James M. Trauer

Cognitive Behavioral Therapy for Chronic Insomnia: A Systematic Review and Meta-analysis

Insomnia is a prevalent condition, with 5% to 15% of adults meeting formal diagnostic criteria for chronic insomnia (15) (now termed insomnia disorder [6]) and one third reporting dissatisfaction with sleep. Insomnia is associated with both medical and psychiatric comorbidity, being linked to anxiety; depression (7); chronic health problems, such as hypertension (8, 9) and type 2 diabetes (10); health care use; nonmotor vehicle accidents; pain (11); and use of medication and alcohol (1215). Symptoms of insomnia have functional consequences even in the absence of a formal diagnosis (16), with the high economic burden of the condition largely mediated through the productivity cost of work absenteeism (17). Hypnotics, such as benzodiazepines and related drugs, are the most commonly used treatment for insomnia, with around 6% to 10% of U.S. adults using hypnotics in 2010 (18) and 27 daily doses of such drugs being taken per 1000 U.S. persons (19). In Australia, around 90% of primary care encounters for insomnia result in hypnotic prescription (20). Furthermore, despite a lack of evidence, use of second-generation antipsychotics (such as quetiapine) is also increasing, possibly due to patient and physician dissatisfaction with available treatments and a perceived lack of alternatives (21, 22). In this context, considering nonpharmacologic treatment options for insomnia disorder is important. Cognitive behavioral therapy for insomnia (CBT-i) is an effective nonpharmacologic treatment that improves sleep outcomes with minimal adverse effects (23) and is preferred by patients to drug therapy (24). The approach to CBT-i has been refined in recent years, and it is now most commonly studied as a combined cognitive and behavioral treatment incorporating some or all of 5 components. The components are described in Table 1, and although the precise efficacy of each has not been determined, the package of care is more effective than separate delivery of the cognitive or behavioral components (25). Although previous meta-analyses have been performed (2629), no recent meta-analysis has assessed the efficacy of this now-established package of care. We present a meta-analysis of the efficacy of CBT-i on sleep diary outcomes, compared with control, for the treatment of adults with chronic insomnia. Table 1. Components of CBT-i Methods We performed a systematic review and meta-analysis in accordance with the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines, using methods from the Cochrane Handbook for Systematic Reviews of Interventions. The predetermined methods were registered online with PROSPERO (CRD42012002863) (30), with full methods presented in section 1 of the Supplement. Supplement. Supplementary Material Data Sources and Searches We searched MEDLINE, EMBASE, PsycINFO, CINAHL, the Cochrane Library, and PubMed Clinical Queries from inception to 31 March 2015 with the terms sleeplessness, chronic insomnia, insomniac, insomnia, insomni*, sleep initiation and maintenance disorders, cognitive behavioural therapy, cognitive behavioral therapy, cognitive behavioural therapies, cognitive behavioral therapies, sleep hygiene, stimulus control, relaxation, relaxation techniques, behavior modification, behavior therapy, cognitive therapy, imagery, and psychotherapy in any language. We also reviewed the reference lists of 4 review articles on the topic (2628, 31) and briefly screened references by using the same search strategy without limitation to randomized, controlled trials. Study Selection Eligible studies were randomized, controlled trials involving CBT-i in adults (aged 18 years) with chronic insomnia. We defined CBT-i as multimodal therapy delivered in person on at least 2 occasions and incorporating at least 2 of the 5 most widely accepted components of CBT-i: cognitive therapy, stimulus control, sleep restriction, sleep hygiene, and relaxation therapy. For the primary analysis, acceptable control groups included sham therapy, waiting list, no treatment, sleep hygiene, or information provision. Studies were excluded if medical, sleep, or psychiatric comorbid conditions were listed as inclusion criteria, but they were not excluded on the basis of the frequency of comorbid conditions in included patients. We adopted this approach because excluding all studies that allowed patients with comorbid conditions would have markedly depleted the number of included studies, and because patients with chronic insomnia seen in clinical practice are likely to have a range of noninteracting comorbid conditions. Moreover, because only a subgroup of included studies reported the proportion of patients with comorbid conditions, we wished to avoid penalizing studies that reported in greater detail. Data Extraction and Quality Assessment Two authors independently confirmed the eligibility of studies, with all discrepancies resolved by