Navigating Health Equity Challenges: An Epidemiologist’s Perspective on Environmental Surveillance

We met with Dr David Larsen, Associate Professor of Public Health in the David B. Falk College of Sport and Human Dynamics at Syracuse University. He’s a distinguished epidemiologist with a PhD and MPH from Tulane University’s School of Public Health and Tropical Medicine. His extensive expertise encompasses large data analysis, spatial statistics, and study design, focusing on global health issues such as mosquito-borne illnesses, mHealth, and sanitation. Dr. Larsen has been a pivotal figure in critical initiatives, including the control of Zika and dengue transmission, malaria elimination, and the campaign to end open defecation.

In your experience, how can epidemiologists effectively integrate health equity considerations into designing and implementing environmental surveillance systems? What challenges might arise in this process?

From my perspective, achieving health equity through environmental surveillance presents a complex challenge. Let’s break down the key components I’ve observed:

First and foremost, we need to address the issue of coverage. The fundamental question here is, ‘Are we effectively monitoring wastewater from vulnerable and marginalized communities?’ The answer varies from place to place. A case in point is New York State, where urban areas tend to be part of the surveillance network because they’re connected to sewer systems. However, rural communities, often lacking sewer access, may be left out. This isn’t just a local concern; it’s a global one. Notably, even large communities can be omitted from surveillance if we solely focus on formal sewer systems. This underscores the urgent need to address concerns around equity in coverage within environmental surveillance.

Transitioning to another crucial aspect, catchment size plays a pivotal role in our sampling efforts. As I’ve observed, the more diluted a sample is, the more challenging it becomes to detect specific signals, such as those indicating COVID-19. To illustrate, consider a scenario with a million people contributing to a single treatment plant within a community. In such cases, we’d require a thousand infections before any signal becomes discernible. These delays could potentially result in severe outbreaks. Conversely, in smaller communities, say those with 5,000 people, we’re more likely to detect a single infection, enabling swift intervention.

Addressing these formidable challenges necessitates a focus on expanding our coverage. However, I must emphasize that this is easier said than done. Communities without sewer connections introduce their unique sampling challenges. Meanwhile, large treatment plants with extensive catchment areas present complexities of their own. In light of these challenges, part of the solution may involve exploring upstream efforts to reduce the size of these catchment areas.

In summary, as an epidemiologist, health equity in environmental surveillance revolves around the dual goals of inclusion and timely threat detection. It’s undoubtedly a complex undertaking, but one that holds immense importance for safeguarding public health.

Could you provide examples of instances where disparities in health outcomes were identified through environmental surveillance data analysis? How were these disparities addressed, and what were the outcomes?

In our collaboration with local health departments, it’s evident that these departments have an understanding of their communities. They’re well aware that for communities that have limited access to healthcare, many structural factors play a significant role including poverty, systemic racism and injustice, and lack of access to health care. Therefore, it’s not surprising to local health departments that these areas exhibit higher COVID-19 rates. This points to a treatment gap that is very real in identifying the source from wastewater, informing the health department and for them to implement it.

When we examine the relationship between COVID-19 levels, wastewater data, and clinical surveillance measures, we observe a more pronounced gap in communities with limited healthcare access. This stark contrast can serve to underscore existing health disparities. Local health departments may already have an inkling of these disparities, but it reinforces the fact that they are missing a considerable number of individuals who are being affected as the data collected might not be complete and accurate. This understanding is particularly crucial for contact tracing efforts.

However, it’s important to note that environmental surveillance doesn’t directly address these disparities. Instead, it can bring them to the forefront, serving as a reminder of the work that needs to be done. The hope is that this heightened awareness will drive systemic changes aimed at addressing these disparities more comprehensively.

How can collaboration between epidemiologists, environmental scientists, and policymakers be structured to ensure that health equity considerations are woven into decisions and actions arising from environmental surveillance findings?

As an epidemiologist, I find my role to be that of an advisor to policymakers. My expertise lies in laying out the scientific findings and projections based on the available data for policymakers, whether they’re publicly elected officials in New York or any other place, such that they can make informed decisions that affect public health.

Ultimately, the responsibility for public health decisions lies in the hands of public representatives, which is the government here. However, as an epidemiologist, I see it as my duty to advise the public elected representatives regarding such decisions and to warn them about the potential outcomes based on our scientific insights.

In essence, my perspective is that epidemiologists and environmental scientists should present scientific evidence objectively and clearly. The democratic process should then take its course. The decisions ultimately rest with the voters and their elected officials.

Regarding environmental surveillance, my role is to ensure the data’s robustness and the value of projections and models derived from that data. I offer advice and input, but I don’t make the policy decisions. That’s a responsibility that belongs to the public officials, who should be well-informed by the science we provide.

What ethical implications should epidemiologists be mindful of when dealing with environmental surveillance and how can these concerns be translated into responsible and transparent data analysis practices?

When it comes to ethical infectious disease surveillance, there are explicit principles to follow. The World Health Organization (WHO) laid out these principles in a 2019 report, which serves as a valuable starting point. Let’s delve into some of these key principles:

Value to Local Health Departments and Decision Makers: The data collected should have practical value and inform the actions and responses of local health departments and decision-makers.

Transparency: Surveillance data should be open and accessible, allowing for scrutiny and understanding of the processes involved.

Privacy: Privacy is a paramount concern, and here’s where wastewater surveillance has an advantage. Wastewater data is inherently private, especially when dealing with larger populations. However, as the sample size decreases, particularly in smaller catchment areas, there may be identifiable aspects. Any attempt to identify an individual from wastewater data is ethically wrong and should be immediately halted. Unfortunately, such cases have been reported in the United States.

Public Disclosure: Wastewater surveillance primarily operates at the community level, which raises privacy concerns as catchment areas shrink. To address this, certain data may need to be modified when shared publicly. Specifically, building-level data should never be disclosed publicly, especially for smaller catchment areas. Instead, data should be aggregated to protect privacy.

As an experienced epidemiologist, what advice do you have for young researchers aiming to contribute to the understanding of health equity through their work in environmental surveillance and data analysis? Are there specific areas or methodologies you recommend they focus on?

I often encourage young researchers to start with a clear research question. It’s crucial to determine what they want to figure out. From there, they should assess whether the necessary data already exist or if data collection is required. This evaluation process may involve creative thinking about data collection methods.

Regarding specific areas of research and methodologies, my suggestion is to begin with established norms, especially in infectious disease surveillance. Building on this foundation, researchers can explore nuances or deviations as needed. Methodological choices should be influenced by the available data and the community under study. There’s considerable room for qualitative research in this domain.

One area that holds promise is the design of systems for communities with limited access to sewer infrastructure. Whether it involves pit latrines or alternative sanitation solutions, understanding their impact is essential. We must consider whether missing data on individuals without proper sanitation facilities matters significantly in disease transmission. It’s vital to approach this from an equity perspective, acknowledging that perfect representation of all communities in environmental surveillance may not be feasible due to infrastructure and logistical challenges.

In essence, we must remember that environmental surveillance is a tool to better grasp disease transmission dynamics. The question is, can we achieve a comprehensive understanding of infectious disease transmission, even if some communities are not included in our surveillance systems? Currently, the answer is uncertain. Therefore, we must work to either validate or refute this hypothesis, ultimately ensuring that our surveillance efforts contribute to equitable health outcomes.