Pritam Chordiya

Behavioral Programs for Type 2 Diabetes Mellitus: A Systematic Review and Network Meta-analysis

In 2012, 29.1 million Americans had diabetes with costs of

Behavioral Programs for Type 1 Diabetes Mellitus: A Systematic Review and Meta-analysis

245 billion (1), representing 11% of the total U.S. health care expenditure (2). Although tight glycemic control may reduce the risk for microvascular complications in type 2 diabetes mellitus (T2DM) (3), behavioral and pharmacologic management of body weight, blood pressure, and cholesterol levels are often needed to reduce the risk for mortality and macrovascular complications. Moreover, other patient-centered outcomes, such as diabetes-related distress and depression, are important to address (4). Health care experts recommend that anyone with diabetes adopt and adhere to multiple self-care behaviors, including healthy eating, being active, monitoring, taking medication, problem-solving, healthy coping, and reducing risks (5). Approaches to support behavior change include diabetes self-management education (DSME) with or without an added support (clinical, behavioral, psychosocial, or educational) phase, and lifestyle programs. Because knowledge acquisition insufficiently promotes behavioral changes (6), recommendations for DSME have shifted from traditional didactic educational services to more patient-centered methodologies that incorporate interaction, problem-solving, and other behavioral approaches. Although evidence shows that diabetes-specific behavioral interventions can be effective, which combination of program components and delivery mechanisms is most effective is unclear (711). We conducted a network meta-analysis to identify factors related to program components and delivery mechanisms that moderate the effectiveness of multicomponent behavioral programs for T2DM. Methods Key informants, a technical expert panel, and public commentary informed our methods. A protocol and a peer- and public-reviewed technical report were produced for the Agency for Healthcare Research and Quality (AHRQ) and are available online (www.ahrq.gov/research/findings/evidence-based-reports/). Data Sources and Searches A research librarian searched the following bibliographic databases from 1993 to January 2015: Ovid MEDLINE (Appendix Table 1) and Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials via the Cochrane Library, EMBASE via Ovid, CINAHL Plus with Full Text via EBSCOhost, PsycINFO via Ovid, Scopus, and PubMed via the National Center for Biotechnology Information Databases. We reviewed the reference lists of relevant systematic reviews and of all included studies. We also searched ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform, relevant conference proceedings (2011 through 2014), and the U.S. Federal Register. Appendix Table 1. Search Strategy for MEDLINE* Study Selection We included studies conducted in highly developed countries published in English after 1993 (because medical management for diabetes intensified after this time). We included randomized, controlled trials done in community or outpatient health settings and involving adults that compared a behavioral program with usual care (medical management provided to all participants), an active control (intervention not meeting our definition of behavioral program), or another behavioral program (comparative effectiveness study). A behavioral program was a multicomponent, diabetes-specific program that included repeated interactions with trained individuals over at least 4 weeks, and that consisted of DSME using a behavioral approach or another program format including at least a structured dietary or physical activity intervention with another component (Appendix). We excluded abstracts and studies in which the intervention was a disease or care management program (for example, with active adjustment of diabetes-related medications) (12) or a quality improvement program incorporating strategies targeting health systems or providers (13). Other exclusion criteria included studies 1) focusing on patients with newly diagnosed (1 year) disease; 2) with no outcome of interest to this review (for example, only C-reactive protein), or in which the only difference between the study groups was a factor outside of the reviews scope (for example, low- vs. high-fat diet); and 3) in which 25% or more of the participants had type 1 diabetes mellitus (unless results were reported for those with T2DM). Two reviewers independently screened all titles and abstracts, and the full text of any publication marked for inclusion was retrieved. Two reviewers independently assessed the full texts by using a priori inclusion criteria and a standard form. We resolved disagreements by consensus or consultation with a third reviewer. Data Extraction and Quality Assessment One reviewer extracted data by using a structured form created in the Systematic Review Data Repository (available at http://srdr.ahrq.gov/) (14); a second reviewer verified data. Two reviewers independently applied the Cochrane risk of bias tool (15). Discrepancies were resolved through discussion. Data Synthesis and Analysis With input from technical experts, we categorized behavioral programs by various component and delivery factors (Table). We separated DSME and DSME plus support, in recognition that the support phase of the latter was often of lower intensity (less frequent contacts) and focused on different content, such as psychosocial support. Table. Categorization of Program Components and Delivery Factors To serve as an overview of program effectiveness and help interpret our primary analysis of program moderation, we performed pairwise meta-analyses by using the HartungKnappSidikJonkman random-effects model (16, 17) for multiple behavioral, clinical, and health outcomes, as well as health care utilization and program acceptability (the full report is available at www.ahrq.gov/research/findings/evidence-based-reports/). We defined thresholds for clinical importance where there was guidance: For hemoglobin A1c (HbA1c), we used a reduction of at least 0.4% (for example, 7.6% vs. 8.0%) (18); for quality-of-life measures and other patient-reported outcomes, we used a conservative value of one-half SD (19, 20). We then conducted a network meta-analysis that allowed simultaneous evaluation of a suite of comparisons and considered both direct and indirect evidence while preserving the within-study randomization. To assure the transitivity within the network, we categorized all behavioral programs and comparators into nodes. The nodes for behavioral programs were formed on the basis of different combinations of variables in our program categorization (Appendix Table 2); we identified all plausible nodes differing by only one variable (for example, a level within the intensity category) and then filled the nodes with the applicable interventions on the basis of our coding. The nodes for the comparator groups were categorized as usual care, active non-DSME control (education interventions not meeting our criteria), and active other control (for example, stand-alone dietary or physical activity interventions). Appendix Table 2. Characteristics of Studies of Behavioral Programs for T2DM Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued Appendix Table 2Continued The analysis was conducted by using a Bayesian network model to compare all interventions simultaneously and to use all available information on treatment effects in a single analysis (21, 22). These methods ensure that correlation in multigroup trials is preserved. Mean differences (MDs) were modeled using noninformative prior distributions. A normal prior distribution with mean 0 and large variance (10000) was used for each of the trial means, whereas their between study variance had a uniform prior with range 0 to 2. These priors were checked for influence with sensitivity analyses. Markov chain Monte Carlo simulations using WinBugs software were performed to obtain simultaneous estimates of all interventions compared with placebo, as well as estimates of which interventions were the best. A burn-in sample of 20000 iterations was followed by 300000 iterations used to compute estimates. A sensitivity analysis that thinned the amount of used data to every 10th iteration was also conducted to check for proper chain convergence. The analysis was checked for consistency by contrasting direct and indirect estimates in each triangular and quadratic loop by using the methods described by Veroniki and colleagues (23). Results are presented as estimates of the treatment effects (MD) relative to usual care, with 95% credible intervals. To examine different population subgroups, we conducted subgroup analyses of the pairwise meta-analysis results for HbA1c at longest follow-up in comparison with usual care and active controls; subgroups were defined on the basis of study-level baseline HbA1c (<7% vs. 7%), age (<65 vs. 65 years), and ethnicity (75 vs. <75% nonwhite), according to categories that were defined a priori. For baseline HbA1c level and age, we performed subgroup analyses of the network meta-analysis; the analysis was rerun for studies having a mean baseline HbA1c level of 7% or greater and for those with a mean participant age younger than 65 years. For subgroups based on race/ethnicity, the number of trials in either subgroup was insufficient to perform a network meta-analysis. Role of the Funding Source This project was funded under contract 290-2012-000131 from the AHRQ, U.S. Department of Health and Human Services. Staff at AHRQ participated in development of the scope of the work and reviewed drafts of the manuscript. Approval by AHRQ was required before the manuscript could be submitted for publication, but the authors are solely responsible for its content and the decision to submit for publication. AHRQ staff did not participate in the conduct of the review,

