NHS National Data Opt-Outs: trends and potential consequences for health data research.

BACKGROUND: The English National Health Service (NHS) data opt-out allows people to prevent use of their health data for purposes other than direct care. In 2021, the number of opt-outs increased in response to government-led proposals to create a centralised pseudonymised primary care record database. AIM: To describe the potential impact of NHS National Data Opt-outs in 2021 on health data research. DESIGN & SETTING: We conducted a descriptive analysis of opt-outs using publicly available data and discuss the potential consequences on research. METHOD: Trends in opt-outs in England were described by age, sex and region. Using a hypothetical study, we explored statistical and epidemiological implications of opt-outs. RESULTS: During the lead up to a key government-led deadline for registering opt-outs (from 31 May 2021 to 30 June 2021), 1,339,862 national data opt-outs were recorded; increasing the percentage of opt-outs in England from 2.77% to 4.97% of the population. Amongst females, percentage opt-outs increased by 83% (from 3.02% to 5.53%) compared to 75% in males (2.51%-4.41%). Across age-groups, the highest relative increase was among people aged 40-49 years which rose from 2.89% to 6.04%. Considerable geographical variation was not clearly related to deprivation. Key research consequences of opt-outs include reductions in sample size and unpredictable distortion of observed measures of the frequency of health events or associations between these events. CONCLUSIONS: Opt-out rates varied by age, sex and place. The impact of this and variation by other characteristics on research is not quantifiable. Potential effects of opt-outs on research and consequences for health policies based on this research must be considered when creating future opt-out solutions.

Inequalities in the recovery of colorectal cancer services during the COVID-19 pandemic: a national population-based study.

AIM: Evidence is lacking on whether there were inequalities in the recovery of colorectal cancer (CRC) services within the English National Health Service (NHS) following the COVID-19 pandemic. The aim of this study was to evaluate recovery according to patient age and socioeconomic status. METHOD: Using routinely collected data, CRC patients diagnosed and treated in the English NHS were identified for two timeframes: the 'initial pandemic period' (April-June 2020) and the 'pandemic period' (April 2020-March 2022). Poisson models evaluated changes in numbers of diagnoses, major resections, adjuvant chemotherapy and neoadjuvant radiotherapy use for each timeframe, relative to the equivalent pre-pandemic timeframe (April-June 2019 and April 2018-March 2020, respectively), stratified by age and socioeconomic status. Tumour stage at presentation was evaluated over time. RESULTS: Substantial deficits in diagnoses, major resections and adjuvant chemotherapy were identified in the initial pandemic period, whilst the use of neoadjuvant radiotherapy increased. Overall, these deficits recovered. Patients outside screening age, and in the most deprived group, had greater deficits in diagnoses and major resections. There was no evidence of stage migration by June 2021. CONCLUSIONS: CRC services showed recovery to baseline during the pandemic. However, evident inequalities must be addressed in ongoing recovery efforts. Long-term outcomes will fully establish the impact of the pandemic on CRC patients.

Improving risk models for patients having emergency bowel cancer surgery using linked electronic health records: a national cohort study.

BACKGROUND: Life-saving emergency major resection of colorectal cancer (CRC) is a high-risk procedure. Accurate prediction of postoperative mortality for patients undergoing this procedure is essential for both healthcare performance monitoring and preoperative risk assessment. Risk-adjustment models for CRC patients often include patient and tumour characteristics, widely available in cancer registries and audits. The authors investigated to what extent inclusion of additional physiological and surgical measures, available through linkage or additional data collection, improves accuracy of risk models. METHODS: Linked, routinely-collected data on patients undergoing emergency CRC surgery in England between December 2016 and November 2019 were used to develop a risk model for 90-day mortality. Backwards selection identified a 'selected model' of physiological and surgical measures in addition to patient and tumour characteristics. Model performance was assessed compared to a 'basic model' including only patient and tumour characteristics. Missing data was multiply imputed. RESULTS: Eight hundred forty-six of 10 578 (8.0%) patients died within 90 days of surgery. The selected model included seven preoperative physiological and surgical measures (pulse rate, systolic blood pressure, breathlessness, sodium, urea, albumin, and predicted peritoneal soiling), in addition to the 10 patient and tumour characteristics in the basic model (calendar year of surgery, age, sex, ASA grade, TNM T stage, TNM N stage, TNM M stage, cancer site, number of comorbidities, and emergency admission). The selected model had considerably better discrimination compared to the basic model (C-statistic: 0.824 versus 0.783, respectively). CONCLUSION: Linkage of disease-specific and treatment-specific datasets allowed the inclusion of physiological and surgical measures in a risk model alongside patient and tumour characteristics, which improves the accuracy of the prediction of the mortality risk for CRC patients having emergency surgery. This improvement will allow more accurate performance monitoring of healthcare providers and enhance clinical care planning.

Linkage of multiple electronic health record datasets using a 'spine linkage' approach compared with all 'pairwise linkages'.

BACKGROUND: Methods for linking records between two datasets are well established. However, guidance is needed for linking more than two datasets. Using all 'pairwise linkages'-linking each dataset to every other dataset-is the most inclusive, but resource-intensive, approach. The 'spine' approach links each dataset to a designated 'spine dataset', reducing the number of linkages, but potentially reducing linkage quality. METHODS: We compared the pairwise and spine linkage approaches using real-world data on patients undergoing emergency bowel cancer surgery between 31 October 2013 and 30 April 2018. We linked an administrative hospital dataset (Hospital Episode Statistics; HES) capturing patients admitted to hospitals in England, and two clinical datasets comprising patients diagnosed with bowel cancer and patients undergoing emergency bowel surgery. RESULTS: The spine linkage approach, with HES as the spine dataset, created an analysis cohort of 15 826 patients, equating to 98.3% of the 16 100 patients identified using the pairwise linkage approach. There were no systematic differences in patient characteristics between these analysis cohorts. Associations of patient and tumour characteristics with mortality, complications and length of stay were not sensitive to the linkage approach. When eligibility criteria were applied before linkage, spine linkage included 14 509 patients (90.0% compared with pairwise linkage). CONCLUSION: Spine linkage can be used as an efficient alternative to pairwise linkage if case ascertainment in the spine dataset and data quality of linkage variables are high. These aspects should be systematically evaluated in the nominated spine dataset before spine linkage is used to create the analysis cohort.

Outcomes of colorectal cancer resection in patients with inflammatory bowel disease: a national population-based analysis in England and Wales.

AIM: The aim was to compare early postoperative outcomes and 2-year cancer-specific mortality following colorectal cancer (CRC) resection in patients with and without inflammatory bowel disease (IBD) in England and Wales. METHOD: Records for patients in the National Bowel Cancer Audit who had major CRC resection between April 2014 and December 2017 were linked to routinely collected hospital level administrative datasets and chemotherapy and radiotherapy datasets. Multivariable regression models were used to compare outcomes with adjustment for patient and tumour characteristics. RESULTS: In all, 63 365 patients were included. 1285 (2.0%) had an IBD diagnosis: 839 (65.3%) ulcerative colitis, 435 (33.9%) Crohn's disease and 11 (0.9%) were indeterminate. IBD patients were younger, had more advanced cancer staging and a higher proportion of right-sided tumours. They also had a higher proportion of emergency resection, total/subtotal colectomy, open surgery and stoma formation at resection, with longer hospital admissions and higher rates of unplanned readmission and reoperation. Fewer rectal cancer patients with IBD received neoadjuvant radiotherapy (24.8% vs. 36.0%, P = 0.005) whilst similar proportions of Stage III colon cancer patients received adjuvant chemotherapy. Ninety-day postoperative mortality was similar, but unadjusted 2-year cancer-specific mortality was significantly higher in patients with IBD (subdistribution hazard ratio 1.35, 95% CI 1.18-1.55). Risk adjustment for patient and tumour factors reduced this association (adjusted subdistribution hazard ratio 1.22, 95% CI 1.05-1.43). CONCLUSION: Patients with IBD and CRC are a distinct patient group who develop CRC at a younger age and undergo more radical surgery. They have worse cancer survival, with the difference in prognosis appearing after the early postoperative period.

Surgical Treatment and Outcomes of Colorectal Cancer Patients During the COVID-19 Pandemic: A National Population-based Study in England.

To compare the management and outcomes of colorectal cancer (CRC) patients during the first 2 months of the COVID-19 pandemic with the preceding 6 months. BACKGROUND: The pandemic has affected the diagnosis and treatment of CRC patients worldwide. Little is known about the safety of major resection and whether creating "cold" sites (COVID-free hospitals) is effective. METHODS: A national study in England used administrative hospital data for 14,930 CRC patients undergoing surgery between October 1, 2019, and May 31, 2020. Mortality of CRC resection was compared before and after March 23, 2020 ("lockdown" start). RESULTS: The number of elective CRC procedures dropped sharply during the pandemic (from average 386 to 214 per week), whereas emergency procedures were hardly affected (from 88 to 84 per week). There was little change in characteristics of surgical patients during the pandemic. Laparoscopic surgery decreased from 62.5% to 35.9% for elective and from 17.7% to 9.7% for emergency resections. Surgical mortality increased slightly (from 0.9% to 1.2%, P = 0.06) after elective and markedly (from 5.6% to 8.9%, P = 0.003) after emergency resections. The observed increase in mortality during the first phase of the pandemic was similar in "cold" and "hot" sites (P > 0.5 elective and emergency procedures). CONCLUSIONS: The pandemic resulted in a 50% reduction in elective CRC procedures during the initial surge and a substantial increase in mortality after emergency resection. There was no evidence that surgery in COVID-free "cold" sites led to better outcomes in the first 2 months.

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 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.

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.

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.

Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes.

BACKGROUND: Multivariate meta-analysis (MVMA) jointly synthesizes effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates, it could be argued that it is redundant. METHODS: We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 and 2013 with two or more binary outcomes with at least three studies and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML). RESULTS: Eighty CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in three of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other seven reviews. Statistical significance changed in two reviews: In one, this was due to a very small change in P value; in the other, the MVMA result for one outcome suggested that previous univariate results may be vulnerable to small-study effects and that the certainty of clinical conclusions needs consideration. CONCLUSIONS: MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth and can be difficult to apply because of missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.