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How Flu Vaccinations Vary Among Racial Communities
Deborah Lin
Dr. Andrew Noymer
June 11, 2012
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Introduction
Influenza is a leading cause of mortality in adults. Overall, each year numerous patients
die from influenza-related illness and influenza-associated causes. Despite the benefits of
influenza vaccine, and their relative ease to obtain, each year a large percentage of adults go
unvaccinated. Disparities in immunization rates likely translate into disparities in health,
especially along racial lines. Potential reasons in immunization rates can be traced to racial
disparities. Prior studies on immunization rates in the Medicare population has found that older
age, poorer health status, more education, higher income, more knowledge about positive
attitudes toward immunization, private secondary insurance coverage, and a normal source of
care are all associated with higher rates. Although these factors help explain some of the racial
disparities in immunization rates, none of them, either individually or in a combination, fully
explains these differences or the root cause of the disparity.
Vaccination significantly reduces influenza-related mortality rates in adults. Currently, it
is the most effective means to prevent the consequences of influenza. The flu vaccination serves
the dual purpose of reducing public health care costs and minimizing influenza-related deaths,
but some studies show reduced incidence as well. Vaccination can not only protect individuals
against the influenza virus but also indirectly protect unvaccinated individuals if a sufficient
number of people get vaccinations (if fewer people become infected with the influenza virus, it
reasons that even those who go unvaccinated have a lower chance of coming into contact with
someone currently suffering from influenza). The main objective of vaccination in the elderly is
not reduce the incidence of flu itself, but to reduce the risk of complications in more vulnerable
individuals (like the elderly). Although the vaccine is not purely designed to prevent the flu, it is
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effective in the minimization of hospitalization, and the reduction of deaths from influenza and
all causes. However, despite the presence of a safe and effective vaccine, long-standing
recommendations to vaccinate all elderly individuals and government support for vaccination of
the elderly population, vaccination levels are still not optimal.
Efforts aimed at increasing influenza vaccine coverage have focused primarily on
increasing vaccination in physicians’ offices, such as computer reminders and advertising
campaigns, but these efforts may have diminishing returns. Working individuals or those without
any other reason to see a physician may be less willing to endure the inconvenience of making an
appointment in advance or taking time off work. Alternative locations, such as retail stores,
pharmacies and doctors or nurses coming to workplaces, could be an increasingly viable solution
because of the increasing number of retail clinics and prevalence of influenza immunization at
worksites. Interestingly, current research indicates that elderly people have a mistrust of doctors
and prefer to receive their vaccines from their neighborhood drugstore (Walgreen Co., 2012).
Drugstores are easily accessible and are in close proximity to most neighborhoods and
communities. Additionally, the elderly may feel more comfortable working with the same
pharmacy technicians that they have frequent interaction with, rather than going to a doctor’s
office.
To guide this study, data was used from the nationally representative National Health
Interview Survey of the U.S. adult population in 2010 (the most complete and up-to-date study)
to better understand the potential for alternative locations to improve influenza vaccination rates.
This survey site contains statistics related to national trends covering a wide arrange of health
topics in the United States. The framework of the study consists of patient factors, such as
socioeconomic status, health-seeking attitudes and behaviors, and accessibility to physician
3
services. The goal of this study was to analyze the characteristics of patients who did receive the
influenza vaccine and determine who among that subset had little contact with physicians and
therefore could benefit from an alternative location (so that they are more likely to continue
immunization in the future). This study also analyzed the characteristics of patients who were
vaccinated in different locations. Such comparisons can help identify whether alternative
locations are serving traditionally “under-served” populations that are not being vaccinated at the
same rate as traditional health care sites. This study seeks to offer approaches to increase
vaccination rates in order for all populations and communities to benefit from the vaccine and
the resulting improved quality of life for the community at large.
Theoretical Frameworks
Theories of Managed Care Inequality
In recent years, it is become clear that there are substantial racial disparities in the quality
of health care in the United States, particularly for preventative services (Schneider et al. 2001).
Despite the availability of vaccines and increased influenza rates, African Americans are still
persistently less likely to receive flu vaccinations than Whites. Pre-paid health plans could play
an important role in both increasing vaccination rates and reducing racial disparity.
However, those same pre-paid health plans have a financial incentive to increase delivery and
potentially cost-saving preventative services, such as flu vaccinations, to elderly people and other
high-risk populations instead of minorities. By targeting the elder, pre-paid health plans, may
reduce health costs in the short-term, to the company, but does a disservice to the community at
large. These plans encourage compliance with federal and state law, but little beyond basics.
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Limited access to health care or poor health education reduces African Americans’ use of
preventative services. However, if health plans address these issues, racial disparity could be
minimized for enrollees in managed care than for those with fee-for-service insurance. Despite
the importance of the issue, few studies have compared racial disparity in cases of minorities
with managed care or fee-for-service insurance. Thus, analyzing influenza vaccination offers an
opportunity to examine whether health plans increase use of preventative services compared with
fee-for-service insurance and whether they reduce racial disparity in preventative service use. A
better understanding of how to fix racial disparities can have wide ranging effects on Medicare
and the medical field in general.
Socioeconomic Factors
Drawing from theories of facilitators and barriers to vaccination in ethnic populations,
some medical experts have sought to understand associations between influenza vaccination
statuses. Specifically, medical experts continually assist all patients in understanding the
effectiveness of influenza vaccination and its benefits. It would be beneficial to analyze how
medical experts measure vaccination usage rates and patient knowledge. Medical experts have
found that barriers do exist between patients and vaccine receipt. Previous studies identified a
few common patient barriers, such as fear of the pain of injection or needles, concerns about
vaccine effectiveness and perceived lack of social influences of physician recommendation for
vaccination. Additional studies have also found that different cultural and economic situations
have been used to predict similar vaccination behaviors as well. Demographic factors, such as
marital status, sex, education, and household income were related to influenza vaccination status.
Married couples were more likely to have received the influenza vaccination than in other
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marital categories (Nowalk et al. 2004). Significant differences existed among groups, such as
attitudes and perceived consequences of obtaining influenza vaccine. Specifically, individuals
that received vaccines were more likely to report that their physician and family or friends
thought that their significant others should be vaccinated. Compared to the unvaccinated
individuals, individuals who were more willing to receive the influenza vaccination also believed
in its effectiveness. Individuals were also more willing to receive the flu vaccination because of
its effectiveness. Another contributing factor to receipt of vaccine is physician recommendation.
Of those who did not receive the vaccination, only half recalled that their physician
recommended it (Nowalk et al. 2004). Even though existing research cannot ensure the accuracy
of patient recall of physicians’ recommendation, it is imperative that physicians take every
opportunity to recommend flu vaccinations to all eligible adult individuals so that all racial
communities can understand the importance of preventing influenza and other diseases. By
capitalizing on every vaccination opportunity, immunization rates will increase across all
minority communities.
