Epidemiology of Spontaneous Cervical Artery Dissection: Population-Based Study.

BACKGROUND: Cervical artery dissection (CeAD) represents up to 15% to 25% of ischemic strokes in people under the age of 50 years. Noninvasive vessel imaging is increasingly used in clinical practice, but the impact on the frequency of detection of CeAD is unknown. In 2006, the yearly incidence rate of CeAD was estimated at 2.6 per 100 000 person-years, but the current incidence is unknown. METHODS: In this population-based retrospective observational cohort study, we utilized the resources of the Rochester Epidemiology Project to ascertain all adult residents of Olmsted County, MN, diagnosed with internal carotid artery dissection and common carotid artery dissection or vertebral artery dissection from 2002 to 2020. Patients with only intracranial involvement or CeAD following major trauma were excluded. Age-adjusted sex-specific and age- and sex-adjusted incidence rates were estimated using the US White 2010 decennial census, with rates expressed per 100 000 person-years. We assessed longitudinal trends by dividing the data into 5-year time intervals, with the last being a 4-year interval. RESULTS: We identified 123 patients with a diagnosis of CeAD. There were 63 patients with internal carotid artery dissection, 54 with vertebral artery dissection, 2 with concurrent internal carotid artery dissection and vertebral artery dissection, and 4 with common carotid artery dissection. There were 63 (51.2%) female patients and 60 (48.8%) male patients. The average age at diagnosis was 50.2 years (SD, 15.1 [95% CI, 20.1-90.5] years). The incidence rate of spontaneous CeAD encompassing all locations was 4.69 per 100 000 person-years (2.43 for internal carotid artery dissection and 2.01 for vertebral artery dissection). The incidence rate increased from 2.30 per 100 000 person-years from 2002 to 2006 to 8.93 per 100 000 person-years from 2017 to 2020 (P<0.0001). The incidence rate for female patients rose from 0.81 per 100 000 person-years from 2002 to 2006 to 10.17 per 100 000 person-years from 2017 to 2020. CONCLUSIONS: The incidence rate of spontaneous CeAD increased nearly 4-fold over a 19-year period from 2002 to 2020. The incidence rate in women rose over 12-fold. The increase in incidence rates likely reflects the increased use of noninvasive vascular imaging.

Wastewater Surveillance for Identifying SARS-CoV-2 Infections in Long-Term Care Facilities, Kentucky, USA, 2021-2022.

Persons living in long-term care facilities (LTCFs) were disproportionately affected by COVID-19. We used wastewater surveillance to detect SARS-CoV-2 infection in this setting by collecting and testing 24-hour composite wastewater samples 2-4 times weekly at 6 LTCFs in Kentucky, USA, during March 2021-February 2022. The LTCFs routinely tested staff and symptomatic and exposed residents for SARS-CoV-2 using rapid antigen tests. Of 780 wastewater samples analyzed, 22% (n = 173) had detectable SARS-CoV-2 RNA. The LTCFs reported 161 positive (of 16,905) SARS-CoV-2 clinical tests. The wastewater SARS-CoV-2 signal showed variable correlation with clinical test data; we observed the strongest correlations in the LTCFs with the most positive clinical tests (n = 45 and n = 58). Wastewater surveillance was 48% sensitive and 80% specific in identifying SARS-CoV-2 infections found on clinical testing, which was limited by frequency, coverage, and rapid antigen test performance.

Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?

Variable selection in regression models is a particularly important issue in epidemiology, where one usually encounters observational studies. In contrast to randomized trials or experiments, confounding is often not controlled by the study design, but has to be accounted for by suitable statistical methods. For instance, when risk factors should be identified with unconfounded effect estimates, multivariable regression techniques can help to adjust for confounders. We investigated the current practice of variable selection in 4 major epidemiologic journals in 2019 and found that the majority of articles used subject-matter knowledge to determine a priori the set of included variables. In comparison with previous reviews from 2008 and 2015, fewer articles applied data-driven variable selection. Furthermore, for most articles the main aim of analysis was hypothesis-driven effect estimation in rather low-dimensional data situations (i.e., large sample size compared with the number of variables). Based on our results, we discuss the role of data-driven variable selection in epidemiology.

Geographic Variation, Economic Activity, and Labor Market Characteristics in Trajectories of Suicide in the United States, 2008-2020.

Suicide rates in the United States have increased over the past 15 years, with substantial geographic variation in these increases; yet there have been few attempts to cluster counties by the magnitude of suicide rate changes according to intercept and slope or to identify the economic precursors of increases. We used vital statistics data and growth mixture models to identify clusters of counties by their magnitude of suicide growth from 2008 to 2020 and examined associations with county economic and labor indices. Our models identified 5 clusters, each differentiated by intercept and slope magnitude, with the highest-rate cluster (4% of counties) being observed mainly in sparsely populated areas in the West and Alaska, starting the time series at 25.4 suicides per 100,000 population, and exhibiting the steepest increase in slope (0.69/100,000/year). There was no cluster for which the suicide rate was stable or declining. Counties in the highest-rate cluster were more likely to have agricultural and service economies and less likely to have urban professional economies. Given the increased burden of suicide, with no clusters of counties improving over time, additional policy and prevention efforts are needed, particularly targeted at rural areas in the West.

Invited Commentary: Combining Information to Answer Epidemiologic Questions About a Target Population.

Epidemiologists are attempting to address research questions of increasing complexity by developing novel methods for combining information from diverse sources. Cole et al. (Am J Epidemiol. 2023;192(3)467-474) provide 2 examples of the process of combining information to draw inferences about a population proportion. In this commentary, we consider combining information to learn about a target population as an epidemiologic activity and distinguish it from more conventional meta-analyses. We examine possible rationales for combining information and discuss broad methodological considerations, with an emphasis on study design, assumptions, and sources of uncertainty.

Selection Bias Requires Selection: The Case of Collider Stratification Bias.

In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.

Confounder Adjustment Using the Disease Risk Score: A Proposal for Weighting Methods.

Propensity score analysis is a common approach to addressing confounding in nonrandomized studies. Its implementation, however, requires important assumptions (e.g., positivity). The disease risk score (DRS) is an alternative confounding score that can relax some of these assumptions. Like the propensity score, the DRS summarizes multiple confounders into a single score, on which conditioning by matching allows the estimation of causal effects. However, matching relies on arbitrary choices for pruning out data (e.g., matching ratio, algorithm, and caliper width) and may be computationally demanding. Alternatively, weighting methods, common in propensity score analysis, are easy to implement and may entail fewer choices, yet none have been developed for the DRS. Here we present 2 weighting approaches: One derives directly from inverse probability weighting; the other, named target distribution weighting, relates to importance sampling. We empirically show that inverse probability weighting and target distribution weighting display performance comparable to matching techniques in terms of bias but outperform them in terms of efficiency (mean squared error) and computational speed (up to >870 times faster in an illustrative study). We illustrate implementation of the methods in 2 case studies where we investigate placebo treatments for multiple sclerosis and administration of aspirin in stroke patients.

Characterizing Imbalance in the Tails of the Propensity Score Distribution.

Understanding characteristics of patients with propensity scores in the tails of the propensity score (PS) distribution has relevance for inverse-probability-of-treatment-weighted and PS-based estimation in observational studies. Here we outline a method for identifying variables most responsible for extreme propensity scores. The approach is illustrated in 3 scenarios: 1) a plasmode simulation of adult patients in the National Ambulatory Medical Care Survey (2011-2015) and 2) timing of dexamethasone initiation and 3) timing of remdesivir initiation in patients hospitalized for coronavirus disease 2019 from February 2020 through January 2021. PS models were fitted using relevant baseline covariates, and tails of the PS distribution were defined using asymmetric first and 99th percentiles. After fitting of the PS model in each original data set, values of each key covariate were permuted and model-agnostic variable importance measures were examined. Visualization and variable importance techniques were helpful in identifying variables most responsible for extreme propensity scores and may help identify individual characteristics that might make patients inappropriate for inclusion in a study (e.g., off-label use). Subsetting or restricting the study sample based on variables identified using this approach may help investigators avoid the need for trimming or overlap weights in studies.

A Design and Analytical Strategy for Monitoring Disease Positivity and Biomarker Levels in Accessible Closed Populations.

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g., in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g., means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Invited Commentary: Raising a High-Pressure Alarm About Pediatric Hypertension.

