Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros.

We consider measurement error models for two variables observed repeatedly and subject to measurement error. One variable is continuous, while the other variable is a mixture of continuous and zero measurements. This second variable has two sources of zeros. The first source is episodic zeros, wherein some of the measurements for an individual may be zero and others positive. The second source is hard zeros, i.e., some individuals will always report zero. An example is the consumption of alcohol from alcoholic beverages: some individuals consume alcoholic beverages episodically, while others never consume alcoholic beverages. However, with a small number of repeat measurements from individuals, it is not possible to determine those who are episodic zeros and those who are hard zeros. We develop a new measurement error model for this problem, and use Bayesian methods to fit it. Simulations and data analyses are used to illustrate our methods. Extensions to parametric models and survival analysis are discussed briefly.

Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data.

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Airway inflammation accelerates pulmonary exacerbations in cystic fibrosis.

Airway inflammation underlies cystic fibrosis (CF) pulmonary exacerbations. In a prospective multicenter study of randomly selected, clinically stable adolescents and adults, we assessed relationships between 24 inflammation-associated molecules and the future occurrence of CF pulmonary exacerbation using proportional hazards models. We explored relationships for potential confounding or mediation by clinical factors and assessed sensitivities to treatments including CF transmembrane regulator (CFTR) protein synthesis modulators. Results from 114 participants, including seven on ivacaftor or lumacaftor-ivacaftor, representative of the US CF population during the study period, identified 10 biomarkers associated with future exacerbations mediated by percent predicted forced expiratory volume in 1 s. The findings were not sensitive to anti-inflammatory, antibiotic, and CFTR modulator treatments. The analyses suggest that combination treatments addressing RAGE-axis inflammation, protease-mediated injury, and oxidative stress might prevent pulmonary exacerbations. Our work may apply to other airway inflammatory diseases such as bronchiectasis and the acute respiratory distress syndrome.

Diagnosis of human leptospirosis: systematic review and meta-analysis of the diagnostic accuracy of the Leptospira microscopic agglutination test, PCR targeting Lfb1, and IgM ELISA to Leptospira fainei serovar Hurstbridge.

BACKGROUND: Leptospirosis is an underdiagnosed infectious disease with non-specific clinical presentation that requires laboratory confirmation for diagnosis. The serologic reference standard remains the microscopic agglutination test (MAT) on paired serum samples. However, reported estimates of MAT's sensitivity vary. We evaluated the accuracy of four index tests, MAT on paired samples as well as alternative standards for leptospirosis diagnosis: MAT on single acute-phase samples, polymerase chain reaction (PCR) with the target gene Lfb1, and ELISA IgM with Leptospira fainei serovar Hurstbridge as an antigen. METHODS: We performed a systematic review of studies reporting results of leptospirosis diagnostic tests. We searched eight electronic databases and selected studies that tested human blood samples and compared index tests with blood culture and/or PCR and/or MAT (comparator tests). For MAT selection criteria we defined a threshold for single acute-phase samples according to a national classification of leptospirosis endemicity. We used a Bayesian random-effect meta-analysis to estimate the sensitivity and specificity of MAT in single acute-phase and paired samples separately, and assessed risk of bias using the Quality Assessment of Studies of Diagnostic Accuracy Approach- 2 (QUADAS-2) tool. RESULTS: For the MAT accuracy evaluation, 15 studies were included, 11 with single acute-phase serum, and 12 with paired sera. Two included studies used PCR targeting the Lfb1 gene, and one included study used IgM ELISA with Leptospira fainei serovar Hurstbridge as antigen. For MAT in single acute-phase samples, the pooled sensitivity and specificity were 14% (95% credible interval [CrI] 3-38%) and 86% (95% CrI 59-96%), respectively, and the predicted sensitivity and specificity were 14% (95% CrI 0-90%) and 86% (95% CrI 9-100%). Among paired MAT samples, the pooled sensitivity and specificity were 68% (95% CrI 32-92%) and 75% (95% CrI 45-93%) respectively, and the predicted sensitivity and specificity were 69% (95% CrI 2-100%) and 75% (2-100%). CONCLUSIONS: Based on our analysis, the accuracy of MAT in paired samples was not high, but it remains the reference standard until a more accurate diagnostic test is developed. Future studies that include larger numbers of participants with paired samples will improve the certainty of accuracy estimates.

Social-environmental phenotypes of rapid cystic fibrosis lung disease progression in adolescents and young adults living in the United States.

Background: Cystic fibrosis (CF) is a genetic disease but is greatly impacted by non-genetic (social/environmental and stochastic) influences. Some people with CF experience rapid decline, a precipitous drop in lung function relative to patient- and/or center-level norms. Those who experience rapid decline in early adulthood, compared to adolescence, typically exhibit less severe clinical disease but greater loss of lung function. The extent to which timing and degree of rapid decline are informed by social and environmental determinants of health (geomarkers) is unknown. Methods: A longitudinal cohort study was performed (24,228 patients, aged 6-21 years) using the U.S. CF Foundation Patient Registry. Geomarkers at the ZIP Code Tabulation Area level measured air pollution/respiratory hazards, greenspace, crime, and socioeconomic deprivation. A composite score quantifying social-environmental adversity was created and used in covariate-adjusted functional principal component analysis, which was applied to cluster longitudinal lung function trajectories. Results: Social-environmental phenotyping yielded three primary phenotypes that corresponded to early, middle, and late timing of peak decline in lung function over age. Geographic differences were related to distinct cultural and socioeconomic regions. Extent of peak decline, estimated as forced expiratory volume in 1 s of % predicted/year, ranged from 2.8 to 4.1 % predicted/year depending on social-environmental adversity. Middle decliners with increased social-environmental adversity experienced rapid decline 14.2 months earlier than their counterparts with lower social-environmental adversity, while timing was similar within other phenotypes. Early and middle decliners experienced mortality peaks during early adolescence and adulthood, respectively. Conclusion: While early decliners had the most severe CF lung disease, middle and late decliners lost more lung function. Higher social-environmental adversity associated with increased risk of rapid decline and mortality during young adulthood among middle decliners. This sub-phenotype may benefit from enhanced lung-function monitoring and personalized secondary environmental health interventions to mitigate chemical and non-chemical stressors.

Dynamic updating of clinical survival prediction models in a changing environment.

BACKGROUND: Over time, the performance of clinical prediction models may deteriorate due to changes in clinical management, data quality, disease risk and/or patient mix. Such prediction models must be updated in order to remain useful. In this study, we investigate dynamic model updating of clinical survival prediction models. In contrast to discrete or one-time updating, dynamic updating refers to a repeated process for updating a prediction model with new data. We aim to extend previous research which focused largely on binary outcome prediction models by concentrating on time-to-event outcomes. We were motivated by the rapidly changing environment seen during the COVID-19 pandemic where mortality rates changed over time and new treatments and vaccines were introduced. METHODS: We illustrate three methods for dynamic model updating: Bayesian dynamic updating, recalibration, and full refitting. We use a simulation study to compare performance in a range of scenarios including changing mortality rates, predictors with low prevalence and the introduction of a new treatment. Next, the updating strategies were applied to a model for predicting 70-day COVID-19-related mortality using patient data from QResearch, an electronic health records database from general practices in the UK. RESULTS: In simulated scenarios with mortality rates changing over time, all updating methods resulted in better calibration than not updating. Moreover, dynamic updating outperformed ad hoc updating. In the simulation scenario with a new predictor and a small updating dataset, Bayesian updating improved the C-index over not updating and refitting. In the motivating example with a rare outcome, no single updating method offered the best performance. CONCLUSIONS: We found that a dynamic updating process outperformed one-time discrete updating in the simulations. Bayesian updating offered good performance overall, even in scenarios with new predictors and few events. Intercept recalibration was effective in scenarios with smaller sample size and changing baseline hazard. Refitting performance depended on sample size and produced abrupt changes in hazard ratio estimates between periods.

Accuracy of the direct agglutination test for diagnosis of visceral leishmaniasis: a systematic review and meta-analysis.

BACKGROUND: Parasitological investigation of bone marrow, splenic or lymph node aspirations is the gold standard for the diagnosis of visceral leishmaniasis (VL). However, this invasive test requires skilled clinical and laboratory staff and adequate facilities, and sensitivity varies depending on the tissue used. The direct agglutination test (DAT) is a serological test that does not need specialised staff, with just minimal training required. While previous meta-analysis has shown DAT to have high sensitivity and specificity when using parasitology as the reference test for diagnosis, meta-analysis of DAT compared to other diagnostic techniques, such as PCR and ELISA, that are increasingly used in clinical and research settings, has not been done. METHODS: We conducted a systematic review to determine the diagnostic performance of DAT compared to all available tests for the laboratory diagnosis of human VL. We searched electronic databases including Medline, Embase, Global Health, Scopus, WoS Science Citation Index, Wiley Cochrane Central Register of Controlled Trials, Africa-Wide Information, LILACS and WHO Global Index. Three independent reviewers screened reports and extracted data from eligible studies. A meta-analysis estimated the diagnostic sensitivity and specificity of DAT. RESULTS: Of 987 titles screened, 358 were selected for full data extraction and 78 were included in the analysis, reporting on 32,822 participants from 19 countries. Studies included were conducted between 1987-2020. Meta-analysis of studies using serum and DAT compared to any other test showed pooled sensitivity of 95% (95%CrI 90-98%) and pooled specificity of 95% (95%CrI 88-98%). Results were similar for freeze-dried DAT and liquid DAT when analysed separately. Sensitivity was lower for HIV-positive patients (90%, CrI 59-98%) and specificity was lower for symptomatic patients (70%, CrI 43-89%). When comparing different geographical regions, the lowest median sensitivity (89%, CrI 67-97%) was in Western Asia (five studies). CONCLUSIONS: This systematic review and meta-analysis demonstrates high estimated pooled sensitivity and specificity of DAT for diagnosis of VL, although sensitivity and specificity were lower for different patient groups and geographical locations. This review highlights the lack of standardisation of DAT methods and preparations, and the lack of data from some important geographical locations. Future well-reported studies could provide better evidence to inform test implementation for different patient populations and use cases. PROSPERO REGISTRATION: CRD42021240830.

Prioritising cardiovascular disease risk assessment to high risk individuals based on primary care records.

OBJECTIVE: To provide quantitative evidence for systematically prioritising individuals for full formal cardiovascular disease (CVD) risk assessment using primary care records with a novel tool (eHEART) with age- and sex- specific risk thresholds. METHODS AND ANALYSIS: eHEART was derived using landmark Cox models for incident CVD with repeated measures of conventional CVD risk predictors in 1,642,498 individuals from the Clinical Practice Research Datalink. Using 119,137 individuals from UK Biobank, we modelled the implications of initiating guideline-recommended statin therapy using eHEART with age- and sex-specific prioritisation thresholds corresponding to 5% false negative rates to prioritise adults aged 40-69 years in a population in England for invitation to a formal CVD risk assessment. RESULTS: Formal CVD risk assessment on all adults would identify 76% and 49% of future CVD events amongst men and women respectively, and 93 (95% CI: 90, 95) men and 279 (95% CI: 259, 297) women would need to be screened (NNS) to prevent one CVD event. In contrast, if eHEART was first used to prioritise individuals for formal CVD risk assessment, we would identify 73% and 47% of future events amongst men and women respectively, and a NNS of 75 (95% CI: 72, 77) men and 162 (95% CI: 150, 172) women. Replacing the age- and sex-specific prioritisation thresholds with a 10% threshold identify around 10% less events. CONCLUSIONS: The use of prioritisation tools with age- and sex-specific thresholds could lead to more efficient CVD assessment programmes with only small reductions in effectiveness at preventing new CVD events.

Emulated trial investigating effects of multiple treatments: estimating combined effects of mucoactive nebulisers in cystic fibrosis using registry data.

INTRODUCTION: People with cystic fibrosis (CF) are often on multiple long-term treatments, including mucoactive nebulisers. In the UK, the most common mucoactive nebuliser is dornase alfa (DNase). A common therapeutic approach for people already on DNase is to add hypertonic saline (HS). The effects of DNase and HS used alone have been studied in randomised trials, but their effects in combination have not. This study investigates whether, for people already prescribed DNase, adding HS has additional benefit for lung function or use of intravenous antibiotics. METHODS: Using UK CF Registry data from 2007 to 2018, we emulated a target trial. We included people aged 6 years and over who were prescribed DNase without HS for 2 years. We investigated the effects of combinations of DNase and HS over 5 years of follow-up. Inverse-probability-of-treatment weighting was used to control confounding. The period predated triple combination CF transmembrane conductance regulator modulators in routine care. RESULTS: 4498 individuals were included. At baseline, average age and forced expiratory volume in 1 s (FEV1%) predicted were 21.1 years and 69.7 respectively. During first year of follow-up, 3799 individuals were prescribed DNase alone; 426 added HS; 57 switched to HS alone and 216 were prescribed neither. We found no evidence that adding HS improved FEV1% at 1-5 years, or use of intravenous antibiotics at 1-4 years, compared with DNase alone. CONCLUSION: For individuals with CF prescribed DNase, we found no evidence that adding HS had an effect on FEV1% or prescription of intravenous antibiotics. Our study illustrates the emulated target trial approach using CF Registry data.

Diagnostic performance of the IMMY cryptococcal antigen lateral flow assay on serum and cerebrospinal fluid for diagnosis of cryptococcosis in HIV-negative patients: a systematic review.

BACKGROUND: The incidence of cryptococcosis amongst HIV-negative persons is increasing. Whilst the excellent performance of the CrAg testing in people living with HIV is well described, the diagnostic performance of the CrAg LFA has not been systematically evaluated in HIV-negative cohorts on serum or cerebrospinal fluid. METHODS: We performed a systematic review to characterise the diagnostic performance of IMMY CrAg® LFA in HIV-negative populations on serum and cerebrospinal fluid. A systematic electronic search was performed using Medline, Embase, Global Health, CENTRAL, WoS Science Citation Index, SCOPUS, Africa-Wide Information, LILACS and WHO Global Health Library. Studies were screened and data extracted from eligible studies by two independent reviewers. A fixed effect meta-analysis was used to estimate the diagnostic sensitivity and specificity. RESULTS: Of 447 records assessed for eligibility, nine studies met our inclusion criteria, including 528 participants overall. Amongst eight studies that evaluated the diagnostic performance of the IMMY CrAg® LFA on serum, the pooled median sensitivity was 96% (95% Credible Interval (CrI) 68-100%) with a pooled specificity estimate of 96% (95%CrI 84-100%). Amongst six studies which evaluated the diagnostic performance of IMMY CrAg® LFA on CSF, the pooled median sensitivity was 99% (95%CrI 95-100%) with a pooled specificity median of 99% (95%CrI 95-100%). CONCLUSIONS: This review demonstrates a high pooled sensitivity and specificity for the IMMY CrAg® LFA in HIV-negative populations, in keeping with findings in HIV-positive individuals. The review was limited by the small number of studies. Further studies using IMMY CrAg® LFA in HIV-negative populations would help to better determine the diagnostic value of this test.

Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models.

Longitudinal observational data on patients can be used to investigate causal effects of time-varying treatments on time-to-event outcomes. Several methods have been developed for estimating such effects by controlling for the time-dependent confounding that typically occurs. The most commonly used is marginal structural models (MSM) estimated using inverse probability of treatment weights (IPTW) (MSM-IPTW). An alternative, the sequential trials approach, is increasingly popular, and involves creating a sequence of "trials" from new time origins and comparing treatment initiators and non-initiators. Individuals are censored when they deviate from their treatment assignment at the start of each "trial" (initiator or noninitiator), which is accounted for using inverse probability of censoring weights. The analysis uses data combined across trials. We show that the sequential trials approach can estimate the parameters of a particular MSM. The causal estimand that we focus on is the marginal risk difference between the sustained treatment strategies of "always treat" vs "never treat." We compare how the sequential trials approach and MSM-IPTW estimate this estimand, and discuss their assumptions and how data are used differently. The performance of the two approaches is compared in a simulation study. The sequential trials approach, which tends to involve less extreme weights than MSM-IPTW, results in greater efficiency for estimating the marginal risk difference at most follow-up times, but this can, in certain scenarios, be reversed at later time points and relies on modelling assumptions. We apply the methods to longitudinal observational data from the UK Cystic Fibrosis Registry to estimate the effect of dornase alfa on survival.

Estimating sensitivity and specificity of diagnostic tests using latent class models that account for conditional dependence between tests: a simulation study.

BACKGROUND: Latent class models are increasingly used to estimate the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and are commonly fitted using Bayesian methods. These models allow us to account for 'conditional dependence' between two or more diagnostic tests, meaning that the results from tests are correlated even after conditioning on the person's true disease status. The challenge is that it is not always clear to researchers whether conditional dependence exists between tests and whether it exists in all or just some latent classes. Despite the increasingly widespread use of latent class models to estimate diagnostic test accuracy, the impact of the conditional dependence structure chosen on the estimates of sensitivity and specificity remains poorly investigated. METHODS: A simulation study and a reanalysis of a published case study are used to highlight the impact of the conditional dependence structure chosen on estimates of sensitivity and specificity. We describe and implement three latent class random-effect models with differing conditional dependence structures, as well as a conditional independence model and a model that assumes perfect test accuracy. We assess the bias and coverage of each model in estimating sensitivity and specificity across different data generating mechanisms. RESULTS: The findings highlight that assuming conditional independence between tests within a latent class, where conditional dependence exists, results in biased estimates of sensitivity and specificity and poor coverage. The simulations also reiterate the substantial bias in estimates of sensitivity and specificity when incorrectly assuming a reference test is perfect. The motivating example of tests for Melioidosis highlights these biases in practice with important differences found in estimated test accuracy under different model choices. CONCLUSIONS: We have illustrated that misspecification of the conditional dependence structure leads to biased estimates of sensitivity and specificity when there is a correlation between tests. Due to the minimal loss in precision seen by using a more general model, we recommend accounting for conditional dependence even if researchers are unsure of its presence or it is only expected at minimal levels.

Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations.

Rationale: Studies estimating the rate of lung function decline in cystic fibrosis have been inconsistent regarding the methods used. How the methodology used impacts the validity of the results and comparability between studies is unknown. Objectives: The Cystic Fibrosis Foundation established a work group whose tasks were to examine the impact of differing approaches to estimating the rate of decline in lung function and to provide analysis guidelines. Methods: We used a natural history cohort of 35,252 individuals with cystic fibrosis aged ⩾6 years in the Cystic Fibrosis Foundation Patient Registry (CFFPR), 2003-2016. Modeling strategies using linear and nonlinear forms of marginal and mixed-effects models, which have previously quantified the rate of forced expiratory volume in 1 second (FEV1) decline (percent predicted per year), were evaluated under clinically relevant scenarios of available lung function data. Scenarios varied by sample size (overall CFFPR, medium-sized cohort of 3,000 subjects, and small-sized cohort of 150), data collection/reporting frequency (encounter, quarterly, and annual), inclusion of FEV1 during pulmonary exacerbation, and follow-up length (<2 yr, 2-5 yr, entire duration). Results: Rate of FEV1 decline estimates (percent predicted per year) differed between linear marginal and mixed-effects models; overall cohort estimates (95% confidence interval) were 1.26 (1.24-1.29) and 1.40 (1.38-1.42), respectively. Marginal models consistently estimated less rapid lung function decline than mixed-effects models across scenarios, except for short-term follow-up (both were ∼1.4). Rate of decline estimates from nonlinear models diverged by age 30. Among mixed-effects models, nonlinear and stochastic terms fit best, except for short-term follow-up (<2 yr). Overall CFFPR analysis from a joint longitudinal-survival model implied that an increase in rate of decline of 1% predicted per year in FEV1 was associated with a 1.52-fold (52%) increase in the hazard of death/lung transplant, but the results exhibited immortal cohort bias. Conclusions: Differences were as high as 0.5% predicted per year between rate of decline estimates, but we found estimates were robust to lung function data availability scenarios, except short-term follow-up and older age ranges. Inconsistencies among previous study results may be attributable to inherent differences in study design, inclusion criteria, or covariate adjustment. Results-based decision points reported herein will support researchers in selecting a strategy to model lung function decline most reflective of nuanced, study-specific goals.

Comparative effectiveness of BNT162b2 versus mRNA-1273 covid-19 vaccine boosting in England: matched cohort study in OpenSAFELY-TPP.

OBJECTIVE: To compare the effectiveness of the BNT162b2 mRNA (Pfizer-BioNTech) and mRNA-1273 (Moderna) covid-19 vaccines during the booster programme in England. DESIGN: Matched cohort study, emulating a comparative effectiveness trial. SETTING: Linked primary care, hospital, and covid-19 surveillance records available within the OpenSAFELY-TPP research platform, covering a period when the SARS-CoV-2 delta and omicron variants were dominant. PARTICIPANTS: 3 237 918 adults who received a booster dose of either vaccine between 29 October 2021 and 25 February 2022 as part of the national booster programme in England and who received a primary course of BNT162b2 or ChAdOx1. INTERVENTION: Vaccination with either BNT162b2 or mRNA-1273 as a booster vaccine dose. MAIN OUTCOME MEASURES: Recorded SARS-CoV-2 positive test, covid-19 related hospital admission, covid-19 related death, and non-covid-19 related death at 20 weeks after receipt of the booster dose. RESULTS: 1 618 959 people were matched in each vaccine group, contributing a total 64 546 391 person weeks of follow-up. The 20 week risks per 1000 for a positive SARS-CoV-2 test were 164.2 (95% confidence interval 163.3 to 165.1) for BNT162b2 and 159.9 (159.0 to 160.8) for mRNA-1273; the hazard ratio comparing mRNA-1273 with BNT162b2 was 0.95 (95% confidence interval 0.95 to 0.96). The 20 week risks per 1000 for hospital admission with covid-19 were 0.75 (0.71 to 0.79) for BNT162b2 and 0.65 (0.61 to 0.69) for mRNA-1273; the hazard ratio was 0.89 (0.82 to 0.95). Covid-19 related deaths were rare: the 20 week risks per 1000 were 0.028 (0.021 to 0.037) for BNT162b2 and 0.024 (0.018 to 0.033) for mRNA-1273; hazard ratio 0.83 (0.58 to 1.19). Comparative effectiveness was generally similar within subgroups defined by the primary course vaccine brand, age, previous SARS-CoV-2 infection, and clinical vulnerability. Relative benefit was similar when vaccines were compared separately in the delta and omicron variant eras. CONCLUSIONS: This matched observational study of adults estimated a modest benefit of booster vaccination with mRNA-1273 compared with BNT162b2 in preventing positive SARS-CoV-2 tests and hospital admission with covid-19 20 weeks after vaccination, during a period of delta followed by omicron variant dominance.

Built environment factors predictive of early rapid lung function decline in cystic fibrosis.

BACKGROUND: The extent to which environmental exposures and community characteristics of the built environment collectively predict rapid lung function decline, during adolescence and early adulthood in cystic fibrosis (CF), has not been examined. OBJECTIVE: To identify built environment characteristics predictive of rapid CF lung function decline. METHODS: We performed a retrospective, single-center, longitudinal cohort study (n = 173 individuals with CF aged 6-20 years, 2012-2017). We used a stochastic model to predict lung function, measured as forced expiratory volume in 1 s (FEV1 ) of % predicted. Traditional demographic/clinical characteristics were evaluated as predictors. Built environmental predictors included exposure to elemental carbon attributable to traffic sources (ECAT), neighborhood material deprivation (poverty, education, housing, and healthcare access), greenspace near the home, and residential drivetime to the CF center. MEASUREMENTS AND MAIN RESULTS: The final model, which included ECAT, material deprivation index, and greenspace, alongside traditional demographic/clinical predictors, significantly improved fit and prediction, compared with only demographic/clinical predictors (Likelihood Ratio Test statistic: 26.78, p < 0.0001; the difference in Akaike Information Criterion: 15). An increase of 0.1 µg/m3 of ECAT was associated with 0.104% predicted/yr (95% confidence interval: 0.024, 0.183) more rapid decline. Although not statistically significant, material deprivation was similarly associated (0.1-unit increase corresponded to additional decline of 0.103% predicted/year [-0.113, 0.319]). High-risk regional areas of rapid decline and age-related heterogeneity were identified from prediction mapping. CONCLUSION: Traffic-related air pollution exposure is an important predictor of rapid pulmonary decline that, coupled with community-level material deprivation and routinely collected demographic/clinical characteristics, enhance CF prognostication and enable personalized environmental health interventions.

Estimating distribution of length of stay in a multi-state model conditional on the pathway, with an application to patients hospitalised with Covid-19.

Multi-state models are used to describe how individuals transition through different states over time. The distribution of the time spent in different states, referred to as 'length of stay', is often of interest. Methods for estimating expected length of stay in a given state are well established. The focus of this paper is on the distribution of the time spent in different states conditional on the complete pathway taken through the states, which we call 'conditional length of stay'. This work is motivated by questions about length of stay in hospital wards and intensive care units among patients hospitalised due to Covid-19. Conditional length of stay estimates are useful as a way of summarising individuals' transitions through the multi-state model, and also as inputs to mathematical models used in planning hospital capacity requirements. We describe non-parametric methods for estimating conditional length of stay distributions in a multi-state model in the presence of censoring, including conditional expected length of stay (CELOS). Methods are described for an illness-death model and then for the more complex motivating example. The methods are assessed using a simulation study and shown to give unbiased estimates of CELOS, whereas naive estimates of CELOS based on empirical averages are biased in the presence of censoring. The methods are applied to estimate conditional length of stay distributions for individuals hospitalised due to Covid-19 in the UK, using data on 42,980 individuals hospitalised from March to July 2020 from the COVID19 Clinical Information Network.

Trajectories of early growth and subsequent lung function in cystic fibrosis: An observational study using UK and Canadian registry data.

BACKGROUND: Understanding the pulmonary impact of changes in early life nutritional status over time in a paediatric CF population may help inform how to use nutritional assessment to guide clinical care. National registry data provides an opportunity to study patterns of weight gain over time at the level of the individual, and thus to gain detailed understanding of the relationship between early weight trajectories and later lung function in children with Cystic Fibrosis (CF). METHODS: Using data from the United Kingdom (UK) and Canadian CF Registries, a mixed effects linear regression model was used to describe children's weight and BMI z-score trajectories from age 1 to 5 years. The intercept (weight-for-age at age 1) and slope (weight-for-age trajectory) from this model were then used as covariates in a linear regression of first lung function measurement at age 6 years. RESULTS: In both the UK and Canadian data, greater weight-for-age z-score at age 1 year and greater change in weight-for-age over time were associated with higher FEV1% predicted. A greater weight-for-age z-score at age 1 year was associated with a higher FEV1% predicted (UK: 3.78% (95% CI: 1.76; 4.70); Canada: 3.20% (95%CI: 1.76, 4.70)). These associations were reproduced for BMI z-scores and FVC% predicted. CONCLUSIONS: Early weight-for-age, specifically at age 1 year, and weight-for-age trajectories across early childhood are associated with later lung function. This relationship persists after adjustment for potential confounders. Current guidelines may need to be updated to place less emphasis on a specific cut-off (such as the 10th percentile) and encourage tracking of weight-for-age over time.