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Author: Julia Shipman

Effects of Outdoor Wildfire PM2.5 on Alzheimer’s Disease and Related Dementia

Written by Julia Shipman on . Posted in Extreme Weather, Thematic Areas.

Effects of Outdoor Wildfire PM2.5 on Alzheimer’s Disease and Related Dementia

Investigators:

Jennifer Stowell, Chad Milando, and Greg Wellenius

Funding:

NIA R61AG086854 (CACHE)

Data sources:

Measures:

  • Health Measures: AD/ADRD ED visits and hospitalizations will be identified using the International Classification of Diseases versions 9 and 10.
  • Aging Measures: Age of event is recorded for all patients, and the analysis is restricted to persons aged at least 40 years.
  • Climate Measures: Daily WFS-specific PM2.5, heat, and relative humidity will allow us to examine un-biased associations between wildfire smoke and AD/ADRD. Meteorology will include multiple measures of temperature (i.e. absolute, heat index, wet bulb globe, etc.).

Project Summary:

We will examine the impact of exposure to WFS-specific PM2.5 on emergency department visits and hospitalizations for incident AD/ADRD or exacerbations of AD/. We will link population-weighted exposure, meteorology, and demographic variables to AD/ADRD events across the contiguous US for 2006-2023 using a large medical claims dataset. We will accomplish this using distributed lag nonlinear models (DLNM) and conditional Poisson regression. We will explore multiple lag lengths to account for delays in exposure effects. These analyses will be conducted using a case-control study design where each case is matched to non-case days within the same month, year, and on the same day of the week. This design inherently controls for all time invariant confounders, and all models will include terms for confounding variables such as temperature, relative humidity, and holidays. We will repeat our analyses stratifying on measures of individual and community-level social determinants of health (SDOH) using age, sex, and select ACS variables.

This research will help to increase our understanding of the environmental factors associated with AD/ADRD. Our results will provide actionable evidence for public health practitioners, clinicians, and policymakers in future efforts to mitigate the impacts of climate change on AD/ADRD. Our future research will build on these results and inform an R01 proposal to examine the potential synergistic impacts of multiple extreme weather events (i.e. wildfire, drought, heat, etc.) and mixtures of pollutants on AD/ADRD in US adults.

Outputs:

  • Poster presentations
  • Grant proposal
  • Code
  • Future publications

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What am I Reading? A Life Course Approach to Brain Health in a Changing Climate

Written by Julia Shipman on . Posted in What Am I Reading?.

What am I reading? A Life Course Approach to Brain Health in a Changing Climate

If you’re interested in contributing a short What Am I Reading post, we’d love to hear from you! Email us at cache@colorado.edu

Written by Kelly Perry and Jenna Merenstein

Calling for an exposome-informed approach to brain health across the life course

Older adults are a key group at risk from climate-related threats, including extreme heat, poor air quality, and flooding (EPA). While these hazards were once primarily seen as risks to the heart and lungs, new evidence shows they also play a significant role in brain health, aging, and an increased risk of dementia (Jones et al 2025). We highlight two recent notable studies that explore the links between adverse environmental exposures and impaired brain health and aging, with a broad theme underscoring the need for more equitable, justice-centered, and exposome-informed community-level and occupational interventions to mitigating environment-related acceleration of neurocognitive aging across the life course.

What does a life course, exposome-informed, justice-centered approach to understanding and protecting brain health look like in practice? First, it demands investment in transdisciplinary research that bridges cognitive science, neuroscience, urban planning, and environmental science. Second, it requires designing policies that protect the brain from environmental harm at all stages of life—from improved maternal housing to urban greening strategies for older adults. Third, it calls for integrating neuroimaging-based measures of brain structure and brain function into exposome-focused studies to identify early biomarkers of environmental impact. Doing so would help identify targetable properties for future exposome-based intervention work and ultimately help slow or delay the negative effects of the exposome on neurocognitive aging.

A quick, non-exhaustive review of recent articles discussing adverse environmental exposures and impaired brain health, with implications for an exposome-informed life course approach

Jones and colleagues (2025) conducted an umbrella review and meta-analysis of longitudinal studies that investigated environmental risk factors for dementia, and identified that exposure to the following nine factors was associated with increased relative risk of all-cause dementia (dementia resulting from any combination of underlying causes) compared to those who were unexposed: fine particulate matter (PM; e.g., PM less than or equal to 2.5 µg/m3), particulate matter (PM less than or equal to 10 µg/m3), nitrogen dioxide (NO2), nitrogen oxides (NOx), carbon monoxide (CO), shift work, night shift work, chronic noise, and extremely low-frequency magnetic fields (ELF-MF). They also highlighted community-level factors that were associated with a lower relative risk for dementia, such as neighborhood greenness. Regarding specific types of dementia, Jones and colleagues found the following factors were associated with increased relative risk of dementia of the Alzheimer’s type: PM2.5, ELF-MF, sulfur dioxide (SO2), chronic noise, and pesticides. Similarly, PM2.5, PM10, and chronic noise were associated with increased relative risk of vascular dementia.

