Particulate air pollution and respiratory disease in Anchorage, Alaska.

This paper examines the associations between average daily particulate matter less than 10 microns in diameter (PM10) and temperature with daily outpatient visits for respiratory disease including asthma, bronchitis, and upper respiratory illness in Anchorage, Alaska, where there are few industrial sources of air pollution. In Anchorage, PM10 is composed primarily of earth crustal material and volcanic ash. Carbon monoxide is measured only during the winter months. The number of outpatients visits for respiratory diagnoses during the period 1 May 1992 to 1 March 1994 were derived from medical insurance claims for state and municipal employees and their dependents covered by Aetna insurance. The data were filtered to reduce seasonal trends and serial autocorrelation and adjusted for day of the week. The results show that an increase of 10 micrograms/m3 in PM10 resulted in a 3-6% increase in visits for asthma and a 1-3% increase in visits for upper respiratory diseases. Winter CO concentrations were significantly associated with bronchitis and upper respiratory illness, but not with asthma. Winter CO was highly correlated with automobile exhaust emissions. These findings are consistent with the results of previous studies of particulate pollution in other urban areas and provide evidence that the coarse fraction of PM10 may affect the health of working people.

This paper exaines the associations between averge daily particulate matter less than 10 pm in diameter (PM1o) and mpure th daily outpaent visits for respiratory disease inDg asthma, bronchitis -and uppC rspiy ilness in Anchorage, Alaska (1996) Recent studies have reported that particulate pollution in ambient air is associated with increased mortality (1-3) and morbidity (4,5). Studies have been done in cities where the primary source of particulate pollution is combustion products. Studies of areas with high industrial particulate pollution show increases in asthma symptoms and hospital admissions. The present study examined the association between particulate pollution and the incidence of acute respiratory diseases as measured by outpatient visits for specific respiratory diagnoses in an area without significant industrial pollution, Anchorage, Alaska. Anchorage is a city of 240,000 people located in a "bowl" surrounded by mountains and sea coast. Wood smoke is not a major contributor to particulate pollution in this area because wood is not commonly used as fuel due to its high cost. Electric power plants are fueled by natural gas. The main sources of particulate pollution are unpaved roads, road sanding, vehicular traffic, and ashfall from volcanic eruptions. On 18 August 1992, during the period of this study, Mt. Spurr, 60 miles west of Anchorage, erupted and rained ash on the city. Hourly measurements of particulate matter with aerodynamic diameter less than 10 pm (PM10) reached a maximum level of 3000 pg/m3. The 24-hr average concentration was 565 pg/m3 on the day after the eruption. Computer-controlled scanning electron microscopy (CCSEM) was used to determine the composition of particles from 10 random samples taken before and after the volcano erupted (6). Over 80% of the particle mass was between 2.5 and 10 pm. The composition was mainly silica and silica-aluminum. CCSEM showed that less than 5% by weight of the filter mass was carbonaceous particles. This is consistent with source apportionment studies by chemical mass balance done 7 years previously (7), which concluded that more than 85% of the total suspended particulates (TSP) in Anchorage was earth crustal material. Size-fractionated mass measurements below 15 pm were made in Anchorage by the U.S. EPA in the early 1980s using dichotomous samplers. Historic data collected by EPA during 1983 also suggest a high coarse-particle mass fraction.
Average fine [aerodynamic diameter (d) < 2.5 pm] to coarse (2.5 pm < da< 15 pm) particle mass ratios in the summer of 1983 were 0.14 ± 0.05. The overall median ratio of fine to coarse PM1 was calculated to be 0.26. This is in istinct contrast with the 0.4 to 0.7 PM2 5/ PM15 ratios reported for 6 communities in the lower 48 states by Spengler and Thurston in 1983 (8). We investigated the relationship between respiratory illness treated on an outpatient basis and ambient particulate PM10 pollution using a health insurance database. Working people and their dependents, generally considered a healthy group, are the sample population used in this analysis. The sample size was approximately 6% of the population of the city of Anchorage.

