Annals of Epidemiology - Articles in Press

Variability of the Date of HIV Diagnosis: A Comparison of Self-Report, Medical Record, and HIV/AIDS Surveillance Data - Corrected Proof

2010-07-12

Purpose: We sought to describe and quantify differences among the year of first positive HIV test from patient report, the medical record, and HIV/AIDS surveillance data.Methods: We merged two clinic-based studies with overlapping HIV-infected participant populations in North Carolina with the HIV/AIDS Reporting System (HARS) and examined the first positive HIV test year from patient report, the medical record, and HARS. Matches were considered the same year of diagnosis.Results: The self-reported year of diagnosis had high agreement with the medical record (67% matched exactly and 19% differed by 1 year, weighted kappa = 0.85), although there were wide 95% limits of agreement (–4.0 earlier to 3.9 years later). On average, the dates of diagnosis from patient report and the medical record were earlier than HARS with wide 95% limits of agreement (7.5 years earlier to 6.0 years later for patient report vs. HARS, 7.7 years earlier to 6.0 years later for medical record vs. HARS).Conclusions: These measures could not reliably be used interchangeably as there was wide variability in both directions. Although collection of data from patient report or existing sources is convenient, cost effective, and efficient, there is significant variability between sources.

Short Sleep Duration Is Associated with the Development of Impaired Fasting Glucose: The Western New York Health Study - Corrected Proof

2010-07-12

Purpose: To examine whether sleep duration was associated with incident-impaired fasting glucose (IFG) over 6 years of follow-up in the Western New York Health Study.Methods: Participants (N = 1,455, 68% response rate) who were free of type 2 diabetes and known cardiovascular disease at baseline (1996–2001) were reexamined in the period 2003–2004. A nested case-control study was conducted. Cases had fasting plasma glucose (FPG) less than 100 mg/dL at baseline and 100 to 125 mg/dL at follow-up: controls (n = 272) had FPG less than 100 mg/dL at both exams. Cases (n = 91) were individually matched to three controls (n = 272) on sex, race, and year of study enrollment. Average sleep duration was categorized as short (<6 hours), mid-range (6 to 8 hours), and long (>8 hours).Results: In multivariate conditional logistic regression after adjustment for several diabetes risk factors, the odds ratio (OR) of IFG among short sleepers was 3.0 (95% confidence limit [CL]: 1.05, 8.59) compared to mid-range sleepers. There was no association between long sleep and IFG: OR 1.6 (95% CL: 0.45, 5.42). Adjustment for insulin resistance attenuated the association only among short sleepers: OR 2.5 (95% CL: 0.83, 7.46).Conclusions: Short sleep duration was associated with an elevated risk of IFG. Insulin resistance appears to mediate this association.

Size Does Matter: Adolescent Build and Male Reproductive Success in the Guangzhou Biobank Cohort Study - Corrected Proof

2010-07-12

Purpose: Women usually report attributes of masculinity as attractive. These are attributes are metabolically expensive. We examined the trade off of a key attribute of masculinity, muscularity, proxied by recalled adolescence build, with lifetime reproductive success in the developing country setting of Southern China.Methods: We used poisson multivariable regression in 19,168 older (≥50 years) Chinese from the Guangzhou Biobank Cohort Study (phases 2 and 3) to examine the sex-stratified, adjusted associations of recalled adolescent relative weight (light (n = 6730), average (n = 9344), and heavy (n = 3094)) with number of offspring.Results: Among men, recalled heavy adolescent weight compared with light was associated with an incident rate ratio for offspring of 1.08 (95% confidence interval [CI] 1.04–1.13) adjusted for age. This estimate was unchanged by adjustment for life course socio-economic position. There was no such association in women.Conclusions: Male physical attractiveness, possibly representing levels of testosterone, was rewarded by lifetime reproductive success, despite potential costs. Socio-economic development may facilitate an inevitable move toward environmentally driven higher levels of testosterone with corresponding public health implications for any conditions or societal attributes driven by testosterone. Further investigation is warranted.

Walking Pace, Leisure Time Physical Activity, and Resting Heart Rate in Relation to Disease-Specific Mortality in London: 40 Years Follow-Up of the Original Whitehall Study. An Update of Our Work with Professor Jerry N. Morris (1910–2009) - Corrected Proof

2010-06-28

Purpose: To examine the association of leisure time physical activity, walking pace and resting heart rate with disease-specific mortality in a prospective cohort study by reporting updated analyses of an earlier report we produced with the British epidemiologist, Jerry N. Morris (1910–2009).Methods: In the original Whitehall study, 19,019 male, nonindustrial, London-based government employees, aged from 40 to 69 years in 1967 and 1970, participated in a medical examination during which data on leisure time physical activity (N = 6715), self-rated walking pace (N = 6729), and resting heart rate (N = 1183) were collected. Cox proportional hazards analyses were used to estimate hazard ratios for the relation between these exposures and disease-specific mortality.Results: In models adjusted for a range of covariates including socioeconomic status, smoking, and obesity, high resting heart rate was associated with a modestly elevated rate of mortality from all causes (hazard ratio; 95% confidence interval: tertile 3 vs. tertile 1: 1.17; 0.99, 1.37 p[trend]: 0.07) and respiratory disease (1.69; 1.04, 2.76 p[trend]: 0.03). Of the two markers of physical activity, walking pace was inversely related to mortality ascribed to all causes (slow vs. high walking pace 1.71; 1.53, 1.91 p[trend]: <0.001]), coronary heart disease (2.03; 1.68, 2.47 p[trend]: <0.001), and total cancers (1.25; 0.98, 1.59 p[trend]: 0.04). The corresponding associations for leisure time activity were typically weaker. For other mortality endpoints—respiratory disease (walking pace: 1.96; 1.48, 2.60 p[trend]: <0.001]), hematopoietic cancer (walking pace: 1.36; 0.52, 3.51 p[trend]: 0.03), stomach cancer (inactive versus active leisure time: 1.53; 0.88, 2.64 p[trend]: 0.04), and rectal cancer (walking pace: 4.85; 1.70, 13.8 p[trend]: 0.007)—individual activity indices revealed effects, but not both.Conclusions: Higher levels of physical activity indexed by the various markers herein appeared to confer protection against a range of mortality outcomes.

