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Health checks and cardiovascular risk factor values over six years' follow-up: Matched cohort study using electronic health records in England.

ABSTRACT: BACKGROUND:The National Health Service (NHS) in England introduced a population-wide programme for cardiovascular disease (CVD) prevention in 2009, known as NHS Health Checks. This research aimed to measure the cardiovascular risk management and cardiovascular risk factor outcomes of the health check programme during six years' follow-up. METHODS AND FINDINGS:A controlled interrupted time series study was conducted. Participants were registered with general practices in the Clinical Practice Research Datalink (CPRD) in England and received health checks between 1 April 2010 and 31 December 2013. Control participants, who did not receive a health check, were matched for age, sex, and general practice. Outcomes were blood pressure, body mass index (BMI), smoking, and total cholesterol (TC) and high-density lipoprotein cholesterol (HDL). Analyses estimated the net effect of health check by year, allowing for the underlying trend in risk factor values and baseline differences between cases and controls, adjusting for age, sex, deprivation, and clustering by general practice. There were 127,891 health check participants and 322,910 matched controls. Compared with controls, health check participants had lower BMI (cases mean 27.0, SD 4.8; controls 27.3, SD 5.6, Kg/m2), systolic blood pressure (SBP) (cases 129.0, SD 14.3; controls 129.3, SD 15.0, mm Hg), and smoking (21% in health check participants versus 27% in controls), but total and HDL cholesterol were similar. Health check participants were more likely to receive weight management advice (adjusted hazard ratio [HR] 5.03, 4.98 to 5.08, P < 0.001), smoking cessation interventions (HR 3.20, 3.13 to 3.27, P < 0.001), or statins (HR 1.24, 1.21 to 1.27, P < 0.001). There were net reductions in risk factor values up to six years after the check for BMI (-0.30, -0.39 to -0.20 Kg/m2, P < 0.001), SBP (-1.43, -1.70 to -1.16 mm Hg, P < 0.001), and smoking (17% in health check participants versus 25% in controls; odds ratio 0.90, 0.87 to 0.94, P < 0.001). The main study limitation was that residual confounding may be present because randomisation was not employed; health check-associated measurement introduced differential recording that might cause bias. CONCLUSIONS:Our results suggest that people who take up a health check generally have lower risk factor values than controls and are more likely to receive risk factor interventions. Risk factor values show net reductions up to six years following a health check in BMI, blood pressure, and smoking, which may be of public health importance.

SUBMITTER: Alageel S

PROVIDER: S-EPMC6667114 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Health checks and cardiovascular risk factor values over six years' follow-up: Matched cohort study using electronic health records in England.

Alageel Samah S Gulliford Martin C MC

PLoS medicine 20190730 7

<h4>Background</h4>The National Health Service (NHS) in England introduced a population-wide programme for cardiovascular disease (CVD) prevention in 2009, known as NHS Health Checks. This research aimed to measure the cardiovascular risk management and cardiovascular risk factor outcomes of the health check programme during six years' follow-up.<h4>Methods and findings</h4>A controlled interrupted time series study was conducted. Participants were registered with general practices in the Clinic ...[more]

PMID: 31361740

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Project description:BackgroundIn England, almost all general practices (GPs) have implemented GP online services such as electronic personal health records (ePHRs) that allow people to schedule appointments, request repeat prescriptions, and access parts of their medical records. The overall adoption rate of GP online services has been low, reaching just 28% in October 2019. In a previous study, Abd-Alrazaq et al adopted a model to assess the factors that influence patients' use of GP online services in England. According to the previous literature, the predictive power of the Abd-Alrazaq model could be improved by proposing new associations between the existing variables in the model.ObjectiveThis study aims to improve the predictive power of the Abd-Alrazaq model by proposing new relationships between the existing variables in the model.MethodsThe Abd-Alrazaq model was amended by proposing new direct, mediating, moderating, and moderated mediating effects. The amended model was examined using data from a previous study, which were collected by a cross-sectional survey of a convenience sample of 4 GPs in West Yorkshire, England. Structural equation modeling was used to examine the theoretical model and hypotheses.ResultsThe new model accounted for 53% of the variance in performance expectancy (PE), 76% of the variance in behavioral intention (BI), and 49% of the variance in use behavior (UB). In addition to the significant associations found in the previous study, this study found that social influence (SI) and facilitating conditions (FCs) are associated with PE directly and BI indirectly through PE. The association between BI and UB was stronger for younger women with higher levels of education, income, and internet access. The indirect effects of effort expectancy (EE), perceived privacy and security (PPS), and SI on BI were statistically stronger for women without internet access, patients with internet access, and patients without internet access, respectively. The indirect effect of PPS on BI was stronger for patients with college education or diploma than for those with secondary school education and lower, whereas the indirect effect of EE on BI was stronger for patients with secondary school education or lower than for those with college education or a diploma.ConclusionsThe predictive power of the Abd-Alrazaq model improved by virtue of new significant associations that were not examined before in the context of ePHRs. Further studies are required to validate the new model in different contexts and to improve its predictive power by proposing new variables. The influential factors found in this study should be considered to improve patients' use of ePHRs.

