Objective: To investigate for subtypes of bipolar depression using latent class analysis (LCA). Method: Participants were recruited through a bipolar disorder (BD) clinic. LCA was undertaken using: (i) symptoms reported on the SCID-IV for the most severe lifetime depressive episode; (ii) lifetime illness features such as age at first depressive and hypo/manic episodes; and (iii) family history of BD and unipolar depression. To explore the validity of any demonstrated 'classes', clinical, demographic and treatment correlates were investigated. Results: A total of 243 BD subjects (170 with BD-I and 73 with BD-II) were included. For the combined sample, we found two robust LCA solutions, with two and three classes respectively. There were no consistent solutions when the BD-I and BD-II samples were considered separately. Subjects in class 2 of the three-class solution (characterised by anxiety, insomnia, reduced appetite/weight loss, irritability, psychomotor retardation, suicidal ideation, guilt, worthlessness and evening worsening) were significantly more likely to be in receipt of government financial support, suggesting a particularly malign pattern of symptoms. Conclusion: Our study suggests the existence of two or three distinct classes of bipolar depression and a strong association with functional outcome.
Objective: e-Mental health services have been shown to be effective and cost-effective for the treatment of depression. However, to have optimal impact in reducing the burden of depression, strategies for wider reach and uptake are needed. Method: A review was conducted to assess the evidence supporting use of e-mental health programmes for treating depression. From the review, models of dissemination and gaps in translation were identified, with a specific focus on characterising barriers and facilitators to uptake within the Australian healthcare context. Finally, recommendations for promoting the translation of e-mental health services in Australia were developed. Results: There are a number of effective and cost-effective e-health applications available for treating depression in community and clinical settings. Four primary models of dissemination were identified: unguided, health service-supported, private ownership and clinically guided. Barriers to translation include clinician reluctance, consumer awareness, structural barriers such as funding and gaps in the translational evidence base. Conclusion: Key strategies for increasing use of e-mental health programmes include endorsement of e-mental health services by government entities, education for clinicians and consumers, adequate funding of e-mental health services, development of an accreditation system, development of translation-focused activities and support for further translational research. The impact of these implementation strategies is likely to include economic gains, reductions in disease burden and greater availability of more interventions for prevention and treatment of mental ill-health complementary to existing health and efficient evidence-based mental health services.
Published online ; Journal Article ; This is the final version of the article. Available from Nature Publishing Group via the DOI in this record. ; Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10(-21)), left fusiform gyrus (d=-0.288; P=8.25 × 10(-21)) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10(-19)). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.Molecular Psychiatry advance online publication, 2 May 2017; doi:10.1038/mp.2017.73. ; The ENIGMA Bipolar Disorder working group gratefully acknowledges support from the NIH Big Data to Knowledge (BD2K) award (U54 EB020403 to PMT). We thank the members of the International Group for the Study of Lithium Treated Patients (IGSLi) and Costa Rica/Colombia Consortium for Genetic Investigation of Bipolar Endophenotypes. We also thank research funding sources: The Halifax studies have been supported by grants from Canadian Institutes of Health Research (103703, 106469, 64410 and 142255), the Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship to TH. TOP is supported by the Research Council of Norway (223273, 213837, 249711), the South East Norway Health Authority (2017-112), the Kristian Gerhard Jebsen Stiftelsen (SKGJ‐MED‐008) and the European Community's Seventh Framework Programme (FP7/2007–2013), grant agreement no. 602450 (IMAGEMEND). Cardiff is supported by the National Centre for Mental Health (NCMH), Bipolar Disorder Research Network (BDRN) and the 2010 NARSAD Young Investigator Award (ref. 17319) to XC. The Paris sample is supported by the French National Agency for Research (ANR MNP 2008 to the 'VIP' project) and by the Fondation pour la Recherche Médicale (2014 Bio-informarcis grant). The St. Göran bipolar project (SBP) is supported by grants from the Swedish Medical Research Council, the Swedish foundation for Strategic Research, the Swedish Brain foundation and the Swedish Federal Government under the LUA/ALF agreement. The Malt-Oslo sample is supported by the South East Norway Health Authority and by generous unrestricted grants from Mrs. Throne-Holst. The UT Houston sample is supported by NIH grant, MH085667. The UCLA-BP study is supported by NIH grants R01MH075007, R01MH095454, P30NS062691 (to NBF), K23MH074644-01 (to CEB) and K08MH086786 (to SF). Data collection for the UMCU sample is funded by the NIMH R01 MH090553 (PI Ophoff). The Oxford/Newcastle sample was funded by the Brain Behavior Research Foundation and Stanley Medical Research Institute. The University of Barcelona sample is supported by the CIBERSAM, the Spanish Ministry of Economy and Competitiveness (PI 12/00910), and the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya (2014 SGR 398). The KCL group is supported by a MRC Fellowship MR/J008915/1 (PI Kempton). The NUIG sample was supported by the Health Research Board (HRA_POR/2011/100). The Sydney sample was funded by the Australian National Medical and Health Research Council (Program Grant 1037196; project grant 1066177) and the Lansdowne Foundation and supported by philanthropic donations from Janette O'Neil and Paul and Jenny Reid. SF was supported by the National Institute of Mental Health under grant R01MH104284. DD is partially supported by a NARSAD 2014 Young Investigator Award (Leichtung Family Investigator) and a Psychiatric Research Trust grant (2014). The Münster Sample was funded by the German Research Foundation (DFG), grant FOR2107, DA1151/5-1 to UD. The Penn sample was funded by NIH grants