US FGM/C model suggests earlier estimates undercounted affected women and overcounted girls at risk

A modelling study of female genital mutilation and cutting (FGM/C) in the United States estimated that earlier estimates had likely underestimated the number of women already living with FGM/C while overestimating the number of girls considered at risk. The study was reported by Sean Callaghan in PLOS One and applied a model to 1,248,776 women and girls associated with 21 countries of origin in the United States.

The analysis did not directly count individuals’ FGM/C status. Instead, it combined population and prevalence data to estimate how many people were likely to have undergone the practice and how many remained potentially at risk. Population estimates came from the 2015–2019 American Community Survey Public Use Microdata Sample. Unlike earlier US studies that primarily selected people by place of birth, the study selected its population primarily by ancestry.

Age-disaggregated prevalence estimates were derived from Multiple Indicator Cluster Surveys, Demographic and Health Surveys and other nationally representative surveys using methods developed in earlier work by Callaghan. The included survey samples were described as nationally representative at the 95% confidence level, with reported FGM/C error margins between 0.2% and 1.78%.

Earlier US estimates used relatively simple calculations. Jones and colleagues multiplied the relevant population by an average prevalence measure, while Goldberg and colleagues used age-based prevalence assumptions. European studies frequently applied an additional adjustment for migration and acculturation, commonly estimating that native-born girls had half the risk of foreign-born girls. Callaghan’s model aimed to represent variation that could be concealed by combining these elements into a single adjustment and by assuming a common risk boundary for all communities.

The new model incorporated a coefficient for socio-economic selective migration, drawing on differences between national prevalence and prevalence among more educated, wealthier or urban groups. It also introduced an Ethno-religious Loading Factor for Nigerian and Egyptian populations. This factor was used in sensitivity analyses to illustrate how ethnic and religious selection might affect estimates, rather than to claim an exact measured effect, and results were presented both with and without it.

Three approaches were used to estimate the proportion of FGM/C risk occurring before migration, after migration and in the future. The first used a universal risk boundary of age 19, consistent with an earlier framework. The second replaced that boundary with an age above which FGM/C was considered rare in each relevant community. The third fitted a generalised gamma distribution to age-of-cutting data, providing a continuous probability curve for estimating risk at individual ages. All fitted quartiles were within the 90% confidence level of the original quartiles, and 95.2% were within the 95% confidence level.

The third approach assumed that the age distribution of FGM/C was unimodal, although this may be less suitable for Liberia, where survey data suggested a bimodal distribution. The model also did not include the possible effects of long-term residence in refugee camps before migration, transnational travel or acculturation. The Ethno-religious Loading Factor values for Nigeria and Egypt were explicitly described by the author as speculative and intended to demonstrate a mechanism’s potential impact rather than predict its exact scale.

All three migration-timing approaches produced the same total estimate of 587,386 women and girls in the affected population. However, the more detailed methods classified more people as already living with FGM/C and fewer as remaining at risk. Relative to the age-19 approach, the generalised gamma model reclassified 85,512 women and girls from the at-risk category to the living-with category, representing a 66.8% reduction in the estimated number considered at risk.

Under the hypothesised deterrence scenario, represented by a Prevention Impact Factor of 0.25 and incorporating the hypothetical effect of ethnic and religious selective migration, the model estimated that the Jones method had overstated the overall impacted population by 12.9%, or 79,817 people, while the Goldberg method had overstated it by 6.3%, or 36,388. In the generalised gamma results, 94.1% of the estimated affected population, or 508,953 people, were classified as likely already living with FGM/C; 5.9%, or 31,826, were classified as potentially at risk. These figures describe a model-based population classification rather than a direct prevalence survey. The findings change the composition of the modelled population rather than its total size, and should not be interpreted as proof that earlier studies directly misclassified individual women’s status.

Subject of Research: Modelling migration-related FGM/C prevalence and risk among US immigrant and diaspora populations

Article Title: Modelling the impact of migration on FGM/C prevalence and risk in immigrant and diaspora populations

Article References: Callaghan, S. (2026). Modelling the impact of migration on FGM/C prevalence and risk in immigrant and diaspora populations. PLOS One, 21(10), e0360038. https://doi.org/10.1371/journal.pone.0360038

Image Credits: AI Generated

DOI: 10.1371/journal.pone.0360038

Keywords: female genital mutilation, FGM/C, migration, diaspora populations, United States, selective migration, age of cutting, prevalence modelling, acculturation, American Community Survey, Modelling, impact