SciELO - Scientific Electronic Library Online

 
vol.117 número6Trayectorias sanitarias durante 4 años de niños expuestos prenatalmente a cocaína y/o cannabis. Estudio de cohorte retrospectivo en La Pampa, ArgentinaFunción del índice de volumen plaquetario medio/linfocitos en el diagnóstico de la apendicitis durante la niñez índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

  • Não possue artigos citadosCitado por SciELO

Links relacionados

  • Não possue artigos similaresSimilares em SciELO

Compartilhar


Archivos argentinos de pediatría

versão impressa ISSN 0325-0075versão On-line ISSN 1668-3501

Arch. argent. pediatr. vol.117 no.6 Buenos Aires dez. 2019  Epub 01-Dez-2019

http://dx.doi.org/10.5546/aap.2019.368 

Original articles

Effects of contamination and climate in the Pediatric Emergency Department visits for acuterespiratory infection in the City of Buenos Aires

Fernando Ferreroa 

Rosana Abrutzkyb 

María F Ossorioc 

Fernando Torresc 

aDepartment of Medicine, Hospital General de Niños "Pedro de Elizalde", Autonomous City of Buenos AiresHospital General de Niños "Pedro de Elizalde"

bGino Germani Research Institute, Universidad de Buenos Aires

cDepartment of Education and Research, Hospital General de Niños"Pedro de Elizalde,"Autonomous City of Buenos Aires

ABSTRACT

Introduction

Pollution and climate have an impact on pediatric respiratory diseases; fewstudies have assessed this in the Autonomous City of Buenos Aires.

Objective

To assess the impact of the interaction between air pollutants and climate on the Emergency Department visits for acute lowerrespiratory tract infection (ALRTI) in a children'shospital.

Methods

Ecological, time-series study with generalized additive models that included totalvisits and visits for ALRTI to the Emergency Department between 2012 and 2016. A serieswith 7-day moving averages for ALRTI visits was founded as a bias control measure. Predictors were daily levels of air pollutants (carbon monoxide, nitrous dioxide, particulatematter < 10 p) and meteorological variables (temperature, humidity). Pollutants weremeasured at three monitoring stations. Temporalvariables (day of the week, warm/cold semester) were controlled.

Results

There were 455 256 total visits; 17 298 accounted for visits for ALRTI. A correlation wasestablished only between total visits and day ofthe week (Mondays and Saturdays, more visits;Thursdays, less visits). Less visits for ALRTI were recorded in the warm semester comparedto the cold semester (relative risk = 0.23; 95 %confidence interval: 0.29-0.18; p < 0.001). One monitoring station did not show any correlation; the other two stations showed aweak correlation between carbon monoxide andparticulate matter < 10 p and visits for ALRTI.

Conclusion

The season accurately accounts forthe increased number of total visits and visits for ALRTI. Although there was a correlation betweenthe level of certain pollutants and the number ofvisits, its impact was irrelevant.

Key words Air pollution; Climate; Respiratory tract infections; Child

INTRODUCTION

Acute lower respiratory tract infections (ALRTIs) are a major causeof pediatric morbidity and mortalityand the most common reason forvisits to the Emergency Departmentin pediatrics.1

In recent years, two threats have emerged worldwide: air pollutionand climate change.2 The relationbetween pollution, climate, and visitsfor respiratory diseases in children, especially in urban areas, is wellknown.3,4 An association has beenestablished between the season andrespiratory syncytial virus (RSV) circulation at a local level.5

The World Health Organization (WHO) has estimated that urbanpollution caused 3 700 000 prematuredeaths in 2012 (80 %, in middle- andlow-income countries). The pediatricpopulation is a vulnerable group toclimate factors.7

Few studies have assessed the interaction among environmentalpollution, climatic variations, andrespiratory disease in the Autonomous City of Buenos Aires (CABA). Most studies have been conductedin Europe or the United States of America (USA), but their results maynot be extrapolated to our setting dueto differences in pollutants, climaticaspects, and the health status of theexposed population.8,9

There is local evidence that air pollutants, in combination withclimate, may constitute a healthhazard in the adult population.10 Itis necessary to assess the impact ofthese outcome measures on the healthof particularly vulnerable subgroups.

