Epidemiology最新文献

Background: Childhood maltreatment is associated with elevated adult weight. It is unclear whether this association extends to pregnancy, a critical window for the development of obesity.

Methods: We examined associations of childhood maltreatment histories with prepregnancy body mass index (BMI) and gestational weight gain among women who had participated for >20 years in a longitudinal cohort. At age 26-35 years, participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about prepregnancy weight and gestational weight gain for previous pregnancies (n = 656). Modified Poisson regression models were used to estimate associations of maltreatment history with prepregnancy BMI and gestational weight gain z -scores, adjusting for sociodemographics. We used multivariate imputation by chained equations to adjust outcome measures for misclassification using data from an internal validation study.

Results: Before misclassification adjustment, results indicated a higher risk of prepregnancy BMI ≥30 kg/m 2 in women with certain types of maltreatment (e.g., emotional abuse risk ratio = 2.4; 95% confidence interval: 1.5, 3.7) compared with women without that maltreatment type. After misclassification adjustment, estimates were attenuated but still modestly elevated (e.g., emotional abuse risk ratio = 1.7; 95% confidence interval: 1.1, 2.7). Misclassification-adjusted estimates for maltreatment associations with gestational weight gain z -scores were close to the null and imprecise.

Conclusions: Findings suggest an association of maltreatment with prepregnancy BMI ≥30 kg/m 2 but not with high gestational weight gain. Results suggest a potential need for equitable interventions that can support all women, including those with maltreatment histories, as they enter pregnancy.

Background: The COVID-19 pandemic, and subsequent policy responses aimed at curbing disease spread and reducing economic fallout, had far-reaching consequences for maternal health. There has been little research to our knowledge on enduring disruptions to maternal health trends beyond the early pandemic and limited understanding of how these impacted pre-existing disparities in maternal health.

Methods: We leveraged rigorous interrupted time-series methods and US National Center for Health Statistics Vital Statistics Birth Data Files of all live births for 2015-2021 (N = 24,653,848). We estimated whether changes in maternal health trends after the onset of the COVID-19 pandemic (March 2020) differed from predictions based on pre-existing temporal trends. Outcomes included gestational diabetes, hypertensive disorders of pregnancy, gestational weight gain, and adequacy of prenatal care.

Results: We found an increased incidence of gestational diabetes (December 2020 peak: 1.7 percentage points (pp); 95% confidence interval [CI]: 1.3, 2.1), hypertensive disorders of pregnancy (January 2021 peak: 1.3 pp; 95% CI: 0.4, 2.1), and gestational weight gain (March 2021 peak: 0.1 standard deviation; 95% CI: 0.03, 0.1) and declines in inadequate prenatal care (January 2021 nadir: -0.4 pp; 95% CI: -0.7, -0.1). Key differences by subgroups included greater and more sustained increases in gestational diabetes among Black, Hispanic, and less educated individuals.

Conclusion: These patterns in maternal health likely reflect not only effects of COVID-19 infection but also changes in healthcare access, health behaviors, remote work, economic security, and maternal stress. Further research about causal pathways and longer-term trends will inform public health and clinical interventions to address maternal disease burden and disparities.

Background: Increased lung-cancer risks for low socioeconomic status (SES) groups are only partially attributable to smoking habits. Little effort has been made to investigate the persistent risks related to low SES by quantification of potential biases.

Methods: Based on 12 case-control studies, including 18 centers of the international SYNERGY project (16,550 cases, 20,147 controls), we estimated controlled direct effects (CDE) of SES on lung cancer via multiple logistic regression, adjusted for age, study center, and smoking habits, and stratified by sex. We conducted mediation analysis by inverse odds ratio weighting to estimate natural direct effects (NDE) and natural indirect effects via smoking habits. We considered misclassification of smoking status, selection bias, and unmeasured mediator-outcome confounding by genetic risk, both separately as well as by multiple quantitative bias analysis, using bootstrap to create 95% simulation intervals (SI).

