To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
A proportion of patients with bipolar disorder (BD) manifests with only unipolar mania (UM). This study examined relevant clinical features and psychosocial characteristics in UM compared with depressive-manic (D-M) subgroups. Moreover, comorbidity patterns of physical conditions and psychiatric disorders were evaluated between the UM and D-M groups.
This clinical retrospective study (N = 1015) analyzed cases with an average of 10 years of illness duration and a nationwide population-based cohort (N = 8343) followed up for 10 years in the Taiwanese population. UM was defined as patients who did not experience depressive episodes and were not prescribed adequate antidepressant treatment during the disease course of BD. Logistic regression models adjusted for relevant covariates were used to evaluate the characteristics and lifetime comorbidities in the two groups.
The proportion of UM ranged from 12.91% to 14.87% in the two datasets. Compared with the D-M group, the UM group had more psychotic symptoms, fewer suicidal behaviors, a higher proportion of morningness chronotype, better sleep quality, higher extraversion, lower neuroticism, and less harm avoidance personality traits. Substantially different lifetime comorbidity patterns were observed between the two groups.
Patients with UM exhibited distinct clinical and psychosocial features compared with patients with the D-M subtype. In particular, a higher risk of comorbid cardiovascular diseases and anxiety disorders is apparent in patients with D-M. Further studies are warranted to investigate the underlying mechanisms for diverse presentations in subgroups of BDs.
The Brain Health Test-7 (BHT-7) is a revised tool from the original BHT, containing more tests about frontal lobe function. It was developed with theaim of identifying patients with mild cognitive impairment (MCI) and early dementia.
Here we report the validity of the BHT-7 versus the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in differentpsychiatry or neurology clinics.
Patients with memory complaints were recruited in this study from the outpatient clinic of psychiatry or neurology in 3 different kinds of hospitals. Allpatients underwent the evaluation of the BHT-7, MMSE, MoCA, and clinical dementia rating (CDR). The clinical diagnosis (normal, MCI, dementia) was made by consensus meeting, taking into account all available data.
Demographic data and the scores of the MMSE, MoCA, and BHT-7 between groups were compared. Logistic regression was adopted for analysis of optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating characteristic (ROC) curve,and the area under the ROC curve (AUC).
We enrolled a total of 1090 subjects (normal 402, MCI 317, dementia 371); of them, 705 (64.7%) were female. There was a statistically significant differencein age, years of education, and 3 cognitive test scores among the 3 groups.
Compared with the MMSE and MoCA, the BHT-7 performed slightly betterthan MMSE and MoCA in differentiating MCI or dementia from the normalcontrols (Table 1). For BHT- 7, the cutoff point was 17 between normal andMCI, and 14 between normal and dementia. These cutoff points for BHT-7were consistent through 3 different clinical settings, but inconsistent for MMSE and MoCA. The testing time for the BHT-7 was about 5-7 minutes, shorter than that of the MMSE and MoCA.
Compared with MMSE and MoCA, the BHT-7 showed slightly better performance in differentiating normal from MCI or dementia subjects. The testing time for the BHT-7 was shorter, and its cutoff points were consistent through different outpatient clinic settings. The results support that BHT-7 is auseful cognitive screening tool for MCI or early dementia in various hospital settings.
Comparisons of the performance of BHT-7, MMSE, MoCA
The association between dietary Fe intake and diabetes risk remains inconsistent. We aimed to explore the association between dietary Fe intake and type 2 diabetes mellitus (T2DM) risk in middle-aged and older adults in urban China. This study used data from the Guangzhou Nutrition and Health Study, an on-going community-based prospective cohort study. Participants were recruited from 2008 to 2013 in Guangzhou community. A total of 2696 participants aged 40–75 years without T2DM at baseline were included in data analyses, with a median of 5·6 (interquartile range 4·1–5·9) years of follow-up. T2DM was identified by self-reported diagnosis, fasting glucose ≥ 7·0 mmol/l or glycosylated Hb ≥ 6·5 %. Cox proportional hazard models were used to estimate hazard ratios (HR) and 95 % CI. We ascertained 205 incident T2DM cases during 13 476 person-years. The adjusted HR for T2DM risk in the fourth quartile of haem Fe intake was 1·92 (95 % CI 1·07, 3·46; Ptrend = 0·010), compared with the first quartile intake. These significant associations were found in haem Fe intake from total meat (HR 2·74; 95 % CI 1·22, 6·15; Ptrend = 0·011) and haem Fe intake from red meat (HR 1·86; 95 % CI 1·01, 3·44; Ptrend = 0·034), but not haem Fe intake from processed meat, poultry or fish/shellfish. The association between dietary intake of total Fe or non-haem Fe with T2DM risk had no significance. Our findings suggested that higher dietary intake of haem Fe (especially from red meat), but not total Fe or non-haem Fe, was associated with greater T2DM risk in middle-aged and older adults.
