FACTORS ASSOCIATED WITH PRESCRIBING ANTIVIRAL MEDICATIONS FOR
TREATMENT OF INFLUENZA AMONG PRIMARY CARE PHYSICIANS IN THE
UNITED STATES
by
PAYAL H. PATEL
ANDREW C. RUCKS, COMMITTEE CHAIR
W. JACK DUNCAN
MICHAEL MAETZ
LESLIE A. MCCLURE
NIR MENACHEMI
A DISSERTATION
Submitted to the graduate faculty of The University of Alabama at Birmingham,
in partial fulfillment of the requirements for the degree of
Doctor of Public Health
BIRMINGHAM, ALABAMA
2011
Copyright by
Payal H. Patel
2011
FACTORS ASSOCIATED WITH PRESCRIBING ANTIVIRAL MEDICATIONS FOR
TREATMENT OF INFLUENZA AMONG PRIMARY CARE PHYSICIANS IN THE
UNITED STATES
PAYAL H. PATEL
PUBLIC HEALTH
ABSTRACT
Influenza is a contagious respiratory illness that affects millions each year. The
illness, if not contained, can be a paramount public health threat in the U.S. and the
world. Primary care physicians play an essential role in administering antiviral
medications as a treatment for influenza yet the factors that influence their prescribing of
these medications remain unclear.
The present study aims to identify factors associated with physicians’ prescribing
of antiviral medications in the ambulatory care setting. The data utilized for this study
were obtained from the National Ambulatory Medical Care Survey for each influenza
season between the years of 2005-2008. The sample population included primary care
physicians in the U.S. who diagnosed patients with influenza according to the
International Classification of Diseases, 9th Revision, Clinical Modification code 487.
First, descriptive characteristics of the sample population were computed. Next,
bivariate analyses were performed to identify relationships between the dependent and
independent variables. This was followed by multivariable logistic regression analyses
with two models: one with no fixed effects and the other including year as a fixed effect.
Summary statistics included descriptive statistics, odds ratios, 95% confidence intervals,
and cross tabulations in order to assess physician, patient, and health facility
characteristics associated with the prescribing of antiviral medications.
iii
A large proportion of physicians who diagnosed patients with influenza were in
the specialty of family practice. The majority of patients with influenza were female and
non-Hispanic. Multivariable logistic regression analysis revealed that race, type of health
facility setting, employment status, and metropolitan location were all significantly
associated with prescribing antiviral medications. Patients’ expected source of payment
and geographical location of the health facility were marginally associated with
prescribing antiviral medications.
By identifying factors associated with physicians’ prescribing practices of
antiviral medications, a more timely diagnosis and treatment of influenza can occur.
Efforts should be targeted to improve physician education and awareness of the illness.
Interventions may be implemented to improve the prescribing of antiviral medications
and potentially inappropriate prescribing practices.
Keywords: influenza, antiviral medications, public health
iv
DEDICATION
I dedicate this work to my husband, Sorush. Thank you so much for your continuous
love, support, guidance, and confidence. You have been my support throughout the years and
this dissertation would not have been possible without you. I love you! To my Mom and Dad,
thank you for your love and support and for providing the means for me to pursue my education
and to accomplish anything I desire. I love you both so very much. You both will always be my
inspiration. To my grandfather, thank you from the bottom of my heart. It is because of your
perseverance and hard work that allowed your grandchildren to have a better life and to have the
opportunity to pursue our dreams. It is because of your courage, wisdom, and dedication that led
me to pursue doctoral studies. Lastly, thank you God for making all of this possible.
v
ACKNOWLEDGMENTS
I would like to acknowledge the University of Alabama at Birmingham School of Public
Health for providing me the opportunity to pursue doctoral work. This work would not have been
possible without the support and guidance of the faculty in the Department of Health Care
Organization and Policy (HCOP). I would like to acknowledge my committee members Dr.
Andrew Rucks, Dr. Nir Menachemi, Dr. Leslie McClure, Dr. Jack Duncan, and Dr. Michael
Maetz. Thank you so much for all of your help, support, advice, and guidance throughout the
entire dissertation process. Thank you so much for your time and commitment. This dissertation
would not have been possible without you all. I would like to acknowledge the continuous
support and guidance of Dr. Nir Menachemi from the beginning until the end of my dissertation.
You have helped me transition from a student to a professional and I truly feel fortunate to have
been taught by you. You have instilled in me the skills and knowledge to succeed in academia
and have helped me transition from a student to a professional. Thank you! Lastly, I would like
acknowledge the staff in the HCOP department for all of your support.
vi
TABLE OF CONTENTS
Page
ABSTRACT.................................................................................................................................... iii
DEDICATION .....................................................................................................................v
ACKNOWLEDGMENTS ................................................................................................. vi
LIST OF TABLES ...............................................................................................................x
LIST OF FIGURES .......................................................................................................... xii
CHAPTER
1 INTRODUCTION ..........................................................................................................1
Statement of the Problem .........................................................................................1
Background of the Problem .....................................................................................3
Diagnosis and Treatment .........................................................................................5
Purpose of the Study ................................................................................................8
Significance of the Study .........................................................................................9
Research Questions ..................................................................................................9
2 REVIEW OF THE LITERATURE ...............................................................................11
Background of Antiviral Medications ...................................................................11
Effectiveness of Treatment in Healthy Adults .................................................12
Effectiveness of Treatment in the Elderly .......................................................13
Effectiveness of treatment in Children ............................................................13
Antiviral Medications for Prophylaxis.............................................................13
Conceptual Framework ..........................................................................................16
Eisenberg’s Framework of Clinical Decision-Making ...................................16
Patient Characteristics .....................................................................................18
Physician Characteristics ................................................................................19
Physician’s Interaction with Professional Environment .................................19
Doctor-Patient Relationship ............................................................................20
vii
Factors Associated with Physicians’ Decisions to Prescribe Antiviral
Medications ..............................................................................................................23
Patient Characteristics ........................................................................................24
Physician Characteristics ...................................................................................26
Doctor-Patient Relationship ...............................................................................30
Chapter Summary ..............................................................................................32
3 RESEARCH METHODOLOGY...................................................................................36
Survey Instrument ..................................................................................................37
Sample................................................................................................................37
Inclusion and Exclusion Criteria........................................................................39
Data Collection ..................................................................................................40
Confidentiality ...................................................................................................40
Estimation Procedure .........................................................................................41
Variables ................................................................................................................43
Outcome Variable ..............................................................................................43
Independent Variables of Interest ......................................................................44
Analyses Procedures ..............................................................................................46
Chapter Summary ..................................................................................................47
4 RESULTS ......................................................................................................................49
Descriptive Characteristics ....................................................................................49
Sample Size.......................................................................................................50
Patient Characteristics .......................................................................................51
Physician Characteristics ..................................................................................53
Health Facility Characteristics ..........................................................................54
Prescribing Rates of Antiviral Medications ......................................................55
Bivariate Analyses .................................................................................................58
Patient Characteristics .......................................................................................58
Physician Characteristics ..................................................................................59
Health Facility Characteristics ..........................................................................59
Interaction Analyses...............................................................................................61
Multivariable Logistic Regression Analyses .........................................................61
Testing Assumptions........................................................................................61
Logistic Regression Results .............................................................................63
viii
Fixed Effects ....................................................................................................65
Chapter Summary ..................................................................................................68
5 CONCLUSIONS............................................................................................................69
Summary ................................................................................................................69
Discussion ..............................................................................................................70
Descriptive Characteristics ....................................................................................72
Patient Characteristics .......................................................................................72
Physician Characteristics ..................................................................................73
Health Facility Characteristics ..........................................................................74
Prescription Rates of Antiviral Medications .....................................................75
Multivariable Logistic Regression Findings ...........................................................77
Patient Characteristics ......................................................................................77
Physician Characteristics .................................................................................78
Health Facility Characteristics .........................................................................78
Year as a Fixed Effect ......................................................................................79
Strengths and Limitations .......................................................................................81
Policy Implications .................................................................................................82
Future Research ......................................................................................................84
Conclusions .............................................................................................................85
LIST OF REFERENCES ...................................................................................................87
APPENDICES
A Correlation Matrix...................................................................................................98
B NAMCS Patient Survey Instrument ......................................................................100
C NAMCS Physician Survey Induction Form ..........................................................102
D Institutional Review Board Approval ...................................................................127
ix
LIST OF TABLES
Table
Page
1 Recommended Dosage of Neuraminidase Inhibitors According to Age ......................15
2 Review of Studies Pertaining to Antiviral Prescribing by Physicians ...........................34
3 Names of Antiviral Medications and Corresponding Codes .........................................43
4 Measurements of Independent Variables .......................................................................44
5 Frequencies and Percentages of Physicians by Year and Specialty ..............................50
6 Sample Physician Frequency for 2005-2008 .................................................................51
7 Frequencies and Percentages of Patient Characteristics ................................................52
8 Frequencies and Percentages of Physician Characteristics ............................................53
9 Frequency and Percentages of Health Facility Characteristics ......................................54
10 Weighted Antiviral Prescription Summary Statistics by Year ....................................56
11 Antiviral Medication Prescriptions by Specialty ..........................................................57
12 Bivariate Analysis of Demographic Characteristics Between the Prescription and
Non-Prescription of Antiviral Medications ..................................................................59
13 Logistic Model Pearson Chi-Square Goodness-of-Fit Test .........................................62
14 Logistic Model Hosmer and Lemeshow Goodness-of-Fit Test ...................................62
15 Multivariable Logistic Regressions of Factors Associated with Prescribing of
Antiviral Medications ..................................................................................................64
16 Results of Year as a Fixed Effect with 2008 as Reference Group ...............................66
x
17 Results of Multivariable Logistic Model With and Without Year as a Fixed Effect ..67
xi
LIST OF FIGURES
Figure
Page
1 Conceptual Model of Factors Influencing Physicians’ Decision Making .....................22
2 Prescriptions for Treatment of Influenza By Year .........................................................56
3 Antiviral Medication Prescribing by Specialty ............................................................. 57
4 ROC Curve.....................................................................................................................63
xii
CHAPTER 1
INTRODUCTION
In April 2009, the emergence of novel Influenza A virus also known as H1N1 was
announced by the World Health Organization (WHO, 2009). This was the start of the
first influenza pandemic since the “Hong Kong Flu” of 1969 (CDC, 2004). The 2009
novel H1N1 is responsible for approximately 99% of influenza viruses for the 2009-10
influenza season (CDC, 2009). In order to develop immunity to the virus, vaccinations
are provided but they are not 100% effective (CDC, 2009). The Centers for Disease
Control and Prevention (CDC) recommend the use of antiviral medications for treatment
and chemoprophylaxis of influenza to primary care providers; however the use of these
medications remains underutilized (Gaglia, Cook, Kraemer, & Rothberg, 2007).
According to Anantham, McHugh, O‟Neil, & Farrow (2008), primary care physicians
play a critical role in diagnosis, treatment, and containment of influenza making their
prescribing practices of antiviral medications of great interest to public health.
Statement of the Problem
Accurate diagnosis and treatment of influenza are essential to ensure containment
and reduce transmissibility. Seasonal influenza affects as many as twenty percent of the
American population and causes approximately 226,000 hospitalizations and 36,000
deaths annually (CDC, 2009). Influenza may spread from one country to another
resulting in a pandemic where the number of illnesses and deaths are greatly exacerbated
1
(WHO, 2009). The impact can range from mild to severe leading to thousands of deaths
annually. Additionally, an extremely large economic burden is associated with the
illness. Children, healthy adults, and the elderly create different influenza scenarios
which require an effective response uniquely targeted to the specific population. Hence,
physicians must be fully knowledgeable and be able to adapt accordingly to treat healthy
adults or the more susceptible populations.
Although recommendations and guidelines for diagnosis and treatment are
available from the CDC, the use of antiviral medications remains suboptimal (CDC,
2009). Additionally, when these medications are prescribed, in many cases this is done
so inappropriately (Fazio et al., 2008). Influenza symptomotology can range from a mild
upper respiratory infection to an acute illness (CDC, 2009). Misdiagnosing or
inadequately treating a patient presenting with influenza-like illness may result in severe
complications, especially for those who are considered to be at high-risk such as certain
vulnerable populations.
Currently, little is known regarding physicians‟ approaches to treating the illness.
More importantly, inappropriate prescribing occurs where antibiotics are commonly
prescribed for treatment of influenza rather than antiviral medications (Linder, Nieva, &
Blumentals, 2009). Therefore, it is necessary to identify factors associated with
physicians‟ decisions to prescribe antiviral medications as they remain unclear. The
utilization of antiviral medications depends on prompt rapid influenza diagnostic testing
and providing an accurate diagnosis, although use of this testing remains suboptimal
(CDC, 2010). Physicians‟ response through the effective use of antiviral medications is a
key component in addressing and controlling influenza. Many researchers have focused
2
On identifying best practices to treat influenza (Hammond, 2002; Bartlett, 2006; Stiver,
2003; Stiver, 2007; Fiore et al., 2007) but very few have focused on assessing physicians‟
practices and approaches to treating the illness (Rothberg et. al, 2006; Linder et al.,
2009). More research is needed to identify factors associated with physicians‟
prescribing practices of antiviral medications.
Background of the Problem
Primary care providers are often first in line to respond to seasonal and pandemic
influenza, providing an accurate diagnosis and treatment to children, adults, and the
elderly (Gaglia et al., 2007; Mueller et al., 2010). However, providers are often faced
with uncertainty regarding diagnosis, best treatment options for individual patients, and
evidence of what the best treatment may be (Chapman, Durieux, & Walley, 2004). A
decision to prescribe in the absence of a clear diagnosis may be a way to reduce the
degree of uncertainty experienced by the provider (Chapman et al., 2004).
The severity and mortality of influenza are most common in those individuals
who are considered vulnerable or at-risk. According to the Department of Health and
Human Services (2009), at-risk individuals are defined as “children, pregnant women,
senior citizens and others who have special needs in the event of a public health
emergency” (DHS 2007). This population group needs special health care efforts as they
are exposed to adverse outcomes if treatment of influenza is not timely (Hutchins,
Truman, Merlin, & Redd, 2009). Many studies have documented morbidity in children
during epidemics of seasonal influenza (Heikkinen et al., 2004; Izurieta et al., 2000;
Neuzil, Mellen, Wright, Mitchel, & Griffinet, 2000). Children, especially those who are
3
aged 2-4, experience influenza-related hospitalization rates similar to adults who are
considered to be at high-risk for complications from the illness (Uyeki, 2003).
The elderly represent a group that needs special care during an influenza
outbreak. Elderly individuals have a weakened immune system and may have other
health conditions which may lead to severe complications from the illness. According to
McClure (1999), 90% of those who die each year as a result of influenza are over the age
of 65. Although vaccinations are strongly recommended for this population as a
prophylactic measure, only 20% to 60% are vaccinated (Cox, Khan, Schweinle,
Okamoto, & McLaughlin, 2000). Additionally, pregnant women face an increased risk of
complications and hospitalizations. According to Jamieson and colleagues (2009),
pregnancy increases a woman‟s risk of hospitalization by four-fold. Physicians must be
able to provide a timely diagnosis and initiate adequate treatment to this group so
complications of infection are prevented. As a result, the direct and indirect economic
impact resulting from hospitalizations can be reduced.
A comparison of 15 economic studies that assessed cost-effectiveness of
treatment of influenza revealed that an influenza attack rate of 35% causes a total
economic burden of $71.3 billion dollars annually (Lynd, Goeree, & O‟Brien, 2005).
Approximately 80% of the economic burden is indirect resulting from a loss of
productivity. Additionally, children and the elderly are more susceptible to the illness
resulting in an increased economic burden due to cost of treatment and loss of
productivity of caretakers. According to Lynd and colleagues (2006), the average total
cost associated with each child experiencing hospitalization due to influenza was
$13,159. If children were admitted to an ICU, the average cost per child was $39, 792.
4
Moreover, according to a retrospective analysis of 18,000 patients with diagnosed
influenza in a managed care setting, the total cost of treating influenza was $12.8 million.
Inpatient care accounted for over 35% of this amount (Cox et al., 2000). Sullivan (1996)
stated that hospitalizations comprise the greatest component of direct medical costs for
the treatment of influenza.
Based on epidemiological data, 24.7 million cases of influenza were diagnosed in
2003. From these data, Molinari and colleagues (2007) systematically estimated the
annual impact of seasonal influenza by measuring influenza burden and related direct and
indirect costs. Their results indicated that the economic burden resulting from influenza
remained high. Across all age groups, the annual economic burden of seasonal influenza
was $87.1 billion. The average economic burden when examining only lost income
resulting from lost lives and lost days of production was $26.8 billion. More specifically,
64% of the total economic burden was attributed to cases in the elderly and the analysis
was highly sensitive to the number of deaths in this group. Lastly, the economic burden
resulting from influenza was 1% of the U.S. Gross Domestic Product of $11 trillion in
2003. Since influenza epidemics occur on an annual basis, the economic burden results
in an adverse effect on the rate of economic growth (Molinari et al., 2007).
Diagnosis and Treatment
In order to provide an accurate diagnosis of influenza, physicians are encouraged
to conduct rapid influenza diagnostic testing of patients presenting with influenza-like
illness (CDC, 2009) which is defined as having a fever plus two of the following:
headache, myalgia, fatigue, headache, cough, or sore throat (Monto, Gravenstein, Elliott,
5
Colopy, & Schweinle, 2000). These tests are used to detect influenza outbreaks and
guide diagnosis and treatment of the illness. Results of the test can typically be seen
within 15 minutes (CDC, 2010). However, physicians‟ frequency of use of rapid testing
remains unknown (Uyeki, 2003). Once a proper diagnosis has been made, antiviral
medications can be administered to serve as a prompt treatment for the illness.
Antiviral medications are a class of medications used specifically for treatment of
viral infections (CDC, 2009). In the United States, four prescription antiviral
medications in two classes are available for treatment of influenza: adamantanes
(amantadine and rimantadine) and neuraminidase inhibitors (oseltamivir and zanamivir).
Among the Influenza A viruses, high levels of resistance have been seen with the use of
adamantanes; therefore, neuraminidase inhibitors are recommended for treatment (Katz,
Lamias, Shay, & Uyeki 2009). Many studies have examined the efficacy of antiviral
medications for treatment and chemoprophylaxis and conclude they are effective if
administered properly (Jefferson, Jones, Doshi, & Del Mar, 2009; Treanor et al., 2000).
Sintchenko and colleagues (2002) compared two strategies for treatment of
influenza; empirical antiviral therapy versus therapy resulting from a positive result of
rapid testing. The results showed that patients who present with influenza-like illness but
are not considered to be at high-risk for complications should be tested to provide a
confirmatory diagnosis. Prompt antiviral treatment was recommended for high-risk
patients, although it would be more costly. When comparing the two strategies, each
choice involved a tradeoff. The test result-based strategy exhibited higher specificity
while the empirical treatment was likely to be safer. Lastly, the analysis illustrated that
6
treatment based on a clinical diagnosis of influenza was the favored strategy (Sintchenko,
Gilbert, Colera, & Dwyer, 2002).
On the contrary, Apisarnthanarak and Mundy (2008) studied health care
providers‟ knowledge and attitudes with respect to avian influenza, also known as the
H5N1 virus that also infects birds. The study occurred in an H5N1 endemic setting
hospital in Thailand. They showed that there was a gap between knowledge of the illness
and effective influenza control and treatment practices, although practitioners responding
to the event felt positive with respect to the response. Moreover, approximately 15% of
respondents indicated that influenza was not a severe illness and antiviral treatment was
not necessary, and that personal protective equipment was worn by only 33% of
respondents suggesting that knowledge of the illness was limited. The study concluded
with promoting education for improved effectiveness of influenza treatment
(Apisarnthanarak & Mundy, 2008).
The response and role of the public health sector was a key consideration in the
H5N1 influenza outbreak. In April 2008, WHO issued recommendations on infection
control for patients and health care facilities that ranged from the use of protective
equipment, treatment, and waste disposal. The recommendations were based on best
practices and experiences of health care providers and facilities in different countries
(WHO, 2009). These recommendations and guidelines also targeted widespread
awareness building among health care providers to ensure responsiveness to possible and
actual influenza cases. The rationale was that the effectiveness of policies and guidelines
on influenza infection and control depended largely on the efficiency of diagnosing
7
clinical cases and the speed of administering and distributing antiviral medications
(Ferguson et al., 2005).
In order to prevent transmission of the illness, administration of antiviral
medications must be timely in addressing both seasonal and pandemic influenza (WHO,
2009). Although antiviral medications are available, it is necessary to explore
physicians‟ prescribing practices and the factors that impact these prescribing practices.
By doing so, associated factors may be identified, explaining why antiviral medications
are underutilized for treatment of influenza. Exploring physicians‟ prescribing practices
of antiviral medications during time periods of seasonal influenza may help determine
preventive and control outcomes.
Purpose of the Study
Most research with respect to antiviral medications has focused primarily on their
efficacy and effectiveness rather than on assessing physicians‟ prescribing practices of
these medications. For example, the literature examines the effectiveness of antiviral
medications as a treatment or chemoprophylaxis (Falagas et al., 2010; Jefferson et al.,
2009) yet little is known about factors associated with prescribing of antiviral
medications in the U.S. The main purpose of the present study is to identify
characteristics associated with prescribing antiviral medications among primary care
physicians in the U.S. To meet this purpose, the research will identify primary care
physicians‟ prescribing practices of antiviral medications for treatment of influenza and
explore characteristics associated with these prescribing practices. The research will also
determine if there are any differences in the prescribing of antiviral medications over
8
time and by specialty. The long-term objective of this study is to identify factors
associated with physicians‟ decisions to prescribe antiviral medications for treatment of
influenza. It is important for public health professionals to understand the relationships
among factors that contribute to effectively treating the illness.
Significance of the Study
The findings of this study may benefit many stakeholders including physicians,
hospital administrators, and state and local health departments. By identifying factors
associated with physicians‟ prescribing of antiviral medications, federal guidelines can be
enhanced. Hospital administrators, in coordination with state and local health
departments, can make necessary improvements in the influenza planning process.
Findings from this study will contribute to the literature pertaining to prescribing
practices of antiviral medications. By identifying factors that may impact physicians‟
approaches to treating influenza, areas of misconception can be addressed. The data
gathered from the results of this study shall serve as a guide for improving the efficacy of
prescribing antiviral medications, implementing appropriate diagnostic algorithms, and
strengthening current influenza treatment recommendations and guidelines.
Additionally, results from this study may help in designing continuing education
programs for physicians regarding influenza and its treatment.
Research Questions
The present study will help characterize physicians‟ prescribing practices of
antiviral medications in response to influenza infection. Questions addressed include:
9
1. What are the factors associated with prescribing antiviral medications for
treatment of influenza among primary care physicians?
2. Are there differences in prescribing practices among the primary care specialties?
3. Are there differences in prescribing practices over time?
This study aims to provide findings that may result in improvements in
awareness, education, and training of providers with respect to influenza and its
treatment. Recommendations and guidelines regarding diagnosis and treatment are
provided at the federal level. Therefore, this study utilizes nationally representative data
to identify factors associated with prescribing antiviral medications among primary care
physicians in the U.S.
Previous literature with respect to physicians‟ prescribing practices of antiviral
medications does not provide a framework to conceptualize physicians‟ prescribing of
antiviral medications. However, the literature has illustrated that prescribing practices
relate to decision making by physicians and there are many factors that influence the
decision to prescribe accurately aside from patient symptomotology (Benson, 1983). The
conceptual framework and model illustrating various influences will be discussed next in
Chapter 2.
10
CHAPTER 2
REVIEW OF THE LITERATURE
The Centers for Disease Control and Prevention (CDC) and the World Health
Organization (WHO) provide specific guidelines and recommendations to health care
providers for treatment of influenza. These recommendations and guidelines are
provided to ensure a timely diagnosis and treatment of the illness. The purpose of this
chapter is to review the literature regarding antiviral medications, to review the
conceptual framework that is applied to this research, and to examine the literature
pertinent to this dissertation which will help identify gaps that set the stage for this study.
Background of Antiviral Medications
There are two classes of antiviral medications that are available for treatment of
influenza: neuraminidase inhibitors and adamantanes. The adamantanes are effective in
treating influenza A viruses but have been associated with adverse side effects and drugresistance (CDC, 2009). Oseltamivir and zanamivir constitute the neuraminidase
inhibitors and have been associated with effective treatment of influenza with little
adverse effects if no pre-existing illness is present (CDC, 2009). These medications work
11
by interfering with the release of the influenza virus from the host cells. The influenza
virus replicates in the respiratory tract between 24 and 72 hours after its onset.
Therefore, the neuraminidase inhibitors must be administered as early as possible after
the onset of influenza-like illness since they act at the stage of viral replication (Moscona,
2005). Treatment recommendations and guidelines vary with respect to children, healthy
adults, and the elderly as the vulnerable population is more likely to develop secondary
complications. Antiviral medications should be administered according to the CDC
recommendations presented in Table 1.
Effectiveness of Treatment in Healthy Adults
According to Hayden and colleagues, (1997), influenza cases treated with
zanamivir resulted in a one-day reduction to alleviate symptoms in healthy adults.
Additionally, many studies have illustrated the effectiveness of neuraminidase inhibitors
for treatment of influenza in healthy adults (Cooper et al., 2003; Treanor et al., 2000;
Whitley et al., 2001). While these studies examined the time from onset of symptoms to
start of therapy, the Immediate Possibility to Access Treatment (IMPACT) study
examined the relationship between time of neuraminidase therapy initiation and duration
of influenza as well as other efficacy measures (Aoki et al., 2003). According to the
IMPACT study, treatment that was initiated within 12 hours after onset of fever led to a
three-day reduction in the duration of the illness. The study concluded that treatment
initiated at 36 to 48 hours after onset of influenza-like symptoms does not result in highly
favorable outcomes as compared to early initiation of treatment (Aoki et al., 2003).
12
Effectiveness of Treatment in the Elderly
While treatment with neuraminidase inhibitors is effective in healthy adults, a
study of the elderly in Canadian long-term care facilities revealed that elderly treated
with oseltamivir within 48 hours of onset of influenza-like illness were less likely to be
hospitalized or experience death (Bowles et al, 2002). The study concluded that
oseltamivir was safe as well as effective when used as a prophylaxis in the elderly
(Bowles et al., 2002).
Effectiveness of Treatment in Children
Many studies have explored antiviral treatment of influenza in children (Hedrick
et al, 2000; Hayden et al., 1997; Makela et al., 2000; Rodriguez et al., 2002). Whitley
and colleagues (2001) found that treatment with a neuraminidase inhibitor in children
with influenza-like illness reduced the length of the illness by 1.5 days if treatment was
administered within 48 hours of illness onset. Most of the above studies found that
treatment with zanamivir reduced the median duration of influenza by 1.25-2.5 days.
Antiviral Medications for Prophylaxis
Oseltamivir and zanamivir may both be used for chemoprophylaxis for prevention
of seasonal influenza in healthy adults, elderly, and children (Hayden et al, 1999;
Moscona, 2005). The neuraminidase inhibitors are highly effective (70-90 percent) in
preventing influenza when used as prophylaxis in healthy adults (Cooper et al., 2003;
Oxford et al., 2004). Although both drugs are effective in prevention of the illness, only
oseltamivir is approved by the FDA for use as a prophylaxis.
Prevention of influenza in children and elderly is essential as they are at high-risk
for developing complications. With respect to children, oseltamivir is approved for
13
prophylaxis in children ages 13 and older (CDC, 2009). In the elderly, seasonal
prophylaxis for influenza is important in controlling outbreaks in residential homes and
long-term care facilities (Bowles et al., 2002). A double-blinded, placebo-controlled,
randomized study found that the incidence of influenza in the elderly living in residential
homes decreased by 92% from the use of oseltamivir as prophylaxis (Peters, Gravenstein,
& Norwood, 2001). Although many of the elderly residents had received influenza
vaccinations, the administration of antiviral medications provided additionally protection
against influenza.
14
Table 1.
Recommended Dosage of Neuraminidase Inhibitors According to Age (CDC, 2009)
Antiviral
Drug
Treatment
(5 days)
Chemoprophylaxis
(10 days)
1-6 yr
7-12 yr
13-64 yr
≥65 yr
≥13 yr
Oseltamivir
Body weight: <15 kg – 30
mg twice daily
Body weight: 15-23 kg- 45
mg twice daily
Body weight: 23-40 kg –
60 mg twice daily
Body weight: >40 kg – 75
mg twice daily
Body weight: <15 kg – 30mg
twice daily
Body weight: 15-23 kg – 45mg
twice daily
Body weight: 23-40 kg – 60 mg
twice daily
Body weight: > 40 kg – 75 mg
twice daily
75 mg twice
daily
75 mg twice
daily
75 mg once daily
Zanamivir
NA
10 mg (equivalent to 2
inhalations) twice daily
10 mg
(equivalent to
2 inhalations)
twice daily
10 mg
(equivalent to
2 inhalations)
twice daily
NA
15
Conceptual Framework
The adequate prescribing of antiviral medications and treatment of influenza
involves a plethora of factors. Literature in the social sciences has illustrated that many
factors outside of medicine influence clinical decision-making (McKinlay et al., 1996).
By thoroughly understanding these factors and their associations with treatment of
influenza, physicians may be able to prescribe antiviral medications more adequately.
The factors that can impact inappropriate prescribing may include patient characteristics
and clinical symptomotology, physician characteristics, or health facility characteristics
(McKinlay, 1996). The high incidence of influenza, the increases seen in morbidity and
mortality, and the suboptimal use of antiviral medications suggest that influenza needs to
be treated more effectively (Wilkins et al., 2003). Therefore, it is imperative that efforts
be made to examine these factors so that physicians‟ prescribing practices can be more
effective. If needed, interventions that address physicians‟ prescribing practices may be
implemented to ensure adequate prescribing of antiviral medications and mitigate
prescribing errors.
Eisenberg’s Framework of Clinical Decision Making
A majority of literature regarding decision-making pertains to normative
concepts. Normative concepts describe how decisions should be made rather than how
decisions are made. A majority of decision theory is normative where the decision maker
is fully informed, rational, and able to make the most accurate decision (Bramson, 2007).
Normative concepts do not consider the impact of various characteristics and their
16
influences on the decision-making process (Albert, 1978). Few authors briefly examine
sociocultural factors on decision-making (Schwartz et al., 1973).
Decision-making is a complex process. The amount of knowledge possessed by
the decision-maker about outcomes of a performed action varies amongst individuals
(Albert, 1978). There are three types of decisions that are made by individuals:
1. Decisions of certainty
2. Decisions of risk or uncertainty
3. Decisions of ignorance
As examined by Albert (1978), decisions of certainty refer to having a well-specified
outcome for each course of action performed. Decisions of risk or uncertainty refer to
having a well-specified set of outcomes along with a probability of occurrence form each
action performed. Lastly, decisions of ignorance refer to having a range of possible
outcomes along with an unspecified probability of occurrence with each action performed
(Albert, 1978).
In the clinical setting, decision-making involves much risk and uncertainty
(Farnan et al., 2008; Eisenberg, 2002).
Therefore, any characteristic that may affect the
physician‟s decision making process needs to be examined. According to Waitzkin and
Stoeckle (1972), it is necessary to study characteristics such as patient characteristics and
doctor-patient relationships as these may play an essential role in the overall decisionmaking process.
In response to much of decision theory consisting of normative concepts,
Eisenberg (1979) proposed that decision-making by physicians should involve the
interaction between patient, physician, and sociodemographic characteristics. According
17
to Eisenberg, decision-making in the clinical setting is based not only on scientific
outcomes, but patient and physicians characteristics may also have an influence in the
clinical decision-making environment (Eisenberg, 1979).
After reviewing much of the literature examining characteristics that influence
decision-making by physicians, Eisenberg summarized these characteristics as “the
sociologic characteristics of the patient, the sociologic characteristics of the physician,
the physician‟s interaction with his profession and the health system, and the physician‟s
interpersonal relationship with the patient” (Eisenberg, 1979).
Patient Characteristics
Patient characteristics that may influence physicians‟ decision-making include the
patient‟s income and ethnic background, social class, sex, physical appearance, and
insurance status (Eisenberg, 1979). For example, in a study examining race in the
patient-physician relationship, African American patients rated their physician visits as
less participatory compared to whites (Patrick et al., 1999). According to the authors, if
patients visited physicians of their own race, they rated the physician as more
participatory. Other authors have indicated that physicians‟ clinical decision-making is
based on gender prejudices (Lennane & Lennane, 1973). In a study examining physician
behaviors with patients, females were provided better information and empathy compared
to males (Hooper et al., 1982). In addition to gender, physical appearance seems to be
another factor that influences physician behavior (Hooper et al., 1982).
Insurance status also plays an important role in physicians‟ decision-making.
According to Mort and colleagues, physicians are more likely to recommend additional
services to insured patients compared to uninsured patients (Mort et al., 1996).
18
Physician Characteristics
Characteristics of the physician are also essential to examine as these may play a
critical role in clinical decision-making. Physician characteristics include factors such as
specialty, education, and age. Eisenberg (1979) identifies physician specialty as a factor
that influences clinical decision-making as those in different specialties may have varying
levels of knowledge and attitudes regarding specific conditions or illnesses.
Additionally, age is an important factor to consider. For example, physicians who are of
a younger age tend to prescribe medications more appropriately than older physicians
(Hemminki, 1975; Pineault, 1977; Freeborn, Baer, Greenlick, & Bailey, 1972).
However, these younger physicians also order an increased number of laboratory tests
compared to older physicians. While many specialties do prescribe appropriately, family
physicians prescribe more inappropriately than other physicians in general (Eisenberg,
1979).
Physician’s Interaction with Professional Environment
Clinical decision-making may also be influenced by the physician‟s interaction
with the professional environment. Characteristics such as interactions with colleagues
and the setting in which decision-making is conducted may act as influences (Eisenberg,
1979; Eisenberg, 2002; McKinlay et al., 1996). If the physician‟s practice is a solo
practice where there is not much interaction with other physicians, then there may not be
much influence to consider new activities in the practice such as adopting new drugs.
The setting where physicians interact with patients is said to have more influence on
quality of care compared to physician training (Eisenberg, 1979).
19
Doctor-Patient Relationship
Lastly, the doctor-patient relationship is another factor that may influence clinical
decision-making. Eisenberg (1979) stated that the patient and physician comprise a
social system. Within the framework of this social interaction is where clinical decisionmaking occurs. There are three patterns of social interaction that influence physicians‟
decision making. First, there is the interaction known as active-passive relationship
where the physician is in control of the entire interaction between the physician and
patient and the patient is silent. Second, there is the guidance-cooperation relationship
where the physician provides guidance to the patient. The patient is expected not to
question the advice that is provided and simply comply with the physician‟s guidance.
Lastly, there is the relationship known as the mutual participation relationship. In this
type of interaction, the physician and patient are both active consumers of the patient‟s
health (Eisenberg, 1979). The physician serves as a mentor to the patient and helps the
patient improve along the way.
Physician specialty is known to be a strong influence on the doctor-patient
relationship (Einsberg, 1979). Additional factors known to influence doctor-patient
relationship include interpersonal factors such as uncooperativeness or unfriendliness and
prognostic factors (Einsberg, 1979). In a more recent article by Eisenberg (2002),
patient demand and convenience were identified as additional factors that may influence
physicians‟ decision-making.
The conceptual model of factors influencing a physician‟s decision to prescribe
antiviral medications was derived from constructs of Eisenberg‟s framework of clinical
20
decision-making. Figure 1 depicts these four constructs and their relationship to clinical
decision-making.
The four categories of characteristics examined above illustrate the types of
impacts various factors may have on clinical decision-making. This framework does not
neglect outside influences on decision-making as do normative decision theories. By
utilizing this framework in the current study, we can adequately identify factors
associated with physicians‟ prescribing practices of antiviral medications.
21
Patient Characteristics
Physician Characteristics
Patient-Physician Interaction
Health Facility Characteristics
(a) Age, Sex, Race, Ethnicity
(b) Clinical & Behavioral
(c) Insurance Status
(a) Specialty
(b) Education
(c) Years in practice
(a) Physician specialty/training
(b) Prognostic factors
(c) Interpersonal factors
(a) Practice setting
(b) Organizational factors
(c) Peer relationships
Decision-Making Process
(a) Prescribe antiviral medications
(b) Do not prescribe antiviral medications
-Adapted from Eisenberg‟s Conceptual Framework (Eisenberg, 1979)
Figure 1.
Conceptual Model of Factors Influencing Physicians‟ Decision-Making
22
Factors Associated with Physicians‟ Decisions to Prescribe Antiviral Medications
In recent years, there have been an increasing number of studies focusing on
identifying physicians‟ approaches to treating influenza. The subject of many of these
studies is related to prescribing practices of antiviral medications and knowledge of
complications associated with the illness (Rothberg et al. 2006; Linder et al., 2009;
Daugherty et al., 2009). A timely diagnosis and accuracy in prescribing antiviral
medications are essential for effective treatment of influenza and containment. Diagnosis
of influenza can be complicated as many other respiratory illnesses produce similar
symptoms to that of influenza. Additionally, rapid influenza testing may not be
performed to confirm the illness. Lack of awareness along with other factors such as lack
of insurance, patient refusal, cost of antiviral medications, and doubts of medication
efficacy have been identified as reasons for not prescribing antiviral medications (Fazio
et al, 2008; Rothberg et al, 2006; Linder, Chan, & Bates, 2006). The following section
reviews studies related to physicians‟ approaches to prescribing antiviral medications.
This review will help identify factors that contribute to our understanding of the issue and
help identify gaps in the literature.
Several factors may affect a physician‟s decision to prescribe antiviral mediations
for treatment of influenza. Table 2 presents results of selected studies that identify
factors associated with prescribing antiviral medications. The literature suggests that
patient characteristics, patient expectations and demands, and physician characteristics
affect prescribing practices (Eisenberg, 2002). Knowledge and awareness of influenza
and its treatment may also play an essential role in the decision-making process (Omar,
23
Sullivan, Petersen, Islam, & Majeed, 2008). In order to better understand this statement,
studies were reviewed that attempted identify factors associated with physicians‟
prescribing of antiviral medications.
Patient Characteristics
Many studies have focused on the treatment of antiviral medications in children
and the elderly, as they are more likely to develop complications from influenza.
Unimmunized children, in whom attack rates are >30%, face a much greater risk of
morbidity and mortality resulting from influenza (Heikkinen, Silvennoinen, Peltola,
Ziegler, & Vainionpaa, 2004). Influenza in children can affect multiple body organs and
can result in major complications. Additionally, the clinical signs of influenza are less
apparent in adults making diagnosis and treatment questionable (Peltola, Ziegler, and
Ruuskanen, 2003).
Due to the similarity of symptoms of other co-circulating viruses with influenza,
it is necessary to have a confirmed diagnostic laboratory test of influenza before initiating
treatment with antiviral medications (Katz, Lamias, Shay, & Uyeki, 2009; Uyeki, 2003).
Although amantadine has shown to be ineffective in adults, it is said to be effective in
children age ≥ 1 year. If antiviral treatment was initiated within 48 hours of onset of
influenza-like illness, a reduction of illness was observed in children (Uyeki, 2003). Due
to the low accuracy of clinical diagnosis of influenza in children, it is necessary to
conduct rapid influenza testing before initiating treatment
The elderly are also considered high-risk and are susceptible to secondary
complications from influenza. A study of spinal cord injury patients diagnosed with
influenza indicated that practitioners prescribed antiviral medications to 21% of patients
24
(Evans et al., 2006). Amantadine was frequently prescribed for treatment (64%).
Patients typically experience high-risk of respiratory complications after spinal cord
injury yet antiviral medications were rarely used for treatment of influenza in this
population (Evans et al, 2006).
Further examining influenza in the elderly, Gupta and colleagues (2007)
conducted a survey of influenza-like illness in long-term care facilities in the U.K. The
results indicated that of the 20 responses, half reported outbreaks of influenza in nursing
homes. The average attack rate was 41%, with a total of 31 deaths. Seven facilities
indicated that a policy was in place for management of influenza. Of the seven facilities,
only four reported using neuraminidase inhibitors for treatment of influenza. Variations
with respect to treatment were seen in practice settings at the local level (Gupta et al.,
2007).
In order to assess the relationship between patient characteristics and prescribing
practices, Linder, Bates, and Platt (2005) examined antiviral prescribing for patients
diagnosed with influenza at the national level. The results of their analysis indicated that
during the time between October 1995 and May 2002, there were 6.4 million
prescriptions of antiviral medications of which 5.9 million were prescribed during
influenza season. Physicians prescribed antiviral medications in 19% of ambulatory
visits and adamanatanes were prescribed for 14% of patients with a diagnosis of
influenza. Independent predictors of antiviral prescribing were patient age (OR=2.1) and
Medicare insurance (OR=0.1) compared to private insurance (Linder et al., 2005).
Specifically, physicians were more likely to be prescribe antiviral medications to adults
compared to children and less likely to prescribe to those with Medicare compared to
25
private insurance (Linder, 2005). The focus of that study was patient characteristics but
in order to accurately characterize factors associated with prescribing antiviral
medications, physician and treatment setting characteristics need to be examined as well.
Physician Characteristics
Knowledge of influenza and its treatment influences physicians‟ decisions to
prescribe antiviral medications. According to Aronson (2006), a number of events must
take place before writing a prescription. These events involve decisions. First, a
diagnosis must be made with certainty through an understanding of basic
pathophysiology of the illness. Second, the physician must weigh the benefit and harm of
a specific treatment to the patient. Third, choosing the right drug from alternatives is
essential. For example, a neuraminidase inhibitor versus an adamantane for treatment of
influenza would be a decision-making scenario. However, if an accurate diagnosis has
been made for influenza and the physician thinks antibiotics may work best, then the
decision is to prescribe either an antiviral or antibiotic for treatment (Aronson, 2006).
Several studies have examined factors associated with physicians‟ prescribing of
antiviral medications. For example, Rothberg and colleagues (2006) conducted the first
study to assess physicians‟ reported use of antiviral therapy for treatment of influenza and
identified possible determinants of prescribing in the absence of specific
recommendations. The majority of respondents (87%) reported that antiviral medications
shorten the course of illness, but only 28% believed that the medication prevents bacterial
complications. Physicians also exhibited concerns regarding cost of antiviral medications
and held the opinion that influenza is self-limited and therefore does not require treatment
(Rothberg et al, 2006). Although the study captured physicians‟ self-reported prescribing
26
of antiviral medications, it did not consider patient characteristics such as age or preexisting conditions that may impact prescribing. Additionally, the sample population of
this study was limited to two medical centers which limit the generalizability of the
results to a specific region or nation.
While the above study illustrated opinions held by physicians, Linder, Chan, and
Bates (2006) sought to assess the appropriateness of prescribing antiviral medications.
According to results of their retrospective analysis, of the patients who claimed to have
influenza, 19% were actually diagnosed. Additionally, physicians prescribed antiviral
medications for 15% of the diagnosed influenza cases of which 30% of prescriptions
were considered inappropriate. Knowledge and awareness was a concern in this study as
physicians exhibited unfamiliarity with the medications and believed that influenza was a
self-limited illness. The authors suggested that physicians may be unaware of the 2-day
efficacy of antiviral medications. Physicians may also be fulfilling their own desire to
„do something‟ for the patient. Lastly, physicians may be prescribing antiviral
medications to maintain patient satisfaction. The authors indicated that the retrospective
nature of the study was a limitation. Additionally, the small sample size (n=102) limited
the power to detect differences between groups and to identify predictors of inappropriate
antiviral prescribing (Linder et al, 2006).
Alongside knowledge of antiviral medications, attitudes, and practices comparing
pediatricians and internists have also been studied. Since children are at high-risk for
developing complications from influenza, it is important to understand pediatricians‟
behavior in treating the illness. In a study by Rothberg and colleagues (2008)
pediatricians were more likely to believe that antiviral medications could decrease
27
mortality compared to internists (53% vs. 38%). Most pediatricians limited prescribing
antiviral medications to high-risk patients whereas internists prescribed equally to highrisk and average-risk patients. The most common reason cited for not prescribing
antiviral medications was that patients presented too late for treatment followed by the
uncertainty of diagnosis. The belief that influenza is self-limited was a common reason
given by pediatricians and internists for not prescribing antiviral medications (Rothberg
et al., 2008). Similarly, in a study of family practitioners and pediatricians, 92% of the
physicians were unable to identify conditions that are associated with “high-risk” group
children. Furthermore, 50% of respondents were able to identify pregnancy as a category
of “high-risk.” (Dominguez & Daum, 2005).
Aside from the knowledge possessed by physicians, associations between
physician specialty and prescribing have also been found. In a study of Connecticut,
Minnesota, New Mexico, and New York, family practitioners (64%) prescribed antiviral
medications to a greater number of patients compared to internists (58.7%), and
pediatricians (41.7%) (Fazio et al., 2008). In the same study, authors found that the
prescription of amantadine was highest in New Mexico (43.2%), despite
recommendations from the CDC to discontinue its use due to resistance development
(CDC, 2009). The study used random sampling of physicians from specific hospitals in
the four states limiting generalizability to all primary care physicians.
Mueller and colleagues (2010) examined antiviral prescribing practices among
emergency department physicians in nine states for the 2007 influenza season. The
results of their study indicated that 55.7% of clinicians prescribed antiviral medications
for treatment of influenza. Additionally, rapid influenza testing was positively associated
28
with prescribing antiviral medications. These clinicians were also more likely to
prescribe antiviral medications for treatment to patients presenting with onset of
influenza-like symptoms within 48 hours and a positive rapid influenza test compared to
patients who had no testing. Lastly, the clinicians prescribed antiviral medications to
54.5% of pediatric patients who had a positive rapid influenza test (Mueller et al., 2010).
That study, however, did have limitations. The study sample consisted of emergency
department clinicians only, limiting generalizability to other specialties. The study also
had a low response rate suggesting the potential of response bias.
Studies examining associations of various physician characteristics with
prescribing antiviral medications were reviewed. One important area of the literature
may explain why antiviral medications are underprescribed and that is the prescribing of
antibiotics as treatment for influenza. Antibiotics are known for treating bacterial
infections, and inappropriate use of antibiotics may cause individuals to become resistant
to its effectiveness of treating pathogenic bacteria (Ciesla, Leader, & Stoddard, 2004).
Many studies have demonstrated that antibiotics are prescribed for treatment of influenza.
According to a study of national ambulatory care visits, patients who had a
primary diagnosis of influenza were prescribed antibiotics for 38% of all visits while
antiviral medications were prescribed to 30% of visits. This figure illustrates the high
rate of antibiotic prescribing for influenza, resulting in an increase in antibiotic resistance.
The authors suggest interventions such as increasing education of physicians and
improving diagnostic methods that may result in decreasing the use of antibiotics (Ciesla
et al., 2004). Additionally, multivariable modeling revealed that influenza season, sex,
29
and visits to emergency departments were independent predictors of antibiotic
prescribing (Linder et al., 2006).
Physicians also need to be cautious when prescribing antibiotics to children with
no secondary bacterial infection. In order to reduce the inappropriate prescribing of
antibiotics, the CDC has put forth much effort to promote their correct use. According to
ambulatory data analyzed from 1995-98, children diagnosed as having upper respiratory
tract infections were less likely to be prescribed antibiotics in 1998 than in previous years
(Nash, Harman, Wald, & Kelleher, 2002). According to the findings, physician specialty
was a significant factor, as non-pediatricians were more likely to prescribe antibiotics
than pediatricians.
Another study examining antibiotics for treatment of influenza compared clinical
and societal outcomes in patients using antibiotics for the treatment of influenza.
Antibiotics were prescribed to 42% of patients presenting with influenza-like illness
(Carrat, Schwarzinger, Housset, & Valleron, 2004). The rate of antibiotic treatment was
higher in the elderly and vaccinated patients. Antiviral medications were prescribed in
conjunction with antibiotics to 7% of patients. No correlation was found between
antibiotic treatment and clinical/societal outcomes such as duration of illness, number of
secondary visits, lost workdays, and time it took to return to normal daily activities
(Carrat et al., 2004).
Doctor-Patient Relationship
Physicians are faced with a choice when prescribing drugs for treatment of an
illness or condition. The choice involves whether or not to prescribe a drug, the selection
of drug, and the dosage of the drug. The combination of choices results in the best
30
possible outcome for the patient. Physicians typically experience risk when making such
decisions. Prescribers of medications tend to be influenced by patient pressure for a
specific drug, financial incentives by pharmaceutical companies, or past experiences
(Chapman, Durieux, & Walley, 2004). For example, physicians‟ knowledge of a
condition and its preferred treatment does not infer appropriate treatment. An example is
of providing an antibiotic for treatment of an upper respiratory tract infection with no
secondary bacterial infection which is commonly seen (Chapman et al., 2004).
Patient communication and expectation plays an essential role in physicians‟
decision-making to prescribe medication. According to Britten, Jenkins, Barber, Bradley,
and Stevenson (2003), a study of 24 general practitioners and 186 patients revealed that
the majority of patients want to take an active role in their treatment decision.
Additionally, 42% of patients indicated that they either wanted or expected to be
provided a prescription for their main problem. Approximately 9% of general
practitioners reported feeling uncomfortable regarding their prescribing decisions due to
patient pressure (Britten et al., 2003),
Physicians may feel uncomfortable with their prescribing decisions because
patients may demand a treatment that is not suited for the condition. Additionally,
physicians may feel frustrated when patients undermine their knowledge and ability.
According to Fagnan (2000), patients‟ expectations are important when deciding whether
to prescribe an antiviral medication for influenza or upper respiratory tract infection.
Physicians want to satisfy patients whose expectation is to be prescribed an antibiotic.
(Fagnan, 2000). Patients who expected antibiotics for treatment of a viral infection were
three times more likely to be prescribed these medications by physicians compared to
31
those who did not specifically want them. Those patients who did not receive antibiotics
upon request were more likely to express dissatisfaction (Macfarlane, Holmes,
Macfarlane, & Britten, 1997).
The study by Fagnan (2000) leads us to believe that patients‟ beliefs regarding
antibiotics may be misconstrued. According to a survey of knowledge, attitudes, and
practice of viral infections, the general public has high misconceptions of antibiotic use
which may lead to inappropriate prescribing due to patient expectation (Belongia, Naimi,
Gale, & Besser, 2002). Approximately 50% of parents and adults surveyed were
prescribed antibiotics for a nonbacterial infection. Due to the high patient demand for
antibiotics, physicians‟ decision-making for prescription of an antiviral medication is
complicated and uncertainty exists that may lead the physician to make an inappropriate
decision (Belongia et al., 2002).
Many studies have examined associations with patient and physician
characteristics but very few have examined the effects of health facility characteristics.
Also, a majority of these studies‟ results are not generalizable to primary care physicians
practicing in the United States. This dissertation aims to fill these gaps by examining
various characteristics in the ambulatory care setting that may impact physicians‟
decision to prescribe antiviral medications ensuring generalizability of results to primary
care physicians in the U.S.
Chapter Summary
The literature review has illustrated associations of patient characteristics and
physician characteristics with prescribing of antiviral medications; however factors
32
associated with prescribing practices still remain unclear. Very few studies have
examined additional factors such as health facility characteristics. Additionally, designs
and/or settings of many of these studies limited generalizability. A need exists for
research that specifically examines factors associated with the prescribing of antiviral
medications by primary care physicians. By doing so, appropriate measures and
interventions may be implemented to mitigate inappropriate prescribing of antiviral
medication for treatment of influenza.
33
Table 2.
Review of Studies Pertaining to Antiviral Prescribing by Physicians
Author
Year
Sample
Research Design
Results
Statistical significance p<.05
Rothberg et al
2006
336
Physicians from two
clinics
Cross-sectional
Two states:
Massachusetts and Texas
March-June 2004
More likely to prescribe antivirals
Texas vs. Massachusetts; OR= 3.5; CI, 2.2–5.9
Higher patient volume (OR= 2.0; CI, 1.5–2.6)
Use of rapid testing (OR= 1.8; CI, 1.1–3.0)
Belief that antivirals shortens illness
(OR= 4.9; CI, 1.8–13.2)
Belief that antivirals decreases mortality rates (OR= 3.0; CI, 1.7–
5.5).
Mueller et al
2007
1,055
Emergency department
clinicians
Cross-sectional
Nine-states: California,
Colorado, Georgia,
Minnesota, New Mexico,
New York, Oregon, and
Tennessee
May – September 2007
Less likely to prescribe antivirals
Academic hospitals (OR=0.2; CI, 0.1-0.4)
Was not aware if facility had written antiviral policy (OR= 0.2, CI,
0.2-0.9)
Fazio, Laufer,
Meek, & Palumbo
2008
Metropolitan area
primary care
physicians at
Emerging Infections
Program sites
Cross-sectional
Four states: Connecticut,
Minnesota, New Mexico,
and New York
Influenza season of 200607
More likely to prescribe antivirals
10 -17 years of training (OR= 1.7; CI, 1.1-2.7)
18 or more years of training (OR= 1.9; CI, 1.1-3.1)
Rapid testing (OR=17.8; CI, 9.3-34.2)
Prescribing behavior
40.7% in New Mexico prescribed adamantanes
Rapid testing decreased later in the influenza season (51.7% vs.
28.2% in pediatrics; 48.6% vs. 24.3% in adults; p<.0001)
Physicians practicing more than 10 years were less likely (66.4%) to
order an influenza test than those in practice <10 years (76.0%)
(p<0.05).
53.8% prescribed antiviral agents to at least some patients with ILI.
Differences by specialty: family practice, 66.4%; internal medicine,
58.7%; and pediatrics, 41.7%.
34
Katz, Lamias,
Shay, & Uyeki
2009
2,649
Physician members of
four medical societies
Cross-sectional
United States
Influenza season of 200405
Physicians more likely to order rapid tests if patient privately
insured (68.3% vs. 33.9%, p<.0001)
Suburban and rural practice more likely to use rapid tests (OR= 1.8;
CI, 1.5-2.1; and OR= 3.5; CI, 2.7- 4.5)
Use of rapid testing more likely to prescribe (OR= 2.9; CI 2.3-3.6)
21.7% do not prescribe due to skepticism about antiviral
effectiveness
Rothberg et al
2008
107 pediatricians
103 internists
Cross-sectional
Massachusetts and Texas
Influenza season of 200304
More likely to prescribe antivirals
Use amantadine, pediatricians vs. internists (56% vs. 23%, p<.0001)
Belief that antivirals could decrease mortality, pediatricians vs.
internists (38% vs. 22%, p=0.01).
Treat only high-risk patients, pediatricians vs. internists (86% vs.
53%, p<.0001).
Linder, Nieva, &
Blumentals
2009
958
Clinically-diagnosed
patients of influenza
21 primary care clinics
Retrospective analysis
Eastern Massachusetts
1999-2007
Independent predictors of antiviral prescribing
Symptom duration of 2 or fewer days (OR= 12.4; 95% 8.3-18.6)
Year (OR= 1.4 for each successive influenza season; CI, 1.3- 1.7)
Patient age (OR= 1.3 per decade; CI, 1.2-1.5
Compared to having no influenza testing, having a negative
influenza test (OR=5.5; CI, 3.4-9.1) or a positive influenza test
(OR= 11.4; CI, 6.7-19.3).
Hsieh et al
2010
12,140,727
Influenza patient visits
Cross-sectional
United States
2002-06
Anivirals were prescribed in 21.7% of visits
Before 2006 CDC guidelines (43.3%-adamananes; 56.5%oseltamivir)
After 2006 CDC guidelines (2.3%-adamantanes; 97.7%-oseltamivir)
35
CHAPTER 3
RESARCH METHODOLOGY
This chapter describes the methods that were utilized to answer the following
research questions:
1.
What are the factors associated with prescribing antiviral medications for
treatment of influenza among primary care physicians?
2. Are there differences in prescribing practices among primary care specialties?
3. Are there differences in prescribing practices over time?
This chapter includes the research methodology associated with this dissertation.
Data for this study were obtained from the National Ambulatory Medical Care Survey
(NAMCS) for the years of 2005-2008. Information presented in this chapter regarding
the NAMCS was obtained from its corresponding downloadable documentation file
available at http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm.
This chapter includes a description of the NAMCS, which includes the sample
selection process for the survey and the data collection process. The estimation
procedures are then presented along with the description of the outcome variable of
interest. The chapter concludes with identifying the independent variables of interest and
the analyses procedures.
,
36
Survey Instrument
The NAMCS is an ongoing national survey that was conducted annually from the
years of 1973-81, in 1985, and annually since 1989. The survey is conducted to provide
reliable information with respect to the provision and use of ambulatory medical care
services in the U.S. Results of the survey are based on a sample consisting of nonfederal, employee-based physicians whose primary involvement is in providing direct
patient care. According to the American Medical Association (AMA) and the American
Osteopathic Association (AOA), visits to non-federally employed physicians are
classified as office-based, patient care. Additionally, the data are extremely useful in
understanding patterns of healthcare in the U.S. NAMCS is sponsored by the National
Center for Health Statistics (NCHS).
Prior to physicians‟ participation in the survey, trained interviewers visited the
sample of physicians to provide information and instructions with respect to properly
completing the survey and additional forms. Physicians were randomly assigned to a one
week reporting period where data were recorded by the physician or staff on an encounter
form. Various types of data were recorded such as symptoms experienced by the patient,
primary diagnosis of the physician, and any medications prescribed (NAMCS, 2005).
Additionally, demographic characteristics of the patients were recorded such as age, race,
ethnicity, and gender.
Sample
The present study used data from influenza seasons between October 1, 2005 and
May 31, 2008. The year of 2005 was chosen as the starting point because the physician
weight variable was publicly available beginning in 2005 and most recent survey year
37
available was in 2008. Additionally, the CDC provided specific recommendations to
discontinue the use of adamantanes for the treatment of influenza on January 14, 2006.
By choosing the above time frame, the study can assess physicians‟ prescribing practices
of adamantanes before and after the CDC recommendation. Consistent with the purpose
of the study, the sample included data on primary care physicians in the U.S. According
to the AMA, primary care physicians are defined as those specializing in family practice,
internal medicine, obstetrics/gynecology, and general pediatrics. This sample is selected
because primary care physicians are typically first in line to respond to influenza (Linder
et al., 2006; Rothberg et. al., 2008; Mueller et. al, 2010).
The sampling unit in this survey was identified by the encounter between
physicians and patients. The NAMCS used a multistage probability design that consisted
of three stages of sampling. The first stage was comprised of a multistage probability
design involving probability samples of primary sampling units (PSU). PSUs are
geographic clusters that include counties, groups of counties, or towns. Additionally,
PSUs should contain only adjacent counties and contain a minimum measure of size.
Generally, PSUs are formed to minimize costs incurred through data collection and to
maximize heterogeneity within PSUs. Solving the following formula results in the
minimum measure of size for a PSU:
MIN (M α) ≥ [ (fo) (∑ M α) / a],
MIN (M α) represents the minimum measure of size for PSUs, fo represents the largest
overall sampling rate, M α represents the measure of size for PSUs, and a represents the
number of PSUs to be selected (NAMCS, 2005).
38
The first-stage sample consisted of 112 PSUs. The second stage sample utilized
master files from the AMA and AOA. Practicing physicians were selected using a
probability sample from these files within the PSU. Physicians eligible for the survey
were stratified into 15 groups. These groups include general and family practice,
osteopathy, internal medicine, pediatrics, general surgery, obstetrics and gynecology,
orthopedic surgery, cardiovascular diseases, dermatology, urology, psychiatry,
neurology, ophthalmology, otolaryngology, and a category for other specialties
(NAMCS, 2005). For the purpose of this study, primary care physicians were used for
data analysis. Primary care physicians were divided into four categories including
General and Family Practice, Internal Medicine, General Pediatrics, and
Obstetrics/Gynecology (OB/GYN). All participating physicians were required to
complete the physician induction form (See Appendix C).
The last and final stage involved the selection of patient visits from the sample
physicians. This process consisted of two steps. First, the sample of physicians was
divided randomly into 52 subsamples each of which were of equal size. Each subsample
was then assigned randomly to each of the 52 weeks in the survey year. Second, each
physician was assigned a one-week reporting period. Patient visits were selected from a
systematic random sample of visits during their respective reporting week (NAMCS,
2005).
Inclusion and Exclusion Criteria
The present study was restricted to primary care physicians only. Physician
diagnoses with an International Classification of Diseases, Ninth Revision, Clinical
Modification (ICD-9-CM) code of 487, 487.0, 487.1, and 487.8 were included in the
39
study. These codes represent the diagnoses of influenza, influenza with pneumonia,
influenza with other respiratory manifestations, and influenza with other manifestations
respectively. Additionally, influenza season begins October 1st of each year and
continues through May 31st of the following year. Therefore, only patient visits made
during influenza seasons between October 1, 2005 and May 31, 2008 were included for
data analysis.
Data Collection
Data collection for the survey was carried out by physicians and/or qualified
office staff. The U.S. Bureau of the Census served as the field data collection agent. To
ensure proper data collection, physicians were required to keep a daily log of all patient
visits during the 1-week assigned period. The log included scheduled and unscheduled
visits. Data obtained by physicians were recorded on 8 x 14 inch Patient Record forms
(See Appendix B). The field staff ensured completeness of forms and conducted clerical
edits for central processing of data. Constella Group, Inc performed all medical and drug
coding. Error rates with respect to keying and coding were extremely low, between zero
and one percent. Nonresponse rates for survey items were five percent or less. This is
described in detail in the NAMCS documentation (NAMCS, 2005).
