Variations in dementia diagnosis in England and association with general practice characteristics

Walker, Ian; Lord, Paul A; Farragher, Tracey

doi:10.1017/s146342361700007x

Cited by 14 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While we were able to link pollution exposures to the primary care record to obtain diagnoses of dementia, there are concerns around the variability of dementia diagnoses in UK primary care, 24 and a recent review has concluded that dementia diagnoses on primary care databases may not be an accurate reflection of the true prevalence. 25 Under-recording is thought to be a common issue, as the diagnosis is associated with a stigma for many, and GPs may be reluctant to diagnose dementia unless highly certain.…”

Section: Discussionmentioning

confidence: 99%

Are noise and air pollution related to the incidence of dementia? A cohort study in London, England

et al. 2018

View full text Add to dashboard Cite

ObjectiveTo investigate whether the incidence of dementia is related to residential levels of air and noise pollution in London.DesignRetrospective cohort study using primary care data.Setting75 Greater London practices.Participants130 978 adults aged 50–79 years registered with their general practices on 1 January 2005, with no recorded history of dementia or care home residence.Primary and secondary outcome measuresA first recorded diagnosis of dementia and, where specified, subgroups of Alzheimer’s disease and vascular dementia during 2005–2013. The average annual concentrations during 2004 of nitrogen dioxide (NO2), particulate matter with a median aerodynamic diameter ≤2.5 µm (PM2.5) and ozone (O3) were estimated at 20×20 m resolution from dispersion models. Traffic intensity, distance from major road and night-time noise levels (Lnight) were estimated at the postcode level. All exposure measures were linked anonymously to clinical data via residential postcode. HRs from Cox models were adjusted for age, sex, ethnicity, smoking and body mass index, with further adjustments explored for area deprivation and comorbidity.Results2181 subjects (1.7%) received an incident diagnosis of dementia (39% mentioning Alzheimer’s disease, 29% vascular dementia). There was a positive exposure response relationship between dementia and all measures of air pollution except O3, which was not readily explained by further adjustment. Adults living in areas with the highest fifth of NO2 concentration (>41.5 µg/m3) versus the lowest fifth (<31.9 µg/m3) were at a higher risk of dementia (HR=1.40, 95% CI 1.12 to 1.74). Increases in dementia risk were also observed with PM2.5, PM2.5 specifically from primary traffic sources only and Lnight, but only NO2 and PM2.5 remained statistically significant in multipollutant models. Associations were more consistent for Alzheimer’s disease than vascular dementia.ConclusionsWe have found evidence of a positive association between residential levels of air pollution across London and being diagnosed with dementia, which is unexplained by known confounding factors.

show abstract

Section: Discussionmentioning

confidence: 99%

Are noise and air pollution related to the incidence of dementia? A cohort study in London, England

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Diagnostic accuracy and awareness of different symptoms may play a role in the association between the severity and nature of cognitive function and diagnosis; a meta-analysis of physician accuracy identified severity of dementia as the main predictor for accurate diagnosis. 48…”

Section: Severity and Nature Of Cognitive Impairmentmentioning

confidence: 99%

“…62 Evidence suggests that some GPs recognise and manage patients as having dementia without recording them in primary care record systems as having dementia. 19,48 It is possible that recent increases in the number of people diagnosed with dementia simply reflect an improvement in the coding of existing knowledge; further ongoing epidemiological studies such as CFAS and CADDY are needed to monitor this.…”

Section: Inpatient Hospital Visitsmentioning

confidence: 99%

Undiagnosed dementia in primary care: a record linkage study

Aldus

Arthur

Dennington-Price

et al. 2020

Health Serv Deliv Res

View full text Add to dashboard Cite

Background The number of people living with dementia is greater than the number with a diagnosis of dementia recorded in primary care. This suggests that a significant number are living with dementia that is undiagnosed. Little is known about this group and there is little quantitative evidence regarding the consequences of diagnosis for people with dementia. Objectives The aims of this study were to (1) describe the population meeting the criteria for dementia but without diagnosis, (2) identify predictors of being diagnosed and (3) estimate the effect of diagnosis on mortality, move to residential care, social participation and well-being. Design A record linkage study of a subsample of participants (n = 598) from the Cognitive Function and Ageing Study II (CFAS II) (n = 7796), an existing cohort study of the population of England aged ≥ 65 years, with standardised validated assessment of dementia and consent to access medical records. Data sources Data on dementia diagnoses from each participant’s primary care record and covariate and outcome data from CFAS II. Setting A population-representative cohort of people aged ≥ 65 years from three regions of England between 2008 and 2011. Participants A total of 598 CFAS II participants, which included all those with dementia who consented to medical record linkage (n = 449) and a stratified sample without dementia (n = 149). Main outcome measures The main outcome was presence of a diagnosis of dementia in each participant’s primary care record at the time of their CFAS II assessment(s). Other outcomes were date of death, cognitive performance scores, move to residential care, hospital stays and social participation. Results Among people with dementia, the proportion with a diagnosis in primary care was 34% in 2008–11 and 44% in 2011–13. In both periods, a further 21% had a record of a concern or a referral but no diagnosis. The likelihood of having a recorded diagnosis increased with severity of impairment in memory and orientation, but not with other cognitive impairment. In multivariable analysis, those aged ≥ 90 years and those aged < 70 years were less likely to be diagnosed than other age groups; those living with a spouse (odds ratio 2.38, 95% confidence interval 1.04 to 5.41) were more likely to be diagnosed than people living alone. The median time to diagnosis from first meeting the criteria for dementia was 3 years. Diagnosis did not affect survival or the probability of a move to residential care. Limitations People with moderate to severe dementia at baseline could not consent to record linkage. The small numbers in some groups limited power to detect effects. Conclusions The lack of relationship between severity of non-memory impairment and diagnosis may reflect low awareness of other symptoms of dementia. There remains little objective evidence for benefits of diagnosis for people with dementia. Future work Potential benefits of diagnosis can be realised only if effective interventions are accessible to patients and carers. Future work should focus on improving support for people living with cognitive impairment. Study registration National Institute for Health Research Clinical Research Network Central Portfolio Management System (CPMS 30655). Funding This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 20. See the NIHR Journals Library website for further project information.

show abstract

“…Examples of the above scenarios are mental health conditions, such as anxiety and depression, stigmatized neurological conditions such as dementia, and chronic conditions which may have a "silent" onset, such as diabetes (18,19). Figures from a range of studies and two meta-analyses shown in Table 1, they show that estimated rates of under-diagnosis for dementia, depression and anxiety, average about 50% (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). Further assessments of completeness of EHR data, such as the review by Chan et al (36), show that missingness of parameters such as blood pressure and smoking status can be as high as 38-51%, but are less likely to be missing in populations where these parameters are important for clinical care, such as a high risk cardiovascular disease cohort.…”

Section: Dimensions Of Health Information Quality In Electronic Healtmentioning

confidence: 99%

Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data

Ford

Rooney

Hurley

et al. 2020

Front. Public Health

View full text Add to dashboard Cite

Background: Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias toward the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterized by under-diagnosis. Methods: Study 1: We created synthetic data, produced to mimic structured EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic. Results: Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR. Conclusions: The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult Ford et al. Bayesian Analysis for EHR Data to find. Our simple model made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrate the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missing diagnoses in EHR data.

show abstract

Variations in dementia diagnosis in England and association with general practice characteristics

Cited by 14 publications

References 15 publications

Are noise and air pollution related to the incidence of dementia? A cohort study in London, England

Are noise and air pollution related to the incidence of dementia? A cohort study in London, England

Undiagnosed dementia in primary care: a record linkage study

Can the Use of Bayesian Analysis Methods Correct for Incompleteness in Electronic Health Records Diagnosis Data? Development of a Novel Method Using Simulated and Real-Life Clinical Data

Contact Info

Product

Resources

About