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. 2020 Nov 26;756:144020. doi: 10.1016/j.scitotenv.2020.144020

Air pollution, sociodemographic and health conditions effects on COVID-19 mortality in Colombia: An ecological study

Laura A Rodriguez-Villamizar a,⁎, Luis Carlos Belalcázar-Ceron b, Julián Alfredo Fernández-Niño c, Diana Marcela Marín-Pineda d, Oscar Alberto Rojas-Sánchez e, Lizbeth Alexandra Acuña-Merchán f, Nathaly Ramírez-García f, Sonia Cecilia Mangones-Matos b, Jorge Mario Vargas-González b, Julián Herrera-Torres b, Dayana Milena Agudelo-Castañeda g, Juan Gabriel Piñeros Jiménez h, Néstor Y Rojas-Roa b, Victor Mauricio Herrera-Galindo i
PMCID: PMC7688425  PMID: 33279185

Abstract

Objective

The present study aimed to determine the association between chronic exposure to fine particulate matter (PM2.5), sociodemographic aspects, and health conditions with COVID-19 mortality in Colombia.

Methods

We performed an ecological study using data at the municipality level. We used COVID-19 data obtained from government public reports up to and including July 17th, 2020. We defined PM2.5 long-term exposure as the 2014–2018 average of the estimated concentrations at municipalities obtained from the Copernicus Atmospheric Monitoring Service Reanalysis (CAMSRA) model. We fitted a logit-negative binomial hurdle model for the mortality rate adjusting for sociodemographic and health conditions.

Results

Estimated mortality rate ratios (MRR) for long-term average PM2.5 were not statistically significant in either of the two components of the hurdle model (i.e., the likelihood of reporting at least one death or the count of fatal cases). We found that having 10% or more of the population over 65 years of age (MRR = 3.91 95%CI 2.24–6.81), the poverty index (MRR = 1.03 95%CI 1.01–1.05), and the prevalence of hypertension over 6% (MRR = 1.32 95%CI1.03–1.68) are the main factors associated with death rate at the municipality level. Having higher hospital beds capacity is inversely correlated to mortality.

Conclusions

There was no evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality rate at the municipality level in Colombia. Demographics, health system capacity, and social conditions did have evidence of an ecological effect on COVID-19 mortality. The use of model-based estimations of long-term PM2.5 exposure includes an undetermined level of uncertainty in the results, and therefore they should be interpreted as preliminary evidence.

Keywords: COVID-19, Coronavirus, Air pollution, Particulate matter, Mortality, Colombia

Graphical abstract

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Highlights

  • •

    There was not a significant association between long-term exposure to PM2.5 and COVID-19 mortality in Colombia.

  • •

    Demographic, health system, and social conditions are related to COVID-19 mortality.

  • •

    Population over 65 years, poverty index, and prevalence of hypertension are associated to the death rate.

1. Introduction

The SARS-CoV-2 is a new coronavirus responsible for the human coronavirus disease 2019 (COVID-19) initially reported in Wuhan, China, in December 2019. The rapid global spread of COVID-19 made the World Organization of Health (WHO) declare it a public health emergency of international concern (World Health Organization, 2020b). Up to and including July 20th 2020, 14,530,563 cases and 606,741 deaths have been reported in 188 countries (Johns Hopkins Coronavirus Resource Center, 2020). Approximately 80% of COVID-19 confirmed cases reported mild to moderate disease, and the average case fatality rate is 4.6%, with a wide variation across countries (Wang et al., 2020a).

Efforts to determine modifiable factors that could increase transmission, exacerbate symptoms, and increase the risk of COVID-19 mortality remain essential to guide public policies. Individual conditions such as age above 65 years and underlying chronic diseases, including diabetes, hypertension, cardiovascular disease, chronic lung disease, kidney failure, and cancer, have shown to increase the risk of mortality (Ruan et al., 2020; Lippi and Wong, 2020; Hussain et al., 2020; Zhou et al., 2020; Cheng et al., 2020). Environmental factors have also been explored with evidence of COVID-19 airborne transmission (Lu et al., 2020; Setti et al., 2020; Prather et al., 2020).

