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Title Predictors of No-Show Appointment Status in the Prenatal Genetic Counseling Setting

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Author Sabrowsky, Sonia Marie

Publication Date 2021

Peer reviewed|Thesis/dissertation

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UNIVERSITY OF CALIFORNIA, IRVINE

Predictors of No-Show Appointment Status in the Prenatal Genetic Counseling Setting

THESIS

submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

in Genetic Counseling

by

Sonia Marie Sabrowsky

Thesis Committee: Professor Emeritus Moyra Smith, MD, PhD, Chair Adjunct Professor Pamela Flodman, MSc, MS Assistant Clinical Professor Rebecca LeShay, MS

2021

© 2021 Sonia Marie Sabrowsky

DEDICATION

To

the ones I call family

both those that I share genes with, and all the others I share my life with.

ii TABLE OF CONTENTS

Page

LIST OF FIGURES v

LIST OF TABLES vi

ACKNOWLEDGMENTS vii

ABSTRACT OF THESIS viii

I. INTRODUCTION 1 1.1 Prenatal genetics 1 1.2 Prenatal genetic counseling protocol 3 1.3 Barriers to genetic counseling 5 1.4 Barriers to attending scheduled clinic visits 6 1.5 Impact of non-attendance on patients and the healthcare system 7 1.6 Aims of this study 9

II. METHODS 11 2.1 IRB protocol 11 2.2 Retrospective chart review 11 2.2.1 Patient chart selection 11 2.2.2 Data collected from patient records in the medical records database 12 2.2.3 Additional data cleaning and corrections 15 2.3 Data analysis 17

III. RESULTS 18 3.1 Descriptive data 18 3.1.1 Demographics of participants 18 3.1.2 Clinical characteristics of participants 20 3.2 Univariate analysis of factors that may predict patient attendance to scheduled visits 35 3.3 Multivariate analysis 41

iii IV. DISCUSSION 48 4.1 Evaluation of no-show rate and factors associated with attendance status 48 4.2 Limitations of the study and future research directions 57 4.3 Conclusions 59

V. REFERENCES 60

APPENDIX A: Frequencies of races/ethnicities among patients 65

APPENDIX B: Frequencies of preferred languages among patients 66

APPENDIX C: Distribution of gestational age in weeks among patients 67

APPENDIX D: Frequencies of referral indications among patients 68

APPENDIX E: Frequencies of modes of counseling 69

APPENDIX F: Backward stepwise logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling, Step 5 70

iv LIST OF FIGURES

Page

Figure 1 Distribution of age at estimated date of delivery (EDD) among patients 23

Figure 2 Frequencies of races/ethnicities among patients 24

Figure 3 Frequencies of preferred languages among patients 25

Figure 4 Distribution of distance from patients’ residence to clinic 26

Figure 5 Distribution of median family income by patients’ ZIP codes 27

Figure 6 Frequencies of gestational age by trimester among patients 28

Figure 7 Distribution of number of total pregnancies among patients 29

Figure 8 Distribution of number of living children among patients 30

Figure 9 Frequencies of primary referral indications among patients 31

Figure 10 Frequencies of referring provider types among patients 32

Figure 11 Frequencies of primary payor types among patients 33

Figure 12 Distribution of lead time for scheduled visits 34

v LIST OF TABLES

Page

Table 1 Demographic characteristics of the study sample 19

Table 2 Clinical characteristics of the study sample 21

Table 3 Comparison of demographic characteristics between subjects who attended and did not attend their scheduled clinic visits 37

Table 4 Comparison of clinical characteristics between subjects who attended and did not attend their scheduled clinic visits 38

Table 5 Comparison of demographic and clinical characteristics with combined categories between subjects who attended and did not attend their scheduled clinic visits 40

Table 6 Full logistic regression of no-show status on characteristics of Pregnant patients scheduled for genetic counseling 43

Table 7 Significance of backward stepwise regression at each step 46

Table 8 Final reduced logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling 46

vi ACKNOWLEDGEMENTS

I would first like to thank my thesis committee for their combined brainpower and continued support throughout this research process. I am thankful for my committee chair, Dr. Moyra Smith, who consistently challenged me to think big picture and consider how our research can serve our patients’ best interests. Her humbleness, kindness, and brilliance have been invaluable contributions to this research project, my graduate training, and the larger medical community. I am also grateful for Pamela Flodman and her countless hours of one-on-one mentorship, not only in this research effort but in my broader academic and clinical training. Any statistical understanding I have gained, as well as any professional successes I may achieve, are due in large part to her guidance. Additionally, I would like to thank Rebecca LeShay for contributing her clinical expertise in guiding the development of this project and interpretation of results. I appreciate the gentle, yet immensely constructive feedback in both this research and in the clinic, which encouraged me to think more deeply and expand the limits of my comfort zone.

I would also like to thank Dr. Kathy Osann for her statistical expertise and willingness to answer my endless questions with patience and kindness. Although not formally on my thesis committee, she has been an invaluable member of the team.

To my parents, who have supported me in every personal, academic, and professional decision, thank you for providing a solid foundation for me to grow and encouraging me to take risks when I would have rather played it safe. Any positivity I contribute to the world is a reflection of your love and guidance.

I am grateful for my friends who have endured countless phone calls when the idea of completing a master’s thesis seemed an impossible feat. I am especially thankful for my best friend, Courtney, who has kept me grounded since the sixth grade and inspires me to care for my patients as much as she cares for hers. I am also particularly grateful for Meghan; I couldn’t have asked for a better roommate, friend, and mentor to navigate graduate school and survive a global pandemic with.

Finally, I would like to thank my classmates. Your adaptability, resilience, and ingenuity inspires me every day. Thanks to each and every one of you for being dependable sounding boards, reliable birthday planners, and enthusiastic cheerleaders. I am so proud to be part of the UCI Genetic Counseling Class of 2021.

vii ABSTRACT OF THESIS

Predictors of No-Show Appointment Status in the Prenatal Genetic Counseling Setting

by

Sonia Marie Sabrowsky

Master of Science in Genetic Counseling

University of California, Irvine, 2021

Dr. Moyra Smith, Chair

The negative effects of missed clinic appointments are well-studied, and certain predictive factors, such as longer lead times and lack of private insurance, have been associated with no-show behavior across a breadth of clinical specialties. However, little is known about the relationship between patient attendance and clinical characteristics specific to the prenatal genetic counseling setting. The current study examined demographic and clinical data from

3,461 pregnant patients scheduled for prenatal genetic counseling to explore factors that may predict whether a patient is more or less likely to no-show to their scheduled appointment.

Logistic regression analyses demonstrated that patients were more likely to no-show if they were in the third trimester of pregnancy, had two or more living children, had a lead time of two weeks or more, were referred primarily for a personal or family history of a possible genetic condition, and/or were referred by an outside provider. Patients were less likely to no-show if they were referred for an abnormal ultrasound, had multiple referral indications, had an ultrasound scheduled the same day, and/or their visit was being paid for by a private

viii insurance plan. This study provides additional support to current literature on no-show behavior and introduces variables specific to the unique patient population served by prenatal genetic counselors. Understanding predictors of patient attendance allows for the identification of patients at the highest risk to no-show to their scheduled appointments and provides insight for the potential development of targeted interventions to effectively mitigate this risk.

ix I. INTRODUCTION

1.1 Prenatal genetics

With an increasing number of settings in which genetics specialists play an important role in patient care, the expanding development of prenatal screening and diagnostic tools continues to highlight the importance of prenatal genetic counseling. Genetic counseling can be described as a communication process between patient and counselor that focuses on education and decision making related to the risk and implications of a genetic disorder in a family (American

Society of Human Genetics, 1975). In the prenatal setting, genetic counselors perform risk assessments based on pregnancy and family history information, help patients make informed decisions regarding prenatal screening and diagnostic testing, and facilitate patient understanding of relevant genetic diagnoses (NSGC, 2008). While prenatal genetic counselors typically conduct sessions independently, they collaborate closely with other members of the patient’s care team and use their communication skills to assist in the clear, empathetic delivery of difficult news in the event of abnormal results or ultrasound findings (Menzel, et al., 2018). There are several reasons a pregnant patient or expecting couple may be referred for genetic counseling.

Indications for prenatal genetic counseling may include (Norton & Chard, 2019):

● Maternal age of 35 years or older at the estimated date of delivery

● Maternal exposure to substance(s) that may cause physical or functional birth defects

● History of infertility or recurrent (two or more) pregnancy losses

● Consanguinity

● Personal or family history of a genetic disorder

● Previous child with a genetic condition or birth defect

● Abnormal ultrasound findings or prenatal screening results

1 Demand for prenatal genetic counseling is driven by the fact that significant complications during pregnancy are not uncommon. It is estimated that major structural or genetic birth defects are seen in 3-5% of live births, with chromosome anomalies affecting approximately 1 in 150 live births (CDC, 2008; Nussbaum et al., 2007). Additionally, the risk of a chromosomal anomaly increases with maternal age, and there has been a societal shift toward later motherhood in recent decades (Dailey et al., 1996; Pew Research Center, 2018). With this steady trend, there is an increasing number of women who may benefit from the education and options provided during genetic counseling to make informed decisions about their pregnancies.

Prenatal testing to assess for abnormalities can generally be categorized as screening or diagnostic. Screening tests such as carrier screening, ultrasound, serum screening, and cell-free

DNA are non-invasive and aim to assess fetal risk for birth defects and genetic disorders

(Carlson & Vora, 2017). Carrier screening can evaluate parental carrier status for a variety of autosomal recessive conditions, such as cystic fibrosis and spinal muscular atrophy, as well as several X-linked conditions, such as fragile X syndrome and Duchenne muscular dystrophy.

Fetal ultrasounds have the ability to detect soft anatomic markers, which may suggest increased risk for chromosomal aneuploidy, as well as many birth defects (Rayburn, Jolley, & Simpson,

2015). Screening for aneuploidy can also be performed via collection of a maternal blood sample to assess fetal risk. This may entail measurement of specific maternal serum analytes sampled during a defined gestational age window to provide a risk estimate for certain conditions, or it may involve the molecular evaluation of cell-free DNA (cfDNA) from the placenta to identify pregnancies at significantly increased risk for common chromosome anomalies (Carlson & Vora,

2017). Among screening tests, cfDNA has the highest available detection rate of trisomy 13, 18,

2 21, and sex chromosome abnormalities (Gil et al., 2017). However, screening cannot provide a fetal diagnosis of a genetic disorder or rule out a genetic disorder.

In contrast, invasive diagnostic testing, such as chorionic villus sampling (CVS) and amniocentesis, allows for the direct analysis of placental or fetal cells with the highest accuracy possible prenatally to inform patients whether their pregnancy is affected by a particular genetic condition (Carlson & Vora, 2017). Due to the invasive nature of diagnostic testing, there are procedure-related miscarriage risks, with rates typically quoted around 1 in 455 for CVS and 1 in

900 for amniocentesis (Akolekar et al., 2015). However, recent research suggests the actual risks may be much lower than traditionally quoted to patients (Salomon et al., 2019). Given the growing variety of prenatal screening and testing methods, accurate counseling that incorporates discussion of the benefits and limitations of each option, along with integration of the patient’s goals and values, is a key aspect of the informed decision making process when patients are faced with the personal challenge to choose how much information is the right amount of information during pregnancy.

1.2 Prenatal genetic counseling protocol

The information discussed during a genetic counseling session may vary depending on a patient’s referral indication, but sessions often include (NSGC, 2008):

● Identification of patient questions and concerns

● Review of pregnancy, exposure, medical, and family history

● Risk assessment and explanation of the chance to have a child with a given genetic

condition or birth defect

● Discussion of and assistance with making decisions about screening and testing options

3 ● Explanation of screening or testing results

● Provision of short-term counseling and emotional support

● Referral to support and community resources, as needed

These responsibilities of the genetic counselor allow for the identification of pregnancies at increased risk for genetic conditions, with the goal of informing reproductive decisions

(Ioannides, 2017).

One important decision pregnant patients face is whether or not to pursue further genetic screening or testing, and if so, what kind. While there may be a historical perception that the purpose of prenatal testing is focused solely on the option of pregnancy termination, many healthcare providers agree that the goal of prenatal genetic testing is to improve outcomes for women and families by providing them with the ability to choose the outcome that is best for them (Dukhovny & Norton, 2018). For some patients, pregnancy termination may result in the best outcome given their values, goals, and personal situation. But the benefits of prenatal testing may also include reassurance in the event of normal results, the ability to identify disorders where prenatal treatment may be effective, or the option to preemptively plan for any changes in delivery location or medical personnel that may help optimize neonatal outcome (ACOG 2016).

The American College of Obstetricians and Gynecologists (ACOG) provides guidance through

Practice Bulletins and Committee Opinions for healthcare providers who are responsible for discussing prenatal screening and diagnostic testing with patients. Obstetric care providers, such as genetic counselors and obstetrician-gynecologists, should discuss the benefits and limitations of the available screening and diagnostic testing with all patients so each one can make a personal, informed choice whether to accept or decline testing based on accurate information, their unique clinical context, and their values and goals (Rose et al., 2020). This guideline

4 applies in the context of screening or diagnostic testing for fetal chromosome abnormalities, as well as carrier screening (ACOG, 2017).

One way in which patients are given the opportunity to make personal choices about their testing options is through nondirective counseling. ACOG advises that both pretest and posttest counseling should be conducted clearly, objectively, and in a nondirective manner in order to provide patients with the ability to understand the information provided and make informed decisions (ACOG, 2017). However, this does not mean patients are alone in making difficult decisions. Shared decision making is a mutually beneficial approach in healthcare where providers and patients collaborate so providers can understand what matters most to individual patients and patients can feel educated, supported, and guided toward making informed decisions consistent with their values (Elwyn et al., 2012). Through shared decision making, genetic counselors can help increase patient knowledge, reduce decisional conflict, and improve patient healthcare experience related to prenatal screening and testing (Birch et al., 2018).

1.3 Barriers to genetic counseling

Despite the described benefits of genetic counseling and shared decision making when faced with prenatal screening and testing options, there may be barriers to accessing or accepting genetic counseling services for some patients. These barriers have been more frequently studied in the cancer genetic counseling setting where patients may be seen for suspected hereditary cancer syndromes. At Baylor University Medical Center, one research study found that a primary reason high risk patients with active cancer may not receive genetic counseling and testing was due to a lack of referral from their physician (Swink et al., 2019). Another study showed that among patients who received referral letters mailed both to their homes and to their primary care

5 providers, only 8% completed a genetic counseling appointment within the following year (Kne et al., 2017). The same study identified patient-perceived obstacles that deterred those who did not complete genetic counseling from utilizing their referral, including limited utility, limited understanding of genetic counseling, concerns about the process, and concerns about the expense

(Kne et al., 2017).

Additionally, given the history of low participation of under-represented minority populations in clinical genetic services, there are some challenges that may disproportionately affect minority populations (Halbert & Harrison, 2018). In addition to limited awareness of genetic counseling, specific concerns such as unfamiliarity with Western medicine, the potential for inappropriate use of genetic information, and the prioritization of familial responsibilities over personal healthcare have been identified as barriers to genetic counseling and testing among different ethnic minority groups (Glenn, Chawla, & Bastani, 2012). In the prenatal setting specifically, Asian women have self-identified potential cultural factors such as lack of available resources, societal shame and stigma, and family pressure which influence attitude toward topics of prenatal testing and termination that may be anticipated to be discussed during a genetic counseling visit (Tsai et. al, 2017).

1.4 Barriers to attending scheduled clinic visits

Once a referral is made and an appointment is scheduled, there can be barriers to successfully attending scheduled clinic visits, as well. No-show appointments, or appointments where the scheduled patient fails to attend, occur across medical specialties. No-show rates vary dramatically across clinic specialties and locations, with one systematic literature review revealing an average published no-show rate of 23%, ranging from 4.0% at intravenous therapy

6 clinics and 79.2% at physiotherapy clinics (Dantas et al., 2018). Patient demographics, appointment characteristics, and other relevant factors have been studied to investigate potential predictors of no-show behavior. Across specialties, younger adult age, lower socioeconomic status, further travel distance to clinic, and lack of private insurance have been found to be more frequently associated with failure to attend scheduled clinic visits (Dantas et al., 2018).

Additionally, one of the most commonly reported significant predictors of non-attendance is high lead time, or the time a patient must wait between making their appointment and being seen in clinic (McMullen & Netland, 2015; Dantas et al., 2018; Shaw et al., 2018). Prior no-show history is the other most commonly reported determinant of non-attendance (Dantas et al., 2018).

Focusing on patients with a history of non-attendance to 3 or more clinic appointments, one study identified age, race, and income as factors significantly related to repeated no-show appointments (Miller et al., 2015). Thus far, published information is not available to provide insight into predictive factors of non-attendance in the prenatal genetic counseling setting specifically, where unique time-sensitive factors such as gestational age are relevant.

1.5 Impact of non-attendance on patients and the healthcare system

It is well understood that missed clinic appointments impact both the clinic and its patients. Clinic productivity and workflow is negatively impacted by missed clinic appointments, with one family practice residency clinic reporting that no-show appointments accounted for

31.1% of scheduled appointments and up to 14% loss of total clinic revenue (Moore et al., 2001).

When providers are made to wait for late or absent patients, access is not only delayed for patients who arrive on time, but these clinic slots could otherwise be offered to other individuals in need of services. In clinics where medical professionals are training, missed clinic

7 appointments reduce educational opportunities, as well. Patients are also negatively impacted as high no-show rates can contribute to longer appointment lead times and result in reduced patient satisfaction and quality of care (LaGanga & Lawrence, 2007). Additionally, given that race and income have been previously identified as predictive factors of no-show behavior, the negative implications of non-attendance may disproportionately impact certain populations.

Clinics may employ multiple strategies in an effort to minimize no-show rates. Strategies that target patient behavior may include patient education to encourage understanding of the importance of their visit, reminder notifications to reduce the likelihood of forgetting a scheduled appointment, and patient sanctions, such as no-show or late-cancellation fees, to disincentivize patients from no-showing (Johnson, Mold, & Pontious, 2007). Other strategies, such as overbooking and short lead-time scheduling, also known as advanced or open access, rely on purposefully adjusting the clinic schedule itself to minimize the impact of missed appointments on clinic efficiency (Murray & Berwick, 2003). By understanding factors associated with no- show behavior, one research team actively applied no-show probabilities to demonstrate how combining patient no-show models with advanced scheduling methods can improve clinic efficiency and optimize daily clinic attendance (Daggy et al., 2010). This integrated scheduling method, referred to as Mu-Law, was applied at a Midwestern VA medical center and allowed

12% more patients to be successfully served compared to a one-patient-per-slot scheduling method (Daggy et al., 2010).

Furthermore, telehealth has become increasingly popular as an option for improving access to care and potentially decreasing no-show rates. Particularly in the field of genetics, the use of live video consultations, or telegenetics, is a successful and satisfactory mode of service for clients and clinicians (Vrecar, Hristovski, & Peterlin, 2016). The nature of telegenetics allows

8 patients to receive high quality care with increased convenience and reduced travel-associated time and costs (Zilliacus et al., 2010). However, conflicting data are available regarding the impact of telegenetics on patient attendance. One recent study on the rapid shift to telephone cancer genetic counseling due to the COVID-19 pandemic showed a 2.2% decrease in patient no-show rate at one academic medical center (Shannon et al., 2020). Alternatively, another study of four rural oncology clinics showed that patients were significantly less likely to attend telegenetics cancer genetic counseling visits than in-person visits, with lower attendance rates being statistically associated with lesser computer comfort (Buchanana et al., 2015). It is also important to consider that for some populations in which access to internet or phone services is limited, the option of telehealth may not significantly affect attendance rates because the ability to successfully participate in a remote visit would also be limited. While recent research studies help provide insight into the impact of telegenetics on cancer genetic counseling clinics, published information regarding attendance rates in the prenatal genetic counseling setting remains unavailable.

1.6 Aims of this study

Currently, published literature has not analyzed factors specific to the prenatal genetic counseling setting that may predict non-attendance to scheduled clinic visits. Descriptive information about the proportion of individuals who no-show to appointments also remains unpublished. Defining the predictive factors of non-attendance will allow for the future identification of patients who may be at an increased risk for failure to attend scheduled clinic visits. Gaining insight into this patient population is the first step toward developing targeted interventions to increase clinic attendance rates among these patients, which could help

9 maximize efficient use of clinic resources and minimize healthcare disparities. This retrospective chart review aims to investigate demographic factors, pregnancy history, and referral details from patients scheduled for prenatal genetic counseling over the last five years in order to:

1. Determine the proportion of patients who fail to attend their scheduled prenatal genetic counseling visits.

2. Identify factors that predict patient attendance or non-attendance to scheduled prenatal genetic counseling sessions.

The results of this study will provide insight into patient factors unique to the prenatal genetic counseling clinic as well as factors shared with other specialties that may be most strongly associated with no-show behavior. The hypothesis is that factors such as referral indication, referring provider, gestational age, number of living children, travel distance to clinic, and appointment type significantly influence patient attendance. These variables, along with others related to patient demographics, pregnancy history, and referral, will be compared between patients who successfully attended their scheduled clinic visits and those who failed to attend.

10 II. METHODS

2.1 IRB protocol

This research protocol was reviewed by the University of California, Irvine Institutional

Review Board and was approved on November 24, 2020 under Expedited Review: Category 5

HS# 2020-6238.

2.2 Retrospective chart review

2.2.1 Patient chart selection

The initial study sample included all pregnant patients aged 18 years and older scheduled to be seen by a prenatal genetic counselor at the UCI Center for Fetal Evaluation between

January 1, 2015 and November 24, 2020. These patients were identified through review of the

FileMaker prenatal electronic medical records database. There were 3,713 patients who met this initial criteria. Of these 3,713 patients, 145 were excluded because they cancelled their visits in advance without rescheduling and could not be appropriately categorized as showing or no- showing to their appointments. Another 107 patients were seen for preconception counseling and excluded from this study as they were not pregnant at the time of their appointments. This resulted in a final number of 3,461 patient charts reviewed. To keep identifiable patient information separate from the research information, a unique identification number (pdx#) was used for each patient. Identifiable patient information including dates and zip codes were used to calculate new variables and subsequently stored within the secured UCI Health Sciences network separately from the research data.

11 2.2.2. Data collected from patient records in the medical records database

Demographic information, pregnancy history, and appointment details for each patient were collected from the electronic medical record database, including the variables listed below.

Demographic information collected from each patient’s chart includes:

❖ Age at estimated date of delivery.

❖ Preferred language for medical appointments.

❖ Race or ethnicity as documented by the referring provider or genetic counselor. For this

study, patient race or ethnicity were categorized per the Revisions to the Standards for the

Classification of Federal Data on Race and Ethnicity as follows (NIH, 2015):

➢ American Indian or Alaska Native. A person having origins in any of the

original peoples of North and South America (including Central America), and

who maintains tribal affiliation or community attachment.

➢ Asian. A person having origins in any of the original peoples of the Far East,

Southeast Asia, or the Indian subcontinent including, for example, Cambodia,

China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand,

and Vietnam.

➢ Black or African American. A person having origins in any of the black racial

groups of Africa. Terms such as "Haitian" or "Negro" can be used in addition to

"Black or African American."

➢ Hispanic or Latino. A person of Cuban, Mexican, Puerto Rican, South or Central

American, or other Spanish culture or origin, regardless of race. The term,

"Spanish origin," can be used in addition to "Hispanic or Latino."

12 ➢ Native Hawaiian or Other Pacific Islander. A person having origins in any of

the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.

➢ White. A person having origins in any of the original peoples of Europe, the

Middle East, or North Africa.

❖ Home ZIP code, which was used to approximate the travel distance to clinic by

determining the straight-line distance between the patient’s ZIP code and the ZIP code

for the UCI CFE (92868). Data from the United States Census Bureau’s 2015-2019

American Community Survey was also used to record the median family income within

each patient’s ZIP code of residence.

❖ Primary payor, which indicates how the genetic counseling services were paid for. This

may or may not be the same as the patient’s primary insurance. Payor categories included

the California Prenatal Screening Program, an HMO or PPO insurance plan, MediCal

public health insurance, or other funding source, such as self-payment.

Details of pregnancy history collected from each patient’s chart includes:

❖ Estimated date of delivery which was used to calculate the expected gestational age of the

patient’s pregnancy (in weeks) at the date of the scheduled appointment. Gestational age

was further categorized by trimester, with 0-13.9 weeks gestation being first trimester,

14.0-27.9 weeks gestation being second trimester, and 28.0 or more weeks gestation

being third trimester.

❖ Primary referral indication for the scheduled appointment, which was categorized as

advanced maternal age, positive prenatal screening result, abnormal ultrasound, personal

or family history of a potentially genetic condition, possible teratogen exposure, or other.

13 Referral indications classified as “Other” included situations such as patient desire for

NIPT or late prenatal care.

❖ Secondary and tertiary referral indications for the scheduled appointment, which were

used to determine whether a patient had multiple referral indications.

❖ The total number of pregnancies the patient has had, including the current pregnancy at

the time of the scheduled appointment.

❖ The number of living children the patient has.

Details collected from each chart regarding the patient’s scheduled appointment include:

❖ The date of the scheduled appointment and the referral date, which refers to the date the

patient was initially contacted to schedule the appointment. These dates were used to

calculate lead time, or the number of days between the scheduling of the visit and the

scheduled visit itself. The scheduled appointment date was also used to determine

whether the patient was scheduled for genetic counseling before or during the COVID-19

pandemic. March 11, 2020 was used to define the pandemic start date as declared by the

World Health Organization (WHO).

❖ The patient’s referring provider, which was used to determine whether the patient was

referred internally through UCI by an obstetrician-gynecologist or maternal-fetal

medicine specialist, or referred externally by an outside provider or other UCI specialist.

❖ The service mode of the appointment, which was categorized as in-person, by video,

video-attempted, or by phone. Video, video-attempted, and phone visits were further

combined into remote visits.

14 ❖ Whether or not the patient had a concurrent ultrasound appointment scheduled on the

same day as their genetic counseling visit.

❖ Whether the patient attended or no-showed to their scheduled appointment.

2.2.3 Additional data cleaning and corrections

Of note, minor corrections were made to account for a proportionally small number of data entry errors in the FileMaker database. When a potential error was identified, information was cross-verified and corrected using the patient’s official electronic medical record in the UCI

Health Epic system. Within the different variables collected, the following number of errors were identified and subsequently corrected or excluded from the analysis.

❖ Age at estimated date of delivery: Three entries were blank due to an incomplete entry

for the patient’s birth date in the database. Birth dates for these patients were verified in

Epic and their ages at estimated date of delivery were able to be determined.

❖ Estimated date of delivery: Twenty-four entries required correction due to a single

mistyped digit in the date year (ex. 2015 instead of 2016), typically when the dates were

near a new year change.

❖ Referring provider: Ten entries were blank in the database. Nine of these missing

referring providers could be identified in Epic and corrected. One referring provider of a

no-show patient could not be identified and remained a missing data point.

❖ Primary referral indication: Three entries were blank in the database. Each of these

missing referral indications were able to be identified in Epic and corrected.

15 ❖ ZIP code: Thirty-two entries were missing, and 16 were identified as incorrect. Incorrect

entries were due to a single mistyped digit and identified because those ZIP codes did not

exist. All 48 incorrect or missing values were identified in Epic and corrected.

❖ Referral date: Fifteen entries were missing, and 48 were identified as incorrect due to

impossible resulting lead times during a single pregnancy (i.e. lead times of more than 9

months or indicating that the referral date was after the scheduled visit date). Twenty-

seven of these entries could be reasonably adjusted due to a single mistyped digit in the

date year (ex. 2015 instead of 2016), typically when the dates were near a new year

change. One additional entry was incorrect due to the patient’s birth year being input as

the referral date year, and could be reasonably corrected. The remaining 35 entries were

unable to be corrected, and those entries were further interpreted as missing data points.

❖ Race/ethnicity: Four entries were missing, and 17 entries were documented as “other” or

“unknown,” which could not be reasonably categorized into one of the NIH-defined

groups. These 21 entries were further interpreted as missing data points.

Additionally, there was one patient record that encompassed two subsequent pregnancies.

This record was reconstructed to reflect the first referral in which the patient no-showed to her scheduled visit. The patient’s first interview date, total number of pregnancies, age at estimated date of delivery, and ultrasound appointment were adjusted based on the information available in her Epic records. All other variables were confirmed to be consistent with the FileMaker record.

16 2.3 Data Analysis

Descriptive and inferential analyses were performed using IBM SPSS Statistics software version 27 (IBM 2020). Descriptive analyses were used to calculate distributions of variables related to patient demographic data, pregnancy history information, and appointment details.

Inferential analyses included Fisher’s exact tests and Chi-square tests for association in 2xn tables to compare the proportion of individuals who failed to attend scheduled clinic appointments between categories. Statistical significance is reported for each analysis as a nominal p-value. Significance was considered for p-values less than 0.05. No corrections were made for multiple comparisons.