Hypertension is a common "silent killer" in adult medicine, but epidemiologic estimates of elevated blood pressure in children and adolescents are challenged by under-diagnosis and resultant low utilization of relevant administrative or billing codes. In the article by Horgan et al (Am J Epidemiol 2024), children and adolescents with hypertension and elevated blood pressure were identified using direct assessment of blood pressure measurements available in the electronic health record from both inpatient and outpatient visits ("clinical cohort") in comparison to diagnosis codes ("claims-based cohort"). The study population included 3.75 million pediatric healthcare visits available in the US Food and Drug Administration's Sentinel System. While the study applied a relatively novel methodology to interrogate available clinical data within the EHR to better understand the prevalence of pediatric hypertension and raised concern for a higher occurrence of hypertension among children and adolescents than previously realized using claims codes, the utility of the prevalence estimates may be limited by the potential for misclassification bias inherent in EHR data. However, these data raise important concerns about relaying solely on ICD-9-CM/ICD-10-CM codes to quantify the epidemiology of pediatric hypertension and highlight opportunities to address elevated blood pressure in children that could improve long-term cardiovascular health.

Efficient risk-based collection of biospecimens in cohort studies: Designing a prospective study of diagnostic performance for multicancer detection tests.

In cohort studies, it can be infeasible to collect specimens on an entire cohort. For example, to estimate sensitivity of multiple Multi-Cancer Detection (MCD) assays, we desire an extra 80mL of cell-free DNA (cfDNA) blood, but this much extra blood is too expensive for us to collect on everyone. We propose a novel epidemiologic study design that efficiently oversamples those at highest baseline disease risk from whom to collect specimens, to increase the number of future cases with cfDNA blood collection. The variance reduction ratio from our risk-based subsample versus a simple random (sub)sample (SRS) depends primarily on the ratio of risk model sensitivity to the fraction of the cohort selected for specimen collection subject to constraining the risk model specificity. In a simulation where we chose 34% of Prostate, Lung, Colorectal, and Ovarian Screening Trial cohort at highest risk of lung cancer for cfDNA blood collection, we could enrich the number of lung cancers 2.42-fold and the standard deviation of lung-cancer MCD sensitivity was 31-33% reduced versus SRS. Risk-based collection of specimens on a subsample of the cohort could be a feasible and efficient approach to collecting extra specimens for molecular epidemiology.

Deep Learning - Methods to Amplify Epidemiological Data Collection and Analyses.

Deep learning is a subfield of artificial intelligence and machine learning based mostly on neural networks and often combined with attention algorithms that has been used to detect and identify objects in text, audio, images, and video. Serghiou and Rough (Am J Epidemiol. 0000;000(00):0000-0000) present a primer for epidemiologists on deep learning models. These models provide substantial opportunities for epidemiologists to expand and amplify their research in both data collection and analyses by increasing the geographic reach of studies, including more research subjects, and working with large or high dimensional data. The tools for implementing deep learning methods are not quite yet as straightforward or ubiquitous for epidemiologists as traditional regression methods found in standard statistical software, but there are exciting opportunities for interdisciplinary collaboration with deep learning experts, just as epidemiologists have with statisticians, healthcare providers, urban planners, and other professionals. Despite the novelty of these methods, epidemiological principles of assessing bias, study design, interpretation and others still apply when implementing deep learning methods or assessing the findings of studies that have used them.

Exposure to per- and polyfluoroalkyl substances and breast cancer risk: a systematic review and meta-analysis of epidemiologic studies.

We synthesized the epidemiologic evidence on the associations between per- and polyfluoroalkyl substances (PFAS) exposure and breast cancer risk. Our systematic review and meta-analysis included 18 and 11 articles, respectively, covering studies up to February 2023. The summary relative risks (RRs) estimated by random-effects meta-analyses did not support an association between PFAS and overall breast cancer risk (eg, a natural log (ln)-unit increase in serum/plasma concentrations [ng/mL] for perfluorooctanoate [PFOA] RR = 0.95; 95% CI, 0.77-1.18; perfluorooctane sulfonate [PFOS] RR = 0.98; 95% CI, 0.87-1.11). However, when limiting to studies that assessed exposures prior to a breast cancer diagnosis, we observed a positive association with PFOA (a ln-unit increase, RR = 1.16; 95% CI, 0.96-1.40). We also observed some possible heterogeneous associations by tumor estrogen and progesterone receptor status among postmenopausal breast cancer cases. No meaningful changes were observed after excluding the studies with high risk of bias (Tier 3). Based on the evaluation tool developed by the National Toxicology Program, given the heterogeneity across studies and the variability in timing of exposure measurements, the epidemiologic evidence needed to determine the association between PFAS exposure and breast cancer remains inadequate. Our findings support the need for future studies with improved study designs to determine this association.

