Socioeconomic Inequality in Unintended Pregnancy in Egypt: A Decomposition Analysis

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M. Zaky, Mohamed Helmy, Mohamed Ali H. Aboakrab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453953/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study aims to examine and quantify disparities in unintended pregnancies in Egypt utilizing data from the 2021 Egypt Family Health Survey (EFHS). The inequality in unintended pregnancy is assessed by using the concentration curve, the Wagstaff normalized concentration index (WCI). This study uses decomposition analysis to identify the factors contributing to unintended pregnancy inequality. The concentration curve confirms that the primary driver is socioeconomic disparity in unintended pregnancies, with mothers from disadvantaged economic backgrounds bearing a disproportionate burden. The decomposition analysis also reveals that household economic status and maternal education are the primary determinants of unintended pregnancy inequalities. Furthermore, the findings indicate that a substantial number of disparities in unintended pregnancies were caused by the usage of contraceptives, residency, number of children, age of the current mother, and father's educational attainment. Decomposition Socioeconomic factors Inequality unintended pregnancies Egypt Figures Figure 1 Contributions to the literature Although numerous studies have investigated the determinants of unwanted pregnancies among married women in Egypt, limited attention has been given to the role of socioeconomic factors to inequality in unintended pregnancies in Egypt. The study draws on data from the Egypt Family Health Survey (EFHS) 2021–2022, the most recent and comprehensive national dataset available, offering updated insights into socioeconomic disparities across the country. Most previous studies employed logistic regression models to examine factors associated with unintended pregnancy. One study focused exclusively on the use of the concentration index to assess inequality in unintended pregnancy. Building on this foundation, the present study adopts a more comprehensive analytical approach by combining the concentration index with the concentration curve and decomposition analysis to provide a deeper understanding of socioeconomic disparities. Introduction Unintended pregnancy constitutes a significant public health concern due to its impact on mother and child health, in addition to societal outcomes (Jalaly et al. 2015 ; Khajehpour et al. 2013 ; Petersen & Moos 1997 ). Estimates of the exact number of unintended pregnancies are needed for governments to understand how common this problem is among different groups of people. This helps them make smart choices about how to spend money on reproductive health care (Kost and Zolna 2019 ; Sully et al. 2020; UNFPA 2022). More than 60 percent of unintended pregnancies result in abortion, with approximately 45 percent of these procedures being unsafe, therefore contributing significantly (5–13%) to global maternal mortality and posing as a substantial barrier to the achievement of the Sustainable Development Goals (UNFPA 2022). Globally, unintended pregnancies reached approximately 121 million annually from 2015 to 2019, accounting for over half of all pregnancies. Unintended pregnancy rates demonstrate substantial regional disparities. From 2015 to 2019, the rate of unintended pregnancy in Middle Africa was 258 per 1,000 reproductive-age women, indicating that one in four women had an unintended pregnancy, compared to a rate of 35 per 1,000 in Western Europe. In the Arab region, two out of five pregnancies are unintended (Bearak et al., 2023 ; UNFPA 2018; UNFPA 2022). The global rate of unplanned pregnancies decreased worldwide from 1990 to 2019; the extent of this decline varied significantly across regions. Europe and North America experienced the most significant reductions, with rates declining by approximately fifty percent compared to the early 1990s. In contrast, sub-Saharan Africa experienced the least significant decline, with a reduction of approximately 12 percent (Bearak et al., 2023 ; UNFPA 2022). Although numerous studies have investigated the determinants of unwanted pregnancies among married women in Egypt [El-Sharqawy & El-Nemer, 2020; Metwaly et al., 2018 ; Mohamed et al., 2019 ; Mohamed & Awadein, 2024; Shaheen et al., 2007 ], limited attention has been given to the role of socioeconomic inequality in shaping these outcomes. Khadr ( 2009 ) examined three rounds of the Egypt Demographic and Health Survey (EDHS) from 1995 to 2005, focusing primarily on level of inequality in unintended pregnancies, without explicitly analyzing socioeconomic disparities. Therefore, the present study aims to assess the relative contribution of socioeconomic factors to inequality in unintended pregnancies in Egypt. Previous studies on unintended pregnancy in Egypt have relied on varying data sources. Several investigations collected primary data using questionnaires administered to selected samples of married women in one or two governorates [El-Sharqawy & El-Nemer, 2020; Metwaly et al., 2015; Metwaly et al., 2018 ; Mohamed et al., 2019 ; Mohamed & Awadein, 2024; Youssef et al., 2002 ]. Other studies utilized secondary data from the Egypt Demographic and Health Survey (EDHS) to examine unintended pregnancy at national level (Khadr, 2009 ; Shaheen et al., 2007 ). In contrast, the present study draws on data from the Egypt Family Health Survey (EFHS) 2021–2022, the most recent and comprehensive national dataset available, offering updated insights into socioeconomic disparities across the country. Most previous studies employed logistic regression models to examine factors associated with unintended pregnancy [Metwaly et al., 2015; Metwaly et al., 2018 ; Mohamed et al., 2019 ; Shaheen et al., 2007 ; Youssef et al., 2002 ]. In contrast, Khadr ( 2009 ) focused exclusively on the use of the concentration index to assess inequality in unintended pregnancy. Building on this foundation, the present study adopts a more comprehensive analytical approach by combining the concentration index with the concentration curve and decomposition analysis to provide a deeper understanding of socioeconomic disparities. To summarize, this study contributes to existing literature in three key ways. First, it aims to quantify the extent to which socioeconomic inequality contributes to disparities in unintended pregnancies in Egypt. Second, it applies the Wagstaff decomposition method, a technique that, to the best of our knowledge, has not been previously employed in this context in Egypt. Third, the analysis is based on the most recent nationally representative dataset available in Egypt thereby providing timely and policy-relevant insights. Data Source The data for this study was obtained from the Egypt Family Health Survey (EFHS) conducted in 2021–2022. The EFHS is a nationally representative survey administered under the supervision of the Central Agency for Public Mobilization and Statistics (CAPMAS). The main objective of the survey is to provide estimates on indicators like fertility, mortality, morbidity, maternal health, child health, women's empowerment, family planning, and violence. In the EFHS, a total of 10,996 women aged 15 to 49 were surveyed. The selection of variables and the overall modeling strategy in this study were guided by the proximate determinants of fertility framework proposed by Bongaarts ( 1978 ; 2015 ). This conceptual model offers a structured approach to identifying the direct mechanisms influencing fertility outcomes. While the original framework includes biological, behavioral, and socio-contextual factors, our study adapted it to align with the structure and limitations of the available dataset. Unintended pregnancy was used as the dependent variable, dichotomized as “1” for yes and “0” for no, given its relevance as a proximate fertility-related indicator. The independent variables were selected based on both the Bongaarts model and supporting empirical literature (Anand et al., 2024 ; Dixit et al., 2012 ; Omani-Samani et al., 2018 ; Singh et al., 2012 ), and included a range of socio-demographic and reproductive health factors. These comprised maternal education, household economic status, maternal age, paternal education, place of residence, number of living children, maternal employment, current contraceptive method, history of abortion, access to health facilities, difficulty accessing health services, and internet use. Methodology Inequality Measurement The concentration curve is a diagrammatical measure of inequality. It establishes the cumulative percentage of the health variable (y-axis) against the cumulative percentage of the sample, ordered by economic status, beginning with the poorest, and concluding with the richest (x-axis). If all individuals, irrespective of their living standards, possess identical values for the health variable, the concentration curve will be a 45-degree line extending from the bottom left corner to the top right corner; this can be referred to as the equality line. If, by contrast, the health sector variable takes elevated (lower) values amongst disadvantaged individuals, the concentration curve will position itself above (below) the equality line. The greater the distance of the curve from the line of equality, the more concentrated the health variable is among those with limited resources (Wagstaff et al., 1991 ; Kakwani et al., 1997 ; Wagstaff, 2000 ). The concentration index is a relative measure of inequality that reflects the degree to which a health indicator is distributed amongst the disadvantaged or the privileged. The value of the concentration index can vary between − 1 and + 1. The concentration index yields negative values when the concentration curve is situated above the diagonal line. It yields positive values when it is positioned below the diagonal. The negative values imply that a variable is concentrated among disadvantaged individuals, whereas positive values suggest the contrary. In the absence of inequality, the concentration index will equal 0 (Wagstaff et al., 1991 and Wagstaff et al., 1997). The concentration index is calculated as twice the (weighted) covariance of a health variable and a relative economic rank variable, divided by the mean of the health variable, as follows Wagstaff et al. ( 2003 ): $$\:C\hspace{0.33em}=\frac{2}{\mu\:}\hspace{0.33em}Co{v}_{w}\left({y}_{i\hspace{0.33em}},{R}_{i}\right)$$ 1 Where y i is unintended pregnancy, µ is its mean, R i is the fractional rank of i th individual (for weighted data) within the socioeconomic distribution defined by wealth and Cov w denotes the weighted covariance. Wagstaff ( 2005 ) suggested that, in the case of a binary variable, the concentration index could be normalized by dividing it through by either the reciprocal of the mean of the health variable (unintended pregnancy) or the bound of the concentration index. The Wagstaff normalized concentration index (WCI) can be written as (Wagstaff, 2005 ): $$\:WCI\hspace{0.33em}=\frac{C}{1-\mu\:}\hspace{0.33em}$$ 2 Decomposition of Inequality A decomposition analysis allows estimating how determinants proportionally contribute to inequality in unintended pregnancy. Wagstaff et al. ( 2003 ) showed that for any linear regression model linking the health variable of interest y (unintended pregnancy), to a set of k determinants, x k : $$\:{y}_{i}\hspace{0.33em}=\hspace{0.33em}\alpha\:\hspace{0.33em}+{\sum\:}_{k=1}^{n}{\beta\:}_{k}{X}_{ki}+\hspace{0.33em}{\epsilon\:}_{i}$$ 3 Where ε is an error term. Given the relationship between y i and x ki , the concentration index for y (C) can be written as: $$\:C\hspace{0.33em}={\sum\:}_{k=1}^{n}\left(\left.\frac{{\beta\:}_{k}^{m}⥂{\stackrel{̄}{x}}_{k}}{\mu\:}\right)\right.{C}_{k}\hspace{0.33em}+\hspace{0.33em}\frac{G{C}_{\epsilon\:}}{\mu\:}$$ 4 Where µ is the mean of y. \(\:{\stackrel{̄}{x}}_{k}\) is the mean of x k ; C k is the concentration index for x k . In the last term (which can be computed as a residual), GC ε is the generalized concentration index for ε i . C consists of two components. The first is the deterministic, or ‘explained’, component. The second is a residual, or ‘unexplained’, component that reflects the inequality that cannot be explained by systematic variation in the x k across socioeconomic groups. Results Table 1 presents summary statistics regarding unintended pregnancy and its determinants among Egyptian women. The study indicates that 22 percent of Egyptian women encountered unexpected pregnancies, whereas 78 percent had wanted pregnancies. Majority of the mothers lived in rural regions (64 percent), while 60 percent attained higher education. As shown in Table 1 , the majority of the mothers were aged between 19 and 39 years, with 71 percent having less than three children. Two-thirds utilized contraception, and the majority had no prior experience of abortion (72 percent). The data indicates that around 87 percent of women were unemployed. Furthermore, 60 percent of women experienced restricted access to health facilities, but 53 percent indicated availability of internet access. Table 1 Summary statistics about unintended pregnancy and its determinants in Egypt, (2021) Variable % N Unintended pregnancy intended unintended 78.2 21.8 8602 2394 Place of Residence Urban Rural 36.1 63.9 3969 7027 Current Age 19–29 30–39 40–49 48.26 45.64 8.53 5307 5019 938 Educational attainment No education primary secondary Higher 11.46 3.62 22.92 62 1260 398 2520 6818 Household Economic Status Poorest Poorer Middle Richer Richest 17.5 19.6 21 21.9 20.1 1921 2151 2314 2405 2205 Husband's education No education and primary secondary Higher 20.59 51.6 18.02 2264 5674 1981 How difficult is the distance to the health facility to get medical care for yourself Big problem Not a big problem 24.3 75.7 2676 8320 Number of Children Less than 3 Greater than or equal 3 71.13 28.87 7821 3175 Mother’s Not working Work Working 86.77 13.23 9541 1455 Using Internet No Yes 46.74 53.26 5140 5856 Having Abortion No Yes 72 28 7919 3077 Contraceptive use No Yes 34 66 3734 7262 Total 100 10996 Figure 1 presents the concentration curve for unintended pregnancy, illustrating wealth-related inequality. The curve's position above the line of equality indicates that unintended pregnancies are more concentrated among poorer Egyptian mothers. This reveals that there is a pro-poor inequality in unintended pregnancy in Egypt. As shown in Table 2 , the value of Wagstaff's normalized concentration index (WCI) for unintended pregnancy is -0.210, with a 95% confidence interval ranging from − 0.184 to -0.114. This result confirms that unintended pregnancies are significantly more concentrated among Egyptian mothers from lower socioeconomic backgrounds. Table 2 Wagstaff’s normalized concentration index (WCI) (95% confidence interval, standard error and P-value) for unintended pregnancy in Egypt Unintended Pregnancy CI Std. Err. 95% Conf. interval for CI WCI P-value -0.164 0.010 (− 0.184, − 0.144) -0.210 < 0.001 Table 3 shows the decomposition of unintended pregnancy determinants. Several factors concentrated among lower socioeconomic status women include lack of maternal and paternal education, living in poor households, having had an abortion, and difficulty accessing health facilities. This study decomposed the concentration index of unintended pregnancy against wealth related characteristics to determine the relative share of each independent variable to inequality. Household's economic status has the highest share to unintended pregnancy inequality (21 percent), followed by mother's education (16 percent). Other notable contributors included contraceptive use (12 percent), rural residence (11 percent), and number of children less than three (9 percent). Each of the variables father’s education, current mother’s age, and having an abortion—each contributed equally (7 percent) in the inequality of unintended pregnancy. Difficulty accessing health facilities has the lowest share (6 percent) to unintended pregnancy inequality in Egypt. Table 3 Decompositions of concentration index for the determinants of unintended pregnancy in Egypt Variables βm k WCI s elasticity share % share Mother's education No education -0.130 -0.269 0.115 -0.068 0.018 -4.4 Primary -0.165 -0.014 0.036 -0.027 0.000 -0.1 Secondary -0.327 0.255 0.229 -0.341 -0.087 20.9 Higher a Sum 16 Household economic status Poorest -0.842 -0.023 0.175 -0.668 0.015 -3.7 Poorer 0.631 -0.129 0.196 0.561 -0.073 17.5 Middle 0.305 -0.103 0.210 0.292 -0.030 7.2 Richer 0.017 0.063 0.219 0.017 0.001 -0.3 Richest a Sum 21 Current Mother's age 19–29 -0.058 0.849 0.4826 -0.127 -0.108 16.1 30–39 -0.044 -0.681 0.46 -0.091 0.062 -9.2 40-49 a Sum 7 Father's education No education and Primary -0.312 -0.015 0.206 -0.292 0.004 -1.1 Secondary -0.162 0.084 0.516 -0.379 -0.032 7.6 Higher a Sum 7 Rural residence -0.120 0.132 0.639 -0.349 -0.046 11 Number of Children less than 3 -0.021 0.523 0.711 -0.068 -0.036 9 Working mother 0.015 -0.216 0.132 0.009 -0.002 0.5 Contraceptive use 0.115 -0.150 0.660 0.345 -0.052 12 Have abortion 0.160 -0.146 0.280 0.204 -0.030 7 Distance to health facilities 0.881 -0.026 0.243 0.975 -0.026 6 Using internet -0.010 0.099 0.533 -0.025 -0.002 1 Discussion The findings from the concentration curve clearly demonstrate that unintended pregnancies were more prevalent among mothers from lower socioeconomic backgrounds. These findings are consistent with the literature from India and Iran (Anand et al., 2024 ; Khoramrooz et al., 2019 ; Omani-Samani et al., 2018 ). Our research findings allign with previous studies (Anand et al., 2024 ; Font-Ribera et al., 2008; Islam et al., 2022 ; Khoramrooz et al., 2019 ; Omani-Samani et al., 2018 ), indicating that the economic condition of households significantly influences the inequality in unintended pregnancies. Research in Bangladesh (Bishwajit et al., 2017 ) identified poverty as a significant factor contributing to uintended pregnancies, since women of lower socioeconomic class face restricted access to contraception due to financial constraints (Bishwajit et al., 2017 ; Font-Ribera et al., 2008). Multiple studies (Anand et al., 2024 ; Bearak et al., 2018 ; Habib et al., 2024; Khoramrooz et al., 2019 ; Sarder et al., 2021 ) have consistently indicated that place of residence is a significant factor contributing to inequalities in unplanned pregnancies, corroborating our findings. Studies indicate that rural women are more prone to unwanted pregnancies compared to those who live in urban areas. This disparity may be attributed to differences in sociodemographic factors, including inadequate awareness of family planning programs, a preference for having larger families, and restricted access to family services among rural women (Dixit et al. 2012 ; Khademi and Cooke 2003 ; Sarder et al., 2021 ). Our results align with existing studies (Anand et al., 2024 ; Dutta et al. 2015 ; Khoramrooz et al., 2019 ; Sarvestani et al., 2017 ) indicating that mother's education significantly contributed to the inequality in unintended pregnancy. The likelihood of unintended pregnancy is higher among less educated women. This perhaps is because of a lack of awareness and knowledge about family planning methods (Cakmak and Ertem 2005 ; Dixit et al. 2012 ). Additionally, our findings indicate