consensus. One of these 2 authors extracted data, which were verified by a third author. We contacted the corresponding author of all included studies published after 1 January 2000 to request clarification of data and methods. Study quality assessments were performed independently by the 2 authors who extracted and verified data using the Cochrane Collaboration tool for assessing risk of bias (32). Data Synthesis and Analysis Our main outcome measures of interest were sleep diary measures of sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE%) (Table 2). These end points were assessed at 3 time points that we defined for the purpose of this analysis: immediately after treatment, early follow-up (4 weeks to <6 months after completion of the intervention), and late follow-up (6 to 12 months after completion of the intervention). Because studies most frequently reported results as the mean and SD at a point in time rather than the SD of the mean change over time, the SD of the change over time was imputed in most cases. All analyses used random-effects models, with heterogeneity assessed using the I 2 statistic and publication bias assessed with funnel plots and the Egger test (33, 34). Table 2. Glossary Six sensitivity analyses were performed that were limited to studies with particular intervention characteristics. First, we limited the analysis to studies incorporating sleep restriction because this may be among the most effective components of CBT-i (35). Second, because the optimal dosage of CBT-i is unknown but may be 4 sessions (36), we limited the analysis to studies involving at least 4 in-person contacts. Third, to consider the incremental effect of CBT-i incorporating a greater number of components, we limited the analysis to studies involving at least 4 components. Fourth, we limited the analysis to studies using a comparator group other than sleep hygiene because this may or may not be an effective stand-alone treatment (31). Fifth, we restricted the analysis to studies delivering treatment on an individual basis only, rather than in a group setting. Finally, to determine whether a tendency existed for studies with significant results to follow patients longer, we restricted the posttreatment analysis to studies with follow-up time points. In addition, we performed 3 sensitivity analyses in which we varied the correlation coefficients used to impute SDs. All statistical tests were 2-tailed, with P values less than 0.05 considered statistically significant. Statistical analyses were performed using Stata, version 13.0 (StataCorp), and R, version 3.1.3 (R Foundation for Statistical Computing). Role of the Funding Source This study received no funding. Results Our formal search strategy identified 292 references for review of the title and abstract. Of these, the full text was obtained and reviewed for 91 articles that were considered potentially appropriate for inclusion, and 20 studies met all inclusion criteria (although only 19 contributed data to the pooled estimates presented). The study flow diagram with reasons for exclusion is presented in Figure 1. Figure 1. Summary of evidence search and selection. CBT-i = cognitive behavioral therapy for insomnia; RCT = randomized, controlled trial. * Restricted to references not returned on MEDLINE search. Study Characteristics Table 3 shows descriptive data for the 20 included studies, which involved a total of 1162 patients (range, 20 to 201 patients), with values presented for only the groups that contributed data to this meta-analysis when possible. Most study populations were of late or middle age (mean age, 55.6 years), and 9 studies incorporated age restrictions as exclusion criteria. Sex was predominantly female (64.3%), and most studies were performed in developed countries (n= 19). Of the studies excluded on the basis of the population studied, 5 enrolled hypnotic-dependent patients, 1 enrolled obese persons, 3 enrolled patients with any comorbid condition, 1 enrolled patients with moderate to severe hot flashes, and 3 did not require a formal diagnosis of insomnia. Most studies referenced accepted definitions for insomnia (n= 17), most often an edition of the Diagnostic and Statistical Manual of Mental Disorders (n= 13), the International Classification of Sleep Disorders (n= 7), or both (Table 3). Nineteen studies excluded patients on the basis of presence of comorbid conditions, with all of these excluding patients with psychiatric or sleep-related comorbid conditions, 17 excluding those with medical comorbid conditions, 17 excluding those with medication or drug use, 4 excluding pregnant women, and 3 excluding shift workers. However, approaches differed in whether any comorbid condition was sufficient for exclusion or whether only patients with severe, unstable, or treatment-requiring conditions were excluded. Fourteen studies screened all patients with polysomnography at baseline to detect unrecognized sleep disorders. Table 3. Characteristics of Rando

