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

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

/pdf/experimental-design-and-data-analysis-for-biologists-8ybva8d53x.pdf

Experimental Design and Data Analysis for Biologists

This report updates the 2002 recommendations by the Advisory Committee on Immunization Practices (ACIP) on the use of influenza vaccine and antiviral agents (CDC. Prevention and Control of Influenza: Recommendations of the Advisory Committee on Immunization Practices [ACIP]. MMWR 2002;51 [No. RR-3]:1-31). The 2003 recommendations include new or updated information regarding 1) the timing of influenza vaccination by age and risk group; 2) influenza vaccine for children aged 6-23 months; 3) the 2003-2004 trivalent inactivated vaccine virus strains: A/Moscow/10/99 (H3N2)-like, A/New Caledonia/20/99 (H1N1)-like, and B/Hong Kong/330/2001-like antigens (for the A/Moscow/10/99 [H3N2]-like antigen, manufacturers will use the antigenically equivalent A/Panama/2007/99 [H3N2] virus, and for the B/Hong Kong/330/2001-like antigen, manufacturers will use either B/Hong Kong/330/2001 or the antigenically equivalent B/Hong Kong/1434/2002); 4) availability of certain influenza vaccine doses with reduced thimerosal content, including single 0.25 mL-dose syringes; and 5) manufacturers of influenza vaccine for the U.S. market. Although the optimal time to vaccinate against influenza is October and November, vaccination in December and later continues to be strongly recommended A link to this report and other information regarding influenza can be accessed at http://www.cdc.gov/ncidod/diseases/flu/fluvirus.htm.

https://stacks.cdc.gov/view/cdc/6625/cdc_6625_DS1.pdf

Prevention and control of influenza : recommendations of the Advisory Committee on Immunization Practices (ACIP)

Although the methodological literature is replete with advice regarding the development and validation of multi-item scales based on reflective measures, the issue of index construction using formative measures has received little attention. The authors seek to address this gap by (1) examining the nature of formative indicators, (2) discussing ways in which the quality of formative measures can be assessed, and (3) illustrating the proposed procedures with empirical data. The aim is to enhance researchers’ understanding of formative measures and assist them in their index construction efforts.

https://statmath.wu.ac.at/courses/r-se-mbr/dl/M1b_Diamantopoulos_2001.pdf

Index Construction with Formative Indicators: An Alternative to Scale Development

1 Introduction 11 Analytical problems 12 Errors in qunatitative analysis 13 Types of error 14 Random and systematic errors in titrimetric analysis 15 Handling systematic errors 16 Planning and design of experiments 17 Calculators and computers in statistical calculations 2 Statistics of Repeated Measurements 21 Mean and standard deviation 22 The distribution of repeated measurements 23 Log-normal distribution 24 Definition of a 'sample' 25 The sampling distribution of the mean 26 Confidence limits of the mean for large samples 27 Confidence limits of the mean for small samples 28 Presentation of results 29 Other uses of confidence limits 210 Confidence limits of the geometric mean for a log-normal distribution 211 Propagation of random errors 212 Propagation of systematic errors 3 Significance Tests 31 Introduction 32 Comparison of an experimental mean with a known value 33 Comparison of two experimental means 34 Paired t-test 35 One-sided and two-sided tests 36 F-test for the comparison of standard deviations 37 Outliers 38 Analysis of variance 39 Comparison of several means 310 The arithmetic of ANOVA calculations 311 The chi-squared test 312 Testing for normality of distribution 313 Conclusions from significance tests 314 Bayesian Statistics 4 The Quality of Analytical Measurements 41 Introduction 42 Sampling 43 Separation and estimation of variances using ANOVA 44 Sampling strategy 45 Quality control methods - Introduction 46 Stewhart charts for mean values 47 Stewhart charts for ranges 48 Establishing the process capability 49 Average run length: cusum charts 410 Zone control charts (J-charts) 411 Proficiency testing schemes 412 Method performance studies (collaborative trials) 413 Uncertainty 414 Acceptable sampling 415 Method validation 5 Calibration Methods in Instumental Analysis 51 Introduction: instrumentational analysis 52 Calibration graphs in instrumental analysis 53 The product-moment correlation coefficient 54 The line of regression of y on x 55 Errors in the slope and intercept of the regression line 56 Calculation of a concentration and its random error 57 Limits of detection 58 The method of standard additions 59 Use of regression lines for comparing analytical methods 510 Weighted regression lines 511 Intersection of two straight lines 512 ANOVA and regression calculations 513 Curvilinear regression methods - Introduction 514 Curve fitting 515 Outliers in regression 6 Non-parametric and Robust Methods 61 Introduction 62 The median: initial data analysis 63 The sign test 64 The Wald-Wolfowitz runs test 65 The Wilcoxon signed rank test 66 Simple tests for two independent samples 67 Non-parametric tests for more than two samples 68 Rank correlation 69 Non-parametric regression methods 610 Robust methods: introduction 611 Simple robust methods: trimming and winsorization 612 Further robust estimates of location and spread 613 Robust ANOVA 614 Robust regression methods 615 Re-sampling statistics 616 Conclusions 7 Experiimental Design and Optimization 71 Introduction 72 Randomization and blocking 73 Two-way ANOVA 74 Latin squares and other designs 75 Interactions 76 Identifying the important factors: factorial designs 77 Fractional factorial designs 78 Optimization: basic principles and univariate methods 79 Optimization using the alternating variable search method 710 The method of steepest ascent 711 Simplex optimization 712 Simulated annealing 8 Multivariate Analysis 81 Introduction 82 Initial analysis 83 Prinicipal component analysis 84 Cluster analysis 85 Discriminant analysis 86 K-nearest neighbour method 87 Disjoint class modelling 88 Regression methods 89 Multiple linear regression 810 Principal component regression 811 Partial least squares regression 812 Natural computation methods artificial neural networks 813 Conclusions Solutions to Exercises Appendix 1 Commonly used statistical significance tests Appendix 2 Statistical tables Index

