1. Introduction
Schizophrenia (SCZ) is a multifaceted mental disorder characterized by core symptoms that are intricately connected to erratic dysfunction in the emotional, cognitive, and perceptual realms, posing a growing threat to global public health [1,2,3]. Schizophrenia was estimated to affects 24 million individuals in 2019, equating to 1 in every 300 people worldwide [4]. The Global Burden of Disease study in 2019 revealed that the raw prevalence, incidence, and burden of schizophrenia have been on the rise since 1990 [5]. In 2016, schizophrenia was positioned ninth among the primary contributors to years lived with disability in China [6]. Therefore, it is essential to study the risk factors of schizophrenia for early detection, intervention, treatment, and prevention of the disease. This proactive approach can not only lower the occurrence of schizophrenia but also alleviate the burden on patients and their families, ultimately enhancing the overall health of society.
Existing evidence suggests that the onset of schizophrenia is significantly influenced by genetic, socioeconomic, and behavioral risk factors [7,8,9]. Additionally, environmental factors, such as ambient air pollution, are recognized as potential risk triggers of this mental health disorder [10,11], through the biological pathways of oxidative stress on and systemic inflammation in the neuropsychiatric system [12]. Ambient air pollution is a significant global public health concern [13], with up to 92% of the population residing in areas where air quality exceeds the safe limits established by the World Health Organization in 2016. Extensive research in recent decades has underscored the adverse effects that prolonged exposure to ambient air pollution on mental health can have [14,15,16]. There is limited research on the effects of short-term exposure to air pollutants on schizophrenia, in particular among populations from low- and middle-income countries. Furthermore, evidence on the effects of short-term exposure to air pollutants remains inconsistent for schizophrenia. For instance, a study in Anhui, China, demonstrated a significant association between short-term exposure to NO2 and increased hospital admissions for schizophrenia [6]. In contrast, a study in California, USA, did not find evidence for associations of schizophrenia with NO2 and CO exposure [17]. Consequently, further research from diverse regions is imperative to furnish additional evidence for assessing the influence of air pollution on schizophrenia.
In this study, we performed a case-crossover study in central China, primarily aiming to comprehensively investigate the short-term effects of six air pollutants on the risk of hospital admission for schizophrenia. The secondary purpose was to depict the concentration-response (C-R) associations and identify potential effect modifiers via stratified analyses. Our findings may help to better understand the association between short-term exposure to ambient air pollution and schizophrenia hospitalization in low-to-middle income countries.
2. Materials and Methods
2.1. Study Area
Jingmen City (111°51′–113°29′ E, 30°32′–31°36′ N) is situated in the center of Hubei Province, central China, and spans a total area of 12,400 square kilometers. Due to its location in the mid-latitude zone, the city experiences a north subtropical monsoon climate characterized by four distinct seasons. As of the end of 2023, the permanent population in Jingmen city amounted to 2.55 million.
2.2. Hospitalization and Environmental Data
Daily records of hospital admission for schizophrenia from 1 January 2015 to 31 December 2017 were extracted from the electronic medical record database of Jingmen Mental Health Center in Hubei, China. We collected information on schizophrenia patients including sex, age, and date of hospitalization. A schizophrenia case was determined on the basis of comprehensive medical history collection, mental examination, symptom assessment, and the elimination of other obstacles, and was coded according to the International Statistical Classification of Diseases and Related Health Problems 10th revision (ICD-10: F20, F21). As only anonymized aggregated data were used in the analysis, and we did not collect personal details that could lead to individual identification; thus, ethical approval was not required for this study.
Daily average concentrations of six ambient air pollutants in the criteria, including particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5), particulate matter with an aerodynamic diameter ≤ 10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), during 2015–2017 were collected from 5 urban monitoring sites administrated by the Jingmen Environmental Monitoring Centre. Daily meteorological details including mean temperature (°C), relative humidity (RH, %), and barometric pressure (BP, hPa) were obtained from 1 urban station in Jingmen city administrated by the China Meteorological Data Network.
