The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England

Antihypertensive Medications and Eczematous Dermatitis in Older Adults

Importance

Rates of physician-diagnosed eczema have been increasing among older adults, but little is known regarding the pathophysiologic processes and best treatments in this subgroup. Preliminary data suggest that medications-antihypertensive medications in particular-may contribute to eczematous dermatitis; however, there are limited population-based data on the proportion of eczematous dermatitis diagnoses among older adults that may be attributed to antihypertensive drugs.

Objectives

To determine whether antihypertensive drug use is associated with eczematous dermatitis in older adults.

Design, Settings, and Participants

This was a longitudinal cohort study of a population-based sample of individuals 60 years and older without a diagnosis of eczematous dermatitis at baseline. It was conducted at primary care practices participating in The Health Improvement Network in the United Kingdom from January 1, 1994, to January 1, 2015. Data analyses were performed from January 6, 2020, to February 6, 2024.

Exposure

Exposure date by first prescription for an antihypertensive drug within each drug class.

Main outcome measures

Newly active eczematous dermatitis was based on the first date for 1 of the 5 most common eczema codes used in a previously validated algorithm.

Results

Among the total study sample of 1 561 358 older adults (mean [SD] age, 67 [9] years; 54% female), the overall prevalence of eczematous dermatitis was 6.7% during a median (IQR) follow-up duration of 6 (3-11) years. Eczematous dermatitis incidence was higher among participants receiving antihypertensive drugs than those who did not (12 vs 9 of 1000 person-years of follow-up). Adjusted Cox proportional hazard models found that participants who received any antihypertensive drugs had a 29% increased hazard rate of any eczematous dermatitis (hazard ratio [HR], 1.29; 95% CI, 1.26-1.31). When assessing each antihypertensive drug class individually, the largest effect size was observed for diuretic drugs (HR, 1.21; 95% CI, 1.19-1.24) and calcium channel blockers (HR, 1.16; 95% CI, 1.14-1.18), and the smallest effect sizes were for angiotensin-converting enzyme inhibitors (HR, 1.02; 95% CI, 1.00-1.04) and β-blockers (HR, 1.04; 95% CI, 1.02-1.06).

Conclusions and Relevance

This cohort study found that antihypertensive drugs were associated with a small increased rate of eczematous dermatitis, with effect sizes largest for calcium channel blockers and diuretic drugs, and smallest for angiotensin-converting enzyme inhibitors and β-blockers. Although additional research is needed to understand the mechanisms underlying the association, these data could be helpful to clinicians to guide management when a patient presents with eczematous dermatitis in older age.

Develop and Validate a Computable Phenotype for the Identification of Alzheimer's Disease Patients Using Electronic Health Record Data

INTRODUCTION

Alzheimer's Disease (AD) are often misclassified in electronic health records (EHRs) when relying solely on diagnostic codes. This study aims to develop a more accurate, computable phenotype (CP) for identifying AD patients by using both structured and unstructured EHR data.

METHODS

We used EHRs from the University of Florida Health (UF Health) system and created rule-based CPs iteratively through manual chart reviews. The CPs were then validated using data from the University of Texas Health Science Center at Houston (UT Health) and the University of Minnesota (UMN).

RESULTS

Our best-performing CP is " patient has at least 2 AD diagnoses and AD-related keywords " with an F1-score of 0.817 at UF, and 0.961 and 0.623 at UT Health and UMN, respectively.

DISCUSSION

We developed and validated rule-based CPs for AD identification with good performance, crucial for studies that aim to use real-world data like EHRs.

Dementia risk in patients with type 2 diabetes: Comparing metformin with no pharmacological treatment

INTRODUCTION

Metformin has been suggested as a therapeutic agent for dementia, but the relevant evidence has been partial and inconsistent.

METHODS

We established a national cohort of 210,237 type 2 diabetes patients in the UK Clinical Practice Research Datalink. Risks of incident dementia were compared between metformin initiators and those who were not prescribed any anti-diabetes medication during follow-up.

RESULTS

Compared with metformin initiators (n = 114,628), patients who received no anti-diabetes medication (n = 95,609) had lower HbA1c and better cardiovascular health at baseline. Both Cox regression and propensity score weighting analysis showed metformin initiators had lower risk of dementia compared to those non-users (adjusted hazard ratio = 0.88 [95% confidence interval: 0.84-0.92] and 0.90 [0.84-0.96]). Patients on long-term metformin treatment had an even lower risk of dementia.