Detecting Differentially Expressed Genes: Minimizing Burden of Testing and Maximizing Number of Discoveries - Corrected Proof

Wen-Chung Lee — 2010-06-28

Purpose: Recent progress in DNA microarray technologies allows researchers to perform genome-wide screening to detect differentially expressed genes. Under the paradigm of false discovery rate control, this paper presents sample size methods.Methods: The author considers the following two scenarios: 1) planning the sample size to keep the ‘burden of testing’ (defined as the expected number of genes that have to be tested before a true discovery can be made) below a certain level, and 2) given a fixed amount of budget, balancing the number of subjects to be recruited and the number of genes to be tested to maximize the total number of true discoveries.Results: The study calculates sample sizes to minimize the burden of testing or to maximize the number of discoveries.Conclusions: The present approach to sample size calculation bears more direct relevance to gene-discovery studies.

Does Ignoring Model Selection When Assessing the Effect of PM on Mortality Make Us Too Vigilant? - Corrected Proof

Steven Roberts, Michael A. Martin — 2010-06-04

Purpose: To investigate the extent to which standard errors can be underestimated in time-series studies of the association between particulate matter air pollution (PM) and mortality if model selection variation is not accounted for.Methods: Actual-time series data from Cook County, Illinois, and Salt Lake County, Utah, for the period 1987 to 2000 were used to generate mortality time series. These series were used to examine the overconfidence resulting from ignoring variability introduced by the model selection process.Results: When variation associated with a model selection process is not accounted for, we found that the estimated standard errors for the effect of PM on mortality were substantially smaller than the true standard errors that necessarily incorporate model selection variability. Because of this, the true standard errors are approximately 70% larger than the reported standard errors. We also found that not accounting for model selection effects can result in the observed size of tests of no association between PM and mortality being up to about five times the nominal significance level.Conclusions: Failing to account properly for the effect of model selection can reduce the accepted burden of proof for concluding a statistically significant association between PM and mortality.

A Multiphase Method for Estimating Cohort Effects in Age-Period Contingency Table Data - Corrected Proof

Katherine M. Keyes, Guohua Li — 2010-06-03

Purpose: Understanding the effects of age, period, and cohort on disease morbidity and mortality may help identify etiological factors and inform prevention programs. We illustrate a three-phase method that conceptualizes the cohort effect as a partial interaction between age and period. As an example of application, we analyze homicide mortality data for males in the United States from 1935 through 2004.Methods: The three-phased method begins with graphical inspection; second, a median polish is used to remove the log-additive components of age and period effects; third, a linear regression of residuals from the median polish is modeled to quantify the relative magnitude of the cohort effect.Results: Individuals born after 1960 have a significantly increased rate of homicide relative to those born between 1920 and 1924. After removal of the log-additive effects of age and period, the estimated homicide rate for men born between 1980 and 1984 is more than twice the rate for men born between 1920 and 1924 (rate ratio, 2.11; 95% confidence interval, 1.98–2.25).Conclusion: The three-phase method presented herein offers several advantages, the foremost being an alternative conceptualization of the cohort effect not as an independent component of age and period effects, but as a partial interaction. In addition, the strengths of the method include computational simplicity, interpretability, and reliability.

Nicotine Dependence Predicts Repeated Use of Prescribed Opioids. Prospective Population-based Cohort Study - Corrected Proof

Svetlana Skurtveit, Kari Furu, Randi Selmer, Marte Handal, Aage Tverdal — 2010-06-03

Purpose: The aim of this study was to evaluate prospectively smoking dependence as a predictor of repeated use of prescribed opioids in non-cancer patients.Methods: We conducted a prospective population-based study cohort of 12,848 men and 15,894 women 30–75 years of age in health surveys in Norway during 2000–2002 with repeated opioid prescriptions (12+, during 2004–2007) recorded in the Norwegian Prescription Database as the outcome measure. Information on history of smoking and potential confounders was obtained at baseline by self-administered questionnaires. For smoking, participants were divided into categories: never; previously heavy (stopped maximum of 5 years earlier; 10+ cigarettes daily); daily not heavy (1–9 cigarettes); dependent daily smokers (10+ cigarettes), and other (previously and/or not daily). Odds ratios (ORs) with 95% confidence intervals (CIs) were estimated by logistic regression.Results: During follow-up, 335 (1.5%) of survey participants were registered with 12+ prescriptions of opioids during the period 2004–2007. The prevalence of repeated prescription frequency of opioids was higher for men and women with a history of smoking. The adjusted OR for prescribed opioids for dependent daily smokers was 3.1 (95% CI: 2.3–4.1), for daily non-heavy smokers 1.8 (1.2–2.7), and for previous heavy smokers 1.8 (1.1–3.0), compared with never-smokers as reference.Conclusions: Results of the study suggest that smoking dependence may predict more frequent use of opioids.