Variable Selection When Estimating Effects in External Target Populations.

External validity is an important part of epidemiologic research. To validly estimate effects in specific external target populations using a chosen effect measure (i.e., "transport"), some methods require that one account for all effect measure modifiers [EMMs]. However, little is known about how including other variables that are not EMMs (i.e., non-EMMs) in adjustment sets impacts estimates. Using simulations, we evaluated how inclusion of non-EMMs affected estimation of the transported risk difference (RD) by assessing impacts of covariates that A) differ (or not) between the trial and the target, B) are associated with the outcome (or not), and C) modify the RD (or not). We assessed variation and bias when covariates with each possible combination of these factors were used to transport RDs using outcome modeling or inverse odds weighting. Including variables that differed in distribution between the populations but were non-EMMs reduced precision, regardless of whether they were associated with the outcome. However, non-EMMs associated with selection did not amplify bias resulting from omitting necessary EMMs. Including all variables associated with the outcome may result in unnecessarily imprecise estimates when estimating treatment effects in external target populations.

Visualizing External Validity: Graphical Displays to Inform the Extension of Treatment Effects from Trials to Clinical Practice.

BACKGROUND: In the presence of effect measure modification, estimates of treatment effects from randomized controlled trials may not be valid in clinical practice settings. The development and application of quantitative approaches for extending treatment effects from trials to clinical practice settings is an active area of research. METHODS: In this article, we provide researchers with a practical roadmap and four visualizations to assist in variable selection for models to extend treatment effects observed in trials to clinical practice settings and to assess model specification and performance. We apply this roadmap and visualizations to an example extending the effects of adjuvant chemotherapy (5-fluorouracil vs. plus oxaliplatin) for colon cancer from a trial population to a population of individuals treated in community oncology practices in the United States. RESULTS: The first visualization screens for potential effect measure modifiers to include in models extending trial treatment effects to clinical practice populations. The second visualization displays a measure of covariate overlap between the clinical practice populations and the trial population. The third and fourth visualizations highlight considerations for model specification and influential observations. The conceptual roadmap describes how the output from the visualizations helps interrogate the assumptions required to extend treatment effects from trials to target populations. CONCLUSIONS: The roadmap and visualizations can inform practical decisions required for quantitatively extending treatment effects from trials to clinical practice settings.

Urinary metabolite concentrations of phthalate and plasticizers in infancy and childhood in the UNC Baby Connectome Project.

INTRODUCTION: Existing evidence suggests that exposure to phthalates is higher among younger age groups. However, limited knowledge exists on how phthalate exposure, as well as exposure to replacement plasticizers, di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH) and di-2-ethylhexyl terephthalate (DEHTP), change from infancy through early childhood. METHODS: Urine samples were collected across the first 5 years of life from typically developing infants and young children enrolled between 2017 and 2020 in the longitudinal UNC Baby Connectome Project. From 438 urine samples among 187 participants, we quantified concentrations of monobutyl phthalate (MnBP), mono-3-carboxypropyl phthalate (MCPP), monoisobutyl phthalate (MiBP), monoethyl phthalate (MEP) monobenzyl phthalate (MBzP), and metabolites of di(2-ethylhexyl) phthalate (DEHP), diisonoyl phthalate (DiNP), DINCH and DEHTP. Specific gravity (SG) adjusted metabolite and molar sum concentrations were compared across age groups. Intraclass correlation coefficients (ICCs) were calculated among 122 participants with multiple urine specimens (373 samples). RESULTS: Most phthalate metabolites showed high detection frequencies (>80% of samples). Replacement plasticizers DINCH (58-60%) and DEHTP (>97%) were also commonly found. DiNP metabolites were less frequently detected (<10%). For some metabolites, SG-adjusted concentrations were inversely associated with age, with the highest concentrations found in the first year of life. ICCs revealed low to moderate reliability in metabolite measurements (ρ = 0.10-0.48) suggesting a high degree of within-individual variation in exposure among this age group. The first 6 months (compared to remaining age groups) showed an increased ratio of carboxylated metabolites of DEHP and DEHTP, compared to other common metabolites, but no clear age trends for DINCH metabolite ratios. CONCLUSION: Metabolites of phthalates and replacements plasticizers were widely detected in infancy and early childhood, with the highest concentrations observed in the first year of life for several metabolites. Higher proportions of carboxylated metabolites of DEHP and DEHTP in younger age groups indicate potential differences in metabolism during infancy.

Individual and joint effects of prenatal PM2.5 and maternal stress on child temperament.

Prenatal fine particulate matter (PM2.5) and maternal psychological functioning have been associated with child cognitive outcomes, though their independent and joint impacts on earlier behavioral outcomes remains less studied. We used data from 382 mother-child pairs from a prospective birth cohort in Mexico City. Temperament was measured at 24 months using the Carey Toddler Temperament Scale (TTS). Exploratory factor analysis (EFA) was used to update the factor structure of the TTS. During pregnancy, mothers completed the Crisis in Family Systems-Revised, Edinburgh Depression Scale, pregnancy-specific anxiety scale, and the Perceived Stress Scale. Pregnancy PM2.5 was assessed using estimates from a satellite-based exposure model. We assessed the association between prenatal maternal stress and PM2.5 on temperament, in both independent and joint models. Quantile g-computation was used to estimate the joint associations. Models were adjusted for maternal age, SES, education, child sex, and child age. In EFA, we identified three temperament factors related to effortful control, extraversion, and negative affect. Our main results showed that higher levels of PM2.5 and several of the maternal psychological functioning measures were related to both effortful control and negative affect in the child, both individually and as a mixture. For instance, a one quartile increase in the prenatal mixture was associated with higher negative affect scores in the child (0.34, 95% CI: 0.16, 0.53). We observed modification of these associations by maternal SES, with associations seen only among lower SES participants for both effortful control (-0.45, 95% CI: -0.70, -0.20) and negative affect outcomes (0.60, 95% CI: 0.35, 0.85). Prenatal PM2.5 and maternal psychological functioning measures were associated with toddler temperament outcomes, providing evidence for impacts of chemical and non-chemical stressors on early child health.

How Choice of Effect Measure Influences Minimally Sufficient Adjustment Sets for External Validity.

Epidemiologic researchers generalizing or transporting effect estimates from a study to a target population must account for effect-measure modifiers (EMMs) on the scale of interest. However, little attention is paid to how the EMMs required may vary depending on the mathematical nuances of each effect measure. We defined 2 types of EMMs: a marginal EMM, where the effect on the scale of interest differs across levels of a variable, and a conditional EMM, where the effect differs conditional on other variables associated with the outcome. These types define 3 classes of variables: class 1 (conditional EMM), class 2 (marginal but not conditional EMM), and class 3 (neither marginal nor conditional EMM). Class 1 variables are necessary to achieve a valid estimate of the RD in a target population, while an RR requires class 1 and class 2 and an OR requires classes 1, 2, and 3 (i.e., all variables associated with the outcome). This does not mean that fewer variables are required for an externally valid RD (because variables may not modify effects on all scales), but it does suggest that researchers should consider the scale of the effect measure when identifying an EMM necessary for an externally valid treatment effect estimate.

Iterative Causal Forest: A Novel Algorithm for Subgroup Identification.

Precisely and efficiently identifying subgroups with heterogeneous treatment effects (HTEs) in real-world evidence studies remains a challenge. Based on the causal forest (CF) method, we developed an iterative CF (iCF) algorithm to identify HTEs in subgroups defined by important variables. Our method iteratively grows different depths of the CF with important effect modifiers, performs plurality votes to obtain decision trees (subgroup decisions) for a family of CFs with different depths, then finds the cross-validated subgroup decision that best predicts the treatment effect as a final subgroup decision. We simulated 12 different scenarios and showed that the iCF outperformed other machine learning methods for interaction/subgroup identification in the majority of scenarios assessed. Using a 20% random sample of fee-for-service Medicare beneficiaries initiating sodium-glucose cotransporter-2 inhibitors (SGLT2i) or glucagon-like peptide-1 receptor agonists (GLP1RA), we implemented the iCF to identify subgroups with HTEs for hospitalized heart failure. Consistent with previous studies suggesting patients with heart failure benefit more from SGLT2i, iCF successfully identified such a subpopulation with HTEs and additive interactions. The iCF is a promising method for identifying subgroups with HTEs in real-world data where the potential for unmeasured confounding can be limited by study design.

