Epidemiology最新文献

Introduction: There is lack of consensus regarding whether a second screening in Rhesus-positive pregnant women is worthwhile, with different guidelines, recommendations, and practices. We aimed to estimate the number and timing of missed alloimmunizations in Rhesus-positive pregnancies screened once and weigh the relative burden of additional screening and monitoring versus the estimated reduction in adverse pregnancy outcomes.

Methods: We extracted information on maternal, pregnancy, and screening results for 682,126 pregnancies for 2003-2012 from Swedish national registers. We used data from counties with a routine second screening to develop and validate a logistic model for a positive second test after an earlier negative. We used this model to predict the number of missed alloimmunizations in counties offering only one screening. Interval-censored survival analysis identified an optimal time window for a second test. We compared the burden of additional screening with estimated adverse pregnancy outcomes avoided.

Results: The model provided an accurate estimate of positive tests at second screening. For counties with the lowest screening rates, we estimated that a second screening would increase the alloimmunization prevalence by 33% (from 0.19% to 0.25%), detecting the 25% (304/1222) of cases that are currently missed. The suggested timing of a second screen was gestational week 28.For pregnancies currently screened once, the estimated cost of a second test followed by maternal monitoring was approximately 10% the cost incurred by the excess adverse pregnancy outcomes.

Conclusion: Investment in routine second screening can identify many alloimmunizations that currently go undetected or are detected late, with the potential for cost savings.

It has become standard in medical treatment to base dosage on evidence in randomized trials. Yet it has been rare to study how outcomes vary with dosage. In trials to obtain drug approval, the norm has been to compare some dose of a new drug with an established therapy or placebo. Standard trial analysis views each trial arm as qualitatively different, but it may be credible to assume that efficacy and adverse effects weakly increase with dosage. Optimization of patient care requires joint attention to both, as well as to treatment cost. This paper develops methodology to use limited trial evidence to choose dosage when efficacy and adverse effects weakly increase with dose. I suppose that dosage is an integer t ∊ (0,1, . ,T), T being a specified maximum dose. I study dosage choice when trial evidence on outcomes is available for only K dose levels, where K < T+1. Then the population distribution of dose response is partially identified. I show that the identification region is a convex polygon. I characterize clinical and population decision making using the minimax-regret criterion. A simple analytical solution exists when T = 2. Computation is tractable when T is larger.

Background: Colorectal cancer (CRC) is a common, fatal cancer. Identifying subgroups who may benefit more from intervention is of critical public health importance. Previous studies have assessed multiplicative interaction between genetic risk scores and environmental factors, but few have assessed additive interaction, the relevant public health measure.

Methods: Using resources from colorectal cancer consortia including 45,247 CRC cases and 52,671 controls, we assessed multiplicative and additive interaction (relative excess risk due to interaction, RERI) using logistic regression between 13 harmonized environmental factors and genetic risk score including 141 variants associated with CRC risk.

Results: There was no evidence of multiplicative interaction between environmental factors and genetic risk score. There was additive interaction where, for individuals with high genetic susceptibility, either heavy drinking [RERI = 0.24, 95% confidence interval, CI, (0.13, 0.36)], ever smoking [0.11 (0.05, 0.16)], high BMI [female 0.09 (0.05, 0.13), male 0.10 (0.05, 0.14)], or high red meat intake [highest versus lowest quartile 0.18 (0.09, 0.27)] was associated with excess CRC risk greater than that for individuals with average genetic susceptibility. Conversely, we estimate those with high genetic susceptibility may benefit more from reducing CRC risk with aspirin/NSAID use [-0.16 (-0.20, -0.11)] or higher intake of fruit, fiber, or calcium [highest quartile versus lowest quartile -0.12 (-0.18, -0.050); -0.16 (-0.23, -0.09); -0.11 (-0.18, -0.05), respectively] than those with average genetic susceptibility.

Conclusions: Additive interaction is important to assess for identifying subgroups who may benefit from intervention. The subgroups identified in this study may help inform precision CRC prevention.

Background: Intersectionality, or the multidimensional influence of social identity and systems of power, may drive increased morbidity and mortality for adults of color with Down syndrome. We documented racial and ethnic differences in death and hospitalizations among Medicaid enrolled adults with Down syndrome and assessed interaction of racial-ethnic group and Down syndrome.

Methods: Our sample consisted of 119,325 adults with Down syndrome and >3.2 million adults without intellectual disability enrolled in Medicare at any point from 2011-2019. We calculated age-adjusted mortality and hospitalization rates by racial-ethnic group among those with Down syndrome. We examined additive interaction between Down syndrome and racial and ethnic group on mortality and hospitalization rates.

Results: Among those with Down syndrome, age-adjusted mortality rate did not differ between Black and White racial groups (rate ratio: 0.96, 95%CI: 0.92, 1.01) while mortality rate was lower for Pacific Islander (0.80), Asian (0.71), Native (0.77), and Mixed-race groups (0.50). Hospitalization rates were higher for all marginalized groups compared to the White group. When assessing the interaction between racial-ethnic group and Down syndrome, Black, Native Americans, and Mixed-race groups exhibited a negative additive interaction for mortality rate and all groups except Native Americans exhibited positive additive interaction for hospitalization.

Conclusions: Increased hospitalization rates for adults with Down syndrome from marginalized racial and ethnic groups suggest worse health and healthcare. Similar mortality rates across racial and ethnic groups may result from increased infant mortality rate in marginalized groups with Down syndrome leading to reduced mortality among those surviving to adulthood.

When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.

Difference-in-differences (DiD) is a powerful, quasi-experimental research design widely used in longitudinal policy evaluations with health outcomes. However, DiD designs face several challenges to ensuring reliable causal inference, such as when policy settings are more complex. Recent economics literature has revealed that DiD estimators may exhibit bias when heterogeneous treatment effects, a common consequence of staggered policy implementation, are present. To deepen our understanding of these advancements in epidemiology, in this methodologic primer, we start by presenting an overview of DiD methods. We then summarize fundamental problems associated with DiD designs with heterogeneous treatment effects and provide guidance on recently proposed heterogeneity-robust DiD estimators, which are increasingly being implemented by epidemiologists. We also extend the discussion to violations of the parallel trends assumption, which has received less attention. Last, we present results from a simulation study that compares the performance of several DiD estimators under different scenarios to enhance understanding and application of these methods.

Background: Sibling studies of maternal smoking during pregnancy and subsequent risk of depression have produced mixed results. A recent study identified not considering the amount of maternal smoking and age of onset as potentially masking a true association. We examine these issues and also the amount of maternal smoking during pregnancy as a determinant of the severity of depressive symptoms.

Methods: We analyzed data from the community-based National Longitudinal Survey of Youth (US, 1994-2016). Mothers reported smoking during pregnancy (none, <1 pack/day, ≥1 pack/day). We assessed offspring's lifetime depression (i.e., ≥8 symptoms) and symptom counts with the Centers for Epidemiologic Studies Depression scale. We estimated the risk of these two outcomes in the full sample (n = 7172) and among siblings (n = 6145) using generalized linear mixed-effects models with random intercepts by family and family-averaged means for sibling analyses.

Results: Among siblings, we observed dose-dependent elevations for both risk of depression (smoking during pregnancy <1 pack/day adjusted risk ratio [aRR] = 1.18; 95% confidence interval [CI] = 1.07, 1.30; smoking ≥1 aRR = 1.36; 95% CI = 1.19, 1.56) and severity of depressive symptoms (smoking <1 pack/day aRR = 1.12; 95% CI = 1.08, 1.16); smoking ≥1 pack/day aRR = 1.25; 95% CI = 1.18, 1.31). Among both samples, the P for trend was <0.01. In analysis limited to offspring diagnosed before age 18, results for severity were attenuated.

Conclusions: This evidence supports the existence of an independent association between maternal smoking during pregnancy and both the risk of depression and the severity of depressive symptoms. The results highlight the utility of considering the amount of smoking, severity of symptoms, and age of onset.

Background: Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method.

Methods: We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors.

Results: Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups.

Conclusion: Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.

Background: Gestational diabetes is associated with adverse outcomes such as preterm birth (<37 weeks). However, there is no international consensus on screening criteria or diagnostic levels for gestational diabetes, and it is unknown whether body mass index (BMI) or obesity modifies the relation between glucose level and preterm birth.

Methods: We studied a pregnancy cohort restricted to two Danish regions from the linked Danish Medical Birth Register to study associations between glucose measurements from the 2-hour postload 75-g oral glucose tolerance test (one-step approach) and preterm birth from 2004 to 2018. In Denmark, gestational diabetes screening is a targeted strategy for mothers with identified risk factors. We used Poisson regression to estimate rate ratios (RR) of preterm birth with z-standardized glucose measurements. We assessed effect measure modification by stratifying analyses and testing for heterogeneity.

Results: Among 11,337 pregnancies (6.2% delivered preterm), we observed an adjusted preterm birth RR of 1.2 (95% confidence interval [CI] = 1.1, 1.3) for a one-standard deviation glucose increase of 1.4 mmol/l from the mean of 6.7 mmol/l. There was evidence for effect measure modification by obesity, for example, adjusted RR for nonobese (BMI, <30): 1.2 (95% CI = 1.1, 1.3) versus obese (BMI, ≥30): 1.3 (95% CI = 1.2-1.5), P = 0.05 for heterogeneity.

Conclusion: Among mothers screened for gestational diabetes, increased glucose levels, even those below the diagnostic level for gestational diabetes in Denmark, were associated with increased preterm birth risk. Obesity (BMI, ≥30) may be an effect measure modifier, not just a confounder, of the relation between blood glucose and preterm birth risk.