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Statistical approach to the impact of air pollution on the otolaryngology system diseases

  • March 2021
Authors:
Barbara HELENA Jasiulis-Gołdyn at University of Wroclaw
Barbara HELENA Jasiulis-Gołdyn
Dominik Nowakowski at Medical University of Bialystok
Dominik Nowakowski
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Abstract

The World Health Organization considers pollen mixtures PM2.5 to be the most harmful to health among other types of atmospheric pollution. Particles with a smaller diameter more easily enter the body. The first contact of these dusts with the human body occurs in the respiratory tract. Our main goal is to analyze the impact of air pollution indicator and meteorological data on the otolaryngological system on the basis of diagnosed diseases in residents of Wrocław (Poland). Finally, we use R software to create a GLM (Generalized Linear Model) and semiparametric GLM to predict the number of temporary incapacitated workers and students unable to learn due to otolaryngological diseases based on the air pollution factors and meteorological data.
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Statistical approach to the impact of air
pollution on the otolaryngology system diseases
Barbara Jasiulis-Gołdyn∗Dominik Nowakowski∗
March 2021
Key words: Air pollution, Correlation analysis, Dependence modeling,
Generalized linear model, Global health
Abstract
The World Health Organization considers pollen mixtures PM2.5 to be
the most harmful to health among other types of atmospheric pollution.
Particles with a smaller diameter more easily enter the body. The first
contact of these dusts with the human body occurs in the respiratory
tract.
Our main goal is to analyze the impact of air pollution indicator and
meteorological data on the otolaryngological system on the basis of diag-
nosed diseases in residents of Wrocław (Poland).
Finally, we use R software to create a GLM (Generalized Linear Model)
and semiparametric GLM to predict the number of temporary incapaci-
tated workers and students unable to learn due to otolaryngological dis-
eases based on the air pollution factors and meteorological data.
1 Introduction
Research is ongoing around the world on the impact of air pollution factors on
human health. Our main goal is to create a model that explain the impact
of environmental factors (mainly air pollutants) on the otolaryngology system,
which is responsible for diseases of the throat, nose, ears and larynx. We in-
vestigate the impact of air quality factors on the daily number of cases, as our
airways are primarily exposed to the negative effects of dust. Since the weather
conditions seem to be significant, we also consider here the impact of the aver-
age air temperature or average humidity. We analyze data from the National
Health Fund, which was collected in 2015 and are directly related to the city of
Wrocław.
Our task will be to clarify the number of daily cases throughout the year. This
will give us an overview of the year-round data compilation and we will be able
to predict the intensity of the otolaryngological disease group in the coming
years.
∗Institute of Mathematics, University of Wrocław, pl. Grunwaldzki 2/4, 50-384 Wrocław,
Poland
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Proceedings 63rd ISI World Statistics Congress, 11 - 16 July 2021, Virtual
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2 Optimalization
At the very beginning, we need to find a day in the past in which the concen-
tration of a given factor has significantly influenced the number of diagnosed
diseases of a particular day. Finally, we take into account data on the magni-
tude of factors from the past. For this reason we use Spearman’s correlation
coefficients. We investigate how many days back specific air quality and me-
teorological data should be taken into account to maximise the absolute value
of Spearman’s correlation between the number of diagnosed diseases and the
factor in question. The results below indicate that the factor usually affects the
number of cases with a maximum delay of three days. This applies to dust.
Variable Spearman’s correlation shift days
As.PM10. 0.38663090 0
BaA.PM10. 0.76864594 3
BaP.PM10. 0.74069835 7
BbF.PM10. 0.76625105 3
BjF.PM10. 0.77488086 3
BkF.PM10. 0.74174381 3
Cd.PM10. 0.69833691 1
DBaH.PM10. 0.77021759 3
IP.PM10. 0.75942023 3
Ni.PM10. -0.09422864 11
NOx.PM10. 0.27659045 5
O3 -0.56525307 12
Pb.PM10. 0.68218795 0
PM10 0.42772200 0
CO 0.51433960 12
NO2 0.15307808 5
C6H6 0.44700977 0
PM2.5 0.51059693 0
average air temperature -0.77688749 2
average humidity 0.41433302 12
station-level pressure 0.16831916 0
sunshine -0.44431827 14
Table 1: Air pollutants as variables with shifted day optimalization
Now let’s look at the correlation structure of our data set. Since we have a
counting variable here that takes values only from a set of natural numbers, we
use Spearman correlation values, which use actions on the ranks. The relation-
ship between variables can be seen in the Figure 1.
