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Epidemiology poster session 4: Modelling
P1-S4.15 Revisiting HIV epidemic appraisals for assisting in the design of effective HIV prevention programs
  1. S Mishra1,
  2. S K Sgaier2,
  3. L Thompson3,
  4. S Moses3,
  5. B M Ramesh4,
  6. M Alary5,
  7. J F Blanchard3
  1. 1Imperial College, London, UK
  2. 2Bill & Melinda Gates Foundation, India
  3. 3Centre for Global Public Health, Canada
  4. 4Karnataka Health Promotion Trust, Bangalore, India
  5. 5Centre de recherche FRSQ du CHA universitaire de Québec, Québec, Canada


Background There is substantial heterogeneity in the size and trajectory of HIV, driven largely by differences in the population sexual structure, which determines overall HIV transmission dynamics. Two standard methods have been developed to appraise epidemics and guide prevention strategies. The numerical proxy method classifies epidemics based on HIV prevalence thresholds. The Modes of Transmission (MOT) model estimates the distribution of incidence over 1 year among subgroups. Neither approach explicitly captures the drivers of the epidemic and can therefore lead to misguided prevention priorities. Using data from India, we explored the limitations of current methods and propose an alternative approach.

Methods We compared outputs of the traditional methods in five countries with published results, and applied the numeric and MOT model to India, and to six districts within India. We developed an alternative approach based on a qualitative understanding of local epidemic drivers, the Transmission Dynamics Epidemic Classification (TDEC) scheme, and demonstrated its application. Where data permitted, we calculated the population attributable fraction of paid sex for HIV infection among males to assist in TDEC classification.

Results Country and district level analysis illustrated three main limitations of the numeric and MOT methods: (1) their results misinterpreted underlying transmission dynamics and were inconsistent; (2) they were difficult to apply to local epidemics with heterogeneity across districts ; and (3) the MOT model was highly sensitive to input parameters, many of which required extraction from non-regional sources. The TDEC method offered a logical algorithm to characterise local sexual structures that likely sustain onward HIV transmission; it required minimal but key input data.

Conclusion Traditional appraisals of HIV epidemics can misdirect prevention programming if the goal is long-term HIV control. By characterising local transmission dynamics, the TDEC approach provides a potentially more effective tool with which policy makers can design intervention programs.

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