Methods
Database Particulates are measured daily as 24-hr PM1O samples using an Anderson head sampler at a central location in Anchorage, the Gambell site. Measurements are made intermittently at two other sites within the city. The Pearson correlation coefficient between sites ranges from 0.76 to 0.81. The Gambell site is located close to a major highway, and PM1O concentrations are 43-76% higher than concentrations measured at other sites. Other than PM10, few pollutants are routinely monitored in Anchorage. Carbon monoxide monitoring is conducted hourly and daily between October and March. CO is routinely monitored at five locations in the Anchorage area. Only two sites exceeded the 9 ppm CO standard during the period of this study: the Seward Highway site, located four blocks from the Gambell PM1O monitoring site, and the Garden site, a residential area about 2 miles from the Gambell PM1O monitoring site. Measurement of criteria pollutants, sulfur dioxide, nitrogen dioxide, and ozone are only done occasionally as they remain quite low.
Available 8-hr maximum CO concentrations measured at each of the five CO monitoring sites in Anchorage between 1 October 1992 and 31 March 1993 and between 1 October 1993 and 31 March 1994 were obtained from the Municipality of Anchorage Environmental Services Division. These data were processed to generate the average 8-hr maximum CO concentration for each day, which was then used in the analysis.
Daily claims made for outpatient visits for respiratory illness were obtained from Aetna Insurance Company, which processes the health insurance claims for both employees of the State of Alaska and employees of the Municipality of Anchorage. Both groups have comprehensive health insurance with low deductibles for employees and dependents. We analyzed data from a 22-month period from 1 May 1992 to 1 March 1994. All outpatient visits that were submitted to insurance, whether they occurred in doctors' offices or in emergency rooms, were captured by this method. The diagnosis code recorded for the visit was based on the International Classification of Diseases 9th Revision (ICD-9) coding. ICD-9 codes were grouped to identify upper respiratory problems such as sore throat, earaches, sinusitis, rhinitis, and other nonspecific upper airway problems. This whole group of illnesses is referred to as upper respiratory illness (URI). The second group, referred to as bronchitis, includes lower airway diseases such as bronchitis, tracheitis, and nonspecific cough. Pneumonia was not included, as it is frequently treated on an inpatient basis. The third respiratory category, referred to as asthma, included all reactive airway disease, bronchospasm, and asthma ICD-9 codes. Diarrhea, a common diagnosis presumably unrelated to air pollution, was recorded as a control diagnosis. The ICD-9 codes used were: for asthma, 519 Reiterations of the insurance data collection were done until we were confident of a stable claims report. Only visits where both patient and provider had an Anchorage zip code were included in the analysis. There were approximately 11,000 State of Alaska and 3000 municipal employees and dependents eligible for health insurance in Anchorage during the time of the study.

Analytical Methods
Daily outpatient visits, temperature, and PM1O series exhibit seasonal cycles, some of which are common. Unless adjusted for long-term cycles, shared seasonal or monthly cycles among outpatient visits and environmental variables could confound results. Adopting the technique used in Kinney and Ozkaynak (8), a weighted 19day moving average filter developed by Shumway (9) was used to detrend the pollution and meteorological series. The method involves subtracting the weighted moving average of each variable (X) from itself on each observation. In other words, the X, on day t = i is filtered as: where wi is the filter weights shown in Shumway (9). This process of filtering removes the long-term cycles but not the short-term cycles (i.e., high frequencies). When a linear filter such as this is applied to both the predictor and predicted variables before regression analysis, linear regression relationships among variables are preserved and can be estimated without bias. In addition, this filter efficiently removes the autocorrelation in the pollution and the outpatient visit series. Autocorrelation functions were examined to detect any remaining temporal structure in the filtered data, and none was found.
We computed descriptive statistics for all the filtered and unfiltered data. We used a generalized linear model procedure to test statistical differences in the daily outpatient visits by day of the week. Crosscorrelations between filtered outpatient visits (e.g., for asthma) and filtered PM1O were calculated to determine the importance of the relationship between doctors visits and same-day (or lag 0), previousday (or lag 1) and 2-days prior (or lag 2) PM1O measurements. We analyzed the daily outpatient visit (OV) counts and pollution data using time-series and regression modeling techniques implemented with SAS software (SAS Institute Inc., Cary, North Carolina).
