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In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993–2005) and the Hispanic Community Health Study/Study of Latinos (2008–2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error. [ABSTRACT FROM AUTHOR]

The Preconception Period Analysis of Risks and Exposures Influencing Health and Development (PrePARED) Consortium creates a novel resource for addressing preconception health by merging data from numerous cohort studies. In this paper, we describe our data harmonization methods and results. Individual-level data from 12 prospective studies were pooled. The crosswalk-cataloging-harmonization procedure was used. The index pregnancy was defined as the first postbaseline pregnancy lasting more than 20 weeks. We assessed heterogeneity across studies by comparing preconception characteristics in different types of studies. The pooled data set included 114,762 women, and 25,531 (22%) reported at least 1 pregnancy of more than 20 weeks' gestation during the study period. Babies from the index pregnancies were delivered between 1976 and 2021 (median, 2008), at a mean maternal age of 29.7 (standard deviation, 4.6) years. Before the index pregnancy, 60% of women were nulligravid, 58% had a college degree or more, and 37% were overweight or obese. Other harmonized variables included race/ethnicity, household income, substance use, chronic conditions, and perinatal outcomes. Participants from pregnancy-planning studies had more education and were healthier. The prevalence of preexisting medical conditions did not vary substantially based on whether studies relied on self-reported data. Use of harmonized data presents opportunities to study uncommon preconception risk factors and pregnancy-related events. This harmonization effort laid the groundwork for future analyses and additional data harmonization. [ABSTRACT FROM AUTHOR]

The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last few decades in the medical literature. However, these methods have been criticized, especially because of the data-driven modeling process, which involves statistical decision-making. In this paper, we propose an approach that uses the bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigation of the statistical validity and uncertainty of the groups identified in the original data by checking to see whether the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replicationwise variability. We evaluated the ability of 3 commonly used adequacy criteria (average posterior probability, odds of correct classification, and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrate the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes. [ABSTRACT FROM AUTHOR]

Estimating the fraction of dementia cases in a population attributable to a risk factor or combination of risk factors (the population attributable fraction (PAF)) informs the design and choice of dementia risk-reduction activities. It is directly relevant to dementia prevention policy and practice. Current methods employed widely in the dementia literature to combine PAFs for multiple dementia risk factors assume a multiplicative relationship between factors and rely on subjective criteria to develop weightings for risk factors. In this paper we present an alternative approach to calculating the PAF based on sums of individual risk. It incorporates individual risk factor interrelationships and enables a range of assumptions about the way in which multiple risk factors will combine to affect dementia risk. Applying this method to global data demonstrates that the previous estimate of 40% is potentially too conservative an estimate of modifiable dementia risk and would necessitate subadditive interaction between risk factors. We calculate a plausible conservative estimate of 55.7% (95% confidence interval: 55.2, 56.1) based on additive risk factor interaction. [ABSTRACT FROM AUTHOR]

Cancer is an important cause of childhood mortality, yet the etiology is largely unknown. A combination of pre- and postnatal factors is thought to be implicated, including maternal medication use. We aimed to provide: 1) a systematic review of peer-reviewed publications on associations between maternal medication use and childhood cancer, with a focus on study design and methodology; and 2) suggestions for how to increase transparency, limit potential biases, and improve comparability in studies on maternal medication use and childhood cancer. We conducted a systematic search in the PubMed, Embase, Scopus, Cochrane, and Web of Science databases to June 8, 2020. Altogether, 112 studies were identified. The reviewed studies were heterogeneous in study design, exposure, and outcome classification. In 21 studies (19%), the outcome was any childhood cancer. Of the 91 papers that reported on specific types of cancer, 62% did not report the cancer classification system. The most frequently investigated medication groups were sex hormones (46 studies, excluding fertility medications), and antiinfectives (37 studies). Suggestions for strengthening future pharmacoepidemiologic studies on maternal medication use and childhood cancer relate to choice of cancer classification system, exposure windows, and methods for identification of, and control for, potential confounders. [ABSTRACT FROM AUTHOR]

The number of operations that surgeons have previously performed is associated with their patients' outcomes. However, this association may not be causal, because previous studies have often been cross-sectional and their analyses have not considered time-varying confounding or positivity violations. In this paper, using the example of surgeons who perform coronary artery bypass grafting, we describe (hypothetical) target trials for estimation of the causal effect of the surgeons' operative volumes on patient mortality. We then demonstrate how to emulate these target trials using data from US Medicare claims and provide effect estimates. Our target trial emulations suggest that interventions on physicians' volume of coronary artery bypass grafting operations have little effect on patient mortality. The target trial framework highlights key assumptions and draws attention to areas of bias in previous observational analyses that deviated from their implicit target trials. The principles of the presented methodology may be adapted to other scenarios of substantive interest in health services research. [ABSTRACT FROM AUTHOR]

Two frequently encountered but underrecognized challenges for causal inference in studying the long-term health effects of disasters among survivors include 1) time-varying effects of disasters on a time-to-event outcome and 2) selection bias due to selective attrition. In this paper, we review approaches for overcoming these challenges and demonstrate application of the approaches to a real-world longitudinal data set of older adults who were directly affected by the 2011 Great East Japan Earthquake and Tsunami (n  = 4,857). To illustrate the problem of time-varying effects of disasters, we examined the association between degree of damage due to the tsunami and all-cause mortality. We compared results from Cox regression analysis assuming proportional hazards with those derived using adjusted parametric survival curves allowing for time-varying hazard ratios. To illustrate the problem of selection bias, we examined the association between proximity to the coast (a proxy for housing damage from the tsunami) and depressive symptoms. We corrected for selection bias due to attrition in the 2 postdisaster follow-up surveys (conducted in 2013 and 2016) using multivariable adjustment, inverse probability of censoring weighting, and survivor average causal effect estimation. Our results demonstrate that analytical approaches which ignore time-varying effects on mortality and selection bias due to selective attrition may underestimate the long-term health effects of disasters. [ABSTRACT FROM AUTHOR]

Within a school grade, children who are young for grade are at increased risk of psychiatric diagnoses, but the long-term implications remain understudied, and associations with students who delay or accelerate entry underexplored. We used Norwegian birth cohort records (birth years: 1967–1976, n  = 626,928) linked to records in midlife. On-time school entry was socially patterned; among those born in December, 23.0% of children in the lowest socioeconomic position (SEP) delayed school entry, compared with 12.2% among the highest SEP. Among those who started school on time, there was no evidence for long-term associations between birth month and psychiatric/behavioral disorders or mortality. Controlling for SEP and other confounders, delayed school entry was associated with increased risk of psychiatric disorders and mortality. Children with delayed school entry were 1.31 times more likely to die by suicide (95% confidence interval: 1.07, 1.61) by midlife, and 1.96 times more likely to die from drug-related death (95% confidence interval: 1.59, 2.40) by midlife than those born late in the year who started school on time. Associations with delayed school entry are likely due to selection, and results thus underscore that long-term health risks can be tracked early in life, including through school entry timing, and are highly socially patterned. [ABSTRACT FROM AUTHOR]

