The All-Cause Mortality and a Screening Tool to Determine High-Risk Patients among Prevalent Type 2 Diabetes Mellitus Patients

An Elaboration on Sample Size Planning for Performing a One-Sample Sensitivity and Specificity Analysis by Basing on Calculations on a Specified 95% Confidence Interval Width

Sample size calculation based on a specified width of 95% confidence interval will offer researchers the freedom to set the level of accuracy of the statistics that they aim to achieve for a particular study. This paper provides a description of the general conceptual context for performing sensitivity and specificity analysis. Subsequently, sample size tables for sensitivity and specificity analysis based on a specified 95% confidence interval width is then provided. Such recommendations for sample size planning are provided based on two different scenarios: one for a diagnostic purpose and another for a screening purpose. Further discussion on all the other relevant considerations for the determination of a minimum sample size requirement and on how to draft the sample size statement for performing sensitivity and specificity analysis are also provided.

The effect of pay-for-performance program on infection events and mortality rate in diabetic patients: a nationwide population-based cohort study

Background

Diabetes mellitus is a known risk factor for infection. Pay for Performance (P4P) program is designed to enhance the comprehensive patient care. The aim of this study is to evaluate the effect of the P4P program on infection incidence in type 2 diabetic patients.

Methods

This is a retrospective longitudinal cohort study using data from the National Health Insurance Research Database in Taiwan. Diabetic patients between 1 January 2002 and 31 December 2013 were included. Primary outcomes analyzed were patient emergency room (ER) infection events and deaths.

Results

After propensity score matching, there were 337,184 patients in both the P4P and non-P4P cohort. The results showed that patients’ completing one-year P4P program was associated with a decreased risk of any ER infection event (27.2% vs. 29%; subdistribution hazard ratio [HR] 0.87, 95% confidence interval [CI] 0.86–0.88). While the number needed to treat was 58 for the non-P4P group, it dropped to 28 in the P4P group. The risk of infection-related death was significantly lower in the P4P group than in the non-P4P group (4.1% vs. 7.6%; HR 0.46, 95% CI 0.45–0.47). The effect of P4P on ER infection incidence and infection-related death was more apparent in the subgroups of patients who were female, had diabetes duration ≥5 years, chronic kidney disease, higher Charlson’s Comorbidity Index scores and infection-related hospitalization in the previous 3 years.

Conclusions

The P4P program might reduce risk of ER infection events and infection-related deaths in type 2 diabetic patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06091-2.

Cost-Effectiveness of Point-of-Care A1C Tests in a Primary Care Setting

Objective: We evaluated the cost-effectiveness of the point-of-care A1c (POC-A1c) test device vs. the traditional laboratory dosage in a primary care setting for people living with type 2 diabetes. Materials and Methods: The Markov model with a 10-year time horizon was based on data from the HealthRise project, in which a group of interventions was implemented to improve diabetes and hypertension control in the primary care network of the urban area of a Brazilian municipality. A POC-A1c device was provided to be used directly in a primary care unit, and for a period of 18 months, 288 patients were included in the point-of-care group, and 1,102 were included in the comparison group. Sensitivity analysis was performed via Monte Carlo simulation and tornado diagram. Results: The results indicated that the POC-A1c device used in the primary care unit was a cost-effective alternative, which improved access to A1c tests and resulted in an increased rate of early control of blood glucose. In the 10-year period, POC-A1c group presented a mean cost of US$10,503.48 per patient and an effectiveness of 0.35 vs. US$9,992.35 and 0.09 for the traditional laboratory test, respectively. The incremental cost was US$511.13 and the incremental effectiveness was 0.26, resulting in an incremental cost-effectiveness ratio of 1,947.10. In Monte Carlo simulation, costs and effectiveness ranged between $9,663.20-$10,683.53 and 0.33-0.37 for POC-A1c test group, and $9,288.28-$10,413.99 and 0.08-0.10 for traditional laboratory test group, at 2.5 and 97.5 percentiles. The costs for nephropathy, retinopathy, and cardiovascular disease and the probability of being hospitalized due to diabetes presented the greatest impact on the model's result. Conclusion: This study showed that using POC-A1c devices in primary care settings is a cost-effective alternative for monitoring glycated hemoglobin A1c as a marker of blood glucose control in people living with type 2 diabetes. According to our model, the use of POC-A1c device in a healthcare unit increased the early control of type 2 diabetes and, consequently, reduced the costs of diabetes-related outcomes, in comparison with a centralized laboratory test.

The mortality in infectious inpatients with type 2 diabetes compared with non-diabetic population: Infection in type 2 diabetes

The aim of the study was to determine the mortality in infectious inpatients with type 2 diabetes mellitus (T2DM) compared with non-diabetic population.A retrospective study was conducted on 13,916 infectious inpatients in The First Hospital of Qinhuangdao. Diabetic types were classified using International classification of Diseases-10. Mortality records were collected.The mortality was higher in patients with T2DM than patients without T2DM (T2DM 4.3% vs non-diabetes 1.7%, χ = 59.560, P < .001). In multiple logistic regression analysis, T2DM was an independent risk factor of death in infectious inpatients (OR = 1.539, 95% CI: 1.181∼2.006, P = .001). The mortalities between those with T2DM and those without T2DM were stratified by age. The mortalities of patients with T2DM were 0.0% in ∼39 years, 0.0% in 40 to 49 years, 2.7% in 50 to 59 years, 3.1% in 60 to 69 years, 4.1% in 70 to 79 years and 8.7% in 80∼ years groups. The mortalities of patients without T2DM were 0.2% in ∼39 years, 0.1% in 40 to 49 years, 0.6% in 50 to 59 years, 1.0% in 60 to 69 years, 3.1% in 70 to 79 years and 5.9% in 80∼ years groups. T2DM was an independent risk factor of death only in 60 to 69 years groups (OR = 2.323, 95% CI: 1.234∼4.372, P = .009).The increase of mortality appears earlier in patients with T2DM. Infectious inpatients with T2DM are at increased risk of death and brings heavy economic burden to patients, society and government.