Differential Employment Quality and Educational Inequities in Mental Health: A Causal Mediation Analysis.

BACKGROUND: In the United States, inequities in mental distress between those more and less educated have widened over recent years. Employment quality, a multidimensional construct reflecting the relational and contractual features of employer-employee relationships, may mediate this inequity throughout adulthood, yet no study has examined the extent of this mediation in the United States, or how it varies across racialized and gendered populations. METHODS: Using the information on working-age adults from the 2001 to 2019 Panel Study of Income Dynamics, we construct a composite measure of employment quality via principal component analysis. Using this measure and the parametric mediational g-formula, we then estimate randomized interventional analogs for natural direct and indirect effects of low baseline educational attainment (≤high school: no/yes) on the end-of-follow-up prevalence of moderate mental distress (Kessler-6 Score ≥5: no/yes) overall and within subgroups by race and gender. RESULTS: We estimate that low educational attainment would result in a 5.3% greater absolute prevalence of moderate mental distress at the end of follow-up (randomized total effect: 5.3%, 95% CI = 2.2%, 8.4%), with approximately 32% of this effect mediated by differences in employment quality (indirect effect: 1.7%, 95% CI = 1.0%, 2.5%). The results of subgroup analyses across race and gender are consistent with the hypothesis of mediation by employment quality, though not when selecting on full employment (indirect effect: 0.6%, 95% CI = -1.0%, 2.6%). CONCLUSIONS: We estimate that approximately one-third of US educational inequities in mental distress may be mediated by differences in employment quality.

Identifying and estimating effects of sustained interventions under parallel trends assumptions.

Many research questions in public health and medicine concern sustained interventions in populations defined by substantive priorities. Existing methods to answer such questions typically require a measured covariate set sufficient to control confounding, which can be questionable in observational studies. Differences-in-differences rely instead on the parallel trends assumption, allowing for some types of time-invariant unmeasured confounding. However, most existing difference-in-differences implementations are limited to point treatments in restricted subpopulations. We derive identification results for population effects of sustained treatments under parallel trends assumptions. In particular, in settings where all individuals begin follow-up with exposure status consistent with the treatment plan of interest but may deviate at later times, a version of Robins' g-formula identifies the intervention-specific mean under stable unit treatment value assumption, positivity, and parallel trends. We develop consistent asymptotically normal estimators based on inverse-probability weighting, outcome regression, and a double robust estimator based on targeted maximum likelihood. Simulation studies confirm theoretical results and support the use of the proposed estimators at realistic sample sizes. As an example, the methods are used to estimate the effect of a hypothetical federal stay-at-home order on all-cause mortality during the COVID-19 pandemic in spring 2020 in the United States.

Variability and Longitudinal Trajectories of Phthalate and Replacement Biomarkers across Pregnancy in the Human Placenta and Phthalates Study.

Human exposure to phthalates is widespread, but assessment of variability across pregnancy has been hampered by short half-lives of phthalate biomarkers and a few repeated measures in prior studies. We aimed to characterize the variability and longitudinal profiles of phthalate and replacement biomarkers across pregnancy. Within the Human Placenta and Phthalates Study, 303 pregnant women provided urine samples at up to 8 visits across gestation. Concentrations of 14 metabolites of phthalates and 4 metabolites of replacements were quantified in each sample, and subject-specific averages within each trimester were calculated. We examined variability in individual biomarker concentrations across the 8 visits, within trimesters, and across trimester-specific averages using intraclass correlation coefficients (ICCs). To explore longitudinal exposure biomarker profiles, we applied group-based trajectory modeling to trimester-specific averages over pregnancy. Pooling multiple visits into trimester-specific averages improved the ICCs for all biomarkers. Most biomarkers generally showed stable concentrations across gestation, i.e., high-, medium-, and low-concentration profiles, with small proportions of participants falling into the "high"-exposure groups. Variability over pregnancy is likely attributable to random fluctuations around a baseline exposure rather than true changes in concentrations over time.

