Edward A. Frongillo
Other affiliations: Ministry of Health (New South Wales), Cornell University, University of Michigan ...read more
Bio: Edward A. Frongillo is an academic researcher from University of South Carolina. The author has contributed to research in topics: Population & Food security. The author has an hindex of 82, co-authored 422 publications receiving 25739 citations. Previous affiliations of Edward A. Frongillo include Ministry of Health (New South Wales) & Cornell University.
Papers published on a yearly basis
TL;DR: This primer will equip both scientists and practitioners to understand the ontology and methodology of scale development and validation, thereby facilitating the advancement of the understanding of a range of health, social, and behavioral outcomes.
Abstract: Scale development and validation are critical to much of the work in the health, social, and behavioral sciences. However, the constellation of techniques required for scale development and evaluation can be onerous, jargon-filled, unfamiliar, and resource-intensive. Further, it is often not a part of graduate training. Therefore, our goal was to concisely review the process of scale development in as straightforward a manner as possible, both to facilitate the development of new, valid, and reliable scales, and to help improve existing ones. To do this, we have created a primer for best practices for scale development in measuring complex phenomena. This is not a systematic review, but rather the amalgamation of technical literature and lessons learned from our experiences spent creating or adapting a number of scales over the past several decades. We identified three phases that span nine steps. In the first phase, items are generated and the validity of their content is assessed. In the second phase, the scale is constructed. Steps in scale construction include pre-testing the questions, administering the survey, reducing the number of items, and understanding how many factors the scale captures. In the third phase, scale evaluation, the number of dimensions is tested, reliability is tested, and validity is assessed. We have also added examples of best practices to each step. In sum, this primer will equip both scientists and practitioners to understand the ontology and methodology of scale development and validation, thereby facilitating the advancement of our understanding of a range of health, social, and behavioral outcomes.
TL;DR: It is demonstrated that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.
Abstract: Objective. This study investigates associations between food insufficiency and cognitive, academic, and psychosocial outcomes for US children and teenagers ages 6 to 11 and 12 to 16 years. Methods. Data from the Third National Health and Nutrition Examination Survey (NHANES III) were analyzed. Children were classified as food-insufficient if the family respondent reported that his or her family sometimes or often did not get enough food to eat. Regression analyses were conducted to test for associations between food insufficiency and cognitive, academic, and psychosocial measures in general and then within lower-risk and higher-risk groups. Regression coefficients and odds ratios for food insufficiency are reported, adjusted for poverty status and other potential confounding factors. Results. After adjusting for confounding variables, 6- to 11-year-old food-insufficient children had significantly lower arithmetic scores and were more likely to have repeated a grade, have seen a psychologist, and have had difficulty getting along with other children. Food-insufficient teenagers were more likely to have seen a psychologist, have been suspended from school, and have had difficulty getting along with other children. Further analyses divided children into lower-risk and higher-risk groups. The associations between food insufficiency and children9s outcomes varied by level of risk. Conclusions. The results demonstrate that negative academic and psychosocial outcomes are associated with family-level food insufficiency and provide support for public health efforts to increase the food security of American families.
TL;DR: This study provides the strongest empirical evidence to date that food insecurity is linked to specific developmental consequences for children, and that these consequences may be both nutritional and nonnutritional.
Abstract: Food insecurity has been associated with diverse developmental consequences for U.S. children primarily from cross-sectional studies. We used longitudinal data to investigate how food insecurity over time related to changes in reading and mathematics test performance, weight and BMI, and social skills in children. Data were from the Early Childhood Longitudinal Study-Kindergarten Cohort, a prospective sample of approximately 21,000 nationally representative children entering kindergarten in 1998 and followed through 3rd grade. Food insecurity was measured by parent interview using a modification of the USDA module in which households were classified as food insecure if they reported > or =1 affirmative response in the past year. Households were grouped into 4 categories based on the temporal occurrence of food insecurity in kindergarten and 3rd grade. Children's academic performance, height, and weight were assessed directly. Children's social skills were reported by teachers. Analyses examined the effects of modified food insecurity on changes in child outcomes using lagged, dynamic, and difference (i.e., fixed-effects) models and controlling for child and household contextual variables. In lagged models, food insecurity was predictive of poor developmental trajectories in children before controlling for other variables. Food insecurity thus serves as an important marker for identifying children who fare worse in terms of subsequent development. In all models with controls, food insecurity was associated with outcomes, and associations differed by gender. This study provides the strongest empirical evidence to date that food insecurity is linked to specific developmental consequences for children, and that these consequences may be both nutritional and nonnutritional.
TL;DR: The role of malnutrition in child mortality is not revealed by these conventional methods, despite the long-standing recognition of the synergism between malnutrition and infectious diseases as discussed by the authors, and suggests that strategies involving only the screening and treatment of the severely malnourished will do little to address this impact.
Abstract: Conventional methods of classifying causes of death suggest that about 70% of the deaths of children (aged 0-4 years) worldwide are due to diarrhoeal illness, acute respiratory infection, malaria, and immunizable diseases. The role of malnutrition in child mortality is not revealed by these conventional methods, despite the long-standing recognition of the synergism between malnutrition and infectious diseases. This paper describes a recently-developed epidemiological method to estimate the percentage of child deaths (aged 6-59 months) which could be attributed to the potentiating effects of malnutrition in infectious disease. The results from 53 developing countries with nationally representative data on child weight-for-age indicate that 56% of child deaths were attributable to malnutrition's potentiating effects, and 83% of these were attributable to mild-to-moderate as opposed to severe malnutrition. For individual countries, malnutrition's total potentiating effects on mortality ranged from 13% to 66%, with at least three-quarters of this arising from mild-to-moderate malnutrition in each case. These results show that malnutrition has a far more powerful impact on child mortality than is generally appreciated, and suggest that strategies involving only the screening and treatment of the severely malnourished will do little to address this impact. The methodology provided in this paper makes it possible to estimate the effects of malnutrition on child mortality in any population for which prevalence data exist.
TL;DR: The data presented provide a baseline for assessing progress and help identify countries and regions in need of populationwide interventions and approaches to lower child malnutrition should be based on successful nutrition programmes and policies.
Abstract: Nutritional status is the best global indicator of well-being in children. Although many surveys of children have been conducted since the 1970s, lack of comparability between them has made it difficult to monitor trends in child malnutrition. Cross-sectional data from 241 nationally representative surveys were analysed in a standard way to produce comparable results of low height-for-age (stunting). Multilevel modelling was applied to estimate regional and global trends from 1980 to 2005. The prevalence of stunting has fallen in developing countries from 47% in 1980 to 33% in 2000 (i.e. by 40 million), although progress has been uneven according to regions. Stunting has increased in Eastern Africa, but decreased in South-eastern Asia, South-central Asia and South America; Northern Africa and the Caribbean show modest improvement; and Western Africa and Central America present very little progress. Despite an overall decrease of stunting in developing countries, child malnutrition still remains a major public health problem in these countries. In some countries rates of stunting are rising, while in many others they remain disturbingly high. The data we have presented provide a baseline for assessing progress and help identify countries and regions in need of populationwide interventions. Approaches to lower child malnutrition should be based on successful nutrition programmes and policies.
01 Apr 2000
TL;DR: Reading a book as this basics of qualitative research grounded theory procedures and techniques and other references can enrich your life quality.
Abstract: In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feel confused to get those things. Feeling the limited of experience and sources to be better is one of the lacks to own. However, there is a very simple thing that can be done. This is what your teacher always manoeuvres you to do this one. Yeah, reading is the answer. Reading a book as this basics of qualitative research grounded theory procedures and techniques and other references can enrich your life quality. How can it be?
01 Jan 1998
University of Washington1, Sapienza University of Rome2, Mekelle University3, University of Texas at San Antonio4, King Saud bin Abdulaziz University for Health Sciences5, Debre markos University6, Emory University7, University of Oxford8, University of Cartagena9, United Nations Population Fund10, University of Birmingham11, Stanford University12, Aga Khan University13, University of Melbourne14, National Taiwan University15, University of Cambridge16, University of California, San Diego17, Public Health Foundation of India18, Public Health England19, University of Peradeniya20, Harvard University21, National Institutes of Health22, Tehran University of Medical Sciences23, Auckland University of Technology24, University of Sheffield25, University of Western Australia26, Karolinska Institutet27, Birzeit University28, Brandeis University29, American Cancer Society30, Ochsner Medical Center31, Yonsei University32, University of Bristol33, Heidelberg University34, Vanderbilt University35, South African Medical Research Council36, Jordan University of Science and Technology37, New Generation University College38, Northeastern University39, Simmons College40, Norwegian Institute of Public Health41, Boston University42, Chinese Center for Disease Control and Prevention43, University of Bari44, University of São Paulo45, University of Otago46, University of Crete47, International Centre for Diarrhoeal Disease Research, Bangladesh48, Fred Hutchinson Cancer Research Center49, Teikyo University50, Bhabha Atomic Research Centre51, University of Tokyo52, Finnish Institute of Occupational Health53, Heriot-Watt University54, University of Alabama at Birmingham55, Griffith University56, National Center for Disease Control and Public Health57, University of California, Irvine58, Johns Hopkins University59, New York University60, University of Queensland61, Universidade Federal de Minas Gerais62, National Research University – Higher School of Economics63, University of Bergen64, Columbia University65, Shandong University66, University of North Carolina at Chapel Hill67, Fujita Health University68, Korea University69, Chongqing Medical University70, Zhejiang University71
TL;DR: The global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013 is estimated using a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs).
Abstract: Summary Background In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013. Methods We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19 244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Findings Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m 2 or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4–29·3) to 36·9% (36·3–37·4) in men, and from 29·8% (29·3–30·2) to 38·0% (37·5–38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9–24·7) of boys and 22·6% (21·7–23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7–8·6) to 12·9% (12·3–13·5) in 2013 for boys and from 8·4% (8·1–8·8) to 13·4% (13·0–13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Interpretation Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Funding Bill & Melinda Gates Foundation.
TL;DR: The new curves are closely aligned with the WHO Child Growth Standards at 5 years, and the recommended adult cut-offs for overweight and obesity at 19 years.
Abstract: Objective To construct growth curves for school-aged children and adolescents that accord with the WHO Child Growth Standards for preschool children and the body mass index (BMI) cut-offs for adults. Methods Data from the 1977 National Center for Health Statistics (NCHS)/WHO growth reference (1–24 years) were merged with data from the under-fives growth standards’ cross-sectional sample (18–71 months) to smooth the transition between the two samples. State-of-the-art statistical methods used to construct the WHO Child Growth Standards (0–5 years), i.e. the Box-Cox power exponential (BCPE) method with appropriate diagnostic tools for the selection of best models, were applied to this combined sample. Findings The merged data sets resulted in a smooth transition at 5 years for height-for-age, weight-for-age and BMI-for-age. For BMI-for-age across all centiles the magnitude of the difference between the two curves at age 5 years is mostly 0.0 kg/m² to 0.1 kg/m². At 19 years, the new BMI values at +1 standard deviation (SD) are 25.4 kg/m² for boys and 25.0 kg/m² for girls. These values are equivalent to the overweight cut-off for adults (> 25.0 kg/m²). Similarly, the +2 SD value (29.7 kg/m² for both sexes) compares closely with the cut-off for obesity (> 30.0 kg/m²). Conclusion The new curves are closely aligned with the WHO Child Growth Standards at 5 years, and the recommended adult cut-offs for overweight and obesity at 19 years. They fill the gap in growth curves and provide an appropriate reference for the 5 to 19 years age group. Bulletin of the World Health Organization 2007;85:660–667.