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THE COLLATERAL HEALTH IMPACT OF SARS IN TAIWAN
Daniel Bennett (University of Chicago)Chun-Fang Chiang (National Taiwan University)David Meltzer (University of Chicago)
June 29, 2012
Introduction
The SARS epidemic in 2003 lasted for 3 months and led to 312 confirmed cases and 82 deaths in Taiwan.
However, the health impact of the SARS epidemic is not limited to people infected with SARS.
Huge decline in both outpatient visits and inpatient visits. Any consequences of these missing visits?
Introduction
How many non-SARS deaths did SARS cause?
Which groups experienced greater mortality?
Any long run health impact due to missing hospital visits?
Context
National Health Insurance in Taiwan -- high coverage rate (96%) -- low copayments -- frequent hospital visits SARS 2003 in Taiwan -- first case : 3/15 -- first big event: 4/23 -- first death case: 5/1 -- removed on WHO list : 7/ 3
Implications
Health impact due to panic/fear caused by infectious disease
Welfare analysis of health care system. ( If fewer visits do not worsen health,
then health care services may be wasteful: shopping and and unnecessary visits )
Literature— Medical Care Utilization and Mortality
Less utilization higher mortality
-- Card, Dobkin and Maestas (2009)
Medicare eligibility (65 years old); Sample: around age 65, admitted to hospitals through emergency departments
nearly 1-percentage-point drop in 7-day mortality for patients
-- Ken Chay (2012) Canada data
-- Some studies find no effect:
-- Finkelstein and McKnight(2008) . Medicare in 1965-1975
-- Generous insurance coverage : no effect
Most studies find some effects. Freeman (2008)
Literature— Changes during/after the SARS epidemic Decline in outpatient and inpatient hospital visits
Admission rates for most chronic ambulatory-care sensitive conditions (ACS), except for diabetes, did not change after the SARS epidemic. (Huang, Lee and Hsiao)
Shifting childbirth services from advanced hospitals to local community hospitals during SARS epidemic did not increase neonatal mortality
Data
Population death records -- month of death, age, cause of death,
township BNHI panel of one million people -- outpatient and inpatient records ICD9 code, expenditure -- birthday, sex -- linked with death records (month of death) -- use the date out of the insurance in the
same month to identify the date of death
The 2003 SARS Epidemic in Taiwan
1 2 3 4 5 6 7 8 9 10 11 120
500
1000
1500
2000
2500
SARS Incidence by Month During 2003
Reported Cases Suspected Cases Probable Cases
Month
Nu
mb
er
of
Ca
se
s
Outpatient Visits: Ratio of 2003 to Other Years by Month
1 2 3 4 5 6 7 8 9 10 11 120.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Townships with positive SARS incidence Townships with zero SARS incidence
Month
Ra
tio
Inpatient Visits: Ratio of 2003 to Other Years by Month
1 2 3 4 5 6 7 8 9 10 11 120.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
Townships with positive SARS incidence Townships with zero SARS incidence
Month
Ra
tio
Mortality: 2003 and 2000-2002.0
004
4.0
004
6.0
004
8.0
005
.00
05
2.0
005
4
1 2 3 4 5 6 7 8 9 10 11 12Month of death
mortality of 2003 mortality of 2000-2002
Mortality (age>=65).0
03
.00
32
.00
34
.00
36
.00
38
.00
4
1 2 3 4 5 6 7 8 9 10 11 12Month of death
mortality of 2003 (old) mortality of 2000-2002 (old)
Mortality (age<65).0
001
7.00
01
75.
00
01
8.00
01
85.
00
01
9.00
01
95
1 2 3 4 5 6 7 8 9 10 11 12Month of death
mortality of 2003(young) avg mortality of 2000-2002(young)
Alternative Explanations
Economic Shocks -- unemployment rate didn’t increase -- less activity less mortality (Evans
and Moore 2009)
