# Evaluating the harmonisation potential of diverse cohort datasets

Sarah Bauermeister, Mukta Phatak, Kelly Sparks, Lana Sargent, Michael Griswold, Caitlin Mchugh, Mike Nalls, Simon Young, Joshua Bauermeister, Paul Elliott, et al.

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► To cite this version:

Sarah Bauermeister, Mukta Phatak, Kelly Sparks, Lana Sargent, Michael Griswold, et al. Evaluating the harmonisation potential of diverse cohort datasets. European Journal of Epidemiology, 2023, 38, pp.605-615. 10.1007/s10654-023-00997-3. hal-04140812

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Submitted on 26 Jun 2023

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# Evaluating the harmonisation potential of diverse cohort datasets

Sarah Bauermeister<fnref n="1" /> ID · Mukta Phatak<fnref n="2" /> · Kelly Sparks<fnref n="3" /> · Lana Sargent<fnref n="4" /> · Michael Griswold<fnref n="5" /> · Caitlin McHugh<fnref n="2" /> · Mike Nalls<fnref n="6" /> · Simon Young<fnref n="1" /> · Joshua Bauermeister<fnref n="1" /> · Paul Elliott<fnref n="7" /> · Andrew Steptoe<fnref n="8" /> · David Porteous<fnref n="9" /> · Carole Dufouil<fnref n="10" /> · John Gallacher<fnref n="1" />

Received: 3 August 2022 / Accepted: 22 March 2023 / Published online: 26 April 2023

© The Author(s) 2023

## Abstract

Data discovery, the ability to find datasets relevant to an analysis, increases scientific opportunity, improves rigour and accelerates activity. Rapid growth in the depth, breadth, quantity and availability of data provides unprecedented opportunities and challenges for data discovery. A potential tool for increasing the efficiency of data discovery, particularly across multiple datasets is data harmonisation. A set of 124 variables, identified as being of broad interest to neurodegeneration, were harmonised using the C-Surv data model. Harmonisation strategies used were simple calibration, algorithmic transformation and standardisation to the Z-distribution. Widely used data conventions, optimised for inclusiveness rather than aetiological precision, were used as harmonisation rules. The harmonisation scheme was applied to data from four diverse population cohorts. Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity. Although harmonisation is not an exact science, sufficient comparability across datasets was achieved to enable data discovery with relatively little loss of informativeness. This provides a basis for further work extending harmonisation to a larger variable list, applying the harmonisation to further datasets, and incentivising the development of data discovery tools.

**Keywords** Data harmonisation · Cohort · Data visualisation · C-surv data model · Data discovery · Datasets

## Background

Data discovery, the ability to find data assets relevant to an analysis, is a critical component of a productive research environment. Efficient data discovery increases opportunity, improves rigour, and accelerates activity. For cohort studies, the complexity and variety of longitudinal data presents particular discovery challenges as data structures and labeling conventions are highly variable and typically underdocumented. The growth of data repositories [1–5] providing global 3rd party researcher access to multiple datasets, increases the value of efficient data discovery.

A pre-requisite for efficient data discovery is harmonisation. The goal of harmonisation is to achieve comparability (inferential equivalence) between two or more variables by inferring a latent construct that the variables are considered to represent. For example, different reaction time tasks may be judged to represent a latent construct of cognitive processing speed. However, latent constructs are purpose-specific, as a latent construct of processing speed based on

<figure><img src="image_3.png" /></figure>

<footnote>Sarah Bauermeister
sarah.bauermeister@psych.ox.ac.uk</footnote>

<footnote n="1">¹ Dementias Platform UK, Oxford, UK</footnote>

<footnote n="2">² Alzheimer Disease Data Initiative, Kirkland, Washington,
USA</footnote>

<footnote n="3">³ Evaluserve, Bengaluru, India</footnote>

<footnote n="4">⁴ National Institute of Aging, Bethesda, USA</footnote>

<footnote n="5">⁵ University of Mississippi, Oxford, USA</footnote>

<footnote n="6">⁶ Data Tecnica International LLC, Washington, USA</footnote>

<footnote n="7">⁷ Imperial College, London, England</footnote>

<footnote n="8">⁸ University College London, London, England</footnote>

<footnote n="9">⁹ University of Edinburgh, Edinburgh, Scotland</footnote>

<footnote n="10">¹⁰ University of Bordeaux, Bordeaux, France</footnote>
simple reaction time may be inadequate for a hypothesis based on choice reaction time. Harmonisation is easily con- flated with standardisation where data are organised (struc- tured and annotated) according to a standard model. Clearly, standardisation is a step towards harmonisation, but they are not coterminous. Standardisation is about conformability whilst harmonisation is about comparability.

