Although previous studies have found a protective association between attendance at religious services and depression, the extent to which this association is driven by depressed persons’ dropping out of religious activities is not clear. The authors examined whether early onset of a major depressive episode (MDE) predicted a subsequent decrease in religious service attendance. Data came from 3 follow-up studies of the National Collaborative Perinatal Project birth cohort (mean age = 37 years at last follow-up; n = 2,097; 1959–2001). The generalized estimating equations method was used to calculate the impact of an early MDE diagnosis (before age 18 years) on the likelihood of change in level of religious service attendance from childhood to adulthood. Twenty-seven percent of study participants met the criteria for lifetime MDE (n = 567), of whom 31% had their first onset prior to age 18 years. Women with early MDE onset were 1.42 times more likely (95% confidence interval: 1.19, 1.70) than women with adult-onset MDE or no lifetime MDE to stop attending religious services by the time of the first adult follow-up wave. No significant associations were observed among men. These findings suggest that women are more likely to stop attending religious services after onset of depression. Selection out of religious activities could be a significant contributor to previously observed inverse correlations between religious service attendance and psychopathology during adulthood.

Extant analyses of the relation between economic conditions and population health were often based on annualized data and were susceptible to confounding by nonlinear time trends. In the present study, the authors used generalized additive models with nonparametric smoothing splines to examine the association between economic conditions, including levels of economic activity in New York State and the degree of volatility in the New York Stock Exchange, and monthly rates of death by suicide in New York City. The rate of suicide declined linearly from 8.1 per 100,000 people in 1990 to 4.8 per 100,000 people in 1999 and then remained stable from 1999 to 2006. In a generalized additive model in which the authors accounted for long-term and seasonal time trends, there was a negative association between monthly levels of economic activity and rates of suicide; the predicted rate of suicide was 0.12 per 100,000 persons lower when economic activity was at its peak compared with when it was at its nadir. The relation between economic activity and suicide differed by race/ethnicity and sex. Stock market volatility was not associated with suicide rates. Further work is needed to elucidate pathways that link economic conditions and suicide.

Menstrual bleeding patterns are considered relevant indicators of reproductive health, though few studies have evaluated patterns among regularly menstruating premenopausal women. The authors evaluated self-reported bleeding patterns, incidence of spotting, and associations with reproductive hormones among 201 women in the BioCycle Study (2005–2007) with 2 consecutive cycles. Bleeding patterns were assessed by using daily questionnaires and pictograms. Marginal structural models were used to evaluate associations between endogenous hormone concentrations and subsequent total reported blood loss and bleeding length by weighted linear mixed-effects models and weighted parametric survival analysis models. Women bled for a median of 5 days (standard deviation: 1.5) during menstruation, with heavier bleeding during the first 3 days. Only 4.8% of women experienced midcycle bleeding. Increased levels of follicle-stimulating hormone (β = 0.20, 95% confidence interval: 0.13, 0.27) and progesterone (β = 0.06, 95% confidence interval: 0.03, 0.09) throughout the cycle were associated with heavier menstrual bleeding, and higher follicle-stimulating hormone levels were associated with longer menses. Bleeding duration and volume were reduced after anovulatory compared with ovulatory cycles (geometric mean blood loss: 29.6 vs. 47.2 mL; P = 0.07). Study findings suggest that detailed characterizations of bleeding patterns may provide more insight than previously thought as noninvasive markers for endocrine status in a given cycle.

Pleiotropy across the 8q24 region is perhaps the most intriguing of the genome-wide association findings relating to cancer. This region of chromosome 8 is a gene desert, far from any recognized genes. Guarrera et al., whose work is reported in this issue (Am J Epidemiol. 2012;175(6):479–487), took an epidemiologic approach to learn more about the 8q24 region. They capitalized on their ascertainment of other endpoints in members of the cohort at the Turin site of the European Prospective Investigation Into Cancer and Nutrition to investigate multiple outcomes for additional pleiotropic effects in the 8q24 region. Alternative design options might involve genotyping of more variants, incorporation of more cases, or use of a single control group close to the size of the most common case group. Their analytic methods reflect the uncertainty of the underlying biology. The findings sharpen the scientific question about how variation in the 8q24 region affects pathogenesis. The genome-wide association effort is possible because of the economy of scale afforded by extremely dense genotyping. Strict adherence to the hypothesis-driven approach would ignore information that is obtainable at a trivial cost. The genome-wide association strategy tests whether agnostic data-mining methods can advance knowledge alongside or even in place of the standard hypothesis-driven approach, which is the conventional scientific method children learn in kindergarten and onward, even through graduate school and beyond.

