Abstract The risk of costly complications and the externalities of contagious dis-eases lead many countries provide free flu shots to certain populations. In 2008, theNetherlands expanded their flu shot program to cover all individuals over the age of60, instead of 65. We investigate the effects of the expansion and examine those factorsthat influence people to change their behavior. We find that the main barrier to takeup of free flu shots is labor force participation. Expansion of the program did littleto change the behavior of those at increased risk, primarily because these individualswere already getting flu shots.
Keywords Preventive health care · Flu shot · Dutch policy · Coverage expansion
JEL Classification I10 · I18
Part of this work was completed while both authors were working at Tilburg University. We are grateful totwo anonymous referees for their comments. Additionally we thank Peter Kooreman, Miquelle Marchand,Annette Scherpenzeel, Joost Timmermans and Corrie Vis for helpful comments. This paper draws on dataof the LISS panel of CentERdata. Supporting materials, including a translation in English of the survey inDutch, are available from the authors.
K. G. CarmanRAND Corporation, 1776 Main St, Santa Monica, CA 90405, USAe-mail: [email protected]
I. Mosca (B)Institute of Health Policy and Management, Erasmus University Rotterdam,PO Box 1738, 3000 DR Rotterdam, The Netherlandse-mail: [email protected]
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1 Introduction
Influenza can affect 10–30 % of the population each year resulting in lost work daysand higher health costs, not to mention pain and suffering for those who are ill andpossible death, especially among high risk populations. In recent years, infection ratesin the Netherlands have approached epidemic levels (RIVM 2009; De Jong et al.2011). To combat the risks of influenza, the Dutch government provides free influenzavaccinations (flu shots) to certain high risk groups, including the elderly, diabetics,and those with heart disease. Other countries recommend that many more individualsget flu shot; for example, the United States Centers for Disease Control and Prevention(CDC) now recommends that everyone get a flu shot, in order to promote so-calledherd immunity.
While it may not be necessary for everyone to have a flu shot, the preventive benefitsare clear, especially for high risk populations who are more likely to suffer compli-cations from influenza, including death. These preventive benefits are the primarymotivation for providing free flu shots in the Netherlands. But flu shots can also pro-vide a positive externality. As with all vaccinations, flu shots reduce the prevalence ofdisease and therefore the likelihood that individuals will come into contact with andpossibly contract influenza.
Economic theory shows that without government intervention individuals wouldunderinvest in this type of prevention. This means that some population-based pre-vention and promotion programs must be financed by the state.
This paper examines the flu shot program in the Netherlands. By using a representa-tive sample of Dutch individuals (LISS panel data), we aim to understand who respondsto the availability of free flu shots. In 2008, the Netherlands expanded their free flu shotprogram to include all individuals age 60 and older, instead of all individuals age 65and older. In both regimes, flu shots are provided free of charge by the National Insti-tute of Public Health and the Environment (RIVM). Prior to the expansion, 32 % ofour sample between the ages of 60 and 64 got flu shots. After the expansion, this growsto 61 %. This paper investigates the effectiveness of the expanded flu shot program byfocusing on who responds to the new expanded program. We examine the factors thatmay influence people to change their behavior, particularly focusing on the impact ofworking and subjective explanations of take up, such as perceived side effects and timeconstraints. We also conduct a back of the envelope cost benefit analysis to investigatewhether the expansion of the flu shot program was worth while.
The paper proceeds as follows. Section 2 briefly discusses literature on preventivehealth care and on flu shots in particular. Section 3 describes the institutional settingof the Dutch flu shot program. Section 4 focuses on the data description and themethodology used. Empirical results are presented in Sect. 5. Section 6 concludes.
2 Literature on Preventive Health Care and Flu Shots
There is an extensive literature on the decision to take up a preventive care program ifthe expected present value of the reduction of getting sick and the probability of death
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is greater than the opportunity costs of intervention. See Grossman (1972), Selden(1993); Chang (1996) for a further description of this notion.
Several empirical works examine the factors that make people decide to invest inpreventive care. Trivedi et al. (2008) study the effect of an increase in patients’ share ofhealth care costs on the use of important preventive programs such as mammography.An increase in the cost sharing is significantly associated with lower mammographyrates. Particularly women with low income and educational level are worse off whenco-payments are in place. Kenkel (1994) finds that the probability of women will havePap smears and mammograms increases with schooling and insurance coverage anddecreases with age. Belkar et al. (2006) state that women’s awareness of Pap testsincreases their propensity to ever screen for cervical cancer. The role of awareness ispivotal in determining who uses preventive care programs and failing to account forit can bias the measurement of other effects.