DISCUSSION

Metformin may act beyond its glycemic effect and reduce dementia risk to an even lower level than that of patients with milder diabetes and better health profiles.

HIGHLIGHTS

Metformin initiators had a significantly lower risk of dementia compared with patients not receiving anti-diabetes medication. Compared with metformin initiators, diabetes patients not receiving pharmacological treatment had better glycemic profiles at baseline and during follow-up. Patients on long-term metformin treatment had an even lower risk of subsequent dementia incidence. Metformin may act beyond its effect on hyperglycemia and has the potential of being repurposed for dementia prevention.

Impact of dementia and drug compliance on patients with acute myocardial infarction

BACKGROUND

In South Korea, the number of people with dementia is rising at a worrisome rate, and many of them also have acute myocardial infarction (AMI), a disease with a high mortality rate.

HYPOTHESIS

We speculated that dementia and drug compliance have significant impact on the mortality of patients with AMI.

METHODS

The study derived data from the National Health Insurance Service-Senior for a retrospective cohort study. The total number of patients diagnosed with AMI for the first time between 2007 and 2013 was 16 835, among whom 2021 had dementia. Medication possession ratio (MPR) was used to assess medication adherence.

RESULTS

AMI patients with dementia had unfavorable baseline characteristics; they had significantly higher risk of all-cause mortality (hazard ratio [HR]: 2.49; 95% confidence interval [CI]: 2.34-2.66; p < .001) and lower MPR (aspirin: 21.9% vs. 42.8%; p < .001). AMI patients were stratified by presence of dementia and medication adherence, and the survival rate was the highest among those with no dementia and good adherence, followed by those with no dementia and poor adherence, those with dementia and good adherence, and those with dementia and poor adherence. The multivariable analysis revealed that dementia (HR: 1.64; 95% CI: 1.53-1.75; p < .001) and poor adherence to medication (HR: 1.60; 95% CI: 1.49-1.71; p < .001) had a significant association with all-cause mortality in AMI patients.

CONCLUSIONS

AMI patients with dementia have a higher mortality rate. Their prognosis is negatively affected by their poorer medication adherence than patients without dementia.

Early prediction of Alzheimer's disease and related dementias using real-world electronic health records

INTRODUCTION

This study aims to explore machine learning (ML) methods for early prediction of Alzheimer's disease (AD) and related dementias (ADRD) using the real-world electronic health records (EHRs).

METHODS

A total of 23,835 ADRD and 1,038,643 control patients were identified from the OneFlorida+ Research Consortium. Two ML methods were used to develop the prediction models. Both knowledge-driven and data-driven approaches were explored. Four computable phenotyping algorithms were tested.

RESULTS

The gradient boosting tree (GBT) models trained with the data-driven approach achieved the best area under the curve (AUC) scores of 0.939, 0.906, 0.884, and 0.854 for early prediction of ADRD 0, 1, 3, or 5 years before diagnosis, respectively. A number of important clinical and sociodemographic factors were identified.

DISCUSSION

We tested various settings and showed the predictive ability of using ML approaches for early prediction of ADRD with EHRs. The models can help identify high-risk individuals for early informed preventive or prognostic clinical decisions.

A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model

Background and Aims

As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care.

Methods

This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis.

Results

The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment.

Conclusion

The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.

[General aspects of dementia disorders]

The prevalence of dementia increases with age. In rare cases, people younger than 65 years old are also affected, with substantial consequences for the professional life. The symptoms depend on the form of dementia and can vary individually. Impairment of short-term memory is not always in the foreground and other neurocognitive domains, such as the disturbance of executive functions can have a significant impact on the ability to cope with everyday life. Pathophysiologically, neurodegenerative dementias with the major forms of Alzheimer's dementia, Lewy body dementia, and frontotemporal dementia are distinguished from vascular dementias. Mixed forms are common. There is no curative treatment, but progression can be slowed by nonpharmacological measures and, especially in Alzheimer's dementia, by pharmacological treatment. Appropriate measures can promote independence and autonomy for as long as possible; however, in the course of the disease restrictions in the extended activities of independent living will initially occur, such as banking transactions, use of means of transport and, in the further course, also in the basic activities of daily living. Legal capacity and the ability to consent to health interventions are restricted sooner or later; however, this must always be evaluated for the specific situation and is not generally the case with the diagnosis of dementia. Instruments such as living wills, identification of a health care proxy, and advanced care planning should be used at an early stage. To decrease family caregiver burden with the increased risk of developing depression, supportive, accompanying measures and education are of great importance.

