EL1: Intro

Overwiev of this part of the course

Important aspects

Data (where does is come from, what does it contain)
Ethics and legal (how to handle sensitive data, what laws and regulations apply)
Project management (how to plan and execute a data project, version control, reproducibility, R specific packages for efficient data handling)

Data – what is it?

EU Data Act | Article 2, Definitions:

For the purposes of this Regulation, the following definitions apply:

‘data’ means any digital representation of acts, facts or information and any compilation of such acts, facts or information, including in the form of sound, visual or audio-visual recording;

‘metadata’ means a structured description of the contents or the use of data facilitating the discovery or use of that data;

‘personal data’ means personal data as defined in Article 4, point (1), of Regulation (EU) 2016/679;

‘non-personal data’ means data other than personal data;

Course structure

Lectures on different data sources/registers
🧑‍💻 Exercises on data management and analysis
- R with some additional tools (Git, GitHub, targets, data.table)
A data project with written report and presentation
Final exam 😱
Instruction web page in addition to Canvas
Litterature: accessible through GU library (O’Reilly Learning for Higher Education) or otherwise shared (no need to purchase books)

Different types of data

🩻 Images
- Statistical image analysis
🧪 Lab samples
📝 Unstructured medical records
- Natural Language Processing
📺 Sensor data
- Time series (“big data”)
💻 EHRs (electronic health records)
- Structured but hierarchical rather than tabular
💻 Structured medical records
- tabular data

Usages

👩🏼‍🔬 Research
📒 Quality control/improvement
🔖 Administration/reporting
📰 News coverage
🧑‍💻 Building prediction models and tools

Register data

Three types of health care registers:

Administrative registers
Health care registers
Quality registers

💶 Administrative data

(As found in all types of registers)

Billing codes
- Direct (what something actually cost)
- Estimated (DRG codes for different types of procedures)
Claims data
- Primary for reimbursement (insurance company or other payer)
- Secondarily for Health economy/epidemiology
How to contact patients, health care providers etc
Dates and times for visits, procedures etc

🏥 Hospital background data

hospital characteristics
staffing
resources
geographical area
level of specialization
private, public

🩺 Clinical data

health care registers
- Mandatory (by law)
- eg: National patient register, cancer register (diagnoses)
quality registers
- Optional for health care providers
- (Mandatory within organizations joining)
- conditions (diabetes, cancer, etc)
- procedures (total hip arthroplasty)
- Diagnoses, treatments, health status, questionnaires (PROM/PREM)

🤓 Individual background data

socioeconomic data
education
income
occupation
family relations
migration status
Mortality data
- date of death
- cause of death

🗺️ Aggregated data

“Micro” vs. “macro” data.

population data
neighborhood characteristics
pollution
crime rates

Inclusion/exclusion criteria

👍 Defines the target study/register population
👎 Define exceptions to the general rules

🦿Simple example

“Every Swedish resident who had total hip arthroplasty performed in Sweden”

Include: all ages, all hospitals, all reasons for the prosthesis, all types of prosthesis
Exclude: Swedish residents with surgery performed in other countries. Non-Swedish residents with the procedure performed in Sweden.

🥴 Complicated example

The National Quality Register for Ovarian Cancer

Inclusion
1. Epithelial borderline tumours of the ovary
  - Topography code according to ICD-O/2: C56.9.
  - Morphology code according to ICD-O/2 ≥80103 and <85900.
  - Borderline tumours with 5th digit 3 in the morphology code according to ICD-O/2 and benign behaviour flag = 3.
2. Epithelial ovarian cancer:
  - Topography code according to ICD-O/2: C56.9.
  - Morphology code according to ICD-O/2 ≥80103 and <85900.
  - Malignant tumours with 5th digit 3 in the morphology code according to ICD-O/2 and benign behaviour flag blank.
3. Non-epithelial ovarian cancer:
  - Topography code according to ICD-O/2: C56.9.
  - Morphology code according to ICD-O/2 ≥85903 and <95900, with the exception of mesotheliomas with ICD-O/2 codes in the interval ≥90500 and <90600.
  - Malignant tumours with digit 3 as the fifth digit in the morphology code according to ICD-O/2.
  - Exception for granulosa cell tumours, where all cases with morphology codes according to ICD-O/2 in the interval ≥86200 and ≤86223 are included.
4. Malignant tumours of the fallopian tube:
  - Topography code according to ICD-O/2: C57.0.
  - Morphology code according to ICD-O/2 ≥80003 and <95900, with the exception of mesotheliomas with ICD-O/2 codes in the interval ≥90500 and <90600.
  - Malignant tumours with digit 3 as the fifth digit in the morphology code according to ICD-O/2.
Exclusion
- Epithelial ovarian cancer and borderline tumours of the ovary
  Cases with behavior codes 0, 1, 2, 6, or 9 as the fifth digit in the ICD-O/2 morphology code are excluded.
  Morphology codes according to ICD-O/2 <80103 and ≥85900 are excluded.
- Non-epithelial ovarian cancer
  Cases with digits 0, 1, 2, 6, or 9 as the fifth digit in the ICD-O/2 morphology code are excluded, with the exception of granulosa cell tumours, for which cases with ICD-O/2 morphology codes in the interval ≥86200 and ≤86223 are included even when the final digit is 0, 1, 2, or 3.
  Morphology codes according to ICD-O/2 <85903, as well as codes in the intervals ≥90500 and <90600 (mesotheliomas) and ≥95900, are excluded.
- Tumours of the fallopian tube
  Cases with behaviour codes 0, 1, 2, 6, or 9 as the fifth digit in the ICD-O/2 morphology code are excluded.
  Morphology codes according to ICD-O/2 in the intervals ≥90500 and <90600 (mesotheliomas) and ≥95900 are excluded.
- For all diagnoses, cases are excluded if the diagnosis is based solely on:
  - clinical examination (basis of diagnosis 1),
  - imaging procedures including radiography, scintigraphy, ultrasound, MRI, CT (or equivalent examinations) (basis of diagnosis 2),
  - autopsy with or without histopathological examination (basis of diagnosis 4 or 7),
  - surgery without histopathological examination (basis of diagnosis 6), or
  - other laboratory investigations (basis of diagnosis 8).
  - cases with age <18 years are excluded.

