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A novel framework for validating and applying standardized small area measurement strategies

Abstract
Local measurements of health behaviors, diseases, and use of health services are critical inputs into local, state, and national decision-making. Small area measurement methods can deliver more precise and accurate local-level information than direct estimates from surveys or administrative records, where sample sizes are often too small to yield acceptable standard errors. However, small area measurement requires careful validation using approaches other than conventional statistical methods such as in-sample or cross-validation methods because they do not solve the problem of validating estimates in data-sparse domains. Results: All model types have substantially higher concordance correlation coefficient and lower root mean square than the direct, single-year Behavioral Risk Factor Surveillance System estimates. In addition, the inclusion of relevant domain-specific covariates generally improves predictive validity, especially at small sample sizes, and their leverage can be equivalent to a five- to tenfold increase in sample size.

Comments
This paper discusses the relative scarcity of accurate and precise local-level public health measurements. The authors describe approaches that can address this deficiency, such as small area measurement methods. This includes a suite of statistical methods aimed at filling the need for better local information,  direct domain estimation, indirect domain estimation, and small area modeling. Indirect estimation implicitly makes assumptions about how domains are related in time and/or space to increase the effective sample size for small domains, for example. Small area modeling is explicit about the assumptions of relatedness in space and/or time and addresses the limited availability of survey and administrative data in three ways: pooling data over several years; borrowing strength in space by exploiting spatial correlations ; using structured relationships with co-variates to predict the quantity of interest. The authors illustrate the approach by estimating Type 2 diabetes prevalence for all counties in the US for 2008.

Citation
Srebotnjak, T., Mokdad, A., Murray, C., “A novel framework for validating and applying standardized small area measurement strategies,” Population Health Metrics,” Vol. 8, 2010, p. 1 – 13

Expertise Level
Intermediate

Professional Field
Health Metrics, Public Health

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