The authors conducted a time-series analysis to examine seasonal variation of mortality risk in association with particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) and chemical species in Xi’an, China, using daily air pollution and all-cause and cause-specific mortality data (2004–2008). Poisson regression incorporating natural splines was used to estimate mortality risks of PM2.5 and its chemical components, adjusting for day of the week, time trend, and meteorologic effects. Increases of 2.29% (95% confidence interval: 0.83, 3.76) for all-cause mortality and 3.08% (95% confidence interval: 0.94, 5.26) for cardiovascular mortality were associated with an interquartile range increase of 103.0 μg/m3 in lagged 1–2 day PM2.5 exposure. Stronger effects were observed for the elderly (≥65 years), males, and cardiovascular diseases groups. Secondary components (sulfate and ammonium), combustion species (elemental carbon, sulfur, chlorine), and transition metals (chromium, lead, nickel, and zinc) appeared most responsible for increased risk, particularly in the cold months. The authors concluded that differential association patterns observed across species and seasons indicated that PM2.5-related effects might not be sufficiently explained by PM2.5 mass alone. Future research is needed to examine spatial and temporal varying factors that might play important roles in modifying the PM2.5–mortality association.

The authors examined the impact of race/ethnicity on responses to the Everyday Discrimination Scale, one of the most widely used discrimination scales in epidemiologic and public health research. Participants were 3,295 middle-aged US women (African-American, Caucasian, Chinese, Hispanic, and Japanese) from the Study of Women’s Health Across the Nation (SWAN) baseline examination (1996–1997). Multiple-indicator, multiple-cause models were used to examine differential item functioning (DIF) on the Everyday Discrimination Scale by race/ethnicity. After adjustment for age, education, and language of interview, meaningful DIF was observed for 3 (out of 10) items: “receiving poorer service in restaurants or stores,” “being treated as if you are dishonest,” and “being treated with less courtesy than other people” (all P's < 0.001). Consequently, the “profile” of everyday discrimination differed slightly for women of different racial/ethnic groups, with certain “public” experiences appearing to have more salience for African-American and Chinese women and “dishonesty” having more salience for racial/ethnic minority women overall. “Courtesy” appeared to have more salience for Hispanic women only in comparison with African-American women. Findings suggest that the Everyday Discrimination Scale could potentially be used across racial/ethnic groups as originally intended. However, researchers should use caution with items that demonstrated DIF.

Prospective epidemiologic studies have characterized major risk factors for incident diabetes by a variety of diabetes case definitions. Whether different definitions alter the association of diabetes with risk factors is largely unknown. Using 1987–1998 data from the ongoing Atherosclerosis Risk in Communities (ARIC) Study, the authors assessed the relation of traditional risk factors with 3 different diabetes case definitions and 4 fasting glucose categories. They compared the study protocol case definition with 2 nested case definitions, self-reported diabetes and a multiple-evidence definition. Significant differences in risk factor associations by case definition and by screening cutpoints were observed. Specifically, the magnitude of the association between the risk factors (baseline metabolic syndrome, fasting glucose, blood pressure, body mass index, and serum insulin) and incident diabetes differed by case definition. Associations with these risk factors were weaker with a case definition based on self-report compared with other definitions. These results illustrate the potential limitations of case definitions that rely solely on self-report or those that incorporate measured glucose values to ascertain undiagnosed cases. Although the ability to identify risk factors of diabetes was consistent for the case definitions studied, tests of novel risk factors may result in different estimates of effect sizes depending on the definition used.

