Validity and Reliability of the Assessment of Quality of Life (AQoL)-8D Multi-Attribute Utility Instrument

OBJECTIVE: The purpose of this paper was to report tests of the validity and reliability of a new instrument, the Assessment of Quality of Life (AQoL)-8D, which was constructed to improve the evaluation of health services that have an impact upon the psychosocial aspects of the quality of life. METHODS: Australian and US data from a large multi-instrument comparison survey were used to conduct tests of convergent, predictive and content validity using as comparators five other multi-attribute utility (MAU) instruments—the EQ-5D, SF-6D, Health Utilities Index (HUI) 3, 15D and the Quality of Well-Being (QWB)—as well as four non-utility instruments—the SF-36 and three measures of subjective well-being (SWB). A separate three part Australian survey was used to assess test–retest reliability. RESULTS: Results indicate that AQoL-8D correlates more highly with both the SWB instruments and the psychosocial dimensions of the SF-36, and that it is similar to the other MAU instruments in terms of its convergent and predictive validity. The second Australian survey demonstrated high test–retest reliability. CONCLUSIONS: The results indicate that the AQoL-8D is a reliable and valid instrument which offers an alternative to the MAU instruments presently used in economic evaluation studies, and one which is particularly suitable when psychosocial elements of health are of importance

Assessing the validity of the ICECAP-A capability measure for adults with depression

BACKGROUND: Effectiveness and cost-effectiveness are increasingly important considerations in determining which mental health services are funded. Questions have been raised concerning the validity of generic health status instruments used in economic evaluation for assessing mental health problems such as depression; measuring capability wellbeing offers a possible alternative. The aim of this study is to assess the validity of the ICECAP-A capability instrument for individuals with depression. METHODS: Hypotheses were developed using concept mapping. Validity tests and multivariable regression analysis were applied to data from a cross-sectional dataset to assess the performance of ICECAP-A in individuals who reported having a primary condition of depression. The ICECAP-A was collected alongside instruments used to measure: 1. depression using the depression scale of the Depression, Anxiety and Stress Scale (DASS-D of DASS-21); 2. mental health using the Kessler Psychological Distress Scale (K10); 3. generic health status using a common measure collected for use in economic evaluations, the five level version of EQ-5D (EQ-5D-5L). RESULTS: Hypothesised associations between the ICECAP-A (items and index scores) and depression constructs were fully supported in statistical tests. In the multivariable analysis, instruments designed specifically to measure depression and mental health explained a greater proportion of the variation in ICECAP-A than the EQ-5D-5L. CONCLUSION: The ICECAP-A instrument appears to be suitable for assessing outcome in adults with depression for resource allocation purposes. Further research is required on its responsiveness and use in economic evaluation. Using a capability perspective when assessing cost-effectiveness could potentially re-orientate resource provision across physical and mental health care services. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12888-017-1211-8) contains supplementary material, which is available to authorized users

Construction of the descriptive system for the assessment of quality of life AQoL-6D utility instrument

BackgroundMulti attribute utility (MAU) instruments are used to include the health related quality of life (HRQoL) in economic evaluations of health programs. Comparative studies suggest different MAU instruments measure related but different constructs. The objective of this paper is to describe the methods employed to achieve content validity in the descriptive system of the Assessment of Quality of Life (AQoL)-6D, MAU instrument.MethodsThe AQoL program introduced the use of psychometric methods in the construction of health related MAU instruments. To develop the AQoL-6D we selected 112 items from previous research, focus groups and expert judgment and administered them to 316 members of the public and 302 hospital patients. The search for content validity across a broad spectrum of health states required both formative and reflective modelling. We employed Exploratory Factor Analysis and Structural Equation Modelling (SEM) to meet these dual requirements.Results and DiscussionThe resulting instrument employs 20 items in a multi-tier descriptive system. Latent dimension variables achieve sensitive descriptions of 6 dimensions which, in turn, combine to form a single latent QoL variable. Diagnostic statistics from the SEM analysis are exceptionally good and confirm the hypothesised structure of the model.ConclusionsThe AQoL-6D descriptive system has good psychometric properties. They imply that the instrument has achieved construct validity and provides a sensitive description of HRQoL. This means that it may be used with confidence for measuring health related quality of life and that it is a suitable basis for modelling utilities for inclusion in the economic evaluation of health programs.<br /

Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments

ANTECEDENTES La versión revisada del Cuestionario de Impacto de Fibromialgia (FIQR) es uno de los cuestionarios específicos más utilizados en estudios de FM. Sin embargo, este cuestionario no permite el cálculo de los AVAC, ya que no es una medida basada en preferencias. El objetivo de este estudio fue desarrollar un algoritmo de mapeo que permita que los puntajes de FIQR se transformen en puntajes de utilidad que se pueden usar en los análisis de costo de utilidad. METODOS Se realizó una encuesta transversal. Se pidió a ciento 92 mujeres españolas con fibromialgia que completaran cuatro cuestionarios generales de calidad de vida, es decir, EQ-5D-5 L, 15D, AQoL-8D y SF-12, y un instrumento específico para la enfermedad, el FIQR. Se adoptó un enfoque de mapeo directo para derivar algoritmos de mapeo entre el FIQR y cada uno de los cuatro instrumentos de utilidad de atributos múltiples (MAU). La utilidad del estado de salud se trató como la variable dependiente en el análisis de regresión, mientras que la puntuación de FIQR y la edad fueron factores predictivos. RESULTADOS Las puntuaciones medias de utilidad oscilaron entre 0,47 (AQoL-8D) y 0,69 (15D). Todas las correlaciones entre la puntuación total de FIQR y las puntuaciones de utilidad de los instrumentos de MAU fueron altamente significativas (p <0,0001) con magnitudes mayores que 0,5. Aunque se encontraron diferencias muy leves en el error absoluto promedio entre el estimador de mínimos cuadrados ordinarios (OLS) y el modelo lineal generalizado (GLM), los modelos basados en GLM fueron mejores para EQ-5D-5 L, AQoL-8D y 15D. CONCLUSIÓN Los algoritmos de mapeo desarrollados en este estudio permiten la estimación de valores de utilidad a partir de puntajes en un cuestionario específico de fibromialgia.BACKGROUND The revised version of the Fibromyalgia Impact Questionnaire (FIQR) is one of the most widely used specific questionnaires in FM studies. However, this questionnaire does not allow calculation of QALYs as it is not a preference-based measure. The aim of this study was to develop mapping algorithm which enable FIQR scores to be transformed into utility scores that can be used in the cost utility analyses. METHODS A cross-sectional survey was conducted. One hundred and 92 Spanish women with Fibromyalgia were asked to complete four general quality of life questionnaires, i.e. EQ-5D-5 L, 15D, AQoL-8D and SF-12, and one specific disease instrument, the FIQR. A direct mapping approach was adopted to derive mapping algorithms between the FIQR and each of the four multi-attribute utility (MAU) instruments. Health state utility was treated as the dependent variable in the regression analysis, whilst the FIQR score and age were predictors. RESULTS The mean utility scores ranged from 0.47 (AQoL-8D) to 0.69 (15D). All correlations between the FIQR total score and MAU instruments utility scores were highly significant (p < 0.0001) with magnitudes larger than 0.5. Although very slight differences in the mean absolute error were found between ordinary least squares (OLS) estimator and generalized linear model (GLM), models based on GLM were better for EQ-5D-5 L, AQoL-8D and 15D. CONCLUSION Mapping algorithms developed in this study enable the estimation of utility values from scores in a fibromyalgia specific questionnaire.• Ministerio de Economía y Competitividad. Becas DEP2012–39828 y DEP2015–70356 (I+D+i) • Ministerio de Educación, Cultura y Deporte. Beca PRX14 / 00751, para Narcis Gusi Fuertes • Ministerio de Educación, Cultura y Deporte. Beca FPU14 / 01283, para Daniel Collado Mateo • Fundación Tatiana Pérez de Guzmán el Bueno. Beca predoctoral para Daniel Collado MateopeerReviewe

Fasting glucose and body mass index as predictors of activity in breast cancer patients treated with everolimus-exemestane: the EverExt study

Evidence on everolimus in breast cancer has placed hyperglycemia among the most common high grade adverse events. Anthropometrics and biomarkers of glucose metabolism were investigated in a observational study of 102 postmenopausal, HR + HER2- metastatic breast cancer patients treated with everolimus-exemestane in first and subsequent lines. Best overall response (BR) and clinical benefit rate (CBR) were assessed across subgroups defined upon fasting glucose (FG) and body mass index (BMI). Survival was estimated by Kaplan-Meier method and log-rank test. Survival predictors were tested in Cox models. Median follow up was 12.4 months (1.0-41.0). The overall cohort showed increasing levels of FG and decreasing BMI (p &lt; 0.001). Lower FG fasting glucose at BR was more commonly associated with C/PR or SD compared with PD (p &lt; 0.001). We also observed a somewhat higher BMI associated with better response (p = 0.052). More patients in the lowest FG category achieved clinical benefit compared to the highest (p &lt; 0.001), while no relevant differences emerged for BMI. Fasting glucose at re-assessment was also predictive of PFS (p = 0.037), as confirmed in models including BMI and line of therapy (p = 0.049). Treatment discontinuation was significantly associated with changes in FG (p = 0.014). Further research is warranted to corroborate these findings and clarify the underlying mechanisms

Neotropical xenarthrans: a data set of occurrence of xenarthran species in the neotropics

Xenarthrans -anteaters, sloths, and armadillos- have essential functions for ecosystem maintenance, such as insect control and nutrient cycling, playing key roles as ecosystem engineers. Because of habitat loss and fragmentation, hunting pressure, and conflicts with domestic dogs, these species have been threatened locally, regionally, or even across their full distribution ranges. The Neotropics harbor 21 species of armadillos, 10 anteaters, and 6 sloths. Our data set includes the families Chlamyphoridae (13), Dasypodidae (7), Myrmecophagidae (3), Bradypodidae (4), and Megalonychidae (2). We have no occurrence data on Dasypus pilosus (Dasypodidae). Regarding Cyclopedidae, until recently, only one species was recognized, but new genetic studies have revealed that the group is represented by seven species. In this data paper, we compiled a total of 42,528 records of 31 species, represented by occurrence and quantitative data, totaling 24,847 unique georeferenced records. The geographic range is from the southern United States, Mexico, and Caribbean countries at the northern portion of the Neotropics, to the austral distribution in Argentina, Paraguay, Chile, and Uruguay. Regarding anteaters, Myrmecophaga tridactyla has the most records (n = 5,941), and Cyclopes sp. Have the fewest (n = 240). The armadillo species with the most data is Dasypus novemcinctus (n = 11,588), and the fewest data are recorded for Calyptophractus retusus (n = 33). With regard to sloth species, Bradypus variegatus has the most records (n = 962), and Bradypus pygmaeus has the fewest (n = 12). Our main objective with Neotropical Xenarthrans is to make occurrence and quantitative data available to facilitate more ecological research, particularly if we integrate the xenarthran data with other data sets of Neotropical Series that will become.Fil: Marques Santos, Paloma. Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas; BrasilFil: Bocchiglieri, Adriana. Universidade Federal de Sergipe; BrasilFil: Garcia Chiarello, Adriano. Universidade de Sao Paulo; BrasilFil: Pereira Paglia, Adriano. Universidade Federal de Minas Gerais. Instituto de Ciências Biológicas; BrasilFil: Moreira, Adryelle. Amplo Engenharia e Gestão de Projetos ; BrasilFil: Abba, Agustin Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Estudios Parasitológicos y de Vectores. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo. Centro de Estudios Parasitológicos y de Vectores; ArgentinaFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Universidad Nacional de Misiones. Instituto de Biología Subtropical; ArgentinaFil: Gatica, Ailin. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; ArgentinaFil: Ochoa, Ana Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto Multidisciplinario de Investigaciones Biológicas de San Luis; ArgentinaFil: de Angelo, Carlos Daniel. Universidad Nacional de Rio Cuarto. Facultad de Cs.exactas Fisicoquimicas y Naturales. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Cordoba. Instituto de Ciencias de la Tierra, Biodiversidad y Ambiente.; ArgentinaFil: Tellaeche, Cintia Gisele. Universidad Nacional de Jujuy. Facultad de Ciencias Agrarias. Centro de Estudios Ambientales Territoriales y Sociales; Argentina. Universidad Nacional de Jujuy. Instituto de Ecorregiones Andinas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Ecorregiones Andinas; ArgentinaFil: Varela, Diego Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Vanderhoeven, Ezequiel Andres. Ministerio de Salud. Instituto Nacional de Medicina Tropical; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Caruso, María Flavia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Administración de Parques Nacionales. Delegación Regional del Noroeste; ArgentinaFil: Arrabal, Juan Pablo. Secretaria de Gobierno de Salud. Instituto Nacional de Medicina Tropical - Sede Puerto Iguazú Misiones; Argentina. Centro de Investigaciones del Bosque Atlántico; ArgentinaFil: Iezzi, María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Di Bitetti, Mario Santiago. Centro de Investigaciones del Bosque Atlántico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Cruz, Paula Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. Centro de Investigaciones del Bosque Atlántico; ArgentinaFil: Reppucci, Juan Ignacio. Administración de Parques Nacionales. Delegación Regional del Noroeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Benito Santamaria, Silvia. Centro de Investigaciones del Bosque Atlántico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Quiroga, Verónica Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Diversidad y Ecología Animal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto de Diversidad y Ecología Animal; ArgentinaFil: Di Blanco, Yamil Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Marás, Gustavo Arnaldo. Administración de Parques Nacionales. Delegación Regional del Noroeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Camino, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; ArgentinaFil: Perovic, Pablo Gastón. Administración de Parques Nacionales. Delegación Regional del Noroeste; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martínez Pardo, Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Costa, Sebastián Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; ArgentinaFil: Pinheiro, Fabiana. Universidade Federal do Rio Grande do Sul; BrasilFil: Volkmer de Castilho, Pedro. Universidade Federal de Santa Catarina; BrasilFil: Bercê, William. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Camara Assis, Julia. Universidade Estadual Paulista Julio de Mesquita Filho. Faculdade de Engenharia.; BrasilFil: Rodrigues Tonetti, Vinicius. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Alves Eigenheer, Milene. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Chinem, Simonne. Universidade de Sao Paulo; BrasilFil: Honda, Laura K.. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Bergallo, Helena de Godoy. Universidade do Estado de Rio do Janeiro; BrasilFil: Alberici, Vinicius. Universidade de Sao Paulo; BrasilFil: Wallace, Robert. Wildlife Conservation Society; Estados UnidosFil: Ribeiro, Milton Cezar. Universidade de Sao Paulo; BrasilFil: Galetti, Mauro. Universidade Estadual Paulista Julio de Mesquita Filho; Brasi