ArticleBMC medical informatics and decision making2013
Methodological challenges in using the Finnish Hospital Discharge Register for studying fire-related injuries leading to inpatient care.
Article in BMC medical informatics and decision making, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Hospital admissions for non-fatal drowning in Finland, 2002-2023: a nationwide population-based register study.Injury epidemiology · 2025Article
- Determinants of hospitalizations for pneumonia among Finnish drug users.SAGE open medicine · 2018Article
- Burden of Fire Injuries in Finland: Lost Productivity and Benefits.Journal of public health research · 2016Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundThe objective was to examine feasibility of using hospital discharge register data for studying fire-related injuries.
methodsThe Finnish National Hospital Discharge Register (FHDR) was the database used to select relevant hospital discharge data to study usability and data quality issues. Patterns of E-coding were assessed, as well as prominent challenges in defining the incidence of injuries. Additionally, the issue of defining the relevant amount of hospital days accounted for in injury care was considered.
resultsDirectly after the introduction of the ICD-10 classification system, in 1996, the completeness of E-coding was found to be poor, but to have improved dramatically around 2000 and thereafter. The scale of the challenges to defining the incidence of injuries was found to be manageable. In counting the relevant hospital days, psychiatric and long-term care were found to be the obvious and possible sources of overestimation.
conclusionsThe FHDR was found to be a feasible data source for studying fire-related injuries so long as potential challenges are acknowledged and taken into account. Hospital discharge data can be a unique and powerful means for injury research as issues of representativeness and coverage of traditional probability samples can frequently be completely avoided.
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