The Rate Stabilizing Tool: Generating Stable Local-Level Measures of Chronic Disease.
Prev Chronic Dis
; 16: E38, 2019 03 28.
Article
en En
| MEDLINE
| ID: mdl-30925140
ABSTRACT
Accurate and precise estimates of local-level epidemiologic measures are critical to informing policy and program decisions, but they often require advanced statistical knowledge, programming/coding skills, and extensive computing power. In response, we developed the Rate Stabilizing Tool (RST), an ArcGIS-based tool that enables users to input their own record-level data to generate more reliable age-standardized measures of chronic disease (eg, prevalence rates, mortality rates) or other population health outcomes at the county or census tract levels. The RST uses 2 forms of empirical Bayesian modeling (nonspatial and spatial) to estimate age-standardized rates and 95% credible intervals for user-specified geographic units. The RST also provides indicators of the reliability of point estimates. In addition to reviewing the RST's statistical techniques, we present results from a simulation study that illustrates the key benefit of smoothing. We demonstrate the dramatic reduction in root mean-squared error (rMSE), indicating a better compromise between accuracy and stability for both smoothing approaches relative to the unsmoothed estimates. Finally, we provide an example of the RST's use. This example uses heart disease mortality data for North Carolina census tracts to map the RST output, including reliability of estimates, and demonstrates a subsequent statistical test.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Disparidades en el Estado de Salud
/
Análisis Espacial
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Prev Chronic Dis
Asunto de la revista:
SAUDE PUBLICA
Año:
2019
Tipo del documento:
Article