Leukocyte telomere length (LTL) is a potential indicator of cellular aging; however, its relation to physical activity and sedentary behavior is unclear. The authors examined cross-sectionally associations among activity, sedentary behavior, and LTL among 7,813 women aged 43–70 years in the Nurses’ Health Study. Participants self-reported activity by questionnaire in 1988 and 1992 and sedentary behavior in 1992. Telomere length in peripheral blood leukocytes, collected in 1989–1990, was measured by quantitative polymerase chain reaction. The least-squares mean telomere length (z-score) was calculated after adjustment for age and other potential confounders. For total activity, moderately or highly active women had a 0.07-standard deviation (SD) increase in LTL (2-sided Ptrend = 0.02) compared with those least active. Greater moderate- or vigorous-intensity activity was also associated with increased LTL (SD = 0.11 for 2–4 vs. <1 hour/week and 0.04 for ≥7 vs. <1 hour/week; 2-sided Ptrend = 0.02). Specifically, calisthenics or aerobics was associated with increased LTL (SD = 0.10 for ≥2.5 vs. 0 hours/week; 2-sided Ptrend = 0.04). Associations remained after adjustment for body mass index. Other specific activities and sitting were unassociated with LTL. Although associations were modest, these findings suggest that even moderate amounts of activity may be associated with longer telomeres, warranting further investigation in large prospective studies.

C-reactive protein (CRP) is one of the most commonly used markers of acute phase reaction in clinical settings and predictors of cardiovascular risk in healthy women; however, data on its physiologic regulation in premenopausal women are sparse. The objective of this study was to evaluate the association between endogenous reproductive hormones and CRP in the BioCycle Study (2005–2007). Women aged 18–44 years from western New York were followed prospectively for up to 2 menstrual cycles (n = 259). Serum levels of CRP, estradiol, progesterone, luteinizing hormone, and follicle-stimulating hormone were measured up to 8 times per cycle, timed by fertility monitors. CRP levels varied significantly across the cycle (P < 0.001). More women were classified as being at elevated risk of cardiovascular disease (CRP, >3 mg/L) during menses compared with other phases (12.3% vs. 7.4%; P < 0.001). A 10-fold increase in estradiol was associated with a 24.3% decrease in CRP (95% confidence interval: 19.3, 29.0). A 10-fold increase in luteal progesterone was associated with a 19.4% increase in CRP (95% confidence interval: 8.4, 31.5). These results support the hypothesis that endogenous estradiol might have antiinflammatory effects and highlight the need for standardization of CRP measurement to menstrual cycle phase in reproductive-aged women.

To estimate the association of antiretroviral therapy initiation with incident acquired immunodeficiency syndrome (AIDS) or death while accounting for time-varying confounding in a cost-efficient manner, the authors combined a case-cohort study design with inverse probability-weighted estimation of a marginal structural Cox proportional hazards model. A total of 950 adults who were positive for human immunodeficiency virus type 1 were followed in 2 US cohort studies between 1995 and 2007. In the full cohort, 211 AIDS cases or deaths occurred during 4,456 person-years. In an illustrative 20% random subcohort of 190 participants, 41 AIDS cases or deaths occurred during 861 person-years. Accounting for measured confounders and determinants of dropout by inverse probability weighting, the full cohort hazard ratio was 0.41 (95% confidence interval: 0.26, 0.65) and the case-cohort hazard ratio was 0.47 (95% confidence interval: 0.26, 0.83). Standard multivariable-adjusted hazard ratios were closer to the null, regardless of study design. The precision lost with the case-cohort design was modest given the cost savings. Results from Monte Carlo simulations demonstrated that the proposed approach yields approximately unbiased estimates of the hazard ratio with appropriate confidence interval coverage. Marginal structural model analysis of case-cohort study designs provides a cost-efficient design coupled with an accurate analytic method for research settings in which there is time-varying confounding.

