The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial

McGregor, Joanna; Brooks, Caroline; Chalasani, Padmaja; Chukwuma, Jude; Hutchings, Hayley; Lyons, Ronan A.; Lloyd, Kenneth E.

doi:10.1186/1745-6215-11-39

Cited by 40 publications

(41 citation statements)

References 21 publications

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“…To create the CMD subcohort, we used an algorithm designed to identify cases of patients with CMDs (anxiety and depression), with a sensitivity of 0.32, a specificity of 0.96 and a positive predictive value of 0.74. 45,46 We took the decision to first identify diagnosed individuals because it was necessary to ensure that outcomes were not confounded by treatments prescribed for other reasons. For example, respiratory symptoms related to viral infections are not affected by household conditions.…”

Section: Methodsmentioning

confidence: 99%

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

et al. 2018

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BackgroundPoor-quality housing adversely affects residents’ health but there is a paucity of high-quality evidence to support this.ObjectiveThis research investigated the health impact of bringing housing to a national quality standard.DesignA natural experiment of improvements to housing quality analysed using repeated measures of health-care utilisation and economic outcomes at an individual person level.SettingCarmarthenshire, UK.ParticipantsA total of 32,009 residents registered for a minimum of 60 days at 8558 social homes that received housing improvements between January 2005 and March 2015.InterventionsMultiple internal and external housing improvements, including wall and loft insulation, windows and doors, heating system upgrades, new kitchens and bathrooms, garden path safety improvements and electrical system upgrades (adding power sockets, and extractor fans in kitchens and bathrooms).Main outcome measuresEmergency hospital admissions for cardiorespiratory conditions and injuries. Primary health-care utilisation for respiratory and common mental health disorders, emergency department injury attendances and health-care utilisation costs.Data sourcesCarmarthenshire County Council home address and intervention records were anonymously linked within the Secure Anonymous Information Linkage databank to demographic information from the Welsh Demographic Service data set; hospital admission data from the Patient Episode Dataset for Wales; primary care contacts and prescribed medications from general practice data; emergency department attendances from the Emergency Department Data Set; and deaths from the Office for National Statistics mortality register.MethodsThe study used a longitudinal panel design to examine changes in standard of eight housing cointervention from intervention records, and linked to individuals registered at intervention homes. Health outcomes were obtained retrospectively for each individual in a dynamic cohort and were captured for up to 123 consecutive months. An additional local authority region could not be utilised as a comparator owing to different reporting pressures resulting in the recording of a different intervention. The exposure group for each cointervention was compared with an internal reference group of people living in homes that did not receive the cointervention during their tenancy. A multilevel modelling approach was used to account for repeated observations for individuals living in intervention homes. Counts of health outcomes were analysed using negative binomial regression models to determine the effect of each cointervention that reached housing quality standards during an individual’s period of tenancy, compared with those living in properties that did not. We adjusted for potential confounding factors and for background trends in the regional general population. A cost–consequences analysis was conducted as part of the health economic evaluation.ResultsResidents aged ≥ 60 years living in homes in which electrical systems were upgraded were associated with 39% fewer admissions than those living in homes in which they were not [incidence rate ratio (IRR) 0.61, 95% confidence interval (CI) 0.53 to 0.72;p < 0.01]. Reduced admissions were also associated with windows and doors (IRR 0.71, 95% CI 0.63 to 0.81;p < 0.01), wall insulation (IRR 0.75, 95% CI 0.67 to 0.84;p < 0.01) and gardens and estates (IRR 0.73, 95% CI 0.64 to 0.83;p < 0.01) for those living in homes in which these cointervention were upgraded. There were no associations of change in emergency admissions with upgrading heating (IRR 0.91, 95% CI 0.82 to 1.01;p = 0.072), loft insulation (IRR 0.98, 95% CI 0.86 to 1.11;p = 0.695), kitchens (IRR 0.98, 95% CI 0.83 to 1.17;p = 0.843) or bathrooms (IRR 0.93, 95% CI 0.81 to 1.06;p = 0.287).LimitationsThere was no randomisation, there were incomplete data on the scale of the intervention for individual households and there were no estimates for the impact of the whole programme.ConclusionsThis complex interdisciplinary study found that hospital admissions could be avoided through improving housing quality standards.Future workAt their initiation, future non-health projects should have a built-in evaluation to allow intervention exposures to be randomly allocated to residents, with the simultaneous analysis of multiple health outcomes in one statistical model.FundingThe National Institute for Health Research Public Health Research programme.

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Section: Methodsmentioning

confidence: 99%

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

et al. 2018

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“…A previous study demonstrated that patients with depression can be accurately identified for inclusion in a trial through their primary care records 17. More recently John et al 18 investigated a number of algorithms for identifying adults with CMD using electronic primary care data using the five-item Mental Health Inventory (MHI-5) as a reference standard.…”

Section: Introductionmentioning

confidence: 99%

Defining adolescent common mental disorders using electronic primary care data: a comparison with outcomes measured using the CIS-R

et al. 2016

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ObjectiveTo compare the prevalence of common mental disorders (CMDs) derived from data held in primary care records with that measured using the revised Clinical Interview Schedule (CIS-R) in order to assess the potential robustness of findings based only on routinely collected data.Design and settingComparison study using linkage between the Avon Longitudinal Study of Parents and Children (ALSPAC) and electronic primary care records.ParticipantsWe studied 1562 adolescents who had completed the CIS-R in ALSPAC at age 17–18 years and had linkage established to their primary care records.Outcome measuresOutcome measures from ALSPAC were whether or not an individual met International Classification of Diseases-10 criteria for a diagnosis of (1) a CMD or, specifically, (2) depression. Lists of Read codes corresponding to diagnoses, symptoms and treatments were used to create 12 definitions of CMD and depression alone using the primary care data. We calculated sensitivities and specificities of these, using CIS-R definitions as the reference standard.ResultsSensitivities ranged from 5.2% to 24.3% for depression and from 3.8% to 19.2% for CMD. The specificities of all definitions were above 98% for depression and above 96% for CMD.For both outcomes, the definition that included current diagnosis, treatment or symptoms identified the highest proportion of CIS-R cases.ConclusionsMost individuals meeting case definitions for CMD based on primary care data also met CIS-R case definitions. Conversely many individuals identified as cases using the CIS-R had no evidence of CMD in their clinical records. This suggests that clinical databases are likely to yield underestimates of the burden of CMD in the population. However, clinical records appear to yield valid diagnoses which may be useful for studying risk factors and consequences of CMD. The greatest epidemiological value may be obtained when information is available from survey and clinical records.

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“…In recent years, a number of systems have been developed to improve clinical research recruitment using data from electronic health records (EHRs). Proof-of-concept trials have demonstrated that routinely collected primary care data could be used to identify potential participants for a depression trial [10], and a chronic obstructive pulmonary disease (COPD) trial [11] had good sensitivity and specificity. Clinical trial alert systems that drew information from the EHRs have been piloted successfully to identify potential study participants at the point of care [12,13].…”

Section: Introductionmentioning

confidence: 99%

Managing clinical research permissions electronically: A novel approach to enhancing recruitment and managing consents

et al. 2013

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Background One mechanism to increase participation in research is to solicit potential research participants’ general willingness to be recruited into clinical trials. Such research permissions and consents typically are collected on paper upon patient registration. We describe a novel method of capturing this information electronically. Purpose The objective is to enable the collection of research permissions and informed consent data electronically to permit tracking of potential research participants’ interest in current and future research involvement and to provide a foundation for facilitating the research workflow. Methods The project involved systematic analysis focused on key areas, including existing business practices, registration processes, and permission collection workflows, and ascertaining best practices for presenting consent information to users via tablet technology and capturing permissions data. Analysis was followed by an iterative software development cycle with feedback from subject matter experts and users. Results An initial version of the software was piloted at one institution in South Carolina for a period of 1 year, during which consents and permission were collected during 2524 registrations of patients. The captured research permission data were transmitted to a clinical data warehouse. The software was later released as an open-source package that can be adopted for use by other institutions. Limitations There are significant ethical, legal, and informatics challenges that must be addressed at an institution to deploy such a system. We have not yet assessed the long-term impact of the system on recruitment of patients to clinical trials. Conclusions We propose that by improving the ability to track willing potential research participants, we can improve recruitment into clinical trials and, in the process, improve patient education by introducing multimedia to informed consent documents.

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The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial

Cited by 40 publications

References 21 publications

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

Health impact, and economic value, of meeting housing quality standards: a retrospective longitudinal data linkage study

Defining adolescent common mental disorders using electronic primary care data: a comparison with outcomes measured using the CIS-R

Managing clinical research permissions electronically: A novel approach to enhancing recruitment and managing consents

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