Determinants of the choice of health care provider in Nigeria

Determinants of the choice of health care provider in Nigeria

Lloyd Ahamefule Amaghionyeodiwe

Received: 7 February 2007 /Accepted: 24 September 2007 / Published online: 23 October 2007# Springer Science + Business Media, LLC 2007

Abstract The study investigated the determinants ofhouseholds’ choice of health care provider in Nigeria.Individual and household based questionnaires were usedfor the study. Using a multinomial logit model, the findingsrevealed that both distance and money prices are significantfactors in discouraging individuals from seeking modernhealth care services but money prices was less important asa determinant of the choice of health care provider. Andfrom the descriptive analysis, money price was a majorreason why many low income households opted for theself-care option. Furthermore, the study showed that olderpeople tend to patronize public and private hospitals.

Keywords Provider choice . Health care .

Public sector health care . Health care utilization

JEL Classification I10

1 Introduction

Having faced difficult times in recent years, the Nigerianeconomy nose-dived in terms of its performance, and thehealth sector was not speared. The public sector healthsector in Nigeria has been faced with depleting resourceswith which to achieve health sector objectives. This isworsened by the increasing demand for public sector healthcare services. Thus, user fees were introduced, whereindividuals now have to pay a token for the services

rendered. Given the inability of the government to meet theever-increasing demand for health care within its con-strained resources, the quality of health services beingprovided by public sector and the subsequent introductionof user charges in this ‘difficult’ period when the economyis plagued by an ever-increasing inflation, unemploymentand poverty and the persistent fall in household's realincome, has led to poor health performance of the Nigerianeconomy. The health sector suffered disproportionately,with the health needs of many Nigerians (particularly therural and urban poor) remaining unmet.

For instance, mortality rate remained high. As at 1995,the infant and under-five mortality rates stood at 195 and191 per 100,000 live births respectively. The average lifeexpectancy at birth for both males and females was 50years [34] while only 56.5% of Nigerians had access to oneform of health care or the other [11]. In terms of theownership of health care facilities, available data shows thatboth public sector and private sector ownership of healthcare facilities in Nigeria have increased overtime. As suchthere has been a diverse spread in the usage of health carefaculties in Nigeria. While some individuals and house-holds opted for public sector health facilities, some optedfor the private sector owned health facilities while manyothers opted for self care. The question now is what reallydrives health care provider choice in Nigeria?

In line with this question, Acton [1]; Christianson [9];Grossman [17]; Akin et al. [2]; Heller [19]; Dor and Vander Gaag [10]; Gertler et al. [15]; Gertler and van der Gaag[14]; and Bolduc et al. [6] showed that a typical problem indeveloping countries is that even in the absence of userfees, access to health services are not equal due to non-monetary factors such as travel time. Dor and Vander Gaag[10] and Gertler and van der Gaag [14], using a conditionalutility function, demonstrated that economic variables such

Health Care Manage Sci (2008) 11:215–227DOI 10.1007/s10729-007-9038-3

L. A. Amaghionyeodiwe (*)Department of Economics, Faculty of the Social sciences,University of the West Indies,Mona, Kingston 7, Jamaicae-mail: [email protected]

as household income and price do have an influence onhealth care decisions. In their own studies, Heller [19] andChernichovsky and Meesok [8] showed that price, income,and distance are important determinants of the choice ofhealth provider.

Larsen [23]; Mwabu [29]; Gilson et al. [16] and Tembon[33] emphasized the importance of quality of care, whichthey agreed is significant, in the choice of health careproviders. Collaborating the significance of quality of care,Hall and Dornan [18] avowed that though patients may notbe perfect judges of medical competence, but they dorecognize training and a facility’s technical capacity andthey can make subjective judgments about quality. Theyevaluate the professional qualifications and capabilities ofthe clinicians based on an image of the provider that reflectsthe societal definition and sub-cultural expectations of thatrole, as well as the conceptions formed by the patientthrough prior experiences or from hearing about experi-ences of other people.

Heller [19] and Chernichovsky and Meesok [8], amongothers, affirmed that price, income, and distance areimportant determinants of the choice of health provider.Patients seek inexpensive and physically proximate healthproviders, and the poor are especially likely to seek lessexpensive and closer facilities. Empirically, price is oftencorrelated with quality: more expensive facilities tend tohave higher quality. Since there are few high qualityfacilities, on average patients are further away from suchproviders and have to pay more both for travel and for care.There is therefore a trade-off between quality and cost.

More specifically, Anderson [3], Kroeger [22], Newboldet al. [31], Henderson et al. [20], Fosu [12] categorized thefactors that influence the demand for health care servicesinto three: predisposing factors (social or demographiccharacteristics), enabling factors (access for health care),and the need for care (characteristics of perceived illness).

Given the above, this study investigated what factorsdrives the choice of health care provider in Nigeria. Therest of the paper is organized as follows. Section 2 containsthe methodology and estimation issues as well as datarequirements and sources. Section 3 looks at the character-istics and facility assessment of respondents. Section 4 isthe estimation results and discussion while Section 5concludes the study.

2 Health care system in Nigeria

2.1 Structure of the Nigerian health care system

Presently, modern health care services in Nigeria areprovided by the federal, state and local governments aswell as private non-governmental (profit and non-profit)

organizations. The three-tiers of government operatethrough a network of primary, secondary and tertiary levelfacilities.1

The primary health care is largely the responsibility ofthe local governments with the support of state ministries ofhealth. It provides general health services at preventive,curative, and promotive levels to the populace. It also actsas the entry point of the health care system. Private medicalpractitioners provide health care services at this level. Also,noting that traditional medicine is widely used, the localhealth authorities, where applicable, seek the collaborationof the traditional practitioners in promoting their healthprogrammes such as nutrition, environmental sanitation,personal hygiene, family planning and immunization.

