Currently Being Prepared
Additional Developments - Being Revised and Updated
Continuing Research During 2024 and 2025
Background
The former Division of Government Research (DGR) at UNM developed a special purpose statewide gravity model for measuring geographic access to health care facilities and providers in New Mexico. This work was performed for the former New Mexico Health Policy Commission (NM HPC) from 1998 through 2002 as an addition to comprehensive statistical work with New Mexico's health care data. The results of this preliminary work were only published on DGR's former web page and also in a limited distribution publication by the NM HPC ( HPC Quick Facts 2003 - color extract). A special poster presentation was also prepared that won the poster contest at the 2002 ESRI SWUG Conference held in Taos, New Mexico ( now Esri Southwest User Conference).
Many academic and applied research studies have demonstrated the utility of a GIS (Geographic Information System) and spatial statistical methods (spatial analysis) such as gravity models for public health (Selected References and Esri Health and Human Services). These evolving methods (GIS-Based Accessibility Measures and Application) have provided an improved higher resolution understanding of geographic accessibility (potential and relative spatial access) than the official (traditional epidemiological) lower resolution regional availibility methods routinely used by government agencies. However there is more research needed to help the selection of an appropriate model(s) to apply in a particular place. New Mexico has some very unique social, economic, political, and topographic characteristics that need to be considered when developing and applying these methodologies. This research will consider these factors and hopefully result in the selection of an appropriate and useful model(s) to measure geographic accessibility to health care providers and facilities.
Previous and Ongoing Developments
This page is focused on the development of an example social determinants of health (SDOH) index for New Mexico that can be used for testing and evaluating the utility of various gravity models developed for measuring geographic accessibility to health care providers and facilities. For more background information and results from previous preliminary research please see ( Geographic Acces to New Mexico Health Care Providers and Facilities - original page). A more comprehensive update focused on data acquisition, preparation, description and visualization has also been previously prepared (see Geographic Acces to New Mexico Health Care Providers and Facilities - data preparation page). Another more recent page focused on data visualization and preliminary analyses has already been developed (see Geographic Acces to New Mexico Health Care Providers and Facilities - analyses page). In addition, a new page with analyses results is currently being developed (see Geographic Acces to New Mexico Health Care Providers and Facilities - analyses and developments page).
The primary purpose of the previous and this continuing research is to allow other researchers to review these results and to make suggestions to improve the interpretation of these results. Hopefully with the cooperation of others, especially researchers with a public health, statistical, and demographic background, portions of this interdisciplinary research may eventully be published in an appropriate academic journal and presented at both academic and applied users confrences. The findings of this research should also help promote the application of these methods in New Mexico by various state governement agencies to assist policymakers in the NM Legislature to make more informed data-driven decisions when allocating resources to help alleviate disparities and inequalities.
I have only a limited background in population health although I have a strong geography background plus a basic background in statistics and computing. In order to facilitate my current long-term geographic access to healthcare research project I have realized that obtaining a stronger population health background is necessary. I currently plan on continuing to take various population health classes from UNM's College of Population Health to improve my understanding using my retiree tuition remission benefits. My goal as a non-traditional student is to complement my pevious education, work, and research experience with these additional population health classes. I think that this will be a good continuing education program similar to the recently developed academic programs being offered at other universities such as the Master of Applied Sciences (MAS) in Spatial Analysis for Public Health at the Johns Hopkins Bloomberg School of Public Health. I have decided to start with some of the undergraduate online classes for a good background (completed PH 101 - Introduction to Population Health and PH 102 - Global Health Challenges and Responses) and hope to eventually get permission to enroll in the Minor in Public Health Program. This program is designed as a minor field of study for students currently pursuing a graduate degree from other disciplines. As a non-degree graduate student with a previous master's degree I can't enroll in any of these classes without advisor permission.
An Example SDOH Index for New Mexico
I previously developed an example SDOH for New Mexico that did not include the health care access and quality domain but did include aspects of the other four domains. This development allowed for the statistical comparision of various gravity model measures of health care accessibility to this test composite SDOH index based on the other four domains. I think this technique can help the selection of a useful gravity model(s) for New Mexico. A graphical PowerPoint slide that illustrates this development was prepared for a recent public health class (see PDF - A NM Test SDOH Index). In addition, I used spatial statistical regression models such as Geographically Weighted Regression (GWR) to explore the possibility that these models could help identify areas of the state with potential health disparities based on the strength of the statistical relationships.
I have also realized that a more complete SDOH for New Mexico can be developed with the addition of the gravity model measurements of health care accessibility plus other quality measures (the fifth domain). An improved example SDOH composite index can be used to better identify and map local communities in the state that exhibit various levels of healthcare disparities. I plan to compare this more comprehensive method to identify communities with health disparities to the spatial statistical regression methods that I previously used.
