Surveillance Systems to Track and Evaluate Obesity Prevention Efforts

Hoelscher, Deanna M.; Ranjit, Nalini; Pérez, Adriana

doi:10.1146/annurev-publhealth-031816-044537

Cited by 15 publications

(16 citation statements)

References 73 publications

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“…This study highlights the value of B4SC data as a monitoring tool for childhood obesity and as a source of feedback for communities so that they can measure progress on pre‐school obesity targets. Monitoring and surveillance is considered a key part of obesity prevention and management strategies 28,29 . A whole population monitoring system exists in the United Kingdom where the National Child Measurement Programme (NCMP) measures height and weight when children start primary school (age four‐five years), a similar age to the B4SC measure in NZ, and obesity prevalence is reported at community (local authority) level 30 .…”

Section: Discussionmentioning

confidence: 99%

“…Monitoring and surveillance is considered a key part of obesity prevention and management strategies. 28,29 A whole population monitoring system exists in the United Kingdom where the National Child Measurement Programme (NCMP) measures height and weight when children start primary school (age four-five years), a similar age to the B4SC measure in NZ, and obesity prevalence is reported at community (local authority) level. 30 In contrast to NZ, however, the NCMP measures height and weight again when children start secondary school (age 10-11 years).…”

Section: Models 3 and 4 Ofmentioning

confidence: 99%

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Child obesity prevalence across communities in New Zealand: 2010–2016

Gibb

Shackleton

Audas

et al. 2019

Australian and New Zealand Journal of Public Health

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Objective: To assess community‐level differences in four‐year‐old obesity prevalence in New Zealand (NZ), trends over time, and the extent to which differences can be explained by ethnicity, deprivation and urbanicity. Methods: Obesity measures from the Ministry of Health’s B4 School Check were available for 72–92% of NZ four‐year‐olds for fiscal years 2010/11–2015/16. Ethnicity, deprivation and urbanicity data for the 78 communities were obtained by linking to administrative records. Growth models were used to examine variability in obesity levels and trends over time, and the extent to which ethnicity, deprivation and urbanicity contributed to differences between communities. Results: There were large variations in obesity across communities (range 8.4% to 28.8%). A decline in the prevalence of childhood obesity was observed in most (48 of 78) communities from 2010/11 to 2015/16 (average change=0.2%, range=‐2.0% to 1.9%). Around 50% of the variance in obesity between territorial authorities could be explained by differences in socioeconomic deprivation and ethnic composition. Conclusions: Child obesity varies between NZ communities, but most territorial authorities have experienced a decrease in obesity over the period 2010/11–2015/16. Implications for public health: Addressing deprivation and ethnic inequalities in obesity could substantially reduce community‐level differences in obesity in NZ.

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Section: Discussionmentioning

confidence: 99%

Section: Models 3 and 4 Ofmentioning

confidence: 99%

Child obesity prevalence across communities in New Zealand: 2010–2016

Gibb

Shackleton

Audas

et al. 2019

Australian and New Zealand Journal of Public Health

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“…In Brazil, one of the commitments of the Political Nacional de Alimentação e Nutrição (PNAN, National Food and Nutrition Policy) is the continuous and systematic monitoring of the population's food and nutritional situation [3]. Periodic food consumption data allows researchers to know the main changes and temporal trends, to identify eating behaviors which could be harmful to the health of the population, to monitor the effectiveness of interventions and to subsidize public policies [4].…”

Section: Introductionmentioning

confidence: 99%

Trends in food consumption of schoolchildren from 2nd to 5th grade: a panel data analysis

et al. 2020

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Objective To identify trends in food consumption among schoolchildren (2nd-5th grades) from public schools in the city of Florianópolis, Brazil, in a period of three years. Methods Three cross-sectional surveys were carried out in 2013 (n=1,942), 2014 (n=1,989) and 2015 (n=2,418). Dietary intake data were obtained using the Web-Based Food Intake and Physical Activity of Schoolchildren questionnaire. Food items were aggregated to eight food groups. Kruskal-Wallis heterogeneity and trend tests were used to analyze the differences and trends among the mean intake frequency of food groups. Results There were trends to decrease the mean intake frequency of sweets in the total sample (2013: 0.72±0.91; 2014: 0.68±0.87; 2015: 0.67±0.89, p=0.03) which was determined by children between 7-9 years old (2013: 0.69±0.88; 2014: 0.64±0.85; 2015: 0.62±0.87, p=0.02), and boys (2013: 0.75±0.90; 2014: 0.70±0.86; 2015: 0.68±0.88, p=0.03). Younger children also tended to increase the mean intake frequency of fruits and vegetables (2013: 1.03±1.35; 2014: 1.16±1.45; 2015: 1.17±1.41, p=0.03) and those aged ten-12 years decreased their intake of dairy products (2013: 1.32±1.25; 2014: 1.23±1.18; 2015: 1.20±1.20, p=0.05). Conclusion The results suggest positive trends for younger children, with an increased consumption of fruits and vegetables in both sexes and decreased consumption of sweets for boys. Older children reduced their consumption of dairy products over the three-year period of this study.

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“…At a minimum, these should include frequency of consumption of fruit; vegetables (usually excluding potatoes); sugar-sweetened beverages (including fruit juices); snack foods, usually grouped as those high in salt (potato chips, corn chips, savoury biscuits); and sugar (sweet biscuits, cakes, muffins, donuts), and also “fast foods” or takeaway foods that include pizza, pastries, and hamburgers. Composite measures such as “food risk scores” [ 56 ] or “junk food indicies” [ 57 ] can be derived from these measures.…”

Section: Weight-related Behavioursmentioning

confidence: 99%

Elements of Effective Population Surveillance Systems for Monitoring Obesity in School Aged Children

Hardy

Mihrshahi

2020

IJERPH

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The continuing high prevalence of child overweight and obesity globally means that it remains the most common chronic health condition in children. Population-based child obesity surveillance systems are critical for monitoring trends in obesity and related behaviours, and determining the overall effect of child obesity prevention strategies. Effective surveillance systems may vary in methods, scope, purpose, objectives, and attributes, and our aim was to provide an overview of child obesity surveillance systems globally, and to highlight main components and other types of survey data that can enhance our understanding of child obesity. Measures of adiposity, including body mass index and waist circumference are essential, but effective surveillance must also include measures of weight-related behaviours, including diet, physical activity, sedentary time, and sleep. While objective measures are desirable, the variability in psychometrics and rapid evolution of wearable devices is potentially problematic for examining long-term trends over time and how behaviours may change. Questionnaires on self-reported behaviours are often used but also have limitations. Because the determinants of obesity are not only functioning at the individual level, some measures of the broader environmental and commercial determinants, including the built and food environments, are useful to guide upstream policy decisions.

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Surveillance Systems to Track and Evaluate Obesity Prevention Efforts

Cited by 15 publications

References 73 publications

Child obesity prevalence across communities in New Zealand: 2010–2016

Child obesity prevalence across communities in New Zealand: 2010–2016

Trends in food consumption of schoolchildren from 2nd to 5th grade: a panel data analysis

Elements of Effective Population Surveillance Systems for Monitoring Obesity in School Aged Children

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