Many thanks to everyone who contributed to the session for their valuable insights and engaging discussions!💡

Thank you, Vanessa! 🙌

Maybe these functions are useful for forecasting fertility? Or for jointly modeling fertility and mortality? We do not fully know yet—but there’s plenty of exciting applied work ahead. Give our paper a read, try out the functions, and let us know what you think! 👇 6/n
doi.org/10.1080/0032...

What are the density, survival, and hazard functions good for? They capture fertility age patterns without individual birth histories, complex models, or parity-specific data. All you need are age-specific fertility rates, e.g. from the Human Fertility Database or UN World Population Prospects. 5/n

What’s the difference between density, survival, and hazard functions based on birth counts vs fertility rates? We show that birth counts carry information on mortality in the maternal cohort; the fertility rates do not. That means if mortality is high, the two sets of functions will differ. 4/n

The 'Born once, die once' framework described above uses birth counts. Most demographers, however, work with fertility rates. Can we apply the 'life table logic' directly to these rates to get density, survival, and hazard functions of fertility? Yes! And we show exactly how in the paper. 3/n

Every person dies only once—and is also born only once. We apply life table logic to all births in a cohort of women, treating birth (not death) as the event, and using maternal age as the time axis. The result: density, survival, and hazard functions that capture the timing of reproduction. 2/n

Thanks so much, @drjenndowd.bsky.social! Fortunately, one thing I learned during my time at @oxforddemsci.bsky.social is that you never really leave. Here's to a long collaboration! 🤓

Thanks so much for this, @evangelinewarren.com! Could you please add me to the list? Thank you. ☺️

You can also read Abrams et al.’s response here: https://doi.org/10.1073/pnas.2320028121
Let's keep the conversation going! 🗨️ #mortality #demography #lifeexpectancy

Different counterfactuals provide different perspectives, neither is ‘right’ or ‘wrong’. What do you think is the best way to analyze US life expectancy stagnation? Read our letter: https://doi.org/10.1073/pnas.2318276121
@leverhulme.bsky.social @popassocamerica.bsky.social @eapsphd.bsky.social (6)

BOTTOM LINE: Working-age mortality is still an important driver of US life expectancy stagnation globally, but as Abrams et al. rightfully point out, we need to explore older-age slowdowns too. 📉 (5)

We calculate an alternative counterfactual: What would US life expectancy be if 2010-19 age-specific mortality improved like in other high-income countries? 🌍 We find the opposite pattern: 25-64 mortality is more important than 65+ for explaining counterfactual differences. (4)

We question whether this within-US counterfactual tells the whole story for 2 reasons:
- 25-64 US mortality was already stagnating in 2000-09, so less room to get worse.
- 2010-19 slowdowns in 65+ mortality improvements were common globally, but rising 25-64 mortality wasn’t. (3)

Abrams et al. calculated counterfactual US life expectancy in 2010-19 if annual age-specific mortality changes followed 2000-09 trends. Surprise finding: Slowdowns in 65+ mortality improvements explain more post-2010 stagnation than 25-64 mortality. https://doi.org/10.1073/pnas.2308360120 (2)