Interventions of Impact of Type 1 Diabetes and its Prevention

            Hippisley-Cox, Coupland, Vinogradova, Robson, and Brindle (2008) explain that in the UK, there are actually 3.5 million individuals suffering from diabetes. With the increasing levels of aging population and obesity, the growth of diabetes is also increasing. Lifestyle interventions have been targeted at the individuals with severe risk of Type 2 diabetes and it has been determined to be effective in decreasing its occurrence. The threat of Type 2 diabetes is affected by many factors which include family history, physical activity, age, and obesity. Individuals from some specific population groups and communities are seemingly at a higher risk. The algorithm of QRISK2 was utilized for estimating the likelihood of the CVD or cardiovascular disease even conditional on the diabetes, deprivation, ethnicity, and smoking data was included in the equation. The events of CVD were actually allocated for either vascular disease, stroke, transient ischaemic attack, unstable angina, and stable angina in accordance with the distributions of probability utilized in the previous of Assessment of Health Technology. It is believed by the authors that various factors detail the differences which are present in the incremental costs. First of all, the model of SPHR involved a very wide range of outcomes related to health like colorectal cancer, osteoarthritis, and depression which weren’t included in the previous calculations.

            The cost of individual screening for Type 2 diabetes at a very high risk because of the hyperglycaemia wasn’t included in the SPHR model’s version. The model’s main drivers are the effect of the intervention in decreasing CVD and diabetes. A sufficient portion of the incremental costs can be attributed to the cost-saving which is related with CVD and diabetes. The analysis of deterministic sensitivity actually highlights that the results of the model are most sensitive to the changes in the incidence of baseline of these conditions. In the analysis of authors, six high-risk groups were investigated separately. However, it is highly probable that combining the overall criteria would increase the allocation of resource to a subpopulation with even higher gain in cost-savings and gains. The model of SPHR actually can changed for evaluating the combined criteria of treatment, in addition to various other policies for the prevention of Type 2 diabetes. The policymakers of the UK can utilized this model for deciding just which populations they want to target with the interventions of lifestyle in accordance with overall objectives whether they are short or long-term gains, preventing diabetes or CVD, or equity (Hippisley-Cox, et al. 2008).

        According to Kalyani, Saudek, Brancati, and Selvin, (2010), evidence is rising that the disease of diabetes is related to some other comorbidities which include prognosis, cancer risk, fracture risk, incontinence, and cognitive impairment, the significance of both comorbidities diabetes will continue to increase with the increasing age of populations. Therapies which have seemingly proven to decrease the macrovascular and microvascular complications will require to be assessed under the consideration of comorbidities which are identified newly.

        The change in lifestyle has proven to be successful in delaying or preventing the Type 2 diabetes’ onset in individuals who are at a high-risk. On the basis of this, new approaches of public health are emerging that might have competent monitoring a national level. For instance, the research trial of Diabetes Prevention Program demonstrated that the intervention of lifestyle had its effect in the adults who were older and it was successful all ethnic and racial groups. The usual studies regarding this work have demonstrated that lifestyle intervention’s delivery the settings of group at a level of community are also successful in reducing the risk of Type 2 diabetes. The program of National Diabetes Prevention has been established now for implementing this intervention of lifestyle in the whole nation.

        It can be said that another merging issue is the impact of new criteria which is based on laboratory on public health like introducing the utilization of A1c for the diagnosis of this disease of Type 2 diabetes. These changes might affect the number of people who are undiagnosed while facilitating Type 2 diabetes introduction. It has been suggested by several studies that process indicators like eye exams and foot exams might not be sufficiently sensitive for capturing every aspect of quality of care that results ultimately in lower morbidity. New quality of care indicators of diabetes are actually under development and might assist in determining whether evidence-based, timely, and appropriate care is connected to the reduction of risk factor. Additionally, the scientific evidence that the Type 2 diabetes can be somehow delayed or prevented has seemingly stimulated a new study into the best approaches and markers for referring and identifying individuals at a high-risk to prevention programs in diabetes or its complications by affecting several factors of behavioral risk like certain dietary choices, which haven’t been experimented in trials which are controlled and randomized (Kalyani, et al. 2010).

                According to Zhuo, Zhang, and Hoerger (2013), diabetes’ progressive nature and the resulting reliance on medicines seem to pose very significant challenges for many healthcare systems and patients. In spite of the improvements in safety and efficacy of the prevalent medications, the issue with adherence still is there. Recognizing the determinants of sticking and adherence might have the system, provider, and patient-focused strategies for ensuring and encouraging optimized adherence and ultimately medical or clinical results. Recent developments and advances in the technology might have also helped in lifting the weight which is related to nonadherence and methods for the adjustments in doses. Medication adherence of patient is a very significant component to successful treatment, and adherence to the therapies which are prescribed can delay or prevent the complications’ onset, decrease the hospitalization risk, and reduce the direct costs of healthcare. This adherence seems to go beyond the passive act of simply following a recommendation of a prescriber. It also includes an agreement by patients for purchasing medications and taking them in accordance with the schedule and does which is prescribed by the healthcare expert. But lack of adherence to medication is not always the mistake or fault of the patient. The adherence barriers can simply be classified into 3 categories including external factors, provider factors, and patient factors.

