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Code linking NCHS mortality data with GFD flood event data

Written by Julia Shipman on . Posted in Code & Data, Uncategorized.

Code linking NCHS mortality data with GFD flood event data

Link to code

Click here

Authors/Creators/ Team Members: Victoria D. Lynch, Jonathan A. Sullivan, Aaron B. Flores, Xicheng Xie, Sarika Aggarwal, Rachel C. Nethery, Marianthi-Anna Kioumourtzoglou, Anne E. Nigra, and Robbie M. Parks

Specific purpose of code: This code links National Center for Health Statistics (NCHS) mortality data with Global Flood Database (GFD) flood event data from 2001 – 2018 by US county. We used the NCHS data to identify monthly total and cause-specific deaths by age group, sex, and county and used Global Human Settlement Layer (GHSL) population data to calculate county-level flood exposure and mortality rates. We used a Bayesian formulation of the conditional quasi-Poisson model to analyze the county-level association between the number of flood events per month and monthly death rates, accounting for overdispersion in the mortality data. The conditional approach examines differences within matched strata (here, county-months) like a case-crossover study design, which removes confounding bias due to factors that vary across strata. Bayesian inference enables the ‘borrowing of information’ across county units and for the full distributional estimation of the parameters of interest.
All-cause and cause-specific mortality associated with flood events is likely differential by flood cause and severity; therefore, we conducted analyses separately by all-cause and cause-specific mortality (cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases), flood cause (all floods, heavy rain, tropical cyclone, snowmelt, and ice jam or dam break), and flood severity (mild, moderate, severe, and very severe). Because very severe flood events were most strongly associated with increased mortality across all flood causes and mortality groups, we further analyzed associations stratified by age group (0-64 and 65+ years) and sex (female and male) for very severe floods only.

General Application: This code links county-level flood exposure, categorized by flood type, with county-level mortality rates for the six primary causes of death in the US: cancers, cardiovascular disease, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases. The code could be used with any county-level health outcome and, with modification, with other county-level environmental exposures. The code specifically categorized exposure by flood type and severity, which would not apply to other exposures.

How does or could this code allow researchers to assess research questions related  to aging or life course?: The code is written to assess the association between flood exposure and mortality by age category; in our paper, we specifically stratified by age category (0-64 years old and 65+ years old) to examine flood exposure-related mortality among older adults. The NCHS data include individual-level age at death and would enable analyses with any subset of age groups.

Data sets used: 

  • Population, socioeconomic, or health data:
    National Center for Health Statistics (NCHS) mortality data; Global Human Settlement Layer (GHSL) population exposure.
  • Climate, weather, disaster or environment data:
    Global Flood Database (GFD) flood event data; Dartmouth Flood Observatory (DFO) flood classification data; Parameter-elevation Regression on Independent Slopes Model (PRISM) temperature data.

Are all the data publicly available or are some restricted-access? Data on flood exposure are available without restrictions for individual flooding events. Temperature data and population data are also publicly available.

NCHS mortality data are restricted. To access the NCHS mortality data, applicants must submit a project review form:(https://www.cdc.gov/nchs/data/nvss/nchs-research-review-application.pdf) to nvssrestricteddata@cdc.gov and allow four to six weeks for processing.

Links to data:

  1. National Center for Health Statistics, Mortality Data
  2. Global Flood Database and Global Human Settlement Layer (downloaded together)
  3. Parameter-elevation Regression on Independent Slopes Mode

Coding Language:  R 

Tools and Packages used:

R: acs, BiocManager, dlnm, dplyr, ecm, Epi, fiftystater, foreign, fst, ggpubr, ggplot2, graph, graticule, haven, here, janitor, lubridate, mapproj, maptools, mapview, MetBrewer, pipeR, raster, RColorBrewer, readxl, rgdal, rgeos, rnaturalearth, rnaturalearthdata, scales, sf, sp, sqldf, survival, splines, table1, tidycensus, tidyverse, totalcensus, usmap, zipcodeR, zoo, INLA, Rgraphviz, fmesher

Output(s): Exploratory data analysis of flood and mortality data (maps, figures, tables), and output of statistical analysis (figures, tables)

Spatial extent: United States

Temporal extent: 2001-2018

Published papers that use this code: Lynch, Victoria D., et al. “Large floods drive changes in cause-specific mortality in the United States.” Nature Medicine 31.2 (2025): 663-671. doi: https://doi.org/10.1038/s41591-024-03358-z

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What am I Watching? CAFE University: Considerations for temperature in public health studies Auto Draft

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

What am I Watching? CAFE University: Considerations for temperature in public health studies 

Link to webinar

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 and Deborah Balk, CUNY Institute for Demographic Research

Researchers interested in studying the impacts of temperature on health face decisions on how to operationalize and measure exposures. This webinar “CAFE University: Considerations for temperature in public health studies” by Lauren Mock and Shreya Nalluri explores the myriad choices required when using temperature data to study health outcomes, from choice of metric and dataset to the study design.  

The webinar includes an overview of various temperature metrics including land surface temperature (LST), air temperature (AT), heat index, wet bulb globe temperature (WGBT), and universal thermal comfort index (UTCI). A fuller discussion  about choice of metrics can be found in this resource from the World Resources Institute (Engel et al. 2025), also referenced in the webinar. (It is useful to note that these are all measures of outdoor temperature. Measurement of indoor vs outdoor exposure is explored in another post on air pollution). 

Each metric is more suited for specific purposes and contexts than for others, and each has drawbacks. For example, air temperature is widely available but doesn’t include factors that influence how humans perceive temperature, such as humidity. Both WGBT and UTCI do consider humidity.  

Other dataset considerations include the availability of multiple metrics such as daily maximums, minimums, or averages. (All of the metrics discussed in the webinar provide daily temperature resolution, but some sensors have more fine-grain temporal data, such as hourly. See Table below.)  

Additionally, researchers may choose to use raw temperature values versus standardized values such as percentiles, which can account for acclimatization.  

Source: CAFE University 

Another aspect of operationalizing exposure is considering the timing of exposure relative to an outcome of interest – the webinar gives an example of defining the exposure period as the day of, day prior to, or three days prior to an event.  