Previous exercises in the harmonisation of cohort data have focussed on hypothesis-specific testing across multiple datasets. The Maelstrom consortium has adopted a differ- ent approach by developing multiple harmonised datasets available for 3rd party use [6]. The Integrative Analysis of Longitudinal Studies of Aging (IALSA) consortium also supports a broad range of multi-cohort analyses; making the harmonisation code available for many of them [7]. The CLOSER consortium provides an extensive programme of topic-specific harmonisation initiatives across 19 cohorts [8], designed to test a broad range of hypotheses. Other ini- tiatives are more focussed. Adhikari et al. describe harmo- nising 20 variables from two pregnancy cohorts to better understand risk factors for pre-term birth [9], whilst Almeida et al. describe and validate a pipeline for neurodegenera- tion analyses, also using two population cohorts [10]. Fur- ther larger harmonisation collaborative projects include the Research Advancement through Cohort Cataloguing and Harmonization (ReACH) [11], the EU Child Cohort Net- work [12] and the Melbourne Children's LifeCourse (Life- Course) [13] initiatives.

Here we focus on harmonisation for data discovery. Focussing on data discovery requires fewer assumptions about likely future use of the harmonised data, enabling the use of more generic latent constructs. It is also sympathetic to relatively simple harmonisation rules. Value lies in ena- bling the availability of relevant data from multiple datasets, to be assessed accurately and efficiently, prior to an access request. This reduces search costs for data discovery and reduces the risk of accessing and processing uninforma- tive data. The work was conducted within the Dementias Platform UK Data Portal (DPUK) [1]. This is an integrated research environment for bona fide researchers interested in accessing cohort data for secondary analysis. The Portal provides tools for data discovery, an access management sys- tem and a virtual desktop interface (VDI) for analysis. The VDI provides a virtual desktop with preinstalled statistical programmes (e.g., Stata, Python, SPSS, R, MATLAB) for analysing complex multi-modal datasets. The data discovery tools (Cohort Matrix, Cohort Directory, Cohort Explorer) provide researchers with the ability to investigate appropri- ate cohorts at increasing levels of granularity. The Cohort Explorer (https://portal.dementiasplatform.uk/CohortExpl orer) is premised on the harmonisation of a select number of 30 variables focused on neurodegeneration.

To evaluate the harmonisation potential of population cohort data for data discovery, members of the Alzheimer's Disease Data Initiative (ADDI) [4] and DPUK formed a Data Harmonisation Group, to attempted the harmonisation of a comprehensive set (124 variables) of neurodegeneration related variables, across four diverse population cohort data- sets. The aim of this project is to inform the development of discovery tools across the ADDI Workbench and the DPUK Data Portal.

## Methods

### Variable selection

A set of 124 variables optimised for neurodegeneration was identified by consensus within the ADDI Data Harmonisa- tion group. Variables were selected to reflect the frequency of being requested in Dementias Platform UK (DPUK) data access proposals [1], to cover a range of data modalities, and to include modifiable and non-modifiable risk factors.

### Standardisation

Datasets were curated to a common structure and labelling conventions using C-Surv as the data model [14]. C-Surv is a simple four level acyclic taxonomy intended to capture the breadth of data typically collected in research cohorts. The tiered structure supports grouped and individual variable discovery. C-Surv comprises 18 data themes (level 1) lead- ing to > 146 data ‘domains’ (level 2), > 500 data ‘families’ (level 3) and then to a growing number of data ‘objects’ (level 4) i.e. the measured variable. C-Surv has been adopted by DPUK [1], Dementias Platform Australia [3], and the ADDI workbench [4]. Other models, developed for other purposes were available, such as the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model for administrative health data [15], and CDISC Clinical Data Interchange Standards Consortium (CDISC) for trials data [16], but these have structural and semantic complexity that is alien to the cohort study design.

### Schema development

The harmonisation schema was optimised to be inclusive of datasets by using relatively simple harmonisation rules and widely used value-labelling conventions. Three strategies for harmonisation, as described in the Maelstrom harmonisation guidelines [17] were used.

Simple calibration, using direct mapping between the source variable and the harmonised variable, was adopted for widely used standard metrics such as weight or height. Direct mapping, including cut-off points was used for

<figure><img src="image_4.png" /></figure>
validated clinical scales. The Gregorian calendar was used for dates and conventional units were used for age (years), durations (hours), concentrations (mg/ml), volumes (mm³), etc.

Algorithmic transformation was used for non-clinical questionnaire responses including lifestyle. The algorithm was selected to be inclusive by using a relatively simple transformation and was developed iteratively as it was applied to each dataset. Gender was transformed as male, female; smoking as ‘current, past, and never’, and ethnicity as white, black, Asian, mixed, other. Cohabitation was coded as single, married/cohabiting, divorced/separated, widowed, whilst education was considered as educational experience and transformed into junior or less, secondary, degree or equivalent, postgraduate or equivalent. For type of accommodation a straightforward transformation was house/bungalow, apartment, sheltered/residential, other.

Non-clinical cognitive performance scores were stand-
ardised into z-scores by default, with an option for refining
this rule on a scale-by-scale basis according to the variable
distribution. More sophisticated methods such as latent
variable modelling or multiple imputation were not used.

Schema evaluation

The utility of the harmonisation rules was tested using four
DPUK collaborating cohorts. These were selected on the
basis of having diverse primary scientific objectives, pro-
viding longitudinal multimodal data, and being frequently
requested by DPUK users. The cohorts were the Airwave
Health Monitoring Study (Airwave); an occupational
cohort [18], the English Longitudinal Study of Ageing
(ELSA); a social science focussed study [19], Generation
Scotland; a genetics cohort [20], and Memento; a neuro-
degeneration cohort [21]. The coverage of each cohort and
overlap of variables across cohorts was assessed, along
with the utility of the harmonisation rules. All analyses
were conducted within the DPUK Data Portal [22].

Results

Core variables

The variable list comprised a range of modifiable and
non-modifiable risk factors and metadata (Table 1). Of
the 124 variables, most variables (n = 103) were present
in the baseline data. However, for ELSA data, 18 vari-
ables were collected in subsequent waves. For Memento,
two outcomes were collected through linkage to health
records. For both ELSA and Memento, genetics data are

available independently of study wave. The variables cov-
ered 15 out of the 18 data themes represented by C-Surv
data model (Table 1). Themes not represented were link-
age data (theme 14), healthcare utilisation data (theme 15),
and device data (theme 18).

Representation and distribution

Most variables (n = 120; 97%) were found in one or more
cohorts. Memento, being primarily designed to investi-
gate neurodegeneration, included most variables (n = 92).
The other cohorts, designed to address a broader range of
questions had fewer neurodegeneration-focused variables
(Table 2). Of the 4 variables that were not found in any
cohort, one was related to air pollution (pm₂.₅ concentration)
and another was loneliness assessment. That Mild Cognitive
Impairment (MCI) status was not available in any cohort
reflects the difficulty of capturing these data in a population
setting. That ADAS-Cog score was not available reflects the
use of this scale primarily in trials than in cohorts.

The distribution of variables across cohorts also varied,
with 34 variables being common to all cohorts, 10 in three
cohorts, 30 in two cohorts and 46 in one cohort (Fig. 1). This
shows the diversity of the selected cohorts and reflects the
range of scientific purpose underlying these datasets. For
example, that ELSA and Memento include 13 and 26 unique
variables respectively reflects the distinctive scientific foci of
these studies; ELSA being focussed on social factors under-
lying ageing, and Memento focussed more specifically on
neurodegeneration.

Utility

Of the 120 variables that were represented in one or more
datasets, 61 (51%) were directly mapped. Direct mapping
was generally straightforward but did involve truncation of
dates, and the interpretation of text for primary cause of
death and medications. For alcohol consumption, although
using units per week is translatable with most datasets an
'other' option was allowed for when consumption was
present but not quantifiable. The harmonisation rules for
each variable are shown in the supplementary materials
(table S1).

Fifty three (44%) variables were transformed by algo-
rithm. For 44 of these, this was a matter of using a ‘yes/
no’ (present/absent) format. For ELSA, the presence or
absence of a medical condition was inferred from the data
of diagnosis, or a symptom rating score. For Generation
Scotland the presence or absence of angina and myocar-
dial infarction was inferred from self-reported heart dis-
ease. For Memento several outcomes were indicated as
present by interpreting a rating scale score or by a clinical
diagnosis. The remaining nine variables required more
<table><caption>**Table 1** Core variable list</caption><thead><tr><th>#</th><th>C-Surv theme</th><th>Variable</th><th>Strategy</th><th>Harmonisation rule</th></tr></thead><tbody><tr><td>1</td><td>Administration:theme 1</td><td>Cohort ID</td><td>SC</td><td>Anonymised by cohort</td></tr><tr><td>2</td><td></td><td>Assessment date</td><td>SC</td><td>Gregorian calendar (yyyy-mm-dd)</td></tr><tr><td>3</td><td></td><td>Date of birth</td><td>SC</td><td>Gregorian calendar (yyyy-mm-dd)</td></tr><tr><td>4</td><td></td><td>Date of death</td><td>SC</td><td>Gregorian calendar (yyyy-mm-dd)</td></tr><tr><td>5</td><td></td><td>Cause of death</td><td>SC: text</td><td>ICD-11 categories 1–18</td></tr><tr><td>6</td><td></td><td>DNA extracted</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>7</td><td></td><td>Plasma collected</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>8</td><td></td><td>Serum collected</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>9</td><td></td><td>CSF collected</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>10</td><td>Sociodemographic:theme 2</td><td>Age</td><td>SC</td><td>Value: years 1–130</td></tr><tr><td>11</td><td></td><td>Gender</td><td>SC</td><td>1 male; 2 female</td></tr><tr><td>12</td><td></td><td>Ethnicity</td><td>AT</td><td>1 white; 2 Black; 3 Asian; 4 mixed 5 other</td></tr><tr><td>13</td><td></td><td>Cohabitation</td><td>AT</td><td>1 single; 2 married/cohabiting; 3 separated/divorced; 4 widowed other</td></tr><tr><td>14</td><td></td><td>Years education</td><td>SC</td><td>Value: years range</td></tr><tr><td>15</td><td></td><td>Educational level</td><td>AT</td><td>1 postgrad; 2 degree; 3 secondary; 4 junior or less</td></tr><tr><td>16</td><td></td><td>Income</td><td>AT</td><td>Quantiles using local currency</td></tr><tr><td>17</td><td>Early life experience:theme 3</td><td>Childhood physical abuse</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>18</td><td></td><td>Adolescent physical abuse</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>19</td><td></td><td>Sexual abuse</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>20</td><td></td><td>Parental smoking behaviour</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>21</td><td>Medical history:theme 4</td><td>Type 1 diabetes diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>22</td><td></td><td>Type 2 diabetes diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>23</td><td></td><td>AD diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>24</td><td></td><td>AD FTD diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>25</td><td></td><td>AD mixed diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>26</td><td></td><td>VaD diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>27</td><td></td><td>PD diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>28</td><td></td><td>Depression diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>29</td><td></td><td>Self-report visual difficulty</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>30</td><td></td><td>Self-report hearing difficulty</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>31</td><td></td><td>Angina diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>32</td><td></td><td>MI diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>33</td><td></td><td>Hypertension diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>34</td><td></td><td>Stroke diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>35</td><td></td><td>Head injury</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>36</td><td></td><td>COPD diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>37</td><td></td><td>Arthritis diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>38</td><td></td><td>Current pain</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>39</td><td></td><td>Self-report general health</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>40</td><td></td><td>Medications</td><td>SC: text</td><td>Value: number prescribed</td></tr><tr><td>41</td><td>Family disease history: theme 5</td><td>Dementia parent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>42</td><td></td><td>Dementia grandparent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>43</td><td></td><td>Dementia sibling</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>44</td><td></td><td>AD parent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>45</td><td></td><td>AD grandparent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>46</td><td></td><td>AD sibling</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>47</td><td></td><td>VaD parent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>48</td><td></td><td>VaD grandparent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>49</td><td></td><td>VaD sibling</td><td>SC</td><td>1 Yes; 0 No</td></tr></tbody></table>

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>#</th><th>C-Surv theme</th><th>Variable</th><th>Strategy</th><th>Harmonisation rule</th></tr></thead><tbody><tr><td>50</td><td></td><td>PD parent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>51</td><td></td><td>PD grandparent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>52</td><td></td><td>PD sibling</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>53</td><td></td><td>CHD parent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>54</td><td></td><td>CHD grandparent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>55</td><td></td><td>CHD sibling</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>56</td><td></td><td>Stroke parent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>57</td><td></td><td>Stroke grandparent</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>58</td><td></td><td>Stroke sibling</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>59</td><td rowspan="10">Psychological status: theme 6</td><td>GHQ score</td><td>AT</td><td>Scale score</td></tr><tr><td>60</td><td>Self-report depression</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>61</td><td>Loss of interest</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>62</td><td>Depression score</td><td>AT</td><td>Scale score</td></tr><tr><td>63</td><td>EPQ Neuroticism</td><td>AT</td><td>Scale score</td></tr><tr><td>64</td><td>EQP Extraversion</td><td>AT</td><td>Scale score</td></tr><tr><td>65</td><td>Life satisfaction score</td><td>AT</td><td>Scale score</td></tr><tr><td>66</td><td>Job satisfaction score</td><td>AT</td><td>Scale score</td></tr><tr><td>67</td><td>Quality of Life score</td><td>AT</td><td>Scale score</td></tr><tr><td>68</td><td>Loneliness scale score</td><td>AT</td><td>Scale score</td></tr><tr><td>69</td><td rowspan="9">Cognitive status: theme 7</td><td>Immediate recall score</td><td>S</td><td>Z score</td></tr><tr><td>70</td><td>Delayed recall score</td><td>S</td><td>Z score</td></tr><tr><td>71</td><td>Digit symbol substitution score</td><td>S</td><td>Z score</td></tr><tr><td>72</td><td>Verbal fluency score</td><td>S</td><td>Z score</td></tr><tr><td>73</td><td>Choice reaction time mSec</td><td>S</td><td>Z score</td></tr><tr><td>74</td><td>Fluid intelligence score</td><td>S</td><td>Z score</td></tr><tr><td>75</td><td>MMSE score</td><td>SC</td><td>Scale score</td></tr><tr><td>76</td><td>ADAS cog total score</td><td>SC</td><td>Scale score</td></tr><tr><td>77</td><td>CDR total score</td><td>SC</td><td>Scale score</td></tr><tr><td>78</td><td></td><td>Subjective memory complaint</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>79</td><td></td><td>MCI diagnosis</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>80</td><td rowspan="10">Lifestyle: theme 8</td><td>Alcohol consumption</td><td>AT</td><td>Alcohol units per week, other</td></tr><tr><td>81</td><td>Smoking status</td><td>AT</td><td>0 never smoked; 1 past smoker; 2 current</td></tr><tr><td>82</td><td>Vigorous exercise</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>83</td><td>Moderate exercise</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>84</td><td>Walking</td><td>AT</td><td>1 Yes; 0 No</td></tr><tr><td>85</td><td>Sleep quality scale</td><td>AT</td><td>Scale score</td></tr><tr><td>86</td><td>Sleep hours per night</td><td>SC</td><td>Hours per night</td></tr><tr><td>87</td><td rowspan="2">Life functionality: theme 9</td><td>ADL score</td><td>AT</td><td>Scale score (higher value higher independence)</td></tr><tr><td>88</td><td>IADL score</td><td>AT</td><td>Scale score (higher value higher functioning)</td></tr><tr><td>89</td><td