            Barriers which related with patients include sociocultural factors, educational levels, ethnicity, gender, age, health beliefs, fear, comorbid condition, and marital status. It is highlighted by an interesting study just how a mix of demographics can impact the adherence differentially. A research on Hispanic women and men with the disease of diabetes highlighted that there were less probable in receiving the support of treatment and facing great obstacles to the treatment in comparison with the Hispanic men. Among the Hispanic women, the perceived support was associated importantly with self-efficacy which is better, which in turn was associated significantly with the adherence of better self-care. Medication beliefs and health literacy together with the knowledge of patients of the illness and its treatment also have a part in whether patients will be adhering to the regimen of treatment. The attitudes which are positive tend to be related to better outcomes of adherence. A survey focusing on patients with the T2D demonstrated that beliefs about the benefit of treatment were associated significantly with the adhering intent to the treatment while beliefs which are negative were related to the reduced adherence to medication. The education about disease plays a significant role in just how the patient seems to perceive the disease and its threat. However, the information on diabetes along can be referred to as not sufficient or enough for self-management which is effective. Research on patients with the diabetes consistently demonstrates that the information on diabetes is not an individual factor which is associated with adherence to medication, exercise, diet, and self-care. Access, motivation, and confidence towards care also play significant and critical roles.
The providers of healthcare play a significant part in the adherence to treatment through their association with the patient. Creating a successful environment that centers the patient, provides patient education, and ensures open communication can assist the patients effectively in overcoming the barriers. Making sure that patients meet their appointments of follow-up and have a sufficient support along with monitoring of patient are significant elements in delivering successful healthcare while encouraging the patients for adhering to the treatment. Systematic methods of patient assessment like the MTM or medication therapy management, Spider WEB, can assist the training clinicians in integrating adherence considerations consistently into care plans which are patient-centers that promote and don’t hinder the adherence of patient.
        External factors can actually be associated with the treatment or disease itself. The potency of the disease, its response, and duration in terms of treatment all seem to play a significant role in influencing the perception of patients regarding the diseases and their adherence. Factors which are associated with the medication are some of the most common factors for non-adherence to the treatment of insulin. Such factors include adverse effects, access to care, and cost. A research regarding 507 students with the disease of diabetes highlighted that only twenty-two percent of the patients with diabetes of 1 type and twenty-four percent of the patients with T2D had a high adherence to the treatment. Adverse eveents which are associated with the treatment including weight gain, feeling bad after the injection of insulin, and reactions of injection-site were associated significantly with low adherence. Difficulties, cost, and insulin shortage in preparing the injection also were importantly related with low adherence.

        The cost of treatment, specifically with a low status of socioeconomic might be a factor which is limiting to the medication adherence. Researches have actually shown that costs which are related to the treatment of diabetes might seemingly result in patients that are diagnosed newly not seeking care after diagnosis, or inappropriate, or inconsistent usage of medication. A regression and bivariate analysis of the 2011-13 Survey of Medical Expenditure Panel highlighted that among the adults who are privately insured with diabetes that compared low deductible with no-deductible group. The group which is low-deductible has twenty-seven percent of the primary visits of care. Thirty-nine percent of checkups and seventy-seven percent fewer visits of specialty care (Zhuo, Zhang and Hoerger 2013).

        According to Ikonen, Sund, Venermo, and Winell (2010), in Finland, the presence of Type 2 diabetes have actually been raised quite considerably recently and the figures from the Association from Finnish Diabetes estimates that there three-hundred and thousand individuals diagnosed with Type 2 diabetes and there are countless others with diabetes which are undiagnosed. The increasing and high availability with the Type 2 diabetes management in both growing and substantial.

        In Finland, the healthcare systems offer universal coverage, even though this is organized largely on a municipal level instead of national level, resulting is some type of variation in the allocation of resource among different municipalities. Diabetes treatment is normally reimbursed fully in Finland and it is actually estimated that the management of related complications and diabetes now accounts for almost twelve to fifteen percent of the total healthcare investment.

