First trimester plasma PER- AND Polyfluoroalkyl Substances (PFAS) and blood pressure trajectories across the second and third trimesters of pregnancy

BACKGROUND: Evidence suggests that exposure to per- and polyfluoroalkyl substances (PFAS) increases risk of high blood pressure (BP) during pregnancy. Prior studies did not examine associations with BP trajectory parameters (i.e., overall magnitude and velocity) during pregnancy, which is linked to adverse pregnancy outcomes. OBJECTIVES: To estimate associations of multiple plasma PFAS in early pregnancy with BP trajectory parameters across the second and third trimesters. To assess potential effect modification by maternal age and parity. METHODS: In 1297 individuals, we quantified six PFAS in plasma collected during early pregnancy (median gestational age: 9.4 weeks). We abstracted from medical records systolic BP (SBP) and diastolic BP (DBP) measurements, recorded from 12 weeks gestation until delivery. BP trajectory parameters were estimated via Super Imposition by Translation and Rotation modeling. Subsequently, Bayesian Kernel Machine Regression (BKMR) was employed to estimate individual and joint associations of PFAS concentrations with trajectory parameters - adjusting for maternal age, race/ethnicity, pre-pregnancy body mass index, income, parity, smoking status, and seafood intake. We evaluated effect modification by age at enrollment and parity. RESULTS: We collected a median of 13 BP measurements per participant. In BKMR, higher concentration of perfluorooctane sulfonate (PFOS) was independently associated with higher magnitude of overall SBP and DBP trajectories (i.e., upward shift of trajectories) and faster SBP trajectory velocity, holding all other PFAS at their medians. In stratified BKMR analyses, participants with ≥ 1 live birth had more pronounced positive associations between PFOS and SBP velocity, DBP magnitude, and DBP velocity - compared to nulliparous participants. We did not observe significant associations between concentrations of the overall PFAS mixture and either magnitude or velocity of the BP trajectories. CONCLUSION: Early pregnancy plasma PFOS concentrations were associated with altered BP trajectory in pregnancy, which may impact future cardiovascular health of the mother.

Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model.

Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our model showed improved identifiability of the true population pattern and prevalence in simulation. We focus application of this model to identify the intake patterns of adults living at or below the 130% poverty income level. Five dietary patterns were identified and characterised by reproducible code/data made available to encourage further research.

Examining Generalizability of Nutrient-Based Food Patterns and Their Cross-Sectional Associations with Cardiometabolic Health for Hispanic/Latino Adults in the US: Results from the National Health and Nutrition Examination Survey (NHANES) and the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

Background: Ethnicity, cultural background, and geographic location differ significantly amongst the US Hispanic/Latino population. These characteristic differences can greatly define measured diet and its relationship with cardiometabolic disease, thus influencing generalizability of results. Objective: We aimed to examine dietary patterns of Hispanic/Latino adults and their association with cardiometabolic risk factors (high cholesterol, hypertension, obesity, diabetes) across two representative studies with differing sampling strategies. Methods: Data were collected from Mexican or Other Hispanic adult participants from 2007-2012 National Health and Nutrition Examination Survey (NHANES, n=3,209) and 2007-2011 Hispanic Community Health Survey/Study of Latinos (HCHS/SOL, n=13,059). Nutrient-based food patterns (NBFPs) were derived using factor analysis on nutrient intake data estimated from 24-hour dietary recalls and interpreted using common foods prominent in these nutrients. Cross-sectional association between NBFPs (quintiles) and cardiometabolic risk factors, defined by clinical measures and self-report, were estimated using survey-weighted logistic regression. Results: Five NBFPs were identified in both studies: (1) meats, (2) grains/legumes, (3) fruits/vegetables, (4) dairy, and (5) fats/oils. Association to cardiometabolic risk factors differed by NBFP and study. In HCHS/SOL, persons in the highest quintile of meats NBFP had higher odds of diabetes (OR=1.43, 95%CI: 1.10, 1.86) and obesity (OR=1.36, 95%CI: 1.14, 1.63). Those in the lowest quintile of grains/legumes NBFP (OR=1.22, 95%CI: 1.02, 1.47) and the highest quintile of fats/oils (OR=1.26, 95%CI: 1.03, 1.53) also had higher odds of obesity. In NHANES, NBFPs associated with higher odds of diabetes included those in the lowest quintile of dairy (OR=1.66, 95%CI: 1.01, 2.72) and highest quintile of grains/legumes (OR=2.10, 95%CI: 1.26, 3.50). Persons in the fourth quintile of meats (OR=0.68, 95%CI: 0.47, 0.99) had lower odds of cholesterol. Conclusion: Diet-disease relationships among Hispanic/Latino adults vary according to two representative studies. These differences have research and practical implications when generalizing inferences on heterogeneous underrepresented populations.

