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A Cost-Effectiveness Analysis of Preimplantation Genetic Screening with In Vitro Fertilization In Ontario
by
Rhonda Gina Zwingerman
A thesis submitted in conformity with the requirements for the degree of Master of Science
Institute of Health Policy, Management & Evaluation,
University of Toronto
© Copyright by Rhonda Gina Zwingerman 2017
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A Cost-Effectiveness Analysis of Preimplantation Genetic Screening
with In Vitro Fertilization in Ontario
Rhonda Gina Zwingerman
Master of Science
Institute of Health Policy, Management & Evaluation
University of Toronto
2017
Abstract
Background: Preimplantation genetic screening (PGS) is an increasingly common addition to IVF
cycles in an effort to improve upon current embryo selection techniques by limiting the transferrable
cohort of embryos to those that are euploid. Objective: To examine the cost-effectiveness of
preimplantation genetic screening (PGS) with IVF compared to IVF alone in terms of additional live
births. Methods: A decision analytic model was created using TreeAge Pro to compare IVF with
PGS to IVF alone for women of different age groups with one to ten blastocysts from the societal
perspective. Sequential single embryo transfers of blastocysts from a single oocyte retrieval were
modelled until all blastocysts were exhausted, treatment was discontinued or a live birth was
achieved. Results: PGS became both incrementally less costly and more effective as the number of
blastocysts and the age of the woman increased. IVF alone was the superior strategy – more effective
and less costly - for all women under age 35. This was also true when three or fewer blastocysts were
available, regardless of age. Sensitivity analysis showed that the model was most sensitive to the
implantation rates and early pregnancy loss rates of both IVF with PGS and IVF alone. Conclusions:
Using currently available data, PGS appears to be more costly and less effective than IVF alone in
the majority of scenarios. However, PGS may be less costly and more effective for older women with
a large number of blastocysts. Future research should focus on improving available population-based
PGS outcome data.
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Dedicated to Benjamin.
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Acknowledgments
I would like to wholeheartedly thank, first and foremost, my thesis committee, Dr. Okun, Dr. Shapiro
and Dr. Isaranuwatchai, for their mentorship, guidance, commitment and support throughout this
entire process. Without each of them – both individually and collectively – this work would not have
been possible. I would also like to sincerely thank my internal and external reviewers, Dr. Kolomietz
and Dr. Hitkari, for taking the time out of their very busy schedules to help me realize this goal. I
would like to express my gratitude to all the faculty and students at IHPME who taught, supported
and guided me along this journey and widened my field of vision beyond what is taught in medical
school and residency.
None of this would have been possible without the continued support and encouragement of my
friends, family and, of course, my husband, Jeremy. Thank you all from the bottom of my heart. I
could not have done this without you.
I would like to thank and acknowledge BORN Ontario for access to CARTR Plus / BORN Ontario
data and the Department of Obstetrics and Gynaecology, Mount Sinai Hospital, Toronto for their
generous funding support.
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Table of Contents
Acknowledgments.......................................................................................................................... iv
Table of Contents .............................................................................................................................v
List of Tables ................................................................................................................................. ix
List of Figures ..................................................................................................................................x
List of Abbreviations ..................................................................................................................... xi
1 Introduction .................................................................................................................................1
1.1 Infertility in Canada .............................................................................................................1
1.2 In Vitro Fertilization ............................................................................................................1
1.3 Female Age & Embryo Aneuploidy ....................................................................................2
1.4 The Rationale behind Preimplantation Genetic Screening ..................................................2
1.4.1 Improving IVF Outcomes ........................................................................................2
1.4.2 Elective Single Embryo Transfer .............................................................................3
1.5 The Rationale behind Economic Evaluations ......................................................................3
2 Review of the Literature..............................................................................................................7
2.1 A Brief History of PGS ........................................................................................................7
2.1.1 Original Methods of Embryo Biopsy & Aneuploidy Screening ..............................7
2.1.2 Trophectoderm Biopsy.............................................................................................8
2.1.3 Comprehensive Chromosome Screening .................................................................9
2.1.3.1 Array Comparative Genomic Hybridization .............................................9
2.1.3.2 Single Nucleotide Polymorphism Analysis ...............................................9
2.1.3.3 Real-Time Quantitative PCR ...................................................................10
2.1.3.4 Next Generation Sequencing ...................................................................10
2.2 The Efficacy of Comprehensive Chromosome Screening .................................................10
2.3 The Cost-Effectiveness of PGS .........................................................................................11
2.3.1 CEAs of Older Methods of PGS ............................................................................11
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2.3.2 CEAs of PGS for Recurrent Pregnancy Loss ........................................................12
2.3.3 CEAs of PGS using CCS .......................................................................................12
2.4 Study Objective ..................................................................................................................14
3 Methods .....................................................................................................................................16
3.1 Study Design ......................................................................................................................16
3.2 Model Design & Structure .................................................................................................16
3.3 Clinical Parameters ............................................................................................................17
3.3.1 CARTR Plus / BORN Ontario ...............................................................................18
3.3.2 Pregnancy & Pregnancy Loss Rates ......................................................................19
3.3.3 Treatment Discontinuation.....................................................................................19
3.4 Cost Parameters – Societal Perspective .............................................................................20
3.4.1 Assisted Reproductive Technologies & Medication Costs ....................................20
3.4.2 Productivity Costs ..................................................................................................21
3.4.3 Hospital and Physician Costs .................................................................................22
3.4.4 Calculating Costs within the Decision Tree...........................................................23
3.4.4.1 Initial Costs: Health States ......................................................................23
3.4.4.2 Incremental Costs: Health States .............................................................25
3.4.4.3 Markov Model Transition Costs ..............................................................25
3.5 Model Assumptions ...........................................................................................................27
3.6 Alternative Perspectives.....................................................................................................28
3.7 Sensitivity Analyses ...........................................................................................................29
3.7.1 One-way Sensitivity Analysis ................................................................................29
3.7.2 Probabilistic Sensitivity Analysis ..........................................................................30
3.8 Ethics Approval .................................................................................................................30
4 Results .......................................................................................................................................31
4.1 Societal Perspective ...........................................................................................................31
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4.1.1 Incremental Cost ....................................................................................................31
4.1.2 Incremental Effect ..................................................................................................31
4.1.3 Incremental Cost-Effectiveness Ratios ..................................................................32
4.1.4 Sensitivity Analyses ...............................................................................................32
4.1.4.1 One Way Sensitivity Analysis .................................................................32
4.1.4.2 Probabilistic Sensitivity Analysis ............................................................33
4.2 Alternative Perspectives.....................................................................................................34
4.2.1 Direct Health Care .................................................................................................34
4.2.2 Hypothetical MOHLTC .........................................................................................35
4.2.3 Patient Self-Pay ......................................................................................................35
5 Discussion .................................................................................................................................36
5.1 Key Findings ......................................................................................................................36
5.2 Comparison with Other Studies .........................................................................................38
5.3 Strengths ............................................................................................................................40
5.4 Limitations .........................................................................................................................41
5.4.1 Clinical Parameters ................................................................................................41
5.4.2 Cost Data ................................................................................................................42
5.4.3 Model Design .........................................................................................................43
5.5 CARTR Plus / BORN Ontario ...........................................................................................43
5.5.1 Data Quality ...........................................................................................................44
5.5.2 Data Accessibility ..................................................................................................47
5.6 Non-Economic Advantages of PGS...................................................................................48
5.7 Future Directions ...............................................................................................................49
5.8 Conclusions ........................................................................................................................50
6 References .................................................................................................................................51
7 Tables ........................................................................................................................................62
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7.1 Description of clinical inputs included in the model. ........................................................62
7.2 Detailed description of all costs included in the model. ....................................................63
7.3 Costs included under the four different perspectives examined. .......................................66
7.4 Point estimates for C, E and ICER by age category and number of blastocysts
(societal perspective). ........................................................................................................68
7.5 Results of the probabilistic sensitivity analysis by age category and number of
blastocysts (societal perspective). ......................................................................................69
7.6 Point estimates for C, E and ICER by age category and number of blastocysts
(alternative perspectives). ..................................................................................................70
7.7 Causes of inadequate PGS data quality and suggestions for data quality improvement
in the CARTR Plus / BORN Ontario registry. ...................................................................73
8 Figures .......................................................................................................................................74
8.1 The cost-effectiveness plane. .............................................................................................74
8.2 Simplified diagram of the Markov model used within the IVF alone arm of the full
decision tree. ......................................................................................................................75
8.3 Simplified diagram of the IVF/PGS arm of the decision model, including the
embedded Markov model and its clones. ...........................................................................76
8.4 Full decision tree comparing IVF/PGS to IVF alone. ........................................................77
8.5 One-way sensitivity analysis: tornado diagrams by age group and number of
blastocysts, from a societal perspective. ............................................................................78
8.6 Probabilistic sensitivity analysis: scatter plots by age group, from a societal
perspective. ........................................................................................................................80
8.7 Probabilistic sensitivity analysis: scatter plots by age group, from alternative
perspectives. .......................................................................................................................81
8.8 Components of data usability.............................................................................................83
9 Appendices ................................................................................................................................84
9.1 Categorization of causes of inadequate data quality in medical registries. .......................84
ix
List of Tables
1. Description of clinical inputs included in the model.
2. Detailed description of all costs included in the model.
3. Costs included under the four different perspectives examined.
4. Point estimates for C, E and ICER by age category and number of blastocysts (societal
perspective).
5. Results of the probabilistic sensitivity analysis by age category and number of blastocysts
(societal perspective).
6. Point estimates for C, E and ICER by age category and number of blastocysts
(alternative perspectives).
7. Causes of inadequate PGS data quality and suggestions for data quality improvement in
the CARTR Plus / BORN Ontario registry.
x
List of Figures
1. The cost-effectiveness plane.
2. Simplified diagram of the Markov model used within the IVF alone arm of the full
decision tree.
3. Simplified diagram of the IVF/PGS arm of the decision model, including the embedded
Markov model and its clones.
4. Full decision tree comparing IVF/PGS to IVF alone.
5. One-way sensitivity analysis: tornado diagrams by age group and number of blastocysts,
from a societal perspective.
6. Probabilistic sensitivity analysis: scatter plots by age group, from a societal perspective.
7. Probabilistic sensitivity analysis: scatter plots by age group, from alternative
perspectives.
8. Components of data usability.
xi
List of Abbreviations
aCGH – array comparative genomic hybridization
ARA – advanced reproductive age
BORN – Better Outcomes Registry & Network
CARTR – Canadian Assisted Reproductive Technologies Register
CCS – comprehensive chromosome screening
CEA – cost-effectiveness analysis
CGH – comparative genomic hybridization
CLBR – cumulative live birth rate
COH – controlled ovarian hyperstimulation
DET – double embryo transfer
EPL – early pregnancy loss (used here in lieu of the terms miscarriage or spontaneous abortion)
eSET – elective single embryo transfer
ET – embryo transfer
FET – frozen embryo transfer
FISH – fluorescent in situ hybridization
ICER – incremental cost-effectiveness ratio
ICSI – intracytoplasmic sperm injection
IVF – in vitro fertilization
LBR – live birth rate
MOHLTC – Ministry of Health and Long Term Care [of Ontario]
NGS – next generation sequencing
OCCI – Ontario Case Costing Initiative
OPU – ovum pickup (a.k.a. oocyte retrieval)
PGD – preimplantation genetic diagnosis
PGS – preimplantation genetic screening
PSA – probabilistic sensitivity analysis
qPCR – quantitative real-time polymerase chain reaction
RPL – recurrent pregnancy loss
SART – Society of Assisted Reproductive Technology
SNP – single nucleotide polymorphism
WTP – willingness-to-pay
1
1 Introduction
1.1 Infertility in Canada
The prevalence of infertility in Canada has been increasing over time, with recent studies estimating
that up to sixteen percent of Canadian couples are affected (1). The inability to conceive is associated
not only with significant emotional and psychological hardship but is all too often accompanied by
social stigma and relationship conflict as well (2). And while treatment options for infertility have
increased both in number and sophistication over the past decades, those treatments are often
stressful, invasive, disruptive and expensive (2).
1.2 In Vitro Fertilization
In vitro fertilization (IVF) was pioneered in the 1970s as a treatment for tubal disease, with the first
live birth occurring in England in 1978 (3). In its original incarnation, IVF involved the laparoscopic
retrieval of an oocyte from a single dominant follicle, the fertilization of that oocyte by sperm in
vitro, the culture of the resulting embryo to the cleavage stage of development and the transfer of that
embryo back into a woman’s uterus in the hopes of achieving a pregnancy (3). Since 1978, the
process as well as the indications for IVF have evolved significantly. IVF is now used to help people
struggling with infertility due to almost any cause (4). It is also used to help single and LGBTQ
individuals and couples build their families. Modern IVF begins with controlled ovarian
hyperstimulation (COH) with injectable gonadotropins to encourage the development of multiple
mature oocytes. The oocytes are then retrieved transvaginally under ultrasound guidance – a
procedure that is often referred to as an oocyte pick-up (OPU). Those oocytes are then fertilized
either traditionally, where tens of thousands of sperm are incubated with each oocyte and fertilization
is allowed to occur on its own, or by intracytoplasmic sperm injection (ICSI), where an individual
sperm is injected into each oocyte. The resultant embryos are then cultured to the blastocyst stage,
which, in humans, is reached five to six days after fertilization. Blastocysts are either transferred into
a woman’s uterus or cryopreserved for future use. Since 1978, millions of babies worldwide have
been born through IVF (4). Despite these advances, the overall success rates of IVF remain low. In
Canada in 2014, the chance of live birth per cycle started was only 22.3% (5).
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1.3 Female Age & Embryo Aneuploidy
Aneuploid embryos are embryos that have an incorrect number of chromosomes. Conversely,
euploid embryos are those that have the correct chromosomal complement. In humans, euploid cells
have 22 pairs of autosomes plus 2 sex chromosomes. Human gametes, oocytes and sperm, each have
half the euploid number of chromosomes – 22 autosomes and one sex chromosome.
It is now well recognized that incidence of aneuploidy in gametes and embryos is high and that
aneuploidy is a significant cause of the relatively low fecundability in humans (6,7). Aneuploidy
rates in human oocyte and embryos are strongly correlated with oocyte age, and therefore, female
age. Increasing aneuploidy rates with female age parallel a rise in spontaneous early pregnancy loss
(EPL) rates and a decline in natural fertility (4). Studies have shown fewer than 30% of blastocysts
created for use in IVF are aneuploid in women aged 26 to 30 but that this number rises to
approximately 85% for women over 43 years of age (8).
1.4 The Rationale behind Preimplantation Genetic Screening
1.4.1 Improving IVF Outcomes
Currently, morphological characteristics of embryos are used to select the best embryo(s) for transfer
in an IVF cycle. While there does exist a correlation between embryo morphology and chromosomal
status, with higher quality blastocysts more likely to be euploid, this correlation is moderate at best
(9). Many high quality blastocysts are in fact aneuploid, particularly in women of advanced
reproductive age (ARA) (9). The transfer of aneuploid embryos is a significant cause of failure to
achieve pregnancy (implantation failure) and of EPL after IVF (10). For women or couples with
multiple embryos available to transfer, the repeated transfer of embryos that unknowingly lack the
capacity to lead to a live birth is not only time consuming and costly, but also psychologically
difficult. This is particularly true when those embryos result in nonviable pregnancies.
Preimplantation genetic screening (PGS) is a process whereby embryos that are created in vitro are
biopsied in order to determine their chromosomal status. That information is then used to select the
most appropriate embryo or embryos for transfer rather than relying on embryo morphology alone.
Because transferred aneuploid embryos lead to implantation failure or EPL, it follows that restricting
transferred embryos to those that are euploid should improve implantation and live birth rates (LBR)
per embryo transferred (10,11).
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1.4.2 Elective Single Embryo Transfer
In addition to improving embryo selection, PGS also has the potential to increase the acceptability of
elective single embryo transfer (eSET). Because of low implantation rates per embryo transferred, it
has long been accepted practice to transfer multiple embryos at a time. This practice has resulted in
very high rates of twins and higher order multiples for those undergoing IVF and consequently
unacceptably high rates of obstetrical and neonatal complications associated with multiple
pregnancies, most notably the complications associated with prematurity. More recently there has
been a movement to promote increased use of eSET, particularly in younger women with favourable
chances of success (12,13). And while some jurisdictions have tied IVF funding to eSET policies,
these have applied almost exclusively to younger women, with double embryo transfer (DET)
policies remaining the standard for many (14,15). With the improved implantation rates of confirmed
euploid embryos, physicians and patients alike may be more willing to practice eSET in
circumstances where DET is the current standard, including in women of ARA (16). In fact, the
American Society of Reproductive Medicine recently released a statement recommending that all
euploid blastocyst transfers be single embryo transfers regardless of the age of the woman (17).
1.5 The Rationale behind Economic Evaluations
In order for new treatments or technologies to be funded by insurance companies or governmental
health ministries a number of factors must be considered. These include the clinical effectiveness of
the proposed new intervention, the budgetary impact of its implementation, potential political
ramifications, and its cost-effectiveness. Although the cost-effectiveness of a given intervention is
often portrayed as an intrinsic quality of that intervention, it is more accurately conceptualized as a
relative construct (18), meaning it is better judged in relation to other available interventions rather
than in isolation. A new intervention may be considered cost-effective by decision-makers in
comparison to current treatment X but not in comparison to alternative treatment Y. A novel health
technology is often compared to the current standard of care to determine its cost-effectiveness under
a specific set of circumstances. Economic evaluations, therefore, are methodological attempt to
quantify the trade-off between those costs and health effects (19–21). A cost-effectiveness analysis
(CEA) is a type of economic evaluation where the health effects are quantified in terms of natural
health units gained rather than in quality-adjusted life years (as in a cost-utility analysis) or dollars
(as in a cost-benefit analysis) (19,22,23).
4
There are two common summary statistics used to report CEA results in medicine: the average cost-
effectiveness ratio (ACER) and the incremental cost-effectiveness ratio (ICER) (23,24). The ACER
is calculated as the average cost of a treatment divided by the average effect; therefore, a comparison
of current practice to a new intervention yields two ACERs which are then compared to each other to
determine their relative cost-effectiveness.
𝐴𝐶𝐸𝑅 = 𝐶𝑎𝑣𝑔
𝐸𝑎𝑣𝑔
where Cavg = the average cost of an intervention; Eavg = the average effectiveness of an intervention
The ICER, on the other hand, is calculated as the difference in costs between the two interventions
divided by the difference in effects between the two interventions.
𝐼𝐶𝐸𝑅 =𝐶𝑛𝑒𝑤 − 𝐶𝑠𝑡𝑑
𝐸𝑛𝑒𝑤 − 𝐸𝑠𝑡𝑑=
𝐶
𝐸
where Cnew = the cost of the new intervention; Enew = the effectiveness of the new intervention; Cstd = the cost of
the standard intervention; Estd = the effectiveness of the standard intervention
While the ACER may be simpler to understand and is commonly reported in publications, it can lead
to inappropriate conclusions. The ACER obscures what is happening at the margins because it
“distributes the difference in costs over all subjects and assumes all outcomes are produced at
equivalent costs” (23). It also does not explicitly compare one intervention to another. For these
reasons, it has been recommended that the ICER be the primary summary statistic used in CEAs
(23).
ICERs are easiest to understand diagrammatically in the context of the cost-effectiveness plane
(Figure 8.1). This is a plane divided into four quadrants through the origin with E on the x-axis and
C on the y-axis (19,22). ICERs that fall within each of the four planes can be interpreted as follows:
1. Northwest quadrant: Here the new intervention is dominated by the current standard of care.
This is because the new intervention is both less effective and more expensive than the
current standard. In this document, this quadrant will be shaded red.
2. Northeast quadrant: Here the new intervention is more effective but also more expensive than
the current standard of care. Many new health technologies and interventions fall into this
quadrant. The ICER is most useful in this quadrant. Ultimately, whether or not a new
intervention with an ICER in the northeast quadrant is deemed to be cost-effective depends
5
on decision-makers’ willingness-to-pay for additional health gains. In this document, this
quadrant will be shaded blue.
3. Southwest quadrant: Here the new intervention is both less effective and less expensive than
the current standard of care. ICERs in this quadrant appear positive because both the E and
C are negative. Unlike the northeast quadrant, it is more difficult to interpret ICERs in this
quadrant. It is also more difficult for decision-makers to consider policies that would result in
worse health outcomes for the sake of financial savings. In this document, this quadrant will
be shaded yellow.
4. Southeast quadrant: Here the new intervention is said to be dominant over the current
standard of care. This is because the new intervention is both more effective and less
expensive than the current standard. Interventions in this quadrant should be readily adopted
into clinical practice. In this document, this quadrant will be shaded green.
An ICER is most helpful for conveying the results of a CEA when the new intervention is more
effective and more costly than the current standard of care, in other words, when the ICER falls in
the northeast quadrant of the cost-effectiveness plane (Figure 8.1). Negative ICERs, i.e. those in the
northwest and southeast quadrants, are not only conceptually difficult to understand but difficult to
analyze. Because the ICER is a ratio statistic, it is not possible to know whether a negative ICER in
isolation is negative because of a negative numerator (C) or because of a negative denominator
(E) (25). Furthermore, ICERs made of up both negative numerators and denominators, i.e. those in
the southwest quadrant, will appear positive. This is problematic as their interpretation is entirely
different from a positive ICER made up of both a positive numerator and denominator. ICERs in the
southwest quadrant indicate that the new treatment is both less expensive and less effective than the
current treatment. The societal pressures and values involved in deciding to adopt a new strategy that
will save money at the expense of health are vastly different than those involved in deciding to spend
more money in order to drive better outcomes.
