NMI - Nordic Multimorbidity Index
Weighted comorbidity score for register-based studies
NMI (Nordic Multimorbidity Index) is a weighted comorbidity score developed for Nordic register data. It combines 50 predictors - 29 diagnosis-based and 21 prescription-based - and gives a continuous score reflecting the overall comorbidity burden.
Article: Kristensen et al. Clinical Epidemiology 2022 - doi:10.2147/CLEP.S353398
What does NMI give you?
A continuous score per person that can be used as: - A covariate in regression models (adjusting for comorbidity) - A descriptive variable in Table 1
NMI is validated to predict 5-year mortality in Nordic populations and is more fine-grained than e.g. the Charlson Comorbidity Index.
When is it used?
- When you want to adjust for comorbidity in a Cox or logistic regression
- When you want to describe the comorbidity burden in your population
- As an alternative to Charlson or CCI, particularly in Nordic cohort studies
Algorithm under development
An R implementation of the NMI algorithm for this project is under development and coming soon. This page will be updated with code examples when the algorithm is ready.
Existing implementation: Stata .do file The SDU Pharmacoepidemiology Unit has published a Stata implementation of NMI: pharmacoepi.sdu.dk/nmi/
No official R package is known. Consider contacting SDU directly to ask whether they have R code under development or are interested in collaborating on an R version.
NMI requires: - LPR (somatic + psychiatric LPR2, LPR3) - 5-year lookback, A/B/G diagnoses - LMDB: 5-year lookback, ATC codes
Scores are assigned per predictor based on weights in Table 2 of the article. One predictor is excluded in dementia studies: dx_F00_G30 and rx_N06D are avoided to prevent circular logic, as dementia is the study outcome.
The 5-year lookback can be an inclusion criterion. NMI requires at least 5 years of register data before the index date. If you make full lookback an inclusion criterion (only people with at least 5 years of prior residency/coverage in Denmark), it must be applied already when you build your cohort, not first here. The same requirement must be applied to any comparison cohort, so both groups have the same depth of data before index. Decide on it in Phase 1 and apply it in Phase 10.
Example: make the 5-year lookback an inclusion criterion (conceptual)
The idea is to require that each person has resided in Denmark (and is therefore covered by the registers) for at least 5 years before their index date. You check this against residence/migration data (VNDS/BEF) and apply it as a filter before you match a comparison cohort:
# Conceptual - adapt to your residence data
kohort <- kohort %>%
mutate(lookback_ok = as.numeric(index_date - residence_start) >= 365.25 * 5) %>% # at least 5 years before index
filter(lookback_ok) # keep only full lookbackApply the same requirement to the comparison cohort’s assigned index dates, so the two groups are comparable.
More information
See the article for the full predictor list and weights. See Overview of registers for confirmed column names in LPR and LMDB.