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Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009–2018) Cover

Short-term effects of air pollution on hospital admissions for cardiovascular diseases and diabetes mellitus in Sofia, Bulgaria (2009–2018)

Archives of Industrial Hygiene and Toxicology
Volume 74 (2023): Issue 1 (March 2023)
By: Angel M. Dzhambov,  Krasimira Dikova,  Tzveta Georgieva,  Teodor I. Panev,  Plamen Mukhtarov and  Reneta Dimitrova  
Open Access
|Apr 2023

Figures & Tables

Figure 1

Daily hospital admission counts for ischaemic heart disease (IHD), cerebral infarction (CI), and type 2 diabetes mellitus (T2DM) in Sofia, Bulgaria from 2009 to 2018
Daily hospital admission counts for ischaemic heart disease (IHD), cerebral infarction (CI), and type 2 diabetes mellitus (T2DM) in Sofia, Bulgaria from 2009 to 2018

Figure 2

Daily concentrations of air pollutants in Sofia, Bulgaria from 2009 to 2018. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Daily concentrations of air pollutants in Sofia, Bulgaria from 2009 to 2018. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Figure 3

Risk of hospital admissions for ischaemic heart disease associated with air pollution levels over seven days. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence intervals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Risk of hospital admissions for ischaemic heart disease associated with air pollution levels over seven days. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence intervals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Figure 4

Risk of hospital admissions for cerebral infarction associated with air pollution levels over seven days. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence intervals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Risk of hospital admissions for cerebral infarction associated with air pollution levels over seven days. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence intervals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Figure 5

Risk of hospital admissions for type 2 diabetes mellitus associated with air pollution levels over seven days. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Risk of hospital admissions for type 2 diabetes mellitus associated with air pollution levels over seven days. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Figure 6

Risk of hospital admissions for ischemic heart disease associated with air pollution levels over seven days stratified by gender and age. Legend: circles – male <65 yrs.; squares – female <65 yrs.; triangles – male ≥65 yrs.; diamonds – female ≥65 yrs. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Risk of hospital admissions for ischemic heart disease associated with air pollution levels over seven days stratified by gender and age. Legend: circles – male <65 yrs.; squares – female <65 yrs.; triangles – male ≥65 yrs.; diamonds – female ≥65 yrs. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Figure 7

Risk of hospital admissions for cerebral infarction associated with air pollution levels over seven days stratified by gender and age. Legend: circles – male <65 yrs.; squares – female <65 yrs.; triangles – male ≥65 yrs.; diamonds – female ≥65 yrs. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Risk of hospital admissions for cerebral infarction associated with air pollution levels over seven days stratified by gender and age. Legend: circles – male <65 yrs.; squares – female <65 yrs.; triangles – male ≥65 yrs.; diamonds – female ≥65 yrs. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Figure 8

Risk of hospital admissions for type 2 diabetes mellitus associated with air pollution levels over seven days stratified by gender and age. Legend: circles – male <65 yrs.; squares – female <65 yrs.; triangles – male ≥65 yrs.; diamonds – female ≥65 yrs. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide
Risk of hospital admissions for type 2 diabetes mellitus associated with air pollution levels over seven days stratified by gender and age. Legend: circles – male <65 yrs.; squares – female <65 yrs.; triangles – male ≥65 yrs.; diamonds – female ≥65 yrs. Lag models for the effects of pollutant concentrations include mutually adjusted 0–7 day lags, averaged over 3 and 7 days (lags 0–3 and 0–7), and the cumulative effect of lags 0–7 (net sum of lagged effects). All models are adjusted for time trend, day of the week, deciles of temperature, and relative humidity. Pollutants are tested one-at-a-time (single-pollutant models). Coefficients shown are incidence rate ratios (IRR) with 95 % confidence inter vals, where intervals not crossing the horizontal reference line indicate statistically significant estimates. CO – carbon monoxide; NO2 – nitrogen dioxide; O3 – ozone; PM10 – particulate matter ≤10 µm; PM2.5 – particulate matter ≤2.5 µm; SO2 – sulphur dioxide

