Air Pollution & COPD Exacerbations in Bogota: A Time Series Study

Air Quality, Atmosphere & Health (2024) 17:2775–2787
https://doi.org/10.1007/s11869-024-01600-8
Short‑term effect of air pollution exposure on COPD exacerbations:
a time series study in Bogota, Colombia
Astrid Berena Herrera López1 · Carlos A. Torres‑Duque2 · María Patricia Arbeláez1 · Néstor Yezid Rojas Roa3 ·
Horacio Riojas‑Rodríguez4 · José Luis Texcalac Sangrador4 · Víctor Herrera5 · Laura Andrea Rodríguez‑Villamizar5
Received: 10 April 2024 / Accepted: 12 June 2024 / Published online: 13 August 2024
© The Author(s) 2024
Abstract
Introduction Air pollution poses a risk for people with Chronic Obstructive Pulmonary Disease (COPD). This study estimated the short-term effect of variations in air pollutant concentrations on exacerbations of COPD (COPD-E) in Bogotá,
Colombia.
Methods We performed an ecological time series study from 2014 to 2021 to evaluate the short-term effect of fine
particulate matter ­(PM2.5), nitrogen dioxide (­ NO2), and ozone ­(O3) levels on COPD-E treated in the emergency and
hospitalization services. Daily counts of patients with COPD-E discharge diagnoses were obtained from the National
Health Information System, and daily measurements of ­PM2.5, ­NO2, and ­O3 concentrations and meteorological data
were obtained from air monitoring stations. A Generalized Additive Model was used with Distributed Lag Non-Linear
Models to control for confounders.
Results An increase of 10 μg/m3 in ­PM2.5 and ­O3 was associated with increased COPD-E admissions (lagged 0-3 days) with
Relative Risk (RR) of 1.04 (95%CI: 1.02 -1.07) and RR:1.03 (95%CI:1.01 – 1.04), respectively. During the rainy season
and minimum temperature of the series, for every 10 μg/m3 increase in P
­ M2.5 concentration, COPD-E admissions (lagged
0-3 days) increased with RR 1.03 (95%CI: 1.01-1.06). A higher magnitude of association was observed in men (­ PM2.5, 1.04
95%CI:1.01 – 1.06 and ­O3, 1.04 95%CI:1.02 – 1.05, lag 0-7 days) than in women.
Conclusions A higher air pollution was associated with more COPD-E. These results highlight the importance of actions
aimed at improving air quality.
Keywords Ambiental air pollution · Chronic obstructive pulmonary disease · COPD exacerbations
1
Facultad Nacional de Salud Pública, Universidad de
Antioquia, Medellín, Antioquia, Colombia
Carlos A. Torres‑Duque
[email protected]
2
CINEUMO, Fundación Neumológica Colombiana, Bogotá,
Colombia
María Patricia Arbeláez
[email protected]
3
Departamento de Ingeniería Química y Ambiental,
Universidad Nacional de Colombia, Bogotá, Colombia
Néstor Yezid Rojas Roa
[email protected]
4
Dirección de Salud Ambiental, Centro de Investigación
en Salud Poblacional, Instituto Nacional de Salud
Pública. Cuernavaca, Morelos, México
5
Departamento de Salud Pública, Escuela de Medicina,
Universidad Industrial de Santander, Bucaramanga,
Colombia
* Astrid Berena Herrera López
[email protected]; [email protected]
Horacio Riojas‑Rodríguez
[email protected]
José Luis Texcalac Sangrador
[email protected]
Víctor Herrera
[email protected]
Laura Andrea Rodríguez‑Villamizar
[email protected]
Vol.:(0123456789)
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Introduction
Air Pollution continues to be a global public health and
environmental problem (Dominski et al. 2021). It is one
of the significant health risk factors, to which 5.15 to
2.35 Years of Life Lost were attributed between 1990 and
2019 worldwide (Abbafati et al. 2020; Bălă et al. 2021).
Outdoor air pollution due to 2.5-micron particulate matter ­(PM2.5) was responsible for 4.14 million deaths (118
Million Years of Life Lost), and ­O3 was responsible for
365,000 premature deaths (6.21 Million Potential Years
of Life Lost). Both air pollutants were responsible for 1 in
9 deaths worldwide in 2019 (Health Effects Institute and
Institute for Health Metrics and Evaluation’s Global Burden of Desease project 2020). For the same year, 18% of
premature deaths in patients with chronic obstructive pulmonary disease (COPD) were due to air pollution (World
Health Organization WHO 2022; Soriano et al. 2020).
Air quality in low- and middle-income countries has
deteriorated with accelerating urbanization, the development of fossil-fuel-based economies, disparities in exposure to air pollution, and the use of solid fuels, mainly
biomass, for cooking and domestic heating (World Health
Organization WHO 2021; Stoner et al. 2021). Similarly, as
a result of aging populations and changing lifestyles, the
prevalence of chronic non-communicable diseases (NCDs)
is increasing in these countries. The burden of morbidity
and mortality from NCDs is therefore increased by poor
air quality (World Health Organization WHO 2021).
COPD, a pathological disease characterized by airflow
obstruction and respiratory symptoms, is characterized by
the occurrence of COPD exacerbations (COPD-E), which
are episodes of worsening symptoms that require additional treatment and adversely affect the prognosis and
mortality of the disease (Gold Science Committee 2023;
Celli et al. 2021; Soler-Cataluña and Lopez-Campos
2022). Causes of COPD-E are diverse and include bacterial and viral infections, exposure to tobacco and biomass
smoke, poor adherence to treatment, other concomitant
diseases, and COPD-E's history and severity (Gold Science Committee 2023; Lee et al. 2019).
Although there is no conclusive causal relationship,
emerging evidence suggests that exposure to air pollution
may also adversely affect COPD (Yan et al. 2019; Li et al.
2016). Increasing evidence links short-term air pollution
with hospital admissions and emergency department visits
for COPD-E (Yan et al. 2019; Hoffmann et al. 2022; Jin et al.
