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The Coronavirus Disease 2019 (COVID-19) has had a continuous and robust impact on world health. The resulting COVID-19 pandemic has had a devastating physical, mental and fiscal impact on the millions of people living with noncommunicable diseases (NCDs). In addition to older age, people living with CVD, stroke, obesity, diabetes, kidney disease, and hypertension are at a particularly greater risk for severe forms of COVID-19 and its consequences. Meta-analysis indicates that hypertension, diabetes, chronic kidney disease, and thrombotic complications have been observed as both the most prevalent and most dangerous co-morbidities in COVID-19 patients. And despite the nearly incalculable physical, mental, emotional, and economic toll of this pandemic, forthcoming public health figures continue to place cardiovascular disease as the number one cause of death across the globe in the year 2020. The world simply cannot wait for the next pandemic to invest in NCDs. Social determinants of health cannot be addressed only through the healthcare system, but a more holistic multisectoral approach with at its basis the Sustainable Development Goals (SDGs) is needed to truly address social and economic inequalities and build more resilient systems. Yet there is reason for hope: the 2019 UN Political Declaration on UHC provides a strong framework for building more resilient health systems, with explicit calls for investment in NCDs and references to fiscal policies that put such investment firmly within reach. By further cementing the importance of addressing circulatory health in a future Framework Convention on Emergency Preparedness, WHO Member States can take concrete steps towards a pandemic-free future. As the chief representatives of the global circulatory health community and patients, the Global Coalition for Circulatory Health calls for increased support for the healthcare workforce, global vaccine equity, embracing new models of care and digital health solutions, as well as fiscal policies on unhealthy commodities to support these investments.

Title from spine. ; On the indications for the use of picric acid / Samuel A. Jones -- Particular visibilities / Sam'l Potter -- A few thoughts on inflammation considered in the light of pathology and pathogenesis / E.C. Franklin -- Medical legislation in the United States / J.P. Dake -- The logical basis of the high potency question / Samuel Potter -- Michigan mendacity / Sam'l Potter -- A critical analysis of some high potency practice / S.A. Jones -- The latest system in medicine / H.E. Beebe -- Three lectures on homoeopathic pharmaceutics / F.E. Boericke -- Report of the Bureau of Organization, Registration and Statistics of the American Institute of Homoeopathy. ; Trial of William Bushnell, M.D. [et al.] . for practising homoeopathy while they were members of the Massachusetts Medical Society . -- Clinical facts / D.A. McLachlan -- To the members of the homoeopathic profession on the Pacific Coast / California State Homoeopathic Medical Society -- Lithaemia / J.W. Dowling -- Proceedings of the Michigan Institute of Homoeopathy held at Ann Arbor, June 14, 1855 -- Circular letter to the Honorable, the Members of the Senate and House of Representatives of the United States / Office of the Chairman of the Committee on Legislation, American Institute of Homoeopathy. ; Mode of access: Internet.

[This corrects the article DOI: 10.1371/journal.pone.0256740.].

