About 33 million people were estimated to be HIV infected worldwide in 2007 
. Despite the relative ease of diagnosing HIV in adults, even in developed countries, the exact number of HIV infected people is unknown because not all those contacted in population surveys or surveillance systems will consent to HIV testing 
. Traditionally population estimates of HIV prevalence in sub-Saharan Africa have been based on sentinel surveillance of pregnant women attending antenatal clinics 
. These data remain widely available and used particularly in resource poor settings, even though they are known to be biased due to lower fertility of HIV positive women, and in some countries, by unrepresentative selection of surveillance clinics 
Many other sources have been utilised in more recent times such as regional or national household surveys 
, surveys among high-risk populations 
, and population-based surveillance studies 
. A common feature of many of these surveys is non-response, and a major concern for analysis and generalisation is that the level of non-response can result in substantial biases in the population HIV prevalence estimates 
. This is of particular concern if differential response rates are associated with specific characteristics of the population or high-risk groups 
and if these data are used as inputs for deriving demographic, social and economic impacts of HIV.
We previously examined mortality patterns and levels by HIV infection status in rural South Africa using data from three annual population-based HIV surveys conducted between 2003 and 2006 
. The age-adjusted mortality rate in 2005 among HIV-infected adults (15–54 years) was reported at 53.9 deaths per 1,000 person-years and among HIV uninfected adults as 4.6 deaths per 1,000 person-years; the age-adjusted mortality among adults contacted but who refused to test in the HIV surveillance was estimated to be 26.2 deaths per 1,000 person-years. In the analysis here we use results of the HIV surveillance conducted by the Africa Centre in 2005 in the Umkhanyakude area of KwaZulu-Natal in which 58% eligible people refused to participate in the surveillance, to suggest a method for estimating the effect of gender-specific refusal rates on HIV prevalence estimates.
High levels of test refusal are not unique to our research area, and are common in HIV surveillance in South Africa, as highlighted by García-Calleja et. al. 
. A review of 20 population-based HIV prevalence surveys conducted between 2001 and 2005 in 19 sub-Saharan countries including South Africa showed that the proportion of women who refused HIV testing in the surveys ranged from 1% to 17% in 18 of the 19 countries but in South Africa the refusal rate was 30% (women) and 35% (men) 
. Further, within South Africa, a recent nationally representative HIV survey found KwaZulu-Natal province (where our study area is located) to have the highest refusal to HIV testing at 37% (excluding absentees and non-contacts) 
The aim of this paper is to describe a simple model which uses mortality rates by HIV status to estimate HIV prevalence among the population who refused to participate in the HIV surveillance. The estimated prevalence among those who refused testing is then used to adjust the overall prevalence in the total population in the area. We use longitudinal demographic surveillance data from rural South Africa to derive mortality rates for persons who tested negative and positive during HIV surveillance in the same area in 2005 (testers), and for those who refused to participate in the 2005 HIV surveillance (non-testers) and for whom HIV status was thus unknown to us. The model uses these mortality rates, and assumes that mortality among the HIV status unknown group is a weighted average of the mortality rates of those whose HIV status was known, to estimate HIV prevalence among non-testers. Given that the data are from a longitudinal demographic surveillance with routine recording of deaths and the population at risk, the resulting mortality data could provide a tool for adjusting HIV prevalence in the study population to allow for refusal to test. This method could be applicable in other demographic surveillance sites, with relatively high rates of refusal to participate in HIV testing but reliable mortality data for the population which can be categorised by HIV status.
We compare observed HIV prevalence rates (from those who consented to testing) to the adjusted HIV prevalence rates by age and sex, and document a significant underestimate in the observed prevalence rates.