4.1 Main findings
The MoT model, which provides an estimate of the distribution of new infections in the next year, was applied in three countries of the Latin American and Caribbean region. The results suggest that despite signs of a feminisation of the epidemic in Mexico and Peru (decreasing male to female ratio in terms of HIV cases), the majority of infections still occur among MSM and their partners. These accounted for estimates of 65% and 62% in Mexico and Peru respectively when they represent less than 2.5% and 4% of the total adult population. It is known from a very extensive body of information, that this population is very heterogeneous in terms of both behaviours and prevalence implying the need for more precise modelling to adequately address this issue and provide more reliable estimates of incidence. Epidemic spread among the heterosexual population was estimated to remain quite low although its contribution is more important in Peru than in Mexico. A considerable percentage of estimated new infections occurring among women are directly related to sexual contact with members of key populations. In Peru, these were evenly spread between groups making it harder to direct prevention efforts towards a particular section of the population. Partner notification services might prove particularly efficacious in this context. Sex work accounted for over 8% of estimated new infections in Peru and 5% in Mexico. As noted previously, although condom use is thought to be high among FSW and their clients, sex work results in secondary infections through sex between clients and their stable partners. In fact, in both countries the majority of infections attributable to sex work were estimated to occur among client partners. In Mexico, IDU is growing as a localised problem with approximately 9% of new infections occurring among this group despite having a relatively low prevalence (5%). Although opioid traffic from Mexico to the U.S. has been known for the last century, local consumption is a relatively recent phenomenon.
The estimated distribution of incidence obtained for the Dominican Republic exhibits a different pattern. No group contributes to the majority of infections with MSM and the low risk group each accounting for 30% of new infections. However, it is important to highlight that while the low risk group represents 41% of the total population, MSM represent just over 2%. Commercial sex plays an important role in the epidemic. Even with the high rates of condom use considered in the model, it accounts for about 12% of new infections. This can be explained by the relatively high number of people involved and by the frequency of exposure among clients. Residents of Bateyes contribute to nearly 9% of new infections when they account for less than 4% of the total population, highlighting the importance of
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considering populations specific to a country that are outside the classic list of key populations. With the modified version of the model, it was estimated that over 11% of infections would occur among NIDU and over 6% among women with no education. The former is likely to be an over-estimate as over half of NIDU were assumed to have sex with men but reminds us about the overlap in risks among key populations. Lowering levels of drug use among MSM in DR might be efficient in decreasing transmission levels. Transwomen were also shown to be at high risk with a prevalence of 17%. These share a mode of transmission and it would be simple to integrate them in the model. However, data was insufficient to parameterise the model and so a simple inference was made estimating that 0.4% to 4.3% of all infections could arise in this population. Considering its potentially high contribution to incidence in comparison to its small size, efforts should be made to reach this population with prevention programmes.
4.2 Data availability
Regarding data availability, there was variation between countries but some major gaps emerged from the analysis. Information on partners of key populations (IDU, FSWs’ clients, MSM and individuals engaging in CHS) is missing in all countries. These are very hard to reach as many may be unaware of their own risk. Partner notification programmes could provide a means to identify and characterise them through short behavioural surveys. The limitation is that only partners of STI/HIV positive individuals would be reached through this path. More importantly, this depends on individuals being willing to disclose their infection or even their suspicion of infection to their partner. Routine sexual health check-ups should be promoted through campaigns to normalise this practice and facilitate the process. In fact, this lack of data reflects one of the main problems hindering STI control among stable partners which is the disclosure over other partnerships, especially among married MSM. Estimates of the size of key populations rarely originate from rigorous studies designed for this purpose. This information is also of relevance for the design of prevention interventions and for the planning of health services provision in general so should be high in the list of priorities of national HIV programmes. Data on the number of sex acts or injections per partner was also largely unavailable. Considering the important uncertainty around the per act transmission probability of HIV, it might be more appropriate to use the per partnership probability in the model and circumvent this problem. In all countries data on clients of sex workers was scarce. A better understanding of this population, especially in DR would help preventing secondary transmission to other partners. Regarding each country, there was a lack of information on IDU, in Peru attributed to the low prevalence of this practice. It is
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important to ensure that this is in fact the case. In Mexico, the information on MSM is not optimal considering their important contribution to the epidemic and more information on their STI prevalence and sexual practices is necessary.
