Walter Mojica

Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care

Key Summary Points Health information technology has been shown to improve quality by increasing adherence to guidelines, enhancing disease surveillance, and decreasing medication errors. Much of the evidence on quality improvement relates to primary and secondary preventive care. The major efficiency benefit has been decreased utilization of care. Effect on time utilization is mixed. Empirically measured cost data are limited and inconclusive. Most of the high-quality literature regarding multifunctional health information technology systems comes from 4 benchmark research institutions. Little evidence is available on the effect of multifunctional commercially developed systems. Little evidence is available on interoperability and consumer health information technology. A major limitation of the literature is its generalizability. Health care experts, policymakers, payers, and consumers consider health information technologies, such as electronic health records and computerized provider order entry, to be critical to transforming the health care industry (1-7). Information management is fundamental to health care delivery (8). Given the fragmented nature of health care, the large volume of transactions in the system, the need to integrate new scientific evidence into practice, and other complex information management activities, the limitations of paper-based information management are intuitively apparent. While the benefits of health information technology are clear in theory, adapting new information systems to health care has proven difficult and rates of use have been limited (9-11). Most information technology applications have centered on administrative and financial transactions rather than on delivering clinical care (12). The Agency for Healthcare Research and Quality asked us to systematically review evidence on the costs and benefits associated with use of health information technology and to identify gaps in the literature in order to provide organizations, policymakers, clinicians, and consumers an understanding of the effect of health information technology on clinical care (see evidence report at www.ahrq.gov). From among the many possible benefits and costs of implementing health information technology, we focus here on 3 important domains: the effects of health information technology on quality, efficiency, and costs. Methods Analytic Frameworks We used expert opinion and literature review to develop analytic frameworks (Table) that describe the components involved with implementing health information technology, types of health information technology systems, and the functional capabilities of a comprehensive health information technology system (13). We modified a framework for clinical benefits from the Institute of Medicines 6 aims for care (2) and developed a framework for costs using expert consensus that included measures such as initial costs, ongoing operational and maintenance costs, fraction of health information technology penetration, and productivity gains. Financial benefits were divided into monetized benefits (that is, benefits expressed in dollar terms) and nonmonetized benefits (that is, benefits that could not be directly expressed in dollar terms but could be assigned dollar values). Table. Health Information Technology Frameworks Data Sources and Search Strategy We performed 2 searches (in November 2003 and January 2004) of the English-language literature indexed in MEDLINE (1995 to January 2004) using a broad set of terms to maximize sensitivity. (See the full list of search terms and sequence of queries in the full evidence report at www.ahrq.gov.) We also searched the Cochrane Central Register of Controlled Trials, the Cochrane Database of Abstracts of Reviews of Effects, and the Periodical Abstracts Database; hand-searched personal libraries kept by content experts and project staff; and mined bibliographies of articles and systematic reviews for citations. We asked content experts to identify unpublished literature. Finally, we asked content experts and peer reviewers to identify newly published articles up to April 2005. Study Selection and Classification Two reviewers independently selected for detailed review the following types of articles that addressed the workings or implementation of a health technology system: systematic reviews, including meta-analyses; descriptive qualitative reports that focused on exploration of barriers; and quantitative reports. We classified quantitative reports as hypothesis-testing if the investigators compared data between groups or across time periods and used statistical tests to assess differences. We further categorized hypothesis-testing studies (for example, randomized and nonrandomized, controlled trials, controlled before-and-after studies) according to whether a concurrent comparison group was used. Hypothesis-testing studies without a concurrent comparison group included those using simple prepost, time-series, and historical control designs. Remaining hypothesis-testing studies were classified as cross-sectional designs and other. We classified quantitative reports as a predictive analysis if they used methods such as statistical modeling or expert panel estimates to predict what might happen with implementation of health information technology rather than what has happened. These studies typically used hybrid methodsfrequently mixing primary data collection with secondary data collection plus expert opinion and assumptionsto make quantitative estimates for data that had otherwise not been empirically measured. Cost-effectiveness and cost-benefit studies generally fell into this group. Data Extraction and Synthesis Two reviewers independently appraised and extracted details of selected articles using standardized abstraction forms and resolved discrepancies by consensus. We then used narrative synthesis methods to integrate findings into descriptive summaries. Each institution that accounted for more than 5% of the total sample of 257 papers was designated as a benchmark research leader. We grouped syntheses by institution and by whether the systems were commercially or internally developed. Role of the Funding Sources This work was produced under Agency for Healthcare Research and Quality contract no. 2002. In addition to the Agency for Healthcare Research and Quality, this work was also funded by the Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services, and the Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services. The funding sources had no role in the design, analysis, or interpretation of the study or in the decision to submit the manuscript for publication. Data Synthesis Literature Selection Overview Of 867 articles, we rejected 141 during initial screening: 124 for not having health information technology as the subject, 4 for not reporting relevant outcomes, and 13 for miscellaneous reasons (categories not mutually exclusive). Of the remaining 726 articles, we excluded 469 descriptive reports that did not examine barriers (Figure). We recorded details of and summarized each of the 257 articles that we did include in an interactive database (healthit.ahrq.gov/tools/rand) that serves as the evidence table for our report (14). Twenty-four percent of all studies came from the following 4 benchmark institutions: 1) the Regenstrief Institute, 2) Brigham and Womens Hospital/Partners Health Care, 3) the Department of Veterans Affairs, and 4) LDS Hospital/Intermountain Health Care. Figure. Search flow for health information technology ( HIT ) literature. Pediatrics Types and Functions of Technology Systems The reports addressed the following types of primary systems: decision support aimed at providers (63%), electronic health records (37%), and computerized provider order entry (13%). Specific functional capabilities of systems that were described in reports included electronic documentation (31%), order entry (22%), results management (19%), and administrative capabilities (18%). Only 8% of the described systems had specific consumer health capabilities, and only 1% had capabilities that allowed systems from different facilities to connect with each other and share data interoperably. Most studies (n= 125) assessed the effect of the systems in the outpatient setting. Of the 213 hypothesis-testing studies, 84 contained some data on costs. Several studies assessed interventions with limited functionality, such as stand-alone decision support systems (15-17). Such studies provide limited information about issues that todays decision makers face when selecting and implementing health information technology. Thus, we preferentially highlight in the following paragraphs studies that were conducted in the United States, that had empirically measured data on multifunctional systems, and that included health information and data storage in the form of electronic documentation or order-entry capabilities. Predictive analyses were excluded. Seventy-six studies met these criteria: 54 from the 4 benchmark leaders and 22 from other institutions. Data from Benchmark Institutions The health information technology systems evaluated by the benchmark leaders shared many characteristics. All the systems were multifunctional and included decision support, all were internally developed by research experts at the respective academic institutions, and all had capabilities added incrementally over several years. Furthermore, most reported studies of these systems used research designs with high internal validity (for example, randomized, controlled trials). Appendix Table 1 (18-71) provides a structured summary of each study from the 4 benchmark institutions. This table also includes studies that met inclusion criteria not highlighted in this synthesis (26, 27, 30, 39, 40, 53, 62, 65, 70, 71). The data supported 5 primary themes (3 directly r

Meta-analysis: surgical treatment of obesity.

Context The effectiveness of surgical therapy in the treatment of obesity is unclear. Contribution Many published studies of obesity surgery have significant limitations, and case series make up much of the evidence. Evidence is complicated by the heterogeneity of procedures studied. However, surgery can result in substantial amounts of weight loss (20 to 30 kg) for markedly obese individuals. One cohort study documented weight loss for 8 years with associated improvements in comorbid conditions, such as diabetes. Complications of surgery appear to occur in about 20% of patients. Implications Those considering surgical treatment for obesity should understand that, although patients who have surgery can lose substantial amounts of weight, the evidence base for these treatments is limited. The Editors The prevalence of obesity in the United States is reaching epidemic proportions. An estimated 30% of individuals met the criteria for obesity in 19992002 (1, 2), and many industrialized countries have seen similar increases. The health consequences of obesity include heart disease, diabetes, hypertension, hyperlipidemia, osteoarthritis, and sleep apnea (3-7). Weight loss of 5% to 10% has been associated with marked reductions in the risk for these chronic diseases and with reducing the incidence of diabetes (8-14). The increasing numbers of obese individuals have led to intensified interest in surgical treatments to achieve weight loss, and a variety of surgical procedures have been used (Figure 1). Bariatric surgery was first performed in 1954 with the introduction of the jejunoileal bypass, which bypasses a large segment of small intestine by connecting proximal small intestine to distal small intestine. With this procedure, weight loss occurs secondary to malabsorption from reduction of upstream pancreatic and biliary contents. However, diarrhea and nutritional deficiencies were common, and this procedure was discontinued because of the complication of irreversible hepatic cirrhosis. With the development of surgical staplers came the introduction of gastroplasty procedures by Gomez in 1981 (15) and Mason in 1982 (16). In these early procedures, the upper portion of the stomach was stapled into a small gastric pouch with an outlet (that is, a stoma) to the remaining distal stomach, which limited the size of the meal and induced early satiety. These procedures were prone to staple-line breakdown or stoma enlargement and were modified in turn by the placement of a band around the stoma (vertical banded gastroplasty). Figure 1. Surgical procedures. The first gastric bypass was reported in 1967 by Mason and Ito (17). It combined the creation of a small gastric pouch with bypassing a portion of the upper small intestine. Additional modifications resulted in the Roux-en-Y gastric bypass (RYGB), a now common operation that involves stapling the upper stomach into a 30-mL pouch and creating an outlet to the downstream small intestine. The new food limb joins with the biliopancreatic intestine after a short distance. This procedure, performed laparoscopically or by using an open approach, generates weight loss by limiting gastric capacity, causing mild malabsorption, and inducing hormonal changes. A second common technique, particularly outside of the United States, is the laparoscopic adjustable gastric band. This device is positioned around the uppermost portion of the stomach and can be adjusted to allow tailoring of the stoma outlet, which controls the rate of emptying of the pouch and meal capacity. Another procedure, preferred by a number of surgeons, is the biliopancreatic bypass, which combines a limited gastrectomy with a long Roux limb intestinal bypass that creates a small common channel (that is, an intestine where food and biliopancreatic contents mix). This procedure can be combined with a duodenal switch, which maintains continuity of the proximal duodenum with the stomach and uses a long limb Roux-en-Y bypass to create a short common distal channel. These latter 2 procedures generate weight loss primarily through malabsorption. Recent worldwide survey data from 2002 and 2003 show that gastric bypass is the most commonly performed weight loss procedure (65.1%) (18). Slightly more than half of gastric bypasses are done laparoscopically. Overall, 24% of cases are laparoscopic adjustable band procedures; 5.4% are vertical banded gastroplasties; and 4.9% are biliopancreatic diversion, with or without the duodenal switch. In California, the number of bariatric cases increased 6-fold between 1996 and 2000 (19), from 1131 cases to 6304; an estimated 140000 procedures were performed in the United States in 2004. With this escalation in the number of procedures, there have been reports of high postoperative complication rates (20-24). Because of these reports and the increasing use of obesity surgery, we were asked to review the literature to estimate the effectiveness of bariatric surgery relative to nonsurgical therapy for weight loss and reduction in preoperative obesity-related comorbid conditions. We were also asked to compare outcomes of surgical techniques. This paper is part of a larger evidence report titled Pharmacological and Surgical Treatment of Obesity, which was prepared for the Agency for Healthcare Research and Quality and is available at www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=hstat1a.chapter.19289. Methods Literature Search and Selection We began with an electronic search of MEDLINE on 16 October 2002, followed by a search of EMBASE and subsequent periodic search updates (on 22 May, 2 June, 12 June, and 3 July 2003). We also assessed existing reviews of surgical therapy for obesity (10, 25, 26). Three reviewers independently reviewed the studies, abstracted data, and resolved disagreements by consensus (2 reviewers per study). The principal investigator settled any unresolved disagreements. We focused on studies that assessed surgery and used a concurrent comparison group. This category includes randomized, controlled trials (RCTs); controlled clinical trials; and cohort studies. A brief scan of the literature showed that these types of studies were rare. Therefore, we also elected to include case series with 10 or more patients, since these studies can be used to assess adverse events and could potentially augment the efficacy data from comparative studies. Publication bias is one potential limitation of analyzing the available literature because poor or negative results are not as likely to be reported as are successes or positive results. Extraction of Study-Level Variables We abstracted data from the articles, including number of patients and comorbid conditions, adverse events, types of outcome measures, and time from intervention until outcome. Detailed data were also collected on characteristics of the study samples, including median age, percentage of women, median baseline weight (in kilograms or body mass index [BMI]), percentage of patients with comorbid conditions at baseline (diabetes, hypertension, dyslipidemia, and sleep apnea), percentage of improvement or resolution of preexisting comorbid conditions, and median follow-up time. We also recorded whether the case series studies reported on consecutive patients. Choice of Outcomes The main outcomes of interest were weight loss, mortality, complication rates, and control of obesity-related comorbid conditions. We used the most commonly reported measurement of weight loss, that is, kilograms, which allowed us to include the greatest number of studies. Among 111 surgical studies reporting weight loss, 43 reported weight loss in kilograms or pounds, 17 reported excess weight loss or some variant, 46 reported both of these outcomes, and 5 reported neither. A total of 89 studies had sufficient data to be included in the weight loss analysis. Because weight loss achieves health benefits primarily by reducing the incidence or severity of weight-related comorbid conditions, we also compared the effects on these outcomes. Quality of life, an important outcome in assessing tradeoffs between benefits and risks, was reported infrequently. Statistical Analyses Because we included both comparative studies and case series, we conducted several types of analyses. The vast number of types of surgical procedures and technical variations required that we aggregate those that were clinically similar and identify the comparisons that were of most interest to the clinical audience. On the basis of discussions with bariatric surgeons, we categorized obesity surgery procedures by procedure type (for example, gastric bypass, vertical banded gastroplasty), laparoscopic or open approach, and specific surgical details such as length of Roux limb (see the larger evidence report for details). Analysis of the Efficacy of Surgical Weight Loss We extracted the mean weight loss and standard deviation at 12 postoperative months and at the maximum follow-up time (36 months). These times were chosen because they are clinically relevant and are most commonly reported. Of the 89 weight loss studies, 71 reported baseline BMI (average, 47.1 kg/m2), 16 reported baseline weight in kilograms or pounds (average, 123.3 kg), and 2 did not report either. The average age of patients was 38 years, and more than three quarters were women. For comparative studies that reported a within-study comparison of 2 procedures, a mean difference was calculated. Mean differences were pooled by using a random-effects model, and 95% CIs were estimated; the same method was used to determine a pooled mean weight loss for each group considering all studies combined. However, mean difference in weight loss was not calculated. Analysis of Surgery Mortality We recorded the number of deaths observed and the total number of patients in each procedure group. If the study self-identified the deaths as early or postoperative or as occurring within 30 days of the surgery, we termed these early deaths. If the

Interventions for the prevention of falls in older adults: systematic review and meta-analysis of randomised clinical trials

Abstract Objective To assess the relative effectiveness of interventions to prevent falls in older adults to either a usual care group or control group. Table 2 Components of multifactorial falls risk assessment Trial Orthostatic blood pressure Vision Balance and gait Drug review Instrumental activities of daily living or activities of daily living Cognitive evaluation Environmental hazards Other Carpenter 1990w4 No No No No Yes No No Fabacher 1994w13 Yes Yes Yes Yes Yes Yes Yes Assessment of hearing and depression Rubenstein 1990w30 Yes Yes Yes Yes Yes Yes Yes Neurological and musculoskeletal examination, laboratory tests, 24 hour heart monitor Tinetti 1994w37 Yes No Yes Yes No No Yes Muscle strength and range of motion Wagner 1994w39 No Yes No Yes No No Yes Hearing, assessment of alcohol misuse, assessment of physical activity Gallagher 1996w15 Yes Yes Yes Yes Yes Yes Yes List of health problems Coleman 1999w7 No No No Yes No No No Self management skills, health assessment Close 1999w6 Yes Yes Yes Yes Yes Yes Yes Affect, carotid sinus studies (if clinical suspicion) McMurdo 2000w21 Yes Yes No Yes No No No Review of lighting in environment Van Haastregt 2000w38 No No No Yes Yes Yes Yes Physical health, psychosocial functioning Millar 1999w24 Yes Yes No Yes No No No Review of lighting in environment Crome 2000w8* Jensen 2002w17 No Yes Yes Yes Yes Yes Yes Hearing, review of lighting in environment, assistive device (for example, cane, walker), review of use of device, and repair of device if needed See table A on bmj.com for details of references. * No specific components stated. Design Systematic review and meta-analyses. Data sources Medline, HealthSTAR, Embase, the Cochrane Library, other health related databases, and the reference lists from review articles and systematic reviews. Data extraction Components of falls intervention: multifactorial falls risk assessment with management programme, exercise, environmental modifications, or education. Results 40 trials were identified. A random effects analysis combining trials with risk ratio data showed a reduction in the risk of falling (risk ratio 0.88, 95% confidence interval 0.82 to 0.95), whereas combining trials with incidence rate data showed a reduction in the monthly rate of falling (incidence rate ratio 0.80, 0.72 to 0.88). The effect of individual components was assessed by meta-regression. A multifactorial falls risk assessment and management programme was the most effective component on risk of falling (0.82, 0.72 to 0.94, number needed to treat 11) and monthly fall rate (0.63, 0.49 to 0.83; 11.8 fewer falls in treatment group per 100 patients per month). Exercise interventions also had a beneficial effect on the risk of falling (0.86, 0.75 to 0.99, number needed to treat 16) and monthly fall rate (0.86, 0.73 to 1.01;2.7). Conclusions Interventions to prevent falls in older adults are effective in reducing both the risk of falling and the monthly rate of falling. The most effective intervention was a multifactorial falls risk assessment and management programme. Exercise programmes were also effective in reducing the risk of falling.

