Allocating Costs and Benefits in Disease

Prevention Programs: An Application to

Cervical Cancer Screening

May 1981

NTIS order #PB81-221806
CASE STUDY # 7

THE IMPLICATIONS OF

COST-EFFECTIVENESS

ANALYSIS OF

MEDICAL TECHNOLOGY

JUNE 1981

BACKGROUND PAPER #2: CASE STUDIES OF

MEDICAL TECHNOLOGIES

CASE STUDY #7: ALLOCATING COSTS AND BENEFITS IN

DISEASE PREVENTION PROGRAMS: AN APPLICATION TO

CERVICAL CANCER SCREENING

Bryan R. Luce, Ph. D.

Senior Analyst, Health Program

Office of Technology Assessment, U.S. Congress

OTA Background Papers are documents that contain information believed to be

useful to various parties. The information undergirds formal OTA assessments or is

an outcome of internal exploratory planning and evaluation. The material is usually

not of immediate policy interest such as is contained in an OTA Report or Technical

Memorandum, nor does it present options for Congress to consider.

CONGRESS OF THE UNITED STATES

Office Technology Assessment

Washington, D C 20510
Library of Congress Catalog Card Number 80-600161

For sale by the Superintendent of Documents,

U.S. Government Printing Office, Washington, D.C. 20402
Foreword

This case study is one of 17 studies comprising Background Paper #2 for OTA’S

assessment, The in-zpiications of Cost-Effectiveness Analysis of Medical Technology.

That assessment analyzes the feasibility, implications, and value of using cost-effectiveness and cost-benefit analysis (CEA/CBA) in health care decisionmaking. The major, policy-oriented report of the assessment was published in August 1980. In addition

to Background Paper #2, there are four other background papers being published in

conjunction with the assessment: 1) a document which addresses methodological

issues and reviews the CEA/CBA literature, published in September 1980; 2) a case

study of the efficacy and cost-effectiveness of psychotherapy, published in October

1980; 3) a case study of four common diagnostic X-ray procedures, to be published in

summer 1981; and 4) a review of international experience in managing medical technology, published in October 1980. Another related report was published in

September of 1979: A Review of Selected Federal Vaccine and immunization Policies.

The case studies in Background Paper #2: Case Studies of Medical Technologies

are being published individually. They were commissioned by OTA both to provide

information on the specific technologies and to gain lessons that could be applied to

the broader policy aspects of the use of CEA/CBA. Several of the studies were specifically requested by the Senate Committee on Finance.

Drafts of each case study were reviewed by OTA staff; by members of the advisory panel to the overall assessment, chaired by Dr. John Hogness; by members of

the Health Program Advisory Committee, chaired by Dr. Frederick Robbins; and by

numerous other experts in clinical medicine, health policy, Government, and economics. We are grateful for their assistance. However, responsibility for the case studies remains with the authors,

#

/yd~

. ●

JOHN H. GIBBONS

Director
Advisory Panel on The Implications of

Cost= Effectiveness Analysis of Medical Technology

John R. Hogness, Panel Chairman

President, Association of Academic Health Centers

Stuart H. Altman Sheldon Leonard

Dean Manager

Florence Heller School Regulatory Affairs

Brandeis University General Electric Co.

James L. Bennington

Chairman

Department of Anatomic Pathology and

Clinical Laboratories

Children’s Hospital of San Francisco

John D. Chase

Associate Dean for Clinical Affairs

University of Washington School of Medicine

Joseph Fletcher

Visiting Scholar

Medical Ethics

School of Medicine

University of Virginia

Clark C. Havighurst

Professor of Law

School of Law

Duke University

Barbara J. McNeil

Department of Radiology

Peter Bent Brigham Hospital

Robert H. Moser

Executive Vice President

American College of Physicians

Frederick Mosteller

Chairman

Department of Biostatistics

Harvard University

Robert M. Sigmond

Advisor on Hospital Affairs

Blue Cross and Blue Shield Associations

Jane Sisk Willems

VA Scholar

Veterans Administration
OTA Staff for Background Paper #2

Joyce C. Lashof, Assistant Director, OTA

Health and Life Sciences Division

H. David Banta, Health Program Manager

Clyde J. Behney, Project Director

Kerry Britten Kemp, * Editor

Virginia Cwalina, Research Assistant

Shirley Ann Gayheart, Secretary

Nancy L. Kenney, Secretary

Martha Finney, * Assistant Editor

Other Contributing Staff

Bryan R. Luce Lawrence Miike Michael A. Riddiough

Leonard Saxe Chester Strobel’

OTA Publishing Staff

John C. Holmes, Publishing Officer

John Bergling Kathie S. Boss Debra M. Datcher Joe Henson

● OTA contract personnel,
Preface

This case study is one of 17 that comprise

Background Paper #2 to the OTA project on the

Implications of Cost-Effectiveness Analysis of

Medical Technology. * The overall project was

requested by the Senate Committee on Labor

and Human Resources. In all, 19 case studies of

technological applications were commissioned

as part of that project. Three of the 19 were specifically requested by the Senate Committee on

Finance: psychotherapy, which was issued separately as Background Paper #3; diagnostic Xray, which will be issued as Background Paper

#5; and respiratory therapies, which will be included as part of this series. The other 16 case

studies were selected by OTA staff.

In order to select those 16 case studies, OTA,

in consultation with the advisory panel to the

overall project, developed a set of selection

criteria. Those criteria were designed to ensure

that as a group the case studies would provide:

●

●

●

●

●

●

●

examples of types of technologies by function (preventive, diagnostic, therapeutic,

and rehabilitative);

examples of types of technologies by physical nature (drugs, devices, and procedures);

examples of technologies in different stages

of development and diffusion (new, emerging, and established);

examples from different areas of medicine

(such as general medical practice, pediatrics, radiology, and surgery);

examples addressing medical problems that

are important because of their high frequency or significant impacts (such as

cost );

examples of technologies with associated

high costs either because of high volume

(for low-cost technologies) or high individual costs;

examples that could provide informative

material relating to the broader policy and

methodological issues of cost-effectiveness

or cost-benefit analysis (CEA/CBA); and

*Office of Technology Assessment, U.S. Congress, The Implications of Cost-Effectiveness Analysis of Medical Technology, GPO

stock No. 052-003 -00765-7 (Washington, D. C.: U.S. Government

Printing Off Ice, August 1980).

● examples with sufficient evaluable literature.

On the basis of these criteria and recommendations by panel members and other experts,

OTA staff selected the other case studies. These

16 plus the respiratory therapy case study requested by the Finance Committee make up the

17 studies in this background paper.

All case studies were commissioned by OTA

and performed under contract by experts in academia. They are authored studies. OTA subjected each case study to an extensive review

process. Initial drafts of cases were reviewed by

OTA staff and by members of the advisory

panel to the project. Comments were provided

to authors, along with OTA’S suggestions for

revisions. Subsequent drafts were sent by OTA

to numerous experts for review and comment.

Each case was seen by at least 20, and some by

40 or more, outside reviewers. These reviewers

were from relevant Government agencies, professional societies, consumer and public interest

groups, medical practice, and academic medicine. Academicians such as economists and decision analysts also reviewed the cases. In all,

over 400 separate individuals or organizations

reviewed one or more case studies. Although all

these reviewers cannot be acknowledged individually, OTA is very grateful for their comments and advice. In addition, the authors of

the case studies themselves often sent drafts to

reviewers and incorporated their comments.

These case studies are authored works

commissioned by OTA The authors are responsible for the conclusions of their specific case study. These cases are not statements of official OTA position. OTA does

not make recommendations or endorse particular technologies. ,During the various

stages”of the review and revision process,

therefore, OTA encourage the athors to 

present balanced information and to recgnize divergent points of view. In two cases. In two cases,

OTA decided ”that in order to more fully

present divergent views on particular technologies a commentary should be added to

the case study. Thus, following the case
The case studies were selected and designed to

fulfill two functions. The first, and primary,

purpose was to provide OTA with specific information that could be used in formulating

general conclusions regarding the feasibility and

implications of applying CEA/CBA in health

care. By examining the 19 cases as a group and

looking for common problems or strengths in

the techniques of CEA/CBA, OTA was able to

better analyze the potential contribution that

these techniques might make to the management

of medical technologies and health care costs

and quality. The second function of the cases

was to provide useful information on the specific technologies covered. However. this was

Some of the case studies are formal CEAS or

CBAS; most are not. Some are primarily concerned with analysis of costs; others are more

concerned with analysis of efficacy or effectiveness. Some, such as the study on end-stage

renal disease, examine the role that formal

analysis of costs and benefits can play in policy

formulation. Others, such as the one on breast

cancer surgery, illustrate how influences other

than costs can determine the patterns of use of a

technology. In other words, each looks at evaluation of the costs and the benefits of medical

technologies from a slightly different perspective. The reader is encouraged to read this study

in the context of the overall assessment’s objectives in order to gain a feeling for the potential

role that CEA/CBA can or cannot play in health

care and to better understand the difficulties and

complexities involved in applying CEA/CBA to

specific medical technologies.

The 17 case studies comprising Background

Paper #2 (short titles) and their authors are:

Artificial Heart: Deborah P. Lubeck and John P.

Bunker

Automated Multichannel Chemistry Analyzers:

Milton C. Weinstein and Laurie A. Pearlman

Bone Marrow Transplants: Stuart O. Schweitzer and C. C. Scalzi

Breast Cancer Surgery: Karen Schachter and

Duncan Neuhauser

Cardiac Radionuclide Imaging: William B.

Stason and Eric Fortess

Cimetidine and Peptic Ulcer Disease: Harvey V.

Fineberg and Laurie A. Pearlman

Colon Cancer Screening: David M. Eddy

CT Scanning: Judith L. Wagner

Elective Hysterectomy: Carol Korenbrot, Ann

B. Flood, Michael Higgins, Noralou Roos,

and John P. Bunker

End-Stage Renal Disease: Richard A. Rettig

Gastrointestinal Endoscopy: Jonathan A. Showstack and Steven A. Schroeder

Neonatal Intensive Care: Peter Budetti, Peggy

McManus, Nancy Barrand, and Lu Ann

Heinen

Nurse Practitioners: Lauren LeRoy and Sharon

Solkowitz

Orthopedic Joint Prosthetic Implants: Judith D.

Bentkover and Philip G. Drew

Periodontal Disease Interventions: Richard M.

Scheffler and Sheldon Rovin

Selected Respiratory Therapies: Richard M.

Scheffler and Morgan Delaney

These studies will be available for sale by the

Superintendent of Documents, U.S. Government Printing Office, Washington, D.C. 20402.

Call OTA’S Publishing Office (224-8996) for

availability and ordering information.

Vlll
Case Study #7

Allocating Costs and Benefits in Disease

Prevention Programs:Application to , . ,

Cervical Cancer Screening, 

Bryan R. Luce, Ph. D.

Senior Analyst, Health Program

Office of Technology Assessment

Us.Congress

AUTHOR’S ACKNOWLEDGMENTS .

I acknowledge many debts of gratitude, but the primary one is to individual faculty members at the University of California at Los Angeles. Dr. Stuart Schweitzer

worked with me throughout the entire project and spent numerous hours with me

discussing the economic and theoretical constructs of the study; Dr. Shari Cretin was

extremely helpful, particularly during the process of modeling the disease; Dr. Elizabeth Stern served as the resident expert on cervical cancer epidemiology—much of my

data was drawn from her excellent work; Dr. Lester Breslow provided a valuable overview of the entire project; Dr. Robert Andrews helped a great deal, not only with the

cost analysis, but also as an effective critic from outside the health field; finally, Dr.

Michael GranfieId was instrumental in the reformulation of the financial analysis.