Incidence, morbidity, mortality and disparities in dementia: A population linked electronic health records study of 4.3 million individuals

INTRODUCTION

We report dementia incidence, comorbidities, reasons for health-care visits, mortality, causes of death, and examined dementia patterns by relative deprivation in the UK.

METHOD

A longitudinal cohort analysis of linked electronic health records from 4.3 million people in the UK was conducted to investigate dementia incidence and mortality. Reasons for hospitalization and causes of death were compared in individuals with and without dementia.

RESULTS

From 1998 to 2016 we observed 145,319 (3.1%) individuals with incident dementia. Repeated hospitalizations among senior adults for infection, unknown morbidity, and multiple primary care visits for chronic pain were observed prior to dementia diagnosis. Multiple long-term conditions are present in half of the individuals at the time of diagnosis. Individuals living in high deprivation areas had higher dementia incidence and high fatality.

DISCUSSION

There is a considerable disparity of dementia that informs priorities of prevention and provision of patient care.

Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.

Adult atopic eczema and the risk of dementia: A population-based cohort study

BACKGROUND

Chronic inflammatory conditions have been linked to dementia, but little is known about the role of atopic eczema, an inflammatory condition recently recognized to be common among older adults.

OBJECTIVE

To determine whether active atopic eczema is associated with incident dementia.

METHODS

A longitudinal cohort study of 1,767,667 individuals aged 60 to 99 years registered with The Health Improvement Network, a primary care cohort in the United Kingdom. The diagnoses of atopic eczema and dementia were identified using medical record codes.

RESULTS

The incidence of dementia was 57 per 10,000 person-years among those with atopic eczema during follow-up (12.1% of the population) compared with 44 per 10,000 person-years in the control group. This translated to a 27% increased risk of dementia (hazard ratio, 1.27; 95% CI, 1.23-1.30) in adjusted Cox proportional hazard models. Similar associations were observed in subgroup analyses of vascular dementia and Alzheimer's disease. The association persisted after additionally adjusting for the use of systemic corticosteroids (hazard ratio, 1.29; 95% CI, 1.26-1.33) and potential mediators (hazard ratio, 1.19; 95% CI, 1.16-1.22). More severe eczema was associated with a higher risk of dementia.

LIMITATIONS

Lack of detailed data on severity.

CONCLUSION

Atopic eczema was associated with a small but increased risk of incident dementia. The association increased with the severity of atopic eczema.

Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning

BACKGROUND

Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable.

METHODS

We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer's disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets.

RESULTS

We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42-73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters.

CONCLUSION

Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.

What gets recorded, counts: dementia recording in primary care compared with a specialist database

BACKGROUND

databases of electronic health records are powerful tools for dementia research, but data can be influenced by incomplete recording. We examined whether people with dementia recorded in a specialist database (from a mental health and dementia care service) differ from those recorded in primary care.

METHODS

a retrospective cohort study of the population covered by Lambeth DataNet (primary care electronic records) between 2007 and 2019. Documentation of dementia diagnosis in primary care coded data and linked records in a specialist database (Clinical Records Interactive Search) were compared.

RESULTS

3,859 people had dementia documented in primary care codes and 4,266 in the specialist database, with 2,886/5,239 (55%) documented in both sources. Overall, 55% were labelled as having Alzheimer's dementia and 29% were prescribed dementia medication, but these proportions were significantly higher in those documented in both sources. The cohort identified from the specialist database were less likely to live in a care home (prevalence ratio 0.73, 95% confidence interval 0.63-0.85), have multimorbidity (0.87, 0.77-0.98) or consult frequently (0.91, 0.88-0.95) than those identified through primary care codes, although mortality did not differ (0.98, 0.91-1.06).

DISCUSSION

there is under-recording of dementia diagnoses in both primary care and specialist databases. This has implications for clinical care and for generalizability of research. Our results suggest that using a mental health database may under-represent those patients who have more frailty, reflecting differential referral to mental health services, and demonstrating how the patient pathways are an important consideration when undertaking database studies.

Markers of dementia-related health in primary care electronic health records

OBJECTIVES

Identifying routinely recorded markers of poor health in patients with dementia may help treatment decisions and evaluation of earlier outcomes in research. Our objective was to determine whether a set of credible markers of dementia-related health could be identified from primary care electronic health records (EHR).

METHODS

The study consisted of (i) rapid review of potential measures of dementia-related health used in EHR studies; (ii) consensus exercise to assess feasibility of identifying these markers in UK primary care EHR; (iii) development of UK EHR code lists for markers; (iv) analysis of a regional primary care EHR database to determine further potential markers; (v) consensus exercise to finalise markers and pool into higher domains; (vi) determination of 12-month prevalence of domains in EHR of 2328 patients with dementia compared to matched patients without dementia.

RESULTS

Sixty-three markers were identified and mapped to 13 domains: Care; Home Pressures; Severe Neuropsychiatric; Neuropsychiatric; Cognitive Function; Daily Functioning; Safety; Comorbidity; Symptoms; Diet/Nutrition; Imaging; Increased Multimorbidity; Change in Dementia Drug. Comorbidity was the most prevalent recorded domain in dementia (69%). Home Pressures were the least prevalent domain (1%). Ten domains had a statistically significant higher prevalence in dementia patients, one (Comorbidity) was higher in non-dementia patients, and two (Home Pressures, Diet/Nutrition) showed no association with dementia.

CONCLUSIONS

EHR captures important markers of dementia-related health. Further research should assess if they indicate dementia progression. These markers could provide the basis for identifying individuals at risk of faster progression and outcome measures for use in research.

Estimating the lifetime risk of dementia using nationwide individually linked cause-of-death and health register data

BACKGROUND

Previous estimates of the lifetime risk of dementia are restricted to older age groups and may suffer from selection bias. In this study, we estimated the lifetime risk of dementia starting at birth using nationwide integral linked health register data.

METHODS

We studied all deaths in The Netherlands in 2017 (n = 147 866). Dementia was assessed using the cause-of-death registration, individually linked with registers covering long-term care, specialized mental care, dispensed medicines, hospital discharges and claims, and primary care. The proportion of deaths with dementia was calculated for the total population and according to age at death and sex.

RESULTS

According to all data sources combined, 24.0% of the population dies in the presence of dementia. This proportion is higher for females (29.4%) than for males (18.3%). Using multiple causes of death only, the proportion with dementia is 17.9%. Sequential addition of long-term care and hospital discharge data increased the estimate by 4.0 and 1.5%-points, respectively. Further addition of dispensed medicines, hospital claims and specialized mental care data added another 0.6%-points. Among persons who die at age ≤65-70 years, the proportion with dementia is ≤6.2%. After age 70, the proportion rises sharply, with a peak of 43.9% for females and 33.1% for males at age 90-95 years.

CONCLUSIONS

Around one-fourth of the Dutch population is diagnosed with dementia at some point in life and dies in the presence of dementia. It is a major challenge to arrange optimal care for this group.

Risk factors for dementia development, frailty, and mortality in older adults with epilepsy - A population-based analysis

OBJECTIVE

Although the prevalence of comorbid epilepsy and dementia is expected to increase, the impact is not well understood. Our objectives were to examine risk factors associated with incident dementia and the impact of frailty and dementia on mortality in older adults with epilepsy.

METHODS

The CALIBER scientific platform was used. People with incident epilepsy at or after age 65 were identified using Read codes and matched by age, sex, and general practitioner to a cohort without epilepsy (10:1). Baseline cohort characteristics were compared using conditional logistic regression models. Multivariate Cox proportional hazard regression models were used to examine the impact of frailty and dementia on mortality, and to assess risk factors for dementia development.

RESULTS

One thousand forty eight older adults with incident epilepsy were identified. The odds of having dementia at baseline were 7.39 [95% CI 5.21-10.50] times higher in older adults with epilepsy (n = 62, 5.92%) compared to older adults without epilepsy (n = 88, 0.86%). In the final multivariate Cox model (n = 326), age [HR: 1.20, 95% CI 1.09-1.32], Charlson comorbidity index score [HR: 1.26, 95% CI 1.10-1.44], and sleep disturbances [HR: 2.41, 95% CI 1.07-5.43] at baseline epilepsy diagnosis were significantly associated with an increased hazard of dementia development over the follow-up period. In a multivariate Cox model (n = 1047), age [HR: 1.07, 95% CI 1.03-1.11], baseline dementia [HR: 2.66, 95% CI 1.65-4.27] and baseline e-frailty index score [HR: 11.55, 95% CI 2.09-63.84] were significantly associated with a higher hazard of death among those with epilepsy. Female sex [HR: 0.77, 95% CI 0.59-0.99] was associated with a lower hazard of death.

