Five‐year cost‐effectiveness of the Patient Empowerment Programme (PEP) for type 2 diabetes mellitus in primary care

This study evaluated the short‐term cost‐effectiveness of the Patient Empowerment Programme (PEP) for diabetes mellitus (DM) in Hong Kong. Propensity score matching was used to select a matched group of PEP and non‐PEP subjects. A societal perspective was adopted to estimate the cost of PEP. Outcome measures were the cumulative incidence of all‐cause mortality and diabetic complication over a 5‐year follow‐up period and the number needed to treat (NNT) to avoid 1 event. The incremental cost‐effectiveness ratio (ICER) of cost per event avoided was calculated using the PEP cost per subject multiplied by the NNT. The PEP cost per subject from the societal perspective was US$247. There was a significantly lower cumulative incidence of all‐cause mortality (2.9% vs 4.6%, P < .001), any DM complication (9.5% vs 10.8%, P = .001) and CVD events (6.8% vs 7.6%, P = .018), in the PEP group. The costs per death from any cause, DM complication or case of CVD avoided were US$14 465, US$19 617 and US$30 796, respectively. The extra amount allocated to managing PEP was small and it appears cost‐effective in the short‐term as an addition to RAMP.     

Direct medical costs for patients with type 2 diabetes in Sweden

OBJECTIVES: To estimate the total direct medical costs to society for patients with type 2 diabetes in Sweden and to investigate how different factors, for example diabetic late complications, affect costs. DESIGN: Cross-sectional data regarding health care utilization, clinical characteristics and quality of life, were collected at a single time-point. Data on resource use cover the 6-month period prior to this time point. SETTING: Patient recruitment and data collection were performed in nine primary care centres in three main regions in Sweden. SUBJECTS: Only patients with an age at diabetes diagnosis >/= 30 years (type 2 diabetes) were included (n = 777). RESULTS: The total annual direct medical costs for the Swedish diabetes type 2 population were estimated at about 7 billion SEK (Swedish Kronor) in 1998 prices, which is about 6% of the total health care expenditures and more than four times higher than the former Swedish estimate obtained when using diabetes as main diagnosis for calculating costs. The annual per patient cost was about 25 000 SEK. The largest share of this cost was hospital inpatient care. Costs increased with diabetes duration and were higher for patients treated with insulin compared to those treated with oral hypoglycaemic drugs or with life style modification only. Patients with both macro- and microvascular complications had more than three times higher costs compared with patients without such complications. CONCLUSIONS: Type 2 diabetes is a serious and expensive disease and the key to reducing costs seems to be intensive management and control in order to prevent and delay the associated late complications.     

Trends and disparities in telehealth use among Louisiana Medicaid beneficiaries with type 2 diabetes

Aim

To examine trends in telehealth use among Medicaid beneficiaries with type 2 diabetes (T2D) before and during the coronavirus disease 2019 (COVID-19) pandemic and identify factors related to telehealth use.

Methods

We compared monthly proportions of outpatient visits delivered by telehealth by race/ethnicity, geography and age among Louisiana Medicaid beneficiaries with T2D using claims data from January 2018 to August 2021. We also examined the changes in provider types delivering telehealth. Multivariable logistic regression was conducted to identify individual level and zip code-level factors associated with telehealth use during the COVID-19 pandemic.

Results

The monthly proportion of outpatient visits delivered by telehealth was low (< 1%) before the pandemic, spiked in April 2020 (> 15%), then remained at approximately 5%. Telehealth use varied across different racial/ethnic groups, geography and age groups over years. Older beneficiaries were less probable to use telehealth during the pandemic (adjusted odds ratio [AOR] = 0.874, 95% confidence interval [CI]: 0.831-0.919). Females used more telehealth than males (AOR = 1.359, 95% CI: 1.298-1.423). Black beneficiaries used more telehealth than White beneficiaries (AOR = 1.067, 95% CI: 1.000-1.139). More telehealth services were used by Medicaid beneficiaries who were living in urban areas, with more primary care utilization, and with more chronic conditions at baseline.

Conclusions

We found disparities in the uptake of telehealth during the COVID-19 pandemic, but they might have been narrowed for some groups (Hispanic and rural) among Louisiana Medicaid beneficiaries with T2D. Future studies should explore strategies to improve access to telehealth services and reduce related disparities for the low-income population.     

Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm

Aim

To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm.

Materials and Methods

Data from adults and children with type 1 diabetes and more than two diabetes-related visits were analysed from the Diabetes Prospective Follow-up Registry. Q-Finder, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH less than 7.3 during a hospitalization event.

Results

Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q-Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast-acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the number of risk profiles matching patients’ characteristics.

Conclusions

Q-Finder confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA.