Visits For Respiratory Diseases Research Articles

Changes in primary care visits for respiratory illness during the COVID-19 pandemic: a multinational study by the International Consortium of Primary Care Big Data Researchers (INTRePID).

The majority of patients with respiratory illness are seen in primary care settings. Given COVID-19 is predominantly a respiratory illness, the INTernational ConsoRtium of Primary Care BIg Data Researchers (INTRePID), assessed the pandemic impact on primary care visits for respiratory illnesses. Definitions for respiratory illness types were agreed on collectively. Monthly visit counts with diagnosis were shared centrally for analysis. Primary care settings in Argentina, Australia, Canada, China, Norway, Peru, Singapore, Sweden and the United States. Over 38 million patients seen in primary care settings in INTRePID countries before and during the pandemic, from January 1st, 2018, to December 31st, 2021. Relative change in the monthly mean number of visits before and after the onset of the pandemic for acute infectious respiratory disease visits including influenza, upper and lower respiratory tract infections and chronic respiratory disease visits including asthma, chronic obstructive pulmonary disease, respiratory allergies, and other respiratory diseases. INTRePID countries reported a marked decrease in the average monthly visits for respiratory illness. Changes in visits varied from -10.9% [95% confidence interval (CI): -33.1 to +11.3%] in Norway to -79.9% (95% CI: -86.4% to -73.4%) in China for acute infectious respiratory disease visits and - 2.1% (95% CI: -12.1 to +7.8%) in Peru to -59.9% (95% CI: -68.6% to -51.3%) in China for chronic respiratory illness visits. While seasonal variation in allergic respiratory illness continued during the pandemic, there was essentially no spike in influenza illness during the first 2 years of the pandemic. The COVID-19 pandemic had a major impact on primary care visits for respiratory presentations. Primary care continued to provide services for respiratory illness, although there was a decrease in infectious illness during the COVID pandemic. Understanding the role of primary care may provide valuable information for COVID-19 recovery efforts and planning for future global emergencies.

Community-Acquired Pneumonia Diagnosis Following Emergency Department Visits for Respiratory Illness.

Community-acquired pneumonia (CAP) is often considered for children presenting to the emergency department (ED) with respiratory symptoms. It is unclear how often children are diagnosed with CAP following an ED visit for respiratory illness. We performed a retrospective case-control study to evaluate 7-day CAP diagnosis among children 3 months to 18 years discharged from the ED with respiratory illness from 2011 to 2021 and who receive care at 4 hospital-affiliated primary care clinics. Logistic regression was performed to assess for predictors of 7-day CAP diagnosis. Seventy-four (0.7%, 95% confidence interval [CI] = 0.6%, 0.9%) of 10 329 children were diagnosed with CAP within 7 days, and fever at the index visit was associated with increased odds of diagnosis (odds ratio [OR] = 3.32, 95% CI = 1.75-6.28). Community-acquired pneumonia diagnosis after discharge from the ED with respiratory illness is rare, even among children who are febrile at time of initial evaluation.

Adherence to dietary guidelines associated with lower medical service utilization in preschoolers: a longitudinal study

ObjectiveWe aimed to evaluate the association between dietary guideline adherence and overall, outpatient, and emergency medical service utilization in Taiwanese preschoolers.MethodsWe selected 614 preschoolers (2–6 years) who had one day of 24-h dietary recall data from the 2013–2016 Nutrition and Health Survey in Taiwan. The Taiwanese Children Healthy Eating Index (TCHEI) was developed on the basis of Taiwanese Food-Based Dietary Guidelines; it assesses dietary adequacy and eating behavior. Data on the participants’ outpatient and emergency medical service utilization were obtained for 2013–2018 from the National Health Insurance Research Database. A multivariable generalized linear model was used to evaluate the association between the TCHEI and medical service utilization for all disease and respiratory diseases.ResultsAfter adjustment for confounding factors, children aged 2–3 years in the Tertile (T) 2 and T3 groups of the TCHEI exhibited 25% (95% CI 0.69–0.83) and 16% (95% CI 0.77–0.92) lower overall medical visits, respectively. The same pattern was noted in the outpatient and emergency visits for all diseases and respiratory diseases. The children aged 4–6 years in the T2 group exhibited 15% (95% CI 0.80–0.91) and 11% (95% CI 0.82–0.97) lower overall visits and visits for respiratory diseases, respectively. Moreover, preschoolers in the T2 group exhibited lower overall medical expenditures than did those in the T1 group.ConclusionsTCHEI score was positively correlated with better nutritional status. Optimal dietary intake associated with lower medical service utilization among Taiwan preschoolers.

On the short-term impact of pollution: The effect of PM 2.5 on emergency room visits.

In this paper, we study the short-term effect of fine particulate matter (PM 2.5) exposure on respiratory emergency room (ER) visits in Chile, a middle-income country with high levels of air pollution. To instrument for PM 2.5, we use wind speed at different altitudes (pressure levels). Unlike previous papers, our data allow us to study the impact of high pollution levels across all age groups. We find that a 1μg per cubic meter (μg/m3 ) increase in PM 2.5 exposure for 1day increases ER visits for respiratory illness by 0.36%. The effect is positive and significant for all age groups. Furthermore, the coefficients on government environmental alerts suggest that avoidance behavior becomes increasingly significant across all age groups as restrictions become more severe.

Different volatile organic compounds in local point source air pollution pose distinctive elevated risks for respiratory disease-associated emergency room visits

Air pollution increases risk of respiratory disease but prior research has focused on particulate matter and criteria air pollutants, and there are few studies on respiratory effects of volatile organic compounds (VOC). We examined zip code level relationships between emergency room (ER) visits for respiratory illness and VOC pollution in New York State from 2010 to 2018. Detailed information on VOC pollution was derived from the National Emissions Inventory, which provides point source information on VOC emissions at the zip code level. We considered four respiratory diseases: asthma, acute upper respiratory infections, chronic obstructive pulmonary disease (COPD), and lower respiratory disease, using mixed effects regression with a random intercept to account for county level variability in single pollutant models, and Random Forest Regression (RFR) to assess relative importance of VOC exposures when considered together in multipollutant models.Single pollutant models show associations between respiratory-related ER visits with all pollutants of interest across all study years, even after adjusting for poverty and smoking by zip code. The largest relative single pollutant effect sizes considered included benzene, ethylbenzene, and total (summed) VOCs. Results from RFR including all VOC exposures indicate that ethylbenzene has the greatest variable importance for asthma, acute upper respiratory infections, and COPD, with toluene and benzene most important for lower respiratory ailments. RFR results also demonstrate presence of pairwise interactive effects between VOC pollutants. Our findings show that local VOC pollution may offer a significant contribution to the risk of respiratory disease-related ER visits, and that effects vary by illness and by VOC compound. ER visit rates for respiratory illness were elevated in high poverty zip codes, although this may be attributable to the fact that the poor lack basic access to health care and use ERs more frequently for routine care.

Ambient carbon monoxide and relative risk of daily hospital outpatient visits for respiratory diseases in Lanzhou, China.

At present, evidence of the associations between carbon monoxide (CO) and respiratory diseases (RD) in Northwest China is limited and controversial. The aim of this study is to evaluate the impact of ambient CO on outpatient visits for RD in Lanzhou, China. The daily amount of outpatient visits for total and cause-specific RD, air pollutant, and weather variables were collected in Lanzhou, China from 1st January 2013 to 31st December 2019. A generalized additive model and distributed lag nonlinear model were used to assess associations between CO and outpatient visits for RD. During the study period, a total of 1,623,361 RD outpatient visits were recorded. For each interquartile range (IQR) (0.77mg/m3) increase in CO, the relative risk (RR) was 1.163 (95% CI: 1.138, 1.188) for total RD at lag07, 1.153 (95% CI: 1.128,1.179) for upper respiratory tract infection (URTI) at lag07, 1.379 (95% CI: 1.338,1.422) for pneumonia at lag07, 1.029 (95% CI: 0.997,1.062) for chronic obstructive pulmonary disease (COPD) lag04, 1.068 (95% CI: 1.028,1.110) for asthma lag03, and 1.212 (95% CI: 1.178,1.247) for bronchitis lag07, respectively. In the subgroup analyses, the impacts of CO were more pronounced on total RD, pneumonia, COPD, and bronchitis in males than females, while the opposite was true in URTI and asthma. The impact of CO on RD was the strongest for children under 15years-of-age. We also found significantly stronger effects during cold seasons compared to warm seasons. In addition, we observed a roughly linear exposure-response curve between CO and RD with no threshold effect. This study in Lanzhou revealed a remarkable association between CO level and an elevated risk of total and cause-specific RD outpatient visits, especially for pneumonia.