Results: Mediation analysis of lung-cancer risks for SES estimated mean proportions of 43% in men and 33% in women attributable to smoking. Bias analyses decreased direct effects of SES on lung cancer, with selection bias showing the strongest reduction in lung-cancer risk in the multiple bias analysis. Lung-cancer risks remained increased for lower SES groups, with higher risks in men [4th versus 1st (highest) SES quartile: CDE 1.50 (SI 1.32-1.69)] than women [CDE 1.20 (SI 1.01-1.45)]. NDE were similar to CDE, particularly in men.

Conclusions: Bias adjustment lowered direct lung-cancer risk estimates of lower SES groups. However, risks for low SES remained elevated, likely attributable to occupational hazards or other environmental exposures.

Background: Evidence for long-term mortality risks of PM2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM2.5-mortality associations in the UK Biobank cohort using detailed information on confounders and exposure.

Methods: We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM2.5 concentrations from spatio-temporal machine learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions.

Results: In fully adjusted models, an increase of 10 μg/m³ in PM2.5 was associated with hazard ratios (HRs) of 1.27 (95%CI: 1.06-1.53) for all-cause, 1.24 (1.03-1.50) for non-accidental, 2.07 (1.04-4.10) for respiratory, and 1.66 (0.86-3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (HR=0.88, 95%CI: 0.59-1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates.

Conclusions: We found associations of long-term PM2.5 exposure with all-cause, non-accidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM2.5 and mortality.

Purpose: "Positive epidemiology" emphasizes strengths and assets that protect the health of populations. Positive mental health refers to a range of social and emotional capabilities that may support adaptation to challenging circumstances. We examine the role of positive mental health in promoting adolescent health during the crisis phase of the COVID-19 pandemic.

Methods: We used four long-running Australian and UK longitudinal cohorts: Childhood to Adolescence Transition Study (CATS; analyzed N=809; Australia); Longitudinal Study of Australian Children (LSAC) - Baby (analyzed N=1,534) and Kindergarten (analyzed N=1,300) cohorts; Millennium Cohort Study (MCS; analyzed N=2,490; UK). Measures included: (Pre-pandemic exposure): Positive mental health (parent-reported, 13-15 years) including regulating emotions, interacting well with peers, and caring for others; and pandemic outcomes: psychological distress, life satisfaction, and sleep and alcohol use outside of recommendations (16-21 years; 2020). We used two-stage meta-analysis to estimate associations between positive mental health and outcomes across cohorts, accounting for potential confounders.

Results: Estimates suggest meaningful effects of positive mental health on psychosocial outcomes during the pandemic, including lower risk of psychological distress (Risk Ratio [RR]=0.83 95%CI=0.71, 0.97) and higher life satisfaction (RR=1.1, 95%CI=1.0, 1.2). The estimated effects for health behaviors were smaller in magnitude (sleep: RR=0.95, 95%CI=0.86, 1.1; alcohol use: RR=0.97, 95%CI=0.85, 1.1).

Conclusions: Our results are consistent with the hypothesis that adolescents' positive mental health supports better psychosocial outcomes during challenges such as the COVID-19 pandemic, but relevance for health behaviors is less clear. These findings reinforce the value of extending evidence to include positive health states and assets.

Background: To our knowledge, no agreed-upon best practices exist for joining U.S. Census ZIP Code Tabulation Areas (ZCTAs) and U.S. Postal Service ZIP Codes (ZIPs). One-to-one linkage using 5-digit ZCTA identifiers excludes ZIPs without direct matches. "Crosswalk" linkage may match a ZCTA to multiple ZIPs, avoiding losses.

Methods: We compared non-crosswalk and crosswalk linkages nationally and for mortality and health insurance in California. To elucidate selection implications, generalized additive models related sociodemographics to whether ZCTAs contained non-matching ZIPs.

Results: Nationwide, 15% of ZCTAs had non-matching ZIPs, i.e., ZIPs dropped under non-crosswalk linkage. ZCTAs with non-matching ZIPs were positively associated with metropolitan core location, lower socioeconomics, and non-white population. In California, 34% of ZIPs in the mortality and 25% in the health insurance data had ZCTAs with non-matching ZIPs; however, these ZIPs constitute only 0.03% of total mortality and 0.44% of total insurance enrollees.

Conclusions: Our study findings support the use of crosswalk linkages and ZCTAs as a unit of analysis. One-to-one linkage may cause bias by differentially excluding ZIPs with more disadvantaged populations, although affected population sizes appear small.

Background: Observational studies link high midlife systolic blood pressure to increased dementia risk. However, synthesis of evidence from randomized controlled trials has not definitively demonstrated that antihypertensive medication use reduces dementia risk. Here, we emulate target trials of antihypertensive medication initiation on incident dementia using three cohort studies, with attention to potential violations of necessary assumptions.

Methods: We emulated trials of antihypertensive medication initiation on incident dementia using data from the Atherosclerosis Risk in Communities (ARIC) study, Cardiovascular Health Study (CHS), and Health and Retirement Study (HRS). We used data-driven methods to restrict participants to initiators and non-initiators with overlap in propensity scores and positive control outcomes to look for violations of positivity and exchangeability assumptions.

Results: Analyses were limited by the small number of cohort participants who met eligibility criteria. Associations between antihypertensive medication initiation and incident dementia were inconsistent and imprecise (ARIC: HR = 0.30 [0.05, 1.93]; CHS: HR = 0.66 [0.27, 1.64]; HRS: HR = 1.09 [0.75, 1.59]). More stringent propensity score restriction had little effect on findings. Sensitivity analyses using a positive control outcome unexpectedly suggested antihypertensive medication initiation increased risk of coronary heart disease in all three samples.

Conclusions: Positive control outcome analyses suggested substantial residual confounding in effect estimates from our target trials, precluding conclusions about the impact of antihypertensive medication initiation on dementia risk through target trial emulation. Formalized processes for identifying violations of necessary assumptions will strengthen confidence in target trial emulation and avoid inappropriate confidence in emulated trial results.

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 to 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 (LEEP) 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 LEEP model-based approach and Vital Statistics direct estimates approach, though life expectancy at older ages (75 and older) showed weak correlations.

Conclusion: 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 policy making aimed at improving population health outcomes.

Background: Education is strongly associated with cognitive outcomes at older ages, yet the extent to which these associations reflect causal effects remains uncertain due to potential confounding.

Methods: Leveraging changes in historical measures of state-level education policies as natural experiments, we estimated the effects of educational attainment on cognitive performance over 10 years in 20,248 non-Hispanic Black and non-Hispanic White participants, aged 45+ in the REasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (2003-2020) by (1) using state- and year- specific compulsory schooling laws, school-term length, attendance rate, and student-teacher ratio policies to predict educational attainment for US Census microsample data from 1980 and 1990, and (2) applying policy-predicted years of education (PPYEd) to predict memory, verbal fluency, and a cognitive composite. We estimated overall and race- and sex-specific effects of PPYEd on level and change in each cognitive outcome using random intercept and slope models, adjusting for age, year of first cognitive assessment, and indicators for state of residence at age 6.

Results: Each year of PPYEd was associated with higher baseline cognition (0.11 standard deviation [SD] increase in composite measure for each year of PPYEd, 95% confidence interval [CI]: 0.07, 0.15). Subanalyses focusing on individual cognitive domains estimate the largest effects of PPYEd on memory. PPYEd was not associated with rate of change in cognitive scores. Estimates were similar across Black and White participants and across sex.

Conclusions: Historical policies shaping educational attainment are associated with better later life memory, a major determinant of dementia risk.

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 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 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: 41,065 individuals were infected, 5,203 were hospitalized and 1,543 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 non-urban 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 (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 ozone reversed (HR=1.32, 1.17-1.49), yielding to 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 SARS-CoV-2 pandemic in urban areas.