- Email: [email protected]
Visit planning by the rural family planning worker
Socio-&on. Ph. Sci Vol. IS. No. 5. pp. 243-248, 1981 Printed in Great Britain
MM~12l/Sl/OSO243~~o6$o2.oo1O Pergamon Press Ltd
VISITPLANNINGBYTHERURALFAMILYPLANNINGWORKER Indian Institute of Management, Ahmedabad-380015,India EReceiced ft January 1981) A~~~~t-Tbis paper discusses a modet of adoption of family planning methods in which the necessary conditions for acceptance are inferred from survey data. The dynamics of the adoption process is modelled as a Markov process. A dynamic programming formulation is used to find an optimal visit pattern for the field worker. The optimal solution recommends selectivity in visits. For the optimal strategy the relationship between total visit effort and the rate of acceptance is shown to be non tinear and selectivity in visiting is shown to doubte acceptance. The strategy of selectivity was implemented in a field unit experiment, where the performance of the worker and the field unit was shown to improve considerably.
The relevance of government sponsored famiiy welfare programmes in the develaped countries is well established. The Indian family planning programme which began in 1951 has passed through many phases. The performance of the prog~mme is characterised by a large gap between different regions, in spite of a standard pattern of input. Since field worker is the major resource of the programme his effectiveness needs to be improved. Recent introduction of muit~purpose worker (MPW) scheme has limited the population per worker to 5000 and enhanced his status by allowing him to dispense medicine and provide many other services in addition to family planning advice. observation of workers in the field has revealed that even the MPW continues to be ineffective in his family planning work. This paper discusses how the visits of a MPW can be planned for family plannjng work both in terms of profile of the client that should be visited and the content of communication. First a methodology is described to determine a minimal set of necessary characteristics which distinguish an acceptor of family planning from a nonacceptor. The worker needs to manipulate some of these characteristics in moving clients from awareness of family planning to acceptance. Since the coverage in terms af popuiation per worker is about 5000, the targets for acceptors are two per month and the fact that family planning is one of several services to be provided, selectivity has to be maintained. The paper discusses a model of adoption which provides the criteria for this sdection, Finalily, the implementation of some of the suggestions regarding visit planning in a primary health centre (covering 0.1 million population) is described. METHODOLOGY
OF IDENTIFYING
CHARACTERISTICS
NECESSARY
FOR ACCEPTANCE
In disciplines like marketing and innovation, several models have been proposed through analysis of recap data, mostly from acceptors of innovations which identify the stages an individual must pass through before accepting an innovation[lZ]. The reliability of recall data is tow and therefore its use is criticised. The set of s&h-
Gent condition for adoption proposed in this paper are based on data on 1700 couples from rural Western India. This data included all the important attitudinal, socioeconomic and programme variables which have shown some association with family planning use. The basic objective of discovering the necessary characteristics could be considered equivalent to specifyingaminimum set of terminal states with knowndescriptors, from which most of the users could be expected to come. The technique that was used for this purpose-Automatic Interaction Detector (AID)[3] has the ability to classify the population of respondents into sub groups through sequential binary splits on chosen variables such that the within group variance (Wan) on the dependent variatrte is minimised. The within group variance of a tree is defined by eqn (I).
where N is the size of the parent group, P the proportion of users in the parent group, ni the size of the ith terminal (dependent variable is dichotonous) group, pi the proportion of users in the ith group and g the final count of terminal groups. The variables emerging as the descriptors of terminal. groups with high proportion of users could be taken as the necessary conditions for acceptance. A single application of AID to the data was not found adequate and therefore many alternative trees were generated by the use of a simple heuristic. The final tree which represents a set of necessary conditions for adoption is shown in Fig. 1. Favourable attitude, no desire for more children, knowledge of methods, and intention to act can be seen as the 4 sign~~~ant characteristics that an individual must possess before finally accepting a family planning method. The proposed mode! (based on the data of 1700 coupies which included 201 users) was able to explain the use behaviour of 156 user couples (78% of the user couples). The three variables explained 30% of the variance and another 10% was explained by variables which intervened between the stage of knowledge and the final 243
244
S.C.BHATNAGAR
G
78
(0)
'G 152 (2) M 270 (IO) Mp423(4)
G
49
M Mp
63 (0) 213 (3)
G M
29 38
(0) (8)
Mp
19
(1)
G 56 M 128 Mp 169 G
G
335
(59)
M
718
(110)
Mp 647
(32)
M
G ,M
183 448
Mp 224
237
(0) (1) (0)
I8
(2)
M 41 Mp 22
(1) (0)
G M
54 80
(16) (9)
Mp
77
(2)
(87) G 74 M I57 Mp 50
(57)
(40) (78) (23)
(100) (28) k Mp
Legend
(01
22 80 78
(0) (II (2)
33 131
(I) (12)
: G M
A - Attitude to Family Planning D - Desire for more children K - Knowledge of Family Planning G - Gujarat M - Maharashtra Mp- Madhya Pradesh Numbers : Respondents (users)
Mp
19
(1)
Fig. 1. Characteristicsof adopters. adoption of a family planning method. Thus it could be said that the model possesses a high degree of internal validity. To study the external validity of the model structure, the three characteristics namely attitude, desire and knowledge were imposed separately on the component data of three different states in Western India (Fig. I). In each state, the model was able to explain the use behaviour of about 70% of the user couples even when the acceptance rates for the three states differed widely (Gujarat: 17.6%, Maharasbtra: 15.3% and Madhya Pradesh: 4.9%). STATE-SPACEDESCIUPTIONOFCOUPLES
A Markov Process model, specified by a transition matrix P where each element Pijrepresents the probability of movement from state i to state j, was used to describe the dynamics of the process of adoption of family planning. The states could be defined in terms of various combinations of the 4 characteristics found to be necessary for adoption.
tance for each couple. Two sets of transition probabilities need to be estimated to represent the movement of couples between states-when a couple is visited by a field worker and when a couple is not visited by a field worker. In the first quarter, no special effort was made to contact the couples. At the end of the quarter, only 25% of the sample had been contacted by the worker. In the second quarter, a special effort was made to contact about 70% of the sample. The results of the three surveys were pooled separately for couples visited and couples not visited to estimate the transition probabilities. In linking only 4 of the 12 combinations of attitude, desire and knowledge with use and 6 with intention to act, the stage model ruled out the existence of 14 states. Thus the state-space was defined by the remaining 22 states. Transition probabilities were estimated by using the formula in eqn (2).
P(i/j, k) =
ESTIMATIONOFTRANSITIONMATIUCES
Time ordered micro data was generated from a panel study consisting of three successive surveys of a panel of 255 respondents repeated after approximately one quarter (three months). The surveys were designed to measure the four characteristics necessary for accep-
where T is the number of time periods for which observations were made and n(i/j, k) is the number of responder .s who moved from the jth state to the ith state during one time period under the kth alternative (k = 1
Visit planning by the rural family planning worker Table 1. Matrix of transition probabilities-not statss
1
2
3
4
266 66 1. 2 58 235 50 3 583250 4 500 5005* 62 _ 6 12s soil 7 76 76 76 8 *---___ gS----____ 250lo* 1 _-____ ,5312* 13' ‘10_______ 15* _ l6.w --____-____-_ 400-______-_______ 179 ,2518 125IF20 mo _ 2, . --_-_ 22 __" -
oescription 1. 2. 3. 4. 5. 6. 7. 5. 9. 10. 11.
5
6
7
66 5s 500 307
66 62 *w76
_ 62
-
-
_
_ _
,66-
8
9
IO
1,
12
13
14
visited 1s
66 266 66 66 66 176 11'7 11: 53 58 58 83..-__ -" _ _ _ _ _ 62 62 -_-"_-_-..___ ,53_ _ _ ,53,ooo_ _ _ _ _ _ 23076 692 _ 253 250 250 c I 500_-,:,_____ 500 _ _ 533 66 _ 133 66 666__"_______ _ _ 333 0 0 __ 400 200 200 333?66166500
_ _ 62...._-_ 200 2QO_
_ _
100"
-
"
16
17
qe19_---zr___-
_ -
93.. _ -
_ -
____ _ " l25-
_ -
" ~
_ _ _
I" -
1 _
I _ _
-
-
-
-
125 62 200 3"O
66
-
200 166125 687 62 _ _ 133 wo 303 __-.-
_
IO0
100".
-
-
-
:: _ -
-
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-
_ -
c 375 _ -
-
_
_ 125
-
125. -
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-
-
_ _ ,2$_ _ -
_
500
22 ~ ._ -
-
400 _ "
_
__-_-______-
-
245
200 200 370 1x1
.-
of states 22. 13. 14, 15. 16. 17. ID. 19. 20. 21. 22.
Fav. att. no desire, know. no in+.. non use Fsv. att. no deslra, know. int. non use Fav. att. no dsatie, knw. ,,ee Fw. att. ne desire, nc knou. no lnt. non "am FW. att. deeire, know, no int. non USB Fw. att. desire, know. use FIN. att. desire, knw. non use Fav. att. desir., no !:nrw. int. non UBB Wfev. att. no desire, kna#, na fnt. non ~88 Unfav. att. no d-ire, knau. !nt. ~3" use Unfav. att. no desire, no knm. no int. non UBB
Unfav. att. desire, know. no int. nan use Unfav. att. desire, knw. int. non use Unfav. att. desire, no &ma'. no fnt. non "88 Neut. att. no desire, kncu. no int. non use Neut. att. no desire, know. int. "cr. USC Meut. att. desCrn, no kmw. no int. non "8~ h'eut. att. desire. OD knw. na fnt. ncn ~88 Fleut, att, desire, knar. no fnt. non we Naot. z&t. desire, know. int. non usa Nwt. att. desire, know wo tile&. &t, no desire,,knw. WE
Table 2. Matrix of transition probabilities-visited 1
2
3
4
5
6
1 2 3 4
333 83 83 176 552 176 58 600 40 40 160 5on---~o-_-_-___,bc~______ __ 538 153 250 125 : 125 375 7---500_ _ *_ 9 142 239 1 ;7 625 125 IO I,-_---_-__ 12 71 142 250 13 200 200 14 500 _ _ _ 15 500-..-16 _ 4OI-_-__-__---_____ *7 333-__I" $1, 19 20600..----_---___, Zl5oo--IIswII::~III::~II____ 22
_' " _ _
*
7
a
9
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11
12
1s
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15
16
77
18
19
8, 58 40
83 SE 40
83 58 -
83 -
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-
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66
-
_ 40-
_
I%_ -
76 125 -
999-
" -
47
_ 250 -
: -
. f" ,25125 --_--_-___ 47 142 _ _ _ 999.. _--_____ 6OD_ 11 71 750-____________I _ 400 200 250_--___ 250: 5lJo---..--~-_-__,_
_
*_----"-__ ,1,-
-
All Probabilftiee *arka
_ 333 ,25,25-
in
-
_
_ _ 500-
2,
22
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-
142-
I
:
I
:
1
:
200 333 222 500
" 111 _ -
222 _ 500
333 111 -
111 .#
-
:
-
Tables 1 L 2 have been multiplied by ,000
in Table 1 denote states r.cmmendti
for couples visited by the field worker (Tabte 1) and k = 2 for couples not visited by the field worker (Table 2). It was assumed that once a couple accepts sterilization, no further movement is possible. Therefore one of the states is an absorbing state and the remanning 21 states are transient states. An analysis of the behavior of the couples revealed that the rate of movement into the absorbing state was almost stable after five quarters. PREDICTIVE VALIDITYOFTHEMODEL
The existing pattern of worker visits was used to predict the acceptance rates of family planning methods. The proportion of couples visited in each state was SEPSVolIS. No. J--D
-
to
for . visit by the uork,r.
calculated by analysing the distribution of visits prior to the first survey and between the first and second survey. In the existing pattern of visits, there was no evidence of a planned effort to reach a particular profile of couples, though a slight bias towards reaching ready couples (with a favourable attitude, no desire for more children and knowledge) existed. Starting from the initial distribution of couples a successive multiplication of the transition matrix (for the current pattern) was done to predict the distribution of couples for 12 quarters. From this distribution, acceptance rates were calculated. Average acceptance rates of 5% for sterilization and 11.4%for condoms were predicted. The actual acceptance rates in this community
246
SC. BHATNAGAR
over a period of five years prior to the study for sterilization and the condom were 4.8% and 13% respectively. Thus the predicted rates were close to the actual. OPTIMIZATIONOF A FIELDWORKER'S VISITPATTERN
Rewards were computed from the costs and benefits of each transition. Benefits result when a couple accepts one of the family planning methods and births are averted. Therefore, transitions into only such states results in benefits. Official estimates of the births prevented by the acceptance of family planning methods and the consequent economic benefits were used: These are: Sterilization averts 1.6 births; Use of the condom for one quarter averts 0.09 birth; Economic benefit of a birth averted is Rs.1500 ($180). A cost is incurred when a field worker visits a couple. This cost has many components like worker’s salary, cost of administrative support, cost of supervision and fixed cost of establishment which could be allocated differently for estimating the cost of a visit. The resource conversion sub system was modelled in detail to obtain the cost estimates which ranged from Rs 9 to 21 for a visit. DYNAMICPROGRAMMINGFORMULATION
The following dynamic programming formulation was used to optimize the pattern of worker visits. Let p(i/j, k) be the probability of transition from the jth state to the ith state under alternative k. Let r(i/j, k) be the reward associated with a transition from the jth state to the ith state under alternative k. Then 22
qik= 2 P(j/i, k) x r(j/i, k) i=l
is the immediate reward for a transition from state i. Let d,(t) be the number of the alternative in the ith state that will be followed in time period t. When di(t) is specified for all i and all t, a policy is determined. The optimal policy is one that maximizes total expected reward from each i and t. Let w(f) be the total expected reward in t periods starting from state i if an optimal policy is followed. Then the recursive relationship in eqn (4) holds [4]
sterilization the optimal pattern of visits will result in an average acceptance rate of 12% a year. This was approximately two and a half times the existing acceptance rate. The acceptance of spacing methods (condom and IUCD) will be on an average 18%. This rate was 50% more than the existing acceptance rate of 13%. The variance in the acceptance rates of family planning arises from errors in estimates of individual probabilities and from the stochastic nature of the transition matrices. The latter was estimated by using a method suggested by Bartholomew[5]. The basic assumption is that nj(t), the number of respondents in state j at time t has a multi-nominal distribution with parameters N (the total number of respondents) and pi (the proportion of respondents in state j) for all t. The variance was computed for the expected distribution in the 22 states under the optimal policy. The standard deviation for the expected number of sterilization cases at the end of the tenth quarter was 5.7, less than 10% of the number of expected sterilization cases at the end of the tenth quarter. This visit pattern was expected to result in a steady decline in resource consumption. For the sample population, 58% of the couples are expected to be visited in the first quarter. By the end of the twelfth quarter, only 28% of the couples are expected to be visited. The average proportion of couples visited each quarter was 40% as compared with the existing visit effort of 25%. The cost sensitivity of the optimal strategy was studied. As earlier mentioned, the cost estimates for a visit may vary depending upon the allocation of overhead costs. Also, the acceptance rates for different amounts of visit efforts were estimated. The optimal decision rule to select couples to visit was insensitive to cost changes within the range of Rs. O-21. As the cost of a visit was increased, the optimal amount of average visit effort needed declined. The acceptance rates measured in equivalent sterilizations (a composite measure used by the Government of India) for different visit efforts is plotted in Fig. 2). The non-linear relationship between the visit effort and acceptance rates is evident (Fig. 2). The acceptance rates were higher when the optimal pattern of visits was followed; this was due to two factors-selectivity in couples contacted and higher visit effort. The existing visit effort (B) of 25% results in 7.5% acceptance. If this effort is optimally spent, then 14.5% acceptance is
w,(t + 1) m;x {qi’ + 5 p(i/j, k) x w:(t)). j=l
The recursive equation given above can be solved to specify the optimal alternative to be used in each state at each stage (period) of the process. OPTlMALVISITPATTERN
The optimal visit pattern was calculated by using the dynamic programming formulation given above. Visits to selective couples were found to be optimal. Only couples in 11states of the total 22 states should be visited. These states are marked in Table 1. It was predicted that for
0
20
40
60
80
JO0
Fig. 2. Acceptancerates vs workervisit effort.
247
Visit planning by the rural family planning worker achieved: an increase of 7%. The increase in acceptance
from an optimal 25% to an optimal 40% visit effort is only 2%. Thus most of the increase was due to selectivity. In fact, the acceptance rates declined to 11% (C) if all the couples are visited each quarter.
The recommendation that selectivity be used in the couples visited assumed that the data on descriptors of couples needed to classify them is available. To get this data a suitable information system needs to be developed. Couple registers are maintained currently for the population. However their classification scheme is based on demographic parameters only. These registers need to be modified to maintain information necessary for the classification scheme suggested here. Answers to only five simple questions are necessary for this classification. Therefore, the cost of maintaining that information system will not be large. Selectivity in couples visited is a well recognized principle. A United Nations Commission report@] cautioned against non-selectivity. This paper provides the selection criteria. Some of the characteristics of the couples to be visited appeared to be counter-intuitive. For instance, it is recommended that couples who have expressed an “intention to act” should not be visited. A field worker’s visit seems to be effective only in preparing a couple for adoption by bringing about attitude and knowledge changes. However for the final act of adoption, the worker is not effective. Worker~lient transactions were observed. The approach of a field worker to a couple who is ready to accept a family planning
method, needs to be altered. Also, the efforts of the worker should be augmented by using other channels which may be effective during the last stage of adoption The analysis treated the current work methodology as fixed. As an experiment the basic family register maintained by every field worker in a PHC was altered, to include 3 additional pieces of information. “Knowledge” was dropped because in the area of experiment it was widespread. Training was provided to the workers to deal with clients selectively and to tailor their communication to the profile being visited. The communication objective for different profile is shown in Table 3. The performance of the PHC as reported in the routine evaluation improved considerably after the institution of the new pattern of field visits[7]. Observation of worker client transactions after the training, revealed that the confidence of the worker in tackling the client had increased to a great extent and skills had improved marginally. The report on the experiment concluded that the suggested pattern of field visits could lead to definite improvement in family planning performance and it could easily be dovetailed into the existing activity pattern of the PHCS.
REFERENCES I. M, S. Alba, Microanalysis of the socia-dynamics of diffusion
of innovation. Unpublished Doctoral Dissertation, North Western University, 1967. 2. D. B. Learner, Profit maximization through new product marketing planning and control. Applicafion of fhe Sciences in ~u~efi~g ~u~uge~e~f (Edited by Frank M. Bass et a!.). Wiley, New York (1968).
Table 3. Workerclient communicationobjective (checklist) Climt
fWour&l~
Attitudtds, NO
No intmtim Nwtral Attitude, NO intontlm
Cbnunioetion
Proflla
dssiro
NO desire
Objsctiw
isethocb
SovKt on knosledgo of nd petfmmanca. R*Mons for non-intention (witing rot child to qrw up &,c. - wqgrat Nir,,dh
Pill.) Favovrab1.s Attitud,, ~a dry&-• Intnt~on N*utra,l Attitude. No desire, 1ntPntion SSll pill knarladqs
Fwourabla ettitude, Desire PIarm then 3 Children Neutral sttittda, desire Flors thin 3 children
Niredh
but first
pxwld,
Why desira nora children?Check health of mother, nutrition of children, tmily income and direuads Proa havinq ~lfs childru,. Cheek ~easonb Pm m~avourabl8 .ttitude, pouid* knwlodqe Build rrrpwrt by rttwMling to nick children, mother .te. Bring in contaEt or a "SW, crmulity lsaler ato. depending on the ,!.~a,, -dialike Fm auailnblemethod., reliqiaw 0ppDaitim ltc. Roibt difficult md hostile EM.* - brinq in contact with aupsrvision. Both amall far&y norm snd FP m&hods ewe not acceptad.
248
S. C. BHATNAGAR
3. .I. N. Morgan and J. A. Sonquist, Problems in the analysis of survey data and a proposal. I. Am. Statist. Assoc. 58.415-434 (1963).
4. R. A. Howard, Dynamic Programming and Markov Processes. MIT Press, Cambridge, Mass. (1966). 5. D. J. Bartholomew, StuchQstic Modems for Social Processes. Wiley, London (1967).
6. United Nations Advisory Mission, An ~vu~~a~jo~~~fhe~am;/y Planning Proaramme of the Government of’ India, p. 8. UN Commis&ione;for Technical Cooperation, frew Yo;k (1969). 7. S. C. Bhatnagar and S. Sengupta, An experiment in activity planning at Singhpur primary health centre. PSG Monograph, Indian Institute of Management, Ahmedabad, April 1980.
