InterventionEffects of diabetes self-management programs on time-to-hospitalization among patients with type 2 diabetes: A survival analysis model
Introduction
Diabetes continues to be one of the leading causes of morbidity and mortality for adults living in America, particularly Hispanics and African Americans. Current estimates suggest that 25.6 million adults aged 20 or older have a diagnosis of diabetes [1], and additional persons, mostly immediate family members, are adversely affected by the disease and its complications. The impacts of diabetes and associated comorbidities on healthcare utilization are striking as diabetes often progresses to diverse microvascular, macrovascular, and neuropathic complications that drive up healthcare utilization, and result in significant morbidity and premature mortality.
Different approaches to improving glycemic control, which is the hallmark of diabetes treatment, have involved enhancing diabetes self-care processes using behavioral and technological programs. As part of strategic measures to combat the growing diabetes burden, the American Diabetes Association (ADA) and American Association of Clinical Endocrinologists (AACE) developed rigorous guidelines and recommendations for diabetes treatment and management. Diabetes self-management programs (DSMP) including diabetes self-management education (DSME) and information technology have been identified as critical elements of managing diabetes and improving patient outcomes [2], [3], [4].
However, current literature provides mixed results on the effectiveness of self-care interventions in glycemic control and other related health measures. Some studies have documented positive impacts of various DSMPs on outcomes such as: glycated hemoglobin and fasting blood glucose levels [5], [6], [7]; rates of hospitalization [8]; lifestyle outcomes [9] and quality of life [10]. Other studies found no significant improvements in clinical outcomes [11], [12], [13] or quality of life [7]. These conflicting findings have been attributed to different study designs and settings, as well as the differences in the levels of severity of uncontrolled diabetes in the patients studied. Even in studies with evidence that self-management training is effective, most reviews have documented treatment decay or limited long-term effects, largely due to study attrition and censoring [13], [14]. Consequently, there is the need for further research that account for decay effects and study attrition.
When analyzing diabetes outcomes, complex research methodologies, such as longitudinal analysis, and survival analysis models, provide researchers with greater opportunity for analyzing patient-centered outcomes. Survival analysis models are designed to address study attrition and censoring by estimating the time-to-event for an outcome of interest, and assessing the relationship between various covariates and the time-to-event.
To date, very few research studies have focused on time-to-hospitalization in a disease management context, while accounting for censoring among patients with Type 2 diabetes (T2DM). By taking time-to an event of interest into account, researchers are able to obtain additional information rather than just a binary yes–no for an intervention of interest. Time-to-event, or survival analyses models, therefore improve power and precision of a study by addressing censoring and attrition to include subjects who “survived” the program without experiencing the event (in this case, hospitalization), left the program prematurely, or were lost to follow-up [15].
This study compared time-to-hospitalization among T2DM patients randomized to one of four study arms: personal digital assistant hand held device (PDA), Chronic Disease Self-Management Program (CDSMP), combined PDA and CDSMP (COM), and usual care (UC). We sought to determine whether DSMP enhanced the probability of healthier outcomes and prolonged the time to first hospitalization within any of the treatment groups, after controlling for relevant demographic and clinical variables.
Section snippets
Data
A retrospective cohort analysis was conducted using secondary data from a recently concluded randomized controlled trial (RCT) of T2DM self-management interventions in Texas [12]. They study lasted 2007–2012. Enrolled individuals in the RCT were recruited from seven participating clinics of a large university-affiliated healthcare system. Potential participants were identified within the healthcare system through electronic medical records (EMR) if they: (1) had a diagnosis of T2DM; (2) were 18
Descriptive and univariate analysis
Overall, subjects in the four study arms were comparable across baseline demographic and clinical characteristics (Table 1). Most subjects were females (55%) and 72 percent of subjects had greater than high school education. Approximately 64 percent of subjects identified as non-Hispanic whites, 20 percent were Hispanics and 16 percent were non-Hispanic Blacks. Over 64 percent of subjects reported income ranges less than $50,000 per annum, of which most people (37%) indicated they fell within
Discussion
In this study, we sought to determine whether three types of diabetes self-management intervention programs prolonged the time to hospitalization among participants enrolled in a T2DM randomized controlled trial. Although several studies have documented the impacts of diabetes self-management on hospitalization rates in the United States, our study is the first, to our knowledge, to compare time-to-hospitalization among subjects randomized to different diabetes self-management groups.
After
Acknowledgements
Secondary data for the study came from a National Institutes of Health (NIH) funded randomized, controlled trial (RCT). Award No: 1P20MD002295. Title: Employing Diabetes Self-Management Models to Reduce Health Disparities in Texas. Dates: 9/30/2007–9/29/2012.
Trial Registration: clinicaltrials.gov Identifier: NCT01221090
References (26)
- et al.
Mediating the effect of self-care management intervention in type 2 diabetes: a meta-analysis of 47 randomised controlled trials
Patient Educ Couns
(2010) National Diabetes Fact Sheet: National estimates and general information on diabetes and prediabetes in the United States, 2011
(2011)- et al.
