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Projecting disease when death is likely

D R Hoover1, Y Peng, A J Saah

  • 1Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD, USA.

American Journal of Epidemiology
|May 1, 1996
PubMed
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This study introduces a new model to accurately project disease incidence and mortality, especially for individuals at high risk of death. It addresses limitations in traditional survival analyses for better disease burden estimation.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Accurate disease incidence and prevalence projections are crucial, particularly for individuals with competing risks of death.
  • Traditional survival analyses may yield misleading results when censoring disease-free individuals at death.

Purpose of the Study:

  • To develop and introduce useful quantifications of disease and death for settings with competing risks.
  • To present a novel four-stage disease/death model (A, B, C, D) for improved projection accuracy.

Main Methods:

  • Utilized Kaplan-Meier functions within a four-stage disease/death model (A: living without disease, B: dead without disease, C: living with disease, D: dead with disease).
  • Applied the model to project cytomegalovirus (CMV) disease in a cohort of human immunodeficiency virus type 1 (HIV-1) infected individuals from the Multicenter AIDS Cohort Study (MACS).

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Main Results:

  • At 3 years post-CD4+ count below 100/microliters, probabilities for stages A, B, C, and D were 18.7%, 46.3%, 5.3%, and 29.9%, respectively.
  • The average CMV morbidity during this period was 0.28 years for an affected individual.

Conclusions:

  • The proposed model provides a more accurate method for disease and death quantification in the presence of competing risks.
  • This approach enhances the projection of disease incidence, prevalence, and net morbidity, particularly in vulnerable populations.