Effect of different cold air intensities and their lagged effects on outpatient visits for respiratory illnesses in Handan in different seasons

The authors analyzed cold air processes of different intensities and their effect on the number of patients with respiratory illnesses visiting a hospital outpatient department in Handan City in different seasons from 2016 to 2019. For cold air events of the same intensity (apart from cold waves), the number of patients with respiratory illnesses visiting the outpatient department was highest in winter. During the cold waves, the number of patients increased sharply to greater than 55 patients per day, and the number of patients was higher in spring than in autumn. The probability density of the number of patients with respiratory illnesses visiting the outpatient department gradually changed to a positive skewness as the cold air intensity increased. Although the incidence of respiratory illnesses was the highest in winter and lowest in summer, the impact of cold air on respiratory illnesses was the largest in summer and spring, when the number of people with respiratory illnesses visiting the outpatient department increased by 18.4% and 13.3% two and five days after a cold air day. By contrast, the number people visiting the outpatient department with respiratory illnesses in winter only increased by 3.2% three days after a cold air day. The lag time of the health impact decreased with increasing cold air intensity in summer, autumn and winter, whereas the lag time was always long in spring. These findings provide a targeted basis for a scientific response to the risks to human health caused by global climate change.摘要本文利用2016年到2019年邯郸市气象要素和呼吸系统疾病门诊数据, 分析了不同季节不同强度的冷空气过程及其对呼吸系统疾病的影响. 结果显示: 尽管呼吸系统疾病在冬季高发, 夏季最低, 但冷空气对呼吸系统疾病的影响在夏, 春季最大, 就诊人数分别在冷空气日后两天和五天增加18.4%和13.3%, 而冬季就诊人数在冷空气日后三天仅增加3.2%.冷空气对疾病影响的滞后时间在夏, 秋和冬季随冷空气强度的增加而减少, 而春季的滞后时间总是很长. 这些发现可为科学应对气候异常导致的人群健康风险提供针对性依据.

Impact of Pollen Counts and Air Pollution Over Hospital Visits for Respiratory Illnesses in North Delhi Region

Impact of Pollen Counts and Air Pollution Over Hospital Visits for Respiratory Illnesses in North Delhi Region

Interactive Effects between Temperature and Humidity on Outpatient Visits of Respiratory Diseases in Lanzhou, China

AbstractThis study assessing the lag and interactive effects between the daily average temperature and relative humidity on respiratory disease (RD) morbidity in Lanzhou, China, using data from daily outpatient visits for RD between 2014 and 2017 and meteorological and pollutant data during the same period analyzed with Poisson generalized linear model and distributed lag nonlinear models; the effects are further explored by classifying the RD by gender, age, and disease type. The results showed that the effect of temperature and relative humidity on outpatient visits of different populations and types of RD is nonlinear, with a significant lag effect. Relative to 11°C, every 1°C decrease in temperature is associated with 10.98% [95% confidence interval (CI): 9.87%–12.11%] increase for total RD. Chronic obstructive pulmonary disease is affected only by low temperature, upper respiratory tract infection is affected by both low and high temperatures, and asthma is influenced by high temperature. When the relative humidity is less than 32%, every 1% decrease in relative humidity is associated with 6.00% (95% CI: 3.00%–9.11%) increase for total RD; relative humidity has different effects on the outpatient risk of different types of RD. Temperature and relative humidity have an obvious interactive effect on different types and populations of RD: when both temperature and humidity are at low levels, the number of outpatient visits for RD is higher. When the relative humidity is ≤50% and the temperature is ≤11°C, total RD outpatient visits increase by 4.502% for every 1°C drop in temperature; that is, a dry environment with low temperature has the most significant impact on RD.

