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Variance Estimation for a Complex Life Table Quantity: Disease- Free Life Expectancy

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Variance Estimation for a Complex Life Table Quantity: Disease- Free Life Expectancy University of Pennsylvania ScholarlyCommons PSC Working Paper Series 4-27-2015 Variance Estimation for a Complex Life Table Quantity: Disease- free Life Expectancy Ezra Fishman University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/psc_working_papers Part of the Demography, Population, and Ecology Commons Fishman, Ezra, "Variance Estimation for a Complex Life Table Quantity: Disease-free Life Expectancy" (2015). PSC Working Paper Series. 60. https://repository.upenn.edu/psc_working_papers/60 Fishman, Ezra. 2015. "Variance Estimation for a Complex Life Table Quantity: Disease-free Life Expectancy." PSC Working Paper Series, WPS 15-2, http://repository.upenn.edu/psc_working_papers/60. This paper is posted at ScholarlyCommons. https://repository.upenn.edu/psc_working_papers/60 For more information, please contact [email protected]. Variance Estimation for a Complex Life Table Quantity: Disease-free Life Expectancy Abstract Background: In the last decade, adult mortality in the United States has continued its long-run decline, while diabetes prevalence has increased. It is unknown whether the additional person-years lived in the adult population have mostly been spent in a diseased or a disease-free state. Furthermore, although illness and death are stochastic processes, little is known about the variance in diabetes-free life expectancy (DFLE) when compared across ages. More generally, methods of obtaining the variance of complex life table quantities are under-explored. Objective: Estimate DFLE and its variance in the United States in 2000 and 2010. Methods: Data on diabetes prevalence for ages 20+ come from the National Health and Nutrition Examination Surveys (NHANES), 1999-2000 (n=4,205) and 2009-2010 (n=5,752). Diabetes prevalence was defined as HbA1c at least 6.5% or taking diabetes medication. Deaths and population counts yb age and sex come from the Human Mortality Database, covering the entire U.S. population. DFLE was estimated using Sullivan’s method. Three methods of estimating variance in DFLE were explained and compared: the delta method, Monte Carlo simulation, and bootstrapping. Results: Although life expectancy at age 20 rose by approximately 3 years for both males and females between 2000 and 2010, DFLE at age 20 did not change during this decade. At age 70, life expectancy rose by 2.5 years for males and 2.7 years for females, but DFLE rose only 0.7 years for males and 0.8 years for females. For all methods, both sexes and in both years, variance in DFLE was larger at younger ages (males, 2000, age 20, delta method: 0.020) than at older ages (males, 2000, age 70, delta method: 0.012). For any given age/sex/year, the delta method produced the smallest estimates of variance of DFLE, followed by Monte Carlo. Bootstrapping produced variance estimates that were by far the largest, often ten times larger than the Monte Carlo variances. Differences across methods in the variance in estimated diabetes prevalence accounted for most of the differences across methods in the variance of DFLE. Conclusions: The vast majority of the person-years of life gained by the U.S. adult population between 2000 and 2010 were spent with diabetes. Variance in DFLE arises mostly from variance in estimated disease prevalence. The variance of life-table quantities can be obtained using multiple methods, and the appropriate method for a given research problem will vary. Keywords Diabetes, Life Expectancy, Sullivan Method, National Health and Nutrition Examination Survey (NHANES), Statistical Demography Disciplines Demography, Population, and Ecology | Social and Behavioral Sciences | Sociology Comments Fishman, Ezra. 2015. "Variance Estimation for a Complex Life Table Quantity: Disease-free Life Expectancy." PSC Working Paper Series, WPS 15-2, http://repository.upenn.edu/psc_working_papers/60. This working paper is available at ScholarlyCommons: https://repository.upenn.edu/psc_working_papers/60 Variance estimation for a complex life table quantity: Disease-free life expectancy Ezra Fishman May 2015 Contact information: Ezra Fishman Population Studies Center, University of Pennsylvania 3718 Locust Walk 239 McNeil Building Philadelphia, PA 19104 [email protected] Tel: 215-898-6441 1 Abstract Background: In the last decade, adult mortality in the United States has continued its long-run decline, while diabetes prevalence has increased. It is unknown whether the additional person-years lived in the adult population have mostly been spent in a diseased or a disease-free state. Furthermore, although illness and death are stochastic processes, little is known about the variance in diabetes-free life expectancy (DFLE) when compared across ages. More generally, methods of obtaining the variance of complex life table quantities are under-explored. Objective: Estimate DFLE and its variance in the United States in 2000 and 2010. Methods: Data on diabetes