| Summary: | UTAH'S HEALTH: AN ANNUAL REVIEW JUNE 2011 | VOLUME 16 www.matheson.utah.edu/UHReview UH REVIEW 2011 Utah's Health: An Annual Review Original Research Articles 10 Racial and Ethnic Disparities in Seasonal Influenza Vaccination Among Utah Adults, 2000-2008 Andrew E. Burger, BA; EricN. Reither, PhD; David W. Ramos, BS; & Sun Young Jeon, BS 21 Sexual Activity and Contraceptive Use: A survey of University of Utah Undergraduate Students Aged 18-20 Janet C. Jacobson, MD; Sara E. Simonsen, CNM, MSPH; Katherine Morgan Ward DNP, WHNP; Ashley Lena Havlicak; & David K. Turok, MD/MPH 30 Protocol Use in Disease Outbreak Investigations: Applying a Technical Systems Solution to a Natural System Problem Heidi S. Kramer, MS; LaverneA. Snow, MPH; Matthew Samore, MD; and FrankA. Drews, PhD 35 May We Speak to the Lady of the House? Are Women Really the Ones Who Look for Health Information? Kathleen Digre, MD, Sally Patrick, MLS, Sara Simonsen, CNM, MSPH, Brenda Ralls, PhD, Michael Varner, MD, and Patricia Murphy, PhD 40 Preliminary Findings from a Pilot Integrative Obesity and Eating Disorder Intervention Justine J. Reel, PhD, LPC, CC-AASP; Sonya S00H00, PhD; Carlie Ashcraft, MS; & Rachel Lacy, MS 47 Cancer Survival in LTtah: Female Breast, Prostate, Colorectal, Lung and Bronchus, and Melanoma of the Skin, 1995-2006 Antoinette M. Stroup, PhD; C Janna Harrell, MS; Kimberly A. Herget, BA; Rosemary Dibble, CTR Perspectives 58 Awareness of Radon-Associated Health Risks in LTtah Wallace Aker ley, Chris Keyser, Sandie Edwards, Rob Wilson, Terry Van Du-ren, Dylan Akerley, Sarah Tranter, Susan Sharry 61 Community Readiness to Prevent Intimate Partner Violence: A LTniversity Needs Assessment to Health Education Practice Jacqueline R. Barco, MS & Justine J. Reel, PhD, LPC, CC-AASP 70 Working with Individuals with Intellectual Disabilities in Healthcare Settings: Body Image and Eating Disorder Concerns Justine J. Reel, PhD, LPC, CC-A4SP & Robert A. Bucciere, MSW, LCSW T7 2011 Utah Legislative Review 89 2011 Utah Health Data Review UTHE UNIVERSITY OF UTAH® Utah's Health: An Annual Review Executive Editors JB Flinders, MPH, MBA Editor-in-Chief Articles Editor Michelle Everill-Flinders Managing Editor Priti Shah Data Editor Sarah Watts-Justice, MHA, MPA Production Editor Jason Fox, MPA Legislative Editor Editorial Board Members Kyle Burningham Breanna Johnson Ryan Vanderwerff Anthony Tran Caroline Harris Zane Partridge Kimberly Judd Gregg Jones Acknowledgement We would like to thank Dr. Richard Sperry for his continued support and guidance, Sue Dean for her timely and unwavering assistance, the University of Utah Publications Council, ASUU, and the Governor Scott M. Matheson Center for Healthcare Studies for their financial support. Faculty Advisor Richard Sperry, MD, PhD Governor Scott M. Matheson Presidential Endowed Chair in Health Policy Management Advisory Board Members Marlene Egger, PhD Professor, Family & Preventive Medicine, University of Utah Leslie Francis, PhD Dean, College of Humanities, Alfred C. Emery Professor of Law Robert Paul Huefner, PhD Professor Emeritus, Political Science, University of Utah Pamela S. Perlich, PhD Senior Research Economist, Bureau of Economic and Business Research, University of Utah Debra Scammon, PhD Emma Eccles Jones Professor of Marketing, David Eccles School of Business, University of Utah Tawna Skousen, PhD Executive Vice President, Sawyer Technologies Natalie Stillman-Webb, PhD Assistant Professor (Lecturer), English & the University Writing Program, University of Utah Julia Summerhays, PhD Assistant Professor, Health Promotion and Education, University of Utah Norman J. Waitzman, PhD Professor, Department of Economics, University of Utah Utah's Health: An Annual Review 2011 Volume 16 www.uhreview.com A Publication of The University of Utah Utah's Health: An Annual Review | The University of Utah Governor Scott M. Matheson Center for Healthcare Studies 175 North Medical Drive East, Salt Lake City, Utah 84132 © 2011 The University of Utah. All Rights Reserved Introduction and Editor's Note It is with great pleasure that I, on behalf of the 2010-2011 Editorial Board, present the sixteenth volume of Utah's Health: An Annual Review. Utah's Health is dedicated to publishing original and timely health-related research relating to the State of Utah, and providing an analysis of important health-related data. It is a vehicle for health policy dialogue at both state and national levels and is designed to aid students, researchers, legislators, and health-related professionals in the continual pursuit of health-related knowledge and practice. Utah's Health also serves as a health education resource to the general public, and is available online at www.matheson.utah.edu. As in previous years, Utah's Health is comprised of four main sections: Original Research Articles, Perspectives, a Legislative Review, and a Data Review. The Original Research Articles submitted this year are of the utmost qual-ity and demonstrate a high caliber of peer-reviewed scientific research that relates to the health of Utahns. I am most grateful for all of the wonderful submissions that were received. Journals are a complicated and time-consuming process. They involve perseverance, patience, and sacrifice on the part of numerous individuals and organizations. Appreciation is due to many individuals, not only those involved in the journal directly, but those that continue to engage in research, data collection, and the practice of health itself. First and foremost, I would like to thank the diligent group of authors, contributors, and volunteers that have sac-rificed their time and effort to make this journal possible. Their commitment to the research and analysis of health related issues in Utah is the impetus behind the quality of this edition. I am extraordinarily fortunate to have, and extremely thankful for, the guidance of a fantastic group of advisory board members. Their insight and expertise in providing expert reviews and revisions to the numerous articles and data pages is invaluable. I would also like to ex-tend my deepest gratitude to Dr. Richard Sperry for his unwavering support and direction as our faculty advisor. I greatly appreciate the contributions of a remarkable group of fellow students and editorial board members who excelled in the creation of this work. As the Editor-in-Chief, I extend to each one of them a sincere and heartfelt thank you for their hard work and commitment to the success of this publication. My extra special thanks to Mrs. Sarah Watts-Justice for her diligence and guidance throughout the revision and publication process. In her hands, the jour-nal truly becomes an outstanding blend of art and science. As a final note, I continue to be surprised at the vast information we have regarding our health and health behaviors, and how little of it we truly take to heart. We as practitioners, researchers, and educators should always remember that in order to truly create and maintain healthy behaviors in our families, communities, and organizations, we must first do so in our own lives. Again this year, this volume is dedicated to the friends, colleagues, relatives, and loved ones we have lost over the past year. May we continue to use our gifts of knowledge, research, and practice for the health, safety, and ever-improving quality of life in our communities, our families, and within ourselves. JB Flinders, MPH, MBA Editor-in-Chief Utah's Health: An Annual Review-Volume XVI, 2011 UTAH'SHEALTH: ANANNUAL REVIEW2011 Authors and Contributors Wallace Akerley, MD, is Director of Community Oncology Research, Medical Director of the Clinical Trials Office, co-director o f the Thoracic Cancer Program at Huntsman Cancer Institute (HCI) and Professor o f Medicine at the University of Utah, both in Salt Lake City, Utah. Dr. Akerley earned his medical degree from the Brown University School of Medicine in Providence, Rhode Island. He held his residency in internal medicine at the LAC-USC Medical Center in Los Angeles, California. He is Chairman of the Data and Safety Monitoring Committee at the Huntsman Cancer Center