Even so, a recent paper (Cruz et al., 2025) proposes a framing of heat as a chronic phenomenon and argues that chronic exposures pose different risks that are not captured by existing research that evaluates the health impacts of acute exposures to extreme heat. 

The webinar also explores the use of several gridded temperature datasets, including ERA5, gridMET, and PRISM, and how to link these with health data (e.g., calculating zonal statistics and population-weighting).  

The webinar provides strengths, limitations, and use cases for each dataset, which vary in their spatial and temporal resolution, as well as the measurements that can be calculated using each dataset. For example, gridMET and PRISM have high-resolution daily averages but do not have the additional measurement components to calculate UTCI or WBGT. ERA5 has higher resolution temporally with hourly data but is coarser spatially and provides global coverage with components to calculate more metrics, including UTCI and WBGT. Though, the ERA5-Land data is not well-suited for coastal studies.  

In general, gridded data are also limited by the data underlying them, a point that was touched on but not explored in depth in the webinar.  It was noted that “gridded products inherit uncertainty from models and stations,” and these limitations are important to keep in mind when choosing a dataset and measures.   

Regardless of which dataset is used, another consideration is how the resolution of the temperature data compares to the resolution of the health or socioeconomic data or outcome of interest. Using spatial resolution as an example, when a researcher wants to integrate temperature data to an administrative unit (say, county or municipality) associated with health records or survey respondents’ residence, they may care whether the temperature grid data in question is more or less coarse than those administrative units. At mismatched spatial scales, there may be implications for errors in measurement or attribution when averaging temperature into the administrative units. As an example, the figure below shows gridMET and ERA5 data for the same day (July 31, 2025) in the Philadelphia, PA and surrounding NJ area.  Averaging across finer resolution data would likely be more suitable for finer-scale (smaller) units such as administrative units that represent urban areas and seen in the center of the figure below.  

Finer scale data may also be more suitable when within administrative unit variation in temperature is high, or the analyst wants to capture that variation. For coarser administrative units, if additional information such as where population is concentrated is not available, use of the coarser temperature data may be as suitable as finer-grained data, noting of course that any sub-unit variation over these administrative areas is simply unobserved (Porter & Howell, 2016) or was reallocated with ancillary data (Zoraghein and Leyk, 2018, Uhl et al. 2018).  

In the context of gridded population data, Leyk et al. (2019) provide a review of products available and a guide for understanding underlying data used in each gridded product, including the population data (spatial coarseness and how change over time is handled), use of ancillary data (such as settlements, roads or land cover), and methods used to allocate population within a defined grid “cell size”. All of these impact the suitability of use in particular applications. Fitness-for-use guidelines for these global population gridded dataset may depend on spatial and temporal resolution, scale and setting (rural vs urban), and mechanism of interest as noted in Leyk et al. (2019) and the same principles can act as a lens when evaluating which climate data set is most suitable for a given analysis.  

Materials from a CACHE demonstration project “Heat, Disability in older adults and Care” from El Colegio de Mexico provides code and guidance for calculating UTCI using ERA5 data, producing municipality-level estimates of number of severe heat days:   https://agingclimatehealth.org/severe-heat-days-using-the-universal-thermal-comfort-index/  

 

References 

Cruz, M., Mach, K. J., Turek-Hankins, L. L., Ashad-Bishop, K. C., Bailey, Z. D., Evans, S. D., Fanning, A., Fernandez-Burgos, M., Gilbert, J., Howard, B., Mahabir, M., Marturano, J., Murphy, L. N., Muse, N., Pérodin, J., & Clement, A. C. (2025). Where heat does not come in waves: A framework for understanding and managing chronic heat. Environmental Research: Climate, 4(2), 023002. https://doi.org/10.1088/2752-5295/adc827 

 

Engel, R. E. Mackres, M. Palmieri and E. Anzilotti (2025). Beyond the Thermometer: 5 Heat Metrics That Drive Better Decision-Making, World Resources Institute Insights March 17, 2025 https://www.wri.org/insights/beyond-thermometer-measuring-heat  

 

Leyk, S., Gaughan, A. E., Adamo, S. B., De Sherbinin, A., Balk, D., Freire, S., Rose, A., Stevens, F. R., Blankespoor, B., Frye, C., Comenetz, J., Sorichetta, A., MacManus, K., Pistolesi, L., Levy, M., Tatem, A. J., & Pesaresi, M. (2019). The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use. Earth System Science Data, 11(3), 1385–1409. https://doi.org/10.5194/essd-11-1385-2019 

 

Porter, J. R., & Howell, F. M. (2016). A spatial decomposition of county population growth in the United States: Population redistribution in the rural-to-urban continuum, 1980–2010. In Recapturing space: New middle-range theory in spatial demography (pp. 175-198). Cham: Springer International Publishing. 

Uhl, J. H., Zoraghein, H., Leyk, S., Balk, D., Corbane, C., Syrris, V., & Florczyk, A. J. (2020). Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective. International Journal of Digital Earth, 13(1), 22–44. https://doi.org/10.1080/17538947.2018.1550120  

 

Zoraghein, H., & Leyk, S. (2018). Enhancing areal interpolation frameworks through dasymetric refinement to create consistent population estimates across censuses. International Journal of Geographical Information Science, 32(10), 1948–1976. https://doi.org/10.1080/13658816.2018.1472267  

 

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What am I reading: Sunny-day Floods and their Health Risks  

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

What am I reading? Sunny-day Floods and their Health Risks

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 Kathryn Foster, Cornell University 

As sea-level rise worsens the impacts of hurricanes and storm surges, it also leads to more frequent tidal and seasonal floods in coastal areas, commonly referred to as sunny-day, blue sky, or nuisance flooding. The annual number of days with sunny-day flooding has more than doubled since 2000, with projections that it will triple by 2050, averaging 45-85 days a year nationwide (NOAA, 2025). Current research, such as Mueller et al. (2024), is beginning to document the many impacts of sunny-day flooding and other types of flooding on residential health.  