2022; Ross et al. 2023; Liu et al. 2022; Du et al. 2021; Tang
et al. 2021; Dong et al. 2023; Ting et al. 2021). However,
most epidemiological studies have been conducted in North
America, Asia, and Europe, and there is little information
on its association in Latin American countries, where it is
Air Quality, Atmosphere & Health (2024) 17:2775–2787
recognized that the concentrations of air pollutants are generally higher than in North America and Europe. It is not
possible to fully and accurately extrapolate the short-term
effects of air pollution on COPD patients in Colombia from
estimates of effects in high-income countries, given the heterogeneity in the levels and components of air pollutants
(Health Effects Institute and Institute for Health Metrics and
Evaluation’s Global Burden of Desease project 2020).
A previous study in Colombia by Morantes and Fajardo
showed that high concentrations of P
­ M2.5 and P
­ M10 with a lag
time of 48 hours in hospitalized patients were associated with
increased sputum volume (OR: 4. 74; 95%CI: 1.02-21.90),
purulence (OR: 6.58; 95%CI: 2.51-17.20), pleuritic pain
(OR: 3.62; 95%CI: 1.27-10.37), use of antibiotics (OR: 2.87;
95%CI: 1.17-7.07) and corticosteroids (OR: 2.62; 95%CI:
1.07-6.44) (Morantes-Caballero and Fajardo Rodriguez
2019). However, this study was only an assessment of COPD
symptoms. Therefore, it is essential to approach the question
from the perspective of healthcare utilization and more severe
clinical outcomes, as assessing emergency department admissions and hospitalizations for COPD is relevant.
As seen in the GOLD 2022 definition of COPD (Gold
Science Committee 2022) there is a paradigm shift in the
taxonomy of COPD, as it is now recognized that it may
be caused by non-infectious risk factors, such as poor outdoor or indoor air quality (Roque et al. 2023; Stolz et al.
2022), that may play a clinically relevant role for the disease. Documentation and understanding of the relationship
between COPD-E and pollutant levels is crucial because it
may increase COPD patients' survival and quality of life
and reduce healthcare costs and disease burden. In addition,
reducing exacerbations is one of the most essential treatment
targets in COPD from a clinical and economic perspective
(Gold Science Committee 2023; Hoogendoorn et al. 2017)
Bogotá is a megacity in Latin America that has experienced high levels of air pollution during the last decades.
However, there is little information on the relationship
between outdoor air pollution and COPD-E and its behavior
among the inhabitants of this high-altitude city. This study
aimed to determine the short-term effect of variations in air
pollutants on COPD-E between 2014 and 2021 in Bogotá,
Colombia, hypothesizing that at least one pollutant is associated with emergency care and hospitalizations for COPD-E.
Methods
Study design and location
A retrospective ecological time series study was conducted
between 1 January 2014 and 31 December 2021 in Bogotá,
Colombia. The city is located in the central zone between
Air Quality, Atmosphere & Health (2024) 17:2775–2787
4°35'56"north latitude and 74°04'51" east longitude, with an
altitude of 2,582 m asl, classified as high altitude (≥2,500
m) (Figure S1).
Data sources
Health Data
Daily counts were obtained from emergency and hospital
admissions with a primary discharge diagnosis of COPDE (ICD-10 J44.1, J440). Daily counts were also obtained
from visits for other acute lower respiratory tract infections
(ICD-10 J20-J22) and other acute upper respiratory tract
infections (ICD-10 J30-J39). Data were obtained from the
Social Protection Information System (Sistema de Information de la Protection Social - SISPRO) in the Individual Service Delivery Records (Registers Individuals de Prestation
de Services - RIPS) module of the Ministry of Health and
Social Protection (Ministerio de Salud y Protección Social)
(Ministerio de Salud y ProtecciónSocial 2022). Daily counts
of confirmed COVID-19 cases were obtained from the public database available on the National Health Institute website (Instituto Nacional de Salud - INS).
Pollutants and meteorological data
Hourly data on the city's daily concentrations of P
­ M2.5, ­NO2,
and ­O3, as well as meteorological variables, were obtained
from the Bogotá Air Quality Monitoring Network (Red de
Monitoreo de Calidad de Aire de Bogotá - RMCAB). This
network consists of 20 air quality monitoring stations (Figure S1). The District Environment Office (Secretaría Distrital del Ambiente) provided data for the period studied. The
24-hour average was calculated for P
­ M2.5 and N
­ O2, and the
maximum of the 8-hour moving average for O
­ 3. Estimates
were obtained from temporal representativeness greater than
or equal to 75% of the hourly record for each pollutant.
The study was approved by the Ethics Committee of
the National School of Public Health of the University of
Antioquia (Comité de Ética de la Facultad Nacional de Salud
Pública de la Universidad de Antioquia) (CEI- 2103000200162). Informed consent was waived according to international standards (CIOMS, OMS 2017).
Statistical analysis
The trend, seasonality, pollutant variation, and the Spearman
correlation between pollutants, meteorological variables,
and daily counts of COPD-E patients were described. Time
series regression analysis was performed to evaluate the relationship between daily counts of people treated for COPD-E
and daily average concentrations for each pollutant, adjusted
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for meteorological variables, dry and rainy weather periods,
holidays, COVID-19 cases, days of mandatory isolation, and
daily counts of people treated for upper and lower respiratory tract infections. Generalized Additive Models (GAMs)
with quasi-Poisson distribution and non-linear distributed
lag were used (Gasparrini and Armstrong 2011a; Gasparrini
and Armstrong 2011b). In this case, the effect of pollutants
was assumed to be linear, while the relationship with meteorological variables was modeled by a natural cubic spline
with 5 degrees of freedom. The time series model was built
assuming an over disperse Poisson distribution, including a
time function with 4 degrees of freedom (df)/year (to correct
for seasonality and long-term trend) and day of week as a
factor. Sensitivity analyses were performed by varying the
degrees of freedom between 4 and 8. To assess the lagged
effect of pollutant variations on COPED-E (Li et al. 2022)
we developed models with different lagged structures including lagged effects up to 15 days (lag 0-3, lag 0-7 and lag
0-15). We used the following model specification:
(
)
[ ( )]
Log E Yt = intecerpt + CB air pollutant PM 2.5 , NO2 , O3
+ ns(Time, 4df ∕per year) + ns(Minimum temperature)
+ ns (Precipitation) + ns( Humidity) + ns(Solar radiation)
+ Dow + Holiday + COVID − 19 cases + ICD10( J20 − J22)
+ ICD10 (J30 − J39) + 𝜀t , family = quasipoisson
Where ­Yt represents the number of people with COPD-E on
day t, "intercept" is the intercept of the model; "CB (crossbasis) air pollutant" is a two-dimensional spatial function
in which the relationships of COPD-E concentrations and
COPD-E lag are combined, assuming a linear and polynomial function, respectively. Meteorological variables of
ns (temperature), ns (precipitation), ns (humidity), and ns
(solar radiation) were included as covariates in the multivariate model with natural cubic smoothing functions (natural
splines-ns) to control for non-linear confounding effects of
the trends of the meteorological variables included in the
model with 5 degrees of freedom. The smoothing allows
maximum likelihood estimation.
