Long-Term Outcomes After Lung Transplantation in Children With Intellectual Disabilities.

BACKGROUND: The United Network for Organ Sharing (UNOS) started recording data on intellectual disability status in 2008. This study aimed to characterize the long-term outcomes for children with intellectual disabilities (IDs) undergoing lung transplantation. METHODS: All pediatric patients (under 18 years old) undergoing bilateral lung transplantation were identified using the UNOS database. The patients were grouped into the following categories: no cognitive delay, possible cognitive delay, and definite cognitive delay. The primary endpoint was graft survival at 3-year posttransplantation. Multivariate Cox proportional hazards modeling was used to estimate the independent effect of cognitive disability on graft survival. RESULTS: Five hundred four pediatric patients who underwent lung transplantation between March 2008 and December 2022 were retrospectively analyzed. 59 had a definite cognitive delay (12%), 23 had a possible delay (5%), and 421 had no delay (83%). When comparing these three groups, there was no significant difference in 60-day graft survival (p = 0.4), 3-year graft survival (p = 0.6), 3-year graft survival for patients who survived at least 60-day posttransplantation (p = 0.9), distribution of causes of death (p = 0.24), nor distribution treatment of rejection within 1-year posttransplantation (p = 0.06). CONCLUSIONS: Intellectual disability does not impact long-term outcomes after bilateral lung transplantation. Intellectual disability should not be a contraindication to bilateral lung transplantation on the basis of inferior graft survival.

Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables.

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation and double machine learning. To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment. We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.

Weight Matching in Infant Heart Transplantation: A National Registry Analysis.

BACKGROUND: Infants account for a significant proportion of pediatric heart transplantation but also suffer from a high waitlist mortality. Donor oversizing by weight-based criteria is common practice in transplantation and is prevalent in this group. We sought to analyze the impact of oversizing on outcomes in infants. METHODS: Infant heart transplantations reported to the United Network for Organ Sharing from January 1994 to September 2019 were retrospectively analyzed. 2384 heart transplantation recipients were divided into quintiles (Q1-Q5) on the basis of donor-to-recipient weight ratio (DRWR). Multivariate Cox regression was used to estimate the effect of DRWR. The primary end point was graft survival at 1 year. RESULTS: The median DRWR for each quintile was 0.90 (0.37-1.04), 1.17 (1.04-1.29), 1.43 (1.29-1.57), 1.74 (1.58-1.97), and 2.28 (1.97-5.00). Pairwise comparisons showed improved survival for Q3 and Q4 over each of the bottom 2 quintiles and the top quintile. Regression analyses found that Q3 and Q4 were protective against graft failure compared with the bottom 2 quintiles. There was no difference in hazard among the top 3 quintiles. Significant covariates included primary diagnosis, ischemia time, serum bilirubin level, transplantation year, mechanical ventilation at transplantation, and extracorporeal membrane oxygenation at transplantation. Sex, female-to-male transplantation, and mechanical circulatory support at transplantation were not significant in univariate analyses. CONCLUSIONS: Modest oversizing by DRWR (1.29-1.97) is associated with increased survival and lower risk in infant heart transplantation. Additional investigation is needed to establish best practices for size matching in this population.

Disparities in hepatocellular carcinoma survival by Medicaid-status: A national population-based risk analysis.

BACKGROUND: Previous studies have demonstrated disparities in survival surrounding hepatocellular carcinoma (HCC) across a variety of socio-demographic factors; however, the relationship between Medicaid-status and HCC survival is poorly understood. METHODS: We constructed 5-year, disease-specific survival curves using the Kaplan-Meier method and performed an adjusted survival analysis using multivariate Cox-proportional hazard regression. RESULTS: We analyzed 17,059 non-elderly patients (12,194 non-Medicaid, 4875 Medicaid) diagnosed between 2006 and 2013 and found that Medicaid status was not associated with higher risk of diseases-specific death compared to other insurance types (p = .232, aHR 1.02, 95% CI: 0.983-1.07) after for controlling for a variety of co-variates (ie. marital status, urbanicity, etc.). We found no difference in the risk of death between patients enrolled in Medicaid for more than three years versus those enrolled for less than three years. In all models, rurality and unmarried status were also associated with an increased risk of death (aHR 1.11, 95% CI: 1.03-1.18, p = .002 and aHR 1.18, 95% CI: 1.13-1.23, p < .001, respectively). DISCUSSION: Those enrolled in Medicaid prior to HCC diagnosis may not be associated with a higher risk of disease-specific death compared to non-Medicaid enrolled patients.

Development and evaluation of a novel dietary bisphenol A (BPA) exposure risk tool.