that father’s education level is an important contributing factor to the inequality of unintended pregnancy, as confirmed by Omani-Samani et al. ( 2018 ). Consistent with existing literature (Anand et al., 2024 ; Khoramrooz et al., 2019 ), our analysis suggests that the number of children ever born majorly contributes to the inequality in unintended pregnancies. Women with more children are more likely to experience unintended pregnancies, as demonstrated by Kassa et al. ( 2012 ). Our decomposition analysis indicates that maternal age is a contributing factor to the inequality in unintended pregnancies. Older women are more likely to experience unintended pregnancies, aligning with the findings of previous studies (Faghihzadeh et al., 2003; Khoramrooz et al., 2019 ; Omani-Samani et al., 2018 ; Sarvestani et al., 2017 ). The decomposition analysis of inequality in our study highlights the important role of contraceptive use in explaining the disparities in unintended pregnancies. This finding is consistent with previous research conducted in Iran (Khoramrooz et al., 2019 ), which demonstrated women who have lower rates of contraceptive use are more likely to experience unintended pregnancies. The findings of this paper indicate that the distance to health facilities has a considerable contribution in explaining the inequality in unintended pregnancies. This result is consistent with previous studies (Aragaw et al., 2023 ; Bekele & Fekadu 2021 ; Getu et al., 2016; Kassa et al., 2012 ) which showed that the likelihood of unintended pregnancy is higher among mothers with a big problem with distance to health facilities than mothers who do not have not a big problem. A possible reason might be that the problem of distance to health facilities can limit women's ability to obtain necessary healthcare, such as contraception, potentially leading to unintended pregnancies (Sato et al., 2021). Conclusion This article assessed inequalities in unintended pregnancies and analyzed the different factors in Egypt utilizing data from the 2021 Egypt family health survey (EFHS). This study adopted the decomposition approach to investigate the relative share of demographic and socioeconomic determinants to inequality in unintended pregnancy. The decomposition analysis revealed that the wealth index was the predominant factor contributing to inequality in unplanned pregnancies, followed by the mother's education. Additional factors such as contraceptive use, place of residence, number of children, current mother's age, and father's education significantly contribute to this inequality. These findings may have programmatic implications specially to support Egypt’s goal to reach replacement level by 2030. It is advisable to provide educational seminars for women and their partners to enhance awareness of unintended pregnancies and provide information on contraception. Efforts supporting the enhancement of accessing family planning services to prevent unplanned pregnancies are highly encouraged. Further research is needed to investigate the impact of unplanned pregnancies on the health of the mother and child in Egypt. The Egypt 2030 Population Strategy adopts a cross-sectoral approach that targets both educational and economic determinants of unplanned fertility. Key policy interventions include improving access to education for girls, especially in rural and low-income communities, by expanding school infrastructure, offering scholarships, and reducing direct and indirect costs of schooling. The strategy also emphasizes incorporating reproductive health and family planning education into school curricula to promote early awareness and informed decision-making. Unintended pregnancy is a significant barrier to Egypt's development goals, particularly in the context of the National Population and Development Strategy 2023–2030. This strategy aims to reduce Egypt’s total fertility rate (TFR) to 2.4 children per woman by 2030. Achieving this target requires the implementation of a coordinated, multi-sectoral framework that engages key government institutions and international development partners. The Ministry of Health and Population, the Ministry of Education, the National Population Council, and organizations such as UNFPA are collaboratively integrating population policy with education and social development initiatives. The health sector focuses on expanding access to family planning services and postnatal contraception. The education sector is promoting girls’ education and revising curricula to include population-related content. In parallel, the social development sector addresses underlying structural drivers of fertility by combating poverty and working to eliminate child marriage. The media sector plays a critical role by raising public awareness about the benefits of smaller families and the importance of women’s empowerment. This coordinated approach reflects a comprehensive strategy that aligns demographic goals with broader development objectives (Egypt Ministry of Planning, 2023 ; UNFPA, 2023 ). This study has notable strengths as well as certain limitations. One major strength is the use of the concentration index and concentration curve analysis - rather than traditional analytical methods - to measure and decompose inequality in unintended pregnancy, marking a first in this area of research. Another strength is the use of the most recent nationally representative data from Egypt, drawn from the 2021 Demographic and Health Survey (DHS) conducted by CAPMAS, which enhances the relevance and currency of the findings. However, a key limitation of the study is the exclusion of important predictors of inequality in unintended pregnancy, such as paternal age, fertility preferences, and maternal body mass index (BMI). Future research should incorporate these variables to offer a more comprehensive understanding of the factors contributing to inequality in unintended pregnancy. Declarations - Corresponding author: Hassan Zaky ( [email protected] ) - Ethics approval and consent to participate: The data set used is secondary data. All ethics approval and consent to participate were handled by the Central Agency for Public Mobilization and Statistics (CAPMAS). - Consent for publication: Not applicable - Competing interests: The authors declare no competing interests. - Funding: There is no funding - Availability of data and materials: Data is available upon request from the Central Agency for Public Mobilization and Statistics (CAPMAS). CAPMAS is the government agency that collected the data. More details about the data set can be found at https://censusinfo.capmas.gov.eg/Metadata-ar-v4.2/index.php/catalog/1843 References Anand, A., Mondal, S., & Singh, B. (2024). Changes in Socioeconomic Inequalities in Unintended Pregnancies Among Currently Married Women in India. Global Social Welfare , 11 (1), 85-96. Aragaw, F. M., Amare, T., Teklu, R. E., Tegegne, B. A., & Alem, A. Z. (2023). Magnitude of unintended pregnancy and its determinants among childbearing age women in low and middle-income countries: evidence from 61 low and middle income countries. Frontiers in Reproductive Health , 5 , 1113926. Bearak, J., Popinchalk, A., Alkema, L., & Sedgh, G. (2018). Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: estimates from a Bayesian hierarchical model. The Lancet Global Health , 6 (4), e380-e389. Bearak, J. M., Alkema, L., Kantorová, V., & Casterline, J. (2023). Alignment between Desires and Outcomes among Women Wanting to Avoid Pregnancy: A Global Comparative Study of “Conditional” Unintended Pregnancy Rates. Studies in Family Planning , 54 (1), 265-280. Bekele, Y. A., & Fekadu, G. A. (2021). Factors associated with unintended pregnancy in Ethiopia; further analysis of the 2016 Ethiopian demographic health survey data. BMC Pregnancy and Childbirth , 21 , 1-7. Bishwajit, G., Tang, S., Yaya, S., & Feng, Z. (2017). Unmet need for contraception and its association with unintended pregnancy in Bangladesh. BMC pregnancy and childbirth , 17 , 1-9. Bongaarts J. (1978). A framework for analyzing the proximate determinants of fertility. Popul Dev Rev 1978; 4: 105. Bongaarts J. (2015). Modeling the fertility impact of the proximate determinants: time for a tune-up. Demogr Res 2015; 33: 535–60 . Cakmak, A., & Ertem, M. (2005). An important opportunity for family planning education: The mother of patient receiving medical treatment in a children’s hospital in Turkish. Journal of Dicle Medical , 4 , 190-195. Central Agency for Public Mobilization and Statistics (CAPMAS 2022): Egypt Family Health Survey–2021: Cairo, CAPMAS – December 2022. Dixit, P., Ram, F., & Dwivedi, L. K. (2012). Determinants of unwanted pregnancies in India using matched case-control designs. BMC pregnancy and childbirth , 12 , 1-12. Dutta, M., Shekhar, C., & Prashad, L. (2015). Level, trend and correlates of mistimed and unwanted pregnancies among currently pregnant ever married women in India. PloS one , 10 (12), e0144400. Egypt Ministry of Planning. (2023). National Population and Development Strategy 2023–2030. Cairo: Government of Egypt. El-shrqawy, E., & El-Nemer, A. (2020). Unintended Pregnancy: Associated Factors and outcomes among pregnant women. Mansoura Nursing Journal , 7 (2), 227-240. Faghih Zadeh, S., Babaee Rochee, G., Lmyian, M., Mansourian, F., & Rezasoltani, P. (2003). Factors associated with unwanted pregnancy. Journal of Sex &Marital Therapy , 29 (2), 157-164. Getu Melese, K., Gebrie, M. H., Berta Badi, M., & Fekadu Mersha, W. (2016). Unintended Pregnancy in Ethiopia: Community Based Cross-Sectional Study. Obstetrics and gynecology international , 2016 (1), 4374791. Habib, M. A., Raynes-Greenow, C., Nausheen, S., Soofi, S. B., Sajid, M., Bhutta, Z. A., & Black, K. I. (2017). Prevalence and determinants of unintended pregnancies amongst women attending antenatal clinics in Pakistan. BMC pregnancy and childbirth , 17 , 1-10. Islam, M. A., Rahim, A., & Jabbar, A. (2022). Prevalence and triggering factors of unintended pregnancies among women in India: Evidence from Indian Demographic and Health Survey 2015–2016. Clinical Epidemiology and Global Health , 13 , 100949. Jalaly, P., Ghodsi, Z., & Hojjatoleslami, S. (2015). Health Behavior's Deference in Intended and Unintended Pregnancies. Procedia-Social and Behavioral Sciences , 185 , 156-159. Kakwani, N.C., Wagstaff, A., and Doorlsaer, E.V. (1997), "Socioeconomic inequalities in health: Measurement, computation and statistical inference", Journal of Econometrics , 77(1), 87-104. Kassa, N., Berhane, Y., & Worku, A. (2012). Predictors of unintended pregnancy in Kersa, Eastern Ethiopia, 2010. Reproductive health , 9 , 1-7. Khademi, S., & Cooke, M. S. (2003). Comparing the attitudes of urban and rural Iranian women toward menopause. Maturitas , 46 (2), 113-121. Khadr, Z. (2009). Monitoring socioeconomic inequity in maternal health indicators in Egypt: 1995-2005. International Journal for Equity in Health , 8 , 1-12. Khajehpour, M., Simbar, M., Jannesari, S., Ramezani-Tehrani, F., & Majd, H. A. (2013). Health status of women with intended and unintended pregnancies. Public health , 127 (1), 58-64. Khoramrooz, M., Rezapour, A., Shirinbakhsh, S., & Khosravi, A. (2019). Understanding changes in socioeconomic inequality of unintended pregnancy among Iranian married women: a Blinder-Oaxaca decomposition analysis. Health Scope , 8 (4). Kost, K., & Zolna, M. (2019). Challenging unintended pregnancy as an indicator of reproductive autonomy: A response. Contraception , 100 (1), 5-9. Metwally, A. M., Saleh, R. M., Abdelhamed, A. M., Salama, S. I., Mores, C. W., Shaaban, F. A., & Azmy, O. M. (2015). Determinants of unintended pregnancy and its impact on the health of women in some governorates of Upper Egypt. Journal of the Arab Society for Medical Research , 10 (1), 1-8. Metwaly, H., Nour Eldin, S., & Gonied, A. (2018). Determinants and health related outcomes of unintended pregnancy in Zagazig city and nursing implication. Mansoura Nursing Journal , 5(2), 143-156. Mohamed, E. A. E. B., Hamed, A. F., Yousef, F. M., & Ahmed, E. A. (2019). Prevalence, determinants, and outcomes of unintended pregnancy in Sohag district, Egypt. Journal of the Egyptian Public Health Association , 94 , 1-9. Mohamed Hassan, H., & Awadein Ali, L. (2024). Knowledge of emergency contraception and acceptability of unplanned pregnancy among married women in reproductive age in Beni-Suef. Egyptian Journal of Health Care , 15 (1), 1603-1618. Omani-Samani, R., Amini Rarani, M., Sepidarkish, M., Khedmati Morasae, E., Maroufizadeh, S., & Almasi-Hashiani, A. (2018). Socioeconomic inequality of unintended pregnancy in the Iranian population: a decomposition approach. BMC Public Health , 18 , 1-8. Petersen, R., & Moos, M. K. (1997). Defining and measuring unintended pregnancy: issues and concerns. Women's health issues , 7 (4), 234-240. Sarder, A., Islam, S. M. S., Maniruzzaman, Talukder, A., & Ahammed, B. (2021). Prevalence of unintended pregnancy and its associated factors: evidence from six south Asian countries. PLoS One, 16(2), e0245923. Sarvestani, K. A., Ahmadi, A., Enayat, H., & Movahed, M. (2017). Level and factors related to unintended pregnancy with a brief review of new population policies in Iran. Iranian journal of public health , 46 (7), 973. Sato, R., Rohr, J., Huber-Krum, S., Esmer, Y., Okçuoğlu, B. A., Karadon, D., ... & Canning, D. (2021). Effect of distance to health facilities and access to contraceptive services among urban Turkish women. The European Journal of Contraception & Reproductive Health Care , 26 (5), 374-382. Shaheen, A. A., Diaaeldin, M., Chaaya, M., & El Roueiheb, Z. (2007). Unintended pregnancy in Egypt: evidence from the national study on women giving birth in 1999. Singh, A., Chalasani, S., Koenig, M. A., & Mahapatra, B. (2012). The consequences of unintended births for maternal and child health in India. Population Studies, 66(3), 223–39 . Sully, E. A., Biddlecom, A., Darroch, J. E., Riley, T., Ashford, L. S., Lince-Deroche, N., ... & Murro, R. (2020). Adding it up: investing in sexual and reproductive health 2019. UNFPA. (2023). National Strategy for Population and Development 2023–2030 – Egypt. Retrieved from: https://egypt.unfpa.org United Nations Population Fund (UNFPA 2018). Addressing unintended pregnancy in the Arab region. United Nations Population Fund Arab States Regional Office. United Nations Population Fund (UNFPA 2022). Seeing the Unseen: The case for action in the neglected crisis of unintended pregnancy. www.unfpa.org/swp2022 Wagstaff, A. (2000). Socioeconomic inequalities in child mortality: comparisons across nine developing countries. Bulletin of the World Health Organization , 78 , 19-29. Wagstaff, A. (2005). The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. Health economics , 14 (4), 429-432. Wagstaff, A., Van Doorslaer, E., & Watanabe, N. (2003). On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam. Journal of Econometrics , 112 (1), 207-223. Wagstaff, A., Paci, P., & Van Doorslaer, E. (1991). On the measurement of inequalities in health. Social science & medicine , 33 (5), 545-557. Youssef, R. M., Moubarak, I. I., Gaffar, Y. A., & Atta, H. Y. (2002). Correlates of unintended pregnancy in Beheira governorate, Egypt. Eastern Mediterranean Health Journal , 8 (4-5), 521-536. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Zaky","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAwYGZgaGCmShA0RpOQPjJhCrhbGNFC3m/IcfG/POs0vsb2+/Jl34g0GO70YCfi2WDceMk3m3JSfOOHOmTHpGAoOxJCEtBgcbjA/zbmNO3CCRkybNk8CQuIGglsPsnw/zzqlP3CD/BqylnrCWYzxAhzUcBtrCfgykJcGAoJYzPMWGc44dN55xJofZekaahOHMMw8IaDl/fLPEm5pq2f724w9vF9jYyPMdJ2ALCDDxgCkeE2kGBgnCykGA8QeYYn/8mTj1o2AUjIJRMNIAAPjuRs51GcT/AAAAAElFTkSuQmCC","orcid":"","institution":"American University in Cairo","correspondingAuthor":true,"prefix":"","firstName":"Hassan","middleName":"H. 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1","display":"","copyAsset":false,"role":"figure","size":159891,"visible":true,"origin":"","legend":"\u003cp\u003eConcentration curve of unintended pregnancy in Egypt (2021)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7453953/v1/e1351a10422801e23c363e1b.png"},{"id":94645653,"identity":"7f9ae895-4cbd-4f64-a6fe-baaba42df12d","added_by":"auto","created_at":"2025-10-29 08:39:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":807362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7453953/v1/644cb2a4-8d5f-4a0d-9a8c-dcb7f3aebc29.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Socioeconomic Inequality in Unintended Pregnancy in Egypt: A Decomposition Analysis","fulltext":[{"header":"Contributions to the literature","content":"\u003cul\u003e\n \u003cli\u003eAlthough numerous studies have investigated the determinants of unwanted pregnancies among married women in Egypt, limited attention has been given to the role of socioeconomic factors to inequality in unintended pregnancies in Egypt.\u003c/li\u003e\n \u003cli\u003eThe study draws on data from the Egypt Family Health Survey (EFHS) 2021–2022, the most recent and comprehensive national dataset available, offering updated insights into socioeconomic disparities across the country.\u003c/li\u003e\n \u003cli\u003eMost previous studies employed logistic regression models to examine factors associated with unintended pregnancy. One study focused exclusively on the use of the concentration index to assess inequality in unintended pregnancy. Building on this foundation, the present study adopts a more comprehensive analytical approach by combining the concentration index with the concentration curve and decomposition analysis to provide a deeper understanding of socioeconomic disparities.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eUnintended pregnancy constitutes a significant public health concern due to its impact on mother and child health, in addition to societal outcomes (Jalaly et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khajehpour et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Petersen \u0026amp; Moos \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Estimates of the exact number of unintended pregnancies are needed for governments to understand how common this problem is among different groups of people. This helps them make smart choices about how to spend money on reproductive health care (Kost and Zolna \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sully et al. 2020; UNFPA 2022).\u003c/p\u003e\u003cp\u003eMore than 60 percent of unintended pregnancies result in abortion, with approximately 45 percent of these procedures being unsafe, therefore contributing significantly (5–13%) to global maternal mortality and posing as a substantial barrier to the achievement of the Sustainable Development Goals (UNFPA 2022).\u003c/p\u003e\u003cp\u003eGlobally, unintended pregnancies reached approximately 121\u0026nbsp;million annually from 2015 to 2019, accounting for over half of all pregnancies. Unintended pregnancy rates demonstrate substantial regional disparities. From 2015 to 2019, the rate of unintended pregnancy in Middle Africa was 258 per 1,000 reproductive-age women, indicating that one in four women had an unintended pregnancy, compared to a rate of 35 per 1,000 in Western Europe. In the Arab region, two out of five pregnancies are unintended (Bearak et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UNFPA 2018; UNFPA 2022).\u003c/p\u003e\u003cp\u003eThe global rate of unplanned pregnancies decreased worldwide from 1990 to 2019; the extent of this decline varied significantly across regions. Europe and North America experienced the most significant reductions, with rates declining by approximately fifty percent compared to the early 1990s. In contrast, sub-Saharan Africa experienced the least significant decline, with a reduction of approximately 12 percent (Bearak et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UNFPA 2022).\u003c/p\u003e\u003cp\u003eAlthough numerous studies have investigated the determinants of unwanted pregnancies among married women in Egypt [El-Sharqawy \u0026amp; El-Nemer, 2020; Metwaly et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohamed et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohamed \u0026amp; Awadein, 2024; Shaheen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e], limited attention has been given to the role of socioeconomic inequality in shaping these outcomes. Khadr (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) examined three rounds of the Egypt Demographic and Health Survey (EDHS) from 1995 to 2005, focusing primarily on level of inequality in unintended pregnancies, without explicitly analyzing socioeconomic disparities. Therefore, the present study aims to assess the relative contribution of socioeconomic factors to inequality in unintended pregnancies in Egypt.\u003c/p\u003e\u003cp\u003ePrevious studies on unintended pregnancy in Egypt have relied on varying data sources. Several investigations collected primary data using questionnaires administered to selected samples of married women in one or two governorates [El-Sharqawy \u0026amp; El-Nemer, 2020; Metwaly et al., 2015; Metwaly et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohamed et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohamed \u0026amp; Awadein, 2024; Youssef et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e]. Other studies utilized secondary data from the Egypt Demographic and Health Survey (EDHS) to examine unintended pregnancy at national level (Khadr, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Shaheen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In contrast, the present study draws on data from the Egypt Family Health Survey (EFHS) 2021–2022, the most recent and comprehensive national dataset available, offering updated insights into socioeconomic disparities across the country.\u003c/p\u003e\u003cp\u003eMost previous studies employed logistic regression models to examine factors associated with unintended pregnancy [Metwaly et al., 2015; Metwaly et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohamed et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shaheen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Youssef et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e]. In contrast, Khadr (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) focused exclusively on the use of the concentration index to assess inequality in unintended pregnancy. Building on this foundation, the present study adopts a more comprehensive analytical approach by combining the concentration index with the concentration curve and decomposition analysis to provide a deeper understanding of socioeconomic disparities.\u003c/p\u003e\u003cp\u003eTo summarize, this study contributes to existing literature in three key ways. First, it aims to quantify the extent to which socioeconomic inequality contributes to disparities in unintended pregnancies in Egypt. Second, it applies the Wagstaff decomposition method, a technique that, to the best of our knowledge, has not been previously employed in this context in Egypt. Third, the analysis is based on the most recent nationally representative dataset available in Egypt thereby providing timely and policy-relevant insights.\u003c/p\u003e\n\u003ch3\u003eData Source\u003c/h3\u003e\n\u003cp\u003eThe data for this study was obtained from the Egypt Family Health Survey (EFHS) conducted in 2021–2022. The EFHS is a nationally representative survey administered under the supervision of the Central Agency for Public Mobilization and Statistics (CAPMAS). The main objective of the survey is to provide estimates on indicators like fertility, mortality, morbidity, maternal health, child health, women's empowerment, family planning, and violence. In the EFHS, a total of 10,996 women aged 15 to 49 were surveyed.\u003c/p\u003e\u003cp\u003eThe selection of variables and the overall modeling strategy in this study were guided by the proximate determinants of fertility framework proposed by Bongaarts (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This conceptual model offers a structured approach to identifying the direct mechanisms influencing fertility outcomes. While the original framework includes biological, behavioral, and socio-contextual factors, our study adapted it to align with the structure and limitations of the available dataset. Unintended pregnancy was used as the dependent variable, dichotomized as “1” for yes and “0” for no, given its relevance as a proximate fertility-related indicator. The independent variables were selected based on both the Bongaarts model and supporting empirical literature (Anand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dixit et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Omani-Samani et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and included a range of socio-demographic and reproductive health factors. These comprised maternal education, household economic status, maternal age, paternal education, place of residence, number of living children, maternal employment, current contraceptive method, history of abortion, access to health facilities, difficulty accessing health services, and internet use.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Methodology","content":"\u003ch2\u003eInequality Measurement\u003c/h2\u003e\u003cp\u003eThe concentration curve is a diagrammatical measure of inequality. It establishes the cumulative percentage of the health variable (y-axis) against the cumulative percentage of the sample, ordered by economic status, beginning with the poorest, and concluding with the richest (x-axis). If all individuals, irrespective of their living standards, possess identical values for the health variable, the concentration curve will be a 45-degree line extending from the bottom left corner to the top right corner; this can be referred to as the equality line. If, by contrast, the health sector variable takes elevated (lower) values amongst disadvantaged individuals, the concentration curve will position itself above (below) the equality line. The greater the distance of the curve from the line of equality, the more concentrated the health variable is among those with limited resources (Wagstaff et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Kakwani et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Wagstaff, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe concentration index is a relative measure of inequality that reflects the degree to which a health indicator is distributed amongst the disadvantaged or the privileged. The value of the concentration index can vary between − 1 and + 1. The concentration index yields negative values when the concentration curve is situated above the diagonal line. It yields positive values when it is positioned below the diagonal. The negative values imply that a variable is concentrated among disadvantaged individuals, whereas positive values suggest the contrary. In the absence of inequality, the concentration index will equal 0 (Wagstaff et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1991\u003c/span\u003e and Wagstaff et al., 1997). The concentration index is calculated as twice the (weighted) covariance of a health variable and a relative economic rank variable, divided by the mean of the health variable, as follows Wagstaff et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e):\u003c/p\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:C\\hspace{0.33em}=\\frac{2}{\\mu\\:}\\hspace{0.33em}Co{v}_{w}\\left({y}_{i\\hspace{0.33em}},{R}_{i}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere y\u003csub\u003ei\u003c/sub\u003e is unintended pregnancy, µ is its mean, R\u003csub\u003ei\u003c/sub\u003e is the fractional rank of i\u003csup\u003eth\u003c/sup\u003e individual (for weighted data) within the socioeconomic distribution defined by wealth and Cov\u003csub\u003ew\u003c/sub\u003e denotes the weighted covariance.\u003c/p\u003e\u003cp\u003eWagstaff (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) suggested that, in the case of a binary variable, the concentration index could be normalized by dividing it through by either the reciprocal of the mean of the health variable (unintended pregnancy) or the bound of the concentration index. The Wagstaff normalized concentration index (WCI) can be written as (Wagstaff, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e):\u003c/p\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:WCI\\hspace{0.33em}=\\frac{C}{1-\\mu\\:}\\hspace{0.33em}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003ch2\u003eDecomposition of Inequality\u003c/h2\u003e\u003cp\u003eA decomposition analysis allows estimating how determinants proportionally contribute to inequality in unintended pregnancy. Wagstaff et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) showed that for any linear regression model linking the health variable of interest y (unintended pregnancy), to a set of k determinants, x\u003csub\u003ek\u003c/sub\u003e:\u003c/p\u003e\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}\\hspace{0.33em}=\\hspace{0.33em}\\alpha\\:\\hspace{0.33em}+{\\sum\\:}_{k=1}^{n}{\\beta\\:}_{k}{X}_{ki}+\\hspace{0.33em}{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere ε is an error term. Given the relationship between y\u003csub\u003ei\u003c/sub\u003e and x\u003csub\u003eki\u003c/sub\u003e, the concentration index for y (C) can be written as:\u003c/p\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:C\\hspace{0.33em}={\\sum\\:}_{k=1}^{n}\\left(\\left.