Because of low daily counts for some categories of doctors visits (e.g., asthma, bronchitis), we examined two different methods of modeling the pollution-health effect relationships. Both ordinary and Poisson regression models were fitted to filtered outpatient visit, temperature, and pollution data. Consistency of results and normality of model residuals were examined. In all cases, results from Poisson and multiple regression models were almost identical. Moreover, residuals from the multiple regression models were very nearly normally distributed. Consequently, for technical and practical reasons, we chose multiple regression modeling framework in the analysis. Basic analysis involved fitting multiple regression models to four filtered morbidity variables (i.e., doctors visits diagnosed as asthma, bronchitis, diarrhea, and upper respiratory infections ) using filtered same-day or previous day PM10 and temperature as explanatory variables. The diarrhea category was selected for analysis as a control category. The form of the basic regression model (model I) was: where OV F is the filtered daily outpatient visits, X CF is the filtered same-day or previous-day daily temperature and PM1O measurements, and E is the error term. Other models were also done. Model 2 added a weekend/weekday indicator variable (WID) as an additional explanatory variable to Equation 2. Model 3 was a regression specification using as the dependent variable outpatient visits that were both filtered and weekend/weekday adjusted. Specifically, model 3 was written as: OV_R=OV_F-(a +a1W J)D In models 1-3, same-day temperature and same-day PM1O were included. We also ran models with different lags of temperature and PM10. We present results from one of these, Model 4, where previous day's PM1O (or lag 1 PM1O) instead of same-day PM10 is included in the specification. The models were run for all ages combined and separately for three age groups (<10 years,  years, and 46+). Due to sample size limitations, male and female outpatient visits were combined. Finally, we also examined potential statistical confounding of results due to other pollutants of health concern and the influence of variations in the PM1O composition over time. We included the available wintertime CO measurements independently, as well as jointly with PMIO data, in the regression models tested. Potential changes in the seasonal composition of PM10 and the influence of the volcanic eruption that occurred on 18 August 1992 were also modeled using nested regression modeling methods. In this case, we estimated separate PM1O slopes for winter versus summer seasons and periods strongly influenced by volcanic eruption (18 August 1992-31 December 1992) versus the remaining period less influenced by volcano ash (i.e., 1 May 1992-17 August 17 1992; 1 January 1993-1 March 1994).
Environmental Health Perspectives * Volume 104, Number 3, March 1996 Articles -Gordian et al. Figure 1 displays the daily PMIO measurements collected at the Gambell site in Anchorage. Both the original and filtered series are shown. The influence of volcanic eruptions on PM1O levels during the fall of 1992 are clear. After detrending, long-term cycles and seasonal patterns are no longer apparent. Daily counts for outpatient visits for asthma, bronchitis, and URI are shown in Figures 2-4. Again, the 19-day moving average filter detrends the observations for respiratory illness visits. Because of the relatively young age of the sample population, CHF and COPD visits were infrequent, and no analysis of these was done. Table 1 presents the summary statistics for the analysis variables: temperature, PM1O, CO, visits for illnesses of asthma, bronchitis, diarrhea, and URI. Correlation of 8-hr maximum CO measurements among the five different sites was quite high (p = 0.8). Consequently, using the data from all monitors, we calculated the 8-hr maximum CO value in the Anchorage area for each day data were collected. The correlation between daily PM1O and daily average maximum CO was found to be small (p = 0.15). Table 1 also provides a breakdown of the statistics by different age categories. Clearly, most of the visits are recorded in the largest age category, 11-45 year olds. Because only active employee insurance records were analyzed, most of the population at risk were under 65 years of age.

Results
Ordinary regression models were run for all outpatient visit categories. Table 2 presents the results for the basic model (model 1) for the three respiratory illness categories: asthma, bronchitis, and URI. All of the estimated PMIO regression coefficients were significant for these illness categories. However, a generalized linear model analysis indicated substantial weekend/ weekday differences in the recorded outpatient visits. Because most doctors' offices are closed on the weekends, typical weekend visit counts for all causes of illness were five times lower than during the weekdays. Moreover Statistically significant associations were found between both same-day and previous-day PM1O (lag 0, lag 1) and asthma visits and between same-day PM1O and URI diagnosed outpatient visits based on model 3 and 4 specifications. The statistical association found between lag 1 PM1O and visits for asthma was stronger and more significant than the association found between same-day PM1O and visits for asthma (Tables 2 and 4). Other lags (i.e., lag 2,3) of PMIO were also studied but not found to be significant in the models tested. Using the coefficients from model 3 and model 4, the magnitude of the projected PM1O effect on outpatient visits for each 10 pg/m3 increase in PM10 is 2.5-3.5% excess outpatient visits for asthma and 1.2% excess outpatient visits for URI ( Table 2).
