The Norwegian Armed Forces Health Registry conscription board health examinations 1968-2018.
Elin Anita FadumLeif Aa StrandInger RudvinMari L HæreidEinar K BorudPublished in: Scandinavian journal of public health (2020)
Aim: The aim of the study is to encourage further research initiatives and collaborations based on Norwegian Armed Forces Health Registry (NAFHR) data by presenting basic information on the data contained therein. Methods: We describe how conscription board health examinations (CBHEs) are carried out, how results are recorded in the NAFHR, and the completeness of NAFHR data that are electronically available for research purposes. Results: In December 2018, the NAFHR contained data on nearly 1.5 million Norwegian citizens (95% men) who attended CBHE in 1968-2018 at the age of 17-19 years. The percentage of persons included from each birth cohort has varied as the Armed Forces' personnel requirements and filing procedures have changed, increasing from 73% of eligible men born in 1950 to 95% of eligible men born in 1960-1991. In 2010 a preselection of candidates was implemented wherefore less than half of men born in 1992-2000 are registered in the NAFHR. Information on aerobic fitness, cognitive general ability, height and weight is registered for approximately 95% of individuals included in the NAFHR. The NAFHR contains more detailed health information for CBHEs that took place as from 1980, and information included from 2011 onwards is the most detailed. Unique, national personal identification numbers may be used to link the NAFHR to other health registries or data sources for public health research. Conclusions: The NAFHR contains CBHE data on the majority of Norwegian men and a substantial number of women born since 1950. NAFHR data represent a valuable resource for research collaborations.
Keyphrases
- health information
- healthcare
- electronic health record
- public health
- mental health
- big data
- social media
- body mass index
- middle aged
- type diabetes
- gestational age
- physical activity
- health promotion
- machine learning
- low birth weight
- quality improvement
- pregnant women
- body composition
- polycystic ovary syndrome
- data analysis
- artificial intelligence
- metabolic syndrome
- insulin resistance
- drinking water
- weight gain
- skeletal muscle
- climate change