Discussion Paper No. 711

ANALYZING THE DECISIONTO GET FLU SHOT:AN EMPIRICAL STUDY

Yoshiro Tsutsui, Uri Benzion,
Shosh Shahrabani and Gregory Yom Din

March 2008

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Analyzing the Decision to Get Flu Shot: An Empirical Study
January 08, 2008

Yoshiro Tsutsui1 , Uri Benzion2, Shosh Shahrabani3, Gregory Yom Din4

1

Yoshiro Tsutsui, PhD, Institute of Social and Economic Research, Osaka University, 6-1
Mihogaoka, Ibaraki, Osaka, 567-0047 JAPAN
2
Uri Benzion, PhD, Department of Economics, Ben-Gurion University, Beer-Sheva 84105,
Israel uriusa@gmail.com and The Economics and Management Department, The Max Stern
Academic College of Emek Yezreel, Emek Yezreel 19300, Israel.
3
Shosh Shahrabani, D.Sc., The Economics and Management Department, The Max Stern
Academic College of Emek Yezreel, Emek Yezreel 19300, Israel. shoshs@yvc.ac.il
4
Gregory Yom Din, PhD, Golan Research Institute, University of Haifa, P.O.B. 97, Katzrin,
12900, Israel, rres102@research.haifa.ac.il
Corresponding author: Uri Benzion, Department of Economics, Ben-Gurion University, BeerSheva 84105, Israel uriusa@gmail.com and The Economics and Management Department,
The Max Stern Academic College of Emek Yezreel, Emek Yezreel 19300, Israel.Fax:97246423522, Tel: 972-522848321.

Abstract
Influenza vaccination has been shown to be cost effective in reducing morbidity andmortality and in decreasing work absenteeism and use of health-care resources. The
purpose of this study was to identify predictors and beliefs regarding people’s
vaccination decision against the influenza. It was hypothesized that Health Belief
Model (HBM) categories, such as severity of illness, vaccine effectiveness and side
effects of the vaccine, affect the decision to get flu shot. In addition, we examined
psychological effects, such as time preference, subjective probability of flu, andattitude toward risk. A questionnaire surveys was conducted in the USA, in 2004. The
questions included HBM categories and the psychological effects. The results indicatethat the main predictors of past immunization against influenza are: the estimatedeffectiveness of the vaccination, periodic blood test, perceived severity of flu illness,side effects of vaccine (negative effect), having health anxieties, and subjective
probability of being infected. Based upon these results, it is recommended toenlarging people’s knowledge regarding the influenza illness, its potential risks, andthe potential benefits of the vaccine.

1Key Words: Flu shot, Health Belief Model, Survey

1. Introduction

Influenza vaccination has been shown to be cost effective in reducing morbidity andmortality in the older adult population, and in decreasing morbidity, workabsenteeism and use of healthcare resources among the working healthy adult
population (Bridges et al., 2000; Campbell et al., 1997; Nichol et al., 2003; Lee et al.,2002). Influenza vaccine prevents influenza illness in approximately 70%−90% of
healthy adults under the age of 65 years and in 58% among persons over 60 years ofage (Wilde et al., 1999; Bridges et al., 2000; Demicheli et al., 2000; Govaert et al.,1994). Although influenza vaccination levels increased substantially during the 1990s,
further improvements in vaccination coverage levels are needed. For example, in
2004, estimated vaccination coverage among adults with high-risk conditions aged
18−49 years and 50−64 years was 26% and 46%, respectively, substantially lower
than the Healthy People 2000 and Healthy People 2010 objectives of 60% (CDC,2006). Vaccination levels (doses distributed/1000 population) for 2003 were 286 in
the USA, 230 in Japan, and 344 in Canada (MIV, 2005).

Based on a large national survey in USA, the current study sought to analyze theimpact of health, behavioral predictors, and demographic factors on people’s
vaccination decision against the flu. A 2004 survey of 4,979 people from different
states in the USA was used as the data source. The survey questionnaire comprisedquestions on various subjects, most of them including numerous items, and among
them attitudes toward the flu and flu shots.

2

In the model, we hypothesized that beliefs, according to the Health Belief Model(HBM) (Rosenstock et al., 1988), such as severity of illness, vaccine effectiveness,and vaccine side effects, as well as other behavioral variables, such as subjective
probability of flu, and time preference, would explain the vaccination decision.

