3.3 The Mapping of FGM/C Risks in Outline
3.3.7 Mapping Prevalence of FGM/C in Selected Countries
use this spatial information for campaigns to eliminate the practice of FGM and planning purposes, which is gaining increasing importance in policy circles that attempt to focus the allocation of public resources to the most at risk population.
Multivariate Bayesian geo-additive regression models were used to evaluate the significance of the POR determined for the fixed effects and spatial effects between prevalence of FGM in Senegal. Each factor was considered separately in unadjusted models using conventional logistic regression models. Next, fully adjusted multi- variate Bayesian geo-additive regressions analyses were performed to look again for a statistically significant correlation between these variables, but this time fur- ther controlling for any influence from individual (age), ethnic, educational and religious factors. A P-value of <0.05 was considered indicative of a statistically significant difference.
tives, organization, sample design and questionnaires used in the 2008 EDHS and 1995–2008 EDHS are described elsewhere (Yoder et al. 2004; Creel 2001). The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki as reflected in a priori approval by the institution’s human research committee. The Ethics Committee of the National Statistical Office of Egypt granted ethical approval.
The sampling strategy for each survey was designed to be nationally representa- tive enough to provide information for each governorate. A two-stage sampling pro- cess was employed. In the first stage clusters were selected from a list of enumeration areas with probability proportional to size. In the second stage, a complete house- hold listing was completed in each selected cluster, followed by the random selec- tion of households per cluster. In each household, all women aged 15–59 were interviewed. For instance, in 2008 survey data were collected from a total of 12,008 women aged 15–59. The collective 1995–2008 surveys collected data from 61,834 women aged 15–59. Further details on sampling can be found elsewhere in the final reports of each survey year. For all surveys, there were few participants with miss- ing data for FGM/C and other covariates; thus, data analysis on FGM/C was based on 12,008 women in 2008 and 61,834 women in the 1995–2008 samples with a complete set of data.
We studied FGM as the main outcome in terms of “whether a participant had had FGM performed on her”. This question was converted into a binary variable, with two categories defined as 1 if the participant was cut and 0 if the participant had no FGM performed on her. The main exposure variable in the analysis was the “gover- norate of residence” (of which there are 27 in 2008 as shown in Figs. 3.2b, 3.3, and 3.4 in 2008), in addition to various control variables on socio-demographic factors potentially associated with FGM: sex, age, education level, marital status, place of residence (urban vs. rural). Age was recorded as a continuous variable and was re- coded into a categorical variable of 5-year age cohorts in the preliminary analysis.
For the modelling of the prevalence of FGM, we examine the cohort’s effect of age of the respondent as a continuous variable using a flexible nonlinear function to estimate the age-related trend of FGM risk among women in the surveys. Education level was categorized as “None”, “Primary”, “Secondary” and “Higher”.
In terms of results about Egypt, the unweighted baseline socio-demographic characteristics are shown in Table 3.1, and by FGM/C status (whether circumcised or not) in Table 3.2. The overall prevalence of FGM/C differs slightly between the surveys (91.9% in 2008 and 96.1% in 1995–2008). Before investigating factors associated with FGM/C and trends across the four surveys, we examined compa- rability of women sampled in each survey. The survey populations are similar in terms of the mean ages of women (for 2008 the mean age was 32.4 years and in 1995–2008 the mean age of the sample was 33.1 years). Most of the population sampled lived in rural settings (56.5% in 2008 and 56.3% in 1995–2008) and 67.6% were married in 2008 with more at 87.6% in 1995–2008. A total of 43.6%
of women in the 2008 population had a secondary education while 29.8% had no education compared to 35.3% with secondary education and 36.4% no education in 1995–2008. Women with FGM/C were mostly married (64.5% vs 87.3%), with
Table 3.1 Baseline characteristics of the study population of women (Egypt DHS, 1995–2008)a
Variable N = 12,008 (2008) N = 61,834 (1995–2008)
Mean ageb (SD) respondent 32.6 33.1
Circumcised (%)
Yes 91.9 96.1
No 8.1 3.9
Place of residence (%)
Urban 44.0 43.7
Rural 56.0 56.3
Married (%)
Yes 63.6 87.6
No 36.4 12.4
Education (%)
No education 21.6 36.4
Primary education 14.1 18.2
Secondary education 47.9 35.3
Higher education 16.4 10.0
Religion (%)
Muslim 94.9
Other 5.1
Governorates of residence (%)
Matrouh 12.2
Alexandria 6.5
Beheira 0.8
Kafr el-Sheikh 0.8
Dakahlia 1.3
Damietta 6.6
Port Said 6.1
North Sinai 5.8
Gharbia 4.1
Monufia 6.2
Qalyubia 4.6
Al Sharqia 7.6
Ismailia 1.2
Giza 8.7
Faiyum 2.8
Cairo 2.6
Suez 5.6
South Sinai 4.6
Beni Suef 4.7
Minya 3.6
New Valley 1.6
Asyut 0.5
Red Sea 0.3
mostly secondary education in 2008 (39.9%) and no education from 1995 to 2008 (38.4%), lived in rural areas (54.2% vs 55.6%), were living in urban Lower Egypt and urban Upper Egypt (31.3%, 22.6% vs 30.9%, 24.4%) in 2008 and 1995–2008 respectively. Notably, in all surveys, women’s age group is significantly associated with FGM/C. Thus, there is evidence of change in rates of FGM/C across age cohorts.
