Dataset Information

Estimating smoking prevalence in general practice using data from the Quality and Outcomes Framework (QOF).

ABSTRACT:

Objectives

To determine to what extent underlying data published as part of Quality and Outcomes Framework (QOF) can be used to estimate smoking prevalence within practice populations and local areas and to explore the usefulness of these estimates.

Design

Cross-sectional, observational study of QOF smoking data. Smoking prevalence in general practice populations and among patients with chronic conditions was estimated by simple manipulation of QOF indicator data. Agreement between estimates from the integrated household survey (IHS) and aggregated QOF-based estimates was calculated. The impact of including smoking estimates in negative binomial regression models of counts of premature coronary heart disease (CHD) deaths was assessed.

Setting

Primary care in the East Midlands.

Participants

All general practices in the area of study were eligible for inclusion (230). 14 practices were excluded due to incomplete QOF data for the period of study (2006/2007-2012/2013). One practice was excluded as it served a restricted practice list.

Measurements

Estimates of smoking prevalence in general practice populations and among patients with chronic conditions.

Results

Median smoking prevalence in the practice populations for 2012/2013 was 19.2% (range 5.8-43.0%). There was good agreement (mean difference: 0.39%; 95% limits of agreement (-3.77, 4.55)) between IHS estimates for local authority districts and aggregated QOF register estimates. Smoking prevalence estimates in those with chronic conditions were lower than for the general population (mean difference -3.05%), but strongly correlated (Rp=0.74, p<0.0001). An important positive association between premature CHD mortality and smoking prevalence was shown when smoking prevalence was added to other population and service characteristics.

Conclusions

Published QOF data allow useful estimation of smoking prevalence within practice populations and in those with chronic conditions; the latter estimates may sometimes be useful in place of the former. It may also provide useful estimates of smoking prevalence in local areas by aggregating practice based data.

SUBMITTER: Honeyford K

PROVIDER: S-EPMC4120299 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating smoking prevalence in general practice using data from the Quality and Outcomes Framework (QOF).

Honeyford Kate K Baker Richard R Bankart M John G MJ Jones David R DR

BMJ open 20140716 7

<h4>Objectives</h4>To determine to what extent underlying data published as part of Quality and Outcomes Framework (QOF) can be used to estimate smoking prevalence within practice populations and local areas and to explore the usefulness of these estimates.<h4>Design</h4>Cross-sectional, observational study of QOF smoking data. Smoking prevalence in general practice populations and among patients with chronic conditions was estimated by simple manipulation of QOF indicator data. Agreement betwee ...[more]

PMID: 25031192

Similar Datasets

Project description:ObjectivesTo investigate the effect of withdrawing incentives on recorded quality of care, in the context of the UK Quality and Outcomes Framework pay for performance scheme.DesignRetrospective longitudinal study.SettingData for 644 general practices, from 2004/05 to 2011/12, extracted from the Clinical Practice Research Datalink.ParticipantsAll patients registered with any of the practices over the study period-13,772,992 in total.InterventionRemoval of financial incentives for aspects of care for patients with asthma, coronary heart disease, diabetes, stroke, and psychosis.Main outcome measuresPerformance on eight clinical quality indicators withdrawn from a national incentive scheme: influenza immunisation (asthma) and lithium treatment monitoring (psychosis), removed in April 2006; blood pressure monitoring (coronary heart disease, diabetes, stroke), cholesterol concentration monitoring (coronary heart disease, diabetes), and blood glucose monitoring (diabetes), removed in April 2011. Multilevel mixed effects multiple linear regression models were used to quantify the effect of incentive withdrawal.ResultsMean levels of performance were generally stable after the removal of the incentives, in both the short and long term. For the two indicators removed in April 2006, levels in 2011/12 were very close to 2005/06 levels, although a small but statistically significant drop was estimated for influenza immunisation. For five of the six indicators withdrawn from April 2011, no significant effect on performance was seen following removal and differences between predicted and observed scores were small. Performance on related outcome indicators retained in the scheme (such as blood pressure control) was generally unaffected.ConclusionsFollowing the removal of incentives, levels of performance across a range of clinical activities generally remained stable. This indicates that health benefits from incentive schemes can potentially be increased by periodically replacing existing indicators with new indicators relating to alternative aspects of care. However, all aspects of care investigated remained indirectly or partly incentivised in other indicators, and further work is needed to assess the generalisability of the findings when incentives are fully withdrawn.

Project description:BackgroundAcute care services are currently overstretched in many high income countries. Overcrowding also plays a major role in acute care in the Netherlands. In a region of the Netherlands, the general practice cooperative (GPC) and ambulance service have begun to integrate their care, and the rapid and complete transfer of information between these two care organisations is now the basis for delivering appropriate care. The primary aim of this mixed-methods study is to evaluate the Netherlands Triage System (NTS) merger project and answering the question: What is the added value of implementing a digital NTS merger in terms of healthcare use and healthcare costs? A secondary question is: What are the experiences of patients and care professionals in different acute healthcare organisations following implementation of the digital NTS merger?MethodsPatients who made an acute care request during the 12 months before the NTS merge intervention (control period) were compared with matched patients in the 12 months following the start of the NTS merge. Outcomes included difference in healthcare use 30 days after an acute event and patient' and care professional' experiences during the intervention period. To assess healthcare costs, we used reference prices updated to 2021.ResultsCompared to patients in the control period, patients in the intervention period were hospitalized less often (52.9% vs 64.4%, p = 0.061) and had fewer emergency department (ED) visits (58.7% vs 69.3%, p = 0.074) in the 30 days following the acute care request. The ED costs were significantly lower during the intervention period compared to the control period (p = 0.042). Furthermore, patients in the intervention period were very satisfied overall with the acute care network (4.63 of 5) and care professionals were fairly satisfied with the cooperation to date (2.73 of 4).ConclusionThe Triple Aim for acute care can be met using relatively simple interventions, but medical data merging is a prerequisite for achieving more robust results covering on the various aspects of the Triple Aim. These successes should be communicated so that a common language can be developed that will support the successful further implementation of larger scale initiatives.

Project description:ObjectivesTo evaluate a county-wide deincentivisation of the Quality and Outcomes Framework (QOF) payment scheme for UK General Practice (GP).SettingIn 2014, National Health Service England signalled a move towards devolution of QOF to Clinical Commissioning Groups. Fifty-five GPs in Somerset established the Somerset Practice Quality Scheme (SPQS)-a deincentivisation of QOF-with the goal of redirecting resources towards Person Centred Coordinated Care (P3C), especially for those with long-term conditions (LTCs). We evaluated the impact on processes and outcomes of care from April 2016 to March 2017.Participants and designThe evaluation used data from 55 SPQS practices and 17 regional control practices for three survey instruments. We collected patient experiences ('P3C-EQ'; 2363 returns from patients with 1+LTC; 36% response rate), staff experiences ('P3C-practitioner'; 127 professionals) and organisational data ('P3C-OCT'; 36 of 55 practices at two time points, 65% response rate; 17 control practices). Hospital Episode Statistics emergency admission data were analysed for 2014-2017 for ambulatory-sensitive conditions across Somerset using interrupted time series.ResultsPatient and practitioner experiences were similar in SPQS versus control practices. However, discretion from QOF incentives resulted in time savings in the majority of practices, and SPQS practice data showed a significant increase in P3C oriented organisational processes, with a moderate effect size (Wilcoxon signed rank test; p=0.01; r=0.42). Analysis of transformation plans and organisational data suggested stronger federation-level agreements and informal networks, increased multidisciplinary working, reallocation of resources for other healthcare professionals and changes to the structure and timings of GP appointments. No disbenefits were detected in admission data.ConclusionThe SPQS scheme leveraged time savings and reduced administrative burden via discretionary removal of QOF incentives, enabling practices to engage actively in a number of schemes aimed at improving care for people with LTCs. We found no differences in the experiences of patients or healthcare professionals between SPQS and control practices.

Dataset Information

Estimating smoking prevalence in general practice using data from the Quality and Outcomes Framework (QOF).

Objectives

Design

Setting

Participants

Measurements

Results

Conclusions

Publications

Estimating smoking prevalence in general practice using data from the Quality and Outcomes Framework (QOF).

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets