Viral hepatitis in all its forms is a major public health problem throughout the world, affecting several hundreds of millions of people. Viral hepatitis is a cause of considerable morbidity and mortality both from acute infection and chronic sequelae which include, in the case of hepatitis B, C and D, chronic active hepatitis and cirrhosis. Hepatocellular carcinoma, which is one of the 10 commonest cancers worldwide, is closely associated with hepatitis B and, at least in some regions of the world, with hepatitis C virus. This timely monograph is a distillation of knowledge of hepatitis B, C and D, based on a review of 1000 studies by a small group of scientists. (It is interesting to note in passing that some 5000 papers on viral hepatitis are published annually in the world literature.) The epidemiological, clinical and experimental data on the association between infection with hepatitis B virus and primary liver cancer in humans are reviewed in a readable and succinct format. The available information on hepatitis C and progression to chronic infection is also evaluated and it is concluded (perhaps a little prematurely) that hepatitis C virus is carcinogenic. However, it is concluded that hepatitis D virus, an unusual virus with a number of similarities to certain plant viral satellites and viroids, cannot be classified as a human carcinogen. There are some minor criticisms: there are few illustrations and some complex tabulations (for example, Table 6) and no subject index. A cumulative cross index to IARC Monographs is of little value and occupies nearly 30 pages. This small volume is a useful addition to the overwhelming literature on viral hepatitis, and the presentation is similar to the excellent World Health Organisation Technical Reports series on the subject published in the past. It is strongly recommended as a readable up-to-date summary of a complex subject; and at a cost of 65 Sw.fr (approximately £34) is excellent value. A J ZUCKERMAN

https://jcp.bmj.com/content/jclinpath/48/7/691.1.full.pdf

IARC Monographs on the Evaluation of Carcinogenic Risks to Humans

There is a simple robust variance estimator for cluster-correlated data. While this estimator is well known, it is poorly documented, and its wide range of applicability is often not understood. The estimator is widely used in sample survey research, but the results in the sample survey literature are not easily applied because of complications due to unequal probability sampling. This brief note presents a general proof that the estimator is unbiased for cluster-correlated data regardless of the setting. The result is not new, but a simple and general reference is not readily available. The use of the method will benefit from a general explanation of its wide applicability.

A Note on Robust Variance Estimation for Cluster‐Correlated Data

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Libri Ricevuti: IARC Monographs on the Evaluation of Carcinogenic Risks to Humans

BACKGROUND Electronic health records have the potential to improve the delivery of health care services. However, in the United States, physicians have been slow to adopt such systems. This study assessed physicians' adoption of outpatient electronic health records, their satisfaction with such systems, the perceived effect of the systems on the quality of care, and the perceived barriers to adoption. METHODS In late 2007 and early 2008, we conducted a national survey of 2758 physicians, which represented a response rate of 62%. Using a definition for electronic health records that was based on expert consensus, we determined the proportion of physicians who were using such records in an office setting and the relationship between adoption and the characteristics of individual physicians and their practices. RESULTS Four percent of physicians reported having an extensive, fully functional electronic-records system, and 13% reported having a basic system. In multivariate analyses, primary care physicians and those practicing in large groups, in hospitals or medical centers, and in the western region of the United States were more likely to use electronic health records. Physicians reported positive effects of these systems on several dimensions of quality of care and high levels of satisfaction. Financial barriers were viewed as having the greatest effect on decisions about the adoption of electronic health records. CONCLUSIONS Physicians who use electronic health records believe such systems improve the quality of care and are generally satisfied with the systems. However, as of early 2008, electronic systems had been adopted by only a small minority of U.S. physicians, who may differ from later adopters of these systems.

/pdf/electronic-health-records-in-ambulatory-care-a-national-18cvf5g9fd.pdf

Electronic Health Records in Ambulatory Care — A National Survey of Physicians

There is increasing interest in estimating and drawing inferences about risk or prevalence ratios and differences instead of odds ratios in the regression setting. Recent publications have shown how the GENMOD procedure in SAS (SAS Institute Inc., Cary, North Carolina) can be used to estimate these parameters in non-population-based studies. In this paper, the authors show how model-adjusted risks, risk differences, and risk ratio estimates can be obtained directly from logistic regression models in the complex sample survey setting to yield population-based inferences. Complex sample survey designs typically involve some combination of weighting, stratification, multistage sampling, clustering, and perhaps finite population adjustments. Point estimates of model-adjusted risks, risk differences, and risk ratios are obtained from average marginal predictions in the fitted logistic regression model. The model can contain both continuous and categorical covariates, as well as interaction terms. The authors use the SUDAAN software package (Research Triangle Institute, Research Triangle Park, North Carolina) to obtain point estimates, standard errors (via linearization or a replication method), confidence intervals, and P values for the parameters and contrasts of interest. Data from the 2006 National Health Interview Survey are used to illustrate these concepts.

Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

SUDAAN language manual, release 10.0

Long-term evaluation of drug abuse resistance education☆

Experimental studies of prevention programs often randomize clusters of individuals rather than individuals to treatment conditions. When the correlation among individuals within clusters is not accounted for in statistical analysis, the standard errors are biased, potentially resulting in misleading conclusions about the significance of treatment effects. This study demonstrates the generalized estimating equations (GEE) method, focusing specifically on the GEE-independent method, to control for within-cluster correlation in regression models with either continuous or binary outcomes. The GEE-independent method yields consistent and robust variance estimates. Data from project DARE, a youth substance abuse prevention program, are used for illustration.

Analysis of prevention program effectiveness with clustered data using generalized estimating equations.

This paper demonstrates the use of the delta method for estimating the variance of ratio statistics derived from animal carcinogenicity experiments. The Cochran-Armitage test (Cochran, 1954, Biometrika 10, 417-451; and Armitage, 1955, Biometrics 11, 375-386) is routinely applied to carcinogenicity data as a test for linear trend in lifetime tumor incidence rates. The computing formula for this test derives from the assumption that the denominators of the quantal response rates are fixed. However, when time-at-risk weights are introduced to correct for treatment-related differences in survival, the denominators of the quantal response rates are subject to random variation. The delta method and weighted least squares techniques are applied here to approximate the variance of such ratio statistics and test for a linear dose-response relationship among treatments. This technique is compared to that of Bailer and Portier (1988, Biometrics 44, 417-431), who introduced a survival-adjusted quantal response test for trend in lifetime tumor incidence rates. Their test modifies the usual Cochran-Armitage computing formula by weighting the denominators of the response rates to reflect less-than-whole-animal contributions to risk. Within the framework of a weighted least squares linear regression model that underlies the Cochran-Armitage test, the time-at-risk weights of Bailer and Portier are incorporated using the delta method. Although the delta method approach is slightly more computationally intensive, small-sample simulations indicate that it has superior operating characteristics over the Poly-3 trend test of Bailer and Portier when background tumor incidence rates are low (under 3%) and survival patterns differ markedly across treatments.(ABSTRACT TRUNCATED AT 250 WORDS)

Gayle Bieler

Papers

Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data.

SUDAAN language manual, release 10.0

Long-term evaluation of drug abuse resistance education☆

Analysis of prevention program effectiveness with clustered data using generalized estimating equations.

Ratio estimates, the delta method, and quantal response tests for increased carcinogenicity.