Summary Background Comprehensive and comparable estimates of health spending in each country are a key input for health policy and planning, and are necessary to support the achievement of national and international health goals. Previous studies have tracked past and projected future health spending until 2040 and shown that, with economic development, countries tend to spend more on health per capita, with a decreasing share of spending from development assistance and out-of-pocket sources. We aimed to characterise the past, present, and predicted future of global health spending, with an emphasis on equity in spending across countries. Methods We estimated domestic health spending for 195 countries and territories from 1995 to 2016, split into three categories—government, out-of-pocket, and prepaid private health spending—and estimated development assistance for health (DAH) from 1990 to 2018. We estimated future scenarios of health spending using an ensemble of linear mixed-effects models with time series specifications to project domestic health spending from 2017 through 2050 and DAH from 2019 through 2050. Data were extracted from a broad set of sources tracking health spending and revenue, and were standardised and converted to inflation-adjusted 2018 US dollars. Incomplete or low-quality data were modelled and uncertainty was estimated, leading to a complete data series of total, government, prepaid private, and out-of-pocket health spending, and DAH. Estimates are reported in 2018 US dollars, 2018 purchasing-power parity-adjusted dollars, and as a percentage of gross domestic product. We used demographic decomposition methods to assess a set of factors associated with changes in government health spending between 1995 and 2016 and to examine evidence to support the theory of the health financing transition. We projected two alternative future scenarios based on higher government health spending to assess the potential ability of governments to generate more resources for health. Findings Between 1995 and 2016, health spending grew at a rate of 4·00% (95% uncertainty interval 3·89–4·12) annually, although it grew slower in per capita terms (2·72% [2·61–2·84]) and increased by less than $1 per capita over this period in 22 of 195 countries. The highest annual growth rates in per capita health spending were observed in upper-middle-income countries (5·55% [5·18–5·95]), mainly due to growth in government health spending, and in lower-middle-income countries (3·71% [3·10–4·34]), mainly from DAH. Health spending globally reached $8·0 trillion (7·8–8·1) in 2016 (comprising 8·6% [8·4–8·7] of the global economy and $10·3 trillion [10·1–10·6] in purchasing-power parity-adjusted dollars), with a per capita spending of US$5252 (5184–5319) in high-income countries, $491 (461–524) in upper-middle-income countries, $81 (74–89) in lower-middle-income countries, and $40 (38–43) in low-income countries. In 2016, 0·4% (0·3–0·4) of health spending globally was in low-income countries, despite these countries comprising 10·0% of the global population. In 2018, the largest proportion of DAH targeted HIV/AIDS ($9·5 billion, 24·3% of total DAH), although spending on other infectious diseases (excluding tuberculosis and malaria) grew fastest from 2010 to 2018 (6·27% per year). The leading sources of DAH were the USA and private philanthropy (excluding corporate donations and the Bill & Melinda Gates Foundation). For the first time, we included estimates of China's contribution to DAH ($644·7 million in 2018). Globally, health spending is projected to increase to $15·0 trillion (14·0–16·0) by 2050 (reaching 9·4% [7·6–11·3] of the global economy and $21·3 trillion [19·8–23·1] in purchasing-power parity-adjusted dollars), but at a lower growth rate of 1·84% (1·68–2·02) annually, and with continuing disparities in spending between countries. In 2050, we estimate that 0·6% (0·6–0·7) of health spending will occur in currently low-income countries, despite these countries comprising an estimated 15·7% of the global population by 2050. The ratio between per capita health spending in high-income and low-income countries was 130·2 (122·9–136·9) in 2016 and is projected to remain at similar levels in 2050 (125·9 [113·7–138·1]). The decomposition analysis identified governments' increased prioritisation of the health sector and economic development as the strongest factors associated with increases in government health spending globally. Future government health spending scenarios suggest that, with greater prioritisation of the health sector and increased government spending, health spending per capita could more than double, with greater impacts in countries that currently have the lowest levels of government health spending. Interpretation Financing for global health has increased steadily over the past two decades and is projected to continue increasing in the future, although at a slower pace of growth and with persistent disparities in per-capita health spending between countries. Out-of-pocket spending is projected to remain substantial outside of high-income countries. Many low-income countries are expected to remain dependent on development assistance, although with greater government spending, larger investments in health are feasible. In the absence of sustained new investments in health, increasing efficiency in health spending is essential to meet global health targets. 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.

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. © 2020 Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Université de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Fundação para a Ciência e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council ofAustralia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 "Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Funding Information: This work was conducted in part using the resources of the Advanced Computing Center for Research and Education at Vanderbilt University, Nashville, TN. KS, BL, CH were supported by the National Institutes of Health under award numbers R01EB017230, and T32EB001628, and in part by ViSE/VICTR VR3029 and the National Center for Research Resources, Grant UL1 RR024975-01. This work was also possible thanks to the support of the Institutional Research Chair in NeuroInformatics of Universit? de Sherbrooke, NSERC and Compute Canada (MD, FR). MP received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 754462. The Wisconsin group acknowledges the support from a core grant to the Waisman Center from the National Institute of Child Health and Human Development (IDDRC U54 HD090256). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, NIH NIBIB 1R01EB029272-01, and a Microsoft Faculty Fellowship to F.P. LF acknowledges the support of the Cluster of Excellence Matters of Activity. Image Space Material funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany?s Excellence Strategy ? EXC 2025. SW is supported by a Medical Research Council PhD Studentship UK [MR/N013913/1]. The Nottingham group's processing was performed using the University of Nottingham's Augusta HPC service and the Precision Imaging Beacon Cluster. JPA, MA and SMS acknowledges the support of FCT - Funda??o para a Ci?ncia e a Tecnologia within CINTESIS, R&D Unit (reference UID/IC/4255/2013). MM was funded by the Wellcome Trust through a Sir Henry Wellcome Postdoctoral Fellowship [213722/Z/18/Z]. EJC-R is supported by the Swiss National Science Foundation (SNSF, Ambizione grant PZ00P2 185814/1). CMWT is supported by a Sir Henry Wellcome Fellowship (215944/Z/19/Z) and a Veni grant from the Dutch Research Council (NWO) (17331). FC acknowledges the support of the National Health and Medical Research Council of Australia (APP1091593 and APP1117724) and the Australian Research Council (DP170101815). NSF OAC-1916518, NSF IIS-1912270, NSF IIS-1636893, NSF BCS-1734853, Microsoft Faculty Fellowship to F.P. D.B. was partially supported by NIH NIMH T32-MH103213 to William Hetrick (Indiana University). CL is partly supported by NIH grants P41 EB027061 and P30 NS076408 ?Institutional Center Cores for Advanced Neuroimaging. JYMY received positional funding from the Royal Children's Hospital Foundation (RCH 1000). JYMY, JC, and CEK acknowledge the support of the Royal Children's Hospital Foundation, Murdoch Children's Research Institute, The University of Melbourne Department of Paediatrics, and the Victorian Government's Operational Infrastructure Support Program. C-HY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support. LC acknowledges support from CONACYT and UNAM. ARM acknowledges support from CONACYT. LJO, YR, and FZ were supported by NIH P41EB015902 and R01MH119222. AJG was supported by P41EB015898. NM was supported by R01MH119222, K24MH116366, and R01MH111917. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No. 785907 & 945539 (HBP SGA2 & SGA3), and from the ANR IFOPASUBA- 19-CE45-0022-01. PG, CR, NL and AV were partially supported by ANID-Basal FB0008 and ANID-FONDECYT 1190701 grants. We would like to acknowledge John C Gore, Hiromasa Takemura, Anastasia Yendiki, and Riccardo Galbusera for their helplful suggestions regarding the analysis, figures, and discussions. Publisher Copyright: © 2021 ; White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols foreach fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process. ; Peer reviewed

Background: In the midst of the coronavirus pandemic, the International Chiropractors Association (ICA) posted reports claiming that chiropractic care can impact the immune system. These claims clash with recommendations from the World Health Organization and World Federation of Chiropractic. We discuss the scientific validity of the claims made in these ICA reports. Main body: We reviewed the two reports posted by the ICA on their website on March 20 and March 28, 2020. We explored the method used to develop the claim that chiropractic adjustments impact the immune system and discuss the scientific merit of that claim. We provide a response to the ICA reports and explain why this claim lacks scientific credibility and is dangerous to the public. More than 150 researchers from 11 countries reviewed and endorsed our response. Conclusion: In their reports, the ICA provided no valid clinical scientific evidence that chiropractic care can impact the immune system. We call on regulatory authorities and professional leaders to take robust political and regulatory action against those claiming that chiropractic adjustments have a clinical impact on the immune system.

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.).

Imagine being able to target an audience made up of highly qualified and purchase-ready prospects and easily building them into loyal clients by anticipating their needs and hence offering true value. This is the power of big data for digital marketing. Big Data: A Roadmap for Successful Digital Marketing explores recent trends in the use of big data to predict consumer behavior, strategies to engage online customers, integration of big data with other data sources, and its applications in social media analytics, mobile marketing, search engine optimization and customer relationship management. As the marketing world moves into a data-focused future, the success of marketing efforts will be wholly based on attention to detail in data analysis and effectively acting on insights in order to implement changes that will deliver improved results. This book will help professionals succeed in their digital marketing efforts as well as provide food for thought for students and researchers in the fields of digital marketing, customer behavior and big data analytics.

