Bayesian semiparametric model for sequential treatment decisions with informative timing.

We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.

Ten-Year Trends of Persistent Mortality With Gallstone Disease: A Retrospective Cohort Study in New Jersey.

BACKGROUND AND AIMS: Recent trends in mortality with gallstone disease remain scarce in the United States. Yet multiple changes in clinical management, such as rates of endoscopy, cholecystectomy, and cholecystostomy, and insurance access at the state level, may have occurred. Thus, we evaluated recent secular trends of mortality with gallstone disease in New Jersey. METHODS: We performed a retrospective, cohort study of mortality from 2009 to 2018 using the National Center for Health Statistics, Restricted Mortality Files. The primary outcome was any death with an International Classifications of Disease, 10th Revision, Clinical Modification diagnosis code of gallstone disease in New Jersey. Simple linear regression was used to model trends of incidence of death. RESULTS: 1580 deaths with diagnosed gallstone disease (dGD) occurred from 2009 to 2018. The annual trend of incidence of death was flat over 10 years. The incidence of death with dGD relative to all death changed only from 0.21% to 0.20% over 10 years. These findings were consistent also in 18 of 20 subgroup combinations, although the trend of death with dGD in Latinos 65 years or older increased [slope estimate 0.93, 95% confidence limit 0.42-1.43, P = .003]. CONCLUSION: The rate of death with dGD showed little change over the recent 10 years in New Jersey. This needs to be reproduced in other states and nationally. A closer examination of the changes in clinical care and insurance access is needed to help understand why they did not result in a positive change in this avoidable cause of death.

Pneumonitis in advanced non-small cell lung cancer: no interaction between immune checkpoint inhibition and radiation therapy.

Background: Radiation pneumonitis and immune-related pneumonitis have been studied independently, but little information has emerged on the interactions between radiation therapy (RT) and immune checkpoint inhibition (ICI). We examine whether RT and ICI are synergistic in causing pneumonitis. Methods: A retrospective cohort was assembled using the Surveillance, Epidemiology, and End Results-Medicare database, including Medicare beneficiaries diagnosed with American Joint Committee on Cancer 7th ed. (AJCC) stages IIIB-IV NSCLC between 2013-2017. Exposures to RT and ICI were determined by evaluating for treatment within 12 months of diagnosis (RT group and ICI group) and for a second exposure (e.g., ICI after RT) within 3 months after the first exposure (RT + ICI group). Untreated controls were matched to treated patients who were diagnosed in the same three-month window. A validated algorithm for identifying cases of pneumonitis in claims data was used to evaluate for the outcome within 6 months after treatment. The primary outcome was the relative excess risk due to interaction (RERI), a quantitative measure of additive interaction between two treatments. Results: There were 18,780 patients included in the analysis with 9,345 (49.8%), 7,533 (40.2%), 1,332 (7.1%), and 550 (2.9%) in the control, RT, ICI, and RT + ICI groups, respectively. Relative to controls, the hazards ratios of pneumonitis were 11.5 (95% CI: 7.9 to 17.0), 6.2 (95% CI: 3.8 to 10.3), and 10.7 (95% CI: 6.0 to 19.2) in the RT, ICI, and RT-ICI groups, respectively. The RERIs were -6.1 (95% CI: -13.1 to -0.6, P=0.97) and -4.0 (95% CI: -10.7 to 1.5, P=0.91) in the unadjusted and adjusted analyses, respectively, consistent with no evidence of additive interaction (RERI ≤0) between RT and ICI. Conclusions: In this study of Medicare beneficiaries with advanced NSCLC, RT and ICI were, at most, additive rather than synergistic in causing pneumonitis. Pneumonitis risk in patients treated with RT and ICI is not more than could be expected from each therapy alone.

Decreased Emergency Cholecystectomy and Case Fatality Rate, Not Explained by Expansion of Medicaid.

INTRODUCTION: Population data on longitudinal trends for cholecystectomies and their outcomes are scarce. We evaluated the incidence and case fatality rate of emergency and ambulatory cholecystectomies in New Jersey (NJ) and whether the Medicaid expansion changed trends. MATERIALS AND METHODS: A retrospective population cohort design was used to study the incidence of cholecystectomies and their case fatality rate from 2009 to 2018. Using linear and logistic regression we explored the trends of incidence and the odds of case fatality after versus before the January 1, 2014 Medicaid expansion. RESULTS: Overall, 93,423 emergency cholecystectomies were performed, with 644 fatalities; 87,239 ambulatory cholecystectomies were performed, with fewer than 10 fatalities. The 2009 to 2018 annual incidence of emergency cholecystectomies dropped markedly from 114.8 to 77.5 per 100,000 NJ population (P < 0.0001); ambulatory cholecystectomies increased from 93.5 to 95.6 per 100,000 (P = 0.053). The incidence of emergency cholecystectomies dropped more after than before Medicaid expansion (P < 0.0001). The odds ratio for case fatality among those undergoing emergency cholecystectomies after versus before expansion was 0.85 (95% CI, 0.72-0.99). This decrease in case fatality, apparent only in those over age 65, was not explained by the addition of Medicaid. CONCLUSIONS: A marked decrease in the incidence of emergency cholecystectomies occurred after Medicaid expansion, which was not accounted for by a minimal increase in the incidence of ambulatory cholecystectomies. Case fatality from emergency cholecystectomy decreased over time due to factors other than Medicaid. Further work is needed to reconcile these findings with the previously reported lack of decrease in overall gallstone disease mortality in NJ.

Rheumatoid Arthritis Disease Activity and Hospitalized Infection in a Large US Registry.

OBJECTIVES: The association between disease activity and infection risk among patients with rheumatoid arthritis (RA) is not clear, and it is challenging to determine because of confounding due to the effects of RA treatments and comorbidities. METHODS: Using patients with RA in the CorEvitas registry with Medicare coverage in 2006-2019, we identified eligible patients who had at least 1 visit with moderate disease activity based on the Clinical Disease Activity Index (CDAI; CDAI >10 and ≤22). Follow-up started at the subsequent CorEvitas visit. Hospitalized infection during follow-up was assessed in linked Medicare data. We calculated the incidence rate of hospitalized infection for patients in remission, and low and moderate disease activity, and estimated the effect of time-varying CDAI on hospitalized infection by controlling for baseline and time-dependent confounders using marginal structural models (MSMs). RESULTS: A total of 3,254 patients with RA were eligible for analysis, among which 529 hospitalized infections were identified during follow-up. The crude incidence of hospitalized infection was 3.8 per 100 person-years for patients in remission, 6.6 for low disease activity, and 8.0 for moderate disease activity. Using MSMs and compared with being in remission, the hazard ratio of hospitalized infection associated with low disease activity was 1.60 (95% confidence interval [95% CI] 1.13-2.28) and with moderate disease activity was 1.83 (95% CI 1.30-2.64). CONCLUSION: The risk of hospitalized infection was higher for patients with RA in low or moderate disease activity than for those in remission after accounting for the interplay of disease activity, RA treatments, treatment switching, and other potential confounders.

Hierarchical Bayesian bootstrap for heterogeneous treatment effect estimation.

A major focus of causal inference is the estimation of heterogeneous average treatment effects (HTE) - average treatment effects within strata of another variable of interest such as levels of a biomarker, education, or age strata. Inference involves estimating a stratum-specific regression and integrating it over the distribution of confounders in that stratum - which itself must be estimated. Standard practice involves estimating these stratum-specific confounder distributions independently (e.g. via the empirical distribution or Rubin's Bayesian bootstrap), which becomes problematic for sparsely populated strata with few observed confounder vectors. In this paper, we develop a nonparametric hierarchical Bayesian bootstrap (HBB) prior over the stratum-specific confounder distributions for HTE estimation. The HBB partially pools the stratum-specific distributions, thereby allowing principled borrowing of confounder information across strata when sparsity is a concern. We show that posterior inference under the HBB can yield efficiency gains over standard marginalization approaches while avoiding strong parametric assumptions about the confounder distribution. We use our approach to estimate the adverse event risk of proton versus photon chemoradiotherapy across various cancer types.

Use of a New Prevention Model in Acute Care Surgery: A Population Approach to Preventing Emergency Surgical Morbidity and Mortality.

Whether patients undergo the more morbid and costly emergent rather than an elective type of surgery, may depend on many factors. Since tertiary prevention (preventing poor outcomes from emergency surgery) carries a much higher mortality than secondary prevention (preventing emergency surgery) or primary prevention (preventing the disease requiring surgery), the overall United States mortality might be reduced significantly, if emergency surgery could be avoided via high-quality primary prevention and non-surgical therapy or increasing elective surgery at the expense of emergency procedures, e.g., secondary prevention. The practice and study of acute care surgery then has the potential to broaden from a focus on the patient in the hospital emergency and operating rooms to the patient who no longer requires either, whose disease is treated or prevented in his/her/their community.

Respiratory Microbiome Disruption and Risk for Ventilator-Associated Lower Respiratory Tract Infection.

BACKGROUND: Ventilator-associated lower respiratory tract infection (VA-LRTI) is common among critically ill patients and has been associated with increased morbidity and mortality. In acute critical illness, respiratory microbiome disruption indices (MDIs) have been shown to predict risk for VA-LRTI, but their utility beyond the first days of critical illness is unknown. We sought to characterize how MDIs previously shown to predict VA-LRTI at initiation of mechanical ventilation change with prolonged mechanical ventilation, and if they remain associated with VA-LRTI risk. METHODS: We developed a cohort of 83 subjects admitted to a long-term acute care hospital due to their prolonged dependence on mechanical ventilation; performed dense, longitudinal sampling of the lower respiratory tract, collecting 1066 specimens; and characterized the lower respiratory microbiome by 16S rRNA sequencing as well as total bacterial abundance by 16S rRNA quantitative polymerase chain reaction. RESULTS: Cross-sectional MDIs, including low Shannon diversity and high total bacterial abundance, were associated with risk for VA-LRTI, but associations had wide posterior credible intervals. Persistent lower respiratory microbiome disruption showed a more robust association with VA-LRTI risk, with each day of (base e) Shannon diversity <2.0 associated with a VA-LRTI odds ratio of 1.36 (95% credible interval, 1.10-1.72). The observed association was consistent across multiple clinical definitions of VA-LRTI. CONCLUSIONS: Cross-sectional MDIs have limited ability to discriminate VA-LRTI risk during prolonged mechanical ventilation, but persistent lower respiratory tract microbiome disruption, best characterized by consecutive days with low Shannon diversity, may identify a population at high risk for infection and may help target infection-prevention interventions.

Validation of a claims-based algorithm for identifying non-infectious pneumonitis in patients diagnosed with lung cancer.

PURPOSE: Non-infectious pneumonitis (NIP) is a common complication of treatments for lung cancer. We know of no existing validated algorithm for identifying NIP in claims databases, limiting our ability to understand the morbidity and mortality of this toxicity in real-world data. METHODS: Electronic health records (EHR), cancer registry, and administrative data from a National Cancer Institute-designated comprehensive cancer center were queried for patients diagnosed with lung cancer between 10/01/2015-12/31/2020. Health insurance claims were searched for ICD-10-CM codes that indicate an inpatient or outpatient diagnosis with possible NIP. A 20-code (Algorithm A) and 11-code (Algorithm B) algorithm were tested with and without requiring prescription with corticosteroids. Cases with a diagnosis of possible NIP in the 6 months before their first lung cancer diagnosis were excluded. The algorithms were validated by reviewing the EHR. The positive predictive value (PPV) for each algorithm was computed with 95% confidence intervals (CI). RESULTS: Seventy patients with lung cancer had a diagnosis code compatible with NIP: 36 (51.4%) inpatients and 34 (48.6%) outpatients. The PPV of Algorithm A was 77.1% (95% CI: 65.6-86.3). The PPV of Algorithm B was 86.9% (95% CI: 75.8-94.2). Requiring a documented prescription for a systemic corticosteroid improved the PPV of both Algorithm A and Algorithm B: 92.5% (95% CI: 79.6-98.4) and 100.0% (95% CI: 90.0-100.0), respectively. CONCLUSIONS: This study validated ICD-10-CM and prescription-claims-based definitions of NIP in lung cancer patients. All algorithms have at least reasonable performance. Enriching the algorithm with corticosteroid prescription records results in excellent performance.

Net benefit separation and the determination curve: A probabilistic framework for cost-effectiveness estimation.

Considerations regarding clinical effectiveness and cost are essential in comparing the overall value of two treatments. There has been growing interest in methodology to integrate cost and effectiveness measures in order to inform policy and promote adequate resource allocation. The net monetary benefit aggregates information on differences in mean cost and clinical outcomes; the cost-effectiveness acceptability curve was developed to characterize the extent to which the strength of evidence regarding net monetary benefit changes with fluctuations in the willingness-to-pay threshold. Methods to derive insights from characteristics of the cost/clinical outcomes besides mean differences remain undeveloped but may also be informative. We propose a novel probabilistic measure of cost-effectiveness based on the stochastic ordering of the individual net benefit distribution under each treatment. Our approach is able to accommodate features frequently encountered in observational data including confounding and censoring, and complements the net monetary benefit in the insights it provides. We conduct a range of simulations to evaluate finite-sample performance and illustrate our proposed approach using simulated data based on a study of endometrial cancer patients.

Risk of HCC With Hepatitis B Viremia Among HIV/HBV-Coinfected Persons in North America.

BACKGROUND AND AIMS: Chronic HBV is the predominant cause of HCC worldwide. Although HBV coinfection is common in HIV, the determinants of HCC in HIV/HBV coinfection are poorly characterized. We examined the predictors of HCC in a multicohort study of individuals coinfected with HIV/HBV. APPROACH AND RESULTS: We included persons coinfected with HIV/HBV within 22 cohorts of the North American AIDS Cohort Collaboration on Research and Design (1995-2016). First occurrence of HCC was verified by medical record review and/or cancer registry. We used multivariable Cox regression to determine adjusted HRs (aHRs [95% CIs]) of factors assessed at cohort entry (age, sex, race, body mass index), ever during observation (heavy alcohol use, HCV), or time-updated (HIV RNA, CD4+ percentage, diabetes mellitus, HBV DNA). Among 8,354 individuals coinfected with HIV/HBV (median age, 43 years; 93% male; 52.4% non-White), 115 HCC cases were diagnosed over 65,392 person-years (incidence rate, 1.8 [95% CI, 1.5-2.1] events/1,000 person-years). Risk factors for HCC included age 40-49 years (aHR, 1.97 [1.22-3.17]), age ≥50 years (aHR, 2.55 [1.49-4.35]), HCV coinfection (aHR, 1.61 [1.07-2.40]), and heavy alcohol use (aHR, 1.52 [1.04-2.23]), while time-updated HIV RNA >500 copies/mL (aHR, 0.90 [0.56-1.43]) and time-updated CD4+ percentage <14% (aHR, 1.03 [0.56-1.90]) were not. The risk of HCC was increased with time-updated HBV DNA >200 IU/mL (aHR, 2.22 [1.42-3.47]) and was higher with each 1.0 log10 IU/mL increase in time-updated HBV DNA (aHR, 1.18 [1.05-1.34]). HBV suppression with HBV-active antiretroviral therapy (ART) for ≥1 year significantly reduced HCC risk (aHR, 0.42 [0.24-0.73]). CONCLUSION: Individuals coinfected with HIV/HBV on ART with detectable HBV viremia remain at risk for HCC. To gain maximal benefit from ART for HCC prevention, sustained HBV suppression is necessary.

A Bayesian nonparametric model for zero-inflated outcomes: Prediction, clustering, and causal estimation.

Researchers are often interested in predicting outcomes, detecting distinct subgroups of their data, or estimating causal treatment effects. Pathological data distributions that exhibit skewness and zero-inflation complicate these tasks-requiring highly flexible, data-adaptive modeling. In this paper, we present a multipurpose Bayesian nonparametric model for continuous, zero-inflated outcomes that simultaneously predicts structural zeros, captures skewness, and clusters patients with similar joint data distributions. The flexibility of our approach yields predictions that capture the joint data distribution better than commonly used zero-inflated methods. Moreover, we demonstrate that our model can be coherently incorporated into a standardization procedure for computing causal effect estimates that are robust to such data pathologies. Uncertainty at all levels of this model flow through to the causal effect estimates of interest-allowing easy point estimation, interval estimation, and posterior predictive checks verifying positivity, a required causal identification assumption. Our simulation results show point estimates to have low bias and interval estimates to have close to nominal coverage under complicated data settings. Under simpler settings, these results hold while incurring lower efficiency loss than comparator methods. We use our proposed method to analyze zero-inflated inpatient medical costs among endometrial cancer patients receiving either chemotherapy or radiation therapy in the SEER-Medicare database.

A practical introduction to Bayesian estimation of causal effects: Parametric and nonparametric approaches.

Substantial advances in Bayesian methods for causal inference have been made in recent years. We provide an introduction to Bayesian inference for causal effects for practicing statisticians who have some familiarity with Bayesian models and would like an overview of what it can add to causal estimation in practical settings. In the paper, we demonstrate how priors can induce shrinkage and sparsity in parametric models and be used to perform probabilistic sensitivity analyses around causal assumptions. We provide an overview of nonparametric Bayesian estimation and survey their applications in the causal inference literature. Inference in the point-treatment and time-varying treatment settings are considered. For the latter, we explore both static and dynamic treatment regimes. Throughout, we illustrate implementation using off-the-shelf open source software. We hope to leave the reader with implementation-level knowledge of Bayesian causal inference using both parametric and nonparametric models. All synthetic examples and code used in the paper are publicly available on a companion GitHub repository.

Functional clustering methods for longitudinal data with application to electronic health records.

We develop a method to estimate subject-level trajectory functions from longitudinal data. The approach can be used for patient phenotyping, feature extraction, or, as in our motivating example, outcome identification, which refers to the process of identifying disease status through patient laboratory tests rather than through diagnosis codes or prescription information. We model the joint distribution of a continuous longitudinal outcome and baseline covariates using an enriched Dirichlet process prior. This joint model decomposes into (local) semiparametric linear mixed models for the outcome given the covariates and simple (local) marginals for the covariates. The nonparametric enriched Dirichlet process prior is placed on the regression and spline coefficients, the error variance, and the parameters governing the predictor space. This leads to clustering of patients based on their outcomes and covariates. We predict the outcome at unobserved time points for subjects with data at other time points as well as for new subjects with only baseline covariates. We find improved prediction over mixed models with Dirichlet process priors when there are a large number of covariates. Our method is demonstrated with electronic health records consisting of initiators of second-generation antipsychotic medications, which are known to increase the risk of diabetes. We use our model to predict laboratory values indicative of diabetes for each individual and assess incidence of suspected diabetes from the predicted dataset.

Nested g-computation: A causal approach to analysis of censored medical costs in the presence of time-varying treatment.

Rising medical costs are an emerging challenge in policy decisions and resource allocation planning. When cumulative medical cost is the outcome, right-censoring induces informative missingness due to heterogeneity in cost accumulation rates across subjects. Inverse-weighting approaches have been developed to address the challenge of informative cost trajectories in mean cost estimation, though these approaches generally ignore post-baseline treatment changes. In post-hysterectomy endometrial cancer patients, data from a linked database of Medicare records and the Surveillance, Epidemiology, and End Results program of the National Cancer Institute reveal substantial within-subject variation in treatment over time. In such a setting, the utility of existing intent-to-treat approaches is generally limited. Estimates of population mean cost under a hypothetical time-varying treatment regime can better assist with resource allocation when planning for a treatment policy change; such estimates must inherently take time-dependent treatment and confounding into account. In this paper, we develop a nested g-computation approach to cost analysis to address this challenge, while accounting for censoring. We develop a procedure to evaluate sensitivity to departures from baseline treatment ignorability. We further conduct a variety of simulations and apply our nested g-computation procedure to two-year costs from endometrial cancer patients.

HIV RNA, CD4+ Percentage, and Risk of Hepatocellular Carcinoma by Cirrhosis Status.

BACKGROUND: Despite increasing incidence of hepatocellular carcinoma (HCC) among HIV-infected patients, it remains unclear if HIV-related factors contribute to development of HCC. We examined if higher or prolonged HIV viremia and lower CD4+ cell percentage were associated with HCC. METHODS: We conducted a cohort study of HIV-infected individuals who had HIV RNA, CD4+, and CD8+ cell counts and percentages assessed in the Veterans Aging Cohort Study (1999-2015). HCC was ascertained using Veterans Health Administration cancer registries and electronic records. Cox regression was used to determine hazard ratios (HR, 95% confidence interval [CI]) of HCC associated with higher current HIV RNA, longer duration of detectable HIV viremia (≥500 copies/mL), and current CD4+ cell percentage less than 14%, adjusting for traditional HCC risk factors. Analyses were stratified by previously validated diagnoses of cirrhosis prior to start of follow-up. RESULTS: Among 35 659 HIV-infected patients, 302 (0.8%) developed HCC over 281 441 person-years (incidence rate = 107.3 per 100 000 person-years). Among patients without baseline cirrhosis, higher HIV RNA (HR = 1.25, 95% CI = 1.12 to 1.40, per 1.0 log10 copies/mL) and 12 or more months of detectable HIV (HR = 1.47, 95% CI = 1.02 to 2.11) were independently associated with higher risk of HCC. CD4+ percentage less than 14% was not associated with HCC in any model. Hepatitis C coinfection was a statistically significant predictor of HCC regardless of baseline cirrhosis status. CONCLUSION: Among HIV-infected patients without baseline cirrhosis, higher HIV RNA and longer duration of HIV viremia increased risk of HCC, independent of traditional HCC risk factors. This is the strongest evidence to date that HIV viremia contributes to risk of HCC in this group.

Concordance of hospitalizations between Clinical Practice Research Datalink and linked Hospital Episode Statistics among patients treated with oral antidiabetic therapies.

PURPOSE: The ability of the Clinical Practice Research Datalink (CPRD) to ascertain all-cause hospitalizations remains unknown. We determined the proportion of hospitalizations in CPRD that were also recorded in Hospital Episode Statistics (HES), and vice versa, among patients initiating oral antidiabetic (OAD) therapy. METHODS: We conducted a retrospective cohort study from October 2009 to September 2012 among OAD-treated patients registered with general practitioners who contribute to CPRD and consent to HES linkage. In CPRD, we identified initial hospitalizations for each calendar year by an Inpatient Referral, Consultation Type code, or Read code indicating an inpatient episode and determined if an admission date was recorded in HES within ±30 days. We then identified initial HES admission dates and determined if a hospitalization was documented in CPRD within ±30 days. Sensitivity analyses were conducted utilizing HES discharge, rather than admission, dates. RESULTS: Among 8574 OAD-treated HES-linked patients in CPRD, 6574 initial hospitalizations across the study period were identified in CPRD, and 5188 (78.9% [95% CI, 77.9%-79.9%]) were confirmed by a HES admission date within ±30 days (median difference, ±3 days [IQR, 1-7 days]). Among 8609 initial hospital admissions in HES, 4803 (55.7% [95% CI, 54.7%-56.8%]) hospitalizations were recorded in CPRD within ±30 days (median difference, ±4 days [IQR, 1-9 days]). Similar results were observed using HES discharge dates. CONCLUSION: A substantial minority of patient-level hospitalization data are nonconcordant between HES and CPRD. Pharmacoepidemiologic studies within CPRD that seek to identify hospitalizations should consider linkage with HES to ensure adequate ascertainment of inpatient events.

Determinants of Liver Complications Among HIV/Hepatitis B Virus-Coinfected Patients.

BACKGROUND: Hepatitis B virus (HBV) infection is a leading cause of end-stage liver disease (ESLD) and hepatocellular carcinoma (HCC) in HIV. Factors contributing to the high rates of liver complications among HIV/HBV-coinfected individuals remain unknown. SETTING: North American AIDS Cohort Collaboration on Research and Design. METHODS: We performed a retrospective cohort study among HIV/HBV-coinfected patients in 10 US and Canadian cohorts of the North American AIDS Cohort Collaboration on Research and Design that validated ESLD (ascites, spontaneous bacterial peritonitis, variceal hemorrhage, and/or hepatic encephalopathy) and HCC diagnoses from 1996 to 2010. Multivariable Cox regression was used to examine adjusted hazard ratios [aHRs with 95% confidence interval (CIs)] of liver complications (first occurrence of ESLD or HCC) associated with hypothesized determinants and with increasing durations of HIV suppression (≤500 copies/mL). RESULTS: Among 3573 HIV/HBV patients with 13,790 person-years of follow-up, 111 liver complications occurred (incidence rate = 8.0 [95% CI: 6.6 to 9.7] events/1000 person-years). Rates of liver complication were increased with non-black/non-Hispanic race [aHR = 1.76 (1.13-2.74)], diabetes mellitus [aHR = 2.07 (1.20-3.57)], lower time-updated CD4 cell count [<200 cells/mm: aHR = 2.59 (1.36-4.91); 201-499 cells/mm: aHR = 1.75 (1.01-3.06) versus ≥500 cells/mm], heavy alcohol use [aHR = 1.58 (1.04-2.39)], and higher FIB-4 at start of follow-up [>3.25: aHR = 9.79 (5.73-16.74); 1.45-3.25: aHR = 3.20 (1.87-5.47) versus FIB-4 <1.45]. HIV suppression for ≥6 months was associated with lower liver complication rates compared with those with unsuppressed HIV [aHR = 0.56 (0.35-0.91)]. CONCLUSIONS: Non-black/non-Hispanic race, diabetes, lower CD4 cell count, heavy alcohol use, and advanced liver fibrosis were determinants of liver complications among HIV/HBV patients. Sustained HIV suppression should be a focus for HIV/HBV-coinfected patients to reduce the risks of ESLD/HCC.

Improved Quality of Life With Anti-TNF Therapy Compared With Continued Corticosteroid Utilization in Crohn's Disease.

BACKGROUND: Corticosteroids (CS) and anti-TNF drugs are used to treat Crohn's disease (CD). In this study, we assessed the net health benefit of initiating anti-TNF therapy relative to additional CS use in CD using a novel combination of a retrospective cohort study and a simulation model. METHODS: Using Medicaid data from 2001 to 2005 and Medicare data from 2006 to 2013, beneficiaries were identified with CD and CS use who subsequently received either an anti-TNF or reached a cumulative dose of >3000 mg CS during the year. By using overall and latent class-specific remission-time equivalent (RTE) estimates derived from discrete-choice experiments, mean 12-month cumulative RTEs were calculated after propensity score adjustment for baseline characteristics. A Markov model was constructed using transition probabilities derived from the retrospective cohort to perform additional sensitivity analyses of RTE estimates, analytic assumptions, and transition probabilities. Cumulative RTEs were calculated via Monte Carlo simulation in this model. RESULTS: In the retrospective cohort, 1563 new anti-TNF initiators and 1563 propensity score-matched prolonged CS users were identified. Anti-TNF use was associated with greater mean RTEs at the end of 1 year (5.34 vs 4.54, incremental benefit: 0.79; 95% CI, 0.53-1.07). This benefit persisted in all latent classes. In the Markov model, anti-TNF therapy was the preferred strategy, and the results were robust in multiple sensitivity analysis and latent class analysis. CONCLUSIONS: In both a retrospective cohort study and a simulation model, anti-TNF use was associated with improved quality of life, measured as RTEs, when compared with continued CS utilization for CD.

Risk maps for cities: Incorporating streets into geostatistical models.

Vector-borne diseases commonly emerge in urban landscapes, and Gaussian field models can be used to create risk maps of vector presence across a large environment. However, these models do not account for the possibility that streets function as permeable barriers for insect vectors. We describe a methodology to transform spatial point data to incorporate permeable barriers, by distorting the map to widen streets, with one additional parameter. We use Gaussian field models to estimate this additional parameter, and develop risk maps incorporating streets as permeable barriers. We demonstrate our method on simulated datasets and apply it to data on Triatoma infestans, a vector of Chagas disease in Arequipa, Peru. We found that the transformed landscape that best fit the observed pattern of Triatoma infestans infestation, approximately doubled the true Euclidean distance between neighboring houses on different city blocks. Our findings may better guide control of re-emergent insect populations.