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"Fusion" study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information. [ABSTRACT FROM AUTHOR]

Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives. [ABSTRACT FROM AUTHOR]

Monitoring survival in cancer is a common concern for patients, physicians, and public health researchers. The traditional cohort approach for monitoring cancer prognosis has a timeliness problem. In this paper, we propose a survivorship-period-cohort (SPC) model for examining the effects of survivorship, period, and year-of-diagnosis cohort on cancer prognosis and for predicting future trends in cancer survival. We used the developed SPC model to evaluate the relative survival (RS) of patients with liver cancer in Taiwan (diagnosed from 1997 to 2016) and to predict future trends in RS by imputing incomplete follow-up data for recently diagnosed patient cohorts. We used cross-validation to select the extrapolation method and bootstrapping to estimate the 95% confidence interval for RS. We found that 5-year cumulative RS increased for both men and women with liver cancer diagnosed after 2003. For patients diagnosed before 2010, the 5-year cumulative RS rate for men was lower than that for women; thereafter, the rates were better for men than for women. The SPC model can help elucidate the effects of survivorship, period, and year-of-diagnosis cohort effects on cancer prognosis. Moreover, the SPC model can be used to monitor cancer prognosis in real time and predict future trends; thus, we recommend its use. [ABSTRACT FROM AUTHOR]

Principled methods with which to appropriately analyze missing data have long existed; however, broad implementation of these methods remains challenging. In this and 2 companion papers (Am J Epidemiol. 2018;187(3):576-584 and Am J Epidemiol. 2018;187(3):585-591), we discuss issues pertaining to missing data in the epidemiologic literature. We provide details regarding missing-data mechanisms and nomenclature and encourage the conduct of principled analyses through a detailed comparison of multiple imputation and inverse probability weighting. Data from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are used to create a masked data-analytical challenge with missing data induced by known mechanisms. We illustrate the deleterious effects of missing data with naive methods and show how principled methods can sometimes mitigate such effects. For example, when data were missing at random, naive methods showed a spurious protective effect of smoking on the risk of spontaneous abortion (odds ratio (OR) = 0.43, 95% confidence interval (CI): 0.19, 0.93),while implementation of principled methods multiple imputation (OR = 1.30, 95% CI: 0.95, 1.77) or augmented inverse probability weighting (OR = 1.40, 95% CI: 1.00, 1.97) provided estimates closer to the "true" full-data effect (OR = 1.31, 95% CI: 1.05, 1.64). We call for greater acknowledgement of and attention to missing data and for the broad use of principled missing-data methods in epidemiologic research. [ABSTRACT FROM AUTHOR]

Medication use is often underreported in paper-based questionnaires or interviews.Web-based questionnaires may improve recall ofmedication use, but data on their validity are currently lacking. Participants in the Pregnancy and Infant Development (PRIDE) Study (2014-2016; n = 557) and the Pregnancy Drug Registry (pREGnant) (2015-2016; n = 169) completed a 6-week paper-based medication diary during gestational weeks 19-24 or 26-31. In week 34, they completed a Web-based questionnaire with questions on medication names, time period and frequency of use, and quantity taken. To assess the degree of underreporting, we calculated the questionnaire's sensitivity using the medication diary as the reference standard. Sensitivity was high formany medication groups, including antiepilepticmedication (sensitivity (Sn) = 0.96, 95% confidence interval (CI): 0.89, 1.00), antacids (Sn = 0.89, 95% CI: 0.86, 0.93), and iron preparations (Sn = 0.81, 95% CI: 0.64, 0.98). However, medications for short-term use were underreported more frequently, with sensitivities of 0.54 (95% CI: 0.35, 0.72) for antihistamines, 0.63 (95% CI: 0.57, 0.69) for analgesic and antipyretic agents, and 0.57 (95% CI: 0.51, 0.64) for acetaminophen. Shortening the period of time between exposure and questionnaire administration increased sensitivity substantially. In conclusion, underreporting in Web-based questionnaires is limited for many medication groups. In prospective studies, underreporting of medications for short-term usemay be reduced by decreasing the interval between consecutive questionnaires. medication use; pregnancy; Pregnancy Drug Registry (pREGnant); Pregnancy and Infant Development (PRIDE) [ABSTRACT FROM AUTHOR]

Epidemiologists often compare the observed number of deaths in a cohort with the expected number of deaths, obtained by multiplying person-time accrued in the cohort by mortality rates for a reference population (ideally, a reference that represents the mortality rate in the cohort in the absence of exposure). However, if exposure is hazardous (or salutary), this calculation will not consistently estimate the number of deaths expected in the absence of exposure because exposure will have affected the distribution of person-time observed in the study cohort. While problems with interpretation of this standard calculation of expected counts were discussed more than 2 decades ago, these discussions had little impact on epidemiologic practice. The logic of counterfactuals may help clarify this topic as we revisit these issues. In this paper, we describe a simple way to consistently estimate the expected number of deaths in such settings, and we illustrate the approach using data from a cohort study of mortality among underground miners. [ABSTRACT FROM AUTHOR]