United Kingdom's Wider Determinants of Health (WDH). Contains data on the individual, social and environmental factors which influence the health of the population and impact on inequalities in health. The WDH model framework and tool is an ongoing project that is being revised and developed. There are currently six domains topics that focus on various themes and contains selected indicators. The current domains are: natural and built environment, work and the labour environment, income and vulnerability, crime, education, and the Marmont indicators. The current version excludes a specific domain related to access and the provision of healthcare services. This exclusion recognizes that factors outside the healthcare system heavily influence health. As the United Kingdom has a well-developed National Health Service (NHS) that provides good healthcare coverage, emphasis on the other domains is appropriate. Aspects of this model should be evaluated to see how they can be applied in New Mexico. Although a comparison with or without the healthcare access domain should be conducted.
Canada - Social Determinants of Health and Health Inequalities (Public Health Agency of Canada). A discussion of the social and economic influences on health in Canada. Also ongoing measures to address the social determinants of health (SDOH) plus funding opportunities and multisectorial actions.
U.S. Social Determinants of Health Database ( Agency for Healthcare Research and Quality). This database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis. U.S. Social Determinants of Health View ( Centers for Medicare & Medicaid Services). The SDOH View provides a user-friendly way to view social determinants of health across various domains and geographies. Geographic granularity is available at county and Census Tract levels. State-level data is available as part of the Population View. (Use of the Chrome browser is recommended.)US Social Determinants of Health Atlas ( Web Posting by Carto, Inc.). Describes an elaborate statistical study by the Center for Spatial Data Science at the University of Chicago that developed four indexes derived by a combination of 2014 American Community Survey social factors for all the US census tracts (Kolak et al., 2020). Also see (The U.S. Social Determinants of Health Atlas).
Arizona - The Arizona Department of Health Services maintains an extensive public health data portal (ADHS PDHP) that contains interactive maps of many of the factors from the five key social determinants of health (SDOH) areas or domains. However, there is not a currently developed special SDOH composite index that combines several factors. Although the (Arizona Health Improvement Plan) is concerned with the SDOH and the impact of place on health in Arizona.
California - The California Department of Public Health (CDPH) has a web page that provides links to various programs in California that are focused on disparities ( CDPH Disparities). There is also a special study focused on developing a preparedness index ( Community Outbreak Preparadness Index (COPI), Presentation and Community Outbreak Preparadness Index (COPI), Technical Report). The COPI study and some other studies have developed special indexes such as the Race and Ethnic Polarization Indexes using selected SDOH elements. I did not see that there was a currently developed special SDOH composite index in use. However, a nonprofit organization the Public Health Alliance of Southern California has developed a Healthy Places Index (HPI) that maps data on social conditions that drive health — like education, job opportunities, clean air and water, and other indicators that are positively associated with life expectancy at birth. The Technical Report describes the methods used to develop this index that is composed of various social determinants of health (SDOH) indicators plus other releavant indicators such as health outcomes, climate change exposures, social vulnerabilities, and race/ethnicity data.
Colorado - The Colorado Department of Health & Environmenat maintains a dedicated web page ( Social Determinants of health research) that contains links to SDOH research and data. There is also another web page ( Maps and GIS for health and environment) presenting some SDOH related data. A platform for visualizing geographic disparities for selected social determinants of health and other health data has been developed ( CDPHE Community Health Equity Map) plus an ArcGIS Hub web page that provides access to open data such as geospatial data sets, maps, visualization tools, and web-based mapping applications (CDPE Open Data) is available. I did not see that there was a currently developed special SDOH composite index in use.
Nevada - The Nevada Department of Health and Human Services has produced various studies that include several social determinants of healts (SDOH) factors that are available from the Nevada Office of Analytics - Data Dashboards & Reports Catalog. However, I did not see that there was a currently developed special SDOH composite index in use.
New Mexico - The New Mexico Department of Health has developed the New Mexico's Indicator Based Information System ( NM-IBIS) that contains various SDOH data elements that can be displayed on GIS maps and downladed. There is also a nonprofit organization ( New Mexico Social Drivers of Health Collaborative) that has a discussion and GIS maps of several SDOH factors. However, both of these sources for New Mexico's health related data currently have not developed a composite SDOH index.
Oregon - The Oregon Health Authority (OHA) maintains various health related Data and Statistics and a social determinants of health (SDOH) screening program. These data are available plus there are some maps of these data ( Data and Maps ). However, I did not see that there was a currently developed special SDOH composite index in use.
Texas - I could not find any specific discussions or presentations of SDOH factors on the Texas Department of State Health Services web site. Although there is an extensive GIS Gallery ( CHS GIS) with maps and access to some of the SDOH data elements. There are also some developed indexs for health related data, but no SDOH index has been developed.
Utah - The Utah Department of Health & Human Services (DHHS) has developed the Utah Healthy Place Index in cooperation with the Public Health Alliance of Southern California. The Utah DHHS has been recently focused on the social determinants of health (SDOH) and the roles they play in health outcomes. They originally developed a composite measure of SDOH by geographic area, the Utah health improvement index (HII). This work was expanded with the continuing development of the Utah Healthy Place Index (HPI). There is a well-developed interactive map tool, the Utah HPI Map that displays and explains the the HPI score and the various community characteristics that were combined to create it.
