- Open Access
Neighborhood environmental factors linked to hospitalizations of older people for viral lower respiratory tract infections in Spain: a case-crossover study
Environmental Health volume 21, Article number: 107 (2022)
Lower respiratory tract viral infection (LRTI) is a significant cause of morbidity-mortality in older people worldwide. We analyzed the association between short-term exposure to environmental factors (climatic factors and outdoor air pollution) and hospital admissions with a viral LRTI diagnosis in older adults.
We conducted a bidirectional case-crossover study in 6367 patients over 65 years of age with viral LRTI and residential zip code in the Spanish Minimum Basic Data Set. Spain’s State Meteorological Agency was the source of environmental data. Associations were assessed using conditional logistic regression. P-values were corrected for false discovery rate (q-values).
Almost all were hospital emergency admissions (98.13%), 18.64% were admitted to the intensive care unit (ICU), and 7.44% died. The most frequent clinical discharge diagnosis was influenza (90.25%). LRTI hospital admissions were more frequent when there were lower values of temperature and O3 and higher values of relative humidity and NO2. The regression analysis adjusted by temperatures and relative humidity showed higher concentrations at the hospital admission for NO2 [compared to the lag time of 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)] and O3 [compared to the lag time of 3-days (q-value< 0.001), 1-week (q-value< 0.001), and 2-weeks (q-value< 0.001)] were related to a higher odds of hospital admissions due to viral LRTI. Moreover, higher concentrations of PM10 at the lag time of 1-week (q-value = 0.023) and 2-weeks (q-value = 0.002), and CO at the lag time of 3-days (q-value = 0.023), 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)], compared to the day of hospitalization, were related to a higher chances of hospital admissions with viral LRTI.
Unfavorable environmental factors (low temperatures, high relative humidity, and high concentrations of NO2, O3, PM10, and CO) increased the odds of hospital admissions with viral LRTI among older people, indicating they are potentially vulnerable to these environmental factors.
Lower respiratory tract infections (LRTIs) are a significant cause of hospitalizations, morbidity, and mortality worldwide, particularly in older adults over 65 years old . Older people are at increased risk due to a less efficient immune system (immunosenescence), a progressive decrease in respiratory function, a greater predisposition to respiratory infections, and being more prone to develop complications [2, 3]. LRTI includes acute bronchiolitis, bronchitis, and pneumonia, causing severe respiratory failure and leading to hospitalization and possible death .
The incidence of bacterial LRTIs in older people has substantially decreased in the last decades due to vaccination programs and antibiotic treatment. Thus, the most frequent etiological agents responsible for LRTIs are respiratory viruses, such as the respiratory syncytial virus (RSV) and influenza virus (Flu), among others [1, 4]. Viral LRTI leads to an uncontrolled host immune response, damaging the lung epithelium and decreasing respiratory gas exchange .
Knowing all the risk factors for viral LRTIs can help prevent and improve their management in the healthcare system. Environmental factors are crucial in developing lung diseases, including respiratory infections [2, 6, 7]. The air pollutants for public health concerns are carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter up to 10 μm in size (PM10), and sulfur dioxide (SO2), among others [2, 6]. Several studies have shown associations between short-term exposure to outdoor air pollutants and hospitalizations, emergency room visits, or home health visits for respiratory morbidity (pneumonia, chronic obstructive pulmonary disease, asthma) in people aged 65 years and older .
Climatic factors are other contributors to human morbidity and mortality from lung disease. Viral LRTIs have a seasonal pattern in older people [9, 10]. Seasonal environmental changes (temperature, wind speed, humidity, thunderstorms) are associated with a higher probability of infections . Seasonal temperature fluctuations and relative humidity, particularly in winter, affect the transmission of respiratory viruses and host susceptibility .
Several studies have shown that climatic changes can affect air quality and significantly impact the concentration of air pollutants, their mixture, and how they are dispersed or deposited . The interaction of air pollution and climate factors affects pathogens’ survival time, increasing the host’s susceptibility [13, 14]. This relationship between environmental factors (climatic factors and outdoor air pollution) and viral LRTIs in the older population may not be uniform, requiring multivariate analysis considering the maximum number of climatic and environmental factors.
Scientific publications on environmental pollution and respiratory diseases are abundant. Air pollutant exposure may alter respiratory system responses against viral infection by modulating appropriate host defense responses and increasing viral LRTI and associated morbidity . However, information about the older population with viral LRTI is scarce . This study aimed to analyze the association between short-term exposure to environmental factors and hospital admissions with viral LRTI diagnosis in older people aged 65 years or older before the Coronavirus Disease 2019 (COVID-19) era.
Material and methods
We conducted a bidirectional case-crossover study (all patients serve as their controls) in individuals aged 65 years or older who had a hospital admission due to viral LRTI in Spain during 2013–2015. This study was approved by the Research Ethics Committee (Comité de Ética de la Investigación; CEI PI 81_2021) of the Instituto de Salud Carlos III (Madrid, Spain). All the extracted information was completely anonymous and did not require the patients’ consent.
Epidemiological and clinical data
Clinical information was obtained from the Spanish Minimum Basic Data Set (MBDS) facilitated by the Health Information Institute of the Spanish Ministry of Health, Consumer Affairs and Social Welfare (MHCSW), previously described . This anonymized database provides discharge diagnoses, procedures, and epidemiological information. Diagnoses are made following the standardized methods of each hospital. Data were recorded using the International Classification of Diseases, 9th ed, Clinical Modification (ICD-9-CM). One of the fields that the MBDS provides is the residential zip code. However, in some registries, this field was incorrectly collected or absent in 30% of the patients, so it was impossible to geolocate them.
ICD-9-CM codes of the outcome variable
We selected older patients who had a primary diagnosis of viral LRTI [Flu (487.0, 487.1, 488.01, 488.02, 488.11, 488.12, 488.81, and 488.82), RSV (079.6, 466.11, and 480.1), viral pneumonia (480.0, 480.1, 480.2, 480.8, and 480.9), and acute bronchiolitis (466.11 and 466.19)] or a primary diagnosis of acute respiratory failure (518.81) with a secondary diagnosis of viral LRTI (Supplementary Table 1).
The outcome variable was hospital admission with viral LRTI. Patients were never hospitalized in the three control times before hospital admission (3 days, 1-week, and 2-weeks).
Environmental information was collected from the Spanish State Meteorological Agency (AEMET; http://www.aemet.es/en/), which provides data from 880 meteorological stations throughout the Spanish territory  as geolocation, climatic data, and outdoor air pollutants. The quality of the AEMET data meets the criteria of the European Environment Agency . The environmental data for each patient were obtained from the weather station closest to the patient’s residential zip code.
The link in space-time between environmental factors and MBDS data was established as follows: i) The environmental data [temperature (°C) and relative humidity (%)] and ambient air pollutants [SO2 (μg/m3), CO (μg/m3), NO2 (μg/m3), O3 (μg/m3), PM10 (μg/m3)] from the meteorological stations distributed throughout the territory were geolocated in space as a reference point (latitude-longitude). ii) Each patient in the study had their spatial location through the residential zip code (geographical area), from which the centroid was extracted, and geolocation in space as a reference point (latitude-longitude) was obtained. iii) Once both data sources were geolocated in space, each patient was linked to the meteorological station closest to their home. iv) The MBDS had the date of hospital admission and each meteorological station of the measurement date, so the link of dates was simple. The mean distance from each residential zip code to its nearest meteorological station was 8.99 km 95% CI (8.69, 9.28).”
In this bidirectional case-crossover analysis, three short control times (3 days, 1-week, and 2-weeks before hospital admission), compared to the day of hospital admission, were used to estimate the risk of hospital admission with viral LRTI related to acute exposure to environmental factors . A symmetric bidirectional sampling with control periods before and after hospital admission was used to adjust the long-term trend and seasonality impact on variable exposures [20, 21]. Since the time between infection, symptoms, and hospital admission is unknown in our study and may vary over a wide range, these three control times were analyzed separately to cover the wide range of days at risk. In each of these three control times, we calculated the mean value of each environmental factor in a bidirectional way taking into account three days (one day before, the day of the study, and one day after) to mitigate extreme levels. With this design, it was unnecessary to include invariant confounding factors because each individual is their control [20, 21].