Population surveillance approach to detect and respond to new clusters of COVID-19.

To maintain control of the coronavirus disease 2019 (COVID-19) epidemic as lockdowns are lifted, it will be crucial to enhance alternative public health measures. For surveillance, it will be necessary to detect a high proportion of any new cases quickly so that they can be isolated, and people who have been exposed to them traced and quarantined. Here we introduce a mathematical approach that can be used to determine how many samples need to be collected per unit area and unit time to detect new clusters of COVID-19 cases at a stage early enough to control an outbreak. We present a sample size determination method that uses a relative weighted approach. Given the contribution of COVID-19 test results from sub-populations to detect the disease at a threshold prevalence level to control the outbreak to 1) determine if the expected number of weekly samples provided from current healthcare-based surveillance for respiratory virus infections may provide a sample size that is already adequate to detect new clusters of COVID-19 and, if not, 2) to determine how many additional weekly samples were needed from volunteer sampling. In a demonstration of our method at the weekly and Canadian provincial and territorial (P/T) levels, we found that only the more populous P/T have sufficient testing numbers from healthcare visits for respiratory illness to detect COVID-19 at our target prevalence level-assumed to be high enough to identify and control new clusters. Furthermore, detection of COVID-19 is most efficient (fewer samples required) when surveillance focuses on healthcare symptomatic testing demand. In the volunteer populations: the higher the contact rates; the higher the expected prevalence level; and the fewer the samples were needed to detect COVID-19 at a predetermined threshold level. This study introduces a targeted surveillance strategy, combining both passive and active surveillance samples, to determine how many samples to collect per unit area and unit time to detect new clusters of COVID-19 cases. The goal of this strategy is to allow for early enough detection to control an outbreak.

Childhood Respiratory Outpatient Visits Correlate With Socioeconomic Status and Drive Geographic Patterns in Antibiotic Prescribing.

Reducing geographic disparities in antibiotic prescribing is a central public health priority to combat antibiotic resistance, but drivers of this variation have been unclear. We measured how variation in outpatient visit rates (observed disease) and antibiotic prescribing rates per visit (prescribing practices) contributed to geographic variation in per capita antibiotic prescribing in Massachusetts residents younger than 65 years between 2011 and 2015. Of the difference in per capita antibiotic prescribing between high- and low-prescribing census tracts in Massachusetts, 45.2% was attributable to variation in outpatient visit rates, while 25.8% was explained by prescribing practices. Outpatient visits for sinusitis, pharyngitis, and suppurative otitis media accounted for 30.3% of the gap in prescribing, with most of the variation in visit rates concentrated in children younger than 10 years. Outpatient visits for these conditions were less frequent in census tracts with high social deprivation index. Interventions aimed at reducing geographic disparities in antibiotic prescribing should target the drivers of outpatient visits for respiratory illness and should account for possible underutilization of health services in areas with the lowest antibiotic consumption. Our findings challenge the conventional wisdom that prescribing practices are the main driver of geographic disparities in antibiotic use.

Where Virtual Care Was Already a Reality: Experiences of a Nationwide Telehealth Service Provider During the COVID-19 Pandemic.

BackgroundThe COVID-19 pandemic has led to an increase in the use of and demand for telehealth services.ObjectiveHere, we describe the utilization of telehealth services provided by Doctor On Demand, Inc., a well-known telehealth company in the United States, before and during the COVID-19 pandemic. We also explore how the number of virtual visits, reasons for visits, and patients served changed over time.MethodsWe reported data as a percentage change from the baseline week during 2 distinct time periods: February-June 2019 and February-June 2020 based on 4 categories of visits: respiratory illness, unscheduled behavioral health, scheduled behavioral health, and chronic illness.ResultsIn 2020, the total visit volume increased considerably from March through April 7, 2020 (59% above the baseline) and then declined through the week of June 2 (15% above the baseline). Visits for respiratory illnesses increased through the week of March 24 (30% above the baseline) and then steadily declined through the week of June 2 (65% below the baseline). Higher relative increases were observed for unscheduled behavioral health and chronic illness visits through April (109% and 131% above the baseline, respectively) before a decline through the week of June 2 (69% and 37% above the baseline, respectively). Increases in visit volume among rural residents were slightly higher than those among urban residents (peak at 64% vs 58% above the baseline, respectively).ConclusionsAlthough this telehealth service provider observed a substantial increase in the volume of visits during the COVID-19 pandemic, it is interesting to note that this growth was not fueled by COVID-19 concerns but by visits for behavioral health and chronic illness. Telehealth services may play a role as a “safety valve” for patients who have difficulty accessing care during a public health emergency.

The Impact of Fossil-Fuels and Biomass Combustion on Outdoor PM2.5 Mass Associations with Respiratory Emergency Department Visits in Dhaka, Bangladesh

Introduction: Developed world studies report associations between particle air pollution and increased risk of emergency department (ED) visits for respiratory diseases, but few were conducted in the south Asia region, where PM2.5 composition is different. Differences are from greater biomass and residue crop-burning, which may have differing health impacts from past PM2.5 studies. Despite that fact, an “equal toxicity” assumption has been made in most global PM2.5 effect estimates. We evaluated this assumption in Dhaka.Methods: The acute effects of PM2.5 and source component’s on 38,233 respiratory ED visits during 2014-2017 were assessed using time-series modeling, adjusted for temporal trend, day of week, holidays, relative humidity, and temperature. Daily counts of respiratory ED visits were obtained from National Institute of Chest Diseases and Hospital. Analyses were conducted to test for seasonal effect modification by including dummy variables for monsoon vs. non-monsoon seasons.Results: PM2.5 yielded statistically significant association with increased respiratory ED visits [Excess Risk, ER=0.88%, 95%CI: 0.37%, 1.39%] per 10 μg/m3 increase. The effect of PM2.5 air pollution per 10 µg/m3 varies with season, with the highest during monsoon season [2.84%, 95%CI: 0.74, 4.91] vs. non-monsoon season [0.77%, 95%CI: 0.23%, 1.31%], suggesting that the effect size per unit mass is much larger when fossil-fuel combustion sources dominated exposures, with effect per unit mass declining when crop-burning and dust sources dominated exposures. A source-specific health effect analysis model using elemental source tracers provided consistent results, with fossil-fuel PM2.5 having a larger ER per 10 μg/m3 than biomass-combustion PM2.5.Conclusion: This study found that the respiratory health in Dhaka is significantly affected by particle air pollution, with greater impact by fossil-fuel PM2.5. This disparity in health impact by PM2.5 source origin should be considered in PM2.5 risk assessment, PM2.5 mitigation measure prioritization, and clinical advice.

Temporal analysis of atmospheric nitrogen dioxide pollution and pediatric outpatient visits for respiratory illnesses in Yuexiu District, Guangzhou, China

Temporal analysis of atmospheric nitrogen dioxide pollution and pediatric outpatient visits for respiratory illnesses in Yuexiu District, Guangzhou, China

Association of PM2.5 concentration with health center outpatient visits for respiratory diseases of children under 5\u2009years old in Lima, Peru

BackgroundLima is one of the more polluted cities in Latin America. High levels of PM2.5 have been shown to increase health center outpatient visits of respiratory diseases.MethodsHealth center outpatient visits for children < 5 years for childhood respiratory disease (acute lower respiratory infections (ALRI), pneumonia and acute bronchiolitis/asthma) from 498 public clinics in Lima were available on a weekly basis from 2011 to 2015 from Peru’s Ministry of Health (MINSA). The association between the average weekly concentrations of PM2.5 was evaluated in relation to the number of weekly health center outpatient visits for children. Weekly PM2.5 values were estimated using a recently developed model that combined data observed from ground monitors, with data from space satellite and meteorology. Ground monitoring data came from 10 fixed stations of the Peruvian National Service of Meteorology and Hydrology (SENAMHI) and from 6 mobile stations located in San Juan de Miraflores by Johns Hopkins University. We conducted a time-series analysis using a negative binomial model.ResultsWe found a significant association between exposure to PM2.5 and all three types of respiratory diseases, across all age groups. For an interquartile increase in PM2.5, we found an increase of 6% for acute lower respiratory infections, an increase of 16–19% for pneumonia, and an increase of 10% for acute bronchiolitis / asthma.ConclusionsHigher emissions of environmental pollutants such as PM2,5 could be a trigger for the increase of health center outpatients visits for respiratory diseases (ALRI, pneumonia and asthma), which are themselves risk factors for mortality for children in Lima province, Peru.

A New Deep Learning Algorithm for Detecting the Lag Effect of Fine Particles on Hospital Emergency Visits for Respiratory Diseases

There exists a time lag between short-term exposure to fine particulate matter (PM2.5) and incidence of respiratory diseases. The quantification of length of the time lag is significant for preparation and allocation of relevant medical resources. Several classic lag analysis methods have been applied to determine this length. However, different models often lead to distinct results and which one is better is subtle. The prerequisite of obtaining the reliable length is that the model can truly reveal the underlying pattern hidden in the above relationship. Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning, whose strong capability makes it widely applied in many fields. In this study, we manage to exploit it to acquire the time-lag length in the exposure-response relationship. The relationship between exposure and response is assumed as linear and non-linear, and models with and without confounding factors are performed under these two assumptions. Results of DLNM model show that the best hospital emergency visit prediction appears in 3 lag days, with the maximum RR value of 1.004357 (95% CI: 1.000938-1.009563). Then, a vary of LSTM models with different time steps are performed, which are evaluated by mean absolute error (MAE), the mean absolute percentage error (MAPE), the root of mean square error (RMSE) and R square (R 2 ). The results show that LSTM of time step 3 achieves the lowest MAE (33), MAPE (9.86), RMSE (42) and the highest R 2 (0.78), consistent with the result of DLNM model. Also, the proposed model is compared with ARIMA model, one of the commonly used forecasting models, showing better accuracy. This demonstrates that LSTM can be used as a new method to detect the lag effect of PM2.5 on respiratory diseases.

Vaccination Status and Resource Use During Hospital Visits for Respiratory Illnesses.

To evaluate variation in resource use for children with acute respiratory tract illness (ARTI) by vaccination status. We conducted a retrospective cohort study of children 0 to 16 years of age at 5 children's hospitals with 1 of 4 ARTI diagnoses (pneumonia, croup, asthma, and bronchiolitis) between July 2014 and June 2016. The predictor variable was provider-documented up-to-date (UTD) vaccination status (yes or no). Outcomes were receipt of each of the following tests or treatments (yes or no): complete blood cell count, blood cultures, C-reactive protein (CRP) level testing, viral testing, influenza testing, pertussis testing, chest radiographs, neck radiographs, antibiotics, and corticosteroids. We generated multivariable logistic regression models to examine the associations between our predictor and outcomes. Of the 2302 participants included in analysis, 568 (25%) were diagnosed with pneumonia, 343 (15%) were diagnosed with croup, 653 (28%) were diagnosed with asthma, and 738 (32%) were diagnosed with bronchiolitis. Most (92%) vaccination statuses were documented as UTD. Across conditions, children whose vaccination status was documented as not UTD had higher adjusted odds of receiving a complete blood cell count, blood culture, CRP level testing, and influenza testing (P < .001). Children with pneumonia whose vaccination status was documented as not UTD had higher adjusted odds of receiving CRP level testing and influenza testing (P < .001). Children with croup whose vaccination status was documented as not UTD had higher adjusted odds of receiving blood cultures (P < .001). Children with ARTI whose vaccination status was documented as not UTD had higher odds of undergoing laboratory testing compared with children whose vaccination status was documented as UTD.

The health effect of PM2.5 on daily outpatient visits for respiratory diseases in central Taiwan

The health effect of PM2.5 on daily outpatient visits for respiratory diseases in central Taiwan

The impact of outdoor air pollutants on outpatient visits for respiratory diseases during 2012\u20132016 in Jinan, China

BackgroundFew studies have investigated the associations between outdoor air pollution and outpatient visits for respiratory diseases (RDs) in general population.MethodsWe collected daily outpatient data of primary RDs from five hospitals in Jinan during January 2012 and December 2016, as well as daily measurements of air pollutants from the Jinan Environmental Monitoring Center and daily meteorological variables from the China Meteorological Data Sharing Service System. A generalized additive model (GAM) with quasi-Poisson regression was constructed to estimate the associations between daily average concentrations of outdoor air pollutants (PM2.5,PM10, SO2, NO2, CO and O3) and daily outpatient visits of RDs after adjusting for long-time trends, seasonality, the “day of the week” effect, and weather conditions. Subgroup analysis stratified by gender, age group and the type of RDs was conducted.ResultsA total of 1,373,658 outpatient visits for RDs were identified. Increases of 10 μg/m3 in PM2.5, PM10, NO2, CO and O3 were associated with0.168% (95% CI, 0.072–0.265%), 0.149% (95% CI, 0.082–0.215%), 0.527% (95% CI, 0.211–0.843%), 0.013% (95% CI, 0.003–0.023%), and 0.189% (95% CI, 0.032–0.347%) increases in daily outpatient visits for RDs, respectively. PM2.5 and PM10 showed instant and continuous effects, while NO2, CO and O3 showed delayed effects on outpatient visits for RDs. In stratification analysis, PM2.5 and PM10 were associated with acute RDs only.ConclusionsExposure to outdoor air pollutants including PM2.5, PM10, NO2, CO and O3 associated with increased risk of outpatient visits for RDs.

Impact of heatwaves on daily outpatient visits of respiratory disease: A time-stratified case-crossover study

ObjectivesThis study aims to estimate the impact of heatwaves from July 2010 to October 2012 on daily outpatient visits for respiratory disease (RD) in Cangnan, China and identify vulnerable populations. MethodsThe definition of heatwave was a period at least 3 consecutive days with maximum temperature exceeding 35 °C in this study. A time-stratified case-crossover design was conducted to examine the relationship between heatwaves and outpatient visits for RD. Patient data for the period from 2010 to 2012 were collected from the Third People's Hospital of Cangnan and daily meteorological data for the same period were collected from the China Meteorological Data Service Center. Data regarding the air pollution index (API), a composite indicator of air pollution, were collected from the Data Center of the Chinese Ministry of Environmental Protection. RD were identified based on the 10th revision International Classification of Diseases (ICD-10) codes (J00-J99). A conditional Poisson regression model was applied to examine the heatwave-RD association using the Relative Risk (RR) while adjusting for meteorological and air pollution factors including temperature, rainfall, wind speed, pressure, humidity, sunshine hours and API. ResultsDuring the study period, 4 heatwaves occurred and a total of 1732 outpatient visits for RD were reported. Heatwaves increased the frequency of RD outpatient visits and the highest RR of total RD was 1.155% and 95% Confidence Intervals (95% CI) was 1.084–1.232 at Lag 0. For subcategories, heatwaves increased the risk of infectious RD (Lag 0: RR =1.182, 95% CI: 1.106–1.263) and decreased the risk of non-infectious RD ((Lag 6: RR =0.750, 95% CI: 0.568–0.990). Moreover, heatwaves showed adverse effects on acute upper respiratory infection (Lag 0: RR =1.306, 95% CI: 1.177–1.450). The RR of outpatient visits for RD was statistically significant in females (Lag 0: RR =1.161, 95% CI: 1.046–1.298), males (Lag 4: RR =1.161, 95% CI: 1.096–1.261), young people aged 4–17 years (Lag 0: RR =1.741, 95% CI: 1.524–1.990) and elders aged 65 years or older (Lag 5: RR =1.412, 95% CI: 1.111–1.794) during heatwaves. ConclusionsHeatwaves had a significant harmful impact on daily outpatient visits for RD in Cangnan, especially for vulnerable population identified. These results can be used not only to strengthen the health education and protection of these vulnerable populations, but also to assist relevant organizations with developing intervention programmes and improving disease prevention and community care.