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In diagnostic medicine, the true disease status of a patient is often represented on an ordinal scale—for example, cancer stage (0, I, II, III, or IV) or coronary artery disease severity measured using the Coronary Artery Disease Reporting and Data System (CAD-RADS) scale (none, minimal, mild, moderate, severe, or occluded). With advances in quantitation of diagnostic images and in artificial intelligence (AI), both supervised and unsupervised algorithms are being developed to help physicians correctly grade disease. Most of the diagnostic accuracy literature deals with binary disease status (disease present or absent); however, tests diagnosing ordinal-scaled diseases should not be reduced to a binary status just to simplify diagnostic accuracy testing. In this paper, we propose different characterizations of ordinal-scale accuracy for different clinical use scenarios, along with methods for comparing tests. In the simplest scenario, just the proportion of correct grades is considered; other scenarios address the magnitude and direction of misgrading; and at the other extreme, a weighted accuracy measure with weights based on the relative costs of different types of misgrading is presented. The various scenarios are illustrated using a coronary artery disease example where the accuracy of AI algorithms in providing patients with the correct CAD-RADS grade is assessed. [ABSTRACT FROM AUTHOR]

We evaluated the performance of a semiquantitative food frequency questionnaire (SFFQ), the Automated Self-Administered 24-Hour Dietary Recall (ASA24), and 7-day dietary records (7DDRs), in comparison with biomarkers, in the estimation of nutrient intakes among 627 women in the Women's Lifestyle Validation Study (United States, 2010-2012). Two paper SFFQs, 1 Web-based SFFQ, 4 ASA24s (beta version), 2 7DDRs, 4 24-hour urine samples, 1 doubly labeled water measurement (repeated among 76 participants), and 2 fasting blood samples were collected over a 15-month period. The dietary variables evaluated were energy, energy-adjusted intakes of protein, sodium, potassium, and specific fatty acids, carotenoids, a-tocopherol, retinol, and folate. In general, relative to biomarkers, averaged ASA24s had lower validity than the SFFQ completed at the end of the data-collection year (SFFQ2); SFFQ2 had slightly lower validity than 1 7DDR; the averaged SFFQs had validity similar to that of 1 7DDR; and the averaged 7DDRs had the highest validity. The deattenuated correlation of energy-adjusted protein intake assessed by SFFQ2 with its biomarker was 0.46, similar to its correlation with 7DDRs (deattenuated r = 0.54). These data indicate that the SFFQ2 provides reasonably valid measurements of energy-adjusted intake for most of the nutrients assessed in our study, consistent with earlier conclusions derived using 7DDRs as the comparison method. The ASA24 needs further evaluation for use in large population studies, but an average of 3 days of measurement will not be sufficient for some important nutrients. [ABSTRACT FROM AUTHOR]

In a 1989 paper, Marchbanks et al. (Am J Epidemiol. 1989; 130(2):259-267) noted inconsistent definitions of infertility across research and clinical practice and examined differences in prevalence estimates across definitions. Since their study, there have been substantial changes in society, technology, and clinical practice related to female reproductive health. In response, we revisited the original paper using data from a recent study among reproductive-aged women. Internal comparisons across various definitions of infertility were made by assessing how many and which women were classified as infertile, their age at infertility, and the probability of spontaneous pregnancy after infertility. Results were also compared with Marchbanks et al. Black women were more likely to be classified as infertile than white women based on the definition "12 months of unprotected intercourse" (40.1% vs. 33.7%) but less likely by "12 months of attempting pregnancy" (14.3% vs. 21.8%) and "visiting a doctor for help getting pregnant" (8.4% vs. 19.7%). After unprotected intercourse for 12 months, 36.1% of women who were attempting pregnancy spontaneously conceived by 6 months compared with 13.5%of women who were not attempting pregnancy. While our results for most infertility definitions were similar to those of Marchbanks et al., prevalence estimates continued to differ across demographic groups by definition. [ABSTRACT FROM AUTHOR]

In 2019, the National Institutes of Health combined the Multicenter AIDS Cohort Study (MACS) and the Women's Interagency HIV Study (WIHS) into the MACS/WIHS Combined Cohort Study (MWCCS). In this paper, participants who made a study visit during October 2018–September 2019 (targeted for MWCCS enrollment) are described by human immunodeficiency virus (HIV) serostatus and compared with people living with HIV (PLWH) in the United States. Participants include 2,115 women and 1,901 men with a median age of 56 years (interquartile range, 48–63); 62% are PLWH. Study sites encompass the South (18%), the Mid-Atlantic/Northeast (45%), the West Coast (22%), and the Midwest (15%). Participant race/ethnicity approximates that of PLWH throughout the United States. Longitudinal data and specimens collected for 35 years (men) and 25 years (women) were combined. Differences in data collection and coding were reviewed, and key risk factor and comorbidity data were harmonized. For example, recent use of alcohol (62%) and tobacco (28%) are common, as are dyslipidemia (64%), hypertension (56%), obesity (42%), mildly or severely impaired daily activities (31%), depressive symptoms (28%), and diabetes (22%). The MWCCS repository includes serum, plasma, peripheral blood mononuclear cells, cell pellets, urine, cervicovaginal lavage samples, oral samples, B-cell lines, stool, and semen specimens. Demographic differences between the MACS and WIHS can confound analyses by sex. The merged MWCCS is both an ongoing observational cohort study and a valuable resource for harmonized longitudinal data and specimens for HIV-related research. [ABSTRACT FROM AUTHOR]

Estimation of causal effects of time-varying exposures using longitudinal data is a common problem in epidemiology. When there are time-varying confounders, which may include past outcomes, affected by prior exposure, standard regression methods can lead to bias. Methods such as inverse probability weighted estimation of marginal structural models have been developed to address this problem. However, in this paper we show how standard regression methods can be used, even in the presence of time-dependent confounding, to estimate the total effect of an exposure on a subsequent outcome by controlling appropriately for prior exposures, outcomes, and time-varying covariates. We refer to the resulting estimation approach as sequential conditional mean models (SCMMs), which can be fitted using generalized estimating equations. We outline this approach and describe how including propensity score adjustment is advantageous. We compare the causal effects being estimated using SCMMs and marginal structural models, and we compare the two approaches using simulations. SCMMs enable more precise inferences, with greater robustness against model misspecification via propensity score adjustment, and easily accommodate continuous exposures and interactions. A new test for direct effects of past exposures on a subsequent outcome is described. [ABSTRACT FROM AUTHOR]

There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate themethods with data from the Ovarian Cancer Association Consortium. [ABSTRACT FROM AUTHOR]

Longitudinal data sources offer new opportunities for the evaluation of sequential interventions. To adjust for time-dependent confounding in these settings, longitudinal targeted maximum likelihood based estimation (TMLE), a doubly robust method that can be coupled with machine learning, has been proposed. This paper provides a tutorial in applying longitudinal TMLE, in contrast to inverse probability of treatment weighting and g-computation based on iterative conditional expectations. We apply these methods to estimate the causal effect of nutritional interventions on clinical outcomes among critically ill children in a United Kingdom study (Control of Hyperglycemia in Paediatric Intensive Care, 2008-2011). We estimate the probability of a child's being discharged alive from the pediatric intensive care unit by a given day, under a range of static and dynamic feeding regimes. We find that before adjustment, patients who follow the static regime "never feed" are discharged by the end of the fifth day with a probability of 0.88 (95% confidence interval: 0.87, 0.90), while for the patients who follow the regime "feed from day 3," the probability of discharge is 0.64 (95% confidence interval: 0.62, 0.66). After adjustment for time-dependent confounding, most of this difference disappears, and the statistical methods produce similar results. TMLE offers a flexible estimation approach; hence, we provide practical guidance on implementation to encourage its wider use. [ABSTRACT FROM AUTHOR]

Internal validity of a risk model can be studied efficiently with bootstrapping to assess possible optimism in model performance. Assumptions of the regular bootstrap are violated when the development data are clustered. We compared alternative resampling schemes in clustered data for the estimation of optimism in model performance. A simulation study was conducted to compare regular resampling on only the patient level with resampling on only the cluster level and with resampling sequentially on both the cluster and patient levels (2-step approach). Optimism for the concordance index and calibration slope was estimated. Resampling of only patients or only clusters showed accurate estimates of optimism in model performance. The 2-step approach overestimated the optimism in model performance. If the number of centers or intraclass correlation coefficient was high, resampling of clusters showed more accurate estimates than resampling of patients. The 3 bootstrap schemes also were applied to empirical data that were clustered. The results presented in this paper support the use of resampling on only the clusters for estimation of optimism in model performance when data are clustered. [ABSTRACT FROM PUBLISHER]

There are several measures that summarize the mortality experience of a population. Of these measures, life expectancies are generally preferred based on their simpler interpretation and direct age standardization, which makes them directly comparable between different populations. However, traditional life expectancy estimations are highly inaccurate for smaller populations and consequently are seldom used in small-area applications. In this paper, the authors compare the relative performance of traditional life expectancy estimation with a Bayesian random-effects approach that uses correlations (i.e., borrows strength) between different age groups, geographic areas, and sexes to improve the small-area life expectancy estimations. In the presented Monte Carlo simulations, the Bayesian random-effects approach outperforms the traditional approach in terms of bias, root mean square error, and coverage of the 95% confidence intervals. Moreover, the Bayesian random-effects approach is found to be usable for populations as small as 2,000 person-years at risk, which is considerably smaller than the minimum of 5,000 person-years at risk recommended for the traditional approach. As such, the proposed Bayesian random-effects approach is well-suited for estimation of life expectancies in small areas. [ABSTRACT FROM AUTHOR]

To date, Web-based 24-hour recalls have not been validated using objective biomarkers. From 2006 to 2009, the validity of 6 Web-based DietDay 24-hour recalls was tested among 115 black and 118 white healthy adults from Los Angeles, California, by using the doubly labeled water method, and the results were compared with the results of the Diet History Questionnaire, a food frequency questionnaire developed by the National Cancer Institute. The authors performed repeated measurements in a subset of 53 subjects approximately 6 months later to estimate the stability of the doubly labeled water measurement. The attenuation factors for the DietDay recall were 0.30 for blacks and 0.26 for whites. For the Diet History Questionnaire, the attenuation factors were 0.15 and 0.17 for blacks and whites, respectively. Adjusted correlations between true energy intake and the recalls were 0.50 and 0.47 for blacks and whites, respectively, for the DietDay recall. For the Diet History Questionnaire, they were 0.34 and 0.36 for blacks and whites, respectively. The rate of underreporting of more than 30% of calories was lower with the recalls than with the questionnaire (25% and 41% vs. 34% and 52% for blacks and whites, respectively). These findings suggest that Web-based DietDay dietary recalls offer an inexpensive and widely accessible dietary assessment alternative, the validity of which is equally strong among black and white adults. The validity of the Web-administered recall was superior to that of the paper food frequency questionnaire. [ABSTRACT FROM PUBLISHER]

Dialysis has been used in the treatment of patients with end-stage renal disease since the 1960s. Recently, several large observational studies have been conducted to assess whether early initiation of dialysis prolongs survival, as compared with late initiation. However, these studies have used analytic approaches which are likely to suffer from either lead-time bias or immortal-time bias. In this paper, the authors demonstrate that recently developed methods in the causal inference literature can be used to avoid both types of bias and accurately estimate the ideal time for dialysis initiation from observational data. This is illustrated using data from a nationwide population-based cohort of patients with chronic kidney disease in Sweden (1996–2003). [ABSTRACT FROM PUBLISHER]

The authors conducted a meta-analysis of the association between smoking before a first pregnancy, when undifferentiated breast tissue may be vulnerable to tobacco carcinogens, and the risk of breast cancer. A search of the published literature through August 2010 identified 23 papers reporting on associations between smoking before a first pregnancy and breast cancer. Odds ratios or hazard ratios and 95% confidence intervals, adjusted for known or suspected breast cancer risk factors, were abstracted from each study. Data were pooled using both fixed- and random-effects models. The fixed-effect summary risk ratio for breast cancer among the women who smoked before their first pregnancy versus women who had never smoked was 1.10 (95% confidence interval: 1.07, 1.14); the random-effects estimate was similar. The separate fixed-effect risk ratios for smoking only before the first pregnancy (5 studies) or only after the first pregnancy (16 studies) were both 1.07, providing no evidence that breast tissue is more susceptible to malignant transformation from smoking before the first pregnancy. While these small summary risk ratios may represent causal effects, residual confounding could readily produce estimates of this size in the absence of any causal effect. [ABSTRACT FROM AUTHOR]

Arsenic in drinking water causes increased mortality from several cancers, ischemic heart disease, bronchiectasis, and other diseases. This paper presents the first evidence relating arsenic exposure to pulmonary tuberculosis, by estimating mortality rate ratios for Region II of Chile compared with Region V for the years 1958–2000. The authors compared mortality rate ratios with time patterns of arsenic exposure, which increased abruptly in 1958 in Region II and then declined starting in 1971. Tuberculosis mortality rate ratios in men started increasing in 1968, 10 years after high arsenic exposure commenced. The peak male 5-year mortality rate ratio occurred during 1982–1986 (rate ratio = 2.1, 95% confidence interval: 1.7, 2.6; P < 0.001) and subsequently declined. Mortality rates in women were also elevated but with fewer excess pulmonary tuberculosis deaths (359 among men and 95 among women). The clear rise and fall of tuberculosis mortality rate ratios in men following high arsenic exposure are consistent with a causal relation. The findings are biologically plausible in view of evidence that arsenic is an immunosuppressant and also a cause of chronic lung disease. Finding weaker associations in women is unsurprising, because this is true of most arsenic-caused health effects. Confirmatory evidence is needed from other arsenic-exposed populations. [ABSTRACT FROM PUBLISHER]

Life-course epidemiology relies on specifying complex (causal) models that describe how variables interplay over time. Traditionally, such models have been constructed by perusing existing theory and previous studies. By comparing data-driven and theory-driven models, we investigated whether data-driven causal discovery algorithms can help in this process. We focused on a longitudinal data set on a cohort of Danish men (the Metropolit Study, 1953–2017). The theory-driven models were constructed by 2 subject-field experts. The data-driven models were constructed by use of the temporal Peter-Clark (TPC) algorithm. The TPC algorithm utilizes the temporal information embedded in life-course data. We found that the data-driven models recovered some, but not all, causal relationships included in the theory-driven expert models. The data-driven method was especially good at identifying direct causal relationships that the experts had high confidence in. Moreover, in a post hoc assessment, we found that most of the direct causal relationships proposed by the data-driven model but not included in the theory-driven model were plausible. Thus, the data-driven model may propose additional meaningful causal hypotheses that are new or have been overlooked by the experts. In conclusion, data-driven methods can aid causal model construction in life-course epidemiology, and combining both data-driven and theory-driven methods can lead to even stronger models. [ABSTRACT FROM AUTHOR]

The aim of this study was to update and validate the Pregnancy Physical Activity Questionnaire (PPAQ), using novel and innovative accelerometer and wearable camera measures in a free-living setting, to improve the measurement performance of this method for self-reporting physical activity. A prospective cohort of 50 eligible pregnant women were enrolled in early pregnancy (mean = 14.9 weeks' gestation). In early, middle, and late pregnancy, participants completed the updated PPAQ and, for 7 days, wore an accelerometer (GT3X-BT; ActiGraph, Pensacola, Florida) on the nondominant wrist and a wearable camera (Autographer; OMG Life (defunct)). At the end of the 7-day period, participants repeated the PPAQ. Spearman correlations between the PPAQ and accelerometer data ranged from 0.37 to 0.44 for total activity, 0.17 to 0.53 for moderate- to vigorous-intensity activity, 0.19 to 0.42 for light-intensity activity, and 0.23 to 0.45 for sedentary behavior. Spearman correlations between the PPAQ and wearable camera data ranged from 0.52 to 0.70 for sports/exercise and from 0.26 to 0.30 for transportation activity. Reproducibility scores ranged from 0.70 to 0.92 for moderate- to vigorous-intensity activity and from 0.79 to 0.91 for sports/exercise, and were comparable across other domains of physical activity. The PPAQ is a reliable instrument and a valid measure of a broad range of physical activities during pregnancy. [ABSTRACT FROM AUTHOR]

Statistical approaches to adaptive treatment strategies (ATS) can be used to mimic the sequential decision-making inherently found in clinical practice. To illustrate the use of a statistical ATS approach, we emulated a target trial of different blood pressure (BP) control plans for the prevention of cardiovascular events among individuals with hypertension at high cardiovascular risk, inspired by the Systolic Blood Pressure Intervention Trial (SPRINT). We included 103,708 patients with hypertension and a "QRISK3" estimated 10-year risk of cardiovascular disease of ≥20% who initiated an antihypertensive drug between 1998 and 2018. Dynamic marginal structural models estimated the comparative effects of treating patients with intensive (target BP: 130/80 mm Hg), standard (140/90 mm Hg), and conservative (150/90 mm Hg) BP control strategies. The adjusted hazard ratios (HRs) for the intensive versus standard strategy were 0.96 (95% confidence interval (CI): 0.92, 1.00) for major adverse cardiovascular events and 0.93 (95% CI: 0.88, 0.97) for death from cardiovascular causes. For the conservative versus standard strategy, they were 1.06 (95% CI: 1.02, 1.10) and 1.08 (95% CI: 1.03, 1.13), respectively. These results are largely compatible with SPRINT. ATS can be used to emulate randomized controlled trials of complex treatment strategies in an observational setting and represents an alternative approach for situations where randomized controlled trials are not feasible. [ABSTRACT FROM AUTHOR]

Studying causal exposure effects on dementia is challenging when death is a competing event. Researchers often interpret death as a potential source of bias, although bias cannot be defined or assessed if the causal question is not explicitly specified. Here we discuss 2 possible notions of a causal effect on dementia risk: the "controlled direct effect" and the "total effect." We provide definitions and discuss the "censoring" assumptions needed for identification in either case and their link to familiar statistical methods. We illustrate concepts in a hypothetical randomized trial on smoking cessation in late midlife, and emulate such a trial using observational data from the Rotterdam Study, the Netherlands, 1990–2015. We estimated a total effect of smoking cessation (compared with continued smoking) on 20-year dementia risk of 2.1 (95% confidence interval: −0.1, 4.2) percentage points and a controlled direct effect of smoking cessation on 20-year dementia risk had death been prevented of −2.7 (95% confidence interval: −6.1, 0.8) percentage points. Our study highlights how analyses corresponding to different causal questions can have different results, here with point estimates on opposite sides of the null. Having a clear causal question in view of the competing event and transparent and explicit assumptions are essential to interpreting results and potential bias. [ABSTRACT FROM AUTHOR]

Social capital has been conceptualized as features of social organization, such as networks, and norms that facilitate coordination and cooperation for mutual benefit. Because of long-standing anti-Black structural oppression in the United States, social capital may be associated with health differently for Black people than for other racial/ethnic groups. Our aim was to examine the psychometric properties of social capital indicators, comparing responses from Black and White people to identify whether there is differential item functioning (DIF) in social capital according to race. DIF examines how items are related to a latent construct and whether this relationship differs across groups such as different racial groups. We used data from respondents to the Southeastern Pennsylvania Household Health Survey in 2004, who lived in Philadelphia (n  = 2,048), a city with a large Black population. We used item response theory analysis to test for racial DIF. We found DIF across the items, indicating measurement error, which could be related to the way these items were developed (i.e. based on cultural assumptions tested in mainstream White America). Hence, our findings underscore the need to interrogate the assumptions that underly existing social capital items through an equity-based lens, and to take corrective action when developing new items to ensure that they are racially and culturally congruent. [ABSTRACT FROM AUTHOR]

In first-line antiretroviral therapy (ART) for human immunodeficiency virus (HIV) treatment, some subgroups of patients may respond better to an efavirenz-based regimen than an integrase strand transfer inhibitor (InSTI)-based regimen, or vice versa, due to patient characteristics modifying treatment effects. Using data based on nearly 16,000 patients from the North American AIDS Cohort Collaboration on Research and Design from 2009–2016, statistical methods for precision medicine were employed to estimate an optimal treatment rule that minimizes the 5-year risk of the composite outcome of acquired immune deficiency syndrome (AIDS)-defining illnesses, serious non-AIDS events, and all-cause mortality. The treatment rules considered were functions that recommend either an efavirenz- or InSTI-based regimen conditional on baseline patient characteristics such as demographic information, laboratory results, and health history. The estimated 5-year risk under the estimated optimal treatment rule was 10.0% (95% confidence interval (CI): 8.6, 11.3), corresponding to an absolute risk reduction of 2.3% (95% CI: 0.9, 3.8) when compared with recommending an efavirenz-based regimen for all patients and 2.6% (95% CI: 1.0, 4.2) when compared with recommending an InSTI-based regimen for all. Tailoring ART to individual patient characteristics may reduce 5-year risk of the composite outcome compared with assigning all patients the same drug regimen. [ABSTRACT FROM AUTHOR]