Canning and colleagues (2025) followed people from midlife to older age (43–69 years) to study how long-term exposure to air pollution affects the brain. They looked at common pollutants such as PM2.5, PM10, and nitrogen oxides (NOx), and measured cognitive thinking skills and acquired brain scans (i.e., structural magnetic resonance imaging, or MRI). A strength of this study is its long follow-up period and inclusion of adults over 65—an age group that is growing quickly worldwide but often excluded from studies of environmental exposure and neurocognitive aging. The team found that people exposed to more air pollution in mid-to-late life had lower cognitive performance, slower thinking speed, and greater age-related decreases in brain volume. Compared to individuals with lower exposure to NO2, NOx, and PM10, higher exposure to these pollutants was linked to larger brain ventricles and smaller hippocampal volume—and these changes are tied to memory abilities and overall brain health.

Interestingly, the team did not observe links between environmental exposures and measures of verbal memory, or between white matter hyperintensities (a marker for cardiovascular damage). These null findings could suggest that environmental risks may impact only certain aspects of how we think about the underlying neurobiology, although additional research is needed to confirm this. The authors also emphasize that these findings should be viewed within the bigger picture—where environmental exposures interact with genetics, cognition, and brain changes starting before birth and accumulating over the life course.

Researchers are now leveraging the idea of the “exposome”—the sum of environmental exposures an individual experiences across their lifetime—to offer a more holistic lens to brain health. For example, Legaz and colleagues (2025) propose an exposome framework that combines both social factors (e.g., education quality) and physical factors (e.g., air pollution) and connects them to brain outcomes measured with tools such as MRI (as shown by Canning et al., 2025). Legaz and colleagues’ framework highlights how systemic inequities—structures that promulgate an unequal distribution of resources and opportunities in communities—shape the pathways of brain aging. For instance, they report that greater structural inequities are associated with adverse brain outcomes, such as lower brain volume and reduced connectivity among different brain regions, and these outcomes have negative impacts on our cognitive abilities. These impacts are magnified in older adults and those living with dementia. We refer the reader to Legaz et al. for a descriptive figure of this model.

The takeaway for an exposome-informed approach for brain health over the life course

Taking an “exposome” approach means putting environmental justice at the center of studying brain health over the life course. Marginalized communities—often low-income or racially maligned—face the highest exposure to environmental risks while having the least resources to cope. As Legaz and colleagues (2025) emphasize, addressing this imbalance is both ethically imperative and essential to people’s health and wellbeing. Solutions include expanding equitable access to restorative green spaces (Besser et al., 2023), enforcing clean air and water quality standards in underserved areas, and investing in community-led initiatives. Implementing such steps would reduce harmful exposures, build community-level resilience, and promote brain health equity for all.

As air pollution, toxic environmental exposures, and climate change converge with a rapid increase in the global population of older adults (GBD Lancet 2022), we are at a critical juncture: such convergence demands a fundamental rethinking of how we understand and protect brain health across the life course.

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Daily Temperature Data Processing and Analysis: An Example for New York City

Written by Julia Shipman on . Posted in Extreme Weather, Thematic Areas.

Daily Temperature Data Processing and Analysis: An Example for New York City

Link to code

Click here

Date: September 2025

Authors/Creators/ Team Members: 

Author: Selen Ozdogan

Team Members: Frank Heiland, Deborah Balk, Jennifer Brite, Peter Marcotullio

Specific purpose of code:

This code aims to provide a comprehensive guide to acquiring and cleaning daily temperature and precipitation data for New York City between 2015-2022 using two primary data sources: Global Historical Climatology Network daily (GHCNd) from the U.S. National Centers for Environmental Information and ERA5-Land Reanalysis from the European Union’s Copernicus Project.

From these data sources, the code assembles daily air temperature and precipitation. It also calculates wet bulb temperature and creates temperature exposure variables with varying temporal resolutions. The extent of this example is New York City (NYC). Aggregation of the input data is necessary to generate estimates for all NYC.  

The code is embedded in an R Markdown pdf file.

General Application:

This is a guide to obtaining climate data and creating different temperature measures and temporal exposure lags. With minor tweaks, the code could be used for other locations/time-periods and can be merged with any daily data set for data analysis. Note that our example here is from 2015-2022, but that time period can be extended (as we also did in the underlying research); a short time period is given in this R Markdown package to facilitate the demonstration.

How does or could this code allow researchers to assess research questions related  to aging or life course?:

The output from this code, daily climate data, could be merged with any daily (or more aggregated temporal frequency) data to study the impact of extreme weather events on aging populations, so long as the underlying spatial resolution of the climate data and population data (from either administrative, census or survey data) are spatially and temporally compatible.

Data sets used: 

  • Climate, weather, disaster or environment data:

    Global Historical Climatology Network daily (GHCNd) – point location format.

    ERA5-Land Reanalysis data – grid format

  • All data are publicly available

Links to data:

  1. Global Historical Climatology Network daily (GHCNd)
  2. Climate Data Store

Coding Language:  R, Python

Tools and Packages used:

R: tidyverse, lubridate, magrittr, here, sf, raster, exactextractr, openxlsx, fixest, slider

Python: os, cdsapi, time, Path

Output(s): Dataset

Spatial extent: New York City (roughly 300 sq. miles or 778 sq. km.)

Temporal extent: 2015-2022

Comments: Replication package for the Demography article will be available here. 

Published papers that use this code:

Forthcoming paper “Extreme Weather and Mortality of Vulnerable Urban Populations:  An Examination of Temperature and Unclaimed Deaths in New York City”, in Demography (2026).

Link to PAA Poster

Related Content: 

Demonstration Project: Impact of Extreme Weather on Hard-to-Capture, Vulnerable Populations: Evidence from Hart Island — New York’s Public Burial Ground

Seminar: Measuring Extreme Temperatures and Thermal Comfort in Aging and Demographic Reseach 

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Linking SVRGIS with FEMA Disaster Declarations and Census/ACS

Written by Julia Shipman on . Posted in Code & Data, Disasters, Thematic Areas.

