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Incidence rates for diseases are widely used in the field of medical research because they lead to clear and simple physical and clinical interpretations. In this study, we propose an efficient estimation method that incorporates auxiliary subgroup information related to the incidence rate into the estimation of the Cox proportional hazard model. The results show that utilizing the incidence rate information improves the efficiency of the estimation of regression parameters based on the double empirical likelihood method compared to that for conventional models that do not incorporation such information. We show that estimators of regression parameters asymptotically follow a multivariate normal distribution with a variance-covariance matrix that can be consistently estimated. Simulation results indicate that the proposed estimators significantly increase efficiency. Finally, an example of the effects of type 2 diabetes on stroke is applied to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

This study explored heterogeneous treatment effects (HTEs) of the real‐world use of dipeptidyl peptidase‐4 inhibitors (DPP‐4is) vs. sulfonylureas (SUs) on cardiovascular diseases (CVDs) and mortality in patients with type 2 diabetes. Utilizing Taiwan's National Health Insurance Research Database, 19,853 propensity score‐matched pairs of DPP‐4i and SU stable users were identified. Classification and regression tree analyses and Cox models were applied to explore HTEs, according to various patient characteristics, on the composite CVDs, three‐point major adverse cardiovascular event (MACE), and all‐cause mortality. The absolute risk difference (ARD), hazard ratio (HR), and 95% confidence interval (CI) were estimated for comparing treatment effects. CVD history, ischemic stroke, or transient ischemic attack (IS/TIA) history, and age at treatment initiation were significant treatment effect modifiers. Patients with prior IS/TIA but without any other prior CVDs benefited most in reduced risks of composite CVDs from using DPP‐4i vs. SU (ARD −4.31%, 95% CI −7.48% to −1.14%, HR 0.81, 95% CI 0.69 ~ 0.95), followed by those without prior IS/TIA and CVDs and initiated with DPP‐4i at age < 69.3 years (ARD −0.90%, 95% CI −1.47% to −0.32%, HR 0.86, 95% CI 0.77 ~ 0.97). Patients with prior IS/TIA benefited most in reduced risks of three‐point MACE from using DPP‐4i vs. SU (ARD −4.22%, 95% CV −6.66% to −1.78%, HR 0.80, 95% CI 0.69 ~ 0.93), followed by those without prior IS/TIA and initiated with DPP‐4i at age < 69.3 years (ARD −0.68%, 95% CI −1.08% to −0.29%, HR 0.81, 95% CI 0.70 ~ 0.93). Consideration of CVD and IS/TIA histories and age could facilitate individualized diabetes management of using DPP‐4i vs. SU. Future studies are warranted given the hypothesis‐generating nature in this exploratory research. [ABSTRACT FROM AUTHOR]

Objective: Developing country-specific unit-cost catalogs is a key area for advancing economic research to improve medical and policy decisions. However, little is known about how health care costs vary by type 2 diabetes (T2D) complications across time in Asian countries. We sought to quantify the economic burden of various T2D complications in Taiwan.Research Design and Methods: A nationwide, population-based, longitudinal study was conducted to analyze 802,429 adults with newly diagnosed T2D identified during 1999-2010 and followed up until death or 31 December 2013. Annual health care costs associated with T2D complications were estimated, with multivariable generalized estimating equation models adjusted for individual characteristics.Results: The mean annual health care cost was $281 and $298 (2017 U.S. dollars) for a male and female, respectively, diagnosed with T2D at age <50 years, with diabetes duration of <5 years, and without comorbidities, antidiabetic treatments, and complications. Depression was the costliest comorbidity, increasing costs by 64-82%. Antidiabetic treatments increased costs by 72-126%. For nonfatal complications, costs increased from 36% (retinopathy) to 202% (stroke) in the event year and from 13% (retinopathy or neuropathy) to 49% (heart failure) in subsequent years. Costs for the five leading costly nonfatal subtype complications increased by 201-599% (end-stage renal disease with dialysis), 37-376% (hemorrhagic/ischemic stroke), and 13-279% (upper-/lower-extremity amputation). For fatal complications, costs increased by 1,784-2,001% and 1,285-1,584% for cardiovascular and other-cause deaths, respectively.Conclusions: The cost estimates from this study are crucial for parameterizing diabetes economic simulation models to quantify the economic impact of clinical outcomes and determine cost-effective interventions. [ABSTRACT FROM AUTHOR]

Background: To assess the associations of various HbA1c measures, including a single baseline HbA1c value, overall mean, yearly updated means, standard deviation (HbA1c-SD), coefficient of variation (HbA1c-CV), and HbA1c variability score (HVS), with microvascular disease (MVD) risk in patients with type 2 diabetes. Methods: Linked data between National Cheng Kung University Hospital and Taiwan's National Health Insurance Research Database were utilized to identify the study cohort. The primary outcome was the composite MVD events (retinopathy, nephropathy, or neuropathy) occurring during the study follow-up. Cox model analyses were performed to assess the associations between HbA1c measures and MVD risk, with adjustment for patients' baseline HbA1c, demographics, comorbidities/complications, and treatments. Results: In the models without adjustment for baseline HbA1c, all HbA1c variability and mean measures were significantly associated with MVD risk, except HVS. With adjustment for baseline HbA1c, HbA1c-CV had the strongest association with MVD risk. For every unit of increase in HbA1c-CV, the MVD risk significantly increased by 3.42- and 2.81-fold based on the models without and with adjustment for baseline HbA1c, respectively. The associations of HbA1c variability and mean measures with MVD risk in patients with baseline HbA1c < 7.5% (58 mmol/mol) were stronger compared with those in patients with baseline HbA1c ≥ 7.5% (58 mmol/mol). Conclusions: HbA1c variability, especially HbA1c-CV, can supplement conventional baseline HbA1c measure for explaining MVD risk. HbA1c variability may play a greater role in MVD outcomes among patients with relatively optimal baseline glycemic control compared to those with relatively poor baseline glycemic control. [ABSTRACT FROM AUTHOR]

Background: Evidence is limited on excess risks of cardiovascular diseases (CVDs) associated with ambient air pollution in diabetic populations. Survival analyses without considering the spatial structure and possible spatial correlations in health and environmental data may affect the precision of estimation of adverse environmental pollution effects. We assessed the association between air pollution and CVDs in type 2 diabetes through a Bayesian spatial survival approach.Methods: Taiwan's national-level health claims and air pollution databases were utilized. Fine individual-level latitude and longitude were used to determine pollution exposure. The exponential spatial correlation between air pollution and CVDs was analyzed in our Bayesian model compared to traditional Weibull and Cox models.Results: There were 2072 diabetic patients included in analyses. PM2.5 and SO2 were significant CVD risk factors in our Bayesian model, but such associations were attenuated or underestimated in traditional models; adjusted hazard ratio (HR) and 95% credible interval (CrI) or confidence interval (CI) of CVDs for a 1 μg/m3 increase in the monthly PM2.5 concentration for our model, the Weibull and Cox models was 1.040 (1.004-1.073), 0.994 (0.984-1.004), and 0.994 (0.984-1.004), respectively. With a 1 ppb increase in the monthly SO2 concentration, adjusted HR (95% CrI or CI) was 1.886 (1.642-2.113), 1.092 (1.022-1.168), and 1.091 (1.021-1.166) for these models, respectively.Conclusions: Against traditional non-spatial analyses, our Bayesian spatial survival model enhances the assessment precision for environmental research with spatial survival data to reveal significant adverse cardiovascular effects of air pollution among vulnerable diabetic patients. [ABSTRACT FROM AUTHOR]