Vital and

Health Statistics

Social Cognition

Approach to

Reporting Chronic

Conditions in

Surveys

Series 6:

Cognition

No, 3

and Survey

Health

Measurement

This report describes a study, using methods derived from social cognition,

designed to gain a better understanding of how the lay person organizes

illnesses into natural categories and how these categories are linked to other

knowledge about self and others,

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Centers for Disease Control

National Center for Health Statistics

Hyattsvllle, Maryland

October 1989

DHHS Publication No. [PHS) 89-1078

Reprinted November 1992
Copyright Information

All material appearing in this report IS in the public domain and may be

reproduced or copied without permission; citation as to source, however, is

apprecmted.

Suggested Citation

Brev~er MB. Dull VT, Jobe JB. Social cognition approach to reporting chronic

conditions in health surveys National Center for Health Statistics. Vital Health

Stat 6(3). 1989.

Library of Congress Cataloging-in-Publication Data

Brewer, Manlynn B.

Socral cogrvt!on approach to reporlng chrome conditions m health

surveys / Manlynn B. Brewer, Valerie T. Dull, Jared B. Jobe.

p. cm - (vital & health statmt[cs. Series 6, Cognition and survey

measurement ; no. 3) (DHHS publ[cabon : no. (PHS) 89-1078)

“This research was a part of a larger project entitled “Laboratory-based

Research on the Cogmtlon Aspects of Survey Methodology” being conducted

by the National Center for Health Stat@cs under a grant #SES a4-03415 from

the National Science Foundation.”

“DHHS contract no, 282–21 23.”

Bibliography: p. ISBN 0-8406–041 4-9

1. Health surveys. 2. National Health Interview Surveys (U.S.) 3, Chronic

diseases- Reporling. 4. Cognition. 5. Memory. 1.Dull, Valerie T, Il. Jobe Jared

B. III. National Center for Health Statistics (U.S.) IV. National Science

Foundation (U.S.) V. Ttle. V1. Series. V1l. Series: DHHS publication ; no. (PHS)

89-1078.

[DNLM: 1. Chronic Disease–epidemiology 2. Cogmtlon. 3. Data

Collection–methods. 4 Health Surveys-United States. W 30 B847s]

RA408.5.B74 1989

614.4’2–dc19

DNLM/DLC

for Library of Congress 89–600021

CIP
National Center for Health Statistics

Manning Feinleib, M.D., Dr.P.H., Director

Robert A. Israel, Deputy Director

Jacob J. Feldman, Ph.D., Associate DirectorforAnalysis

and Epidemiology

Gail F. Fisher, Ph.D., Associate Directorfor Planning and

ExtramuralPrograms

Peter L. Hurley, Associate Directorfor V7taland Health

Statistics Systems

Stephen E. Nieberding, Associate Directorfor Management

Charles J. Rothwell, Associate Directorfor Data Processing

and Sen’ices

Monroe G. Sirken, Ph.D., Associate Directorfor Research

and Methodology

Office of Research and Methodology

Monroe G. Sirken, Ph.D., Associate Director

Kenneth W. Harris, SpecialAssistant for Program

Coordinationand Statistical Standards

Lester R. Curtin, Ph.D., Chief StatisticcdMethods Stafi

James T. Massey, Ph.D., Chiej Swvey Design Stajf

Andrew A. White, Ph.D., Chie$ Statistical Technology

Staff

This research was part of a larger project entitled, “Laboratoxybawd Research on the Cognitive Aspects of Suxvey Methodology,”

being conducted by the National Center for Health Statistics under

grant SES 84-03415 from the National Science Foundation (NSF).

Monroe G. Sirken, Ph.D., was the principal investigator for this grant.

Murray Abom, Ph.D., was the NSF program director.

This research was performed under DHHS contract 282-85-2123

from NCHS to the University of California, Los Angeles. Marilynn

Brewer, Ph.D., was the principal investigator. Patricia Royston was the

NCHS project officer. Jared B. Jobe, Ph.D., is the technical editor and

reviewer for Series 6.

The authors thank MAXICARE for making patient medical files

available and gratefully acknowledge the work of research assistants

Sherry Schneider and Jane Asawa.
Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Cognitive analysis ofthe NHIS checklist procedure . . . . . . . . . . . . . . . . . . . . . .

Theory ofnatural categories

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... .OOO..

Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . - . . . . . .

Result s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Initial familiarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Free-sort analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Comparative analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Discussion . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Appendixes

I. Recruitment procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

H. EsTerimental instructions and instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . . . . . . . . .

111, Definitions ofchroni ceonditions included in study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

IV. Analysis techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of tables

1,

2.

3.

4.

5,

6.

7.

Chronicconditions,b yorganization in National Health Interview Survey checklist . . . . . . . . . . . . . . . . . . . . . . . . .

Chronicconditions,b ypercent ofrespondents indicating initial unfamiliarity with condition . . . . . . . . . . . . . . . . .

Means for each ofseven rating scales, by cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chronicconditions, by organization resulting from hierarchical cluster analysis— 12-cluster solution . . . . . . . . . . .

Chronic conditions, by organization in International Classification of Diseases, 9th Revirion (ICD-9) and

National HeaM~InterviewS urvey (NHIS) checklist group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Statistics from quadratic assignment paradigm analysis comparing natural clustering with International Classification ofDiseases, 9th Revision (ICD-9) and National Health Interview Survey (NHIS) groupings of chronic

diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chronicconditions,b yorganization resulting from profile cluster analysis-4-cluster solution . . . . . . . . . . . . . . .

List of figures

1. Associative network model of the representation ofchronic conditions in memory . . . . . . . . . . . . . . . . . . . . . . . . .

1

2

3

4

4

4

6

6

6

9

12

13

15

17

29

33

2

6

7

8

9

9

11

2

...

Ill
Social Cognition

Approach to Reporting

Chronic Conditions in

Health Surveys

by Marilynn B. Brewer, Ph. D., and Valerie T.

Dull, Ph. D., Institute for Social Science

Research, University of California, Los

Angeles, and Jared B. Jobe, Ph. D., Office of

Research and Methodology

Introduction

Since 1957 the National Center for Health Statistics

has annually conducted the National Health Interview Survey (NHIS), with a sample of 40,00&50,000 households.

The core content of the NHIS survey instrument is used to

elicit information on the frequency and impact of acute

health problems, the presence of chronic illnesses and disabilities, and experiences with the health care system for

respondents and other members of the household. The

specific content of the interview questionnaire varies somewhat from year to year as supplemental sections on specific

health problems change (National Center for Health

Statistics, 1967, 1973, 1987).

The NHIS interview is conducted in the respondent’s

home, Each adult responds for himself or herselfi adults

are permitted to give responses for children and for all

relatives in the household who are either unavailable at the

time of the interview or are unable or unwilling to participate. Thus, information on chronic conditions is obtained

about all members of the household, either through selfreport or by proxy responses from a related member of the

same household.

One of the purposes of NHIS is to produce statistics on

the prevalence of chronic illness and disability in the U.S.

population. In attempting to maximize the accuracy and

completeness of reports of chronic conditions, many

changes have been made to NHIS since the survey began.

The changes have primarily been of two types: (1) Changing from a “condition approach” to a “person approach” in

January 1969, after an experimental comparison was made

of the two approaches, and (2) changing the chronic disease

checklists used in the condition approach.

The condition approach utilizes a short series of direct

questions to obtain information from respondents about

recent occurrences of illnesses and injuries, followed by a

chronic condition checklist. Examples are: “Last week or

the week before, did you have any accidents or injuries,

either at home or away from home?” and “At the present

time, do you have any ailments or conditions that have

continued for a long time?” The questions were structured

to elicit information about any departure from a state of

physical or mental well-being resulting from disease or

injury (National Center for Health Statistics, 1972).

The current person approach, on the other hand, utilizes probe questions on health-related actions, such as

restricting activities and making health care visits, with followup questions on the illnesses and injuries that caused

these actions. An example is: “During the past 2 weeks, did

— stay in bed all or most of the day because of any

illness or injury?” Neither the condition approach nor the

person approach is a pure condition or person approach

but a hybrid approach, employing some aspects of the other

approach.

The major change in the condition checklists was made

in January 1985, when 100 chronic conditions were divided

into six separate lists based on a general differentiation into

body systems or parts. One checklist is randomly assigned

to each household. Several minor modifications to this

approach occurred in January 1982. A more detailed

account of the changes made in NHIS is contained in work

by Gleeson (National Center for Health Statistics, 1972),

and a summary of NHIS methods and instruments is contained in work by Jabine (National Center for Health

Statistics, 1987). The organization of the six chronic condition checklists is contained in table 1.

Among other things, the NHIS interview is designed to

tap into the respondent’s episodic memory-in this case,

recall of specific events involving illness or contact with

health care facilities within a specified time period. Whenever a particular health-related event is reported using the

person approach, the respondent is probed for further

information concerning the health-related event that may

reveal the presence of a chronic health condition. The

respondent, therefore, is required first to retrieve information about specific events or experiences in the recent past

and then to recall information relevant to the diagnosis of

existing chronic conditions.

As part of the person approach, the NHIS interview

also contains items used to tap into the respondent’s general (semantic) knowledge about his or her chronic

illnesses and personal health status using one of the six

1
Table 1. Chronic conditions, by organization in National Health

Interview Survey checklist

Group 1

&oitla or rheumatism

Lumbago

Sciatica

Slipped disk

Bursttls

Skin cancer

Eczema or psoriasis

Acne

Dermatitis

Bunions

Group 2

Cataracts

Glaucoma

Cleft palate

Cerebral palsy

Curvature of spine

Clubfoot

Group 3

Gallstones

Cirrhosis ,

Hepatitis

Jaundice

Uicer

Hernia

Gastritis

Enteritis

Diverticulitis

Colitis

Cancer of stomach, etc.

Group 4

Goiter

Diabetes

Anemia

Epilepsy

Muiliple sclerosis

Group 4-Con.

Migraine

Neuralgia or neuritis

Nephritis

Kidney stones

Breast cancer

Cancer of uterus

Cancer of prostate

Herpes

Syphiiis

Group 5

Rheumatic fever

Hardening arteries

Coronary heart disease

Hypertension

Stroke

Brain hemorrhage

Angina pectoris

Myocardlal infarctlonheart attack

Damaged heart valves

Tachycardia

Heart murmur

Aneurysm

Blood CIOtS

Varicose veins

Leukemia

Hemorrhoids

Phlebnis

Group 6

BronctW

Asthma

Hay fever

Sinusitis

Tonsilliils

Lung cancer

Emphysema

Pleurisy

Tuberculosis

highly structured chronic condition checklists described

earlier. In this method respondents are read a list of

specific chronic conditions and asked to indicate, for each,

whether they (and related adults and children for whom

they serve as proxy respondents) have that condition. With

this method, the name of the chronic condition is used as a

cue and the respondents are asked to recognize whether

that cue is applicable to themselves or related household

members.

Cognitive analysis of the NHIS

checklist procedure

In cognitive psychology, the survey respondent is

viewed as a processor of information, much the same as

a computer is a processor of information. Cognitive psychology focuses on the respondent’s thought processes through

four stages in the question-answering process. These stages

are comprehension, information retrieval, judgment, and

response.

The success with which information about health conditions is elicited depends in part on how that information is

encoded and organized in the respondent’s long-term memory and what search strategies are employed to retrieve that

information from memory. From this perspective, the current checklist procedure being used in the NHIS interview

has two potential drawbacks.

First, the organization of chronic conditions into six

subsets (checklists) is based on methodological considerations (grouping by disease type), and the organization of

some checklists may not necessarily reflect the knowledge

of the lay public. As a result, the context in which the

chronic conditions occur may or may not match the way

they are represented in the individual respondent’s hierarchical memory structure. Therefore, the knowledge that

oneself or a family member has a particular condition may

be less likely to be retrieved from memory.

Second, the retrieval strategy associated with the

checklist procedure relies on “bottom up” processing, starting from the condition name as a retrieval cue and working

up through various associative pathways to respondents’

general knowledge about themselves and other members of

their households. From what is known about the retrieval

process involved in such recognition memory, it appears

that the NHIS approach does not take advantage of associative connections or higher order categorizations that may

cue memory for multiple conditions at the same time.

A simplified schematic representation of the issues

involved in retrieving information from a general knowledge structure is provided in figure 1. Suppose, for

example, that the individual has organized his or her knowledge of chronic illnesses into general categories (conditions

that involve chronic pain, stomach troubles, and so on).

Such categories are represented as A, B, and C in the

figure, Any particular chronic condition (cl) is stored as a

member of one of these categories, with associative links to

other members of the same categozy and to the category as

a whole. In turn, the categories are linked to other knowledge about self, friends, and relatives. In figure 1, only one

of the categories (A) is associated with the respondent’s

8

CategoV

B

C5 G G

igure 1. Associative network model of the representation of

hronic conditions in memory

2
self. For instance, the respondent may think of himself or

herself as a person who has stomach problems but no heart

condition (represented as Category B). Category C in the

figure is not associated with the self but is linked to some

other household member (such as the knowledge that “my

husband has a lung disease”).

If figure 1 is an accurate representation of one way in

which memory about chronic diseases is structured, then it

has implications for how such knowledge will be retrieved

from memory. When retrieval starts from a specific cue (for

example, C2),successful recognition depends on whether all

of the associative paths that link the cue to the target (in

this case, self) are activated during the retrieval process, In

terms of the model shown here, this would involve linking

up through the system from the cue to the category and

from the category to the self. If either of these linkages

failed to be activated, recognition would not occur and the

condition would not be reported. On the other hand, if the

retrieval process started from the more general categories

(such as endocrine disorders or heart and blood disorders),

direct linkages would be available both down to specific

conditions and up to the self, thus possibly increasing the

chances that complete linkage between self and any specific

condition would be made.

If this model is correct, the efficiency and accuracy of

reporting of chronic conditions could be improved by

knowledge of how the lay public tends to organize illnesses

into natural categories and how these categories are linked

to knowledge about self and others. Specifically, organizing

the condition categories for NHIS in the same way that

respondents typically organize them in memory would

increase the probability of respondents’ recognizing and

report ing conditions of self and others. Because research in

the area of social cognition is particularly concerned with

how information about self and others is structured in

memory, it seems that the insights and methods of social

cognition research would be applicable to understanding

the problems associated with reporting chronic conditions.

Theory of natural categories

Outside of medical anthropoloW (for example,

D’Andrade et al., 1972), relatively little research has been

conducted on how health-related conditions are categorized by the lay population as part of shared social

knowledge. Recent advances in the theory of natural object

cat egories (Rosch and Lloyd, 1978; Smith and Medin,

19S1) and its extension to social categories (Cantor and

Mischel, 1979; Brewer, Dull, and Lui, 1981) provide a

framework for studying lay knowledge of chronic health

conditions. From this perspective, natural categories differ

from formal category systems in that they are made up of

“fuzzy sets” (that is, groups lacking sharp boundaries or

formal rules of inclusion), with categories being defined in

terms of multiple overlapping resemblances among category members rather than in terms of single defining

features. Thus, the categorization of health conditions that

individuals develop from personal experience and social

communication may be quite different from the organizational structure represented in formal classification systems

such as In temational Classification of Diseases, 9th Revision

(lCD-9) diagnostic coding system of the World Health

Organization (1977). For example, whereas diagnostic

codes are organized around physiological systems, lay categories may be organized around more behaviorally relevant

features, such as degree of debility, embarrassment, or

prevalence. The degree of correspondence between lay categorizations and the ICD-9 system has obvious relevance to

the future design of health survey questionnaires.

Knowledge about an individual’s implicit system of categorization within a particular domain can be derived from

judgments of similarity among representative items from

that domain. One method used in social cognition research

to obtain such similarity judgments is the free-sort procedure. Subjects are given a large set of items representing

the domain under investigation and asked to sort these

items into groupings in anyway they see fit. This technique

has the advantage of imposing no a priori structure on

subjects’ judgments, thus permitting spontaneous associations and clusterings to be represented in the final sortings.

When several individual subjects sort the same set of items,

the frequency with which specific items are grouped

together in the same category becomes a measure of perceived similarity among those items. Analysis techniques

such as multidimensional scaling and cluster analysis can

then be applied to these similarity judgments to determine

the structure underlying the shared perceptions.

The research reported here is part of a larger programmatic effort to apply laborato~-based research methods

from cognitive psychology to issues of survey research.

Within that larger framework, the objective of the present

study was to assess the utility of procedures derived from

social cognition research on natural categories to achieving

a better understanding of how individuals store and retrieve

information about chronic health conditions. The results

could be applied to a redesign of the NHIS checklist categories to more closely reflect the manner in which

respondents organize the categories in their memory,

thereby producing more accurate retrieval and better estimates of the prevalence of chronic conditions. In the study,

standard laboratory methods for the assessment of natural

categories were applied to the domain of chronic diseases.
Method

Subjects Procedure

Participants were recruited forthestudy by sampling

from a list of subscribers to MAXICARE, a health maintenance organization that services the Greater Los Angeles

area. A health maintenance organization was selected

because the medical records of participants could be used

to validate their responses. Letters with return cards

enclosed were mailed to 750 subscribers selected at random who matched the following criteria.

. Were 45–64 years of age.

. Lived in the Los Angeles area.

. Had telephone access.

. Had insurance coverage for multiple persons.

. Had medical records that indicated a history of a

chronic condition from at least one of these categories:

Neoplasms, metabolic disease, blood or circulatory

disease, respiratory problems, digestive or genitourinary disease, and musculoskeletal problems.

On the return cards, sample persons were asked to provide

a printed name, signature, and phone number of the

person in the household interested in volunteering for the

study. After the cards had been returned, each household

was called so that further screening could be conducted

and a session scheduled for the laboratory. (See appendix

I for a detailed explanation of recruitment and return

rates.)

This recruitment procedure resulted in participation by

70 individuals (34 males and 36 females). In the initial

design, all participants were to be interviewed in pairs,

which required that two adult members from the same

household participate. (The reason for this requirement

was to obtain validation data.) However, because of difficulties encountered in recruiting couples, some participants

were interviewed individually. The final group of 70 was

composed of 62 paired participants (31 pairs) and 8 singles.

All experimental sessions were conducted in the Institute

for Social Science Research (ISSR) laborato~ at the University of California, Los Angeles, by trained graduate

student experimenters, with the exception of two sessions

conducted in participants’ homes, Respondents were paid

$20.00 for their participation and reimbursed for parking.

Participants arrived at the ISSR laborato~ for their

scheduled session and were greeted by the two research

assistants. For those sessions involving pairs of participants,

the two experimenters were randomly assigned to the two

family members, who were interviewed in separate rooms

of the laboratory to ensure independent responding. At the

beginning of the session, each participant was asked to read

and sign an informed consent form, which described the

nature of the experiment and its general purpose. (See

appendix II). An experimental session consisted of three

tasks, administered in a fixed order.

Free-sort task

Participants were given a stack of 68 index cards, each

with the name of a specific chronic condition printed on it.

The 64 conditions were selected from the 1985 NHIS

checklist, with approximately an equal number of conditions selected from each of the six checklist categories. All

nonspecific diseases were omitted. In addition to conditions taken directly from the interview checklist, the experiment included four conditions (leukemia, cancer of the

uterus, herpes, and syphilis) that were added to increase the

range of conditions represented in the set to be studied,

(Appendix III contains the complete list of conditions used

in the experiment, including ICD-9 codings and descriptions for each.)

After participants read through the entire set of cards,

they were asked whether they were familiar with the term

used to describe each condition. If participants indicated

that they had not heard of the term or were not sure what it

was, a standardized definition was read to them from a

prepared list of descriptions of all conditions included in

the study (appendix III).

After they had read the entire set of cards for initial

familiarity, participants were given a new set of shuffled

cards and were asked to sort those into “piles based on how

you think they should go together.” No constraints were

placed on the number of piles to be generated or the basis

on which those sortings should be made. Participants were

encouraged to use their own judgment in deciding what

conditions should be grouped together and were assured

that there were no right or wrong answers for this task.
These procedures were designed to impose minimal

structure on the participants’ judgments of similarity

among the chronic conditions.

The participants were given an unlimited amount of

time to complete their sortings. When they indicated that

they had finished, they were asked to go through the stacks

one last time to evaluate whether each condition had been

placed where it belonged. When the participant had verified

the final sorting, the experimenter went on to the next

phase,

Dimensional-sorting task

Following the free sort, participants were asked to sort

the 6S conditions again in a set of structured-sorting tasks.

This phase of the experiment consisted of seven different

sortings along preselected rating scales. The scales were

selected to represent dimensions judged by the research

team (in consultation with National Center for Health

Statistics staff) to be relevant to the perception of chronic

conditions. The seven scales, administered in a fixed order,

wtxw

1,

2,

3.

4.

.5.

6.

7.

A 4-point scale of perceived incidence, ranging from

“extremely common” to “extremely rare”

A 4-point scale of perceived painfulness, from “very

painful” to “not at all painful”

A 4-point scale of perceived seriousness, from “major”

to “minor”

A 4-point scale of perceived severity, from “disabling”

to “not at all disabling”

A 3-point scale of embarrassment, from “definitely

embarrassing” to “not at all embarrassing”

A 3-point scale of perceived controllability, from “definitely controllable” to “definitely not controllable”

A 4-point scale of subjective likelihood of the respondent contracting the condition, from “very unlikely” to

“already has the condition (or has had it in the past).”

This last scale constituted our primary measure of the

accuracy of self-reporting with respect to each condition. When participants assigned a card to level 4 on

this scale, they were recorded as having that specific

condition.

For each of these structured sortings, participants were

given a new deck of shuffled index cards with the 68 different conditions and were instructed to place each condition

at the appropriate point along the rating scale. Plywood

boxes partitioned into labeled sections were provided for

this purpose. Each box also had a slot labeled “don’t know”

for placement of those conditions that the participant found

difficult to evaluate on that particular scale. As each sorting

was completed, the box was set aside for later recording.

Postsorting interview

Following the eight sorting tasks, a brief, structured,

free-response interview was conducted with each participant to allow him or her to talk about personal experience

with chronic illness. This interview included a section in

which participants were asked to indicate which of the

conditions in the study their partners now had or had in the

past. Thus, for each pair of interviewees in the experiment,

we obtained both a self-report of chronic conditions and a

proxy report from the partner.

At the close of the session, participants were thanked

for their participation and were requested to sign two forms

authorizing the release of their medical record information

to the project. They were then given the remuneration

agreed on and parking reimbursements. After the participants’ departure, the research assistants recorded the

results from all of the sortings, entering the data directly on

an IBM PC/AT microcomputer.
As a result of the selection and recruitment procedures, the participants in the laboratory study constituted

an older than average and more middle-class sample of the

Los Angeles area population. The 36 female and 34 male

participants ranged in age from 41 to 67years, with a mean

age of 56 years. They came from households containing

from one to six persons, with a mean household size of

three. Median annual family income of our participant

group was $46,809.

Initial familiarity

Table 2 shows the chronic condition terms by the percent of participants who initially indicated that they were

unfamiliar with them, (As indicated in the Method section,

when this response occurred, the participant was provided

with a standardized description of the specific condition.)

Because of the selection procedures used to recruit participants for this study, all had had direct or indirect

experience with chronic illness and had received some form

of medical treatment within the last year. Thus, our participant sample might not be representative of the population

at large, in that they were relatively experienced with illness

conditions in general. Nevertheless, a few of the terms on

our list of 68 were familiar to only one-half of our participants, and two items (enteritis and nephritis) were

unfamiliar to more than 75 percent of the participants. By

far the majority of terms, however, had unfamiliarity

responses of less than 20 percent.

In general, participants who were initially unfamiliar

with a particular term were satisfied with the descriptive

information provided and did not show any reluctance to

incorporate those conditions in the sorting task. (See table

3 and appendix IV for further analyses of “don’t know”

responses to the dimensional sorting task.)

Free-sort analyses

The average number of groupings produced by sortings

of the 68 items was 19.54, with a standard deviation of 7.64

and a range of 6–38. A measure of perceived similarity was

derived by computing the frequency with which chronic

conditions co-occurred with others in the participant sortings. If a condition was put in the same category as another

Table 2. Chronic conditions, by percent of respondents

indicating initiai unfamiliarity with condition

[Number of respondents = 701

H&h Moderate Low

unfamiliarity Percent unfamiliarity Percent unfamiliarity Percent

Enteritis 87

Nephritis 76

Diverticulitis 54

Phlebitis 49

Sciatica 49

Lumbago 46

Neuralgia or

neuritis 43

Angina

pectorls 39

Dermatitis 37

Aneurysm 34

Pleurisy 33

colitis 30

Gastrltls

Multiple sclerosis

Goiter

Hepatitis

SInusltls

Cerebral palsy

Glaucoma

Jaundice

Rheumatic fever

Gout

Myocardlal infarctionheart attack

Tachycardia

Bursitis

Eczema or psoriasis

Leukemia

Heart murmur

Anemia

Bunions

Cancer of prostate

Coronary heart

disease

Damaged heart

valves

Herpes

27

27

24

21

21

20

20

20

20

17

17

16

14

14

14

11

10

10

10

10

10

10

10

Cleft palate

Hernia

Cirrhosis

Cwvature of spine

Gallstones

Hardening arteries

Cataracts

Brain hemorrhage

Tuberculosis

Arthritis

or rheumatism

Cancer of

stomach, elc

Emphysema

Stroke

Acne

Asthma

Bronchitis

Clubfoot

Epilepsy

Hypertension

Skin cancer

Syphilis

Cancer of uterus

Diabetes

Hay fever

Hemorrhoids

Varicose veins

Biood clots

Breast cancer

Kidney stones

Lung cancer

Migraine

Sllpped disk

Tonsiliil!s

Ulcer

9

9

7

7

7

7

6

6

6

4

4

4

4

3

3

3

3

3

3

3

3

1

1

1

1

1

0

0

0

0

0

0

0

0

condition, the pairing received a co-occurrence score of 1,

and the co-occurrence scores were tabulated for each oft he

70 participants. When all sortings had been scored in this

way, the result was a unique 68 X 68 co-occurrence matrix

for each of the 70 sortings. These co-occurrence scores

were then summed across all participants using matrix

addition, which produced an aggregate symmetric 68 x 68

co-occurrence matrix with numbers of participants (7o) on

the diagonal. The data in the aggregate matrix represented

the extent to which participants agreed that pairs of conditions were similar to each other. A high co-occurrence

value in the matrix meant that the chronic conditions were

placed in the same category with high frequency.

6
Table 3. Means for each of seven rating scales, by cluster

Unlikely

Embarrasslng- Conirollabk- io get ii

Common- Palnfut- Major- Dlsabl[ng- not not oneself–

rare not painful minor not disabling ambarrasslng controllable have i!

Cluster and condition (l-4) (1-4) (l-4) (1-4) (1-.3) (1+ (1-4)

Arlhrllls or rheumatism . . . . . . .

Lumbago . . . . . . . . . . . . . . . .

