TABLE 7: DESCRIPTIVE STATISTICS FOR 2005 SURVEY

Variable type and

level Variable name % n Mean Min Max

Individual level

Continuous Average age of HH members

(years) 53,790 23 0 117

Continuous Average education of HH

members aged >=6 years (years passed) 36,925 2.8 0 18 Categorical Religion Muslim Hindu Buddhist 91.6 6.1 2.3 49,281 3,284 1,225 Categorical Nationality Bengali Rakhain (tribal) 98.3 1.7 52,875 915 Household level

Continuous Average household size 7,855 6.9 1 38

Categorical Household size grouped

1-5 members 6-10 members 10+ members 32.7 57.9 9.4 2,571 4,548 736

Categorical Sex of main earner

Male

Female 89.8 10.2 7,044 803

Categorical Socioeconomic status

Better-off HH

Poor HH 34.5 65.5 2,707 5,134

Categorical NGO membership

Member HH

Non member HH 44.6 55.4 3,494 4,342

Categorical Presence of pregnant women

At least one pregnant woman

No Pregnant woman in HH 16.8 83.2 1,316 6,531

Categorical Presence of Village Health

Post VHP village Non-VHP village 4.8 95.2 74,78 377

Categorical Presence of under-five

children

Atleast one under-five child No under-five child

65.2

34.8 5,113 2,734

D

A

1999,2004

2005S

Both bivariate and multivariate analysis was carried out in investigating the factors that influence membership in the health card scheme. Bivariate analysis was carried out to observe one-to-one relationship between membership status and the influencing factors. Data was analysed using STATAIC 12 software. The list of influencing factors included socioeconomic and demographic profile of the household, proximity of the households to the health centres, presence of pregnant women in household, presence of under-five children in household, and

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membership at development programmes offered by non-government organizations. These factors were chosen, based on literature review on this field and on suggestion by the programme people of the MHI scheme. Variables were mostly at household level as membership at the scheme was per household. The scheme as mentioned above was household based and for that we expected household size to play a role in decision to enrol. Comparatively larger households had to pay no extra fee for getting the regular services under the healthcare scheme for each of the household members. Among the other factors socioeconomic status was included as a person’s current wealth is expected to influence his or her decision to invest in any scheme. At the same time its important to judge whether the scheme is able to address issues with social exclusion from access to social protection (like MHI) and the influence of socioeconomic status on membership can help us understand this aspect (19- 22). Education provides access to information that helps people to understand the importance of securing oneself against the uncertain health risks (20, 23). For this we included the mean level of household education as one of the independent variables. As official age of children joining school in rural Bangladesh is six years we excluded members aged less than six years in calculating mean household education. Age has been found to play important role in individual’s decision to insure against health risks (24). Ill health is most often found to be positively correlated with age and therefore we presumed that households with higher mean age of its members would have greater incentive to join the scheme. The other demographic factor included was the sex of the main income earner of a household. It’s been found that male-headed households are comparatively in better-off socioeconomic position which gives them access to fund needed to join MHI schemes (25, 26). However, its also true that women participation in development programmes has been encouraging (27-29). Therefore the interest to observe the impact of gender in decision to enroll in MHI scheme, lead us to include this variable in our analysis. The services provided at the VHPs were mainly primary level care. However, vaccination and healthcare for the children were also provided at the VHPs. We anticipated this aspect of service provision would attract families with children under the age of

50

five to become members of the health card scheme. Travel distance to the healthcare centre has historically played a deciding role in seeking healthcare from any particular facility (30-34). The health card scheme operated through 7 VHPs covering 8 villages. Thus the presence of VHP in a village is expected to influence household membership in the health card as the scheme only provided services through these health centres. Presence of pregnant women in the household was included in the list as the scheme benefits included pregnancy and delivery related care in addition to the primary healthcare services. Membership of households in development programmes offered by NGOs was taken as an influencing factor. Studies conducted earlier have shown that households with membership of the development programmes especially the micro finance ones are more likely to join MHI programmes in developing countries (35).

The multivariate analysis to show the influence of each of the explanatory variables on the response variable holding all other variables constant is explained in the following section.

A

M

M

A

From economic perspective the villagers’ choice to enroll in the health card scheme can be explained by the expected utility theory where they compare the expected utility from having health insurance with that of not having any insurance. According to the theory the villagers will enroll into the scheme only when they find the expected utility of joining at a given premium to be greater than that of not joining. Based on the choice villagers make all our study participants can be grouped into two categories ‘members’ and ‘non-members’. In search of factors that influence uptake of health insurance scheme the dependent variable of concern in our current study is therefore defined as a binary choice variable. A logistic regression model has been used in explaining the relationship between the dependent variable and the explanatory variables as the dependent variable in our case has a binary distribution and not a normal distribution. Our variable of interest is a probability, i.e. we are interested in finding out how the independent variables are linked with the probability of a

51

household being member of the health card scheme or not. Probabilities are different from continuous variables in the sense that they are bounded by the values 0 and 1. Therefore we can not assume normality for a probability. The logistic regression model analyses the influence of various independent variables on a dichotomous/binary outcome by estimating the probability of the event occurring. It does so by examining relationship between the independent variables and the log odds of the dichotomous variable by estimating changes in the log odds of the dependent as opposed to the dependent variable itself. The log odds ratio is a summary measure of the relationship between the two variables and is expressed as the ratio of two odds.

The central mathematical concept underlying logistic regression model is the logit, known to be the natural logarithm of an odds ratio. It takes the following form:

Logit (Y)= natural log (odds) = ln [ ] = α +βiXi---(1)

Where

Y=outcome of interest, i.e. in our case health card membership

Xi= i number of independent variables, i.e. influencing factors for membership

p=probability of event occuring, i.e. in our case probability of households having health card membership

α = intercept

βi = regression coefficients

Taking antilog of equation (1) on both sides we can predict the probability of occurrence of the outcome of interest, i.e probability of households having health card membership. This would look like:

p=Probability (Y=outcome of interest | Xi=xi, values of Xi) =

In this model the value of the coefficient β determines the strength of relationship between the independent variable(X) and the logit of the dependent

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variable (Y). When β is greater than zero, larger (or smaller) X values are associated with larger (or smaller) logits of Y. Conversely, if β is less than zero, larger (or smaller) X values are associated with smaller (or larger) logits of Y (36).

Details on logistic regression analysis can be found elsewhere (36-38).

INDEPENDENT VARIABLES INCLUDED IN THE LOGISTIC REGRESSION MODEL

The influence of various indicators on uptake of the MHI scheme in Chakaria was analyzed taking health card membership as the dependent variable. The independent variables were the same set of factors that has been used in the bi- variable analysis. These were a mix of categorical and continuous variables. A summary of the dependent and independent variables used in the multivariate analysis is given in Table 8.