Treatment of hypertension in rural Cambodia: results of a 6-year programme

This study was aimed to describe the outcomes of a hypertension treatment programme in two outpatient clinics in Cambodia. We determined proportions of patients who met the optimal targets for blood pressure (BP) control and assessed the evolution of mean systolic and diastolic BP (SBP/DBP) over time. Multivariate analyses were used to identify predictors of BP decrease and risk factors for LTFU. A total of 2858 patients were enrolled between March 2002 and June 2008 of whom 69.2% were female, 30.5% were aged >/=64years and 32.6% were diabetic. The median follow-up time was 600 days. By the end of 2008, 1642 (57.4%) were alive-in-care, 8 (0.3%) had died and 1208 (42.3%) were lost to follow-up. On admission, mean SBP and DBP were 162 and 94 mm Hg, respectively. Among the patients treated, a significant SBP reduction of 26.8 mm Hg (95% CI: 28.4-25.3) was observed at 6 months. Overall, 36.5% of patients reached the BP targets at 24 months. The number of young adults, non-overweight patients and non-diabetics reaching the BP targets was more. Older age (>64 years), uncontrolled DBP (>/=90 mm Hg) on last consultation and coming late for the last consultation were associated with LTFU, whereas non-diabetic patients were 1.5 times more likely to default than diabetics (95% CI: 1.3-1.7). Although the definite magnitude of the BP decrease due to antihypertension medication over time cannot be assessed definitely without a control group, our results suggest that BP reduction can be obtained with essential hypertension treatment in a large-scale programme in a resource-limited setting

The Effectiveness of Telephone Follow-Up for Diabetes Management

Approved May 2020 by faculty of UMKC in partial fulfillment of the requirements for the degree of Doctor of NursingDiabetes is the seventh leading cause of death in the United States with 30.3 million Americans having the disease and a cost of $327 billion. Approximately 300,000 people are affected in Kansas, a cost of$ 2.6 billion, and $6.7 billion in Missouri. The burden of diabetes to society includes increased resource expenditure and reduced productivity. This quasi-experimental quality improvement study, one cohort, with pre and post-test included eighteen adult participants with diabetes at one clinic in the central Midwest. Participants received telephone calls and short message services weekly for four weeks and then every two weeks for eight weeks. Fasting blood glucose levels, hemoglobin A1C, and a Summary of Diabetes Self-Care Activities Scale were collected pre-and-post intervention. Telephone follow-up reduced hemoglobin A1C and fasting blood glucose levels, and significantly increased adherence to diet, exercise, blood glucose testing, and foot care. Telephone follow-up to patients with uncontrolled diabetes can improve adherence to diabetes self-management skills

IoMT innovations in diabetes management: Predictive models using wearable data

Diabetes Mellitus (DM) represents a metabolic disorder characterized by consistently elevated blood glucose levels due to inadequate pancreatic insulin production. Type 1 DM (DM1) constitutes the insulin-dependent manifestation from disease onset. Effective DM1 management necessitates daily blood glucose monitoring, pattern recognition, and cognitive prediction of future glycemic levels to ascertain the requisite exogenous insulin dosage. Nevertheless, this methodology may prove imprecise and perilous. The advent of groundbreaking developments in information and communication technologies (ICT), encompassing Big Data, the Internet of Medical Things (IoMT), Cloud Computing, and Machine Learning algorithms (ML), has facilitated continuous DM1 management monitoring. This investigation concentrates on IoMT-based methodologies for the unbroken observation of DM1 management, thereby enabling comprehensive characterization of diabetic individuals. Integrating machine learning techniques with wearable technology may yield dependable models for forecasting short-term blood glucose concentrations. The objective of this research is to devise precise person-specific short-term prediction models, utilizing an array of features. To accomplish this, inventive modeling strategies were employed on an extensive dataset comprising glycaemia-related biological attributes gathered from a large-scale passive monitoring initiative involving 40 DM1 patients. The models produced via the Random Forest approach can predict glucose levels within a 30-minute horizon with an average error of 18.60 mg/dL for six-hour data, and 26.21 mg/dL for a 45-minute prediction horizon. These findings have also been corroborated with data from 10 Type 2 DM patients as a proof of concept, thereby demonstrating the potential of IoMT-based methodologies for continuous DM monitoring and management.Funding for open Access charge: Universidad de Málaga / CBUA. Plan Andaluz de Investigación, Desarrollo e Innovación (PAIDI), Junta de Andalucía, Spain. María Campo-Valera is grateful for postdoctoral program Margarita Salas – Spanish Ministry of Universities (financed by European Union – NextGenerationEU). The authors would like to acknowledge project PID2022-137461NB-C32 financed by MCIN/AEI/10.13039/501100011033/FEDER(“Una manera de hacer Europa”), EU

The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain

Evaluation of a Comprehensive Diabetes Mellitus Protocol at a Rural, Federally Qualified Health Center in Southern West Virginia

Background: Diabetes mellitus is a chronic disease that affects nearly 34 million Americans. In rural Appalachia, the population is affected disproportionately at a rate of 14% compared to the national average of 10%. Diabetes is a lifelong, chronic condition managed best by a multidisciplinary team-based approach to achieve optimal disease control. Best practices in the care of diabetes support the use of evidenced based care protocols and leveraging technology to decrease the burden of disease. Type 2 diabetes mellitus (T2DM) is the most common type, making it the focal population for evaluation. Purpose: The purpose of this project was to evaluate the impact of a standardized diabetes mellitus protocol for patients with T2DM at a rural federally qualified health center (FQHC) in rural southern West Virginia. Program evaluation completes the care cycle. This information can inform stakeholders about a protocol’s effectiveness, thus leading to recommendations for change to improve T2DM education and outcomes in healthcare delivery. Intervention and Methods: Program Evaluation was completed using a retrospective chart review and a provider survey. Objective 1 was to evaluate the diabetes protocol using seven core quality measures (hemoglobin A1c, blood pressure, low density lipoprotein [LDL] cholesterol, diabetes self-management education (DSME), annual urine microalbumin, retinopathy, and neuropathy exams) over three years (pre-protocol T1 and post-protocol T2 and T3). Objective 2 utilized a provider survey to determine behaviors regarding Type 2 Diabetes Mellitus (T2DM) protocol and diabetes education team awareness and utilization. Results: Results for Objective 1 found statistically significant improvement at T3 for diastolic blood pressure and annual microalbumin, but not for other metrics. Overall, most metrics noted improvement or stabilization over all time periods despite the evaluation taking place during the COVID-19 pandemic. Results for Objective 2 found that majority of providers were aware of the T2DM protocol and utilized the diabetes education accreditation program (DEAP) team regularly. Conclusion: The evaluation provided valuable insight on the current efforts to reduce the burden of diabetes mellitus at the facility in rural West Virginia. Over half of all core quality measures met facility benchmarks, however measures for DSME referral, A1c, retinopathy and neuropathy exams are still lower than expected. All providers agree that COVID-19 had a negative impact on patient care. Recommendations for improvements in practice include a patient-individualized approach to care with increasing utilization of the DEAP team, and continuous provider support of DSME in the management of patients with T2DM

Diabetes-Related Complication in Canada; Prevalence of Complication, Their Association with Determinants and Future Potential Cost-Effectiveness of Pharmacy-Based Intervention

