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1. Investing in the foundation of sustainable development: pathways to scale up for early childhood development

Author

Richter, Linda M, Daelmans, Bernadette, Lombardi, Joan, Heymann, Jody, Boo, Florencia Lopez, Behrman, Jere R, Lu, Chunling, Lucas, Jane E, Perez-Escamilla, Rafael, Dua, Tarun, Bhutta, Zulfiqar A, Stenberg, Karin, Gertler, Paul, Darmstadt, Gary L, and Committee, Paper 3 Working Group and the Lancet Early Childhood Development Series Steering

Subjects
Biomedical and Clinical Sciences, Health Sciences, Pediatric, Prevention, Generic health relevance, Quality Education, Child Development, Child Health Services, Child Protective Services, Child, Preschool, Developing Countries, Early Intervention, Educational, Financing, Government, Humans, Maternal Health Services, Politics, Poverty, Paper 3 Working Group and the Lancet Early Childhood Development Series Steering Committee, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract

Building on long-term benefits of early intervention (Paper 2 of this Series) and increasing commitment to early childhood development (Paper 1 of this Series), scaled up support for the youngest children is essential to improving health, human capital, and wellbeing across the life course. In this third paper, new analyses show that the burden of poor development is higher than estimated, taking into account additional risk factors. National programmes are needed. Greater political prioritisation is core to scale-up, as are policies that afford families time and financial resources to provide nurturing care for young children. Effective and feasible programmes to support early child development are now available. All sectors, particularly education, and social and child protection, must play a role to meet the holistic needs of young children. However, health provides a critical starting point for scaling up, given its reach to pregnant women, families, and young children. Starting at conception, interventions to promote nurturing care can feasibly build on existing health and nutrition services at limited additional cost. Failure to scale up has severe personal and social consequences. Children at elevated risk for compromised development due to stunting and poverty are likely to forgo about a quarter of average adult income per year, and the cost of inaction to gross domestic product can be double what some countries currently spend on health. Services and interventions to support early childhood development are essential to realising the vision of the Sustainable Development Goals.

Published
2017

2. Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Author

Xavier Rossello, Jannick AN Dorresteijn, Arne Janssen, Ekaterini Lambrinou, Martijn Scherrenberg, Eric Bonnefoy-Cudraz, Mark Cobain, Massimo F Piepoli, Frank LJ Visseren, Paul Dendale, null This paper is a co-publication betw, Centro Nacional de Investigaciones Cardiovasculares Carlos III [Madrid, Spain] (CNIC), Instituto de Salud Carlos III [Madrid] (ISC), Centro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV), University Medical Center [Utrecht], Jessa Ziekenhuis [Hasselt], Cyprus University of Technology, Hasselt University (UHasselt), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hospices Civils de Lyon (HCL), Service de Cardiologie Lyon (Hôpital Louis Pradel [CHU - HCL]), Hôpital Louis Pradel [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Imperial College London, University of Southern California (USC), Rossello, Xavier/0000-0001-6783-8463, Rossello, Xavier, Dorresteijn, Jannick A. N., JANSSEN, Arne, Lambrinou, Ekaterini, SCHERRENBERG, Martijn, Bonnefoy-Cudraz, Eric, Cobain, Mark, Piepoli, Massimo F., Visseren, Frank L. J., DENDALE, Paul, CarMeN, laboratoire, Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Male, Time Factors, Epidemiology, [SDV]Life Sciences [q-bio], Allied Health Personnel, Disease, 030204 cardiovascular system & hematology, Critical Care and Intensive Care Medicine, Medical and Health Sciences, 0302 clinical medicine, prevention, Risk Factors, cardiovascular disease, Preventive Health Services, Advanced and Specialised Nursing, 030212 general & internal medicine, Cardiovascular nursing, Societies, Medical, Aged, 80 and over, biology, risk assessment, General Medicine, Middle Aged, Prognosis, Risk prediction, 3. Good health, [SDV] Life Sciences [q-bio], Preventive cardiology, Europe, Primary Prevention, Medical–Surgical Nursing, Cardiovascular Diseases, Practice Guidelines as Topic, Female, patient, Cardiology and Cardiovascular Medicine, Risk assessment, Algorithms, Adult, Cardiovascular Nursing, medicine.medical_specialty, Critical Care, Clinical Decision-Making, Cardiology, Decision Support Techniques, 03 medical and health sciences, Predictive Value of Tests, Health Sciences, Medical–Surgical, medicine, Humans, Medical history, Intensive care medicine, Association (psychology), Aged, Advanced and Specialized Nursing, patient Keywords Risk prediction, Acca, Models, Statistical, business.industry, biology.organism_classification, Lifetime risk, business, Forecasting
Abstract

Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of - usually interactive and online available - tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this paper was produced within the framework of the ESC Prevention of Cardiovascular Disease Programme which is led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP). The ESC Prevention of Cardiovascular Disease Programme is supported by unrestricted educational grants. The authors received no financial support for the research, authorship, and/or publication of this article. Rossello, X (corresponding author), Ctr Nacl Invest Cardiovasc CNIC Carlos III, Melchor Fernandez Almagro 3, Madrid 28029, Spain. fjrossello@cnic.es

Published
2020
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3. Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study

Author

Agogo, George O, der Voet, Hilko van, Veer, Pieter Van't, Ferrari, Pietro, Leenders, Max, Muller, David C, Sánchez-Cantalejo, Emilio, Bamia, Christina, Braaten, Tonje, Knüppel, Sven, Johansson, Ingegerd, van Eeuwijk, Fred A, Boshuizen, Hendriek, LS IRAS EEPI GRA (Gezh.risico-analyse), IRAS RATIA-SIB, Risk Assessment of Toxic and Immunomodulatory Agents, [Agogo,GO, Boshuizen,H] National Institute for Public Health and the Environment, Bilthoven, The Netherlands. [Agogo,GO, Voet,HV, Eeuwijk,FAV, Boshuizen,H] Biometris, Wageningen University and Research Center, Wageningen, The Netherlands.[Veer,PV, Boshuizen,H] Department of Human Nutrition, Wageningen University and Research Center, Wageningen, The Netherlands. [Ferrari,P] Nutritional Epidemiology Group, International Agency for Research on Cancer, Lyon, France. [Leenders,M] Department of Gastroenterology and Hepatology, University Medical Center Utrecht, Utrecht, The Netherlands. [Muller,DC] Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France. [Sánchez-Cantalejo,E] Andalusian School of Public Health, Granada, Spain. CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain. [Bamia,C] WHO Collaborating Center for Food and Nutrition Policies, Department of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece. [Braaten,T] Department of Community Medicine, University of Tromsø, Tromsø, Norway. [Knüppel,S] Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Potsdam, Germany. [Johansson,I] Department of Odontology, Umea University, Umea, Sweden., However, the publication cost will be covered by Wageningen University and Research Centre, Biometris, P.O. Box 100, 6700 AC WAGENINGEN should the paper be accepted for a publication., LS IRAS EEPI GRA (Gezh.risico-analyse), IRAS RATIA-SIB, and Risk Assessment of Toxic and Immunomodulatory Agents

Subjects
Male, Parametric Analysis, Medicin och hälsovetenskap, Nutrition and Disease, Calibration (statistics), Test Statistics, lcsh:Medicine, markers, Overfitting, Statistical Inference, outcomes, Medical and Health Sciences, Wiskundige en Statistische Methoden - Biometris, Mathematical and Statistical Techniques, Neoplasms, Surveys and Questionnaires, Voeding en Ziekte, Statistics, Medicine, Prospective Studies, lcsh:Science, Multidisciplinary, Maximum Likelihood Estimation, Regression analysis, Estudios Prospectivos, Replicate, Diseases::Neoplasms [Medical Subject Headings], Middle Aged, PE&RC, Neoplasias, Diet Records, Europe, Survival Rate, Biometris, nutrition, Physical Sciences, Calibration, instruments, Regression Analysis, Dieta, Female, dietary self-report, Statistics (Mathematics), Bayesian Statistics, Research Article, Adult, Heteroscedasticity, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies::Prospective Studies [Medical Subject Headings], General Science & Technology, Logit, Phenomena and Processes::Physiological Phenomena::Nutritional Physiological Phenomena::Diet [Medical Subject Headings], Research and Analysis Methods, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Data Collection::Nutrition Assessment [Medical Subject Headings], Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies::Cohort Studies::Longitudinal Studies [Medical Subject Headings], Covariate, MD Multidisciplinary, Humans, cancer, Statistical Methods, Statistical Hypothesis Testing, Mathematical and Statistical Methods - Biometris, Aged, VLAG, disease, Observational error, Models, Statistical, business.industry, lcsh:R, Evaluación Nutricional, Feeding Behavior, Estudios Longitudinales, Estudios Epidemiológicos, Survival Analysis, Nutrition Assessment, lcsh:Q, Food Habits, business, Mathematics, Analytical, Diagnostic and Therapeutic Techniques and Equipment::Investigative Techniques::Epidemiologic Methods::Epidemiologic Study Characteristics as Topic::Epidemiologic Studies [Medical Subject Headings], Generalized Linear Model
Abstract

Journal Article; In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model. Yes

Published
2014