Patterns of Medication Use and Related Health Care Service Utilization Associated with Vaginal Estrogen Therapy in Medicaid-Enrolled Women

Home , Atrophic vaginitis, Equilin, Estradiol acetate, Vaginal estrogen

Dissertation

Presented in Partial Fulfillment of the Requirements for the Doctor of Philosophy Degree in the Graduate School of the Ohio State University

Marjorie V. Neidecker, MEng, RN

Graduate Program in Pharmacy

The Ohio State University

2009

Dissertation Committee:

Rajesh Balkrishnan, PhD, Advisor

Deborah A. Levine, MD, MPH

Milap C. Nahata, MS, PharmD

Sharon Schweikhart, PhD

Marjorie V. Neidecker

2009

Abstract

Background : Vaginal atrophy, or atrophic vaginitis, is a symptom of menopause experienced by up to 40% to 50% of postmenopausal women. Symptoms include vaginal dryness, itching, and irritation; dyspareunia; successive urinary tract infections; and urinary incontinence. The most effective therapy for atrophic vaginitis is vaginal estrogen, available in cream and tablet forms. Not all forms have received equal acceptance, potentially resulting in sub-optimal medication adherence and persistence.

The objective of this study was to understand the medication-taking behavior of vaginal estrogen users in clinical practice and measure associated healthcare utilization and cost outcomes.

Methods : Retrospective claims of women enrolled in the North Carolina Medicaid program between January 2003 and December 2007 were analyzed. Inclusion criteria were occurrence of at least one prescription claim for vaginal estrogen and continuous health plan enrollment for 12 months prior to and 12 months following vaginal estrogen initiation. Multivariate regression analyses were used to estimate the effect of form of vaginal estrogen on medication adherence, medication persistence, and health services utilization and cost outcomes. Adherence was measured using the medication possession ratio (MPR); persistence was measured using treatment duration. Additional outcomes

ii explored included prescribing provider specialty, proportion of initial prescriptions refilled, and switching to the other form of vaginal estrogen. Covariates included age, race, Charlson Comorbidity Index, mammography, prior and current use of systemic estrogen, and the number of outpatient office visits in the year prior to vaginal estrogen initiation.

Results : 1,505 women (30% black, age 40 - 64 years, mean age 53.2 years) met all inclusion criteria. 89.6% of women used vaginal cream products; 10.4% used vaginal tablets. An estimated 23.3% of vaginal estrogen prescriptions were prescribed by obstetrician/gynecologists. Mean MPR was 0.31 for vaginal tablet users; 0.27 for vaginal cream users (p = 0.117). Treatment duration over two years of follow-up was similar for both forms of vaginal estrogen (296 days for vaginal tablets vs. 280 days for vaginal cream, p = 0.607). Tablet users refilled their first prescription at a significantly higher rate (48.8% for vaginal tablets, 35.3% for vaginal cream, p = 0.001) but more often switched to the other form of vaginal estrogen (4.4% vs. 1.7%, p = 0.023). Annual obstetrician/gynecologist and primary care physician costs and vaginal estrogen prescription costs for tablets and creams were similar. Multivariate regression models suggest that use of vaginal tablets compared to vaginal creams is significantly associated with more often refilling the initial prescription (odds ratio 1.77, p = 0.001), higher number of annual vaginal estrogen prescriptions (β = 0.322, p < 0.001), and lower

vaginal estrogen prescription costs (β = -0.133, p = 0.019).

Conclusion : Women on vaginal tablet therapy were more likely to refill their initial

prescription and, although not significant over a 2 year window, to have longer treatment

iii duration, indicating a preference for this treatment modality. At the same time, healthcare utilization in terms of physician visits and costs were no higher for vaginal tablet users than vaginal cream users, and were significantly lower in vaginal estrogen prescription costs.

Dedication

To the growing ranks of post-menopausal women.

Acknowledgements

My very sincere thanks must first be extended to my advisor, Dr. Rajesh

Balkrishnan, Merrell Dow Professor, for inviting me to join his lab and mentoring me through the doctoral program. Along with providing many valuable academic opportunities, he been most encouraging and supportive. He may never fully know the extent of my gratitude for his concern and support during this past very challenging year.

I hope this is not the end of our work together, but the beginning of new collaborations to come where I may continue to learn from his expertise. I wish him all the best as he takes his next step forward in his bright and accomplished career.

I would also like to thank my dissertation committee members, Drs. Deborah

Levine, Milap Nahata, and Sharon Schweikhart for their contributions to this report. Dr.

Levine deserves special thanks for her insightful comments on the study methodology and clinical considerations. I am most grateful, too, to Dr. Schweikhart, who several years ago had confidence in my abilities and opened the door to my work in the College of Public Health, eventually leading to this doctoral degree.

Several others deserve my thanks. Fabian Camacho, biostatistician and SAS programmer extraordinaire , has been tremendously patient and truly invaluable in guiding me with his expert knowledge through the dataset creation and the statistical

vi analyses. Kathy Brooks, in her calm and kind way, has been most helpful in navigating the many administrative pathways required at such a large university.

Finally, and most importantly, I would like to extend gratitude from the bottom of my heart to my family for their unwavering support in this circuitous journey. From my mid-life announcement that I would enter the College of Nursing graduate entry program, to my interim work in the College of Public Health, to the completion of this doctoral program in the College of Pharmacy, they have backed me 100 percent. My parents,

John and Marie Vermeulen, have encouraged me from the start in their calm and loving way to aim high. My amazing husband, Tom, and wonderful children, Marita, Derek,

Peter, and Elise, have sacrificed much in these five years, for which I am forever indebted. I have learned more than they will ever know from the selfless and unconditional love they have shown me.

vii

Vita

November 17, 1960 Born – Columbus, Ohio

1980-81 Industrial Engineering Intern (summers) General Motors, Detroit Diesel Allison Div.; Indianapolis

1982 B.S. Mechanical Engineering Industrial Engineering Concentration University of Notre Dame; Notre Dame, Indiana

1983 M.Eng. Operations Research and Industrial Engineering Cornell University; Ithaca, New York

1982-1984 International Systems Engineer AT&T Bell Laboratories; Holmdel, New Jersey

1984-1985 Logistics Management Consultant Cleveland Consulting Associates; Cleveland, Ohio

1985-1990 Analytic Products Project Manager The Fair, Isaac Companies; San Rafael, California

2006 R.N. licensure, Graduate-Entry Nursing Program The Ohio State University; Columbus, Ohio

2006 Bachelor’s Plus Program in Education Ashland University; Columbus, Ohio

2006 Long-Term Substitute Mathematics Teacher Southwestern City Schools; Grove City, Ohio

2006 - 2007 Research Specialist Center for Health Outcomes, Policy and Evaluation Studies The Ohio State University; Columbus, Ohio

viii

2007 Lecturer, College of Public Health The Ohio State University; Columbus, Ohio

2008 Graduate Teaching Associate, College of Pharmacy The Ohio State University; Columbus, Ohio

2008 – present Graduate Research Associate, College of Pharmacy The Ohio State University; Columbus, Ohio

Fields of Study

Major Field: Pharmacy

Minor Field: Nursing

Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgements ...... vi

Vita ...... viii

Table of Contents ...... x

List of Tables ...... xiv

List of Figures ...... xvi

Chapter 1: Introduction ...... 1

1.1 Background ...... 1

1.1.1 Atrophic Vaginitis ...... 1

1.1.2 Vaginal Estrogen ...... 2

1.2 Importance of Assessing Medication Use Behavior and Need for Research

...... 3

1.3 Study Objectives ...... 4

Chapter 2: Literature Review and Theoretical Framework ...... 6

2.1 Literature Review...... 6

2.1.1 Atrophic Vaginitis ...... 6

2.1.2 Prevalence and Treatment Rates ...... 8

2.1.3 Treatment Guidelines for Atrophic Vaginitis: Hormone and Non-

Hormone Therapy ...... 9

2.1.4 Vaginal Estrogen Preparations ...... 12

2.1.5 Efficacy of Vaginal Estrogen Preparations ...... 16

2.1.6 Vaginal Estrogen Medication Use: Adherence, Duration, and

Acceptability ...... 16

2.1.7 Health Services Utilization and Associated Cost...... 18

2.1.8 Women’s Attitudes toward and Knowledge of Menopause and

Hormone Therapy ...... 19

2.2 Theoretical Framework ...... 20

2.2.1 Becker and Maiman’s Modified Health Belief Model ...... 21

2.2.2 Andersen’s Revised Behavioral Model of Health Services Use ... 24

2.2.3 Study Conceptual Framework...... 26

Chapter 3: Methodology ...... 29

3.1 Study Design ...... 29

3.1.1 Data Source: North Carolina Medicaid Claims ...... 29

3.1.2 Study Perspective ...... 31

3.1.3 Medicaid Database Elements ...... 32

3.1.4 Study Population ...... 33

3.1.5 Analytical Dataset ...... 34

3.2 Study Variables ...... 36

3.2.1 Patient Characteristics ...... 37

3.2.2 Vaginal Estrogen Therapy ...... 41

3.2.3 Medication Use Behavior ...... 42

3.2.4 Health Care Service Utilizations and Costs ...... 44

3.3 Statistical Methods ...... 45

3.3.1 Descriptive Analyses ...... 45

3.3.2 Prescribing Physician Specialty ...... 46

3.3.3 Patterns of Medication Use ...... 47

3.3.4 Health Care Service Utilization ...... 48

3.4 Regression Diagnostics and Other Statistical Considerations ...... 50

3.4.1 Nonlinear Relationships ...... 50

3.4.2 Non-Normal Distribution of Residuals ...... 51

3.4.3 Heteroskedasticity ...... 51

3.4.4 Autocorrelation ...... 51

3.4.5 Multicollinearity ...... 52

3.4.6 Model Specification: Model Goodness of Fit Tests and Model

Adequacy Tests ...... 52

3.4.7 Missing Data ...... 53

Chapter 4: Results ...... 54

4.1 Patient Characteristics ...... 54

4.2 Prescribing Physician Specialty ...... 57

4.3 Patterns of Medication Use ...... 59

xii

4.4 Health Care Utilization ...... 68

Chapter 5: Discussion ...... 74

5.1 Discussion ...... 74

5.2 Policy Implications ...... 79

5.3 Future Studies ...... 80

5.4 Limitations ...... 81

5.5 Conclusion ...... 83

References ...... 84

Appendix A: Institutional Review Board Approval ...... 95

Appendix B: Access to North Carolina Medicaid Data Approval ...... 97

Appendix C: Systemic Estrogen NDC Numbers ...... 99

xiii

List of Tables

Table 2.1: Vaginal estrogen preparations available in the United States 14

Table 2.2: Advantages and disadvantages of each vaginal estrogen form 18

Table 3.1: Select elements extracted from the Medicaid database 31

Table 3.2: Steps in creation of analytical dataset 36

Table 3.3: Conditions, ICD-9-CM codes, and corresponding weights included in the Deyo adaptation of Charlson Comorbidity Index 39

Table 3.4: Codes for identifying care settings 45

Table 4.1: Pre-Treatment Characteristics of Women on Vaginal Estrogen Therapy 56

Table 4.2: Prescribing Physician Specialty (OB/GYN) by Vaginal Estrogen Form 57

Table 4.3: Unadjusted and Adjusted Parameter Estimates for Prescribing/Receiving Tablet vs. Cream Vaginal Estrogen 58

Table 4.4: Medication Use Outcomes by Vaginal Estrogen Form: Refill Patterns, Medication Possession Ratio (MPR), and Treatment Duration 60

Table 4.5: Unadjusted and Adjusted Parameter Estimates for Predicting Index Medication Refill 64

Table 4.6: Parameter Estimates for Predicting Medication Adherence to Index Medication 66

Table 4.7: Parameter Estimates for Predicting Treatment Duration with Index Medication 67

xiv

Table 4.8: Health Care Service Utilization and Cost Outcomes, One Year Post-Index by Vaginal Estrogen Form 68

Table 4.9: Health Care Service Utilization and Cost Outcomes, One Year Post-Index by Vaginal Estrogen Form 69

Table 4.10: Parameter Estimates for Predicting Health Care Utilization: Number of Index Prescriptions 70

Table 4.11: Parameter Estimates for Predicting Health Care Utilization: Cost of OB/GYN + PCP Visits 72

Table 4.12: Parameter Estimates for Predicting Health Care Utilization: Cost of VE Prescriptions 73

List of Figures

Figure 2.1: Becker and Maiman’s Modified Health Belief Model 23

Figure 2.2: Andersen’s Revised Behavioral Model of Health Services Use 25

Figure 2.3: Study Conceptual Framework 28

Figure 4.1: Time to vaginal estrogen treatment discontinuation during the 1 st year 61

Figure 4.2: Time to vaginal estrogen treatment discontinuation for women with 2 or more years of continuous eligibility and at least one prescription refill 62

xvi

Chapter 1: Introduction

1.1 Background

1.1.1 Atrophic Vaginitis

Vaginal atrophy, the thinning of the vaginal epithelium, is a symptom of menopause experienced by up to 40% to 50% of postmenopausal women.2, 3 When

accompanied by inflammation, vaginal atrophy is also referred to as atrophic vaginitis.

Atrophy from estrogen depletion also affects the vulvar region and is associated with

lower urinary tract changes, and thus is often called vulvovaginal atrophy or urogenital

atrophy. The decline in estrogen during the menopausal transition is responsible for

vaginal atrophy which includes symptoms of vaginal dryness, itching, and irritation, and

often leads to dyspareunia (painful intercourse).4 Unlike vasomotor symptoms, atrophic vaginitis does not resolve after the menopausal transition. Women may also experience vaginal atrophy as a result of radiation therapy or chemotherapy, concurrent with use of medications containing anti-estrogen properties (e.g., Tamoxifen (Nolvadex)), during lactation, or following an oophorectomy (surgical removal of the ovaries) performed before natural menopause.5, 6

Most women hesitate to discuss embarrassing urogenital complaints with their providers. Only 20% to 25% of affected women discuss their symptoms with their

1 provider because they believe their symptoms are a necessary consequence of menopause.6, 7 Despite many women’s hesitance to discuss urogenital symptoms with their providers, urogenital aging is becoming more widely recognized because of increasing life expectancy, more open discussion of the topic among women and their providers, and the availability of effective therapies. Urogenital aging is a public health concern because of its high prevalence and the potential with treatment to prevent serious medical conditions and quality of life symptoms such as dyspareunia, uterine prolapse, and urinary incontinence.5

1.1.2 Vaginal Estrogen

The most effective therapy for atrophic vaginitis is estrogen.8 While oral

hormone therapy is effective in treating many types of menopause-related symptoms,

local vaginal estrogen is recommended for the treatment of atrophic vaginitis alone

because it is very low dose and thought to minimize breast and cardiovascular risks

associated with oral estrogen.9 Vaginal estrogen is available in cream, tablet, and ring

forms, and in varying dosages. A meta-analysis of 19 clinical trials concluded all forms

of vaginal estrogen are effective in the treatment of symptoms related to vaginal atrophy.

However, tablets relieved some symptoms significantly better.10 Not all forms have

received equal acceptance by women. Many women find creams to be messy and

application times of creams and tablets difficult to remember, potentially resulting in sub-

optimal adherence.1 In randomized control trials, adherence to treatment with the ring or

tablets was greater than with creams, and the ring was favored as a treatment delivery

system.10

1.2 Importance of Assessing Medication Use Behavior and Need for Research

Poor adherence to drug therapy for chronic illnesses has been associated with

higher health care service utilization and costs to the health care system, including

diabetes;11 HIV; 12 hypertension, hypercholesterolemia, and congestive heart failure;13

asthma;14 and several others. It is unknown if adherence to a particular form of vaginal estrogen therapy for atrophic vaginitis in perimenopausal and postmenopausal women produces similar results. Only one study to date has been performed to compare use of different forms of vaginal estrogen in “real world” clinical practice in terms of medication adherence and treatment duration. 15. No study has measured service utilization or cost outcomes for vaginal estrogen products. Use of an effective and convenient treatment has the potential to result in lower health care utilization in terms of primary care and obstetrician/gynecologist visits and associated costs.

Complicating the adherence issue are the results of the Women’s Health Initiative published in recent years indicating oral hormone therapy has been shown to increase the risk of adverse cardiovascular outcomes and gynecological cancers, including endometrial cancer.16-18 It is not known, however, if these same risks occur with vaginal estrogen use. As a consequence, the Food and Drug Administration recommends all hormone therapy, including vaginal estrogen, be prescribed at the lowest dose and for the shortest duration required to relieve symptoms.19 In light of this change in hormone

therapy strategy and the long-term need for atrophic vaginitis symptom relief for a large

proportion of postmenopausal women, it is important to understand the medication-taking

behavior of vaginal estrogen users in clinical practice.

1.3 Study Objectives

In this study, the objectives and alternative hypotheses for examining vaginal estrogen use in Medicaid enrolled women are:

Aim 1 : To examine the association between select patient baseline characteristics, such as

age, race, comorbidities, outpatient office visits, previous systemic estrogen use,

and mammography screening, of Medicaid enrollees with atrophic vaginitis using

various forms of vaginal estrogen (cream, tablet, and ring).

H1: Younger women and white women will be more likely to use the tablet

and ring forms than older women and Black women with atrophic

vaginitis.

Aim 2 : To examine the relationship between type of provider (obstetrician/gynecologist

(OB/GYNs) vs. other type of physician) and form of vaginal estrogen prescribed.

H1: OB/GYNs will more often prescribe vaginal estrogen tablets and rings

than cream compared to other providers.

Aim 3 : To evaluate refill patterns by form of vaginal estrogen, and to examine the

differences in medication adherence and medication persistence by form of

vaginal estrogen.

H1: Tablet users will refill their prescription more often, and tablet users will

less often switch to cream than vice versa. Medication adherence and

persistence will be greater for vaginal estrogen tablet users than for cream

users.

Aim 4 : To examine the differences in health care service utilization (number of OB/GYN

and primary care provider (PCP) visits) and cost (vaginal estrogen cost, and

OB/GYN and PCP visit costs) between different forms of vaginal estrogen.

H1: Health care service utilization and costs will be greatest for women using

vaginal estrogen cream and least for tablets.

Chapter 2: Literature Review and Theoretical Framework

This chapter will provide an overview of the literature related to atrophic vaginitis, its pharmacotherapeutic treatment alternatives, and corresponding medication use patterns.

The theoretical framework for this study is also presented.

2.1 Literature Review

2.1.1 Atrophic Vaginitis

The changes in estrogen levels during menopause result in unpleasant symptoms for many women, often affecting quality of life. Menopausal symptoms include vasomotor symptoms (hot flashes), vaginal atrophy symptoms, sleep disturbances, sexual dysfunction, and fatigue. Vasomotor symptoms are most common, affecting 50% or more of women,20 followed by symptoms of vaginal atrophy experienced by up to 40% to

50% of postmenopausal women.2, 3 While the duration of acute menopausal symptoms such as vasomotor symptoms and fatigue typically subside when estrogen levels are no longer erratic, symptoms of vaginal atrophy typically become increasingly apparent during the menopausal transition and, for many women, persist indefinitely.8

In premenopausal women, the ovaries produce the majority of circulating

estrogen. Estrogen helps maintain thick, healthy layers of cells in the vagina, allowing

the epithelium to remain thick and moist and the vagina supple and elastic.10 After

6 menopause, when the ovaries no longer produce estrogen, the vaginal epithelium atrophies and the vagina becomes shorter, narrower, and loses much of its moistness and elasticity, resulting in vaginal atrophy. Atrophy from estrogen depletion also occurs in the vulvar region and is associated with lower urinary tract changes, and thus is often called vulvovaginal atrophy or urogenital atrophy. In addition to menopause, other low estrogen conditions that may cause vaginal atrophy include breastfeeding, surgical removal of the ovaries before natural menopause, and estrogen-lowing medications such as those used to treat uterine fibroids, endometriosis, or breast cancer. 21

Atrophic vaginitis occurs when vaginal atrophy is accompanied by inflammation.

Although the term vaginitis is used, it is a misnomer because the inflammation is not

caused by an exogenous pathogen, but instead is an involutional change resulting from

estrogen deficiency. Symptoms include vaginal dryness, burning, pruritus (itching),

leucorrhea (white discharge), dyspareunia (painful intercourse), and vaginal bleeding or

spotting.22, 23 Because the lower part of the vagina and urethra have a common

embryological origin and are subject to similar hormone effects, urinary problems may

also occur as a result of estrogen loss. 5 In a healthy pre-menopausal adult female, the vaginal pH ranges from 3.5 to 5.0, helping to protect the urogenital area from vaginal and urinary tract infections. 24 During post-menopause, the vaginal pH increases to more than

5.0, 6, 24 reducing the presence of protective bacteria and permitting the growth of pathogens. 22 Because of these changes as well as the thinning of the vaginal epithelium,

post-menopausal women are prone to recurrent vaginitis and recurrent urinary tract

7 infections. 5 While urinary incontinence is not directly related to urogenital atrophy, it is more common in women with urogenital atrophy.

Women who remain sexually active and have greater mean levels of androgens

(testosterone and androstenedione) have been shown to experience less severe atrophic vaginitis in the perimenopausal transition. 25 Other factors contributing to increasing

severity of vaginal atrophy include cigarette smoking 26 and never having given birth

vaginally. 6

2.1.2 Prevalence and Treatment Rates

The median age menstrual irregularity begins is between 45 and 47.5 years and

the menopausal transition that follows has an average duration of 5 years. 27-30 In Western society, the average age of spontaneous (natural) menopause is 51.4 years. 6 With the

increase in human life expectancy over the last several decades, women can now expect

to live well into their 70s or 80s. Most women will live almost one-third of their lives

post-menopause, in an estrogen-deprived state. 6 Symptoms of atrophic vaginitis are experienced by up to 40% to 50% of postmenopausal women. 2, 3 The percentage of

women experiencing vaginal dryness and other symptoms of vaginal atrophy increases

with age throughout the menopausal transition and, for many women, persists

indefinitely. 20 Given that 42 million American women were age 50 and older according to the 2000 U.S. Census, 31 an estimated 17 to 21 million suffer symptoms of atrophic

vaginitis.

Despite this high prevalence, only 20% to 25% of symptomatic women seek

treatment from a physician, either because of embarrassment or because of a lack of

8 awareness regarding available therapies.6, 32 Many women make difficult lifestyle

changes to cope with their symptoms such as discontinuing sexual activity. Cultural,

religious, and societal beliefs may also contribute to the low rate of women seeking

treatment for atrophic vaginitis. 6

2.1.3 Treatment Guidelines for Atrophic Vaginitis: Hormone and Non-Hormone

Therapy

According to treatment guidelines published by the North American Menopause

Society and the Agency for Healthcare Research and Quality, first-line therapies for women with vaginal atrophy include non-hormonal vaginal lubricants and moisturizers. 33,

34 Several over-the-counter vaginal moisturizer and lubricant products are available to treat vaginal dryness, which is an appropriate option for women with minor symptoms, or those who are concerned about hormone therapy or who are not candidates for estrogen treatment. The efficacy of non-hormonal treatments, however, have not been well studied. 21 Replens, a water-based bioadhesive vaginal gel, creates a moist film that

adheres to the vaginal wall and typically provides symptom relieve for two to three days,

according to the manufacturer.35 In clinical studies, Replens has been shown to

significantly improve vaginal dryness, itching, irritation, dyspareunia, and to lower pH. 36,

37 Vaginal lubricants have also been shown to be helpful in relieving dryness during

intercourse, although shorter-acting than moisturizers. The most commonly used

lubricants are either water-based (K-Y Jelly or Astroglide) or silicone-based (ID

Millennium). 4 While over-the-counter vaginal moisturizer and lubricant products have

9 been found to be helpful in treating symptoms of vaginal atrophy, none have been found to be as effective and long lasting as prescription estrogen therapy.

The treatment guidelines by The North American Menopause Society further state that for symptomatic vaginal atrophy that does not respond to non-hormonal vaginal lubricants and moisturizers, prescription therapy may be required. Estrogen is the most consistently effective therapy for many menopausal symptoms, including atrophic vaginitis.8 Prior to 2002, physicians commonly prescribed hormone replacement therapy not only to treat menopausal symptoms, but also to help prevent coronary heart disease and osteoporosis-related fractures.38 In 2002, the Women’s Health Initiative, a large-

scale multi-site randomized clinical trial to study the effects of oral hormone replacement

therapy, was halted before completion. For postmenopausal women with intact uteri, the

study investigators found that estrogen combined with progestin increased the relative

risks of breast cancer and coronary heart disease events, outweighing the benefits.16

Prescriptions for hormone replacement therapy dropped dramatically, from 90 million

annually in 1999 to an estimated 58 million annually in 2003.39 This study examined

vaginal estrogen use after the halt of both arms of the Women’s Health Initiative.

While oral hormone therapy is still prescribed short term to treat such menopausal

symptoms as vaginal dryness, irregular menses, vasomotor symptoms, and decreased

libido,40 standard doses of systemic estrogen are not sufficient to eliminate symptoms of atrophic vaginitis in 10 to 25 percent of patients.41 Some women require higher dosages

or coadministration of vaginal estrogen. 7 To treat symptoms of vaginal atrophy alone,

vaginal estrogen is an effective alternative. The FDA advises, “When prescribing solely

10 for the treatment of vulvar and vaginal atrophy, topical vaginal products should be considered.” 42 Because topical estrogen, including vaginal estrogen, avoids hepatic first-

pass metabolism, lower doses are required to relieve symptoms compared to oral

estrogen. As a result, vaginal estrogen has the advantage of being very low dose and

generally causes fewer systemic adverse events, such as endometrial stimulation,

bleeding, breast tenderness, increased breast cancer risk, and increased cardiovascular

risks.9 Disadvantages of vaginal estrogen therapy include patient dislike of vaginal manipulation, less prevention of postmenopausal bone loss and vasomotor symptoms, decreased control of absorption with vaginal creams compared to oral and transdermal delivery, and irregular treatment intervals that may cause patients to forget to administer the treatment. 43

One major concern regarding vaginal estrogen is whether endometrial overstimulation and hyperplasia, potential precursors to cancer of the uterus, may result from the low vaginal estrogen dose. Although the data were not conclusive in a

Cochrane Collaborative meta-analysis of vaginal estrogen therapy for vaginal atrophy, a small number of cases of endometrial overstimulation and hyperplasia with vaginal estrogen therapy in clinical trials give rise to the concern that women with an intact uterus may need progesterone in addition to estrogen when using vaginal estrogen for more than six months.10 Because no study has followed the long term effects of vaginal

estrogen therapy beyond six to twelve months, some clinicians advise discontinuing

vaginal estrogen therapy after six months of use.9 Anecdotally, many physicians

prescribe vaginal estrogen for periods longer than six months. 44

Accordingly, the FDA advises that vaginal estrogen therapy, like oral and transdermal hormone therapy, be used at the lowest effective dose and for the shortest duration to reach treatment goals.9, 42 But unlike most other menopausal symptoms which improve when estrogen levels have stabilized, symptoms of vaginal atrophy are a long term problem which cannot be expected to improve with time and will continue until treated. Up to 24 months of therapy may be required to completely eliminate vaginal dryness, and some patients may not fully respond even with coadministration of systemic and local estrogen therapy. 5 The effects of discontinuing vaginal estrogen therapy for

vaginal atrophy have not been documented by published studies. However, a survey at

the conclusion of the Women’s Health Initiative found that, in women ages 60-69,

discontinuation of oral hormone replacement therapy after an average of 5.7 years of

medication use resulted in a return of vaginal or genital dryness for 34.6% women.45 In

conflict with the FDA recommendations, the North American Menopause Society

recommends vaginal estrogen therapy should be continued for women as long as

distressful symptoms of vaginal atrophy remain. 33

2.1.4 Vaginal Estrogen Preparations

Vaginal estrogen preparations use estrogen in a variety of forms. Estrogen is a

generic term for any of the estrus-producing compounds (female sex hormones) including

estradiol, estriol, and estrone. Estradiol is the principal intracellular estrogen in

premenopausal women; it is the most potent naturally occurring ovarian and placental estrogen in mammals. Conjugated estrogens is a mixture of the sodium salts of the sulfate esters of estrone (a metabolite of estradiol) and equilin (an estrogen occurring in

12 the urine of pregnant mares).46 Vaginal estrogen products are produced from estradiol,

conjugated estrogens, or estradiol acetate.

There are currently five vaginal estrogen preparations available in the United

States, produced in cream, tablet, and ring forms, and in varying dosages (Table 2.1).

Estrace vaginal cream, Estring vaginal ring, and Femring vaginal ring are indicated for

the treatment of vulvar and vaginal atrophy.47-49 Estring has an additional indication for atrophy of the lower urinary tract while Femring is also indicated for moderate to severe vasomotor symptoms associated with the menopausal transition. Premarin vaginal cream and Vagifem vaginal tablets are indicated for atrophic vaginitis. 50, 51 Premarin has the additional indication for kraurosis vulvae (atrophy of the female external genitalia, resulting in dryness and shriveling, with leukoplakic patches on the mucosa and intense itching) and dyspareunia. Because Femring is a higher dose vaginal estrogen preparation intended for systemic distribution, it is outside the scope of this study and excluded from the analysis.

Vaginal creams have been available since the late 1970s (Premarin) and early

1980s (Estrace) and are the most widely used form of local estrogen. Estring vaginal ring was introduced in 1996. Vaginal tablets (Vagifem) are the newest form of vaginal estrogen, approved in 1999. No generic products are available for any of the vaginal estrogen drugs. 52

Preparation Trade Name Indications Strength National Drug 2008 Dosing Days Active Ingredient Code 53 Average Supply (Manufacturer) Wholesale of One Year Approved 52 Price 54 Package (30-day) Vaginal Premarin 50 1. Treatment of atrophic 0.625 mg/gm 00046-0872-01 (discontinued 0.5-2gm daily 28-112 cream Conjugated vaginitis and kraurosis cream; 2/27/2004) x 3 wks, then estrogens vulvae 42.5 gm tube 1 wk off (Wyeth 2. Treatment of Pharmaceuticals moderate to severe 00046-0872-93 $100.60 Inc.) dyspareunia, a symptom 1978 of vulvar and vaginal atrophy, due to menopause Estrace 47 Treatment of vulvar and 0.1 mg/gm cream; 00430-3754-11 (discontinued Start 2-4gm 10-297 Estradiol vaginal atrophy 12 gm tube 11/18/2003) daily x 1-2 14 (Warner Chilcott wks;

(US), LLC) 0.1 mg/gm cream; 00430-3754-14 $100.35 then 1-2gm 1984 daily x 1- 42.5 gm tube 2wks; continue 1gm, 1-3/wk Vaginal Vagifem 51 Treatment of atrophic 25 mcg 18 tablets 00169-5173-04 $88.88 Start 1 tab 28 tablets Estradiol vaginitis daily x 2 wks; (Novo Nordisk 25 mcg 8 tablets 00169-5173-03 $39.52 continue 2 Pharmaceuticals, tabs /wk Inc.) 1999

Continued

Table 2.1: Vaginal estrogen preparations available in the United States.

