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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

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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

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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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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

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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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What Am I Watching? Understanding the Health Impacts of Wildfire Smoke Exposure

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

What am I Watching? Understanding the health impacts of wildfire smoke exposure

Link to video recording

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 Elizabeth Sorensen Montoya, Ph.D. University of Colorado Boulder  www.elizabethsorensenmontoya.com

If you live in the Eastern U.S. or the Midwest, you’ve probably spent the last few days breathing in that now-familiar sign of summer: Canadian wildfire smoke. But this isn’t just a North American problem. In recent years, wildfires have become more frequent, more intense, and harder to suppress. Because wildfire smoke can travel long distances, the health impacts often reach far beyond the burn zone.  

So, what does all this smoke actually mean for our health? 
 
As part of the Climate and Health Research Coordinating Center’s (CAFÉ RCC) State of the Science webinar series, Dr. Michael Brauer, professor at the School of Population and Public Health at the University of British Columbia, delivered an excellent talk exploring just that. You can watch the full seminar here. 
 
Below is a quick, high-level overview of some key takeaways from the presentation: 

  • The “new normal”: Wildfires are becoming more frequent, larger, and harder to suppress. Not only that, but they’ve begun to extend beyond what we have traditionally thought of as “fire season”, with smoke events occurring well outside traditional summer months. 
  • Health impacts: The talk covered a wide range of health outcomes linked to wildfire smoke exposure, from respiratory and cardiovascular impacts to emerging evidence on effects like dementia, reduced cognitive performance, and ambulance dispatches. A particularly interesting piece of the talk focused on recent research into the delayed impacts of wildfire smoke. For example, one study by Landguth and colleagues shows that smoke exposure during the summer can increase the risk of flu during the following winter.  
  • Looking ahead: Dr. Brauer talked about how wildfire smoke could change in the years to come, not only as a result of climate change but also our response to it. 
  • What can be done? Dr. Brauer ended the talk by outlining several approaches for reducing exposure, from individual-level interventions to community-level planning and preemptive actions.  

The seminar is well worth watching in full. Dr. Brauer does a fantastic job of weaving together scientific evidence, real-world case studies, and forward-looking perspectives.  

As wildfires continue to affect communities around the world, it’s increasingly important to understand the health risks and how we might reduce them. Dr. Brauer’s talk is a great starting point for those curious about wildfire smoke and health and a valuable resource for those already working in that field.    

 

 

References: 

Brauer, M. (2024) Understanding the health impacts of wildfire smoke exposure. Presented as part of the CAFÉ RCC State of the Science webinar series, 15 May. Available at: https://www.youtube.com/watch?v=2CViMQ-Xjuo 

Landguth, E.L., Holden, Z.A., Graham, J., Stark, B., Mokhtari, E.B., Kaleczyc, E., Anderson, S., Urbanski, S., Jolly, M., Semmens, E.O. and Warren, D.A., 2020. The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the USA. Environment international, 139, p.105668. 

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What Am I Reading: Frameworks on Climate, Health, and Aging

Written by Tania Ochoa on . Posted in Blog, Other, Thematic Areas, What Am I Reading?.

What am I reading? Frameworks on Climate, Health, and Aging

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

Frameworks, tools that can be used to inform research and develop interventions, can help conceptualize scientific problems and visualize complex relationships between factors that influence health. In the past several years, several new frameworks have been developed regarding the interactions between environmental factors such as climate change and health, paying special attention to the impacts on an aging population. Malecki et al. (2022) and Tipaldo, Balk & Hunter (2024) present more general frameworks regarding how environmental, social, and biological factors interact to influence the health of aging populations focusing on disease and adverse health outcomes, while Prina et al. (2024) also present a general framework based on these factors but expanding on a prior framework of healthy aging. Zuelsdorff & Limaye (2024) focus on how similar factors more specifically impact the risk and health burden of Alzheimer’s disease and related dementias (ADRD).  

The frameworks developed in Zuelsdorff & Limaye (2024) and Tipaldo, Balk, and Hunter (2024) illustrate potential moderating and mediating factors on the pathways from climate-sensitive exposures to health outcomes, with the former honing in on ADRD and the latter considering various conditions and outcomes. Similarly, yet from a different perspective, Prina et al. (2024) consider the many factors and interactions of said factors that contribute to healthy aging, emphasizing positive outcomes for aging individuals facing climate hazards. Taking a different approach, Malecki et al. (2022) build their framework on a traditional toxicological dose-response model to show the development of disease and expand it to show factors that influence vulnerability at different stages. For example, environmental and social factors are theorized to influence hazard and exposure levels while individual-level biological and social factors influence biologically effective dose and effects.  

