Effect of diurnal temperature range on outpatient visits for common cold in Shanghai, China

Association between diurnal temperature range and outpatient visits for urticaria disease in Lanzhou, China: a distributed lag nonlinear analysis

BACKGROUND

A growing number of epidemiological studies have shown that daily temperatures are associated with urticaria. However, the relationship between daily changes in temperature and urticaria is unclear.

OBJECTIVES

To assess the diurnal temperature difference (DTR) effects on urticaria outpatient visits in Lanzhou, China.

METHODS

Urticaria outpatient visits data during 2011-2019 were collected from three major tertiary hospitals in Lanzhou. Daily temperature data from the official website of China Meteorological Administration. Assessment of the relationship between urticaria outpatient volume and DTR in Lanzhou City using a distributed lag nonlinear model.

RESULTS

A total of 83,022 urticaria visits were enrolled. There was a nonlinear relationship between DTR and urticaria outpatient visits and a lagged effect of DTR impact. The effects of high DTR on urticaria visits were not seen in all populations but in the male population and in the 15-59 age group. High DTR (P95: 18.2 °C) was associated with a 27% (95% CI: 0.01, 60.53%) and 31% (95% CI: 1.60, 68.99%) increase in the number of urticaria visits in the 21-day lag effect for the male cohort and the 15-59 year old cohort, respectively, compared with 11.5 °C, respectively.

CONCLUSIONS

Our study suggests that DTR is a potential risk factor for urticaria. The results of this study may provide a scientific basis for local governments to improve preventive measures in the health care system.

Effects of apparent temperature on daily outpatient and inpatient visits for cause-specific respiratory diseases in Ganzhou, China: a time series study

BACKGROUND

Non-optimum temperatures are associated with increased risk of respiratory diseases, but the effects of apparent temperature (AT) on respiratory diseases remain to be investigated.

METHODS

Using daily data from 2016 to 2020 in Ganzhou, a large city in southern China, we analyzed the impact of AT on outpatient and inpatient visits for respiratory diseases. We considered total respiratory diseases and five subtypes (influenza and pneumonia, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), asthma and chronic obstructive pulmonary disease [COPD]). Our analysis employed a distributed lag nonlinear model (DLNM) combined with a generalized additive model (GAM).

RESULTS

We recorded 94,952 outpatients and 72,410 inpatients for respiratory diseases. We found AT significantly non-linearly associated with daily outpatient and inpatient visits for total respiratory diseases, influenza and pneumonia, and URTI, primarily during comfortable AT levels, while it was exclusively related with daily inpatient visits for LRTI and COPD. Moderate heat (32.1 °C, the 75.0th centile) was observed with a significant effect on both daily outpatient and inpatient visits for total respiratory diseases at a relative risk of 1.561 (1.161, 2.098) and 1.276 (1.027, 1.585), respectively (both P < 0.05), while the results of inpatients became insignificant with the adjustment for CO and O3. The attributable fractions in outpatients and inpatients were as follows: total respiratory diseases (24.43% and 18.69%), influenza and pneumonia (31.54% and 17.33%), URTI (23.03% and 32.91%), LRTI (37.49% and 30.00%), asthma (9.83% and 3.39%), and COPD (30.67% and 10.65%). Stratified analyses showed that children ≤5 years old were more susceptible to moderate heat than older participants.

CONCLUSIONS

In conclusion, our results indicated moderate heat increase the risk of daily outpatient and inpatient visits for respiratory diseases, especially among children under the age of 5.

Relationship between temperature variability and daily hospitalisations in Hong Kong over two decades

Background

Studies have highlighted the impacts of temperature variability (TV) on mortality from respiratory diseases and cardiovascular diseases, with inconsistent results specifically in subtropical urban areas than temperate ones. We aimed to fully determine TV-associated health risks over a spectrum of diseases and various subgroups in a subtropical setting.

Methods

Using inpatient data from all public hospitals in Hong Kong from 1999 to 2019, we examined the TV-hospitalisation associations by causes, ages, and seasons by fitting a quasi-Poisson regression. We presented the results as estimated percentage changes of hospitalisations per interquartile range (IQR) of TV.

Results

TVs in exposure days from 0-5 days (TV0-5) to 0-7 days (TV0-7) had detrimental effects on hospitalisation risks in Hong Kong. The overall population was significantly affected over TV0-5 to TV0-7 in endocrine, nutritional and metabolic (from 0.53% to 0.58%), respiratory system (from 0.38% to 0.53%), and circulatory systems diseases (from 0.47% to 0.56%). While we found no association with seasonal disparities, we did observe notable disparities by age, highlighting older adults' vulnerability to TVs. For example, people aged ≥65 years experienced the highest change of 0.88% (95% CI = 0.34%, 1.41%) in hospitalizations for injury and poisoning per IQR increase in TV0-4.

Conclusions

Our population-based study highlighted that TV-related health burden, usually regarded as minimal compared to other environmental factors, should receive more attention and be addressed in future relevant health policies, especially for vulnerable populations during the cold seasons.

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

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.