2.3. Study Design
A time-stratified case-crossover (TSCC) design was utilized to investigate the association between short-term exposure to ambient air pollution and hospital admissions for schizophrenia. Case-crossover designs are widely used in environmental epidemiology to assess the acute health effects of exposure to air pollution [18]. This approach has a distinctive feature where each case serves as an individual baseline, helping to account for the confounding effects of stable individual characteristics over the short term. Factors such as demographic differences, socioeconomic background, and specific behavioral risk factors are effectively controlled in this method. For each schizophrenia case, the case day was defined as the day of hospital admission for schizophrenia, and corresponding control days (n = 3 or 4) were selected from the same day of the week in the same calendar year and month [19,20]. Such a TSCC design could well mitigate the influence of temporal trends on study outcomes and minimize bias associated with such trends [21]. By comparing exposure to air pollutants on case days vs. control days, we were able to evaluate the potential correlation between air pollution and hospitalization risk on the strength of the self-matched case-control design. The current analysis encompassed 4079 case days and 13896 control days in total.
2.4. Statistical Analysis
Short-term effects of exposure to ambient air pollution on the risk of schizophrenia hospitalization were estimated using the conditional logistic regression (CLR) model [22]. To account for the delayed or cumulative effects of air pollution, the analysis assessed exposures using multiple single-day lags (lag-0 to lag-4 days) and moving average lags (lag-01 to lag-04 days). For instance, lag-0 day represents the day of admission, and lag-01 day is the two-day moving average exposure of the admission day and the day before admission. A natural cubic spline (ns) function with 3 degrees of freedom (df) was utilized to smooth the terms of the 3-day moving average temperature (Temp0_2) and current-day RH and BP in line with prior investigations, so as to capture any potential nonlinear confounding effects of meteorological conditions. The constructed model is shown below:
where P denotes the conditional probability of the included case being admitted to hospital for schizophrenia; β0 represents the intercept; β is the estimated coefficient of interest; ρ indicates the air pollutant exposure level (μg/m3 or mg/m3).Using maximum likelihood estimation methods, we conducted separate assessments on the associations between schizophrenia hospitalization and exposure to ambient air pollutants across multiple days of lag. We estimated the odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) associated with the per interquartile range (IQR) of exposure [23]. Each pollutant was smoothed using the ns function with df = 3 to illustrate the curvilinear relationships between exposure and response. We utilized likelihood ratio tests to assess the possible nonlinear C-R function [24,25].
To identify potentially vulnerable groups, we conducted subgroup analyses stratified by sex (male and female) and age group (≤44 years and ≥45 years). Additionally, we investigated seasonal patterns in the relationship between ambient air pollutants and schizophrenia admissions by dividing the survey period into cool (October to March of the next year) and warm (April to September) months. We assessed the significance of differences in estimated effects between subgroups using two-sample z-tests [26,27,28].
Co-pollutant analyses were conducted to check the robustness of the estimated associations. We only performed two-pollutant analyses to avoid strong multicollinearity, and two-sample z-tests were utilized to check the effect heterogeneity in single- and two-pollutant models. All analyses were carried out using R software (version 4.0.5, R Foundation for Statistical Computing, Vienna, Austria). Two-sided tests with p < 0.05 were considered statistically significant.
3. Results
Table 1 displays the descriptive statistics of schizophrenia cases in the study. The average age of 4079 schizophrenia patients was 38.1 years, and 48.0% were males. More than half of the patients (68.0%) were under 45 years of age, and 51.0% of cases occurred in cold months. Slightly higher concentrations of air pollutants were seen on case days compared with control days, with mean estimates of 57.4 μg/m3 vs. 57.0 μg/m3 for PM2.5, 99.7 μg/m3 vs. 98.4 μg/m3 for PM10, 20.3 μg/m3 vs. 19.9 μg/m3 for SO2, 34.5 μg/m3 vs. 34.0 μg/m3 for NO2, and 100.4 μg/m3 vs. 99.7 μg/m3 for O3 (Table 2). Pairwise correlations between meteorological factors and particulate and gaseous pollutants are shown in Figure 1. We observed positive correlations between air pollutants with an exception of O3, and Spearman’s correlation coefficients ranged from 0.33 to 0.89.
Figure 2 estimates the odds of schizophrenia risk associated with the per IQR increase in ambient air pollutants at different time lags. The results showed a significant rise in schizophrenia risk related to NO2 and SO2 exposure, while no associations were found with other air pollutants (i.e., PM2.5, PM10, CO, and O3). On lag-0 day, for instance, per IQR increases in SO2 and NO2 were associated with odds of 1.112 (95% CI: 1.033, 1.196) and 1.112 (95% CI: 1.033, 1.197) of schizophrenia hospitalization, respectively. We did not identify evidence for the effect heterogeneity in SO2- and NO2-related risk increases between single- and two-pollutant analyses (Table 3). Visually, a well-established linear C-R association was observed for both exposures to SO2 (p = 0.911 for nonlinearity, Figure 3) and NO2 (p = 0.230 for nonlinearity, Figure 3).
Figure 4 illustrates subgroup analyses for air pollution-schizophrenia associations stratified by sex, age, and season. We identified more evident exposure-related risks of schizophrenia admission among the elderly, while no sex difference was observed. For an IQR increase in PM10, a significantly greater risk of schizophrenia (p = 0.016 for difference) was seen among individuals aged over 45 (OR = 1.153, 95% CI: 1.037, 1.283) compared with young individuals (OR = 0.982, 95% CI: 0.911, 1.058). Seasonal analyses provided suggestive evidence for those more pronounced effects of air pollutants on schizophrenia during the cold months (October to March of the next year). For instance, an IQR increase in NO2 exposure was associated with odds of 1.161 (95% CI: 1.062, 1.268) in the cold season and 1.001 (95% CI: 0.871, 1.151) in the warm season.
4. Discussion
Based on data from schizophrenia hospitalization records and air pollution concentrations, this hospital-based study revealed a nexus between short-term exposure to SO2 and NO2 and elevated odds of schizophrenia admissions. For both pollutants, the largest effects occurred on lag-0 day. The link between air pollution exposure and schizophrenia was particularly salient among in individuals aged 45 years and above and during cold seasons. These results may offer further support for the epidemiologic relationship between air pollution and schizophrenia during developing nations.
Our single-city study investigated the potential correlation between six distinct air pollutants and hospital admissions attributed to schizophrenia in central China. The results highlighted significant negative impacts of SO2 and NO2 on the same (lag-0) day, with the estimated ORs of 1.112 (95% CI: 1.033, 1.196) and 1.112 (95% CI: 1.033, 1.197) per IQR increase in exposure. These results generally aligned with several previous investigations into the Chinese population. In Xi’an city of western China, 1.374% (95% CI: 0.723%, 2.025%) and 1.881% (95% CI: 0.957%, 2.805%) increases in outpatient consultations for schizophrenia were linked with every 10-μg/m3 rise in SO2 and NO2 concentration on lag-0 day [29]. Also, a time series study in Hefei, central China, revealed an estimated risk of 1.10 (95% CI: 1.01, 1.18) for schizophrenia hospitalization associated with the per IQR increase in NO2 on lag-01 day [6]. These findings consistently emphasized the great public health significance of early warnings of gaseous pollution events in future environmental risk research on individuals with mental disorders. A thorough understanding of exposure-response curves holds paramount importance in formulating effective public policies and conducting risk assessments. Given the ubiquity of air pollution exposure, which is extraordinarily unlike other risk factors, achieving the ambitious goals of having clean air and a blue sky would have great health potential in alleviating the burden of schizophrenia.