Meta-analysis : Pharmacologic treatment of obesity

Context The effectiveness of pharmacologic therapy in the treatment of obesity is unclear. Contribution This review of 79 clinical trials involving diet plus the obesity drugs sibutramine, orlistat, fluoxetine, sertraline, bupropion, topiramate, or zonisamide shows that these medications can lead to modest weight reductions of approximately 5 kg or less at 1 year. Available evidence is lacking on the effect of these drugs on long-term weight loss, health outcomes such as cardiovascular events and diabetes, and adverse effects. Implications Those considering pharmacologic treatment for obesity should understand that these drugs can lead to modest weight loss at 1 year, but data on long-term effectiveness and safety are lacking. The Editors Obesity has been defined as excess body fat relative to lean body mass (1) and, in humans, is the result of interactions of the environment with multiple genes. The age-adjusted prevalence of obesity was 30.5% in 19992000 (2). Although it is difficult to precisely estimate the change in prevalence of obesity over time because of changing definitions, nearly all clinical authorities agree that obesity is reaching epidemic proportions (2-13). Obesity is currently defined as a body mass index (BMI) of 30 kg/m2 or greater. Individuals whose BMI falls between 25 kg/m2 and 29.9 kg/m2 are considered overweight. Attempts to meet the body weight goal of the Healthy People 2000 initiative (7)to reduce the prevalence of overweight among adults to less than 20% of the populationdid not succeed. Still, many Americans are trying. According to a national survey (14), about 40% of women and 25% of men reported that they were currently trying to lose weight. However, most weight loss attempts consist of 6 months of loss followed by gradual regain to baseline (15). The health consequences of obesity include some of the most common chronic diseases in our society. Obesity is an independent risk factor for heart disease (16). Type 2 diabetes mellitus, hypertension and stroke, hyperlipidemia, osteoarthritis, and sleep apnea are all more common in obese individuals (17-19). A recent prospective study involving 900000 U.S. adults reported that increased body weight was associated with increased death rates for all cancer combined and for cancer at multiple specific sites (20). Adult weight gain is associated with increased risk for breast cancer in postmenopausal women (21). Weight loss of 5% to 10% can be associated with marked reductions in the risk for these chronic diseases (22). In the Diabetes Prevention Program, weight loss of about 5% to 6% among persons with a BMI of 34 kg/m2, along with increased physical activity, resulted in a 58% reduction in the incidence of diabetes (23). In response to the increase in obesity, pharmaceutical treatments for obesity have become both more numerous and more commonly used. Drugs prescribed for weight loss can be divided into 2 categoriesappetite suppressants and lipase inhibitorson the basis of their putative mechanisms of action. Appetite suppressants can be further subdivided on the basis of the neurotransmitters they are believed to affect. This article, which reviews the available evidence on medications used as obesity treatment in adults (Table 1), is part of a larger evidence report prepared for the Agency for Healthcare Research and Quality titled Pharmacological and Surgical Treatment of Obesity. The larger report is available at www.ncbi.nlm.nih.gov/books/bv.fcgi?rid=hstat1a.chapter.19289. Table 1. Prescription Medications Used for Weight Loss Methods Literature Search and Selection Our search for controlled human studies of pharmacologic treatments of obesity began with an electronic search of MEDLINE on 16 October 2002. Subsequently, our librarian conducted current awareness search updates on 22 May, 2 June, 12 June, and 3 July 2003. We also searched the Cochrane Controlled Clinical Trials Register Database and existing systematic reviews. Full details of the search strategy are available in the larger evidence report. To be accepted for analysis, a study of drug therapy had to be a controlled clinical trial that assessed the effect of one of the pharmaceutical agents in humans and reported at least 6-month weight loss outcomes in pounds or kilograms. We made an exception for topiramate, for which most trials reported only percentage of weight loss. Patients in included studies needed to have a BMI of 27 kg/m2 or greater (Appendix Table). The technical expert panel for our evidence report determined which pharmaceutical agents would be included. The panel chose sibutramine, orlistat, phentermine, and diethylpropion, all of which have been approved by the U.S. Food and Drug Administration, as well as other medications being used for weight loss, including fluoxetine, bupropion, sertraline, topiramate, and zonisamide. Extraction of Study-Level Variables and Results Three reviewers, working in groups of 2, extracted