Identifying patient profiles of disparate care in resectable pancreas cancer using latent class analysis.

BACKGROUND AND OBJECTIVES: Disparities in pancreas cancer care are multifactorial, but factors are often examined in isolation. Research that integrates these factors in a single conceptual framework is lacking. We use latent class analysis (LCA) to evaluate the association between intersectionality and patterns of care and survival in patients with resectable pancreas cancer. METHODS: LCA was used to identify demographic profiles in resectable pancreas cancer (n = 140 344) diagnosed from 2004 to 2019 in the National Cancer Database (NCDB). LCA-derived patient profiles were used to identify differences in receipt of minimum expected treatment (definitive surgery), optimal treatment (definitive surgery and chemotherapy), time to treatment, and overall survival. RESULTS: Minimum expected treatment (hazard ratio [HR] 0.69, 95% confidence interval [CI]: 0.65, 0.75) and optimal treatment (HR 0.58, 95% CI: 0.55, 0.62) were associated with improved overall survival. Seven latent classes were identified based on age, race/ethnicity, and socioeconomic status (SES) attributes (zip code-linked education and income, insurance, geography). Compared to the referent group (≥65 years + White + med/high SES), the ≥65 years + Black profile had the longest time-to-treatment (24 days vs. 28 days) and lowest odds of receiving minimum (odds ratio [OR] 0.67, 95% CI: 0.64, 0.71) or optimal treatment (OR 0.76, 95% CI: 0.72, 0.81). The Hispanic patient profile had the lowest median overall survival-55.3 months versus 67.5 months. CONCLUSIONS: Accounting for intersectionality in the NCDB resectable pancreatic cancer patient cohort identifies subgroups at higher risk for inequities in care. LCA demonstrates that older Black patients and Hispanic patients are at particular risk for being underserved and should be prioritiz for directed interventions.

Identifying US County-level characteristics associated with high COVID-19 burden.

BACKGROUND: Identifying county-level characteristics associated with high coronavirus 2019 (COVID-19) burden can help allow for data-driven, equitable allocation of public health intervention resources and reduce burdens on health care systems. METHODS: Synthesizing data from various government and nonprofit institutions for all 3142 United States (US) counties, we studied county-level characteristics that were associated with cumulative and weekly case and death rates through 12/21/2020. We used generalized linear mixed models to model cumulative and weekly (40 repeated measures per county) cases and deaths. Cumulative and weekly models included state fixed effects and county-specific random effects. Weekly models additionally allowed covariate effects to vary by season and included US Census region-specific B-splines to adjust for temporal trends. RESULTS: Rural counties, counties with more minorities and white/non-white segregation, and counties with more people with no high school diploma and with medical comorbidities were associated with higher cumulative COVID-19 case and death rates. In the spring, urban counties and counties with more minorities and white/non-white segregation were associated with increased weekly case and death rates. In the fall, rural counties were associated with larger weekly case and death rates. In the spring, summer, and fall, counties with more residents with socioeconomic disadvantage and medical comorbidities were associated greater weekly case and death rates. CONCLUSIONS: These county-level associations are based off complete data from the entire country, come from a single modeling framework that longitudinally analyzes the US COVID-19 pandemic at the county-level, and are applicable to guiding government resource allocation policies to different US counties.