The literature on the relationship between foreign aid and institutions has found that the effects of aid vary across different donor characteristics and delivery mechanisms. This article focuses on China's resource-related development projects, which have been considered controversial due to the relative lack of conditionality. By distinguishing between vertical and horizontal dimensions of political accountability, the study finds that China's resource-related projects are particularly detrimental to the accountability of recipient countries' horizontal (legislative and judicial) institutions. These projects are often delivered to resource-rich countries, in the form of packaging access to resources and infrastructure construction, to improve China's own energy access. Local officials may be tempted to weaken horizontal institutions so that the projects can be implemented quickly. Nevertheless, these projects have little effect on vertical accountability, as China has less intention and capacity to fundamentally restrain electoral competition in recipient countries.
Schizotypy refers to schizophrenia-like traits below the clinical threshold in the general population. The pathological development of schizophrenia has been postulated to evolve from the initial coexistence of ‘brain disconnection’ and ‘brain connectivity compensation’ to ‘brain connectivity decompensation’.
In this study, we examined the brain connectivity changes associated with schizotypy by combining brain white matter structural connectivity, static and dynamic functional connectivity analysis of diffusion tensor imaging data and resting-state functional magnetic resonance imaging data. A total of 87 participants with a high level of schizotypal traits and 122 control participants completed the experiment. Group differences in whole-brain white matter structural connectivity probability, static mean functional connectivity strength, dynamic functional connectivity variability and stability among 264 brain sub-regions of interests were investigated.
We found that individuals with high schizotypy exhibited increased structural connectivity probability within the task control network and within the default mode network; increased variability and decreased stability of functional connectivity within the default mode network and between the auditory network and the subcortical network; and decreased static mean functional connectivity strength mainly associated with the sensorimotor network, the default mode network and the task control network.
These findings highlight the specific changes in brain connectivity associated with schizotypy and indicate that both decompensatory and compensatory changes in structural connectivity within the default mode network and the task control network in the context of whole-brain functional disconnection may be an important neurobiological correlate in individuals with high schizotypy.
To develop an equation that can estimate the 24-h urinary Na excretion by using casual spot urine specimen for older hypertensive participants in rural Ningxia and further to compare with the INTERSALT method, Kawasaki method and Tanaka method.
Older hypertensive participants in rural Ningxia provided their casual spot urine samples and 24-h urine samples between January 2015 and February 2017. Sex-specific equation was developed using linear forward stepwise regression analysis. Model fit was assessed using adjusted R2. Approximately half of all participants were randomly selected to validate the equation. Mean differences, intraclass correlation coefficients and Bland–Altman plots were used to evaluate the performance of all methods.
Pingluo County and Qingtongxia County in Ningxia Hui Autonomous Region, China.
Older hypertensive participants in rural Ningxia.
Totally, 807 of 1120 invited participants provided qualified 24-h urine samples and spot urine samples. There was no statistical difference comparing the laboratory-based method against the new method and the INTERSALT method, while Kawasaki method had the largest bias with a mean difference of 40·81 g/d (95 % CI 39·27, 42·35 g/d). Bland–Altman plots showed similar pattern of the results.
The INTERSALT method and the new equation have the potential to estimate the 24-h urinary Na excretion in this study population. However, the extrapolation of the results to other population needs to be careful. Future research is required to establish a more reliable method to estimate 24-h urinary Na excretion.
Scholars and policy makers need systematic assessments of the validity of the measures produced by V-Dem. In Chapter 6, we present our approach to comparative data validation – the set of steps we take to evaluate the precision, accuracy, and reliability of our measures, both in isolation and compared to extant measures of the same concepts. Our approach assesses the degree to which measures align with shared concepts (content validation), shared rules of translation (data generation assessment), and shared realities (convergent validation). Within convergent validity, we execute two convergent validity tests. First, we examine convergent validity as it is typically conceived – examining convergence between V-Dem measures and extant measures. Second, we evaluate the level of convergence across coders, considering the individual coder and country traits that predict coder convergence. Throughout the chapter, we focus on three indices included in the V-Dem data set: polyarchy, corruption, and core civil society. These three concepts collectively provide a “hard test” for the validity of our data, representing a range of existing measurement approaches, challenges, and solutions.
This chapter sets forth the conceptual scheme for the V–Dem project. We begin by discussing the concept of democracy. Next, we lay out seven principles by which this key concept may be understood – electoral, liberal, majoritarian, consensual, participatory, deliberative, and egalitarian. Each defines a “variety“ of democracy, and together they offer a fairly comprehensive accounting of the concept as used in the world today. Next, we show how this seven-part framework fits into our overall thinking about democracy, including multiple levels of disaggregation – to components, subcomponents, and indicators. The final section of the chapter discusses several important caveats and clarifications pertaining to this ambitious taxonomic exercise.
This chapter recounts how a project of this scale came together and why it has succeeded. Five main factors were responsible for V–Dem’s success: timing, inclusion, deliberation, administrative centralization, and fund–raising. First, planning for V-Dem began at a time when both social scientists and practitioners were realizing that they needed better democracy measures. This made it possible to recruit collaborators and find funding. Second, the leaders of the project were always eager to expand the team to acquire whatever expertise they lacked and share credit with everyone who contributed. Third, the project leaders practiced an intensely deliberative decision–making style to ensure that all points of view were consulted and only decisions that won wide acceptance were adopted. Fourth, centralizing the execution of the agreed–upon tasks helped tremendously by streamlining processes and promoting standardization, documentation, professionalization, and coordination of a large number of intricate steps. Finally, successful fund–raising from a mix of both research foundations and bilateral and multilateral organizations has been critical.
In this chapter we focus on the measurement of five key principles of democracy – electoral, liberal, participatory, deliberative, and egalitarian. For each principle, we discuss (1) the theoretical rationale for the selected indicators, (2) whether these indicators are correlated strongly enough to warrant being collapsed into an index, and (3) the justification of aggregation rules for moving from indicators to components and from components to higher–level indices. In each section we also (4) highlight the top– and bottom–five countries on each principle of democracy in early (1812 or 1912) and late (2012) years of our sample period, as well as the aggregate trend over the whole time period 1789–2017 (where applicable). Finally, we (5) look at how the different principles are intercorrelated in order to assess the trade–offs involved between the conceptual parsimony achieved by aggregating to a few general concepts and the retention of useful variation permitted by aggregating less.
Four characteristics of V-Dem data present distinct opportunities and challenges for explanatory analysis: (1) the large number of democracy indicators (i.e., variables), (2) the measurement of concepts by multiple coders filtered through the V-Dem measurement model, (3) the large number of years in the data set, and 4) the ex ante potential for dependence across countries (generically referred to as spatial dependence). This chapter discusses 3 challenges and 10 opportunities that are implied by these characteristics. At the end of this chapter, we also discuss three assumptions that are implicit in most analyses of observational indicators of macro-features at the national level, which aim to draw conclusions about causal relationships.
Varieties of Democracy is the essential user's guide to The Varieties of Democracy project (V-Dem), one of the most ambitious data collection efforts in comparative politics. This global research collaboration sparked a dramatic change in how we study the nature, causes, and consequences of democracy. This book is ambitious in scope: more than a reference guide, it raises standards for causal inferences in democratization research and introduces new, measurable, concepts of democracy and many political institutions. Varieties of Democracy enables anyone interested in democracy - teachers, students, journalists, activists, researchers and others - to analyze V-Dem data in new and exciting ways. This book creates opportunities for V-Dem data to be used in education, research, news analysis, advocacy, policy work, and elsewhere. V-Dem is rapidly becoming the preferred source for democracy data.
Users of V–Dem data should take care to understand how the data are generated because the data collection strategies have consequences for the validity, reliability, and proper interpretation of the values. Chapters 4 and 5 explain how we process the data after collecting the raw scores and how we aggregate the most specific indicators into more general indices. In this chapter we explain where the raw scores come from. We distinguish among the different types of data that V–Dem reports and describe the processes that produce each type and the infrastructure required to execute these processes.
V-Dem relies on country experts who code a host of ordinal variables, providing subjective ratings of latent – that is, not directly observable – regime characteristics. Sets of around five experts rate each case, and each rater works independently. Our statistical tools model patterns of disagreement between experts, who may offer divergent ratings because of differences of opinion, variation in scale conceptualization, or mistakes. These tools allow us to aggregate ratings into point estimates of latent concepts and quantify our uncertainty around these estimates. This chapter describes item response theory models that can account and adjust for differential item functioning (i.e., differences in how experts apply ordinal scales to cases) and variation in rater reliability (i.e., random error). We also discuss key challenges specific to applying item response theory to expert–coded cross-national panel data, explain how we address them, highlight potential problems with our current framework, and describe long-term plans for improving our models and estimates. Finally, we provide an overview of the end–user–accessible products of the V-Dem measurement model.