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Impact of air pollution on hospital admissions with a focus on respiratory diseases: a time-series multi-city analysis

  • Alessandro Slama
  • Andrzej Śliwczyński
  • Jolanta Woźnica
  • Maciej Zdrolik
  • Bartłomiej Wiśnicki
  • Jakub Kubajek
  • Olga Turżańska-Wieczorek
  • Dariusz Gozdowski
  • Waldemar Wierzba
  • Edward FranekEmail author
Open Access
Research Article
  • 479 Downloads

Abstract

Together with the growing availability of data from electronic records from healthcare providers and healthcare systems, an assessment of associations between different environmental parameters (e.g., pollution levels and meteorological data) and hospitalizations, morbidity, and mortality has become possible. This study aimed to assess the association of air pollution and hospitalizations using a large database comprising almost all hospitalizations in Poland. This time-series analysis has been conducted in five cities in Poland (Warsaw, Białystok, Bielsko-Biała, Kraków, Gdańsk) over a period of almost 4 years (2014–2017, 1255 days), covering more than 20 million of hospitalizations. The hospitalizations have been extracted from the National Health Fund registries as daily summaries. Correlation analysis and distributed lag nonlinear models have been used to investigate for statistically relevant associations of air pollutants on hospitalizations, trying by various methods to minimize potential bias from atmospheric parameters, days of the week, bank holidays, etc. A statistically significant increase of respiratory disease hospitalizations has been detected after peaks of particulate matter concentrations (particularly PM2.5, between 0.9 and 4.5% increase per 10 units of pollutant increase, and PM10, between 0.9 and 3.5% per 10 units of pollutant increase), with a typical time lag between the pollutant peak and the event of 2 to 6 days. For other pollution parameters and other types of hospitalizations (e.g., cardiovascular events, eye and skin diseases, etc.), a weaker and ununiform correlations were recorded. Ambient air pollution exposure increases are associated with a short-term increase of hospitalizations due to respiratory tract diseases. The most prominent effect was recorded with the correlation of PM2.5 and PM10. There is only weak evidence indicating that such short-term associations exist between peaks of air pollution concentrations and increased hospitalizations for other (e.g., cardiovascular) diseases. The obtained information could be used to better predict hospitalization patterns and costs for the healthcare system and perhaps trigger additional vigilance on particulate matter pollution in the cities.

Keywords

Air pollution Respiratory health Hospital admissions Multi-city time-series analysis Particulate matter 

Abbreviations

COPD

Chronic obstructive pulmonary disease

DLNM

Distributed lag nonlinear model

ICD

International Classification of Diseases

NHF

National Healthcare Fund

PM

Particulate matter

PM2,5

Particulate matter 2.5 μm or less in diameter, generally described as fine particles

PM10

Particulate matter 10 μm or less in diameter

SD

Standard deviation

Introduction

Ambient air pollution is recognized to adversely affect health (Arbex et al. 2012). Several studies conducted in almost all parts of the world have found that day-to-day increases in pollution levels are associated with different pathologies, respiratory tract disease (Kim et al. 2018; Cerezo Hernández et al. 2018), asthma (Zheng et al. 2015), increased COPD exacerbations (Moore et al. 2016), cardiovascular (Analitis et al. 2006) and cerebrovascular diseases—stroke (Tian et al. 2017), etc. Several theories on the pathogenesis of these effects of ambient pollution have been put forward (Bernstein et al. 2004), but overall, the area remains poorly understood, and there is no consensus on which constituents (Kampa and Castanas 2008) of air pollution are most harmful (Brunekreef and Holgate 2002). The large Polish (Nabrdalic and Samora 2018) cities and in general Eastern European cities are recognized to have poorer air quality relative to other cities in Europe (Katsouyanni et al. 1996; Zmirou et al. 1998). Higher pollution levels in Polish cities are caused, in part, by sources that include coal-powered electricity generating stations and heating sources. Despite the recognition that Polish cities have perhaps poorer air quality than other Western European cities, up to date only few large-scale statistical analysis have been performed (Pac et al. 2013; Haluszka et al. 1998; Niepsuj et al. 1998) on the potential impact associations between day-to-day fluctuations in air pollution levels and hospitalizations. At this time, we know of no study that analyses data from more than 20 million hospitalizations and ED visits (ED visits which turn into hospitalizations and/or ED visits which require a hospital diagnostic or specialized medical visit or intervention are logged into the database with an ICD-10 classification and therefore will be reported as “hospitalizations”—these data will include the specialized ambulatory care) in the study area. The goal of this analysis was to investigate the association between different air pollutants and hospitalizations in a multi-city time-series observation, considering the potential influence of key meteorological parameters. This data analysis has been possible thanks to the access to the electronic registry of the Polish National Healthcare fund, to the data of the Institute of Meteorology and Water Management and Chief Inspectorate of Environmental Policy.

Methods

Core data source

The data related to the number of hospitalizations in the cities of Warsaw, Białystok, Bielsko-Biała, Kraków, and Gdańsk were obtained from the reporting system of the NHF (in Polish: Narodowy Fundusz Zdrowia) and covered a period of almost 4 years (2014–2017, 1255 days). The International Classification of Diseases 10th (ICD-10) revision coding was used to identify the different diagnoses at admission (the following ICD-10 categories were considered: F, mental and behavioral disorders; G, diseases of the nervous system; H, dis. of the eye, adnexa, of the ear mastoid process; I, diseases of the circulatory system; J, diseases of the respiratory system; L, diseases of the skin and subcutaneous tissue; S/T, injury, poisoning, and other cons. of ext. causes) for the period between January 1, 2014, and August 1, 2017.

Data on the concentration of air pollution were obtained from the Chief Inspectorate for Environmental Protection (GIOS) and included NO, NOx, NO2, O3, SO2, PM2.5, PM10, PM10_24, and PM2.5_24. Daily (obtained from manual stations) and hourly data (obtained from automatic stations, coded as 24) have been used in the analysis. Meteorological data have been gathered from the Institute of Meteorology and Water Management (IMGW) that have beacons in the Polish cities and included temperature, main wind speed, and precipitations.

