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Hyak Mortality Monitoring System Innovative Sampling and Estimation Methods

Jonathan C Wakefield and Tyler Harris McCormick

September 2012 CSSS Working Paper #118



Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low-to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space- what we term data melding. Data sources amendable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. There are very few useful demonstrations of `data melding', and the two of which we are aware both relate to HIV prevalence. 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:
* Data Melding: a sampling and surveillance component that organizes two data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the HDSS sites using informed sampling so as to capture as many events as possible;
* Cause of Death: verbal autopsy to characterize the distribution of deaths by cause at the population level; and
* 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 health and demographic surveillance system site in South Africa. Compared to traditionally cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.

We compare the relative cost and precision of Hyak to traditional repeated cluster samples to measure mortality. We find that in as short as two years Hyak is substantially more cost-effective and accurate than current systems.