Mortality: Research shows that in Florida, a 20-mm (0.79 in.) increase in tidal flooding depth raises mortality rates by 0.46% to 0.60% among adults 65 and older. Sea-level rise could contribute to an additional 130 elderly deaths annually in Florida relative to 2019 (Mueller et al. 2024).  

Similarly, longer-lasting floods have greater impacts on mortality than other types of floods, such as flash floods (Lynch et al., 2025). An increase in frequency and severity of seasonal tidal flooding could increase the risk of mortality by blocking roads to medical services, such as regular doctor’s appointments, pharmacies, and hospitals. Other research supports these conclusions, finding that with anticipated intensified flooding, elderly populations will become more vulnerable to morbidity and mortality risks, largely due to mobility constraints among aging adults (Paavola, 2017; Hu et al., 2018; Sheahan et al., 2025). The mortality effects of flooding primarily affect those who require at least 8.85 minutes to reach the nearest hospital (Mueller et al. 2024).  

Infectious Diseases: Seasonal tidal flooding may also increase the incidence of waterborne and infectious diseases in the United States. As flooding increases, communities are exposed to standing water around their homes for longer periods. Seasonal floods are strongly associated with increased hospitalizations for Legionnaire’s disease, a type of pneumonia caused by Legionella bacteria that grows in warm, moist climates (Lynch and Shaman 2022). Enteric infections may also arise from drinking water contamination and sewerage disruption (Carr et al., 2024; Wright et al., 2018), and mosquito-borne diseases and infections may occur from exposure to polluted flood waters (Wright et al., 2018).  

Previous research on coastal storms finds that these events are associated with the spread of certain infectious diseases, such as E. Coli, Legionnaires’, Cryptosporidiosis, Paratyphoid fever, and Dengue in certain areas (Lynch and Shaman 2023; Zheng et al., 2017). These studies similarly posit that the spread of infectious disease will increase alongside predicted increases in coastal storms due to climate change. Although not yet explicitly studied, researchers hypothesize that the spread of infectious diseases associated with coastal storms will also be linked to seasonal tidal flooding (Lynch and Shaman 2022). This provides ground for future research to examine how increasing tidal floods relate to the rise in infectious diseases, like that of coastal storms.   

Other Impacts: Furthermore, other hazards, such as snakebites and wound infections, may occur with tidal flooding and prolonged inundation of residential areas (Wright et al., 2018). Flooding increases residents’ exposure to venomous snakes, as snakes try to enter homes to find dry land or as water snakes are found in inundated streets, leading to a potential health hazard if bitten (Ochoa et al., 2018). Furthermore, as residents document having to walk through flooded streets during seasonal tidal flooding, wounds may become infected by the floodwater, or unseen hazards in the water may cause wounds (Wright et al., 2018). While these hazards are beginning to be documented, little research has examined their relationship to tidal flooding; future research could fill this gap by examining the association between reported snake bites and wound infections and tidal flooding.  

From Research to Policy: These studies highlight that the costs of climate change go beyond heat impacts, emphasizing that the increased frequency and severity of seasonal tidal flooding may directly affect residents’ health. Mueller and colleagues (2024) suggest that communities design programs to improve transportation options for the elderly during the sunny-day flooding season. Furthermore, other researchers suggest that public health initiatives should inform clinicians and residents alike about the health risks of seasonal flooding (Lynch and Shaman, 2022). Alongside public-health initiatives, more effective and equitable preparations for flood risk should be put in place to better mitigate seasonal flooding-related health risks, especially for elderly adults in these communities (Lynch et al., 2025). Such initiatives could include increasing flood-risk awareness by hosting community classes on flood preparation, stockpiling essential medical supplies (Yodsubana and Nuntaboot, 2021), coordinating resource and information sharing with government agencies, healthcare providers, and community groups for elderly residents (Madani Hosseini et al., 2024), and constructing seawalls and elevating roads to prevent road closures (Mueller et al., 2024).   

References:

Hu, P., Zhang, Q., Shi, P., Chen, B., & Fang, J. (2018). Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors. Science of the Total Environment, 643, 171-182. 

Lynch, V. D., & Shaman, J. (2022). The effect of seasonal and extreme floods on hospitalizations for Legionnaires’ disease in the United States, 2000–2011. BMC Infectious Diseases, 22(1), 550. 

Lynch, V. D., & Shaman, J. (2023). Waterborne infectious diseases associated with exposure to tropical cyclonic storms, United States, 1996–2018. Emerging infectious diseases, 29(8), 1548. 

Lynch, V. D., Sullivan, J. A., Flores, A. B., Xie, X., Aggarwal, S., Nethery, R. C., … & Parks, R. M. (2025). Large floods drive changes in cause-specific mortality in the United States. Nature Medicine, 31(2), 663-671.  

Madani Hosseini, M., Zargoush, M., & Ghazalbash, S. (2024). Climate crisis risks to elderly health: strategies for effective promotion and response. Health Promotion International, 39(2), daae031. 

Mahmoudi, S., Moftakhari, H., Muñoz, D. F., Radfar, S., Sweet, W., & Moradkhani, H. (2025). Escalating high tide flooding along the Atlantic and Gulf Coast of the United States due to sea level rise. Earth’s Future, 13(9), e2024EF005328. 

Mueller, V., Hauer, M., & Sheriff, G. (2024). Sunny-day flooding and mortality risk in coastal Florida. Demography, 61(1), 209-230. 

NOAA Office for Coastal Management High tide flooding.. (n.d.). https://coast.noaa.gov/states/fast-facts/recurrent-tidal-flooding.html#:~:text=Here%20are%20some%20statistics%20on%20high%20tide,inches%20(21%20to%2024%20centimeters)%20since%201880  

Paavola, J. (2017). Health impacts of climate change and health and social inequalities in the UK. Environmental Health, 16 (Suppl 1), 113. 

Wright, L. D., D’Elia, C. F., & Nichols, C. R. (2018). Impacts of coastal waters and flooding on human health. In Tomorrow’s Coasts: Complex and Impermanent (pp. 151-166). Cham: Springer International Publishing. 

