New Horizons in the use of routine data for ageing research

Abstract The past three decades have seen a steady increase in the availability of routinely collected health and social care data and the processing power to analyse it. These developments represent a major opportunity for ageing research, especially with the integration of different datasets across traditional boundaries of health and social care, for prognostic research and novel evaluations of interventions with representative populations of older people. However, there are considerable challenges in using routine data at the level of coding, data analysis and in the application of findings to everyday care. New Horizons in applying routine data to investigate novel questions in ageing research require a collaborative approach between clinicians, data scientists, biostatisticians, epidemiologists and trial methodologists. This requires building capacity for the next generation of research leaders in this important area. There is a need to develop consensus code lists and standardised, validated algorithms for common conditions and outcomes that are relevant for older people to maximise the potential of routine data research in this group. Lastly, we must help drive the application of routine data to improve the care of older people, through the development of novel methods for evaluation of interventions using routine data infrastructure. We believe that harnessing routine data can help address knowledge gaps for older people living with multiple conditions and frailty, and design interventions and pathways of care to address the complex health issues we face in caring for older people.

[1]  C. Gale,et al.  Cohort profile: The Myocardial Ischaemia National Audit Project (MINAP). , 2019, European heart journal. Quality of care & clinical outcomes.

[2]  Arturo Gonzalez-Izquierdo,et al.  UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER , 2019, J. Am. Medical Informatics Assoc..

[3]  Alan R. Moody,et al.  From Big Data to Precision Medicine , 2019, Front. Med..

[4]  Samuel M Brown,et al.  Can Big Data Deliver on Its Promises?-Leaps but Not Bounds. , 2018, JAMA network open.

[5]  Diogo M. Camacho,et al.  Next-Generation Machine Learning for Biological Networks , 2018, Cell.

[6]  J. Murphy The General Data Protection Regulation (GDPR) , 2018, Irish medical journal.

[7]  A. Street,et al.  Development and validation of a Hospital Frailty Risk Score focusing on older people in acute care settings using electronic hospital records: an observational study , 2018, The Lancet.

[8]  E. Nøhr,et al.  How to investigate and adjust for selection bias in cohort studies , 2018, Acta obstetricia et gynecologica Scandinavica.

[9]  Liang‐Kung Chen,et al.  Standard set of health outcome measures for older persons , 2018, BMC Geriatrics.

[10]  Hadi Kharrazi,et al.  Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study , 2017, BMC Geriatrics.

[11]  J. Hippisley-Cox,et al.  Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study , 2017, British Medical Journal.

[12]  Carlos Sáez,et al.  Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances , 2017, Statistical methods in medical research.

[13]  A. R. Tate,et al.  Quality of recording of diabetes in the UK: how does the GP's method of coding clinical data affect incidence estimates? Cross-sectional study using the CPRD database , 2017, BMJ Open.

[14]  Michael J Pencina,et al.  Controlling for Informed Presence Bias Due to the Number of Health Encounters in an Electronic Health Record. , 2016, American journal of epidemiology.

[15]  Mhairi Aitken,et al.  Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies , 2016, BMC medical ethics.

[16]  G. Collins,et al.  Prediction models for cardiovascular disease risk in the general population: systematic review , 2016, British Medical Journal.

[17]  S. Goodacre,et al.  Patient and public involvement in emergency care research , 2016, Emergency Medicine Journal.

[18]  John Young,et al.  Development and validation of an electronic frailty index using routine primary care electronic health record data , 2016, Age and ageing.

[19]  P. Martus,et al.  Baseline participation in a health examination survey of the population 65 years and older: who is missed and why? , 2016, BMC Geriatrics.

[20]  D. Chambers,et al.  What evidence is there on the effectiveness of different models of delivering urgent care? A rapid review , 2015 .

[21]  David Moher,et al.  The REporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) Statement: Methods for Arriving at Consensus and Developing Reporting Guidelines , 2015, PloS one.

[22]  William Stafford Noble,et al.  Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.

[23]  N. Freemantle,et al.  Making inferences on treatment effects from real world data: propensity scores, confounding by indication, and other perils for the unwary in observational research , 2013, BMJ.

[24]  Chris Dyer,et al.  Improving recruitment of older people to research through good practice. , 2011, Age and ageing.

[25]  Nick Freemantle,et al.  Effect of the quality and outcomes framework on diabetes care in the United Kingdom: retrospective cohort study , 2009, BMJ : British Medical Journal.

[26]  Tom Chan,et al.  Problems with primary care data quality: osteoporosis as an exemplar. , 2004, Informatics in primary care.

[27]  J. Norrie,et al.  Pragmatic Trials. , 2016, The New England journal of medicine.

[28]  T. Foley,et al.  The Potential of Learning Healthcare Systems , 2015 .