Another strain of the literature focuses on the determinants of those individualsgetting a flu shot. Mullay (1999) examines the microeconomic determinants of beingimmunized against influenza. He finds that the propensity to receive a vaccine dependson a number of both individual characteristics, such as schooling and age, and envi-ronmental factors, such as insurance coverage. Chi and Neuzil (2004) investigate howpatient attitudes, beliefs, knowledge, and sociodemographic factors relate to influenzavaccine acceptance in an older population. Receipt of vaccination is associated witha discussion about the influenza shot with the health care provider and a positiveattitude towards the flu shot. A history of side effects and negative attitude towardinfluenza vaccine are associated with failure to receive the shot. Shahrabani and Ben-zion (2006) look at the socioeconomic factors affecting the decision to take a flu shotin Israel, where vaccination rates remain relatively low compared to other countries.Chronic illness, previous hospitalizations, and age increase the chance to take the flushot. McCaul et al. (2002) test the effect of cues to action, i.e. messages intended toincrease flu immunization. In North Dakota, counties use remind letters, action letters,or no letters at all within the flu shot program. The authors show that the reminder typeused does not significantly affect the immunization rate. However, the action messagesworked better (28.2 % participate with the message) than no message (19.6 %). Denton(1997) examines the importance of communication in prevention programs. Researchshows that high-risk patients who should take the vaccine are more likely to do so ifthey understand its efficacy and absence of side effects.
According to the CDC1 the flu shot is 30–70 % points effective in preventing hos-pitalization for pneumonia (a lung infection) and influenza among elderly persons notliving in chronic-care facilities (such as nursing homes) and those persons with long-term (chronic) medical conditions (such as asthma, diabetes, or heart disease). Dutchresearchers find similar results. The flu shots offer protection to the elderly, especiallyif flu shots are taken every year (Simonsen et al. 2007).
In addition to these direct benefits, there are indirect benefits as well. If othersreceive flu shots, and therefore do not get influenza, the risk to the whole population candecline. Widespread use of flu shots can potentially lead to so-called herd immunity,
1 For further information see http://www.cdc.gov/flu/about/qa/vaccineeffect.htm.
where enough people can be vaccinated that the overall risk of contact with the diseaseis nearly eliminated. Along these lines, the US CDC has recommended since 2011that everyone, regardless of age, receive a flu shot.
3 Dutch Policy Change
The Dutch government finances some preventive care programs through the NationalInstitute of Public Health and the Environment (RIVM), including flu shots. Eachyear the Health Council of the Netherlands classifies the criteria of high-risk groups inthe population who are eligible to receive a free flu shot. General practitioners (GPs,huisartsen) then provide most immunizations.2 Through RIVM’s program, GPs sendletters in the fall to all of their patients who are eligible for these free flu shots invitingthem to come in for their vaccination. There are two primary groups covered by thefree flu shots: those over the age of 65 (or 60) and those at high risk due to otherchronic illnesses, such as diabetes and heart disease. Outside of these groups, peoplecan still receive a flu shot from their doctor. In this case the out-of-pocket price willdepend on their specific health insurance package.
In 2008, the flu shot program was expanded to the group of 60–64 year olds after theHealth Council (Gezondheidsraad 2007) positively advised on the beneficial effectsof the flu shot for this extended group. Scientific research showed that the flu shoteffectiveness for this targeted group is highly reliable because the immune systemof these individuals responds by producing specific antibodies against influenza. Inthe Netherlands, there is a constant increase in the number of people eligible for thefree flu vaccination program. This is due to the ageing population and to the betterregistration of patients with chronic conditions.
Take up of flu shots following this policy change was studied by Tacken et al. (2009).The National GP Information Network (Landelijk Informatie Netwerk Huisartsenzorg)is a representative national network of GP practices that uses electronic medical recordsto record patients’ information. All 72 practices of this network have been approachedin this study. The electronic medical records software registers the vaccine given to thepatient, those who refused to get a flu shot, those who do not react to the invitation toget a flu shot, and those who do not belong to the high-risk population but are willingto pay out-of-pocket for their vaccination because they are excluded from the eligiblegroup (e.g. younger than 60).
According to Tacken et al. the degree of vaccination in the “enlarged” group, thoseindividuals aged between 60 and 65, was generally low (51.9 % points); just slightlymore than the half of the high-risk patients aged between 60 and 65 decided to geta flu shot. There was no significant difference compared to previous years with thedegree of vaccination of high-risk individuals.
In other words, their study indicates that the policy change did not have any realinfluence on the vaccination program. In 2009 there was a slight increase in the per-centage (54.7) of people aged between 60 and 65 who got the flu vaccine. Also, the
2 The bulk of flu shots are indeed given at the GP practice. However, employers can offer such an immu-nization program as well.
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number of individuals eligible for the flu shot increased because of the vaccinationagainst the influenza A H1N1. Generally women make more use of the free vaccinationprogram than men (73.7 vs. 69.0 % points in 2008) as the high-risks living in betterboroughs do than high-risk individuals in poor boroughs (71.7 and 68.7 % points in2008).
Our paper makes several contributions relative to Tacken et al.. First, we make use ofmultivariate regressions while they limit their analysis to cross tabs. Second, becausewe use survey data we are able to control for many more characteristics than they areable to observe in administrative data. Survey data allows us to collect detailed demo-graphic information and investigate the reasons behind individuals’ choices. Whiletheir observations of take up of flu shots may be more accurate, to fully understandbehavior, both methodologies are necessary. Third, their data comes from a specificgroup of doctors who may not be representative. In contrast, our data is based on a rep-resentative sample of the population. Finally, we study in more detail the heterogeneityof flu shot take up. In particular we carefully consider the role of past flu shot take up.They essentially treat their data as repeated cross sections while we take advantage ofpanel data to compare changes in behavior for specific individuals over time.
