According to 2012 data, diabetes is a serious chronic disease affecting 29.1 million people in the United States. It can lead to conditions such as kidney failure, blindness, and heart disease. Diabetes also makes patients vulnerable to infections that require amputation (CDC, 2014). In Arizona, which has the third largest population of AIs in the country, almost 16% of AIs reported having diabetes, especially T2DM (Bass, Bailey, Gieszl, & Gouge 2015). In Southern Arizona, the CDC estimates that about 24.1% of adult AIs have diabetes. The state’s distribution of T2DM is caused by a combination of genetic and environmental factors.
Behavioral risk factors such as smoking, alcoholism, sedentary lifestyles, weight gain, and poor diets can be classified as environmental factors of T2DM and were observed among Navajo Nation and Pima Indians (Arizona Department of Health Services, Bureau of Tobacco and Chronic Disease [AZDHS], 2011; Murea, Ma, & Freedman, 2012). Exposure to pollutants is another environmental factor that can be associated with T2DM because pollutants affect insulin sensitivity and glucose metabolism (Eze et al., 2015). Genetic factors include a family history of obesity or diabetic vascular complications. Individuals with such a family history are at high risk of getting Type 2 diabetes (Murea, Ma, & Freedman, 2012).
The evaluation of epidemiological and environmental data about T2DM in AI communities has revealed several gaps in knowledge. To begin with, most epidemiological data about AIs by federal agencies such as the CDC do not have information on populations living in Indian reservations as reservations are independent governmental entities (AZDHS, 2011). Moreover, further evaluation is needed on the effects of exposure to environmental pollutants; most studies tend to focus on behavioral risk factors. These gaps in knowledge can cause health disparities among urban AIs and those living in reservations, thereby making it difficult to identify chronic disease patterns.
Furthermore, there is a need for further evaluation of sociocultural and linguistic factors that often prevent people from accessing health care. The concept of cultural competence (see Appendix, Terms and Definitions) is imperative if the GHFHC wishes to successfully implement a population health improvement plan that will address the various needs of AI communities.
Health Improvement Plan to Address Diabetes Among American Indians
Among the many models adopted into PHM efforts, the collaborative chronic care model (CCM) framework is successful at managing diabetes and other chronic diseases among affected populations. There are six elements that are essential to the CCM: (a) health systems, (b) delivery system design, (c) decision support, (d) clinical information systems, (e) community resources and policies, and (f) self-management support (Improving Chronic Illness Care, 2003; see Appendix, Terms and Definitions). The GHFHC’s will follow the CCM for its health improvement plan based on certain assumptions about the plan. These assumptions are that the plan (a) needs to be sustained for a long time, (b) needs to comply with evidence-based guidelines for patient care, (c) needs to focus on patient education and lifestyle improvement, (d) needs to provide affordable and cost-effective care for AIs, and (e) needs to be culturally sensitive and equitable for disadvantaged community members.
The CCM’s six elements complement these assumptions and when the model is implemented in a PHM, the CCM will allow an informed, active community to productively interact with a proactive, prepared clinical team to achieve improved outcomes. Key components of the plan that are consistent with the CCM elements and the GHFHC’s assumptions are as follows: (a) establishing a system for collecting data and tracking health outcomes among AI patients; (b) establishing an operational leadership that will change staff management policies to ethnically match ethnicity and language of AI patients; (c) training all health care professionals on the CCM and cultural and linguistic competence; (d) sharing reports, lab-work, and epidemiological data with local health systems; (e) identifying local resources such as community health centers, YMCAs, religious centers, and senior centers that can help connect patients with the GHFHC; and (f) planning regular meetings for all stakeholders to resolve issues, discuss outcomes, and make recommendations.
The different components in the plan will enable health care professionals in diagnosing widespread diabetes in the AI community and ensure that cultural competence is deployed at the patient, health care professional, organizational, and systems levels. The next
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