The relationship between gaseous air pollution and schizophrenia involves complex biological mechanisms, and a number of studies have shed light on evidence. Firstly, SO2 and NO2, prominent components of motor vehicle exhaust emissions, could generate reactive oxygen species and reactive nitrogen species that function as oxidants [30,31]. These substances can interact with various biomolecules in the body, such as proteins, lipids, and DNA, initiating an oxidative stress response that results in cellular damage and dysfunction [12]. For example, previous studies have shown that NO2 exposure could activate microglia upon entering the brain, initiating an inflammatory process mediated by these cells. This process can set off a cascade of events encompassing secondary neuronal degeneration, diminished neurogenesis, synaptic impairments, and modifications in the cerebral architecture [32,33]. As for SO2, SO2 has been confirmed to have neurotoxic effects in animal experiments. Evidence suggests that SO2 has toxic effects on the hippocampus and is related to neuronal apoptosis, DNA-protein cross-linking, and protein oxidation [34]. These pathological changes directly compromise or indirectly perturb the normal function of the brain, thereby increasing the risk of schizophrenia or causing the recurrence of the disease. Secondly, exposure to gaseous air pollutants could enhance the expression of transcription factors [35] and disrupt neurotransmitter balance in the brain, thus leading to mental or behavioral changes linked to increased neuroinflammation in specific brain regions [36,37]. Neurotransmitters occupy a pivotal position in the transmission of information in the brain, and patients with schizophrenia often have an imbalance in neurotransmitter secretion in the brain [38]. Current research on the acute effects of pollutants on schizophrenia often lacks definitive evidence of causal relationships. The specific mechanisms underlying these effects remain unclear and warrant further in-depth investigation and research for clarification.
Familial genetic aggregation, brain structural abnormalities, and acquired environmental factors may all play a crucial role in the development of schizophrenia [39,40]. Among environmental risks, gaseous pollutants may interact with other factors to collectively influence the risk of schizophrenia. By exploring the interplay between high genetic susceptibility and environmental pollution in the development of schizophrenia, personalized prevention strategies can be devised for individuals at risk, including recommendations to take protective masks and adhere to healthy lifestyles. Moreover, individuals may exhibit varying sensitivities and responses to gaseous air pollution in triggering the onset and symptoms of schizophrenia. Therefore, future research efforts should focus on a more in-depth understanding of the relationship between gaseous pollutants and schizophrenia, considering individual susceptibility variations and the interplays of multiple risk factors. In the current study, it was noted that the most prominent associations between SO2, NO2, and schizophrenia were observed on lag-0 day and remained statistically significant in the cumulative lag models. One possible explanation for this finding is the prompt access to healthcare in China, allowing schizophrenia patients to seek immediate assistance without prior appointments [29]. It is suggested that in the daily care of patients with schizophrenia, an emphasis should be placed on their living environment, particularly air quality. Ideally, patients should be relocated to an area with improved air quality, or efforts should be made to decrease gaseous pollutants in their surroundings.
According to sex-subgroup analyses, sex differences were not evidently observed in our study. Nevertheless, previous research has suggested sex disparities in the association between air pollution and schizophrenia. In a comprehensive meta-analysis encompassing 17 studies from multiple countries [41], a more pronounced link between gaseous pollutants and schizophrenia was seen among females compared with males. The underlying causes for this susceptibility discrepancy may be complex, but two speculations have been put forward. Firstly, it could be potentially related to the sex differences in the gene and behavioral patterns. For instance, women may typically spend more time engaging in outdoor activities that should extend exposure periods and raise the likelihood of exposure. Secondly, alterations in enzyme expression were different between sexes. Animal studies have uncovered differences in the expression of enzymes between male and female mice, which impart females with a heightened sensitivity to air pollutants [42,43]. While our study did not identify any evidence for gender differences, gender-stratified analyses are still highly warranted in further investigations on the relationship between air pollution and schizophrenia. Exploring the varying sensitivities of individuals of different genders to air pollution could provide significant insights to facilitate the development of more targeted prevention and treatment approaches.
