This article was downloaded by: [North Carolina State University] On: 22 April 2015, At: 05:25 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
Archives of Environmental Health: An International Journal Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/vzeh20
Multipollutant Exposures and Health Responses to Particulate Matter a
a
Michael D. Lebowitz Ph.D. , James J. Quackenboss , Dr. Michal Krzyzanowski a
O'Rourke & Carl Hayes
a c
, Mary Kay
b
a
Health and Environmental Program Respiratory Sciences Center , University of Arizona , Tucson, Arizona, USA b
United States Environmental Protection Agency , Research Triangle Park, North Carolina, USA
c
Department of Medical Statistics , National Institute of Hygiene , Warsaw, Poland Published online: 03 Aug 2010.
To cite this article: Michael D. Lebowitz Ph.D. , James J. Quackenboss , Dr. Michal Krzyzanowski , Mary Kay O'Rourke & Carl Hayes (1992) Multipollutant Exposures and Health Responses to Particulate Matter, Archives of Environmental Health: An International Journal, 47:1, 71-75, DOI: 10.1080/00039896.1992.9935947 To link to this article: http://dx.doi.org/10.1080/00039896.1992.9935947
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Multipollutant Exposures and Health Responses
Downloaded by [North Carolina State University] at 05:26 22 April 2015
to Particulate Matter
MICHAEL D. LEBOWITZ JAMES J.QUACKENBOSS MICHAL KRZVUNOWSKI' MARY M Y O'ROURKE Health and Environmental Program Respiratory ScienceS Center Universihy of Arizona Tucson, Arizona CARL HAYES United States Environmental Pmtection Agency Research Triangle Park, North C a d n a
ABSTRACT. Epidemiobgkd methods provide opportunities to study interactions of pollutants in complex environments. Duringthe study of health and the environment and the evaluation of particulate matter in Tucson, we found that type, location, and teinporality of particulate nutter apowm w c ~ crit&al r with respect to the various interactions that dated to health effects. Indoor padalate matter interacted with ather cmnponents of particulate matter found in tobacco smoke, as evidenced by lung functbn. The interaction of environmental tobacco smoke with indoor fonn;rldehyde causal v a h r symptoms. Other interadons occumd betweem indoor and outdoor fonns of particulate matter, which causal symptoms in some of the subjects.
THE INDOOR ENVIRONMENT'S contribution to total exposure is substantial for many contaminants, e.g., combustion products, particulate matter (PM), and formaldehyde (HCHO).' Evaluation of total exposures requires knowledge of the distributions of concentrations of each class of contaminants, and, as contaminants interact, their joint distributions. It is difficult to evaluate pollutant interactions because many contaminant concentrations vary temporally and spatially. Therefore, the various associated impacts on health are more complex and require more intensive analyses and models. The overall health impact from such exposures is estimated to be sufficiently substantial'' to necessitate exposure-response studies. The selection of pollutants for measurement should be based primarily on the Januarykbruary1992 [Val. 47 (No. l)]
known or suspected health effects. Because contaminants may have combined effects, exposure to them and the factors that influence them should be studied in representative environmental and epidemiological ~arnples.~-~ Quantitative estimates of the contributing effea-modifier(s)factors, and the number of individuals exposed to different combinations of these factors, are required for selection of the study populations and for the efficient allocation of resources needed to assess exposures and health effects. There is a need to study mixed sources for given pollutants, e.g., environmental tobacco smoke (ETS); *Dr. Krzyzanowski is currently affiliated with the Department of Medical Statistics, National Institute d Hygiene, Warsaw, Poland.
71
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other combustion sources produce PM, carbon monoxide (CO), nitrogen dioxide (NO,), and HCHO. More important, when epidemiological studies are designed and interpreted, it should be noted that several multiple pollutants from various sources have similar, and possibly synergistic, effects.”’ For example, NO, and HCHO produce irritation; PM from environmental tobacco smoke (ETS) and HCHO irritate the eye and mucus membranes; PM, HCHO, and allergens produce “allergic” symptoms; NO, ozone (OJ, PM, and HCHO affect pulmonary function; CO, NG, and HCHO impact cognitive skills;’,’ CO, NO,, nitrates, and methylene chloride influence carboxyhemoglobin and methemoglobin levels and cardiac arrhythmias; and radon and tobacco smoke-ETS play a role in the develop ment of lung cancer. These special characteristics of exposure-response relationships necessitate the development of epidemiological designs that differ from traditional epidemiological studies.2 Specifically, nested and stratified samples are required to insure that monitoring and modeling provide for exposure assessment.6,’ This will be demonstrated by using a combination of instruments that are required to estimate personal exposures to PM.’,’ The effects on ventilatory function and respiratory and allergic symptoms in a community p o p ulation sample are evaluated over time.
