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 the 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% of 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.

Immortal time may arise in survival analyses when individuals are assigned to treatment strategies based on post-eligibility information or selected based on post-assignment eligibility criteria. Selection based on eligibility criteria applied after treatment assignment results in immortal time when the analysis starts the follow-up at assignment. Misclassification of assignment to treatment strategies based on treatment received after eligibility results in immortal time when the treatment strategies are not distinguishable at the start of follow-up. Target trial emulation prevents the introduction of immortal time by explicitly specifying eligibility and assignment to the treatment strategies, and by synchronizing them at the start of follow-up. We summarize analytic approaches that prevent immortal time when longitudinal data are available to emulate the target trial from the time of treatment assignment. The term "immortal time bias" suggests that the source of the bias is the immortal time, but it is selection or misclassification that generates the immortal time, leading to bias.

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 article develops a 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: Asthma is a phenotypically complex disease requiring nuanced data to generate clinically and scientifically robust real-world evidence. A quantitative measure of data quality is important for variables key to the research questions at hand. Using electronic health record (EHR) data, this study compared accuracy for asthma features between traditional real-world evidence approaches using structured data and advanced approaches applying artificial intelligence technologies to unstructured clinical data.

Methods: We extracted 18 protocol-defined features from 6037 healthcare encounters among 3481 patients. Features included asthma severity subtypes, comorbidities, symptoms, findings, and procedures. We created a manual reference standard through chart abstraction, with two annotators reviewing each record. We assessed interrater reliability using Cohen's kappa score and accuracy against the reference standard as an F1-score.

Results: In the traditional study arm, average recall was 40.8%, precision 72.5%, and F1-score across features was 52.2%. In the advanced study arm, average recall was 95.7%, precision 93.8%, and F1-score was 94.7%. There was an absolute increase of 42.5% and a relative increase of 81.4% in the F1-score between traditional and advanced approaches. Cohen's kappa score indicated 0.80 inter-rater reliability, reflecting a credible reference standard.

Conclusions: Use of advanced approaches can enable high-quality real-world data sets in asthma, including granular clinical features such as disease subtypes and symptomatic outcomes. Data quality can be measured and, when high, can support generation of high-validity real-world evidence using routinely collected healthcare data.

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 CRC 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 body mass index (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/nonsteroidal anti-inflammatory drugs 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: To our knowledge, no agreed-upon best practices exist for joining U.S. Census ZIP Code Tabulation Areas (ZCTAs) and U.S. Postal Service ZIP Codes (ZIPs). One-to-one linkage using 5-digit ZCTA identifiers excludes ZIPs without direct matches. "Crosswalk" linkage may match a ZCTA to multiple ZIPs, avoiding losses.

Methods: We compared noncrosswalk and crosswalk linkages nationally and for mortality and health insurance in California. To elucidate selection implications, generalized additive models related sociodemographics to whether ZCTAs contained nonmatching ZIPs.

Results: Nationwide, 15% of ZCTAs had nonmatching ZIPs, i.e., ZIPs dropped under noncrosswalk linkage. ZCTAs with nonmatching ZIPs were positively associated with metropolitan core location, lower socioeconomics, and non-White population. In California, 34% of ZIPs in the mortality and 25% in the health insurance data had ZCTAs with nonmatching ZIPs; however, these ZIPs constitute only 0.03% of total mortality and 0.44% of total insurance enrollees.

Conclusions: Our study findings support the use of crosswalk linkages and ZCTAs as a unit of analysis. One-to-one linkage may cause bias by differentially excluding ZIPs with more disadvantaged populations, although affected population sizes seem small.

Background: Evidence for long-term mortality risks of PM 2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM 2.5 -mortality associations in the UK Biobank cohort using detailed information on confounders and exposure.

Methods: We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM 2.5 concentrations from spatiotemporal machine-learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual- and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions.

Results: In fully adjusted models, an increase of 10 μg/m³ in PM 2.5 was associated with hazard ratios of 1.27 (95% confidence interval: 1.06, 1.53) for all-cause, 1.24 (1.03, 1.50) for nonaccidental, 2.07 (1.04, 4.10) for respiratory, and 1.66 (0.86, 3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (hazard ratio = 0.88, 95% confidence interval: 0.59, 1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates.

Conclusions: We found associations of long-term PM 2.5 exposure with all-cause, nonaccidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM 2.5 and mortality.

Purpose: "Positive epidemiology" emphasizes strengths and assets that protect the health of populations. Positive mental health refers to a range of social and emotional capabilities that may support adaptation to challenging circumstances. We examine the role of positive mental health in promoting adolescent health during the crisis phase of the COVID-19 pandemic.

Methods: We used four long-running Australian and UK longitudinal cohorts: Childhood to Adolescence Transition Study (CATS; analyzed N = 809; Australia); Longitudinal Study of Australian Children (LSAC) - Baby (analyzed N =1,534) and Kindergarten (analyzed N = 1,300) cohorts; Millennium Cohort Study (MCS; analyzed N = 2,490; United Kingdom). Measures included prepandemic exposure: positive mental health (parent reported, 13-15 years) including regulating emotions, interacting well with peers, and caring for others; and pandemic outcomes: psychological distress, life satisfaction, and sleep and alcohol use outside of recommendations (16-21 years; 2020). We used a two-stage meta-analysis to estimate associations between positive mental health and outcomes across cohorts, accounting for potential confounders.

Results: Estimates suggest meaningful effects of positive mental health on psychosocial outcomes during the pandemic, including lower risk of psychological distress (risk ratio [RR] = 0.83, 95% confidence interval [CI] = 0.71, 0.97) and higher life satisfaction (RR = 1.1, 95% CI = 1.0, 1.2). The estimated effects for health behaviors were smaller in magnitude (sleep: RR = 0.95, 95% CI = 0.86, 1.1; alcohol use: RR = 0.97, 95% CI = 0.85, 1.1).

Conclusions: Our results are consistent with the hypothesis that adolescents' positive mental health supports better psychosocial outcomes during challenges such as the COVID-19 pandemic, but the relevance for health behaviors is less clear. These findings reinforce the value of extending evidence to include positive health states and assets.

Background: Education is strongly associated with cognitive outcomes at older ages, yet the extent to which these associations reflect causal effects remains uncertain due to potential confounding.

Methods: Leveraging changes in historical measures of state-level education policies as natural experiments, we estimated the effects of educational attainment on cognitive performance over 10 years in 20,248 non-Hispanic Black and non-Hispanic White participants, aged 45+ in the Reasons for Geographic and Racial Disparities in Stroke cohort (2003-2020) by (1) using state- and year-specific compulsory schooling laws, school-term length, attendance rate, and student-teacher ratio policies to predict educational attainment for US Census microsample data from 1980 and 1990, and (2) applying policy-predicted years of education (PPYEd) to predict memory, verbal fluency, and a cognitive composite. We estimated overall and race- and sex-specific effects of PPYEd on level and change in each cognitive outcome using random intercept and slope models, adjusting for age, year of first cognitive assessment, and indicators for state of residence at age 6.

Results: Each year of PPYEd was associated with higher baseline cognition (0.11 standard deviation [SD] increase in composite measure for each year of PPYEd, 95% confidence interval [CI] = 0.07, 0.15). Subanalyses focusing on individual cognitive domains estimate the largest effects of PPYEd on memory. PPYEd was not associated with the rate of change in cognitive scores. Estimates were similar across Black and White participants and across sex.

Conclusions: Historical policies shaping educational attainment are associated with better later-life memory, a major determinant of dementia risk.

Background: The increased prevalence of physical diseases among individuals with mental illness contributes to their increased risk of mortality. However, the mediating role of specific diseases in the effect of mental illness on mortality is not well understood.

Method: We conducted a longitudinal causal mediation analysis using data from beneficiaries of a South African medical insurance scheme from 2011 to 2020. We estimated the overall effect of major depressive disorder (MDD) on mortality and evaluated reductions in this overall effect through hypothetical interventions on the risks of mediating physical diseases using an interventional effects approach. Monte Carlo simulation-based g-computation was used for estimation.

Results: Among 981,540 individuals, 143,314 (14.6%) were diagnosed with MDD. Mortality risk after 8 years was 6.5% under MDD, and 5.3% under no MDD (risk ratio 1.23, 95% CI = 1.19, 1.26). Overall, 43.4% of this disparity could be attributed to higher rates of physical comorbidities due to MDD. Cardiovascular diseases accounted for 17.8%, followed by chronic respiratory diseases (8.6%), cancers (7.5%), diabetes and chronic kidney disease (5.8%), tuberculosis (4.3%), and HIV (2.7%).

Conclusion: Within the privately insured population of South Africa, MDD is associated with increased mortality. We found that noncommunicable diseases, rather than infectious diseases, are important mediators of the effect of MDD on mortality.