With respect to regression analysis results, we have presented adjusted and fully adjusted marginal odds ratios in Table 3.3. In 2008 factors associated with FGM/C in the unadjusted analysis were: 50–54 years of age (OR = 10.1, 95% CI = 5.87–
17.3), rural place of residence (OR = 3.73, 95% CI = 3.03–4.59), being married (OR = 3.77, 95% CI = 3.08–4.61), with “no education” (OR = 15.7, 95% CI = 11.4–
21.7) and living in Sharqia (OR = 18.1, 95% CI = 4.32–75.5) or Qina (OR = 11.3, 95% CI = 3.52–36.3). After adjusting for all other factors (fully adjusted model), the likelihood of FGM/C occurring remained statistically most significant in women 50–54 years old, living rural, being married and with no education. Women with no education were 9.5 times more likely to be circumcised than all higher educated persons (95% CI = 6.08–14.8); women living in rural communities were 3.5 times more likely to have undergone FGM/C (95% CI = 2.61–4.70) than women living in urban areas and 1.53 times more likely to be married than not. Women with FGM/C were least likely to live in Matrouh and North Sinai and most likely in Sharqia and Qina. In the Spatial analysis the place of residence, being married, with primary education and living in Sharqia and Qina were the most significant factors associ- ated with FGM/C. Living rural meant 3.34 times more likelihood of being cut than living urban (95% CI = 2.56–4.46), being married made the women 1.51 times more
Table 3.1 (continued)
Variable N = 12,008 (2008) N = 61,834 (1995–2008)
Sohag 0.3
Qena 0.5
Luxor 0.3
Aswan 0.1
Region of residence (%)
Urban governorate 19.5
Lower Egypt—urban 11.8
Lower Egypt—rural 31.3
Upper Egypt—urban 11.7
Upper Egypt—rural 24.6
Frontier governorate 1.2
Year of survey (%)
1995 23.9
2000 25.2
2005 31.5
2008 19.4
aData is expressed as a mean (standard deviation) or as percentages
bAge ranges from 18 to 97 years
Table 3.2 Baseline characteristics of the women study population by circumcision status (Egypt DHS, 1995–2008)a
Variable
2008 1995–2008: 13 years
Not circumcised (N=)
Circumcised
(N=6571) P-valueb Not circumcised (N=)
Circumcised
(N=) P-valueb
Age group (%) P < 0.001 p < 0.001
15–19 years 19.6 80.5 9.6 90.4
20–24 years 13.2 86.8 5.5 94.5
25–29 years 8.0 92.0 4.9 95.1
30–34 years 6.3 93.7 4.5 95.5
35–39 years 5.1 94.9 4.0 96.0
40–44 years 4.4 95.6 4.1 95.9
45–49 years 4.1 95.9 3.3 96.7
50–54 years 2.8 97.3 4.1 95.9
55–59 years 3.4 96.6 3.5 96.5