Sample preparation

In this time-series analysis, to account for the great data variability encountered on the different week days (Faryar 2013; De Pablo Dávila et al. 2013; Sun et al. 2009; Tai et al. 2006), we normalized the data sample per week day, season, and bank holidays, calculating a ratio of observed number of patients by mean number of patients in the particular day of the week, or holiday. In addition, and specifically for the analysis explained in the “Cardiovascular and respiratory test” section below, 7-day averages for weather and air pollution data have been used and holiday periods and bank holidays have been omitted from the sample.

From a preliminary data correlation analysis, it was evident, that at least for some ICD-10 categories (mainly ICD-10 = J, respiratory diseases), a strong correlation between temperature (Chan et al. 2013) and hospitalizations was present. To account for this fact, we normalized the data set also by temperature.

No further significant correlations with other meteorological values—windspeed, precipitations, pressure, and humidity—has been found (Zhang et al. 2014), and therefore, no further normalizations have been added to the dataset.

Correlation analysis and DLNM

On such normalized data set, the hypothesized association between air pollution and number of hospitalizations was analyzed using at first a simple correlation analysis. As the association between air pollution and respiratory illness may be delayed in time (Zhang et al. 2018; Sinclair and Tolsma 2004), a potential lag effect from 0 to 10 days has been taken into consideration (Taj et al. 2017; Lall et al. 2011). To further explore the lag effect, on the data that showed bigger potential association, the correlation analyses was combined with a distributed lag nonlinear model (DLNM) (Gasparrini et al. 2010, 2012). The lag cumulative effect was considered over all lags from 0 to 10 days. The chosen method was the Almon method (Almon 1965), which can handle DLNM (Almon lag model 2018), is largely used, and for which several open softwares are available. The distributed lag analysis has been performed in Statistica software using the Almon lag model (Statistica 2018).

The model can be shortly written as

\( y(t)=\sum \limits_{i=\phi}^k{\beta}_{{}_ix\left(t-i\right)+\varepsilon (t)} \)

where the xi predictor variables of y used in the model represent observations made periodically during a continuous time period beginning at some time before y was observed and ending at the time of observation of y. Models of this kind are known as distributed lag models and are useful when changes in the independent variable x have an effect on the value of y over many samples of y. Typically, in this bivariate distributed lag model, if x and y are observed at identical periods at the same frequency, t, bivariate observations will be made of y(t) and x(t). The percentage of number of patients’ increase was calculated based on the results of multiple regression, where response variable was number of patients (normalized by the day) and independent variables were pollution level and temperature. The increase of number of patients was estimated using coefficient of regression (slope) for pollution level multiplied by 10 (number of units of pollutants).

Cardiovascular and respiratory test

A subset of data has been selected (cardiovascular diseases and respiratory diseases—ICD-10: I10–I15, I20–I24, I26, I40, I41, I44–I49, I50, I60–I68, I74, I80–I82, J00–J46) to focus the analysis on both a broader dataset first, and narrowing data next with a higher probability of association, as well as to test the sensitivity of the results. The pollutant (PM2.5 and PM10) levels and data on weather conditions were computed as a 7-day moving average. Furthermore, data points of Saturday, Sunday, and holidays were omitted. Regression analysis was further performed using the following variables, separately for each city:
  1. (a)

    Logarithm values from the average of the last 7 days for particulate matter concentrations PM10 and PM2.5—where PM logarithm: y = (/100)% x)

     
  2. (b)

    Average of the last 7 days for weather data (temperature—n °C), maximum wind speed (in 10 m/s), humidity (in %), pressure (in hPa), and sum of precipitation (in 10 mm)

     
  3. (c)

    The values of average squares from the last 7 days for weather variables

     
  4. (d)

    Zero variables for days of the week

     

Results

Descriptive statistics of the study setting

The hospitalizations statistics per ICD category are displayed in Table 1.
Table 1

Mean hospitalizations per day per ICD-10

Mean visits per day

Hospitalization diagnosis (ICD-10)

 

F (mean)

SD

G (mean)

SD

H (mean)

SD

I (mean)

SD

J (mean)

SD

L (mean)

SD

S (mean)

SD

T (mean)

SD

Białystok

315.8

216.9

68.7

42.6

50.8

29.9

548.9

367.9

940.7

687.7

66.3

43.2

207.4

105.6

3.6

2.3

Bielsko-Biała

143.5

98.4

38.0

25.1

24.3

13.9

345.0

231.1

499.8

380.4

47.3

31.3

114.4

56.2

3.3

1.29

Gdańsk

456.2

296.3

100.0

65.9

59.0

28.1

953.0

636.1

1282.0

862.1

92.5

59.7

278.7

137.6

3.6

2.16

Kraków

733.7

495.8

163.2

110.8

87.5

48.6

1432.5

969.4

2160.4

1491.0

130.6

83.4

389.0

182.7

0.0

0.0

Warszawa

1852.8

986.6

269.3

169.3

230.1

127.9

3517.8

2335.9

3539.3

2317.4

239.7

148.1

1034.8

480.8

17.2

7.4

A proportionally large variability due to seasonality and day of the week was clearly observed (large SDs).

The air pollutant statistics are displayed in Table 2. The cities that displayed the highest pollution index were Krakow (mean NO2 65.34 ppb, PM2.5 68.38 μg/m3, PM10 89.85 μg/m3) and Warsaw (mean NO2 61.82 ppb, PM2.5 38.09 μg/m3, PM10 57.15 μg/m3). For several cities, the air pollutant values have often crossed significantly the EU guideline “Air Quality Standards” level, and in some cases (particulate matter and NO2), the mean value during the study period was already above such limits (Air Quality Standards 2008; Dąbrowiecki et al. 2018).
Table 2

Descriptive statistics of air pollution

 

Gdańska

 

Białystoka

 

Bielsko-Białaa

 

Krakówb

 

Warszawac

 

AQS

Variable

Mean

Data range (from-to)

Mean

Data range (from-to)

Mean

Data range (from-to)

Mean

Data range (from-to)

Mean

Data range (from-to)

 

NO (ppb)

21.61

0.82–223.73

9.48

0.1–173.01

22.08

0–325.47

365.42

20.54–906.76

67.73

3.52–401.22

 

NOx (ppb)

74.78

6.54–655.63

36.58

1.39–314.2

73.12

0–611.14

247.86

20.00–1506.31

158.04

16.23–759.58

 

NO2 (ppb)

32.94

4.72–102.78

24.98

2.01–93.6

42.40

0–141.02

65.34

13.00–160.15

61.82

9.57–177.68

50

O3 (ppb)

66.90

3.6–146.28

73.68

0–174

76.28

0–180.92

68.35

0.00–187.74

67.03

1.83–178.94

120

SO2 (ppb)

10.68

1.4–420.99

6.35

0.97–58.14

17.69

0–140.19

9.97

1.31–68.00

13.00

0.98–118.40

125

PM2.5 (mg/m3)

24.35

0–126

36.64

0–261

0.00

0–0

68.38

10.55–448.12

38.09

5.45–165.58

25

PM10 (mg/m3)

63.65

3.24–859.25

54.01

0–402.6

76.65

0–462.37

89.85

14.82–522.92

57.15

9.92–277.56

40

PM10_24 (mg/m3)

26.60

0–145

21.82

0–129.7

37.09

0–319.7

33.86

0.00–279.30

23.23

4.54–128.10

25

PM2.5_24 (mg/m3)

14.41

0–104

20.14

0–134.17

29.80

0–280.3

47.64

0.00–333.80

33.22

0.00–151.80

40

AQS Air Quality Standards for the EU

aOne thousand two hundred fifty-five measure days (except PM2.5_24 and PM10_24 1216 days)

bOne thousand twelve measure days (except PM2.5_24 and PM10_24 990 days)

cOne thousand forty-seven measure days (except PM2.5_24 and PM10_24 1016 days)

In Figs. 1 and 2, the hospitalization statistics and the air pollution values are graphically displayed for the largest city, Warsaw. It has been chosen as a representative city because of the highest number of inhabitants and highest pollution grade.
Fig. 1

Mean weekly pollutant concentrations in Warsaw. SDs are presented as error bars

Fig. 2

Mean number of patients per day in Warsaw based on weekly data for various types of ICD-10 categories. SDs are presented as error bars

The meteorological statistics are displayed in Table 3. The values are coherent with a continental climate region with relatively little wind—in comparison with other climates, e.g., Mediterranean—so with a limited chance for the weather to dilute the air pollutants.
Table 3

Meteorological values statistics

 

Daily data

Mean

SD

Min

P25

Median

P75

Białystok

Temperature °C

7.7

8.2

− 17.9

1.8

6.8

14.4

Main wind speed (km/h)

9

3.4

2

6.7

8.3

10.6

Precipitation (mm/month)

65.3

394.4

0

0

0

2

Bielsko-Biała

Temperature °C

9.4

7.8

− 18.1

3.6

9.2

15.5

Main wind speed (km/h)

9.6

5.6

2.4

5.9

7.6

11.3

Precipitation (mm/month)

51.5

347.9

0

0

0

2.8

Gdańsk

Temperature °C

8.9

6.7

− 9.6

3.8

7.9

14.8

Main wind speed (km/h)

14.2

5.3

3.9

10.6

13.1

17

Precipitation (mm/month)

46.2

333.5

0

0

0

1.5

Kraków

Temperature °C

9.2

8

− 19.8

3.4

8.5

15.6

Main wind speed (km/h)

10.8

5.9

0.9

6.7

9.4

13.5

Precipitation (mm/month)

52.8

355

0

0

0

1.8

Warszawa

Temperature °C

9.5

8.4

− 15.6

3.6

8.5

16.2

Main wind speed (km/h)

13

5.2

3.7

9.3

12.2

15.9

Precipitation (mm/month)

54.7

361.9

0

0

0

1.5

Relationship of meteorological values with hospitalizations

To evaluate the relationships between weather variables and the number of hospitalized patients, we calculated the corresponding correlation coefficients. Through this, we evaluated the strength of relationships and the direction (negative or positive) of their influence. Squared values of correlation coefficients (R2) in case of temperature were recorded between 35 and 50% (depending on the ICD-10 category), for wind speed 1–3% and for precipitation only about 1%. As an example, the correlation coefficients for all the measured meteorological variables for ICD-1 = J (i.e., respiratory diseases) are reported in Table 4.
Table 4

Correlation coefficients between weather variables and number of patients ICD-10 = J (normalized by day of the week)

City

Temperature °C

Wind speed km/h

Precipitation (mm)

Białystok

− 0.6247

0.1724

0.0589

 

p = 0.000

p = .000

p = 0.037

Bielsko-Biała

− 0.6010

0.1790

− 0.0625

 

p = 0.000

p = 0.000

p = 0.027

Gdańsk

− 0.7198

0.1134

− 0.0950

 

p = 0.000

p = 0.000

p = 0.001

Kraków

− 0.7124

0.1044

− 0.0229

 

p = 0.000

p = 0.000

p = 0.418

Warszawa

− 0.7114

0.1102

0.1195

 

p = 0.000

p = 0.000

p = 0.000

Relationship of different pollutants and effects on hospitalizations

For each pollutant and for each city, a correlation table was generated plotting the ICD-10 hospitalization diagnosis versus the lag (days from 0 to 10). The highest absolute value of the correlation coefficients recorded by this methodology is reported in Table 5: the highlighted values represent the highest 25th percentile. The highest recorded correlation is clearly identified in the ICD-10 J column (i.e., respiratory diseases).
Table 5

Correlation coefficients between pollutants and patients hospitalized in the different ICD-10 categories

  