Theories of Government Policy
Health care reform has become a hot-button issue among political leaders. Currently,
approximately forty-five million Americans are estimated to be without health insurance at some
point during each year. Because of this staggering statistic, President Obama signed the Patient
Protection and Affordable Care Act, known as “Obamacare”, which would require firms and
small businesses to share the responsibility of paying for employee health insurance
(HealthCare.gov, 2009) (currently “Obamacare” is facing serious legal hurdles and may never
become a fully effectuated law, however it has been passed into law and will be examined for
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purposes of this theory). Under Obamacare companies would be obliged to offer healthcare
plans, which include complementary flu vaccinations to almost all employees. Ultimately,
Obamacare would reform certain aspects of the private health insurance industry and public
health insurance programs, increase insurance coverage of pre-existing conditions, and extend
the access to health insurance to thirty million Americans. In effect, it would increase projected
national medical spending and lower projected Medicare spending. Firms and small business
would continue having the option of comparing policies and premiums and buy insurance with
governmental assistance. Low income families would also be eligible for Federal government
assistance. Co-payments, co-insurance, and deductibles would be eliminated from a select
number of pre-paid health plans and would be considered to be part of an “essential benefits
package”. Another change under Obamacare would be the restricting of Medicare reimbursement
from “fee-for-service” to “bundled payments”. Additional support from this bill would go
towards medical research and the National Institute of Health.
Hypothesis
For this analysis, I hypothesize that
(1) Minorities have lower access to health care, and therefore lower flu vaccination
rates.
(2) Married couples have greater access to health care and therefore higher flu
vaccination rates.
(3) Working individuals have higher uptake of flu vaccinations.
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Data
This research project analyzed data from the National Health Interview Survey (NHIS).
The NHIS is a household survey of non-military and non-institutionalized individuals in the
United States. It is the principal source of data collection programs sponsored by the National
Center for Health Statistics. The NHIS randomly samples approximately 35,000 to 40,000
households. Sampling and interviewing occur continuously throughout the year and the NHIS is
nationally-representative of all households in the United States.
This study used variables from the Sample Adult File (2010) Data Release. The Sample
Adult File consists of data with age-standardized distributed vaccination rates. In the Sample
Adult File, one adult per family is randomly selected to participate. For the 2010 survey, 16,157
individuals were interviewed, ranging from age 18 to 85. The NHIS Sample Adult File was
collected through the use of clustering and sampling of data through a multi-stage probability
design.
The hypothesis revolves around basic information on health status, access to health care
services, and health behaviors on individuals. In order to account for the probability of selection
and non-response from historically under-represented groups, the NHIS oversamples certain
demographic groups, such as Black, Asian and Hispanics, so that the weighted estimates in this
research project can be generalized to the entire adult civilian population of the United States.
Demographic Variables
This project examined data on sex, age, gender, race/ethnicity, marital status, and
currently employment status over the past 12 months. Race/ethnicity was limited to White,
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Black, American Indian/Alaskan-native (AIAN), Asian and multiple race. Similarly, marital
status was limited to married, widowed, divorced, separated, never married, and cohabitating.
Lastly, employment status was limited to working for pay at a company, with company but not at
work, looking for work, working for family and not working. Race not released, unknown
marital status, and refused to answer and don’t know responses were eliminated from the
analysis because of the statistically insignificant values.
Dependent Variables
The hypothesis of the linear regression revolved around the differential percentage take-
rate of recipients of the flu vaccination over the past year. This project relied on voluntary
recipients of vaccination from the National Health Interview Survey (NHIS). Survey data on
receipt of the flu vaccination have been found to be more complete than other sources.
Statistical Analysis
This project used Stata for statistical analysis for the comprehensive design of the NHIS.
The specific categories that this study focused on was: race (Whites vs. Non-Hispanic Blacks),
marital status (Married couples vs non-married), and employment status (working individuals in
a company vs. non-working). The reference groups are Whites, married couples, and working
individuals. The analysis used the logistic regression model to calculate the unadjusted odd-
ratios to identify basic group differences for each specific category without controls. The
unadjusted odd ratio is also known as the crude odds ratio. The crude odds ratio is the ratio that
is not stratified (by sex, age or age²). On the basis of previous research, this project adjusted for
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variables that were most likely to influence flu vaccinations. Variables that carried non-statistical
significance to the previous models and preliminary analyses were eliminated.
For the specific category of race, the analysis compared the prevalence of influenza
vaccination using unadjusted odd-ratios for Whites to other minority races, such as Black,
American Indian/Alaskan Native, Asian, and Multiple Race. Similarly, for the specific category
of marital status, the analysis compared the prevalence of influenza vaccination using unadjusted
odd-ratios for Married Couples to Non-Married statuses, such as Widowed, Divorced, Separated,
Never-Married (Single) and Cohabitating. Finally, for the specific category of employment
status, analysis compared the prevalence of influenza vaccination using unadjusted odd-ratios for
Working Individuals with a Company to Non-Working Individuals, such as With Company but
Not At Work, Looking for Work, Working for Family, and Not Working.
Sex, age and age2
were added as controls to observe the group differences using adjusted
odds ratios. The above listed variables are kept constant to prevent their influence on
independent variables and the dependent variable. If the adjusted odds ratio equals one, then
there is no association, if one is included in the confidence interval, then it is possible that the
odds ratio equals one, and it is not statistically significant. Odds ratios may be adjusted for
confounding factors. The analysis used similar multivariate logistic procedures for each specific
category, designating the same reference groups.
Results
Basic Characteristics
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The unadjusted sample consisted of 15,840 observations of adults. The adjusted sample
consisted of 16, 157 observations of adults.
Table 2 shows the unadjusted prevalence of flu vaccination in Whites and by other
individual ethnic groups. Whites were more likely to be vaccinated compared with Blacks. In
general, there are racial/ethnic differences of flu vaccination among, and between, all minority
groups. However, there were no significant differences between the unadjusted and adjusted odd
ratios for flu vaccinations observed.
The adjusted prevalence of flu vaccination stratified by sex, age and age², is shown in
Table 3. Sex, age and age² are the controlled variables and are kept constant to prevent their
influence on the effect of the independent variable on the dependent. The adjusted prevalence of
flu vaccination was the highest for American Indian/Alaskan Native and Asian populations.
Blacks (p<0.001) have the lowest vaccination rate out of all minorities because of the current
barriers to quality healthcare. Generally, they are not able to afford proper healthcare, which
includes flu vaccinations (Scheider et al. 2001). American Indian/Alaskan Native have a
relatively high vaccination rate, higher than Whites, because the Federal Government provides
healthcare for most federally recognized tribes (at last count there were 564 federally recognized
tribes) (IHS, 2010). According to the Census, 60% of the American Indian population has access
to health facilities which provide flu vaccinations. However, due to the small sample size,
American Indians and Alaskan Natives are not statistically significant. Asians have the highest
vaccination rate; this can be explained that Asians make up the highest percentage of
immigration over the last two decades, generally for higher-level jobs or educational
opportunities. Given the reasons for immigration, Asians tend to be given more opportunities
through their educational institutions or workplace to take better care of their health (i.e. to be
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vaccinated). (Wessling, 2004). Social status and/or overall economic well-being logically leads
to an increase in access to resources that enable them to receive vaccinations. Multiple race
category becomes insignificant because it has too many variables involved (CDC, 2012). The
multiple variables cause it to become insignificant and therefore cannot be predicted.