Regarding the gathering of data and information, I am particularly appreciative

of Vicky Reyes, Administrator of the Porta-Pap Clinic at Los Angeles County/University of Southern California Medical Center. Her knowledge and insight were invaluable to the estimation of costs associated with the clinic model.

I also thank Sue Luce who spent hours editing the manuscript.
Contents

Page

Summarry 3

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Background:CervicalCancer . . . . . . . . . . . . . . . 5

DiseaseEtiology .. .. .., .. .. 5

Incidence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Detection. . . . . . . . . . . . . . . 7

Cost-EffectiveDetection .. .. ... 10

Methodolog y . . . . . . . . . . . . . . . . . . . . . . . . . 1.

DiseaseTransitionProbabilitim. . . . . . . . . . . . 11

ScreeningPolicies .. .... . . . . . . . . . . . . . . . . . . . . . .12 

costs . . . . . . . . . . . .......,......0.,.,.. 13

Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

PolicyAnalysis . . . .. ... . . . . . . . . . . . . . . 14

SensitivityAnalysis. . . . . . . . . . . . . . . . . . . . . 15

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

BenefitsandCosts . . . . . . . . . . . . . . . . . . . . . . 15

DiscountRateSensitivity . . . . . . . . . . . . . . . . 17

PapTestErrorRateSensitiviV . . . . . . . . . . . . 17

JointProductionEffects. . . . . . . . . . . . . . . . . . 17

MigrationEffects. . ....... .. .. . . . . . . . . . 18

Low-CostModel, . . . . . . . . . . . . . . . . . . . . . . 18

High-Risk, High-Caw-Finding-Cost Population 18

Discussion . . . . . .. .. ... ... . .. . . . . . .. . . . . . . . . . . . . . . . . . . . .. . . . . . . .. 19

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

DiseaseTransition. . . . . . . . . .. . . . . . . . . . . 22

Sreening and Treatrnent. .. ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Benefits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

AppendixA,-Glossary ofTerms . . . . . . . . . . . . 27

AppendixB. —Health StateVectorsfor

ScreeningPolicies . ........... ... . . . . . . . . . . . . . 27

Appendix C.-Cost Formulations . . . . . . . . . . . . 28

AppendixD. —Values for Base Case Analysis . . . 29

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

LIST OF TABLES

TableNo. page

1.

2.

3.

4.

5.

6.

7.

8.

9.

ProgressiveStagesofCervical Cancer. ... . . . . . . .5

Annual Incidence of Carcinoma InSitu . . . . . 7

AnnuaI Incidence of Invasive Carcinoma . . . 7

Sample Disease Progression Chart. . . . . . . . . 12

Benefits and Costs. . . . . . . . . . . . . . . . . . . . . 15

Cost-EffectivenessRatiosf or Society . . . . . . . 16

Cost-EffectivenessRatiosf or Philanthropic

Organizations . . . . . . . . . .. . .. . .. . . . . . ......... 17

Discount Rate Sensitivity: Total Cost Per

YearofLifeSaved. . . . . . . . . . . . . . . . . . . . . 17

PapTest Error Rate Sensitivity: Total Cost

per Year of Life Saved . . . . . . . . . . . . . . . . . . 19

10. Joint Production Effects. . . . . . . . . . . . . . . . . 19

11

12

13

14

MigrationEffects. . . . . . . ........21

Cost-EffectivenessRatiosforSociety:

Low-CostModel. . . . . . . . . . . . . . . . . . . . . . 21

Cost-EffectivenessRatiosforph~anthropic

Organizations: Low-CostModel . . . . . . . . . . 21

Cost-EffectivenessCornparisons: HighCost Low-Cost and High-Risk Models . . . . 21

LIST OF FIGURES

FigureNo. Page

l. Decision Tree forCervical Cancer Screening

and Treatment . . . . . . . . . . . . . ,

. . . . . . . . . . 8

2. Decision Tree for Cervical Screening

and Treatment (simplified for analysis). .... 13

3. Benefits: Lives Saved andYears of LifeSaved. 15

4. TotalCosts. . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5. Cost-EffectivenessRatios for Society.. ............ 16

6. Discount Rate Sensitivity: Total Cost per year

ofLife Saved . . . . . . . . . . . . . . . . . . . . . . . . . . 18

7. Joint Production Effect 5: Total Cost per year

of Life Saved. . . . . . . . . . . . . . . . . . . . . . . . . . 20
Case Study #7:

Allocating Costs and Benefits in Disease

Prevention Programs: An Application to

Cervical Cancer Screening

Bryan R. Luce, Ph. D.

Senior Analyst, Health Program

Office of Technology Assessment

U.S. Congress

SUMMARY

This study is an attempt to advance the state

of the art of cost-effectiveness analysis (CEA) as

the technique pertains to disease prevention and

health promotion programs. Cervical cancer

screening is used as the subject of analysis to

demonstrate the application of CEA to a disease

prevention program. First, the disease process is

modeled using a Markov chain technique to

“age” a simulated population of 30- to 39-yearold women for 10 years, using disease transition

probabilities reported in the literature. Then,

the cost effectiveness of screening at different intervals, ranging from no screening to annual

screening for the 10-year period, is calculated.

The effects of the following are evaluated: 1) different migration patterns, 2) different risk

groups, 3) different modes of administering Pap

tests, and 4) joint production considerations.

The sensitivity of the results both to various discount rates and to a range of Pap test error rates

are also tested.

The results of the analysis indicate that a private party always has a financial incentive to

postpone screening, whereas society finds it

more cost effective to screen than not to screen,

but only at infrequent intervals. The cost effectiveness of screening is markedly affected when

a more efficient (i. e., less costly) delivery mode

is simulated, and it is significantly affected when

joint production effects are considered; however, it is not very sensitive to small changes in

the discount rate, which initially was set at 10

percent, nor to varying assumptions regarding

Pap test error rates.

Since private parties who may be interested in

offering health promotion/disease prevention

programs would incur the full cost of such programs while deriving only a portion of the benefits, we conclude that if society wants the private sector to screen for cervical cancer at a

societally determined optimal rate, then society

must be willing to subsidize the cost of the

screening. We further conclude that the cost effectiveness of cervical cancer screening is much

more affected by the costs assigned to screening

than by different assumptions regarding the discount and error rates. These conclusions suggest

that more attention should be paid to providing

such programs at the most efficient level, that

proper accounting measures should be used,

and that less attention should be paid to the

precise rates of discount and error.
4 ● Background paper #2: Case Studies of Medical Technologies

INTRODUCTION

Prevention and early disease detection—The

words alone suggest the saving of life, limb,

misery, and heartbreak. Certainly, research has

shown that prevention and early detection of

disease can be effective, To mention just a few

effective prevention programs, for example, decreasing the speed on our highways saves lives

and limbs, as well as property; decreasing

smoking and weight lowers general mortality;

immunization prevents illness and death; and

fluoride treatment decreases dental caries.

Belloc and Breslow (2) have shown that moderate changes in basic lifestyle are associated

with lower overall mortality. In addition, over

the past century, there has been overwhelming

evidence that much of the success in limiting

disease and extending life is attributable to basic

preventive activities.

Nevertheless, the vast majority of health

dollars are spent on acute medical care that produces little change in overall mortality or morbidity rates, and there is every reason to believe

that the future promises more of the same (26),

Why does the health care sector persist in remaining wedded to the treatment mode of medical care to the near exclusion of the preventive

mode? We know prevention can be effective,

but can it be cost effective?

One reason for the neglect of preventive care

may have to do with the manner in which the

costs of health promotion/disease prevention

programs are incurred and the benefits are accrued. The costs are usually certain, incurred in

the present, and not borne by the recipient of

many of the benefits. The benefits, on the other

hand, are ordinarily uncertain and are generally

accrued in the future.

Suppose, for example, that a health insurer

such as Blue Cross is considering whether to include hypertension screening in its insurance

package. Part of its decision will be based on a

determination of whether such a program is efficient, i.e., whether the savings (benefits) due to

the future decreased cardiovascular disease will

be equal to or greater than the costs of the

screening program. Most of the costs of the program can be estimated rather accurately, will be

incurred in the present, and will be borne only

by Blue Cross. However, the benefits of screening will accrue to: 1) the individual, in terms of

more earnings, less morbidity, and longer life;

2) the life insurer, in terms of postponed death

benefit payments and more premium payments;

3) the employer, in terms of a healthier, more

productive work force, lower employee turnover, and lower disability payments; 4) the government at all levels, in terms of lower medical

costs (for those “saved” individuals who do not

have heart disease while on medicare), higher

tax revenues and fewer unemployment benefits;

5) society in general, in terms of higher general

prosperity; and, finally, 6) Blue Cross, in terms

of lower future medical costs due to heart disease.1 Furthermore, many of the benefits that

might otherwise accrue to Blue Cross will be

lessened because some individuals who participate in the initial screening programs will not

continue treatment and others will change insurance plans owing to dissatisfaction, job

change, or advanced age leading to medicare eligibility. The medical care cost savings which accrue to Blue Cross will do so over many years

and consequently must be discounted at a substantial rate (probably 10 percent). The net effect of all these factors may make the hypertension screening program financially unattractive to Blue Cross.

Most other health promotion/disease prevention programs can be analyzed in a similar manner, with the analysis producing similar results.

Consequently, health insurers such as Blue

Cross and medicare tend not to include health

promotion/disease prevention coverage in their

plans, irrespective of whether the benefits in

their aggregate outweigh (or justify) the costs in

their aggregate,

Nevertheless, many health promotion/disease prevention programs do exist outside of the

country’s mainstream—private fee-for-service,

third-party reimbursed—health care system.

The military, for instance, strongly encourages

1

This` enumeration of benefits Includes much double counting,
Case Study #7: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening ● 5

preventive measures such as regularly scheduled

physical examinations and immunizations, as

well as programs in weight control, exercise,

drug counseling, venereal disease control, and

safety. Unlike Blue Cross, the military has many

natural incentives to maintain a healthy population, becuase although the military incurs the

costs of the aforementioned programs, it also

accrues many of the benefits.

We also observe an increasing number of

health promotion programs in the private sector

with industry’s exercise activities, antismoking

clinics, alcoholism programs, etc., and a proliferation of independent, health-oriented organizations such as Weight Watchers, Smokenders, Alcoholics Anonymous, health spas, and

organic food stores. The thriving existence of

these organizations indicates that some employers and many individuals believe that sufficient

benefits accrue to them to justify their paying

for particular preventive programs.

Essentially, then, many different private parties are evaluating health programs independBACKGROUND: CERVICAL CANCER

Disease Etiology

The etiology of cervical cancer has been difficult to trace with precision, because the evidence confirming the presence of this disease interrupts its natural course. Nevertheless, there is

considerable circumstantial and epidemiological

evidence that suggests the pathway that cervical

cancer takes.

A recent and important long-term study was

done and reported by a Canadian task force appointed by the Conference of Deputy Ministers

of Health (38). After many years of data gathering and a 1ong history of cervical screening, the

task force concluded that the progressiveness of

the disease is suggested, as indicated in table 1.

(See app. A for definitions of technical medical

terms. ) The Canadian task force reported the

following (38):

ently and are allocating resources on the basis of

their own perception of the programs’ value.

Since the benefits are often accrued by other and

unrelated parties, however, society may be able

to further the public welfare by encouraging

those programs whose total benefits to society

are proven to exceed total costs to society.

Thus, evaluation methods need to be developed

and used to help society determine which programs should be encouraged, for whom, and at

what level.

This study attempts to shed light on some of

the economic and other policy issues related to

health promotion and disease prevention. A basic cost-effectiveness methodology for cervical

cancer screening is developed, and various costs

and benefits are gathered in different combinations to simulate the objective functions of the

many potential financiers of this particular disease prevention program. We ask: For whom is

cervical cancer screening cost effective, under

what conditions, and at what screening intensity?

1. Dysplasia:

a. The annual progression rate from dysplasia to cancer is 5 to 6.4 percent.

b. The incidence of carcinoma in situ (CIS)

in a population with dysplastic changes

is 49/1,000 but is only 0.04/1,000 in a

population without them.

c. The spontaneous regression rate from

dysplasia to normal is 30 to 40 percent,

but dysplastic lesions are more apt to

return in a reverted normal population

than they would be in a completely norTable 1 .—Progressive Stages of Cervical Cancer

Disease stage Mean age at diagnosis

Dysplasia . . . . . . . . . . . . . . . . . . . . . . . 24.0

Carcinoma in situ (CIS) . . . . . . . . . . . . 34.0

CIS with microinvasion . . . . . . . . . . . . 43.7

Occult carcinoma of cervix. . . . . . . . . 48.6

Clinical carcinoma of cervix . . . . . . . . 52.0
6 ● Background Paper #.?: Case Studies of Medical Technologies

mal population. Also, the greater the

degree of dysplasia, the better the

chance CIS and invasive carcinoma will

develop.

2. Carcinoma in situ:

a.

b.

c.

d.

Progression rates from CIS to invasive

carcinoma have been reported as ranging from 25 to 70 percent, but the progression rate is very difficult to observe

because CIS is generally treated when

discovered.

The phase lasts from 1 to 20 years, depending on the study cited.

The disease regresses in O to 25 percent

of cases, depending on the study cited.

It is believed that, except in rare cases,

CIS precedes invasive carcinoma. z

The task force also reported that there is some

evidence which shows that the incidence of clinical cancer of the cervix began declining prior to

the screening program (38). Although it is not

possible to prove that screening has had an effect on this decline in incidence, data the task

force examined do tend to indicate that screening has had a significant effect on the decline in

mortality from carcinoma of the uterus, a finding confirmed by many others (4,8,28). In contrast to these investigators, Dickenson, et al.

(12) claim that the incidence of clinical carcinoma of the cervix is increasing, and Boyes (3)

claims it is remaining constant; and both report

their data as showing an increasing incidence of

CIS.

A study from Finland (18) found results with

respect to the progression of the disease that

somewhat resembled the results of the Canadian

task force. It reported that the probability of a

preinvasive lesion’s progressing to invasive

cancer was 0.28 to 0.39, while the probability

that an invasive cancer was either not preceded

by a preinvasive stage or was preceded by a

preinvasive stage of less than 5 years was O to

0.38. The latter finding is not surprising when

one considers that other studies have also

demonstrated that the preinvasive stages of

2

1t should be noted that other studies not cited i n the task force’s

report have pointed to a small percentage of cases in which either

no preinvasive lesion or one of very short duration preceded invasive carcinoma (18).

older women are of relatively short duration.

Dunn (14), for example, estimated the duration

of CIS to be approximately 16 years for 20- to

30-year-old women but less than 5 years for

postmenopausal women. This finding may be

due to the fact that the site of the lesions on the

cervix in older women is not as exposed as it is

in younger women and is consequently less

available for scraping.

Incidence

Although much research has been conducted

in an attempt to determine the true incidence of

cervical cancer, the findings reported in the literature are not consistent. Nevertheless, there is

general agreement that the incidence of cervical

cancer is, at least secondarily, highly correlated

with age and socioeconomic status. Because of

this correlation, much of the work using a single

incidence/prevalence rate for a demographically diverse population is not very helpful.3

Tables 2 and 3 are presented below to demonstrate the variability of CIS and invasive carcinoma incidence rates reported in the literature

(some of the data have been combined or interpolated for purposes of comparison). The rates

vary enormously across categories of age and

socioeconomic status and across studies. However, the trends seem to be more consistent.

Note that the incidence of CIS is highest in the

middle years, whereas the incidence of invasive

carcinoma rises continuously with age until well

after menopause. Note also that CIS, not invasive disease, accounts for most of the variability

and high frequency. When cultural lines are

compared, the incidence of CIS is seen to be

significantly higher in blacks than in whites,

in non-Latins than in Latins, and in poor than

nonpoor.

The reason for the extreme variability of CIS

incidence rates within categories and across

studies is unknown. However, some of the variation may be due to test error and to the timing

of incidence rate calculations. For instance, if

the false-negative rate is very high, any subsequent screening will be “contaminated” by the

“leftover prevalence” cases. One theoretical

‘Incidence refers to new cases; prevalence refers to existing

cases.
Case Study #7: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening ● 7

Table 2.—Annual Incidence of Carcinoma in Situa (cases per 100,000 population)

British

Alameda Alameda El Paso El Paso Columbia

Mayo Co., Calif. Co., Calif. Connect- Nevada (13) (13) (38) Miami (2) Miami (2)

Age Clinic (12) (13) (white) (13) (black) icut (13) (13) (Latin) (non-Latin) 1966-70 (indigent) (private)

15 to 19. . . . . . . . 0 4 176 2 0 0 o – 1,880 0

20 to 24. . . . . . . . 94 121 838 42 297 0 55 200

2,024 165

25 to 29. . . . . . . . 194 390 1,240 131 860 197 294 350

30 to 34. . . . . . . . 163 729 1,308 176 903 310 600 285

1,020 100

35 to 39. . . . . . . . 163 704 590 197 992 316 499 200

40 to 44. . . . . . . . 57 557 609 171 736 229 528 150

790 310

45 to 54. . . . . . . . 78 287 144 88 443 180 285 80

1,772 312

597 187

55 to 64. . . . . . . . 17 183 117 36 183 153 190 45

65+ . . . . . . . . . . 10 83 129 21 83 30 71 43

a Numbers in parentheses refer to the references in the Iist at the end of this case study

Table 3.—Annual Incidence of Invasive Carcinoma’ (cases per 100,000 population)

British

Alameda Alameda El Paso El Paso Alameda Columbia

Mayo Co., Calif. Co., Calif. Connect- Nevada (13) (13) Co. (28) (38)

Age Clinic (12) (13) (white) (13) (black) icut (13) (13) (Latin) (non-Latin) 1968-69 1966-70 Mean

15 to 19. ., . . . . . 0 0 0 0 0 0 o – – 0

20 to 24. . . . . . . . 5 5 1 5 0 0 5 2

25 to 29. ., . . . . . 0 o 7 24 13 8 20 9

30 to 34. . . . . . . . 0 30 23 10 44 35 10 26 28 23

35 to 39. . . . . . . . 18 42 46 18 51 61 20 15 50 36

40 to 44. . . . . . . . 26 34 45 22 55 86 33 43 68 46

45 to 54. . . . . . . . 36 34 85 24 55 105 40 31 65 53

55 to 64. . . . . . . . 26 50 109 26 71 92 55 38 103 63

65+ . . . . . . . . . . 27 45 85 29 59 113 89 44 73 63

a Numbers in parentheses refer to the references in the Iist at the end of this case study

model has been developed (7) which demonstrates that subsequent tests will asymptotically

approach the true incidence. Therefore, if the

study in question is calculating an incidence rate

using second or even third test screening data

and its test errors are high, the reported incidence will be proportionally overstated. It is

also probably safe to speculate that the studies

reporting very high incidence rates are unwittingly including many prevalence cases, especially in light of the much lesser variation in invasive incidence.

In light of the discussion above, it is interesting to note that Dickenson’s study at the

Mayo Clinic (12) reported both a low incidence

rate (see table 2) and a low false-negative rate

(3.3 percent). The clinic has been screening since

1947, so presumably the asymptote had been

reached by the time the incidence was calculated

on the respective cohorts.

Detection

Decision Tree Analysis

With minor variations, most authors agree on

the general decision strategy for the appropriate

method of cervical cancer diagnosis (23,27,34,

41). The consensus is that an atypical or abnormal Pap test should be followed by colposcopy. Kuptsow (23) suggests two positive Paps

for mild dysplasia prior to examination of the

patient by colposcopy. Nyirjesy (27) recommends that anti-infectious treatment be given to

all patients with atypical Pap smears before a

second Pap, whose positive finding would then

lead to colposcopy. He reports that 84 percent
8 ● Background Paper #2: Case Studies of Medical Technologies

of those with atypical Pap smears are presented

as normal after anti-infectious treatment.

In any case, most authors (23,34,41) suggest

that biopsy should follow colposcopy when indicated. Nyirjesy (27) recommends immediate

biopsy under colposcopic control. Yates, et al.

(41) and Stafl, et al. (34) agree that an unsatisfactory colposcopic examination should be followed by a diagnostic cone biopsy.

Appropriate treatment for confirmed diagnosis is indicated in figure 1. An expanded form of

the Stafl, et al. (34) decision tree will be used as

the appropriate course of action in this study.

Pap Test Error Rates

There has been a considerable amount of disagreement concerning the precise magnitude of

Pap test error rates. Part of the reported variation is due to definitional problems, part to the

nature of Pap testing itself, and the rest to the

fact that, for ethical reasons, the error rates can

never be proven empirically.

DEFINITIONAL PROBLEMS

The first definitional problem is the category

into which Pap test results must be placed. The

classification of Pap test results is generally

regarded as consisting of the following (27):

Class 1 Negative

Class II Atypical

Class 111 Suspicious

Class IV Strongly suggestive of malignancy

Class V Conclusive for malignancy

The few researchers who state where they distinguish the negative from the positive pole

make the distinction between classes II and III—

Figure 1 .- Decision Tree for Cervical Cancer Screening and Treatment”

Positive 

Normal \

mihls a See app. A for deflnitionsof medical terms that appear in this figure
Case Study #7: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening ● 9

but the subjective nature of those classifications

hints at the obscurity of the Pap test result. The

Pap test result is not clearly positive or negative,

but is regarded as an intermediate step leading

to more conclusive results. As was previously

mentioned, Nyirjesy (27) reported that in his

study 84 percent of class 11s (atypicals) reverted

to negative after anti-infectious treatment; 10

percent were confirmed positive by biopsy. Ny -

irjesy further reported that 84 percent of class

111s (suspicious) were confirmed positive by biopsy. Although the specific results differ from

study to study, it is clear that a significant proportion of class 11s will be positive and a significant proportion of class 111s will be negative.

Since the purpose of the Pap test is to discover

true positives, and since the purpose of calculating the false-negative rate is to estimate the

true number of missed positives, it is wise to

consider the findings of the class 11s and 111s

before calculating the sensitivity and specificity

of the test. As was indicated previously, both

findings are ordinarily followed up by either

anti-infectious treatment (class 11s) and then

retesting, or by biopsy (class 111s). Therefore,

true positives and true negatives are much more

likely to surface, and error rates can be calculated which better reflect their conceptual

meaning.

The next major definitional problem is the

category—positive or negative—into which

confirmed biopsy results must be placed. Biopsy

results are often classified as follows:

Negative

Dysplasia

Mild

Moderate

Severe

Carcinoma in situ (CIS)

CIS with microinvasion

Invasive carcinoma

At which point is a result considered to be

positive? AS May (25) pointed out, if the purpose of the Pap test is to detect carcinoma of the

cervix, only CIS and the more serious categories

must be considered as positive. If one considers

that the purpose of the test is to detect a precursor to cancer, however, then dysplasia must

also be considered positive. Some researchers

seem to draw the line between positive and negative results within the dysplasia category,

which further complicates the analysis of the literature. The biggest problem in all of these definitional difficulties is that many authors fail to

state their assumptions, thereby making it hazardous to compare data. May (25) partially

avoided the difficulty by doing his calculations

twice—considering dysplasia once as negative

and once as positive.

THE NATURE OF THE TEST

False Negatives. —Since there is much human

involvement in scraping the cervix, preparing

the slide, and reading the microscopic results of

the Pap test, as well as in clerical work, it is not

surprising that different organizations report

different errors. The false-negative rates reported range from 2.8 to 45 percent (1,7,12,19,

22,23,25,27,29,32,33,34,35,37,38,41,42). Watchel (37) estimates that the false-negative rate is

6 to 10 percent in specialized labs and 20 to 25

percent in mass screening centers. The literature

appears to bear this out, with many average

labs reporting figures somewhere in the middle,

i.e., false-negative rates of 10 to 20 percent.

However, Coppleson, et al. (7), using an analytical technique for assessing multiple screens

and using various sources of data, have estimated false-negative rates of the Pap test to be

40 percent for dysplasia, 20 to 45 percent for

CIS, and 24 percent for preclinical invasive carcinoma.

False Positives. —For several reasons, false

positives do not raise the concern that false

negatives do. First of all, the false-positive rate

appears to be low except in the class 111 category

(suspicious) which, as discussed previously,

probably should not be considered until after

further workup. Second, colposcopy and subsequent biopsy are accurate and not terribly expensive in terms of dollars or morbidity. Finally, the disease is so life threatening that to

suspect a nonexistent cancer pales into insignificance compared to missing cancerous tissue. In

any case, there seldom are estimates of the falsepositive rate in the literature. Those few studies

which either estimate a false-positive rate or

provide sufficient data for the reader to estimate
10 ● Background Paper #2: Case Studies of Medical Technologies

one (25,32) suggest that the rate is less than 2

percent.

Cost-Effective Detection

Clearly, if one is to formulate a rational rescreening strategy, it must be tailored to the

various risk categories. Several researchers have

developed screening schedules tailored to risk

categories, but few have employed economics in

a cost-effective approach. Anderson and Gunn

(1) developed a family of incidence curves corresponding to age and socioeconomic variables,

but their rescreening criteria were based on an

arbitrary incidence level (the overall reported

national average). Walton, et al. (38) recommended a similar schedule based on risk factors,

but did not indicate how they determined the

screening interval length.

Knox (21) has used a comprehensive and

complex computer simulation model which adjusts disease progression transition probabilities

until the model predicts reported incidence,

prevalence, and mortality rates. A simplified

variation is described in a later article (20). The

output of this model is the determination of the

“optimal” ages for screening. Economic factors

are implied only by considering the number of

screening tests per lives saved.

Galliher (17) has applied benefit-cost analysis

to a somewhat simplified disease etiology to determine optimal ages for screening. He and

Knox both use a cohort design, simulating a

group of individuals through an entire lifetime.

Studies on the cost effectiveness of cervical

METHODOLOGY

cancer screening have also been published by

Coppleson and Brown (5,6). Their model, using

Markov chains to simulate a disease process,

like Knox’s model “correctly” predicts reported

prevalence data. However, the problem with

this approach is that it ignores the effect of

previous screening efforts which “purifies” the

population, resulting in an understatement of

the prevalence of invasive cervical cancer. Knox

attempts to correct for this by including screening and treatment effects in the model. Eddy (15)

similarly has used mathematical modeling to

analyze lifetime screening strategies and to compare screening programs across different cancer

sites.

Other studies have analyzed the cost effectiveness of one strategy over another (9,11,32),

but these studies have used overall incidence

rates rather than risk-related ones, and they

have often omitted critical variables such as discounting factors or error rates. In none of the

aforementioned studies has there been an appropriate or complete economic analysis: None

of the studies has concerned itself with joint production effects (e.g., a visit for a Pap test usually includes other examinations as well), emigration rates (e. g., disenrollment from an insurance

plan), the value of time costs, nor even with an

estimation of the cost effectiveness of employing

nonphysician personnel to perform routine procedures. Neither has anyone attempted to determine who actually incurs the different costs involved in cervical cancer screening and who accrues the many benefits. Our CEA presented below considers all of the aforementioned factors.

The nature of cervical cancer and the state of various intervals,

medical ethics preclude our determining the cost benefits for each

effectiveness of alternative screening strategies

through a true experimental situation: Once CIS

is detected, it is imperative that medical intervention take place; and such intervention

prevents observations over a longer period.

Therefore, using our model, we simulate

screening the population twice (see app. B), aging it for 11 years while simulating screening at

and calculating the costs and

screening policy tested. Ten

possible screening policies are examined—from

screening every year to screening only in the

loth year. All costs of screening and of treating

disease associated with cervical cancer are totaled for each policy. Benefits are calculated by

comparing lives and years of life saved for each

policy compared to no screening for the 11

years. Finally, many different cost-effectiveness

ratios are devised for society’s interested parties
Case Study #i’: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening 11

(e.g., a philanthropic organization, a health insurer, society itself).

Disease Transition Probabilities

A mathematical modeling technique known

as a Markov chain process is used to simulate

the aging of a population at risk for cervical

cancer, so that a population is “exposed” to a

disease process over a number of periods and

undergoes changes in health status according to

a set of disease transition probabilities, each of

which is the probability of changing from a particular health state to another in one period.

For purposes of our analysis, 13 health states

are defined as folIows:

Normal

Reverted normal—having regressed from a

previous state of dysplasia

Dysplasia

CIS

Invasive carcinoma of the cervix

High-risk, disease-free

High-risk, dysplasia

High-risk, CIS

High-risk, invasive carcinoma

Hysterectomy

Emigration

Death due to cervical cancer

Death due to all other causes

The probability of annual progression and

regression from one state to another is shown in

the disease progression chart in table 4. According to that matrix, a 30- to 39-year-old

woman who is normal (Hi) in one period has a

0.00110 probability of developing dysplasia

within 1 year, a probability of 0.00116 of dying

from causes other than cervical cancer, and a

probability of 0.99718 of remaining disease free.

A woman who is disease free but who previously had dysplasia (H,) has a much higher probability, 0.32000, of contracting dysplasia again.

Most of the transition probabilities presented in

table 4 are estimates from various sources, but

these probabilities are actually variables themselves. We have chosen many of the particular

values shown because their use corresponds to

published prevalence and mortality rates

(1,32,36).

The high-risk states, H6, H7, H8, and H9, are

included for accounting purposes only; that is,

once a woman is identified as high risk, she will

automatically be placed on a more intensive Pap

testing sequence than those women not so identified, as discussed further below. Transition

probabilities for the high-risk states H6

, H7, H8,

and H9 are assumed to be the same as those for

H2

, H3, H4, and H5, respectively. States H,10

through H13 serve as collecting states and essentially do not allow outward transition. The hysterectomy state (H10) is included, because a hysterectomy ordinarily ensures that the patient is

no longer at risk for cancer of the cervix. Emigration (Hi, ) simulates women who have left the

program

4 and approximates the position of a

health insurer who incurs screening expenses

without deriving future benefits; the health insurer’s position is opposed to that of society,

because society would accrue the benefits of

screening regardless of migration patterns. Initially, we assume no emigration, but we subsequently test several migration rates to analyze

the effects which enrollee loss has on the costeffective solution for different parties. Our

model accepts the feature of emigration by accumulating in the emigration state (Hll) a fixed

proportion of individuals from each alive state,

sending away, for example, 10 percent per year

(depending on the migration rate) and acting as

though the health status of those persons does

not change—and from the health insurer’s perspective, that is exactly what happens. The feature of emigration is ignored in calculating society’s cost-effectiveness ratios.

Screening Policies

Ten different policies are analyzed, beginning

with the policy of screening every year and ending with the policy of screening only in the l0th

year. The costs and benefits of the entire Ii-year

period are totaled and compared. A simplified

decision tree for screening and treating preclinical disease (see figure 2) is used in our model.

Each time a screen is simulated, lesions (H3, H4)

are detected (minus error) and treated, and the

cases are placed in the high-risk, disease-free

health state (H,), which has defined probabilities of transition according to the matrix in

4

Emigration is used here in the broad sense, with any movement

out of an enrollment considered emigration.
12 ● Background Paper #2: Case Studies of Medical Technologies

000 0
Case Study #i’: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening 13

Figure 2.— Decision Tree for Cervical Cancer

Screening and Treatment (simplified for analysis)

False, true

table 4. Invasive cases (H5) discovered by

screening are treated and placed in the hysterectomy state (H1O). The undetected lesions remain in their previous health state continuing to

age, also as defined by the matrix.

Note that the matrix indicates that one period

later, half of the remaining invasive cases will

have received a hysterectomy and will be

“saved;” the other half will also receive a

hysterectomy but will not survive. The literature confirms this survival rate (32).

The assumption is made that all this will occur within 1 year. That assumption is an oversimplification, but adopting it makes calculation easier; furthermore it results in erring on

the medically conservative side, making more

frequent screening more attractive.

By using the high-risk states, we are able to

age high- and regular-risk groups simultaneously and to differentiate between the two, simulating separate screening policies. This differentiation between high- and regular-risk groups

is important, since women who have experienced precursors to cancer and who have been

treated, as well as women who have had noncancerous but atypical Pap smears, are usually

scheduled for repeat smears at short, regular intervals of time. Since our model has this feature,

costs can be appropriately calculated.

costs

Costs are accumulated by screening, diagnostic, and treatment categories in order to assign

them to the appropriate parties, For discounting

purposes, all costs are calculated at the time

they are incurred, which requires calculation of

the screening and treatment costs only when

screening is performed, and calculation of the

costs of treating invasive cancer as it occurs

(i.e., yearly).

Two basic cost formulations are applied: a

low-cost model and a high-cost model (see app.

C for further details). The low-cost model is a

hypothetical health clinic in which licensed

vocational nurses (L. V.N. S) perform the Pap

testing, a registered nurse practitioner (R. N. P.)

performs the colposcopic examinations and biopsies, and a part-time physician who is on call

for professional staff support performs all cryosurgeries. This model uses a reputable cytology

laboratory, which bills the clinic periodically,

thus taking advantage of economies of scale.

Conservative treatment with the costs calculated from a composite of treatments as described by Lacey and Townsend (24), is assumed for all CIS discovered: $10 percent cryosurgery and 10 percent ionization. Costs of

hospitalization are as described in the Third National Cancer Survey (10). The high-cost model

is a hypothetical private, fee-for-service practice

in a university teaching hospital that has physicians take Pap smears and perform colposcopic

examinations and biopsies. This model uses a

university hospital laboratory that bills each patient individually. More radical treatment for

CIS is assumed: 10 percent cryosurgery and 90

percent ionization. All hospitalization costs are

calculated in the same way as that described for

the low-cost model.

In order to simulate joint production effects

of the high-cost model, the cost of the Pap smear
 ● Background Paper #2: Case Studies of Medical Technologies

procedure is systematically reduced as the

screening interval Lengthens. s This feature is included because other evaluations are also being

performed when a woman has a Pap smear. If

she were to have an annual smear, for instance,

it would be assumed that since the main purpose

of the visit would be for the Pap test, the total

physician charge should be allocated only to the

Pap. However, if the policy were for women to

wait 4 to 5 years for a “checkup,” at which point

a Pap would also be administered, perhaps only

a small fraction of the physician’s charge should

be allocated to the screening procedure, since

the probability of such visits’ taking place—

even without a procedure as a Pap smear—

would be high. One may, in fact, assume that a

significant proportion of women with gynecological symptoms will visit a physician within

some specified time and that the Paps that

would be taken would not be primarily a screening measure at all; consequently, the proportion

of the cost allocated to cervical cancer screening

is further diminished.

We assume an inverse linear relationship between the amount of cost allocated to the smear

and the length of interval between smears (i. e.,

the longer the interval, the lower the cost). Since

the low-cost clinic operates for the sole purpose

of Pap testing, this joint production feature is

not included. However, its omission is not necessary because the L.V.N. S can be adequately

trained to perform pelvic and breast examinations, take general history, as well as to do

numerous other health promotion/disease prevention activities (30),

Actual costs can and do vary between the two

extreme models suggested here, and other estimates will affect the cost-effective solution,

depending on the system employed and assumptions made.

Our simulation model also has other features.

One is its capability of including high case-finding costs for high-risk women who are difficult

to reach. Another is its capability of including

estimates of time opportunity costs for women

to undergo not only the Pap test but also the

other diagnostic and treatment procedures.

5

The cost of the lab is not included,

As stated previously, costs are calculated and

discounted as they occur; therefore, the population is screened, and costs are calculated according to the screening policy (i. e., screening

every year, every second year, every third year,

etc. ). Those cases identified as high-risk are

screened annually, and costs are assigned as

they occur. Likewise, clinically detected invasives accrue costs as the disease becomes clinically apparent.

Benefits

Number of Lives Saved.—To calculate the

number of lives saved for each screening policy

compared to no screening, the number of deaths

incurred in each year for each of the 10 policies

is subtracted from the number of deaths in the

respective years when no screening occurs for 11

years. Each life saved is discounted at the time

accrued. This latter calculation weighs the state

of health in which each policy leaves its population after 11 years, b

Number of Years of Life Saved.—It is assumed that each woman saved will live to her

average life span as indicated by standard life

tables. Next, the stream of benefits is discounted

as if it were an annuity of 1 life-year for x years.

Although this simulation process is not as accurate as using a life table process whereby 

the

life saved has a probability of loss of life associated with each year of life (as opposed to our

method which assumes that the survivor will

live until the average life span and then die), it is

much easier to calculate and results in a higher

(i.e., more conservative) estimate of benefits.

Policy Analysis

The prevention and the financing of preventive programs are social decisions as well as a

series of private decisions, and costs are incurred and benefits are accrued in different

mixes depending on the perspective of the parties involved. Therefore, the cervical cancer

screening program is analyzed to determine

6

This is necessary because each screening policy leaves the population with differing degrees of morbidity. For instance, a policy

which last screened women at age 36 will result in many more precancerous and cancerous lesions a t age 41 than would a policy

which last screened women at age 39 or 40.
whether a private party must be subsidized by

society in order to induce the party to carry out

social policy. If benefits to the private party are

already greater than zero, a pareto solution

(i.e., a solution whereby no one can be made

better off without making someone worse off)

can be reached by society’s subsidization.

Another policy explored is that of financing a

case-finding effort for a high-risk but isolated

population, the cost effectiveness of which is

compared to more inexpensive programs for a

normal-risk population.

A third policy option is embodied in the lowand high-cost models that are presented. If all

RESULTS

Except where noted, the results presented below are calculated from our “base case” set of

parameters (see app. D). Every comparison consists of 10 data points, each one representing a

different screening policy (i. e., screening every

year, every second year, etc. ).

Benefits and Costs

Benefits and costs, discounted at the same

rate (40), clearly indicate the declining function

of both as the interval between screens lengthens

(see table 5). Although benefits decline rather

rapidly in near linear fashion (see figure 3), costs

Table 5.— Benefits and Costs”

Cervical cancer screening: women, aged 30 to 39

(per 100,000 screened)

Policy Total costs

(screening (screening,

intervals Years of diagnosis &

in years) Lives saved life saved treatment)

1 . . . . . . . . . . . . . . 32 348 $34,006,000

2. . . . . . . . . . . . . . 30 323 13,397,000

3. . . . . . . . . . . . . . 27 295 8,423,000

4. . . . . . . . . . . . . . 25 269 5,919,000

5 24 257 5,330,000

6: : : : : : : : : : : : : : 21 223 3,394,000

7. . . . . . . . . . . . . . 20 218 3,185,000

8. . . . . . . . . . . . . . 19 210 2,993,000

9. . . . . . . . . . . . . . 18 199 2,819,000

10. . . . . . . . . . . . . . 17 186 2,662,000

a Base case, discount rate = 10%

other factors are the same, the low-cost model

must be more cost effective, but the sensitivity

of different possible error rates (i. e., a differential in quality) must be examined.

Sensitivity Analysis

Each of the major variables is estimated at all

reasonable levels, ranging from minimum to

maximum values, in order to provide an interval of confidence. The specific variables that are

tested include: 1) error rates, 2) transition probabilities, 3) emigration rates, 4) costs, 5) discount rates, and 6) prevalence rates.

1

4(

3(

20

10

c

Figure 3.— Benefits: Lives Saved and

Years of Life Saved”

Cervical cancer screening: women, aged 30 to 39

(per 1OO,OOO-screened)

Years of life saved

Lives saved

1 I I I 1 1 I

1234567 8 9 10

Screening policy (screening intervals in years)

aBase Case, discount rate = 10%

decline at a decreasing rate (see figure 4). This

is an expected phenomenon, because a change

from policy 1 (screening every year) to that of

policy 2 (screening every second year) results in

50-percent fewer screens, whereas a change

from policy 7 to policy 8 results in simply post-
16 ● Bacground Paper #.?: Case Studies of Medical Technologies

Figure 4.—Total Costs (screening, diagnosis,

and treatment -

Cervical cancer screening: women, aged 30 to 39

(per 100,000 screened)

40

30

20

10

0

1 1 1 1 1 I 1

12345 6 7 8 9 10

Screening policy (screening intervals in years)

a Base case; discount rate = 10%.

poning screening for 1 year. The different

shapes of the benefit and cost curves, as well as

their decreasing functions, form the basis of the

changing natures of the calculated cost-effectiveness ratios.

Society’s cost-effectiveness curve (see table 6

and figure 5) indicates that the cost effectiveness

increases markedly as the interval lengthens but

increases at a decreasing rate. The final upward

break of the curve suggests that the minimum

screening policy for society under these conditions and assumptions is to screen once in the

ninth year. That is, although it is more cost effective to postpone cervical cancer screening until the ninth year, postponing screening an additional year increases societal costs.

A health insurer’s curve, simulated by total

medical costs (see figure 4), however, never

breaks upward. This curve indicates that it is

always financially beneficial to postpone screening. The reason is that although more serious

Table 6.—Cost.Effectiveness Ratios for Societya

Cervical cancer screening: women, aged 30 to 39

Policy Total Total cost

(screening intervals cost per per year of

in years) life saved b

life saved b

1 . . . . . . . . . . . . . . . . $1,064,800

2

$97,600

. . . . . . . . . . . . . . . 452,600 41,500

3 . . . . . . . . . . . . . . . . 311,100 28,600

4 . . . . . . . . . . . . . . . . 239,600 22,000

5 . . . . . . . . . . . . . . . . 224,900 20,700

6 . . . . . . . . . . . . . . . . 164,900 15,200

7 . . . . . . . . . . . . . . . . 158,300 14,600

8 . . . . . . . . . . . . . . . . 154,400 14,300

9 153,100 14,200

10 : : : : : : : : : : : : : : : : 153,900 14,300

a Base; discount rate = 10%.

B

Total cost IS the cost of screening, diagnosis, and treatment

Figure 5.—Cost-Effectiveness Ratios for Society

Cervical cancer screening: women, aged 30 to 39

1,000

900

800

700

600

500

400 -

300 -

Total cost/life savedb

(read from left axis)

200

100

90

80

70

60

50

30

20

100 10

0 1 I 1

1 2 3 4 5 6 7 8 9 10

Screening policy (screening intervals in years)

a Base case; discount rate = 10%.

Total cost is the cost of screening, diagnosis, and treatment.
Case Study #7: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening ● 17

disease must be treated as screening is postponed, the resulting costs never become so large

that funding a screening program at any level

becomes financially advantageous to the organization.

Unlike health insurers, patient-education-oriented philanthropic organizations such as the

American Cancer Society seek to reduce lives or

years of life lost due to cancer, without bearing

any of the costs of doing so. The benefit curve

(see figure 3 and table 7) is continually falling,

indicating that the optimal screening strategy

from this perspective is to screen annually (or

even more frequently).

Discount Rate Sensitivity

A discount rate of 10 percent was selected as

appropriate for our analysis, because it is apTable 7.- Cost= Effectiveness Ratios for

Philanthropic Organizations’

Cervical cancer screening: women, aged 30 to 39

Policy Initial screening

(screening intervals cost per

in years) life saved

1 . . . . . . . . . . . . . . . . $850,000

2 . . . . . . . . . . . . . . . . 386,000

3 . . . . . . . . . . . . . . . . 251,000

4 . . . . . . . . . . . . . . . . 182,000

5 . . . . . . . . . . . . . . . . 166,000

6 . . . . . . . . . . . . . . . . 108,000

100,000

 ::::::::::::::: : 94,000

9 90,000

10 : : : : : : : : : : : : : : : : 87,000

Initial screening

cost per year

of life saved

$77,957

34,463

23,101

16,757

15,282

9,911

9,215

8,694

8,322

8,068

a Basecase; discount rate = 10%

proximately the present rate of return on investments in the private market. Table 8 and

figure 6 indicate that the sensitivity of our

results to small changes in the discount rate is

minimal and decreases rapidly for policies less

frequent than annual screening. As expected,

however, the significantly lower cost-effectiveness values for very low discount rates (i. e., O or

5 percent) reflect the facts that costs tend to be

incurred in the present and benefits tend to be

accrued in the future.

Pap Test Error Rate Sensitivity

False-negative rates are reported over a wide

range of values. For our base case analysis, we

chose an overall false-negative rate of 0.2, a

compromise between the frequently reported

rate of 0.1 and the analytically derived rates of

0.4 for dysplasia, 0.3 for CIS, 0.24 for invasive

disease (7). Sensitivity analysis (see table 9, columns 1 through 3) indicates that the results are

affected little by varying the error rates over this

range.

False-positive rates are rarely reported (16)

but are often thought to be no higher than 0.10

(25,32). Our analysis (table 9, columns 4

through 6), indicates little sensitivity to the

precise error rate used.

Joint Production Effects

The proportion of the cost of a gynecological

visit which should be allocated to the Pap test is

Table 8.—Discount Rate Sensitivity: Total Cost per Year of Life Saved” b

Cervical cancer screening: women, aged 30 to 39

Policy (screening

intervals in years) O% 50/0 80/0 9 % 10% 11%` 120/0

$18,473 $51,928 $78,530 $87,985 $97,632 $107,424 $117,323

8,598 23,039 33,971 37,746 41,537 45,321 49,081

3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6,049 15,971 23,436 26,003 28,574 31,131 33,662

4 4,770 12,404 18,116 20,073 22,028 23,968 25,880

5: : : : : : : : : : :: : : : : : : : : : : : : :: : : : :: 5,309 12,575 17,524 19,136 20,706 22,220 23,673

6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,447 8,683 12,561 13,884 15,201 16,501 17,777

7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,655 8,718 12,276 13,454 14,609 15,730 16,811

8 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3,987 8,946 12,220 13,267 14,273 15,230 16,134

9 4,506 9,409 12,402 13,318 14,176 14,973 15,705

10: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 5,349 10,190 12,847 13,610 14,302 14,921 15,468

aBase case except for discount rates as indicated.

b

Total cost is the cost of screening, diagnosis, and treatment
18 ● Backgrourld Paper #2: Case Studies of Medical Technologies

Figure 6.—Discount Rate Sensitivity: Total Cost

per Year of Life Saved” ab

Cervical cancer screening: women, aged 30 to 39

‘

o o

~

90

80

70

60

50

40

30

20

10

0

10VO

1 2 3 5 6 7 8 9 10

Screening policy (screening Intervals in years)

a Base case, except for discount rates as indicated

b

Total COSt IS the cost of screening, diagnosis, and treatment

not known, but it is hypothesized here that the

proportion must decline as the screening interval lengthens. With the assumption of a IO-percent linear reduction in cost as the screening interval increases by 1 year, both cost per life

saved and total cost stabilize at about half the

value of the cost when no joint production effects are considered (see table 10 and figure 7).

Migration Effects

When the effects of migration are simulated

(see table 11), we again note the familiar pattern

of highest sensitivity for the most frequent

screening policies and little difference as the interval lengthens. As was expected, higher migration rates result in each screening policy’s

being less cost effective.

Low=Cost Model

When minimum cost estimates are assigned

for screening, diagnostic workups, and minor

treatment procedures, all cost-effective indicators improve dramatically (see tables 12 and

13). Cost per year of life saved drops below

$10,000 by policy 2 (screening every second

year) and quickly stabilizes between $4,000 and

$5,000 (see table 12). The optimum screening interval is policy 7 (screening only in the seventh

year), since screening either more or less often

saves less money.

An evaluation of cost-effectiveness ratios for

philanthropic organizations (see table 13) yields

the familiar continually declining function, because no savings are associated with lower cancer morbidity, although relatively low costs per

year of life saved are quickly reached.

High= Risk, High= Case-Finding=Cost

Population

The women who are at highest risk for cervical cancer are often both the most difficult to

find and the most reluctant to participate in a

screening program. To evaluate the cost effectiveness of screening this special population, we

modify our variables in the following manner:

1.

2.

3.

All transition probabilities from the disease-free state (Hl

) to dysplasia (H3), CIS

(Hal), and invasive carcinoma of the cervix

(H,) are doubled.

Disease prevalence of the initial population vector is doubled for disease states

H2

, H3, H4, and H,.

The low-cost model is used, except that an

additional $20, which is intended to simulate a case-finding cost, is assigned for the

Pap.

Results with this high-risk model are compared to those of the high- and low-cost, regular-risk models (see table 14). At every screening level, the cost per year of life saved is less
Case Study #7: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening 19

Table 9.—Pap Test Error Rate Sensitivity: Total Cost per Year of Life Saved a b

Cervical cancer screening: women, aged 30 to 39

Test error rates c

e = 0.1 el = 0.2 e1= 0.4 e1= 0.2 e1= 0.2 e1= 0.2

e2 = 0.1 e2 = 0.2 e2 = 0.3 e2 = 0.2 e2 = 0.2 e2 =0.2

Policy (screening e3= 0.1 e 3

= 0 . 2 e3 = 0.24 e3= 0.2 e3 = 0.2 e3 = 0.2

intervals in years) e4 = 0.05 e4 = 0.05 e4 =0.05 e4 = 0.01 e4 = 0.05 e4 = 0.10

$113,800 $113,300 $113,500 $108,600 $113,300 $119,100

40,900 41,500 43,300 39,700 41,500 43,800

3. . . . . . . . . . . . . . . . . . . . . . . . . 27,600 28,600 30,800 27,400 28,600 30,100

4. . . . . . . . . . . . . . . . . . . . . . . . . 21,000 22,000 24,400 21,200 22,000 23,100

5. . . . . . . . . . . . . . . . . . . . . . . . . 19,600 20,800 23,100 20,000 20,800 21,700

6. . . . . . . . . . . . . . . . . . . . . . . . . 14,200 15,200 17,500 14,700 15,200 15,900

13,600 14,600 16,900 14,100 14,600 15,200

 : : : : : : : : : : : : : : : : : : : : : : : : 13,300 14,300 16,500 13,800 14,300 14,900

9 13,200 14,200 16,500 13,700 14,200 14,700

10: : : : : : : : : : : : : : : : : : : : : : : : : 13,300 14,300 16,700 13,900 14,300 14,900

aBase case, except for error values as indicated.

b

Total cost is the cost of screening, diagnosis, and treatment.

cError rates: e, = false-negative rate for dysplasia; = false-negative rate for CIS; e, = false-negative rate for preclinical invasive carcinoma; e4 = false-positive rate.

Table 10.—Joint Production Effects”

Cervical cancer screening: women, aged 30 to 39

Total cost per year Total cost per

of life saved b 100,000 screened b

Policy (screening No joint Joint No joint Joint

intervals in years) production production production product ion

1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . $97,600 $97,600 $34,006,000

2

$34,006,000

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41,500 30,600 13,397,000 9,861,000

3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28,600 17,000 8,423,000 5,004,000

4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22,000 11,400 5,919,000 3,064,000

5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20,700 9,600 5,330,000 2,460,000

6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15,200 7,400 3,394,000 1,647,000

7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,600 7,100 3,185,000 1,537,000

14,300 7,000 2,993,000 1,472,000

:: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 14,200 7,200 2,819,000 1,424,000

10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14,300 7,400 2,662,000 1,387,000

a Base case; discount rate= 10%; and the cost of the Pap test for the joint production columns was decreased by 10% for each yearly increase of screening internal.

b Total cost is the cost of screening, diagnosis, and treatment.

than half that of the high-cost, regular-risk

model and is only slightly higher than that of

the low-cost model.

Discussion

The worth of cervical cancer screening is affected far more by the method of analysis and

the point of view taken than by the sensitivity of

disputed values such as the precise discount rate

or the correct error rates. Traditionally, the latter two parameters are the principal ones considered if, indeed, any sensitivity analysis is performed at all.

The results of our study are affected most by

varying the costs. This finding is not necessarily

unexpected for a couple of reasons. First, only

the costs of cervical cancer screening and the

more simple diagnostic and treatment procedures are varied–as opposed to the costs of

treating invasive carcinoma. This approach

seems appropriate, since one can more easily

employ less costly personnel and resources for
20 ● Background Paper #2: Case Studies of Medical Technologies

Figure 7.- Joint Production Effects:

Total Cost per Year.of Life Saveda b

Cervical cancer screening: women, aged 30 to 39

1 2 3 4 5 6 7 8 9 10

Screening policy (screening intervals in years)

● Base case; discount rate = 10%; and the cost of the Pap test for joint production was decreased by 10% for each yearly increase of screening interval.

total coat is the cost of screening, diagnosis, and treatment.

the former than for the latter. Second, the disease is not very common; consequently, many

women must be screened in order to discover a

single case. Although most screening in this

country is not performed the way depicted in

the low-cost model, the proper method of

analyzing the cost effectiveness of any procedure is by using the most efficient possible

method.

Nevertheless, efficiency of operation is only

one of many aspects to consider when evaluating the cost effectiveness of health programs.

Another consideration is the effect of joint production, since clearly the entire cost of a gynecological visit should not be allocated to the Pap

test. In fact, our results show that as the yearly

interval increases, a cost reduction of only 10

percent makes screening more than twice as cost

effective as it would be if joint production were

not taken into account.

Furthermore, analyzing the worth of a health

program solely from the point of view of society

is insufficient, because the decisionmakers in

this country are very often private parties or

private individuals. A health insurer, for instance, is apt to make policy decisions primarily

on a financial basis. A consideration of only the

financial aspects of this problem indicates that it

is good policy for all private parties to put

screening off an additional year, regardless of

the model or assumptions used. Consequently,

if society wants certain segments of the private

sector to have incentives to screen, it may have

to subsidize cervical cancer screening programs.

This conclusion is further substantiated when

migration effects are considered, since the financial rewards of reduced cancer morbidity are

lost when screened members move away or

change insurance plans.

Finally, simulation of high case-finding costs

for a high-risk population of women indicates

that such a program is indeed cost effective if it

employs the low-cost option. The increased cost

assigned to case-finding is apparently offset by

the increased morbidity which is discovered and

cured. This finding is particularly interesting,

since the same high case-finding cost is assigned

to all Pap testing, even for policies in which the

women receive multiple tests over the Ii-year

period.

The results of our study demonstrate that

there are many ways to evaluate a health program, even when using basic cost-effectiveness

techniques. The interpretation of the results

depends on which benefits and costs are germane to the evaluator and how the program is

organized and financed.
Case Study #7 Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening ● 21

Table 11 .—Migration Effects”

Cervical cancer screening: women, aged 30 to 39

Policy

(screening

intervals in Total cost per year of life saved b Total cost per 100,000 screenedb

years) No migration 10% migration/yr 30 %migration/yr No migration 1O% migration/yr 30% migration/yr

1 . . . . . . . . $97,600 $148,500 $383,900 $34,006,000 $23,231,000 $13,378,000

2 . . . . . . . . 41,500 57,100 107,800 13,397,000 8,053,000 3,123,000

3 . . . . . . . . 28,600 38,900 69,200 8,423,000 4,931,000 1,710,000

4 . . . . . . . . 22,000 29,600 49,200 5,919,000 3,384,000 1,063,000

5 . . . . . . . . 20,700 25,670 37,600 5,330,000 2,753,000 734,000

6 15,200 19,900 29,500 3,394,000 1,873,000 520,000

7::::::: : 14,600 18,139 24,900 3,185,000 1,638,000 413,000

8 . . . . . . . . 14,300 16,818 21,900 2,993,000 1,444,000 344,000

9 . . . . . . . . 14,200 15,825 19,800 2,819,000 1,284,000 299,000

10 . . . . . . . . 14,300 15,079 18,500 2,662,000 1,153,000 271,000

aBase case, discount rate = 10O%.

bTotal COSt IS the cost of screening, diagnosis, and treatment

Table 12.—Cost”Effectiveness Ratios for Society:

Low”Cost Model’

Cervical cancer screening: women, aged 30 to 39

Policy

(screening intervals Total cost per Total cost per year

in years) life saved b of life savedb

1 . . . . . . . . . . . . . . . . $298,000 $27,300

2 . . . . . . . . . . . . . . . . 101,700 9,300

3 . . . . . . . . . . . . . . . . 72,800 6,700

4 . . . . . . . . . . . . . . . . 59,000 5,400

5 . . . . . . . . . . . . . . . . 57,000 5,200

6 . . . . . . . . . . . . . . . . 46,500 4,300

7 . . . . . . . . . . . . . . . . 45,800 4,200

8 . . . . . . . . . . . . . . . . 46,000 4,200

9 . . . . . . . . . . . . . . . . 47,100 4,400

10 . . . . . . . . . . . . . . . . 49,300 4,600

aBaSe Case With Iow.cost screening and treatment procedures, discount rate

= 10%

bTotal cost IS the cost of screening, diagnosis. and treatment

Table 13.—Cost-Effectiveness Ratios for

Philanthropic Organizations: Low-Cost Model’

Cervical cancer screening: women, aged 30 to 39

Policy

(screening intervals Screening cost Screening cost per

in years) per life saved year of life saved

1 . . . . . . . . . . . . . . . . $175,700 $16,110

2 77,300 7,090

3::::::::::::::: : 50,300 4,620

4 . . . . . . . . . . . . . . . . 36,400 3,350

5 . . . . . . . . . . . . . . . . 33,200 3,060

6 . . . . . . . . . . . . . . . . 21,500 1,980

7 20,000 1,840

8:::::;::::::::: : 18,800 1,740

9 18,000 1,670

10 : : : : : : : : : : : : : : : : 17,400 1,610

aBaSe Case with Iow.COSt screening and treatment procedures, discount rate

= 10%.

Table 14.—Cost”Effectiveness Comparisons:

High-Cost, Low”Cost, and High.Risk Models

(total cost per year of life saved)”

Cervical cancer screening: women, aged 30 to 39

Policy

1 . . . . . . . .

2, . . . . . . .

3 . . . . . . . .

4 . . . . . . . .

5 . . . . . . . .

6 . . . . . . . .

7 . . . . . . . .

8, . . . . . . .

9

10 : : : : : : : :

High-cost

model b

$97,600

41,500

28,600

22,000

20,700

15,200

14,600

14,300

14,200

14,300

Low-cost

model

$27,300

9,300

6,700

5,400

5,200

4,300

4,200

4,200

4,400

4,600

High-risk,

low-cost model

$36,600

14,600

10,300

8,200

7,800

6,200

6,000

6,000

6,100

6,200

aTotal COSt IS the cost of screening, diagnosls, and treatment

b The high.co5t model IS the base case model, discount rate = 1O%
22 ● Background Paper #2: case Studies of Medical Technologies

LIMITATIONS

There are numerous limitations to a study

such as ours, most of them related to the inability to examine and control variables as one could

in a true experimental design. This problem is

most clear when one realizes that neither has it

been proved that dysplasia and CIS are precursors to cancer, nor has it been proved that

screening for and treating of these conditions

have led to decreased cervical cancer mortality.

Sufficient inferential evidence is available to

warrant screening at some level, but sufficient

uncertaint y is present to question the optimal

periodicity. Therefore, in simulating the disease

process and its diagnosing, the best we can do is

test the various estimates of prevalence, incidence, screening, error, and death rates reported by clinicians and researchers in order to

produce results that seem reasonable.

Disease Transition

One of the more severe limitations of our

study stems from the model’s inherent assumptions. For instance, the transition probabilities

from one disease state to another, regardless of

a woman’s prior medical history, are assumed

to be constant, which almost certainly 

is not the

case. In fact, logic alone would dictate that time

in certain disease states be positively correlated

with the probability of transition to the next

higher state. To overcome this restriction, highrisk categories have been included which allow

for higher transition rates for selected groups of

individuals who are at higher risk. Also, the

Markov technique does not allow for the possibility of a threshold effect (i.e., the disease’s

having a zero probability of progressing to the

next state in one period), whereas it does allow

for the nonzero probability of progressing

within two, three, or more periods.

Since apparently not every disease progresses

to invasive cancer, there is a distinct possibility

that this phenomenon is not a random procedure, but rather the result of two or more separate disease processes—one which progresses

and one which does not, and possibly even a

third which regresses. Obviously, this is not so

much a limitation of the modeling process as it

is a statement of the lack of understanding of the

disease process itself.

Other simplifying assumptions also are made

regarding transition probabilities. The literature, for instance, suggests that treatment of

clinically invasive carcinoma results in about a

50-percent overall survival rate (24), which is

ordinarily couched in terms of 1, 2, or 5 years.

Our model assumes that clinically invasive cases

transit directly, within the single period of 1

year, to the permanent states of either hysterectomy or death. To mitigate this oversimplification, the simulated screening process identifies a given proportion of invasive cases and

transfers them directly to the hysterectomy category, thus mimicking the discovery of microinvasive cases and simulating a higher survival

rate.

Screening and Treatment

A simplifying assumption in our model is that

a single algorithm is available to practitioners

for screening and treatment of preclinical disease. Actual practitioners, though, may use several different possible treatments for the same

diagnosis. For example, although the model

assumes treatment of early dysplasia by cryosurgery, actual practitioners may in some instances not treat this condition immediately, in

some treat it by laser carbon dioxide, and in

others treat it by only the biopsy 

itself. Actual

practitioners may also use the same treatment

for very different diagnoses. Although medical

authorities currently recommend hysterectomy

for only the more severe cases, practitioners actually perform this surgery based on factors

such as the woman’s interest in bearing children

(24) or the availability of beds (31). We attempt

to account for these possibilities by simulating

conservative therapy for the low-cost model and

more radical therapy for the high-cost model.

Thus, actual costs should lie somewhere in between these extremes.

Another possibility which our analysis ignores altogether is that of medical complications

following therapy, Although complications
may be expensive, produce illness, and result in

deaths, they are relatively rare. In addition,

they are only associated with the radical forms

of treatment, and their exclusion is not expected

to affect this study’s conclusions.

Finally, since there is considerable uncertainty regarding error rates, the results were calculated with a wide range of rates. Such a sensitivity analysis requires the reader to interpret

the findings with appropriate caution, although

the model is not very sensitive to the rates used.

costs

Assigning costs to any production process is

usually a difficult task and often results in arbitrary allocation decisions. In that regard, our

study is no exception. For instance, since a fee is

not recovered for- each service performed in the

low-cost clinic model, we decided that all fixed

and variable costs, with the exception of 25 percent of the physician’s and 50 percent of the

R.N. ’s time, would be allocated to the Pap procedure. This decision was reached after many

hours of discussion with health clinic personnel.

The practitioners’ remaining times are allocated

to colposcopy and cryosurgery; for the purpose

of simplicity, an average “fee” was assigned to

these treatments, resulting in slightly overestimated costs for- some policies and underestimated costs for others.

The joint production phenomenon is a related

cost allocation problem. Joint production

should be considered, because multiple activi ties occur during any patient/practitioner contact. When a Pap smear is administered during a

visit, the Pap may or may not be the primary

reason for the visit; the patient may also be formally or informally “screened” for problems

such as cervicitis, hypertension, breast cancer,

anemia, and wife battering. Whatever the case,

we at tempted to account for this phenomenon

by assuming an inverse linear relationship between the cost allocated to the screening program and the length of interval between screens,

a relationship which appears to be as reasonable

an assumption as any other without the benefit

of a very careful study of the activities involved.

Such a study was clearly beyond the scope of

this research. The inverse relationship probably

holds more strongly for the shorter intervals

and becomes weaker as the interval lengthens.

Nevertheless, this assumption certainly approximates reality more closely than do most other

cost-effectiveness studies, which seldom even

consider the phenomenon of joint production.

Benefits

Benefits are even more difficult to estimate

and allocate than are costs. As stated several

times previously, there is no irrefutable evidence that any benefits associate positively with

cervical cancer screening, not to mention the

lack of consensus regarding the magnitude of

benefits attributable to different levels of

screening.

Nevertheless, one of our principal tasks was

to quantify estimated benefits as expressed in

terms of: 1) number of lives saved, 2) years of

life saved, and 3) related economic consequences for each screening policy. The first

problem with this method is that it considers

only saving lives, although there are many

lesser benefits such as the prevention of pain,

suffering, and mourning, as well as the psychological and physical consequences of losing

one’s childbearing ability. Most of these other

effects are too difficult to measure adequately,

and we fared no better in this effort than other

investigators.

The second major problem with this approach, that of comparing benefits of each

screening policy with no screening for 10 years,

arises because the purpose of our analysis is to

measure and compare both costs and benefits

from the perspective of the potential financier

who does not ordinarily finance preventive

health care (the few major exceptions being

prepaid group practice plans, military health

systems, and individuals paying for screening out-of-pocket). Therefore, comparing one

screening policy with no screening seems to be

the most appropriate choice. One could argue

that it is reasonable to compare a given screening policy with the annual screening often

recommended by cancer control organizations
24 ● Background Paper #.?: Case Studies of Medical Technologies

and cancer specialists. We suspect, however,

that the final ratios would be substantially the

same. A related concern is that benefits from the

last screen for each of the policies continue past

the llth year, because each policy leaves its

population in a unique health status mix; as a

result, future benefits will accrue differentially

for each policy. We chose to stop at the llth

year partly for convenience, but with the expectation that it would still be possible to obtain a

reasonably accurate picture of the relative merits of one policy over another and with the realization that the discounting mechanism significantly dilutes benefits which accrue far into the

future.

CONCLUSIONS

Regardless of the assumptions made or measures used, the cost effectiveness of cervical cancer screening increases as the screening interval

length increases until some maximum value is

reached, This function is largely due to the effect

of discounting, since costs are incurred in the

present and benefits are delayed, accruing in the

future. As indicated in figure 6, the higher the

discount rate, the more advantageous it is to

postpone screening. However, since the functions observed are nonlinear, decreasing at a

decreasing rate, it is much more cost effective to

postpone screening 1 additional year when the

intervals are frequent than it is when the intervals are infrequent. For example, at a 10-percent

discount rate, if the established policy were to

screen biennially, then changing to a policy of

screening every year would cost an extra

$56,000 per year of life saved; however, changing from a policy of screening every 8 years to a

policy of screening every 7 years would cost

only an extra $300 per year of life saved. Cervical cancer screening is already an accepted

practice, so the most pertinent issue is the appropriate screening interval length. The most

useful way to address this would be a marginal

analysis such as that just described. A CEA does

not provide the desirable level of screening,

because there is no information regarding the

Summary

As must be evident by now, there are numerous limitations to this study’s simulation process

(the more important ones have been discussed).

However, reality never can be—nor need be—

perfectly duplicated, even if all elements are

known. The value of a simulation model is

measured by determining both its usefulness in

gaining new insights into a complex process and

its ability to successfully predict events, and

simplifying assumptions are permitted so long

as they do not unduly bias the results.

value of life. Once one assigns a value, one can

easily read the interval from the appropriate

table.

The analysis does indicate that the results are

not very sensitive to error rates or to small

changes in the discount rate, as long as one accepts the premise that the true discount rate currently lies somewhere near 10 percent. However, much more attention must be paid to the

true costs of operating a screening program. For

instance, when costs are decreased in recognition of the effects of joint production and in

recognition of the obvious fact that benefits

(other than averting death due to cervical

cancer) are accrued, the cost effectiveness of

the program increases approximately twofold

throughout most of the interval range under

consideration (see table 10). Likewise, when we

simulate a minimal cost screening program, still

maintaining acceptable standards of care, we

note a tremendous difference in cost effectiveness, making every screening level more than

three times more attractive. If the effects of joint

production were evaluated in the low-cost model, as they were in the base case (high-cost) model, cervical cancer screening would be even

more appealing.
Case Study #7: Allocating Costs and Benefits in Disease Prevention Programs: An Application to Cervical Cancer Screening ● 25

One of the purposes of this study was to examine the cost effectiveness of cervical cancer

screening in light of the perspectives of different

individuals and institutions within society,

since, ordinarily, cost-effectiveness studies are

performed with only society’s objectives in

mind. The results of our analysis show a continuous decline in total medical costs as the

screening interval lengthens (see table 5 and

figure 4), indicating that an institution whose

primary objective is financial will always

benefit from postponing screening. To a health

insurer, for example, cervical cancer screening

always costs more than it saves, which may explain why preventive benefits are often not

covered by insurance policies. But at least one

other thing that makes cervical cancer screening

even less attractive to a private insurer is the

fact that subscribers leave the plan. Table 11 indicates that as emigration rates increase, screening becomes less attractive, particularly if the

screenings are frequent. The combined effects of

screening costs being larger than medical cost

savings and the loss of subscribers (and with

them, future benefits) may help explain the

reluctance of the private sector to finance

screening.

This study has also demonstrated the offsetting costs and benefits associated with outreach

programs designed to increase access to health

care for a high-risk population. Although the

cost of finding these women was substantial (an

additional $20 for each visit), their increased

risk for cervical cancer (twice the risk) was sufficient to keep the cost-effective function nearly

identical to that for the low-cost, normal-risk

group. This finding implies that the increased

costs of outreach programs can be justified on

the basis of decreased cervical cancer mortality

alone. If these high-risk women are also assumed to be at high risk for diseases other than

cervical cancer, one can hypothesize that significant additional dividends can be gained from

the gynecological visit. These additional benefits have been labeled “joint production effects. ” The added costs of performing other tests

and evaluations will not be large, but the additional health benefits could be substantial.

Owing to the way costs and benefits are evaluated, though, an outreach program will probably not be undertaken. Where a private party

may place emphasis on the increased screening

and induced treatment costs, a public party may

be interested only in the cost per woman

screened. Thus, an outreach program may be

attractive to neither party. If all costs and all

benefits are taken into account, however, it may

become very appealing, particularly from a

societal point of view.

Policy Considerations.—We conclude from

our study that simple totaling and comparing all

benefits and all costs of disease prevention/

health promotion programs will not provide

sufficient information for society to allocate its

resources efficiently. This is because many of

the decisions are made by individuals in the private sector rather than by those in the public

sector, and because the key decisionmaker may

not be personally affected by the benefits.