A standard approach to analysis of case-cohort data involves fitting log-linear models. In this paper, we describe how standard statistical software can be used to fit a broad class of general relative rate models to case-cohort data and derive confidence intervals. We focus on a case-cohort design in which a roster has been assembled and events ascertained but additional information needs to be collected on explanatory variables. The additional information is ascertained just for persons who experience the event of interest and for a sample of the cohort members enumerated at study entry. One appeal of such a case-cohort design is that this sample of the cohort may be used to support analyses of several outcomes. The ability to fit general relative rate models to case-cohort data may allow an investigator to reduce model misspecification in exposure-response analyses, fit models in which some factors have effects that are additive and others multiplicative, and facilitate estimation of relative excess risk due to interaction. We address model fitting for simple random sampling study designs as well as stratified designs. Data on lung cancer among radon-exposed men (Colorado Plateau uranium miners, 1950–1990) are used to illustrate these methods. [ABSTRACT FROM AUTHOR]

The objective of this systematic review and meta-analysis was to examine the association between maternal chronic medical conditions (CMCs) and peripartum mental illness. MEDLINE, Embase, CINAHL, and PsycINFO were searched to September 2017. Data were extracted and quality was assessed using standardized instruments. We generated unadjusted and adjusted pooled odds ratios and 95% confidence intervals using DerSimonian and Laird random effects models. The review included 16 papers representing 12 studies and 1,626,260 women. CMCs overall were associated with peripartum mental illness overall (adjusted pooled odds ratios (aPOR) = 1.43, 95% confidence interval (CI): 1.25, 1.63). CMCs overall were associated with antepartum (aPOR = 1.41, 95% CI: 1.10, 1.81) and postpartum mental illness separately (aPOR = 1.44, 95% CI: 1.13, 1.85) and with peripartum depression (aPOR = 1.45, 95% CI: 1.25, 1.67) and anxiety separately (aPOR = 1.63, 95% CI: 1.35, 1.95). No studies examined bipolar or psychotic disorders. Diabetes (aPOR = 1.34, 95% CI: 1.07, 1.69), hypertension/heart disease (aPOR = 1.60, 95% CI: 1.05, 2.45), migraine (aPOR = 1.75, 95% CI: 1.20, 2.54), and other neurological disorders (aPOR = 1.45, 95% CI: 1.19, 1.77), but not asthma, were each associated with peripartum mental illness. Findings suggest that mental health resources should be integrated in medical settings where pregnant and postpartum women with CMCs are treated. [ABSTRACT FROM AUTHOR]

In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike's Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators' performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006-2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms. [ABSTRACT FROM AUTHOR]

While a growing body of research is examining the impacts of prolonged occupational sitting on cardiovascular and other health risk factors, relatively little work has examined the effects of occupational standing. The objectives of this paper were to examine the relationship between occupations that require predominantly sitting and those that require predominantly standing and incident heart disease. A prospective cohort study combining responses to a population health survey with administrative health-care records, linked at the individual level, was conducted in Ontario, Canada. The sample included 7,320 employed labor-market participants (50% male) working 15 hours a week or more and free of heart disease at baseline. Incident heart disease was assessed using administrative records over an approximately 12-year follow-up period (2003-2015). Models adjusted for a wide range of potential confounding factors. Occupations involving predominantly standing were associated with an approximately 2-fold risk of heart disease compared with occupations involving predominantly sitting. This association was robust to adjustment for other health, sociodemographic, and work variables. Cardiovascular risk associated with occupations that involve combinations of sitting, standing, and walking differed for men and women, with these occupations associated with lower cardiovascular risk estimates among men but elevated risk estimates among women. [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]

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemio-logic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed healthcare databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases. [ABSTRACT FROM AUTHOR]

In this paper, we examine patterns of self-reported diagnosis of noncommunicable diseases (NCDs) and prevalences of algorithm/measured test-based, undiagnosed, and untreated NCDs in China, Ghana, India, Mexico, Russia, and South Africa. Nationally representative samples of older adults aged ≥50 years were analyzed from wave 1 of the World Health Organization's Study on Global Ageing and Adult Health (2007-2010; n = 34,149). Analyses focused on 6 conditions: angina, arthritis, asthma, chronic lung disease, depression, and hypertension. Outcomes for these NCDs were: 1) self-reported disease, 2) algorithm/measured test-based disease, 3) undiagnosed disease, and 4) untreated disease. Algorithm/measured test-based prevalence of NCDs was much higher than self-reported prevalence in all 6 countries, indicating underestimation of NCD prevalence in low- and middle-income countries. Undiagnosed prevalence of NCDs was highest for hypertension, ranging from 19.7% (95% confidence interval (CI): 18.1, 21.3) in India to 49.6% (95% CI: 46.2, 53.0) in South Africa. The proportion untreated among all diseases was highest for depression, ranging from 69.5% (95% CI: 57.1, 81.9) in South Africa to 93.2% (95% CI: 90.1, 95.7) in India. Higher levels of education and wealth significantly reduced the odds of an undiagnosed condition and untreated morbidity. A high prevalence of undiagnosed NCDs and an even higher proportion of untreated NCDs highlights the inadequacies in diagnosis and management of NCDs in local health-care systems. [ABSTRACT FROM AUTHOR]