Methods A study of indoor-outdoor (total) exposures and their respiratory effects is underway in Tucson, Arizona. The objective of the study is to evaluate responses of bronchial responsive subjects (those with greater than normal bronchial lability who are likely to respond more to environmental stimuli) and matched healthy subjects to total and combined air pollutant exposures. Of special interest are the combinations of pollutants from several sources, including combustion emissions (gas, wood burning, ETS), HCHO (from materials), and outdoor sources. The quality assurance and analytical procedures are based on previous studies6*’@12 and are described elsewhere.’ Exposure assessment. Each day individuals recorded in a diary the total time that they spent in five location categories: (1) home; (2) worklschool; (3) outside, near roads; (4) outside, away from roads; and (5) other indoors. Individuals spent more than 65% of their time at home, and the average time spent outdoors was 3 hld. Basic environmental inventory questionnaires and weekly household activity records were completed for each home so that presence and use of source and removal factors could be identified.” The PM2,’ and PM, samples were collected in the main living area in 50% of the homes; in 10% of the homes, these samples were collected outside of the main living area. Stratified samples were taken from homes with and without ETS. Details of these procedures are described Median PM, values are shown, by season and ETS classification, in Table 1. Indoor and outdoor levels were correlated (r = .43), and outdoor PM,, was always less than 65 C(B/m3.l4Outdoor micro PM,, correlated with estimated 72
regional PM,, (r = .63). The PM, concentrations, which were estimated for the 50% of homes in which samples were not collected, were calculated with multiple regression equations that related measured PM to information from the environmental inventory and household activity questionnaires and to outdoor PM levels and weather data. Collection of PM,, occurred almost daily at two regional stations and every sixth day at six local stations, which were located closer to the homes. PM, data obtained from local stations were compared with the two daily monitors and climatological data obtained from the National Weather Service.I6 A daily index of total personal exposure was estimated for PM,, and it was based on estimated or measured concentrations (Ci) in the five location categories (i), weighted by the daily proportion of time spent at each (Pi). Exposures to PM,, in the home were based on measured or estimated concentrations in the main living area. The exposure while “outside, away from roads” was assumed to be the same as the measurement or estimate provided by the local station, whereas exposure while ”outside, near roads” was represented by the regional PM measured downtown. Concentration estimates for PM, in two indoor locations that were not measured (workkhool and other indoors) were calculated by applying the median indoorloutdoor (IlO) ratio from nonsource homes by season to the daily local (PM,d outdoor concentrations. Because this assumed that there were no sources in these locations, exposures could have been underestimated. A daily estimate of each person’s total, time-weighted average exposure was calculated as PE = Pi x Ci.8,14,15 Seasonal concentrations, averaged NOs by season, and type of stove used are shown in Table 1.16 Outdoor O3 never exceeded 0.12 ppm; the mean 8-h maximum was 44 ppb. Ozone concentrations were higher during late spring through early fall. However, concentrations were uniform throughout the basin, and indoor levels were always less than 0.035 pprn.” The use of outdoor regional air pollutant concentrations could be of concern because of so-called ecological biases. However, the only significant source of O3 is outdoors, and the concentrations are uniform throughout the basin.” The PM,, measurements we used a p proximated the concentrations in the residence areas, and they were homogeneous for these areas. Furthermore, exposure assessments were based on information for each individual, i.e., time spent in each location. As ecological analysis principles state,” when small, homogeneous geographical areas are used to represent individuals within those areas, the correspondence is good. (Asymptotically, with decreasing size and increasing homogeneity, the ecological results and individual analyses are the same.”) Health status assessment. The methods used to assess specific health effects are dependent on the characteristics of exposures and on the anticipated responses. These methods and their quality control have been discussed extensively.’-’ Basic individual characteristics were determined from a modified standard health questionnaire.” The questionnaire also solicited information about higher education level and index Archives of EnvironmentalHealth
I
Table l.-lndoor
Season Winter SpringlFall Summer
I
Average PMt0(rdm3 and N 4 (ppb), by Sswn and Source
No. homes
PMIo medians With ETS (PMld No. homes
24
38
49
80.8 43.3 35.3
NO, means (kitchen)
Gas dove
Without ETS (PMld
No. homes
(NQd
No. homes
31.4 30.0 17.5
20 22 37
25.3 23.9 17.7
54
26 37 49
65 77
Elec. stove (NO,) 10.1 8.2 9.4
Sou~es:Quackenboss et al.i43
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Note: Measurementswere inteerated weekly.
used for socioeconomic status (SES), and it contained additional sections in which normal activity patterns and symptomatic responses to environmental factors could be identified. Peak expiratory flow rates (PEFR) were measured with mini-Wright peak flow meters, and measurements occurred up to four time periods each day: (1) morning (on rising), (2) near noon, (3) evening (4-7 P.M.), and (4) prior to bedtime. Each sub ject was trained to use the peak flow meter. They were to perform three tests during each time period (i.e., morning, noon, evening, bedtime), and they recorded the largest measurement in their diary. Symptoms, medication use, and type of medication taken each day were also recorded in the diaries. Statistical methods. Pollutant concentrations and exposure estimates were evaluated as distributions over time.14 Typical descriptive analyses were performed prior to the application of univariate and multivariate ANOVAlANCOVA models. These were also used to analyze daily variations in values. The relationship of PEFR to the exposure variables was analyzed with a version of the random effect longitudinal mode1,25,26 which also accounted for the autocorrelation of PEFR measurements in each subjects. The application of this model to these data has been described e1se~here.l~ The relationship between symptoms and exposure was analyzed with a regressive logistic model," which is a version of logistic regression that allows for multiple data per individual. Observation of a symptom each day was adjusted for the occurrence of the symptom the previous day and for the proportion of days during the preceding study period that the symptom was o h served.
Results Indoor/outdoor PM data. As found previously, the indoor levels of PM, and PM, were markedly higher in homes where cigarettes were smoked at the time of the survey than in homes with no ETS, especially during the winter (Table 1). The indoor/outdoor ratio in homes without I T S was about 0.5 in the summer and increased to 0.75 in the spring and fall; the ratio during the winter was 0.8.14In homes with ETS, the variability of PM levels was much greater than in homes without smokers; interquartile ranges were 40.8-104.9 and 24.3-42.9, respectively, during the winter. This reflectJanuaylFebruary1992 [Vd. 47 (No.111
ed the variability in the frequency of smoking indoors. Thus, the estimates of daily total personal exposure were markedly higher for subjects who lived in homes with ETS than for residents of homes in which there were no smokers at the time of the survey. Previous findings. The prevalence rates of acute respiratory illness symptoms, after adjusting for age and sex, were related to indoor PMZ-5; higher rates were experienced by younger female^.^ These period prevalence rates were related significantly to U S in households where there was a lower SES index; the relative risk ratio for acute respiratory illness symptoms was up to 2.18 when more than one pack of cigarettes was smoked each day in these homes.16 Prevalence rates of nonspecific symptoms in children and their mothers were also related to Prevalence rates of allergidirritant symptoms were also related to a combination/interaction of ETS and bedroom HCHO levels.'6 The regressive logistic approach was used to remove autocorrelations in symptoms, and the only significant increase in the risk of symptoms from outdoor PM was for allergic symptoms in asthmatic children. This effect was limited to the spring months, during which the odds ratio was 1.92 (95% confidence interval, 0.90-4.20 per 10 Ccg/m3increase of PMl,,"). Daily variability in PEFR-mostly the result of ETS-was also related significantly to indoor PMz.5; age and sex were controlled for by using the log linear model.16 After adjusting for age and sex," diurnal (bronchial) responsiveness was related to ETS in homes where there were higher PM,o exposures. C u m t analyses. The random effects model (REM)" enabled us to detect the effects (significant) of exposure for only 30 children who had a current diagnosis of asthma (based on 674 PEFR measurements). The PEFR in the morning was significantly lower in children who lived in homes where there were higher concentrations of indoor PM (relationshipwith PM.5 was slightly better than with PM,,). The PEFR in the morning was further decreased in inhabitants of houses where cigarettes were smoked in the bedrooms. These effects were not modified when cold or asthma medication use data were included in the model, although the latter factor was significantly related to the improvement of PEFR during the morning following the use of medications. The calculated index of daily total PMl0 exposure was also tested. The estimated effects (using the REM) on 73
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morning PEFR in the asthmatic children were significant (p < .05) when the index was the only variable that corresponded to the exposure in the model. The coefficient was -1.12 + 0.30 I/min/(Ccg/m3)for the personal PM, exposure index. However, the models with the measured PM data were better in terms of likelihood function, and when both the index and measurements were included to the models, only the latter remained significant predictors of PEFR." Analysis of daily population changes in symptoms and PEFR. There were 1 007 d of observation during which we had information on an average of 14 subjects per day, indoor ETS and gas range use (an average of 30% of subjects), time-activity budgets, work, and outdoor exposures. A total of 16.7% of the population were active smokers, and 25% (average) of the entire population were under age 15 y; during a typical day, approximately 39% of the population were exposed to ETS. ANCOVA was used to control for these factors, and it was shown that daily rates of acute respiratory (ARI) symptoms were related to outdoor temperature and PM,. If the maximum temperature exceeded 36 "C (95 OF), and if PM, was less than 45 pg/m3, the daily ARI prevalence rate was only 10%; however, this increased to 14% (45-70 pg/m3) and then to 17.7% when PM, exceeded 70 &m3. An increase in allergidirritant symptoms was related to the interaction between outdoor PM, and 0,. When the previous day's 8-h maximum average 0, exceeded 56 ppb, the symp tom prevalence rates were increased from 21.6% to 35.1 %, which corresponded to PM, concentrations below and above 50 pg/m3, respectively. (Other meteorologic, pollutant, and confounding factors were not significant in these relationships.) There was an independent effect of outdoor PM, on PEFR. PM, with temperature, caused an even more significant modification in evening PEFRs (Table 2). Standard normal deviates V scores) were used and were adjusted for season, proportion of children in the population, other significant pollutants, weather, and confounding variables; the resulting change was greater than 1 standard deviation unit. After adjusting for other variables (Table 3), PM, independently and significantly affected PEFR values in the morning, although not to the same extent. Again, there was an interaction with temperature, but this time it was with the
previous day's maximum. High values of the two combined (> 50 pg/m3 and > 95 OF) were associated with the greatest reduction in PEFR. (A slight significant effect of PM, on bedtime PEFR was also.observed.)