Many studies encounter clustering due to multicenter enrollment and nonmortality outcomes, such as quality of life, that are truncated due to death—that is, missing not at random and nonignorable. Traditional missing-data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) carried out among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e. the survivor average causal effect (SACE)). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete-case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and software code to support future research. [ABSTRACT FROM AUTHOR]

Despite promising results from individual-level studies, state-level studies of the effectiveness of comprehensive background-check (CBC) policies in reducing firearm fatalities have yielded null results in multiple states. These prior studies focused on CBC laws adopted in the 1990s, when record keeping was far less complete. We estimated the effect of the implementation of CBC policies on state-level firearm homicide and suicide rates in states implementing CBC policies from 2013 to 2015 (Colorado, Delaware, Oregon, and Washington). We compared age-adjusted firearm homicide and suicide rates, measured annually from 15 years prior to policy implementation until 2019, in each treated state to rates in control groups constructed using the synthetic control group method. Differences in firearm homicide rates for Colorado, Oregon, and Washington post treatment were all small (0.09 to 0.18 per 100,000 residents per year) and not well distinguished from natural variation. Oregon had on average 0.80 per 100,000 fewer firearm suicides per year than did synthetic Oregon post treatment. However, these results were inconsistent across modeling approaches and not well distinguished from natural variation. Our models produced poor fit for Delaware. Coupled with previous null results from Indiana, California, Maryland, Pennsylvania, and Tennessee, the present results suggest that extending background check requirements to private transfers alone and implementing these policies as is currently done is not sufficient to achieve significant state-level reductions in firearm fatalities. [ABSTRACT FROM AUTHOR]

Target trial emulation (TTE) applies the principles of randomized controlled trials to the causal analysis of observational data sets. One challenge that is rarely considered in TTE is the sources of bias that may arise if the variables involved in the definition of eligibility for the trial are missing. We highlight patterns of bias that might arise when estimating the causal effect of a point exposure when restricting the target trial to individuals with complete eligibility data. Simulations consider realistic scenarios where the variables affecting eligibility modify the causal effect of the exposure and are missing at random or missing not at random. We discuss means to address these patterns of bias, namely: 1) controlling for the collider bias induced by the missing data on eligibility, and 2) imputing the missing values of the eligibility variables prior to selection into the target trial. Results are compared with the results when TTE is performed ignoring the impact of missing eligibility. A study of palivizumab, a monoclonal antibody recommended for the prevention of respiratory hospital admissions due to respiratory syncytial virus in high-risk infants, is used for illustration. [ABSTRACT FROM AUTHOR]

We compared the performance of prognostic tools for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using parameters fitted either at the time of hospital admission or across all time points of an admission. This cohort study used clinical data to model the dynamic change in prognosis of SARS-CoV-2 at a single hospital center in the United Kingdom, including all patients admitted from February 1, 2020, to December 31, 2020, and then followed up for 60 days for intensive care unit (ICU) admission, death, or discharge from the hospital. We incorporated clinical observations and blood tests into 2 time-varying Cox proportional hazards models predicting daily 24- to 48-hour risk of admission to the ICU for those eligible for escalation of care or death for those ineligible for escalation. In developing the model, 491 patients were eligible for ICU escalation and 769 were ineligible for escalation. Our model had good discrimination of daily risk of ICU admission in the validation cohort (n  = 1,141; C statistic: C  = 0.91, 95% confidence interval: 0.89, 0.94) and our score performed better than other scores (National Early Warning Score 2, International Severe Acute Respiratory and Emerging Infection Comprehensive Clinical Characterisation Collaboration score) calculated using only parameters measured on admission, but it overestimated the risk of escalation (calibration slope = 0.7). A bespoke daily SARS-CoV-2 escalation risk prediction score can predict the need for clinical escalation better than a generic early warning score or a single estimation of risk calculated at admission. [ABSTRACT FROM AUTHOR]

Cross-sectional studies of total gestational weight gain (GWG) and perinatal outcomes have used different approaches to operationalize GWG and adjust for duration of gestation. Using birth records from California (2007–2017), Nevada (2010–2017), and Oregon (2008–2017), we compared 3 commonly used approaches to estimate associations between GWG and cesarean delivery, small-for-gestational-age birth, and low birth weight (LBW): 1) the Institute of Medicine–recommended GWG ranges at a given gestational week, 2) total weight gain categories directly adjusting for gestational age as a covariate, and 3) weight-gain-for-gestational-age z scores derived from an external longitudinal reference population. Among 5,461,130 births, the 3 methods yielded similar conclusions for cesarean delivery and small-for-gestational-age birth. However, for LBW, some associations based on z scores were in the opposite direction of methods 1 and 2, paradoxically suggesting that higher GWG increases risk of LBW. This was due to a greater proportion of preterm births among those with high z scores, and controlling for gestational age in the z score model brought the results in line with the other methods. We conclude that the use of externally derived GWG z scores based on ongoing pregnancies can yield associations confounded by duration of pregnancy when the outcome is strongly associated with gestational age at delivery. [ABSTRACT FROM AUTHOR]

Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007–2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities. [ABSTRACT FROM AUTHOR]

Noncompliance, a common problem in randomized clinical trials (RCTs), can bias estimation of the effect of treatment receipt using a standard intention-to-treat analysis. The complier average causal effect (CACE) measures the effect of an intervention in the latent subpopulation that would comply with their assigned treatment. Although several methods have been developed to estimate the CACE in analyzing a single RCT, methods for estimating the CACE in a meta-analysis of RCTs with noncompliance await further development. This article reviews the assumptions needed to estimate the CACE in a single RCT and proposes a frequentist alternative for estimating the CACE in a meta-analysis, using a generalized linear latent and mixed model with SAS software (SAS Institute, Inc.). The method accounts for between-study heterogeneity using random effects. We implement the methods and describe an illustrative example of a meta-analysis of 10 RCTs evaluating the effect of receiving epidural analgesia in labor on cesarean delivery, where noncompliance varies dramatically between studies. Simulation studies are used to evaluate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Evaluating the impacts of population-level interventions (e.g. changes to state legislation) can be challenging as conducting randomized experiments is often impractical and inappropriate, especially in settings where the intervention is implemented in a single, aggregate unit (e.g. a country or state). A common nonrandomized alternative is to compare outcomes in the treated unit(s) with unexposed controls both before and after the intervention. However, the validity of these designs depends on the use of controls that closely resemble the treated unit on before-intervention characteristics and trends on the outcome, and suitable controls may be difficult to find because the number of potential control regions is typically limited. The synthetic control method provides a potential solution to these problems by using a data-driven algorithm to identify an optimal weighted control unit—a "synthetic control"—based on data from before the intervention from available control units. While popular in the social sciences, the method has not garnered as much attention in health research, perhaps due to a lack of accessible texts aimed at health researchers. We address this gap by providing a comprehensive, nontechnical tutorial on the synthetic control method, using a worked example evaluating Florida's "stand your ground" law to illustrate methodological and practical considerations. [ABSTRACT FROM AUTHOR]

