Study on the correlation between ambient environment-meteorological factors and the number of visits of acute otitis media, Lanzhou, China

Air pollutants, seasonal influenza, and acute otitis media in children: a population-based analysis using 22-year hospitalization data

BACKGROUND

Acute otitis media (AOM) is a prevalent childhood acute illness, with 13.6 million pediatric office visits annually, often stemming from upper respiratory tract infections (URI) and affected by environmental factors like air pollution and cold seasons.

METHODS

Herein, we made use of territory-wide hospitalization data to investigate the relationships between meteorological factors, air pollutants, influenza infection, and AOM for children observed from 1998 to 2019 in Hong Kong. Quasi-Poisson generalized additive model, combined with a distributed-lag non-linear model, was employed to examine the relationship between weekly AOM admissions in children and weekly influenza-like illness-positive (ILI +) rates, as well as air pollutants (i.e., oxidant gases, sulfur dioxide, and fine particulate matter), while accounting for meteorological variations.

RESULTS

There were 21,224 hospital admissions due to AOM for children aged ≤ 15 years throughout a 22-year period. The cumulative adjusted relative risks (ARR) of AOM were 1.15 (95% CI, 1.04-1.28) and 1.07 (95% CI, 0.97-1.18) at the 95th percentile concentration of oxidant gases (65.9 ppm) and fine particulate matter (62.2 μg/m3) respectively, with reference set to their medians of concentration. The ARRs exhibited a monotone increasing trend for all-type and type-specific ILI + rates. Setting the reference to zero, the cumulative ARRs of AOM rose to 1.42 (95% CI, 1.29-1.56) at the 95th percentile of ILI + Total rate, and to 1.07 (95% CI, 1.01-1.14), 1.19 (95% CI, 1.11-1.27), and 1.22 (95% CI, 1.13-1.32) for ILI + A/H1N1, A/H3N2, and B, respectively.

CONCLUSIONS

Our findings suggested that policy on air pollution control and influenza vaccination for children need to be implemented, which might have significant implications for preventing AOM in children.

Effects of air pollution on emergency visits for acute otitis media among children: a case-crossover study in Chongqing, China

Background

Many epidemiological studies have demonstrated the short-term effects of air pollution on acute otitis media (AOM) in children, but few studies have explored the association between AOM and air pollution in Chinese children. This study aimed to analyze the effects of air pollution on emergency visits for AOM among children through a time-stratified case-crossover design in Chongqing, China.

Methods

The outpatient medical records of children from nine main urban districts who presented with AOM between December 22, 2018 and December 21, 2021 were collected from the Children's Hospital of Chongqing Medical University. Data for air pollution variables, including the air quality index (AQI), particulate matter ≤ 10 μm (PM10), PM2.5, SO2, CO, NO2 and O3 from 17 monitoring sites were collected. Data for meteorological factors as confounding variables also were collected. Conditional logistic regression was used to analyze the data with single-pollutant models, multi-pollutant models, and stratified analyses.

Results

Increases in AQI, PM10, PM2.5, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in single-pollutant models and stratified analyses. Increases in PM10, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in multi-pollutant models. NO2 had the most statistically significant OR values in all models, whereas significant effects of O3 were observed only in seasonal stratification. In single-pollutant models, we found that the best lag periods were lag 0-7 for air pollution variables except for O3 and the largest OR values were 1.185 (95%CI: 1.129-1.245) for SO2 in single-pollutant models. In stratified analyses, there were no difference between groups in these statistically significant OR values through gender and age stratification, while the differences between seasons in these OR values of PM10, SO2, CO, NO2 and O3 were statistically significant. Children aged 0 years and 3-5 years represented the most susceptible population, and among the seasons, susceptibility was greater during Winter and Spring.

Conclusion

Short-term exposure to air pollution can increase emergency visits for AOM among children in Chongqing, China.

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.

Fluctuations in emergency department visits related to acute otitis media are associated with extreme meteorological conditions

Background

Climate change has been associated with a higher frequency of extreme weather events, resulting in an overall increase in morbidity and mortality. Acute otitis media (AOM) is one of the most common otolaryngological infections and accounts for 1.5% of emergency department visits. This study aimed to identify associations between extreme weather events and the immediate and delayed risks for AOM-related emergency department visits (EV).

Methods

A total of 1,465 AOM-related EVs were identified in the Vienna General Hospital between 2015 and 2018. A distributed lag non-linear model was applied to evaluate the relationship between extreme weather conditions and the total number of AOM-related EVs per day. The relative risk (RR) and cumulative RR (cRR) of single-day events and extended weather events over three days were analyzed over a lag period of 14 days.

Results

AOM-related EVs showed a pronounced seasonality, with the highest occurrence during winter. Single-day weather events affected AOM-related EVs only at high relative humidity. Prolonged extreme weather conditions over three days, however, significantly increased the cRR for AOM-related EVs to 3.15 [1.26-7.88; p = 0.014] and 2.14 [1.14-4.04; p = 0.018] at mean temperatures of -4°C (1^st-percentile - p₁) and 0°C (p₅) on the same day. Relative humidity of 37% (p₁) decreased RR to 0.94 [0.88-0.99; p = 0.032] on day 7, while extremely high humidity of 89% (p₉₉) led to an increased cRR of 1.43 [1.03-2.00; p = 0.034] on day 7. Heavy prolonged precipitation of 24mm (p₉₅) reduced cRR beginning day 4 up until day 14 to 0.52 [0.31-0.86; p = 0.012]. Prolonged low atmospheric pressure events of 985hPa (p₅) reduced the RR to 0.95 [0.91-1.00; p = 0.03], whereas extremely high atmospheric pressure events of 1013hPa (p₉₉) increased the RR to 1.11 [1.03-1.20; p = 0.008]. Extremely low wind speeds significantly diminished the RR of AOM-related EVs.

