Geospatial
Analysis

Place matters to personal and population health. We can help you understand YOUR data and local, regional, and national measures of health, economics, and demographics.

 

HealthLandscape is dedicated not only to data democratization and data visualization, but also to research related to health, health care and social determinants of health. We are actively:

  • Researching how our social circumstances affect our health and access to health care;
  • Applying geospatial and statistical techniques to help answer questions about disparities in health outcomes; and
  • Creating new visualization methods.

Our Geospatial Research Briefs highlight these interests. These short papers will cover a variety of topics that are intended to demonstrate the power of geospatial analysis and tools for better understanding important issues related to healthcare. The research briefs will emphasize the use of publicly accessible datasets as well as data visualization and mapping tools, while focusing on the key areas of health disparities, population health, primary care, and value-based payment models.

Distribution of Multigenerational Households by Race and Ethnicity, and Implications for COVID-19 Planning

August, 2020 - Mark Carrozza, MA

While social determinants such as institutional racism, access to care, and wealth play a part in the differences in death rates, this brief focuses on geospatial and racial differences in multigenerational households. Households with multiple generations are particularly susceptible to intra-household COVID-19 spread. Difficulty in maintaining proper distancing for suspected or confirmed cases puts members of these households at a higher risk of becoming seriously ill or dying as a result of COVID-19.

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Where are Areas of Greatest Need of New Health Centers? A Spatial Empirical Bayes Approach

July, 2017 - Michael Topmiller, PhD & Jennifer Rankin, PhD

Geospatial methods can also be integrated with Bayesian approaches to account for spatial variation and variance instability in regards to population. This brief illustrates the use of a spatial empirical Bayes approach to identify high-need areas based on low-income populations not served by the federally-funded Health Center Program (HCP).

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Exploring Competition and Proximity: A Comparison of Basic Methods

October, 2016 - Jené Grandmont, MA

When planning for the expansion of services and determining areas of need, estimating people’s ability to reach health care services is an important issue. In order to accurately identify the areas in need of additional health care providers, while avoiding service area overlap, it is necessary to understand the practical accessibility of other nearby providers. While there is a wealth of literature exploring definitions and measures of potential access (Apparicio et al, 2008, Topmiller, 2013), this brief illustrates the importance of local context in choosing the right measure by exploring the relationship between potential health care access and utilization.

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Where are “bright spots” for appropriate Diabetes preventive care?

July, 2016 - Michael Topmiller, PhD

Research has shown that higher rates of appropriate Diabetes preventive care can lead to better health outcomes, fewer hospitalizations, and lower spending (Gray et al., 2012; Kralewski et al., 2013). Our previous work has demonstrated that geographic variation exists for Medicare spending, hospitalizations, and preventive care, while also identifying priority regions for improving care (Topmiller, 2016). However, little is known about the strategies that lead to higher rates of preventive care and why rates vary so much across geographic regions. Finding the “bright spots,” regions with higher than expected rates of appropriate Diabetes preventive care, can assist researchers and policy makers in identifying successful strategies for producing higher rates. This brief utilizes a two-step geospatial approach for identifying regions that are appropriate Diabetes preventive care “bright spots.”

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Where are “hot spots” of Medicare spending and preventable hospitalizations and “cold spots” for preventive care?

May, 2016 - Michael Topmiller

Many healthcare reform efforts are underway that are working towards achieving the triple aim of better care, better health, and lower costs. However, questions still remain about how reforms take into account the significant geographic variation of healthcare spending and utilization. Recognizing the importance of geography, researchers have developed hot-spot and cold-spot approaches for targeting healthcare super-utilizers and high need communities, offering potential models for identifying priority regions where policymakers can target scarce resources. Hot-spotting and cold-spotting has also been used in the field of geospatial analysis, where hot spots are defined as clusters of high values and cold spots as clusters of low values. Thus, we could think of clusters of counties with low rates of preventive care as cold spots. This brief details an approach for identifying priority geographic regions for improving preventive care.

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Do Regions with More Preventive Care Have Lower Spending and Fewer Hospitalizations?

March, 2016 - Michael Topmiller, PhD

Complications from Diabetes are a major cause of hospitalizations and high Medicare spending, with about 27% of Medicare beneficiaries diagnosed with the disease (CMS, 2013). Appropriate Diabetes preventive care such as annual hemoglobin A1C tests, blood lipids LDL‐C tests, and eye exams have been shown to reduce complications with Diabetes and improve quality of life (Gray et al., 2012). Despite the evidence for positive outcomes associated with more preventive care, few studies have explored the relationship between Diabetes preventive care, utilization, and spending.
This brief explores the relationship of appropriate Diabetes preventive care to preventable hospitalization rates and Medicare spending per enrollee (age‐sex‐race adjusted).

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