Place of residence (%)
p < 0.001
Urban 15.4 84.6 7.4 92.6
Rural 4.7 95.3 2.7 97.3
Married (%) p < 0.001 p < 0.001
Yes 5.9 94.1 4.3 95.7
No 15.8 84.2 8.4 91.6
Education (%) p < 0.001 p < 0.001
No education
4.2 95.8 3.3 96.7
Primary education
4.8 95.2 2.0 98.0
Secondary education
9.2 90.8 4.4 95.6
Higher education
24.8 75.2 18.4 81.6
Religion (%) p < 0.001 p < 0.001
Muslim 6.9 88.0 71.0 29.0
Other 1.2 3.9 91.0 9.0
Governorates of residence (%)
p < 0.001
Matrouh 77.9 22.1
Alexandria 14.4 85.6
Beheira 7.5 92.5
Kafr el-Sheikh
4.9 95.1
Dakahlia 10.3 89.7
Damietta 14.7 85.3
Table 3.2 (continued)
Variable
2008 1995–2008: 13 years
Not circumcised (N=)
Circumcised
(N=6571) P-valueb Not circumcised (N=)
Circumcised
(N=) P-valueb
Port Said 26.1 73.9 North Sinai 29.4 70.6
Gharbia 12.1 87.8
Monufia 2.8 97.1
Qalyubia 2.1 97.9
Al Sharqia 0.6 99.4
Ismailia 5.7 94.3
Giza 11.0 89.0
Faiyum 5.3 94.7
Cairo 10.4 89.6
Suez 9.6 90.4
South Sinai 19.2 80.8
Beni Suef 1.3 98.7
Minya 11.7 88.3
New Valley 5.3 94.7
Asyut 7.1 92.9
Red Sea 3.0 97.0
Sohag 4.1 95.9
Qena 1.0 99.0
Luxor 4.1 95.9
Aswan 1.5 98.5
Region of residence (%)
p < 0.001 Urban
governorate
7.9 92.1
Lower Egypt—
Urban
4.9 95.1
Lower Egypt—
Rural
1.1 98.9
Upper Egypt—
Urban
4.5 95.5
Upper Egypt—
Rural
1.2 98.8
Frontier Governorate
27.0 73.0
(continued)
Table 3.2 (continued)
Variable
2008 1995–2008: 13 years
Not circumcised (N=)
Circumcised
(N=6571) P-valueb Not circumcised (N=)
Circumcised
(N=) P-valueb
Year of survey (%)
p < 0.001
1995 4.5 95.5
2000 3.5 96.5
2005 4.4 95.6
2008 9.1 90.9
aData are expressed as mean (standard deviation) or as percentages
bCut-off significant P-value is <0.05
Table 3.3 Unadjusted and fully adjusted odds ratios of women’s circumcision across selected covariates (Egypt DHS, 1995–2008)
Variable
2008 1995–2008: 13 years
Unadjusted OR and 95% CIa
Fully adjusted OR and 95% CIb
Unadjusted OR and 95%CIa
Fully adjusted OR and 95% CIb Age
15–19 years 1.00 1.00 1.00 1.00
20–24 years 1.69(1.31, 2.19) 2.19(1.55, 3.10) 0.93(0.80, 1.08) 1.90(1.54, 2.34) 25–29 years 3.98(2.85, 5.56) 5.72(3.63, 8.99) 1.00 2.49(2.02, 3.07) 30–34 years 4.76(3.20, 7.08) 5.29(3.17, 8.82) 0.94(0.80, 1.10) 2.37(1.91, 2.94) 35–39 years 6.33(4.00, 10.0) 5.96(3.37, 10.5) 1.12(0.95, 1.33) 2.36(1.87, 2.98) 40–44 years 6.00(3.64, 9.89) 5.50(2.98, 10.2) 1.02(0.85, 1.23) 2.51(1.99, 3.15) 45–49 years 5.78(3.42, 9.78) 6.57(3.41, 12.7) 1.08(0.88, 1.33) 2.54(1.99, 3.24) 50–54 years 10.1(5.87, 17.3) 11.4(6.04, 21.6) 1.28(1.06, 1.54) 3.78(2.15, 6.65)