Yodsuban, P., & Nuntaboot, K. (2021). Community-based flood disaster management for older adults in southern of Thailand: A qualitative study. International journal of nursing sciences, 8(4), 409-417. 

Zheng, J., Han, W., Jiang, B., Ma, W., & Zhang, Y. (2017). Infectious diseases and tropical cyclones in Southeast China. International journal of environmental research and public health, 14(5), 494. 

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Processing NVDI and VIIRS vegetation data for use in population health research

Written by Julia Shipman on . Posted in Code & Data, Uncategorized.

Processing NVDI and VIIRS vegetation data for use in population health research 

Link to code

Click here

Prepared by: Alex Mikulas, PhD, CACHE postdoctoral associate 

Date: March 16, 2026 

Original Authors: 

Finn Roberts, IPUMS Senior Data Analyst 

Rebecca Luttinen, IPUMS Global Health Data Analyst 

Devon Kristiansen, IPUMS Global Health Research Manager 

Jude Mikal, Senior Research Fellow, University of Minnesota College of Pharmacy 

Specific purpose of code:  The below code resources offer a comprehensive outline for downloading, processing, aggregating, and integrating global vegetation coverage data for use in demographic and health research. Vegetation data come from the Normalized Difference Vegetation Index (NVDI), the Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODUS).  

Ultimately, these resources allow users to aggregate environmental data into spatially relevant scales and integrate it into a variety of social and health data sources to better measure environmental context or exposure.  

The IPUMS DHS Spatial Analysis and Health Research Hub has numerous resources on using environmental data in health research. While many resources in the hub are used with DHS data integration, the data, code, and analysis resources can be altered for data integration into any spatially identified aging and health dataset.  

Link to code:  

General Application: This code and associated resources allow researchers to build a vegetation coverage dataset that can be integrated into any individual or aggregate dataset that has temporal and spatial specificity. The data extend from 1981 to current, with 10 to 20-day increments and up to 20-meter raster resolution. 

How does or could this code allow researchers to assess research questions related to aging or life course?: This code can be used to create environmental context and exposure to greenspace and vegetation variables that can be used cross-sectionally or longitudinally, and at spatially detailed scales. It can be integrated into health surveys to provide environmental context, aggregated data to identify locations with high concentrations of aging adults and changing vegetation or greenspace, etc. In longitudinal datasets, researchers could chart an individual’s longitudinal exposure to vegetation and other relevant environmental features over the life course.  

Data sets used:  

Publicly available climate and weather data.  

Links to data (also repeated in code examples):  

Coding Language: R 

Tools and Packages used: terra, sf, dplyr, ggplot2, ggspatial, patchwork, lubridate (likely others) 

Output(s): datasets, maps 

Spatial extent: Global dataset, raster data at 20 – 250-meter resolution 

Temporal extent: 1981 to current; 10-20 day increments 

Published papers that use this code:  

Moisa, M., Roba, Z., Purohit, S., Deribew, K., & Gemeda, D. (2025). Evaluating the impact of land use and land cover change on soil moisture variability using GIS and remote sensing technology in southwestern Ethiopia. Environmental Monitoring and Assessment, 197. https://doi.org/10.1007/s10661-025-14301-1 

Grace, K., Kristiansen, D., Boyle, E. H., & Luetke, M. (2023). Investigating Seasonal Agriculture, Contraceptive Use, and Pregnancy in Burkina Faso. The Professional Geographer. https://www.tandfonline.com/doi/full/10.1080/00330124.2023.2199316 

 

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Resources and Data from the IPUMS DHS Spatial Analysis and Health Research Hub 

Written by Julia Shipman on . Posted in Code & Data, Uncategorized.

Resources and Data from the IPUMS DHS Spatial Analysis and Health Research Hub 

Link to data

Click here

Prepared by: Alex Mikulas, PhD, CACHE postdoctoral associate 

Date: March 16, 2026 

Author: The IPUMS DHS Spatial Analysis and Health Research Hub is designed to be a resource for researchers who are familiar with IPUMS DHS population health survey data but new to weather, environment, and disaster research that uses spatial data sources. Such resources include conceptual frameworks for environment/health research, introductions to datasets, spatial data processing, and analysis code. The code and data resources in the hub use R scripting to demonstrate basic spatial data processing techniques for integrating numerous environmental and weather-related data with social and health data in the DHS.  

IPUMS Demographic and Health Surveys (IPUMS-DHS) is a database of thousands of consistently coded variables on the health and well-being of men, women, children, and births of randomly selected households in 42 African countries and 9 Asian countries. Data include records of all household members, effectively capturing social and demographic data across the life course and age groups for low- and middle-income countries.   

The guides and resources in the IPUMS-DHS Spatial Analysis and Health Research Hub are oriented toward data integration with IPUMS-DHS data. However, many scripts can be applied to any other social, health, and aging datasets that have geographic data identifiers. This includes datasets that have variables for administrative geographies (unique identifiers or spatial data polygons), respondent address or lat/long variables, or other gridded and raster datasets examining aging and health. 

To support such research, the IPUMS Global Health team received a 2023 supplemental grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, or NICHD (3R01HD069471-12S1). 

Data are available on: Hub resources include data integration walkthroughs, dataset explanations, spatial data processes, and more. Datasets referenced include CHIRPS, CHIRTS, NVDI, VIIRS, and more. See a sample of the numerous resources below: 

Citation: IPUMS. (2026, March 16). Supporting Research on Extreme Weather and Health. IPUMS DHS Spatial Analysis and Health Research Hub. https://tech.popdata.org/dhs-research-hub/about.html 

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Uncovering the Exposome: A Pilot Study of Aging and Environmental Exposure in Malawi 

Written by Julia Shipman on . Posted in Uncategorized.

Uncovering the Exposome: A Pilot Study of Aging and Environmental Exposure in Malawi 

Investigators:

Helene Purcell 

Funding:

CACHE Seed Funding 

Data sources:

  • Long-term individual-level panel data from the Malawi Longitudinal Study of Families and Health (MLSFH), with repeated cognitive and health measures spanning over 15 years;
  • Geocoded administrative and infrastructure data from the National Statistics Office (NSO) of Malawi.
  • Environmental and other Geospatial Information Systems (GIS) datasets from NOAA and other environmental data sources.

Measures:

  • Climate Measures: historic rainfall and drought data
  • Physical Environment: road access, sanitation and water sources
  • Social environment: family structure and social network data
  • Policy environment: exposure to fertilizer subsidies, access to Anti-retroviral therapy (ART) for HIV/AIDS
  • Community services: proximity to health facilities and schools
  • Life experiences: economic shocks, migration history, adverse childhood experiences (ACEs)
  • Longitudinal MLSFH socioeconomic and health data: cognitive assessments, epigenetic clock measurements, other mental and physical health/aging measurements(frailty, activities of daily living (ADLs), blood pressure, etc.)

Project Summary:

In recent years, the exposome, encompassing the totality of environmental, socioeconomic, and health-related exposures throughout one’s life,1 has emerged as a pivotal yet understudied dimension in the understanding of aging trajectories, longevity, and Alzheimer’s Disease and Alzheimer’s Disease-Related Dementias (AD/ADRD) risk, resilience, and disparities. This project addresses a critical gap in global aging research by extending exposome science to a low-income Sub-Saharan African population that faces high climate vulnerability, socioeconomic change, and health system constraints. The overarching goal is to build foundational infrastructure and analytic methods to study how cumulative, multi-domain exposures shape cognitive aging and Alzheimer’s Disease and Alzheimer’s Disease-Related Dementia (AD/ADRD) risk in this context.

This includes developing a modular, geospatially-coded exposome database in Malawi, which will link historical census, environmental/climate data, and other administrative data to longitudinal household data from the Malawi Longitudinal Study of Families and Health (MLSFH) that are precisely geo-coded for integration. By launching the development of this database, we can begin to evaluate the association between exposomal factors and the aging process, with a particular focus on cognitive decline and AD/ADRD, using the Harmonized Cognitive Assessment Protocol (HCAP) survey measures in the MLSFH, forthcoming epigenetic data, and other ADRD risk factors.

Outputs:

Phase 1: Data cleaning, geocoding, aggregation of NSO and MLSFH data; construct first version of exposome database with documentation

Phase 2: Link exposures to longitudinal cognition data and implement analytic strategies to evaluate cumulative and life course effects

Phase 3: Finalize modeling of cognitive decline and indicators for ADRD risk, draft manuscript, and prepare data products for dissemination

References:

[1] Christopher Paul Wild. The exposome: from concept to utility. International Journal of Epidemiology, 41(1):24–32, 01 2012. doi: 10.1093/ije/dyr236.

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What am I Reading? The Role of Housing for Health in an Era of Environmental Change

Written by Julia Shipman on . Posted in Uncategorized.

What am I Reading? The Role of Housing for Health in an Era of Environmental Change

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.

Post written by Alex Mikulas, PhD, University of Colorado-Boulder 

Introduction 

Housing is an important protective force and has important impacts on health and wellbeing , especially during rapid environmental change and disasters. Due to the health, economic, and social vulnerabilities of aging adults, housing may serve an especially important role in shaping their health and wellbeing. In both research and policy settings, it is critical to identify how several social and material features of housing shape health risks and outcomes for aging adults before, during, and after environmental change and disasters.  

From housing to health 

One framework outlined by health and housing scholars Carloyn Swope and Diana Hernández identifies four features of housing that impact health outcomes(2019). Housing conditions are the characteristics of a home itself that shape health and wellbeing directly – such as the protective power of air conditioning or the danger of mold exposure. Housing consistency is the long- and short-term experiences of housing that have major health implications over time, such as residential instability, crowding, or chronic homelessness. Housing costs are household financial aspects of housing. These include factors beyond affordability, such as how mortgage status or tenant protections may impact economic well-being after a job loss or weathering a medical emergency. Finally, the larger housing context – a home’s neighborhood characteristics and physical location – shapes access to health and emergency services, and important local amenities. 

Health scholars Jennifer Molinsky and Anne Forsyth introduce timing as another important consideration for how housing shapes climaterelated health risks (2023). Even before a disaster or acute environmental change, housing shapes who is at higher risk of exposure. During an event, housing influences how sensitive someone might be by providing varying degrees of shelter and protection from immediate danger.

Finally, after disaster, housing shapes the recovery and resiliency of residents, serving as an economic asset, or as a financial liability. In an era of increasing environmental change and disasters, a comprehensive conceptual approach to housing – its layered dimensions and their timing – is necessary for addressing the health needs of aging adults. 

Aging adults, housing, and health before, during, and after disaster 

The three examples below use these frameworks to illustrate how different features of housing influence health and wellbeing, particularly for aging adults.  

The housing market shapes the risk of exposure to environmental change and disasters before they occur 

The housing market is a primary force in shaping vulnerability to future disasters and environmental change by placing people into more or less risky locations. Take renters and rental markets, for example. In urban landscapes, affordable rental housing options are more likely to be located in high climate-risk areas of cities, and this pattern is expected to increase (Buchanan et al. 2020; JCHS 2022). Because lower income renters have fewer options for affordable housing in low-risk locations and limited economic resources to relocate, the limits of the rental market structure low-income and other disadvantaged populations in higher-risk areas.  

For homeowners, another pattern is emerging. Due to social change and emerging risk disclosure policies, home buyers have a growing awareness of climate risk and are altering their mobility and purchasing decisions. Home prices and prized residential locations are now partly determined by their resilience to disasters and environmental risks in a process called climate gentrification (Thompson et al. 2023).  At the same time, high-risk regions are among the fastest growing on the United States, driven in large part by retirees moving to warmer areas (Schuetz 2024). More broadly, some researchers believe that many homes are overvalued due to the potential risk of climate-related market changes (Gourevitch et al. 2023; Gourevitch and Kousky 2025). Federal mortgage buyout programs exist to help residents relocate from high-risk areas, but the programs are unevenly applied across states and social groups (Shi et al. 2022). More research is needed to determine if patterns of weather-related migration will begin to place “climate handcuffs” on aging adults wishing to sell homes in high-risk or less desirable places. 