In the 21st century, diabetes mellitus (DM) emerged as one of the most prevalent non-communicable diseases, and poses a major problem for every health system in the world. Its global prevalence has more than doubled in the last three decades. As diabetes has become more prevalent, the health programming designed to target diabetes patients has remained inadequate and only heightened the burden. This heightened burden has manifested itself in the increased risk of complications common among patients with diabetes. These complications vary widely, and are typically categorized as either micro-vascular or macro-vascular depending upon the size of blood vessels that are compromised. Due to the havoc that can ensue by either type of complication, the increased risk of diabetes-related complications has been recognized as a serious threat to population health. To gain insight into the threat posed and how it will likely present in the Canadian population, patient’s data from the diabetes component of Survey on Living with Chronic Diseases in Canada (SLCDC-DM-2011) was analyzed. This analysis revealed that among Canadian diabetes patients, 80.26 percent reported having at least one type of diabetes-related complication. The most frequently reported complications were high blood pressure (54.65%), cataracts (29.52%), poor circulation (21.68%), and heart disease (19.4%). This analysis also revealed the predictive role of socio-economic factors associated with diabetes-related complications in Canada. Being married, having a higher income, and having a higher level of education were protective against most complications. In contrast, low levels of physical activity and high levels of HbA1C were important risk factors for many diabetes–related complications. Identifying common diabetes-related complications, protective factors and risk factors is useful for combating the threat posed by diabetes-related complications. To combat this threat in practice, healthcare professionals will play a significant role in the control and management of diabetes and its complications. Diabetes is a chronic disease that needs long-term treatment, and thus multi-disciplinary teams will be required. Increasingly, pharmacists are being determined as having a prominent position on these teams due to their accessibility to the Canadian population, and their expanding scope of practice. This profession has contributed positively to the long-term prognosis of patients with diabetes, in part, by aiding in the control and management of the disease. This aid oftentimes comes in the form of pharmacy-based interventions. Pharmacy-based interventions include a variety of services aimed at enabling patients with diabetes to have better control of their condition. I conducted a systematic review and meta-analysis to evaluate the effects of pharmacy-based interventions on clinical and non-clinical outcomes associated with diabetes-related complications. Four main databases were searched. Based upon my meta-analysis, the standardized absolute mean difference in reduction of HbA1C (%) from baseline to the time of the last follow-up significantly favoured patients in the pharmacy-based intervention group compared to those receiving care as usual (0.96%; 95% CI 0.71: 1.22, P<0.001). In addition, the standardized absolute mean difference in reduction of BMI unit (kg/m2) was 0.61 (95% CI 0.20: 1.03, P<0.001) in favour of the pharmacy-based intervention group. Both of these results demonstrate the positive effect pharmacy-based interventions can have on clinical outcomes. However, there is a dearth of evidence about the effects of pharmacy-based interventions on non-clinical outcomes, including health care utilization and quality of life. Therefore, it was not possible to evaluate non-clinical outcomes associated with diabetes-related complications in the same way. Each year healthcare expenses incurred from diabetes and its complications total more than US$827 billion. This health care cost is significant, and is only expected to grow alongside diabetes’ increasing prevalence. In light of this, a debate over the comparative effectiveness of the different strategies used to manage diabetes and its complications has been sparked. The development of analytic models that can be used as tools in determining budget prioritization and cost-effectiveness of interventions is beginning to be prioritized. To conduct an economic evaluation of these interventions, simulation models are necessary. These models estimate health outcomes, such as life years saved or Quality Adjusted Life Years (QALYs) gained, and account for the costs and health consequences associated with diabetes, its complications and risk factors. I developed a hybrid (agent-based/system dynamic) individual-level micro simulation model using 2,931 patient records from the SLCDC-2011. This model extrapolated the effects of pharmacy-based interventions on health outcomes, costs and health-related quality of life (HRQOL) over time through time-varying risk factors of diabetes-related complications. The treatment effects of pharmacy-based interventions were modeled as reductions in HbA1c levels, BMI, systolic blood pressure and LDL, all of which can affect the risk of progressing long-term complications. The annual costs of diabetes-related complications, as well as, costs associated with pharmacy-based intervention from a societal prospective, were also considered. Using this data, the micro-simulation model was able to estimate the expected number of major health events (heart failure, stroke, amputation, and blindness), QALYs over a patient’s lifetime, the patient’s economic burden on the health care system, and the extent to which pharmacy-based intervention can modify these outcomes. Deterministic and probabilistic sensitivity analyses were conducted to evaluate the uncertainty around the results. Based on the results from my micro-simulation model, a pharmacy–based intervention could avert a total of 155 deaths associated with complications, 19 heart failures, 159 strokes, 24 amputations and 29 blindness events in a population of 2,931 patients over the next 50 years. In addition, the intervention could add 1,246 additional life-years (0.42 per patients) and 953 additional quality-adjusted life-years (0.32 per patients). The intervention would also be cost-effective in comparison to usual care, as indicated by the incremental discounted cost per QALY gained ($3928). Overall, these results suggest that an integrated pharmacy-based intervention could be a cost-effective strategy to control and manage diabetes-related complications in Canada. This is promising and has important public health implications that should not be ignored