Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

Propensity scores using missingness pattern information: a practical guide.

Electronic health records are a valuable data source for investigating health-related questions, and propensity score analysis has become an increasingly popular approach to address confounding bias in such investigations. However, because electronic health records are typically routinely recorded as part of standard clinical care, there are often missing values, particularly for potential confounders. In our motivating study-using electronic health records to investigate the effect of renin-angiotensin system blockers on the risk of acute kidney injury-two key confounders, ethnicity and chronic kidney disease stage, have 59% and 53% missing data, respectively. The missingness pattern approach (MPA), a variant of the missing indicator approach, has been proposed as a method for handling partially observed confounders in propensity score analysis. In the MPA, propensity scores are estimated separately for each missingness pattern present in the data. Although the assumptions underlying the validity of the MPA are stated in the literature, it can be difficult in practice to assess their plausibility. In this article, we explore the MPA's underlying assumptions by using causal diagrams to assess their plausibility in a range of simple scenarios, drawing general conclusions about situations in which they are likely to be violated. We present a framework providing practical guidance for assessing whether the MPA's assumptions are plausible in a particular setting and thus deciding when the MPA is appropriate. We apply our framework to our motivating study, showing that the MPA's underlying assumptions appear reasonable, and we demonstrate the application of MPA to this study.Economic and Social Research Council [Grant Number ES/J5000/21/1]; Medical Research Council [Project Grant MR/M013278/1]; Health Data Research UK [Grant Number EPNCZO90], which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcom

A randomised controlled trial of a care home rehabilitation service to reduce long-term institutionalisation for elderly people

Objectives: to evaluate the effect of a care home rehabilitation service on institutionalisation, health outcomes and service use. Design: randomised controlled trial, stratified by Barthel ADL index, social service sector and whether living alone. The intervention was a rehabilitation service based in Social Services old people's homes in Nottingham, UK. The control group received usual health and social care. Participants: 165 elderly and disabled hospitalised patients who wished to go home but were at high risk of institutionalisation (81 intervention, 84 control). Main outcome measures: institutionalisation rates, Barthel ADL index, Nottingham Extended ADL score, General Health Questionnaire (12 item version) at 3 and 12 months, Health and Social Service resource use. Results: the number of participants institutionalised was similar at 3 months (relative risk 1.04, 95% confidence intervals 0.65–1.65) and 12 months (relative risk 1.23, 95% confidence intervals 0.75–2.02). Barthel ADL Index, Nottingham Extended ADL score and General Health Questionnaire scores were similar at 3 and 12 months. The intervention group spent significantly fewer days in hospital over 3 and 12 months (mean reduction 12.1 and 27.6 days respectively, P < 0.01), but spent a mean of 36 days in a care home rehabilitation service facility. Conclusions: this service did not reduce institutionalisation, but diverted patients from the hospital to social services sector without major effects on activity levels or well-being

Estimating treatment effects with partially observed covariates using outcome regression with missing indicators.

Missing data is a common issue in research using observational studies to investigate the effect of treatments on health outcomes. When missingness occurs only in the covariates, a simple approach is to use missing indicators to handle the partially observed covariates. The missing indicator approach has been criticized for giving biased results in outcome regression. However, recent papers have suggested that the missing indicator approach can provide unbiased results in propensity score analysis under certain assumptions. We consider assumptions under which the missing indicator approach can provide valid inferences, namely, (1) no unmeasured confounding within missingness patterns; either (2a) covariate values of patients with missing data were conditionally independent of treatment or (2b) these values were conditionally independent of outcome; and (3) the outcome model is correctly specified: specifically, the true outcome model does not include interactions between missing indicators and fully observed covariates. We prove that, under the assumptions above, the missing indicator approach with outcome regression can provide unbiased estimates of the average treatment effect. We use a simulation study to investigate the extent of bias in estimates of the treatment effect when the assumptions are violated and we illustrate our findings using data from electronic health records. In conclusion, the missing indicator approach can provide valid inferences for outcome regression, but the plausibility of its assumptions must first be considered carefully

Probabilistic linkage without personal information successfully linked national clinical datasets.