Hospitalization ICD10 categories

Pollutant

City

F

G

H

I

J

L

S

T

  NOx

Krakow

0.039

0.067

0.058

0.078*

0.191**

0.071

0.071

0.000

Gdansk

0.032

0.076*

0.097*

0.109*

0.115*

0.076*

0.040

0.040

Bielsko Biala

0.046

0.032

0.058

0.057

0.176**

0.054

0.081*

0.070

Bialystok

0.028

0.087*

0.066

0.044

0.158**

0.068

0.063

0.052

Warsaw

0.085*

0.092*

0.038

0.037

0.180**

0.079*

0.093*

0.058

  NO

Krakow

0.057

0.085*

0.032

0.076*

0.221**

0.053

0.084*

0.000

Gdansk

0.057

0.054

0.085*

0.096*

0.147*

0.093*

0.066

0.063

Bielsko Biala

0.042

0.029

0.077*

0.025

0.145*

0.053

0.082*

0.063

Bialystok

0.023

0.098*

0.042

0.054

0.120*

0.045

0.065

0.048

Warsaw

0.069

0.112*

0.036

0.054

0.170**

0.060

0.095*

0.057

  NO2

Krakow

0.041

0.054

0.071

0.108*

0.228**

0.072

0.155**

0.000

Bielsko Biala

0.033

0.052

0.047

0.053

0.232**

0.060

0.118*

0.052

Bialystok

0.044

0.081*

0.064

0.089*

0.148*

0.055

0.060

0.074*

Warsaw

0.076*

0.076*

0.045

0.031

0.129*

0.067

0.135*

0.066

  SO2

Krakow

0.024

0.028

0.068

0.109*

0.202**

0.094

0.126*

0.000

Gdansk

0.050

0.045

0.093*

0.053

0.033

0.066

0.082*

0.063

Bielsko Biala

0.053

0.039

0.048

0.078*

0.228**

0.073*

0.080*

0.056

Bialystok

0.026

0.044

0.033

0.085*

0.208**

0.047

0.058

0.055

Warsaw

0.065

0.104

0.075*

0.110*

0.168**

0.078*

0.065

0.082*

  PM2.5

Krakow

0.035

0.025

0.065

0.062

0.175**

0.060

0.095*

0.000

Gdansk

0.080*

0.054

0.047

0.099*

0.220**

0.070

0.074*

0.042

Bialystok

0.042

0.053

0.061

0.081*

0.245**

0.051

0.060

0.048

Warsaw

0.062

0.067

0.060

0.042

0.279**

0.047

0.084*

0.098*

  PM10

Krakow

0.042

0.026

0.065

0.033

0.209**

0.051

0.085*

0.000

Gdansk

0.079*

0.076*

0.099

0.116

0.055

0.044

0.058

0.041

Bielsko Biala

0.042

0.047

0.072

0.041

0.214**

0.067

0.109*

0.050

Bialystok

0.045

0.045

0.075*

0.074*

0.178**

0.040

0.062

0.048

Warsaw

0.082

0.054

0.049

0.021

0.233**

0.062

0.106*

0.092*

  PM2.5 24 h

Krakow

0.038

0.036

0.047

0.047

0.163**

0.048

0.081*

0.000

Gdansk

0.050

0.084*

0.047

0.053

0.185**

0.083*

0.049

0.043

Bielsko Biala

0.067

0.039

0.081*

0.046

0.162**

0.054

0.111*

0.070

Bialystok

0.016

0.066

0.050

0.071

0.248**

0.020

0.101*

0.053

Warsaw

0.025

0.064

0.061

0.033

0.276**

0.049

0.102*

0.079*

  PM10 24 h

Krakow

0.034

0.027

0.054

0.024

0.189**

0.045

0.101*

0.000

Gdansk

0.063

0.106*

0.040

0.077*

0.202**

0.085*

0.038

0.033

Bielsko Biala

0.051

0.019

0.070

0.042

0.160**

0.051

0.120*

0.058

Bialystok

0.080*

0.075*

0.038

0.097*

0.166**

0.034

0.060

0.031

Warsaw

0.081*

0.022

0.055

0.036

0.265**

0.049

0.119*

0.071

  O3

Bielsko Biala

0.075*

0.066

0.080*

0.059

0.145*

0.087*

0.051

0.044

Bialystok

0.038

0.045

0.056

0.047

0.130*

0.108*

0.080*

0.058

Warsaw

0.031

0.016

0.105*

0.019

0.139*

0.169**

0.131*

0.052

25th percentile

0.035

0.039

0.047

0.042

0.148

0.049

0.063

0.040

*Significant correlation at 0.01 significance level; **Highlighted values represent the strongest (and significant) correlation coefficient > 0.15

Respiratory disease hospitalizations

The deeper analysis using the Almon model algorithm has been applied to the respiratory disease sub-data, and the P values for the distributed lag model (Almond method) were plotted, where the pollutant was the independent variable (cause) and the number of events (hospitalizations) was the dependent variable (always with lags from 0 to 10 days). The results of the highest recorded correlation/lag day, as well as the calculated coefficient of % increase per each 10 units of increased pollutant at the specific identified lag are displayed in Table 6.
Table 6

Percent increase of hospital admissions for respiratory disease/lag (days)

 

Gdańsk

Białystok

Bielsko-Biała

Kraków

Warszawa

All citiesa

Variable

%

Lag (days)

%

Lag (days)

%

Lag (days)

%

Lag (days)

%

Lag (days)

%

NO (ppb)

1.0

6

1.9

4

1.4

6

0.2

2

0.8

6

0.3

NOx (ppb)

0.30%

3

1.3

4

0.7

5–6

0.3

2

0.5

5–6

0.4

NO2 (ppb)

A

 

2.9*

4

3.5

4

2.6

1–2

1.3*

4

2.4

O3 (ppb)

A

 

− 1.7*

9

− 2.1*

10

A

 

− 1.6*

9

− 1.2

SO2 (ppb)

0.3*

3

12.7

0

5.4

5–7

7.5*

3

3

8

1.6

PM2.5 (mg/m3)

3.1

5

2.4

5–6

A

 

0.8*

2

3.4

7

1.3

PM10 (mg/m3)

0.1*

3

1

5

1.1

6

0.9

3

1.6

7

0.6

PM10_24 (mg/m3)

3.1

7

2.8

5–6

1.7

5–6

1.4

3

3.5

7

1.9

PM2.5_24 (mg/m3)

3.6

7

4.5

6–7

1.9

5

1.4

4

4.5

7

2.3

All p values are below 0.000 with exception Gdańsk values of SO2 (p value 0,237) and PM10 (p value 0.054)

% % increase of hospitalizations per each 10 additional pollutant units, Lag days intersection of the lowest P value from Almon model and the strongest correlation coefficient, A measurements not available

*Highlighted values represent a lower correlation coefficient (as seen in Table 5)

aThe results for all cities together were calculated using multiple linear regression were city was treated as dummy variable

Several pollutants show a statistically significant and correlated increase in hospitalizations, with the largest effect (as well as consistent among the different cities) being the one of the particulate matter, PM2.5 and PM10.