Statistical approaches to adaptive treatment strategies (ATS) can be used to mimic the sequential decision-making inherently found in clinical practice. To illustrate the use of a statistical ATS approach, we emulated a target trial of different blood pressure (BP) control plans for the prevention of cardiovascular events among individuals with hypertension at high cardiovascular risk, inspired by the Systolic Blood Pressure Intervention Trial (SPRINT). We included 103,708 patients with hypertension and a "QRISK3" estimated 10-year risk of cardiovascular disease of ≥20% who initiated an antihypertensive drug between 1998 and 2018. Dynamic marginal structural models estimated the comparative effects of treating patients with intensive (target BP: 130/80 mm Hg), standard (140/90 mm Hg), and conservative (150/90 mm Hg) BP control strategies. The adjusted hazard ratios (HRs) for the intensive versus standard strategy were 0.96 (95% confidence interval (CI): 0.92, 1.00) for major adverse cardiovascular events and 0.93 (95% CI: 0.88, 0.97) for death from cardiovascular causes. For the conservative versus standard strategy, they were 1.06 (95% CI: 1.02, 1.10) and 1.08 (95% CI: 1.03, 1.13), respectively. These results are largely compatible with SPRINT. ATS can be used to emulate randomized controlled trials of complex treatment strategies in an observational setting and represents an alternative approach for situations where randomized controlled trials are not feasible. [ABSTRACT FROM AUTHOR]

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008–2011), self-reported information on diet was collected from all participants (n  = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014–2017) and third (2020–2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n  = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010–2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration–based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R 2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes. [ABSTRACT FROM AUTHOR]

In first-line antiretroviral therapy (ART) for human immunodeficiency virus (HIV) treatment, some subgroups of patients may respond better to an efavirenz-based regimen than an integrase strand transfer inhibitor (InSTI)-based regimen, or vice versa, due to patient characteristics modifying treatment effects. Using data based on nearly 16,000 patients from the North American AIDS Cohort Collaboration on Research and Design from 2009–2016, statistical methods for precision medicine were employed to estimate an optimal treatment rule that minimizes the 5-year risk of the composite outcome of acquired immune deficiency syndrome (AIDS)-defining illnesses, serious non-AIDS events, and all-cause mortality. The treatment rules considered were functions that recommend either an efavirenz- or InSTI-based regimen conditional on baseline patient characteristics such as demographic information, laboratory results, and health history. The estimated 5-year risk under the estimated optimal treatment rule was 10.0% (95% confidence interval (CI): 8.6, 11.3), corresponding to an absolute risk reduction of 2.3% (95% CI: 0.9, 3.8) when compared with recommending an efavirenz-based regimen for all patients and 2.6% (95% CI: 1.0, 4.2) when compared with recommending an InSTI-based regimen for all. Tailoring ART to individual patient characteristics may reduce 5-year risk of the composite outcome compared with assigning all patients the same drug regimen. [ABSTRACT FROM AUTHOR]

The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort Study (EWC), a collaborative research design comprising 69 cohorts in 31 consortia, was funded by the National Institutes of Health (NIH) in 2016 to improve children's health in the United States. The EWC harmonizes extant data and collects new data using a standardized protocol, the ECHO-Wide Cohort Data Collection Protocol (EWCP). EWCP visits occur at least once per life stage, but the frequency and timing of the visits vary across cohorts. As of March 4, 2022, the EWC cohorts contributed data from 60,553 children and consented 29,622 children for new EWCP data and biospecimen collection. The median (interquartile range) age of EWCP-enrolled children was 7.5 years (3.7–11.1). Surveys, interviews, standardized examinations, laboratory analyses, and medical record abstraction are used to obtain information in 5 main outcome areas: pre-, peri-, and postnatal outcomes; neurodevelopment; obesity; airways; and positive health. Exposures include factors at the level of place (e.g. air pollution, neighborhood socioeconomic status), family (e.g. parental mental health), and individuals (e.g. diet, genomics). [ABSTRACT FROM AUTHOR]

Respondent-driven sampling (RDS) is a potential strategy for addressing challenges in accurate estimation of abortion incidence, but it relies on often untested assumptions. We conducted an RDS study to estimate the cumulative lifetime incidence of abortion in Soweto, Johannesburg, South Africa (April–December 2018), to evaluate whether RDS assumptions were met, and to compare RDS estimates of incidence with estimates adjusted for employment and age based on census data. A total of 849 participants were recruited from 11 seed participants between April and December 2018. The assumption that individuals can identify target population members and the assumption of approximation of sampling with replacement was met. There were minor violations of the assumptions of seed independence from the final sample and reciprocity of ties. Assumptions of accurate degree reporting and random recruitment were not met. Failure to meet assumptions yielded a study sample with different employment characteristics than those of the target population; this could not be resolved by standard RDS methods. The RDS estimate of cumulative lifetime abortion incidence was 12.1% (95% confidence interval: 9.8, 14.3), and the employment-adjusted estimate was 16.9% (95% confidence interval: 12.8, 22.1). We caution researchers in using RDS for representative estimates of abortion incidence. Use of postsurvey weights to adjust for differences in characteristics between the sample and the target population may yield more representative results. [ABSTRACT FROM AUTHOR]

Populations that are reliant on subsistence farming are particularly vulnerable to climatic effects on crop yields. However, empirical evidence on the role of the timing of exposure to crop yield deficits in early-life development is limited. We examined the relationship between child survival and annual crop yield reductions at different stages of early-life development in a subsistence farming population in Burkina Faso. Using shared frailty Cox proportional hazards models adjusting for confounders, we analyzed 57,288 children under 5 years of age followed by the Nouna Health and Demographic Surveillance System (1994–2016) in relation to provincial food-crop yield levels experienced in 5 nonoverlapping time windows (12 months before conception, gestation, birth–age 5.9 months, ages 6.0 months–1.9 years, and ages 2.0–4.9 years) and their aggregates (birth–1.9 years, first 1,000 days from conception, and birth–4.9 years). Of the nonoverlapping windows, point estimates were largest for child survival related to food-crop yields for the time window of 6.0 months–1.9 years: The adjusted mortality hazard ratio was 1.10 (95% confidence interval: 1.03, 1.19) for a 90th-to-10th percentile yield reduction. These findings suggest that child survival in this setting is particularly vulnerable to cereal-crop yield reductions during the period of nonexclusive breastfeeding. [ABSTRACT FROM AUTHOR]

Metal exposure has been suggested as a possible environmental risk factor for Parkinson disease (PD). We searched the PubMed, EMBASE, and Cochrane databases to systematically review the literature on the relationship between metal exposure and PD risk and to examine the overall quality of each study and the exposure assessment method. A total of 83 case-control studies and 5 cohort studies published during the period 1963–July 2021 were included, of which 73 were graded as being of low or moderate overall quality. Investigators in 69 studies adopted self-reported exposure and biomonitoring after disease diagnosis for exposure assessment approaches. The meta-analyses showed that concentrations of copper and iron in serum and concentrations of zinc in either serum or plasma were lower, while concentrations of magnesium in CSF and zinc in hair were higher, among PD cases as compared with controls. Cumulative lead levels in bone were found to be associated with increased risk of PD. We did not find associations between other metals and PD. The current level of evidence for associations between metals and PD risk is limited, as biases from methodological limitations cannot be ruled out. High-quality studies assessing metal levels before disease onset are needed to improve our understanding of the role of metals in the etiology of PD. [ABSTRACT FROM AUTHOR]

