Patient experience of access to primary care: identification of predictors in a national patient survey.

BACKGROUND: The 2007/8 GP Access Survey in England measured experience with five dimensions of access: getting through on the phone to a practice, getting an early appointment, getting an advance appointment, making an appointment with a particular doctor, and surgery opening hours. Our aim was to identify predictors of patient satisfaction and experience with access to English primary care. METHODS: 8,307 English general practices were included in the survey (of 8,403 identified). 4,922,080 patients were randomly selected and contacted by post and 1,999,523 usable questionnaires were returned, a response rate of 40.6%. We used multi-level logistic regressions to identify patient, practice and regional predictors of patient satisfaction and experience. RESULTS: After controlling for all other factors, younger people, and people of Asian ethnicity, working full time, or with long commuting times to work, reported the lowest levels of satisfaction and experience of access. For people in work, the ability to take time off work to visit the GP effectively eliminated the disadvantage in access. The ethnic mix of the local area had an impact on a patient's reported satisfaction and experience over and above the patient's own ethnic identity. However, area deprivation had only low associations with patient ratings. Responses from patients in small practices were more positive for all aspects of access with the exception of satisfaction with practice opening hours. Positive reports of access to care were associated with higher scores on the Quality and Outcomes Framework and with slightly lower rates of emergency admission. Respondents in London were the least satisfied and had the worst experiences on almost all dimensions of access. CONCLUSIONS: This study identifies a number of patient groups with lower satisfaction, and poorer experience, of gaining access to primary care. The finding that access is better in small practices is important given the increasing tendency for small practices to combine into larger units. Consideration needs to be given to ways of retaining these and other benefits of small practice size when primary care services are reconfigured. Differences between population groups (e.g. younger people, ethnic minorities) may be due to differences in actual care received or different response tendencies of different groups. Further analysis is needed to determine whether case-mix adjustment is required when comparing practices serving different populations.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Applications of simple and accessible methods for meta-analysis involving rare events: A simulation study

From SAGE Publishing via Jisc Publications RouterHistory: epub 2021-06-17Publication status: PublishedMeta-analysis of clinical trials targeting rare events face particular challenges when the data lack adequate number of events and are susceptible to high levels of heterogeneity. The standard meta-analysis methods (DerSimonian Laird (DL) and Mantel–Haenszel (MH)) often lead to serious distortions because of such data sparsity. Applications of the methods suited to specific incidence and heterogeneity characteristics are lacking, thus we compared nine available methods in a simulation study. We generated 360 meta-analysis scenarios where each considered different incidences, sample sizes, between-study variance (heterogeneity) and treatment allocation. We include globally recommended methods such as inverse-variance fixed/random-effect (IV-FE/RE), classical-MH, MH-FE, MH-DL, Peto, Peto-DL and the two extensions for MH bootstrapped-DL (bDL) and Peto-bDL. Performance was assessed on mean bias, mean error, coverage and power. In the absence of heterogeneity, the coverage and power when combined revealed small differences in meta-analysis involving rare and very rare events. The Peto-bDL method performed best, but only in smaller sample sizes involving rare events. For medium-to-larger sample sizes, MH-bDL was preferred. For meta-analysis involving very rare events, Peto-bDL was the best performing method which was sustained across all sample sizes. However, in meta-analysis with 20% or more heterogeneity, the coverage and power were insufficient. Performance based on mean bias and mean error was almost identical across methods. To conclude, in meta-analysis of rare binary outcomes, our results suggest that Peto-bDL is better in both rare and very rare event settings in meta-analysis with limited sample sizes. However, when heterogeneity is large, the coverage and power to detect rare events are insufficient. Whilst this study shows that some of the less studied methods appear to have good properties under sparse data scenarios, further work is needed to assess them against the more complex distributional-based methods to understand their overall performances

Outcome-sensitive multiple imputation: a simulation study.

BACKGROUND: Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should be imputed. Similarly no clear recommendations exist on: the utility of incorporating a secondary outcome, if available, in the imputation model; the level of protection offered when data are missing not-at-random; the implications of the dataset size and missingness levels. METHODS: We used realistic assumptions to generate thousands of datasets across a broad spectrum of contexts: three mechanisms of missingness (completely at random; at random; not at random); varying extents of missingness (20-80% missing data); and different sample sizes (1,000 or 10,000 cases). For each context we quantified the performance of a complete case analysis and seven multiple imputation methods which deleted cases with missing outcome before imputation, after imputation or not at all; included or did not include the outcome in the imputation models; and included or did not include a secondary outcome in the imputation models. Methods were compared on mean absolute error, bias, coverage and power over 1,000 datasets for each scenario. RESULTS: Overall, there was very little to separate multiple imputation methods which included the outcome in the imputation model. Even when missingness was quite extensive, all multiple imputation approaches performed well. Incorporating a secondary outcome, moderately correlated with the outcome of interest, made very little difference. The dataset size and the extent of missingness affected performance, as expected. Multiple imputation methods protected less well against missingness not at random, but did offer some protection. CONCLUSIONS: As long as the outcome is included in the imputation model, there are very small performance differences between the possible multiple imputation approaches: no outcome imputation, imputation or imputation and deletion. All informative covariates, even with very high levels of missingness, should be included in the multiple imputation model. Multiple imputation offers some protection against a simple missing not at random mechanism

Relationship between quality of care and choice of clinical computing system: Retrospective analysis of family practice performance under the UK's quality and outcomes framework

OBJECTIVES: To investigate the relationship between performance on the UK Quality and Outcomes Framework pay-for-performance scheme and choice of clinical computer system. DESIGN: Retrospective longitudinal study. SETTING: Data for 2007–2008 to 2010–2011, extracted from the clinical computer systems of general practices in England. PARTICIPANTS: All English practices participating in the pay-for-performance scheme: average 8257 each year, covering over 99% of the English population registered with a general practice. MAIN OUTCOME MEASURES: Levels of achievement on 62 quality-of-care indicators, measured as: reported achievement (levels of care after excluding inappropriate patients); population achievement (levels of care for all patients with the relevant condition) and percentage of available quality points attained. Multilevel mixed effects multiple linear regression models were used to identify population, practice and clinical computing system predictors of achievement. RESULTS: Seven clinical computer systems were consistently active in the study period, collectively holding approximately 99% of the market share. Of all population and practice characteristics assessed, choice of clinical computing system was the strongest predictor of performance across all three outcome measures. Differences between systems were greatest for intermediate outcomes indicators (eg, control of cholesterol levels). CONCLUSIONS: Under the UK's pay-for-performance scheme, differences in practice performance were associated with the choice of clinical computing system. This raises the question of whether particular system characteristics facilitate higher quality of care, better data recording or both. Inconsistencies across systems need to be understood and addressed, and researchers need to be cautious when generalising findings from samples of providers using a single computing system

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

Impact of the English Directly Enhanced Service (DES) for Learning Disability

Final Report to the Department of Healt