981 resultados para Health Expectancy


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Whereas research has demonstrated that phobic or fearful individuals overestimate the likelihood of incurring aversive consequences from an encounter with feared stimuli, it has not yet been systematically investigated whether these individuals also overestimate the likelihood (i.e., the frequency) of such encounters. In the current study, spider-fearful and control participants were presented with background information that allowed them to estimate the overall likelihood that different kinds of animals (spiders, snakes, or birds) would be encountered. Spider-fearful participants systematically overestimated the likelihood of encountering a spider with respect to the likelihood of encountering a snake or a bird. No such expectancy bias was observed in control participants. The results thus strengthen our idea that there indeed exist two different types of expectancy bias in high fear and phobia that can be related to different components of the fear response. A conscientious distinction and examination of these two types of expectancy bias are of potential interest for therapeutic applications.

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Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.

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OBJECTIVES To determine life expectancy for older women with breast cancer. DESIGN Prospective longitudinal study with 10 years of follow-up data. SETTING Hospitals or collaborating tumor registries in four geographic regions (Los Angeles, California; Minnesota; North Carolina; Rhode Island). PARTICIPANTS Women aged 65 and older at time of breast cancer diagnosis with Stage I to IIIA disease with measures of self-rated health (SRH) and walking ability at baseline (N = 615; 17% aged ≥80, 52% Stage I, 58% with ≥2 comorbidities). MEASUREMENTS Baseline SRH, baseline self-reported walking ability, all-cause and breast cancer-specific estimated probability of 5- and 10-year survival. RESULTS At the time of breast cancer diagnosis, 39% of women reported poor SRH, and 28% reported limited ability to walk several blocks. The all-cause survival curves appear to separate after approximately 3 years, and the difference in survival probability between those with low SRH and limited walking ability and those with high SRH and no walking ability limitation was significant (0.708 vs 0.855 at 5 years, P ≤ .001; 0.300 vs 0.648 at 10 years, P < .001). There were no differences between the groups in breast cancer-specific survival at 5 and 10 years (P = .66 at 5 years, P = .16 at 10 years). CONCLUSION The combination of low SRH and limited ability to walk several blocks at diagnosis is an important predictor of worse all-cause survival at 5 and 10 years. These self-report measures easily assessed in clinical practice may be an effective strategy to improve treatment decision-making in older adults with cancer.

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PURPOSE OF REVIEW Improved virological and immunological outcomes and reduced toxicity of antiretroviral combination therapy (ART) raise the hope that life expectancy of HIV-positive persons on ART will approach that of the general population. We systematically review the literature and summarize published estimates of life expectancy of HIV-positive populations on ART. We compare their life expectancy with the life expectancy of the general or, in sub-Saharan Africa, HIV-negative populations, by time period and gender. RECENT FINDINGS Ten relevant studies were published from 2006 to 2015. Three studies were from Canada, two from European countries, three from sub-Saharan Africa and two were multicountry studies. Life expectancy increased over time in all studies and regions. Expressed as the percentage of life expectancy in the HIV-negative or general population, estimated life expectancy at age 20 years in HIV-positive people on ART ranged from 60.3% (95% CI 58.0-62.6%) in Rwanda (2008-2011) to 89.1% (95% CI 84.7-93.6%) in Canada (2008-2012). The percentage of life expectancy in the HIV-negative or general population achieved was higher in HIV-positive women than in HIV-positive men in all countries, except for Canada wherein the opposite was the case. SUMMARY Life expectancy in HIV-positive people on ART has improved worldwide in recent years, but important gaps remain compared with the general and HIV-negative population, and between regions and genders.