BACKGROUND: Exposure to endocrine disrupting chemicals such as bisphenol A (BPA) is primarily from the diet through canned foods. Characterizing dietary exposures can be conducted through biomonitoring and dietary surveys; however, these methods can be time-consuming and challenging to implement. METHODS: We developed a novel dietary exposure risk questionnaire to evaluate BPA exposure and compared these results to 24-hr dietary recall data from participants (n = 404) of the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) study, a dietary clinical trial, to validate questionnaire responses. High BPA exposure foods were identified from the dietary recalls and used to estimate BPA exposure. Linear regression models estimated the association between exposure to BPA and questionnaire responses. A composite risk score was developed to summarize questionnaire responses. RESULTS: In questionnaire data, 65% of participants ate canned food every week. A composite exposure score validated that the dietary exposure risk questionnaire captured increasing BPA exposure. In the linear regression models, utilizing questionnaire responses vs. 24-hr dietary recall data, participants eating canned foods 1-2 times/week (vs. never) consumed 0.78 more servings (p < 0.001) of high BPA exposure foods, and those eating canned foods 3+ times/week (vs. never) consumed 0.89 more servings (p = 0.013) of high BPA exposure foods. Participants eating 3+ packaged items/day (vs. never) consumed 62.65 more total grams of high BPA exposure food (p = 0.036). CONCLUSIONS: Dietary exposure risk questionnaires may provide an efficient alternative approach to 24-hour dietary recalls to quantify dietary BPA exposure with low participant burden. TRIAL REGISTRATION: The trial was prospectively registered at clinicaltrials.gov as NCT01826591 on April 8, 2013.

Evaluating predicted heart mass in adolescent heart transplantation.

BACKGROUND: Predicted Heart Mass (PHM) has emerged as an attractive size matching metric in adult cardiac transplantation. However, since PHM was derived from a healthy adult cohort, its generalizability to the pediatric population is unclear. We hypothesize that PHM can be extended to older adolescents, and potentially broaden the donor pool available to this group. METHODS: The United Network for Organ Sharing database was retrospectively analyzed for patients aged 13 to 18 undergoing heart transplantation. Recipients were divided into quintiles (Q1-Q5) based on donor-to-recipient predicted heart mass ratios (PHMR). Primary end-point was graft survival at 5 years. RESULTS: Two thousand sixty-one adolescent heart transplant recipients between January 1994 and September 2019 were retrospectively analyzed. The median PHMR's for each quintile was 0.84 (0.59-0.92), 0.97 (0.92-1.02), 1.08 (1.02-1.14), 1.21 (1.14-1.30), and 1.44 (1.30-2.31). Kaplan-Meier survival curves demonstrated comparable survival across all quintiles of PHMR (p = 0.9). Multivariate Cox regression showed no significant difference in graft failure of the outer quintiles when compared to the middle quintile (Q1: 1.04 HR, p = 0.80; Q2: 1.02 HR, p = 0.89; Q4: 1.19 HR, p = 0.28; Q5: 1.02 HR, p = 0.89). Significant covariates included transplant year (HR: 0.95, p < 0.0001), serum bilirubin (HR: 1.04, p = 0.0004), ECMO at transplantation (HR: 2.85, p < 0.0001), and underlying diagnosis of dilated cardiomyopathy (vs congenital heart disease, HR: 0.66, p = 0.0004). CONCLUSIONS: Matching by PHM is not associated with survival or risk in adolescent heart transplant recipients. Our results underscore the ongoing need to develop an improved size-matching method in pediatric heart transplantation.

Geographic variation in the use of continuous outpatient inotrope infusion therapy and beta blockers.

BACKGROUND: Continuous outpatient inotrope infusion therapy (COIIT) can be used as palliative or interim treatment in patients with advanced heart failure (AHF). Despite widespread use, there is a relative lack of data informing best practices. This study aimed to examine whether patterns of COIIT use differed by region and to explore whether observed differences influenced clinical outcomes. METHODS: Retrospective study of AHF patients receiving COIIT from May 2009 through June 2016. The primary outcome was regional difference, the secondary outcome was persistence (duration) on therapy. Cox proportional hazards model was used to calculate hazard ratios for treatment regimens. RESULTS: There were 3,286 patients, mean (SD) age 61.9 (14.4) years and 74.0% (2,433) male. Inotrope selection and beta blocker use varied by region by chi square (χ2 (21) = 166.9, p < 0.001). Persistence was greater on milrinone compared to dobutamine (HR (for discontinuation) 0.54, CI 0.41-0.70, p < 0.001). Concurrent beta-blocker was associated with greater persistence for patients receiving milrinone (HR 0.13, CI 0.08-0.20, p < 0.001) and dobutamine (HR 0.36, CI 0.18-0.71, p < 0.001). CONCLUSIONS: Patterns of COIIT use varied by region, and variations in use were associated with differences in clinical outcomes.