Interactions and intersections between the various social and environmental factors are explicitly shown in the frameworks of both Zuelsdorff & Limaye (2024) and Tipaldo, Balk, and Hunter (2024), and are also discussed by Malecki et al. (2022). Notably, Zuelsdorff & Limaye (2024) also show policy responses such as adaptation and mitigation in the form of a feedback loop from the built environment to impact climate change, which in turn impacts factors of the built and social environments, interpersonal and individual processes, and biomedical factors. 

While emphasizing the need to better understand mechanisms, these four recent framework papers identify myriad research gaps and directions for future research. Tipaldo, Balk, and Hunter (2024) detail recommended next steps for data collection efforts such as a need for longitudinal studies and studies that investigate cumulative impacts and factors across the life course. This suggestion is echoed by both Prina et al. (2024) and Malecki et al. (2022), which also note how big “–omics” data can be harnessed to enhance understanding. With a scope limited to the U.S., Malecki et al. (2022) call for more holistic research in various populations, and both Prina et al. (2024) and Tipaldo, Balk, and Hunter (2024) extend this by citing a need for more research in low-and-middle income and Global South settings. Together, these papers suggest a need for collaborative research across disciplines and regions to address the complex and pressing challenges faced by older adults due to climate hazards. This speaks to the goals of the 2022 NIH Climate Change and Health Initiative and Strategic Framework, which calls for the development of transdisciplinary efforts at the climate-health intersection (Woychik et al., 2022). 

Importantly, all of these framework papers on climate/environment and aging-related health emphasize the need for research to help inform action. Prina et al. (2024) summarizes from a review of the literature potential strategies for mitigation and adaptation. Zuelsdorff & Limaye (2024) and Malecki et al. (2022) highlight a need to translate research findings into policy and actionable interventions for both practitioners and community members. Tipaldo, Balk, and Hunter (2024) note that systematic studies can help inform effective intervention strategies, building on the need for systematic studies on various climate hazards that is noted by the other framework papers. 

References 

  • Malecki KMC, Andersen JK, Geller AM, et al. Integrating Environment and Aging Research: Opportunities for Synergy and Acceleration. Front Aging Neurosci. 2022;14:824921. doi:10.3389/fnagi.2022.824921 
  • Prina M, Khan N, Akhter Khan S, et al. Climate change and healthy ageing: An assessment of the impact of climate hazards on older people. J Glob Health. 2024;14:04101. doi:10.7189/jogh.14.04101 
  • Tipaldo JF, Balk D, Hunter LM. A framework for ageing and health vulnerabilities in a changing climate. Nat Clim Chang. 2024;14(11):1125-1135. doi:10.1038/s41558-024-02156-2 
  • Woychik RP, Bianchi DW, Gibbons GH, et al. The NIH Climate Change and Health Initiative and Strategic Framework: addressing the threat of climate change to health. The Lancet. 2022;400(10366):1831-1833. doi:10.1016/S0140-6736(22)02163-8 
  • Zuelsdorff M, Limaye VS. A Framework for Assessing the Effects of Climate Change on Dementia Risk and Burden. Gaugler JE, ed. The Gerontologist. 2024;64(3):gnad082. doi:10.1093/geront/gnad082 

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US County-to-County Migration Flow Data, 1990-2010

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

US County-to-County Migration Flow Data, 1990-2010

Background:

The Internal Revenue Service’s (IRS) county-to-county migration data are an incredible resource for understanding migration in the United States. Produced annually since 1990 in conjunction with the US Census Bureau, the IRS migration data represent 95 to 98 percent of the tax filing universe and their dependents, making the IRS migration data one of the largest sources of migration data. However, any analysis using the IRS migration data must process at least seven legacy formats of these public data across more than 2000 data files — a serious burden for migration scholars.

Objective:

To produce a single, flat data file containing complete county-to-county IRS migration flow data and to make the computer code used to process the migration data available.

Methods:

This paper uses R to process more than 2,000 IRS migration files into a single, flat data file for use in migration research.

Contribution:

To encourage and facilitate the use of this data, we provide a single, standardized, flat data file containing county-to-county migration flows for the period 1990-2010 and provide the full R script to download, process, and flatten the IRS migration data.

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