and Director of the SEER Utah Cancer Registry as part of the Department o f Health in the State of Utah. Dr. Akerley is a member o f the American Society of Clinical Oncology, the International Association for the Study of Lung Cancer, and the Society of Utah Medical Oncologists, as well as nu-merous cancer management committees. Carlie Ashcraft is a graduate student in the Department o f Health Promotion and Education. She works at the Salt Lake Valley Health Department. Jacqueline R. Barco, MS, is a doctoral candidate in Counseling Psy-chology at the University o f Utah. She is a researcher and advocate in the field of mental health and gender studies, particularly as they relate to the social etiology of gender-based violence and its effects on women. Robert A. Bucciere, MSW, LCSW, is the Lead Licensed Clinical Social Worker for the University Health Care: Neurobehavior HOME Program at the University of Utah that serves clients with intellectual disabilities. He treats youth and adults with disabilities and is a member o f ATSA for the specialization in sexual offender treatment. Rosemary Dibble, CTR, is the Director o f Operations at the Utah Cancer Registry. During her more than 20-year tenure as Director of Operations, the Utah Cancer Registry has consistently been one of the top ranked SEER Reg-istries in the nation and has received numerous awards. Frank Drews, PhD - Associate Professor, Department of Psychology, University o f Utah. C. Janna Harrell has a Master o f Sci-ence in Family Ecology from the Univer-sity of Utah with an emphasis in Demog-raphy. She is the Senior Research Analyst at the Utah Cancer Registry. Kim Herget has a Bachelor's of Arts in Psychology and is a Masters of Statistics student in the Sociology department at the University of Utah. She is a Biostatis-tician at the Utah Cancer Registry. Heidi S. Kramer, MS - Gradu-ate Student, Department of Psychology, University o f Utah. Rachel Lacy is a graduate student in the Department o f Health Promotion and Education. She works at the Salt Lake Valley Health Department. Justine J. Reel, PhD, LPC, CC-AASP, is an Assistant Professor in the Department o f Health Promotion and Education at the University of Utah. She directs the eating disorder and obesity prevention graduate track and is the Founder and Faculty Advisor o f Students Promoting Eating disorder Awareness and Knowledge (SPEAK). Matthew Samore, MD - Professor of Medicine, Adjunct Professor of Bio-medical Informatics. Dr. Samore is the Director of the Salt Lake IDEAS Center and Chief of the Division o f Epidemiology at the University of Utah. Laverne A. Snow, MPH - Graduate Student, Biomedical Informatics Depart-ment, University of Utah. Sonya SooHoo, PhD, is a Research Associate at the Center for Health Care Evaluation, VA Palo Alto Health Care System located in Menlo Park, CA. She received her doctoral degree from Uni-versity o f Utah. Antoinette M. Stroup, PhD, has a Master of Science in Family Ecology from the University of Utah and a PhD in Epidemiology from the University of California. She is the Director of the Utah Cancer Registry and is a Research Assis-tant Professor in the Division of Epidemi-ology, Department of Internal Medicine, School o f Medicine at the University of Utah. UTAH'SHEALTH: ANANNUAL REVIEW2011 Contents Original Research Articles Racial and Ethnic Disparities in Seasonal Influenza Vaccination Among Utah Adults, 2000-2008 Andrew E. Burger, BA; Eric N. Reither, PhD; David W. Ramos, BS; & Sun Young Jeon, BS Sexual Activity and Contraceptive Use: A survey of University of Utah Undergraduate Students Aged 18-20 Janet C. Jacobson, MD; Sara E. Simonsen, CNM, MSPH; Katherine Morgan Ward DNP, WHNP; Ashley Lena Havlicak; & David K. Turok, MD/MPH Protocol Use in Disease Outbreak Investigations: Applying a Technical Systems Solution to a Natural System Problem Heidi S. Kramer, MS; Laverne A. Snow, MPH; Matthew Samore, MD; and Frank A. Drews, PhD May We Speak to the Lady of the House? Are Women Really the Ones Who Look for Health Information? Kathleen Digre, MD, Sally Patrick, MLS, Sara Simonsen, CNM, MSPH, Brenda Ralls, PhD, Michael Varner, MD, and Patricia Murphy, PhD Preliminary Findings from a Pilot Integrative Obesity and Eating Disorder Intervention Justine J. Reel, PhD, LPC, CC-AASP; Sonya SooHoo, PhD; Carlie Ashcraft, MS; & Rachel Lacy, MS Cancer Survival in Utah: Female Breast, Prostate, Colorectal, Lung and Bronchus, and Melanoma of the Skin, 1995-2006 Antoinette M. Stroup, PhD; C Janna Harrell, MS; Kimberly A. Herget, BA; Rosemary Dibble, CTR Perspectives Awareness of Radon-Associated Health Risks in Utah Wallace Akerley, Chris Keyser, Sandie Edwards, Rob Wilson, Terry Van Duren, Dylan Akerley, Sarah Tranter, Susan Sharry Community Readiness to Prevent Intimate Partner Violence: A University Needs Assessment to Health Education Practice Jacqueline R. Barco, MS & Justine J. Reel, PhD, LPC, CC-AASP Working with Individuals with Intellectual Disabilities in Healthcare Settings: Body Image and Eating Disorder Concerns Justine J. Reel, PhD, LPC, CC-AASP & Robert A. Bucciere, MSW, LCSW 2011 Utah Legislative Review 2011 Utah Health Data Review 10 21 30 35 40 47 58 61 70 77 89 UTAH'SHEALTH: ANANNUAL REVIEW2011 Original Research Articles 2011 Utah's Health: An Annual Review 2011 UTAH'S HEALTH: AN ANNUAL REVIEW CORRESPONDENCE Andrew E. Burger sociology Graduate student Utah state University department of ssw&A 0730 old Main Hill Logan, UT 84322-0730 phone (435) 227-5241 cell (208) 569-4354 fax (435) 797-1240 email andrew.burger@aggiemail.usu. edu k e yw o r d s influenza, vaccination, health disparities, race/ethnicity, socioeconomic status (s e s ) Racial and Ethnic Disparities in Seasonal Influenza Vaccination among Utah Adults, 2000-2008 Andrew E. Burger, BA; Eric N. Reither, phD; David w. Ramos, Bs; & sun Young Jeon, Bs a b s t r a c t Health inequalities have long been observed among racial/ethnic groups in the United States. Despite initiatives to address health disparities, substantial differences remain. One important area o f concern is racial/ethnic disparities in vaccination for the seasonal flu. The seasonal flu is responsible for thousands o f deaths each year in the United States, and even more hospitalizations, with billions o f dollars drained from the economy due to illness and lost productivity. Seasonal vaccination remains the most simple and effective means of preventing the flu, but millions go unvaccinated every year with notable differ-ences across racial/ethnic groups. Using the Behavioral Risk Factor Surveillance System (BRFSS), we examine vaccination rates among adults from various racial/ethnic groups in Utah during the 2000-2008 influenza seasons. Our analyses demonstrate that flu vaccina-tions increased significantly for non-Hispanic Whites over this period, but appear to have declined somewhat among Hispanics. Through a series o f logistic regression models, we discovered that lower odds of vaccination among Hispanics disappeared after controlling for healthcare coverage and other socioeconomic characteristics. These findings suggest that seasonal influenza vaccination rates can be improved among racial/ethnic minorities in Utah by addressing structural barriers to receiving the seasonal flu vaccination, espe-cially access to healthcare coverage. 10 r a c ia l a n d e t h n ic d is p a r i t ie s i n s e a s o n a l i n f l u e n z a v a c c in a t io n UTAH'S HEALTH: AN ANNUAL REVIEW 2011 Every year influenza infections and related comorbidities account for thousands of deaths in the United States. Effective and safe vaccines for seasonal influenza have been developed and promise to substantially reduce the mortali-ty and morbidity burden of influenza viruses. However, millions go unvaccinated every year in the United States. Past research has identified the existence o f disparities in seasonal influenza vaccination, with racial/ethnic minorities experiencing lower vaccination rates than non-Hispanic Whites (Egede and Zheng 2003; Fiscella 2005; Fiscella et al. 2007; Fiscella et al. 2002; Linn, Guralnik, and Patel 2010; Logan 2009; Zimmerman et al. 2003). While inequalities in vaccination have been observed in the past, they are typically based on single year observations. To better understand racial/ethnic disparities and trends in those disparities, this study will examine nine consecutive flu seasons beginning in the year 2000 in the state o f Utah. Given the increasingly diverse population in Utah - particularly with a rapidly growing Hispanic population - it is important to under-stand recent trends and disparities in flu vaccination, which will help identify opportunities to improve public health in the state. DISEASE BURDEN OF SEASONAL INFLUENZA Mortality In a recent publication issued by the Centers for Disease Con-trol and Prevention (CDC), mortality estimates were provided for the 1976-2007 influenza seasons in the United States (CDC 2010a). Deaths resulting from seasonal influenza were estimat-ed to have ranged from a low of 3,349 during the 1986-1987 flu season to a high of 48,614 deaths during the 2003-2004 season. The average level o f mortality from influenza during 1976-2007 was around 23,607 deaths per flu season. Using public data available for analysis online from the CDC (2003), we found that the mortality burden of the seasonal flu and pneumonia, which are often associated (see Klugman, Chien, and Madhi 2009), is so great that it was listed as the eighth leading cause of death in the United States from 1999-2007, with an estimated total of 553,629 deaths during that time period. This places influenza and pneumonia above suicide, homicide, liver disease, hypertension, and AIDS in terms of the estimated total number of deaths in the United States during that time period (CDC 2003). Molinari et al. (2007) estimate that 610,660 life-years are lost per annum in the U.S. due to the seasonal flu. Morbidity While the mortality burden associated with the seasonal flu has been well characterized in the scientific literature, the morbidity burden, while likely to be great, is harder to estimate. Hospitalizations due to influenza during each flu season may help in estimating the virulence of various flu strains. From 1979 to 2000, an average o f nearly 200,000 people was hospi-talized each year due to influenza-related illnesses (Thompson et al. 2004). However, because of the varying severity of the sea-sonal flu, estimates o f hospitalizations ranged anywhere from 157,911 during the 1990-1991 flu season to 430,960 during the 1997-1998 flu season. While hospitalization rates were found to be highest among the elderly, young children under the age o f 5 also experienced high hospitalization rates - similar, in fact, to those experienced by 50-64 year olds. Economic Burden The total economic burden o f the seasonal flu is estimated to be nearly $87 billion annually in the United States (Molinari et al. 2007). An estimated $6 billion is spent on influenza related hospitalizations, $6.8 billion on outpatient care, and more than $16 billion in lost earnings due to illness and loss of life. With an estimated annual 44 million days lost from work due to influ-enza, the impact of the seasonal flu in terms o f lost productivity, absenteeism, and related costs for employers is also substantial (Akazawa, Sindelar, and Paltiel 2003). Although these figures are striking, the actual disease burden of the seasonal flu is likely to be larger than previously esti-mated. Several reporting issues contribute to the underreport-ing of influenza related illnesses and deaths. One reason for the underreporting of deaths from influenza is that "states are not required to report individual seasonal flu cases or deaths of people older than 18 years o f age" to the CDC (CDC, 2010d, p. 1). Furthermore, influenza is rarely listed on death certificates of individuals who die from flu related complications, such as pneumonia. Additionally, even when the International Classifi-cation of Disease (ICD) codes are implemented to track mortal-ity, research has shown that many deaths caused by influenza tend to be missed, such as cardiovascular or circulatory deaths caused by influenza-related complications (Monto 2008). e f f e c t iv e n e s s o f i n f l u e n z a v a c c in a t io n The high mortality, morbidity, and economic costs associated with seasonal influenza could be reduced through vaccination, which is an effective way to prevent infection (Nichol 2008, CDC 2010b). The CDC recommends that obtaining a flu vac-cination should be the first step in preventing the seasonal flu (CDC 2010d). Vaccination against the seasonal flu provides substantial benefits for mothers and young infants (Zaman et al. 2008), as well as healthy children (Jefferson et al. 2005, Man-zoli et al. 2007). Among working U.S. adults, vaccination also has substantial health benefits, decreasing upper respiratory ill-ness by 25% and reducing absenteeism due to upper respiratory illness by 43% (Nichol et al. 1995). Vaccination also provides significant benefits for the elderly populations which are par-r a c ia l a n d e t h n ic d is p a r i t ie s i n s e a s o n a l i n f l u e n z a v a c c in a t io n 11 2011 U tah's h e a l th : an a n n u a l re v iew ticularly vulnerable to influenza (Nichol et al. 2007, Gross et al. 1995). Even during years where the influenza vaccine (which is prepared before the onset of each flu season) is a poor antigenic match for that season's particular flu strain, health benefits can still be gained through vaccination (Herrera et al. 2007, CDC 2010b). Given the substantial health benefits provided by influ-enza vaccination, the CDC revised its guidelines in 2010-2011 to recommend that all individuals 6 months o f age and older receive an influenza vaccination (CDC 2010b). Reaching this ambitious new standard will be difficult. Prior goals set by the CDC for vaccinating recommended age groups have been hard to attain (Nichol 2008). Before the 2010-2011 change in protocol, the CDC recommended influenza vaccina-tion only for select groups of the population, such as the elderly or those at particular risk of complications due to the flu (Nichol 2007). However, Lu et al. (2008) found that from 1989-2005, when the CDC focused on these high-risk populations, vaccina-tion attainment goals were rarely met. Indeed, only 69.5% of persons aged 65 and older received the flu vaccine in the United States during the 2007 flu season (Linn, Guralnik, and Patel 2010), demonstrating that considerable gains need to be made in order to achieve the new 2010-2011 standard of universal vaccination o f the entire population age six months and older. Important in understanding the barriers to attainment of the CDC goals is an examination o f the substantial differences in influenza vaccination rates by race/ethnicity. b a r r ie r s t o v a c c in a t io n Consistently, race and ethnicity prove to be strongly associated with seasonal influenza vaccination (Egede and Zheng 2003, Chen et al. 2007). Fiscella (2005) estimates that if racial/eth-nic disparities were eliminated, an additional 1 million elderly minority persons in the U.S. would receive an influenza vaccina-tion each year. Eliminating vaccination disparities could yield remarkable improvements in population health. To illustrate, eliminating vaccination disparities could save an estimated 33,000 years o f life per annum among racial/ethnic minorities in the U.S. (Fiscella 2007) Preventive healthcare services such as flu vaccination are often underutilized by racial/ethnic minorities (Logan 2009). Chen et al. (2007) found that Hispanics tend to cite structural barriers that prevent receipt of the seasonal flu vaccination, in-cluding insufficient access to preventive services, lack of trans-portation, not knowing where to go, and economic costs. These researchers also found that health insurance was a significant predictor o f vaccination among Hispanics. In addition to structural barriers, racial/ethnic minorities may be less informed regarding the severity of the seasonal flu and the benefits of vaccination. According to Chen et al. (2007), one o f the most common explanations among racial/ethnic minorities for not receiving the flu vaccine was a lack o f concern about contracting the flu. This suggests that some racial/eth-nic minorities may be less likely to go to a health care facility with the intent o f receiving just an influenza vaccine (Link et al. 2006). Misinformation and inadequate education about the seasonal flu among some racial/ethnic minorities may contrib-ute to lower rates of vaccination. Language barriers can also deter vaccination, especially among Hispanics and other racial/ethnic groups with large numbers of recent migrants. Fiscella et al. (2002) presents evidence showing that English-speaking Hispanics with health insurance did not differ significantly from their non-Hispanic White counterparts in terms o f receiving an influenza vaccina-tion. However, Spanish-speaking Hispanics with health insur-ance received flu vaccinations at lower rates than non-Hispanic Whites with insurance. m e t h o d s a n d p r o c e d u r e DATA sOURCE To identify racial/ethnic disparities in flu vaccination in Utah, this study will utilize the Behavioral Risk Factor Surveillance System (BRFSS), which is an annual health survey sponsored by the CDC. The BRFSS is the largest ongoing telephone based survey tracking health-related information of non-institution-alized U.S. adults over the age o f 18 (CDC 2008). The BRFSS is administered in Utah by the Department of Health; data are collected monthly on a range of different health topics (Utah Department o f Health). d e pe n d e n t v a r ia bl e The BRFSS measures influenza vaccination by asking respon-dents if they have received a flu shot during the past 12 months. The respondents' responses were coded as "Yes", "No", "Don't Know", or "Refused." Beginning in 2004, the BRFSS additional-ly asked respondents if they had received an influenza vaccina-tion through a nasal spray. Since the principal interest o f this research is vaccination, not the mode of vaccination, the two variables were combined so that if the respondent responded "Yes" to either or both, they were coded as having been vacci-nated during the last 12 months. Flu Seasons in the BRFSS The BRFSS presents some unique challenges in accurately linking reports of seasonal flu vaccination to the appropriate flu season. Given the seasonal timing of flu epidemics and the retrospective wording of the flu vaccine question in the BRFSS, 12 RACIAL AND ETHNIC DIs p ARITIEs IN sEAsONAL INFLUENzA vACCINATION UTAH'S HEALTH: AN ANNUAL REviEw 2011 it becomes difficult to identify which flu season the respondent is referring to in his or her responses about vaccinations. Previous studies o f BRFSS data have addressed this issue in a variety of ways. For example, Linn et al. (2010) include all responses from the 2008 BRFSS in their analysis o f flu vaccina-tion rates during the 2007 flu season. While this method likely does primarily capture individuals from the 2007 flu season, it certainly also includes respondents who were referring to either the 2006 or the 2008 flu seasons. Furthermore, using any given year of the BRFSS to estimate the previous year's flu season may prove inaccurate since vaccination rates may vary across flu sea-sons in response to the virulence of flu strains, economic condi-tions, and other factors. For instance, enhanced media coverage during a given flu season could increase vaccination rates as a larger segment of the population becomes aware o f the flu (Ma et al. 2006). Other factors, such as the influenza vaccine short-age of 2004 - in which there was a nearly 50% reduction in the supply o f flu vaccine - could also play a role in seasonal differ-ences in vaccination (Zimmerman et al. 2006). Until the 2009 BRFSS, respondents were only asked whether they had received a flu vaccination in the previous 12 months. Beginning with the 2009 BRFSS, however, information was gathered regarding the month and year of the respondent's last reported flu vaccination. With that information we can ac-curately determine during which flu season respondents were vaccinated. Flu seasons typically begin in late October or No-vember and can last until the next year's summer (CDC 2010c). Since public influenza vaccinations typically begin before the flu season starts, we will consider respondents who received their vaccine from September o f any given year through August of the next year as being vaccinated for that particular flu season. For example, in Table 1 approximately 78% (n = 3486) of those who reported receiving a flu vaccine in Utah did so during the 2008 flu season (sometime between September 2008 and August 2009). Approximately 20% (n = 902) reported having received their flu vaccine during the 2009 influenza season, and a little more than 1% (n = 56) reported having received their flu vaccine during the 2007 influenza season. Taken together, roughly 22% of respondents referred to flu seasons other than 2008, mean-ing that they would be misclassified using the methodology adopted by Linn et al. (2010). Clearly, it is necessary to exercise caution when making as-sumptions about the ability of a single wave o f the BRFSS to accurately depict vaccination rates for a particular flu season. However, dramatic gains in accuracy can be made when re-stricting the sample by interview month. Among respondents who were interviewed from January to September o f the 2009 Utah BRFSS, nearly 98% reported receiving their vaccination during the 2008 flu season. By excluding individuals who were interviewed from October to the end of the 2009 BRFSS, we greatly reduce the number of vaccinations reported for the 2009 flu season, which increases our ability to portray seasonal vac-cination rates accurately. Because vaccination dates are not available in the BRFSS prior to 2009, we propose an alternate method of measuring seasonal vaccination rates based on the respondents' month of interview. Our analyses suggest that by restricting the sample to those interviewed from January to September of each survey year, we will estimate the previous year's seasonal flu vac-cination rates with greater precision. To illustrate, we will use responses from individuals interviewed during the months o f January through September of the 2001 BRFSS to estimate vaccination rates during the 2000 flu season. Subsequent flu seasons will be coded in a like manner. This is an imperfect solution, as restricting the sample by in-terview month will result in the exclusion o f about a quarter of the respondents in each survey year. However, those individuals who are excluded are likely to be reporting a flu vaccine for a different flu season and their inclusion would produce error and significantly reduce our ability to evaluate specific flu seasons. TABLE 1. Influenza seasons in which vaccination was reported, 2009 Utah BRFSS. Entire Sample Interview Month 01/09 to 01/10 Restricting Vaccination by Interview Month 01/09 to 09/09 Restricting Vaccination by Interview Month 10/09 to 01/10 Flu Season n % Flu Season n % Flu Season n % 2007a 56 1 .2% 2007a 56 1 .6% 2007a 0 0.0% 2008b 3486 78.4% 2008b 3341 97.9% 2008b 145 14.0% 2009c 902 20.3% 2009c 14 0.4% 2009c 888 85.9% Total 4444 100% Total 3411 100% Total 1033 100% a. Received influenza vaccination from 01/08-08/08. b. Received influenza vaccination from 09/08-08/09. c. Received influenza vaccination from 09/09-12/09. r a c ia l a n d e t h n ic d is p a r i t ie s i n s e a s o n a l i n f l u e n z a v a c c in a t io n 13 2011 U tah's h e a l th : an a n n u a l re v iew Moreover, exploratory analyses indicate that the data are not biased in any particular fashion by eliminating respondents who were interviewed later in the year. Like previous research (Lu, Euler, and Callahan 2009), our study selects respondents by time of interview. However, whereas the methodology outlined by Lu et al. (2009) includes respondents interviewed from February to August, we include respondents over a wider interval o f time - January through September. This decision is rooted in our analyses of 2009 BRFSS data for Utah, which reveal that our technique retains fully 95.8% of respondents reporting vaccination for the 2008 flu season, compared to 89.3% using the previous standard. We retain these additional BRFSS participants without compromis-ing our ability to categorize them into the correct flu season; 97.9% o f the respondents in our sample report receiving their vaccination for the 2008 flu season, which results in a low rate of error that is comparable to the alternative approach. To en-sure that this finding is not anomalous, we compared our results against national BRFSS data. The benefits o f using interview months January through September are even more pronounced in national data, corroborating our findings for Utah. i n d e pe n d e n t v a r ia bl e s Our chief independent variable is race/ethnicity. Within the BRFSS, racial/ethnic background is coded as White non-His-panic, Black non-Hispanic, other race non-Hispanic, multiracial non-Hispanic, and Hispanic. However, due to the low num-ber of respondents in the Black non-Hispanic and multiracial non-Hispanic categories during our period of study, we com-bine them with the other race non-Hispanic category to create three exhaustive and mutually exclusive racial/ethnic catego-ries: White non-Hispanic, Hispanic, and other non-Hispanic. Although it is difficult to identify precisely which racial/ethnic groups comprise the other non-Hispanic category, we retain them in our analyses for comparative purposes and also to maximize statistical power for some analyses. Other independent variables of interest include age, sex, level of education, household income, and healthcare coverage. Age is collapsed into six categories including: 18-24, 25-34, 35-44, 4 5-5 4, 55-6 4 , and 65+. Educational attainment is recoded into five categories including: "Less than High School", "High School Graduate", "Some College or Technical School", and "College Graduate." To create the "Less than High School" category we combined three different responses: "Never Attended School or Only Kindergarten", "Elementary School (Grades 1-8)" and "Some High School (grades 9-11)." Household income is collapsed into seven different categories. Whether or not the respondent participated in some sort of healthcare plan was assessed through the question, "Do you have any kind o f health care coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare?" to which the FIGURE 1. Directed acyclic graph (DAG) showing mediated effects of race/ethnicity on the odds of seasonal influenza vaccination. 14 RACIAL AND ETHNIC DIs p ARITIEs IN sEAsONAL INFLUENzA vACCINATION U ta h 's h e a l t h : a n a n n u a l r e v ie w 2011 respondent replied "Yes", "No", "Don't Know", or "Refused." ANALYsIs To illustrate plausible mechanisms through which race/eth-nicity influences the odds of receiving an influenza vaccination, we have constructed a directed acyclic diagram (Greenland, Pearl, and Robins 1999; Shrier and Platt 2008). As shown in Figure 1, we propose that the race effect is mediated primarily through healthcare coverage and indicators o f socioeconomic status (specifically household income and education); the "direct" influence o f race/ethnicity is therefore expected to at-tenuate substantially after controlling for these three mediators. Note that we also include a handful of demographic measures as control variables, to account for potential differences between racial/ethnic groups. For instance, Hispanic respondents tend to be younger than their non-Hispanic White counterparts, which could partially account for gross differences in influenza vaccination rates observed between these groups. All analyses were performed using SPSS 18 and Microsoft Excel 2007. Additionally, the SPSS Complex Samples Module was used to generate point estimates and produce variance estimates. Vaccination rates were estimated for the 2000- 2008 influenza seasons for various sociodemographic groups. Difference o f proportions tests were subsequently conducted to determine if the differences observed between racial/ethnic groups were statistically significant for each sociodemographic subgroup (e.g., females). Linear regression was also used to summarize trends in vaccination rates over this period of observation for non-Hispanic Whites and Hispanics. Trends in seasonal vaccination rates among other non-Hispanics were generally similar to Whites and are not shown in the results. Finally, a series of logistic regression models were estimated to assess potential mechanisms through which race/ethnic-ity affects the odds o f influenza vaccination. Model 1 includes demographic control variables, which likely account for some of the raw differences in the odds o f influenza vaccination ob-served between racial/ethnic groups. Model 2 includes presence of healthcare coverage, which potentially represents the single most important structural barrier to influenza vaccination for disadvantaged racial/ethnic groups. Model 3 includes socioeco-nomic factors (household income and education), which may represent additional mechanisms through which race/ethnicity influences the odds o f influenza vaccination. r e su l t s Influenza vaccination percentages are presented in Table 2 for the 2000-2008 flu seasons by race/ethnicity and sociode-mographic subgroups. For most Hispanic sociodemographic subgroups, vaccination rates were significantly lower than their non-Hispanic White counterparts. To illustrate, Hispanic males experienced substantially lower influenza vaccination rates than White males (23.1% vs. 34.6%). A similar disparity was observed for Hispanic females (31.9% vs. 39.5%). Significant differences between Hispanics and Whites were also observed across all income levels, with Hispanics experiencing lower vac-cination rates except for the $50,000-$74,999 category where Hispanic vaccination rates were higher. Noteworthy differences 2000 2001 2002 2003 2004 2005 2006 2007 2008 RACIAL AND ETHNIC DIs pARITIEs IN sEAsONAL INFLUENzA vACCINATION 15 2011 UTAH'S HEALTH: AN ANNUAL REviEw Table 2. Influenza vaccination coverage in sociodemographic subgroups by race: Utah, 2000-2008. Total Sample White, non-Hispanic Hispanic Other, non-Hispanic Characteristics --- 1 --- n % 95% CI n % 95% CI n % 95% CI n % 95% CI Age 18-24 2,682 24.5 22.4-26.7 2,278 25 22.8-27.5 267 19.9 14.1-27.3 137 25.8 17.2-36.7 25-34 6,353 25.8 24.5-27.1 5,509 25.9 24.6-27.3 567 23.2 19.2-27.7 277 29.1 22.5-36.8 35-44 6,554 27.3 25.9-28.7 5,749 27.4 26.0-28.9 492 24.5 19.8-29.8 313 30.3 24.3-37.0 45-54 6,646 35.9 34.4-37.5 6,070 36.2 34.6-37.8 339 33.7 27.7-40.2 237 34.7 27.4-42.8 55-64 5,541 48.4 46.6-50.1 5,148 49 47.1-50.8 214 39.2*** 30.9-48.1 179 44.6 35.2-54.4 65+ 7,245 74.2 72.8-75.6 6,857 74.1 72.7-75.5 218 0.749 66.9-81.5 170 78.2 69.9-84.8 Sex Male 15,188 33.5 32.5-34.5 13,699 34.6 33.5-35.7 877 23.1*** 19.6-27.0 612 32.1 27.5-37.1 Female 19,987 38.8 37.9-39.7 18,043 39.5 38.5-40.5 1228 31 9* * * 28.3-35.7 716 36.4 31.4-41.7 Household Income <$14,999 2,435 31.4 28.8-34.1 1,984 33.5 30.5-36.7 295 24.4** 18.6-31.4 156 23.8* 16.7-32.7 $15,000-$24,999 4,593 35 32.9-37.1 3,853 37.5 35.2-39.8 531 25 5* * * 20.4-31.3 209 28.5* 21.6-36.7 $25,000-$34,999 3,860 36.9 34.8-39.0 3,400 38.9 36.7-41.1 292 21.6* * * 16.2-28.3 168 37.3 26.8-49.1 $35,000-$49,999 5,915 34.5 32.9-36.2 5,376 35.3 33.6-36.9 311 28.5* 21.4-36.8 228 31.5 24.5-39.4 $50,000-$74,999 6,466 34.7 33.2-36.3 6,085 34.4 32.9-36.0 212 43.4** 35.2-52.0 169 33.1 23.7-44.0 >$75,000 8,311 39.1 37.7-40.5 7,894 39.3 37.8-40.7 184 30.4* 23.0-39.0 233 42.2 33.6-51.3 Education Less than HS 2,162 28.3 25.6-31.1 1,436 32.5 29.0-36.1 603 21 8* * * 17.4-26.8 123 26.8 17.7-38.4 High School 9,983 34.1 32.8-35.4 8,912 35.2 33.8-36.6 695 25.2*** 21.2-29.6 376 31.6 25.3-38.7 1-3 Years College 11,702 35.4 34.3-36.6 10,795 35.5 34.3-36.8 476 34.9 28.9-41.4 431 34.2 28.4-40.6 4+ Years College 11,280 41 39.8-42.1 10,559 41.3 40.1-42.5 324 34.8* 28.7-41.4 397 38.7 32.4-45.5 Healthcare Coverage Yes 30,903 39.4 38.7-40.1 28,508 39.7 38.9-40.5 1338 35.9* * 32.2-39.8 1057 37.2 33.3-41.3 No 4,168 17.8 16.3-19.5 3,147 18.4 16.6-20.5 763 15.8 12.8-19.5 258 18.6 13.4-25.1 t = unweighted sample size. CI = confidence interval. % and CI are calculated from weighted values. p values from difference of proportion test (White, non-Hispanic vs. Hispanic/White, non-Hispanic vs. Other, non-Hispanic). *p<0.05; **p<0.01; ***p<0.001. between Hispanics and Whites were also observed by education level. For instance, Hispanics with a High School degree or less had vaccination rates that were roughly 10% lower than Whites with similar education. Significant differences were also ob-served between Hispanics and Whites with four or more years of education, with Hispanics experiencing significantly lower rates of vaccination than their White counterparts. Age appears to play a very important role in vaccination across all racial/ethnic groups, with younger groups experienc-ing substantially lower vaccination rates than older respon-dents. Across all racial/ethnic groups, vaccination rates jump more than 25% from the 55-64 to the 65+ categories. From ages 18-54, estimated vaccination rates were similar among racial/ ethnic groups. Across all races/ethnicities, vaccination rates increased with rising age, household income, education, and membership in some form o f healthcare coverage plan. Figure 2 shows flu vaccination trends in Utah for non-His-panic Whites and Hispanics during the 2000-2008 flu seasons. In general, non-Hispanic Whites tend to exhibit higher annual vaccination rates than Hispanics. Over this period of obser-vation, non-Hispanic Whites also experienced a statistically significant increase in vaccination rates (p < 0.05). Somewhat disconcerting is the negative linear trend observed among His-panics (p > 0.05), which shows that disparities have widened over the past decade. Hispanic vaccination rates generally declined from their high of 35.4% during the 2000 flu season to their lowest point of 21.8% during the 2006 flu season, after which it rebounded. In Table 3, we present results from a series o f logistic re-gression models that examine the effect of race/ethnicity on vaccination, while controlling for various sociodemographic characteristics. In Model 1 we examined the effect of race/eth-nicity while controlling for sex, age, and period of observation. This model indicates that the odds o f receiving a flu vaccine were about 18% lower among Hispanics than non-Hispanic Whites (p < 0.01). Statistically significant differences by sex were also observed; the odds o f flu vaccination were about 17% lower among men (p < 0.001). Age was also significantly associ-ated with flu vaccination, with odds of vaccination dramatically increasing with age. Relative to the oldest age group (65+), the odds o f vaccination among those in the 55-64 age group were nearly 70% lower (p < 0.001). Finally, consistent with our find- 16 r a c ia l a n d e t h n ic d is p a r i t ie s i n s e a s o n a l i n f l u e n z a v a c c in a t io n U ta h 's h e a l t h : a n a n n u a l r e v ie w 2011 Table 3. Logistic regression estimates of the effect of race/ethnicity and sociodemographic factors on influenza vaccinations for Utah adults, 2000-2008. Sociodemographic Factors Model 1 Model 2 Model 3 AORt 95% CIt AOR 95% CI AOR 95% CI Race/ethnicity White 1.000 1.000 1.000 Hispanic 0.819** 0.708 0.947 0.999 0.859 1.161 1.131 0.959 1.333 Other 1.082 0.909 1.288 1.089 0.916 1.293 1.067 0.890 1.279 Sex Female 1.000 1.000 1.000 Male 0.824*** 0.772 0.879 0.843*** 0.789 0.900 0.810*** 0.755 0.869 Age 65+ 1.000 1.000 1.000 55-64 0.323*** 0.292 0.358 0.338*** 0.305 0.374 0.301*** 0.269 0.338 45-54 0.195*** 0.177 0.215 0.204*** 0.185 0.226 0.180*** 0.161 0.202 35-44 0.132*** 0.119 0.146 0.140*** 0.127 0.155 0.124*** 0.110 0.139 25-34 0.120* * * 0.109 0.133 0.132*** 0.120 0.146 0.119*** 0.106 0.133 18-24 0.114*** 0.099 0.130 0.130*** 0.113 0.149 0.128*** 0.110 0.150 Healthcare Coverage No 1.000 1.000 Yes 2.182*** 1.927 2.471 1.911*** 1.662 2.197 Education Less than HS 1.000 High School 1.254* 1.034 1.520 1-3 Years College 1.338** 1.106 1.619 4+ Years College 1.593*** 1.316 1.928 Household Income <$14,000 1.000 $15,000-$24,999 1.083 0.909 1.290 $25,000-$34,999 1.213* 1.020 1.442 $35,000-$49,999 1.139 0.964 1.345 $50,000-$74,999 1.180 0.999 1.395 >$75,000 1.324** 1.119 1.567 Period 2000-2008 1.065*** 1.052 1.079 1.069*** 1.055 1.082 1.066*** 1.051 1.080 Valid n.f 35,021 34,920 31,437 * p <0.05; **p <0.01; * **p <0.001. t AOR, adjusted odds ratio; CI, confidence interval. t Unweighted sample size. RACIAL AND ETHNIC DIspARITIEs IN sEAsONAL INFLUENzA vACCINATION 17 2011 U tah's h e a l th : an a n n u a l re v iew ings for non-Hispanic Whites in Figure 2, the overall trend in flu vaccination over this period of observation was positive and statistically significant (p < 0.001). In Model 2, we extend the previous model by controlling for healthcare coverage. Most interesting is the disappearance of any statistically significant effect of Hispanic ethnicity on the odds of vaccination (p = 0.99) when controlling for healthcare coverage. Among respondents with some form of healthcare, the odds o f receiving an influenza vaccination were over two times greater than respondents without healthcare coverage (p < 0.001). Model 3 includes all variables in Models 1 and 2 and also adds education and household income. In this model, the non-significant impact of race/ethnicity is maintained. However, it is interesting to note that controlling for healthcare coverage, education and household income causes the odds o f vaccina-tion among Hispanics to reverse relative to Model 1. That is, in Model 3 the odds of vaccination are about 13% higher among Hispanics than non-Hispanic Whites - and this effect ap-proaches a marginal level o f statistical significance (p = 0.14). d i s c u s s i o n As shown through these analyses, there were significant dif-ferences in flu vaccination rates across racial/ethnic groups in Utah during the 2000-2008 flu seasons. The main finding to emerge from our study is that Hispanics in Utah were generally vaccinated at lower rates than non-Hispanic Whites. Unfortu-nately, over the past decade disparities between non-Hispanic Whites and Hispanics in seasonal influenza vaccination have in-creased. While Hispanic ethnicity appears to play an important role in determining influenza vaccination, its effect is driven primarily by access to some form of healthcare coverage, as well socioeconomic factors. This is promising news, as it suggests that policies and programs designed to address basic structural barriers like health insurance and education can potentially overcome certain racial/ethnic health disparities, including widening gaps in influenza vaccine coverage in the state o f Utah. Another key finding in our study is the jump in vaccina-tion rates for each racial/ethnic group that occurs at the age of 65. Aside from being more susceptible to influenza related complications (which could motivate individuals to seek im-munization), a likely explanation for the large increase in flu vaccination rates for individuals ages 65 and older is Medicare coverage, which starts at the age o f 65 and usually covers the cost o f influenza vaccinations. Additionally, since the target age populations for the influenza and pneumococcal vaccination overlap, the CDC strongly recommends that health-care officials administer the vaccines concurrently which may also increase vaccination rates among those 65 and older (CDC 2002). We think it is important to note that the sharp drop in vac-cination coverage for non-Hispanic Whites during the 2004 season was expected, since that season experienced a serious shortage in flu vaccination supplies (Zimmerman et al. 2006). It is interesting to note however, that the 2004 vaccine short-age did not drive Hispanic vaccination rates any lower - in fact, our estimates suggest that they rose somewhat and the disparity between Hispanics and non-Hispanic Whites narrowed. One ex-planation for relatively stable vaccination rates among Hispan-ics during the vaccine shortage could be greater efforts to reach vulnerable populations during this period of time. That is, 2004 could be interpreted as a public health success story. Unfortu-nately, during subsequent years vaccination rates continued to decline for Hispanics. It wasn't until the 2007 influenza season, when vaccinations