Epidemiology最新文献

Electronic health record (EHR) systems capture patient information inconsistently, with patients generally contributing more data when they are sick than healthy. This creates "informed presence," systematic differences between captured and non-captured data, potentially biasing association estimates. There is growing interest in methods that account for informed presence, but practical approaches for conceptualizing, identifying, and addressing this bias in applied EHR research have received limited attention. Focusing on longitudinal settings, we present a conceptual framework for informed presence bias, which arises when data capture depends on exposure and outcome and thus the visit process acts as a collider. We then illustrate methods that aim to reduce bias by reweighting or resampling observed data to approximate conditional independence between the visit process and the outcome. We illustrate these methods using longitudinal EHR data from pediatric solid organ transplant recipients (N=271) to examine the association between steroids and cytomegalovirus viremia, where the frequency of cytomegalovirus testing varies across patients and over time. Incidence rate ratios decreased from 1.83 (95% CI 1.02, 3.28) in a naïve analysis to 1.37 (0.73, 2.57) when accounting for informed presence using inverse intensity weighting. Incidence rate ratio estimates from bootstrapped inverse intensity weighting were 1.37 (0.71, 2.27) and 1.40 (0.73, 2.68) from multiple outputation. These results show the anticipated attenuation of effect estimates after accounting for informed presence bias. When analyzing irregularly measured EHR data, we recommend (1) identifying the expected observation process using conceptual diagrams, (2) assessing dependence in the observation process, and (3) accounting for outcome dependence in statistical analysis.

Background: The use of a standard body mass index (BMI) cut-point (e.g., BMI ≥ 30 kg/m2) to define obesity across the lifespan does not reflect our current understanding of changes in adiposity that occur throughout the aging process. The objective of the present study was to examine the validity of BMI-for-age percentiles as a measure of obesity in older adults, compared to an objective gold-standard measure of obesity status, total body fat percent (BF%) measured by Dual Energy X-ray Absorptiometry scan.

Methods: Using data from the Canadian Longitudinal Study on Aging (n = 28,764), sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were calculated for BMI ≥ 85th percentile and other thresholds against BF% ≥ 35%, 38%, and 40%, respectively.

Results: When comparing obesity defined by BMI percentile values (≥ 85th percentile) to BF% (≥ 40% BF) as a measure of true obesity status, Se, Sp, PPV, and NPV values were 0.32 (0.31, 0.33), 0.98 (0.98, 0.98), 0.94 (0.93, 0.95) and 0.61 (0.60, 0.62) in women, and 0.85 (0.81, 0.89), 0.86 (0.85, 0.86), 0.14 (0.13, 0.16), and 1.00 (0.99, 1.00) in men, respectively.

Conclusion: Incorporating age and sex information, BMI-for-age percentile is a highly specific and moderately sensitive measure of obesity in older adults, with a potential as a valuable screening tool. Further research on BMI-for-age percentiles could help develop more accurate obesity screening tools than the current standard BMI cut-points.

Growing evidence suggests that health impacts of a given increment of fine particulate matter (PM2.5) are changing over time. While these trends could influence environmental health disparities, how temporal trends vary across locations and subpopulations remain unknown. Using case-crossover analyses, we analyzed how PM2.5-mortality associations varied over time (2001-2016) in two U.S. states (North Carolina and Michigan) among persons 65+ years of age. We further characterized temporal variation in the PM2.5-mortality association by sociodemographic variables including age, sex, race/ethnicity, educational attainment, urbanicity, and sub-state region. From the 2001-2008 study period to the 2009-2016 study period, the odds of mortality per 10g/m 3 PM2.5 decreased by 0.77% in North Carolina but increased by 0.28% in Michigan. Using a non-linear temporal model, the associations between PM2.5 concentration and mortality were variable over time in North Carolina and steadily increased over time in Michigan. We also found suggestive evidence of widening disparities in PM2.5-mortality odds ratios over time. For example, PM2.5-mortality odds in Michigan increased for more dramatically for non-Hispanic Black (1.71% [-1.17, 4.67%]) than for non-Hispanic White (0.14% [95% confidence interval: -0.93, 1.22%]) subpopulations. In North Carolina, these groups experienced a 0.40% (-3.56, 2.85%) and 0.96% (-2.52, 0.62%) reduction in PM2.5-mortality, respectively. Our results suggest that PM2.5-related mortality impacts may be changing over time, with different trends by location and subpopulation, potentially exacerbating environmental injustice.

Background: Extreme heat and high air pollution levels may co-occur and act synergistically. The synergistic health effects of extreme heat and NO2, as well as the fine-scale spatial heterogeneity in such joint effects, remain unclear. We aimed to investigate the synergistic effect of extreme heat and NO2 exposures on cardiorespiratory hospitalizations in California and explore its spatial heterogeneity.

Methods: Daily ZIP Code Tabulation Area (ZCTA)-level hospitalization data, 2000-2019, was obtained from the California Department of Health Care Access and Information. Extreme heat and NO2 exposures were defined as days when the heat index and NO2 concentration exceeded a ZCTA-specific threshold, respectively. We first investigated the state-level synergistic effects with a case-crossover analysis and then estimated the ZCTA-specific synergistic effects using a within-community matched design combined with a spatial Bayesian hierarchical model. Finally, we explored how community characteristics modify these effects using meta-regressions.

Results: We found mild synergistic effects of extreme heat and NO2 exposures on cardiorespiratory hospitalizations at the state level, with a relative excess risk due to interaction (RERI) of 0.005 (95% confidence interval: -0.002, 0.011). Yet, great spatial heterogeneity was observed, with ZCTA-specific RERIs ranging from -1.08 to 2.22. Higher RERIs were observed in communities with lower socioeconomic status, higher population density, reduced green space, greater proportions of racial and ethnic minority residents, and higher historical temperatures.

Conclusions: Our findings underscore the need for adaptation policies that integrate compound exposures to heat and air pollution and inform the development of targeted intervention strategies to protect vulnerable communities.

Background: It remains unclear why cool temperatures cause more persistent adverse health effects compared to hot weather. Fluid homeostasis may constitute a causal link between past temperatures and adverse health effects. In this study we investigated the association between past outdoor temperatures and current fluid homeostasis.

Methods: We studied participants from five cohorts during three decades in Sweden (total n=29,755, age 18-86 years, 50.4% women). We quantified fluid homeostasis through indicators of hormonal regulation (vasopressin biomarker plasma copeptin), urine concentration (urine osmolality), and replenishment of fluid loss (total water intake) and related these parameters to past outdoor temperatures (21 days) using distributed non-linear lag models.

Results: Past temperatures were non-linearly associated with current copeptin and urine osmolality.Cool temperatures during days and weeks prior contributed to distinct patterns of high copeptin with concomitant influence on urine osmolality. Overall, a scenario of temperatures of 0 °C for 21 days showed 14.9% (95% confidence interval 11.5-18.3%) higher copeptin levels compared to reference temperatures of 14.3 °C for 21 days. Associations between copeptin and warm temperatures were less complex and of shorter duration, linking elevated temperatures within 24 hours with higher copeptin compared to the reference temperature.

Conclusions: Alterations in human fluid homeostasis may partly explain the observed link between moderately cool outdoor temperatures and adverse health effects weeks later. If so, avoiding altered water balance through moderately increased water intake might mitigate adverse health effects of cool weather.

Air quality regulations and programs are vital for protecting the public from harms caused by air pollution. To support these actions, numerous epidemiological studies have sought to identify the pollutants most responsible for adverse outcomes. These studies often used statistical adjustments for co-pollutants in outcome regression models, a practice also commonly applied to assess interactions between co-pollutants. Here, we highlight possible pitfalls of multi-pollutant analyses. Indiscriminate co-pollutant adjustment can induce noncausal associations through collider adjustment, distorting effect estimates for individual air pollutants. We describe the underlying mechanisms and provide empirical evidence on how such bias may realistically influence the relationships between air pollution and health outcomes from a well-characterized Canadian national cohort alongside a simulation study. Additionally, we discuss strategies to mitigate the impact of this bias. Given the widespread interest in multi-pollutant approaches among the scientific and policy communities, greater caution is needed when conducting and interpreting research on multiple pollutants.

We discuss generalizability analyses under a partially nested trial design, where part of the trial is nested within a cohort of trial-eligible individuals, while the rest of the trial is not nested. This design arises, for example, when only some centers participating in a trial are able to collect data on non-randomized individuals, or when data on non-randomized individuals cannot be collected for the full duration of the trial. Our work is motivated by the Necrotizing Enterocolitis Surgery Trial, which compared initial laparotomy versus peritoneal drain for infants with necrotizing enterocolitis or spontaneous intestinal perforation. During the first phase of the study, data were collected from randomized individuals as well as consenting non-randomized individuals; during the second phase of the study, however, data were only collected from randomized individuals, resulting in a partially nested trial design. We propose methods for generalizability analyses with partially nested trial designs. We describe identification conditions and propose estimators for causal estimands in the target population of all trial-eligible individuals, both randomized and non-randomized, in the part of the data where the trial is nested while using trial information spanning both parts. We evaluate the estimators in a simulation study and provide an illustration using the Necrotizing Enterocolitis Surgery Trial study.

Background: Data on lactation status for research are often collected through surveys. Information on human milk feeding collected at routine healthcare visits and stored in electronic health records (EHR) is an emerging source of data for lactation research. We compared information on milk feeding obtained from structured EHR fields with survey data.

Methods: We included participants from five US healthcare systems in the Managing Our Mood survey. Individuals had a live birth (March 2022-October 2023), depression diagnosis during pregnancy, and ≥1 record of human milk feeding information in their or their infant's EHR. We compared information from EHR data up to ten months after delivery with survey data collected 3-4 months after delivery as the reference. We assessed agreement on lactation status (human milk feeding ever and at survey) using percent agreement, Cohen's kappa, sensitivity, specificity, positive predictive value, and negative predictive value overall and by characteristics.

Results: According to survey data, the prevalence of human milk feeding ever was 93.2% and was 73.0% at the time of survey among 281 eligible individuals. Agreement between data sources for ever and for human milk feeding at the survey was ≥92% with kappas ≥0.77. EHR and survey data agreed on human milk feeding ever for 97.3% (95% confidence interval: 94.6%, 98.7%) and on human milk feeding at the time of the survey for 98.0% (95% confidence interval: 95.1%, 99.2%) of those who reported yes to these practices on the survey. These measurements were lower among individuals with fewer records.

Conclusions: There was substantial agreement on lactation status between EHR and survey data. These findings suggest that lactation information from structured EHR may be used for epidemiologic research.

Background: School closures during the coronavirus disease 2019 pandemic disrupted children's education, socialization, and access to mental health resources, raising concerns about long-term effects on childrens' mental health. The objective was to evaluate the impacts of pandemic-era school reopenings on children's mental health and healthcare expenditures. Variation in the timing of school reopenings created a unique quasi-experiment.

Methods: We used difference-in-differences analysis to examine how staggered implementation of school reopenings affected diagnoses with depression, anxiety, and attention-deficit/hyperactivity disorder, and related healthcare spending among school-aged children during March 2020-June 2021 across 24 California counties. Data were drawn from medical claims from the second largest private health insurer in the state (N = 185,735).

Results: School reopening was associated with a 1.2%-point drop in monthly prevalence of mental health diagnoses [95% confidence interval (CI) = -1.59, -0.74], and a 10.6% (95% CI = -13.4%, -7.8%) drop in related healthcare spending. The mental health conditions that saw the largest differential between in-person and remote school districts were anxiety and depression. Effects were strongest among girls.

Conclusions: In-person learning is an important component of children's mental health. These results are informative for future policymaking during public health crises, to balance infection risk with the need for socialization and other critical resources that schools provide to children.