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      Economic burden of childhood overweight and obesity: A systematic review and meta‐analysis

      1 , 2 , 3 , 1
      Obesity Reviews
      Wiley

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          Abstract

          <p id="d1090168e247">To update existing literature and fill the gap in meta‐analyses, this meta‐analysis quantitatively evaluated the worldwide economic burden (in 2022 US $) of childhood overweight and obesity in comparison with healthy weight. The literature search in eight databases produced 7756 records. After literature screening, 48 articles met the eligibility criteria. The increased annual total medical costs were $237.55 per capita attributable to childhood overweight and obesity. Overweight and obesity caused a per capita increase of $56.52, $14.27, $46.38, and $1975.06 for costs in nonhospital healthcare, outpatient visits, medication, and hospitalization, respectively. Length of hospital stays increased by 0.28 days. Annual direct and indirect costs were projected to be $13.62 billion and $49.02 billion by 2050. Childhood obesity ascribed to much higher increased healthcare costs than overweight. During childhood, the direct medical expenditures were higher for males than for females, but, once reaching adulthood, the expenditures were higher for females. Overall, the lifetime costs attributable to childhood overweight and obesity were higher in males than in females, and childhood overweight and obesity resulted in much higher indirect costs than direct healthcare costs. Given the increased economic burden, additional efforts and resources should be allocated to support sustainable and scalable childhood obesity programs. </p>

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          ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

          Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.
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            The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration

            Systematic reviews and meta-analyses are essential to summarise evidence relating to efficacy and safety of healthcare interventions accurately and reliably. The clarity and transparency of these reports, however, are not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (quality of reporting of meta-analysis) statement—a reporting guideline published in 1999—there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realising these issues, an international group that included experienced authors and methodologists developed PRISMA (preferred reporting items for systematic reviews and meta-analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this explanation and elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA statement, this document, and the associated website (www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.
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              Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

              Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                Obesity Reviews
                Obesity Reviews
                Wiley
                1467-7881
                1467-789X
                February 2023
                November 27 2022
                February 2023
                : 24
                : 2
                Affiliations
                [1 ]Michigan State University College of Nursing East Lansing Michigan USA
                [2 ]Georgia Southwestern State University School of Health Sciences Americus Georgia USA
                [3 ]Department of Communication University of Tennessee at Chattanooga Chattanooga Tennessee USA
                Article
                10.1111/obr.13535
                4a7f726f-52a5-4261-95b1-33ef1b04920e
                © 2023

                http://creativecommons.org/licenses/by-nc-nd/4.0/

                http://doi.wiley.com/10.1002/tdm_license_1.1

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