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Mathematical models are shown to be useful for making qualitative predictions about …
Biology Articles » Biomathematics » The future of the HIV pandemic » Risk assessment
The concentrated nature of the HIV epidemics in most Asian and Eastern European countries suggests the utility of a novel approach to the management of HIV in these countries. The techniques of risk assessment, routinely used by environmental epidemiologists, can be used to map exposure to HIV. Behavioural surveillance can identify the high risk behaviours that transmit HIV and can be used to map the distribution of vulnerable populations within a country (16). The need for mapping emerges from the heterogeneous distribution of risk behaviours and hence HIV within countries. For example, in India, injecting drug use is concentrated in the north-east of the country and in urban centres such as Chennai, Delhi and Mumbai, where HIV transmission associated with needle sharing has led to significant epidemics of HIV (17, 18). These maps may be supplemented by surveillance of HIV-related knowledge and of biological markers of risk, such as the sexually transmitted infections that facilitate HIV transmission (e.g. herpes simplex virus type-2), or bloodborne infections such as hepatitis C. Procedures for the estimation of the size of the vulnerable populations can then define the magnitude of exposure to HIV by the different transmission routes in different parts of the country (12).
A key step in risk assessment is to identify what is an acceptable risk. In the United Kingdom, for example, the Health and Safety Executive considers a one-in-a-million excess risk of death per year from environmental pollutants as the level of acceptable risk at which no further improvement in safety needs to be made (19). It is often illuminating to compare policy on acceptable risk with currently accepted risk revealed in morbidity and mortality statistics. For example, using estimates of cause-specific death rates (20) and HIV case reports (21), it is possible to estimate the average time it takes a 15–24-year-old living in the Russian Federation to acquire a one-in-a-million chance of death from specific causes (Table 2). The accepted risk of death from AIDS revealed by these statistics should highlight the need for HIV prevention strategies to bring the accepted risk in line with what is considered acceptable. In reality, this average masks a great deal of heterogeneity in risk. Moral judgements tend to be made about the culpability of those becoming infected with HIV, and the concept of acceptable risk for these people implicitly revised.
Effective surveillance of vulnerable populations identifies prevalent HIV infections. However, the incidence of new HIV infections can show a markedly different pattern. For example, in 2002 in Indonesia, although prevalent infections were equally distributed between injecting drug users, and sex workers and their clients (Table 1), the majority of incident infections were among injecting drug users (22). Patterns of incidence for a given year can be crudely estimated from simple measures of risk behaviour without the need for a complex dynamic model. For example, the average number of visits to different sex workers reported by male clients (e.g. x per year), together with the probability of HIV transmission per sex worker visited (P), and HIV prevalence among sex workers (y), can give an estimate of the expected number of new infections among clients that year » N-N(1-Py)x, where N is the number of clients. Simple estimates of expected incidence based on similar calculations for different vulnerable groups can act as a useful guide for HIV prevention (22). Interventions that focus on HIV prevention among vulnerable populations have been found in the past to be both effective and cost-effective, even where HIV transmission has become more generalized (23, 24).
The relationship between simple measures of risk behaviour and HIV incidence is analogous to the dose–response relationship central to the assessment of health risks in environmental epidemiology. Dose–response relationships need to account for the timing of health outcomes, and this is especially important for infectious diseases with long incubation and infectious periods. The relationship is simplified by focusing on HIV status rather than on health outcomes, and it is possible that measures of risk at the population level can be linked to HIV prevalence some years later. For vulnerable populations the relationship may be reasonably well defined, since high prevalence of unsafe behaviour such as needle sharing typically results in rapid HIV transmission and an epidemic that saturates at high prevalence. Of course, policy-makers are also (and often solely) concerned with risk of HIV to the wider adult population. In sub-Saharan Africa there is evidence, albeit limited, that living in a community with a certain risk profile — economically active, mobile population, many female bar workers, close to a large town — increases an individual's risk of infection over and above his or her own sexual behaviour (8). In Asia and Eastern Europe, although analogous research has not been carried out, models suggest that adults living where injecting drug use and sex work are common are at a higher risk of infection irrespective of their own sexual behaviour (5). Thus, risk in the general population is likely to be correlated with risk in other groups as well as with patterns of sexual behaviour. Epidemiological theory suggests a threshold relationship, such that below a certain level of sexual activity in the population the risk may be minimal (25). This contrasts with linear relationships seen, for example, between risks of ischaemic heart disease and blood pressure, cholesterol or obesity (26). As noted earlier, however, this relationship is not well understood, and therefore dose–response curves for the general adult population will be poorly defined. Furthermore, as the time frame expands, the problems inherent in forecasts of HIV prevalence will return. What role, then, can be played by transmission dynamic models?
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