Suchergebnisse

Introduction: The objective was to develop and validate an instrument that measures different determinants of people's food choices and simultaneously accounts for a variety of factors: health, emotions, price and availability, society and culture, environment and politics, and marketing and advertising. Methods: This is a cross-sectional study focusing on food choice determinants. It was carried out in 16 countries in 2017 and 2018. This study included 11,960 volunteer adult participants from different countries. The data was validated using Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM). Results: Validation using CFA with SEM revealed that multi-factor modelling produced first- and second-order models that could be used to define the EATMOT scale, the first presenting better fitting indices, with the goodness-of-fit and comparative-fit indices very close to 1, as well as root-mean-square-error-of-approximation, root-mean-square-residual and standardised-root-mean-square-residual at practically zero. Conclusion: The validated EATMOT scale guarantees confidence in the information obtained through this instrument, and can be used in future studies to better understand food choice determinants in different geographical areas and help plan strategies to improve healthy eating patterns and diminish the burden of non-communicable diseases. ; info:eu-repo/semantics/publishedVersion

7‐11 November 2010, Tenth International Congress on Drug Therapy in HIV Infection, Glasgow, UK

This is the final version. Available from the National Academy of Sciences via the DOI in this record. ; The input data and R code are available on ForestPlots (https://doi.org/10.5521/forestplots.net/2021_4). ; The responses of tropical forests to environmental change are critical uncertainties in predicting the future impacts of climate change. The positive phase of the 2015-2016 El Niño Southern Oscillation resulted in unprecedented heat and low precipitation in the tropics with substantial impacts on the global carbon cycle. The role of African tropical forests is uncertain as their responses to short-term drought and temperature anomalies have yet to be determined using on-the-ground measurements. African tropical forests may be particularly sensitive because they exist in relatively dry conditions compared with Amazonian or Asian forests, or they may be more resistant because of an abundance of drought-adapted species. Here, we report responses of structurally intact old-growth lowland tropical forests inventoried within the African Tropical Rainforest Observatory Network (AfriTRON). We use 100 long-term inventory plots from six countries each measured at least twice prior to and once following the 2015-2016 El Niño event. These plots experienced the highest temperatures and driest conditions on record. The record temperature did not significantly reduce carbon gains from tree growth or significantly increase carbon losses from tree mortality, but the record drought did significantly decrease net carbon uptake. Overall, the long-term biomass increase of these forests was reduced due to the El Niño event, but these plots remained a live biomass carbon sink (0.51 ± 0.40 Mg C ha-1 y-1) despite extreme environmental conditions. Our analyses, while limited to African tropical forests, suggest they may be more resistant to climatic extremes than Amazonian and Asian forests. ; Natural Environment Research Council (NERC) ; Natural Environment Research Council (NERC) ; European Research Council (ERC) ; The Royal Society ; Belgian Science Policy Office (BELSPO) ; Belgian Science Policy Office (BELSPO) ; Belgian Science Policy Office (BELSPO) ; Belgian Science Policy Office (BELSPO) ; Flemish Interuniversity Council VLIR-UOS ; Flemish Interuniversity Council VLIR-UOS ; Natural Environment Research Council (NERC) ; The Gordon and Betty Moore Foundation ; European Union ; Natural Environment Research Council (NERC) ; Natural Environment Research Council (NERC) ; Natural Environment Research Council (NERC) ; Gabon's National Parks Agency ; Leverhulme Trust ; The David and Lucile Packard Foundation ; CIFOR

ObjectiveTo generate a global reference for caesarean section (CS) rates at health facilities. DesignCross-sectional study. SettingHealth facilities from 43 countries. Population/SampleThirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10045875 women giving birth from 43 countries for model testing. MethodsWe hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Main outcome measuresArea under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. ResultsAccording to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (). ConclusionsThis article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. Tweetable abstractThe C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. Tweetable abstract The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. ; NICHD NIH HHS ; World Health Organization ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Social Med, Av Bandeirantes, BR-3900 Ribeirao Preto, Brazil ; WHO, World Bank Special Programme Res Dev & Res Traini, UNDP UNFPA UNICEF WHO, Dept Reprod Hlth & Res, CH-1211 Geneva, Switzerland ; Univ Paris 05, Sorbonne Paris Cite, UMR 216, Inst Dev Res, Paris, France ; WHO Reg Off Amer, Women & Reprod Hlth CLAP WR, Latin Amer Ctr Perinatol, Montevideo, Uruguay ; Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA 30322 USA ; Paris Descartes Univ, Ctr Epidemiol & Biostat, Obstetr Perinatal & Pediat Epidemiol Res Team, Inserm U1153, Paris, France ; Natl Inst Publ Hlth, Ctr Populat Hlth Res, Cuernavaca, Morelos, Mexico ; Univ Technol, Fac Hlth, Sydney, NSW, Australia ; Natl Ctr Child Hlth & Dev, Dept Hlth Policy, Tokyo, Japan ; Ctr Rosarino Estudios Perinat, Rosario, Argentina ; Lindsay Stewart R&D Ctr, Off Res & Clin Audit, Royal Coll Obstetricians & Gynaecologists, London, England ; London Sch Hyg & Trop Med, Dept Hlth Serv Res & Policy, London WC1, England ; Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Shanghai Key Lab Childrens Environ Hlth,Minist Ed, Shanghai 200030, Peoples R China ; Univ Estadual Campinas, Sch Med Sci, Dept Obstet & Gynaecol, Campinas, SP, Brazil ; Family Hlth Bur, Minist Hlth, Colombo, Sri Lanka ; Fiocruz MS, ENSP, BR-21045900 Rio De Janeiro, Brazil ; Natl Inst Hlth & Welf, Helsinki, Finland ; Univ Tokyo, Grad Sch Med, Dept Paediat, Tokyo, Japan ; Bayer Krankenhausgesellschaft, Bayer Arbeitsgemeinschaft Qualitatssicherun Stati, Munich, Germany ; Khon Kaen Univ, Fac Med, Dept Obstet & Gynecol, Khon, Kaen, Thailand ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Obstet & Gynaecol, BR-14049 Ribeirao Preto, Brazil ; Minist Sante, Direct Sante Famille, Ouagadougou, Burkina Faso ; Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98195 USA ; Univ Mongolia, Hlth Sci, Sch Publ Hlth, Ulaanbaatar, Mongol Peo Rep ; GLIDE Tech Cooperat & Res, Ribeirao Preto, SP, Brazil ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Paediat, BR-14049 Ribeirao Preto, SP, Brazil ; Univ Calif San Francisco, Dept Obstet & Gynaecol & Global Hlth Sci, San Francisco, CA 94143 USA ; Khon Kaen Univ, Fac Publ Hlth, Dept Biostat & Demog, Khon Kaen, Thailand ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; Inter Amer Dev Bank, Social Protect & Hlth Div, Mexico City, DF, Mexico ; Fortis Mem Res Inst, Gurgaon, Haryana, India ; Hosp Nacl Itaugua, Itaugua, Paraguay ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; NICHD NIH HHS: T32 HD052460 ; World Health Organization: 001 ; Web of Science

ObjectiveTo generate a global reference for caesarean section (CS) rates at health facilities. DesignCross-sectional study. SettingHealth facilities from 43 countries. Population/SampleThirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10045875 women giving birth from 43 countries for model testing. MethodsWe hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Main outcome measuresArea under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. ResultsAccording to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (). ConclusionsThis article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. Tweetable abstractThe C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. Tweetable abstract The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems. ; NICHD NIH HHS ; World Health Organization ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Social Med, Av Bandeirantes, BR-3900 Ribeirao Preto, Brazil ; WHO, World Bank Special Programme Res Dev & Res Traini, UNDP UNFPA UNICEF WHO, Dept Reprod Hlth & Res, CH-1211 Geneva, Switzerland ; Univ Paris 05, Sorbonne Paris Cite, UMR 216, Inst Dev Res, Paris, France ; WHO Reg Off Amer, Women & Reprod Hlth CLAP WR, Latin Amer Ctr Perinatol, Montevideo, Uruguay ; Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA 30322 USA ; Paris Descartes Univ, Ctr Epidemiol & Biostat, Obstetr Perinatal & Pediat Epidemiol Res Team, Inserm U1153, Paris, France ; Natl Inst Publ Hlth, Ctr Populat Hlth Res, Cuernavaca, Morelos, Mexico ; Univ Technol, Fac Hlth, Sydney, NSW, Australia ; Natl Ctr Child Hlth & Dev, Dept Hlth Policy, Tokyo, Japan ; Ctr Rosarino Estudios Perinat, Rosario, Argentina ; Lindsay Stewart R&D Ctr, Off Res & Clin Audit, Royal Coll Obstetricians & Gynaecologists, London, England ; London Sch Hyg & Trop Med, Dept Hlth Serv Res & Policy, London WC1, England ; Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Shanghai Key Lab Childrens Environ Hlth,Minist Ed, Shanghai 200030, Peoples R China ; Univ Estadual Campinas, Sch Med Sci, Dept Obstet & Gynaecol, Campinas, SP, Brazil ; Family Hlth Bur, Minist Hlth, Colombo, Sri Lanka ; Fiocruz MS, ENSP, BR-21045900 Rio De Janeiro, Brazil ; Natl Inst Hlth & Welf, Helsinki, Finland ; Univ Tokyo, Grad Sch Med, Dept Paediat, Tokyo, Japan ; Bayer Krankenhausgesellschaft, Bayer Arbeitsgemeinschaft Qualitatssicherun Stati, Munich, Germany ; Khon Kaen Univ, Fac Med, Dept Obstet & Gynecol, Khon, Kaen, Thailand ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Obstet & Gynaecol, BR-14049 Ribeirao Preto, Brazil ; Minist Sante, Direct Sante Famille, Ouagadougou, Burkina Faso ; Univ Washington, Inst Hlth Metr & Evaluat, Seattle, WA 98195 USA ; Univ Mongolia, Hlth Sci, Sch Publ Hlth, Ulaanbaatar, Mongol Peo Rep ; GLIDE Tech Cooperat & Res, Ribeirao Preto, SP, Brazil ; Univ Sao Paulo, Ribeirao Preto Med Sch, Dept Paediat, BR-14049 Ribeirao Preto, SP, Brazil ; Univ Calif San Francisco, Dept Obstet & Gynaecol & Global Hlth Sci, San Francisco, CA 94143 USA ; Khon Kaen Univ, Fac Publ Hlth, Dept Biostat & Demog, Khon Kaen, Thailand ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; Inter Amer Dev Bank, Social Protect & Hlth Div, Mexico City, DF, Mexico ; Fortis Mem Res Inst, Gurgaon, Haryana, India ; Hosp Nacl Itaugua, Itaugua, Paraguay ; Univ Fed Sao Paulo, Sch Med Sao Paulo, Dept Obstet, Sao Paulo, Brazil ; NICHD NIH HHS: T32 HD052460 ; World Health Organization: 001 ; Web of Science