Logistic regression was also performed to further investigate the patient demographic and clinical characteristics found to be significantly related to non-attendance through univariate analysis. Backward stepwise elimination of variables was then completed to determine a reduced regression model that best explains the data. These statistical tests were performed with estimation of odds ratios and 95% confidence intervals.

17 III. RESULTS

3.1 Descriptive data

3.1.1 Demographics of participants

The demographics of the study population, consisting of 3,461 pregnant adults scheduled to be seen by a prenatal genetic counselor at the UC Irvine Center for Fetal Evaluation between

January 1, 2015 and November 24, 2020, are summarized in Table 1. The patients’ ages at their estimated delivery dates ranged from 18-56 years, with a mean age of 34 and a standard deviation of 6 (Figure 1). Regarding race and ethnicity, 751 (21.7%) patients were categorized as being non-Hispanic White, 1863 (53.8%) were categorized as Hispanic or Latina, and 662

(19.1%) were categorized as Asian (Figure 2). Of the patients studied, 2,521 (72.8%) patients’ preferred language was English and 829 (24.0%) patients’ preferred language was Spanish

(Figure 3). The mean estimated distance between patients’ places of residence and the UCI clinic was 10.2 miles with a standard deviation of 37.0 miles and a range of 0-1,732.7 miles, given that a few scheduled patients resided out of state (Figure 4). Patients lived in ZIP code areas where the median family income ranged from $37,853-$250,001, with an average median family income of $86,614 and standard deviation of $27,280 (Figure 5).

18 Table 1. Demographic characteristics of the study sample. Descriptive statistics (N = 3,461 participants)

Demographics (categorical) n (%)

Race/ethnicity Non-Hispanic White 751 (21.7%) Hispanic or Latina 1863 (53.8%) Asian 662 (19.1%) Black or African American 68 (2.0%) American Indian or Alaska Native 4 (0.1%) Native Hawaiian or Pacific Islander 5 (0.1%) Multiethnic 87 (2.5%) Unknown/Missing 21 (0.6%)

Preferred Language English 2521 (72.8%) Spanish 829 (24.0%) Vietnamese 56 (1.6%) Other 55 (1.6%)

Demographics (continuous)

Age at estimated date of delivery, years Mean (S.D.) 34 (6) Median 35 Range 18-56

Median family income by ZIP code, dollars Mean (S.D.) 86,614 (27,280) Median 75,930 Range 37,853-250,001

Distance to clinic, miles Mean (S.D.) 10.2 (37.0) Median 6.0 Range 0-1732.7

19 3.1.2 Clinical characteristics of participants.

The clinical characteristics of the study population are summarized in Table 2. With regard to gestational age at their scheduled visit, 1,102 patients (31.8%) were in the first trimester of pregnancy, 2,110 (61.0%) were in the second trimester, and 249 (7.2%) were in the third trimester (Figure 6). Patients’ number of living children ranged from 0-9, with a median of

1, and total number of pregnancies ranged from 1-15, with a median of 3 (Figures 7-8).

Patients were most frequently referred for advanced maternal age (39.5%), with 937

(27.1%) referred for a positive screening result, 523 (15.1%) referred for a personal or family history of a possible genetic condition, 429 (12.4%) being referred for abnormal ultrasound findings, and 148 (4.3%) referred for possible teratogen exposure (Figure 9). Additionally, 1,957

(56.5%) of patients had more than one referral indication. Patients were most frequently referred by an obstetrician outside of the UCI medical system (47.1%), while 1,147 (33.2%) patients were referred by a UCI general obstetrician and 543 (15.7%) referred by a UCI maternal fetal medicine specialist (Figure 10). In terms of payment for genetic counseling services, 911

(26.3%) visits would have been covered by the California Prenatal Screening Program, 1,065

(30.8%) covered by an HMO/PPO insurance plan, and 1,472 (42.5%) covered by MediCal public health insurance (Figure 11).

The lead time for scheduled visits ranged from 0-104 days, with a mean lead time of 11 days and a standard deviation of 10 days (Figure 12). Additionally, 1,637 (47.3%) patients had an ultrasound scheduled the same day as their scheduled genetic counseling visit. Due to the

COVID-19 pandemic, during which time 319 (9.2%) patients were scheduled, 243 (7.0%) patients were scheduled for telehealth via video or telephone rather than in-person counseling. A subset of 262 (7.6%) patients were seen previously for genetic counseling at UCI.

20 Table 2. Clinical characteristics of the study sample Descriptive statistics (N = 3,461 participants)

Clinical characteristics (categorical) n (%) Trimester on scheduled visit date 1st 1102 (31.8%) 2nd 2110 (61.0%) 3rd 249 (7.2%)

Primary referral indication Advanced maternal age 1367 (39.5%) Abnormal ultrasound 429 (12.4%) Personal/family history of possible genetic condition 523 (15.1%) Positive screen result 937 (27.1%) Teratogen exposure 148 (4.3%) Other 57 (1.6%)

Multiple referral indications No 1957 (56.5%) Yes 1504 (43.5%)

Ultrasound same day No 1637 (47.3%) Yes 1824 (52.7%)

Mode of counseling In-person 3218 (93.0%) Remote 243 (7.0%)

Scheduled during COVID-19 pandemic No 3142 (90.8%) Yes 319 (9.2%)

Previously had genetic counseling No 3199 (92.4%) Yes 262 (7.6%)

21 Table 2 (Continued). Clinical characteristics of the study sample Referring provider UCI OB 1147 (33.2%) UCI MFM 543 (15.7%) Outside OB 1630 (47.1%) Other 140 (4.0%) Missing 1 (< 0.1%)

Primary payor HMO/PPO 1065 (30.8%) MediCal 1472 (42.5%) California Prenatal Screening Program 911 (26.3%) Other 13 (0.4%)

Clinical characteristics (continuous) Total pregnancies Mean (S.D) 3 (2) Median 3 Range 1-15

Living children Mean (S.D) 1 (1) Median 1 Range 0-9

Lead time, days Mean (S.D) 11 (10) Median 7 Range 0-104

22 Mean = 34 Std. Dev. = 6 N = 3,461

Figure 1. Distribution of age at estimated date of delivery (EDD) among patients. N=3,641 pregnant patients scheduled for prenatal genetic counseling.

23 Figure 2. Frequencies of races/ethnicities among patients. N=3,440 pregnant patients scheduled for prenatal genetic counseling. Races/ethnicities were categorized per the Revisions to the Standards for the Classification of Federal Data on Race and Ethnicity (NIH, 2015). Appendix A can be referenced for a more detailed distribution of patient races/ethnicities as documented by the referring provider or genetic counselor.

24 Figure 3. Frequencies of preferred languages among patients. N=3,461 pregnant patients scheduled for prenatal genetic counseling. Appendix B can be referenced for a more detailed distribution of preferred languages, including those categorized as “Other.”

25 Mean = 10.3 Std. Dev. = 37.0 N = 3,461

Figure 4. Distribution of distance from patients’ residence to clinic. N=3,641 pregnant patients scheduled for prenatal genetic counseling. Travel distance to clinic was approximated by determining the straight-line distance between the patient’s resident ZIP code and the ZIP code for the UCI CFE (92868).

26 Mean = 86,613 Std. Dev. = 27,280 N = 3,352

Figure 5. Distribution of median family income by patients’ ZIP codes. N=3,352 pregnant patients scheduled for prenatal genetic counseling. Data from the United States Census Bureau’s 2015-2019 American Community Survey was used to record the median family income within each patient’s ZIP code of residence.

27 Figure 6. Frequencies of gestational age by trimester among patients. N=3,461 pregnant patients scheduled for prenatal genetic counseling. First trimester was defined as 0-13.9 weeks gestation, second trimester was defined as14.0-27.9 weeks gestation, and third trimester was defined as 28.0 or more weeks gestation. Appendix C can be referenced for a distribution of gestational age in weeks among patients.

28 Mean = 3 Std. Dev. = 2 N = 3,438

Figure 7. Distribution of number of total pregnancies among patients. N=3,438 pregnant patients scheduled for prenatal genetic counseling. The total number of pregnancies includes the patient’s current pregnancy at the time of the scheduled appointment

29 Mean = 1 Std. Dev. = 1 N = 3,406

Figure 8. Distribution of number of living children among patients. N=3,406 pregnant patients scheduled for prenatal genetic counseling.

30 Figure 9. Frequencies of primary referral indications among patients. N=3,461 pregnant patients scheduled for prenatal genetic counseling. “AMA” refers to advanced maternal age, and “Personal/Family Hx” refers to a personal or family history of a potentially genetic condition. Referral indications classified as “Other” included situations such as patient desire for NIPT or late prenatal care. Appendix D can be referenced for a more detailed distribution of primary referral indications, including subcategories of Positive Screen referrals.

31 Figure 10. Frequencies of referring provider types among patients. N=3,460 pregnant patients scheduled for prenatal genetic counseling. “OB” refers to obstetrician, and “MFM” refers to maternal fetal medicine specialist. “Other” includes any other referral source, such as a referral from another specialty and self-referrals.

32 Figure 11. Frequencies of primary payor types among patients. N=3,461 pregnant patients scheduled for prenatal genetic counseling. The primary payor of a patient’s visit indicates how the genetic counseling services were paid for. This may or may not be the same as the patient’s primary insurance. Payor categories included the California Prenatal Screening Program (“CPSP”), an HMO or PPO insurance plan, MediCal public health insurance, or “Other” funding source, such as self-payment.

33 Mean = 11 Std. Dev. = 10 N = 3,426

Figure 12. Distribution of lead time for scheduled visits. N=3,426 pregnant patients scheduled for prenatal genetic counseling. “Lead time” is defined as the number of days between the scheduling of a patient’s visit and the date of the scheduled visit itself.

34 3.2 Univariate analysis of factors that may predict patient attendance to scheduled visits

Patients who successfully attended their scheduled clinic visit and those who failed to attend were compared based on their demographic and clinical characteristics using Fisher’s exact tests (2-sided) and Chi-Square tests for association when Fisher’s exact tests could not be completed due to insufficient computational ability. Due to small group sizes, race and ethnicity categories were combined into three categories: non-Hispanic White, Hispanic or Latina, and

Other. The following variables were divided into four groups by quartiles: age at estimated delivery date (18-30 years, 31-35 years, 36-38 years, and 39-56 years), median family income by

ZIP code (less than $65,896; $65,896-$75,930; $75,931-$103,454; and greater than $103,454), distance to clinic (less than 3.9 miles, 3.9-6.0 miles, 6.1-9.7 miles, and greater than 9.7 miles), number of total pregnancies (1, 2, 3-4, more than 4), number of living children (0, 1, 2, more than 2), and lead time (0-3 days, 4-7 days, 8-14 days, greater than 14 days).

Significant relationships were identified between attendance status and preferred language (p=0.007), primary referral indication (χ 2 (5 d.f.) =52.281, p<0.001), referring provider (p<0.001), and primary payor (p<0.001). Additionally, the proportion of no-show patients significantly decreased with increasing median family income by ZIP code (p=0.012) and further distance from clinic (p=0.003). The proportion of no-show patients significantly increased with increasing gestational age by trimester (p<0.001), higher number of pregnancies

(p<0.001), higher number of living children (p<0.001), and longer lead time (p<0.001). The proportion of no-show patients was also significantly lower among those with multiple referral indications (p<0.001) and those with an ultrasound scheduled the same day as their genetic counseling visit (p<0.001). There was also a trend indicating a smaller proportion of no-show patients among those scheduled for counseling via telehealth compared to those scheduled for in-

35 person counseling, although this comparison did not quite reach statistical significance

(p=0.058). The relationships between attendance status and race or ethnicity, age at estimated date of delivery, whether a patient previously had genetic counseling, and whether a patient was scheduled during the COVID-19 pandemic were not significant. These results are shown in

Tables 3 and 4.

When no-show rates did not differ between categories within the same variable, categories were combined for the multivariate model. Referral indication and primary payor categories of “Other” were also excluded from further analysis. Previously established significant relationships between attendance status and demographic and clinical characteristics maintained significance upon combination or exclusion of categories as shown in Table 5.

36 Table 3. Comparison of demographic characteristics between subjects who attended and did not attend their scheduled clinic visits. Attendance Status (N = 3,461 participants) p-value Show No-Show N = 3,159 N = 302

Demographic comparisons n (%) n (%) Race/ethnicity 0.076 Non-Hispanic White (N=751) 682 (90.8%) 69 (9.2%)

Hispanic or Latina (N=1,863) 1,688 (90.6%) 175 (9.4%) Other (N=847) 789 (93.2%) 58 (6.8%)

Preferred Language 0.007 English (N=2,521) 2,322 (92.1%) 199 (7.9%) Spanish (N=829) 735 (88.7%) 94 (11.3%) Vietnamese (N=56) 49 (87.5%) 7 (12.5%) Other (N=55) 53 (96.4%) 2 (3.6%)

Age at estimated date of delivery 18-30 (N=881) 788 (89.4%) 93 (10.6%) 0.134 31-35 (N=917) 839 (91.5%) 78 (8.5%) 36-38 (N=902) 827 (91.7%) 75 (8.3%) 39-56 (N=761) 705 (92.6%) 56 (7.4%)

Median family income by ZIP code 0.012 < $65,896 (N=938) 846 (90.2%) 92 (9.8%) $65,896-$75,930 (N=791) 710 (89.8%) 81 (10.2%) $75,931-$103,454 (N=812) 744 (91.6%) 68 (8.4%) > $103,454 (N=811) 761 (93.8%) 50 (6.2%)

Distance to clinic 0.003 < 3.9 mi (N=940) 844 (89.8%) 96 (10.2%) 3.9-6.0 mi (N=795) 714 (89.8%) 81 (10.2%) 6.1-9.7 mi (N=889) 813 (91.5%) 76 (8.5%) > 9.7 mi (N=837) 788 (94.1%) 49 (5.9%)

37 Table 4. Comparison of clinical characteristics between subjects who attended and did not attend their scheduled clinic visits. Attendance Status (N = 3,461 participants) p-value Show No-Show N = 3,159 N = 302

Clinical characteristic comparisons n (%) n (%) Trimester on scheduled clinic date < 0.001 1st (N=1102) 1,023 (92.8%) 79 (7.2%) 2nd (N=2110) 1,927 (91.3%) 183 (8.7%) 3rd (N=249) 209 (83.9%) 40 (16.1%) Number of total pregnancies <0 .001

1 (N=700) 652 (93.1%) 48 (6.9%)

2 (N=828) 771 (93.1%) 57 (6.9%) 3-4 (N=1245) 1,137 (91.3%) 108 (8.7%) ≥5 (N=665) 577 (86.8%) 88 (13.2%) Number of living children <0 .001 0 (N=1066) 999 (93.7%) 67 (6.3%) 1 (N=1003) 938 (93.5%) 65 (6.5%) 2 (N=697) 621 (89.1%) 76 (10.9%) ≥3 (N=640) 553 (86.4%) 87 (13.6%)

Primary referral indication < 0.001a Advanced maternal age (N=1367) 1,235 (90.3%) 132 (9.7%) Abnormal ultrasound (N=429) 406 (94.6%) 23 (5.4%) Personal/family history of possible genetic condition (N=523) 458 (87.6%) 65 (12.4%)

Positive screen result (N=937) 890 (95.0%) 47 (5.0%) Teratogen exposure (N=148) 125 (84.5%) 23 (15.5%) Other (N=57) 45 (78.9%) 12 (21.1%)

Multiple referral indications < 0.001 No (N=1,957) 1,750 (89.4%) 207 (10.6%) Yes (N=1,504) 1,409 (93.7%) 95 (6.3%) Ultrasound same day < 0.001 No (N=1,637) 1,433 (87.5%) 204 (12.5%) Yes (N=1,824) 1,726 (94.6%) 98 (5.4%)

38 Table 4 (Continued). Comparison of clinical characteristics between subjects who attended and did not attend their scheduled clinic visits. Mode of counseling 0.058 In-person (N=3,218) 2,929 (91.0%) 289 (9.0%) Remote (N=243) 230 (94.7%) 13 (5.3%)

Scheduled during COVID-19 pandemic 0.176

No (N=3,142) 2,861 (91.1%) 281 (8.9%) Yes (N=319) 298 (93.4%) 21 (6.6%)

Previously had genetic counseling 0.648 No (N=3,199) 2,922 (91.3%) 277 (8.7%) Yes (N=262) 237 (90.5%) 25 (9.5%)

Referring provider < 0.001 UCI obstetrician (N=1,147) 1,065 (92.9%) 82 (7.1%) UCI maternal fetal medicine specialist (N=543) 513 (94.5%) 30 (5.5%) Outside obstetrician (N=1,630) 1,458 (89.4%) 172 (10.6%) Other (N=140) 123 (87.9%) 17 (12.1%)

Primary payor < 0.001

HMO/PPO (N=1,065) 1,009 (94.7%) 56 (5.3%) MediCal (N=1,472) 1,277 (86.8%) 195 (13.2%) California Prenatal Screening Program (N=911) 864 (94.8%) 47 (5.2%) Other (N=13) 9 (69.2%) 4 (30.8%)

Lead time < 0.001 0-3 days (N=779) 736 (94.5%) 43 (5.5%) 4-7 days (N=971) 887 (91.3%) 84 (8.7%) 8-14 days (N=887) 817 (92.1%) 70 (7.9%) ≥15 days (N=789) 689 (87.3%) 100 (12.7%) a p-value based on Chi-Square test for association. Fisher’s exact test could not be completed due to insufficient memory.

39 Table 5. Comparison of demographic and clinical characteristics with combined categories between subjects who attended and did not attend their scheduled clinic visits. Attendance Status (N = 3,461 participants) p-value Show No-Show N = 3,159 N = 302

Demographic comparisons n (%) n (%) Preferred Language 0.005 English (N=2,521) 2,322 (92.1%) 199 (7.9%) Other (N=940) 837 (89.0%) 103 (11.0%) Median family income by ZIP code 0.005 < $75,931 (N=1,729) 1,556 (90.0%) 173 (10.0%) $75,931-$103,454 (N=812) 744 (91.6%) 68 (8.4%) > $103,454 (N=811) 761 (93.8%) 50 (6.2%) Distance to clinic 0.001 < 6.1 mi (N=1,735) 1,558 (89.8%) 177 (10.2%) 6.1-9.7 mi (N=889) 813 (91.5%) 76 (8.5%) > 9.7 mi (N=837) 788 (94.1%) 49 (5.9%) Clinical characteristic comparisons n (%) n (%) Number of total pregnancies < 0.001 1-2 (N=1,528) 1,423 (93.1%) 105 (6.9%) 3-4 (N=1,245) 1,137 (91.3%) 108 (8.7%) ≥5 (N=665) 577 (86.8%) 88 (13.2%) Number of living children < 0.001 0-1 (N=2,069) 1,937 (93.6%) 132 (6.4%) 2 (N=697) 621 (89.1%) 76 (10.9%) ≥3 (N=640) 553 (86.4%) 87 (13.6%) Primary referral indication < 0.001 Advanced maternal age (N=1367) 1,235 (90.3%) 132 (9.7%) Abnormal ultrasound (N=429) 406 (94.6%) 23 (5.4%) Personal/family history of possible genetic condition (N=523) 458 (87.6%) 65 (12.4%) Positive screen result (N=937) 890 (95.0%) 47 (5.0%)

