NEISA Publications

Hassane, A., R. Gittell, R. Woodward, and C.P. Wake (2005) Exploring the link between asthma hospital services and air quality in New Hampshire. Academy Health Conference, Boston, June 26-28.

Abstract

Research Objective: This study used the spatial, temporal, and age-specific patterns of asthma admissions to identify potential asthma triggers.

Study Design: We merged hospital admissions data with data on air pollutants such as Ozone (O3), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) obtained from local Environmental Protection Agency and University monitoring sites. To analyze the relationship between asthma admission and air pollution, we employed two different methods. First, we ran multiple OLS regression of asthma admissions on air pollutants. Second, for various age groups, locations, and years we compared cumulative daily asthma admissions relative to cumulative daily concentrations of air pollutants. We reported the admissions as an annualized rate per 10,000 people to provide a common basis for inter age group comparisons. Pollutants were measured in parts per billion with data on 1hour daily maximum for O3 and CO and 1hour daily average for SO2.

Population Studied: We collected hospital admissions data from three New Hampshire Seacoast area hospitals.

Principal Findings: Our study confirmed the findings of previous studies, such as Bates et al (1998) and Silverman (2003), which identify low summer and high fall asthma admissions. We found that the lowest admissions in the summer occurred at different times in different years, from June to August. Similarly, the peak in the fall can occur in September, October, or November. We also observed that children 0-4 years of age and young adults 18-24 years of age had the highest rates of asthma and the largest seasonal variations.

The multiple OLS regression of daily asthma on pollutants (CO, SO2 and O3), controlling for daily outside temperature and relative humidity, revealed only a significant and negative relationship between asthma admission and O3 (-0.005, P=0.08). An increase of 1 part per billion in the daily 1hr maximum O3 led to a .6% decrease in the number of daily asthma admissions.

However, the second and more detailed method of analyzing the cumulative daily asthma admissions relative to the cumulative daily concentration of air pollutants showed no evidence of a link between asthma admission and levels of air pollutants. The changes in the slope of the cumulative daily asthma graphs were not explained by changes in the concentration level of CO, SO2 and O3.

Conclusions: In contrast to the substantial literature establishing a link between air quality and asthma, our more detailed, but preliminary analyses found little confirmation of the causal relationship. The finding that high levels of ozone were associated with lower asthma admissions is surprising. It is, however, similar to findings by Neisdell (2004). He argued that the issuance of smog alerts in period of high ozone led to an increase in avoidance behavior. Individuals restricted activities, which led to better health. We are extending our analysis to include more data on air quality indicators such as Nitrogen Dioxide (NO2), Particulate Matter PM10, and pollen.

Implications for Policy, Delivery or Practice: There has been an increase in the prevalence of asthma in the US in the past few years (Redd, 2002). Studies have linked asthma admissions and emergency department visits to poor air quality (Garty, et al, 1998 and Schwartz, et al 1993). Our study has the potential of providing more evidence on the link between air pollution and asthma. Health services providers can use these findings to issue guidelines to populations at risk. Management teams of hospitals can use air quality information and forecast to determine the level of staff needed.

Primary Funding Source: Grant from the National Oceanic and Atmospheric Administration (NOAA).