4 Data and Methodology
The data for this study comes from a survey administered through the LISS panel.3
The LISS panel includes a representative sample of Dutch households who answermonthly surveys accessed through the internet. In order to ensure representativeness,households without internet access are provided with a computer and internet access.While there are some problems attracting the oldest elderly to participate in the survey,this is primarily an issue among those over 80 and therefore not relevant for ourstudy, which focuses on the ages of 60–64 for the main analysis and ages 55–59 and65–69 as controls. We focus on those individuals who become eligible in 2008 due tothe expanded program. Our primary interest is in how the policy expansion affectedbehavior, not just how eligibility for free flu shots affects behavior. Our main analysisconsists of 484 individuals; an additional 604 are between the ages of 55 and 59 and391 between 65 and 69. There are a number of advantages of the LISS panel. First,the LISS panel regularly collects household demographic data, thus these questionsneed not be asked during our survey. Second, as it is a panel, it is easy to return torespondents at a later date to ask follow up questions.
Our study was conducted in the LISS panel in September 2008 and January 2009. InSeptember respondents were asked about their past use of flu shots, whether they had aflu shot in 2007, whether they received an invitation for a flu shot in 2007, contact withhealth care providers, and their perceptions regarding flu shots. In January, respondentswere asked whether they had received a flu shot during the fall of 2008. The timing ofthe initial survey was selected to occur prior to the mailing of invitations for flu shotsfor the Winter 2008/2009 season.
3 The full survey and dataset in both English and the original Dutch are available from lissdata.nl. Thesurvey is labeled “33 Disease Prevention”.
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Table 1 Flu shots in 2007 and2008
Tables present number ofindividuals in each cell andpercent of total in each cell
No flu shotin 2008
Flu shotin 2008
Total
Panel A: ages 60–64
No flu shot in 2007 184 150 334
38 % 31 % 69 %
Flu shot in 2007 3 148 151
1 % 31 % 31 %
Total 187 298 485
39 % 61 % 100 %
Panel B: ages 55–59
No flu shot in 2007 391 44 435
72 % 8 % 80 %
Flu shot in 2007 14 96 110
3 % 18 % 20 %
Total 405 140 545
74 % 26 % 100 %
Panel C: age 65
No flu shot in 2007 24 22 46
24 % 22 % 47 %
Flu shot in 2007 0 52 52
0 % 53 % 53 %
Total 24 74 98
24 % 76 % 100 %
Panel D: age 66–69
No flu shot in 2007 44 17 61
22 % 8 % 30 %
Flu shot in 2007 6 136 142
3 % 67 % 70 %
Total 50 153 203
25 % 75 % 100 %
The primary interest is whether individuals who would not have been eligible underthe 2007 guidelines choose to get a flu shot in 2008 after the expansion of eligibility.Panel A of Table 1 presents a cross tab of flu shot take up in 2007 and 2008. Severalimportant factors can be gleaned from this table. First, participation in the flu shotprogram for the affected age group increased dramatically from 2007 to 2008; nearlytwice as many people get flu shots in 2008. Second, among those who got a flu shotin 2007, nearly all of them continue to do so in 2008. Only three individuals switchfrom getting a flu shot to not getting a flu shot. Panels B, C and D of Table 1 presentthe same information for those between 55 and 59, age 65, and ages 66–69. Like thosebetween 60 and 64, people aged 65 were not eligible for flu shots in 2007 but are noweligible in 2008. What we learn from these comparison groups is that if nothing elsechanges (as in Panels B and D), most people continue to do what they did before. If flu
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shots become available for free (as in Panels A and C), roughly half of those who didnot get a flu shot in 2007 get one in 2008. These results suggest that in the multivariateanalysis it is important to consider past behavior.
Respondents were also asked about risk factors that may make them more prone tocomplications due to influenza. These include diabetes, high blood pressure, and heartdisease. Individuals suffering from these chronic conditions should also have receivedan invitation for a flu shot, thus an invitation received by someone outside of the normalage range is likely to indicate that an individual has some additional risk factors.
When discussing influenza with a general population, one concern is that the word“flu” (griep) may be interpreted to mean something other than influenza. Thus respon-dents are instructed to consider actual influenza.4 The informal use of the word mayinfluence how people respond to questions about influenza. In our sample, the aver-age number of times one has had influenza in the last 5 years is 0.51. We also askindividuals about whether or not they agree or disagree (on a 5 point scale) with anumber of subjective statements that explain who might or might not get a flu shot.Individuals are asked about their perceptions of the protective effects of flu shots, theside effects, their own risk, and the costs.5 These questions are designed to address themain reasons individuals may have for not getting a flu shot. In particular, this allowsus to identify what factors are most related to take up or non-take up of flu shots.
Table 2 presents summary statistics for the main demographic variables and for theother control variables that will be considered in the multivariate analysis. Summarystatistics are presented for all individuals between 60 and 64 compared to two controlgroups, those between 55 and 59 and those between 65 and 69. The control groupsare significantly different from the treatment group in terms of age, previous flu shotinvitations, and working status. Differences in gender and marital status are not sta-tistically significant. The older control group is more similar to the treatment group interms of education, number of doctor visits in the past year, and frequency of influenzain the past year, while the younger group is more educated, has had fewer visits to thedoctor in the past year, and a lower incidence of influenza in the previous 5 years.