How Safe Are Common Analgesics for the Treatment of Acute Pain for Children? A Systematic Review

Type 1 diabetes mellitus (T1DM), one of the most common chronic diseases in childhood and adolescence, is increasing in prevalence in the United States (1). The landmark DCCT (Diabetes Control and Complications Trial) and its related longitudinal study (EDIC [Epidemiology of Diabetes Interventions and Complications]) found that intensive glycemic control prevents development and progression of micro- and macrovascular complications (2, 3) and death (4). However, the intervention was initiated early (duration of T1DM <3 years for prevention group) in relatively young (mean age, 27 years), healthy patients. A meta-analysis of 12 trials of intensive control in diverse patient populations confirmed only a reduction in development of microvascular complications. Authors of that analysis stressed that benefits may apply only for interventions initiated early and should be weighed against risks for severe hypoglycemia (5). Factors other than glycemic control appear necessary to improve outcomes. For instance, intensive lowering of blood pressure has reduced major cardiovascular events by 11% (6). In addition, findings from 2 large cross-national studies support interventions to address other outcomes of importance for patients, such as diabetes-related distress (7). All patients with diabetes are encouraged to adopt and adhere to many self-care behaviors (8, 9). This is particularly challenging for those with T1DM, who require lifelong insulin therapy and therefore should undertake rigorous self-monitoring and regulation of blood glucose levels through frequent adjustments to insulin dose, diet, and physical activity (10). Approaches for supporting patients to change several behaviors include diabetes self-management education (DSME) with or without added support (11) and lifestyle programs (12). Because knowledge acquisition alone is insufficient for behavioral changes (13, 14), the focus for DSME has shifted from traditional didactic approaches to more patient-centered methods that incorporate interaction, problem-solving, and other behavioral approaches and techniques (11, 1517). Moreover, programs need to be tailored to the needs of the target population, such as developmental milestones in children or unique personal challenges during adolescence or adulthood (18). Few systematic reviews on education and training in T1DM have been conducted over the past decade (1921). Most reviews assessed only the effects on glycemic control, included highly didactic interventions, or reviewed interventions conducted outside the health care setting (such as summer camps) (1923). All focused on children and adolescents. When calculated, effect sizes demonstrated very modest improvement at longest follow-up (21, 23). An updated evaluationone that focuses on programs incorporating behavioral approaches and targeting several behaviorsis required to determine whether shifts in practice have translated into better outcomes for patients of all ages with T1DM. Anticipating high diversity in program content and delivery mechanisms, our evaluation also explores effect modification by program factors. Methods With assistance from key informants, a technical expert panel, and public commentary, we developed and followed a standard protocol. A peer- and public-reviewed technical report with additional details is available online on the Agency of Healthcare Research and Qualitys (AHRQs) Effective Healthcare Web site (24). Data Sources and Searches Our librarian searched the following bibliographic databases from 1993 to 15 January 2015: Ovid MEDLINE and Ovid MEDLINE In-Process & Other Non-Indexed Citations, Cochrane Central Register of Controlled Trials via Cochrane Library, EMBASE via Ovid, CINAHL Plus with Full Text via EBSCOhost, PsycINFO via Ovid, and PubMed (2014 only) via the National Center for Biotechnology Information Databases (MEDLINE strategy is presented in Appendix Table 1). On 3 June 2015, we updated the search in MEDLINE. We reviewed the reference lists of relevant systematic reviews and all included studies, searched ClinicalTrials.gov and the World Health Organization International Clinical Trials Registry Platform, and searched relevant conference proceedings (2011 through 2014) and the U.S. Federal Register. Appendix Table 1. Search Strategy for MEDLINE* Study Selection We included studies that were conducted in highly developed countries (25) and were published in English after 1993 (reflecting intensification of medical management based on the DCCT) (2). We included prospective comparative studies (that is, randomized, controlled trials [RCTs]; nonrandomized controlled trials; prospective cohort studies; controlled beforeafter studies) that enrolled participants of any age and compared a behavioral program with usual care (that is, medical management provided to all participants), an active control (intervention beyond usual care but not meeting our definition of a behavioral program), or another behavioral program. A behavioral program was operationally defined as a multicomponent, diabetes-specific program with repeated interactions by trained individuals, a duration of 4 weeks or longer, and DSME that entailed a behavioral approach or another program format that included at least a structured dietary or physical activity intervention with another component (Appendix Table 2). Appendix Table 2. Operational Definitions of Behavioral Program and Comparators We excluded studies in which the intervention was a disease or care management program (for example, those with active adjustment of diabetes-related medications) (26) or other quality improvement programs targeting health systems or providers (27). Studies were also excluded if they 1) focused on newly diagnosed (1 year) patients, 2) focused on psychological counseling or treatment without explicitly targeting several diabetes self-care behaviors, 3) had no outcomes of interest to this review (for example, reporting on only insulin sensitivity), 4) had study groups that differed only by a factor outside the reviews scope (for example, low- vs. high-fat diet), and 5) included a study sample in which 25% of participants had type 2 diabetes (unless results were reported for T1DM). Two reviewers independently screened all titles and abstracts. We retrieved the full text of any publications marked for inclusion by either reviewer. Two reviewers independently assessed the full texts using a priori inclusion criteria and a standard form. We resolved disagreements by consensus or by consulting another team member. Data Extraction and Quality Assessment One reviewer extracted data by using a structured form created in the Systematic Review Data Repository (http://srdr.ahrq.gov/) (28); a second reviewer verified all data. Two reviewers independently assessed methodological quality. Discrepancies were resolved through discussion. We used the Cochrane Risk of Bias tool (29) for RCTs and nonrandomized controlled trials and used the NewcastleOttawa Scale (30) for prospective cohort studies and controlled beforeafter studies. Data Synthesis and Analysis Characteristics of included studies are presented in summary tables. Our key outcomes were glycemic control (that is, glycosylated hemoglobin [HbA1c]); quality of life; development of micro- and macrovascular complications; all-cause mortality; adherence to diabetes self-management behaviors; and changes in body composition, physical activity, or dietary or nutrient intake. Secondary outcomes included episodes of severe hypo- or hyperglycemia, depression, anxiety, control of blood pressure and lipids, health care utilization, and program acceptability (via participant attrition). Harms included activity-related injury. We defined thresholds for clinical importance when the literature provided guidance; for HbA1c we used a between-group difference of 0.4percentage point change (for example, 7.6% vs. 8.0%) (31); for patient-reported outcomes represented by continuous data, we used a one-half SD based on the mean SD from the pooled studies (32, 33). With input from our technical experts, we categorized the behavioral programs by various component and delivery factors (Appendix Table 3). Programs not classified as DSME or DSME with added support (both incorporating education or training on several diabetes self-care behaviors) were considered lifestyle because they generally consisted of structured dietary and physical activity interventions. Appendix Table 3. Categorization of Program Components and Delivery Factors When possible we used (or computed) change from baseline values. If SDs were not given, they were computed from P values, 95% CIs, z statistics, or t statistics or were estimated from upper-bound P values, ranges, interquartile ranges, or (as a last resort) imputation using the largest reported SD from the other studies in the same meta-analysis. When computing SDs for change from baseline values, we assumed a correlation of 0.5; we conducted post hoc sensitivity analyses using correlations of 0.25 and 0.75. We pooled results for all ages and for subgroups based on age (that is, youth [aged 18 years] and their families, young adults [aged 19 to 30 years], adults [aged 31 to 64 years], and older adults (aged 65 years]) when there was more than 1 trial in each age category. We used the HartungKnappSidikJonkman random-effects model (34, 35) using Stata 11.2 (Stata Corp.) and Excel 2010 (Microsoft) software. We calculated weighted mean differences (MDs) or standardized mean differences (SMDs), as appropriate, with corresponding 95% CIs. We analyzed outcomes at the end of intervention to 1-month follow-up (EOI), and at 1 to no more than 6 months (6-month), more than 6 to 12 months (12-month), more than 12 to 24 months (12-month), and more than 24 months (24-month) after the intervention. If a study included more than 1 follow-up time point in each stratum, we used the longer follow-up. We did not include observational stud

Behavioral Programs for Diabetes Mellitus

Background. Fear of adverse events and occurrence of side effects are commonly cited by families and physicians as obstructive to appropriate use of pain medication in children. We examined evidence comparing the safety profiles of three groups of oral medications, acetaminophen, nonsteroidal anti-inflammatory drugs, and opioids, to manage acute nonsurgical pain in children (<18 years) treated in ambulatory settings. Methods. A comprehensive search was performed to July 2015, including review of national data registries. Two reviewers screened articles for inclusion, assessed methodological quality, and extracted data. Risks (incidence rates) were pooled using a random effects model. Results. Forty-four studies were included; 23 reported on adverse events. Based on limited current evidence, acetaminophen, ibuprofen, and opioids have similar nausea and vomiting profiles. Opioids have the greatest risk of central nervous system adverse events. Dual therapy with a nonopioid/opioid combination resulted in a lower risk of adverse events than opioids alone. Conclusions. Ibuprofen and acetaminophen have similar reported adverse effects and notably less adverse events than opioids. Dual therapy with a nonopioid/opioid combination confers a protective effect for adverse events over opioids alone. This research highlights challenges in assessing medication safety, including lack of more detailed information in registry data, and inconsistent reporting in trials.