prevalence for ages 20+ come from the National Health and Nutrition Examination Surveys (NHANES), 1999-2000 (n=4,205) and 2009-2010 (n=5,752). Diabetes prevalence was defined as HbA1c at least 6.5% or taking diabetes medication. Deaths and population counts by age and sex come from the Human Mortality Database, covering the entire U.S. population. DFLE was estimated using Sullivan’s method. Three methods of estimating variance in DFLE were explained and compared: the delta method, Monte Carlo simulation, and bootstrapping. Results: Although life expectancy at age 20 rose by approximately 3 years for both males and females between 2000 and 2010, DFLE at age 20 did not change during this decade. At age 70, life expectancy rose by 2.5 years for males and 2.7 years for females, but DFLE rose only 0.7 years for males and 0.8 years for females. For all methods, both sexes and in both years, variance in DFLE was larger at younger ages (males, 2000, age 20, delta method: 0.020) than at older ages (males, 2000, age 70, delta method: 0.012). For any given age/sex/year, the delta method produced the smallest estimates of variance of DFLE, followed by Monte Carlo. Bootstrapping produced variance estimates that were by far the largest, often ten times larger than the Monte Carlo variances. Differences across methods in the variance in estimated diabetes prevalence accounted for most of the differences across methods in the variance of DFLE. Conclusions: The vast majority of the person-years of life gained by the U.S. adult population between 2000 and 2010 were spent with diabetes. Variance in DFLE arises mostly from variance in estimated disease prevalence. The variance of life-table quantities can be obtained using multiple methods, and the appropriate method for a given research problem will vary. Key words Diabetes, Life Expectancy, Sullivan Method, National Health and Nutrition Examination Survey (NHANES), statistical demography 2 Introduction and Background The most basic indicator of mortality in a population is life expectancy (LE), which is calculated from a set of age-specific mortality rates using a single-decrement life table [1]. When using current (cross- sectional) mortality rates, LE at birth measures the average length of time a newborn would live if he were subject to the current mortality risks for his entire life. For decades, population health researchers have been interested in methods to combine information on mortality with information on health into single indices to facilitate the comparison of health and mortality across populations and over time. Prominent among these indices is disease-free life expectancy (DFLE), also known as healthy life expectancy, which measures the average length of time a disease-free individual at a given age would live in a disease-free state, if she were subject to the current risks of mortality and disease for her remaining life. DFLE can refer to expected life without any disease or expected life without a particular disease, such as diabetes, the subject of the analysis in this paper. A fairly simple method of calculating DFLE was developed by Sullivan [2], in which the person-years lived at each age in the life table population are apportioned to diseased and disease-free states based on the age-specific prevalence of disease. Consistent with the basic life table, Sullivan’s method uses a synthetic cohort approach, meaning it subjects the population at every age to current rates of disease and death, rather than observing the rates experienced by actual cohorts as they age. The synthetic cohort approach thus facilitates comparisons of the combined mortality-health states of a population at a point in time with the combined mortality-health states of the population at a different point in time. Historically, demography has been more interested in measures of central tendency, such as means (of which LE and DFLE are examples), than in variances. Neither of the two major demography textbooks published in the last 15 years discusses the estimation of variances of life table parameters [1, 3]. And in fact, if one were to obtain information on disease presence from every member of the population, as well as complete records of mortality and age-specific population counts, one could calculate DFLE without consideration of sampling variation. However, in reality, disease prevalence in a population is almost always estimated from sample data in the form of queries from national health care databases with incomplete coverage [4] or from health surveys [5]. The variance of estimated DFLE, or some other measure of its uncertainty, such as a 95% confidence interval, is therefore of interest. More fundamentally, even if the investigator had data on the entire population, one might consider the actual events that took place (deaths and disease cases) to be random draws from underlying, unobserved stochastic processes of mortality and disease. In this case, even quantities in the basic life table, such as LE, would have variances associated with them [6]. This paper will explore three methods of estimating variances of estimated DFLE.
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