Table 2.1 continued Preparation Trade Name Indications Strength National Drug 2008 Dosing Days Active Ingredient Code 53 Average Supply (Manufacturer) Wholesale of One Year Approved 52 Price 54 Package (30-day) Vaginal ring Estring 48 Treatment of moderate to 2 mg ring 00013-2150-36 $145.69 one 7.5 mcg 90 Estradiol severe urogenital symptoms 7.5 (3- month ring every 3 (Pharmacia and due to postmenopausal atrophy mcg/24hr supply) months Upjohn Company) of the vagina (such as dryness, 1996 burning, pruritus and dyspareunia) and/or the lower urinary tract (urinary urgency and dysuria) Femring 49 1. Treatment of moderate to 50 00430-6201-40 $159.01 one 50 mcg 90

15 Estradiol acetate severe vasomotor symptoms mcg/24hr 00430-6201-95 (3- month or 100 mcg

(Warner Chilcott associated with the menopause supply) ring every 3 (US), LLC) 2. Treatment of moderate to 100 00430-6202-95 $169.43 months 2003 severe symptoms of vulvar and mcg/24hr 00430-6202-40 (3- month vaginal atrophy associated with supply) the menopause

2.1.5 Efficacy of Vaginal Estrogen Preparations

A Cochrane Collaborative meta-analysis of 19 clinical trials concluded all forms

of low-dose, local, prescription vaginal estrogen (creams, tablets, rings, and pessaries –

available outside the U.S.) to be effective in the treatment of symptoms related to vaginal

atrophy. 1, 10, 36, 37, 55-70 However, there were significant differences in symptom

improvement between forms of vaginal estrogen. Vaginal tablets, compared to the

vaginal ring, relieved symptoms of dyspareunia, frequency, dryness, and burning and

itching significantly better. Between vaginal tablets and creams, there was a significant

difference in relieving vaginal dryness favoring the tablets. There were no significant

differences in symptom relief between the cream and ring. These results, however, must

be interpreted with caution, as the trials entailed wide variation in design, small trial

numbers, and significant heterogeneity in some results (i.e., excessive variation in

treatment effects).

2.1.6 Vaginal Estrogen Medication Use: Adherence, Duration, and Acceptability

The abovementioned Cochrane review compared adherence and acceptability of the three categories of vaginal estrogen in randomized clinical trials (RCTs). 10

Adherence to treatment with the ring versus cream was significantly higher (two RCTs;

OR 2.23, 95% CI 1.31 to 3.80). Comparing tablets versus cream, tablets had a significantly higher adherence rate (two RCTs; OR 3.48, 95% CI 1.64 to 7.38).

Adherence to ring versus tablets was higher but not significant (one RCT; OR1.69, 95%

CI 0.66 to 4.31). Consistent with the Cochrane review, anecdotal evidence of medication compliance with vaginal creams is not high, as many women report this form of vaginal estrogen to be messy. 4

One recent study explored treatment duration of vaginal estrogen tablets and cream in “real world” clinical practice using managed care claims from 57 commercial health plans in the United States. Patients (n = 13,074) were selected who initiated vaginal estrogen treatment between January and June 2004 and followed for 10 months.

Women using vaginal estrogen tablets had a significantly longer average treatment duration compared to those using vaginal estrogen creams (149.5 ± 101.1 days vs. 91.6 ±

30.0 days, respectively, p < 0.01). Among those receiving multiple prescriptions, the

treatment duration for vaginal tablets versus cream continued to be significantly longer

(198.5 ± 82.4 days vs. 177.1 ± 86.7 days, p < 0.01). Medication adherence, as measured

by medication possession ratio greater or equal to 80%, was higher among vaginal tablet

than in vaginal cream users (74% ± 27% vs. 52% ± 32%, p < 0.01; OR 3.24, 95% CI

2.84-3.70). 15 The study did not consider vaginal estrogen rings.

In measuring treatment acceptability, the Cochrane review found the ring was

favored to cream in overall acceptability (one RCT; OR 5.45, 95% CI 2.66 to 11.16) and

as a delivery system (two RCTs; OR 6.08, 95% CI 3.70 to 10.00). The ring was also

favored to tablets as a delivery system (four RCTs; OR 4.55, 95% CI 3.17 to 6.53). 10

However, only one of these studies followed patients beyond six months, which had a

duration of 48 weeks. 70

Patient preference for the method of vaginal estrogen delivery is extremely

important. Understanding the advantages and disadvantages of each product and

consideration of patient symptoms, lifestyle, and individual requirements help in

choosing a treatment modality that will increase adherence. 71 Some advantages and

disadvantages of the three forms of vaginal estrogen are shown in Table 2.2.

Vaginal Estrogen Advantages Disadvantages Formulation Creams Soothing Potential difficulty with insertion Can also be applied to Messy vulva Leakage may result in less accurate dose Rings 3-month dosing is May sense the ring during extremely convenient intercourse Ease of use Associated with excessive vaginal Comfort discharge Possible expulsion during urination or defecation Tablets Convenient application May find twice-weekly dosing Less messy than creams difficult to remember Low side effect profile

Table 2.2: Advantages and disadvantages of each vaginal estrogen form. 44, 71

2.1.7 Health Services Utilization and Associated Cost

Poor adherence to drug therapy has been associated with higher service utilization

and costs to the health care system for several chronic illnesses, including diabetes;11, 72-76

HIV; 12 hypertension, hypercholesterolemia, and congestive heart failure;13 asthma;14 overactive bladder;77 and psoriasis.78 Studies in these disease areas have shown lower

medication adherence is associated with higher health care service utilization, such as

hospitalizations and emergency room visits, and lower health care costs. At present, no

published study has evaluated the health care utilization and related costs for patients

using vaginal estrogen therapy. Although atrophic vaginitis is a chronic condition, health

care utilization in the form of hospitalization or an emergency room visit is unlikely, in

18 contrast to many of the chronic illnesses listed above. However, use of an effective and convenient treatment has the potential to result in lower health care utilization in terms of reduced primary care and obstetrician/gynecologist visits and associated costs for atrophic vaginitis and urinary tract infections.

2.1.8 Women’s Attitudes toward and Knowledge of Menopause and Hormone

Therapy

Several factors affect a woman’s choice of treatment for menopausal symptoms, including attitudes towards menopause and hormone therapy. Studies have shown attitudes toward menopause differ across ethnic groups and stage of menopause. In the

Study of Women’s Health Across the Nation survey of more than 16,000 women during

1995 and 1996, women in the later stages of menopause tended to show more positive attitudes. Among racial and ethnic groups, African American women showed a more positive attitude toward menopause and aging than white, Hispanic, Japanese American, and Chinese American women, despite having symptoms similar in frequency and severity.79 While African American women tended to view the cessation of menses and

the burden of worrying about pregnancy as a relief, white women tended to perceive

menopause as a change of life that should be treated medically. African American women

were less likely to discuss their symptoms with their physicians and, among those who

did, less likely to have hormone replacement therapy recommended by their physicians. 80

Thus race and ethnicity may influence a woman’s attitudes toward and acceptance of

menopause, and consequently affect her expectations for and adherence to treatment. 81

Following the halt of the estrogen/progestin arm of the Women’s Health Initiative in 2002, attitudes toward and use of oral hormone therapy changed dramatically. One

19 year later, in a survey of 127 predominantly white, well-educated Midwestern women

(ages 50-70 years), belief patterns about hormone therapy were, in order of frequency:

‘use is risky’, ‘vindication of prior beliefs’, benefit to me outweigh risk’, and ‘unaware of new recommendations’. Sixty-four percent of women previously taking hormone therapy had discontinued therapy, although 24% of those who had stopped chose to resume use.

Common responses with emotional overtones expressed worry, confusion, anger, and grief. 82 In June 2004, two years after publication of the first Women’s Health Initiative findings, a nationally representative survey of 781 women (ages 40-60 years) drawn from the Knowledge Networks Internet panel found only 29% of women were aware of the

Women’s Health Initiative results. Only 40% of women had a positive aggregate knowledge score of the impact of hormone therapy on seven key disease outcomes.

Knowledge scores were lower for African American women (odds ratio, 0.4; 95% CI:

0.2-0.6) and among women with less education (odds ratio, 0.5; 95% CI: 0.3-0.9).83

These findings suggest age, race, and education, along with attitudes toward and knowledge of menopause and hormone therapy may all affect a woman’s decision to seek treatment for menopausal symptoms.

2.2 Theoretical Framework

This study examined the effect of the form of vaginal estrogen therapy on patients’ medication use behavior after adjusting for demographic, clinical, and economic factors. It further examined the influence of vaginal estrogen form on patients’ health care utilization. The decision to use vaginal estrogen and the resulting use of health care services is influenced by a complex mix of patient and physician related factors. The theoretical framework for this study was based on two classic models of health behavior:

20 a modification of the Health Belief Model by Becker and Maiman 84 and Andersen’s revised Behavioral Model of Health Services Use.85 The modified Health Belief Model presents social-psychological and related variables which predict medication use behavior. The revised Behavioral Model of Health Services Use proposes determinants of health care utilization and medication use behavior. Within these models, form of vaginal estrogen therapy used can be viewed as a determinant of patient health behavior.

These models were combined to evaluate the potential association between vaginal estrogen medication use behavior and health care utilization.

2.2.1 Becker and Maiman’s Modified Health Belief Model

Becker and Maiman’s modified Health Belief Model, constructed using

Rosenstock’s Health Belief Model 86 as a foundation, attempts to explain and predict compliance with health and medical care recommendations, specifically preventative care. The model was derived from an extensive review of the literature available at the time (1975), which suggested the following sociobehavioral interventions for compliance improvement are the most promising: 84

• certain health beliefs (especially personal estimate of vulnerability to and

seriousness of the disease, and faith in the efficacy of care),

• health-related motivations,

• perceptions of psychological and other costs of the recommended action,

• various aspects of the doctor-patient relationship, and

• social influence.

The resulting model illustrates the relationship between three primary components for

predicting and explaining compliance behavior: 1) readiness to undertake recommended

21 compliance behavior, 2) modifying and enabling factors, and 3) compliant behaviors.

These components are further described in Figure 2.1.

READINESS TO UNDERTAKE MODIFYING AND COMPLIANT BEHAVIORS RECOMMENDED ENABLING FACTORS COMPLIANCE BEHAVIORS Motivations : • Concern about (salience of) health matters in Demographic (very young or old) general

• Willingness to seek and accept medical direction Structural (cost, duration, • Intention to comply complexity, side effects, • Positive health activities accessibility of regimen, need for new patterns of behavior) Likelihood of Compliance with Value of Illness Threat Reduction preventative health Attitudes (satisfaction with visit, • Subjective estimates of: recommendations and prescribed physician, other staff, clinic o Susceptibility or resusceptibility (including regimens (e.g., screening, procedures and facilities) 23 belief in diagnosis) immunizations, prophylactic o Vulnerability to illness in general Interaction (length, depth, exams, drugs, diet, exercise, o Extent of possible bodily harm continuity, mutuality of personal and work habits, follow- o Extent of possible interference with social expectation, quality, and type of up tests, referrals and follow-up roles doctor-patient relationship; appointments, entering or continuing a treatment program) • Presence of (or past experience with) symptoms physician agreement with patient; feedback to patient) Probability That Compliant Behavior Will Reduce the Enabling (prior experience with Threat : action, illness or regimen; source • Subjective estimates of: of advice and referral (including o The proposed regimen’s safety social pressure)) o The proposed regimen’s efficacy to prevent, delay, or cure (including “faith in doctors and medical care” and “chance of recovery”)

Figure 2.1: Becker and Maiman’s Modified Health Belief Model.84

While vaginal estrogen therapy for the treatment of atrophic vaginitis is not considered preventative care, the model offers several factors potentially associated with medication adherence. Many of the variables presented in the model, such as motivations, subjective estimates of the proposed regimen safety, and type of doctor- patient relationship, are not available in the data used for this study.

2.2.2 Andersen’s Revised Behavioral Model of Health Services Use

The framework for this study also draws on Andersen’s revised Model for Health

Service Utilization (Figure 2.2). 85 The model was originally conceived in the 1960s and has been revised several times, as recently as 1995, through collaboration and research with other prominent medical sociologists, most notably Lu Ann Aday.87, 88 The model

explains an individual’s health care practices and health services utilization as a function

of three characteristics: predisposing characteristics, enabling resources, and need. The

health outcomes resulting from personal health behaviors are in the form of perceived

health status, evaluated heath status, and consumer satisfaction. The characteristics in

the model that influence health behavior are used in this study to select predictor

variables of health outcomes. These characteristics are:

1. Predisposing characteristics – a person’s predisposition to use services.

Tendency to use health care services can be predicted by characteristics which

exist prior to onset of illness. Predisposing patient characteristics include

demographic factors such as age, gender, race/ethnicity, education, income, and

geographic region. Physician-related characteristics include ownership status,

specialty, and practice environment.

2. Enabling resources – factors which enable or impede use. Despite being

predisposed to using health care services, an individual may not have the means to

do so. Enabling factors are those that affect the ability to gain access to services

such as health insurance, proximity to care, and quality of care.

3. Need factors – a person’s need for care, as perceived by the individual or the

health care provider. Need factors include perceived health status, medical

condition, severity of disease, number of comorbidities, and quality of life.

The individual, given his or her predisposing characteristics, must sense the need and have the enabling resources to seek health care services. The health behavior that follows, in the form of personal health practices and the use of the health care system, affects such outcomes as health status and patient satisfaction.

ENVIRONMENT POPULATION CHARACTERISTICS HEALTH OUTCOMES BEHAVIOR

Health Care Personal Perceived System Health Health Status Practices

Evaluated Predisposing Enabling Need Characteristics Resources Health Status

External Consumer Environment Use of Satisfaction Health Services

Figure 2.2: Andersen’s Revised Behavioral Model of Health Services Use 85

2.2.3 Study Conceptual Framework

Combining select elements of Becker and Maiman’s modified Health Belief

Model and Andersen’s revised Behavioral Model of Health Services Use, a conceptual framework for the study has been developed (Figure 2.3). In this study, the population characteristics and health behavior modules of Andersen’s model served as the framework while Becker and Maiman’s model was consulted to select available variables for each component. The resulting model allowed the examination of the impact of determinants (demographics, pre-existing clinical factors, and quality of care variables) and prescribing patterns (provider specialty and form of vaginal estrogen initially used) on health behaviors (medication adherence and persistence and health care service utilization). The modifying and enabling factors identified from the Health Belief Model included the demographic variables of age and race, a clinical variable of disease severity measured by previous use of systemic estrogen, comorbidity variables measured by the

Charlson-Deyo comorbidity index and number of unique prescriptions, an access to care variable identified by number of outpatient visits, and a quality of care variable measured by having had an annual mammogram. These variables allowed risk adjustment between the cohorts of patients using various forms of vaginal estrogen. Two variables related to vaginal estrogen therapy were included: the specialty of the prescribing physician

(obstetrician/gynecologist or other) and the form of vaginal estrogen, which was the primary variable of interest in this study. The outcome variables in this study were specific patient health behaviors. Medication adherence was measured using Medication

Possession Ratio (MPR) by evaluating prescription refill patterns. Medication persistence was calculated by examining the time between the first vaginal estrogen

26 prescription fill and the end of the days supply of the last vaginal estrogen prescription refill. Economic outcomes evaluated were number and cost of primary care and

OB/GYN visits and number and cost of vaginal estrogen prescriptions.

