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To improve health it is important that care and prevention activities are focused on real problems and real need. Rational decisions about health strategies and interventions should be based on reliable and timely knowledge of the distribution of disease, which is only available with good surveillance. For a disease like AIDS, convincing statistics are necessary in estimating the extent of the spread of HIV and the associated demographic, social, and economic costs. However, there are problems with HIV surveillance: the long latent period means that disease is a reflection of historic rather than current spread; the infection is particularly present where resources for surveillance are limited; and there are biases in who comes forward for testing, be it in anonymous women attending antenatal clinics or those seeking a diagnosis in voluntary counselling and testing. These challenges have led to the development of surveillance methods and the theoretical tools to interpret surveillance data, which are based on an understanding of the problems and use the best available data and models to provide timely and practical information for users.
The devastating costs of the disease and the initial alarm followed by limited spread in many industrial countries conspire to generate scepticism among the public, politicians, and professionals alike about the scale of the HIV pandemic.1 Against such scepticism convincing estimates can have a powerful and timely advocacy effect.2 However, for these estimates to be convincing, they need to have a sound empirical basis and be based on transparent well accepted methods. This is the only antidote against the common tendency to overestimate the spread and consequences of a disease to generate more resources for the response to the epidemic. Although overestimation may have public health benefits in the short run, its long term effects will undoubtedly be counterproductive.
The availability …
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