Short-term air pollutant concentrations, specifically particular matter (PM), might contribute to the spread of the pandemic by transporting viruses in aerosols (airborne transmission) to longer distances than the usual involved in close contacts transmitted through droplets (Zhang et al., 2020). In China, Zhu et al. (2020) conducted a time-series study with data of 120 cities during January and February of 2020. They found a positive association between the daily count of confirmed cases and concentrations of fine and coarse PM (PM2.5 and PM10, respectively), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2) the two weeks before (lag 0–14). Similar findings for PM2.5 and O3 were reported in Italy by Borro et al. (2020) and Zoran et al. (2020) in studies analyzing data from 110 Italian provinces between February and March and data from Milan comparing correlations before and beyond lockdown. In the United States, Adhikari (2020) also found positive short-term associations between air pollutants and confirmed cases in New York. These studies suggest a significant relationship between air pollution and COVID-19 infection and PM's potential effect in airborne transmission. The role of PM as a potential carrier of the SARS-CoV-2 was analyzed for the pandemic in Italy. The authors compared PM10 concentrations and events in Lombardy (the region with a higher number of cases and deaths) and Piedemont (located near Lombardy with less affectation) before and during the first peak of the pandemic. The results showed that the cities in the Piedemont region had even more PM10 pollution events than the cities in the Lombardy region, suggesting that short-term concentrations of PM10 do not fully explain the spread and severity of the pandemic (Bontempi, 2020).

Long-term exposure to atmospheric pollution has been hypothesized as a contributing factor for explaining mortality related to COVID-19. This hypothesis is based on the evidence that chronic exposure to air pollutants is associated with chronic inflammatory response and overexpression of inflammatory cytokines and chemokines (Gouda et al., 2018) and with the development of chronic respiratory and cardiovascular diseases (Brunekreef and Holgate, 2002; Brook, 2008). These factors might increase infected people's susceptibility to SARS-CoV-2 and therefore, might mediate the pathway between chronic exposure to air pollution and COVID-19 mortality (See Supplementary material Fig. S1).

Italy was the first country affected by the pandemic in Europe, with an outbreak and mortality larger than the one observed in the city of Wuhan. The regions in Northern Italy exhibited the higher mortality rates for COVID-19 coinciding with the regions with higher air pollutant concentrations, suggesting that chronic exposure to air pollution might contribute to SARS-Cov-2 lethality (Conticini et al., 2020). Fattorini and Regoli (2020) assessed the correlation between chronicity of exposure to air pollution and COVID-19 mortality by using a regional distribution of the mean concentration NO2, PM10, PM2.5, and O3 from 2016 to 2019, the number of days per year in which the regulatory limits of PM10 and O3 were exceeded, and the number of years during the last decade (2010–2019) in which limit value of PM10 was exceeded for at least 35 days. They found significant correlations between all three measurements, supporting early and preliminary evidence on the role of chronic exposure to air pollution on COVID-19 mortality. These studies provided meaningful results; however, they did not control for potential confounding factors involved in the relationship between chronic exposure to air pollution and COVID-19 mortality. Ecological studies conducted in China and United States controlling for a variety of sociodemographic and health conditions showed that chronic air pollution exposure, mainly to NO2, PM2.5 SO2, increase the COVID-19 mortality risk by 11.2% (CI95%: 3.4%–19.5%), 15% (CI95%:5% - 25%) and 17.2% (CI95%:0.5%–36.9%), respectively (Wu et al., 2020; Yao et al., 2020; Liang et al., 2020).

The pandemic dynamics have had geographic transitions, from China to Europe and then to America, with a growing impact in Latin America and the Caribbean (World Health Organization, 2020a; Kirby, 2020). In developing countries of Latin America, average of air pollutant concentrations are usually higher than levels reported in high-income countries of Europe and North America, especially for PM2.5 (World Health Organization, 2020c). Despite the known effect of chronic exposure to air pollution on the burden of cardiovascular and respiratory diseases (Cesaroni et al., 2014; Ballesteros-González et al., 2020), its potential effect on COVID-19 mortality has not been fully elucidated, particularly in low-and-middle-income countries. This study aimed to determine the association between chronic exposure to PM2.5, and COVID-19 mortality in Colombia, South America, using an ecological approach and controlling for potential socioeconomic and health conditions confounders. The purpose of this study is to provide results to assess the hypothesis of the long-term effect of PM2.5 on COVID-19 mortality in countries with different socioeconomic contexts and pollution levels.

