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THE AMERICAN JOURNAL OF MANAGED CARE® VOL. 23, NO. 7 429

MANAGERIAL

A lthough advances in screening and vaccination tech-

nologies have substantially lowered the risk of cervical

cancer among women, it still accounts for more than

4000 deaths per year in the United States.1 There are also per-

sistent disparities in incidence and mortality rates, not only by

socioeconomic status and geography, but also by ethnicity and

race.2,3 Hispanic women have a higher risk of cervical cancer

than other major ethnic/racial groups and are more likely to

be diagnosed at a later stage.4 In urban areas, reducing cervical

cancer morbidity and mortality is particularly difficult as the

success of cancer prevention programs requires knowledge and

self-control that few patients possess.5

Patient navigation refers to “the support and guidance offered to

persons with abnormal cancer screening or a new cancer diagnosis

in accessing the cancer care system, overcoming barriers, and

facilitating timely, quality care provided in a culturally sensitive

manner.”6 In public health practice, patient navigation may include

a variety of specific services and interventions, such as scheduling

appointments with culturally sensitive caregivers, providing trans-

portation or interpretation services, and assisting participants with

childcare during scheduled appointments.6 The results of several

randomized controlled trials have shown that patient navigation is

effective in increasing patient satisfaction, decreasing the anxiety

associated with screening processes and procedures, and improv-

ing cancer screening uptake and adherence.7,8 However, there is

still limited evidence supporting the efficacy of patient navigation

in improving patient outcomes over the long term or assessing the

cost-effectiveness (CE) of specific patient navigation programs.9

In this study, we explored the implementation and results

from a community-based patient navigation program (designed

to increase the cervical cancer screening [Pap test] rate) in San

Antonio, Texas, for an underserved Hispanic female population

18 years or older. The program was multilevel and included some

elements and principles from behavioral economics.10 Because

the benefits of cervical cancer screening are hardly observed in

the short term, we also used an evidence-based microsimulation

Cost-Effectiveness of a Patient Navigation Program to Improve Cervical Cancer ScreeningYan Li, PhD; Erin Carlson, DrPH; Roberto Villarreal, MD; Leah Meraz, MA; and José A. Pagán, PhD

ABSTRACT

OBJECTIVES: To assess the cost-effectiveness of a community-based patient navigation program to improve cervical cancer screening among Hispanic women 18 or older in San Antonio, Texas.

STUDY DESIGN: We used a microsimulation model of cervical cancer to project the long-term cost-effectiveness of a community-based patient navigation program compared with current practice.

METHODS: We used program data from 2012 to 2015 and published data from the existing literature as model input. Taking a societal perspective, we estimated the lifetime costs, life expectancy, and quality-adjusted life-years and conducted 2-way sensitivity analyses to account for parameter uncertainty.

RESULTS: The patient navigation program resulted in a per-capita gain of 0.2 years of life expectancy. The program was highly cost-effective relative to no intervention (incremental cost-effectiveness ratio of $748). The program costs would have to increase up to 10 times from $311 for it not to be cost-effective.

CONCLUSIONS: The 3-year community-based patient navigation program effectively increased cervical cancer screening uptake and adherence and improved the cost-effectiveness of the screening program for Hispanic women 18 years or older in San Antonio, Texas. Future research is needed to translate and disseminate the patient navigation program to other socioeconomic and demographic groups to test its robustness and design.

Am J Manag Care. 2017;23(7):429-434

430 JULY 2017 www.ajmc.com

MANAGERIAL

model to assess improvements in long-term patient outcomes, and

to evaluate the CE of the program versus the status quo. Finally, to

ensure the robustness of the CE analysis, we conducted sensitivity

analyses to assess several key cost and effectiveness parameters.

METHODSProgram Description

This study focused on a 3-year patient navigation program for cervi-

cal cancer screening implemented by the Bexar County Hospital

District (University Health System) in San Antonio, Texas, from 2012

to 2015. The program targeted an urban female Hispanic population

18 years or older enrolled in CareLink, a financial assistance pro-

gram for the uninsured population in San Antonio. This population

has a particularly high risk for cervical cancer: in 2009, approxi-

mately 67% of women aged at least 18 years who were enrolled in

CareLink had not had the recommended Pap test within the past

3 years.11 In addition, these women faced a range of cultural and

socioeconomic barriers to undergoing cancer screening (eg, lack of

financial resources to access screening services, fear of embarrass-

ment, and concerns about provider sensitivity to patient comfort).

The patient navigation program is recognized as a major com-

ponent of a community-based, culturally competent, secondary

cancer prevention program described in a previous study.11 It

was designed to provide personalized social communication by

encouraging participants to call “Claudia,” a bilingual female con-

tact person who would act as a program navigator in disseminating

health information. This is consistent with the behavioral econom-

ics principle of relying on social and cultural norms because using

the same Hispanic name as a contact person helps participants

recall similar events in memory in a culturally competent way.10

The program also included participant reminders to call Claudia

within newsletters, public service announcements, and automated

messages. Afterward, these patient navigators provided assess-

ments of the cervical cancer and screening knowledge of patients

they had spoken with, as well as personalized education about the

potential benefits of screening.

