Cape economics unit 2 multiple choice

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Cape economics unit 2 multiple choice

View Large In relation to outpatient visits, the available information on total visits must be converted into annual utilization rates, i. This simple calculation can be refined to account for seasonal variations, as the total annual visits calculated based on the household survey will be under-reported over stated when the month of reporting is less more prone to specific diseases or health conditions.

Seasonal adjustments can be made by taking into account the month s in which the survey was conducted and accessing information on total visits to health care facilities per month in the year of the survey. Where there is a strong indication that the seasonal pattern of use is relatively similar over recent years, and there are no current monthly utilization figures, the year in which data on monthly utilization are available can be used.

This is often the case as monthly health utilization figures for the year of study may not be immediately available. Many countries have some form of health information system HIS that documents total visits to individual public sector facilities in each month. We were also able to get information on utilization of private sector services in each month directly from private health insurance organizations, as these services are primarily used by those with private insurance cover.

A seasonality index, comparing utilization of each type of service in the month in which household survey data were collected with the average monthly utilization over a full year, can be calculated as follows: SIjk is the seasonal index for month j the month in which the survey was conductedUik is the total visits to a specified facility k in month i, Ujk is the total visits to facility k in month j.

We found that the seasonality adjustment did not dramatically affect our results. The more important issue is trying to get comprehensive utilization data rather than only information on one visit during a recall period. In addition, recall bias may result in household surveys under-reporting total utilization levels.

It is advisable to triangulate total utilization estimates from the household survey with those available from other sources, such as a health ministry HIS, and adjust the household survey estimates appropriately.

Collecting and analysing cost data Although detailed primary costing of different types of services can be undertaken for a BIA, most studies draw on datasets of total expenditure on different types of service and total utilization of those services.

A key issue here is to ensure that expenditure rather than budget data are used. Frequently, data on expenditure by type of service e.


This is then supplemented with total utilization data, such as from a HIS, if available. Some BIA studies simply obtain information on expenditure from a NHA or Ministry of Health and divide this by the total utilization reported in the household survey.

This approach would be appropriate if there is no alternative source of accurate data on total actual utilization of health services such as a well-maintained national HIS, and if utilization estimates from the household survey have not been adjusted to equal actual utilization levels derived from a HIS as recommended in the previous section.

Whichever approach is used, there are several issues that need to be addressed in the calculation of unit costs. In calculating unit costs, the degree of disaggregation by category of health service is dependent on what data are available from the household survey.

Most household surveys merely distinguish between public and private sector services, and within the public sector category, between clinics or similar primary care facilities and hospitals. As noted earlier, greater disaggregation by type of hospital e. This is understandable as survey respondents may not know the category of hospital.

In our South African study, we asked for the name of the hospital used both for outpatient visits and inpatient admissions and later coded each response according to a list of all hospitals and their categories. This is time consuming and may not be feasible in resource-constrained environments.

However, if one is undertaking a household survey specifically for the BIA, it is an exercise that is well worthwhile. Figure 1 illustrates what the BIA results for public sector hospitals would have been if we only had aggregated information on utilization of a public hospital and which would have assumed that unit costs were the same irrespective of which hospital was visited compared with the results we did obtain with the disaggregated level of care hospital data.

What this highlights is that, because higher income groups in South Africa and probably in many other countries tend to use services at higher level hospitals e.

While the distribution of utilization of services is pro-poor certainly in terms of public hospital outpatient serviceswhen this is translated into benefits using information on utilization of different levels of care and the relative unit costs at these different types of hospital, it is clear that in fact the benefit incidence of use of public hospitals is pro-rich in the South African context.

The differences arising from using disaggregated data are particularly marked for the top and the bottom quintiles in Figure 1. Figure 1 Illustrating the impact of aggregated hospital level data on benefit incidence results.

Analysis of SACBIA dataset Another factor that could be taken into account is the potential for variation in resourcing of health services across geographic areas.Multiple Choice Practice Tests in Mathematics for CXC by Ali, Fayad W See more like this.

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Cape economics unit 2 multiple choice

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