Subset analysis on cardiovascular disease and respiratory disease hospitalizations

The results of the subset data analysis on cardiovascular disease and respiratory tract disease with the 7-day average pollutant values and the method described in “Cardiovascular and respiratory test” section have provided a similar result, displayed in Table 7. In Fig. 3, the sample plot of the respiratory patients in Warsaw (ICD-10=J) versus the 7 day particulate matter concentration average (linear and logarithm) is being displayed.
Table 7

Percent increase in patients per each 10-unit increase in pollutant concentration

  

Białystok

Bielsko-Biała

Gdańsk

Kraków

Warszawa

All citiesa,b

 

Pollutant

%

N

%

N

%

N

%

N

%

N

%a

N b

Respiratory

PM10

1.70%

25

2.30%

18.3

1.40%

26.8

1.40%

46.3

2.10%

111.7

1.78%

228.1

Disease

PM2.5

1.60%

23.2

1.27%

9.9

0.94%

17.6

1.08%

35.3

2.00%

106

1.51%

192

Cardiovascular

PM10

0.70%

5.7

0.70%

3.5

0.50%

7.3

0.50%

9.7

0.01%

0.3

0.27%

26.5

Disease

PM2.5

0.90%

6.9

0.40%

1.9

0.20%

3

0.10%

2.9

0.44%

22.9

0.37%

37.6

% increase of the percent of patients/day per each 10 units of increase in pollutant concentration, N number of additional patients increase per day per each 10 units of pollutant concentration increase

aIncrease of the patients per day for all cities was calculated as weighted average where the weight was the number of patients per total study period

bNumber of additional patients increase per day for all cities was calculated as the sum of all increases/day

For all examined cities, the impact of changes in the average PM10 concentration level from the last 7 days on hospital admissions due to respiratory diseases is statistically significant at the significance level of 0.01. With 10% increase in PM10 concentration from the mean, for example, the number of patients increases on average by 25 patients (1.7% of the average number patients) in Bialystok, 26.8 (1.4% of mean) in Gdansk, 46 (1.4%) in Krakow, 18 patients (2.3% of average) in Bielsko-Biała, and 111.7 people (2.1%) in Warsaw. The average PM10 concentration from the previous 7 days had a statistically significant impact on hospital admissions due to cardiovascular system diseases for all cities studied except of Warsaw. Although statistically significant, correlations were weak. Similar dependencies apply to the model for PM2.5 and cardiovascular disease. The biggest effect of the increase in concentration particulate matter by 10% is an increase of 0.9% in the number of patients in Bialystok
Fig. 3

Relationships between the number of patients with respiratory diseases (J) and PM particulate matter concentration level in Warsaw (logarithm of 7-day average)

Discussion

Key results

In this analysis, we have found positive associations between ambient levels of pollutants (mainly PM2.5 and PM10) and hospitalizations. A positive association between air pollution and acute respiratory disease health impact/hospitalization was to be expected (Zhang et al. 2018; Sinclair and Tolsma 2004, Sinclair et al. 2010; Vahedian et al. 2017). The pollution levels at which these results were recorded, even though on a proportionally high pollution range (Air Quality Standards; Directive 2008; WHO Air Quality 2005; WHO 2013), were still not in a “critical” range similar to the London smog 1952 or the Asian smoke-haze event of 1997 (Bell and Davis 2001and Bell et al. 2004, Heil and Goldammer 2001) (see Table 2).

The peak effect on hospitalizations increase has been found with a time lag of 3–6 (sometimes up to 7) days (see Table 6). Such lag effect for respiratory disease hospitalizations show similarities with earlier findings (Sinclair and Tolsma 2004; Sinclair et al. 2010; de Souza et al. 2014).

Several mechanisms have been suggested (Esposito,  Tenconi et al., 2014) to explain the adverse effects of air pollutants. The most consistent and most widely accepted explanation (Chauhan and Johnston 2003; Arbex et al. 2012) is that, once in contact with the respiratory epithelium, high concentrations of oxidants and pro-oxidants in environmental pollutants such as PM of various sizes and compositions and in gases cause the formation of oxygen and nitrogen-free radicals, which in turn induce oxidative stress in the airways. In other words, an increase in free radicals that are not neutralized by antioxidant defenses initiate an inflammatory response with release of inflammatory cells and mediators (cytokines, chemokines, and adhesion molecules) that reach the systemic circulation, leading to subclinical inflammation, which not only has a negative effect on the respiratory system but also causes systemic effects. These processes may take a discrete amount of days to lead to clinically relevant symptoms that require, due to their severity, medical attention and/or hospitalization.

A more limited correlated and statistically significant association has been found between the different pollutant levels and cardiovascular disease (CVD). Several years ago, in a study covering some Eastern European cities, on a smaller sample case (Katsouyanni et al. 1997) and looking at mortality rate, and not on hospitalization rates, a somehow similarly trending result has been reported (Samoli et al. 2001).

To some extent, the result of poorer correlation with CVD could be explained first in a high preexisting baseline, i.e., the underlying relatively high prevalence of cardiovascular disease which shows a relatively high baseline rate of CVD hospitalizations (Szafraniec-Burylo et al. 2016), rendering the peaks which might be generated by the excess exposure to pollutants less visible. Another possible input in the study results is the source of the pollutant. In general, the “smoke” pollution in Poland is particularly influenced by a high degree of utilization of charcoal as heating (Nabrdalic and Samora 2018). In all the European Union, 80% of private homes using coal are in Poland. Scientific debate is currently ongoing (Hime et al. 2018) on the health effects on particulate pollution depending on the source of pollutant. The charcoal burning fumes would contain PM particles with a higher SOx-bound component than, for example, diesel exhaust and might therefore have a greater influence on the respiratory tract (sulfur oxides are toxic urticants). In addition, other bias factors such as underlying morbidity, age, etc., could have had an impact to the results.