rowspan="2">Physical environment: theme 10</td><td>Number of house occupants</td><td>SC</td><td>Value (occupants)</td></tr><tr><td>90</td><td>Number of rooms</td><td>SC</td><td>Value (rooms)</td></tr><tr><td>91</td><td></td><td>Type of accommodation</td><td>AT</td><td>1 house/bungalow, 2 apartment, 3 residential/sheltered/ other</td></tr><tr><td>92</td><td></td><td>Pollution (grime in house)</td><td>SC</td><td>1 Yes; 0 No</td></tr><tr><td>93</td><td rowspan="3">Social environment: theme 11</td><td>Number of contacts/month</td><td>SC</td><td>Value (number of social contacts)</td></tr><tr><td>94</td><td>Social media sites used</td><td>SC</td><td>Value (number of sites used)</td></tr><tr><td>95</td><td>Social media use daily</td><td>SC</td><td>Value (types used daily)</td></tr><tr><td>96</td><td rowspan="4">Physical examination: theme 12</td><td>Height</td><td>SC</td><td>Value (cm)</td></tr><tr><td>97</td><td>Weight</td><td>SC</td><td>Value (kg)</td></tr><tr><td>98</td><td>BMI</td><td>SC</td><td>Value (ratio m²/kg)</td></tr><tr><td>99</td><td>Grip strength</td><td>SC</td><td>Value (kg)</td></tr></tbody></table>
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>#</th><th>C-Surv theme</th><th>Variable</th><th>Strategy</th><th>Harmonisation rule</th></tr></thead><tbody><tr><td>100</td><td></td><td>Gait (walking) speed</td><td>SC</td><td>Value (m/sec)</td></tr><tr><td>101</td><td></td><td>Systolic BP</td><td>SC</td><td>Value (mm/hg)</td></tr><tr><td>102</td><td></td><td>Diastolic BP</td><td>SC</td><td>Value (mm/hg)</td></tr><tr><td>103</td><td>Imaging: theme 13</td><td>White matter volume</td><td>SC</td><td>Value (mm³ standardised)</td></tr><tr><td>104</td><td></td><td>Grey matter volume</td><td>SC</td><td>Value (mm³ standardised)</td></tr><tr><td>105</td><td></td><td>Left hippocampal volume</td><td>SC</td><td>Value (mm³ standardised)</td></tr><tr><td>106</td><td></td><td>Right hippocampal volume</td><td>SC</td><td>Value (mm³ standardised)</td></tr><tr><td>107</td><td></td><td>WM hyperintensities</td><td>SC</td><td>Value (mm³ standardised)</td></tr><tr><td>108</td><td></td><td>Amyloid PiB SUVR</td><td>SC</td><td>Ratio</td></tr><tr><td>109</td><td>Biosample assays: theme 16</td><td>Haemoglobin</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>110</td><td></td><td>White cell count</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>111</td><td></td><td>RBC count</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>112</td><td></td><td>Total cholesterol</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>113</td><td></td><td>HDL cholesterol</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>114</td><td></td><td>Creatinine</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>115</td><td></td><td>Glucose</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>116</td><td></td><td>CRP</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>117</td><td></td><td>Cortisol decrease</td><td>SC</td><td>Value (mg/dl)</td></tr><tr><td>118</td><td></td><td>Abeta 1-42</td><td>SC</td><td>Value (pg/ml)</td></tr><tr><td>119</td><td></td><td>Abeta 1-40</td><td>SC</td><td>Value (pg/ml)</td></tr><tr><td>120</td><td></td><td>Abeta 1-42</td><td>SC</td><td>Value (pg/ml)</td></tr><tr><td>121</td><td></td><td>Abeta 1-40</td><td>SC</td><td>Value (pg/ml)</td></tr><tr><td>122</td><td></td><td>Total tau</td><td>SC</td><td>Value (pg/ml)</td></tr><tr><td>123</td><td></td><td>P tau</td><td>SC</td><td>Value (pg/ml)</td></tr><tr><td>124</td><td>Molecular: theme 17</td><td>APOE status</td><td>SC</td><td>1 2/2; 2 2/3; 3 2/4; 4 3/3; 5 3/4; 6 4/4)</td></tr></tbody></table>

SC Simple calibration, AT Algorithmic transformation, S Standardisation

C-Surv themes not represented: Linkage data (theme 14), Healthcare utilisation data (theme 15), and Device data (theme 18)

<table><caption>**Table 2 Distribution of core variables across cohorts**</caption><thead><tr><th colspan="2">Harmonised dataset</th><th colspan="4">Number of variables per cohort</th></tr><tr><th>C-Surv theme</th><th>Variables included: n</th><th>Airwave</th><th>ELSA</th><th>Generation scotland</th><th>Memento</th></tr></thead><tbody><tr><td>Administration</td><td>9</td><td>6</td><td>7</td><td>6</td><td>9</td></tr><tr><td>Sociodemographic</td><td>7</td><td>6</td><td>7</td><td>7</td><td>6</td></tr><tr><td>Early life environment</td><td>4</td><td>0</td><td>4</td><td>0</td><td>0</td></tr><tr><td>Medical history</td><td>20</td><td>10</td><td>13</td><td>10</td><td>18</td></tr><tr><td>Family disease history</td><td>18</td><td>0</td><td>0</td><td>15</td><td>12</td></tr><tr><td>Psychological status</td><td>10</td><td>3</td><td>7</td><td>3</td><td>5</td></tr><tr><td>Cognitive status</td><td>11</td><td>4</td><td>5</td><td>4</td><td>6</td></tr><tr><td>Lifestyle</td><td>7</td><td>3</td><td>7</td><td>2</td><td>6</td></tr><tr><td>Life functionality</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td></tr><tr><td>Physical environment</td><td>4</td><td>1</td><td>2</td><td>3</td><td>2</td></tr><tr><td>Social environment</td><td>3</td><td>0</td><td>3</td><td>0</td><td>0</td></tr><tr><td>Physical examination</td><td>7</td><td>5</td><td>7</td><td>5</td><td>6</td></tr><tr><td>Imaging</td><td>6</td><td>0</td><td>0</td><td>0</td><td>6</td></tr><tr><td>Biosample assays</td><td>15</td><td>8</td><td>8</td><td>4</td><td>13</td></tr><tr><td>Molecular data</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Totals</td><td>124</td><td>47</td><td>73</td><td>60</td><td>92</td></tr></tbody></table>

<figure><img src="image_6.png" /></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 1** Distribution of core vari-ables across cohorts</figcaption></figure>

interpretation (Table 3). For smoking, there was close concordance between the harmonisation rules and the raw data with some interpolation required for ELSA data. This could have been easily addressed by simplifying the scale to a binary ‘ever smoked: yes/no’ format, although this would have been less informative for most of the datasets. Ethnicity was missing in Memento as by law these data are not permitted to be collected in France. Harmonising education was difficult as all the cohorts used qualifications as the index and these varied in detail and across jurisdiction (UK and France). The decision to harmonise on the basis of educational experience rather than qualifications provided a basis for greater integration, although it may be argued that the harmonised scale is less informative. Similarly, for cohabitation and housing type, where simplified scales were applied to the more detailed raw data. For household income local currency was used and aggregated into four quantiles of annual income. For exercise (vigorous, moderate and walking) a simple quantification was not possible due to the diversity of measurement and harmonisation was limited to presence or absence.

The six cognitive performance scores were standardised to the Z distribution. The distributions for immediate recall (skew = -0.42), delayed recall (skew = -0.42), digit symbol

substitution (skew = -0.11), verbal fluency (skew = 0.31), were sufficiently Gaussian for Z-scores to be meaningful. For choice reaction time (skew = 1.09), a loge normalisation was used before transforming to z-scores. For this exercise, fluid intelligence is an interpretation of the ELSA numeracy score from ELSA (skew = -0.54).

## Discussion

For a set of 124 variables, selected for relevance to neurodegeneration, a harmonisation schema designed for data discovery, was applied to data from four diverse population cohorts. Of the 120 variables that were found in the datasets, correspondence between the harmonised data schema and cohort-specific data models was complete or close for 111 (93%). For the remainder, harmonisation was possible with a marginal a loss of granularity. Overall, this demonstrated the feasibility and utility of using relatively simple harmonisation procedures for the purpose of data discovery.

