The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Mark Daniel - One of the best experts on this subject based on the ideXlab platform.
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are changes in depressive symptoms general health and residential area socio economic status associated with trajectories of waist circumference and body mass index
PLOS ONE, 2020Co-Authors: Theo Niyonsenga, Suzanne J Carroll, Neil T Coffee, Anne W Taylor, Mark DanielAbstract:Objective: This study sought to assess whether changes in depressive symptoms, general health, and area-level socio-economic status (SES) were associated to changes over time in waist circumference and body mass index (BMI). Methods: A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative Location Factor, was derived from hedonic regression models using residential property features but blind to Location. Growth curve models with latent variables were fitted to data. Results: Waist circumference, BMI and depressive symptoms increased over time. General health and relative Location Factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative Location Factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only. Conclusions: Worsening depressive symptoms and general health predict increasing adiposity and suggest the development of unhealthful adiposity might be prevented by attention to negative changes in mental health and overall general health.
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relative residential property value as a socio economic status indicator for health research
International Journal of Health Geographics, 2013Co-Authors: Neil T Coffee, Mark Daniel, Tony Lockwood, Graeme Hugo, Catherine Paquet, Natasha J HowardAbstract:Residential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value. Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between Location and residential property value, and six cardiometabolic disease risk Factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk Factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score. RLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to Location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk Factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models. A statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk Factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group. This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.
Neil T Coffee - One of the best experts on this subject based on the ideXlab platform.
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are changes in depressive symptoms general health and residential area socio economic status associated with trajectories of waist circumference and body mass index
PLOS ONE, 2020Co-Authors: Theo Niyonsenga, Suzanne J Carroll, Neil T Coffee, Anne W Taylor, Mark DanielAbstract:Objective: This study sought to assess whether changes in depressive symptoms, general health, and area-level socio-economic status (SES) were associated to changes over time in waist circumference and body mass index (BMI). Methods: A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative Location Factor, was derived from hedonic regression models using residential property features but blind to Location. Growth curve models with latent variables were fitted to data. Results: Waist circumference, BMI and depressive symptoms increased over time. General health and relative Location Factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative Location Factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only. Conclusions: Worsening depressive symptoms and general health predict increasing adiposity and suggest the development of unhealthful adiposity might be prevented by attention to negative changes in mental health and overall general health.
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relative residential property value as a socio economic status indicator for health research
International Journal of Health Geographics, 2013Co-Authors: Neil T Coffee, Mark Daniel, Tony Lockwood, Graeme Hugo, Catherine Paquet, Natasha J HowardAbstract:Residential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value. Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between Location and residential property value, and six cardiometabolic disease risk Factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk Factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score. RLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to Location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk Factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models. A statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk Factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group. This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.
Catherine Paquet - One of the best experts on this subject based on the ideXlab platform.
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relative residential property value as a socio economic status indicator for health research
International Journal of Health Geographics, 2013Co-Authors: Neil T Coffee, Mark Daniel, Tony Lockwood, Graeme Hugo, Catherine Paquet, Natasha J HowardAbstract:Residential property is reported as the most valuable asset people will own and therefore provides the potential to be used as a socio-economic status (SES) measure. Location is generally recognised as the most important determinant of residential property value. Extending the well-established relationship between poor health and socio-economic disadvantage and the role of residential property in the overall wealth of individuals, this study tested the predictive value of the Relative Location Factor (RLF), a SES measure designed to reflect the relationship between Location and residential property value, and six cardiometabolic disease risk Factors, central obesity, hypertriglyceridemia, reduced high density lipoprotein (HDL), hypertension, impaired fasting glucose, and high low density lipoprotein (LDL). These risk Factors were also summed and expressed as a cumulative cardiometabolic risk (CMR) score. RLF was calculated using a global hedonic regression model from residential property sales transaction data based upon several residential property characteristics, but deliberately blind to Location, to predict the selling price of the property. The predicted selling price was divided by the actual selling price and the results interpolated across the study area and classified as tertiles. The measures used to calculate CMR were collected via clinic visits from a population-based cohort study. Models with individual risk Factors and the cumulative cardiometabolic risk (CMR) score as dependent variables were respectively tested using log binomial and Poisson generalised linear models. A statistically significant relationship was found between RLF, the cumulative CMR score and all but one of the risk Factors. In all cases, participants in the most advantaged and intermediate group had a lower risk for cardio-metabolic diseases. For the CMR score the RR for the most advantaged was 19% lower (RR = 0.81; CI 0.76-0.86; p <0.0001) and the middle group was 9% lower (RR = 0.91; CI 0.86-0.95; p <0.0001) than the least advantaged group. This paper advances the understanding of the nexus between place, health and SES by providing an objective spatially informed SES measure for testing health outcomes and reported a robust association between RLF and several health measures.
Pizhong Qiao - One of the best experts on this subject based on the ideXlab platform.
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Identifying damage in honeycomb fiber-reinforced polymer (FRP) composite sandwich bridge decks
Advanced Composites in Bridge Construction and Repair, 2014Co-Authors: Pizhong Qiao, Wei FanAbstract:Abstract: A strain energy-based damage identification method for plate-type structures is presented. The damage identification method employs a damage Location Factor matrix and a damage severity correction Factor (DSCF) matrix for damage localization and quantification. An experimental modal test of an as-manufactured fiber-reinforced polymer sandwich deck panel is conducted in the laboratory to demonstrate the applicability and effectiveness of the proposed DSCF-based damage identification method. The composite deck panel at healthy and three damaged stages is tested using a surface-bonded polyvinylidene fluoride sensor array and impact hammer system. The present damage identification method can be used as a viable and effective technique for damage localization and quantification of plate-type structures.
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a strain energy based damage severity correction Factor method for damage identification in plate type structures
Mechanical Systems and Signal Processing, 2012Co-Authors: Pizhong QiaoAbstract:Abstract A strain energy-based damage identification method for plate-type structures is presented. The concepts of a damage Location Factor (DLF) matrix and a damage severity correction Factor (DSCF) matrix, which can be derived from the elemental modal strain energy, are proposed. The damage identification method using the DLF and DSCF is developed for damage localization and quantification in plate-type structures. The method consists of three steps: sensitive mode selection, damage localization, and damage quantification. The proposed method is a response-based damage identification technique which requires the modal frequencies and curvature mode shapes before and after damage. Numerical study demonstrates its viability to correctly detect the damage, approximate the damage area, and estimate the damage severity under high measurement noise and low damage severity conditions. The possibility of damage identification using the partial modal strain energy from the modal strain/curvature mode shape in one direction of plate is also explored based on the numerically simulated data. The method is further implemented on the experimental modal testing data to identify damage at three stages of increasing damage severity on a fiber-reinforced plastic (FRP) sandwich deck panel using a surface-bonded PVDF sensor array. The present DSCF-based damage identification method, as demonstrated in this study, can be used as a viable and effective technique for damage localization and quantification of plate-type structures.
Frédérique Bertoncello - One of the best experts on this subject based on the ideXlab platform.
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The Long and Winding Road: Combining Least Cost Paths and Network Analysis Techniques for Settlement Location Analysis and Predictive Modelling
2014Co-Authors: Tom Brughmans, Laure Nuninger, Frédérique Bertoncello, Philip Verhagen, Graeme EarlAbstract:In this paper, we describe an exploratory analysis of the possibilities of combining least cost path analysis and network analysis techniques. Accessibility is a potentially important site Location Factor. So far, the cost paths. However, these methods do not provide direct information on the foci of movement. Starting from networks created from least cost paths, network analysis and space syntax were used to obtain additional information on the structural features of the network. It is concluded that both techniques can be used with least cost path-based networks, and will provide new insights into the characteristics of the network. For most applications however the space syntax measures that take the geographical dimension into account seem to be preferable to the simple network analysis measures used here.
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The long and winding road: combining least cost paths and network analysis techniques for settlement Location analysis and predictive modelling
2012Co-Authors: Philip Verhagen, Tom Brughmans, Laure Nuninger, Frédérique BertoncelloAbstract:In this paper, we describe an exploratory analysis of the possibilities of combining least cost path analysis and network analysis techniques. Accessibility is a potentially important site Location Factor. So far, the definition of accessibility has been approached through the creation of accumulated cost surfaces and least cost paths. However, these methods do not provide direct information on the foci of movement. Starting from networks created from least cost paths, network analysis and space syntax were used to obtain additional information on the structural features of the network. It is concluded that both techniques can be used with least cost path-based networks, and will provide new insights into the characteristics of the network. For most applications however the Space syntax measures that take the geographical dimension into account seem to be preferable to the simple network analysis measures used here.