Identifying socio-ecological drivers of common cold in Bhutan: a national surveillance data analysis

The common cold is a leading cause of morbidity and contributes significantly to the health costs in Bhutan. The study utilized multivariate Zero-inflated Poisson regression in a Bayesian framework to identify climatic variability and spatial and temporal patterns of the common cold in Bhutan. There were 2,480,509 notifications of common cold between 2010 and 2018. Children aged < 15 years were twice (95% credible interval [CrI] 2.2, 2.5) as likely to get common cold than adults, and males were 12.4% (95 CrI 5.5%, 18.7%) less likely to get common cold than females. A 10 mm increase in rainfall lagged one month, and each 1 °C increase of maximum temperature was associated with a 5.1% (95% CrI 4.2%, 6.1%) and 2.6% (95% CrI 2.3%, 2.8%) increase in the risk of cold respectively. An increase in elevation of 100 m and 1% increase in relative humidity lagged three months were associated with a decrease in risk of common cold by 0.1% (95% CrI 0.1%, 0.2%) and 0.3% (95% CrI 0.2%, 0.3%) respectively. Seasonality and spatial heterogeneity can partly be explained by the association of common cold to climatic variables. There was statistically significant residual clustering after accounting for covariates. The finding highlights the influence of climatic variables on common cold and suggests that prioritizing control strategies for acute respiratory infection program to subdistricts and times of the year when climatic variables are associated with common cold may be an effective strategy.

Analysis of the effect of meteorological factors on hemorrhagic fever with renal syndrome in Taizhou City, China, 2008–2020

Background

Hemorrhagic fever with renal syndrome (HFRS) is endemic in Zhejiang Province, China, while few studies have concentrated on the influence of meteorological factors on HFRS incidence in the area.

Methods

Data on HFRS and meteorological factors from January 1, 2008 to December 31, 2020 in Taizhou City, Zhejiang Province were collected. Multivariate analysis was conducted to the relationship between meteorological factors including minimum temperatures, relative humidity, and cumulative rainfall with HFRS.

Results

The HFRS incidence peaked in November and December and it was negatively correlated with average and highest average temperatures. Compared with median of meteorological factors, the relative risks (RR) of weekly average temperature at 12 ℃, weekly highest temperature at 18 ℃relative humidity at 40%, and cumulative rainfall at 240 mm were most significant and RRs were 1.41 (95% CI: 1.09–1.82), 1.32 (95% CI: 1.05–1.66), 2.18 (95% CI: 1.16–4.07), and 1.91 (95% CI: 1.16–2.73), respectively. Average temperature, precipitation, relative humidity had interactions on HFRS and the risk of HFRS occurrence increased with the decrease of average temperature and the increase of precipitation.

Conclusion

Our study results are indicative of the association of environmental factors with the HFRS incidence, probable recommendation could be use of environmental factors as early warning signals for initiating the control measure and response. 

Epidemiology of rhinovirus under the COVID‐19 pandemic in Guangzhou, China, 2020

Background

Methods

Results

Conclusion

Temporal cross-correlations between air pollutants and outpatient visits for respiratory and circulatory system diseases in Fuzhou, China

BACKGROUND

Previous studies have suggested that there is an association between air pollutants and circulatory and respiratory diseases; however, relatively few have analyzed the association between air pollutants and outpatient visits based on the mortality, hospitalization rates, etc., especially in areas with relatively good air quality. Therefore, we conducted this study to research the association between air pollutants and outpatient visits in Fuzhou, China.

METHODS

We used a generalized linear Poisson model to study the association between air pollution and outpatient visits for respiratory and circulatory diseases from 2016 to 2018 in Fuzhou, China.

RESULTS

In the single pollutant model, nitrogen dioxide (NO2) had a significant effect. For lag day 0 to lag day 5, the effect decreased with every 10 μg/L increase in NO2. The daily maximum 8-h mean ozone (O3-8h) and upper respiratory outpatient visits were positively associated during the cold period [lag2, excess risk (ER) (95% confidence interval (CI)): 1.68% (0.44-2.94%)], while O3-8h and respiratory disease were positively associated during the warm period [lag5, ER (95% CI): 1.10% (0.11-2.10%) and lag4, ER (95% CI): 1.02% (0.032-2.02%)]. Similarly, particulate matter (PM) with an average aerodynamic diameter of less than 10 μm (PM10) and lower respiratory diseases were positively associated during the warm period [lag0, ER (95% CI): 1.68% (0.44-2.94%)]. When the concentration of O3-8h was higher than 100 μg/L, there was a positive effect on circulatory [lag5, ER (95% CI): 2.83% (0.65-5.06%)], respiratory [lag5, ER (95% CI): 2.47% (0.85-4.11%)] and upper respiratory [lag5, ER (95% CI): 3.06% (1.38-4.77%)] outpatient visits. The variation in O3-8h changed slightly when we adjusted for other air pollutants, and after adjusting for O3-8h, the ERs of the other air pollutants changed slightly. After adjusting for PM with an average aerodynamic diameter of less than 2.5 μm (PM2.5), the ERs of the other air pollutants increased, and after adjusting for NO2, the ER of PM decreased.

CONCLUSION

Exposure to ambient NO2, O3, PM2.5 and PM10 was associated with an increase in respiratory and circulatory system-related outpatient visits in Fuzhou, China.