• DOI:10.1098/RSTB.1991.0006
• Corpus ID: 33644270

Drugs, sex and HIV: a mathematical model for New York City.

@article{Blower1991DrugsSA,
  title={Drugs, sex and HIV: a mathematical model for New York City.},
  author={Sally M. Blower and Diana M. Hartel and Hadi Dowlatabadi and R. M. Anderson and Robert M. May},
  journal={Philosophical transactions of the Royal Society of London. Series B, Biological sciences},
  year={1991},
  volume={331 1260},
  pages={
          171-87
        }
}

• S. Blower, D. Hartel, +2 authors R. May
• Published 28 February 1991
• Biology
• Philosophical transactions of the Royal Society of London. Series B, Biological sciences

A data-based mathematical model was formulated to assess the epidemiological consequences of heterosexual, intravenous drug use (IVDU) and perinatal transmission in New York City (NYC). The model was analysed to clarify the relationship between heterosexual and IVDU transmission and to provide qualitative and quantitative insights into the HIV epidemic in NYC. The results demonstrated the significance of the dynamic interaction of heterosexual and IVDU transmission. Scenario analysis of the model was used to suggest a new explanation for the stabilization of the seroprevalence level that has been observed in the NYC IVDU community; the proposed explanation does not rely upon any IVDU or sexual behavioural changes. Gender-specific risks of heterosexual transmission in IVDUs were also explored by scenario analysis. The results showed that the effect of the heterosexual transmission risk factor on increasing the risk of HIV infection depends upon the level of IVDU. The model was used to predict future numbers of adult and pediatric AIDS cases; a sensitivity analysis of the model showed that the confidence intervals on these prediction estimates were extremely wide. This prediction variability was due to the uncertainty in estimating the values of the models' thirty variables (twenty biological-behavioural transmission parameters and the initial sizes of ten subgroups). However, the sensitivity analysis revealed that only a few key variables were significant in contributing to the AIDS case prediction variability; partial rank correlation coefficients were calculated and used to identify and to rank the importance of these key variables. The results suggest that long-term precise estimates of the future number of AIDS cases will only be possible once the values of these key variables have been evaluated accurately.