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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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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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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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The Problem With Estimating Public Health Spending.

Jonathon P Leider1

  • 1De Beaumont Foundation, Bethesda; and Office of Public Health Practice and Training, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.

Journal of Public Health Management and Practice : JPHMP
|May 1, 2013
PubMed
Summary

Understanding US public health spending is vital. Different data sources yield varied estimates due to differing methods and definitions, highlighting the need for standardized reporting. This study compares these key datasets.

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Area of Science:

  • Public Health Finance
  • Health Services Research
  • Governmental Health Expenditures

Background:

  • Accurate public health spending data is crucial for planning, demonstrating value, and assessing cost-effectiveness.
  • Current national public health finance data sources are limited and produce divergent estimates.
  • Discrepancies hinder effective resource allocation and policy evaluation.

Purpose of the Study:

  • To compare and contrast four major national public health finance datasets.
  • To identify reasons for significant differences in public health expenditure estimates.
  • To inform efforts toward systematizing public health spending and revenue reporting.

Main Methods:

  • Comparative analysis of data from Trust for America's Health, Association of State and Territorial Health Officials (ASTHO), National Association of County and City Health Officials (NACCHO), and Census Bureau.
  • Examination of data collection methodologies, definitions, and inclusion/exclusion criteria.
  • Analysis of FY2008 public health spending estimates across different governmental levels.

Main Results:

  • Significant variations exist in public health spending estimates. For FY2008, ASTHO reported $24 billion for state health agencies, while Census data estimated $60 billion for all state governmental agencies.
  • Census data indicated local governments spent $87 per capita, contrasting with NACCHO's estimate of $64 per capita for local health departments.
  • Differences stem from varying data collection methods, definitions, and scope (e.g., inclusion of all agencies vs. specific departments, behavioral health, disability, clinical care).

Conclusions:

  • Discrepancies in public health expenditure data arise from methodological and definitional differences among reporting organizations.
  • Harmonization efforts and a standardized expenditure reporting system are essential for accurate and systematic reporting of public health finances.
  • Further analysis of underutilized Census administrative data is recommended.