Recovery of CD4-positive T lymphocyte count after initiation of antiretroviral therapy (ART) has been thoroughly examined among people with human immunodeficiency virus infection. However, immunological response after restart of ART following care interruption is less well studied. We compared CD4 cell-count trends before disengagement from care and after ART reinitiation. Data were obtained from the East Africa International Epidemiology Databases to Evaluate AIDS (IeDEA) Collaboration (2001–2011; n  = 62,534). CD4 cell-count trends before disengagement, during disengagement, and after ART reinitiation were simultaneously estimated through a linear mixed model with 2 subject-specific knots placed at the times of disengagement and treatment reinitiation. We also estimated CD4 trends conditional on the baseline CD4 value. A total of 10,961 patients returned to care after disengagement from care, with the median gap in care being 2.7 (interquartile range, 2.1–5.4) months. Our model showed that CD4 cell-count increases after ART reinitiation were much slower than those before disengagement. Assuming that disengagement from care occurred 12 months after ART initiation and a 3-month treatment gap, CD4 counts measured at 3 years since ART initiation would be lower by 36.5 cells/μL than those obtained under no disengagement. Given that poorer CD4 restoration is associated with increased mortality/morbidity, specific interventions targeted at better retention in care are urgently required. [ABSTRACT FROM AUTHOR]

Many studies encounter clustering due to multicenter enrollment and nonmortality outcomes, such as quality of life, that are truncated due to death—that is, missing not at random and nonignorable. Traditional missing-data methods and target causal estimands are suboptimal for statistical inference in the presence of these combined issues, which are especially common in multicenter studies and cluster-randomized trials (CRTs) carried out among the elderly or seriously ill. Using principal stratification, we developed a Bayesian estimator that jointly identifies the always-survivor principal stratum in a clustered/hierarchical data setting and estimates the average treatment effect among them (i.e. the survivor average causal effect (SACE)). In simulations, we observed low bias and good coverage with our method. In a motivating CRT, the SACE and the estimate from complete-case analysis differed in magnitude, but both were small, and neither was incompatible with a null effect. However, the SACE estimate has a clear causal interpretation. The option to assess the rigorously defined SACE estimand in studies with informative truncation and clustering can provide additional insight into an important subset of study participants. Based on the simulation study and CRT reanalysis, we provide practical recommendations for using the SACE in CRTs and software code to support future research. [ABSTRACT FROM AUTHOR]

We describe a new method for presenting and interpreting linear trends in health inequalities, and present a proof-of-concept analysis of inequalities in smoking among adolescents in Europe. We estimated the regression line of the assumed linear relationship between smoking prevalence in low– and high–socioeconomic status (SES) youth over time. Using simulation, we constructed a 95% confidence interval (CI) for the smoking prevalence in low-SES youth for when this would be 0% in high-SES youth, and we calculated the likelihood of eradicating smoking inequality (<5% for both low and high SES). This method was applied to data on adolescents aged 15–16 years (n  = 250,326) from 23 European countries, derived from the 2003–2015 European Survey Project on Alcohol and Other Drugs. Smoking prevalence decreased more slowly among low- than among high-SES adolescents. The estimated smoking prevalence was 9.4% (95% CI: 6.1, 12.7) for boys and 5.4% (95% CI: 1.4, 9.2) for girls with low SES when 0% with high SES. The likelihood of eradicating smoking inequality was <1% for boys and 37% for girls. We conclude that this novel methodological approach to trends in health inequalities is feasible in practice. Applying it to trends in smoking inequalities among adolescents in Europe, we found that Europe is currently not on track to eradicate youth smoking across SES groups. [ABSTRACT FROM AUTHOR]

Comparing parasite genotypes to inform parasitic disease outbreak investigations involves computation of genetic distances that are typically analyzed by hierarchical clustering to identify related isolates, indicating a common source. A limitation of hierarchical clustering is that hierarchical clusters are not discrete; they are nested. Consequently, small groups of similar isolates exist within larger groups that get progressively larger as relationships become increasingly distant. Investigators must dissect hierarchical trees at a partition number ensuring grouped isolates belong to the same strain; a process typically performed subjectively, introducing bias into resultant groupings. We describe an unbiased, probabilistic framework for partition number selection that ensures partitions comprise isolates that are statistically likely to belong to the same strain. We computed distances and established a normalized distribution of background distances that we used to demarcate a threshold below which the closeness of relationships is unlikely to be random. Distances are hierarchically clustered and the dendrogram dissected at a partition number where most within-partition distances fall below the threshold. We evaluated this framework by partitioning 1,137 clustered Cyclospora cayetanensis genotypes, including 552 isolates epidemiologically linked to various outbreaks. The framework was 91% sensitive and 100% specific in assigning epidemiologically linked isolates to the same partition. [ABSTRACT FROM AUTHOR]

Epidemiologists face a unique challenge in measuring risk relationships involving time-varying exposures in early pregnancy. Each week in early pregnancy is distinct in its contribution to fetal development, and this period is commonly characterized by shifts in maternal behavior and, consequently, exposures. In this simulation study, we used alcohol as an example of an exposure that often changes during early pregnancy and miscarriage as an outcome affected by early exposures. Data on alcohol consumption patterns from more than 5,000 women in the Right From the Start cohort study (United States, 2000–2012) informed measures of the prevalence of alcohol exposure, the distribution of gestational age at cessation of alcohol use, and the likelihood of miscarriage by week of gestation. We then compared the bias and precision of effect estimates and statistical power from 5 different modeling approaches in distinct simulated relationships. We demonstrate how the accuracy and precision of effect estimates depended on alignment between model assumptions and the underlying simulated relationship. Approaches that incorporated data about patterns of exposure were more powerful and less biased than simpler models when risk depended on timing or duration of exposure. To uncover risk relationships in early pregnancy, it is critical to carefully define the role of exposure timing in the underlying causal hypothesis. [ABSTRACT FROM AUTHOR]

Knowledge regarding deaths due to suicide or alcohol- or drug-related causes may be limited by inconsistent and/or restrictive case definitions, resulting in concerns regarding validity of findings and underestimates of burden. In this proof-of-concept study, we assessed varying case definitions (suicide, alcohol-related, and drug-related mortality using underlying-cause-of-death (UCOD) versus multiple-cause-of-death (MCOD) International Classification of Diseases, Tenth Revision (ICD-10) codes) on the basis of counts and rates among Colorado veterans who died (2009–2020). Suicide, alcohol-related, or drug-related ICD-10 codes were identified, and 2 case definitions were compared: UCOD (qualifying ICD-10 code listed as the UCOD) and MCOD (qualifying ICD-10 code in any cause-of-death field). Of 109,314 decedents, the number of deaths and the age-adjusted mortality rate (per 100,000 persons) significantly increased when MCOD codes were included: n  = 4,930 (110.3 deaths/100,000 persons) for UCOD versus n  = 6,954 (138.4 deaths/100,000 persons) for MCOD. While rates of suicide mortality did not change, rates of alcohol-related mortality doubled with the more inclusive case definition: 1,752 (27.3 deaths/100,000 persons) for UCOD versus 3,847 (59.8 deaths/100,000 persons) for MCOD. Alcohol-use disorder codes accounted for 71% of additional alcohol-related deaths captured with the MCOD definition. Studies that rely on UCOD codes may be underestimating the burden of deaths, especially alcohol-related deaths. Increased effort is required to reevaluate current classifications of deaths associated with suicide, alcohol use, or drug use. [ABSTRACT FROM AUTHOR]

