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Mortality rates are rising in the United States with significant community and regional variations. MortalityMinder (MM) is a web-based visualization tool that enables interactive exploration of social, economic and geographic factors associated with premature mortality among mid-life adults ages 25-64 across the United States. Using authoritative data from the CDC and other sources, MM is a freely available, publicly-accessible, open source, and easily maintained tool. The goal of MortalityMinder (MM) is to enable healthcare researchers, providers, payers, and policy makers to gain actionable insights into how, where, and why midlife mortality rates are rising in the United States (US). It is designed to help healthcare payers, providers and policymakers at the national, state, county and community levels identify and address unmet healthcare needs, healthcare costs, and healthcare utilization.
MM links and supporting info are at:
- http://mortalityminder.idea.rpi.edu (primary site)
- https://olyerickson.shinyapps.io/mortalityminder/ (backup site)
- http://bit.ly/mortalityminder_video_final (Demonstration Video)
- http://bit.ly/mortalityminder_slides (Mortality Minder Summary Slides)
MortalityMinder uses county-level mortality rates and social and economic factors measurements available from well-known public portals. Mortality rates from 2000-2017 are obtained through the CDC WONDER portal, the definitive source of mortality information in the United States. Social factors data for 2015-2017 are obtained through County Health Rankings (CHR), an aggregate of county-level data curated by the Robert Wood Johnson Foundation. MM considers 168 factors from twenty (20) sources, including datasets such as AHRF, BRFSS, the Bureau of Labor Statistics, the FBI, and many others. This version of MM focuses on midlife deaths attributed to leading causes of death including 'Deaths of Despair', 'Cardiovascular', 'Cancer' and 'All Cause', but the approach can be readily generalized to any health problem of interest. The ICD-10 definitions for the causes were taken Stein et al, "The Epidemic of Despair Among White Americans: Trends in the Leading Causes of Premature Death, 1999–2015", 2017.
Age-specific mortality rates were calculated in three-year chunks for each cause of death at the county, state, and national levels. To privacy CDC Wonder suppresses rates for counties with too few deaths, so calculating rates over three year periods ensures more rates are reported. MM aims to capture the actual experience of mortality of American ages 25 to 64 at a community-level, so our analysis does not age-adjusted. All deaths in a community are treated equally. In the future, age-adjustment could easily be added to MM. To provide for more complete data for effective visualizations, county mortality rates that are suppressed to preserve privacy by CDC WONDER were imputed using mortality rates within a state and the Amelia package for R. Multiple imputation could be added to the analysis in the future. Details of data sources and preparation are available at: https://github.com/TheRensselaerIDEA/MortalityMinder/wiki/data/
We gathered factors addressing health behaviors, clinical care, education, employment, social supports, community safety and physical environment domains from CHR. We filtered the original 168 factors from CHR to a set of approximately 70 factors that were relevant to at least one cause of death at the national level. We first kept only factors that were rates or other measurements that represented rates on other measurements that did no directly reflect county population size. Then we performed at factor association study to determine the associations with all 4 causes of mortality, corrected for multiple hypothesis testing using the Benjamin-Hochberg Method. Thus only potential relevant factors to at least one cause of death were deployed in the app.
MortalityMinder (MM) dramatically illustrates midlife mortality rate increases reported in (Wolf and Schoomaker, JAMA 2019), while providing greater insight into community-level variations and their associated factors to help determine remedies. Using authoritative data from the CDC and other sources, MM is designed to help health policy decision makers in the public and private sectors identify and address unmet healthcare needs, healthcare costs, and healthcare utilization. Innovative analysis divides counties into risk groups for visualization and correlation analysis using K-Means clustering and Kendall correlation. For each State and Cause of Death, MM dynamically creates three analysis and visualization infographics each address a different question:
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Nationwide View: What are the trends in midlife mortality rates for "Cause of Death" across the United States and in "State"? Nationwide View reveals midlife mortality rates through time and compares state and national trends.
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State View: How do midlife mortality rates for "Cause of Death" vary by county across and why? State View categorizes counties into risk groups based on their midlife mortality rates over time. The app determines correlations of factors to risk groups and visualizes the most significant protective and destructive factors.
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Factor View: How are county-level social and economic factors associated with midlife mortality rates for "Cause of Death" in "State"? Factor View enables users to explore individual factors including their relation to the selected cause at a county level for each state and the distribution of those factors within each state.
