Epidemiology最新文献

Background: Many organizations recommend lifestyle modifications for cancer survivors. Effect estimates for these interventions are often based on observational data and are challenging to interpret due to vaguely defined causal questions, design-induced biases, and lack of comparability between individuals.

Methods: We outlined a three-step procedure to address these challenges: target trial specification, emulation, and modification to explore lack of comparability due to unmeasured confounding or positivity violations. We illustrated this procedure by specifying the protocols of two target trials that estimate the effects of adhering to seven physical activity and dietary recommendations and abstaining from alcohol on 20-year mortality among adults with breast or prostate cancer. We emulated these target trials using data from the Nurses' Health Study, Nurses' Health Study II, and Health Professionals Follow-up Study.

Results: In the main analysis, we included 9,107 adults (5,840 with breast cancer, 3,267 with prostate cancer) and 1,791 deaths occurred. After we modified the target trials, mortality risk differences (95% confidence intervals) comparing the physical activity and dietary intervention versus no intervention ranged from -4.8% (-7.5%, -2.3%) to -13.0% (-15.8%, -9.8%) for breast cancer and from -3.0% (-7.4%, 0.9%) to -12.8% (-17.6%, -7.6%) for prostate cancer. Risk differences comparing no alcohol consumption versus no intervention ranged from 1.3% (0.1%, 2.4%) to 3.6% (2.5%, 4.9%) for breast cancer and from -1.7% (-4.3%, 1.0%) to 6.4% (4.0%, 9.0%) for prostate cancer.

Conclusions: We described a three-step procedure that improves the interpretability of observational estimates of the effects of lifestyle interventions and showed how estimates varied under different modifications.

Unmeasured confounding is a major concern in obtaining credible inferences about causal effects from observational data. Proximal causal inference is an emerging methodological framework to detect and potentially account for confounding bias by carefully leveraging a pair of negative control exposure and outcome variables, also known as treatment and outcome confounding proxies. Although regression-based proximal causal inference is well-developed for binary and continuous outcomes, analogous proximal causal inference regression methods for right-censored time-to-event outcomes are currently lacking. In this paper, we propose a novel two-stage regression proximal causal inference approach for right-censored survival data under an additive hazard structural model. We provide theoretical justification for the proposed approach tailored to different types of negative control outcomes, including continuous, count, and right-censored time-to-event variables. We illustrate the approach with an evaluation of the effectiveness of right heart catheterization among critically ill patients using data from the SUPPORT study. Our method is implemented in the open-access R package "pci2s."

Reproductive complications tend to recur. The risk of gestational diabetes is much higher in the second pregnancy if it occurred in the first. Such recurrence risks are regarded as reflecting heterogeneity among couples in their inherent risk. Pregnancy complications not only predict their own recurrence but have been shown to be associated with different later health problems like hypertension and heart disease. Epidemiologically considering reproductive history as a risk factor has been challenging, however, because women vary in their number of pregnancies and there's no obvious way to account for both prior occurrences and prior nonoccurrences. We propose a simple empirical Bayes approach, the Beta Approach for Risk Summarization (BARS). We apply BARS to retrospective data reported at enrollment in a large cohort, the Sister Study, to estimate propensity to gestational diabetes, and use that to predict subsequent occurrences of gestational diabetes based on successively updated pregnancy histories. We assess the calibration of our predictive model for gestational diabetes and demonstrate that it works well. We then apply the method to prospective data from the Sister Study, revisiting an earlier paper that linked gestational diabetes to the risk of breast cancer, but now using BARS and additional person time.

Background: Pathogen transmission of an outbreak generally begins well before it is identified by a surveillance system, particularly for infectious diseases in which a high proportion of cases are subclinical, as is the case for arboviruses. We aimed to ascertain the most likely date of the primary case (the first infection, whether detected or not) in an outbreak.

Methods: Using data from a 2019 dengue outbreak in a rural, riverine town in Northwestern Ecuador, we investigated potential undetected dengue virus transmission before the outbreak detected in mid-May. The outbreak was preceded by four reported cases on 9 February, 13 February, 28 March, and 2 May. Using a hidden Markov model, we estimate the most likely date of the primary case for different assumed case reporting fractions.

Results: For all reporting fractions, the most likely primary case occurred near the 2 February candidate index cases, ranging from 7 February to 12 February, over 2 months before the main outbreak. Individual simulations showed that earlier and later primary cases were also possible. Our results suggest that the dengue virus was circulating in the community for around 3 months before the outbreak.

Conclusions: Surveillance systems that can detect low-level transmission in the early stages of an outbreak can provide time to intervene before the exponential phase of the outbreak, with the potential to substantially reduce transmission and disease burden.

Trial engagement effects are effects of trial participation on the outcome that are not mediated by treatment assignment. Most work on extending (generalizing or transporting) causal inferences from a randomized trial to a target population has, explicitly or implicitly, assumed that trial engagement effects are absent, allowing evidence about the effects of the treatments examined in trials to be applied to nonexperimental settings. Here, we define novel causal estimands and present identification results for generalizability and transportability analyses in the presence of trial engagement effects. Our approach allows for trial engagement effects under assumptions of no causal interaction between trial participation and treatment assignment on the absolute or relative scales. We show that under these assumptions, even in the presence of trial engagement effects, the trial data can be combined with covariate data from the target population to identify average treatment effects in the context of usual care as implemented in the target population (i.e., outside the experimental setting). The identifying observed data functionals under these no-interaction assumptions are the same as those obtained under the stronger identifiability conditions that have been invoked in prior work. Therefore, our results suggest a new interpretation for previously proposed generalizability and transportability estimators. This interpretation may be useful in analyses under causal structures where background knowledge suggests that trial engagement effects are present but interactions between trial participation and treatment are negligible.

Background: Time-series models for count outcomes are routinely used to estimate short-term health effects of environmental exposures. The dispersion parameter is universally assumed to be constant over the study period.

Objective: The aim is to examine whether dispersion depends on time-varying covariates in a case study of emergency department visits in Atlanta during 1999-2009 and to evaluate approaches for addressing time-varying dispersion.

Methods: Using the double generalized linear model framework, we jointly modeled the Poisson log-linear mean and dispersion to estimate associations between emergency department visits for respiratory diseases and daily ozone concentrations. We conducted a simulation study to evaluate the impact of time-varying overdispersion on health effect estimation when constant overdispersion is assumed and developed an analytic code for implementing double generalized linear model using R.

Results: We found dispersion to depend on calendar date and meteorology. Assuming constant dispersion, the relative risk (RR) per interquartile range increase in 3-day moving ozone exposure was 1.037 (95% confidence interval: 1.024, 1.050). In the multivariable dispersion model, the RR was reduced to 1.029 (95% confidence interval: 1.020, 1.039), but with a large (26%) reduction in log RR standard error. The positive associations for ozone were robust against different dispersion model specifications. Simulation study results also demonstrated that when time-varying dispersion is present, it can lead to a larger standard error assuming constant dispersion.

Conclusion: When the outcome exhibits large dispersion in a time-series analysis, allowing for covariate-dependent time-varying dispersion can improve inference, particularly by increasing estimation precision.

Background: Exposure to fine particulate matter (PM 2.5 ) during pregnancy has been associated with adverse birth outcomes. However, limited evidence exists on the effects of specific PM 2.5 components. We investigated the association of prenatal exposure to PM 2.5 and its components with birth outcomes and mortality at age <5 years in four metropolitan cities in South Korea.

Methods: We obtained data from Statistic Korea linking birth records for 2013-2015 to death records under age 5 years. Data for PM 2.5 and 10 of its components were collected from four monitoring stations. We calculated exposures during pregnancy and each trimester for a total of 324,566 births. We used logistic regression to estimate the associations between exposure and risk of preterm birth (PTB) (<37 weeks), low birth weight (<2.5 kg), small for gestational age (birth weight <10 th percentile for the same gestational age), and under-5 mortality.

Results: An interquartile range (8.7 µg/m 3 ) increase in exposure to PM 2.5 during the entire pregnancy was associated with increased odds of PTB (odds ratio [OR] = 1.17; 95% confidence interval [CI] = 1.11, 1.23). We observed no association with low birth weight, small for gestational age, or under-5 mortality for the entire pregnancy exposure. Elemental carbon and secondary inorganic aerosols showed higher effect estimates for PTB than did other components.

Conclusions: In urban populations of South Korea, exposure to PM 2.5 during pregnancy was associated with an increased risk of PTB. Different components showed varying associations with adverse birth outcomes.