Unexplained mortality during the US COVID-19 pandemic: retrospective analysis of death certificate data and critical assessment of excess death calculations.

OBJECTIVES: Cause-of-death discrepancies are common in respiratory illness-related mortality. A standard epidemiological metric, excess all-cause death, is unaffected by these discrepancies but provides no actionable policy information when increased all-cause mortality is unexplained by reported specific causes. To assess the contribution of unexplained mortality to the excess death metric, we parsed excess deaths in the COVID-19 pandemic into changes in explained versus unexplained (unreported or unspecified) causes. DESIGN: Retrospective repeated cross-sectional analysis, US death certificate data for six influenza seasons beginning October 2014, comparing population-adjusted historical benchmarks from the previous two, three and five seasons with 2019-2020. SETTING: 48 of 50 states with complete data. PARTICIPANTS: 16.3 million deaths in 312 weeks, reported in categories-all causes, top eight natural causes and respiratory causes including COVID-19. OUTCOME MEASURES: Change in population-adjusted counts of deaths from seasonal benchmarks to 2019-2020, from all causes (ie, total excess deaths) and from explained versus unexplained causes, reported for the season overall and for time periods defined a priori: pandemic awareness (19 January through 28 March); initial pandemic peak (29 March through 30 May) and pandemic post-peak (31 May through 26 September). RESULTS: Depending on seasonal benchmark, 287 957-306 267 excess deaths occurred through September 2020: 179 903 (58.7%-62.5%) attributed to COVID-19; 44 022-49 311 (15.2%-16.1%) to other reported causes; 64 032-77 054 (22.2%-25.2%) unexplained (unspecified or unreported cause). Unexplained deaths constituted 65.2%-72.5% of excess deaths from 19 January to 28 March and 14.1%-16.1% from 29 March through 30 May. CONCLUSIONS: Unexplained mortality contributed substantially to US pandemic period excess deaths. Onset of unexplained mortality in February 2020 coincided with previously reported increases in psychotropic use, suggesting possible psychiatric or injurious causes. Because underlying causes of unexplained deaths may vary by group or region, results suggest excess death calculations provide limited actionable information, supporting previous calls for improved cause-of-death data to support evidence-based policy.

Where Do We Go From Here? A Framework for Using Susceptible-Infectious-Recovered Models for Policy Making in Emerging Infectious Diseases.

OBJECTIVES: Throughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making. METHODS: We identify and describe 5 broad standards: transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards. RESULTS: We graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent. CONCLUSIONS: There is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.

A US Population Health Survey on the Impact of COVID-19 Using the EQ-5D-5L.

BACKGROUND: The COVID-19 pandemic has resulted in negative impacts on the economy, population health, and health-related quality-of-life (HRQoL). OBJECTIVE: To assess the impact of COVID-19 on US population HRQoL using the EQ-5D-5L. DESIGN: We surveyed respondents on physical and mental health, demographics, socioeconomics, brief medical history, current COVID-19 status, sleep, dietary, financial, and spending changes. Results were compared to online and face-to-face US population norms. Predictors of EQ-5D-5L utility were analyzed using both standard and post-lasso OLS regressions. Robustness of regression coefficients against unmeasured confounding was analyzed using the E-Value sensitivity analysis. SUBJECTS: Amazon MTurk workers (n=2776) in the USA. MAIN MEASURES: EQ-5D-5L utility and VAS scores by age group. KEY RESULTS: We received n=2746 responses. Subjects 18-24 years reported lower mean (SD) health utility (0.752 (0.281)) compared with both online (0.844 (0.184), p=0.001) and face-to-face norms (0.919 (0.127), p<0.001). Among ages 25-34, utility was worse compared to face-to-face norms only (0.825 (0.235) vs. 0.911 (0.111), p<0.001). For ages 35-64, utility was better during pandemic compared to online norms (0.845 (0.195) vs. 0.794 (0.247), p<0.001). At age 65+, utility values (0.827 (0.213)) were similar across all samples. VAS scores were worse for all age groups (p<0.005) except ages 45-54. Increasing age and income were correlated with increased utility, while being Asian, American Indian or Alaska Native, Hispanic, married, living alone, having history of chronic illness or self-reported depression, experiencing COVID-19-like symptoms, having a family member diagnosed with COVID-19, fear of COVID-19, being underweight, and living in California were associated with worse utility scores. Results were robust to unmeasured confounding. CONCLUSIONS: HRQoL decreased during the pandemic compared to US population norms, especially for ages 18-24. The mental health impact of COVID-19 is significant and falls primarily on younger adults whose health outcomes may have been overlooked based on policy initiatives to date.