GENERALIZED ADDITIVE MODEL FOR COUNT TIME SERIES: AN APPLICATION TO QUANTIFY THE IMPACT OF AIR POLLUTANTS ON HUMAN HEALTH

The associations between ambient air pollutants and tuberculosis seasonality are unclear. We assessed the temporal cross-correlations between ambient air pollutants and tuberculosis seasonality. Monthly tuberculosis incidence data and ambient air pollutants (PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2)) and air quality index (AQI) from 2013 to 2017 in Shanghai were included. A cross-correlogram and generalized additive model were used. A 4-month delayed effect of PM2.5 (0.55), PM10 (0.52), SO2 (0.47), NO2 (0.40), CO (0.39), and AQI (0.45), and a 6-month delayed effect of O3 (−0.38) on the incidence of tuberculosis were found. The number of tuberculosis cases increased by 8%, 4%, 18%, and 14% for a 10 μg/m3 increment in PM2.5, PM10, SO2, and NO2; 4% for a 10 unit increment in AQI; 8% for a 0.1 mg/m3 increment in CO; and decreased by 4% for a 10 μg/m3 increment in O3. PM2.5 concentrations above 50 μg/m3, 70 μg/m3 for PM10, 16 μg/m3 for SO2, 47 μg/m3 for NO2, 0.85 mg/m3 for CO, and 85 for AQI, and O3 concentrations lower than 95 μg/m3 were positively associated with the incidence of tuberculosis. Ambient air pollutants were correlated with tuberculosis seasonality. However, this sort of study cannot prove causality.

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Study of the Effects of Air Pollutants on Human Health Based on Baidu Indices of Disease Symptoms and Air Quality Monitoring Data in Beijing, China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16061014 ◽

2019 ◽

Vol 16 (6) ◽

pp. 1014 ◽

Cited By ~ 13

Author(s):

Shaobo Zhong ◽

Zhichen Yu ◽

Wei Zhu

Keyword(s):

Air Pollution ◽

Air Quality ◽

Human Health ◽

Air Pollutants ◽

Pearson Correlation ◽

Additive Model ◽

Health Impact ◽

Cerebrovascular Diseases ◽

Relative Risks ◽

The Impact

There is an increasing body of evidence showing the impact of air pollutants on human health such as on the respiratory, and cardio- and cerebrovascular systems. In China, as people begin to pay more attention to air quality, recent research focused on the quantitative assessment of the effects of air pollutants on human health. To assess the health effects of air pollutants and to construct an indicator placing emphasis on health impact, a generalized additive model was selected to assess the health burden caused by air pollution. We obtained Baidu indices (an evaluation indicator launched by Baidu Corporation to reflect the search popularity of keywords from its search engine) to assess daily query frequencies of 25 keywords considered associated with air pollution-related diseases. Moreover, we also calculated the daily concentrations of major air pollutants (including PM10, PM2.5, SO2, O3, NO2, and CO) and the daily air quality index (AQI) values, and three meteorological factors: daily mean wind level, daily mean air temperature, and daily mean relative humidity. These data cover the area of Beijing from 1 March 2015 to 30 April 2017. Through the analysis, we produced the relative risks (RRs) of the six main air pollutants for respiratory, and cardio- and cerebrovascular diseases. The results showed that O3 and NO2 have the highest health impact, followed by PM10 and PM2.5. The effects of any pollutant on cardiovascular diseases was consistently higher than on respiratory diseases. Furthermore, we evaluated the currently used AQI in China and proposed an RR-based index (health AQI, HAQI) that is intended for better indicating the effects of air pollutants on respiratory, and cardio- and cerebrovascular diseases than AQI. A higher Pearson correlation coefficient between HAQI and RRTotal than that between AQI and RRTotal endorsed our efforts.

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Impact of Influenza on Outpatient Visits, Hospitalizations, and Deaths by Using a Time Series Poisson Generalized Additive Model

PLoS ONE ◽

10.1371/journal.pone.0149468 ◽

2016 ◽

Vol 11 (2) ◽

pp. e0149468 ◽

Cited By ~ 6

Author(s):

Ru-ning Guo ◽

Hui-zhen Zheng ◽

Chun-quan Ou ◽

Li-qun Huang ◽

Yong Zhou ◽

...