Teratogen exposure (N=148) 125 (84.5%) 23 (15.5%)

40 Table 5 (Continued). Comparison of demographic and clinical characteristics with combined categories between subjects who attended and did not attend their scheduled clinic visits. Referring provider < 0.001

UCI OB or MFM specialist (N=1,690) 1,578 (93.4%) 112 (6.6%) Outside Referral (N=1770) 1,581 (89.3%) 189 (10.7%)

Primary payor < 0.001 MediCal (N=1,472) 1,277 (86.8%) 195 (13.2%) HMO/PPO (N=1,065) 1,009 (94.7%) 56 (5.3%) California Prenatal Screening Program (N=911) 864 (94.8%) 47 (5.2%)

Lead time < 0.001 0-3 days (N=779) 736 (94.5%) 43 (5.5%) 4-14 days (N=1,858) 1,704 (91.7%) 154 (8.3%) ≥15 days (N=789) 689 (87.3%) 100 (12.7%)

3.3 Multivariate analysis

Patient demographic and clinical characteristics found to be significantly associated with attendance status by univariate analyses were incorporated into a full logistic regression model to investigate the importance of each individual factor in predicting attendance status when all other factors were accounted for. Patients in the third trimester of pregnancy were found to be 1.95 times more likely to no-show compared to patients in the first trimester (p=0.008; 95% CI: 1.18,

3.21). Patients with two living children were 2.10 times more likely to no-show (p=0.005; 95%

CI: 1.25, 3.54) and patients with three or more living children were 2.39 times more likely to no- show compared to those with one or no living children (p=0.002; 95% CI: 1.36, 4.19). In terms of referral indication, patients referred for a personal or family history of a possible genetic condition were 1.48 times more likely to no-show compared to those primarily referred for

41 advanced maternal age (p=0.031; 95% CI: 1.03, 2.13), while individuals referred for an abnormal ultrasound finding were 0.42 times less likely to no-show compared to to advanced maternal age referrals (p=0.002; 95% CI: 0.24, 0.72). Patients who had more than one referral indication were also 0.50 times less likely to no-show compared to those with a single reason for referral

(p<0.001; 95% CI: 0.37, 0.68). Additionally, individuals referred by a provider outside the UC

Irvine obstetrics and maternal fetal medicine team were 2.33 times more likely to no-show to their scheduled genetic counseling visit (p<0.001; 95% CI: 1.72, 3.15). For patients with an ultrasound scheduled on the same day as their genetic counseling visit, they were 0.24 times less likely to no-show to genetic counseling (p<0.001; 95% CI: 0.18, 0.32). When patients’ visits were funded by an HMO or PPO insurance plan, they were 0.55 times less likely to no-show compared to patients whose visits were covered by MediCal public insurance (p=0.004; 95% CI:

0.37, 0.83). Finally, patients with a lead time of more than two weeks were 1.96 times more likely to no-show to their scheduled visits compared to those with a lead time of three days or less (p=0.004; 95% CI: 1.23, 3.12). After accounting for all other significant demographic and clinical characteristics, the patient’s preferred language, median family income by ZIP code, distance to clinic, and total number of pregnancies were not found to significantly predict patient attendance status. These findings are displayed in Table 6, where “Exp(B)” represents the odds ratio for each comparison.

42

Table 6. Full logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling. (n = 3,197) 95% C.I. for Exp(B) Variables B S.E. df Sig. Exp(B) Lower Upper

Preferred Language Other than English -0.001 0.161 1 0.995 0.99 0.72 1.37

Median Family Income by ZIP Code 2 0.814 Reference: < $75,931

$75,931 - $103,454 0.007 0.179 1 0.970 1.00 0.70 1.42

> $103,454 -0.122 0.209 1 0.560 0.88 0.58 1.33

Distance to Clinic 2 0.497 Reference: < 6.1 mi

6.1 – 9.7 mi 0.082 0.178 1 0.646 1.08 0.76 1.53

> 9.7 mi -0.193 0.234 1 0.409 0.82 0.52 1.30

Trimester 2 0.031 Reference: 1st

2nd 0.201 0.159 1 0.205 1.22 0.89 1.66

3rd 0.670 0.254 1 0.008 1.95 1.18 3.21

Total Pregnancies 2 0.068 Reference: 1 - 2

3 – 4 -0.495 0.254 1 0.051 0.60 0.37 1.00

≥ 5 -0.177 0.299 1 0.555 0.83 0.46 1.50

Living Children 2 0.006 Reference: 0 - 1

2 0.745 0.265 1 0.005 2.10 1.25 3.54

≥ 3 0.874 0.285 1 0.002 2.39 1.36 4.19

43 Table 6 (Continued). Full logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling. (n = 3,197)

95% C.I. for Exp(B) Variables B S.E. df Sig. Exp(B) Lower Upper

Primary Referral Indication 4 < 0.001 Reference: Advanced maternal age

Abnormal ultrasound -0.868 0.279 1 0.002 0.42 0.24 0.72

Personal/family history of possible genetic condition 0.396 0.184 1 0.031 1.48 1.03 2.13

Positive screen result -0.845 1.022 1 0.408 0.43 0.05 3.18

Teratogen exposure 0.387 0.286 1 0.176 1.47 0.84 2.58

Multiple Referral Indications -0.676 0.152 1 < 0.001 0.50 0.37 0.68

Ultrasound Same Day -1.418 0.152 1 < 0.001 0.24 0.18 0.32

Outside Referral 0.846 0.154 1 < 0.001 2.33 1.72 3.15

Primary Payor 2 0.015 Reference: MediCal

HMO/PPO -0.590 0.207 1 0.004 0.55 0.37 0.83

California Prenatal Screening Program 0.295 1.024 1 0.773 1.34 0.18 9.99

Lead Time 2 0.015 Reference: 0 – 3 days

4 – 14 days 0.379 0.210 1 0.071 1.46 0.96 2.20

≥ 15 days 0.674 0.237 1 0.004 1.96 1.23 3.12

44 Non-significant factors were removed from the model in a backward stepwise approach to determine a reduced model that best explains the data. Patients’ preferred language, median family income by ZIP code, distance to clinic, and total number of pregnancies could be eliminated without significantly affecting the fit of the model. When primary payor was removed from the model due to an association between the California Prenatal Screening Program paying for the vast majority of genetic counseling visits for positive prenatal screening results, the fit of the model was significantly affected (p<0.001). This indicates that the primary payor of a patient’s visit is a significant predictor of attendance status above and beyond its correlation with the primary referral indication, and should therefore remain included in the final model. Of note, when primary payor was removed from the model, it was determined that patients referred due to a possible teratogen exposure were 1.75 times more likely to no-show compared to those referred for advanced maternal age (p=0.044; 95%CI: 1.01, 2.24). Though not quite significant at the 0.05 level, it is also notable that individuals referred for a positive screen result were found to be 0.68 times less likely to no-show compared to advanced maternal age referrals when primary payor was removed from the model (p=0.078; 95% CI: 0.45, 1.04). These steps are summarized in

Table 7, and the final reduced regression model can be seen in Table 8.

45 Table 7. Significance of backward stepwise regression at each step. (n = 3,197)

-2 log Δ -2 log df df p value likelihood likelihood

Full Model 22 1573.426 ------

Step 1 Remove Distance to Clinic 20 1574.842 1.416 2 0.493

Step 2 Remove Median Family Income 18 1575.638 0.796 2 0.672 by ZIP Code

Step 3 17 1575.646 0.008 1 0.929 Remove Preferred Language

Step 4 15 1581.417 5.771 2 0.056 Remove Total Pregnancies

Step 5 13 1595.592 14.175 2 < 0.001 Remove Payor

Table 8. Final reduced logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling. (n = 3,197) 95% C.I. for Exp(B) Variables B S.E. df Sig. Exp(B) Lower Upper

Trimester 2 0.021 Reference: 1st

2nd 0.204 0.158 1 0.196 1.22 0.90 1.67

3rd 0.703 0.252 1 0.005 2.01 1.23 3.31

Living Children 2 < 0.001 Reference: 0 - 1

2 0.423 0.174 1 0.015 1.52 1.08 2.14

≥ 3 0.707 0.176 1 < 0.001 2.02 1.43 2.86

46 Table 8 (Continued). Final reduced logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling. (n = 3,197) 95% C.I. for Exp(B) Variables B S.E. df Sig. Exp(B) Lower Upper

Primary Referral Indication 4 < 0.001 Reference: Advanced maternal age

Abnormal ultrasound -0.862 0.277 1 0.002 0.42 0.24 0.72

Personal/family history of possible genetic condition 0.395 0.181 1 0.029 1.48 1.04 2.11

Positive screen result -0.946 1.039 1 0.362 0.38 0.05 2.97

Teratogen exposure 0.419 0.284 1 0.140 1.52 0.87 2.65

Multiple Referral Indications -0.676 0.149 1 < 0.001 0.50 0.38 0.68

Ultrasound Same -1.429 0.151 1 < 0.001 0.24 0.17 0.32 Day

Outside Referral 0.857 0.153 1 < 0.001 2.35 1.74 3.18

Primary Payor 2 0.001 Reference: MediCal

HMO/PPO -0.671 0.187 1 <0.001 0.51 0.35 0.73

California Prenatal Screening Program 0.367 1.041 1 0.773 1.44 0.18 11.10

Lead Time 2 0.021 Reference: 0 – 3 days

4 – 14 days 0.359 0.209 1 0.086 1.43 0.95 2.16

≥ 15 days 0.645 0.236 1 0.006 1.90 1.20 3.02

47 IV. DISCUSSION

Missed clinic appointments affect all healthcare systems, with wide variation of no-show rates across specialties and locations. Regardless of setting, missed clinic appointments have been known to significantly impact clinic efficiency and continuity of care, which affects patients, providers, and the clinic function as a whole. In general, factors that have been repeatedly identified to predict no-show behavior include lower socioeconomic status, lack of private insurance, greater travel distance to clinic, and high lead time (Dantas et al., 2018).