55–59 years 5.42(3.08, 9.56)
Place of residence
Urban 1.00 1.00 1.00 1.00
Rural 3.73(3.03, 4.59) 3.35(2.48, 4.52) 5.14(4.62, 5.72) 0.29(0.25, 0.35) Married
Yes 3.77(3.08, 4.61) 1.64(1.22, 2.19) 2.29(2.02, 2.60) 1.61(1.37, 1.89)
No 1.00 1.00 1.00 1.00
Education
No education 15.7(11.4, 21.7) 9.27(5.92, 14.5) 21.2(18.5, 24.2) 14.1(12.0, 16.5) Primary
education
9.39(5.96, 14.8) 7.18(4.17, 12.4) 22.6(18.5, 27.8) 17.2(13.9, 21.5) Secondary
education
3.51(2.80, 4.40) 4.47(3.33, 5.99) 5.85(5.21, 6.57) 5.51(4.85, 6.26) Higher
education
1.00 1.00 1.00 1.00
Religion
Muslim 3.94(2.95, 5.27) 7.27(4.83, 10.9) 4.10(2.92, 5.77) 2.52(1.61, 3.96)
Other 1.00 1.00 1.00 1.00
Table 3.3 (continued)
Variable
2008 1995–2008: 13 years
Unadjusted OR and 95% CIa
Fully adjusted OR and 95% CIb
Unadjusted OR and 95%CIa
Fully adjusted OR and 95% CIb Governorates of residence
Matrouh 1.00 1.00
Alexandria 0.69(0.44, 1.08) 0.46(0.27, 0.79) Beheira 0.33(0.20, 0.53) 0.21(0.12, 0.36) Kafr el-Sheikh 1.09(0.60, 2.00) 0.95(0.48, 1.86) Dakahlia 0.67(0.39, 1.16) 0.18(0.09, 0.32) Damietta 1.01(0.63, 1.62) 0.33(0.19, 0.57) Port Said 18.1(4.32, 75.5) 7.18(1.65, 31.3) North Sinai 5.42(2.28, 12.9) 2.75(0.98, 7.71) Gharbia 2.23(1.18, 4.21) 0.69(0.33, 1.42) Monufia 0.84(0.54, 1.30) 0.28(0.17, 0.48) Qalyubia 3.89(1.72, 8.77) 1.47(0.63, 3.44) Al Sharqia 1.44(0.89, 2.33) 0.26(0.14, 0.45) Ismailia 1.92(0.92, 4.00) 1.05(0.46, 2.41) Giza 0.94(0.59, 1.50) 0.56(0.32, 0.97) Faiyum 8.51(3.04, 23.9) 3.04(0.99, 9.32) Cairo 2.05(1.08, 3.87) 0.58(0.28, 1.21) Suez 0.88(0.56, 1.36) 0.23(0.13, 0.40) South Sinai 1.52(0.92, 2.52) 0.85(0.48, 1.51) Beni Suef 2.73(1.55, 4.81) 1.87(0.90, 3.91) Minya 11.3(3.52, 36.3) 7.35(2.89, 18.7) New Valley 7.76(2.39, 25.2) 5.81(1.61, 21.0) Asyut 2.70(1.20, 6.03) 1.92(0.80, 4.59) Red Sea 3.71(0.87, 15.8) 5.24(1.16, 23.8) Sohag 2.08(0.63, 6.92) 0.84(0.20, 3.48) Qena 0.03(0.02, 0.05) 0.003(0.002,
0.007) Luxor 0.28(0.17, 0.46) 0.07(0.04, 0.13) Aswan 0.49(0.17, 1.40) 0.19(0.06, 0.56) Region of residence (%)
Urban governorate
1.00 1.00
Lower Egypt—urban
2.03(1.74, 2.36) 2.08(1.77, 2.45) Lower
Egypt—rural
9.62(8.00, 11.6) 22.0(17.1, 28.5) Upper
Egypt—urban
1.62(1.39, 1.89) 1.56(1.33, 1.83) (continued)
likely to have undergone FGM (95% CI = 1.13–1.99) and women with primary edu- cation were now 8.38 times more at risk of cutting than those higher educated (95%
CI = 4.82–14.7).
