Epidemiology最新文献

Background: We evaluated air emissions of industrial compounds, many of which have carcinogenic or endocrine disrupting properties, in relation to breast cancer incidence.

Methods: Using the United States Environmental Protection Agency's Toxics Release Inventory, we quantified air emissions of 28 compounds near Sister Study participants' residences during the 10 years leading up to study enrollment (2003-2006; n=46,150). We used Cox proportional hazards regression to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for associations of residential emission levels of single pollutants with incident breast cancer. We assessed pollutant mixtures using an Exposure Continuum Mapping (ECM) framework and characterized associations using a joint-exposure response function.

Results: During follow-up (median=13.4 years), we identified 4,155 breast cancer cases. We observed non-monotonic but elevated associations with breast cancer for emissions within 3km of the residence for nickel compounds (HRquintile5vs.none = 1.3; 95% CI 1.0, 1.6) and trichloroethylene (HRquintile5vs.none = 1.3; 95% CI 1.0, 1.6). ECM identified 25 mixture profiles that explained 72% of the variance in emissions patterns, with most participants experiencing relatively low emissions profiles. The joint-exposure response function suggested that higher incidence of breast cancer occurred among individuals with relatively rare, high emissions profiles; however, the overall trend was not associated with breast cancer (p=0.09).

Conclusions: In our study, breast cancer incidence was associated with air emissions of certain industrial carcinogens. Although the overall emissions mixture did not show a trend related to breast cancer, this may not reflect the importance of individual compounds or specific emissions sources.

During the coronavirus disease (COVID-19) pandemic, researchers attempted to estimate the number of averted and avertible outcomes due to vaccination campaigns to quantify public health impact. However, the estimands used in these analyses have not been previously formalized. It is also unclear how these analyses relate to the broader framework of direct, indirect, total, and overall causal effects under interference. Here, using potential outcome notation, we adjust the direct and overall effects to accommodate analyses of averted and avertible outcomes. We use this framework to interrogate the commonly held assumption that vaccine-averted outcomes via direct impact among vaccinated individuals (or vaccine-avertible outcomes via direct impact among unvaccinated individuals) is a lower bound on vaccine-averted (or -avertible) outcomes overall. To do so, we describe a susceptible-infected-recovered-death model stratified by vaccination status. When vaccine efficacies wane, the lower bound fails for vaccine-avertible outcomes. When transmission or fatality parameters increase over time, the lower bound fails for both vaccine-averted and -avertible outcomes. Only in the simplest scenario where vaccine efficacies, transmission, and fatality parameters are constant over time, outcomes averted via direct impact among vaccinated individuals (or outcomes avertible via direct impact among unvaccinated individuals) is a lower bound on overall impact. In conclusion, the lower bound can fail under common violations to assumptions on time-invariant vaccine efficacy, pathogen properties, or behavioral parameters. In real data analyses, estimating what seems like a lower bound on overall impact through estimating direct impact may be inadvisable without examining the directions of indirect effects.

Background: Improvements in breast cancer therapy since the randomized controlled trials of mammography screening might have reduced the screening benefit. Most observational studies of mammography effectiveness would be confounded by these improvements and other factors. Using a design resistant to this confounding, we evaluated whether mammography in asymptomatic women reduces breast cancer mortality during the treatment era succeeding the trials.

Methods: We designed a quasi-experimental cohort study in regions of Denmark without organized screening. We predicted the number of expected mammograms for each general practice based on observed numbers of mammograms and individual risk factors. Regardless of a woman's individual exposure to mammography, we assigned her the ratio of observed to expected mammograms of her general practice as her instrumental variable. We employed this potential instrumental variable as mammography exposure status and followed women from January 1, 2006 until death, emigration, or December 31, 2014, whichever came first.

Results: We included 169,197 women aged 50-66 from 738 general practices and without previous breast cancer as of January 1, 2006. Women affiliated with a practice referring more women than expected, compared with less, had a lower hazard of breast cancer death (hazard ratio 0.80, 95% confidence interval 0.68, 0.95). Negative control associations were near null, suggesting no confounding bias.

Conclusions: This quasi-experimental study estimated a continued protective effect of mammography in women where most were presumably asymptomatic. In contrast to conventional observational studies, the use of practice referral ratio as an instrumental variable may avoid bias from uncontrolled confounding.

Epidemiologic studies recruiting individuals with higher-than-population-average mortality can be affected by "truncation by death", whereby the outcome of interest (e.g. quality of life) is considered not to be defined for individuals who die before the end of follow-up. Here, we use the potential outcomes framework and principal stratification to derive conditions under which the survivor average causal effect, an estimand defined for the "always-survivors" stratum, is modified by a variable that represents a possible common cause of survival and the outcome of interest, and by a variable that only affects survival. Further, we show that this principal effect can be expressed as a weighted average of this treatment effect for individuals with each level of these variables, and that these weights depend not only on the relative frequencies of the levels in the total population, but also in the "always-survivors" principal stratum. We also discuss the implications of this work for the transportability of the survivor average causal effect.

Background: We examined interactions, to our knowledge not yet explored, between long-term exposures to particulate matter (PM 10 ) with nitrogen dioxide (NO 2 ) and ozone (O 3 ) on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infectivity and severity.

Methods: We followed 709,864 adult residents of Varese Province from 1 February 2020 until the first positive test, COVID-19 hospitalization, or death, up to 31 December 2020. We estimated residential annual means of PM 10 , NO 2 , and O 3 in 2019 from chemical transport and random-forest models. We estimated the interactive effects of pollutants with urbanicity on SARS-CoV-2 infectivity, hospitalization, and mortality endpoints using Cox regression models adjusted for socio-demographic factors and comorbidities, and additional cases due to interactions using Poisson models.

Results: In total 41,065 individuals were infected, 5203 were hospitalized and 1543 died from COVID-19 during follow-up. Mean PM 10 was 1.6 times higher and NO 2 2.6 times higher than WHO limits, with wide gradients between urban and nonurban areas. PM 10 and NO 2 were positively associated with SARS-CoV-2 infectivity and mortality, and PM 10 with hospitalizations in urban areas. Interaction analyses estimated that the effect of PM 10 (per 3.5 µg/m 3 ) on infectivity was strongest in urban areas [hazard ratio (HR) = 1.12; 95% CI =1.09, 1.16], corresponding to 854 additional cases per 100,000 person-years, and in areas at high NO 2 co-exposure (HR = 1.15; 1.08, 1.22). At higher levels of PM 10 co-exposure, the protective association of O 3 reversed (HR =1.32, 1.17, 1.49), yielding 278 additional cases per µg/m 3 increase in O 3 . We estimated similar interactive effects for severity endpoints.

Conclusions: We estimate that interactive effects between pollutants exacerbated the burden of the SARS-CoV-2 pandemic in urban areas.

Background: Attending clinic appointments supports HIV viral suppression, yet racial disparities are documented. We assessed whether multilevel resilience resources were associated with appointment attendance among African American/Black (AA/B) adults living with HIV in the United States.

Methods: We ascertained data from 291 AA/B clinical cohort participants from 2018 to 2021. We assessed resilience using the Multilevel Resilience Resource Measure. Binary outcomes were a nonrepeated indicator of attending ≥87.5% of scheduled HIV appointments over 12 months (i.e., visit adherence) and a repeated measure of attending appointments during two sequential 6-month follow-up windows (i.e., clinic attendance). Modified Poisson models estimated adjusted risk ratios (aRRs).

Results: The aRR for clinic attendance among participants with greater versus lesser multilevel resilience resource endorsement was 0.95 (95% confidence interval: 0.88, 1.0). The aRR for visit adherence among participants with greater versus lesser multilevel resilience resource endorsement was 1.2 (0.95, 1.4).

Conclusions: This analysis is one of the first to assess appointment attendance as a function of resilience. Findings should be confirmed in larger cohorts.

Background: Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy at the congressional district level to derive local estimates, but such an approach has not been validated.

Methods: Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010-2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland-Altman plots to visualize the agreement between the two measures.

Results: We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric Life Expectancy Estimates Project model-based approach and the Vital Statistics direct estimates approach, though life expectancy at older ages (75 years and older) showed weak correlations.

Conclusions: This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policymaking aimed at improving population health outcomes.

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.

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.

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.