Missouri - I think the Center for Applied Research and Engagement Systems (University of Missouri Extension) prepared an ArcGIS Online presentation of selected factors related to the SDOH in Missouri (Exploring the Social Determinants of Health). Also present the results of a public survey to measure opinions about which factors are driving community well-being. Plus a discussion about the impacts of non-profit organizations interventions that impact the social determinants and population health along with a map providing the location of non-profit organizations. This organization has performed various GIS and health related projects throughout the country. I need to review more of their projects to see if they have developed any SDOH composite indexes.
North Carolina - The North Carolina Department of Health and Human Services( State Center for Health Statistics) developed a SDOH composite index. I adapted their work and developed an example SDOH composite index for New Mexico that is modeled on the North Carolina Social Determinants of Health (also PDF) Z-score based index. There is also a North Carolina Data Portal that provides access to data, maps and tools to support community health assessment and other public health activities. This data portal is a cooperative project with the Center for Applied Research and Engagement Systems (University of Missouri Extension).
Washington - The Washington State Department of Health Maintains a series of dashbords based on county, ACH (Accountable Communities of Health), and census tract geographies for the state of Washington's Social Determinants of Health (SDOH) Data. ACHs are collections of regional organizations/agencies across the state whose goal is to improve the health and health equity of their communities. Interactive and hardcopy Geographic Information System (GIS) maps of selected SDOH elements and other health data are available. These data can also be downloaded. However, I did not see that there was a currently developed special SDOH composite index in use.
Connecticut - The Connecticut Department of Health has a very well-developed web site. There is a Population Statistics page with access to various SDOH related data. A recent Power Point Presentation ( Measuring What Matters) presents maps of various health indicators. There are also various other SDOH related publications although I did not see any that were focused on developing a composite SDOH index. Connecticut is an eastern mostly urban and suburban state with a township based geographical and political organizational structure typical of other New England states. In addition, a local non-profit (Connecticut's DataHaven) has many SDOH factors that are included in various interactive maps, specialized reports, and available data for download. However, I did not see any specific focus on developing a social determinants of health (SDOH) composite index. Note: I chose this nonprofit organization as an example of how access to data and the innovative analyses of these data can be used to promote the health, well-being, equity, and quality of life for the population in various communities. I am impressed and happy to see all the great work that is being performed in my former home state and by this dedicated organization located in my former hometown.
An in-depth scoping review of studies related to medical deserts in Western countries was prepared by ( Flinterman et al., 2023). This review points out the problem of identifying medical deserts based on population-based characteristics such as distance from providers and population density, It also shows that there is no agreed upon method to define rural areas. In addition, it presents the various approaches that have been suggested and used to mitigate the healthcare workforce shortage by policymakers such as incentives to locate in rural areas. It also considers the work-related and lifestyle-related factors that influence the decisions of healthcare professionals to work or not work in rural areas.
An extensive study in West Virginia ( Hong et al., 2022) demonstrates the development of an integrated index that combined both spatial and aspatial or socio-economic accessibility indices. This paper clearly shows how to assemble the necessary data and how to conduct the appropriate spatial-statistical analyses to foster a better understanding of the spatial and social determinants that shape access to primary healthcare services.
A study in Los Angeles County, California focused on access to pharmacies ( Wisseh et al., 2020) based on both distance and also their social determinants of health (SDOH) characteristics. This study identified two distinct types of pharmacy deserts based on a statistical clustering analysis (K-Means) of the SDOH indices. This methodology can be used to help understand the underlying social inequities that are important defining characteristics of helthcare deserts.
An elaborate statistical study "Quantification of Neighborhood-Level Social Social Determinants of Health in the Continental United States" (Kolak et al., 2020). Using an exploratory factor analysis this study developed four indexes derived by a combination of 2014 American Community Survey social factors for all the US census tracts. In addition, seven neighborhood and community types were identified that can be statistically compared using regression analysis with various health outcomes such as mortality rates. This study provides an excellent example of how spatial analysis of available data can be used to better understand the relationships between economic and social inequalities and health outcome disparities. Performed by the Center for Spatial Data Science at the University of Chicago.
A earlier study that focuses on developing composite measures of specific aspects of health, health services, and health performance in Manitoba, Canada ( Metge et al., 2009). These health performance indices can be useful to statistically compare with other SDOH incices to evaluate potential health outcome disparities.
A recent project conducted by Agency for Healthcare Research and Quality's (AHRQ, See Chisolm et al., 2023). The objective was to describe health equity research priorities for health care delivery systems and delineate a research and action agenda that generates evidence-based solutions to persistent racial and ethnic inequities in health outcomes. A recommendation to support research on health care delivery system consolidation and access. This research should address how consolidations and closings are changing the geographic distribution of health care and the access inequities that are potentially created.
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