  Traditionally health statistics are derived from civil and/or vital
registration. Civil registration in low-income countries varies from partial
coverage to essentially nothing at all. Consequently the state of the art for
public health information in low-income countries is efforts to combine or
triangulate data from different sources to produce a more complete picture
across both time and space - data amalgamation. Data sources amenable to this
approach include sample surveys, sample registration systems, health and
demographic surveillance systems, administrative records, census records,
health facility records and others.
  We propose a new statistical framework for gathering health and population
data - Hyak - that leverages the benefits of sampling and longitudinal,
prospective surveillance to create a cheap, accurate, sustainable monitoring
platform. Hyak has three fundamental components:
  1) Data Amalgamation: a sampling and surveillance component that organizes
two or more data collection systems to work together: a) data from HDSS with
frequent, intense, linked, prospective follow-up and b) data from sample
surveys conducted in large areas surrounding the Health and Demographic
Surveillance System sites using informed sampling so as to capture as many
events as possible;
  2) Cause of Death: verbal autopsy to characterize the distribution of deaths
by cause at the population level; and
  3) SES: measurement of socioeconomic status in order to characterize poverty
and wealth.
  We conduct a simulation study of the informed sampling component of Hyak
based on the Agincourt HDSS site in South Africa. Compared to traditional
cluster sampling, Hyak's informed sampling captures more deaths, and when
combined with an estimation model that includes spatial smoothing, produces
estimates mortality that have lower variance and small bias.