The conditional logistic regression (CLR) was used to evaluate the association between environmental factors (climatic factors and ambient air pollutants) and hospital admissions with viral LRTI by univariate analysis (each environmental factor separately) and multivariate analysis (adjusted by temperature and relative humidity). This test provides the measure of effect, called the odds ratio (OR), and its 95% confidence interval (95%CI), calculated using the exact method by dividing the exposure level at the hospital admission by the exposure level at the control time, implying only individuals with variations in variable exposures provide information. Thus, an OR > 1 indicated higher odds when the environmental variable was augmented at the hospital admission or reduced at the control lag time, and an OR < 1 showed higher odds when the environmental feature was increased at the control lag time or decreased at the hospital admission. Many OR values tended to 1, making interpretation difficult, which is why relative humidity (%)], SO2 (μg/m3), CO (μg/m3), NO2 (μg/m3), O3 (μg/m3), and PM10 (μg/m3) were log2-transformed. The assumption of linearity was confirmed by plotting the Martingale residuals on the Y axis against continuous covariates on the X axis.
Statistical analysis was performed using the R statistical software v3.5.2 , with the clogit function from a package for survival analysis (version 3.4–0, https://CRAN.R-project.org/package=survival) . All tests were two-tailed, and p-values were corrected by the Benjamini and Hochberg procedure’s false discovery rate (q-values).
We found 6367 hospital admissions of patients over 65 with viral LRTI and residential zip code. The patients’ median was 78 years old, and half were men (50.51%). The Charlson comorbidity index was 2.14. The two most frequent comorbidities were congestive heart failure (19.63%) and diabetes (32.73%). Almost all were emergency hospital admissions (98.13%), 18.64% were admitted to the intensive care unit (ICU), and 7.44% died. The most frequent clinical discharge diagnosis was Flu (90.25%) (Table 1).
Environmental features and viral LRTI hospital admissions
LRTI hospital admissions were more frequent with low temperatures and high relative humidity (Fig. 1). In the univariate regression analysis (Table 2), the temperature showed significant values of OR > 1, indicating lower values at the lag time of 3-days (q-value = 0.014), 1-week (q-value< 0.001), and 2-weeks (q-value< 0.001) than at the day of hospitalization were related to a higher odds of hospital admissions due to viral LRTI. Moreover, the relative humidity showed a significant value of OR < 1, indicating higher values of relative humidity at the lag time of 2-weeks (q-value< 0.001) before the day of the hospitalization increased the odds of LRTI hospital admission.
We also clearly observed that more LRTI hospital admissions were found when there were higher levels of NO2 and lower levels of O3. At the same time, SO2, PM10, and CO did not show a clear relationship between pollutants and LRTI hospital admissions (Fig. 2). The univariate and adjusted regression analysis between ambient air pollutants and LRTI hospital admissions showed similar OR values (Supplementary Table 2). The regression analysis adjusted by temperatures and relative humidity showed higher concentrations at the hospital admission for NO2 [compared to the lag time of 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)] and O3 [compared to the lag time of 3-days (q-value< 0.001), 1-week (q-value< 0.001), and 2-weeks (q-value< 0.001)] were related to a higher odds of hospital admissions due to viral LRTI (Fig. 3). Moreover, higher concentrations of PM10 at the lag time of 1-week (q-value = 0.023) and 2-weeks (q-value = 0.002), and CO at the lag time of 3-days (q-value = 0.023), 1-week (q-value< 0.001) and 2-weeks (q-value< 0.001)], compared to the day of hospitalization, were related to a higher chances of hospital admissions with viral LRTI (Fig. 3).
This study indicates that low temperatures, high relative humidity, and high concentrations of NO2, O3, PM10, and CO are associated with increased hospital admissions due to viral LRTI in patients 65 or older. Our data support the monitoring of environmental factors to assess the risk of hospital admissions and advise minimizing exposure to air pollutants in older people.
This study was performed for all 12 months, instead of only the colder months (December–March), when there were more hospitalized patients than during the warmer months (April–November). It is so because we wanted to analyze if there were associations between outdoor environmental pollution and LRTI hospitalizations at any time of the year (cold and warm seasons). As we showed, the epidemiological wave of viral LRTI occurs during the cold months (December–March), but there were also LRTI viral infections in the other months of the year, including summer.
Changes in weather conditions affect the respiratory system enabling the spread of infection-causing pathogens [7, 11]. These changes can increase the risk of viral LRTI and cause pneumonia, bronchitis, and other respiratory tract pathologies in older adults [9, 10]. An increase in the number of inflammatory cells and fibrinogen concentration has been observed during cold exposure, damaging the respiratory system, and leading to urgent hospitalizations and possible death [7, 11]. Besides, lower temperatures increase pathogens’ stability, abundance, survival, and infectivity . High humidity increases the infectivity of viruses because humidity stabilizes the droplets that carry the pathogen from person to person through the air . Our study found a higher risk of hospitalization for viral LRTI among older adults ≥65 years exposed to low temperatures and high relative humidity before hospital admission. Low temperatures and high humidity are associated with a higher risk of viral LRTI [24,25,26]. Our data agree with previous data showing that ambient temperatures below the reference levels potentiate respiratory tract infections and increase hospital admissions in older adults [7, 9, 10]. However, some studies show discordant data on temperature concerning our research [27, 28], partially justified because not all regions of the world have the same seasonal pattern of LRTI, finding differences in the circulation of respiratory viruses according to geographic characteristics [29,30,31].