Finally we asked about a number of risk factors. Those with risk factors such asdiabetes, high blood pressure, and heart disease are more likely to get a flu shot, inboth years. This suggests first, that many individuals with risk factors were gettingflu shots before the expansion of the program. Second, there is a slight decrease indiabetes and high blood pressure among those who do not get a flu shot, suggestingthat a few people with risk factors started getting flu shots after the expansion of theprogram. Other risk facts, such as perceived risk and self-assessed health, also point inthe expected directions. These differences reported in Table 2 are merely indicative.
4 In the questionnaire, the following is reported: “The first few questions are about the flu. By flu, we meanactual flu or influenza, not a cold or stomach flu. With influenza you become sick very fast, with achingmuscles all over your body, a high temperature and usually have a pounding headache”.5 The four statements considered in our models are:
I think that a flu shot provides good protection against the flu.Flu shots have unpleasant side effects.I think I am at high risk to get the flu.Flu shots are too expensive for me.
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Table 2 Summary statistics
Variable Treatedage 60–64
Control 1age 55–59
Control 2age 65–69
t Test for.treated-control1 = 0
t Test for.treated-control2 = 0
Flu shot in 2007 0.323 0.202 0.665 −4.728 11.003
[0.468] [0.402] [0.473] [0.000] [0.000]
Flu shot in 2008 0.614 0.257 0.768 −12.412 4.750
[0.487] [0.437] [0.423] [0.000] [0.000]
Age 61.906 56.997 66.754 −60.697 53.537
[1.349] [1.394] [1.398] [0.000] [0.000]
Dummy if male 0.526 0.482 0.517 −1.512 −0.293
[0.5] [0.5] [0.5] [0.131] [0.770]
Dummy if partnered 0.802 0.806 0.777 0.177 −0.925
[0.399] [0.396] [0.417] [0.859] [0.355]
Dummy if working 0.269 0.682 0.061 15.352 −8.416
[0.444] [0.466] [0.24] [0.000] [0.000]
Dummy if retired 0.365 0.033 0.767 −15.767 13.279
[0.482] [0.179] [0.423] [0.000] [0.000]
Dummy if MBO 0.132 0.195 0.118 2.881 −0.675
[0.339] [0.397] [0.323] [0.004] [0.500]
Dummy if HBO or WO 0.243 0.301 0.261 2.216 0.616
[0.429] [0.459] [0.44] [0.027] [0.538]
Number of DoctorVisits in LastYear
2.557 2.397 2.673 −0.841 0.613
[2.962] [3.455] [2.68] [0.400] 0.540
Dummy ifReceived anInvitation forFlu Shot
0.381 0.31 0.831 −2.561 15.350
[0.486] [0.463] [0.375] [0.011] [0.000]
Ever had flu in last5 years
0.267 0.318 0.23 1.906 −1.276
[0.443] [0.466] [0.421] [0.057] [0.202]
Observations 551 604 391
Columns 1–3 mean followed by standard deviation in parentheses. For columns 4–5 t statistics followedby p value in parentheses
In Sect. 5 we will consider multivariate analysis that allows us to control for all factorsat the same time.
In our analysis, we use two estimation techniques to study the effect of the pol-icy change in flu shot. We start with a difference-in-differences (DD) model anddifference-in-difference-in-differences (DDD) model. Both models are largely usedin the literature when comparing outcomes for two (or more) groups in two timeperiods. We implement the DD by running a probit regression of the following form:
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Flu Shot = f (β0 + β1 T reated + β2 T ime + β3 T reated ∗ T ime + β4 X + ε)
where Flu Shot is a dummy indicating if the individual received a flu shot, Treatedis a dummy indicating whether the individual is in age group that became eligible in2008, and Time is a dummy equal to one in the 2008 after the change is implemented.In this case, β3 represents the effect of the new policy on the treated population. X isa vector containing all other characteristics; in this case we control for age, gender,marital status, working status, retirement, and education. We control for the numberof doctor visits in the previous year, whether they had influenza in the past 5 years,and whether they previously received a flu shot invitation as proxies for health. Forthose under 65, past flu shot invitations indicate that the individual has a risk factorfor influenza complications, such as heart disease or diabetes.
However, a key assumption of the DD methodology is that an appropriate controlgroup can be found. Individuals over 65 and under 60 might not be good control groupsfor individuals between 60 and 64. The potential problem with this DD analysis is thatother factors unrelated to the new policy change might affect the health of the elderlyrelative to the younger individuals, for example, major changes such as retirement.This makes the groups less comparable. Another important difference is that healthstatus is likely to decline with age and thus the potential complications of influenzawill increase. Differences between the treatment and control groups that directly relateto the behavior of interest are likely to violate the most important assumption of theDD model: the assumption of parallel trends. Because our data include only one yearbefore and after the policy change we cannot test this assumption directly.