Despite promising results from individual-level studies, state-level studies of the effectiveness of comprehensive background-check (CBC) policies in reducing firearm fatalities have yielded null results in multiple states. These prior studies focused on CBC laws adopted in the 1990s, when record keeping was far less complete. We estimated the effect of the implementation of CBC policies on state-level firearm homicide and suicide rates in states implementing CBC policies from 2013 to 2015 (Colorado, Delaware, Oregon, and Washington). We compared age-adjusted firearm homicide and suicide rates, measured annually from 15 years prior to policy implementation until 2019, in each treated state to rates in control groups constructed using the synthetic control group method. Differences in firearm homicide rates for Colorado, Oregon, and Washington post treatment were all small (0.09 to 0.18 per 100,000 residents per year) and not well distinguished from natural variation. Oregon had on average 0.80 per 100,000 fewer firearm suicides per year than did synthetic Oregon post treatment. However, these results were inconsistent across modeling approaches and not well distinguished from natural variation. Our models produced poor fit for Delaware. Coupled with previous null results from Indiana, California, Maryland, Pennsylvania, and Tennessee, the present results suggest that extending background check requirements to private transfers alone and implementing these policies as is currently done is not sufficient to achieve significant state-level reductions in firearm fatalities. [ABSTRACT FROM AUTHOR]

The United States faces rapidly rising rates of violent crime committed with firearms. In this study, we sought to estimate the impact of changes to laws that regulate the concealed carrying of weapons (concealed-carry weapons (CCW) laws) on violent crimes committed with a firearm. We used augmented synthetic control models and random-effects meta-analyses to estimate state-specific effects and the average effect of adopting shall-issue CCW permitting laws on rates of 6 violent crimes: homicide with a gun, homicide by other means, aggravated assault with a gun, aggravated assault with a knife, robbery with a gun, and robbery with a knife. The average effects were stratified according to the presence or absence of several shall-issue permit provisions. Adoption of a shall-issue CCW law was associated with a 9.5% increase in rates of assault with a firearm during the first 10 years after law adoption and was associated with an 8.8% increase in rates of homicide by other means. When shall-issue laws allowed violent misdemeanants to acquire CCW permits, the laws were associated with higher rates of gun assaults. It is likely that adoption of shall-issue CCW laws has increased rates of nonfatal violent crime committed with firearms. Harmful effects of shall-issue laws are most clear when provisions intended to reduce risks associated with civilian gun-carrying are absent. [ABSTRACT FROM AUTHOR]

Despite repeated calls by scholars to critically engage with the concepts of race and ethnicity in US epidemiologic research, the incorporation of these social constructs in scholarship may be suboptimal. This study characterizes the conceptualization, operationalization, and utilization of race and ethnicity in US research published in leading journals whose publications shape discourse and norms around race, ethnicity, and health within the field of epidemiology. We systematically reviewed randomly selected articles from prominent epidemiology journals across 5 periods: 1995–1999, 2000–2004, 2005–2009, 2010–2014, and 2015–2018. All original human-subjects research conducted in the United States was eligible for review. Information on definitions, measurement, coding, and use in analysis was extracted. We reviewed 1,050 articles, including 414 (39%) in our analyses. Four studies explicitly defined race and/or ethnicity. Authors rarely made clear delineations between race and ethnicity, often adopting an ethnoracial construct. In the majority of studies across time periods, authors did not state how race and/or ethnicity was measured. Top coding schemes included "Black, White" (race), "Hispanic, non-Hispanic" (ethnicity), and "Black, White, Hispanic" (ethnoracial). Most often, race and ethnicity were deemed "not of interest" in analyses (e.g. control variables). Broadly, disciplinary practices have remained largely the same between 1995 and 2018 and are in need of improvement. [ABSTRACT FROM AUTHOR]

We sought to determine whether machine learning and natural language processing (NLP) applied to electronic medical records could improve performance of automated health-care claims-based algorithms to identify anaphylaxis events using data on 516 patients with outpatient, emergency department, or inpatient anaphylaxis diagnosis codes during 2015–2019 in 2 integrated health-care institutions in the Northwest United States. We used one site's manually reviewed gold-standard outcomes data for model development and the other's for external validation based on cross-validated area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and sensitivity. In the development site 154 (64%) of 239 potential events met adjudication criteria for anaphylaxis compared with 180 (65%) of 277 in the validation site. Logistic regression models using only structured claims data achieved a cross-validated AUC of 0.58 (95% CI: 0.54, 0.63). Machine learning improved cross-validated AUC to 0.62 (0.58, 0.66); incorporating NLP-derived covariates further increased cross-validated AUCs to 0.70 (0.66, 0.75) in development and 0.67 (0.63, 0.71) in external validation data. A classification threshold with cross-validated PPV of 79% and cross-validated sensitivity of 66% in development data had cross-validated PPV of 78% and cross-validated sensitivity of 56% in external data. Machine learning and NLP-derived data improved identification of validated anaphylaxis events. [ABSTRACT FROM AUTHOR]

Pooled testing has been successfully used to expand SARS-CoV-2 testing, especially in settings requiring high volumes of screening of lower-risk individuals, but efficiency of pooling declines as prevalence rises. We propose a differentiated pooling strategy that independently optimizes pool sizes for distinct groups with different probabilities of infection to further improve the efficiency of pooled testing. We compared the efficiency (results obtained per test kit used) of the differentiated strategy with a traditional pooling strategy in which all samples are processed using uniform pool sizes under a range of scenarios. For most scenarios, differentiated pooling is more efficient than traditional pooling. In scenarios examined here, an improvement in efficiency of up to 3.94 results per test kit could be obtained through differentiated versus traditional pooling, with more likely scenarios resulting in 0.12 to 0.61 additional results per kit. Under circumstances similar to those observed in a university setting, implementation of our strategy could result in an improvement in efficiency between 0.03 to 3.21 results per test kit. Our results can help identify settings, such as universities and workplaces, where differentiated pooling can conserve critical testing resources. [ABSTRACT FROM AUTHOR]

Wastewater surveillance for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to be a valuable source of information regarding SARS-CoV-2 transmission and coronavirus disease 2019 (COVID-19) cases. Although the method has been used for several decades to track other infectious diseases, there has not been a comprehensive review outlining all of the pathogens that have been surveilled through wastewater. Herein we identify the infectious diseases that have been previously studied via wastewater surveillance prior to the COVID-19 pandemic. Infectious diseases and pathogens were identified in 100 studies of wastewater surveillance across 38 countries, as were themes of how wastewater surveillance and other measures of disease transmission were linked. Twenty-five separate pathogen families were identified in the included studies, with the majority of studies examining pathogens from the family Picornaviridae, including polio and nonpolio enteroviruses. Most studies of wastewater surveillance did not link what was found in the wastewater to other measures of disease transmission. Among those studies that did, the value reported varied by study. Wastewater surveillance should be considered as a potential public health tool for many infectious diseases. Wastewater surveillance studies can be improved by incorporating other measures of disease transmission at the population-level including disease incidence and hospitalizations. [ABSTRACT FROM AUTHOR]

Multimorbidity (≥2 chronic conditions) is a common and important marker of aging. To better understand racial differences in multimorbidity burden and associations with important health-related outcomes, we assessed differences in the contribution of chronic conditions to hospitalization, skilled nursing facility admission, and mortality among non-Hispanic Black and non-Hispanic White older adults in the United States. We used data from a nationally representative study, the National Health and Aging Trends Study, linked to Medicare claims from 2011–2015 (n  = 4,871 respondents). This analysis improved upon prior research by identifying the absolute contributions of chronic conditions using a longitudinal extension of the average attributable fraction for Black and White Medicare beneficiaries. We found that cardiovascular conditions were the greatest contributors to outcomes among White respondents, while the greatest contributor to outcomes for Black respondents was renal morbidity. This study provides important insights into racial differences in the contributions of chronic conditions to costly health-care utilization and mortality, and it prompts policy-makers to champion delivery reforms that will expand access to preventive and ongoing care for diverse Medicare beneficiaries. [ABSTRACT FROM AUTHOR]

Follow-up of US cohort members for incident cancer is time-consuming, is costly, and often results in underascertainment when the traditional methods of self-reporting and/or medical record validation are used. We conducted one of the first large-scale investigations to assess the feasibility, methods, and benefits of linking participants in the US Radiologic Technologists (USRT) Study (n  = 146,022) with the majority of US state or regional cancer registries. Follow-up of this cohort has relied primarily on questionnaires (mailed approximately every 10 years) and linkage with the National Death Index. We compared the level of agreement and completeness of questionnaire/death-certificate–based information with that of registry-based (43 registries) incident cancer follow-up in the USRT cohort. Using registry-identified first primary cancers from 1999–2012 as the gold standard, the overall sensitivity was 46.5% for self-reports only and 63.0% for both self-reports and death certificates. Among the 37.0% false-negative reports, 27.8% were due to dropout, while 9.2% were due to misreporting. The USRT cancer reporting patterns differed by cancer type. Our study indicates that linkage to state cancer registries would greatly improve completeness and accuracy of cancer follow-up in comparison with questionnaire self-reporting. These findings support ongoing development of a national US virtual pooled registry with which to streamline cohort linkages. [ABSTRACT FROM AUTHOR]

We estimated the degree to which language used in the high-profile medical/public health/epidemiology literature implied causality using language linking exposures to outcomes and action recommendations; examined disconnects between language and recommendations; identified the most common linking phrases; and estimated how strongly linking phrases imply causality. We searched for and screened 1,170 articles from 18 high-profile journals (65 per journal) published from 2010–2019. Based on written framing and systematic guidance, 3 reviewers rated the degree of causality implied in abstracts and full text for exposure/outcome linking language and action recommendations. Reviewers rated the causal implication of exposure/outcome linking language as none (no causal implication) in 13.8%, weak in 34.2%, moderate in 33.2%, and strong in 18.7% of abstracts. The implied causality of action recommendations was higher than the implied causality of linking sentences for 44.5% or commensurate for 40.3% of articles. The most common linking word in abstracts was "associate" (45.7%). Reviewers' ratings of linking word roots were highly heterogeneous; over half of reviewers rated "association" as having at least some causal implication. This research undercuts the assumption that avoiding "causal" words leads to clarity of interpretation in medical research. [ABSTRACT FROM AUTHOR]

We compared the performance of prognostic tools for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using parameters fitted either at the time of hospital admission or across all time points of an admission. This cohort study used clinical data to model the dynamic change in prognosis of SARS-CoV-2 at a single hospital center in the United Kingdom, including all patients admitted from February 1, 2020, to December 31, 2020, and then followed up for 60 days for intensive care unit (ICU) admission, death, or discharge from the hospital. We incorporated clinical observations and blood tests into 2 time-varying Cox proportional hazards models predicting daily 24- to 48-hour risk of admission to the ICU for those eligible for escalation of care or death for those ineligible for escalation. In developing the model, 491 patients were eligible for ICU escalation and 769 were ineligible for escalation. Our model had good discrimination of daily risk of ICU admission in the validation cohort (n  = 1,141; C statistic: C  = 0.91, 95% confidence interval: 0.89, 0.94) and our score performed better than other scores (National Early Warning Score 2, International Severe Acute Respiratory and Emerging Infection Comprehensive Clinical Characterisation Collaboration score) calculated using only parameters measured on admission, but it overestimated the risk of escalation (calibration slope = 0.7). A bespoke daily SARS-CoV-2 escalation risk prediction score can predict the need for clinical escalation better than a generic early warning score or a single estimation of risk calculated at admission. [ABSTRACT FROM AUTHOR]

Growth in early infancy is hypothesized to affect chronic disease risk factors later in life. To date, most reports draw on European-ancestry cohorts with few repeated observations in early infancy. We investigated the association between infant growth before 6 months and lipid levels in adolescents in a Hispanic/Latino cohort. We characterized infant growth from birth to 5 months in male (n  = 311) and female (n  = 285) infants from the Santiago Longitudinal Study (1991–1996) using 3 metrics: weight (kg), length (cm), and weight-for-length (g/cm). Superimposition by translation and rotation (SITAR) and latent growth mixture models (LGMMs) were used to estimate the association between infant growth characteristics and lipid levels at age 17 years. We found a positive relationship between the SITAR length velocity parameter before 6 months of age and high-density lipoprotein cholesterol levels in adolescence (11.5, 95% confidence interval; 3.4, 19.5), indicating higher high-density lipoprotein cholesterol levels occurring with faster length growth. The strongest associations from the LGMMs were between higher low-density lipoprotein cholesterol and slower weight-for-length growth, following a pattern of associations between slower growth and adverse lipid profiles. Further research in this window of time can confirm the association between early infant growth as an exposure and adolescent cardiovascular disease risk factors. [ABSTRACT FROM AUTHOR]

