XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Summary 1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed. 2. Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake. 3. Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations. 4. Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/j.2041-210X.2009.00001.x

A protocol for data exploration to avoid common statistical problems

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

https://aasldpubs.onlinelibrary.wiley.com/doi/pdf/10.1002/hep.29367

The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases.

To revise 1998 recommendations on childhood obesity, an Expert Committee, comprised of representatives from 15 professional organizations, appointed experienced scientists and clinicians to 3 writing groups to review the literature and recommend approaches to prevention, assessment, and treatment. Because effective strategies remain poorly defined, the writing groups used both available evidence and expert opinion to develop the recommendations. Primary care providers should universally assess children for obesity risk to improve early identification of elevated BMI, medical risks, and unhealthy eating and physical activity habits. Providers can provide obesity prevention messages for most children and suggest weight control interventions for those with excess weight. The writing groups also recommend changing office systems so that they support efforts to address the problem. BMI should be calculated and plotted at least annually, and the classification should be integrated with other information such as growth pattern, familial obesity, and medical risks to assess the child’s obesity risk. For prevention, the recommendations include both specific eating and physical activity behaviors, which are likely to promote maintenance of healthy weight, but also the use of patient-centered counseling techniques such as motivational interviewing, which helps families identify their own motivation for making change. For assessment, the recommendations include methods to screen for current medical conditions and for future risks, and methods to assess diet and physical activity behaviors. For treatment, the recommendations propose 4 stages of obesity care; the first is brief counseling that can be delivered in a health care office, and subsequent stages require more time and resources. The appropriateness of higher stages is influenced by a patient’s age and degree of excess weight. These recommendations recognize the importance of social and environmental change to reduce the obesity epidemic but also identify ways healthcare providers and health care systems can be part of broader efforts.

/pdf/expert-committee-recommendations-regarding-the-prevention-38kyxmdgpa.pdf

Expert Committee Recommendations Regarding the Prevention, Assessment, and Treatment of Child and Adolescent Overweight and Obesity: Summary Report

Fatty liver (FL) is the most frequent liver disease in Western countries. We used data from the Dionysos Nutrition & Liver Study to develop a simple algorithm for the prediction of FL in the general population. 216 subjects with and 280 without suspected liver disease were studied. FL was diagnosed by ultrasonography and alcohol intake was assessed using a 7-day diary. Bootstrapped stepwise logistic regression was used to identify potential predictors of FL among 13 variables of interest [gender, age, ethanol intake, alanine transaminase, aspartate transaminase, gamma-glutamyl-transferase (GGT), body mass index (BMI), waist circumference, sum of 4 skinfolds, glucose, insulin, triglycerides, and cholesterol]. Potential predictors were entered into stepwise logistic regression models with the aim of obtaining the most simple and accurate algorithm for the prediction of FL. An algorithm based on BMI, waist circumference, triglycerides and GGT had an accuracy of 0.84 (95%CI 0.81–0.87) in detecting FL. We used this algorithm to develop the "fatty liver index" (FLI), which varies between 0 and 100. A FLI < 30 (negative likelihood ratio = 0.2) rules out and a FLI ≥ 60 (positive likelihood ratio = 4.3) rules in fatty liver. FLI is simple to obtain and may help physicians select subjects for liver ultrasonography and intensified lifestyle counseling, and researchers to select patients for epidemiologic studies. Validation of FLI in external populations is needed before it can be employed for these purposes.

/pdf/the-fatty-liver-index-a-simple-and-accurate-predictor-of-1rxiltoz3l.pdf

The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population

http://www.giorgiobedogni.it/reprints/poj49.pdf

Prevalence of and risk factors for nonalcoholic fatty liver disease: The Dionysos nutrition and liver study†‡

Non-alcoholic fatty liver disease (NAFLD) is rapidly becoming the most common liver disease worldwide. The prevalence of NAFLD in the general population of Western countries is 20–30%. About 2–3% of t

http://www.giorgiobedogni.it/reprints2/2010_fl_dd.pdf

Epidemiology of Non-Alcoholic Fatty Liver Disease

Clinical Prediction Models—a Practical Approach to Development, Validation and Updating

Background: The study of the relationship between anthropometry and visceral adipose tissue (VAT) is of great interest because VAT is associated with many risk factors for noncommunicable diseases and anthropometry is easy to perform in clinical practice. The studies hitherto available for children have, however, been performed on small sample sizes.

Design: Pooling of the data of studies published from 1992 to 2004 as indexed on Medline.

Aims: To assess the relationship between anthropometry and VAT and subcutaneous adipose tissue (SAT) as measured by magnetic resonance imaging (MRI) in children and to analyze the effect of age, gender, pubertal status and ethnicity.

Subjects and methods: Eligible subjects were 7–16 year-old, with availability of VAT and SAT, gender, ethnicity, body mass index (BMI) and waist circumference (WC). A total of 497 subjects were collected from seven different investigators and 407 of them (178 Caucasians and 229 Hispanics) were analyzed.

Results: Despite ethnic differences in MRI data, BMI, WC and age, no difference in VAT was found between Caucasians and Hispanics after correction for SAT and BMI. Univariate regression analysis identified WC as the best single predictor of VAT (64.8% of variance) and BMI of SAT (88.9% of variance). The contribution of ethnicity and gender to the unexplained variance of the VAT–WC relationship was low (3%) but significant (P0.002). The different laboratories explained a low (4.8%) but significant (P<0.0001) portion of the unexplained variance of the VAT–WC and SAT–BMI relationships. Prediction equations for VAT (VAT (cm2)=1.1 WC (cm)-52.9) and SAT (SAT (cm2)=23.2 BMI (kg/m2)-329) were developed on a randomly chosen half of the population and crossvalidated in the remaining half. The pure error of the estimate was 13 cm2 for VAT and 57 cm2 for SAT.

Conclusions: WC can be considered a good predictor of VAT as well as BMI of SAT. The importance of ethnicity and gender on VAT estimation is not negligible.

Giorgio Bedogni

Papers

The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population

Prevalence of and risk factors for nonalcoholic fatty liver disease: The Dionysos nutrition and liver study†‡

Epidemiology of Non-Alcoholic Fatty Liver Disease

Clinical Prediction Models—a Practical Approach to Development, Validation and Updating

Crossvalidation of anthropometry against magnetic resonance imaging for the assessment of visceral and subcutaneous adipose tissue in children.