Linking SVRGIS with FEMA Disaster Declarations and Census/ACS

Link to code

Click here

Date: September 2026

Author/Creator: Amy Read

Specific purpose of code: This code provides examples of how to pull, filter, and merge data from the NOAA/NWS Severe Weather GIS Database (SVRGIS), OpenFEMA Disaster Declarations Summaries, and data from the Decennial Census and American Community Survey (ACS). Walkthroughs are provided for A) merging FEMA disaster declarations to SVRGIS tornado paths data based on incident date and location, B) performing a spatial join between event paths and Census geographies (intersection of line and polygon) to identify geographic areas that were exposed to tornadoes, hail, and/or wind during the user-specified timeframe.

General Application: This template can be extended to access and merge other OpenFEMA datasets based on incident (such as Public Assistance or Individual Assistance summary data), and any other data on Census geographic boundaries that is of interest to the researcher. This code also allows the user to identify FEMA disaster declarations for tornado events at smaller geographic levels (tracts, block groups, etc.).

How does or could this code allow researchers to assess research questions related  to aging or life course?: This code could be used with any of the ACS/Census data subset by age group. Since this code focuses on the spatial join between tornado/wind/hail event paths and Census geographies, any other demographic/health datasets tracked by state, county, tract, block group, etc. could be merged by FIPS code into these data the same way one would combine them with Census data alone.

Data sets used: 

  • Population, socioeconomic, or health data: Decennial Census, ACS
  • Climate, weather, disaster or environment data: SVRGIS (Tornadoes, Wind, Hail) and FEMA Disaster Declarations Summaries

Are all the data publicly available or are some restricted-access? All are publicly available.  

Links to data:

SVGIS: https://www.spc.noaa.gov/gis/svrgis/
Census/ACS: https://data.census.gov/

Coding Language:  R 

Tools and Packages used: tidycensus, rfema, sf, tidyverse

Output(s): Merged dataset saved to .Rds format

Spatial extent: Contiguous United States

Temporal extent: Example focuses on 2000-2010 but explains how to filter/extend beyond that. SVRGIS data is available from 1950 for tornadoes and from 1955 for hail and wind. FEMA disaster declarations are available from 1953. 

Comments: This is a revised, streamlined, and more generalized version of the code used for the manuscript below. That code is also available on the author’s GitHub.

Published papers that use this code: Read, A. (2025). Repeated disaster and the economic valuation of place: Temporal dynamics of tornado effects on housing prices in the United States, 1980–2010. Population and Environment, 47(3), 29. https://doi.org/10.1007/s11111-025-00502-w

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Weathering the Impact: ENSO-Driven Disasters, Power Disruption, and Health Outcomes in Medicare Populations

Written by Julia Shipman on . Posted in Code & Data, Disasters, Thematic Areas.

Weathering the Impact: ENSO-Driven Disasters, Power Disruption, and Health Outcomes in Medicare Populations

 

Investigators:

Sara Curran, Jeff Stanaway, Luanne Thompson, June Yang, Emmanuela Gakidou

Data sources:

ENSO indices from the National Oceanic and Atmospheric Administration Climate Prediction Center from 1980-2025.

SHELDUS: Special hazard events and losses database for the United States 1970 – 2023. Available by County and day.

Center for Medicare & Medicaid Services claim and beneficiary data from 1990-2023. Available by County and day.

HHS emPOWER: Count of medicare beneficiaries who rely on electricity-dependent durable medical and assistive equipment and devices from 2016-2025. Available by County and month.

DOE-417 archive data: Electric Emergency Incident and Disturbance Report from 2000 to 2023. Available by County and Day.

Spatial Coverage: United States

Temporal Coverage: 2016 – 2023.

Measures:

  • Climate Measurements:

    • ENSO Indices (NOAA): Seasonal measurements of sea surface temperature anomalies used to classify El Niño and La Niña events.
    • SHELDUS: Provides detailed records of natural disaster events (including type, date, and location), allowing alignment of local disasters with ENSO periods. It also includes loss data (e.g., property damage, crop loss, injuries) to quantify event severity.

    Power Infrastructure Measurements:

    • HHS emPOWER Dataset: Monthly, county-level counts of Medicare beneficiaries who rely on electricity-dependent durable medical equipment (DME), indicating vulnerability to power disruptions.
    • DOE OE-417 Archive: Incident-level reports on electrical disturbances, including type of event, date/time, geographic area, and population affected.

    Health Outcomes:

    • CMS Claims and Quality Measures: Data on Medicare beneficiaries covering:
      • Hospital readmission rates
      • Emergency department (ED) and inpatient utilization
      • Home health services
      • Minimum Data Set (MDS) indicators from nursing homes
      • Preventable hospitalizations (e.g., ambulatory care-sensitive conditions)

    Demographic and Socioeconomic Context:

    • County-Level Medicare Population Characteristics: Including race/ethnicity, age, sex, and dual eligibility for Medicare and Medicaid, enabling analysis of differential impacts across vulnerable groups.