The data of a recent study that assessed the relationship between

environmental pollutants, climate, and visits to the Emergency Department (some of whichare included in this report) indicate that climatewas the main determinant of the number of totalvisits.11 However, due to the influence of airpollution and climate on respiratory health,12 itis possible that visits for such specific conditiondevelope a different pattern.

The objective of this study was to assess if there was a correlation among air pollution levelsin the CABA, climatic variables, and the numberof visits for ALRTI to the Emergency Departmentof a children's hospital.

METHODS

Design: Ecological, time-series study with generalized additive models (GAMs).

Population: Patients who visited the Emergency Department of Hospital General de Niños "Pedro de Elizalde" (HGNPE) in the studyperiod (2012-2016).

Climate data: These were obtained from the National Weather Service, Central Station ("Villa Ortúzar").

Pollution data: These were obtained from the Environmental Protection Agency of the Autonomous City of Buenos Aires, as recordedby three automatic monitoring stations located indifferent areas of the CABA: "Parque Centenario"(residential area), "Avenida Córdoba" (high-traffic area), and "La Boca" (urban industrialarea).13 Twenty-four-hour averages were used.14

Visit data: These were obtained from the record books of visits to the Emergency Department and the registry of visits for ALRTI to the Emergency Department of HGNPE.

Study outcome measures:

  • Carbon monoxide (CO): CO levels in outsideair, in parts per billion (ppb)/parts per million (ppm). Daily average.

  • Nitrous dioxide (NO2): NO2 levels in outsideair, in parts per million (ppm). Daily average.

  • Particulate matter < 10 n (PM10): Solid orliquid particles with a diameter of less than10 microns, expressed as µ g/m3 . Dailyaverage.

  • Mean temperature: Average temperaturesrecorded in the 24 hours of each day, indegrees Celsius (°C).

  • Maximum temperature: Highest temperaturevalue recoded in the 24 hours of each day. Indegrees Celsius (°C).

  • Minimum temperature: Lowest temperaturevalue recorded in the 24 hours of each day. Indegrees Celsius (°C).

  • Relative humidity: Percentage of water vaporin the air relative to the amount of water vaporin the air that is required to saturate the air inthe same pressure and temperature conditions.

  • Cold/warm semester: The National Weather Service definition was used. For the CABA, April, May, June, July, August, and Septembermake up the cold semester, and the rest of themonths, the warm semester.

Outcome variables:

Visits for ALRTI: Number of unscheduled visits to the Emergency Department of HGNPE recorded as ALRTI each day. In addition, aseries with moving averages was developed; this procedure is used in time-series analysis tosmooth potential biases, considering the averageof a specific period as the value for each moment. In this case, a 7-day moving mean was used, considering a potential load bias related to thedays of the week.

Total visits: Number of unscheduled visits to the Emergency Department of HGNPE each day, regardless of diagnosis.

Analysis: A time-series analysis was done based on the regression analysis betweentemporal variables (pollution, climate), takenas independent variables, and number of visits, as dependent variables.15 Lags of up to 7 dayswere considered (a lag was defined as the periodbetween the change in a variable and its effectson other variables; in this case, between a changein pollutants or temperature and the change inthe number of visits). GAMs16 were used for nonparametric as well as parametric smoothing. Different models were tested and GAMs witha quasi-Poisson distribution showed the bestresults.

Ethical aspects: Approval was requested and obtained from the Research Ethics Committee of HGNPE. The study was registered in the Central Registry of Research Projects of the Ministry of Health of the Autonomous City of Buenos Aires (199/2017).

RESULTS

Complete series of visits to the Emergency Department were used, total and for ALRTI. Series of 1697 records were established (Table 1).

Table 1 General characteristics of primary variables (daily values) 

The series of primary outcome measures (total visits and visits for ALRTI) displayed differentbehaviors in relation to temporal variables (day, month, station, and warm/cold semester). Theseries of total visits showed a correlation with theday of the week; Mondays and Saturdays with ahigher average of visits (relative risk [RR] = 1.13;95 % confidence interval [CI]: 1.09-1.18; and RR = 1.13; 95 % CI: 1.09-1.17, respectively), and Thursdays with a lower average (RR = 0.94;95 % CI: 0.98-0.90). No variation was observedin relation to the visits for ALRTI and the day ofthe week.