Discussion Estimates of personal exposure to PM,, correlated well with indoor home estimates. This was expected because people spend more than 65% of their time at h~me.'""'~,'~The measured indoor PM concentration was a predictor of morning PEFR, as was the index of ETS in the homes. The interaction of the two was related to increased bronchial responsiveness. There was an increased prevalence of acute respiratory symptoms as indoor ETS and PM increased, especially in lower SES households; an increased incidence of nonspecific symptoms was related to increased ETS. Normally, the incidence of ARls decreased as the temperature increased. However, if PM, or 0, was high, this did not occur; in fact, ARI symptoms increased as temperature increased. Increases in allergidirritant symptoms, which were observed during summer and fall, were related to PM, and temperature. Significant increases in allergic symptoms, resulting from the HCHO-ETS interaction indoors, were noted, and these increases were also present on spring days when there were higher outdoor levels of PM. This latter increase can be related to a greater concentration of PM allergens during the blooming season.29 Furthermore, pollen interacts with PM and 0, outdoors, thus further exacerbating asthmatic and allergic symptoms.' Thus, the composition of PM needs to be characterized better, both physically/chernicallyand biologically. The relationship between outdoor PM and 0,with a decrease in PEFR, originally reported previ~usly,~,~ was confirmed. There was a surprisingly significant interaction' between indoor ETS and outdoor 0, in the REM analysis for PEFR.17 Further, when the percentage of children in the population was considered, and when temperature was controlled for, outdoor PM, interacted significantly with PEFR. Also, ETS and outdoor temperature appeared to interact similarly. Thus, different forms of outdoor and indoor PM interact with each other and with certain gases. Surprisingly, other expected interactions were not seen. Formaldehyde
Table l.-Effect of PM,, (During Spring, Dry Summer, and Fall) on Evening PERFS, as Adjusted* Deviations from the Grand Mean by ANOVA PM1o (ccB/m3)
< 50 > 50 Grand mean
Adjusted deviations
With max. temp > 95 O F adjusted deviations
Max. temp ( O F alone)
Adjusted deviations
+ 0.58
+ 3.39 + 25.60
70-950
+3.39 - 2.46
-
-0.91
> 95O
458.86
*Percentage of children (significant); smoking, work exposure, indoor gas, and outdoor NOz (all nonsignificant).
74
Alchiws of EnvironmentalHealth
Table 3.-Effect of Tanpmture on Moming PEFR (Vmin) Temperature (OF) 70-95 >9 5
PMl0
< 70
< 50
437.9 461.4
>50
448.2 448.3
Adjusted deviations. from thegrand mean
452.1 419.3
+ 1.14 -1.76
OANCOVA, p < .036.
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did not interact with pollen or NOs in relation to bronchial responsiveness went unaffected; nor did PM interact with NOz, as evidenced by the lack of symptoms. These and other interactions of indoor and outdoor PM, and interactions with other indoor or outdoor pollutants, need further exploration when the data set is larger.
********** This study has been supported by EPA Cooperative Agreement wCR811806 and EPRl Contract No. RP2822-1. Michal Krzyzanowski was the recipient of International Fogaw Fellowship, National Institutes of Health, grant #l-FO5-TW03940. Although the research described in this article has been funded in part by the EPA, it has not been subjected to the agency's required peer and policy review and, therefore, does not necessarily d e c t the views of the agency, and no olfcial endorsement should be inferred. Informed consent was o b tained from each person who participated in the study after the nature of the study and study procedures were explained fully. Requests for reprints should be sent to: Michael D. Lebowitz, Ph.D., Respiratory Sciences Center, University of Arizona College of Medicine, 1501 N. Campbell Avenue, Tucson, AZ 85724.
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**********
1. National Academy of Sciences (NAS). Indoor Pollutants. Washington, D.C.: National Academy Press, 1981. 2. World Health Organization WHO). Methodology in indoor air quality research. WHO/EURO Report 103. GenevatCopenhagen: WHO, 1986. 3. World Health Oganization WHO). Guidelines on studies in environmental epidemiology. Geneva: WHO (Environmental Health Criteria #27), 1983. 4. World Health Oganization WHO). Indoor air pollutants: exposure and health effects. Copenhagen: WHO/EURO Reports and Studies #78. 5. Cassell E, Lebowitz MD. The utility of the multiplex variable in understandingcausality. Perspect Bio Med 1976; 19(3):338-41. 6. Leaderer BP, Zagraniski RT, Berwick M. Stdwijk JAJ.Assessment of exposure to indoor air contaminants from combustion sources: methodology and application. Am J Epidemiol 1986; 12427589. 7. Quackenboss JJ, Lebowitz MD, Hayes C. Epidemiological study of respiratory responsesto indoorloutdoor air qualii. Environ International 1989; 15:493-502. 8. Spender ID, Treitman RD, Tostem TD, Mage DT, S w e k ML. Personal exposures to respirable particulates and implications for air pollution epidemiology. Environ Sci Technol 1985; 1 9
januay/Febmay 1992 [Vd. 47 (No. l)]
