How do dataset characteristics affect the performance of propensity score methods and regression for controlling confounding in observational studies?:A simulation study

In observational studies, researchers must select a method to control for confounding. Options include propensity score methods and regression. It remains unclear how dataset characteristics (size, overlap in propensity scores, exposure prevalence) influence the relative performance of the methods, making it difficult to select the best method for a particular dataset. A simulation study to evaluate the role of dataset characteristics on the performance of propensity score methods, compared to logistic regression, for estimating a marginal odds ratio in the presence of confounding was conducted. Outcomes were simulated from logistic and complementary log-log models, and size, overlap in propensity scores, and prevalence of the exposure were varied. Regression showed poor coverage for small sample sizes, but with large sample sizes it was more robust to imbalance in propensity scores and low exposure prevalence than were propensity score methods. Propensity score methods frequently displayed suboptimal coverage, particularly as overlap in propensity scores decreased. These problems were exacerbated at larger sample sizes. Power of matching methods was particularly affected by lack of overlap, low prevalence of exposure, and small sample size. Performance of inverse probability of treatment weighting depended heavily on dataset characteristics, with poor coverage and bias with low overlap. The advantage of regression for large data size was less clear in sensitivity analysis with a complementary log-log outcome generation mechanism and unmeasured confounding, with superior bias and error but lower coverage than nearest neighbour and caliper matching

Long-term glycemic variability and risk of adverse outcomes: a systematic review and meta-analysis

OBJECTIVE: Glycemic variability is emerging as a measure of glycemic control, which may be a reliable predictor of complications. This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes. RESEARCH DESIGN AND METHODS: Medline and Embase were searched (2004–2015) for studies describing associations between HbA1c variability and adverse outcomes in patients with type 1 and type 2 diabetes. Data extraction was performed independently by two reviewers. Random-effects meta-analysis was performed with stratification according to the measure of HbA1c variability, method of analysis, and diabetes type. RESULTS: Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08–2.25], two studies), cardiovascular events (1.98 [1.39–2.82]), and retinopathy (2.11 [1.54–2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15–1.57], two studies), macrovascular events (1.21 [1.06–1.38]), ulceration/gangrene (1.50 [1.06–2.12]), cardiovascular disease (1.27 [1.15–1.40]), and mortality (1.34 [1.18–1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability. CONCLUSIONS: HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment

Modelling Conditions and Health Care Processes in Electronic Health Records : An Application to Severe Mental Illness with the Clinical Practice Research Datalink

BACKGROUND: The use of Electronic Health Records databases for medical research has become mainstream. In the UK, increasing use of Primary Care Databases is largely driven by almost complete computerisation and uniform standards within the National Health Service. Electronic Health Records research often begins with the development of a list of clinical codes with which to identify cases with a specific condition. We present a methodology and accompanying Stata and R commands (pcdsearch/Rpcdsearch) to help researchers in this task. We present severe mental illness as an example. METHODS: We used the Clinical Practice Research Datalink, a UK Primary Care Database in which clinical information is largely organised using Read codes, a hierarchical clinical coding system. Pcdsearch is used to identify potentially relevant clinical codes and/or product codes from word-stubs and code-stubs suggested by clinicians. The returned code-lists are reviewed and codes relevant to the condition of interest are selected. The final code-list is then used to identify patients. RESULTS: We identified 270 Read codes linked to SMI and used them to identify cases in the database. We observed that our approach identified cases that would have been missed with a simpler approach using SMI registers defined within the UK Quality and Outcomes Framework. CONCLUSION: We described a framework for researchers of Electronic Health Records databases, for identifying patients with a particular condition or matching certain clinical criteria. The method is invariant to coding system or database and can be used with SNOMED CT, ICD or other medical classification code-lists

rEHR: An R package for manipulating and analysing Electronic Health Record data

Research with structured Electronic Health Records (EHRs) is expanding as data becomes more accessible; analytic methods advance; and the scientific validity of such studies is increasingly accepted. However, data science methodology to enable the rapid searching/extraction, cleaning and analysis of these large, often complex, datasets is less well developed. In addition, commonly used software is inadequate, resulting in bottlenecks in research workflows and in obstacles to increased transparency and reproducibility of the research. Preparing a research-ready dataset from EHRs is a complex and time consuming task requiring substantial data science skills, even for simple designs. In addition, certain aspects of the workflow are computationally intensive, for example extraction of longitudinal data and matching controls to a large cohort, which may take days or even weeks to run using standard software. The rEHR package simplifies and accelerates the process of extracting ready-for-analysis datasets from EHR databases. It has a simple import function to a database backend that greatly accelerates data access times. A set of generic query functions allow users to extract data efficiently without needing detailed knowledge of SQL queries. Longitudinal data extractions can also be made in a single command, making use of parallel processing. The package also contains functions for cutting data by time-varying covariates, matching controls to cases, unit conversion and construction of clinical code lists. There are also functions to synthesise dummy EHR. The package has been tested with one for the largest primary care EHRs, the Clinical Practice Research Datalink (CPRD), but allows for a common interface to other EHRs. This simplified and accelerated work flow for EHR data extraction results in simpler, cleaner scripts that are more easily debugged, shared and reproduced

Excess mortality in England and Wales during the first wave of the COVID-19 pandemic

Background Deaths during the COVID-19 pandemic result directly from infection and exacerbation of other diseases and indirectly from deferment of care for other conditions, and are socially and geographically patterned. We quantified excess mortality in regions of England and Wales during the pandemic, for all causes and for non-COVID-19-associated deaths. Methods Weekly mortality data for 1 January 2010 to 1 May 2020 for England and Wales were obtained from the Office of National Statistics. Mean-dispersion negative binomial regressions were used to model death counts based on pre-pandemic trends and exponentiated linear predictions were subtracted from: (i) all-cause deaths and (ii) all-cause deaths minus COVID-19 related deaths for the pandemic period (week starting 7 March, to week ending 8 May). Findings Between7Marchand8May2020,therewere 47 243 (95% CI: 46 671 to 47 815) excess deaths in England and Wales, of which 9948 (95% CI: 9376 to 10 520) were not associated with COVID-19. Overall excess mortality rates varied from 49 per 100 000 (95% CI: 49 to 50) in the South West to 102 per 100 000 (95% CI: 102 to 103) in London. Non-COVID-19 associated excess mortality rates ranged from −1 per 100 000 (95% CI: −1 to 0) in Wales (ie, mortality rates were no higher than expected) to 26 per 100 000 (95% CI: 25 to 26) in the West Midlands. Interpretation The COVID-19 pandemic has had markedly different impacts on the regions of England and Wales, both for deaths directly attributable to COVID-19 infection and for deaths resulting from the national public health response

Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: a national cohort study

Background: To estimate excess mortality for care home residents during the COVID-19 pandemic in England, exploring associations with care home characteristics. Methods: Daily number of deaths in all residential and nursing homes in England notified to the Care Quality Commission (CQC) from 1 January 2017 to 7 August 2020. Care home-level data linked with CQC care home register to identify home characteristics: client type (over 65s/children and adults), ownership status (for-profit/not-for-profit; branded/independent) and size (small/medium/large). Excess deaths computed as the difference between observed and predicted deaths using local authority fixed-effect Poisson regressions on pre-pandemic data. Fixed-effect logistic regressions were used to model odds of experiencing COVID-19 suspected/confirmed deaths. Results: Up to 7 August 2020, there were 29,542 (95% CI 25,176 to 33,908) excess deaths in all care homes. Excess deaths represented 6.5% (95% CI 5.5 to 7.4%) of all care home beds, higher in nursing (8.4%) than residential (4.6%) homes. 64.7% (95% CI 56.4 to 76.0%) of the excess deaths were confirmed/suspected COVID-19. Almost all excess deaths were recorded in the quarter (27.4%) of homes with any COVID-19 fatalities. The odds of experiencing COVID-19 attributable deaths were higher in homes providing nursing services (OR 1.8, 95% CI 1.6 to 2.0), to older people and/or with dementia (OR 5.5, 95% CI 4.4 to 6.8), amongst larger (vs. small) homes (OR 13.3, 95% CI 11.5 to 15.4) and belonging to a large provider/brand (OR 1.2, 95% CI 1.1 to 1.3). There was no significant association with for-profit status of providers. Conclusions: To limit excess mortality, policy should be targeted at care homes to minimise the risk of ingress of disease and limit subsequent transmission. Our findings provide specific characteristic targets for further research on mechanisms and policy priority

Depression and anxiety predict health-related quality of life in chronic obstructive pulmonary disease: systematic review and meta-analysis.