Hair relaxers are used by millions of black women, possibly exposing them to various chemicals through scalp lesions and burns. In the Black Women’s Health Study, the authors assessed hair relaxer use in relation to uterine leiomyomata incidence. In 1997, participants reported on hair relaxer use (age at first use, frequency, duration, number of burns, and type of formulation). From 1997 to 2009, 23,580 premenopausal women were followed for incident uterine leiomyomata. Multivariable Cox regression was used to estimate incidence rate ratios and 95% confidence intervals. During 199,991 person-years, 7,146 cases of uterine leiomyomata were reported as confirmed by ultrasound (n = 4,630) or surgery (n = 2,516). The incidence rate ratio comparing ever with never use of relaxers was 1.17 (95% confidence interval (CI): 1.06, 1.30). Positive trends were observed for frequency of use (Ptrend < 0.001), duration of use (Ptrend = 0.015), and number of burns (Ptrend < 0.001). Among long-term users (≥10 years), the incidence rate ratios for frequency of use categories 3–4, 5–6, and ≥7 versus 1–2 times/year were 1.04 (95% CI: 0.92, 1.19), 1.12 (95% CI: 0.99, 1.27), and 1.15 (95% CI: 1.01, 1.31), respectively (Ptrend = 0.002). Risk was unrelated to age at first use or type of formulation. These findings raise the hypothesis that hair relaxer use increases uterine leiomyomata risk.

Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976–1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984–1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.

The authors evaluated the association between smoking and the incidence of psoriasis among 185,836 participants from a cohort of older women (the Nurses’ Health Study, 1996–2008), a cohort of younger women (the Nurses’ Health Study II, 1991–2005), and a cohort of men (Health Professionals’ Follow-up Study, 1986–2006). Information on smoking was collected biennially during follow-up. The authors identified a total of 2,410 participants with incident psoriasis. Compared with never smokers, past smokers had a relative risk of incident psoriasis of 1.39 (95% confidence interval (CI): 1.27, 1.52) and current smokers had a relative risk of 1.94 (95% CI: 1.64, 2.28). For current smokers who smoked 1–14 cigarettes/day, the relative risk was 1.81 (95% CI: 1.38, 2.36); for those who smoked 15–24 cigarettes/day, the relative risk was 2.04 (95% CI: 1.68, 2.47); and for those who smoked 25 or more cigarettes/day, the relative risk was 2.29 (95% CI: 1.74, 3.01). There was a trend toward an increased risk of psoriasis with increasing pack-years or duration of smoking (Ptrend < 0.0001). The risk was highest among smokers who had 65 or more pack-years of smoking (relative risk = 2.72, 95% CI: 2.05, 3.60) and among those with a smoking duration of 30 or more years (relative risk = 1.99, 95% CI: 1.75, 2.25). The authors observed a graded reduction of risk with an increase in time since smoking cessation (Ptrend <0.0001). In this study, smoking was found to be an independent risk factor for psoriasis in both women and men. Psoriasis risk was particularly augmented for heavy smokers and persons with longer durations of smoking.

Information on dietary supplements, medications, and other xenobiotics in epidemiologic surveys is usually obtained from questionnaires and is subject to recall and reporting biases. The authors used metabolite data obtained from hydrogen-1 (or proton) nuclear magnetic resonance (1H NMR) analysis of human urine specimens from the International Study of Macro-/Micro-Nutrients and Blood Pressure (INTERMAP Study) to validate self-reported analgesic use. Metabolic profiling of two 24-hour urine specimens per individual was carried out for 4,630 participants aged 40–59 years from 17 population samples in Japan, China, the United Kingdom, and the United States (data collection, 1996–1999). 1H NMR-detected acetaminophen and ibuprofen use was low (∼4%) among East Asian population samples and higher (>16%) in Western population samples. In a comparison of self-reported acetaminophen and ibuprofen use with 1H NMR-detected acetaminophen and ibuprofen metabolites among 496 participants from Chicago, Illinois, and Belfast, Northern Ireland, the overall rate of concordance was 81%–84%; the rate of underreporting was 15%–17%; and the rate of underdetection was approximately 1%. Comparison of self-reported unspecified analgesic use with 1H NMR-detected acetaminophen and ibuprofen metabolites among 2,660 Western INTERMAP participants revealed similar levels of concordance and underreporting. Screening for urinary metabolites of acetaminophen and ibuprofen improved the accuracy of exposure information. This approach has the potential to reduce recall bias and other biases in epidemiologic studies for a range of substances, including pharmaceuticals, dietary supplements, and foods.

