TESTING THE PREVALENCE ELASTICITY OF DEMAND FOR HPV
VACCINATION
May 2016
Rae Staben
Department of Economics
Stanford University
Stanford, CA 94305
[email protected]
Under the direction of Prof. Jay Bhattacharya
ABSTRACT
I investigate how HPV vaccination decisions respond to changes in the prevalence of cervical
cancer using state-level cancer incidence and mortality data from the United States Cancer
Statistics and individual-level vaccination data from the National Immunization Survey. I use a
linear probability model, a logistic model, and a Cox proportional hazard model. Across all
specifications, I find that cervical cancer prevalence has little to no effect on HPV vaccination.
This unresponsiveness to prevalence may be due to a lack of awareness of HPV and its causal
link to cervical cancer.
Keywords: Prevalence elasticity, human papillomavirus, vaccination, immunization, rational
disease dynamics
Acknowledgments:
I would like to thank my advisor, Professor Jay Bhattacharya, for his guidance throughout this
process. Thanks to Professor Marcelo Clerici-Arias for supporting everyone in the honors
program. I am also grateful to my friends and family for their help and kind words.
Table of Contents
INTRODUCTION...............................................................................................................................3
MEDICAL BACKGROUND..............................................................................................................4
HPV DISEASE BURDEN............................................................................................................................4
HPV PREVENTION....................................................................................................................................5
PREVENTION OF CERVICAL CANCER........................................................................................................5
Screening.............................................................................................................................................5
Vaccination..........................................................................................................................................5
LITERATURE REVIEW...................................................................................................................8
EPIDEMIOLOGICAL EXPLANATIONS OF LOW RATES OF HPV VACCINATION...........................................8
PREVALENCE ELASTICITY......................................................................................................................10
PREDICTIONS FROM ECONOMIC EPIDEMIOLOGY..............................................................14
USING THE SIR MODEL..........................................................................................................................14
EVIDENCE FOR HPV’S PREVALENCE ELASTICITY.................................................................................16
METHODS.......................................................................................................................................18
DATA......................................................................................................................................................18
ECONOMETRIC FRAMEWORK.................................................................................................................22
RESULTS.........................................................................................................................................30
LINEAR PROBABILITY ANALYSIS...........................................................................................................30
Current Prevalence Rates..................................................................................................................30
Lagged Prevalence Rates..................................................................................................................31
LOGISTIC ANALYSIS...............................................................................................................................33
Current Prevalence Rates..................................................................................................................33
Lagged Prevalence Rates..................................................................................................................34
COX PROPORTIONAL HAZARD ANALYSIS..............................................................................................35
Current Prevalence Rates..................................................................................................................35
Lagged Prevalence Rates..................................................................................................................36
KNOWLEDGE OF HPV-CERVICAL CANCER LINK.................................................................37
CONCLUSION.................................................................................................................................43
REFERENCES.................................................................................................................................45
APPENDIX.......................................................................................................................................49
ADDITIONAL INFORMATION...................................................................................................................49
FULL MODEL OUTPUT............................................................................................................................54
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Introduction
Economic epidemiology incorporates human behavior into epidemiological models of the
spread of disease. Prevalence elasticity is a theory from economic epidemiology, which
hypothesizes that individuals respond to changes in the number of people sickened by a disease.
As more people become ill, the threat of infection rises and people take more preventive action
to avoid the disease. Similarly, people take fewer preventive steps if the disease becomes less
common in a population because the risk of getting the disease falls. For example, an epidemic
of a vaccine-preventable infection may prompt more people to get vaccinated. People weigh the
financial and personal costs of vaccination with an estimate of their benefits from receiving the
vaccine. Risk perception affects this cost-benefit analysis. As the risk of infection increases,
people assess the vaccine’s benefits more favorably and thus should be more likely to get
vaccinated.
To test this hypothesis, I examine the relationship between the prevalence of cervical cell
carcinoma and the uptake of the human papillomavirus (HPV) vaccine. In 2006, the FDA
approved the first vaccine to prevent HPV, a sexually transmitted infection that can cause certain
cancers and genital warts. The HPV vaccine offers a new context to empirically test for
prevalence-elastic behavior. Previous prevalence elasticity research has focused on diseases with
a short time between getting infected and becoming symptomatic (such as measles and
influenza). By contrast, there is a long time lag between contracting HPV and dealing with any
consequences of the disease, such as cervical cancer. While girls can get the HPV vaccine from
age 9 to 26, the median age at cervical cancer diagnosis is 48 (CDC 2012). Other HPV
associated cancers have even higher median diagnosis ages. Therefore, people’s response to
HPV prevalence may be overshadowed by their discounting of the future. This temporal
separation makes HPV an interesting disease for studying prevalence elasticity.
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Additionally, previous prevalence elasticity analyses focused on responses to epidemics,
like the flu, measles, or HIV. While HPV is the most common sexually transmitted infection,
cervical cancer is a relatively low prevalence disease, averaging around 11,000 cases a year.
Though it is a serious, possibly life-threatening condition, few women actually get cervical
cancer. It is not clear if women will demonstrate the same prevalence elasticity observed in
responses to high-prevalence diseases. Either HPV vaccination will still exhibit prevalence
elastic behavior due to the virus’s potential for severe consequences, or the low frequency of
cervical cancer will not cause individuals to alter their behavior as HPV’s threat changes.
This paper investigates how the prevalence of cervical cancer affects demand for HPV
vaccination from 2006-2012 in the United States. Cervical cancer prevalence varies across states
and years. This variation can be exploited to estimate the effect of cervical cancer prevalence on
vaccine demand. I estimate the magnitude of prevalence elasticity using cancer prevalence data
from the United States Cancer Statistics and HPV immunization records from the National
Immunization Survey of Teens. I use a linear probability model, a logistic model, and a Cox
proportional hazard model. My results show that cervical cancer prevalence likely does not
influence individuals’ HPV vaccination decisions. The prevalence elasticity of demand for the
HPV vaccine appears to be inconsequential, which may be due to a lack of knowledge about
HPV and its link to various cancers.
Medical Background
HPV Disease Burden
HPV is the most common sexually transmitted infection (STI) in the United States (CDC
2014a), with about one in four people currently infected (CDC 2015b). HPV refers to a family of
over 150 related viruses. Most HPV infections are asymptomatic and go away on their own, but
some strains of HPV can cause genital warts and certain cancers. The Centers for Disease
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Control (CDC) estimate that all HPV strains combined probably cause 91% of the 11,000
cervical cancer cases in the United States (CDC 2014b, CDC 2014a). Beyond cervical cancer,
HPV exposure is associated with about 65-75% of the cancers of the anus, oropharynx, penis,
vagina, and vulva (CDC 2014b).
HPV Prevention
Preventative measures can decrease morbidity and mortality from HPV and cervical
cancer. HPV is transmitted through skin-to-skin contact, so practicing abstinence or using a
condom can reduce transmission risk. However, a condom does not eliminate the risk of
contracting HPV because the virus can spread to areas that are not covered by the condom (CDC
2016b).
Prevention of Cervical Cancer
Screening
Additionally, women can reduce their risk of HPV-related cervical cancer by getting
routine cervical cancer screenings. The CDC recommends women age 21-65 get routine cervical
cancer screening to detect cervical cancer or pre-cancerous cells (CDC 2014a). Through the
1940s, cervical cancer was a large cause of death for women of childbearing age in the United
States (NIH 2013). However, with the invention in the 1950s of the Pap smear, which tests for
precancerous and cancerous cells on the cervix, cervical cancer has become much less common.
From 1955 to 1992, cervical cancer incidence and mortality fell by over 60% due to pap smears
allowing earlier detection of cancer (NIH 2013).
Vaccination
The pap smear has reduced deaths from cervical cancer tremendously, and is a great
success story in cancer screening. But, the success of the pap smear program doesn’t eliminate
the need to expand prevention of cervical cancer through HPV vaccination. Vaccination reduces
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the risk of contracting oncogenic strains of HPV, thereby preventing women from getting the
causative agent of most cervical cancers.
Currently, there are three slightly different vaccines for HPV. Gardasil was the first HPV
vaccine, approved for use in females in 2006 (FDA 2006) and for males in 2009 (CDC 2010).
The FDA approved another vaccine, Cervarix, for females age 10 through 25 in 2009 (FDA
2009). Cervarix is not approved for use in males. Both Gardasil and Cervarix protect against
HPV types 16 and 18, which cause 70% of cervical cancer cases in the world (CDC 2015c) In
2014, the FDA approved Gardasil 9, which covers five additional HPV types (31, 33, 45, 52, and
58). Gardasil 9 protects against previously omitted HPV strains that collectively cause 20% of
cervical cancers (FDA 2014). Gardasil and Gardasil 9 also protect against HPV types 6 and 11,
which are associated with more than 90% of anogenital warts (CDC 2015a).
Though the HPV vaccine1 targets many types of HPV that cause cancer and genital warts,
people may resist vaccination because of its personal costs. The HPV vaccine costs between
$140-$180/dose (CDC 2016c), though health insurers frequently reimburse this cost. The HPV
vaccine also has a risk of adverse effects, such as swelling at the vaccination site, fever,
headache, or fainting. Still, the HPV vaccine is considered extremely safe, and severe allergic
reactions are rare2 (CDC 2013a). However, if perceived benefits to the vaccine are low, these
financial and physical costs may be enough to inhibit vaccination.
The CDC has weighed these costs and benefits, and decided to recommend HPV
vaccination for all preteens. The CDC recommends that boys and girls get vaccinated at age 11
1
Though there are multiple vaccines to prevent HPV, my analysis does not differentiate between
them and I will use the term “the HPV vaccine” to refer to any of the vaccines.
2
80 million doses of Gardasil were distributed from 6/2006-9/2015, with only 32,925 adverse
event reports (CDC 2016a).
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or 12 because the vaccine is most effective if given before they become sexually active. Women
and men can receive the vaccine until they are age 26 and 21, respectively (CDC 2015g).
Despite these recommendations, HPV vaccination rates remain low, particularly when
compared to other vaccines for adolescents. Comparing the vaccination rates for the Tdap and
HPV vaccines is instructive because both vaccines are recommended for preteens. The Tdap
vaccine prevents tetanus, diphtheria, and pertussis. The Tdap vaccine is typically given at age 11
or 12 (CDC 2013c), and so teens could get the HPV vaccine at the same appointment. 88% of
teens age 13-17 had at least one Tdap dose in 2014 (CDC 2014c), while only 60% of females age
13-17 had at least one dose of the HPV vaccine and 41.7% of males age 13-17 had at least one
dose of the HPV vaccine (CDC 2014d). The higher rate of Tdap vaccination implies many
missed opportunities to administer the HPV vaccine to adolescents. Figure 1 shows the HPV
vaccination coverage from 2008-2014 by gender.
Figure 1
EstimatedVaccinationCoverage,≥1DoseofHPV
VaccineAmongAdolescents Aged13-17Years
HPVVaccination Rate
70
60
50
40
30
44.3
48.7
53.8
53
60
Females
41.7
37.2
34.6
20
Males
20.8
10
0
2007
57.3
8.3
2008
2009
2010
2011
2012
2013
2014
2015
Source: National Immunization Survey 2008-2014
Increasing the rate of HPV vaccination among women would produce significant
decreases in morbidity and mortality. The CDC estimates that raising female completion rates of
the 3-dose HPV vaccine sequence from current levels to 80% would prevent 53,000 future
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cervical cancer cases over the lifetimes of girls in the United States who were 12 and under in
2013 (NCI 2014, p. 9). Because the HPV vaccine has the potential to avoid so many cases of
cancer, understanding the low rate of vaccination against HPV is important.
Literature Review
Epidemiological Explanations of Low Rates of HPV vaccination
Epidemiologists have proposed several reasons why the HPV vaccine has not had a
swifter uptake. Holman et al. (2014) present a review of the literature about potential barriers to
HPV vaccination. A parent’s receipt of a doctor’s recommendation to vaccinate their child is
associated with higher rates of vaccination. However, healthcare providers often recommend the
vaccine based on their own perception of a patient’s risk of contracting HPV and are less likely
to recommend the vaccine for younger patients. Thus, parents of young adolescents may not
receive a doctor’s recommendation to vaccinate their child, even though the vaccine is most
effective at younger ages before the initiation of sexual activity. If a parent does not receive a
doctor’s recommendation, they will be less likely to vaccinate their child. Parents face several
other hurdles to their decision to vaccinate their children with the HPV vaccine – parents often
cite a lack of knowledge about the vaccine, concerns that the vaccine will increase their child’s
sexual activity, and high vaccine cost as reasons to not vaccinate (Holman et al., p. 78).
Additionally, teens interact with the healthcare system less frequently than than other age groups
(Holman et al., p. 80); Holman et al. find that preventive care visits and more interaction with the
health care system are both associated with higher rates of starting the HPV vaccine series (p.
79).
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Gilkey et al. (2015a) surveyed a nationally representative sample of pediatricians and
family physicians3 in the United States to better understand how doctors communicate about the
HPV vaccine to adolescents in their practice. Receipt of a doctor’s recommendation is a strong
predictor of HPV vaccination (p. 181). However, doctors recommend HPV less strongly than
other adolescent vaccines such as Tdap or the meningococcal vaccine. They also report that
discussing the HPV vaccine with parents takes about twice the time needed to discuss Tdap (p.
184). The amount of time required to allow patients to make a decision about receiving the HPV
vaccine may inhibit doctors from talking about the HPV vaccine with their patients. While
about three-fourths of doctors perceived Tdap as highly important to parents, only 13% of
doctors perceived the HPV vaccine as highly important to parents. Furthermore, 95% of doctors
recommend Tdap as highly important for 11-12 year olds, while about three-fourths of doctors
recommend the HPV vaccine as highly important for the same age group. Most doctors also
report discussing the HPV vaccine last during conversations about an adolescent’s vaccination.
Parents may perceive a doctor’s recommendation as relatively temperate, particularly when
compared to the strong recommendations for other vaccines like Tdap. These communication
difficulties between patients, their parents, and doctors may explain why HPV vaccination is low
when compared with other vaccines for adolescents.
Another study by Gilkey et al. (2015b) surveyed pediatricians and family physicians4
about how and when they recommend the HPV vaccine. About a quarter of the doctors reported
that they do not strongly recommend the vaccine. Few doctors follow the recommended HPV
vaccination schedule of administering the vaccine around age 12. A quarter of doctors do not
3
4
Sample size = 776
Sample size = 776
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recommend the vaccine to girls by age 11-12, and roughly 40% do not recommend it to boys by
age 11-12. Additionally, about 60% recommend the vaccine using a risk-based approach, rather
than consistently recommending the vaccine to every patient. Doctors appear to be tentative and
inconsistent in their delivery of recommendations for HPV.
Prevalence Elasticity
Though all of these issues likely depress HPV vaccination coverage, another factor in the
low vaccine uptake may be a response to the prevalence of HPV. While traditional epidemiology
assumes that there is no relationship between vaccination and disease prevalence, economic
epidemiology theorizes that vaccination rates should increase if the prevalence of the associated
disease increases. I have not found any research on the prevalence elasticity of demand in the
context of HPV. To understand how demand for HPV vaccination changes due to cervical cancer
prevalence, I will build off of previous work that focuses on how disease prevalence affects
people’s demand for self-protection for other diseases.
Since the development of the Susceptible-Infected-Recovered (SIR) model by Kermack
and Mckendrick (1927), the model has been applied to understand how disease prevention
influences the spread of disease. The theory of prevalence elasticity states that demand for
protection from a disease has a negative relationship with respect to price, and a competing
positive relationship with disease prevalence (Philipson 2000). A disease outbreak will limit
itself – as the number of infected people rises, susceptible people will seek more protection.
However, as the number of infected decreases, susceptible people will seek less protection.
Therefore, public health measures to stop the spread of a disease are also self-limiting. If the
preventative measures succeed in reducing the number of cases of a disease, then demand for
protection will also fall. As an example, price subsidies and mandatory vaccination programs
reduce incentives for people outside of the program to get vaccinated. As the subsidy or
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mandate stimulates demand for those covered, it dampens demand for those outside of the
program. After enough people have been vaccinated, the disease’s prevalence will fall.
Consequently, the unvaccinated will not want to get vaccinated because the infection threat is
reduced with the new, lower prevalence. This negative feedback between prevalence and demand
for protection complicates total eradication of a disease.
Previous research has been done to empirically estimate the prevalence elasticity for
protective behavior. Philipson (1996) exploited the variation in measles prevalence across states
during the U.S. measles epidemic of 1989-1991. This study used a proportional hazard model to
estimate how prevalence affects the age that children get the measles vaccine. Using individuallevel data from the National Health Interview Study (NHIS) merged with yearly state prevalence
data, Philipson finds that parents in states hit harder by the measles epidemic brought their
children in for vaccination at younger ages than parents in low prevalence states. Before the
measles epidemic, vaccination timing did not differ between states differentially affected by the
epidemic. This study concluded that different prevalence levels during the epidemic changed
parents’ vaccination decisions.
Mullahy (1999) explores determinants of demand for flu shots, focusing on the effects of
employment status and perceived infection risk. Mullahy uses individual-level data from the
1991 NHIS and measures perceived infection risk by including several flu risk factor variables,
including the number of weeks of widespread flu activity during the prior year’s flu season, selfreported health status, age, and an indicator of anyone in the household being a health care
worker. Individuals’ propensity to get vaccinated is significantly positively related to the
number of weeks of widespread flu in the previous year. Furthermore, the elderly have a larger
positive response to the lagged disease threat than the non-elderly. In addition, people in worse
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health are more likely to get vaccinated. These findings are consistent with prevalence elastic
demand. People respond to recent increases in flu prevalence, and are more likely to get
vaccinated if they perceive the risks from an infection to be greater due to increased age or worse
baseline health.
Li et al. (2004) test how people’s perception of a disease’s severity influences their
decision to seek protection. They hypothesize that the elderly will be more likely to receive a
pneumococcal bacteria or influenza virus vaccine as their perceived threat of disease increases,
measured by the disease’s mortality rate. They propose that lagged disease threat, or the
disease’s mortality rate in previous years, may influence perceptions of the present disease
threat. The pneumococcal vaccine does not protect against the type of pneumonia that becomes
more common during influenza outbreaks. Thus, during flu season, the increased threat of
influenza infection should cause increases in influenza vaccination rates but not in the
pneumococcal vaccination rate. If people respond to the increased threat of influenza infection
by also getting pneumococcal vaccines, people may be misinterpreting the threat from the
influenza virus. They test the effect of threat misperception by using lagged mortality rates for
influenza. The flu virus mutates significantly each year, requiring a new vaccine each year.
Therefore, people who understand the true flu threat should not respond to a lagged flu mortality
rate; last year’s flu is essentially a different disease, so last year’s mortality rate should be
irrelevant to one’s current vaccination decision. Their results show mixed evidence for
responsiveness to misperceptions of disease threats. They find that people were not significantly
more likely to receive the pneumococcal vaccine during an influenza epidemic. However, they
find that people did respond to one-year lagged mortality rates, but stopped responding by the
second year. Perceived disease threat may be particularly relevant for the HPV vaccine’s demand
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because of the long time lag between getting HPV and cervical cancer diagnosis.
Prevalence elasticity has also been studied in the context of sexually-transmitted
infections. The STI context is unique because an individual’s behavior affects their risk. If
someone wishes to completely eliminate the risk of contracting an STI, they can practice
abstinence. Behavioral modifications such as using condoms, selecting lower-risk partners, or
practicing monogamy can also reduce STI risk. To lower the chance of contracting HPV,
individuals can practice any combination of the previous behaviors, and can additionally choose
to get the HPV vaccine to protect themselves from certain types of HPV. Though I have not
found any articles focused on the HPV vaccine, several researchers have studied behavioral
responses to HIV/AIDS.
Geoffard and Philipson (1996) analyze the relationship between the rate of new HIV
infections and HIV’s prevalence using data from the San Francisco Men’s Health Study from
1983-1992. They measured HIV prevalence as the percentage of study participants who were
HIV positive in each cycle of the study. They find that the rate people in San Francisco became
infected with HIV fell as the prevalence of HIV increased. Geoffard and Philipson interpret this
observation as evidence that people’s demand for protection against HIV increased as the disease
become more widespread in San Francisco. Alternative explanations exist because information
about how HIV spread and how to protect oneself became more common at the same time.
Thus, while Geoffard and Philipson cannot identify a causal relationship between prevalence and
infection rate, their analysis suggests that the HIV epidemic exhibits prevalence elasticity.
Ahituv et al. (1996) examine empirical evidence of prevalence elasticity in the context of
AIDS and demand for condoms. As AIDS became more common, the risk of infection from
unprotected sex rose. Economic epidemiology predicts that individuals will substitute away
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from unprotected sex towards safer sex as AIDS prevalence increases. In 1984, before AIDS
became widespread, condom demand did not differ significantly throughout the United States.
As the disease developed, it affected states differentially and condom usage became more
common in states with more cases of AIDS. They use logistic regressions to estimate how
individual’s probability of condom use depends on per capita AIDS prevalence in an individual’s
state of residence, controlling for various state and individual characteristics. They find that an
increase in prevalence of AIDS in a person’s state of residence increases their likelihood of using
a condom. This study demonstrates that the prevalence of a disease can alter individual decisions
to take prophylactic measures against STIs.
Predictions from Economic Epidemiology
Using the SIR Model
I will use the SIR model to demonstrate prevalence elasticity mathematically. The SIR
model divides a population between three states – susceptible to infection (S), infected (I), and
recovered (R). Without HPV vaccination, people travel from S to I to R; if I allow for
vaccination, people can bypass I and transition directly from S to R. People will decide to get
vaccinated only if the perceived benefits of vaccination outweigh the perceived costs of time,
money, and effort spent on getting vaccinated.
𝑆" , 𝐼" , 𝑅" are the proportions of the total population that are susceptible, infected, and
recovered at time t, respectively. Thus, 𝑆" + 𝐼" + 𝑅" = 1. Differential equations show the rates of
change in the relative size of the three states at time t.
𝑑𝑆
= − 𝛽𝐼" 𝑆" − 𝑣 𝐼" , 𝑝 𝑆"
𝑑𝑡
𝑑𝐼
= 𝛽𝐼" 𝑆" − 𝑟𝐼"
𝑑𝑡
𝑑𝑅
= 𝑣 𝐼" , 𝑝 𝑆 + 𝑟𝐼"
𝑑𝑡
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where
•
𝑣 𝐼" , 𝑝 is the vaccination rate, as a function of 𝐼" and price of the HPV vaccine, p.
•
𝛽 is a constant representing the infectivity of HPV.
•
𝛽𝐼" is HPV’s infection rate, which is the product of 𝛽 and 𝐼" .
•
r is the recovery rate of HPV.
Let S*, I*, R* represent the steady-state proportions of the susceptible, infected, and
recovered populations. In the steady-state, none of the state’s relative sizes are changing, though
people continue to transition between the states.
In the steady-state,
12 ∗
1"
=
14 ∗
1"
=
15 ∗
= 0.
1"
As long as I* ≠ 0,
𝑆∗ =
𝐼∗ =
𝑅∗ =
𝑟
𝛽
−𝑣 𝐼 ∗ , 𝑝
𝛽
𝛽 + 𝑣 𝐼∗ , 𝑝 − 𝑟
𝛽
I*, or the steady-state prevalence of HPV, decreases as HPV vaccination rates increase.
However, the decrease in HPV due to vaccination is bounded if the vaccine’s demand is
prevalence elastic. As vaccination increases, fewer people will get HPV, thereby reducing
incentives for the unvaccinated to become vaccinated.
To understand how a vaccine subsidy changes the steady-state prevalence of HPV, I take
the derivative of I* with respect to the vaccine price, p.
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May 5, 2016
15
14 ∗
18
=
9:
9;
9:
<= ∗
9>
The model assumes people decrease their demand for the vaccine as the price increases,
so
1?
18
< 0. Additionally, if the demand for the HPV vaccine is prevalence elastic,
vaccine price subsidy will be more effective for smaller values of
1?
14 ∗
1?
14 ∗
> 0. A
, meaning that a subsidy
will stimulate vaccine demand more if demand responds less to the disease prevalence. If
vaccine demand is not very price elastic, there will not be an opposing decrease in vaccine
demand in response to the decreased disease prevalence that the subsidy causes. To summarize,
prevalence elastic vaccine demand responds to both price of the vaccine and the pervasiveness of
the disease. Prevalence and price will have opposing effects on vaccine demand. If the price
increases, vaccine demand falls. In contrast, if prevalence increases, vaccine demand rises. The
above equation for
14 ∗
18
demonstrates this relationship.
Evidence for HPV’s Prevalence Elasticity
The pattern of HPV vaccination in the United States suggests that the vaccine may be
prevalence-elastic. In particular, the HPV vaccine exhibits a unique trend among racial/ethnic
groups. Relative to all other racial/ethnic groups in 2014, Hispanic girls had the highest rates of
completion for the three-shot HPV sequence (CDC 2015e). From 1999-2012, Hispanic women
also had the highest incidence rate of cervical cancer in every year but 2010, relative to other
racial/ethnic groups (CDC 2015d). This relationship is possible evidence for the non-zero
prevalence elasticity of the HPV vaccine; Hispanic girls or their mothers may respond to the high
cervical cancer incidence rates in their community and be more likely to get the vaccine.
HPV vaccination rates also differ between genders. Since the HPV vaccine’s approval
for boys in 2009, HPV vaccine coverage has been much lower for males than females (see
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Figure 1). HPV-associated cancers that men are susceptible to typically have much lower
prevalence than HPV-associated cancers females may get, with the exception of oropharyngeal
cancer. The CDC estimates the average number of HPV-attributable cancers per year for each
gender (see Table 1). The total number of HPV-attributable cancers diagnosed in females each
year is slightly less than two times the total for males. Potentially boys and their parents see the
low prevalence of these cancers and conclude that the HPV vaccine’s costs outweigh its benefits,
given the current level of male HPV-attributable cancers.
Table 1
Cancer Site
Anus
Cervix
Oropharynx
Penis
Vagina
Vulva
Total
Male
1,400
7,200
700
9,300
Female
2,600
10,400
1,800
600
2,200
17,600
Source: CDC 2014b
Many explanations other than prevalence-elastic demand exist to explain these trends.
For example, many Hispanic girls are likely poor and therefore eligible for the Vaccines for
Children program, a federal program to fund vaccinations recommended by the CDC. Hispanic
girls may have higher coverage because many of them are being provided with subsidized HPV
vaccination. Males may have lower vaccination rates because the HPV vaccine was approved
for boys in 2009, three years after it was approved for use in girls. Boys may be vaccinated less
because awareness about male HPV vaccination is still catching up. Nevertheless, the trends in
HPV vaccination among ethnic groups and genders are consistent with prevalence elastic vaccine
demand, justifying the need for the following empirical analysis to estimate how demand for the
HPV vaccine responds to cervical cancer prevalence.
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Methods
Data
This analysis uses two national datasets to examine prevalence elasticity for HPV. Data
on state cervical cancer mortality and incidence rates comes from the United States Cancer
Statistics (USCS) Incidence and Mortality Web-based Report produced by the CDC and the
National Cancer Institute (NCI). Individual-level data on HPV vaccination is from the National
Immunization Survey (NIS) of Teens.
HPV is linked to over 90% of cervical cancer cases (CDC 2014b). HPV can cause several
types of cancer, but cervical cancer is the most common of the HPV-associated cancers.
Additionally, cervical cancer prevention is often cited as the motivation for increasing HPV
vaccination coverage. For all of these reasons, cervical cancer makes an appropriate proxy for
HPV prevalence. The USCS provides official statistics of cancer incidence in states and regions
that meet data quality standards5. I use these state-level cervical cancer incidence and mortality
rates to measure the prevalence of HPV in a state. Cervical cancer rates are available from 1999
to 2012. USCS uses medical records for incidence data, and death certificates filed in the United
States for mortality data. Rates are per 100,000 persons and are age-adjusted to the 2000 U.S.
standard population.
The NIS-Teen estimates vaccination coverage of teenagers in the United States. It is a
random-digit-dialed telephone survey of parents or guardians of teens age 13-17 in all 50 states
and Washington D.C. The NIS-Teen added cell phones to the survey methodology in 2011 to
account for households that have no landline. Individual sociodemographic data is collected
during phone interviews of the parents. At the end of the phone interview, parents are also asked
for contact information for their child’s vaccination provider. The provider is then mailed a
5
See Table 10 for a list of state cancer registries that did not meet USCS publication criteria
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questionnaire to collect information on the teen’s vaccination status and dates of vaccination.
Provider-reported vaccination histories are used to estimate vaccination rates in the population.
However, many teens in the sample lack adequate provider data because parents do not
give consent to contact the child’s vaccine providers, there is inadequate contact information for
a provider, or their provider does not respond to the survey. The CDC cautions that teens with
adequate provider data likely differ from teens without provider data. Evidence from the NISChild survey suggests that children with adequate provider data are more likely to be up-to-date
with their vaccinations. They are also more likely to live in households with higher family
income, have a white mother, and live outside a central city of a Metropolitan Statistical Area.
Additionally, a child with inadequate provider data is less likely to have a parent who could
locate a shot card and is less likely to live in the state where the mother lived when the child was
born. These elements point to discontinuity of health care and are associated with lower
vaccination rates (CDC 2013b, p. 34). Thus, the NIS recommends using an NIS-developed
sampling weight to adjust for bias from provider non-response. Using this weight will reduce
bias from differences in teens with and without provider data. I include the weight in my
analysis.
The NIS-Teen records a teen’s age when they received their first dose of HPV from
provider immunization reports, as well as the respondent’s age when they participated in the
NIS-Teen. I expand the dataset using each respondent’s age, so I observe each individual at
every age from birth until they are surveyed by the NIS. Then, I merge the cervical cancer
prevalence data for the individual’s state in a given year onto each observation of the individual
in the same year. I create an indicator of HPV vaccination status, which is equal to 0 until the
individual receives their first dose of the HPV vaccine. This vaccination indicator serves as the
Rae Staben
May 5, 2016
19
dependent variable in my models. See Appendix, Figure 4 for a visual explanation and STATA
code.
In the immunization data, people are observed in a state as a teenager (anywhere from 1317). Their state of residence is recorded when the survey observes them. I assume that they lived
in the same state for all of their lives. For example, if a 16-year-old who lives in Montana is
surveyed by the NIS-Teen and responds that she received her first HPV vaccine dose at age 12, I
assume that she was living in Montana at age 12 as well. Thus, when I merged the cervical
cancer data to the immunization survey, I assume that she is making her vaccine decision in the
context of Montana’s cervical cancer prevalence. It is possible that she lived in a different state
at the time, however.
Before reshaping the data, there were 103,034 teens without adequate provider data and
141,533 teens with adequate provider data. I dropped all individuals that lacked adequate
provider data from my analysis. I also dropped individuals with adequate provider data who
were missing age at first HPV shot, so my sample includes only people who eventually receive
one dose of the vaccine. After restricting my sample to only teens with necessary data, there are
419,123 unweighted observations of 27,974 individuals in the dataset. Table 2 shows weighted
summary statistics for the variables I use in my analysis.
Table 2: Summary Statistics
Variables
Dependent Variable
Vaccinated (if age at first shot ≤ age)
Explanatory Variables
Cervical cancer mortality rate (per
100,000)
Cervical cancer incidence rate (per
100,000)
Individual Characteristics
Rae Staben
Number of
Observations
(weighted)
Mean
Standard
Deviation
Minimum
Maximum
541,000,000
0.19
0.39
0
1
541,000,000
2.49
0.51
1
5.8
541,000,000
8.41
1.36
4.3
13.8
May 5, 2016
20
Female
Race/ethnicity
Hispanic
White, non-Hispanic
Black, non-Hispanic
Other, non-Hispanic + Multiple
Mother's education level
Less than 12 years
12 Years
More than 12 years, non-college grad
College graduate
Number of children under 18 in household
One
Two or Three
Four or more
Family Income
$0 - $7500
$7501 - $10000
$10001 - $17500
$17501 - $20000
$20001 - $25000
$25001 - $30000
$30001 - $35000
$35001 - $40000
$40001 - $50000
$50001 - $60000
$60001 - $75000
$75001+
HPV vaccination required for school entry
Age at first HPV shot
Year
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
State
Alabama
Arizona
Arkansas
California
Colorado
Delaware
District of Columbia
Florida
Georgia
Hawaii
Rae Staben
541,000,000
0.74
0.44
0
1
541,000,000
541,000,000
541,000,000
541,000,000
0.26
0.5
0.15
0.09
0.44
0.5
0.36
0.28
0
0
0
0
1
1
1
1
541,000,000
541,000,000
541,000,000
541,000,000
0.17
0.25
0.25
0.34
0.38
0.43
0.43
0.47
0
0
0
0
1
1
1
1
541,000,000
541,000,000
541,000,000
0.3
0.57
0.13
0.46
0.5
0.33
0
0
0
1
1
1
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
0.05
0.05
0.09
0.05
0.06
0.06
0.04
0.05
0.07
0.05
0.07
0.36
0.02
13.05
0.21
0.21
0.29
0.22
0.24
0.24
0.2
0.21
0.26
0.22
0.26
0.48
0.13
1.78
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
18
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
0.06
0.07
0.07
0.08
0.08
0.08
0.08
0.08
0.08
0.08
0.07
0.07
0.06
0.05
0.23
0.25
0.26
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.26
0.25
0.23
0.21
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
0.01
0.03
0.01
0.17
0.02
0
0
0.05
0.03
0
0.12
0.16
0.08
0.37
0.13
0.03
0.01
0.23
0.18
0.04
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
May 5, 2016
21
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wisconsin
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
541,000,000
0
0.04
0.02
0.01
0.01
0.01
0.02
0
0.02
0.03
0.03
0.02
0.01
0.02
0
0.01
0
0.03
0.01
0.07
0.03
0.04
0.01
0.01
0.05
0
0.01
0.01
0.09
0
0.02
0.03
0.01
0.02
0.05
0.2
0.14
0.1
0.09
0.11
0.13
0.05
0.13
0.16
0.18
0.13
0.07
0.13
0.07
0.09
0.06
0.17
0.09
0.25
0.18
0.19
0.12
0.11
0.21
0.03
0.11
0.11
0.28
0.05
0.13
0.16
0.07
0.14
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Notes: Data are from the NIS-Teen 2008-2012 and the USCS Incidence and Mortality Web-based Report 1999-2012. Data from
the National Immunization Survey is available for 2008-2014, but cervical cancer prevalence data is only available from the
USCS for 1999-2012. Thus, the most recent data used is from 2012. Additionally, this table uses the NIS-Teen recommended
weight as a frequency weight to generate weighted summary statistics.
Econometric Framework
In my analysis, I include several sociodemographic controls, such as family income,
gender, and mother’s education level. I modeled the selection of my controls after Philipson
(1996). I also include an indicator of whether a state requires HPV vaccination for school entry.
Few states have such a requirement. In 2008 Virginia required female students to complete 3
doses of the vaccine, with the first dose before entering 6th grade. Parents can still choose for the
child not to receive the HPV vaccine (Virginia Department of Health). In 2007, D.C. enacted
Rae Staben
May 5, 2016
22
legislation to mandate the HPV vaccine for school (NCSL 2016).6 Vaccination status acts as the
dependent variable; an individual is considered vaccinated if the age at first HPV shot is less than
or equal to their age in a given year.
I use three model specifications to identify the relationship between vaccination status
and cancer prevalence – a linear probability model, a logistic model, and a Cox proportional
hazard model. I use a linear probability model due to its ease of interpretation. However, a linear
probability model can give predicted probabilities less than 0 or greater than 1. Therefore, I also
use a logistic model, which restricts the predicated probabilities of vaccination to be between 0
and 1. I also include a Cox proportional hazard model because my data is well-suited to a hazard
model, with vaccination serving as the ‘failure’ event. I use the Cox proportional hazard model
to test if teens in high cervical cancer prevalence states get vaccinated earlier than teens in low
cervical cancer prevalence states. A hazard model corrects for attrition from the sample.
However, I have no attrition from the study because I expanded my dataset back in time. People
only are removed from the analysis once they get vaccinated. Thus, the logistic model and Cox
proportional hazards model should not differ greatly in their predictions. I exploit variation in
cervical cancer incidence and mortality rates between states and years. Figure 2 and
Figure 3 show the cervical cancer incidence and mortality rates of states by percentile. A
list of states in each percentile is included in the Appendix Table 11 and Table 12.
6
Rhode Island requires HPV vaccination for all seventh graders, as of September 2015. The
data for my analysis ends in 2012, so this mandate is not reflected. (NCSL 2016)
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May 5, 2016
23
Figure 2
StateCervicalCancerIncidenceRates,
bypercentile,1999-2012
Casesper100,000people
18
16
14
12
10
8
6
4
2
0
1999
2001
2003
25th%
2005
50th%
2007
75th%
2009
2011
99th%
Notes: Data are from USCS Incidence and Mortality Web-based Report 1999-2012.
Figure 3
StateCervicalCancerMortalityRates,
bypercentile,1999-2012
Casesper100,000people
7
6
5
4
3
2
1
0
1999
2001
2003
25th%
2005
50th%
2007
75th%
2009
2011
99th%
Notes: Data are from USCS Incidence and Mortality Web-based Report 1999-2012.
Rae Staben
May 5, 2016
24
I use state fixed effects to control for unobserved differences between states. I also
include year fixed effects because the HPV vaccine was not approved for public use throughout
the entire period of my analysis so time may have a large effect on vaccination likelihood.
Though I include specifications with no fixed effects, state fixed effects only, and year fixed
effects only for each of the three model types, the most comprehensive specification includes
both state and year fixed effects. All lagged prevalence models include state and year fixed
effects, but vary in the inclusion of prevalence rates from specific years. The preferred
specification for the lagged prevalence models includes state and year fixed effects, as well as
cervical cancer prevalence rates from each of the past three years.
Linear probability model:
𝑦CD" = 𝛽E + 𝛽F 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D" + 𝛽L 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D" + 𝛼D + 𝛿" + 𝑿𝒊𝒔𝒕 𝛾 + 𝜖CD"
where
𝑦CD" is an indicator equal to 1 if individual i is vaccinated in year t and state s, and is equal to 0
otherwise
𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D" is the cervical cancer mortality rate in deaths per 100,000 in year t and state s
𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D" is the cervical cancer mortality rate in cases per 100,000 in year t and state s
𝛼D is the unobserved, time-invariant fixed effects of state s
𝛿" is the unobserved fixed effects of year t
𝑿𝒊𝒔𝒕 is a vector of covariates for individual i in year t in state s (specifically gender,
race/ethnicity, mother’s education level, number of children under age 18 in the
household, household income)
Rae Staben
May 5, 2016
25
𝛽F and 𝛽L are the coefficients of interest, showing how an individual’s probability of vaccination
changes with respect to the cervical cancer mortality and incidence rates in a state.
With these coefficients, I can calculate the prevalence elasticity of demand for the HPV
vaccine. I use the mean mortality or incidence rate and the mean vaccination rate to calculate
prevalence elasticity.
Using the mortality rate as the measure of cervical cancer prevalence,
mortality elasticity = 𝛽F ∗
𝑚𝑒𝑎𝑛 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒
𝑚𝑒𝑎𝑛 𝑦
Using the incidence rate as the measure of cervical cancer prevalence,
incidence elasticity = 𝛽L ∗
𝑚𝑒𝑎𝑛 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒
𝑚𝑒𝑎𝑛 𝑦
Prevalence Lags with Linear Probability Model:
𝑦CD" = 𝛽E + +𝜃F 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D,"YZ +𝜃L 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D,"Y[ + 𝛼D + 𝛿" + 𝑿𝒊𝒔𝒕 𝛾 + 𝜖CD"
where
𝑦CD" , 𝛼D , 𝛿" , and 𝑿𝒊𝒔𝒕 are defined as above.
𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D,"YZ is the cervical cancer mortality rate in deaths per 100,000 in year t-k and state s
for k = {1,2,3}.
𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D,"Y[ is the cervical cancer incidence rate in cases per 100,000 in year t-m and
state s for m = {1,2,3}, where k = m.
𝜃F and 𝜃L are the coefficients of interest, showing how an individual’s probability of vaccination
changes with respect to the cervical cancer mortality and incidence rates in a state from k years
ago.
Rae Staben
May 5, 2016
26
Though I include these models with lagged rates from each of the previous three years
individually, my preferred specification includes the mortality and incidence rates from the
previous 3 years (shown below).
]
𝑦CD" = 𝛽E +
]
𝜋Z 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒CD,"YZ +
Z^F
𝜌Z 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒CD,"YZ + 𝛼D + 𝛿" + 𝑿𝒊𝒔𝒕 𝛾 + 𝜖CD"
Z^F
With this model, 𝜋Z and 𝜌Z show how vaccination responds to lagged mortality and incidence
rates, respectively, for k = {1, 2, 3}.
Logistic model:
Let 𝑧 = 𝜏E + 𝜏F 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D" + 𝜏L 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D" + 𝛼D + 𝛿" + 𝑿𝒊𝒔𝒕 𝛾
𝑦CD" =
exp 𝑧
1 + exp 𝑧
where
𝑦CD" represents individual i’s probability of vaccination in year t and state s
𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D" is the cervical cancer mortality rate in deaths per 100,000 in year t and state s
𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D" is the cervical cancer mortality rate in deaths per 100,000 in year t and state s
𝛼D is the unobserved, time-invariant fixed effects of state s
𝛿" is the unobserved fixed effects of year t
𝑿𝒊𝒔𝒕 is a vector of covariates for individual i in year t in state s (specifically gender,
race/ethnicity, mother’s education level, number of children under age 18 in the
household, household income)
𝜏F and 𝜏L are the coefficients of interest, showing how an individual’s probability of vaccination
changes with respect to the cervical cancer mortality and incidence rates in a state.
