Apr 9, 2010 - Milwaukee, Wisconsin. Bo Li. Department of Statistics, Purdue university. Joint work with: Steve Sain, Linda Mearns, Henry A. Anderson, Sari ...
Statistical Aspects of Environmental Risk, SAMSI, NC
The Impact of Heat Waves on Morbidity in Milwaukee, Wisconsin Bo Li Department of Statistics, Purdue university
Joint work with: Steve Sain, Linda Mearns, Henry A. Anderson, Sari Kovats, Krisitie Ebi, Jonathan Patz
Apr. 9, 2010
Outline
• Background • Data: Health, climate, pollution • Statistical model to quantify the association between hot weather and morbidity • Sensitivity analysis to temperature change • Results • Discussion
Background • Heat waves cause substantial number of deaths – 1995 upper Midwest heat wave resulted in 700 deaths in Chicago, 91 deaths and 95 paramedic emergency medical services in Milwaukee – 2003 European heat wave killed an estimated 22,000 to 35,000 people
• Future heat waves will become more intense, more frequent, and longer lasting in the second half of the 21st century. (Meehl and Tebaldi, 2004) • Hayhoe et al. (2004) estimated that heat waves and extreme heat in Los Angeles would quadruple in frequency while heat-related mortality will increase two to three times.
Previous findings • Many studies have documented heat related mortality (Kalkstein and Greene, 1997; Keatinge et al., 2000; Hajat et al., 2004; Curriero et al., 2002; ONeill et al., 2003) • Few time-series studies have addressed the effects of weather on morbidity – Excess admissions for various disease and age groups were documented during the 1995 Chicago heat wave (Semenza et al., 1999) – Hot temperature was associated with increased hospital admissions for cardiovascular disease based on the Medicare data (only persons > 65) across 12 US cities (Schwartz, Samet and Patz, 2004)
Previous findings
• Few time-series studies have addressed the effects of weather on morbidity (Continue) – However, Michelozzi (2009) found that in European cities high temperatures did not increase cardiovascular admissions – High temperatures is shown to increase emergency hospital admissions for respiratory causes, particularly in the elderly (Kovats et al., 2004) – Admissions for endocrine and renal disorders increases during heat waves events (Knowlton et al., 2009)
Objectives
• Estimate the impact of hot weather on morbidity in Milwaukee, WI. • Use these exposure-response functions to estimate the potential effects of climate change on additional admissions due to increased hot weather.
Migrating State Climates
Data
Time window: 1989-2005
• Hospital inpatient discharge data: – Reported by all of Wisconsin’s acute care nonfederal hospitals with the exception of the federal Veterans Administration hospitals – Demographic data including age, ICD9 code, admission and discharge date, etc. – Strong holiday effects and weekly pattern (more admissions on Mondays and Tuesdays and less on Saturdays and Sundays)
Data • Daily meteorological data: – National Climate Data Center (NCDC) cooperative daily meteorological data – Includes max/min temperature and max/min Relative Humidity – No missing values.
• Pollution data: EPA Air Quality System database. – Daily ozone between May - October, 286 days have missing values - remove those days – PM10 at every 5 to 6 days - Interpolate to daily data by natural cubic spline
Data Annual average inpatients by zipcodes in WI
Milwaukee
ozone monitor ●
PM10 monitor ●
*
weather station
Data analysis Evaluate relationships between daily temperature and hospital admissions following Kovats et al. (2004) and Hajat et al. (2007) log(µ) = β0 + µyear + µdow + µholiday + β1OZ + β2P M 10 +s(doy) + s(RH) + s(Tavg ) µ is the expectation of daily admission counts dow: day of week; doy: day of year OZ, RH: daily average of ozone and relative humidity P M 10: spline interpolation to get the daily PM10 Tavg : average temperature over the index and previous two days s(•): smooth function of •
The smoothed relationship between hospital admissions and average temperatures over the index and previous two days for selected causes of admission
The smoothed relationship between hospital admissions and average temperatures over the index and previous two days for eight age groups
Quantify high temperature effects Assume a log linear increase in risk when temperature is above th, in addition to the smooth relationship between the risk and the average temperature Tavg . log(µ) = β0 + µyear + µdow + µholiday + β1OZ + β2P M 10 +β3(T − th)I(T −th≥0) + s(doy) + s(RH) + s(Tavg ) T : daily average temperature th: temperature threshold I(•): indication function of • Question: How to estimate th?
Quantify high temperature effects • To estimate this threshold, we in turn assign th seven different values (24.7, 26.1, 26.65, 27.2, 27.8, 28.9, 29.45), which correspond to the (90, 95, 96, 97, 98, 99, 99.5)th percentiles of summer daily temperatures of the entire 17 years, and then for each value we assess the significance and examine the sign of the estimated βb3. • A temperature value is considered a reasonable threshold only if βb3 are significantly positive for all the other temperatures in the list that are greater than or equal to such value.
The p-values for βb3 for selected causes
Thresholds and slopes for relation between high temperature and hospital admission by causes
Estimating future temperatures • Estimate the monthly temperature change after 70 years using the GFDL simulation data (1971-2000 and 2041-2070) • Apply the estimated difference to the real data (19892005) to project them to 2059-2075. • Only use the simulation data (1989-2000 and 20592070) to estimate the temperature change. • Note: This is by no means the precise temperature during 2059-2075, but rather a reasonable approach to approximate what could be the future temperature given the available information.
Contrast of past and future climate Geophysical Fluid Dynamics Laboratory (GFDL): Timeslice experiments at approximately 50 km resolution Max/min daily temperature 1971-2000 and 2041-2070
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Sensitivity analysis to temperature changes Evaluate temperature change and estimate the future temperature by delta method - Calculate monthly difference between 1989-2000 and 2059-2070 in GFDL temperatures Monthly average temperature over 12 years
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- Apply monthly mean difference to temperatures in 1989-2005 to project temperatures at 20592075
Sensitivity analysis to temperature changes in both mean and variance Std. dev. of temperatures in 1989-2000 and 2059-2070 14
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Monthly std. dev. of detrended temperature over 12 years
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Project temperatures at 1989-2005 to 2059-2075 by applying the differences in mean and variance
Sensitivity analysis to temperature changes in both mean and variance
Statistical summary of daily temperature of future projection (2059-2075) Estimate hospital admissions with the fitted model and the projected temperature (assuming no changes in RH and Pollutions)
Estimated daily hospital admissions (daily adm.) by selected causes corresponding to projected temperatures in 2059-2075 by adjusting either only the mean or both mean and variance of temperatures in 1989-2005
Conclusion • High temperatures had a significant effect on several causes of hospital admissions and age groups in Milwaukee, Wisconsin for the 1989-2005 time period. – Five causes of admission (endocrine, genitourinary, renal, accidents, and self-harm)
∗ Consistent with previous studies: renal, endocrine ∗ Not very consistent: cardiovascular, mental health – Three age groups (15-64, 75-84, >85 years) • Our predictions of increased number of heat-related hospital admissions are consistent with other climate projections and have implications for public health interventions.
Discussion Limitations:
• Only consider primary diagnoses but not secondary diagnoses (This can bias the result, e.g., cardiovascular disease) • No consideration of harvesting effect and acclimatization • Assume RH and pollution remain the same in the future
