A Study of the Influence of Meteorological and Environmental Factors on Otitis Media with Effusion in Lanzhou

Abstract

In observational studies, a possible correlation between atmospheric environmental factors and the number of daily outpatient visits by Otitis media with effusion(OME) patients has been observed. However, the causal relationship is not clear.To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases. The levels of AQI、PM2.5、PM10、NO₂、O₃、SO₂、CO、AP、RH、W and T were obtained from local monitor stations. Data of patients with OME were collected from two Grade A Level hospitals in Lanzhou from January 1, 2014 to December 31, 2016. Descriptive analysis of data was carried out for the study subjects. Spearman correlation coefficients between atmospheric environmental factors and daily visits of patients with OME were calculated by SPSS statistical software. Lag effects, relative risks(RR) and exposure-response curves were calculated by generalized additive model (GAM) with R software. (1) The incidence of OME in winter and spring was more than that in summer and autumn, which was consistent with the seasonal variation of meteorological environmental factors of Lanzhou. That was, the meteorological conditions and air quality in winter and spring were poor, while in summer and autumn they were relatively good. (2) The number of male outpatients were 1.05, 1.08 and 1.09 times of female outpatients during the period 2014–2016, respectively. And aged 0–10 years old outpatients accounted for 31% of the total OME outpatients. (3) Exposure-response curve showed that PM2.5, PM10, NO₂ and SO₂ were positively correlated with OME, T was negatively correlated with OME. When concentration < 1mg/m³, CO was positively correlated with OME. When concentration>1mg/m³, CO was negatively correlated with OME. When concentration<30 ug/m³, O₃ was positively correlated with OME. When concentration>30ug/m³, O₃ was negatively correlated with OME. (4) The factors we studied could significantly affect the number of OME outpatients within 2–3 days of single lag effects, and 3–4 days of cumulative lag effects.(5) The influential factors on OME were as follows: PM2.5、NO₂、SO₂、O₃、 CO and T. The daily average number of OME patients in different seasons was different in major region of Lanzhou city, with more in winter、spring and fewer in summer、autumn. Age and sex were the important factors affecting the daily average number of OME patients. Males were more susceptible to OME than females and children awere moresusceptible to OME than adults. The change of OME patients was related to air quality, air pressure and temperature. The worse the air quality, the higher the air pressure, the lower the temperature, the more the average daily number of OME patients. Meteorological environmental factors affected the visits of OME, and the lagging effect time of different factors were different. Most of the research factors within 3–4 days had a significant impact on the number of patients of OME. 1.The number of OME visits in the Lanzhou was more seasonal in winter and spring than in summer and fall. 2.Age and sex were the most important factors affecting the number of patients with OME. According to the prevalence of OME in Lanzhou, children were more likely to have OME than adults and men were more likely to have OME than women. 3.The number of OME patients was related to air quality, air pressure and temperature. 4.The meteorological factors have a delayed effect on the onset of OME, and the time of delayed effect is different for different factors. The single delayed effect of 2–3 days and the cumulative delayed effect of 3–4 days have a significant effect on the change of the number of patients with OME. To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases.

Introduction

Otitis media with effusion (OME) is a frequent disorder which can lead to hearing loss. The diagnosis is based on otoscopy and tympanometry. Although a certain number of medications can be used to treat OME, they are not reliably effective and rarely provide long-term relief. As a common disease in otorhinolaryngology, the manifestations of OME mainly include middle ear effusion, conductive hearing loss, tinnitus and ear stuffiness. If diagnosis and treatment are not timely, the disease can be prolonged into a chronic course. Especially in young children, the disease is often not easy to be found, conductive hearing loss in young children is one of the main causes [1]. It has been pointed out [2, 3]that the occurrence of OME is related to upper respiratory tract infection, age and adenoid hypertrophy. The pathogenesis of OME is the result of interaction of multiple risk factors. It is a complex disease involving multiple genetic and environmental factors [4]. OME shows a consistent time relation with upper respiratory tract infections, with a peak 3 to 4 days after the onset of nasal symptoms and upper respiratory tract infection [5].The global estimate that annual incidence of OM is 740 million, with nearly 21,000 deaths attributable to complications of OM [6]. Up to 80% of children experience at least one episode of OME [7].

Therefore, how to improve the diagnostic efficacy, in-depth analysis of its related risk factors can be better from diagnosis to prevention and treatment, to achieve more scientific and effective treatment. The purpose of this study was to explore the related factors of OME, and to propose preventive and therapeutic measures.

