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Measuring socio-economic impacts of HIV/AIDS & malaria

It seems straightforward, but a second-long pause reveals serious hitches especially when you intend to carry out this measurement in a developing country. Inwent (read as Invent), a Germany based, non-profit organisation with a major focus on Capacity Building with worldwide operations dedicated to human resource development, advanced training and dialogue delved into this important, but often ignored subject in Africa. Dr. Bernd Gutterer facilitated the three months online workshop and later the on-site discussion in Accra, Ghana with participation from representatives from different African countries.

Impact measurement studies for health related indicators normally make the following assumptions. (i) That measurement of socio-economic impacts in the strict sense should be subjected to a relatively longer time period otherwise such studies are measuring effects. This implies, therefore that if a study is about impact measurement of Malaria, unlike HIV/AIDS, for example, the ultimate sample would be composed of mortality due to malaria. Thus, such studies are difficult because they would be focusing on  forensic autopsy. Otherwise, malaria disease is known to have a cure with known average treatment period, but also known to recur and kill. (ii) That the sample should focus on identical populations based on age, sex, location, household income, accessibility to a health unit and where possible, other genetically-related considerations such as blood groups. (iii) That the basic sampling unit be a person and not a household because different people although live in the same household present different scenarios.

Developing countries present a very difficult case in measuring HIV/AIDS & Malaria and their socio-economic impacts. Among the challenges presented are deriving the actual indirect costs of a disease.  Most studies that attempt to measure the socio-economic impacts establish that the indirect costs are actually greater than the direct cost of treatment! This is unrealistically unacceptable, but practically true for most African countries. The second challenge is estimation of the real economic productivity of someone who has either been unemployed or partially employed for a very long time! And the third challenge is both quantification and estimation of social benefits accumulated by the patient whose social behavior is stochastic.

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