Short-term impact of noise, other air pollutants and meteorological factors on emergency hospital mental health admissions in the Madrid region

Does the new European Union air quality directive really protect health? A nationwide case study in Spain

In October 2024, the European Union (EU) issued a Directive establishing a series of limit values, which, once transposed into national law by EU Member States, will become mandatory. This Directive is less restrictive than the WHO guideline values published in 2021. Using dose-response functions specifically calculated for every Spanish province, this study sought to ascertain how many short-term, all-cause hospital emergency admissions (ICD-9: 1-799 and ICD-10: A00-R99) were attributable to NO2, PM10 and PM2.5 concentrations across the period 2013-2018, and what the ensuing reduction in admissions would be, if the EU Directive and WHO guideline values were applied respectively. The results obtained indicate that here in Spain some 38,790 admissions per year are attributable to NO2, PM10 and PM2.5 pollution, at a total cost of €548 million. These admissions would be reduced by 478 (1.23 %) if the EU Directive were observed, and by 4496 (11.59 %) if the WHO guideline values were met. While compliance with the EU limit values would have almost no effect in terms of a decrease in attributable admissions, such an effect would indeed be seen in the event of compliance with the WHO guideline values and would entail a saving of over €63 million nationwide. This study only focused on short-term effects of PM and NO2.

Economic estimation and impact of air pollution and temperature extremes on emergency hospital admissions in Spain

Air pollution and extreme temperatures are important public health problems but their effects on morbidity are not usually measured jointly. The objective was thus to analyse the effect of short-term exposure to both air pollution and to heat and cold waves on emergency hospital admissions at a provincial level, and estimate the economic cost. Time series study analysing emergency hospital admissions due to natural causes (ICD-9:1-799 and ICD-10: A00-R99) across the period 2013-2018. The independent variables were daily mean concentrations of NO2, PM2.5, PM10 and ozone, the maximum daily temperature in heat waves, and the minimum daily temperature in cold waves. Generalised linear models with a Poisson regression link were used to calculate the relative risks and attributable risks and estimated the related economic cost. In relation to air pollution, exposure to NO2 showed the greatest association with the number of provinces (39 %) and the highest number of attributable admissions (27,823;95%CI:14181-42,610) and an annual cost of €393,25 million. For the O3, the association with attributable admissions was 22,858; 95%CI:5986-39,683 and an annual cost of €312,76 million. The exposure to PM showed the lowest association with the attributable admissions, 11,203 (95%CI:4470-17,504) and an annual cost of €152,95 million. In the case of extreme temperatures their impact was very much less (5377; 95CI:2347-8373) than that of air pollution and their annual cost of €76.0 million. According the results found, a considerable number of emergency hospital admissions are mainly attributable to short-term exposure to air pollution instead of the extreme temperatures. The extreme temperatures prevention plans should be implemented to address the impact on health of air pollution and temperatures jointly.

Short-term effects of fine particulate matter from biomass combustion and Saharan dust intrusions on emergency hospital admissions due to mental and behavioural disorders, anxiety and depression in Spain

Climate change is affecting both the frequency and scale of wildfires, as well as the increase in the number of days with Saharan dust intrusions. Traditionally, studies have focused on the extent to which the increase in fine particulate matter (PM) has had an impact on cardio-respiratory diseases, but (apart from PM) not on how the meteorological and pollution conditions in these situations affect other diseases, such as those linked to mental health. This study therefore sought to ascertain how daily mean PM10, PM 2.5, NO2, O3 concentrations and daily maximum temperature in heat waves influence daily emergency hospital admissions in Spain caused by mental and behavioural disorders, depression and anxiety on days with PM from biomass combustion and/or Saharan dust intrusions, as compared to days without such conditions, across the period 2009-2018. Our results indicate that on days on which there is biomass combustion, PM concentrations have a statistically significant effect on emergency admissions due to mental disorders, probably related with the toxicity of these particles. Yet on days with intrusions of Saharan dust rather than PM, it is the other variables considered in the analysis that are most closely linked to these types of admissions. The results of this study thus point to the need to implement public health prevention plans which take into account the joint effect of various environmental risk factors that act synergistically in given situations.