SIGNIFICANCE

The odds of having dementia were higher in older adults with incident epilepsy. A higher comorbidity burden acts as a risk factor for dementia, while prevalent dementia and increasing frailty were associated with mortality.

Glycemic Control, Diabetic Complications, and Risk of Dementia in Patients With Diabetes: Results From a Large U.K. Cohort Study

OBJECTIVE

Type 2 diabetes is an established risk factor for dementia. However, the roles of glycemic control and diabetic complications in the development of dementia have been less well substantiated. This large-scale cohort study aims to examine associations of longitudinal HbA_1c levels and diabetic complications with the risk of dementia incidence among patients with type 2 diabetes.

RESEARCH DESIGN AND METHODS

Data of eligible patients with diabetes, aged ≥50 years in the U.K. Clinical Practice Research Datalink from 1987 to 2018, were analyzed. Time-varying Cox regressions were used to estimate adjusted hazard ratios (HRs) and 95% CIs for dementia risk.

RESULTS

Among 457,902 patients with diabetes, 28,627 (6.3%) incident dementia cases were observed during a median of 6 years' follow-up. Patients with recorded hypoglycemic events or microvascular complications were at higher risk of dementia incidence compared with those without such complications (HR 1.30 [95% CI 1.22-1.39] and 1.10 [1.06-1.14], respectively). The HbA_1c level, modeled as a time-varying exposure, was associated with increased dementia risk (HR 1.08 [95% CI 1.07-1.09] per 1% HbA_1c increment) among 372,287 patients with diabetes with postdiagnosis HbA_1c records. Similarly, a higher coefficient of variation of HbA_1c during the initial 3 years of follow-up was associated with higher subsequent dementia risk (HR 1.03 [95% CI 1.01-1.04] per 1-SD increment).

CONCLUSIONS

Higher or unstable HbA_1c levels and the presence of diabetic complications in patients with type 2 diabetes are associated with increased dementia risk. Effective management of glycemia might have a significant role in maintaining cognitive health among older adults with diabetes.

The rising tide of dementia deaths: triangulation of data from three routine data sources using the Clinical Practice Research Datalink

BACKGROUND

Dementia is currently the leading certified underlying cause of death in England. We assess how dementia recording on Office for National Statistics death certificates (ONS) corresponded to recording in general practice records (GP) and Hospital Episode Statistics (HES).

METHODS

Retrospective study of deaths (2001-15) in 153 English General Practices contributing to the Clinical Practice Research Datalink, with linked ONS and HES records.

RESULTS

Of 207,068 total deaths from any cause, 19,627 mentioned dementia on the death certificate with 10,253 as underlying cause; steady increases occurred from 2001 to 2015 (any mention 5.3 to 15.4 %, underlying cause 2.7 to 10 %). Including all data sources, recording of any dementia increased from 13.2 to 28.6 %. In 2015, only 53.8 % of people dying with dementia had dementia recorded on their death certificates. Among deaths mentioning dementia on the death certificate, the recording of a prior diagnosis of dementia in GP and HES rose markedly over the same period. In 2001, only 76.3 % had a prior diagnosis in GP and/or HES records; by 2015 this had risen to 95.7 %. However, over the same period the percentage of all deaths with dementia recorded in GP or HES but not mentioned on the death certificate rose from 7.9 to 13.3 %.

CONCLUSIONS

Dementia recording in all data sources increased between 2001 and 2015. By 2015 the vast majority of deaths mentioning dementia had supporting evidence in primary and/or secondary care. However, death certificates were still providing an inadequate picture of the number of people dying with dementia.

Socio-economic predictors of time to care home admission in people living with dementia in Wales: A routine data linkage study

OBJECTIVES

Limited research has shown that people with dementia (PwD) from lower socio-economic backgrounds can face difficulties in accessing the right care at the right time. This study examined whether socio-economic status (SES) and rural versus urban living location are associated with the time between diagnosis and care home admission in PwD living in Wales, UK.

METHODS/DESIGN

This study linked routine health data and an e-cohort of PwD who have been admitted into a care home between 2000 and 2018 living in Wales. Survival analysis explored the effects of SES, living location, living situation, and frailty on the time between diagnosis and care home admission.