2. Material and methods

2.1. Study population

Colombia is a country located in the extreme north of South America, consisting of 32 departments, 1122 municipalities. According to the National Administrative Department of Statistics (DANE, for its initials in Spanish), the population of Colombia in 2020 is estimated to be 50,372,424 inhabitants (Departamento Administrativo Nacional de Estadistica DANE, 2018). Estimations based on the national census of 2018 show that 51.2% of the total population in Colombia are women, 77.1% reside in urban areas, and 68.2% are between 15 and 64 years old.

2.2. Data sources

2.2.1. Air pollution data

Ninety-two out of 1122 municipalities measure air quality regularly in Colombia (Instituto de Hidrología MyEAI, 2017). Large cities such as Bogota, Medellin, Bucaramanga, Cali, and Barranquilla have air quality monitoring networks. Medium-size and smaller cities perform periodic manual measurements that are not readily available. Because of the scarcity of surface measurements in the country, we retrieved PM2.5 surface concentrations from the Copernicus Atmospheric Monitoring Service CAMS Reanalysis (CAMSRA) and CAMS Near Real-Time (CAMSNRT) for this study. CAMSRA uses four-dimensional variational data assimilation techniques, combining satellite observations with a global scale atmospheric model to produce aerosol concentrations and mixing ratios of several gases at the surface and vertical gridded data (Flemming et al., 2017; European Centre for Medium-Range Weather Forecasts, 2020). CAMSNRT is evaluated every quarter, and evaluation reports are available at the COPERNICUS website (Copernicus Atmosphere Monitoring Service CAMS, 2020). We downloaded surface CAMSRA concentrations over Colombia for PM2.5 using the ECMWF WebAPI and the Python script provided at this platform. We retrieved monthly average gridded data at a 0.125-degree resolution from January 2014 to December 2018. We estimated PM2.5 concentrations at the centroid of each municipality by using a mathematical interpolation from the nearest four retrieved CAMSRA concentrations. Additionally, in order to evaluate the responsiveness of CAMS-based estimation of PM2.5 concentrations, as a support for data validation, we evaluated exposure data for the quarantine period (between March 1 and August 31, 2020) using CAMSNRT.

2.2.2. Population and socioeconomic data

Total population, population by age groups, and area of residence (urban/rural) were retrieved at the municipality level from the estimation of population 2020 based on the Colombian census DANE 2018. We obtained cartographic information and maps from the DANE Geoportal public website (Departamento Administrativo Nacional de Estadistica DANE, 2020a), and the spatial data were created in ArcGIS 10.6.1® using the projection of Colombia in mode Custom Azimuth Equidistant and Datum WGS 1984. We used the Multidimensional Poverty Index as a socioeconomic ecologic measure at the municipality level. The poverty index ranges from 0 to 100 with higher percentages meaning privation of more indicators and dimensions. The higher the index, the higher the socioeconomic deprivation (Departamento Administrativo Nacional de Estadistica DANE, 2020b).

2.2.3. Health data

We obtained data related to the number of confirmed cases and deaths for COVID-19 and the number of RT-PCR tests to confirm positive cases of infected people from the National Institute of Health (INS) website (www.ins.gov.co). The database includes case-by-case information of report date, diagnosis date, date of first symptoms, department and municipality of origin, age, sex, clinical condition, and death date for fatality cases. Information about the number of tests was available at the department level. We used the crude period prevalence of arterial hypertension, diabetes mellitus, and chronic kidney disease data from the High-Cost Account created by the Ministry of Health and Social Protection; we calculated prevalences for the period between July 1st, 2018 and June 30th, 2019 for the 1122 municipalities of Colombia. Hospital beds capacity was measured as intermediate and intensive care beds per 100,000 inhabitants for each municipality, as a surrogate of the health system capacity. We obtained the data from the publicly available national registry of healthcare providers (Registro Especial de Prestadores de Servicios de Salud - REPS https://prestadores.minsalud.gov.co/habilitacion/).