In addition to the services provided by patient navigators, the

program also implemented multilevel strategies designed to

increase the uptake and adherence of cancer

screening within the target population. For

example, the program relied on a mass media

health promotion campaign, which allowed

women to align their subjective assessment

of cervical cancer risk with their actual risk

by receiving health education and informa-

tion messages provided by patient navigators

who are similar to, or representative of, the

target population. The program also provided

patients with accurate information related to cervical cancer risk

to address unrealistic expectations (ie, individuals may have

unreasonably low or high estimates of their cervical cancer risk).10

Lastly, as an incentive to each program participant, all screening

tests were free. The patient navigation program was designed to

be multilevel and to integrate general principles of behavioral eco-

nomics by taking into account key factors that patients consider

when making screening decisions.

The patient navigation program demonstrated its effectiveness

at improving cervical cancer screening through interviews and

focus groups that took place between program staff members

and participants. In particular, 94% of program staff (including

patient navigators, care providers, and others who participated in

the implementation of the program) agreed that it had addressed

the needs of cervical cancer screening among Hispanic women and

participants were either very satisfied or satisfied working with the

program. In addition, patients reported a positive experience using

the program services provided, including increased knowledge

about cervical cancer and HPV and stronger motivation to partici-

pate in cancer screenings. Overall, the program has navigated 4500

women in the target population and increased the 3-year screening

rate from 65% to 80% during the 3-year study period.

Model Structure

Although empirical studies based on actual data may produce

important insights into the CE of a given patient navigation pro-

gram, they are costly or may not be able to assess the long-term

impact of cervical cancer screening strategies.12 Simulation model-

ing—particularly microsimulation models—offers a more flexible,

cost-effective approach to conducting economic evaluations and

making informed decisions compared with studies based on actual

behavioral observations.12 By incorporating the best available bio-

logical, clinical, and epidemiological evidence, a microsimulation

model of cervical cancer enables researchers to simulate a popula-

tion of interest, capture the disease progression of each individual,

predict the long-term consequences of different interventions

within a virtual environment, and provide insights into the CE of

different strategies designed for cervical cancer prevention.

Our model structure is as follows (a detailed model descrip-

tion can be found in the eAppendix [available at ajmc.com]): our

TAKEAWAY POINTS

Community-based patient navigation programs may improve cervical cancer screening uptake, especially among Hispanic women. This study provides healthcare managers with knowledge about patient navigation programs that are multilevel and include some elements and principles from behavioral economics to improve cancer screening.

› This study adds to the existing literature by assessing the long-term cost-effectiveness of a community-based patient navigation program for Hispanic women.

› This study promotes the implementation of patient-centered cancer screening services.


Cost-Effectiveness of a Patient Navigation Program

microsimulation model incorporates state-

of-the-art knowledge from previous cervical

cancer decision models, and estimated param-

eters based on our specific program.10,13,14 The

natural history of cervical cancer was modeled

using 16 states, including: well (healthy) HPV

infection, low- and high-grade squamous

intraepithelial lesions (SILs), hysterectomy

for benign disease, undetected and detected

cervical cancer stages I to IV, survival from can-

cer, and death due to cervical cancer or other

causes (Figure 1). Transitions between health

states were governed by transition probabili-

ties that were dependent on age, SILs level,

cancer stage, and screening or vaccination

strategies. The basic cycle length was 1 year.

Each year, women in the simulation model

could be infected with HPV or stay uninfected.

We assumed all cases of cervical cancer start

with HPV infection, which is consistent with

the epidemiologic finding that HPV causes a

majority of cervical cancer cases.15,16 HPV infec-

tion, clearance, and progression to low- or

high-grade SILs is a complex process that var-

ies depending on HPV virus type and patient

characteristics, such as age and immune sta-

tus. We used average transition probabilities

for all virus types (ie, we did not distinguish

between different types of HPV), which sim-

plified our model without losing important

information. The incidence of HPV infection

was modeled to be a function of age, and the

parameters of the incidence function did not

change throughout the simulation.

Women infected with HPV could regress to

well, stay unchanged, or progress to low- or

high-grade SILs. Similarly, women with low-

or high-grade SILs could undergo regression,

no change, or progression to stage I cancer

without symptoms. Current knowledge

about the natural history of cervical cancer

suggests that most HPV infections will regress on their own and

some persistent HPV infections may progress to high-grade SILs

and cancer.17,18

Women with asymptomatic stage I cancer either become symp-

tomatic or progress to higher stages without detection. Once cancer

becomes symptomatic or is detected by screening, the patient will

undergo medical treatment. Women without cancer may undergo

a hysterectomy due to other causes19 and all women could die from

causes outside those identified in the study.

Parameter Estimation

Tables 1 and 2 summarize all input parameters in the microsimu-

lation model. We estimated the incidence of HPV and transition

probabilities among different health states from the published

literature13,14,19-21 and age-specific female mortality rates from

other causes by subtracting the rates due to cervical cancer from

age-specific, all-cause mortality rates obtained from 2010 US life

tables.22 Quality of life weights were determined by either age (for

women without cervical cancer) or cancer stage (women with).23-25

FIGURE 1. Simplified Model of HPV Infection and Cervical Cancer Progression

HPV indicates human papillomavirus; SIL, squamous intraepithelial lesion.

High-grade SILsLow-grade SILsHPV

Death due to cervical cancer

Well

Undiagnosed cancer stage I

Undiagnosed cancer stage II

Undiagnosed cancer stage III

Undiagnosed cancer stage IV

Diagnosed cancer stage I

Diagnosed cancer stage II

Diagnosed cancer stage III

Diagnosed cancer stage IV

TABLE 1. Cervical Cancer Natural History Model Parameters and Sources

Measures Values

(transition probabilities) Source

Incidence and transitions among precancerous states

Incidence of HPV infection Age-specific Kulasingam et al (2006)27

Incidence of hysterectomy Age-specific Merrill et al (2008)19

HPV regression Age-specific Myers et al (2000)14

HPV to low-grade SIL 0.054 Myers et al (2000)14

HPV to high-grade SIL 0.006 Myers et al (2000)14

Low-grade regression and progression

Age-specific Myers et al (2000)14

High-grade SIL regression 0.03 Myers et al (2000)14

High-grade SIL progression Age-specific Myers et al (2000)14

Transitions among cancerous states

Symptom onset Varies with cancer stage Myers et al (2000)14

Cancer progression Varies with cancer stage Myers et al (2000)14

Mortality

Mortality due to cancerVaries with stage and time since diagnosis

Ries et al (2007)20

All-cause mortality Age-specific Murphy et al (2013)22

HPV indicates human papillomavirus; SIL, squamous intraepithelial lesion.


MANAGERIAL

We used a societal perspective in the CEA by incorporating both

program and treatment costs; the patient navigation and screening

program costs were estimated to be $311 per person. We calculated

this figure by adding all costs incurred in the program ($1,399,815),

including navigation and screening-related program staff salaries,

health promotion media and outreach costs, and Pap test cost, then

dividing the total by the number of women (4500) who received

patient navigation and screening services. We estimated annual

treatment costs for different cancer stages from the published

literature.26-29 Women with HPV infection or low-grade SILs do not

need treatment, and thus, did not incur additional costs.

Lastly, the sensitivity and specificity of the screening test were

estimated to be 80% and 95%, respectively, based on the published

literature.25,30,31 Although we expected that

the prevalence of HPV infection in the San

Antonio metropolitan area would be higher

than the national average, we still used the

national average (26.8%) in our model due

to a lack of population-specific data in our

community of interest.17 All input data and

parameters can be found in the eAppendix.

Experiments

Our model followed 100,000 simulated

women with the same age distribution and

prevalence of HPV infection as the population

of interest (Hispanic women 18 years or older)

throughout their lifetime, with and without

the patient navigation program. Simulating

a large population provides stable estimates

of long-term outcomes for each simulation

scenario. In the CEA, we assumed that women

who were successfully navigated through the

study received Pap tests at suitable intervals,

appropriate diagnostic procedures (eg, col-

poscopy, biopsy), and treatment based on the

results of screening. Specifically, women with

low-grade SILs were reexamined every 6 to 12

months until they had 3 negative screening

test results.32 In addition, women with con-

firmed high-grade SILs or cancer were treated

according to published guidelines.32

We tracked the overall costs, life expec-

tancy, and quality-adjusted life-years (QALYs)

of the simulated population for each scenario

and discounted them by 3% (a widely accepted

discount rate in CEA) annually. We then mea-

sured the performance of the program by

estimating the incremental cost-effectiveness

ratio (ICER) between the patient navigation

program and no intervention. We implemented the microsimu-

lation model using the software package AnyLogic 7.1 (AnyLogic

North America; Chicago, Illinois).

RESULTSBaseline Cost-Effectiveness Analysis

Table 3 presents the CE estimates of the patient navigation program

for cervical cancer screening compared with no intervention. Our

results show that the program costs an average of $45 more per per-

son than the no intervention scenario. Also, the screening program

showed an increase in the life expectancy of the studied population

TABLE 2. Quality of Life and Cost Parameters and Sources

Measures Values Sources

Utility weights

Without cancer Age-specific Hanmer et al (2006)23

Local cancer (stage I) 0.68Kim et al (2002)24

Goldhaber-Fiebert et al (2008)25

Regional cancer (stages II and III) 0.56Kim et al (2002)24


Distant cancer (stage IV) 0.48Kim et al (2002)24


Costs, $

Program cost ($/person) 311 The patient navigation program

Treatment cost ($/[person × year])

High-grade SIL 3221

Bidus et al (2006)26 Kulasingam et al (2006)27

Insinga et al (2004)28 Insinga et al (2005)29

Local cancer (stage I) 24,477



Regional cancer (stages II and III) 26,197



Distant cancer (stage IV) 41,959



Other parameters

Sensitivity of the screening test 80%Goldhaber-Fiebert et al

(2008)25 Cuzick et al (2006)30 Solomon et al (2003)31

Specificity of the screening test 95%Goldhaber-Fiebert et al

(2008)25 Cuzick et al (2006)30 Solomon et al (2003)31

Initial population characteristics – The patient navigation program

Discount rate for costs and utilities 3% –

SIL indicates squamous intraepithelial lesion.


