25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Importance Between 1980 and 2000, the prevalence of obesity increased significantly among adult men and women in the United States; further significant increases were observed through 2003-2004 for men but not women. Subsequent comparisons of data from 2003-2004 with data through 2011-2012 showed no significant increases for men or women. Objective To examine obesity prevalence for 2013-2014 and trends over the decade from 2005 through 2014 adjusting for sex, age, race/Hispanic origin, smoking status, and education. Design, Setting, and Participants Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height. Exposures Survey period. Main Outcomes and Measures Prevalence of obesity (body mass index ≥30) and class 3 obesity (body mass index ≥40). Results This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21 013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity ( P  = .004) and for class 3 obesity ( P  = .01) but not among men ( P  = .30 for overall obesity; P  = .14 for class 3 obesity). Conclusions and Relevance In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity in 2013-2014 was 35.0% among men and 40.4% among women. The corresponding values for class 3 obesity were 5.5% for men and 9.9% for women. For women, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014; there were no significant trends for men. Other studies are needed to determine the reasons for these trends.

https://jamanetwork.com/journals/jama/articlepdf/2526639/joi160058.pdf

Trends in Obesity Among Adults in the United States, 2005 to 2014.

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

Introduction to the practice of statistics, (3rd edition), by David S. Moore and George P. McCabe. Pp. 825 (with appendices and CD-ROM). £27.95. 1999. ISBN 0 7167 3502 4 (W. H. Freeman).

(2008). Introduction to Variance Estimation. Journal of the American Statistical Association: Vol. 103, No. 483, pp. 1324-1325.

Introduction to Variance Estimation

Introduction: Predictors of lung function decline in systemic sclerosis (SSc) are unknown. Serum pneumoprotein levels, surfactant protein-D (SP-D) and Krebs von den Lungen-6 (KL-6), correlate with pulmonary damage. We aimed to test whether levels can predict rapid lung function decline in SSc. Methods: SSc patients who had serial pulmonary function tests (PFT) were analyzed for SP-D and KL-6 levels by enzyme linked immunosorbent assay. Levels were correlated with an annual rate of decline in % predicted forced vital capacity (FVC) of >﹣2% (out-come); controls did not experience this FVC decline. Uni- and multi-variate analysis, adjusting for age, disease duration, gender, baseline % predicted FVC, SP-D, and KL-6, was performed. Results are reported as mean ± SD. Results: Thirty three cases and 25 controls had a disease duration of 8.8 ± 7.3 and 8.3 ± 6.1 years, respectively. In adjusted analyses, lung function decline correlated with greater baseline FVC OR = 1.03 [95% CI of 1.00-1.07]; a trend towards significance was observed for greater levels of SP-D with FVC decline, OR = 1.37 [95% CI of 0.96-2.12]. Conclusion: Our data provide evidence that SSc patients with long-standing disease are still at risk for lung function decline and SP-D levels may predict lung function decline.

/pdf/predicting-lung-function-decline-with-serum-pneumoproteins-a-1dvif4w0b6.pdf

Predicting Lung Function Decline with Serum Pneumoproteins: A Case Control Study

Risk of Gambling Onset in Youth Who are Younger than Same-Grade Peers

The Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved. New COVID-19 cases are supplied by the “Trajectoire de sante publique” information system, geolocated to civic addresses and then aggregated by day and dissemination area. To target community-level clusters, cases identified as residents of congregate living settings are excluded from the cluster detection analysis. Detection is performed using the space-time scan statistic and Poisson statistical model, and implemented in the SaTScan
 software. Information on detected clusters is disseminated daily via an online interactive mapping interface. The number of clusters detected tracked with the number of new cases. Slightly more than 4900 statistically significant (p ≤ 0.01) space-time clusters were detected over 14 health regions from May to October 2020. The Montreal region was the most affected. Considering the objective of timely cluster detection, the use of near-real-time health surveillance data of varying quality over time and by region constitutes an acceptable compromise between timeliness and data quality. This tool serves to supplement the epidemiologic investigations carried out by regional public health authorities for purposes of COVID-19 management and prevention.

/pdf/detection-of-covid-19-case-clusters-in-quebec-may-october-4n2c8vedr4.pdf

Ernest Lo

Papers

Predicting Lung Function Decline with Serum Pneumoproteins: A Case Control Study

Risk of Gambling Onset in Youth Who are Younger than Same-Grade Peers

Detection of COVID-19 case clusters in Québec, May-October 2020.

Maternal Substance Use Disorders and Accidental Drug Poisonings in Children.

Perron et al. Respond