Identifying US Counties with High Cumulative COVID-19 Burden and Their Characteristics.

Identifying areas with high COVID-19 burden and their characteristics can help improve vaccine distribution and uptake, reduce burdens on health care systems, and allow for better allocation of public health intervention resources. Synthesizing data from various government and nonprofit institutions of 3,142 United States (US) counties as of 12/21/2020, we studied county-level characteristics that are associated with cumulative case and death rates using regression analyses. Our results showed counties that are more rural, counties with more White/non-White segregation, and counties with higher percentages of people of color, in poverty, with no high school diploma, and with medical comorbidities such as diabetes and hypertension are associated with higher cumulative COVID-19 case and death rates. We identify the hardest hit counties in US using model-estimated case and death rates, which provide more reliable estimates of cumulative COVID-19 burdens than those using raw observed county-specific rates. Identification of counties with high disease burdens and understanding the characteristics of these counties can help inform policies to improve vaccine distribution, deployment and uptake, prevent overwhelming health care systems, and enhance testing access, personal protection equipment access, and other resource allocation efforts, all of which can help save more lives for vulnerable communities. SIGNIFICANCE STATEMENT: We found counties that are more rural, counties with more White/non-White segregation, and counties with higher percentages of people of color, in poverty, with no high school diploma, and with medical comorbidities such as diabetes and hypertension are associated with higher cumulative COVID-19 case and death rates. We also identified individual counties with high cumulative COVID-19 burden. Identification of counties with high disease burdens and understanding the characteristics of these counties can help inform policies to improve vaccine distribution, deployment and uptake, prevent overwhelming health care systems, and enhance testing access, personal protection equipment access, and other resource allocation efforts, all of which can help save more lives for vulnerable communities.

Robust Clustering with Subpopulation-specific Deviations.

The National Birth Defects Prevention Study (NBDPS) is a case-control study of birth defects conducted across 10 U.S. states. Researchers are interested in characterizing the etiologic role of maternal diet, collected using a food frequency questionnaire. Because diet is multi-dimensional, dimension reduction methods such as cluster analysis are often used to summarize dietary patterns. In a large, heterogeneous population, traditional clustering methods, such as latent class analysis, used to estimate dietary patterns can produce a large number of clusters due to a variety of factors, including study size and regional diversity. These factors result in a loss of interpretability of patterns that may differ due to minor consumption changes. Based on adaptation of the local partition process, we propose a new method, Robust Profile Clustering, to handle these data complexities. Here, participants may be clustered at two levels: (1) globally, where women are assigned to an overall population-level cluster via an overfitted finite mixture model, and (2) locally, where regional variations in diet are accommodated via a beta-Bernoulli process dependent on subpopulation differences. We use our method to analyze the NBDPS data, deriving pre-pregnancy dietary patterns for women in the NBDPS while accounting for regional variability.

Empirically Derived Dietary Patterns Using Robust Profile Clustering in the Hispanic Community Health Study/Study of Latinos.

