Selected Publications

Recent research on the US opioid epidemic has focused on the white or total population and has largely been limited to data after 1999. However, understanding racial differences in long-term trends by opioid type may contribute to improving interventions. We analyzed age-standardized opioid mortality rates, by race and opioid type, for the US resident population from 1979 to 2015. The long-term trends in opioid-related mortality for non-Hispanic blacks and non-Hispanic whites went through three successive waves. In the first wave, from 1979 to the mid-1990s, the epidemic affected both populations and was driven by heroin. In the second wave, from the mid-1990s to 2010, the increase in opioid mortality was driven by natural/semi-synthetic opioids (i.e., codeine, morphine, hydrocodone, or oxycodone) among whites, while there was no increase in mortality for blacks. In the current wave, increases in opioid mortality for both populations have been driven by heroin and synthetic opioids (i.e., fentanyl and its analogues).

We present a model for estimating neonatal mortality rates for all countries. Neonatal mortality is an important indicator to track progess towards the Sustainable Development Goals. The model is used by the United Nations Inter-agency Group for Child Mortality Estimation.
Demographic Research

We present a Bayesian hierarchical model to estimate age-specific mortality at the subnational level. The model framework overcomes issues with estimating mortality in small populations, is flexible enough to be implemented in a wide variety of situations, and produces estimates of different measures of inequality across regions.

Recent Publications

More Publications

  • Trends in Black and White Opioid Mortality in the United States, 1979–2015

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  • Deaths without denominators: using a matched dataset to study mortality patterns in the United States

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  • Estimating Subnational Populations of Women of Reproductive Age

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University of California, Berkeley

  • Instructor, Formal Demography Workshop, June 2017
  • Instructor, Formal Demography Workshop, August 2015
  • Graduate Student Instructor, Demographic Methods, Fall Semester, 2014

University of Tasmania

  • Tutor, Calculus and Applications I, Semester 1, 2009
  • Tutor, Data Handling and Statistics I, Semester 2, 2008
  • Demonstrator, Chemistry I, Semester 1, 2008


Recent Posts

More Posts

Introduction This is a tutorial on estimating age-specific mortality rates at the subnational level, using a model similar to that described in our Demography paper. There are four main steps, which will be described below: Prepare data and get it in the right format Choose and create a mortality standard Fit the model Analyze results from the model A few notes on this particular example: I’ll be fitting the model to county-level mortality rates in California over the years 1999 to 2016.


PhDs are hard. They are incredibly fulfilling, but mentally challenging and emotionally draining. You meet some amazing people, but also have to deal with some difficult people and difficult situations. During my time as a PhD student, a lot of things went better than I imagined, but I also made a fair few mistakes. The following are a few thoughts after my experience. They are based on being involved in the demographic research field — a relatively small and supportive academic community — but the comments are pretty general.


Professional Experience

University of Massachusetts, Amherst

Graduate Student Researcher, January 2017 – June 2018

World Health Organization

Consultant, September 2016 – June 2017

Data Science for Social Good

Fellow, May 2016 – September 2016

Human Mortality Database

Graduate Student Researcher, January 2015 – May 2016

UNICEF Technical Advisory Group

Consultant, March 2014 – December 2015

The Centre for Aboriginal Economic Policy Research

Research Officer, April 2012 – December 2014

Reserve Bank of Australia

Analyst/Senior Analyst, February 2010 – June 2013