K23MH098130 (to TDS), K23MH085096 (to DHW), R01MH107703 (to TDS) and R01MH101111 (to DHW), as well as support from the Brain and Behavior Research Foundation. The Tulsa studies were supported by the William K. Warren Foundation. Partial support was also received from the NIMH (K01MH096077). The Pittsburgh sample was funded by 5R01MH076971 (PI Phillips) and the Pittsburgh Foundation (Phillips). The Sao Paulo (Brazil) studies have been supported by grants from FAPESP-Brazil (#2009/14891-9, 2010/18672-7, 2012/23796-2 and 2013/03905-4), CNPq-Brazil (#478466/2009 and 480370/2009), the Wellcome Trust (UK) and the Brain & Behavior Research Foundation (2010 NARSAD Independent Investigator Award granted to GFB). MB and AP received support from the German Federal Ministry of Education and Research (BMBF) within the framework of the BipolLife research network on bipolar disorders. Data from the AMC was supported by the Organization for Health Research and Development (ZonMw), program Mental Health, education of investigators in mental health (OOG; #100-002-034). MMR used the e-Bioinfra Gateway to analyze data from the AMC (see Shahand et al. (2012): A grid-enabled gateway for biomedical data analysis. Journal of Grid Computing 1–18). The CliNG study sample was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via the Clinical Research Group 241 'Genotype-phenotype relationships and neurobiology of the longitudinal course of psychosis', TP2 (PI Gruber; http://www.kfo241.de; grant number GR 1950/5-1). The FIDMAG Germànes Hospitalàries Research Foundation sample is supported by the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya (2014-SGR-1573) and several grants funded by Instituto de Salud Carlos III (Co-funded by European Regional Development Fund/European Social Fund) "Investing in your future"): Miguel Servet Research Contract (CPII16/00018 to E. P.-C.), Sara Borrell Contract grant (CD16/00264 to M.F.-V.) and Research Projects (PI14/01148 to E.P.-C. and PI15/00277 to E.C.-R.).
Background Non-fatal outcomes of disease and injury increasingly detract from the ability of the world's population to live in full health, a trend largely attributable to an epidemiological transition in many countries from causes affecting children, to non-communicable diseases (NCDs) more common in adults. For the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015), we estimated the incidence, prevalence, and years lived with disability for diseases and injuries at the global, regional, and national scale over the period of 1990 to 2015. Methods We estimated incidence and prevalence by age, sex, cause, year, and geography with a wide range of updated and standardised analytical procedures. Improvements from GBD 2013 included the addition of new data sources, updates to literature reviews for 85 causes, and the identification and inclusion of additional studies published up to November, 2015, to expand the database used for estimation of non-fatal outcomes to 60 900 unique data sources. Prevalence and incidence by cause and sequelae were determined with DisMod-MR 2.1, an improved version of the DisMod-MR Bayesian meta-regression tool first developed for GBD 2010 and GBD 2013. For some causes, we used alternative modelling strategies where the complexity of the disease was not suited to DisMod-MR 2.1 or where incidence and prevalence needed to be determined from other data. For GBD 2015 we created a summary indicator that combines measures of income per capita, educational attainment, and fertility (the Socio-demographic Index [SDI]) and used it to compare observed patterns of health loss to the expected pattern for countries or locations with similar SDI scores. Findings We generated 9·3 billion estimates from the various combinations of prevalence, incidence, and YLDs for causes, sequelae, and impairments by age, sex, geography, and year. In 2015, two causes had acute incidences in excess of 1 billion: upper respiratory infections (17·2 billion, 95% uncertainty interval [UI] 15·4–19·2 billion) and diarrhoeal diseases (2·39 billion, 2·30–2·50 billion). Eight causes of chronic disease and injury each affected more than 10% of the world's population in 2015: permanent caries, tension-type headache, iron-deficiency anaemia, age-related and other hearing loss, migraine, genital herpes, refraction and accommodation disorders, and ascariasis. The impairment that affected the greatest number of people in 2015 was anaemia, with 2·36 billion (2·35–2·37 billion) individuals affected. The second and third leading impairments by number of individuals affected were hearing loss and vision loss, respectively. Between 2005 and 2015, there was little change in the leading causes of years lived with disability (YLDs) on a global basis. NCDs accounted for 18 of the leading 20 causes of age-standardised YLDs on a global scale. Where rates were decreasing, the rate of decrease for YLDs was slower than that of years of life lost (YLLs) for nearly every cause included in our analysis. For low SDI geographies, Group 1 causes typically accounted for 20–30% of total disability, largely attributable to nutritional deficiencies, malaria, neglected tropical diseases, HIV/AIDS, and tuberculosis. Lower back and neck pain was the leading global cause of disability in 2015 in most countries. The leading cause was sense organ disorders in 22 countries in Asia and Africa and one in central Latin America; diabetes in four countries in Oceania; HIV/AIDS in three southern sub-Saharan African countries; collective violence and legal intervention in two north African and Middle Eastern countries; iron-deficiency anaemia in Somalia and Venezuela; depression in Uganda; onchoceriasis in Liberia; and other neglected tropical diseases in the Democratic Republic of the Congo. Interpretation Ageing of the world's population is increasing the number of people living with sequelae of diseases and injuries. Shifts in the epidemiological profile driven by socioeconomic change also contribute to the continued increase in years lived with disability (YLDs) as well as the rate of increase in YLDs. Despite limitations imposed by gaps in data availability and the variable quality of the data available, the standardised and comprehensive approach of the GBD study provides opportunities to examine broad trends, compare those trends between countries or subnational geographies, benchmark against locations at similar stages of development, and gauge the strength or weakness of the estimates available. Funding Bill & Melinda Gates Foundation.