Finally, as discussed in a CACHE post by Jenna Tipaldo, those who wish to age in their longstanding residence are often doing so in areas at greater risk to wildfire and other disasters (Winkler and Mockrin 2025). Personal preference for aging in place, as well as constraints imposed by the difficulty in selling homes in risky locations, means that populations are aging faster and clustering in high-risk locations compared to lower-risk places. The market and social features of housing – cost, changing preferences, and the desire to age in place – are stratifying the risk of environmental change and disaster by both age and socioeconomic status.  

During a disaster, physical shelters shape the experience and severity of environmental change and disasters 

Housing characteristics and materials – the sheltering capacity of a home – are critically important for shaping the immediate health impact of a disaster or environmental change. Take, for example, the importance of home heating and cooling during extreme temperatures. A Toronto-based research team found that during periods of extreme heat, living in a nursing home with air conditioning lowered the risk of death compared to living in a nursing home without air conditioning (Katz et al. 2025). Other studies find similar evidence for the protective capacity of air conditioning during extreme heat (Sera et al. 2020). 

On the opposite end of the spectrum, old or sub-standard housing stock is inefficient, difficult, and expensive to heat during extreme cold. For example, mobile homes are at greater risk of disaster and have minimal sheltering capabilities compared to other structures (Rumbach, Sullivan, and Makarewicz 2020). Meeting energy costs (Hernández 2013) and the disruption of utility services (Peterson et al. 2024) are growing concerns as energy costs increase and as energy grids are tested during extreme temperatures. In a poignant illustration of the basic importance of housing as shelter, one CACHE demonstration project details how deaths of vulnerable – likely unhoused – New York City residents increase on days with extreme heat. 

Ultimately, because older populations are much more physiologically vulnerable to environmental conditions, and only slightly more likely to seek shelter outside the home when disaster strikes (Behr and Diaz 2013; Malik et al. 2018), the sheltering capacity of their homes is paramount as disasters and extreme weather become more common (Smith and Swacina 2017). And yet, the physical limitations and fixed incomes of many aging adults present challenges to retrofitting existing homes with new, climate resilient features like air conditioning, water filtration, and fire mitigation (Forsyth and Molinsky 2021).  

After change and disaster, the economics of housing shape recovery and adaptability 

Finally, for homeowners, disasters can often result in the loss of their largest economic assets (Tagtachian and Balk 2023). Homes are often under-insured or have no coverage at all for increasingly common disasters like wildfires or floods. Rising insurance premiums make formal protection out of reach for many. Also, disasters can result in a cascadeof economic and health risks if households do not have sufficient coverage or independent wealth to rebuild or start anew (Rhodes and Besbris 2022). 

Take for example, the 2024 Hurricane Helene flooding in North Carolina. Flooding damaged ordestroyed mortgaged homes, resulting in homeowners being responsible for payments on unusable assets. In many cases, these households relocated to substandard temporary housing that is vulnerable to the elements, or they continued to live in their flood-damaged homes. Such housing conditions increase health risks related to extreme temperature, toxic debris and mold exposure, and substandard living conditions (Albert 2026). The situation is similar in the immediate wake of the 2025 Los Angeles urban wildfires (Borunda 2026; Copley 2026). Because impacted households may be unable to sell damaged or lost homes at reasonable prices, they may divert any financial resources away from health-promotion and towards rent or repair efforts, all while covering outstanding mortgages.  

As for renters, inattentive or predatory landlords may neglect to make their housing units climate resilient and delay timely repairs after a disaster. Many renters lack formal protections against such negligence, and against evictions and rent hikes in the aftermath of disasters (Lee and Van Zandt 2019). Often, disaster or rapid environmental change displaces renter households across town or across the country. Sudden or extreme changes in housing circumstances have extensive health impacts due to healthcare disruptions, loss of community and social support, extreme stress and mental health challenges, and more (Fussell and Lowe 2014; Tapsell and Tunstall 2008). There are greater impacts of displacement on aging populations (McDermott et al. 2019; Prohaska and Peters 2019). 

Final remarks 

The complex and layered features of housing – from market forces and financial relationships to the structure’s characteristics and location – shape health and wellbeing before, during, and after environmental change and disasters. Because of aging people’s additional vulnerabilities in health, mobility, and finances, housing is and will remain a crucial element for how they weather a changing and more disaster-prone future. Researchers and policy makers should focus attention on particular and relevant features of housing and health, and their occurrence along the timeline of environmental change and disasters. Such efforts will help uncover unknown risks and possible adaptive solutions to better support and protect the growing and increasingly climate-vulnerable aging population. 

Additional conceptual resources 

  • Housing and health – (Meltzer and Schwartz 2016; Taylor 2018) 
  • Climate gentrification – (Thompson et al. 2023) 
  • Aging in place – (Forsyth and Molinsky 2021) 
  • Rebuilding homes after disaster – (Rhodes and Besbris 2022) 
  • NPR and NYT coverage of emerging impacts of Hurricane Helene and the Los Angeles urban wildfires –  (Albert 2026; Borunda 2026; Copley 2026; Kaysen 2025) 

 

References 

Albert, Gerard III. 2026. “Winter Is Tough on People Still Living in RVs after Helene in Asheville, N.C.” NPR, January 27. 

Behr, Joshua G., and Rafael Diaz. 2013. “Disparate Health Implications Stemming From the Propensity of Elderly and Medically Fragile Populations to Shelter in Place During Severe Storm Events.” Journal of Public Health Management and Practice 19:S55. doi:10.1097/PHH.0b013e318297226a. 

Borunda, Alejandra. 2026. “The Long-Term Health Impacts from the LA Wildfires Are Just Becoming Clear.” NPR, January 14. 

Buchanan, Maya K., Scott Kulp, Lara Cushing, Rachel Morello-Frosch, Todd Nedwick, and Benjamin Strauss. 2020. “Sea Level Rise and Coastal Flooding Threaten Affordable Housing.” Environmental Research Letters 15(12):124020. doi:10.1088/1748-9326/abb266. 