data from the same articles and resolved disagreements by consensus. A senior physician resolved any remaining disagreements. We used the Jadad score to evaluate the quality of the studies, using information on study design, method of random assignment, blinding, and withdrawal (34). Jadad scores range from 0 (lowest quality) to 5 (highest quality). We also collected information on withdrawal and dropout rates and calculated the percentage of attrition by dividing the number of patients providing follow-up data by the number of patients initially enrolled. Of the medications we assessed, 3 had up-to-date existing meta-analyses (sibutramine, phentermine, and diethylpropion) and 4 others had a sufficient number of new studies to justify a new meta-analysis (orlistat, fluoxetine, bupropion, and topiramate). However, because heterogeneity was too great for the fluoxetine studies, they are summarized narratively. Selection of Trials for Meta-Analysis The outcome of interest was weight loss between baseline and follow-up. To make our analyses comparable, we stratified them in the same manner as did the recent meta-analysis on sibutramine (35). We defined data collected at 6 months to be data collected at any point between 16 and 24 weeks; likewise, 1-year follow-up data were those collected at any point between 44 and 54 weeks. If a study presented data for 2 or more time points in an interval, for example, 16 and 18 weeks, we chose the longest follow-up measurement for our analysis. Mean Difference For each trial, we extracted the follow-up mean weight loss for the control group, the follow-up mean weight loss for the medication group, and the standard deviation for each group. We then calculated a mean difference for each study, which was the difference between follow-up mean weight loss in the control group versus the medication group. Sensitivity Analyses We conducted sensitivity analyses on 4 study dimensions: Jadad quality score (2 vs. 3), year of publication (1998 or earlier vs. 1999 or later), completion rate (<80% vs. 80% and <70% vs. 70%), and dosage. We tested for differences between subgroups (for example, high-quality vs. lower-quality studies) by conducting a meta-regression analysis using a single dichotomous variable to indicate subgroup membership. We conducted sensitivity analyses to determine the possible impact of dropouts. In these analyses, we assumed that all patients who dropped out had a weight loss of zero. The mean weight loss for a particular study was then recalculated on the basis of the complete sample of both responders and dropouts. We assumed that the standard deviation of weight loss for a study did not change and recalculated the standard error on the basis of the complete sample size. We then conducted a pooled analysis for each medication and follow-up time as performed in the original approach. Meta-Analysis of Weight Loss For the 6-month and 12-month analyses, we estimated a pooled DerSimonianLaird random-effects estimate (36) of the overall mean difference. The mean differences in the individual trials are weighted by both within-study variation and between-study variation in this synthesis. We also report P values derived from the chi-square test of heterogeneity based on the Cochran Q-test (37), and the I2 statistic (38). This latter statistic represents the percentage of study variability that is due to heterogeneity rather than chance and is independent of the number of studies and the effect size metric. Publication Bias We assessed the possibility of publication bias by evaluating a funnel plot. We also conducted an adjusted rank correlation test (39) as a formal statistical test for publication bias. Extraction of Data on Adverse Events We assessed evidence of adverse events from randomized, controlled trials (RCTs) only. We did not include observational studies or case series data. Each trial included in the weight loss analysis was examined to determine whether it reported data on adverse events. Adverse events were recorded as the number of events or the number of people, depending on how the trial chose to report events. Most trials recorded the number of events rather than the number of unique people who experienced the event. Each event was counted as if it represented a unique individual. Because a single individual might have experienced more than 1 event, this assumption may have overestimated the number of people who had an adverse event. Meta-Analysis of Adverse Events For subgroups of events that occurred in 2 or more trials, at least once in the medication group and at least once in the control group, we performed a meta-analysis to estimate the pooled odds ratio and its associated 95% CI. Given that many of the events were rare, we used exact conditional inference to perform the pooling rather than applying the usual asymptotic methods that assume normality. For interpretability, for any significant pooled odds ratio greater than 1 (which indicates that the odds of the adverse

Meta-analysis: chronic disease self-management programs for older adults

Context Do self-management programs improve outcomes of adults with chronic conditions? Contribution This meta-analysis summarizes data from 53 randomized, controlled trials of self-management interventions for adults with diabetes mellitus, hypertension, or osteoarthritis. Self-management helped reduce hemoglobin A1c and blood pressure levels in diabetes and hypertension, respectively, but had minimal effect on pain and function in patients with arthritis. The authors could not identify any self-management program characteristics that predicted successful outcomes. Cautions The authors found evidence of possible publication bias. Implications Self-management programs may improve some outcomes in patients with some chronic diseases, but how to design an optimal program is not yet clear. The Editors Chronic diseases are conditions that are usually incurable. Although often not immediately life-threatening, they place substantial burdens on the health, economic status, and quality of life of individuals, families, and communities (1). In 1995, 79% of noninstitutionalized persons who were 70 years of age or older reported having at least 1 of 7 of the most common chronic conditions affecting this age group: arthritis, hypertension, heart disease, diabetes mellitus, respiratory disease, stroke, and cancer (1). Of these 7 conditions, arthritis is most prevalent, affecting more than 47% of individuals 65 years of age and older (2). Hypertension affects 41% of this population, and 31% of this group has some form of heart disease (of which ischemic heart disease and a history of myocardial infarction are major components). Diabetes mellitus affects approximately 10% of persons 65 years of age and older and increases the risk for other chronic conditions, including ischemic heart disease, renal disease, and visual impairment (2). Enthusiasm is growing for the role of self-management programs in controlling and preventing chronic disease complications (3-5). Despite this enthusiasm, experts do not agree on the definition of what constitutes a chronic disease self-management program, which elements of self-management programs are essential regardless of the clinical condition, or which elements are important for specific conditions. Several recent reviews on chronic disease self-management interventions have been published, including 2 Cochrane collaborations (6-13). Almost all have been disease-specific. One Cochrane review (12) concluded that there was insufficient evidence to assess the benefit of dietary treatment for type 2 diabetes mellitus programs, but exercise programs led to improved hemoglobin A1c values. A second Cochrane review of self-management for hypertension (11) used unpooled results to conclude that a reduction in the frequency of medication dosage increased adherence. There was not, however, consistent evidence of decreased blood pressure. Almost all previous reviews have been disease-specific or addressed specific intervention components within specific disease conditions (14-17). Two recent reviews assessed self-management programs across conditions. The first review provided a qualitative evaluation of self-management interventions across 3 conditions: type 2 diabetes mellitus, arthritis, and asthma (18). This review, which presented an overall optimistic assessment of self-management interventions, did not, however, include a quantitative synthesis of the data, nor did it address the issue of publication bias. The second review quantitatively assessed 71 trials (both randomized and nonrandomized) that included a self-management education program for patients with asthma, arthritis, diabetes mellitus, hypertension, and miscellaneous other conditions. Meta-analysis found statistically significant benefits for some outcomes within conditions. The authors could not detect meaningful differences in the effectiveness of the programs because of the varying intervention characteristics, such as the use of a formal syllabus, the type of program facilitator, the number of program sessions in which patients participated, and the duration of the program (19). In our review, we sought to quantitatively assess chronic disease self-management programs for older adults within and across disease conditions. We used empirical data from the literature to address 2 research questions: First, do chronic disease self-management programs result in improved disease-related outcomes for specific chronic diseases of high prevalence in older adults? Second, if self-management interventions are effective, are there specific components that are most responsible for the effect, within or across disease conditions? To address these questions, we focused on evaluating the effect of self-management programs for the 3 chronic conditions that have been most commonly studied in controlled trials of older adults: osteoarthritis, diabetes mellitus, and hypertension. Methods Conceptual Model Because there is no accepted definition of what constitutes a chronic disease self-management program, we used an intentionally broad definition to avoid prematurely excluding relevant studies. On the basis of a conceptual framework derived from the clinical literature and from discussions with social scientists with expertise in self-management, we defined chronic disease self-management as a systematic intervention that is targeted toward patients with chronic disease. The intervention should help them actively participate in either or both of the following: self-monitoring (of symptoms or of physiologic processes) or decision making (managing the disease or its impact through self-monitoring). We attempted to understand the characteristics particular to chronic disease self-management programs that may be most responsible for their effectiveness. On the basis of the literature and expert opinion, we postulated 5 hypotheses regarding the effectiveness of chronic disease self-management programs that feature the following characteristics: Tailoring. Patients who receive interventions tailored to their specific needs and circumstances are likely to derive more benefit than those receiving interventions that are generic. roup setting. Patients are more likely to benefit from interventions received within a group setting that includes others affected by the same condition than from an intervention provided in some other setting. Feedback. Patients are more likely to derive benefit from a cycle of intervention followed by some form of individual review with the provider of the intervention than from interventions where no such review exists. Psychological emphasis. Patients are more likely to derive benefit from a psychological intervention than from interventions where there is no psychological emphasis. Medical care. Patients who receive interventions directly from their medical providers (physicians or primary care providers) are more likely to derive benefit than those who receive interventions from nonmedical providers. Outcome Measures From the literature, we identified outcomes of interest to include the following: clinical outcomes, such as pain and function for osteoarthritis; measures that have strong links to clinical outcomes, such as hemoglobin A1c levels, fasting blood glucose levels, and patient weight for diabetes and blood pressure for hypertension; and intermediate outcomes, such as knowledge, feeling of self-efficacy, and health behaviors that are postulated to be related to clinical outcomes. Databases for Literature Search We used several databases and published documents to identify existing research and potentially relevant evidence for this report. For our primary source of citation information from 1980 until 1995, we used An Indexed Bibliography on Self-Management for People with Chronic Disease (20), published by the Center for Advancement of Health in association with the Group Health Cooperative of Puget Sound; we obtained any studies not listed in the bibliography (including those published later than 1995) by searching MEDLINE, PsycINFO, and CINAHL. We also used the Cochrane Library (its database of systematic reviews and the central register of controlled trials); the Assessment of Self-Care Manuals, published by the Evidence-based Practice Center at the Oregon Health Sciences University (21); and 77 other previously completed reviews relevant to this project. We retrieved all relevant documents referenced in these publications, and we updated our search in September 2004. Each review discussed at least 1 intervention aimed at chronic disease self-management. We also searched the Health Care Quality Improvement Projects database, maintained by the U.S. Centers for Medicare & Medicaid Services. This database contains reports known as narrative project documents, each of which describes an individual research project conducted by a Medicare Peer Review Organization; most projects in this database are not published elsewhere. Each report includes the projects background, aims, quality indicators, collaborators, sampling methods, interventions, measurement, and results. A complete description of our literature search has been reported elsewhere (22). Article Selection and Data Abstraction Two trained physician reviewers, working independently, conducted the article selection, quality assessment, and data abstraction; disagreements were resolved by consensus or third-party adjudication. Articles were not masked. We included all randomized trials that assessed the effects of an intervention or interventions relative to either a group that received usual care or a control group among the elderly and for our 3 conditions. Most studies compared their intervention with usual care or with a control intervention designed to account for the added attention received in the intervention (such as attending classes on vehicle safety instead of attending classes on self-management). Because our analysis was funded by the Centers for M