supplies had fully recovered, that vaccination rates among Hispanics improved noticeably. Between non-Hispanic Whites and Hispanics, one o f the most important gaps in coverage appears in the 55-64 age group. In this age category, about 39% o f Hispanics reported receiv-ing a flu vaccine, as opposed to 49% o f non-Hispanic Whites. Addressing this particular racial/ethnic disparity is important given the increased susceptibility of older individuals to flu complications (Nichol 2007). Men of Hispanic descent were also substantially less likely than other groups to receive the flu vaccine, which may point to a need for outreach programs targeted at Hispanic males. Similarly, persons with low income, little education, and no form of healthcare coverage are gener-ally less likely to receive influenza vaccination. Public health stakeholders should take note o f these high-risk groups. c o n c l u s io n Reduced disease burden and improved population health can be achieved through routine vaccination for seasonal influenza. Unfortunately, this study demonstrates that there are signifi-cant racial/ethnic and sociodemographic disparities in vaccina-tion rates in the state of Utah. Moreover, estimated disparities between non-Hispanic Whites and Hispanics have widened substantially over the past decade. Importantly however, the impact of Hispanic ethnicity on the odds o f vaccination ap-pears to be a function of healthcare coverage, education and household income. This lends support to the findings o f Chen et al. (2006) which found that structural barriers (such as lack of health insurance) were the greatest impediment to Hispanics in obtaining a seasonal flu vaccine. With only 58% of Utah Hispanics reporting some form of 18 RACIAL AND ETHNIC DIspARITIEs IN sEAsONAL INFLUENzA vACCINATION UTAH'S HEALTH: AN ANNUAL REviEw 2011 healthcare coverage as opposed to 88% of non-Hispanic Whites during the period of observation in this study, it is clear that important gains in influenza vaccination coverage can be made by increasing healthcare coverage among Hispanics. Further-more, continued efforts to better inform Hispanic males about the benefits o f vaccination would be worthwhile. Across all race/ethnicities vaccination rates were very high for the 65+ age group during the 2000-2008 flu seasons. While Utah has done remarkably well in vaccinating this vulnerable age group regardless of race/ethnicity, it has fared less well in terms of reducing racial/ethnic health disparities, which is a primary public health objective outlined in Healthy People 2020 (U.S. Department o f Health and Human Services 2010). The seasonal flu is a serious disease that carries substantial mortality, morbidity, and economic burdens for the state of Utah. Addressing racial/ethnic disparities in influenza vaccina-tion, especially among Hispanics, will reduce these burdens while simultaneously helping achieve nationally prominent public health objectives. By focusing on initiatives that improve access to healthcare and health insurance and that increase the overall socioeconomic condition of the Utah Hispanic popula-tion, the disparity between Hispanics and non-Hispanic Whites in terms o f seasonal influenza vaccination could be greatly diminished. r e f e r e n c e s 1. Akazawa, M., Sindelar, J., & Paltiel, A. (2003). Economic costs of influenza related work absenteeism. Value in Health, 6(2), 107- 115. 2. Blumenshine, P., Reingold, A., Egerter, S., Mockenhaupt, R., Braveman, P., & Marks, J. (2008). Pandemic influenza planning in the United States from a health disparities per-spective. Emerging Infectious Diseases, 14(5), 709-715. 3. Centers for Disease Control and Preven-tion. (2010). Estimates of deaths associated with seasonal influenza - United States, 1976 - 2007. MMWR Weekly: Morbidity and Mortality Weekly Report 59(33), 1057-1062. 4. Centers for Disease Control and Preven-tion. (2010). Prevention and control of influen-za with vaccines: recommendations of the Ad-visory Committee on Immunization Practices (ACIP), 2010. MMWR Weekly: Morbidity and Mortality Weekly Report, 59(RR-8), 1-62. 5. Centers for Disease Control and Pre-vention. (2010b). The flu season. Accessed December 4, 2010. http://www.cdc.gov/flu/ about/season/flu-season.htm. 6. Centers for Disease Control and Preven-tion. (2010c). Estimating seasonal influenza-associated deaths in the United States: CDC study confirms variability of flu. Accessed December. 3, 2010. http://www.cdc.gov/flu/ about/disease/us_flu-related_deaths.htm. 7. Centers for Disease Control and Preven-tion. (2010d). CDC says "Take 3" actions to fight the flu. Accessed January 20, 2011. http://www.cdc.gov/flu/protect/preventing. htm. 8. Centers for Disease Control and Preven-tion. (2008). Behavioral Risk Factor Surveil-lance System. In About the BRFSS. Accessed November 25, 2010. http://www.cdc.gov/ brfss/about.htm. 9. Centers for Disease Control and Preven-tion. Web-based Injury Statistics Query and Reporting System (WISQARS) [Online]. (2003). National Center for Injury Prevention and Control, Centers for Disease Control and Prevention. Accessed October 11, 2010 from http://www.cdc.gov/ncipc/wisqars. 10. Centers for Disease Control and Pre-vention. (2002). Prevention and control of influenza: Recommendations of the advisory committee on immunization practices (ACIP), 2002. MMWR Weekly: Morbidity andMor-tality Weekly Report 51(1-31). 11. Chen, E., Martin, A. D., & Matthews, K. A. (2006). Understanding health disparities: the role of race and socioeconomic status in children's health. American Journal of Public Health, 96(4), 702-708. 12. Egede, L., & Zheng, D. (2003). Racial/ ethnic differences in influenza vaccination cov-erage in high-risk adults. American Journal of Public Health, 93(12), 2074-2078. 14. Fiscella, K., Dressler, R., Meldrum, S., & Holt, K. (2007). Impact of influenza vaccina-tion disparities on elderly mortality in the United States. Preventive Medicine, 45(1), 83-87. 15. Fiscella, K., P. Franks, et al. (2002). "Dis-parities in health care by race, ethnicity, and language among the insured: findings from a national sample." Medical Care 40(1), 52-59. Fiscella, K. (2005). Commentary-anatomy of racial disparity in influenza vaccination. Health Services Research, 40(2), 539-550. 16. Gross, PA, AW Hermogenes, HS Sacks, J Lau, and RA Levandowski. 1995. "The efficacy of influenza vaccine in elderly persons." Annals of Internal Medicine 123:518. 17. Greenland, S., Pearl, J., & Robins, J.M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37-48. Gross, P. A., A. W. Hermogenes, et al. (1995). "The efficacy of influenza vaccine in elderly persons." Annals o f Internal Medicine 123(7), 518-527. 18. Herrera, G. A., Iwane, M. K., Cortese, M., Brown, C., Gershman, K., Shupe, A., . . . Bridges, C. B. (2007). Influenza vaccine effec-tiveness among 50-64-year-old persons during a season of poor antigenic match between vaccine and circulating influenza virus strains: Colorado, United States, 2003-2004. Vaccine, 25(1), 154-160. 19. Jefferson, T., Smith, S., Demicheli, V., Harnden, A., Rivetti, A., & Di Pietrantonj, C. (2005). Assessment of the efficacy and effectiveness of influenza vaccines in healthy children: systematic review. The Lancet, 365(9461), 773-780. 20. Keech, M., & Beardsworth, P. (2008). The impact of influenza on working days lost: a review of the literature. Pharmacoeconomics, 26(11), 911-924. 21. Klugman, K., Chien, Y., & Madhi, S. (2009). Pneumococcal pneumonia and influenza: A deadly combination." Vaccine, 27 (Supplement 3), C9-C14. 22. Link, M. W., I. B. Ahluwalia, et al. (2006). "Racial and ethnic disparities in influenza vaccination coverage among adults during the 2004-2005 season." American Journal of Epidemiology 163(6), 571-578. 23. Linn, S. T., Guralnik, J. M., & Patel, K. V. (2010). Disparities in influenza vaccine cover-age in the United States, 2008. Journal o f the American Geriatrics Society, 58(7), 1333- 1340. 24. Logan, J. (2009). Disparities in influenza immunization among U.S. adults. Journal of the National Medical Association, 101(2), 161- 166. 