Project Summary:

Disruptions driven by the El Niño-Southern Oscillation (ENSO), such as flooding, drought, and extreme temperatures, have long been implicated in public health threats across the United States. For example, during negative phases of ENSO (La Nina events) there is typically an increase in the number of hurricanes that can result in major floods. This also occurs during El Nino events in the Southwest US.  Major floods have been shown to elevate hospitalization rates among older adults for skin conditions, neurological illnesses, musculoskeletal disorders, and injuries in the weeks following exposure (Aggarwal et al. 2025). In addition, other impacts include health care disruptions due to displacement from homes and communities or through damage to health infrastructure. These might be particularly important for those requiring frequent care for chronic illnesses.

However,  mechanisms linking ENSO-related disasters with health outcomes remains poorly understood. For instance, the role of power infrastructure failure, such as prolonged outages disrupting electricity-dependent medical care is a plausible pathway for detrimental impacts on healthcare particularly for the elderly who are less mobile than the general population..

Our U.S.-based project investigates how ENSO-related disasters influence health outcomes among older adults, focusing especially on power disruptions as a critical explanatory pathway. By aligning the data sources mentioned above, we aim to trace if and how power outages amplify the health impact of ENSO-driven disasters on older adults.

We will employ a combination of panel regression models, Difference-in-Difference analysis, and causal mediation analysis to estimate the direct and indirect effects of ENSO-driven disasters, to quantify the role of infrastructure failure and identify populations at higher risk.

Comments

Papers on impacts of El Nino in the U.S. 

Outputs:

Peer-reviewed publications, grant proposals, conference presentations

References:

Aggarwal, Sarika, Jie K. Hu, Jonathan A. Sullivan, Robbie M. Parks, and Rachel C. Nethery. 2025. “Severe Flooding and Cause-Specific Hospitalisation among Older Adults in the USA: A Retrospective Matched Cohort Analysis.” The Lancet Planetary Health 9(7):101268. doi:10.1016/S2542-5196(25)00132-9.

Fussell, Elizabeth, Sara R. Curran, Matthew D. Dunbar, Michael A. Babb, Luanne Thompson, and Jacqueline Meijer-Irons. 2017. “Weather-Related Hazards and Population Change: A Study of Hurricanes and Tropical Storms in the United States, 1980–2012.” The ANNALS of the American Academy of Political and Social Science 669(1):146–67. doi:10.1177/0002716216682942.

Salas, Renee N., Laura G. Burke, Jessica Phelan, Gregory A. Wellenius, E. John Orav, and Ashish K. Jha. 2024. “Impact of Extreme Weather Events on Healthcare Utilization and Mortality in the United States.” Nature Medicine 30(4):1118–26. doi:10.1038/s41591-024-02833-x.

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What Am I Reading: Disasters and Aging in Place

Written by Julia Shipman on . Posted in Disasters, Thematic Areas, What Am I Reading?.

What am I reading? Disasters and Aging in Place

Link to article

If you’re interested in contributing a short What Am I Reading post, we’d love to hear from you! Email us at cache@colorado.edu

Written by Jenna Tipaldo, CUNY School of Public Health and CUNY Institute for Demographic Research, jenna.tipaldo09@sphmail.cuny.edu

A new report from Winkler & Mockrin (2025) entitled “Aging and wildfire risk to communities” explores the exposure of older populations to wildfires. A main findings is that “Nearly all (87 percent) of the population growth in higher wildfire risk locations between 2010 and 2020 was among people over the age of 60, many of whom had been living in higher risk places for years and are growing older (i.e., aging in place).” Each is relevant when thinking about older adults and exposure, vulnerability, and resilience to disasters.  Wildfires are just one of type of disaster to consider: recent evidence also suggests that coastal zones – areas at risk of storms and other seaward hazards such as flooding and tsunamis – are also aging faster than areas farther inland (Bukvic et al., 2018; Hauer et al., 2020; Tagtachian and Balk, 2023). 

No relocation: Forsyth & Molinsky (2020) note that to some, aging in place signifies remaining in their home while to others it may mean moving but within the same community, such as downsizing. Based on recent shifts in age distribution in Census blocks with “moderate-to-high” wildfire risk, Winkler & Mockrin (2025) conclude that the increase in older adult populations in fire-prone regions is likely attributable to populations aging in place rather than in-migration (Figure 4). They also note important spatial variation and also uncertainty about the relative contributions of migration and death. They also note that aging in place seems to be the “primary mechanism” in higher risk rural areas (Winkler & Mockrin, 2025). 

Source: Winkler & Mockrin (2025)

Health and Health Care : Winkler & Mockrin (2025) summarize the various ways in which older adults can be at higher risk due to wildfires including 1) physical limitations that are barriers to preparation or response, 2) factors like social isolation which can impact access to information and resources, and 3) higher rates of chronic diseases which are risk factors for adverse health outcomes due to fires and smoke. Furthermore, disasters can be disruptive to healthcare, not only by damaging facilities and displacing people from their homes but also by disrupting care which relies on movement. Examples include when patients are unable to travel to hospitals or medical providers, or if healthcare workers can‘t get to a patient’s home due to inaccessible roads (Tarabochia‐Gast et al., 2022) or suspended public transit systems. Rural areas face additional challenges with longer travel times for healthcare access, especially with high levels of hospital closures (Miler et al., 2020; McCarthy et al., 2021). Such patterns negatively impact health care access, emergency medical response, and transport times (GAO, 2021; Kaufman et al., 2016). On average, rural residents must travel about 20 miles farther for typical health care services – in non-disaster times (GAO 2021). While those miles may seem trivial, in emergencies they can mean loss of access to care and treatment. 

