Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

Prevalence of chronic conditions – Stroke

IPH has estimated and forecast clinical diagnosis rates of stroke among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of adults who report that they have experienced doctor-diagnosed stroke in the previous 12 months. Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data are based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of adults who report that they have experienced doctor-diagnosed stroke at any time in the past. Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.

Prevalence of chronic conditions – Diabetes

IPH has estimated and forecast clinical diagnosis rates of diabetes among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed diabetes in the previous 12 months (annual clinical diagnosis).  Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. Note that an adjustment was made for diabetes medication use recorded in the SLÁN physical examination sub-group of 45+ year olds. In Northern Ireland, the data is based on the Health and Social Wellbeing Survey 2005/06 . The data describe the number of people who report that they have experienced doctor-diagnosed diabetes at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland.Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past. Differences between IPH estimates and reference study estimates: The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.  

Making Chronic Conditions Count: 5. Chronic Airflow Obstruction

Chronic conditions are responsible for a significant proportion of early deaths. They reduce qualityof life in many of the adults living with them, represent substantial financial costs to patients andthe health and social care system, and cause a significant loss of productivity to the economy.This report contains estimates and forecasts of the population prevalence of chronic airflowobstruction, and it shows how it varies across the island and what change is expected between2007, 2015 and 2020.

Making Chronic Conditions Count: 1. Diabetes

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of diabetes, and it shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Making Chronic Conditions Count: 2. Coronary Heart Disease (Angina and Heart Attack)

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of coronary heart disease (angina and heart attack), and it shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Making Chronic Conditions Count: 3. Hypertension

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of hypertension and shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Making Chronic Conditions Count: 4. Stroke

Chronic conditions are responsible for a significant proportion of early deaths. They reduce quality of life in many of the adults living with them, represent substantial financial costs to patients and the health and social care system, and cause a significant loss of productivity to the economy. This report contains estimates and forecasts of the population prevalence of stroke, and it shows how it varies across the island and what change is expected between 2007, 2015 and 2020.

Musculoskeletal Conditions Briefing

Musculoskeletal conditions (MSCs) are a group of diseases that affect the body’s bones, joints, muscles and the tissues that connect them. Common MSCs include back pain, rheumatoid arthritis, osteoarthritis, osteoporosis, and spinal disorders. MSCs are the most common cause of severe long term pain and physical disability in developed countries. They significantly affect the psychosocial wellbeing of individuals as well as their families and carers. They are responsible for substantial costs to the health and social care system and the economy. They are a leading cause of absence from work and lost productivity at work. MSCs comprise a diverse group of conditions. Some have a specific medical diagnosis (eg rheumatoid arthritis) but others have no clear medical diagnosis (eg back pain). Risk factors for the development and progression of MSCs include age, sex, family history, obesity, physical inactivity, injury and biomechanical occupational health issues.

Musculoskeletal Conditions Briefing: Technical Documentation

Musculoskeletal conditions (MSCs) are a group of diseases that affect the body’s bones, joints, muscles and the tissues that connect them. Common MSCs include back pain, rheumatoid arthritis, osteoarthritis, osteoporosis, and spinal disorders. MSCs are the most common cause of severe long term pain and physical disability in developed countries. They significantly affect the psychosocial wellbeing of individuals as well as their families and carers. They are responsible for substantial costs to the health and social care system and the economy. They are a leading cause of absence from work and lost productivity at work. MSCs comprise a diverse group of conditions. Some have a specific medical diagnosis (eg rheumatoid arthritis) but others have no clear medical diagnosis (eg back pain). Risk factors for the development and progression of MSCs include age, sex, family history, obesity, physical inactivity, injury and biomechanical occupational health issues. This document details the methods used to calculate the estimates and forecasts.

IPH response to NI Health and Social Care Board 'Tranforming Your Care' consultation

The 'Transforming Your Care (TYC)' consultation relates to proposals for changes in the delivery of Health and Social Care in Northern Ireland in the context of the TYC report published in December 2011. TYC is about making changes to ensure safe, high quality and sustainable services for patients, service users and staff. TYC sets out proposals in respect of how health and social services will need to adapt and be organised to best meet the needs associated with population ageing, increasing long-term conditions and other challenges. Key points from IPH response include: IPH welcomes the HSC commitment to transform health and social care services to meet Northern Ireland’s changing population health needs Inequalities are a dominant feature of health service utilisation patterns in Northern Ireland – for example hospital admission rates for self-harm and alcohol-related admissions in the most deprived areas are double the regional figure. IPH recommends that

IPH response to a Draft Policy Framework for supporting people in Northern Ireland living with long term (or chronic) conditions

The Department of Health, Social Services and Public Safety recently consulted on a draft Policy Framework for supporting people in Northern Ireland living with long term (or chronic) conditions

IPH response to the Department for Social Development Draft Regeneration and Housing Bill

IPH recognise that housing and regeneration initiatives are key determinants of health and have responded to the Department for Social Development (DSD), Draft Regeneration and Housing Bill.

Social Interaction Effects on Fertility: Intentions and Behaviors

The existing literature shows that social interactions in individuals' networks affect their reproductive attitudes and behaviors through three mechanisms: social influence, social learning, and social support. In this paper, we discuss to what extent the Theory of Planned Behavior (TPB), an individual based theorization of intentions and behavior used to model fertility, takes these social mechanisms into account. We argue that the TPB already integrates social influence and that it could easily accommodate the two other social network mechanisms. By doing so, the theory would be enriched in two respects. First, it will explain more completely how macro level changes eventually ends in micro level changes in behavioral intentions. Indeed, mechanisms of social influence may explain why changes in representations of parenthood and ideal family size can be slower than changes in socio-economic conditions and institutions. Social learning mechanisms should also be considered, since they are crucial to distinguish who adopts new behavioral beliefs and practices, when change at the macro level finally sinks in. Secondly, relationships are a capital of services that can complement institutional offering (informal child care) as well as a capital of knowledge which help individuals navigate in a complex institutional reality, providing a crucial element to explain heterogeneity in the successful realization of fertility intentions across individuals. We develop specific hypotheses concerning the effect of social interactions on fertility intentions and their realization to conclude with a critical review of the existing surveys suitable to test them and their limits.

Prevalence of chronic conditions – Hypertension

IPH has estimated and forecast clinical diagnosis rates of hypertension among adults for the years 2010, 2015 and 2020. In the Republic of Ireland, the data are based on the Survey of Lifestyle, Attitudes and Nutrition (SLÁN) 2007. The data describe the number of people who report that they have experienced doctor-diagnosed hypertension in the previous 12 months (annual clinical diagnosis). Data are available by age and sex for each Local Health Office of the Health Service Executive (HSE) in the Republic of Ireland. In Northern Ireland, the data is based on the Health and Social Wellbeing Survey 2005/06. The data describe the number of people who report that they have experienced doctor/nurse-diagnosed hypertension at any time in the past (lifetime clinical diagnosis). Data are available by age and sex for each Local Government District in Northern Ireland. Clinical diagnosis rates in the Republic of Ireland relate to the previous 12 months and are not directly comparable with clinical diagnosis rates in Northern Ireland which relate to anytime in the past.   The IPH estimated prevalence per cents may be marginally different to estimated prevalence per cents taken directly from the reference study. There are two reasons for this: 1) The IPH prevalence estimates relate to 2010 while the reference studies relate to earlier years (Northern Ireland Health and Social Wellbeing Survey 2005/06, Survey of Lifestyle, Attitudes and Nutrition 2007, Understanding Society 2009). Although we assume that the risk of the condition in the risk groups do not change over time, the distribution of the number of people in the risk groups in the population changes over time (eg the population ages).  This new distribution of the risk groups in the population means that the risk of the condition is weighted differently to the reference study and this results in a different overall prevalence estimate. 2) The IPH prevalence estimates are based on a statistical model of the reference study. The model includes a number of explanatory variables to predict the risk of the condition. Therefore the model does not include records from the reference study that are missing data on these explanatory variables. A prevalence estimate for a condition taken directly from the reference study would include these records.