Confidentiality
The NAMCS complies with Privacy Rule of the Health Insurance Portability and
Accountability Act (HIPAA). The surveys do not collect personal identifiers such as
patients‟ name, address, or social security number. Collection of data for the surveys was
authorized by the Public Health Service Act, Section 306, (Title 42, U.S. Code, 242k).
40
Additionally, this research was approved by the University of Alabama at Birmingham
Institutional Review Board for Non-Human Subjects Research (See Appendix D).
Estimation Procedure
The estimation procedure presented here was obtained from the NAMCS
documentation. The results of the survey can be extrapolated to represent national
findings. A multistage estimation procedure was used to derive statistics. Four steps are
involved to produce national estimates that are unbiased.
1) Inflation by reciprocals of the probabilities of selection
2) Adjustment for nonresponse
3) Ratio adjustment for fixed totals
4) Weight smoothing
First, the survey incorporated a three-stage sample design as discussed in the
“Sample” section. Therefore, the three probabilities associated with the sample design
are the probability of selecting the PSU, the probability of selecting a physician within a
PSU, and the probability of selecting a patient visit within the physician‟s practice
(NAMCS, 2005).
Second, adjustments accounting for nonresponse were made. Physicians who did
not submit Personal Record Forms needed to be taken into consideration with respect to
bias. Therefore, weights of nonresponding physicians were shifted to responding
physicians. Overall annual estimates were not affected by the shift in nonresponse
adjustment. Item nonresponse rates were less than five percent (NAMCS, 2005). For
those items with missing data, these items were imputed by assigning a value from the
41
Patient Record Forms that exhibited similar characteristics based on a number of
variables. The items were randomly assigned their values.
Third, a ratio adjustment was made within each of the 15 physician specialty
groups for the overall NAMCS data. The ratio adjustment was a multiplication factor
where the numerator consisted of the number of physicians in the universe grouped by
specialty. The values for the numerator were obtained from the AMA and AOA. The
denominator consisted of the estimated number of physicians in the respective specialty
group (NAMCS, 2005).
Lastly, the estimation procedure involved weight smoothing. During each survey
year, there were a small number of physicians whose final visits are relatively large
compared to the rest of the sample. The uneven weight may affect the true representation
of the resulting statistics and also increase variance. Although the small sample of
extreme weights can be eliminated from the study, this would lead to an inaccurate count
of total visits indicating bias. In order to resolve the uneven weights of visits, weight
smoothing is used where the extreme weight visits are shifted to the smaller weight visits
(NAMCS, 2005).
The “patient visit weight” is an essential component involved in producing
national estimates from the sample data. The downloadable data represent only a small
portion of patients and thus does not include all visits that occurred in the U.S. In order
to obtain national estimates from the patient visits reported annually in the survey, an
inflation factor referred to as the “patient visit weight” is assigned to each recorded
response. By aggregating the patient visit weights to the sample records for each year,
the national estimates can be obtained for the U.S.
42
Variables
Outcome Variable
The dichotomous dependent variable in this study was the prescribing of antiviral
medications for treatment of influenza. Primary care physicians and/or office staff who
reported data were allowed to enter a maximum of eight medications that were either
provided or prescribed for treatment of influenza or influenza-like illness on the Patient
Record form.
The National Drug Code Directory was used to code the therapeutic class of
drugs. Antimicrobials (code 0300) were the drug class that contained antiviral
medications (code 0388). Additionally, drug data collection and generic codes were
recorded using a NCHS, 5-digit drug code that was applied to each entry in the NAMCS.
The drug and generic names and relative codes of each of the four antiviral medications
used for treatment of influenza are presented in Table 3.
Table 3.
Names of Antiviral Medications and Corresponding Codes
Drug Name
Drug Code
Generic Name
Drug Code
Tamiflu
00002
Oseltamivir
70102
Relenza
99085
Zanamivir
70031
Symmetrel
30305
Amantadine
50165
Flumadine
94003
Rimantadine
57100
43
Independent Variables of Interest
The independent variables for analysis are categorized into three groups: patient
characteristics, physician characteristics, and health facility characteristics. These
categories are based on Eisenberg‟s conceptual framework. Due to the secondary nature
of the data, patient-physician interaction could not be analyzed. A list of independent
variables and their level of measurement are provided in Table 4.
Table 4.
Measurements of Independent Variables
Variable
Level of Measurement
Coding/Description
NAMCS Year
Categorical
1 = 2005
2 = 2006
3 = 2007
4 = 2008
Age
Age Recode
Continuous
Categorical
Years
1 = Under 15 years
2 = 15-24 years
3 = 25-44 years
4 = 45-64 years
5 = 65+ years
Sex
Categorical
1 = Female
2 = Male
Race
Categorical
1 = White
2 = Black/American
3 = Asian
4 = Native Hawaiian/Other Pacific
Islander
5 = American Indian/Alaska
Native
6 = More than one race reported
Ethnicity
Categorical
1 = Hispanic or Latino
2 = Not Hispanic or Latino
Expected source of payment
Categorical
1 = Private insurance
2 = Medicare
3 = Medicaid/SCHIP
4 = Worker‟s Compensation
5 = Self-pay
6 = No charge/charity
7 = Other
8 = Unknown
Patient characteristics
44
Patient status
Categorical
1 = New patient
2 = Established patient
Type of training
Categorical
1 = M.D. – Doctor of Medicine
2 = D.O. – Doctor of Osteopathy
Physician Characteristics
Specialty
Categorical
1 = General and family practice
2 = Internal medicine
3 = General Pediatrics
4 = Obstetrics/Gynecology
Employment status
Categorical
1 = Owner
2 = Employee
3 = Contractor
4 = Blank
Ownership status
Categorical
1 = Physician or physician group
2 = HMO
3 = Medical/Academic health
center
4 = Other hospital
5 = Other health care corporation
6 = Other
Diagnosis
Categorical
ICD-9-CM codes
Type of setting
Categorical
1 = Private solo or group practice
2 = Free standing clinic
3 = Federally qualified health
center
4 = Mental health center
5 = Non-federal government clinic
6 = Family planning clinic
7 = Health maintenance
organization (HMO) or other
prepaid practice
8 = Faculty practice plan
9 = Other
Solo practice
Dichotomous
1 = yes
2 = no
Lab testing
Dichotomous
1 = Yes
2 = No
Physician characteristics
Health facility characteristics
45
Type of location
Dichotomous
1 = Metropolitan Statistical Area
(MSA)
2 = Non-MSA
Geographical location
Categorical
1 = Northeast
2 = Midwest
3 = South
4 = West
Analyses Procedures
This study was a retrospective serial cross-sectional analysis of NAMCS data for
the influenza seasons 2005 through 2008. The statistical software STATA version 11.0
was used for the purpose of data management and analyses. The survey component of
STATA commands, svyset, was used to analyze the complex survey data. The study
accounted for the complex sampling design of NAMCS. Datasets for analysis were
publicly available and downloadable from
http://www.cdc.gov/nchs/ahcd/ahcd_questionnaires.htm. Descriptive statistics were
calculated in the preliminary phase of data analysis. The descriptive statistics of the
population are presented as frequency statistics and percentages composed in a table.
The distribution of each independent variable was examined and if the frequency in any
of the categories was small, these values were collapsed across categories.
The prevalence of prescribing antiviral medications was calculated as a
dichotomous variable by the creation of a dummy variable, where “one” represents
prescribed antiviral medications and “zero” represents did not prescribe antiviral
medications. Bivariate analysis was conducted in order to identify any relationships
between the dependent variable and the independent factors thought to be associated with
as presented in Table 3.2. Significance for the bivariate analysis was at the 0.10 level.
46
The variables that were significant in the bivariate analysis were included in the
multivariable logistic regression model. Additionally, variables that were deemed
meaningful from the literature review presented in Chapter 2 were also included in the
model. Diagnostic tests were performed to ensure that the model complied with basic
assumptions of the logistic regression. In order to ensure that the model fit the data over
all covariate patterns, a residual analysis was performed to assess model fit.
Additionally, the area under the Receiver Operating Characteristic (ROC) curve was
observed to determine the calibration levels of the model. In order to control for any
differences in prescribing practices over time (2005-2008) that may have occurred, the
variable year was included as a fixed effect. Lastly, interaction analyses were conducted
to identify differences, if any, in prescribing practices among specialty and over time.
Specifically, each of the independent variables of interest was interacted with physician
specialty and year. The following equation was used to determine any interactions at the
0.05 level of significance where b0 represents antiviral prescribing, b1 is the variable of
interest that is to be interacted with b2, which is physician specialty or year:
logistic b0+ b1+ b2 i. b1* b2
Calculated statistics included the prevalence odds ratios, 95% confidence intervals, and
p-values.
Chapter Summary
This chapter presented the methodology associated with this cross-sectional
retrospective analysis. The survey methodology used by NAMSC was discussed
followed a list of independent variables. The inclusion criterion was primary care
47
physicians who diagnosed patients for influenza based on the ICD-9-CM codes during
the influenza seasons between 2005 and 2008. The primary form of analysis used in this
study was multivariable logistic regression analyses to identify factors associated with
prescribing antiviral medications. The following chapter will present the results from the
data analysis.
48
CHAPTER 4
RESULTS
This chapter presents the results of the data analysis as discussed in the previous
chapter. Descriptive, bivariate, and multivariable logistic regression analyses were
conducted to identify factors associated with prescribing antiviral medications for
treatment of influenza. Interactions between the variables of interest and specialty and
year were conducted to identify any differences in prescribing practices among specialty
and by year. In this chapter, descriptive statistics are presented followed by results of the
bivariate analyses. Results of the interaction analyses are then provided. The chapter
concludes with results of the multivariable logistic regression analysis.
Descriptive Characteristics
All analyses performed with the data used weighted observations as indicated in
the NAMCS documentation sampling weights. Significance was established at the alpha
level of 0.10 for the bivariate analysis and 0.05 for the multivariable logistic regression
analyses. Table 5 depicts the number of physicians by specialty and year that were
included in the NAMCS between 2005 and 2008. The numbers in the first column
represent the unweighted sample and in the second column represent the weighted
sample. As shown in Table 5, general/family practice comprised the largest specialty
followed by internal medicine. Pediatrics and Obstetrics and gynecology comprised the
49
smallest portions of primary care physicians in the NAMCS during the survey years of
interest.
Table 5.
Frequencies and Percentages of Physicians by Year and Specialty
Specialty
Unweighted Sample
Weighted Sample
Estimated
Proportions (%)
127
62
92
67
59,225
48,446
32,699
22,406
36.4
29.8
20.1
13.8
348
162,776
142
70
72
84
56,642
39,487
30,698
24,065
368
150,892
150
72
95
92
58,556
44,168
31,322
25,752
2005
General/family practice
Internal medicine
Pediatrics
Obstetrics and gynecology
Total
2006
General/family practice
Internal medicine
Pediatrics
Obstetrics and gynecology
Total
2007
General/family practice
Internal medicine
Pediatrics
Obstetrics and gynecology
Total
409
157,798
2008
General/family practice
Internal medicine
Pediatrics
Obstetrics and gynecology
140
86
106
79
63,158
44,648
33,755
27,362
Total
411
168,923
37.5
26.2
20.3
15.9
36.6
27.6
19.6
16.1
37.4
26.4
20.0
16.2
* Percentages based on the weighted sample
Sample Size
Table 6 depicts the sample size of the study which was 169 primary care
physicians who treated patients diagnosed with influenza during the influenza seasons of
2005-2008. The number of physicians who treated influenza outside of the influenza
50
season was relatively small (n=9), therefore diagnoses during the influenza seasons on
interest were included in the study. The weighted number of observations was 68,164.
The sample size was identified according to the inclusion criteria of this study which was
primary care physicians only who treated patients with a diagnosis of influenza according
to the ICD-9-CM codes of 487, 487.0, 487.1, or 487.8. The sample included 11 strata
and 87 population sampling units (PSU). The specialty of family practice comprised the
largest percentage of primary care physicians who treated patients with an influenza
diagnosis (55.7%). General pediatrics comprised 31.5% of primary care physicians
followed by internal medicine who comprised 12.8% of primary care physicians who
treated patients with an influenza diagnosis during the survey years of 2005-2008. The
smallest specialty of primary care physicians who treated patients with influenza was
Obstetrics and gynecology (1.8%). Due to the extremely small number of physicians in
this specialty, the category was excluded from all analyses.
Table 6.
Sample Physician Frequency for 2005-2008
Specialty
Unweighted Sample
Weighted Sample
Estimated
Proportions (%)
87
22
60
3
37,960
8,743
21,461
1,106
55.7
12.8
31.5
1.8
169
68,164
100
Family practice
Internal medicine
General Pediatrics
Obstetrics and gynecology
Total after exclusion
* Percentages based on the weighted sample
Patient Characteristics
The number of patients with an influenza diagnosis according to the ICD-9-CM
code 487, 487.0, 487.1, and 487.8 was 176 and the weighted number of patients was
5,711,127 patients. As described in Table 7, the mean age for patients presenting with
51
influenza was 25.3 with a standard deviation of 20.3. The age variable included patients
ranging from under one year (121 days) to 89 years of age. Approximately 55.6% of
patients were female and 44.4% were male. The majority of patients with influenza were
white (84.2%) while African Americans represented 11.8% of the influenza patient
population. Additionally, 18.9% of patients were classified as Hispanic or Latino. A
majority of influenza patients (92.3%) had established relationships with their physicians
while a small percentage were new patients (7.7%). The expected source of payment for
a large proportion of patients was private insurance (68.1%), while 8.9% of patients
utilized Medicare, and 5.9% used Medicaid as payment for medical services. Payment
sources for the remaining 17.2% of patients were self-pay and unknown.
Table 7.
Frequencies and Percentages of Patient Characteristics
Variable
Frequency
(Pre-weighted)
Frequency
(Weighted)
Estimated
Proportions (%)
25.3 (mean)
20.3 (SD)
95% CI (21.2-29.4)
25.3 (mean)
20.3 (SD)
95% CI (21.2-29.4)
Age Recode
Under 15 years
15-24 years
25-44 years
45-64 years
65+ years
68
31
48
19
10
2,088,913
1,011,633
1,775,707
482,016
352,858
36.6
17.7
31.1
8.4
6.2
Gender
Female
Male
98
78
3,371,234
2,339,893
59.0
41.0
Race
White
Black
Other
152
18
6
5,181,521
258,026
271,580
90.7
4.5
4.8
Ethnicity
Hispanic or Latino
32
1,157,047
20.3
Not Hispanic or
144
4,554,080
79.7
Patient characteristics (n=176)
Age
Latino
52
Patient Status
Established patient
New patient
Expected Source of Payment
Private insurance
Medicare
Medicaid
Self-pay
Other
Unknown
* Percentages based on the weighted sample
158
18
5,324,188
386,939
93.2
6.8
118
17
12
5
1
23
3,681,205
520,794
326,995
312,647
85,717
783,769
64.5
9.1
5.7
5.5
1.5
13.7
Physician Characteristics
Table 8 depicts the descriptive characteristics for primary care physicians
included in the study. Most primary care physicians reported having a Doctor of
Medicine degree (85.2%). Furthermore, 55.7% of primary care physicians specialized in
family practice followed by general pediatrics (31.5%), internal medicine (12.8%).
Approximately 69.1% of primary care physicians were owners of their practice while
29.4% were employees. Lastly, 52.9% of primary care physicians were non-solo
practitioners.
