Population Health Research and PICOT Statement.
Population Health Research and PICOT Statement
In elderly patients with type 1 diabetes mellitus (T1DM), how does continuous glucose monitoring (CGM), when compared to the traditional self-monitoring of blood glucose (SMBG) levels decrease hypoglycemic events, improve glycemic control, and reduce hospital readmissions or admissions in the long-term.Population Health Research and PICOT Statement.
Demographic Description of Health Population
Diabetes is one of the leading causes of death in the United States. The National Diabetes Statistics Report revealed that there are 88 million adults with prediabetes and 34.2 million with diagnosed diabetes. Out of the diagnosed cases, 26.8% were older patients aged over 65 years, followed by those between 45 and 64 years (Center for Disease Control and Prevention, 2020). However, in Texas, the condition is more prevalent in patients between 45 and 64 years, followed by patients over 76 y/o. The prevalence of the disease in the state is higher than the national average. Rusk, Wichita, Waller, and Nueces Counties have higher prevalence rates than the State and national average. About 2.3 million Texans were diagnosed with diabetes, 11.2% of whom were adults. The condition is disproportionately concentrated in East Texas, in older patients and ethnic minorities in the region (Texas Demographic Center, 2018).Population Health Research and PICOT Statement.
The condition is also nationally prevalent among American Indians/Alaska Natives and people of Hispanic origin in the country. About 1.4 million of the diagnosed adults have type 1 diabetes (T1D), while type 2 diabetes (T2D) accounts for 95% of all the diagnosed cases. The education level and socioeconomic status of patients significantly influence the prevalence of the condition. According to the Center for Disease Control, 13.3% of those diagnosed with diabetes have less than high school education, 9.7% with at least a high school education, and 7.5% with tertiary education (Center for Disease Control and Prevention, 2020). Complications associated with the condition include chronic kidney disease, stroke, heart diseases, lower-limb amputations, and adult-onset blindness.
Explain How Nursing Science, Health Determinants, Epidemiologic, Genomic, and Genetic Data Impact Population Health Management for the Selected Population
Health determinants typically fall into multiple broad categories: Social factors, health services, biology and genetics, individual behavior, and policymaking. Policies at federal, state, and local levels impact population and personal health for diabetic patients. Social health determinants typically reflect the physical conditions and social factors of an individual’s environment, and they affect a broad range of functioning, quality-of-life, and health outcomes. Examples of social health determinants that impact adult diabetic patients in Texas include the accessibility to resources that meet daily needs such as healthy foods, living wages, job, and educational opportunities, social attitudes, and norms towards the diabetes management, social support and interactions, socioeconomic conditions, for instance, poverty, and transport options. Physical health determinants include the built environment, e.g., transportation, exposure to toxic substances, and neighborhoods, housing, and homes.
Accessing to health services can affect the health of diabetic patients. Limited access or lack of access to health services greatly impacts the health status of diabetic patients. Adult diabetic patients in Texas encounter various barriers to healthcare access; these barriers include language barriers, particularly among the Hispanic community, lack of insurance coverage, high diabetes care costs, and limited access to specialized care. The individual behavior of diabetic patients also impacts their health outcomes. For instance, a diabetic patient who does not smoke is physically active, takes his/her medication, as per the physician’s instructions, and observes a healthy diet has minimal risk for rehospitalization or hospital admissions. Various genetic and biological factors impact specific populaces more than the others. Type 1 diabetes is an autoimmune condition characterized by the destruction of pancreatic β cells that produce insulin. The genetic background of the patient is an essential component in the destruction of beta cells. Diabetes Type 1 is linked to the HLA class II genes, which account for 30-50% of the gene-associated risk factors. Environmental factors may trigger the immune-mediated destruction of the β cells. Although patients with T1D may lack a family history of the condition, the presence of HLA genotype and insulin gene polymorphism increases susceptibility to the condition. The probability of developing T1D with no family history is 0.4%, relation with affected mother is 1% to 4%, relation with affected father is 3% to 8%, and a relationship with both parents affected is 30%.Population Health Research and PICOT Statement.
Nursing science impacts the management of diabetes in the populace by influencing the development of practical conceptualizations and theories for improving how clinicians/healthcare providers and patients administer diabetes care and manage the condition. Nursing science typically merges the worlds of human, applied, and natural science holistically into a multidimensional lens that explores new and improved ways to deliver healthcare services to diabetes patients. Furthermore, it emphasizes the importance of patient-centered care, and it contributes to the discovery and research of innovative approaches that aim to improve the health outcomes of diabetic patients. Nurses understand their patients best, and, according to AUTHOR, the communication and trust between nurses and their patients promote better patient experiences, diagnoses, and treatment.Population Health Research and PICOT Statement.
In a world of the ever-increasing technological diagnoses and empirically/practically infinite/interminable data points, nursing science maintains a crucial human aspect in the balance of diabetes care. For instance, telenursing puts cutting-edge technology in nurses’ hands, allowing them to monitor patients with chronic diseases, e.g., diabetes and to offer critical care to patients located in remote regions. Telenursing removes/eliminates the burden of transportation and distance, thereby increasing care beyond hospital admissions, providing healthcare access to patients with mobility issues, and even minimizing response times. Moreover, telenursing decreases costs by structuring therapy sessions, allowing self-test, and sorting patients as per urgency prior to them showing up at the care facility. Epidemiological data promotes the identification of the distribution of the disease, i.e., diabetes, the factors that underlie its cause and source, and methods/approaches for its control. Genetic and genomic data, on the other hand, facilitate researchers’ capacity to predict who might develop the disease (i.e., diabetes), the personalization of treatment, and the likelihood of identifying the genetic correlation between phenotype and genotype.Population Health Research and PICOT Statement.
Potential solution and PICOT statement
The Use of Continuous Glucose Monitoring
Measuring glycated hemoglobin has been a gold standard for the management of diabetes. However, these methods do not reflect the intra and interday glycemic levels, which increase the risk of microvascular and macrovascular complications. Intermittently-viewed CGM and continuous glucose monitoring can be used to address such complications. Patients can opt to use continuous glucose monitors or self-monitoring testing strips to measure and monitor the concentration of blood sugar glucose.Population Health Research and PICOT Statement.
Although SMBG has been proven to be helpful or correlates with the efficient management of diabetes in non-insulin treated and insulin-treated diabetes, it has notable drawbacks. First, according to Danne, Nimri, Battelino, Bergenstal, Close, and Garg (2017), the aforementioned approach requires a fingerstick to get a blood sample. Furthermore, SMBG only offers a single “point-in-time” measurement that provides no indication of the rate or direction of the change in glucose levels; therefore, utilizing SMBG data solely may trigger inappropriate treatment decisions (for instance administering correction insulin in cases where there is a decrease in glucose levels). Secondly, securing glucose data using SMBG depends on the self-monitoring decision of the patient. Thirdly, SMBG typically fails to detect asymptomatic and nocturnal hypoglycemia.Population Health Research and PICOT Statement.
Intermittently-viewed CGM, according to Danne et al. (2017), usually provides the current glucose value as well as retrospective glucose information for a particular period upon “scanning.” Two surveys using iCGM have shown statistically significant improvements in hypoglycemia, user satisfaction, glycemic variability, and time-in-range (Danne et al., 2017). RT-CGM (a CGM medical device) in unblinded mode offers real-time graphical and numerical data regarding a patient’s current glucose trends, glucose level, and the rate/direction of change of glucose. CGM devices with programmable alarms/alerts that warn users about the current or/and impending low or high glucose provide additional safety benefits. Population Health Research and PICOT Statement.Several studies have demonstrated that the utilization of RT-CGM improves the quality-of-life and glycemic control in both adults and children with T1D with either multiple daily insulin injection therapies or subcutaneous insulin infusion (Welsh, 2018). The use of rtCGM, according to Danne et al. (2017), aids in improving HbA1c, decreasing the period spent in hyperglycemia and hypoglycemia, and decreasing moderate-to-severe hypoglycemia. RT-CGM’s benefits are typically observed in patients who utilize these devices frequently. The cost-efficacy of rtCGM over SMBG has also been reported using a large populace-based model (Danne et al., 2017). Furthermore, in a lifetime evaluation/analysis, the use of RT-CGM was shown to reduce overall complications related to diabetes.Population Health Research and PICOT Statement.