Longitudinal data are necessary to reveal changes within an individual as he or she ages. However, rarely will a single cohort study capture data throughout a person's entire life span. Here we describe in detail the steps needed to develop life-course trajectories from cohort studies that cover different and overlapping periods of life. Such independent studies are probably from heterogenous populations, which raises several challenges, including: 1) data harmonization (deriving new harmonized variables from differently measured variables by identifying common elements across all studies); 2) systematically missing data (variables not measured are missing for all participants in a cohort); and 3) model selection with differing age ranges and measurement schedules. We illustrate how to overcome these challenges using an example which examines the associations of parental education, sex, and race/ethnicity with children's weight trajectories. Data were obtained from 5 prospective cohort studies (carried out in Belarus and 4 regions of the United Kingdom) spanning data collected from birth to early adulthood during differing calendar periods (1936–1964, 1972–1979, 1990–2012, 1996–2016, and 2007–2015). Key strengths of our approach include modeling of trajectories over wide age ranges, sharing of information across studies, and direct comparison of the same parts of the life course in different geographical regions and time periods. We also introduce a novel approach of imputing individual-level covariates of a multilevel model with a nonlinear growth trajectory and interactions. [ABSTRACT FROM AUTHOR]

Assortativity is the tendency of individuals connected in a network to share traits and behaviors. Through simulations, we demonstrated the potential for bias resulting from assortativity by vaccination, where vaccinated individuals are more likely to be connected with other vaccinated individuals. We simulated outbreaks of a hypothetical infectious disease and vaccine in a randomly generated network and a contact network of university students living on campus. We varied protection of the vaccine to the individual, transmission potential of vaccinated-but-infected individuals, and assortativity by vaccination. We compared a traditional approach, which ignores the structural features of a network, with simple approaches which summarized information from the network. The traditional approach resulted in biased estimates of the unit-treatment effect when there was assortativity by vaccination. Several different approaches that included summary measures from the network reduced bias and improved confidence interval coverage. Through simulations, we showed the pitfalls of ignoring assortativity by vaccination. While our example is described in terms of vaccines, our results apply more widely to exposures for contagious outcomes. Assortativity should be considered when evaluating exposures for contagious outcomes. [ABSTRACT FROM AUTHOR]

Morbidity and mortality have been increasing among middle-aged and young-old Americans since the turn of the century. We investigated whether these unfavorable trends extend to younger cohorts and their underlying physiological, psychological, and behavioral mechanisms. Applying generalized linear mixed-effects models to data from 62,833 adults from the National Health and Nutrition Examination Surveys (1988–2016) and 625,221 adults from the National Health Interview Surveys (1997–2018), we found that for all sex and racial groups, physiological dysregulation has increased continuously from Baby Boomers through late-Generation X and Generation Y. The magnitude of the increase was higher for White men than for other groups, while Black men had a steepest increase in low urinary albumin (a marker of chronic inflammation). In addition, Whites underwent distinctive increases in anxiety, depression, and heavy drinking, and they had a higher level than Blacks and Hispanics of smoking and drug use in recent cohorts. Smoking is not responsible for the increasing physiological dysregulation across cohorts. The obesity epidemic contributes to the increase in metabolic syndrome but not in low urinary albumin. The worsening physiological and mental health profiles among younger generations imply a challenging morbidity and mortality prospect for the United States, one that might be particularly inauspicious for Whites. [ABSTRACT FROM AUTHOR]

The Autism and Developmental Disabilities Monitoring (ADDM) Network conducts population-based surveillance of autism spectrum disorder (ASD) among 8-year-old children in multiple US communities. From 2000 to 2016, investigators at ADDM Network sites classified ASD from collected text descriptions of behaviors from medical and educational evaluations which were reviewed and coded by ADDM Network clinicians. It took at least 4 years to publish data from a given surveillance year. In 2018, we developed an alternative case definition utilizing ASD diagnoses or classifications made by community professionals. Using data from surveillance years 2014 and 2016, we compared the new and previous ASD case definitions. Compared with the prevalence based on the previous case definition, the prevalence based on the new case definition was similar for 2014 and slightly lower for 2016. Sex and race/ethnicity prevalence ratios were nearly unchanged. Compared with the previous case definition, the new case definition's sensitivity was 86% and its positive predictive value was 89%. The new case definition does not require clinical review and collects about half as much data, yielding more timely reporting. It also more directly measures community identification of ASD, thus allowing for more valid comparisons among communities, and reduces resource requirements while retaining measurement properties similar to those of the previous definition. [ABSTRACT FROM AUTHOR]

We developed and validated a synthetic cohort approach to examine numbers of cardiovascular risk factors (CRFs) and adverse clinical events, including incident cardiovascular disease and all-cause mortality, across the life span from ages 20 years to 90 years. The current analysis included 40,875 participants from 7 large, population-based longitudinal epidemiologic studies (1948–2016). On the basis of a joint multilevel imputation model, we multiply imputed each participant's life-span numbers of CRFs and events using available records. To validate the imputed values, we partially removed the observed data and then compared the imputed and observed values. The complete life-span synthetic data set reflected the original observed data trends well. In our validation sample, the distributions of imputed CRFs and events were close to the observed distributions but with less variability. Bland-Altman plots indicated that there was a slightly negative trend in general, and the agreement bias was relatively small for the continuous CRFs. The hypothetical linear regression model suggested that the relationships between the CRFs and events were preserved in the imputed data set. This approach generated valid estimates of CRFs and events across the life span for African-American and White participants. The synthetic cohort may be sufficiently accurate to be useful in assessing the origins and timing of accumulating cardiovascular risk that can inform efforts to avoid cardiovascular disease development. [ABSTRACT FROM AUTHOR]

Life expectancy for US White men and women declined between 2013 and 2017. Initial explanations for the decline focused on increases in "deaths of despair" (i.e. deaths from suicide, drug use, and alcohol use), which have been interpreted as a cohort-based phenomenon afflicting middle-aged White Americans. There has been less attention on Black mortality trends from these same causes, and whether the trends are similar or different by cohort and period. We complement existing research and contend that recent mortality trends in both the US Black and White populations most likely reflect period-based exposures to 1) the US opioid epidemic and 2) the Great Recession. We analyzed cause-specific mortality trends in the United States for deaths from suicide, drug use, and alcohol use among non-Hispanic Black and non-Hispanic White Americans, aged 20–64 years, over 1990–2017. We employed sex-, race-, and cause-of-death–stratified Poisson rate models and age-period-cohort models to compare mortality trends. Results indicate that rising "deaths of despair" for both Black and White Americans are overwhelmingly driven by period-based increases in drug-related deaths since the late 1990s. Further, deaths related to alcohol use and suicide among both White and Black Americans changed during the Great Recession, despite some racial differences across cohorts. [ABSTRACT FROM AUTHOR]

Among 626 participants of the Men's Lifestyle Validation Study (2011–2013), we evaluated the validity and reproducibility of a self-administered 152-item semiquantitative food frequency questionnaire (SFFQ) using two 7-day dietary records (7DDRs), 4 Automated Self-Administered 24-hour dietary recalls (ASA24s), four 24-hour urine samples, 1 doubly labeled water measurement (repeated in 104 participants), and 2 fasting blood samples, collected over 15 months. Compared with 7DDRs, SFFQs underestimated energy intake, macronutrients, and sodium intake but overestimated some micronutrients. The mean of the Spearman correlation coefficients was 0.66 (range, 0.38–0.88) between 46 energy-adjusted nutrients estimated from 7DDRs and the final SFFQ, deattenuated for within-person variation in the 7DDRs. These deattenuated correlations were similar using ASA24s as the comparison. Relative to biomarkers, SFFQs underestimated energy, sodium, and protein intakes, as well as the sodium:potassium ratio. The energy-adjusted correlations between the final SFFQ and the biomarkers were slightly lower than the correlations between the SFFQ and 7DDRs. Using the method of triads to calculate validity coefficients, the median validity coefficient between SFFQ and true intake was 0.65 and 0.69 using 7DDRs and ASA24s, respectively, as the third method. These data indicate that this SFFQ provided reasonably valid estimates for a wide range of nutrients when evaluated by multiple comparison methods. [ABSTRACT FROM AUTHOR]

Michigan experienced a significant measles outbreak in 2019 amidst rising rates of nonmedical vaccine exemptions (NMEs) and low vaccination coverage compared with the rest of the United States. There is a critical need to better understand the landscape of nonvaccination in Michigan to assess the risk of vaccine-preventable disease outbreaks in the state, yet there is no agreed-upon best practice for characterizing spatial clustering of nonvaccination, and numerous clustering metrics are available in the statistical, geographical, and epidemiologic literature. We used school-level data to characterize the spatiotemporal landscape of vaccine exemptions in Michigan for the period 2008–2018 using Moran's I , the isolation index, the modified aggregation index, and the Theil index at 4 spatial scales. We also used nonvaccination thresholds of 5%, 10%, and 20% to assess the bias incurred when aggregating vaccination data. We found that aggregating school-level data to levels commonly used for public reporting can lead to large biases in identifying the number and location of at-risk students and that different clustering metrics yielded variable interpretations of the nonvaccination landscape in Michigan. This study shows the importance of choosing clustering metrics with their mechanistic interpretations in mind, be it large- or fine-scale heterogeneity or between- and within-group contributions to spatial variation. [ABSTRACT FROM AUTHOR]

The field of genetic epidemiology is relatively young and brings together genetics, epidemiology, and biostatistics to identify and implement the best study designs and statistical analyses for identifying genes controlling risk for complex and heterogeneous diseases (i.e., those where genes and environmental risk factors both contribute to etiology). The field has moved quickly over the past 40 years partly because the technology of genotyping and sequencing has forced it to adapt while adhering to the fundamental principles of genetics. In the last two decades, the available tools for genetic epidemiology have expanded from a genetic focus (considering 1 gene at a time) to a genomic focus (considering the entire genome), and now they must further expand to integrate information from other "-omics" (e.g., epigenomics, transcriptomics as measured by RNA expression) at both the individual and the population levels. Additionally, we can now also evaluate gene and environment interactions across populations to better understand exposure and the heterogeneity in disease risk. The future challenges facing genetic epidemiology are considerable both in scale and techniques, but the importance of the field will not diminish because by design it ties scientific goals with public health applications. [ABSTRACT FROM AUTHOR]

Effect estimates from randomized trials and observational studies might not be directly comparable because of differences in study design, other than randomization, and in data analysis. We propose a 3-step procedure to facilitate meaningful comparisons of effect estimates from randomized trials and observational studies: 1) harmonization of the study protocols (eligibility criteria, treatment strategies, outcome, start and end of follow-up, causal contrast) so that the studies target the same causal effect, 2) harmonization of the data analysis to estimate the causal effect, and 3) sensitivity analyses to investigate the impact of discrepancies that could not be accounted for in the harmonization process. To illustrate our approach, we compared estimates of the effect of immediate with deferred initiation of antiretroviral therapy in individuals positive for the human immunodeficiency virus from the Strategic Timing of Antiretroviral Therapy (START) randomized trial and the observational HIV-CAUSAL Collaboration. [ABSTRACT FROM AUTHOR]

In the United States, hepatitis C virus (HCV) transmission is rising among people who inject drugs (PWID). Many regions have insufficient prevention intervention coverage. Using modeling, we investigated the impact of scaling up prevention and treatment interventions on HCV transmission among PWID in Perry County, Kentucky, and San Francisco, California, where HCV seroprevalence among PWID is >50%. A greater proportion of PWID access medication-assisted treatment (MAT) or syringe service programs (SSP) in urban San Francisco (established community) than in rural Perry County (young, expanding community). We modeled the proportion of HCV-infected PWID needing HCV treatment annually to reduce HCV incidence by 90% by 2030, with and without MAT scale-up (50% coverage, both settings) and SSP scale-up (Perry County only) from 2017. With current MAT and SSP coverage during 2017–2030, HCV incidence would increase in Perry County (from 21.3 to 22.6 per 100 person-years) and decrease in San Francisco (from 12.9 to 11.9 per 100 person-years). With concurrent MAT and SSP scale-up, 5% per year of HCV-infected PWID would need HCV treatment in Perry County to achieve incidence targets—13% per year without MAT and SSP scale-up. In San Francisco, a similar proportion would need HCV treatment (10% per year) irrespective of MAT scale-up. Reaching the same impact by 2025 would require increases in treatment rates of 45%–82%. Achievable provision of HCV treatment, alongside MAT and SSP scale-up (Perry County) and MAT scale-up (San Francisco), could reduce HCV incidence. [ABSTRACT FROM AUTHOR]

Estimation of causal effects from observational data is a primary goal of epidemiology. The use of multiple methods with different assumptions relating to exchangeability improves causal inference by demonstrating robustness across assumptions. We estimated the effect of antiretroviral therapy (ART) on mortality in rural KwaZulu-Natal, South Africa, from 2007 to 2011, using 2 methods with substantially different assumptions: the regression discontinuity design (RDD) and inverse-probability–weighted (IPW) marginal structural models (MSMs). The RDD analysis took advantage of a CD4-cell-count–based threshold for ART initiation (200 cells/μL). The 2 methods yielded consistent but nonidentical results for the effect of immediate initiation of ART (RDD intention-to-treat hazard ratio (HR) = 0.66, 95% confidence interval (CI): 0.35, 1.26; RDD complier average causal effect HR = 0.56, 95% CI: 0.41, 0.77; IPW MSM HR = 0.49, 95% CI: 0.42, 0.58). Although RDD and IPW MSM estimates have distinct identifying assumptions, strengths, and limitations in terms of internal and external validity, results in this application were similar. The differences in modeling approaches and the external validity of each method may explain the minor differences in effect estimates. The overall consistency of the results lends support for causal inference about the effect of ART on mortality from these data. [ABSTRACT FROM AUTHOR]