700-07. 9. Quackenboss JJ, Lebowitz MD, Crutchfield CD. Indooratdoor relationships for particulate matter: exposure classification and health effects. Environ International 115:353-60. 10. Holberg Cj, ORourke MK, Lebowitz MD. Multivariate analysis of ambient environmental factors and respiratory effeas. Int J Epidemiol 1 987; 16399-410. 11. Lebowitz MD, Collins L, Holbeg C. Time series analysis of respiratory responses to i n d p and outdoor environmental phenomena. Environ Res 1987; 4333241. 12. Quackenboss JJ, Spengler ID, Letz R, Duffy CP. Personal exposure to nitrogen dioxide: relationship to indoorloutdoor air quality and activity patterns. Environ Sci and Technol 1% 20(8):775-83. 13. Lebowitz MD, Quackenboss JJ, Kollander M, Souek ML, Colome S. The new standard questionnaire for estimation d indoor concentrations. J Air Pollut Control Assoc 1989; 391411-19. 14. Quackenboss JJ, Krzyzanomki M, Lebowitz MD. Exposure assessment approaches to evaluate respiratory health effects of particulate matter and nitrogen dioxide. J Expos Assess Environ Epidemiol 1 (1):83-107. 15. Quackenboss I], Spengler ID, Kanarek MS, Letz R, Duffy CP. Personal exposure to nitrogen dioxide: relationship to indoorloutdoor air quality and activity patterns. Environ Sci Technol 1986; 2 0 775-83. 16. Quackenboss JJ, Lebowitz MD, Hayes C, Young CL. Respiratory responses to indoorloutdoor air pollutants: combustion pollutants, formaldehyde, and particulate matter. In: Harper JP Ed. Combustion processes and the quality of the indoor e n v i m ment. Pittsburgh: AWMA, 1989;280-93. 17. Krzyzanmki M, Quackenboss JJ, Lebowitz MD. Acute respiratory effects of prolonged ambient ozone. Arch Environ Health (in Press). 18. Lebowitz MD. A critical examination of factorial ecology and social area analysis for epidemiological research. J Ariz Acad Sci 1977; 12B6-W. 19. Epidemiology Standardization Project. Am Rev Respir Dis 1978 1 la(SUppl.). 20. Wright BM. A miniature Wright peak flow meter. Br Med J 1978; 211627-28. 21. Perks WH, Tams IP, Thompson DA, Prowse K. An evaluation of the mini-Wright peak flow meter. Thorax 1979; 3479-81. 22. Lebowitz MD, Knudson RJ, Robertson G, Burraws 6. Significance of intraindividual changes in maximum expiratory flow volume and peak expiratory flow measurements. Chest 1981; 81:%-70. 23. Quackenboss JJ, Lebowitz MD, Krzyzarowtki M. The m a 1 range of diurnal changes in peak expiratory flow rates: relationship to symptoms and respiratory disease. Am Rev Respir Dis 1991; 143~323-30. 24. Lebowitz MD. The use of peak expiratory flow rate measurements in respiratory disease. A stated-the-art review. Ped Pulmonol 1991; 11:166-74. 25. Jones RH. Time series analysk with unequally spaced data. In: Hannan EJ, Krishnaiah PR, Rao MM, Eds. Handbook of statistics. North-Holland, 1987a; 157-77 (vol. 5). 26. Jones RH. Serial correlation in unbalanced mixed models. Bull Int Stat Inst. Proceedings of the 46th Session of International Statistical Institute. Tokyo: September 8-16, 1987; 105-22 (Book 4). 27. Bonney GE. Logistic regression for dependent binary observations. Biometrics 1987; 43951. 28. Lebowitz MD, Quackenboss JJ. The effectson environmental tobacco smoke on pulmonary function. Int Arch Occup Envim Health (Suppl.) 1990, 147-52. 29. ORourke MK, Quackenboss JJ, Lebowii MD. Respiratay disease response in sensitive individuals to indoor and outdoor pollen exposure in Tucson, Arizona. Aerobiologica 1991 (in press).
7s
Archives of Environmental Health: An International Journal Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/vzeh20
Multipollutant Exposures and Health Responses to Particulate Matter a
a
Michael D. Lebowitz Ph.D. , James J. Quackenboss , Dr. Michal Krzyzanowski a
O'Rourke & Carl Hayes
a c
, Mary Kay
b
a
Health and Environmental Program Respiratory Sciences Center , University of Arizona , Tucson, Arizona, USA b
United States Environmental Protection Agency , Research Triangle Park, North Carolina, USA
c
Department of Medical Statistics , National Institute of Hygiene , Warsaw, Poland Published online: 03 Aug 2010.
To cite this article: Michael D. Lebowitz Ph.D. , James J. Quackenboss , Dr. Michal Krzyzanowski , Mary Kay O'Rourke & Carl Hayes (1992) Multipollutant Exposures and Health Responses to Particulate Matter, Archives of Environmental Health: An International Journal, 47:1, 71-75, DOI: 10.1080/00039896.1992.9935947 To link to this article: http://dx.doi.org/10.1080/00039896.1992.9935947
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions
Multipollutant Exposures and Health Responses
Downloaded by [North Carolina State University] at 05:26 22 April 2015
to Particulate Matter
MICHAEL D. LEBOWITZ JAMES J.QUACKENBOSS MICHAL KRZVUNOWSKI' MARY M Y O'ROURKE Health and Environmental Program Respiratory ScienceS Center Universihy of Arizona Tucson, Arizona CARL HAYES United States Environmental Pmtection Agency Research Triangle Park, North C a d n a
ABSTRACT. Epidemiobgkd methods provide opportunities to study interactions of pollutants in complex environments. Duringthe study of health and the environment and the evaluation of particulate matter in Tucson, we found that type, location, and teinporality of particulate nutter apowm w c ~ crit&al r with respect to the various interactions that dated to health effects. Indoor padalate matter interacted with ather cmnponents of particulate matter found in tobacco smoke, as evidenced by lung functbn. The interaction of environmental tobacco smoke with indoor fonn;rldehyde causal v a h r symptoms. Other interadons occumd betweem indoor and outdoor fonns of particulate matter, which causal symptoms in some of the subjects.
THE INDOOR ENVIRONMENT'S contribution to total exposure is substantial for many contaminants, e.g., combustion products, particulate matter (PM), and formaldehyde (HCHO).' Evaluation of total exposures requires knowledge of the distributions of concentrations of each class of contaminants, and, as contaminants interact, their joint distributions. It is difficult to evaluate pollutant interactions because many contaminant concentrations vary temporally and spatially. Therefore, the various associated impacts on health are more complex and require more intensive analyses and models. The overall health impact from such exposures is estimated to be sufficiently substantial'' to necessitate exposure-response studies. The selection of pollutants for measurement should be based primarily on the Januarykbruary1992 [Val. 47 (No. l)]
known or suspected health effects. Because contaminants may have combined effects, exposure to them and the factors that influence them should be studied in representative environmental and epidemiological ~arnples.~-~ Quantitative estimates of the contributing effea-modifier(s)factors, and the number of individuals exposed to different combinations of these factors, are required for selection of the study populations and for the efficient allocation of resources needed to assess exposures and health effects. There is a need to study mixed sources for given pollutants, e.g., environmental tobacco smoke (ETS); *Dr. Krzyzanowski is currently affiliated with the Department of Medical Statistics, National Institute d Hygiene, Warsaw, Poland.
71
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other combustion sources produce PM, carbon monoxide (CO), nitrogen dioxide (NO,), and HCHO. More important, when epidemiological studies are designed and interpreted, it should be noted that several multiple pollutants from various sources have similar, and possibly synergistic, effects.”’ For example, NO, and HCHO produce irritation; PM from environmental tobacco smoke (ETS) and HCHO irritate the eye and mucus membranes; PM, HCHO, and allergens produce “allergic” symptoms; NO, ozone (OJ, PM, and HCHO affect pulmonary function; CO, NG, and HCHO impact cognitive skills;’,’ CO, NO,, nitrates, and methylene chloride influence carboxyhemoglobin and methemoglobin levels and cardiac arrhythmias; and radon and tobacco smoke-ETS play a role in the develop ment of lung cancer. These special characteristics of exposure-response relationships necessitate the development of epidemiological designs that differ from traditional epidemiological studies.2 Specifically, nested and stratified samples are required to insure that monitoring and modeling provide for exposure assessment.6,’ This will be demonstrated by using a combination of instruments that are required to estimate personal exposures to PM.’,’ The effects on ventilatory function and respiratory and allergic symptoms in a community p o p ulation sample are evaluated over time.
Methods A study of indoor-outdoor (total) exposures and their respiratory effects is underway in Tucson, Arizona. The objective of the study is to evaluate responses of bronchial responsive subjects (those with greater than normal bronchial lability who are likely to respond more to environmental stimuli) and matched healthy subjects to total and combined air pollutant exposures. Of special interest are the combinations of pollutants from several sources, including combustion emissions (gas, wood burning, ETS), HCHO (from materials), and outdoor sources. The quality assurance and analytical procedures are based on previous studies6*’@12 and are described elsewhere.’ Exposure assessment. Each day individuals recorded in a diary the total time that they spent in five location categories: (1) home; (2) worklschool; (3) outside, near roads; (4) outside, away from roads; and (5) other indoors. Individuals spent more than 65% of their time at home, and the average time spent outdoors was 3 hld. Basic environmental inventory questionnaires and weekly household activity records were completed for each home so that presence and use of source and removal factors could be identified.” The PM2,’ and PM, samples were collected in the main living area in 50% of the homes; in 10% of the homes, these samples were collected outside of the main living area. Stratified samples were taken from homes with and without ETS. Details of these procedures are described Median PM, values are shown, by season and ETS classification, in Table 1. Indoor and outdoor levels were correlated (r = .43), and outdoor PM,, was always less than 65 C(B/m3.l4Outdoor micro PM,, correlated with estimated 72
regional PM,, (r = .63). The PM, concentrations, which were estimated for the 50% of homes in which samples were not collected, were calculated with multiple regression equations that related measured PM to information from the environmental inventory and household activity questionnaires and to outdoor PM levels and weather data. Collection of PM,, occurred almost daily at two regional stations and every sixth day at six local stations, which were located closer to the homes. PM, data obtained from local stations were compared with the two daily monitors and climatological data obtained from the National Weather Service.I6 A daily index of total personal exposure was estimated for PM,, and it was based on estimated or measured concentrations (Ci) in the five location categories (i), weighted by the daily proportion of time spent at each (Pi). Exposures to PM,, in the home were based on measured or estimated concentrations in the main living area. The exposure while “outside, away from roads” was assumed to be the same as the measurement or estimate provided by the local station, whereas exposure while ”outside, near roads” was represented by the regional PM measured downtown. Concentration estimates for PM, in two indoor locations that were not measured (workkhool and other indoors) were calculated by applying the median indoorloutdoor (IlO) ratio from nonsource homes by season to the daily local (PM,d outdoor concentrations. Because this assumed that there were no sources in these locations, exposures could have been underestimated. A daily estimate of each person’s total, time-weighted average exposure was calculated as PE = Pi x Ci.8,14,15 Seasonal concentrations, averaged NOs by season, and type of stove used are shown in Table 1.16 Outdoor O3 never exceeded 0.12 ppm; the mean 8-h maximum was 44 ppb. Ozone concentrations were higher during late spring through early fall. However, concentrations were uniform throughout the basin, and indoor levels were always less than 0.035 pprn.” The use of outdoor regional air pollutant concentrations could be of concern because of so-called ecological biases. However, the only significant source of O3 is outdoors, and the concentrations are uniform throughout the basin.” The PM,, measurements we used a p proximated the concentrations in the residence areas, and they were homogeneous for these areas. Furthermore, exposure assessments were based on information for each individual, i.e., time spent in each location. As ecological analysis principles state,” when small, homogeneous geographical areas are used to represent individuals within those areas, the correspondence is good. (Asymptotically, with decreasing size and increasing homogeneity, the ecological results and individual analyses are the same.”) Health status assessment. The methods used to assess specific health effects are dependent on the characteristics of exposures and on the anticipated responses. These methods and their quality control have been discussed extensively.’-’ Basic individual characteristics were determined from a modified standard health questionnaire.” The questionnaire also solicited information about higher education level and index Archives of EnvironmentalHealth
I
Table l.-lndoor
Season Winter SpringlFall Summer
I
Average PMt0(rdm3 and N 4 (ppb), by Sswn and Source
No. homes
PMIo medians With ETS (PMld No. homes
24
38
49
80.8 43.3 35.3
NO, means (kitchen)
Gas dove
Without ETS (PMld
No. homes
(NQd
No. homes
31.4 30.0 17.5
20 22 37
25.3 23.9 17.7
54
26 37 49
65 77
Elec. stove (NO,) 10.1 8.2 9.4
Sou~es:Quackenboss et al.i43
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Note: Measurementswere inteerated weekly.