A real-world survey conducted by Charleer, Mathieu, Nobels, Block, Radermecker, and Hermans (2018) which aimed to evaluate the effect of RT-CGM in actual-world settings on QOL, work absenteeism, hospital admissions, and glycemic control revealed that sensor-augmented pump therapy in T1D patients aid in improving HbA1c, QOL, and hypoglycemia and in reducing hospital admissions due to acute diabetes complications and work absenteeism. Sensor-augmented pump therapy has also been ascertained to be cost-effective for the treatment of T1D (Danne et al., 2017). A review by Rodbard (2017) also demonstrates the importance of CGM in improving glycemic outcomes. According to Rodbard (2017), CGM has been shown to be clinically valuable, decreasing risks for hyperglycemia and hypoglycemia, improving patients’ QOL for a broad range of patient populaces and clinical indications, and providing glycemic variability. Rodbad (2017) further argues that the use of CGM can aid in reducing mean glucose and HbA1c. In a systematic review, Janapala, Jayaraj, Fathima, Kashif, Usman, and Dasari (2019) conclude that the utilization of CGM in T2D is beneficial because it significantly decreases HbA1c compared to the traditional SMBG method. Danne et al. (2017) recommend the use of CGM in conjunction with HbA1c during the assessment of glycemic status and therapy adjustment for T1D patients and T2D patients treated with intensive insulin therapy who aren’t attaining glucose targets, particularly if the patient experiences problematic hypoglycemia.Population Health Research and PICOT Statement.
Two experimental studies demonstrated that CGM could reduce the risk of hypoglycemia by 33% to 50%. Apart from the recent improvements in the accuracy of the calibration of the CGMs, the tools are FDA approved for non-adjuvant use. A recent crossover randomized study demonstrated that CGM is useful for T1D management in special health populations, including pregnant women, hypoglycemic elderly with poor glycemic control, and hospitalized patients (Rodbard, 2017).Population Health Research and PICOT Statement.
How the solution incorporates health policies and goals that support health care equity
The management of diagnosed diabetes is estimated to cost $327 billion annually (Janapala et al., 2019). Cost is a major influencing factor, not just inequitable healthcare but in diabetic management. Most insurance plans, including Medicare, only cover a portion of the total cost of healthcare, forcing patients to copay in total healthcare costs. The healthcare system in the United States is marked by significant inequalities across economic, gender, and racial lines. There are approximately 29 million uninsured Americans (Glantz, Duncan, Ahmed, Fan, Reed, & Kalirai, 2019). A recent study showed that the cost of diabetics for Hispanics and older patients in the US is significantly higher than in other health populations.Population Health Research and PICOT Statement. Medicare Part B covers CMG monitors, test strips, lancet devices, and glucose control solutions for all diabetic patients regardless of whether they use insulin or not. Patients on insulin receive a maximum of 300 test strips and lancets after every three months, while those not on insulin receive a maximum of 100 test strips, lancets, and other testing supplies recommended by a healthcare professional (American Diabetes Association, n.d.). The use of Continuous Glucose Monitoring incorporates the concepts of health equity because they are not only cost-effective but also user friendly for any health population regardless of their education level, the socioeconomic and ethnic background of the patient (Janapala et al., 2019).Population Health Research and PICOT Statement.
Although patients with T1DM know the advantages of having optimal blood glucose control, most patients have suboptimal blood glucose control. Poor control increases micro and macrovascular risks, which subsequently increases healthcare costs and reduces the QoL (Wan et al., 2018). A major barrier to ensuring intensive blood glucose management is increased hypoglycemic risk, which negatively impacts the patient’s QoL and management of diabetes. Population Health Research and PICOT Statement.
CGM emerged as a more precise and accurate method to achieve glycemic control with improved decision-making in the management of diabetes. Compared with SMBG, CGM decreases HbA1C without increasing hypoglycemia with the highest HbA1c levels at baseline. Current evidence suggests that good glycemic control decreases and prevents complications in individuals with T1DM (Polonsky et al., 2017). Presently, the most common CGM systems are either connected to insulin pumps or are standalone systems that interrupt the administration of insulin to 2 hours when the concentration of glucose goes below a given threshold.Population Health Research and PICOT Statement.
This paper reviews literature to find evidence that supports CGM as the most effective intervention compared to SMBG to improve blood glucose control, prevent hypoglycemic events, and reduce admissions among patients with T1DM. It describes the methods used by the author to search for literature, and an analysis of the methods and findings of each article. It also compares the limitations, differences, and similarities of literature and discusses the implications for future research.Population Health Research and PICOT Statement.
The author conducted an initial search for evidence-based literature in Cochrane, PubMed, and Medline databases. The author used the following keywords; T1DM, CGM, SMBG, and glycemic control. For a more refined search outcome, the author used ‘or’ and ‘and’ Boolean search operators. The general search yielded 12 articles. To obtain the most recent and best articles, the author used the following exclusion and inclusion criteria:
The author selected articles published within the last five years in English. These studies included both qualitative and quantitative articles with experimental and non-experimental designs. The search focused on articles whose study sample had type 1 DM and used CGM as a primary intervention and SBGM as a comparison with the following outcome measures; decreased hospital admissions and readmissions, reduced hypoglycemic events, and improved glycemic control. The author also considered articles with other significant outcome measures, such as improved QoL or cost-effectiveness.Population Health Research and PICOT Statement.
The author excluded articles that were not published in English and were beyond the last five years. Besides, articles whose population sample focused on patients with type 2 DM or other types of diabetes, did not use CGM and SBGM as the primary and comparison interventions respectively were excluded. The final search yielded five articles.
The article by Charleer et al. (2018) is a quantitative study with a multicenter prospective, observational cohort design whose purpose was to examine how CGM impacts glycemic control, absenteeism, hospital admission, and QoL. The study included 515 adult participants with type 1 DM who were on CGM (intervention). The researchers analyzed data from all patients initiated in a CGM reimbursement program. Charleer et al. (2018) found that 81 %( 417) participants used CGM for 12 months. At the start of the program, the baseline HbA1Cwas 7.7 (9.8mmol/mol) and reduced to 7.4.
There was a potential reduction in the HbA1C levels of participants who began CGM due to poor blood glucose control at 4, 8, and 12 months in comparison to those who began CGM due to pregnancy or hypoglycemia. Besides, 16% of the participants were admitted for severe ketoacidosis or hypoglycemia in comparison to 4% (P < 0.0005) after the intervention (Charleer et al., 2018). Besides, days of admission reduced (P < 0.0005) (54 to 18 per 100 patient-years) Within the same period, there was a significant improvement in the QoL and a decrease in absenteeism and hypoglycemic fear (Charleer et al., 2018).
Beers et al. (2016) conducted RCTs in two medical clinics to determine how CGM impacts T1DM patient’s hypoglycemia awareness. The study included 52 patients between 18-75 years, diagnosed with T1DM. The researchers randomly assigned participants to an intervention group, CGM (16 weeks) group including a 12-week washout and 16 weeks SMBG or to a comparison group, SMBG (16 weeks) group with 12 weeks washout and 16 weeks CGM (Beers et al., 2016). The intervention group (CGM-SMBG) had 26 participants, while the comparison group (SMBG-CGM) had 26 participants. These researchers noted a decrease in hypoglycemic time and hyperglycemia (p<0·0001, and 5·0%, 3·1–6·9; p<0·0001) respectively. Besides, there was a decrease hypoglycemic events in the CGM than the comparison phase (SBMG) (p=0·033).Population Health Research and PICOT Statement.
Wan et al. (2018) conducted a DIAMOND RCTs to evaluate how cost-effective CGM was in patients with T1DM. They randomized 158 patients with HbA1C exceeding 7.5% and T1DM to either CGM (intervention) or a control group. Participants filled surveys after six months, and complications were simulated using a modified TID policy model. The primary outcomes were costs per QALY (quality-adjusted life-year). Wan et al. (2018) noted that the QALYs in the intervention and control groups were similar (0.462 vs. 0.455, P = 0.61).
The study by Polonsky et al. (2017) is a quantitative article with an RCT design whose purpose was to establish whether CGM improves QoL. The researchers conducted a prospective RCT to assess CGM vs. SMBG among 158 adult participants with poorly controlled T1DM. The participants completed a QoL measure before the study and a CGM satisfaction survey after the study. QoL changes were analyzed using linear regression. The researchers further assessed relationships between outcomes of QoL changes and CGM satisfaction. Polonsky et al. (2017) found a significant increase in confidence and a marked reduction in distress (P=0.01) in the intervention group than the comparison group. However, there was no difference in fear of hypoglycemia and well-being. Satisfaction with CGM had no association glycemic changes but was linked to decreases in hypoglycemic fear (P=0.002) and diabetic distress and (P < 0.001) and increased hypoglycemic confidence and well-being (P=0.01).
The article by Olafsdottir et al. (2018) is a quantitative study with an RCT design that examined how CGM impacts hypoglycemic confidence, daytime, and nocturnal hypoglycemia, and glycemic variability in patients with T1DM. The researchers performed evaluations from GOLD RCTs with 161 participants over 69 weeks, where a comparison was made between CGM and SMBG in participants with T1DM. The researchers conducted evaluations using the hypoglycemia confidence questionnaire and masked CGM. Olafsdottir et al. (2018) found a 48 % (P<0.001) decrease in nocturnal hypoglycemia (<70mg/dL glucose levels), 65% decrease in glycemic levels (<54mg/dL) (P < 0.001). Daytime hypoglycemia also decreased by 40% (P < 0.001) compared with 54% (P < 0.001) (Olafsdottir et al., 2018). Besides, CGM increased hypoglycemia confidence (P=0.016). When using CGM, the participants reported a lot of confidence (P = 0.0033) in identifying and responding to hypoglycemia.Population Health Research and PICOT Statement.
A major similarity among all the studies is that the researchers focused on adult patients with type 1 DM as the population sample with CGM as the intervention and SMBG as the comparison. However, in the study by Wan et al. (2018), apart from having a diagnosis of T1DM, the participants also had an HbA1C >7.5%. Polonsky et al. (2017) included adult patients aged >25 years with an HbA1C 7.5–10.0%. Beers et al. (2016) included patients 18-75 years managed with either SMBG or CGM, while Olafsdottir et al. (2018) included patients aged 18 years with T1DM and HbA1C >7.5%.
A limitation of the study by Charleer et al. (2018) was that other factors such as education, training, and frequent contact with healthcare providers at the initiation of CGM contributed to the outcome benefits. Besides, since it was observational, the study did not have a prospective comparison group. Beers et al. (2016) used CGM devices whose accuracy differed, yet when interpreting CGM derived data, researchers should take accuracy to account. Besides, the CGM devices that the researchers used could be outdated as the CGM devices entered the market when the study was ongoing.
Wan et al. (2018) used patient-reported descriptions of NSHEs (nonsevere hypoglycemic events), which differ with the international description of 54mg/Dl achievable by a CGM device for 20 successive minutes. Besides, there were possible inaccuracies in the number of days worked with a productivity of 50%, missed days of work, and hours devoted to the self-management of diabetes care daily (Wan et al., 2018). Although these limitations had minor implications on the statistical significance, they had no clinical significance. The statistical implications, however, cannot limit the applicability of the outcomes general population settings. Besides, since all studies only incorporated adult patients diagnosed with T1DM, researchers cannot apply the findings to other patient groups, especially patients with T2DM.Population Health Research and PICOT Statement.
Areas of Further Study
The findings of this literature review reveal that CGM is a common, precise, and more accurate method to achieve glycemic control with improved decision-making in the management of diabetes compared to SMBG. CGM reduces hospital admissions and readmissions, decreases hypoglycemic events, and improves blood glucose control in T1DM. Further studies should examine whether similar outcomes can be observed in other patient groups such as those aged 17 years and younger with T1DM or patients with T2DM.
The findings from the literature review recommend using CGM as an effective intervention to reduce hospital admissions and readmissions, decrease hypoglycemic events, and improve blood glucose control in T1DM. A major similarity of the studies is that the population samples were adult patients diagnosed with T1DM, and CGM was the primary intervention with SMBG as the comparison method. Further studies should examine whether similar outcomes can be observed in other patient groups such as those aged 17 years and younger with T1DM or patients with T2DM.Population Health Research and PICOT Statement.