American journal of epidemiology最新文献

Traumatic brain injury (TBI) is a major public health concern affecting millions of people each year. Disparities in TBI outcomes based on social determinants of health (SDoH), such as race and socioeconomic position, highlight the need to explore the causative structural inequities. We employed a socio-epidemiologic approach, with particular focus on the putative role of structural racism, to investigate the prevalence, sociodemographic patterns, and neuropsychiatric outcomes of TBI in the All of Us database. This study included 11 286 individuals with a documented TBI diagnosis, determined based on a curated phenotype definition using the International Statistical Classification of Diseases Clinical Modification criteria. Outcome measures included TBI prevalence and sociodemographic distribution; TBI severity; and neuropsychiatric diagnoses related to TBI. Nearly equivalent TBI prevalences were observed across racial categories. Black participants with TBI had higher socioeconomic deprivation indices and higher prevalence of certain neuropsychiatric conditions, such as substance use disorders and headache disorders, compared to White participants. This study underscores the importance of considering SDoH, particularly race and socioeconomic position, in TBI research. These findings highlight the need for efforts to address structural inequities that impact disparities in TBI and call for future research investigating how healthcare practices relate to disparities in TBI outcomes. This article is part of a Special Collection on Methods in Social Epidemiology.

Emergency department (ED) visits during influenza seasons represent a critical yet less examined indicator of the acute burden of influenza. This study investigates the burden of influenza-associated ED visits in 6 US cities during influenza seasons from 2005-2006 to 2016-2017. Using a time-series design, we estimated associations between daily ED visits and weekly influenza activity data from the Influenza Hospitalization Surveillance Network (FluSurv-NET). A counterfactual approach was then used to calculate attributable expected ED visits. Highest influenza-associated rates were observed among the youngest (0-4 years) and oldest (65+ years) age groups. Combining estimates across seasons, the influenza-associated ED visit rate for respiratory diseases was almost 6 times larger compared to the subset of ED visits that resulted in hospitalization: 364 per 100 000 population (95% CI, 294-435) for total ED visits vs 58 per 100 000 population (95% CI, 45-71) for hospitalization. This difference was particularly large for the 0-4 years age group: 911 per 100 000 population (95% CI, 558-1263) for total ED visits vs 43 per 100 000 population (95% CI, 15-71) for hospitalization. This study highlights the substantial burden of influenza on emergency health care services and the importance of integrating such data into public health planning and influenza management strategies.

Obesity increases cardiovascular disease (CVD) and cancer mortality risk, with prevalence rising globally over recent decades. In the United States, steep obesity increases contributed to adverse trends in obesity-related mortality and to slowing decline in overall CVD mortality, particularly among younger generations. Switzerland experienced slower obesity increases, but the contribution of obesity to mortality trends remains uncharacterized. We analyzed all adult deaths recorded in Swiss mortality statistics between 1995-2019. Obesity-related CVD and cancer deaths were identified using multiple cause of death approaches. Annual changes in age-standardized mortality rates were estimated via segmented regression. Age-period-cohort models assessed cohort variations. Overall, CVD mortality declined steadily while cancer mortality decline attenuated after 2005, primarily reflecting slower declines in obesity-unrelated cancer mortality. Obesity-related mortality increased from 1995-2005 and then decreased, while obesity-unrelated rates decreased throughout 1995-2019. These diverging trends did not slow overall CVD mortality decline. Age-period-cohort modeling revealed lower obesity-related mortality rates in younger versus older generations. In Switzerland, unlike in the United States, trends in obesity-related mortality did not slow the decline of overall CVD mortality. Obesity-related mortality rates did not increase in younger generations, highlighting the role of reduced childhood obesity prevalence and improved management of obesity-related conditions in Switzerland.

Multiple imputation (MI) models can be improved with auxiliary covariates (ACs), 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 ACs were created using structured and NLP-derived features, and we mimicked scenarios where AFib was unobserved by omitting it from all analyses. Using the least absolute shrinkage and selection operator, 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. High-dimensional MI 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 square error (RMSE) of 0.173 and 94% coverage. Natural language processing-derived AC alone did not outperform baseline MI. High-dimensional MI approaches may decrease bias in studies where confounder missingness depends on unobserved factors.

Food environment near schools (FENS) influence children's dietary habits and contribute to obesity. Socioeconomic characteristics of schools and school neighborhoods play a role in determining FENS. We compare the availability of fast-food restaurants (FFRs) and convenience stores (CSs) across schools' socioeconomic characteristics: whether the school is public or private and the school neighborhood's median household income. We obtained the number of FFRs and CSs within a 0.75-mile network buffer from schools' locations and the names of the outlets. Negative binomial regression models, stratified by urbanicity, were used to estimate the association between the number of outlets near schools and schools' socioeconomic characteristics. We explored brand names and types of outlets. Private schools' neighborhoods had more FFRs and CSs than public schools across all income and urbanization levels. Private and public schools in low-income urban neighborhoods had more outlets compared to those in higher-income urban areas. While the names of FFRs and CSs near both school types were broadly similar, private schools had more non-chain outlets. Programs and policies to promote healthy eating and reduce obesity- and diet-related diseases should target food environments near both private and public schools, especially those located in urban areas and low-income communities.

Mendelian randomization (MR) is a technique that uses genetic variation to address causal questions about how modifiable exposures influence health. For some time-varying phenotypes, genetic effects may have differential importance at different periods in the lifecourse. MR studies often employ conventional instrumental variable (IV) methods designed to estimate average lifetime effects. Recently, several extensions of MR have been proposed to investigate time-varying effects, including structural mean models (SMMs). SMMs exploit IVs through g-estimation and circumvent some of the parametric assumptions required by other MR methods. In this study, we applied g-estimation of SMMs within an MR framework to estimate the period effects of adiposity measured at two life stages, childhood and adulthood, on cardiovascular disease (CVD), type 2 diabetes (T2D), and breast cancer. We found persistent period effects of higher adulthood adiposity on increased risk of CVD and T2D. Higher childhood adiposity had a protective period effect on breast cancer risk. We compared this approach with an inverse variance weighted multivariable MR method, which also uses multiple IVs to assess time-varying effects but relies on a different set of assumptions. We highlight the strengths and limitations of each approach and conclude by emphasizing the importance of underlying methodological assumptions in the application of MR to lifecourse research.

The COVID Infection Survey monitored daily positivity through the COVID-19 pandemic from April 26, 2020 to March 13, 2023. In total, 451 079 participants in private residential households were enrolled in England and tested at regular intervals for SARS-CoV-2. Here, we estimated the cumulative incidence of polymerase chain reaction-positive infections using a multilevel regression and poststratification model to obtain estimates of daily positivity, combined with a distribution of the duration of positivity from regular testing data. We estimated cumulative incidence by epoch (approximated by the dominance of successive SARS-CoV-2 variants) and calculated the corresponding infection hospitalization ratios. We found that cumulative incidence was relatively low during pre-Alpha and Alpha-dominant epochs, rose steadily during the Delta-dominant epoch, and was highest during successive Omicron-dominant epochs. High cumulative incidences in successive Omicron-dominant epochs are consistent with lack of protection from previous infections. However, infection hospitalization ratios, whilst higher at the start of the pandemic, remained low after the Delta-dominant epoch and vaccine introduction. Stratified estimates show hospitalization risk was consistently very low for younger age groups, increasing with age. Surveys with random sampling and longitudinal designs facilitate direct estimation of prevalence and incidence, however, should be complemented by dense sampling to estimate duration of infection to maximize their value.