Presentation on theme: "Chief Engineer’s Office The Mont Belvieu Puzzle; JJ& RN April 8, 2010 Page 1 Asthma-related Hospital Admissions vs Ozone Concentrations in Texas CEO/AQD."— Presentation transcript:
Chief Engineer’s Office The Mont Belvieu Puzzle; JJ& RN April 8, 2010 Page 1 Asthma-related Hospital Admissions vs Ozone Concentrations in Texas CEO/AQD John Jolly May xx, 2010 Draft deliberative document – do not quote or cite
Chief Engineer’s Office The Mont Belvieu Puzzle; JJ& RN April 8, 2010 Page 2 Research Questions 1.In ten of Texas’ most populated counties, how well do ozone concentrations and hospital admissions (due to asthma) correlate? 2.How are hospital admission rates and ozone concentrations changing over time in each of these counties? 3.Using all 31 counties with regulatory ozone monitors, do counties with higher summertime ozone concentrations have higher summertime hospital admissions due to asthma?
Methods Hospital admissions – Obtained age-adjusted asthma-related hospital admissions (AA rate) data by quarter and county, , from Texas Health Care Information Collection (THCIC), Center for Health Statistics, Texas Dept of State Health Services (DSHS) Ozone – Obtained daily peak 8-hr ozone measurements, from TCEQ MOTHER database, for all 82 regulatory monitors in Texas from – Calculated, for each quarter/year at each of these monitors, average peak 8-hr ozone – Averaged these values by county for the 31 counties with regulatory monitors * * The 32 nd county with a regulatory monitor, Brewster County, was excluded from analysis.
Uncertainties Hospital admissions data are by quarter and county – No finer temporal/spatial resolution available – Therefore cannot determine if hospitals nearer to higher ozone levels see more asthma admissions Ozone concentration data – By averaging across a county, we lose spatial resolution – unable to determine ozone gradients within the county – By averaging data by quarter, we lose temporal resolution – there may be episodes of crucial importance to health that are masked by the other days in the quarter
Analyses 1. Time Series Graphs of Ozone vs Hospital Admissions 2. Least Squares Linear Regression – Average Ozone vs AA Rate for 10 heavily-populated and/or highly industrialized Texas counties
El Paso County
Summary (analyses 1 and 2) Asthma-related admissions rate (AA rate) inversely correlated with average ozone in 9 of the 10 counties – 8 counties show highest AA rate in Q4 and Q1, for most or all years, with highest ozone almost always in Q2/Q3 Travis county had highest AA rate Q2, Q3, Q4 – Of the 9 counties, 6 have inverse correlations that are statistically significant at.05 level – Cameron county is an outlier: its AA rate and average ozone are positively correlated (see bullet on next slide) due to unusual ozone seasonal patterns Changes seen in average ozone across time period – 8 of 10 counties showed decreases in Q3 ozone in vs – 3 of 10 counties appear to show overall decreases in AA rate, but this has not been quantified
Summary (analyses 1 and 2) Cameron County has very different ozone seasonality than the other counties – Its lowest average quarterly ozone is consistently in Q3 (July – September) – markedly lower than other quarters Harris County surprises – According to the ozone metric used here (average daily peak 8-hr ozone, by quarter), Harris shows lower ozone than might be expected (ranks 19 of 31 monitored counties) – By this metric, Q2 ozone is markedly higher than Q3 in most years, even though this county typically shows largest number of exceedance days in August/September – Less seasonal difference in AA rate (along with Travis County) than the other 8 counties
Analyses 3. Relationship of 3 rd -Quarter Mean Peak Daily Ozone and 3 rd -Quarter Asthma-related Hospital Admissions All 31 Counties Combined
Kleberg and McLennan counties have 2 quarters apiece of 3 rd -quarter data. The other 29 counties represented here have 4 years of 3 rd -quarter data.
Summary (analysis 3) What is relationship between ozone and AA rate during peak ozone season, when all 31 counties combined? – 3 rd quarter (July – September) is expected to be peak ozone season Answer: When all counties are combined, essentially no statistical relationship between average 3 rd quarter ozone and 3 rd quarter AA rate The lack of relationship suggests that something other than ambient ozone is driving summertime hospital admissions
Analyses 4. Relationship of Quarterly Mean Peak Daily Ozone and Quarterly Asthma-related Hospital Admissions All 31 Counties Analyzed Separately
Statistics on Mean Quarterly Peak Daily Ozone vs Quarterly AA Rate, , for 31 Texas Counties with Regulatory Air Monitors These are sorted by descending R square. (Statistics for counties in yellow were also seen in slides 7-25.) Note that these 31 counties roughly break down into 3 groups: 1.Strongly inversely correlated (first 7 counties) – all are significant at.05 level 2.Weakly correlated (R- square.10 to.24) – no correlations are statistically significant – of these 11 counties, 3 are positively correlated 3.Essentially uncorrelated (R-square <.10) – 13 monitors with AA rate essentially unrelated to average peak ozone.
Summary (overall) Based on quarterly average data, evidence presented here suggests that asthma-related hospital admissions are either inversely correlated, or uncorrelated, with ozone levels – Counties with higher ozone levels show stronger anticorrelation between ozone levels and AA rate – Counties with lower ozone levels show little correlation between AA rate and ozone This suggests ambient ozone concentrations are not the primary cause of asthma-related hospital admissions
Possible Future Analyses 1.Alter the metric used to represent high ozone – Currently the “Mean Peak 8-hr Ozone” favors monitors with consistently elevated peak ozone but few/none very high days (e.g. DFW) rather than monitors with some low/modest days and a minority of very high days (e.g. HGB) – This results in Harris county having lower mean ozone than the 4 principal DFW counties – Could do geometric mean – better covers data that are skewed, e.g. Houston – Would an area with low/modest ozone, and a minority of very high days be expected to show more asthma-related health effects than an area with +/- consistently elevated, but rarely high, ozone concentrations? 2.Look at 1999 – 2004 data – Summertime ozone levels higher in those years than later years – Those data are not presently ready to analyze, but could be made so if needed