Sciallca . . . . . . . . . . . . . . . . .

Burs[lls . . . . . . . . . . . . . . . . .

Miqralne . . . . . . . . . . . . . . . .

Nw.rralgiaorneurlfls . . . . . . . .

Sllppeddlsk . . . . . . . . . . . . . .

13un[ons. . . . . . . . . . . . . . . . .

Cleftpalate, . . . . . . . . . . . . . .

Curvalureofsplne. . . . . . . . . .

Clubfoot . . . . . . . . . . . . . . . .

Eczemaorpsorlasis. . . . . . . . .

Acne . . . . . . . . . . . . . . . . . .

Dermatitis . . . . . . . . . . . . . . .

Hares . . . . . . . . . . . . . . . . .

Syphllls . . . . . . . . . . . . . . . . .

Gallstones . . . . . . . . . . . . . . .

Cirrhosis . . . . . . . . . . . . . . . .

HePWt[s.. . . . . . . . . . . . . . .

Jaundice . . . . . . . . . . . . . . . .

Nephrllls . . . .. . . . . . . . . . . . .

Kldrwystones. . . . . . . . . . . . .

ulcer . . . . . . . . . . . . . . . . . .

Hmia.. . . . . . . . . . . . . . . . .

Gaslnlis. . . . . . . . . . . . . . . . .

Enteritis . . . . . . . . . . . . . . . . .

Dlvertlwlltls . . . . . . . . . . . . . .

Colltls. . . ! . . . ! . . . . . . . . . .

Hemorrhoids . . . . . . . . . . . . . .

Brorwhllis . . . . . . . . . . . . . . .

Asthma . . . . . . . . . . . . . . . . .

Hayfever . . . . . . . . . . . . . . . .

S[nusiUs, . . . . . . . . . . . . . . . .

Tonslllilis . . . . . . . . . . . . . . . .

Emphysema . . . . . . . . . . . . . .

Pleurisy . . . . . . . . . . . . . . . . .

Tuberculosis . . . . . . . . . . . . . .

Ant?mki . . . . . . . . . . . . . . . . .

Bloodclots. . . . . . . . . . . . . . .

Varlcoseveins . . . . . . . . . . . . .

Phlebitis . . . . . . . . . . . . . . . .

Cataracts . . . . . . . . . . . . . . . .

Glaucoma, , . . . . . . . . . . . . .

Cerehralpally. . . . . . . . . . . .

Dlabeles . . . . . . . . . . . . . . . .

Epilepsy . . . . . . . . . . . . . . . .

Multlplesclerosls. . . . . . . . . . .

Rheumallcfever . . . . . , . . . .

Hardtmlng arteries. ., . . . . . . .

Coronaryheartdisease. . . . . . .

Hypertension . . . . . . . . . . . . .

Stroke . . . . . . . . . . . . . . . . . .

Brairrhemorrhage . . . . . . . . . .

Anglnapecloris . . . . . . . . . . . .

Myocardlal lnfarcllonheartatlack . . . . . . . . . . . . . .

Damaged ht?artvalves. . . . . . . .

Tachycardla . . . . . . . . . . . . . .

Heartmurmur . . . . . . . . . . . . .

Aneurysm . . . . . . . . . . . . . . .

SIdncancer . . . . . . . . . . . . . .

Cancerofstomach, etc,, ,.., . .

Breaslcancer . . . . . . . . . . . . .

Cancerofprostate. . . . . . . . . .

Lungcancer . . . . . . . . . . . . . .

Cancerofuterus . . . . . . . . . . .

Leukemia.. . . . . . . . . . . . . . .

Gaul. . . . . . . . . . . . . . . . . . .

Goner. . . . . . . . . . . . . . . . . .

1.4

2.2

2.4

1.7

k:

2.0

1.8

3.4

2.6

3.5

2.0

1.4

1.7

2.3

2.5

1.8

2.2

2.4

2.7

2.7

2.1

1.5

I .e

1.8

2.6

2.5

2.1

1.4

1.6

1.7

1.3

1.4

1.4

2.0

2.4

2.9

2.0

2.2

1.7

2.6

1.8

2.1

2.9

1.9

2.e

2.7

2.8

1.6

1.6

1.3

1.8

2.6

2.0

1,7

2.2

2,1

2.0

2.3

::

1.9

2.1

1.9

1.9

2.5

2.7

2.9

1.3

1.7

1.6

1.7

1.2

1.9

1.2

2.3

3.8

2.7

3.6

3.3

3.7

3.2

2.7

2.9

1.4

2.5

2.e

3.7

2.3

1.2

1.5

2.0

1.8

2.3

2.1

2.0

E

2.5

3,2

2.4

2.1

2.2

2.0

2.7

3.8

2.5

2.6

1.9

3.6

3.3

2.8

3.5

3.2

2.6

2.5

3.0

R

1.9

1.6

1.6

1.2

2.2

3.2

3.5

2.0

3.2

1,3

2.5

2.0

1.8

2.1

2.7

1.7

3.1

2.7

3.2

3.2

3.4

3.2

3.4

2.6

3.9

3.6

3.1

3.6

3.7

3.9

3.8

2.8

2.1

2.4

1.4

2.2

2.6

2.6

2.3

2.3

2.9

3.0

3.0

2.8

2.e

3.3

3.0

2.6

3.7

3.6

3.6

1.7

2.8

1.8

2.7

E

2.3

3.1

2.6

2.1

1.8

2.4

1,6

2.0

1.7

1.2

1.7

1.2

1.0

1.8

1.1

1.4

2.3

2.6

1.3

2.4

1.1

1.2

1.3

1.0

1.2

&

2.8

2.0

2.e

2.5

2.5

2.4

2.8

1.6

3.3

3.3

2.6

2.9

3.5

3.8

3.6

2.9

2.4

2.3

1.8

2.2

2.8

2.6

2.3

2.4

2.5

2.6

2.7

2.6

2.5

2.9

2.8

2.4

3.3

3.2

3.1

1.6

2.6

1.6

2.7

U

2.1

2.1

2.1

1.6

2.3

2.0

1.4

2.0

1.9

1.4

2.2

1.2

1.1

1.9

1.1

1.6

2.3

2.6

1.4

3.0

1.3

2.0

1.8

1.3

1.7

1.3

2.6

2.9

2.6

2.8

2.8

2.8

2.8

2.9

2.e

2.6

1.7

2.4

1.8

2.1

2.1

2.3

1.4

1.1

2.9

2.0

2.2

2.4

2.e

2.8

2.8

2.6

2.5

2.7

2.5

2.4

1.8

2.8

2.7

2.9

2.8

2.9

2.4

2.7

2.1

2.9

2.7

2.4

2.6

2.8

2.8

2.0

2.8

1.5

2.0

2.8

2.7

2.6

2.9

2.4

2.4

2.7

2.6

2.7

2.8

2.8

2.7

2.3

2.1

2.0

1.9

2.5

2.1

2.6

2.6

2.4

2.1

1.9

1.9

?.8

1.9

1.8

1.9

1.4

2.0

2.3

2.3

1.8

1.6

1.5

2.1

1.6

1.6

2.3

1.6

1.6

1.8

1.6

1.5

1.4

1.6

1.7

1.6

1.6

1.5

1.5

‘1.8

1.6

1.6

1.4

2.2

1.7

1.6

1.4

1.9

1.9

1.8

1.6

1.6

2.5

1.6

2.0

2.6

1.9

2.2

2.0

1.5

2.5

2.7

2.0

2.3

2.0

2.0

2.1

2.3

1.6

2.2

2.0

2.0

2.3

2.0

2.4

1.7

1.6

2.9

2.0

2.0

2.7

2.3

2.2

2.2

2.3

1.0

1.8

1.1

2.0

2.2

2.0

1.4

1.2

2.2

1.5

1.7

1.9

1.7

2.0

2.3

2.1

2.5

1.8

2.0

2.0

3.1

2.9

1.8

2.4

2.9

2.9

1.7

2.2

1.5

2.2

2.1

2.5

2.0

2.1

2.0

1.3

2.0

1.3

1.4

1.5

2.2

2.2

2.6

2.1

2.0

2.1

2.2

1.9

2.1

2.0

1.9

2.2

2.0

1.6

1.6

1.7

1.4

1.6

1.7

1.7

7
The aggregate co-occurrence matrix was analyzed

using several nonparametric techniques to determine what

underlying structure could be derived from participants’

similarity judgments. The co-occurrence matrix was first

subjected to a complete-link hierarchical cluster analysis

using the CLUSTAN computer program package, version

2.1. (See appendix IV for a detailed explanation of this

computer package.) In this method all objects in the stimulus set begin as separate, single-member clusters. Then,

based on similarity values, the most similar items are joined

to each other in various stages. The final stage occurs when

all objects are contained within a single large cluster.

When the co-occurrence matrix for 68 chronic conditions was subjected to this analysis, the result was a solution

of 12 separate clusters, shown in table 4. Descriptively, the

final cluster solution had a distinct grouping of the chronic

conditions on the basis of body location, with some consideration of symptomatology as well. The most cohesive large

Table 4. Chronic conditions, by organization resulting from

hierarchical cluster analysis-12-cluster solution

Cluster 1 Cluster 7

Arlhritis or rheumatism Rheumatic fever

Lumbago Hardening arteries

Sciatica Coronary heart disease

Bursitis Hypertension

Migraine Stroke

Neuralgia or neuritis Brain hemorrhage

Angina pectoris

Cluster 2 Mvocardlal lnfarctlonSllpped disk

Bunions

Curvature of spine

Cleft palate

Clubfoot

~eart attack

Damaged heart valves

Tachycardla

Heart murmur

Aneurysm

Cluster 3 Cluster 8

Eczema or psoriasis Anemia

Acne Blood clots

Dermatiis Varicose veins

Herpes Phlebitis

Syphilis

Cluster 4 Cluster 9

Gallstones Cataracts

Cirrhosis Glaucoma

Hepatis

Jaundice Cluster 10

Nephritis

IOdney stones Cerebral palsy

Diabetes

Epilepsy

Cluster 5 Multiple sclarosis

Ulcer

Hernia Cluster 11

Gastritis

Enteritis

Diverticulitis

Colitis

Hemorrhoids

Skin cancer

Cancer of the stomach, etc.

Breast cancer

Cancer of prostate

Cancer of uterus

Lung cancer

Cluster 6 Leukemia

Bronchtils

Asthma Cluster 12

Hay fever

Sinusitis

Tonsillitis

Emphysema

Pleurisy

Tuberculosis

Gout

Goiter

cluster groups pertained to cancers, heart disease, and

respiratory ailments. Less cohesive clusters involved conditions pertaining to stomach-digestive ailments, liver-kidney

problems, and musculoskeletal conditions.

Symptomatology evidently contributed to the aggregate

sorting solution for Clusters 1, 3, and 10. Pain and nerverelated conditions were grouped in Cluster 1, which would

explain the presence of migraine in this cluster. Although

herpes and syphilis joined Cluster 3 rather late in the linkage process, they probably co-occurred in some subjects’

sortings because of their associated skin symptomatolob~.

Similarly, Cluster 10 includes conditions that cause tremors

and, in the case of diabetes and epilepsy, loss of

consciousness.

The two small (two-item) clusters in the solutirm varied

widely in their level of similarity. Cataracts and glaucoma

were among the first conditions to link, with a

co-occurrence score of 60 (that is, 60 of 70 participants

placed these conditions in the same category). On the other

hand, the pairing of gout and goiter represents a

co-occurrence score of only 14. Both gout and goiter had

insufficiently high overall similarities with other conditions

in previously formed clusters to be able to link them with

those preexisting groups. This cluster probably indicates a

confusion on the part of participants as to the proper body

system and symptoms to use in assigning gout and goiter.

Reliability of aggregate cluster solution

Although the cluster analysis of the a~regate cooccurrence matrix resulted in meaningful clusterings of the

68 chronic conditions, it is always possible that solutions

obtained from aggregated matrixes bear little resemblance

to the structure of the sorting produced at the individual

level. To test whether our a~egate cluster solution was

representative of the participants’ own groupings, a correlation was computed between the 12-cluster structure and

the individual co-occurrence matrix obtained from each

respondent’s free sort. The majority of participants produced more than 12 groupings of the stimulus items in their

free sorts, indicating that individuals made somewhat finer

differentiations than were reflected in the aggregate Clustering. Overall, however, correspondence between individual and a~egate matrixes was quite high.

The mean correlation (using Fisher’s z-transformation)

was .454, with individual correlations ranging from .09 to

.71. All the correlations were positive and significantly

higher than .00. Only seven participants produced sortings

that had correlations of less than .25 with the a~reg~le

cluster solution. An examination of these seven sortings

indicated that they were deviant from the typical sorting in

that they had either a large number of single-item groupings or a small number of categories in which almost all of

the conditions were lumped together. For those pmticipants who sorted the conditions more systematically, there

was generally a close correspondence between individual

sorting and the aggregated data structure. No indication of

meaningful individual differences in the nature of the

8
underlying categorization of chronic conditions was evident

from this correlation analysis.

Comparative analyses

For purposes of better understanding the nature of our

participants’ subjective categorization of chronic conditions, the results of the sorting analysis were compared with

the categorizations derived from other methods. First, the

natural clusterings were compared with formal categorizations based on physiological subsystems. Second, the results

of cluster analyses from the free-sort data were compared

with clusterings derived from similarities among the diseases in their placement on the dimensional sortings. (A

third comparison, based on multidimensional scaling, is

reported in appendix IV.)

Comparing natural clusterings with formal

classification systems

A nonparametric analysis known as the quadratic assignment paradigm (QAP) was employed to assess the

match between our participants’ clustering of the 68

chronic conditions and those generated from more formal

classification systems. (See appendix IV for a technical

description.) Two comparisons were conducted, one involving classifications based on ICD-9 codes and the second

involving the disease classifications represented in the

NEWSchecklists.

In order to conduct these analyses, two new similarity

matrixes were constructed to represent the clustering of

conditions within the ICD-9 and checklist systems. The

checklist matrix was constructed by assigning a similarity

score of 1 to those pairs of conditions that fell within the

same checklist subcategory in the NHIS interview schedule

and assigning a Oto pairings not in the same subcategory. A

similar procedure was followed to generate a similarity

matrix representing the ICD-9 categories. All conditions

that are grouped together at the two-digit level in the

ICD-9 coding scheme were assigned pair similarity scores

of 1, and conditions that do not share codes at this level

were assigned similarity scores of O. The NHIS and ICD

groupings used to generate these two formal similarity

matrixes are displayed in tables 1 and 5, respectively.

The QAP analysis is done by performing a cross multiplication of the data matrix (the 68 X 68 a~egate

co-occurrence matrix) and the comparison similarity

matrix. If the comparison matrix mirrors the data matrix,

then the sum of these cross-products should be high; the

large values of co-occurrence would remain in the summation, but the small values of co-occurrence would drop out

as a result of being multiplied by O. This summed crossproduct can be thought of as an unnormalized correlation

coefficient. To test against randomness, the expected value

and variance of this index was calculated (Hubert and

Levin, 1976) and a z-score derived. This score was then

compared with a normal distribution in the usual manner to

find the appropriate p-value for the obtained index.

Table 5. Chronic conditions, by organization in International

Classification of Diseases, 9th Revision (ICD-9) and National

Health Interview Survey (NHIS) checklist group

ICD-9 cdegoty NHIS group ICD-9 categoiy NHIS group

Category I

Arthritis or rheumatism

Gout

Lumbago

Sciatica

Slipped disk

Bursitis

Bunions

Cleft palate

Curvature of spine

Clubfoot

Neuralgia or Neuritis

Category II

Eczema or psoriasis

Acne

Category Ill

Nephritis

Kidney stones

Category IV

Gallstones

Cirrhosis

Hepatitis

Ulcer

Hernia

Gastritis

Enteritis

Divertlculttis

colitis

Category V

Bronchlfis

Asthma

Hay fever

Sinusitis

Tonslllitls

Emphysema

1

1

1

1

1

1

1

2

2

2

4

1

1

1

4

4

3

3

3

3

3

3

3

3

3

6

6

6

6

6

6

6

Category VI

Rheumatic fever

Hardening arteries

Coronary heart disease

Hypertension

Stroke

Brain hemorrhage

Angina pectoris

Myocardial lnfarcfionheart attack

Damaged heart valves

Tachycardia

Heart murmur

Aneurysm

Blood CIOtS

Varicose veins

Hemorrhoids

Phlebttls

Category Vll

Cataracts

Glaucoma

Cerebral palsy

Epilepsy

Multiple sclerosis

Migraine

Categoy Vlll

.Jaundlce

Goiter

Diabetes

Anemia

Category IX

Leukemia

Skin cancer

Cancer of stomach, etc.

Breast cancer

Cancer of prostate

Lung cancer

Cancer of uterus

Catego~ X

Tuberculosis

Herpes

Syphilis

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

5

2

2

2

4

4

4

3

4

4

4

5

1

3

4

4

6

4

5

4

4

Results of these analyses are shown in table 6.

Although both of the formal classification matrixes fit the

obtained co-occurrences significantly better than chance,

the ICD-9 codes show a better fit than the NHIS checklist

categories, probably because NHIS has only 6 categories to

simplitj administration, compared with 10 categories for

Table 6. Statistics from quadratic assignment paradigm analysis

comparing natural clustering with hrfemat;ona/ C/assificafion of

Diseases, 9th Revision (ICD-9) and National Health Interview

Survey (NHIS) groupings of chronic diseases

Statistk checklist ICD-9 NH/S checklist

Slmllarity index. . . . . . . . . . . . . . . 13,110.0 12,796.0

r. . . . . . . . . . . . . . . . . . . . . . . .690 .513

z . . . . . . . . . . . . . . . . . . . . . . 31.86 24.57

P . . . . . . . . . . . . . . . . . .00001 .00001

9
the ICD-9. The checklist organizational structure differs

from the ICD-9 system in the placement of the various

cancers. In the ICD-9 system, all cancers (neoplasms) are

coded together at the two-digit level, but in the checklist

grouping, the various cancers are separated according to

the body part affected. In our participant sorting, the cancers were grouped together in a tight cohesive cluster, thus

resembling the ICD-9 classification system more closely.

Clearly the physiological systems that form the basis of

the ICD-9 categories are also a significant component of

the participants’ natural categorization. Although

participant sortings did match the ICD-9 categories quite

closely, there were several exceptions with interesting implications. For example, diabetes was placed by our

participants with nervous system conditions, although it is

classified in the ICD-9 categories as a metabolic condition.

Migraine is a nervous sybtem condition, but participants

placed it with musculoskeletal-ccmnective tissue problems

such as arthritis. If participants had strictly adhered to the

etiology underlying the ICD-9 organization, herpes and

syphilis would have been sorted with tuberculosis because

they are all from the infectious-parasitic disease category.

The sorting cluster that violates the ICD-9 categorization to

the greatest degree is the cluster including gallstones, cirrhosis, and kidney stones (Cluster 4). Three different

ICD-9 categories are represented in this cluster—digestive,

endocrine-metabolic, and genitourinary-indicating the

extent to which participant categories crosscut ICD-9

classifications.

Where participant sortings deviate from ICD-9 codes,

the differences tend to reflect differences between the

external symptomatic characteristics of a condition and its

underlying systemic base, with participants focusing on

external symptomatic characteristics. Herpes and syphilis,

for instance, are seen by participants as most similar to

other skin conditions, and tuberculosis is grouped with lung

disorders and congestive conditions. Hemorrhoids are seen

by our research participants as more similar to diseases of

the intestine than to circulatory conditions. Diabetes is classified by participants among the nervous system conditions,

in comparison to its classification as an endocrinologic condition in the ICD-9 coding. Research participants also drew

more distinction between spinal and skeletal disorders than

are reflected in the ICD-9 classification, as well as somewhat different distinctions between heart and circulato~

conditions, In general, however, the aggregate clustering of

respondent sortings corresponds remarkably closely to

ICD-9 classification at the two-digit level.

Profile analyses

In analyses of the free-sort data, an index of similarity

between chronic conditions is derived from the frequency

with which they are sorted into the same catego~ (cooccurrence). Another method for calculating similarity

scores is to assess the degree to which pairs of conditions

have similar patterns of ratings on relevant dimensions.

Such “profile” simiku-ity measures have been used in earlier research on the natural classification of diseases, such

as work by D’Andrade et al. (1972). After mean ratings

have been obtained on selected rating dimensions, the

differences between conditions in their ratings across the dimension can be calculated, and this index can be subjected

to cluster analysis in a manner parallel to analysis of

free-sort co-occurrences. The results of the dimensional

sortings in our study were subjected to this type of profile

analysis for purposes of comparing the structure of the

free-sort clusterings with the structure of groupings derived

more indirectly through the rating scales, based on their

placements in the structured sorting task, The ratings obtained for each condition on each scale are reported in

table 3 (along with the frequency with which participants

were unfamiliar with each condition). These ratings reflect

our participants’ general knowledgeability regarding

chronic disorders, particularly with respect to painfulness,

severity, and relative frequency of occurrence. In general,

participants rated most chronic disorders as moderately

controllable, with the exception of various cancers, multiple

sclerosis, heart attacks, strokes, and birth defects. Chronic

diseases were also generally evaluated as not particularly

embarrassing (with the exception of epilepsy, syphilis,

herpes, and some deformities, such as cleft palate and

clubfoot). Among our research participants, ah bough syphilis was rated as the most embarrassing condition, epilepsy

was almost as high in embarrassment rating,

The mean ratings on the seven dimensions were

treated as a profile for each condition in the study. A

squared Euclidean distance measure was computed as an

index of similarity between the profiles of each pair of

conditions. The 68 X 68 matrix of similarity (distance)

scores was subjected to cluster analysis for purposes of

comparison with the clusters derived from the free-sort

co-occurrences.

In general, the profile similarity scores did not produce

as good a cluster solution as the free-sort measure. The

optimal profile clustering was found to be 4 groupings (as

opposed to 12 for the aggregate free-sort matrix), and the

correlation between the two solutions was not high

(appendix IV). The groupings generated by the four-cluster

profile solution are presented in table 7. Cluster 1 is characterized by relatively low ratings on the severity and

seriousness scales and high ratings on relative frequency.

Cluster 4, on the other hand, is characterized by high ratings on seriousness and Cluster 3 by relatively high

embarrassment ratings. Cluster 2 was distinguished primarily by moderate ratings on most of the scales. Although

these groupings have some intuitive appeal in terms of

single dimensions of distinction among conditions, they collapse across many other interesting differences and do not

produce an optimal categorization in terms of cat egoV size

or intercategory differentiation. Only the free-sort procedure produced meaningful cohesive clusterings that appear

to correspond to natural categories.

10
Table 7. Chronic conditions, by organization resulting from

profiie cluster anaiysis-Four-cluster solution

Cluster 1: Minor, freauent Pleurlsv

Arthritis

Bunions

Bursltls

Cataracts

Skin cancer

ulcer

Acne

Hernia

Dermalills

Gaslrills

Anemia

Mlgralne

Hypertension

Varicose veins

Hemorrhoids

Bronchllls

Hay fever

Slnusitls

Tonslllllls

Eczema or psarlasls

Cluster 2: Moderate

Gout

Lumbago

Sclatlca

Slipped disk

Glaucoma

Prostate cancer

Curvature of spine

Gallstones

Hepatitis

Jaundice

Enteritis

Dlverticulllls

colitis

Goner

Tubercilosls

Neuralgla or neurit[s

Nephritis

Kidney stones

Rheumatic fever

Tachycardla

Heart murmur

Phlebttis

Cluster 3 Embarrassing

Cleft palate

Clubfoot

Epilepsy

Herpes

Syphilis

Cluster 4: Major

Cerebral palsy

Clrrhosls

Cancer of stomach, etc.

Mullple sclerosis

Breast cancer

Emphysema

Leukemia

Cancer of uterus

Lung cancer

Arteriosclerosis

Coronary heart disease

Stroke

Brain hemorrhage

Angina pectorls

Myocardial lnfarcllonheart attack

Damaged hearl valves

Aneurysm

Blood clots

11
Discussion

The present study served two general purposes. The

main contribution of this study is methodological; it represents a unique application of social cognition research

methods to health problems. Specifically, classical social

cognition methods of free sort and multidimensional scaling were used to gain insights into the natural

categorization of chronic health conditions by the lay public. The results of the cluster analyses of this study suggest

that respondents with different personal medical histories

may be quite similar in their subjective categorization of

chronic illnesses. If this preliminary finding is confirmed by

subsequent research, such conditions could be grouped into

cohesive natural categories that have considerable generality across individuals in this culture. Even with the small

sample size used here, the lay categories show overlap with

the systemic-based classification of chronic conditions represented in ICD-9 codes, although the natural

categorizations crosscut ICD-9 coding categories in a

number of cases.

The fact that the laboratory methods revealed a coherent natural category system makes it possible to test

whether a categoxy-based approach would be useful in surveys designed to elicit information on the prevalence of

chronic conditions. The checklist recognition procedure

currently employed in NHIS requires respondents to

answer “yes” or “no” as to whether they or household

members have a series of conditions read from a checklist.

This method may not be the most efficient for eliciting such

information because it does not make use of subjective

categorizations to guide memory retrieval. The results of a

small unpublished pilot study demonstrated the feasibility

of an alternative interview procedure involving open-ended

responses to a series of hierarchically structured questions

for obtaining self and proxy reports on specific chronic

conditions. A larger scale field study would be needed to

determine whether the reports obtained from the hierarchical interview are more accurate, when cross-validated

against medical records, than are reports obtained from the

checklist procedure.

Another important methodological feature of this

study is the use of health maintenance organization patients

as respondents. The medical records of these patients are

readily accessible. Although the records themselves are

subject to inaccuracies, respondent-reported conditions can

be validated against their medical records to the extent that

conditions have been professionally diagnosed. In this way,

reasonably accurate estimates of underreporting or overreporting can be determined, and the effects of cognitive or

other independent variables can be measured.

Finally, the free-sort methodology may also apply to

other kinds of questions. These include questions on food

groups in dietary intake studies and kinds of physical activity in lifetime exercise studies. Further research will

determine if this methodology is applicable to these and

other areas.

12
References

Alkndcrfer, M. S., and R. K. Blashfield. 1984. Cluster Analysis.

Beverly Hills, California: Sage Publications.

Ikdwr, F. B., L. J. Hubert, and J. V. Schultz. 1977. Quadratic Assignment Program (Q4P) Technical Manual. Laborato~ of Experimented Design, University of Wisconsin.

lkwer, M. J3., V. Dull, and L. Lui. 1981. Perceptions of the

cklcrly: Stemoqpes as prototypes. J. Pem SW Psychol. 41:656-

67(}.

C.mtor, N., and W. Mischel. 1979. Categorization Processes in

the Pcrccption of People. In Advances in Experimental Social

I!SyChCJCJLr\ edited by L. Berkowitz. Vol. 10. New York Academic

Press.

D’Andradc, R., N. Quinn, S. Nerlove, and A. K Romney. 1972.

C:~tcgorics of Disease in American-English and Mexican-Spanish.

In A. K Romney, R. Shepard, and S. Nerlove (eds.) il4ultidimensional Scaling: lheories and Applicafi”on in the Behavioral Sciences.

Vol. 2. Ncw York Seminar Press.

Hulwrt, L., and J. Levin. 1976. Evaluating object set

p:lrt itions: Free-sort analysis and some generalizations. Journal of

1‘cthal Lcaming and Verbal Behavior 15:459-470.

National Center for Health Statistics, W. G. Madow. 1967. Interview dutu on chronic conditions compared with information derived from medical records. Vital and Health Statistics. Series 2,

No. 23. PHS Pub. No. 1000. Public Health Service. Washington:

U.S. Government Printing Office.

Nutional Center for Health Statistics, G. A. Gleeson. 1972.

lntcrvicwing methods in the Health Interview Survey. Vital and

lA’aldl Statistics. Series 2, No. 48. DHEW Pub. No. (HSM)

72-1048. Health Services and Mental Health Administration.

Washington: U.S. Government Printing Office.

Nationrd Center for Health Statistics, W. G. Madow. 1973. Net

differences in interview data on chronic conditions and information derived from medical records. P7tal and Health Statistics.

Series 2, No. 57. DHEW Pub. No. (HSM) 73-1331. Washington:

U.S. Government Printing Office.

National Center for Health Statistic& T. B. Jabine. 1987. Reporting chronic conditions in the National Health Interview Survey, a

review of tendencies from evaluation studies and methodological

test. Wal and Health Statktics. Series 2, No. 105. DHHS Pub. No.

(PHS) 87-1379. Public Health Service. Washington: U.S. GovernmentPrinting Office.

Rosch, E., and B. B. Lloyd. 1978. Cognition and Categotiation.

Hilkdale, New Jersey Erlbaum.

Smith, E. E., and D. L. Medin. 1981. Categories and Concepts.

Cambridge, Massachusetts: Harvard University Press.

Wishart, D. 1982. CL fJSTAN User Manual. Release 2.1. Interuniversity/Research Councils Series. Report No. 47. Edinburgh:

Edinburgh University.

World Health Organization. 1977. Manual of the International

Statistical Classification of Diseases, Injuries, and Causes of Death.

Based on the Recommendations of the Ninth Revision Conference, 1975. Geneva World Health Organization.

Young, F. W., and R. Lewyckyj. 1981. A Guide for Users of

ALSCAL-4: A Nonmetric Multidimensional Scaling and Unfolding

Program with Several Individual Differences Options. Chapel Hill,

N.C.: Psychometric Laboratory, University of North Carolina.

13
Appendixes

I.

IL

III.

w.

Recruitment procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Summary of recruitment outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Experimental instructions and instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Informed consent form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Medical record release form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Experimental instructions and script . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Postsession interview questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Definitions of chronic conditions included in study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Analysis techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Cluster analysis and the CLUSTAN computer package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Note on sortings of unfamiliar conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Comparison with multidimensional scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Quadratic assignment paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Profile analysis procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Appendix figure

I. Chronic condition proximities-nonmetric analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15

15

17

17

18

19

25

29

33

33

33

33

35

36

34

14
Appendix I

Recruitment procedures

Summary of recruitment outcomes

At the beginning of the study, MAXICARE, a health

care provider, referred the Institute for Social Science

Research (ISSR) to the Hawthorne Community Medical

Group (HCMG), their data management firm. Recruitrmmt for the sample was subsequently performed through

this firm.

Recruitment was initially designed to target 35 individuals who satisfied the following requirements:

● Were 45-64 years of age

● Had insurance coverage for multiple persons (including one other person in the 45–64 age range)

. Lived in the immediate Los Angeles area

● Had access to a telephone

● Had a history of one of the following types of conditions or had a partner with such a history

Neoplasms

Metabolic diseases

Blood and circulatory problems

Respiratory ailments

Digestive or gastrourinary disease

Musculoskeletal problems

Subjects were recruited from multiple-person households to participate in pairs in order that each member of

the pair could act as a self-respondent and proxy responclcnt. The 35 recruited subjects with their partners would

result in 70 participants.

On January 27, 1986, HCMG mailed 500 letters to

health care subscribers in the Greater Los Angeles area.

Their names and addresses had been selected randomly

from the HCMG medical data files of individuals who met

the outlined criteria. By returning a card enclosed with the

letter, subscribers could volunteer for a University of California, Los Angeles (UCLA) study called HealthInfo.

These cards were addressed directly to ISSR.

In the last week of March, it became apparent that the

goal of 35 pairs of subjects would not be met by the initial

mailing of 500 letters. As of April 1, 63 cards had been

returned (a 13-percent return rate). Of these 63, 42 had

already been successfully contacted. Of these 42, 14 pairs

agreed to participate, and 28 had refused or had not met

the criteria. In summary, 33 percent of those contacted

prior to April 1 had agreed to participate.

Based on the rate of return and participation from the

first 500 cards, an additional 250 cards were deemed sufficient to complete the study. The second mailing was

completed March 27, 1986.

A total of 750 letters were mailed for recruitment purposes. All subsequent figures reported are based on a

mailing of 750 and a return of 101 cards (a 14-percent

return rate).

After the cards had been received at ISSR, the volunteers were called and screened, using a standardized form.

It required approximately 257 telephone calls to recruit the

number of participants necessary for the study. This is an

average of 2.5 calls per participant, or an average of 6.4

calls for every scheduled session (40 sessions in all). These

calls took place predominantly on weekdays from 7:00 p.m.

to 9:30 p.m. (Los Angeles is a city of commuters; calls

made earlier than 7:00 p.m. were usually picked up by an

answering machine.)

A total of 101 cards were returned. Screening resulted

in the following breakdown:

●

●

●

●

28 single females

12 single males

58 couples

3 persons of unknown status (no telephone number

given or telephone out of service)

Of the 58 couples:

● In 10 cases, one partner refused to participate

● Five lived too far away (perceived or actual)

● Two were too busy to participate

● Two were in the medical profession

. One spouse was out of town for the study

. In one case, the spouse did not speak English

● In six cases, miscellaneous problems or excuses occurred

Thus, 27 couples were unable to participate. This left 31

eligible participants.

To obtain these 31 eligible pairs, the screening criteria

were, of necessity, relaxed such that one partner could be

out of the age range by plus or minus 5 years.

A pervasive problem throughout the screening process

was the lack of cooperation from the spouse who did not

volunteer for the study. The project director phoned

12 couples (10 “partner refused” and 2 “too busy”) to

15
attempt refusal conversions; in all cases the spouse

remained unpersuaded. It seemed that the major obstacle

that prevented these couples from participating was the

time required to travel to UCLA, find parking, and participate in a 2-hour session. (Unfortunately, UCLA has a

reputation for its lack of parking and confusing campus

structure.)

To overcome this problem and to complete the study in

a timely manner, the study was expanded to include several

individuals who would participate without partners. The

final breakdown in participants in the experiment was as

follows:

31 couples = 62 participants

8 singles = 8 participants

Total = 70 participants

During the course of the study, most sessions were

conducted after 5:00 p.m. and on weekends to accommodate participants’ work schedules. Several steps were taken

to reduce the number of cancellations and persons who did

not arrive for scheduled appointments (no shows). Despite

these efforts, there were 5 no shows and 11 last-minute

cancellations. One couple was scheduled three times before

actually participating. Participants were generally scheduled 1-4 weeks in advance. Those scheduled more than

1 week in advance were sent campus maps with detailed

parking instructions. Those scheduled 3-4 weeks in

advance were usually telephoned to confirm their appointments. Those couples who canceled or missed their

appointments were called immediately and asked to

reschedule. Of 5 no shows, 3 couples subsequently reschecluled, and of 11 la$t-minute cancellations, 6 were

rescheduled.

Even with written and oral instructions, participants

reported that they found negotiating an unfamiliar college

campus a somewhat frustrating experience (as any college

freshman can attest). For this reason, two sessions were

conducted in the households of the volunteers. One couple

was unable to negotiate the UCLA campus because of the

husband’s recent knee surgery, and another volunteer was a

single male who could not travel to UCLA because of some

recent surgeq that had arisen from complications with his

diabetes. Conducting the interview in the home of the single person went smoothly. However, finding adequate space

and privacy to interview each member of the pair independently was more difficult in a small house than it would

have been in the laboratory.

16
Appendix II

Experimental instructions

and instruments

Informed consent form

HEALTHINFO STUDY

Date: August 20.,1985

INFOtiD CONSENT FORM

By signing this form, I agree to participate in the research

called ‘rHealthInfo”under the following conditions:

I Understand that during this 1 1/2 to two-hour session, I will be

asked to provide my ideas about the nature and causes of health

problems. I also understand that I will.be paid $20.00 for

my participation.

I understand that my participation is purely voluntary and

that I am free not to answer any particular ”questions.

~ understand ”thatinformation gathered-from any of the tasks

performed during this experimental session wIII be held fn

strict confidence .by,~he researchers at.ISSR and the National

Center for Health Statistics.

I further understand that the information gathered in this

session will be used only for the purposes of evaluating

techniques of health surveys and will not involve risk or

discomfort.

1 understand that I may refuse to participate or may withdraw

from this study at any time without any negative consequences.

Also, the investigator may stop the study at any time. I also

understand that no information which identifies me will be

released without my separate consent, and that all identifiable

information will be protected to the limits allowed by law. If

the study design or the use of the data is to be changed, I will

be so informed and my consent reobtained. I understand that

if I have any questions, comments, or concerns about the study

or the informed consent process, I may write or call the office

of the Vice Chancellor-ResearchPrograms, 3134 M~phy Hall,

UCLA, Los Angeles, CA goo24, (213) 825-8714. I acknowledge that

I have received a copy of this form.

Signed: Date:

We are sure that you will find the session interesting. Your

participation will be a contribution to a valuable research project.

You may request further information about this study by contacting

Dr. Marilynn B. Brewer or Dr. Valerie T.-Dull at (213) 825-0711,

Institute for Social.Science Research, Bunche Hall, UCLA, Los Angeles,

CA 90024.

17
Medical record release form

AUTHORIZATION TO RECEIVE OR RELEASE MEDICAL INFORMATION

The signing of this form allows my health maintenance organization

to release medical information, under terms authorized by Section

306 of the Public Health Service Act (42 U.S.C. 242k).

“I authorize my health maintenance organization to release to

representatives of the Institute for Social Science Research at

UCLA records pertaining to my medical history and any medical

treatments I have<received, for the purpose of research.

This authorization becomes effective immediately and expires

on July 31, 1986..

I understand that the Institute for Social Science Research at

UCLA will not release any of my medical information to.any other

person or group, and that the same restrictions for receipt,

or -releaseof medical information apply.to the Institute for

Social Science Research as to my health maintenance organization

and that no further authorization is Indicated in this form.

I understand that no permanent records will be kept in a

that would permit inditiidualidentificationand that all

information will be used solely for’statisticalresearch

report purposes.

NAME:

SIGNATURE:

DATE:

WITNESS:

DATE:

form

and

18
Experimental instructions and

script

HEALTHINT’OSTUDY SCRIPT

[EXPERIMENTERSWELCOME BOTH SUBJECTS TO THE SESSION AND TAKE THEM

TO SEPARATE ROOMS OF THE LAB WHERE THEY BEGIN THE SESSION.]

Thank you for comingto UCLA today. To startthis session,I’d

like you to read this form and if you

please do not hesitateto ask me.

[ALLOW AMPLE TIME FOR SUBJECT TO READ

have any questions about it,

THE INFORMED CONSENT FORM.]

Now, do you have any question’s?At this point,I’d like you to

sign this form If you understand the nature of your participation

during this session.

[LET SUBJECT SIGN FORM AND GIVE THEM A COPY.1

To start off, I want to tell you what this study is about. Today

we will be concernedwith your ideas about illness. We w1ll be

askingyou to perform a variety of tasks during this time in order

to find out about your thoughts regarding disease and illness. We

want to stress that during this whole time, there will be no

right or wrong answers. This 1s not at all a test of any kind.

If ,thereis a point at which you do not understand the instructions,

please feel free to ask.

To begin, here are some index cards. Each one has a name of’a

diseaseor illnesstyped on it. Oftenmedicalterms are not

famillar. Since wetll .beusing these for the rest of the

session, I~d like you to first read each term. Then, tell me if

Outve heard of the term and definitel~ know what It is, you have

heard of the term and are pretty sure Vou know what ft G, or me

not heard of the term or you are not sure Vou ft is.

[E GOES THROUGHEACH CARD WITH SU13JECTANDRECO~S FAMILIARITY

RATINGS FOR EACH ITEM. (SEE RECORDING SHEET) IF SUBJECT INDICATES

THEY HAVE NOT HEARD OF TERM, E SHOULD READ FROM DEFINITIONLIST.]

19
These are a set with the same conditions written on them but

they have been shuffled. I’d like you to sort.these cards into

piles based on how you think they shouldgo together. Each pile

will represent those illnesses that you think are similar “insome

way. You can use as many different piles as you think are

necessary. In other words, I?d like you to freely sort these cards

into groups based on how you think the illnesses should be grouped

together. Again, keep in mind that we are interested in what

.

u think these illnesses are like. There are no~$orrect answers.

Do you have any questions about this task? Please begin your

sorting, (IFNECESSARY READ: We would like.you to put these

illnesses into-groups based on something that they have in

common, In other words,

they are similar in some

you will put them in the same pile if

way.)

WHEN SUBJECT IS FINISHED: Do you want to go through each stack

to see that everything belongs there?

[WHEN THE soRT TASK IS FINISHED, RUBBER BAND EACH sTAcK TOGETHER

AND PLACE ON THE SIDE FOR RECORDING AT END OF SESSION.]

Now, we are going to.ask you to”go through these same cardsagain.

This time we want you to tell us some specific things about

each illness. First, we are interested~nhow common or rare

you t~k each of these illnesses are. You may feel that some

these diseases are ones that are extremely common and others

extremely rare. If you feel that the illness is extremely

common, put the card here. If yau feel it is extremely rare

it here. You may feel that others of these

what common or somewhat rare, in which case

cards here or here. If you feel you do not

of

are

place

~llneses are only someyou would place the

know enough about a

20
particular illness to make a guess aboutu&?therit is common or rare,

use this donrt know slot. But remember, there are no right or

wrong answers and we would”prefer that you indicate what you

“thinkeven if you are not sure.

[THROUGHOUT THIS PORTION OF THE SCRIpT, THE E SHOULD POINT TO

THE APPARAT’O$ANDSHOW THE PHYSICAL LOCATIONS OF THE SCALE AS

THEY TALK. DEMONSTRATE THIS PROCEDURE BY WALKING THROUGH IT

STEP-BY-STEP.]

Now, we~d like you to think about how physically painful these

illnesses might be. Some of these condi60ns are physically

very painful while others are not at all painful. Using these

slots again, please place-% illness here if you think it is

very painful or here if it is not at all painful. If you think

the illness is somewhat painful put it here or if you think

that it is slightly painful put it here. Here iS the don’t

&JQJWslot but,please try to make a guess about what you think, even

if you are not sure.

some of.these illnesses might cause life-threateningsituations

for certain people. This time, please think about how lifethreatening these diseases are for people that have them.. If you

think they are major illnesses put them here. If they are minor

illnesses place them here. Then, if they are somewhat majoi

put them here or somewhat minor place them here. (IF NECESSARY:

Here is the don~t

wrong answers and

are not sure.)

know slot but remember there are no right or

we are lmterested in what you think, even if You

21
These illnesses could also cause problems in the way people lead

their lives. This time, please think about how disabling each

of these Illnessesare. Then, if they are very disabling put them

in this slot. If they , are not~~ disabling put them in this

slot. If’the illnesses could be disabling put them here. If

they are not likely to be disabling put them here. (IF NECESSARY:

Here is the don~t know slot but remember there are no right or wrong. ..

Please think about how embarrassingsome of these illnesses might

be. Some could definitely be seen as embarrassing,others could

be seen as not at all embarrassing. Still others may be somewhat

‘embarrassing.Please take these cards and once more put them

in the slot where you think they should go. (IF NECESSARY:

Here is the don~t know slot but remember there are no right or ...)

Certain of these.illnesses could definitely Qe controllable or

some of these could def{&tely not be ‘controllable. So, if someone - —— .

has the ‘illness,it My or may not be possible to control ~ {i-6

0Ciui+~j2fffJr<+

P

Other illnessesmight only be partly controllable.

——

Once again, please take these cards and put them in the slot

where you believe they should go. (IF NECESSARY: Here iS the...)

22
Lastly, I would like for you to think about your own chances of

getting each of these illnesses. If you think it is very unlikely

that you will get this illness or there is no c-hance at all,

put the card Zn this slot. If you think you get this illness

put it in here.. If’you thin~ It is vpry likely that you will’get

this illness put the card here. If the illness 3s something you

already have at this time, put the card here. Remember, this

slot is for illneses you k’nowyou have.

Thank you. I see the conditions in this ,pfle are condibns you have

indicatedyou have. We would like to be able to ask you some.further

questions about these particular illnesses later i,fthatts ok

with YOU. We have

last task involves

now completed all the sorting tasks. The

some questions we would like to ask you.

These questions have to do with your feelings regarding illness

.

and the impact tt has had on your life and your partner’s life

a% well. This information is of course -. completely confidential.

[ADMINISTERPosTsEssIoN INTERVIEW QUESTIONNAIRE. E mcoms ALL

RESPONSES VERBATIM (OR AS CLOSE AS POSSIBLE)]

23
We would like to thank you very much for your participation. As

a final requests may we ask you to read this authorization to

release medical information? As part of this study, we need to

have,access to the formal code numbers for any illnesses for

which you have been treated. It.will help us to determine how accurate

our findi.ngsabout illness are.

(IF NECESSARY READ: We are interested in only the official code

numbers that the doctor wr$tes., Ve are not interested in any

background information or history. We need to know only the

code numbers for the precision of this research project. It will

be used for research purposes only.)

[PAY AND THANK SUBJECTS. ESCORT OUTSIDE. RECORD DATA IMMEDIATELY.]

24
Possession interview

questionnaire

!.D. Number

POSTSESSION INTERVIEW QUESTIONNAIRE

NOTICk:

OMB NO.

Exp. Date

Information contained on this form which would permit identification

of any individual or establishnienthas been collected with a guarantee

thatit will,beheld in strict confidence, will be used only for

purposed stated for this study, and will not be disclosed or released

to others without the consent-of the individual or the establishment

in accordance with section 308(d) of the Public Health Service Act

(42 USC 242k).

Background information:

1.

2.

3;

4*

5.

6.

7.

Sex (Record without asking]

Mhat is your date of birth? ———//

.. . .

Iihatis your”relationship to (Name of partner*~?

What is the highest grade in schoolthat you completed?” “

How many individuals are living in your household? This number includes

you, your spouse/partner,any childrenor other financiallydependent

relatives.

Number

What is the gross annual income, before taxes, for your household? This

would include wages and salariesbefore deductions,as well as fncome from

sourcessuch as SocialSecurity,unemploymentinsurance,pensions,welfares

alimony or child support.

$

Doyou consider

Excellent

~ Good

Fair

— Poor

your general health to be:

“Partner” refers to the paired household member

25
Target Condition of the Self: (If no apriori target condition, skip

this section.)

N9te: Subject must have disclosed this target condition during

the session. If not disclosed, then replace with a condition

that has been disclosed.

1, Do you have an idea or theory”about how you developed (target

condition?) Where it came from? Why? 1

[open ended]

If you had to say a specific thing that is responsible for your

illness, which ONE would you choose?

2.

_Yourself (own actions}

_Someone else (specif?icperson,

Environment (pollution, stress

‘Heredity

‘Chance (God, fate, etC.) —

actions)

, etc.)

3. Do you think you can control the ‘episodes, attacks or spells that

you experience with your illness?

Not at all (Skip Sa)

‘Slightly

‘Somewhat

—‘Very much

3a. If so, how do you control it?

[open ended]

4.

5.

When your illness flares up, what do you have to do?

[open ended]

Rate how much effect youfve experienced in these different

aspects of your life because of (target condition).

No Effect Slight

Effect

Some

Effect

Alot of

Effect

N/A or

DK

Athletic Activity/

Exercise 1

a.

2 3 4 8

b. Reliance on

Medication 1 4

4

4

4

4

2 3

3

3

3

.3

8

8

8

8

8

c. Amount of Sleep/Rest i

d.

e.

2

Personality 1 2

Personal appearance 1 2

f. Work/Career Activities 1 2

26
~. Family Life

h. Social Life

No Effect Sltght Some Alot of N/A 0$

Effect Effect Effect” DK

1 2 3 4 8

1 2 3 4 8

Proxy Interview Section: (Subjectnow answers for partner)

6. Of the conditions we have been discussing, are there any that

(partner)has?

[open endedj

NOTE: The following questions should be asked for partnerts target

condition (if any AND if disclosed in the question above.)

7. Do you have an idea or theory about how (partner)developed

(target condition)? Where it came from? Why?

[open ended]

8. If you had to say a specific thing that is responsible for

(partner’s)illness, which ONE would you choose?

Partner (their own actions)

‘Someone else (specificperson, actions)

‘Environment (pollution,stress, etc.)

~Heredity

_Chance (God, fate, etc.)

9. Do you think (partner)can control the episodes, attacks or spells

that they experience with their illness?

Not at all (Skip 9a)

‘Slightly ❑Somewhat

— Very much

ga. If S05 how do they control it?

[open ended]

10. When their illness flares up, what do they have to do?

[open ended]

27
11. Rate how much effect (partner)has experienced in these

different aspects of their life because of (partner’s

target condition).

No )Zffect Slight Some Alot of N/A or

Effect Effect Effect DK

a. Athletic Activity/

Exercise 1 2 3 4 8

b. Reliance”on

Medication 1 2 3 4 8

Amount of Sleep/

Rest

c.

4

4

4

8

8

8

1

1

1

2

2

2

3

d. 3

e.

f.

Personality

Personal.appearance 3

Work/Career

Activities 1 2

2

2

3

3

3

4

4

4

8

8

8

Family life 1 g.

h. Social life 1

28
Appendix Ill

Definitions of chronic

conditions included in

study

01: Arthritis of any kind or rheumatism

A general term for inflammation of a joint which

causes pain, swelling, stiffness and often changes the shape

of the joint, (714, 715) (729.0)

02: Gout

A disease caused by build up of a waste deposited in

and around joints. Symptoms are pain, inflammation, redness and swelling around the joints, especially the big toe.

(712, 274)

03: Lumbago

A dull ache in the lower back—may be caused by

ovmvorking muscles in the area or by problems with one of

the disks between the vertebrae. (724.2)

04: Sciatica

Severe pain in the back of the thigh along the length of

the large sciatic nerve running down the leg. It is caused by

inflammation or damage to the nerve. Symptoms are a

sharp pain running down the back of the thigh, numbness,

and tingling. (724.3)

05: A slipped or ruptured disk

Damage to the cartilage disks between the vertebrae of

the backbone due to too much pressure put on it. If severe

pressure is put on a disk suddenly, the disk may break open

or be moved from its correct position. It may cause severe

pain because of pressure on nerves running between vertebrae. (722)

06: Bursitis

An inflammation of the fluid sac found around joints

and over bones near the skin. It results in pain and swelling

of the area, Locations often affected are shoulder, elbow,

kmx, and big toe. (726, 727.2, 727.3)

07: Skin cancer

An abnormal growth on the skin. Often caused by

overexposure to harmful sun rays. (172.8, 172.9, 173)

OS:Eczema or psoriasis

Skin disorder in which there may be inflammation,

itching, swelling, blistering, scaling, a rash, or discharge. The

cause is usually unknown but in some cases it may be

caused by an allergy or fungal infection. Psoriasis is a skin

irritation with redness and scaling of the outer surfaces of

arms and legs which lasts a long time, (696)

09: Acne

A disease of the skin which results in small red bumps

caused by an inflammation in the gland at the bottom of a

hair in the skin. (706)

10: Dermatitis

A general term for inflammation of the skin with

itching, redness, and perhaps bumps. (681.8, 692)

11: Bunions

Inflammation of the joint of the big toe causing pain

and swelling. Sometimes caused by wearing shoes too tight

for many years. (727.1)

12: Cataracts

A clouding of the crystalline lens of the eye so that light

rays can’t pass through, causing poor vision and perhaps

blindness. Cataracts may occur in older people as part of

aging or in younger people because of disease. (366)

13: Glaucoma

Disease of the eye in which there is an increase in the

pressure inside the eye causing the eye to become hard and

perhaps causing blindness. A person with glaucoma may

have pain in the eyes, headaches, and fuzzy vision. (365)

14: A cleft palate or harelip

A tuck or split of the upper lip or roof of mouth caused

by a birth defect. (749)

15: Palsy or cerebral palsy

Abnormal development of the nervous system because

of damage to the brain, sometimes at birth. The symptoms

include poor muscle control and coordination. (343)

16: Curvature of the spine

A bending of the spine forward, backward, or to either

side. (737)

17: A clubfoot

A condition caused by contraction of one or more

muscles of the foot causing an abnormal foot

position. (754.7)

18: Gallstones

A small stone formed in the gallbladder from the

concentrated bile stored there. They can plug the duct

going to the small intestine, causing pain and digestion

problems. (574)

29
19: Cirrhosis of the liver

Chronic disease of the liver in which the cells are

destroyed, scarring occurs and the organ becomes hard and

doesn’t work well. (571.2, 571.5, 571.6)

20 Hepatitis

Inflammation of the liver caused by a viral infection or

poisoning. A person with hepatitis will usually have yellow

colored skin, an enlarged sore liver, a fever, be very weak,

nauseous, and lose weight. (571.4) (573.3)

21: Jaundice

Yellowness of the skin, eyes, and body fluids caused by

too much of a yellow substance in the blood and tissues.

This substance is made from worn-out red blood cells.

(277.4)

22: Ulcer

An open sore on the skin or on an internal organ.

