American journal of epidemiology最新文献

During epidemics, emergency department (ED) syndromic surveillance of patient arrivals provides timely but non-virus-specific assessment of epidemic intensity. Surveillance of severe infection outcomes (intensive care admission or death) is less timely because outcomes can take weeks to occur. Time series models can be used to estimate the frequency of severe infection outcomes due to viruses. We developed and evaluated daily time series modelling applied to linked ED, infection and outcomes data from Australia to better predict population and health system burden during acute respiratory virus epidemics. In retrospective daily surveillance emulation, generalised additive models nowcasted (short-term forecast) the frequency of ED arrivals attributable to each of influenza and COVID-19 that will have a severe infection outcome within 28 days. Daily nowcasts spanned days -29 to -4 from each date for which surveillance was emulated. To validate the method, nowcasts were compared with subsequently observed severe infection outcome frequencies for December 2021 through February 2023. During this period, the mean daily day -4 nowcast error was 2.7 (34.2%), compared with 3.5 (43.8%) if outcomes known at day -1 were used. With increasing real-world data availability, this method could improve rapid, automated epidemic assessment for timely public health action.

When using real-world data to construct an external comparator arm for a single-arm trial, there may be differences in how and when patients are assessed for disease between trial and real-world settings. Such differences can generate outcome measurement error when comparing time to event endpoints and lead to biased findings. Recent methods have been developed to mitigate measurement error bias in real-world endpoints; however, they rely on the existence of a validation sample, ie, data on a set of patients where both the "true" trial-like and "mis-measured" real-world measures are collected. We demonstrate how novel statistical methods can be leveraged as quantitative bias analyses (QBA) to contextualize real-world evidence findings when outcome measurement error is of concern, but validation samples are infeasible to collect. QBA allows researchers to set plausible ranges for the amount of error when not directly measurable. We highlight how to conduct QBA with two recent methods, Cumulative Incidence Curve Correction and Survival Regression Calibration, and illustrate how to generate plausible parameter values through simulation. We provide an illustrative QBA example in a cohort of real-world patients with Newly Diagnosed Multiple Myeloma and provide practical guidance to apply QBA for outcome measurement error and interpret results.

Observational studies evaluating the effectiveness of cancer screening are often biased due to nonalignment at time zero, which can be avoided by target trial emulation (TTE). We aimed to illustrate this by evaluating site-specific effectiveness of screening colonoscopy regarding colorectal cancer (CRC) incidence. Based on a German health care database, we assessed the effect of screening colonoscopy vs no screening colonoscopy in preventing CRC in the distal and the proximal colon over 12 years of follow-up in 55-69-year-old persons. We compared four different study designs: cohort and case-control study, each with/without alignment at time zero. In both analyses with time zero-alignment, screening colonoscopy showed a rather similar effectiveness in reducing the incidence of distal and proximal CRC (cohort analysis: 32% (95% CI, 27%-37%) vs 28% (20%-35%); case-control analysis: 27% vs 33%). Both analyses without alignment suggested a difference by site: Incidence reduction regarding distal and proximal CRC, respectively, was 65% (61%-68%) vs 37% (31%-43%) in the cohort analysis and 77% (67%-84%) vs 46% (25%-61%) in the case-control analysis. Violations of basic design principles can substantially bias the results of observational studies. In our example, it falsely suggested a much stronger preventive effect of colonoscopy in the distal vs the proximal colon. Our study illustrates that TTE avoids such design-induced biases.

Population aging in the Asia-Pacific will not proceed along the paths already followed by more developed countries, but differences in later-life health across the Asia-Pacific region are poorly understood. Using data from five harmonized longitudinal surveys in the region, we examine gender and cross-national differences in life expectancy (LE) and health expectancies (HEs) at age 50 in Australia, Japan, South Korea, China, and Indonesia. We adopt a microsimulation-based multistate life table model to provide estimates of HEs across four dimensions of health, including life expectancy free of poor self-related health, ADL disability, functional limitations, and chronic diseases. We find that older adults in the Asia-Pacific are experiencing substantially different regimes of health in later life, with cross-national differences arising across measures of health, over age, and between men and women. Older adults China and Indonesia experience more lifetime with physical limitations compared to those in Australia or Japan. Older adults in China spend a markedly higher proportion of remaining LE with chronic diseases compared to other countries. Our results provide much-needed evidence examining current conditions across the region, deepening understanding of how Asia-Pacific populations are currently fairing in terms of later-life health and functioning.

Despite widespread implementation of mask mandates for COVID-19 transmission control, studies examining their effectiveness have yielded mixed results ranging from strong benefits to no effect. These inconsistencies may arise from a variety of methodological and measurement challenges, including the implicit assumption that mandates truly modify masking behavior-the essential mechanism for transmission interruption. Here, we leverage self-reported mask adherence data from >34 000 individuals collected via a digital participatory surveillance platform between June 2, 2020, and January 1, 2021, to examine this assumption. Using an interrupted time series approach, we aggregate masking observations at the county level to analyze the effect of mandates on masking uptake across 555 diverse U.S. counties. We evaluate masking during the 14 days premandate and postmandate issuance, finding a modest 1-3 percentage point overall increases in masking. However, substantial heterogeneity was observed, with larger changes seen in counties initially exhibiting low mask adherence, the U.S. West, and on masking uptake in public compared to private settings. Our findings suggest that conflicting estimates of the effect of mandates on transmission reduction may reflect modification by heterogeneity in the mandates' alteration of masking behavior. Future interventions should tailor mandates to local context and baseline adherence for maximal behavioral change.

Our objective was to examine the role of structural racism and economic disadvantage in perinatal health inequities using the Environmental influences on Child Health Outcomes Cohort. Participants' addresses were linked to area-level measures of life expectancy, education, unemployment, health insurance, jail rate, segregation, and housing cost burden. We created absolute measures to represent economic disadvantage and relative measures comparing values for Black or Latinx people to White people in the same area to represent structural racism. We used quantile G-computation to estimate the effects of a one-quartile increase in all exposures simultaneously on fetal growth and gestational age measures. A one-quartile increase in economic disadvantage was associated with a reduction in birthweight [(-25.65 grams, 95% CI (-45.83, -5.48)], but not gestational age [-0.02 weeks, 95% CI (-0.13, 0.09)]. With a one-quartile increase in Latinx-White structural racism, we observed reductions in birthweight [-80.83, 95% CI (-143.42, -18.23)] among Latinx participants. A one-quartile increase in Black-White structural racism was weakly associated with lower birthweight among Black participants [-15.70, 95% CI (-82.89, 51.48)] but was associated with higher birthweight among White participants [57.47, 95% CI (13.26, 101.67)]. Our findings suggest co-occurring forms of structural inequity likely influence racialized disparities in fetal growth outcomes.

Exposure to fine particulate matter (PM2.5) is associated with cardiometabolic risk among World Trade Center Health Program general responders, but current studies focus mainly on PM2.5 mass. We studied the associations between annual source-apportioned PM2.5 exposures, and self-reported diabetes or repeated blood glucose measurements among general responders enrolled between 2003 and 2019 (n = 34 764), residing in Tri-state area. We used non-negative matrix factorization to attribute PM2.5 component to sources (ie, biomass burning, oil combustion, metal industry, other industries, and motor vehicles). We used multivariable mixed-effect models to estimate the simultaneous associations of the source-apportioned PM2.5 exposures with the outcomes. We found (% change [95% CIs]) an interquartile range increase in PM2.5 attributed to metal industry sources (0.42 μg/m3) to be associated with an 8.35% (1.39%, 15.79%) higher risk of diabetes and a 1.31% (0.80%, 1.82%) increase in glucose levels. Source-specific associations with glucose were modified by sex, showing larger associations with biomass burning (1.08% [0.32%, 1.85%] per 0.44 μg/m3) and motor vehicle (1.34% [0.76%, 1.93%] per 0.92 μg/m3) pollution among women, and larger associations with oil-combustion (0.68% [0.03%, 1.34%] per 1.74 μg/m3) pollution among men. These findings can inform policies and interventions targeting emissions from these specific sources, particularly for general responders with a history of extreme air pollution exposures.

Health visiting is a complex public health intervention in which specialist nurses work with families to support the healthy development of children up to 5 years of age. Using routinely collected administrative health data, we emulated a target trial to estimate the effect of enhanced health visiting services on potentially avoidable hospital admissions for children born in 10 local areas in England between 2016 and 2019. We found that receiving additional support from the health visiting team in the early weeks of life was associated with an increased odds of a child experiencing a potentially avoidable hospitalization (OR, 1.28; 95% CI, 1.02-1.60). Health visiting may encourage families to seek secondary health care, for example by building confidence in public services or heightening parental anxiety about the risks of childhood health conditions. However, qualitative research and sensitivity analyses indicated that our effect estimate may have been subject to residual confounding, selection bias or both. An in-depth understanding of the intervention and the mechanisms through which treatments are assigned is essential for generating valid estimates of causal effects.