With the rapid development of China’s economy and the acceleration of urbanization, environmental pollution which changes from traditional coal-burning to mixed pollution has become increasingly severe. Climate warming has a profound impact on human survival and development. The World Health Organization (WHO) listed 10 threats to global health, with air pollution cited as the biggest environmental risk to health.The first reason is that air pollution is also a major contributor to climate change.Large number of epidemiological studies from domestic and foreign scholars have found that the physical and mental health of human beings is closely related to the ambient air environment and meteorological factors. Air pollution is related to the health effect of the population, including the mortality of cardiovascular and respiratory diseases, and so on. Human physical and mental health is closely related to the surrounding atmospheric environment and meteorological factors. Despite the studies assessing the impact of ambient air pollution exposures on upper respiratory infections, the potential relationships between OME and ambient air pollution exposure have not been examined in detail. The relationship between ambient atmospheric environment and OME is complicated, which has aroused widespread concern. Considerable scale epidemiological studies have been concerned on air pollution and OM [8, 9]. But to our knowledge, no earlier study has examined the relationship between daily outpatient clinic visits for OME and daily concentration of air pollutants. Such studies require a sufficient number of patients with a clinically confirmed OME diagnosis. However, the existing research area is limited to China’s southeast coast and developed areas [10].

Evidence has shown that temperature, humidity, wind speed, rainfall and thunderstorm weather are all associated with respiratory diseases [11–14] .

While several studies analyzing seasonal effects of air pollutants were focused on mortality/morbidity of allergic rhinitis [15], few studies considered the seasonal effect of air pollutants and meteorological factors on OME. So, it’s essential to make extended models for time series data on environmental climate factors and daily outpatient visits for OME to incorporate time-varying effects, in Lanzhou City, China.Lanzhou is the political, economic and cultural center of Gansu Province. In recent years, Lanzhou has become one of the most serious air pollution cities in China with the development of urbanization, the raging of dust storms, havoc and the increase of the number of motor vehicles, as well as the unique basin terrain and special meteorological conditions. It is of great social and economic benefit and practical significance to improve human physical condition, reduce unnecessary medical expenses and realize scientific prevention and treatment to study the relationship between OME and meteorological-environmental factors and make timely diagnosis, so as to standardize the treatment and popularize the knowledge of prevention and treatment.

We support the hypothesis that OME onset is triggered by exposure to ambient air pollution and meteorological factors. Taking Lanzhou City as the target region, this paper analyzed the relationship between meteorological-environmental factors and OME from January 1, 2014 to December 31, 2016. We reviewed epidemiologic studies and discussed the lag effects of air pollutants and meteorological factors on OME in Northwestern China. The time series analysis based on non-parametric generalized additive model (GAM) was used to quantitatively analyze the exposure-response relationship between environmental climate factors and daily outpatient visits for OME, after controlling for multiple revisits and holiday effects. The key to quantitative analysis and evaluation of the health risk of pollutants lies in the establishment of exposure-response relationship between pollution and the end point of the health effect of the population. In addition, relevant prediction models were established to explore the prediction model for OME in Lanzhou City, providing scientific basis for better disease prevention and healthy travel promotion in the local area.

Through the above research work, the highest impact factors were obtained in different seasons, we aimed to provide a scientific basis for disease prevention of susceptible population. Our results and methods should be applicable to other cities in China, and be intended to also contribute to improve global change, urbanization and health.

Materials and Methods

Meteorological and Environmental Conditions

Lanzhou lies at the geometric centre of the Chinese geography, in a transitional zone between the Some Random Place Somewhere and the Tibetan Plateau. Located in the northwest of China(36°03’ north latitude,103°40’ east longitude), Lanzhou City has four distinct seasons and belongs to a typical temperate continental monsoon climate.The diurnal temperature range within a year is large. The valley topography and inversion layer weakens the diffusion of polluted air, which aggravates the air pollution in Lanzhou City. Therefore, the changes of air pollution factors in the four seasons of the year have obvious seasonal characteristics. It plays an important strategic role in driving the economy of the whole province. It is also an important transportation hub and a golden city for the Belt and Road of our country. Its south, north and east side surrounded by mountains, the Yellow River from west to east across the entire city, is a typical belt-shaped valley city. Due to the development of chemical industry, which produces a large amount of industrial waste gas, domestic pollution and motor vehicle exhaust, the air pollution is serious. In addition, Lanzhou is located in the lower reaches of the Hexi Corridor, on the main migration path of dust storms in East Asia, so a large number of particles are transported here every year from the upper reaches (western and northwestern Gansu), especially in spring. Lanzhou is an industrial city, and with the continuous promotion of the “One Belt, One Road” strategy in China, the dense population produces large pollutant discharge. In addition, the valley topography formed by its special geographical location results in the characteristics of high quiet wind frequency and thick inversion layer in the urban area, resulting in poor diffusion and dilution conditions of pollutants and high pollutant concentration, especially in winter. In addition, frequent sandstorms in spring will lead to a large number of foreign particles entering Lanzhou. Therefore, both natural and human factors make Lanzhou one of the cities with the most serious air pollution.