Secondary health care is available at the state levels.Each government secondary health facility serves as theadministrative headquarters supervising health care activi-ties of the primary health care units. Secondary health careprovides specialized services to patients referred from theprimary health care level. It operates in- and out-patienthealth care services. Tertiary health care consists of highlyspecialized services, which are provided by teaching andother specialist hospitals. These hospitals provide care forspecific diseases like orthopaedic, ophthalmic (eye), psy-chiatric and other infectious ailments. The referral system isto help ensure that the primary health care services areappropriately supported. The major aim of the referralsystem is to enable all Nigerians have easy access not onlyto primary health care but also to both secondary andtertiary health care.

Basically, from the foregoing, there are three basictransactors within the health care delivery system inNigeria. They are: The Consumers—these are the patientswho need the services of the health care industry. Peoplewho are not sick also require some health interventionprogrammes, which improve their general well-being. Suchhealth intervention programmes include immunization,water and sanitation programmes, etc.; The Producers—these are the doctors and other health professionals orpersonnel as well as the health facilities, which offerservices for the care of the patients.; The Facilitators—these are institutions, public and private, which intermedi-ate in one form or the other between the consumers and theproducers. The forms of intermediation include under-writing the cost of health care received by patients, eitherpartially or fully. Some of these facilitators also play dualrole akin to that of the producer. These transactors arerelated and the linkage between them is summarized inFig. 1.

1 The Nigerian government clearly defined the roles and functions tobe performed by each tier of government and this was entrenched inthe 1979 and 1999 Constitutions of Nigeria.

216 Health Care Manage Sci (2008) 11:215–227

2.2 Ownership of health care facilities in Nigeria

Ownership of registered health institutions in Nigeria hasbeen decentralized with each tier of government owningand operating various health facilities within its area ofjurisdiction. The trend in the ownership of registered healthfacilities in Nigeria has been on the increase.

For instance, between 1987 and 1990, local govern-ments’ and states’ ownership of health facilities increasedmarginally by 9.6% and 2.4% respectively. For the localgovernments the figures stood 6,760 health facilities in1987, and 7,412 in 1990 while that of the state governmentsfor this period stood at 2,982 and 3,353, respectively. By1993, the ownership by the state governments fell to by33.3% while those of the local governments increasedfurther by 12.9%. This expansion was partly due to theemphasis on PHC. And by 2000, there were 10,149 primaryhealth institutions and 937 stated-owned health institutionsin Nigeria. The federal government ownership of healthfacilities between 1987 and 1990, showed an increase of29.7% when it rose from158 and 205. This later fell by9.7% in 1993 and kept falling till 2000 with the federalgovernment owning only 51 health institutions.

In Nigeria, private organizations, missions, privateindividuals, etc also own health care facilities. By 1987,

1,905 health care facilities were privately owned, thenumber rose to 2,295 in 1991 and 3,061 in 1993respectively. This represents 15.1%, 16.4%, and 21.3% ofthe total health care facilities in Nigeria for thecorresponding years. By 2000, it rose to 3,987. Privately-owned health care facilities complement the publicly-owned ones in enhancing the quantity and quality of healthcare services available. The observed growth in theownership of registered health facilities tends to reflect theemphasis on PHC which can be seen as one of the variousmeans of decentralizing health care delivery to thegrassroots. Though, there was a steady growth of healthfacilities in Nigeria, there does not seem to be acorresponding increase in coverage or quality of healthcare services provided.

3 Methodology and estimation issues

3.1 The model

Patient’s choice of health care provider is a discretedecision, consistent with quantitative choice models. Inthis study we assume a heterogeneous group of patients N,which, consists of n types or patient groups. A patient

C

The Facilitator - Payment of services on behalf of Consumers - Receipt for premium - Provision of medical facilities and supplies - Offer of services to Consumers - Research and training. - Referral center, specialist, teaching institutions - Government

A

The Consumer -Need for health care -Self Care -Payment for health care -Payment for Premium

B

The Producer - Offer of services to Consumers - Receipt of payment for services rendered - Provision of medical facilities and supplies *Software, e.g. drugs * Hardware, e.g. buildings/equipment -Research and training

Fig. 1 Transcators within theNigeria health system

Health Care Manage Sci (2008) 11:215–227 217

group is indexed by i=1, 2, ..., n. We assumed that in theevent of an illness, a patient would seek help from a healthcare system characterized by many providers. The patientor his/her relative is further assumed to choose medicaltreatment from five types of health care providers from theset F, indexed by j=1, 2, 3, 4, 5 with:

✓ F1: Self-treatment (includes traditional medicalpractice);✓ F2: Government hospitals✓ F3: Government clinics✓ F4: Private hospitals (including mission hospitals)✓ F5: Private health clinics (including mission clinics)

The probability that a particular alternative is chosenequals the probability that this choice yields the highestutility among all the alternatives. Thus, the maximumexpected utility associated with each treatment option (tothe client) is conditional on: the characteristics of facilitiesassociated with each treatment option [F1, F2, F3, F4, or F5]and the socioeconomic characteristics of the person makingthe choice.

A health care provider is characterized by:

➢ A set V of health service system and accessvariables, with V=(v1, …,vd);

For a patient to derive utility from visiting a health careprovider, he must make a choice out of the five possibilities.The potential benefits from each provider are determined bythe interaction between the provider’s characteristics asperceived by the patient, and the patient’s characteristics.The patient’s characteristics are described as follows.

A patient group is characterized by:

A set X of individual and household characteristics,with X=(x1, ..., xq);A set Y of illness characteristics, which describes thepatient group’s perception of the actual need to makeuse of a health care facility, with Y=(y1, ..., yr);

A patient i in N has a utility function defined on thespace of health care providers, which are conditioned by theset of factors that determine the value of a health careservice for that patient. Since the patient visits only oneprovider, he or she chooses the provider that maximizesutility, which provider then determines her utility. So foreach patient i in N,

uj ¼ F1;F2;F3;F4;F5

�� Xi; Yi;Vj;� � ¼ max ui1; ui2; ui3; ui4; ui5f g

ð1ÞWhere

uij ¼ uj Fj

�� Xi; Yi;Vj;� � ¼ αjXi þ βjYi þ ϕiVj ð2Þ

The parameters αj, βj, and ϕi are vectors with the samedimension as the variables Xj, Yj, and Vi. So for the firstelement:

ajXi ¼Xq

h¼1

ajhxih

From Eq. 2, the first two parameters are individualspecific and the last one is choice specific because itincorporates only attribute aspects of the facilities andcalled conditional in the logit framework. So an increase ofthe variable xik in some aspect k of individual i, will changethe utility of that individual patient (when choosing a healthcare provider j) with a factor αjk. On the other hand, anincrease of the variable Vjk in some aspect k of health careprovider j, will change the utility of patient i (whenchoosing that health care provider) with a factor ϕjk.

These parameters are estimated by applying techniquesthat are developed for a logit model with both multinomialand conditional aspects. The variables are then randomvariables and a stochastic term is added in Eq. 2.

Uij ¼ ajXi þ bjYi þ ϕiVj þ "ij ð3ÞA person will choose provider j=m if and only if it

offers, of all the available choices, the highest level ofutility. So if Fi is a random variable whose value (j=1, 2, 3,4, 5) indicates the choice of provider made by person i, theprobability that person i will choose alternative m is

Pr Fi ¼ mð Þ ¼ Pr Uim < Uij

� �: For all j ¼ 1; 2; 3; 4; 5 with j 6¼ m

So Pr(uim+ɛim>uij+ɛij) and Pr e"ij � "im < ueim � uij� �

,for all j=1, 2, 3, 4, 5 with j≠m.

McFadden [27] has shown that if the five error terms ɛijare independent and identically distributed with Weibulldistribution F "ij

� � ¼ exp exp �"ij� �

, then

Pr Fi ¼ mð Þ ¼ exp Uimð ÞP3

j¼1exp Uij

� � ð4Þ

Since the probabilities sum over the five facilities to 1,this generalized logit model has to be normalized to a systemof four equations in the five −1 unknown probabilities.

3.2 Estimation and prediction

The most widely used quantitative choice model is logitand since the patient’s alternative choices are more thantwo, a multinomial logit model (MNL) was adopted for thestudy. Each of the N observations on the dependent variableFi (i=1,...,n) is treated as a single draw from a multinomialdistribution with five outcomes. Define a dummy variable


δij=1 if patient i makes choice j and δij=0 otherwise, j=1,...,5. Then the likelihood function is

log L ¼Xm

i¼1

X3

j¼i

dij Pr Fi ¼ jð Þ ð5Þ

Maximizing this function with respect to the parametersαj, βj, γj, ϕi, and ψi produces the parameter estimatesbaj; bbj; bϕi. Given these estimates one can compute estimatesof patient i’s utility

buij ¼ bajXi þ bbjYi þ bϕiVj ð6ÞFurthermore, probability predictions can be computed. For

each patient, the estimated probabilities must sum to unityacross the five outcomes, of course. Borooah [7] distinguishestwo ways of establishing the effect of a change in the value ofa variable upon the outcome probabilities. For a multinomialmodel, the most effective way is to compare the computedprobabilities before and after the change with the values ofthe other variables left unchanged.

We follow the alternative way of keeping the values of theother variables unchanged while changes to the values of avariable are being analysed, is to set the values of the othervariables to their mean values, Xr ¼

Pn

i¼1

Xir=n. Then define

;u1j ¼ θjr þX

s 6¼r

θjsXs and ;u0j ¼ θjr þX

s6¼r

θjsXs ð7Þ

and use Eq. 4 to compute the predicted probabilities, firstusing uij ¼ u1j (to obtain bp1j ) and then using uij ¼ ;u0j (toobtain bp0j ). The difference between bp1j and bp0j is the effect ofa change in the value of the rth-determining variable for the“average person” (defined as a person with average valuesfor all other variables) on the probability of observingoutcome j (j=1, 2, 3, 4, 5) for that person.

Applied to the multinomial logit model, the probabilityof a person’s choice for a health care provider, relative tothe self-care option, is expressed as follows:

ln Pr F2ð Þ=Pr F1ð Þð Þ ¼ bθ2 þ ba2Xi þ bβ2Yi þ bϕiV2 ð8Þ



ln Pr F5ð Þ=Pr F1ð Þð Þ ¼ bθ5 þ ba5Xi þ bβ5Yi þ bϕiV5 ð11ÞThe variables X, Y, and V are specified as follows: X is

household and individual characteristics (is a vector ofindividual’s characteristics such as age, seriousness of illness,education, sex, religion etc.), Y is characteristics of perceived

illness (is a random variable, which represents the unob-served individual characteristics such as severity andcomplexity of illness that may affect the providers’ marginalproductiveness relative to self-care.); V is characteristics ofthe health service (is a vector of characteristics of alternativej facing individual i, these included proximities of healthfacilities, probability of being seen by a physician, quality ofservice etc.).

In this study a multinomial logit model is used becausewe assumed that the alternative options provide distinctchoices, have different attributes and can be considered to bemutually exclusive. This is in line with nearly all studies thatconcentrate on provider choice. They utilize the multinomiallogit specification, or the less restrictive nested multinomiallogit specification (see [28, 33, 21, 10, 5]).

3.3 Data requirements and sources

The study utilized mainly primary data. The primary dataused were individual and household based. Both stratifiedrandom and cluster-sampling techniques were utilized. Theabove was supplemented with a health facility survey.Facilities were selected for interviewing on the basis ofproximity to a household cluster (this is a geographic areasuch as a village or neighborhoods of a city).

The survey collected socio-economic information such ashousehold consumption, demographic characteristics, timeuse, income and consumption, education and health status.The health statistics provided a detailed description of healthcare and the incidence of morbidity thirty days prior to thesurvey date including information on the length of illness,choice of treatment, expenditure on consultation and drugs,travel time and cost. Other information sort includeshousehold spending per visit, household total health spend-ing, composition of household's spending on health services,number of times the self treatment option was chosen whenan illness occurs. The household sample included bothindividuals who do not report as well as those who reportedan illness within four weeks of the survey.

The facility survey collected information about infra-structure, personnel, availability of health services anddrugs and fees charged. Other information about theaverage consultation time, net expenditure per visit,percentage of people reported ill or injured, governmenthealth spending and the cost recovered were also sorted.

Providers’ quality2 was measured by the availability ofessential drug; the number of medical staff as an indicator

2 Very few studies in health economics and bio-medical literatureprovide useful guidelines for building or constructing health qualityindices from facility level data. Garner et al. [13] and Peabody et al.[32] attempted to deal with this problem.


of the level of human resources available at the facilitywhich may reflect the sophistication and range of healthservices provided; the provision of basic adult and childhealth services measured by the availability of a function-ing laboratory, the ability to vaccinate children and theability to provide prenatal, postnatal and child growthmonitoring services (grouped together as ‘mother and babycare’); the availability of essential infrastructures likeelectricity and running water.

The measure of health status prior to treatment was thenature of illness, which was measured by “illness” dummyvariables. This includes what the individual health problemis. Provider dummies were also constructed to capture thevariations in provider characteristics. Income was measuredas total household income in the month prior to the surveywhile the per capita monthly income was calculated usingdata for members of the household.

The prices for each provider where not directly availablewere constructed from the money price and/or costinformation for each provider given by care recipients.For those who utilized care, these money price and/or costdata were available, but unavailable for those who did notutilize health care. Thus, for each provider, the available(money) cost information was used to estimate cost/price.The non-monetary access utilization price was measured bytravel and service time of the providers.

3.4 Data gathering procedure

The six geo-political zones in Nigeria, namely Southwest,Southeast, South-south, Northeast, Northwest, and North-central, comprised the entire study’s population and wereused as strata. From each strata, samples were randomlydrawn. These were made independently in the differentstrata. The samples were then drawn into clusters. It wasfrom these clusters that the households were randomlychosen. A state each was randomly selected from each zone.From each state, three local government areas (including oneurban and one rural) were randomly selected. In all, 18 localgovernment areas were sampled. Enumerators in these LGAshelped in the gathering of these data.

From each state, 1,650 (750 for rural; 600 for urban and300 for the third LGA) households were randomly selectedand in all a total of 9,900 households were interviewed(involving 29,700 individuals) using questionnaires. Out ofthis, 7,920 valid questionnaires (that is, those properlyanswered) were returned. This represents a response rate of80%. Accordingly, this total of 7,920 questionnaires wasused for the analysis.

Households were randomly selected from both rural andurban areas. The questionnaires were administered in such away that information obtained from the nearest cluster, waslinked to a specific household in the cluster. The data were

for both outpatients and inpatients in both rural and urbanareas. Households and/or patients were randomly selectedfrom each public sector health facility. The above house-hold survey was supplemented by a health facility survey.

4 Characteristics and facility assessment of respondents

4.1 Socio-demographic characteristics

The spread of the respondents covers the various tribes inNigeria with the three major ones namely Ibo, Yoruba andHausa constituting a respective 21.7, 20.6 and 23.5%. Also,82.8% of the households were headed by the father (male)while 12.2% was headed by the mother (female) and othersconstitutes 3.5% with 1.5% not giving any response.Majority of the respondents (58.2%) live in urban areaswhile 41.8% live in rural areas. Children constituted alarger percentage of those who were sick, for instance,59.0% of the household members who reported sick werechildren. This shows that many of those who reported sickwere those in the active service and this might be anindicator of the healthiness of the country.

Table 1 also indicates that majority of the respondents(52.6%) belonged to the lowest income group, earning anaverage monthly income of 4,885.98. This amount is lessthan the minimum wage of public civil servants which is5,500. Only about 3.7% of the respondents belonged to thehighest income group earning on average, a monthlyincome of 58,106.20. This tend to indicate that a highpercentage of the low-income earners are often sickcompared to the high-income earners. This may be adducedto the fact that high-income earners are exposed to betternutrition, shelter, environment etc., and they (the high-income earners) have a higher ability to pay for betterhealth care services than the low-income earners. Also,majority of the households within the lowest incomequartile spend less than an average of 507.21 on healthcare monthly, while their counterparts from higher incomequartile spend between 1,070.11 and 2,198.77.3

In terms of the type of illness experienced by therespondents, illness that can easily be treated through thefirst point of contact with the curative health care deliverysystem tend to predominate. This is because most of therespondents who reported ill indicated that they sufferedfrom Mild Fever and/or Malaria. This tends to justify theposition of the National Health Policy, which laidemphasis on primary health care. This also agrees withthe findings of Mbanefoh and Soyibo [26] as well as

3 It is important to note that the monthly health care spending dependsmore on the frequency of occurrence of an illness in a household.


reflecting the concern of the World Health Organization(WHO) that fever, especially malaria is one of the majorkiller diseases in sub-Saharan African in general andNigeria in particular.

4.2 Subjective reasons for the choice of health care provider

Both private and public sector health care facilitiesdominated in the choice of health care provider utilized.This may be linked to the type of illness experienced by thehousehold members as well as other various reasons whichinclude distance (nearness of facility to their homes),quality of services (better treatment and available of qualitycare), household’s income, money price and/or cost of

treatment etc., But overall distance dominated all otherreasons. This result contrasts that of Mbanefoh and Soyibo[26], where cost is ranked as the most important reason,followed by quality. The result however agrees with otherempirical studies that distance is a significant determinantof the choice of health care provider.

Given that the preferred health care facility may notessentially be the health care provider chosen, and coupledwith the fact that the choice of health care provider maychange over time, we seek to know what influencesrespondents’ choice of the health care provider to use andalso whether or not there was a change in the health careprovider chosen within the first and second illness episode.4

Table 2 further confirmed that distance and better treatmentdominated in the factors that influence the choice of ahealth care provider. Another finding in the survey is thedifference in the choice of treatment during the secondillness episode. By the second illness episode, there was anincrease in the number of respondents seeking care fromgovernment and private facilities. Surprisingly the usage ofgovernment health facility (47.8%) was more than that ofprivate facilities (40.5%).

The reasons for the change in the choice of providerduring the second illness episode include better treatment(30.8%), cost and/or money price of treatment (14.2%),distance (33.0%), income (3.2%) and time factor (4.2%)(See Table 3). This suggests that distance ranks more thanthe others when the choice of treatment is being made and itis closely followed by better treatment (quality of service).Also, some of the respondents who changed their choice ofhealth care provider to that of government hospital did sobecause of the consultation time, which they claimed wasconvenient to them and well as their having easy access tothe health facility while those who changed theirs to privatehealth facilities said they did so because of the convinceand that the facility was less crowded.

Table 2 Choice of health care provider by illness episode

Health Provider 1st Episode(%)

2nd Episode(%)

% Change

Self treatment 7.4 7.9 6.8Traditional healer 13.5 2.9 −78.8Private facility 39.6 40.5 2.3Government facility 32.6 47.4 45.3Pharmacy/drug shop 3.3 3.1 −5.3Others 1.4 1.2 −12.5No response 2.3 2.3 0

Table 1 Socio-demographic characteristics of the respondents

Tribe of Respondents (%)

Ibo 21.7Yoruba 20.6Hausa 23.5Othersa 34.2

Head of household (%)Male 82.8Female 12.2Othersb 3.5

Location (settlement area) of respondents (%)Urban 58Rural 42

Age group of sick respondents (%)c

Children 59.0Adult 37.6

Mean household monthly income by income quartile (in NigerianNaira)Quartile 1 (lowest) 4,885.95Quartile 2 14,762.48Quartile 3 29,116.02Quartile 4 (highest) 58,106.20

Mean household monthly health care spending by income quartile (inNigerian Naira)Quartile 1 (lowest) 507.21Quartile 2 907.01Quartile 3 1,070.11Quartile 4 (highest) 2,198.77

Types of illness experienced by the households (%)d

Mild fever/malaria 82.0Diarrhoea 7.6Respiratory infection 3.6Delivery 1.1Measles 0.6Others 2.2Number of respondents 7,920

a This includes other tribes like Tiv, Idoma, Itsekiri, Efik, Ibibio, Edo etc.b No response constituted 1.5% of the respondents.c No response constituted 3.4% of the respondents.c No response constituted 3.0% of the respondents.

4 Illness episode is defined as the duration of the illness. Illness thatbegan prior to the survey calendar and was present at the first day ofthis survey was used as the first illness episode was excluded whileillnesses that are ongoing on the day prior to interview are included asthe second illness episode.


Education plays a vital role on health conditions as wellas on the choice of treatment. And it is assumed that loweducation breeds ignorance that heightens the effect ofcultural barriers and superstitions [4]. It was not surprisingthen, when the survey showed that many of those whochose self-care (including traditional medicine/healers)rather than seeking outside medical assistance were thosewith low education (See Table 4). Also, high-incomeearners tend to have the will power to take good care ofthemselves. This is reflected in their choice of health careprovider, where most of them chose private facilities whilemany of the low-income group chose government facilitywhere the fees changed are relatively cheaper to those ofthe private facilities.

Given that many of the traditional medicine healersreside in rural areas, and that there were more health postsand health centres in these areas (though, many lack thebasic infrastructure) the residents therefore have easieraccess to these facilities than those in the urban areas, nowonder then that most of them (the rural households) preferto utilize mainly government facilities, traditional healersand self-care than those in the urban areas.

Majority of those with higher education changed theirchoice of treatment because of the quality of care received(better treatment). On the contrary, many of those whoselevel of income made to change their choice of health careprovider during the second illness episode were those withlittle or no education. It can thus, be deduced that the levelof education attained by individual households, influencestheir decision on which choice of health care provider touse, (See Table 5).

Also, 13.1%, 19.6%, 29.1% and 4.1% of the urbanpopulace, changed their choice of treatment due to the costand/or money price of treatment, time factor, better treatment(quality) and income respectively as compared to 19.8%,12.4%, 20.6% and 15.9% from the rural area. This againindicates that distance and better treatment (quality of care)ranked high in the choice of treatment during the secondillness episode both in the rural and urban areas.

5 Estimation results and discussion

5.1 Choice of health care provider

The Nested Multinomial Logit (NMNL) model wasestimated using full maximum likelihood estimation meth-od. The NMNL nested the choice of provider within thechoice of whether to seek care at all. Variables werecategorization follows from Anderson [3], Kroeger [22],Newbold et al. [31], Henderson et al. [20], Fosu [12] whocategorized the factors that influence the demand for healthcare services into three: predisposing factors (social ordemographic characteristics), enabling factors (access forhealth care), and the need for care (characteristics ofperceived illness). The results are presented in Table 6.5

Age was positively related to the demand for publicsector and private sector hospitals. In contrast, it isnegatively related to the demand for public and privateclinics. And in the case of children aged 14 years andbelow, the relationships are significant at 1% level, whilefor the older population, especially, those aged greater than60 years, it was significant at 5%. This implies that olderpeople tend to patronize public and private hospitals. Incontrast, young people patronize public as well as privatehealth clinics. This may be due to the fact that morespecialized services needed by older people are available inpublic and private hospitals. On the contrary, much of thehealth care requirements of the younger people fall withinthe purview of PHC and hence are available in public andprivate clinics, thus the observed result of the estimation.This result was similar to Gertler et al. [15] but slightlydifferent from that of Lavy and Germain [24].

Income was not a significant determinant of health careprovider’s choice except for the middle income groupwhere it was significant for private clinics and privatehospitals. This implies the existence of income differentialsin the choice of health provider, especially with regard toprivate health care facilities. The high income earners tendto patronize the private facilities much more than those inthe lower income group. This can be traced to theirperception that private health facilities provide more qualitycare and is less time-wasting (that is, less waiting andconsultation time) than the public health facilities.

Education is also positively related to the demand forboth the public and private hospital. In contrast, it wasnegatively related to the demand for private sector andpublic sector clinics. In all the cases, the relationships were

Table 3 Reasons for the change in the choice of provider used duringthe last illness episode

Reason Percentage

Better treatment 30.8Cost (money price) of treatment 14.2Accessibility/proximity (distance) 33.0Income 3.2Delay in treatment (time factor) 4.2Not applicable 14.4Total 100.0

Better treatment encompasses the availability of the required healthcare services as well as the availability of quality care (that is,availability of qualified personnel, infrastructure, drug, etc.

5 The inclusion of non-linear forms of some variables like age andincome is to justify that there could be non-linearity in theirrelationship with the choice of health care provider. For instance,whether or not there is decreasing health care demand in the very highage groups. The same applies to income.


significant at 1%. The implication of this is that theeducated households tend to patronize both public andprivate hospitals more than the clinics while the lesseducated ones patronize private and public clinics morethan the hospitals. This observed result of the estimationmight be due to the perceived quality of care as well as theavailability of specialized services being provided by boththe public and private hospitals. This result slightly tallieswith those of Gertler and Van der Gaag [14], for Coted'Ivoire. They found that the choice of government healthfacilities was not influenced by education but it influencesthe choice of a private facility. This they attributed to thesmall variation in education in the sample. But the study byMwabu et al. [28] however reported a significant result,using household and family data from Kenya on a sampleof adults.

Traditionally in Nigeria, the father, as the head of familyor household should be the main decision-maker for hisfamily. This also applies to the choice of health careprovider when a member of the household is sick. Themothers in this category who took such decisions did somostly when the father of the house was not around whenthe illness occurred and the life of the household memberwas in danger. For the female-headed-households, themothers were the major decision-makers. This was due tothe fact that most of the mothers headed the household

because they were either widows or divorcee (in this casetheir wards were living with them). It was found that insome households where the father was the head, somemother took decisions because of the fact that the husbandworks and reside in another location (state) different fromwhere his family lives.

The result, thus shows that household head was asignificant determinant of which health care provider touse when a household member is sick. This is in terms ofwho makes the decision of which health care provider toutilize when a member of the household is sick. The resultshows that the father as the decision maker is positivelyrelated to the demand for public sector health care facilitiesand negatively related to private hospitals. This positiverelationship with public sector health facilities can be tracedto the fact that majority of the household heads in thesurvey were government employees as they enjoy someincentives (subsidy) when utilizing public health facilities.This result is also similar to the households that are headedby the mother.

On the nature of illness (emergencies) the coefficientshows that it has a positive relationship with private andpublic hospitals as well as private clinics. In contrast, it isnegatively related to public clinics. For all the cases, therelationships were significant but the significance leveldiffers. For both private and public hospitals, the signifi-

Table 5 Education and reasons for change in the choice of provider

Reasons of Change (%)

Level of Education Distance Cost and/or Money Price of Treatment Time Factor Better Treatment Income Not Applicable

No education 16.6 20.3 9.9 6.1 40.9 12.5Primary 22.1 12.3 11.1 9.9 37.1 10.9Secondary 9.0 12.3 13.1 7.4 5.1 15.1Modern II 13.6 16.0 10.3 12.5 7.2 25.1N.C.E 15.3 13.1 12.2 15.1 2.1 11.3OND/HND 18.0 14.5 15.0 20.5 2.3 10.0University 5.4 10.3 25.0 27.4 1.0 10.0Others 10.0 1.2 3.4 1.1 4.3 5.1Total 100.0 100.0 100.0 100.0 100.0 100.0

Table 4 Education and choice of provider

Choice of Treatment (%)

Level of Education Self Care Traditional Healers Private Facility Government Facility Pharmacy/drug Shop Others No Response Total

No education 31.6 46.9 0.5 7.1 9.3 1.5 3.1 100.0Primary 10.1 3.6 0.9 48.6 33.3 2.9 0.7 100.0Secondary 15.8 28.2 12.5 36.5 0.7 3.5 2.8 100.0Modern II 3.6 8.2 20.8 48.9 13.9 3.1 1.5 100.0N.C.E 13.0 0.7 40.2 36.5 8.1 1.0 0.5 100.0OND/HND 7.6 0.0 48.1 40.6 2.7 1.0 0.0 100.0University 3.6 0.0 60.3 31.6 1.1 2.2 1.2 100.0Others 14.8 12.5 25.4 17.3 12.4 13.0 4.6 100.0


cance level is 1% while for the private and public clinicsthe significance level is 5%. This might be traced to the factthat private and public hospitals may have more specializedand modern equipment as well as more qualified personnelthan the clinics, thus, their having a comparative advantage(in treating these emergencies (acute illness)) over thepublic and private clinics or self care.

Table 6 also shows that distance (travel time) isnegatively but significantly different related to the demandfor both private and public health care facilities. It wassignificance at 1%. This effect of distance was estimatedwith the coefficients been restricted to be equal acrossequations. The reason for this is to help reduce or minimizethe disparity in the opportunity cost of time that will beincurred in traveling to the health facility. This has beenargued to be a more important cost incurred in traveling tothe health facility [24] and it is equal to the time lost duringtravel (proportional to distance) multiplied by the hourly

wage of the individual (which, on average, is proportionalto income). If the opportunity cost is large, it discourageshouseholds from seeking professional care but if it isrelatively less or small, it encourages the demand forprofessional care by households. This shows that theprobability of seeking professional care would significantlyincrease if health care were more accessible. Also, anyincrease in non-monetary access cost may reduce thedemand for professional care. This result tallies with thatof Lavy and Quigley [25].

The price of consultation is negatively related to thedemand for modern health care. This can be attributed tothe fact that higher prices (in most cases) discouragedemand. Thus as price increases the probability of seekingmodern health care reduces. This result agrees slightly withthat of Mwabu and Mwangi [30] that showed, amongothers, that the demand for health care services in publicsector clinic is highly sensitive to changes in relative money

Table 6 Multinomial logit estimates of choice of health care provider

Variable Public Clinic Public Hospital Private Clinic Private Hospital

Social/demographic characteristicsConstant −2.34(−6.08)*** −1.98(−5.51)*** −1.86(−4.46)*** −1.77(−5.31)***Age −0.69(−2.61)*** 0.33(2.12)*** −0.55(−2.15)*** 0.75(2.13)***Age2 −0.24(−1.62)** 0.53(0.86) −0.44(1.02)* 0.28(1.23)Age3 −0.31 (0.93) 0.45(1.03)* −0.18(0.32) 0.48(1.56)**Income 0.52(0.22) 0.63(0.39) 0.53(0.25) 0.60(0.42)Income2 0.55(0.23) 0.10(0.31) −0.32(−2.12)*** −0.68(−3.34)***Education −1.01(−2.06)*** 1.04(2.53)*** −0.99(−3.04)*** 0.05(3.03)***Head of household*Father 1.16(2.41)*** 2.01(2.45)*** 1.11(3.60)*** −0.02(−0.10)*Mother 1.84(2.68)*** 1.96(2.31)*** −0.56(0.31) −0.71(−0.34)*Others 2.01(4.12)*** 1.88(2.16)*** −0.63(0.44) −0.42(−0.04)Characteristics of perceived illnessNature of illness acute illness (emergencies). 0.67(3.92)*** 0.43(5.52)*** 0.81(1.19)** −028(−1.23)**Access for health care*Price factorsDistance (travel time)b −1.25(−3.63)*** −1.25(−3.63)*** −1.25(−3.63)*** −1.25(−3.63)***Price of consultationc −0.33(−3.86)*** −0.89(−3.93)*** −1.28(−2.88)*** −2.67(−4.10)***Government employee 0.85(2.41)*** 1.12(3.08)*** −0.00(0.31) −0.00(0.35)

*Quality of facilityDrugs availability 1.34(5.81)*** 1.87(2.05)*** 0.32(6.40)*** 0.89(5.60)***Infrastructure 0.73(2.06)*** 0.21(2.13)*** 0.55(3.25)*** 0.79(2.16)***Personnel 1.08(2.04)*** 0.54(2.05)*** 1.02(4.21)*** 0.47(5.60)***Services 0.35(4.34)*** 0.26(2.89)*** 0.11(3.11)*** 0.61(4.01)***

Summary statistics:Number of observations: 7,920R2—McFadden: 0.782−2 log likelihood: 1,121.691Chi-square: 1,060.56P-value: 0.0000

‘t’—statistics are in brackets.‘b’—the coefficients are restarted to be equal across equations.‘c’—price of consultation covers the fee for the service in Nigeria.(***), (**) Significant at 1% level and 5% level respectively.


prices. The price of consultation is also significant at 1%for all the sources of care used namely private and publicsector hospital as well as private and public sector clinics.This result implies that any increase in the price ofconsultation (a form of user fees being charged) will havetwo major effects namely a reduction in the probability ofthat facility being chosen and secondly, a reduction in theprobability of choosing a modern or professional care.

For the government employee variable, it is positivelyrelated to the demand for the public sector facility butnegatively related to the demand for private facility. It wassignificant at 1% for public sector health facilities while forprivate sector facilities it was not significant. This resultcould be a manifestation of the policy, which entailsgovernment employees, and their families to have accessto health care services at public sector health facilities freeof charge or at a subsidized price. This implies that withsuch a government policy, the probability of governmentemployees and their relatives choosing a public sectorhealth facility becomes higher relative to that of a privatefacility. With this result, the government employeedummy can be interpreted as a price effect and not asa quality effect. This result was similar to that of Lavyand Germain [30].

Finally, all the quality variables used in the analysis(drug availability, infrastructure, Personnel and Services)were positively related to the demand for professional care.Also, they were all significant at 1%. This implies thathouseholds prefer health facilities with adequate andqualified health personnel, up-to-date (modern and wellfunctioning) infrastructure, quality, adequate and diversehealth services. The consequence of this is that householdswill opt for health facilities where drug and diverse servicesare available, infrastructure are functioning well and thereare qualified personnel (doctors and nurses), which they,the households, will prefer to treat them. The implication ofthis is that households take into account the variousdimension of quality of care in making their choices ofwhich health facility to use. Thus, the probability of ahousehold choosing a health facility with better infrastruc-ture as well as where drug and diverse services are availablewill be higher than where these quality variables arelacking. Also, the estimation result shows that householdsattach more importance to the probability of their beingtreated by qualified personnel (doctor or nurse).

6 Conclusion and policy recommendation

The study revealed that both distance and price aresignificant determinants of the choice of health careprovider. But with the results from the descriptive analysis,distance ranked highest among other factors such as better

treatment, income, cost of care, etc. This shows that theprobability of seeking professional care would significantlyincrease if health care were more accessible thoughaccessibility can also depend on the facility type.