The paper is organized as follows: Section 2 reviews the literature, Section 3
describes the survey data, and Section 4 describes the model and the methods. Section
5 presents the major results. Finally, Section 6 summarizes and concludes.

2. Literature Review

The current study is based on the Health Belief Model (HBM). The HBM, developedby Rosenstock et al. (1988), is a systematic method to explain and predict preventive
health behavior in terms of certain belief patterns. It focuses on the relationship ofhealth behaviors and utilization of health services. The model has been adapted to
explore a variety of long- and short-term health behaviors, including vaccinations(Rhodes and Hergenrather, 2003; Hyman et al., 1994; Champion, 1999; Blue andValley, 2002).
According to the HBM, the acceptance of an influenza vaccine depends on thefollowing groups of predictors: (a) perception of susceptibility to influenza, (b) beliefs
about the severity of influenza, (c) perceived benefits of the vaccine in preventing
influenza, (d) perceived barriers to accepting a vaccine (such as: inconvenient,3

expensive, unpleasant, and painful), (d) influence by cues to actions, such asrecommendation to take the vaccine.

In support of the HBM, it was shown that those individuals who received theinfluenza vaccine, as opposed to those who did not receive the vaccine, believed morestrongly that influenza is a serious illness and that receiving the influenza vaccine
would provide them with health benefits (Blue and Valley, 2002; Nexoe et al., 1999).
Reasons cited for not receiving influenza vaccine were similar across studies withreference to perceived barriers to the vaccine. Among the reasons were concern aboutside effects or vaccine safety, perceptions of effectiveness of the vaccine inpreventing illness, lack of awareness, and effectiveness in avoiding illness (Chapman
and Coups, 1999b; Heimberger, 1995; Nichol, 1997).

Socio-demographic background, economic status, and health status also have an
impact on an individual’s decision to be vaccinated. In an empirical study conductedin the U.S.A., Wu (2003) found that people with more education, higher incomes, andbetter insurance coverage are more likely to get flu shots, among various other typesof medical preventive treatments. The author also found that individuals with existing
health difficulties are more likely to get flu shots. Doebbeling et al. (1997) showedthat older individuals, those with higher socioeconomic status, and those employed
longer are more likely to get the influenza vaccine. Moreover, enabling factors suchas income, health insurance, and physician visits exhibited a strong relationship with
influenza vaccination status as well. Shahrabani and Benzion (2006) showed thatliving in a densely populated household and smoking heavily are also importantfactors in predicting the decision not to be vaccinated.

4

Time preference is another factor that may affect the flu shot decision, since
vaccination involves immediate costs and delayed benefits. Time preference is theextent to which decision makers value future outcomes relative to immediate ones.
Consequently, people with future-oriented time preferences should be more likely toadopt preventive measures (Shahrabani et al., 2007). Chapman and Coups (1999a)
provide some evidence that individuals’ time preference patterns can explain
preventive health behavior; in particular, monetary time preferences were found to
predict whether people took flu shots.

The current study combines HBM categories with other behavioral aspects such as:
time preference, the attitude towards risk, and subjective probability of illness, toexamine the main factors affecting the decision to get influenza vaccination.

3. Survey data
A 2004 survey of 4,979 people from different states in the USA was used as a sourceof data. One of the authors has been conducting large questionnaire surveys in theUSA, which comprised over 100 questions. In the 2004 survey, questions on theattitudes toward the flu and flu shots are included, which is utilized in this paper.
Table 1 summarizes the general characteristics of the survey sample. For example, theproportion of women in the sample was about 55%, the proportion of subjects over 60
years old was 25%, and the proportion of those who received the flu shot in the past
was 59%. The percentage of those who took the vaccine was much higher (78%)

5among subjects over 60 years old than among subjects under the age of 60 (53%). In
the next section, we consider the data in an analytical framework.
---------------------------------Insert Table 1 about here----------------------------------

4. The Model

4.1 Variables
The dependent variable (dEXINJ ) was a dichotomous variable equal to one if an
individual has had a flu shot in the past , and zero if not. The explanatory variables
included three groups: (a) HBM categories including: susceptibility, seriousness,benefits and barriers, (b) other psychological effects like subjective probability,
attitude toward risk, time preference and health motivation, and (c) control variables
including demographic variables.

The list of variables, their definitions and short description of the survey questions arepresented in Table 2. In addition the table shows the formulas and range we used foreach variable and specifies the expected effect direction of each variable on thedecision to get the vaccine. A detailed explanation for these variables and hypotheses
are given in the following section.