Construction of a mathematical model for tuberculosis transmission in highly endemic regions of the Asia-pacific

We present a mathematical model to simulate tuberculosis (TB) transmission in highly endemic regions of the Asia-Pacific, where epidemiology does not appear to be primarily driven by HIV-coinfection. The ten-compartment deterministic model captures many of the observed phenomena important to disease dynamics, including partial and temporary vaccine efficacy, declining risk of active disease following infection, the possibility of reinfection both during the infection latent period and after treatment, multidrug resistant TB (MDR-TB) and de novo resistance during treatment. We found that the model could not be calibrated to the estimated incidence rate without allowing for reinfection during latency, and that even in the presence of a moderate fitness cost and a lower value of R0, MDR-TB becomes the dominant strain at equilibrium. Of the modifiable programmatic parameters, the rate of detection and treatment commencement was the most important determinant of disease rates with each respective strain, while vaccination rates were less important. Improved treatment of drug-susceptible TB did not result in decreased rates of MDR-TB through prevention of de novo resistance, but rather resulted in a modest increase in MDR-TB through strain replacement. This was due to the considerably greater relative contribution of community transmission to MDR-TB incidence, by comparison to de novo amplification of resistance in previously susceptible strains.

Feasibility of achieving the 2025 WHO global tuberculosis targets in South Africa, China, and India: a combined analysis of 11 mathematical models.

Summary Background The post-2015 End TB Strategy proposes targets of 50% reduction in tuberculosis incidence and 75% reduction in mortality from tuberculosis by 2025. We aimed to assess whether these targets are feasible in three high-burden countries with contrasting epidemiology and previous programmatic achievements. Methods 11 independently developed mathematical models of tuberculosis transmission projected the epidemiological impact of currently available tuberculosis interventions for prevention, diagnosis, and treatment in China, India, and South Africa. Models were calibrated with data on tuberculosis incidence and mortality in 2012. Representatives from national tuberculosis programmes and the advocacy community provided distinct country-specific intervention scenarios, which included screening for symptoms, active case finding, and preventive therapy. Findings Aggressive scale-up of any single intervention scenario could not achieve the post-2015 End TB Strategy targets in any country. However, the models projected that, in the South Africa national tuberculosis programme scenario, a combination of continuous isoniazid preventive therapy for individuals on antiretroviral therapy, expanded facility-based screening for symptoms of tuberculosis at health centres, and improved tuberculosis care could achieve a 55% reduction in incidence (range 31–62%) and a 72% reduction in mortality (range 64–82%) compared with 2015 levels. For India, and particularly for China, full scale-up of all interventions in tuberculosis-programme performance fell short of the 2025 targets, despite preventing a cumulative 3·4 million cases. The advocacy scenarios illustrated the high impact of detecting and treating latent tuberculosis. Interpretation Major reductions in tuberculosis burden seem possible with current interventions. However, additional interventions, adapted to country-specific tuberculosis epidemiology and health systems, are needed to reach the post-2015 End TB Strategy targets at country level. Funding Bill and Melinda Gates Foundation

Palliative Care and Hematological Malignancies: Increased Referrals at a Comprehensive Cancer Centre

BACKGROUND Current evidence indicates that patients with hematological malignancies are less likely to receive input from specialist palliative care services compared to those with solid tumors. AIM We aimed to analyze data for referrals to our palliative care service, in order to assess trends in the number and proportion of referrals received from hematology, and changes in the characteristics of these referrals. DESIGN Prospective information was collected for all referrals to the Department of Pain and Palliative Care (DPPC) over a four-year period. This included inpatient/outpatient status, diagnosis, symptoms, and goals of the referring clinician; and information linked to hospital inpatient data to obtain date and location of death. SETTINGS/PARTICIPANTS All hematology referrals were from January 2007 to December 2010. RESULTS Hematology referrals constituted 11.6% of all referrals received during the study period. Outpatient referrals increased significantly with each year, as did the proportion of patients referred for symptom control. The median time from referral to death was 34 days, with poorest survival seen in acute leukemia and inpatients. Overall, 54% of inpatient hematology deaths had consultation from the DPPC, with these patients less likely to die in the intensive care unit. CONCLUSIONS Over recent years, collaboration between hematology and palliative care has resulted in increased referral numbers, with potentially positive results for patients.