Patient Characteristics Initial Vaginal Estrogen Health Behaviors Prescription PREDISPOSING CHARACTERISTICS Demographics : HEALTH BEHAVIORS: • Age Vaginal Estrogen Therapy PERSONAL HEALTH PRACTICES • Race/ethnicity Prescribed : • VE form: cream, ring, VE Medication Use Behavior tablet (primary • Medication Adherence NEED CHARACTERISTICS independent variable) • Medication Persistence • • Number of VE refills Clinical Factors Prescribing physician (OB/GYN vs. other) • Switch to alternate form of • Severity of comorbidities • vaginal estrogen o Charlson-Deyo Year prescribed • comorbidity index Systemic estrogen use

28 • Severity of menopausal

symptoms HEALTH BEHAVIORS: o Sys temic estrogen use USE OF HEALTH SERVICES

ENABLING CHARACTERISTICS Health Care Service Utilization : • Number and cost of Access to Care : outpatient visits • Number of outpatient visits • Number and cost of OB/GYN visits and PCP visits Quality of Care : • Number and cost of all vaginal • Mammography estrogen prescription claims

Abbreviations: HT = hormone therapy, OB/GYN = obstetrician/gynecologist, PCP = primary care provider, VE = vaginal estrogen

Figure 2.3: Study Conceptual Framework

Chapter 3: Methodology

This chapter provides information on the study data source as well as the methodology, hypotheses testing, and statistical analyses employed to evaluate the aims listed in

Section 1.3. The analytical dataset was created from the Medicaid claims database using

SAS statistical software; all statistical analyses were performed using Stata 10.

3.1 Study Design

The study employed a retrospective, longitudinal cohort design to analyze North

Carolina Medicaid enrollees newly starting vaginal estrogen therapy between January

2004 and December 2006. Claims from January 2003 to December 2007 were analyzed.

The study was approved by the Ohio State Institutional Review Board (Appendix A).

3.1.1 Data Source: North Carolina Medicaid Claims

Patient data were extracted from North Carolina Medicaid administrative claims by the North Carolina Division of Medical Assistance (NC DMA). The program, which provides complete coverage, including full prescription benefits, to all enrollees who maintain eligibility, is nearly entirely fee-for-service payment. North Carolina's

Medicaid program serves approximately one out of every five people residing in the state. In 2006-2007, Medicaid served approximately 1.7 million children, aged, blind and/or disabled individuals. The budget for the 2007-2008 Medicaid program is

29 approximately $11.3 billion, supported by a combination of federal Medicaid funds and state appropriations.89

The North Carolina Medicaid program serves low-income individuals and families who cannot afford health insurance. Coverage varies with the different needs of various groups: aged, blind, and disabled; infants, children, and families; long-term care; and Medicare recipients. To be eligible for Medicaid, one must also be a U.S. citizen or provide proof of eligible immigration status and be a resident of North Carolina.

Medicaid helps pay for certain medical expenses such as: doctor, clinic, and hospital charges; prescriptions (excluding prescriptions for Medicare beneficiaries); vision and dental care, Medicare premiums, nursing home care, medical equipment, home health services, mental health care, and most medically necessary services for children under age 21. Medicaid recipients whose income is greater than the Medicaid income limit must pay a deductible before Medicaid pays for medical expenses. Typical prescription drug co-payments for recipients below the Medicaid income limit is $1 for a generic drug, $1 for a brand drug for which no generic is available, and $3.00 for brand drug for which there is a generic available. For those above the income limit, a co-pay of $10 is charged for a brand drug for which there is a generic available. A co-pay is also charged for physician, dental, and non-emergency emergency room visits.89

The database consisted of medical claims (both outpatient and inpatient services),

prescriptions claims, long-term care claims, and eligibility records for each Medicaid

recipient. Longitudinal information for each Medicaid recipient was linked using a

Medicaid enrollee identifier available on each claim and eligibility record, scrambled by

NC DMA so as to not be able to identify the recipient. Demographic, clinical, and utilization-related variables were retrieved from the medical claim files, while details on prescription medication use were assessed using variables retrieved from the pharmacy claims files. Medication use behavior related to vaginal estrogen therapy was examined using prescription refill patterns. Patients’ health care utilization patterns and costs both before and after starting medication therapy were assessed. Eligibility and enrollment details were evaluated using details found in the eligibility/enrollment file. Records were provided by the NC DMA for years 1998-2007, although for this study only years 2003-

2007 were analyzed as one form of vaginal estrogen, the tablet, was rarely prescribed before 2004. The North Carolina Medicaid dataset provides self-reported information on patient race, of which there is a larger representation of minorities than in the general population, allowing for a statistically strong comparison between racial and ethnic groups.

3.1.2 Study Perspective

The study prospective is that of the payer, the North Carolina Medicaid program.

Because benefits associated with improved medication use behavior for vaginal estrogen use could potentially improve quality of life, healthcare utilization for the treatment of bothersome symptoms associated with atrophic vaginitis may be reduced. Understanding which forms of vaginal estrogen appear more acceptable to women will help in developing treatment recommendations to women with atrophic vaginitis.

3.1.3 Medicaid Database Elements

Table 3.1 lists the database elements provided by the NC DMA for this study.

Medicaid Record Type Database element Eligibility/enrollment data: Enrollee ID (scrambled) Enrollment to date Enrollment from date State aid category Eligibility information Birth year Gender Race/ethnicity Medical Claims Enrollee ID (scrambled) Date service incurred Diagnosis codes Procedure codes Place of service Category of service Provider specialty Service from date Service to date Type of provider Cost/reimbursement amount Diagnosis related group (DRG) Admission date Discharge date Pharmacy claims Enrollee ID (scrambled) Date service incurred/prescription fill date NDC codes Quantity dispensed Days supplied Cost/reimbursement amount Drug therapy class Co-payment

Table 3.1: Select elements extracted from the Medicaid database.

3.1.4 Study Population

Women with at least one prescription claim for vaginal estrogen were selected for this study. Vaginal estrogen is indicated for the relief of symptoms associated with atrophic vaginitis or urogenital atrophy. While it would have been desirable to include as a reference group women diagnosed with atrophic vaginitis but not receiving vaginal estrogen therapy, the diagnosis of atrophic vaginitis is seldom coded in the medical claims. Additionally, no indication is coded for prescription claims. Therefore, the study population is limited to women prescribed vaginal estrogen and assumed to have atrophic vaginitis. Women ages 40 to 64 years were included in the study. Medicaid enrollees under age 40 were excluded to limit the sample to peri- and postmenopausal women.

Women ages 65 and over are likely dual eligible for Medicaid and Medicare, and would not be guaranteed to have complete claims history in the Medicaid database. Similarly, enrollees under age 65 who showed evidence of dual eligibility, as indicated by crossover claims, were also excluded for the same reason.

The inclusion and exclusion criteria for the claims dataset provided by the NC

DMA are summarized as follows:

Inclusion Criteria:

1. Claims between January 1, 2003 to December 31, 2007

2. Female

3. Age 18 to 64

4. At least one claim of vaginal estrogen medication, identified by the National

Drug Codes (NDC) listed below:

a. Estrace (cream): NDC = 00430-3754-11 or 00430-3754-14

b. Premarin (cream): NDC = 00046-0872-01 or 00046-0872-93

c. Estring (ring): NDC = 00013-2150-36

d. Femring (ring): NCD = 00430-6201-40, 00430-6201-95, 00430-6202-95,

or 00430-6202-40

e. Vagifem (tablets): NDC = 00169-5173-03 or 00169-5173-04

Exclusion Criteria:

1. Crossovers: Claim Type = O (professional crossover), or

W (outpatient crossover), or

X (inpatient crossover).

2. Management fee claims

3.1.5 Analytical Dataset

An index date was identified for each eligible vaginal estrogen user, indicated by the first date a vaginal estrogen prescription was filled. Twelve months of continuous

Medicaid eligibility was required prior to the index date to ensure no previous vaginal estrogen prescriptions had been filled and to ensure any previous absence of health care services use was not due to lack of insurance. This time period served to control for baseline characteristics before initiating therapy. Enrollees were also required to have a minimum of 12 months continuous eligibility after the index date for evaluation of medication use behavior and health care utilization outcomes. Definitions for index- related time terminology are:

1. Index date : Date of first prescription filled for vaginal estrogen medication

during January 1, 2004 to December 31, 2006

2. Index or initial prescription : The first new vaginal estrogen prescription

filled by the patient during January 1, 2004 to December 31, 2006

3. Pre-index, pre-initiation, or pre-treatment period : 12 months prior to the

index date (during January 1, 2003 to December 31, 2005)

4. Post-index, post-initiation, or treatment period : 12 months following the

index date (during January 1, 2004 to December 31, 2007).

From the dataset provided by NC DMA, further refinements were made to create the final analytical dataset as described by the steps and resulting patient counts in Table

3.2. Exclusions for therapy initiation before 2004, use of the product Femring, and women under age 40 are described in the following section.

Patients in Steps in Creating the Analytical Dataset Dataset Enrollees with claims provided by NC DMA 15,815 Step 1: Select newly started cases of vaginal estrogen therapy, identified 10,355 by the National Drug Codes (NDC) listed in section 1.1.2 Step 2: Select enrollees with continuous enrollment in Medicaid 6-12 months before and 12 months after the initial vaginal estrogen 6,399 prescription Step 3: Starting therapy during a time window January 1999 to December 5,994 2006 Step 4: Aid Category Code = QB (catastrophic) 5,249 Step 5: Exclude patients with less than 12 months continuous enrollment 4,578 prior to the initial vaginal estrogen prescription Step 6: Exclude patients whose initial prescription is Femring (systemic 4,513 estrogen therapy) Step 7: Select newly started cases of vaginal estrogen therapy January 1, 2004 and later (before vaginal estrogen tablets were routinely 1,836 prescribed) Step 8: Exclude patients whose initial prescription is Estring (due to 1,812 small sample in dataset) Step 9: Select patient age 40 years and older 1,505

Table 3.2: Steps in creation of analytical dataset.

3.2 Study Variables

The analytical framework and variables for the study can be summarized as follows:

• Target population : women age 40-64 years prescribed vaginal estrogen

therapy for the treatment of atrophic vaginitis and receiving care in the North

Carolina Medicaid setting.

• Outcome variables : medication use behavior (medication adherence and

persistence) and healthcare service utilization (number and cost of PCP plus

OB/GYN visits, and number and cost of vaginal estrogen prescriptions filled).

• Primary variable of interest : form of vaginal estrogen prescribed (cream vs.

ring vs. tablet).

• Covariates : other variables included in the analysis to help explain

variability in the outcomes are:

o Demographic factors : patient’s age (at the index date) and

race/ethnicity

o Clinical factors (in the pre-index period): severity of menopausal

symptoms : use of systemic estrogen; comorbidity severity : Charlson-

Deyo comorbidity index and number of unique prescriptions

o Access to care factors (in the pre-index period): number of outpatient

visits

o Quality of care factors (in the pre-index period): mammography

o Medication related factors (at the index date): prescribing physician

(OB/GYN vs. other specialist or primary care physician) and year

index medication prescribed.

3.2.1 Patient Characteristics

Patient demographic characteristics available in the database include birth date, which was converted to age at the index date, and race/ethnicity. Race categories were

American Indian/Alaskan (1.9%), Asian (0.8%), black/African (30.4%), Pacific Islander

(<0.1%), white (56.2%), and unreported (10.6%). Ethnicity was divided into Hispanic

(1.1%), Non-Hispanic (58.7%), and unreported (40.3%). Because few Hispanics and few non-white and non-black races were indentified in the data base, ethnicity was not considered and race was grouped by black, white, and other/unreported.

The comorbidity burden of each patient in the 12-month pre-index period was measured using the Deyo adaptation of the Charlson Comorbidity Index (CCI). The CCI was originally designed as a weighted index to estimate the risk of death from 19 comorbid diseases recorded in medical records for use in longitudinal studies. 90 The

Deyo adaptation to the CCI maps International Classification of Diseases, Ninth

Revision, Clinical Modification (ICD-9-CM) diagnosis and procedure codes to 17

categories of comorbidities for application in administrative databases. It has been

validated for several clinical outcomes and resource utilization other than death. 91 Table

3.3 lists the comorbidity ICD-9-CM codes included in the index with the corresponding weights.

Conditions ICD-9-CM Codes Weight Myocardial infarction 410, 412 1 Congestive heart failure 398, 402, 428 1 Peripheral vascular disease 440-447 1 Cerebrovascular disease 430-433, 435 1 Dementia 290, 291, 294 1 Chronic pulmonary disease 491-493 1 Rheumatologic disease 710, 714, 725 1 Peptic ulcer disease 531-534 1 Mild liver disease 571, 573 1 Diabetes (mild to moderate) 250.xx 2 1 Diabetes with chronic complications N/A 2 Hemiplegia or paraplegia 342, 434, 436, 437 2 Moderate or severe renal disease 403, 404, 580-586 2 Any malignancy including leukemia and lymphoma Any tumor 140.xx – 195.xx 2 Leukemia 204, 205, 206, 207, 208 Lymphoma 200, 202, 203 Moderate or severe liver disease 070, 570, 572 3 Metastatic solid tumor 196-199 6 Autoimmune deficiency syndrome (AIDS) 042-044 6

Table 3.3: Conditions, ICD-9-CM codes, and corresponding weights included in the

Deyo adaptation of Charlson Comorbidity Index .

Systemic estrogen use was selected to measure severity of menopausal symptoms in the 12- month pre-index period. While a more appropriate measure for this study may

39 have been the presence of an office visit for symptoms related to vaginal atrophy, such as atrophic vaginitis or dyspareunia, these diagnoses are rarely recorded. Systemic estrogen use was identified by the NDC numbers listed in Appendix C. All forms of hormone therapy (oral, transdermal, and implants) were included, as were both estrogen only and estrogen plus progestin formulations.

As a proxy for access to care, the number of outpatient visits during the 12-month pre-index period was tabulated. This measure has been used in previous Medicaid claims studies 92 to approximate the patient’s opportunity to receive health care services.

Number of days of visits to physicians in the outpatient setting as well as to optometrists,

psychologists, and other health care practitioners were considered outpatient visits.

Outpatient visits were identified by the following claim type and provider type

combinations, and then summed to determine the 12-month total: J:022, M:060, J:011,

J:082, J:020, J:074, M:076, J:052, J:112, J:067, M:104, J:033, J:055, J:030, J:032, P:028,

J:110, J:028, J:051, M:090, P:087, J:087, J:089, J:109, J:061, J:085, P:022, J:083, J:029,

M:068, P:020, J:041, M:069, L:022, L:020, or L:052.

Given that atrophic vaginitis is primarily a condition affecting peri- and post-

menopausal women, and hence the majority of the patients in this gender-specific study

are over age 40 years, event of mammography was selected as a quality of care variable.

Recommended guidelines for the frequency of breast cancer screening vary slightly

between two national groups. The National Cancer Institute recommends women age 40

and older have a mammogram every 1 to 2 years, 93 whereas the American College of

Obstetricians and Gynecologists recommends women aged 40 to 49 years have screening

40 mammography every 1 to 2 years and annually for women age 50 years and over. 94 In this study, event of mammography is calculated over the two year period spanning 12 months pre-index and 12-months post-index to allow for women complying with the once in 2 years screening recommendation. Mammography was identified by the presence of one of the following codes: Current Procedural Terminology (CPT) = 77051, 77052,

77055, 77056, 77057, 76082, 76083, 76090, 76091, 76092, or 76085; ICD-9-CM =

V76.11 or V76.12; or Healthcare Common Procedure Coding System (HCPCS) =

G0202, G0204, or G0206.