We assessed the impacts of Tanzania's adolescent-focused Cash Plus intervention on depression. In this pragmatic cluster-randomized controlled trial, 130 villages were randomly allocated to an intervention or control arm (1:1). Youth aged 14–19 years living in households receiving governmental cash transfers were invited to participate. The intervention included an intensive period (a 12-session course) and an aftercare period (9 months of mentoring, productive grants, and strengthened health services). We examined intervention impacts on a depressive symptoms scale (10-item Center for Epidemiologic Studies Depression Scale score (range, 0–30)) and rates of depressive symptomatology (score ≥10 points on the scale), recorded at study baseline (April–June 2017), midline (May–July 2018), and endline (June–August 2019). Using intention-to-treat methodology, we employed logistic and generalized linear models to estimate effects for binary and continuous outcomes, respectively. Quantile regression was used to estimate effects across the scale. From 2,458 baseline participants, 941 intervention and 992 control adolescents were reinterviewed at both follow-ups. At endline, the intervention reduced the odds of depressive symptomatology (adjusted odds ratio = 0.67, 95% confidence interval: 0.52, 0.86), with an undetectable mean scale difference (risk difference = −0.36, 95% confidence interval: –0.84, 0.11). Quantile regression results demonstrated an intervention effect along the upper distribution of the scale. Integration of multisectoral initiatives within existing social protection systems shows potential to improve mental health among youth in low-resource settings. [ABSTRACT FROM AUTHOR]

The authors discuss a sampling algorithm for obtaining estimates from data such as those given by Google via the Google Trends Extended for Health (GT-E) API. Highlights include an outline of different samplings based on width of sampling range, a Visual Basic for Applications code sheet provided for the algorithm, and the use of the algorithm with the unscaled GT-E data in favor of the scaled public facing data.

Poor mental health among the unemployed—the long-term unemployed in particular—is established, but these associations may be driven by confounding from unobserved, time-invariant characteristics such as past experiences and personality. Using longitudinal register data on 2,720,431 residents aged 30–60 years, we assessed how current unemployment and unemployment history predict visits to specialized care due to psychiatric conditions and self-harm in Finland in 2008–2018. We used linear ordinary-least-squares and fixed-effects models. Prior to adjusting for time-invariant characteristics, current unemployment was associated with poor mental health, and the risk increased with longer unemployment histories. Accounting for all time-invariant characteristics with the fixed-effects models, these associations attenuated by approximately 70%, yet current unemployment was still associated with a 0.51 (95% confidence interval: 0.48, 0.53) percentage-point increase in the probability of poor mental health among men and women. Longer unemployment histories increased the probability among men in their 30s but not among older men or among women. The results indicate that selection by stable characteristics may explain a major part of the worse mental health among the unemployed and especially the long-term unemployed. However, even when controlling for this selection, current unemployment remains associated with mental health. [ABSTRACT FROM AUTHOR]

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with exposure to repetitive head impacts such as those from American football. Our understanding of this association is based on research in autopsied brains, since CTE can only be diagnosed postmortem. Such studies are susceptible to selection bias, which needs to be accounted for to ensure a generalizable estimate of the association between repetitive head impacts and CTE. We evaluated the relationship between level of American football playing and CTE diagnosis after adjusting for selection bias. The sample included 290 deceased male former American football players who donated their brains to the Veterans Affairs–Boston University–Concussion Legacy Foundation (VA-BU-CLF) Brain Bank between 2008 and 2019. After adjustment for selection bias, college-level and professional football players had 2.38 (95% simulation interval (SI): 1.16, 5.94) and 2.47 (95% SI: 1.46, 4.79) times the risk of being diagnosed with CTE as high-school–level players, respectively; these estimates are larger than estimates with no selection bias adjustment. Since CTE is currently diagnosed only postmortem, we additionally provide plausible scenarios for CTE risk ratios for each level of play during the former players' lifetime. This study provides further evidence to support a dose-response relationship between American football playing and CTE. [ABSTRACT FROM AUTHOR]

The worldwide shortage of vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection while the pandemic still remains uncontrolled has led many countries to the dilemma of whether or not to vaccinate previously infected persons. Understanding the level of protection conferred by previous infection compared with that of vaccination is important for policy-making. We analyzed an updated individual-level database of the entire population of Israel to assess the protection provided by both prior infection and vaccination in preventing subsequent SARS-CoV-2 infection, hospitalization with coronavirus disease 2019 (COVID-19), severe disease, and death due to COVID-19. Outcome data were collected from December 20, 2020, to March 20, 2021. Vaccination was highly protective, with overall estimated effectiveness of 94.5% (95% confidence interval (CI): 94.3, 94.7) for documented infection, 95.8% (95% CI: 95.2, 96.2) for hospitalization, 96.3% (95% CI: 95.7, 96.9) for severe illness, and 96.0% (95% CI: 94.9, 96.9) for death. Similarly, the overall estimated level of protection provided by prior SARS-CoV-2 infection was 94.8% (95% CI: 94.4, 95.1) for documented infection, 94.1% (95% CI: 91.9, 95.7) for hospitalization, and 96.4% (95% CI: 92.5, 98.3) for severe illness. Our results should be considered by policy-makers when deciding whether or not to prioritize vaccination of previously infected adults. [ABSTRACT FROM AUTHOR]

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) have been associated with earlier natural menopause; however, the underlying mechanisms are not well understood, particularly the extent to which this relationship is mediated by sex hormones. We analyzed data (1999–2017) on 1,120 premenopausal women from the Study of Women's Health Across the Nation (SWAN). Causal mediation analysis was applied to quantify the degree to which follicle-stimulating hormone (FSH) and estradiol levels could mediate the associations between PFAS and incident natural menopause. Participants with higher PFAS concentrations had shorter times to natural menopause, with a relative survival of 0.82 (95% confidence interval (CI): 0.69, 0.96) for linear perfluorooctane sulfonate (n-PFOS), 0.84 (95% CI: 0.69, 1.00) for sum of branched-chain perfluorooctane sulfonate (Sm-PFOS), 0.79 (95% CI: 0.66, 0.93) for linear-chain perfluorooctanoate (n-PFOA), and 0.84 (95% CI: 0.71, 0.97) for perfluorononanoate (PFNA), comparing the highest tertile of PFAS concentrations with the lowest. The proportion of the effect mediated through FSH was 8.5% (95% CI: −11.7, 24.0) for n-PFOS, 13.2% (95% CI: 0.0, 24.5) for Sm-PFOS, 26.9% (95% CI: 15.6, 38.4) for n-PFOA, and 21.7% (6.8, 37.0) for PFNA. No significant mediation by estradiol was observed. The effect of PFAS on natural menopause may be partially explained by variations in FSH concentrations. [ABSTRACT FROM AUTHOR]

Adolescent internalizing symptoms have increased since 2010, whereas adequate sleep has declined for several decades. It remains unclear how self-reported sleep attainment has affected internalizing-symptoms trends. Using 1991–2019 data from the Monitoring the Future Study (n  ~ 390,000), we estimated age-period-cohort effects in adolescent internalizing symptoms (e.g. loneliness, self-esteem, self-derogation, depressive affect) and the association with yearly prevalence of a survey-assessed, self-reported measure of attaining ≥7 hours of sleep most nights. We focused our main analysis on loneliness and used median odds ratios to measure variance in loneliness associated with period differences. We observed limited signals for cohort effects and modeled only period effects. The feeling of loneliness increased by 0.83% per year; adolescents in 2019 had 0.68 (95% CI: 0.49, 0.87) increased log odds of loneliness compared with the mean, which was consistent by race/ethnicity and parental education. Girls experienced steeper increases in loneliness than boys (P  < 0.0001). The period-effect median odds ratio for loneliness was 1.16 (variance = 0.09; 95% CI: 0.06, 0.17) before adjustment for self-reported frequency of getting ≥7 hours sleep versus 1.07 (variance = 0.02; 95% CI: 0.01, 0.03) after adjustment. Adolescents across cohorts are experiencing worsening internalizing symptoms. Self-reported frequency of <7 hours sleep partially explains increases in loneliness, indicating the need for feasibility trials to study the effect of increasing sleep attainment on internalizing symptoms. [ABSTRACT FROM AUTHOR]

