American journal of epidemiology最新文献

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately impacted Black nursing home (NH) residents. Alzheimer's Disease and Related Dementias (ADRD) may exacerbate disparities, however little empirical evidence exists on the degree to which race and ADRD intersect to impact COVID-19-related outcomes. We conducted a cohort study (April-December 2020) leveraging electronic health records from 12 United States NH corporations. We used parametric g-formula to obtain standardized estimates of incident COVID-19 infection and 30-day COVID-19-associated hospitalization or death by race, both overall and within strata of ADRD status. The cohort comprised 127,913 resident-episodes, including 15,379 incident COVID-19 infections, 1,522 deaths, and 2,548 hospitalizations. Black residents were more likely than White residents to experience incident COVID-19 and subsequent hospitalization, but not more likely to subsequently die. Disparities in hospitalization and a combined endpoint of hospitalization or death were more pronounced among residents with ADRD compared to residents without ADRD. These results suggest the presence of disparities in COVID-19 outcomes by race and provide evidence that ADRD status may exacerbate racial disparities in COVID-19 outcomes among nursing home residents. Our findings offer valuable insights for current and future preparedness efforts in NHs in the United States and countries with similarly under-resourced long-term care settings.

Background: Mounting evidence suggests that early-life lead exposure alters immune system functions, including T-cell dependent antibody responses to childhood immunizations. However, no studies have identified critical windows of susceptibility to lead exposure.

Aim: To identify perinatal critical windows of lead exposure that are associated with antibody responses to anti-MMR (anti-measles, -mumps, and -rubella virus) and anti-DTP (anti-diphtheria, -tetanus, and -pertussis toxoids) vaccinations in Hispanic school-aged (mean± standard deviation: 4.8±0.6 years) children.

Methods: Weekly lead exposure-from 16 weeks before to 14 weeks after birth-was measured in deciduous teeth from 271 children enrolled in the PROGRESS study. Serum levels of anti-MMR and anti-DTP antibodies were measured by a Luminex-multiplexed-microbead-array immunoassay. Time-varying associations between log2-transformed dentine lead concentrations and log2-transformed antibody levels were estimated by fitting distributed lag non-linear models.

Results: A two-fold higher dentine lead concentration in the first three weeks postpartum was associated with an average -4.29% lower anti-tetanus level (95%confidence interval(CI):-8.22,-0.20). A perinatal (one week before to one week after birth) critical window of lead exposure demonstrated an average -3.44% (95%CI:-7.05;0.30) lower anti-diphtheria antibody level.

Conclusions: Our study suggests that early-life lead exposure may contribute to immune dysfunction by reducing children's antibody responses to scheduled vaccinations.

One of the key limitations of electronic health records (EHR) data is that not all health care encounters are observed. The degree to which patient information is captured is referred to as observability. Poor observability, and in particular differential observability, can lead to biased estimates and inference. As such, understanding the degree of observability is important in EHR based studies. In this study, we propose using external data with known observability to assess the degree of overall observability in EHR. We also construct a test for differential observability in the target EHR dataset. Using principles from the transportability literature, we show that we can use a balancing score based weight to estimate the observability of our target outcome. We conduct a series of simulation experiments to understand the conditions under which dataset features must be required to obtain proper inference. To illustrate this, we consider hospital readmissions among patients with end stage renal disease as our outcome of interest. We use administrative claims data, where the outcome is fully observed, as our external data.

Background: Child maltreatment is a persistent public health problem in the United States. Child Protective Services (CPS) data are the de facto data source for child maltreatment surveillance, despite these data's established limitations. Maltreatment-related mortality could be a complementary source of child maltreatment surveillance data.

Methods: We calculated trends over time, and patterns across state and by race/ethnicity, comparing child maltreatment report rates to child maltreatment-related mortality rates, between 2005 and 2020.

Results: These two measures of maltreatment show different time trends and patterns by state and race/ethnicity. Time trends in maltreatment-related mortality decreased slightly across the study period for all racial/ethnic groups, while maltreatment report rates increased, particularly for Non-Hispanic Black children. Reports and mortality data revealed very different pictures of which states had the highest and lowest maltreatment rates, overall and by race/ethnicity. Only 14 states had report and mortality rates in the same tertile, with less alignment when stratified by race/ethnicity.

Discussion: Patterns in child maltreatment report rates and death rates do not align. Future work should consider additional sources of data to improve maltreatment surveillance. These findings highlight the need to identify a valid and consistent approach to capture patterns of maltreatment in the United States.

We examined the association between ambient air pollution exposure and risk of spontaneous abortion (SAB) using Georgia state-wide fetal death records from 2005-2014. Each SAB case was matched to four non-SAB pregnancies by maternal residential county and conception month. Daily concentrations of ten pollutants were estimated and linked to maternal residential census tracts. Cox regression was used to estimate hazard ratios (HR) across four prenatal exposure windows (first month, weekly, cumulative weekly average over the first trimester, cumulative weekly average over the second trimester). Our dataset contained 47,649 SABs with a median gestational age of nine weeks. Carbon monoxide (CO) showed the strongest association, with an HR of 1.12 (1.05, 1.20) per 0.43 ppm increase in average first month exposure, and 1.06 (1.02, 1.10) per 0.42 ppm increase in average weekly exposure. Nitrogen dioxide (NO2) also exhibited elevated HRs. Other pollutants like nitrate compounds (NO3), nitrogen oxides (NOx), and organic carbon (OC) showed positive associations, while ozone (O3), PM2.5, PM10, elemental carbon (EC), and ammonium ions (NH4) were null. Early pregnancy exposure to traffic-related pollutants may increase SAB risk, highlighting potential benefits of air pollution regulation.

Area economic inequality may underlie social disparities in suicide mortality (SM). Differences in measuring inequality contribute to variability across empirical evidence. We contrasted common income measures - absolute poverty, Gini inequality index, Index of Concentration at the Extremes (ICE) - and examined their associations with age- and sex-standardized SM across 1381 US counties. We used the 2006-2019 National Violent Death Reporting System linked to 2006-2010 administrative data on socioeconomic factors and a Bayesian spatial multilevel approach. Compared to affluent areas, poorer areas had the highest relative risk (RR) of SM (ICE RR: 1.24, 95% Credible Interval (CI): 1.17 - 1.31 and absolute poverty RR: 1.33, CI: 1.25 - 1.41). Gini inequality was not linearly associated with SM. Cross-classifying Gini x ICE showed highest-risk areas had concentrated poverty (ICE) but varying Gini inequality. These high-risk poverty-segregated areas were more often medically underserved, had lower population density and high unemployment. African American or Indigenous suicide decedents often resided in high Gini inequality areas, while older, White decedents, with military backgrounds more often resided in lower Gini areas. The choice of inequality measure can lead to varied conclusions about social disparities in SM. Comparative approach offers more nuanced understanding of underlying socioeconomic marginalization.

Multiple imputation (MI) models can be improved with auxiliary covariates (AC), but their performance in high-dimensional data remains unclear. We aimed to develop and compare high-dimensional MI (HDMI) methods using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation with acute kidney injury as outcome and simulated 100 cohorts with a null treatment effect, incorporating creatinine labs, atrial fibrillation (AFib), and other investigator-derived confounders in the outcome generation. Missingness was imposed on creatinine based on creatinine itself and AFib. Different HDMI candidate AC were created using structured and NLP-derived features and we mimicked scenarios where AFib was unobserved by omitting it from all analyses. Using LASSO, we selected HDMI covariates for MI and propensity score models. The treatment effect was estimated after propensity score matching in MI datasets, and HDMI methods were compared to baseline imputation and complete case analysis. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency with a root-mean-squared-error of 0.173 and 94% coverage. NLP-derived AC alone did not outperform baseline MI. HDMI approaches may decrease bias in studies where confounder missingness depends on unobserved factors.

Non-vaccination and under-vaccination with the COVID-19 vaccine may be attributed to multifaceted barriers including hesitancy and access issues. Using data from the CDC's Research and Development Survey, a nationally representative survey fielded from November 3, 2022 - December 12, 2022 (n=6,821), we examined the adjusted population attribution fraction (PAF) of COVID-19 non-vaccination and under-vaccination attributed to vaccine hesitancy by sociodemographic characteristics. Overall, the adjusted PAF of non-vaccination attributed to vaccine hesitancy was 84.8%, and the adjusted PAF of under-vaccination attributed to vaccine hesitancy decreased with increasing COVID-19 vaccine doses (76.0%, 41.0%, and 16.9% for ≥2, ≥3, and ≥4 doses, respectively). The proportion of adults who considered the social benefit of the vaccine, risk of contracting COVID-19, and information received from a medical provider increased with greater number of COVID-19 vaccine doses received. In contrast, the proportion of adults who were concerned about long-term impacts, speed of vaccine development and personal risk of getting vaccinated decreased with greater number of COVID-19 vaccine doses received. Understanding the PAF estimates from the acute phase of the pandemic serves as an important comparison for post-pandemic vaccination estimates, and is needed for messaging as COVID-19 cases, hospitalizations, and deaths resurge in the fall of 2024.