3.2.2 Vaginal Estrogen Therapy

The focus of this study was to investigate the use of various forms of vaginal estrogen. Three forms were available in the dataset: vaginal estrogen cream (VC), vaginal estrogen ring (VR), and vaginal estrogen tablets (VT). Estrace and Premarin creams have been found in a previous administrative claims database study to have similar medication adherence and persistence;15 therefore, the two cream products were combined in the VC group. Because Femring vaginal ring is higher dose and indicated for the treatment of vasomotor symptoms as well as other systemic menopausal symptoms, it was excluded from this study. Consequently, Estring vaginal ring was the only ring product included in the VR group. Only 24 Estring users qualified for the study, and hence the VR was excluded from the study analyses. Vagifem vaginal estrogen tablet was the sole product in the VT group. The NDC numbers for each drug are listed in table 2.1.

An attempt was made to identify the specialty of the prescriber to test the hypothesis that OB/GYNs would more often prescribe the newer VR and VT products than the older VC products. Because provider specialty was not recorded on the prescription claim, an algorithm was devised. It was assumed that patients would fill their prescriptions within 15 days of the office visit where the prescription was received.

If a visit to a provider with the specialty code ‘016’ occurred 15 days prior to filling a vaginal estrogen prescription, the prescriber was assumed to be an OB/GYN; otherwise, the provider was coded as “other”.

3.2.3 Medication Use Behavior

Medication use behavior was evaluated using two common measures: medication adherence and medication persistence. Medication adherence, also called medication compliance, may be defined as “the extent to which a patient acts in accordance with the prescribed interval and dose of a dosing regimen,”95 Persistence may be defined as “the

duration of time from initiation to discontinuation of therapy”.95 Adherence is measured over a period of time and is reported as a percentage; whereas persistence is based upon the time the therapy was available and is reported in number of days. As is common in retrospective assessments, adherence in this study was measured by the medication possession ratio (MPR), 96 the number of doses dispensed in relation to the dispensing

period, determined by the equation:

MPR = Number of days supply of vaginal estrogen medication in the post-index period . Number of days in the post-index period

By definition, at least one refill (i.e., two prescription fills) must occur to calculate MPR.

Thus in this study, MPR was only calculated for patients refilling at least once. An

42 alternate denominator for this equation is the time period between first and last prescription filled. The entire 12-month post-index period was selected to avoid over estimating adherence in persons who discontinue treatment early by not refilling their prescription. This approach assumes vaginal estrogen therapy is intended to continue the entire post-index period, as is required to obtain and maintain relief of symptoms. It is feasible, though, that therapy was intended to be short term, in which case adherence would be under estimated. A major assumption when measuring MPR using pharmacy claims data is that the dispensed prescription is actually consumed. 97 While there are

many other methods to assess medication adherence, such as blood and urine assays,

patient interviews, pill counts, prescriptions refills, and electronic monitoring, 98, 99

pharmacy records have been shown to have predictive validity. 96, 100

Medication persistence provided a measure of treatment duration, calculated by the equation:

Treatment duration = (date of last vaginal prescription refill + days supply of last vaginal estrogen refill) – date of first vaginal prescription fill.

Typically, persistence analyses contain a minimum allowable gap in days between prescription refills, based upon the time a patient could go without medication and not experience suboptimal outcomes. 101 In this study, because of the wide range in values for days supply in the database and the variable and flexible dosing of VC products, a minimum allowable gap was not specified for the treatment duration calculation. To calculate treatment duration and MPR, the medication dispensed, date of dispensing,

43 quantity dispensed, and days supply of medication was extracted from pharmacy claim records.

3.2.4 Health Care Service Utilizations and Costs

Health care service utilization in the form of number of outpatient visits,

OB/GYN visits, PCP visits, emergency room visits, and inpatient hospital days were determined for patients in the 12-month pre-index period as potential covariates in evaluating the study aims. To assess outcomes of health care service utilization of women using vaginal estrogen, number and cost of OB/GYN plus PCP visits were tabulated for the 12-month post-index period, since women traditionally seek treatment for atrophic vaginitis from these provider types. Hospitalizations, emergency room visits, and all outpatient visits were not considered as outcomes because women do not receive their care for atrophic vaginitis in those settings. While a more appropriate measure for this study may have been the sum of office visits related to atrophic vaginitis, this diagnosis is rarely recorded. Utilization measures were derived by summing the events of interest during the observation period using claim type, procedure code, provider type, and provider specialty data as listed in Table 3.4.

Care Setting Database variable and value Outpatient visits Claim type and Provider type combinations: J:022, M:060, J:011, J:082, J:020, J:074, M:076, J:052, J:112, J:067, M:104, J:033, J:055, J:030, J:032, P:028, J:110, J:028, J:051, M:090, P:087, J:087, J:089, J:109, J:061, J:085, P:022, J:083, J:029, M:068, P:020, J:041, M:069, L:022, L:020, or L:052 OB/GYN visits Claim type = J and Provider specialty = 016 PCP visits Claim type = J and Provider specialty = 001 or 011 Emergency room visits Claim type = S and Procedure code = RC45*, where * is 0-9 Inpatient hospital days for Claim type = S: Admission date – Discharge date

Table 3.4: Codes for identifying care settings.

3.3 Statistical Methods

All statistical analyses were performed using Stata 10, conducted at an a priori

significance level of 0.05. The statistical approaches implemented to investigate the aims of

the study are described in the following sections.

3.3.1 Descriptive Analyses

Aim 1 : To examine the association between select patient baseline characteristics, such as

age, race, comorbidities, outpatient office visits, previous systemic estrogen use,

and mammography screening of Medicaid enrollees with atrophic vaginitis using

various forms of vaginal estrogen (cream, tablet, and ring).

H1: Younger women and white women will be more likely to use the tablet

form than older women and black women with atrophic vaginitis.

Baseline patient characteristics were compared by vaginal estrogen form using descriptive statistics. Means were computed for continuous variables and proportions

(percentages) for categorical variables to understand the distributions. To compare categorical variables among vaginal estrogen forms, the Pearson’s chi-square test was applied. For continuous variables, the t test was employed to compare two forms of

vaginal estrogen.

3.3.2 Prescribing Physician Specialty

Aim 2 : To examine the relationship between type of provider (obstetrician/gynecologist

vs. other type of physician) and form of vaginal estrogen prescribed.

H1: OB/GYNs will more often prescribe vaginal estrogen tablets than cream

compared to other providers.

The proportion of women prescribed each form of vaginal estrogen by an

OB/GYN was compared by applying the chi-square test. A multivariable logistic regression was performed to evaluate the likelihood of prescribing VT versus VC with

OB/GYN provider (yes/no) as the primary independent variable. Covariates in the model included demographic factors (age, race), clinical factors (CCI, systemic estrogen use, mammography), and index year. The model was as follows:

VT (yes/no) = β0 + β1(OB/GYN) + βi(Covariate) i + ε,

where i = 1, … , number of covariates; ε is the error term.

3.3.3 Patterns of Medication Use

Aim 3 : To evaluate refill patterns by form of vaginal estrogen, and to examine the

differences in medication adherence and medication persistence by form of

vaginal estrogen.

H1: Tablet users will refill their prescription more often, and tablet users will

less often switch to cream than vice versa. Medication adherence and

persistence will be greater for vaginal estrogen tablets than for cream.

Descriptive statistics were calculated for several outcomes of medication use patterns to aid in understanding and comparing women’s acceptance of each form of vaginal estrogen:

• Proportion of women refilling their index prescription at least once,

• Proportion of women switching to another form of vaginal estrogen,

• Mean and standard deviation of MPR during the first year post-index,

• Mean and standard deviation of treatment duration during the first year post-

index,

• Mean and standard deviation of treatment duration during the first two years

post-index, for those patients with two years of continuous eligibility after

initiating vaginal estrogen therapy. This measure is important to assess true

treatment duration in “real world” clinical practice, since all clinical trials to

date have been 12 months or less.

To compare categorical variables, the Pearson’s chi-square test was applied; for continuous variables, the t test was employed. Survival analysis was used to evaluate

47 persistence and a Kaplan-Meier curve was constructed to compare treatment discontinuation among vaginal estrogen forms. Multivariable logistic regression was performed to evaluate the binary outcome of whether or not a prescription was refilled, after adjusting for covariates, and considering form of vaginal estrogen as the primary independent variable. The model included the same covariates listed in section 3.3.2., with the addition of OB/GYN prescriber (yes/no). The model was as follows:

Refill (yes/no) = β0 + β1(Vaginal estrogen form) + βi(Covariate) i + ε,

where i = 1, … , number of covariates; ε is the error term.

To evaluate the continuous variables of MPR and treatment duration, ordinary least

squares (OLS) regression analysis was performed. Again, form of vaginal estrogen was

considered the primary independent variable, and the corresponding models were:

MPR = β0 + β1(Vaginal estrogen form) + βi(Covariate) i + ε, and

Treatment duration = β0 + β1(Vaginal estrogen form) + βi(Covariate) i + ε,

where i = 1, … , number of covariates; ε is the error term.

3.3.4 Health Care Service Utilization

Aim 4 : To examine the differences in health care service utilization (OB/GYN plus PCP

visits) and cost (vaginal estrogen cost and OB/GYN plus PCP visit costs) across

different forms of vaginal estrogen.

H1: Health care service utilization and costs will be greatest for women using

vaginal estrogen cream and least for tablets.

To evaluate resource utilization, the number and cost of OB/GYN plus PCP visits and the number and cost of vaginal estrogen prescriptions in the post-index year were

48 tabulated, along with the mean and standard deviation statistics for each. Negative binomial regression models were constructed to analyze the effect of vaginal estrogen form on the number of visits and the number of prescriptions. Because the data were over dispersed, that is, the variance much larger than the mean, a negative binomial regression model was preferred to the Poisson regression.102 To analyze the effect of vaginal

estrogen form on the cost of visits and the cost of prescriptions, Ordinary Least Squares

(OLS) regression analyses were used. As the cost distributions were skewed, cost

outcomes were log-transformed. Again, vaginal estrogen form was the primary

independent variable and covariates described in section 3.2.2 were included. The

resulting models were:

Number of OB/GYN + PCP visits = β0 + β1(Vaginal estrogen form) + βi(Covariate) i + ε,

Number of vaginal estrogen prescriptions = β0 + β1(Vaginal estrogen form) +

βi(Covariate) i + ε,

ln(Cost of OB/GYN + PCP visits) = β0 + β1(Vaginal estrogen form) +

βi(Covariate) i + ε,

ln(Cost of vaginal estrogen prescriptions) = β0 + β1(Vaginal estrogen form) +

βi(Covariate) i + ε,

where i = 1, … , number of covariates; ε is the error term.

3.4 Regression Diagnostics and Other Statistical Considerations

Validity of logistic and OLS regression is dependent on how well the data meet

the assumptions of the models. OLS regression assumes the following assumptions are

satisfied: 103

• Linearity – the relationship between the independent (predictor) variables and

dependent (outcome) variable is linear,

• Normality – the errors (residuals) are normally distributed; this assumption is only

required when performing hypothesis testing, as is the case for this study,

• Homoscedasticity (homogeneity of the variance) – the variance of the errors is

constant, and

• Independence – the errors of the observations are not correlated with one another

(this is also an assumption of logistic regression).

Other concerns for both OLS and logistic regression addressed in the analyses include:

• Model specification – the proper regression model is used,

• Influence of individual observations on the coefficients, and

• Collinearity of predictor variables.

The analytical methods used to address each of the assumptions and concerns are described in the following section.

3.4.1 Nonlinear Relationships

Linear regression assumes the relationship between the dependent variable and any continuous independent variables is linear. To check for nonlinear relationships, the standardized residuals were plotted against each of the continuous independent variables

50 in this study, age, CCI, and number of outpatient visits, and observed for an obvious nonlinear pattern. If a nonlinear relationship was observed, transformation of the independent variable was considered. Transformation of the variables was only performed if a change in the overall model results occurred. 102

3.4.2 Non-Normal Distribution of Residuals

For valid hypothesis testing, the residuals must be normally distributed to assure the p-values associated with the t-tests and F-tests are valid. Normality of the residuals was checked by creating a kernel density plot (similar to a histogram with narrow bins and a moving average). The plot was compared against an overlaid normal density for deviations from normality. 103

3.4.3 Heteroskedasticity

OLS regression assumes the variance of the error term is constant for all

observations or any value of the independent variable (homoscedasticity).

Heteroskedasticity occurs when the variance of the error term varies by observation,

causing the standard errors of the coefficients to be large and estimates of significance to

be less accurate. One common method to test heteroskedasticity is to plot the residuals

against the fitted (predicted) values. If there is no pattern to the scatter plot, i.e., the plot

is random, then the model is well-fitted. 102

3.4.4 Autocorrelation

Regression analysis also assumes independence of observations. When data are

collected over time, as is the case in a longitudinal claims database, a variable may be

correlated with itself over successive time intervals, known as autocorrelation. A

Durbin-Watson test for correlated residuals was used to test for the assumption of independence over time. The Durbin-Watson statistic has a range of 0 to 4, with a midpoint of 2. 103 A Durbin–Watson statistic substantially less than 2 is evidence of autocorrelation.

3.4.5 Multicollinearity

A potential problem in multiple regression models is multicollinearity, including two predictor variables that are near perfect linear combination of each other. This effect causes unstable model estimates of the coefficients and wildly inflated standard errors.

The variance inflation factor (VIF) was computed to check for multicollinearity.

Tolerance, defined as 1/VIF, of a variable lower than 0.1 is considered by many researchers to indicate the variable may be a linear combination of other independent variables. 104

3.4.6 Model Specification: Model Goodness of Fit Tests and Model Adequacy

Tests

To examine adequate fit of the multiple logistic regression models, the Hosmer-

Lemeshow Chi-square test was conducted. The Receiver Operating Characteristics

(ROC) curves were also plotted to evaluate the discriminating power of the logistic models. An area under the curve greater than 0.75 indicated good discrimination.102 The area under the ROC curve is also known as the c-statistic, measuring the proportion of observations correctly classified. 102 For the standard negative binomial regression, goodness-of-fit was measured by the likelihood ratio test of alpha. A p value less than

0.05 indicated the negative binomial model was a better fit than the poisson regression model.105

3.4.7 Missing Data

In the study dataset, missing data occurred in only a small fraction of observations, as is typical in an administrative claims database. No attempt was made to impute values for missing variables. In performing calculations, STATA ignores observations for which values are missing; consequently, all regression models included only those patients for whom all values of the model variables were available.103

Chapter 4: Results

4.1 Patient Characteristics

Baseline characteristics of women prescribed tablet or cream forms of vaginal estrogen are shown in Table 4.1. 1,505 women satisfied the inclusion criteria for the study, of which 90% used vaginal estrogen creams (VC) and 10% vaginal estrogen tablets (VT). The average age of all women using vaginal estrogen therapy was 49.4 years; however, after exclusion of women under age 40, the average age was 53.2 years

(standard deviation (SD) 6.6). No significant difference in mean age was seen across forms of vaginal estrogen, or in the distribution of women by age category. In this population, 30% were of black race, 56% white, and 3% other race. Race was not reported for 11% of the sample. Sample size of ethnic minorities such as Hispanic was small (1.3%) and often unreported (36.3%); therefore, ethnicity was not considered in this report.

In the pre-treatment year, comorbidities were small, on average a total of 1.2 (SD

1.2) comorbidities and a Charlson-Deyo comorbidity index of 1.6 (SD 1.9), and did not vary significantly across vaginal estrogen forms. Mammography, a measure of quality of care, was received by 42% of women in the pre-treatment year and 59% of women in the

2-year time period covering both the pre-treatment year and the year following initiating

54 vaginal estrogen therapy. Systemic estrogen therapy in the pre-treatment period was received by 23% of VT users compared to 16% of VC users, a significant difference among patient baseline characteristics. Measures of health care utilization such as number of outpatient visits, emergency room visits, hospitalizations, and prescriptions proved to be similar for both vaginal estrogen forms. VT users had significantly more

OB/GYN visits, and VC users had significantly more PCP visits. Overall, the number of women initiating vaginal estrogen therapy decreased annually from 2004 to 2006; however, vaginal tablet use peaked in 2005.