Association of outdoor air and noise pollution with unprovoked seizures and new onset epilepsy: A systematic review and meta-analysis

Research has indicated that certain environmental exposures may increase the risk of unprovoked seizures and new onset epilepsy. This study aimed to synthesize the literature that has estimated the associations between short- and long-term exposure to outdoor air and noise pollution and the risk of unprovoked seizures and new onset epilepsy. We searched Embase, MEDLINE, Scopus, Web of Science, BIOSIS Previews, Latin American and Caribbean Health Sciences Literature, Proquest Dissertations and Theses, conference abstracts, and the gray literature and conducted citation tracing in June 2023. Observational and ecological studies assessing the associations of air and noise pollution with unprovoked seizures or new onset epilepsy were eligible. One reviewer extracted summary data. Using fixed and random effects models, we calculated the pooled risk ratios (RRs) for the studies assessing the associations between short-term exposure to air pollution and unprovoked seizures. Seventeen studies were included, 16 assessing the association of air pollution with seizures and one with epilepsy. Eight studies were pooled quantitatively. Ozone (O3; RR = .99, 95% confidence interval [CI] = .99-.99) and nitrogen dioxide (NO2) exposure adjusted for particulate matter (RR = 1.02, 95% CI = 1.01-1.02) on the same day, and carbon monoxide (CO) exposure 2 days prior (RR = 1.12, 95% CI = 1.02-1.22), were associated with seizure risk. A single study of air pollution and epilepsy did not report a significant association. The risk of bias and heterogeneity across studies was moderate or high. Short-term exposure to O3, NO2, and CO may affect the risk of seizures; however, the effect estimates for O3 and NO2 were minimal. Additional research should continue to explore these and the associations between outdoor air pollution and epilepsy and between noise pollution and seizures and epilepsy.

Impact of drought on mental and behavioral disorders, contributions of research in a climate change context. A narrative review

Mental and behavioral disorders are an important public health problem and constitute a priority for the WHO, whose recommendations include the surveillance of their risk factors. On the other hand, drought episodes have been increasing in frequency and severity in Europe since 1980. Therefore, to review the present knowledge about the impact of drought on mental and behavioral disorders, in the present climate change context, and to underline potential research gaps, could be of major interest. Thus, we performed a narrative review using online academic databases with the aim of identifying relevant literature about the impact of drought on mental and behavioral disorders. To the best of our knowledge, no study in Europe quantifies the potential association between drought and mental disorders. A limited number of studies have found significant associations between droughts (with different temporal ranges) and various measures of mental health. However, according to our review, only three of them quantified the association between drought and objective mental health outcomes, such as number of emergencies due to clinically diagnosed mental disorders or suicides. Additionally, few studies used specific indices as a measure of drought; and finally, as far as authors are aware, none of them has analyzed this relationship adjusting for various other potential environmental confounders. Moreover, the eventual association could vary between different geographical areas within the same country. Therefore, national and regional studies would be especially necessary. Thus, there is a need for specific national and regional studies, in Europe and globally, that assess the impact of specific indices of drought (with different temporal ranges) on objective mental health outcomes controlling for potential environmental confounders. Moreover, the quantification of its cost would be necessary for health prioritization, evidence-based policies and strategic health planning.

A Systematic Review for Indoor and Outdoor Air Pollution Monitoring Systems Based on Internet of Things

Global population growth and increasing pollution levels are directly related. The effect does not just apply to outdoor spaces. Likewise, the low indoor air quality is also having a negative impact on the health of the building residents. According to the World Health Organization, indoor air pollution is a leading cause of 1.6 million premature deaths annually. Tackling this public health issue, due to the direct relationship between air pollution levels and mortality and morbidity rates as well as overall comfort, is mandatory. Many companies have begun to build inexpensive sensors for use in Internet of Things (IoT)-based applications to pollution monitoring. The research highlights design aspects for sustainable monitoring systems including sensor types, the selected parameters, range of sensors used, cost, microcontrollers, connectivity, communication technologies, and environments. The main contribution of this systematic paper is the synthesis of existing research, knowledge gaps, associated challenges, and future recommendations. Firstly, the IEEE database had the highest contribution to this research (48.51%). The results showed that 87.1%, 66.3%, and 36.8% of studies focused on harmful gas monitoring, thermal comfort parameters, and particulate matter levels pollution, respectively. The most studied harmful gases were CO2, CO, NO2, O3, SO2, SnO2, and volatile organic compounds. The cost of the sensors was suitable for people with limited incomes and mostly under USD 5, rising to USD 30 for specific types. Additionally, 40.35% of systems were based on ESP series (ESP8266 and ESP32) microcontrollers, with ESP8266 being preferred in 34 studies. Likewise, IoT cloud and web services were the preferred interfaces (53.28%), while the most frequent communication technology was Wi-Fi (67.37%). Indoor environments (39.60%) were the most studied ones, while the share for outdoor environments reached 20.79% of studies. This is an indication that pollution in closed environments has a direct impact on living quality. As a general conclusion, IoT-based applications may be considered as reliable and cheap alternatives for indoor and outdoor pollution monitoring.

A novel correction method for modelling parameter-driven autocorrelated time series with count outcome

BACKGROUND

Count time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for modelling parameter-driven count time series can obtain consistent and reliable standard error of parameter estimates, causing potential inflation of type I error rate.

METHODS

We proposed a new maximum significant ρ correction (MSRC) method that utilizes information of significant autocorrelation coefficient ρ estimate within 5 orders by moment estimation. A Monte Carlo simulation was conducted to evaluate and compare the finite sample performance of the MSRC and classical unbiased correction (UB-corrected) method. We demonstrated a real-data analysis for assessing the effect of drunk driving regulations on the incidence of road traffic injuries (RTIs) using MSRC in Shenzhen, China. Moreover, there is no previous paper assessing the time-varying intervention effect and considering autocorrelation based on daily data of RTIs.

RESULTS

Both methods had a small bias in the regression coefficients. The autocorrelation coefficient estimated by UB-corrected is slightly underestimated at high autocorrelation (≥ 0.6), leading to the inflation of the type I error rate. The new method well controlled the type I error rate when the sample size reached 340. Moreover, the power of MSRC increased with increasing sample size and effect size and decreasing nuisance parameters, and it approached UB-corrected when ρ was small (≤ 0.4), but became more reliable as autocorrelation increased further. The daily data of RTIs exhibited significant autocorrelation after controlling for potential confounding, and therefore the MSRC was preferable to the UB-corrected. The intervention contributed to a decrease in the incidence of RTIs by 8.34% (95% CI, -5.69-20.51%), 45.07% (95% CI, 25.86-59.30%) and 42.94% (95% CI, 9.56-64.00%) at 1, 3 and 5 years after the implementation of the intervention, respectively.

CONCLUSIONS

The proposed MSRC method provides a reliable and consistent approach for modelling parameter-driven time series with autocorrelated count data. It offers improved estimation compared to existing methods. The strict drunk driving regulations can reduce the risk of RTIs.

Acute effects of military aircraft noise on sedative and analgesic drug administrations in psychiatric patients: A case-time series analysis

BACKGROUND

Existing evidence suggests that psychiatric patients are highly noise sensitive, and that noise exposure increases the risk for adverse mental health outcomes, such as psychiatric hospitalizations and even suicide. To investigate acute effects of noise in this vulnerable population, we assessed short-term associations between fighter jet noise and on-demand sedative and analgesic drug administrations in a psychiatric clinic located close to a military airfield in Switzerland.

METHODS

We applied a case time series analysis with an hourly time resolution using distributed-lag models. Analysis was adjusted for long-term and seasonal trends, day of week, time of day, time-varying weather conditions and the week of stay. Noise exposure (hourly A-weighted equivalent continuous sound pressure levels (LAeq)) was modelled using detailed flight plans and noise footprints for different fighter jet and route combinations. Outcome data were available from the clinic's records.

OUTCOMES

During the study period (06/2016-12/2021), 23,486 flights occurred. 5,968 clinical stays with a median length of 41 days (IQR: 28d, 50d) were recorded. The odds ratio (OR) for medication administration over the lag period of 3 hours after exposure was 1.016 (95 %CI: 1.006, 1.026) per 10 dB LAeq for sedatives and 1.032 (95 %CI: 1.016, 1.048) per 10 dB for analgesics. Effects were larger in multimorbid patients.

INTERPRETATION

Case time series analysis is a novel method to investigate transient associations in observational data while minimizing risk of bias. Using an objectively recorded outcome measure, our results demonstrate that psychiatric patients are a vulnerable population, in which noise exposure can lead to symptom exacerbations and adverse events.

The relationship between toxic air pollution, health expenditure, and economic growth in the European Union: fresh evidence from the PMG-ARDL model

Air pollution is a danger to economies throughout the European Union. Industry, population expansion, a building boom owing to housing and infrastructure development, increasing vehicle traffic, crowded streets, a lack of availability of clean fuel, and ineffective control programs are the primary causes. Toxic air is a double-edged sword for a country's health since it affects just a tiny fraction of Europe's population. The financial burden and healthcare expenses for people rise when health expenditures rise. The present research looks at how dangerous air levels, healthcare costs, and the expansion of the European Union's economy are all connected. The findings are based on data collected over 29 years and account for the abovementioned variables. The results of the unit root test have the significant probability values of all variables: health expenditures (HE), gross domestic product (GDP), nitrous oxides (NOX), and carbon dioxides (CO2) emissions at both level and first difference. We used the Johansen, Kao, and Pedroni cointegration tests to test the null hypothesis of no cointegration to see that sample variables had a long-term association. The PMG-ARDL test was used to get these findings. The results confirmed the significant probability values of dependent variables in long- and short-run results that GDP has a positive and significant effect on health expenditure, while NOX and CO2 emissions have a negative and significant impact on (HE), in the European Union. To verify the results, we applied the robustness test, fully modified OLS (FMOLD), and dynamic OLS (DOLS); the robustness test results validated the PMG-ARDL test results. Environmental pollution (CO2, NOX) has a significant and negative impact on healthcare expenditures and a significant effect on GDP (HE) in the EU region. The findings of this research have implications for a wide range of parties, including those who would examine the link between factors in a study meant to improve air quality, distribute health resources, or develop strategies for economic development.

Medical cost of environmental pollution: evidence from the Chinese Social Survey

Environmental pollution impairs residents' health, while the pursuit of health is highly correlated to medical costs. Understanding how environmental pollution affects medical costs is closely linked to the welfare of society. Based on theoretical analysis, this paper uses data from 5112 households of the Chinese Social Survey (CSS) in 2019, constructs a composite indicator to quantify environmental pollution using respondents' evaluations, and empirically investigates the causal effect of environmental pollution on household medical cost and the mechanism. The conclusions are shown as follows. First, environmental pollution can increase household medical costs, and this estimation result still holds after dealing with the endogeneity problem and other robustness tests. Second, there is heterogeneity in the impact of environmental pollution on household medical costs, households in the upper socioeconomic class, with heavy pension burdens or with strong health insurance coverage are more sensitive to environmental pollution and incur relatively higher household medical costs. Third, environmental pollution reduces residents' satisfaction with their spiritual life, which adversely affects their physical and mental health and can increase household medical costs. Residents' satisfaction with their spiritual life is an important mechanism for environmental pollution to affect household health care expenditures. Therefore, governments should enhance the enforcement of environmental protection and governance, strengthen the awareness of green issues and health education, and increase the supply of facilities for leisure and sports, thus reducing medical costs due to environmental pollution and easing the medical burden of residents.