Next we examined the age dependence of the results by repeating the model 3  under the three age categories: <10 years, between 11 and 45 years, and >46 years (but typically less than 65 years). Table 3 presents these regression results. Due to the small number of daily counts, some of the age-specific regression estimates were not significant. For asthma visits, the effect estimate for the 11to 45-years age group (2.6% excess visits) was not significant (p = 0.14) but similar in magnitude to one previously found for the all ages combined. However, statistically significant associations were found between PM1O and URIrelated outpatient visits for children under 10 years of age and adults over 46 years of age. The predicted PM1O effect on URI visits associated with an increase of 10 pg/rm3 PM1O was 1.9% and 1.2%, respectively, in these two age categories. Outpatient visits for diarrhea were not significant either in models 2 or 3.
We examined the assocition between daily CO and outpatient visits using the regression models (i.e., models 3 and 4). tions. Models with lag 1, 2, or 3 coefficient loses its significance when the ables did not result in statistically data set is split by summer and winter seant coefficients. Same-day CO was son. However, the magnitude of the estiignificantly associated with outpa-mated summer and winter coefficients -its for bronchitis and URI using remain similar to the PM1 coefficient estilable CO series, obtained during mated from the full data set (see Tables 4  ter of

Discussion
We analyzed 22 months of daily PM1O, temperature, and daily cause-specific outpatient visit data from Anchorage, Alaska, to study the acute relationship between PM1O and respiratory illnesses. The health data were obtained from a large health insurance provider to state and municipal employees in Anchorage. Even though the coverage was only partial (80-90%) and records may have included repeat visits to a doctor by the same individuals, the data set is considered to be representative. Furthermore, we applied conservative statistical methods to control for potential seasonal, weekly, and daily confounders of PM1O health effects. In particular, we controlled for potential influences of temperature on daily outpatient visits. In Anchorage, continuous records for other pollutants such as ozone (O) sulfur dioxide (SO2), and nitrogen dioxide (NO2) were not available for the period of analysis. It is unlikely that these omitted variables could confound potential associations between PM1O and outpatient visits.
Limited monitoring data available for these pollutants indicate very low levels [below the National Ambient Air Quality Standards (NAAQS) for 03, NO2 and SO2. SO2 concentrations measured in 1983-1985 were less than 10% of the NAAQS. The maximum hourly 03 was 40 ppb, one-third the NAAQS during the 2 years of monitoring in 1983-1985. Although only 6 months of NO2 data are available for Anchorage, levels of this pollutant did not exceed one-third the NAAQS. SO2 was measured at the base of the volcano during the eruption, but not in the city of Anchorage. The measurement taken at the base of the volcano 60 miles from Anchorage was considered low for a volcanic eruption (750 tons/day) and unlikely to affect Anchorage. Two pollutants do occur in Anchorage in significant amounts. They are CO and benzene. A year-long monitoring study of ambient air for volatile organic compounds in Anchorage was completed in 1994. The study showed that the levels of benzene in the winter in Anchorage were higher than the levels reported in any other U.S. city in a national study done in 1987 (10). Benzene monitoring was done at the CO monitoring sites and was highly correlated with CO concentrations (p = 0.97) (11). Both benzene and CO are exhaust emis- sions of incomplete combustion of Alaskan gasoline, which is high in benzene (5%) and other aromatic compounds. An increase of 1 ppm CO is equivalent to an increase of 3 ppb benzene in Anchorage. CO measurements at five sites in Anchorage were available for October-March during fall and winter of 1992 and 1993. CO measurements were not correlated with PM1O measured at the Gambell site (R2 = 0.14). Nevertheless, potential confounding of PM1O associations due to CO were examined by running the outpatient visit-pollution models using average maximum CO as an independent exposure variable. Models were also run with both CO and PM1o together in model 3 and model 4 specification. Results indicated that CO and PM1O associations with outpatient visits are independent of each other. Because CO data are only collected in the cold season, the sample size for the CO models was about half of that used in the PM1O models. Therefore, we have less confidence in the associations detected for CO than those found for PM1O. Nevertheless, the significant associations detected for CO are intriguing. These results suggest that wintertime emissions from automobiles, CO, NO2, fine particles, and volatile organic compounds (VOCs), may significantly contribute to bronchitis and URI in the Anchorage population. Because CO is a surrogate for many of the vehicular emissions, it is not possible to directly link CO with the inferred respiratory effects. Available epidemiologic