Due to the natural degradation of immune system function in the elderly, their defenses against air pollution are significantly reduced, heightening their vulnerability to the adverse health consequences of airborne contaminants. This perspective is well supported by numerous epidemiological studies [44,45], and our age subgroup analysis offers additional insight into this knowledge. The research findings indicated that individuals aged 45 and above were observed to face a greater likelihood of developing schizophrenia upon exposure to ambient air pollutants, in comparison with younger age groups. This discovery is largely consistent with previous air pollution-schizophrenia studies indicating the heightened vulnerability of the elderly population to air pollutants [29,46]. Given the vulnerability of older adults to air pollutants, ensuring better air quality should be a top priority for the elderly to ensure they have access to a clean and healthy living environment in the context of China’s accelerating population aging and national strategy for healthy aging. This present study suggests that air pollution-related schizophrenia risk seems to be more pronounced during the colder season compared with the warmer months, in line with a prior survey in a Chinese megacity [45]. This emphasizes the potentially intricate temporal relationship between air pollution and schizophrenia. Variations in temperature between warmer and colder seasons have a noteworthy influence on the emission, dispersion, dilution, and chemical transformation of air pollutants, potentially serving as a contributing factor to their overall environmental impact [47]. In polluted days particularly in winter, public health practitioners (e.g., governmental bodies and medical institutions) should efficiently incorporate population-based environmental health and toxicology evidence when providing services for schizophrenia prevention and risk management. Meanwhile, it is of great public health necessity for community stakeholders (e.g., health service centers and family physicians) to provide regular health education on household air cleaning and personal defensive measures, so as to collaboratively reduce the recurrence risk of schizophrenia associated with ambient air pollution.
It is crucial to recognize and acknowledge the limitations of our research. Firstly, the data are limited to a solitary city in central China. Considering that climate, environment, economic and social conditions vary greatly in various locations, cautious interpretation is required when extrapolating the findings of the current study to other regions. Future studies should consider expanding the data sources to encompass a broader geographical range. Secondly, the exposure assessment of our analyses was on the basis of city-wide daily average levels of air pollutants. It is important to note that these average levels may not fully capture individual actual exposure levels, as the within-city distribution of air pollutants can be affected by factors such as geographic location, meteorological conditions, and population density [48,49]. Therefore, misclassification of exposure levels is inevitable, which may lead to certain biases in our assessment of the correlation between air pollution and schizophrenia. Thirdly, schizophrenia is an extremely complex disease, and its pathogenesis involves genetic, environmental, biological, and other factors at multiple levels. Air pollution is just one of these factors and may interact with others to collectively impact the risk of developing schizophrenia. While acknowledging that individual-specific factors, such as genetics, lifestyle patterns, and social surroundings, might have influenced our findings, we refrained from considering their potential impact due to their minimal variability within a short timeframe and their feasibility for control in a case-crossover design. In future research, more comprehensive environmental risk analysis can greatly help deepen our understanding of their complex interplay.
5. Conclusions
This case-crossover study added novel evidence for the nexus between the risk of schizophrenia and short-term air pollution in central China. Our findings provide support for the negative effects of short-term exposure to gaseous air pollutants (SO2 and NO2) on hospital admission for schizophrenia. Additionally, we observed a stronger connection between air pollution and schizophrenia among individuals aged 45 and above, with a particularly notable impact during colder months. In recent years, the volume of research literature exploring the correlation between air pollution and schizophrenia has significantly expanded, attributable to the rapid progress and advances in the accessibility of environmental health data. However, significant gaps in knowledge persist regarding the specific mechanism linking air pollution to increased risk of schizophrenia, as well as how air pollution may differentially affect schizophrenia individuals with various socioeconomic statuses. To more deeply comprehend the mental health hazards linked to air pollution, further epidemiological and mechanistic investigations are imperative. Additionally, this type of research can serve valuable information for environmental policy and public health strategies for schizophrenia prevention and risk management.
Y.Z. (Yuwei Zhou) and J.Y. performed Writing—original draft; J.Z. and Y.W. performed Data curation, Formal analysis, Visualization; J.S., Y.Z. (Yalin Zhang) and Y.T. performed Writing—review and editing; Y.Z. (Yunquan Zhang) and C.H. performed Writing—review and editing, Methodology, Software, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.