2.2.4. Statistical analysis

The Colombian municipalities with at least one confirmed case of COVID-19 constituted the analytic sample. We calculated population-time at risk as the total population multiplied by the number of days since the first symptom for the first confirmed case at each municipality. We computed the mortality rate using the population-time at risk as a denominator. We described the geographic distribution of the deaths counts, and explored its fit to a Poisson distribution, using the variance test (VT) and the O2 test. Based on the mean and variance of the death counts we rejected the null hypothesis in both tests (p > 0.01) finding evidence of overdispersion (range: 0–1402) and the presence of inflated zeros (58.5%).

Considering the high number of zeros and that from an epidemiological point of view, the first death of COVID-19 represents a phase of the pandemic for one specific municipality; we decided to fit a hurdle model regression for the death counts. We interpreted Hurdle models as a two-part model integrated into one model. The first part is typically a binary response model (logit), and the second part is usually a truncated-at-zero count model (Cameron and Tivedi, 1998). We fit a logit-negative binomial hurdle model and use the population-time at risk as the “offset” variable in the regression models. We used the continuous long-term average of PM2.5 as the primary independent variable in the hurdle model. We performed a sensitivity analysis, running models using the PM2.5 average as categorized variable and as a modeled variable with restricted cubic splines using three knots. We used the Akaike criterion to compare the models. We adjusted the effect of long-term PM2.5 using the following confounding variables identified in the directed acyclic diagram (DAG, see supplementary material Fig. S1): percentage of population 65 years or older, percentage of the urban population, population density, poverty index, hospital beds capacity, number of COVID-19 tests at the department level, and prevalence (percentage) of hypertension, diabetes, and chronic renal failure. These variables were used as covariates. We ran the analysis clustered by department to account for potential correlation in municipalities within the same department. We conducted secondary analyses excluding the capital district of Bogotá, which holds the highest count of deaths, excluding Medellin, the city among three capitals for which CAMSRA underestimates land-based concentrations (see supplementary material Fig. S3a), and excluding municipalities with less than ten confirmed cases. Furthermore, to validate CAMS-based estimations, we analyzed their correlation with surface measurements of PM2.5 during the study period as well as their responsiveness to the quarantine period (between March and August) in Bogota, Barranquilla, and Medellin. We ran all the analyses using STATA 15.

3. Results

There were 182,140 confirmed cases and 6288 confirmed deaths for COVID-19 in Colombia up to and including July 17th. Colombia had confirmed COVID-19 cases in 772 out of 1122 municipalities (68.8%) and deaths for COVID-19 were reported in 320 (41.5% of municipalities with cases). Table 1 summarizes the characteristics of the municipalities with COVID-19 confirmed cases. Mortality proportion for COVID-19 varies widely across municipalities with confirmed cases from 0 to 197.0 per 100,000; the mortality rate (using person-time at risk as the denominator) ranged between 0 and 38.4 per 1,000,000. Fig. 1(a) presents the mortality rate for COVID-19 by municipality. The long-term average exposure to PM2.5 (2014–2018) in municipalities with COVID-19 confirmed cases was 20.0 μg/m3 raging between 9.1 and 37.5 μg/m3. Fig. 1(b) presents the long-term average exposure to PM2.5 at the municipality level.

Table 1.

Characteristics of the municipalities with COVID-19 cases in Colombia up to and including July 17th, 2020 (mean and SD).

Variable Total
772 municipalities		 PM2.5 < 20 μg/m3a
393 municipalities		 PM2.5 ≥20 μg/m3a
379 municipalities
COVID-19 mortality rate (per 1,000,000) 0.75 (2.23) 0.75 (2.60) 0.74 (1.76)
Time since symptoms in first case 66.15 (37.58) 62.48 (36.68) 69.96 (38.18)
% Population 65 or older 10.03 (3.64) 9.89 (3.75) 10.17 (3.53)
% Urban population 49.26 (24.71) 41.74 (22.53) 57.07 (24.48)
Population density per Km2 229.98 (899.66) 116.38 (421.68) 347.78 (1119.66)
Poverty index 39.85 (17.73) 42.56 (18.72) 37.03 (16.19)
Hospital beds capacity (per 100,000) 71.95 (78.22) 70.78 (77.73) 73.15 (78.81)
% Hypertension 6.11 (3.74) 5.68 (3.19) 6.57 (4.19)
% Diabetes 1.57 (1.42) 1.43 (1.04) 1.73 (1.71)
%Chronic renal failure 0.94 (1.26) 0.86 (1.00) 1.04 (1.48)
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a

Mean 2014–2018 from Copernicus Atmospheric Monitoring Service Reanalysis model.