Fewer visits were recorded in the warm semester than in the cold semester, both total (RR = 0.79; 95 % CI: 0.77-0.81; p < 0.001) and for ALRTI (RR = 0.23; 95 % CI: 0.29-0.18; p < 0.001).

The series of pollutants (Tables 2 and3) measured at "Parque Centenario" station didnot show significant correlations with any of theoutcome variables. The analysis of the other twostations showed a correlation between increased CO and NO2 and total visits, with different lagsby station and compound. The most importanteffect was related to CO: a 10-ppb CO increaseat "Avenida Córdoba" station correlated with amore than 2 % increase in the number of visitsbetween the following day and day 7, whichpeaked on day 6 with a 2.5 % increase in thenumber of visits. The increase in CO levelsmeasured at "La Boca" station peaked on day 3, when a 2.3 % increase in the number of total visitswas confirmed. NO2 levels measured at "La Boca"station also showed a correlation with the totalvisits: 2 days after a 10-ppb NO2 increase, thenumber of visits increased 3.5 %. PM10 showednegative correlations with the total visits for allthe lags analyzed at "Avenida Córdoba" and "La Boca" stations.

Table 2 Risk for general visits and visits for acute lower respiratory tract infection based on pollutant increase, considering each monitoring station (same day and 1-, 2-, and 3-day lags) 

Table 3 Risk for general visits and visits for acute lower respiratory tract infection based on pollutant increase, considering each monitoring station (4-, 5-, 6-, and 7-day lags) 

In relation to visits for ALRTI, the series showed correlations with CO, which increased5.2 % 3 days after the 10-ppb increase measuredat "Avenida Córdoba" station and 9.5 % after6 days. The 10-ppb CO increase at "La Boca"station correlated with the 13 % increase in visitsfor ALRTI after 5 days, but this was not consistentover the days. NO2 exhibited negative correlationsat "Avenida Córdoba" station and not significantcorrelations at "La Boca" station. Finally, PM10showed a weak but consistent correlation in thesame and the following day, with a 5.5 % increasein the same day and a mildly lower increase thefollowing day after an increase of 10 pg/m in PM10 levels at "Avenida Córdoba" station.

DISCUSSION

Our study has verified that the season accurately accounts for the increased numberof total visits to the Emergency Department ofa children's hospital, specifically for ALRTI, whereas, although air CO and NO2 levels show a correlation with the number of visits, their impact on this factor is not relevant.

There is evidence that air pollution may affect children's health, particularly respiratory tractdiseases. A study conducted in Sao Paulo (Brazil) observed that exposure to air pollutants increasedthe number of visits to an emergency departmentfor asthma and bronchiolitis in children, both onthe same day and with some lags.17 However, a previous study carried out in Buenos Airesobserved that the increase in the studied pollutantlevels did not have a significant impact onthe number of total visits to the emergencydepartment of a children's hospital.11 In this case, it was also verified that this did not occur whenconsidering visits for ALRTI specifically.

The CABA automatic monitoring network includes three stations, which are strategicallylocated in three areas with distinct characteristicsthat reflect different environmental scenarios: residential, urban industrial, and high-traffic areas.13 The circulation of pollutants in the cityand their dispersion to the surrounding areasis influenced by the wind and conditioned bythe height of the temperature inversion layer, and varies from day to day. However, the dataobtained from the stations, both in average andseparately, provide information about the qualityof the air to which the city and surrounding areapopulations are exposed.

More accurate studies should be done, which would require pollutant dispersion models and/or specific monitoring, but this is beyond thescope of this study. It is worth noting that, in anecological study of this type, the research subjectis the population as a whole, so it bears thelimitations entailed by it.18

Although it was not possible to verify it in our study, it does not mean that air quality does not affect patients' health, but that the impact of daily variations is not reflected on the number ofvisits. Most likely, it is influenced by the size ofthe studied population.

It should be noted that the levels of environmental pollutants in the CABA are usuallywithin acceptable ranges: as mentioned before, inthe study period, very few times, pollutant levelsexceeded the limits established by the nationallegislation (9 ppm in 8-hour averages and 35 ppmin 1-hour averages for CO, 220 ppb in 1-houraverages for NO2, and 150 µ g/m in 24-houraverages for PM10).19

In addition, on the one side, the geographic and wind characteristics of this region restrictthe accumulation of air components; on the otherside, the impact of climate (winter) on pediatricmedical visits is so big that it may mask anyother consideration.20 A study that analyzedthe information about patient's exact place ofresidence would provide additional elements todescribe the relationship between air pollutionand health in the area.

It is clear that visits for ALRTI increase significantly in the winter. This is consistent withthe fact that, in a city with temperate climatelike Buenos Aires, the main causative agents of ALRTI in children (RSV and influenza virus) circulate in the winter.21 Such seasonal variationfor different diseases (pneumonia, acute otitismedia, upper respiratory tract infections) has alsobeen described recently in a study carried out inthe USA.22

The difference observed in total visits per day of the week may be influenced by socialdynamics.23,24 The fact that visits for ALRTI do notfollow the same pattern supports this.

The establishment of a series with 7-day moving averages for the visits for ALRTI helpedto mitigate the impact of the poor quality ofcertain data.25 However, the study evidencedthe weakness of the current recording systemfor its implementation in this type of research. Possibly, the ongoing transition to electronicclinical records26 taking place in health facilitiesrun by the Government of the Autonomous Cityof Buenos Aires will remarkably improve this.27

Our results confirm, once more, the evidence of the association between the winter season andthe increase in the number of pediatric medicalvisits, and its implications for the managementof health resources.

CONCLUSION

Although a correlation was observed between the level of certain pollutants and the numberof visits, its impact is not relevant. The numberof visits to the Emergency Department of achildren's hospital for acute respiratory infectionsincreased significantly in the winter.

REFERENCIAS

1. World Health Organization. The global burden of disease: 2004 update. Geneva: WHO; 2008; [acceso 29 de mayo de 2019]. Disponible en: https://www.who.int/healthinfo/global_burden_disease/GBD_report_2004update_full.pdfLinks ]

2. Rosenzweig C, Karoly D, Vicarelli M, Neofoitis P, et al. Attributing physical and biological impacts to anthropogenicclimate change. Nature. 2008;453(7193):353-7. [ Links ]

3. Bono R, Romanazzi V, Bellisario V, Tassinari R, et al. Air pollution, aeroallergens and admissions to pediatricemergency room for respiratory reasons in Turin, northwestern Italy. BMC Public Health. 2016;16(1):722. [ Links ]

4. Bernstein AS, Rice MB. Lungs in a warming world: climatechange and respiratory health. Chest. 2013;143(5):1455-9. [ Links ]

5. Ferrero F, Torres F, Abrutzky R, Ossorio MF, et al. Circulación del virus sincicial respiratorio en Buenos Aires. Su relación con el cambio climático global. Arch Argent Pediatr. 2016;114(1):52-5. [ Links ]

6. Organización Mundial de la Salud. Calidad del aire y salud. [acceso 20 de febrero de 2017]. Disponible en: http://www.who.int/mediacentre/factsheets/fs313/es/Links ]

7. Xu Z, Etzel RA, Su H, Huang C, et al. Impact of ambienttemperature on children's health: a systematic review. Environ Res. 2012;117:120-31. [ Links ]

8. Largeron Y, Staquet C. Persistent inversion dynamics andwintertime PM10 air pollution in Alpine valleys. Atmos Environ. 2016;135:92-108. [ Links ]

9. Wanka E, Bayerstadler A, Heumann C, Nowak D, et al. Weather and air pollutants have an impact on patients withrespiratory diseases and breathing difficulties in Munich, Germany. Int J Biometeorol. 2014;58(2):249-62. [ Links ]

10. Abrutzky R, Dawidowski L, Matus P, Romero-Lankao P. Health Effects of Climate and Air Pollution in Buenos Aires: A First Time Series Analysis. J Environ Prot. 2012;3:262-71. [ Links ]

11. Abrutzky R, Torres FA, Ossorio MF, Ferrero F. Impacto de la contaminación atmosférica y el clima en las consultas a un departamento de emergencias pediátrico en la Ciudad de Buenos Aires. Rev Fac Cienc Med Córdoba. 2017;74(4):365-71. [ Links ]

12. Barry M, Annesi-Maesano I. Ten principles for climate, environment and respiratory health. Eur Respir J. 2017;50(6):1701912. [ Links ]

13. Agencia de Protección Ambiental de la Cuidad de Buenos Aires. Red de Monitoreo. [acceso 17 de abril de 2019]. Disponible en: https://www.buenosaires.gob.ar/areas/med_ambiente/apra/calidad_amb/red_monitoreo/mapa.php?menu_id=32434Links ]

14. Costa S, Ferreira J, Silveira C, Costa C, et al. Integrating healthon air quality assessment--review report on health risks oftwo major European outdoor air pollutants: PM and NO2. J Toxicol Environ Health B Crit Rev. 2014;17(6):307-40. [ Links ]

15. Peng R, Dominici F, Louis T. Model choice in time seriesstudies of air pollution and mortality. J R Stat Soc Ser A. 2006;169(2):179-203. [ Links ]

16. Pepió Viñals M. Series temporales. Barcelona: Universitat Politecnica de Catalunya; 2001. [ Links ]

17. Schvartsman C, Amador Pereira L, Ferreira Braga A, Farhat SC. Seven-day cumulative effects of air pollutants increaserespiratory ER visits up to threefold. Pediatr Pulmonol. 2017;52(2):205-12. [ Links ]

18. Morgenstern H. Uses of ecologic analysis in epidemiologicresearch. Am J Public Health. 1982;72(12):1336-44. [ Links ]

19. Decreto N.° 198/06, Anexos, reglamentario de la Ley 1356/04. In: Boletín Oficial de la Ciudad de Buenos Aires. 2394 Buenos Aires, Argentina: 8 de marzo de 2006; [acceso 23 de octubre de 2018]. Disponible en: http://www.buenosaires.gob.ar/areas/leg_tecnica/sin/imagen.php?idn=83624&idf=1Links ]

20. Abrutzky R, Dawidowski L, Murgida A, Natenzon CE. Contaminación del aire en la Ciudad Autónoma de Buenos Aires: el riesgo de hoy o el cambio climático futuro, unafalsa opción. Cien Saude Colet. 2014;19(9):3763-73. [ Links ]

21. Marcone DN, Durand LO, Azziz-Baumgartner E, Vidaurreta S, et al. Incidence of viral respiratory infectionsin a prospective cohort of outpatient and hospitalizedchildren aged <5 years and its associated cost in Buenos Aires, Argentina. BMC Infect Dis. 2015;15:447. [ Links ]

22. Lipsett SC, Monuteaux MC, Fine AM. Seasonality of Common Pediatric Infectious Diseases. Pediatr Emerg Care. 2018. [ Links ]

23. Faryar KA. The Effects of Weekday, Season, Federal Holidays, and Severe Weather Conditions on Emergency Department Volume in Montgomery County, Ohio. Wright State University;Dayton, Ohio: 2013; [acceso 18 de octubre de 2018]. Disponible en: https://corescholar.libraries.wright.edu/cgi/viewcontent.cgi?article=1094&context=mphLinks ]

24. Sun Y, Heng BH, Seow YT, Seow E. Forecasting dailyattendances at an emergency department to aid resourceplanning. BMC Emerg Med. 2009;9:1. [ Links ]

25. Bello PLD, Martinez CS. Una metodología de series detiempo para el área de la salud: caso práctico. Rev Fac Nac Salud Pública. 2007;25(2):117-22. [ Links ]

26. Tillmann T, Gibson AR, Scott G, Harrison O, et al. Systems Medicine 2.0: potential benefits of combining electronichealth care records with systems science models. J Med Internet Res. 2015;17(3):e64. [ Links ]

27. Ley N.° 5669 de Historia Clínica Electrónica. In: Boletín Oficialde la Ciudad de Buenos Aires. 5019. Buenos Aires, Argentina: 27 de octubre de 2016; [acceso 18 de octubre de 2018]. Disponible en: http://www2.cedom.gob.ar/es/legislacion/normas/leyes/ley5669.htmlLinks ]

Received: October 24, 2018; Accepted: May 27, 2019

Conflict of interest

None.

Creative Commons License Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons