Announcements

Name: Dr. Ricardo Noriega Espinoza
Affiliations: ¹ Department of Public Health, Brigham Young University, Provo, UT 84602, USA;
² Paso del Norte Health Information Exchange, El Paso, TX 79912, USA
Interests: prediabetes; type 2 diabetes; comorbidity; prevalence; Hispanic 

“Comorbidity Prevalence in Prediabetes and Type 2 Diabetes: A Cross-Sectional Study in a Predominantly Hispanic U.S.–Mexico Border Population”
by Ricardo X. Noriega, Juan J. Nañez, Emily F. Hartmann, John D. Beard, Chantel D. Sloan-Aagard and Evan L. Thacker
Int. J. Environ. Res. Public Health 2025, 22(5), 673; https://doi.org/10.3390/ijerph22050673
Available online: https://www.mdpi.com/1660-4601/22/5/673 

The following is an interview with Dr. Ricardo Noriega Espinoza: 

1. Congratulations on your recent publication! Could you briefly introduce yourself and your current research focus?
   I'm doctor Ricardo Noriega. I'm originally from Ecuador where I was trained as a physician and I later earned a Master of Public Health with an emphasis in epidemiology in the US from Brigham Young University. Right now, I'm working as an adjunct professor at this university, BYU, and I also work as an epidemiologist and research consultant at PHIX, a health information exchange non-profit organization based in El Paso. Most of my work involves analyzing health data to identify trends and translating clinical information into actionable public health insights. Lately, I've been especially focused on understanding comorbidities in people with prediabetes and type 2 diabetes. That's the main point of my research, and how early detection combined with localized data can help to drive more effective prevention. So, this current research looks at over 100,000 adult medical records in El Paso, which is a predominantly Hispanic bordered community. We found that people with prediabetes had significantly fewer comorbidities compared to those with type 2 diabetes. That’s really reinforced how important early intervention is, especially in underserved populations where diabetes risk is high.
2. Your work focuses on chronic diseases in diverse populations. What’s one surprising insight about community health behaviors that changed how you approach prevention strategies?
   I think one thing that really surprised me was just how low awareness of prediabetes is even among people who regularly visit their healthcare provider. So, for example, the CDC estimates that over 97,000,000 US adults have prediabetes, but more than 80% of them don't know it. In our own study we saw the same patterns, like many patients have a diagnosis of prediabetes in their medical records, but they didn't seem to be engaged in any prevention effort. There’s a gap between having a diagnosis and actually doing something about it. It really shifted how I think about health behavior regarding prediabetes. I think it's not just about motivation. It is also about how risk is communicated. I think that all people involved in providing healthcare should ask, are we making the message culturally relevant? Is it clear, practical or truly connected with the patient in a way that feels personal or meaningful? Because of that, I have started focusing more on a systemic-level solution. An example is improving how medical records follow up on prediabetes and, for example, partnership with local organizations to deliver lifestyle programs that really resonate with the people we are trying to help. And while our results that people with diabetes have more comorbidity than those with prediabetes may not be surprising, there actually hasn't been much larger-scale research quantifying that difference, especially using real world data from Hispanic communities. So, in that sense, our study helped to fill a gap. It provides us a way to track population health over time, compare between communities, and evaluate whether prevention strategies are really working where they are needed most. 
3. Chronic diseases (like diabetes, heart disease) affect millions. How can spatial or environmental data reveal hidden patterns in these diseases that traditional methods might miss?
   That’s a great question. Even though we did not publish any results about these using these methods, we have been working on this. We consider that spatial and environmental data can really open eyes to patterns that we might have otherwise missed, especially when we are just looking at broad averages. For example, in El Paso we have observed that areas with limited green spaces, poor air quality, and higher heat exposure tend to have higher rates of cardiovascular and respiratory conditions, so these hot spots are invisible when the data are aggregated by city. But when we map clinic visits or health outcomes against environmental metrics, this becomes clear. As part of our effort to study diabetes at PHIX, we mapped the prevalence of diabetes by ZIP code and we found that areas closer to the US–Mexico border consistently show higher prevalence. This geographic gradient highlights how social and structural factors like access to care, poverty, housing, and transportation interact with the health outcomes. To better understand these disparities right now we are refining our analysis using the census track and block level. That adds a level of detail that we believe will give us an even clearer picture of local disparities and help guide more targeted intervention in and also resource allocation strategies in El Paso. We have also developed dashboards that layer health outcomes with environmental data such as heat exposure and air quality. These two factors have been especially helpful for our public health partners, enabling them to identify high-risk neighborhoods and take focused actions. Some of them are identifying cooling centers, for example, expanding shaded green areas, or coordinating local outreach efforts. This kind of place-based analysis helps us to shift from reacting to health problems to preventing them via the community level where the impact is most direct. 
4. When combining environmental data (e.g., air quality, green spaces) with health outcomes, what’s a common oversight people make—and how do you address it?
   I think that probably a common oversight is treating environmental exposure as static, which means averaging data over large areas or long time periods without accounting for individual variability or cumulative exposure. We cannot assume that everyone in a given city experiences the environment in the same way, right? People don't live in averages; they live in specific microenvironments that can vary drastically by the hour, the block, or even where they work versus where they live. So, to address this, we have advocated for using fine-grained, time-linked data and when possible incorporated mobility patterns. For example, aligning daily air quality data with emergency department visits helps us to detect acute environmental triggers. Rather than identify disease patterns, the idea is to understand exposure in a way that reflects life experience. The strategies will be designed not just in a data-driven way, but also in a way that is relevant and practical for the communities we serve. Working closely with environmental health experts and utilizing data such as syndromic surveillance systems and GIS could enable El Paso and other communities to respond with greater precision and impact.
5. What appealed to you about the journal that made you want to submit your paper? How was your experience submitting to IJERPH?
   Well, we were drawn to this journal because of the interdisciplinary approach and global reach.
   One of my co-authors has published with the journal before and he spoke highly of the experience. So, when we began looking for the right place to submit our work, he suggested it as a strong fit and after reviewing the journal’s scope, we agreed with that. The study was well aligned with the “Infectious Diseases, Chronic Diseases, and Disease Prevention” Section, particularly because it addresses the core priorities that are understanding the burden of chronic diseases and also identifying the opportunities for early intervention. Although our main focus was on chronic diseases, we also included data on infection and respiratory conditions, including COVID-19, and its prevalence differences between individuals with prediabetes and those with type 2 diabetes, which made the study especially relevant to the scope of this Section.
   And as for the submission and peer review process, we believe that it was efficient and highly constructive. The reviewers provided thoughtful feedback that helped us to improve our arguments and clarify the results. Something that I also really appreciate is the journal’s open access model which ensures that the findings are available for clinicians, researchers and community leaders. So, thank you so much for that.