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Statistical methods are developed which assess survival data for two attributes; (1) prolongation of life, (2) quality of life. Health state transition probabilities correspond to prolongation of life and are modeled as a discrete-time semi-Markov process. Imbedded within the sojourn time of a particular health state are the quality of life transitions. They reflect events which differentiate perceptions of pain and suffering over a fixed time period. Quality of life transition probabilities are derived from the assumptions of a simple Markov process. These probabilities depend on the health state currently occupied and the next health state to which a transition is made. Utilizing the two forms of attributes the model has the capability to estimate the distribution of expected quality adjusted life years (in addition to the distribution of expected survival times). The expected quality of life can also be estimated within the health state sojourn time making more flexible the assessment of utility preferences. The methods are demonstrated on a subset of follow-up data from the Beta Blocker Heart Attack Trial (BHAT). This model contains the structure necessary to make inferences when assessing a general survival problem with a two dimensional outcome. ^

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This study described home infusion techniques and practices, measured the perceived risk of HIV and hepatitis transmission to self and others, and measured the outcome expectancy of following risk reduction guidelines for 90 hemophilia patients and/or their infusion assistants. It also assessed general knowledge of HIV and hepatitis information for the same population.^ The study subjects were hemophilia patients or their infusion assistants from the Gulf States Hemophilia Center in Houston, the El Paso Satellite Hemophilia Clinic in El Paso, or Texas members of the Women Outreach Network of the National Hemophilia Foundation (WONN) group. Each subject was interviewed either by telephone or in person. The questionnaire used was developed for the study and consisted of 60 items. These items assessed general demographics for the patients and assistants, including questions about their training to do infusions as well as the actual practices, measured perceived personal risk for the transmission of HIV or hepatitis to the assistants, perceived risk of transmission of HIV or hepatitis to others for assistants and self-infusers, and the outcome expectancy for following recommended risk reduction guidelines also for both groups.^ The theoretical framework used assumed that perceived risk and outcome expectancy would be predictive of behavior. The findings did not support this theory. Instead, the findings suggest that infusion behavior is habitual in nature; most respondents perform exactly the same behavior for every infusion. Since none of the variables selected were predictive of the compliance behavior for home infusion the teaching method should be directed towards mastery learning, or learning that will incorporate the correct behavior into a habitual pattern of home infusion. ^

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Evaluation of the impact of a disease on life expectancy is an important part of public health. Potential gains in life expectancy (PGLE) that can properly take into account the competing risks are an effective indicator for measuring the impact of the multiple causes of death. This study aimed to measure the PGLEs from reducing/eliminating the major causes of death in the USA from 2001 to 2008. To calculate the PGLEs due to the elimination of specific causes of death, the age-specific mortality rates for heart disease, malignant neoplasms, Alzheimer disease, kidney diseases and HIV/AIDS and life table constructing data were obtained from the National Center for Health Statistics, and the multiple decremental life tables were constructed. The PGLEs by elimination of heart disease, malignant neoplasms or HIV/AIDS continued decreasing from 2001 to 2008, but the PGLE by elimination of Alzheimer's disease or kidney diseases revealed increased trends. The PGLEs (by years) for all race, male, female, white, white male, white female, black, black male and black female at birth by complete elimination of heart disease 2001–2008 were 0.336–0.299, 0.327–0.301, 0.344–0.295, 0.360–0.315, 0.349–0.317, 0.371–0.316,0.278–0.251, 0.272–0.255, and 0.282–0.246 respectively. Similarly, the PGLEs (by years) for all race, male, female, white, white male, white female, black, black male and black female at birth by complete elimination of malignant neoplasms, Alzheimer's disease, kidney disease or HIV/AIDS 2001–2008 were also uncovered, respectively. Most diseases affect specific population, such as, HIV/AIDS tends to have a greater impact on people of working age, heart disease and malignant neoplasms have a greater impact on people over 65 years of age, but Alzheimer's disease and kidney diseases have a greater impact on people over 75 years of age. To measure the impact of these diseases on life expectancy in people of working age, partial multiple decremental life tables were constructed and the PGLEs were computed by partial or complete elimination of various causes of death during the working years. Thus, the results of the study outlined a picture of how each single disease could affect the life expectancy in age-, race-, or sex-specific population in USA. Therefore, the findings would not only assist to evaluate current public health improvements, but also provide useful information for future research and disease control programs.^