Study limitations

Limitation of this study are represented by the missing stratification of the hospitalizations between age groups and gender, which could help identifying clearer trends and additional subanalysis could have been made. In addition, as the data source consisted of aggregated daily records of hospitalizations, the statistical significance of the results is weaker. As a last point, while the temperature effect on hospitalization (e.g., flu season related hospitalizations) has been tackled normalizing the sample data (as described in the “Sample preparation” section), such normalization does not totally separate the potential cause/effect or combined effect of pollution/season from the results.

Generalizability and further analysis

The overall number of hospitalizations captured by the analysis (over 20 million) is large compared to literature (Moore et al. 2016; Zhang et al. 2018; Stieb et al. 2009), in particular in Europe, and the results per se could therefore be interesting to also help predict hospitalization trends and quantify the needs for environmental preventive measures to help minimize the healthcare impact and costs associated with such events. It is important to note, however, that the aggregated daily records’ data source do constitute a limitation to the strength of the statistical analysis.

Also for this reason, on the same dataset, further analysis could be performed using different statistical methodologies, e.g., a case-crossover data setup (Lu and Zeger 2007) or artificial neural networks (Fang 2018; Polezer et al. 2018) to further test the sensitivity of these results.

Conclusions

Ambient air pollution exposure increases were associated with an increase of hospitalizations due to respiratory tract diseases in a large time-series observation in five major polish cities in the years 2014–2017. The most prominent effect was recorded with the correlation of PM2.5 and PM10. There was weak evidence of short-term associations between peaks of air pollution concentrations and increased hospitalizations for cardiovascular diseases. A further work on a dataset enabling a better stratification of the sample (e.g., age, gender, and likewise a detailed admission analysis, e.g., sub-stratum for COPD, lower respiratory tract infection, asthma, etc.) would provide a better insight on the subject matter.

Notes

Acknowledgements

This research has been carried out thanks to the hospitalization data provided by the Polish NHF (in Polish: Narodowy Fundusz Zdrowia). The concentrations of air pollutants were obtained from the Chief Inspectorate for Environmental Protection (GIOS) and the Meteorological data have been provided by the Institute of Meteorology and Water Management (IMGW).