-------------------------------Insert Table 2 about here----------------------------------

6

4.2 Method
We used the OLS regression model to examine the factors affecting past
immunization. In doing this, we speculate the following hypotheses, and test them. 1

Hypothesis 1: People, who perceived higher seriousness or severity of illness(SEVERILL), have higher subjective probability to get the illness (SBJPROB), andhave higher scores for the perceived bother to the family when ill (BOTHRILL), will
possibly tend to get the vaccine. The variables in this category include: SBJPROB − An individual's subjective probability of being infected byinfluenza:Seriousness of illness- including: SEVERILL − perceived severity of
influenza illness, and BOTHRILL − bother to family and friends in case ofillness.

Hypothesis 2: People who think benefit from flu vaccination is larger have higher
tendency to be vaccinated. As for the benefit from vaccination, we adopt: EFFECT −Effectiveness of vaccination:

we expect that higher scores for effectiveness of

vaccination will positively affect the decision to get the vaccine.

Hypothesis 3: Barriers to accepting a vaccine: people who have higher levels of
barriers tend not to take the vaccine. Barriers include MNYCOST − the perceived

1

Hypotheses 1-4 essentially follows the idea of HBM.

7

cost of flu shot, and SIDEEFF -estimated side effects of flu shot. People who havehigher scores costs and higher levels of side effects, may tend not to take the vaccine.

Hypothesis 4: According to the susceptibility item of HBM, we expect that those withhigher levels of anxieties about their health will be more highly motivated to take thevaccine. The variable that indicates susceptibility is: HEALTH − Have anxieties
about health.

Hypothesis 5: Those who have higher time discount rate (present –orientation) haveless tendency to get the vaccine, because they discount future benefits of vaccination
more and compare them with the immediate costs of vaccination. 2 The variable thatindicates time preference is TDR − (Referring to the question: "want to postpone
pleasure for later"): we expect that a present-oriented attitude will negatively affectthe tendency to get the vaccine.

Hypothesis 6: People who are more risk averse are more likely to take the vaccine.
The variable that indicates the risk attitude is: RAIN − Referring to the question:"How high does must probability of rain be for you to take an umbrella?" The lower
value may represent the people’s risk aversion. We expect that individuals with higher
reversed score (lower threshold probability for taking the umbrella) will also havehigher incentive to get the vaccine.

Hypothesis 7: People who have higher levels of health motivation will tend to takethe vaccine. The variable that indicates health motivation is: dBLDTSTP − Periodic
2

Chapman and Coups (1999a) provide some evidence that time preference patterns can explain
preventive health behavior;

8

blood test. Some evidence indicates that preventive behaviors may be highly
correlated with one another (Fukunaga et al., 1997); therefore, we expect that those
who took periodic blood tests will have a higher probability of taking the vaccine.
Control variables: dGENDER− gender; AGE−age; dMARIT −marital status;
EDUC−education level, dEXILL- Whether infected by influenza during the last
two years. dBLDTSTS − Blood test following suspicion of illness, which may
indicate individuals in a risk group.

We estimated the OLS regression for the following equation:(1) dEXINJ = (dEXILL, SBJPROB, SEVERILL, dBLDTSTP, dBLDTSTS,HEALTH, EFFECT, SIDEEFF, MNYCOST, BOTHRILL, TDR,
RAIN, dGENDER, dMARIT , EDUC, AGE)

Table 3 summarizes the mean values of the explanatory variables. The data showthat the mean values of the variables, perceived effectiveness of vaccination, anddegree of anxiety about health are higher for the group that received the vaccine in thepast than for those that never received it, while the mean value of the variable ofestimated side effects of the flu shot is higher for the second group.
---------------------------------Insert Table 3 about here----------------------------------

5. Results9Table 4 summarizes the results for the OLS model. The results indicate that the main
predictors (based on higher scores) of past immunization against influenza are: theestimated effectiveness of the vaccination, and periodic blood test. This means that
individuals who perceived the vaccine as relatively effective tend to get the vaccine.
In addition, individuals who executed periodic bloods, which indicates higher levelsof health motivation, also tend to take the vaccine. These results confirm hypotheses 2
and 7.