Systematic Review: Comparative Effectiveness of Treatments to Prevent Fractures in Men and Women with Low Bone Density or Osteoporosis

Context Sorting through the proven benefits and harms of the agents available for treating osteoporosis is difficult. Contribution This systematic review of 76 randomized trials and 24 meta-analyses found good evidence that multiple agents, including alendronate, zoledronic acid, and estrogen, prevented vertebral and hip fractures more than placebo. Harms included increased risk for thromboembolic events with raloxifene, estrogen, and estrogenprogestin and increased gastrointestinal symptoms with bisphosphonates. No large trials directly compared 2 or more agents and established superiority of any agent. Implication Available data insufficiently characterize the benefits and harms of various therapies for osteoporosis relative to one another. The Editors Osteoporosis is a systemic skeletal disease characterized by low bone mass and microarchitectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture (1). Approximately 44 million people in the United States are affected by osteoporosis and low bone mass (2). The clinical complications include fractures, disability, and chronic pain. About 54% of women age 50 years or older will have an osteoporotic fracture during their lifetime (3). Furthermore, approximately 4% of patients older than 50 years of age who have a hip fracture die while in the hospital and 24% die within 1 year after the hip fracture (4). The economic burden of osteoporosis is large and growing. Most estimates are based on the cost of fracture alone: A 1995 estimate of costs incurred by osteoporotic fractures in the United States was

Systematic review of the effects of n−3 fatty acids in inflammatory bowel disease

13.8 billion (5). A 2003 review estimated the total costs in the United States at

The Efficacy of Omega–3 Fatty Acids on Cognitive Function in Aging and Dementia: A Systematic Review

17 billion (6). Although the bulk of these costs were incurred by retired individuals older than age 65 years, direct costs and work loss are significant among employed postmenopausal women (7). The increasing prevalence and cost of osteoporosis have heightened interest in the efficacy and safety of the many agents available to treat the loss of bone mineral associated with osteoporosis. This systematic review, developed under the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care Program, describes the benefits in fracture reduction and the harms from adverse events among and within the various classes of pharmacotherapies for osteoporosis. The agents evaluated were bisphosphonates (alendronate, etidronate, ibandronate, pamidronate, risedronate, and zoledronic acid), calcitonin, estrogen, teriparatide, selective estrogen receptor modulators (raloxifene and tamoxifen), testosterone, and vitamins (vitamin D) and minerals (calcium). Methods We followed a standardized protocol for the review. The full technical report (8) provides detailed methods, evidence tables, and risk estimates for individual studies. The full report also enumerates studies included in the meta-analyses described in this review. Data Sources and Study Selection We searched MEDLINE (1966 to December 2006), the ACP Journal Club database, the Cochrane Central Register of Controlled Trials (no date limits), the Cochrane Database of Systematic Reviews (no date limits), and the Web sites of the National Institute for Health and Clinical Excellence (no date limits) and Health Technology Assessment Programme (January 1998 to December 2006) for materials pertaining to the specified agents, limiting our searches to English-language publications and human studies. We first identified systematic reviews and meta-analyses of trials that reported pooled estimates of the effect of the agents on fracture risk. When such reviews were identified for specific agents, we truncated our searches for randomized trials to include only those published after the last search date used in the review or meta-analysis. We manually searched reference lists of all review articles obtained for any reports of original research not already identified, and we reviewed U.S. Food and Drug Administration (FDA) medical and statistical reviews, scientific information packets from pharmaceutical companies, and additional studies recommended by our technical expert panel and by stakeholders during a public review period. To supplement the information in systematic reviews on estrogen, we reviewed the Womens Health Initiative and Heart and Estrogen/progestin Replacement Study trials, as suggested by our technical expert panel. Finally, we conducted an additional search for large observational studies that reported any of the following adverse events: 1) cardiovascular events (myocardial infarction and stroke); 2) thromboembolic events (pulmonary embolism and venous thromboembolic events); 3) malignant conditions (breast cancer, colon cancer, lung cancer, and osteosarcoma); 4) upper gastrointestinal events (perforations, ulcers, bleeding, and esophageal ulcerations); and 5) osteonecrosis. The search was updated for this paper, but not for the full report, by searching MEDLINE (1 January 2007 to 10 November 2007) for large clinical trials that reported fracture outcomes for the specified agents. For information on efficacy, we selected meta-analyses that reported pooled risk estimates for fracture and randomized trials that compared any of the agents with placebo or with each other and reported fracture outcomes. For information on harms, we selected systematic reviews, randomized trials, and large casecontrol or cohort studies with more than 1000 participants. We also reviewed cases of osteonecrosis at AHRQs request. Data Extraction and Study Quality Two physicians independently abstracted data about study populations, interventions, follow-up, and outcome ascertainment by using a structured form. For each group in a randomized trial, a statistician extracted the sample size and number of persons who reported fractures. Two reviewers, under the supervision of the statistician, independently abstracted information about adverse events. Disagreements were resolved by the statistician or the principal investigator. Adverse events were recorded onto a spreadsheet that identified numbers of participants in each trial group and the description of the adverse event as listed in the original article. Each event was counted as if it represented a unique individual. Because an individual may have experienced more than 1 event within a category of adverse events (for example, both stroke and myocardial infarction), this assumption may have overestimated the number of people who had an adverse event in that category. If a trial report mentioned a particular type of adverse event but did not report data on it, we did not include the trial in that particular events analysis. In other words, we did not assume an occurrence of zero events unless it was specifically reported as such. By taking this approach, we may have overestimated the number of patients for whom a particular adverse event was observed. We used predefined criteria to assess the quality of systematic reviews and randomized trials, based on internal and external validity assessment detailed in the QUOROM (Quality of Reporting of Meta-Analyses) statement (9), and items related to randomization, blinding, and accounting for withdrawals and dropouts (10, 11). Each element is detailed in appendices to the full report (8). For this review, we characterized the overall strength of evidence for estimating fracture risk as good, fair, or weak on the basis of the characteristics previously described, as well as the number of studies, total number of participants across studies, whether fractures were a primary outcome, reproducibility of results across studies, and precision of the CIs surrounding the point estimates. Evidence was classified as good if the total sample size was greater than 1000, the results across all studies were consistent, and the studies were of high methodological quality. Evidence was classified as fair if results were inconsistent across the studies. The evidence was classified as weak if no studies assessed fracture as a primary outcome, the total sample size across studies was less than 500, and the CIs around the point estimates were wide and crossed null. Data Synthesis and Statistical Analysis Comparisons of interest were single agent versus placebo and single agent versus another agent for agents within the same class and across classes. We also compared estrogenprogestin versus placebo or single drugs. Studies that included either calcium or vitamin D in all study groups were classified as comparisons between the other agents in each group; for example, alendronate plus calcium versus risedronate plus calcium would be classified as alendronate versus risedronate. In this review, we summarize data on vertebral, nonvertebral, and hip fractures; data on total, wrist, and humerus fractures are included in the full report (8). The number of people with at least 1 fracture was our primary outcome of interest. Because fractures rarely occurred and zero events were often observed in at least 1 treatment group, we calculated odds ratios (ORs) by using the Peto method (12). Trials with zero events in both groups have an undefined OR. Because fractures are rare events, the OR approximates the relative risk (RR) for fracture. We combined data from multiple study groups in an individual study to calculate a single OR for comparisons of interest. In these instances, the same outcome had been reported for each group, and the individuals in each group were unique. For example, to develop an OR for the risk for vertebral fractures regardless of dose, we combined the participants in the various dose groups and compared them with those in the placebo group. We conducted the meta-analysis by using StatXact PROCs (Cytel, Cambridge, Massachusetts) for SAS software (SAS Institute, Cary, North Carolina). Recognizing that characteristics of the study population may affect risk for fracture, we defined risk groups to categorize the