Statistics and Chemometrics for Analytical Chemistry

Applied regression analysis and other multivariable methods, 3rd ed.

For the first wave of pandemic influenza or a bioterrorist influenza attack, antiviral agents would be one of the few options to contain the epidemic in the United States until adequate supplies of vaccine were available. The authors use stochastic epidemic simulations to investigate the effectiveness of targeted antiviral prophylaxis to contain influenza. In this strategy, close contacts of suspected index influenza cases take antiviral agents prophylactically. The authors compare targeted antiviral prophylaxis with vaccination strategies. They model an influenza pandemic or bioterrorist attack for an agent similar to influenza A virus (H2N2) that caused the Asian influenza pandemic of 1957-1958. In the absence of intervention, the model predicts an influenza illness attack rate of 33% of the population (95% confidence interval (CI): 30, 37) and an influenza death rate of 0.58 deaths/1,000 persons (95% Cl: 0.4, 0.8). With the use of targeted antiviral prophylaxis, if 80% of the exposed persons maintained prophylaxis for up to 8 weeks, the epidemic would be contained, and the model predicts a reduction to an illness attack rate of 2% (95% Cl: 0.2, 16) and a death rate of 0.04 deaths/1,000 persons (95% CI: 0.0003, 0.25). Such antiviral prophylaxis is nearly as effective as vaccinating 80% of the population. Vaccinating 80% of the children aged less than 19 years is almost as effective as vaccinating 80% of the population. Targeted antiviral prophylaxis has potential as an effective measure for containing influenza until adequate quantities of vaccine are available.

Containing Pandemic Influenza with Antiviral Agents

Eventually, you will utterly discover a extra experience and carrying out by spending more cash. still when? reach you say you will that you require to get those all needs behind having significantly cash? Why don't you attempt to acquire something basic in the beginning? That's something that will lead you to understand even more nearly the globe, experience, some places, bearing in mind history, amusement, and a lot more?

Applied Regression Analysis and Multivariable Methods

Population-wide benefits of routine vaccination of children against influenza.

The causative agent of cholera, Vibrio cholerae, has been shown to be autochthonous to riverine, estuarine, and coastal waters along with its host, the copepod, a significant member of the zooplankton community. Temperature, salinity, rainfall and plankton have proven to be important factors in the ecology of V. cholerae, influencing the transmission of the disease in those regions of the world where the human population relies on untreated water as a source of drinking water. In this study, the pattern of cholera outbreaks during 1998-2006 in Kolkata, India, and Matlab, Bangladesh, and the earth observation data were analyzed with the objective of developing a prediction model for cholera. Satellite sensors were used to measure chlorophyll a concentration (CHL) and sea surface temperature (SST). In addition, rainfall data were obtained from both satellite and in situ gauge measurements. From the analyses, a statistically significant relationship between the time series for cholera in Kolkata, India, and CHL and rainfall anomalies was determined. A statistically significant one month lag was observed between CHL anomaly and number of cholera cases in Matlab, Bangladesh. From the results of the study, it is concluded that ocean and climate patterns are useful predictors of cholera epidemics, with the dynamics of endemic cholera being related to climate and/or changes in the aquatic ecosystem. When the ecology of V. cholerae is considered in predictive models, a robust early warning system for cholera in endemic regions of the world can be developed for public health planning and decision making.

https://www.pnas.org/content/pnas/105/46/17676.full.pdf

Azhar Nizam

Papers

Applied regression analysis and other multivariable methods, 3rd ed.

Containing Pandemic Influenza with Antiviral Agents

Applied Regression Analysis and Multivariable Methods

Population-wide benefits of routine vaccination of children against influenza.

Environmental signatures associated with cholera epidemics