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 public 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 public health background is necessary. I currently plan on continuing to take various public health classes from UNM's College of Public 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 public 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 instructor and perhaps departmental 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 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.
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).
Missouri - 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.
Washington - A series of dashbords based on county, ACH (Accountable Communities of Health), and census tract geographies for the state of Washington (Social Determinants of Health Data). ACHs are collections of regional organizations/agencies across the state whose goal is to improve the health and health equity of their communities.
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.
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