BOOKS OF RtfiDIfGS - PAHO/WHO

Inquiry,/Volume XIV, September 1977
well-established statistical thceory that specifies
the confidence one can have in their
rcsults (c.g., the various methods for appraising
the dispersion ot data about their
central tendency). This tlieoretical supersltructure
is very useful for drawing atten,tion
to potential sources of imprecision or
diffusion of focus in particular kinds of
trend cxtrapolations.
Possible Weaknesses:
O Apparent trends in the data have no life
of their own. Their continuity over time
may be very tenuous. Though the temptation
exista, such trends cannot be taken
for granted.
D No fomrn meas exists for taking "new"
trends or !.-tor cmerging in thc future
into account ila advance. Inded, the ch
may be inhibited by the perceived "objectivity"
of extrapolad techniques.
O Most quantitative techniquaes require data
that are quite precise and reliable if their
major strengths are to be capitalized upon.
Data most frequeantly available to areawide
plannrs tend 1, be ol uncertain reliability
and valdity.
O IU thc uawc~ of the lorocuatig problcm
canot be expreUed in quantitative terms
Md in termo flr which the data re available
on a year-to-year bast, most available
techniquaes are only partially uble.
Morpl o Ancyus id Rd e Trem
1e ov l insat af metioda f~lli under
thiese two nbica s ato mae a foroaati ng prob.
hm more man= ubl by bre it into subproblem.
One seeks to discen the relative
imporence of each of the subprobkm and to
improve prodicion accuracy by working with
more cfcúively dched subproblems Specifc
attention is paid to enumerating the set
d *il itgificn t (fcusble) facon (alterativa)
for explaining some outaome (echieving
aome obioctive). Thae lctan eo altea=tives
may be defined b~ally or throqb ime of inductive
mathematical techniques, such multiple
regreio or tbc compul#~id Aulmatic
Interaction Detector (AID, pragram. The
_ '?4 _
ovcrall process is called morphological analysis;
the graphic display of subproblems showing
their interrelationships is called a relevance
tree 7.
Consider the following hypothetical example:
An HSA was concerned with the future
effect on total demand for services that will
result from a rapid expansion of ambulatory
care facilities. A large amount of data on both
the working behavior of physicians and on
factors contributing to demand for local ambulatory
facilities was analyzed using the AID
program. This program worked through the
data, establishing "factors" through a series of
dichotomous splits. These factors were chosen
by the program because the dichotomy splits
that were selected maximized the betweengroup
variance. The graphic display of the
series of these dichotomized factors represented
a "picture" of all factors that would contribute
to changes in demand for ambulatory care, expreased
in terma of relative importance. Tlcse
factors then were appraised by "experts" .ho
rendered judgments about the extent to which
each factor would change over the next few
year. Taken together, the graphic display and
expert judgment resulted in the forecast that
demand for outpatient services would increase
directly with an expeanion in such facilities,
Lcading to a relative decline in use of key inpatient
servicea.
Whether graphic or quantitative, morphological
analyis has not yet seen many applicatiom
in the health care context. Its strengths
Lrgely remain to be tested, but might include
the following:
Sreunguhs Claiumed:
O Brosd, large-scale forecasting problems are
much easiar to handie in "pieces."
O Relevance trees readily show what is
(thought to be) contingent upon what, thus
chains of causality, potential arenas for
conflicts of interes, and areas where organizational
change are necessary (or are likely)
can be shown more clearly.
O Recombiastion of subproblems may show
a varicty of ways in which changes might
occur and might thus dispel possible tendencis
to fall into "single possibility" ruts.