Female genital mutilation/cutting (FGM/C) coding capacities in Swiss university hospitals using the International Classification of Diseases (ICD)

Abstract

Background The real prevalence and incidence of women living with or at risk of female genital mutilation/cutting (FGM/C) is unknown in Switzerland and many parts of Europe, as there are no representative surveys similar to DHS or MICS for European countries. Indirect estimates are commonly used to estimate the number of women with FGM/C in high-income countries, but may not reflect the actual FGM/C prevalence among migrants. Direct measures may provide more accurate estimates that could guide policy- and clinical decision-making. Swiss hospital data may provide a sample of patients that can be used to describe the prevalence of FGM/C in Swiss hospitals. Our study assesses the number of inpatient women and girls in Swiss university hospitals from countries with high FGM/C prevalence, and of inpatients with a coded diagnosis of FGM/C. Methods We conducted an exploratory descriptive study in Switzerland to assess the number of women and girls admitted to Swiss university hospitals between 2016 and 2018 from 30 FGM/C practicing countries, as well as inpatients with a coded diagnosis of FGM/C using anonymized data. We calculated indirect estimates for inpatient women and girls living with or at risk of FGM/C and compared them with the number of inpatients with a coded diagnosis of FGM/C. Results 8720 women and girls from FGM/C practicing countries were admitted. 207 patients had a coded diagnosis of FGM/C, including 7 with a nationality outside the 30 targeted countries, corresponding to an overall prevalence of 2.3% (95%CI, 2.0–2.6). The number of FGM/C cases by hospital was significantly different across years (P < 0.001), with a higher proportion of cases collected in Geneva, Switzerland. Conclusions The comparison between indirect estimates of inpatients with or at risk of FGM/C and the low number of FGM/C cases coded, suggests low recording and coding capacities of FGM/C. Tweetable abstract The capacity of coding primary and secondary diagnosis of FGM/C in Swiss university hospitals seems low. Protocol number: 2018–01851: SwissEthics Committee, Canton of Geneva, Switzerland.

Diagnoses and procedures of inpatients with female genital mutilation/cutting in Swiss University Hospitals: a cross-sectional study

“…In four Swiss university hospitals, 207 inpatients had a primary (n = 22, 10.6%) or secondary (n = 185, 89.4%) diagnosis of FGM/C coded at admission between 2016 and 2018 [ 26 ]. As discussed in our related paper on Swiss university hospitals’ capacities of coding FGM/C, this was much less than expected when compared with the number of inpatients who could have undergone FGM/C based on their nationality and indirect estimates (n = 4947) [ 26 ]. Either fewer women than expected have undergone FGM/C, or healthcare professionals did not identify and/or record it, or professional coders failed to code FGM/C, resulting in suboptimal coding.…”

“…Future studies could assess the prevalence of FGM/C and associated health outcomes in all hospitals, and study regional variations, such as in areas near asylum centres. Application of our method is mostly limited by undercoding of FGM/C, which most likely results from insufficient training about FGM/C [ 26 ]. Besides gynaecology and obstetrics, health professionals working in paediatrics, travel medicine, infectious diseases, primary care, and migrant health programmes, could benefit from such training.…”

“…Furthermore, Swiss basic health insurance covers most pregnancy-related costs, facilitating access to healthcare [ 36 ]. Obstetricians and gynaecologists routinely perform genital examinations and are more likely trained to diagnose FGM/C [ 26 ]. FGM/C is also more likely to be recorded in obstetrics charts, because it can influence childbirth [ 1 ].…”

The global prevalence of female genital mutilation/cutting: A systematic review and meta-analysis of national, regional, facility and school-based studies

“…Prevalence within these migrant populations ranged from 0.32% (of a sample of 145,492) [104] to 99% (of a sample of 191) [105] (S8 Results: S14 Table ). Type II [106][107][108][109][110][111][112][113][114] and Type III [104,105,[115][116][117][118][119][120] were most common in 9 studies each, followed by Type I (8 studies) [121][122][123][124][125][126][127][128], and Type IV (three studies) [129][130][131] (S7 Results: S15 Table ).…”

“…Prevalence within these migrant populations ranged from 0.32% (of a sample of 145,492) [104] to 99% (of a sample of 191) [105] (S8 Results, S14 Table ). Type III [104][105][106][107][108][109][110][111][112][113] was the most common type in 10 studies, followed by Type II in 9 studies [114][115][116][117][118][119][120][121][122],…”