Personal choice: Aging in place can be a personal choice in support of maintaining one’s agency and independence by staying in one’s own home and community (Forsyth & Molinsky, 2020). Even so, staying in one’s home can also result from lack of choice due to limited resources and/or few desirable and affordable options. Modifications are expensive too. Even older adults who are relatively better off can struggle to pay for downsizing or modifying a new dwelling for care needs (Forsyth & Molinsky, 2020).  

From research to policy 

To help support healthy aging in place, Winkler & Mockrin (2025) suggest that existing programs that support older adults could be expanded to include wildfire risk reduction. An example is the USDA’s Section 504 Home Repair program which supports older low-income homeowners. In addition, organizations such as the AARP provide useful material for aging in place such as a checklist for people who are prepping their home. Such resources should be expanded to include disaster risk as a consideration.  

 

References:  

  • Bukvic, A., Gohlke, J., Borate, A., and Suggs, J. 2018. “Aging in Flood-Prone Coastal Areas: Discerning the Health and Well-Being Risk for Older Residents.” International Journal of Environmental Research and Public Health 15(12):2900. https://doi.org/10.3390/ijerph15122900.   
  • Hauer, Mathew E., Elizabeth Fussell, Valerie Mueller, Maxine Burkett, Maia Call, Kali Abel, Robert McLeman, and David Wrathall. 2020. “Sea-Level Rise and Human Migration.” Nature Reviews Earth & Environment 1(1):28–39. https://doi.org/10.1038/s43017-019-0002-9.  
  • Kaufman, B.G., Thomas, S.R., Randolph, R.K., et al. The rising rate of rural hospital closures. The Journal of Rural Health. 2016;32(1):35-43. https://doi.org/10.1111/jrh.12128  
  • McCarthy, S., Moore, D., Smedley, W. A., Crowley, B. M., Stephens, S. W., Griffin, R. L., Tanner, L. C., & Jansen, J. O. (2021). Impact of Rural Hospital Closures on Health-Care Access. Journal of Surgical Research, 258, 170–178. https://doi.org/10.1016/j.jss.2020.08.055 
  • Miller, K.E.M., James, H.J., Holmes, G.M., Van Houtven, C.H. The effect of rural hospital closures on emergency medical service response and transport times. Health Serv Res. 2020;55(2):288-300. https://doi.org/10.1111/1475-6773.13254  
  • Tagtachian, D. and Balk, D., 2023. Uneven vulnerability: characterizing population composition and change in the low elevation coastal zone in the United States with a climate justice lens, 1990–2020. Frontiers in Environmental Science, 11, p.1111856. 
  • Tarabochia‐Gast, A. T., Michanowicz, D. R., & Bernstein, A. S. (2022). Flood Risk to Hospitals on the United States Atlantic and Gulf Coasts From Hurricanes and Sea Level Rise. GeoHealth, 6(10), e2022GH000651. https://doi.org/10.1029/2022GH000651 
  • Winkler, R. L., & Mockrin, M. H. (2025). Aging and wildfire risk to communities (Report No. EIB-284). U.S. Department of Agriculture, Economic Research Service. https://doi.org/10.32747/2025.9015828.ers 

 

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Severe Heat Days using the Universal Thermal Comfort Index

Written by Julia Shipman on . Posted in Code & Data, Extreme Weather, Heat, Thematic Areas.

Severe Heat Days using the Universal Thermal Comfort Index

Link to code

Click here

Date: September 2025

Authors/Creators/ Team Members: Marcial Yangali (marcialy@colmex.mx)

Reviewers: Landy Sánchez and Emerson Baptista

Specific purpose of code: It demonstrates how to construct severe heat measures using the UTCI data (ERA5-HEAT ). It starts by showing data manipulation from raster (grid data) to a tabular dataset that obtains UTCI values for each municipality in Mexico. Then, it presents how to map and analyze such data. Finally, it shows how to count the number of days of severe heat (32°C UTCI and above). This script is part of the demonstration CACHE project “Heat, Disability in older adults and Care” from El Colegio de Mexico.

General Application: This develops familiarity with heat data and fundamental skills to construct an environmental dataset that can be easily integrate with demographic information.

How does or could this code allow researchers to assess research questions related  to aging or life course?: This code help to analyze exposure to severe heat days by age structure at a subnational level. Particularly, to understand how aging realtes to higher exposure to extreme weather. Historical UTCI measures could be employ to assess cumulative heat impacts across the life course.

Data sets used: 

  • Two publicy available datasets are used:

    • Universal Thermal Climate Index (UTCI). Copernicous ERA5-HEAT
    • 2020 Mexican Municipalities and States boundaries. INEGI (Census Bureau office)

Coding Language:  R 

Tools and Packages used: R packages for data manipulation (mainly dplyr but also janitor, stringr, tidyr and forcats), visualization (ggplot2, patchwork), and geospatial operations (sf).

Output(s): Analysis results, maps and graphs

Spatial extent:Mexico (33.0°N (North), -118.5°E (East), 14.0°N (South), and -86.0°E (West)

Temporal extent: May 2019

Key words: heat, severe weather, thermal index, Mexico

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Aging and disability in the Mexican Population

Written by Julia Shipman on . Posted in Code & Data, Other, Thematic Areas.