Keyword(s):

Time Series ◽

Generalized Additive Model ◽

Additive Model ◽

Outpatient Visits

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Air pollutants and hospitalization due to pneumonia among children. An ecological time series study

Sao Paulo Medical Journal ◽

10.1590/1516-3180.2014.00122601 ◽

2015 ◽

Vol 133 (5) ◽

pp. 408-413 ◽

Cited By ~ 9

Author(s):

Tassia Soldi Tuan ◽

Taís Siqueira Venâncio ◽

Luiz Fernando Costa Nascimento

Keyword(s):

Time Series ◽

Air Pollutants ◽

Hospital Admissions ◽

Population Attributable Fraction ◽

Additive Model ◽

Cost Savings ◽

Time Series Study ◽

Avoidable Hospitalizations ◽

Relative Risks ◽

Series Study

ABSTRACT CONTEXT AND OBJECTIVE: Exposure to air pollutants is one of the factors responsible for hospitalizations due to pneumonia among children. This has considerable financial cost, along with social cost. A study to identify the role of this exposure in relation to hospital admissions due to pneumonia among children up to 10 years of age was conducted. DESIGN AND SETTING: Ecological time series study using data from São José dos Campos, Brazil. METHODS: Daily data on hospitalizations due to pneumonia and on the pollutants CO, O3, PM10 and SO2, temperature and humidity in São José dos Campos, in 2012, were analyzed. A generalized additive model of Poisson's regression was used. Relative risks for hospitalizations due to pneumonia, according to lags of 0-5 days, were estimated. The population-attributable fraction, number of avoidable hospitalizations and cost savings from avoidable hospitalizations were calculated. RESULTS: There were 539 admissions. Exposure to CO and O3 was seen to be associated with hospitalizations, with risks of 1.10 and 1.15 on the third day after exposure to increased CO concentration of 200 ppb and ozone concentration of 20 µg/m3. Exposure to the pollutants of particulate matter and sulfur dioxide were not shown to be associated with hospitalizations. Decreases in CO and ozone concentrations could lead to 49 fewer hospitalizations and cost reductions of R$ 39,000.00. CONCLUSION: Exposure to certain air pollutants produces harmful effects on children's health, even in a medium-sized city. Public policies to reduce emissions of these pollutants need to be implemented.

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Assessment of the impact on human health of exposure to urban air pollutants: an Indian case study

International Journal of Environmental Studies ◽

10.1080/0020723042000275123 ◽

2005 ◽

Vol 62 (2) ◽

pp. 201-214 ◽

Cited By ~ 2

Author(s):

MRINAL K. GHOSE ◽

R. PAUL ◽

S.K. BANERJEE

Keyword(s):

Human Health ◽

Air Pollutants ◽

Urban Air ◽

The Impact

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The impact of European legislative and technology measures to reduce air pollutants on air quality, human health and climate

Environmental Research Letters ◽

10.1088/1748-9326/11/2/024010 ◽

2016 ◽

Vol 11 (2) ◽

pp. 024010 ◽

Cited By ~ 21

Author(s):

S T Turnock ◽

E W Butt ◽

T B Richardson ◽

G W Mann ◽

C L Reddington ◽

...

Keyword(s):

Air Quality ◽

Human Health ◽

Air Pollutants ◽

The Impact

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The Effect of the COVID-19 Vaccine on Daily Cases and Deaths Based on Global Vaccine Data

Vaccines ◽

10.3390/vaccines9111328 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1328

Author(s):

Zhiwei Li ◽

Xiangtong Liu ◽

Mengyang Liu ◽

Zhiyuan Wu ◽

Yue Liu ◽

...

Keyword(s):

Random Effects ◽

Generalized Additive Model ◽

Meta Analysis ◽

Additive Model ◽

Global Pandemic ◽

Daily Data ◽

Fair Distribution ◽

The Impact ◽

The Relationship

Background: Coronavirus disease 2019 (COVID-19), a global pandemic, has caused over 216 million cases and 4.50 million deaths as of 30 August 2021. Vaccines can be regarded as one of the most powerful weapons to eliminate the pandemic, but the impact of vaccines on daily COVID-19 cases and deaths by country is unclear. This study aimed to investigate the correlation between vaccines and daily newly confirmed cases and deaths of COVID-19 in each country worldwide. Methods: Daily data on firstly vaccinated people, fully vaccinated people, new cases and new deaths of COVID-19 were collected from 187 countries. First, we used a generalized additive model (GAM) to analyze the association between daily vaccinated people and daily new cases and deaths of COVID-19. Second, a random effects meta-analysis was conducted to calculate the global pooled results. Results: In total, 187 countries and regions were included in the study. During the study period, 1,011,918,763 doses of vaccine were administered, 540,623,907 people received at least one dose of vaccine, and 230,501,824 people received two doses. For the relationship between vaccination and daily increasing cases of COVID-19, the results showed that daily increasing cases of COVID-19 would be reduced by 24.43% [95% CI: 18.89, 29.59] and 7.50% [95% CI: 6.18, 8.80] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Daily increasing deaths of COVID-19 would be reduced by 13.32% [95% CI: 3.81, 21.89] and 2.02% [95% CI: 0.18, 4.16] with 10,000 fully vaccinated people per day and at least one dose of vaccine, respectively. Conclusions: These findings showed that vaccination can effectively reduce the new cases and deaths of COVID-19, but vaccines are not distributed fairly worldwide. There is an urgent need to accelerate the speed of vaccination and promote its fair distribution across countries.