NO2 is an irritating pollutant related to the high traffic that penetrates deep into the lung, causing respiratory diseases, including viral LRTI [2, 6]. NO2 causes an imbalance in the Th1/Th2 differentiation (increased IL-4/IFN-γ ratio) and the activation of the JAK-STAT pathway, damaging the lung cell membrane and increasing airway inflammation . Our study found an elevated risk of hospital admissions due to viral LRTI associated with short-term exposure to NO2 in older people. Our findings are consistent with other reports on short-term  and long-term  exposure to outdoor NO2 and COVID-19 in older people with respiratory failure. It may be due to NO2 inhalation oxidizing proteins and lipids and altering the immune system . However, discordant studies did not show any association between outdoor NO2 and LRTI in older people , suggesting that outdoor NO2 may impact viral LRTI in combination with other environmental pollutants rather than NO2 itself [37, 38].
O3 is a potent and toxic oxidizing gas that arises in the stratosphere or the troposphere after various reactions from photochemical smog [2, 6]. Its absorption usually occurs by inhalation, which can penetrate deeply into the lungs due to its low solubility in water. O3 reacts with cells lining the airways, stimulating their receptors and nerve endings and leading to oxidative stress, inflammation, and decreased total lung capacity . Our findings are consistent with previous reports that found significant associations between short-term exposure to ambient O3 and increased risk of pneumonia hospital admissions among older adults [40, 41]. However, discordant studies did not find a relationship between outdoor O3 and LRTI hospital admissions [42, 43].
In our study, O3 was the most critical environmental factor because it was strongly associated with viral LRTI hospital admissions, increasing with longer delay times. Interestingly, the epidemiological wave of viral LRTI occurred during the cold months (December–March), when O3 levels were lower compared to the warmer and hotter months (May–September) when older people spend much more time outdoors. The impact of O3 on the LRTI severity depends on several factors, such as viral epidemiological characteristics and O3 exposure (outdoor activities, O3 concentrations, exposure time, and susceptibility to air pollutants). The O3 sources in winter are practically the same as in summer, mainly for chemical reactions between O3 precursors in the atmosphere, such as NOX and volatile organic compounds from combustion associated with cars, planes, trains, power plants, oil refineries, factories, or evaporation of organic compounds from standard consumer products (paints, cleaning products, and solvents) . O3 levels increase when their precursor emissions react in the presence of sunlight, warm temperatures, and light winds (warm seasons). When winter arrives, the temperature and solar radiation decrease, and most of the warm air rise, displacing O3 to the upper layers of the atmosphere . However, it should also be noted that Spain has a Mediterranean climate characterized by hot summers, low winds, and intense solar radiation; and cool winters that are slightly cloudy and rainy. It affects the physical-chemical processes of O3 formation, which is why O3 continues to be generated in the cold months, with production peaks on specific days when the temperature and solar radiation are higher .
PM10 can be inhaled through small liquid or solid droplets that invade the lungs and cause long-term severe respiratory problems. PM10 has a long half-life, allowing it to spread to distant destinations, where people become exposed [2, 6]. PM10 causes lung damage by increasing inflammation and airspace epithelial permeability . Several studies have demonstrated an association between particulate matter up to 2.5 μm in size (PM2.5) and emergency visits for severe viral respiratory diseases in older patients [34, 47, 48]. Unlike our study, other studies reported no increase in LRTI hospitalizations related to PM10 , likely due to varying ambient PM10, weather conditions, and co-pollutants in different geographic areas.