Fig. 1.

Fig. 1

Fig. 1

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Mortality for COVID-19 and PM2.5 long-term average in Colombia at municipality level.

(a) Mortality rate for COVID-19 by municipality in Colombia up to and including July 17th, 2020.

(b) Long-term average of PM2.5 concentrations (2014–2018) in Colombia (in μg/m3).

The geographic pattern of COVID-19 mortality proportion and the long-term average of PM2.5 does not seem to have a good overlap at visual inspection as some municipalities with low levels of PM2.5 exhibit high mortality proportions. The regions with higher mortality proportion are located in the Atlantic and Pacific coasts, and the Amazonian region. Fig. 2 shows the visual inspection of the relation between the estimated long-term mean of PM2.5 and the COVID-19 mortality rate logarithm. The patterns did not follow a linear trend, and increased log of mortality rates for COVID-19 are present at the lowest levels of mean PM2.5. Using a binomial approach (having or no having deaths), the relation with mean PM2.5 did not follow a linear trend but a line with different inflection points (See Supplementary material Fig. S2). Restricted cubic splines of PM2.5 with three knots identified those points to be 12.6, 19.3, and 26.6 μg/m3.

Fig. 2.

Fig. 2

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Relation between mortality rate for COVID-19 and PM2.5 in municipalities with COVID-19 confirmed cases in Colombia up to and including July 17th, 2020.

We present the results of our primary analysis using hurdle models in Table 2 . Estimated mortality rate ratios (MRR) for long-term average PM2.5 were not statistically significant in either of the two components of the model: the logit component modeling the change of no having deaths to have at least one death (MRR = 1.00; 95%CI 0.92–1.08), and the negative binomial model of counts of deaths (MRR = 1.00; 95%CI 0.95–1.06). In the logit component, we found that in municipalities having 10% or more of the population over 65 years of age, the mortality rate is almost four times the mortality rate in municipalities with fewer percentages of the older population. The prevalence of hypertension over 6% is the other main factor associated with the death rate at the municipality level (RR = 1.32; 95% CI 1.03–1.68). On the other hand, having a higher percentage of urban population and higher hospital beds capacity are negatively correlated to mortality (Table 2). Once the municipality reaches at least one COVID-19 death, the main factors associated with the mortality rate are the percentage of urban population and the poverty index, which increases the mortality rate in 2% and 3%, respectively (Table 2). Also, a significant cluster (department) effect was identified in the data (p < 0.01).

Table 2.

Mortality rate ratios in the main analysis using hurdle models for municipalities with COVID-19 cases in Colombia up to and including July 17th, 2020.

Variable Component logit
(0-at least one death)
 Component negative binomial
(counts of deaths>1)
MRR 95% CI P-value MRR 95% CI P-value
Long-term average PM2.5 (μg/m3) 1.00 0.92–1.08 0.973 1.00 0.95–1.06 0.747
% Population 65 or older>10% 3.91 2.24–6.81 0.000 0.51 0.23–1.14 0.100
% Urban population 0.96 0.94–0.98 0.000 1.02 1.01–1.03 0.000
Population density
(per Km2)		 0.99 0.99–1.00 0.129 1.00 0.99–1.00 0.033
Poverty index 0.99 0.97–1.02 0.928 1.03 1.01–1.05 0.001
Hospital beds capacity (per 100,000) 0.99 0.98–0.99 0.000 1.00 0.99–1.01 0.052
% Hypertension
spline 1 (2.09–5.97)
spline 2 (5.97–10.37)
0.75
1.32
0.60–0.94
1.03–1.68
0.013
0.026
0.96
1.00
0.73–1.28
0.70–1.45
0.804
0.960
% Diabetes>4% 0.29 0.01–9.97 0.495 1.74 0.43–6.95 0.434
%Chronic renal failure>3% 1.25 0.35–4.39 0.726 0.42 0.15–1.17 0.096
Number of test at department level 1.00 0.00–1.00 0.056 1.00 0.99–1.00 0.565
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MRR: mortality rate ratio; CI: confidence interval.

