PubMed · 14124965
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T KOLLER, E T RIPPMANN. 1964. [STATISTICAL DATA ON TUBERCULOSIS].. https://pubmed.ncbi.nlm.nih.gov/14124965/
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The interest of social scientists in complexity theory has developed rapidly in recent years. Here, I consider briefly the primary characteristics of complexity theory, with particular emphasis given to relations and networks, non-linearity, emergence, and hybrids. I assess the 'added value' compared with other, existing perspectives that emphasise relationality and connectedness. I also consider the philosophical underpinnings of complexity theory and its reliance on metaphor. As a vehicle for moving away from reductionist accounts, complexity theory potentially has much to say to those interested in research on health inequalities, spatial diffusion, emerging and resurgent infections, and risk. These and other applications in health geography that have invoked complexity theory are examined in the paper. Finally, I consider some of the missing elements in complexity theory and argue that while it is refreshing to see a fruitful line of theoretical debate in health geography, we need good empirical work to illuminate it.
National governments and international agencies, including programmes like the Global Alliance for Vaccines and Immunizations and the Global Fund to Fight AIDS, Tuberculosis and Malaria, have committed to scaling up health interventions and to meeting the Millennium Development Goals (MDGs), and need information on costs of scaling up these interventions. However, there has been no systematic attempt across health interventions to determine the impact of scaling up on the costs of programmes. This paper presents a systematic review of the literature on the costs of scaling up health interventions. The objectives of this review are to identify factors affecting costs as coverage increases and to describe typical cost curves for different kinds of interventions. Thirty-seven studies were found, three containing cost data from programmes that had already been scaled up. The other studies provide either quantitative cost projections or qualitative descriptions of factors affecting costs when interventions are scaled up, and are used to determine important factors to consider when scaling up. Cost curves for the scaling up of different health interventions could not be derived with the available data. This review demonstrates that the costs of scaling up an intervention are specific to both the type of intervention and its particular setting. However, the literature indicates general principles that can guide the process: (1) calculate separate unit costs for urban and rural populations; (2) identify economies and diseconomies of scale, and separate the fixed and variable components of the costs; (3) assess availability and capacity of health human resources; and (4) include administrative costs, which can constitute a significant proportion of scale-up costs in the short run. This study is limited by the scarcity of real data reported in the public domain that address costs when scaling up health interventions. As coverage of health interventions increases in the process of meeting the MDGs and other health goals, it is recommended that costs of scaling up are reported alongside the impact on health of the scaled-up interventions.