Methodology on the Determinants of Maternal and Child Health Care Utilisation

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Allin (2006) measured health care utilisation by number of outpatient visits to the physician, general practitioner (GP), specialist and dental visits. Questions that capture visits were asked, for instance, “in the past 12 months, have you been a patient over night in a hospital and nursing home?”( Allin, 2006). Indicators of health care need include age, sex, self-assessed health in five categories (excellent, very good, good, fair and poor) and the presence of chronic condition and activity limitations.

Hotckkiss et al (2010) controlled for need of family planning services by generating questions on the desire for children at the time of the survey. A woman was said to have need for family planning if she wanted a child not sooner than two years following the survey, "wanted a child but was unsure of the timing", "did not want more children and currently pregnant". Women that are barren and those that want a child within the next two years were seen as not to have need of contraceptives.

Van de poel et al (2011) proxy need through rich array of self reported health problems and symptoms of chronic illness for medical condition. The medical conditions include; arthritis, angina asthma, depression, psychosis and tuberculosis.

Non-need determinants of utilisation include marital status, education and employment. Socioeconomic status is measured by principal component score from analysis of asset ownership and household dwelling characteristics including sanitation facilities.

Bonfruer et al (2012) proxy medical care need by a set of self-reported health problems. Self assed health is measured on a point scale running from good to very bad for six chronic diseases such as arthritis, angina, asthma, depression, psychosis, and diabetes. The non-need related determinants of health care utilisation consist of marital status and occupational status. Bonfruer et al (2012) modeled maternal and child health care utilisation by constructing an indicator of whether the child‟s mother has received sufficient antenatal care defined as at least four antenatal visits to a medically trained skilled health worker and whether there are skilled birth attendants like doctors, nurses, or midwife.

3.4.2 Methodology on the determinants of maternal and child health care

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utilisation will be reviewed. The technique ranges from the logit model, descriptive statistics, the poison model, the negative binomial model and the two-part model.

3.4.2.1 The logit, poison, negative binomial, and two-part model.

The logit model has been widely used in estimating the determinants of maternal and child health care utilisation. Some studies used the logit model to estimate the determinants of antenatal care and skilled delivery (Babalola and Fatusi; 2007, Goland et al ; 2012, Nketiah-Amponsah et al; 2012, Arthur; 2012), some studies used the poison or negative binomial model to estimate the determinant of antenatal care (Nwosu et al, 2012 ) while others used the two- part model ( Ortiz, 2007 ) to estimate the determinants of antenatal care utilisation.

3.4.2.2 The Two-part model

The two part model is an econometrics model for estimating health care demand. The utilisation of health care services in general has two important characteristics that are vital in selecting the appropriate estimation method. Health care demand depends on two decision processes; in the first stage, the individual decides to either utilise the health care services or not. In the second stage, the individual and the health care provider decide on the intensity of use of the health care. For health care service utilisation like the antenatal care which is usually measured by number of visits, the use of negative binomial or poison regressions provides the appropriate method of estimations since the number of antenatal visits are counted.

However, in terms of estimation of antenatal visits using the negative binomial or poison regressions, the distribution of antenatal visits takes only non negative integer values and ignores all the zeros. This implies that, the decision of individuals with no antenatal visits are not taken into consideration in the analysis, while others with single or multiple visits are overrepresented in the model during the survey.

Conceptually, the two part model can solve the problem of excess zeros and is a more appropriate model than using negative binomial or poison model. Comparing the poison model and the negative binomial model, the poison model assumes that the mean is equal to the variance and every count is independent of each other. The variance of health care utilisation often exceeds the mean, as one visit to a physician or one stay at a hospital may relate to the subsequent visits. The zero negative binomial

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model relax the independence assumption and allows for over-dispersion (Asada and Kephart, 2007). The negative binomial regression model provides a better fit to health care utilisation than the binomial or poison models. Given the peculiar nature of antenatal visits in Nigeria where over 40 percent of respondents do not go for antenatal care, there exists the problem of excess zeros as such, the use of only logit model or negative binomial only in the analysis of antenatal care utilisation may lead to inconsistent parameter estimates and hence misinterpretation. This is the weakness found in Nwosu et al (2012) and other past studies on the determinants of antenatal care utilisation in Nigeria.

Secondly, according to Ortiz (2007), health care utilization partly involves agency problem in terms of demand inducement. In this case, the patient takes decision of attending the first medical visit but further decisions are decided by patient and medical doctors where each one maximises her utility function and takes advantage of some information asymmetry problems. To overcome this problem, the two part model on health care demand has been widely used to separate the two decisions of first deciding to seek medical care, and then to determine the frequency of visits. In this study, the individual in this case; the pregnant woman decides whether or not to seek antenatal care. In the second stage, the health professional determines the frequency of visits which is usually at least four antenatal visits according to the WHO standard.

The two-part model entails that, the first stage of the decision process is empirically measured by the logit model that predicts use of antenatal care. Then in the second stage, the negative binomial model is used to estimate the intensity or frequency of antenatal visit. Based on Gerdtham's (1996) two stage hurdle model, Deb et al (1999) as well as Nunez and chi (2013), two equations are estimated the first is specified as a binary logit model for the probability that a woman attends antenatal care or not. This is written as;

Prob(antenatal visits>0) = 1/(1+exp(-Xi*a) ... (1) Where Xi is a row vector of k given individual characteristics (e.g gender, age) and a is a set of parameters to be estimated.

In the second equation, a negative binomial model is specified to model the frequency of antenatal visits by a woman. This is specified as;

(λi | λi >0) = exp(∑bj Xji)exp(ei),... (2)

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Where Xi is a row vector of K given individual characteristics, b is a set of parameter to be estimated and ei is the error term.

3.5 lessons learned from literature review and value additions of reviewed