Public health practitioners are frequently faced with having to make programmatic decisions with less than perfect data. It is seldom the case that a high-quality study exists that can provide answers to everyday public health questions directly, and often we must rely upon imperfect surveillance data to guide policy and programmatic decisions. This has been particularly the case for HIV, where the urgency of the epidemic coupled with enormously scaled-up international resources demand rapid responses in planning and targeting prevention and care programs. Further, the last several years have seen a massive increase in not only HIV surveillance data but also in programmatic data, quantitative and qualitative research studies, and local and national experience and expertise. While the volume of information has increased exponentially, its use and analysis to inform program, planning and policy have been lagging. Diverse sources of data are rarely presented together, and gathering, synthesizing and interpreting them has become increasingly challenging.
"Triangulation" is a term that can broadly refer to an approach to synthesizing multiple, diverse sources of data at the level of interpretation. However, the word itself has come to mean different things in different fields. Social scientists have used the term "triangulation" since the late 1960 s . By triangulation they mean examining multiple data sources to validate results, increase credibility and gain a more detailed understanding of findings [2, 3]. The term has been used to refer to methods for establishing both internal and external validity by decreasing the uncertainty of a single measurement by making multiple observations . Denzin describes four types of triangulation: data triangulation, in which data gathered through different samples and at different times are compared; investigator triangulation, in which more than one investigator examines the same question and results are compared; theory triangulation, in which different theoretical constructs are applied to the same observed data; and method triangulation, in which phenomena are examined using different methods . The term triangulation has also been used in nursing research to refer to mixed qualitative and quantitative methods to explain complex phenomena [6–8], akin to Denzin's method triangulation. In these contexts, "triangulation" often implies that the approach was created at the design stage, where the investigator could exert control over the study methods and measures. Triangulation is similar to evidence mapping [9, 10], and realist synthesis [11, 12]. These, however, tend to be more forward-looking and less concerned about explaining what has transpired in the past, creating the present day situation. It is also similar to narrative reviews but tends to have a broader focus than individual interventions . Triangulation, as used in the context of public health, is probably most closely related to critical interpretive synthesis, which has as its focus the generation of theory to interpret observations and is not nearly as methodologically constrained as meta-analysis . However, as discussed below, triangulation extends the construct of critical interpretive synthesis by addressing not only how we got to where we are but also what we do next.
More recently the term has been used in the context of public health to refer to the process of reviewing and interpreting secondary data from multiple data sets that bear on the same question to make public health decisions, combining elements of both data triangulation and method triangulation [15, 16]. The work of Stoneburner and Low-Beer in Uganda offers an early example of this approach to triangulation . Their basic question was whether or not the declining trends in HIV prevalence seen in women attending antenatal clinics in Kampala and elsewhere in the early to mid 1990 s were the result of declines in HIV incidence or the result of mortality. The scope of the question and the timeframe encompassed did not allow for the design of a single prospective study that could answer this question directly and definitively. The changes during this period were substantial with HIV prevalence falling from 21.1% to 9.8% between 1991 and 1998 and to 6.4% in 2001  based on data from antenatal clinic sites. They were able to confirm similar trends in other national datasets, such as a decline in HIV prevalence in male army recruits and blood donors and declines in directly measured HIV incidence in large cohort studies in the country . They also identified a series of data sets that bore on variables in the chain of events that led to prevalent HIV infection in women, including age at sexual debut, risk factors for exposure to HIV (men's and women's numbers of partners, communication about avoidance of risk), risk factors for transmission (use of condoms), incident HIV infection and mortality. They were able to show that an increase in age of sexual debut and decreasing numbers of sexual partners, which would have led to contraction of sexual networks, temporally preceded the decline in HIV prevalence . Moreover, data from neighboring countries with HIV epidemics in a similar stage as Uganda did not show comparable declines in early sexual debut and number of sex partners nor subsequent declines in HIV prevalence, further suggesting that behavior change rather than mortality was the cause of the decline in HIV prevalence seen in Uganda . Additionally, prevention programs at the time emphasized partner reduction or "zero grazing". The success of Uganda was heralded as evidence that national level responses could produce sweeping impact on the HIV epidemic. In this example, we highlight the success of the triangulation approach in assembling and interpreting diverse existing data sources to provide evidence in support of the underlying cause of the national epidemic trend. There are several other examples where public health triangulation has been used to understand trends, for instance, from Cambodia, Thailand and the United States [21–25], and the 5-year evaluation of the Global Fund to Fight AIDS, Tuberculosis and Malaria  is using not dissimilar methods
In this paper we propose a standard approach to public health triangulation and suggest scenarios in which it can enhance understanding of the national and local HIV epidemics and prove useful in programmatic decision-making. We define public health triangulation as the process of reviewing and interpreting existing data and trends in those data from multiple data sources that bear on different facets of a broad public health question in order to identify factors that underlie the observed data and to assist with public health decision-making and actions.