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Forecasting Other Characteristics

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Cohort Change Ratios and their Applications

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Abstract

Chapter 7 focused on forecasts of school enrollment size and composition. In this chapter we focus on forecasts of other population-related characteristics such as households, family structure, labor force, obesity, and disability. These and similar variables are needed for planning, budgeting, policy analysis, and program administration. Because these characteristics are strongly affected by population size and demographic composition, forecasts of a population’s age structure (and, to a lesser extent, its sex and race/ethnicity structure) provide a basis for forecasting them. This chapter uses the participation-rate (or prevalence-rate) method in which forecasts of other characteristics are derived from forecasts of demographic characteristics through the use of rates. We illustrate the application of this method using forecasts of alcohol consumption, diabetes, cigarette use and consumption, labor force, and households and related variables.

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Notes

  1. 1.

    Obese is defined as a body mass index greater than or equal to 30.0 kg/m2. Extremely obese is a body mass index greater than or equal to 40 kg/m2.

  2. 2.

    The birth cohorts were: 1911–1920, 1921–1930, 1931–1940, and 1941–1950. The surveys analyzed were the National Health Examination Survey 1959–1962, National Health and Nutrition Examination Survey (NHANES) I 1971–1973, NHANES II 1976–1980, NHANES III 1988–1994, and NHANES 1999–2000.

  3. 3.

    Current use is at least one drink in the past 30 days. Binge use is five or more drinks on the same occasion (i.e., at the same time or within a couple of hours of each other) on at least one day in the past 30 days. Heavy use is five or more drinks on the same occasion each of five or more days in the past 30 days.

  4. 4.

    The number of alcohol users from the 2013 Drug Survey (136.9 million) is about two percent lower than our estimate. The Drug Survey estimate is based on the non-institutional population, whereas our estimate is based on the total population. The total population in the United States is roughly two percent higher than the non-institutional population.

  5. 5.

    The latest prevalence rates provided by the National Center for Health Statistics were for 2011. To estimate rates for 2013, we adjusted the 2011 prevalence rates so when applied to the 2013 population by age the result would match the latest estimate of the number of persons diagnosed with diabetes in the United States from the Centers for Disease Control and Prevention (CDCP), adjusted upward for the difference in population definition (21.6 million). CDCP numbers are based on non-institutional population and we are using total population.

  6. 6.

    If an area has large college group quarters or prison/jail populations, they should be forecast separately from other civilian group quarters for the same reasons the military and civilian group quarters populations handled distinctly.

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Authors and Affiliations

  1. Health Fitness Corporation, Lake Forest, IL, USA

    Jack Baker

  2. University of California Riverside, Riverside, CA, USA

    David A. Swanson

  3. University of California San Diego, San Diego, CA, USA

    Jeff Tayman

  4. Western Washington University, Bellingham, WA, USA

    Lucky M. Tedrow

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  1. Jack Baker
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  2. David A. Swanson
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  3. Jeff Tayman
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  4. Lucky M. Tedrow
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Baker, J., Swanson, D.A., Tayman, J., Tedrow, L.M. (2017). Forecasting Other Characteristics. In: Cohort Change Ratios and their Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-53745-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-53745-0_8

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