Cream All Tablet p Variable (Estrace & Patients (Vagifem) value Premarin) n (% of total) 1,505 160 (10.4) 1,345 (89.6) Age (years) at index, years, 53.2 (6.6) 53.6 (6.4) 53.1 (6.6) 0.426 mean (SD) 40-44 years, n (%) 182 (12.1) 18 (11.3) 164 (12.2) 45-49 years, n (%) 292 (19.4) 30 (18.8) 262 (19.5) 50-54 years, n (%) 337 (22.4) 36 (22.5) 301 (22.4) 0.993 55-59 years, n (%) 377 (25.0) 42 (26.3) 335 (24.9) 60-64 years, n (%) 317 (21.1) 34 (21.3) 283 (21.0) Black race, n (%) 458 (30.4) 56 (35.0) 402 (29.9) White race, n (%) 846 (56.2) 82 (51.3) 764 (56.8) 0.555 Other race, n (%) 42 (2.8) 5 (3.1) 37 (2.8) Unreported race, n (%) 159 (10.6) 17 (10.6) 142 (10.6) Charlson-Deyo comorbidity index, 1.6 (1.9) 1.7 (2.1) 1.5 (1.8) 0.381 mean (SD) Mammography in pre-initiation year 624 (41.5) 77 (48.1) 547 (40.7) 0.070 (yes/no), n (%) Mammography in either pre- or post- 882 (58.6) 109 (68.1) 773 (57.5) 0.010 initiation year (yes/no), n (%) Systemic estrogen use (yes/no), n (%) 299 (16.3) 43 (22.8) 252 (15.5) 0.009 No. of outpatient visits, mean (SD) 44.7 (62.1) 38.3 (52.5) 45.4 (63.1) 0.173 OB/GYN visits, mean (SD) 0.7 (1.8) 1.0 (1.9) 0.7 (1.8) 0.029 PCP visits, mean (SD) 8.6 (8.7) 7.3 (7.1) 8.7 (8.8) 0.045 OB/GYN + PCP visits, mean (SD) 9.3 (8.9) 8.3 (7.5) 9.4 (9.0) 0.126 Index year 2004, n (%) 598 (39.7) 51 (31.9) 547 (40.7) 2005, n (%) 580 (38.5) 66 (41.3) 514 (38.2) 0.070 2006, n (%) 327 (21.7) 43 (26.9) 284 (21.1)

Table 4.1: Pre-Treatment Characteristics of Women on Vaginal Estrogen Therapy.

4.2 Prescribing Physician Specialty

OB/GYNs prescribed approximately 23% of vaginal estrogen index medications and prescribed a greater proportion of the VT prescriptions than VC prescriptions (25% vs. 19%, p = 0.069) (Table 4.2). After adjusting for confounding variables, OB/GYNs were no more likely to prescribe VTs than other prescribers (Table 4.3). However, women who used systemic estrogen therapy in the pre-treatment year were more likely to use VTs than VCs (odds ratio (OR) 1.8, p = 0.008), as were women who had received mammography in the pre- or post-treatment years (OR 1.5, p = 0.022). Compared to the index year (2004), VTs were also more likely to be prescribed in later years (2005, OR

1.5, p = 0.042; 2006, OR 1.6; p = 0.042).

Cream Tablet p Outcome All Patients (Estrace & (Vagifem) value Premarin) n (% of total) 1,505 160 (10.6) 1,345 (89.4) OB/GYN prescriber, n (%) 295 (23.3) 40 (25.0) 255 (19.0) 0.069

Table 4.2: Prescribing Physician Specialty (OB/GYN) by Vaginal Estrogen Form.

Tablet (Cream referent) Variable n = 1,505 Unadjusted Adjusted* Odds Ratio p value Odds Ratio p value OB/GYN prescriber 1.146 0.306 1.360 0.123 Age, years 1.016 0.057 1.014 0.281 Race white (referent) 1 - 1 - Black 0.961 0.826 0.848 0.543 Other/unreported 1.179 0.173 0.764 0.149 Charlson-Deyo comorbidity index 0.950 0.083 1.064 0.167 in pre-treatment year Number of outpatient visits 1.000 0.760 0.997 0.055 in pre-treatment year Systemic estrogen use 1.202 0.212 1.769 0.008 in pre-treatment year Mammography 1.151 0.196 1.519 0.022 in pre- or post-treatment year Index year, 2004 (referent) 1 - 1 - 2005 0.802 0.068 1.501 0.042 2006 0.810 0.140 1.574 0.042

* Multivariable logistic regression analysis

Table 4.3: Unadjusted and Adjusted Parameter Estimates for Prescribing/Receiving

Tablet vs. Cream Vaginal Estrogen.

4.3 Patterns of Medication Use

Table 4.4 shows various measures of medication use patterns. Compared to VC users, VT users more often refilled their first prescriptions (VT: 48.8%, VC: 35.3%, p = 0.001). Interestingly, VT users also more often switched to VC compared to VC users switching to VTs (VT: 4.4%, VC: 1.7%, p = 0.023). Among those who refilled their index prescription at least once in the first year post-index, VT users showed a higher but not significant MPR (VT: mean 0.309, SD 0.193; VC: mean 0.274, SD 0.178; p = 0.117) and a significantly higher treatment duration (VT: mean 136.6 days, SD 138.2;

VC: mean 111.4 days, SD 127.6; p = 0.020). However, among those who refilled their index prescription at least once over a 2 year time window, there was no significant difference in treatment duration (VT: mean 296.3 days, SD 222.2; VC: mean 279.7 days,

SD 223.3; p = 0.607). Kaplan Meier curves of time to treatment discontinuation

(equivalently, treatment duration) are shown in Figure 4.1 for vaginal estrogen treatment during the first year (any number of prescriptions fills) and in Figure 4.2 for treatment among women with 2 or more years of continuous eligibility and at least one prescription refill. Figure 4.1 illustrates that, among all women with a vaginal estrogen prescription, there is a 25% probability that VC users will discontinue treatment at approximately 175 days, whereas VT users have a 25% chance of discontinuing treatment at approximately

275 days. But as Figure 4.2 shows, for women who refill their vaginal estrogen prescription at least once, treatment duration measured over two years shows very little difference in the discontinuation rate between VT and VC users.

Index Medication Outcome Tablet Cream p n = 160 n = 1,345 value Refilled index prescription at least once in first 78 (48.8) 475 (35.3) 0.001 year post-index, n (%) Switched to the other form of VE in first year 7 (4.4) 23 (1.7) 0.023 post-index, n (%) MPR during 1 year post index continuous n=72 n=461 eligibility, 2+ fills Mean (SD) 0.309 (0.193) 0.274 (0.178) 0.117 Median 0.230 0.167 Range [min, max] [0.088, 0.899] [0.107, 1] Treatment duration (days), 1 year post-index n=160 n=1,345 continuous eligibility, 1+ fills Mean (SD) 136.6 (138.2) 111.4 (127.6) 0.020 Median 52.5 30 Range [min, max] [28, 365] [30, 365] Treatment duration (days), 2 years post-index n=56 n=351 continuous eligibility, 2+ fills Mean (SD) 296.3 (222.2) 279.7 (223.3) 0.607 Median 213.5 206 Range [min, max] [51, 730] [37, 730]

Table 4.4: Medication Use Outcomes by Vaginal Estrogen Form: Refill Patterns,

Medication Possession Ratio (MPR), and Treatment Duration.

Kaplan-Meier survival estimates 1.00 0.75 0.50 0.25 0.00 0 100 200 300 400 analysis time

cream tablet

Figure 4.1: Time to vaginal estrogen treatment discontinuation during the 1 st year.

Kaplan-Meier survival estimates 1.00 0.75 0.50 0.25 0.00 0 200 400 600 800 analysis time

cream tablet

Figure 4.2: Time to vaginal estrogen treatment discontinuation for women with 2 or more years of continuous eligibility and at least one prescription refill.

Adjusting for confounding variables (Table 4.5), VT users remained more likely to refill their index prescription than VC users (OR 1.8, p = 0.001). Older age was also significantly associated with increasing odds of refilling the initial prescription (OR 1.02, p = 0.025; that is, 2% greater odds of refilling for every 1 year increase in age).

Reducing the odds of refilling the index prescription was a higher Charlson Comorbidity

Index (OR 0.94, p = 0.050). Also, women initiating therapy in 2005 were significantly less likely to refill their index prescription compared to 2004 (OR 0.8, p = 0.044).

Refilled Prescription (yes/no) Variable n = 1,505 Unadjusted Adjusted* Odds p p Coefficient Ratio value value Tablet (cream referent) 1.742 0.001 1.766 0.001 Age, years 1.016 0.057 1.019 0.025 Race white (referent) 1 - 1 - Black 0.961 0.826 0.935 0.711 Other/unreported 1.179 0.173 1.185 0.168 Charlson-Deyo comorbidity index in pre- 0.950 0.083 0.941 0.050 treatment year Number of outpatient visits 1.000 0.760 1.000 0.757 in pre-treatment year OB/GYN prescriber 1.146 0.306 1.124 0.391 Mammography in either pre-treatment year or 1.151 0.196 1.110 0.348 post index year Systemic estrogen use 1.202 0.212 1.044 0.823 in pre-treatment year Systemic estrogen use 1.249 0.120 1.225 0.278 in post-index year Index year, 2004 (referent) 1 - 1 - 2005 0.802 0.068 0.780 0.044 2006 0.810 0.140 0.762 0.063

* Multivariable logistic regression analysis

Table 4.5: Unadjusted and Adjusted Parameter Estimates for Predicting Index

Medication Refill.

MPR was slightly higher for VT users in the first year post-index, although insignificant (Table 4.6). If fact, none of the available variables proved significant in predicting MPR. Similarly, treatment duration among women with two years of continuous eligibility post-index and having at least one refill did not show a significant difference between VT and VC users (Table 4.7). Women receiving their vaginal estrogen index prescription from an OB/GYN showed significantly higher treatment duration, approximately 60 more days (p = 0.033). However, compared to 2004, women receiving their index prescription in 2005 used their vaginal estrogen product 64 days less

(p < 0.001).

MPR in 1st year, 2 or more Index Variable Prescription Fills Unadjusted Adjusted* Odds p value Coefficient p value Ratio Tablet (cream referent) 0.036 0.117 0.039 0.092 Age, years 0.001 0.434 0.001 0.338 Race white (referent) 0 - 0 - Black -0.022 0.400 -0.022 0.409 Other/unreported 0.016 0.382 0.022 0.229 Charlson-Deyo comorbidity index in pre- 0.005 0.325 0.005 0.312 treatment year Number of outpatient visits <0.001 0.500 <0.001 0.460 in pre-treatment year OB/GYN prescriber -0.018 0.353 -0.022 0.263 Mammography in either pre-treatment 0.017 0.279 0.015 0.371 year or post index year Systemic estrogen use 0.035 0.100 0.045 0.100 in pre-treatment year Systemic estrogen use 0.015 0.469 -0.011 0.663 in post-index year Index year, 2004 (referent) 0 - 0 - 2005 -0.030 0.086 -0.034 0.058 2006 -0.001 0.705 -0.015 0.479 Constant - - 0.195 0.004

* OLS regression analysis

Table 4.6: Parameter Estimates for Predicting Medication Adherence to Index

Medication.

Treatment Duration (days) during First 2 Years, Variable 2 or More Index Prescription Fills Unadjusted Adjusted* Odds Ratio p value Coefficient p value Tablet (cream referent) 16.535 0.607 20.072 0.536 Age, years -2.382 0.156 -2.248 0.181 Race white (referent) 0 - 0 - Black 50.569 0.183 49.655 0.188 Other/unreported 26.895 0.279 35.108 0.159 Charlson-Deyo comorbidity index -7.531 0.222 -7.360 0.239 in pre-treatment year Number of outpatient visits -0.074 0.687 0.015 0.935 in pre-treatment year OB/GYN prescriber 60.814 0.029 59.608 0.033 Mammography in either pre- 31.491 0.160 28.314 0.215 treatment year or post index year Systemic estrogen use 4.794 0.881 -1.813 0.963 in pre-treatment year Systemic estrogen use -4.220 0.888 -9.500 0.797 in post-index year Index year, 2004 (referent) 0 - 0 - 2005 -67.252 0.002 -64.269 0.004 2006 - - - - Constant - - 387.762 <0.001

* OLS regression analysis

Table 4.7: Parameter Estimates for Predicting Treatment Duration with Index

Medication.

4.4 Health Care Utilization

Unadjusted outcomes of health care utilization measures in the first year post- index (Table 4.8) are slightly higher, but insignificant, for VC compared to VT index prescriptions, in terms of both number of OB/GYN plus PCP visits and costs, and cost of vaginal estrogen prescriptions. However, the number of vaginal estrogen prescriptions filled is significantly higher among VT users (VT: mean 2.55, SD 2.43; VC: mean 1.85,

SD 1.86; p < 0.001).

Tablet Cream p Outcome n=160 n=1345 value 7.17 7.43 Number of OB/GYN + PCP visits, mean (SD) 0.720 (9.39) (8.65) Number of vaginal estrogen index medication 2.55 1.85 <0.001 prescriptions, mean (SD) (2.43) (1.86) $717 $627 Cost of OB/GYN + PCP visits, mean (SD) 0.330 ($1,255) ($1,087) Cost of vaginal estrogen index medication $125 $130 0.619 prescriptions, mean (SD) ($123) ($133)

Table 4.8: Health Care Service Utilization and Cost Outcomes, One Year Post-

Index by Vaginal Estrogen Form.

After adjusting for confounders, VT users showed no significant difference in number of OB/GYN plus PCP visits compared to VC users (Table 4.9). Several covariates, however, contributed to higher OB/GYN plus PCP visits: black race

(coefficient ( β) = 0.278, p = 0.011), Charlson Comorbidity Index (β=0.135, p < 0.001),

OB/GYN prescriber ( β=0.506, p < 0.001), mammography ( β=0.307, p < 0.001), and 68 index prescription in year 2006 ( β=0.389, p < 0.001). Increasing age resulted in fewer

OB/GYN plus PCP visits ( β=-0.018, p < 0.001).

Number of OB/GYN + PCP Visits* Variable Unadjusted Adjusted* Coefficient p value Coefficient p value Tablet (cream referent) -0.036 0.753 -0.165 0.132 Age, years -0.012 0.029 -0.018 <0.001 Race white (referent) 0 - 0 - Black 0.262 0.022 0.278 0.011 Other/unreported 0.224 0.004 0.230 0.002 Charlson-Deyo comorbidity index 0.134 <0.001 0.135 <0.001 in pre-treatment year Number of outpatient visits 0.002 <0.001 0.001 0.074 in pre-treatment year OB/GYN prescriber 0.516 <0.001 0.506 <0.001 Mammography in either pre- 0.308 <0.001 0.307 <0.001 treatment year or post index year Systemic estrogen use 0.035 0.721 -0.086 0.449 in pre-treatment year Systemic estrogen use 0.173 0.068 0.113 0.309 in post-index year Index year, 2004 (referent) 0 - 0 - 2005 -0.107 0.173 -0.124 0.101 2006 0.401 <0.001 0.389 <0.001 Constant N/A N/A 2.142 <0.001

* Negative binomial regression analysis

Health Care Service Utilization and Cost Outcomes, One Year Post-Index by

Vaginal Estrogen Form.

