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 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

Outpatient Visits: Ratio of 2003 to Other Years by Month

Inpatient Visits: Ratio of 2003 to Other Years by Month

Mortality: 2003 and

Mortality (age>=65)

Mortality (age<65)

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  Number of deaths: 2415  Mortality from diseases, however, did not increase

Mortality for all causes of death 2003 v.s avg v.s. 1999

Mortality (disease or natural death)

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

Table 1: Changes in Mortality during 2003 SARS period Dependent variable:Non-SARS Mortality ( by thousand) Category:AllOLDYoung (1) (2)(3) March (0.0187)(0.1401)(0.0089) April (0.0187) (0.1401)(0.0089) May **0.4053*** (0.0187)(0.1401)(0.0089) June (0.0187)(0.1401)(0.0089) July (0.0187)(0.1401) (0.0089) Month fixed effectsYes Year fixed effectsYes R-squared

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

Table 2: Mortality by history of hospital visits Dependent variable:Mortality ( t ) Category:OldYoungGroup H Group L (1)(2)(3) (4) Week9* Week10* Week11* Week12* * * 0.01 Week13* * ** Week14* * Week15* Week16* * Week17* Week18* Week19* ** ** Week20* Week21* ** * Week22* Week23* Week24* Week25* Week26* Sample size R-squared

Change in mortality by disease  Cancer v.s. Diabetes

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/ /3 & survived the Sars epidemic

Specification I

Specification II

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.