Conclusions

While single-day extreme weather events had little impact on the occurrence of AOM-related EVs, extended periods of extreme temperatures, relative humidity, precipitation, wind speeds and atmospheric pressure significantly impacted the RR for AOM-related EVs. These findings could help improve healthcare resource allocation in similar climates and aid in educating patients about the role of environmental factors in AOM.

Air pollution and coronary heart disease-related hospital visits in Beijing, China: time-series analysis using a generalized additive model

To investigate correlations between environmental and meteorological factors and frequency of presentation for coronary heart disease (CHD) in Beijing. Daily measurements of levels of six atmospheric pollutants were made, data relating to meteorological conditions collected, and CHD-related outpatient visits recorded from January 2015 to December 2019 in Beijing. A time-series analysis was made, using a generalized additive model with Poisson distribution, and R 3.6.3 software was used to estimate relationships among levels of atmospheric pollutants, ambient temperature, and visits occasioned by CHD. Results were controlled for time-dependent trend, other weather variables, day of the week, and holiday effects. Lag-response curves were plotted for specific and incremental cumulative effects of relative risk (RR). The aim was to correlate meteorological-environmental factors and the daily number of CHD-related hospital visits and to quantify the degree of correlation to identify any pathological associations. Response diagrams and three-dimensional diagrams of predicted exposure lag effects were constructed in order to evaluate relationships among the parameters of air pollution, temperature, and daily CHD visits. The fitted model was employed to predict the lag RR and 95% confidence interval (95% CI) for specific and incremental cumulative effects of random air pollutants at random concentrations. This model may then be used to predict effects on the outcome variable at any concentration of any defined pollutant, giving flexibility for public health purposes. The overall lag-response RR curves for the specific cumulative effects of the pollutants, particulate matter (PM)2.5, PM10, SO2, CO, and NO2, were statistically significant and for PM2.5, PM10, CO, and NO2, the overall lag-response RR curves for the incremental cumulative effect were statistically significant. When PM2.5, PM10, SO2, CO, and NO2 concentrations were above threshold values and the temperature was below 45 °F (reference value 70 °F), the number of CHD-related hospital visits increased with a time lag effect. The outpatient volume of CHD was predicted by the model to guide the flexible distribution of medical resources. Elevated PM2.5, PM10, SO2, CO, and NO2 concentrations in the atmosphere combined and low ambient temperature increased the risk of CHD with a time lag effect.

Effects of ambient particulate exposure on blood lipid levels in hypertension inpatients

Background

With modernization development, multiple studies of atmospheric particulate matter exposure conducted in China have confirmed adverse cardiovascular health effects. However, there are few studies on the effect of particulate matter on blood lipid levels in patients with cardiovascular disease, especially in southern China. The purpose of this study was to investigate the association between short- and long-term exposure to ambient particulate matter and the levels of blood lipid markers in hypertension inpatients in Ganzhou, China.

Methods

Data on admission lipid index testing for hypertension inpatients which were divided into those with and without arteriosclerosis disease were extracted from the hospital's big data center from January 1, 2016 to December 31, 2020, and air pollution and meteorology data were acquired from the China urban air quality real time release platform from January 1, 2015 to December 31, 2020 and climatic data center from January 1, 2016 to December 31, 2020, with data integrated according to patient admission dates. A semi-parametric generalized additive model (GAM) was established to calculate the association between ambient particulate matter and blood lipid markers in hypertension inpatients with different exposure time in 1 year.

Results

Long-term exposure to particulate matter was associated with increased Lp(a) in three kinds of people, and with increased TC and decreased HDL-C in total hypertension and hypertension with arteriosclerosis. But particulate matter was associated with increased HDL-C for hypertension inpatients without arteriosclerosis, at the time of exposure in the present study. It is speculated that hypertension inpatients without arteriosclerosis has better statement than hypertension inpatients with arteriosclerosis on human lipid metabolism.

Conclusion

Long-term exposure to ambient particulate matter is associated with adverse lipid profile changes in hypertension inpatients, especially those with arteriosclerosis. Ambient particulate matter may increase the risk of arteriosclerotic events in hypertensive patients.

Association between Particulate Matter Pollution Concentration and Hospital Admissions for Hypertension in Ganzhou, China

Fine particulate matter (PM_2.5) and respirable particulate matter (PM₁₀) are two major air pollutants with toxic effects on the cardiovascular system. Hypertension, as a chronic noncommunicable cardiovascular disease, is also a risk factor for several diseases. We applied generalized linear models with a quasi-Poisson link to assess the effect of air pollution exposure on the number of daily admissions for patients with hypertension. In addition, we established a two-pollutant model to evaluate PM_2.5 and PM₁₀ hazard effect stability by adjusting the other gaseous pollutants. Results showed that during the study period, 24 h mean concentrations of ambient PM_2.5 and PM₁₀ at 38.17 and 59.84 μg/m³, respectively, and a total of 2,611 hypertension hospital admissions were recorded. Air pollution concentrations significantly affected the number of hospitalizations for hypertension approximately 2 months after exposure. For each 10 μg/m³ increase in PM_2.5 and PM₁₀ in single-pollutant models, the number of hospitalizations for hypertension increased by 7.92% (95% CI: 5.48% to 10.42%) and 4.46% (95% CI: 2.86% to 5.65%), respectively, at the lag day with the strongest effect. NO₂, O₃, CO, and SO₂ had different significant effects on the number of hospitalizations over the same time period, and PM_2.5 and PM₁₀ still showed robust significant effects after adjustment of gas pollutants through a two-pollutant model. These findings may contribute to a better understanding of the health effects of ambient particulate matter.