Randomized clinical trials (RCTs) are usually the preferred strategy with which to generate evidence of comparative effectiveness, but conducting an RCT is not always feasible. Though observational studies and RCTs often provide comparable estimates, the questioning of observational analyses has recently intensified because of randomized-observational discrepancies regarding the effect of postmenopausal hormone replacement therapy on coronary heart disease. Reanalyses of observational data that excluded prevalent users of hormone replacement therapy led to attenuated discrepancies, which begs the question of whether exclusion of prevalent users should be generally recommended. In the current study, the authors evaluated the effect of excluding prevalent users of statins in a meta-analysis of observational studies of persons with cardiovascular disease. The pooled, multivariate-adjusted mortality hazard ratio for statin use was 0.77 (95% confidence interval (CI): 0.65, 0.91) in 4 studies that compared incident users with nonusers, 0.70 (95% CI: 0.64, 0.78) in 13 studies that compared a combination of prevalent and incident users with nonusers, and 0.54 (95% CI: 0.45, 0.66) in 13 studies that compared prevalent users with nonusers. The corresponding hazard ratio from 18 RCTs was 0.84 (95% CI: 0.77, 0.91). It appears that the greater the proportion of prevalent statin users in observational studies, the larger the discrepancy between observational and randomized estimates.

Regression calibration has been described as a means of correcting effects of measurement error for normally distributed dietary variables. When foods are the items of interest, true distributions of intake are often positively skewed, may contain many zeroes, and are usually not described by well-known statistical distributions. The authors considered the validity of regression calibration assumptions where data are non-Gaussian. Such data (including many zeroes) were simulated, and use of the regression calibration algorithm was evaluated. An example used data from Adventist Health Study 2 (2002–2008). In this special situation, a linear calibration model does (as usual) at least approximately correct the parameter that captures the exposure-disease association in the “disease” model. Poor fit in the calibration model does not produce biased calibrated estimates when the “disease” model is linear, and it produces little bias in a nonlinear “disease” model if the model is approximately linear. Poor fit will adversely affect statistical power, but more complex linear calibration models can help here. The authors conclude that non-Gaussian data with many zeroes do not invalidate regression calibration. Irrespective of fit, linear regression calibration in this situation at least approximately corrects bias. More complex linear calibration equations that improve fit may increase power over that of uncalibrated regressions.

With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1–12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America’s Table Study (1997–1998), the authors found that 4–6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2–4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4–6 administrations of the 24HR should be considered along with administration of an FFQ.

This study examined associations between mortality and demographic and risk characteristics among young injection drug users in San Francisco, California, and compared the mortality rate with that of the population. A total of 644 young (<30 years) injection drug users completed a baseline interview and were enrolled in a prospective cohort study, known as the UFO (“U Find Out”) Study, from November 1997 to December 2007. Using the National Death Index, the authors identified 38 deaths over 4,167 person-years of follow-up, yielding a mortality rate of 9.1 (95% confidence interval: 6.6, 12.5) per 1,000 person-years. This mortality rate was 10 times that of the general population. The leading causes of death were overdose (57.9%), self-inflicted injury (13.2%), trauma/accidents (10.5%), and injection drug user-related medical conditions (13.1%). Mortality incidence was significantly higher among those who reported injecting heroin most days in the past month (adjusted hazard ratio = 5.8, 95% confidence interval: 1.4, 24.3). The leading cause of death in this group was overdose, and primary use of heroin was the only significant risk factor for death observed in the study. These findings highlight the continued need for public health interventions that address the risk of overdose in this population in order to reduce premature deaths.

