should be paid to the effects of temperature variability and change on human health. Furthermore, different weather and climate metrics, such as TV, should be considered in understanding the climate component of the epidemiology of these (and other diseases), especially in light of climate change, where a wider range and extreme climate events are expected to occur in future.

Short-term effects of personal exposure to temperature variability on cardiorespiratory health based on subclinical non-invasive biomarkers

Growing literatures have explored the cardiorespiratory health effects of the daily temperature, but such effects of temperature variability remain unclear. We investigated the acute associations of personal levels of temperature variability with cardiorespiratory biomarkers. This is a panel study with four repeated measurements among forty eligible college students in Hefei, Anhui Province, China. We collected personal-level temperature data using temperature/humidity data loggers. Temperature variability parameters included diurnal temperature range (DTR), the standard-deviation of temperature (SDT) and temperature variability (TV). Cardiorespiratory health indicators included three BP parameters [systolic BP (SBP), diastolic BP (DBP) and mean article pressure (MAP)], fractional exhaled nitric oxide (FeNO), and four saliva biomarkers [C-reactive protein (CRP), cortisol, alpha-amylase and lysozyme]. Linear mixed-effect models were then used to assess the associations of temperature variability with these cardiorespiratory biomarkers. We found that short-term exposure to the three temperature variability parameters was associated with these cardiorespiratory biomarkers. The magnitude, direction and significance of these associations varied by temperature variability parameters, by biomarkers and by lags of exposure. Specifically, temperature variability parameters were inversely associated with BP and saliva lysozyme; positively associated with airway inflammation biomarkers (FeNO and saliva CRP) and stress response biomarkers (saliva cortisol and alpha-amylase). The results were robust to further control for air pollutants, and these associations were more prominent in females and in subjects with abnormal body mass index. Our findings suggested that acute exposure to temperature variability could significantly alter cardiorespiratory biomarker profiles among healthy young adults in China.

Impact of temperature changes between neighboring days on cardiovascular disease hospital admissions among suburban farmers in Qingyang, Northwest China

BACKGROUND

No studies focused on impact of temperature changes between neighboring days (TCN) on cardiovascular disease (CVD) hospital admissions among suburban farmers although CVD has been the main cause to global mortality and disability especially in undeveloped and developing countries/areas.

METHOD

Daily data of CVD hospital admissions on suburban farmers and daily data of meteorology in Qingyang (China) were collected during 2011-2015. A distributed lag non-linear model (DLNM) was applied to explore the exposure-response relations and lagged effects between TCN and CVD hospital admissions with stratified analyses by age and gender. Extreme low TCN effects and burden analysis were conducted.

RESULTS

Based on 25,984 cases in Qingyang (China) during 2011-2015 among suburban farmers, we found that, first, nonlinear relationship was observed between TCN and CVD hospital admissions and adverse impact in negative TCN (temperature dropping between neighboring days) while protective effect in positive TCN (temperature rising between neighboring days) were discovered; third, during lag0-27, the cumulative relative risk (RR) for extreme low TCN (5th percentile, - 3.5 °C) and extreme high TCN (95th percentile, 3 °C) was 29.55 (95% CI 4.709-185.436) and 0.040 (95% CI 0.009-0.169), respectively; fourth, the age < 65 and females were more vulnerable to negative TCN than the age ≥ 65 and males among suburban farmers, respectively; last, moderate low TCN contributed the most fractions and numbers on CVD hospital admissions.

CONCLUSIONS

Among Qingyang suburban farmers in Northwest China, negative TCN should be paid more attention.