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.

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

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.

Philadelphia Beverage Tax's Impact on Beverage Ad Expenditures and Number of Ads Purchased.

INTRODUCTION: On January 1, 2017, Philadelphia implemented a beverage excise tax. The study's objective was to determine whether beverage advertising expenditures and the number of beverage ads purchased changed in Philadelphia compared to Baltimore because of this tax. METHODS: Monthly beverage ad expenditures and the number of beverage ads purchased by brand from January 2016 through December 2019 were obtained. Ads were coded as being for taxed or not taxed beverages and analyzed in 2023. The primary outcomes were quarterly taxed beverage ad expenditures and number of ads purchased. A controlled interrupted time series design on segmented linear regression models was used. Models (aggregated and stratified by internet, spot TV, and local radio) compared whether levels and trends in the outcomes changed from pre- to post-tax in Philadelphia compared to Baltimore. RESULTS: There were no significant differences in taxed beverage advertising expenditures between Philadelphia and Baltimore for trends pretax, at implementation, or post-tax. There were 0.13 (95% CI: -0.25, -0.003) fewer quarterly taxed beverage ads purchased per 100 households in Philadelphia versus Baltimore at baseline. Among internet advertising, there were 0.42 (95% CI: -0.77, -0.06) fewer quarterly taxed beverage ads purchased per 100 households in Philadelphia versus Baltimore immediately post-tax. For spot TV ads, the percentage of taxed beverages ads purchased per quarter was greater at baseline in Philadelphia by 28.0 percentage points (95% CI: 1.9, 54.1). CONCLUSIONS: This study found little evidence of changes in mass media advertising on the examined platforms between 2016 and 2019 due to the Philadelphia beverage tax.

Bayesian estimation methods for survey data with potential applications to health disparities research.

Understanding how and why certain communities bear a disproportionate burden of disease is challenging due to the scarcity of data on these communities. Surveys provide a useful avenue for accessing hard-to-reach populations, as many surveys specifically oversample understudied and vulnerable populations. When survey data is used for analysis, it is important to account for the complex survey design that gave rise to the data, in order to avoid biased conclusions. The field of Bayesian survey statistics aims to account for such survey design while leveraging the advantages of Bayesian models, which can flexibly handle sparsity through borrowing of information and provide a coherent inferential framework to easily obtain variances for complex models and data types. For these reasons, Bayesian survey methods seem uniquely well-poised for health disparities research, where heterogeneity and sparsity are frequent considerations. This review discusses three main approaches found in the Bayesian survey methodology literature: 1) multilevel regression and post-stratification, 2) weighted pseudolikelihood-based methods, and 3) synthetic population generation. We discuss advantages and disadvantages of each approach, examine recent applications and extensions, and consider how these approaches may be leveraged to improve research in population health equity.

Differences in Serum miRNA Profiles by Race, Ethnicity, and Socioeconomic Status: Implications for Developing an Equitable Ovarian Cancer Screening Test.

Serum miRNAs are promising biomarkers for several clinical conditions, including ovarian cancer. To inform equitable implementation of these tests, we investigated the effects of race, ethnicity, and socioeconomic status on serum miRNA profiles. Serum samples from a large institutional biobank were analyzed using a custom panel of 179 miRNA species highly expressed in human serum, measured using the Abcam Fireplex assay via flow cytometry. Data were log-transformed prior to analysis. Differences in miRNA by race and ethnicity were assessed using logistic regression. Pairwise t tests analyzed racial and ethnic differences among eight miRNAs previously associated with ovarian cancer risk. Pearson correlations determined the relationship between mean miRNA expression and the social deprivation index (SDI) for Massachusetts residents. Of 1,586 patients (76.9% white, non-Hispanic), compared with white, non-Hispanic patients, those from other racial and ethnic groups were younger (41.9 years ± 13.2 vs. 51.3 ± 15.1, P < 0.01) and had fewer comorbidities (3.5 comorbidities ± 2.7 vs. 4.6 ± 2.8, P < 0.01). On logistic regression, miRNAs predicted race and ethnicity at an AUC of 0.69 (95% confidence interval, 0.66-0.72), which remained consistent when stratified by most comorbidities. Among eight miRNAs previously associated with ovarian cancer risk, seven significantly varied by race and ethnicity (all P < 0.01). There were no significant differences in SDI for any of these eight miRNAs. miRNA expression is significantly influenced by race and ethnicity, which remained consistent after controlling for confounders. Understanding baseline differences in biomarker test characteristics prior to clinical implementation is essential to ensure instruments perform comparably across diverse populations. PREVENTION RELEVANCE: This study aimed to understand factors affecting miRNA expression, to ensure we create equitable screening tests for ovarian cancer that perform well in diverse populations. The goal is to ensure that we are detecting ovarian cancer cases earlier (secondary prevention) in women of all races, ethnic backgrounds, and socioeconomic means.

The measurement of racism in health inequities research.

There is limited literature on the measures and metrics used to examine racism in the health inequities literature. Health inequities research is continuously evolving, with the number of publications increasing over time. However, there is limited knowledge on the best measures and methods to examine the impact of different levels of racism (institutionalized, personally mediated, and internalized) on health inequities. Advanced statistical methods have the potential to be used in new ways to examine the relationship between racism and health inequities. In this review, we conduct a descriptive examination of the measurement of racism in the health inequities epidemiologic literature. We examine the study design, methods used for analysis, types of measures used (e.g., composite, absolute, relative), number of measures used, phase of research (detect, understand, solutions), viewpoint (oppressor, oppressed), and components of structural racism measures (historical context, geographical context, multifaceted nature). We discuss methods (e.g., Peters-Belson, latent class analysis, difference in differences) that have demonstrated potential for future work. The articles reviewed were limited to the detect (25%) and understand (75%) phases, with no studies in the solutions phase. Although the majority (56%) of studies had cross-sectional designs, many authors pointed to the need for longitudinal and multilevel data for further exploration. We examined study design features as mutually exclusive elements. However, racism is a multifaceted system and the measurement of racism in many studies does not fit into a single category. As the literature grows, the significance of methodological and measurement triangulation to assess racism should be investigated.

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.

Racial and ethnic heterogeneity in diets of low-income adult females in the United States: results from National Health and Nutrition Examination Surveys from 2011 to 2018.

BACKGROUND: Poor diet is a major risk factor of cardiovascular and chronic diseases, particularly for low-income female adults. However, the pathways by which race and ethnicity plays a role in this risk factor have not been fully explored. OBJECTIVES: This observational study aimed to identify dietary consumption differences by race and ethnicity of US female adults living at or below the 130% poverty income level from 2011 to 2018. METHODS: A total of 2917 adult females aged 20 to 80 years from the National Health and Nutrition Examination Survey (2011-2018) living at or below the 130% poverty income level with at least one complete 24-hour dietary recall were classified into 5 self-identified racial and ethnic subgroups (Mexican, other Hispanic, non-Hispanic [NH]-White, NH-Black, and NH-Asian). Dietary consumption patterns were defined by 28 major food groups summarized from the Food Pattern Equivalents Database and derived via a robust profile clustering model, which identifies foods that share consumption patterns across all low-income female adults and foods that differ in consumption patterns based on the racial and ethnic subgroups. RESULTS: All food consumption patterns were identified at the local level, defined by racial and ethnic subgroups. Legumes and cured meats were the most differentiating foods identified across all racial and ethnic subgroups. Higher consumption levels of legumes were observed among Mexican-American and other Hispanic females. Higher consumption levels of cured meat were observed among NH-White and Black females. NH-Asian females had the most uniquely characterized patterns with a higher consumption of prudent foods (fruits, vegetables, and whole grains). CONCLUSIONS: Differences among the consumption behaviors of low-income female adults were found along racial and ethnic lines. Efforts to improve the nutritional health of low-income female adults should consider racial and ethnic differences in diets to appropriately focus interventions.

Comparison of Enhanced Recovery After Surgery (ERAS) metrics by race among gynecologic oncology patients: Ensuring equitable outcomes.

