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Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
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The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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Bayesian Models to Generate Small Area Estimates of Population Health: Tutorial for Using Rate Stabilizing Tools and

David DeLara1, Ryan Zomorrodi2, Harrison Quick3

  • 1Division for Heart Disease and Stroke Prevention, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Highway, Atlanta, GA, United States, 1 770-488-8976.

JMIR Public Health and Surveillance
|January 30, 2026
PubMed
Summary
This summary is machine-generated.

New tools, the Rate Stabilizing Toolbox (RSTbx) for ArcGIS Pro and the Rate Stabilizing Tool (RSTr) for R, simplify small area estimation for public health professionals. These tools enhance local health data analysis and community health program planning.

Keywords:
Bayesian statisticsGISRgeographic information systemsmall area estimatessoftwarespatial analysisspatiotemporal models

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Area of Science:

  • Public Health Data Analysis
  • Geospatial Statistics
  • Health Informatics

Background:

  • High-quality local-level population health data are crucial for community health initiatives.
  • Traditional small area estimation methods, often using Bayesian techniques, are computationally complex and inaccessible to many public health professionals.
  • There is a need for accessible tools to generate reliable small area estimates.

Purpose of the Study:

  • To introduce and demonstrate two user-friendly tools, the Rate Stabilizing Toolbox (RSTbx) and the Rate Stabilizing Tool (RSTr), for calculating small area estimates.
  • To showcase the benefits of these tools in improving the reliability and utility of local health data.
  • To provide a tutorial for public health professionals on using these tools for data analysis.

Main Methods:

  • Development of the Rate Stabilizing Toolbox (RSTbx) as an ArcGIS Pro plugin.
  • Development of the Rate Stabilizing Tool (RSTr) as an R package.
  • Demonstration of tool usage with census tract-level mortality data from North Carolina and hospitalization data from Rhode Island.

Main Results:

  • The tools decrease the number of geographic units with suppressed estimates.
  • Users can flexibly set thresholds for statistical reliability.
  • Credible intervals generated by the tools aid in identifying statistically significant differences between geographic units.
  • Both tools include built-in age-standardization capabilities.

Conclusions:

  • The Rate Stabilizing Toolbox and Rate Stabilizing Tool for R are powerful, accessible resources for generating high-quality local-level health data.
  • These tools can inform public health programs and tailor health promotion activities to specific community needs.
  • The tools facilitate the enhancement of community health across the country through improved data analysis.