Two frequently encountered but underrecognized challenges for causal inference in studying the long-term health effects of disasters among survivors include 1) time-varying effects of disasters on a time-to-event outcome and 2) selection bias due to selective attrition. In this paper, we review approaches for overcoming these challenges and demonstrate application of the approaches to a real-world longitudinal data set of older adults who were directly affected by the 2011 Great East Japan Earthquake and Tsunami (n  = 4,857). To illustrate the problem of time-varying effects of disasters, we examined the association between degree of damage due to the tsunami and all-cause mortality. We compared results from Cox regression analysis assuming proportional hazards with those derived using adjusted parametric survival curves allowing for time-varying hazard ratios. To illustrate the problem of selection bias, we examined the association between proximity to the coast (a proxy for housing damage from the tsunami) and depressive symptoms. We corrected for selection bias due to attrition in the 2 postdisaster follow-up surveys (conducted in 2013 and 2016) using multivariable adjustment, inverse probability of censoring weighting, and survivor average causal effect estimation. Our results demonstrate that analytical approaches which ignore time-varying effects on mortality and selection bias due to selective attrition may underestimate the long-term health effects of disasters. [ABSTRACT FROM AUTHOR]

In the causal mediation framework, several parametric-regression–based approaches have been introduced in the last decade for estimating natural direct and indirect effects. For a binary outcome, a number of proposed estimators use a logistic model and rely on specific assumptions or approximations that may be delicate or not easy to verify in practice. To circumvent the challenges prompted by the rare outcome assumption in this context, an exact closed-form natural-effects estimator on the odds ratio scale was recently introduced for a binary mediator. In this work, we further push this exact approach and extend it for the estimation of natural effects on the risk ratio and risk difference scales. Explicit formulas for the delta method standard errors are provided. The performance of our proposed exact estimators is demonstrated in simulation scenarios featuring various levels of outcome rareness/commonness. The total effect decomposition property on the multiplicative scales is also examined. Using a SAS macro (SAS Institute, Inc. Cary, North Carolina) we developed, our approach is illustrated to assess the separate effects of exposure to inhaled corticosteroids and placental abruption on low birth weight mediated by prematurity. Our exact natural-effects estimators are found to work properly in both simulations and the real data example. [ABSTRACT FROM AUTHOR]

Increasingly often in epidemiologic research, associations between survival time and predictors of interest are measured by differences between distribution functions rather than hazard functions. For example, differences in percentiles of survival time, expressed in absolute time units (e.g., weeks), may complement the popular risk ratios, which are unitless measures. When analyzing time to an event of interest (e.g., death) in prospective cohort studies, the time scale can be set to start at birth or at study entry. The advantages of one time origin over the other have been thoroughly explored for the estimation of risks but not for the estimation of survival percentiles. In this paper, we analyze the use of different time scales in the estimation of survival percentiles with Laplace regression. Using this regression method, investigators can estimate percentiles of survival time over levels of an exposure of interest while adjusting for potential confounders. Our findings may help to improve modeling strategies and ease interpretation in the estimation of survival percentiles in prospective cohort studies. [ABSTRACT FROM AUTHOR]

In this issue of the Journal, Isanaka et al. (Am J Epidemiol. 2016;184(12):861-869) set out to update an incidence correction factor used for estimating numbers of cases of severe acute malnutrition (SAM) in children aged 6-59 months. The total number of current SAM cases (prevalent cases) increases by the number of new (incident) cases and decreases as a result of recovery or death. Prevalence estimates are obtained from cross-sectional surveys. Calculation of incidence typically requires longitudinal data, which evidently are rarely collected for SAM, and so a correction factor is applied instead. Isanaka et al. pool and meta-analyze data from longitudinal and community programs in 3 West African countries (Mali, Niger, and Burkina Faso), covering the period 2009-2012. Heterogeneity and the ongoing lack of data undermine the use of a single incidence correction factor for SAM estimates. Routine data collection is recommended as a way forward and aligns with recommendations of the World Health Organization. This commentary helps to outline a context for the use of such data and provide some perspective on the inadequacy of data, relative to the importance of the issue. [ABSTRACT FROM AUTHOR]

Ozone is associated with adverse health; however, less is known about vulnerable/sensitive populations, which we refer to as sensitive populations. We systematically reviewed epidemiologic evidence (1988-2013) regarding sensitivity to mortality or hospital admission from short-term ozone exposure. We performed meta-analysis for overall associations by age and sex; assessed publication bias; and qualitatively assessed sensitivity to socioeconomic indicators, race/ethnicity, and air conditioning. The search identified 2,091 unique papers, with 167 meeting inclusion criteria (73 on mortality and 96 on hospitalizations and emergency department visits, including 2 examining both mortality and hospitalizations). The strongest evidence for ozone sensitivity was forage. Per 10-parts per billion increase in daily 8-hour ozone concentration, mortality risk for younger persons, at 0.60% (95% confidence interval (Cl): 0.40, 0.80), was statistically lower than that for older persons, at 1.27% (95% Cl: 0.76,1.78). Findings adjusted for publication bias were similar. Limited/suggestive evidence was found for higher associations among women; mortality risks were 0.39% (95% Cl: -0.22,1.00) higher than those for men. We identified strong evidence for higher associations with unemployment or lower occupational status and weak evidence of sensitivity for racial/ethnic minorities and persons with low education, in poverty, or without central air conditioning. Findings show that some populations, especially the elderly, are particularly sensitive to short-term ozone exposure. [ABSTRACT FROM AUTHOR]

Mumps is a potentially severe viral infection. The incidence of mumps has declined dramatically in high-income countries since the introduction of mumps antigen-containing vaccines. However, recent large outbreaks of mumps in highly vaccinated populations suggest waning of vaccine-induced immunity and primary vaccine failure. In this paper we present a simple method for identifying geographic regions with high outbreak potential, demonstrated using 2006 mumps seroprevalence data from Belgium and Belgian vaccination coverage data. Predictions of the outbreak potential in terms of the effective reproduction number in future years signal an increased risk of new mumps outbreaks. Literature reviews on serological information for both primary vaccine failure and waning immunity provide essential information for our predictions. Tailor-made additional vaccination campaigns would be valuable for decreasing local pockets of susceptibility, thereby reducing the risk of future large-scale mumps outbreaks. [ABSTRACT FROM PUBLISHER]

In this paper, we propose new methods for analyzing cases of vaccine adverse events spontaneously reported to a surveillance database. The methods use the self-controlled case series approach, extended in several ways with parametric and nonparametric assumptions to account for the specific features of the data (large amount of underreporting and variation of reporting with time since vaccination). This work was motivated by the documented risk of intussusception after RotaShield vaccination (Wyeth-Lederle Vaccines, Radnor, Pennsylvania) and used worldwide spontaneous reports of intussusception occurring after Rotarix vaccination (GlaxoSmithKline Biologics, Research Triangle Park, North Carolina) collected between January 2004 and February 2010. The estimated risk during the 3- to 7-day period after vaccination was approximately 5 times higher after dose 1 of Rotarix than after dose 2, which is similar to published findings on the same topic. We undertook a large simulation study to evaluate the performance of the method in different scenarios, including its robustness to different sample sizes and time-dependent reporting functions. The bias was generally small, the type I error rate was correctly controlled, and the power to detect a risk ratio of 4 was satisfactory, provided that the sample size was over 100. The proposed methods are an effective way to explore and quantify vaccine safety signals from spontaneous reports. [ABSTRACT FROM PUBLISHER]

Although there is strong evidence that short-term exposure to particulate matter is associated with health risks, less is known about whether some subpopulations face higher risks. We identified 108 papers published after 1995 and summarized the scientific evidence regarding effect modification of associations between short-term exposure to particulate matter and the risk of death or hospitalization. We performed a meta-analysis of estimated mortality associations by age and sex. We found strong, consistent evidence that the elderly experience higher risk of particular matter-associated hospitalization and death, weak evidence that women have higher risks of hospitalization and death, and suggestive evidence that those with lower education, income, or employment status have higher risk of death. Meta-analysis showed a statistically higher risk of death of 0.64% (95% confidence interval (Cl): 0.50, 0.78) for older populations compared with 0.34% (95% Cl: 0.25, 0.42) for younger populations per 10 ng/m³ increase of particulate matter with aerodynamic diameter ≤10 µm. Women had a slightly higher risk of death of 0.55% (95% Cl: 0.41,0.70) compared with 0.50% (95% Cl: 0.34, 0.54) for men, but these 2 risks were not statistically different. Our synthesis on modifiers for risks associated with particulate matter can aid the design of air quality policies and suggest directions for future research. Studies of biological mechanisms could be informed by evidence of differential risks by population, such as by sex and preexisting conditions. [ABSTRACT FROM AUTHOR]

Relationships between some risk factors and breast cancer incidence are known to vary by tumor subtype. However, breast tumors can be classified according to a number of markers, which may be correlated, making it difficult to identify heterogeneity of risk factors with specific tumor markers when using standard competing-risk survival analysis. In this paper, we propose a constrained competing-risk survival model that allows for assessment of heterogeneity of risk factor associations according to specific tumor markers while controlling for other markers. These methods are applied to Nurses’ Health Study data from 1980–2006, during which 3,398 incident invasive breast cancers occurred over 1.4 million person-years of follow-up. Results suggested that when estrogen receptor (ER) and progesterone receptor (PR) status are mutually considered, some risk factors thought to be characteristic of “estrogen-positive tumors,” such as high body mass index during postmenopause and increased height, are actually significantly associated with PR-positive tumors but not ER-positive tumors, while other risk factors thought to be characteristic of “estrogen-negative tumors,” such as late age at first birth, are actually significantly associated with PR-negative rather than ER-negative breast cancer. This approach provides a strategy for evaluating heterogeneity of risk factor associations by tumor marker levels while controlling for additional tumor markers. [ABSTRACT FROM PUBLISHER]

