Talk:Birth - Caesarean Delivery

Trends and determinants of caesarean sections births in Queensland, 1997-2006. Howell S, Johnston T, Macleod SL. Aust N Z J Obstet Gynaecol. 2009 Dec;49(6):606-11. PMID: 20070708

"In Queensland, in 2006, CS rates were 26.9 and 48.0% among public and private sector patients, respectively. ....The CS rate in Queensland in 2006 was higher than in any other Australian state. The increase in Queensland's CS rates can be attributed to both the rising number of primary caesarean births and the rising number of repeat caesareans."

Risk adjustment for inter-hospital comparison of primary cesarean section rates: need, validity and parsimony.

BMC Health Serv Res. 2006 Aug 15;6:100.

PMID: 16911770

http://www.biomedcentral.com/1472-6963/6/100

Cesarean section rates is often used as an indicator of quality of care in maternity hospitals. The assumption is that lower rates reflect in developed countries more appropriate clinical practice and general better performances. Hospitals are thus often ranked on the basis of caesarean section rates. The aim of this study is to assess whether the adjustment for clinical and sociodemographic variables of the mother and the fetus is necessary for inter-hospital comparisons of cesarean section (c-section) rates and to assess whether a risk adjustment model based on a limited number of variables could be identified and used. METHODS: Discharge abstracts of labouring women without prior cesarean were linked with abstracts of newborns discharged from 29 hospitals of the Emilia-Romagna Region (Italy) from 2003 to 2004. Adjusted ORs of cesarean by hospital were estimated by using two logistic regression models: 1) a full model including the potential confounders selected by a backward procedure; 2) a parsimonious model including only actual confounders identified by the "change-in-estimate" procedure. Hospital rankings, based on ORs were examined. RESULTS: 24 risk factors for c-section were included in the full model and 7 (marital status, maternal age, infant weight, fetopelvic disproportion, eclampsia or pre-eclampsia, placenta previa/abruptio placentae, malposition/malpresentation) in the parsimonious model. Hospital ranking using the adjusted ORs from both models was different from that obtained using the crude ORs. The correlation between the rankings of the two models was 0.92. The crude ORs were smaller than ORs adjusted by both models, with the parsimonious ones producing more precise estimates. CONCLUSION: Risk adjustment is necessary to compare hospital c-section rates, it shows differences in rankings and highlights inappropriateness of some hospitals. By adjusting for only actual confounders valid and more precise estimates could be obtained.