View at national and county-levels are included as well. Selecting 'United States' for State initiates nationwide analysis. MM prioritizes and visualizes the most significant factors associated with higher risks for each cause of death and allows the user to explore individual factors include its relation to the selected cause at a county level for each state and the distribution of those factors within each state.
Demonstrations and usability testing by our advisory panel of health care experts and other testers have shown that MortalityMinder is compelling and highly engaging. The page-oriented organization of the app lends itself to user-driven investigation and storytelling, like a highly interactive slideshow.
MortalityMinder provides a compelling and engaging tool to investigate the social and economic determinants of mortality. MM:
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Documents the disturbing rise in midlife Deaths of Despair due to suicide, overdose, and self-harm and other national/regional increases in midlife mortality rates due to All Causes, Cancer, and Cardiovascular Disease.
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Highlights potential social determinants through statistical analysis of factors associated with disparities in regional trends in midlife mortality rates.
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Provides county-level confirmation of trends and hypothesized causes.
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Yields insights that can be used to create region-specific interventions and best practices to meet unmet healthcare needs.
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Enables rigorous reproducible analysis of potential determinants of health by local, state, and national healthcare organizations to support development of programs, policies, and procedures to improve longevity.
MortalityMinder is an open source R project freely available with full documentation via a GitHub repository. R was chosen for its powerful environment for statistical computing and graphics using standard packages.
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MM utilizes the R Shiny and FullPage Javascript frameworks for web interactivity. Source data preparation is documented on the GitHub Wiki. Data Loader scripts enable new data sources and preparations to be easily incorporated. Data may be downloaded under 'DOWNLOAD SOURCE DATA'. Missing county mortality rates are imputed using state-wide rates and Amelia R Package.
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MM can be run from the public web locations or installed locally.
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Code is easily customized, extended, and maintained. The app continuously evolves in an agile framework to incorporate user feedback and introductions of new data streams, analyses, visualization, and health care problems.
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App design based on formal usability study of 20+ users and recommendations from our advisory board of healthcare and design professionals.
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The innovative visualizations and analytics in MortalityMinder can be adapted into other applications or formats by using the provided code and data.
The focus of MortalityMinder development from July 2019 thru December 2019 was on data source identification for multiple causes, beyond our Phase 1 example of 'Deaths of Despair'; social factor selection; refinement of our analytic techniques; expansion of our visualization choices; implementation of our interactive, web-based framework; and refinement of the user experience, with input from our external experts, graphic designers, media experts and a formal usability study.
MortalityMinder is currently published via two publicly-accessible web locations. Our open source R code is freely available via a github repository. Source data and generated results may be downloaded from within the app. MM developed using the R language and environment for statistical computing and graphics, incorporating best practices and using well-known packages whenever possible. Data Loader scripts enable additional years and types of data to be easily integrated. The result is a robust, extensible package that can be maintained and grown over time as either an open source package or within organizations such as AHRQ.
MM can be run from the public web locations; no user installation is required to test the application. Alternatively, the GitHub repository may be cloned and run immediately in the user's [RStudio environment (https://rstudio.com/), either on a server or on a personal machine.
MM utilizes the R Shiny platform for web interactivity; most of the visualizations presented in MM are generated in real-time based on data loaded when the app is launched. Data analysts and software engineers familiar with the R language and reactive coding via Shiny will have no problem customizing the code as required. MM's overall web layout utilizes a standard Javascript UI framework which is easily customized to modify the overall style of the app.
MortalityMinder was created by undergraduate and graduate students in the Health Analytics Challenge Lab at Rensselaer Polytechnic Institute with generous support from the United Health Foundation and the Rensselaer Institute for Data Exploration and Applications (IDEA). MortalityMinder was directed by Kristin P. Bennett and John S. Erickson.
The MortalityMinder Team would like to thank our advisory board, including Ms. Anne Yau, United Health Foundation; Dr. Dan Fabius, Continuum Health; Ms. Melissa Kamal, New York State Department of Health; and Dr. Tom White, Capital District Physicians' Health Plan (CDPHP). We would also like to thank the communication and design professionals that help with design,
Kristin P. Bennett and John S. Erickson, "MortalityMinder: An R-based web app enabling healthcare researchers, providers, payers, and policy makers gain actionable insights into how, where, and why midlife mortality rates are rising in the United States (US)." (Version v1.0). http://doi.org/10.5281/zenodo.3575089