4.2.1 The Epi-Tool
To ensure that countries have enough data and of acceptable quality to complete the model, a stage previous to the application of the MoT has recently been conceived by UNAIDS. This consists of a systematic and standardised review of the information available in the country for each of the model’s inputs and for each sub-population. This information is entered in a formatted worksheet named the “Epi-tool” which allows scoring the sources for each input. If the total score is below a certain threshold then the country’s data is considered insufficient to carry on the MoT. Apart from serving as a filter to prevent unreliable results from being produced it also serves as a tool to highlight gaps in data availability and therefore to set priorities for data collection activities. Countries that do not have enough data to complete the model will carry out other activities suggested by UNAIDS that will help improving the understanding of their epidemic. The Epi-tool was tested on the MoT for Mexico as this was the only country that had not finalised its process when the tool was developed.
The first stage of the Epi-tool analysis requires determining which subpopulations are relevant to the epidemic in the country and indicating whether there is data available for each of the inputs describing prevalence and risk behaviours characterising each population (see Figure 13). Once the table is filled in, the tool calculates a score rating the data availability. The score obtained for Mexico when tested was 64%.
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Figure 13. Screenshot of the Epi-review checklist for Mexico.
Note: Blue squares indicate that there is data available, yellow squares indicate there is no data available.
Following this, detail on the sources available for each subpopulation has to be entered (Figure 14). The number of data sources, the year the study took place, the estimate, the confidence interval and the reference of each source have to be given for each of the inputs. Based on this, the user determines whether the quality of the data for each input is good, limited, poor. The woksheet for MSM is shown as an example.
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The results of the data quality scoring are summarised in the Results worksheet shown in Figure 15 below.
Figure 15. Screenshot of the Epi-review results sheet for Mexico
The data is considered to be sufficient to complete the MoT if the availability score is above 50% and the quality score for the subpopulations is generally above 1.
In this case based on the Epi-tool, the availability score was above 50% and the quality score was above 1 for the low risk group, the casual heterosexual risk group and all the key populations but it was below 1 for all the partners of members of key populations. This would therefore be borderline acceptable to complete the MoT as 1/3 of the subpopulations have a quality score below 1. This contrasts with UNAIDS experts’ opinion at the time the Mexican data was evaluated for the MoT. In comparison to other countries data availability was much greater for Mexico and so it seemed evident that it could carry out the MoT.
4.3 Limitations
Aside from gaps in data availability, the model itself has its shortcomings and has been subject to substantial criticism in the past couple of years. The model structure does not take into account overlap in risk behaviours and thereby assumes assortative mixing between or within groups. Overlap in risk behaviours is an important facilitator of transmission as it can efficiently work as a bridge for the virus to pass from one population to another (for instance FSW who inject drugs can spread infection from other IDU to their clients). In particular, this can lead to over-estimations of the impact of prevention interventions among a specific group as other modes of exposure are
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ignored [275]. In the same way, it does not incorporate heterogeneities in behaviour within risk groups. Using averages for entire groups can substantially affect the results, which is why many mathematical models of infectious disease transmission include heterogeneities in risk and define contact mixing patterns. IDU for instance might share injecting equipment only within a specific cluster while some FSWs clients might only visit a particular type of FSW. Similarly, the model ignores geographic heterogeneity (in prevalence and behaviours) which, as seen for Mexico and Peru, can be important. These issues can be addressed by disaggregating groups when data availability permits it and by applying the model to specific regions or cities if it is considered more appropriate. The model does not incorporate characteristics of the natural history of HIV infection such as differences in transmission probability during the course of infection or under anti-retroviral treatment. A revised version of the MoT has been developed to incorporate the impact of ART treatment. It assumes that prevalence remains constant during the period simulated which can be a problem if the epidemic is at a stage of rapid change but this is only a short term projection so it should not be an important source of error.