        Additionally, almost ninety percent of the medical costs which are direct are related to diabetes can be attributed to the management of complications which are related to diabetes with an annual direct costs for the patients with Type 2 diabetes with complications which are related to diabetes and is being almost twenty times higher than for the patients without any type of complications. Complications which are macrovascular in particular are the complications which are the leading cause of mortality and morbidity in patients who are suffering from diabetes, and these complications seem to account for a sufficient proportion for the complete utilization of healthcare resource. For instance, in 2002, in Finland, it was measured that twenty-five percent of all the myocardial infractions and additional fifty-four to sixty percent of the amputations of lower limb occurred in individuals suffering from diabetes.

        The analysis was carried out utilizing the Diabetes Model of IQVIA CORE ICDM which is actually a validated long-term model of cost-effectiveness that can be used for analyzing either Type 2 diabetes or Type 1 diabetes. It is simply based on a series of sub-models which are inter-dependent in a structural way including renal, ophthalmic, and cardio-vascular complications; diabetic food, neuropathy, and peripheral vascular illness complications; along with acute events which include hypoglycemic events.

        The sub-models actually have a structure of semi-Markov together with diabetes which depend on probabilities which are derived from the published literature for simulating the progression of disease. Simulation of Monte Carlo using the tracker variable for overcoming the properties which are memory-less of a standard model of Markovf and allows for interaction and interconnectivity among sub-models. For each simulation of model a cohort of almost thousand simulated patients was operated through the model which used the first-order simulation of Monte Carlo.

        In Finland, it can be said that CSII is more likely to highlight a treatment alternative which is cost-effective for patients with the Type 2 diabetes and poor or inefficient glycemic control in spite of the MDI optimization. CSII is seemingly related to clinical outcomes which are associated with MDI with a high costs of acquisition partly offset by a decreased lifetime cost and incidence of complications which are related to diabetes (Ikonen, et al. 2010).

        Arora, Peters, Burner, Lam, and Menchine (2014) explains that interventions of text message have a potential for a very high population; need minimal resources and it can be translated into existing services and routine practice. Ninety-six percent of adults in Australia utilize phones and it has been determined that this utilization is quite high amongst individuals from remote, rural, and socioeconomic areas and those with a higher index of body mass, health comprises lower levels and behaviors of health which is self-rated.

        Characteristics which are socio-demographic align quite closely with the ones having quite a high percent of diabetes which are Type 2, suggesting that the intervention of text message could be quite an authentic method for supporting the self0management of their conditions. It has been shown by research that interventions of text message can optimize cardiac patients’ short-term health, help in promoting the cessation of smoking, and help in the weight loss by offering prompts, support, reminders, and information. Researches have also determined that the text messaging can help in lowering the blood pressure while improving the adherence to medication, weight loss, physical activity, and glycaemic control. However, these researches have actually been limited because of the factors such as a short duration of study, lack of theories, and small size of sample (Arora, et al. 2014).

        The trial which is randomized controlled will seemingly include 2 parallel arms, a control arm, and an intervention. The six-month intervention of arm will be received in the arm. Both of the arms will obtain the usual care from their health professionals and treating doctor. The intervention of text message was actually developed by an expert panel of health promotion staff, academics, and clinicians using guidelines which are based on evidence along with recommendations. The content of the designed messages actually has a readability grade of 5-6. Each and every message was seemingly limited to one-hundred and sixty characters and no message was repeated during the process of intervention. Furthermore, the messages were tested with twenty individuals with Type 2 diabetes in a pilot program of 5-week pilot for determining the message acceptability and ensuring the content of message, structure, and tone was authentic. And adjustments to the messages simply were made on the basis of feedback of participant pilot. The text messages of DTEXT were just designed utilizing the system of COM-B from the Wheel of Behavior Change (Liang, et al. 2011).

    This system actually raises the motivation, opportunity, and capability of an individual for enhancing their self-management of lifestyle and health associated with behaviors with the objective of optimizing the control of diabetes. Behavior change of an individual is actually a critical component for achieving the conditions’ self-management (Borus and Laffel 2010).

Future Implications of Impact of Type 1 Diabetes and its Prevention

        One of the most common issues that have been identified while reading the reviews and conducting a systematic literature review on finding the human and financial impact of Type 2 diabetes is that there weren’t suitable resources that allowed the authors to conduct their study on a better and larger scale. Diabetes is actually an epidemic that is increasing at a drastic pace and as the time is passing, patients with diabetes are increasing. Authors require enough resources for conducting their study and researching the issues. These resources include time, cost, and work. Considering the fact that the topic is all about massive international issue like diabetes, it is understandable that the authors have to not only conduct research but they also have to study the past works. Some of the theories and models in the studies are not accessible and they require the authors to invest their money in purchasing the sources from which they can gain information. Furthermore, finding the suitable resources for credible information also needs a lot of time because not all the sources are available on the internet. Therefore, authors also have to search manually by visiting libraries and other societies, where they can get the required information. This whole process is very time-costly and it is quite a long process because authors cannot access all the require data so quickly. Another important complication is that there is a lack of practicality in the models which have been designed and implemented. Models need to be implemented on patients suffering from Type 2 diabetes and it hasn’t been possible in most of the studies. This is a major limitation that has been observed in the articles utilized for finding the data. Studies that focus on such a big and international disease must have sufficient resources along with a large size of sample on which the proposed model can be applied and findings can be achieved for suggesting the results.

        For improving the potential of the studies, it is important to use more theories in the research and attempt to use the information which incorporates facts or data with the statements because it demonstrates that statements are not based on evidence. Furthermore, it is critical for the authors to utilize larger sample sizes for conducting their surveys and implementing their models to gain the information which can be relied upon for extending the studies. It can be said that this study will be used as a foundation for conduction further studies and conducting research on the Type 2 diabetes which has become one of the most common non-communicable diseases.

References of Impact of Type 1 Diabetes and its Prevention

Breeze, P. R., C. Thomas, H. Squires, A. Brennan, C. Greaves, Peter J. Diggle, E. Brunner, A. Tabak, L. Preston, and J. Chilcott. 2017. "The impact of Type 2 diabetes prevention programmes based on risk‐identification and lifestyle intervention intensity strategies: a cost‐effectiveness analysis." Diabetic Medicine 34 (5): 632-640.

CDC. 2019. Diabetes—A Major Health Problem. https://www.cdc.gov/diabetes/ndep/pdfs/ppod-guide-diabetes-major-health-problem.pdf.

Foos, Volker, Nebibe Varol, Bradley H Curtis, Kristina S. Boye, David Grant, James L Palmer, and Phil McEwan. 2015. "Economic impact of severe and non-severe hypoglycemia in patients with Type 1 and Type 2 diabetes in the United States." Journal of Medical Economics 18 (6): 420-432.

Haghparast-Bidgoli, Hassan, Sanjit Kumar Shaha, Abdul Kuddus, Md Alimul Reza Chowdhury, Hannah Jennings, Naveed Ahmed, Joanna Morrison, et al. 2018. "rotocol of economic evaluation and equity impact analysis of mHealth and community groups for prevention and control of diabetes in rural Bangladesh in a three-arm cluster randomised controlled trial." BMJ Open 8 (8).

Healthypeople. 2019. Diabetes. https://www.healthypeople.gov/2020/topics-objectives/topic/diabetes.

Hippisley-Cox, Julia, Carol Coupland, Yana Vinogradova, John Robson, and P. Brindle. 2008. "Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study." Heart 94 (1): 34-39.

Ikonen, Tuija S., Reijo Sund, Maarit Venermo, and Klas Winell. 2010. "Fewer major amputations among individuals with diabetes in Finland in 1997–2007: a population-based study." Diabetes care 33 (12): 2598-2603.

Kalyani, Rita Rastogi, Christopher D. Saudek, Frederick L. Brancati, and Elizabeth Selvin. 2010. "Association of diabetes, comorbidities, and A1C with functional disability in older adults: results from the National Health and Nutrition Examination Survey (NHANES), 1999–2006." Diabetes care 33 (5): 1055-1060.

Liang, Xiaohua, Qianqian Wang, Xueli Yang, Jie Cao, Jichun Chen, Xingbo Mo, Jianfeng Huang, Lu Wang, and Dongfeng Gu. 2011. "Effect of mobile phone intervention for diabetes on glycaemic control: a meta‐analysis." Diabetic medicine 28 (4): 455-463.

Morello, Candis M., and Jan D. Hirsch. 2017. "Utilizing Advances in Diabetes and Targeting Medication Adherence to Enhance Clinical Outcomes and Manage Costs for Type 2 Diabetes Posttest."

Roze, Stephané, Jayne Smith-Palmer, Alexis Delbaere, Karita Bjornstrom, Simona de Portu, William Valentine, and Mikko Honkasalo. 2019. "Cost-Effectiveness of Continuous Subcutaneous Insulin Infusion Versus Multiple Daily Injections in Patients with Poorly Controlled Type 2 Diabetes in Finland." Diabetes Therapy 1-12.

Waller, Karen, Susan Furber, Adrian Bauman, Margaret Allman-Farinelli, Paul van den Dolder, Alison Hayes, and Franca Facci et al. 2019. "DTEXT–text messaging intervention to improve outcomes of people with type 2 diabetes: protocol for randomised controlled trial and cost-effectiveness analysis." BMC public health 19 (1): 262.

Zhuo, Xiaohui, Ping Zhang, and Thomas J. Hoerger. 2013. "Lifetime direct medical costs of treating type 2 diabetes and diabetic complications." American journal of preventive medicine 45 (3): 253-261.