Table 8.
Frequencies and Percentages of Physician Characteristics
Variable
Frequency
(Pre-weighted)
Frequency
(Weighted)
Estimated Proportions
(%)
Physician characteristics (n=169)
Type of Training
Doctor of Medicine
Doctor of Osteopathy
144
25
58,083
10,081
85.2
14.8
Specialty
Family Practice
Internal Medicine
General Pediatrics
87
22
60
337,960
8,743
21,461
55.7
12.8
31.5
Employment Status
Owner
Employee
Contractor
123
45
1
47,115
20,047
402
69.1
29.4
0.6
53
Solo practice?
Yes
No
89
80
32,078
36,086
47.1
52.9
* Percentages based on the weighted sample
Health Facility Characteristics
Descriptive characteristics for health facilities are shown in Table 9. The majority
of primary care physicians (81.7%) practiced in a private solo or group setting while
10.1% practiced in a free standing clinic. The facilities were largely owned by the
physician or physician group (89.4%). Over half of the health facilities conducted on-site
laboratory testing (58.6%). With respect to geographic location, the majority of health
facilities were located in a metropolitan statistical area (87%), while 13% were located in
a non-metropolitan statistical area. Lastly, 36.1% of facilities were located in the south
followed by 33.1% in the west, 18.9% in the northeast and 11.8% in the midwest.
Table 9.
Frequency and Percentages of Health Facility Characteristics
Variable
Frequency
(Pre-weighted)
Frequency
(Weighted)
Estimated Proportions
(%)
Type of setting
Private solo or group practice
Free standing clinic
Federally qualified health clinic
Other
138
17
7
7
55,663
6,857
2,822
2,822
81.7
10.1
4.1
4.1
Ownership status
Physician or physician group
Health maintenance organization
Medical/academic health center
Other
151
6
7
5
60,905
2,420
2,822
2,018
89.4
3.6
4.1
3.0
99
70
39,930
28,234
58.6
41.4
147
22
59,289
8,875
87.0
13.0
Health Facility Characteristics
Lab Testing
Yes
No
Metropolitan Location
MSA
Non MSA
54
Geographical Location
Northeast
Midwest
South
West
32
20
61
56
12,903
8,064
24,600
22,590
18.9
11.8
36.1
33.1
* Percentages based on the weighted sample
Prescribing Rates of Antiviral Medications
The pre-weighted and weighted prescription rates for antiviral medications are
described in Table 10. There were approximately 1,872,322 antiviral medication
prescriptions for treatment of influenza in the U.S during the 2005-2008 influenza
seasons, according to the weighted analysis of prescriptions. Neuraminidase inhibitors
comprised 12.9% of all prescriptions for treatment of influenza in 2005. Beginning in
2006, neuraminidase inhibitors were prescribed much more frequently (36.1%), remained
steady in 2007 (35%), and were prescribed less frequently in 2008 (25%). Adamantanes
comprised 10.7% of all prescriptions for treatment of influenza in 2005. Beginning in
2006, the adamantanes were prescribed much less frequently (2.8%). Prescription of
adamantanes was 6.8% in 2007 and 5.0% in 2008. The “other” category comprised the
largest portion of prescriptions for treatment of influenza (76.4%). The “other” category
included antibiotics (41.3%), ibuprofen and acetaminophen (10.6%), antitussins (8.5%),
bronchodilators (5.2%), and allergy medications (3.1%). The remaining 7.7% of
medications were not listed.
55
Table 10.
Weighted Antiviral Prescription Summary Statistics by Year
Antiviral Category
2005
N (%)
2006
N (%)
2007
N (%)
2008
N (%)
Neuraminidase Inhibitors
7 (187,495)
(12.9)
13 (566,505)
(36.1)
7 (232,196)
(35.0)
11(503,959)
(25.0)
Adamantanes
4 (156,232)
(10.7)
1 (47,209)
(2.8)
1 (45,775)
(6.9)
4 (100,792)
(5.0)
Other
24 (1,114,520)
(76.4)
22 (1,038,593)
(61.1)
10 (325,737)
(49.1)
20 (1,411,086)
(70.0)
Total
33 (1,458,247)
36 (1,573,626)
20 (663,417)
80 (2,015,837)
Figure 2 provides a visual representation of the percentages of antiviral medications
compared to other medications prescribed using the weighted numbers from Table 11.
We can see that “other” medications were commonly prescribed and the prescription of
neuraminidase inhibitors increased over time but began to decrease by 2008.
80
Neuraminidase
Inhibitors
Adamantanes
60
40
20
Other
0
2005
2006
2007
2008
Figure 2. Prescriptions for Treatment of Influenza by Year
Numbers of physicians who prescribed antiviral medications for treatment of
influenza are summarized in Table 11. Among the specialties, 40.5% of family practice
physicians prescribed antiviral medications to patients presenting with influenza while
59.5% did not prescribe antiviral medications. General pediatricians represented 28.3%
56
of primary care physicians who prescribed antiviral medications for treatment of
influenza while 71.7% did not prescribe. Lastly, 27.3% of internists prescribed antiviral
medications while 72.7% did not prescribe. Overall, a large percentage in each of the
specialties did not prescribe antiviral medications for treatment of influenza as seen in
Figure 3.
Table 11.
Antiviral Medication Prescriptions by Specialty
Specialty
Unweighted
prescribed
antivirals
(N)
Weighted
prescribed
antivirals
(N)
Proportion
prescribed
antivirals
(%)
Unweighted did
not prescribe
antivirals (N)
50
Weighted
did not
prescribe
antivirals
(N)
21,928
Proportion
Did Not
Prescribe
Antivirals
(%)
59.5
Family
practice
34
14,926
40.5
Internal
medicine
6
2,689
27.3
16
7,160
72.7
General
pediatrics
17
6,073
28.3
43
15,388
71.7
* Percentages based on the weighted values
80
60
Prescribed Antivirals
40
20
Did not prescribe
antivirals
0
Family
Practice
Internal
Medicine
General
Pediatrics
OB/GYN
Figure 3. Antiviral Medication Prescribing by Specialty
57
Bivariate Analyses
In order to identify the relationship between patient, physician, and health facility
characteristics and the prescribing of antiviral medications, bivariate analyses were
conducted. The results of the analyses are presented in Table 12. Independent variables
that had a statistical significance of 0.10 or less were chosen to be included into the
multivariable logistic regression model. Additionally, variables that had been indicated
in the literature to be meaningful to analyses were also included in the model regardless
of statistical significance. According to the results, five factors were found to have a
significant relationship with prescribing antiviral medications. These factors were patient
age, patient race, patient status, type of physician training, and physician employment
status. Additional factors that were included in the model due to significance in the
literature were patient gender, ethnicity, expected source of payment, type of setting, solo
practice, geographical location, and metropolitan location.
Patient Characteristics
With respect to patient characteristics, age (p=0.032), race (p=0.012), and patient
status (p=0.021) were found to have a significant relationship with prescribing antiviral
medications. Physicians prescribed antiviral medications more often to children and
young adults than to older individuals presenting with influenza. Additionally,
physicians prescribed antiviral medications more often to whites than any other race.
Established patients were prescribed antiviral medications more often than new patients
(p=0.021). There were no relationships between antiviral prescribing and gender or
ethnicity.
58
Physician Characteristics
Type of training and employment status had a significant relationship with
physicians‟ prescribing of antiviral medications. Physicians whose medical training was
Doctor of Osteopathy prescribed antiviral medications more often than physicians whose
training was Doctor of Medicine (p=0.043). Additionally, physicians who were owners
of their practice prescribed more antiviral medications compared to employees of the
practice (0.093). There were no differences in physician specialty but this variable was
included in the multivariable logistic regression model because it was deemed
meaningful to the analyses according to the literature presented in Chapter 2 (Rothberg et
al., 2008).
Health Facility Characteristics
Type of setting, lab testing performed, geographical location, and metropolitan
location did not have any significant relationships with the prescribing of antiviral
medications. However, these variables were included in the multivariable logistic
regression model because they deemed to be meaningful for the analyses.
Table 12.
Bivariate Analysis of Demographic Characteristics Between the Prescription and Non-Prescription of
Antiviral Medications
Characteristics
Age (years)
Under 15
15-24
25-44
45-64
65+
Gender
Female
Male
N
(unweighetd)
Prescribed
antivirals
(row %)
Did not prescribe
antivirals
(row %)
p-value
61
31
48
19
10
26.13
29.96
39.07
55.37
15.95
73.87
70.04
60.93
44.63
84.05
0.032
94
75
34.03
30.98
65.97
69.02
0.761
59
Race
White
Black
Other
Ethnicity
Hispanic or Latino
Not Hispanic or
Patient Status
Latino
Established patient
New patient
Expected source of
Private insurance
payment
Medicare
Medicaid
Self-pay
Other
Unknown
Physician
Type of training
Characteristics
Doctor of Medicine
Doctor of Osteopathy
Specialty
Family Practice
Internal Medicine
Pediatrics
Employment status
Owner
Employee
Contract
Health Facility
Type of setting
Characteristics
Private solo or group
Freestanding clinic
practice
Federally qualified
Other
health clinic
Solo practice
Yes
No
Ownership status
Physician/physician
HMO
group
Medical/academic
Other
health
center
Lab testing
Yes
No
143
20
6
31.69
21.7
84.7
68.31
78.3
15.3
0.012
32
137
27.82
34.05
72.18
65.95
0.615
156
13
34.76
5.59
65.24
94.41
0.021
115
15
10
5
1
23
27.31
47.40
3.71
64.66
76.1
40.83
72.69
52.60
96.29
35.34
23.9
59.17
0.120
144
25
30.07
51.43
69.93
48.57
0.043
87
22
60
40.15
27.84
26.65
59.85
72.16
73.35
0.361
135
33
1
34.93
26.05
0.00
65.07
73.95
100
0.093
138
17
7
29.34
34.75
24.33
70.66
65.25
75.67
0.272
7
50.32
49.68
95
74
37.31
28.32
62.69
67.22
0.301
151
6
7
5
0.35
0.26
0.00
0.21
0.65
0.74
100
0.79
0.594
95
74
32.77
32.81
67.23
67.19
0.892
60
Geographical location
Northeast
32
Midwest
30
South
61
West
56
Metropolitan location
MSA
147
Non-MSA
22
* Percentages based on the weighted values
34.24
15.74
31.46
39.95
65.76
84.26
68.54
60.05
0.453
31.30
41.53
68.70
58.47
0.296
Interaction Analyses
With respect to research questions pertaining to identifying differences in
prescribing practices among specialty and over time, interaction analyses were conducted
with each of the variables listed in Table 12. According to the results, there were no
significant differences in prescription rates with physician specialty or year. However,
there were some associations that should be noted. Even though prescribing of antiviral
medications was not significantly different by specialty and year, family practice
physicians prescribed less antiviral medications for treatment of influenza in 2007
compared to other years (19.7%). Additionally, family practice physicians prescribed
more antiviral medications to females compared to other specialties (67.2%). Lastly, in
2007, primary care physicians practicing in the south prescribed more than their
counterparts in other regions (51.8%).
Multivariable Logistic Regression Analyses
Testing Assumptions
The assumptions associated with multivariable logistic regression were tested and
the results are presented below. The correlation of the data was assessed (See Appendix
A). After observing the data, no multocollinearity was present. Table 13 depicts the
61
values of the goodness-of-fit test. The Pearson chi-square test was performed on the
unweighted data to assess goodness-of-fit. There were 169 observations and the number
of covariate patterns was 167. The Pearson chi-square test statistic was 166.7.
Table 13.
Logistic Model Pearson Chi-Square Goodness-of-Fit Test
Number of Observations
169
Number of Covariate Patterns
167
Pearson Chi-Square
166.7
P-value
0.261
Since the number of covariate patterns was closely approaching the number of
observations, the Hosmer and Lemeshow goodness-of-fit test for grouped data was
performed to provide better goodness-of-fit estimates. The results are shown in Table 14.
Again, there were 169 observations grouped into ten groups of data based on ordering of
the fitted values. The Hosmer and Lemeshow chi-square with eight degrees of freedom
was 9.18. The p-value was 0.33 suggesting that the model is a good fit.
Table 14.
Logistic Model Hosmer and Lemeshow Goodness-of-Fit Test
Number of Observations
169
Number of Groups
10
Hosmer and Leneshow chi-square (df=8)
9.18
P-value
0.335
Figure 4 illustrates the ROC curve. The area under the ROC curve was 0.798 indicating
that the model was able to distinguish between the outcome of interest and the
alternative.
62
1.00
0.75
Sensitivity
0.50
0.25
0.00
0.00
0.25
0.50
1 - Specificity
0.75
1.00
Area under ROC curve = 0.7975
Figure 4 ROC Curve
Logistic Regression Results
The results of the multivariable logistic regression analyses assessing factors
associated with prescribing antiviral medications for treatment of influenza among
primary care physicians in the U.S. are presented in Table 15. The results of this analysis
assist in answering the first research question, which was to identify the prescribing
practices of antiviral medications. Most factors presented in Table 16 were statistically
significant at the alpha level of 0.10 in the bivariate analyses. The logistic regression
model included patient demographic variables (age, sex, ethnicity, and race) and
physician/treatment facility characteristics (expected source of payment, patient status,
region, MSA location, training, type of setting, solo practice, and employment status).
After multivariable adjustment, race, metropolitan location, type of setting, and
ownership status were significantly associated with prescribing antiviral medications.
More specifically, physicians were more likely to prescribe antiviral medications to
whites compared to African-Americans (OR, 3.60; CI, 1.47, 7.74). With respect to type
of health facility setting, primary care physicians were more likely to prescribe antiviral
medications if their practice was a private solo/group practice compared to a free63
standing clinic (OR, 1.44; CI, 1.09, 2.18). Primary care physicians who were owners of
health facility were more likely to prescribe antiviral medications compared to physician
employees of the health facility (OR, 1.65; CI, 1.21, 1.69). Lastly, physicians whose
health facility was located in a metropolitan statistical area were more likely to prescribe
antiviral medications compared to facilities located in a non-metropolitan statistical area
(OR, 1.97; CI, 1.12, 2.01). Patient‟s expected source of payment (private insurance) was
marginally associated with prescribing antiviral medications and a midwest geographic
location was marginally negatively associated with prescription of antiviral medications.
Table 15.
Multivariable Logistic Regressions of Factors Associated with Prescribing of Antiviral Medications
Factor
Odds
Ratio
0.97
95% Confidence
Interval
[0.93, 1.01]
P-value
0.65
[0.24, 1.79]
0.412
1.18
[0.36, 3.91]
0.689
3.60
[1.47, 7.74]
0.023
Private insurance (self-pay r)
1.12
[0.84, 1.23]
0.071
Medicare
1.82
[0.62, 2.30]
0.272
Medicaid
0.20
[0.03, 1.65]
0.147
Other
1.04
[0.87, 1.39]
0.131
1.01
[0.89, 1.03]
0.656
1.69
[0.42, 1.8]
0.162
Private solo/group practice (freestanding clinic r)
1.44
[1.09, 2.18]
0.041
Federally qualified health clinic
1.07
[0.96, 1.59]
0.187
0.59
[0.19, 1.82]
0.360
Age
Sex
Male (female r)
Ethnicity
Hispanic (non-Hispanic r)
Race
White (African-American r)
0.124
Expected source of payment
Patient status
Established (new r)
Training
M.D. (D.O r)
Type of setting
Solo practice
Yes (no r)
64
Employment status
Owner (employee r)
1.65
[1.21, 1.69]
0.046
Contractor
0.89
[0.58, 1.21]
0.214
South (West r)
0.93
[0.60, 1.44]
0.400
Northeast
0.87
[0.38, 1.99]
0.342
Midwest
0.60
[0.36, 1.02]
0.056
1.97
[1.12, 2.01]
0.032
Geographical location
Metropolitan location
MSA (non-MSA r)
*r represents the reference category
Fixed Effects
After conducting the multivariable logistic regression analyses, year as a fixed
effect was included in the model. By including year as a fixed effect, the model was able
to control for differences across the survey years in any observable or unobservable
factors. By doing so, the threat of omitted variable bias can be reduced. The results of
the fixed effects regression analysis are presented in Table 16.