To overcome this issue we then estimate a Probit model that allows us to considerwhat characteristics are most associated with take up of the flu shot following thepolicy expansion. We focus on the group affected by the policy expansion, i.e. the60–64 year olds.
5 Results
This section presents the main results of the paper. First, we consider a DD modelcomparing the treated age group, 60–64 year olds, with those between 55 and 59 andthen with those between 65 and 69. Second, we use a Probit model to focus on thosebetween 60 and 64, and investigate what factors influenced take up of the free flu shotamong those eligible due to the policy expansion.
Table 3 presents the results of the DD models. In column 1 the coefficient on thetreatment group interacted with a dummy for 2008 tells us how take up of the flu shotincreased among 60–64 year olds following the expansion of the free flu shot program.Column 1 compares 60–64 year olds with 55–59 year olds. Column 3 compares 60–64 year olds with 65-69 year olds. These regressions control for whether or not therespondent received an invitation for a free flu shot in 2007—for those under 65 thiswould typically indicate that they have risk factors, such as heart disease or diabetes,that increase their likelihood of complications. The regressions also control for thenumber of doctor visits in the previous year as a proxy for individual health. Theseresults show that among the treated, take up of flu shots increased by 30.8 % points
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Table 3 DD and DDD regressions predicting who gets a flu shot
Model 1 Model 2 Model 3 Model 4
Dummy for 2008 0.109*** 0.109*** 0.159*** 0.157***
(0.024) (0.024) (0.029) (0.029)
Dummy if in treatment group (age 60–64) −0.049 −0.186*** −0.063 −0.328***
(0.054) (0.066) (0.067) (0.085)
Treatment interacted with prior invitation 0.255*** 0.407***
(0.080) (0.067)
Treatment group interacted with 2008 0.308*** 0.560*** 0.265*** 0.519***
(0.043) (0.053) (0.037) (0.046)
Treatment interacted with priorinvitation and with 2008
−0.276*** −0.557***
(0.014) (0.023)
Age 0.030*** 0.030*** 0.012 0.016
(0.010) (0.010) (0.012) (0.013)
Dummy if male 0.004 0.009 0.023 0.028
(0.029) (0.028) (0.037) (0.038)
Dummy if partnered −0.010 −0.010 0.070* 0.074*
(0.033) (0.032) (0.040) (0.041)
Dummy if working −0.081** −0.080** −0.078 −0.078
(0.034) (0.033) (0.055) (0.058)
Dummy if retired −0.006 −0.009 0.010 0.010
(0.042) (0.040) (0.046) (0.049)
Dummy if MBO −0.031 −0.032 −0.045 −0.047
(0.035) (0.035) (0.054) (0.057)
Dummy if HBO or WO −0.089*** −0.085*** −0.051 −0.053
(0.032) (0.030) (0.042) (0.044)
Number of doctor visits in last year 0.022*** 0.022*** 0.045*** 0.047***
(0.005) (0.005) (0.007) (0.008)
Dummy if received an invitation for flu shot 0.628*** 0.648*** 0.590*** 0.556***
(0.027) (0.031) (0.029) (0.046)
Ever had flu in last 5 years 0.037 0.041 0.033 0.039
(0.031) (0.031) (0.038) (0.041)
Constant 0.109*** 0.109*** 0.159*** 0.157***
(0.024) (0.024) (0.029) (0.029)
Control group ages 55–59 55–59 65–69 65–69
Observations 2,177 2,177 1,777 1,777
R-squared 0.454 0.478 0.400 0.427
Standard errors clustered at the individual level. Standard errors in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1
when 55–59 year olds are used as the control and by 26.5 % points when 65–69 yearolds are used as the control. These results suggest that the program led to large increasesin take up, but by no means did everyone in the treated group start getting flu shots.
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Columns 2 and 4 in Table 3 consider a DDD model. This allows us to investigatewhether the impact of the reform differed among those who had previously receivedan invitation for a flu shot relative to those who were newly eligible following thechange in policy. The coefficient on the treatment group interacted with a dummy for2008 shows the effect of the reform on the newly eligible group. These individualsincreased take up of flu shots by 56.0 % points when 55–59 year olds are used as thecontrol and by 51.9 % points when 65–69 year olds are used as the control. For thosewho were previously invited for flu shots the effect of the policy can be found byadding the coefficient on interaction term between treatment group and the dummyfor 2008 to the coefficient on the triple interaction term between treatment group, year,and receipt of a previous invitation. For this group we see that the take up increasedby 28.4 % points when compared to 55–59 year olds and decreased by 3.8 percentagepoints when compared to 65–69 year olds. These results suggest that much of theimpact was on those who received invitations for the first time, i.e. those for whom thepolicy change represented a new opportunity. For those who had previously receivedinvitation, mostly due to chronic health conditions, the policy change had a muchsmaller and perhaps even negative effect. Since respondents who receive invitationsboth for flu shots covered due to age and due to chronic conditions get invitations fromtheir doctor, it is possible that those who received invitations prior to the expansiondid not even realize that there was any change.