We choose explanatory variables, which are the most correlated with individual
calls described by the ICD-10 codes and not correlated with another explanatory
variables.
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Proceedings 63rd ISI World Statistics Congress, 11 - 16 July 2021, Virtual
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Figure 1: Matrix of Spearman correlation value for shifted days.
3 Generalized linear models and data analysis
At the beginning, we use GLM (Generalized Linear Model) with the Poisson
distribution. Next, we will extend the model by adding a nonlinear factor that
will be time-related creating such called semiparametric GLM (for more details
on the construction see [6]). By comparing these two models, we use the AIC
and BIC information criteria with values presented below:
Model AIC BIC
GLM 14486.36 14609.81
semiparametric GLM 6149.657 6350.27
Table 2: Value of information criteria for both models. A better model turns
out to be semiparametric GLM than the classical GLM because it has smaller
values for both criteria.
The results of our analyzes one can find in the Figure 2. Unfortunatelly, the
explicit formula for the semiparametric model is much more complicated but
easy to describe by numeric methods. It follows that we present here the GLM
model for analyzed data having friendly analytical formula. The prediction of
daily number of cases at time t (notation DNofCt) described by GLM is given
by the following formula (with parametres estimated by the MLE method):
DNofCt= exp{6.418 −0.089 BaAt−3+ 0.0065 BaPt−7+ 0.2 BbFt−3
−0.33 BjFt−3−0.058BkFt−3+ 0.047 Cdt−1+ 0.049 DBaHt−3
−0.001 O3t12 + 0.32 Pbt−0.001 PM10t+ 0.001 PM2.5t
+ 0.022 COt−12 −0.02 AATt−2+ 0.0009 AHt−12
−0.0002 sunshinet−14 + 0.001 STPt+ 0.006 C6H6t}
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Proceedings 63rd ISI World Statistics Congress, 11 - 16 July 2021, Virtual
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Figure 2: Prediction of daily number of cases in Wrocław in 2015 based on
environmental factors and air pollutants.
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Associations of outdoor fine particulate air pollution and cardiovascular disease in 157 436 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study
Article
Full-text available
  • Jun 2020
Background Most studies of long-term exposure to outdoor fine particulate matter (PM2·5) and cardiovascular disease are from high-income countries with relatively low PM2·5 concentrations. It is unclear whether risks are similar in low-income and middle-income countries (LMICs) and how outdoor PM2·5 contributes to the global burden of cardiovascular disease. In our analysis of the Prospective Urban and Rural Epidemiology (PURE) study, we aimed to investigate the association between long-term exposure to PM2·5 concentrations and cardiovascular disease in a large cohort of adults from 21 high-income, middle-income, and low-income countries. Methods In this multinational, prospective cohort study, we studied 157 436 adults aged 35–70 years who were enrolled in the PURE study in countries with ambient PM2·5 estimates, for whom follow-up data were available. Cox proportional hazard frailty models were used to estimate the associations between long-term mean community outdoor PM2·5 concentrations and cardiovascular disease events (fatal and non-fatal), cardiovascular disease mortality, and other non-accidental mortality. Findings Between Jan 1, 2003, and July 14, 2018, 157 436 adults from 747 communities in 21 high-income, middle-income, and low-income countries were enrolled and followed up, of whom 140 020 participants resided in LMICs. During a median follow-up period of 9·3 years (IQR 7·8–10·8; corresponding to 1·4 million person-years), we documented 9996 non-accidental deaths, of which 3219 were attributed to cardiovascular disease. 9152 (5·8%) of 157 436 participants had cardiovascular disease events (fatal and non-fatal incident cardiovascular disease), including 4083 myocardial infarctions and 4139 strokes. Mean 3-year PM2·5 at cohort baseline was 47·5 μg/m³ (range 6–140). In models adjusted for individual, household, and geographical factors, a 10 μg/m³ increase in PM2·5 was associated with increased risk for cardiovascular disease events (hazard ratio 1·05 [95% CI 1·03–1·07]), myocardial infarction (1·03 [1·00–1·05]), stroke (1·07 [1·04–1·10]), and cardiovascular disease mortality (1·03 [1·00–1·05]). Results were similar for LMICs and communities with high PM2·5 concentrations (>35 μg/m³). The population attributable fraction for PM2·5 in the PURE cohort was 13·9% (95% CI 8·8–18·6) for cardiovascular disease events, 8·4% (0·0–15·4) for myocardial infarction, 19·6% (13·0–25·8) for stroke, and 8·3% (0·0–15·2) for cardiovascular disease mortality. We identified no consistent associations between PM2·5 and risk for non-cardiovascular disease deaths. Interpretation Long-term outdoor PM2·5 concentrations were associated with increased risks of cardiovascular disease in adults aged 35–70 years. Air pollution is an important global risk factor for cardiovascular disease and a need exists to reduce air pollution concentrations, especially in LMICs, where air pollution levels are highest. Funding Full funding sources are listed at the end of the paper (see Acknowledgments).
Effects of Ambient Air Pollution on Symptoms of Asthma in Seattle-Area Children Enrolled in the CAMP Study
Article
  • Jan 2001
We observed a panel of 133 children (5-13 years of age) with asthma residing in the greater Seattle, Washington, area for an average of 58 days (range 28-112 days) during screening for enrollment in the Childhood Asthma Management Program (CAMP) study. Daily self-reports of asthma symptoms were obtained from study diaries and compared with ambient air pollution levels in marginal repeated measures logistic regression models. We defined days with asthma symptoms as any day a child reported at least one mild asthma episode. All analyses were controlled for subject-specific variables [age, race, sex, baseline height, and FEV(1) PC(20) concentration (methacholine provocative concentration required to produce a 20% decrease in forced expiratory volume in 1 sec)] and potential time-dependent confounders (day of week, season, and temperature). Because of variable observation periods for participants, we estimated both between- and within-subject air pollutant effects. Our primary interest was in the within-subject effects: the effect of air pollutant excursions from typical levels in each child's observation period on the odds of asthma symptoms. In single-pollutant models, the population average estimates indicated a 30% [95% confidence interval (CI), 11-52%] increase for a 1-ppm increment in carbon monoxide lagged 1 day, an 18% (95% CI, 5-33%) increase for a 10-microg/m(3) increment in same-day particulate matter < 1.0 microm (PM(1.0)), and an 11% (95% CI, 3-20%) increase for a 10-microg/m(3) increment in particulate matter < 10 microm (PM(10)) lagged 1 day. Conditional on the previous day's asthma symptoms, we estimated 25% (95% CI, 10-42%), 14% (95% CI, 4-26%), and 10% (95% CI, 3-16%) increases in the odds of asthma symptoms associated with increases in CO, PM(1.0), and PM(10), respectively. We did not find any association between sulfur dioxide (SO(2)) and the odds of asthma symptoms. In multipollutant models, the separate pollutant effects were smaller. The overall effect of an increase in both CO and PM(1. 0) was a 31% (95% CI, 11-55%) increase in the odds of symptoms of asthma. We conclude that there is an association between change in short-term air pollution levels, as indexed by PM and CO, and the occurrence of asthma symptoms among children in Seattle. Although PM effects on asthma have been found in other studies, it is likely that CO is a marker for vehicle exhaust and other combustion by-products that aggravate asthma.
Human health effects of air pollution
Article
Hazardous chemicals escape to the environment by a number of natural and/or anthropogenic activities and may cause adverse effects on human health and the environment. Increased combustion of fossil fuels in the last century is responsible for the progressive change in the atmospheric composition. Air pollutants, such as carbon monoxide (CO), sulfur dioxide (SO(2)), nitrogen oxides (NOx), volatile organic compounds (VOCs), ozone (O(3)), heavy metals, and respirable particulate matter (PM2.5 and PM10), differ in their chemical composition, reaction properties, emission, time of disintegration and ability to diffuse in long or short distances. Air pollution has both acute and chronic effects on human health, affecting a number of different systems and organs. It ranges from minor upper respiratory irritation to chronic respiratory and heart disease, lung cancer, acute respiratory infections in children and chronic bronchitis in adults, aggravating pre-existing heart and lung disease, or asthmatic attacks. In addition, short- and long-term exposures have also been linked with premature mortality and reduced life expectancy. These effects of air pollutants on human health and their mechanism of action are briefly discussed.
Wand Semiparametric Regression with R
  • Jan 2018
  • J Harezlak
  • D Ruppert
J. Harezlak, D. Ruppert, M. Wand Semiparametric Regression with R, Springer (2018)