Timothy Riffe

Data Resource Profile: The Human Mortality Database (HMD)

Data Resource Profile: The Human Mortality Database (HMD) Magali Barbieri,* John R Wilmoth, Vladimir M Shkolnikov, Dana Glei, Domantas Jasilionis, Dmitri Jdanov, Carl Boe, Timothy Riffe, Pavel Grigoriev and Celeste Winant Department of Demography, University of California, Berkeley, CA, USA, French Institute for Demographic Studies, Paris, France, Population Division, United Nations, Department of Economic and Social Affairs, New York, NY, USA, Max Planck Institute for Demographic Research, Rostock, Germany, New Economic School, Moscow, Russia and Georgetown University, Washington, DC, USA

The force of mortality by life lived is the force of increment by life left in stationary populations

Background The age distribution and remaining lifespan distribution are identical in stationary populations. The life table survival function is proportional to the age distribution in stationary populations. Objective We provide an alternative interpretation of the life table when viewed by remaining years of life. Conclusions The functions describing the mortality of birth cohorts over age are identical to the functions describing the growth of death cohorts as time to death decreases in stationary populations.

A unified framework of demographic time

Demographic thought and practice is largely conditioned by the Lexis diagram, a two-dimensional graphical representation of the identity between age, period, and birth cohort. This relationship does not account for remaining years of life, total length of life, or time of death, whose use in demographic research is both underrepresented and incompletely situated. We describe an identity between these six demographic time measures and describe the sub-identities and diagrams that pertain to this identity. We provide an application of this framework to the measurement of late-life morbidity prevalence. We generalize these relationships to higher order identities derived from an arbitrary number of events in calendar time. Our examples are based on classic human demography, but the concepts we present can reveal patterns and relationships in any event history data, and contribute to the study of human or non-human population dynamics measured on any scale of calendar time.

A Cause-of-Death Decomposition of Young Adult Excess Mortality

We propose a method to decompose the young adult mortality hump by cause of death. This method is based on a flexible shape decomposition of mortality rates that separates cause-of-death contributions to the hump from senescent mortality. We apply the method to U.S. males and females from 1959 to 2015. Results show divergence between time trends of hump and observed deaths, both for all-cause and cause-specific mortality. The study of the hump shape reveals age, period, and cohort effects, suggesting that it is formed by a complex combination of different forces of biological and socioeconomic nature. Male and female humps share some traits in all-cause shape and trend, but they also differ by their overall magnitude and cause-specific contributions. Notably, among males, the contributions of traffic and other accidents were progressively replaced by those of suicides, homicides, and poisonings; among females, traffic accidents remained the major contributor to the hump.

Trends in avoidable mortality over the life course in Mexico, 1990–2015: a cross-sectional demographic analysis

Objective To analyse average lifespan and quantify the effect of avoidable/amenable mortality on the difference between state-specific mortality and a low-mortality benchmark in Mexico during 1990–2015. Design Retrospective cross-sectional demographic analysis using aggregated data. Setting Vital statistics from the Mexican civil registration system. Participants Aggregated national data (from 91.2 million people in 1995 to 119.9 in 2015) grouped in 64 populations (32 Mexican states (including Mexico City) by sex) with cause-of-death data. Main outcome measures Cause-specific contributions to the gap in life expectancy with a low-mortality benchmark in three age groups (0–14, 15–49 and 50–84 years). Results Infants and children under the age of 15 years show improvements towards maximal survival in all states. However, adult males aged 15 to 49 years show deterioration after 2006 in almost every state due to increasing homicides, and a slow recovery thereafter. Out of 35 potential years, females and males live on average 34.57 (34.48 to 34.67) and 33.80 (33.34 to 34.27), respectively. Adults aged 50 to 84 years show an unexpected decrease in the low mortality benchmark, indicating nationwide deterioration among older adults. Females and males in this age group show an average survival of 28.59 (27.43 to 29.75) and 26.52 (25.33 to 27.73) out of 35 potential years, respectively. State gaps from the benchmark were mainly caused by ischaemic heart diseases, diabetes, cirrhosis and homicides. We find large health disparities between states, particularly for the adult population after 2005. Conclusions Mexico has succeeded in reducing mortality and between-state inequalities in children. However, adults are becoming vulnerable as they have not been able to reduce the burden of violence and conditions amenable to health services and behaviours, such as diabetes, ischaemic heart diseases and cirrhosis. These trends have led to large health disparities between Mexican states in the last 25 years.