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  1. Lee JY, Lee SWH
    Diabetes Technol Ther, 2018 Jul;20(7):492-500.
    PMID: 29812965 DOI: 10.1089/dia.2018.0098
    BACKGROUND: Telemedicine has been utilized increasingly worldwide for diabetes management, due to its potential to improve healthcare access and clinical outcomes. Few studies have assessed the economic benefits of telemedicine, which may contribute to underfunding in potentially important programs. We aim to systematically review the literature on economic evaluations of telemedicine in diabetes care, assess the quality, and summarize the evidence on driver of cost-effectiveness.

    MATERIALS AND METHODS: A literature search was performed in 10 databases from inception until February 2018. All economic evaluations assessing the economic evaluation of telemedicine in diabetes were eligible for inclusion. We subsequently evaluated the study quality in terms of effectiveness measures, cost measure, economic model, as well as time horizon.

    RESULTS: Of the 1877 studies identified, 14 articles were included in our final review. The healthcare providers' fees are a major predictor for total cost. In particular, the use of telemedicine for retinal screening was beneficial and cost-effective for diabetes management, with an incremental cost-effectiveness ratio between $113.48/quality-adjusted life year (QALY) and $3,328.46/QALY (adjusted to 2017 inflation rate). Similarly, the use of telemonitoring and telephone reminders was cost-effective in diabetes management.

    CONCLUSIONS: Among all telemedicine strategies examined, teleophthalmology was the most cost-effective intervention. Future research is needed to provide evidence on the long-term experience of telemedicine and facilitate resource allocation.

    Matched MeSH terms: Diabetes Mellitus/economics*
  2. Upadhyay DK, Ibrahim MI, Mishra P, Alurkar VM, Ansari M
    Daru, 2016 Feb 29;24:6.
    PMID: 26926657 DOI: 10.1186/s40199-016-0145-x
    BACKGROUND: Cost is a vital component for people with chronic diseases as treatment is expected to be long or even lifelong in some diseases. Pharmacist contributions in decreasing the healthcare cost burden of chronic patients are not well described due to lack of sufficient evidences worldwide. In developing countries like Nepal, the estimation of direct healthcare cost burden among newly diagnosed diabetics is still a challenge for healthcare professionals, and pharmacist role in patient care is still theoretical and practically non-existent. This study reports the impact of pharmacist-supervised intervention through pharmaceutical care program on direct healthcare costs burden of newly diagnosed diabetics in Nepal through a non-clinical randomised controlled trial approach.
    METHODS: An interventional, pre-post non-clinical randomised controlled study was conducted among randomly distributed 162 [control (n = 54), test 1 (n = 54) and test 2 (n = 54) groups] newly diagnosed diabetics by a consecutive sampling method for 18 months. Direct healthcare costs (direct medical and non-medical costs) from patients perspective was estimated by 'bottom up' approach to identify their out-of-pocket expenses (1USD = NPR 73.38) before and after intervention at the baseline, 3, 6, 9 and 12 months follow-ups. Test groups' patients were nourished with pharmaceutical care intervention while control group patients only received care from physician/nurses. Non-parametric tests i.e. Friedman test, Mann-Whitney U test and Wilcoxon signed rank test were used to find the differences in direct healthcare costs among the groups before and after the intervention (p ≤ 0.05).
    RESULTS: Friedman test identified significant differences in direct healthcare cost of test 1 (p 
    Matched MeSH terms: Diabetes Mellitus/economics*
  3. Dagenais GR, Gerstein HC, Zhang X, McQueen M, Lear S, Lopez-Jaramillo P, et al.
    Diabetes Care, 2016 05;39(5):780-7.
    PMID: 26965719 DOI: 10.2337/dc15-2338
    OBJECTIVE: The goal of this study was to assess whether diabetes prevalence varies by countries at different economic levels and whether this can be explained by known risk factors.