Warfarin's complex dosing is a significant barrier to measurement of its exposure in observational studies using population databases. Using population-based administrative data (1996–2019) from British Columbia, Canada, we developed a method based on statistical modeling (Random Effects Warfarin Days' Supply (REWarDS)) that involves fitting a random-effects linear regression model to patients' cumulative dosage over time for estimation of warfarin exposure. Model parameters included a minimal universally available set of variables from prescription records for estimation of patients' individualized average daily doses of warfarin. REWarDS estimates were validated against a reference standard (manual calculation of the daily dose using the free-text administration instructions entered by the dispensing pharmacist) and compared with alternative methods (fixed window, fixed tablet, defined daily dose, and reverse wait time distribution) using Pearson's correlation coefficient (r), the intraclass correlation coefficient, and the root mean squared error. REWarDS-estimated days' supply showed strong correlation and agreement with the reference standard (r  = 0.90 (95% confidence interval (CI): 0.90, 0.90); intraclass correlation coefficient = 0.95 (95% CI: 0.94, 0.95); root mean squared error = 8.24 days) and performed better than all of the alternative methods. REWarDS-estimated days' supply was valid and more accurate than estimates from all other available methods. REWarDS is expected to confer optimal precision in studies measuring warfarin exposure using administrative data. [ABSTRACT FROM AUTHOR]

Cerebral microvascular dysfunction may contribute to depression via disruption of brain structures involved in mood regulation, but evidence is limited. The retina allows for visualization of a microvascular bed that shares similarities with the cerebral microvasculature. We investigated the associations between baseline retinal arteriolar and venular calibers (central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE), respectively) and incident depressive symptoms in the Multi-Ethnic Study of Atherosclerosis (MESA). We used longitudinal data on 4,366 participants (mean age = 63.2 years; 48.5% women, 28.4% Black) without baseline depressive symptoms. Depressive symptoms, defined as Center for Epidemiologic Studies Depression Scale score ≥16 and/or use of antidepressant medication, were determined between 2002 and 2004 (baseline; MESA visit 2) and at 3 follow-up examinations conducted every 1.5–2 years thereafter. Fundus photography was performed at baseline. After a mean follow-up period of 6.1 years, 21.9% (n  = 958) had incident depressive symptoms. After adjustment for sociodemographic, lifestyle, and cardiovascular factors, a 1–standard-deviation larger baseline CRVE was associated with a higher risk of depressive symptoms (hazard ratio = 1.10, 95% confidence interval: 1.02, 1.17), and a 1–standard-deviation larger baseline CRAE was not statistically significantly associated with incident depressive symptoms (hazard ratio = 1.04, 95% confidence interval: 0.97, 1.11). In this study, larger baseline CRVE, but not CRAE, was associated with a higher incidence of depressive symptoms. [ABSTRACT FROM AUTHOR]

Maternal childhood adversity and trauma may elicit biological changes that impact the next generation through epigenetic responses measured in DNA methylation (DNAm). These epigenetic associations could be modified by the early postnatal environment through protective factors, such as early childhood home visiting (HV) programs that aim to mitigate deleterious intergenerational effects of adversity. In a cohort of 53 mother-child pairs recruited in 2015–2016 for the Pregnancy and Infant Development Study (Cincinnati, Ohio), we examined the association between maternal adverse childhood experiences (ACEs) and neonatal DNAm in the secretogranin V gene (SCG5), which is important in neuroendocrine function. We examined prenatal HV as an effect modifier. Mothers completed a questionnaire on ACEs during pregnancy, and infant buccal samples were collected 1 month postpartum. Multivariable linear regression was used to examine the association between maternal ACEs and neonatal DNAm expressed as M-values averaged across 4 cytosine-phosphate-guanine dinucleotide sites. A higher number of maternal ACEs (>3) was associated with a 5.79–percentage-point lower offspring DNAm (95% confidence interval: −10.44, −1.14), and the association was modified by the number of home visits received during pregnancy. In a population of at-risk mother-child dyads, preliminary evidence suggests that maternal ACEs have a relationship with offspring SCG5 DNAm that differs by the amount of prenatal HV. [ABSTRACT FROM AUTHOR]

Population-based seroprevalence surveys can provide useful estimates of the number of individuals previously infected with serious acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and still susceptible, as well as contribute to better estimates of the case-fatality rate and other measures of coronavirus disease 2019 (COVID-19) severity. No serological test is 100% accurate, however, and the standard correction that epidemiologists use to adjust estimates relies on estimates of the test sensitivity and specificity often based on small validation studies. We have developed a fully Bayesian approach to adjust observed prevalence estimates for sensitivity and specificity. Application to a seroprevalence survey conducted in New York State in 2020 demonstrates that this approach results in more realistic—and narrower—credible intervals than the standard sensitivity analysis using confidence interval endpoints. In addition, the model permits incorporating data on the geographical distribution of reported case counts to create informative priors on the cumulative incidence to produce estimates and credible intervals for smaller geographic areas than often can be precisely estimated with seroprevalence surveys. [ABSTRACT FROM AUTHOR]

When an entire cohort of patients receives a treatment, it is difficult to estimate the treatment effect in the treated because there are no directly comparable untreated patients. Attempts can be made to find a suitable control group (e.g. historical controls), but underlying differences between the treated and untreated can result in bias. Here we show how negative control outcomes combined with difference-in-differences analysis can be used to assess bias in treatment effect estimates and obtain unbiased estimates under certain assumptions. Causal diagrams and potential outcomes are used to explain the methods and assumptions. We apply the methods to UK Cystic Fibrosis Registry data to investigate the effect of ivacaftor, introduced in 2012 for a subset of the cystic fibrosis population with a particular genotype, on lung function and annual rate (days/year) of receiving intravenous (IV) antibiotics (i.e. IV days). We consider 2 negative control outcomes: outcomes measured in the pre-ivacaftor period and outcomes among persons ineligible for ivacaftor because of their genotype. Ivacaftor was found to improve lung function in year 1 (an approximately 6.5–percentage-point increase in ppFEV1), was associated with reduced lung function decline (an approximately 0.5–percentage-point decrease in annual ppFEV1 decline, though confidence intervals included 0), and reduced the annual rate of IV days (approximately 60% over 3 years). [ABSTRACT FROM AUTHOR]

