It would be great to see more work 📄 filling these gaps and extending this approach to other events, contributing to improve our understanding of the short- and long-term impacts of different natural disasters on mortality and other health outcomes #publichealth #demography #Demography

While our study is an important first step, we could not look into which groups were more severely affected, e.g. by socioeconomic status or neighbourhood, we only looked at the first 4 weeks, and we only considered mortality rather than including other health outcomes

Demographers could play an important role in this area by leveraging their expertise on mortality data and modeling to make estimates like the ones in our paper available for other natural disasters

The contrast between 30 direct fatalities and >400 excess deaths underscores the importance of complementing cause-of-death investigation (to identify direct deaths) with techniques more suited to capture both direct and indirect mortality impacts of wildfires and other natural disasters

We find that mortality was higher 📈 than expected in the first four weeks after the wildfires (440 more deaths than expected). We performed several sensitivity analyses to rule out other mechanisms

In this work with @astokespop.bsky.social and Rafeya Raquib at @busph.bsky.social , we use mortality models to estimate expected deaths 📈 in the absence of the wildfires. We then compare observed and expected deaths to quantify how many excess deaths ☠️ are likely attributable to the wildfires🔥

But of course future can't explain the past, so if the slowdown appeared much earlier in some state-metro combinations, then other mechanisms have to be responsible 8/8🧵

Beyond demographic curiosity, these findings have implications for how we think about explaining the US mortality stagnation. If 2010 or 2014 are meaningful thresholds then explanations focusing on what happened shortly before (e.g. the Great Recession) seem more plausible 7/8🧵

For example, female mortality showed very little improvement past 2005 in Iowa and Kansas. At the same time, metropolitan counties in California, Texas, and New York show only moderate slowdowns in mortality declines over the entire period 6/8🧵

Another interesting finding is that while nationally mortality declines have slowed down starting in 2010, and stagnated after 2014, these are not obvious thresholds at the state-metro level 5/8🧵

Even in the late 2010s, 8 states had lower female and male mortality in nonmetropolitan than metropolitan areas, highlighting that national-level trends and patterns can hide significant heterogeneity 4/8🧵

While we are accustomed to think of a nonmetropolitan mortality disadvantage, we show that in the early 2000s 19 states for females and 10 for males had lower mortality in nonmetropolitan than in metropolitan areas 3/8🧵

This is a descriptive study (with publicly available data) but I think it shows that deep description of a phenomenon can provide valuable insights even if it does not directly explore explanations for the observed patterns 2/8🧵

Studies like the one by Jacob Bor and colleagues are of great help in quantifying the extent of misreporting and the detailed misclassification ratios they report that can be applied by other teams that do not have access to the restricted ACS-NVSS linked data

Survey data linked with mortality records are great but 1) publicly available data is limited (NHIS, NHANES, some NLMS), 2) sample sizes are small and a lot of spatial and temporal granularity is lost, and 3) linkages with vital statistics are updated with long delays (NHIS now goes to 2019)

We faced a similar problem with the reporting of education on death certificates (and developed an potential solution) but there are few general purpose strategies that would work for all characteristics reported on death certificates jamanetwork.com/journals/jam...

This greatly limits the availability of official mortality statistics for AI/AN populations and is of course a barrier to tackling existing inequalities (since we can't even measure them!)

Thank you! Your Demography article 📖 was a big inspiration read.dukeupress.edu/demography/a...