Dataset Information

Assessing the Effect of Clinical Inertia on Diabetes Outcomes: a Modeling Approach.

ABSTRACT: BACKGROUND:There are an increasing number of newer and better therapeutic options in the management of diabetes. However, a large proportion of diabetes patients still experience delays in intensification of treatment to achieve appropriate blood glucose targets-a phenomenon called clinical inertia. Despite the high prevalence of clinical inertia, previous research has not examined its long-term effects on diabetes-related health outcomes and mortality. OBJECTIVE:We sought to examine the impact of clinical inertia on the incidence of diabetes-related complications and death. We also examined how the impact of clinical inertia would vary by the length of treatment delay and population characteristics. DESIGN:We developed an agent-based model of diabetes and its complications. The model was parameterized and validated by data from health surveys, cohort studies, and trials. SUBJECTS:We studied a simulated cohort of patients with diabetes in San Antonio, TX. MAIN MEASURES:We examined 25-year incidences of diabetes-related complications, including retinopathy, neuropathy, nephropathy, and cardiovascular disease. KEY RESULTS:One-year clinical inertia could increase the cumulative incidences of retinopathy, neuropathy, and nephropathy by 7%, 8%, and 18%, respectively. The effects of clinical inertia could be worse for populations who have a longer treatment delay, are aged 65 years or older, or are non-Hispanic whites. CONCLUSION:Clinical inertia could result in a substantial increase in the incidence of diabetes-related complications and mortality. A validated agent-based model can be used to study the long-term effect of clinical inertia and, thus, inform clinicians and policymakers to design effective interventions.

SUBMITTER: Correa MF

PROVIDER: S-EPMC6420509 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing the Effect of Clinical Inertia on Diabetes Outcomes: a Modeling Approach.

Correa Maria F MF Li Yan Y Kum Hye-Chung HC Lawley Mark A MA

Journal of general internal medicine 20181218 3

<h4>Background</h4>There are an increasing number of newer and better therapeutic options in the management of diabetes. However, a large proportion of diabetes patients still experience delays in intensification of treatment to achieve appropriate blood glucose targets-a phenomenon called clinical inertia. Despite the high prevalence of clinical inertia, previous research has not examined its long-term effects on diabetes-related health outcomes and mortality.<h4>Objective</h4>We sought to exam ...[more]

PMID: 30565149

Similar Datasets

Project description:BackgroundFood system function is vulnerable to disruption from a variety of sources. Disruption of the processes required for food provision may result in decreases in food security in affected communities. Currently, there are few tools that quantitatively predict or analyze food system vulnerabilities to contribute to food system resilience analysis. This work presents a prototype version of one such tool, a fault tree, which can be used conceptually and for future modeling work. Fault tree analysis is an engineering tool used to illustrate basic and intermediate factors that can cause overall system failures.MethodsThe fault tree defines food system functioning as food security at the community level and maps the components of the food system onto three main tenets of food security - accessibility, availability, and acceptability. Subtrees were populated using a top down approach guided by expertise, extant literature, and 36 stakeholder interviews.ResultsThe food system is complex, requiring 12 subtrees to elaborate potential failures. Subtrees comprising accessibility include physical accessibility of the vending point and economic accessibility among community members. Food availability depends on the functioning of the food supply chain, or, in the case of individuals who rely on donated food, the food donation system. The food supply chain includes processing, wholesale operations, distribution systems, and retail center subtrees. Elements of acceptability include the medical appropriateness, nutritional adequacy, and cultural acceptability of food. Case studies of the effects of Winter Storm Jonas of 2016 and the 2013-2017 California drought in Baltimore City illustrate the utility of the fault tree model.ConclusionFTA of potential routes to food system failure provides a tool that allows for consideration of the entirety of the food system; has potential to provide a quantitative assessment of food system failure and recovery; and is able to capture short-term and long-term hazards in a single framework. This systems modeling approach highlights an extensive list of vulnerability points throughout the food system, and underscores the message that reducing food system vulnerabilities requires action at all levels to protect communities from the risks of short-term and long-term threats to food security.

Project description:BackgroundClinical inertia can lead to poor glycemic control among type 2 diabetes patients. However, there is paucity of information on clinical inertia in low- and middle-income countries including Malaysia. This study aimed to determine the time to treatment intensification among T2D patients with HbA1c of ≥7% (≥53 mmol/mol) in Malaysian public health clinics. The proportion of patients with treatment intensification and its associated factors were also determined.Material and methodsThis was a five-year retrospective open cohort study using secondary data from the National Diabetes Registry. The study setting was all public health clinics (n = 47) in the state of Negeri Sembilan, Malaysia. Time to treatment intensification was defined as the number of years from the index year until the addition of another oral antidiabetic drug or initiation of insulin. Life table survival analysis based on best-worst case scenarios was used to determine the time to treatment intensification. Discrete-time proportional hazards model was fitted for the factors associated with treatment intensification.ResultsThe mean follow-up duration was 2.6 (SD 1.1) years. Of 7,646 patients, the median time to treatment intensification was 1.29 years (15.5 months), 1.58 years (19.0 months) and 2.32 years (27.8 months) under the best-, average- and worst-case scenarios respectively. The proportion of patients with treatment intensification was 45.4% (95% CI: 44.2-46.5), of which 34.6% occurred only after one year. Younger adults, overweight, obesity, use of antiplatelet medications and poorer HbA1c were positively associated with treatment intensification. Patients treated with more oral antidiabetics were less likely to have treatment intensification.ConclusionClinical inertia is present in the management of T2D patients in Malaysian public health clinics. We recommend further studies in lower- and middle-income countries to explore its causes so that targeted strategies can be developed to address this issue.

Project description:BackgroundIn recent years there is increasing interest in modeling the effect of early longitudinal biomarker data on future time-to-event or other outcomes. Sometimes investigators are also interested in knowing whether the variability of biomarkers is independently predictive of clinical outcomes. This question in most applications is addressed via a two-stage approach where summary statistics such as variance are calculated in the first stage and then used in models as covariates to predict clinical outcome in the second stage. The objective of this study is to compare the relative performance of various methods in estimating the effect of biomarker variability.MethodsA joint model and 4 different two-stage approaches (naïve, landmark analysis, time-dependent Cox model, and regression calibration) were illustrated using data from a large multi-center randomized phase III trial, the Ocular Hypertension Treatment Study (OHTS), regarding the association between the variability of intraocular pressure (IOP) and the development of primary open-angle glaucoma (POAG). The model performance was also evaluated in terms of bias using simulated data from the joint model of longitudinal IOP and time to POAG. The parameters for simulation were chosen after OHTS data, and the association between longitudinal and survival data was introduced via underlying, unobserved, and error-free parameters including subject-specific variance.ResultsIn the OHTS data, joint modeling and two-stage methods reached consistent conclusion that IOP variability showed no significant association with the risk of POAG. In the simulated data with no association between IOP variability and time-to-POAG, all the two-stage methods (except the naïve approach) provided a reliable estimation. When a moderate effect of IOP variability on POAG was imposed, all the two-stage methods underestimated the true association as compared with the joint modeling while the model-based two-stage method (regression calibration) resulted in the least bias.ConclusionRegression calibration and joint modelling are the preferred methods in assessing the effect of biomarker variability. Two-stage methods with sample-based measures should be used with caution unless there exists a relatively long series of longitudinal measurements and/or strong effect size (NCT00000125).

Dataset Information

Assessing the Effect of Clinical Inertia on Diabetes Outcomes: a Modeling Approach.

Publications

Assessing the Effect of Clinical Inertia on Diabetes Outcomes: a Modeling Approach.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets