José Luis Ayuso-Mateos

Bio: José Luis Ayuso-Mateos is an academic researcher from Autonomous University of Madrid. The author has contributed to research in topics: Population & Mental health. The author has an hindex of 60, co-authored 280 publications receiving 14956 citations. Previous affiliations of José Luis Ayuso-Mateos include World Health Organization & University of Cantabria.

Global burden of depressive disorders in the year 2000

Abstract: Background The initial Global Burden of Disease study found that depression was the fourth leading cause of disease burden, accounting for 3.7% of total disability adjusted life years (DALYs) in the world in 1990. Aims To presentthe new estimates of depression burden for the year 2000. Method DALYs for depressive disorders in each world region were calculated, based on new estimates of mortality, prevalence, incidence, average age at onset, duration and disability severity. Results Depression is the fourth leading cause of disease burden, accounting for 4.4% of total DALYs in the year 2000, and it causes the largest amount of non-fatal burden, accounting for almost 12% of all total years lived with disability worldwide. Conclusions These data on the burden of depression worldwide represent a major public health problem that affects patients and society.

Validity and reliability of the Functioning Assessment Short Test (FAST) in bipolar disorder.

Abstract: Background: Numerous studies have documented high rates of functional impairment among bipolar disorder (BD) patients, even during phases of remission. However, the majority of the available instruments used to assess functioning have focused on global measures of functional recovery rather than specific domains of psychosocial functioning. In this context, the Functioning Assessment Short Test (FAST) is a brief instrument designed to assess the main functioning problems experienced by psychiatric patients, particularly bipolar patients. It comprises 24 items that assess impairment or disability in six specific areas of functioning: autonomy, occupational functioning, cognitive functioning, financial issues, interpersonal relationships and leisure time. Methods: 101 patients with DSM-IV TR bipolar disorder and 61 healthy controls were assessed in the Bipolar Disorder Program, Hospital Clinic of Barcelona. The psychometric properties of FAST (feasibility, internal consistency, concurrent validity, discriminant validity (euthymic vs acute patients), factorial analyses, and testretest reliability) were analysed. Results: The internal consistency obtained was very high with a Cronbach's alpha of 0.909. A highly significant negative correlation with GAF was obtained (r = -0.903; p < 0.001) pointing to a reasonable degree of concurrent validity. Test-retest reliability analysis showed a strong correlation between the two measures carried out one week apart (ICC = 0.98; p < 0.001). The total FAST scores were lower in euthymic (18.55 ± 13.19; F = 35.43; p < 0.001) patients, as compared with manic (40.44 ± 9.15) and depressive patients (43.21 ± 13.34). Conclusion: The FAST showed strong psychometrics properties and was able to detect differences between euthymic and acute BD patients. In addition, it is a short (6 minutes) simple interview-administered instrument, which is easy to apply and requires only a short period of time for its application.

Depressive disorders in Europe: prevalence figures from the ODIN study.

Abstract: Background This is the first report on the epidemiology of depressive disorders from the European Outcome of Depression International Network (ODIN) study. Aims To assess the prevalence of depressive disorders in randomly selected samples of the general population in five European countries. Method The study was designed as a cross-sectional two-phase community study using the Beck Depression inventory during Phase 1, and the Schedule for Clinical Assessment in Neuropsychiatry during Phase 2. Results An analysis of the combined sample ( n =8.764) gave an overall prevalence of depressive disorders of 8.56% (95% CI 7.05-10.37). The figures were 10.05% (95% CI 7.80-12.85) for women and 6.61% (95% CI 4.92-8.83) for men. The centres fall into three categories: high prevalence (urban Ireland and urban UK), low prevalence (urban Spain) and medium prevalence (the remaining sites). Conclusions Depressive disorder is a highly prevalent condition in Europe. The major finding is the wide difference in the prevalence of depressive disorders found across the study sites.

Functional outcome in bipolar disorder: the role of clinical and cognitive factors

Abstract: Introduction: Few studies have examined the clinical, neuropsychological and pharmacological factors involved in the functional outcome of bipolar disorder despite the gap between clinical and functional recovery. Methods: A sample of 77 euthymic bipolar patients were included in the study. Using an a priori definition of low versus good functional outcome, based on the psychosocial items of the Global Assessment of Functioning (GAF, DSM-IV), and taking also into account their occupational adaptation, the patients were divided into two groups: good or low occupational functioning. Patients with high (n = 46) and low (n = 31) functioning were compared on several clinical, neuropsychological and pharmacological variables and the two patient groups were contrasted with healthy controls (n = 35) on cognitive performance. Results: High- and low-functioning groups did not differ with respect to clinical variables. However, bipolar patients in general showed poorer cognitive performance than healthy controls. This was most evident in low-functioning patients and in particular on verbal memory and executive function measures. Conclusions: Low-functioning patients were cognitively more impaired than highly functioning patients on verbal recall and executive functions. The variable that best predicted psychosocial functioning in bipolar patients was verbal memory.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: A systematic analysis for the Global Burden of Disease Study 2015

Abstract: BACKGROUND The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. METHODS We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). FINDINGS Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6-58·8) of global deaths and 41·2% (39·8-42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. INTERPRETATION Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. FUNDING Bill & Melinda Gates Foundation.