Cost-Effectiveness of Glucose-Lowering Therapies as Add-on to Standard Care for People With Type 2 Diabetes in Malaysia

OBJECTIVES

This study aims to evaluate the cost-effectiveness of various glucose-lowering therapies as add-on to standard care for people with type 2 diabetes (T2D) in Malaysia.

METHODS

A state-transition microsimulation model was developed to compare the clinical and economic outcomes of 4 treatments: standard care, dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter-2 inhibitors (SGLT2is), and glucagon-like peptide-1 receptor agonists. Cost-effectiveness was assessed from a healthcare provider's perspective over a lifetime horizon with 3% discount rate in a hypothetical cohort of people with T2D. Data input were informed from literature and local data when available. Outcome measures include costs, quality-adjusted life-years, incremental cost-effectiveness ratios, and net monetary benefits. Univariate and probabilistic sensitivity analyses were performed to assess uncertainties.

RESULTS

Over a lifetime horizon, the costs to treat a person with T2D ranged from RM 12 494 to RM 41 250, whereas the QALYs gains ranged from 6.155 to 6.731, depending on the treatment. Based upon a willingness-to-pay threshold of RM 29 080 per QALY, we identified SGLT2i as the most cost-effective glucose-lowering treatment, as add-on to standard care over patient's lifetime, with the net monetary benefit of RM 176 173 and incremental cost-effectiveness ratios of RM 12 279 per QALY gained. The intervention also added 0.577 QALYs and 0.809 LYs compared with standard care. Cost-effectiveness acceptability curve showed that SGLT2i had the highest probability of being cost-effective in Malaysia across varying willingness-to-pay threshold. The results were robust to various sensitivity analyses.

CONCLUSIONS

SGLT2i was found to be the most cost-effective intervention to mitigate diabetes-related complications.

Disparities in COVID-19 related outcomes in the United States by race and ethnicity pre-vaccination era: an umbrella review of meta-analyses

Background

Meta-analyses have investigated associations between race and ethnicity and COVID-19 outcomes. However, there is uncertainty about these associations' existence, magnitude, and level of evidence. We, therefore, aimed to synthesize, quantify, and grade the strength of evidence of race and ethnicity and COVID-19 outcomes in the US.

Methods

In this umbrella review, we searched four databases (Pubmed, Embase, the Cochrane Database of Systematic Reviews, and Epistemonikos) from database inception to April 2022. The methodological quality of each meta-analysis was assessed using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR-2). The strength of evidence of the associations between race and ethnicity with outcomes was ranked according to established criteria as convincing, highly suggestive, suggestive, weak, or non-significant. The study protocol was registered with PROSPERO, CRD42022336805.

Results

Of 880 records screened, we selected seven meta-analyses for evidence synthesis, with 42 associations examined. Overall, 10 of 42 associations were statistically significant (p ≤ 0.05). Two associations were highly suggestive, two were suggestive, and two were weak, whereas the remaining 32 associations were non-significant. The risk of COVID-19 infection was higher in Black individuals compared to White individuals (risk ratio, 2.08, 95% Confidence Interval (CI), 1.60-2.71), which was supported by highly suggestive evidence; with the conservative estimates from the sensitivity analyses, this association remained suggestive. Among those infected with COVID-19, Hispanic individuals had a higher risk of COVID-19 hospitalization than non-Hispanic White individuals (odds ratio, 2.08, 95% CI, 1.60-2.70) with highly suggestive evidence which remained after sensitivity analyses.

Conclusion

Individuals of Black and Hispanic groups had a higher risk of COVID-19 infection and hospitalization compared to their White counterparts. These associations of race and ethnicity and COVID-19 outcomes existed more obviously in the pre-hospitalization stage. More consideration should be given in this stage for addressing health inequity.

Effects of ketogenic diet on health outcomes: an umbrella review of meta-analyses of randomized clinical trials

BACKGROUND

Systematic reviews and meta-analyses of randomized clinical trials (RCTs) have reported the benefits of ketogenic diets (KD) in various participants such as patients with epilepsy and adults with overweight or obesity. Nevertheless, there has been little synthesis of the strength and quality of this evidence in aggregate.

METHODS

To grade the evidence from published meta-analyses of RCTs that assessed the association of KD, ketogenic low-carbohydrate high-fat diet (K-LCHF), and very low-calorie KD (VLCKD) with health outcomes, PubMed, EMBASE, Epistemonikos, and Cochrane database of systematic reviews were searched up to February 15, 2023. Meta-analyses of RCTs of KD were included. Meta-analyses were re-performed using a random-effects model. The quality of evidence per association provided in meta-analyses was rated by the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) criteria as high, moderate, low, and very low.

RESULTS

We included 17 meta-analyses comprising 68 RCTs (median [interquartile range, IQR] sample size of 42 [20-104] participants and follow-up period of 13 [8-36] weeks) and 115 unique associations. There were 51 statistically significant associations (44%) of which four associations were supported by high-quality evidence (reduced triglyceride (n = 2), seizure frequency (n = 1) and increased low-density lipoprotein cholesterol (LDL-C) (n = 1)) and four associations supported by moderate-quality evidence (decrease in body weight, respiratory exchange ratio (RER), hemoglobin A_1c, and increased total cholesterol). The remaining associations were supported by very low (26 associations) to low (17 associations) quality evidence. In overweight or obese adults, VLCKD was significantly associated with improvement in anthropometric and cardiometabolic outcomes without worsening muscle mass, LDL-C, and total cholesterol. K-LCHF was associated with reduced body weight and body fat percentage, but also reduced muscle mass in healthy participants.

CONCLUSIONS

This umbrella review found beneficial associations of KD supported by moderate to high-quality evidence on seizure and several cardiometabolic parameters. However, KD was associated with a clinically meaningful increase in LDL-C. Clinical trials with long-term follow-up are warranted to investigate whether the short-term effects of KD will translate to beneficial effects on clinical outcomes such as cardiovascular events and mortality.

Using artificial intelligence methods for systematic review in health sciences: A systematic review

The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods. A search was conducted in 4 databases (Medline, Embase, CDSR, and Epistemonikos) up to April 2021 for systematic reviews and other related reviews implementing AI methods. To be included, the review must use any form of AI method, including machine learning, deep learning, neural network, or any other applications used to enable the full or semi-autonomous performance of one or more stages in the development of evidence synthesis. Twelve reviews were included, using nine different tools to implement 15 different AI methods. Eleven methods were used in the screening stages of the review (73%). The rest were divided: two in data extraction (13%) and two in risk of bias assessment (13%). The ambiguous benefits of the data extractions, combined with the reported advantages from 10 reviews, indicating that AI platforms have taken hold with varying success in evidence synthesis. However, the results are qualified by the reliance on the self-reporting of the review authors. Extensive human validation still appears required at this stage in implementing AI methods, though further evaluation is required to define the overall contribution of such platforms in enhancing efficiency and quality in evidence synthesis.