Study Populations and Definitions

To evaluate the health effect of air pollution and meteorological factors in Lanzhou, data on daily hospital outpatient visits for OME from 1 January 2014 to 31 December 2016 were collected from Department of Otolaryngology-Head and Neck Surgery, two Grade A of Level III Hospitals in Lanzhou. All clinical data were collected through the computer-managed outpatient and emergency treatment system. The above two hospitals are large comprehensive Class III hospitals, with advanced inspection equipment, experienced clinicians, sufficient disease sources and complete electronic medical record system, which can ensure the credibility of the collected case data. Guidelines for Clinical Application of OME (Revised 2004 edition) as diagnostic criteria. At the same time, according to the standard diagnosis of the tenth edition of the International Classification of Diseases (ICD-10), patients diagnosed with OME after visiting the outpatient or emergency department of the above two hospitals between January 1, 2014 and December 31, 2016 were searched. The case collection has the following explanation: through the electronic medical record retrieval system, searches in the diagnosis"The secretory otitis media, the exudative otitis media” the note, retrieves altogether 11,466 to see a doctor the patient. According to the inclusion and exclusion criteria, 7,697 cases were included in the study. We chose the patients with middle ear effusion in the absence of acute symptoms and tympanic membrane perforation [15, 16]. The data included gender, age, chief complaint, specialist physical examination, auxiliary examination and disease diagnosis.

Inclusion Criteria

The 2016 guidelines for the clinical application of OME (2004 revision) stated that, OME was diagnosed with the following signs: air-bulging otoscopy for tympanic membrane invagination, bulging, opacity, decreased or disappeared light cone, middle ear effusion (air-liquid level or air bubble), and poor diminished or reduced mobility of tympanic membrane; acoustic immittance examination, if the middle ear shows a negative pressure state or the tympanogram is“B/C”type. On the basis of the above diagnostic criteria, OME patients without tympanic membrane congestion, perforation, drainage and other symptoms were included in this study [15, 16]. If the patients can not cooperate or the diagnosis is still unclear, tympanostomy or tympanotomy can be performed. Thin-section CT scan of the mastoid process of the temporal bone showing the middle ear effusion and excluding the space occupying the parapharyngeal space can diagnose OME.

Exclusion Criteria

1. OME patients with a history of upper respiratory tract allergic diseases such as allergic rhinitis and asthma.

2. OME patients with history of radiotherapy for nasopharyngeal carcinoma and head and neck tumor.

3. OME patients with laryngopharyngeal reflux disease.

4. OME patients with underlying systemic diseases such as tuberculosis and diabetes mellitus.

5. OME patients with barotrauma of the ear or other traumatic factors described in the medical history were included to ensure lateral unity of study subjects.

6. Patients with OME who had repeated visits, such as a second visit within 5 days of the first visit and only a first visit, were included in the study data to exclude the effect of the second visit on the number of visits per day.

Air Pollution, Meteorological Data

The air quality monitoring data from 5 monitoring stations in Lanzhou (Lanlian Hotel-Xigu District, Biologics Factory-Chengguan District, Railway Design Institute-Chengguan District, Staff Hospital-Qilihe District, Yuzhong campus of Lanzhou University- suburb) were used to study the influence of air pollution on human body, including the daily average concentration of 6 main pollutants (daily average w (SO2), w (NO2), w (PM10), w (PM2.5), w (CO), w (O3) and air pollution index AQI), in Lanzhou city from January 1, 2014 to December 31, 2016. The air quality index(AQI), is a quantitative index for describing the air quality.By monitoring the average concentrations of six pollutants (PM2.5, PM10, SO2, NO2, CO, O3) in a certain period of time, the corresponding AQI subindices of these six pollutants can be obtained.

The meteorological data was derived from Gansu Meteorological Bureau, including the daily ground meteorological observation data. The data were hourly surface meteorological observations, including mean temp (T,°C), average air pressure(AP, HPa), wind speed (W, m/s), relative humidity (RH,%) were obtained. The seasonal division adopted the meteorological season division method, which was from December last year to February for winter, March to May for spring, June to August for summer, and September to November for autumn.

Quality Control

1. OME patients at second outpatient visit within 5 days from first. Only one visit per individual patient per day was included in the tabulation of the daily visit counts.

2. OME patients with an associated diagnosis, non-specified otoscopic observation, barotraumas, or other traumatic factors described in medical history.

3. Patients with an impaired immune system, malformations (cleft palate), or neoplastic rhino-pharyngeal diseases.

4. Selected hospitals were open regularly all day from Monday to Friday, however could be close on holidays and weekends. Excluded the impact of the holidays and weekends on the number of daily visits.

Statistical Analysis

This study was designed as a cross-sectional study.To study the impact of meteorological and environmental factors on the number of OME patients in the main urban areas of Lanzhou, data on AQI, daily mean air pollutant concentrations (PM2.5, PM10, SO2, NO2, CO and O3), daily mean meteorological condition levels (T, AP, RH and W), and daily mean number of visits to OME clinics and emergency departments were collected from January 1,2014, to December 31,2016, in Lanzhou.

The time Series Generalized Addition Model (GAM)

GAM was used to further understand and analyze the complicated dynamic mechanism and pollution characteristics of concentration changes. GAM is a non-parametric development of Generalized linear Model that deals with complex relationships between independent and dependent variables. Compared with the total population, the outpatient visits for OME was a small probability event and typically followed a Poisson distribution. Therefore, GAM model with Poisson regression(PGAM) were used to combine the distributed linear and non-linear lag models(DLNM) (Gasparrini2013, 2014),as well as the libraries R statistics mgcv [17]. In controlling the long-term trend, the day of the week and the holiday effect, we considered the influence of the interaction of meteorological-environmental factors on the incidence of OME. A smooth function was used to control the mixing of holiday and weekend to enter the model according to the minimum value of AIC (Akaike information criterion, AIC). After the core model was established, the auto correlation variables and degrees of freedom were selected according to the Partial Correlation Function (PACF). In this model, the number of daily outpatient visits for OME was designed as dependent variable and the spline smoothing function was used to fit the nonlinear independent variables in the time series data. Independent variables were included in the models: climate variables T, RH, AP and W; air pollutants variables PM10, PM2.5, CO, SO2, NO2, O3, and AQI. The degree of freedom(df) was selected according to the partial auto correlation (PACF) value.

Lag Effect

Based on recent studies [18], we examined the lag effect of air pollutants and meteorological factors with single-day lag (from L0 to L6) and multi-day lag (L01 to L10). L0 corresponded to the current-day, and L1 referred to the previous-day. L03 corresponded to 4 day moving average of pollutant concentration and meteorological factors of the current and the previous 3 days. The cumulative measure was mean (lags 0–1; L01), mean (lags 0–2; L02), mean (lags 0–3; L03), respectively. Finally, chose the specific day with the strongest effect, as shown in Table  1 and 2.

Table 1.

Statistical description of AQI in 2014-2016 years in Lanzhou

Relative Risk

RR was used to quantitatively evaluate the effect of independent variables on disease incidence.When RR>1, it indicated that the risk of disease increased with the increase of exposure value, while RR<1 showed that the risk of disease decreased with the increase of exposure value.

Spearman Correlation Coefficient

Using SPSS21.0 software to calculate Spearman correlation coefficient between two variables, using R3.5.0 software to run generalized additive model(GAM), the relative risk and exposure-response curves of single-lag and cumulative-lag were calculated.

Results

Gender Specific Effects

According to the statistics of sex and age group, 3930 cases of male visits, accounted for 50.6% of the total outpatients. 3757 cases of female visits, accounted for 49.4% of the total outpatients. The average monthly visiting number of male patients with OME was` obviously greater than that of women, and the changing degree of the number of patients in male patients was greater than that of women. The total number of male patients of OME (2014–2016) was 1.05, 1.08 and 0.99 times of female patients, respectively. The annual changes in the number of male and female patients’ daily visits showed a bimodal pattern.

Age Specific Effects

Patients < 10 years old accounted for 31% of the total OME patients included in the study. 11–20 years old accounted for 12%, 21–30 years old accounted for 11%, 31–40 years old accounted for 13%, 41–50 years old accounted for 15%, 51–60 years old accounted for 11%, 61–70 years old accounted for 5%, 71–80 years old accounted for 2%, > 81 years old accounted for less than 1%. The analysis results showed that the number of patients < 10 years old was the most, followed by 41–50 years old, and > 80 years old was the least.

Seasonal Specific Effects

As shown in Fig. (1-a), the number of patients with OME showed volatility in the structure changes from 2014 to 2016. The number of patients with OME was higher in alternation of spring and summer, autumn and winter, while the variation in other months was less volatile. It can be seen that the daily number of outpatient visits for OME was higher in November, December and January; and decreased gradually, reaching a minimum in August.