Cross-national studies of the linkage of health-care spending with population health have found surprisingly limited evidence of benefits. In this study, we investigated associations between national health spending and key health resources (numbers of hospital beds, physicians, and nurses) and utilization of cost-effective health services (antenatal care, attendance of trained staff at childbirth, and measles vaccination), sometimes in ways that curtail the benefits of that expenditure. Using annual panel data from 1990–2014 covering 140 countries, we show that variation in health spending as a share of gross domestic product is not associated with decreased mortality rates. It is also very weakly associated with increased health-care resources and health service utilization (elasticity smaller than 0.08), with the association being close to 0 in low-income countries. In addition, countries with a higher share of out-of-pocket spending have a significantly lower level of health resources and service utilization. These findings, rather than the ineffectiveness of health care, could explain the lack of impact of health spending. In contrast, gross domestic product per capita is significantly associated with increased health resources, a higher rate of service utilization, and lower mortality rates, suggesting that income is an important determinant of public health. [ABSTRACT FROM AUTHOR]

North America has been contending with an unregulated street drug supply in which opioids are often adulterated with illicitly manufactured fentanyl. The unpredictability of composition may result in an increased risk of overdose due to unexpected elevated concentrations of the high-potency drug. Using data from a community-based drug-checking project, we evaluated trends in fentanyl concentration of illicit opioids in the context of an overdose epidemic. Using a quantification model for fentanyl hydrochloride, historical Fourier-transform infrared spectra from opioid drug-checking samples were analyzed to determine fentanyl concentrations. Median monthly fentanyl concentrations were plotted, and polynomial and autoregressive time-series analyses were performed to examine trends over time. A total of 3,621 fentanyl-positive samples were included in the study, spanning November 2017 to December 2019. Monthly median fentanyl concentrations ranged from 4.5% to 10.4%. Time-series analyses indicated that a third-degree polynomial model fit the data well (R 2 = 0.639), suggesting a cyclical pattern in median concentration over time. Notably, absolute variance in fentanyl concentration decreased by an average 0.1% per month (P  < 0.001). Future research should explore the relationship between fentanyl concentration and overdose to identify potential targeted harm-reduction interventions that can respond to changes in observed fentanyl concentration. [ABSTRACT FROM AUTHOR]

Effect measure modification is often evaluated using parametric models. These models, although efficient when correctly specified, make strong parametric assumptions. While nonparametric models avoid important functional form assumptions, they often require larger samples to achieve a given accuracy. We conducted a simulation study to evaluate performance tradeoffs between correctly specified parametric and nonparametric models to detect effect modification of a binary exposure by both binary and continuous modifiers. We evaluated generalized linear models and doubly robust (DR) estimators, with and without sample splitting. Continuous modifiers were modeled with cubic splines, fractional polynomials, and nonparametric DR-learner. For binary modifiers, generalized linear models showed the greatest power to detect effect modification, ranging from 0.42 to 1.00 in the worst and best scenario, respectively. Augmented inverse probability weighting had the lowest power, with an increase of 23% when using sample splitting. For continuous modifiers, the DR-learner was comparable to flexible parametric models in capturing quadratic and nonlinear monotonic functions. However, for nonlinear, nonmonotonic functions, the DR-learner had lower integrated bias than splines and fractional polynomials, with values of 141.3, 251.7, and 209.0, respectively. Our findings suggest comparable performance between nonparametric and correctly specified parametric models in evaluating effect modification. [ABSTRACT FROM AUTHOR]

We compared 3 hypothetical trajectories of change in both general and coronavirus disease 2019 (COVID-19)-specific anxiety during the first wave of the spread in the state of Israel: panic (very high anxiety, either from the outset or rapidly increasing), complacency (stable and low anxiety), and threat-sensitive (a moderate, linear increase compatible with the increase in threat). A representative sample of 1,018 Jewish-Israeli adults was recruited online. A baseline assessment commenced 2 days prior to the identification of the first case, followed by 6 weekly assessments. Latent mixture modeling analyses revealed the presence of 3 trajectories: 1) "threat-sensitivity" (29% and 66%, for general and virus-specific anxiety, respectively), 2) panic (12% and 25%), and 3) complacency (29% and 9%). For general anxiety only, a fourth class representing a stable mid-level anxiety was identified ("balanced": 30%). For general anxiety, women and the initially anxious—both generally and specifically from the spread of the virus—were more likely to belong to the panic class. Men and older participants were more likely to belong to the complacency class. Findings indicate a marked heterogeneity in anxiety responses to the first wave of the spread of COVID-19, including a large group evincing a "balanced" response. [ABSTRACT FROM AUTHOR]

The association between early-life greenness and child cognition is not well understood. Using prospective data from Project Viva (n  = 857) from 1999–2010, we examined associations of early-life greenness exposure with mid-childhood cognition. We estimated residential greenness at birth, early childhood (median age 3.1 years), and mid-childhood (7.8 years) using 30-m resolution Landsat satellite imagery (normalized difference vegetation index). In early childhood and mid-childhood, we administered standardized assessments of verbal and nonverbal intelligence, visual-motor abilities, and visual memory. We used natural splines to examine associations of early life-course greenness with mid-childhood cognition, adjusting for age, sex, race, income, neighborhood socioeconomic status, maternal intelligence, and parental education. At lower levels of greenness (greenness <0.6), greenness exposure at early childhood was associated with a 0.48% increase in nonverbal intelligence and 2.64% increase in visual memory in mid-childhood. The association between early-childhood greenness and mid-childhood visual memory was observed after further adjusting for early childhood cognition and across different methodologies, while the association with nonverbal intelligence was not. No other associations between early life-course greenness and mid-childhood cognition were found. Early childhood greenness was nonlinearly associated with higher mid-childhood visual memory. Our findings highlight the importance of nonlinear associations between greenness and cognition. [ABSTRACT FROM AUTHOR]

Placental abruption and cardiovascular disease (CVD) have common etiological underpinnings, and there is accumulating evidence that abruption may be associated with future CVD. We estimated associations between abruption and coronary heart disease (CHD) and stroke. The meta-analysis was based on the random-effects risk ratio (RR) and 95% confidence interval (CI) as the effect measure. We conducted a bias analysis to account for abruption misclassification, selection bias, and unmeasured confounding. We included 11 cohort studies comprising 6,325,152 pregnancies, 69,759 abruptions, and 49,265 CHD and stroke cases (1967–2016). Risks of combined CVD morbidity-mortality among abruption and nonabruption groups were 16.7 and 9.3 per 1,000 births, respectively (RR = 1.76, 95% CI: 1.24, 2.50; I 2 = 94%; τ2 = 0.22). Women who suffered abruption were at 2.65-fold (95% CI: 1.55, 4.54; I 2 = 85%; τ2 = 0.36) higher risk of death related to CHD/stroke than nonfatal CHD/stroke complications (RR = 1.32, 95% CI: 0.91, 1.92; I 2 = 93%; τ2 = 0.15). Abruption was associated with higher mortality from CHD (RR = 2.64, 95% CI: 1.57, 4.44; I 2 = 76%; τ2 = 0.31) than stroke (RR = 1.70, 95% CI: 1.19, 2.42; I 2 = 40%; τ2 = 0.05). Corrections for the aforementioned biases increased these estimates. Women with pregnancies complicated by placental abruption may benefit from postpartum screening or therapeutic interventions to help mitigate CVD risks. [ABSTRACT FROM AUTHOR]