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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

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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)

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Table 3-Whites are More Likely to have Higher Flu Vaccinations than Blacks, 2010

(adjusted)

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Table 4-Married Couples are More Likely to Receive Flu Vaccinations than the Non-

Married, 2010 (unadjusted)

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Table 5-Married Couples are More Likely to Receive Flu Vaccinations than the Non-

Married, 2010 (adjusted)

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Table 6-Working Individuals are More Likely to Receive Flu Vaccinations than Non-

Working Individuals (unadjusted)

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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]

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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]

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