Although these findings indicate value for data discovery, harmonisation is not an exact science and we have not described a mature process. The selection of variables, relevant to neurodegeneration was a reasonable starting point
<table><caption>**Table 3** Application of algorithmic transformations across cohorts</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Transformation</th><th colspan="4">Cohort</th></tr><tr><th>Airwave</th><th>ELSA</th><th>Generation Scotland</th><th>Memento</th></tr></thead><tbody><tr><td rowspan="3">Smoking</td><td>Never</td><td>Never</td><td>Ever smoked Yes/no</td><td>Never</td><td>Never</td></tr><tr><td>Ex</td><td>Ex</td><td>–</td><td>Ex</td><td>Ex</td></tr><tr><td>current</td><td>current</td><td>–</td><td>current</td><td>current</td></tr><tr><td>Smoking?</td><td>Yes</td><td></td><td></td><td></td><td></td></tr><tr><td></td><td>no</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Ethnicity</td><td>White</td><td>White</td><td>White</td><td>White</td><td>–</td></tr><tr><td>Black</td><td>Other</td><td>Non white</td><td>–</td><td>–</td></tr><tr><td>Asian</td><td>–</td><td>–</td><td>Asian</td><td>–</td></tr><tr><td>Mixed</td><td>–</td><td>–</td><td>Mixed</td><td>–</td></tr><tr><td rowspan="5">Education</td><td>Post grad equivalent (ISCED 7/8)*</td><td>Post grad</td><td>–</td><td>–</td><td>Higher dipl</td></tr><tr><td>Degree equivalent (ISCED 4–6)</td><td>Deg. equiv</td><td>Deg. Equiv</td><td>College/Uni</td><td>Degree</td></tr><tr><td>Secondary (ISCED 2/3)</td><td>–</td><td>Higher ed</td><td>–</td><td>General Bac</td></tr><tr><td></td><td>A level NVQ3</td><td>NVQ3</td><td>Highers</td><td>Tech Bac</td></tr><tr><td></td><td>GCSE NVQ2</td><td>NVQ2</td><td>Standards</td><td>CAP/BEP</td></tr><tr><td></td><td>NVQ1</td><td>NVQ1</td><td>CSE equivalent</td><td>Elementary</td></tr><tr><td></td><td>–</td><td>Foreign/other</td><td>–</td><td>Certificate</td><td>Primary</td></tr><tr><td></td><td>&lt; = Primary (ISCED 1)</td><td>–</td><td>–</td><td>Certificate</td><td>Primary</td></tr><tr><td rowspan="2">Cohabitation</td><td></td><td>No qualification</td><td>No qualification</td><td>No qualification</td><td>No qualification</td></tr><tr><td>Single</td><td>Single</td><td>Single</td><td>Are you living as a couple? yes/no</td><td>Single</td></tr><tr><td rowspan="5">Housing type</td><td>Married/cohabiting</td><td>Married</td><td>Married</td><td>–</td><td>Married/cohabiting</td></tr><tr><td></td><td>–</td><td>Remarried</td><td>–</td><td>–</td></tr><tr><td>cohabiting</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Divorced/separated</td><td>Divorced</td><td>Divorced</td><td>–</td><td>Divorced/separated</td></tr><tr><td>Separated</td><td>Separated</td><td>Separated</td><td>–</td><td>–</td></tr><tr><td></td><td>widowed</td><td>–</td><td>Widowed</td><td>–</td><td>Widowed</td></tr><tr><td></td><td>Other</td><td>Other</td><td>–</td><td>–</td><td>–</td></tr><tr><td rowspan="5">Household income</td><td>House/bungalow</td><td>–</td><td>–</td><td>House/bungalow</td><td>Single family dwelling</td></tr><tr><td>Apartment</td><td>–</td><td>–</td><td>Apartment/flat</td><td>Apartment</td></tr><tr><td>Sheltered/residential</td><td>–</td><td>–</td><td>Hostel</td><td>Residential</td></tr><tr><td></td><td>–</td><td>–</td><td>Mobile/caravan</td><td>Sheltered</td></tr><tr><td></td><td>–</td><td>–</td><td>Sheltered</td><td>Religious community</td></tr><tr><td></td><td>Other</td><td>–</td><td>–</td><td>Homeless</td><td>Care home</td></tr><tr><td></td><td>Four quantiles using local currency</td><td>Annual: &lt; £25,999, 26,000–37,999, 38,000–59,999, 600,000+</td><td>Gross monthly and annual in Pounds Sterling</td><td>–</td><td>Other</td></tr><tr><td rowspan="2">Vigorous exercise</td><td rowspan="2">Yes/No</td><td rowspan="2">–</td><td rowspan="2">Do you attend sports clubs, gym, exercise classes?</td><td rowspan="2">–</td><td>Monthly: €400–&lt; 800</td></tr><tr><td>800–&lt; 1200</td></tr><tr><td rowspan="2">Moderate exercise</td><td rowspan="2">Yes/No</td><td rowspan="2">–</td><td rowspan="2">–</td><td rowspan="2">–</td><td>1200–&lt; 1800</td></tr><tr><td>1800–&lt; 2500</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>2500 &lt; 4000</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>4000–&lt; 6000</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>6000+</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Days per week</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Hours per day</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Minutes per day</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>No vigorous exercise</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Days per week</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Hours per day</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>Minutes per day</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td>No moderate exercise</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>
<table><caption>**Table 3** (continued)</caption><thead><tr><th rowspan="2">Variable</th><th rowspan="2">Transformation</th><th colspan="3">Cohort</th><th rowspan="2">Memento</th></tr><tr><th>Airwave</th><th>ELSA</th><th>Generation Scotland</th></tr></thead><tbody><tr><td>Walking</td><td>Yes/No</td><td>—</td><td>—</td><td>—</td><td>Days per week<br>Hours per day<br>Minutes per day<br>No walking</td></tr></tbody></table>

as it framed a specific use-case. Undoubtedly, the selection of specific variables reflected the research interest of the DPUK scientific community and the ADDI Data Harmonisation Group. However, a broad range of variables of generic interest were represented. From this limited variable-set a strong case can be made for incrementally expanding the range of harmonised variables. For a small number of variables (n=9) using less granular harmonisation rules would have increased inclusiveness. Offering a selection of rules for investigators to choose which best suits their purpose would be straightforward.

Our model was developed using only four cohorts and not all cohorts had data on all variables. Given the use of relatively generic harmonisation rules, the addition of further cohorts with different patterns of 'missingness' is unlikely to materially affect the schema as described, but would inform its extension to other variables. For many variables the 'Yes/No' indicator was sufficiently generic that whether these variables should be construed as directly mapped or algorithmically transformed is moot. For the processing of free text, the manual interpretation of free text data used here is not scalable or necessarily consistent; the potential of natural language processing for rapid and consistent textual interpretation should be explored. The availability of biosamples was included in the variable list. Technically these are metadata, but were considered informative for data discovery. For cognitive performance, although the harmonisation process was straightforward, without claiming aetiological commonality, grouping tests according to widely used cognitive domains was judged a pragmatic solution. Harmonisation was not applied to longitudinal data. This was intentional to simplify the problem. However, the inclusive and generic nature of the harmonisation schema suggest that applying it longitudinally would be relatively straightforward.

