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Air Quality and Pace of Aging among Older Adults in the United States

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

Air Quality and Pace of Aging among Older Adults in the United States

Investigators:

Arun Balachandran, Daniel W Belsky

Funding:

NIA R61AG086854 (CACHE)

Data sources:

Measures:

  • Health Measures: The Pace of Aging health measures used in the study is obtained from three blood biomarkers (HbA1c, C-reactive protein, cystatin-C), three physical assessments (diastolic blood pressure, peak-flow lung-function testing, waist circumference), and three functional tests (gait speed, balance, grip strength) available in the US Health and Retirement Study.
  • Aging Measures: Pace of Aging Age for all participants in HRS 2006-2016 who has at least two follow-ups of biomarker data (N=13, 358).
  • Climate Measures: Annual data of 5 exposure during 2002-16.

Project Summary:

Air pollution is emerging as a central public health threat to aging populations. Living in more polluted areas is associated with increased risk for a wide range of aging related diseases. There is emerging evidence that air pollution may accelerate the aging process itself, shortening healthy lifespans in already aging populations. Efforts are underway to reduce pollution and its harms. Metrics to monitor the impact of those efforts on population health are needed. Passively accumulated data such as hospitalizations for asthma or heart attacks are limited because they capture only the tip of the iceberg of latent morbidity caused by pollution. More sensitive and comprehensive measures are needed. If air pollution really does hasten the aging process, new methods to quantify the pace of biological aging could provide the answer.

The central hypothesis of this pilot proposal is that air pollution accelerates the pace of biological aging. We propose a one-year study to test this hypothesis and generate proof-of-concept for a method to monitor population health impacts of air pollution and efforts to reduce/mitigate it. Successful completion of this pilot study will position us to apply for R01 grants to expand our project to global scale, to develop an interactive toolkit for researchers and policymakers can use to evaluate how changes in air pollution levels will impact population aging, and to investigate the role of air pollution in social gradients in biological aging in the US.

Our pilot will analyze data from the US Health and Retirement Study (HRS), an ongoing longitudinal study of adults aged 50 and older and their spouses in the United States. HRS is ongoing since 1992. Survey data are collected every two years. Since 2006, biomarker data are collected every four years. Refresher panels are recruited periodically to replace study members who have died. The HRS has so far collected data on around 40,000 individuals, with roughly 20,00 participating at any given assessment wave. Our analysis will focus on a sample of 13,000 adults for whom we have previously conducted analysis to phenotype Pace of Aging, a longitudinal measurement of the rate of decline in the integrity of multiple bodily systems2. We will link Pace of Aging, sociodemographic, and morbidity and mortality data with small-area air pollution data within the HRS Contextual Data Resource (HRS-CDR) hosted within the Michigan Center for Demography of Aging (MiCDA) accessed via their Virtual Desktop Infrastructure (VDI). HRS-CDR is a collection of analysis-ready datasets that link HRS participant-level data with small-area characteristics relevant to health and wellbeing. Air pollution exposure data consist of average annual concentrations of PM 2.5 (mg/m3) and O3 (mg/m3) at the census-tract level for the period 2002-2016.

On the creation of the weather variables:

The air pollution data are integrated at the census-tract level, within the server of the Health and Retirement Study at Michigan Centre on Demography of Aging (MiCDA). The Virtual Data Enclave (VDE) supported by MiCDA gives the PM 2.5 (mg/m3) and O3 (mg/m3) at the census-tract level for the period 2002-2016, and the researchers made use of this.

Outputs:

  • Poster
  • Future publications and code

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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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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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US Census Bureau Household Pulse Survey – Disaster Displacement

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

US Census Bureau Household Pulse Survey – Disaster Displacement

Link to code

Click here

Date: July 2025

Authors/Creators/ Team Members: Ther W. Aung, Ashwini R. Sehgal

Specific purpose of code: The purpose of this code is to analyze the questions regarding displacement due to disasters in US Census Bureau’s Household Pulse Survey. The code is applied to a single dataset.

General Application: This code is an example of analyzing nationally-representative survey data using weights and replicate weights.

How does or could this code allow researchers to assess research questions related  to aging or life course?: This code could be used or applied to analyze nationally-representative survey data, which tend to have age as a variable and may either span the entire population or target a particular demographic. The Household Pulse Survey has participants aged 18 and above, with age top-coded at 88 (age is calculated by subtracting year of birth from the survey year).

Data sets used: 

Coding Language: Stata

Tools and Packages used: svyset, svy

Output(s): Dataset, tables, multivariate covariates of displacement and collinearity assessment

Spatial extent: US

Temporal extent: 2022-2023

Published papers that use this code: Aung, T. W., & Sehgal, A. R. (2025). Prevalence, Correlates, and Impacts of Displacement Because of Natural Disasters in the United States From 2022 to 2023. American Journal of Public Health, 115(1), 55–65. https://doi.org/10.2105/AJPH.2024.307854

Related Papers: Depression and Anxiety Symptoms in Adults Displaced by Natural Disasters

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Code linking the American Community Survey (ACS) microdata with the Spatial Hazards Events and Losses Database for the United States (SHELDUS)

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

Code linking the American Community Survey (ACS) microdata with the Spatial Hazards  Events and Losses Database for the United States (SHELDUS).

Authors: Deborah Balk, Kytt MacManus, Hieu Tran, Camilla Greene, Shemontee Chowdhury

Specific purpose of code: This  code links the American Community Survey (ACS) microdata with the Spatial Hazards  Events and Losses Database for the United States (SHELDUS).] 

Integration of Python programming with ArcGIS API, IPUMS API to pull Decennial Census data, create interactive maps, find insights about the changes in population and how badly the disabilities seniors’ indicators in Community Resilience Estimates (CRE) layer exposed to the Low Elevation Coastal Zone (LECZ) in Puerto Rico.

Link to code:  https://github.com/hieutrn1205/CACHED_estimate_social_vulnerability/blob/master/estimate_social_vulnerability.ipynb

General Application:

This is a lesson on linking Census data with LECZ Merit-DEM dataset on housing vacancies and population changes. In the lesson, we disseminate the regional and local data (county-level and tract level) on overlaying with the CRE layer to show how vulnerability of seniors who are currently living in the LECZ’s zones. The code will give the audience ability to estimate of many percentages’ social vulnerabilities people in the exposed zone also. The margin of error and statistical tests are 90 percent in CRE which gives us a solid statistically belief to the social vulnerabilities’ population.

How does or could this code allow researchers to assess research questions related  to aging or life course?:  This code could be used with the Decennial data to asses any 5 year age groups from under 5 to 85+ years of age, I had gathered the visualization on the 5 year-age groups in the pyramid chart for 2010 and 2020 to assess the mean of each Age/Sex group at admin level 0 (national).

Data sets used:

  • Population, socioeconomic, or health data: Decennial Census Data on Age/Sex, Occupancy Status (Vacancy), Social Vulnerabilities in Community Resilience Estimates (CRE).
  • Climate, weather, disaster or environment data: Low Elevation Coastal Zone (LECZ)

• Are all the data publicly available or are some restricted-access [choose one]:

• Which are restricted access? : Community Resilience Estimates (CRE). I have talked with the personnels at U.S Census regarding the restrictions. Please refer the users to the first question Community Resilience Estimates Frequently Asked Questions and “if the person is a potential researcher, they are able to access the data with an approved project through the Federal Statistical Research Data Centers. If they would like to go that route, please share my email address (sehsd.cre@census.gov). I’d be happy to get them started, or they can go here: Federal Statistical Research Data Centers.”

Coding Language:  Python

Tools and Packages used:  Pandas, Numpy, Matplotlib, ipumspy, arcgis

Output(s):  Maps, Graphs

Spatial extent: Puerto Rico

Temporal extent: 2010-2020

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Code linking SHELDUS with ACS data

Written by Julia Shipman on . Posted in Code & Data, Other, SHELDUS + ACS.

Code linking SHELDUS with ACS data 

Link to code

Click here

Date: June 2025

Authors/Creators/ Team Members:  Jenna Tipaldo, Deborah Balk, Dylan Connor, Helen Wilson Burns, Lori Hunter, Melanie Gall 

Specific purpose of code: This code links the American Community Survey (ACS) microdata with the Spatial Hazards Events and Losses Database for the United States (SHELDUS). 