In order to control for short-term weekly variations and
seasonal trends, the day of the week ("Dow") and public holiday ("Holiday"). Other covariates considered in the model
were COVID-19 cases, and other lower (ICD10- J20-J22)
and upper (ICD10- J30-J39) respiratory tract infections.
Seasonal patterns were assessed using Dickey-Fuller,
Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests and trends
using a simple linear regression model and the KPSS test
for trend. Autocorrelation was calculated for 15-day lags. A
stratified analysis was performed for seasonality, temperature extremes (minimum and maximum daily temperature),
and sex. Quasi Akaike's Information Criterion (QAIC) and
Root Mean Squared Error (RMSE), R2, minimization of
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autocorrelation, and evaluation of residuals were used to
select the best model, to identify deviations from model
assumptions, and to generate a priori hypotheses based on
the epidemiological literature. Results are presented as a
relative risk of daily COPD-E per an increase of 10 μg/m3
­PM2.5, ­NO2, and ­O3. Statistical tests were two-sided, and
95% confidence intervals were calculated for each estimated
effect. R software version 4.3.1 (The R Foundation for Statistical Computing 2023) with the DLNM library (Gasparrini 2021) was used for statistical analyses.
Results
Air quality and admission to emergency
and inpatient departments for COPD
Over the eight years (2,922 days), the daily average concentration of ­PM2.5 decreased by 43.15% (from 21.76 μg/m3
in 2014 to 15.20 μg/m3 in 2021), the concentration of ­NO2
decreased by 10.87% (from 31. 80 μg/m3 in 2014 to 28.68
μg/m3 in 2021), while the maximum 8 h moving average
concentration of ­O3 increased by 12.77% (from 36.32 μg/
m3 in 2014 to 41.64 μg/m3 in 2021) (Table 1). The daily
pollutant averages were 18.43 ± 8.1 μg/m3 for ­PM2.5, 31.35
± 10.1 μg/m3 for ­NO2, and 36.56 ± 15.27 μg/m3 for O
­ 3. The
annual average concentrations of P
­ M2.5 did not reach the
annual intermediate objective of the World Health Organization (WHO 2005) guideline in force during the study period
(10 μg/m3 for ­PM2.5), but they did for N
­ O2 and O
­ 3. However,
they did not reach the limit values set by the WHO for 2021
(5 μg/m3 for P
­ M2.5, 10 μg/m3 for N
­ O2 and O
­ 3 of 100 μg/
3
m ). Meteorological variables showed variations, especially
temperature and solar radiation, with means and standard
deviations of 14.87 ± 0.77 °C and 178.33 ± 49.01 ­Wm2,
respectively, over the period.
The correlation variables of the series showed seasonal
behavior. The mean and variance of the time series were
constant, and the Dickey-Fuller test (p-value=0.010) indicated rejection of the null hypothesis of non-stationarity.
Therefore, a non-linear distributed lag model with a Poisson
link (quasi-poisson) was constructed to model the COPDE counts. The presence of increasing (­ O3) and decreasing
­(PM2.5 and N
­ O2) trends in the behavior of the pollutants
was confirmed (Figure S2, S3, and S4). The P
­ M2.5, ­NO2,
and ­O3 series showed a more pronounced seasonality than
the meteorological variable series. The autocorrelation or
serial correlation was positive for all three pollutants, and
the risk estimates were higher for the lags in the first seven
days (Figure S5).
In total, 37,968 people over 40 years of age were discharged from the emergency and hospitalization services
with a diagnosis of COPD-E out of 46,373 admissions
Air Quality, Atmosphere & Health (2024) 17:2775–2787
during the same period, for a ratio of 1.22 admissions to
cases, with a maximum of 48 days in 2019 and a minimum
of 20 in 2021. Although a decreasing trend was observed,
this series showed three peaks (2014, 2018, and 2019)
throughout the period analyzed. For 2014, the daily mean
was 16.85 ± 6.10, of which 52.92% were female and 47.04%
were male (Table 1).
The most positive and highest Spearman correlations were found between ­P M 2.5 and ­NO 2 (0.65); relative humidity and precipitation (0.57); ­O3 and maximum
temperature (0.51) and ­P M 2.5 and ­O 3 (0.44); a positive
correlation was found between COPD-E and P
­ M2.5, ­NO2
and maximum temperature concentrations (0.12, 0.20,
0.09 respectively) and inverse correlation with pollutant
­O3 (-0.03) (Fig. 1).
Association of air pollutants and admission
to the emergency department and hospital
for COPD‑E
Figure 2 shows the Relative Risk (RR) and 95% confidence
intervals (95%CI) for COPD-E for 10 μg/m3 increments of
­PM2.5 and ­O3 with lags from 0 to 15 days. The effects of the
pollutants decreased after the seventh day and then increased
after 14 days. The O
­ 3 effect was more significant on the same
day than ­PM2.5, which was more significant at the 3-day lag.
The effect of ­NO2 was null in the first three days and decreased
from the fourth to the thirteenth day, showing an inverse proportionality with the occurrence of COPD-E (Fig. 2) (Figure
S5). The effects of P
­ M2.5 and O
­ 3 on COPD-E were significant,
especially during the first three days of the lag.
