The Anti-Vaxxer movement

The Anti Vaxxer Movement: finding patterns in demographics 1

The Anti-Vaxxer Movement:

Finding Patterns in Demographics

Rebecca Stephanie Lemick

Whitworth University

Spring 2014


Introduction:

This paper aims to look at the trends between the group dubbed by the media as “Anti-

Vaxxers”, which is a group consisting mainly of parents who are against vaccinations for various

reasons such as health concerns of ingredients contained inside vaccines, correlation of the

Measles, Mumps and Rubella [MMR] vaccine and autism, and other conspiracy theories. While

measles was announced in 2000 to be officially eliminated in the U.S (CDC, 2015), as a result of

unvaccinated children wandering the Disneyland’s California adventure park, anti-vaxxers have

been making headlines recently due to an outbreak of measles (Aliferis, 2015) bringing up

criticisms of the legality of not vaccinating children per the CDC’s recommendation. However

the concepts of anti-vaxxers itself not new and dates back to 19th century when vaccinations were

first introduced to the public (Wolfe, R.M, 2002).

Present arguments against vaccinations by parents are astoundingly similar to those of

the 19th century. Vaccinations became mandatory circa the 1840’s and due to parent’s concerns

of ingredients found within the vaccines, many anti-vaccination groups arose the 1870s. Leading

to the vaccination law amendment in 1898, allowing parents to choose to have their children be

exempt from mandatory vaccination. Similarly, California, Washington, Oregon and 17 other

states by law allow children to be exempt from vaccines due to the “personal beliefs” of parents.

These laws have come under intense scrutiny by the media and legislators following the

nationwide outbreak of measles. A recently proposed bill by pediatrician and Senator Dr.

Richard Pan, was designed to enforce vaccination for the State of California know as Senate Bill


227 and remove controversial exemption. However that came to a standby when legislators could

not decide whether or not unvaccinated children should be denied access to public schools.

The issue of mandatory vaccinations is a hot topic, with many parents fearful of the health risks

imposed by their children coming into contact with unvaccinated children. Whereas anti-vaxxers

fear vaccinating their children due to development of possible side effects, health risks by being

exposed to formaldehyde, thimerosal and MSG’s, which are believed to be lethal if injected. The

autism argument correlated to the MMR vaccine for anti-vaxxers may be the most commonly

known, however most anti-vaxxers base their decision based upon the side effects as a result of

the ingredients contained within vaccines, which contains valid concerns (Bachmair, 2012).

However some arguments are quite ludicrous and contain the general notion that the U.S

government in conjunction with large corporations such as McDonalds and Coca Cola are

attempting to mind control society.

Regardless of the arguments against vaccinations, the pro-vaccination argument merits

larger health concerns and safety. Diseases such as measles are preventable and left untreated

can pose serious health risks and high infant mortality rates, especially for children infected

under the age of 15 months. These children unfortunately are too young to receive the 3 dose

vaccinations recommended by the CDC and are thereby vulnerable to the disease. Albeit that

measles rarely kills in industrialized nations, it is important to note that measles is prevalent and

strives in overpopulated and underdeveloped regions. For example, in Africa child mortality

rates due to measles are high enough that every one minute a child dies.

So if measles carries such a large death toll and health, why are we allowing it to

resurface? The answer goes beyond politics and is better understood in terms of economics and


income demographics. Anti-vaxxers are simply white upper-middle class Americans that can

afford to take such risks with their children health’s’, disregarding the public externalities that

arise from their personal choice.

Literature Review:

Finding academic literature supporting the anti-vaxxer movement is like finding a needle

in a haystack. Despite the article’s retraction from the Lancet in 2010, Wakefield’s 19998 study

is the basis of the autism and MMR vaccine argument by anti-vaxxers. Wakefield’s study was

initially meant to observe the correlation of gastrointestinal disorders with vaccines. The study

developed into observing behavior as well and from the sample of 12 children, the study

concluded that 8 developed the symptoms and characteristics of autism and developmental

regression after exposure to the MMR vaccine, in previously normal children (Wakefield et al,

1998). The problem with Wakefield’s study was that it provided too small of a sample size, and

not enough methodology describing the methods used by its clinical psychologists identifying

what these symptoms or characteristics of a developmental regression disorder. Furthermore

without the methodology, the questions could have been loaded so that they may have caused an

answer that would jump to conclusions of how these children developed autistic characteristics.

Following Wakefield’s study, the Lancet published another article examining evidence of autism

linked as a result of vaccination with a larger sample size (Taylor et al, 1999). The sample size

consisted of 498 causes of autism: 261 of core autism, 166 of atypical autism and 71 of


Asperger’s syndrome. Within that sample 293 were confirmed by clinical psychologist as

autistic, and showed no developmental regression after MMR vaccine or causal association

between MMR vaccines and Autism.

Preceding the retraction of Wakefield’s study, the Clinical Infectious Diseases published

a study discussing the three theories regarding the combination of MMR vaccines causing autism

by damaging the intestines, thimerosal a toxic preservative found commonly in vaccine, and the

simultaneous administration of vaccines weakening the immune system. The sample population

here was 50,000 British children who received the MMR vaccine and the study performed a time

trend analysis of 3 million people during 1988 and 1999, with 20 studies being performed in

several countries by different investigators. The study concluded that there is no epidemiologic

or statistical data that show that either thimerosal or MMR is a cause for autism (Gerber, 2009).

Similarly Blume’s study in 2006 examined specifically why parents in industrialized

nations were choosing not vaccinate their children using a sample from population data of parent

population in UK during the late 19th century which was the rise of the anti-vaccination

movement, and comparing it to a sample a century later from the Netherlands during the 1970s.

Blume also focused on income and found that parents with a lower income vaccinated their

children as mandated by law of the state, whereas those with higher incomes did not, as they

could afford the risk of being fined (Blume, 2006). The major finding of Blume’s study was that

“Vaccination is recommended by the State for the benefit of the community, and where it causes

injury, the State ought to provide compensation as part of the cost of providing protection for the

community as a whole, (Blume p. 642, 2006). Concluding that vaccinations are good and tend to


fight diseases rather than cause them, and that furthermore supporting the movement, as a social

movement will have only yield a negative effect, giving more power to the anti-vaccination

movement rather than discrediting it. This study really focused on why the choice of vaccinating

your children is important, and why its important for people to be well informed rather than

blindly following social movements and only having one-sided information.

Kata’s article in Vaccine Journal examined how anti-vaccination groups due to the

availability of the internet are mixing information and personal opinions with scientific evidence

to support their stance against vaccines (Kata p. 3778, 2012). As many people use the Internet to

research information online, the Internet is thus a medium that is allowing the resurfacing of the

anti-vaccination movement as more people become misinformed and misconstrue data thereby

making the decision to not vaccinate their children. While Kata’s article doesn’t go into depth in

obtaining statistics or providing a sample, the author does look at the 4 tropes that anti-vaxxers

use in constructing and validating their arguments and present an academic analysis at the

arguments presented. These 4 tropes are how the anti-vaxxers skew science, shift hypotheses,

censor and attack the opposition. Kata concludes her argument stating that ““With the anti-

vaccination movement embracing the postmodern paradigm, which inherently questions an

authoritative, science-based approach, “facts” may be reinterpreted as just another “opinion”.

This issue is as much about the cultural context surrounding healthcare, perceptions of risk, and

trust in expertise, as it is about vaccines themselves. For these reasons it is possible the minds of

deeply invested anti-vaccine activists may never be changed; therefore it is for both the

laypersons with genuine questions or worries about vaccines and the healthcare professionals


who work to ease their fears that keeping abreast of the methods of persuasion discussed here is

essential. Recognizing anti-vaccine tactics and tropes is imperative, for an awareness of the

disingenuous arguments used to cajole and convert audiences gives individuals the tools to think

critically about the information they encounter online. It is through such recognition that truly

informed choices can then be made, (Kata p. 3789, 2012)

Further discrediting Wakefield and stemming of Kata is Camargo’s 2015 study published

in the American Journal of Public Health. The article examined the recent resurgence of

preventable diseases that had been previously eradicated as a result of the high percentage of

vaccination rates. However due to the anti-vaxxer movement spreading via social media and

online, public health is also affected and not just individuals’ children. This article went into

depth disproving Wakefield with statistical data with a large sample size of 27,749 Canadian

participants. It again concluded as most scientific studies have that vaccines do not cause autism,

but illustrated that despite this well published fact the problem goes beyond science as the anti-

vaxxers have a general mistrust of science and are over-estimating their competence as informed

parents due to the information that can be found via the internet (Camargo p.234, 2015).

While insightful and full with relevant data, most of these studies all are biased for

vaccinations and many are aimed at disproving Wakefield’s study but few to none studies are

aimed at the other arguments presented by anti-vaxxers. Only 2 really touched on the ingredient

thimerosal, which is contained in vaccines, but none observe the effect of MSG’s or

concentrations of formaldehyde in vaccines which vary from vaccine to vaccine. Further adding

to the bias of the scientific community is that there is no other academic study focusing on the

negative effects of vaccines, despite multiple anti-vaccination advocated proclaiming that there


are. In my attempts to reach out the anti-vaccination community to find more literature, I was

only referenced to one book (Bachmair, 2012) which contains sample data that I used for my

regression analysis. Albeit the controversial nature of an academic study illustrating the negative

effects of vaccinations, I would have greatly like to have found one which would have greatly

aided in my research and helping form a less biased literature review.

Methodology:

For my data, I have collected statistics provided by the CDC regarding vaccination rates

by state, demographic information of those vaccinated for MMR, insurance coverage and types

of those vaccinated and economic data such as the total amount collected by each State’s

personal income tax. All of the data from the CDC include all of the vaccinations recommended

by the CDC for children collected through the 2013’s National Health Survey and the GeoFred

website. Taking these excel sheets, I have compressed them into one excel file that I will use to

run linear regressions via STATA focusing on independent variables being which states

vaccines have the lowest vaccination rates, which have the lowest vaccination rates, which have

the highest vaccination rates, which demographic group has the lowest vaccination rates,

vaccination rates by those below poverty level, and the dependent variable being the State’s

personal income tax collection. Despite having nationwide data and my compiled data included

all 50 states, I focused my research to the domestic 48 states and did not include U.S territories

such as Guam, Puerto Rico, or the Virgin Islands. I hypothesize that there is a correlation

between income, race and vaccination rates. My null hypothesis is that those of other ethnicities,


State personal income tax and insurance will play a significant role in vaccination rates, whereas

those who are white the state personal income tax will play a minimal role, as will insurance. My

alternative hypothesis is thus that those who are below the poverty line will have higher

vaccination rates than white demographic.

Data:

Figure 1

Figure 2


Figure 3

Figure 4


Figure 5

Results:

The data at first was very hard to format and took time to format correctly due to how it

was imported into STATA, however once the data was re-entered manually from excel the data

began to run properly in the regressions. graphed fine. As you can see in figure 4, there is no

statistical significance in being white and being vaccinated, however there is a correlation of

being Hispanic and being vaccinated than there is over being black. I believe this gap in the data

however comes from the importation from the CDC, as some data for some states was not

available and was entered in automatically as 0. Furthermore I lacked binary variables in my

data, and all data was ranked on percentage out of 100. In the appendix of this paper, you will

find my data from my consolidated excel file.


In figure 5 it is clear that the total number of white’s not vaccinated is not statistically

significant with state income tax which was actually quite surprising, however all the other

independent variables were statistically significant. Poverty line here however was also not

statistically significant for white people not being vaccinated which thus proved further to me

that those who do not vaccinate are not dependent on income level.

Conclusion:

This data required several accounting for errors, due to lack of information for some

demographics, and lack of personal income tax for states that don’t collect, I understand that

there are some errors in my data. That being said I do believe my data still points in the right

direction, despite my learning curve working with STATA, my original imported excel data still

shows that states that have higher personal income taxes have lower vaccination rates for white

demographics. If I could modify my data to include the states subcategory I would but it kept

being omitted whenever I attempted to run my regression through STATA. I believe that if I

could fix that data, my regression would show more statistical significances in relation to types

of insurances coverages, demographics and income levels. I also recommend that legislators

need to look into demographics of students in public schools that are not vaccinated, as if

majority of those who are part of the anti-vaxxer group are indeed upper middle class income as

my data and the literature review suggest, then it is more likely that those students attend private

schools and not public schools. If parents can gamble to risk exposure of diseases to other

students it should be done so in a setting, where other parents are willingly paying for the

education their kids receive.


Bibliography:

Aliferis, L. (2015, January 22). Disneyland Measles Outbreak Hits 59 Cases And Counting. Retrieved March 14, 2015, from http://www.npr.org/blogs/health/2015/01/22/379072061/disneyland-measles-outbreak-hits-59-cases-and-counting

Bachmair, A., & Ryazanova, A. (2012). Appendix. In Vaccine free: 111 stories of unvaccinated children. CreateSpace Independent Publishing Platform.

Behrmann, Jason. (2010, January). The anti-vaccination movement and resistance to allergen-immunotheraphy: A guide for clinical allergists. Allergy, Asthma & Clinical Immunology, 6:26. 1-11.

Blume, Stewart. (2006, February). Anti-Vaccination Movements and their interpretations. Social Science & Medicine. 62:3, 628-642.

Camargo, Jr. Kenneth, Grant, Roy. (2015, February). Public Health, Science and Policy Debate. American Journal of Public Health, 105:2. 232-235

CDC. (2015, April 17) Frequently Asked Questions about Measles in the U.S. (Retrieved April 22, 2015, from http://www.cdc.gov/measles/about/faqs.html

Gerber, Jeffrey. Offit, Paul. (2009). Vaccines and Autism: A Tale of Shifting Hypotheses. Clinical Infectious Diseases, 48:4. 456-461.

Kata, Anna. (2012, May). Anti-vaccine activists, Web 2.0, and the postmodern paradigm – An overview of tactics and tropes used online by the anti-vaccination movement. Vaccine, 30:25.3778-3789

Taylor, B. Miller, E., Farrington CP, Petropoulos MC, Favot-Mayaud I, Li, J and Waight,Pa. (1999, June) Autism and measles, mumps, and rubella vaccine: no epidemiological evidence for acausal association. Lancet, 353:9169. 2026-2029

Wakefield, Aj. Et Al. (1998, July). Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. Lancet, 352: 9123. 234-235

Wolfe, R. M., & Sharp, L. K. (2002). Anti-vaccinationists past and present.BMJ : British Medical Journal, 325(7361), 430–432.


Appendix:

Region CovInsured NoCov StatePersTax TotNoVacc TotVacc NoVaccWhiteUS TOTAL 85.9 14.1 $310,828,815.00 9.0 91.0Alabama 85.2 14.8 $3,206,583.00 4.5 95.5Alaska 81.0 19.0 $0.00 5.9 94.1Arizona 82.0 18.0 $3,462,413.00 12.3 87.7Arkansas 81.6 18.4 $2,602,160.00 5.8 94.2California 82.1 17.9 $67,995,659.00 14.6 85.4Colorado 86.3 13.7 $5,658,457.00 14.0 86.0Connecticut 91.9 8.1 $7,772,602.00 4.0 96.0Delaware 89.2 10.8 $1,040,341.00 1.8 98.2District of Columbia 92.1 7.9 $0.00 0.7 99.3Florida 78.5 21.5 $0.00 6.6 93.4Georgia 80.8 19.2 $8,965,572.00 6.1 93.9Hawaii 92.3 7.7 $1,745,461.00 3.4 96.6Idaho 84.1 15.9 $1,338,075.00 8.9 91.1Illinois 86.4 13.6 $16,058,396.00 5.5 94.5Indiana 86.6 13.4 $4,896,317.00 4.4 95.6Iowa 89.9 10.1 $3,197,578.00 1.6 98.4Kansas 87.4 12.6 $2,511,660.00 5.9 94.1Kentucky 84.3 15.7 $3,749,258.00 5.4 94.6Louisiana 81.7 18.3 $2,753,680.00 6.8 93.2Maine 90.5 9.5 $1,414,110.00 4.5 95.5Maryland 87.6 12.4 $7,773,773.00 0.3 99.7Massachusetts 95.9 4.1 $13,246,221.00 0.6 99.4Michigan 89.1 10.9 $7,874,712.00 10.8 89.2Minnesota 91.7 8.3 $9,528,454.00 3.7 96.3Mississippi 84.7 15.3 $1,667,344.00 1.8 98.2Missouri 86.7 13.3 $5,361,976.00 4.9 95.1Montana 81.9 18.1 $1,063,261.00 7.5 92.5Nebraska 86.7 13.3 $2,124,164.00 7.5 92.5Nevada 76.5 23.5 $92,743.00 6.1 93.9New Hampshire 88.0 12.0 $11,973,673.00 6.3 93.7New Jersey 86.0 14.0 $1,297,493.00 4.4 95.6New Mexico 78.1 21.9 $42,964,774.00 6.3 93.7New York 88.7 11.3 $10,390,520.00 2.2 97.8North Carolina 82.8 17.2 $498,528.00 4.0 96.0


North Dakota 88.5 11.5 $8,424,843.00 4.8 95.2Ohio 87.7 12.3 $2,962,128.00 14.0 86.0Oklahoma 82.8 17.2 $6,649,418.00 10.2 89.8Oregon 84.6 15.4 $10,809,736.00 15.0 85.0Pennsylvania 88.0 12.0 $1,088,405.00 6.7 93.3Rhode Island 87.7 12.3 $3,422,532.00 4.4 95.6South Carolina 85.7 14.3 $0.00 5.5 94.5South Dakota 85.6 14.4 $0.00 6.9 93.1Tennessee 86.1 13.9 $239,219.00 7.7 92.3Texas 75.4 24.6 $0.00 4.5 95.5Utah 85.6 14.4 $2,889,912.00 11.0 89.0Vermont 93.0 7.0 $675,240.00 12.8 87.2Virginia 87.5 12.5 $10,877,689.00 4.4 95.6Washington 86.4 13.6 $0.00 10.4 89.6West Virginia 85.4 14.6 $1,770,466.00 8.2 91.8Wisconsin 90.3 9.7 $6,793,269.00 6.8 93.2Wyoming 84.6 15.4 $0.00 5.6 94.4

Region White Black Hisp

PovLine

NoPovLine

TotInsured

Public

Private

Mixed Other

US TOTAL 90.4 93.4 94.6 92.6 7.4 91.9 93.3 93.6 94.3 86.5

Alabama 90.1 0.0 0.0 100.0 0.0 89.7 0.0 96.7 0.0 0.0Alaska 90.0 0.0 0.0 98.7 1.3 90.5 96.6 90.4 0.0 95.5

Arizona 79.1 0.0 98.6 96.5 3.5 91.4 99.3 92.9 0.0 0.0

Arkansas 91.5 0.0 0.0 99.2 0.8 88.3 98.8 0.0 93.9 0.0

California 80.0 0.0 97.0 0.0 0.0 90.7 0.0 92.6 93.4 0.0

Colorado 83.1 0.0 98.0 98.0 2.0 86.0 0.0 86.0 0.0 0.0

Connecticut 80.2 0.0 97.

6 0.0 0.0 91.4 0.0 91.7 0.0 0.0

Delaware 95.6 98.5 99.6 97.9 2.1 94.8 0.0 96.0 0.0 93.3

District of Columbia 99.5 94.7 95.

1 97.7 2.3 96.2 95.6 98.5 0.0 92.0

Florida 90.4 0.0 95.5 95.5 4.5 93.4 0.0 91.7 0.0 0.0

Georgia 95.4 95.0 0.0 99.0 1.0 93.9 0.0 96.6 0.0 0.0

Hawaii 0.0 0.0 97.8 89.9 10.1 92.8 0.0 91.9 0.0 94.2


Idaho 89.7 0.0 95.3 98.6 1.4 91.1 0.0 93.5 94.3 91.7

Illinois 91.1 93.4 98.6 96.7 3.3 91.4 94.0 92.7 97.0 0.0

Indiana 93.3 0.0 0.0 99.4 0.6 92.0 94.2 92.7 94.9 88.3Iowa 96.7 0.0 0.0 100.0 0.0 94.5 0.0 95.0 98.0 0.0

Kansas 90.2 0.0 0.0 96.5 3.5 89.4 97.7 92.5 0.0 0.0Kentucky 89.7 0.0 0.0 94.6 5.4 89.5 0.0 92.0 96.1 0.0Louisiana 87.6 0.0 0.0 93.1 6.9 88.1 0.0 91.3 0.0 0.0

Maine 89.6 0.0 0.0 88.7 11.3 91.0 0.0 94.2 95.1 88.3Maryland 98.3 99.7 0.0 0.0 0.0 95.3 0.0 95.6 0.0 98.4Massachu

setts 96.1 0.0 0.0 0.0 0.0 95.8 0.0 98.0 0.0 0.0

Michigan 88.5 0.0 0.0 0.0 0.0 89.2 0.0 96.0 0.0 0.0Minnesota 93.7 0.0 0.0 0.0 0.0 90.8 0.0 86.3 0.0 96.5Mississipp

i 93.9 98.7 0.0 97.1 2.9 95.2 97.1 96.1 98.2 0.0

Missouri 87.0 0.0 0.0 99.2 0.8 89.8 0.0 91.1 94.8 0.0Montana 88.4 0.0 0.0 96.9 3.1 87.3 0.0 96.3 96.3 89.1Nebraska 94.2 0.0 0.0 92.2 7.8 92.5 0.0 94.5 0.0 90.5

Nevada 88.4 0.0 97.8 96.4 3.6 90.4 95.7 95.2 95.9 0.0

New Hampshire 96.9 0.0 0.0 97.4 2.6 96.3 0.0 98.7 98.7 97.3

New Jersey 96.2 0.0 95.

6 92.0 8.0 95.6 0.0 96.0 0.0 0.0

New Mexico 86.6 0.0 96.

3 99.2 0.8 89.1 0.0 94.6 95.3 0.0

New York 93.5 98.8 100.0 98.0 2.0 95.3 0.0 96.0 0.0 94.4

North Carolina 93.7 0.0 96.

3 93.4 6.6 96.0 0.0 99.3 0.0 0.0

North Dakota 92.0 0.0 0.0 91.9 8.1 91.4 0.0 0.0 96.7 91.6

Ohio 87.5 0.0 0.0 0.0 0.0 86.0 0.0 89.2 93.7 0.0

Oklahoma 84.5 0.0 97.3 94.2 5.8 89.8 0.0 94.2 93.7 86.2

Oregon 85.6 0.0 98.6 0.0 0.0 89.4 0.0 93.1 92.1 0.0

Pennsylvania 91.9 93.5 95.

9 98.8 1.2 93.3 97.0 95.6 96.3 88.1

Rhode Island 96.1 0.0 91.

7 92.0 8.0 95.6 0.0 95.8 0.0 0.0

South 90.6 0.0 0.0 98.5 1.5 89.2 0.0 91.2 0.0 90.4


CarolinaSouth Dakota 92.6 0.0 0.0 0.0 0.0 93.1 97.1 97.9 0.0 0.0

Tennessee 96.9 0.0 93.4 95.2 4.8 92.3 0.0 94.0 93.0 0.0

Texas 94.1 0.0 97.2 97.4 2.6 92.7 93.2 94.4 96.7 0.0

Utah 92.4 0.0 0.0 91.8 8.2 92.6 0.0 94.3 97.9 0.0Vermont 90.9 0.0 0.0 0.0 0.0 91.2 0.0 93.1 0.0 92.0

Virginia 91.2 0.0 96.1 0.0 0.0 88.6 0.0 88.1 96.3 0.0

Washington 91.4 0.0 0.0 93.3 6.7 93.5 0.0 93.8 0.0 98.5

West Virginia 85.6 0.0 0.0 94.4 5.6 86.0 0.0 88.1 90.7 0.0

Wisconsin 92.4 0.0 0.0 0.0 0.0 93.2 0.0 92.1 0.0 96.4Wyoming 87.1 0.0 0.0 0.0 0.0 89.0 94.1 96.5 0.0 0.0

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