Ulcers are areas of dead tissues which heal slowly and

sometimes bleed and make pus. The most common are

those in the digestive system caused by digestive enzymes

and acids attacking and digesting the lining of the esophagus, stomach, or small intestine. (531, 532, 533)

23: Hernia

The pushing of an organ or part of an organ through

the wall of the area it belongs in; a rupture. Hernias are

sometimes caused by straining, as when lifting something

heavy. (550) (552, 553)

24: Gastritis

Inflammation of the stomach. Symptoms are pain or

tenderness, nausea, vomiting, or a coated tongue. (535)

25: Enteritis

Inflammation of the intestines. (555)

26: Diverticulitis

Inflammation of the sacs and pouches

intestine. (562)

27: Colitis

of the large

Inflammation of colon (large intestine) usually causing

pain and diarrhea. (556, 558)

28: Cancer of the stomach, intestines, colon, or rectum

An abnormal, malignant cell growth found in the stomach, intestines, colon, or rectum. Cancer cells don’t work

correctly, use up the body’s energy, and replace the normal

cells in these areas so that they don’t work properly. (151,

152, 153, 154)

29: Goiter

An enlargement of thyroid which causes a swelling at

the front of the throat. Some persons with goiter may also

be nervous, tremble, sweat a lot, be skinny, and have a high

heart rate. (240, 241, 242) (246)

30: Diabetes

A disorder of carbohydrate metabolism, due to an

absence or lack of insulin produced from the pancreas. It

30

may be more common in certain families or be developed

later in life. A person with diabetes has extra sugar in the

blood and urine, needs to urinate often, is thirsty, loses

weight, and is nervous and weak. (250)

33: Anemia

A shortage of red blood cells in the body or not enough

hemoglobin inside the red blood cells carrying oxygen to

tissues. Symptoms are paleness, weakness, and shortness of

breath. (280, 282,283, 284)

34 Epilepsy

A disease in which the brain doesn’t work correctly, at

times resulting in a partial or complete loss of consciousness and in some cases convulsions. (345)

35: Multiple sclerosis

A disease of the nervous system which slowly

progresses. It affects a person by causing scars to form on

the nerves of the brain and spinal cord. Usually affects

young adults with symptoms of double vision, slow, difficult

speech, weakness, and trembling. (340)

36: Migraine

A very severe throbbing headache which affects a person from time to time. The headache will usually be on the

side of the head and the person may also have vision problems, be unable to stand light, have an upset stomach, and

be sweating. (346)

3% Neuralgia or neuritis

Sudden attacks of pain along the course of one or more

nerves and inflammation of a nerve. (729,2)

38: Nephritis

Inflammation of the kidneys. Symptoms may be fever,

dull pain in the back, water retention, rapid pulse, vomiting,

and discoloration of urine. (582)

39: Kidney stones

A hard stone-like object made from minerals in the

urine that forms in the kidneys and may stop the flow of

urine if it gets into a ureter and plugs it. (592.0)

40: Breast cancer

An abnormal, malignant growth found in the breast,

Cancer cells don’t work correctly, use up the body’s energy

and replace the normal cells in these areas so that they

don’t work properly. (174)

41: Cancer of the prostate

An abnormal, malignant cell growth found in the prostate gland. This gland is found in men and is responsible for

making a milky fluid that is put into semen. Cancer cells

don’t work correctly, use up the body’s energy and replace

the normal cells in these areas so that they don’t work

properly. (185)

42: Rheumatic fever

Rheumatic fever can result from a strep throat infection. The person will suddenly feel joint pain and fever and
inflammation of the heart may result. At this point the

valves of the heart are commonly damaged. (390, 392, 393,

398)

41: Hardening of the arteries or arteriosclerosis

Thickening and hardening of the walls of blood vessels,

especially the arteries, which make blood flow more difficult, (440)

42: Corona~ heart disease

Disease of the artery that goes around or encircles the

heart. (411, 414.0)

43: Hypertension, sometimes called high blood pressure

Higher than normal force of the blood pushing against

the walls of the arteriex high pressure symptoms may be

dizziness, headaches, bleeding from small blood vessels,

vision problems, and kidney inflammation. (401, 402, 403,

404, 405)

44 A stroke

A sudden, severe attack, especially one caused by the

bursting of a blood vessel or a clot in the brain. Symptoms of

a brain stroke are sudden unconsciousness, loud and difficult breathing, paralysis on one side of the body, a cold

sweat on the body, a below-normal temperature, and speech

problems. (436)

45: A hemorrhage of the brain

Massive bleeding in brain tissue. (431, 432)

46: Angina pectoris

A severe pain in the chest, usually around the heart,

which may spread to the arms, neck, or jaw. The person will

have a feeling of pressure around the heart, will not move,

may be sweating and very afraid. It’s caused by the heart

not getting enough blood. (413)

47: A myocardial infarction, heart attack

The dying of a part of the heart muscle because the

blood flow through an artery bringing fresh blood to the

heart is decreased or stopped. The person will have a severe

squeezing pain in the chest, irregular heartbeats, may go

into shock and the heart may stop beating. (410, 412, 414.8)

4& Damaged heart valves

Injury of valves in the heart between the different

chambers on each side that keep the blood flowing in the

correct direction as the heart muscle contracts. (394, 395,

396, 397) (424)

49: Tachycardia or rapid heart

Abnormal rapidity of heartbeat or pulse rate above 100

pcr minute. (427.0, 427.1, 427.2) (785.0)

50: A heart murmur

An abnormal, soft blowing sound which maybe heard

when listening to the heart or the large blood vessels around

it, Murmurs are heard as the blood moves through a valve

in the heart not working properly, an abnormal narrowing

or enlargement of one of the vessels near the heart, or

openings in the heart where they should not be. (785.2)

51: An aneurysm

h enlargement in one spot of a blood vessel, usually

the large artery leading from the heart, caused by a weakness in the vessel’s wzdl;it may cause a rupture. (414.1, 441,

442) (437.3)

52: Blood clots

Jelly-like mass of blood which doesn’t flow normally

through blood vessels. (453)

53: Varicose veins

Swollen, twisted, and knotted veins. Most common in

legs. Veins have little valves along their length that allow

blood to move along them in only one direction, preventing

it from backing up. If a vein enlarges, the valves won’t close

together and blood can then flow back and pool in the vein.

(454) (456)

54: Hemorrhoids or piles

A group of enlarged veins around the anus and rectum.

These veins may bleed, be painful or itchy. (455)

55: Phlebitis or thrornbophlebitis

Inflammation of a vein. Symptoms are pain along the

vein, swelling below the inflammation, and a change in the

color of the skin. (451)

56: Bronchitis

A inflammation of the lining of the tubes which take

air into the lungs. Symptoms are pain in the chest, a cough,

and fever. (491)

57: Asthma

A condition in which the person coughs a great deal

and has trouble breathing because the tubes taking air from

the main branch of the lungs tighten and swell. (493)

58: Hay fever

A allergy to pollen or dust, causing the nose, eyes, and

upper airways to become irritated. A person with this condition will sneeze, have a runny nose, and sore, red, watery

eyes during certain times of the year, especially spring and

fall. (477)

59: Sinusitis

Inflammation of a sinus membrane in the nose. May

result in difficulty in breathing through nose, pain, or fever.

(473)

60: Tonsillitis

Inflammation of the tonsils.

throat, fever, headache, chills, and

the neck. (474)

61: Lung cancer

Symptoms are a sore

swollen lymph nodes in

An ~bnormal, malignant cell growth found in the lungs.

Cancer cells don’t work correctly, use up the body’s energy

31
and replace the normal cells in these areas so that they

don’t work properly. (162)

62: Emphysema

A chronic lung disease in which the air spaces in the

lungs become enlarged and the tissue between them

becomes stiff. The person will have difficulty breathing and

a cough. The lungs will become larger than normal, causing

a barrel-shaped chest. (492)

63: Pleurisy

Inflammation of the thin membrane covering the outside of the lungs, the inside of the chest, and the top of the

diaphragm. There are several different kinds of pleurisy but

symptoms are usually temperature, painful breathing, and

maybe a collection of pus, blood, or clear fluid in the chest.

(511)

64: Tuberculosis

Infectious disease caused by bacteria which usually

affects the lungs but can affect other areas of the body.

Symptoms vary, depending on type. Small bumps usually

form in the infected area and there is fever, sweating, tissue

death, scarring, weight loss, and weakness. If it is affecting

the lungs, the person will usually have a cough, chest pain,

and blood in their mucus and spit. (011, 012)

65: Leukemia

Disease which affects blood-making organs, causing

too many white cells to be made. Symptoms may include

poor appetite, weakness, enlarged spleen and liver, swollen

lymph nodes, anemia, pain in bones, bleeding, and an

inability to fight off infection. (204, 205, 206, 207, 208)

66: Cancer of the uterus

An abnormal, malignant cell growth found in the

uterus. Cancer cells don’t work correctly, use up the body’s

energy and replace the normal cells in these areas so that

they don’t work properly. (179, 180, 182)

67: Herpes

An infectious skin disease caused by one of the herpes

viruses. The virus causes eruptions of the skin or mucous

membranes, painful blisters usually occur on the head or

face or genitals. If it returns, it’s usually in the same spot as

before. (054)

68: Syphilis

A severe, contagious venereal disease caused by bacteria, spread mostly by sexual contact or through infected

blood. If the disease is not treated in the early stages, it may

cause sores on the skin, bones, and liver and may cause

heart damage, paralysis, or insanity. (091.0)

32
Appendix IV

Analysis techniques

Cluster analysis and the CLUSTAN

computer package

Cluster analysis is a method of classifying objects into

groupings based on some similarity measure. In this study,

the similarity measures were the co-occurrence scores from

the aggregate free-sort matrix. The hierarchy procedure

from the CLUSTAN computer package was used to analyze these data. In a hierarchical clustering procedure, all

the objects begin as single entities unlinked to any others.

Then, based on similarity scores, the most similar objects

are linked together in a step-by-step process until all

objects end up in one large cluster. For every step in the

analysis, one linkage occurs. Once an object has joined a

particular cluster, it remains there for the rest of the analysis, This sequence of linkages, or fusions, can be

represented in a tree diagram that graphically displays

which objects have joined each other for every step.

The procedure began with 68 objects—each chronic

condition in a cluster by itself. In each of the subsequent

fusions, similarity was judged by the frequency of

co-occurrence in the free-sort task, with high values indicating greater similarity between chronic conditions. For this

study, a complete linkage option was selected as the

method for evaluating similarity among the conditions.

Basically, every object in a cluster, using this analysis, is

completely linked to every other member of the cluster.

(See Aldenderfer and Blashfield (1984) for a detailed

explanation of the fusion criterion.)

The output is a sequence of linkages with their associated fusion coefficients (the numerical co-occurrence

values at which various clusters merge). Based on these

output results, one is able to see a history of the cluster

linkages from 68 single-member clusters to 1 cluster with

68 members. From these results, a dendrogram figure in

which the chronic conditions were graphed by their fusion

values, was plotted using the tree procedure.

To determine the optimal number of clusters in this

analysis, the fusion values were plotted against the number

of clusters. This heuristic is commonly used as a method to

show how many clusters best represent the data. When the

graph appears to flatten, it su~ests that no new information is being contributed to the linkages that follow.

However, researchers also suggest that this heuristic be

used in conjunct ion with “subjective inspection”

(Aldenderfer and Blashfield, 1984).

Note on sortings of unfamiliar

conditions

For the most part, participants seemed comfortable

sorting the diseases with which they were initially unfamiliar based on the definitions read to them earlier in the

experiment. These participants generally sorted the unfamiliar diseases into clusters similar to clusters formed by

those who said they knew of those diseases initially. In other

words, participants did not seem to sort all of their

unknown diseases into one pile or to assign them haphazardly to groupings. Those diseases that many participants

were unfamiliar with (enteritis, diverticulitis, and nephritis)

were frequently sorted into single card categories and,

therefore, did not affect the co-occurrence cluster solution.

In conclusion, the participants were able to place initially

unfamiliar diseases into meaningful groups based on a brief

description; therefore, their sortings of these unfamiliar

diseases probably did not affect the integrity of the cooccurrence cluster solution.

Comparison with multidimensional

scaling

Multidimensional scaling (MDS) was used as another

nonparametric method of analysis to represent the similarity data. A classical nonparametric MDS analysis was

performed on the co-occurrence matrix. Only the lower half

of the matrix was used because similarity between pairs was

transitive. The diagonal values of the matrix were removed

because they were not necessary for the calculation of interpoint distances.

Using this method, the co-occurrences were transformed into “proximities,” which were represented in a

dimensional space. The proximities, or distances, were

derived from a ranking of the interobject similarities. They

were then represented graphically as configurations of

points in an n-dimensional space, thus showing the similarity or dissimilarity among the chronic conditions.

In order to evaluate whether a dimensional solution

might be appropriate for the data being analyzed, tsvo factors should be considered. The first is whether the elements

are arrayed in a dimensional manner, having unique values,

and with points spread rather evenly along dimensions. This

is especially true for solutions using many elements, such as

33
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Figure L Chronic condition proximities-nonmetric analysis

the 68 chronic conditions used in this study. The second

factor to consider when analyzing the appropriateness of

MDS is the actual interpretability of the dimensions within

the solution.

The ALSCAL procedure within the SAS statistical

package was used as the data analysis computer package

(Young and Lewyckyj, 1981). The result was a

two-dimensional MDS solution, whichis shown in figureI.

Most of theccmditions are spread along the two dimensions. However, there is some “clumping” for the heart

conditions, such as heart attack, tachycardia, and angina, as

well as for musculoskeletal conditions, such as bursitis and

lumbago. Certain of these conditions are so close together

that they were not printed by the computer program

because they had identical proximity values.

In addition, although many of the points have been

fitted along the dimensions, it is not clear just what conceptsare being represented inthe data solution. Coronary

heart disease has the most extreme x-axis negative value

(-1.83) in the solution; colitis has the most extreme positive

value (1.41). Potential substantive labels that come to mind,

such as “inherited-contracted” or “major-minor,” are

readily dismissed when noting that many cancers are neutral on this dimension, as are tuberculosis, pleurisy, and

asthma. Similarly, the y axis does not appear to be interpretable. Cancers of the prostate, breast, and stomach are

the most positively extreme (coordinates around 1.85);

glaucoma, arthritis, and curvature of the spine are the

most negative (with coordinates ranging from –1 .47 to

–1.71). Akhough “severity” might seem an appropriate

34
label at first, it is an unlikely choice because brain hemorrhage and heart attack are neutral with respect to this

dimension.

A simple way to compare the adequacy of the similarity

solution for the MDS and the clustering analysis is to

embed the clusters for the free-sort task into the MDS

solution obtained for the same data matrix. If both soh.ltions are representing the data with high agreement, then

the clusters should form’ neat concentric circles around the

conditions that are near each other when superimposed in

the two-dimensional space. Such a pattern would indicate

that the cluster and MDS solutions matched in their degree

of association among chronic conditions. A poor match

would lead to messy elongations within the

two-dimensional space when the clusters are embedded.

Taken together with the other characteristics of the MDS

solution, such a poor match would be further confirmation

that MDS is not a very good means of representing the

underlying similarity structure of the obtained data.

Figure I displays the clusters as they appear when

superimposed in the two-dimensional space. Elongations in

the figure are clearly evident, with the exception of two

strong clusters—heart conditions and certain cancers—

both of which lie near poles of the two dimensions.

Based on this comparison, it would seem that MDS

does not do a very effective job of reflecting the inherent

structure of the similarity data. Although one might argue

that increasing the number of dimensions in the solution

would help the interpretability of the solution, such was not

the case in our analysis. Unfortunately, MDS is limited as

an analysis tool after about three dimensions because it is

difficult to conceptualize elements in a space with four or

more dimensions. For these reasons, MDS was not performed with more than three dimensions.

The cluster analysis solution would appear to be the

best means of representing the similarity structure in these

data because of its emphasis on grouping similar entities

rather than distancing dissimilarities, as is the case in MDS.

In addition, clusters were formed on the basis of subjective

similarity, without regard to any particular dimensions.

MDS, on the other hand, entailed fitting the similarities to a

few (2-4) meaningful dimensions. Using this type of evaluative process to judge similarity does not appear to be the

strategy employed by respondents in the free-sort task. Further analyses, therefore, were focused on interpreting the

cluster analysis solution as the better representation of similarity judgments.

Quadratic assignment paradigm

The quadratic assignment paradigm was the technique

used to examine the organizational structure that subjects

mapped onto the chronic conditions used in the study, The

paradigm assumes that there is some independent way to

organize or structure the set of objects a priori. (In this

study, the set of objects was the 68 chronic conditions typed

on individual cards,) This analysis enables an investigator to

test several known organizational structures against the

data actually obtained. Some possible a priori organizations

of these chronic conditions are the National Health Interview Survey (NHIS) checklist groups and the 9th Revision

International Classification of Diseases (ICD-9) coding categories. A brief explanation of this analysis is discussed

next.

For this study, subjects were asked to sort 68 cards into

groups or piles according to “how they should go together.”

The task was not limited in any way, so subjects had no

restrictions as to the number of groupings created or the

number of cards within each grouping. Following this freesort task, the number of groupings were counted and the

conditions that had been placed within them were recorded.

Scoring was performed by giving every pair of conditions

within a group a co-occurrence score of 1. Every subject’s

free-sort solution was scored in this way, according to what

cards had been placed together within the same pile. If a

pile contained a single member, the chronic condition

within it received a score of O with all other conditions

because it had not co-occurred with others within that particular subject’s free-sort solution.

When all sort solutions had been scored in this way, the

result was a unique 68 X 68 co-occurrence matrix for each

of the 70 free-sort solutions. These co-occurrence scores

were then summed across all subjects, using matrix addition, which produced an a~egate and symmetric

co-occurrence matrix with the same dimensions and N (70)

on the diagonal. This data matrix represented the degree to

which conditions were seen as being similar to other conditions. Thus, the higher the co-occurrence values, the

greater the degree of similarity as judged by subjects who

placed them in the same category with high regularity.

In addition to this matrix, hvo structure matrixes were

specified such that the checklist groupings and the ICD-9

categories for these conditions could be tested against the

obtained data matrix. The checklist structure matrix was

constructed by giving a score of 1 to the pairings of conditions within each of the six groupings of the checklist and a

Oto pairings not in the same grouping. The same procedure

was followed for a second structure matrix constructed in

the same manner but with O, 1 pairings based on the ICD-9

categories. The analysis issue, then, involved the extent to

which these two structure matrixes fit the data matrix.

The analysis in this paradigm was done by performing a

cross-multiplication of the data matrix and the relevant

structure matrix. If the structure matrix mirrors the data

matrix, then the sum of these cross-products should be

high; the large values of co-occurrence would remain in the

summation, but the small values of co-occurrence would

drop out as a result of being multiplied by O.This summed

cross-product can be thought of as an unnormalized correlation coefficient. For an approximate test against

randomness, the expected value and variance of this index

can be obtained by formulation (rather than unwieldy permutation) and a z-score calculated. This score can then be

compared with a normal distribution in the usual manner

to find the appropriate p-value for the obtained

35
index. (See Hubert and Levin, (1976) for expected value

and variance formulas for the index,)

This analysis was performed using a customized computer package called the Quadratic Assignment Program

(Baker, Hubert, and Schultz, 1977) available at the University of California, Santa Barbara,

The results of this analysis and the degree of association between each of the two a priori groupings and the

obtained data matrix are reported in the body of this report.

Profile analysis procedures

The 70 participants were asked to rate the 68 diseases

(on scales from 1-3 or 1-4) on seven dimensions: commonrare, painful-not painful, major-minor, disabling-not

disabling, embarrassing-not embarrassing, controllable-not

controllable, and unlikely to get the disease-have it already.

To cluster the 68 diseases using these attributes, the mean

rating for each disease on each of the seven attributes was

used to create a profile matrix.

Attribute

1234567

Disease 1

2

68

The CLUSTAN statistical package was used to perform the

cluster analysis. Squared Euclidean distance was the similarity coefficient chosen. Procedure CORREL was used to

create the similarity matrix between diseases, and procedure HIERARCHY (Wishart, 1982, p. 31) created the

fusion hierarchy of the similarity matrix. Procedure TREE

(Wishart, 1982, p. 37) was used to plot the dendrogram

produced by HIERARCHY.

To find the best cluster solution, several agglomerative

hierarchical algorithms were explored. Procedure COMPARE (Wishart, 1982, p. 18) was used to compare the

hierarchical classifications produced by CLUSTAN with

the original similarity matrix. The resulting cophenetic correlations werti

Complete linkage .56

Single linkage ,55

Average linkage .65

Ward’s method .52

(The similarity coefficient was sums of squares instead of

Euclidean distance.)

The higher the cophenetic correlation, the greater is

the agreement between the original data and the solution.

Because the average linkage solution had the highest cophenetic correlation, its solution was utilized for further

analyses.

Procedure RULES in CLUSTAN (Wishart, 1982, p.

14) was used to test for the significant number of clusters in

the hierarchical clustering sequence. The “upper tail” rule

(commonly referred to as Mojena’s Rule) in RULES was

used to compute z-scores for each level of hierarchy. The

first level to reach a z-score greater than 1.96 is considered

a significant number of clusters. For the 68-disease profile

solution, n = 4 clusters was significant.

In order to find the optimal four-cluster solution, the

k-means procedure was performed on the four-cluster

average-linkage solution. In the k-means procedure, the

similarity of an object to other objects in its cluster and to

all other clusters is tested, and the object is relocated if it is

more similar to another cluster. Program RELOCATE

(Wishart, 1982, p. 43), which performs the k-means analysis, relocates until all objects remain stable in their current

clusters.

To verify the k-means relocation of the average-linkage

solution, we also had k-means relocate the diseases from a

random four-cluster start. (The computer arbitrarily

assigned objects to the initial four-clusters.) The randomstart solution was identical to the k-means average-linkage

solution, verifying that the diseases were being meaningfully relocated to each of the four clusters.

The k-means average-linkage solution is the profile

solution that is discussed in the body of this report, It

yielded four clusters that have intuitive appeal: the minor

disease profile, the moderate profile, the major profile, and

the embarrassing profile.

36

* U.S. GOVERNMENT PRINTING OFFICE 1992 –3 42 -327 I 60 0 17
.

Vital and Health Statistics

series descriptions

SERIES 1

SERIES 2,

SERIES 3,

sERIES 4,

SERIES 5.

SERIES G.

SERIES lIJ

SERIES 11,

SERIES 12.

SERIES 13,

Programs and Collection Procedures–These reports

ds!scmbe the data collechon programs of the National Center

for Health Statistics They Include descriptions of the

methods used to collect and process the data, definitions.

and other material necessav for understanding Ihe data

Data Evaluation and Methods Research These reporis

me studws of new statlst!cal methods and Include analyhcal

techniques, oblectwe evaluations of relrabllrtv of collected

data, and contributions to statmtlcal theory These studies

also include experimental tests of new survey methods and

comparisons of U.S. methodology with those of other

countnes

Analytical and Epidemiological Studies–These reports

present analylcal or mterpretlve studies based on vital and

health statlst!cs These reports cary the analyses furlher than

the expository types of reports m the other series

Documents and Committee Reports- These are fjnal

reports of malor committees concerned with vital and health

statcstlcs and documents such as recommended model wfal

rsgstrat]on laws and rewsed birth and death certificates

International Vital and Health Statistics Reports –These

reports are analylcal or descnptwe reports that compare U S

Wal and health stat! stlcs with those of other countries or

present other Irrternat[onal data of relevance to the health

statistics system of the Unrted States.

Cognition and Survey Measurement–These reports are

from the Nattonal Laboratory for Collaborate Research n

Cognmon and Survey Measurement, They use me!hods of

cogn!twe smence to design, evaluate, and test survey

Instruments.

Data From the National Health Interview Survey- These

reports contain stat] stlcs on Illness, unintentional lnjurles,

dlsablhty; use of hospital, medical, and other health serwces,

and a wide range of special current health topics covering

many aspects of health behaviors, health status, and health

care utlllzatlon They are based on da!a collected In a

contmumg national household !ntervew survey

Data From the National Health Examination Survey, the

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination Survey

Data from drect exammatlon, testing, and measurement on

representatwe samples of the clvlran nonlnstltut lonallzed

population prov!de the basis for (1) medlcallv defined total

prevalence of spec!fc diseases or conditions in Ihe United

Stat@s and the drstnbutlons of the population Wh respect to

physmal, phywologlcal, and psychological charac:enstlcs,

,and (2) analyses of trends and relationships among various

measurements and between survay periods

Data From the Institutionalizad Population Surveys

Discontinued In 1975. Reports from these surveys are

included m Series 13.

Data From the National Health Care Survey–These

reports contain statistics on health resources and the publlc’s

use of health care resources mcludmg ambulatory, hospital,

and long-term care servces based on data collected directly

from health care providers and provider records

SERIES 14

SERIES 15

SERIES 16

SERIES 20

SERIES 21

SERIES 22

SERIES 23

SERIES 24

Data on Health Resources: Manpower and Facilities–

Dlscontnued In 1990 Reports on the numbers, geographc

dlatr[butlon, and character: st’cs of health resources are no’.v

,nc, Lded (n Serle5 13

Data From Special Surveys -These reports co fltan

stattst[cs on health and hea:fh. relaled topics collected In

special surveys that are not part of the continuing data

systems of the National Center for Health Statlsttcs

Compilations of Advance Data From Vital and Health

Statistics –Advance Data Reports prowde early release of

!nformatlon from the Nallonal Center for Health Statlstlcs’

health and demographic surveys They are compiled In the

order jn which they are publlshed Some of these releases

may be foliowed by detailed reports In Series 10–13

Data on Mortality–These reports conta!n stat{stlcs on

mortally that are not Included n regular, annual, or monthly

reports Special analyses by cause of death, age, other

demographic variables, and geographc and trend znzlyses

are mcluc!ed

Data on Natality, Marriage, and Divorce–These reports

contz, n statlatics orT na:allt,y, mamage, and divorce that are

not nc’uded In regular, annual, or monthly reports. Specal

anal>ses by health and demographic :arables and

geographc and trend analyses are Included

Data From the National Mortality and Natality Surveys–

D+scontlnued In 1975 Repjrts from these sample surveys,

based on vl!al records, are now publshed m Serls% 20 or 21

Data From the National Survey of Family Growth –These

reports contain statlstcs on factors that affect birth rates,

ncludlng contraception, lnfer\W/ty, cohabtatton, mamage,

dvorce, and remarrage, adoption; use of medcal care for

famly planr?mg and mferl:ty and related maternal and Infant

health IopIcs These statistics are based on national surveys

of chlldbearng age

Compilations of Data on Natality, Mortality, Mafriage,

Divorce, and Induced Terminations of Pregnancy–

These include advance reports of b!rfhs, deaths, marrleges,

and a:vorces breed on flnai data from the National Wfal

Statlstcs Sys!em that ‘:/ere publshed as supplements to the

fl,kmfh/}J L/If2/ Sra!,SrICS Re,00II (tJVSR) These rsports prowde

hgbl ghts and sur’nmarles Qf detal!ed da!a subsequently

pub’shed In \’fa/ Sfat/sf/cs of (be Un/led Sfates Other

SUPP emen+s to the ~.l\’SR publlshed here pro/tale selected

fnd’ngs based on fnal c!a!a from the h~at)onal Mtal Statlstlcs

System and nay be fo!la:~ed by detailed reports fn Ser{e: 20

m 21

For ansbvers to qLestlcns about this report or for a list of reports publljhed

m these series, contact

Sclentflc and Technical Information Branch

!Jatonal Center for Health StatWcs

Centers for Dsease Control

Pubic Health Servce

6525 Belcrest Road, Room 1064

Hyattsvlle, MD 20782

(301) 436-8500
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HEALTH & HUMAN SERVICES

Public Health Service

Centers for Disease Control

National Center for Health Statistics

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