Aging and disability in the Mexican Population

Link to code

Click here

Date: September 2025

Authors/Creators/ Team Members: Marcial Yangali (marcialy@colmex.mx) and Mariana Ramos

Reviewers: Emerson Baptista and Landy Sánchez

Specific purpose of code: To show how to construct disability measures using the international recommendation of the Washington Group. It also demonstrates how to evaluate age and sex composition of the population with disability and their territorial distribution.

General Application: This demonstrates basic manipulation of demographic data with R, particularly useful for those with limited familiarity with population measures.

How does or could this code allow researchers to assess research questions related  to aging or life course?: This code examines age structure in census data. Specifically, explores how the prevalence of disability condition increases with age.

Data sets used: 

  • All are publicy available datasets:

    • 2020 Mexican Housing and Population Data (IPUMS International)

Coding Language:  R 

Tools and Packages used: R packages for data manipulation (mainly dplyr but also stringr, ipumsr, tidyr, srvyr, gtsummary, purr, and labelled), visualization (ggplot2, patchwork, geofacet, scales and plotly), and geospatial operations (sf, biscale, ggtern).

Output(s): Analysis results, maps and graphs

Spatial extent: Mexico (33.0°N (North), -118.5°E (East), 14.0°N (South), and -86.0°E (West)

Temporal extent: 2022

Key words: disability, Mexico, age structure, sex composition, aging

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Joining ACAG Annual Estimates of PM2.5 with Social Determinants of Health (SDOH) data

Written by Julia Shipman on . Posted in Air Polllution, Code & Data, Other, Thematic Areas.

Joining ACAG Annual Estimates of PM2.5 with Social Determinants of Health (SDOH) data

Link to code

Click here

Date: July 2025

Authors/Creators/Team Members: Zoé Haskell-Craig and Priyanka deSouza

Specific purpose of code: This code aggregates gridded annual average PM2.5 concentration estimates produced by the Atmospheric Composition Analysis Group (ACAG) to the census tract level, producing a variable containing the average PM2.5 exposure for each tract. This is then combined with socioeconomic and demographic data available at the tract level from the social determinants of health (SDOH) database produced by the Agency for Healthcare Research and Quality (AHRQ).

General Application: This code takes advantage of the `tigris` package to aggregate high resolution (fine spatial scale) modelled estimates of PM2.5 pollution to the administrative boundaries at which demographic and SDOH data are available. As an example, here we demonstrate computing the annual average PM2.5 concentrations in 2020 for census tracts and combining this with SDOH data on race/ethnicity and income. With minor changes, this code can be used for other years and temporal resolutions (i.e. monthly estimates of PM2.5) and for other administrative units (ZCTAs, blockgroups, counties, etc).

How does or could this code allow researchers to assess research questions related to aging or life course?: While the dataset output from this code does not contain variables on age, the raw SDOH dataset contains census information on age which could be included with minor edits to the code. Also, the aggregation of PM2.5 exposure to the census tract (or other administrative units) allows researchers to combine this exposure with other information available from the census, such as income by age breakdowns.

Data sets used: 

Are all the data publicly available or are some restricted-access? Publicly available 

Coding Language:  R 

Tools and Packages used: tidyverse, readxl, sf, raster, ncdf4, exactextractr, tigris, viridis

Output(s): Dataset with census tracts as unit of analysis and a map of the average PM2.5 per census tract in 2020. 

Spatial extent: Continental US

Temporal extent: Single-year, 2020 (code can be modified to produce data for any year from 1998 – 2023, or monthly for any month in that period). 

Additional Comments: Journal article using this code is forthcoming. 

Published papers that use this code: Zoé Haskell-Craig, Kevin P. Josey, Patrick L. Kinney, and Priyanka deSouza. (2025). Equity in the Distribution of Regulatory PM2.5 Monitors. Environmental Science & Technology. DOI: 10.1021/acs.est.4c12915

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What am I Reading? Disentangling the Impacts of Extreme Heat on Biological Aging: Measuring the Hidden Toll on Our Body

Written by Julia Shipman on . Posted in Extreme Weather, Heat, Thematic Areas, What Am I Reading?.

What am I reading? Disentangling the Impacts of Extreme Heat on Biological Aging: Measuring the Hidden Toll on Our Body

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Written by Eun Young Choi, Postdoctoral Associate, University of Southern California

High temperatures are heating not only our neighborhoods and homes, but also our very biology, as we suggested in a recent study (Choi & Ailshire, 2025). Among the many ways extreme heat threatens public health, its subtle yet significant impact on the body’s aging processes has begun to draw attention. I’ve been exploring how we can quantify the biological consequences of extreme heat, and why these hidden impacts matter for aging research.

Why Focus on Biological Aging?

Much of the climate-health literature has understandably focused on observable health outcomes: hospitalizations, respiratory and cardiovascular diseases, and death (Bunker et al., 2016; Hajat et al., 2010). These are essential indicators, but the effects of extreme heat might not immediately manifest as diagnosable conditions; instead, they may exert a silent toll at the cellular and molecular level. Over time, this biological strain can accumulate, increasing the risk of disability and disease. Biological age, measured using biomarkers that reflect physiological wear and tear, offers a promising way to identify early signs of changes in the body before chronic conditions are formally diagnosed. These include measures based on clinical biomarkers, cellular markers, and, more recently, molecular signatures such as DNA methylation (Chen et al., 2023 for review). Each of these tools provides a useful lens into how the aging process may advance more rapidly than expected based on chronological age, and how it may be shaped by external stressors like heat. Recent studies show that accelerated biological aging is associated with elevated risk of cardiovascular disease, cognitive decline, and mortality (Emami et al., 2022; Fransquet et al., 2019; Zhou et al., 2022). As such, these clocks provide a useful intermediate endpoint that can be particularly valuable when studying environmental exposures, which often unfold gradually and exert diffuse effects across multiple body systems. In this context, biological age can help us understand not just whether heat is harmful, but how it does its damage.