Also, the study reveals that older people tend topatronize public and private hospitals while young peoplepatronize public as well as private health clinics. This wecan link to the fact that most hospitals provide specializedservices. With respect to education, the educated house-holds tend to patronize both public and private hospitalsmore than the clinics while the less educated ones patronizeprivate and public clinics more than the hospitals. Thecoefficient of the nature of illness (emergencies) showeddiverse relationship with various health care providers. Thiswe traced to the fact that some of the health facilities mayhave more specialized and modern equipment as well asmore qualified personnel than others.

In terms of price, any increase in the price ofconsultation will either reduce the probability of thatfacility being chosen or reduce the probability of choosinga modern or professional care. Also, households considerthe government subsidization of treatment in a publicfacility to mean better quality, especially in terms of gettingmore service for the actual fee paid and this tend toencourage them to choose a public health facility relative toself care or private facility. Households try to evaluate thequality of care in health facilities before making theirchoices. This evaluation includes availability of betterinfrastructure, drug and diverse services. Thus, choosingpublic sector health care facilities will be met with mixedfeelings. The reason being that public sector health carefacilities in Nigeria are known for not having drugs in stockthereby, resulting in patients purchasing drugs from nearbypharmacy or drug shops.

Based on these findings, it was recommended amongothers that: given the high travel cost and travel time asshown by the findings of this study, the government shouldendeavour to bring public sector health facilities closer tothe people especially in the rural areas. This can be donethrough the establishment of new public sector health carefacilities. Though, recently, the federal government builtmore public sector health centers across the country, morepublic health centers can still be established and should beeffectively managed. Also, government should improve onits provision of basic essential drugs in public sector healthcare facilities. Efforts should be on how to procure theseessential drugs, especially, those that are generic in nature.Bulk purchasing can be embarked upon, this is likely toreduce cost. Also, local manufacturing of drugs should beencouraged. This can be through fiscal incentives and itwill help to reduce the high cost of drugs, which are usuallyimported and the high cost of foreign exchange (mostly dueto high exchange rate depreciation) added to the price.


Given the perception of people about the quality of carein our public sector health facilities, which many believedis poor, there is the need for the government to improve onthe quality of care in her health facilities. Also, sincehouseholds are willing to pay for improved quality of carein public sector hospitals and the government has not mademuch progress in increasing fees in public hospital forquality improvements, the government should identifyexisting deficiencies in the management of its facilitiesand then try to take care of these deficiencies. Emphasis inthis respect can be laid on employee motivation andmanagement systems and control. Given our findings thatthe rich has a higher tendency of utilizing private healthfacilities, the government should help encourage them toincrease their patronage of these private sector facilities andalso encourage the private sector health providers toimprove on their facilities as a way of pulling away therich from public sector health facilities. This will enable thepoor and the more vulnerable groups in the society to bebetter targeted and provided for.

Appendix

The states sampled for the study are Imo, Oyo, Borno,River, Benue and Kano

The list of sampled LGAs include: Ibadan North, IbadanNorth West and Oluyole LGAs for Oyo state; KanoMunicipal, Tudun Wada and Nassarawa LGAs for Kanostate; Owerri Municipal, Owerri North and Okigwe LGAsfor Imo state; Port-Harcourt, Degema and Bonny LGAs forRiver state; Markurdi, Gboko and Kastina-Ala LGAs forBenue state; Maiduguri, Damboa and Biu LGAs for Borno.

The total of 7,920 questionnaires used for the study’sanalysis was made up as follows: Oyo state, 1,402; ImoState, 1,350; Borno State, 1,265; River State, 1,340, BenueState, 1,258; and Kano State, 1,305.

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Further Reading

1. Amaghionyeodiwe AL (2005) User charges and the willingness topay for Public Sector Health Care Services in Nigeria. A Ph.Dthesis submitted to the Department of Economics, University ofIbadan, Nigeria

2. Ching P (1995) User fees, demand for children’s health care andaccess across income groups: the Philippine case. Soc Sci Med 41(1):37–46

3. Gertler P, Glewwe P (1989) The willingness to pay for education inDeveloping Countries: Evidence from Rural Peru. LSMS workingpaper, No.54, Washington D.C.. The World Bank

4. Gertler P, Glewwe P (1990) The willingness to pay for socialservices in Developing Countries. LSMS working paper. No.29.The World Bank, Washington, D.C

5. Gertler P, Van der Gaag J (1988) Measuring the willingness to payfor social services in developing countries. Living StandardMeasurement Study (LSMS) working paper, No.45, The Worldbank, Washington

6. Gilson L (1985) Government health charges: is equity abandoned.Discussion papers No.15 London: London School of Hygiene andTropical Medicine, Evaluation and Planning Center for Health care

7. Mbanefoh GF, Soyibo A, Anyanwu JC (1996a) Sources of healthcare financing. Paper presented at the training workshop on healthCare financing in Nigeria issues and options, organized by theHealth Policy Training and Research Programme, University ofIbadan. January

8. Mbanefoh GF, Soyibo A, Anyanwa JC (1996b) Demand for healthcare in Nigeria: an econometric analysis. A Final Research ReportPresented at the Writers/Author Workshop Organised by IHPP atthe World Bank, March 25 to April 2

9. Mwabu G, Ainsworth M, Nyamete A (1996) The effect of prices,service quality and availability on the demand on the demand formedical care: Insights from Kenya. In Shaw RP, Ainsworth M(eds.) Financing health services through user fees and insurance:Case studies from Sub-Saharan Africa. World Bank DiscussionPapers 294, Africa Technical Department Series. Washington, D.C.,World Bank.

10. Ndeso-Atanga S. (2003) Health care quality and the choice of careproviders: Cameroon II Africa’s changing markets for health andveterinary services: The New Institutional Issues. UCIAS EditedVolume 5 http://repositories.cdlib.org/uciaspubs/editedvolumes/5/5


http://repositories.cdlib.org/uciaspubs/editedvolumes/5/5

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Determinants of the choice of health care provider in Nigeria