We also found that, individual who perceived higher levels of severity of illness,
lower levels of perceived side effects , higher levels of health anxieties and higher
levels of subjective probability of being infected, received the flu vaccination in thepast. These findings confirm hypothesis 4, which refers to the perceived susceptibility(higher scores of health anxieties), and confirm hypothesis 1 regarding the severity ofillness (except for the variable of perceived bother to the family when ill). Yet, theresults in table 4 are not compatible with hypotheses 5 and 6, which refer to time
preference and attitude toward risk.

The significant control variables affecting the decision to be vaccinated were: age,education, and blood tests (because of suspicion of disease). Older individuals withhigher levels of education that executed blood tests (which may indicate individuals ina risk group) took the vaccine in the past.

---------------------------------Insert Table 4 about here----------------------------------

10

6. Summary and Conclusions
Influenza vaccination has been shown to be cost effective in reducing morbidity andmortality and in decreasing work absenteeism and use of health-care resources.
Although influenza vaccination levels have increased substantially during the 1990s,
further improvements in vaccination coverage levels are needed.

The current study was conducted to identify the behaviors and beliefs regarding thedecision to get the flu shot. It was hypothesized that subjective factors affect thedecision of individuals to be vaccinated.

The main results of the study show that individuals who received the vaccine, as
opposed to those who did not received it had stronger beliefs that (a) influenza is aserious illness, (b) the vaccine is effective, and (c) there are minor side effects to thevaccine. These results are compatible with the HBM results. Other researchers havealso found influenza vaccine acceptance to be influenced by perceptions ofeffectiveness of the vaccine in preventing illness, and likelihood of vaccine side
effects, (Chapman, 1999; Fiebach and Viscoli, 1991; Heimberger, 1995; Nichol,
1997; Blue and Valley, 2002). In addition, we found that individuals who received
the vaccine have more anxieties about their health, have higher subjective
probabilities to be infected, and have more precautionary motivation. The study adds
to the existing literature by combining behavioral factors of the HBM withpsychological effects, such as subjective probability of illness.

11

Our conclusion from this study is that subjective probabilities, in addition to
behavioral factors may affect the decision to get the vaccine. Although this decision
has direct effect on the individual itself, it has also direct and indirect effects on thesociety since influenza is an epidemic disease.

Based upon these results, werecommend enlarging people’s knowledge regarding the influenza illness, its potential
risks, and the potential benefits of the vaccine.

127. References

1. Blue CL and Valley JM. Predictors of influenza vaccine: Acceptance among
healthy adult workers. AAOHN Journal 2002; 50(5); 227-235.
2. Bridges CB, Thompson WW, Meltzer MI, et al. Effectiveness and cost-benefit
of influenza vaccination of healthy working adults: A randomized controlled
trial. The Journal of the American Medical Association 2000; 284(13); 16551663.
3. Campbell DS, Run-dey MH. Cost-effectiveness of the influenza vaccine in ahealthy, working-age population. Journal of Occupational & Environmental
Medicine 1997; 39(5); 408-414.
4. CDC, 2006. Prevention and Control of Influenza Recommendations of theAdvisory Committee on Immunization Practices (ACIP). United States
Department of Health and Human Services. Centers for Disease Control andPrevention (CDC). available: http://www.cdc.gov/
5. Champion VL. Revised susceptibility, benefits, and barriers scale formammography screening. Research in Nursing & Health 1999; 22(4); 341-348.
6. Chapman GB, Coups EJ. Time preferences and Preventive Health Behavior:Acceptance of the Influenza Vaccine. Medical Decision Making 1999a; 19(3);307-314.
7. Chapman GB, Coups EJ. Predictors of influenza vaccine acceptance among
healthy adults. Preventive Medicine 1999b; 29(4); 249-62.
8. Demicheli V, Jefferson T, Rivetti D, Deeks J. Prevention and early treatment
of influenza in healthy adults. Vaccine 2000; 18; 957-1030.

13

9. Doebbeling BN, Edmond MB, Davis CS, Woodin JR, & Zeitler RR. Influenza
vaccination of health care workers: Evaluation of factors that are important inacceptance. Preventive Medicine 1997; 26(1); 68-77.
10. Fiebach NH, Viscoli CM. Patient acceptance of influenza vaccination.
American Journal of Medicine 1991; 91(4); 393-400.
11. Fukunaga I, Jitsunari F, Takeda N, Asakawa F, Maruyama Y. A study ofhealth behavior of the elderly - correlation between participation in healthexaminations and health behavior. Japanese Journal of Hygiene 1997; 52(2);490–503.
12. Govaert TM, Thijs CT, Masurel N, Sprenger MJ, Dinant GJ, Knottnerus JA.
Efficacy of influenza vaccination in elderly individuals: a randomized doubleblind placebo-controlled trial. The Journal of the American Medical
Association 1994; 272(21); 1661-1665.
13. Heimberger T, Chang HG, Shaikh M, Crotty L, Morse D, & Birkhead G.
Knowledge and attitudes of healthcare workers about influenza: Why are they
not getting vaccinated? Infection Control and Hospital Epidemiology 1995;
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14. Hyman RB, Baker S, Ephraim R, Moadel A, and Philip J. Health belief model
variables as predictors of screening mammography utilization. Journal of
Behavioral Medicine 1994; 17; 391-406.
15. Lee PY, Matchar DB, Clements DA, Huber J, Hamilton JD, Peterson ED.
Economic analysis of influenza vaccination and antiviral treatment for healthy
working adults. Annals of Internal Medicine 2002; 137(4); 225-31.

14

16. MIV - Macroepidemiology of Influenza Vaccination study group. The
macroepidemiology of influenza vaccination in 56 countries, 1997-2003.
Vaccine 2005; 23(44); 5133-5143.
17. Nexoe J, Kragstrup J, Sogaard J. Decision on influenza vaccination among theelderly: A questionnaire study based on the Health Belief Model and theMultidimensional Locus of Control Theory. Scandinavian Journal of Primary
Health Care 1999; 17(2); 105-110.
18. Nichol KL, Hauge M. Influenza vaccination of healthcare workers. Infection
Control and Hospital Epidemiology 1997; 18; 189-94.
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Vaccination Behavior Among Young Men Who Have Sex with Men: Stages of
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Health Belief Model. Health Education Quarterly 1988; 15(2); 175-183.
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Plausible

Behavioral

Explanations,forthcoming.

15

The

AmericanEconomist2008,

24. Wilde JA, McMillan JA, Serwint J, Butta J, O'Riordan MA, Steinhoff MC.
Effectiveness of influenza vaccine in health care professionals: a randomized
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16

Tables

Table 1. General characteristics of the survey sample
men

women age >60

age <=

received flu shot60

infected by flu
duringthe last two years

never
men

2,231

women
age > 60

2,748

ever

never

ever

498

1,541

920 1,311

1,809

422

661

1,965

1,119 1,629

2,195

553

908

1,047

112

1,646 1,860

2,704

802

2,039

1,604

435

2,400

540

1,159

age <=

251
3,506

60
received flu shot
never

2,940

ever
infected by flu during the last two years

4,004

never

975

ever

17

Table 2. Codes and explanation of the variables, linkage to the questionnaire
Code
SBJPROB
SEVERILL
BOTHRILL
dEXINJ
dEXILL
EFFECT
MNYCOST
SIDEEFFdBLDTSTP
dBLDTSTSRAIN
TDR
OVERCON
HEALTHAGE
dGENDERdMARITEDUC

Explanation

Question

Formula, range

directionsubjective probability of illness

Q50 Estimate your chance to be infected by flu during the next 12 months.

Q50, 0 to 100

increaseseverity of illness

Q44 How serious a disease do you think the flu is?

7-Q44, 1 to 6

increase

bother to your family when ill

Q47 When infected, to what extent do you bother your family and friends?

5-Q47, 1 to 4

increaseever received a flu shot

Q41 Have you ever received (1) a flu shot?

No - 0, yes - 1

increase

infected during the last 2 years

Q43 Have you been infected (1) by the flu during the last two years?

No - 0, yes - 1

increase

effectiveness of vaccination

Q49 How effective do you think the flu shot is?

6-Q49, 1 to 5

increasecost of flu shot

Q45 How much do you think a flu shot costs?

Q45, 0 to 50,000$

increaseside effect of the vaccine

Q46 How serious do you think the side effects caused by a flu shot are?

8-Q46, 1 to 7

increase

periodic blood test

Q51 Undergo blood test in the last 12 months as part of a periodic test

No - 0, yes - 1

increase

blood test because of suspicion

Q51 Undergo blood test in the last 12 months because of suspicion of diseaseNo - 0, yes - 1

increasethreshold for action

100-Q21, 0 to 100

decreasetime preference

Q21 How high does the probability of rain have to be in order for you to takean umbrella?
Q2.5 I want to postpone joys for later

6-Q2.5, 1 to 5

decreaseoverconfidence

Q2.6 I will never be robbed

6-Q2.6, 1 to 5

increase*

have anxieties about healthQ2.12 I have anxieties about my health

6-Q2.12, 1 to 5

increaseage

Q57.1 Your birth year?