OBJECTIVES: Race and ethnicity are not routinely audited in Enhanced Recovery After Surgery (ERAS) pathways. Given known racial disparities in outcomes in gynecologic oncology, the purpose of this study was to compare differences in ERAS implementation and outcomes by race. METHODS: A cohort study was performed among gynecologic oncology patients enrolled in an ERAS pathway at one academic institution from March 2017 to December 2021. Compliance with ERAS metrics, postoperative complications, 30-day survival, reoperations, intensive care unit (ICU) transfers, and readmissions within 30 days were compared by race. RESULTS: Of 1083 patients (17.0% non-white), non-white women were younger (54.2 years ±13.1 vs. 60.7 years ±13.6, p < 0.001) and proportionally fewer spoke English (75.0% vs. 97.8%, p < 0.001). Fewer non-white women received preadmission ERAS education (73.4% vs. 79.9%, p = 0.05). There were no differences in ERAS implementation by race, including similar rates of preoperative nutritional assessment, carbohydrate loading, antibiotic and thrombosis prophylaxis, and unplanned surgeries by race. There were no differences in complications, reoperations, ICU transfers, or readmissions by race on univariate and multivariate analysis. Four non-white (2.2%) and two white women (0.2%, p = 0.009) died within 30 days of surgery. CONCLUSIONS: Fewer non-white women received preadmission education, possibly due to language barriers. ERAS compliance, postoperative complications, readmissions, reoperations, and ICU transfers did not differ by race. There were two additional deaths within 30 days postoperatively among non-white women compared to white women - which is difficult to interpret given the rarity of perioperative mortality - but appeared unlikely to be related to differences in ERAS protocol implementation. ERAS programs should ensure educational materials are translated into various languages and audit metrics by race to ensure equitable outcomes.

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.

Public attitudes about lung cancer: stigma, support, and predictors of support.

INTRODUCTION: Lung cancer is the leading cause of cancer death, yet public engagement with efforts against lung cancer is low. Public engagement with a cancer is critical to efforts to combat it, yet the reasons for low support for efforts against lung cancer have not been systematically characterized. METHODS: We conducted a telephone survey of 1,071 people to determine levels of engagement and attitudes that might potentially drive engagement. These were then analyzed by univariate and multivariate analysis. RESULTS: Eight percent of participants were involved with a lung cancer organization and 12% chose it among cancers to receive more support. Most participants felt that lung cancer was principally caused by external factors, that it could be cured if caught early, and that lung cancer patients were at least partly to blame for their illness. In multivariate analysis, participants who were supportive in some way of efforts against lung cancer were more likely to be employed, live in suburbia, and to be unsure of the cause of lung cancer. Potential supporters were more likely to be employed, female, younger, have higher income, to believe that genetics is the primary cause of lung cancer, and to believe that lung cancer can be cured when caught early. Participants frequently noted that they supported a particular cancer because of knowing someone affected by that cancer. CONCLUSION: As the lung cancer movement attempts to grow and increase its impact, the most successful recruitment efforts will be targeted to these groups.

Incidence of cetuximab-related infusion reactions in oncology patients treated at the University of North Carolina Cancer Hospital.

PURPOSE: The primary purpose of this study was to determine the rate of infusion reactions to cetuximab in oncology patients treated at the University of North Carolina Cancer Hospital. Secondarily, we sought to evaluate predictors of grade 3-4 hypersensitivity, including geography. METHODS: Data were collected by retrospective chart review for patients treated with cetuximab at the University of North Carolina Cancer Hospital between 15 November 2006 and 31 December 2010. Data were analyzed for occurrence of hypersensitivity reaction in 125 patients with various cancer types. RESULTS: Of the 125 subjects, 31 (24.8%) experienced an infusion reaction of any grade. Of 125, 18 (14.4%) experienced a grade 3 or 4 reaction. The odds ratio for patients with an allergy history having a grade 3 or 4 reaction was 2.57 (95% CI 0.93 to 7.09, p = 0.07). Pretreatment with steroids was associated with absence of grade 3 or 4 reaction with an odds ratio of 0.21 (95% CI 0.05 to 0.83, p = 0.04). Mapping of reaction rates by county revealed higher rates in some of the more rural counties of North Carolina, however, statistical power was lacking. CONCLUSIONS: Rates of hypersensitivity reaction at UNC are similar to rates seen in other areas of the southeastern United States and higher than in other regions of the United States and Europe. Rates of both hypersensitivity reactions and grade 3 to 4 hypersensitivity reactions have not substantially changed over time. Geography, allergy history, and perhaps smoking or cancer type may help predict who will react to cetuximab. Steroids should be strongly considered as premedication in addition to diphenhydramine.

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.