Cost-Effectiveness of a Patient Navigation Program

by 0.2 years and an increase in QALYs by 0.06

years, which results in an ICER of $748 per

QALY for the patient navigation program

versus no intervention. We used $50,000 per

QALY as the CE threshold to determine that the

patient navigation program was considered

to be cost-effective in the baseline scenario.

Sensitivity Analysis

We studied the robustness of the baseline results by conducting

sensitivity analyses that accounted for uncertainties in the cost and

effectiveness of the patient navigation program. Figure 2 reports

the results of a 2-way sensitivity analysis of the program cost per

participant and screening rate. Again, we used $50,000 per QALY

as the CE threshold. Specifically, if a combination of program cost

per participant and the screening rate falls below the CE frontier

shown in Figure 2, the ICER of the patient navigation program rela-

tive to the status quo is less than $50,000 per QALY, which means

the program is more cost-effective; otherwise, the status quo is

more cost-effective. The results show that, because 80% of program

participants received cervical cancer screening tests, the cost of the

program could increase up to 10 times—from $311 to $3312—before

the program becomes less cost-effective. In addition, even if the

patient navigation program only resulted in a screening rate of

70% (a 5% increase from the status quo), the program is considered

cost-effective as long as its price tag remains below $2353 per person.

DISCUSSIONLarge disparities in cancer screening uptake and outcomes exist

across many socioeconomic and demographic groups in the United

States and, despite substantial progress to reduce these differences

by developing new cancer screening initiatives, these gaps in can-

cer screening stubbornly persist.11 Community-based, multilevel

patient navigation programs have shown promise in improving

adherence to cancer screening processes and protocols. When

these programs can further incorporate some principles of behav-

ioral economics—with a focus on understanding the heuristics

individuals use to make decisions—they can address patient biases

in cancer risk and decision making surrounding cancer screening

and optimize the appropriate architecture for individuals who are

considering undergoing cancer screening.

Our results showed that a specific community-based patient

navigation program for cervical cancer screening was cost-effective

in increasing the screening rate and improving the long-term health

outcomes of the target population. We estimated that an average

program participant would gain an additional life expectancy of

0.2 years and an additional 0.06 QALYs. Under the baseline scenario,

the patient navigation program costs $748 for each additional QALY

gained with respect to no intervention, indicating that the program

is highly cost-effective. Sensitivity analyses showed the robust-

ness of the CE of the patient navigation program: 1) the program

costs would have to increase up to 10 times from $311 before the

program ceased to be cost-effective, and 2) the program would be

cost-effective even if the screening rate only increased from 65%

to 70% instead of the observed 80% after program implementation.

Similar community-based patient navigation programs have

been shown to improve the cervical cancer screening rate of

Hispanic women and the colorectal cancer (CRC) screening rate

of Hispanic men. (In a previous study, we showed that a patient

navigation program increased the CRC screening rate among target

Hispanic men from 16% to 80% in data collected from 2011-2013.)33

The navigation program would reduce the lifetime overall cost

to its participants (due to significantly reduced cancer risk) and

thus achieve cost-savings compared with no intervention.33 Given

these promising results, healthcare providers may consider testing

and evaluating similar patient navigation programs to improve

screening for other types of cancer (eg, breast cancer and prostate

cancer) in other populations of interest.

Limitations

The cervical cancer natural history model was developed based on

parameters that reflect the general US population, not specifically

the Hispanic population, which was the target population of this

TABLE 3. Cost-Effectiveness of the Patient Navigation Program Compared With the Status Quo

Prevention Program Cost ($) Life Expectancy (years) QALY ICER ($/QALY)

Program 642.80 36.49 22.29 748.33

Status quo 597.90 36.29 22.23 –

Increment 44.90 0.20 0.06 –

ICER indicates incremental cost-effectiveness ratio; QALY, quality-adjusted life-year.

FIGURE 2. Two-Way Sensitivity Analysis for the Choice of Status Quo or Screening Programa

QALY indicates quality-adjusted life-year.aCost-effectiveness threshold is $50,000 per QALY.

2000

2500

3000

3500

4000

4500

70% 75% 85% 80%

$50,000/QALY thresholdcost-effectiveness frontier

Status Quo

Pro

gram

Cos

t per

Par

tici

pant

($)

Screening Rate


MANAGERIAL

study. We also did not have local cost estimates for cervical cancer

treatment; thus, we relied on national average costs estimated

from the existing literature. We will update the CE results as more

local data for model parameterization become available and con-

duct more comprehensive sensitivity analyses to address these

parameter uncertainties.

We did not model the way in which different population char-

acteristics or socioeconomic factors would influence the choice

of screening in the model. One of our next steps will be to model

a decision-making process for each individual participating in the

simulation regarding whether to undergo screening, which would

increase the realism of the model by considering individual het-

erogeneity and potentially improve the validity of the CE results.

Finally, we did not examine the effect of varying screening

intervals or HPV vaccination on the projected outcomes. Modeling

these additional scenarios is a next step, as our model can be easily

adapted to incorporate a different screening interval or include

HPV vaccination as part of cervical cancer prevention practices.

CONCLUSIONS Our study results demonstrate how a health system serving a low-

income, urban, and minority (Hispanic) population was able to

develop a cost-effective patient navigation program for cervical

cancer screening. Although our findings are promising, the patient

navigation program results presented here need to be translated

and disseminated to other socioeconomic and demographic groups

to test the robustness and design of the program, particularly in

terms of how to carefully calibrate behavioral change components

and understand which program attributes are most promising. n

AcknowledgmentsThe authors thank David Siscovick, MD, MPH, for his constructive comments.

Author Affiliations: Center for Health Innovation, The New York Academy of Medicine (YL, JAP), New York, NY; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai (YL, JAP), New York, NY; College of Nursing and Health Innovation, The University of Texas at Arlington (EC), Arlington, TX; Research and Information Management, University Health System (RV, LM), San Antonio, TX; Leonard Davis Institute of Health Economics, University of Pennsylvania (JAP), Philadelphia, PA.

Source of Funding: This study was funded by the Cancer Prevention and Research Institute of Texas (award grant ID PP120111).

Author Disclosures: The authors report no relationship or financial inter-est with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (YL, JAP); acquisition of data (YL, RV, EC, LM); analysis and interpretation of data (YL, JAP); drafting of the manuscript (YL); critical revision of the manuscript for important intel-lectual content (YL, RV, LM, JAP); statistical analysis provision of patients or study materials (EC); obtaining funding (RV); and administrative, technical, or logistic support (EC, LM).

Address Correspondence to: Yan Li, PhD, Center for Health Innovation, The New York Academy of Medicine, 1216 Fifth Ave, New York, NY 10029. E-mail: [email protected].

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Full text and PDF at www.ajmc.com

eAppendix

Introduction

In this study, we evaluated the cost-effectiveness of cervical cancer screening compared

with the status quo. Cost-effectiveness analysis is a type of decision analysis that compares the

relative health and economic consequences of different interventions (including no intervention).

Decision-makers can assess the cost incurred to achieve a unit gain of health improvement. The

rationale behind cost-effectiveness analysis is that resources are limited, and thus, should be used

as efficiently as possible to maximize health benefits. Cost-effectiveness analysis helps

policymakers maximize population health and studies are conducted based on a societal

perspective, which requires decision makers to incorporate all direct and indirect costs and health

benefits associated with an intervention.

We developed a microsimulation model of cervical cancer to conduct the cost-

effectiveness analysis. Although empirical studies based on actual data may produce reliable

findings, they are costly or may not be useful or able to assess long-term impacts. Simulation

modeling, however, is a more flexible, cost-effective approach to conducting economic evaluation

to help inform decision-making compared with studies based on actual behavioral observations.

By incorporating the best available biological, clinical, and epidemiological evidence, our

simulation model of cervical cancer enables us to simulate a population of interest; capture the

disease progression of each individual; predict the long-term consequences of different

interventions in a virtual environment; and provide insight into the cost-effectiveness of the

interventions. We refer to Goldie et al for a more comprehensive discussion on the use of

simulation modeling to inform policymaking for cervical cancer prevention.1 This technical report

provides details about our model.

Natural History Model

The natural history of cervical cancer was modeled using 16 states, including well; HPV

infection; low- and high-grade squamous intraepithelial lesions (SIL); hysterectomy for benign

disease; undetected and detected cervical cancer states I-IV; survival from cancer; and death due to

cervical cancer or other causes (eAppendix Figure 1). Transitions between health states were

governed by transition probabilities that depend on age, SIL level, cancer stage, and screening or

vaccination strategies. We used 1 year as a basic cycle length.

Each year, women in the simulated model could be infected with HPV or stay uninfected.

We assumed all cases of cervical cancer start from HPV infection, which is consistent with the

epidemiologic finding that HPV causes the majority of cervical cancer cases.2,3 HPV infection,

clearance, and progression to low- or high-grade SIL is a complex process that varies, depending

on HPV virus type and patient characteristics, such as age and immune status. We used average

transition probabilities for all virus types and thus, we did not need to distinguish different types of

HPV.

This simplified our model without losing important information. We modeled the incidence

of HPV infection as a function of age, and assumed the incidence function did not change

throughout the simulation.

Women infected with HPV can regress to infected, stay unchanged, or progress to low- or

high-grade SIL. Similarly, women with low-grade SIL can undergo regression to uninfected or

infected, no change, or progression to high-grade SIL. Women with high-grade SIL can regress,

stay the same, or progress to stage 1 cancer without symptoms. Current knowledge about the

natural history of cervical cancer suggests that most HPV infections will regress on their own

without any treatment, as some persistent HPV infections may progress to high-grade SIL and

eventually, cervical cancer.4,5

Women in stage 1 cancer without symptoms either become symptomatic or progress to

higher stages of cancer without detection. Once cancer becomes symptomatic or is detected by

screening, the patient will undergo medical treatment. Both the probability of survival and the

probability of mortality due to cancer are stage-specific; a higher stage of cancer will typically

result in lower probability of survival with or without treatment, and a higher mortality rate.

Women without cancer have age-specific probabilities of undergoing a hysterectomy due to

other causes.6 It is important to include hysterectomy in the model because it will significantly alter

the natural history of cervical cancer. In addition, all women could die due to other causes other

than those included in this study. We use age-specific mortality rates from national vital statistics

data.7

We assumed that women in our studied population received their screening tests (Pap tests)

at the appropriate interval, and also received appropriate diagnostic procedures (eg, colonoscopy

and biopsy) and treatment based on the results of the screening tests. Specifically, women with

low-grade SIL were re-examined every 6 to 12 months until they had 3 negative screening test

results.8 In addition, women with confirmed high-grade SIL or cancer were treated according to

published guidelines.8

Parameter Estimation

Incidence of HPV infection. The probabilities for HPV incidence, regression, and progression were

based on averages for all virus types given that our model did not distinguish between different

types of the HPV virus.9 Table 1 of the eAppendix presents the age-specific estimates for HPV

incidence. The table shows HPV incidence reaches a peak from age 17 to 21, which is consistent

with the epidemiologic finding among women nationwide. Note that our model would have more

accuracy if we could use population-specific HPV incidence rates, but these data are not available

for the study.

Transition probabilities among precancerous states. We obtained age-specific annual transition

probabilities among precancerous states from published literature.6,9,10 Table 2 of the eAppendix

presents parameter values and the corresponding literature sources. The table shows that the

majority of women infected with HPV will regress, and only a small proportion will progress every

year.

Also, the regression rates decrease significantly as age increases. Women with high-grade

SIL who are older than 30 years have an average of 4 times higher greater probability of

progressing to cancer compared with women with high-grade SIL who are younger than 30 years.

These data are also in consistent with the Surveillance, Epidemiology, and End Results (SEER)

data.11

Transition probabilities among cancer states. Women with asymptomatic cervical cancer have a

stage-specific probability of having symptoms and progressing to the more advanced cancer stage

(eAppendix Table 3). It is evident that a more advanced cancer stage is associated with a greater

likelihood of symptoms. For example, the annual probability of symptom onset ranges from 0.15

for stage 1 cancer to 0.9 for stage 4 cancer. Table 3 of the eAppendix also presents probabilities of

death due to cancer, which is a function of both cancer stage and years following diagnosis. The

parameters were originally estimated from the SEER data collected from the National Cancer

Institute, and were also used in other studies.9-11 We assumed that there was no mortality due to

cancer after 5 years postdiagnosis. This assumption was consistent with other cost-effectiveness

analysis studies and clinical findings.9,10,12 We estimated age-specific female mortality rates due to

other causes by subtracting age-specific mortality rates due to cervical cancer from age-specific

all- cause mortality rates obtained from the US life tables in 2010.7

Quality of life weights. We used quality of life weights (QALYs) to measure the effectiveness of

different prevention programs in preventing cervical cancer. We not only considered morbidity and

mortality when calculating QALYs, but also incorporated the effect of aging. For example, a

healthy woman who is less than 20 years old has a quality of life weight of 1, and a healthy woman

older than 79 years has a quality of life weight of 0.724. We obtained the age-specific quality of

life weights based on nationally representative values.16 When a woman had cancer, her quality of

life weight would be determined based on her cancer stage rather than her age. We obtained the

stage-specific QOL weights from published studies.14,15 The QOL weight is 0 when a person is in a

death state. Table 4 of the eAppendix presents the age- and stage-specific estimates of QOL

weights.

Costs and other parameters. Cost calculation in our model includes both program costs and

treatment costs (eAppendix Table 5). Specifically, the screening program costs $311 per person.

We calculated this figure by adding up all costs incurred in the program ($1,399,815), including

Pap test costs, program staff salaries, and health promotion media and outreach cost, and dividing

the total cost by the number of women (4500) who received screening. We estimated annual

treatment costs for high-grade SIL, local cervical cancer (stage 1), regional cervical cancer (stages

2 & 3), and distant cervical cancer (stage 4) from published literature.16-19 Women with HPV

infection or low-grade SIL do not treatment and thus, do not incur additional costs.

Table 5 of the eAppendix also includes other parameters required to assess the cost-

effectiveness of different prevention programs. In particular, we estimated the screening test

characteristics (sensitivity and specificity) from published literature.15,20,21 Although we expect that

the prevalence of HPV infection in Bexar County is higher than the national average, we still used

the national average (26.8%) in our model due to a lack of population-specific data.4 Finally, we

discounted both costs and QOL by 3% annually.

User Interface

Figures 2 and 3 of the eAppendix demonstrate the model input and output interfaces. The

input interface enables users (eg, policymakers) to easily assess the cost and effectiveness of either

A Su Salud Pap screening program or HPV vaccination program for a user-defined length of time.

Through the input interface, users can perform “What if” analyses by varying age distribution,

prevalence of HPV infection for the initial population, probabilities of receiving Pap tests or HPV

vaccinations, and the per capita cost of each program. This feature is especially useful when there

are uncertainties in the estimation of parameters. The output interface enables users to visualize

dynamic changes of several simulation outcomes, including yearly prevalence of HPV, incidence

of low- and high-grade SIL, cancer incidence and mortality, and cost and effectiveness measures

(ie, mean cost, mean QALY, life expectancy).

Comparing simulated time series data with actual time series statistics would help to

conceptually validate model predictions and calibrate model parameters. We designed the user-

friendly interfaces so that the cervical cancer prevention economic evaluation model could be

readily used when there are updates to the screening and vaccination parameters or the program is

implemented in another population.

REFERENCES

1. Goldie SJ, Goldhaber-Fiebert JD, Garnett GP. Public health policy for cervical cancer

prevention: The role of decision science, economic evaluation, and mathematical modeling.

Vaccine. 2006;24:S155-S163.

2. Herrero R. Epidemiology of cervical cancer. J Natl Cancer Inst Monogr. 1995;(21):1-6.

3. Schiffman MH, Bauer HM, Hoover RN, et al. Epidemiologic evidence showing that human

papillomavirus infection causes most cervical intraepithelial neoplasia. J Natl Cancer Inst.

1993;85(12):958-964.

4. Dunne EF, Unger ER, Sternberg M, et al. Prevalence of HPV infection among females in the

United States. Jama. 2007;297(8):813-819.

5. Giuliano AR, Harris R, Sedjo RL, et al. Incidence, prevalence, and clearance of type-specific

human papillomavirus infections: The Young Women’s Health Study. J Infect Dis.

2002;186(4):462-469.

6. Merrill RM. Hysterectomy surveillance in the United States, 1997 through 2005. Med Sci Monit.

2008;14(1):CR24-CR31.

7. Murphy SL, Xu J, Kochanek KD, others. National vital statistics reports. Natl Vital Stat Rep.

2013;61(4). http://www.documentingreality.com/forum/attachments/f227/520621d1394038065-

lists-death-suicide-unsolved-missing-nvsr61_04-20-281-29.pdf. Accessed August 19, 2015.

8. Wright Jr TC, Cox JT, Massad LS, Twiggs LB, Wilkinson EJ, others. 2001 consensus guidelines

for the management of women with cervical cytological abnormalities. Jama. 2002;287(16):2120-

2129.

9. Kulasingam SL, Havrilesky L, Ghebre R, Myers ER. Screening for cervical cancer: a decision

analysis for the US Preventive Services Task Force. 2011.

http://www.ncbi.nlm.nih.gov/books/NBK92546. Accessed August 19, 2015.

10. Myers ER, McCrory DC, Nanda K, Bastian L, Matchar DB. Mathematical model for the

natural history of human papillomavirus infection and cervical carcinogenesis. Am J Epidemiol.

2000;151(12):1158-1171.

11. Ries LAG, Eisner MP, Kosary CL, et al. SEER Cancer Statistics Review, 1975-2004. National

Cancer Institute; Bethesda, MD: 2007. Available Seer Cancer Govcsr1975-2001. 2007.

12. Russell AH, Shingleton HM, Jones WB, et al. Diagnostic Assessments in Patients with

Invasive Cancer of the Cervix: A National Patterns of Care Study of the American College 1 of

Surgeons. Gynecol Oncol. 1996;63(2):159-165.

13. Hanmer J, Lawrence WF, Anderson JP, Kaplan RM, Fryback DG. Report of nationally

representative values for the noninstitutionalized US adult population for 7 health-related quality-

of-life scores. Med Decis Making. 2006;26(4):391-400.

14. Kim JJ, Wright TC, Goldie SJ. Cost-effectiveness of alternative triage strategies for atypical

squamous cells of undetermined significance. Jama. 2002;287(18):2382-2390.

15. Goldhaber-Fiebert JD, Stout NK, Salomon JA, Kuntz KM, Goldie SJ. Cost-effectiveness of

cervical cancer screening with human papillomavirus DNA testing and HPV-16, 18 vaccination. J

Natl Cancer Inst. 2008;100(5):308-320.

16. Bidus MA, Maxwell GL, Kulasingam S, et al. Cost-effectiveness analysis of liquid-based

cytology and human papillomavirus testing in cervical cancer screening. Obstet Gynecol.

2006;107(5):997-1005.