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Life expectancy has consistently increased over the last 150 years due to improvements in nutrition, medicine, and public health. Several studies found that in many developed countries, life expectancy continued to rise following a nearly linear trend, which was contrary to a common belief that the rate of improvement in life expectancy would decelerate and was fit with an S-shaped curve. Using samples of countries that exhibited a wide range of economic development levels, we explored the change in life expectancy over time by employing both nonlinear and linear models. We then observed if there were any significant differences in estimates between linear models, assuming an auto-correlated error structure. When data did not have a sigmoidal shape, nonlinear growth models sometimes failed to provide meaningful parameter estimates. The existence of an inflection point and asymptotes in the growth models made them inflexible with life expectancy data. In linear models, there was no significant difference in the life expectancy growth rate and future estimates between ordinary least squares (OLS) and generalized least squares (GLS). However, the generalized least squares model was more robust because the data involved time-series variables and residuals were positively correlated. ^

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Negli ultimi anni i modelli VAR sono diventati il principale strumento econometrico per verificare se può esistere una relazione tra le variabili e per valutare gli effetti delle politiche economiche. Questa tesi studia tre diversi approcci di identificazione a partire dai modelli VAR in forma ridotta (tra cui periodo di campionamento, set di variabili endogene, termini deterministici). Usiamo nel caso di modelli VAR il test di Causalità di Granger per verificare la capacità di una variabile di prevedere un altra, nel caso di cointegrazione usiamo modelli VECM per stimare congiuntamente i coefficienti di lungo periodo ed i coefficienti di breve periodo e nel caso di piccoli set di dati e problemi di overfitting usiamo modelli VAR bayesiani con funzioni di risposta di impulso e decomposizione della varianza, per analizzare l'effetto degli shock sulle variabili macroeconomiche. A tale scopo, gli studi empirici sono effettuati utilizzando serie storiche di dati specifici e formulando diverse ipotesi. Sono stati utilizzati tre modelli VAR: in primis per studiare le decisioni di politica monetaria e discriminare tra le varie teorie post-keynesiane sulla politica monetaria ed in particolare sulla cosiddetta "regola di solvibilità" (Brancaccio e Fontana 2013, 2015) e regola del GDP nominale in Area Euro (paper 1); secondo per estendere l'evidenza dell'ipotesi di endogeneità della moneta valutando gli effetti della cartolarizzazione delle banche sul meccanismo di trasmissione della politica monetaria negli Stati Uniti (paper 2); terzo per valutare gli effetti dell'invecchiamento sulla spesa sanitaria in Italia in termini di implicazioni di politiche economiche (paper 3). La tesi è introdotta dal capitolo 1 in cui si delinea il contesto, la motivazione e lo scopo di questa ricerca, mentre la struttura e la sintesi, così come i principali risultati, sono descritti nei rimanenti capitoli. Nel capitolo 2 sono esaminati, utilizzando un modello VAR in differenze prime con dati trimestrali della zona Euro, se le decisioni in materia di politica monetaria possono essere interpretate in termini di una "regola di politica monetaria", con specifico riferimento alla cosiddetta "nominal GDP targeting rule" (McCallum 1988 Hall e Mankiw 1994; Woodford 2012). I risultati evidenziano una relazione causale che va dallo scostamento tra i tassi di crescita del PIL nominale e PIL obiettivo alle variazioni dei tassi di interesse di mercato a tre mesi. La stessa analisi non sembra confermare l'esistenza di una relazione causale significativa inversa dalla variazione del tasso di interesse di mercato allo scostamento tra i tassi di crescita del PIL nominale e PIL obiettivo. Risultati simili sono stati ottenuti sostituendo il tasso di interesse di mercato con il tasso di interesse di rifinanziamento della BCE. Questa conferma di una sola delle due direzioni di causalità non supporta un'interpretazione della politica monetaria basata sulla nominal GDP targeting rule e dà adito a dubbi in termini più generali per l'applicabilità della regola di Taylor e tutte le regole convenzionali della politica monetaria per il caso in questione. I risultati appaiono invece essere più in linea con altri approcci possibili, come quelli basati su alcune analisi post-keynesiane e marxiste della teoria monetaria e più in particolare la cosiddetta "regola di solvibilità" (Brancaccio e Fontana 2013, 2015). Queste linee di ricerca contestano la tesi semplicistica che l'ambito della politica monetaria consiste nella stabilizzazione dell'inflazione, del PIL reale o del reddito nominale intorno ad un livello "naturale equilibrio". Piuttosto, essi suggeriscono che le banche centrali in realtà seguono uno scopo più complesso, che è il regolamento del sistema finanziario, con particolare riferimento ai rapporti tra creditori e debitori e la relativa solvibilità delle unità economiche. Il capitolo 3 analizza l’offerta di prestiti considerando l’endogeneità della moneta derivante dall'attività di cartolarizzazione delle banche nel corso del periodo 1999-2012. Anche se gran parte della letteratura indaga sulla endogenità dell'offerta di moneta, questo approccio è stato adottato raramente per indagare la endogeneità della moneta nel breve e lungo termine con uno studio degli Stati Uniti durante le due crisi principali: scoppio della bolla dot-com (1998-1999) e la crisi dei mutui sub-prime (2008-2009). In particolare, si considerano gli effetti dell'innovazione finanziaria sul canale dei prestiti utilizzando la serie dei prestiti aggiustata per la cartolarizzazione al fine di verificare se il sistema bancario americano è stimolato a ricercare fonti più economiche di finanziamento come la cartolarizzazione, in caso di politica monetaria restrittiva (Altunbas et al., 2009). L'analisi si basa sull'aggregato monetario M1 ed M2. Utilizzando modelli VECM, esaminiamo una relazione di lungo periodo tra le variabili in livello e valutiamo gli effetti dell’offerta di moneta analizzando quanto la politica monetaria influisce sulle deviazioni di breve periodo dalla relazione di lungo periodo. I risultati mostrano che la cartolarizzazione influenza l'impatto dei prestiti su M1 ed M2. Ciò implica che l'offerta di moneta è endogena confermando l'approccio strutturalista ed evidenziando che gli agenti economici sono motivati ad aumentare la cartolarizzazione per una preventiva copertura contro shock di politica monetaria. Il capitolo 4 indaga il rapporto tra spesa pro capite sanitaria, PIL pro capite, indice di vecchiaia ed aspettativa di vita in Italia nel periodo 1990-2013, utilizzando i modelli VAR bayesiani e dati annuali estratti dalla banca dati OCSE ed Eurostat. Le funzioni di risposta d'impulso e la scomposizione della varianza evidenziano una relazione positiva: dal PIL pro capite alla spesa pro capite sanitaria, dalla speranza di vita alla spesa sanitaria, e dall'indice di invecchiamento alla spesa pro capite sanitaria. L'impatto dell'invecchiamento sulla spesa sanitaria è più significativo rispetto alle altre variabili. Nel complesso, i nostri risultati suggeriscono che le disabilità strettamente connesse all'invecchiamento possono essere il driver principale della spesa sanitaria nel breve-medio periodo. Una buona gestione della sanità contribuisce a migliorare il benessere del paziente, senza aumentare la spesa sanitaria totale. Tuttavia, le politiche che migliorano lo stato di salute delle persone anziane potrebbe essere necessarie per una più bassa domanda pro capite dei servizi sanitari e sociali.

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Background: The liberalisation of trade in services which began in 1995 under the General Agreement on Trade in Services (GATS) of the World Trade Organisation (WTO) has generated arguments for and against its potential health effects. Our goal was to explore the relationship between the liberalisation of services under the GATS and three health indicators – life expectancy (LE), under-5 mortality (U5M) and maternal mortality (MM) - since the WTO was established. Methods and Findings: This was a cross-sectional ecological study that explored the association in 2010 and 1995 between liberalisation and health (LE, U5M and MM), and between liberalisation and progress in health in the period 1995–2010, considering variables related to economic and social policies such as per capita income (GDP pc), public expenditure on health (PEH), and income inequality (Gini index). The units of observation and analysis were WTO member countries with data available for 2010 (n = 116), 1995 (n = 114) and 1995–2010 (n = 114). We conducted bivariate and multivariate linear regression analyses adjusted for GDP pc, Gini and PEH. Increased global liberalisation in services under the WTO was associated with better health in 2010 (U5M: 20.358 p,0.001; MM: 20.338 p = 0.001; LE: 0.247 p = 0.008) and in 1995, after adjusting for economic and social policy variables. For the period 1995–2010, progress in health was associated with income equality, PEH and per capita income. No association was found with global liberalisation in services. Conclusions: The favourable association in 2010 between health and liberalisation in services under the WTO seems to reflect a pre-WTO association observed in the 1995 data. However, this liberalisation did not appear as a factor associated with progress in health during 1995–2010. Income equality, health expenditure and per capita income were more powerful determinants of the health of populations.