Fine particulate matter and incident coronary heart disease events up to 10 years of follow-up among Deepwater Horizon oil spill workers.

BACKGROUND: During the 2010 Deepwater Horizon (DWH) disaster, in-situ burning and flaring were conducted to remove oil from the water. Workers near combustion sites were potentially exposed to burning-related fine particulate matter (PM2.5). Exposure to PM2.5 has been linked to increased risk of coronary heart disease (CHD), but no study has examined the relationship among oil spill workers. OBJECTIVES: To investigate the association between estimated PM2.5 from burning/flaring of oil/gas and CHD risk among the DWH oil spill workers. METHODS: We included workers who participated in response and cleanup activities on the water during the DWH disaster (N = 9091). PM2.5 exposures were estimated using a job-exposure matrix that linked modelled PM2.5 concentrations to detailed DWH spill work histories provided by participants. We ascertained CHD events as the first self-reported physician-diagnosed CHD or a fatal CHD event that occurred after each worker's last day of burning exposure. We estimated hazard ratios (HR) and 95% confidence intervals (95%CI) for the associations between categories of average or cumulative daily maximum PM2.5 exposure (versus a referent category of water workers not near controlled burning) and subsequent CHD. We assessed exposure-response trends by examining continuous exposure parameters in models. RESULTS: We observed increased CHD hazard among workers with higher levels of average daily maximum exposure (low vs. referent: HR = 1.26, 95% CI: 0.93, 1.70; high vs. referent: HR = 2.11, 95% CI: 1.08, 4.12; per 10 µg/m3 increase: HR = 1.10, 95% CI: 1.02, 1.19). We also observed suggestively elevated HRs among workers with higher cumulative daily maximum exposure (low vs. referent: HR = 1.19, 95% CI: 0.68, 2.08; medium vs. referent: HR = 1.38, 95% CI: 0.88, 2.16; high vs. referent: HR = 1.44, 95% CI: 0.96, 2.14; per 100 µg/m3-d increase: HR = 1.03, 95% CI: 1.00, 1.05). CONCLUSIONS: Among oil spill workers, exposure to PM2.5 from flaring/burning of oil/gas was associated with increased risk of CHD.

Toxic metal mixtures in private well water and increased risk for preterm birth in North Carolina.

BACKGROUND: Prenatal exposure to metals in private well water may increase the risk of preterm birth (PTB) (delivery < 37 weeks' gestation). In this study, we estimated associations between arsenic, manganese, lead, cadmium, chromium, copper, and zinc concentrations in private well water and PTB incidence in North Carolina (NC). METHODS: Birth certificates from 2003-2015 (n = 1,329,071) were obtained and pregnancies were assigned exposure using the mean concentration and the percentage of tests above the maximum contaminant level (MCL) for the census tract of each individuals' residence at the time of delivery using the NCWELL database (117,960 well water tests from 1998-2019). We evaluated associations between single metals and PTB using adjusted logistic regression models. Metals mixtures were assessed using quantile-based g-computation. RESULTS: Compared with those in other census tracts, individuals residing in tracts where > 25% of tests exceeded the MCL for lead (aOR 1.10, 95%CI 1.02,1.18) or cadmium (aOR 1.11, 95% CI 1.00,1.23) had an increased odds of PTB. Conversely, those residing in areas with > 25% MCL for zinc (aOR 0.77 (95% CI: 0.56,1.02) and copper (aOR 0.53 (95% CI: 0.13,1.34)) had a reduced odds of PTB. A quartile increase in the concentrations of a mixture of lead, cadmium, and chromium was associated with a small increased odds for PTB (aOR 1.02, 95% CI 1.01, 1.03). This metal mixture effect was most pronounced among American Indian individuals (aOR per quartile increase in all metals: 1.19 (95% CI 1.06,1.34)). CONCLUSIONS: In a large study population of over one million births, lead and cadmium were found to increase the risk of PTB individually and in a mixture, with additional mixtures-related impacts estimated from co-exposure with chromium. This study highlights critical racial and ethnic health disparities in relation to private well water thereby emphasizing the urgent need for improved private well water quality to protect vulnerable populations.

