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A model for the management of a family planning system online

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LIBRARY

OF THE

MASSACHUSETTS INSTITUTE
OF TECHNOLOGY



FEB 5 1974




A MJDEL FOR THE MANAGEMENT OF
A FAMILY PLANNING SYSTEM

613 - 72

Glen L. Urban
Revised June, 1973



INSTITUTE OF TECHNOLOGY



CAMBRIDGE, MASSACHUSETTS 0213!



A MODEL FOR THE MANAGEMENT OF
A FAMILY PLANNING SYSTEM

613 - 72

Glen L. Urban
Revised June, 1973






J A?: 2S 1974 ■



M. I. T. .:.,



A MODEL FOR THE MANAGEMENT OF
A FAMILY PLANNING SYSTEM

ABSTRACT

This paper describes a planning model designed to be used by
managers of family planning systems to improve understanding, forecast-
ing, and planning. The macro-flow model describes the patient movement
through post partum and non-post partura programs. The flows model the
phenomena of: outreach recruitment, continuance, post partum checkups,
switching methods, referral, migration, contraceptive use experience, pri-
vate protection, method effectiveness, advertising response, follow up,
abortion, and medical services. Strategic variables can be linked to
the flow parameters to produce capacity requirements and budgetary impli-
cations. The model output includes benefit measures of total active
patients, couple years of protection, "births protected", and unwanted
births prevented. The fertility aspects of births prevented are modeled
through a non-stationary Markov process submodel which considers demographic
phenomena without burdening the basic flow structure. The input proce-
dures used to process patient visit, outreach, clinic survey, and experi-
mental data are discussed and some empirical results are reported. The
combination of data based estimates and subjective judgment is done by
"fitting" the model to past observed data. Testing and control are done by
"tracking" model performance through conditional prediction, diagnosis,
and updating.

The model is implemented in an on-line, conversational program that
facilitates evolutionary model building by allowing the user to specify his
model options. The application and testing of the model in the Atlanta
Area Family Planning System are discussed and the experiences of managers
in using the model to gain new insights , forecast , budget , and plan are reported.



A MODEL FOR THE MANAGEMENT OF
A FAMILY PLANNINC SYSTEM

by Glen L. Urban

INTRODUCTION
From a macro point of view, the question in population is "What
should the population growth rate be?" Growth rates in the U.S.A. in
the last five years imply our population will double each 70 to 80 years
— a rate considered excessive by many. At the micro level, the ques-
tion a family faces is, "How can we have the number of children we want
and at the times we want them?" Families are not very successful In
planning births. Fifty percent of births do not occur when wanted (i.e.,
timing failures) and twenty percent are unwanted births. In the poor
and near poor groups where private medical care and contraceptives are

generally not available, the problem is more severe with forty percent

2
of births being unwanted. These births can and usually do produce

undesirable sociological, psychological, or medical effects on the child
or mother. This paper will address the problem indigent families face
in planning their families in the United States.

The need for family planning has been recognized by Congress and
the President. Over one hundred million dollars per year have been appro-
priated through the Tydings bill in order to make contraceptives available
to the indigent. The National Center for Family Planning Services of the
Department of Health, Education, and Welfare grants money to metropolitan
and rural areas to develop local family planning services. In addition,
state health departments, county health departments, hospitals, and pri-
vate groups, such as planned Parenthood, provide funds and services



2.



at the local level.

The recipients of the grants have the task of planning and control-
ling a system which best serves the needs of its clientele who wish to
prevent unwanted births. Managers plan and budget for post partum and
non-post partum programs. They allocate resources for recruiting new
patients through outreach workers, but trade off this allocation
against resources used to maintain high rates of continuing contracep-
tive usage. Tlu'y determine the contraceptive methods to be used and Imple-
ment policies on such matters as abortion and sterilization.

The purpose of this paper is to describe a model designed to
be used by managers of family planning delivery systems to: help them
better understand their systems; enable them to make better forecasts;
and, provide them with a tool for planning. The paper will begin with
a description of the model structure, output, and input procedures.
Then the evolutionary implementation of the model will be discussed and
an application of the model to the Atlanta Area Family Planning System
will be presented. The paper will close with a discussion of future work
and the applicability of the model methodology to developing countries.



MODEL STRUCTURE



The basic approach of this work is to build a macro process

3

model. This type of model is a deterministic flow model that allows

an effective evolutionary approach to implementation and a reasonable
trade-off between the richness of behavioral content and the difficulty
of model estimation and testing. The process notions are particularly
attractive in this setting since the basic clientele behavior can be
represented by a patient flow that managers can understand and inter-
nalize. This type of model is feasible since in most U.S. family plan-
ning programs, a record is made of each patient visit and therefore,
detailed flow parameters can be estimated. The process model traces
movement from the target group population through post partum and non-
post partum family planning program events. It links strategic resource
and policy variables to the flow so that after data basing the model
parameters, overall acceptance and birth rate effects can be encompassed,



OVERALL FLOWS

The model begins with the concept of a target group. This is
the population that managers define for program development and attempt
to serve. For example, the target group may be all fertile women ages 15
to 45 who live in a specific metropolitan area and are poor by O.E.O.
standards. The model divides the target group into two basic sections:
(1) those active in the family planning system and (2) those not active
in the family planning system. "Active" is defined as those who accepted
contraceptive supplies at their last visit (e.g., accepted a three month



J



supply of pills or retained their lUD) and have not missed their next
appointment. The not-active group is divided into pregnant and not
pregnant. The flow between target group sections that occur within
one period are shown in Figure One.