17. Kulasingam SL, Kim JJ, Lawrence WF, et al. Cost-effectiveness analysis based on the atypical

squamous cells of undetermined significance/low-grade squamous intraepithelial lesion Triage

Study (ALTS). J Natl Cancer Inst. 2006;98(2):92-100.

18. Insinga RP, Glass AG, Rush BB. The health care costs of cervical human papillomavirus–

related disease. Am J Obstet Gynecol. 2004;191(1):114-120.

19. Insinga RP, Dasbach EJ, Elbasha EH. Assessing the annual economic burden of preventing and

treating anogenital human papillomavirus-related disease in the US. Pharmacoeconomics.

2005;23(11):1107-1122.

20. Cuzick J, Mayrand MH, Ronco G, Snijders P, Wardle J. New dimensions in cervical cancer

screening. Vaccine. 2006;24:S90-S97.

21. Solomon D. Role of triage testing in cervical cancer screening. J Natl Cancer Inst Monogr.

2003;31:97-101.

22. Fornos LB, Urbansky KA, Villarreal R. Increasing cervical cancer screening for a multiethnic

population of women in South Texas. J Cancer Educ. 2014;29(1):62-68.

1.

eAppendix Table 1. Age-Specific Annual Incidence for HPV Incidence4

Age Value

12 0

13 0.01

14 0.05

15 0.1

16 0.1

17 0.12

18 0.15

19 0.17

20 0.15

21 0.12

22 0.1

23 0.1

24-29 0.05

30-49 0.01

50 0.005

HPV indicates human papillomavirus.

eAppendix Table 2. Annual Transition Probabilities Among Precancerous States

Parameter Age Value

(transition

probabilities)

Source

HPV to Well

15-24 0.552

4,5

25-29 0.37

30-39 0.175

40-49 0.103

50+ 0.034

HPV to Low-grade SIL -- 0.054

HPV to High-grad SIL -- 0.006

Low-grade SIL to Well 15-34 0.09

35+ 0.054

Low-grade SIL to HPV 15-34 0.01

35+ 0.006

Low-grade SIL to High-grade

SIL

15-34 0.02

35+ 0.06

High-grade SIL to Well -- 0.03

High-grade SIL to Low-grade

SIL

-- 0.03

High-grade SIL to Cancer

12-29 0.01

30+ 0.04

Well, HPV, Low- and High-

grade SIL to Hysterectomy

18-44 0.005

10 45-64 0.006

65+ 0.002

HPV indicates human papillomavirus; SIL, squamous intraepithelial lesions.

eAppendix Table 3. Annual Transition Probabilities Among Cancer States and

Mortality Rates

Parameter Value Source

Probability of symptoms for Cancer stage 1 0.15

4,5




Cancer Stage 1 to Cancer stage 2 0.438

Cancer Stage 2 to Cancers stage 3 0.536

Cancer Stage 3 to Cancer stage 4 0.684

Mortality rates for Cancer stage 1

14

Year 1 0.014

Year 2 0.042

Year 3 0.062

Year 4 0.071

Year 5 0.087

Mortality rates for Cancer stage 2 & 3

Year 1 0.138

Year 2 0.292

Year 3 0.379

Year 4 0.438

Year 5 0.464

Mortality rates for Cancer Stage 4

Year 1 0.484

Year 2 0.698

Year 3 0.78

Year 4 0.834

Year 5 0.842

All-cause mortality rates for women Age-Specific 11

eAppendix Table 4. Estimates of Quality of Life Weights


Quality weights by age for people without

cancer

16

<20 year 1.000

20-29 year 0.913

30-49 year 0.893

50-59 year 0.837

60-69 year 0.811

70-79 year 0.771

>79 year 0.724

Quality weights by cancer stage

13,17 Local cervical cancer (stage 1) 0.680

Regional cervical cancer (stages 2 & 3) 0.560

Distant cervical cancer (stage 4) 0.480

eAppendix Table 5. Estimates of Cost and Other Parameters


Program cost ($/person) Program

data Pap test screening 311

Treatment cost ($ /(person x year))

18–21

High-grade SIL 3,221

Local cervical cancer (stage 1) 24,477

Regional cervical cancer (stages 2 & 3) 26,197

Distant cervical cancer (stage 4) 41,959

Other Parameters

Screening test characteristics

13,22,23 Sensitivity 80%

Specificity 95%

Probability of Pap test screening Program

data Status quo 65%

Program 80%

Age distribution Program-specific 2

Prevalence of HPV infection 26.8% 8

Discount rate for costs and

quality of life weights 3%


eAppendix Figure 1. Natural History of HPV Infection and Cervical Cancer

HPV indicates human papillomavius; SIL, squamous intraepithelial lesions.

eAppendix Figure 2. Cervical Cancer Microsimulation Model Input Interface


eAppendix Figure 3. Cervical Cancer Microsimulation Model Output Interface

HPV indicates human papillomavirus; QALY, quality-adjusted life-year.

cost-effectiveness of a patient navigation program to ... · navigation program effectively...

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