eLearning: eLearning for Nurses

The functionality tested with the Kenyan nursing programme is the provision of certification-level learning courses for health workers through telecommunications means.

We have summarised the assessed annual impacts on Kenya of the Nursing training programme on key health needs as illustrated in Table 21.

Table 21: Estimated Monetised Benefits for Kenya

Kenyan health impact Lives Saved Single Year Value Lifetime Value

AIDS 850 $1,739,000

Malaria 15 $31,000

Tuberculosis 50 $110,000

Diarrhoea and Childhood Disease 30 $55,000 $2,758,000

Maternal Health 40 $86,000 $2,832,000

Perinatal Health 30 $59,000 $3,113,000

TOTAL 1,020 $2,079,000 $8,703,000

We have undertaken individual calculations for 44 sub-Saharan African countries for which we had the data required. Table 22 shows the aggregate results for a single year of benefits.

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Table 22: Estimated Impact Across Sub-Saharan Africa

Sub-Saharan Africa health impact Lives Saved Single Year Value Lifetime Value

AIDS 54,700 $94 million

Malaria 4,000 $7 million

Tuberculosis 5,400 $8 million

Diarrhoea and Childhood Disease 7,600 $13 million $593 million

Maternal Health 6,000 $11 million $281 million

Perinatal Health 7,400 $12 million $570 million

TOTAL 85,100 $145 million $1,444 million

For the eLearning case, we have assessed the impact by assuming there is an impact on effective health worker supply equivalent to the share of nurses being trained by rolling out across Kenya’s and sub-Saharan Africa’s population that is outside the current mobile phone network. This is then applied to interventions where we have published information on the rate of mortality decline associated with them. We have calculated the impact of the provision of information to health workers through the following calculation:

 We begin with the reported outputs from the Kenyan example of training 3,038 certified nurses across the country;

 We assume the impact of these investments is equivalent to increasing the effective health workers labour force across the country of 3,038 nurses out of a total nursing population of 37,113 as an 8.2% increase in the effective health workforce;

 We have then applied this 8.2% increase in effective health worker capacity across three high-profile diseases (HIV/AIDS, malaria and tuberculosis) and three population groups (mothers, newborns and children under five years of age). The impact on health outcomes have been taken from published articles as explained below. For HIV/AIDS, we have used the 81% reduction in annual death rates among anti-retroviral treatments in Uganda from Mermin et al 2008. For tuberculosis, we have used the 38% reduction in mortality through treatment by a specifically-trained health professional in Khan et al 2006. Diarrhoea and childhood disease reduction has been modelled using the Integrated Management of Childhood Illness in Tanzania reported by Schellenberg 2003. Malaria morbidity has been modelled at 12% from Ngasala et 2008’s evidence from Tanzania. Maternal mortality has been modelled on the 40% reduction found in Ronsmans et al 2003 across West Africa. Perinatal mortality is based on the 29% reduction through the use of trained personnel from Jokhio et al 2005’s study in Pakistan;

 We have calculated the number of lives saved through the different interventions by first calculating the share of the population who would require satellite communication (proxied by the share of the population outside the existing mobile telephone network). Using these population data, we have then calculated the expected number of deaths from AIDS, malaria, tuberculosis, maternal health problems and childhood disease using the December 2004 WHO

mortality rates per 100,000 persons for these diseases and groups. We have then estimated the number of deaths prevented by multiplying the total number of estimated deaths by the percentage decrease in mortality from the relevant interventions from the published data and also by the 8.2% change in health worker capacity; and

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 A monetarised benefit has been calculated for the first year of benefits for all six health categories using a value of three times national output per capita for the additional year of life. Lifetime impacts have only been calculated from diarrhoea and childhood diseases, maternal health and prenatal health as we could calculate the total estimated additional years of life that would be expected to be saved through these interventions. To generate these total life years saved we have assumed that the patients live to the current average life expectancy from their current age after their interventions. We have assumed that the average current age for mothers is twenty and the average age of children is 2.5 years. For AIDS and tuberculosis, this approach would not be appropriate as the evidence base used was solely on annual mortality rates and patients would be expected to continue to suffer from a chronic disease which would reduce their overall lifespan. For malaria, we have not calculated a lifetime impact as we did not have the average age of all malarial patients – both children and adults – which would be required to calculate the number of years of life saved.

We have also estimated that there is currently a shortfall of 588,000 trained nurses in remote areas of sub-Saharan Africa to meet the MDG target of 2.5 health workers per 1,000 inhabitants. We have calculated this figure based on the total number of nurses in each country, the population for each country in areas outside of the mobile network and assuming that the nurse-to-population density in these areas is 2.5 less dense than in other areas. Extending the proportion of training that Kenya has achieved, the gain in certified nurses across Sub-Saharan African would be 44,085. This would be equivalent to 8% of the current SSA nursing population. However, it would only reflect 7% of the estimated need for SSA rural nurses to meet the WHO’s 2.5 health workers per 1,000 inhabitants goal.