Frontmatter -- Índice -- Fútbol y sociedad en América Latina: a manera de introducción -- FÚTBOL, HISTORIA, SOCIEDAD -- Contar la historia del fútbol en América Latina -- Construyendo el campo sociológico del fútbol en América Latina -- PRIMERA PARTE IDENTIDADES Y ESTILOS -- Introducción -- La invención del fútbol latinoamericano: cinco relatos y un silencio -- Narrativas do estilo: uma história das fronteiras nacionais pelo futebol -- Verdeamarelismo na ponta das chuteiras: reconfiguração da identidade nacional brasileira e Golpe de Estado (2013-2018) -- La orureñidad en el fútbol, del campamento minero a la diáspora: colectividades nómadas e identidades migratorias -- SEGUNDA PARTE FUTBOLISTAS E HINCHADAS -- Introducción -- Campeonatos de fútbol y ecuavoley en Reuterpark-Haus der Jugend (Bonn). Redes transnacionales y prácticas de ciudadanía de migrantes latinoamericanos a través de la apropiación informal del espacio público -- Experiencias itinerantes: territorios, fronteras y disputas de una barra argentina en movimiento -- Ritos, identidades y colonialismo en hinchadas transnacionales en Colombia y México -- Fútbol globalizado: nuevas identidades y formas de asociación. Un análisis de los hinchas del F. C. Barcelona y del Real Madrid en Lima -- TERCERA PARTE. PODER POLÍTICO -- Introducción -- Fútbol y política/política y fútbol: aspectos históricos de unas relaciones peligrosas -- #VamosColombia: selección, nación y Twitter. El uso de Twitter para el nacionalismo deportivo del presidente Juan Manuel Santos -- Polític as e formas de torcer: novas faces do associativismo torcedor no Brasil -- Goles y cárteles de la droga: la influencia de estructuras criminales en el fútbol de América Latina -- CUARTA PARTE. DESIGUALDAD DE GÉNERO -- Introducción -- #Déjala trabajar: el fútbol y el feminismo en Brasil -- Aproximaciones al desarrollo del fútbol practicado por mujeres en Colombia -- Estrategias transculturales y de género en el fútbol de migrantes andinos en Brasil -- A "guerra dos sex os" nos Jogos Olímpicos 2016 -- QUINTA PARTE. Patrimonio cultural e indigenización -- Introducción -- Entre un pasado lejano y el presente: supervivencias de un juego de pelota mesoamericano -- The evolution of the mesoamerican ballgames through time -- El duelo entre la Escuela-Ayllu de W arisata y los vecinos de Achacachi: la indigenización del fútbol en el departamento de La Paz (1900-1940) -- Pelota mixteca: pertenencia, convivencia y ancestralidad -- SEXTA PARTE. LOS MEDIOS DE COMUNICACIÓN -- Introducción -- Cien años de goledad: medios, literatura y fútbol globalizado -- Cómo leer los clásicos… de fútbol -- Entre la naturalización y la visibilización: fútbol, racismo y prensa en Perú. Discurso sobre el racismo de la prensa escrita deportiva peruana a través del caso Tinga -- Brasil, Alema nha e Futebol: confrontos, mediações e multiculturalismo no jornalismo internacional -- SÉPTIMA PARTE. REPRESENTACIONES EN LITERATURA Y CINE -- Introducción -- La múltiple instrumentalización del fútbol -- Radiografía del potrero: Borocotó y los orígenes de la ficción de fútbol en Argentina -- Marcaje al hombre: fútbol y cine en la Venezuela del siglo XXI -- "Geraldinos": cultura popular e e xclusão social no novo Maracanã (Rio de Janeiro) -- Sobre las autoras, los autores, la editora y los editores

Frontmatter -- CONTENTS -- ACKNOWLEDGMENTS -- INTRODUCTION -- PART I DEVELOPMENT -- Introduction -- 1 BUNTY SINGH BUILDER OF DREAMS -- 2 IMRAN HOUSING CONTRACTOR -- 3 DALPAT MANAGER OF SERVICES -- 4 MEHMOODBHAI TOILET OPERATOR -- 5 KAUSHAL LAND AGGLOMERATOR -- 6 JANU SISTER-SUPERVISOR OF MIGRANT CONSTRUCTION WORKERS -- PART II PROPERTY -- Introduction -- 7 DR. K MIDDLE-CLASS SOCIAL WORKER -- 8 ASHOK RAVAT SHIVAJI PARK'S SENTINEL -- 9 SHAZIA PROOF MAKER -- 10 NIRMALA KAMATHIPURA'S GATEKEEPER -- 11 FARHAD "SUE MAKER" -- PART III BUSINESS -- Introduction -- 12 RAMITA SURROGACY AGENT -- 13 MUHAMMAD REVALORIZER OF E-WASTE -- 14 DEEPAK MAKING MUMBAI (IN CHINA) -- 15 LUBAINA FRAMING "DEVELOPMENT" -- 16 SHANKAR DELIVERING AUTHENTICITY -- 17 MANAL-MUNA COOKING UP VALUE -- 18 RAMJI BUSINESS ENERGIZER -- PART IV DIFFERENCE -- Introduction -- 19 BHIMSEN GAIKWAD SINGER OF JUSTICE -- 20 SULTAN IMAGE MANAGER -- 21 RAJ CARTING COSMOPOLITANISM -- 22 LAXMI DEALER IN EMOTION -- 23 DHARAMSEY ASSEMBLER OF TRADITION -- 24 DALVI SPEAKER OF CITIES -- PART V PUBLICS -- Introduction -- 25 SHASHI DOT CONNECTOR -- 26 ANIL PRAKASH AMPLIFIER OF CINEMA-INDUSTRIAL CONNECTIONS -- 27 GAURAVPANT MISHRA CROWD MAKER -- 28 SRINIVASAN KINGMAKER -- 29 MADHU DOOR OPENER -- 30 POORNIMA DESIGNING RELATIONS -- PART VI TRUTH -- Introduction -- 31 RAJANI PANDIT DETECTOR OF "TRUTHS" -- 32 AFZAL TAXIMAN RUMOR NAVIGATOR -- 33 PAWAN PRISON MASTER -- 34 SUJIT MASTER COMMUNICATOR -- 35 CHADDA REPORT MAKER -- 36 PRAKASH DATA ENTREPRENEUR -- CONCLUSION OTHER PLACES, OTHER TIMES -- GLOSSARY -- ABOUT THE CONTRIBUTORS -- INDEX