Copley, Michael. 2026. “California Fire Victims Say Fighting with Insurance Companies Has Delayed Rebuilding.” NPR, January 13. 

Forsyth, Ann, and Jennifer Molinsky. 2021. “What Is Aging in Place? Confusions and Contradictions.” Housing Policy Debate 31(2):181–96. doi:10.1080/10511482.2020.1793795. 

Fussell, Elizabeth, and Sarah R. Lowe. 2014. “The Impact of Housing Displacement on the Mental Health of Low-Income Parents after Hurricane Katrina.” Social Science & Medicine 113:137–44. doi:10.1016/j.socscimed.2014.05.025. 

Gourevitch, Jesse D., and Carolyn Kousky. 2025. “New Homeowners Insurance Data Reveals Insights into Market Trends and Suggests Future Research Needs.” Risk Management and Insurance Review 28(2):324–38. doi:10.1111/rmir.70010. 

Gourevitch, Jesse D., Carolyn Kousky, Yanjun (Penny) Liao, Christoph Nolte, Adam B. Pollack, Jeremy R. Porter, and Joakim A. Weill. 2023. “Unpriced Climate Risk and the Potential Consequences of Overvaluation in US Housing Markets.” Nature Climate Change 13(3):250–57. doi:10.1038/s41558-023-01594-8. 

Hernández, Diana. 2013. “Energy Insecurity: A Framework for Understanding Energy, the Built Environment, and Health Among Vulnerable Populations in the Context of Climate Change.” American Journal of Public Health 103(4):e32–34. doi:10.2105/AJPH.2012.301179. 

JCHS, Joint Center for Housing Studies of Harvard University. 2022. America’s Rental Housing 2022. Joint Center for Housing Studies of Harvard University. https://www.jchs.harvard.edu/sites/default/files/reports/files/Harvard_JCHS_Americas_Rental_Housing_2022.pdf. 

Katz, Gabrielle M., Kevin A. Brown, Vasily Giannakeas, and Nathan M. Stall. 2025. “Air Conditioning in Nursing Homes and Mortality During Extreme Heat.” JAMA Internal Medicine. doi:10.1001/jamainternmed.2025.6595. 

Kaysen, Ronda. 2025. “L.A. Faces Pressure From Wealthy Residents as Pacific Palisades Rebuilds.” The New York Times, February 4. 

Lee, Jee Young, and Shannon Van Zandt. 2019. “Housing Tenure and Social Vulnerability to Disasters: A Review of the Evidence.” Journal of Planning Literature 34(2):156–70. doi:10.1177/0885412218812080. 

Malik, Sidrah, David C. Lee, Kelly M. Doran, Corita R. Grudzen, Justin Worthing, Ian Portelli, Lewis R. Goldfrank, and Silas W. Smith. 2018. “Vulnerability of Older Adults in Disasters: Emergency Department Utilization by Geriatric Patients After Hurricane Sandy.” Disaster Medicine and Public Health Preparedness 12(2):184–93. doi:10.1017/dmp.2017.44. 

McDermott, -Levy Ruth, Ann Marie Kolanowski, Donna Marie Fick, and Michael E. Mann. 2019. “Addressing the Health Risks of Climate Change in Older Adults.” Journal of Gerontological Nursing 45(11):21–29. doi:10.3928/00989134-20191011-04. 

Meltzer, Rachel, and Alex Schwartz. 2016. “Housing Affordability and Health: Evidence From New York City.” Housing Policy Debate 26(1):80–104. doi:10.1080/10511482.2015.1020321. 

Molinsky, Jennifer, and Ann Forsyth. 2023. “Climate Change, Aging, and Well-Being: How Residential Setting Matters.” Housing Policy Debate 33(5):1029–54. doi:10.1080/10511482.2022.2109711. 

Peterson, Sara K. E., Susan Spierre Clark, Michael A. Shelly, and Samantha E. M. Horn. 2024. “Assessing the Household Burdens of Infrastructure Disruptions in Texas during Winter Storm Uri.” Natural Hazards 120(8):7065–7104. doi:10.1007/s11069-024-06480-w. 

Prohaska, Thomas R., and Karen E. Peters. 2019. “Impact of Natural Disasters on Health Outcomes and Cancer Among Older Adults.” The Gerontologist 59(Supplement_1):S50–56. doi:10.1093/geront/gnz018. 

Rhodes, Anna, and Max Besbris. 2022. Soaking the Middle Class: Suburban Inequality and Recovery from Disaster. Russell Sage Foundation. 

Rumbach, Andrew, Esther Sullivan, and Carrie Makarewicz. 2020. “Mobile Home Parks and Disasters: Understanding Risk to the Third Housing Type in the United States.” Natural Hazards Review 21(2):05020001. doi:10.1061/(ASCE)NH.1527-6996.0000357. 

Schuetz, Jenny. 2024. “How Will US Households Adjust Their Housing Behaviors in Response to Climate Change?” Real Estate Economics 52(3):596–617. doi:10.1111/1540-6229.12486. 

Sera, Francesco, Masahiro Hashizume, Yasushi Honda, Eric Lavigne, Joel Schwartz, Antonella Zanobetti, Aurelio Tobias, Carmen Iñiguez, Ana M. Vicedo-Cabrera, Marta Blangiardo, Ben Armstrong, and Antonio Gasparrini. 2020. “Air Conditioning and Heat-Related Mortality: A Multi-Country Longitudinal Study.” Epidemiology 31(6):779. doi:10.1097/EDE.0000000000001241. 

Shi, Linda, Anjali Fisher, Rebecca M. Brenner, Amelia Greiner-Safi, Christine Shepard, and Jamie Vanucchi. 2022. “Equitable Buyouts? Learning from State, County, and Local Floodplain Management Programs.” Climatic Change 174(3):29. doi:10.1007/s10584-022-03453-5. 

Smith, David A., and Paul J. Swacina. 2017. “The Disaster Evacuation or Shelter-in-Place Decision: Who Will Decide?” Journal of the American Medical Directors Association 18(8):646–47. doi:10.1016/j.jamda.2017.05.004. 