People flow from pregnant to active or not-active by accep-
tance or non-acceptance of family planning upon delivery at a hospital
with a post partum program. Movement from not-active/not-pregnant to
active occurs due to new patient requests for contraceptives or outreach
generated acceptance. Actives return to the not-active class by discon-
tinuing contracepion (not returning for an appointment) or by becoming
pregnant. Likewise , not-active/not-pregnant people may become pregnant.
The final flows are actives switching methods, referral between
agencies, and migration rates into and out of the target group.

For purposes of model development, the not-active section of the
target group is denoted by NSTATE and further divided into mutually
exclusive and collectively exhaustive subsections as follows:
NSTATE^ ^^ - nuabar of p%opU at tla* t la «t4f ■

8-1 Pregnant

s"2 Never active in system

s-3 Ever active (where active at one time but not now) and
have no negative attltuda towards contraception

8-A Outreach exposure (visited by outreach work«r but did not

accept an appointment or did not appear for an appointment)

8-5 Adverclaing sware (cware of app««I of sssaafte)

s-5 + m Ever active and have a negative attitude with respect
to method n (nf-1,2. . .NM)

NS is defined as the last state and NS - 5 + NM. In this notation s=2 to

8-5 + NM are the not-active/not pregnant group. The division into these additioi



5.







deliver and accept post partuip.








out




deliver and




accept by request








migration

1 \




not accept
post partum




or outreacli










\


/ .


s/ S X




, —




Pregnant


Not Active
Not Pregnant


Active




"1

i






-7


\ ^


"t


r


^










- 1

i


in






become




discontinue








switch


migration




pregnant




method








methods










or










agencies








contraceptive failure











and become pregnant



FIGURE ONE: Target Group Sections and Interaction



states Is done since people who have had differential experience in the
system will behave differently in terms of acceptance and continuance.
For example, those never in the system (s=2) may respond differently to a
visit from an outreach visitor than those who had been in the system and
dropped out (s=3) or those who had negative experience with a method
(s=5+m) . Those who are aware of advertising (s=5) may be more likely to
request an appointment at a family planning clinic. Likewise, those who
are visited by an outreach worker and did not accept an appointment (s-4)
may be more likely to request an appointment. This is an indirect out-
reach effect due to the receipt of communication, but the reluctance to
commit to an appointment at that time. The state of being pregnant is
8=1 and It contains all people currently pregnant.

NON POST-PARTIjM FLOW

The detailed non post-partum flow is represented in figure two.
New patients enter from the not-active/not-pregnant group as the result
of a home visit from an outreach worker or a request for an appointment.
The flow traces the initial acceptance and contintiance process.

Outreach Recruitment : Outreach workers are usually women who are similar
to the members of the target group, but who have been trained in family
planning. These women work in the community. For example, they may go
door to door in a low cost housing development. If they find someone home
who is in the target group, they talk to them about family planning. The
number of people seen by outreach workers from agency a, who are in an
eligible state 3 (s=2, 3, . . .NS) (see box 1 in figure two) is:



7.



Not Acti ve / Not Pregnant

^ ~



tj



Request

Appointment g



Seen by Outreach
Worker 1



Do not
come to
appointment



come to
appointment



come as a res
offollowup



1



iUlt j



Jl



T



accept
appointment



do not accei
appointmeni



rlf



come
appol



.ntment k-



do not
accept
method



accept
method
11



@



do not come
to appointment



come as a result
of follow up c



chose
sterili-
zation



accept
method



chos
sterll
zatlon



'" 6 I



do not
accept method



not
referred



13



referred
to new
agency jj



®



come to
next appointment j^



^ ^ referred from
other agencies



jf \ do not

accept



do not come to
next appointment

I



f



come as the result
of followup jc



accept
method



16



=i



switch
method



-e



not
referred



referred to

new agency



chose ^^

sterilization _J



® Return to active group as sterilized
^ ?rT^J° ^'^'" ^" not-actlve/not-pregnant group

® To - ^-,/^«-y^cH., .^^^^^^^^^ referral ©come to agency because of referra:
^ ^wu. _ Non-Po8t Partum Agency Flow Structure



8.



(1) OUrSEE^ - NKCALL PtTIND HSIKSE^ /TARGP



OUTSEE ■ number of peoole in state s that outr each workers
t,a,8 from agency a see in month t

NRCALL - number of r_ecruitment outreach calls made in month t
• by agency a

PRFIND = £e_rcent of outreach calls of agency a that result in
find ing a person in target group

NSTATE " number of people at time t in state s
t,s

TARGP - number of people in the targe t grou2. at time t



The number of calls are reduced by the percent of people found that

are in the target group (PRFIND). The states of those called upon are

determined in proportion to the number of people in each state relative

to the target group (see third term of equation 1). This assumes a

random calling pattern with respect to states within the target group.

Equation 1 also reduces effectiveness by the fraction of ineligible

people (active or pregnant) since NSTATE over s - 2,3,"", NS does not

t »s

include the active or pregnant sections of the target group.

After reaioving tho»« seen from each state (NSTATB) , the number
who make an appointment with the outreach worker (see box 2 in figure 2)
is specified as :

NS

(2) OUTAPT^ = Y OUTSEE PDESIR

t,a i - t,a,s a,s
s=/

OUTAPT = number of people visited by outr each worker who



t,a



make an appointment in month t at agency



PDESIR » £ercent of people visited who are in state s and
who desire an appointment at agency a



9.



P.ESXK U ,„.3...p.ea ., ,.,... .,„,, ^,„^,^ „^^ ^^^^^__^ ^^^^^^^^^^^

dlff««,ce. bet„«„ outreach workers of agencle,.

TKe .™,er „ho c..e to rHe appo.„..e.r ^,e ..rou^h .he ourreacH
worker before any follow up effort (see box 6) la:

(3) COHO^_, . o„I«,^_^ ,^^

'" "■" -» ^. .djuste. for folio, up l) ,
equation 13 can be used to refer people of any depth of experience when
PREF is further subscripted by d. Such alternative on-liae subscripting
will be discussed later in this paper.

Switching of methods (see box 16) is simply modeled by a first
order Markov transition from one method to another within an agency where
the rate of switching can have different values for women using different
methods and with different numbers of past acceptances of the method.

Continuance : Continuation is modeled by specifying the number of people
who will return for their next visit. The alternative would be to use
time as the basic unit of continuance (i.e., percent of patients active
n months after acceptance). In this model, visit continuation is pr-vferred
since (1) costs and service are incurred at visits, (2) client data is



15.



visit based, (3) phenomena such as referral and switching are visit
related, and (4) managers think in terms of the visit as the underlying
program event. In situations where patient visit records are not
available, time dependent rates could be derived from survey data and
be converted into visit to visit rates by dividing the time axis into
visit intervals and calculating the percent who continue from visit d to
d + 1.

The number of people who return for their next visit (see
box 13) is:
(14) COMC^ . 4 ^ - ACCPT^. „ , . ?CC»iC



^^^t m A H " number of people coming to their continuing appointment

agency a



» ' • in mcnth t having laat accepted method m d times at



ACCPT



t-A.B.«.d



- w^ber of people «^ f cc^j^ed in aoath t-A
satfaod B at agancy a for the dth tiae



'COMC^ 4 d " -B*'*^"*' **^ paopla vho «jg« for sontlnulng ap-
' ' pointaenta for method m at agency a after d
visits



^-APT . , - Interval between appointment
method m at agency a



This number Is then updated to reflect follow up (see box 14 ) in a
similar manner to equation 4 to define:



CONCOM



, - after outreach follow up the number of people

^•'"•^•'^ with C2a^^ "^

to agenc;
method m



• * * with continuing appointTaents coraine in month t
to agency a having accepted d times and using



Those who do not come may have lost interest in contraception or they
may have had a negative experience with their method. Those who do
not come are divided into net negative and negative groups and ref.t'.med to the
appropriate states. The updating for state 3 which has been defined as ever
in system but not negative" is:



16.



ND

(15) NSTATE^^3- J^ ^A^CPT^.A.m.a.d * C°^COM^.™. a.d> ^^-^^^^^^.^^ "^ ^^TATE^^ 3

PERNEC , - per cent of people who have accepted d times and

' last accepted method ra who have a negative experience

The first term defines those due for an appointment in month t (ACCPT^ . ,)

t-A,m,a,d

less those who came (CONCOM) , while the second term defines the non-negative
percentage, For the negative states (s=5+n, m=l, . . . , NM, where NM is the

number of methods) :

ND

(16) NSTATE c^ - ^ (ACCPT^ a ™ o h" CONCOM, .) PEKNEG ,

z,yrm *^ t-A,m,a,d t,m,a,d m,d

Those who come may accept (see box 15 and equation 7), switch

methods (box 16), and be referred (see box 17 and equation 13). Those

who do not come to their appointment may obtain contraceptives through

private channels and they are identified as a separate group, but they are

not included in the public system manager's definition of "active". At

the end of the period all people are in a non-active (NSTATE ) or

active (ACCPT^ ,) so in the next period, equations 1 to 15 again
t,m,a,a r- . -i

process them through the non-post partum flow.


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Online LibraryGlen L UrbanA model for the management of a family planning system → online text (page 1 of 4)