<b>MortalitySmooth</b>: An<i>R</i>Package for Smoothing Poisson Counts with P-Splines

Camarda, Carlo Giovanni

doi:10.18637/jss.v050.i01

Cited by 140 publications

(140 citation statements)

References 27 publications

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“…Birth counts and corresponding exposures by single year of age and calendar time were downloaded from the Human Fertility Database (2017). The estimates were (again) generated with Camarda's R package for smoothing surfaces with P-splines (Camarda 2012(Camarda , 2015. The lower panel shows rates of fertility improvement, where improvement means an increase in fertility.…”

Section: Discussionmentioning

confidence: 99%

“…We selected the so-called P-spline approach, originally developed by Eilers and Marx (1996), adapted to the analysis of mortality by Currie et al (2004) and further refined by Camarda (2008). The author, Carlo Giovanni Camarda, also provides the R extension package "MortalitySmooth" (Camarda 2012), which makes it easy and straightforward to apply the method. At its core, the model assumes Poisson distributed death counts with the (log-)exposures as an offset to account for changing population sizes over time and/or age.…”

Section: From Raw Death Rates To Smoothed Death Ratesmentioning

confidence: 99%

“…5 to obtain the required death rates: Raw death rates, based on death counts and corresponding exposure times from the Human Mortality Database (HMD, 2017), were smoothed assuming Poisson distributed death counts using Camarda's MortalitySmooth package (Camarda 2012(Camarda , 2015. The life table functions l x , L x , and T x were estimated using the approach outlined in Chapter 3 of Preston et al (2001).…”

Section: How To Estimate Age-specific Contributions To the Change In mentioning

confidence: 99%

“…Our estimates for single year and age for breast cancer as well as for all other cancer sites have been smoothed, again using P-Splines as outlined in Chap. 5 Eilers and Marx (1996);Camarda (2012Camarda ( , 2015.…”

Section: Acknowledgmentsmentioning

confidence: 99%

See 3 more Smart Citations

Visualizing Mortality Dynamics in the Lexis Diagram

Rau

Bohk-Ewald

Muszyńska

et al. 2018

The Springer Series on Demographic Methods and Population Analysis

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In recent decades, there has been a rapid development of demographic models and methods and an explosive growth in the range of applications of population analysis. This series seeks to provide a publication outlet both for high-quality textual and expository books on modern techniques of demographic analysis and for works that present exemplary applications of such techniques to various aspects of population analysis.Topics appropriate for the series include:• General demographic methods Volumes in the series are of interest to researchers, professionals, and students in demography, sociology, economics, statistics, geography and regional science, public health and health care management, epidemiology, biostatistics, actuarial science, business, and related fields.More information about this series at

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Section: Discussionmentioning

confidence: 99%

Section: From Raw Death Rates To Smoothed Death Ratesmentioning

confidence: 99%

Section: How To Estimate Age-specific Contributions To the Change In mentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

See 2 more Smart Citations

Visualizing Mortality Dynamics in the Lexis Diagram

Rau

Bohk-Ewald

Muszyńska

et al. 2018

The Springer Series on Demographic Methods and Population Analysis

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“…1 we smoothed the death rates for the hunter-gatherer populations using P-splines, because of small sample sizes in these populations, following the proposal of Eilers and Marx (58). We used the implementation by Camarda (59,60) in R, which has been tailored for smoothing mortality in a Poisson framework. We also smoothed the data for historic Sweden (1751-1759).…”

Section: Box 1 Demographic Distributions and Measuresmentioning

confidence: 99%

The emergence of longevous populations

Colchero

Rau

Jones

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

151

165

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The human lifespan has traversed a long evolutionary and historical path, from short-lived primate ancestors to contemporary Japan, Sweden, and other longevity frontrunners. Analyzing this trajectory is crucial for understanding biological and sociocultural processes that determine the span of life. Here we reveal a fundamental regularity. Two straight lines describe the joint rise of life expectancy and lifespan equality: one for primates and the second one over the full range of human experience from average lifespans as low as 2 y during mortality crises to more than 87 y for Japanese women today. Across the primate order and across human populations, the lives of females tend to be longer and less variable than the lives of males, suggesting deep evolutionary roots to the male disadvantage. Our findings cast fresh light on primate evolution and human history, opening directions for research on inequality, sociality, and aging.biodemography | equality | lifespan | pace and shape | senescence L ongevous populations have two characteristics: The average length of life is long and relative variation in lifespans is low. For example, life tables for contemporary Sweden and Japan indicate that most deaths occur at ages between the late 70s and early 90s. Our primate relatives, in contrast, have lifespans that are highly variable in length but short on average and rarely longer than 30 y (Fig. 1). An association between the average length of life and its variability has been found for industrialized societies (1, 2). However, detailed knowledge is lacking about whether and how this association varies across species separated by millions of years of primate evolution or whether it has changed over the past several centuries of unprecedented social progress in human populations. Fuller comprehension of the relationship between rising lifespans and reduced lifespan variability across evolution and history holds potential insights that might illuminate past, current, and future longevity.We pose three related questions aimed at filling this knowledge gap: How long and variable are lifespans for humans compared with nonhuman primates, for humans today compared with the past, and for males compared with females? We provide answers to these questions by applying a powerful framework that simultaneously examines changes in both the average length of life in a population or species-the "pace" of life-and relative variation in the length of life, i.e., the "shape" of the distribution of ages at death (3-5). Studying variation in lifespan links to increasing interest in social, economic, and health inequalities and to key sociological findings that relate social factors-including high social status and social integration-to longer, healthier lifespans in human and animal societies (6-10).Estimating the average length of life (here measured by life expectancy, the mean age at death) and variation in lifespans relative to the average (measured here as "lifespan equality"; Box 1) requires data on the ages at death of individuals...

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Insight into stagnating adult life expectancy: Analyzing cause of death patterns across socioeconomic groups

2020

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This study analyzes the complexity of female longevity improvements. As socioeconomic status is found to influence health and mortality, we partition all individuals, at each age in every year, into five socioeconomic groups based on an affluence measure that combine an individual's income and wealth. We identify the particular socioeconomic groups that have been driving the standstill for Danish females at older ages. Within each socioeconomic group, we further analyze the cause of death patterns. The decline in life expectancy for Danish females is present for four out of five subgroups, however, with particular large decreases for the low-middle and middle-affluence groups. Cancers, smoking-related lung and bronchus causes, and other diseases particularly contribute to the stagnation. For four of the five socioeconomic groups only small cardiovascular improvement are experienced during the period of stagnating life expectancy compared to an equally long and subsequent period.

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MortalitySmooth: AnRPackage for Smoothing Poisson Counts with P-Splines

Cited by 140 publications

References 27 publications

Visualizing Mortality Dynamics in the Lexis Diagram

Visualizing Mortality Dynamics in the Lexis Diagram

The emergence of longevous populations

Insight into stagnating adult life expectancy: Analyzing cause of death patterns across socioeconomic groups

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