Swope, Carolyn B., and Diana Hernández. 2019. “Housing as a Determinant of Health Equity: A Conceptual Model.” Social Science & Medicine 243:112571. doi:10.1016/j.socscimed.2019.112571. 

Tagtachian, Daniela, and Deborah Balk. 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. doi:10.3389/fenvs.2023.1111856. 

Tapsell, S. M., and S. M. Tunstall. 2008. “‘I Wish I’d Never Heard of Banbury’: The Relationship between ‘Place’ and the Health Impacts from Flooding.” Health & Place 14(2):133–54. doi:10.1016/j.healthplace.2007.05.006. 

Taylor, Lauren A. 2018. Housing And Health: An Overview Of The Literature. Policy Brief. Health Affairs Health Policy Brief. https://www.healthaffairs.org/do/10.1377/hpb20180313.396577/full/. 

Thompson, Joshua J., Robert L. Wilby, John K. Hillier, Richenda Connell, and Geoffrey R. Saville. 2023. “Climate Gentrification: Valuing Perceived Climate Risks in Property Prices.” Annals of the American Association of Geographers 113(5):1092–1111. doi:10.1080/24694452.2022.2156318. 

Winkler, Richelle, and Miranda H. Mockrin. 2025. Aging and Wildfire Risk to Communities. EIB-284. Washington, D.C: U.S. Department of Agriculture, Economic Research Service. doi:10113/9015828,%2010.32747/2025.9015828.ers. 

 

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Behavioral Response to Extreme Temperatures among the Elderly

Written by Julia Shipman on . Posted in Uncategorized.

Behavioral Response to Extreme Temperatures among the Elderly

Investigators:

Kathryn Grace, Sarah Flood, David Van Riper

Funding:

CACHE demonstration project; NIA-UMN LCC pilot grant (P30AG066613)

Data sources:

  • Time use data (2003-2024) from the American Time Use Survey (ATUS). Data on how people spend their time are collected throughout the year and across the United States using daily time diaries. Through linkage with the monthly Current Population Survey (CPS), county of residence is available for a subset of ATUS respondents.
  • Daily temperature variables for the contiguous US from GridMET.

Measures:

  • Time use measurements
    1. Daily totals of the amount of time spent in various activities (e.g., sleep, exercise), with others, inside, and outside
    2. Data are collected throughout the year
  • Temperature measurements
    1. Mean, minimum, and maximum of the GridMET daily temperature variables aggregated to the county scale.

Project Summary:

Extreme temperature events represent the leading cause of weather-related mortality in the U.S., and age is a key risk factor for severe health outcomes (e.g., death, hospitalizations) from exposure to extreme temperatures. Older adults who are economically disadvantaged, of color, and have underlying health conditions or mobility limitations are most severely impacted by extreme temperatures, exacerbating health disparities. Behavior is a significant pathway through which social inequality leads to health disparities. Despite consensus that behavioral modifications can ameliorate the deleterious health outcomes for older adults caused by extreme temperatures, there is a dearth of interdisciplinary, scientifically based, rigorous studies of older adult behavior and temperature, especially during extreme temperature events.

This project examines variability in older adult behavior across the temperature spectrum (i.e., temperature-behavior relationship), focusing on both hot and cold seasons and extreme temperatures within those seasons. A socioecological framework guides this investigation of multilevel and heterogeneous impacts of temperature on health behavior. The results will inform interventions to reduce vulnerability and save lives.

To accomplish the proposed work, the project team will build a unique dataset to examine individual-level behavior—protective and deleterious—under varying temperature conditions. The dataset combines information on (1) daily behavior and well-being from the 2003-2024 American Time Use Survey; and (2) day- and location-specific temperature data. This novel dataset enables analyses of the determinants of behavioral responses to temperature changes. We will estimate the temperature-behavior relationship and assess variation in vulnerability by age, socioecological characteristics, and factors related to exposure and assess the significance of time and place for the impact of temperatures on behavior.

Outputs:

Presentations, future publications, and code to construct the dataset for analysis.

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Test

Written by Tania Ochoa on . Posted in Uncategorized.

The multi-faceted, publicly-available disaster data platform from the Federal Emergency Management Agency (FEMA)

About:

OpenFEMA is the “agency’s data delivery platform which provides datasets to the public in open, industry standard, machine-readable formats. Datasets are available in multiple formats, including downloadable files and through an easily digestible Application Programming Interface (API). Each page includes information about the specific dataset, links to downloadable files, a data dictionary describing each field, and an endpoint link (if applicable for those datasets available via the API).”

Data are available on:

Disasters: declaration denials; disaster declaration summaries; FEMA web declarations; FEMA web disaster declarations; FEMA web disaster summaries; mission assignments.

Emergency management: annual NRIRS public data (National Fire Incidence Reporting System); emergency management performance grants; IPAWS archived alerts; national household survey; non-disaster and assistance to firefighter grants; Disaster Relief Appropriations Act of 2013 (Hurricane Sandy)

Individual assistance: FEMA assists individuals and households through the coordination and delivery of Individual Assistance (IA) programs. IA includes a number of programs, including the Individuals and Households Program (IHP) which is comprised of Housing Assistance (HA) and Other Needs Assistance (ONA). 

  • Data include: housing assistance program data (owners and renters); large disasters registration intake and household program.

Public assistance: Public Assistance (PA) is FEMA’s largest grant program providing funds to assist communities responding to and recovering from major declared disasters or emergencies.

  • Data include: Public Assistance Applicants; Public Assistance Applicants Program Deliveries; Funded Project Details; Funded Projects Summaries; Grant Award Activities; Second Appeals Tracker

Hazard mitigation: Hazard Mitigation Assistance (HMA) is for actions taken to reduce or eliminate long term risk to people and property from natural disasters.

  • Data include: Assistance Mitigated Properties; projects; grant program; disaster summaries; mitigation plan statuses, communities; transactions; and more

National Flood Insurance Program (NFIP) aims to reduce the impact of flooding on private and public structures.

  • Data include: NFIP reinsurance placement information; community layer; redacted claims and policies; status

Misc

  • FEMA regions; Datasets; Fields

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