VT users filled significantly more index prescriptions than did VC users

(β=0.322, p < 0.001) (Table 4.10). Older age also contributed to a higher number of

index prescription fills ( β=0.009, p = 0.008). Compared to 2004, fewer index

prescriptions were filled in 2005 ( β=-0.170, p = 0.001) and 2009 ( β=0.121, p = 0.038).

Number of Index Prescriptions* Variable Unadjusted Adjusted* Coefficient p value Coefficient p value Tablet (cream referent) 0.319 <0.001 0.322 <0.001 Age, years 0.008 0.018 0.009 0.008 Race white (referent) 0 - 0 - Black -0.064 0.388 -0.066 0.371 Other/unreported 0.094 0.059 0.103 0.038 Charlson-Deyo comorbidity index -0.005 0.692 -0.010 0.423 in pre-treatment year Number of outpatient visits <0.001 0.616 <0.001 0.286 in pre-treatment year OB/GYN prescriber 0.019 0.735 0.001 0.992 Mammography in either pre- 0.079 0.076 0.057 0.208 treatment year or post index year Systemic estrogen use 0.138 0.020 0.080 0.292 in pre-treatment year Systemic estrogen use 0.124 0.032 0.078 0.293 in post-index year Index year, 2004 (referent) 0 - 0 - 2005 -0.153 0.002 -0.170 0.001 2006 -0.091 0.116 -0.121 0.038 Constant N/A N/A 0.110 0.563

* Negative binomial regression analysis

Table 4.10: Parameter Estimates for Predicting Health Care Utilization: Number of Index Prescriptions.

Models for the log-transformed cost of the utilization measures show similar patterns (Table 4.11 and 4.12). The adjusted cost of OB/GYN plus PCP visits (Table

4.11) is 45% lower for VT users than VC users after adjusting for confounders

(β=-0.590, p = 0.042). Several covariates contributed to a higher cost of visits: higher

Charlson Comorbidity Index ( β=0.222, p < 0.001), OB/GYN prescriber ( β=2.381, p <

0.001), mammography ( β=1.487, p < 0.001), and index prescription obtained in 2006 compared to 2004 ( β=1.623, p < 0.001). Older age ( β=-0.062, p < 0.001) and index

prescription obtained in 2005 compared to 2004 ( β=-1.169, p < 0.001) significantly

reduced the cost of OB/GYN plus PCP visits.

Natural Log (Cost of OB/GYN + PCP Visits)* Variable Unadjusted Adjusted* Coefficient p value Coefficient p value Tablet (cream referent) -0.249 0.444 -0.590 0.042 Age, years -0.063 <0.001 -0.062 <0.001 Race white (referent) 0 - 0 - Black 0.450 0.172 0.527 0.071 Other/unreported 0.051 0.823 0.112 0.578 Charlson-Deyo comorbidity index 0.227 <0.001 0.222 <0.001 in pre-treatment year Number of outpatient visits 0.002 0.155 0.001 0.570 in pre-treatment year OB/GYN prescriber 2.840 <0.001 2.381 <0.001 Mammography in either pre- 1.842 <0.001 1.487 <0.001 treatment year or post index year Systemic estrogen use 0.263 0.350 -0.383 0.230 in pre-treatment year Systemic estrogen use 0.657 0.016 0.482 0.121 in post-index year Index year, 2004 (referent) 0 - 0 - 2005 -1.346 <0.001 -1.169 <0.001 2006 1.917 <0.001 1.623 <0.001 Constant N/A N/A 5.374 <0.001

* OLS regression analysis

Table 4.11: Parameter Estimates for Predicting Health Care Utilization: Cost of

OB/GYN + PCP Visits.

The adjusted cost of VT prescriptions is approximately 12% lower than VC prescriptions after adjusting for confounders ( β=0-.133, p = 0.019), despite more

prescriptions being filled (Table 4.12). No other covariates demonstrated a significant

72 effect on the cost of vaginal estrogen prescriptions except race coded in the claims data base as either “other” or “unreported.”

Natural Log (Cost of VE Prescriptions)* Variable Unadjusted Adjusted* Coefficient p value Coefficient p value Tablet (cream referent) -0.126 0.025 -0.133 0.019 Age, years 0.004 0.170 0.005 0.054 Race white (referent) 0 - 0 - Black -0.045 0.430 -0.055 0.335 Other/unreported 0.095 0.016 0.088 0.026 Charlson-Deyo comorbidity index -0.004 0.643 -0.004 0.646 in pre-treatment year Number of outpatient visits <0.001 0.767 <0.001 0.762 in pre-treatment year OB/GYN prescriber 0.061 0.166 0.059 0.183 Mammography in either pre- 0.032 0.364 0.031 0.384 treatment year or post index year Systemic estrogen use 0.082 0.093 0.056 0.372 in pre-treatment year Systemic estrogen use 0.087 0.067 0.054 0.378 in post-index year Index year, 2004 (referent) 0 - 0 - 2005 -0.054 0.168 -0.051 0.198 2006 -0.008 0.857 -0.016 0.732 Constant N/A N/A 4.269 <0.001

* OLS regression analysis

Table 4.12: Parameter Estimates for Predicting Health Care Utilization: Cost of

VE Prescriptions.

Chapter 5: Discussion

In this final section, the findings from this study are discussed, as well as study limitations, policy implications, and the need for future research.

5.1 Discussion

To the author’s knowledge, this is the first study to explore the relationship between form of vaginal estrogen and the medication-taking behavior of women with atrophic vaginitis in a Medicaid population. In the North Carolina, 1,505 women met the inclusion criteria, of which 1,345 used VCs and 160 VTs. While VCs became available in the 1970s and 1980s, and VTs entered the market 20 years later in 1999, the smaller number of VT prescriptions nonetheless seems disproportionate. In terms of demographic characteristics and health care service utilization, the patients in each treatment group were not significantly different. Among clinical characteristics, treatment groups were again similar, except for a significantly higher rate of systemic estrogen use among VT users (22.8% vs. 15.5%, p = 0.009) and mammography received in either the pre- or post- initiation year (68.1% vs. 57.5%, p = 0.010). A possible explanation for this difference may lie in the characteristics of the prescribers. Given that

VTs are a newer and highly effective therapy for atrophic vaginitis, clinicians who prescribe VT may be more savvy in their treatment approach to this condition. Patient

74 characteristics may also be a factor. VT users may have more severe atrophic vaginitis symptoms and require concurrent use of systemic and local estrogen to effectively relieve symptoms, or potentially the patients themselves are more savvy and willing to treat their symptoms more aggressively. Unfortunately, the influence of these prescriber and patient characteristics on use of systemic estrogen cannot be measured in a claims database study.

The results of this study showed VTs appeared to be associated with better medication-taking behavior among women with atrophic vaginitis compared to VCs.

Consistent with a recent study by Shulman and colleagues,15 VT compared to VC therapy is associated with greater medication adherence and persistence, as demonstrated by a higher rate of refilling the first prescription, greater MPR, and greater treatment duration.

These patterns appeared in both studies, despite differences in study design and methodology. While Shulman followed patients for 10 months post initiation of vaginal estrogen therapy, this study followed patients 12 months to measure MPR and the rate of refilling the first prescription, and 24 months to measure treatment duration.

Additionally, Shulman’s algorithm for medication persistence calculated the time between the first and last prescription fill, omitting the day’s supply of the last vaginal estrogen prescription fill,95 thus providing a more conservative estimate of treatment

duration. Given these methodological differences, one would expect the Shulman study

to estimate higher medication adherence and lower persistence figures. As expected,

MPR was greater when compared to this study: for VT, the Shulman study estimated

MPR as 0.82 (SD 0.26) over 10 months follow-up versus 0.31 (SD 0.19) over 12 months

75 for this study, and for VC, 0.79 (SC 0.33) versus 0.27 (SD 0.18). Similarly, treatment duration in patients with multiple vaginal estrogen prescriptions in the Shulman study was 149.1 days (SD 101.1) over 10 months versus 296.3 days (SD 222.2) over 2 years in this study for VT and 96.1 days (SD 30.0) versus 279.7 days (SD 223.3) for VC.

While some differences in MPR between this study and the Shulman study were expected, likely contributing factors to the large disparity in results are the characteristics of the study population. The Shulman study was conducted in a commercial claims database; this study employed Medicaid claims. Medicaid recipients are low income and/or disabled, making access to care and the burden of greater comorbidities obstacles in ideal medication-taking behavior. Additionally, the Medicaid population has a higher proportion of minorities than the general public. African Americans, 30% of the study population, had an average treatment duration that was 17 days less than whites.

Previous studies in the North Carolina Medicaid population have shown lower medication adherence and persistence in African American compared to whites for other chronic diseases.92 In a study of 1992 – 1994 Georgia Medicaid claims, 106 white women

had a 70% chance of being compliant with oral hormone therapy for 3 years, whereas

black women had a 60% compliance rate.

This study was the first to measure treatment duration over two years in clinical

practice. Clearly, the advantage of the two year follow-up period allowed for a more

accurate description of real world treatment duration of vaginal estrogen use than a 10

month window. Women used vaginal estrogen approximately 9 months on average and

up to the full 2 years of follow-up, confirming anecdotal reports of long-term use.

Clinical trials to date averaged 165 days, 15 a substantially shorter duration than the results of either this study or the Shulman study. Some clinicians recommend limiting the use of vaginal estrogen to 6 months, 9 the length of most clinical trials to date measuring endometrial safety. For the same reason, the FDA recommends using vaginal estrogen at the lowest dose and shortest duration required to reach treatment goals.19 However, the

North American Menopause Society recommends continuing vaginal estrogen therapy as

long as distressful symptoms remain and providing close surveillance of women with

concerning symptoms (such as spotting and breakthrough bleeding) or who may

otherwise be at high risk for endometrial cancer. 33 Observing the pattern in this study

that many women use vaginal estrogen for longer periods of time raises concern over

how carefully these patients are monitored for signs of endometrial cancer.

The measurement of MPR and treatment duration had some serious shortcomings.

The treatment duration calculation in both this study and in Shulman’s study did not

consider a maximum permissible gap on the number of days between refills and thus

raises questions about its accuracy as a measure of continuous therapeutic treatment. 95 A maximum permissible gap was not assigned here due to the wide variation in, and sometimes unrealistic reporting of, the days supply on the vaginal estrogen pharmacy claims. Additionally, clinicians may prescribe a dosing range, such as administering the tablet 1 – 3 times per week or applying 2 – 4 grams of the cream daily. Or, patients may self-adjust the dosage depending on the patients’ needs. For these reasons, together with recommendations to use vaginal estrogen for a minimal period of time, MPR and treatment duration do not appear to be a highly meaningful and appropriate measure of

77 medication use behavior for vaginal estrogen. Contrary to typical chronic disease medication use studies, women who use vaginal estrogen therapy for a shorter duration or at lower MPR could actually be thought of as compliant.

To fully understand medication-taking behavior in patients with chronic conditions, adherence and persistence are typically both measured. 95 In the case of

vaginal estrogen, additional measures such as the presence of prescription refills and

switching to another therapy are perhaps more important in providing a comprehensive

characterization of medication-taking behavior because of the difficulty in measuring

medication adherence and persistence. This study found VT users, compared to VC

users, were more likely to refill their first prescription (48.8% vs. 35.3%, p = 0.001) but

also more likely to switch to the other form of vaginal estrogen in the first year (4.4% vs.

1.7%, p = 0.023). Nonetheless, despite the many uncertainties in employing the

traditional measures of adherence and persistence for vaginal estrogen use, when taken

collectively with refill rate patterns, the medication-taking behavior measures suggest a

tendency for women to prefer the use of vaginal tablets. This data from clinical practice

support results of clinical trials evaluated in a Cochrane Collaboration meta-analysis 10 showing VT was favored to VC in ease of administration, comfort of administration, and overall acceptability. Furthermore, this study showed a 12% lower prescription costs for

VTs in the first year of use, after adjusting for the model covariates. Despite these advantages, VTs are prescribed at an 88% lower rate than VCs in the North Carolina

Medicaid population.

Vaginal estrogen medication-taking behavior is likely a result of a combination of many factors. In addition to ease of use and administration, severity of symptoms and level of symptom relief realized by different treatment modalities may influence a woman’s decision to choose to continue or discontinue with a therapy. Some women may choose to discontinue therapy because the inconveniences or risks of the medication outweigh the benefits. The estrogen plus progestin arm of the Women’s Health Initiative was halted in 2002, and later the estrogen-only arm halted in 2004, influencing many women in their decision of whether or not to initiate or halt their own hormone therapy.107 In this population, initiation of vaginal estrogen therapy decreased from 2004 to 2006, consistent with the trend for oral hormone therapy following the halt of the

Women’s Health Initiative.39

5.2 Policy Implications

The low rate of prescribing VTs along with the documented preference for VTs

over VCs among most women suggest many clinicians are not aware of this alternate

form of vaginal estrogen therapy. Lack of clinician expertise in treatment options,

however, is just one shortcoming in the process of providing women with satisfactory

treatment for atrophic vaginitis. Leading clinician-investigators report there is a need for

more open discussion between health care providers and patients regarding both the

availability of treatment for atrophic vaginitis and the treatment alternatives. 7, 108 And

before women seek treatment for atrophic vaginitis, women must first: 1) become aware

that treatments for atrophic vaginitis exist and atrophic vaginitis need not be a necessary

part of menopause, and 2) overcome embarrassment and concern about clinician

79 reactions to a discussion of such an intimate topic. Clinicians, in turn, must be willing to overcome reluctance in discussing potentially embarrassing symptoms with their patients.

Clinicians should also be aware that patient treatment preferences are very individual and may differ by race. Education of both clinicians and peri- and post-menopausal women is needed. Finally, women who use vaginal estrogen for longer than 6 -12 months require careful surveillance for signs of endometrial hyperplasia and endometrial cancer, such as spotting and break-through bleeding. Considering most women will live almost one-third of their lives post-menopause in an estrogen-deprived state, greater attention should be given to this undertreated condition.

5.3 Future Studies

While this study suggests an association between VT use and improved medication-taking behavior over VC use, further research is warranted, particularly in the areas of understanding patients’ preferences and the safety of vaginal estrogen.

Clinical studies have already measured short-term efficacy, clinical assessment, safety, and acceptability for the various forms of vaginal estrogen. Further understanding of patient preferences may be obtained from both observational studies and surveys.

Observational studies should explore prescription refill patterns of switching estrogen products, both local and systemic, and measure duration of use among those who switch.

Patterns of use by age group, race, and among patients with various diagnoses (i.e., breast cancer, postpartum, etc.) will help understand patients’ preferences for specific subgroups. A survey tool may be especially helpful to learn more about several important patient issues, for both those on vaginal estrogen therapy and those diagnosed

80 with atrophic vaginitis but not on therapy. Areas to explore include: 1) patients’ views on menopause and vaginal estrogen use, 2) awareness of atrophic vaginitis treatment options, 3) safety concerns, 4) reasons why patients chose to start and/or stop vaginal estrogen therapy, and 5) advantages and disadvantages of each form of vaginal estrogen.

Because systemic estrogen use has been associated with several health risks, particularly gynecological cancers and cardiovascular events, many researchers and clinicians fear vaginal estrogen may pose the same risks. A long-term clinical trial of vaginal estrogen use to evaluate these risks would be deemed unethical. However, a claims database study may shed some light on these safety concerns. The study may require a larger sample than the North Carolina Medicaid claims database and should have a minimum of ten years of longitudinal data to allow time for adverse events to present. To obtain adequate study power, a composite or global index could be developed. The index should include both adverse events and precursors to those events, such as diagnoses for suspicious symptoms and procedures to test for adverse outcomes.

5.4 Limitations

There are some limitations to this study worth noting. Because this is an observational study, the subjects are not randomly assigned to vaginal estrogen therapy groups and differences in patient characteristics among cohorts can be expected.

