Self-Reported Substantial 1-Year Weight Change Among Men and Women in the United States

Population-based data have not been readily available on relatively short-term changes in weight. Therefore, we sought to determine the nature of self-reported substantial (> 10%) weight change over one year in a representative sample of the US population which participated in the 1989 National Health Interview Survey (NHIS). Across all ages, a larger proportion of women than men reported both weight loss as well as weight gain of any amount (18.9% vs. 16.1% for weight loss and 20.0% vs. 16.1% for weight gain). In sex-specific logistic regression analyses, significant risk factors common to both sexes for substantial weight loss included divorced/separated marital status, smoking, increased number of blood pressure checks, increased BMI (body mass index) and increased number of bed days. Black race reduced the risk of weight loss for both men and women. Sex-specific risk factors for weight loss in men only were widowhood or never married marital status, while increasing age was a protective factor in women only. Concerning weight gain > 10% over the past year, increased number of blood pressure checks and having one or more diabetic parents were significant risk factors among both men and women; while never being married, increased age, BMI, and education exerted a protective effect in both sexes. For women only, risk factors for weight gain included black race, increased number of contacts with a health professional, and being unemployed. Intention to lose weight was associated with both weight gain and weight loss in both sexes, although it did not serve as a confounder in any of these relationships. A greater likelihood of substantial weight loss among women relative to men was diminished for persons with higher BMI, higher number of blood pressure checks, being widowed, divorced or separated, and intention to lose weight. A greater likelihood of substantial weight gain among women relative to men was diminished for persons with low BMI. The results of this cross-sectional study of weight change, involving a one-year follow-up period, generally correspond with the results obtained by longitudinal studies involving a longer follow-up.     

Exploiting nonsystematic covariate monitoring to broaden the scope of evidence about the causal effects of adaptive treatment strategies

In studies based on electronic health records (EHR), the frequency of covariate monitoring can vary by covariate type, across patients, and over time, which can limit the generalizability of inferences about the effects of adaptive treatment strategies. In addition, monitoring is a health intervention in itself with costs and benefits, and stakeholders may be interested in the effect of monitoring when adopting adaptive treatment strategies. This paper demonstrates how to exploit nonsystematic covariate monitoring in EHR‐based studies to both improve the generalizability of causal inferences and to evaluate the health impact of monitoring when evaluating adaptive treatment strategies. Using a real world, EHR‐based, comparative effectiveness research (CER) study of patients with type II diabetes mellitus, we illustrate how the evaluation of joint dynamic treatment and static monitoring interventions can improve CER evidence and describe two alternate estimation approaches based on inverse probability weighting (IPW). First, we demonstrate the poor performance of the standard estimator of the effects of joint treatment‐monitoring interventions, due to a large decrease in data support and concerns over finite‐sample bias from near‐violations of the positivity assumption (PA) for the monitoring process. Second, we detail an alternate IPW estimator using a no direct effect assumption. We demonstrate that this estimator can improve efficiency but at the potential cost of increase in bias from violations of the PA for the treatment process.     

Living with observational data in biological anthropology

The establishment of cause and effect relationships is a fundamental objective of scientific research. Many lines of evidence can be used to make cause–effect inferences. When statistical data are involved, alternative explanations for the statistical relationship need to be ruled out. These include chance (apparent patterns due to random factors), confounding effects (a relationship between two variables because they are each associated with an unmeasured third variable), and sampling bias (effects due to preexisting properties of compared groups). The gold standard for managing these issues is a controlled randomized experiment. In disciplines such as biological anthropology, where controlled experiments are not possible for many research questions, causal inferences are made from observational data. Methods that statisticians recommend for this difficult objective have not been widely adopted in the biological anthropology literature. Issues involved in using statistics to make valid causal inferences from observational data are discussed.     

Negative and Non-Positive Epidemiological Studies

Aim. To identify and discuss validity aspects on so called negative and non-positive studies. Methods. Arguments and examples are drawn from experiences in occupational health epidemiology regarding the interpretation of more or less equivocal study results. Results and conclusions. A negative study may be defined as showing a result that goes against the investigated hypothesis of an increased (or prevented) risk. Traditionally, studies with a risk estimate (relative risk or odds ratio) above but close to unity are also referred to as negative, given a narrow confidence interval (CI) that includes unity. A risk estimate above unity with the CI including unity is non-positive, however, but an estimate below unity with upper CI bond exceeding unity might be seen as possibly negative or non-negative. A weaker “significance” than usually required should perhaps be accepted when evaluating serious hazards. In contrast to positive studies, the negative and non-positive studies tend to escape criticism in spite of questionable validity that may have obscured existing risks (or preventive effects). Even stronger arguments can be made in criticising negative and non-positive studies than positive studies, for example, regarding selection phenomena, and observational problems regarding exposure and outcome. Negative confounding should be considered although usually weak. In case-control studies, so-called over-matching may obscure an existing risk as could the “healthy worker effect” in cohort studies. Small scale non-positive studies should be made available for meta-analyses and when considering studies that do not convincingly show a risk; those who are exposed should be given the “benefit of the doubt.”     

Targeted On‐line SPE‐LC‐MS/MS Assay for the Quantitation of 12 Apolipoproteins from Human Blood

Laborious sample pretreatment of biological samples represents the most limiting factor for the translation of targeted proteomics assays from research to clinical routine. An optimized method for the simultaneous quantitation of 12 major apolipoproteins (apos) combining on‐line SPE and fast LC‐MS/MS analysis in 6.5 min total run time was developed, reducing the manual sample pretreatment time of 3 μL serum or plasma by 60%. Within‐run and between‐day imprecisions below 10 and 15% (n = 10) and high recovery rates (94–131%) were obtained applying the high‐throughput setup. High‐quality porcine trypsin was used, which outperformed cost‐effective bovine trypsin regarding digestion efficiency. Comparisons with immunoassays and another LC‐MS/MS assay demonstrated good correlation (Pearson's R: 0.81–0.98). Further, requirements on sample quality concerning sampling, processing, and long‐term storage up to 1 year were investigated revealing significant influences of the applied sampling material and coagulant on quantitation results. Apo profiles of 1339 subjects of the LIFE‐Adult‐Study were associated with lifestyle and physiological parameters as well as establish parameters of lipid metabolism (e.g., triglycerides, cholesterol). Besides gender effects, most significant impact was seen regarding lipid‐lowering medication. In conclusion, this novel highly standardized, high‐throughput targeted proteomics assay utilizes a fast, simultaneous analysis of 12 apos from least sample amounts.     

Comments on recent reports on infrared spectral detection of disease markers in blood components

The search for disease markers in whole blood, or easily accessible blood components by spectral methods is a highly important aspect in the field of biophotonic research for disease diagnostics and screening, since it promises a minimally invasive approach to assess an individual's state of health. Fourier transform infrared spectroscopy, in particular, promises to be a fast, inexpensive method to search for markers of disease, since it detects variation in the proteome, lipidome and metabolome of biofluids, or activation of immune cells. However, the analysis of any materials by spectral methods is confounded by external factors such as those related to sample deposition and data acquisition, and by inherent variations in blood plasma concentration of small molecules (lactate, carbonate, phosphate, glucose) that varies between individual subjects and even for a given individual, as a function of time. Furthermore, observed differences in spectral patterns between patient samples and the control group may be due to the body's immune response (in particular, to the albumin to globulin ratio) and therefore, may not be specific to disease. These factors need to be accounted for in any effort to reliably detect much smaller variations in the concentration of disease‐specific markers.     

混杂试验的多元分析

本文首次提出混杂试验的多元统计分析方法,并以香茅肥料试验为实例,论述平方和与乘积和矩阵的分解及总自由度的裂分方法,各种WilksA统计量的构造及其显著性测验法．最后给出了笔者编制的SAS统计分析程序．     

Correlations with Self-Reported Weight Loss in Overweight U.S. Adults

The authors investigated two issues among overweight men and women in the U.S.: 1) what is the influence of the self-expressed intention to lose weight in the presence of other potential predictors of loss and 2) what are easily identifiable predictors of intentional weight loss during a 1-year recall period. The sample consisted of 1996 overweight men (body mass index (BMI ≥ 27.8 kg/m²) and 2586 overweight women (BMI ≥ 27.3 kg/m²) who answered questions regarding 1-year weight change in a Current Health Topic supplement of the population-based 1989 National Health Interview Survey. Of these overweight persons, 56.8% of men and 72.1% of women attempted to lose weight during the previous year. The most important characteristic associated with weight loss was the expressed intention itself. For any weight loss, the odds ratios (95% confidence intervals) for intention were 4.6 (3.6?5.9) for men and 3.8 (2.8?5.0) for women. Controlling for other factors reduced the odds only slightly, to 4.3 for men and 3.5 for women. Among women, older age, having a greater frequency of blood pressure checks, and being in poorer health reduced the influence of intent as a predictor of loss. To address the second objective, the identification of predictors of intentional 1-year weight loss, analysis was restricted to overweight persons who attempted to lose weight. For both sexes, statistically significant predictors (p<0.05) included never being married, smoking, higher BMI, being diabetic, and having a higher number of blood pressure checks. Being divorced or separated was predictive of weight loss in men only. Also, men were more likely to achieve weight loss than women. In conclusion, 1-year weight loss among the overweight was primarily a function of the intention to lose weight, although other factors contributed to determine whether weight loss was achieved.