RESULTS

In 34,514 PwD, the average time between diagnosis and care home admission was 1.5 (±1.4) years. Cox regression analysis showed that increased age, living alone, frailty, and living in less disadvantaged neighbourhoods were associated with faster rate to care home admission. Living in rural regions predicted a slower rate until care home admission.

CONCLUSIONS

This is one of the first studies to show a link between socio-economic factors on time to care home admission in dementia. Future research needs to address variations in care needs between PwD from different socio-economic and geographical backgrounds.

Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review

Background

Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research.

Objective

This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions.

Methods

A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines.

Results

A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance.

Conclusions

Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Indicators of dementia disease progression in primary care: An electronic health record cohort study

BACKGROUND AND PURPOSE

The objectives were to assess the feasibility and validity of using markers of dementia-related health as indicators of dementia progression in primary care, by assessing the frequency with which they are recorded and by testing the hypothesis that they are associated with recognised outcomes of dementia. The markers, in 13 domains, were derived previously through literature review, expert consensus, and analysis of regional primary care records.

METHODS

The study population consisted of patients with a recorded dementia diagnosis in the Clinical Practice Research Datalink, a UK primary care database linked to secondary care records. Incidence of recorded domains in the 36 months after diagnosis was determined. Associations of recording of domains with future hospital admission, palliative care, and mortality were derived.

RESULTS

There were 30,463 people with diagnosed dementia. Incidence of domains ranged from 469/1000 person-years (Increased Multimorbidity) to 11/1000 (Home Pressures). An increasing number of domains in which a new marker was recorded in the first year after diagnosis was associated with hospital admission (hazard ratio for ≥4 domains vs. no domains = 1.24; 95% confidence interval = 1.15-1.33), palliative care (1.87; 1.62-2.15), and mortality (1.57; 1.47-1.67). Individual domains were associated with outcomes with varying strengths of association.

CONCLUSIONS

Feasibility and validity of potential indicators of progression of dementia derived from primary care records are supported by their frequency of recording and associations with recognised outcomes. Further research should assess whether these markers can help identify patients with poorer prognosis to improve outcomes through stratified care and targeted support.

Data-driven discovery of probable Alzheimer's disease and related dementia subphenotypes using electronic health records

Introduction

We sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.

Methods

A retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5‐fold cross‐validation. XGBoost was used to rank the variable importance.

Results

Four subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took anti‐dementia drugs and had sensory problems, such as deafness and hearing impairment.

The 0‐year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764 (SD: 0.02); the 6‐month model, 0.751 (SD: 0.02); the 1‐year model, 0.752 (SD: 0.02); the 2‐year model, 0.749 (SD: 0.03); and the 3‐year model, 0.735 (SD: 0.03), respectively. Based on variable importance, the top‐ranked comorbidities included depression, stroke/transient ischemic attack, hypertension, anxiety, mobility impairments, and atrial fibrillation. The top‐ranked medications included anti‐dementia drugs, antipsychotics, antiepileptics, and antidepressants.

Conclusions

Four subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan.

An Overview of Treating People with Comorbid Dementia: Implications for Cancer Care

With increasing prevalence of both cancer and dementia in the UK, due to an ageing population, oncology healthcare professionals will experience higher numbers of people with both conditions. As dementia is highly heterogeneous and symptoms vary from individual to individual, it presents specific challenges for healthcare professionals, people with dementia and caregivers alike. This overview will describe current theories that explain the association between cancer and dementia, report prevalence rates and highlight the evidence on the impact of having a diagnosis of dementia on outcomes along the cancer pathway from cancer symptom detection to cancer treatment outcomes. It suggests that although prevalence rates of cancer and dementia are typically lower than other comorbidities, people with cancer and dementia have poorer cancer-related outcomes. This includes later stage cancer diagnoses, fewer cancer treatment options and an increased risk of death compared with people who have cancer alone or other comorbid conditions. Considerations for cancer treatment decision making and management are proposed to improve patient experience for this group.

Prevalence and geographical variation of dementia in New Zealand from 2012 to 2015: Brief report utilising routinely collected data within the Integrated Data Infrastructure

OBJECTIVES

There are no national dementia epidemiological studies using New Zealand (NZ) data. NZ routinely collects health-care data within the Integrated Data Infrastructure (IDI). The study objectives were to 1) investigate late-onset dementia estimates using the IDI between 2012-2015 and compare these with 2) published estimates, and 3) variations between North and South Islands and ethnicity.