Exposure to secondhand smoke (SHS) is known to be harmful to health. However, the association between household SHS and cognitive function among middle-aged and older women in China is understudied. Lagged dependent variable regression was used to examine the association between household SHS exposure and the cognitive function of married women who had been exposed to SHS, using data from 2 waves of the China Health and Retirement Longitudinal Study (CHARLS, 2011-2013). Controlling for age, educational attainment, geographic residence, household expenditures, and chronic conditions (i.e., hypertension, diabetes, and depressive symptoms), the results indicated that longer SHS exposure was associated with a greater decline in memory over 2 years. After comparing differences across age groups, this pattern was significant for women aged 55-64 years. Furthermore, those who were illiterate, lived in rural areas, and reported depressive symptoms had a greater decline in memory. With evidence linking household SHS exposure with a higher risk of cognitive decline, effective education and public health intervention programs are urgently needed. Stronger tobacco control regulations and education about the dangers of household SHS are viable strategies to reduce the impending dementia epidemic in China. [ABSTRACT FROM AUTHOR]

Flaws in the design of randomized trials may bias intervention effect estimates and increase between-trial heterogeneity. Empirical evidence suggests that these problems are greatest for subjectively assessed outcomes. For the Risk of Bias in Evidence Synthesis (ROBES) Study, we extracted risk-of-bias judgements (for sequence generation, allocation concealment, blinding, and incomplete data) from a large collection of meta-analyses published in the Cochrane Library (issue 4; April 2011). We categorized outcome measures as mortality, other objective outcome, or subjective outcome, and we estimated associations of bias judgements with intervention effect estimates using Bayesian hierarchical models. Among 2,443 randomized trials in 228 meta-analyses, intervention effect estimates were, on average, exaggerated in trials with high or unclear (versus low) risk-of-bias judgements for sequence generation (ratio of odds ratios (ROR) = 0.91, 95% credible interval (CrI): 0.86, 0.98), allocation concealment (ROR = 0.92, 95% CrI: 0.86,0.98), and blinding (ROR = 0.87, 95% CrI: 0.80,0.93). In contrast to previous work, we did not observe consistently different bias for subjective outcomes compared with mortality. However, we found an increase in between-trial heterogeneity associated with lack of blinding in meta-analyses with subjective outcomes. Inconsistency in criteria for risk-of-bias judgements applied by individual reviewers is a likely limitation of routinely collected bias assessments. Inadequate randomization and lack of blinding may lead to exaggeration of intervention effect estimates in randomized trials. [ABSTRACT FROM AUTHOR]

This review examines the conduct and reporting of observational studies using propensity score-based methods to compare coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or medical therapy for patients with coronary artery disease. A systematic selection process identified 48 studies: 20 addressing CABG versus PCI; 21 addressing bare-metal stents versus drug-eluting stents; 5 addressing CABG versus medical therapy; 1 addressing PCI versus medical therapy; and 1 addressing drug-eluting stents versus balloon angioplasty. Of 32 studies reporting information on variable selection, 7 relied exclusively on statistical criteria for the association of covariates with treatment, and 5 used such criteria to determine whether product or nonlinear terms should be included in the propensity score model. Twenty-five (52%) studies reported assessing covariate balance using the estimated propensity score, but only 1 described modifications to the propensity score model based on this assessment. The over 400 variables used in the 48 propensity score models were classified into 12 categories and 60 subcategories; only 17 subcategories were represented in at least half of the propensity score models. Overall, reporting of propensity score-based methods in observational studies comparing CABG, PCI, and medical therapy was incomplete; when adequately described, the methods used were often inconsistent with current methodological standards. [ABSTRACT FROM AUTHOR]

We examined the effectiveness of human papillomavirus vaccination by dose number and spacing against incident genitalwarts in a cohort of 64, 517 female health-plan enrollees in the United States during 2006-2012. Eligible recipients were classified into groups by regimen: 0, 1, 2 (<6 months apart), 2 (≥6 months apart), or 3 doses. They were followed until a genital wart diagnosis, loss to follow-up, or the end of study. Propensity score weights were used to balance baseline differences across groups. To account for latent genital warts before vaccination, we applied 6- and 12-month buffer periods from last and first vaccine dose, respectively. Incidence rates and hazard ratios were calculated using Poisson regression and Cox models. The propensity score-weighted incidence rate per 100, 000 person-years was 762 among unvaccinated participants. Using 6- and 12-month buffer periods, respectively, incidence rates were 641 and 257 for 1 dose, 760 and 577 for the 2-dose (<6-month interval) regimen, 313 and 194 for the 2-dose (≥6-month interval) regimen, and 199 and 162 among 3-dose vaccinees; vaccine effectiveness was 68%and 76%for the 2-dose (≥6-month interval) regimen and 77% and 80% in 3-dose vaccinees compared with unvaccinated participants. Vaccine effectiveness was not significantamong vaccinees receiving 1-dose and 2-dose (<6-month interval) regimens compared with unvaccinated participants. Our findings contribute to a better understanding of the real-world effectiveness of HPV vaccination. [ABSTRACT FROM AUTHOR]

Comparability of population surveys across countries is key to appraising trends in population health. Achieving this requires deep understanding of the methods used in these surveys to examine the extent to which the measurements are comparable. In this study, we obtained detailed protocols of 8 nationally representative surveys from 2007-2013 from Brazil, Chile, Colombia, Mexico, the United Kingdom(England and Scotland), and the United States--countries that that differ in economic and inequity indicators. Data were collected on sampling frame, sample selection procedures, recruitment, data collection methods, content of interview and examination modules, and measurement protocols. We also assessed their adherence to the World Health Organization's "STEPwise Approach to Surveillance" framework for population health surveys. The surveys, which included half a million participants, were highly comparable on sampling methodology, survey questions, and anthropometric measurements. Heterogeneity was found for physical activity questionnaires and biological samples collection. The common age range included by the surveys was adults aged 18--64 years. The methods used in these surveys were similar enough to enable comparative analyses of the data across the 7 countries. This comparability is crucial in assessing and comparing national and subgroup population health, and to assisting the transfer of research and policy knowledge across countries. [ABSTRACT FROM AUTHOR]