Although the model is statistically adjusted for several covariates such as age, gender, race, and a set of comorbidities and clinical factors, other confounders may still be present. The study lacks clinically important data to fully adjust for differences in severity of atrophic vaginitis between cohorts and duration of distressful symptoms.

Omitted personal health behavior characteristics which may be potential confounders include over the counter preparations, sexual activity, diet, exercise, alcohol use, smoking, and illicit drug use. This study also was not able to account for patients’ beliefs regarding menopause and treatment with hormone therapy. Similarly, physician characteristics are not available which may play a significant role in their patients’ medication use behavior. The use of sample prescriptions provided by clinicians could not be measured. The observational study design does not permit causal inference of the findings.

The study employed encounter claims from an administrative database, which may lead to misclassification errors because the accuracy of the diagnosis codes and drug codes has not been validated. The values for the days of medication supply was often less than 2 weeks, occasionally as low as 1 day, and was therefore adjusted by the author to be at least one month’s supply. The level of accuracy of calculations based on days supply, such as MPR and treatment duration, is questionable. Additionally, the study assumes that dispensed prescriptions were actually administered. Some dispensed prescription drugs may be only partially consumed or not at all.

Generalizability of the study results is limited to populations similar to that of the study. This analysis uses North Carolina Medicaid data, which is a health care program for low income and disabled populations. Medicaid patients included in this study are under age 65, whereas many women over age 65 still require relieve from atrophic vaginitis. Results may not be generalizable to a non-Medicaid population or another U.S. region.

5.5 Conclusion

Women on VT therapy were shown to be more likely to refill their initial prescription and, although not significant over a 2 year window, to have longer treatment duration, indicating a preference for this treatment modality. At the same time, health care utilization in terms of physician visits and costs were lower for VT users than VC users, and were significantly lower in vaginal estrogen prescription costs, despite number of VT prescriptions being higher. Given the increasing prevalence of atrophic vaginitis with the growing and aging population, the number of women for whom symptom relief and quality of life could be improved by using vaginal estrogen tablets has the potential to be great.

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Appendix A: Institutional Review Board Approval

From: "Schulte, Janet" To: [email protected]; [email protected] Sent: Friday, February 22, 2008 9:34:23 AM Subject: MEDICATION ADHERENCE AND RELATED ECONOMIC OUTCOMES ASSOCIATED WITH VAGINAL ESTROGEN THERAPY IN MEDICAID- ENROLLED WOMEN WITH ATROPHIC VAGINITIS

Dear Investigators,

The above project has been determined to be exempt from IRB review. The project number is 2008E0137. You may begin your data collection.

• The project “MEDICATION ADHERENCE AND RELATED ECONOMIC OUTCOMES ASSOCIATED WITH VAGINAL ESTROGEN THERAPY IN MEDICAID-ENROLLED WOMEN WITH ATROPHIC VAGINITIS” has been determined to be exempt in category # 4 - Research, involving the collection or study of existing data, documents, records, pathological specimens, or diagnostic specimens, if these sources are publicly available or if the information is recorded by the investigator in such a manner that subjects cannot be identified directly or through identifiers linked to the subjects.

• You are reminded that all persons accessing medical records under this protocol must sign the waiver of HIPAA authorization and must have completed CITI training. Records will not be released to investigators without the proper approvals.

• You are reminded that you must promptly report any problems to the Office of Responsible Research Practices.

• No procedural changes may be made in exempt research.

Janet

Janet Schulte, CIP Administrator, Office of Responsible Research Practices 614 688-0389 Phone 614 688-0366 Fax [email protected]

Appendix B: Access to North Carolina Medicaid Data Approval

Appendix C: Systemic Estrogen NDC Numbers

Ingredient Trade Name NDC Numbers ESTROGENS, CENASTIN 51129 -3199 51285 -*444 51285 -*441 51285 -*442 CONJUGATED TABLETS 51285-*443 51285-*446 54868-4879 54868-5415 SYNTHETIC A 55154-6352 62584-*173 ESTROGENS, CONJUGATED 58087 -*013 68258 -9088 44514 -*493 53002 -*365 CONJUGATED ESTROGEN 55045-1131 58087-*011 58087-*012 67228-0185 TABLETS 68258-9087 68258-9089 PREMARIN 53978 -3440 11822 -5200 67228 -0118 67228 -0119 TABLETS 67228-0120 26053-0130 26053-0131 26053-0132 26053-0133 55887-*702 00008-0867 00008-0868 00008-1100 00008-1101 00008-1102 00008-1103 00008-1104 00046-1100 00046-1101 00046-1102 00046-1103 00046-1104 00179-1172 00179-1173 00179-1239 00179-1240 00179-1779 00179-1857 00179-1905 00406-1100 00440-8170 00440-8171 00615-0588 00855-1621 11822-5207 12280-*039 12634-*409 24236-*917 35356-*109 35356-*249 35356-*250 35356-*251 51129-1972 51129-2106 51129-2441 51129-2992 51129-3623 51129-3624 51129-3834 51655-*452 52959-*222 52959-*223 53918-0190 53978-0189 53978-0190 54569-0812 54569-0813 54868-0365 54868-0451 54868-0452 54868-0453 54868-2702 54868-4865 55045-1951 55154-0211 55154-0213 55289-*047 55289-*123 55289-*943 57866-6680 57866-6681 57866-6682 58016-*745 58016-*948 58056-*159 58056-*160 58056-*161 58056-*353 58295-0867 58864-*422 60491-*514 60491-*515 60491-*516 60491-*517 61392-*619 61392-*987 62584-*864 62584-*865 62584-*866 62584-*867 62584-*868 64579-*065 64725-0270 65084-*169 65084-*225 66116-*285 66267-*174 66267-*246 66267-*247 66267-*902 66336-*599 67801-*326 67801-*427 68115-*858 68788-0866 68788-0867 PREMARIN 00046 -0749 INJECTION KIT PREMARIN 68225 -*007 INTRAVENOUS INJECTION ESTROGENS, EEMT TABLETS 26053 -0383 26053 -0135 15310 -*020 15310 -*010 ESTERIFIED ESTERIFIED 53746 -*078 53746 -*077 51991 -*078 51991 -*079 METHYL- ESTROGEN AND 13273-2805 13273-2806 68462-*173 68462-*174 TESTOSTERONE METHYL- 00527-1410 00527-1409 TESTOSTERONE TABLETS

100

Ingredient Trade Name NDC Numbers ESSIAN TABLETS 61916 -*043 66993 -*921 61916 -*044 66993 -*920 ESTRATEST H S 00032 -1023 62559 -0887 54868 -3564 58016 -*154 TABLETS 54868-3565 62559-0880 FEMTEST 10135 -4700 10135 -4690 TABLETS SYNTEST TABLETS 54868 -4771 66576 -*231 66576 -*230 ESTROGENS, ENJUVIA TABLETS 51285 -*408 51285 -*410 51285 -*407 51285 -*406 CONJUGATED 51285-*409 SYNTHETIC B ESTROGENS, ESTRATAB 12634 -*509 51129 -1483 55289 -*735 57866 -6685 ESTERIFIED TABLETS 58087-*072 MENEST TABLETS 51129 -1768 51129 -4492 53978 -3426 55045 -3484 58864-*951 60793-*837 60793-*838 60793-*839 60793-*840 61570-*072 61570-*073 61570-*074 61570-*075 ESTROGENS NATURAL 10715 -0037 ESTROGENIC SUBSTANCE PRIMESTRIN 00684 -0130 AQUEOUS ESTROGENS, PREMPHASE 54868 -3800 60491 -*903 CONJUGATED TABLETS MEDROXY- PREMPRO TABLET 51129 -2118 00046 -0875 00046 -0937 00046 -0938 PROGESTERONE 00046-0975 35356-*276 35356-*277 35356-*279 ACETATE 54868-3799 54868-5047 54868-5540 55045-2561 60491-*904 ESTRADIOL - ACTIVELLA 00169 -5174 00169 -5175 00420 -5174 00420 -5175 NORETHINDRONE TABLETS ACETATE COMBIPATCH 54868 -4831 00078 -0377 00078 -9377 00078 -0378 TRANSDERMAL 00078-9378 57616-*051 57616-*061 SYSTEM ESTRADIOL AND 51991 -*474 61916 -*050 NORTHINDRONE ACETATE TABLETS ESTRADIOL ALORA ESTRADIOL 52544 -*471 52544 -*472 52544 -*473 52544 -*884 TRANSDERMAL SYSTEM CLIMARA 00089 -0310 00089 -0309 00089 -0311 00089 -0312 ESTRADIOL 00089-0313 50419-*451 50419-*452 50419-*453 SYSTEM 50419-*454 00089-0308 54868-4089 55045-2673 TRANSDERMAL 50419-*456 50419-*459 54868-4813 00378-3350 00378-3352 59490-*350 59490-*352 59490-*360 00378-3349 00378-3351 00378-3360 00378-3361 59490-*349 59490-*351 59490-*361 101

Ingredient Trade Name NDC Numbers DIVIGEL GEL 52483 -0882 52483 -0080 ELESTRIN GEL 00482 -4900 63094 -4900 ESTRACE TABLETS 00087 -0021 00087 -0755 00087 -0756 00430 -0720 00430-0721 00430-0722 00555-1027 15548-*021 15548-*755 15548-*756 54868-0494 54868-0495 54868-4430 55154-2012 55154-2013 55289-*101 55289-*396 58016-*039 58056-*294 58056-*295 60491-*255 60491-*257 62584-*755 ESTRADERM 00078 -0480 00078 -0481 00083 -2310 00083 -2320 TRANSDERMAL 60491-*258 60491-*259 SYSTEM ESTRADIAL 55887 -*266 TABLET ESTRADIOL 26053 -0128 26053 -0129 55887 -*863 00179 -1457 TABLET 00339-6237 00339-6402 00339-6404 00378-1452 00378-1454 00378-1458 00555-0886 00591-0487 00591-0488 00591-0528 00603-3556 12634-*536 12634-*772 13411-*337 13411-*338 13411-*465 23490-0487 49999-*083 51129-2224 51129-3508 52555-*718 54348-*701 54348-*703 54569-4907 54569-4908 54868-4030 54868-4031 54868-4370 55045-2739 55289-*761 57866-3989 58016-*527 58016-*649 58864-*803 58864-*804 66267-*092 66267-*093 66267-*267 68115-*387 68115-*496 00093-1057 00093-1058 00093-1059 00555-0887 00555-0899 00603-3557 00603-3558 00904-5177 00904-5178 00904-5179 15548-*025 15548-*026 15548-*027 51285-*501 51285-*502 51285-*504 52555-*717 55160-*129 55160-*130 55160-*131 59564-*121 ESTRASORB 15456 -*325 66500 -*325 TOPICAL ESTROGEL 00051 -1028 65592 -1028 17139 -*617 EVAMIST 63094 -0215 ESTRADIOL TRANSDERMAL SPRAY GYNODIOL 00421 -0158 00421 -0748 00421 -0768 00421 -1259 TABLETS 51285-*503 VIVELLE PATCHES 57616 -*015 00083 -2325 00083 -2326 00083 -2327 TRANSDERMAL 00083-2328 54868-3795 54868-3796 57616-*011 57616-*012 57616-*013 57616-*014 VIVELLE DOT 54569 -5581 57616 -*085 00078 -0365 54868 -4862 TRANSDERM 54868-4242 54868-4243 54868-4244 54868-4920 102

Ingredient Trade Name NDC Numbers PATCH 57616 -*081 57616 -*082 57616 -*083 57616 -*084 00078-0343 00078-0344 00078-0345 00078-0346 00078-7343 00078-9344 00078-9345 00078-9346 MENOSTAR 00089 -0315 50419 -*455 ESTRADIOL TRANSDERMAL SYSTEM PATCH ESCLIM PATCH 64248 -0310 64248 -0320 64248 -033 1 64248 -0340 64248-0350 ESTRADIOL CLIMARA PRO 00089 -0327 50419 -*491 LEVONORGESTREL ESTRADIOL LEVONORGESTREL SYSTEM PATCH ESTRADIOL CLINAGEN LA 40 55553 -*244 VALERATE INJECTION DELESTROGEN 61570 -*181 61570 -*180 61570 -*182 00003 -0251 INJECTION 00003-0330 00003-0343 42023-*110 42023-*111 42023-*112 ESTRA VAL 20 30727 -*381 INJECTION ESTRADIOL 00223 -7605 00223 -7606 00223 -7607 10715 -0244 VALERATE 51193-*244 ESTRADIOL 00781 -3029 00781 -3030 00781 -3031 10715 -0026 VALERATE 10715-0027 54643-1071 54643-1072 54643-1070 INJECTION ESTRATE LA 51698 -*027 51698 -*244 INJECTION ESTRO SPAN 00684 -0118 00684 -0132 00684 -0143 ESTRADIOL DEPESTRATE 51698 -*254 CYPIONATE INJECTION DEPO ESTRADIOL 00009 -0271 54868 -1729 CYPIONATE INJECTION ESTRA -D 11001 -*254 ESTRADIOL 10715 -0254 51193 -*254 CYPIONATE ESTRADIOL 10715 -*254 10892 -*254 66466 -6851 66467 -6851 CYPIONATE INJECTION ESTRAGYN LA 5 55553 -*254 INJECTION ESTRO SPAN C 00684 -0218 ESTRADIOL DEPO TESTADIOL 00009 -0253 51698 -*257

103

Ingredient Trade Name NDC Numbers CYPIONATE INJECTION TESTOSTERONE TESTOSTERONE 10892 -*257 10715 -0257 CYPIONATE CYPIONATE - ESTRADIOL CYPIONATE INJECTION ESTRADIOL DUO -SPAN 00684 -0202 00684 -0102 VALERATE TESTOSTERONE ENANTHATE ESTRADIOL FEMTRACE 00430 -0391 61916 -*040 61916 -*041 61916 -*042 ACETATE TABLETS 00430-0389 00430-0390 ESTRADIOL ORTHO PREFEST 00107 -1840 NORGESTIMATE TABLETS PREFEST TABLETS 51285 -*088 ESTRADIOL TESTOSTERONE 51193 -*360 VALERATE ENANTHATE TESTOSTERONE ESTRADIOL ENANTHATE VALERATE INJECTION TESTRADIOL L A 00463 -1085 INJECTION ESTROPIPATE ESTROPIPATE 55887 -*324 00378 -4551 00378 -4553 00555 -0727 TABLET 00555-0728 00591-0414 00591-0415 00591-0416 00603-3559 00603-3560 00603-3561 13411-*466 13411-*467 51129-1940 51129-2049 51129-4248 51129-4490 51285-*010 51285-*876 54868-4149 54868-4761 66267-*094 66336-*977 00555-0729 00677-1508 00677-1509 55160-*134 55160-*135 55160-*136 OGEN TABLET 55154 -3927 00009 -3772 00009 -3773 00009 -3774 00074-3943 00074-3946 00074-3951 00339-5269 12634-*512 51129-1568 54868-1261 54868-1262 55045-2059 58056-*357 58056-*358 60491-*476 60491-*477 60491-*478 ORTHO EST 00062 -1800 00062 -1801 54868 -3672 54868 -3673 TABLETS 64248-*101 64248-*102 DROSPIRENONE ANGELIQ TABLETS 12866 -0483 50419 -*483 64259 -0483 ETHINYL ESTRADIOL ETHINYL ESTINYL TABLET 65084 -*165 62584 -*298 62584 -*299 ESTRADIOL ETHINYL FEMHRT TABLETS 00071 -0144 00430 -0145 00430 -0544 51285 -*144 ESTRADIOL 51285-*145 104

Ingredient Trade Name NDC Numbers NORETHINDRONE ACETATE

105