However, little is known about how factors specific to the prenatal genetic counseling setting, such as gestational age or type of referral indication, relate to no-show behavior. This setting is unique in that pregnant patients are faced with a variety of prenatal screening and testing options, and they must make decisions regarding these options within a short and structured timeline.

When patients fail to attend their scheduled genetic counseling visits, they are missing an opportunity to receive personalized care from a provider who is specially trained to educate, answer questions, and facilitate decision-making related to these options. This study aimed to determine the proportion of patients who failed to attend their scheduled prenatal genetic counseling visits, as well as identify factors that predict non-attendance. Understanding which patients are at the highest risk to no-show to their scheduled visits may provide valuable insight for the development of targeted interventions or solutions to help reduce that risk.

4.1 Evaluation of no-show rate and factors associated with attendance status

This study documented visit attendance for adult patients with prenatal genetic counseling visits scheduled between January 1, 2015 and November 24, 2020. The clinic no- show rate during this time period was determined to be 8.7%, as 302 of the 3,461 patients who

48 met criteria for this study failed to attend their scheduled visits. Previous literature has identified varying no-show rates across specialties and locations, ranging from 4.0% at intravenous therapy clinics to 79.2% at physiotherapy clinics (Dantas et al., 2018). The no-show rate at the studied prenatal genetic counseling clinic fits well within the lower end of the published range. To better understand the proportion of patients who missed their appointments, a number of demographic and clinical factors were investigated to determine which, if any, of those factors significantly predict attendance status.

In this study sample, the patient’s race or ethnicity, preferred language, age, number of pregnancies, travel distance to clinic, and the median family income within their resident ZIP code area did not significantly predict whether it would be more or less likely for a patient to attend their scheduled genetic counseling visit. Because data related to patient insurance information was not available for many patients in this sample, data from the United States

Census Bureau’s 2015-2019 American Community Survey was used to record the median family income within each patient’s ZIP code of residence as a proxy to reflect socioeconomic status.

Previous literature has shown that individuals who live further away from their clinic site and those of lower socioeconomic status are less likely to attend their scheduled visits (Dantas et al.,

2018). Although not significant in the multivariate model, univariate analysis showed a significant trend of decreasing no-show rate with increasing median family income and with further distance to clinic. While it seems counter-intuitive that those who live further away from the clinic site may be more likely to attend their visits, it is important to consider that those ZIP codes further away from the site include higher income areas. This suggests that socioeconomic status may have a more influential role in predicting attendance status than travel distance.

Additionally, mode of transportation to clinic was not explored in this study, which could be

49 interesting data to obtain and analyze in future research to gain further insight into how travel- related barriers may impact patient attendance.

Other factors that did not significantly predict whether a patient would be more or less likely to attend their scheduled genetic counseling visit included whether they previously had genetic counseling at UCI, whether they were scheduled during the COVID-19 pandemic, and whether they were scheduled for in-person or telehealth counseling. Of note, the no-show rate among patients with telehealth visits was 5.3% compared to 9.0% of those with in-person visits

(p=0.058). Although this relationship between attendance status and the mode of counseling is not quite statistically significant, it is interesting to compare this relationship to the relationship between attendance status and whether a patient was scheduled before or during the COVID-19 pandemic (p=0.176). Telehealth visits became an option at UCI only after the start of the

COVID-19 pandemic, so we would expect that these two variables would be very similar in terms of their relationship with attendance status. However, the lower no-show rate of 5.6% among patients with telehealth visits compared to the overall no-show rate of 6.6% among patients scheduled during the pandemic suggests that the mode of counseling may be an interesting variable to investigate further as a potential means to decrease no-show rates. At the studied clinic site, patients who are late to their scheduled telehealth appointments receive at least two phone calls from the genetic counselor to determine whether the patient is still available for the visit. In instances where a patient may have forgotten their visit, they may still be available and able to join the genetic counseling session immediately. This is much less feasible for patients who forget an in-person appointment. Even prior to the recent pandemic, telehealth has become an increasingly popular option for potentially decreasing no-show rates and improving access to care. Previous research has suggested the increased convenience of

50 telehealth visits with successful and satisfactory outcomes among patients, particularly in the field of genetics (Vrecar, Hristovski, & Peterlin, 2016; Zilliacus et al., 2010). Specifically related to the COVID-19 pandemic, another academic medical center experienced a 2.2% decrease in patient no-show rate after the rapid shift to telephone cancer genetic counseling (Shannon et al.,

2020). If telehealth remains an option for patients after the pandemic’s end, it may be useful to follow-up with further investigation of patients’ interest in and adherence to telehealth appointments compared to in-person counseling.

Several clinical characteristics were found to significantly predict whether a patient would be more or less likely to attend their scheduled genetic counseling visit. In terms of gestational age, patients in the third trimester of pregnancy were significantly more likely to no- show than those who were scheduled to be seen earlier in pregnancy. One possible explanation is that referral indications may be different in the third trimester compared to the first or second and act as a confound when evaluating the relationship between gestational age and attendance status. Patients scheduled in the third trimester may be more likely to be late transfers of care for previously identified abnormalities, or late referrals to prenatal care in general with missed opportunities for certain screening options earlier in pregnancy. Other reasons why patients in the third trimester may be more likely to no-show could be due to possible situations such as early labor, or a perception that prenatal options may be more limited or less relevant this far along in pregnancy, with individuals failing to recognize the remaining value of genetic counseling. While there are fewer screening and testing options available later in pregnancy, patients in the third trimester may still benefit from genetic counseling as counselors can provide information regarding what prenatal options are still available, testing options or management

51 recommendations that may be relevant at delivery, psychosocial support, and recurrence risks or other information for planning future pregnancies.

In addition to patients of later gestational age, individuals with two or more children were significantly more likely to no-show than those with one child or no children. Patients with two children were 1.5 times more likely to no-show, and those with three or more children were twice as likely to no-show. In one previous research study, patients’ self-reported reasons for missed clinic appointments included a lack of childcare or the presence of a sick child (Campbell et al., 2000). These previously reported reasons may provide possible explanations for why no- show rates are higher among patients with more children. In these situations, alternative scheduling options may prove beneficial in reducing a patient’s risk to no-show. For example, telehealth may be an appealing option for some individuals as a way to successfully participate in the scheduled visit from their own home, or scheduling related appointments together at the same time may minimize the frequency of needing to find childcare for numerous separate visits.

As demonstrated in previous studies, longer lead times were significantly associated with higher no-show rates. Patients whose visits were scheduled more than 14 days after they were initially contacted to book the appointment were almost twice as likely to no-show compared to those whose visits were scheduled within three days of initial contact. Of note, higher risk referral indications, such as a positive screening result or abnormal ultrasound finding, are more likely to be scheduled on shorter notice. This could result in a possible interaction between referral indication and lead time, and it may be interesting to examine whether the effect of longer lead times on attendance status differs depending on referral indication. One previously self-reported reason why patients may miss their scheduled visit is due simply to forgetting the appointment was scheduled (Campbell et al., 2000). It would be reasonable to expect that the

52 more time that passes between the scheduling of a visit and the visit date itself, the more likely a person may be to forget about the appointment or forget about the importance of the appointment and prioritize their other daily activities. At this clinic in particular, patients receive an automated phone call reminder about their visit approximately two days before the appointment date. Given the diversity of preferred languages among patients served by this clinic, it is possible that some patients may not understand the automated reminder. It is also possible that some patients do not have voicemail set up or do not know how to check their voicemail messages, and they may not hear the reminder at all unless they answer the phone at the time of the call. It may be worthwhile to reevaluate this reminder process, particularly among patients for whom it has been more than two weeks since they were initially contacted, to address these potential barriers and consider briefly reminding the patients about the expected benefits and importance of genetic counseling.

Patients who had an ultrasound scheduled the same day as their genetic counseling visit were approximately half as likely to no-show compared to those who did not have a concurrent ultrasound. For those with in-person visits, genetic counseling on the same day as a scheduled ultrasound allows patients to make a single trip to clinic for two related appointments, improving the convenience of both visits and potentially reducing the likelihood of forgetting either appointment. In terms of scheduling, this result could imply that scheduling genetic counseling visits on the same day as planned ultrasounds whenever possible may help reduce no-show rates.

In the future, it could be interesting to further stratify a study sample to compare the prediction of attendance status by concurrent ultrasound between those with telehealth and in-person appointments. This would allow for determination of whether the relationship is comparable in

53 both groups, or if those with a concurrent ultrasound are only less likely to no-show if scheduled for in-person counseling.

In terms of referral indications, patients with multiple referral indications were about half as likely to no-show compared to those with a single referral indication. Additionally, individuals primarily referred for an abnormal ultrasound were less than half as likely to no-show compared to those referred for advanced maternal age (AMA). Abnormal ultrasound indications ranged from relatively low-risk findings, such as isolated echogenic intracardiac focus or choroid plexus cysts, to serious anomalies, such as anencephaly, or multiple anomalies. Alternatively, patients with a referral for a personal or family history of a possible genetic condition, including many possibilities such as cystic fibrosis or a congenital heart defect, were nearly twice as likely to no- show compared to those with an AMA referral. These differences in no-show rates based on referral indication may be due to the patient’s perceived risk to the pregnancy or anticipated usefulness of testing or management options related to the referral. It is possible that for patients referred due to a personal or family history, prior familiarity with the condition in question may reduce the perceived risk related to the indication, compared to patients hearing new information about an unfamiliar abnormal ultrasound finding. Other emotions or coping mechanisms, such as fear or denial related to the condition in question and the implications for their ongoing pregnancy, may also play a role in why some patients may be more likely to no-show to their genetic counseling appointments.