Table 4 shows the unadjusted prevalence of flu vaccinations in Married Couples and
Non-Married Couples, Widowed, Divorced, Separated, Never-Married, and Cohabitating.
Married couples are more likely to receive flu vaccination than Non-Married. Widowed couples
have a relatively high flu vaccination rate because they qualify and are permitted to receive
Medicare hospital insurance (Social Security Administration, 2012). Divorced couples also have
a relatively high vaccination rate because there are usually children involved (those with children
generally tend to care more about their own health than those without). Even though the
government does not compel parents to vaccinate their children, co-parenting will encourage
vaccinations for children as a means to provide the best possible care for the child. Typically, in
divorced households, the realization that the benefit of vaccination is usually in the best interest
for both parent and child. Separated individuals have a relative low vaccination rate because they
may not have access to health maintenance organizations, such as Non-VA healthcare facilities
(Straits-Tröster, 2006). Never-married (Single) individuals have the lowest vaccination rate
because this group of people may not be as worried about their health and will have a lower
vaccination rate. Lastly, cohabitating individuals have a relatively low vaccination rate, possibly
due to low income levels or less pressure from their significant other. Since the existing research
does not focus on some of the groups, many of these observations are speculative. The data was
not affected by the analysis.
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The adjusted prevalence of flu vaccination stratified by sex, age and age2
, is shown in
Table 5. The adjusted prevalence of flu vaccination was significantly higher for married couples
than in non- married couples. Widowed and Divorced couples have higher flu vaccination rates
possibly due to the fact that this group may consist of the elderly high-risk population that are
highly susceptible to the flu and other diseases (particularly widowed couples). Separated
individuals receive the lowest vaccination rates possibly because their marriage is unstable, they
are going through some type of financial unrest and they are probably not as worried about
receiving vaccines or as concerned about their health as a married person. This is only a
speculation as little research has been done on this category. However, the data did not affect the
analysis. Never-Married (Single) individuals have a relatively low vaccination rate because the
age group of a single person is usually younger. Younger people are less worried about their
health and/or have less financial wherewithal and thus, may not be as worried about receiving
vaccinations (Lochner and Wynne, 2011). Those cohabitating may have a higher vaccination
rates than those who are widowed or divorced because they are more similar to the married
couple group and are likely to be more involved in each other’s health. However, one difference
is that those who are married are able to receive benefits from their partners work and/or
provider which generally results in cheaper health insurance. These benefits may not be
available to cohabitating individuals (Walton, 2012). So the likelihood of cohabitating couples
receiving vaccinations is not as high as married couples.
Table 6 shows the unadjusted prevalence of flu vaccinations in Working Individuals and
Non-Working Individuals, With a Company but Not At Work, Looking for Work, Working for
Family, and Not Working. Working individuals were more likely to be vaccinated compared to
non-working individuals. Overall, there are no differences in flu vaccinations among those
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employed between the unadjusted and adjusted odd ratios. However, there were significant
statistical differences between individual employment status groups.
The adjusted prevalence of flu vaccination stratified by sex, age and age2
, is shown in
Table 7. The adjusted prevalence of flu vaccination was significantly higher for Working
Individuals, currently employed and working at a company, than in Non-Working Individuals.
Most employees in a company are able to receive medical insurance, which covers flu
vaccinations (B.Y.Lee et al., 2009). Individuals whom are looking for work may be able to
receive flu vaccinations because they are financially stable and are able to pay for the vaccines
themselves or may have COBRA (Henrich and Holmes, 2009). COBRA gives workers and their
families the right to choose to continue group health benefits for limited periods of time under
certain circumstances such as transition between jobs or other life events. However, working for
family individuals may not be able to receive flu vaccinations because they usually are not able
to receive health insurance like other employees. They must pay for their own health services if
they need it. Not working individuals may be individuals that are unemployed at time of survey
and thus may be receiving unemployment benefits that provide for flu vaccinations (Gilbert,
2009).
Discussion
The results from this study provide three important additions to current knowledge about
varying flu vaccination rates among all communities and the existing racial/ethnic inequalities in
influenza vaccination. First, as a percentage, African Americans receive fewer vaccinations than
Whites. Asians are considered a minority but they do not fit the minority group characteristics.
There was no difference in vaccination rates between the unadjusted and adjusted odd ratios.
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Second, married couples, as compared to all other marital statuses, are more likely to be
vaccinated. There are differences in vaccination rates between unadjusted and adjusted odd
ratios. Finally, working individuals are more likely to receive vaccines. While looking for work,
respondents are likely to be on some form of unemployment benefits or COBRA and thus are
more likely to receive vaccinations. Financial independence plays a significant role in access to
healthcare and vaccinations. There was no difference in vaccination rates between the unadjusted
and adjusted odd ratios.
Policy Implications
With the developed analysis, this project can advocate for policy changes to the current
regulations to create a more effective Medicare system. For minority communities, creating
better and more readily accessible access to flu vaccinations would allow for a higher percentage
of minorities to stay healthier. At the same time, health care costs are also rising. If the
government created a system to assist minority communities financially, perhaps everyone could
receive the vaccination. Perhaps if the government created mobile immunization clinics that
travel throughout the country, minorities would be more inclined to be vaccinated. Minorities
would then receive a higher percentage of vaccinations because the clinics are easily accessible
to the community. However, this solution does come with one serious drawback, as there is a
common distrust of the government within many minority communities, particularly regarding
the effectiveness of government sponsored vaccinations and the underlying “true” purpose of the
vaccination. Currently, subsidized vaccinations are only offered to select communities and only a
limited number of pre-paid health plans are available for Whites. Perhaps if the government
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provided flu vaccinations at employment agencies and housing assistance programs, it would
encourage more people to receive vaccines because it is easily accessible.
Future Research
A future study could analyze how income correlates with flu vaccination rates. An
extended study on disaggregating the data to observe the data based on individual states would
also be valuable to many communities. Additionally, each state has different laws regarding
health insurance and flu vaccines. For example, the Vermont State Health Commissioner is
currently attempting to take away the right of Vermont parents to make vaccine decisions for
their children. Colorado has passed flu shot mandates for all healthcare workers. Since the
analysis was based on the sample adult file, studying the child data release could prove
significant. Since health care costs are rising, it could be insightful to track the data, analysis, and
results over time and compare those to historic data.