Table 2 presents summary statistics for both the treatment and control groups andincludes t-tests for equality of means. One key and unsurprising difference is thatindividuals age 60–64 are much more likely to be working than the older group andmuch less likely to be working than the younger group. While age 65 is the normalretirement age, many retire early. In column 1 of Table 3, we can see that with theyounger control group, working is a significant impediment to flu shot take up.
Because of the potential problems with the DD model, we also consider the behavioronly of those between age 60 and 64, those affected by the change. These probitregressions allow us to consider what characteristics are most associated with take upof the flu shot following the policy expansion. In all cases we consider four modelsdesigned to identify which individuals are most likely to get a flu shot following theexpansion of the program. All regressions report marginal effects. First, we regress adummy for receiving a flu shot in 2008 on demographic characteristics and whetherthe individual received a flu shot in 2007. This model provides insight into whether ornot individuals continue to make the same choice as in the past. Second, we regressa dummy for receiving a flu shot in 2008 on demographic characteristics and do notcontrol for whether the individual received a flu shot in 2007. This model identifieswho is most likely to get a flu shot, regardless of past behavior. Third, we rerun thesecond specification but limit our sample only to those who did not get a flu shot inthe past. Because the descriptive statistics suggest that only a very small number ofindividuals stop getting a flu shot, this focuses attention on those who start to receivea flu shot in the year of the policy change. Fourth, we change the dependent variableto receiving a flu shot in 2007, to investigate what characteristics are most associatedwith past flu shot take up. In all cases we limit our sample to those between the agesof 60 and 64, the newly targeted age group.
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Table 4 Base case probit regressions predicting who gets a flu shot
Dependent variable Model 1 Model 2 Model 3 Model 4Flu shot in2008
Flu shot in2008
Flu shot in2008
Flu shot in2007
Flu shot in 2007 0.594***
(0.041)
Age 0.006 0.010 0.008 0.015
(0.017) (0.018) (0.021) (0.019)
Dummy if male −0.014 0.005 0.004 0.050
(0.047) (0.048) (0.058) (0.051)
Dummy if partnered 0.045 0.047 0.018 0.040
(0.057) (0.057) (0.067) (0.055)
Dummy if working −0.156*** −0.145** −0.156** −0.016
(0.060) (0.058) (0.064) (0.057)
Dummy if MBO −0.051 −0.040 −0.079 0.037
(0.075) (0.074) (0.084) (0.080)
Dummy if HBO or WO 0.011 −0.032 0.012 −0.075
(0.053) (0.055) (0.065) (0.051)
Number of doctorvisits in last year
0.029** 0.043*** 0.035** 0.031***
(0.013) (0.012) (0.016) (0.012)
Dummy ifreceived aninvitation for flushot
−0.241*** 0.297*** −0.251*** 0.762***
(0.087) (0.043) (0.077) (0.034)
Sample Whole sample Whole sample Individuals whodid not receive aflu shot in 2007
Whole sample
Observations 484 484 333 484
Pseudo R-squared 0.283 0.134 0.0480 0.608
Table presents marginal effects. Standard errors in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1
Table 4 presents the base case specifications. In column 1, we see that if you receiveda flu shot in 2007, you are nearly 60 % points more likely to receive one again in 2008.Individuals who are currently working are 15.6 % points less likely to receive a flushot. Individuals who visit the doctor more often are more likely to get a flu shot, withan increase of approximately 3 % points per visit.
Those who received an invitation in 2007 are actually 24.1 % points less likelyto receive a flu shot. This somewhat counterintuitive result is easily understood bycomparing column 1 to column 2. When we do not control for past behavior, invitationsincrease the likelihood of a flu shot in general. The positive and significant coefficienton past behavior in column 1 indicates that, controlling for whether you receivedan invitation in the past, individuals tend not to change their behavior following theexpansion of the program. This result can be interpreted to indicate that once you havedeclined an invitation for a flu shot, you are likely to continue to reject opportunities
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for a flu shot. The results in column 3, limited to those who declined a flu shot in 2007are largely in line with those in column 1.
Finally column 4 considers past take up of a flu shot. The main indicator of take upprior to the expansion of the program was receipt of an invitation for a flu shot. Doctorvisits also increase take up. In this case, invitations and doctor visits can be interpretedas an indicator of the individual’s underlying health and risk. Those who had healthproblems were likely to receive flu shots in the past (and to continue after the newexpansion). Interestingly, labor supply does not predict past take up. This suggests thatlabor supply may not be an impediment for receiving a flu shot for those at the highestrisk. However comparing this result to the other columns, we see that working is animpediment to flu shot take up among those at lower risk. Interestingly, the pseudoR-squared is much higher (0.608 vs. 0.048–0.283) for 2007 than for any of the 2008specifications. Prior to the program expansion, invitations were very effective; wheninvitations were targeted to those with potential co-morbidities, invitations signaledmore importance. After the expansion, many who received invitations probably didnot see the reason to receive a flu shot. Prior to the expansion, it was easy to explainwho would get a flu shot: anyone who got an invitation. However, after the expansionflu shot take up is more random.
Table 5 investigates the subjective reasons for and against flu shot take up. Demo-graphic characteristics are excluded from the tables but are included in all regressions.These coefficients were not qualitatively affected by the addition of the subjective rea-sons. As in Table 4, four models are included for each set of independent variables.6
Perceptions of effectiveness of flu shots in protecting against the flu are a significantpredictor of take up of flu shots. In 2008 and 2007, moving up one point on the Likertscale, for example from agree to strongly agree that flu shots provide good protectionagainst influenza, increases the likelihood of a flu shot by roughly 14–20 % pointsdepending on the specification. Those who perceive more unpleasant side effects areless likely to receive flu shots. Moving up one point on the Likert scale decreases thelikelihood of a flu shot by 8.4–12.6 % points depending on the specification. When wedo not control for past behavior, we see that perceptions of risk affect take up. Thosewho strongly agree that they are at high risk to get the flu are 7.1–7.4 % points morelikely to get a flu shot than those who only agree, in models 2 and 4. However in mod-els 1 and 3, which explicitly or through sample limitations control for past behavior,the effect is (in most cases) not significant. Similar effects are found for not havingenough time for a flu shot. In models 2 and 4 this reduces the likelihood of getting a flushot by 7.6–8.5 % points. But no effects are found in models 1 and 3. Finally, peoplewho feel that flu shots are too expensive were less likely to get a flu shot in 2007 butmore likely to get a flu shot in 2008. This suggests that individuals who agreed withthis statement would have liked to have a flu shot but found costs to be too high. Whencosts decline (to zero monetary cost) they are now able to have a flu shot.
The significant differences in take up of flu shots in 2008 between working andnonworking individuals aged 60–64 merits further investigation. While our sample size
6 We have also considered a broader list of subjective explanations for having or not having a flu shot.Many are never significant. For example, fear of doctors or needles and lack of knowledge have no impacton flu shot take up. The broader results are available from the authors.
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Table 5 Probit regressions predicting who gets a flu shot: subjective explanations
Dependent variable Model 1 Model 2 Model 3 Model 4Flu shot in2008
Flu shot in2008
Flu shot in2008
Flu shot in2007
Flu shot in 2007 0.502***
(0.052)
I think that a flu shot providesgood protection against theflu
0.139*** 0.195*** 0.180*** 0.135***
(0.033) (0.033) (0.041) (0.035)
Flu shots have unpleasantside effects
−0.084*** −0.126*** −0.088** −0.088***
(0.029) (0.030) (0.037) (0.027)
I think I am at high risk to getthe flu
0.031 0.074** 0.053 0.071***
(0.029) (0.029) (0.037) (0.025)
Flu shots are too expensive for me 0.090** 0.087** 0.112** −0.029
(0.036) (0.038) (0.044) (0.034)
I don’t have time for a flu shot −0.045 −0.085** −0.041 −0.076*
(0.039) (0.041) (0.048) (0.040)
Sample Whole Whole Limited Whole
Observations 484 484 333 484
Pseudo R-squared 0.352 0.284 0.142 0.788
Table presents marginal effects. Regressions also control for age, gender, partnered status, working, educa-tion, doctor visits in previous year, and invitation in previous year. Coefficients for excluded variables aresimilar to those in Table 4. Standard errors in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1
is too limited to provide power for a full set of interaction terms, the only interactionterm that is consistently significant is an interaction between working and stating“I don’t have time for a flu shot”.7 Table 6 modifies the results from Table 5 toinclude this interaction term. While other variables are largely unchanged, we see thatthe significant effect of working disappears. Furthermore, time constraints have nosignificant effects for those who are not working, while the interaction term betweentime constraints and working is significant in all models of flu shot take up in 2008.For example in model 1, those who strongly agree that they don’t have time for a flushot and are currently working are nearly 13 % points less likely to receive a flu shotthan those who only agree with the statement. These effects are qualitatively similarin models 2 and 3. In Model 4, which looks at take up of flu shots in 2007, we donot see the same effect. There take up continues to largely be related to receiving aninvitation, which is a strong signal of health status. These results suggest that perceivedtime constraints are an important barrier to the take up of free flu shots for workingindividuals who are not otherwise at high risk.
7 Additional interaction terms were investigated but none were consistently significant. These results areavailable from the authors upon request.
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Table 6 Probit regressions predicting who gets a flu shot: subjective explanations
Dependent variable Model 1 Model 2 Model 3 Model 4Flu shot in2008
Flu shot in2008
Flu shot in2008
Flu shot in2007
Flu shot in 2007 0.500***
(0.053)
Dummy if working 0.104 0.177 0.149 0.332
(0.125) (0.122) (0.178) (0.244)
I don’t have time for a flu shot −0.006 −0.033 0.005 −0.037
(0.045) (0.048) (0.055) (0.045)
I don’t have time for a flushot × dummy ifworking
−0.129* −0.164** −0.155* −0.106
(0.073) (0.076) (0.090) (0.072)
Sample Whole Whole Limited Whole
Observations 484 484 333 484
Pseudo R-squared 0.357 0.291 0.149 0.792
Table presents marginal effects. Regressions also control for age, gender, partnered status, working, edu-cation, doctor visits in previous year, invitation in previous year, and variables for “I think that a flu shotprovides good protection against the flu”, “Flu shots have unpleasant side effects”, “I think I am at highrisk to get the flu” and “Flu shots are too expensive for me”. Coefficients for excluded variables are similarto those in Tables 4 and 5. Standard errors in parentheses*** p < 0.01, ** p < 0.05, * p < 0.1
We have also considered the effect of potential co-morbidities by adding controlsfor whether the individual had influenza in the past 5 years, whether they have dia-betes, high blood pressure or heart disease. Tacken et al. (2009) find that the programexpansion did not significantly affect the behavior of high risk individuals relative tolow risk individuals. In our data, we find that these risk factors have no effect on takeup of the flu shot.8 Invitations to high risk individuals were very effective; most alreadygot a flu shot in the past and did not change their behavior after the change in policy.