With 2008 as the reference group, there were four significant factors that were
associated with prescribing antiviral medications. We see that primary care physicians
were more likely to prescribe antiviral medications to whites than to African Americans
(OR, 2.89; CI, 1.43, 2.85). With respect of the type of health facility, physicians
practicing in a private solo/group practice setting were more likely to prescribe compared
to those practicing in a free-standing clinic (OR, 1.57; CI, 1.09, 2.26). Primary care
physicians who were owners of their facility were more likely to prescribe compared to
employees (OR, 1.57; CI, 1.09, 2.26). Health facilities that were located in a
metropolitan statistical area were more likely to prescribe compared to a nonmetropolitan statistical area (OR, 2.34; CI, 1.47, 2.79). In 2007, primary care physicians
prescribed less antiviral medications compared to 2008 (OR, 0.67; CI, 0.61, 0.98).
65
Furthermore, geographical location had a marginal association with prescribing antiviral
medications. Physicians practicing in the midwest were less likely to prescribe antiviral
medications compared to the south (OR, 0.59; CI, 0.41, 1.43).
Table 16.
Results of Year as a Fixed Effect with 2008 as Reference Group
Factor
Odds
Ratio
0.96
95% Confidence
Interval
[0.94, 1.02]
P-value
0.74
[0.36, 1.54]
0.123
1.36
[0.54, 3.38]
0.423
2.89
[1.43, 2.98]
0.037
1.24
[0.91, 1.34]
0.079
1.52
[0.95, 1.61]
0.161
1.39
[0.51, 3.84]
0.124
1.57
[1.09, 2.26]
0.016
0.73
[0.31, 1.70]
0.470
1.57
[1.09, 2.26]
0.042
South (West r)
1.08
[0.79, 1.49]
0.221
Northeast
0.84
[0.45, 1.87]
0.317
Midwest
0.59
[0.41, 1.03]
0.051
2.34
[1.47, 2.79]
0.028
Dummy 2005 (Dummy 2008 r)
1.13
[0.87, 1.42]
0.142
Dummy 2006
1.78
[0.97, 2.14]
0.063
Dummy2007
0.67
[0.61, 0.98]
0.049
Age
Sex
Male (female r)
Ethnicity
Hispanic (non-Hispanic r)
Race
White (African-American r)
0.176
Expected source of payment
Private insurance (self-pay r)
Patient status
Established (new r)
Training
M.D. (D.O r)
Type of setting
Private solo/group practice (freestanding clinic r)
Solo practice
Yes (no r)
Employment status
Owner (employee r)
Geographical location
Metropolitan location
MSA (non-MSA r)
Year
* r represents the reference category
66
Table 17 compares the significant results of the multivariable logistic regression
model with the same model having year as a fixed effect. Certain observable and
unobservable factors may have occurred over time that could not be included in the
logistic model. By controlling for these factors, we see that there were some slight
changes in prescribing practices of antiviral medications throughout 2005-2008.
Table 17.
Results of Multivariable Logistic Model With and Without Year as a Fixed Effect
Factor
Age
Sex
Male (female r)
Ethnicity
Hispanic (non-Hispanic r)
Race
White (African-American r)
Odds Ratio
p-value
Odds Ratio
without fixed
without
with fixed
effect
fixed effect
effect
0.97
0.124
0.96
p-value with
fixed effect
0.176
0.65
0.412
0.74
0.123
1.18
0.689
1.36
0.423
3.60
0.023
2.89
0.037
1.12
0.071
1.24
0.079
1.01
0.656
1.52
0.161
1.69
0.162
1.39
0.124
1.44
0.041
1.57
0.016
0.59
0.360
0.73
0.470
1.65
0.046
1.57
0.042
South (West r)
0.93
0.400
1.08
0.221
Northeast
0.87
0.342
0.84
0.317
Midwest
0.60
0.056
0.59
0.051
1.97
0.032
2.34
0.028
Expected source of payment
Private insurance (self-pay r)
Patient status
Established (new r)
Training
M.D. (D.O r)
Type of setting
Private solo/group practice
(freestanding clinic r)
Solo practice
Yes (no r)
Employment status
Owner (employee r)
Geographical location
Metropolitan location
MSA (non-MSA r)
67
Chapter Summary
This chapter presented the results from the biavariate and multivariable logisstic
regression analyses. According to the bivariate analyses, five factors were found to have
a significant relationship with antiviral medication prescribing. These factors were age,
race, patient status, physician training, and physician employment status. All
assumptions of the logistic regression were met and the model was a good fit. According
to the main findings of the multivariable logistic regression analysis, four significant
associations were found. First, primary care physicians were more likely to prescribe
antiviral medications to whites compared to African-Americans. They were also more
likely to prescribe if the health facility was a private solo/group practice setting.
Additionally, primary care physicians who were owners of their facility were more likely
to prescribe. Lastly, physicians whose health facility was located in a metropolitan
statistical area were more likely to prescribe antiviral medications for treatment of
influenza compared to health facilities located in a non-metropolitan statistical area. In
the next and final chapter, further elaboration of the results will be presented along with
implications of findings, strengths and limitations of the study, policy implications, future
research, and conclusions.
68
CHAPTER 5
CONCLUSIONS
This chapter presents the discussion of the results and implications for future
research. First, a brief summary of the previous chapters is presented. The descriptive
characteristics and results of analyses are presented followed by a discussion of the
factors associated with prescribing antiviral medications for treatment of influenza.
Thereafter, a section describing the study strengths and limitations will follow. The final
section will present the conclusions and implications for future research.
Summary
Influenza is a contagious respiratory illness that affects millions annually.
Treatment of influenza is initiated through the use of antiviral medications. Physicians
play an essential role in the diagnosis and treatment of the illness, yet their prescribing
practices of antiviral medications remain unclear. The purpose of this dissertation was to
identify factors associated with prescribing antiviral medications for treatment of
influenza among primary care physicians in U.S. The conceptual framework presented in
Chapter 2 was used to show that several factors may contribute to clinical decisionmaking. Data analyses were conducted in order to answer the following research
questions:
1. What are the factors associated with prescribing antiviral medications for
treatment of influenza among primary care physicians?
69
2. Are there differences in prescribing practices among primary care specialties?
3. Are there differences in prescribing practices over time?
Descriptive, bivariate, and multivariable logistic regression analyses were
conducted to answer the research questions. The results indicated that race, patient
status, type of training, and employment status were significantly associated with
prescribing antiviral medications for treatment of influenza. Additionally, expected
source of payment, and geographical location were marginally associated with
prescribing antiviral medications. There were no significant differences in prescribing
antiviral medications by physician specialty and over time.
Discussion
This study found that the tendency for primary care physicians to prescribe
antiviral medications for treatment of influenza is associated with a variety of factors
such as race, type of health facility setting, physician employment status, and
metropolitan location. Patient symptomotology alone was not a key indicator for
prescribing antiviral medications. Perhaps most importantly, however, primary care
physicians prescribed antiviral medications to 32.2% of patients with an influenza
diagnosis while antibiotics were prescribed to 41.3% of patients with an influenza
diagnosis.
Neuraminidase inhibitors are an effective treatment for influenza by decreasing
the length of the illness and preventing further bacterial complications. Additionally, the
CDC has provided specific guidelines and recommendations for the proper use and
dosage of the antiviral medications. Potential barriers to adequately prescribing antiviral
70
medications are physician knowledge and education and the influence of external
characteristics. The race of the patient and the type of health insurance were important
factors in the decision-making process of prescribing antiviral medications. The type of
health facility setting and its geographical location were also associated with physicians‟
prescribing of antiviral medications. Furthermore, an extremely small percentage of
antiviral medications prescribed (1%) were not associated with an influenza diagnosis,
suggesting that prophylactic prescribing was very limited.
Approximately 40% of primary care physicians believe that influenza is selflimited and does not require treatment (Rothberg et al., 2006). The previous finding
shows that primary care physicians need to be adequately informed regarding the illness
and its diagnosis and treatment. The current study demonstrated that many factors are
associated with prescribing antiviral medications. Thus, diagnoses should be made on the
basis of patient symptomotology, and physicians should receive the proper education
regarding influenza and its treatment.
The conceptual framework used for this study, as presented in Chapter 2, was
derived from concepts developed by Eisenberg (1979). A visual representation of this
framework was shown in Figure 2.1. The conceptual framework suggests that there were
other factors in addition to patient symptomotology that affected physicians‟ decision
making. In the current study, the impact of patient, physician, and health facility
characteristics on the prescribing of antiviral medications were examined. The results
demonstrated that various patient, physician, and health facility characteristics did play a
role in the decision to prescribe antiviral medications for treatment of influenza. It is
71
important from a policy perspective that every effort is made to ensure proper prescribing
of antiviral mediations in order to reduce the spread and transmission of the illness.
Descriptive Characteristics
Patient characteristics
In the current study, the smallest group of influenza patients was in the 65+ age
group (5.9%). This finding suggests that the elderly may use inpatient services rather
than outpatient service due to the increased likelihood of developing secondary
complications (Nichol, Baken, & Nelson, 1999). Additionally, the elderly are considered
a high-risk population and have a weakened immune system that may be easily
susceptible to various other complications. The elderly are also at an increased risk of
developing morbidity and increased mortality from influenza compared to other age
groups (Nichol et al., 1999).
With respect to gender, 55.6% of influenza patients were female which is
consistent with findings from previous studies (Linder et al., 2005; Linder et al., 2006;
Linder et al., 2009; Hsieh et al., 2010). The majority of influenza patients were white
(84.2%) followed by African Americans who represented 11.8%. This finding may
suggest that whites are more likely to seek medical care for treatment of influenza
compared to other races. Additionally, the issue of health disparity may be relevant
where the minority races may not have access to care.
The majority of influenza patients‟ ethnic origin was non-Hispanic (81.07%).
According to the CDC, approximately 35.3 million individuals identify themselves as
Hispanic in the U.S. and approximately 49% receive influenza vaccinations compared to
72
the national average of 69% (CDC, 2008). This population may not be seeking medical
care due to factors such as education and poverty or lack of access to health care facilities
(CDC, 2008). On the contrary, a majority of the U.S. population is non-Hispanic so this
may be a reason for the small proportion of Hispanic influenza patients.
The expected source of payment for influenza patients was private insurance
(68.1%) followed by Medicare (8.9%). This finding corroborates that of Mort and
colleagues (1996), who found that physicians are more likely to recommend health
services to insured patients compared to uninsured patients. Physicians may also be more
likely to conduct additional influenza testing if the patient is privately insured and less
likely to do so for patients on Medicaid, on other coverage or with no coverage (Katz et
al., 2009).
Physician Characteristics
Physicians whose specialty was family practice comprised the largest proportion
of primary care physicians (48.5%). Temte (2005) found that 11% of patient visits in a
family practice were due to influenza or influenza-like illness. Approximately 52.9% of
physicians who treated influenza were non-solo physicians. This finding is consistent
with that of Moore and Coddington (2008) who state that physicians and health systems
are continuing to combine for strategic, economic, and financial reasons. A few decades
ago, solo practices were more commonly seen whereas today, physicians are more
interested in employment opportunities rather than private practice opportunities (Moore
& Coddington, 2008). With respect to employment status, 69.1% of primary care
physicians who treated patients with influenza had an ownership position in their practice
or health facility.
73
Health Facility Characteristics
The type of work setting for a majority of physicians was private solo or group
practice (81.7%). As mentioned previously, physicians have been merging their solo
practices into group practices for a variety of reasons (Moore & Coddington, 2008).
Approximately 89% of health facilities were owned by the physician or physician group.
Recent research has shown that financial incentives involved with owners of health
facilities resulted in a significant change in the practice patterns of the physicians.
Practice pattern changes were not evident among physician non-owners (Mitchell, 2008).
For example, owners of health practices increased the use of diagnostic procedures,
surgery, and ancillary services compared to physician non-owners (Mitchell, 2008).
Approximately 59% of primary care physicians who treated patients with
influenza performed laboratory testing in their practice. Rapid influenza testing takes
approximately 15 minutes to obtain diagnosis results. Physicians practicing in facilities
with laboratory testing and rapid influenza testing may be more likely to provide an
accurate diagnosis of the illness and as a result, more appropriate treatment (Ozkaya et
al., 2009). Furthermore, influenza testing in the pediatric department may allow
pediatricians to prescribe more appropriately and reduce over prescriptions of antibiotics
to children (Ozkaya et al., 2009). Additionally, Mueller et al. (2009) found a positive
association between influenza testing and prescribing antiviral medications.
Lastly, a large proportion of physicians practiced in a metropolitan location in the
U.S (87.0%). Primary care physicians (36.1%) treated the greatest number of influenza
patients in the south followed by the west (33.1%). This finding supports the findings of
74
the CDC indicating that the south has a higher incidence of influenza compared to other
regions (CDC 2009).
Prescription Rates of Antiviral Medications
The prescription rates of antiviral medications varied across the survey years of
2005-2008. The neuraminidase inhibitors were prescribed for 12.9% of patients, while
the adamantanes comprised 10.7% of all prescriptions for treatment on influenza in 2005.
In 2006, the prescription of neuraminidase inhibitors (36.1%) greatly increased from the
previous year while the prescription of adamantanes decreased (2.8%). This trend is
attributed to the CDC guidelines and recommendations to discontinue the use of
adamantanes that were released in January 2006 (CDC, 2009). Overall, the “other”
category comprised the largest proportion of prescription for treatment of influenza
during the years of 2005-2008. Altogether, prescriptions for antiviral medications during
2005-2008 comprised 37% of all prescriptions for treatment of influenza. This finding is
inconsistent with the work of Linder et al. (2005) where physicians prescribed antiviral
medications to a small number of patients (19%) for treatment of influenza from 19952002. Perhaps the number of influenza diagnoses increased or physician awareness of
the illness may have been improved after 2002, leading to increased number of antiviral
prescriptions.
A possible reason for the small number of antiviral prescriptions may be that
patients who had an influenza diagnosis may have had a secondary bacterial complication
since a large portion of medications prescribed by primary care physicians for treatment
of influenza were antibiotics. The largest group of influenza patients was children under
the age of 15. Approximately 24% of children presenting with influenza were prescribed
75
antiviral medications while 28% of children were prescribed antibiotics for treatment of
influenza. This finding supports the work of Kozyrski and colleagues (2004) who found
that pediatricians and general practitioners exhibit nonadherance when prescribing
antiviral medications for viral respiratory infections prevalent in children. Antibiotic
treatment was commonly initiated leading to increased resistance amongst the children
(Kozyrsky et al., 2004). According to the literature review presented in Chapter 2,
antiviral medications were the most common treatment for children with influenza and
have been shown to be very effective (Hendrick et al., 2000), yet they were
underprescribed. Prescriptions other than antiviral medications were provided to patients
presenting with influenza regardless of age while CDC guidelines recommend the
prescription of neuraminidase inhibitors as the primary treatment for the illness (CDC,
2008).