The authors declare no competing interests.
Footnotes
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Enlarge this image.Figure 1. Spearman correlation coefficients between daily mean concentrations of ambient air pollutants and meteorological factors. Abbreviations: PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm; PM10, particulate matter with aerodynamic diameter ≤ 10 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; RH, relative humidity; Temp, temperature; BP, barometric pressure. Notes: p for all pairwise correlations <0.05. The color gradient from dark green to dark orange indicates the transition from a negative to positive correlation.
Enlarge this image.Figure 2. Estimated odds of hospital admissions for schizophrenia per IQR increase in ambient air pollutants at various lag days. Abbreviations: PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm; PM10, particulate matter with aerodynamic diameter ≤ 10 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; OR, odds ratio; CI, confidence interval; IQR, interquartile range. Notes: * p < 0.05, ** p < 0.01, IQR estimates: PM2.5 = 41 μg/m3; PM10 = 56 μg/m3; SO2 = 12 μg/m3; NO2 = 18 μg/m3; O3 = 54 μg/m3; CO = 0.3 mg/m3.
Enlarge this image.Figure 3. Exposure-response associations of ambient air pollutants on lag-0 day with hospital admission for schizophrenia. Abbreviations: PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm; PM10, particulate matter with aerodynamic diameter ≤ 10 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; OR, odds ratio; CI, confidence interval.
Enlarge this image.Figure 4. Stratified estimates of odds for hospital admissions for schizophrenia associated with per IQR increase in exposure to ambient air pollutants (A–C). Abbreviations: PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm; PM10, particulate matter with aerodynamic diameter ≤ 10 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; OR, odds ratio; CI, confidence interval; IQR, interquartile range. Notes: * p < 0.05, ** p < 0.01, *** p < 0.001, IQR estimates: PM2.5 = 41 μg/m3; PM10 = 56 μg/m3; SO2 = 12 μg/m3; NO2 = 18 μg/m3; O3 = 54 μg/m3; CO = 0.3 mg/m3.
Summary statistics of study population for hospital admissions for schizophrenia, 2015–2017.
| Characteristics | Statistic |
|---|---|
| Hospital admission for schizophrenia, No. | 4079 |
| Sex, No. (%) | |
| Male | 1966 (48.0%) |
| Female | 2113 (52.0%) |
| Age, years | |
| Mean (SD) | 38.1 (13.2) |
| Median (IQR) | 56.0 (20.0) |
| Age, No. (%) | |
| 0–44 year | 2784 (68.0%) |
| 45+ year | 1295 (32.0%) |
| Season, No. (%) | |
| Warm (April to September) | 2001 (49.0%) |
| Cold (October to March of the next year) | 2078 (51.0%) |
Abbreviations: SD, standard deviation; IQR, interquartile range.
Descriptive distributions of exposure to ambient air pollutants and meteorological conditions on case days and control days.