Hurricane Florence and suicide mortality in North Carolina: a controlled interrupted time-series analysis.

BACKGROUND: Natural disasters are associated with increased mental health disorders and suicidal ideation; however, associations with suicide deaths are not well understood. We explored how Hurricane Florence, which made landfall in September 2018, may have impacted suicide deaths in North Carolina (NC). METHODS: We used publicly available NC death records data to estimate associations between Hurricane Florence and monthly suicide death rates using a controlled, interrupted time series analysis. Hurricane exposure was determined by using county-level support designations from the Federal Emergency Management Agency. We examined effect modification by sex, age group, and race/ethnicity. RESULTS: 8363 suicide deaths occurred between January 2014 and December 2019. The overall suicide death rate in NC between 2014 and 2019 was 15.53 per 100 000 person-years (95% CI 15.20 to 15.87). Post-Hurricane, there was a small, immediate increase in the suicide death rate among exposed counties (0.89/100 000 PY; 95% CI -2.69 to 4.48). Comparing exposed and unexposed counties, there was no sustained post-Hurricane Florence change in suicide death rate trends (0.02/100 000 PY per month; 95% CI -0.33 to 0.38). Relative to 2018, NC experienced a statewide decline in suicides in 2019. An immediate increase in suicide deaths in Hurricane-affected counties versus Hurricane-unaffected counties was observed among women, people under age 65 and non-Hispanic black individuals, but there was no sustained change in the months after Hurricane Florence. CONCLUSIONS: Although results did not indicate a strong post-Hurricane Florence impact on suicide rates, subgroup analysis suggests differential impacts of Hurricane Florence on several groups, warranting future follow-up.

Amplification of Bias Due to Exposure Measurement Error.

Observational epidemiologic studies typically face challenges of exposure measurement error and confounding. Consider an observational study of the association between a continuous exposure and an outcome, where the exposure variable of primary interest suffers from classical measurement error (i.e., the measured exposures are distributed around the true exposure with independent error). In the absence of exposure measurement error, it is widely recognized that one should control for confounders of the association of interest to obtain an unbiased estimate of the effect of that exposure on the outcome of interest. However, here we show that, in the presence of classical exposure measurement error, the net bias in an estimate of the association of interest may increase upon adjustment for confounders. We offer an analytical expression for calculating the change in net bias in an estimate of the association of interest upon adjustment for a confounder in the presence of classical exposure measurement error, and we illustrate this problem using simulations.

A Bespoke Instrumental Variable Approach to Correction for Exposure Measurement Error.

A covariate-adjusted estimate of an exposure-outcome association may be biased if the exposure variable suffers measurement error. We propose an approach to correct for exposure measurement error in a covariate-adjusted estimate of the association between a continuous exposure variable and outcome of interest. Our proposed approach requires data for a reference population in which the exposure was a priori set to some known level (e.g., 0, and is therefore unexposed); however, our approach does not require an exposure validation study or replicate measures of exposure, which are typically needed when addressing bias due to exposure measurement error. A key condition for this method, which we refer to as "partial population exchangeability," requires that the association between a measured covariate and outcome in the reference population equals the association between that covariate and outcome in the target population in the absence of exposure. We illustrate the approach using simulations and an example.

The Case for Case-Cohort: An Applied Epidemiologist's Guide to Reframing Case-Cohort Studies to Improve Usability and Flexibility.

When research questions require the use of precious samples, expensive assays or equipment, or labor-intensive data collection or analysis, nested case-control or case-cohort sampling of observational cohort study participants can often reduce costs. These study designs have similar statistical precision for addressing a singular research question, but case-cohort studies have broader efficiency and superior flexibility. Despite this, case-cohort designs are comparatively underutilized in the epidemiologic literature. Recent advances in statistical methods and software have made analyses of case-cohort data easier to implement, and advances from casual inference, such as inverse probability of sampling weights, have allowed the case-cohort design to be used with a variety of target parameters and populations. To provide an accessible link to this technical literature, we give a conceptual overview of case-cohort study analysis with inverse probability of sampling weights. We show how this general analytic approach can be leveraged to more efficiently study subgroups of interest or disease subtypes or to examine associations independent of case status. A brief discussion of how this framework could be extended to incorporate other related methodologic applications further demonstrates the broad cost-effectiveness and adaptability of case-cohort methods for a variety of modern epidemiologic applications in resource-limited settings.