In the collated surveys of 1998–2008, factors associated with FGM/C in the unadjusted analysis were: rural place of residence (OR = 5.14, 95% CI = 4.62–
5.72); being married (OR = 2.29, 95% CI = 2.02–2.60); education, with the category
“Primary education” highest (OR = 22.6, 95% CI = 18.5–27.8), followed by no education (OR = 21.2, 95% CI = 18.5–24.2) and secondary (OR = 5.85, 95%
CI = 5.21–6.57) vs people with higher education, living in rural Lower Egypt (OR = 9.62, 95% CI = 8.00–11.6) and being interviewed in 2000 (OR = 3.19, 95%
CI = 2.77–3.67). After adjusting for all other factors the likelihood of FGM/C remained statistically significant. A statistically significant effect remained for all factors: being 55–59 years old made you 5.42 times more likely to be cut than being 15–19 years of age (95% CI = 3.08–9.56), living rural suddenly had a greatly reduced likelihood of 0.29 (95% CI = 0.25–0.35), being married made FGM 1.61 times more likely (95% CI = 1.37–1.89) than not being, having primary education made women 17.2 times more likely to be cut than higher educated persons (95%
CI = 13.9–21.5), living in rural Lower Egypt made it 22 times more likely (95%
CI = 17.1–28.5) than living in the Urban governorate and women being interviewed in 2000 were 2.09 times more affected than in 2008. Women with FGM/C were least likely to live in the Frontier governorate and most likely to live in rural Lower Egypt.
Table 3.3 indicates the shift in the prevalence of FGM at the regional level and across age cohorts during the 13-year-period. When exploring the overall national prevalence of FGM/C between the 13-year-period, we noted only a slight decrease of 4% points from the 96.1% average between 1995 and 2008 to 91.9% in 2008 (unweighted average). What holds true here, however, is that aggregate national figures on FGM/C prevalence conceal important spatial variations at the regional
Variable
2008 1995–2008: 13 years
Unadjusted OR and 95% CIa
Fully adjusted OR and 95% CIb
Unadjusted OR and 95%CIa
Fully adjusted OR and 95% CIb Upper
Egypt—rural
6.65(5.64, 7.85) 11.6(9.14, 14.8) Frontier
governorate
0.26(0.24, 0.29) 0.24(0.21, 0.28) Year of survey (%)
1995 2.88(2.47, 3.36) 1.61(1.34, 1.94)
2000 3.19(2.77, 3.67) 2.09(1.76, 2.49)
2005 2.04(1.80, 2.31) 1.39(1.18, 1.63)
2008 1.00 1.00
aUnadjusted marginal odds ratio (OR) from standard logistic regression models
bAdjusted marginal odds ratio (OR) from standard logistic regression models. Adjusted for women’s age, region and rural/urban location
Table 3.3 (continued)
level within the survey periods. . Unadjusted marginal odds ratios shown in Table 3.3 indicate that in 2008 the highest risk of FGM/C was in Sharqia (OR = 18.1, 95%
CI = 4.32–75.5) and Qina (OR = 11.3, 95% CI = 3.52–36.3), and the lowest risk in Matrouh (OR = 0.03, 95% CI = 0.02–0.05) and North Sinai (OR = 0.28, 95%
CI = 0.17–0.46). Regarding the effect of age, the unadjusted marginal odds ratios show a rise in FGM/C risk across age cohorts of women, suggesting that there is a secular ascent in FGM/C risk.
In terms of Bayesian spatial analysis results relating to Egypt, we introduced and controlled for spatial and nonlinear factors associated with higher FGM/C risk in all years. Governorate of residence was modelled as a spatial variable in Figs. 3.2b, 3.3, and 3.4, and age of the respondent at the time of interview was modelled as a con- tinuous variable using a flexible nonlinear curve in Fig. 3.1. The modelled covariate results confirmed what was observed in the logistic regression analysis but the pat- terns differ markedly with governorate of residence and age remaining significant risk factors in both surveys. Overall, results of 2008 (Fig. 3.2b) show that after accounting for (1) sampling error in the observed data; (2) relationships with covari- ates and the uncertainty in the form of these relationships); (3) uncertainty in the spatial autocorrelation structure of the outcome variable, the Egyptian regions with the highest FGM/C risk included South Sinai, Suez, Ismailia, Sharqia, Fayoum and Qina and significant positive spatial effects were observed in South Sinai, Ismailia, Sharqia, Fayoum and Qina but not Suez. In the adjusted Figs. 3.3 and 3.4, the high- est risk regions were still South Sinai, Ismailia, Suez, Sharqia, Fayoum and Qina but there were no significant spatial effects.
With regard to the shift of FGM/C by regions, in both samples, the spatial analy- sis has captured the substantial variation in FGM/C risk across regions observed in the marginal regression analyses. The results shown in Figs. 3.2b, 3.3, and 3.4 are
2
1
0
–1
–2
10 20 30
Women’s age in years, EDHS2008
40 50 60
Effect of women’s age
Fig. 3.1 Left: Estimated non-parametric trend of women’s FGM risk by women’s age cohort in 2008. Shown is the posterior mean within 80% of credible regions (EDHS 2008)
a
b
Fig. 3.2 (a) Map of Egypt showing governorates. (b) Left: Adjusted total residual spatial effects for women’s circumcision, at governorate level in Egypt in 2008. Shown are the posterior odds ratios. Right: Corresponding posterior probabilities at 90% nominal level (EDHS 2008). Red- coloured—highrisk. Green-coloured—low risk. Black-coloured—significant positive spatial effect. White-coloured—significant negative spatial effect. Grey-coloured—no significant effect
Fig. 3.4 Left: Adjusted structured random residual spatial effects for female circumcision at gov- ernorate level in Egypt in 2008. Shown are the posterior odds ratios. Right: Corresponding poste- rior probabilities at 90% nominal level (EDHS 2008). Red-coloured—highrisk. Green-coloured—low risk. Black-coloured—significantpositive spatial effect. White-coloured—significant negative spa- tial effect. Grey-coloured—no significant effect
Fig. 3.3 Left: Adjusted unstructured random residual spatial effects for female circumcision, at governorate level in Egypt in 2008. Shown are the posterior odds ratios. Right: Corresponding posterior probabilities at 90% nominal level (EDHS 2008). Red-coloured—high risk. Green- coloured—low-risk. Black-coloured—significantpositive spatial effect. White-coloured—signifi- cant negative spatial effect Grey-coloured—no significant effect
in other words covariate-adjusted region FGM/C spatial variation captured by the global total residual region effects (i.e. the sum of the unstructured and structured spatial effect). There is a clear pattern of regions with higher risk of FGM/C, mostly the governorates of South Sinai, Ismailia, Sharqia, Fayoum and Qina in 2008, which were associated with a higher risk of FGM/C, while states such as Damietta, Alexandria, Dakahlia, North Sinai, Qalyubia, Giza, Cairo and Asyut in 2008 were associated with a lower risk of FGM/C. These spatial patterns confirm some of the observed marginal model findings shown in Table 3.3 while running opposite to others.
Specifically, the left-hand map in Fig. 3.3 shows estimated posterior total resid- ual region odds of FGM/C for each governorate in 2008, ranging from a lower POR of 0.03 (0.02, 0.05) in Matrouh to a higher POR of 18.1 (4.32, 75.5) in Sharqia, with red color indicating the higher risk recorded and green color denoting lower risk.
The right-hand map shows the 95% posterior probability map of FGM/C, which indicates the statistical significance associated with the total excess risk. White indi- cates a negative spatial effect (associated with reduced risk of FGM/C prevalence), black a positive effect (an increased risk) and grey a non-significant effect. However, the total spatial residuals in Figs. 3.2b, 3.3, and 3.4 shows that much of the variation in FGM/C likelihood remains to be explained. Overall, the results indicate that across surveys, certain high prevalence regions remain “hot spots” regarding FGM/C risk. These include Sharqia, Ismailia, South Sinai, Fayoum and Qina. Risk remained non-significant in the high prevalence regions of Aswan and Menoufia.