data on respiratory effects of CO and VOCs are quite limited. A recent article by Morris et al. (12) showed that ambient CO levels were positively associated with hospital admissions for CHF among elderly people in seven large U.S. cities. Ware et al. (13) had shown respiratory and irritant health effects associated with ambient VOCs in Kanawha Valley, West Virginia. Both petroleum or auto-related compounds and chemical manufacturing emissions were determined to be the likely source of ambient VOCs and the estimated health effects in that Volume 104, Number 3, March 1996 * Environmental Health Perspectives Articles * Particulate air pollution in Alaska   sources. Because of the high ratio of coarse to fine particles in Anchorage, our analyses inity. Analyses of many years of ent exposures by staying indoors or limiting is somewhat unique in that it suggests that ortality and daily air pollution in their outdoor activities. Businesses closed, some morbidity may be related to coarse geles as well as in Toronto, Canada, work was curtailed, and events were post-particles (>2.5 pm in diameter) that are Uso shown statistically significant poned immediately after the eruption. Many primarily of geologic origin. This is consis-:ions between CO, NO2, index of people wore dust masks. Consequently, use tent with a study done in rural, western rticulate or carbonaceous air polluof ambient PM1O concentrations following Washington state, where PM10 also has a Ld daily total mortality (14,15).
the period of volcanic eruption can lead to predominantly earth crustal component have found statistically significant misclassification of personal exposures to (23). If our results are confirmed through ions between either the same-day or PM1O. Past studies have shown that poten-additional studies, they would bear signifis-day PM1O and outpatient visits for tial pulmonary toxicity of volcanic ash may cantly on some of the critical scientific due to asthma and URI, in a loca-be quite low. Beck et al. (16) showed that uncertainties facing the U.S. EPA in its Lere the primary sources of PM1O are based on short-term animal bioassays, toxiongoing review of the ambient air quality mbustion or secondary aerosols. city of Mt. St. Helens volcanic ash was low standard for PM1O.
:rustal and volcanic ash sources are and similar to responses to aluminum We believe that it is important to furl to dominate the respirable particle oxide, a dust considered to be relatively ther examine the robustness of these results Anchorage. However, the associainert. This result is also compatible with by analyzing a longer series of available und between PM1O and asthmathe possibility that volcanic ash is not toxic records for outpatient visits and PM10 in doctors visits seems to be higher until after mixing with combustion-related Anchorage. It is also important to extend ,nificant only during the period fine particles. the analysis to more vulnerable segments of ng volcanic activity. These findings Based on our findings, the toxicity of the population such as persons 65 years of )e explained by various exposure or the aerosol mixture in Anchorage seems to age and older. The association found related factors. After the volcanic be comparable to that inferred from other between CO       gestive heart failure. Additional years of data should be analyzed to confirm that the results are not influenced by limited sample size. It is also important to better characterize the combustion or petroleum pollutants by collecting and analyzing long-term measurements of fine particles (PM25 <da<2.5 pm), coarse particles (2.5 pmda< 10 pm), NO2, CO, selected VOCs (e.g., benzene, toluene, xylenes), and trace elements of combustion (Br, Pb, V, Ni, etc.). Finally, the feasibility of examining other health records from Anchorage, such as emergency room visits, hospital admissions, and Articles * Particulate air pollution in Alaska mortality should also be considered in subsequent studies.

Conclusions
The results from analysis of 22 months of daily PMIO and outpatient visits for respiratory illness in Anchorage, Alaska showed that an increase of 10 pg/m3 in PM1O is associated with a 3-6% increase in medical visits for asthma and a 1-3% increase in medical visits for upper respiratory illness. This study is one of few which shows that silicaceous or earth crustal coarse particulate pollution may have an acute, adverse effect on respiratory health even at relatively low ambient concentrations. It also suggests that the increased morbidity is associated not just with a vulnerable segment of the population, but with a relatively young, healthy working group as well. These findings could have important implications to U.S. EPA in the ongoing review of the ambient air quality standard for PMIo. Whereas most of the past epidemiologic studies have linked particulate air pollution with daily health effects in urban settings, our results suggest that anthropogenic sources are not the only sources that may have an impact on respiratory health. Additional studies in Alaska or other environments with similar aerosol composition are highly recommended to confirm the statistical associations found between respiratory illness and coarse particle dominated PM1O.