Weight Loss Surgery is More Effective Than Diet and Exercise in Helping Severely Obese People Lose Weight

BACKGROUND n-3 Fatty acids are purported to have health effects in patients with inflammatory bowel disease (IBD), but studies have reported mixed results. OBJECTIVE We aimed to synthesize published and unpublished evidence to determine estimates of the effect of n-3 fatty acids on clinical outcomes in IBD and whether n-3 fatty acids modify the effects of or need for treatment with other agents. DESIGN Computerized databases were searched for studies of n-3 fatty acids in immune-mediated diseases from 1966 to 2003. We also contacted experts in the nutraceutical industry to identify unpublished studies; however, none were identified. RESULTS Reviewers identified 13 controlled trials that assessed the effects of n-3 fatty acids on clinical, sigmoidoscopic, or histologic scores; rates of induced remission or relapse; or requirements for steroids and other immunosuppressive agents in Crohn disease or ulcerative colitis. Most clinical trials were of good quality. Fewer than 6 were identified that assessed the effects of n-3 fatty acids on any single outcome of clinical, endoscopic, or histologic scores or remission or relapse rates. Consistent across 3 studies was the finding that n-3 fatty acids reduce corticosteroid requirements, although statistical significance was shown in only 1 of these studies. CONCLUSION The available data are insufficient to draw conclusions about the effects of n-3 fatty acids on clinical, endoscopic, or histologic scores or remission or relapse rates.

Effects of Omega-3 Fatty Acids on Cancer Risk: A Systematic Review

We systematically reviewed the published literature on the effects of omega–3 fatty acids on measures of cognitive function in normal aging, incidence and treatment of dementia. Computerized databases were searched for published literature to identify potentially relevant studies with the intent to conduct a meta-analysis. We screened 5,865 titles, reviewed 497 studies of which 49 underwent a detailed review, and found 5 studies that pertained to our objectives. We included controlled clinical trials and observational studies, including prospective cohort, case-control, and case series designs; we excluded case reports. We had no language restrictions. We abstracted data on the effects of omega–3 fatty acids and on study design, relevant outcomes, study population, source, type, amount, and duration of omega–3 fatty acid consumption, and parameters of methodological quality. A single cohort study has assessed the effects of omega–3 fatty acids on cognitive function with normal aging and found no association for fish or total omega–3 consumption. In four studies that assessed the effects of omega–3 fatty acids on incidence and treatment of dementia, a trend in favor of omega–3 fatty acids (fish and total omega–3 consumption) toward reducing risk of dementia and improving cognitive function was reported. The available data are insufficient to draw strong conclusions about the effects of omega–3 fatty acids on cognitive function in normal aging or on the incidence or treatment of dementia. However, limited evidence suggests a possible association between omega–3 fatty acids and reduced risk of dementia.