Eur J Public Health. 2025 Nov 29:ckaf217. doi: 10.1093/eurpub/ckaf217. Online ahead of print.
ABSTRACT
There is a need to efficiently identify groups at risk of unmet health service needs. In response, we developed and evaluated the performance of a regression model to assess unmet health service needs in the Finnish population. The study population consisted of population-based Healthy Finland 2022-cohort participants (N = 18 442), aged 20-104. The primary outcome was self-reported unmet need for physician’s or nurse’s services. A total of 38 potential risk factors were evaluated. Statistical models were developed using bootstrap-enhanced LASSO regression (bolasso). Of the participants, 5875 (32%) were classified as experiencing unmet health care need. The C-index from the final model including 15 predictors from the best bolasso models varied between 0.73 and 0.76 and pooled C-index over the imputed data sets was 0.75 (95% CI 0.70-0.79). Fifteen factors-including health-related, socioeconomic variables, heavy alcohol use, experiences with health services, caregiving for others, and language group-were found to be strongly associated with an increased risk of unmet health care needs and may be a useful targets for preventing unmet health care need.
PMID:41351301 | DOI:10.1093/eurpub/ckaf217
