The model described here provides a simple tool to inform the planning of effective, appropriately targeted, country specific intervention programmes. It allows the user to identify those risk groups among whom most of the new HIV infections will occur and the relative orders of magnitude of the incident infections between the different risk groups, which in turn will help countries to focus intervention strategies and to explore current coverage of interventions. The model does not take into consideration the distribution of all behaviours within the risk groups, overlapping risk behaviours, the patterns of mixing of demographic, social, geographic, and economic variables and the influence of specific sexually transmitted infections, and therefore cannot be used to generate accurate predictions. However, it should be pointed out that most countries lack the detailed data that accurate predictions require.
The model was applied to two countries to illustrate its application. Results indicate and confirm that patterns of transmission of HIV vary widely between countries.
The estimated numbers of new infections in Kenya and Thailand based on our model outputs have been confirmed and calibrated by other models. The total number of new infections is estimated to be around 82
400 for Kenya and 17
800 for Thailand. These compare well with the estimates of 86
300 for Kenya from the Spectrum model7
500 for Thailand from the Asian Epidemic model.8
In addition, incidence estimates in some of the risk groups obtained from our model have been confirmed by empirical data. Although incidence obtained from cohort studies on sex workers in Kenya show large variation, estimates over recent years (1998–2003) vary from 1.7% to 8.5% per year with lower estimates in more recent years.9
According to the US Census Bureau, new infections in this population group peaked in the early 1990s, and our model estimate of 1.9% among sex workers in 2005 is plausible. Similarly, our model estimate of incidence among IDUs in Thailand (2.6%) falls within the range of estimates obtained from cohort studies among IDUs in Thailand (0.5% to 10.2% per year) in recent years (1999–2002).9
Results for Kenya show that the majority of new infections in 2005 occurred through heterosexual contact (90% of new infections), of which the majority were in the low risk population and among individuals engaging in casual sex and their partners. The model confirms that sex workers and their clients are extremely vulnerable groups, not only for the acquisition of HIV, but also for the transmission of sexually transmitted infections and HIV.10
Although the relative contribution of sex workers to the total number of new infections was small (1.3%) because the sex worker population is small relative to the total population, the incidence rate in this group was high at 1.9 per 100 per year. Clients of sex workers accounted for 10.5% of all new infections with an incidence rate of 3.6% per year. The model further shows that MSM accounted for 4.5% of new infections, with a high incidence rate of 4.5 per 100 per year. This calculation was based on the assumption that 1% of men in the general population have sex with other men and may be an underestimate of the true MSM situation in Kenya. Statistics on MSM are hard to obtain because homosexuality in Kenya is a criminal offence.11
It is recognised however, that networks of homosexuals are found throughout Africa, although many adopt a heterosexual lifestyle in order to fit in.12
Reports that male homosexuality is fashionable among young men and is practiced in prisons, boarding schools, and colleges,12
as well as studies suggesting homosexual activity among truck drivers, especially between older men and young boys,13
have provided evidence to suggest that sex between men in Kenya is more common than generally believed. Similarly, while sub‐Saharan Africa has generally been considered largely free of injecting drug use, a review of studies from East Africa has shown an increase in injecting drug use in Kenya.14
The study shows that heroin is freely available on the Kenya coast, that 45% of heroin users in Nairobi are injectors, that heroin injectors share injecting equipment and have sex with each other and with non‐users, and that 50% of injecting drug users interviewed in Nairobi were HIV positive.14
Our model shows that while the overall percentage of people injecting drugs in the adult population might be small (estimated to be around 0.3% of the male population), the incidence rate in this risk group is very high (16.3 per 100 per year) and IDU accounted for 4.8% of all new infections.
Many interventions in Kenya, such as the strengthening STI/HIV control project, have implemented community and clinical interventions among selected vulnerable groups to reduce transmission of STIs and HIV infections. While most of these interventions have targeted female sex workers, some projects have also been designed to target both female sex workers and their clients.13
Studies in Kenya have reported high levels of sexual interaction with casual or non‐regular partners13,15
which in addition to low condom use during these contacts lead to high transmission rates13
and should be a priority for targeting interventions. The taboo surrounding homosexuality has impeded the provision of AIDS education and support for these men and there has been no official recognition of the role homosexuals play in transmitting the virus.12
The increasing number of IDUs in Kenya together with the very high HIV infection rate among them, as well as the lack of information about the dangers of injecting, sharing needles, and unprotected sex,14
call for an urgent response and introduction of harm reduction methods in these groups.
The epidemic in Thailand has evolved through different stages over time. The main routes of HIV transmission in the late 1980s and early 1990s in Thailand were injecting drug use and sex work,16,17,18
from which HIV spread in the 1990s to the partners of clients of sex workers.19
However, the government was quick to respond and successfully implemented comprehensive national strategies including practicing safe injection techniques, public AIDS education messages, a 100% condom use campaign, and mobilising all sectors of Thai society.16,20,21,22,23
Thailand was therefore one of the first countries to reduce HIV prevalence by the mid 1990s.24
Our model estimates that the general, low risk heterosexual population accounted for 43% of all new infections in 2005 although the incidence rate in this population was low (0.03 per 100 per year). MSM also accounted for a large proportion of new infections (21%) while sex workers, clients, and partners of clients of sex workers explained a further 18% of new infections. Injecting drug users and their partners accounted for a total of 7% of new infections. The incidence rate among IDUs was estimated to be high at 2.6 per 100 per year.
The HIV epidemic in Thailand has spread beyond vulnerable groups to the general population, while it also appears to be on the rise again in MSM. In addition, the epidemic threatens to regain momentum in communities where complacency has set in (for example, among young people).23
Prevention strategies must therefore be adapted to the changing patterns of risk behaviour and situations involving MSM, IDUs, sex workers, and their clients. More attention should be given to prevention strategies aimed at reducing HIV transmission between regular partners (in particular in young people), one of whom may have been exposed to HIV through buying or selling sex, while sustaining existing prevention efforts targeting sex work.
In many countries with low level or concentrated epidemics the HIV epidemic is initially limited to vulnerable population such as IDUs, sex workers and their clients, and MSM.25
These groups are often hard to reach because of local laws and social stigma and interventions required to reach them will differ. However, as the epidemic progresses, the virus will spread to the sexual partners of vulnerable groups and the size and the composition of the populations to be targeted for effective intervention and care will change, as will the resources that are needed to control the epidemic.25
The spreadsheet model presented here can help countries to assess the change in epidemics, to prioritise target groups for interventions, and to plan more effectively the resources required to implement these interventions.
Attempts to apply this model to specific countries during regional training workshops conducted by UNAIDS and WHO in 2005 showed that country specific data on HIV prevalence or risk behaviour are often lacking, hence limiting the use of the model. Although more countries with generalised epidemics in sub‐Saharan Africa are conducting Demographic and Health Surveys (DHS) that can provide useful information on sexual behaviour in the general population, data on groups that are particularly vulnerable, such as sex workers, MSM, and IDUs, are often limited. The example from Kenya has shown that these groups cannot be ignored when planning and targeting interventions. Similarly, in countries with concentrated or low level epidemics, information may be available from studies targeting specific groups with higher behavioural risk, while there is less information on behaviour among the general population.
In addition to limited availability of data, the quality of the data collection as well as measurement procedures can affect the accuracy of the estimates. For example, women saying that they had received money, goods, or favours in exchange for sex in population based surveys cannot be assumed to have engaged in commercial sex. When assessing studies of behaviour, attention needs to be paid to the measurement of key parameters and to the quality of data collection. Care should also be taken when extrapolating information from a study conducted in a specific part of the country to the rest of the country, for example the reported number of sex work or IDU partners in the capital city of a country would be different from that in more remote parts of the country.
There is an urgent need for improved biological and behavioural surveillance systems to provide more reliable data for planning effective interventions. Given the availability of relevant data, the model presented here provides a simple tool for estimating who are most likely to be infected with HIV in the coming year and what behaviours put them at risk of infection, which will provide governments and national AIDS programmes with the information needed to plan and focus intervention and prevention efforts so as to effectively address the epidemics in their countries.