Risk of Active Tuberculosis in the Five Years Following Infection . . . 15

BACKGROUND It is often stated that the lifetime risk of developing active TB after an index infection is 5% to 10%, one-half of which accrues in the 2 to 5 years following infection. The goal of this study was to determine whether such estimates are consistent with local programmatic data. METHODS This study included close contacts of individuals with active pulmonary TB notified in the Australian state of Victoria from January 1, 2005, to December 31, 2013, who we deemed to have been infected as a result of their exposure. Survival analysis was first performed on the assumption of complete follow-up through to the end of the study period. The analysis was then repeated with imputation of censorship for migration, death, and preventive treatment, using local mortality and migration data combined with programmatic data on the administration of preventive therapy. RESULTS Of 613 infected close contacts, 67 (10.9%) developed active TB during the study period. Assuming complete follow-up, the 1,650-day cumulative hazard was 11.5% (95% CI, 8.9-14.1). With imputation of censorship for death, migration, and preventive therapy, the median 1,650-day cumulative hazard over 10,000 simulations was 14.5% (95% CI, 11.1-17.9). Most risk accrued in the first 5 months after infection, and risk was greatest in the group aged < 5 years, reaching 56.0% with imputation, but it was also elevated in older children (27.6% in the group aged 5-14 years). CONCLUSIONS The risk of active TB following infection is several-fold higher than traditionally accepted estimates, and it is particularly high immediately following infection and in children.

The risk of global epidemic replacement with drug-resistant Mycobacterium tuberculosis strains

OBJECTIVES Multidrug-resistant tuberculosis (MDR-TB) is a threat to tuberculosis (TB) control. To guide TB control, it is essential to understand the extent to which and the circumstances in which MDR-TB will replace drug-susceptible TB (DS-TB) as the dominant phenotype. The issue was examined by assessing evidence from genomics, pharmacokinetics, and epidemiology studies. This evidence was then synthesized into a mathematical model. METHODS This model considers two TB strains, one with and one without an MDR phenotype. It was considered that intrinsic transmissibility may be different between the two strains, as may the control response including the detection, treatment failure, and default rates. The outcomes were explored in terms of the incidence of MDR-TB and time until MDR-TB surpasses DS-TB as the dominant strain. RESULTS AND CONCLUSIONS The ability of MDR-TB to dominate DS-TB was highly sensitive to the relative transmissibility of the resistant strain; however, MDR-TB could dominate even when its transmissibility was modestly reduced (to between 50% and 100% as transmissible as the DS-TB strain). This model suggests that it may take decades or more for strain replacement to occur. It was also found that while the amplification of resistance is the early cause of MDR-TB, this will rapidly give way to person-to-person transmission.

Cost-effectiveness and resource implications of aggressive action on tuberculosis in China, India, and South Africa: a combined analysis of nine models.