Improving diabetes self-care with a PDA in ambulatory care
Telemed J E Health
(2008) - et al.
National Standards for Diabetes Self-Management Education
Diabetes Care
(2009) Internet and information technology use in treatment of diabetes
Int J Clin Pract
(2010)- et al.
Group based training for self-management strategies in people with type 2 diabetes mellitus
Cochrane Database Syst Rev
(2005) - et al.
ROSES: role of self-monitoring of blood glucose and intensive education in patients with Type 2 diabetes not receiving insulin. A pilot randomized clinical trial
Diabetic Med
(2011) - et al.
Self-monitoring of blood glucose in patients with type 2 diabetes mellitus who are not using insulin
Cochrane Database Syst Rev
(2005) - et al.
PS1-35: impact of an ADA-accredited diabetes education on healthcare utilization
Clin Med Res
(2012) - et al.
Group based diabetes self-management education compared to routine treatment for people with type 2 diabetes mellitus. A systematic review with meta-analysis
BMC Health Serv Res
(2012)
Predictors of quality of life gains among people with type 1 diabetes participating in the Dose Adjustment for Normal Eating (DAFNE) structured education programme
Diabetes Res Clin Pract
Blood glucose self-monitoring in type 2 diabetes: a randomised controlled trial
Health Technol Assess
Behavioral and technological interventions targeting glycemic control in a racially/ethnically diverse population: a randomized controlled trial
BMC Public Health
Cited by (22)
Towards a better understanding of self-management interventions in type 2 diabetes: A meta-regression analysis
2021, Primary Care DiabetesCitation Excerpt :This systematic review and meta-regression analysis included 142 interventional studies (115 RCTs and 27 quasi experimental studies) which examined the effectiveness of DSME intervention programs on different levels of outcome in relation to the implementation of the five defined attributes [16]. The interventional studies were conducted of the 39 different countries: Sixty from North America (USA [12,31,36,37,39,41–43,45–92], Canada [93–95], Mexico [96]), twenty nine were from Europe (UK [9,97–102], Netherlands [103–106], Spain [33,35,107,108], Norway [109,110], Sweden [111,112], Turkey [113,114], Belgium [115], Cyprus [116], Finland [117], Germany [118], Greece [119], Iceland [120], Georgia [32], Italy [11]), twenty seven were from the Western Pacific (Australia [44,121–123], China [124–131], Korea [132–135], Hong Kong [136,137], Indonesia [138,139], Japan [140], Philippines [141,142], Taiwan [13,29], Thailand [143,144]), seventeen were from the Middle East and North Africa (Qatar [145], Saudi Arabia [146], Jordan [30,147], Iran [40,148–158], Iraq [28]), four were from Africa (Kenya [159], Mali [160], Ethiopia [161], South Africa [162]), four were from South America (Brazil [163–166]), and one was from South-East Asia (India [167]) (Supplement section Tables B1 & B2). The most commonly reported outcome in this category was knowledge as assessed by several scales (e.g. DKQ [153], DKT [58], SKILL-D [91].
Clinical and socio-demographic determinants of self-care behaviours in patients with heart failure and diabetes mellitus: A multicentre cross-sectional study
2016, International Journal of Nursing StudiesCitation Excerpt :In heart failure, self-care reduces hospitalization rates (Kato et al., 2013), health-care costs (Hamar et al., 2015) and mortality (Smith et al., 2014), and improves quality of life (Clark et al., 2015). In diabetes mellitus, self-care improves metabolic control (Walker et al., 2014; Yuan et al., 2014) and quality of life (Chao et al., 2015; Kargar Jahromi et al., 2015) and reduces cardiovascular risk (Powers et al., 2015; Sicuro et al., 2014), hospitalizations (Adepoju et al., 2014) and disease-related complications (Kargar Jahromi et al., 2015; Shreck et al., 2014; Williams et al., 2014). Unfortunately, self-care is suboptimal in both heart failure and diabetes mellitus patients (Cha et al., 2012a,b; Dunbar et al., 2014; Dungan et al., 2013; Jaarsma et al., 2013; Kato et al., 2013; Kerr et al., 2007; Riegel et al., 2009a,b; Saleh et al., 2014; Ware et al., 2006), and when more than one illness is active, self-care becomes particularly challenging (Gallacher et al., 2011; Schmitt et al., 2014).
The Pre-Adaptation of a Stroke-Specific Self-Management Program Among Older Adults
2023, Journal of Aging and HealthPredictors of self-care behaviors and glycemic control among patients with type 2 diabetes mellitus
2023, Frontiers in Public HealthLearning in a Virtual Environment to Improve Type 2 Diabetes Outcomes: Randomized Controlled Trial
2023, JMIR Formative ResearchHolistic self-management behavior among urban patients with type 2 diabetes
2022, International Journal of Public Health Science