The impact on mortality of heat waves in Budapest, Hungary

R Sari Kovats, Shakoor Hajat, London School of Hygiene and Tropical Medicine,London, United Kingdom

Anna Páldy, Fodor Jozsef National Center for Public Health, National Institute of Environmental Health,

Budapest, Hungary

János Bobvos Capital Institute of State Public Health Service,Budapest, Hungary

Background• Heat wave August 2003 estimated excess deaths

• No standard definition• Events not comparable

– magnitude– duration– time of occurrence

• Methods to create baseline – regression model– episode analysis

• Short term mortality displacement

• France 10,000 excess deaths• Portugal 1,316 excess deaths• Italy reports 20 % more than average in July/Aug• Spain has reported 100 deaths

Objectives• Describe and quantify the

relationship between daily temperature extremes and mortality in Budapest

• Describe any differences between subgroups (by age and cause)

Mortality Mean daily count % of total

All cause 76.6 100%

Cardiovascular 38.6 50.4%Respiratory 2.79 3.6%

All cause: ages 15 to 64 20.3 26.5% ages 65 to 74 18.1 23.6% ages 75+ 37.5 49.0%

Cardiovascular: 38.6 100% ages 15 to 64 6.2 16.2% ages 65 to 74 8.8 22.7% ages 75+ 23.6 61.2%

Variable Mean value Range

Mean temperature (ºC) 11.3 -11, 29Relative humidity (%) 68.8 32, 100

Ozone (μg/m 3) 65.3 9, 199TSP (μg/m 3) 59.3 12, 249

Data

– Budapest residents– Years 1993-2000

Defining heat episodes• Create daily series with mean temperature (lags 0-2)

• Identified days with Temp above the 99th centile (26.6ºC). • Heat waves = three continuous days

Heat wave event Mean temp

(average)

Max temp

(maximum)

28 June to 1 July 1994 4 days 27.0 36.3

30 July to 8 August 1994 10 days 27.5 36.3

22 July to 25 July 1988 4 days 27.4 34.6

3 August to 5 August 1998 3 days 27.6 36.7

13 June to 15 June 2000 3 days 27.5 36.2

20 August to 22 August 2000 3 days 28.1 37.9

date

hw meant

01jan1994 01jan1995 01jan1996 01jan1997 01jan1998 01jan1999 01jan2000

-10

0

10

20

30

40

Methods: Episode analysis

– Baseline = expected mortaltiy – Regression model

• day of week • time of year• air pollution • temperature • relative humidity

– Excess %• [observed - expected]/expected * 100• Confidence intervals

Observed and expected mortality 2000

all cause

date

predicted mean alld Deaths: all causes mean temperature heatwave event

01jun2000 01jul2000 01aug2000 31aug2000

0

50

100

Mean temperature

Heat wave 5

Heat wave 6

Excess mortality [all cause, all ages] for each event

0

10

20

30

40

50

60

70

jun 94 aug 94 jul 98 aug 98 jun 00 aug 00

estimate

lcl

ucl

Results by age groupAll cause

Cardiovascular

June 94 Aug 94 Jul 98 Aug 98 Jun 00 Aug 00

15 to 64 11 32 2 3 31 -365 to 74 10 12 18 -4 14 875 plus 49 50 48 33 57 33TOTAL 69 94 66 32 101 38

June 94 Aug 94 Jul 98 Aug 98 Jun 00 Aug 00

15 to 64 -4 2 -1 -1 8 065 to 74 14 6 14 -4 8 075 plus 51 36 22 25 39 27TOTAL 60 45 35 21 55 27

Number of “excess deaths”

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

June 94 Aug 94 Jul 98 Aug 98 Jun 00 Aug 00

15-64

65-74

75+

-20.0%

-10.0%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

June 94 Aug 94 Jul 98 Aug 98 Jun 00 Aug 00

15-64

65-74

75+

date

predicted mean alld Deaths: all causes heatwave event twoweek

01jun1994 01aug1994

0

50

100

Short term mortality displacement

Heat wave 1

Short-term mortality

displacement

Two week % excess lower CI upper CIJun-94 6.7 1.2 12.3Aug-94 6.0 0.8 11.3Jul-98 6.7 0.8 12.5

Aug-98 -Jun-00 6.7 0.6 12.8Aug-00 1.3 -1.4 4.0

•Is excess compensated completely by “dip” following heat wave? •We estimated the excess over a one week and a two week period, beginning on the day of the heat wave•Excess over two weeks =6-7%

One week % lower CI upper CIJun-94 16.6 8.4 24.8Aug-94 12.9 5.5 20.2Jul-98 11.1 3.7 18.5Aug-98 5.0 -0.1 10.1Jun-00 17.4 8.1 26.6Aug-00 5.7 0.2 11.2

Conclusions Heat waves have significant impact on mortality Heatwaves early in summer have a bigger impact than heatwaves

in late summer harvesting acclimatization

Attributable deaths short term harvesting does not “account” for all excess mortality unknown contribution

Limitations difficulty in identifying episodes estimating “expected mortality” ozone has significant independent effect on mortality in summer.