Preplanned Studies: The Combined Effects of High Temperatures and Ozone Pollution on Medical Emergency Calls — Jinan City, Shandong Province, China, 2013–2019

Epidemiological evidence demonstrates the significant impact of high temperature on both short-term and long-term health outcomes, resulting in increased morbidity and mortality rates for respiratory and circulatory diseases, among other health conditions (1). Simultaneously, there is a growing concern about ozone (O₃) pollution, which poses a pressing challenge to urban development (2). Numerous studies have established a strong association between O₃ pollution and the onset and progression of respiratory and circulatory diseases (3–4). However, there is still a research gap concerning the combined effects of these two variables. Recently, there has been a growing interest in using medical emergency calls (MECs) as indicators to explore the acute effects of high temperature and O₃ pollution (5–7). Addressing this research gap, our study focuses on Jinan City, Shandong Province, China, as a representative urban area grappling with the dual challenge of high temperature and O₃ pollution. We employed a time-stratified case-crossover study design to estimate the impacts of high temperature and O₃ pollution on MECs. The results indicate that high temperature, O₃ pollution, and their combination significantly increase the risk of all-cause MECs and MECs specifically related to neurological diseases. These findings provide essential information for the development of future measures to reduce exposure to high temperature and O₃ pollution. Moreover, emphasizing the necessity to consider the combined effects of high temperature and O₃ pollution is crucial for addressing emerging public health concerns.

For this research study, we collected daily data from the MECs of the Jinan Medical Emergency Center (JMEC) website (http://www.jn120.cn/). The data were collected from May 1 to September 30 for the years 2013–2019. There were minor fluctuations in the MECs rate during the study period, as shown in Supplementary Table S1. The information obtained from the MECs included the patient’s call time, primary statement, and preliminary diagnosis. The primary statement information referred to the main signs and symptoms provided during the telephone call, while the preliminary diagnosis information indicated the disease diagnosis given when the patient was admitted to the hospital’s emergency department. JMEC oversees 66 first-aid service stations that are affiliated with hospitals, providing coverage across all ten administrative districts of Jinan City, as shown in Supplementary Table S2. To classify the MECs, we used the International Classification of Diseases, 10th revision (ICD-10). We specifically screened for ICD-10 codes J00–J99, which are associated with respiratory diseases, I00–I99, which are related to circulatory diseases, and G00–G99, which pertain to neurological diseases.

Meteorological data collected daily throughout the study period were provided by the China Meteorological Science Data Sharing Service Network (http://data.cma.gov.cn/). This data included the daily mean maximum temperature (℃), relative humidity (%), pressure (hPa), and wind speed (m/s). The daily air pollutant data, such as fine particulate matter (PM_2.5), particulate matter (PM₁₀), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and 8-hour ozone (O₃-8h, abbreviated as O₃), were obtained from the Jinan Ecological Environmental Protection Bureau. For this study, we determined the daily pollutant concentration level in Jinan by averaging the readings from all monitoring stations.

In the initial stage of our analysis, we began with a descriptive examination of the compiled data using various indicators, including mean, standard deviation, minimum, maximum, median, first quartile, and third quartile. Following this, correlation analysis was conducted to explore the relationship between temperature, ozone concentration, and MECs, as well as the associations between air pollutants and meteorological factors. Subsequently, a time-stratified case-crossover study design in combination with a conditional logistic regression model was employed to investigate the acute impacts of temperature and ozone pollution on MECs. Detailed information and sensitivity analyses for this core model can be found in the Supplementary Material. Based on a comprehensive review of previous studies and our analytical results, we selected a five-day period for our lag effect study. We examined the effects on the current day (Lag0), single-day lag effects (Lag1–Lag5), and cumulative lag effects (Lag01–Lag05) on MECs to identify the most impactful day and estimate the effect. The effect estimates were presented as odds ratios (ORs) along with their corresponding 95% confidence intervals (CIs). To address diseases with high sensitivity, a subgroup analysis focusing specifically on respiratory, circulatory, and neurological diseases was conducted. The R software (version R 4.2.0; R Studio Inc; the USA) was used for the analysis of time-stratified cross-case data. Statistical significance was considered at a P-value of less than 0.05.

Table 1 presents the daily levels of air pollutants, meteorological factors, and MECs in Jinan from 2013 to 2019. The total number of MECs recorded during this period was 275,868, with an average daily number of all-cause MECs at 258±40 cases. Among these cases, the number of MECs related to respiratory, circulatory, and neurological diseases were 19±9, 35±10, and 39±8, respectively. Furthermore, the average maximum daily temperature reached 30.38±4.06 ℃, while the daily concentration of O₃ was 154.67±47.87 μg/m³.

The chronological diagram presented in Supplementary Figure S1 visually demonstrates consistent trends and potential relationships among temperature, O₃ concentration, and MECs. Additionally, the Spearman correlation analysis (Figure 1) indicates statistically significant correlations between these variables during the study period. Specifically, temperature and MECs, O₃ concentration and MECs, and temperature and O₃ concentration exhibited significant correlations. Notably, a strong positive correlation was observed between O₃ concentration and temperature (r=0.63, P<0.05). Further details on the correlation results between air pollutants and meteorological factors are provided in Supplementary Table S3.

Figure 1. 

Spearman correlation analysis between temperature, O₃, and MECs in Jinan from May to September 2013–2019. (A) represents the correlation analysis between O₃ and temperature; (B) represents the correlation analysis between temperature and MECs; (C) represents the correlation analysis between O₃ and MECs.

Figure 2 presents an analysis of the single-day lag effect concerning temperature, O₃ pollution, and their combined impact. Statistically significant independent effects of both temperature and O₃ pollution on all-cause MECs were noted from Lag0 to Lag3, with the most pronounced effect occurring at Lag0. The OR values for temperature and O₃ pollution were 1.013 (95% CI: 1.010, 1.015) and 1.0005 (95% CI: 1.0002, 1.0006), respectively. Notably, the combined influence of temperature and O₃ pollution demonstrated statistical significance from Lag0 to Lag5, peaking at Lag0, with an OR of 1.017 (95% CI: 1.010, 1.024). A trend of marginal decline in OR values was observed for both independent and combined effects of temperature and O₃ pollution with progressing Lag days. Additionally, subgroup analysis indicated significant impacts on MECs for neurological diseases, with OR values of 1.017 (95% CI: 1.010, 1.024), 1.001 (95% CI: 1.000, 1.002), and 1.022 (95% CI: 1.004, 1.041). However, the effects of MECs on respiratory and circulatory diseases were not statistically significant. The cumulative lag effect analysis of temperature, O₃ pollution, and their combined effect is depicted in Supplementary Figure S2.

Figure 2. 

The lag effects of temperature, ozone (O₃), and their combined effects on all-cause morbidity and morbidity rates for various systemic diseases during the warm season in Jinan from 2013 to 2019.

Note: The variable “Temperature+O₃” in this study represents the combined impact of high temperature and ozone pollution.

Abbreviation: OR=odds ratio; CI=confidence interval; MECs=medical emergency calls.