Predicting Consumption Expenditure for the Analysis of Health Care Financing Equity in Low Income Countries: a Comparison of Approaches

Abstract

The analysis of equity in the distribution of health care payments requires nationally representative income and expenditure surveys, containing information on health care payments and ability to pay. Such national household surveys in developing countries collect limited information on out-of-pocket payments for health care but comprehensive information on household consumption expenditure (a proxy of income). There are also limited nationally representative health surveys to conduct equity analyses requiring an administration of small health-specific surveys to collect detailed information on health care payments. However, collecting household expenditure is expensive and time . This study compares quantile regression to Ordinary Least Square in predicting consumption expenditure. Split sample method and cross validation tests are used to evaluate the prediction methodology. Unlike OLS, the quantile model does not distort the values of, the Gini index, the concentration index and the Kakwani index and is the preferred method for predicting consumption expenditure for financing incidence analysis.

Appendices

Appendix 1: Calculation of Survey Weights in the SHIELD Survey

A two level weight was generated. The first weight (W1) aims to make the sample of insured and the uninsured representative of the population at the district level. Eq. 7 was therefore used to weight both insured and uninsured at the district level separately. The second weight (W2) aims to achieve national representation by making the insured and uninsured populations representative of the national population. The final weight (Wf) was obtained by multiplying W1 and W2. Population data for urban and rural areas were derived from the 2002 national population census. Information on membership of Community Health Fund and National Health Insurance Fund was secured from the Ministry of Health and Social Welfare and the National Health Insurance Fund office, respectively.

The weights were derived as follows;

$${\text{W}}_{1} = \frac{{\mathop \sum \nolimits_{{{\text{i}} = 1}}^{{{\text{n}}^{\text{d}} }} {\text{d}}_{\text{ij}} }}{{\mathop \sum \nolimits_{\text{i}}^{{{\text{m}}^{\text{d}} }} {\text{S}}_{\text{dij}} }},\;{\text{m}}^{\text{d}} \in {\text{n}}^{\text{d}}$$

(7)

where d = district, i = individual in district d, j = insurance status of an individual in district d, sd = district sample.

The second weights was calculated as,

$${\text{W}}_{2} = \frac{{\mathop \sum \nolimits_{{{\text{i}} = 1}}^{{{\text{n}}^{\text{L}} }} {\text{L}}_{\text{ij}} }}{{\mathop \sum \nolimits_{\text{i}}^{{{\text{m}}^{\text{d}} }} {\text{d}}_{\text{ij}} }},\;{\text{m}} \in {\text{n}}$$

(8)

where L = locality (urban or rural).

The final weight was calculated as,

Wf = W1*W2

Appendix 2: Similarities and Differences Between the SHIELD and the HBS Data

The two surveys had similarities and differences in the distribution of a number of variables (Appendix Table 1). For example, the HBS 2007 estimated about 72 % of the Tanzanian population as living in a rural locality while this was approximated to be 75 % in the SHIELD survey. In addition, about 76 and 77 % of the household heads were male in the HBS and SHIELD surveys, respectively. The mean household size was 4.8 in the HBS and 5.1 in the SHIELD survey. There were also similarities in the ownership of assets like radio, tables, watches, and housing characteristics across the two surveys.

Appendix Table 1 Mean distribution of variables used in the prediction process

The two surveys collected information on payments for consultation fees, drugs, and diagnosis. While this information was collected separately for inpatient and outpatient care in the SHIELD survey, expenditure on drugs was collected as a separate category in the HBS and not linked to the type of care sought. Payments in the SHIELD survey were linked to the visit which a household member made in the past 1 year for inpatient care or past 1 month for outpatient care while this information was not linked to visits in the HBS. It is argued that having a more detailed question on the breakdown of health payments has a greater prompting effect, implying less memory loss (Heijink et al. 2011) a fact that may lead to more accurate reporting of out of pocket payments. In addition, the SHIELD survey collected information on transport costs associated with health care utilization while this was not collected in the HBS.