The objective of the present commentary is to suggest that epidemiologists explore the use of anti-Müllerian hormone (AMH) as a new measurement tool in fecundability studies. The authors briefly summarize the advantages and limitations of the 3 current approaches to studies of fecundability. All 3 approaches involve the collection of time-to-pregnancy or attempt-time data, and most are limited to participants who plan their pregnancies. AMH is produced by ovarian follicles during their early growth stages and is measured clinically to assess ovarian reserve (the number of remaining oocytes). Unlike time to pregnancy, serum AMH level can be assessed regardless of pregnancy-attempt status. Measurements are not significantly affected by phase of the menstrual cycle, oral contraceptive use, or early pregnancy. The authors suggest that AMH measurement can be a valuable addition to traditionally designed fecundability studies. In addition, this hormone should be investigated as an independent measure of fecundability in studies that focus on exposures hypothesized to target the ovary.

As with other instrumental variable (IV) analyses, Mendelian randomization (MR) studies rest on strong assumptions. These assumptions are not routinely systematically evaluated in MR applications, although such evaluation could add to the credibility of MR analyses. In this article, the authors present several methods that are useful for evaluating the validity of an MR study. They apply these methods to a recent MR study that used fat mass and obesity-associated (FTO) genotype as an IV to estimate the effect of obesity on mental disorder. These approaches to evaluating assumptions for valid IV analyses are not fail-safe, in that there are situations where the approaches might either fail to identify a biased IV or inappropriately suggest that a valid IV is biased. Therefore, the authors describe the assumptions upon which the IV assessments rely. The methods they describe are relevant to any IV analysis, regardless of whether it is based on a genetic IV or other possible sources of exogenous variation. Methods that assess the IV assumptions are generally not conclusive, but routinely applying such methods is nonetheless likely to improve the scientific contributions of MR studies.

An association of gestational weight gain (GWG) with offspring cognition has been postulated. We used data from the Avon Longitudinal Study of Parents and Children, a United Kingdom prospective cohort (1990 through the present) with a median of 10 maternal weight measurements in pregnancy. These were used to allocate participants to 2009 Institute of Medicine weight-gain categories and in random effect linear spline models. Outcomes were School Entry Assessment score (age, 4 years; n = 5,832), standardized intelligence quotient assessed by Wechsler Intelligence Scale for Children (age, 8 years; n = 5,191), and school final-examination results (age, 16 years; n = 7,339). Offspring of women who gained less weight than recommended had a 0.075 standard deviation lower mean School Entry Assessment score (95% confidence interval: −0.127, −0.023) and were less likely to achieve adequate final-examination results (odds ratio = 0.88, 95% confidence interval: 0.78, 0.99) compared with offspring of women who gained as recommended. GWG in early pregnancy (defined as 0–18 weeks on the basis of a knot point at 18 weeks) and midpregnancy (defined as 18–28 weeks on the basis of knot points at 18 and 28 weeks) was positively associated with School Entry Assessment score and intelligence quotient. GWG in late pregnancy (defined as 28 weeks onward on the basis of a knot point at 28 weeks) was positively associated with offspring intelligence quotient and with increased odds of offspring achieving adequate final-examination results in mothers who were overweight prepregnancy. Findings support small positive associations between GWG and offspring cognitive development, which may have lasting effects on educational attainment up to age 16 years.

Despite the growing burden of chronic disease globally, few studies have examined the socioeconomic patterning of risk across countries. The authors examined differences in the social patterning of body mass index (BMI) and current smoking by urbanicity among 70 countries from the 2002–2003 World Health Surveys. Age-adjusted, gender-stratified ordinary least squares and logistic regression analyses were conducted in each country to assess the relation between education and BMI or smoking. Meta-analytic techniques were used to assess heterogeneity between countries in the education-risk factor relations. Meta-regression was used to determine whether the heterogeneity could be explained by country-level urbanicity. In the least urban countries, persons with higher education had a higher BMI, while the opposite pattern was seen in the most urban countries, with this pattern being especially pronounced among women. In contrast, smoking was consistently concentrated among persons of lower education among all men and among women in the least urban countries. For women in the most urban countries, higher education was associated with higher odds of smoking, although there was substantial variability in this relation. These results highlight a global trend toward an increasing burden of chronic disease risk among persons of lower socioeconomic position as countries become more urban.

One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such “gene-environment-wide interaction studies” have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.