    RESEARCH DESIGN AND METHODS: The prevalence of diabetes, defined as self-reported or fasting glycemia ≥7 mmol/L, was documented in 119,666 adults from three high-income (HIC), seven upper-middle-income (UMIC), four lower-middle-income (LMIC), and four low-income (LIC) countries. Relationships between diabetes and its risk factors within these country groupings were assessed using multivariable analyses.

    RESULTS: Age- and sex-adjusted diabetes prevalences were highest in the poorer countries and lowest in the wealthiest countries (LIC 12.3%, UMIC 11.1%, LMIC 8.7%, and HIC 6.6%; P < 0.0001). In the overall population, diabetes risk was higher with a 5-year increase in age (odds ratio 1.29 [95% CI 1.28-1.31]), male sex (1.19 [1.13-1.25]), urban residency (1.24 [1.11-1.38]), low versus high education level (1.10 [1.02-1.19]), low versus high physical activity (1.28 [1.20-1.38]), family history of diabetes (3.15 [3.00-3.31]), higher waist-to-hip ratio (highest vs. lowest quartile; 3.63 [3.33-3.96]), and BMI (≥35 vs. <25 kg/m(2); 2.76 [2.52-3.03]). The relationship between diabetes prevalence and both BMI and family history of diabetes differed in higher- versus lower-income country groups (P for interaction < 0.0001). After adjustment for all risk factors and ethnicity, diabetes prevalences continued to show a gradient (LIC 14.0%, LMIC 10.1%, UMIC 10.9%, and HIC 5.6%).

    CONCLUSIONS: Conventional risk factors do not fully account for the higher prevalence of diabetes in LIC countries. These findings suggest that other factors are responsible for the higher prevalence of diabetes in LIC countries.

    Matched MeSH terms: Diabetes Mellitus/economics*
  4. Godman B, Wladysiuk M, McTaggart S, Kurdi A, Allocati E, Jakovljevic M, et al.
    Biomed Res Int, 2021;2021:9996193.
    PMID: 34676266 DOI: 10.1155/2021/9996193
    BACKGROUND: Diabetes mellitus rates and associated costs continue to rise across Europe enhancing health authority focus on its management. The risk of complications is enhanced by poor glycaemic control, with long-acting insulin analogues developed to reduce hypoglycaemia and improve patient convenience. There are concerns though with their considerably higher costs, but moderated by reductions in complications and associated costs. Biosimilars can help further reduce costs. However, to date, price reductions for biosimilar insulin glargine appear limited. In addition, the originator company has switched promotional efforts to more concentrated patented formulations to reduce the impact of biosimilars. There are also concerns with different devices between the manufacturers. As a result, there is a need to assess current utilisation rates for insulins, especially long-acting insulin analogues and biosimilars, and the rationale for patterns seen, among multiple European countries to provide future direction. Methodology. Health authority databases are examined to assess utilisation and expenditure patterns for insulins, including biosimilar insulin glargine. Explanations for patterns seen were provided by senior-level personnel.

    RESULTS: Typically increasing use of long-acting insulin analogues across Europe including both Western and Central and Eastern European countries reflects perceived patient benefits despite higher prices. However, activities by the originator company to switch patients to more concentrated insulin glargine coupled with lowering prices towards biosimilars have limited biosimilar uptake, with biosimilars not currently launched in a minority of European countries. A number of activities were identified to address this. Enhancing the attractiveness of the biosimilar insulin market is essential to encourage other biosimilar manufacturers to enter the market as more long-acting insulin analogues lose their patents to benefit all key stakeholder groups.

    CONCLUSIONS: There are concerns with the availability and use of insulin glargine biosimilars among European countries despite lower costs. This can be addressed.

    Matched MeSH terms: Diabetes Mellitus/economics
  5. Chan JCN, Lim LL, Wareham NJ, Shaw JE, Orchard TJ, Zhang P, et al.
    Lancet, 2021 Dec 19;396(10267):2019-2082.
    PMID: 33189186 DOI: 10.1016/S0140-6736(20)32374-6
    Matched MeSH terms: Diabetes Mellitus/economics
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