Q57.1, 1900 to 1990

increase

genderQ54

0 - male, 1 - female

marital status

Q55

1 - married, 0 - othereducation levelQ58.1 The highest level of education completed 1 to 9

Q58.1, 1 to 9

* increase effect of the independent variable overconfidence on the subjective probability of individual to get flu shot.

18

Table 3. Mean values of the variables
Scale
Subjective probability

0-100

Severity of illness

1-6

Bother to family when ill

1-4

Infected during the last 2 years
Vaccination effectiveness(no=0,
yes=1)
1-5

Cost of flu shot

0-50000

Side effects

1-7

Periodic blood
test
Blood test
(suspicion of illness)precautionary motivationTaking an umbrella Probability
Time preference(no=0,
yes=1)(no=0,
yes=1)
0-100

Overconfidence

1-5

Anxieties about health

1-5

Marital status

1-5(not mar.=0 mar. =1)

Age

0-90

Education level

1-9

Whole
sample26.01(0.35)
4.22(0.02)
1.68(0.01)
0.19(0.00)
3.00(0.01)
3.38(0.02)
3.15(0.02)
0.59(0.00)
0.04(0.00)
40.66(0.42)
3.18(0.02)
2.60(0.01)
2.94(0.01)
0.58(0.00)
47.61(0.25)
4.80(0.02)

Received vaccination in the past
Age less than 60 Age 60 and above
28.99
19.20
(0.58)(0.73)
4.35
4.81(0.03)(0.04)
1.73
1.53(0.01)(0.02)
0.22
0.09(0.01)(0.01)
3.11
3.27(0.01)(0.02)
3.50
3.23(0.03)(0.04)
2.97
2.90(0.03)(0.05)
0.61
0.83(0.01)(0.01)
0.05
0.04(0.00)(0.00)
39.63
43.67(0.67)(0.94)
3.16
3.28(0.03)(0.04)
2.56
2.61(0.02)(0.04)
3.04
3.12(0.02)(0.04)
0.65
0.60(0.01)(0.01)
42.25
71.56(0.27)(0.26)
5.02
4.62(0.04)(0.06)+ Standard Error in brackets

19

Never received vaccination in the past
Age less than 60 Age 60 and above
26.82
23.10
(0.61)(1.58)
3.81
4.09(0.03)(0.09)
1.73
1.57(0.02)(0.04)
0.23
0.11(0.01)(0.02)
2.77
2.69(0.01)(0.05)
3.42
3.16(0.03)(0.09)
3.39
3.80(0.03)(0.11)
0.45
0.57(0.01)(0.03)
0.03
0.03(0.00)(0.01)
39.73
39.69(0.77)(1.89)
3.17
3.17(0.03)(0.09)
2.61
2.54(0.02)(0.07)
2.78
2.70(0.03)(0.08)
0.57
0.62(0.01)(0.03)
37.97
68.74(0.28)(0.41)
4.75
4.46(0.04)(0.11)

Table 4. Results of OLS regression model: Dependent variable received or not flu shot in the past ( dEXINJ)
Explanatory
variables(Constant)
SBJPROB
SEVERILL
BOTHRILL
dEXILL
EFFECT
MNYCOST
SIDEEFFdBLDTSTP
dBLDTSTSRAIN
TDR
HEALTHdGENDERdMARIT
AGE
EDUC
R squareAdj. R squareN* p-value < 0.05;

Coeff.

t test-.356*-6.8971.901
10.410-.625
1.671
12.9241.910-10.956

Subjective probability.001*
Severity of illness.049*
Bother family when ill-.005
Infected during the last 2 yrs (no=0,yes=1) .028**
Vaccination effectiveness.109*
Cost of flu shot
.009*
Side effects-.044*
Periodic blood.133*
test (no=0,yes=1)
Blood test (suspicion of illness) (no=0,yes=1) .139*
Uncertainty attitude.000
Time preference.000
Anxieties about health.028*
Gender (0=M, 1=F)-.014
Marital status.018(not mar.=0 mar. =1)
Age
.006*
Education
.012*
.192
.190
4,979

** for p-value <0.1

20

9.451
4.286
.323
-.042
5.208
-1.0831.354
13.7603.492