Nevertheless, as medical costs continue to

escalate, more and more private parties are considering different types of health promotion/

disease prevention programs as potential ways

to save money. Rather than by governmental

exhortations, these programs could be encouraged by carefully planned subsidies that function as transfer payments. Such subsidies could

be in the form of assistance to either suppliers or

consumers, e.g., tax incentives, direct financial

assistance to defray program costs, or redeemable health promotion coupons similar to food

stamps.

A second potentially effective method of promoting these programs might be to encourage

private employers to accept more direct liability

for the health and welfare of their work force,

thus creating health promotion incentives similar to those found in the military (see introduction). Employers could be given tax incentives

to contract with specific organizations for the

provision of health care and possibly to provide

life insurance and increased disability insurance

coverage (39).

By combining these two suggestions, powerful incentives could be created within the private

sector to establish effective programs throughout the country. Programs such as weight control, smoking cessation, and hypertension control, as well as more medically oriented programs such as Pap testing and glaucoma screen-
26 ● Background Paper #2: Case Studies of Medical Technologies

ing, could be more accessible to the general natural incentives and it would combine public

public and could become an accepted part of the and private resources. Without this partnership,

daily working environment. health promotion/disease prevention programs

Such an approach to promoting health would are often too expensive for either the public or

be feasible, because it would capitalize on private sector to undertake independently.
Biopsy .—Removal and examination (usually microscopic) of tissue from the living body, performed

to establish precise diagnosis.

Carcinoma in situ (CIS).—A neoplastic (cancerous)

lesion wherein the tumor cells lie only within the

epitheliums or origin (i. e., preinvasive carcinoma).

Clinical carcinoma .—Carcinoma (cancer) which has

developed to such an extent as to be diagnosed by

overt signs and symptoms. Clinical carcinoma is

normally invasive carcinoma.

Colposcopy.—Visual examination of the vagina and

cervix by means of a colposcope (i. e., a speculum

or instrument inserted into the vagina for the examination of tissues by means of a magnifying

lens).

Ionization.—A large, cone-shaped biopsy which can

be performed for both diagnostic and therapeutic

purposes.

Cryosurgery .—Surgery performed by freezing a portion of the cervix.

Directed biopsy.—A biopsy using a colposcope to

“direct” the procedure.

Dysplasia. —A lesion which is thought to be precancerous when found on the cervix.

False-negative rate,—The number of persons with

disease but not identified by the test (i.e., false

negatives), divided by the total number of persons with the disease.

False-positive rate.—The number of persons without

disease but not classified by the test as being without disease (i. e., false positives), divided by the

total number of persons who are disease free.

Invasive carcinoma.—A mass of cancerous cells

which has “invaded” the surrounding tissue (i. e.,

not confined to the epitheliums, as is carcinoma in

situ ).

Occult carcinoma.—A preclinical stage of invasive

carcinoma.

Pap test.—An exfoliative cytological staining procedure for the detection and diagnosis of various

conditions, particularly malignant and premalignant conditions of the femal genital tract (e. g.,

cancer of the cervix), in which cells from the genital epitheliums are obtained by smears, fixed and

stained, and examined under a microscope for

evidence of pathologic changes.

Precancerous lesion.—Cell abnormality (lesion) that

is thought to be a precursor to cancer. Dysplasia

is considered to be a precancerous lesion.

Preclinical invasive carcinoma. —invasive carcinoma

which has not yet developed to such an extent as

to be diagnosed by overt signs and symptoms.

APPENDIX B.–HEALTH STATE VECTORS FOR

SCREENING POLICIES

Vector after Vector after

Initial vector’ first screening second screening

H1

H2

H3

H4

H5

H6

H7

H8

H9

H10

H11

H12

H13

Normal. . . . . . . . . . . . . . . . . .

Reverted normal. . . . . . . . . . .

Dysplasia . . . . . . . . . . . . . . . .

CIS . . . . . . . . . . . . . . . . . . . . .

Invasive . . . . . . . . . . . . . . . . .

High-risk, disease free . . . . . .

High-risk, dysplasia . . . . . . . ,

High-risk, CIS . . . . . . . . . . . .

High-risk, invasive. . . . . . . . .

Hysterectomy . . . . . . . . . . . . .

Emigration . . . . . . . . . . . . . .

Death due to cervical cancer . .

Death due to all other causes . ,

0.986000

0.003000

0.005000

0.004000

0,002000

0.000000

0.000000

0.000000

0.000000

0.000000

0.000000

0.000000

0.000000

0.986000

0.003000

0.001000

0.000800

0.001840

0.007200

0.000000

0.000000

0.000000

0.000160

0.000000

0.000000

0.000000

0.986000

0.003000

0.000200

0.000160

0.001693

0.003640

0.000000

0.000000

0.000000

0.000307

0.000000

0,000000

0.000000

‘The lnitiaI vector IS screened twice More aging begins

b The vector after the second screening is subequently aged according to the disease progression chart in table 4
28 . Background Paper #2: Case Studies of Medical Technologies

APPENDIX C.–COST FORMULATIONS

Low=Cost Modelt

The low-cost model is not based on fee-for-service,

so costs were assigned by estimating resources required for a hypothetical patient load and by estimating allocation schedules. Listed below are staffing requirements, rent and utility expenses, and furniture

and equipment costs. All expenses other than those

specifically noted are allocated to Pap testing.

Pap Test Cost Calculations

Cost per day

1 physician (10% time @ $20/hr) 2 ... ... ... ... ... $ 4

1 registered nurse practitioner (100% time @ $12/hr)3 72

2 licensed vocational nurses (100% time @ $6.50/hr) 104

1 receptionist/clerk (100% time @ $3.49 /hr). . . . . . . 28

Rent: 1,200 sq ft ( @ $1/sq ft/mo) (3 examination

rooms, office, waiting area, lounge, bath). . . . . . . . 57

Phone and utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Furniture and equipment (see list and prices below)

($18,000 amortized @ 570 for 10 years) . . . . . . . . . 9

Supplies, expendable . . . . . . . . . . . . . . . . . . . . . . . . . 33

Total . . . . . . . . . . . . . . . . . . . . ... ... ... ... .. .$312

Assuming each L.V.N. sees 1 patient every

15 minutes, then 64 patients are seen each day:

312 -64 = . . . . . . . . . . . . . . . . . . . . . . . . .$5 Pap test

Furniture and Equipment Costs

Weight scale (1) . . . . . . . . . . . . . . . .

Exam table with stirrups (3) . . . . . . .

Supply cart (1) . . . . . . . . . . . . . . . . .

Equipment/suppl y table (3) . . . . . . .

Lamps, exam (3). . . . . . . . . . . . . . . .

Stools, exam (3) . . . . . . . . . . . . . . . .

Cabinet, instrument (I) . . . . . . . . . .

Punch, biopsy, uterine (4) . . . . . . . .

Forceps, uterine (4) . . . . . . . . . . . . .

Curette, endocervical (4) . . . . . . . . .

Speculum, endocervical (4) . . . . . . .

Colposcope (2). . . . . . . . . . . . . . . . .

Unit, cryosurgery (1) . . . . . . . . . . . .

Tank, NO . . . . . . . . . . . . . . . . . . . .

Cost per

item

$ 17 7

256

85

224

34

72

310

138

75

28

100

3,650

695

110

Total

cost

$ 177

768

85

672

102

216

310

552

300

112

400

7,300

695

110

Sphygmomanometer (4). . . . . . . . . .

Stethoscope (4) . . . . . . . . . . . . . . . .

Wastebasket with cover (3) . . . . . . .

Desk (4) , . . . . . . . . . . . . . . . . . . . . .

Chair, desk (4) . . . . . . . . . . . . . . . . .

Typewriter, . . . , . . . . . . . . . . . . . . .

Cabinet, filing, 4 drawer (2). . . . . . .

Cabinet, filing, 1 drawer (1). . . . . . .

Bookcase (1) . . . . . . . . . . . . . . . . . .

Boards, bulletin (2) . . . . . . . . . . . . .

Roller deck (1) ., . . . . . . . . . . . . . . .

Chair, comfort/lounge & waiting

room (10) . . . . . . . . . . . . . . . . . . .

Dictionary (4) . . . . . . . . . . . . . . . . .

Dictionary, medical (4) . . . . . . . . . .

Couch (1) . . . . . . . . . . . . . . . . . . . . ,

Table, typewriter (1) . . . . . . . . . . . .

Table, small (4) . . . . . . . . . . . . . . . .

Refrigerator, small (1) . . . . . . . . . . .

Stove, small (1) . . . . . . . . . . . . . . . .

Sinks, installed (5) . . . . . . . . . . . . . .

Wastebasket (3) . . . . . . . . . . . . . . . .

Miscellaneous . . . . . . . . . . . . . . . . .

Total . . . . . . . . . . . . . . . . . . . . . .

Summary of Low-Cost Mode14

Practitioner Lab

Pap. ... ... ... . $ 5 $3

Coloscopy . . . . . . . . 20 —

Biopsy . . . . . . . . . . . 10 12

Cryosurgery . . . . . . 20 —

Ionization. . . . . . . . 400 –

Hysterectomy . . . . . 2,00 0

High-Cost Mode16

Practitioner Lab

Pap. ... ... ... ..$ 2 0 $10

Coloscopy . . . . . . . 50 —

Biopsy . . . . . . . . . . . 25 55

Cryosurgery . . . . . . 55 —

Ionization. . . . . . . . 400 —

Hysterectomy . . . . . 2,000—

Cost per Total

item

33

5

80

156

90

890

75

25

35

50

10

60

15

30

350

25

65

150

200

200

10

500

cost

132

20

240

624

360

890

150

25

35

100

10

600

60

120

350

25

260

150

200

1,000

30

500

$17,680

Hospital Tota l

— $ 8

— 20

— 22

$2760 5 220

5,900’ 7,9005

Hospital Tota l

— $ 30

— 50

— 80

55

$2,360 2,760

5,900 7,900

“Assuming a patient load of 15,360/year (I e., 2 L, V.N. S, each seeing one

patient every 15 minutes for 48 weeks of the year).

‘Except as noted, all items were Identified and priced in consultation with ‘Total hospitalization charges for diagnosis of CIS and invasive cervical

Reyes (3o). Equipment was priced through standard catalogs. cancer, respectively, including multiple admissions. Source: Hospitaliza-

‘Assuming 25 percent of time allocated to Pap test backup. tlons and Payments to Hospitals, Third National Cancer Survey, HEW

‘Assuming 75 percent of time to Pap test backup, which includes patient publication No. 76-1094, March 1976 (costs adjusted to 1979).

health education actvities. ‘The high-cost model is used in the base case (see app. D).
APPENDIX D.–VALUES FOR BASE CASE ANALYSIS

To establish a base against which to make com- 5,

parisons of the effects of assigning alternative values

to variables, a base case is established in which the

study’s variables are assigned the following initial 6.

values. 7.

1.

2.

3.

4,

The initial disease transition matrix is as shown

in table 4.

The initial population vector (i.e., the state of

health of the initial population) is as defined in

appendix B.

The population is screened twice before aging

begins.

The proportion of invasives which are preclinical and therefore are eligible for detection by

Pap test = 0.1.

Discount rate = 10 percent.

Costs (high-cost modeI):

Pap test . . . . . . . ... ... ... ... ... ... $ 20

Pap cytology. ., . . . . . . . . . . . . . . . . . . 10

Colposcopy . . . . . . . . . . . . . . . . . . . . . . 50

Biopsy . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Cryosurgery . . . . . . . . . . . . . . . . . . . . . 55

Ionization . . . . . . . . . . . . . . . . . . . . . . 2,500

Hysterectomy . . . . . . . . . . . . . . . . . . . . 7,900

Error rates: Sensitivity analysis is used to compare the effect of

—False-negative rates for the Pap test: 0.2 for assigning other values.

dysplasia, CIS, and preclinical invasive disease.

—False-positive rate: 0.05.
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