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      Estimating the effect of calorie menu labeling on calories purchased in a large restaurant franchise in the southern United States: quasi-experimental study

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          Abstract

          Objective

          To evaluate whether calorie labeling of menus in large restaurant chains was associated with a change in mean calories purchased per transaction.

          Design

          Quasi-experimental longitudinal study.

          Setting

          Large franchise of a national fast food company with three different restaurant chains located in the southern United States (Louisiana, Texas, and Mississippi) from April 2015 until April 2018.

          Participants

          104 restaurants with calorie information added to in-store and drive-thru menus in April 2017 and with weekly aggregated sales data during the pre-labeling (April 2015 to April 2017) and post-labeling (April 2017 to April 2018) implementation period.

          Main outcome measures

          Primary outcome was the overall level and trend changes in mean purchased calories per transaction after implementation of calorie labeling compared with the counterfactual (ie, assumption that the pre-intervention trend would have persisted had the intervention not occurred) using interrupted time series analyses with linear mixed models. Secondary outcomes were by item category (entrees, sides, and sugar sweetened beverages). Subgroup analyses estimated the effect of calorie labeling in stratums defined by the sociodemographic characteristics of restaurant census tracts (defined region for taking census).

          Results

          The analytic sample comprised 14 352 restaurant weeks. Over three years and among 104 restaurants, 49 062 440 transactions took place and 242 726 953 items were purchased. After labeling implementation, a level decrease was observed of 60 calories/transaction (95% confidence interval 48 to 72; about 4%), followed by an increasing trend of 0.71 calories/transaction/week (95% confidence interval 0.51 to 0.92) independent of the baseline trend over the year after implementation. These results were generally robust to different analytic assumptions in sensitivity analyses. The level decrease and post-implementation trend change were stronger for sides than for entrees or sugar sweetened beverages. The level decrease was similar between census tracts with higher and lower median income, but the post-implementation trend in calories per transaction was higher in low income (change in calories/transaction/week 0.94, 95% confidence interval 0.67 to 1.21) than in high income census tracts (0.50, 0.19 to 0.81).

          Conclusions

          A small decrease in mean calories purchased per transaction was observed after implementation of calorie labeling in a large franchise of fast food restaurants. This reduction diminished over one year of follow-up.

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          Most cited references27

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          Interrupted time series regression for the evaluation of public health interventions: a tutorial

          Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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            Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future.

            Glucose metabolism is normally regulated by a feedback loop including islet β cells and insulin-sensitive tissues, in which tissue sensitivity to insulin affects magnitude of β-cell response. If insulin resistance is present, β cells maintain normal glucose tolerance by increasing insulin output. Only when β cells cannot release sufficient insulin in the presence of insulin resistance do glucose concentrations rise. Although β-cell dysfunction has a clear genetic component, environmental changes play an essential part. Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and β-cell dysfunction, and the potential role of changes in the microbiome. Several new approaches for treatment have been developed, but more effective therapies to slow progressive loss of β-cell function are needed. Recent findings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse effects of these interventions. However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful effects of this disease. Copyright © 2014 Elsevier Ltd. All rights reserved.
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              Adiposity and cancer at major anatomical sites: umbrella review of the literature

              Objective To evaluate the strength and validity of the evidence for the association between adiposity and risk of developing or dying from cancer. Design Umbrella review of systematic reviews and meta-analyses. Data sources PubMed, Embase, Cochrane Database of Systematic Reviews, and manual screening of retrieved references. Eligibility criteria Systematic reviews or meta-analyses of observational studies that evaluated the association between indices of adiposity and risk of developing or dying from cancer. Data synthesis Primary analysis focused on cohort studies exploring associations for continuous measures of adiposity. The evidence was graded into strong, highly suggestive, suggestive, or weak after applying criteria that included the statistical significance of the random effects summary estimate and of the largest study in a meta-analysis, the number of cancer cases, heterogeneity between studies, 95% prediction intervals, small study effects, excess significance bias, and sensitivity analysis with credibility ceilings. Results 204 meta-analyses investigated associations between seven indices of adiposity and developing or dying from 36 primary cancers and their subtypes. Of the 95 meta-analyses that included cohort studies and used a continuous scale to measure adiposity, only 12 (13%) associations for nine cancers were supported by strong evidence. An increase in body mass index was associated with a higher risk of developing oesophageal adenocarcinoma; colon and rectal cancer in men; biliary tract system and pancreatic cancer; endometrial cancer in premenopausal women; kidney cancer; and multiple myeloma. Weight gain and waist to hip circumference ratio were associated with higher risks of postmenopausal breast cancer in women who have never used hormone replacement therapy and endometrial cancer, respectively. The increase in the risk of developing cancer for every 5 kg/m2 increase in body mass index ranged from 9% (relative risk 1.09, 95% confidence interval 1.06 to 1.13) for rectal cancer among men to 56% (1.56, 1.34 to 1.81) for biliary tract system cancer. The risk of postmenopausal breast cancer among women who have never used HRT increased by 11% for each 5 kg of weight gain in adulthood (1.11, 1.09 to 1.13), and the risk of endometrial cancer increased by 21% for each 0.1 increase in waist to hip ratio (1.21, 1.13 to 1.29). Five additional associations were supported by strong evidence when categorical measures of adiposity were included: weight gain with colorectal cancer; body mass index with gallbladder, gastric cardia, and ovarian cancer; and multiple myeloma mortality. Conclusions Although the association of adiposity with cancer risk has been extensively studied, associations for only 11 cancers (oesophageal adenocarcinoma, multiple myeloma, and cancers of the gastric cardia, colon, rectum, biliary tract system, pancreas, breast, endometrium, ovary, and kidney) were supported by strong evidence. Other associations could be genuine, but substantial uncertainty remains. Obesity is becoming one of the biggest problems in public health; evidence on the strength of the associated risks may allow finer selection of those at higher risk of cancer, who could be targeted for personalised prevention strategies.
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                Author and article information

                Contributors
                Role: postdoctoral research fellow
                Role: assistant professor
                Role: research associate
                Role: project manager
                Role: professor
                Role: professor
                Role: professor
                Role: professor
                Role: assistant professor
                Role: associate professor
                Journal
                BMJ
                BMJ
                BMJ-US
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2019
                30 October 2019
                : 367
                : l5837
                Affiliations
                [1 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                [2 ]Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
                [3 ]Division of Health Policy and Insurance Research, Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
                [4 ]Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [5 ]Westbrook College of Health Professions, University of New England, Portland, ME, USA
                [6 ]Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [7 ]Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [8 ]Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
                Author notes
                Correspondence to: J Petimar jsp778@ 123456mail.harvard.edu
                Author information
                http://orcid.org/0000-0002-3025-5931
                Article
                petj050728
                10.1136/bmj.l5837
                6818731
                31666218
                1520d8e0-33ac-4ccc-a5b5-e80bf956e45d
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 24 September 2019
                Categories
                Research
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                Medicine
                Medicine

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