ArticleBMC health services research2013
Risk adjustment for cesarean delivery rates: how many variables do we need? An observational study using administrative databases.
Article in BMC health services research, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Humanizing Birth in a Third-Level Hospital: Revealing the Benefits of Natural Cesarean Sections.Life (Basel, Switzerland) · 2024Article
- Case mix adjustment of health outcomes, resource use and process indicators in childbirth care: a register-based study.BMC pregnancy and childbirth · 2016Article
- Determinants of cesarean delivery: a classification tree analysis.BMC pregnancy and childbirth · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVarious 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.
methodsWe 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.
resultsInformation 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.
conclusionsRecord linkage improves the performance of the models but does not affect inter-hospital comparisons.
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