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]

In this paper, we propose new methods for investigating the extent of heterogeneity in effective contact rates relevant to the transmission of infections. These methods exploit the correlations between ages at infection for different infections within individuals. The methods are developed for serological surveys, which provide accessible individual data on several infections, and are applied to a wide range of infections. We find that childhood infections are often highly correlated within individuals in early childhood, with the correlations persisting into adulthood only for infections sharing a transmission route. We discuss 2 applications of the methods: 1) to making inferences about routes of transmission when these are unknown or uncertain and 2) to estimating epidemiologic parameters such as the basic reproduction number and the critical immunization threshold. Two examples of such applications are presented: elucidating the transmission route of polyomaviruses BK and JC and estimating the basic reproduction number and critical immunization coverage of varicella-zoster infection in Belgium, Italy, Poland, and England and Wales. We speculate that childhood correlations stem from confounding of different transmission routes and represent heterogeneity in childhood circumstances, notably nursery-school attendance. In contrast, it is suggested that correlations in adulthood are route-specific. [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]

In this paper, the authors describe different instrumental variable (IV) estimators of causal risk ratios and odds ratios with particular attention to methods that can handle continuously measured exposures. The authors present this discussion in the context of a Mendelian randomization analysis of the effect of body mass index (BMI; weight (kg)/height (m)2) on the risk of asthma at age 7 years (Avon Longitudinal Study of Parents and Children, 1991–1992). The authors show that the multiplicative structural mean model (MSMM) and the multiplicative generalized method of moments (MGMM) estimator produce identical estimates of the causal risk ratio. In the example, MSMM and MGMM estimates suggested an inverse relation between BMI and asthma but other IV estimates suggested a positive relation, although all estimates had wide confidence intervals. An interaction between the associations of BMI and fat mass and obesity-associated (FTO) genotype with asthma explained the different directions of the different estimates, and a simulation study supported the observation that MSMM/MGMM estimators are negatively correlated with the other estimators when such an interaction is present. The authors conclude that point estimates from various IV methods can differ in practical applications. Based on the theoretical properties of the estimators, structural mean models make weaker assumptions than other IV estimators and can therefore be expected to be consistent in a wider range of situations. [ABSTRACT FROM PUBLISHER]

Doubly robust estimation combines a form of outcome regression with a model for the exposure (i.e., the propensity score) to estimate the causal effect of an exposure on an outcome. When used individually to estimate a causal effect, both outcome regression and propensity score methods are unbiased only if the statistical model is correctly specified. The doubly robust estimator combines these 2 approaches such that only 1 of the 2 models need be correctly specified to obtain an unbiased effect estimator. In this introduction to doubly robust estimators, the authors present a conceptual overview of doubly robust estimation, a simple worked example, results from a simulation study examining performance of estimated and bootstrapped standard errors, and a discussion of the potential advantages and limitations of this method. The supplementary material for this paper, which is posted on the Journal's Web site (http://aje.oupjournals.org/), includes a demonstration of the doubly robust property (Web Appendix 1) and a description of a SAS macro (SAS Institute, Inc., Cary, North Carolina) for doubly robust estimation, available for download at http://www.unc.edu/∼mfunk/dr/. [ABSTRACT FROM PUBLISHER]

In observational studies on cancer patients, progression of performance status over time can be described by using a multistate model in which state-to-state transitions represent changes in a patient's health condition. Although a patient experiences transitions in continuous time, assessments on the patient are often made at irregularly spaced time points. In this paper, the authors formulate a Markov 4-state model for examining longitudinal data on performance status collected under intermittent observation. The cohort consisted of 11,342 patients diagnosed with cancer in Ontario, Canada, from 2007 to 2009. The authors extend the model to estimate the predicted probability of reaching the absorbing state, death, over various time intervals. The authors also illustrate what happens to the estimated transition intensities if the true observational scheme is overlooked. Methods for multistate analysis should be used by epidemiologists, since they prove particularly useful for examining the complexities of disease processes. [ABSTRACT FROM PUBLISHER]

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]

A recent investigation of hormone replacement therapy and breast cancer risk used a method called “floating absolute risks” (FARs) to compute confidence intervals for relative hazards. This method has been used in other medical studies and has received controversy. This controversy stems from the correct implementation of this method. However, there has been no direct comparison of the FAR method, as it is sometimes incorrectly applied and reported, with the conventional approach for computing confidence intervals from proportional hazards regression. In this paper, the author reports simulation results comparing these two methods and demonstrates that the FAR method, when applied incorrectly, can produce confidence intervals that are substantially too narrow. [ABSTRACT FROM AUTHOR]

In case-control studies, the crude odds ratio derived from a 2 × 2 table and the common odds ratio adjusted for stratification variables are staple measures of exposure-disease association. While missing exposure data are encountered in the majority of such studies, formal attempts to deal with them are rare, and a complete-case analysis is the norm. Furthermore, the probability that exposure is missing may depend on true exposure status, so the missing-at-random assumption is often unreasonable. In this paper, the authors present an adjustment to the usual product binomial likelihood to properly account for missing data. Estimation of model parameters without restrictive assumptions requires an additional data collection effort akin to a validation study. Closed-form results are provided to facilitate point and confidence interval estimation of crude and common odds ratios after properly accounting for informatively missing data. Simulations assess performance of the likelihood-based estimates and inferences, and they display the potential for bias in complete-case analyses. An example is presented to illustrate the approach. [ABSTRACT FROM PUBLISHER]

Within a school grade, children who are young for grade are at increased risk of psychiatric diagnoses, but the long-term implications remain understudied, and associations with students who delay or accelerate entry underexplored. We used Norwegian birth cohort records (birth years: 1967–1976, n  = 626,928) linked to records in midlife. On-time school entry was socially patterned; among those born in December, 23.0% of children in the lowest socioeconomic position (SEP) delayed school entry, compared with 12.2% among the highest SEP. Among those who started school on time, there was no evidence for long-term associations between birth month and psychiatric/behavioral disorders or mortality. Controlling for SEP and other confounders, delayed school entry was associated with increased risk of psychiatric disorders and mortality. Children with delayed school entry were 1.31 times more likely to die by suicide (95% confidence interval: 1.07, 1.61) by midlife, and 1.96 times more likely to die from drug-related death (95% confidence interval: 1.59, 2.40) by midlife than those born late in the year who started school on time. Associations with delayed school entry are likely due to selection, and results thus underscore that long-term health risks can be tracked early in life, including through school entry timing, and are highly socially patterned. [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]

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]

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]

Respondent-driven sampling (RDS) is a potential strategy for addressing challenges in accurate estimation of abortion incidence, but it relies on often untested assumptions. We conducted an RDS study to estimate the cumulative lifetime incidence of abortion in Soweto, Johannesburg, South Africa (April–December 2018), to evaluate whether RDS assumptions were met, and to compare RDS estimates of incidence with estimates adjusted for employment and age based on census data. A total of 849 participants were recruited from 11 seed participants between April and December 2018. The assumption that individuals can identify target population members and the assumption of approximation of sampling with replacement was met. There were minor violations of the assumptions of seed independence from the final sample and reciprocity of ties. Assumptions of accurate degree reporting and random recruitment were not met. Failure to meet assumptions yielded a study sample with different employment characteristics than those of the target population; this could not be resolved by standard RDS methods. The RDS estimate of cumulative lifetime abortion incidence was 12.1% (95% confidence interval: 9.8, 14.3), and the employment-adjusted estimate was 16.9% (95% confidence interval: 12.8, 22.1). We caution researchers in using RDS for representative estimates of abortion incidence. Use of postsurvey weights to adjust for differences in characteristics between the sample and the target population may yield more representative results. [ABSTRACT FROM AUTHOR]

Populations that are reliant on subsistence farming are particularly vulnerable to climatic effects on crop yields. However, empirical evidence on the role of the timing of exposure to crop yield deficits in early-life development is limited. We examined the relationship between child survival and annual crop yield reductions at different stages of early-life development in a subsistence farming population in Burkina Faso. Using shared frailty Cox proportional hazards models adjusting for confounders, we analyzed 57,288 children under 5 years of age followed by the Nouna Health and Demographic Surveillance System (1994–2016) in relation to provincial food-crop yield levels experienced in 5 nonoverlapping time windows (12 months before conception, gestation, birth–age 5.9 months, ages 6.0 months–1.9 years, and ages 2.0–4.9 years) and their aggregates (birth–1.9 years, first 1,000 days from conception, and birth–4.9 years). Of the nonoverlapping windows, point estimates were largest for child survival related to food-crop yields for the time window of 6.0 months–1.9 years: The adjusted mortality hazard ratio was 1.10 (95% confidence interval: 1.03, 1.19) for a 90th-to-10th percentile yield reduction. These findings suggest that child survival in this setting is particularly vulnerable to cereal-crop yield reductions during the period of nonexclusive breastfeeding. [ABSTRACT FROM AUTHOR]

Metal exposure has been suggested as a possible environmental risk factor for Parkinson disease (PD). We searched the PubMed, EMBASE, and Cochrane databases to systematically review the literature on the relationship between metal exposure and PD risk and to examine the overall quality of each study and the exposure assessment method. A total of 83 case-control studies and 5 cohort studies published during the period 1963–July 2021 were included, of which 73 were graded as being of low or moderate overall quality. Investigators in 69 studies adopted self-reported exposure and biomonitoring after disease diagnosis for exposure assessment approaches. The meta-analyses showed that concentrations of copper and iron in serum and concentrations of zinc in either serum or plasma were lower, while concentrations of magnesium in CSF and zinc in hair were higher, among PD cases as compared with controls. Cumulative lead levels in bone were found to be associated with increased risk of PD. We did not find associations between other metals and PD. The current level of evidence for associations between metals and PD risk is limited, as biases from methodological limitations cannot be ruled out. High-quality studies assessing metal levels before disease onset are needed to improve our understanding of the role of metals in the etiology of PD. [ABSTRACT FROM AUTHOR]

In this paper, the authors model the nonmonotonic relation between body mass index (BMI) (weight (kg)/height2 (m2)) and mortality in 13,242 black and white participants in the NHANES I Epidemiologic Follow-up Study in order to estimate the BMI at which minimum mortality occurs. The BMI of minimum mortality was 27.1 for black men (95% confidence interval (Cl) 24.8–29.4), 26.8 for black women (95% CI 24.7–28.9), 24.8 for white men (95% CI 23.8–25.9), and 24.3 for white women (95% Cl 23.3–25.4). Each confidence interval included the group average. Analyses conducted by smoking status and after exclusion of persons with baseline illness and persons who died during the first 4 years of follow-up led to virtually identical estimates. The authors determined the range of values over which risk of all-cause mortality would increase no more than 20% in comparison with the minimum. This interval was nine BMI units wide, and it included 70% of the population. These results were confirmed by parallel analyses using quantiles. The model used allowed the estimation of parameters in the BMI-mortality relation. The resulting empirical findings from each of four race/sex groups, which are representative of the US population, demonstrate a wide range of BMIs consistent with minimum mortality and do not suggest that the optimal BMI is at the lower end of the distribution for any subgroup. Am J Epidemiol 1998;147:739–49. [ABSTRACT FROM PUBLISHER]

From 1970 to 1988, thousands of children have been treated with human growth hormone (hGH) to supply pituitary gland deficiency. In France, 51 of the 968 children treated by hGH lots produced between January 1984 and March 1985 had developed Creutzfeldt-Jakob disease (CJD) by the end of 1996. The authors' objective was to investigate which of the 13 hGH lots produced during this period might be implicated in the iatrogenic transmission of CJD. In this paper, the authors describe a model tht was developed to compute the probability for each lot of being contaminated. The validity of the model was assessed by a simulation study that showed a good agreement between the estimated and the simulated number of contaminated lots. The model suggested that about half of the lots distributed during this period might have been contaminated by the infectious agent that causes CJD. The risk of iatrogenic CJD for patients exposed to contaminated hGHlots was estimated to be 0.06 (95% confidence interval 0.05–0.07). [ABSTRACT FROM AUTHOR]

The role of socioeconomic status (SES) across the life course in late-life cognition is unclear. We tested the hypotheses that: 1) High SES in childhood, young adulthood, midlife, and late life have independent causal effects on higher cognition level and slower cognitive decline; 2) Compared with stable low SES (referent), stable high SES has the largest estimated effect for higher cognition level and slower decline among life-course SES combinations. The Rush Memory and Aging Project enrolled 1,940 dementia-free older adults in northeastern Illinois (1997–2018). We used inverse probability–weighted marginal structural models to estimate the joint and independent effect of each life-course SES on global and domain-specific cognition. A total of 1,746 participants had, on average, 6 years of follow-up. High SES at each life-course stage starting in young adulthood had a protective estimated effect on global and domain-specific cognition intercepts. Compared with consistently low SES, consistently high SES (β = 0.64, 95% confidence interval: 0.48, 0.93) and high SES beyond childhood (β = 0.64, 95% confidence interval: 0.47, 0.83) had the largest benefit for global cognition intercepts. None of the life-course SES measures influenced rate of global or domain-specific decline. Additional understanding of life-course SES components influencing cognitive level is warranted. [ABSTRACT FROM AUTHOR]

We describe a new method for presenting and interpreting linear trends in health inequalities, and present a proof-of-concept analysis of inequalities in smoking among adolescents in Europe. We estimated the regression line of the assumed linear relationship between smoking prevalence in low– and high–socioeconomic status (SES) youth over time. Using simulation, we constructed a 95% confidence interval (CI) for the smoking prevalence in low-SES youth for when this would be 0% in high-SES youth, and we calculated the likelihood of eradicating smoking inequality (<5% for both low and high SES). This method was applied to data on adolescents aged 15–16 years (n  = 250,326) from 23 European countries, derived from the 2003–2015 European Survey Project on Alcohol and Other Drugs. Smoking prevalence decreased more slowly among low- than among high-SES adolescents. The estimated smoking prevalence was 9.4% (95% CI: 6.1, 12.7) for boys and 5.4% (95% CI: 1.4, 9.2) for girls with low SES when 0% with high SES. The likelihood of eradicating smoking inequality was <1% for boys and 37% for girls. We conclude that this novel methodological approach to trends in health inequalities is feasible in practice. Applying it to trends in smoking inequalities among adolescents in Europe, we found that Europe is currently not on track to eradicate youth smoking across SES groups. [ABSTRACT FROM AUTHOR]

Mixed evidence exists of associations between mobility data and coronavirus disease 2019 (COVID-19) case rates. We aimed to evaluate the county-level impact of reducing mobility on new COVID-19 cases in summer/fall of 2020 in the United States and to demonstrate modified treatment policies to define causal effects with continuous exposures. Specifically, we investigated the impact of shifting the distribution of 10 mobility indexes on the number of newly reported cases per 100,000 residents 2 weeks ahead. Primary analyses used targeted minimum loss-based estimation with Super Learner to avoid parametric modeling assumptions during statistical estimation and flexibly adjust for a wide range of confounders, including recent case rates. We also implemented unadjusted analyses. For most weeks, unadjusted analyses suggested strong associations between mobility indexes and subsequent new case rates. However, after confounder adjustment, none of the indexes showed consistent associations under mobility reduction. Our analysis demonstrates the utility of this novel distribution-shift approach to defining and estimating causal effects with continuous exposures in epidemiology and public health. [ABSTRACT FROM AUTHOR]

Patients with opioid use disorder (OUD) tend to get assigned to one of 3 medications based on the treatment program to which the patient presents (e.g. opioid treatment programs tend to treat patients with methadone, while office-based practices tend to prescribe buprenorphine). It is possible that optimally matching patients with treatment type would reduce the risk of return to regular opioid use (RROU). We analyzed data from 3 comparative effectiveness trials from the US National Institute on Drug Abuse Clinical Trials Network (CTN0027, 2006–2010; CTN0030, 2006–2009; and CTN0051 2014–2017), in which patients with OUD (n  = 1,459) were assigned to treatment with either injection extended-release naltrexone (XR-NTX), sublingual buprenorphine-naloxone (BUP-NX), or oral methadone. We learned an individualized rule by which to assign medication type such that risk of RROU during 12 weeks of treatment would be minimized, and then estimated the amount by which RROU risk could be reduced if the rule were applied. Applying our estimated treatment rule would reduce risk of RROU compared with treating everyone with methadone (relative risk (RR) = 0.79, 95% confidence interval (CI): 0.60, 0.97) or treating everyone with XR-NTX (RR = 0.71, 95% CI: 0.47, 0.96). Applying the estimated treatment rule would have resulted in a similar risk of RROU to that of with treating everyone with BUP-NX (RR = 0.92, 95% CI: 0.73, 1.11). [ABSTRACT FROM AUTHOR]

Knowledge of and practice around health inequities have been limited by scarce investigations on intersecting forms of structural oppression, including the extent to which their effects are more severe among multiply marginalized groups. We addressed these insufficiencies by adopting a structural intersectionality approach to the study of edentulism (i.e. complete tooth loss), the dental equivalent of mortality. While individual information was gathered from approximately 200,000 adult (ages 18–64 years) respondents to the 2010 US Behavioral Risk Factor Surveillance System survey, state-level data for 2000 and 2010 were obtained from a 2021 study by Homan et al. (J Health Soc Behav. 2021;62(3):350–370) and the US Census. These 3 sources provided information on edentulism, race, sex, structural racism, structural sexism, and income inequality, in addition to multiple covariates. Analyses showed that the intersections between structural sexism and state-level income inequality and structural racism were associated with 1.4 (95% confidence interval: 1.1, 1.9) and 1.5 (95% confidence interval: 1.1, 2.2) times' increased odds of complete tooth loss, respectively. The frequency of edentulism was highest among non-Hispanic Black men residing in states with high structural racism, high structural sexism, and high economic inequality. Based on these and other findings, we highlight the importance of a structural intersectionality approach to research and policy related to health inequities in the United States and elsewhere. [ABSTRACT FROM AUTHOR]

Smoke-free housing policies are intended to reduce the deleterious health effects of secondhand smoke exposure, but there is limited evidence regarding their health impacts. We examined associations between implementation of a federal smoke-free housing rule by the New York City Housing Authority (NYCHA) and pediatric Medicaid claims for asthma, lower respiratory tract infections, and upper respiratory tract infections in the early post–policy intervention period. We used geocoded address data to match children living in tax lots with NYCHA buildings (exposed to the policy) to children living in lots with other subsidized housing (unexposed to the policy). We constructed longitudinal difference-in-differences models to assess relative changes in monthly rates of claims between November 1, 2015, and December 31, 2019 (the policy was introduced on July 30, 2018). We also examined effect modification by baseline age group (≤2, 3–6, or 7–15 years). In New York City, introduction of a smoke-free policy was not associated with lower rates of Medicaid claims for any outcomes in the early postpolicy period. Exposure to the smoke-free policy was associated with slightly higher than expected rates of outpatient upper respiratory tract infection claims (incidence rate ratio = 1.05, 95% confidence interval: 1.01, 1.08), a result most pronounced among children aged 3–6 years. Ongoing monitoring is essential to understanding long-term health impacts of smoke-free housing policies. [ABSTRACT FROM AUTHOR]

The relative excess risk due to interaction (RERI) provides a useful metric of departure from additivity of effects on a relative risk scale. In this paper, the authors show that RERI is identical to the product term in a linear odds ratio or a linear relative risk model. SAS and STATA codes are provided for fitting a linear odds ratio model that directly parameterizes RERI. In addition, this paper presents a method for obtaining likelihood-based 95% confidence bound estimates for RERI. The authors show that likelihood-based confidence intervals may differ substantially from the asymptotic confidence interval estimates advocated by previous authors. The approach presented in this paper should facilitate estimation of RERI and associated likelihood-based confidence bounds, by using standard statistical packages. [ABSTRACT FROM AUTHOR]