Another possible reason for children being prescribed antibiotics may be parent
expectations for prescription of antibiotics and physicians wanting to ensure satisfaction.
Smith and colleagues (2001) found that approximately 50% of parents of children with
respiratory infections expected an antibiotic prescription and physicians perceived an
antibiotic expectation 34% of the time when parents did not make a verbal request (Smith
et al., 2001). Thus, satisfaction of the parents was an important reason for antibiotic
prescriptions to children.
Family physicians prescribed the largest percentage of antiviral medications for
treatment of influenza followed by of general pediatricians. This may be a result of a
large percentage (66.7%) of family physicians ordering rapid influenza tests to confirm
76
influenza diagnosis as indicated by Katz and colleagues (2009). Overall, there were no
significant differences of antiviral prescribing among specialty.
Multivariable Logistic Regression Findings
Research Question 1
The first model of multivariable regression analysis yielded four significant
factors associated with prescribing antiviral medications: patient race, type of setting,
employment status, and metropolitan location. Factors marginally associated with
prescribing antiviral medications were expected source of payment and geographical
location.
Patient Characteristics
Physicians were significantly more likely to prescribe antiviral medications for
treatment of influenza to whites compared to African Americans. This finding indicates
that more whites may be seeking treatment for influenza compared to African-Americans.
Access to care may be a reason as to why African-Americans may not be seeking
treatment. According to researchers who have examined health disparities, physicians
tend to use patients‟ demographic characteristics (gender, race, and socioeconomic status)
as their first step of assessment and base their clinical decision-making on those
characteristics rather than on clinical symptomotology (Lutfey et al., 2008). Race as a
significant factor associated with prescribing antiviral medications corroborates the
findings of Patrick et al. (1999) where race of the patients as well as primary care
physicians was associated with physicians‟ decision making. Specifically, AfricanAmericans rated their physician as less participatory and informative compared to whites.
77
Primary care physicians were also less likely to discuss and offer influenza vaccinations
to African-American patients compared to white patients thus observing further
disparities (Miller, 2010).
Expected source of payment was marginally associated with prescribing antiviral
medications. The implication of this finding is that primary care physicians‟ clinical
decision-making may be affected by patient insurance status. For example, Meyers and
colleagues (2006) found that clinical decision making is often confounded by patients‟
insurance status in the ambulatory care setting. Additionally, physicians tend to alter
their clinical management style in accordance with patient insurance status (Meyers et al.,
2006).
Physician Characteristics
Primary care physicians who practiced in a private solo/group practice were more
likely to prescribe antiviral medications and adhere to the CDC guidelines compared to
freestanding clinics. This finding contradicts the finding of Gillick (2009), who stated
that physicians who worked in a solo/small group practice were less likely to adhere to
recommended guidelines. However, more research is needed to explore the question of
whether physicians in a private solo/group practice are more or less likely to adhere to
practice guidelines. Thus, it may not be working in a solo/group practice per se that
influences physicians‟ adherence to practice guidelines.
Health Facility Characteristics
Physicians who were owners of their practices were more likely to prescribe
antiviral medications for treatment of influenza compared to physicians who were
employees. The implication of this finding may be that owners have more experience to
78
provide effective quality of care to their patients compared to employees. Geographical
location was marginally associated with prescribing antiviral medications. Specifically,
physicians practicing in the midwest were less likely to prescribe antiviral medications.
This finding may indicate that fewer individuals sought treatment for influenza in this
specific region. Again, access to care as well as patient knowledge may be contributing
factors to this finding.
Furthermore, physicians whose health facility was located in a metropolitan
statistical area were more likely to prescribe antiviral medications compared to facilities
located in non-metropolitan or rural areas. The implication of this finding may be that
physicians practicing in a metropolitan area may have a large patient population who is
privately insured (Probst, Samuels, & Moore, 2003). More specifically, individuals
residing in a non-metropolitan (rural) area are generally less educated than those in
metropolitan areas (Probst et al., 2003). Additionally, income levels are lower and
poverty levels are higher in rural areas. With respect to health status, rural residents
generally report having more chronic or acute conditions and less likely to have health
insurance (Probst et al., 2003). These findings from the National Health Interview
Survey show that a combination of factors affects those individuals in rural areas and
may explain the lower number of prescriptions in the non-metropolitan location.
Year as a Fixed Effect
According to the second model of multivariable logistic regression analysis with
year as a fixed effect, similar results were observed as with no fixed effects. Race, type
of health facility setting, physician employment status, and metropolitan location were all
associated with prescribing antiviral medications. Primary care physicians were more
79
likely to prescribe antiviral medications to whites compared to African-Americans.
Comparing this to the model without fixed effects, the magnitude of the association
decreased but overall results were similar. Moreover, physician employment status had a
similar result. By controlling for observed and unobserved characteristics throughout
2005 and 2008, some differences were observed.
According to the year fixed results, the magnitude of the association decreased
where physicians prescribed antiviral medications whites compared to the model with no
fixed effects. Additionally, the magnitude of the association increased where physicians
practicing in a MSA prescribed antiviral medications compared to the model with no
fixed effects. These findings indicate that by controlling for observable and
unobservable characteristics that may have occurred during the years, physicians‟
decision to prescribe antiviral medications was slightly altered. Lastly, physicians
practicing in a private solo/group practice and owners did not experience much difference
in prescribing antiviral medications adjusting for year.
Research Questions 2 and 3
Interaction terms were included in the multivariable logistic regression models in
order to identify differences in prescribing practices by year and specialty. According to
the results, the interaction analysis yielded no overall differences among specialty and by
year. Although there were no overall significant differences, some differences among
variables were observed in the data that should be noted. With respect to specialty,
family physicians prescribed less antiviral medications to influenza patients in 2007
compared to other years. The finding indicates that the number of overall influenza cases
may have been less in 2007 compared to other years. Family physicians in 2007
80
prescribed more antiviral medications to the South compared to other regions. According
to numbers from the Kaiser Foundation, the South had higher number of influenza cases,
deaths, and hospitalizations compared to the national average (Kaiser Foundation).
Strengths and Limitations
A major strength of this study was the ability to use physician weights to identify
prescribing practices of primary care physicians. Once weighted observations were
obtained for the NAMCS data, these observations were nationally representative of the
U.S. The response rate for NAMCS survey was generally high at 95% with a coding
error rate of less than 1% for each of the surveyed years. The data were publicly
available and readily downloadable and contained many variables that were pertinent to
the study. Influenza cases were coded according to the ICD-9-CM coding system, which
provided diagnostic accuracy and consistency. Additionally, the data that were used for
this study were for the four most recent years. The study‟s focus was on physician
characteristics and the physician weight variable was publicly available beginning in
2005 allowing national level estimates.
A limitation of this study is that the pre-weighted sample size was small. This
may suggest that physicians may not be diagnosing influenza correctly due to the
similarity of symptoms from various viral infections. Due to the retrospective nature of
the analyses, the results may not be applicable to physician prescribing practices today as
new guidelines may be emerging. Also, the NAMCS data were not collected specifically
for the purpose of this study. Therefore, the current design of the database was used.
This may lead to conclusions that are tempered by the characteristics of the respondents.
81
Lastly, this study consists of only influenza diagnoses made at ambulatory care centers
and does not include physicians working in emergency departments or physicians who
may have treated influenza patients who had been hospitalized.
Policy Implications
The goal of this study was to identify primary care physician prescribing practices
for antiviral medications used in the treatment of influenza and the factors that are
associated with these practices. It can be seen from the results presented in the previous
chapter that a combination of patient, physician, and treatment facility characteristics are
associated with the prescribing of antiviral medications. The study provided specific
information on the types of factors that are associated with physicians‟ prescribing of
antiviral medications.
From the analyses, it can be concluded that many primary care physicians are not
adhering to antiviral prescribing guidelines and recommendations. In this study, it was
seen that the adamantanes continued to be prescribed even after the CDC guidelines were
released in January 2006. Additionally, primary care physicians may have prescribed on
the basis of factors other than patient symptomotology. Antiviral medications as the
primary treatment for influenza comprised the minority of prescriptions whereas the
“other” category comprised the majority of treatments for influenza. In order to improve
physicians‟ decision-making to prescribe antiviral medications for treatment of influenza,
a significant effort should be made to educate physicians on the importance of adequately
prescribing antiviral medications. Education about the efficacy of antiviral medications
may reduce unnecessary antibiotic use and potentially decrease healthcare costs.
82
Policies that require physicians to participate in active education such as seminars and
workshops should be created. Active education as mentioned above may be more
effective than passive education such as chart reviews and colleague collaboration.
Additionally, although recommendations and guidelines regarding the proper treatment of
influenza are provided by CDC, wider circulation of these guidelines could provide
direction to primary care physicians in their decision-making process.
The CDC should also strengthen the guidelines that require physicians to conduct
rapid influenza testing if a patient presents with influenza-like illness. By doing so,
diagnosis and treatment may be more accurate and in the long-term, rapid-influenza
testing may be more cost-effective as the symptoms will be confirmed and not confused
with other co-circulating viruses that may have similar symptoms.
Furthermore, advances that have occurred in information technology should be
taken advantage of by physicians and health facilities. The inappropriate use of
antibiotics is increasing and it is an important public health priority that antibiotic misuse
be reduced. For example, clinical decision support systems (CDSS) and electronic
prescribing (e-prescribing) may replace hand-written prescriptions by creating,
transmitting, dispensing, and monitoring treatment practices (Gingrich & Hasan, 2010).
E-prescribing may also reduce the number of inappropriate prescriptions by
providing alert messages (Miller, Gardner, & Johnson, 2005). For example, if influenza
has been diagnosed, then entering a prescription for antibiotics may yield an alert
message depending on the patient‟s secondary illness if any. Although research
regarding e-prescribing and appropriateness of prescriptions is limited, the use of eprescribing may help to mitigate prescribing errors. By doing so, patient safety and
83
quality of care may be improved. Policies targeted toward incorporating e-prescribing
systems in health facilities should be implemented.
Future Research
A study should be conducted that utilizes data from the National Hospital
Ambulatory Medical Care Survey (NHAMCS). By doing so, emergency department
visits as well as hospitalizations due to an influenza diagnosis could be included when
identifying factors associated with prescribing practices. The findings from such a study
could be generalizable to primary care physicians in the U.S.
Furthermore, by using the NAMCS and NHAMCS datasets, we can examine if
prescribing practices have changed prior to January 2006 and after the release of the CDC
guidelines in January 2006. As new datasets are released by the National Center for
Health Statistics, multiple years of data can be examined, possibly leading to new
conclusions and contributing to the literature.
It would also be beneficial to examine the utilization of health information
technology (HIT) and appropriateness of antiviral prescribing. Such HIT variables are
only available for NAMCS survey years 2007 and 2008. By conducting such a study, we
can examine if HIT plays a role in the accurate prescribing of antiviral medications
especially among metropolitan vs. non-metropolitan areas.
Lastly, a study designed to evaluate interventions that may improve physician
prescribing practices would need to be conducted in the future. Research has indicated
that in many cases, antiviral medications are prescribed inappropriately or
underprescribed. By implementing interventions that may reduce or eliminate
84
unnecessary or inappropriate prescribing, physicians‟ prescribing practices may be
improved.
A conceptual framework was presented earlier in the literature review that
depicted various characteristics may impact a physician‟s decision to prescribe antiviral
medications. The NAMCS dataset provided many useful variables but failed to include
variables that could be useful in this study such as clinical variables that may be
beneficial. For example, variables such as the number of days the patient had
experienced symptoms before beginning treatment as well as if rapid influenza testing is
performed would be beneficial in future research.
Conclusions
Although CDC recommendations and guidelines are available on the appropriate
use of antiviral medications for treatment of influenza, primary care physicians continue
to exhibit nonadherance. Prescription rates of antiviral medications are not based solely
on antiviral medications but are associated with several factors such as patient race,
health insurance status, type of health facility setting, and metropolitan location. These
factors are particularly important because they may impact physicians‟ decision-making
process.
The implementation of e-prescribing could potentially increase patient safety and
quality of care by decreasing the number of inappropriate prescriptions for treatment of
influenza and ensuring adequate treatment for all populations. By implementing such a
system, factors such as race and health insurance may not play a role in prescribing
antiviral medications.
85
Overall, this study illustrated that a physician‟s decision to prescribe antiviral
medications indeed is associated with many factors. It is of great importance that lessons
learned from this dissertation and previous studies be utilized to further improve
diagnosis and treatment of influenza. It is vital to public health that primary care
physicians be properly educated and the issue of health disparities be addressed. By
doing so, patient diagnosis can be improved and more accurate treatment may be
initiated.
86
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APPENDIX A
CORRELATION MATRIX
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
C15
C16
C17
C18
_________________________________________________________________________________________________________________________
C1
1.00
C2
0.56 1.00
C3
-0.19 -0.21 1.00
C4
-0.07 -0.12 -0.02 1.00
C5
0.01 0.07 -0.05 -0.21 1.00
C6
-0.04 0.01 0.07 -0.11 0.04 1.00
C7
-0.03 -0.04 -0.11 0.16 0.01 -0.09 1.00
C8
0.01 0.05 -0.05 0.06 0.04 -0.06 0.01 1.00
C9
0.21 0.15 0.03 -0.03 -0.01 -0.04 0.16 -0.03 1.00
C10
-0.15 -0.21 0.09 0.031 -0.07 0.14 0.01 0.07 -0.27 1.00
C11
0.04 -0.01 0.04 0.13 -0.05 -0.16 0.02 0.0001 -0.12 0.07 1.00
C12
0.10 -0.01 0.08
C13
0.14 0.04 -0.05 0.16 0.01 -0.01 0.03 0.20 -0.03 0.01 0.36 0.23 1.00
C14
0.05 0.02 -0.03 0.06 0.01 -0.18 -0.04 0.01 -0.08 -0.01 0.55 0.28 0.27 1.00
C15
0.17 0.09 -0.09 -0.05 -0.02 -0.14 -0.06 -0.02 -0.01 -0.04 -0.10 -0.16 -0.09 0.04 1.00
C16
-0.03 -0.02 0.08 -0.01 0.14 -0.09 -0.09 0.01 -0.06 0.02 0.10 -0.09 -0.03 0.13 -0.01 1.00
C17
-0.09 -0.09 0.01 0.08 -0.05 -0.09 0.09 0.15 0.06 -0.19 0.06 0.02 0.11 0.04 0.02 0.03 1.00
C18
0.02 0.08 -0.06 -0.01 0.06 -0.01 -0.01 0.09 0.09 -0.14 -0.14 -0.07 0.13 -0.06 0.04 0.02 0.07 1.00
0.05 -0.03 -0.11 0.07 0.18 0.20 -0.01 0.45 1.00
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APPENDIX A (Continued)
C1: Year
C2: Age
C3: Sex
C4: Ethnicity
C5: Race
C6: Expected source of payment
C7: Patient status
C8: Region
C9: Metropolitan statistical area
C10: Physician specialty
C11: Physician training
C12: Type of setting
C13: Solo practice
C14: Employment status
C15: Ownership status
C16: Laboratory in facility
C17: Age recode
C18: Prescription of antiviral medications
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APPENDIX B
NAMCS PATIENT VISIT DATA COLLECTION FORM
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101
APPENDIX C
NAMCS PHYSICIAN SURVEY INDUCTION FORM
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103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
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APPENDIX D
INSTITUTIONAL REVIEW BOARD APPROVAL
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