Screening Elders for Risk of Hospital Admission (1993)

7/24/2019 Screening Elders for Risk of Hospital Admission (1993)

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Screening Elders

for

Risk of

Hospital Admission

Chad Boult, MD, MPH,* Bryan Dowd, PhD,t David McCaffrey, BA, Lisa Boult, MD, MS,*

Rafael Hernandez, MD,* and Harry Krulewitch, MDS

Objective:To define a set of screening criteria that identifies

elders who are at high risk for repeated hospital admission

in the future.

Design:Longitudinal cohort study. Logistic regression analy-

sis of data from half of the subjects was used to identify risk

factors for repeated hospital admission. The ability of these

risk factors to identify elders who are at high risk for repeated

hospitalization in the future was then tested using data from

the other half of the subjects.

Setting:United States.

Participants: A subsample

n = 5876)

of a multistage prob-

ability sample of all non-institutionalized U.S. civilians who

were

70

years or older in

1984.

Measurements: At baseline (1984), elderly subjects were

asked about their demographic, socioeconomic, medical, and

functional characteristics and about their recent use of health

services. Their subsequent hospital admissions and mortality

were then monitored through the records of the Medicare

program and the National Death Index

(1985-88).

Results:

Among the subjects in the first half of the sample,

eight factors emerged as risk factors for repeated admission:

older age, male sex, poor self-rated general health, availability

of an informal caregiver, having ever had coronary artery

disease, and having had, during the previous year, a hospital

admission, more than six doctor visits, or diabetes. Based on

the presence or absence of these factors in

1984, 7.2%

of the

subjects in the second half of the sample were estimated to

have a high probability of repeated admission

(Pra 0.5)

during 1985-1988. In comparison with subjects estimated to

have a low risk

(Pra14 Bed days in past year

Less

active

in past

year

Health

worse

in

past year

Coronary artery

disease

(CAD)*

Cerebrovascular disease CVD)**

Diabetes in past

year

Hypertension

(ever)

Cancer

(ever)

Arthritis

or

rheumatism

(ever)

Hospital admission in past year

>6 Doctor visits in past year

1

Years

since last doctor

visit

Used visiting nurse

in

past year

No informal caregiver available

Functional limitation***

>1 Fall in past year

Visual impairment

Incontinence (urinary or fecal)

Cognitive impairment#

Hearing

impairment

Predisposing variables

Age

70-74

75-79

80-84

85+

Male

sex

Non-white

race

0-6 Years of education

Lives alone

Has

little control

of

health

< 10,000 Annual income

Enabling

variable

15.8

20.7

30.6

21.1

11.1

13.3

18.6

14.9

16.5

17.7

10.2

44.7

12.5

54.1

21.2

21.4

15.7

2.8

7.0

10.7

10.6

7.9

13.3

26.2

6.5

43.0

30.8

16.5

9.7

42.5

9.3

20.3

36.7

13.6

38.0

~

0.5

1 . 1

9.0

9.0

1.6

2.8

0.7

0.7

0.5

1.4

0 . 0

0.4

0.4

0.3

1.6

1.4

0.8

0.4

0.4

10.0

1.3

0 . 0

0.0

0.0

1.2

0.0

13.6

14.6

* Ever had angina, coronary heart disease, myocardial infarc-

tion or other heart attack.

**

Ever

had arteriosclerosis,hardening of the arteries,

stroke,

or cerebrovascular

accident.

***

Unable to do one or more of five ADLs (eating, using a

toilet, transferring between

a

bed and chair, dressing, bath-

ing) and four IADLs (cooking, shopping, using

a

telephone,

light housecleaning)

without

the assistance of another person

or

special device.

Frequent

or

increasing trouble remembering things or get-

ting confused.

relation

rho)

between:

1)

he error term of a model of

the probability of having repeated admission (inde-

pendent variables = the eight significant risk factors)

and

(2)

the error term of a model of the probability of

having unavailable Medicare records (independent

variables = age, educational level, cognitive impair-

ment, and the use of medical services in the previous

year).22


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_ _

14

BOULT ET AL

RESULTS

The baseline (1984) characteristics of the 5876 sub-

jects for whom 1985-1988 hospitalization data were

available are shown in Table

1.

During the 4-year follow-up interval (1985-1988),

1670 subjects (28.4%) were admitted more than once,

and 646

(11.0 )

died.