METHODS

A population-based, retrospective cohort design was applied to routinely collected de-identified health/administrative IDI data. Dementia was defined by ICD-10-AM dementia codes or anti-dementia drugs.

RESULTS

Approximately 2% of those aged ≥60 years had dementia, lower than published estimates. Dementia was higher in North Island; in 80- to 89-year-olds; among the Māori population when age-standardised, and 9% of all dementia cases had >1 dementia sub-type.

CONCLUSIONS

To our knowledge, this is the first study ascertaining dementia estimates using NZ's whole-of-population IDI data. Estimates were lower than existing NZ estimates, for several reasons. Further work is required, including expanding IDI data sets, to develop future estimates that better reflect NZ's diverse population.

Risk assessment of dementia after hysterectomy: Analysis of 14-year data from the National Health Insurance Research Database in Taiwan

BACKGROUND

Anesthesia and surgery may increase the risk of dementia in the elderly, but the higher prevalence of dementia in women and other evidence suggest that dementia risk increases in younger women undergoing hysterectomy. In this study, we assessed the risk of dementia after hysterectomy.

METHODS

Hysterectomies registered in the National Health Insurance Research Database from 2000 to 2013 were evaluated using a retrospective generational research method. Multivariate Cox regression analysis was used to assess the effect of age at surgery, anesthesia method, and surgery type on the hazard ratio (HR) for the development of dementia.

RESULTS

Among 280 308 patients who underwent hysterectomy, 4753 (1.7%) developed dementia. Age at surgery and anesthesia method were associated with the occurrence of dementia, independent of surgery type. Among patients 30-49 years of age, general anesthesia (GA) was associated with a higher risk of dementia than spinal anesthesia (SA). The HR for GA was 2.678 (95% confidence interval [CI] = 1.269-5.650) and the risk of dementia increased by 7.4% for every 1-year increase in age (HR = 1.074; 95% CI = 1.048-1.101). In patients >50 years of age, the HR for GA was 1.206 (95% CI = 1.057-1.376), and the risk of dementia increased by 13.0% for every 1-year increase in age (HR = 1.130; 95% CI = 1.126-1.134).

CONCLUSION

The risk of dementia in women who underwent hysterectomy was significantly affected by older age at surgery, and the risk might not increase linearly with age, but show instead an S-curve with exponential increase at about 50 years of age. Although less significant, GA was associated with higher risk than SA, and the effect of the anesthesia method was greater in patients <50 years of age. In contrast, the surgical procedure used was not associated to the risk of dementia.

The Secure Anonymised Information Linkage databank Dementia e-cohort (SAIL-DeC)

Introduction

The rising burden of dementia is a global concern, and there is a need to study its causes, natural history and outcomes. The Secure Anonymised Information Linkage (SAIL) Databank contains anonymised, routinely-collected healthcare data for the population of Wales, UK. It has potential to be a valuable resource for dementia research owing to its size, long follow-up time and prospective collection of data during clinical care.

Objectives

We aimed to apply reproducible methods to create the SAIL dementia e-cohort (SAIL-DeC). We created SAIL-DeC with a view to maximising its utility for a broad range of research questions whilst minimising duplication of effort for researchers.

Methods

SAIL contains individual-level, linked primary care, hospital admission, mortality and demographic data. Data are currently available until 2018 and future updates will extend participant follow-up time. We included participants who were born between 1st January 1900 and 1st January 1958 and for whom primary care data were available. We applied algorithms consisting of International Classification of Diseases (versions 9 and 10) and Read (version 2) codes to identify participants with and without all-cause dementia and dementia subtypes. We also created derived variables for comorbidities and risk factors.

Results

From 4.4 million unique participants in SAIL, 1.2 million met the cohort inclusion criteria, resulting in 18.8 million person-years of follow-up. Of these, 129,650 (10%) developed all-cause dementia, with 77,978 (60%) having dementia subtype codes. Alzheimer's disease was the most common subtype diagnosis (62%). Among the dementia cases, the median duration of observation time was 14 years.

Conclusion

We have created a generalisable, national dementia e-cohort, aimed at facilitating epidemiological dementia research.

How well can electronic health records from primary care identify Alzheimer's disease cases?