Identifying Pediatric Hypertension in Observational Data: Comparing Clinical and Claims Cohorts in Real World Data.

We conducted retrospective public health surveillance using data from 2006 to 2016 in seven integrated delivery systems from FDA's Sentinel System. We identified pediatric hypertensive patients by clinical and claims-based definitions and compared demographics, baseline profiles and follow-up time profiles. Among 3,757,803 pediatric patients aged 3 to 17 years, we identified 781,722 children and 551,246 teens with at least three blood pressure measures over 36-months. Of these, 70,315 children (9%) and 47,928 teens (8.7%) met the clinical definition for hypertension and 22,465 (2.8%) children and 60,952 (11%) of teens met the clinical definition for elevated, non-hypertensive blood pressure. Of the 3.7M patients, we identified 3,246 children and 7,293 teens with any claim for hypertension (claims definition). Evidence of hypertension claims among those meeting our clinical definition was poor; 2.2% and 7.3% of clinically hypertensive children and teens had corresponding claims for hypertension. Baseline profiles for claims-based hypertensive patients suggest greater severity of disease compared to clinical patients. Claims-based patients showed higher rates of all-cause mortality during follow-up. Pediatric hypertension in claims-based data sources is under-captured but may serve as a marker for greater disease severity. Investigators should understand coding practices when selecting real-world data sources for future pediatric hypertension work.

Influence of Incomplete Death Information on Cumulative Risk Estimates in United States Claims Data.

Administrative claims databases often do not capture date or fact of death, so studies using these data may inappropriately treat death as a censoring event-equivalent to other withdrawal reasons-rather than a competing event. We examined 1-, 3-, and 5-year inverse-probability-of-treatment-weighted cumulative risks of a composite cardiovascular outcome among 34,527 initiators of telmisartan (exposure) and ramipril (referent) ages ≥55 in Optum claims from 2003 to 2020. Differences in cumulative risks of the cardiovascular endpoint due to censoring of death (cause-specific), as compared to treating death as a competing event (sub-distribution), increased with greater follow-up time and older age, where event and mortality risks were higher. Among ramipril users (selected results), 5-year cause-specific and sub-distribution cumulative risk estimates per 100, respectively, were 16.4 (95% CI 15.3, 17.5) and 16.2 (95% CI 15.1, 17.3) among ages 55-64 (difference=0.2) and were 43.2 (95% CI 41.3, 45.2) and 39.7 (95% CI 37.9, 41.4) among ages ≥75 (difference=3.6). Plasmode simulation results demonstrated the differences in cause-specific versus sub-distribution cumulative risks to increase with increasing mortality rate. We suggest researchers consider the cohort's baseline mortality risk when deciding whether real-world data with incomplete death information can be used without concern.

The role and limitations of electronic medical records versus patient interviews for determining symptoms, underlying comorbidities, and medication usage for patients with COVID-19.

Electronic medical records (EMR) are important for rapidly compiling information to determine disease characteristics (e.g., symptoms) and risk factors (e.g., underlying comorbidities, medications) for disease-related outcomes. To assess EMR data accuracy, agreement between EMR abstractions and patient interviews was evaluated. Symptoms, medical history, and medication usage among COVID-19 patients collected from EMR and patient interviews were compared using overall agreement (same answer in EMR and interview), reported agreement (yes answer in both EMR and interview among those who reported yes in either), and Kappa statistics. Overall, patients reported more symptoms in interviews than in EMR abstractions. Overall agreement was high (≥50% for 20/23 symptoms), but only subjective fever and dyspnea had reported agreement of ≥50%. Kappa statistics for symptoms were generally low. Reported medical conditions had greater agreement with all condition categories (10/10) having ≥50% overall agreement and half (5/10) having ≥50% reported agreement. More non-prescription medications were reported in interviews than in EMR abstractions leading to low reported agreement (28%). Discordance was observed for symptoms, medical history, and medication usage between EMR abstractions and patient interviews. Investigations utilizing EMR to describe clinical characteristics and identify risk factors should consider the potential for incomplete data, particularly for symptoms and medications.

A transversal cross-sectional study of factors related to HPV vaccination status and cancer screening participation among French women aged 25-40.

