Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases
<p>Abstract</p> <p>Background</p> <p>Various studies indicate that inter-hospital comparisons have to take case mix into account and that risk adjustment procedures are necessary to control for potential predictors of cesarean delivery (CD). Different data sources have...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2013-01-01
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| Series: | BMC Health Services Research |
| Online Access: | http://www.biomedcentral.com/1472-6963/13/13 |
| _version_ | 1818789217321353216 |
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| author | Stivanello Elisa Rucci Paola Carretta Elisa Pieri Giulia Fantini Maria P |
| author_facet | Stivanello Elisa Rucci Paola Carretta Elisa Pieri Giulia Fantini Maria P |
| author_sort | Stivanello Elisa |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Various studies indicate that inter-hospital comparisons have to take case mix into account and that risk adjustment procedures are necessary to control for potential predictors of cesarean delivery (CD). Different data sources have been used to retrieve information on potential predictors of CD. The aim of this study was to compare the discrimination capacity and fit of predictive models of CD created using different sources and to assess whether more complex models improve inter-hospital comparisons.</p> <p>Methods</p> <p>We created 4 predictive models of CD. One model included only variables from Hospital Discharge Records of the index hospitalization, one included also information from previous hospitalizations, one also clinical variables from birth certificates (BC) and one also socio-demographic variables. We compared the four models using the Receiver Operator Curve and the Akaike and Bayesian Information Criteria.</p> <p>Results</p> <p>Information from Birth Certificates improved the discrimination and model fit. Adding socio-demographic variables or past comorbidities did not improve the discrimination capacity or the model fit. Hospital-specific CD resulting from the models were highly correlated.</p> <p>Conclusions</p> <p>Record linkage improves the performance of the models but does not affect inter-hospital comparisons.</p> |
| first_indexed | 2024-12-18T14:36:03Z |
| format | Article |
| id | doaj.art-c5f66a5ea61e461297d6e2ef35789776 |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-12-18T14:36:03Z |
| publishDate | 2013-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-c5f66a5ea61e461297d6e2ef357897762022-12-21T21:04:31ZengBMCBMC Health Services Research1472-69632013-01-011311310.1186/1472-6963-13-13Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databasesStivanello ElisaRucci PaolaCarretta ElisaPieri GiuliaFantini Maria P<p>Abstract</p> <p>Background</p> <p>Various studies indicate that inter-hospital comparisons have to take case mix into account and that risk adjustment procedures are necessary to control for potential predictors of cesarean delivery (CD). Different data sources have been used to retrieve information on potential predictors of CD. The aim of this study was to compare the discrimination capacity and fit of predictive models of CD created using different sources and to assess whether more complex models improve inter-hospital comparisons.</p> <p>Methods</p> <p>We created 4 predictive models of CD. One model included only variables from Hospital Discharge Records of the index hospitalization, one included also information from previous hospitalizations, one also clinical variables from birth certificates (BC) and one also socio-demographic variables. We compared the four models using the Receiver Operator Curve and the Akaike and Bayesian Information Criteria.</p> <p>Results</p> <p>Information from Birth Certificates improved the discrimination and model fit. Adding socio-demographic variables or past comorbidities did not improve the discrimination capacity or the model fit. Hospital-specific CD resulting from the models were highly correlated.</p> <p>Conclusions</p> <p>Record linkage improves the performance of the models but does not affect inter-hospital comparisons.</p>http://www.biomedcentral.com/1472-6963/13/13 |
| spellingShingle | Stivanello Elisa Rucci Paola Carretta Elisa Pieri Giulia Fantini Maria P Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases BMC Health Services Research |
| title | Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases |
| title_full | Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases |
| title_fullStr | Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases |
| title_full_unstemmed | Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases |
| title_short | Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases |
| title_sort | risk adjustment for cesarean delivery rates how many variables do we need an observational study using administrative databases |
| url | http://www.biomedcentral.com/1472-6963/13/13 |
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