A series of ICERs all residing in the northeast quadrant of the cost-effectiveness plane can be ordered
in a meaningful way from least to most cost-effective to allow for a relatively intuitive comparison of
options by decision makers. Unfortunately, this is not the case for negative ICERs which cannot be
meaningfully ordered (25). ICERs also become less helpful statistics as the E approaches zero.
Because the ICER is a ratio with E as the denominator, as E approaches zero, the ICER
6
approaches infinity and its absolute value because less useful. When the difference in effectiveness
between the two treatment options is very small or absent, one could choose to simply compare the
costs, although it is preferable to still consider both costs and effects and then employ sensitivity
analysis techniques to characterize the uncertainty around the estimates (19,26).
The ICER, however, is only one piece of information required to determine cost-effectiveness (27).
Ultimately both the budget and the willingness-to-pay (WTP) for additional units of health gained
must be considered as well. The choice of a WTP threshold, or the ICER value above which a
decision maker deems it worthwhile to adopt a given intervention, depends not only on who the
decision maker is but also on how they value particular health gains as well as their risk tolerance for
adverse health outcomes (27). In the case of the current CEA, the unit of health under consideration
is one live birth. Establishing a WTP for this health outcome is complex and remains a pursuit in its
methodological infancy. The theoretical and practical issues surrounding the valuation of an
additional life created as the result of a given treatment is beyond the scope of this paper.
Alternatively, one may conceptualize the outcome of live birth as a successful “cure” of a case of
infertility or childlessness. Despite the fact that the burden of infertility as a disease is known to be
significant and similar to that of other serious chronic diseases (28), there is no agreed upon WTP
threshold, either in the published IVF literature or elsewhere, at which an intervention should clearly
be adopted in order to alleviate this burden. And even if there did exist a consensus WTP threshold,
whether or not an intervention is deemed to be good value for money is only one aspect to consider
when deciding how to allocate limited health care resources. Attention must also be paid to factors
such as equity of access to care, ethics and legislative constraints (27). By their nature, these non-
monetary considerations are arguably more relevant to assisted reproductive technologies compared
to other interventions such as pharmaceuticals or novel surgical approaches.
7
2 Review of the Literature
2.1 A Brief History of PGS
The earliest experiences with the testing of in vitro human embryos for clinical use was for sex
determination for the purposes of avoiding the transmission of X-linked disorders (29,30). This arose
as an alternative to prenatal testing and pregnancy termination for couples at risk of having an
affected child. From these first reports the field of preimplantation genetic diagnosis (PGD) was
born. And as polymerase chain reaction (PCR) techniques improved, the indications for PGD grew
beyond sex determination to include autosomal single-gene disorders and much more, including
translocations and HLA typing (31).
In the early 1990s, as IVF was establishing itself as a mainstream treatment for infertility, experts
began to wonder if PGD could have applications for couples without known genetic diseases but with
anticipated poor outcomes after IVF (6). Knowing that many failed IVF cycles were due to
aneuploidy, PGD techniques were adapted to screen for monosomies and trisomies (32). PGD for
aneuploidy screening was thus born – something that we now refer to as PGS. PGS screens embryos
for aneuploidies when both genetic parents are known or presumed to be chromosomally
normal whereas PGD tests embryos at risk for the presence or absence of a specific genetic
mutation or chromosomal rearrangement (33).
2.1.1 Original Methods of Embryo Biopsy & Aneuploidy Screening
Genetic material for PGD or PGS can be obtained with one of three techniques: a polar body biopsy,
a blastomere biopsy at the cleavage stage or a trophectoderm biopsy at the blastocyst stage (33,34).
Until relatively recently, fluorescence in situ hybridization (FISH) was used to screen biopsy samples
for aneuploidies. FISH is a technique that uses multiple coloured fluorescent probes that bind to
DNA sequences unique to each chromosome (33). The number of different probes used determines
the number of chromosomes screened for and therefore the sensitivity of the test (35). The original
reports of PGS from the early and mid-1990s used polar body biopsies in conjunction with FISH to
screen the sex chromosomes and one, two or three autosomes (32,36). Polar body biopsies have the
advantage of not interfering with fertilization and subsequent embryo development, although the
safety of this technique has not been rigorously evaluated (37). Conversely, polar body biopsies do
not assess the paternal genetic contribution or detect post-zygotic abnormalities and are therefore
associated with unacceptably low detection rates (6,34,35,37).
8
Because of these drawbacks, cleavage stage biopsies of one or two blastomeres combined with FISH
became the standard method of performing PGS up until the mid- to late-2000s (34,35). There were
numerous clinical studies on this method of aneuploidy screening including a Cochrane review (38)
and subsequent meta-analysis (39). The Cochrane review included nine studies that used FISH to
screen between five and nine chromosomes. The LBR was found to be significantly lower in the PGS
arm whereas no difference was found in the rates of multiple pregnancies or early pregnancy losses
(38). The subsequent meta-analysis found strikingly similar results (39).
There are multiple explanations as to why PGS using blastomere biopsies and FISH is associated
with poorer clinical outcomes. First, embryo biopsy at the blastomere stage has been associated with
significantly lower implantation rates compared with either unbiopsied embryos or those biopsied at
the blastocyst stage (40). As such, there is justifiable concern that damage from the biopsy itself
more than offsets any potential gains from knowledge of ploidy status (40). Second, because FISH
screens only a limited number of chromosomes many of the embryos transferred after undergoing
PGS with FISH may in fact still be aneuploid (38). FISH can also lead to false positive results due to
scoring errors of FISH signals which can result in the non-transfer of euploid embryos (39,41). Third,
human cleavage stage embryos are known to have high rates of mosaicism – when two or more cells
from within the same embryo have a different chromosomal complements (42). It follows, therefore,
that PGS results of one or two cells may not, in fact, represent the underlying genetics of the majority
of that embryo. Furthermore, it is felt that mosaic embryos may be able to self-correct and become
euploid between the cleavage and blastocyst stages (39,42,43). Given these facts, it is easy to
understand, in retrospect, why this technology did not herald improvements in embryo selection nor,
ultimately, in IVF outcomes.
2.1.2 Trophectoderm Biopsy
The currently preferred method for obtaining genetic material for PGS is via trophectoderm biopsy at
the blastocyst stage of embryo development. This technique was first described on human embryos in
1990 (44), however the earliest reports of live births after trophectoderm biopsy did not occur until
approximately fifteen years later (45). The shift to trophectoderm biopsy was buoyed by parallel
advances in IVF laboratory techniques. Improvements in embryo culture techniques allowed embryo
culture to the blastocyst stage to become commonplace and the shift from slow freezing to embryo
vitrification allowed for embryos to be dependably cryopreserved while awaiting PGS results without
compromising cycle outcomes (35). Trophectoderm biopsy, unlike blastomere biopsy, has been
shown to preserve the reproductive potential of the embryo (37,40). Furthermore, it provides more
9
genetic material for analysis – five to ten cells compared to one or two – thus reducing the risk of
inaccurate results, particularly relating to mosaicism (35). Finally, the natural embryo selection
process that occurs between the cleavage and blastocysts stages of development results in a more
efficient process as only those embryos that had the intrinsic ability to reach the blastocyst stage are
biopsied (35).
2.1.3 Comprehensive Chromosome Screening
Just as trophectoderm biopsy has supplanted blastomere biopsy as a technique for obtaining
embryonic genetic material, comprehensive chromosome screening (CCS) has supplanted FISH as a
technique for analyzing that material. CCS involves one of a number of techniques all of which share
the same central feature – all chromosomes are evaluated (35,46).
2.1.3.1 Array Comparative Genomic Hybridization
Array comparative genomic hybridization (aCGH) is a technique whereby test and control DNA
samples are amplified and labeled with differently coloured fluorescent probes. The two samples are
then mixed together and allowed to competitively hybridize onto a DNA microarray. The ratios of
hybridization signal intensities are then analyzed to determine copy number (41,47,48). Early studies
of PGS using aCGH relied on blastomeres for source material (49), however the technique was
subsequently validated on trophectoderm samples (50,51). Despite its obvious and substantial
advances over FISH technology, it is limited in its inability to detect polyploidies, uniparental
disomy, structural chromosomal defects or abnormalities in areas of the genome not covered by the
microarray (35,41,48,52).
2.1.3.2 Single Nucleotide Polymorphism Analysis
Single nucleotide polymorphisms (SNPs) are biallelic single base pair variants that occur throughout
the genome. SNP-based CCS microarrays use hundreds of thousands, or millions, of SNPs to
determine copy number variations including aneuploidies and unbalanced translocations (41). This
can be done through qualitatively, quantitatively, or through a combination of both techniques (52).
SNP microarrays cannot consistently detect polyploidy and may not produce a result in the case of
parental consanguinity (48) but unlike aCGH they can detect balanced translocations and uniparental
disomy. Additionally, SNP microarrays have the advantage of being able to detect single gene
defects and screen for aneuploidy simultaneously (41,52,53).
10
2.1.3.3 Real-Time Quantitative PCR
Aneuploidy screening using real-time quantitative PCR (qPCR) followed both aCGH and SNP
microarrays in the chronology of CCS technologies, with the first reports published in 2012 (54).
qPCR involves the amplification of multiple loci spanning all 24 chromosomes followed by rapid
quantification of PCR products to detect aneuploidies (54–56). Because the sample is amplified
directly rather than relying on whole genome amplification, qPCR-based CCS cannot be performed
on single blastomeres and requires a trophectoderm sample (57). qPCR can produce results in only a
few hours, eliminating the need to vitrify blastocysts while awaiting results; this advantage, however,
is somewhat offset by the fact that the analysis of multiple biopsy samples in parallel is very labour
intensive or requires a significant investment in additional equipment (54,57). qPCR can detect
polyploidies but is unable to detect structural chromosomal defects or uniparental disomy (48).
2.1.3.4 Next Generation Sequencing
The newest technology being applied to CCS is next generation sequencing (NGS). NGS involves
the fragmentation and subsequent amplification of DNA, resulting in extremely large numbers of
short DNA sequences. These fragments are sequenced in parallel until a sufficient sequencing depth
is reached. They are then counted and compared with a reference human genome (57,58). This
technology can also be used to detect single gene mutations, polyploidies and uniparental disomy
(48) and, depending on the platform used, large duplications and deletions (48). It is significantly less
expensive than some of the other techniques described and is quickly emerging as the gold standard
in the field of CCS (58,59).
2.2 The Efficacy of Comprehensive Chromosome Screening
There are at least four published systematic reviews that have attempted to summarize the
observational and trial data on PGS using CCS technology (10,60–62). Dahdouh et al. has published
one systematic review and one meta-analysis on three randomized controlled trials (RCTs) that all
included good prognosis patients and used either aCGH or qPCR after trophectoderm biopsy. They
found increased implantation and ongoing pregnancy rates with the transfer of one or two euploid
embryos as compared to an unscreened embryo transfer (10,60). Unlike the RCTs, the eight cohort
studies tended to include poor prognosis patients and included polar body, blastomere and
trophectoderm biopsy samples. A meta-analysis of these studies showed a statistically significant
improvement in implantation rates compared to usual care despite a high degree of heterogeneity
11
(10). Lee et al. reviewed the same three RCTs in addition to five cohort studies and eleven case
series and also found a consistent improvement in implantation rates with the use of CCS (61).
Finally, Chen et al. performed a meta-analysis of four RCTs (three of the four being the same as
above plus one published abstract) and seven cohort studies (62). They also found higher
implantation rates in the RCTs and the observational studies; however, only the cohort studies, not
the RCTs, showed statistically significant increases in clinical and ongoing pregnancy rates and
lower EPL rates. Conversely, the single RCT to report LBR was able to show a benefit with CCS
whereas the pooled estimate from three cohort studies did not (62). Many of the analyses suffered
from very high levels of heterogeneity.
It is worthwhile to note that none of the above studies measured or reported on what is quickly
emerging as the gold standard of IVF outcomes – the cumulative live birth rate (CLBR) per cycle
(63). This statistic captures the outcomes of sequential embryo transfers resulting from one cycle of
COH and OPU, regardless of the number of transfers and whether or not those transfers were of fresh
or frozen embryos. Although the precise methodological details of measuring the CLBR are still
being debated amongst experts in the field (63), this statistic is particularly appropriate for studies of
PGS. First, eSET is more likely to be performed with euploid versus unscreened embryos making it
challenging, and perhaps altogether inappropriate, to interpret implantation and EPL rates after the
first transfer. Second, PGS by its very nature reduces the size of the transferrable cohort of embryos,
the effects of which may not be evident unless all transfers from a given OPU are fully compared.
In summary, while CCS at the blastocyst stage has been shown to improve implantation rates per
embryo transferred, its impact on EPL and LBR have yet to be confirmed in well-controlled large
scale RCTs. High quality randomized trial data comparing CCS using NGS to no PGS has yet to be
published and the impact of PGS on CLBRs has not been established in the literature.
2.3 The Cost-Effectiveness of PGS
2.3.1 CEAs of Older Methods of PGS
There is a single CEA of PGS using blastomere biopsy and FISH technology. This study looked at
women aged 38 – 40 and showed a higher average cost per euploid infant with PGS (ACER =
$118,713) compared to IVF alone (ACER = $68,026) from a societal perspective (64). Given what is
now known about the differences between PGS using blastomeres and FISH and trophectoderm and
CCS (see Section 2.1.1), this study is no longer applicable.
12
2.3.2 CEAs of PGS for Recurrent Pregnancy Loss
One full length publication and one published abstract have looked at the cost-effectiveness of PGS
using CCS for patients with recurrent pregnancy loss (RPL) (65,66). These studies, while worth
reviewing, are not directly applicable to the general infertility population because those suffering
from RPL often do not have concurrent infertility.
The published abstract by Resetkova et al. found a lower average cost per live birth with IVF and
PGS (ACER = $19,416) compared to IVF alone (ACER = $23,184) from a societal perspective (65).
The study by Murugappan et al. compared IVF with PGS (using a combination of blastomere and
trophectoderm biopsies plus aCGH) to expectant management (i.e. spontaneous conception) in
patients with RPL. They found a much higher average cost per live birth in the IVF and PGS arm
(ACER = $45,300) compared with expectant management (ACER = $418). This resulted in an ICER
of $71,906 per additional live birth (66). Furthermore, although this CEA did not model time to
conception, a retrospective cohort study by the same authors found that the time to conception was
significantly shorter in the expectant management group compared with the group that chose to
pursue PGS for RPL (67). Therefore, according to this pair of studies, IVF with PGS for patients
with RPL but without infertility is more expensive, less effective and associated with a longer
duration of childlessness compared to expectant management.
2.3.3 CEAs of PGS using CCS
To date, there are only five published abstracts and one full-length paper that attempt to address the
cost-effectiveness of CCS, with mixed findings. This small body of literature highlights the need for
the current study. These publications are summarized below.
Methods Key Findings Limitations
Abstracts
Resetkova
et al., 2014
(68)
Modelled IVF/PGS using
aCGH compared to IVF
alone to achieve a live
birth. Patient perspective.
Maximum of three
pregnancy attempts.
Average cost per live birth
was higher with PGS vs.
IVF alone. PGS was
approximately $2500
more expensive that IVF
alone and increased the
success rate from 64% to
95%.
Number of embryos per
transfer not specified.
Maximum of three
transfers were modelled.
Unclear if productivity
costs included.
Patounakis
et al., 2014
(69)
Modelled the cost-
effectiveness of euploid
DET vs. unscreened DET,
euploid SET vs.
unscreened SET and
Scenarios in which PGS
required the freezing of all
embryos followed by
frozen embryo transfers
were not cost-effective.
Perspective not specified.
Definition of “cost-
effective” not specified.
Pregnancy and live birth
rates used were obtained
13
euploid SET vs.
unscreened DET. Fresh
vs. frozen transfer with
PGS was also modelled.
When the costs of
multiple births were
included, PGS was most
cost-effective when used
to promote single embryo
transfer.
from a few small studies
that are not generalizable
to the general infertility
population.
Hodes-
Wertz et al.,
2015 (70)
Retrospectively compared
the costs of IVF/PGS and
IVF cycles from 2011 to
2013.
The average cost per
delivery was equivalent
for women < 35 years old,
lower with IVF/PGS for
women aged 35 – 39 and
40 – 42 but much higher
for women > 42 years old.
The multiple gestation rate
was substantially lower
with IVF/PGS.
Number of embryos per
transfer not specified.
Unclear if any patient
factors, such as
diagnosis, reason for PGS
or previous treatment
outcomes, were
controlled for.
Neal et al.,
2016 (71)
Modelled costs of
sequential unscreened
SETs from one OPU until
either live birth was
achieved or no embryos
remained. Model was
applied and compared to a
retrospective cohort of
nearly 12,000 IVF/PGS
cycles.
Average cost per delivery
or exhaustion of all
embryos was
approximately $800
higher in the IVF vs. PGS
group. There were two
and a half times more
transfers in the IVF alone
vs. PGS group.
Estimates from
retrospective cohort may
not be generalizable as
included only those that
actually underwent
IVF/PGS.
Did not include dropout
in the model.
Salem et al.,
2016 (72)
Designed a model to
compare IVF/PGS with
IVF alone to achieve live
birth in women >= 39
years old. Tested false
positive PGS rates from
4% – 16%.
IVF/PGS resulted in lower
live birth rates than IVF
alone for all age groups
studied. The average cost
per live birth was higher
with IVF/PGS compared
to IVF alone.
Perspective not specified.
Maximum number of
transfer attempts unclear.
Full Length Publications
Scriven,
2016 (73)
Modelled sequential
euploid vs. unscreened
SETs up to a maximum of
10 to achieve an ongoing
pregnancy or exhaust
embryo cohort. Both fresh
and freeze-all strategies
tested. Societal
perspective.
IVF/PGS did not improve
the cumulative live birth
rate compared with IVF
alone but did decrease the
number of transfers and
early pregnancy losses.
IVF/PGS resulted in fewer
live births per cycle as its
positive predictive value
decreased.
Included productivity
costs were not
comprehensive. PGS
outcomes estimated from
two small studies (51,74).
Dropout rate assumed to
be zero.
There is significant heterogeneity in both the methodology and findings of these cost-effectiveness
studies. The perspective of the analysis is an important consideration as the majority of the above
studies included only direct ART costs and it is unclear from the abstracts alone if medication costs
14
were included in these estimates. None of these studies included the costs of comprehensive
productivity losses that account for both partners’ time off work due to IVF or its complications. The
number of transfer attempts and the number of embryos per transfer are critical considerations when
evaluating any study of PGS effectiveness or cost-effectiveness as these variables will affect both the
clinical outcomes and the direct costs. Comparing the cost-effectiveness of, for example, three
sequential single unscreened embryo transfers with three sequential single euploid embryo transfers
until a live birth is achieved is not a clinically meaningful comparison because not all embryos are
euploid and therefore the large majority patients with three unscreened embryos available for transfer
will have fewer than three euploid embryos available after PGS with which to attempt to achieve a
pregnancy. With respect to the number of embryos transferred at time, studies looking at the cost-
effectiveness of transferring two euploid embryos at a time are difficult to apply to today’s clinical
practice given the recommendations to always transfer a single embryo at a time when transferring a
euploid embryo (17).
As discussed earlier, studies of PGS effectiveness should ideally examine the CLBR from one cycle
of COH and OPU, at least until the first live birth is achieved or treatment is discontinued by the
patients. Thus far, the published trials of PGS effectiveness have compared only the first transfer in
each group (10,62). Moreover, these trials have included only subsets of either good or poor
prognosis patients and are not representative of the general infertility population. Cost-effectiveness
findings based on clinical inputs from these studies are therefore only applicable to those specific
populations. Finally, none of the above studies modelled the clinical reality that some patients
prematurely discontinue treatment before achieving either a live birth or exhausting their embryo
cohort – either due to financial or psychological reasons.
Given the rapidly expanding use of PGS technology in everyday infertility practice and the scarcity
of publications on its cost-effectiveness, it is not surprising that experts in the field have been calling
for a more robust examination of this topic (61,75,76). The current study addresses many of the
limitations of the studies highlighted above and aims to considerably add to the body of literature on
this subject.
2.4 Study Objective
The objective of this study is to examine the cost-effectiveness of IVF/PGS using CCS technology
compared with IVF alone to achieve a live birth in a general infertility population in Ontario from a
societal perspective. The following female age groups are studied separately: less than 35 years, 35 –
15
37 years, 38 – 40 years, 41 – 42 years and greater than 42 years of age. The study will also examine
the cost-effectiveness of IVF/PGS from three alternative perspectives (direct health care perspective,
hypothetical single-payer government insurer perspective, and patient self-pay perspective). To
achieve this objective, a novel TreeAge Pro model to study CLBRs after IVF will be designed and
constructed.
16
3 Methods
3.1 Study Design
A decision analytic model containing multiple Markov model clones was created using TreeAge Pro
2016 to compare IVF/PGS to IVF alone. The time horizon was one complete IVF cycle, defined as
one cycle of COH and OPU followed by the sequential transfer of all usable embryos created as a
result of that fresh cycle until one of the primary end points was reached. These end points were live
birth, premature treatment discontinuation by the patient or exhaustion of all transferrable embryos.
For the base case analysis costs were calculated from a societal perspective. All embryo transfers
were modelled as single embryo transfers at the blastocyst stage.
This paper follows the Consolidated Health Economic Evaluation Reporting Standards (CHEERS)
recommendations as set out by the ISPOR Health Economic Evaluation Publication Guidelines Good
Reporting Practices Task Force (77).
3.2 Model Design & Structure
The decision tree begins with a decision node which leads to the two competing strategies: IVF/PGS
and IVF alone.
The IVF alone arm leads to a Markov model with three states: embryo transfer, drop out and live
birth. The entire cohort starts out in embryo transfer as this model assumes all subjects have at least
one unscreened blastocyst. The latter two states are absorbing states. Live birth refers to the clinical
scenario where the treatment ends successfully in a live birth. Drop out refers to the clinical scenario
where patients discontinue treatment despite not having achieved a live birth nor having exhausted
transferring all their embryos created from one round of COH and OPU. The embryo transfer state
branches into a subtree with the options of pregnant or not pregnant. The not pregnant arm branches
again into embryo transfer and drop out, both of which lead back to their corresponding states. The
pregnant arm branches into live birth and pregnancy loss. The former branch leads back to its
corresponding state whereas the latter leads to the same two options as not pregnant: embryo transfer
and drop out. This Markov model is shown diagrammatically in Figure 8.2.
17
The IVF/PGS arm leads to a series of nine identical Markov clones as well as a terminal branch
representing the possibility of having no euploid embryos available to transfer. Each Markov clone is
identical in structure to the one described above and shown in Figure 8.2.
The model was designed such that one cycle length equals one embryo transfer. A counter variable
entitled “Total_Blasts” was created and set to equal the total number of unscreened blastocysts
available. The IVF alone arm was made to terminate when the stage counter equaled the value of the
counter variable. The probability of routing towards one of the ten possible branches within the
IVF/PGS arm was determined by two variables: the total number of unscreened blastocysts (i.e. the
“Total_Blasts” variable) and the age category (< 35, 35 – 37, 38 – 40, 41 – 42 and > 42) being
studied. For every age category and starting number of blastocysts, the relative probabilities of
having a specific number of euploid blastocysts was calculated. For example, if the unscreened
embryo cohort size was three, the chances of having zero, one, two or three euploid embryos was
estimated for each of the age categories being studied. These expected euploidy rates were obtained
from the published literature (8). These values were then used to determine the probability of moving
from the initial IVF/PGS node to a specific Markov clone or, in the case of no euploid blastocysts
available, the no euploid terminal branch. For example, for women aged 35 – 37 with 2 unscreened
blastocysts, approximately 12.6% of the cohort would move to the no euploid node while 45.8%
would move to the 1 euploid Markov clone and the remaining 41.6% to the 2 euploid Markov clone.
Each of the nine Markov clones has a termination condition equal to the number of euploid blasts
represented by that clone. For example, the 1 euploid model terminates after one iteration, which
equates to one embryo transfer, while the 9 euploid one terminates after nine iterations, or embryo
transfers.
A simplified version of the IVF/PGS arm of the decision tree including the embedded Markov model
is shown in Figure 8.3. The full decision tree is shown in Figure 8.4.
3.3 Clinical Parameters
All three Markov states – embryo transfer, drop out and live birth – have initial and incremental
effectiveness values of zero. (It is important to note here and throughout Chapter 3 that the meaning
of the term incremental does not refer to the difference in effectiveness between two interventions or
two costs (i.e. the E or the C of the ICER) but rather the effectiveness or costs associated with
spending a Markov cycle in a given Markov state in TreeAge Pro.) The live birth state has a final
18
effectiveness value 1. The embryo transfer and drop out states have final effectiveness values of
zero.
3.3.1 CARTR Plus / BORN Ontario
The Canadian Assisted Reproductive Technologies Register (CARTR) is Canada’s national assisted
reproductive technology database (78). It is run by the Better Outcomes Registry and Network
(BORN) of Ontario – a provincial pregnancy, birth and childhood database. The original intention of
this study was to use Ontario data from 2013 obtained from CARTR Plus / BORN to inform our
model inputs with respect to pregnancy and live birth rates after IVF/PGS and IVF alone. This was
the first in which these two databases were merged therefore allowing the linkage of IVF cycles with
more detailed birth outcomes. Data from more recent years were not available at the time of our data
request.
In total, information from 8608 cycles, both fresh and frozen, was obtained from CARTR Plus /
BORN Ontario. Donor oocyte and gestational carrier cycles were excluded by the data analysts at
BORN Ontario as they were not representative of the general IVF population. We then further
excluded frozen oocyte IVF cycles (26 cases), PGD cycles (3 cases) and embryo or oocyte banking
cycles (29 cases) for the same reason. Cycles with no pregnancy or birth outcome data (19 cases)
were excluded for lack of data. Ectopic pregnancies (44 cases) and stillbirths (10 cases) were
categorized as pregnancy losses. Pregnancy losses were defined as clinical pregnancy losses and
therefore biochemical pregnancies (524 cases) were categorized as failed embryo transfers. There
were cases where the result of the embryo transfer was a clinical intrauterine pregnancy but the
ultimate pregnancy outcome was coded either as unknown (61 cases), missing (29 cases) or left
blank (42 cases). For these, birth outcome was categorized as a live birth if there was a fetal heart
beat present and as a pregnancy loss otherwise.
It became clear after further analysis of the data that there were significant limitations with respect to
how key information, particularly regarding PGS, was collected and/or coded. While CARTR Plus /
BORN Ontario data was used in this study whenever possible, many of the clinical parameters were
ultimately obtained elsewhere. CARTR Plus / BORN Ontario data was used to obtain: the average
dose of FSH for women undergoing either IVF alone or IVF/PGS, the percentage of non-PGS cycles
that used ICSI as the method of fertilization, the percentage of pregnancy losses attributable to
ectopic pregnancies after IVF, and the distribution of modes of delivery (spontaneous vaginal
19
delivery, operative vaginal delivery and Caesarean section) after IVF. A more detailed discussion of
CARTR Plus / BORN Ontario can be found in Chapter 6.
3.3.2 Pregnancy & Pregnancy Loss Rates
Pregnancy and pregnancy loss rates were obtained from the Society of Assisted Reproductive
Technology’s (SART) publicly available online National Summary Report for 2014 (79). Aggregate
data from the “Primary Outcome per Intended Retrieval” tab for patients undergoing conventional
stimulation IVF cycles with patients’ own oocytes were used. The data were filtered to exclude
gestational carrier and frozen oocyte cycles and to include only single embryo transfer cycles on day
5 or 6. PGS/PGS cycles were compared to non-PGS/PGD cycles to obtain model inputs. Table 7.1
shows the clinical input parameters used in the model.
3.3.3 Treatment Discontinuation
It is well known that many patients discontinue infertility treatment before achieving a live birth due
to non-medical reasons such as psychological distress, relationship issues or financial concerns (80–
82). Premature treatment discontinuation or withdrawal is often referred to in the literature as
dropping out. While acknowledging the negative connotations associated with this term (83), the
term dropout will nonetheless be used here for concision and consistency with previous publications.
The dropout rate has traditionally been defined as the proportion of eligible patients not returning for
a subsequent fresh IVF cycle (comprised of COH, OPU and embryo transfer(s)) after the failure to
achieve a live birth in a previous cycle. Studies on dropout rates have consistently found rates
between 15% and 30% (84–90). Premature dropout is, in fact, included in many other decision
models of IVF (for examples see (91–93)). However, nearly all the studies on dropout rates have
focused on this between-cycle dropout rate and, as a result, very little is known about within-cycle
dropout rates – the probability of discontinuing treatment prior to transferring all cryopreserved
embryos from a single cycle of COH and OPU. The data obtained from BORN/CARTR could not be
used to calculate either between-cycle or within-cycle dropout rates for Ontario. Therefore, a
thorough review of the literature was undertaken in an attempt to estimate within-cycle dropout rates
for use in our model.
There are many studies, both quantitative and qualitative, that have examined patients’ decision
making with regards to the disposition of surplus embryos (94–99). These studies included varying
degrees of demographic details, including parity and family status; however, none of these studies
20
provided enough information to determine what percentage of infertile patients with embryos
remaining choose not to use them for another attempt at a pregnancy. A study by Domar et al. looked
at couples who had insurance coverage for up to three cycles of IVF but discontinued treatment
before their third cycle despite not achieving a pregnancy (83). They found that one year after
starting their first cycle 30% of these patients had frozen embryos remaining and that only half of
them returned to use their embryos within the subsequent three years. Therefore, if the between-cycle
dropout rate is approximately 15-30% and the within-cycle dropout rate is approximately 15-30% of
that, it follows that the within-cycle dropout rate can be estimated at 5.60% (range 2.25% to 9.00%).
Additionally, there is some evidence to support the fact that the dropout rate after a pregnancy loss is
higher than after a failed transfer. A recent abstract reported a hazard ratio of 1.18 (90). Therefore,
the within-cycle dropout rate after a pregnancy loss was estimated at 6.61% (range 2.66% to
10.62%).
3.4 Cost Parameters – Societal Perspective
The costs included in the present model, from a societal perspective, encompassed direct health care
costs related to IVF and PGS, health care costs related to prenatal care, and productivity costs related
to time off work for both patients and their partners. Costs are provided in 2015 Canadian dollars. A
discount rate of 0% was applied because the time horizon of the model was short and because it was
modelled in embryo transfer attempts rather than absolute units of time.
3.4.1 Assisted Reproductive Technologies & Medication Costs
The costs of infertility treatments were drawn from fee schedules of Ontario fertility clinics. These
were obtained from publicly available websites as well as through personal communications with
fertility clinic staff wherever possible. Online fee schedules that had not been updated in the previous
two years were excluded as they were assumed to out of date and inaccurate. A list of these costs can
be found in Table 7.1.
Medication costs associated with fertility treatments were calculated in the following manner. The
average dose of FSH used per fresh cycle was obtained from BORN/CARTR. The average FSH dose
was 2903.58 IU for those undergoing IVF alone and 3730.52 IU for those undergoing IVF/PGS.
These values were used regardless of age cohort. The average cost per unit FSH was calculated by
averaging the costs of the two most common FSH formulations used locally: Gonal-f® (EMD
21
Serono) ($350 for 300 IU) and Puregon® (Merck) ($360 for 300 IU). It was assumed that all cycles
were GnRH antagonist cycles with ten days of Estrace® (Acerus) priming (4mg per day) followed by
FSH alone. The cost of Estrace® was set at $0.53 per 2mg. It was assumed that each patient received
four days of a GnRH antagonist at $125.00 per day and one dose of Ovidrel® (EMD Serono) trigger
at $85.00. Finally, it was assumed that each patient took 200mg of Prometrium® (Merck) twice daily
for fourteen days for luteal support. The cost of Prometrium® was set at $1.67 per 100mg. This
protocol and these costs were based on expert opinion and are consistent with local practice.
Frozen embryo transfer (FET) cycles were assumed to be medicated with 4mg of Estrace® twice
daily for fourteen days followed by two weeks of 2mg of Estrace® twice daily and 200mg of
Prometrium® three times daily. Patients who became pregnant after a fresh embryo transfer were
assumed to continue their luteal support until 7 weeks of gestation. Those who became pregnant after
an FET were assumed to continue their luteal support until ten weeks of gestation. These protocols
were based on expert opinion and are consistent with local practice.
It is acknowledged that some IVF cycles, and PGS cycles in particular, may be triggered with a
GnRH agonist rather than hCG and followed up with blood work the next day. Similarly, many other
types of FET cycles exist as do other options for luteal phase support. There is also debate regarding
the optimal duration of luteal phase support. These alternatives were not explicitly modelled in this
study; however, a sensitivity analysis was performed that varied the cost of medications associated
with both fresh and FET cycles (see Section 3.7). In order to estimate a range of values for the
purposes of sensitivity analyses, medication costs were multiplied by 2/3 (minimum) and 4/3
(maximum). The final medication costs used in the model can be found in Table 7.2.
3.4.2 Productivity Costs
Estimates of amount of work missed due to IVF and any resultant pregnancies were estimated based
on a combination of local practice, expert opinion and, wherever available, published resources
(92,100–106). Women undergoing COH and OPU were estimated to miss a total of fifteen hours of
work: six hours over the course of COH plus one eight hours work day for the OPU. Similarly, it was
estimated that a fresh ET would result in four hours of missed work, an FET in five hours of missed
work, routine first trimester care in six hours of missed work and routine second and third trimester
care in a combined fourteen hours of missed work. It was assumed for this study that women
undergoing IVF had partners. It was estimated that these partners would miss ten hours of work
22
during the course of their partner’s COH and OPU and four hours of work for both fresh and frozen
embryo transfers. These estimates were in keeping with other published estimates (92,101–105)
although large differences in study designs, patient characteristics, changes in practice patterns over
time and cultural factors makes direct comparisons and extrapolations challenging. No published
estimates were available from a Canadian setting.
Productivity losses associated with pregnancy loss were obtained from a published economic
evaluation done alongside a British RCT examining various methods of EPL management (106).
Pregnancy losses managed expectantly, medically and surgically were therefore allocated 9.37, 10.63
and 9.86 days off work, respectively (106). Productivity losses associated with ectopic pregnancies
were assumed to be fifteen days, or approximately 1.5 times the number of days associated with EPL,
regardless of whether the ectopic pregnancy was treated medically or surgically.
For the purposes of calculating productivity costs for the present model, all workdays were assumed
to be eight hours in length. Productivity costs associated with missed work for partners of women
undergoing prenatal care, pregnancy complications or labour and delivery were not included.
Estimates of work absences were converted to costs using the average hourly wage rate for females
($25.77/hr.) and males ($29.85/hr.) ages 25 to 54 in Ontario for 2015 (107). Although clearly not
always the case, it was assumed for the purposes of wage rate calculations in this study that patients
were female and their partners were male. In order to estimate a range of values for the purposes of
sensitivity analyses, time off work estimates were multiplied by 2/3 (minimum) and 4/3 (maximum).
The final productivity costs included in the model can be found in Table 7.2.
3.4.3 Hospital and Physician Costs
The Ontario Case Costing Initiative (OCCI) is an initiative of the Ministry of Health and Long-Term
Care of Ontario, Canada that collects cost data for acute inpatient, day surgery and ambulatory care
cases. This initiative has an online Costing Analysis Tool (CAT) that can be used to obtain cost
estimates based on fiscal year, hospital, principal procedure, most responsible diagnosis, age group
and Case Mix Groups (categories developed by the Canadian Institute for Health Information) (108).
The CAT from the most recently available fiscal year (2010/2011) was used to obtain hospital costs
for the following model inputs: dilation and curettage, surgical management of ectopic pregnancy,
medical management of ectopic pregnancy, spontaneous vaginal delivery, operative vaginal delivery,
23
Caesarean section and neonatal costs. All costs were adjusted to 2015 Canadian dollars using the
consumer price index, Health Care only, Ontario only (109).
The Schedule of Benefits, published by the Ontario Ministry of Health and Long Term Care
(MOHLTC), details physician fees for all physician services covered by Ontario’s provincial health
plan (110). This was used to obtain physician costs for the following model inputs: prenatal visits
(assumed 12 throughout pregnancy), first trimester ultrasound, anatomy ultrasound, medical
management of pregnancy loss, dilation and curettage, medical management of ectopic pregnancy,
surgical management of ectopic pregnancy, spontaneous vaginal delivery, operative vaginal delivery
and Caesarean section. For ultrasounds both technical and professional fees were included. Where
applicable, anesthesia fee codes were also included and based on estimates of procedure lengths.
Special visit premiums, including after-hours premiums, were not included.
The cost of routine prenatal screening was obtained based on raw data used in a 2014 publication by
Okun et al. and obtained via personal communication with the author (20). Hospital and physician
costs can be found in Table 7.2.
3.4.4 Calculating Costs within the Decision Tree
The following sections describe how the individual costs described in Table 7.2 were combined and
distributed to calculate the costs associated with different health states, and the movement between
those states, of the Markov models embedded within our decision tree.
3.4.4.1 Initial Costs: Health States
Initial costs are those costs associated with starting out in a particular Markov health state. In the
present model, 100% of subjects started in the embryo transfer state and no subjects started in either
the dropout or live birth states (see Section 3.2). The initial costs associated with the embryo transfer
state can be thought of as the sum of all costs up to and including the first embryo transfer. The initial
costs for the drop out and live birth states were set to zero because no subjects started out in those
states.
The initial cost for the embryo transfer state of the Markov model within the IVF alone arm of the
decision tree was calculated by summing the following individual costs:
- IVF cycle
- IVF cycle medications
24
- COH and OPU productivity
- COH and OPU partner productivity
- fresh embryo transfer productivity
- fresh embryo transfer partner productivity.
It also included the cost of ICSI multiplied by the proportion of cycles that used ICSI, which
according to CARTR Plus / BORN Ontario data, was 66% of all IVF cycles (111).
The initial cost for the embryo transfer state of the Markov model and its clones within the IVF/PGS
arm of the decision tree was calculated by summing the following individual costs:
- IVF cycle
- ICSI
- embryo biopsy and PGS
- embryo cryopreservation
- IVF cycle medications
- COH and OPU productivity
- COH and OPU partner productivity
- FET cycle
- FET medications
- FET productivity
- FET partner productivity.
The key differences between these costs and the initial costs for the embryo transfer state of IVF
alone were: the use of ICSI 100% of the time, the need for embryo biopsy, PGS and
cryopreservation, and the fact that the first embryo transfer was a frozen embryo transfer rather than
a fresh one.
The costs associated with the No Euploid branch of the IVF/PGS arm of the decision tree was
calculated by summing the following individual costs:
- IVF cycle
- ICSI
- embryo biopsy and PGS
- embryo cryopreservation
- IVF cycle medications
- COH and OPU productivity
25
- COH and OPU partner productivity.
The key difference between these costs and the initial costs of the embryo transfer state of the
Markov model within the IVF/PGS arm of the decision tree was the absence of costs associated with
a frozen embryo transfer.
3.4.4.2 Incremental Costs: Health States
As previously mentioned, TreeAge Pro uses the term incremental costs to refer to costs accrued by
spending any cycle other than the initial cycle in a given Markov state. While this terminology may
be confusing to many it is used here nonetheless for consistency with the TreeAge Pro software and
those familiar with it.
For every subsequent Markov cycle spent in the embryo transfer state of the Markov model within
the IVF alone arm accrued costs were calculated as the sum of:
- FET cycle
- FET cycle medications
- FET productivity
- FET partner productivity.
The cost of cryopreservation was included only if it was the second embryo transfer. The cost of an
additional year of embryo storage was included if it was the sixth embryo transfer. This assumed that
those having five or fewer transfers will complete them within one year and those having six or more
will complete them over two years.
The incremental cost associated with the embryo transfer state of the Markov model and its clones
within the IVF/PGS arm of the decision tree was calculated as the sum of:
- FET cycle
- FET cycle medications
- FET productivity
- FET partner productivity.
There were no incremental costs associated with spending time in the other states because they were
both absorbing states.
3.4.4.3 Markov Model Transition Costs
Transition costs are the costs accrued when moving from one Markov state or subtree to another.
26
The costs associated with pregnancy loss were captured when subjects transitioned from pregnant to
pregnancy loss within the subtree of the embryo transfer health state. The overall cost of pregnancy
loss was calculated as the sum of:
- prenatal consult
- early ultrasound
- first trimester luteal support medications (calculated as a weighted average of luteal support
medication costs from both fresh and frozen embryo transfer cycles in the IVF alone arm)
- first trimester care productivity.
The probabilities of the five treatment options (early pregnancy loss treated expectantly, early
pregnancy loss treated medically, early pregnancy loss treated surgically, ectopic pregnancy treated
medically, ectopic pregnancy treated surgically) were multiplied by their associated hospital,
surgeon, anesthesia and productivity costs and then summed and added to the above costs.
Based on data from CARTR Plus / BORN Ontario, 7.9% of all pregnancy losses were due to ectopic
pregnancies (111). Data was not available with respect to what percentage of ectopic pregnancies
were treated medically versus surgically. Two US studies found that approximately one third of
ectopic pregnancies were treated medically whereas two thirds were treated surgically (112,113). A
third study, however, found the opposite – approximately one third were treated surgically and two
thirds medically (114). For the current model, we assumed that half of the ectopic pregnancies, or
3.9% of the pregnancy losses, were treated surgically and the other half medically. With respect to
the management of intrauterine pregnancy losses, the data is equally sparse. Data were not available
to inform the distribution of expectant, medical and surgical management options for EPL in cases
where patients were informed of all three options and subsequently allowed to choose the one that
was most appropriate for them. Therefore, for this study we assumed that one third of all intrauterine
pregnancy losses were treated with each of the three management options.
The costs associated with live birth were captured when subjects transitioned from pregnant to live
birth within the subtree of the embryo transfer health state. The cost was calculated as the sum of:
- prenatal consult
- early ultrasound
- anatomy ultrasound
- prenatal screening
- first trimester care productivity
27
- second and third trimester care productivity
- twelve prenatal visits
- neonatal care.
The cost of first trimester luteal support medication, calculated as a weighted average of luteal
support medication costs from both fresh and frozen embryo transfer cycles for the IVF alone arm
was also included. Finally, costs associated with delivery itself were included. The probability of
each mode of delivery (spontaneous, operative vaginal and Caesarean section) was multiplied by the
sum of costs related to that mode of delivery (hospital, surgeon and, where applicable, anesthesia).
These probabilities were obtained from CARTR Plus / BORN Ontario data (111) and were as
follows: spontaneous vaginal delivery (0.446), operative vaginal delivery (0.123) and Caesarean
section (0.431).
3.5 Model Assumptions
While decision models are extremely useful tools for examining the cost-effectiveness of various
interventions, they are necessarily associated with simplifications of the clinical reality being studied.
It is also frequently necessary to make some assumptions regarding how best to use available clinical
and cost data to inform input parameters. A number of these assumptions are detailed below and
discussed in greater detail in Chapter 6.
The model accounted for an increased dropout rate after EPL compared to after a failed embryo
transfer; however, the model assumed that the dropout rate was independent of the number of
previous embryo transfers and whether or not those transfers were of euploid or unscreened embryos.
There is some data that the between-cycle dropout rate depends on patient factors such as treatment
prognosis, age and treatment history (81,90,115). Comparable data on within-cycle dropout rates was
not available; therefore, static dropout rates were used.
Pregnancy loss and live birth rates were modelled independent of cycle number. In other words, the
chance of having a live birth with a given embryo transfer was assumed to be the same regardless of
whether it was a the first embryo transfer or not. This model also assumed, consistent with current
practice and supported by cohort studies, that the EPL and LBRs were not significantly different
between fresh and frozen embryo transfers (116).
The model structure was designed to accommodate no more than ten available blastocysts. This
number is much higher than many of the other models that have been designed to study PGS in
28
which the maximum number of pregnancy attempts was limited to two (64,66). Furthermore,
according to data from CARTR Plus / BORN Ontario (111), fewer than 5% of all IVF cycles in
Ontario in 2013 resulted in more than ten unscreened blastocysts available for transfer; therefore, this
model encompasses the vast majority of cases encountered in clinical practice.
In this study, we assumed that all surplus embryos successfully survived being cryopreserved and
subsequently rethawed. We also assumed that all pregnancy losses were either first trimester losses
or ectopic pregnancies, rather than stillbirths. According to data from CARTR Plus / BORN Ontario
(111) less than 0.2% of all embryo transfers resulted in stillbirths. This number was too small to
justify its inclusion in the model structure as this would have resulted in much more complex Markov
subtrees.
Finally, the positive predictive value of PGS was assumed to be 100%. In other words, it was
assumed that all embryos with aneuploid PGS results were truly aneuploid. In reality, the true test
performance of PGS is unknown, and although it is generally considered to be very accurate, no test
is perfect. There are numerous methodological challenges associated with determining the
performance characteristics of PGS, including identifying the appropriate gold standard for
comparison and obtaining genetic material from a blastocyst’s inner cell mass. In general, however,
the data does support a sensitivity and specificity of greater than 98% (48,117–120).
3.6 Alternative Perspectives
In addition to the societal perspective, the model was also examined from three alternative
perspectives:
1. Direct health care – this perspective includes all of the costs of the societal perspective with
the exception of productivity costs, which were excluded from this analysis. This perspective
includes costs born both by the government, for example, the cost of prenatal care, and by the
patients, for example, the cost of PGS or fertility medications.
2. Hypothetical MOHLTC – this perspective was designed to simulate an entirely hypothetical
scenario whereby the MOHLTC covers the cost of PGS, but not any of the other costs related
to fertility treatment, in addition to the other direct health care costs that they currently cover.
29
3. Patient self-pay – this perspective includes only the direct health care costs related to fertility
treatments and fertility medications. It does not included any productivity costs nor does it
include any of the costs associated with normal or abnormal pregnancies and their care.
Table 7.3 clearly outlines which costs are included in which perspectives.
3.7 Sensitivity Analyses
In order to explore the uncertainty around the estimates, both one-way and probabilistic sensitivity
analyses were performed. Sensitivity analysis involves altering model inputs to understand how they
impact model results (26). In a one way sensitivity analysis, the value of a single variable is altered
and its impact on the results examined (19,26). With probabilistic sensitivity analysis (PSA), many
variables are changed simultaneously. Each parameter is first assigned a distribution shape and range.
The model is then run repeatedly using Monte Carlo simulation with the point estimates for each
parameter for each run being chosen at random from within the assigned distribution for each
parameter. This allows the joint uncertainty across all parameters to be studied simultaneously
(19,26,121).
3.7.1 One-way Sensitivity Analysis
One-way sensitivity analyses were performed on the variables listed in Tables 7.1 and 7.2 in order to
characterize uncertainty around the estimates and identify key drivers of the model. The ranges used
for clinical and cost inputs can be found in Tables 7.1 and 7.2. Because the SART National Outcome
Summary does not provide ranges or standard deviation estimates, a range of +/- 15% was used. The
range of values for dropout rates (see Table 7.1) were determined from a review of the available
literature (see Section 3.3.3). The minimum and maximum values for fertility treatment costs were
based on the actual range of prices charged at different fertility clinics in Ontario (122). The range of
values for medication costs and productivity costs were determined based on local practice and
expert opinion. Costs obtained from the Ontario MOHLTC Schedule of Benefits, with the exception
of anesthesia-related costs which are partially time based, were not evaluated in one-way sensitivity
analyses as these costs were known precisely. The range of anesthesia costs were varied based on
expert opinion. Minimum and maximum values for hospital costs were obtained from the OCCI.
ICER tornado diagrams were used to summarize the results of the one-way sensitivity analyses.
30
3.7.2 Probabilistic Sensitivity Analysis
In addition to one-way sensitivity analyses, probabilistic sensitivity analysis (PSA) was performed.
All relevant variables were defined with distributions as shown in Tables 7.1 and 7.2. Wherever
possible, costs were defined using gamma distributions as per accepted practice (26). Probabilities
were defined with triangle distributions (rather than beta distributions) because information about
standard deviations was not available. One thousand iterations were performed for each PSA. Scatter
plots of the ICERs from each iteration were displayed on CE planes in order to summarize the results
of the PSAs. Additionally, PSA results were presented in a table detailing what percentage of
iterations resulted in ICERs in each of the four quadrants of the CE plane.
3.8 Ethics Approval
Ethics approval was obtained from the Sinai Health System research ethics board. Approval was also
obtained from the University of Toronto and BORN Ontario.
31
4 Results
Regardless of perspective taken, PGS became increasingly cost-effective – more effective and less
costly – as the number of embryos available and the age of the woman increased.
4.1 Societal Perspective
The point estimates for C, E and ICERs by age group and by the number of blastocysts available
can be found in Table 7.4.
4.1.1 Incremental Cost
When analyzed from the societal perspective, the model found that IVF alone was less costly than
IVF/PGS for women 42 years of age or younger, regardless of the number of blastocysts available.
For women less than 35 years old, the incremental cost of PGS ranged from $4,928 to $6,801. For
women aged 35 to 37 the results were similar with a range of $5,088 to $6,307. The incremental
costs began to decrease in women aged 38 to 40, particularly with larger blastocyst cohorts, with C
ranging from $3,268 to $5,840. This trend continued for women aged 41 to 42 but with an
appreciable widening of the range of values, with a C of $22 with ten blastocysts and $5,632 with
one. IVF/PGS remained more costly than IVF alone for women greater than 42 years of age with
one, two or three blastocysts (C $1,064 to $5,437) but became less costly once four or more
blastocysts available to biopsy. The cost savings ranged from $289 to $4,926.
4.1.2 Incremental Effect
The model found that IVF alone was associated with a higher probability of live birth compared to
IVF/PGS for women less than 35 years of age, regardless of the number of blastocysts available. For
women aged 35 to 37, the difference in live birth rates between the two arms was less than 2% once 6
or more blastocysts were available. For women aged 38 to 40, the difference in live birth rates
favoured IVF alone with 6 or fewer blastocysts and differed by 2% or less once 7 or more blastocysts
were available. For women aged 41 to 42, IVF was approximately 3% more effective than IVF/PGS
alone when 3 or fewer blastocysts were available and differed by 2% or less when 5 to 8 blastocysts
were available. With 9 or 10 blastocysts, IVF/PGS was 3.1% and 4.5% more effective than IVF
alone, respectively. For women greater than 42 years of age, the difference in live birth rates between
the two groups was less than 3% regardless of the number of blastocysts available.
32
4.1.3 Incremental Cost-Effectiveness Ratios
For women under age 35, the ICER fell into the northwest quadrant (meaning that IVF/PGS was
dominated by IVF alone, see Section 1.5 and Figure 8.1) in all cases regardless of the number of
blastocysts available. For women aged 35 to 37, the ICER fell into the northwest quadrant when
seven or fewer blastocysts were available and in the northeast quadrant (meaning that IVF/PGS was
more costly and more effective than IVF alone) when eight or more blastocysts were available. For
women aged 38 to 40, the ICER fell into the northwest quadrant when eight or fewer blastocysts
were available and in the northeast quadrant when nine or ten blastocysts were available. For women
aged 41 to 42, the ICER fell into the northwest quadrant when six or fewer blastocysts were available
and in the northeast quadrant when seven or more blastocysts were available. For women over age
42, the ICER fell into the northwest quadrant when one, two or three blastocysts were available, in
the southwest quadrant (meaning that IVF/PGS was less costly and less expensive than IVF alone)
when four to eight blastocysts were available and in the southeast quadrant (meaning that IVF/PGS
was less costly and more effective) when nine or ten blastocysts were available. The actual ICER
values are shown in Table 7.4.
4.1.4 Sensitivity Analyses
4.1.4.1 One Way Sensitivity Analysis
Figure 8.5 shows the results of one-way sensitivity analyses, in the form of tornado diagrams, for
each of the five age groups and for blastocyst cohort sizes of one, five and ten. For women less than
age 35, the model was most sensitive to the implantation rates of IVF alone and IVF/PGS. It was also
sensitive to the cost of embryo biopsy and PGS. It was less sensitive to the rates of EPL with IVF
alone and IVF/PGS and to the dropout rate. These findings were true regardless of the number of
blastocysts available.
For women aged 35 to 37, the model was most sensitive to the implantation rates of IVF alone and
IVF/PGS when fewer blastocysts were available. When the blastocyst size increased to ten, the
model was most sensitive to the dropout rate, following by the implantation rates and then the EPL
rates of IVF alone and IVF/PGS. For women aged 38 to 40, the model was again most sensitive to
the implantation rates of IVF alone and IVF with PGS regardless of the number of blastocysts
available. As with the younger age groups, the cost of embryo biopsy and PGS was a factor with
33
smaller blastocyst cohort sizes but not larger ones and, conversely, the dropout rate became a more
significant driver of the model results as the blastocyst cohort size increased.
For women aged 41 to 42, the model was most sensitive to the implantation rates of IVF alone and
IVF/PGS when the blastocyst cohort size was very small or very large. With five blastocysts, the
model was most sensitive to the EPL rate of IVF alone, followed next by the implantation rates of
IVF/PGS and IVF alone. For women older than age 42, the model was most sensitive to the
implantation rates of IVF/PGS and IVF alone when the cohort size was small, to the EPL rate of IVF
alone and the implantation rates of IVF/PGS and IVF alone when the cohort size was intermediate,
and to the dropout rate, followed by the EPL and implantation rates of both groups, when the cohort
size was largest.
The model was not sensitive to the costs of COH and OPU nor the costs of fertility medications. The
model was not sensitive to the costs of embryo cryopreservation and storage. The model was not
sensitive to productivity costs of either partner.
4.1.4.2 Probabilistic Sensitivity Analysis
The results of the PSA are shown quantitatively in Table 7.5 and graphically, with scatter plots by
age group, in Figure 8.6.
For women less than age 35, regardless of the number of blastocysts available, the majority of ICER
estimates fell into the northwest quadrant of the CE plane. More than 95% of ICER estimates fell
into the northwest quadrant when seven or fewer blastocysts were available. More than 80% of ICER
estimates fell into the northwest quadrant when eight or more blastocysts were available, with most
of the remaining estimates landing in the northeast quadrant.
For women aged 35 to 37, as the number of blastocysts increased, the ICER estimates began to
migrate from the northwest quadrant towards the northeast quadrant of the CE plane. With three or
fewer blastocysts, more than 90% of the ICER estimates fell into the northwest quadrant. With eight
or more, 36% or fewer of the ICER estimates fell into the northwest quadrant, while 64% or more
fell into the northeast quadrant.
For women aged 37 to 40, the ICER estimates migrated from the northwest to northeast quadrants as
the number of blastocysts increased. More than 95% of ICER estimates fell into the northwest
34
quadrant when three or fewer blastocyst were available. This number fell to 25% when ten
blastocysts were available, with the remainder of the estimates falling into the northeast quadrant.
For women aged 41 to 42 with three or fewer blastocysts, more than 87% of ICER estimates fell into
the northwest quadrant, with most of the remaining estimates falling into the northeast quadrant and
less than 1% falling into the southwest quadrant. In the case of four blastocysts, 77.7% of estimates
fell into the northwest quadrant, 16.6% into the northeast quadrant, 5.6% into the southwest quadrant
and 0.1% into the southeast quadrant. With five or more blastocysts, the ICER estimates spanned all
four quadrants to varying degrees, with an increasing percentage of ICER estimates falling into the
northeast and southeast quadrants as the number of blastocysts increased. With ten blastocysts, there
was a 46.4% chance that IVF/PGS was more effective and more costly than IVF alone and a 40.5%
chance that IVF/PGS was more effective and less costly than IVF alone.
For women older than age 42, more than 95% of ICER estimates fell into the northwest quadrant
when one or two blastocysts were available. This number decreased to 81.1% with three blastocysts.
Once four blastocysts were available, the majority (61.4%) of ICER estimates fell into the southwest
quadrant, although estimates did occupy all four quadrants. This was also true for the case of five
blastocysts. With six blastocysts available, the ICER estimates fell into the southwest quadrant 75%
of the time and into the southeast quadrant 25% of the time. With seven and eight blastocysts, these
numbers shifted to 66% and 33%, and 55% and 45% respectively. With nine or ten blastocysts, the
majority of ICER estimates fell into the southeast quadrant – 58% and 64% respectively.
4.2 Alternative Perspectives
Table 7.3 summarizes the specific costs included in each of the three alternative perspectives. The
point estimates for C, E and ICER by age, number of blastocysts and perspective can be found in
Table 7.6(a-c). Scatter plots of selected PSA results can be found in Figure 8.7(a-b).
4.2.1 Direct Health Care
When productivity costs were excluded from the model, the results were, overall, similar to those of
the societal perspective. IVF/PGS was dominated by IVF alone for women less than age 35
regardless of the number of blastocysts available. IVF/PGS was also dominated by IVF alone for
women with four or fewer blastocysts, regardless of age. IVF/PGS was more effective and more
costly than IVF alone for women aged 35 to 37 with eight or more blastocysts, aged 38 to 40 with
35
nine or more blastocysts, and aged 41 to 42 with seven or more blastocysts. ICER point estimates for
these scenarios ranged from $22,809 to $830,922 per live birth. For women age 42 and older,
IVF/PGS was less effective and less costly than IVF alone for those with five to eight blastocysts.
IVF/PGS dominated IVF alone in this age group when nine or ten blastocysts were available.
4.2.2 Hypothetical MOHLTC
This perspective assumed that the MOHLTC would cover the cost of PGS in addition to the health
care costs related to pregnancy or its complications. From this perspective, IVF/PGS was dominated
by IVF alone when six or fewer blastocysts were available regardless of age and for women under
age 35 regardless of blastocyst cohort size. IVF/PGS was more effective and more expensive than
IVF alone for women ages 35 to 37 with eight or more blastocysts, ages 38 to 40 with nine or more
blastocysts, ages 41 to 42 with seven or more blastocysts, and above age 42 with nine or more
blastocysts. The ICER point estimates for these scenarios with ICERs in the northeast quadrant
ranged from $91,696 to $1,032,609 per live birth. There were no scenarios in which IVF/PGS was
cost saving or in which IVF/PGS dominated IVF alone.
4.2.3 Patient Self-Pay
When the model was analyzed using only those health care costs not covered by the MOHLTC, the
results were, overall, similar to those of the direct health care perspective (Section 4.2.1). IVF/PGS
was dominated by IVF alone for women under age 35 and for women with four or fewer blastocysts
regardless of age. IVF/PGS was more effective and more costly than IVF alone for women aged 35
to 37 with eight or more blastocysts, aged 38 to 40 with nine or more blastocysts, and aged 41 to 42
with seven or more blastocysts. ICER point estimates for these scenarios ranged from $19,716 to
$828,586 per live birth. For women age 42 and older, IVF/PGS was less effective and less costly
than IVF alone for those with five to eight blastocysts. IVF/PGS dominated IVF alone in this final
age group when nine or ten blastocysts were available.
36
5 Discussion
5.1 Key Findings
This study found that IVF/PGS became increasingly more effective and less costly compared to IVF
alone as either the age of the woman or the number of available blastocysts increased. IVF alone,
however, was the dominant strategy for women under age 35 and for women with three or fewer
blastocysts regardless of age. Sensitivity analyses showed that the model was most sensitive to the IR
and EPL rates of IVF/PGS and IVF alone. When only direct health care costs or patient self-pay
costs were considered, the results were very similar to those obtained when considering all costs
from a societal perspective.
IVF/PGS was the dominant strategy over IVF alone (more effective and less costly) only when
advanced reproductive age was coupled with large numbers of blastocysts available for biopsy. This
is a very uncommon clinical scenario. Data from CARTR Plus / BORN Ontario showed that 79% of
cycles in Ontario in 2013 resulted in five or fewer usable blastocysts and 92% of cycles resulted in
eight or fewer usable blastocysts (111). Data from a large study by Franasiak et al., which excluded
those cases in which no blastocysts were available to biopsy, found that half of all of their PGS cases
had three or fewer “evaluable blastocysts” (8). They also found that the mean cohort size of
blastocysts suitable to biopsy was five or fewer for all women aged 34 and above (8).
The present study differed from most economic evaluations in that it did not compare two competing
treatment strategies but rather the impact of a supplemental intervention, namely PGS, on top of the
current treatment approach. PGS is therefore, by its very nature, associated with additional upfront
costs above and beyond those associated with IVF alone. It follows that for IVF/PGS to be less costly
than IVF alone overall, it must result in downstream consequences associated with fewer costs
compared to the downstream consequences of IVF alone. The two downstream consequences that are
best situated to fulfil this criteria are (1) a decrease in the number of FETs needed to achieve a
pregnancy or exhaust an embryo cohort, and (2) a decrease in the EPL rate. The current study found
that the incremental cost-effectiveness of IVF/PGS did in fact improve as the difference in EPL rate
became more pronounced between the two groups and as the number of embryo transfers necessary
to reach the end of a cycle increased.
This study found that the addition of PGS to IVF resulted in lower CLBRs compared to IVF alone in
the majority of scenarios. While this may seem counterintuitive, particularly given the widespread
37
adoption of PGS into clinical practice, it is not unexpected when the data used to inform our model
inputs for implantation and EPL rates are considered in greater detail. This data, obtained from
SART, does not support the magnitude of benefit, either in terms of increased IR or decreased EPL
rates, found in other published studies of PGS outcomes (79). For women less than age 35, there was
only a 3.5% absolute increase in IRs with IVF/PGS per embryo transfer. This increased to 11.5%,
18.4%, 29.4% and 32.9% for the subsequent age groups. Similarly, the absolute decrease in EPL
rates with a pregnancy achieved after the transfer of a euploid embryo compared to a pregnancy
achieved after the transfer of an unscreened embryo transfer was -0.7% in the youngest age group
and 2.6%, 6.9%, 15.2% and 17.3% in the subsequent age groups. These values contrast markedly to
those of, for example, Yang et al. who randomized 112 good prognosis patients under age 35 to
euploid SET versus unscreened SET at the blastocyst stage (51). They found that the IR increased by
25.1% (from 45.8% to 70.9%) and that the EPL rate decreased by 6.5% (from 9.1% to 2.6%) with
IVF/PGS compared to IVF alone (51). It is important to remember that these values represent the
outcomes of the first embryo transfer only and are not cumulative in nature.
The differences between SART and clinical trial data, including the study presented above, could be
explained in part by the fact that studies of PGS are often done on selected groups of fertility
patients, for example, those under 35 years old with a good prognosis or, conversely, those with
previously failed IVF cycles and/or RPL. It is also possible that the patients captured by SART who
underwent IVF/PGS cycles – particularly in the younger age groups – are not representative of the
infertility population as a whole either. Because PGS is not considered the standard of care for all
patients undergoing IVF, patients who underwent IVF/PGS, particularly young woman, may have
been fundamentally different in terms of prognostic factors compared with their equally young
counterparts who did not do PGS. They may have, for example, been more likely to have a history of
previously failed IVF cycles or recurrent implantation failure. This may partly explain the lower than
expected IRs with IVF/PGS in the younger age groups compared with the RCTs of PGS. On the
other hand, patients are often counseled to opt into PGS only if a minimum number of blastocysts are
available. Because women of more advanced reproductive ages are less likely to achieve this number
of blastocysts, it is possible that the patients in the older age groups of the SART database who
underwent IVF/PGS were of better prognosis than their counterparts who underwent IVF alone.
All three currently published RCTs of PGS, and the majority of observational studies, report only on
the results of the first embryo transfer rather than the CLBR from one fresh cycle start – a more
38
clinically meaningful outcome (123,124). There is strong evidence that IVF/PGS is superior to IVF
alone when comparing the IRs of a euploid to an unscreened embryo (62); however, it is much less
clear whether or not this advantage holds true when one compares the CLBR from all usable
embryos obtained from one fresh cycle start. Moreover, many of these studies included the transfer
of multiple embryos at a time in one or both groups. Data from those studies are particularly difficult
to apply to Ontario where the government has instituted a very strict single embryo transfer policy
tied to its recent funding of IVF (15) and where the rate of eSET is already much higher than it is in
the United States (125,126). In addition to these issues with study design, there is a growing body of
evidence that the absolute benefits of PGS may not be as impressive as the original studies suggested
(62,127–130).
Importantly, the per transfer IR and EPL rates are not the only key driver of the CLBR after PGS –
either in our model or in reality. The number of available euploid blastocysts and the percentage of
transferrable euploid embryos relative to the size of the unscreened embryo cohort (i.e. the
aneuploidy rate) are also a key driver of the primary outcome of interest and factors that are often
overlooked in the literature. Our study found that the model became sensitive to the dropout rate as
the blastocyst cohort size increased, particularly in the older age groups. This supports the hypothesis
that a smaller cohort of higher potential blastocysts can decrease dropout rates and therefore improve
the CLBRs. A higher IR per individual embryo transfer also has the ability to decrease the time to
conception, which although not explicitly studied in this model, is extremely important to patients.
5.2 Comparison with Other Studies
As highlighted in section 2.3.3, there are a limited number of other studies that have examined the
cost-effectiveness of PGS using CCS (68–73). These studies have used various methodologies,
looked at different populations of patients, and have had mixed results. In a published abstract, Neal
et al. reported on the costs of performing sequential single euploid versus unscreened embryo
transfers from one cycle of COH and OPU (71). They use individual patient data from a retrospective
cohort of nearly 12,000 IVF/PGS cycles. Unlike the present study, however, they calculated the
direct ART costs associated with each of those IVF/PGS cycles and compared them to the
hypothetical direct IVF costs and transfer outcomes should those cycles have proceeded without
PGS. Their end point was either live birth or exhaustion of the embryo cohort. Neither the average
female age nor the average blastocyst cohort size was described in the abstract. They found that there
were more than double the number of embryo transfers and many more pregnancy losses in the IVF
39
alone arm compared to the PGS arm. They concluded that, overall, the average cost until either live
birth or exhaustion of the embryo cohort was reached was lower with IVF/PGS and that IVF/PGS
was associated with a lower ACER when three or more embryos were available to biopsy (71). The
study did not model the option of dropping out of treatment prematurely, which would have further
favoured IVF/PGS over IVF alone. This study is limited by the fact that it relied upon a population of
patients who not only planned but ultimately completed a PGS cycle. It is reasonable to assume that
this population was different from the general IVF population and therefore the aneuploidy rates
(which were used to calculate the LBRs in their model) may not have been appropriate in their IVF
alone arm. Nonetheless, this study lends strong supports to the widely held belief that PGS can
reduce the number of transfers and therefore the time to cycle completion (either live birth or
exhaustion of embryo cohort) compared to IVF alone. It is difficult to assess, however, the
appropriateness of their included costs and clinical input parameters (particularly their LBR values)
from an abstract alone. Ultimately, the differing results between the Neal et al. abstract and our
present study are most likely due to a combination of differences in key clinical input parameters,
included costs and model structure.
The abstract published by Salem et al., on the other hand, used a model structure and reported results
that were more in keeping with the results of the present study (72). Their economic evaluation was
limited to women aged 39 and up and focused on the impact of varying PGS false positive rates on
the average cost per live birth. They found that even with low false positive rates, IVF/PGS resulted
in lower LBRs per cycle and therefore was less cost-effective than IVF alone (72). Their results, like
ours, were in large part due to the use of SART data to inform their clinical outcome parameters. One
major limitation of their study, however, was the assumption that all women had two blastocysts
suitable to biopsy rather than creating a model that incorporated varying blastocyst cohort sizes.
Scriven et al. recently published the only full length paper on the cost-effectiveness of PGS using
CCS (73). The study used Microsoft Excel to model sequential single embryo transfers from a single
cycle of COH, to a maximum of ten, until either a live birth was achieved or no embryos remained.
He used data from a study by Scott et al. (74) to arrive at LBRs of 25.4% and 43.1% for IVF alone
and IVF/PGS, respectively. Similarly, EPL rates of 8.5% and 5.1% for IVF alone and IVF/PGS,
respectively, were used. An embryo survival rate of 94% was used for cryopreserved embryos. The
study found that IVF/PGS was effective in decreasing the number of transfers and the EPL rate. It
also showed that IVF/PGS was effective at increasing the LBR from the first transfer. As expected
40
and in line with the results of Salem et al. (72), the Scriven study found that the greater the false
positive rate of PGS, the more the model favoured IVF alone. His model also found that regardless of
embryo cohort size, IVF/PGS resulted in fewer live births compared to IVF alone and was therefore
not cost-effective from a societal perspective. This study did not model dropout rates nor did it
include productivity costs associated with ART treatment itself. Furthermore, the study used data
from a single small non-selection study of PGS using aCGH. It does, however, strongly support the
argument that until PGS can be shown to be effective in increasing the CLBR from one fresh cycle of
IVF, it cannot be cost-effective.
5.3 Strengths
For this study, a novel decision model using TreeAge Pro to study the CLBRs from one fresh cycle
of IVF with or without the addition of PGS was designed. This model was specifically created to be
easily adaptable to varying blastocyst cohort sizes and to reflects the clinical reality that the number
of transferrable embryos after PGS is a function of both female age and original embryo cohort size.
Moving forwards, this model could be modified to study other interventions pertaining to IVF – for
example, embryo selection using time-lapse imaging or mitochondrial DNA content. It could also be
updated with new clinical and cost parameters as they become available.
This model is not only novel in its structure but it also includes a number of features absent from
other models used to study cost-effectiveness in the field of infertility. Not only were dropout rates
included in the model, but differential dropout rates were used depending on if an embryo transfer
failed to result in a pregnancy or if it resulted in a pregnancy that was subsequently lost. To our
knowledge, we are not aware of any other studies to date that have included this feature. The present
study did not assume that all EPLs were failed intrauterine pregnancies treated with surgical
management. Costs were divided between expectant, medical and surgical management options for
intrauterine EPLs and between medical and surgical management options for ectopic pregnancies.
Finally, the productivity costs of the partner were included in our study – a societal cost that has been
excluded from the majority of previously published CEAs related to fertility treatments.
The use of population-based data to inform our model means that the results of this study are more
generalizable to the larger infertility population than cost-effectiveness studies that have relied on the
results of a single trial for their model inputs. This, together with the extensiveness of the model
41
design and included costs, makes this study one of the most comprehensive studies of the cost-
effectiveness of PGS to date.
5.4 Limitations
5.4.1 Clinical Parameters
There were a number of limitations in this study related to the values used to inform implantation and
EPL rates with IVF/PGS and IVF alone. The pace at which PGS technology is advancing has made it
challenging for administrative databases to keep up with complete and accurate data collection. The
CARTR Plus / BORN Ontario database does not currently capture the ploidy status of transferred
embryos nor does it accurately or consistently differentiate screened from unscreened embryos at the
time of embryo transfer. For these reason, we were unable to use Ontario data to inform the
implantation and EPL rates in our model (see Section 6.5). Instead this study relied upon population
level data from SART; however, because individual level data was not available, it was not possible
to control for potential differences between the group of patients who underwent IVF/PGS and those
who underwent IVF alone.
Additionally, this study used constant implantation and pregnancy loss rates that were independent of
the number of previous transfers performed. This was done because data on IRs and EPL rates by
number of previous unsuccessful transfers were not available. There is reason to believe, however,
that the LBR may decrease with each successive transfer rather than remaining constant. Similarly,
the present study used a constant dropout rate after failed transfers and a constant, but slightly higher,
dropout rate after EPLs, regardless of the number of previous transfers or remaining transferrable
embryos. The same dropout rates were used in both arms of the model. To our knowledge, there is no
available data to either support or refute these specific assumptions. A dropout rate that increased
with the number of previous unsuccessful transfers would have favoured the IVF/PGS arm of the
model because subjects in that arm underwent fewer total embryo transfers before exhausting their
embryo cohort. To address these limitations, the dropout rates, along with the IR and EPL rates, were
subjected to one-way and probabilistic sensitivity analyses (see Section 4.1.4). Furthermore, the
model was designed such that it can be updated in the future and reanalyzed when these data become
available.
As discussed in section 2.1.3, CCS can be performed using a number of different technologies,
including aCGH and NGS. Much of the currently available clinical data – both in population
42
databases and in published trials – is derived from aCGH-based technologies. Recently, however,
there has been a rapid shift in the global landscape towards NGS-based approaches. Studies
comparing the two technologies (and using NGS as the gold standard) have repeatedly found that
aCGH is associated with a higher false positive rate than initially believed – meaning that aneuploid
(and particularly mosaic aneuploid) embryos were unknowingly being misclassified as euploid and
subsequently transferred (118,131). Mosaic embryos are less likely to implant and more likely to lead
to pregnancy losses compared with euploid embryos (132). This may be one of the reasons why PGS
outcome data from population-based sources such as SART reported differences in EPL rates
between screened and unscreened embryos that were less than expected based on earlier small-scale
studies.
5.4.2 Cost Data
In addition to the limitations described above, there were a number of limitations related to the cost
estimates used in this study. Productivity losses were estimated using the best available data;
however, detailed information relating to the amount of time absent from work due to a fresh IVF
cycle, a frozen embryo transfer cycle, a normal pregnancy, a pregnancy resulting in an intrauterine
EPLs or a pregnancy resulting in an ectopic pregnancy was not available. The data that were
available were not specific to Canada. Productivity losses were therefore estimated using expert
opinion and average hourly wage rates from Statistics Canada. Even less data were available relating
to the productivity losses of partners of individuals going through IVF procedures. We chose to
include these costs, nonetheless, to reflect the clinical reality that the IVF process often results in
work absences for two people rather than one. We stopped short, however, of extending the partners’
productivity losses into pregnancy and delivery. Sensitivity analyses, including the alternative
perspectives analyzed, found that changes in productivity loss estimates did not significantly impact
the results; therefore, it is unlikely that the inclusion of additional productivity losses on behalf of the
partner would have impacted our conclusions.
Cost estimates relating to the management of EPL were limited by the lack of information on the
relative use of expectant management, medical management and surgical management to treat EPL
in Canada. Other studies have chosen to assume that all cases of EPL were managed surgically. This
would overestimate the costs associated with EPL and so in the present study, we used a conservative
estimate and distributed costs equally amongst all three management options. Hospital costs were
obtained from OCCI and as a result, cost estimates for surgical management of EPL and medical and
43
surgical management of ectopic pregnancies did not account for the proportion of those conditions
managed successfully in an outpatient setting. IVF medication costs were calculated based on expert
opinion and local practice; however, it is acknowledged that there are many accepted protocols for
COH, FET cycles, and luteal phase support and that each require different medications for varying
lengths of time. To account for these and other uncertainties, extensive one-way and probabilistic
sensitivity analyses were performed. Ultimately, IR and EPL rates, and to a lesser degree dropout
rates, were found to be much more significant drivers of the model outcome than the costs related to
EPL management or IVF medications.
5.4.3 Model Design
The final group of limitations that must be addressed are those related to the model design itself.
While the present model is more representative and comprehensive than other models of the CLBR
after IVF/PGS, it is not perfect. The model does not include the option of ongoing aneuploid
pregnancies nor the related costs of prenatal diagnosis and genetic termination in either arm. In
reality, IVF/PGS detects embryos with viable aneuploidies and excludes them from the transferrable
cohort. IVF/PGS should therefore result in lower rates of ongoing aneuploid pregnancies and genetic
terminations compared to IVF alone. The costs associated with these pregnancies were not captured
in the present study. In this study, it was assumed that all usable blastocysts were successfully
cryopreserved and thawed without any attrition. Additionally, the model assumed that all transfers
were single embryo transfers and therefore did not capture the benefits – either with respect to health
outcomes or cost savings to the health care system – of a lowering of the multiple birth rate with
IVF/PGS compared to IVF alone.
5.5 CARTR Plus / BORN Ontario
At the outset of this study, the intent was to use data captured in CARTR Plus / BORN Ontario to
inform the clinical input parameters of our model. However, because of a number of key limitations
with respect to how information related to PGS were captured, data was ultimately obtained
elsewhere. It is worthwhile, therefore, to take a moment to discuss medical registries in general, and
CARTR Plus / BORN Ontario specifically, in order to explore ways in which CARTR Plus / BORN
Ontario may consider adapting in order to better meet the needs of its end users.
This analysis builds on the work done by Arts et al. in their 2002 review and synthesis of the
literature surrounding data quality in medical registries (133). In their publication, Arts et al. put
44
forth a categorization of causes of insufficient data quality and propose a framework of procedures in
order to assure high quality data in medical registries. They organized the causes of poor data quality
into three time points (set up of the registry, data collection and quality improvement) and two
locations (central coordinating centre and local sites). Specific errors are further categorized as either
random or systematic in nature. Their complete categorization is shown in Appendix 9.1.
A medical registry can be defined as a “systematic collection of a clearly defined set of health and
demographic data for patients with specific health characteristics, held in a central database for a
predefined purpose” (133). Like other registries, birth registries can be used for several purposes
including to improve patient care and conduct research. In order to be effective, however, its data
must be of a sufficient quality for the needs of its intended users (133). Data quality is generally
considered a function of two constructs – accuracy, the extent to which the data is true, and
completeness, the extent to which all data that could have been included is included (133).
In 2009, with funding from the Ontario MOHLTC, the Ontario Perinatal Surveillance System was
combined with five other partner groups to form BORN Ontario (134). Its mission includes being “an
authoritative source of accurate, trusted and timely information” about maternal, newborn and
childhood health in Ontario (135). In 2012, the medical directors of Canadian fertility clinics entered
into an agreement with BORN Ontario to have it “collect, analyze and report” the data contained
within the CARTR, which was subsequently rebranded as CARTR Plus. This joint undertaking
allows for data from Ontario ART patients to be linked with their pregnancy, birth and childhood
data collected through BORN Ontario (78).
5.5.1 Data Quality
The most significant challenge of working with the individual-level data obtained from CARTR Plus
/ BORN Ontario concerned the identification of individuals undergoing IVF/PGS cycles. In order to
study the effects of PGS (or any other intervention) it is of critical importance to accurately identify
which subjects within the cohort were exposed to the intervention (the treatment group) and which
were not (the control group). Inclusion of too many controls in the treatment group can lead to an
underestimation of treatment effect, or vice versa. When CARTR Plus was set up, the importance of
capturing which cycles included PGS and/or PGD was recognized and these terms were clearly
defined in their data dictionary (available at http://datadictionary.bornontario.ca/encounters-and-
alphabetical-lists/fertility-cartr-plus/). During the design phase of the registry, however, a decision
45
was made to capture this information under the data point “Reason for Treatment Cycle” – a pick list
of twenty items relating to the underlying causes of infertility or reasons for ART. Those options are:
- Male factor
- Endometriosis
- Tubal factor
- PCOS
- Other ovulatory disorder
- Diminished ovarian reserve
- Advanced female age
- Uterine factor
- Peritoneal factor or severe adhesions
- Gonadotoxic therapy
- Other female factor
- No male partner
- No female partner
- Unexplained infertility
- PGD for known genetic factor
- PGS for aneuploidy screening
- Oocyte banking - cancer treatment
- Oocyte banking - social reasons
- Embryo banking - cancer treatment
- Embryo banking - social reasons.
Users can choose as many items from this list as are applicable for a given case.
The primary problem with capturing PGS cycles in this way is that PGS in and of itself is not a
reason for someone to pursue IVF. PGS is better thought of as an additional test that is available to
those people undergoing IVF for another reason. It follows that PGS should be recorded in a method
similar to other tests, tools or procedures such as ICSI, assisted hatching or time-lapse photography.
One possible solution is to separate this pick-list into two separate data points, one entitled “fertility
diagnoses” and one “other indications for treatment” and to remove PGS from this section altogether
and record it elsewhere. Under this proposed scheme, the first list (“Reasons for Treatment cycle -
Fertility Diagnoses”) could include the following:
- Male factor
- Endometriosis
- Tubal factor
- PCOS
- Other ovulatory disorder
- Diminished ovarian reserve
- Advanced female age
- Uterine factor
- Peritoneal factor or severe adhesions
- Gonadotoxic therapy
- Unexplained infertility
46
- Other female factor
- No known fertility issue.
The second list (“Reasons for Treatment Cycle - Other Reasons for Treatment”) could include:
- No male partner
- No female partner
- PGD for known genetic factor
- Oocyte banking – oncology
- Oocyte banking – non-oncology medical
- Oocyte banking – non-medical
- Embryo banking – oncology
- Embryo banking – non-oncology medical
- Embryo banking – non-medical.
Users could choose as many options from each of the two lists as are applicable for a given case.
PGS could be selected regardless of which options are selected as the reasons for treatment. These
changes would allow for data about the reasons for treatment to be collected in a more informative
way.
The second significant issue that limited the ability to accurately differentiate treatment subjects from
controls related to the way in which the data was entered. Although the definition of PGS was clearly
defined in the dictionary, it was unclear to those entering the data how to handle cases in which PGS
was planned but not ultimately performed (T. Nichols, personal communication). It is known that
these cases make up a significant portion of all planned PGS cases because patients and physicians
often set a target number of blastocysts a priori above which PGS will be performed and below
which it will not – and that target is not always met. Without clear instructions about how to input
these cases, there is reason to believe that some users selected “PGS” from the “Reason for
Treatment Cycle” pick-list for all cases in which PGS was planned whereas others selected it only for
those cases that ultimately reached the point of embryo biopsy. There is also the issue of capturing
those cycles in which a fresh embryo transfer was performed and only on surplus blastocysts undergo
PGS. To overcome this issue, CARTR Plus is encouraged to add two data points to capture: (1)
whether or not PGS was planned at the start of an IVF cycle (on all blastocysts or excess blastocysts
only) and (2) whether or not embryo biopsy for the purposes of PGS was ultimately performed. If
PGS was planned but not performed, there could be a pick-list of reasons why it was not – similar to
the “Reason for no ET” data point. Options could include:
- Cycle cancelled prior to OPU
- No oocytes retrieved
- All embryos arrested
- No embryos suitable to biopsy
47
- Unscreened embryo(s) transferred and/or cryopreserved without biopsy
- Other.
A similar system could be put in place for PGD, with the exception that PGD should also remain as
an option under the data point “Reasons for Treatment Cycle – Other Reasons for Treatment.”
The above suggestions would allow data users to accurately differentiate three groups of patients:
those who did not do PGS, those who planned to but did not do PGS, and those who did PGS. And
while this would eliminate one of the major issues around using CARTR Plus / BORN Ontario for
PGS and/or PGD-related research, these changes alone would not be sufficient. That is because the
registry does not currently capture the genetic results of PGS and/or PGD cycles. Until this
information is captured accurately and completely, the utility of CARTR Plus / BORN Ontario for
PGS and/or PGD-related research will remain appreciably compromised. The number of PGS and/or
PGD cycles is rising rapidly and the complete and accurate recording of these results is becoming
increasingly important. In 2012, there were 165 fresh or frozen PGS and/or PGD cycles in Canada
(136). In 2014 that number rose to 762 (126) and in 2015 it jumped to 2,355 (5). CARTR Plus /
BORN Ontario is encouraged to act swiftly to update their registry to include embryo biopsy results
as soon as is feasible. The information captured should include, at a minimum, the following:
- day of embryo biopsy (i.e. Day 3, 5 or 6)
- whether or not the oocytes and/or embryos had been previously frozen prior to biopsy
- platform used (i.e. FISH, aCGH, qPCR, NGS)
- biopsy results (including cases of mosaicism, indeterminate and failure to amplify).
Promisingly, CARTR Plus / BORN Ontario has already updated the data point “Reason for no ET” to
reflect the clinical reality that many freeze-all cycles are a result of PGS. Furthermore, CARTR Plus /
BORN Ontario plans to start reporting CLBR data in 2017 (137). A summary of some of the causes
of inadequate data quality found in CARTR Plus / Born Ontario along with suggestions for
improvement can be found in Table 7.7.
5.5.2 Data Accessibility
For a registry to succeed in achieving its goals the data have to be of good quality (accurate and
complete) and also accessible to its end users. Those end users include not only the stakeholders
involved in a registry’s creation but may also government agencies, policy-makers, academic
institutions, community organizations and individual clinicians and researchers. And just as data
quality can be understood as being a function of accuracy and completeness, data accessibility can be
48
thought of as a function of both timeliness and ease of access. These two constructs are interrelated
because overly bureaucratic access procedures can hinder timeliness. Put together, all these factors
form the basis of data usability. A graphical representation of the components of data usability is
shown in Figure 8.8.
According to its mission, BORN Ontario strives to “mobilize information and expertise to optimize
care” (135). Working with researchers across the province and encouraging high quality research
using their data is consistent with this mission. Therefore, in order to increase the usability of their
data to individual researchers, BORN Ontario may consider improving some features of their data
accessibility. While recognizing that the privacy of Ontarians and the security of personal health
information is paramount, timely access to data is necessary to perform truly impactful research. This
is particularly true in the field of assisted reproductive technologies, where new technologies are
continuously emerging. PGS is a particularly good example of this phenomenon. Only five or six
years ago, PGS was being performed overwhelmingly using FISH technology. A study published
now using PGS data from five years ago, therefore, would be entirely irrelevant in today’s clinical
reality. The same argument could even be made, but to a lesser degree, for the transition that is
currently underway from aCGH to NGS-based CCS. CARTR Plus / BORN Ontario has the potential
to support countless research endeavors and influence numerous policy debates on topics spanning
from the preconception period all the way through early childhood. To realize its goals, however, it
should continue to work towards improving their procedures for efficient and timely access to their
data. They are also encouraged to put in place mechanisms to facilitate end-user feedback and use
that feedback to inform their quality improvement plans. Given the paucity of published population
level data on PGS outcomes, CARTR Plus / BORN Ontario has the ability to uniquely position
themselves to provide answers to critical questions for years to come. I would welcome the
opportunity to work with them towards this goal both now and in the future.
5.6 Non-Economic Advantages of PGS
Cost-effectiveness analyses and other economic evaluations are important tools for quantifying the
relative trade-offs between alternative treatment strategies. However, when it comes to clinical
decision-making, particularly on an individual-patient level, there are other factors to consider when
deciding whether or not to adopt a new technology like PGS. One of the main limitations of the
present study is its inability to measure either the importance of time to conception or the
psychological benefits of reduced duration of infertility. Furthermore, there are also clear
49
psychological benefits to minimizing the incidence of EPL, especially among those already
struggling to conceive. Whether or not fertility patients would be willing to accept a slightly lower
CLBR with the addition of PGS in order to minimize their risk of a pregnancy loss and/or increase
their chance of achieving a pregnancy sooner is unknown and deserves further study. Additionally,
the potential psychological benefits of reduced time to conception and lower EPL rates may also
translate to economic benefits if one assumes that people suffering from infertility are not able to
contribute maximally to society due to the negative effects of their disease. These potential benefits
are not currently being captured in CEAs of PGS. It is important to note, conversely, that a
significant proportion of patients who choose to do PGS will have no euploid embryos available to
transfer and that further study is also required regarding the psychological effects of this outcome.
5.7 Future Directions
This study has highlighted a number of gaps in the literature that could and should be addressed
moving forwards. It has also become clear that this model has great potential to be used in other
capacities to continue to study the cost-effectiveness of PGS. This decision model comparing
IVF/PGS to IVF alone can and should be updated as new clinical and cost data become available.
Specifically, as data on pregnancy and live birth rates after PGS using NGS are published, this model
could be updated and the results reanalyzed with those values. The model could also be expanded to
address a number of the limitations discussed above including the possibility of ongoing aneuploid
pregnancies and of embryo non-survival after cryopreservation.
The process of collecting data for this study has drawn attention to a number of other potential
research opportunities. There is currently a lack of Canadian data regarding productivity losses
around fertility treatment. A study that collected information about time absent from work, for both
women and their partners, as applicable, due to infertility treatments and resulting normal and
abnormal pregnancies would be extremely valuable, not only for economic evaluations related to
PGS but for numerous economic evaluations related to the treatment of infertility in general. A study
that quantified the dropout rates within a single fresh IVF cycle, both with and without the use of
PGS, would also be an extremely valuable addition to the scientific literature and could easily be
done using retrospective records.
Finally, as discussed above, there are many opportunities to study the benefits of PGS that are not
directly addressed by way of economic evaluation including time to conception, time to cycle
50
completion and reduction in EPL rates. Methodologies should be continually developed to
incorporate these values and preferences into future studies of PGS and other embryo selection
techniques.
5.8 Conclusions
In this study, we constructed a novel decision tree to examine the cost-effectiveness of IVF/PGS
compared to IVF alone, in terms of live births, from a societal perspective in Ontario, Canada. The
study found that IVF/PGS became increasingly more effective and less costly compared to IVF alone
as either the age of the woman or the number of available blastocysts increased. IVF alone, however,
remained the dominant strategy for women under age 35 and for women with three or fewer
blastocysts regardless of age. IVF/PGS was the dominant strategy only when advanced reproductive
age was coupled with large numbers of blastocysts available for biopsy – a very uncommon clinical
scenario. Therefore, currently available data does not support the use of IVF/PGS as a cost-effective
intervention except in older women with large embryo cohorts. Because the model was found to be
sensitive to the IR and EPL rates of both IVF/PGS and IVF alone, high quality research is needed in
order to better understand the impact of PGS on CLBRs after IVF. The economic advantages of PGS
resulting from a reduction in the number of FETs and lower EPL rates should be studied alongside
other potential benefits of PGS, such as a reduction in the time to cycle completion and lower
dropout rates.
This study adds considerably to the small but growing body of literature concerning the cost-
effectiveness of PGS by studying the incremental cost-effectiveness of PGS as both a function of
female age and embryo cohort size. This novel, comprehensive model of PGS that we have built can
be used, and improved upon, to enhance our understanding of the potential benefits and drawbacks of
PGS. The results of this study can be used to encourage further discussions regarding the role of PGS
in clinical practice and to shape future research studies on the subject.
51
6 References
1. Bushnik T, Cook JL, Yuzpe AA, Tough S, Collins J. Estimating the prevalence of infertility in
Canada. Hum Reprod. 2012 Mar 1;27(3):738–46.
2. Cousineau TM, Domar AD. Psychological impact of infertility. Best Pract Res Clin Obstet
Gynaecol. 2007 Apr;21(2):293–308.
3. Cohen J. From Pythagoras and Aristotle to Boveri and Edwards: a history of clinical embryology
and therapeutic IVF. In: Textbook of Clinical Embryology [Internet]. Cambridge University Press;
2013. Available from: http://dx.doi.org/10.1017/CBO9781139192736.021
4. Fritz MA, Speroff L. Clinical gynecologic endocrinology and infertility [Internet]. 8th ed.
Philadelphia: Wolters Kluwer Health/Lippincott Williams & Wilkins; 2011.
5. Better Outcomes Registry & Network Ontario. Canadian Assisted Reproductive Technologies
Register (CARTR) Plus: Final treatment cycle and pregnancy outcome data for 2014 &
preliminary treatment cycle data for 2015. CFAS 62nd Annual Meeting; 2016 Sep; Toronto,
Ontario.
6. Wilton L. Preimplantation genetic diagnosis for aneuploidy screening in early human embryos: a
review. Prenat Diagn. 2002 Jun 1;22(6):512–8.
7. Nagaoka SI, Hassold TJ, Hunt PA. Human aneuploidy: mechanisms and new insights into an age-
old problem. Nat Rev Genet. 2012 Jul 1;13(7):493–504.
8. Franasiak J, Forman E, Hong K, Werner M, Upham K, Treff N, et al. The nature of aneuploidy
with increasing age of the female partner: a review of 15,169 consecutive trophectoderm biopsies
evaluated with comprehensive chromosomal screening. Fertil Steril. 2014 Mar;101(3):656–63.
9. Capalbo A, Rienzi L, Cimadomo D, Maggiulli R, Elliott T, Wright G, et al. Correlation between
standard blastocyst morphology, euploidy and implantation: an observational study in two centers
involving 956 screened blastocysts. Hum Reprod. 2014 Jun 1;29(6):1173–81.
10. Dahdouh EM, Balayla J, García-Velasco JA. Comprehensive chromosome screening improves
embryo selection: a meta-analysis. Fertil Steril. 2015 Dec;104(6):1503–12.
11. Forman EJ. Obstetrical and neonatal outcomes from the BEST Trial: single embryo transfer with
aneuploidy screening improves outcomes after in vitro fertilization without compromising delivery
rates. 2014;210(2):157.e1-157.e6.
12. Practice Committee of American Society for Reproductive Medicine. Multiple gestation associated
with infertility therapy: an American Society for Reproductive Medicine Practice Committee
opinion. Fertil Steril. 2012 Apr;97(4):825–34.
13. Practice Committee of the Society for Assisted Reproductive Technology and Practice Committee
of the American Society for Reproductive Medicine. Elective single-embryo transfer. Fertil Steril.
2012 Apr;97(4):835–42.
14. Quebec Commissaire a la sante et au bien-etre. Summary Advisory on Assisted Reproduction in
Quebec [Internet]. 2014 Jun [cited 2014 Oct 9]. Available from:
52
http://www.csbe.gouv.qc.ca/fileadmin/www/2014/Procreation_assistee/CSBE_PA_SummaryAdvi
sory_2014.pdf
15. Greenblatt E, Advisory Process for Infertility Services. Advisory Process for Infertility Services
Key Recommendations Report [Internet]. 2015 Jun [cited 2016 Jan 20] p. 20. Available from:
http://health.gov.on.ca/en/public/programs/ivf/docs/ivf_report.pdf
16. Ubaldi FM, Capalbo A, Colamaria S, Ferrero S, Maggiulli R, Vajta G, et al. Reduction of multiple
pregnancies in the advanced maternal age population after implementation of an elective single
embryo transfer policy coupled with enhanced embryo selection: pre- and post-intervention study.
Hum Reprod. 2015 Sep 1;30(9):2097–106.
17. Practice Committee of the American Society for Reproductive Medicine. Guidance on the limits to
the number of embryos to transfer: a committee opinion. Fertil Steril. 2017 Apr;107(4):901–3.
18. Andrews JR, Lawn SD, Dowdy DW, Walensky RP. Challenges in Evaluating the Cost-
effectiveness of New Diagnostic Tests for HIV-Associated Tuberculosis. Clin Infect Dis Off Publ
Infect Dis Soc Am. 2013 Oct 1;57(7):1021–6.
19. Drummond MF, Sculpher MJ, Claxton K, Stoddart GL, Torrance GW. Methods for the Economic
Evaluation of Health Care Programmes. Fourth Edition edition. Oxford, United Kingdom ; New
York, NY, USA: Oxford University Press; 2015. 464 p.
20. Okun N, Teitelbaum M, Huang T, Dewa CS, Hoch JS. The price of performance: a cost and
performance analysis of the implementation of cell-free fetal DNA testing for Down syndrome in
Ontario, Canada. Prenat Diagn. 2014 Apr;34(4):350–6.
21. Detsky AS, Laupacis A. Relevance of Cost-effectiveness Analysis to Clinicians and Policy
Makers. JAMA. 2007 Jul 11;298(2):221–4.
22. Goodman C. Introduction to Health Technology Assessment. Bethesda, MA: National Library of
Medicine (US); 2014.
23. Hoch JS, Dewa CS. A clinician’s guide to correct cost-effectiveness analysis: Think incremental
not average. Can J Psychiatry. 2008;53(4):267.
24. Detsky AS, Naglie IG. A Clinician’s Guide to Cost-Effectiveness Analysis. Ann Intern Med. 1990
Jul 15;113(2):147–54.
25. Hoch JS, Briggs AH, Willan AR. Something old, something new, something borrowed, something
blue: a framework for the marriage of health econometrics and cost‐effectiveness analysis. Health
Econ. 2002;11(5):415–30.
26. Gray AM, Clarke PM, Wolstenholme J, Wordsworth S. Applied Methods of Cost-effectiveness
Analysis in Healthcare. 1 edition. Oxford University Press; 2010. 328 p.
27. Owens DK. Interpretation of Cost-Effectiveness Analyses. J Gen Intern Med. 1998
Oct;13(10):716–7.
28. Domar AD, Zuttermeister PC, Friedman R. The psychological impact of infertility: a comparison
with patients with other medical conditions. J Psychosom Obstet Gynaecol. 1993;14:45–45.
53
29. Handyside AH, Penketh RJA, Winston RML, Pattinson JK, Delhanty JDA, Tuddenham EGD.
Biopsy of human preimplantation embryos and sexing by DNA amplification. The Lancet. 1989
Feb 18;333(8634):347–9.
30. Handyside AH, Kontogianni EH, Hardy K, Winston RML. Pregnancies from Biopsied Human
Preimplantation Embryos Sexed by Y-Specific DNA Amplification. Nature. 1990 Apr
19;344(6268):768–70.
31. Handyside AH. Preimplantation genetic diagnosis after 20 years. Reprod Biomed Online. 2010
Sep;21(3):280–2.
32. Verlinsky Y, Cieslak J, Freidine M, Lvakhnenko V, Wolf G, Kovalinskaya L, et al. Pregnancies
following pre-conception diagnosis of common aneuploidies by fluorescent in-situ hybridization.
Mol Hum Reprod. 1995 Jul 1;1(5):265–9.
33. Practice Committee of the Society for Assisted Reproductive Technology, Practice Committee of
the American Society for Reproductive Medicine. Preimplantation genetic testing: a Practice
Committee opinion. Fertil Steril. 2008 Nov;90(5, Supplement):S136–43.
34. De Vos A, Van Steirteghem A. Aspects of biopsy procedures prior to preimplantation genetic
diagnosis. Prenat Diagn. 2001 Sep 1;21(9):767–80.
35. Harper JC, Sengupta SB. Preimplantation genetic diagnosis: State of the ART 2011. Hum Genet.
2012 Feb;131(2):175–86.
36. Munne S, Dailey T, Sultan KM, Grifo J, Cohen J. Diagnosing and preventing inherited disease:
The use of first polar bodies for preimplantation diagnosis of aneuploidy. Hum Reprod. 1995 Apr
1;10(4):1014–20.
37. Scott KL, Hong KH, Scott Jr. RT. Selecting the optimal time to perform biopsy for
preimplantation genetic testing. Fertil Steril. 2013 Sep;100(3):608–14.
38. Twisk M, Mastenbroek S, van Wely M, Heineman MJ, Van der Veen F, Repping S.
Preimplantation genetic screening for abnormal number of chromosomes (aneuploidies) in in vitro
fertilisation or intracytoplasmic sperm injection. Cochrane Database Syst Rev [Internet]. 2006.
39. Mastenbroek S, Twisk M, van der Veen F, Repping S. Preimplantation genetic screening: a
systematic review and meta-analysis of RCTs. Hum Reprod Update. 2011 Jul 1;17(4):454–66.
40. Scott RT, Upham KM, Forman EJ, Zhao T, Treff NR. Cleavage-stage biopsy significantly impairs
human embryonic implantation potential while blastocyst biopsy does not: a randomized and
paired clinical trial. Fertil Steril. 2013 Sep;100(3):624–30.
41. Harper JC, Harton G. The use of arrays in preimplantation genetic diagnosis and screening. Fertil
Steril. 2010 Sep 1;94(4):1173–7.
42. Frumkin T, Malcov M, Yaron Y, Ben-Yosef D. Elucidating the origin of chromosomal aberrations
in IVF embryos by preimplantation genetic analysis. Mol Cell Endocrinol. 2008 Jan 30;282(1–
2):112–9.
54
43. Barbash-Hazan S, Frumkin T, Malcov M, Yaron Y, Cohen T, Azem F, et al. Preimplantation
aneuploid embryos undergo self-correction in correlation with their developmental potential. Fertil
Steril. 2009 Sep;92(3):890–6.
44. Dokras A, Sargent IL, Ross C, Gardner RL, Barlow DH. Trophectoderm biopsy in human
blastocysts. Hum Reprod. 1990 Oct 1;5(7):821–5.
45. Kokkali G, Vrettou C, Traeger-Synodinos J, Jones GM, Cram DS, Stavrou D, et al. Birth of a
healthy infant following trophectoderm biopsy from blastocysts for PGD of β-thalassaemia major:
Case report. Hum Reprod. 2005 Jul 1;20(7):1855–9.
46. Treff N, Scott RT Jr. Methods for comprehensive chromosome screening of oocytes and embryos:
capabilities, limitations, and evidence of validity. J Assist Reprod. 2012 May;29(5):381–90.
47. Dahdouh E, Balayla J, Audibert F, Genetics Committee, Society of Obstetricians and
Gynaecologists of Canada. Technical update #323: preimplantation genetic diagnosis and
screening. J Obstet Gynaecol Can. 2015 May;37(5):451–63.
48. Brezina PR, Anchan R, Kearns WG. Preimplantation genetic testing for aneuploidy: what
technology should you use and what are the differences? J Assist Reprod Genet. 2016
Jul;33(7):823–32.
49. Hellani A, Abu-Amero K, Azouri J, El-Akoum S. Successful pregnancies after application of
array-comparative genomic hybridization in PGS-aneuploidy screening. Reprod Biomed Online.
2008;17(6):841–7.
50. Fragouli E, Alfarawati S, Daphnis DD, Goodall N -nek., Mania A, Griffiths T, et al. Cytogenetic
analysis of human blastocysts with the use of FISH, CGH and aCGH: scientific data and technical
evaluation. Hum Reprod. 2011 Feb 1;26(2):480–90.
51. Yang Z, Liu J, Collins GS, Salem SA, Liu X, Lyle SS, et al. Selection of single blastocysts for
fresh transfer via standard morphology assessment alone and with array CGH for good prognosis
IVF patients: results from a randomized pilot study. Mol Cytogenet. 2012;5:24.
52. Munné S. Preimplantation Genetic Diagnosis for Aneuploidy and Translocations Using Array
Comparative Genomic Hybridization. Curr Genomics. 2012 Sep;13(6):463–70.
53. Handyside AH. PGD and aneuploidy screening for 24 chromosomes by genome-wide SNP
analysis: seeing the wood and the trees. Reprod Biomed Online. 2011 Dec;23(6):686–91.
54. Treff NR, Tao X, Ferry KM, Su J, Taylor D, Scott Jr. RT. Development and validation of an
accurate quantitative real-time polymerase chain reaction–based assay for human blastocyst
comprehensive chromosomal aneuploidy screening. Fertil Steril. 2012 Apr;97(4):819–824.e2.
55. Treff N, Scott RJ. Four-hour quantitative real-time polymerase chain reaction-based
comprehensive chromosome screening and accumulating evidence of accuracy, safety, predictive
value, and clinical efficacy. Fertil Steril. 2013 Mar 15;99(4):1049–53.
56. Yang Y-S, Chang S-P, Chen H-F, Ma G-C, Lin W-H, Lin C-F, et al. Preimplantation genetic
screening of blastocysts by multiplex qPCR followed by fresh embryo transfer: validation and
verification. Mol Cytogenet. 2015 Jul 8;8(1):49.
55
57. Handyside AH. 24-chromosome copy number analysis: a comparison of available technologies.
Fertil Steril. 2013 Sep;100(3):595–602.
58. Handyside AH, Wells D. Single Nucleotide Polymorphisms and Next Generation Sequencing. In:
Gardner DK, Sakkas D, Seli E, Wells D, editors. Human Gametes and Preimplantation Embryos
[Internet]. Springer New York; 2013 [cited 2016 May 20]. p. 135–45.
59. Wells D. Next-generation sequencing: the dawn of a new era for preimplantation genetic
diagnostics. Fertil Steril. 2014 May 1;101(5):1250–1.
60. Dahdouh EM, Balayla J, Garcia-Velasco JA. Impact of blastocyst biopsy and comprehensive
chromosome screening technology on preimplantation genetic screening: a systematic review of
randomized controlled trials. Reprod Biomed Online. 2015 Mar;30(3):281–9.
61. Lee E, Illingworth P, Wilton L, Chambers GM. The clinical effectiveness of preimplantation
genetic diagnosis for aneuploidy in all 24 chromosomes (PGD-A): systematic review. Hum
Reprod. 2015 Feb 1;30(2):473–83.
62. Chen M, Wei S, Hu J, Quan S. Can Comprehensive Chromosome Screening Technology Improve
IVF/ICSI Outcomes? A Meta-Analysis. PLOS ONE. 2015 Oct 15;10(10):1–21.
63. Maheshwari A, McLernon D, Bhattacharya S. Cumulative live birth rate: time for a consensus?
Hum Reprod. 2015 Dec 1;30(12):2703–7.
64. Mersereau J, Plunkett B, Cedars M. Preimplantation genetic screening in older women: a cost-
effectiveness analysis. Fertil Steril. 2008 Sep;90(3):592–8.
65. Resetkova N, Tobler KJ, Kearns WG, Werner EF. In vitro fertilization (IVF) with 23-chromosome
pair preimplantation genetic screening (PGS) is cost effective to achieve a live birth compared to
IVF alone for recurrent pregnancy loss (RPL). Fertil Steril. 2013 Sep 1;100(3):S99–100.
66. Murugappan G, Ohno MS, Lathi RB. Cost-effectiveness analysis of preimplantation genetic
screening and in vitro fertilization versus expectant management in patients with unexplained
recurrent pregnancy loss. Fertil Steril. 2015 May;103(5):1215–20.
67. Murugappan G, Shahine LK, Perfetto CO, Hickok LR, Lathi RB. Intent to treat analysis of in vitro
fertilization and preimplantation genetic screening versus expectant management in patients with
recurrent pregnancy loss. Hum Reprod. 2016 Aug;31(8):1668–74.
68. Resetkova N, Tobler KJ, Werner EF, Kearns WG. In vitro fertilization (IVF) with 23-chromosome
pair preimplantation genetic screening (PGS) from trophectoderm biopsy is more cost effective to
achieve a live birth compared to IVF alone. Fertil Steril. 2014 Sep 1;102(3):e176.
69. Patounakis G, Sundheimer LW, DeCherney AH, Hill MJ. Preimplantation genetic screening (PGS)
of embryos prior to transfer: a cost analysis of single embryo transfers (SET) and double embryo
transfers (DET). Fertil Steril. 2014 Sep 1;102(3):e5.
70. Hodes-Wertz B, McCulloh DH, Grifo J. Preimplantation genetic screening is cost effective in cost
per delivery compared to routine in vitro fertilization. Fertil Steril. 2015 Sep 1;104(3):e278.
56
71. Neal S, Morin SJ, Franasiak JM, Juneau CR, Zhan Y, Scott RT. Single embryo transfer (SET)
following comprehensive chromosome screening (CCS) is more cost effective than unscreened
sequential SET. Fertil Steril. 2016 Sep 1;106(3):e19.
72. Salem W, Ho JR, Bendikson KA, Chung K, Paulson R. IVF patients over age 39 experience
decreased cost effectiveness and live birth rates with preimplantation genetic screening: a decision
analytic model and cost effectiveness analysis. Fertil Steril. 2016 Sep 1;106(3):e60–1.
73. Scriven PN. Towards a better understanding of preimplantation genetic screening and cumulative
reproductive outcome: transfer strategy, diagnostic accuracy and cost-effectiveness. AIMS Genet.
2016;3(3):177–95.
74. Scott RJ, Ferry K, Su J, Tao X, Scott K, Treff N. Comprehensive chromosome screening is highly
predictive of the reproductive potential of human embryos: a prospective, blinded, nonselection
study. Fertil Steril. 2012 Apr;97(4):870–5.
75. Scott RJ, Franasiak JM, Forman EJ. Comprehensive chromosome screening with synchronous
blastocyst transfer: time for a paradigm shift☆. Fertil Steril. 2014 Sep 1;102(3):660–1.
76. Dahdouh EM, Balayla J, García-Velasco JA. Preimplantation genetic screening using
comprehensive chromosome screening: evidence and remaining challenges. Hum Reprod. 2015
Jun 1;30(6):1515–6.
77. Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated
Health Economic Evaluation Reporting Standards (CHEERS)—Explanation and Elaboration: A
Report of the ISPOR Health Economic Evaluation Publication Guidelines Good Reporting
Practices Task Force. Value Health. 2013 Mar;16(2):231–50.
78. CARTR Plus [Internet]. [cited 2016 Aug 16]. Available from:
https://www.bornontario.ca/en/partnership-projects/cartr-plus/
79. Society for Assisted Reproductive Technology. SART National Summary Report (2014)
[Internet]. 2016 [cited 2016 Aug 5]. Available from:
https://www.sartcorsonline.com/rptCSR_PublicMultYear.aspx?ClinicPKID=0#patient-first-
attempt
80. Brandes M, van der Steen JOM, Bokdam SB, Hamilton CJCM, de Bruin JP, Nelen WLDM, et al.
When and why do subfertile couples discontinue their fertility care? A longitudinal cohort study in
a secondary care subfertility population. Hum Reprod. 2009 Dec 1;24(12):3127–35.
81. Olivius C, Friden B, Borg G, Bergh C. Why do couples discontinue in vitro fertilization treatment?
a cohort study. Fertil Steril. 2004 Feb;81(2):258–61.
82. Gameiro S, Boivin J, Peronace L, Verhaak CM. Why do patients discontinue fertility treatment? A
systematic review of reasons and predictors of discontinuation in fertility treatment. Hum Reprod
Update. 2012 Nov 1;18(6):652–69.
83. Domar AD, Smith K, Conboy L, Iannone M, Alper M. A prospective investigation into the reasons
why insured United States patients drop out of in vitro fertilization treatment. Fertil Steril. 2010
Sep;94(4):1457–9.
57
84. De Vries MJ, De Sutter P, Dhont M. Prognostic factors in patients continuing in vitro fertilization
or intracytoplasmic sperm injection treatment and dropouts. Fertil Steril. 1999 Oct;72(4):674–8.
85. Schröder AK, Katalinic A, Diedrich K, Ludwig M. Cumulative pregnancy rates and drop-out rates
in a German IVF programme: 4102 cycles in 2130 patients. Reprod Biomed Online. 2004 Jan
1;8(5):600–6.
86. Health Technology & Policy Unit, School of Public Health, Department of Public Health Services.
Assisted Reproductive Technologies (ARTs) Final Report [Internet]. Edmonton, AB: University of
Alberta; 2014 Feb [cited 2014 Nov 20] p. 507. Available from:
http://www.health.alberta.ca/documents/AHTDP-Assisted-Reproductive-Technologies-2014.pdf
87. McDowell S, Murray A. Barriers to continuing in vitro fertilisation--why do patients exit fertility
treatment? J Obstet. 2011 Feb;51(1):84–90.
88. Huppelschoten AG, Dongen AJCM van, Philipse ICP, Hamilton CJCM, Verhaak CM, Nelen
WLDM, et al. Predicting dropout in fertility care: a longitudinal study on patient-centredness.
Hum Reprod. 2013 May 21;det236.
89. Macaldowie A, Lee E, Chambers G. Assisted reproductive technology in Australia and New
Zealand 2013 [Internet]. Sydney, Australia: National Perinatal Epidemiology and Statistics Unit,
University of New South Wales; 2015 Sep [cited 2016 Apr 29] p. 89. Available from:
https://npesu.unsw.edu.au/sites/default/files/npesu/data_collection/Assisted%20reproductive%20te
chnology%20in%20Australia%20and%20New%20Zealand%202013.pdf
90. Karvir H, Hunter Cohn K, Arredondo F, Miller B, Gutmann J, Leondires M, et al. Pregnancy loss
is a major predictor of premature IVF discontinuation. Pacific Coast Reproductive Society; 2016
Mar 9; Palm Springs, CA.
91. Al-Inany HG, Abou-Setta AM, Aboulghar MA, Mansour RT, Serour GI. HMG versus rFSH for
ovulation induction in developing countries: a cost–effectiveness analysis based on the results of a
recent meta-analysis. Reprod Biomed Online. 2006;12(2):163–9.
92. Fiddelers AAA, Dirksen CD, Dumoulin JCM, Montfoort APA van, Land JA, Janssen JM, et al.
Cost-effectiveness of seven IVF strategies: results of a Markov decision-analytic model. Hum
Reprod. 2009 Jul 1;24(7):1648–55.
93. Griffiths A, Dyer SM, Lord SJ, Pardy C, Fraser IS, Eckermann S. A cost-effectiveness analysis of
in-vitro fertilization by maternal age and number of treatment attempts. Hum Reprod. 2010 Apr
1;25(4):924–31.
94. Provoost V, Pennings G, Sutter PD, Gerris J, Velde AV de, Dhont M. To continue or discontinue
storage of cryopreserved embryos? Patients’ decisions in view of their child wish. Hum Reprod.
2011 Apr 1;26(4):861–72.
95. Provoost V, Pennings G, De Sutter P, Dhont M. Decisions on embryo disposition in cross-border
reproductive care: differences between Belgian and Dutch patients at a Belgian fertility center.
Facts Views Vis ObGyn. 2011;3(4):293–301.
58
96. Provoost V, Pennings G, Sutter PD, Velde AV de, Dhont M. Trends in embryo disposition
decisions: patients’ responses to a 15-year mailing program. Hum Reprod. 2012 Feb 1;27(2):506–
14.
97. Lyerly AD, Steinhauser K, Voils C, Namey E, Alexander C, Bankowski B, et al. Fertility patients’
views about frozen embryo disposition: results of a multi-institutional U.S. survey. Fertil Steril.
2010 Jan 15;93(2):499–509.
98. Lyerly AD, Nakagawa S, Kuppermann M. Decisional conflict and the disposition of frozen
embryos: implications for informed consent. Hum Reprod. 2011 Mar 1;26(3):646–54.
99. Cattapan A, Doyle A. Patient Decision-Making About the Disposition of Surplus Cryopreserved
Embryos in Canada. J Obstet Gynaecol Can. 2016 Jan;38(1):60–6.
100. Personal communication with multiple reproductive endocrinology and infertility specialists,
embryologists and fertility nurses, 2016. Toronto, Ontario.
101. Bouwmans CAM, Lintsen BAME, Al M, Verhaak CM, Eijkemans RJC, Habbema JDF, et al.
Absence from work and emotional stress in women undergoing IVF or ICSI: An analysis of IVF-
related absence from work in women and the contribution of general and emotional factors. Acta
Obstet Gynecol Scand. 2008 Jan 1;87(11):1169–75.
102. Kjellberg AT, Carlsson P, Bergh C. Randomized single versus double embryo transfer: obstetric
and paediatric outcome and a cost-effectiveness analysis. Hum Reprod. 2006 Jan 1;21(1):210–6.
103. Fiddelers AAA, Montfoort APA van, Dirksen CD, Dumoulin JCM, Land JA, Dunselman GAJ, et
al. Single versus double embryo transfer: cost-effectiveness analysis alongside a randomized
clinical trial. Hum Reprod. 2006 Aug 1;21(8):2090–7.
104. Kelly J, Hughes CM, Harrison RF. The hidden costs of IVF. Ir Med J. 2006 May;99(5):142–3.
105. Little SE, Ratcliffe J, Caughey AB. Cost of Transferring One Through Five Embryos Per In Vitro
Fertilization Cycle From Various Payor Perspectives: Obstet Gynecol. 2006 Sep;108(3, Part
1):593–601.
106. Petrou S, Trinder J, Brocklehurst P, Smith L. Economic evaluation of alternative management
methods of first-trimester miscarriage based on results from the MIST trial. BJOG Int J Obstet
Gynaecol. 2006 Aug 1;113(8):879–89.
107. Statistics Canada Government of Canada. Table 282-0151 - Labour force survey estimates (LFS),
wages of employees by type of work, National Occupational Classification (NOC), sex, and age
group, unadjusted for seasonality [Internet]. 2016 [cited 2016 May 10]. Available from:
http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=2820151&&pattern=&stByV
al=1&p1=1&p2=38&tabMode=dataTable&csid=
108. Ontario Case Cost Initiative Costing Analysis Tool [Internet]. [cited 2016 Jul 14]. Available from:
https://hsimi.ca/occp/occpreports/
109. Statistics Canada, Government of Canada. Table 326-0021 - Consumer price index, annual
(2002=100 unless otherwise noted) [Internet]. Statistics Canada. 2016 [cited 2016 Apr 22].
Available from: http://www5.statcan.gc.ca/cansim/a26?lang=eng&id=3260021
59
110. Ministry of Health & Long Term Care. Schedule of Benefits, Physician Services Under the Health
Insurance Act [Internet]. 2016 [cited 2016 Mar 10]. Available from:
http://www.health.gov.on.ca/english/providers/program/ohip/sob/physserv/sob_master20160229.p
df
111. CARTR/BORN Data Set.
112. Van Den Eeden SK, Shan J, Bruce C, Glasser M. Ectopic pregnancy rate and treatment utilization
in a large managed care organization. Obstet Gynecol. 2005;105(5, Part 1):1052–1057.
113. Hoover KW, Tao G, Kent CK. Trends in the Diagnosis and Treatment of Ectopic Pregnancy in the
United States: Obstet Gynecol. 2010 Mar;115(3):495–502.
114. Trabert B, Holt VL, Yu O, Van Den Eeden SK, Scholes D. Population-Based Ectopic Pregnancy
Trends, 1993–2007. Am J Prev Med. 2011 May;40(5):556–60.
115. Sharma V, Allgar V, Rajkhowa M. Factors influencing the cumulative conception rate and
discontinuation of in vitro fertilization treatment for infertility. Fertil Steril. 2002 Jul;78(1):40–6.
116. Shapiro BS, Daneshmand ST, Restrepo H, Garner FC, Aguirre M, Hudson C. Matched-cohort
comparison of single-embryo transfers in fresh and frozen-thawed embryo transfer cycles. Fertil
Steril. 2013;99(2):389–392.
117. Fiorentino F, Biricik A, Bono S, Spizzichino L, Cotroneo E, Cottone G, et al. Development and
validation of a next-generation sequencing–based protocol for 24-chromosome aneuploidy
screening of embryos. Fertil Steril. 2014 May;101(5):1375–1382.e2.
118. Grifo J, Colls P, Ribustello L, Escudero T, Liu E, Munne S. Why do array-CGH (ACGH) euploid
embryos miscarry? Reanalysis by NGS reveals undetected abnormalities which would have
prevented 56% of the miscarriages. Fertil Steril. 2015 Sep 1;104(3):e14.
119. Tortoriello DV, Dayal M, Beyhan Z, Yakut T, Keskintepe L. Reanalysis of human blastocysts with
different molecular genetic screening platforms reveals significant discordance in ploidy status. J
Assist Reprod Genet. 2016 Nov 1;33(11):1467–71.
120. Tiegs A, Hodes-Wertz B, McCulloh D, Munné S, Grifo J. Discrepant diagnosis rate of array
comparative genomic hybridization in thawed euploid blastocysts. J Assist Reprod Genet.
2016;33(7):893–7.
121. Briggs AH, Goeree R, Blackhouse G, O’Brien BJ. Probabilistic Analysis of Cost-Effectiveness
Models: Choosing between Treatment Strategies for Gastroesophageal Reflux Disease. Med Decis
Making. 2002 Aug 1;22(4):290–308.
122. Fee Schedules from Ontario Fertility Clinics: Anova Fertility, Astra Fertility Group, Hannam
Fertility Centre, IVF Canada, KARMA, London Health Sciences Centre, Markham Fertility
Centre, Mount Sinai Fertility, Nahal Fertility Program, NewLife Fertility Centre, One Fertility,
Ottawa Fertility Centre, Procrea Fertility Centre, Reproductive Care Centre, RepreMed, TRIO
Fertility.
123. Geraedts J, Sermon K. Preimplantation genetic screening 2.0: the theory. MHR Basic Sci Reprod
Med. 2016 Aug 1;22(8):839–44.
60
124. Gleicher N, Kushnir VA, Barad DH. The impact of patient preselection on reported IVF outcomes.
J Assist Reprod Genet. 2016 Apr 1;33(4):455–9.
125. Center for Disease Control and Prevention. 2014 Assisted Reproductive Technology Fertility
Clinic Success Rates Report (National Summary Table) [Internet]. [cited 2017 Mar 14]. Available
from:
https://ftp.cdc.gov/pub/Publications/art/Clinic_PDFs/2014/ART_9999_2014_Fertility_Clinic_Rep
ort.pdf
126. Better Outcomes Registry & Network Ontario. Canadian Assisted Reproductive Technologies
Register (CARTR) Plus: Preliminary treatment cycle data for 2014 & final treatment cycle and
pregnancy outcome data for 2013. [Internet]. CFAS 2015; 2015 Oct [cited 2016 Jan 21]; Halifax,
NS. Available from: http://www.cfas.ca/images/stories/BORN_CARTR_Plus_presentation.pdf
127. Schufreider A, McQueen D, Mathews J, Liebermann J, Uhler ML, Feinberg EC. Is
preimplantation genetic screening with frozen single embryo transfer superior to fresh in-vitro
fertilization with elective single embryo transfer in a good prognosis population? Fertil Steril.
2016 Sep 1;106(3):e60.
128. Forman E, Hong K, Ferry K, Tao X, Taylor D, Levy B, et al. In vitro fertilization with single
euploid blastocyst transfer: a randomized controlled trial. Fertil Steril. 2013 Jul;100(1):100–7.
129. Gleicher N, Barad DH. A review of, and commentary on, the ongoing second clinical introduction
of preimplantation genetic screening (PGS) to routine IVF practice. J Assist Reprod Genet. 2012
Nov 1;29(11):1159–66.
130. Orvieto R. Preimplantation genetic screening- the required RCT that has not yet been carried out.
Reprod Biol Endocrinol. 2016;14:35.
131. Maxwell SM, Colls P, Hodes-Wertz B, McCulloh DH, McCaffrey C, Wells D, et al. Why do
euploid embryos miscarry? A case-control study comparing the rate of aneuploidy within
presumed euploid embryos that resulted in miscarriage or live birth using next-generation
sequencing. Fertil Steril. 2016 Nov 1;106(6):1414–1419.e5.
132. Scott Jr. RT, Galliano D. The challenge of embryonic mosaicism in preimplantation genetic
screening. Fertil Steril. 2016 May; 105(5):1150-2.
133. Arts DGT, Keizer NF de, Scheffer G-J. Defining and Improving Data Quality in Medical
Registries: A Literature Review, Case Study, and Generic Framework. J Am Med Inform Assoc.
2002 Nov 1;9(6):600–11.
134. BORN Ontario. History of BORN Ontario [Internet]. [cited 2016 Aug 16]. Available from:
https://www.bornontario.ca/assets/documents/History%20of%20BORN%20Ontario_May%20201
4.pdf
135. About BORN [Internet]. BORN Ontario. [cited 2016 Aug 16]. Available from:
https://www.bornontario.ca/en/about-born/
136. Gunby J. Assisted reproductive technologies (ART) in Canada: 2012 results from the Canadian
ART register. [Internet]. Montreal, Quebec: Canadian Fertility and Andrology Society; 2014 [cited
2014 Oct 22]. Available from: http://www.cfas.ca/images/stories/pdf/CARTR_2012.pdf
61
137. 2016 Press Release - Results from the Canadian ART Register [Internet]. Canadian Fertility &
Andrology Society. [cited 2017 Apr 14]. Available from: https://cfas.ca/public-affairs/canadian-
art-register/
62
7 Tables
7.1 Description of clinical inputs included in the model.
Age <
35
Age 35
- 37
Age 38
- 40
Age 41
- 42
Age >
42
Minimum Maximum Distribution Source
Pregnancy rate after a euploid
embryo transfer 0.521 0.539 0.510 0.516 0.444 -15% +15% Triangle (79)
Pregnancy loss rate after IVF
with PGS 0.119 0.113 0.131 0.133 0.182 -15% +15% Triangle (79)
Pregnancy rate after an
unscreened embryo transfer 0.486 0.424 0.326 0.222 0.115 -15% +15% Triangle (79)
Pregnancy loss rate after IVF
alone 0.112 0.139 0.200 0.285 0.355 -15% +15% Triangle (79)
Drop out after failed transfer 0.056 0.023 0.090 Triangle (83–90)
Drop out after pregnancy loss 0.066 0.027 0.106 Triangle (90)
63
7.2 Detailed description of all costs included in the model.
Mean Minimum Maximum Standard Deviation Distribution Source
Fertility Treatment
ICSI $1,600.00 $1,500.00 $2,000.00 $200.00 Gamma (122)
Embryo biopsy and PGS $3,961.02 $3,058.07 $5,100.00 $828.99 Gamma (122)
Cryopreservation and 1 year of storage $971.00 $850.00 $1,230.00 $139.15 Gamma (122)
Frozen embryo transfer cycle $1,975.71 $1,500.00 $2,480.00 $315.95 Gamma (122)
Cost of COH and OPU +/- fresh ET $7,985.71 $6,000.00 $11,000.00 $1,495.16 Gamma (122)
Cost of embryo storage for an additional
year $440.00 $200.00 $500.00 $88.80 Gamma (122)
Medications
IVF cycle $4,077.55 $2,718.37 $5,436.74 $1,109.77 Gamma (100,111)
Frozen embryo transfer cycle $184.63 $123.09 $246.18 $50.25 Gamma (100)
Luteal support after fresh embryo transfer $140.28 $93.52 $187.04 $38.18 Gamma (100)
Luteal support after frozen embryo
transfer $465.19 $310.13 $620.26 $126.61 Gamma (100)
Prenatal Care, Labour & Delivery
Prenatal consult $77.20 $77.20 $77.20 $0.00 n/a (110)
Prenatal visit $33.70 $33.70 $33.70 $0.00 n/a (110)
Early ultrasound $59.85 $59.85 $59.85 $0.00 n/a (110)
Anatomy ultrasound $75.30 $75.30 $75.30 $0.00 n/a (110)
Prenatal screening $183.00 $183.00 $183.00 $0.00 n/a (110)
64
Spontaneous vaginal delivery (obstetrics) $498.70 $498.70 $498.70 $0.00 n/a (110)
Operative vaginal delivery (obstetrics) $535.60 $535.60 $535.60 $0.00 n/a (110)
C-Section (obstetrics) $579.80 $579.80 $579.80 $0.00 n/a (110)
Operative vaginal delivery (anesthesia) $150.10 $135.09 $210.14 $32.43 Gamma (110)
C-Section (anesthesia) $225.15 $165.11 $315.21 $61.69 Gamma (110)
Spontaneous vaginal delivery (hospital) $2,991.18 $64.96 $77,100.67 $2,105.45 Gamma (108)
Operative vaginal delivery (hospital) $3,848.24 $229.50 $43,239.87 $2,410.06 Gamma (108)
C-Section (hospital) $4,538.24 $356.41 $294,479.69 $5,353.91 Gamma (108)
Newborn care $4,032.87 -25% +25% - Triangle (108)
Pregnancy Loss
Medical management (gynecology) $161.15 $161.15 $161.15 $0.00 n/a (110)
Surgical management (gynecology) $112.40 $112.40 $112.40 $0.00 n/a (110)
Medical management of ectopic
pregnancy (gynecology) $207.80 $207.80 $207.80 $0.00 n/a (110)
Surgical treatment of ectopic pregnancy
(gynecology) $306.85 $306.85 $306.85 $0.00 n/a (110)
Follow up visit after medical
management $33.70 $33.70 $33.70 $0.00 n/a (110)
Surgical management (anesthesia) $135.09 $120.08 $150.10 $12.26 Gamma (110)
Surgical treatment of ectopic pregnancy
(anesthesia) $225.15 $195.13 $315.21 $51.02 Gamma (110)
Surgical management (hospital) $3,296.16 $689.20 $21,764.97 $2,155.42 Gamma (108)
Medical management of ectopic
pregnancy (hospital) $1,842.34 $235.10 $12,681.50 $1,733.65 Gamma (108)
65
Surgical treatment of ectopic pregnancy
(hospital) $4,082.32 $539.55 $24,804.43 $1,883.43 Gamma (108)
Productivity
IVF cycle excluding embryo transfer $386.55 $257.70 $515.40 $105.21 Gamma (100)
Fresh embryo transfer $103.08 $68.72 $137.44 $28.05 Gamma (100)
Frozen embryo transfer $128.85 $85.90 $171.80 $35.07 Gamma (100)
First trimester care $154.62 $103.08 $206.16 $42.08 Gamma (100)
Second and third trimester care $360.78 $240.52 $481.04 $98.19 Gamma (100)
Pregnancy loss - expectant management $1,931.72 $1,287.81 $2,575.63 $525.75 Gamma (106)
Pregnancy loss - medical management $2,191.48 $1,460.99 $2,921.97 $596.45 Gamma (106)
Pregnancy loss - surgical management $2,032.74 $1,355.16 $2,710.32 $553.24 Gamma (106)
Ectopic pregnancy $3,092.40 $2,061.60 $4,123.20 $841.64 Gamma (100)
Partner Productivity
IVF cycle excluding embryo transfer $298.50 $199.00 $398.00 $81.24 Gamma (100)
Fresh embryo transfer $119.40 $79.60 $159.20 $32.50 Gamma (100)
Frozen embryo transfer $119.40 $79.60 $159.20 $32.50 Gamma (114)
66
7.3 Costs included under the four different perspectives examined.
Perspective
Societal Direct Health
Care
Hypothetical
MOHLTC
Patient Self-
pay
Fertility Treatment
ICSI
Embryo biopsy and PGS
Cryopreservation and 1 year of storage
Frozen embryo transfer cycle
Cost of COH and OPU +/- fresh ET
Cost of embryo storage for an additional year
Medications
IVF cycle
Frozen embryo transfer cycle
Luteal support after fresh embryo transfer
Luteal support after frozen embryo transfer
Prenatal Care, Labour & Delivery
Prenatal consult
Prenatal visit
Early ultrasound
Anatomy ultrasound
Prenatal screening
Spontaneous vaginal delivery (obstetrics)
Operative vaginal delivery (obstetrics)
C-Section (obstetrics)
Operative vaginal delivery (anesthesia)
C-Section (anesthesia)
Spontaneous vaginal delivery (hospital)
67
Operative vaginal delivery (hospital)
C-Section (hospital)
Newborn care
Pregnancy Loss
Medical management (gynecology)
Surgical management (gynecology)
Medical management of ectopic pregnancy
(gynecology)
Surgical treatment of ectopic pregnancy (gynecology)
Follow up visit after medical management
Surgical management (anesthesia)
Surgical treatment of ectopic pregnancy (anesthesia)
Surgical management (hospital)
Medical management of ectopic pregnancy (hospital)
Surgical treatment of ectopic pregnancy (hospital)
Productivity
IVF cycle excluding embryo transfer
Fresh embryo transfer
Frozen embryo transfer
First trimester care
Second and third trimester care
Pregnancy loss - expectant management
Pregnancy loss - medical management
Pregnancy loss - surgical management
Ectopic pregnancy
Partner Productivity
IVF cycle excluding embryo transfer
Fresh embryo transfer
Frozen embryo transfer
68
7.4 Point estimates for C, E and ICER by age category and number of blastocysts (societal perspective).
< 35 35-37 38-40 41-42 >42
1
C 5881.030 6306.930 5840.490 5631.990 5437.020
E -0.116 -0.057 -0.052 -0.024 -0.014
ICER -50596.320 -111245.150 -112193.070 -233973.630 -391571.290
2
C 4927.600 5180.290 4081.990 3326.520 2675.850
E -0.138 -0.068 -0.072 -0.035 -0.022
ICER -35760.100 -76265.200 -56841.880 -95687.780 -119556.060
3
C 5141.640 5088.110 3531.280 2222.260 1063.750
E -0.124 -0.059 -0.073 -0.036 -0.026
ICER -41612.340 -85688.230 -48538.180 -61633.660 -40274.750
4
C 5521.190 5190.430 3305.190 1443.430 -289.360
E -0.098 -0.044 -0.063 -0.031 -0.027
ICER -56366.290 -117970.850 -52092.630 -46673.990 10810.060
5
C 5907.410 5357.600 3270.060 908.750 -1419.930
E -0.072 -0.028 -0.048 -0.022 -0.024
ICER -81482.040 -192118.010 -66420.590 -42095.040 58860.770
6
C 6216.200 5495.040 3268.240 417.480 -2583.150
E -0.051 -0.014 -0.033 -0.010 -0.019
ICER -121698.940 -402997.950 -98075.830 -43162.870 135563.900
7
C 6472.570 5645.450 3391.210 198.400 -3359.110
E -0.034 -0.002 -0.018 0.004 -0.012
ICER -187952.860 -2925842.820 -192933.490 54513.310 278717.000
8
C 6663.330 5767.680 3524.300 76.760 -3995.120
E -0.022 0.007 -0.003 0.018 -0.004
ICER -301578.890 806008.300 -1183506.860 4385.750 1131496.230
9
C 6800.970 5862.500 3678.320 24.900 -4511.670
E -0.013 0.014 0.010 0.031 0.006
ICER -514876.490 418591.820 368194.220 794.800 -732773.130
10
C 6556.700 5754.010 3805.350 21.870 -4926.470
E -0.029 0.007 0.021 0.045 0.017
ICER -227263.150 784384.270 183719.870 489.180 -294738.490 The table has been colour coded such that red = northwest quadrant, blue = northeast quadrant, yellow = southwest quadrant and
green = southeast quadrant. Northwest quadrant – IVF/PGS more expensive and less effective than IVF alone; Northeast
quadrant – IVF/PGS is more expensive and more effective than IVF alone; Southwest quadrant – IVF/PGS is less expensive and
less effective than IVF alone; Southeast quadrant – IVF/PGS is less expensive and more effective than IVF alone.
69
7.5 Results of the probabilistic sensitivity analysis by age category and number of blastocysts (societal perspective).
Age < 35 1 2 3 4 5 6 7 8 9 10
NW 99.10% 99.50% 98.90% 99.60% 99.30% 98.20% 96.10% 89.10% 81.90% 98.40%
NE 0.00% 0.00% 0.00% 0.00% 0.30% 1.40% 3.60% 10.80% 18.00% 1.50%
SW 0.90% 0.50% 1.10% 0.40% 0.40% 0.40% 0.30% 0.10% 0.10% 0.10%
SE 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Age 35 – 37 1 2 3 4 5 6 7 8 9 10
NW 97.30% 95.50% 91.40% 87.70% 81.00% 68.50% 52.70% 36.00% 20.30% 35.20%
NE 2.20% 4.00% 8.10% 12.10% 18.50% 31.20% 47.30% 64.00% 79.70% 64.70%
SW 0.50% 0.50% 0.50% 0.20% 0.50% 0.30% 0.00% 0.00% 0.00% 0.10%
SE 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Age 37 – 40 1 2 3 4 5 6 7 8 9 10
NW 98.80% 98.20% 96.90% 94.20% 91.10% 82.90% 71.00% 53.90% 36.80% 25.00%
NE 0.70% 0.80% 2.60% 4.90% 8.50% 16.90% 28.80% 45.90% 63.10% 75.00%
SW 0.50% 1.00% 0.50% 0.90% 0.40% 0.20% 0.20% 0.20% 0.10% 0.00%
SE 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
Age 41 – 42 1 2 3 4 5 6 7 8 9 10
NW 96.00% 92.80% 87.90% 77.70% 56.20% 33.80% 22.70% 13.20% 7.90% 4.60%
NE 3.40% 6.40% 11.60% 16.60% 25.00% 30.30% 34.90% 39.20% 41.70% 46.40%
SW 0.60% 0.80% 0.50% 5.60% 15.90% 25.30% 25.20% 18.10% 13.80% 8.50%
SE 0.00% 0.00% 0.00% 0.10% 2.90% 10.60% 17.20% 29.50% 36.60% 40.50%
Age > 42 1 2 3 4 5 6 7 8 9 10
NW 98.10% 96.80% 81.10% 29.70% 7.60% 0.80% 0.30% 0.00% 0.00% 0.00%
NE 1.30% 3.10% 6.20% 4.50% 2.70% 0.50% 1.00% 0.40% 0.10% 0.50%
SW 0.60% 0.10% 12.50% 61.40% 78.20% 74.70% 66.10% 55.00% 42.00% 35.50%
SE 0.00% 0.00% 0.20% 4.40% 11.50% 24.00% 32.60% 44.60% 57.90% 64.00% All numbers are percentages of PSA trial iterations that fall into a given quadrant of the cost-effectiveness plane. NW (northwest
quadrant) – IVF/PGS more expensive and less effective than IVF alone; NE (northeast quadrant) – IVF/PGS is more expensive
and more effective than IVF alone; SW (southwest quadrant) – IVF/PGS is less expensive and less effective than IVF alone; SE
(southeast quadrant) – IVF/PGS is less expensive and more effective than IVF alone. The table has been colour coded such that
red = northwest quadrant, blue = northeast quadrant, yellow = southwest quadrant and green = southeast quadrant.
70
7.6 Point estimates for C, E and ICER by age category and number of blastocysts (alternative perspectives).
(a) Direct Health Care Perspective # Blasts < 35 35-37 38-40 41-42 >42
1
DC 6019.96 6443.49 6050.08 588968 5688.18
DE -0.116 -0.057 -0.052 -0.024 -0.014
ICER -51791.63 -113653.79 -116219.22 -244679.25 -409660.04
2
DC 5119.95 5383.91 4432.15 3797.16 3162.69
DE -0.138 -0.068 -0.072 -0.035 -0.022
ICER -37005.84 -79262.89 -61717.9 -109225.93 -141307.91
3
DC 5295.51 5311.62 3961.19 2851.63 1750.65
DE -0.124 -0.059 -0.073 -0.036 -0.026
ICER -42857.7 -89452.42 -54447.47 -79089.06 -66281.13
4
DC 5641.03 5412.24 3776.55 2189.98 566.85
DE -0.098 -0.044 -0.063 -0.031 -0.027
ICER -57589.69 -123012.31 -59521.64 -70814.17 -21176.38
5
DC 5992.98 5569.24 3759.32 1740.79 -420.88
DE -0.072 -0.028 -0.049 -0.022 -0.024
ICER -82662.42 -199707.19 -76358.37 -80636.34 17446.77
6
DC 6272.8 5694.43 3761.46 1310.92 -1464.06
DE -0.051 -0.014 -0.033 -0.01 -0.019
ICER -122806.92 -418657.58 -112876.72 -135536.34 76833.77
7
DC 6506.58 5833.38 3880.57 1135.09 -2139.57
DE -0.034 -0.002 -0.018 0.004 -0.012
ICER -188940.57 -3023239.06 -220774.26 311881.05 177527.34
8
DC 6680.57 5945.96 4015.73 1043.06 -2691.96
DE -0.022 0.007 -0.003 0.018 -0.004
ICER -302359.06 830922.12 -1344759.74 59596.55 762413.96
9
DC 6806.11 6033.13 4150.41 1010.68 -3139.3
DE -0.013 0.014 0.01 0.031 0.006
ICER -515266.05 430775.35 415450.13 32263.66 509876.48
10
DC 6583.22 5934.35 4268.48 1019.67 -3497.23
DE -0.029 0.007 0.021 0.045 0.017
ICER -228182.53 808968.18 206079.48 22808.93 -209230.92 The table has been colour coded such that red = northwest quadrant, blue = northeast quadrant, yellow = southwest quadrant and
green = southeast quadrant. Northwest quadrant – IVF/PGS more expensive and less effective than IVF alone; Northeast
quadrant – IVF/PGS is more expensive and more effective than IVF alone; Southwest quadrant – IVF/PGS is less expensive and
less effective than IVF alone; Southeast quadrant – IVF/PGS is less expensive and more effective than IVF alone.
71
(b) Hypothetical MOHLTC Perspective # Blasts < 35 35-37 38-40 41-42 >42
1
DC 2922.48 3432.82 3451.55 3683.31 3796.410
DE -0.116 -0.057 -0.052 -0.024 -0.014
ICER -25143.03 -60550.02 -66302.65 -153018.37 -273415.670
2
DC 2726.92 3320.43 3245.39 3538.75 3685.770
DE -0.138 -0.068 -0.072 -0.035 -0.022
ICER -19709.52 -48883.92 -45192.22 -101792.47 -164678.650
3
DC 2861.600 3390.170 3217.310 3488.890 3620.050
DE -0.124 -0.059 -0.073 -0.036 -0.026
ICER -23159.550 -57093.500 -44222.630 -96763.240 -137058.770
4
DC 3092.670 3524.060 3287.480 3504.830 3591.540
DE -0.098 -0.044 -0.063 -0.031 -0.027
ICER -31573.270 -80096.590 -51813.530 -113330.470 -134172.900
5
DC 3321.900 3666.160 3406.050 3564.960 3593.590
DE -0.072 -0.028 -0.049 -0.022 -0.024
ICER -45819.560 -131464.580 -69182.770 -165135.020 -148966.250
6
DC 3514.670 3792.870 3542.770 3653.300 3620.550
DE -0.051 -0.014 -0.033 -0.010 -0.019
ICER -68809.040 -278853.750 -106313.990 -377714.660 -190006.400
7
DC 3664.360 3896.730 3680.030 3758.170 3667.590
DE -0.034 -0.002 -0.018 0.004 -0.012
ICER -106406.970 -2019542.560 -209364.930 1032609.280 -304312.780
8
DC 3775.350 3977.740 3808.280 3871.130 3730.620
DE -0.022 0.007 -0.003 0.018 -0.004
ICER -170870.460 555871.220 -1275290.540 221182.210 -1056584.930
9
DC 3855.250 4038.890 3922.960 3986.230 3806.160
DE -0.013 0.014 0.010 0.031 0.006
ICER -291866.810 288383.100 392683.260 127251.730 618185.820
10
DC 3714.700 3978.840 4018.020 4099.280 3891.260
DE -0.029 0.007 0.021 0.045 0.017
ICER -128756.100 542394.190 193987.520 91696.450 232804.540 The table has been colour coded such that red = northwest quadrant, blue = northeast quadrant, yellow = southwest quadrant and
green = southeast quadrant. Northwest quadrant – IVF/PGS more expensive and less effective than IVF alone; Northeast
quadrant – IVF/PGS is more expensive and more effective than IVF alone; Southwest quadrant – IVF/PGS is less expensive and
less effective than IVF alone; Southeast quadrant – IVF/PGS is less expensive and more effective than IVF alone.
72
(c) Patient Self-Pay Perspective # Blasts < 35 35-37 38-40 41-42 >42
1
DC 7058.500 6971.690 6559.550 6167.390 5852.790
DE -0.116 -0.057 -0.052 -0.024 -0.014
ICER -60726.490 -122970.410 -126005.880 -256216.380 -421515.060
2
DC 6354.060 6024.500 5147.780 4219.440 3427.950
DE -0.138 -0.068 -0.072 -0.035 -0.022
ICER -45925.660 -88693.830 -71683.070 -121372.710 -153606.140
3
DC 6394.930 5882.470 4704.900 3323.760 2091.610
DE -0.124 -0.059 -0.073 -0.036 -0.026
ICER -51755.500 -99066.020 -64669.920 -92183.400 -79190.400
4
DC 6509.380 5849.210 4450.090 2646.170 936.330
DE -0.098 -0.044 -0.063 -0.031 -0.027
ICER -66454.800 -132943.810 -70137.210 -85565.440 -34979.580
5
DC 6632.110 5864.000 4314.290 2136.850 -53.440
DE -0.072 -0.028 -0.049 -0.022 -0.024
ICER -91478.000 -210280.730 -87630.780 -98982.730 2215.420
6
DC 6719.150 5862.580 4179.710 1618.640 -1123.580
DE -0.051 -0.014 -0.033 -0.010 -0.019
ICER -131545.520 -431019.930 -125428.010 -167351.490 58965.750
7
DC 6803.250 5897.670 4161.560 1337.040 -1846.140
DE -0.034 -0.002 -0.018 0.004 -0.012
ICER -197555.150 -3056556.770 -236760.430 367617.290 153180.780
8
DC 6866.240 5929.250 4168.470 1132.950 -2461.560
DE -0.022 0.007 -0.003 0.018 -0.004
ICER -310762.320 828585.960 -1395907.310 64732.420 697161.650
9
DC 6911.880 5955.260 4188.460 985.470 -2984.440
DE -0.013 0.014 0.010 0.031 0.006
ICER -523273.730 425215.380 419259.390 31459.040 -484724.690
10
DC 6829.540 5916.530 4211.480 881.410 -3427.470
DE -0.029 0.007 0.021 0.045 0.017
ICER -236720.220 806538.990 203327.560 19716.120 -205057.290 The table has been colour coded such that red = northwest quadrant, blue = northeast quadrant, yellow = southwest quadrant and
green = southeast quadrant. Northwest quadrant – IVF/PGS more expensive and less effective than IVF alone; Northeast
quadrant – IVF/PGS is more expensive and more effective than IVF alone; Southwest quadrant – IVF/PGS is less expensive and
less effective than IVF alone; Southeast quadrant – IVF/PGS is less expensive and more effective than IVF alone.
73
7.7 Causes of inadequate PGS data quality and suggestions for data quality improvement in the CARTR Plus / BORN Ontario registry.
Likely Cause of Data Quality Issue Proposed Action to Improve Data Quality
Registry set up & organization
- PGS inappropriately captured under
“Reason for Treatment Cycle”
- Divide “Reason for Treatment Cycle” into “Fertility
Diagnoses” and “Other Reasons for ART”
- Remove PGS from “Reason for Treatment Cycle”
and create new section for capturing data related to
PGS
- PGS results not captured
- Create new section for capturing data related to
PGS including whether or not PGS was planned
and/or ultimately performed
- Include precise genetic results for each embryo
screened
- Embryo stage at biopsy and PGS
platform not captured
- Include data points for embryo stage at biopsy and
PGS platform used in new PGS section
Data collection
- Misclassification of PGS and PGD
cases, particularly for cases with
balanced translocations or other
structural abnormalities
- For all cases where PGS and/or PGD is planned,
include a data point for the diagnosis of interest (or
“routine aneuploidy screening”)
- Educate end users regarding the correct way to
input these cases
- Inconsistent handling of cases where
PGS was planned but not completed
- Include data points to capture data related to
whether or not PGS was planned and/or ultimately
performed
- Include data points for reasons why PGS was not
performed if it was planned
- Educate end users regarding the importance of
accurate data entry
Quality improvement
- Lack of feedback mechanism for
end users of the data back to registry
organizers
- Create an easy and efficient mechanism for end
users and researchers to provide feedback to
CARTR Plus / BORN Ontario on an ongoing basis
- Regularly update and improve the database,
particularly as it relates to rapidly evolving
technologies
74
8 Figures
8.1 The cost-effectiveness plane.
From Hoch JS, Briggs AH, Willan AR. Something old, something new, something borrowed, something blue: a
framework for the marriage of health econometrics and cost‐effectiveness analysis. Health Econ. 2002;11(5):415–
30. (25)
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8.2 Simplified diagram of the Markov model used within the IVF alone arm of the full decision tree.
The purple circle represents the Markov node.
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8.3 Simplified diagram of the IVF/PGS arm of the decision model, including the embedded Markov model and its clones.
The purple circle represents the Markov node. The purple ovals with the letter M in the centre of them represent
clones of the illustrated Markov process.
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8.4 Full decision tree comparing IVF/PGS to IVF alone.
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8.5 One-way sensitivity analysis: tornado diagrams by age group and number of blastocysts, from a societal perspective.
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All variables except those costs with known static values were tested in one-way sensitivity analyses; however, only
the variables that had the greatest impact on the resultant ICERs are included in the above tornado diagrams. The
vertical line represents the value of the ICER for that age group and number of blastocysts (see Table 7.4). IR =
implantation rate; EPL = early pregnancy loss; FET = frozen embryo transfer.
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8.6 Probabilistic sensitivity analysis: scatter plots by age group, from a societal perspective.
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8.7 Probabilistic sensitivity analysis: scatter plots by age group, from alternative perspectives.
(a) Hypothetical MOHLTC Perspective
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(b) Patient Self-Pay Perspective
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8.8 Components of data usability.
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9 Appendices
9.1 Categorization of causes of inadequate data quality in medical registries.
From Arts DGT, Keizer NF de, Scheffer G-J. Defining and Improving Data Quality in Medical Registries: A
Literature Review, Case Study, and Generic Framework. J Am Med Inform Assoc. 2002 Nov 1;9(6):600–11. (133)