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ANALYSIS OF NO<sub>2</sub> TROPOSPHERIC COLUMN AMOUNT AT AIRPORTS IN MALAYSIA BEFORE AND DURING COVID-19 PANDEMIC USING SENTINEL-5P TROPOMI DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2021-399-2021 ◽

2021 ◽

Vol XLIII-B3-2021 ◽

pp. 399-403

Author(s):

K. D. Kanniah ◽

N. A. F. Kamarul Zaman ◽

K. Perumal

Keyword(s):

Climate Change ◽

Human Health ◽

Urban Areas ◽

Air Pollutants ◽

Temporal Variations ◽

Data Availability ◽

Spatial And Temporal Patterns ◽

Movement Restriction ◽

Motorized Vehicles ◽

The Impact

Abstract. Air pollution is a serious environmental and health issue in Malaysia due to the recent urbanization processes. The main sources of air pollutants are motorized vehicles in urban areas and airports and industrial activities. At the airports, NO2 is the main pollutant of concern besides aerosols particles, yet gap in data availability prevent studies to describe their patterns and quantify their effects on human health and climate change. In this study NO2 data from TROPOMI sensor on board Sentinel 5-P satellite was used to characterize the spatial and temporal patterns of NO2 tropospheric column amounts at major airports in Malaysia. The results demonstrate that NO2 amounts from aircrafts and ground traffic activities are generally higher and/or similar to the amounts found in urban areas. Total tropospheric column amounts of NO2 during the movement restriction imposed due to Covid-19 pandemic between March and April 2020 was approximately 50% lower the total emission during the same period in 2019 (representing a business as usual period). Assessing the spatial pattern and temporal variations in NO2 (both surface and total vertical profile) is important for monitoring the impact of air pollutants on climate change and human health in Malaysia.

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Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

10.21203/rs.3.rs-27820/v1 ◽

2020 ◽

Author(s):

hazem al-najjar ◽

Nadia Al-Rousan ◽

Ismail A. Elhaty

Keyword(s):

Air Pollution ◽

Time Series ◽

Feature Selection ◽

Wind Speed ◽

Air Pollutants ◽

Time Series Modeling ◽

Climate Variables ◽

Different Seasons ◽

The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

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Importance of applying Mixed Generalized Additive Model (MGAM) as a method for assessing the environmental health impacts: Ambient temperature and Acute Myocardial Infarction (AMI), among elderly in Shanghai, China

PLoS ONE ◽

10.1371/journal.pone.0255767 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255767

Author(s):

Xiaoqian Huang ◽

Weiping Ma ◽

Chikin Law ◽

Jianfeng Luo ◽

Naiqing Zhao

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Time Series ◽

Ambient Temperature ◽

Autocorrelation Function ◽

Risk Ratio ◽

Generalized Additive Model ◽

Seasonal Pattern ◽

Additive Model ◽

Degree Of Freedom

Association between acute myocardial infarction (AMI) morbidity and ambient temperature has been examined with generalized linear model (GLM) or generalized additive model (GAM). However, the effect size by these two methods might be biased due to the autocorrelation of time series data and arbitrary selection of degree of freedom of natural cubic splines. The present study analyzed how the climatic factors affected AMI morbidity for older adults in Shanghai with Mixed generalized additive model (MGAM) that addressed these shortcomings mentioned. Autoregressive random effect was used to model the relationship between AMI and temperature, PM10, week days and time. The degree of freedom of time was chosen based on the seasonal pattern of temperature. The performance of MGAM was compared with GAM on autocorrelation function (ACF), partial autocorrelation function (PACF) and goodness of fit. One-year predictions of AMI counts in 2011 were conducted using MGAM with the moving average. Between 2007 and 2011, MGAM adjusted the autocorrelation of AMI time series and captured the seasonal pattern after choosing the degree of freedom of time at 5. Using MGAM, results were well fitted with data in terms of both internal (R2 = 0.86) and external validity (correlation coefficient = 0.85). The risk of AMI was relatively high in low temperature (Risk ratio = 0.988 (95% CI 0.984, 0.993) for under 12°C) and decreased as temperature increased and speeded up within the temperature zone from 12°C to 26°C (Risk ratio = 0.975 (95% CI 0.971, 0.979), but it become increasing again when it is 26°C although not significantly (Risk ratio = 0.999 (95% CI 0.986, 1.012). MGAM is more appropriate than GAM in the scenario of response variable with autocorrelation and predictors with seasonal variation. The risk of AMI was comparatively higher when temperature was lower than 12°C in Shanghai as a typical representative location of subtropical climate.

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