A statistically significant association was observed for the
effect of pollutants on the number of patients admitted to the
emergency and hospitalization services with a diagnosis of
COPD-E. The RR of COPD-E for each increment of 10μg/m3
were 1,04 (95%CI:1,02 - 1,07) for P
­ M2.5 and 1,03 (95%CI:1,02
- 1,05) for O
­ 3 in the 0–3-day lag (Table 2) for the entire time
series. The same behavior is shown for the lag 0-7 days for
­PM2.5 (RR=1.04; 95%CI:1.01 - 1.06) and ­O3 (RR=1.04;
95%CI: 1.02 - 1,05) and 0 - 15 days for P
­ M2.5 (RR=1.03;
95%CI: 1.01 - 1.06) and ­O3 (RR=1.04; 95%CI: 1.02 - 1.04)
(Table 2). All pollutants except N
­ O2 show a statistically significant risk-increasing effect on admissions for COPD-E (Table 2).
In the stratified analysis by sex, the RRs for P
­ M2.5 and
­O3 at lag 0-3 were higher in men than in women, with a difference of 15,0% and 3.0% for P
­ M2.5 and O
­ 3, respectively.
The increased risk of COPD-E was statistically significant
in men (RR=1.04; 95%CI:1.01- 1.06) but not in women
(RR=0.92; 95%CI:0.89- 0.94) Stratified analysis by season
showed no statistically significant associations were found
during the rainy season for any of the three air pollutants.
However, when minimum rather than mean temperature was
Air Quality, Atmosphere & Health (2024) 17:2775–2787
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Table 1 Description of air pollutant concentrations, meteorological variables, and number of patients admitted to the emergency and hospitalization services in Bogotá D.C. - Colombia, 2014 - 2021
2014
2015
2016
Daily mean concentration of air pollution
Mean ± SD 21.76
18.72 ± 7.07 20.43±9.59
PM 2.5 μg/m3
± 8.49
Median
19.58
16.82 (8.88) 17.84(13.16)
(RIC)
(12.45)
Maximum 52.46
44.29
52.50
Minimum
7.84
7.14
6.84
Mean ± SD 31.80 ±
34.07 ± 8.54 32.90 ±
NO2 μg/ ­m3
7.91
15.85
Median
30.73
32.42 (11.94) 29.27 (22.82)
(RIC)
(10.03)
Maximum 59.82
60.77
98.80
Minimum
12.36
16.25
11.16
Mean ± SD 36.32 ±
36.55±12.84 37.02 ±
O 3 -8h μg/m3
12.95
17.81
Median
34.97
36.55 (17.04) 32.35 (18.58)
(RIC)
(15.37)
Maximum 104.41
82.75
100.16
Minimum
14.68
16.82
10.11
Meteorological
14.64 ± 0.70 15.24 ± 0.89
Temperature°C Mean ± SD 14.60 ±
0.62
Median
14.58
14.73 (0.94) 15.15 (1.2)
(RIC)
(0.83)
Maximum 16.50
16.30
17.74
Minimum
13.00
12.10
12.81
Relative
Mean ± SD 63.45 ±
62.53 ± 5.26 63.01 ± 5.27
humidity %
5.48
Median
63.16 (7.2) 61.94 (6.05) 62.88 (6.29)
(RIC)
Maximum 76.88
78.77
80.85
Minimum
48.62
48.36
46.90
178.00±
198.79±
Solar radiation Mean ± SD 174.42 ±
46.57
47.50
52.71
­Wm2
Median
171.50
171.70
199.41
(IRC)
(56.90)
(56.90)
(72.01)
Maximum 309.65
301.40
338.86
Minimum
76.77
60.60
54.95
Use of health services (daily emergencies and hospitalization)
Mean ± SD 16.85±
10.44 ± 4.93 10.39 ± 4.42
Admission
6.10
COPD-E
Median
16.00
10 (7.00)
10 (6.00)
(IRC)
(8.00)
Maximum 43
27
25
Minimum
5
1
0
2017
2018
2019
2020
2021
18.88
±7.00
17.97
(11.04)
39.49
5.78
28.85 ±
10.30
27.82
(12.2)
70.93
9.37
27.9 6 ±
9.80
25.91
(12.15)
68.60
6.64
17.28
±6.87
15.44
(9.86)
40.84
6.09
31.82 ±
8.43
30.92
(10.02)
60.27
9.69
22.31 ±
7.03
21.24
(10.24)
45.79
8.40
18.22
±8.13
16.47
(11.77)
56.66
6.17
33.32 ±
9.02
32.10
(12.68)
61.52
13.41
41.68 ±
13.71
40.13
(18.45)
89.00
14.48
16.97
±8.92
14.98
(11.44)
56.46
4.91
29.32 ±
8.49
29.14
(12.43)
52.03
10.95
46.72 ±
16.65
43.72
(20.96)
106.45
16.57
15.20 ±6.34
14.80 ±
0.66
14.81
(0.79)
16.72
12.62
65.40 ±
6.41
65.18
(8.61)
81.29
37.71
180.76±
51.19
176.12
(63.38)
369.45
33.39
14.64 ±
0.71
14.60
(0.99)
16.97
12.52
65.93 ±
5.17
65.92
(6.31)
79.13
47.32
168.50 ±
47.43
164.24
(69.40)
286.10
55.11
15.04 ±
0.77
15.05
(1.09)
16.99
12.25
63.60 ±
5.51
63.17
(7.42)
79.99
47.78
172.86±
44.55
168.54
(61.85)
292.24
60.11
15.09 ±
0.73
15.09
(1.09)
17.06
12.43
64.66 ±
5.75
64.56
(6.83)
78.90
42.69
189.39±
49.93
183.33
(70.01)
316.93
82.93
13.24 ±
4.78
13 (6.00)
15.14 ±
6.61
15 (10.00)
22.5 ± 7.10 9.55 ± 7.88 5.85 ± 3.76
22 (8.00)
7 (9.00)
5 (5)
28
3
39
1
48
5
38
1
20
0
14.89 (9.25)
34.35
3.73
28.68 ± 8.20
28.75 (11.89)
53.73
11.89
41.64 ±
12.83
39.47 (17.42)
93.49
16.55
14.90 ± 0.74
14.87 (1.03)
16.53
12.67
67.60 ± 5.61
67.06 (6.96)
81.42
50.50
163.78±
42.47
160.59
(56.50)
274.00
46.36
SD Standard deviation, IQR Interquartile range; emergency department visits and hospitalizations for COPD-E (ICD-10: J44.1, J440), other
acute lower respiratory tract infections (ICD-10: J20-J22), and other acute upper respiratory tract infections (ICD-10: J30-J39); estimated
24-hour daily average of criteria air pollutants for P
­ M2.5, ­NO2, and the maximum of the 8-hour moving average for O
­ 3.