CO is generated mainly during incomplete hydrocarbon combustion from internal combustion engines, waste incinerators, coal power plants, and the oil industry. CO diffuses quickly across the pulmonary membrane triggering proinflammatory responses in the airways . CO is a “silent killer” that binds to hemoglobin in the blood, forming carboxyhemoglobin that displaces oxygen, reduces oxygen-carrying capacity, and decreases the release of oxygen to tissues, increasing the risk of asphyxia-related deaths . Inhalation of CO can be toxic to the respiratory system, causing asthma exacerbation  and increased hospital admission for chronic obstructive pulmonary disease . Our data concord with other studies that found an association between outdoor CO levels and hospital admissions for viral LRTI [53,54,55] and pneumonia . Nevertheless, another report has not shown significant associations between CO and respiratory and LRTI hospital admissions [57,58,59]. These controversial results can be due to densely populated areas, urban congestion, and heavy traffic load, where the predominant air pollutants are NO2 and particulate matter. Therefore, the effects of the CO’s co-emission with these airborne pollutants may confound the contribution of CO in air pollution on health .
Strengths and limitations of the study
Our study also has several strengths that must be considered: (i) this is a nationwide study with a very high number of older adults over 65 years of age with a viral LRTI hospital admission, something challenging to reach with any other database; (ii) we use a bidirectional case-crossover design that minimizes the impact of the absence of fundamental variables in the regression analysis .
The most important limitations are the following: (i) The retrospective design may introduce biases; (ii) the lack of relevant clinical information for the correct interpretation of the data since medical history data (comorbidities and treatments) may affect hospital admission and a stratified analysis would have provided exciting information in this regard; (iii) the diagnostic bias because in the MBDS there was no specific code for the diagnosis of LRTI, and we used ICD-9-CM codes previously used in high impact factor publications [47, 61], but we do not really know the accuracy of the MBDS for LRTI diagnoses; (iv) the MBDS is anonymous and makes it difficult to control whether some older people over 65 have been hospitalized several times; (v) we did not analyze other emerging outdoor air pollutants, such as volatile organic compounds, including benzene; and (vi) lack of indoor air pollution data may have a significant impact on viral LRTIs because most people, especially the older population, spent more time indoors , facilitating the transmission of viral LRTIs among everyone.
Unfavorable environmental factors (low temperatures, high relative humidity, and high concentrations of NO2, O3, PM10, and CO) increased the odds of hospital admissions with viral LRTI among older people, indicating they are potentially vulnerable to these environmental factors.
Availability of data and materials
All relevant data is contained in the article and supplemental files. Those interested can contact Dr. Alejandro Alvaro Meca for additional information at firstname.lastname@example.org.
The MDBS is the property of the MHCSW and the environmental data of the AEMET. Therefore, any researcher can request the data related to this article from MHCSW and AEMET.
95% Confidence interval
State Meteorological Agency
Coronavirus Disease 2019
International Classification of Diseases, 9th ed., Clinical Modification
Intensive care unit
Janus kinase-signal transducer and activator of transcription
Lower respiratory tract infection
Minimum Basic Data Set
Ministry of Health, Consumer Affairs, and Social Welfare
- NO2 :
- O3 :
- PM10 :
Particulate matter up to 10 μm in size
- PM2.5 :
Particulate matter up to 2.5 μm in size
Reactive nitrogen species
Reactive oxygen species
Respiratory syncytial virus
- SO2 :
T helper cell type 1/type 2
Collaborators GL. Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory tract infections in 195 countries: a systematic analysis for the global burden of disease study 2015. Lancet Infect Dis. 2017;17(11):1133–61.
Domingo JL, Rovira J. Effects of air pollutants on the transmission and severity of respiratory viral infections. Environ Res. 2020;187:109650.
Meyer KC. The role of immunity and inflammation in lung senescence and susceptibility to infection in the elderly. Semin Respir Crit Care Med. 2010;31(5):561–74.
Pneumonia Etiology Research for Child Health (PERCH) Study Group. Causes of severe pneumonia requiring hospital admission in children without HIV infection from Africa and Asia: the PERCH multi-country case-control study. Lancet. 2019;394(10200):757–79.
Newton AH, Cardani A, Braciale TJ. The host immune response in respiratory virus infection: balancing virus clearance and immunopathology. Semin Immunopathol. 2016;38(4):471–82.
Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and health impacts of air pollution: a review. Front Public Health. 2020;8:14.
Fares A. Factors influencing the seasonal patterns of infectious diseases. Int J Prev Med. 2013;4(2):128–32.
Simoni M, Baldacci S, Maio S, Cerrai S, Sarno G, Viegi G. Adverse effects of outdoor pollution in the elderly. J Thorac Dis. 2015;7(1):34–45.
