The Development of an Algorithm for Prediabetes Screening and Management in Rural Populations with Low Socioeconomic Status
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The prevalence of prediabetes continues to rise in the United States and without effective intervention, a significant portion of those with prediabetes will go on to develop diabetes. Prediabetes affects approximately one-third of the population (96 million American adults) and 80% of those people are unaware of their prediabetic status. Proper screening for prediabetes could provide patients with access to necessary interventions, such as intensive lifestyle modifications or pharmacologic treatments, that could prevent or delay the onset of diabetes. Preventing diabetes is important because diabetes is responsible for a significant share of the economic burden of health care in the United States, costing over $200 billion annually. Because the prediabetic state is generally asymptomatic, people with low socioeconomic status or in rural areas are more likely to be among those unaware of their status because they are less likely to have been screened. Additionally, people in rural communities and those with low socioeconomic status face overlapping challenges that reduce their access to health care and health screenings. Because of this and other social determinants of health, populations with low socioeconomic status and those in rural areas face disparities in prediabetes and diabetes diagnosis. Appropriate screening and effective management are keys to achieving health equity in care for prediabetes. The purpose of this DNP scholarly project was to create an algorithm based on the best evidence available for advanced practice healthcare providers to guide the screening and management of prediabetes in patients with low socioeconomic status in rural areas. The algorithm was validated using a survey of a panel of advanced practice healthcare providers with expertise in prediabetes and experience working with patients with low socioeconomic status and in rural areas. The survey contained primarily dichotomous questions with optional free-text responses. Data from the survey were collected and analyzed to finalize the algorithm, validate its content, and assess its feasibility. A pilot study for the implementation of the algorithm in clinical practice was proposed and discussed.