How Do We Measure Heat?

Measuring exposure to extreme heat may appear straightforward, but it involves multiple layers of complexity. The most common metric is ambient temperature (technically, the temperature in the surrounding environment), but even this can be defined in various ways—daily maximums, minimums, or averages—and it usually refers to outdoor air temperature. Others use composite indices that better approximate heat stress on the human body. The Heat Index, for example, incorporates humidity and is used to estimate heat-related stress in shaded environments (National Weather Service). The Wet-Bulb Globe Temperature (WBGT) goes further by accounting for wind speed, cloud cover, and sun angle as well to reflect conditions in direct sunlight, making it especially relevant for outdoor or occupational settings. Each of these measures captures different aspects of thermal stress, and the choice of metric should align with the study’s research question and target population. In our study, we used the Heat Index because our sample consisted of older adults, who are generally less likely to work outdoors or spend extended time in full sun. While these metrics can technically be measured indoors, they are typically based on outdoor weather data and do not directly reflect indoor conditions.

How Can We Conceptualize Outdoor Heat Exposure to Better Estimate Health Impacts?

Researchers have considered a variety of factors to understand the magnitude of heat-related health effects. Here, I created a conceptual cube with three axes to synthesize commonly used dimensions: exposure timing, heat intensity, and exposure context. This framework is not a modeling tool, but rather a way to clarify how different features of outdoor heat exposure interact to shape physiological burden.

  • Y-axis: What timeframe is captured?

Exposure can occur over various timeframes: a single hot day, a multi-day heatwave, or repeated seasonal exposures over years. Short-term heat events can trigger immediate physiological stress responses (e.g., inflammation), while longer-term exposure to elevated temperatures may produce cumulative biological wear and tear. Studies use various time windows to model these effects, from several day lags to multi-year averages, depending on the health outcome of interest. While short-term physiological responses to heat stress may be transient, some effects may accumulate over time and this continuous exposure may leave biological imprints detectable by biomarker-based measures. For example, our findings show consistent associations between longer-term heat exposure (e.g., 1-year and 6-year cumulative heat days) and accelerated epigenetic aging, consistent with prior studies reporting robust associations and large effect sizes for longer-term heat exposure (Chiu et al., 2024; Ni et al., 2023).

  • Z-axis. How severe is the exposure?

This axis represents the intensity of the heat experienced. Researchers use both relative and absolute metrics to define extreme heat. Relative measures, such as temperatures above the 95th percentile of the local historical distribution, are useful for identifying deviations from the norm, especially considering that long-term sublethal heat can lead to physiological adaptations and increased thermal tolerance (Carr et al., 2024; Périard et al., 2016). In contrast, absolute thresholds (e.g., >95°F or >35°C) provide a fixed reference point that can be useful for identifying biologically hazardous temperatures and facilitating comparisons across regions. For the Heat Index, for example, the National Weather Service offers a practical classification: 80°F (Caution), 90°F (Extreme Caution), and 103°F (Danger). The choice between relative and absolute measures should be guided by the study’s objective, the population’s vulnerability, and the geographic and climatic context.

  • X-axis. How is exposure experienced?

This axis reflects the context: the individual or situational factors that influence how ambient heat translates into actual personal exposure. Most studies use outdoor temperature data from weather stations or modeled products, and these measures may not fully capture personal exposure – a challenge also seen in air pollution research, where ambient data often serve as proxies for individual exposure (see related discussion). It is therefore important to interpret such data as approximating the potential for exposure rather than direct experience. Key contextual modifiers include time spent outdoors, occupational exposure, housing quality, and access to cooling resources. For instance, older adults living in poorly insulated homes without air conditioning may experience disproportionately high indoor heat, despite similar ambient temperatures. Recent work has also highlighted substantial within-neighborhood variation in heat exposure, reinforcing the need to consider behavioral and environmental mediators when interpreting ambient heat metrics (Li et al., 2023; Reid et al., 2009).

The color gradient in the figure reflects increasing physiological burden, illustrating how these dimensions can intersect to magnify biological stress. For instance, someone who works outdoors (high context), over many years (longer timing), and in areas where temperatures regularly exceed 100°F (high intensity), may experience substantially greater heat-related aging than someone briefly exposed to moderate heat while indoors.

What We Found about Heat and Epigenetic Aging

There is growing evidence from animal studies that heat stress can trigger epigenetic alterations such as DNA methylation, positioning these molecular changes as a plausible biological mechanism linking environmental exposures to long-term health outcomes (Murray et al., 2022). Epigenetic clocks—biological age estimates derived from DNA methylation levels across the genome—can capture cumulative physiological responses to stress and toxins. In our work, we’ve begun to apply these epigenetic measures to understand how outdoor heat exposure may accelerate epigenetic aging. Our findings show that outdoor heat, especially long-term, was significantly associated with greater acceleration in epigenetic age in a diverse national cohort of older adults. These associations persisted even after adjusting for individual-level socioeconomic and behavioral factors, as well as community-level characteristics. Taken together, these findings suggest that chronic heat exposure may not only increase immediate health risks but also disrupt key systems contributing to a steeper trajectory of epigenetic aging, potentially through pathways involving oxidative stress, inflammation, and metabolic reprogramming (Murray et al., 2022). Nonetheless, further evidence is needed to clarify the specific mechanisms linking heat exposure to epigenetic aging acceleration.