6 Conclusions
In sum, the primary determinant of flu shot take up after the expansion of the programwas whether an individual had a flu shot in the previous year. Among those who didnot have a flu shot in 2007, those who work were the least likely to take advantage ofthe new expanded program. Similarly those who expect unpleasant side effects or thatflu shots are not effective are less likely to receive a flu shot. Changing the price ofa flu shot (to free) did influence some individuals to take up the flu shot, particularlythose who felt the price was too high. Finally, the expanded program had no impacton the highest risk groups in the 60–64 year old age group, primarily because theywere already receiving flu shots.
8 These results are available from the authors upon request.
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One barrier to take up is perceptions of effectiveness and side effects. Individualswho think that flu shots cause influenza, a common albeit not totally accurate belief,are unlikely to get a flu shot. Better information about side effects and effectivenesswould likely help to improve take up rates. Finally, it is important that educationprograms help people to understand what can and what can not be prevented by a flushot. Individuals who get a flu shot and then get a bad cold may perceive that theyhave “the flu” and therefore that the flu shot was not effective. Better information aboutinfluenza and influenza vaccinations may help to improve take up.
The largest barrier to take up of free flu shots is participation in the labor force andtime constraints that these individuals feel limit their ability to get flu shots. Manyindividuals stop working before the age of 65, so individuals aged 60 or over who arestill working are likely very dedicated to working. While very few people have jobsthat would prevent them from missing work to visit the doctor, those working pastthe age of 60 may feel that they can not afford to miss work, even if they would beallowed to do so. Unlike many countries, in the Netherlands most individuals must goto the general practitioner if they want a flu shot. Flu shots are not available in grocerystores, drug stores, megamarts, etc. More widely available flu shots, especially afternormal working hours would likely help to increase take up of this program. If thereis concern about availability of flu shots in non-medical locations, flu shots at urgentcare centers (huisartsenpostsen) and hospitals could be introduced.
Given the low take up of the expanded flu shots it is reasonable to question whetheror not the expansion was worthwhile. While a full cost benefit analysis is outside thescope of this paper and has been conducted by others, a back of the envelope calculationparticularly focusing on those still working is worthwhile. Any such calculation shouldfocus on the benefits to those without known risk factors, such as diabetes and heartdisease, since there was no change in eligibility for those with known risk factors. Thebenefits of flu shots for this group will accrue not only to the individual being vaccinatedbut also to their employers. The median income of a working person between 60and 64 is e1800 per month. If an employee misses work for one week, the cost totheir employer is over e450 in wages alone, excluding other costs such as taxes andinsurance premiums paid by the employer. With a conservative assumption of a 10 %chance of influenza that amounts to an expected loss of over e45. And this onlyconsiders costs to the employer not the cost any possible complications or the miseryof being sick. However, benefits will certainly exceed e45.
The costs of influenza vaccination will accrue to the government, the individual, andpotentially employers. Currently each flu shot costs the RIVM approximately e3.50.Costs to the individual will depend on the ease of accessing the flu shot, but willinclude time costs and any disutility associated with receiving a flu shot. Time costswill depend on how easily accessible flu shots are and will accrue to the individualor their employer depending on whether flu shots are available only during workinghours or also outside of working hours. If a flu shot could be obtained in 15 minutesthe time cost would be less than e3, while 2 hours would cost approximately e22.9
9 The median monthly income ise1800. This ise450 per week. Assuming a working day of 8 h this resultsinto less than e3 for 15 minutes and approximately e22 for 2 hours.
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Estimating the costs of the disutility of illness and the disutility of a flu shot requiresa measure of how severe that disutility is. Without this information a complete costbenefit analysis is not possible and measurement of this is beyond the scope of thisresearch as it requires an estimate of the lost Quality Adjusted Life Years due toinfluenza and vaccination. However, the risk of having influenza, which typically lasts1–2 weeks, is significantly worse than the side effects of influenza vaccination, whichtypically involve having a slightly sore arm for one day.
The costs and benefits that we can measure, e45 benefit for prevention of illnessand e6 to e25 cost of vaccination, suggest that benefits exceed the costs. Taking intoaccount the disutility of illness and vaccination would further support this result. Ifless costly and time consuming channels for delivery of vaccination were used, thebenefits would far exceed the costs of the flu shot. Even if we take into account thepossibility that the flu shot may not prevent all cases of influenza, the benefits ofbroader availability are clear. For those who are not working, the avoided costs arelikely to be lower. However, the very low cost of the flu shot would likely make theexpansion of coverage worthwhile.
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