Published onlineJournal ArticleMeta-AnalysisResearch Support, Non-U.S. Gov'tReviewBACKGROUND: The causal association between depression, anxiety, and health-related quality of life (HRQoL) in chronic obstructive pulmonary disease (COPD) is unclear. We therefore conducted a systematic review of prospective cohort studies that measured depression, anxiety, and HRQoL in COPD. METHODS: Electronic databases (Medline, Embase, Cumulative Index to Nursing and Allied Health Literature [CINAHL], British Nursing Index and Archive, PsycINFO and Cochrane database) were searched from inception to June 18, 2013. Studies were eligible for inclusion if they: used a nonexperimental prospective cohort design; included patients with a diagnosis of COPD confirmed by spirometry; and used validated measures of depression, anxiety, and HRQoL. Data were extracted and pooled using random effects models. RESULTS: Six studies were included in the systematic review; of these, three were included in the meta-analysis for depression and two were included for the meta-analysis for anxiety. Depression was significantly correlated with HRQoL at 1-year follow-up (pooled r=0.48, 95% confidence interval 0.37-0.57, P<0.001). Anxiety was also significantly correlated with HRQoL at 1-year follow-up (pooled r=0.36, 95% confidence interval 0.23-0.48, P<0.001). CONCLUSION: Anxiety and depression predict HRQoL in COPD. However, this longitudinal analysis does not show cause and effect relationships between depression and anxiety and future HRQoL. Future studies should identify psychological predictors of poor HRQoL in well designed prospective cohorts with a view to isolating the mediating role played by anxiety disorder and depression.NIHRUniversity of ManchesterCLAHR

Depression and anxiety predict health-related quality of life in chronic obstructive pulmonary disease: systematic review and meta-analysis

Background: The causal association between depression, anxiety, and health-related quality of life (HRQoL) in chronic obstructive pulmonary disease (COPD) is unclear. We therefore conducted a systematic review of prospective cohort studies that measured depression, anxiety, and HRQoL in COPD. Methods: Electronic databases (Medline, Embase, Cumulative Index to Nursing and Allied Health Literature [CINAHL], British Nursing Index and Archive, PsycINFO and Cochrane database) were searched from inception to June 18, 2013. Studies were eligible for inclusion if they: used a nonexperimental prospective cohort design; included patients with a diagnosis of COPD confirmed by spirometry; and used validated measures of depression, anxiety, and HRQoL. Data were extracted and pooled using random effects models. Results: Six studies were included in the systematic review; of these, three were included in the meta-analysis for depression and two were included for the meta-analysis for anxiety. Depression was significantly correlated with HRQoL at 1-year follow-up (pooled r=0.48, 95% confidence interval 0.37–0.57, P<0.001). Anxiety was also significantly correlated with HRQoL at 1-year follow-up (pooled r=0.36, 95% confidence interval 0.23–0.48, P<0.001). Conclusion: Anxiety and depression predict HRQoL in COPD. However, this longitudinal analysis does not show cause and effect relationships between depression and anxiety and future HRQoL. Future studies should identify psychological predictors of poor HRQoL in well designed prospective cohorts with a view to isolating the mediating role played by anxiety disorder and depressio

Chronic morbidity, deprivation and primary medical care spending in England in 2015-16: a cross-sectional spatial analysis

Background Primary care provides the foundation for most modern health-care systems, and in the interests of equity, it should be resourced according to local need. We aimed to describe spatially the burden of chronic conditions and primary medical care funding in England at a low geographical level, and to measure how much variation in funding is explained by chronic condition prevalence and other patient and regional factors. Methods We used multiple administrative data sets including chronic condition prevalence and management data (2014/15), funding for primary-care practices (2015-16), and geographical and area deprivation data (2015). Data were assigned to a low geographical level (average 1500 residents). We investigated the overall morbidity burden across 19 chronic conditions and its regional variation, spatial clustering and association with funding and area deprivation. A linear regression model was used to explain local variation in spending using patient demographics, morbidity, deprivation and regional characteristics. Results Levels of morbidity varied within and between regions, with several clusters of very high morbidity identified. At the regional level, morbidity was modestly associated with practice funding, with the North East and North West appearing underfunded. The regression model explained 39% of the variability in practice funding, but even after adjusting for covariates, a large amount of variability in funding existed across regions. High morbidity and, especially, rural location were very strongly associated with higher practice funding, while associations were more modest for high deprivation and older age. Conclusions Primary care funding in England does not adequately reflect the contemporary morbidity burden. More equitable resource allocation could be achieved by making better use of routinely available information and big data resources. Similar methods could be deployed in other countries where comparable data are collected, to identify morbidity clusters and to target funding to areas of greater need

Stroke following percutaneous coronary intervention : type-specific incidence, outcomes and determinants seen by the British Cardiovascular Intervention Society 2007-12

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