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DAPA Measurement Toolkit

 

Harmonisation

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Introduction

This section outlines a case study illustrating some of the harmonisation concepts described on the general data harmonisation page . The example is from the InterConnect consortium, which has used existing data from different study cohorts across the world. Further case studies are available on the corresponding physical activity (case studies 2 and 3) and anthropometry (case study 4) harmonisation pages.

The InterConnect consortium aims to investigate the association between fish intake and new onset type 2 diabetes. This required the following target variables (all in g/d) to be harmonised across 12 studies:

  • Total fish
  • Fatty/oily fish (fat content > 4%)
  • Lean fish
  • Shellfish (crustaceans and molluscs)
  • Saltwater fish
  • Fresh water fish
  • Fried fish
  • Smoked or salted fish

Two studies assessed fish intake using diet history while the rest used food frequency questionnaires (FFQs). The number of questions on fish intake in the FFQs ranged between 1 and 22. At a basic level, all studies could create total fish; while not all studies contributed to analyses of different types of fish (see Table D.4.1).

Table D.4.1 Harmonisation potential across 12 studies.
Target variables
Participating
cohorts
Total fish Fatty fish Lean fish Shellfish Fresh/ Salt water fish Fried fish Smoked/ salted fish
Study 1 (Australia) Y         Y  
Study 2 (Europe) Y Y Y Y Y   Y
Study 3 (Europe) Y Y          
Study 4 (Europe) Y Y Y Y   Y  
Study 5 (Asia) Y Y Y Y Y   Y
Study 6 (Europe) Y Y Y Y Y    
Study 7 (Asia) Y     Y Y   Y
Study 8 (Europe) Y Y Y Y   Y Y
Study 9 (Europe) Y Y Y Y     Y
Study 10 (Europe) Y Y Y Y   Y  
Study 11 (Europe) Y Y Y Y Y Y Y
Study 12 (Asia) Y

Processing rules to transform the variables from each study into the common target variable format were created and agreed with each study team. Examples of some of the pre-existing study variables and the algorithms or creation rules that were developed to derive target variables are shown below in Figures D.4.1 and D.4.2.

Challenges

  • For some types of fish it was less clear whether these should be classified as lean or fatty fish and often studies have classified these differently.
  • The fat content of certain fishes is variable across the globe; therefore decisions on how to classify specific types of fish might depend on local knowledge.
  • Portion sizes differ across populations. Evidence or knowledge of local portion size is helpful to identify grams per portion or serving and calculate consumption in grams per day, the universal unit.

Fish items in the original cohort (assessed by FFQ)

Fish types (assumption of g/portion)
• White fish (hake, whiting, bream, grouper, sole) (150 g)
• Blue fish (sardines, tuna, bonito, mackerel, salmon) (150 g)

• Cod (150 g)

• Salted or smoked fish (50 g)

• Clam, oyster, mussels (60 g)

• Prawn, king prawn, crayfish (100 g)

• Octopus, squid, cuttlefish (150 g)
Derived:
• Total fish and seafood per day

• Total seafood per day

Frequency
never/almost never;

1-3 times/month;

once a week;

2-4 times/week;

5-6 times/week;

once per day;

2-3 times/day;

4-6 times/day;

more than 6 times/day

Target variable (g/d)

Harmonisation -
categorisation of fish

Harmonisation -
frequency and quantity

Total fish

total fish and seafood per day

For total fish and total seafood, source data are in g/d.

For the remainder, source data are in portion/day. Information on average portion size in grams is available.

For Lean fish: multiply portion/day*150 g

For fatty fish: multiply portion/day*150 g

For Salted/smoked/dried fish: multiply portion/day*50 g

Lean fish

White fish/day + Cod/day

Fatty fish

Blue fish/day

Seafood other than fish

total seafood per day

Fried fish

 Not assessed

Salted/smoked/dried

Salted or smoked fish/day

Freshwater/saltwater fish

 Not assessed

Figure D.4.1 Pre-existing data and algorithms used to derive target variables from a study using a food frequency questionnaire.

Fish items in the original cohort (assessed by diet history)

Fish types
• Total fish

• Cod • Baltic herring with bones; • Baltic herring; • Salmon; • Salmon salted; • Baltic herring salted with bones; • Herring slated; • Smoked Baltic herring with bones; • Sardine; • Smoked redfish • Perch; • Pike; • Flounder; • Bream; • Vendance with bones; • Fish frozen saithe; • Whitefish; • Fish average; • Fish in soup, average; • Roe; • Stockfish; • Vendance, salted with bones; • Smoked vendance with bones; • Smoked lamprey; • Smoked whitefish; • Smoked fish average; • Tuna • Shrimps

Frequency
g/d

Target variable

Harmonisation -
categorisation of fish

Harmonisation -
frequency and quantity

Total fish

Total fish (sum of all available variables) - variables are mutually exclusive

Variables already in g/d

Lean fish

• Cod • Stockfish • Fresh frozen saithe

• Perch • Pike • Flounder • fish, average • fish in soup, average

Fatty fish

• Baltic herring with bones • Baltic herring

• Salmon • Salmon salted • Baltic herring salted with bones • Herring slated • Smoked Baltic herring with bones • Sardine; • Smoked redfish • Whitefish  • Vendance, salted with bones • Smoked vendance with bone • Vendance, with bones • Smoked fish average

Seafood other than fish

• shrimps

Fried fish

 Not assessed

Salted/smoked/dried fish

• Salmon salted • Baltic herring salted with bones

• Herring slalted • Smoked Baltic herring with bones

• Smoked redfish • Vendance, salted with bones
• Smoked vendance with bone • Smoked lamprey
• Smoked whitefish • Smoked fish average: mean of four species • Baltic herring smoked • Vendance smoked • Whitefish smoked • Bream smoked

Freshwater fish

• Perch  • Pike • Bream • fish, average • fish in soup, average • Salmon • Salmon salted • Vendance, salted with bones • Smoked vendance with bone • Vendance, with bones

Saltwater fish

• Baltic herring with bones; • Tuna • Baltic herring

• Baltic herring salted with bones • Herring slalted

• Cod • Stockfish • Fresh frozen saithe • Flounder

• Smoked redfish • Sardine • Smoked fish average

 

Figure D.4.2 Pre-existing data and algorithms used to derive target variables from a study using a diet history.
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