Preface 1. Introduction 2. The concept of risk 3. Overview of count response models 4. Methods of estimation and assessment 5. Assessment of count models 6. Poisson regression 7. Overdispersion 8. Negative binomial regression 9. Negative binomial regression: modeling 10. Alternative variance parameterizations 11. Problems with zero counts 12. Censored and truncated count models 13. Handling endogeneity and latent class models 14. Count panel models 15. Bayesian negative binomial models Appendix A. Constructing and interpreting interactions Appendix B. Data sets and Stata files References Index.

/pdf/negative-binomial-regression-29q4k5nu4u.pdf

Negative Binomial Regression

https://stacks.cdc.gov/view/cdc/6674/cdc_6674_DS1.pdf

Guideline for Hand Hygiene in Health-Care Settings. Recommendations of the Healthcare Infection Control Practices Advisory Committee and the HIPAC/SHEA/APIC/IDSA Hand Hygiene Task Force.

This paper proposes a regression model where the response is beta distributed using a parameterization of the beta law that is indexed by mean and dispersion parameters. The proposed model is useful for situations where the variable of interest is continuous and restricted to the interval (0, 1) and is related to other variables through a regression structure. The regression parameters of the beta regression model are interpretable in terms of the mean of the response and, when the logit link is used, of an odds ratio, unlike the parameters of a linear regression that employs a transformed response. Estimation is performed by maximum likelihood. We provide closed-form expressions for the score function, for Fisher's information matrix and its inverse. Hypothesis testing is performed using approximations obtained from the asymptotic normality of the maximum likelihood estimator. Some diagnostic measures are introduced. Finally, practical applications that employ real data are presented and discussed.

/pdf/beta-regression-for-modelling-rates-and-proportions-1pvlm302rh.pdf

Beta Regression for Modelling Rates and Proportions

The Guideline for Hand Hygiene in Health-Care Settings provides health-care workers (HCWs) with a review of data regarding handwashing and hand antisepsis in health-care settings. In addition, it provides specific recommendations to promote improved hand-hygiene practices and reduce transmission of pathogenic microorganisms to patients and personnel in health-care settings. This report reviews studies published since the 1985 CDC guideline (Garner JS, Favero MS. CDC guideline for handwashing and hospital environmental control, 1985. Infect Control 1986;7:231-43) and the 1995 APIC guideline (Larson EL, APIC Guidelines Committee. APIC guideline for handwashing and hand antisepsis in health care settings. Am J Infect Control 1995;23:251-69) were issued and provides an in-depth review of hand-hygiene practices of HCWs, levels of adherence of personnel to recommended handwashing practices, and factors adversely affecting adherence. New studies of the in vivo efficacy of alcohol-based hand rubs and the low incidence of dermatitis associated with their use are reviewed. Recent studies demonstrating the value of multidisciplinary hand-hygiene promotion programs and the potential role of alcohol-based hand rubs in improving hand-hygiene practices are summarized. Recommendations concerning related issues (e.g., the use of surgical hand antiseptics, hand lotions or creams, and wearing of artificial fingernails) are also included.

/pdf/guideline-for-hand-hygiene-in-health-care-settings-5dgf1j7b1v.pdf

Guideline for Hand Hygiene in Health-Care Settings: recommendations of the Healthcare Infection Control Practices Advisory Committee and the HICPAC/SHEA/APIC/IDSA Hand Hygiene Task Force.

There are two cultures in the use of statistical modeling to reach conclusions from data. One assumes that the data are generated bya given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown. The statistical communityhas been committed to the almost exclusive use of data models. This commit- ment has led to irrelevant theory, questionable conclusions, and has kept statisticians from working on a large range of interesting current prob- lems. Algorithmic modeling, both in theoryand practice, has developed rapidlyin fields outside statistics. It can be used both on large complex data sets and as a more accurate and informative alternative to data modeling on smaller data sets. If our goal as a field is to use data to solve problems, then we need to move awayfrom exclusive dependence on data models and adopt a more diverse set of tools.

https://biostat.jhsph.edu/~mmccall/articles/breiman_2001.pdf

Statistical modeling: The two cultures

This paper demonstrates that the conditional negative binomial model for panel data, proposed by Hausman, Hall, and Griliches (1984), is not a true fixed-effects method. This method—which has been ...

/pdf/fixed-effects-negative-binomial-regression-models-1jm2rrfjq3.pdf

Fixed–Effects Negative Binomial Regression Models

Evaluation of a patient-empowering hand hygiene programme in the UK.

Patient education model for increasing handwashing compliance.

Background: Venous leg ulcers account for 85% of all lower-extremity ulcers, with treatment costs of $3 billion and loss of 2 million workdays per year. The purpose of this study was to validate the clinical efficacy and cost effectiveness of multidisciplinary guidelines for the diagnosis and treatment of venous leg ulcers.

Methods: Eighty (40 retrospective, 40 prospective) patients from the United States and United Kingdom were enrolled.

Results: United States patients were 6.5 times and United Kingdom 2 times more likely to heal if a guideline was followed (P <0.001). A significant decrease was noted in healing time for both the United States and United Kingdom (P <0.01), and the median cost decreased significantly when the guideline was followed (P <0.01).

Conclusions: Implementation of a guideline for diagnosis and treatment of venous leg ulcers resulted in improvement in diagnosis, decrease in healing time, and an increase in healing rates resulting in lower costs.

Validation of venous leg ulcer guidelines in the United States and United Kingdom.

Hand hygiene (HH) is the single most important factor in the prevention of health care-acquired infections. The 3 most frequently reported methods of measuring HH compliance are: (1) direct observation, (2) self-reporting by health care workers (HCWs), and (3) indirect calculation based on HH product usage. This article presents the results of a 12-month multicenter collaboration assessing HH compliance rates at US health care facilities by measuring product usage and providing feedback about HH compliance. Our results show that HH compliance at baseline was 26% for intensive care units (ICUs) and 36% for non-ICUs. After 12 months of measuring product usage and providing feedback, compliance increased to 37% for ICUs and 51% for non-ICUs. (ICU, P = .0119; non-ICU, P < .001). HH compliance in the United States can increase when monitoring is combined with feedback. However, HH still occurs at or below 50% compli- ance for both ICUs and non-ICUs.

/pdf/hand-hygiene-compliance-rates-in-the-united-states-a-one-3mntc4107i.pdf

Richard P. Waterman

Papers

Fixed–Effects Negative Binomial Regression Models

Evaluation of a patient-empowering hand hygiene programme in the UK.

Patient education model for increasing handwashing compliance.

Validation of venous leg ulcer guidelines in the United States and United Kingdom.

Hand Hygiene Compliance Rates in the United States—A One-Year Multicenter Collaboration Using Product/Volume Usage Measurement and Feedback