Sally C. Morton

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

The Care of HIV-Infected Adults in the United States

BACKGROUND AND METHODS In order to elucidate the medical care of patients with human immunodeficiency virus (HIV) infection in the United States, we randomly sampled HIV-infected adults receiving medical care in the contiguous United States at a facility other than military, prison, or emergency department facility during the first two months of 1996. We interviewed 76 percent of 4042 patients selected from among the patients receiving care from 145 providers in 28 metropolitan areas and 51 providers in 25 rural areas. RESULTS During the first two months of 1996, an estimated 231,400 HIV-infected adults (95 percent confidence interval, 162,800 to 300,000) received care. Fifty-nine percent had the acquired immunodeficiency syndrome according to the case definition of the Centers for Disease Control and Prevention, and 91 percent had CD4+ cell counts of less than 500 per cubic millimeter. Eleven percent were 50 years of age or older, 23 percent were women, 33 percent were black, and 49 percent were men who had had sex with men. Forty-six percent had incomes of less than

Efficacy of Angiotensin-Converting Enzyme Inhibitors and Beta-Blockers in the Management of Left Ventricular Systolic Dysfunction According to Race, Gender, and Diabetic Status A Meta-Analysis of Major Clinical Trials

10,000 per year, 68 percent had public health insurance or no insurance, and 30 percent received care at teaching institutions. The estimated annual direct expenditures for the care of the patients seen during the first two months of 1996 were

Spinal Manipulative Therapy for Low Back Pain: A Meta-Analysis of Effectiveness Relative to Other Therapies

5.1 billion; the expenditures for the estimated 335,000 HIV-infected adults seen at least as often as every six months were

Expenditures for the Care of HIV-Infected Patients in the Era of Highly Active Antiretroviral Therapy

6.7 billion, which is about

The Impact of Competing Subsistence Needs and Barriers on Access to Medical Care for Persons with Human Immunodeficiency Virus Receiving Care in the United States

20,000 per patient per year. CONCLUSIONS In this national survey we found that most HIV-infected adults who were receiving medical care had advanced disease. The patient population was disproportionately male, black, and poor. Many Americans with diagnosed or undiagnosed HIV infection are not receiving medical care at least as often as every six months. The total cost of medical care for HIV-infected Americans accounts for less than 1 percent of all direct personal health expenditures in the United States.