BACKGROUND: Latent class models (LCMs) have been used in exploring dietary behaviors over a wide set of foods and beverages in a given population, but are prone to overgeneralize these habits in the presence of variation by subpopulations. OBJECTIVES: This study aimed to highlight unique dietary consumption differences by both study site and ethnic background of Hispanic/Latino populations in the United States, that otherwise might be missed in a traditional LCM of the overall population. This was achieved using a new model-based clustering method, referred to as robust profile clustering (RPC). METHODS: A total of 11,320 individuals aged 18-74 y from the Hispanic Community Health Study/Study of Latinos (2008-2011) with complete diet data were classified into 9 subpopulations, defined by study site (Bronx, Chicago, Miami, San Diego) and ethnic background. At baseline, dietary intake was ascertained using a food propensity questionnaire. Dietary patterns were derived from 132 food groups using the RPC method to identify patterns of the general Hispanic/Latino population and those specific to an identified subpopulation. Dietary patterns derived from the RPC were compared to those identified from an LCM. RESULTS: The LCM identified 48 shared consumption behaviors of foods and beverages across the entire cohort, whereas significant consumption differences in subpopulations were identified in the RPC model for these same foods. Several foods were common within study site (e.g., chicken, orange juice, milk), ethnic background (e.g., papayas, plantain, coffee), or both (e.g., rice, tomatoes, seafood). Post hoc testing revealed an improved model fit in the RPC model [Deviance Information Criterion DICRPC = 2.3 × 104, DICLCM  = 9.5 × 106]. CONCLUSIONS: Dietary pattern behaviors of Hispanics/Latinos in the United States tend to align by ethnic background for some foods and by location for other foods. Consideration of both factors is imperative to better understand their contributions to population health and developing targeted nutrition intervention studies.

Outcomes of pediatric patients with abdominal sepsis requiring surgery and extracorporeal membrane oxygenation using the Extracorporeal Life Support Organization database.

No study describes the use of extracorporeal membrane oxygenation (ECMO) in pediatric patients with abdominal sepsis (AS) requiring surgery. A description of outcomes in this patient population would assist clinical decision-making and provide a context for discussions with patients and families. The Extracorporeal Life Support Organization database was queried for pediatric patients (30 days to 18 years) with AS requiring surgery. Forty-five of 61 patients survived (73.8%). Reported bleeding complications (57.1 vs 48.8%), the number of pre-ECMO ventilator hours (208.1 vs 178.9), and the timing of surgery before (50 vs 66.7%) and on-ECMO (50 vs 26.7%) were similar in survivors and nonsurvivors. Decreased pre-ECMO mean pH (7.1 vs 7.3) was associated with increased mortality (odds ratio, 1.49; 95% confidence interval, 1.04 to 2.14). ECMO use for pediatric patients with AS requiring surgery is associated with increased mortality and an increased rate of bleeding complications compared with all pediatric patients receiving ECMO support. Acidemia predicts mortality and provides a potential target of examination for future studies.

Management of stage IIIA non-small cell lung cancer by thoracic surgeons in North America.

BACKGROUND: Stage IIIA(N2) non-small cell lung cancer is a heterogeneous spectrum ranging from microscopic lymph node metastases to bulky multistation nodal disease. While some favor surgical resection after neoadjuvant therapy, others favor definitive chemoradiation for treatment. Our aim was to determine practice patterns of thoracic surgeons. METHODS: We invited 2,539 active surgeons identified on the Cardiothoracic Surgery Network as expressing interest in general thoracic surgery to participate in an anonymous Web-based survey. The participants evaluated clinical vignettes of a patient with single station N2 disease. RESULTS: In all, 513 surgeons (20%) responded, with 222 (43%) in academic practice. For microscopic N2 disease, 84% (n=430) preferred neoadjuvant therapy followed by surgery. For grossly involved N2 disease, 62% (n=318) favored neoadjuvant therapy followed by surgery if N2 disease was downstaged. In patients with normal pulmonary function tests, requiring pneumonectomy, in the presence of bulky, single station N2 disease, there was less consensus: 32% (n=163) favored neoadjuvant therapy followed by lobectomy (less radical surgery than initially predicted) if feasible and N2 disease had downstaged, 30% (n=159) favored neoadjuvant therapy followed by pneumonectomy if N2 disease downstaged, 12% (n=60) would favor surgery regardless of N2 disease downstaging, and 22% (n=114) favored definitive chemoradiation. If the patient did not have adequate pulmonary function for pneumonectomy but could tolerate lobectomy, 50% favored neoadjuvant therapy followed by reassessment for lobectomy and 41% favored definitive chemoradiation. CONCLUSIONS: There is no clear consensus on management of patients with stage IIIA lung cancer in the United States. Diversity of opinion is greatest in patients with more advanced lung cancer, and limited pulmonary function.