The Breslow-Day analyses suggested that four two-

way interactions had significant effects on the proba-

bility of future admission P

0.10),

so the original model (without

interaction terms) was retained as the final model.

The results

of

the logistic regression of the probabil-

ity of repeated admission on the 28 independent vari-

ables are shown in Table

2.

Of the significant predic-

tors, the need variables tend to make a larger collective

contribution to the probability of future admission than

do the predisposing variables (age and sex), confirming

a pattern that has been observed before.

After the logistic model was constructed using data

from the first half of the sample,

it

was validated using

data from the second half. Second half subjects were

classified on the basis of their 1984 characteristics as

either high-risk

(PraL

0.5,

n

=

204, 7.2%) or low-risk

(Pra< 0.5,

n

=

2623, 92.8%). Table 3 contrasts the

subsequent admission and mortality experiences of

these two groups during 1985-1988. In order to adjust

for the two groups unequal mortality rates, hospital

days and charges are reported per person-year sur-

vived.

Compared with the low-risk subjects, the high-risk

subjects experienced substantially worse outcomes in

every category: repeated admission (41.8% vs 26.1%),

mortality (44.2% vs 19.0%), hospital days per person-

year

(5 .2

vs 2.6), and hospital charges per person-year

($3731 vs $1841). About 70% (69.6) of the identified

high-risk subjects either died or had repeated admis-

sion during the 4 years of follow-up. By comparison,

only 37.4% of the low-risk subjects died or had re-

peated admission.

In order to test the sensitivity of this studys findings

to our definition of repeated admission, we repeated

the studys regression analyses and validity studies

using three alternate definitions of repeated admission:

one admission in

2

years, two admissions in

3

years,

and three admissions in 4 years. In Table 4, the alter-

nate results are compared with those derived using the

original definition of repeated admission.

All four logistic models included similar variables as

significant risk factors for repeated admission (left col-

umn). In the validation studies, the model based on

the original definition of repeated admission identified

the highest proportion of LSOA subjects as being high-

risk (center column). Furthermore, these identified

high-risk subjects subsequently used more hospital

days (right column) than did the high-risk subjects

identified by any of the models based on alternate

definitions. This successful use of a 4-year interval

supports and extends earlier observations that individ-

ual elders concentrated use of hospitals often occurs

during time periods of at least

3

year^.^,^

IAGS-AUGUST 1993-VOL. 42, NO. 8

TABLE 2. LOGISTIC MODEL OF PREDICT ORS OF

REPEATED ADM ISSION WITHIN FOUR YEARS

Adjusted

(95%

C.I.)

Regression Odd s Ratio

Coefficient

Need

variables

General

health

Excellent

Very good

Good

Fair

Poor

>14

Bed days in past year

Less active in past year

Health worse in past year

Coronary

artery disease

Cerebrovascular disease

Diabetes

in

past

year

Hypertension

Cancer

Arthritis

or

rheumatism

Hospital admission in past

year

>6

Doctor visits

in

past

year

1+

Years since last doctor

visit

Used visiting nurse

in

past

year

No informal caregiver

available

Functional

limitation

>1

Fall

in

past year

Visual

impairment

Incontinence (urinary or

Cognitive impairment

Hearing impairment

Predisposing variables

fecal)

Age

70-74

75-79

80-84

85+

Male sex

Non-white race

0-6

Years

of

education

Lives alone

Has

little control of health


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SCREENING FOR RISK OF ADMISSION

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AGS AUGUST 1993 VOL. 1

NO.

8

biased predictions when applied to a general popula-

tion of elderly persons.

DISCUSSION

In conducting these analyses, we were attempting to

find screening criteria that could be used to identify

groups of frail elders that, without intervention, would

experience frequent health-related crises and consume

a disproportionately high level of hospital resources.

The early identification of such elders would allow the

testing of interventions designed to prevent some of

the medical crises and to reduce the use of expensive

hospital services.

Eight screening criteria were identified. When tested,

these criteria classified

7 .2%

of a national sample of

non-institutionalized elders as being at high risk for

repeated hospital admission within 4 years. This high-

risk designation proved to be appropriate; during 4

years of follow-up, the high-risk group experienced

significantly higher rates of repeated admission and

mortality than did the low-risk group. The high-risk

group's hospital days and charges per person-year

survived were twice those

of

the low-risk group.

