TABLE 2
Number of Exacerbations per Air Quality Index (AQI) Category in Rural Counties in Wisconsin From January 1, 2019–June 30, 2022
AQI Levels of Health Concern for 24-hr PM 2.5 (µg/m 3 )
All Days in Study Period # (%)
Lag0 # (%)
Lag1 # (%)
Lag2 # (%)
Lag3 # (%)
Lag4 # (%)
Lag5 # (%)
Lag6 # (%)
Good (0–12.0)
7,847 (89.3)
1,239 (87.0)
1,222 (86.1)
1,209 (85.5)
1,214 (86.2)
1,224 (86.9)
1,218 (86.4)
1,238 (87.9)
Moderate (12.1–35.4) Unhealthy for sensitive groups (35.5–55.4) Unhealthy (55.5–140.4) Very unhealthy (140.5–210.4)
928 (10.6)
184 (12.9)
194 (13.7)
199 (14.1)
191 (13.6)
184 (13.7)
190 (13.5)
168 (11.9)
12 (0.1)
1 (0.1)
2 (0.1)
3 (0.2)
4 (0.3)
0
1 (0.1)
1 (0.1)
4 (0.05)
0 0 0 0
1 (0.1)
3 (0.2)
0 0 0
0 0 0
1 (0.1)
2 (0.1)
0 0
0 0 5
0 0
0 0
0 0
Hazardous (>210.4)
Missing
148
10
15
16
14
15
Total
8,939
1,424 .0191
1,424 .0064
1,424 .0001
1,424 .0026
1,424 .0096
1,424 .0109
1,424 .1945
p -value
–
reported an eect on the total population on lag day 1 and lag days 1–3 for Black individu- als in their study. Moreover, Chi et al. (2019) found an association between PM 2.5 and mul- tiple respiratory diseases on lag days 3–5. Our study, however, was notable in that we found an elevated hazard of asthma exacerba- tions with PM 2.5 levels for people ≥65 years. Some studies have reported inconsistent find- ings—for example, a study by Bozigar et al. (2021) found no association between PM 2.5 and asthma exacerbations. These discrepancies might be due to dierences in study design, study populations, or exposure assessments. The strengths of our study include the use of a case-crossover study design, which is an eective method for assessing time-varying exposures in administrative data sets with only case data included. This study design is advantageous because it uses self-matched referent periods, which naturally control for confounding factors. Another strength is the inclusion of data from multiple rural coun- ties, expanding our knowledge base on the relationship between PM 2.5 and asthma exac- erbations in rural areas. Our study was limited by the following fac- tors. We included data only from seven rural counties in Wisconsin, thus limiting the gen- eralizability of our results to other rural areas of the state. Additionally, we were unable to include wind speed data due to missing observations or humidity data because they
TABLE 3
Multivariate Analysis of the Relationship Between 10-Unit Increases of PM 2.5 and Asthma Exacerbations by Lag Day
Lag Day
Hazard Ratio
95% CI
p -Value
Main model: 10-unit increases Lag0
1.072
[0.942, 1.219]
.3127
Lag1
1.083
[0.951, 1.231]
.2303
1.207
[1.072, 1.370]
.0024
Lag2
Lag3
1.072
[0.942, 1.219]
.2725
Lag4
0.932
[0.817, 1.062]
.2772
Lag5
0.980
[0.860, 1.116]
.7585
Lag6
0.980
[0.860, 1.116]
.7307
Average: Lag0–6
1.127
[0.904, 1.397]
.2959
Main model: 10-unit increases controlling for maximum temperature Lag0 1.041
[0.914, 1.195]
.5434
Lag1
1.041
[0.914, 1.195]
.5352
1.184
[1.051, 1.344]
.0069
Lag2
Lag3
1.062
[0.932, 1.207]
.3810
Lag4
0.914
[0.801, 1.041]
.1918
Lag5
0.970
[0.851, 1.105]
.6534
Lag6
0.970
[0.851, 1.116]
.7007
Average: Lag0–6
1.072
[0.860, 1.344]
.5542
Note. The bolded values indicate significant results as p < .05. CI = confidence interval.
11
January/February 2025 • Journal of Environmental Health
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