In terms of seasonal characteristics, there were more visits in winter and spring, but less in summer and autumn. During the year, the overall visits for OME in the cold season was higher in the warm season. The largest number of patients visited in each season in 2014, followed by 2016 and the lowest number in 2015. (Fig. 1-b)

Fig. 1.

In order from the first one (a-h). a 2014–2016 inter-annual features of OME in Lanzhou City; b Monthly average AQI of Lanzhou during 2014–2016; c Monthly average concentration of PM2.5(ppb) during2014-2016;d Monthly average concentration of PM10(ppb) during 2014–2016;e Monthly average concentration of CO(ppb) during 2014–2016;f Monthly average concentration of NO2 (ppb)during2014-2016;g Monthly average concentration of O3 (ppb)during 2014-2016 h Monthly average concentration of SO2 (ppb)during 2014–2016

The Characteristics of air Quality Distribution in Lanzhou

An AQI value of 50 represented good air quality with little potential to affect public health, while an AQI value over 300 represented hazardous air quality. From 2014 to 2016, air pollution in four different functional areas of Lanzhou City was relatively serious, with light pollution accounting for about 30%. Xigu District was the most serious area, and Yuzhong District had the best air quality. (Table 1)

The annual change of pollution in Lanzhou City was basically bimodal, with serious pollution in spring and winter, followed by autumn and relatively lowest pollution in summer.

In terms of annual doublet characteristics, the first peak was from February to April and the second peak was in December. The AQI was higher in winter than in others.

In 2014–2016, the overall trend of PM2.5 decreased obviously. In December, the concentration reached the highest, and then began to decrease gradually. In March and April, the concentration of PM2.5 had a small rising, then continued to decrease and gradually began to rise in October. The monthly mean concentration of PM2.5 was higher in winter.(Fig. 1-c).

As shown in Fig. 2, there was a similar variation characteristics between PM10 and AQI. The peak time was consistent with the AQI. The average concentration of PM10 in spring and winter was higher than summer and early autumn.

Fig. 2.

(a) Single lag exposure-response. (b) Cumulative lag exposure-response

The average concentration of CO in Lanzhou was consistent with the abnormal increase of SO2 concentration at the same time. The concentration reached the maximum in December –January and with a small increase in the month of July-August. In terms of seasonal variation, the CO concentration was higher in winter than in spring. (Fig. 1-e)

The average concentration of NO2 had an upward trend in the past 2014–2016 years. In terms of annual variation, there were two maximum values: November- January, March-May, and two minimum values: February and June. The average concentration of NO2 in winter was higher than autumn. (Fig. 1-f)

The annual variation of O3 was more complex, showing a “inverted U” type. The concentration of O3 was showing fluctuation trend without special characteristics. (Fig. 1-g)

The trend of SO2 concentration showed a “U type”. The maximum appeared in December–January, and the minimum was in August. In terms of seasonal variation, SO2 concentration was the highest in winter, followed by spring and autumn, and the lowest in summer. (Fig. 1-h)

Spearman Correlation Analysis of Single lag Effects

The response to all of the factors was hysteretic, but there was a certain difference in the lag time among different factors. To identify possible time-delay, we analyzed the single-lag effects of factors on daily number of OME outpatient visits. We calculated lag effects of more than 6 days for the factors, but very few associations were found, so the results of lags of more than 6 days were excluded from the table.

Current day environmental factors had more obvious influence on the daily average number of visits for OME. Not all ambient air pollutants contribute to OME. PM2.5,CO, NO2,SO2 and O3 had a stronger effect on OME compared with other factors and passed the significance test(P < 0.01). PM10 and SO2 on current day showed a negative correlation with the number of patients with OME. O3,T, RH and W revealed a negative correlation during L0-L6.On the contrary, NO2 and AQI showed a positive correlation. The Spearman correlation coefficient of OME and atmospheric factors is less than the result of significant test, especially RH, W and BAR.

As shown in Table 2, the single lag effects of air pollutants were more obvious than meteorological factors. The correlation of the environmental factors had all passed the statistical significance test of 0.01.

Table 2.

Spearman correlation between OME and factors of environment meteorology(single lag)

We also found that the single lag effect of OME on meteorological factors did not pass significant test(P < 0.05) and these factors(RH, W,BAR) were not significantly associated with outpatient visits. Any covariates that were significant at the P < 0.05 level were included in all subsequent analyzed models as confounding factors. On the contrary, the single lag effects on environmental factors were examined by significance test, and the 0–3 day lag effect(L0-L3) was the most obvious(P < 0.01). The single lag effect of OME on all environmental factors was examined by significance test(P < 0.01), and the single lag effect was the most significant on the third day(L3). In general, the effect sizes of these factors showed an increasing trend from L0 to L3, and a decreasing trend after L3; the largest correlation were found in L3.We selected the day with the largest correlation coefficient as the strongest single lag time, while selecting the largest increase in correlation coefficient as the strongest cumulative lag time.(Tables 3 and 4).