Coverage and completeness

🏥 Institutional coverage: proportion of all eligible units/clinics that are connected to the registry
- e.g., 90% of hospitals performing the procedure are connected
- Should be known by the “register holder”
🤒 Case coverage: proportion of patients who should have been reported from connected units that are actually included
- e.g., 85% of eligible patients registered
- The aim is to use 100 % but this is not always possible
Data completeness: proportion of required data fields that are filled in for the registered patients
- 🚬 e.g., 95% of patients have smoking status recorded
- 🩸 e.g., 80% of patients have blood pressure data available

What is recorded?

👩🏻‍⚖️ Some registers are mandated by law and regulations
Quality registers often have a steering committee and register holder
Reseasrh initiated databases according to specific protocols

Data linking

Unique personal identifier
- Not in every country!
- Social security number similar purpose but not as widely used
study specific id number
HSA (“Hälso- och sjukvårdens adressregister” for staff and organizations)

Unique personal identifier

(Swedish: personnummer, reading: (Ludvigsson et al. 2009))

121212-1212 Tolvan Tolvansson

10 (or 12) digits
date of birth-4 digits
assigned at birth or immigration
used in all health care contacts
used for all administrative data
sometimes reused after death
sometimes changed (uncommon)
sometimes inclusion criteria for register
similar in the Nordic countries
- Denmark: CPR number
- Norway: Fødselsnummer
- Finland: Henkilötunnus
- Iceland: Kennitala

Combining data

Similar registries in different areas/regions/countries
- Different individuals but similar data
Same definitions and variables?
Same inclusion criteria?
Don’t get fooled by similar names!
Differences and similarities within the Nordic countries (Laugesen et al. 2021)

Working with health care data

A lot to do before the statistical analysis!

Legalities
- Do I have the right to access this data?
- What am I allowed to do?
- What am I not allowed to do?
Data management
- large datasets
- multiple datasets
- different formats
- missing data
- data cleaning
- data transformation
- data wrangling
- data munging
- data governance
- data engineering
Planning
- What is the purpose?
- How can I achieve my goals?
- What if I change my plans later?
- Can I redo my analysis?
- How do I present/communicate my results?

R as a tool but …

Large files often comes exported from SAS (initially “Statistical Analysis System”)
Comma-Separated Values (csv) or text files
Application Programming Interface (API) calls
Structured Query Language (SQL) databases
Hierarchical data structures (eXtensible Markup Language, XML; JavaScript Object Notation, JSON, …)

Our use of R

{data.table} to handle large data sets efficiently
{targets} to streamline a reproducible pipeline
Git for version control
GitHub for collaboration
Quarto for reporting

Laugesen, Kristina, Jonas F Ludvigsson, Morten Schmidt, Mika Gissler, Unnur Anna Valdimarsdottir, Astrid Lunde, and Henrik Toft Sørensen. 2021. “Nordic Health Registry-Based Research: A Review of Health Care Systems and Key Registries.” Clinical Epidemiology Volume 13 (July): 533–54. https://doi.org/10.2147/CLEP.S314959.

Ludvigsson, Jonas F., Petra Otterblad-Olausson, Birgitta U. Pettersson, and Anders Ekbom. 2009. “The Swedish Personal Identity Number: Possibilities and Pitfalls in Healthcare and Medical Research.” European Journal of Epidemiology 24 (11): 659–67. https://doi.org/10.1007/s10654-009-9350-y.

Nguyen, Andrew. 2022. Hands-on healthcare data: taming the complexity of real-world data. First edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly.