13 results found
Pineda A, 2022, Achieving the 2025 WHO global health body-mass index targets: a modelling study on progress of the 53 countries in the WHO European region, Public Health Science: A National Conference Dedicated to New Research in UK Public Health
Kusuma D, Atanasova P, Pineda E, et al., 2022, Food environment and diabetes mellitus in South Asia: A geospatial analysis of health outcome data, PLoS Medicine, Vol: 19, ISSN: 1549-1277
BACKGROUND: The global epidemic of type 2 diabetes mellitus (T2DM) renders its prevention a major public health priority. A key risk factor of diabetes is obesity and poor diets. Food environments have been found to influence people's diets and obesity, positing they may play a role in the prevalence of diabetes. Yet, there is scant evidence on the role they may play in the context of low- and middle-income countries (LMICs). We examined the associations of food environments on T2DM among adults and its heterogeneity by income and sex. METHODS AND FINDINGS: We linked individual health outcome data of 12,167 individuals from a network of health surveillance sites (the South Asia Biobank) to the density and proximity of food outlets geolocated around their homes from environment mapping survey data collected between 2018 and 2020 in Bangladesh and Sri Lanka. Density was defined as share of food outlets within 300 m from study participant's home, and proximity was defined as having at least 1 outlet within 100 m from home. The outcome variables include fasting blood glucose level, high blood glucose, and self-reported diagnosed diabetes. Control variables included demographics, socioeconomic status (SES), health status, healthcare utilization, and physical activities. Data were analyzed in ArcMap 10.3 and STATA 15.1. A higher share of fast-food restaurants (FFR) was associated with a 9.21 mg/dl blood glucose increase (95% CI: 0.17, 18.24; p < 0.05). Having at least 1 FFR in the proximity was associated with 2.14 mg/dl blood glucose increase (CI: 0.55, 3.72; p < 0.01). A 1% increase in the share of FFR near an individual's home was associated with 8% increase in the probability of being clinically diagnosed as a diabetic (average marginal effects (AMEs): 0.08; CI: 0.02, 0.14; p < 0.05). Having at least 1 FFR near home was associated with 16% (odds ratio [OR]: 1.16; CI: 1.01, 1.33; p < 0.05) and 19% (OR: 1.19; CI: 1.03, 1.38; p < 0.05) increases in the odd
Miraldo M, Atanasova PETYA, Kusuma DIAN, et al., 2022, The impact of the consumer and neighbourhood food environment on dietary intake and obesity-related outcomes: A systematic review of causal impact studies., Social Science and Medicine, Vol: 299, Pages: 1-16, ISSN: 0277-9536
BackgroundThe food environment has been found to impact population dietary behaviour. Our study aimed to systematically review the impact of different elements of the food environment on dietary intake and obesity.MethodsWe searched MEDLINE, Embase, PsychInfo, EconLit databases to identify literature that assessed the relationship between the built food environments (intervention) and dietary intake and obesity (outcomes), published between database inception to March 26, 2020. All human studies were eligible except for those on clinical sub-groups. Only studies with causal inference methods were assessed. Studies focusing on the food environment inside homes, workplaces and schools were excluded. A risk of bias assessment was conducted using the CASP appraisal checklist. Findings were summarized using a narrative synthesis approach.Findings58 papers were included, 55 of which were conducted in high-income countries. 70% of papers focused on the consumer food environments and found that in-kind/financial incentives, healthy food saliency, and health primes, but not calorie menu labelling significantly improved dietary quality of children and adults, while BMI results were null. 30% of the papers focused on the neighbourhood food environments and found that the number of and distance to unhealthy food outlets increased the likelihood of fast-food consumption and higher BMI for children of any SES; among adults only selected groups were impacted - females, black, and Hispanics living in low and medium density areas. The availability and distance to healthy food outlets significantly improved children's dietary intake and BMI but null results were found for adults.InterpretationEvidence suggests certain elements of the consumer and neighbourhood food environments could improve populations dietary intake, while effect on BMI was observed among children and selected adult populations. Underprivileged groups are most likely to experience and impact on BMI. Future research
Atanasova P, Kusuma D, Pineda E, et al., 2022, Food environments and obesity: a geospatial analysis of the South Asia Biobank, income and sex inequalities., SSM - Population Health, Vol: 17, Pages: 101055-101055, ISSN: 2352-8273
Introduction: In low-middle income countries (LMICs) the role of food environments on obesity has been understudied. We address this gap by 1) examining the effect of food environments on adults' body size (BMI, waist circumference) and obesity; 2) measuring the heterogeneity of such effects by income and sex. Methods: This cross-sectional study analysed South Asia Biobank surveillance and environment mapping data for 12,167 adults collected between 2018 and 2020 from 33 surveillance sites in Bangladesh and Sri Lanka. Individual-level data (demographic, socio-economic, and health characteristics) were combined with exposure to healthy and unhealthy food environments measured with geolocations of food outlets (obtained through ground-truth surveys) within 300 m buffer zones around participants' homes. Multivariate regression models were used to assess association of exposure to healthy and unhealthy food environments on waist circumference, BMI, and probability of obesity for the total sample and stratified by sex and income. Findings: The presence of a higher share of supermarkets in the neighbourhood was associated with a reduction in body size (BMI, β = - 3∙23; p < 0∙0001, and waist circumference, β = -5∙99; p = 0∙0212) and obesity (Average Marginal Effect (AME): -0∙18; p = 0∙0009). High share of fast-food restaurants in the neighbourhood was not significantly associated with body size, but it significantly increased the probability of obesity measured by BMI (AME: 0∙09; p = 0∙0234) and waist circumference (AME: 0∙21; p = 0∙0021). These effects were stronger among females and low-income individuals. Interpretation: The results suggest the availability of fast-food outlets influences obesity, especially among female and lower-income groups. The availability of supermarkets is associated with reduced body size and obesity, but their effects do not outweigh the role of fast-food o
Malacarne D, Chandakas E, Robinson O, et al., 2022, The built environment as determinant of childhood obesity: a systematic literature review, Obesity Reviews, Vol: 23, Pages: 1-11, ISSN: 1467-7881
We evaluated the epidemiological evidence on the built environment and its link to childhood obesity, focusing on environmental factors such as traffic noise and air pollution, as well as physical factors potentially driving obesity-related behaviours, such as neighbourhood walkability and availability and accessibility of parks and playgrounds. Eligible studies were i) conducted on human children below the age of 18 years, ii) focused on body size measurements in childhood, iii) examined at least one built environment characteristic, iv) reported effect sizes and associated confidence intervals, and v) were published in English language. A z-Test, as alternative to the meta-analysis, was used to quantify associations due to heterogeneity in exposure and outcome definition. We found strong evidence for an association of traffic-related air pollution (nitrogen dioxide and nitrogen oxides exposure; p<0.001) and built environment characteristics supportive of walking (street intersection density; p<0.01 and access to parks; p<0.001) with childhood obesity. We identified a lack of studies which account for interactions between different built environment exposures or verify the role and mechanism of important effect modifiers such as age.
Pineda A, 2022, Food Taxes for Healthy Eating, UK
Song P, Gupta A, Goon IY, et al., 2021, Data resource profile: Understanding the patterns and determinants of health in South Asians—the South Asia Biobank, International Journal of Epidemiology, Vol: 50, Pages: 717-718e, ISSN: 0300-5771
Pineda E, Brunner EJ, Llewellyn CH, et al., 2021, The retail food environment and its association with body mass index in Mexico, International Journal of Obesity, Vol: 45, Pages: 1215-1228, ISSN: 0307-0565
<jats:title>Abstract</jats:title><jats:sec> <jats:title>Background/Objective</jats:title> <jats:p>Mexico has one of the highest rates of obesity and overweight worldwide, affecting 75% of the population. The country has experienced a dietary and food retail transition involving increased availability of high-calorie-dense foods and beverages. This study aimed to assess the relationship between the retail food environment and body mass index (BMI) in Mexico.</jats:p> </jats:sec><jats:sec> <jats:title>Subjects/Methods</jats:title> <jats:p>Geographical and food outlet data were obtained from official statistics; anthropometric measurements and socioeconomic characteristics of adult participants (<jats:italic>N</jats:italic> = 22,219) came from the nationally representative 2012 National Health and Nutrition Survey (ENSANUT). Densities (store count/census tract area (CTA)) of convenience stores, restaurants, fast-food restaurants, supermarkets and fruit and vegetable stores were calculated. The association of retail food environment variables, sociodemographic data and BMI was tested using multilevel linear regression models.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>Convenience store density was high (mean (SD) = 50.0 (36.9)/CTA) compared with other food outlets in Mexico. A unit increase in density of convenience stores was associated with a 0.003 kg/m<jats:sup>2</jats:sup> (95% CI: 0.0006, 0.005, <jats:italic>p</jats:italic> = 0.011) increase in BMI, equivalent to 0.34 kg extra weight for an adult 1.60 m tall for every additional 10% store density increase (number of convenience stores per CTA (km<jats:sup>2</ja
De Backer C, Teunissen L, Cuykx I, et al., 2021, An Evaluation of the COVID-19 Pandemic and Perceived Social Distancing Policies in Relation to Planning, Selecting, and Preparing Healthy Meals: An Observational Study in 38 Countries Worldwide, FRONTIERS IN NUTRITION, Vol: 7, ISSN: 2296-861X
Pineda E, Bascunan J, Sassi F, 2021, Improving the school food environment for the prevention of childhood obesity: What works and what doesn't, Obesity Reviews, Vol: 22, ISSN: 1467-7881
The food environment has a significant influence on dietary choices, and interventions designed to modify the food environment could contribute to the prevention of childhood obesity. Many interventions have been implemented at the school level, but effectiveness in addressing childhood obesity remains unclear. We undertook a systematic review, a meta-analysis, and meta-regression analyses to assess the effectiveness of interventions on the food environment within and around schools to improve dietary intake and prevent childhood obesity. Estimates were pooled in a random-effects meta-analysis with stratification by anthropometric or dietary intake outcome. Risk of bias was formally assessed. One hundred papers were included. Interventions had a significant and meaningful effect on adiposity (body mass index [BMI] z score, standard mean difference: -0.12, 95% confidence interval: 0.15, 0.10) and fruit consumption (portions per day, standard mean difference: +0.19, 95% confidence interval: 0.16, 0.22) but not on vegetable intake. Risk of bias assessment indicated that n = 43 (81%) of non-randomized controlled studies presented a high risk of bias in the study design by not accounting for a control. Attrition bias (n = 34, 79%) and low protection of potential contamination (n = 41, 95%) presented the highest risk of bias for randomized controlled trials. Changes in the school food environment could improve children's dietary behavior and BMI, but policy actions are needed to improve surrounding school food environments to sustain healthy dietary intake and BMI.
Pineda E, Swinburn B, Sassi F, 2019, Effective school food environment interventions for the prevention of childhood obesity: systematic review and meta-analysis, National Conference on Public Health Science Dedicated to New Research in UK Public Health, Publisher: ELSEVIER SCIENCE INC, Pages: 77-77, ISSN: 0140-6736
Pineda E, Llewellyn CH, Brunner EJ, et al., 2018, P65 Association of food outlet density and obesity: a cross-sectional study of urban areas in mexico, Society for Social Medicine 62nd Annual Scientific Meeting, Hosted by the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, 5–7 September 2018, Publisher: BMJ Publishing Group Ltd
Pineda E, Sanchez-Romero LM, Brown M, et al., 2018, Forecasting Future Trends in Obesity across Europe: The Value of Improving Surveillance, Obesity Facts, Vol: 11, Pages: 360-371, ISSN: 1662-4025
<jats:p><b><i>Objective: </i></b>To project the prevalence of obesity across the WHO European region and examine whether the WHO target of halting obesity at 2010 levels by 2025 is achievable. <b><i>Methods: </i></b>BMI data were collected from online databases and the literature. Past and present BMI trends were extrapolated to 2025 using a non-linear categorical regression model fitted to nationally representative survey data. Where only 1 year of data was available, a flat trend was assumed. Where no data were available, proxy country data was used adjusted for demographics. <b><i>Results: </i></b>By 2025, obesity is projected to increase in 44 countries. If present trends continue, 33 of the 53 countries are projected to have an obesity prevalence of 20% or more. The highest prevalence is projected for Ireland (43%, 95% confidence interval (CI): 28-58%). Lithuania, Finland, and the Netherlands were each estimated to have an absolute increase of 2 percentage points in the prevalence of obesity between 2015 and 2025. <b><i>Discussion: </i></b>The quality of BMI data across Europe is highly variable, with fewer than 50% of the 53 countries having measured nationally representative data and often not enough data to interpret projections meaningfully. Nevertheless, the prevalence of obesity in the European Region appears to be increasing in most countries and, with it, the health and economic burden of its associated diseases. This paints a concerning picture of the future burden of obesity-related noncommunicable diseases across the region. Greater and continued effort for the implementation of effective preventive policies and interventions is required from governments.</jats:p>
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