Space-time smoothing of complex survey data: Small area estimation for child mortality |
Authors: |
Laina D. Mercer, Jon Wakefield, Athena Pantazis, Angelina M. Lutambi, Honorati Masanja, and Samuel Clark |
Source: |
The Annals of Applied Statistics, 9(4):1889–1905, DOI: 10.1214/15-AOAS872 |
Topic(s): |
Childhood mortality
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Country: |
Africa
Tanzania
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Published: |
MAY 2015 |
Abstract: |
Many people living in low- and middle-income countries are not covered
by civil registration and vital statistics systems. Consequently, a wide
variety of other types of data, including many household sample surveys, are
used to estimate health and population indicators. In this paper we combine
data from sample surveys and demographic surveillance systems to produce
small area estimates of child mortality through time. Small area estimates
are necessary to understand geographical heterogeneity in health indicators
when full-coverage vital statistics are not available. For this endeavor spatiotemporal
smoothing is beneficial to alleviate problems of data sparsity. The
use of conventional hierarchical models requires careful thought since the
survey weights may need to be considered to alleviate bias due to nonrandom
sampling and nonresponse. The application that motivated this work is
an estimation of child mortality rates in five-year time intervals in regions of
Tanzania. Data come from Demographic and Health Surveys conducted over
the period 1991–2010 and two demographic surveillance system sites.We derive
a variance estimator of under five years child mortality that accounts for
the complex survey weighting. For our application, the hierarchical models
we consider include random effects for area, time and survey and we compare
models using a variety of measures including the conditional predictive ordinate
(CPO). The method we propose is implemented via the fast and accurate
integrated nested Laplace approximation (INLA). |
Web: |
http://arxiv.org/pdf/1601.08090v1.pdf |
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