Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates
Regional variations in healthcare utilisation rates are ubiquitous and persistent. In settings where an aggregate national health service budget is allocated primarily on a per capita basis, little regional variation in total healthcare utilisation rates will be observed. However, for specific treat...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286998/?tool=EBI |
| _version_ | 1797795967039176704 |
|---|---|
| author | Jan Håkon Rudolfsen Jan Abel Olsen |
| author_facet | Jan Håkon Rudolfsen Jan Abel Olsen |
| author_sort | Jan Håkon Rudolfsen |
| collection | DOAJ |
| description | Regional variations in healthcare utilisation rates are ubiquitous and persistent. In settings where an aggregate national health service budget is allocated primarily on a per capita basis, little regional variation in total healthcare utilisation rates will be observed. However, for specific treatments, large variations in utilisation rates are observed, iymplying a substitution effect at some point in service delivery. The current paper investigates the extent to which this substitution effect occurs within or between specialties, particularly distinguishing between emergency versus elective care. We used data from Statistics Norway and the Norwegian Patient Registry on eight somatic surgeries for all patients treated from 2010 to 2015. We calculated Diagnosis-Related Group (DRG) -weight per capita in 19 hospital regions. We applied principal component analysis (PCA) to demonstrate patterns in DRG-weight, annual relative changes in DRG-weight, and DRG-weight production for elective care. We show that treatments with similar characteristics cluster within regions. Treatment frequency explains 29% of the total variation in treatment rates. In a dynamic model, treatments with a high degree of emergency care are negatively correlated with treatments with a high degree of elective care. Furthermore, when considering only elective care treatments, the substitution effect occurs between specialties and explains 49% of the variation. When designing policies aimed at reducing regional variations in healthcare utilisation, a distinction between elective and emergency care as well as substitution effects need to be considered. |
| first_indexed | 2024-03-13T03:27:01Z |
| format | Article |
| id | doaj.art-2d4e47dcbdb84859b9a86cb1422fbd28 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T03:27:01Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-2d4e47dcbdb84859b9a86cb1422fbd282023-06-25T05:31:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01186Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation ratesJan Håkon RudolfsenJan Abel OlsenRegional variations in healthcare utilisation rates are ubiquitous and persistent. In settings where an aggregate national health service budget is allocated primarily on a per capita basis, little regional variation in total healthcare utilisation rates will be observed. However, for specific treatments, large variations in utilisation rates are observed, iymplying a substitution effect at some point in service delivery. The current paper investigates the extent to which this substitution effect occurs within or between specialties, particularly distinguishing between emergency versus elective care. We used data from Statistics Norway and the Norwegian Patient Registry on eight somatic surgeries for all patients treated from 2010 to 2015. We calculated Diagnosis-Related Group (DRG) -weight per capita in 19 hospital regions. We applied principal component analysis (PCA) to demonstrate patterns in DRG-weight, annual relative changes in DRG-weight, and DRG-weight production for elective care. We show that treatments with similar characteristics cluster within regions. Treatment frequency explains 29% of the total variation in treatment rates. In a dynamic model, treatments with a high degree of emergency care are negatively correlated with treatments with a high degree of elective care. Furthermore, when considering only elective care treatments, the substitution effect occurs between specialties and explains 49% of the variation. When designing policies aimed at reducing regional variations in healthcare utilisation, a distinction between elective and emergency care as well as substitution effects need to be considered.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286998/?tool=EBI |
| spellingShingle | Jan Håkon Rudolfsen Jan Abel Olsen Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates PLoS ONE |
| title | Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates |
| title_full | Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates |
| title_fullStr | Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates |
| title_full_unstemmed | Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates |
| title_short | Related variations: A novel approach for detecting patterns of regional variations in healthcare utilisation rates |
| title_sort | related variations a novel approach for detecting patterns of regional variations in healthcare utilisation rates |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286998/?tool=EBI |
| work_keys_str_mv | AT janhakonrudolfsen relatedvariationsanovelapproachfordetectingpatternsofregionalvariationsinhealthcareutilisationrates AT janabelolsen relatedvariationsanovelapproachfordetectingpatternsofregionalvariationsinhealthcareutilisationrates |