Evaluation of routinely collected records for dementia outcomes in UK: a prospective cohort study

Objectives To evaluate the characteristics of individuals recorded as having a dementia diagnosis in different routinely collected records and to examine the extent of overlap of dementia coding across data sources. Also, to present comparisons of secondary and primary care records providing value for researchers using routinely collected records for dementia outcome capture. Study design A prospective cohort study. Setting and participants A cohort of 25 639 men and women in Norfolk, aged 40–79 years at recruitment (1993–1997) followed until 2018 linked to routinely collected to identify dementia cases. Data sources include mortality from death certification and National Health Service (NHS) hospital or secondary care records. Primary care records for a subset of the cohort were also reviewed. Primary outcome measure Diagnosis of dementia (any-cause). Results Over 2000 participants (n=2635 individuals) were found to have a dementia diagnosis recorded in one or more of the data sources examined. Limited concordance was observed across the secondary care data sources. We also observed discrepancies with primary care records for the subset and report on potential linkage-related selection bias. Conclusions Use of different types of record linkage from varying parts of the UK’s health system reveals differences in recorded dementia diagnosis, indicating that dementia can be identified to varying extents in different parts of the NHS system. However, there is considerable variation, and limited overlap in those identified. We present potential selection biases that might occur depending on whether cause of death, or primary and secondary care data sources are used. With the expansion of using routinely collected health data, researchers must be aware of these potential biases and inaccuracies, reporting carefully on the likely extent of limitations and challenges of the data sources they use.

[1]  M. Molokhia,et al.  What gets recorded, counts: dementia recording in primary care compared with a specialist database , 2021, Age and ageing.

[2]  E. Ford,et al.  Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case–control study using electronic primary care records , 2021, BMJ Open.

[3]  C. Brayne,et al.  Undiagnosed dementia in primary care: a record linkage study , 2020 .

[4]  E. Ford,et al.  Our data, our society, our health: A vision for inclusive and transparent health data science in the United Kingdom and beyond , 2019, Learning health systems.

[5]  C. Sudlow,et al.  Identifying dementia outcomes in UK Biobank: a validation study of primary care, hospital admissions and mortality data , 2019, European Journal of Epidemiology.

[6]  Dan J Stein,et al.  Global, regional, and national burden of Alzheimer's disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016 , 2019, The Lancet Neurology.

[7]  C. Sudlow,et al.  Identifying dementia cases with routinely collected health data: A systematic review , 2018, Alzheimer's & Dementia.

[8]  R. Stewart,et al.  Accuracy of general hospital dementia diagnoses in England: Sensitivity, specificity, and predictors of diagnostic accuracy 2008–2016 , 2018, Alzheimer's & Dementia.

[9]  C. Brayne,et al.  Accuracy of death certification of dementia in population-based samples of older people: analysis over time , 2018, Age and ageing.

[10]  E. Ford,et al.  Predicting dementia from primary care records: A systematic review and meta-analysis , 2018, PloS one.

[11]  I. Deary,et al.  Dementia ascertainment using existing data in UK longitudinal and cohort studies: a systematic review of methodology , 2017, BMC Psychiatry.

[12]  D. Bennett,et al.  Mixed pathologies and neural reserve: Implications of complexity for Alzheimer disease drug discovery , 2017, PLoS medicine.

[13]  Nick C Fox,et al.  Trends in diagnosis and treatment for people with dementia in the UK from 2005 to 2015: a longitudinal retrospective cohort study. , 2017, The Lancet. Public health.

[14]  M. Bennett,et al.  How well are the diagnosis and symptoms of dementia recorded in older patients admitted to hospital? , 2016, Age and ageing.

[15]  M. Boustani,et al.  Dementia Screening in Primary Care , 2017 .

[16]  C. Sudlow,et al.  Comparison of dementia recorded in routinely collected hospital admission data in England with dementia recorded in primary care , 2016, Emerging Themes in Epidemiology.

[17]  N. Adler,et al.  Using Electronic Health Records for Population Health Research: A Review of Methods and Applications. , 2016, Annual review of public health.

[18]  Andrea C. Fernandes,et al.  Cohort profile of the South London and Maudsley NHS Foundation Trust Biomedical Research Centre (SLaM BRC) Case Register: current status and recent enhancement of an Electronic Mental Health Record-derived data resource , 2016, BMJ Open.

[19]  K. Khaw,et al.  Predicting admissions and time spent in hospital over a decade in a population-based record linkage study: the EPIC-Norfolk cohort , 2016, BMJ Open.

[20]  Carole Dufouil,et al.  Guidelines for reporting methodological challenges and evaluating potential bias in dementia research , 2015, Alzheimer's & Dementia.

[21]  K. Bhaskaran,et al.  Data Resource Profile: Clinical Practice Research Datalink (CPRD) , 2015, International journal of epidemiology.

[22]  C. Ritchie,et al.  Dementia trials and dementia tribulations: methodological and analytical challenges in dementia research , 2015, Alzheimer's Research & Therapy.

[23]  G. Livingston,et al.  Diagnostic rates and treatment of dementia before and after launch of a national dementia policy: an observational study using English national databases , 2014, BMJ Open.

[24]  Nicholas J Wareham,et al.  Cohort Profile: A prospective cohort study of objective physical and cognitive capability and visual health in an ageing population of men and women in Norfolk (EPIC-Norfolk 3) , 2013, International journal of epidemiology.

[25]  J. Benito-León,et al.  Under reporting of dementia deaths on death certificates: a systematic review of population-based cohort studies. , 2014, Journal of Alzheimer's disease : JAD.

[26]  N. Steel,et al.  Understanding the dementia diagnosis gap in Norfolk and Suffolk: a survey of general practitioners. , 2014, Quality in primary care.

[27]  Sube Banerjee,et al.  Improving the identification of people with dementia in primary care: evaluation of the impact of primary care dementia coding guidance on identified prevalence , 2013, BMJ Open.

[28]  Amanda Connolly,et al.  Underdiagnosis of dementia in primary care: Variations in the observed prevalence and comparisons to the expected prevalence , 2011, Aging & mental health.

[29]  Ian Scott,et al.  Data Linkage: A powerful research tool with potential problems , 2010, BMC health services research.

[30]  Jorunn L Helbostad,et al.  Unwanted incidents during transition of geriatric patients from hospital to home: a prospective observational study , 2010, BMC health services research.

[31]  I. McKeith,et al.  Epidemiological Pathology of Dementia: Attributable-Risks at Death in the Medical Research Council Cognitive Function and Ageing Study , 2009, PLoS medicine.

[32]  Carol Brayne,et al.  Dementia before Death in Ageing Societies— The Promise of Prevention and the Reality , 2006, PLoS medicine.

[33]  N. Day,et al.  EPIC-Norfolk: study design and characteristics of the cohort. European Prospective Investigation of Cancer. , 1999, British journal of cancer.