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.