used for socioeconomic status (SES), and it contained additional sections in which normal activity patterns and symptomatic responses to environmental factors could be identified. Peak expiratory flow rates (PEFR) were measured with mini-Wright peak flow meters, and measurements occurred up to four time periods each day: (1) morning (on rising), (2) near noon, (3) evening (4-7 P.M.), and (4) prior to bedtime. Each sub ject was trained to use the peak flow meter. They were to perform three tests during each time period (i.e., morning, noon, evening, bedtime), and they recorded the largest measurement in their diary. Symptoms, medication use, and type of medication taken each day were also recorded in the diaries. Statistical methods. Pollutant concentrations and exposure estimates were evaluated as distributions over time.14 Typical descriptive analyses were performed prior to the application of univariate and multivariate ANOVAlANCOVA models. These were also used to analyze daily variations in values. The relationship of PEFR to the exposure variables was analyzed with a version of the random effect longitudinal mode1,25,26 which also accounted for the autocorrelation of PEFR measurements in each subjects. The application of this model to these data has been described e1se~here.l~ The relationship between symptoms and exposure was analyzed with a regressive logistic model," which is a version of logistic regression that allows for multiple data per individual. Observation of a symptom each day was adjusted for the occurrence of the symptom the previous day and for the proportion of days during the preceding study period that the symptom was o h served.
Results Indoor/outdoor PM data. As found previously, the indoor levels of PM, and PM, were markedly higher in homes where cigarettes were smoked at the time of the survey than in homes with no ETS, especially during the winter (Table 1). The indoor/outdoor ratio in homes without I T S was about 0.5 in the summer and increased to 0.75 in the spring and fall; the ratio during the winter was 0.8.14In homes with ETS, the variability of PM levels was much greater than in homes without smokers; interquartile ranges were 40.8-104.9 and 24.3-42.9, respectively, during the winter. This reflectJanuaylFebruary1992 [Vd. 47 (No.111
ed the variability in the frequency of smoking indoors. Thus, the estimates of daily total personal exposure were markedly higher for subjects who lived in homes with ETS than for residents of homes in which there were no smokers at the time of the survey. Previous findings. The prevalence rates of acute respiratory illness symptoms, after adjusting for age and sex, were related to indoor PMZ-5; higher rates were experienced by younger female^.^ These period prevalence rates were related significantly to U S in households where there was a lower SES index; the relative risk ratio for acute respiratory illness symptoms was up to 2.18 when more than one pack of cigarettes was smoked each day in these homes.16 Prevalence rates of nonspecific symptoms in children and their mothers were also related to Prevalence rates of allergidirritant symptoms were also related to a combination/interaction of ETS and bedroom HCHO levels.'6 The regressive logistic approach was used to remove autocorrelations in symptoms, and the only significant increase in the risk of symptoms from outdoor PM was for allergic symptoms in asthmatic children. This effect was limited to the spring months, during which the odds ratio was 1.92 (95% confidence interval, 0.90-4.20 per 10 Ccg/m3increase of PMl,,"). Daily variability in PEFR-mostly the result of ETS-was also related significantly to indoor PMz.5; age and sex were controlled for by using the log linear model.16 After adjusting for age and sex," diurnal (bronchial) responsiveness was related to ETS in homes where there were higher PM,o exposures. C u m t analyses. The random effects model (REM)" enabled us to detect the effects (significant) of exposure for only 30 children who had a current diagnosis of asthma (based on 674 PEFR measurements). The PEFR in the morning was significantly lower in children who lived in homes where there were higher concentrations of indoor PM (relationshipwith PM.5 was slightly better than with PM,,). The PEFR in the morning was further decreased in inhabitants of houses where cigarettes were smoked in the bedrooms. These effects were not modified when cold or asthma medication use data were included in the model, although the latter factor was significantly related to the improvement of PEFR during the morning following the use of medications. The calculated index of daily total PMl0 exposure was also tested. The estimated effects (using the REM) on 73
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morning PEFR in the asthmatic children were significant (p < .05) when the index was the only variable that corresponded to the exposure in the model. The coefficient was -1.12 + 0.30 I/min/(Ccg/m3)for the personal PM, exposure index. However, the models with the measured PM data were better in terms of likelihood function, and when both the index and measurements were included to the models, only the latter remained significant predictors of PEFR." Analysis of daily population changes in symptoms and PEFR. There were 1 007 d of observation during which we had information on an average of 14 subjects per day, indoor ETS and gas range use (an average of 30% of subjects), time-activity budgets, work, and outdoor exposures. A total of 16.7% of the population were active smokers, and 25% (average) of the entire population were under age 15 y; during a typical day, approximately 