BACKGROUND: In 2020, uterine cervical cancer (UCC) was the 12th most common cancer among women in France and the 4th worldwide. French health authorities wanted to increase Human Papilloma Virus (HPV) vaccination and screening rates. There were still many barriers to these measures among young women, their families, and health professionals and teachers. Between 2014 and 2019, international studies found inconsistent effects of HPV vaccination on UCC screening. In 2022, a survey was conducted among women aged 25 to 40 in the Nord-Pas-de-Calais region to assess participation 1) in HPV vaccination and its barriers, 2) in UCC screening as a function of HPV vaccination status. METHODS: Data were collected using an anonymous online questionnaire distributed by QR code in 80 general practices randomly selected in the Nord-Pas-de-Calais region between January and June 2022. Results were analyzed bivariately using the Chi2 test, multivariately when numbers allowed, and in age subgroups (sensitivity analysis). RESULTS: 407 complete questionnaires (for 602 participating women) were analyzed. In our sample, 41% of women aged 25 to 40 in the Nord-Pas-de-Calais region were vaccinated against HPV viruses in 2022. The risk factors for non-vaccination, after multivariable adjustment, were: the periods of eligibility for vaccination in the early days of French vaccination (2007-2012: odds ratio OR = 0.04 [95% CI, 0.02-0.09]; 2012-2017: OR = 0.5 [0.3-0.8]), information received from non-medical sources (OR = 0.3 [0.2-0.6]), and absence of information about vaccination (OR = 0.12 [0.05-0.27]). In our sample, 90% of women were screened for UCC. In bivariate analysis, women at risk of not being screened were those who were youngest, had been vaccinated against HPV, were not heterosexual, lived alone, had gynecological follow-up by their general practitioner, and did not have regular gynecological follow-up. Sensitivity analysis showed that the only risk factor significantly correlated with non-screening regardless of age group was lack of regular gynecological follow-up. CONCLUSIONS: Participation in HPV vaccination and UCC screening is improved by medical education and gynecological follow-up. This multicenter study, limited by the relative youth of vaccination in France, should be repeated after 2037 to assess the possible effect of vaccination on screening.

Social determinants of health and obstetric outcomes: A report and recommendations of the workshop of the Society for Maternal-Fetal Medicine.

This article is a report of a 2-day workshop, entitled "Social determinants of health and obstetric outcomes," held during the Society for Maternal-Fetal Medicine 2022 Annual Pregnancy Meeting. Participants' fields of expertise included obstetrics, pediatrics, epidemiology, health services, health equity, community-based research, and systems biology. The Commonwealth Foundation and the Alliance of Innovation on Maternal Health cosponsored the workshop and the Society for Women's Health Research provided additional support. The workshop included presentations and small group discussions, and its goals were to accomplish the following.

Substance use in a Dutch migraine cohort compared with the general population.

OBJECTIVE: To evaluate self-reported substance user profiles for individuals with migraine and compare these to the general population. BACKGROUND: There is increasing attention to lifestyle influences such as substance use as presumed migraine triggers. METHODS: Data on substance use were collected by survey in a large migraine cohort and from the biannual survey in the general Dutch population for substances. A representative cohort of Dutch patients with migraine (n = 5176) and the Dutch general population (n = 8370) was included. Patients with migraine were subdivided into episodic (EM) and chronic migraine (CM). Substance consumption was compared between the general population and patients with migraine, and between migraine subgroups after standardization for sex and level of education. RESULTS: Included patients with migraine were 83.4% female (4319/5176) and had a mean (standard deviation) age of 44.8 (11.3) years. Patients with migraine reported less illicit drug use (odds ratio [OR] 0.48, 95% confidence interval [CI] 0.42-0.55; p < 0.001), less current and lifetime smoking (OR 0.60, 95% CI 0.55-0.65; p < 0.001 and OR 0.75, 95% CI 0.71-0.79; p < 0.001), and less current alcohol consumption (OR 0.66, 95% CI 0.62-0.70; p < 0.001) compared with the general population. Prevalence of substance use was compared between CM and EM participants and showed higher illicit drug use (OR 1.73, 95% CI 1.11-2.69; p = 0.011), higher current smoking (OR 1.61, 95% CI 1.22-2.11; p < 0.001) but less alcohol use (OR 0.54, 95% CI 0.43-0.68; p < 0.001) for participants with CM compared with EM. No differences were found for a history of smoking (OR 1.18, 95% CI 0.92-1.50, p = 0.19). CONCLUSIONS: Individuals with migraine are less likely to use illicit drugs, smoke, or drink alcohol compared with the general population. Patients with CM less often consume alcohol, while they more often use illicit drugs and smoke compared to those with EM.