Spatio-Temporal Modeling for Small Area Health Analysis

Authored by: Andrew B. Lawson , Ana Corberán-Vallet

Handbook of Discrete-Valued Time Series

Print publication date:  December  2015
Online publication date:  January  2016

Print ISBN: 9781466577732
eBook ISBN: 9781466577749
Adobe ISBN:

10.1201/b19485-23

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Abstract

Small area data arise in a variety of contexts. Usually, arbitrary geographic units (small areas) are the basic observation units in a study carried out within a predefined geographic study area (W). These could be administrative units such as zip codes, postal zones, census tracts, or larger units such as municipalities, counties, parishes, or even states. The study region W could be a predefined area such as a city, county, state, or country, or an arbitrarily defined group of units used for the specific study. It is common for health data to be collected within such units and that the resulting counts of disease are to be the focus of study. Health data usually consist of a particular disease incidence (new counts of disease in a fixed time period), or prevalence (counts within a longer time period). Diseases could range from noninfectious such as diabetes, asthma, or different types of cancers to infectious diseases such as HIV, influenza C, influenza A/H1N1, SARS, or corona virus. In the following, we will confine our attention to disease incidence within small areas and discrete time periods. Note that at a fine level of spatial and temporal resolution (residential location and date of diagnosis) the disease occurrence can form a spatio-temporal point process (Lawson, 2013, ch 12). We do not pursue this form here.

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