  There is growing interest in producing estimates of demographic and global
health indicators in populations with limited data. Statistical models are
needed to combine data from multiple data sources into estimates and
projections with uncertainty. Diverse modeling approaches have been applied to
this problem, making comparisons between models difficult. We propose a model
class, Temporal Models for Multiple Populations (TMMPs), to facilitate
documentation of model assumptions in a standardized way and comparison across
models. The class distinguishes between latent trends and the observed data,
which may be noisy or exhibit systematic biases. We provide general
formulations of the process model, which describes the latent trend of the
indicator of interest. We show how existing models for a variety of indicators
can be written as TMMPs and how the TMMP-based description can be used to
compare and contrast model assumptions. We end with a discussion of outstanding
questions and future directions.
