The Demand for Outpatient The Demand for Outpatient Medical Care in Rural KenyaMedical Care in Rural Kenya

Randall P. EllisRandall P. EllisBoston University USABoston University USA

andandGermano M. MwabuGermano M. Mwabu

University of Nairobi, KenyaUniversity of Nairobi, KenyaMay 2004May 2004

Outline – Skip in short talk!Outline – Skip in short talk!

• Introduction• Model

– Conceptual framework– Choice Process

• Data• Results• Conclusions

Contribution of the paperContribution of the paper

• Structural model of choice among specific providers rather than classes of providers (e.g., public versus private)

• Four stage nested logit model– Report illness– Seek formal care– Choose provider– Take bus or walk

• Calculate demand responsiveness and willingness to pay

ContextContext

• Rural Kenya, 1989, mostly agricultural• Extremely poor region

– < US$200 per capita– 50.2% reported experiencing hunger– High infant mortality

• Poor access to health care – Average 60 minute travel time– 98 minute wait for treatment

• Under-funded “free” public system competing with private health facilities

• Travel costs on a par with treatment costs• Government looking for ways to raise revenue• Implemented user fees while I was there in 1989

Where is Kenya?Where is Kenya?

Where is South Nyanza in Kenya?Where is South Nyanza in Kenya?

South Nyanza

Interesting setting to studyInteresting setting to study

• Diverse set of facilities– Government facilities

• 2 Hospitals• 12 Health centres• 32 Dispensaries

– 34 Missionary and private facilities– Diverse private dispensaries– Informal providers – herbalists, witchcraft,

traditional providers – Not modeled here

Three questions answeredThree questions answered

• How much are consumers willing to pay for an improvement in facility quality?

• Are explainable patterns of utilization of health services due to differences in prevalence of illness or treatment seeking behavior?

• What are implications of modeling transportation mode (bus or walk) decision?

Usual approach: direct utility functionUsual approach: direct utility function

Gertler et al (1987), Dor et al (1987) Uk = Uk (Hk, Ck) Utility from choice k

Hk = h(X, Zk, H0 ) Health production

Ck = I – Pk – wTk Income constraint

Hk = Health after visiting k Ck = consumption of other goods

X = consumer variables Zk = provider characteristics

Pk = price of k, Tk= time cost of k, w = wage, I = income

Approach here: indirect utility functionApproach here: indirect utility function

Mwabu (1986)Uk = Uk (Hk, Ck) Utility from choice k

Hk = h(X, Zk, H0 ) Health production

Ck = C(I, W, Pk, Tk) Demand curve

Different types of time may have different shadow cost to consumerMultiple members of the family may have illness in the same periodAllows assets as well as income to be entered.Allows log of income or wealth to be used.

mi

Choice processChoice process

Uijkmt = U(Xit, Yijt, Zk, Mkm)

+ i + ij + ijk + ijkmt (5)

Pr(i,j,k,m) = Pr(i) Pr(j|i) Pr(k|i,j) Pr(m|i,j,k)

(6)

Choice process starting at last node Choice process starting at last node (Choice of bus versus walking)(Choice of bus versus walking)

Choice process at second to last node Choice process at second to last node (choice of provider)(choice of provider)

Note number of providers to choose from varies by cluster.

Log likelihood functionLog likelihood function

This corresponds to Mcfadden (1978), and Greene (2000), which Heiss (2002) has called the non-normalized nested logit model.

DataData

• WHO “cluster sampling” strategy• 60 clusters (villages) randomly selected• 552 households• 3063 individuals included

– 309 missing data omitted– 34 people hospitalized (1.2% of sample)

• 2720 used for modeling• Used reported income rather than consumption• Transport modes collapsed into “bus” and “walk”

13 measures of quality collapsed using 13 measures of quality collapsed using principal componentsprincipal components

– # of rooms

– # of general beds

– # of maternity beds

– # of medical officers

– # of staff houses

Dummy variables for

– Electricity

– Telephone

– Running water

– Well used

– Maternal child health provided

– Family planning

– Performs surgery

Average PRIN1 values

dispensary = -1.32

health centre = 1.65

District hospital= 2.60

EstimationEstimation

• FIML estimation of joint system using Fortran BHHH algorithm

• Results very different from non-nested logit or sequential estimators

• Explored further interactions (with income and prices) not used in final model

• After testing for inclusive values, could not reject omitted from first stage decision, hence omitted

Implied tradeoffsImplied tradeoffs

• One hour of waiting time revealed to be valued at 22.5 KSh

• One hour of walk time revealed to be valued at 64.3 KSh by average income household

• Upgrading a health facility from a dispensary to a government health centre revealed to be valued at 19.1 KSh

ConclusionsConclusions• More demographic variables affect probability of reporting

an illness than the decision to seek treatment. Income works in opposite directions on these two decisions.

• Choice of transportation is clearly endogenous and affects facility choice.

• Facility quality seriously influences provider choice. • Consumers willing to pay significantly to avoid waits,

travel.• Travel time is a useful method for calculating revealed

preference. • Predicted impact of fee change is substitution, not a

reduction in formal treatment.

Importance of travel timeImportance of travel time

• Varies across individuals, arguably exogenously, hence a valuable instrument

• Affects demand for treatment• May be correlated with supply (rural versus urban)• For emergency room visits, may affect severity

upon arrival (heart attach victims)• Can look at distance to facilities bypassed to

another facility (differential distance)• Can be used to rate facility “quality” or

attractiveness

Using Principal Components/Factor AnalysisUsing Principal Components/Factor Analysis

• Useful for collapsing multiple, highly correlated measures, into one dimension (facility quality)

• Finds optimal weighted sum of all variables that minimizes the orthogonal distances and best explains the entire vector.