General Application: This is an example of linking county-level data on disaster events with PUMA-level micro-data (that is, data on individuals or households) on sociodemographic characteristics. The code could be used with other PUMA-level survey data, or with other county-level environmental data. 

How does or could this code allow researchers to assess research questions related  to aging or life course?: This code could be used with the ACS data to assess any single years of age, so any combination of age group or subsets by age is possible. 

Data sets used: 

  • Population, socioeconomic, or health data: American Community Survey (ACS) from the U.S. Census Bureau
  • Climate, weather, disaster or environment data: Spatial Hazard Events and Losses Database for the United States (SHELDUS)

Are all the data publicly available or are some restricted-access? ACS is publicly available and free to download. SHELDUS is publicly available with data for South Carolina free to download (access to entire SHELDUS dataset requires an agreement with SHELDUS, potentially with compensation).  

Links to data: SHELDUS, ACS 

Coding Language:  R 

Tools and Packages used:  tidyverse, tigris, sf, gganimate, RColorBrewer 

Output(s): Dataset, Maps (static and animated), Graphs 

Spatial extent: South Carolina (Example), United States (application) 

Temporal extent: 2012-2022 

Published papers that use this code: Balk, D., Connor, D., Gall, M., Hunter, L., Tipaldo, J.F., Wilson Burns, H. *authors listed alphabetically. (2025, April) The Changing Demography of Disaster Impact in the US, 2012-2022, Paper presented at the Annual Meeting of the Population Association of America Meeting, April 2025, Washington, DC

https://submissions.mirasmart.com/Verify/PAA2025/Submission/Temp/rad2p2wgqwq.pdf  

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Heat, Disability in Older Adults, and Care

Written by Tania Ochoa on . Posted in Code & Data, Heat, Thematic Areas.

Heat, Disability in Older Adults, and Care

Investigators:

Emerson Baptista, Cecilia Conde, Landy Sánchez (PI)

Funding:

NIA R61AG086854 (CACHE). Partial funding from UN-WOMEN for Care Data

Data sources:

    Project Summary:

    Older adults are particularly vulnerable to heat due to age-related physiological changes and chronic health conditions, which can impair the body’s ability to regulate temperature and maintain homeostasis during heat exposure. This increases the risk of heat-related health issues such as heatstroke and cardiovascular events. Less is known about how exposure to extreme heat can exacerbate disabilities in older adults. On the one hand, exposure to heat increases the difficulty older adults in performing daily activities such as handling transportation, feeding themselves, managing finances, and maintaining the household (Ji et al 2024). On the other hand, extreme heat events could negatively affects physical performance in older adults, impacting gait speed, chair-rise performance, and balance, as well as increasing risk of cardiovascular disease. In addition, comorbilities, particular medications and reduced heat-regulating mechanisms can decrease their ability to adapt to higher temperatures.

    Moreover, while social networks and available care can reduce the heat-related dissability progression, people with disabilities tend to have lower social capital than individuas withouth dissabilities. In Mexico, the vast majority of older adult care is provided by family members, but that is changing due to changes in the population structure and family arregements. Those changes are making formal care more relevant for older adults than in the past. 

    This project seek to understand the exposure of older adults to heat extreme in Mexico and assess their vulnerability by disability status and care availability. 

    1. We evaluate the extreme heat risk for older adults by disability condition, considering sex and age
    2. We estimate future changes in exposure by projecting disabilities under two distinct climate scanerios (2020-2050)
    3. We analyze present and future care needs for older adults under different scenarios of climate and disability projections

    On the creation of the weather variables:

    For present conditions we construct a daily and hourly record of mean and maximum of Universal Thermal Comfort Index (UTCI) and temperature and humidity records by municipality in Mexico (between 1980 and 2024). For future conditions, we estimate expected temperatures under two different climate scenarios (RCP 8.5 and 6.5). Those weather and climate variables are integrated with disability estimations by municipality by type, cause and age.

    Outputs:

    • Estimations of older adult risks to extreme heat events by disabilities condition in Mexico
    • Adjusted measures of thermal comfort indexes
    • Suggested adaptation measures in the care system for older adults given climate scenarios

    Products:

      • Two papers, two conferences presentation, two teaching materials and one policy summary

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