Moving Forward

Advancing this research requires embracing the complexity of both exposures and outcomes. On the exposure side, we must better account for co-occurring environmental stressors. Heat rarely acts alone. For instance, air pollution tends to worsen on hot days, creating compound exposures that may have synergistic effects on biological systems. We also need more attention to contextual variation, how housing, neighborhood infrastructure, and access to cooling resources shape actual heat burden experienced by older adults. This is especially pressing in low-income or racially marginalized communities, where systemic disinvestment may amplify risk. On the outcome side, biological aging is multidimensional. While epigenetic clocks help pinpoint biological responses to heat across systems, they capture only part of the picture. It remains unclear how—and to what extent—heat stress affects specific systems such as the immune, neuro, metabolic, cardiovascular, and renal systems in large human samples. Integrating additional biomarkers such as inflammatory cytokines, neurodegenerative markers, and metabolomic signatures can help build a more comprehensive understanding of how heat “ages” us biologically.

Final Reflections

Extreme heat is no longer exceptional—it is a defining feature of our climate reality. To understand its full toll on aging populations, we must look beyond acute events and capture the long-term, cumulative effects of chronic stress. Biological aging measures offer an important window into these processes, enabling earlier detection, monitoring of intervention impacts, and deeper insight into vulnerability. But how we measure exposure and aging matters. Choices about metrics and methods shape our conclusions—and our ability to act. Integrating high-resolution environmental data with multidimensional biomarkers will be key to building a fuller picture of how climate shapes the aging process.

References

Choi EY, Ailshire JA. Ambient outdoor heat and accelerated epigenetic aging among older adults in the US. Sci Adv. 2025;11(9):eadr0616. doi:10.1126/sciadv.adr0616

Bunker A, Wildenhain J, Vandenbergh A, et al. Effects of Air Temperature on Climate-Sensitive Mortality and Morbidity Outcomes in the Elderly; a Systematic Review and Meta-analysis of Epidemiological Evidence. eBioMedicine. 2016;6:258-268. doi:10.1016/j.ebiom.2016.02.034

Hajat S, O’Connor M, Kosatsky T. Health effects of hot weather: from awareness of risk factors to effective health protection. The Lancet. 2010;375(9717):856-863. doi:10.1016/S0140-6736(09)61711-6

Chen R, Wang Y, Zhang S, et al. Biomarkers of ageing: Current state-of-art, challenges, and opportunities. MedComm – Future Medicine. 2023;2(2):e50. doi:10.1002/mef2.50

Emami M, Agbaedeng TA, Thomas G, et al. Accelerated Biological Aging Secondary to Cardiometabolic Risk Factors Is a Predictor of Cardiovascular Mortality: A Systematic Review and Meta-analysis. Canadian Journal of Cardiology. 2022;38(3):365-375. doi:10.1016/j.cjca.2021.10.012

Fransquet PD, Wrigglesworth J, Woods RL, Ernst ME, Ryan J. The epigenetic clock as a predictor of disease and mortality risk: a systematic review and meta-analysis. Clin Epigenet. 2019;11(1):1-17. doi:10.1186/s13148-019-0656-7

Zhou A, Wu Z, Zaw Phyo AZ, Torres D, Vishwanath S, Ryan J. Epigenetic aging as a biomarker of dementia and related outcomes: a systematic review. Epigenomics. 2022;14(18):1125-1138. doi:10.2217/epi-2022-0209

National Weather Service, Wet Bulb Globe Temperature vs Heat Index. https://www.weather.gov/ict/WBGT.

Chiu KC, Hsieh MS, Huang YT, Liu CY. Exposure to ambient temperature and heat index in relation to DNA methylation age: A population-based study in Taiwan. Environment International. 2024;186:108581. doi:10.1016/j.envint.2024.108581

Ni W, Nikolaou N, Ward-Caviness CK, et al. Associations between medium- and long-term exposure to air temperature and epigenetic age acceleration. Environment International. 2023;178:108109. doi:10.1016/j.envint.2023.108109

Deborah Carr, Giacomo Falchetta, Ian Sue Wing, Population Aging and Heat Exposure in the 21st Century: Which U.S. Regions Are at Greatest Risk and Why?, The Gerontologist, Volume 64, Issue 3, March 2024, gnad050, https://doi.org/10.1093/geront/gnad050

Périard JD, Travers GJS, Racinais S, Sawka MN. Cardiovascular adaptations supporting human exercise-heat acclimation. Autonomic Neuroscience. 2016;196:52-62. doi:10.1016/j.autneu.2016.02.002

Li A, Toll M, Bentley R. Mapping social vulnerability indicators to understand the health impacts of climate change: a scoping review. The Lancet Planetary Health. 2023;7(11):e925-e937. doi:10.1016/s2542-5196(23)00216-4

Reid CE, O’Neill MS, Gronlund CJ, et al. Mapping Community Determinants of Heat Vulnerability. Environ Health Perspect. 2009;117(11):1730-1736. doi:10.1289/ehp.0900683

Murray KO, Clanton TL, Horowitz M. Epigenetic responses to heat: From adaptation to maladaptation. Experimental Physiology. 2022;107(10):1144-1158. doi:10.1113/EP090143

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