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These choices will be signaled globally to our partners and will not affect browsing data. We and our partners process data to: Actively scan device characteristics for identification. I Accept Show Purposes. Table of Contents View All. Table of Contents. Next in Obesity Guide. Frequently Asked Questions Is there an ideal age to begin obesity prevention practices? Key Question 2 What methods have been used to link different population-based data sources?
To determine the categories of variables related to obesity and co-outcomes in each data system, we will take the following approach: Abstract a list of obesity measures, behavioral outcomes, and co-outcomes from each study.
Food environment 2. Physical activity environment 3. Other: e. We will describe the reported units e. For the co- outcomes e. Key Question 4 Which experimental and non-experimental methods have been used in studies of how programs, policies or built environment changes affect or are associated with obesity prevention and control outcomes?
Key Question 5 What are the risks of bias in studies of how programs, policies or built environment changes affect or are associated with obesity prevention and control outcomes? This tool yields an overall classification of risk of bias, and has questions that address the following domains: Selection bias Study design Confounders Blinding Data collection methods Withdrawals and drop-outs We will also abstract detailed information on confounders and types of adjustment.
Below we describe specific analytic methods and context for the non-experimental studies we anticipate identifying in the literature search: Instrumental variable methods are sometimes referred to as "natural experiments" or "randomized encouragement designs". In cases where the intervention under study is not randomized, investigators attempt to identify an "instrument" that is related to receipt of the intervention and randomly distributed or at least hypothetically randomly assigned , but is not related to the outcome.
Bias will be introduced if the instrument shares a common cause with the outcome, directly influences the outcome not just through the treatment of interest , or the instrument is not randomly distributed. We will assess bias specific to instrumental variable methods. Regression discontinuity methods take advantage of existing rules or cutoff points that determine receipt of the intervention of interest e.
Persons just above or just below the cutoff are assumed to be very similar, so comparing these groups allows for a valid estimate of the effect of the intervention. We will assess bias specific to regression discontinuity methods. Propensity score methods. As mentioned above, experimental designs are able to achieve balance between treatment and control groups on all covariates, both observed and unobserved. Propensity score methods attempt to achieve this covariate balance, at least among the observed covariates.
Comparisons can then be made between those who are similar on important factors to isolate the effect of the exposure. We will assess bias specific to propensity score methods. Interrupted time-series methods aim to model changes in the outcome before and after the intervention occurs. The stronger interrupted time-series designs also incorporate a comparison group that did not experience the intervention of interest at any time point; this enables better modeling of trends over time in the absence of the intervention.
Interrupted time-series methods make assumptions about how the outcome of interest would have changed over time in the absence of the intervention. For example, in a simple interrupted time-series the assumption is that the trend in outcome would have continued in the same way as before the intervention time point.
In a comparative interrupted time-series model, the assumption is that the difference in trends between the exposed and comparison groups would have continued in the same way after the intervention time point.
Another assumption is that there is no other change at the time of the intervention that may affect the outcome, as well as a reliance on the model forms modeling the outcome over time. Table 3. The instrument also needs to be at least hypothetically randomized. Gaps are defined as deficiencies in the literature research gaps , and pieces of information necessary for decision making that are unavailable evidence gaps.
Gaps will be abstracted by two independent reviewers during the data abstraction phase of the project. The TEP and stakeholders will be provided a copy of the draft report for review. The TEP and stakeholders will be asked to review the research gaps identified during data abstraction.
The TEP and stakeholders will be asked to discuss the gaps presented to them, and identify additional gaps if any are detected. Using an on-line tool, such as Qualtrics, the Technical Expert Panel TEP and internal advisors will be asked to provide comment on: 1 the gaps identified during data abstraction; 2 the benefits of addressing the gaps in future research; 3 the likelihood of being able to address the gaps; 4 additional gaps not identified by the reviewers. In a conference call, we will first describe the gaps from the review and feedback.
We will then ask the TEP and stakeholders to give additional feedback on the identified gaps, as well as identify any additional gaps. Specifically, we will discuss the following questions: KQ1: What are the important gaps in existing population-based data sources used to estimate the effect of programs, policies, and built environment changes on obesity control? Did we miss any data sources? KQ2: What are the important gaps related to linking population-based data-sources?
KQ3: What are the important gaps related to obesity and obesity-related behavioral outcomes in population-based data sources? What are the important gaps related to other outcomes in the above population-based data sources? KQ4: What are the important gaps related to the methods used to estimate the effect of programs, policies, and built environment changes on obesity prevention and control? KQ5: What are the important gaps related to risks of bias in population-level data sources used to estimate the effects of programs, policies, and built environment changes on obesity prevention and control?
KQ6: Are there additional methodologic or analytic advances that are not addressed in the current literature base that could help strengthen the efforts to estimate the effects of programs, policies, and built environment changes on obesity prevention and control?
During the conference call with our TEP and stakeholders, we will build consensus around the most important gaps to move the field forward. After comparing the important aspects identified by the TEP and stakeholders to the data we found, the team will summarize the gaps identified, and propose means to address these gaps in our report and final presentation. Data Synthesis by Key Question Key Question 1 What population-based data sources have been used in studies of how programs, policies or built environment changes affect or are associated with obesity prevention and control outcomes?
Key Question 3 What obesity measures, dietary and physical behaviors, and other outcomes have been assessed in studies of how programs, policies or built environment changes affect or are associated with obesity prevention and control? Grading the Strength of Evidence for Major Comparisons and Outcomes We will not evaluate the strength of evidence for a particular comparison or outcome as we are not assessing the comparative effectiveness of interventions in this review.
Assessing Applicability We will assess applicability of the evaluation approaches and methods to other settings, policies and populations. Prevalence of childhood and adult obesity in the United States, PMID: WHO overweight and obesity fact sheet.
World Health Organization; Accessed on April 8 Prevalence of obesity and trends in body mass index among US children and adolescents, Prevalence of high body mass index in US children and adolescents, Changing the future of obesity: science, policy, and action.
Measuring health disparities: trends in racial-ethnic and socioeconomic disparities in obesity among 2- to year old youth in the United States, Ann Epidemiol. Based on a systematic review from the The Obesity Expert Panel, Obesity Silver Spring.
Sturm R, Cohen DA. Zoning for health? The year-old ban on new fast-food restaurants in South LA. Health Aff Millwood. Workplace interventions for reducing sitting at work.
Cochrane Database Syst Rev. A retrospective study on changes in residents' physical activities, social interactions, and neighborhood cohesion after moving to a walkable community. Prev Med. Motivating systems-oriented research on environmental and policy changes for obesity prevention.
Pediatric Obesity. Behavioral treatment of obesity in patients encountered in primary care settings: a systematic review. Park Rx: Using Technology to connect us to Nature. Health IT Buzz; Neighborhoods, obesity, and diabetes--a randomized social experiment.
N Engl J Med. Impact of policy and built environment changes on obesity-related outcomes: a systematic review of naturally occurring experiments. Obes Rev. Systematic review and meta-analysis of the impact of restaurant menu calorie labeling. Am J Public Health. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. Measureas Registry Resources. National Collaboration on Childhood Obesity Research; Accessed on September 1, A systematic review of brief dietary questionnaires suitable for clinical use in the prevention and management of obesity, cardiovascular disease and type 2 diabetes.
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