Acknowledgements
I would like to thank my advisor, Dr. Andrew Noymer for his ongoing support for my
personal and professional development. I am also grateful to my mentor, Daisy Carreon, and
supervisors, Dr. Matt Huffman, Dr. Joanne Christopherson, and Dr. Julie DaVanzo whom
contributed to my professional development and offered me ample support. I would like to thank
my fellow 2011 DASA cohorts for their warmth and support. I would also like to thank my
family, and particularly my parents, Mike and Sue Lin for unbelievable generosity. You have
supported me and encouraged me to work hard and succeed in anything and everything I choose
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to do. I am incredibly grateful for what you have given me. I would also like to thank Mark
Sweet, for his warmth, support and friendship and always believing in me. Last but not least, I
would like to thank Raymond Chang for giving me true unconditional comfort, softness, and
laughter. Thank you for reminding me that the light at the end of the tunnel was always closer
than I thought and has been a constant voice of wisdom, guidance, and reassurance.
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Tables and Figures
Table 1-Tables of Descriptives
Number Percent
Total Observations (n): 16,148 (100)
Sex Male: 11,774 (44.18)
Female: 14,876 (55.82)
Race White: 20,056 (75.36)
Black/African American: 4,575 (17.19)
American Indian/Alaskan Native (AIAN): 219 (0.82)
Asian: 1,765 (6.63)
Age Mean : 47.84
Standard Deviation: 18.09
Minimum: 18
Maximum: 85
Marital Status Married: spouse in household: 11,457 (42.99)
Married: spouse not in household: 424 (1.59)
Widowed: 2,478 (9.30)
Divorced: 3,510 (13.17)
Separated: 948 (3.56)
Never Married: 6,288 (23.59)
Cohabitating – living with a partner (1,545)
Employment Status
Working for pay at a job or business: 14,529 (54.52)
With company but not at work: 592 (2.22)
Looking for work: 1,819 (6.83)
Working, but not for pay, for a family: 233 (0.87)
Not working: 9,477 (35.56)
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Table 2-Whites are More Likely to have Higher Flu Vaccinations than Blacks, 2010
(unadjusted)
19
Table 3-Whites are More Likely to have Higher Flu Vaccinations than Blacks, 2010
(adjusted)
20
Table 4-Married Couples are More Likely to Receive Flu Vaccinations than the Non-
Married, 2010 (unadjusted)
21
Table 5-Married Couples are More Likely to Receive Flu Vaccinations than the Non-
Married, 2010 (adjusted)
22
Table 6-Working Individuals are More Likely to Receive Flu Vaccinations than Non-
Working Individuals (unadjusted)
23
Table 7-Working Individuals are More Likely to Receive Flu Vaccinations than Non-
Working Individuals (adjusted)
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Logistic Regression (unadjusted)
_cons .2503982 .0089572 -38.71 0.000 .2334437 .2685841
dont_know 1 (omitted)
not_working 1.33589 .0594405 6.51 0.000 1.224325 1.457622
working_for_family .4792373 .1358945 -2.59 0.009 .2749035 .8354509
looking_for_work .7067375 .0680971 -3.60 0.000 .585115 .8536405
with_company_but_not_at_work 1.587287 .1882193 3.90 0.000 1.258116 2.002582
unknown_marital_status 1 (omitted)
cohab .7552899 .0701799 -3.02 0.003 .6295379 .9061612
never_married .6626411 .0371226 -7.35 0.000 .5937343 .739545
separated .7249493 .087679 -2.66 0.008 .5719514 .9188745
divorced .8798787 .0554259 -2.03 0.042 .7776844 .9955022
widowed 1.038606 .0728698 0.54 0.589 .905169 1.191714
multiple_race .8826554 .142132 -0.78 0.438 .6437602 1.210203
race_not_released .765674 .4820597 -0.42 0.672 .2229139 2.62997
asian 1.318608 .1011326 3.61 0.000 1.134571 1.532498
american_indian_alaskan_native 1.134683 .2451396 0.58 0.559 .7429828 1.732886
black .7465431 .0446385 -4.89 0.000 .6639853 .8393658
flu_vac Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
25
Logistic Regression (adjusted)
_cons .1150904 .0221812 -11.22 0.000 .0788839 .167915
dont_know 1 (omitted)
not_working 1.132731 .0577166 2.45 0.014 1.025074 1.251695
working_for_family .4456625 .1267512 -2.84 0.004 .2552203 .7782103
looking_for_work .7227869 .0698072 -3.36 0.001 .5981371 .8734133
with_company_but_not_at_work 1.549945 .1842552 3.69 0.000 1.227797 1.956617
unknown_marital_status 1 (omitted)
cohab .8319538 .0789396 -1.94 0.053 .6907697 1.001994
never_married .7584034 .0469458 -4.47 0.000 .6717539 .8562299
separated .7246264 .0879605 -2.65 0.008 .571201 .9192621
divorced .8177023 .0521714 -3.15 0.002 .7215833 .926625
widowed .7923613 .0638961 -2.89 0.004 .6765226 .9280346
multiple_race .9114209 .1471263 -0.57 0.566 .6642225 1.250617
race_not_released .7789669 .4919559 -0.40 0.692 .2259141 2.685929
asian 1.334744 .1027667 3.75 0.000 1.147786 1.552155
american_indian_alaskan_native 1.147152 .2484403 0.63 0.526 .7503692 1.753747
black .739404 .0444594 -5.02 0.000 .6572039 .8318853
age2 1.000049 .0000704 0.69 0.490 .9999107 1.000187
age_p 1.00558 .0072309 0.77 0.439 .9915073 1.019853
sex 1.318139 .0559935 6.50 0.000 1.212838 1.432582
flu_vac Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
26
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Lin Final Version - Final

  • 1. 0 How Flu Vaccinations Vary Among Racial Communities Deborah Lin Dr. Andrew Noymer June 11, 2012
  • 2. 1 Introduction Influenza is a leading cause of mortality in adults. Overall, each year numerous patients die from influenza-related illness and influenza-associated causes. Despite the benefits of influenza vaccine, and their relative ease to obtain, each year a large percentage of adults go unvaccinated. Disparities in immunization rates likely translate into disparities in health, especially along racial lines. Potential reasons in immunization rates can be traced to racial disparities. Prior studies on immunization rates in the Medicare population has found that older age, poorer health status, more education, higher income, more knowledge about positive attitudes toward immunization, private secondary insurance coverage, and a normal source of care are all associated with higher rates. Although these factors help explain some of the racial disparities in immunization rates, none of them, either individually or in a combination, fully explains these differences or the root cause of the disparity. Vaccination significantly reduces influenza-related mortality rates in adults. Currently, it is the most effective means to prevent the consequences of influenza. The flu vaccination serves the dual purpose of reducing public health care costs and minimizing influenza-related deaths, but some studies show reduced incidence as well. Vaccination can not only protect individuals against the influenza virus but also indirectly protect unvaccinated individuals if a sufficient number of people get vaccinations (if fewer people become infected with the influenza virus, it reasons that even those who go unvaccinated have a lower chance of coming into contact with someone currently suffering from influenza). The main objective of vaccination in the elderly is not reduce the incidence of flu itself, but to reduce the risk of complications in more vulnerable individuals (like the elderly). Although the vaccine is not purely designed to prevent the flu, it is
  • 3. 2 effective in the minimization of hospitalization, and the reduction of deaths from influenza and all causes. However, despite the presence of a safe and effective vaccine, long-standing recommendations to vaccinate all elderly individuals and government support for vaccination of the elderly population, vaccination levels are still not optimal. Efforts aimed at increasing influenza vaccine coverage have focused primarily on increasing vaccination in physicians’ offices, such as computer reminders and advertising campaigns, but these efforts may have diminishing returns. Working individuals or those without any other reason to see a physician may be less willing to endure the inconvenience of making an appointment in advance or taking time off work. Alternative locations, such as retail stores, pharmacies and doctors or nurses coming to workplaces, could be an increasingly viable solution because of the increasing number of retail clinics and prevalence of influenza immunization at worksites. Interestingly, current research indicates that elderly people have a mistrust of doctors and prefer to receive their vaccines from their neighborhood drugstore (Walgreen Co., 2012). Drugstores are easily accessible and are in close proximity to most neighborhoods and communities. Additionally, the elderly may feel more comfortable working with the same pharmacy technicians that they have frequent interaction with, rather than going to a doctor’s office. To guide this study, data was used from the nationally representative National Health Interview Survey of the U.S. adult population in 2010 (the most complete and up-to-date study) to better understand the potential for alternative locations to improve influenza vaccination rates. This survey site contains statistics related to national trends covering a wide arrange of health topics in the United States. The framework of the study consists of patient factors, such as socioeconomic status, health-seeking attitudes and behaviors, and accessibility to physician
  • 4. 3 services. The goal of this study was to analyze the characteristics of patients who did receive the influenza vaccine and determine who among that subset had little contact with physicians and therefore could benefit from an alternative location (so that they are more likely to continue immunization in the future). This study also analyzed the characteristics of patients who were vaccinated in different locations. Such comparisons can help identify whether alternative locations are serving traditionally “under-served” populations that are not being vaccinated at the same rate as traditional health care sites. This study seeks to offer approaches to increase vaccination rates in order for all populations and communities to benefit from the vaccine and the resulting improved quality of life for the community at large. Theoretical Frameworks Theories of Managed Care Inequality In recent years, it is become clear that there are substantial racial disparities in the quality of health care in the United States, particularly for preventative services (Schneider et al. 2001). Despite the availability of vaccines and increased influenza rates, African Americans are still persistently less likely to receive flu vaccinations than Whites. Pre-paid health plans could play an important role in both increasing vaccination rates and reducing racial disparity. However, those same pre-paid health plans have a financial incentive to increase delivery and potentially cost-saving preventative services, such as flu vaccinations, to elderly people and other high-risk populations instead of minorities. By targeting the elder, pre-paid health plans, may reduce health costs in the short-term, to the company, but does a disservice to the community at large. These plans encourage compliance with federal and state law, but little beyond basics.
  • 5. 4 Limited access to health care or poor health education reduces African Americans’ use of preventative services. However, if health plans address these issues, racial disparity could be minimized for enrollees in managed care than for those with fee-for-service insurance. Despite the importance of the issue, few studies have compared racial disparity in cases of minorities with managed care or fee-for-service insurance. Thus, analyzing influenza vaccination offers an opportunity to examine whether health plans increase use of preventative services compared with fee-for-service insurance and whether they reduce racial disparity in preventative service use. A better understanding of how to fix racial disparities can have wide ranging effects on Medicare and the medical field in general. Socioeconomic Factors Drawing from theories of facilitators and barriers to vaccination in ethnic populations, some medical experts have sought to understand associations between influenza vaccination statuses. Specifically, medical experts continually assist all patients in understanding the effectiveness of influenza vaccination and its benefits. It would be beneficial to analyze how medical experts measure vaccination usage rates and patient knowledge. Medical experts have found that barriers do exist between patients and vaccine receipt. Previous studies identified a few common patient barriers, such as fear of the pain of injection or needles, concerns about vaccine effectiveness and perceived lack of social influences of physician recommendation for vaccination. Additional studies have also found that different cultural and economic situations have been used to predict similar vaccination behaviors as well. Demographic factors, such as marital status, sex, education, and household income were related to influenza vaccination status. Married couples were more likely to have received the influenza vaccination than in other
  • 6. 5 marital categories (Nowalk et al. 2004). Significant differences existed among groups, such as attitudes and perceived consequences of obtaining influenza vaccine. Specifically, individuals that received vaccines were more likely to report that their physician and family or friends thought that their significant others should be vaccinated. Compared to the unvaccinated individuals, individuals who were more willing to receive the influenza vaccination also believed in its effectiveness. Individuals were also more willing to receive the flu vaccination because of its effectiveness. Another contributing factor to receipt of vaccine is physician recommendation. Of those who did not receive the vaccination, only half recalled that their physician recommended it (Nowalk et al. 2004). Even though existing research cannot ensure the accuracy of patient recall of physicians’ recommendation, it is imperative that physicians take every opportunity to recommend flu vaccinations to all eligible adult individuals so that all racial communities can understand the importance of preventing influenza and other diseases. By capitalizing on every vaccination opportunity, immunization rates will increase across all minority communities. Theories of Government Policy Health care reform has become a hot-button issue among political leaders. Currently, approximately forty-five million Americans are estimated to be without health insurance at some point during each year. Because of this staggering statistic, President Obama signed the Patient Protection and Affordable Care Act, known as “Obamacare”, which would require firms and small businesses to share the responsibility of paying for employee health insurance (HealthCare.gov, 2009) (currently “Obamacare” is facing serious legal hurdles and may never become a fully effectuated law, however it has been passed into law and will be examined for
  • 7. 6 purposes of this theory). Under Obamacare companies would be obliged to offer healthcare plans, which include complementary flu vaccinations to almost all employees. Ultimately, Obamacare would reform certain aspects of the private health insurance industry and public health insurance programs, increase insurance coverage of pre-existing conditions, and extend the access to health insurance to thirty million Americans. In effect, it would increase projected national medical spending and lower projected Medicare spending. Firms and small business would continue having the option of comparing policies and premiums and buy insurance with governmental assistance. Low income families would also be eligible for Federal government assistance. Co-payments, co-insurance, and deductibles would be eliminated from a select number of pre-paid health plans and would be considered to be part of an “essential benefits package”. Another change under Obamacare would be the restricting of Medicare reimbursement from “fee-for-service” to “bundled payments”. Additional support from this bill would go towards medical research and the National Institute of Health. Hypothesis For this analysis, I hypothesize that (1) Minorities have lower access to health care, and therefore lower flu vaccination rates. (2) Married couples have greater access to health care and therefore higher flu vaccination rates. (3) Working individuals have higher uptake of flu vaccinations.
  • 8. 7 Data This research project analyzed data from the National Health Interview Survey (NHIS). The NHIS is a household survey of non-military and non-institutionalized individuals in the United States. It is the principal source of data collection programs sponsored by the National Center for Health Statistics. The NHIS randomly samples approximately 35,000 to 40,000 households. Sampling and interviewing occur continuously throughout the year and the NHIS is nationally-representative of all households in the United States. This study used variables from the Sample Adult File (2010) Data Release. The Sample Adult File consists of data with age-standardized distributed vaccination rates. In the Sample Adult File, one adult per family is randomly selected to participate. For the 2010 survey, 16,157 individuals were interviewed, ranging from age 18 to 85. The NHIS Sample Adult File was collected through the use of clustering and sampling of data through a multi-stage probability design. The hypothesis revolves around basic information on health status, access to health care services, and health behaviors on individuals. In order to account for the probability of selection and non-response from historically under-represented groups, the NHIS oversamples certain demographic groups, such as Black, Asian and Hispanics, so that the weighted estimates in this research project can be generalized to the entire adult civilian population of the United States. Demographic Variables This project examined data on sex, age, gender, race/ethnicity, marital status, and currently employment status over the past 12 months. Race/ethnicity was limited to White,
  • 9. 8 Black, American Indian/Alaskan-native (AIAN), Asian and multiple race. Similarly, marital status was limited to married, widowed, divorced, separated, never married, and cohabitating. Lastly, employment status was limited to working for pay at a company, with company but not at work, looking for work, working for family and not working. Race not released, unknown marital status, and refused to answer and don’t know responses were eliminated from the analysis because of the statistically insignificant values. Dependent Variables The hypothesis of the linear regression revolved around the differential percentage take- rate of recipients of the flu vaccination over the past year. This project relied on voluntary recipients of vaccination from the National Health Interview Survey (NHIS). Survey data on receipt of the flu vaccination have been found to be more complete than other sources. Statistical Analysis This project used Stata for statistical analysis for the comprehensive design of the NHIS. The specific categories that this study focused on was: race (Whites vs. Non-Hispanic Blacks), marital status (Married couples vs non-married), and employment status (working individuals in a company vs. non-working). The reference groups are Whites, married couples, and working individuals. The analysis used the logistic regression model to calculate the unadjusted odd- ratios to identify basic group differences for each specific category without controls. The unadjusted odd ratio is also known as the crude odds ratio. The crude odds ratio is the ratio that is not stratified (by sex, age or age²). On the basis of previous research, this project adjusted for
  • 10. 9 variables that were most likely to influence flu vaccinations. Variables that carried non-statistical significance to the previous models and preliminary analyses were eliminated. For the specific category of race, the analysis compared the prevalence of influenza vaccination using unadjusted odd-ratios for Whites to other minority races, such as Black, American Indian/Alaskan Native, Asian, and Multiple Race. Similarly, for the specific category of marital status, the analysis compared the prevalence of influenza vaccination using unadjusted odd-ratios for Married Couples to Non-Married statuses, such as Widowed, Divorced, Separated, Never-Married (Single) and Cohabitating. Finally, for the specific category of employment status, analysis compared the prevalence of influenza vaccination using unadjusted odd-ratios for Working Individuals with a Company to Non-Working Individuals, such as With Company but Not At Work, Looking for Work, Working for Family, and Not Working. Sex, age and age2 were added as controls to observe the group differences using adjusted odds ratios. The above listed variables are kept constant to prevent their influence on independent variables and the dependent variable. If the adjusted odds ratio equals one, then there is no association, if one is included in the confidence interval, then it is possible that the odds ratio equals one, and it is not statistically significant. Odds ratios may be adjusted for confounding factors. The analysis used similar multivariate logistic procedures for each specific category, designating the same reference groups. Results Basic Characteristics
  • 11. 10 The unadjusted sample consisted of 15,840 observations of adults. The adjusted sample consisted of 16, 157 observations of adults. Table 2 shows the unadjusted prevalence of flu vaccination in Whites and by other individual ethnic groups. Whites were more likely to be vaccinated compared with Blacks. In general, there are racial/ethnic differences of flu vaccination among, and between, all minority groups. However, there were no significant differences between the unadjusted and adjusted odd ratios for flu vaccinations observed. The adjusted prevalence of flu vaccination stratified by sex, age and age², is shown in Table 3. Sex, age and age² are the controlled variables and are kept constant to prevent their influence on the effect of the independent variable on the dependent. The adjusted prevalence of flu vaccination was the highest for American Indian/Alaskan Native and Asian populations. Blacks (p<0.001) have the lowest vaccination rate out of all minorities because of the current barriers to quality healthcare. Generally, they are not able to afford proper healthcare, which includes flu vaccinations (Scheider et al. 2001). American Indian/Alaskan Native have a relatively high vaccination rate, higher than Whites, because the Federal Government provides healthcare for most federally recognized tribes (at last count there were 564 federally recognized tribes) (IHS, 2010). According to the Census, 60% of the American Indian population has access to health facilities which provide flu vaccinations. However, due to the small sample size, American Indians and Alaskan Natives are not statistically significant. Asians have the highest vaccination rate; this can be explained that Asians make up the highest percentage of immigration over the last two decades, generally for higher-level jobs or educational opportunities. Given the reasons for immigration, Asians tend to be given more opportunities through their educational institutions or workplace to take better care of their health (i.e. to be
  • 12. 11 vaccinated). (Wessling, 2004). Social status and/or overall economic well-being logically leads to an increase in access to resources that enable them to receive vaccinations. Multiple race category becomes insignificant because it has too many variables involved (CDC, 2012). The multiple variables cause it to become insignificant and therefore cannot be predicted. Table 4 shows the unadjusted prevalence of flu vaccinations in Married Couples and Non-Married Couples, Widowed, Divorced, Separated, Never-Married, and Cohabitating. Married couples are more likely to receive flu vaccination than Non-Married. Widowed couples have a relatively high flu vaccination rate because they qualify and are permitted to receive Medicare hospital insurance (Social Security Administration, 2012). Divorced couples also have a relatively high vaccination rate because there are usually children involved (those with children generally tend to care more about their own health than those without). Even though the government does not compel parents to vaccinate their children, co-parenting will encourage vaccinations for children as a means to provide the best possible care for the child. Typically, in divorced households, the realization that the benefit of vaccination is usually in the best interest for both parent and child. Separated individuals have a relative low vaccination rate because they may not have access to health maintenance organizations, such as Non-VA healthcare facilities (Straits-Tröster, 2006). Never-married (Single) individuals have the lowest vaccination rate because this group of people may not be as worried about their health and will have a lower vaccination rate. Lastly, cohabitating individuals have a relatively low vaccination rate, possibly due to low income levels or less pressure from their significant other. Since the existing research does not focus on some of the groups, many of these observations are speculative. The data was not affected by the analysis.
  • 13. 12 The adjusted prevalence of flu vaccination stratified by sex, age and age2 , is shown in Table 5. The adjusted prevalence of flu vaccination was significantly higher for married couples than in non- married couples. Widowed and Divorced couples have higher flu vaccination rates possibly due to the fact that this group may consist of the elderly high-risk population that are highly susceptible to the flu and other diseases (particularly widowed couples). Separated individuals receive the lowest vaccination rates possibly because their marriage is unstable, they are going through some type of financial unrest and they are probably not as worried about receiving vaccines or as concerned about their health as a married person. This is only a speculation as little research has been done on this category. However, the data did not affect the analysis. Never-Married (Single) individuals have a relatively low vaccination rate because the age group of a single person is usually younger. Younger people are less worried about their health and/or have less financial wherewithal and thus, may not be as worried about receiving vaccinations (Lochner and Wynne, 2011). Those cohabitating may have a higher vaccination rates than those who are widowed or divorced because they are more similar to the married couple group and are likely to be more involved in each other’s health. However, one difference is that those who are married are able to receive benefits from their partners work and/or provider which generally results in cheaper health insurance. These benefits may not be available to cohabitating individuals (Walton, 2012). So the likelihood of cohabitating couples receiving vaccinations is not as high as married couples. Table 6 shows the unadjusted prevalence of flu vaccinations in Working Individuals and Non-Working Individuals, With a Company but Not At Work, Looking for Work, Working for Family, and Not Working. Working individuals were more likely to be vaccinated compared to non-working individuals. Overall, there are no differences in flu vaccinations among those
  • 14. 13 employed between the unadjusted and adjusted odd ratios. However, there were significant statistical differences between individual employment status groups. The adjusted prevalence of flu vaccination stratified by sex, age and age2 , is shown in Table 7. The adjusted prevalence of flu vaccination was significantly higher for Working Individuals, currently employed and working at a company, than in Non-Working Individuals. Most employees in a company are able to receive medical insurance, which covers flu vaccinations (B.Y.Lee et al., 2009). Individuals whom are looking for work may be able to receive flu vaccinations because they are financially stable and are able to pay for the vaccines themselves or may have COBRA (Henrich and Holmes, 2009). COBRA gives workers and their families the right to choose to continue group health benefits for limited periods of time under certain circumstances such as transition between jobs or other life events. However, working for family individuals may not be able to receive flu vaccinations because they usually are not able to receive health insurance like other employees. They must pay for their own health services if they need it. Not working individuals may be individuals that are unemployed at time of survey and thus may be receiving unemployment benefits that provide for flu vaccinations (Gilbert, 2009). Discussion The results from this study provide three important additions to current knowledge about varying flu vaccination rates among all communities and the existing racial/ethnic inequalities in influenza vaccination. First, as a percentage, African Americans receive fewer vaccinations than Whites. Asians are considered a minority but they do not fit the minority group characteristics. There was no difference in vaccination rates between the unadjusted and adjusted odd ratios.
  • 15. 14 Second, married couples, as compared to all other marital statuses, are more likely to be vaccinated. There are differences in vaccination rates between unadjusted and adjusted odd ratios. Finally, working individuals are more likely to receive vaccines. While looking for work, respondents are likely to be on some form of unemployment benefits or COBRA and thus are more likely to receive vaccinations. Financial independence plays a significant role in access to healthcare and vaccinations. There was no difference in vaccination rates between the unadjusted and adjusted odd ratios. Policy Implications With the developed analysis, this project can advocate for policy changes to the current regulations to create a more effective Medicare system. For minority communities, creating better and more readily accessible access to flu vaccinations would allow for a higher percentage of minorities to stay healthier. At the same time, health care costs are also rising. If the government created a system to assist minority communities financially, perhaps everyone could receive the vaccination. Perhaps if the government created mobile immunization clinics that travel throughout the country, minorities would be more inclined to be vaccinated. Minorities would then receive a higher percentage of vaccinations because the clinics are easily accessible to the community. However, this solution does come with one serious drawback, as there is a common distrust of the government within many minority communities, particularly regarding the effectiveness of government sponsored vaccinations and the underlying “true” purpose of the vaccination. Currently, subsidized vaccinations are only offered to select communities and only a limited number of pre-paid health plans are available for Whites. Perhaps if the government
  • 16. 15 provided flu vaccinations at employment agencies and housing assistance programs, it would encourage more people to receive vaccines because it is easily accessible. Future Research A future study could analyze how income correlates with flu vaccination rates. An extended study on disaggregating the data to observe the data based on individual states would also be valuable to many communities. Additionally, each state has different laws regarding health insurance and flu vaccines. For example, the Vermont State Health Commissioner is currently attempting to take away the right of Vermont parents to make vaccine decisions for their children. Colorado has passed flu shot mandates for all healthcare workers. Since the analysis was based on the sample adult file, studying the child data release could prove significant. Since health care costs are rising, it could be insightful to track the data, analysis, and results over time and compare those to historic data. Acknowledgements I would like to thank my advisor, Dr. Andrew Noymer for his ongoing support for my personal and professional development. I am also grateful to my mentor, Daisy Carreon, and supervisors, Dr. Matt Huffman, Dr. Joanne Christopherson, and Dr. Julie DaVanzo whom contributed to my professional development and offered me ample support. I would like to thank my fellow 2011 DASA cohorts for their warmth and support. I would also like to thank my family, and particularly my parents, Mike and Sue Lin for unbelievable generosity. You have supported me and encouraged me to work hard and succeed in anything and everything I choose
  • 17. 16 to do. I am incredibly grateful for what you have given me. I would also like to thank Mark Sweet, for his warmth, support and friendship and always believing in me. Last but not least, I would like to thank Raymond Chang for giving me true unconditional comfort, softness, and laughter. Thank you for reminding me that the light at the end of the tunnel was always closer than I thought and has been a constant voice of wisdom, guidance, and reassurance.
  • 18. 17 Tables and Figures Table 1-Tables of Descriptives Number Percent Total Observations (n): 16,148 (100) Sex Male: 11,774 (44.18) Female: 14,876 (55.82) Race White: 20,056 (75.36) Black/African American: 4,575 (17.19) American Indian/Alaskan Native (AIAN): 219 (0.82) Asian: 1,765 (6.63) Age Mean : 47.84 Standard Deviation: 18.09 Minimum: 18 Maximum: 85 Marital Status Married: spouse in household: 11,457 (42.99) Married: spouse not in household: 424 (1.59) Widowed: 2,478 (9.30) Divorced: 3,510 (13.17) Separated: 948 (3.56) Never Married: 6,288 (23.59) Cohabitating – living with a partner (1,545) Employment Status Working for pay at a job or business: 14,529 (54.52) With company but not at work: 592 (2.22) Looking for work: 1,819 (6.83) Working, but not for pay, for a family: 233 (0.87) Not working: 9,477 (35.56)
  • 19. 18 Table 2-Whites are More Likely to have Higher Flu Vaccinations than Blacks, 2010 (unadjusted)
  • 20. 19 Table 3-Whites are More Likely to have Higher Flu Vaccinations than Blacks, 2010 (adjusted)
  • 21. 20 Table 4-Married Couples are More Likely to Receive Flu Vaccinations than the Non- Married, 2010 (unadjusted)
  • 22. 21 Table 5-Married Couples are More Likely to Receive Flu Vaccinations than the Non- Married, 2010 (adjusted)
  • 23. 22 Table 6-Working Individuals are More Likely to Receive Flu Vaccinations than Non- Working Individuals (unadjusted)
  • 24. 23 Table 7-Working Individuals are More Likely to Receive Flu Vaccinations than Non- Working Individuals (adjusted)
  • 25. 24 Logistic Regression (unadjusted) _cons .2503982 .0089572 -38.71 0.000 .2334437 .2685841 dont_know 1 (omitted) not_working 1.33589 .0594405 6.51 0.000 1.224325 1.457622 working_for_family .4792373 .1358945 -2.59 0.009 .2749035 .8354509 looking_for_work .7067375 .0680971 -3.60 0.000 .585115 .8536405 with_company_but_not_at_work 1.587287 .1882193 3.90 0.000 1.258116 2.002582 unknown_marital_status 1 (omitted) cohab .7552899 .0701799 -3.02 0.003 .6295379 .9061612 never_married .6626411 .0371226 -7.35 0.000 .5937343 .739545 separated .7249493 .087679 -2.66 0.008 .5719514 .9188745 divorced .8798787 .0554259 -2.03 0.042 .7776844 .9955022 widowed 1.038606 .0728698 0.54 0.589 .905169 1.191714 multiple_race .8826554 .142132 -0.78 0.438 .6437602 1.210203 race_not_released .765674 .4820597 -0.42 0.672 .2229139 2.62997 asian 1.318608 .1011326 3.61 0.000 1.134571 1.532498 american_indian_alaskan_native 1.134683 .2451396 0.58 0.559 .7429828 1.732886 black .7465431 .0446385 -4.89 0.000 .6639853 .8393658 flu_vac Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
  • 26. 25 Logistic Regression (adjusted) _cons .1150904 .0221812 -11.22 0.000 .0788839 .167915 dont_know 1 (omitted) not_working 1.132731 .0577166 2.45 0.014 1.025074 1.251695 working_for_family .4456625 .1267512 -2.84 0.004 .2552203 .7782103 looking_for_work .7227869 .0698072 -3.36 0.001 .5981371 .8734133 with_company_but_not_at_work 1.549945 .1842552 3.69 0.000 1.227797 1.956617 unknown_marital_status 1 (omitted) cohab .8319538 .0789396 -1.94 0.053 .6907697 1.001994 never_married .7584034 .0469458 -4.47 0.000 .6717539 .8562299 separated .7246264 .0879605 -2.65 0.008 .571201 .9192621 divorced .8177023 .0521714 -3.15 0.002 .7215833 .926625 widowed .7923613 .0638961 -2.89 0.004 .6765226 .9280346 multiple_race .9114209 .1471263 -0.57 0.566 .6642225 1.250617 race_not_released .7789669 .4919559 -0.40 0.692 .2259141 2.685929 asian 1.334744 .1027667 3.75 0.000 1.147786 1.552155 american_indian_alaskan_native 1.147152 .2484403 0.63 0.526 .7503692 1.753747 black .739404 .0444594 -5.02 0.000 .6572039 .8318853 age2 1.000049 .0000704 0.69 0.490 .9999107 1.000187 age_p 1.00558 .0072309 0.77 0.439 .9915073 1.019853 sex 1.318139 .0559935 6.50 0.000 1.212838 1.432582 flu_vac Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
  • 27. 26 References Aol Money and Finance: Daily Finance. 2012. “Flu shots for the unemployed, uninsured” Retrieved May 16, 2012 http://www.dailyfinance.com/2009/09/01/flu-shots-for-the-u nemployed-uninsured-will-cvs-walgreens-fre/ Centers for Disease Control and Prevention. 2012. “Seasonal Influenza (Flu)” Retrieved June 2, 2012. http://www.cdc.gov/flu/keyfacts.htm Henrich, N. and B Holmes. 2009. “The public’s acceptance of novel vaccines during a pandemic: a focus group study and its application to influenza H1N1” Emerging Health Threats 8: 1-7 Hutchins, Sonju, Kevin Fiscella, Robert S. Levine, Danielle C. Ompad, and Marian McDonald. 2009. “Protection of Racial/Ethnic Minority Populations During an Influenza Pandemic” American Journal of Public Health 99 (S2): S261 – S270 Immunization Action Coalition. 2011. “Influenza: Ask the Experts” Retried May 26, 2012 www.immunize.org/askexperts/experts_inf.asp Indian Health Service. 2011. “Seasonal Influenza.” Retrieved June 2, 2012 (www.ihs.gov) Lee, Bruce Y., Ateev Mehrotra, Rachel M. Burns and Katherine M. Harris. 2009. “Alternative
  • 28. 27 Vaccination locations: Who uses them and can they increase flu vaccination rates?” Vaccine 29: 4252-4256 Lochner, Kimberly and Marc Wynne. 2011. “Flu Shots and the characteristics of unvaccinated elderly Medicare beneficiaries” Medicare and Medicaid Research Review 1 (4): E1-E11 Niren & Associates Family Law Practice. 2009. “H1N1 Flu Vaccinations for the Kids – When Parents Disagree” Retried June 2, 2012 http://www.divorcesupport.ca/divorce-blog/tag/swine-flu-vaccination/ Nowalk, Mary Patricia., Richard Zimmerman, Shunhua Shen, Ilene K. Jewell and Mahlon Raymund. 2004. “Barriers to Pneumococcal and Influenza Vaccination in Older Community-Dwelling Adults (2000-2001)” American Geriatrics Society 52 (1): 25-30 Schneider, Eric C., Paul D. Clearly, Alan M. Zaslavsky and Arnold M. Epstein. 2001. “Racial Disparity in Influenza Vaccination: Does Managed Care Narrow the Gap Between African Americans and Whites” American Medical Association 286 (12): 1455-1460 Social Security Administration. 2012. “Frequently Asked Questions: How to Qualify for Medicare” Retrieved May 28, 2012 http://ssa-custhelp.ssa.gov/app/answers/detail/a_id/400/~/how-to-qualify-for-medicare Straits-Tröster, Kristy A., Leila C. Kahwati, Linda S. Kinsinger, Jean Orelien, Mary B. Burdick
  • 29. 28 and Steven J. Yevich. 2006. “Racial/Ethnic Differences in Influenza Vaccination in Veterans Affairs Healthcare System” American Journal of Preventative Medicine 31 (5): 375-382 Telford, Rose and Anne Rogers. 2003. “What influences elderly peoples’ decisions about whether to accept the influenza vaccination? A qualitative study” Health Education Research: Theory and Practice 16 (6): 743-753 U.S. Department of Health & Human Services. 1999. “The Health Care Law & You” Retrieved May 20, 2012 www.healthcare.gov/law/index.html Walgreens Co., 2012. “Health Topics: Flu” Retrieved June 2, 2012 http://www.walgreens.com/marketing/library/centers/flu/flu_faq.jsp Walton, Alice G. 2012. “The Marriage Problem: Why Many Are Choosing Cohabitation Instead” BMC Public Health 11: 1-7 Wessling, Susan. 2004. “Closing the Immunization Gap” Minority Nurse Winter 2004 www.minoritynurse.com/features/health/02-12-04d.html