Log-transformation of Independent Variables: Must We?

Epidemiologic studies often quantify exposure using biomarkers, which commonly have statistically skewed distributions. Although normality assumption is not required if the biomarker is used as an independent variable in linear regression, it has become common practice to log-transform the biomarker concentrations. This transformation can be motivated by concerns for nonlinear dose-response relationship or outliers; however, such transformation may not always reduce bias. In this study, we evaluated the validity of motivations underlying the decision to log-transform an independent variable using simulations, considering eight scenarios that can give rise to skewed X and normal Y. Our simulation study demonstrates that (1) if the skewness of exposure did not arise from a biasing factor (e.g., measurement error), the analytic approach with the best overall model fit best reflected the underlying outcome generating methods and was least biased, regardless of the skewness of X and (2) all estimates were biased if the skewness of exposure was a consequence of a biasing factor. We additionally illustrate a process to determine whether the transformation of an independent variable is needed using NHANES. Our study and suggestion to divorce the shape of the exposure distribution from the decision to log-transform it may aid researchers in planning for analysis using biomarkers or other skewed independent variables.

Vitamin D Supplement Use and Risk of Breast Cancer by Race-Ethnicity.

BACKGROUND: Vitamin D has anticarcinogenic properties, but a relationship between vitamin D supplement use and breast cancer is not established. Few studies have accounted for changes in supplement use over time or evaluated racial-ethnic differences. METHODS: The Sister Study is a prospective cohort of 50,884 women with 35-74 years of age who had a sister with breast cancer, but no breast cancer themselves at enrollment (2003-2009). We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between vitamin D supplement use and incident breast cancer (3,502 cases; median follow-up 10.5 years). RESULTS: Vitamin D supplement use was common, with 64% reporting ever use (at least once per month) in the year before enrollment. Considering supplement use over time, ever use of vitamin D supplements was not meaningfully associated with breast cancer (HR = 0.96, 95% CI = 0.88, 1.0), relative to never use. However, after adjusting for prior use, recent use of vitamin D supplements ≥1/month was inversely associated with breast cancer (HR = 0.88, 95% CI = 0.78, 1.0), relative to nonrecent use. The inverse association was stronger for ductal carcinoma in situ (HR = 0.67, 95% CI = 0.52, 0.87) than invasive breast cancer (HR = 0.94, 95% CI = 0.72, 1.1, p-for-heterogeneity = 0.02). Supplement use was less common among African American/Black (56%) and non-Black Hispanic/Latina (50%) women than non-Hispanic White women (66%), but there was limited evidence of racial-ethnic differences in HRs (p-for-heterogeneity = 0.16 for ever use, P = 0.55 for recent). CONCLUSIONS: Our findings are consistent with the hypothesis that recent vitamin D use is inversely associated with breast cancer risk.

Associations between mixtures of urinary phthalate metabolite concentrations and oxidative stress biomarkers among couples undergoing fertility treatment.