Our results suggest that these eight screening criteria

could be used to identify members of non-insti tution-

alized elderly populations who are likely to use hospital

resources frequently in the near future, many of whom

might benefit from preventive interventions. Those

identified could, therefore, be considered possible sub-

jects for future studies of the cost-effectiveness of

preventive interventions, such as geriatric evaluation

and management. Because this set of criteria was de-

rived from data collected after the introduction of

prospective payment for hospital care, it is applicable

to today's elderly community-dwelling population. It

offers a current, comprehensive, quantitative method

for identifying

US

elders at high risk of future hospital

admission.

~~

The results we report here do not appear to have

been biased by the exclusion of subjects whose Medi-

care hospitalization records were not available for

analysis. In most cases, the reason that the hospitali-

zation data were missing was because the subjects'

Social Security numbers, through which Medicare rec-

ords were to be traced, either were not provided or

were provided inaccurately (often by a proxy) during

the LSOA interviews (personal communication, M.

G .

Kovar, director of the LSOA). The excluded subjects

tended to be older, sicker, more functionally limited,

and more likely to be female than the subjects whose

hospitalization data were available. Nevertheless, the

relationships

between the eight risk factors and the

probability of repeated admission appear to be similar

among both the included and the excluded subjects.

Predictor Variables

Other investigators have pre-

viously identified as predictors of hospital admission

seven of the eight factors that we report here (all except

Having

he availability of an informal caregiver).',

11-14

an informal caregiver may predict the heavy use of

hospitals because sick people may be more likely to

have identified caregivers.

One factor that has been reported to predict overall

Medicare but is not included among our screen-

ing criteria, is limitation in the ability to perform the

activities of daily living (ADL) and the instrumental

activities of daily living (IADL).We evaluated the effect

of functional limitation on future hospital admission

by constructing a composite variable that reflected the

inability to perform without help one or more of nine

ADL and IADL (see list in footnote to Table 1). In a

univariate analysis, this variable was strongly associ-

ated with subsequent admission. In the multivariate

analyses, however, it was not significant (95%

CI =

0.8-1.8). Presumably, its univariate association with

repeated admission is explained by its collinearity with

other variables that were significant in the regression

model.

TABLE

3.

OBSERVED EXPERIENCES OF HIGH-RISK AND LOW-RISK GROUPS, 1985-88

Predicted to Have Repeated Admission

No (Low-Risk Yes (High-Risk

Group, n

=

204)

roup,

n

= 2623)

Observed Experiences

n

(%)

n

(%)

Repeated admission

584 (26.1) 85 (41.8)*

Mortality

498 (19.0) 90 44.2)*

Hospital charges per person-year survived

$1841 $3731

Hospital

days

per person-year survived

2.6 5 . 2

*

P < 0 0001

TABLE 4. EFFECTS OF USING ALTERNATE DEFINITIONS OF REPEATED ADMISSION

Hospital Days per

Predictors in Elders Classified as 1984-1988, High-

No. of Significant Proportion of Screened Person-Years

Definition Logistic Model High Risk Risk Elders

1+

Admission

in

2 years

8 3 .8 2 .3

2+

Admissions in

3

years

8 2.6 3.4

2+

Admissions in

4

years

(original definition)

3+

Admissions

in

4

vears

8

6

7.2

0

5.2


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816

BOULT ET AL IAGS-AUGUST

1993 VOL.

41, NO 8

Test of Validity In order to test the predictive

validity of the eight risk factors for repeated admission

(two or more admissions in 4 years), we used 1984

interview data to compute, for subjects in the second

half of the sample, the probability of repeated admis-

sion during 1985-1988. The group identified as being

at high risk (Pra2 0.5) did go on to experience consid-

erably higher rates of repeated admission, mortality,

hospital days, and hospital charges than did the low-

risk group (see Table

3).

About half (48.1%)of the false

positives, ie, subjects for whom repeated admission

was predicted but not observed, were subjects who

died during 1985-1988. Of all the subjects who were

predicted to have repeated admission, 69.6% either

died or had repeated admission during the following

4

years. Thus, these screening criteria appear to be es-

pecially accurate in identifying elders at high risk for

one or the other of these undesirable fates. Its predic-

tive validity should now be re-tested on a different

population of non-institutionalized elders.

Use of the Screening Criteria The high-risk seg-

ment of the elderly population identified by our screen-

ing criteria went on to use hospital resources at twice

the rate of their peers. Many such high-risk elders,

except for those who have terminal diseases or unstable

medical conditions, are believed to benefit from pre-

ventive programs like geriatric evaluation and manage-

ment (GEM).24*5 They would, therefore, be appropri-

ate subjects for future research about the cost-effec-

tiveness of GEM and other preventive interventions.

The data required for computing elders probability

of repeated admission (Pra) in the manner described

above are minimal: simple responses to a short self-

administered questionnaire. (Survey materials are

available from the authors upon request.) Alternately,

such data could be extracted from clinical or adminis-

trative records, where available.

An investigator using these screening criteria to iden-

tify high-risk subjects for a study of an intervention

designed to reduce hospital admissions would need to

Spec i f ic i t y (%)

r

. . . . . . .

15

0 2 40 6 8 1

1

-

Spec i f ic i t y

(%)

False Posi t ive Rate)

FIGURE . ROC curve for Pra.The area under the ROC curve (SE)

= 61.0 (1.2) .26

TABLE 5. RELATIONSHIP BETWEEN THRESHOLD FOR HIGH RISK A N D HIGH-RISK ELD ER S OUTCOM ES

High Risk Elders Outcomes, 1985-1988

Hosp. Ch gs. perosp. Days per

Elders at

%

with 2+ Person-Year Person-Year Mor tality

P,, High Risk Adm . Survive d Survive d Rate

0.25 56.1 34.5 3.6 $2515 28.4

0.30 39.2 37.0 4.0 2799 32.3

0.35 27.9 37.9 4.2 2918 34.3

0.40 19.2 40.0 4.6

3127 36.4

0.45 12.7

41.0

4.8 3330 41.0

0.50 7.2 41.8 5.2 3731 44.2

0.55 4.3 39.6 5.7

3820 48.1

0.60 2.7 40.7 5.4

3889 48.3


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SCREENING FOR RISK OF ADMISSION

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AGS-AUGUST

1993 VOL. 41, NO.

8

establish a threshold value for

PI,

bove which elders

would be considered high-risk. Choosing a low thresh-

old would increase the sensitivity of the

PI,,

causing

more elders to be identified as high-risk, whereas

choosing a higher threshold would increase the speci-

ficity of the

PI,,

selecting fewer, but frailer, elders (see

Figure

2).

The threshold value must be chosen with care. As

illustrated in Table

5,

if

Pra= 0.3

were selected as the

threshold,

39.2%

of the population would be classified

as high-risk. The substantial costs of treating such a

large heterogeneous group would be difficult to recover

through reductions in the groups post-treatment use

of hospitals because, even without treatment, its hos-

pital use would be relatively low

( 2799

per person-

year).

At the other extreme, if

Pra =

0.60 were selected,

only 2.7% of the population would be classified as

high-risk. With such a small treatment group, a study

might lack the statistical power necessary to discern

differences in hospital use between the experimental

and control groups. Furthermore, the interventions per

capita costs could not be trimmed through the economy

of scale. Even if significant differences were observed,

the very small market for the intervention, ie, less than

3%

of elderly persons, would limit its potential use in

resolving the larger problem of frequent hospital use

by chronically ill elderly persons.

Future versions of the model that we have described

may be improved by the addition of other variables

(not measured in the LSOA) that reflect life style, use

of medications, psychological conditions, chronic use

of medical services (ie, longer than

1

year), and other

risk factors as yet unrecognized. The predictive validity

of such additional screening criteria should be tested

in the future. Nevertheless, the demonstrated predic-

tive validity of the eight criteria that we report suggests

that they could be used now to estimate elders risk of

repeated hospital admission.

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APPENDIX

Logistic Formula for Estimating the Probability of

Repeated Admission within Four Years

eBx

PI, =

1 + eBx

where:

P,

=

the probability of repeated admission within

four years

e

=

the natural logarithm

BX = Bo + BiXl + B2X2 + . . . . . .

.

+ B13X13

Bo = a constant

X = 1 or 0, the presence or absence of each risk

B

= the logistic regression coefficient of each risk

factor (including five dummy variables)

factor (including five dummy variables)

Screening Elders for Risk of Hospital Admission (1993)

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