Project description:BackgroundIdentifying implausible clinical observations (e.g., laboratory test and vital sign values) in Electronic Health Record (EHR) data using rule-based procedures is challenging. Anomaly/outlier detection methods can be applied as an alternative algorithmic approach to flagging such implausible values in EHRs.MethodsThe primary objectives of this research were to develop and test an unsupervised clustering-based anomaly/outlier detection approach for detecting implausible observations in EHR data as an alternative algorithmic solution to the existing procedures. Our approach is built upon two underlying hypotheses that, (i) when there are large number of observations, implausible records should be sparse, and therefore (ii) if these data are clustered properly, clusters with sparse populations should represent implausible observations. To test these hypotheses, we applied an unsupervised clustering algorithm to EHR observation data on 50 laboratory tests from Partners HealthCare. We tested different specifications of the clustering approach and computed confusion matrix indices against a set of silver-standard plausibility thresholds. We compared the results from the proposed approach with conventional anomaly detection (CAD) approaches, including standard deviation and Mahalanobis distance.ResultsWe found that the clustering approach produced results with exceptional specificity and high sensitivity. Compared with the conventional anomaly detection approaches, our proposed clustering approach resulted in significantly smaller number of false positive cases.ConclusionOur contributions include (i) a clustering approach for identifying implausible EHR observations, (ii) evidence that implausible observations are sparse in EHR laboratory test results, (iii) a parallel implementation of the clustering approach on i2b2 star schema, and (3) a set of silver-standard plausibility thresholds for 50 laboratory tests that can be used in other studies for validation. The proposed algorithmic solution can augment human decisions to improve data quality. Therefore, a workflow is needed to complement the algorithm's job and initiate necessary actions that need to be taken in order to improve the quality of data.

Project description:BackgroundElectronic personal health records (ePHRs) are secure Web-based tools that enable individuals to access, manage, and share their medical records. England recently introduced a nationwide ePHR called Patient Online. As with ePHRs in other countries, adoption rates of Patient Online remain low. Understanding factors affecting patients' ePHR use is important to increase adoption rates and improve the implementation success of ePHRs.ObjectiveThis study aimed to examine factors associated with patients' use of ePHRs in England.MethodsThe unified theory of acceptance and use of technology was adapted to the use of ePHRs. To empirically examine the adapted model, a cross-sectional survey of a convenience sample was carried out in 4 general practices in West Yorkshire, England. Factors associated with the use of ePHRs were explored using structural equation modeling.ResultsOf 800 eligible patients invited to take part in the survey, 624 (78.0%) returned a valid questionnaire. Behavioral intention (BI) was significantly influenced by performance expectancy (PE; beta=.57, P<.001), effort expectancy (EE; beta=.16, P<.001), and perceived privacy and security (PPS; beta=.24, P<.001). The path from social influence to BI was not significant (beta=.03, P=.18). Facilitating conditions (FC) and BI significantly influenced use behavior (UB; beta=.25, P<.001 and beta=.53, P<.001, respectively). PE significantly mediated the effect of EE and PPS on BI (beta=.19, P<.001 and beta=.28, P=.001, respectively). Age significantly moderated 3 paths: PE→BI, EE→BI, and FC→UB. Sex significantly moderated only the relationship between PE and BI. A total of 2 paths were significantly moderated by education and internet access: EE→BI and FC→UB. Income moderated the relationship between FC and UB. The adapted model accounted for 51% of the variance in PE, 76% of the variance in BI, and 48% of the variance in UB.ConclusionsThis study identified the main factors that affect patients' use of ePHRs in England, which should be taken into account for the successful implementation of these systems. For example, developers of ePHRs should involve patients in the process of designing the system to consider functions and features that fit patients' preferences and skills to ensure systems are useful and easy to use. The proposed model accounted for 48% of the variance in UB, indicating the existence of other, as yet unidentified, factors that influence the adoption of ePHRs. Future studies should confirm the effect of the factors included in this model and identify additional factors.

Project description:BackgroundGeneral practices (GPs) in England have recently introduced a nationwide electronic personal health record (ePHR) system called Patient Online or GP online services, which allows patients to view parts of their medical records, book appointments, and request prescription refills. Although this system is free of charge, its adoption rates are low. To improve patients' adoption and implementation success of the system, it is important to understand the factors affecting their use of the system.ObjectiveThe aim of this study is to explore patients' perspectives of factors affecting their use of ePHRs in England.MethodsA cross-sectional survey was carried out between August 21 and September 26, 2017. A questionnaire was used in this survey to collect mainly quantitative data through closed-ended questions in addition to qualitative data through an open-ended question. A convenience sample was recruited in 4 GPs in West Yorkshire, England. Given that the quantitative data were analyzed in a previous study, we analyzed the qualitative data using thematic analysis.ResultsOf the 800 eligible patients invited to participate in the survey, 624 (78.0%) returned a fully completed questionnaire. Of those returned questionnaires, the open-ended question was answered by 136/624 (21.8%) participants. A total of 2 meta-themes emerged from participants' responses. The first meta-theme comprises 5 themes about why patients do not use Patient Online: concerns about using Patient Online, lack of awareness of Patient Online, challenges regarding internet and computers, perceived characteristics of nonusers, and preference for personal contact. The second meta-theme contains 1 theme about why patients use Patient Online: encouraging features of Patient Online.ConclusionsThe challenges and concerns that impede the use of Patient Online seem to be of greater importance than the facilitators that encourage its use. There are practical considerations that, if incorporated into the system, are likely to improve its adoption rate: Patient Online should be useful, easy to use, secure, and easy to access. Different channels should be used to increase the awareness of the system, and GPs should ease registration with the system and provide manuals, training sessions, and technical support. More research is needed to assess the effect of the new factors found in this study (eg, lack of trust, difficulty registering with Patient Online) and factors affecting the continuing use of the system.

Project description:BackgroundUpdatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.MethodsIn this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status.FindingsAmong 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1.InterpretationOur analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources.FundingBritish Heart Foundation Data Science Centre, led by Health Data Research UK.

Project description:IntroductionDisease registers and electronic health records are valuable resources for disease surveillance and research but can be limited by variation in data quality over time. Quality may be limited in terms of the accuracy of clinical information, of the internal linkage that supports person-based analysis of most administrative datasets, or by errors in linkage between multiple datasets.ObjectivesBy linking the National Down Syndrome Cytogenetic Register (NDSCR) to Hospital Episode Statistics for England (HES), we aimed to assess the quality of each and establish a consistent approach for analysis of trends in prevalence of Down's syndrome among live births in England.MethodsProbabilistic record linkage of NDSCR to HES for the period 1998-2013 was supported by linkage of babies to mothers within HES. Comparison of prevalence estimates in England were made using NDSCR only, HES data only, and linked data. Capture-recapture analysis and quantitative bias analysis were used to account for potential errors, including false positive diagnostic codes, unrecorded diagnoses, and linkage error.ResultsAnalyses of single-source data indicated increasing live birth prevalence of Down's Syndrome, particularly in the analysis of HES. Linked data indicated a contrastingly stable prevalence of 12.3 (plausible range: 11.6-12.7) cases per 10 000 live births.ConclusionCase ascertainment in NDSCR improved slightly over time, creating a picture of slowly increasing prevalence. The emerging epidemic suggested by HES primarily reflects improving linkage within HES (assignment of unique patient identifiers to hospital episodes). Administrative data are valuable but trends should be interpreted with caution, and with assessment of data quality over time. Data linkage with quantitative bias analysis can provide more robust estimation and, in this case, stronger evidence that prevalence is not increasing. Routine linkage of administrative and register data can enhance the value of each.

Dataset Information

Health checks and cardiovascular risk factor values over six years' follow-up: Matched cohort study using electronic health records in England.

Publications

Health checks and cardiovascular risk factor values over six years' follow-up: Matched cohort study using electronic health records in England.

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