  There is an increasing focus on reducing inequalities in health outcomes in
developing countries. Subnational variation is of particular interest, with
geographic data used to understand the spatial risk of detrimental outcomes and
to identify who is at greatest risk. While some health surveys provide
observations with associated geographic coordinates, many others provide data
that have their locations masked and instead only report the strata within
which the data resides. How to harmonize these data sources for spatial
analysis has seen previously considered though no method has been agreed upon
and comparison of the validity of methods are lacking. In this paper, we
present a new method for analyzing masked survey data alongside traditional
geolocated data, using a method that is consistent with the data generating
process. In addition, we critique two proposed approaches to analyzing masked
data and illustrate that they are fundamentally flawed methodologically. To
validate our method, we compare our approach with previously formulated
solutions in several realistic simulation environments in which the underlying
structure of the risk field is known. We simulate samples from spatial fields
in a way that mimics the sampling frame implemented in the most common health
surveys in low and middle income countries, the DHS and MICS. In simulations,
the newly proposed approach outperforms previously proposed approaches in terms
of minimizing error while increasing the precision of estimates. The approaches
are subsequently compared using child mortality data from the Dominican
Republic where our findings are reinforced. Accurately increasing precision of
child mortality estimates, and health estimates in general, by leveraging
various types of data improves our ability to implement precision public health
initiatives and better understand the landscape of geographic health
inequalities.
