At least one national daily newspaper used the release of a Department of Health (DoH) report on a national HIV and syphilis sero-prevalence survey to question the validity of official statistics.
The DoH report, wrote a journalist in a front-page lead of a morning daily, 'cast unflattering light on the accuracy of official statistics'. This was, according to the journalist, because the Health Department survey found that between 6,29-million and 6,57-million had been infected with HIV by last year, compared to Stats SA's estimate of 4,5-million.
This approach reflects an unfortunate tendency to sensationalise differences in estimation without exploring the purpose for which data was gathered, and the assumptions and sources used in statistical estimation.
The differences the estimates are related to use of different data sources. Stats SA based its estimates of HIV prevalence on the Nelson-Mandela/Human Sciences Research Council study which indicated a HIV prevalence rate of about 15% among women in their reproductive period (15-49), and on causes of death data derived from death notification forms.
The Department of Health, on the other hand, based its findings on antenatal clinic data. Only pregnant women who attend antenatal care for the first time at a public facility are eligible for inclusion in the DoH study.
As such, the target population of the Department of Health Study is very different from that of the Mandela study (a survey) and the causes of death data published by Stats SA in February 2005 (based on official death notification forms).
In the absence of comprehensive data, all those estimating HIV infection use models and assumptions. All data analysts, whether attached to the Department of Health, Stats SA or any other institution, do this. However, different assumptions tend to underlie different models, and this unsurprisingly gives rise to different findings.
For example, the Department of Health study assumes that the prevalence of HIV among the general population of women 15-49 is the same as all pregnant antenatal clinic attendees. This assumption may be questioned, as there is evidence from some sub-Saharan African countries that prevalence rates based on antenatal clinic data overestimate national prevalence rates.
This does not necessarily undermine the value of the DoH survey. The study is valuable in that it uses the same methodology and the same target population over time (since 1990) to provide an upper limit of the epidemic. It shows that the upper limit of the epidemic may be increasing.
The Health Department acknowledges that the assumptions underlying its estimations are 'crude due to constraints of the survey. The estimates are only as good as the validity of the results and the generalizability of the survey'. Furthermore, the Department has carefully provided confidence intervals for its estimates so that users can see where the significant increases have occurred.
A sober analysis of methodology, assumptions and data sources somewhat undermines attempts to sensationalise differences in estimations of HIV and AIDS infection prevalance. This critical task is complex, and is rarely advanced through the introduction of political tensions, ill-informed outbursts and personal agendas.
It is not just the accuracy of the statistics that is at stake here. It is also an assessment of what is of value in advancing public debate and participation, what informs and what misleads. In this respect, at least, the manner in which differences between Stats SA and Department of Health estimates has been presented, fails this test.
The quality of official statistics depends largely on the cooperation of citizens, enterprises and other respondents in providing appropriate and reliable data to statistical agencies.
In order for the public to trust official statistics, a statistical agency must have a set of fundamental values and principles that earn the respect of the public. These include independence, relevance and credibility as well as respect for the rights of respondents.
Communication with government, other institutions and the public on those conditions amenable to quantitative measurement can be undermined by sensational reportage based on inadequate understanding of the issues under debated.
Subjecting the assumptions underlying the various models used to estimate prevalence levels might better stimulate informed discussion. For example, the assumption that the prevalence of HIV among the general population of women aged 15-49 is the same as pregnant antenatal clinic attendees is open to critical assessment.
These different, and sometimes contradictory, assumptions and models can deepen understanding of HIV-prevalence levels, and thus provide guidance for the development of policy and treatment models. However, sensationalised arguments over whose numbers are more credible might not be as relevant as newspaper headlines suggest.