We found that secondary analysis exhibits similar results to our primary analysis in terms of no evidence of increased risk of COVID-19 mortality rate associated with an increased long-term average of PM2.5 at the municipality level (Table 3 ). Results similar to our primary analysis were also consistent in our sensitivity analysis using different approaches to model PM2.5 long-term average exposure (See supplementary material Table S1).

Table 3.

Mortality rate ratios in secondary analysis using hurdle models for municipalities with COVID-19 cases in Colombia up to and including July 17th, 2020.

Long-term PM2.5 (μg/m3) Component logita
(0 - at least one death)
 Component negative binomiala
(counts of deaths >1)
MRR 95% CI P-value MRR 95% CI P-value
Excluding Bogotá 1.00 0.93–1.08 0.973 1.00 0.96–1.06 0.784
Excluding Medellín 1.00 0.93–1.08 0.973 1.01 0.95–1.06 0.759
Excluding municipalities with less than 10 confirmed COVID-19 cases 1.02 0.96–1.07 0.494 1.03 0.96–1.09 0.347
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MRR: mortality rate ratio; CI: confidence interval.

a

Adjusted for the percentage of population 65 years or older, percentage of urban population, population density, poverty index, hospital beds capacity, number of tests at department level, and prevalence of hypertension, diabetes, and chronic renal failure.

We present a comparison between the estimated monthly average PM2.5 concentration from CAMSRA model and surface levels during the study period (2014–2018) in three cities in the supplementary material (Figs. S3a). The results show that CAMSRA adequately reproduces the trends of surface PM2.5, with correlation coefficients of 0.8 for Bogotá, and 0.6 for Medellín and Barranquilla, with a tendency to underestimate surface levels in Bogotá and Barranquilla. Fig. S3b presents the daily average concentrations of CAMSNRT and surface concentrations during the quarantine period, supporting that CAMSNRT responsiveness to changes in surface levels is adequate, with a tendency to underestimate surface levels at higher PM2.5 surface concentrations.

4. Discussion

Our research presents the first ecologic nationwide study conducted in a developing country assessing the association between COVID-19 mortality and long-term exposure to PM2.5. Our results did not find evidence of an association between higher concentrations of PM2.5 and higher counts of deaths, controlling for nine socioeconomic and health indicators at the municipality level. The effect of socioeconomic and health conditions, such as the proportion of the population over 65 years, the poverty index, and the prevalence of hypertension, showed evidence of increasing the risk of deaths for COVID-19, while the hospital's capacity decreased such risk. The use of model-based estimations of long-term PM2.5 exposure includes an undetermined level of uncertainty in the results, and therefore, they should be interpreted as preliminary evidence.

The first COVID-19 confirmed case occurred in Bogota, the capital district (Instituto Nacional de Salud (Colombia), 2020), where the highest number of cases have been reported, exceeding 45,000 by mid-July. The first cases identified in the capital district were related to returning flights from Europe, which were also related to the first cases identified in other main capital cities, including Medellin and Cali. Then, an additional infection source came from international cruises in the Port of Cartagena, where the epidemic spread to the Atlantic Coast. The epidemic in the Brazilian Amazonian region was the probable source of infection in Leticia, the capital of the Amazonas department, which is the municipality with the highest mortality proportion for COVID-19 in Colombia (195.02 per 100,000 inhabitants). Thus, the COVID-19 spread went from capital cities to small municipalities, where the mortality rates have been higher.

There was no evidence of an association between the long-term average of PM2.5 and the mortality rate for COVID-19 in crude or adjusted models. Our results contrast with the reports of correlational studies conducted in Italy (Conticini et al., 2020; Fattorini and Regoli, 2020) and ecological studies in China (Yao et al., 2020) and the United States (Wu et al., 2020), which found positive associations between PM2.5 and COVID-19 mortality after adjusting for four and 20 potential confounders, respectively. These studies supported the hypothesis that the effect of long-term exposure to PM2.5 on COVID-19 mortality is largely mediated by comorbidities linked to chronic PM-related inflammation (Conticini et al., 2020; Tsai et al., 2019). In this regard, it has been proposed that chronic exposure to PM2.5 causes alveolar ACE-2 receptor overexpression, which may increase viral load in patients exposed to pollutants (Frontera et al., 2020). Our findings revealed a significant effect of aging and poverty on COVID-19 mortality rate, factors related to failure in the mechanisms of acute immune humoral and cellular response at the individual level; and to a higher burden of chronic disease and lower capability of the healthcare system to treat complicated cases of infection, at the municipal level. These findings might suggest that the chronic effect of aging and poverty might have a stronger effect on COVID-19 complications and mortality in developing countries. However, our negative results related to pollution exposure might also be explained by the use of CAMSRA model-based estimations of long-term PM2.5 exposure, which implies a measurement error and an unknown degree of uncertainty in the results.

Another possible explanation for our findings is that long-term exposure to PM2.5 has less impact on biological susceptibility to COVID-19 complications and deaths compared to the effect of other air pollutants such as nitrogen dioxide (NO2). Multipollutant models in Colombia have identified a stronger short-term effect of NO2 on respiratory and cardiovascular morbidity compared to other pollutants (Rodriguez-Villamizar et al., 2019). A country-wide cross-sectional study in the United States using multipollutant models for the effect of PM2.5, NO2, and O3 found a solid positive association between NO2 and COVID-19 fatality and mortality but did not find significant associations with PM2.5 and O3 (Liang et al., 2020). The authors discussed that divergent results with the previous US nationwide study (Wu et al., 2020) are probably due to the use of multi-pollutant models and the adjustment for spatial trends, which might have confounded the findings. Unfortunately, we did not count on reliable NO2 and O3 long-term exposure estimations, so we did not assess this effect in multi-pollutant models.

We found an independent and significant effect of the older age, the poverty index, and the prevalence of hypertension (over 6%) associated with the COVID-19 mortality rate. Several studies reported similar findings related to age and chronic diseases (Ruan et al., 2020; Lippi and Wong, 2020; Hussain et al., 2020; Zhou et al., 2020; Cheng et al., 2020). In Italy Conticini et al. (2020) discussed that factors such as the age structure of the affected population, the great differences between the Italian regional health systems, the capacity of intensive care units in the region, and prevention policies adopted by the government had played a major role in the spread of and mortality for SARS-CoV-2, presumably more than long-term air pollution itself. The effect of poverty on COVID-19 mortality is less described in the literature, but it represents a major risk condition in developing countries, probably related to unstable employment and income, lower health literacy, and limited access to preventive health services (Bong et al., 2020; Proaño, 2020). A few recent ecological studies in the US at the county level have reported a correlation between COVID-19 mortality rate and some social disparities such as poverty status and non-English speaking households and other ethnic minorities (Zhang and Schwartz, 2020; Fielding-Miller et al., 2020).

The strengths of this study include the use of nationwide public government data at the municipality level and the adjustment for nine sociodemographic and health conditions using a hurdle model. The main limitation of this study is the lack of empirical data for the long-term estimation of PM2.5 exposure. The estimation of PM2.5 concentration in this study comes from the CAMSRA model, which has been evaluated using independent measurements available in different world regions at the surface level and in the tropospheric column. These evaluations show that CAMSRA successfully reproduces levels and trends of aerosols and gases (Wang et al., 2020b). A recent research conducted to evaluate the performance of CAMSRA over the cities of Bogota, Medellin, and Barranquilla for PM2.5, CO, and NO2 concentrations comparing measurements from the air quality monitoring networks with retrieved CAMSRA concentrations showed that CAMSRA is able to reproduce PM2.5 levels and trends in these three cities (Fig. S3a). However, the model largely underestimates NO2 and CO concentrations (Vargas, 2020). Additionally, we compared the daily average concentrations of CAMSNRT with surface concentrations in the same cities during the quarantine period (Fig. S3b), and the results also indicate that CAMSNRT adequately reproduces the trends and levels of surface PM2.5, with a tendency to underestimate surface levels at higher PM2.5 concentrations during the dry season.