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

References

  1. Air Quality Standards; Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe; https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:152:0001:0044:EN:PDF; (Accessed: 5.12.2018)
  2. Almon lag model (2018) https://www.le.ac.uk/users/dsgp1/COURSES/TOPICS/Almonlag.pdf (Accessed 18.2.2018)
  3. Analitis A, Katsouyanni K, Dimakopoulou K, Samoli E, Nikoloulopoulos AK, Petasakis Y, Touloumi G, Schwartz J, Anderson HR, Cambra K, Forastiere F, Zmirou D, Vonk JM, Clancy L, Kriz B, Bobvos J, Pekkanen J (2006) Short-term effects of ambient particles on cardiovascular and respiratory mortality. Epidemiology. 17(2):230–233.  https://doi.org/10.1097/01.ede.0000199439.57655.6b CrossRefGoogle Scholar
  4. Arbex MA, de Paula Santos U, Martins LC, Saldiva PH, Pereira LA, Braga AL (2012) Air pollution and the respiratory system. J Bras Pneumol 38(5):643–655.  https://doi.org/10.1590/S1806-37132012000500015 CrossRefGoogle Scholar
  5. Bell ML, Davis DL, Fletcher T (2004) A retrospective assessment of mortality from the London smog episode of 1952: the role of influenza and pollution. Environ Health Perspect 112(1):6–8.  https://doi.org/10.1289/ehp.6539 CrossRefGoogle Scholar
  6. Bell ML, Davis DL (2001) Reassessment of the lethal London fog of 1952: Novel indicators of acute and chronic consequences of acute exposure to air pollution. Environ Health Perspect 109(Suppl 3):389–394.  https://doi.org/10.1289/ehp.01109s3389 CrossRefGoogle Scholar
  7. Bernstein JA, Alexis N, Barnes C, Bernstein IL, Bernstein JA, Nel A, Peden D, Diaz-Sanchez D, Tarlo SM, Williams PB (2004) Health effects of air pollution. J Allergy Clin Immunol 114(5):1116–1123.  https://doi.org/10.1016/j.jaci.2004.08.030 CrossRefGoogle Scholar
  8. Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet. 360(9341):1233–1242.  https://doi.org/10.1016/S0140-6736(02)11274-8 CrossRefGoogle Scholar
  9. Cerezo Hernández A, Ruiz Albi T, Crespo Sedano A, Álvarez González D, López Izquierdo R, Gómez García A, Fernández NA, López Represa C, del Campo Matías F (2018) Influence of air pollution on the number of hospital admissions in a pneumology service. Eur Respir J 52:PA5076.  https://doi.org/10.1183/13993003.congress-2018.PA5076 Google Scholar
  10. Chan EY, Goggins WB, Yue JS, Lee P (2013) Hospital admissions as a function of temperature, other weather phenomena and pollution levels in an urban setting in China. Bull World Health Organ 91(8):576–584.  https://doi.org/10.2471/BLT.12.113035 CrossRefGoogle Scholar
  11. Chauhan AJ, Johnston SL (2003) Air pollution ; and infection in respiratory illness. Br Med Bull 68(1):95–112.  https://doi.org/10.1093/bmb/ldg022 CrossRefGoogle Scholar
  12. Dąbrowiecki P, Czechowski PO, Owczarek T, Chciałowski A, Badyda A (2018) Respiratory diseases admissions due to the smog episode in Warsaw in January 2017. Eur Respir J 52:PA4491.  https://doi.org/10.1183/13993003.congress-2018.PA4491 Google Scholar
  13. De Pablo Dávila F, Soriano Rivas L, Sánchez Llorente JM (2013) Effects of weather types on hospital admissions for respiratory diseases in Castilla-La Mancha Spain. Atmósfera 26(1):95–107.  https://doi.org/10.1016/S0187-6236(13)71064-6 CrossRefGoogle Scholar
  14. de Souza A, Guo Y, Pavão HG, Fernandes WA (2014) Effects of air pollution on disease respiratory: structures lag. Health 6:1333–1339.  https://doi.org/10.4236/health.2014.612163 CrossRefGoogle Scholar
  15. Esposito S, Tenconi R, Lelii M, Preti V, Nazzari E, Consolo S, Patria MF (2014) Possible molecular mechanisms linking air pollution and asthma in children. BMC Pulm Med 14(31).  https://doi.org/10.1186/1471-2466-14-31
  16. Fang X (2018) From Institute of Environmental Medicine Karolinska Institutet Stockholm, Sweden, Use of novel statistical methods in assessing particulate air pollution and evaluating its association with mortality in China Stockholm – Thesis for PhD Degree; https://openarchive.ki.se/xmlui/handle/10616/46343, (Accessed 5.12.2018)
  17. Faryar KA (2013) The effects of weekday, season, federal holidays, and severe weather conditions on emergency department volume in Montgomery County, Ohio; Wright State University, Dayton, Ohio; https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=1094&context=mph; (Accessed on 5.12.2018)
  18. Gasparrini A, Armstrong B, Kenward MG (2010) Distributed lag non-linear models. Stat Med 29:2224–2234.  https://doi.org/10.1002/sim.3940 CrossRefGoogle Scholar
  19. Gasparrini A, Armstrong B, Kenward MG (2012) Multivariate meta-analysis for non-linear and other multi-parameter associations. Stat Med 31:3821–3839.  https://doi.org/10.1002/sim.5471 CrossRefGoogle Scholar
  20. Haluszka J, Pisiewicz K, Miczynski J, Roemer W, & Tomalak W. (1998) Air pollution and respiratory health in children: the PEACE panel study in Krakow, Poland; European Respiratory Review 8 52; - ISSN 0905-9180 - p. 94 - 100Google Scholar
  21. Heil A, Goldammer J (2001) Smoke-haze pollution: a review of the 1997 episode in Southeast Asia. J Reg Environ Chang 2:24–37.  https://doi.org/10.1007/s101130100021 CrossRefGoogle Scholar
  22. Hime NJ, Guy B, Marks GB, Cowie CT (2018) Review: a comparison of the health effects of ambient particulate matter air pollution from five emission sources. Int J Environ Res Public Health 15:1206.  https://doi.org/10.3390/ijerph15061206 www.mdpi.com/journal/ijerph CrossRefGoogle Scholar
  23. Kampa M, Castanas E (2008) Human health effects of air pollution. Environ Pollut 151(2):362–367.  https://doi.org/10.1016/j.envpol.2007.06.012 CrossRefGoogle Scholar
  24. Katsouyanni K, Touloumi G, Spix C, Schwartz J, Balducci F, Medina S, Rossi G, Wojtyniak B, Sunyer J, Bacharova L, Schouten JP, Ponka A, Anderson HR (1997) Short-term effects of ambient sulphur dioxide and particulate matter on mortality in 12 European cities: results from time series data from the APHEA project. Br Med J 314:1658–1663CrossRefGoogle Scholar
  25. Katsouyanni K, Schwartz J, Spix C, Touloumi G, Zmirou D, Zanobetti A, Wojtyniak B, Vonk JM, Tobias A, Pönkä A, Medina S, Bachárová L, Anderson HR (1996) Short term effects of air pollution on health: a European approach using epidemiologic time series data: the APHEA protocol. J Epidemiol Community Health 50(Suppl 1):S12–S18CrossRefGoogle Scholar
  26. Kim D, Chen Z, Zhou L-F, Huang S-X (2018) Air pollutants and early origins of respiratory diseases. Chronic Diseases and Translational Medicine 4:75e94–75e94.  https://doi.org/10.1016/j.cdtm.2018.03.003 CrossRefGoogle Scholar
  27. Lall R, Ito K, Thurston GD (2011) Distributed lag analyses of daily hospital admissions and source-apportioned fine particle air pollution. Environ Health Perspect 119(4):455–460.  https://doi.org/10.1289/ehp.1002638
  28. Lu Y, Zeger SL (2007) On the equivalence of case-crossover and time series methods in environmental epidemiology. Biostatistics 8(2):337–344.  https://doi.org/10.1093/biostatistics/kxl013 Advance Access publication on June 29, 2006CrossRefGoogle Scholar