Risk of hospital admissions associated with air pollution on the same day (lag0), stratified by time of year

PollutantApril — SeptemberOctober — March
IHD(IRR)Cl(IRR)T2DM(IRR)IHD(IRR)Cl(IRR)T2DM(IRR)
PM101.020 (1.000, 1.041)*1.004 (0.984, 1.024)0.999 (0.970, 1.029)0.995 (0.991, 0.999)*0.997 (0.995, 1.000)0.994 (0.990, 0.997)*
PM2.51.010 (0.983, 1.038)1.011 (0.981, 1.041)1.026 (0.980, 1.073)0.992 (0.986, 0.999)*0.997 (0.993, 1.001)0.993 (0.987, 0.999)*
SO20.979 (0.909, 1.055)1.056 (0.990, 1.126)1.001 (0.903, 1.111)0.988 (0.969, 1.008)0.982 (0.960, 1.004)0.964 (0.934, 0.996)*
NO21.038 (1.018,1.057)*1.022 (1.006,1.041)*1.026 (1.000,1.052)*0.996 (0.988, 1.003)0.996 (0.990, 1.003)0.997 (0.988, 1.006)
O30.984 (0.970, 0.997)*0.994 (0.982, 1.007)0.987 (0.968, 1.007)0.995 (0.983, 1.006)1.003 (0.991, 1.015)0.993 (0.976, 1.011)
CO1.071 (0.978, 1.175)0.978 (0.887, 1.078)1.010 (0.874, 1.169)0.964 (0.937, 0.992)*0.978 (0.961, 0.996)*0.969 (0.943, 0.996)*

Risk of hospital admissions associated with above-threshold air pollution levels on the same day (lag0)

PollutantIHD(IRR)CI(IRR)T2DM(IRR)
PM10 ≥45 μg/m31.007 (0.980, 1.034)1.000 (0.973, 1.028)0.991 (0.950, 1.034)
PM 5 ≥15 μg/m31.005 (0.982, 1.029)1.004 (0.979, 1.030)0.997 (0.959, 1.036)
SO ≥40 μg/m30.952 (0.858, 1.056)0.915 (0.796, 1.053)1.003 (0.827, 1.216)
NO ≥25 μg/m31.039 (1.013, 1.066)*1.019 (0.993, 1.045)1.029 (0.991, 1.069)
O ≥60 μg/m30.971 (0.935, 1.008)0.976 (0.941, 1.012)0.944 (0.893, 0.997)*
CO ≥4 mg/m30.929 (0.810, 1.064)0.995 (0.912, 1.085)0.875 (0.771, 0.994)*

Descriptive statistics for the exposure variables in the study

VariableMissing data(N, %)PercentilesMinMax
25th50th75th
PM1045 (1.23)22.3231.2945.383.60601.04
PM2.5204 (5.59)12.2217.7627.100.49485.77
NO223 (0.63)21.4030.0341.700.01184.89
SO225 (0.68)4.626.7210.680.01123.31
O326 (0.71)22.8238.4752.280.3997.27
CO45 (1.23)0.420.610.900.007.83
Temperature20 (0.55)4.7012.4419.19-14.3931.375
Relative humidity20 (0.55)56.2965.8076.2130.5098.26

Risk of hospital admissions associated with air pollution levels on the same day (lag0)

PollutantIHD(IRR)CI(IRR)T2DM(IRR)
PM100.996 (0.992, 1.000)0.998 (0.995, 1.001)0.995 (0.991, 0.999)*
PM2.50.995 (0.989, 1.001)0.998 (0.994, 1.003)0.993 (0.987, 1.000)
SO20.990 (0.967, 1.013)0.985 (0.960, 1.011)0.991 (0.957, 1.003)
NO21.003 (0.996, 1.010)0.999 (0.993, 1.005)1.002 (0.993, 1.011)
O30.991 (0.982, 1.001)0.995 (0.985, 1.004)0.992 (0.978, 1.006)
CO0.974 (0.948, 1.001)0.982 (0.962, 1.002)0.974 (0.947, 1.003)

Risk of hospital admissions associated with deciles of air pollutant levels on the same day (lag0)

DecilesPM10PM2.5SO2NO2O3CO
Ischaemic heart disease (IRR)
D20.993 (0.948, 1.041)1.007 (0.959, 1.058)1.005 (0.959, 1.054)1.019 (0.970, 1.070)1.022 (0.975, 1.072)0.996 (0.953, 1.041)
D30.976 (0.934, 1.019)1.021 (0.977, 1.067)1.001 (0.952, 1.053)0.995 (0.945, 1.048)1.057 (1.004, 1.114)*0.997 (0.949, 1.047)
D40.976 (0.933, 1.022)0.989 (0.945, 1.036)1.014 (0.962, 1.068)1.031 (0.980, 1.086)1.022 (0.968, 1.078)0.981 (0.929, 1.035)
D50.953 (0.908, 1.000)1.004 (0.959, 1.050)1.012 (0.962, 1.065)1.027 (0.977, 1.080)1.02 (0.965, 1.078)0.998 (0.944, 1.054)
D61.004 (0.958, 1.053)1.015 (0.966, 1.067)0.990 (0.937, 1.045)1.046 (0.994, 1.100)1.033 (0.977, 1.092)1.009 (0.954, 1.067)
D70.983 (0.941, 1.027)1.000 (0.954, 1.048)1.011 (0.957, 1.069)1.078 (1.025,1.134)*0.978 (0.923, 1.037)1.012 (0.960, 1.067)
D81.008 (0.965, 1.053)0.998 (0.956, 1.042)0.997 (0.943, 1.053)1.055 (1.003,1.109)*0.982 (0.924, 1.043)1.007 (0.951, 1.067)
D91.022 (0.977, 1.070)1.011 (0.965, 1.060)0.999 (0.940, 1.061)1.067 (1.012,1.125)*0.981 (0.921, 1.045)1.048 (0.994, 1.106)
D100.944 (0.895, 0.997)*1.006 (0.953, 1.062)0.962 (0.895, 1.035)1.039 (0.983, 1.098)0.960 (0.897, 1.027)0.981 (0.922, 1.044)
Cerebral infarction (IRR)
D20.973 (0.929, 1.020)1.023 (0.977, 1.072)0.968 (0.915, 1.024)1.032 (0.981, 1.086)1.027 (0.980, 1.076)0.957 (0.913, 1.003)
D31.016 (0.967, 1.068)0.992 (0.945, 1.042)0.977 (0.924, 1.033)1.033 (0.984, 1.085)1.035 (0.984, 1.089)0.957 (0.908, 1.009)
D40.988 (0.939, 1.040)1.034 (0.984, 1.087)0.942 (0.889, 0.998)*1.042 (0.991, 1.097)1.058 (1.005,1.114)*0.965 (0.913, 1.020)
D51.004 (0.954, 1.057)1.038 (0.987, 1.092)0.971 (0.915, 1.030)1.060 (1.008,1.115)*1.023 (0.970, 1.079)0.957 (0.905, 1.011)
D60.975 (0.927, 1.027)1.018 (0.966, 1.073)0.960 (0.903, 1.020)1.038 (0.988, 1.090)0.986 (0.931, 1.045)0.976 (0.922, 1.034)
D70.994 (0.943, 1.046)1.022 (0.967, 1.080)0.973 (0.914, 1.036)1.053 (1.001,1.107)*1.010 (0.952, 1.073)0.995 (0.936, 1.057)
D81.004 (0.953, 1.058)1.023 (0.971, 1.079)0.982 (0.921, 1.047)1.050 (0.995, 1.108)1.011 (0.950, 1.075)0.963 (0.905, 1.024)
D90.983 (0.931, 1.038)0.981 (0.926, 1.039)0.975 (0.913, 1.040)1.072 (1.018,1.128)*0.987 (0.927, 1.052)0.961 (0.903, 1.023)
D100.965 (0.913, 1.020)1.000 (0.942, 1.060)0.936 (0.869, 1.009)0.997 (0.946, 1.052)0.995 (0.929, 1.067)0.929 (0.872, 0.989)*
Type 2 diabetes mellitus (IRR)
D20.986 (0.918, 1.058)1.019 (0.949, 1.095)0.968 (0.889, 1.055)1.050 (0.971, 1.135)0.992 (0.924, 1.065)0.986 (0.914, 1.063)
D30.933 (0.868, 1.002)0.984 (0.914, 1.059)0.958 (0.876, 1.048)1.009 (0.934, 1.090)1.010 (0.938, 1.087)0.999 (0.921, 1.083)
D40.930 (0.865, 1.000)0.951 (0.883, 1.025)0.991 (0.905, 1.086)1.003 (0.928, 1.085)0.985 (0.913, 1.063)0.947 (0.870, 1.031)
D50.925 (0.858, 0.997)*0.988 (0.916, 1.065)0.971 (0.887, 1.063)1.058 (0.979, 1.144)0.965 (0.890, 1.045)0.944 (0.865, 1.030)
D60.987 (0.916, 1.063)0.988 (0.916, 1.066)0.941 (0.858, 1.033)1.006 (0.930, 1.087)0.985 (0.907, 1.071)0.959 (0.877, 1.048)
D70.952 (0.884, 1.025)0.978 (0.905, 1.056)0.977 (0.888, 1.074)1.094 (1.012,1.182)*0.990 (0.907, 1.080)0.960 (0.877, 1.051)
D80.961 (0.891, 1.036)0.999 (0.924, 1.080)0.956 (0.867, 1.053)1.023 (0.944, 1.108)0.997 (0.910, 1.092)0.977 (0.890, 1.071)
D90.963 (0.891, 1.041)0.986 (0.909, 1.068)1.010 (0.914, 1.116)1.042 (0.961, 1.130)0.982 (0.893, 1.080)0.985 (0.898, 1.081)
D100.941 (0.868, 1.020)0.975 (0.895, 1.063)0.919 (0.823, 1.026)1.059 (0.974, 1.151)0.926 (0.837, 1.025)0.971 (0.882, 1.068)

Spearman’s correlations between the key variables in the study

Variables1.2.3.4.5.6.7.8.9.10.11.
1. IHD1.00
2. CI0.52*1.00
3. T2DM0.83*0.50*1.00
4. PM100.05*0.020.021.00
5. PM250.03-0.010.010.88*1.00
6. SO20.07*-0.010.04*0.50*0.55*1.00
7. NO20.15*0.08*0.13*0.77*0.69*0.42*1.00
8. O3-0.15*-0.07*-0.07*-0.48*-0.43*-0.27*-0.62*1.00
9. CO0.09*-0.010.07*0.66*0.69*0.55*0.66*-0.52*1.00
10. Temperature-0.12*-0.02-0.03-0.20*-0.34*-0.53*-0.21*0.49*-0.42*1.000
11. Relative humidity0.09*-0.020.030.08*0.15*0.12*0.07*-0.50*0.30*-0.53*1.00
DOI: https://doi.org/10.2478/aiht-2023-74-3704 | Journal eISSN: 1848-6312 | Journal ISSN: 0004-1254
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Language: English, Croatian, Slovenian
Page range: 48 - 60
Submitted on: Jan 1, 2023
Accepted on: Mar 1, 2023
Published on: Apr 4, 2023
Published by: Institute for Medical Research and Occupational Health
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
exacerbation,
ischaemic heart diseases,
particulate matter,
stroke,
time series,
type 2 diabetes mellitus
Related subjects:
Medicine,
Basic medical science,
Basic medical science, other

© 2023 Angel M. Dzhambov, Krasimira Dikova, Tzveta Georgieva, Teodor I. Panev, Plamen Mukhtarov, Reneta Dimitrova, published by Institute for Medical Research and Occupational Health
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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