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5.5.1 Policy Implications

Florida is one of the largest states in terms of the percentage of adults with diabetes, and

the three Central Florida counties of Orange, Osceola, and Seminole are close to Florida’s

average of over 10% of adults diagnosed with diabetes in 2013 (Floridacharts.com, 2016) and

more than 26% of adults being obese. In 2014 alone, there were more than 36,000

2013 Central Florida community health needs assessment report, diabetes is the most prevalent

chronic disease in the region, is a leading cause of death, and is therefore considered one of the

top five health priorities in the region (Floridahospital.com, 2013).

Results of this study also showed that the geographical location of diabetic patients with

HG impacts the level of utilization as well as outcome. Recent data about the geographical

distribution of diabetes in Florida shows that 14% of adults in urban areas have diabetes,

compared to 15.5% in suburban areas and 18.7% in rural areas (UnitedHealthFoundation, 2016).

A recent study conducted in eight southeastern states also showed racial disparities in diabetes

hospitalization of rural Medicare beneficiaries (Wan, Lin, & Ortiz, 2016). Therefore, this study

suggests that more effort is needed toward residents in rural areas. These measures might include

conducting community needs assessments to evaluate the availability of healthcare services in

patients’ neighborhoods as well as examining what other challenges are present among those residents that might affect their diabetes-related care.

Another major finding of this study is the great impact of age on utilization and outcome

among diabetic patients with HG. In Florida, most of the state’s counties have older populations

than do counties in the U.S. overall (Reynolds, Gunderson, & Bamford, 2015), and the number

of Floridians aged 85 and older increased by about 4% from 2000 to 2010 (Reynolds et al.,

2015). Additionally, the rate of ED visits for diabetics who are 75 and older increased 44% from

2009 to 2014 (Floridahospital.com, 2013). At the county level, it is estimated that by 2025,

Orange county will have the highest number of people aging 65 and older with over 215,000 and

accounting for 14% of the county’s population (Appendix J, Table 27). In contrast, Seminole

county have the highest percentage of elderly people in Central Florida and it is estimated that

certain races such as Blacks and Hispanics, Osceola county shows the highest percentage of

elderly people among those races (Appendix J, Table 27). Thus, this study indicates a

recommendation for decision makers in Central Florida to develop certain strategies targeting

older diabetic patients in Orange and Seminole counties to improve their health literacy, enhance

their adherence to medications, and be more encouraged to perform regular prevention and

screening visits. In addition, certain strategies can be formulated to target elderly Blacks and

Hispanics living in Osceola county.

Another issue is that the findings of this study show relationships between old age,

comorbidities, HG severity, Socio-economic status, and living in rural areas with the level of

utilization and the severity outcomes. Recent statistics show that Osceola county has the highest

percentage increase of disabled people younger than 65 years from 2011-2015 in Central Florida

(Appendix J, Table 27). Moreover, when compared to the other two counties in Central Florida,

Osceola county has the highest percentage of uninsured people younger than 65 years, the lowest

median household income, and the highest number of persons in poverty (Appendix J, Table 27).

Orange county, on the other hand, has the highest increase in the number and percentage of

veterans from 2011-2015 (Appendix J, Table 27). Therefore, with a transdisciplinary approach,

policy makers might need to consider those issues to implement better strategies to reduce the

growing negative economic and social impact of the HG problem among diabetic Floridians. To

approach such a huge problem, a collaboration among different sectors and stakeholders,

including health care providers, social workers, policy makers, financial experts, health

educators, community leaders, and other experts is called for in order to understand the size of

the problem, discuss the challenges from all perspectives, examine successful strategies

option. This approach will ensure the implementation of better strategies that are economically

and practically feasible. In addition, including experts from different disciples will provide a

larger pool of knowledge, which could reduce potential mistakes and increase success.

Last, there are some difference between the three counties in Central Florida regarding

their racial profile. The 2017 estimates show that Osceola county has the highest percentage of

Hispanic population, Orange county has the highest percentage of African Americans, while

Seminole county has the highest percentage of White people (Appendix J, Table 27). From 2010-

2014, The percentage of Hispanics increased the highest in Osceola county while the number of

Hispanics increased the highest in Orange county (Appendix J, Table 27). Although this study

showed that racial difference did not play a significant role in impacting the utilization and

outcome among HG patients, it is important for policy makers to consider the racial differences

between the three counties especially when developing certain educational or behavioral

modification policies and strategies.

5.5.2 Theoretical Implications

The BM offers a framework for examining the impact of different risk factors for HG

utilization and outcome. Employing SEM to analyze the magnitude of effects of different factors

provided a conceptual understanding of the relationship between all the components of the BM.

Moreover, the BM emphasizes the direct and indirect impact of different components on

utilization and outcome. Thus, the use of advanced statistical methodologies such as SEM was

crucial to examining these relationships and showing the difference between the direct and

indirect impacts of each component on utilization and outcome. In addition, each of the

depth knowledge regarding each of the different risk factors under each component and the

presence of any inter-relations among them.

The BM theoretical base of the study also played a vital role when applying DTREG

analysis. Rather than including all factors into one single inquiry, certain factors were grouped

together based on the component they are related to. This approach provided a

comprehensiveness to each component in terms of the most important factors in each group that

influenced the target variable. Consequently, only high impact factors under each component

were included in the final analysis. This approach provided more accurate and interpretable

results that are also more practical.

5.5.3 Practical Implications

With recent transition toward the value-based payments (VBP), healthcare providers are

being encouraged to deliver more high-quality services at lower cost (CMS.gov, 2017). This is

because their reimbursement is now based on quality rather than quantity of visits or the number

of services provided (Wagner, 2015). In addition, stakeholders impose continuous pressure on

healthcare organizations to provide sustainable, high quality, and cost-effective services

(Ramirez, West, & Costell, 2013). Therefore, understanding which groups are at high risk for

developing complications or using more services for a common disease such as DM is crucial for

healthcare providers (Henkel & Maryland, 2015).

This study revealed that certain groups are at higher risk of longer stays in hospitals,

higher total charges, and worse outcomes. Hospitals can employ a digital predictive model to

help identify those high-risk patients and proactively provide the best care for them. Also,

Knowledge, Attitude, Preventive Practice, and Outcomes (KAP-O) framework to improve their

health status, prevent severe adverse outcomes, and reduce their level of healthcare utilization

(Marathe, Wan, & Marathe, 2016; Wan, 2014). Last, collaborations among different levels of

providers: clinics, hospitals, long-term, and post-acute care organizations might also facilitate

monitoring wearable devices among high-risk DM patients to prevent HG episodes (Clarka ,

Elswickb, Gabrielb, Gurupur, & Wisniewski, 2016).