Controversy exists about breast cancer risk associated with long-term use of calcium channel blockers (CCBs) or angiotensin-converting enzyme inhibitors (ACEis), respectively. Our objective in this study was to separately evaluate associations between duration of CCB or ACEi use and breast cancer in hypertensive women aged ≥55 years at 3 sites in the Kaiser Permanente health-care system (1997-2012). Exposures included CCB or ACEi use of 1-12 years' duration, determined from pharmacy dispensings. Outcomes included invasive lobular or ductal carcinoma. Statistical methods included discrete-time survival analyses. The cohort included 19,674 (17.9%) CCB users and 90,078 (82.1%) ACEi users. Two percent (n = 397) of CCB users and 1.9% (n = 1,733) of ACEi users developed breast cancer. Compared with 1-<2 years of use, in adjusted analysis, there was no association between CCB use for 2-<12 years and breast cancer: All 95% confidence intervals included 1. Increasing duration of ACEi use was associated with reduced breast cancer risk: Compared with 1-<2 years of use, the adjusted hazard ratio was 0.76 (95% confidence interval: 0.63, 0.92) for 5-<6 years of use and 0.63 (95% confidence interval: 0.43, 0.93) for 9-<10 years of use. We conclude that among older women with hypertension, long-term CCB use does not increase breast cancer risk and long-term treatment with ACEis may confer protection against breast cancer. [ABSTRACT FROM AUTHOR]

When investigating the association between brain tumors and use of mobile telephones, accurate data on tumor position are essential, due to the highly localized absorption of energy in the human brain from the radio-frequency fields emitted. We used a point process model to investigate this association using information that included tumor localization data from the INTERPHONE Study (Australia, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, New Zealand, Norway, Sweden, and the United Kingdom). Our main analysis included 792 regular mobile phone users diagnosed with a glioma between 2000 and 2004. Similar to earlier results, we found a statistically significant association between the intracranial distribution of gliomas and the self-reported location of the phone. When we accounted for the preferred side of the head not being exclusively used for all mobile phone calls, the results were similar. The association was independent of the cumulative call time and cumulative number of calls. However, our model used reported side of mobile phone use, which is potentially influenced by recall bias. The point process method provides an alternative to previously used epidemiologic research designs when one is including localization in the investigation of brain tumors and mobile phone use. [ABSTRACT FROM AUTHOR]

At high levels, inorganic arsenic exposure is linked to peripheral arterial disease (PAD) and cardiovascular disease. To our knowledge, no prior study has evaluated the association between low-to-moderate arsenic exposure and incident PAD by ankle brachial index (ABI). We evaluated this relationship in the Strong Heart Study, a large population-based cohort study of American Indian communities. A total of 2,977 and 2,966 PAD-free participants who were aged 45-74 years in 1989-1991 were reexamined in 1993-1995 and 1997-1999, respectively, for incident PAD defined as either ABI <0.9 or ABI >1.4. A total of 286 and 206 incident PAD cases were identified for ABI <0.9 and ABI >1.4, respectively. The sum of inorganic and methylated urinary arsenic species (ΣAs) at baseline was used as a biomarker of long-term exposure. Comparing the highest tertile of ΣAs with the lowest, the adjusted hazard ratios were 0.57 (95% confidence interval (CI): 0.32, 1.01) for ABI <0.9 and 2.24 (95% CI: 1.01, 4.32) for ABI >1.4. Increased arsenic methylation (as percent dimethylarsinate) was associated with a 2-fold increased risk of ABI >1.4 (hazard ratio = 2.04, 95% CI: 1.02, 3.41). Long-term low-to-moderate ΣAs and increased arsenic methylation were associated with ABI >1.4 but not with ABI <0.9. Further studies are needed to clarify whether diabetes and enhanced arsenic metabolism increase susceptibility to the vasculotoxic effects of arsenic exposure. [ABSTRACT FROM AUTHOR]

There are few published examples of absolute risk estimated from epidemiologic data subject to censoring and competing risks with adjustment for multiple confounders. We present an example estimating the effect of injection drug use on 6-year risk of acquired immunodeficiency syndrome (AIDS) after initiation of combination antiretroviral therapy between 1998 and 2012 in an 8-site US cohort study with death before AIDS as a competing risk. We estimate the risk standardized to the total study sample by combining inverse probability weights with the cumulative incidence function; estimates of precision are obtained by bootstrap. In 7,182 patients (83% male, 33% African American, median age of 38 years), we observed 6-year standardized AIDS risks of 16.75% among 1,143 injection drug users and 12.08% among 6,039 nonusers, yielding a standardized risk difference of 4.68 (95% confidence interval: 1.27, 8.08) and a standardized risk ratio of 1.39 (95% confidence interval: 1.12, 1.72). Results may be sensitive to the assumptions of exposure-version irrelevance, no measurement bias, and no unmeasured confounding. These limitations suggest that results be replicated with refined measurements of injection drug use. Nevertheless, estimating the standardized risk difference and ratio is straightforward, and injection drug use appears to increase the risk of AIDS. [ABSTRACT FROM PUBLISHER]

In an accompanying article, Turner et al. (Am J Epidemiol. 2014;180(12):1145–1149) compare the joint effects of smoking and air pollution to make inferences about the reduction in lung cancer mortality achieved when reducing each exposure separately and when reducing both together. In this commentary, we use first principles to quantify the difference between the risk or mortality reduction obtained from reducing each of 2 exposures together and the sum of the risk differences obtained from reducing the 2 exposures separately. Metrics of the impact of joint effects or comparisons of joint effects presented in units of absolute risk, such as Rothman's I, can provide more meaningful quantitative measures of public health impact than unitless metrics (e.g., ratios) and standardized metrics (e.g., the population attributable fraction) of potential interventions for reducing smoking and air pollution exposure. In particular, the venerable attributable community risk metric can provide an estimate of the community impact of such interventions in units of absolute risk. A spreadsheet we provide demonstrates the calculation of the various metrics for hypothetical data similar to those reported by Turner et al. Using algebra, graphics, and examples, we show that positive interaction, or synergy, on the additive scale implies that the impact on risk reduction from a program that applies both interventions will be lesser than the sum of the impacts of the separate interventions. [ABSTRACT FROM PUBLISHER]

To examine the risks of genital herpes and antiherpes treatment during pregnancy in relation to preterm delivery (PTD), we conducted a multicenter, member-based cohort study within 4 Kaiser Permanente regions: northern and southern California, Colorado, and Georgia. The study included 662,913 mother-newborn pairs from 1997 to 2010. Pregnant women were classified into 3 groups based on genital herpes diagnosis and treatment: genital herpes without treatment, genital herpes with antiherpes treatment, and no herpes diagnosis or treatment (unexposed controls). After controlling for potential confounders, we found that compared with being unexposed, having untreated genital herpes during first or second trimester was associated with more than double the risk of PTD (odds ratio (OR) = 2.23, 95% confidence interval (CI): 1.80, 2.76). The association was stronger for PTD due to premature rupture of membrane (OR = 3.57, 95% CI: 2.53, 5.06) and for early PTD (≤35 weeks gestation) (OR = 2.87, 95% CI: 2.22, 3.71). In contrast, undergoing antiherpes treatment during pregnancy was associated with a lower risk of PTD compared with not being treated, and the PTD risk was similar to that observed in the unexposed controls (OR = 1.11, 95% CI: 0.89, 1.38). The present study revealed increased risk of PTD associated with genital herpes infection if left untreated and a potential benefit of antiherpes medications in mitigating the effect of genital herpes infection on the risk of PTD. [ABSTRACT FROM PUBLISHER]