Phthalate exposure has been associated with adverse reproductive outcomes and oxidative stress is a potential mechanism by which they act. However, few human studies have explored co-exposure confounding or joint effects. Furthermore, most studies examine associations between biomarkers of exposure and oxidative stress from the same urine sample. We investigated single-exposure, co-exposure-adjusted, and joint associations between phthalate metabolites and oxidative stress in the Environment and Reproductive Health (EARTH) study among couples undergoing fertility treatment. We examined cross-sectional associations in both women and men, and longitudinal associations in women. Urine was collected in the follicular phase (women only) and at the time of fertility procedure (women and men), and analyzed for 11 phthalate metabolites. Urine from the time of fertility procedure was analyzed for oxidative stress biomarkers, including free 8-iso-prostaglandin F2α (8-iso-PGF2α), its primary metabolite (2,3-dinor-5,6-dihydro-15-F2t-isoprostane [F2-IsoP-M]), and prostaglandin F2α (PGF2α). Linear mixed effects models were used to estimate single-exposure associations. Bayesian Kernel Machine Regression (BKMR) was used to adjust for co-exposures and to estimate joint effects. Among women, we observed positive associations between all phthalate metabolites and oxidative stress biomarkers in single-exposure models, but there was clear co-exposure confounding. For instance, in a single-exposure model, we estimated a 63% (95% confidence interval: 51, 77) increase in the 8-iso-PGF2α metabolite per interquartile range (IQR) difference in mono-n-butyl phthalate (MBP) versus a 34% (95% credible interval: 12, 60) increase in co-adjusted models. However, several phthalate metabolites remained associated with oxidative stress in co-exposure models, and the joint effects of all exposures were high (e.g., an 114% increase in the 8-iso-PGF2α metabolite per IQR difference in all exposures). Longitudinal results were also attenuated compared to cross-sectional results in women; however, the joint effect of all exposures and the 8-iso-PGF2α metabolite remained positive and statistically significant (11% increase per IQR difference in all exposures, 95% credible interval: 0.2, 23). In men, associations were generally less pronounced, although the joint effect of the mixture on 8-iso-PGF2α was above the null. Because oxidative stress is related to reproductive success among couples seeking fertility treatment, mitigating phthalate exposure should be considered as a potentially beneficial measure.

Innovations in suicide prevention research (INSPIRE): a protocol for a population-based case-control study.

BACKGROUND: Suicide deaths have been increasing for the past 20 years in the USA resulting in 45 979 deaths in 2020, a 29% increase since 1999. Lack of data linkage between entities with potential to implement large suicide prevention initiatives (health insurers, health institutions and corrections) is a barrier to developing an integrated framework for suicide prevention. OBJECTIVES: Data linkage between death records and several large administrative datasets to (1) estimate associations between risk factors and suicide outcomes, (2) develop predictive algorithms and (3) establish long-term data linkage workflow to ensure ongoing suicide surveillance. METHODS: We will combine six data sources from North Carolina, the 10th most populous state in the USA, from 2006 onward, including death certificate records, violent deaths reporting system, large private health insurance claims data, Medicaid claims data, University of North Carolina electronic health records and data on justice involved individuals released from incarceration. We will determine the incidence of death from suicide, suicide attempts and ideation in the four subpopulations to establish benchmarks. We will use a nested case-control design with incidence density-matched population-based controls to (1) identify short-term and long-term risk factors associated with suicide attempts and mortality and (2) develop machine learning-based predictive algorithms to identify individuals at risk of suicide deaths. DISCUSSION: We will address gaps from prior studies by establishing an in-depth linked suicide surveillance system integrating multiple large, comprehensive databases that permit establishment of benchmarks, identification of predictors, evaluation of prevention efforts and establishment of long-term surveillance workflow protocols.

Trends in fatal occupational injuries in Latino/a workers relative to other groups, North Carolina 2000-2017.

BACKGROUND: Latino/a workers may experience higher fatal occupational injury rates than non-Latino/a workers. In North Carolina, the Latino/a population more than doubled between 2000 and 2017. We examined fatal occupational injuries among Latino/a and non-Latino/a workers in North Carolina over this period. METHODS: Information on fatal occupational injuries was abstracted from records of the North Carolina Office of the Chief Medical Examiner and the death certificate records held by the North Carolina Office of Vital Records. Estimates of the working population were derived from the decennial census and American Community Survey. Estimates of annual rates of fatal occupational injury for the period January 1, 2000 to December 31, 2017 were derived for Latino/a workers and compared to Black and White workers not identified as Latino/a. RESULTS: Over the study period, 1,783 fatal occupational injuries were identified among non-Latino/a workers and 259 fatal occupational injuries among Latino/a workers in North Carolina. The majority of fatal occupational injuries among Latino/a workers occurred among males employed in construction and agriculture. While the fatal occupational injury rate among Latino/a workers declined over the study period, the rate among Latino/a workers was higher than among non-Latino/a White and Black workers; moreover, fatal occupational injury rates for Latino/a workers trended upwards during the most recent years of the study period. CONCLUSIONS: Latino/a workers in North Carolina have the highest fatal occupational injury rate of any race/ethnicity group.