Advertisement

The HIV Epidemic in New York City; Statistical Methods for Projecting AIDS Incidence and Prevalence

  • Marcello Pagano
  • Victor De Gruttola
  • Samantha MaWhinney
  • Xin Ming Tu

Abstract

Projections of the incidence and prevalence of diagnosed AIDS cases in New York City through 1995 make use of information from the New York City AIDS Surveillance Registry. The projections are done in three steps: First, adjustment of historic incidence data for observed delays in reporting. Second, estimation of the incidence of HIV infection in New York City during the past several years, based on the adjusted incidence data and external estimates of the latency distribution. Third, projection of future incidence of AIDS based on the estimated incidence of HIV infection. Survival after AIDS diagnosis is estimated from dates of diagnosis and death; these survival estimates are combined with estimated AIDS incidence to project prevalence of AIDS. Because little is known of the incidence of HIV infections since 1986, three alternative scenarios are explored: no new infections since 1986, 5,000 new infections per year, and 10,000 new infections per year. These represent the lower bound and two plausible alternative infection rates.

Keywords

Human Immunodeficiency Virus York City Human Immunodeficiency Virus Infection Latency Distribution Harvard School 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Bibliography

  1. Auger I., Thomas P., De Gruttola V., Morse D., Moore D., Williams R., Truman B. and Lawrence C. (1988) “Incubation Period for Pediatric AIDS Patients,” Nature, 333: 515–517.Google Scholar
  2. Bacchetti P. and Moss A. (1989) “Incubation Period of AIDS in San Francisco,” Nature, 338: 251–253.CrossRefPubMedGoogle Scholar
  3. Brookmeyer R. and Gail M.H. (1988), “A Method for Obtaining Short-Term Projections and Lower Bounds on the Size of the AIDS Epidemic,” Journal of the American Statistical Association, 83, 301–308.CrossRefGoogle Scholar
  4. Brookmeyer R. and Goedert J. (1989) “Censoring in an Epidemic with an Application to Hemophilia-Associated AIDS,” Biometrics, 45, 325–335.CrossRefPubMedGoogle Scholar
  5. Kalbfleisch J.D. and Lawless J.F. (1991) “Regression models for right truncated data with application to AIDS incubation times and reporting lags,” Statistica Sinica, 1, 19–32.Google Scholar
  6. Lifson A., Hessol N., Rutherford G., et al. (1989) “The Natural History of HIV Infection in a Cohort of Homosexual and Bisexual Men: Clinical Manifestations, 1979–1989,” Abstract T.A.O.32, Abstracts of the Vth International Conference on AIDS.Google Scholar
  7. MaWhinney S. and Pagano M. (1991a) “Time to AIDS for Children Born to HIV Positive Mothers,” Technical Report, Harvard School of Public Health.Google Scholar
  8. MaWhinney S. and Pagano M. (1991b) “Backcalculation Using Regression Decomposition,” Technical Report Harvard School of Public Health.Google Scholar
  9. Pagano M., Tu X.M., De Gruttola V. and MaWhinney S. (1991) “Analysis of Censored and Truncated Data: Estimating Reporting Delay Distributions and AIDS Incidence from Surveillance Data,” Technical Report, Harvard School of Public Health.Google Scholar
  10. Tu X.M., Meng X. and Pagano M. (1991) “The AIDS Epidemic: Estimating Survival after AIDS Diagnosis from Surveillance Data,” Technical Report, Harvard School of Public Health.Google Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Marcello Pagano
    • 1
  • Victor De Gruttola
    • 1
  • Samantha MaWhinney
    • 1
  • Xin Ming Tu
    • 1
  1. 1.Department of BiostatisticsHarvard School of Public HealthBostonUSA

Personalised recommendations