| Variables | Mean | SD | Percentiles | ||||
|---|---|---|---|---|---|---|---|
| P5 | P25 | P50 | P75 | P95 | |||
| On case days (n = 4079) | |||||||
| Air pollutant | |||||||
| PM2.5, µg/m3 | 57.4 | 36.9 | 16.0 | 32.0 | 47.0 | 74.0 | 125.0 |
| PM10, µg/m3 | 99.7 | 51.7 | 35.0 | 65.0 | 90.0 | 124.0 | 197.0 |
| SO2, µg/m3 | 20.3 | 11.1 | 8.0 | 13.0 | 17.0 | 25.0 | 41.0 |
| NO2, µg/m3 | 34.5 | 13.8 | 17.0 | 24.0 | 32.0 | 42.0 | 61.0 |
| CO, mg/m3 | 0.9 | 0.3 | 0.6 | 0.7 | 0.9 | 1.0 | 1.5 |
| O3, µg/m3 | 100.4 | 36.5 | 45.0 | 73.0 | 98.0 | 126.0 | 161.0 |
| Meteorological condition | |||||||
| Temperature, °C | 16.9 | 8.4 | 3.8 | 9.2 | 17.2 | 24.4 | 29.3 |
| RH, % | 75.7 | 14.6 | 48.0 | 66.0 | 78.0 | 86.0 | 97.0 |
| BP, hPa | 1005.4 | 9.5 | 990.6 | 997.8 | 1004.6 | 1012.7 | 1020.8 |
| On control days (n = 13,896) | |||||||
| Air pollutant | |||||||
| PM2.5, µg/m3 | 57.0 | 37.0 | 16.0 | 32.0 | 47.0 | 73.0 | 125.0 |
| PM10, µg/m3 | 98.4 | 51.0 | 35.0 | 65.0 | 88.0 | 119.0 | 198.0 |
| SO2, µg/m3 | 19.9 | 10.8 | 8.0 | 12.0 | 17.0 | 24.0 | 41.0 |
| NO2, µg/m3 | 34.0 | 13.3 | 16.0 | 24.0 | 31.0 | 41.0 | 60.0 |
| CO, mg/m3 | 0.9 | 0.3 | 0.6 | 0.7 | 0.9 | 1.0 | 1.5 |
| O3, µg/m3 | 99.7 | 36.7 | 45.0 | 72.0 | 97.0 | 125.0 | 162.0 |
| Meteorological condition | |||||||
| Temperature, °C | 16.9 | 8.5 | 3.7 | 9.4 | 17.3 | 24.4 | 29.5 |
| RH, % | 76.4 | 14.5 | 48.0 | 67.0 | 78.0 | 88.0 | 97.0 |
| BP, hPa | 1005.3 | 9.5 | 990.9 | 997.8 | 1004.4 | 1012.6 | 1020.8 |
Abbreviations: SD, standard deviation; PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm; PM10, particulate matter with aerodynamic diameter ≤ 10 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; RH, relative humidity; BP, barometric pressure.
Odds ratios (with 95% CIs) for hospital admissions for schizophrenia associated with per IQR increase in air pollutants using single- and two-pollutant models.
| Pollutant | Model | OR (95% CI) | p for Heterogeneity |
|---|---|---|---|
| SO2 | Single-pollutant | 1.112 (1.033, 1.196) ** | Reference |
| (IQR = 12 µg/m3) | Two-pollutant | ||
| +PM2.5 | 1.105 (1.024, 1.192) * | 0.566 | |
| +PM10 | 1.107 (1.027, 1.194) ** | 0.641 | |
| +NO2 | 1.070 (0.978, 1.171) | 0.149 | |
| +CO | 1.121 (1.037, 1.212) ** | 0.525 | |
| +O3 | 1.114 (1.035,1.198) ** | 0.384 | |
| NO2 | Single-pollutant | 1.112 (1.033, 1.197) ** | Reference |
| (IQR = 18 µg/m3) | Two-pollutant | ||
| +PM2.5 | 1.108 (1.022, 1.201) * | 0.852 | |
| +PM10 | 1.113 (1.026, 1.206) ** | 0.955 | |
| +SO2 | 1.069 (0.976, 1.170) | 0.143 | |
| +CO | 1.150 (1.053, 1.255) ** | 0.170 | |
| +O3 | 1.116 (1.036, 1.202) ** | 0.311 |
Abbreviations: PM2.5, particulate matter with aerodynamic diameter ≤ 2.5 µm; PM10, particulate matter with aerodynamic diameter ≤ 10 µm; SO2, sulfur dioxide; NO2, nitrogen dioxide; CO, carbon monoxide; O3, ozone; OR, odds ratio; CI, confidence interval; IQR, interquartile range. Notes: * p < 0.05, ** p < 0.01.
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; Hu, Chengyang 3
1 Institute of Social Development and Health Management, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Public Health, Wuhan University of Science and Technology, Wuhan 430065, China;
2 School of Public Health, Xiangnan University, Chenzhou 423001, China;
3 Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei 230032, China