Background

Electronic health records (EHR) from primary care are emerging in Alzheimer’s disease (AD) research, but their accuracy is a concern. We aimed to validate AD diagnoses from primary care using additional information provided by general practitioners (GPs), and a register of dementias.

Patients and methods

This retrospective observational study obtained data from the System for the Development of Research in Primary Care (SIDIAP). Three algorithms combined International Statistical Classification of Diseases (ICD-10) and Anatomical Therapeutic Chemical codes to identify AD cases in SIDIAP. GPs evaluated dementia diagnoses by means of an online survey. We linked data from the Register of Dementias of Girona and from SIDIAP. We estimated the positive predictive value (PPV) and sensitivity and provided results stratified by age, sex and severity.

Results

Using survey data from the GPs, PPV of AD diagnosis was 89.8% (95% CI: 84.7–94.9). Using the dataset linkage, PPV was 74.8 (95% CI: 73.1–76.4) for algorithm A1 (AD diagnoses), and 72.3 (95% CI: 70.7–73.9) for algorithm A3 (diagnosed or treated patients without previous conditions); sensitivity was 71.4 (95% CI: 69.6–73.0) and 83.3 (95% CI: 81.8–84.6) for algorithms A1 (AD diagnoses) and A3, respectively. Stratified results did not differ by age, but PPV and sensitivity estimates decreased amongst men and severe patients, respectively.

Conclusions

PPV estimates differed depending on the gold standard. The development of algorithms integrating diagnoses and treatment of dementia improved the AD case ascertainment. PPV and sensitivity estimates were high and indicated that AD codes recorded in a large primary care database were sufficiently accurate for research purposes.

The relationship between sleep duration, cognition and dementia: a Mendelian randomization study

BACKGROUND

Short and long sleep duration have been linked with poorer cognitive outcomes, but it remains unclear whether these associations are causal.

METHODS

We conducted the first Mendelian randomization (MR) study with 77 single-nucleotide polymorphisms (SNPs) for sleep duration using individual-participant data from the UK Biobank cohort (N = 395 803) and summary statistics from the International Genomics of Alzheimer's Project (N cases/controls = 17 008/37 154) to investigate the potential impact of sleep duration on cognitive outcomes.

RESULTS

Linear MR suggested that each additional hour/day of sleep was associated with 1% [95% confidence interval (CI) = 0-2%; P = 0.008] slower reaction time and 3% more errors in visual-memory test (95% CI = 0-6%; P = 0.05). There was little evidence to support associations of increased sleep duration with decline in visual memory [odds ratio (OR) per additional hour/day of sleep = 1.10 (95% CI = 0.76-1.57); P = 0.62], decline in reaction time [OR = 1.28 (95% CI = 0.49-3.35); P = 0.61], all-cause dementia [OR = 1.19 (95% CI = 0.65-2.19); P = 0.57] or Alzheimer's disease risk [OR = 0.89 (95% CI = 0.67-1.18); P = 0.41]. Non-linear MR suggested that both short and long sleep duration were associated with poorer visual memory (P for non-linearity = 3.44e-9) and reaction time (P for non-linearity = 6.66e-16).

CONCLUSIONS

Linear increase in sleep duration has a small negative effect on reaction time and visual memory, but the true association might be non-linear, with evidence of associations for both short and long sleep duration. These findings suggest that sleep duration may represent a potential causal pathway for cognition.

Diagnosing dementia and cognitive dysfunction in the elderly in primary health care: A systematic review

Dementia is a public health issue making the screening and diagnosing of dementia and its prodromal phases in all health settings imperative.

OBJECTIVE

using PRISMA, this systematic review aimed to identify how low-, middle-, and high-income countries establish dementia and cognitive dysfunction diagnoses in primary health care.

METHODS

studies from the past five years in English, Spanish, and Portuguese were retrieved from Scopus, PubMed, Embase, Lilacs, Scielo, and Web of Science. Of 1987 articles, 33 were selected for analysis.

RESULTS

only three articles were from middle-income countries and there were no studies from low-income countries. The most used instrument was the Mini-Mental State Examination (MMSE). Mild Cognitive Impairment (MCI) and dementia criteria were based on experts' recommendation as well as on the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD-10), respectively.

CONCLUSION

differences between these criteria among high- and middle-income countries were observed.

Correction: The diagnosis, burden and prognosis of dementia: A record-linkage cohort study in England

[This corrects the article DOI: 10.1371/journal.pone.0199026.].