Psychological shocks -- compare the pattern and changes
in mortality after SARS with these after 921 earthquake
Psychological shocks
921 Earthquake happened on Sep, 21 in 1999.
Number of deaths: 2415
Mortality from diseases, however, did not increase
Mortality for all causes of death2003 v.s. 2000-2002avg v.s. 1999
.00
04
.00
04
5.0
005
.00
05
5m
ort
alit
y
1 2 3 4 5 6 7 8 9 10 11 12Month of death
2003 avg 2000-20021999
Mortality (disease or natural death)
.00
03
8.0
004
.00
04
2.00
04
4.00
04
6.00
04
8m
ort
alit
y
1 2 3 4 5 6 7 8 9 10 11 12Month of death
2003 avg 2000-20021999
Analysis using Population Death Data
Sample: Monthly mortality from 1999 to 2008
Specification: Include month fixed effects & year fixed
effects to estimate the changes in mortality
MyyyMMMtMy IIIIcmort ,
7
32003, *
Table 1: Changes in Mortality during 2003 SARS period
Dependent variable: Non-SARS Mortality ( by thousand)
Category: All OLD Young
(1) (2) (3)
March 2003 -0.0051 0.0020 -0.0054
(0.0187) (0.1401) (0.0089)
April 2003 0.0061 0.0274 0.0037
(0.0187) (0.1401) (0.0089)
May 2003 0.0455** 0.4053*** 0.0088
(0.0187) (0.1401) (0.0089)
June 2003 0.0096 0.0275 0.0074
(0.0187) (0.1401) (0.0089)
July2003 0.0059 0.0109 0.0056
(0.0187) (0.1401) (0.0089)
Month fixed effects Yes Yes Yes
Year fixed effects Yes Yes Yes
R-squared 0.81 0.82 0.37
Analysis using Population Death Data
From column (1), 1042 non-SARS extra deaths in May 2003 (Population 2003: 22,604,548; 129,878
dead)From column (2), 842 non-SARS extra deaths among old
people in May 2003 (Pop: 2,087,718, 85,778 dead ) SARS death cases: 82
Analysis using NHI one million panel
If missing inpatient hospital visits were responsible for more deaths, we should observe that more deaths from people with higher medical demand.
Time series analysis by group (first look)
Survival analysis using individual data
Analysis I
Group 1: High Medical Demand: # Hospital visits > 11 or hospital stay >
7 days in 2002 Group 2: Low Medical Demand: Sample: Mortality by week and group
starting from 2003
wyyywwttwy IIweekcmort ,
22
9,
Table 2: Mortality by history of hospital visits
Dependent variable: Mortality ( t )
Category: Old Young Group H Group L
(1) (2) (3) (4)
Week9*2003 0.049 -0.004 0.027 -0.0181
Week10*2003 -0.021 -0.008 0.012 -0.041
Week11*2003 0.135 -0.006 0.048 -0.001
Week12*2003 0.154* 0.025 0.061* 0.01
Week13*2003 0.162* -0.000 0.077** -0.017
Week14*2003 0.160* -0.011 0.010 0.026
Week15*2003 0.008 -0.009 0.038 -0.049
Week16*2003 0.008 0.0187 0.056* 0.039
Week17*2003 0.053 -0.006 0.050 -0.004
Week18*2003 0.032 0.001 0.018 0.01
Week19*2003 0.183** 0.008 0.098** 0.011
Week20*2003 0.049 0.009 0.017 -0.001
Week21*2003 0.191** -0.007 0.055* 0.043
Week22*2003 0.108 0.011 0.046 0.011
Week23*2003 0.011 -0.001 0.005 0.012
Week24*2003 0.021 -0.002 -0.003 -0.008
Week25*2003 0.034 -0.002 0.034 -0.017
Week26*2003 -0.051 0.009 0.025 -0.015
Sample size 360 360 240 240
R-squared
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
Group L Group H
Week*2003
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
Old Young
Week*2003
Change in mortality by disease Cancer v.s. Diabetes
.000
11.0
0012
.000
13.0
0014
1 2 3 4 5 6 7 8 9 10 11 12Month of death
2003 avg 2000-2002
.000
03.0
0003
5.0
0004
.000
045
1 2 3 4 5 6 7 8 9 10 11 12Month of death
2003 avg 2000-2002
Findings regarding short run effects While 82 people died of SARS in Taiwan,
we find that the epidemic is associated with around 1000 additional non-SARS deaths.
The health impact is larger among the elderly and those with higher medical demand than others.
Differential effects by disease
Did missing visits cause any long term impacts?
Conditional on being alive after SARS, we would like to estimate the long term impacts of missing visits.
Empirical difficulty: One’s hospital visiting frequency is related with one’s health condition. Those who has decreasing visits could be getting healthier.
Did missing visits cause any long term impacts?
Empirical Strategy: Using instrument variable: Changes in hospital visits of the patient’s hospital
Sample: one million panel Those who had at least one hospital visit from 2003/1 -2003/3 & survived the Sars epidemic
Specification I
I. Probability of dying within the next six years
Change in Visits = -1
Specification II
We can further include the interaction terms of year dummies and change in visits to estimate the differential effects by year.
Instead of using logit, we use linear probability model with instrument variable.
Cut sample by diesase
Effects by disease
Some preliminary findings: -- The long run pattern is different from the pattern of short run effects -- larger impacts on cancer patients, and smaller impacts on diabetes patients. -- The impacts was smaller in later years
Conclusions
We find that SARS epidemic causes more non-SARS deaths than SARS deaths during the SARS epidemic.
We also find that missing hospital visits had long term impacts on those who avoid hospital visits.
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