Colosia AD, Yang J, Hillson E, Mauskopf J, Copley-Merriman C, Shinde V, et al. The epidemiology of medically attended respiratory syncytial virus in older adults in the United States: a systematic review. Plos one. 2017;12(8):e0182321.
Rothberg MB, Haessler SD. Complications of seasonal and pandemic influenza. Crit Care Med. 2010;38(4):e91–7.
Moriyama M, Hugentobler WJ, Iwasaki A. Seasonality of respiratory viral infections. Annu Rev Virol. 2020;7(1):83–101.
Liu Y, Zhou Y, Lu J. Exploring the relationship between air pollution and meteorological conditions in China under environmental governance. Sci Rep. 2020;10(1):14518.
Tilstra MH, Tiwari I, Niwa L, Campbell S, Nielsen CC, Jones CA, et al. Risk and resilience: how is the health of older adults and immigrant people living in Canada impacted by climate- and air pollution-related exposures? Int J Environ Res Public Health. 2021;18(20):10575.
Song WM, Liu Y, Liu JY, Tao NN, Li YF, Liu Y, et al. The burden of air pollution and weather condition on daily respiratory deaths among older adults in China, Jinan from 2011 to 2017. Medicine (Baltimore). 2019;98(10):e14694.
Rebuli ME, Brocke SA, Jaspers I. Impact of inhaled pollutants on response to viral infection in controlled exposures. J Allergy Clin Immunol. 2021;148(6):1420–9.
Subdirección General de Información Sanitaria e Innovación: Registro de Actividad de Atención Especializada. RAE-CMBD. Avalaible on https://www.sanidad.gob.es/estadEstudios/estadisticas/cmbdhome.htm. Accessed 8 Oct 2022.
AirBase - The European air quality database. Avalaible on https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8. Accessed 8 Oct 2022.
Air Quality e-Reporting (AQ e-Reporting). Avalaible on https://www.eea.europa.eu/data-and-maps/data/aqereporting-8. Accessed 8 Oct 2022.
Janes H, Sheppard L, Lumley T. Case-crossover analyses of air pollution exposure data: referent selection strategies and their implications for bias. Epidem. 2005;16(6):717–26.
Jaakkola JJ. Case-crossover design in air pollution epidemiology. Eur Respir J Suppl. 2003;40:81s–5s.
Carracedo-Martinez E, Taracido M, Tobias A, Saez M, Figueiras A. Case-crossover analysis of air pollution health effects: a systematic review of methodology and application. Environ Health Perspect. 2010;118(8):1173–82.
The R Core team. R: a language and environment for statistical computing. Vienna, Austria Europe: The R Foundation for Statistical Computing; 2011.
Therneau T, Grambsch P. Modeling survival data: extending the cox model. In: Statistics for biology and health, vol. 14. New York, USA: Springer; 2000. p. 350.
Xu B, Wang J, Li Z, Xu C, Liao Y, Hu M, et al. Seasonal association between viral causes of hospitalised acute lower respiratory infections and meteorological factors in China: a retrospective study. Lancet Planet Health. 2021;5(3):e154–63.
Huh K, Hong J, Jung J. Association of meteorological factors and atmospheric particulate matter with the incidence of pneumonia: an ecological study. Clin Microbiol Infect. 2020;26(12):1676-83.
Adegboye OA, McBryde ES, Eisen DP. Epidemiological analysis of association between lagged meteorological variables and pneumonia in wet-dry tropical North Australia, 2006-2016. J Expo Sci Environ Epidemiol. 2019;30(3):448-58.
Gronlund CJ, Zanobetti A, Schwartz JD, Wellenius GA, O'Neill MS. Heat, heat waves, and hospital admissions among the elderly in the United States, 1992-2006. Environ Health Perspect. 2014;122(11):1187–92.
Anderson GB, Dominici F, Wang Y, McCormack MC, Bell ML, Peng RD. Heat-related emergency hospitalizations for respiratory diseases in the Medicare population. Am J Respir Crit Care Med. 2013;187(10):1098–103.
Staadegaard L, Caini S, Wangchuk S, Thapa B, de Almeida WAF, de Carvalho FC, et al. Defining the seasonality of respiratory syncytial virus around the world: national and subnational surveillance data from 12 countries. Influenza Other Respir Viruses. 2021;15(6):732–41.
Obando-Pacheco P, Justicia-Grande AJ, Rivero-Calle I, Rodriguez-Tenreiro C, Sly P, Ramilo O, et al. Respiratory syncytial virus seasonality: a global overview. J Infect Dis. 2018;217(9):1356–64.
Chadha M, Hirve S, Bancej C, Barr I, Baumeister E, Caetano B, et al. Human respiratory syncytial virus and influenza seasonality patterns-early findings from the WHO global respiratory syncytial virus surveillance. Influenza Other Respir Viruses. 2020;14(6):638–46.
Ji X, Han M, Yun Y, Li G, Sang N. Acute nitrogen dioxide (NO2) exposure enhances airway inflammation via modulating Th1/Th2 differentiation and activating JAK-STAT pathway. Chem. 2015;120:722–8.
Dales R, Blanco-Vidal C, Romero-Meza R, Schoen S, Lukina A, Cakmak S. The association between air pollution and COVID-19 related mortality in Santiago, Chile: a daily time series analysis. Environ Res. 2021;198:111284.
Kogevinas M, Castano-Vinyals G, Karachaliou M, Espinosa A, de Cid R, Garcia-Aymerich J, et al. Ambient air pollution in relation to SARS-CoV-2 infection, antibody response, and COVID-19 disease: a cohort study in Catalonia, Spain (COVICAT study). Environ Health Perspect. 2021;129(11):117003.
Petit PC, Fine DH, Vasquez GB, Gamero L, Slaughter MS, Dasse KA. The pathophysiology of nitrogen dioxide during inhaled nitric oxide therapy. ASAIO J. 2017;63(1):7–13.
Hesterberg TW, Bunn WB, McClellan RO, Hamade AK, Long CM, Valberg PA. Critical review of the human data on short-term nitrogen dioxide (NO2) exposures: evidence for NO2 NO-effect levels. Crit Rev Toxicol. 2009;39(9):743–81.
Mills IC, Atkinson RW, Anderson HR, Maynard RL, Strachan DP. Distinguishing the associations between daily mortality and hospital admissions and nitrogen dioxide from those of particulate matter: a systematic review and meta-analysis. BMJ Open. 2016;6(7):e010751.
Seaton A, Dennekamp M. Hypothesis: ill health associated with low concentrations of nitrogen dioxide--an effect of ultrafine particles? Thorax. 2003;58(12):1012–5.
Bromberg PA. Mechanisms of the acute effects of inhaled ozone in humans. Biochim Biophys Acta. 2016;1860(12):2771–81.
Luong LMT, Phung D, Dang TN, Sly PD, Morawska L, Thai PK. Seasonal association between ambient ozone and hospital admission for respiratory diseases in Hanoi, Vietnam. Plos one. 2018;13(9):e0203751.
Qiu H, Chuang KJ, Bai CH, Fan YC, Chang TP, Yim SH, et al. Association of ambient ozone with pneumonia hospital admissions in Hong Kong and Taipei: a tale of two southeast Asian cities. Environ Int. 2021;156:106634.
Atkinson RW, Bremner SA, Anderson HR, Strachan DP, Bland JM, de Leon AP. Short-term associations between emergency hospital admissions for respiratory and cardiovascular disease and outdoor air pollution in London. Arch Environ Health. 1999;54(6):398–411.
Phung D, Hien TT, Linh HN, Luong LM, Morawska L, Chu C, et al. Air pollution and risk of respiratory and cardiovascular hospitalizations in the most populous city in Vietnam. Sci Total Environ. 2016;557-558:322–30.
Cape JN. Surface ozone concentrations and ecosystem health: past trends and a guide to future projections. Sci Total Environ. 2008;400(1–3):257–69.
Adame JA, Gutierrez-Alvarez I, Cristofanelli P, Notario A, Bogeat JA, Bolivar JP, et al. Surface ozone trends at El Arenosillo observatory from a new perspective. Environ Res. 2022;214(Pt 1):113887.
Grzywa-Celinska A, Krusinski A, Milanowski J. 'Smoging kills' - effects of air pollution on human respiratory system. Ann Agric Environ Med. 2020;27(1):1–5.
Horne BD, Joy EA, Hofmann MG, Gesteland PH, Cannon JB, Lefler JS, et al. Short-term elevation of Fine particulate matter air pollution and acute lower respiratory infection. Am J Respir Crit Care Med. 2018;198(6):759–66.
Toczylowski K, Wietlicka-Piszcz M, Grabowska M, Sulik A. Cumulative effects of particulate matter pollution and meteorological variables on the risk of influenza-like illness. Viruses. 2021;13(4):556.
Ghio AJ, Smith CB, Madden MC. Diesel exhaust particles and airway inflammation. Curr Opin Pulm Med. 2012;18(2):144–50.
Rose JJ, Wang L, Xu Q, McTiernan CF, Shiva S, Tejero J, et al. Carbon monoxide poisoning: pathogenesis, management, and future directions of therapy. Am J Respir Crit Care Med. 2017;195(5):596–606.
Chatkin J, Correa L, Santos U. External environmental pollution as a risk factor for asthma. Clin Rev Allergy Immunol. 2022;62(1):72–89.
Moore E, Chatzidiakou L, Kuku MO, Jones RL, Smeeth L, Beevers S, et al. Global associations between air pollutants and chronic obstructive pulmonary disease hospitalizations. A systematic review. Ann Am Thorac Soc. 2016;13(10):1814–27.
Tenias JM, Ballester F, Perez-Hoyos S, Rivera ML. Air pollution and hospital emergency room admissions for chronic obstructive pulmonary disease in Valencia, Spain. Arch Environ Health. 2002;57(1):41–7.
Hwang JS, Chan CC. Effects of air pollution on daily clinic visits for lower respiratory tract illness. Am J Epidemiol. 2002;155(1):1–10.
Marques M, Domingo JL. Positive association between outdoor air pollution and the incidence and severity of COVID-19. A review of the recent scientific evidences. Environ Res. 2022;203:111930.
Yee J, Cho YA, Yoo HJ, Yun H, Gwak HS. Short-term exposure to air pollution and hospital admission for pneumonia: a systematic review and meta-analysis. Environ Health. 2021;20(1):6.
Martins LC, Latorre Mdo R, Cardoso MR, Goncalves FL, Saldiva PH, Braga AL. Air pollution and emergency room visits due to pneumonia and influenza in Sao Paulo, Brazil. Rev Saude Publica. 2002;36(1):88–94.
Xie J, Teng J, Fan Y, Xie R, Shen A. The short-term effects of air pollutants on hospitalizations for respiratory disease in Hefei, China. Int J Biometeorol. 2019;63(3):315–26.
Lu W, Tian Q, Xu R, Zhong C, Qiu L, Zhang H, et al. Short-term exposure to ambient air pollution and pneumonia hospital admission among patients with COPD: a time-stratified case-crossover study. Respir Res. 2022;23(1):71.
Chen TM, Gokhale J, Shofer S, Kuschner WG. Outdoor air pollution: nitrogen dioxide, sulfur dioxide, and carbon monoxide health effects. Am J Med Sci. 2007;333(4):249–56.
Alvaro-Meca A, Goez MDC, Resino R, Matias V, Sepulveda-Crespo D, Martinez I, et al. Environmental factors linked to hospital admissions in young children due to acute viral lower respiratory infections: a bidirectional case-crossover study. Environ Res. 2022;212(Pt B):113319.
Graham SE, McCurdy T. Developing meaningful cohorts for human exposure models. J Expo Anal Environ Epidemiol. 2004;14(1):23–43.
We wish to thank the Spanish MHCSW for providing the records of the MBDS and the AEMET of Spain (http://www.aemet.es/) for providing daily environmental data.
This research was supported by CIBER -Consorcio Centro de Investigación Biomédica en Red- (CB 2021), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, and Unión Europea – NextGenerationEU (CB21/13/00044). DS-C is a ‘Sara Borrell’ researcher from ISCIII (grant n° CD20CIII/00001).
Ethics approval and consent to participate
This study was approved by the Research Ethics Committee (Comité de Ética de la Investigación; CEI PI 81_2021) of the Instituto de Salud Carlos III (Madrid, Spain). All the extracted information was completely anonymous and did not require the patients’ consent.
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The authors have no competing interests.
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Supplementary Table 1. Summary of ICD-9-CM coding used for baseline comorbidities investigated in this study.
Supplementary Table 2. Summary of adjusted associations between ambient air pollutants and hospital admissions for lower respiratory tract viral infections in Spain (2013–2015).
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Álvaro-Meca, A., Sepúlveda-Crespo, D., Resino, R. et al. Neighborhood environmental factors linked to hospitalizations of older people for viral lower respiratory tract infections in Spain: a case-crossover study. Environ Health 21, 107 (2022). https://doi.org/10.1186/s12940-022-00928-x
- Older people
- Air pollution
- Respiratory virus
- Lower respiratory tract infection