MM~12l/Sl/OSO243~~o6$o2.oo1O Pergamon Press Ltd
VISITPLANNINGBYTHERURALFAMILYPLANNINGWORKER Indian Institute of Management, Ahmedabad-380015,India EReceiced ft January 1981) A~~~~t-Tbis paper discusses a modet of adoption of family planning methods in which the necessary conditions for acceptance are inferred from survey data. The dynamics of the adoption process is modelled as a Markov process. A dynamic programming formulation is used to find an optimal visit pattern for the field worker. The optimal solution recommends selectivity in visits. For the optimal strategy the relationship between total visit effort and the rate of acceptance is shown to be non tinear and selectivity in visiting is shown to doubte acceptance. The strategy of selectivity was implemented in a field unit experiment, where the performance of the worker and the field unit was shown to improve considerably.
The relevance of government sponsored famiiy welfare programmes in the develaped countries is well established. The Indian family planning programme which began in 1951 has passed through many phases. The performance of the prog~mme is characterised by a large gap between different regions, in spite of a standard pattern of input. Since field worker is the major resource of the programme his effectiveness needs to be improved. Recent introduction of muit~purpose worker (MPW) scheme has limited the population per worker to 5000 and enhanced his status by allowing him to dispense medicine and provide many other services in addition to family planning advice. observation of workers in the field has revealed that even the MPW continues to be ineffective in his family planning work. This paper discusses how the visits of a MPW can be planned for family plannjng work both in terms of profile of the client that should be visited and the content of communication. First a methodology is described to determine a minimal set of necessary characteristics which distinguish an acceptor of family planning from a nonacceptor. The worker needs to manipulate some of these characteristics in moving clients from awareness of family planning to acceptance. Since the coverage in terms af popuiation per worker is about 5000, the targets for acceptors are two per month and the fact that family planning is one of several services to be provided, selectivity has to be maintained. The paper discusses a model of adoption which provides the criteria for this sdection, Finalily, the implementation of some of the suggestions regarding visit planning in a primary health centre (covering 0.1 million population) is described. METHODOLOGY
OF IDENTIFYING
CHARACTERISTICS
NECESSARY
FOR ACCEPTANCE
In disciplines like marketing and innovation, several models have been proposed through analysis of recap data, mostly from acceptors of innovations which identify the stages an individual must pass through before accepting an innovation[lZ]. The reliability of recall data is tow and therefore its use is criticised. The set of s&h-
Gent condition for adoption proposed in this paper are based on data on 1700 couples from rural Western India. This data included all the important attitudinal, socioeconomic and programme variables which have shown some association with family planning use. The basic objective of discovering the necessary characteristics could be considered equivalent to specifyingaminimum set of terminal states with knowndescriptors, from which most of the users could be expected to come. The technique that was used for this purpose-Automatic Interaction Detector (AID)[3] has the ability to classify the population of respondents into sub groups through sequential binary splits on chosen variables such that the within group variance (Wan) on the dependent variatrte is minimised. The within group variance of a tree is defined by eqn (I).
where N is the size of the parent group, P the proportion of users in the parent group, ni the size of the ith terminal (dependent variable is dichotonous) group, pi the proportion of users in the ith group and g the final count of terminal groups. The variables emerging as the descriptors of terminal. groups with high proportion of users could be taken as the necessary conditions for acceptance. A single application of AID to the data was not found adequate and therefore many alternative trees were generated by the use of a simple heuristic. The final tree which represents a set of necessary conditions for adoption is shown in Fig. 1. Favourable attitude, no desire for more children, knowledge of methods, and intention to act can be seen as the 4 sign~~~ant characteristics that an individual must possess before finally accepting a family planning method. The proposed mode! (based on the data of 1700 coupies which included 201 users) was able to explain the use behaviour of 156 user couples (78% of the user couples). The three variables explained 30% of the variance and another 10% was explained by variables which intervened between the stage of knowledge and the final 243
244
S.C.BHATNAGAR
G
78
(0)
'G 152 (2) M 270 (IO) Mp423(4)
G
49
M Mp
63 (0) 213 (3)
G M
29 38
(0) (8)
Mp
19
(1)
G 56 M 128 Mp 169 G
G
335
(59)
M
718
(110)
Mp 647
(32)
M
G ,M
183 448
Mp 224
237
(0) (1) (0)
I8
(2)
M 41 Mp 22
(1) (0)
G M
54 80
(16) (9)
Mp
77
(2)
(87) G 74 M I57 Mp 50
(57)
(40) (78) (23)
(100) (28) k Mp
Legend
(01
22 80 78
(0) (II (2)
33 131
(I) (12)
: G M
A - Attitude to Family Planning D - Desire for more children K - Knowledge of Family Planning G - Gujarat M - Maharashtra Mp- Madhya Pradesh Numbers : Respondents (users)
Mp
19
(1)
Fig. 1. Characteristicsof adopters. adoption of a family planning method. Thus it could be said that the model possesses a high degree of internal validity. To study the external validity of the model structure, the three characteristics namely attitude, desire and knowledge were imposed separately on the component data of three different states in Western India (Fig. I). In each state, the model was able to explain the use behaviour of about 70% of the user couples even when the acceptance rates for the three states differed widely (Gujarat: 17.6%, Maharasbtra: 15.3% and Madhya Pradesh: 4.9%). STATE-SPACEDESCIUPTIONOFCOUPLES
A Markov Process model, specified by a transition matrix P where each element Pijrepresents the probability of movement from state i to state j, was used to describe the dynamics of the process of adoption of family planning. The states could be defined in terms of various combinations of the 4 characteristics found to be necessary for adoption.
tance for each couple. Two sets of transition probabilities need to be estimated to represent the movement of couples between states-when a couple is visited by a field worker and when a couple is not visited by a field worker. In the first quarter, no special effort was made to contact the couples. At the end of the quarter, only 25% of the sample had been contacted by the worker. In the second quarter, a special effort was made to contact about 70% of the sample. The results of the three surveys were pooled separately for couples visited and couples not visited to estimate the transition probabilities. In linking only 4 of the 12 combinations of attitude, desire and knowledge with use and 6 with intention to act, the stage model ruled out the existence of 14 states. Thus the state-space was defined by the remaining 22 states. Transition probabilities were estimated by using the formula in eqn (2).
P(i/j, k) =
ESTIMATIONOFTRANSITIONMATIUCES
Time ordered micro data was generated from a panel study consisting of three successive surveys of a panel of 255 respondents repeated after approximately one quarter (three months). The surveys were designed to measure the four characteristics necessary for accep-
where T is the number of time periods for which observations were made and n(i/j, k) is the number of responder .s who moved from the jth state to the ith state during one time period under the kth alternative (k = 1
Visit planning by the rural family planning worker Table 1. Matrix of transition probabilities-not statss
1
2
3
4
266 66 1. 2 58 235 50 3 583250 4 500 5005* 62 _ 6 12s soil 7 76 76 76 8 *---___ gS----____ 250lo* 1 _-____ ,5312* 13' ‘10_______ 15* _ l6.w --____-____-_ 400-______-_______ 179 ,2518 125IF20 mo _ 2, . --_-_ 22 __" -
oescription 1. 2. 3. 4. 5. 6. 7. 5. 9. 10. 11.
5
6
7
66 5s 500 307
66 62 *w76
_ 62
-
-
_
_ _
,66-
8
9
IO
1,
12
13
14
visited 1s
66 266 66 66 66 176 11'7 11: 53 58 58 83..-__ -" _ _ _ _ _ 62 62 -_-"_-_-..___ ,53_ _ _ ,53,ooo_ _ _ _ _ _ 23076 692 _ 253 250 250 c I 500_-,:,_____ 500 _ _ 533 66 _ 133 66 666__"_______ _ _ 333 0 0 __ 400 200 200 333?66166500
_ _ 62...._-_ 200 2QO_
_ _
100"
-
"
16
17
qe19_---zr___-
_ -
93.. _ -
_ -
____ _ " l25-
_ -
" ~
_ _ _
I" -
1 _
I _ _
-
-
-
-
125 62 200 3"O
66
-
200 166125 687 62 _ _ 133 wo 303 __-.-
_
IO0
100".
-
-
-
:: _ -
-
_
-
_ -
c 375 _ -
-
_
_ 125
-
125. -
"
-
-
_ _ ,2$_ _ -
_
500
22 ~ ._ -
-
400 _ "
_
__-_-______-
-
245
200 200 370 1x1
.-
of states 22. 13. 14, 15. 16. 17. ID. 19. 20. 21. 22.
Fav. att. no desire, know. no in+.. non use Fsv. att. no deslra, know. int. non use Fav. att. no dsatie, knw. ,,ee Fw. att. ne desire, nc knou. no lnt. non "am FW. att. deeire, know, no int. non USB Fw. att. desire, know. use FIN. att. desire, knw. non use Fav. att. desir., no !:nrw. int. non UBB Wfev. att. no desire, kna#, na fnt. non ~88 Unfav. att. no d-ire, knau. !nt. ~3" use Unfav. att. no desire, no knm. no int. non UBB
Unfav. att. desire, know. no int. nan use Unfav. att. desire, knw. int. non use Unfav. att. desire, no &ma'. no fnt. non "88 Neut. att. no desire, kncu. no int. non use Neut. att. no desire, know. int. "cr. USC Meut. att. desCrn, no kmw. no int. non "8~ h'eut. att. desire. OD knw. na fnt. ncn ~88 Fleut, att, desire, knar. no fnt. non we Naot. z&t. desire, know. int. non usa Nwt. att. desire, know wo tile&. &t, no desire,,knw. WE
Table 2. Matrix of transition probabilities-visited 1
2
3
4
5
6
1 2 3 4
333 83 83 176 552 176 58 600 40 40 160 5on---~o-_-_-___,bc~______ __ 538 153 250 125 : 125 375 7---500_ _ *_ 9 142 239 1 ;7 625 125 IO I,-_---_-__ 12 71 142 250 13 200 200 14 500 _ _ _ 15 500-..-16 _ 4OI-_-__-__---_____ *7 333-__I" $1, 19 20600..----_---___, Zl5oo--IIswII::~III::~II____ 22
_' " _ _
*
7
a
9
IO
11
12
1s
lb
15
16
77
18
19
8, 58 40
83 SE 40
83 58 -
83 -
-
" -
-
-
66
-
_ 40-
_
I%_ -
76 125 -
999-
" -
47
_ 250 -
: -
. f" ,25125 --_--_-___ 47 142 _ _ _ 999.. _--_____ 6OD_ 11 71 750-____________I _ 400 200 250_--___ 250: 5lJo---..--~-_-__,_
_
*_----"-__ ,1,-
-
All Probabilftiee *arka
_ 333 ,25,25-
in
-
_
_ _ 500-
2,
22
_ -
_ _
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I -
153.. _ _ -
_ _
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:
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142-
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200 333 222 500
" 111 _ -
222 _ 500
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:
-
Tables 1 L 2 have been multiplied by ,000
in Table 1 denote states r.cmmendti
for couples visited by the field worker (Tabte 1) and k = 2 for couples not visited by the field worker (Table 2). It was assumed that once a couple accepts sterilization, no further movement is possible. Therefore one of the states is an absorbing state and the remanning 21 states are transient states. An analysis of the behavior of the couples revealed that the rate of movement into the absorbing state was almost stable after five quarters. PREDICTIVE VALIDITYOFTHEMODEL
The existing pattern of worker visits was used to predict the acceptance rates of family planning methods. The proportion of couples visited in each state was SEPSVolIS. No. J--D
-
to
for . visit by the uork,r.
calculated by analysing the distribution of visits prior to the first survey and between the first and second survey. In the existing pattern of visits, there was no evidence of a planned effort to reach a particular profile of couples, though a slight bias towards reaching ready couples (with a favourable attitude, no desire for more children and knowledge) existed. Starting from the initial distribution of couples a successive multiplication of the transition matrix (for the current pattern) was done to predict the distribution of couples for 12 quarters. From this distribution, acceptance rates were calculated. Average acceptance rates of 5% for sterilization and 11.4%for condoms were predicted. The actual acceptance rates in this community
246
SC. BHATNAGAR
over a period of five years prior to the study for sterilization and the condom were 4.8% and 13% respectively. Thus the predicted rates were close to the actual. OPTIMIZATIONOF A FIELDWORKER'S VISITPATTERN
Rewards were computed from the costs and benefits of each transition. Benefits result when a couple accepts one of the family planning methods and births are averted. Therefore, transitions into only such states results in benefits. Official estimates of the births prevented by the acceptance of family planning methods and the consequent economic benefits were used: These are: Sterilization averts 1.6 births; Use of the condom for one quarter averts 0.09 birth; Economic benefit of a birth averted is Rs.1500 ($180). A cost is incurred when a field worker visits a couple. This cost has many components like worker’s salary, cost of administrative support, cost of supervision and fixed cost of establishment which could be allocated differently for estimating the cost of a visit. The resource conversion sub system was modelled in detail to obtain the cost estimates which ranged from Rs 9 to 21 for a visit. DYNAMICPROGRAMMINGFORMULATION
The following dynamic programming formulation was used to optimize the pattern of worker visits. Let p(i/j, k) be the probability of transition from the jth state to the ith state under alternative k. Let r(i/j, k) be the reward associated with a transition from the jth state to the ith state under alternative k. Then 22
qik= 2 P(j/i, k) x r(j/i, k) i=l
is the immediate reward for a transition from state i. Let d,(t) be the number of the alternative in the ith state that will be followed in time period t. When di(t) is specified for all i and all t, a policy is determined. The optimal policy is one that maximizes total expected reward from each i and t. Let w(f) be the total expected reward in t periods starting from state i if an optimal policy is followed. Then the recursive relationship in eqn (4) holds [4]
sterilization the optimal pattern of visits will result in an average acceptance rate of 12% a year. This was approximately two and a half times the existing acceptance rate. The acceptance of spacing methods (condom and IUCD) will be on an average 18%. This rate was 50% more than the existing acceptance rate of 13%. The variance in the acceptance rates of family planning arises from errors in estimates of individual probabilities and from the stochastic nature of the transition matrices. The latter was estimated by using a method suggested by Bartholomew[5]. The basic assumption is that nj(t), the number of respondents in state j at time t has a multi-nominal distribution with parameters N (the total number of respondents) and pi (the proportion of respondents in state j) for all t. The variance was computed for the expected distribution in the 22 states under the optimal policy. The standard deviation for the expected number of sterilization cases at the end of the tenth quarter was 5.7, less than 10% of the number of expected sterilization cases at the end of the tenth quarter. This visit pattern was expected to result in a steady decline in resource consumption. For the sample population, 58% of the couples are expected to be visited in the first quarter. By the end of the twelfth quarter, only 28% of the couples are expected to be visited. The average proportion of couples visited each quarter was 40% as compared with the existing visit effort of 25%. The cost sensitivity of the optimal strategy was studied. As earlier mentioned, the cost estimates for a visit may vary depending upon the allocation of overhead costs. Also, the acceptance rates for different amounts of visit efforts were estimated. The optimal decision rule to select couples to visit was insensitive to cost changes within the range of Rs. O-21. As the cost of a visit was increased, the optimal amount of average visit effort needed declined. The acceptance rates measured in equivalent sterilizations (a composite measure used by the Government of India) for different visit efforts is plotted in Fig. 2). The non-linear relationship between the visit effort and acceptance rates is evident (Fig. 2). The acceptance rates were higher when the optimal pattern of visits was followed; this was due to two factors-selectivity in couples contacted and higher visit effort. The existing visit effort (B) of 25% results in 7.5% acceptance. If this effort is optimally spent, then 14.5% acceptance is
w,(t + 1) m;x {qi’ + 5 p(i/j, k) x w:(t)). j=l
The recursive equation given above can be solved to specify the optimal alternative to be used in each state at each stage (period) of the process. OPTlMALVISITPATTERN
The optimal visit pattern was calculated by using the dynamic programming formulation given above. Visits to selective couples were found to be optimal. Only couples in 11states of the total 22 states should be visited. These states are marked in Table 1. It was predicted that for
0
20
40
60
80
JO0
Fig. 2. Acceptancerates vs workervisit effort.
247
Visit planning by the rural family planning worker achieved: an increase of 7%. The increase in acceptance
from an optimal 25% to an optimal 40% visit effort is only 2%. Thus most of the increase was due to selectivity. In fact, the acceptance rates declined to 11% (C) if all the couples are visited each quarter.
The recommendation that selectivity be used in the couples visited assumed that the data on descriptors of couples needed to classify them is available. To get this data a suitable information system needs to be developed. Couple registers are maintained currently for the population. However their classification scheme is based on demographic parameters only. These registers need to be modified to maintain information necessary for the classification scheme suggested here. Answers to only five simple questions are necessary for this classification. Therefore, the cost of maintaining that information system will not be large. Selectivity in couples visited is a well recognized principle. A United Nations Commission report@] cautioned against non-selectivity. This paper provides the selection criteria. Some of the characteristics of the couples to be visited appeared to be counter-intuitive. For instance, it is recommended that couples who have expressed an “intention to act” should not be visited. A field worker’s visit seems to be effective only in preparing a couple for adoption by bringing about attitude and knowledge changes. However for the final act of adoption, the worker is not effective. Worker~lient transactions were observed. The approach of a field worker to a couple who is ready to accept a family planning
method, needs to be altered. Also, the efforts of the worker should be augmented by using other channels which may be effective during the last stage of adoption The analysis treated the current work methodology as fixed. As an experiment the basic family register maintained by every field worker in a PHC was altered, to include 3 additional pieces of information. “Knowledge” was dropped because in the area of experiment it was widespread. Training was provided to the workers to deal with clients selectively and to tailor their communication to the profile being visited. The communication objective for different profile is shown in Table 3. The performance of the PHC as reported in the routine evaluation improved considerably after the institution of the new pattern of field visits[7]. Observation of worker client transactions after the training, revealed that the confidence of the worker in tackling the client had increased to a great extent and skills had improved marginally. The report on the experiment concluded that the suggested pattern of field visits could lead to definite improvement in family planning performance and it could easily be dovetailed into the existing activity pattern of the PHCS.
REFERENCES I. M, S. Alba, Microanalysis of the socia-dynamics of diffusion
of innovation. Unpublished Doctoral Dissertation, North Western University, 1967. 2. D. B. Learner, Profit maximization through new product marketing planning and control. Applicafion of fhe Sciences in ~u~efi~g ~u~uge~e~f (Edited by Frank M. Bass et a!.). Wiley, New York (1968).
Table 3. Workerclient communicationobjective (checklist) Climt
fWour&l~
Attitudtds, NO
No intmtim Nwtral Attitude, NO intontlm
Cbnunioetion
Proflla
dssiro
NO desire
Objsctiw
isethocb
SovKt on knosledgo of nd petfmmanca. R*Mons for non-intention (witing rot child to qrw up &,c. - wqgrat Nir,,dh
Pill.) Favovrab1.s Attitud,, ~a dry&-• Intnt~on N*utra,l Attitude. No desire, 1ntPntion SSll pill knarladqs
Fwourabla ettitude, Desire PIarm then 3 Children Neutral sttittda, desire Flors thin 3 children
Niredh
but first
pxwld,
Why desira nora children?Check health of mother, nutrition of children, tmily income and direuads Proa havinq ~lfs childru,. Cheek ~easonb Pm m~avourabl8 .ttitude, pouid* knwlodqe Build rrrpwrt by rttwMling to nick children, mother .te. Bring in contaEt or a "SW, crmulity lsaler ato. depending on the ,!.~a,, -dialike Fm auailnblemethod., reliqiaw 0ppDaitim ltc. Roibt difficult md hostile EM.* - brinq in contact with aupsrvision. Both amall far&y norm snd FP m&hods ewe not acceptad.
248
S. C. BHATNAGAR
3. .I. N. Morgan and J. A. Sonquist, Problems in the analysis of survey data and a proposal. I. Am. Statist. Assoc. 58.415-434 (1963).
4. R. A. Howard, Dynamic Programming and Markov Processes. MIT Press, Cambridge, Mass. (1966). 5. D. J. Bartholomew, StuchQstic Modems for Social Processes. Wiley, London (1967).
6. United Nations Advisory Mission, An ~vu~~a~jo~~~fhe~am;/y Planning Proaramme of the Government of’ India, p. 8. UN Commis&ione;for Technical Cooperation, frew Yo;k (1969). 7. S. C. Bhatnagar and S. Sengupta, An experiment in activity planning at Singhpur primary health centre. PSG Monograph, Indian Institute of Management, Ahmedabad, April 1980.