BACKGROUND: Probabilistic linkage can link patients from different clinical databases without the need for personal information. If accurate linkage can be achieved, it would accelerate the use of linked datasets to address important clinical and public health questions. OBJECTIVE: We developed a step-by-step process for probabilistic linkage of national clinical and administrative datasets without personal information, and validated it against deterministic linkage using patient identifiers. STUDY DESIGN AND SETTING: We used electronic health records from the National Bowel Cancer Audit and Hospital Episode Statistics databases for 10,566 bowel cancer patients undergoing emergency surgery in the English National Health Service. RESULTS: Probabilistic linkage linked 81.4% of National Bowel Cancer Audit records to Hospital Episode Statistics, vs. 82.8% using deterministic linkage. No systematic differences were seen between patients that were and were not linked, and regression models for mortality and length of hospital stay according to patient and tumour characteristics were not sensitive to the linkage approach. CONCLUSION: Probabilistic linkage was successful in linking national clinical and administrative datasets for patients undergoing a major surgical procedure. It allows analysts outside highly secure data environments to undertake linkage while minimizing costs and delays, protecting data security, and maintaining linkage quality

The c2d Spitzer Spectroscopic Survey Of Ices Around Low-Mass Young Stellar Objects. I. H2O And The 5-8 Mu M Bands

To study the physical and chemical evolution of ices in solar-mass systems, a spectral survey is conducted of a sample of 41 low-luminosity YSOs (L similar to 0.1-10 L-circle dot) using 3-38 mu m Spitzer and ground-based spectra. The sample is complemented with previously published Spitzer spectra of background stars and with ISO spectra of well-studied massive YSOs (L similar to 10(5) L-circle dot). The long-known 6.0 and 6.85 mu m bands are detected toward all sources, with the Class 0-type YSOs showing the deepest bands ever observed. The 6.0 mu m band is often deeper than expected from the bending mode of pure solid H2O. The additional 5-7 mu m absorption consists of five independent components, which, by comparison to laboratory studies, must be from at least eight different carriers. Much of this absorption is due to simple species likely formed by grain surface chemistry, at abundances of 1%-30% for CH3OH, 3%-8% for NH3, 1%-5% for HCOOH, similar to 6% for H2CO, and similar to 0.3% for HCOO- relative to solid H2O. The 6.85 mu m band has one or two carriers, of which one may be less volatile than H2O. Its carrier(s) formed early in the molecular cloud evolution and do not survive in the diffuse ISM. If an NH4+- containing salt is the carrier, its abundance relative to solid H2O is similar to 7%, demonstrating the efficiency of low-temperature acid-base chemistry or cosmic-ray-induced reactions. Possible origins are discussed for enigmatic, very broad absorption between 5 and 8 mu m. Finally, the same ices are observed toward massive and low-mass YSOs, indicating that processing by internal UV radiation fields is a minor factor in their early chemical evolution.NWO SpinozaNOVAEuropean Research Training Network PLANETS HPRN-CT-2002-00308NASA Origins NAG5-13050NASA Hubble Fellowship 01201.01NASA NAS 5-26555Astronom

The impact of the first peak of the COVID-19 pandemic on colorectal cancer services in England and Wales: A national survey.

AIM: The object of this work was to study how National Health Service hospitals in England and Wales aimed to maintain effective and safe colorectal cancer (CRC) services during the first peak of the COVID-19 pandemic (April 2020). METHOD: A national survey was performed among all 148 hospitals providing CRC services. Information was collected about changes in referrals, diagnostic, staging and therapeutic procedures, as well as whether there was access to a 'cold site' (a hospital facility free of COVID-19). Clinicians in each hospital were also asked to give the 'single most important lesson learned' about keeping services safe and effective. RESULTS: Full responses were received from 123 (83%) hospitals, and information about 'cold sites' was available for 146 (99%). Eighty hospitals (54%) had access to a 'cold site' and this was increased in regions with higher COVID-19 infection rates (p <0.001). Of the 123 responding hospitals, 105 (85%) indicated that referrals of patients with suspected CRC had dropped by at least 30%, and 69 (56%) indicated that treatment plans were altered in at least 50% of CRC patients. However, 'cold site' availability protected the capacity for diagnostic colonoscopy (p = 0.013) and CRC resection (p = 0.010). Many 'lessons learned' highlighted the importance of adequate structural service organization, often mentioning 'cold sites' and regional coordination as examples, good communication and triage of patients based on clinical urgency. CONCLUSION: Access to 'cold sites', as well as regional coordination, clear communication and strong leadership, were found to be pivotal in maintaining capacity for diagnosis and treatment of CRC during the COVID-19 surge