In the final regression model that incorporated the primary payor of the patient’s genetic counseling visit, patients referred for a positive screen result or possible teratogen exposure were not significantly more or less likely to show than patients referred for AMA. However, all visits paid for by the California Prenatal Screening Program were for positive screen results, so it is

54 expected that the primary payor and primary referral indication are highly correlated and confound each other’s relationship with attendance status. When primary payor was removed from the regression model in attempt to account for this relationship, it was also seen that individuals referred for possible teratogen exposures were approximately 1.8 times more likely to no-show than those referred for AMA. Alternatively, those referred for a positive screen result were almost significantly less likely to no-show than AMA patients (p=0.078). However, when primary payor was removed from the model, the fit of the overall model was also significantly affected. This indicates that the primary payor of a patient’s visit significantly predicts attendance status beyond its correlation with the patient’s primary referral indication. This is a reasonable finding given that in the final regression model, patients whose visits were paid for by an HMO or PPO insurance plan were nearly half as likely to no-show compared to those with

MediCal public insurance coverage. Prior studies have suggested consistent findings, indicating that a lack of private insurance is more frequently associated with no-show behavior (Dantas et al., 2018). Notably, patients whose visits were paid for by the California Prenatal Screening

Program were not significantly more or less likely to no-show than those whose visits were paid for by MediCal, even though these patients were all referred for a positive screen result and would receive counseling and elected screening or testing covered in full by the program. It would be reasonable to expect a significantly lower no-show rate among California Prenatal

Screening Program patients given their higher risk referral indications and fully funded services, yet this is not indicated by the results. This group of patients consisted both of individuals who have private insurance and those with public insurance, suggesting that the true predictor likely lies in primary insurance type rather than the primary payor of the genetic counseling services, which would be consistent with previous research. To confirm this suspicion, more data would

55 need to be gathered to document the primary insurance type for California Prenatal Screening

Program patients in the future, and further analysis would be required.

Finally, patients who were self-referred or referred by a provider outside the UCI obstetrics (OB) and maternal fetal medicine (MFM) team were nearly 2.4 times more likely to no-show than patients referred internally. Given the other factors included in the logistic regression model, the results indicate that even though patients referred by an MFM specialist may have higher risk referral indications and those with abnormal ultrasounds at UCI may be scheduled to see a genetic counselor the same day, the referring provider significantly predicts how likely a patient is to attend their visit beyond those potential influences. It is possible that patients referred by UCI providers are more likely to use MyChart, an online service that helps patients navigate their scheduled appointments, and are less likely to forget about their visits.

However, it would also make sense that OBs and MFMs within the UCI system have a better understanding of the UCI genetic counseling services and better-developed relationships with the

UCI genetic counselors themselves. With frequent and open communication between the UCI physicians and genetic counselors, it is possible that these referring providers are better equipped to answer patient questions about genetic counseling and explain to patients how these services may improve their prenatal care. This finding potentially highlights the importance of referring provider education about what genetic counseling is, how it can benefit patients, and how this information should be conveyed to patients when making a referral to reduce the chance for a missed visit. This finding may also suggest the importance of the relationship between referring provider and genetic counselor, as providers who know the counselor they are referring to may have a better understanding of their role. One research study that inquired about why patients elected or declined genetic counseling after the identification of a fetal anomaly found that a

56 subset of patients accepted their genetic counseling appointment solely due to their physician’s referral, and another group declined counseling due to unclear information from their physician regarding the etiology of the anomaly (Smith et al., 2021). Previous literature also suggests that access to genetics services often relies on physicians’ abilities to identify at-risk patients and provide either appropriate counseling or make a referral to a genetics specialist who can counsel appropriately (Diamonstein et al., 2017). The findings of the current study potentially further emphasize the need for increased awareness among outside referring providers regarding genetic services and how to best educate patients about the importance of their referrals. One potential method of addressing this need may be through clinic outreach from the genetic counseling team to establish stronger relationships with outside providers, opening the door for future education and communication.

4.2 Limitations of the study and future research directions

Although the results of this study are generally consistent with previous research and further suggest additional prenatal-specific factors that significantly predict no-show behavior, there are several limitations to this study. One limitation is that the study sample was obtained from a single clinic site. Distribution of patient demographic and clinical characteristics may vary across clinic sites in other parts of the United States, and there is limited generalizability when data is obtained from a narrow patient population. In the future, the same characteristics could be investigated from additional prenatal clinics in order to determine whether these results hold true across a broader population of patients scheduled for prenatal genetic counseling services. In particular, it would be interesting to see if similar results related to the impact of

57 referring provider on attendance status were found at a comparable clinic site where referrals are received from both within their own medical center and from outside providers.

Perhaps the most significant limitation of this study is the limited accuracy of data for patients who missed their genetic counseling visits and never successfully rescheduled.

Demographic and clinical details are entered into the patient charts based on the referral received and past medical records available. For patients who successfully attend their visits, this information is verified and inaccuracies within the chart are updated to reflect the correct information per patient report. However, if a patient fails to attend their scheduled visit, there is no opportunity to verify the patient’s information and correct any inaccurate details. For this reason, data available in the charts of patients who attended their visits is inherently more accurate than data available for those who no-showed and subsequently never attended later on.

One additional limitation of this project is that it can only identify which of the studied factors predict whether a patient is more or less likely to no-show compared to patients with different characteristics; these results cannot determine why patients successfully attend or fail to attend their scheduled visits. In order to gain insight into self-identified barriers to attendance, patients could be provided the opportunity through a qualitative survey to disclose their reasons for attending or missing their genetic counseling appointments, as well as their motivations and goals for counseling, or lack thereof. The results of the current study may be applied in designing survey questions that further investigate patient perception of the factors identified to predict non-attendance. In addition to the potential benefit of increased referring provider education to promote more effective communication with patients regarding the importance of their genetic counseling referrals, results of a qualitative survey may spark additional ideas for tackling barriers to attendance.

58

4.3 Conclusions

In conclusion, this study found that prenatal patients in their third trimester of pregnancy, those with more living children, patients referred for a personal or family history of a possible genetic condition, those with higher lead time, and patients referred by an outside provider were significantly more likely to no-show to their scheduled prenatal genetic counseling visits.

Alternatively, patients referred for an abnormal ultrasound, those with multiple referral indications, those with an ultrasound the same day, and patients whose visits are paid for with an

HMO or PPO insurance plan are significantly less likely to no-show. Further, patient race or ethnicity, preferred language, median family income by ZIP code, distance to clinic, number of pregnancies, mode of counseling, whether they were scheduled during the COVID-19 pandemic, and whether they had previous genetic counseling at UCI were not significant predictors of patient no-show status. Several of the examined characteristics are specific to the prenatal clinic, and these results provide new insights into no-show behaviors within this unique patient population. Understanding these factors may allow for identification of patients at an increased risk to no-show to their scheduled visits. This subsequently provides the opportunity to intervene and implement potential solutions that may help decrease no-show rates, such as increased provider awareness about the full spectrum and benefits of genetic counseling services.

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64 APPENDIX A

Frequencies of races/ethnicities among patients. N=3,440 pregnant patients scheduled for prenatal genetic counseling. Patient race/ethnicity categories are as documented by the referring provider or genetic counselor.

65 APPENDIX B

Frequencies of preferred languages among patients. N=3,461 pregnant patients scheduled for prenatal genetic counseling.

66 APPENDIX C

Distribution of gestational age in weeks among patients. N=3,461 pregnant patients scheduled for prenatal genetic counseling.

Mean = 17.9 Std. Dev. = 5.8 N = 3,461

67 APPENDIX D

Frequencies of referral indications among patients. “Personal/Family History” refers to a personal or family history of a potentially genetic condition. Referral indications classified as “Other” included situations such as patient desire for NIPT or late prenatal care.

68 APPENDIX E

Frequencies of modes of counseling. N=3,461 pregnant patients scheduled for prenatal genetic counseling.

69

APPENDIX F

Backward stepwise logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling, Step 5. (n = 3,197)

95% C.I. for Exp(B) Variables B S.E. df Sig. Exp(B) Lower Upper

Trimester 2 0.003 Reference: 1st

2nd 0.280 0.156 1 0.072 1.32 0.97 1.79

3rd 0.855 0.249 1 0.001 2.35 1.44 3.82

Living Children 2 < 0.001 Reference: 0 - 1

2 0.534 0.172 1 0.002 1.70 1.21 2.38

≥ 3 0.900 0.168 1 < 0.001 2.46 1.76 3.42

Primary Referral Indication 4 < 0.001 Reference: Advanced maternal age

Abnormal ultrasound -0.786 0.275 1 0.004 0.45 0.26 0.78

Personal/family history of possible genetic condition 0.457 0.1791 1 0.011 1.58 1.11 2.24

Positive screen result -0.374 0.212 1 0.078 0.68 0.45 1.04

Teratogen exposure 0.564 0.281 1 0.044 1.75 1.01 3.04

Multiple Referral Indications -0.673 0.149 1 < 0.001 0.10 0.38 0.68

70 APPENDIX F (Continued)

Backward stepwise logistic regression of no-show status on characteristics of pregnant patients scheduled for genetic counseling, Step 5. (n = 3,197)

95% C.I. for Exp(B) Variables B S.E. df Sig. Exp(B) Lower Upper

Ultrasound -1.420 0.151 1 < 0.001 0.24 0.18 0.32 Same Day

Outside Referral 0.936 0.152 1 < 0.001 2.55 1.89 3.43

Lead Time 2 0.014 Reference: 0 – 3 days

4 – 14 days 0.351 0.208 1 0.091 1.42 0.94 2.13

≥ 15 days 0.660 0.233 1 0.005 1.93 1.22 3.05

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