Rae Staben
May 5, 2016
27
Prevalence Lags with Logistic Model:
Let w = 𝜏E + 𝜔F 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D,"YZ + 𝜔L 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D,"Y[ + 𝛼D + 𝛿" + 𝑿𝒊𝒔𝒕 𝛾 for k = {1, 2, 3}.
𝑦CD" =
exp 𝑤
1 + exp 𝑤
All independent variables have the same definition as in the linear probability model with lagged
prevalence. 𝜔F and 𝜔L express the relationship between vaccination probability and cervical
cancer mortality and incidence from k years ago.
As with the linear probability model, my preferred specification includes all of the past
three years’ incidence and mortality rates.
Let s = 𝜏E +
]
Z^F 𝜂Z 𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D,"YZ
+
]
Z^F 𝜁Z 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D,"YZ
𝑦CD" =
+ 𝛼D + 𝛿" + 𝑿𝒊𝒔𝒕 𝛾
exp 𝑠
1 + exp 𝑠
With this model, 𝜂Z and 𝜁Z show how vaccination responds to lagged mortality and incidence
rates, respectively, for k = {1, 2, 3}.
Cox Proportional Hazard Model:
𝜆 𝑡 𝑮𝒊𝒔𝒕 ) = 𝜆E 𝑡 ∗ exp(𝑮𝒊𝒔𝒕 ∗ 𝛽)
where 𝜆 𝑡 𝑮𝒊𝒔𝒕 ) is the hazard rate at time t for individual i with covariate vector 𝑮𝒊𝒔𝒕 . The
hazard rate shows the probability that individual i will get vaccinated at time t given they were
not vaccinated before time t.
𝜆E 𝑡 is the baseline hazard rate, or the hazard rate when all of the independent variables are
equal to 0.
𝑮𝒊𝒔𝒕 is a vector of independent variables for individual i in state s at time t, including the
following:
Rae Staben
May 5, 2016
28
𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D" , the cervical cancer mortality rate in deaths per 100,000 in year t and state s
𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D" , the cervical cancer mortality rate in deaths per 100,000 in year t and state s
𝛼D , the unobserved, time-invariant fixed effects of state s
𝛿" , the unobserved fixed effects of year t
Gender, race/ethnicity, mother’s education level, number of children under age 18 in the
household, and household income for individual i in year t in state s
Prevalence Lags with Cox Proportional Hazard Model:
𝜆 𝑡 𝑯𝒊𝒔𝒕 ) = 𝜆E 𝑡 ∗ exp(𝑯𝒊𝒔𝒕 ∗ 𝛽)
𝜆 𝑡 𝑯𝒊𝒔𝒕 ) is the hazard rate at time t for individual i with covariate vector 𝑯𝒊𝒔𝒕 . The hazard rate
shows the probability that individual i will get vaccinated at time t given they were not
vaccinated before time t. However, instead of using the current year’s cervical cancer mortality
and incidence rates, this model includes the rates of the past three years.
𝜆E 𝑡 is the baseline hazard rate, or the hazard rate when all of the independent variables are
equal to 0.
𝑯𝒊𝒔𝒕 is a vector of independent variables, including the following:
𝑚𝑜𝑟𝑡_𝑟𝑎𝑡𝑒D,"YZ , the cervical cancer mortality rate in deaths per 100,000 in year t-k and state s
for k = {1, 2, 3}
𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒D,"Y[ , the cervical cancer mortality rate in deaths per 100,000 in year t-m and
state s for m = {1, 2, 3}, where k = m
𝛼D , the unobserved, time-invariant fixed effects of state s
𝛿" , the unobserved fixed effects of year t
Rae Staben
May 5, 2016
29
Gender, race/ethnicity, mother’s education level, number of children under age 18 in the
household, and household income for individual i in year t in state s
As with the linear and logistic models, my preferred specification includes all of the past three
years’ incidence and mortality rates rather than rates from one of the past three years only.
Results
Linear Probability Analysis
Current Prevalence Rates
If demand for the HPV vaccine is prevalence elastic, the coefficients on mortality and
incidence rates should be positive. Positive coefficients on both measures of prevalence mean an
increase in either measure of prevalence causes an increase in an unvaccinated individual’s
probability of vaccination. Regression results from the linear probability model are shown in
Table 3 (full regression results shown in Appendix, Table 13). Column 1 does not include state
or year fixed effects, column 2 includes only state fixed effects, column 3 includes only year
fixed effects, and column 4 controls for both state and year fixed effects. The prevalence point
estimates vary greatly between columns, demonstrating that the inclusion of fixed effects is
important. Including state and year fixed effects reduces the likelihood of omitted variables bias,
and is therefore closest to representing the true relationship between HPV vaccination and
cervical cancer prevalence.
The first column, without state or year fixed effects, serves as a baseline. With no fixed
effects, an increase in the cervical cancer mortality rate of one-unit raises the probability of
vaccination by 0.0609 while an increase in the cervical cancer incidence rate of one-unit
decreases the probability of vaccination by 0.0879. Prevalence elastic demand does not predict a
negative relationship between vaccination and incidence, but this unexpected relationship
Rae Staben
May 5, 2016
30
disappears when I include state and year fixed effects. With the addition of state and year fixed
effects, the mortality rate is not statistically significantly related to vaccination probability and an
increase in cervical cancer incidence of one-unit increases the vaccination probability by
0.00272.
incidence elasticity = 𝛽L ∗
𝑚𝑒𝑎𝑛 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑐𝑒_𝑟𝑎𝑡𝑒
𝑚𝑒𝑎𝑛 𝑦
Using the above formula, the average incidence elasticity is .119; a 1% increase in the
incidence rate is associated with a 11.9% increase in vaccination.
Table 3
(1)
No Fixed
Effects
(2)
(3)
(4)
State Fixed Year Fixed State and
Effects
Effects
Year Fixed
Effects
0.0609***
-0.131***
-0.00469**
-0.000536
(0.00241)
-0.0879***
(0.00307)
-0.154***
(0.00183)
0.00200**
(0.00253)
0.00272**
(0.000927) (0.00121) (0.000780)
No
Yes
No
No
No
Yes
419,123
419,123
419,123
0.079
0.171
0.470
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(0.00111)
Yes
Yes
419,123
0.471
Cervical cancer mortality
rate (per 100,000)
Cervical cancer incidence
rate (per 100,000)
State FE
Year FE
Observations
R-squared
Note: Each regression also includes gender, race/ethnicity, mother’s education level, number of children under age
18 in the household, household income, and an indicator of whether HPV vaccination is required for school entry in
a state in a given year.
Lagged Prevalence Rates
Table 4 shows results from the lagged linear probability model. I again regress an
indicator of vaccination status on mortality and incidence rates in an individual teen’s state, but
add mortality and incidence rates from the previous three years. Full regression results are
available in Appendix, Table 14. In all four models, people increase their vaccination probability
Rae Staben
May 5, 2016
31
with increases in the lagged incidence rate of cervical cancer while lagged mortality rates are not
statistically significantly related to vaccination. Because so few people die of cervical cancer
each year, cervical cancer incidence may be more visible than cervical cancer mortality. Thus,
people may not respond to changes in the cervical cancer mortality rate. My preferred
specification includes mortality and incidence rates from the past three years in one linear
probability model. In this case (column 4), a one-unit increase in the incidence rate from one
year ago increases vaccination probability by .00274 and a one-unit increase in the incidence rate
from three years ago increase vaccination probability by .00249. The incidence rate from two
years ago has a coefficient in the same range, but is not statistically significantly different from
zero. To summarize, it appears that lagged incidence rates have a small but positive effect on
vaccination rates and mortality rates do not have a statistically significant effect on vaccination.
Table 4
(1)
(2)
(3)
1 Year Lag 2 Year Lag 3 Year Lag
1-year lagged mortality rate
-0.000818
(0.00273)
1-year lagged incidence rate 0.00257**
(0.00118)
2-year lagged mortality rate
2-year lagged incidence rate
0.00296
(0.00299)
0.00191
(0.00124)
3-year lagged mortality rate
0.00116
(0.00320)
3-year lagged incidence rate
0.00275**
(0.00133)
State FE
Yes
Yes
Yes
Year FE
Yes
Yes
Yes
Observations
393,695
364,432
331,944
R-squared
0.464
0.456
0.445
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
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May 5, 2016
(4)
1,2, and 3
Year Lag
-0.00325
(0.00353)
0.00274*
(0.00162)
0.00195
(0.00354)
0.00211
(0.00150)
0.00193
(0.00329)
0.00249*
(0.00139)
Yes
Yes
309,383
0.444
32
Note: Each regression also includes gender, race/ethnicity, mother’s education level, number of children under age
18 in the household, household income, and an indicator of whether HPV vaccination is required for school entry in
a state in a given year.
Logistic Analysis
Current Prevalence Rates
Table 5 includes odds ratios from my models, as well as the average marginal effects of
cervical cancer prevalence on the dependent variable. The average marginal effect is the mean
marginal effect for the sample, holding other variables constant. See Table 15 for full logistic
output. Without fixed effects, a one-unit increase in the cervical cancer mortality rate increases
the probability of vaccination by .0631 and a one-unit increase in the incidence rate decreases the
probability of vaccination by .0911 (see column 2). There is an unintuitive negative relationship
between incidence and vaccination when state and year fixed effects are not included. However,
when I add state and year fixed effects to the model, the relationship between cervical cancer
prevalence and vaccination becomes much weaker. A one-unit increase in the cervical cancer
mortality rate causes a decrease in vaccination probability of .0048 (see column 8). This average
marginal effect is only significant at the 10% level, and is such a small decrease in vaccination
probability it can be thought of as trivial. Additionally, the average marginal effect of incidence
is not statistically significant. Thus, upon controlling for state and year fixed effects, there
appears to be little to no relationship between cervical cancer prevalence and vaccination
probability.
Table 5
VARIABLES
(1)
No Fixed
Effects
Odds Ratio
(2)
No FE
Margins
(3)
State Fixed
Effects
(4)
State FE
Margins
Odds Ratio
(5)
Year Fixed
Effects
Odds Ratio
(6)
Year FE
Margins
(7)
State and
Year Fixed
Effects
Odds Ratio
(8)
State and
Year FE
Margins
Vaccinated (if
age at first
shot <= age)
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May 5, 2016
33
Cervical
cancer
mortality rate
(per 100,000)
Cervical
cancer
incidence rate
(per 100,000)
State FE
Year FE
Observations
1.561***
0.0631***
0.224***
-0.178***
0.927***
-0.00562***
0.937*
-0.00480*
(0.0252)
0.525***
(0.00225)
-0.0911***
(0.00616)
0.162***
(0.00326)
-0.217***
(0.0222)
1.025**
(0.00178)
0.00184**
(0.0330)
0.984
(0.00261)
-0.00120
(0.00366)
No
No
419,123
(0.000948)
(0.00237)
Yes
No
419,123
(0.00156)
(0.0111)
No
Yes
419,123
(0.000804)
(0.0174)
Yes
Yes
419,123
(0.00131)
419,123
419,123
419,123
419,123
Robust standard error form in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
Note: Each model also includes gender, race/ethnicity, mother’s education level, number of children under age 18 in
the household, household income, and an indicator of whether HPV vaccination is required for school entry in a
state in a given year.
Lagged Prevalence Rates
Table 6 shows output from the lagged logistic model, with full logistic output shown in
Table 16. Lagged cervical cancer incidence does not have a statistically significant relationship
to probability of vaccination in any of the proposed logistic models with lagged prevalence rates.
People appear to slightly decrease their vaccination probability in model (1) and model (4).
Model (4) is the most complete specification for the lagged logistic model because it includes
prevalence rates from each of the past three years. In model (4), the odds ratios for the mortality
rate from the past two years are the only statistically significant odds ratios, showing that people
stop responding to lagged cervical cancer mortality rates from more than 2 years ago. Column 8
shows the average marginal effects for the lagged prevalence rates. Holding all else constant, a
one-unit increase in the one-year lagged mortality rate decreases the vaccination probability by
0.00987, or less than 1%. The marginal effect for the two-year lagged mortality rate is only
statistically significant at the 10% level, but shows that a one-unit increase in the two-year
lagged mortality rate decreases vaccination probability by .00677. Though the sign of the
Rae Staben
May 5, 2016
34
average marginal effect is inconsistent with the theory of prevalence elasticity, these are such
small marginal effects that they can be thought of as negligible.
Table 6
VARIABLES
(1)
1 Year Lag
(2)
1 Year Lag
Margins
Odds Ratio
(3)
2 Year Lag
(4)
2 Year Lag
Margins
Odds Ratio
(5)
3 Year Lag
(6)
3 Year Lag
Margins
Odds Ratio
(7)
1,2, 3 Year
Lag
Odds Ratio
(8)
1,2,3 Year
Lag Margins
Vaccinated (if
age at first
shot <= age)
1-year lagged
mortality rate
1-year lagged
incidence rate
0.924**
-0.00623**
0.900***
-0.00987***
(0.0327)
0.994
(0.00280)
-0.000508
(0.0332)
0.993
(0.00346)
-0.000668
(0.0177)
(0.00141)
2-year lagged
mortality rate
2-year lagged
incidence rate
0.966
-0.00294
(0.0187)
0.930*
(0.00177)
-0.00677*
(0.0355)
0.992
(0.00314)
-0.000671
(0.0367)
0.996
(0.00370)
-0.000376
(0.0174)
(0.00150)
3-year lagged
mortality rate
3-year lagged
incidence rate
State FE
Year FE
Observations
0.967
-0.00313
(0.0183)
0.951
(0.00172)
-0.00475
(0.0350)
1.015
(0.00340)
0.00135
(0.0352)
1.024
(0.00347)
0.00218
(0.0166)
(0.00154)
(0.0180)
(0.00165)
Yes
Yes
Yes
Yes
Yes
Yes
392,906
363,578
363,578
328,553
328,553
309,383
309,383
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Each model also includes gender, race/ethnicity, mother’s education level, number of children under age 18 in
the household, household income, and an indicator of whether HPV vaccination is required for school entry in a
state in a given year.
Yes
Yes
392,906
Cox Proportional Hazard Analysis
Current Prevalence Rates
If people respond to cervical cancer prevalence when making HPV vaccination decisions,
the hazard ratio for cervical cancer mortality and incidence rates should be greater than 1. A
hazard ratio above 1 is associated with greater hazard into the failure state, which in this case is
becoming vaccinated. Thus, a hazard ratio for either measure of prevalence above 1 shows an
Rae Staben
May 5, 2016
35
increased likelihood of becoming vaccinated as prevalence increases. Without the inclusion of
fixed effects, cervical cancer mortality is not statistically significantly related to vaccine hazard
and an increase in the incidence rate reduces vaccine hazard. After including state and year fixed
effects, mortality is not statistically significantly related to the vaccination hazard rate and
incidence only slightly decreases the vaccination hazard. Adding state and year fixed effects
dampens the negative relationship between prevalence and the vaccine hazard rate. See
Appendix, Table 17 for full output.
Table 7
(1)
No Fixed
Effects
Cervical cancer
mortality rate
(per 100,000)
Cervical cancer
incidence rate
(per 100,000)
State Fixed
Effects
Year Fixed
Effects
Observations
1.037
(2)
State
Fixed
Effects
(3)
Year
Fixed
Effects
(4)
State and
Year
Fixed
Effects
0.747*** 0.866***
0.953
(0.0238) (0.0243) (0.0198) (0.0301)
0.823*** 0.596*** 0.965*** 0.945***
(0.00846) (0.0102) (0.00995) (0.0152)
No
Yes
No
Yes
No
No
Yes
Yes
1,188,320 1,188,320 1,188,320 1,188,320
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Note: Each model also includes gender, race/ethnicity, mother’s education level, number of children under age 18 in
the household, household income, and an indicator of whether HPV vaccination is required for school entry in a
state in a given year.
Lagged Prevalence Rates
In every Cox proportional hazard model with lagged prevalence rates shown in Table 8,
cervical cancer incidence and mortality are not statistically significantly related to vaccination
Rae Staben
May 5, 2016
36
hazard. My model cannot distinguish the relationship from zero. Accordingly, it is likely that
lagged cervical cancer prevalence does not have a strong effect on HPV vaccination decisions.
Full results are available in Appendix, Table 18.
Table 8
(1)
(2)
(3)
1 Year Lag 2 Year Lag 3 Year Lag
1-year lagged
mortality rate
1-year lagged
incidence rate
0.968
(4)
1,2, and 3
Year Lag
0.969
(0.0307)
0.988
(0.0318)
0.981
(0.0165)
1.025
(0.0176)
1.025
(0.0366)
0.976
(0.0389)
0.973
2-year lagged
mortality rate
2-year lagged
incidence rate
(0.0167)
1.006
(0.0173)
1.013
(0.0349)
1.000
(0.0358)
1.000
(0.0155)
Yes
Yes
(0.0165)
Yes
Yes
1,103,813 1,010,274
911,066
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
855,442
3-year lagged
mortality rate
3-year lagged
incidence rate
State FE
Year FE
Observations
Yes
Yes
Yes
Yes
Note: Each model also includes gender, race/ethnicity, mother’s education level, number of children under age 18 in
the household, household income, and an indicator of whether HPV vaccination is required for school entry in a
state in a given year.
Knowledge of HPV-Cervical Cancer Link
For people to change their behavior in response to a change in a disease’s prevalence,
people must understand how their behavior affects their infection risk. Cervical cancer can
arguably be considered a sexually transmitted disease, with over 90% of cervical cancer cases
Rae Staben
May 5, 2016
37
linked to HPV infection (CDC 2014b). Despite the strong biological link between cervical
cancer and HPV, my results imply that people do not increase vaccination in response to
increases in cervical cancer mortality and incidence in their state of residence. This suggests that
prevalence elasticity does not apply to individual decisions about HPV vaccination. Possibly, the
lack of response is due to the public’s misunderstanding of the consequences of HPV. If people
are unaware of the link between HPV and cervical cancer, and that the HPV vaccine can reduce
their risk of cervical cancer, then it makes sense that their vaccination decisions do not respond
to cervical cancer prevalence.
Past research shows that few people understand the link between HPV and cervical
cancer. Licht et al. (2010) surveyed 406 women ages 18-26 attending two public universities.
About 44% of participants had received one or more vaccine doses. Subjects who successfully
answered that HPV causes genital warts were 1.85 times more likely to have received at least
one HPV vaccine dose (p. 74). Other than knowledge of HPV’s connection to genital warts, there
were no significant differences in HPV knowledge and vaccination status. In general,
participants underestimated their risk of infection with HPV. About 60% of those surveyed rated
their risk of acquiring HPV as low, though it is estimated that at least 50% of sexually active
people are infected with HPV at some point (U.S. HHS 2012). This study suffers from a small
sample size, and results that are difficult to generalize beyond the female college student
population, which is more likely to be knowledgeable about HPV and more likely to be
vaccinated than the general population.
Survey data shows that the general population has a much lower awareness of HPV. The
National Cancer Institute’s Health Information National Trends Survey (HINTS) is a nationally
representative survey of American adults used to track how people use health information and
Rae Staben
May 5, 2016
38
their knowledge about health behaviors. HINTS includes questions about HPV and HPV’s
relationship to various cancers. Tiro et al. (2007) use HINTS 2005 to analyze characteristics
associated with knowledge of HPV and its causation of cervical cancer. In 2005, only 40% of
U.S. women age 18-75 had even heard of HPV, and less than half of those who had heard of
HPV knew that it causes cervical cancer (p. 285). Additionally, Tiro et al. explain that the
HINTS survey encourages guessing because it is a prompted format survey, so HINTS may
overestimate HPV knowledge.
Tiro et al. analyze what characteristics are associated with more HPV awareness.
Women who had previously heard of HPV were more likely to be under age 65, to be nonHispanic White, to have attended or graduated college, and to have a recent Pap test (p. 290).
Women who had heard of HPV were also less likely to say they did not trust at least one source
of health information (p. 290). Women who were aware that HPV can cause cervical cancer
were more likely to be Hispanic and to have attended or graduated from college (p. 290).
People have likely become more knowledgeable about HPV since 2005, so Tiro’s
analysis may underestimate the public’s understanding of HPV in more recent years. I use
HINTS 4 Cycle 4 data to conduct an analysis similar to Tiro et al. (2007), though with data from
2014. My prevalence elasticity analysis uses data up to 2012, so this 2014 data represents an
upper bound of HPV knowledge, while Tiro et al. (2007) can be interpreted as a lower bound of
HPV knowledge. Additionally, Tiro et al. (2007) only uses female respondents because in 2005
the HPV vaccine was only approved for females. HINTS 4 asked HPV-related questions to
males and females because HPV vaccination is now recommended for both genders.
In 2014, about 66% of HINTS respondents had heard of HPV before taking the survey.
Of the people who had heard of HPV before, 78% correctly answered that HPV causes cervical
Rae Staben
May 5, 2016
39
cancer. Out of the overall sample, less than half (48%) of the respondents knew that HPV causes
cervical cancer. Moreover, only 43.8% of those surveyed knew that HPV causes cervical cancer
and had heard of the HPV vaccine previously. Knowledge of other HPV-associated cancers was
particularly low, with 25-30% of the people who had heard of HPV knowing that the virus can
cause penile, anal, and oral cancers. In the total sample, less than 20% was aware that HPV can
cause penile, anal, and oral cancers.
Similar to Tiro et al. (2007), I analyzed which individual characteristics are associated
with greater HPV awareness and knowledge. Detailed results are available in Table 9.
Previously hearing about HPV and knowing about its connection to cervical cancer are both
more common for people under age 50, women, non-Hispanic whites, and people who have
some college education or higher.
HINTS surveys people on how much they trust various sources for cancer information. I
created an indicator variable that is equal to one if an individual trusts all seven common sources
of cancer information (these sources include doctors, family, newspapers and magazines, radio,
the internet, television, and the government). If someone reported that they do not trust at least
one of these sources, the indicator variable is equal to zero. Relative to the overall sample, a
larger proportion of people who had heard of HPV and people who knew HPV causes cervical
cancer trusted all seven sources of cancer information. These groups were also more likely to
have health insurance, to have gotten a pap smear recently, and to have a family history of
cancer. This suggests that people who are more aware of HPV and its consequences are more
attached to the healthcare system through increased trust, health insurance, and past use of
medical services.
Rae Staben
May 5, 2016
40
Respondents who had heard of HPV or who knew that HPV can lead to cervical cancer
also had more HPV-related knowledge in general. Compared to all surveyed adults, a larger
proportion of both groups was aware of the HPV vaccine and of HPV’s connection to other types
of cancer. Finally, a larger percentage of both groups had received a recommendation from a
doctor in the past year to get an HPV vaccine, relative to the total sample.
Table 9: Weighted Percentages of individual characteristics by HPV awareness and knowledge
for individuals ages 18-75 – HINTS 2014
All Adults
n = 3,249
Heard of HPV
n = 2,097
Knew HPV
causes cervical
cancer (of those
who have heard
of HPV)
n = 1,558
18-34
35-49
50-64
65-74
Missing
32.07%
27.82%
26.18%
9.98%
3.95%
35.37%
29.69%
25.15%
7.10%
2.70%
38.26%
29.72%
23.20%
6.29%
2.54%
Female
Male
Missing
50.19%
48.13%
1.68%
57.18%
41.83%
0.98%
58.19%
40.81%
1.00%
Non-Hispanic White
Non-Hispanic Black, African American
Hispanic
Non-Hispanic Asian
Non-Hispanic Other
Missing
Education
Less than High School
High School Graduate
Some College
Bachelor's Degree
Post-Baccalaureate Degree
Missing
Health Information
60.23%
10.80%
14.35%
4.56%
2.10%
7.96%
66.53%
9.66%
14.16%
3.00%
1.98%
4.68%
68.65%
8.46%
13.52%
3.29%
2.23%
3.86%
9.96%
17.18%
29.53%
25.52%
14.51%
3.30%
6.85%
14.42%
31.89%
27.05%
17.29%
2.50%
4.98%
12.26%
32.08%
28.65%
19.51%
2.51%
Sociodemographic
Age
Gender
Race/Ethnicity
Rae Staben
May 5, 2016
41
Trust sources of cancer information
Does not trust one or more sources
Trusts all sources
Health insurance
Yes
No
Missing
Pap test within past two years
Yes
No
Missing
Family History of Cancer
Yes
No
Missing
HPV Knowledge
Have you ever heard of HPV?
Yes
No
Missing
Have you ever heard of the HPV vaccine?
Yes
No
Missing
In the last 12 months, has a doctor
recommended you get the HPV vaccine?
Yes
No
Not sure
Missing
Can HPV cause cervical cancer?
Yes
No
Not sure
Missing
Can HPV cause penile cancer?
Yes
No
Not sure
Missing
Can HPV cause anal cancer?
Yes
No
Not sure
Missing
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43.40%
56.60%
41.11%
58.89%
41.43%
58.57%
85.46%
13.45%
1.08%
87.30%
11.85%
0.85%
87.71%
11.55%
0.75%
34.38%
15.14%
50.48%
40.70%
15.95%
43.35%
42.05%
15.87%
42.07%
64.99%
26.93%
8.08%
70.04%
23.84%
6.12%
71.50%
23.36%
5.14%
66.42%
32.52%
1.06%
100.00%
-
100.00%
-
64.94%
33.30%
1.76%
85.18%
14.66%
0.17%
91.35%
8.53%
0.12%
13.22%
27.93%
10.22%
48.64%
18.35%
28.06%
8.25%
45.34%
19.89%
27.56%
7.73%
44.83%
51.54%
0.61%
13.54%
34.31%
77.59%
0.92%
20.38%
1.10%
100.00%
-
18.99%
10.12%
35.32%
35.58%
28.59%
15.23%
53.17%
3.01%
36.34%
18.19%
42.73%
2.75%
16.68%
11.14%
36.57%
35.61%
25.12%
16.77%
55.05%
3.06%
31.72%
20.30%
45.19%
2.79%
May 5, 2016
42
Can HPV cause oral cancer?
Yes
No
Not sure
Missing
19.36%
11.37%
33.64%
35.63%
29.15%
17.12%
50.65%
3.09%
36.84%
20.66%
39.71%
2.80%
Notes: Table constructed using HINTS 2014 dataset.
Conclusion
Overall, my results do not suggest a strong relationship between cervical cancer
prevalence and the HPV vaccination rate. The prevalence elasticity of the HPV vaccine is likely
close to zero. Thus, future changes in cervical cancer rates are unlikely to have a large effect on
HPV vaccination coverage.
There are several possible explanations for my findings. First, the low mortality and
incidence rates of cervical cancer may explain the low level of prevalence elasticity. Possibly,
the prevalence rates are not high enough to be visible to parents and teens who are deciding
about the HPV vaccine. It is unlikely that many people know someone with cervical cancer or
any other HPV-associated cancer because they are fairly rare in the United States today.
Additionally, the HPV vaccine’s prevalence elasticity may be low because many people do not
know about the HPV vaccine and what diseases the vaccine can prevent. As the HINTS 2014
data shows, awareness of HPV, the HPV vaccine, and HPV’s connection to cervical cancer is
rare. Less than 45% of the sample had both heard of the HPV vaccine before and knew that
HPV can cause cervical cancer. Finally, an abundance of alternatives to prevent HPV and
cervical cancer may weaken the relationship between vaccination and cervical cancer prevalence.
People can prevent HPV infection by practicing abstinence or using condoms. Women can
reduce their risk of serious cervical cancer by getting regular pap smears to detect cancerous
cells earlier. Due to the wealth of options for protection in the United States, people may
consider the HPV vaccine to be unnecessary.
Rae Staben
May 5, 2016
43
Regardless of the reason for the HPV vaccine’s minimal prevalence elasticity,
quantifying the vaccine’s prevalence elasticity is useful for optimally designing programs to
boost HPV vaccine coverage. The SIR model predicts that programs such as subsidies are selflimiting because as the subsidy initially encourages more people to get vaccinated, there is less
incentive for the remaining unvaccinated people to get the vaccine. However, demand for the
HPV vaccine does not appear to depend on cervical cancer prevalence. Thus, HPV vaccine
subsidies or school mandates likely will be effective in increasing HPV vaccination rates.
Estimates of the HPV vaccine’s prevalence elasticity may differ outside of the United
States. Cervical cancer is much more common in developing countries than in the United States.
88% of cervical cancer deaths occur in developing countries (Binagwaho et al., 2012).
Additionally, cervical cancer prevalence varies much more geographically and over time in
developing countries due to the uneven introduction of the HPV vaccine and cheap substitutes
for pap testing. Potentially there is a larger prevalence response in the developing world because
of the higher visibility of cervical cancer, leading to a greater perceived threat of HPV.
Furthermore, cervical cancer screening is less available in developing countries. Thus, people
may see the HPV vaccine as more essential, and may be more responsive to changes in the
prevalence of HPV-related diseases.
Rae Staben
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44
References
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the Local Prevalence of AIDS.” Journal of Human Resources 31.4: pp. 869-898.
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http://www.cdc.gov/cancer/hpv/statistics/age.htm, accessed May 2, 2016.
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2016.
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Data File.” Available at http://www.cdc.gov/nchs/nis/data_files_teen.htm, accessed May
2, 2016.
CDC. 2013c. “Summary of DTaP and Tdap Vaccine Recommendations Across the Lifespan.”
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accessed May 3, 2016.
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http://www.cdc.gov/std/hpv/stdfact-hpv.htm, accessed April 19, 2016.
CDC. 2014b. “How Many Cancers are Linked with HPV Each Year?” Last updated 6/2015.
http://www.cdc.gov/cancer/hpv/statistics/cases.htm, accessed April 19, 2016.
CDC. 2014c. “2014 NIS-Teen Vaccination Coverage Table Data: by State and Selected Area,
Vaccines Routinely Recommended for Adolescents.” Last updated 8/2015.
http://www.cdc.gov/vaccines/imz-managers/coverage/nis/teen/data/tables-2014.html,
accessed April 19, 2016.
CDC. 2014d. “2014 NIS-Teen Vaccination Coverage Table Data: 1 or more doses of HPV by
State and Selected Area.” Last updated 8/2015. http://www.cdc.gov/vaccines/imzmanagers/coverage/nis/teen/data/tables-2014.html, accessed April 19, 2016.
CDC. 2015a. “Human Papillomavirus. Epidemiology and Prevention of Vaccine Preventable
Diseases.” Last updated 8/2015. http://www.cdc.gov/vaccines/pubs/pinkbook/hpv.html,
accessed April 19, 2016.
CDC. 2015b. “Human Papillomavirus (HPV).” Last updated 9/2015. http://www.cdc.gov/hpv/,
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accessed April 19, 2016.
CDC. 2015c. “Other Sexually Transmitted Diseases: Human Papillomavirus.” Last updated
11/2015. http://www.cdc.gov/std/stats14/other.htm, accessed April 19, 2016.
CDC. 2015d. “Cervical Cancer Rates by Race and Ethnicity.” Last updated 8/2015.
http://www.cdc.gov/cancer/cervical/statistics/race.htm, accessed April 20, 2016.
CDC. 2015e. “Morbidity and Mortality Weekly Report: National, Regional, State, and Selected
Local Area Vaccination Coverage Among Adolescents Aged 13-17 Years – United
States, 2014. Table 2.” Last updated 7/2015.
http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6429a3.htm#tab2, accessed May 2,
2016.
CDC. 2015f. “Registries that Met USCS Publication Criteria.” Last updated 8/2015.
http://www.cdc.gov/cancer/npcr/uscs/data/00_data_quality.htm, accessed May 2, 2016.
CDC. 2015g. “HPV Vaccines: Vaccinating Your Preteen or Teen.” Last updated 1/2015.
http://www.cdc.gov/hpv/parents/vaccine.html, accessed May 3, 2016.
CDC. 2016a. “Frequently Asked Questions About HPV Vaccine Safety.” Last updated 3/2016.
http://www.cdc.gov/vaccinesafety/vaccines/hpv/hpv-safety-faqs.html#A2, accessed May
2, 2016.
CDC. 2016b. “Genital HPV Information – Fact Sheet.” Last updated 4/2016.
http://www.cdc.gov/std/HPV/STDFact-HPV.htm#a4, accessed May 2, 2016.
CDC. 2016c. “Vaccines for Children Program (VFC): CDC Vaccine Price List.” Last updated
5/2016. http://www.cdc.gov/vaccines/programs/vfc/awardees/vaccine-management/pricelist/, accessed May 2, 2016.
FDA. 2006. “Gardasil (Human Papillomavirus Vaccine) Questions and Answers – Gardasil, June
8, 2006.”
http://www.fda.gov/BiologicsBloodVaccines/Vaccines/QuestionsaboutVaccines/ucm096
052.htm, accessed April 19, 2016.
FDA. 2009. “FDA Approves New Vaccine for Prevention of Cervical Cancer.” Last updated
4/2013.
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/2009/ucm187048.htm,
accessed April 19, 2016.
FDA. 2014. “FDA Approves Gardasil 9 for Prevention of Certain Cancers Caused by Five
Additional Types of HPV.” Last updated 12/2014.
http://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm426485.htm,
accessed April 19, 2016.
Geoffard, P.Y. and Philipson, T. 1996. “Rational Epidemics and Their Public Control.”
International Economics Review, Vol. 37, No. 3, pp. 603-624.
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Gilkey, M.B., Moss, J.L., Coyne-Beasley, T., Hall, M.E., Shah, P.D., Brewer, N.T. 2015a.
“Physician Communication about Adolescent Vaccination: How is Human
Papillomavirus Vaccine Different?” Preventive Medicine. Volume 77. August 2015. pp.
181-185.
Gilkey, M.B., Malo, T.L., Shah, P.D., Hall, M.E. and Brewer, N.T., 2015b. “Quality of Physician
Communication about HPV Vaccine: Findings from a National Survey.” Cancer
Epidemiology, Biomarkers & Prevention.
Holman D.M., Benard V., Roland K.B., Watson M., Liddon N., Stokley S. 2014. “Barriers to
Human Papillomavirus Vaccination Among US Adolescents: A Systematic Review of the
Literature.” JAMA Pediatrics. 2014;168(1): pp. 76-82.
Kermack, W. O., and A. G. McKendrick. 1927. “A Contribution to the Mathematical Theory of
Epidemics”. Proceedings of the Royal Society of London. Series A, Containing Papers of
a Mathematical and Physical Character 115.772: pp. 700–721.
Li, Y., Norton, E.C. and Dow, W.H. 2004. "Influenza and pneumococcal vaccination demand
responses to changes in infectious disease mortality." Health Services Research 39.4p1:
pp. 905-926.
Licht, A.S., Murphy, J.M., Hyland, A.J., Fix, B.V., Hawk, L.W. and Mahoney, M.C., 2010. Is
use of the human papillomavirus vaccine among female college students related to human
papillomavirus knowledge and risk perception?. Sexually Transmitted Infections, 86(1):
pp.74-78.
Mullahy, J. 1999. “It’ll Only Hurt a Second? Microeconomic Determinants of Who Gets Flu
Shots.” Health Economics 8: pp. 9-24.
National Cancer Institute (NCI). 2014. “Accelerating HPV Vaccine Uptake: Urgency for Action
to Prevent Cancer. A Report to the President of the United States from the President’s
Cancer Panel.” Web-based version available at
http://deainfo.nci.nih.gov/advisory/pcp/annualReports/HPV/index.htm, accessed May 3,
2016.
National Conference of State Legislatures (NCSL). 2016. “HPV Vaccine Policies.”
http://www.ncsl.org/research/health/hpv-vaccine-state-legislation-andstatutes.aspx#2015-2016, accessed May 2, 2016.
National Institutes of Health (NIH). 2013. “Cervical Cancer.” Last updated 3/2013.
https://report.nih.gov/nihfactsheets/viewfactsheet.aspx?csid=76, accessed May 2, 2016.
Philipson,T. 2000. “Chapter 33: Economic epidemiology and infectious diseases,” in Handbook
of Health Economics, Vol. 1, Part B. Elsevier. pp. 1761-1799.
Philipson, T. 1996. “Private Vaccination and Public Health: An Empirical Examination for U.S.
Measles.” Journal of Human Resources 31.3: pp. 611-630.
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Tiro, J.A., Meissner, H.I., Kobrin, S., Chollette, V. 2007 “What do Women in the U.S. Know
about Human Papillomavirus and Cervical Cancer?” Cancer Epidemiology, Biomarkers
and Prevention 16: pp. 288-294.
U.S. Cancer Statistics Working Group. United States Cancer Statistics: 1999–2012 Incidence
and Mortality Web-based Report. Atlanta: U.S. Department of Health and Human
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U.S. Department of Health and Human Services. National Center for Health Statistics. The 20122008 National Immunization Survey – Teen. Available at:
www.cdc.gov/nchs/nis/data_files_teen.htm.
U.S. Department of Health and Human Services. 2012. “Human Papillomavirus Fact Sheet,” last
updated 10/2012. http://www.hhs.gov/opa/reproductive-health/stis/hpv/, accessed May 4,
2016.
Virginia Department of Health. 2015. “School and Day Care Minimum Immunization
Requirements.” Last updated 9/2015.
http://www.vdh.virginia.gov/epidemiology/immunization/requirements.htm, accessed
April 19, 2016.
Rae Staben
May 5, 2016
48
Appendix
Additional Information
Table 10
Registries that did not meet USCS Publication Criteria
2012
Nevada
2011
Nevada
2010
2009
2008
2007
2006
2005
2004
2003
2002
DC, Mississippi, Tennessee, Virginia
2001
Tennessee, Virginia; Mississippi did not
submit
2000
Arkansas, Tennessee, Virginia;
Mississippi, South Dakota did not submit
1999
Arkansas, Tennessee, Virginia;
Mississippi, South Dakota did not submit
Source: CDC 2015f
Figure 4
ID YearSurveyed StateofResidence Age AgeatfirstHPVvaccinedose VaccinationStatus
1
2012
Kentucky
15
15
1
2
2012
California
13
11
1
3
2011
Idaho
13
12
1
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49
ID
1
1
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
Year StateofResidence Age AgeatfirstHPVvaccinedose VaccinationStatus
2012
Kentucky
15
15
1
2011
Kentucky
14
15
0
2010
Kentucky
13
15
0
2009
Kentucky
12
15
0
2008
Kentucky
11
15
0
2007
Kentucky
10
15
0
2006
Kentucky
9
15
0
----------------------------------1998
Kentucky
1
15
0
2012
California
13
11
1
2011
California
12
11
1
2010
California
11
11
1
2009
California
10
11
0
2008
California
9
11
0
----------------------------------2000
California
1
11
0
2011
Idaho
13
12
1
2010
Idaho
12
12
1
2009
Idaho
11
12
0
2008
Idaho
10
12
0
2007
Idaho
9
12
0
----------------------------------1999
Idaho
1
12
0
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50
STATA Commands for Data Expansion on Age
Table 11: Cervical Cancer Mortality Rates (per 100,000 people) by Percentile
Year
1999
25th Percentile
Rate States
2.4 Nevada
New Mexico
Wisconsin
50th Percentile
Rate States
2.8 California
2000
2.3
Maryland
Missouri
2.8
2001
2.5
Michigan
New Hampshire
Ohio
Pennsylvania
Virginia
2.8
2002
2.3
2.6
2003
2.1
Arizona
Iowa
New Hampshire
Virginia
Oregon
Pennsylvania
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2.5
Iowa
New Jersey
New York
California
Georgia
Idaho
Illinois
Kansas
Louisiana
North Carolina
Missouri
Nevada
Pennsylvania
Ohio
Virginia
May 5, 2016
75th Percentile
Rate States
3.2 Georgia
Missouri
New Jersey
New York
North Carolina
3.3 Arkansas
Florida
Illinois
3.1 New Jersey
New Mexico
Oklahoma
South Carolina
3
2.8
Alabama
Louisiana
Oklahoma
Florida
Illinois
99th Percentile
Rate States
5.8 District of Columbia
3.9
Alabama
5.2
Delaware
4.1
Arkansas
Mississippi
4
West Virginia
51
Utah
2004
2
Kentucky
Maine
Wisconsin
2.3
2005
2
Maryland
Oregon
2.6
2006
2.1
Colorado
Washington
2.5
2007
2
Arizona
Hawaii
Michigan
Pennsylvania
2.4
2008
2
2.4
2009
2
2010
1.8
Indiana
Iowa
Maine
Michigan
Nebraska
Idaho
Kansas
Michigan
Colorado
Nevada
2011
1.9
2.3
2012
1.9
Idaho
North Carolina
Washington
Washington
2.4
2.4
2.2
California
New Hampshire
North Carolina
Ohio
Pennsylvania
South Carolina
Georgia
Indiana
New Jersey
Oklahoma
California
Hawaii
Illinois
Indiana
New York
California
Florida
Maryland
New Hampshire
New Mexico
New Mexico
Oregon
West Virginia
2.8
California
Maryland
Pennsylvania
California
New Jersey
Ohio
Rhode Island
Tennessee
Oklahoma
Oregon
Pennsylvania
Tennessee
Indiana
Iowa
Louisiana
Missouri
Georgia
Louisiana
Nevada
West Virginia
3.7
Mississippi
2.9
New Mexico
North Carolina
Texas
3.7
Mississippi
2.8
Florida
New Mexico
Oklahoma
4.7
Mississippi
2.8
Ohio
Tennessee
4.2
Mississippi
2.8
Illinois
Ohio
3.8
Louisiana
2.7
New Mexico
Oklahoma
3.9
Arkansas
2.7
Georgia
Texas
4.3
Delaware
2.7
Florida
Illinois
4.8
West Virginia
2.7
Indiana
5.3
District of Columbia
Table 12: Cervical Cancer Incidence Rates (per 100,000 people) by Percentile
Year
1999
25th Percentile
Rate State
8.2 Alaska
2000
8.2
Iowa
2001
7.5
2002
7.4
Colorado
Wyoming
Colorado
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50th Percentile
Rate State
9.2 Missouri
Wisconsin
9.4 Pennsylvania
South Carolina
8.9
8.2
75th Percentile
State
California
Iowa
10 Alabama
California
New Jersey
New York
9.9 South Carolina
Rate
10.3
Maine
Michigan
Arkansas
May 5, 2016
9.2
New Jersey
99th Percentile
State
District of Columbia
West Virginia
13.9 District of Columbia
Rate
13.8
12.3
West Virginia
10.8
Alabama
52
South Dakota
Minnesota
Rhode Island
Wisconsin
Alaska
Vermont
Arizona
Washington
2003
6.7
2004
6.8
2005
6.9
2006
7
2007
6.5
2008
6.6
Maryland
South Dakota
7.8
2009
6.8
Iowa
2010
6.3
2011
2012
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Montana
Michigan
Tennessee
9.2
Louisiana
16.8
District of Columbia
Ohio
Pennsylvania
Alabama
Indiana
Pennsylvania
Alabama
9.2
Florida
Wyoming
Illinois
New Mexico
13.9
District of Columbia
13
District of Columbia
9
Kentucky
Nevada
10.5
Mississippi
Oklahoma
8.6
Delaware
Georgia
Georgia
Nevada
11.7
District of Columbia
11.1
Louisiana
7.6
Arizona
Ohio
Ohio
Pennsylvania
South Carolina
Washington
10.7
Oklahoma
Nebraska
7.4
Maryland
8.3
11.7
West Virginia
6.2
Arizona
Montana
Virginia
7.3
California
Maine
Nebraska
8.1
13.6
District of Columbia
6.3
6.3
Maryland
Wisconsin
Illinois
8.1
Delaware
Hawaii
Louisiana
Missouri
South Carolina
Alabama
Arkansas
Missouri
New Mexico
Pennsylvania
Alaska
District of Columbia
Oklahoma
Iowa
Rhode Island
Virginia
Virginia
8
7.8
8
8
7.8
7
May 5, 2016
8.8
8.6
8.9
9.6
West Virginia
53
Full Model Output
Table 13: Linear Probability Model
Cervical cancer mortality
rate (per 100,000)
Cervical cancer incidence
rate (per 100,000)
Female
Teen’s Race/Ethnicity
White, non-Hispanic
Black, non-Hispanic
Other, non-Hispanic +
Multiple
Education level of mother
12 Years
More than 12 years, noncollege grad
College graduate
Number of children under
18 in household
Two or Three
Four or more
Family income
$7501 - $10000
$10001 - $17500
$17501 - $20000
$20001 - $25000
(1)
No Fixed
Effects
(2)
State Fixed
Effects
(3)
Year Fixed
Effects
(4)
State and Year
Fixed Effects
0.0609***
-0.131***
-0.00469**
-0.000536
(0.00241)
-0.0879***
(0.00307)
-0.154***
(0.00183)
0.00200**
(0.00253)
0.00272**
(0.000927)
0.131***
(0.00213)
(0.00121)
0.143***
(0.00207)
(0.000780)
0.171***
(0.00176)
(0.00111)
0.171***
(0.00177)
-0.0279***
(0.00310)
0.00132
(0.00384)
-0.0219***
-0.000834
(0.00308)
0.00752*
(0.00389)
-0.000548
0.000149
(0.00238)
0.00886***
(0.00294)
-0.00266
0.00254
(0.00252)
0.0124***
(0.00316)
-0.00214
(0.00451)
(0.00437)
(0.00350)
(0.00361)
0.00128
(0.00394)
-0.00300
-0.000480
(0.00374)
-0.00278
-0.000998
(0.00293)
-0.00533*
-0.000599
(0.00296)
-0.00480
(0.00389)
-0.00974**
(0.00408)
(0.00368)
-0.0113***
(0.00386)
(0.00290)
-0.0174***
(0.00308)
(0.00292)
-0.0166***
(0.00310)
-0.00553**
(0.00217)
-0.0127***
(0.00375)
-0.00612***
(0.00206)
-0.0117***
(0.00357)
-0.00379**
(0.00169)
-0.00948***
(0.00283)
-0.00361**
(0.00169)
-0.00959***
(0.00284)
0.0199***
(0.00707)
0.0144**
(0.00642)
0.0175**
(0.00708)
0.0212***
0.0192***
(0.00670)
0.0138**
(0.00614)
0.0165**
(0.00671)
0.0190***
0.0177***
(0.00538)
0.0129**
(0.00501)
0.0129**
(0.00535)
0.0186***
0.0181***
(0.00539)
0.0132***
(0.00501)
0.0134**
(0.00537)
0.0194***
$25001 - $30000
$30001 - $35000
$35001 - $40000
$40001 - $50000
$50001 - $60000
$60001 - $75000
$75001+
School HPV requirement
State FE
Year FE
Constant
Observations
R-squared
(0.00681)
0.0112
(0.00699)
0.00130
(0.00743)
0.0158**
(0.00712)
0.00809
(0.00670)
0.00688
(0.00673)
0.00294
(0.00635)
-0.00208
(0.00572)
-0.0536***
(0.00765)
No
No
0.702***
(0.00858)
(0.00649)
0.0152**
(0.00667)
0.00753
(0.00711)
0.0192***
(0.00675)
0.0122*
(0.00640)
0.0122*
(0.00645)
0.0150**
(0.00606)
0.00704
(0.00550)
-0.428***
(0.00961)
Yes
No
1.872***
(0.0135)
(0.00522)
0.0162***
(0.00538)
0.00796
(0.00592)
0.0207***
(0.00546)
0.0185***
(0.00522)
0.0170***
(0.00524)
0.0216***
(0.00494)
0.0133***
(0.00449)
0.00867
(0.00606)
No
Yes
-0.147***
(0.00725)
(0.00523)
0.0169***
(0.00538)
0.00761
(0.00593)
0.0206***
(0.00547)
0.0182***
(0.00523)
0.0167***
(0.00525)
0.0211***
(0.00496)
0.0129***
(0.00452)
0.00860
(0.00773)
Yes
Yes
-0.167***
(0.0139)
419,123
419,123
419,123
0.079
0.171
0.470
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
419,123
0.471
Table 14: Lagged Linear Probability Model
(1)
(2)
1 Year Lag 2 Year Lag
1-year lagged mortality rate
1-year lagged incidence rate
0.00116
(0.00320)
0.00275**
(0.00133)
0.212***
(0.00215)
-0.00325
(0.00353)
0.00274*
(0.00162)
0.00195
(0.00354)
0.00211
(0.00150)
0.00193
(0.00329)
0.00249*
(0.00139)
0.212***
(0.00218)
0.00296
(0.00299)
0.00191
(0.00124)
2-year lagged incidence rate
3-year lagged mortality rate
3-year lagged incidence rate
Rae Staben
(4)
1,2, and 3
Year Lag
-0.000818
(0.00273)
0.00257**
(0.00118)
2-year lagged mortality rate
Female
(3)
3 Year
Lag
0.180***
(0.00186)
May 5, 2016
0.194***
(0.00198)
55
Teen’s Race/Ethnicity
White, non-Hispanic
0.00281
(0.00268)
Black, non-Hispanic 0.0134***
(0.00336)
Other, non-Hispanic + Multiple -0.00215
(0.00383)
Education level of mother
12 Years -0.000630
(0.00314)
More than 12 years, non-college grad -0.00517*
(0.00310)
College graduate -0.0175***
0.00295
0.00318
0.00314
(0.00289) (0.00316) (0.00320)
0.0146*** 0.0160*** 0.0160***
(0.00362) (0.00395) (0.00398)
-0.00228
-0.00284
-0.00288
(0.00412) (0.00450) (0.00456)
-0.000475 -0.000620 -0.000537
(0.00338) (0.00369) (0.00373)
-0.00555* -0.00598
-0.00592
(0.00335) (0.00366) (0.00370)
-0.0189***
-0.0205***
0.0207***
(0.00355) (0.00389) (0.00393)
(0.00329)
Number of children under 18 in household
Two or Three -0.00353** -0.00345* -0.00361* -0.00355
(0.00180) (0.00194) (0.00213) (0.00216)
Four or more -0.0101*** -0.0106***
-0.0114***
0.0113***
(0.00302) (0.00326) (0.00356) (0.00362)
Family Income
$7501 - $10000 0.0192*** 0.0211*** 0.0233*** 0.0230***
(0.00573) (0.00617) (0.00674) (0.00681)
$10001 - $17500 0.0138*** 0.0151*** 0.0164*** 0.0163***
(0.00532) (0.00573) (0.00627) (0.00633)
$17501 - $20000 0.0137**
0.0149** 0.0165** 0.0163**
(0.00570) (0.00615) (0.00672) (0.00679)
$20001 - $25000 0.0204*** 0.0228*** 0.0251*** 0.0250***
(0.00555) (0.00599) (0.00655) (0.00662)
$25001 - $30000 0.0177*** 0.0195*** 0.0211*** 0.0211***
(0.00572) (0.00618) (0.00676) (0.00683)
$30001 - $35000 0.00778
0.00872
0.00955
0.00943
(0.00630) (0.00680) (0.00743) (0.00752)
$35001 - $40000 0.0211*** 0.0230*** 0.0254*** 0.0250***
(0.00581) (0.00627) (0.00685) (0.00693)
$40001 - $50000 0.0190*** 0.0210*** 0.0233*** 0.0226***
(0.00556) (0.00600) (0.00656) (0.00663)
$50001 - $60000 0.0174*** 0.0194*** 0.0215*** 0.0212***
(0.00559) (0.00604) (0.00661) (0.00671)
$60001 - $75000 0.0221*** 0.0245*** 0.0269*** 0.0267***
(0.00528) (0.00570) (0.00624) (0.00632)
$75001+ 0.0132*** 0.0149*** 0.0163*** 0.0160***
(0.00480) (0.00519) (0.00568) (0.00574)
School HPV requirement
0.00903
0.0117
0.0130
0.0214*
(0.00853) (0.00951) (0.0108)
(0.0124)
Rae Staben
May 5, 2016
56
State FE
Year FE
Constant
Yes
Yes
-0.167***
(0.0148)
Observations
R-squared
Yes
Yes
-0.181***
(0.0158)
393,695
364,432
0.464
0.456
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Yes
Yes
-0.196***
(0.0173)
Yes
Yes
-0.239***
(0.0299)
331,944
0.445
309,383
0.444
Table 15: Logistic Model
(1)
No Fixed
Effects
No FE
Margins
Odds Ratio
(2)
State Fixed
Effects
State FE
Margins
Odds Ratio
(3)
Year Fixed
Effects
Year FE
Margins
Odds Ratio
(4)
State and Year
Fixed Effects
State and
Year FE
Margins
Odds Ratio
Vaccinated (if age at
first shot <= age)
Cervical cancer
mortality rate (per
100,000)
Cervical cancer
incidence rate (per
100,000)
Female
Teen’s
Race/Ethnicity
White, non-Hispanic
Black, non-Hispanic
Other, non-Hispanic
+ Multiple
Education level of
mother
12 Years
More than 12 years,
non-college grad
College graduate
Number of children
under 18 in
household
Two or Three
Four or more
1.561***
0.0631***
0.224***
-0.178***
0.927***
-0.00562***
0.937*
-0.00480*
(0.0252)
0.525***
(0.00225)
-0.0911***
(0.00616)
0.162***
(0.00326)
-0.217***
(0.0222)
1.025**
(0.00178)
0.00184**
(0.0330)
0.984
(0.00261)
-0.00120
(0.00366)
2.868***
(0.0603)
(0.000948)
(0.00237)
3.924***
(0.0848)
(0.00156)
(0.0111)
9.812***
(0.277)
(0.000804)
(0.0174)
9.884***
(0.280)
(0.00131)
0.809***
(0.0176)
1.012
(0.0271)
0.855***
1.001
(0.0241)
1.077**
(0.0330)
0.989
1.013
(0.0307)
1.139***
(0.0427)
0.962
1.052
(0.0340)
1.200***
(0.0484)
0.972
(0.0271)
(0.0336)
(0.0432)
(0.0451)
1.002
(0.0273)
0.965
0.999
(0.0286)
0.978
0.991
(0.0370)
0.925**
0.997
(0.0376)
0.933*
(0.0260)
0.919***
(0.0262)
(0.0277)
0.904***
(0.0272)
(0.0345)
0.779***
(0.0309)
(0.0352)
0.789***
(0.0316)
0.965**
(0.0146)
0.913***
0.962**
(0.0155)
0.916***
0.966
(0.0208)
0.886***
0.968
(0.0210)
0.885***
Rae Staben
May 5, 2016
57
Family Income
$7501 - $10000
$10001 - $17500
$17501 - $20000
$20001 - $25000
$25001 - $30000
$30001 - $35000
$35001 - $40000
$40001 - $50000
$50001 - $60000
$60001 - $75000
$75001+
School HPV
requirement
State FE
Year FE
Constant
Observations
(0.0242)
(0.0256)
(0.0323)
(0.0325)
1.157***
(0.0571)
1.114**
(0.0506)
1.135**
(0.0561)
1.156***
(0.0552)
1.073
(0.0533)
1.009
(0.0533)
1.116**
(0.0565)
1.061
(0.0501)
1.044
(0.0495)
1.024
(0.0459)
0.990
(0.0403)
0.663***
1.180***
(0.0620)
1.116**
(0.0546)
1.145***
(0.0599)
1.176***
(0.0602)
1.136**
(0.0599)
1.060
(0.0604)
1.169***
(0.0630)
1.127**
(0.0568)
1.118**
(0.0571)
1.147***
(0.0549)
1.071
(0.0470)
0.00792***
1.266***
(0.0871)
1.176**
(0.0767)
1.181**
(0.0818)
1.284***
(0.0864)
1.237***
(0.0876)
1.109
(0.0851)
1.317***
(0.0932)
1.308***
(0.0867)
1.272***
(0.0857)
1.358***
(0.0860)
1.216***
(0.0711)
1.068
1.276***
(0.0882)
1.183**
(0.0773)
1.190**
(0.0829)
1.298***
(0.0879)
1.252***
(0.0889)
1.103
(0.0852)
1.317***
(0.0937)
1.304***
(0.0868)
1.268***
(0.0860)
1.351***
(0.0862)
1.208***
(0.0713)
0.900
(0.0266)
No
No
7.704***
(0.000563)
Yes
No
4.435e+07***
(0.0798)
Yes
Yes
0.000124***
(0.473)
(6.595e+06)
(0.0621)
No
Yes
8.14e05***
(2.33e-05)
419,123
419,123
419,123
419,123
419,123
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(4.36e-05)
419,123
419,123
419,123
Table 16: Lagged Logistic Model
(1)
1 Year
Lag
1 Year Lag
Margins
Odds
Ratio
(2)
2 Year
Lag
2 Year
Lag
Margins
Odds
Ratio
(3)
3 Year
Lag
Odds
Ratio
3 Year
Lag
Margins
(4)
1,2, 3
Year Lag
1,2,3 Year
Lag Margins
Odds
Ratio
Vaccinated
(if age at first
shot <= age)
1-year
lagged
mortality rate
Rae Staben
0.924**
-0.00623**
0.900***
-0.00987***
(0.0327)
(0.00280)
(0.0332)
(0.00346)
May 5, 2016
58
1-year
lagged
incidence
rate
0.994
-0.000508
(0.0177)
(0.00141)
2-year
lagged
mortality rate
2-year
lagged
incidence
rate
Other, nonhispanic +
Multiple
Education
level of
mother
12 Years
More than 12
years, noncollege grad
College
graduate
(0.00177)
-0.00677*
(0.0355)
(0.00314
)
0.000671
(0.0367)
(0.00370)
0.996
-0.000376
(0.00150
)
(0.0183)
(0.00172)
0.992
3-year
lagged
incidence
rate
Black, nonhispanic
(0.0187)
0.930*
-0.00294
3-year
lagged
mortality rate
Teen’s
Race/Ethnici
ty
White, nonhispanic
-0.000668
0.966
(0.0174)
Female
0.993
0.967
-0.00313
0.951
-0.00475
(0.0350)
1.015
(0.00340)
0.00135
(0.0352)
1.024
(0.00347)
0.00218
(0.00154)
(0.0180)
10.07***
(0.291)
(0.00165)
9.942***
(0.282)
10.01***
(0.285)
(0.0166)
10.07***
(0.286)
1.053
1.051
1.050
1.049
(0.0340)
1.202***
(0.0340)
1.203***
(0.0340)
1.201***
(0.0344)
1.201***
(0.0485)
0.974
(0.0486)
0.974
(0.0486)
0.971
(0.0489)
0.971
(0.0451)
(0.0452)
(0.0451)
(0.0457)
0.995
(0.0375)
0.933*
0.997
(0.0376)
0.933*
0.995
(0.0376)
0.934*
0.995
(0.0380)
0.935*
(0.0352)
0.792***
(0.0353)
0.791***
(0.0354)
0.791***
(0.0358)
0.792***
(0.0317)
(0.0317)
(0.0318)
(0.0322)
Number of
children
under 18 in
household
Rae Staben
May 5, 2016
59
Two or
Three
Four or more
Family
Income
$7501 $10000
$10001 $17500
$17501 $20000
$20001 $25000
$25001 $30000
$30001 $35000
$35001 $40000
$40001 $50000
$50001 $60000
$60001 $75000
$75001+
School HPV
requirement
State FE
Year FE
Constant
Observations
Rae Staben
0.969
0.970
0.971
0.971
(0.0210)
0.885***
(0.0325)
(0.0210)
0.886***
(0.0326)
(0.0210)
0.888***
(0.0327)
(0.0213)
0.886***
(0.0332)
1.274***
1.274***
1.276***
1.272***
(0.0882)
1.181**
(0.0883)
1.186***
(0.0886)
1.185***
(0.0890)
1.184**
(0.0772)
1.184**
(0.0776)
1.186**
(0.0776)
1.186**
(0.0783)
1.183**
(0.0826)
1.292***
(0.0828)
1.298***
(0.0829)
1.300***
(0.0835)
1.299***
(0.0875)
1.248***
(0.0880)
1.253***
(0.0883)
1.250***
(0.0891)
1.249***
(0.0886)
1.098
(0.0891)
1.101
(0.0890)
1.099
(0.0899)
1.097
(0.0848)
1.307***
(0.0851)
1.308***
(0.0852)
1.311***
(0.0859)
1.304***
(0.0930)
1.297***
(0.0932)
1.298***
(0.0936)
1.301***
(0.0941)
1.290***
(0.0863)
1.261***
(0.0866)
1.266***
(0.0868)
1.269***
(0.0870)
1.262***
(0.0855)
1.345***
(0.0859)
1.352***
(0.0862)
1.350***
(0.0869)
1.347***
(0.0858)
1.202***
(0.0710)
0.916
(0.0863)
1.207***
(0.0714)
0.944
(0.0863)
1.206***
(0.0714)
0.977
(0.0872)
1.202***
(0.0719)
0.821*
(0.0777)
Yes
Yes
0.000134*
**
(4.27e-05)
(0.0791)
Yes
Yes
0.000145
***
(4.80e05)
(0.0830)
Yes
Yes
0.000127
***
(3.92e05)
(0.0867)
Yes
Yes
0.000246
***
(0.00010
6)
392,906
392,906
363,578
363,578 328,553
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
May 5, 2016
328,553
309,383
309,383
60
Table 17: Cox Proportional Hazards Model
Cervical cancer mortality rate
(per 100,000)
Cervical cancer incidence rate
(per 100,000)
Female
Teen’s Race/Ethnicity
White, non-Hispanic
Black, non-Hispanic
Other, non-Hispanic + Multiple
Education level of mother
12 Years
More than 12 years, non-college
grad
College graduate
Number of children under 18 in
household
Two or Three
Four or more
(1)
No Fixed
Effects
(2)
State Fixed
Effects
(3)
Year Fixed
Effects
1.037
0.747***
0.866***
(4)
State and
Year Fixed
Effects
0.953
(0.0238)
0.823***
(0.0243)
0.596***
(0.0198)
0.965***
(0.0301)
0.945***
(0.00846)
5.027***
(0.128)
(0.0102)
5.093***
(0.129)
(0.00995)
5.191***
(0.129)
(0.0152)
5.217***
(0.129)
0.673***
(0.0193)
0.794***
(0.0286)
0.799***
(0.0362)
0.737***
(0.0225)
0.865***
(0.0332)
0.817***
(0.0389)
0.732***
(0.0212)
0.874***
(0.0321)
0.814***
(0.0367)
0.769***
(0.0240)
0.933*
(0.0370)
0.813***
(0.0387)
0.893***
(0.0333)
0.887***
0.897***
(0.0338)
0.887***
0.878***
(0.0324)
0.828***
0.891***
(0.0337)
0.842***
(0.0330)
0.955
(0.0366)
(0.0336)
0.949
(0.0367)
(0.0309)
0.856***
(0.0326)
(0.0322)
0.875***
(0.0340)
1.163***
(0.0242)
1.129***
(0.0397)
1.161***
(0.0244)
1.138***
(0.0402)
1.121***
(0.0239)
1.064*
(0.0378)
1.126***
(0.0243)
1.078**
(0.0386)
0.975
(0.0643)
0.965
(0.0565)
0.915
(0.0609)
0.886*
(0.0581)
0.868**
(0.0558)
0.721***
0.999
(0.0667)
0.975
(0.0584)
0.934
(0.0627)
0.906
(0.0601)
0.886*
(0.0576)
0.733***
0.989
(0.0669)
0.969
(0.0590)
0.949
(0.0620)
0.923
(0.0610)
0.900
(0.0592)
0.759***
1.014
(0.0692)
0.978
(0.0610)
0.962
(0.0639)
0.937
(0.0632)
0.913
(0.0610)
0.760***
Family Income
$7501 - $10000
$10001 - $17500
$17501 - $20000
$20001 - $25000
$25001 - $30000
$30001 - $35000
Rae Staben
May 5, 2016
61
(0.0530)
(0.0559)
$35001 - $40000 0.759***
0.786***
(0.0510)
(0.0535)
$40001 - $50000 0.688***
0.713***
(0.0421)
(0.0443)
$50001 - $60000 0.594***
0.622***
(0.0371)
(0.0395)
$60001 - $75000 0.597***
0.624***
(0.0350)
(0.0372)
$75001+ 0.718***
0.738***
(0.0375)
(0.0394)
School HPV requirement
0.717***
0.290***
(0.0407)
(0.0248)
State Fixed Effects
No
Yes
Year Fixed Effects
No
No
Observations
1,188,320
1,188,320
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(0.0582)
0.815***
(0.0561)
0.752***
(0.0475)
0.669***
(0.0427)
0.672***
(0.0404)
0.804***
(0.0432)
0.831***
(0.0498)
No
Yes
1,188,320
(0.0603)
0.826***
(0.0577)
0.760***
(0.0488)
0.680***
(0.0442)
0.676***
(0.0415)
0.801***
(0.0442)
0.898
(0.0807)
Yes
Yes
1,188,320
Table 18: Lagged Cox Proportional Hazards Model
(1)
(2)
(3)
(4)
1 Year Lag 2 Year Lag 3 Year Lag 1, 2, and 3
Year Lag
0.968
0.969
(0.0307)
(0.0318)
0.988
0.981
(0.0165)
(0.0176)
1.025
1.025
(0.0366)
(0.0389)
0.976
0.973
(0.0167)
(0.0173)
1.006
1.013
(0.0349)
(0.0358)
1.000
1.000
(0.0155)
(0.0165)
5.228*** 5.239*** 5.251*** 5.254***
(0.130)
(0.130)
(0.131)
(0.133)
1-year lagged mortality rate
1-year lagged incidence rate
2-year lagged mortality rate
2-year lagged incidence rate
3-year lagged mortality rate
3-year lagged incidence rate
Female
Teen’s Race/Ethnicity
White, non-Hispanic
Black, non-Hispanic
Other, non-Hispanic + Multiple
0.768***
(0.0239)
0.933*
(0.0370)
0.814***
(0.0386)
0.767***
(0.0239)
0.931*
(0.0369)
0.813***
(0.0386)
0.766***
(0.0239)
0.929*
(0.0369)
0.809***
(0.0385)
0.768***
(0.0242)
0.931*
(0.0372)
0.810***
(0.0391)
0.890***
0.893***
0.891***
0.893***
Education level of mother
12 Years
Rae Staben
May 5, 2016
62
(0.0336)
0.843***
(0.0322)
0.878***
(0.0340)
(0.0337)
0.843***
(0.0323)
0.875***
(0.0340)
(0.0337)
0.844***
(0.0323)
0.877***
(0.0341)
(0.0341)
0.844***
(0.0327)
0.878***
(0.0345)
1.126***
(0.0243)
1.079**
(0.0386)
1.127***
(0.0243)
1.081**
(0.0387)
1.125***
(0.0243)
1.079**
(0.0387)
1.127***
(0.0247)
1.080**
(0.0394)
1.012
(0.0689)
0.978
(0.0608)
0.960
(0.0636)
0.937
(0.0629)
0.907
(0.0606)
0.757***
(0.0599)
0.820***
(0.0573)
0.757***
(0.0485)
0.677***
(0.0440)
0.676***
(0.0413)
0.797***
(0.0438)
1.014
(0.0882)
Yes
Yes
1.012
(0.0692)
0.983
(0.0613)
0.962
(0.0639)
0.943
(0.0635)
0.910
(0.0610)
0.761***
(0.0604)
0.824***
(0.0577)
0.761***
(0.0489)
0.682***
(0.0444)
0.680***
(0.0417)
0.803***
(0.0444)
1.048
(0.0904)
Yes
Yes
1.015
(0.0693)
0.981
(0.0612)
0.963
(0.0640)
0.942
(0.0634)
0.907
(0.0607)
0.760***
(0.0603)
0.828***
(0.0579)
0.761***
(0.0489)
0.682***
(0.0444)
0.676***
(0.0415)
0.800***
(0.0442)
1.075
(0.0920)
Yes
Yes
1.012
(0.0697)
0.982
(0.0618)
0.964
(0.0645)
0.942
(0.0641)
0.907
(0.0614)
0.759***
(0.0609)
0.826***
(0.0585)
0.760***
(0.0494)
0.679***
(0.0448)
0.674***
(0.0419)
0.799***
(0.0446)
0.987
(0.104)
Yes
Yes
1,103,813 1,010,274
Robust see form in parentheses
*** p<0.01, ** p<0.05, * p<0.1
911,066
855,442
More than 12 years, non-college grad
College graduate
Number of children under 18 in household
Two or Three
Four or more
Family Income
$7501 - $10000
$10001 - $17500
$17501 - $20000
$20001 - $25000
$25001 - $30000
$30001 - $35000
$35001 - $40000
$40001 - $50000
$50001 - $60000
$60001 - $75000
$75001+
School HPV requirement
State FE
Year FE
Observations
Rae Staben
May 5, 2016
63