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"January, 1988."

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Background Estimates of the disease burden due to multiple risk factors can show the potential gain from combined preventive measures. But few such investigations have been attempted, and none on a global scale. Our aim was to estimate the potential health benefits from removal of multiple major risk factors. Methods We assessed the burden of disease and injury attributable to the joint effects of 20 selected leading risk factors in 14 epidemiological subregions of the world. We estimated population attributable fractions, defined as the proportional reduction in disease or mortality that would occur if exposure to a risk factor were reduced to an alternative level, from data for risk factor prevalence and hazard size. For every disease, we estimated joint population attributable fractions, for multiple risk factors, by age and sex, from the direct contributions of individual risk factors. To obtain the direct hazards, we reviewed publications and re-analysed cohort data to account for that part of hazard that is mediated through other risks. Results Globally, an estimated 47% of premature deaths and 39% of total disease burden in 2000 resulted from the joint effects of the risk factors considered. These risks caused a substantial proportion of important diseases, including diarrhoea (92%-94%), lower respiratory infections (55-62%), lung cancer (72%), chronic obstructive pulmonary disease (60%), ischaemic heart disease (83-89%), and stroke (70-76%). Removal of these risks would have increased global healthy life expectancy by 9.3 years (17%) ranging from 4.4 years (6%) in the developed countries of the western Pacific to 16.1 years (43%) in parts of sub-Saharan Africa. Interpretation Removal of major risk factors would not only increase healthy life expectancy in every region, but also reduce some of the differences between regions, The potential for disease prevention and health gain from tackling major known risks simultaneously would be substantial.

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This paper presents an economic model to explain the behavior of life expectancy of both sexes. It explicitly examines the relationship between the gender gap in life expectancy and the gender gap in pay. It shows that as the latter narrows over the course of economic development, the former may initially expand but will eventually shrink. Simulation results from our model accord with the behavior of life expectancy for both sexes since the 1940s in the United States. (C) 2003 Elsevier B.V. All rights reserved.

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This paper develops an overlapping-generations model in which agents invest in health to prolong life in both working and retirement periods. It explores how unfunded social security with or without health subsidies affects life expectancy, economic growth, and welfare. In particular, by extending life at a possible cost of capital accumulation, health subsidies and a pay-as-you-go pension can improve welfare, especially in the short run.

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Objective: To explore the use of epidemiological modelling for the estimation of health effects of behaviour change interventions, using the example of computer-tailored nutrition education aimed at fruit and vegetable consumption in The Netherlands. Design: The effects of the intervention on changes in consumption were obtained from an earlier evaluation study. The effect on health outcomes was estimated using an epidemiological multi-state life table model. input data for the model consisted of relative risk estimates for cardiovascular disease and cancers, data on disease occurrence and mortality, and survey data on the consumption of fruits and vegetables. Results: if the computer-tailored nutrition education reached the entire adult population and the effects were sustained, it could result in a mortality decrease of 0.4 to 0.7% and save 72 to 115 life-years per 100000 persons aged 25 years or older. Healthy life expectancy is estimated to increase by 32.7 days for men and 25.3 days for women. The true effect is likely to lie between this theoretical maximum and zero effect, depending mostly on durability of behaviour change and reach of the intervention. Conclusion: Epidemiological models can be used to estimate the health impact of health promotion interventions.