Table 3.

Strongest lag effect time

Table 4.

Spearman correlation between OME and factors of environment meteorology(cumulative lag)

Spearman Correlation Analysis of Cumulative lag Effects

While running the models, we calculated the cumulative lag correlation coefficient of various factors to OME within twenty days, but very few associations could be found, so the results of lags of more than ten days were excluded from the table. The strongest associations were observed with four-day cumulative measures of factors of environment meteorology (L04) rather than single day or current day lag effect.

The cumulative lag correlation coefficients of AQI showed an upward trend, but the rising speed of L01-L03 was faster than that of L04-L10. The strongest associations were observed with three-day moving average lags of PM10 rather than single-day lags or current day effect. Using the same observation method, we found that L02 was the strongest effect day of PM2.5. On the total the effects of NO2 increased by the moving average days and L04 was its strongest effect day. The effect sizes of SO2 revealed a trend of fluctuations from L01 to L10 and exerted the strongest effect at L03. The cumulative lag effects of CO did not change significantly during L01 - L10 except L04. Different from the positive effects of other factors, O3,T, RH and W revealed obvious negative influence on the daily number of patients for OME and all reached the strongest effect after 4 days. Though some factors’effect(T, RH and W) did not pass the test of significance. The cumulative lag effects of BAR had no obvious change law, but reached the strongest effect when lagged for 6 days.

Analysis of Relative Risks(RRs)

We analyzed the impact of pollutants on OME by using the GAM model, and found patients in Lanzhou were influenced directly by air pollution. The RRs in the number of outpatients for OME with a 10 µg/m3 increase in pollutants for different lag days are shown in Tables 5 and 6. The results were presented as RRs with 95% confidence intervals (Cls). We obtained the RRs of OME related to air pollution by adjusting for risk factors. We calculated adjusted RRs for a change in measured pollution concentration equal to the interquartile ranges of the pollution distribution. Statistically, significance was provided by a P-value < 0.05. The trend of change was not obvious. RRs of AQI, O3 and CO compared with other pollutants were less significant. The RRs of PM10 and PM2.5 for single-day lag effect and moving average lags were increased first and then decreased with time. The largest RRs were found in L04(1.004,1.000 ~ 1.008) and L02(1.006,1.001 ~ 1.010).When the concentration of NO2 increased 10 µg/m3, the number of patients’ largest RR was L04(1.005,1.002 ~ 1.008). The RRs of SO2 for moving average lags increased with days. The largest RRs were found in three-day cumulative measures of SO2 (L3).

Table 5.

RRs in the number of outpatients for single lag effects

Table 6.

RRs in the number of outpatients for cumulative lag effects

Analysis of Single Lag Exposure-Response Relationship

Studying the exposure response curves of all factors at different lag times, it was found that these factors had obvious influence on the incidence of OME, and there was a relatively clear risk threshold concentration, as shown in the following figures. There were smoothed plots of exposure-response relations between factors and outpatient visits for OME in Lanzhou, China (2014–2016). The solid line presents log relative risk of outpatient visits for OME, while the dashed lines present 95% confidence interval (CI) of the log relative risks. For CO, when the concentration was less than 1.5 mg/m3, the RRs between the pollutants and OME was in a relatively safe range. When the concentration was greater than 1.5 mg/m3, the RRs increased along with the concentration increased, and no maximum threshold range was found. For SO2, the curve was “S”type. When the concentration of SO2 was between 20µg/m3 and 40µg/m3, we observed maximum value of RRs. When SO2 concentration exceeded this threshold range, RRs increased with the rise of concentration, and the width of the 95% confidence interval was larger. For O3, The curve showed “M” type in general, and there were two peak threshold ranges, but the second threshold interval was not obvious. When O3 concentration was between 20µg/m3 and 40µg/m3, RRs reached its maximum value. For T, we observed a fundamental monotonic change in the correlation between daily mean temperature and RRs. In particular, RRs continued to be at its maximum value. For BAR, RRs touched the lowest point near 810 hPa. In the 815–820 range, RRs showed an upward trend with the increase of BAR and then decreased. The threshold concentration of other factors were not found and the shape of the exposure-response curve needed further studies.

Analysis of Cumulative Lag Exposure-Response Relationship

Figure 1 displays increased risk for day-specific moving average concentrations from 1 to 10 days for association between ambient air pollutant concentrations and outpatient visits for OME. As for overall outpatient visits, increased RRs were found between outpatient visits and air pollutants consisting of SO2,NO2,O3,CO with highest increase in visits associated with each 10 µg/m3 increase in air pollutants. For SO2, the strongest associations between air pollutants and outpatient visits was found at 20 µg/m3 ~ 40 µg/m3. Furthermore, the shape of the curve was similar to the single lag curve of SO2. For NO2, different from the single lag curve, the curve presented a monotonous increasing trend, and there was no threshold concentration. For O3, the shape of the curve was similar to the wavy line, but there was still a relatively obvious threshold concentration interval at 20 µg/m3 ~ 40 µg/m3. For the other associations (CO), the least RRs corresponding to concentration at 1.0 mg/m3.

Discussion

This study utilized environmental and epidemiological data to focus on the air quality with temporal variation and its health effects on daily outpatient visits for OME in Lanzhou during 2014–2016. We analyzed the daily number of outpatients for OME and attempted to establish time-series relationships between air pollution, as well as meteorological factors, and daily outpatient visits for OME. This study included 11,321 outpatient visits for OME. In addition, the case-crossover study design was used to reduce or eliminate potential confounding effects.

OME is a common illness that is one of the most frequent reasons for medical visits and antibiotic prescriptions [19]. The etiology and pathogenesis are not yet fully elucidated.

3.1. The degree of air pollution in different seasons affects the prevalence of OME.

In this study, we observed significant associations between ambient air pollutants and outpatient visits for OME in Lanzhou City. This association was most notable during November to January and April to June. The two peak periods occurred at the turn of Autumn and Winter, and the turn of Spring and Summer, especially in Winter. Following explanations seemed to attribute to above phenomenon. First, the ambient conditions, such as temperature, humidity and air pollutants were not stable in these periods. The human body could not adapt to the meteorological conditions for external mutation in time. The weak or sensitive people might have allergic diseases, cold and gastrointestinal dysfunction. Second, Lanzhou was an important industrial city in Northwest China. Geography position and human activities were attributed to the unique climatic and environmental characteristics of Lanzhou. In winter, the wind speed was small and more static. The inversion layer was thick. The intensity of the atmosphere was great and the structure of the atmosphere was stable. The special valley basin of Lanzhou was close to the special Valley, so the air pollution in Lanzhou was particularly serious in winter. With the change of industrial structure in Lanzhou, the air pollution in winter was affected by coal and petroleum products. Vehicle exhaust emission was an important source of organic pollutants in the atmosphere.

The result showed that daily concentrations of air pollutants had clear seasonal differences. O3 levels were higher in the warm season than in the cold season. However, concentrations of the other five pollutants were higher in the cold season than in the warm season. Many studies have reported health effects of PM2.5 [20], but O3 also has a strong potentially adverse health effect [21]. Research on harmful effects of O3 is rare in China. De Marco et al. [22] have reported that outdoor NO2 interacted with climate. Villeneuve et al. [23]only studied SO2 and NO2 exposure on the same day of the outpatient visits during the winter period.

3.2. Age difference affects morbidity of OME.

OME visits in children accounted for 31% of all OME visits in the study. First, the middle ear anatomical structures (including Eustachian tube) in infants are shorter, flatter and straighter than those in adults, which is beneficial to the exposure of middle ear cavity, to direct polluted air to the middle ear [24]. Second, Deng [25] suggested that maternal exposure to outdoor air pollutants due to outdoor activities before delivery and exposure to indoor air pollutants due to indoor decoration after delivery were independently associated with the onset of OME in early infancy. Third, children lack of correct language ability, can not accurately complain such as hearing loss, tinnitus, ear pain and other subjective symptoms, resulting in parents can not find the problem in time, timely led to medical treatment. Fourth, the upper airway mucosa acts as the body’s defense barrier to filter and warm inhaled air. Harsh air pressure and temperature can damage the upper airway mucosa and affect the internal environment of the middle ear, inhaled pollutants trigger airway inflammation by producing and releasing inflammatory cytokines. Fifth, the number of infants and young children with upper respiratory tract infections was significantly associated with the severity of air pollution, possibly due to the immature immune system of infants and young children. If the upper respiratory tract inflammation is not effectively controlled, further spread to the Eustachian tube and the middle ear, Eustachian tube swelling and obstruction caused by the middle ear negative pressure and effusion. Sixth, the adenoids are close to the Eustachian tube. The adenoids and the round pillow of the Eustachian tube will swell and fill with blood after being infected by virus and bacteria, thus blocking the eustachian tube orifice, causing negative pressure and fluid accumulation in the middle ear. A combination of these factors could explain the finding that the greatest proportion of visits occurred in the under-10 age group.

3.3. The effect of gender difference on the incidence of OME was not obvious.

Patients of different genders have similar sensitivity to changes in atmospheric environment. Adults perform similar outpatient visit for respiratory diseases after air pollution exposure. From this epidemiological investigation, we found that gender differences had minimal impact on the incidence of OME. Prior evidence suggested that males might be biologically more susceptible than females to air pollution [26]. Exposure to ambient air pollutants is substantially different for individuals depending on personal activities, living environment and others. But our study came up with different results. The reason for this phenomenon might be related to the lag effect of atmospheric environmental factors on OME.

3.4. Analysis of the influence of environmental and meteorological factors on the incidence of OME.

We observed that there was a significant correlation between the level of meteorological environmental dynamics and the average number of visits in the outpatient department and the emergency department. The correlation was stronger between November and January. There were differences in RR between different factors and the average number of visits to OME and ed. These differences may reflect: some factors may have a greater impact than others; different study factors may require different concentration thresholds to aggravate or mitigate adverse effects.

3.5. The main findings and limitations of the research.

Increased risk estimates of ambient air pollutants for OME were driven mainly by the temporal improvement of air quality. There were also inherent limitations to our approach. First, we estimated exposures instead of directly measuring them using personal monitoring. Finally, although we adjusted analyses for a large number of potential risk factors, the possibility for residual confounding remained.

These findings indicate an association between exposure to air pollutants and the incidence of OME. The strong evidence linking OME with exposure to ambient air pollution add further support to our findings. Air pollution exposure may result in a more severe or persistent infection making progression to OME more likely. Alternatively, air pollution may actively promote progression to OME.

Our study demonstrated that the daily number of OME outpatients was correlated with the concentration of air pollution and meteorological factors. All the air pollutants were associated with increased possibility of OME visits with every 10 ug/m3 increase of pollutant concentration. From June to August the meteorological conditions were conducive to dilute and spread air pollutants, therefore the air quality was relatively better during this time of a year. Due to the effects of the prevailing weather conditions and the heating period in winter as well as the impacts of the regular dust-storms from northwest in spring, the air quality was comparatively poor during these periods [27].

RRs in the number of OME outpatients with a 10 µg/m3 increase of air pollutants were different from air pollutions. This may be attributed to the differences of exposure risk factors between districts, including environmental factors, age, gender and exposure time, dose.

3.6. The shape of exposure-response association in investigating potential health effects on air pollutants and meteorological factors. We found nonlinear associations that the relative risk increased at the lower concentrations, but attenuated or even turned negative at higher concentrations. One possible reason is that saturation mechanism, when underlying biochemical and cellular processes become saturated with small doses. Another reason is the small sample size at higher concentrations as can be reflected by the wider confidence intervals.

The effects of O3 on OME were still in debate. However, a human exposure experiment suggested that short-term exposure to O3 can increase the bronchial allergen responsiveness in mild allergic asthma or rhinitis patients. Likewise, Peden et al. [28] also reported enhanced nasal inflammatory responses in OME patients after O3 exposure.

As mentioned above, there are two major mechanisms may account for the increased incidence of OME. Firstly, increased concentration of pollutants lead to airway sensitization and responsiveness to allergens. Next, airway responsiveness to allergens may subsequently aggravate symptoms of OME.

Conclusion

The number of OME visits in the Lanzhou was more seasonal in winter and spring than in summer and fall.

Age and sex were the most important factors affecting the number of patients with OME. According to the prevalence of OME in Lanzhou, children were more likely to have OME than adults and men were more likely to have OME than women.

The number of OME patients was related to air quality, air pressure and temperature.

The meteorological factors have a delayed effect on the onset of OME, and the time of delayed effect is different for different factors. The single delayed effect of 2–3 days and the cumulative delayed effect of 3–4 days have a significant effect on the change of the number of patients with OME.

To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases.

Author Contributions

TH and ZH performed the data analyses and wrote the manuscript; TH and ZCcontributed significantly to analysis and manuscript preparation; ZC helped perform the analysis with constructive discussions; TH, ZH and ZC contributed to the conception of the study. All authors contributed to the article and approved the submitted version.

Declarations

Ethics Approval and Consent to Participate

The database was fully anonymized according to the privacy code. This study was approved by the Ethics Review Board of the hospital. No patient contact was made, and patients could not be traced.

Conflict of Interest

The authors declare no conflict of interests.