  29. Moore E, Chatzidiakou L, Kuku M-O, Jones RL, Smeeth L, Beevers S, Kelly FJ, Barratt B, Quint JK (2016) Global associations between air pollutants and chronic obstructive pulmonary disease hospitalizations—a systematic review. Ann Am Thorac Soc 13(10):1814–1827.  https://doi.org/10.1513/AnnalsATS.201601-064OC Google Scholar
  30. Nabrdalic M, Samora M. (2018) Smotherd by smog, Polish cities rank among Europe’s dirtiest; Article on New York Times April 22, https://www.nytimes.com/2018/04/22/world/europe/poland-pollution.html; (Accessed 5.12.2018)
  31. Niepsuj G, Niepsuj K, Nieroda-Muller A, Rauer R, Krzywiecki A, Borowska M, Hlawiczka S, Brunekreef (1998) Air pollution and respiratory health of children: the PEACE panel study in Katowice, Poland. Eur Respir Rev 8(52):86–93 Ref ID: 480Google Scholar
  32. Polezer G, Tadano YS, Siqueira HV, Godoi AFL, Yamamoto CI, de André PA, Pauliquevis T, Andrade MF, Oliveira A, Saldiva PHN, Taylor PE, Godoi RHM (2018) Assessing the impact of PM2.5 on respiratory disease using artificial neural networks. Environ Pollut 235:394–403.  https://doi.org/10.1016/j.envpol.2017.12.111 CrossRefGoogle Scholar
  33. Pac R, Majewska P, Gorynski (2013) Asthma-related hospital morbidity in relation to air pollution in Malopolska region, Poland. Eur J Pub Health 23(suppl_1):ckt123.128.  https://doi.org/10.1093/eurpub/ckt123.128 Google Scholar
  34. Samoli E, Schwartz J, Wojtyniak B, Touloumi G, Spix C, Balducci F, Medina S, Rossi G, Sunyer J, Bacharova L, Anderson HR, Katsouyanni K (2001) Investigating regional differences in short-term effects of air pollution on daily mortality in the APHEA project: a sensitivity analysis for controlling long-term trends and seasonality. Environ Health Perspect 109(4):349–353.  https://doi.org/10.1289/ehp.01109349 CrossRefGoogle Scholar
  35. Sinclair AH, Tolsma D (2004) Associations and lags between air pollution and acute respiratory visits in an ambulatory care setting: 25-month results from the aerosol research and inhalation epidemiological study. J Air Waste Manage Assoc 54(9):1212–1218CrossRefGoogle Scholar
  36. Sinclair AH, Edgerton ES, Wyzga R, Tolsma D (2010) A two-time-period comparison of the effects of ambient air pollution on outpatient visits for acute respiratory illnesses. J Air Waste Manage Assoc 60:163–175.  https://doi.org/10.3155/1047-3289.60.2.163 CrossRefGoogle Scholar
  37. Stieb DM, Szyszkowicz M, Rowe BH, Leech JA (2009) Air pollution and emergency department visits for cardiac and respiratory conditions: a multi-city time-series analysis. Environ Health 10(8):25.  https://doi.org/10.1186/1476-069X-8-25 CrossRefGoogle Scholar
  38. Sun Y, Heng BH, Seow YT, Seow E (2009) Forecasting daily attendances at an emergency department to aid resource planning. BMC Emerg Med 9:1.  https://doi.org/10.1186/1471-227X-9-1. CrossRefGoogle Scholar
  39. Szafraniec-Burylo SI, Sliwczynski A, Tyszko P, Prusaczyk A, Zuk P, Foryszewska-Witan E, Prusaczyk AS, Guzek M, Wlodarczyk T, Orlewska E (2016) The implementation of integrated care for cardiovascular diseases in Poland. Int J Integr Care 16(6):A368.  https://doi.org/10.5334/ijic.2916 CrossRefGoogle Scholar
  40. Tai CC, Lee CC, Shih CL, Chen SC (2007) Effects of ambient temperature on volume, specialty composition and triage levels of emergency department visits. Emerg Med J 24:641–644 (2006).  https://doi.org/10.1136/emj.2006.045310 CrossRefGoogle Scholar
  41. Taj T, Malmqvist E, Stroh E, Oudin Åström D, Jakobsson K, Oudin A (2017) Short-term associations between air pollution concentrations and respiratory health-comparing primary health care visits, hospital admissions, and emergency department visits in a multi-municipality study. Int J Environ Res Public Health 14(6):E587.  https://doi.org/10.3390/ijerph14060587 CrossRefGoogle Scholar
  42. Tian Y, Xiang X, Wu Y, Cao Y, Song J, Sun K, Liu H, Hu Y (2017) Fine particulate air pollution and first hospital admissions for ischemic stroke in Beijing, China. Sci Rep 7(1):3897.  https://doi.org/10.1038/s41598-017-04312-5
  43. Vahedian M, Khanjani N, Mirzaee M, Koolivand A (2017) Associations of short-term exposure to air pollution with respiratory hospital admissions in Arak, Iran. J Environ Health Sci Eng 15:17.  https://doi.org/10.1186/s40201-017-0277-z CrossRefGoogle Scholar
  44. WHO Air Quality (2005) - WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide Global update; http://apps.who.int/iris/bitstream/handle/10665/69477/WHO_SDE_PHE_OEH_06.02_eng.pdf;jsessionid=125EFA27E84FCF18E7F0A27EDD531C8F?sequence=1 (Accessed 6.12.2018)
  45. WHO REVIHAAP - Review of evidence on health aspects of air pollution – REVIHAAP project: final technical report; WHO/Europe, 2013 http://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report.pdf, (Accessed 6.12.2018)
  46. Zhang Y, Peng L, Kan H, Xu J, Chen R, Liu Y, Wang W (2014) Effects of meteorological factors on daily hospital admissions for asthma in adults: a time-series analysis. PLoS One 9(7):e102475.  https://doi.org/10.1371/journal.pone.0102475 CrossRefGoogle Scholar
  47. Zhang YL, Zhang H, Yi JP, Zhang JJ, Dai XR et al (2018) Effect of air pollution on hospital admissions of respiratory, dermatological, ophthalmic diseases in a coastal city, China. Glob Environ Health Saf 2(1):2 http://www.imedpub.com/articles/effect-of-air-pollution-on-hospital-admissions-of-respiratory-dermatological-ophthalmic-diseases-in-a-coastal-city-china.pdf, (Accessed 5.12.2018)Google Scholar
  48. Zheng X-Y, Ding H, Jiang L-N, Chen S-W, Zheng J-P, Qiu M, Zhou Y-X, Chen Q, Guan W-J (2015) Association between air pollutants and asthma emergency room visits and hospital admissions in time series studies: a systematic review and meta-analysis. PLoS One 18:e0138146.  https://doi.org/10.1371/journal.pone.0138146 CrossRefGoogle Scholar
  49. Zmirou D, Schwartz J, Saez M, Zanobetti A, Wojtyniak B, Touloumi G, Spix C, Ponce de Leon A, Moullec Y, Bacharova L et al (1998) Time-series analysis of air pollution and cause specific mortality: a quantitative summary in Europe (APHEA study). Epidemiology 9:495–503CrossRefGoogle Scholar

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© The Author(s) 2019

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Alessandro Slama
    • 1
  • Andrzej Śliwczyński
    • 2
  • Jolanta Woźnica
    • 3
  • Maciej Zdrolik
    • 3
  • Bartłomiej Wiśnicki
    • 3
  • Jakub Kubajek
    • 3
  • Olga Turżańska-Wieczorek
    • 3
  • Dariusz Gozdowski
    • 4
  • Waldemar Wierzba
    • 2
  • Edward Franek
    • 1
    • 5
    Email author
  1. 1.Central Clinical Hospital MSWiA in WarsawWarsawPoland
  2. 2.University of Humanities and Economics in ŁodzSatellite Campus in WarsawWarsawPoland
  3. 3.Chancellery of the Prime Minister of PolandWarsawPoland
  4. 4.Warsaw University of Life SciencesWarsawPoland
  5. 5.Mossakowski Clinical Research Centre, Polish Academy of SciencesPawinskiego 5, 02-106 WarsawPoland

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