BACKGROUND The post-2015 End TB Strategy sets global targets of reducing tuberculosis incidence by 50% and mortality by 75% by 2025. We aimed to assess resource requirements and cost-effectiveness of strategies to achieve these targets in China, India, and South Africa. METHODS We examined intervention scenarios developed in consultation with country stakeholders, which scaled up existing interventions to high but feasible coverage by 2025. Nine independent modelling groups collaborated to estimate policy outcomes, and we estimated the cost of each scenario by synthesising service use estimates, empirical cost data, and expert opinion on implementation strategies. We estimated health effects (ie, disability-adjusted life-years averted) and resource implications for 2016-35, including patient-incurred costs. To assess resource requirements and cost-effectiveness, we compared scenarios with a base case representing continued current practice. FINDINGS Incremental tuberculosis service costs differed by scenario and country, and in some cases they more than doubled existing funding needs. In general, expansion of tuberculosis services substantially reduced patient-incurred costs and, in India and China, produced net cost savings for most interventions under a societal perspective. In all three countries, expansion of access to care produced substantial health gains. Compared with current practice and conventional cost-effectiveness thresholds, most intervention approaches seemed highly cost-effective. INTERPRETATION Expansion of tuberculosis services seems cost-effective for high-burden countries and could generate substantial health and economic benefits for patients, although substantial new funding would be required. Further work to determine the optimal intervention mix for each country is necessary. FUNDING Bill & Melinda Gates Foundation.

Differential effects of pandemic (H1N1) 2009 on remote and indigenous groups, Northern Territory, Australia, 2009

TOC summary: Vaccination campaigns and public health responses should focus on high-risk groups.

High rates of multidrug-resistant and rifampicin-resistant tuberculosis among re-treatment cases: where do they come from?

BackgroundGlobally 3.9% of new and 21% of re-treatment tuberculosis (TB) cases are multidrug-resistant or rifampicin-resistant (MDR/RR), which is often interpreted as evidence that drug resistance results mainly from poor treatment adherence. This study aims to assess the respective contributions of the different causal pathways leading to MDR/RR-TB at re-treatment.MethodsWe use a simple mathematical model to simulate progression between the different stages of disease and treatment for patients diagnosed with TB. The model is parameterised using region and country-specific TB disease burden data reported by the World Health Organization (WHO). The contributions of four separate causal pathways to MDR/RR-TB among re-treatment cases are estimated: I) initial drug-susceptible TB with resistance amplification during treatment; II) initial MDR/RR-TB inappropriately treated as drug-susceptible TB; III) MDR/RR-TB relapse despite appropriate treatment; and IV) re-infection with MDR/RR-TB.ResultsAt the global level, Pathways I, II, III and IV contribute 38% (28–49, 95% Simulation Interval), 44% (36–52, 95% SI), 6% (5–7, 95% SI) and 12% (7–19, 95% SI) respectively to the burden of MDR/RR-TB among re–treatment cases. Pathway II is dominant in the Western Pacific (74%; 67–80 95% SI), Eastern Mediterranean (68%; 60–74 95% SI) and European (53%; 48–59 95% SI) regions, while Pathway I makes the greatest contribution in the American (53%; 40–66 95% SI), African (43%; 28–61 95% SI) and South-East Asian (50%; 40–59 95% SI) regions.ConclusionsGlobally, failure to diagnose MDR/RR-TB at first presentation is the leading cause of the high proportion of MDR/RR-TB among re-treatment cases. These findings highlight the need for contextualised solutions to limit the impact and spread of MDR/RR-TB.

Controlled fire use in early humans might have triggered the evolutionary emergence of tuberculosis

Significance Tuberculosis is an ancient human disease that continues to affect millions of people worldwide. A crucial component of the origins of the tuberculosis bacterium remains a mystery: What were the conditions that precipitated its emergence as an obligate transmissible human pathogen? Here, we identify a connection between the emergence of tuberculosis and another major event in human prehistory, namely the discovery of controlled fire use. Our results have serious and cautionary implications for the emergence of new infectious diseases—feedback between cultural innovation and alteration of living conditions can catalyze unexpected changes with potentially devastating consequences lasting thousands of years. Tuberculosis (TB) is caused by the Mycobacterium tuberculosis complex (MTBC), a wildly successful group of organisms and the leading cause of death resulting from a single bacterial pathogen worldwide. It is generally accepted that MTBC established itself in human populations in Africa and that animal-infecting strains diverged from human strains. However, the precise causal factors of TB emergence remain unknown. Here, we propose that the advent of controlled fire use in early humans created the ideal conditions for the emergence of TB as a transmissible disease. This hypothesis is supported by mathematical modeling together with a synthesis of evidence from epidemiology, evolutionary genetics, and paleoanthropology.