The value of efficient data discovery is commensurate with growth in 3rd party data access and data complexity. Whilst national projects such as the 'All of Us' [23] and 'UK Biobank' [24] studies are specifically designed for 3rd party access, data discovery in most cohort studies remains challenging. These studies are not resourced to standardise their data, and consensus around how this may be achieved has not been reached. Data platforms, which provide global 3rd

party access across multiple datasets provide an opportunity to develop these solutions, as they are positioned to develop harmonisation pipelines that can be applied systematically and consistently across datasets; enabling discovery at-scale and pace.

Efficient data discovery does not just require harmonisation. It also requires tools that exploit the potential that harmonisation brings. Existing cohort-based data discovery tools range from access to rudimentary spreadsheets, through online data dictionaries [25], to more sophisticated 'shopping basket' approaches offering discovery and selection [26]. The wide range of functionality and complexity of these tools is a strong argument for the development of tools that simplify discovery across datasets, and that follow though into data selection. Using data platforms to consolidate approaches to data discovery and variable selection across multiple datasets incentivises the development of more ergonomic and powerful tooling.

The case for streamlining and standardising data discovery can be difficult to make. However, an example of where this has been transformative is the introduction of reference SNP cluster ID (rs) numbers [27]. By establishing rules around how to annotate genetic data, confidence in the provenance of data is increased, transaction costs of data discovery and access lowered, and rigour improved. The point being that simple solutions can be used to increase scientific opportunity rather than restrict academic freedom. This paper demonstrates that a similar exercise for cohort data is technically feasible and argues that it would be highly valuable. The cohorts who participated in this project have all deposited their data with DPUK and are available upon application through DPUK. The harmonised dataset used for this project will be available as an optional data format in the future, integrated within the DPUK curation programme [11].

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s10654-023-00997-3.

**Acknowledgements** We would like to acknowledge the pioneering work of Professor Isobel Fortier of McGill University in developing the field of data harmonisation for cohort data. Airwave: The Airwave Health Monitoring Study is funded by the Home Office (grant number

<footnote>*ISCED International standard classification of education [28]</footnote>
780-TETRA) with additional support from the National Institute for Health Research (NIHR) Biomedical Research Centre. The Airwave Study uses the computing resources of the UK MEDical BIOinformat- ics Partnership (UK MED-BIO: supported by the Medical Research Council MR/L01632X/1). ELSA: The English Longitudinal Study of Ageing was developed by a team of researchers based at University College London, NatCen Social Research, the Institute for Fiscal Stud- ies, the University of Manchester and the University of East Anglia. The data were collected by NatCen Social Research. The funding is currently provided by the National Institute on Aging in the US, and a consortium of UK government departments coordinated by the National Institute for Health Research. Funding has also been received by the Economic and Social Research Council. Generation Scotland: Generation Scotland received core support from the Chief Scientist Office of the Scottish Government Health Directorates [CZD/16/6] and the Scottish Funding Council [HR03006]. Genotyping of the GS:SFHS samples was carried out by the Genetics Core Laboratory at the GSMAPP Access Policy v6-7 December 2016 FINAL 8 Well- come Trust Clinical Research Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK and the Wellcome Trust (Wellcome Trust Strategic Award “STRatifying Resilience and Depres- sion Longitudinally” (STRADL) Reference 104036/Z/14/Z.Memento: The Memento cohort was supported by a grant from the Fondation Plan Alzheimer (Alzheimer Plan 2008-15 2012) and sponsored by the Bordeaux University Hospital. This work was also conducted by the following: CIC 1401-EC, Bordeaux University Hospital, Inserm, and Bordeaux University Dementias Platform UK: The Medical Research Council supports DPUK through grant MR/T0333771. The PI is John Gallacher Mike Nalls: This research was supported in part by the Intramural Research Program of the NIH, National Institute on Aging (NIA), National Institutes of Health, Department of Health and Human Services; project numberZO1 AG000535, as well as the National Insti- tute of Neurological Disorders and Stroke. Mike Nall’s participation in this project was part of a competitive contract awarded to Data Tecnica International LLC by the National Institutes of Health to support open science research, he also currently serves on the scientific advisory board for Clover Therapeutics and is an advisor to Neuron23 Inc.

**Author contributions** All authors contributed to the conception, crea-
tion and development of the manuscript. Material preparation was con-
ducted by John Gallacher and Sarah Bauermeister. The first draft of the
manuscript was written by John Gallacher and all authors commented
on previous versions of the manuscript. All authors read and approved
the final manuscript.

**Funding** This work was supported by the UK Research and Innovation Medical Research Council [MR/T0333771].

**Data availability** Not applicable.

**Code availability** Not applicable.

Declarations

**Conflicts of interest** The authors declare that they have no conflict of interest.

**Consent to participate** Not applicable.

**Consent for publication** Not applicable.

**Ethics approval** Not applicable.

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