Epidemiology最新文献

Background: Extreme ambient heat is unambiguously associated with a higher risk of illness and death. The Optum Labs Data Warehouse (OLDW), a database of medical claims from US-based patients with commercial or Medicare Advantage health insurance, has been used to quantify heat-related health impacts. Whether results for the insured subpopulation are generalizable to the broader population has, to our knowledge, not been documented. We sought to address this question, for the US population in California from 2012 to 2019.

Methods: We examined changes in daily rates of emergency department encounters and in-patient hospitalization encounters for all-causes, heat-related outcomes, renal disease, mental/behavioral disorders, cardiovascular disease, and respiratory disease. OLDW was the source of health data for insured individuals in California, and health data for the broader population were gathered from the California Department of Health Care Access and Information. We defined extreme heat exposure as any day in a group of 2 or more days with maximum temperatures exceeding the county-specific 97.5th percentile and used a space-time-stratified case-crossover design to assess and compare the impacts of heat on health.

Results: Average incidence rates of medical encounters differed by dataset. However, rate ratios for emergency department encounters were similar across datasets for all causes [ratio of incidence rate ratios (rIRR) = 0.989; 95% confidence interval (CI) = 0.969, 1.009], heat-related causes (rIRR = 1.080; 95% CI = 0.999, 1.168), renal disease (rIRR = 0.963; 95% CI = 0.718, 1.292), and mental health disorders (rIRR = 1.098; 95% CI = 1.004, 1.201). Rate ratios for inpatient encounters were also similar.

Conclusions: This work presents evidence that OLDW can continue to be a resource for estimating the health impacts of extreme heat.

Background: Human papillomavirus (HPV) vaccination has been offered in over a hundred countries worldwide (including the United Kingdom, since September 2008). Controversy around adverse effects persists, with inconsistent evidence from follow-up of randomized controlled trials and confounding by indication limiting the conclusions drawn from larger-scale observational studies. This study aims to estimate the association between receiving a quadrivalent HPV vaccine and the reporting of short-term adverse effects and to demonstrate the utility of regression discontinuity design for examining side effects in routine data.

Methods: We applied a novel regression discontinuity approach to a retrospective population-based cohort using primary care data from the UK Clinical Practice Research Datalink linked to hospital and social deprivation data. We examined the new onset of gastrointestinal, neuromuscular, pain, and headache/migraine symptoms using READ and International Classification of Diseases, tenth revision diagnostic codes. For each year between 2012 and 2017, we compared girls in school year 8 (born July/August) who were eligible to receive the vaccine with girls in year 7 (born September/October) who were not eligible.

Results: Of the 21,853 adolescent girls in the cohort, 10,881 (50%) were eligible for HPV vaccination. There was no evidence of increased new gastrointestinal symptoms (adjusted odds ratio [OR]: 0.99; 95% confidence interval [CI]: 0.85, 1.15), headache/migraine symptoms (OR: 0.84; 95% CI: 0.70, 1.01), or pain symptoms (OR: 1.05; 95% CI: 0.95, 1.16) when comparing those eligible and ineligible for HPV vaccination.

Conclusion: This study found no evidence that HPV vaccination eligibility is associated with reporting short-term adverse effects among adolescent girls.

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: Little attention has been devoted to framing multiple continuous social variables as a "mixture" for social epidemiologic analysis. We propose using the Bayesian kernel machine regression analytic framework that yields univariate, bivariate, and overall exposure mixture effects.

Methods: Using data from the 2023 Survey of Racism and Public Health, we conducted a Bayesian kernel machine regression analysis to study several individual, social, and structural factors as an exposure mixture and their relationships with psychological distress among individuals with at least one police arrest. Factors included racial and economic polarization, neighborhood deprivation, perceived discrimination, police perception, subjective social status, and substance use. We complemented this analysis with a series of unadjusted and adjusted models for each exposure mixture variable.

Results: We found that more self-reported discrimination experiences in the past year (posterior inclusion probability = 1.00) and greater substance use (posterior inclusion probability = 1.00) correlated with higher psychological distress. These associations were consistent with the findings from the unadjusted and adjusted linear regression analyses: past year perceived discrimination (unadjusted b = 2.58, 95% confidence interval [CI]: 1.86, 3.30; adjusted b = 2.20, 95% CI: 1.45, 2.94) and substance use (unadjusted b = 2.92, 95% CI: 2.21, 3.62; adjusted b = 2.59, 95% CI: 1.87, 3.31).

Conclusion: With the rise of big data and the expansion of variables in long-standing cohort and census studies, novel applications of methods from adjacent disciplines are a step forward in identifying exposure mixture associations in social epidemiology and addressing the health needs of socially vulnerable populations.

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.

When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.

Difference-in-differences (DiD) is a powerful, quasi-experimental research design widely used in longitudinal policy evaluations with health outcomes. However, DiD designs face several challenges to ensuring reliable causal inference, such as when policy settings are more complex. Recent economics literature has revealed that DiD estimators may exhibit bias when heterogeneous treatment effects, a common consequence of staggered policy implementation, are present. To deepen our understanding of these advancements in epidemiology, in this methodologic primer, we start by presenting an overview of DiD methods. We then summarize fundamental problems associated with DiD designs with heterogeneous treatment effects and provide guidance on recently proposed heterogeneity-robust DiD estimators, which are increasingly being implemented by epidemiologists. We also extend the discussion to violations of the parallel trends assumption, which has received less attention. Last, we present results from a simulation study that compares the performance of several DiD estimators under different scenarios to enhance understanding and application of these methods.

Background: Sibling studies of maternal smoking during pregnancy and subsequent risk of depression have produced mixed results. A recent study identified not considering the amount of maternal smoking and age of onset as potentially masking a true association. We examine these issues and also the amount of maternal smoking during pregnancy as a determinant of the severity of depressive symptoms.

Methods: We analyzed data from the community-based National Longitudinal Survey of Youth (US, 1994-2016). Mothers reported smoking during pregnancy (none, <1 pack/day, ≥1 pack/day). We assessed offspring's lifetime depression (i.e., ≥8 symptoms) and symptom counts with the Centers for Epidemiologic Studies Depression scale. We estimated the risk of these two outcomes in the full sample (n = 7172) and among siblings (n = 6145) using generalized linear mixed-effects models with random intercepts by family and family-averaged means for sibling analyses.

Results: Among siblings, we observed dose-dependent elevations for both risk of depression (smoking during pregnancy <1 pack/day adjusted risk ratio [aRR] = 1.18; 95% confidence interval [CI] = 1.07, 1.30; smoking ≥1 aRR = 1.36; 95% CI = 1.19, 1.56) and severity of depressive symptoms (smoking <1 pack/day aRR = 1.12; 95% CI = 1.08, 1.16); smoking ≥1 pack/day aRR = 1.25; 95% CI = 1.18, 1.31). Among both samples, the P for trend was <0.01. In analysis limited to offspring diagnosed before age 18, results for severity were attenuated.

Conclusions: This evidence supports the existence of an independent association between maternal smoking during pregnancy and both the risk of depression and the severity of depressive symptoms. The results highlight the utility of considering the amount of smoking, severity of symptoms, and age of onset.

Background: Testing etiologic heterogeneity, whether a disorder subtype is more or less impacted by a risk factor, is important for understanding causal pathways and optimizing statistical power. The study of mental health disorders especially benefits from strategic subcategorization because these disorders are heterogeneous and frequently co-occur. Existing methods to quantify etiologic heterogeneity are not appropriate for noncompeting events in an open cohort of variable-length follow-up. Thus, we developed a new method.

Methods: We estimated risks from urban residence, maternal smoking during pregnancy, and parental psychiatric history, with subtypes defined by the presence or absence of a codiagnosis: autism alone, attention deficit hyperactivity disorder (ADHD) alone, and joint diagnoses of autism + ADHD. To calculate the risk of a single diagnosis (e.g., autism alone), we subtracted the risk for autism + ADHD from the risk for autism overall. We tested the equivalency of average risk ratios over time, using a Wald-type test and bootstrapped standard errors.

Results: Urban residence was most strongly linked with autism + ADHD and least with ADHD only; maternal smoking was associated with ADHD only but not autism only; and parental psychiatric history exhibited similar associations with all subgroups.

Conclusion: Our method allowed the calculation of appropriate P values to test the strength of association, informing etiologic heterogeneity wherein two of these three risk factors exhibited different impacts across diagnostic subtypes. The method used all available data, avoided neurodevelopmental outcome misclassification, exhibited robust statistical precision, and is applicable to similar heterogeneous complex conditions using common diagnostic data with variable follow-up.

Background: Gestational diabetes is associated with adverse outcomes such as preterm birth (<37 weeks). However, there is no international consensus on screening criteria or diagnostic levels for gestational diabetes, and it is unknown whether body mass index (BMI) or obesity modifies the relation between glucose level and preterm birth.

Methods: We studied a pregnancy cohort restricted to two Danish regions from the linked Danish Medical Birth Register to study associations between glucose measurements from the 2-hour postload 75-g oral glucose tolerance test (one-step approach) and preterm birth from 2004 to 2018. In Denmark, gestational diabetes screening is a targeted strategy for mothers with identified risk factors. We used Poisson regression to estimate rate ratios (RR) of preterm birth with z-standardized glucose measurements. We assessed effect measure modification by stratifying analyses and testing for heterogeneity.

Results: Among 11,337 pregnancies (6.2% delivered preterm), we observed an adjusted preterm birth RR of 1.2 (95% confidence interval [CI] = 1.1, 1.3) for a one-standard deviation glucose increase of 1.4 mmol/l from the mean of 6.7 mmol/l. There was evidence for effect measure modification by obesity, for example, adjusted RR for nonobese (BMI, <30): 1.2 (95% CI = 1.1, 1.3) versus obese (BMI, ≥30): 1.3 (95% CI = 1.2-1.5), P = 0.05 for heterogeneity.

Conclusion: Among mothers screened for gestational diabetes, increased glucose levels, even those below the diagnostic level for gestational diabetes in Denmark, were associated with increased preterm birth risk. Obesity (BMI, ≥30) may be an effect measure modifier, not just a confounder, of the relation between blood glucose and preterm birth risk.