39% of the population were exposed to ETS. ANCOVA was used to control for these factors, and it was shown that daily rates of acute respiratory (ARI) symptoms were related to outdoor temperature and PM,. If the maximum temperature exceeded 36 "C (95 OF), and if PM, was less than 45 pg/m3, the daily ARI prevalence rate was only 10%; however, this increased to 14% (45-70 pg/m3) and then to 17.7% when PM, exceeded 70 &m3. An increase in allergidirritant symptoms was related to the interaction between outdoor PM, and 0,. When the previous day's 8-h maximum average 0, exceeded 56 ppb, the symp tom prevalence rates were increased from 21.6% to 35.1 %, which corresponded to PM, concentrations below and above 50 pg/m3, respectively. (Other meteorologic, pollutant, and confounding factors were not significant in these relationships.) There was an independent effect of outdoor PM, on PEFR. PM, with temperature, caused an even more significant modification in evening PEFRs (Table 2). Standard normal deviates V scores) were used and were adjusted for season, proportion of children in the population, other significant pollutants, weather, and confounding variables; the resulting change was greater than 1 standard deviation unit. After adjusting for other variables (Table 3), PM, independently and significantly affected PEFR values in the morning, although not to the same extent. Again, there was an interaction with temperature, but this time it was with the
previous day's maximum. High values of the two combined (> 50 pg/m3 and > 95 OF) were associated with the greatest reduction in PEFR. (A slight significant effect of PM, on bedtime PEFR was also.observed.)
Discussion Estimates of personal exposure to PM,, correlated well with indoor home estimates. This was expected because people spend more than 65% of their time at h~me.'""'~,'~The measured indoor PM concentration was a predictor of morning PEFR, as was the index of ETS in the homes. The interaction of the two was related to increased bronchial responsiveness. There was an increased prevalence of acute respiratory symptoms as indoor ETS and PM increased, especially in lower SES households; an increased incidence of nonspecific symptoms was related to increased ETS. Normally, the incidence of ARls decreased as the temperature increased. However, if PM, or 0, was high, this did not occur; in fact, ARI symptoms increased as temperature increased. Increases in allergidirritant symptoms, which were observed during summer and fall, were related to PM, and temperature. Significant increases in allergic symptoms, resulting from the HCHO-ETS interaction indoors, were noted, and these increases were also present on spring days when there were higher outdoor levels of PM. This latter increase can be related to a greater concentration of PM allergens during the blooming season.29 Furthermore, pollen interacts with PM and 0, outdoors, thus further exacerbating asthmatic and allergic symptoms.' Thus, the composition of PM needs to be characterized better, both physically/chernicallyand biologically. The relationship between outdoor PM and 0,with a decrease in PEFR, originally reported previ~usly,~,~ was confirmed. There was a surprisingly significant interaction' between indoor ETS and outdoor 0, in the REM analysis for PEFR.17 Further, when the percentage of children in the population was considered, and when temperature was controlled for, outdoor PM, interacted significantly with PEFR. Also, ETS and outdoor temperature appeared to interact similarly. Thus, different forms of outdoor and indoor PM interact with each other and with certain gases. Surprisingly, other expected interactions were not seen. Formaldehyde
Table l.-Effect of PM,, (During Spring, Dry Summer, and Fall) on Evening PERFS, as Adjusted* Deviations from the Grand Mean by ANOVA PM1o (ccB/m3)
< 50 > 50 Grand mean
Adjusted deviations
With max. temp > 95 O F adjusted deviations
Max. temp ( O F alone)
Adjusted deviations
+ 0.58
+ 3.39 + 25.60
70-950
+3.39 - 2.46
-
-0.91
> 95O
458.86
*Percentage of children (significant); smoking, work exposure, indoor gas, and outdoor NOz (all nonsignificant).
74
Alchiws of EnvironmentalHealth
Table 3.-Effect of Tanpmture on Moming PEFR (Vmin) Temperature (OF) 70-95 >9 5
PMl0
< 70
< 50
437.9 461.4
>50
448.2 448.3
Adjusted deviations. from thegrand mean
452.1 419.3
+ 1.14 -1.76
OANCOVA, p < .036.
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did not interact with pollen or NOs in relation to bronchial responsiveness went unaffected; nor did PM interact with NOz, as evidenced by the lack of symptoms. These and other interactions of indoor and outdoor PM, and interactions with other indoor or outdoor pollutants, need further exploration when the data set is larger.
********** This study has been supported by EPA Cooperative Agreement wCR811806 and EPRl Contract No. RP2822-1. Michal Krzyzanowski was the recipient of International Fogaw Fellowship, National Institutes of Health, grant #l-FO5-TW03940. Although the research described in this article has been funded in part by the EPA, it has not been subjected to the agency's required peer and policy review and, therefore, does not necessarily d e c t the views of the agency, and no olfcial endorsement should be inferred. Informed consent was o b tained from each person who participated in the study after the nature of the study and study procedures were explained fully. Requests for reprints should be sent to: Michael D. Lebowitz, Ph.D., Respiratory Sciences Center, University of Arizona College of Medicine, 1501 N. Campbell Avenue, Tucson, AZ 85724.
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