25. Lu, P., Bridges, C., Euler, G., & Singleton, r a c ia l a n d e t h n ic d is p a r i t ie s i n s e a s o n a l i n f l u e n z a v a c c in a t io n 19 2011 U tah's h e a l th : an a n n u a l re v iew J. (2008). Influenza vaccination of recom-mended adult populations, US, 1989-2005. Vaccine, 26(14), 1786-1793. 26. Lu, P., Euler, G., & Callahan, D. (2009). "Influenza Vaccination Among Adults with Asthma:: Findings from the 2007 BRFSS Survey." American Journal o f Preventive Medicine 37(2): 109-115. 27. Ma, K. K., Schaffner, W., Colmenares, C., Howser, J., Jones, J., & Poehling, K. A. (2006). Influenza vaccinations of young children increased with media coverage in 2003. Pedi-atrics, 117(2), e157-163. 28. Manzoli, L., Schioppa, F., Boccia, A., & Villari, P. (2007). The efficacy of influenza vaccine for healthy children: a meta-analysis evaluating potential sources of variation in efficacy estimates including study quality. The Pediatric Infectious Disease Journal, 26(2), 97-106. 29. Marmot, M. (2002). The influence of income on health: views of an epidemiologist. Health Affairs, 21(2), 31-46. 30. Molinari, N., Ortega-Sanchez, I., Mes-sonnier, M., Thompson, W., Wortley, P., Weintraub, E., & Bridges, C. (2007). The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine, 25(27), 5086-5096. 31. Monto, A. S. (2008). Epidemiology of in-fluenza. Vaccine, 26(Supplement 4), D45-D48. Nichol, K., Lind, A., Margolis, K., Murdoch, M., McFadden, R., Hauge, M., . . . Drake, M. (1995). The effectiveness of vaccination against influenza in healthy, working adults. New England Journal of Medicine, 333(14), 889-893. 32. Nichol, K., Nordin, J., Nelson, D., Mullooly, J., & Hak, E. (2007). Effectiveness of influenza vaccine in the community-dwelling elderly. New England Journal o f Medicine, 357(14), 1373-1381. 33. Nichol, K. (2008). Efficacy and effective-ness of influenza vaccination. Vaccine, 26, D17-D22. 34. Ross, C. E., & Mirowsky, J. (1999). Refin-ing the Association between Education and Health: The Effects of Quantity, Credential, and Selectivity. Demography, 36(4), 445-460. 35. Smedley, B., Stith, A., & Nelson, A. (2003). Unequal treatment: Confronting racial and ethnic disparities in health care. Washington D.C.: The National Academies Press. 36. Shrier, I., Platt, R.W. (2008). Reduc-ing bias through directed acyclic graphs. BMC Medical Research Methodology, 8(70). Accessed 27 April, 2011 from http://www. biomedcentral.com/1471-2288/8/70 37. Thompson, W. W., D. K. Shay, et al. (2004). "Influenza-associated hospitaliza-tions in the United States." The Journal of the American Medical Association 292(11), 1333- 1340. 38. U.S. Department of Health and Human Services (2010). Healthy people 2020: The road ahead. Retrieved from http://www. health.gov/healthypeople/url/. 39. Utah Department of Health. In Utah and the BRFSS. Accessed 15 November, 2010 from http://health.utah.gov/opha/IBIShelp/brfss/ BRFSSUt.htm. 40. Zaman, K., Roy, E., Arifeen, S., Rahman, M., Raqib, R., Wilson, E., . . . Steinhoff, M. (2008). Effectiveness of maternal influenza immunization in mothers and infants. New England Journal of Medicine, 359(15), 1555- 1564. 41. Zimmerman, R., Santibanez, T., Janosky, J., Fine, M., Raymund, M., Wilson, S., . . . Nowalk, M. (2003). What affects influenza vaccination rates among older patients? An analysis from inner-city, suburban, rural, and Veterans Affairs practices. The American Journal o f Medicine, 114(1), 31-38. 20 RACIAL AND ETHNIC DIspARITIEs IN sEAsONAL INFLUENzA vACCINATION UTAH'S HEALTH: AN ANNUAL REviEw 2011 c o r r e spo n d e n c e Janet c. Jacobson is a Fellow in Family planning in the Department of obstetrics and Gynecology Sara E. Simonsen is a Certified Nurse Midwife and a phD candidate in the University of Utah Division of public Health. Katherine Morgan ward is the program Director for the Nurse-Midwifery and Women's Health Nurse Practitioner program at the University of Utah college o f Nursing. Ashley Lena Havlicak is an undergraduate pre-medical student majoring in psychology. David Turok is a co-Director of the University of Utah's Family planning Fellowship in the Department of obstetrics and Gynecology. k e yw o r d s survey, college student, sexuality, contraception Sexual Activity and Contraceptive Use: A survey of University of Utah Undergraduate Students Aged 18-20 Janet c. Jacobson, MD; Sara E. Simonsen, cNM, MSp H; Katherine Morgan Ward DNP, WHNP; Ashley Lena Havlicak; & David K. Turok, MD/MPH a b s t r a c t b a c k g r o u n d The majority of undergraduate college students <20 years old are sexually active and nearly all wish to protect themselves against the risks o f unplanned pregnancy and sexu-ally transmitted infections (STI). This study investigates levels o f sexual activity among University of Utah undergraduates as well as use of contraception and STI protection. m e t h o d s A convenience sample o f University of Utah students age 18-20 was surveyed using an anonymous web-based questionnaire. RESULTS Of 6,176 eligible students 23.3% completed the survey (n=1,441). Among survey respon-dents, 57.6% reported ever being sexually active and 46.3% reported being currently sexual active. Of those who reported current sexual activity 93.2% were using a method of contraception. However, only 3.7% o f those were using a highly effective method and 4.0% reported using no method. Over half of sexually active students report current use o f two or more methods of contraception and 38.0% report having used emergency contraception (EC). Of sexually active students 5.5% have been (or had a partner who was) pregnant and 4.8% report having had an STI. Over one-fourth (350, 26.8%) of respondents report hav-ing used oral sex in place of vaginal sex and 65 (5.0%) had used anal sex in place of vaginal sex as a method of pregnancy prevention. c o n c l u s io n Sexual activity and associated risks are common among University of Utah undergradu-ates surveyed. Sexually active students report high use o f contraception, multiple methods of contraception, and EC use. There are opportunities for expanding use o f highly effective methods o f contraception, EC and STI education and testing. s e x u a l a c t iv it y a n d c o n t r a c e p t iv e u s e 21 2011 U tah's h e a l th : an a n n u a l re v iew Like all adolescents, teens in Utah mature in a sexu-ally complex society where they are exposed to highly sexualized images in the mainstream media. In the last decade, information for youth on healthy sexuality has become more limited with the expansion o f abstinence-only sexuality education which avoids discussions of healthy sexual relation-ships and pregnancy prevention [1]. Statistics on adolescent sexual practices and outcomes have been reported for decades, but in the current social environment they have generated greater interest. Recent publications on adolescent sexual behavior have expanded to include data on oral sex [2], anal intercourse [3] and masturbation [4]. Data on the sexual behavior o f U.S. adolescents has been generated for decades by large national samples including the Youth Risk Behavior Survey (YRBS) [5], the National Survey of Family Growth (NSFG) [6], and the National Longitudi-nal Study on Adolescent Health [7]. In 1991 54.1% of high-school students reported ever having sexual intercourse with a decrease to 46.0% in 2009. During this time, there was an increase in self-reported condom use at the time o f last inter-course from 46.2% in 1991 to 61.1% in 2009 [5]. Data on U.S. adolescent pregnancies have shown a consistent decline for the last 3 decades [8] but reached a nadir in 2005 with 2006 showing an increase in the U.S. rate [1] and further increases in Utah teen pregnancy rates from 2006-2008 [9]. Information on sexually transmitted infections (STIs) show that nationally and in Utah, rates o f Chlamydia infection have risen sharply over the last decade, and in Utah, nearly two-thirds of new cases are diagnosed in 15-24 year olds [9]. Data on the sexual behavior o f Utah's adolescents and young adults are not available. While the Utah State Department of Health thoroughly reports reproductive health outcomes for ad-ole
|
|---|