Putting the above results in the context of discussion, we note that tracking changes in the prevalence of FGM/C on the basis of nationally representative survey data aggregate figures at the national level may mask important variations across ethnicity or region. To remedy this situation, it is necessary to disaggregate the data, and control for potentially confounding factors. Thus, we used advanced statistical methodology to analyze survey data collected with complex sampling strategies and included possible non-linear covariates. Importantly, this novel approach, developed by Kandala et al. (2009), makes it possible to simultaneously examine individual- level and spatial variability. Overall, among women aged 15–59 in Egypt, the preva- lence of FGM/C has changed little over the 13-year period between successive surveys carried out between 1995 and 2008 (FGM/C prevalence of 91.9% in 2008 and 96.1% between 1995 and 2008). We find that these unadjusted figures do indeed mask important variations at both the regional and individual levels. In the multivari- ate Bayesian geo-additive regression analysis, we controlled for individual- level fac- tors while simultaneously modelling the region of residence as a spatial variable.
The spatial analysis performed in this study reveals that the risk of FGM/C varies across governorates, with the highest risk across the survey periods found in Sharqia and Qina. The question is how we can now interpret these spatial findings given that certain high prevalence regions remained “hot spots” regarding FGM/C risk and oth- ers did not. Importantly, these results show that community-level effects, above and beyond individual-level effects, play a crucial role in determining the likelihood of FGM/C. In other words, the context, in which an individual woman lives, bears an important influence on whether FGM/C is practiced. This finding is consistent with
social convention theory, which predicts that interdependent expectations and social norms shared by community members serve to uphold the practice, making it difficult for individuals to abandon FGM/C without experiencing adverse social sanctions (Mackie and LeJeune 2009). The theory predicts that change is most likely to come about when members of social groups have simultaneously shifted social norms per- taining to FGM/C (Mackie 2000). It may be the case that regional differences in FGM/C risk capture this shift in social norms. At the same time, theory on social norms and conventions does not rule out the possibility of individual- level factors influence on decision-making regarding FGM/C, although the social environment can constrain these choices. Indeed, in our study we find evidence for the simultaneous influence of community- and individual-level factors influencing the risk of FGM/C.
In the fully adjusted model, a number of individual-level factors were found to be associated with the likelihood of FGM/C. In the 2008 survey data these include rural residence, no education and being married. With respect to individual-level predictors of risk of FGM/C among women in Egypt, our most surprising finding concerns the age cohort. Unadjusted estimates of risk of FGM/C showed a signifi- cant rise across age cohorts. The effect of age on the likelihood of FGM/C is highest in women aged 55-59, and decreases with decreasing age. How can we understand this unexpected finding? Yoder and Wang (2013) comment on the possibility of using DHS data to assess the magnitude of reductions in FGM/C, but join several commentators in urging caution about the limitations of self-reported survey data on FGM/C status. Because of the sensitivity of the topic or illegal status, women may be unwilling to disclose having undergone FGM/C (Askew 2005; Shell-Duncan et al. 2013). Additionally, particularly when FGM/C is performed at an early age, women may be unaware of whether they have been cut or the extent of the cutting (Yoder et al. 2004; UNICEF 2013). A number of studies have attempted to deter- mine the reliability of self-reports of FGC status by verifying them through clinical examinations, and have reported variable rates of concordance. While one study in Sudan reported complete agreement between clinical examination and women’s reports of having undergone some form of FGM/C or not (Elmusharaf et al. 2006), others report variable degrees of discrepancy. Morison and colleagues found 3%
disagreement in The Gambia, whereas studies in Tanzania and Nigeria reported disagreements in more than 20% of women (Adinma 1997; Msuya et al. 2002;
Klouman et al. 2005; Snow et al. 2002). A longitudinal study in Ghana afforded a unique opportunity to assess the consistency of women’s self-reports of FGM/C status over repeat surveys (Jackson et al. 2003). The data showed that a substantial number of adolescent girls who initially reported having undergone FGM/C later denied being cut. The authors concluded that denials of having undergone FGM/C were influenced by exposure to anti-FGM/C interventions, and by passage of a law banning FGM/C. In a detailed overview of methodological considerations for mea- suring change in FGM/C, Askew (2005, pp. 472–73) emphasized the need to con- sider the context in which questions of FGM/C status are being asked: “If FGC is widespread, socially acceptable and there is no well-publicized interventions caus- ing people to question its acceptability and legalit, then self-reporting is likely to be valid. If there are reasons why it would not be attractive for respondents to declare