Probabilistic Population Projections for Countries with Generalized HIV/AIDS Epidemics

TL;DR

This paper introduces a simplified probabilistic population projection method for countries with HIV/AIDS epidemics, replacing complex models with a time series and model life tables, aligning with UN methodologies.

Contribution

It proposes a simpler, compatible approach for HIV/AIDS-affected countries that maintains projection accuracy without complex multistate models.

Findings

Method performs well in cross-validation.

Produces similar short-term projections to Spectrum/EPP.

Simplifies population projection process for HIV/AIDS countries.

Abstract

The United Nations (UN) issued official probabilistic population projections for all countries to 2100 in July 2015. This was done by simulating future levels of total fertility and life expectancy from Bayesian hierarchical models, and combining the results using a standard cohort-component projection method. The 40 countries with generalized HIV/AIDS epidemics were treated differently from others, in that the projections used the highly multistate Spectrum/EPP model, a complex 15-compartment model that was designed for short-term projections of quantities relevant to policy for the epidemic. Here we propose a simpler approach that is more compatible with the existing UN probabilistic projection methodology for other countries. Changes in life expectancy are projected probabilistically using a simple time series regression model on current life expectancy, HIV prevalence and ART coverage. These are then converted to age- and sex-specific mortality rates using a new family of model life tables designed for countries with HIV/AIDS epidemics that reproduces the characteristic hump in middle adult mortality. These are then input to the standard cohort-component method, as for other countries. The method performed well in an out-of-sample cross-validation experiment. It gives similar population projections to Spectrum/EPP in the short run, while being simpler and avoiding multistate modeling.