The data used to estimate the inputs comes from a variety of sources that may vary in reliability but, more importantly, that are obtained from different studies, at different times and sometimes in different places and which are applied to a single population. This is because it is rare to have a large study that covers all the questions needed by the model. It is a problem with the MSM population because many studies have been carried out among very high risk groups [276]which are probably not representative of the whole population. However, because there were a variety of data available it was possible to recognise this issue and choose the most appropriate studies to attempt to mitigate this issue. Furthermore, the definition of the populations is not always consistent which also makes the choice of data sources difficult (as in the case of the CHS group). Variations in certain parameters (such as the size of each population as mentioned above) have an important effect on the outputs which can easily lead to different conclusions regarding the groups to be prioritised by prevention. The size of the low risk group is often calculated as the residual of the other groups, so under or over-estimations of the size of key populations will doubly affect the results. This carries important implications as the estimate of the contribution of the low risk group to new infections is a defining number in terms of the characterisation of the epidemic and the subsequent recommendations for prevention. Mishra and colleagues demonstrated this by applying the MoT model to the state of Belgaum in India using different data sources for the estimate of the number of FSWs. They obtained contrasting patterns of incidence distribution [277]. In fact, although some countries such as Kenya have compared the MoT outputs to data [230] the model has not been formally validated. This could be done by applying the MoT to data from the early 1990s and
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comparing the distribution of infections to that of AIDS cases reported 10 years later, preferably in countries where access to ART started late and expanded slowly. Comparing the outputs to more recent data on HIV cases would be a better option since data to parameterise the model has become increasingly available with time, but HIV cases reflect transmission at different times. If however, the distribution of HIV cases by group has remained stable over time, it would be reasonable to do it. Both these potential validation methods carry the limitation of biases in the classification of cases, emerging from both the patients and practitioners.
Another point made in Mishra et al. study is the risk of producing a misleading representation of the epidemic drivers through the MoT. This is especially relevant to countries with generalised epidemics where a high proportion of new infections will be ascribed to the low risk population when in fact, a substantial proportion of these might originate from earlier contact with members of key populations (the most common case being that of clients of sex workers who later on get married and pass on the infection to their partner). As the MoT provides a short term projection it does not take into account individual changes in behaviour over their life course and therefore can ignore the primary source of infection among individuals that are now in the low risk category [277]. In this case, inferring that most infections occur in the low risk group and directing prevention towards this group would not be effective in the long term as the epidemic would constantly be fed by the primary source of infection. Rather than a methodological issue, this has to do with the interpretation of results and highlights the need to combine methods to inform policy.
The MoT is designed to produce a simple message which, in its brevity, can give a false impression of accuracy and validity and this is why it is especially important to be cautious when producing and presenting results obtained with this model. The uncertainty analysis provides a sense of how robust the results are and helps communicating the model limitations to decision makers. However, this too carries important limitations. The model is fit by varying the size and prevalence among the low risk group and therefore does not provide a valid uncertainty range for this group. Parameters can only vary within 100% of their estimate; if this is a small number the maximum and minimum values will not differ much from the point estimate despite attributing it the highest uncertainty range. If this number is applied to a large population, the true uncertainty will be much larger than estimated through this method. Additionally, the method assumes there is no inherent bias in the data and so uncertainty will always be equal on each side of the point estimate. Some parameters such as condom use or the number of partners might be systematically biased as a result of social desirability for instance. It might also be appropriate to assign a larger weight to parameters that have a strong impact on the results such as the number of partners among MSM. A more thorough
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analysis of the implications of these limitations in terms of policy guidance and proposed solutions to optimise the MoT are given in Case et al paper included in appendix.
4.4 Conclusions
In conclusion, clearly, the HIV epidemic is different in each country, but the application of the MoT to these three countries has underlined the need for individual analyses to be carried out if we are to design appropriate measures to control it. Similar epidemic patterns emerged in Mexico and Peru, with MSM contributing to the majority of new infections but the substantial contribution of IDU to the number of new infections in Mexico deserves particular attention. MSM were found to contribute to a large share of new infections in Dominican Republic, comparable to that of the low risk group. This prompts us to suggest that the term “mixed” might be more appropriate than “generalised” to describe this epidemic and that the prevention efforts should be allocated accordingly. In each of these countries, directing prevention efforts towards MSM is likely to have a significant impact on incidence. Conversely, only a small proportion of the resources available for HIV/AIDS have been allocated to prevention among this population while PMTCT services have received larger budgets [3]. This work can be useful for advocacy purposes while keeping in mind the limitations of the model. For instance, the Dominican Republic recently incorporated the results from the MoT to their proposal for the next round of funding from the Global Fund. At this stage it is sensible to trust the magnitude of the results obtained from the MoT, rather than the actual numbers. In fact, the strength of the MoT lies on the process which forces a thorough scrutiny of the available data in the view of producing results. It is only by taking on the task that gaps and issues become apparent and so this is a very useful exercise. The inclusion of the Epi-tool as a preliminary step to completing the model will help strengthening this process and allow a better judgment of the reliability of results. Triangulation between data sources and methods is a good alternative to