Although elevated levels of PM2.5 are observed in urban areas, PM2.5 distribution in Colombia shows that even medium-size and small municipalities have similar or even higher concentrations of PM2.5. This behavior coincides with aerosols' geographical distribution reported in previous studies for Colombia (Ballesteros-González et al., 2020; Luna et al., 2018). These studies indicate that biomass burning is a critical source of PM2.5 in Colombia and that both large and small cities are affected by this source. The PM2.5 geographical distribution and trends presented in our study (Fig. 1b) line up with the results reported in those previous studies. Further research should confirm the validity of information from CAMSRA over other Colombian municipalities and highlights the need for a robust national air quality monitoring network. Even though CAMSRA seems to capture the heterogeneity and trend of concentration across municipalities during the study period, it had a moderate correlation to surface measurements in a selected sample of three of the largest cities. Therefore, the use of CAMSRA to estimate PM2.5 exposure introduced a measurement error and uncertainty in the analysis, which might have partially attenuated any underlying association between PM2.5 and COVID-19 mortality. Therefore, our results should be considered preliminary and need confirmation from further investigations.

Our study has other limitations. First, the ecological study's nature precludes the extrapolation of inferences from the empirical evaluation of hypotheses based on clusters (i.e., municipalities) to the individual level. Therefore, the absence of a relationship between long-term exposure to PM2.5 and mortality among patients diagnosed with SARS-CoV-2 should at best be regarded as provisional. Second, in the absence of reliable NO2 and O3 long-term exposure estimations, we could not incorporate them into the analysis or evaluate the independent association of PM2.5 and mortality in the context of multi-pollutant models. Third, mortality data reflect fatal cases among patients with a confirmatory diagnosis of the infection, excluding deaths among undiagnosed individuals (due to low testing rates or unreliable test results) and those occurring outside of hospitals. Systematic differences in municipalities' capability to comprehensively and correctly identify and register deaths attributable to the infection could have biased our estimate of effect. Although this issue could not be directly addressed in the analysis, adjusting for testing rates and hospital beds capacity should have partially corrected for differential readiness of municipal health systems to cope with the epidemic.

5. Conclusions

There was no evidence of an association between long-term exposure to PM2.5 and mortality rate for COVID-19 at the municipality level in Colombia. Demographics, health system capacity, and social conditions did show an ecological effect on COVID-19 mortality. The use of model-based data to estimate the long-term PM2.5 exposure is an important source of uncertainty in this study, and therefore, results should be considered preliminary evidence. The lack of air pollutants' surface data in most municipalities reveals the need to strengthen the country's air quality monitoring systems.

Funding

This work was supported by the Colombian Ministry of Science and Technology -MINCIENCIAS Grant No. 905–2019. The funder did not have any role in the design, analysis, or interpretation of the study.

Data sharing statement

Data will be made available upon request.

CRediT authorship contribution statement

Laura A. Rodriguez-Villamizar: Conceptualization, Methodology, Formal analysis, Writing - original draft. Luis Carlos Belalcazar-Ceron: Methodology, Validation, Data curation, Writing - original draft. Julián Alfredo Fernández-Niño: Conceptualization, Methodology, Formal analysis, Writing - original draft. Diana Marcela Marín-Pineda: Conceptualization, Methodology, Formal analysis, Writing - original draft. Oscar Alberto Rojas-Sánchez: Conceptualization, Data curation, Writing - original draft. Lizbeth Alexandra Acuña-Merchán: Data curation, Writing - review & editing. Nathaly Ramirez-Garcia: Data curation, Writing - review & editing. Sonia Cecilia Mangones-Matos: Data curation, Writing - review & editing. Jorge Mario Vargas-Gonzalez: Methodology, Validation, Data curation, Writing - review & editing. Julián Herrera-Torres: Methodology, Validation, Data curation, Writing - review & editing. Dayana Milena Agudelo-Castañeda: Writing - review & editing. Juan Gabriel Piñeros Jiménez: Conceptualization, Writing - review & editing. Néstor Y. Rojas-Roa: Conceptualization, Writing - original draft. Victor Mauricio Herrera-Galindo: Conceptualization, Methodology, Formal analysis, Writing - original draft.

Declaration of competing interest

The authors have no conflicts of interest relevant to this article to declare.

Acknowledgments

Acknowledgement

The authors would like to thank Yurley Rojas for her contribution to the generation of maps and Gloria Ramos for updating the COVID-19 public data set for analysis.

Editor: SCOTT SHERIDAN

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2020.144020.

Appendix A. Supplementary data

Supplementary data file contains additional Tables and Figures mentioned in the text that provide further details of the results.

mmc1.docx (585.4KB, docx)

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Supplementary Materials

Supplementary data file contains additional Tables and Figures mentioned in the text that provide further details of the results.

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