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Background  Caesarean section (C-section) rates are often low among the poor and very high among the better-off in low- and middle-income countries. We examined to what extent these differences are explained by medical need in an African context.    Methods  We analyzed electronic records of 12,209 women who gave birth in a teaching hospital in Kenya in 2014. C-section rates were calculated by socioeconomic position (SEP), using maternal occupation (professional, small business, housewife, student) as indicator. We assessed if women had documented clinical indications according to hospital guidelines and if socioeconomic differences in C-section rates were explained by indication.    Results  Indication for C-section according to hospital guidelines was more prevalent among professionals than housewives (16% vs. 9% of all births). The C-section rate was also higher among professionals than housewives (21.1% vs. 15.8% [OR 1.43; 95%CI 1.23–1.65]). This C-section rate difference was largely explained by indication (4.7 of the 5.3 percentage point difference between professionals and housewives concerned indicated C-sections, often with previous C-section as indication). Repeat C-sections were near-universal (99%). 43% of primary C-sections had no documented indication. Over-use was somewhat higher among professionals than housewives (C-section rate among women without indication: 6.6 and 5.5% respectively), which partly explained socioeconomic differences in primary C-section rate.    Conclusions  Socioeconomic differences in C-section rates can be largely explained by unnecessary primary C-sections and higher supposed need due to previous C-section. Prevention of unnecessary primary C-sections and promoting safe trial of labor should be priorities in addressing C-section over-use and reducing inequalities.    Tweetable abstract  Unnecessary primary C-sections and ubiquitous repeat C-sections drive overall C-section rates and C-section inequalities.

Socioeconomic differences in caesarean section – are they explained by medical need? An analysis of patient record data of a large Kenyan hospital