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Fig. 1 Spearman correlation between criteria pollutants, meteorological variables, and patients admitted to the emergency and hospitalization services in Bogotá D.C. - Colombia, 2014 - 2021. NO2S:
Nitrogen Dioxide in μg/m3. PM25S: Particulate matter in 2.5 μg/m3.
TEMPS: Period mean temperature. RGLS: Solar radiation. PRECS:
Precipitation. HRS: Relative humidity. Ozone: Ozone in μg/m3. pepocurghosp: Patients admitted to the emergency and hospitalization
services. tempmin: Period average of minimum temperature. tempmax: Period average for maximum temperature
included in these models, a statistically significant association was found for P
­ M2.5 (RR=1.03; 95%CI: 1.01 - 1.06) in
the 0–3-day lag (Table 2).
According to the sensitivity analyses, the effect estimates
changed little when the degrees of freedom were changed
(Supplementary Table S1).
Table 3 shows the models that recorded the smallest values in the QAIC were the stratification of rainy season and
minimum temperature (12,732.43), rainy season (12,765.13)
and minimum temperature (19,262.25) for the average
­PM2.5, this same behavior of the variables that constituted
the ­O3 models.
Discussion
Our results show a significant association between daily
ambient air pollutant concentrations (­ PM2.5 and O
­ 3) and the
number of emergency room visits or hospitalizations for
COPD-E, mainly in the 0–3-day lag, in Bogotá. There was
no evidence of an increased risk of COPD-E with increasing
­NO2 concentrations. The magnitude of the associations of
COPD-E with ­PM2.5 and ­O3 was higher in men during the
rainy season and when the increase in pollutants occurred
in seasons with lower temperatures.
Air Quality, Atmosphere & Health (2024) 17:2775–2787
In this study, we estimate that for each 10 μg/m3 increase
in ­PM2.5, a 4% (95%CI: 2% -7%) increase in emergency
department admissions and hospitalizations occurred with
a lag of 0 to 3 days. Our findings are consistent in the magnitude and direction with results reported for other cities
(Jin et al. 2022; Dong et al. 2023; Zhang et al. 2018; Santus
et al. 2012; Lin et al. 2020; Devries et al. 2017; Kloog et al.
2014; Ko et al. 2007; Xie et al. 2019). Other studies found no
significant associations between P
­ M2.5 and hospital admissions for COPD (Hoffmann et al. 2022; Belleudi et al. 2010;
Slaughter et al. 2005; Stieb et al. 2009).
Our results also showed that a 10 ug/m3 ­O3 increase was
associated with a 3% (95%CI: 1%-4%) increase in emergency department visits and hospital admissions for COPDE with a lag of 0-3 days. Other studies have demonstrated
this association, such as (Hoffmann et al. 2022), who showed
an increased RR of 1.030 per 10 μg/m3 increase in O
­ 3 for
hospitalization on the day of exposure (95% CI: 1.005 1.056). However, no significant association of O
­ 3 with the
onset of COPD-E was found (Dong et al. 2023).
In contrast to the findings with P
­ M2.5 and O
­ 3, our results
showed no increased risk with N
­ O2 and COPD-E (RR: 0.99;
95%CI: 0.97 – 1,01 per 10 μg/m3) for the lags evaluated.
This association has been shown in other studies (Hoffmann
et al. 2022; Dong et al. 2023; Devries et al. 2017; Peel et al.
2005; Li et al. 2016; Huh et al. 2022; Saki et al. 2020).
A possible reason for the observed differences in exposure
effect estimates could be chance or an unidentified bias
(DeVries et al. 2016). The measured pollutants may be surrogates for another source of air pollution in the city, and
the findings may be an artifact of measurement error at low
concentrations. It could also be explained because the effect
of ­NO2 may have a higher threshold, and short-term exposures are not apparent. Another possible reason is that ­NO2,
being a gaseous traffic pollutant, can rapidly decrease its
effect from the source and the monitoring station.
Our results also showed that in subgroup analyses by sex,
a greater effect with statistical significance was observed in
men than in women, consistent with previous studies (Liang
et al. 2019; Szyszkowicz et al. 2018). The sex difference
may be due to lifestyle factors (tobacco and alcohol intake,
exercise and diet, occupational exposure outside the home)
(Xie et al. 2019) and immune or hormonal responses.
At the same time, we found novel evidence that solar
radiation, precipitation, relative humidity, and temperature
influence COPD exacerbations. In contrast to most studies,
these variables have been little investigated and considered
in previous epidemiologic studies of short-term effects (Huh
et al. 2022; Ferrari et al. 2012)
We found that the effects of ­PM2.5 on emergency department visits and hospitalizations for COPD-E were significant in the rainy season and at minimum temperature,
with a range of 0-3 and 0-15 for P
­ M2.5 increasing by 3%
Air Quality, Atmosphere & Health (2024) 17:2775–2787
2781
Lag of efects NO2 Bogota
Lag of efects O3 Bogota
1.05
1.00
RR
0.95
0.90
0.8
0.90
0.9
1.0
0.95
RR
RR
1.1
1.00
1.2
1.05
1.3
Lag of efects PM2.5 Bogota
0
5
10
0
15
5
10
15
0
5
10
Lag
Lag
Lag of efects PM2.5 Bogota
Lag of efects O3 Bogota
1.00
RR
0.90
0.85
0.85
0.9
0.90
1.0
0.95
0.95
RR
RR
1.00
1.1
1.05
1.05
1.2
1.10
1.10
Lag of efects NO2 Bogota
0
0
5
10
15
Lag
15
0
5
10
5
10
15
15
Lag
Lag
Lag
Lag of efects O3 Bogota
Lag of efects PM2.5 Bogota
1.10
RR
1.05
0.80
0.95
0.95
0.85
1.00
1.00
RR
0.95
0.90
RR
1.05
1.00
1.10
1.05
Lag of efects NO2 Bogota
0
5
10
0
15
5
10
0
15
5
10
Lag of efects PM2.5 Bogota
Lag of efects O3 Bogota
1.15
1.05
1.00
RR
0.95
RR
0.90
0.85
0.85
0.95
0.90
0.90
0.95
1.00
1.00
1.05
1.10
1.10
1.10
1.15
1.15
Lag of efects NO2 Bogota
1.05
RR
15
Lag
Lag
0
5
10
0
15
5
10
15
0
5
1.15
1.05
RR
0.95
0.90
0.85
0
15
1.00
1.05
1.00
RR
0.95
0.90
0.85
10
Lag of efects O3 Bogota
1.10
1.15
Lag of efects NO2 Bogota
1.10
1.15
1.10
1.05
RR
1.00
0.95
5
10
15
0
5
10
15
Lag
Lag
Lag
Lag of efects O3 Bogota
Lag of efects PM2.5 Bogota
Lag of efects NO2 Bogota
1.00
RR
0.90
0.85
0.85
0.9
0.90
1.0
0.95
0.95
RR
RR
1.00
1.1
1.05
1.05
1.2
1.10
1.10
5
15
Lag
Lag of efects PM2.5 Bogota
0
10
Lag
Lag
0
0
5
10
5
10
15
0
5
10
15
15
Lag
Lag
Lag
Fig. 2 Relative risks of exacerbation of chronic obstructive pulmonary disease from short-term exposure to ­PM2.5, ­NO2, and ­O3 at lags of 0-15
days
2782
Air Quality, Atmosphere & Health (2024) 17:2775–2787
Table 2 Association of air pollutants with Emergency Department (ED) and hospital admission for COPD exacerbations, relative risks with generalized additive models using quasi-Poisson regression to estimate associations with lags from lag0 to lag15
Lag/Model
PM2.5 RR (IC95%)
NO2 RR (IC95%)
O3 RR (IC95%)
Main models
Model with mean temperature
E-EPOC ~ cb1. contaminant + cb1.temp+ cb1.HR + cb1.prec + cb1.RLGS + J30_J39IAVSP + J20_J22_IAVI +
COVID case + Day of the week + Weekend
Lag 0-3
1.04 (1.02 - 1.07)
0.99 (0.97 - 1.01)
1.03 (1.01 -1.04)
Lag 0-7
1.04 (1.01 -1.06)
0.97 (0.95 - 0.99)
1.03 (1.01- 1.04)
Lag 0-15
1.03 (0.99 - 1.05)
0.97 (0.95 - 0.99)
1.02 (1.01 -1,03)
Model with Temperature minimum:
E-EPOC ~ cb1. contaminant + cb1.tempmin + cb1.HR + cb1.prec + cb1.RLGS + J30_J39IAVSP + J20_J22_IAVI +
COVID case + Day of the week + Weekend
Lag 0-3
1.04 (1.02 - 1.07)
0.98 (0.96 -1.00)
1.03 (1.02 -1.05)
Lag 0-7
1.04 (1.01 - 1.06)
0.97 (0.95 -0.99)
1.04 (1.02 -1.05)
Lag 0-15
1.03 (1.01 - 1.06)
0.97 (0.95 -0.99)
1.03 (1.02-1.04)
Model with rainy season and minimum
temperature:
E-EPOC ~ cb2.contaminant + cb2.tempmin + cb2.HR + cb2.prec + cb2.RLGS + J30_J39IAVSP + J20_J22_IAVI +
COVID case + Day of the week + Weekend
Lag 0-3
1.03 (1.01 - 1.06)
0.98 (0.96 - 1.00)
1.01 (1.00 -1.03)
Lag 0-7
1.03 (1.00 - 1.05)
0.97 (0.95 - 0.99)
1.01 (1.00 -1.02)
Lag 0-15
1.03 (1.01 - 1.06)
0.97 (0.95 - 0.99)
1.01 (0.99-1.02)
Models for stratified analysis
Model for men:
E-EPOC man ~ cb1. contaminant + cb1.tempc + cb1.HR + cb1.prec + cb1.RLGS + J30_J39IAVSP + J20_J22_IAVI
+ COVID case + Day of the week + Weekend
Lag 0-3
0.94 (0.91 - 0.96)
1.02 (1.00 -1.04)
1.03 (1.02 -1.04)
Lag 0-7
1.04 (1.01 -1.06)
0.97 (0.95 - 0.99)
1.04 (1.02 -1.05)
Lag 0-15
0.90 (0.87 - 0.92)
1.02 (1.00 - 1.04)
1.02 (1.01 -1,04)
Model for women:
E-EPOC woman ~ cb1. contaminant + cb1.tempc + cb1.HR + cb1.prec + cb1.RLGS + J30_J39IAVSP + J20_J22_
IAVI + COVID case + Day of the week + Weekend
Lag 0-3
0.92 (0.89 -0.94)
1.02 (1.00 -1.04)
0.99 (0.98-1.00)
Lag 0-7
0.89 (0.87 - 0.92)
1.01 (0.99 -1.03)
0.99 (0.98 -1.00)
Lag 0-15
0.87 (0.85 - 0.90)
1.01 (0.99 -1.04)
0.98 (0.97 -0.99)
Model for Rainy season:
E-EPOC ~ cb2. contaminant + cb2.tempc + cb2.HR + cb2.prec + cb2.RLGS + J30_J39IAVSP + J20_J22_IAVI +
COVID case + Day of the week + Weekend
Lag 0-3
1.03 (1.00 - 1.05)
0.98 (0.96 -1.01)
1.00 (0.99 -1.02)
Lag 0-7
1.02 (0.99 - 1.05)
0.97 (0.95 -0.99)
1.00 (0.99 -1.01)
Lag 0-15
1.02 (0.99 - 1.05)
0.97 (0.95- 1.00)
0.99 (0.98 -1.01)
Model for dry season
E-EPOC ~ cb2. contaminant + cb2.tempse + cb2.HR + cb2.prec + cb2.RLGS + J30_J39IAVSP + J20_J22_IAVI +
COVID case + Day of the week + Weekend
Lag 0-3
1.03 (1.00 -1.06)
1.06 (1.04 -1.08)
1.07 (1.06 -1.09)
Lag 0-7
1.00 (0.97 - 1.03)
1.04 (1.01 -1.06)
1.07 (1.06 -1.09)
Lag 0-15
0.97 (0.94 - 0.99)
1.01 (0.99 -1.03)
1.06 (1.05 -1.08)
Model including Quarantine period:
E-EPOC ~ cb2.contaminant + cb2.tempmin + cb2.HR + cb2.prec + cb2.RLGS + J30_J39IAVSP + J20_J22_IAVI +
COVID case + Day of the week + Weekend + quarantine
Lag 0-3
1.04 (1.01 - 1.07)
0.97 (0.95 -1.00)
1.04 (1.02 -1.05)
Lag 0-7
1.04 (1.01 - 1.06)
0.97 (0.95 -0.99)
1.04 (1.02 -1.05)
Lag 0-15
1.03 (1.01 - 1.06)
0.97 (0.95 -0.99)
1.03 (1.02 -1.05)
RR Relative risk, IC95% 95% confidence interval, COPD-E Emergency department visit and hospitalization for COPD-E (ICD-10: J44.1, J440),
cb2.pollutant: ­PM2.5, ­NO2, ­O3
cb1.tempc: Temperature
cb2.tempmin: minimum temperature
cb2.HR: Humidity
cb2.prec: Precipitation
cb2.RLGS: Solar radiation
J20_J22_IAVI: Other acute lower respiratory infections (ICD-10 J20-J22) and
J30_J39IAVSP: other acute lower respiratory tract infections (ICD-10 J20-J22);
Casocovid: cases of COVID-19
Holiday: public holiday
Weekend: weekend day
Air Quality, Atmosphere & Health (2024) 17:2775–2787
Table 3 Akaike's QuasiInformation Criterion and the
Root Mean Squared Error
2783
Model
PM2.5
NO2
O3
Mean temperature
Minimum temperature
Man
Woman
Rainy season
Dry season
Rainy season and Temperature minimum
Quarantine
Mean temperature
Minimum temperature
Man
Woman
Rainy season
Dry season
Rainy season and Temperature minimum
Quarantine
Mean temperature
Minimum temperature
Man
Woman
Rainy season
Dry season
Rainy season and Temperature minimum
Quarantine
QAIC
RMSE
R2
19330.37
19262.25
36222.06
42164.74
12765.13
6400.643
12732.43
19258.38
19337.91
19261.76
36476.31
42607.07
12755.54
6409.971
12724.22
19250.36
19305.06
19223.91
36351.66
42517.98
12759.59
6368.184
12732.93
19212.94
5.609
5.580
16.356
20.196
5.463
5.363
5.455
5.579
5.607
5.575
16.457
20.362
5.456
5.373
5.450
5.573
5.591
5.554
16.417
20.363
5.461
5.324
5.451
5.451
0.895
0.898
0.998
1.000
0.885
0.945
0.887
0.898
0.895
0.898
0.895
1.000
0.886
0.945
0.888
0.898
0.896
0.899
0.998
1.000
0.886
0.947
0.887
0.900
RMSE Root Mean Squared Error, QAIC Akaike's Quasi-Information Criterion
(95%CI: 1%-6%) for 10 μg/m3. The variation in the effects
of ­PM2.5 and ­O3 according to the season could be explained
by the fact that it coincides with the increased circulation
of seasonal influenza virus (Sooryanarain and Elankumaran
2015) and that cold temperatures cause bronchoconstriction,
increase the concentration of granulocytes and macrophages
in the lower respiratory tract, reduce mucociliary clearance
(D’Amato et al. 2015) and induce degranulation of mast
cells in the respiratory tract (Proud et al. 1992), with COPD
patients being more prone to COPD-E.
These findings are consistent with those documented
by Liu et al. 2017 (Hwang et al. 2017), for every 10 μg/m3
increase in that of ­PM2.5 during the cold season increased hospitalizations for COPD-E, with an RR of 1.02 (95% CI:1.0071.040, lag 0-1 day). Lam et al. showed a short-term association between COPD-E hospitalization at a cold temperature of
20°C with RR:1.50 (95% CI:1.34-1.68) and the lagged effects
of cold temperature were up to 0-20 days (Lam et al. 2018).
Therefore, during the rainy and cold seasons, the effects of
­PM2.5 and ­O3 on COPD-E hospitalizations and emergencies
should be monitored. Studies should also be conducted to elucidate the underlying mechanisms of the variation in P
­ M2.5
and ­O3 effects by season and temperature. Ehelepola et al.
found that minimum temperature (1 to 5 °C decrease) was
associated with COPD-E (Ehelepola et al. 2018).
The results shown in this study for people diagnosed
for COPD-E with a significant increase of 10 μg/m3 in
­PM2.5 and ­O3 from lag 0 to 7 days are supported by previous meta-analyses by Li et al. 2016, which showed a
significant association with increases of 10 μg/m3 for air
pollutants ­( PM 2.5, ­P M 10, CO, N
­ O 2, ­S O 2, and O
­ 3), especially for N
­ O2 (RR: 1.04; 95%CI: 1.03, 1.06) and ­O3 (RR:
1.03; 95%CI: 1.01, 1.04) from lags 0 to 3 days. Li et al.
2022 also showed that the risk of COPD-E events in the
emergency department and hospitalization increased significantly with an increase of 10 μg/m3 for ­PM2.5 and ­PM10
with a lag of 0 to 7 days. This fact confirms that individual
and cumulative 0 to 7-day lags affect COPD-E, as documented in this study.
Several biological mechanisms may explain the effect
of ­PM2.5 on COPD-E. Among these, airway irritation is
prominent, causing interactions with the immune system
that promote respiratory infections, oxidative stress, and
lung inflammation (Li et al. 2016; Szyszkowicz et al. 2018;
Mu et al. 2014). At the same time, the toxicity and irritancy of the particles aggravate pulmonary infection and
2784
reduce the immune response of the lung (Tsai et al. 2013).
Other effects on the respiratory system from acute or chronic
inhalation of PM, O
­ 3, and Environmentally Persistent Free
Radicals (EPFRs) are the generation of Reactive Oxygen
Species (ROS), which trigger and amplify an inflammatory
process through endogenous production of more ROS (de
Paula Santos et al. 2021).
This study had several limitations. First, we did not evaluate the effects of exposure to other air pollutants on emergency
department admissions and hospitalizations for COPD for example, sulfur dioxide and carbon monoxide. Second, the use of
network data has limited geographic resolution, but in terms of
daily measurements, these are still the best local estimates. In
addition, the location of the monitoring stations in the city of
Bogotá D.C. ensures that the air pollutant concentrations used in
this study are broadly representative of the urban area. Nevertheless, non-differential measurement errors resulting from using
centrally located monitors are likely to attenuate associations.
In addition, personal behaviors such as using heaters, gas fireplaces, or time spent outdoors may affect personal exposure levels. This could affect the magnitude of the observed associations
compared to other locations with different behavioral profiles.
Third, the applied ecological time series study does not consider individual demographic, socioeconomic, or behavioral
characteristics; this could be the subject of further research.
Fourth is aggregation bias, which assigns group characteristics
to all individuals and does not allow identification of individual
sources of risk. Fifth, there is information bias due to possible underreporting of persons treated during the COVID-19
pandemic (Alsallakh et al. 2021; So et al. 2021). Sixth, the
current study adjusted for the effects of temperature, relative
humidity, precipitation, solar radiation, day of week, holidays
and weekends, COVID-19 cases, and care for upper and lower
respiratory tract infections, but not for other variables explaining COPD-E, such as comorbidities, severity of COPD, nutritional and immunological status, COPD severity and access to
health services, due to lack of availability of individual data.
Despite the above limitations, this study has several advantages. The ecological study design with time series allows
us to examine short-term variation in outcomes that changes
in the primary exposure can explain; in our study, whether
­ 3 partly
changes in air concentrations of P
­ M2.5, ­NO2, and O
explain daily changes in the number of people seen for
COPD-E. DNLM considers the delayed effects of exposure
factors and the non-linear relationship between exposure and
response. The current findings help to understand the associations of air pollutants with COPD and provide novel insights
for assessing the potential health effects of air pollution, setting public health priorities, and taking preventive measures
for susceptible subgroups in the future. The advantage of
studying this associations in Bogotá, a capital city with high
altitude located in middle country is that, we are increasingly
unaware of the importance of air quality in these countries.
Air Quality, Atmosphere & Health (2024) 17:2775–2787
Our findings have public policy implications since
­P M 2.5 and O
­ 3 are short-lived air pollutants that cause
global warming and contribute to climate change. COPD
and related hospitalizations generate healthcare costs and
are associated with decreased lung function and disease
progression, reduced quality of life, decreased survival in
COPD patients, and increased disease burden.
Conclusion
This study found that short-term exposure to ­PM2.5, ­O3,
and meteorological variables were associated with COPDE. Concentrations of P
­ M2.5, ­O3, and meteorological factors
(solar radiation and not only temperature) influence the
frequency of emergency room visits and hospitalizations
for COPD. Therefore, more efforts are needed to reduce air
pollution, and efforts should be focused on approaching the
WHO air quality guideline targets.
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 11869-0​ 24-0​ 1600-8.
Acknowledgments To the National Institute of Public Health of Mexico
(Instituto Nacional de Salud de Pública de México) for the research visit
that allowed the methodology developed in this article to be carried out.
To the Subdirectorate of Air, Auditory and Visual Quality of the District
Secretary of Environment of Bogotá D.C. (Subdirección de calidad del
aire, auditiva y visual de la secretaria Distrital de Ambiente de Bogotá
D.C.), for providing the data on criteria air pollutants and meteorological variables. This work was approved by the Ethics Committee of the
National School of Public Health of the University of Antioquia (Comité
de Ética de la Facultad Nacional de Salud Pública de la Universidad de
Antioquia), the Research Committee, and the Ethics Committee of the
Colombian Pneumology Foundation (Comité de Ética de la Fundación
Neumológica Colombiana). The study received no direct funding.
Author contributions Astrid Berena Herrera López: Conceptualization;
Methodology; depuration of data, analysis statistics; write - corridor
original; Visualization; Investigation y Analysis formals.
María Patricia Arbeláez: Supervision; Redaction: revision and edition.
Carlos Arturo Torres-Duque: Supervision; Redaction: revision and edition.
Néstor Yezid Rojas Roa: Supervision; Redaction: revision and
edition.
Víctor Herrera: Supervision; Redaction: revision
José Luis Texcalac Sangrador: Supervision; Redaction: revision and
edition; Analysis formals
Horacio Riojas Rodríguez: Supervision; Redaction: revision and
edition; Analysis formals
Laura Andrea Rodríguez- Villamizar: Conceptualization; Supervision; Redaction: revision and edition; Analysis formals.
Funding Open Access funding provided by Colombia Consortium No
Applicable.
Data availability All data generated or analyzed during this study are
included in this article. Further demand can be directed to the corresponding author in email.
Air Quality, Atmosphere & Health (2024) 17:2775–2787
Declarations
Consent to publication By means of this communication we, the
authors, declare
- That the document is original
- That the document has not been previously published in any printed
or digital media.
- That the document has not been submitted simultaneously to another
publication for study or evaluation.
- That the document is the result of the intellectual collaboration of
the authors.
- That the final version of the document, submitted for evaluation, has
been read and approved by all authors.
- That in order to obtain the data and results shown in this article, the
criteria of scientific ethics were followed, in accordance with the type
of research carried out.
- We confirm that neither the manuscript nor any parts of its content are
currently under consideration or published in another journal.
- All authors have approved the manuscript and agree with its submission to Journal Quality Atmosphere and Health.
Conflicts of Interest The authors declare that they have no competing financial interests or known personal relationships that could have
influenced the work reported in this paper. The authors are solely responsible for the content and writing of the article.
Ethics approval and consent to participate This work was approved by
the Ethics Committee of the National School of Public Health of the
University of Antioquia (Comité de Ética de la Facultad Nacional de
Salud Pública de la Universidad de Antioquia), the Research Committee, and the Ethics Committee of the Colombian Pneumology Foundation (Comité de Ética de la Fundación Neumológica Colombiana). The
study received no direct funding. The ecological data were obtained
from administrative records.
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as you give appropriate credit to the original author(s) and the source,
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