Quality Data for Native American Health Programs
Investigating the Link Between Numbers and People
There is a deficit of high quality health data on the American Indian and Alaska Native (AI/AN) population. Often, the population is missing entirely from our national discussion of public health. It is therefore difficult to define the needs here, but at minimum a lack of health data means we can’t fully understand the reach of diseases or begin to understand their causes. We need strong data before we can begin to think about how to serve this population well and develop responsive health programs and policies.
To address this issue, I’m completing a practicum with the Northern Plains Tribal Epidemiology Center in Rapid City, South Dakota this summer. I found this practicum with the help of the Office of Field Practice. The Office's director, Professor Linda Cushman, has featured opportunities to work with Native American organizations for years.
My work, which draws on my previous experience with Teach For America, is focused on the design and evaluation of health curricula. I’m currently designing a comprehensive training program in gathering and managing quality diabetes data. The data is collected through hospital and clinic visits and is recorded in the Diabetes Management System, an electronic database designed by the Indian Health Service and the Department of Health and Human Services.
Diabetes disproportionately affects the Native American population. In 2014, 17.6 percent of adults over 18 in this population were living with diabetes compared to 7.3 percent of Non-Hispanic Whites. The diabetes epidemic is often tied to current social factors including extreme poverty and food deserts. It’s also tied to historical injustices, such as the displacement of American Indian and Alaska Native people onto reservations, which disrupted their traditional food practices.
The first step in addressing the prevalence of diabetes is to understand the nature of that problem by collecting good data. These data should capture more than the number of people living with diabetes; they should accurately capture history, complications, medications and other useful information to summarize the daily experience of those living with diabetes. In the Public Health Program Planning course I took last spring, we learned that precise data are critical in conducting needs and resources assessments and informing the subsequent logic model of a program. Often, public health practitioners have to collect additional data on an issue, and the presence and accuracy of existent data are foundational to this process.
Currently, physicians and coders are transitioning to using the International Classification of Diseases (10th revision) code set, or ICD-10, to capture all patient diagnoses. This set of codes is far more extensive and allows for increased precision in documenting the status of each patient, which in turn allows public health practitioners to capture more information about an issue.
But upgrading data quality isn’t as simple as switching from one code set to another. Physicians and coders have to know how to navigate the new system efficiently and have to be invested in doing so. In other words, the practitioners have to see the connection between data and programs, problems and solutions, or numbers and people.
The training program in data quality that I’m working on explains both the changing landscape of the coding process as well as invests coders in data quality as it relates to programs. The training is meant to be user-friendly, to the point, technically accurate, and interactive for readers. With this many factors, incorporating all of these elements into a single training has taken many feedback meetings and many drafts.
Before I started my work, however, I had to address my limited knowledge of AI/AN populations. I spent the weeks leading up to my start date researching tribes and cultures in the Great Plains region and continue to do so while in the field. This takes daily efforts, like setting aside time to research, actively listening to my coworkers, and journaling to record what I’ve learned. Similar to the underpinnings of the data quality project, my investment in and knowledge of Native American culture largely informs the quality of my own contributions.
My interest in health disparities is personal: it stems from my experience growing up in an immigrant family and teaching in schools that served low-income minority youth. I brought some level of that investment to this work experience when I began in late May, and my stake has increased substantially alongside the relationships I have established here with mentors and supervisors.
Jennifer Giroux, a Medical Epidemiologist at the Northern Plains Tribal Epidemiology Center (NPTEC) and a commander in the U.S. Public Health Service, has passionately explained the personal side of public health programming in the Rapid City community to me. And PJ Beaudry, a recent graduate of Mailman and the Director of NPTEC, has described his many ideas for program innovation in order to increase the reach and efficacy of current programs and those that are still being designed.
Intellectual and social/emotional competencies play important roles in the work of public health practitioners. I’m learning that balancing these competencies is essential to getting the best data, designing the best programs, and serving people in the best way possible. As a result, the context for my work has been deepened: I’m able to directly connect work products to the people they will serve. Now, I’m more critical of my deliverables.
American Indian and Alaska Native populations continue to experience extreme injustices that have lasting effects on health, and our work to respond to those effects should always be rigorously critiqued, developed, and improved.
By Erica Manoatl, Population and Family Health, MPH '17
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