Summary of Calculated Cost of Scaling Up

Table 43 below presents a summary of the findings associated with the estimated cost of scaling up the above programmes.

Table 43: Summary of Indicative Costs Associated with Scaling Up

CAPEX Associated with scaling up

One-year OPEX for single year

eCare in the clinic $55.7 million $23.57 million

eCare in the village $41.4 million $23.6 million

eLearning $8.6 million $8.3 million

eSurveillance Cost information unavailable

eAdministration/ eGovernance $106 million

It should be noted that the costs of scaling up each individual programme are based on assuming that the programme is replicated in its exact form throughout the region. As explained in Section 2, there are large differences in the socio- economic characteristics of sub-Saharan African countries. For this reason, programmes will need to be tailored to respond to the exact needs of the proposed beneficiaries which will cause the costs of the programmes to differ. For each intervention the estimated one year operating costs are significantly below the estimated single value of the benefits which could be achieved if these programmes were rolled out across sub-Saharan Africa.

6.4

Savings and Opportunity Costs

Although the estimated costs of the programmes delivered with satellite-based technology are more expensive then when delivered through the ADSL or the mobile phone network, these costs are still significantly lower than the estimated benefits associated with these case studies. In addition, if one were to estimate the cost of extending the programmes to those living outside the mobile phone network, the capital costs associated with extending the network would have to be taken into account. Given that the World Bank estimates the total cost of extending the mobile phone network in Africa as $3.5 billion and the cost of providing universal broadband access as $6 billion, the capital costs of using satellite based technology seem relatively low71. When considering the cost implications as described above, it is important to take into

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The source for AIDS patients is the technological remote population in each country multiplied by the WHO morbidity rate for HIV/AIDS for each country. The calculation is based on data: WHO Population Data (WHOSIS, October 2008), ITU African

Telecommunications/ICT Indicators 2008 for mobile network coverage , WHO HIV Morbidity Rates (2005) (WHOSIS, October 2008)

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World Bank (2008), Africa Infrastructure Country Diagnostic: Costing the need for Investment in ICT Infrastructure in Africa, Washington.

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account, not only the estimated health benefits of the programmes, but also the savings which can be incurred through their implementation.

The following areas of savings can be considered:

 Savings through the efficiency optimisation with respect to public expenditures, improving health service quality and geographical availability and temporal continuity;

 Savings in minimizing the movement of patients through avoiding the movement of patients in rural areas to hospitals in the capital. As transportation and subsistence costs are avoided, public expenses and/ or private expenditure on health are consequently reduced;

 Savings through the optimization of organisational costs: the logistics costs for travelling of medical personnel are decreased and human resources can be managed more efficiently. The same number of doctors and nurses will be able to treat an increased number of patients minimizing the waiting time for receiving appropriate care;

 Savings in education programmes through the enablement of distance learning

 Savings in prevention through the more effective monitoring of disease;

 Savings during vaccination campaigns through better management.

In most cases, the move away from paper based systems to ICT based systems has resulted in cost savings. Due to poor infrastructure in Africa, the cost associated with travelling is high as is the amount of time it takes to get from one point to another. In the case of the Uganda Health Information Network Programme, it was estimated that over the first 8 months of the programme, the ICT based programme offered 24.272percent more benefits per unit of spending. As the programme developed, this incremental benefit is likely to increase as efficiencies are gained and health care professionals become more accustomed to using the technology.

For people in remote areas, telemedicine and eHealth are crucial means for accessing medical services that they would otherwise have to travel to the capital to receive. Given the fact that it is people in the most rural locations that are the most vulnerable, scaling up the programme to remote areas is likely to result in incrementally higher benefits than provided above. As economies of scale are achieved one can assume that the costs of the programme will decrease up to the point at which the programme has been extended as far as the current broadband or mobile availability will allow. After that point the cost base will change as satellite technology is employed to extend the service to the rural areas. Although the costs are likely to go up, as we’ve seen, with the use of satellite technology to transmit data, it is important to note that the benefits will also increase significantly given the increased vulnerability of individuals living in the remotest areas. In addition, the cost of travelling from these remote areas into the capital are likely to be prohibitively expensive and as this has not been factored into our benefits analysis the calculated benefits each programme is likely to be higher than currently stated.

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SatelLife (2004), Cost effectiveness study report for the PDA capture and transmission. http://www.healthnet.org/coststudy.php

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7.1

Introduction

The introduction of telemedicine and eHealth along with technological investments will have wider socio-economic benefits beyond the direct improvements in health outcomes. Some of these impacts will be captured in the calculation of value of the additional years of life or health estimated in Section 5. However, we have also considered the nature of these wider impacts and how they manifest themselves through drawing upon available economic literature and considering how the case study cases are functioning. Further analysis would require more specific detail of the applications to be developed and the socio-economic circumstances in which they will operate.

More specifically, investment in health skills and technology raises the general skills and capital levels in the economy leading to higher productivity and therefore higher real economic growth. However, telecommunications investments are unlikely to be a causal driver in economic growth in deprived areas on its own. For more remote and less economically prosperous areas, there needs to be a focus on developing complementary activities in the business environment, the transport network, other infrastructure and education73.

7.2

General Benefits

The telemedicine and eHealth projects set out in the seven case studies have a number of common wider socio- economic impacts which we would expect them all to reflect.

The direct increase in health skills through training and information will be reflected in the quantitative economic benefits presented in Section 5. However, there are likely to be additional skill gains in more general areas of knowledge, such as the awareness of and ability to use technology. These gains would start originally with those who were in direct contact with the programme. However, over time, they would be expected to spread this knowledge through direct instruction and by example. As more people become familiar with new technology and ideas, these become more readily mainstreamed into the general society and improve economic and social outcomes. We would expect that programmes which linked previously technological remote areas through new communications means would be able to see significant positive impacts if the technology was able to be made available to a wider share of the population (for instance, by allowing other groups to access the equipment during non-working periods).

Improved health outcomes increase the effective labour supply in the areas by enabling people to spend more of their lives working and to work more productively. This increase in labour supply should increase the economic output of the impacted area, which in turn increases the resources available to the local community for production and investment. Linked to the increased labour supply is strong evidence that improved expected health outcomes lead to greater investment in education, especially for children and young people. Families who expect their children to live long enough to grow into productive adults will have significantly greater incentive to set aside some of their current earnings to invest in education and also to forego any earnings that the children may have earned by allowing them to remain in school longer. As life and health expectancy increases, demand for more investment in individual children with a similar decline in demand for absolute numbers of children will increase the effective education rate while at the same time lowering population growth.

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Shiu and Lam 2008