Background Achieving universal health coverage (UHC) involves all people receiving the health services they need, of high quality, without experiencing financial hardship. Making progress towards UHC is a policy priority for both countries and global institutions, as highlighted by the agenda of the UN Sustainable Development Goals (SDGs) and WHO's Thirteenth General Programme of Work (GPW13). Measuring effective coverage at the health-system level is important for understanding whether health services are aligned with countries' health profiles and are of sufficient quality to produce health gains for populations of all ages. Methods Based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we assessed UHC effective coverage for 204 countries and territories from 1990 to 2019. Drawing from a measurement framework developed through WHO's GPW13 consultation, we mapped 23 effective coverage indicators to a matrix representing health service types (eg, promotion, prevention, and treatment) and five population-age groups spanning from reproductive and newborn to older adults (≥65 years). Effective coverage indicators were based on intervention coverage or outcome-based measures such as mortality-to-incidence ratios to approximate access to quality care; outcome-based measures were transformed to values on a scale of 0–100 based on the 2·5th and 97·5th percentile of location-year values. We constructed the UHC effective coverage index by weighting each effective coverage indicator relative to its associated potential health gains, as measured by disability-adjusted life-years for each location-year and population-age group. For three tests of validity (content, known-groups, and convergent), UHC effective coverage index performance was generally better than that of other UHC service coverage indices from WHO (ie, the current metric for SDG indicator 3.8.1 on UHC service coverage), the World Bank, and GBD 2017. We quantified frontiers of UHC effective coverage performance on the basis of pooled health spending per capita, representing UHC effective coverage index levels achieved in 2019 relative to country-level government health spending, prepaid private expenditures, and development assistance for health. To assess current trajectories towards the GPW13 UHC billion target—1 billion more people benefiting from UHC by 2023—we estimated additional population equivalents with UHC effective coverage from 2018 to 2023. Findings Globally, performance on the UHC effective coverage index improved from 45·8 (95% uncertainty interval 44·2–47·5) in 1990 to 60·3 (58·7–61·9) in 2019, yet country-level UHC effective coverage in 2019 still spanned from 95 or higher in Japan and Iceland to lower than 25 in Somalia and the Central African Republic. Since 2010, sub-Saharan Africa showed accelerated gains on the UHC effective coverage index (at an average increase of 2·6% [1·9–3·3] per year up to 2019); by contrast, most other GBD super-regions had slowed rates of progress in 2010–2019 relative to 1990–2010. Many countries showed lagging performance on effective coverage indicators for non-communicable diseases relative to those for communicable diseases and maternal and child health, despite non-communicable diseases accounting for a greater proportion of potential health gains in 2019, suggesting that many health systems are not keeping pace with the rising non-communicable disease burden and associated population health needs. In 2019, the UHC effective coverage index was associated with pooled health spending per capita (r=0·79), although countries across the development spectrum had much lower UHC effective coverage than is potentially achievable relative to their health spending. Under maximum efficiency of translating health spending into UHC effective coverage performance, countries would need to reach $1398 pooled health spending per capita (US$ adjusted for purchasing power parity) in order to achieve 80 on the UHC effective coverage index. From 2018 to 2023, an estimated 388·9 million (358·6–421·3) more population equivalents would have UHC effective coverage, falling well short of the GPW13 target of 1 billion more people benefiting from UHC during this time. Current projections point to an estimated 3·1 billion (3·0–3·2) population equivalents still lacking UHC effective coverage in 2023, with nearly a third (968·1 million [903·5–1040·3]) residing in south Asia. Interpretation The present study demonstrates the utility of measuring effective coverage and its role in supporting improved health outcomes for all people—the ultimate goal of UHC and its achievement. Global ambitions to accelerate progress on UHC service coverage are increasingly unlikely unless concerted action on non-communicable diseases occurs and countries can better translate health spending into improved performance. Focusing on effective coverage and accounting for the world's evolving health needs lays the groundwork for better understanding how close—or how far—all populations are in benefiting from UHC. Funding Bill & Melinda Gates Foundation.