\\frac{{\\beta\\:}_{k}^{m}⥂{\\stackrel{̄}{x}}_{k}}{\\mu\\:}\\right)\\right.{C}_{k}\\hspace{0.33em}+\\hspace{0.33em}\\frac{G{C}_{\\epsilon\\:}}{\\mu\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere µ is the mean of y. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{̄}{x}}_{k}\\)\u003c/span\u003e\u003c/span\u003eis the mean of x\u003csub\u003ek\u003c/sub\u003e ; C\u003csub\u003ek\u003c/sub\u003e is the concentration index for x\u003csub\u003ek\u003c/sub\u003e. In the last term (which can be computed as a residual), GC\u003csub\u003eε\u003c/sub\u003e is the generalized concentration index for ε\u003csub\u003ei\u003c/sub\u003e. C consists of two components. The first is the deterministic, or ‘explained’, component. The second is a residual, or ‘unexplained’, component that reflects the inequality that cannot be explained by systematic variation in the x\u003csub\u003ek\u003c/sub\u003e across socioeconomic groups.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents summary statistics regarding unintended pregnancy and its determinants among Egyptian women. The study indicates that 22 percent of Egyptian women encountered unexpected pregnancies, whereas 78 percent had wanted pregnancies. Majority of the mothers lived in rural regions (64 percent), while 60 percent attained higher education.\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the majority of the mothers were aged between 19 and 39 years, with 71 percent having less than three children. Two-thirds utilized contraception, and the majority had no prior experience of abortion (72 percent). The data indicates that around 87 percent of women were unemployed. Furthermore, 60 percent of women experienced restricted access to health facilities, but 53 percent indicated availability of internet access.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary statistics about unintended pregnancy and its determinants in Egypt, (2021)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eUnintended pregnancy\u003c/em\u003e intended\u003c/p\u003e\n \u003cp\u003eunintended\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.2 21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8602\u003c/p\u003e\n \u003cp\u003e2394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePlace of Residence\u003c/em\u003e Urban\u003c/p\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003cp\u003e63.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3969\u003c/p\u003e\n \u003cp\u003e7027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCurrent Age\u003c/em\u003e 19\u0026ndash;29\u003c/p\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.26\u003c/p\u003e\n \u003cp\u003e45.64\u003c/p\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5307\u003c/p\u003e\n \u003cp\u003e5019\u003c/p\u003e\n \u003cp\u003e938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEducational attainment\u003c/em\u003e No education\u003c/p\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003cp\u003esecondary\u003c/p\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.46 3.62\u003c/p\u003e\n \u003cp\u003e22.92\u003c/p\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1260 398\u003c/p\u003e\n \u003cp\u003e2520\u003c/p\u003e\n \u003cp\u003e6818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHousehold Economic Status\u003c/em\u003e Poorest\u003c/p\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003cp\u003eRichest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003cp\u003e21.9\u003c/p\u003e\n \u003cp\u003e20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1921\u003c/p\u003e\n \u003cp\u003e2151\u003c/p\u003e\n \u003cp\u003e2314\u003c/p\u003e\n \u003cp\u003e2405\u003c/p\u003e\n \u003cp\u003e2205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHusband\u0026apos;s education\u003c/em\u003e No education and primary\u003c/p\u003e\n \u003cp\u003esecondary\u003c/p\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.59 51.6\u003c/p\u003e\n \u003cp\u003e18.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2264\u003c/p\u003e\n \u003cp\u003e5674\u003c/p\u003e\n \u003cp\u003e1981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHow difficult is the distance to the health facility to get medical care for yourself\u003c/em\u003e Big problem\u003c/p\u003e\n \u003cp\u003eNot a big problem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003cp\u003e75.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2676\u003c/p\u003e\n \u003cp\u003e8320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNumber of Children\u003c/em\u003e Less than 3\u003c/p\u003e\n \u003cp\u003eGreater than or equal 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.13\u003c/p\u003e\n \u003cp\u003e28.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7821\u003c/p\u003e\n \u003cp\u003e3175\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eMother\u0026rsquo;s\u003c/em\u003e Not working \u003cem\u003eWork\u003c/em\u003e Working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.77\u003c/p\u003e\n \u003cp\u003e13.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9541\u003c/p\u003e\n \u003cp\u003e1455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eUsing Internet\u003c/em\u003e No\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.74\u003c/p\u003e\n \u003cp\u003e53.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5140\u003c/p\u003e\n \u003cp\u003e5856\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHaving Abortion\u003c/em\u003e No\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7919\u003c/p\u003e\n \u003cp\u003e3077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eContraceptive use\u003c/em\u003e No\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3734\u003c/p\u003e\n \u003cp\u003e7262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the concentration curve for unintended pregnancy, illustrating wealth-related inequality. The curve\u0026apos;s position above the line of equality indicates that unintended pregnancies are more concentrated among poorer Egyptian mothers. This reveals that there is a pro-poor inequality in unintended pregnancy in Egypt.\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the value of Wagstaff\u0026apos;s normalized concentration index (WCI) for unintended pregnancy is -0.210, with a 95% confidence interval ranging from \u0026minus;\u0026thinsp;0.184 to -0.114. This result confirms that unintended pregnancies are significantly more concentrated among Egyptian mothers from lower socioeconomic backgrounds.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWagstaff\u0026rsquo;s normalized concentration index (WCI) (95% confidence interval, standard error and P-value) for unintended pregnancy in Egypt\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eUnintended Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% Conf. interval for CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u0026minus;\u0026thinsp;0.184, \u0026minus; 0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the decomposition of unintended pregnancy determinants. Several factors concentrated among lower socioeconomic status women include lack of maternal and paternal education, living in poor households, having had an abortion, and difficulty accessing health facilities.\u003c/p\u003e\n\u003cp\u003eThis study decomposed the concentration index of unintended pregnancy against wealth related characteristics to determine the relative share of each independent variable to inequality. Household\u0026apos;s economic status has the highest share to unintended pregnancy inequality (21 percent), followed by mother\u0026apos;s education (16 percent). Other notable contributors included contraceptive use (12 percent), rural residence (11 percent), and number of children less than three (9 percent). Each of the variables father\u0026rsquo;s education, current mother\u0026rsquo;s age, and having an abortion\u0026mdash;each contributed equally (7 percent) in the inequality of unintended pregnancy. Difficulty accessing health facilities has the lowest share (6 percent) to unintended pregnancy inequality in Egypt.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDecompositions of concentration index for the determinants of unintended pregnancy in Egypt\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;m\u003csub\u003ek\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWCI\u003csub\u003es\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eelasticity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eshare\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% share\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMother\u0026apos;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold economic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRicher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRichest\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent Mother\u0026apos;s age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40-49\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFather\u0026apos;s education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo education and Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Children less than 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking mother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContraceptive use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHave abortion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDistance to health facilities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from the concentration curve clearly demonstrate that unintended pregnancies were more prevalent among mothers from lower socioeconomic backgrounds. These findings are consistent with the literature from India and Iran (Anand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Omani-Samani et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur research findings allign with previous studies (Anand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Font-Ribera et al., 2008; Islam et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Omani-Samani et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), indicating that the economic condition of households significantly influences the inequality in unintended pregnancies. Research in Bangladesh (Bishwajit et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) identified poverty as a significant factor contributing to uintended pregnancies, since women of lower socioeconomic class face restricted access to contraception due to financial constraints (Bishwajit et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Font-Ribera et al., 2008).\u003c/p\u003e\u003cp\u003eMultiple studies (Anand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bearak et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Habib et al., 2024; Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sarder et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have consistently indicated that place of residence is a significant factor contributing to inequalities in unplanned pregnancies, corroborating our findings. Studies indicate that rural women are more prone to unwanted pregnancies compared to those who live in urban areas. This disparity may be attributed to differences in sociodemographic factors, including inadequate awareness of family planning programs, a preference for having larger families, and restricted access to family services among rural women (Dixit et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khademi and Cooke \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sarder et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur results align with existing studies (Anand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dutta et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sarvestani et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) indicating that mother's education significantly contributed to the inequality in unintended pregnancy. The likelihood of unintended pregnancy is higher among less educated women. This perhaps is because of a lack of awareness and knowledge about family planning methods (Cakmak and Ertem \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Dixit et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, our findings indicate that father\u0026rsquo;s education level is an important contributing factor to the inequality of unintended pregnancy, as confirmed by Omani-Samani et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsistent with existing literature (Anand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), our analysis suggests that the number of children ever born majorly contributes to the inequality in unintended pregnancies. Women with more children are more likely to experience unintended pregnancies, as demonstrated by Kassa et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur decomposition analysis indicates that maternal age is a contributing factor to the inequality in unintended pregnancies. Older women are more likely to experience unintended pregnancies, aligning with the findings of previous studies (Faghihzadeh et al., 2003; Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Omani-Samani et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sarvestani et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe decomposition analysis of inequality in our study highlights the important role of contraceptive use in explaining the disparities in unintended pregnancies. This finding is consistent with previous research conducted in Iran (Khoramrooz et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which demonstrated women who have lower rates of contraceptive use are more likely to experience unintended pregnancies.\u003c/p\u003e\u003cp\u003eThe findings of this paper indicate that the distance to health facilities has a considerable contribution in explaining the inequality in unintended pregnancies. This result is consistent with previous studies (Aragaw et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bekele \u0026amp; Fekadu \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Getu et al., 2016; Kassa et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) which showed that the likelihood of unintended pregnancy is higher among mothers with a big problem with distance to health facilities than mothers who do not have not a big problem. A possible reason might be that the problem of distance to health facilities can limit women's ability to obtain necessary healthcare, such as contraception, potentially leading to unintended pregnancies (Sato et al., 2021).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis article assessed inequalities in unintended pregnancies and analyzed the different factors in Egypt utilizing data from the 2021 Egypt family health survey (EFHS). This study adopted the decomposition approach to investigate the relative share of demographic and socioeconomic determinants to inequality in unintended pregnancy.\u003c/p\u003e\u003cp\u003eThe decomposition analysis revealed that the wealth index was the predominant factor contributing to inequality in unplanned pregnancies, followed by the mother's education. Additional factors such as contraceptive use, place of residence, number of children, current mother's age, and father's education significantly contribute to this inequality.\u003c/p\u003e\u003cp\u003eThese findings may have programmatic implications specially to support Egypt\u0026rsquo;s goal to reach replacement level by 2030. It is advisable to provide educational seminars for women and their partners to enhance awareness of unintended pregnancies and provide information on contraception. Efforts supporting the enhancement of accessing family planning services to prevent unplanned pregnancies are highly encouraged. Further research is needed to investigate the impact of unplanned pregnancies on the health of the mother and child in Egypt.\u003c/p\u003e\u003cp\u003eThe Egypt 2030 Population Strategy adopts a cross-sectoral approach that targets both educational and economic determinants of unplanned fertility. Key policy interventions include improving access to education for girls, especially in rural and low-income communities, by expanding school infrastructure, offering scholarships, and reducing direct and indirect costs of schooling. The strategy also emphasizes incorporating reproductive health and family planning education into school curricula to promote early awareness and informed decision-making.\u003c/p\u003e\u003cp\u003eUnintended pregnancy is a significant barrier to Egypt's development goals, particularly in the context of the National Population and Development Strategy 2023\u0026ndash;2030. This strategy aims to reduce Egypt\u0026rsquo;s total fertility rate (TFR) to 2.4 children per woman by 2030. Achieving this target requires the implementation of a coordinated, multi-sectoral framework that engages key government institutions and international development partners. The Ministry of Health and Population, the Ministry of Education, the National Population Council, and organizations such as UNFPA are collaboratively integrating population policy with education and social development initiatives. The health sector focuses on expanding access to family planning services and postnatal contraception. The education sector is promoting girls\u0026rsquo; education and revising curricula to include population-related content. In parallel, the social development sector addresses underlying structural drivers of fertility by combating poverty and working to eliminate child marriage. The media sector plays a critical role by raising public awareness about the benefits of smaller families and the importance of women\u0026rsquo;s empowerment. This coordinated approach reflects a comprehensive strategy that aligns demographic goals with broader development objectives (Egypt Ministry of Planning, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; UNFPA, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has notable strengths as well as certain limitations. One major strength is the use of the concentration index and concentration curve analysis - rather than traditional analytical methods - to measure and decompose inequality in unintended pregnancy, marking a first in this area of research. Another strength is the use of the most recent nationally representative data from Egypt, drawn from the 2021 Demographic and Health Survey (DHS) conducted by CAPMAS, which enhances the relevance and currency of the findings. However, a key limitation of the study is the exclusion of important predictors of inequality in unintended pregnancy, such as paternal age, fertility preferences, and maternal body mass index (BMI). Future research should incorporate these variables to offer a more comprehensive understanding of the factors contributing to inequality in unintended pregnancy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e- Corresponding author: Hassan Zaky ([email protected])\u003c/p\u003e\n\u003cp\u003e- Ethics approval and consent to participate: The data set used is secondary data. All ethics approval and consent to participate were handled by the Central Agency for Public Mobilization and Statistics (CAPMAS).\u003c/p\u003e\n\u003cp\u003e- Consent for publication: Not applicable\u003c/p\u003e\n\u003cp\u003e- Competing interests: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e- Funding: There is no funding\u003c/p\u003e\n\u003cp\u003e- Availability of data and materials: Data is available upon request from the Central Agency for Public Mobilization and Statistics (CAPMAS). CAPMAS is the government agency that collected the data. \u0026nbsp;More details about the data set can be found at https://censusinfo.capmas.gov.eg/Metadata-ar-v4.2/index.php/catalog/1843\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnand, A., Mondal, S., \u0026amp; Singh, B. (2024). Changes in Socioeconomic Inequalities in Unintended Pregnancies Among Currently Married Women in India. \u003cem\u003eGlobal Social Welfare\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 85-96.\u003c/li\u003e\n \u003cli\u003eAragaw, F. M., Amare, T., Teklu, R. E., Tegegne, B. A., \u0026amp; Alem, A. Z. (2023). Magnitude of unintended pregnancy and its determinants among childbearing age women in low and middle-income countries: evidence from 61 low and middle income countries. \u003cem\u003eFrontiers in Reproductive Health\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 1113926.\u003c/li\u003e\n \u003cli\u003eBearak, J., Popinchalk, A., Alkema, L., \u0026amp; Sedgh, G. (2018). Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: estimates from a Bayesian hierarchical model. \u003cem\u003eThe Lancet Global Health\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), e380-e389.\u003c/li\u003e\n \u003cli\u003eBearak, J. M., Alkema, L., Kantorov\u0026aacute;, V., \u0026amp; Casterline, J. (2023). Alignment between Desires and Outcomes among Women Wanting to Avoid Pregnancy: A Global Comparative Study of \u0026ldquo;Conditional\u0026rdquo; Unintended Pregnancy Rates. \u003cem\u003eStudies in Family Planning\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(1), 265-280.\u003c/li\u003e\n \u003cli\u003eBekele, Y. A., \u0026amp; Fekadu, G. A. (2021). Factors associated with unintended pregnancy in Ethiopia; further analysis of the 2016 Ethiopian demographic health survey data. \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e, 1-7.\u003c/li\u003e\n \u003cli\u003eBishwajit, G., Tang, S., Yaya, S., \u0026amp; Feng, Z. (2017). Unmet need for contraception and its association with unintended pregnancy in Bangladesh. \u003cem\u003eBMC pregnancy and childbirth\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 1-9.\u003c/li\u003e\n \u003cli\u003eBongaarts J. (1978). A framework for analyzing the proximate determinants of fertility. \u003cem\u003ePopul Dev Rev 1978; 4: 105.\u0026nbsp;\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eBongaarts J. (2015). Modeling the fertility impact of the proximate determinants: time for a tune-up. \u003cem\u003eDemogr Res 2015; 33: 535\u0026ndash;60\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eCakmak, A., \u0026amp; Ertem, M. (2005). An important opportunity for family planning education: The mother of patient receiving medical treatment in a children\u0026rsquo;s hospital in Turkish. \u003cem\u003eJournal of Dicle Medical\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 190-195.\u003c/li\u003e\n \u003cli\u003eCentral Agency for Public Mobilization and Statistics (CAPMAS 2022): Egypt Family Health Survey\u0026ndash;2021: Cairo, \u003cem\u003eCAPMAS\u003c/em\u003e \u0026ndash; December 2022.\u003c/li\u003e\n \u003cli\u003eDixit, P., Ram, F., \u0026amp; Dwivedi, L. K. (2012). Determinants of unwanted pregnancies in India using matched case-control designs. \u003cem\u003eBMC pregnancy and childbirth\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 1-12.\u003c/li\u003e\n \u003cli\u003eDutta, M., Shekhar, C., \u0026amp; Prashad, L. (2015). Level, trend and correlates of mistimed and unwanted pregnancies among currently pregnant ever married women in India. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(12), e0144400.\u003c/li\u003e\n \u003cli\u003eEgypt Ministry of Planning. (2023). National Population and Development Strategy 2023\u0026ndash;2030. Cairo: Government of Egypt.\u003c/li\u003e\n \u003cli\u003eEl-shrqawy, E., \u0026amp; El-Nemer, A. (2020). Unintended Pregnancy: Associated Factors and outcomes among pregnant women. \u003cem\u003eMansoura Nursing Journal\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 227-240.\u003c/li\u003e\n \u003cli\u003eFaghih Zadeh, S., Babaee Rochee, G., Lmyian, M., Mansourian, F., \u0026amp; Rezasoltani, P. (2003). Factors associated with unwanted pregnancy. \u003cem\u003eJournal of Sex \u0026amp;Marital Therapy\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 157-164.\u003c/li\u003e\n \u003cli\u003eGetu Melese, K., Gebrie, M. H., Berta Badi, M., \u0026amp; Fekadu Mersha, W. (2016). Unintended Pregnancy in Ethiopia: Community Based Cross-Sectional Study. \u003cem\u003eObstetrics and gynecology international\u003c/em\u003e, \u003cem\u003e2016\u003c/em\u003e(1), 4374791.\u003c/li\u003e\n \u003cli\u003eHabib, M. A., Raynes-Greenow, C., Nausheen, S., Soofi, S. B., Sajid, M., Bhutta, Z. A., \u0026amp; Black, K. I. (2017). Prevalence and determinants of unintended pregnancies amongst women attending antenatal clinics in Pakistan. \u003cem\u003eBMC pregnancy and childbirth\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e, 1-10.\u003c/li\u003e\n \u003cli\u003eIslam, M. A., Rahim, A., \u0026amp; Jabbar, A. (2022). Prevalence and triggering factors of unintended pregnancies among women in India: Evidence from Indian Demographic and Health Survey 2015\u0026ndash;2016. \u003cem\u003eClinical Epidemiology and Global Health\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e, 100949.\u003c/li\u003e\n \u003cli\u003eJalaly, P., Ghodsi, Z., \u0026amp; Hojjatoleslami, S. (2015). Health Behavior\u0026apos;s Deference in Intended and Unintended Pregnancies. \u003cem\u003eProcedia-Social and Behavioral Sciences\u003c/em\u003e, \u003cem\u003e185\u003c/em\u003e, 156-159.\u003c/li\u003e\n \u003cli\u003eKakwani, N.C., Wagstaff, A., and Doorlsaer, E.V. (1997), \u0026quot;Socioeconomic inequalities in health: Measurement, computation and statistical inference\u0026quot;, \u003cem\u003eJournal of Econometrics\u003c/em\u003e, 77(1), 87-104.\u003c/li\u003e\n \u003cli\u003eKassa, N., Berhane, Y., \u0026amp; Worku, A. (2012). Predictors of unintended pregnancy in Kersa, Eastern Ethiopia, 2010. \u003cem\u003eReproductive health\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 1-7.\u003c/li\u003e\n \u003cli\u003eKhademi, S., \u0026amp; Cooke, M. S. (2003). Comparing the attitudes of urban and rural Iranian women toward menopause. \u003cem\u003eMaturitas\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 113-121.\u003c/li\u003e\n \u003cli\u003eKhadr, Z. (2009). Monitoring socioeconomic inequity in maternal health indicators in Egypt: 1995-2005. \u003cem\u003eInternational Journal for Equity in Health\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e, 1-12.\u003c/li\u003e\n \u003cli\u003eKhajehpour, M., Simbar, M., Jannesari, S., Ramezani-Tehrani, F., \u0026amp; Majd, H. A. (2013). Health status of women with intended and unintended pregnancies. \u003cem\u003ePublic health\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e(1), 58-64.\u003c/li\u003e\n \u003cli\u003eKhoramrooz, M., Rezapour, A., Shirinbakhsh, S., \u0026amp; Khosravi, A. (2019). Understanding changes in socioeconomic inequality of unintended pregnancy among Iranian married women: a Blinder-Oaxaca decomposition analysis. \u003cem\u003eHealth Scope\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4).\u003c/li\u003e\n \u003cli\u003eKost, K., \u0026amp; Zolna, M. (2019). Challenging unintended pregnancy as an indicator of reproductive autonomy: A response. \u003cem\u003eContraception\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(1), 5-9.\u003c/li\u003e\n \u003cli\u003eMetwally, A. M., Saleh, R. M., Abdelhamed, A. M., Salama, S. I., Mores, C. W., Shaaban, F. A., \u0026amp; Azmy, O. M. (2015). Determinants of unintended pregnancy and its impact on the health of women in some governorates of Upper Egypt. \u003cem\u003eJournal of the Arab Society for Medical Research\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1-8.\u003c/li\u003e\n \u003cli\u003eMetwaly, H., Nour Eldin, S., \u0026amp; Gonied, A. (2018). Determinants and health related outcomes of unintended pregnancy in Zagazig city and nursing implication. \u003cem\u003eMansoura Nursing Journal\u003c/em\u003e, 5(2), 143-156.\u003c/li\u003e\n \u003cli\u003eMohamed, E. A. E. B., Hamed, A. F., Yousef, F. M., \u0026amp; Ahmed, E. A. (2019). Prevalence, determinants, and outcomes of unintended pregnancy in Sohag district, Egypt. \u003cem\u003eJournal of the Egyptian Public Health Association\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e, 1-9.\u003c/li\u003e\n \u003cli\u003eMohamed Hassan, H., \u0026amp; Awadein Ali, L. (2024). Knowledge of emergency contraception and acceptability of unplanned pregnancy among married women in reproductive age in Beni-Suef. \u003cem\u003eEgyptian Journal of Health Care\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 1603-1618.\u003c/li\u003e\n \u003cli\u003eOmani-Samani, R., Amini Rarani, M., Sepidarkish, M., Khedmati Morasae, E., Maroufizadeh, S., \u0026amp; Almasi-Hashiani, A. (2018). Socioeconomic inequality of unintended pregnancy in the Iranian population: a decomposition approach. \u003cem\u003eBMC Public Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 1-8.\u003c/li\u003e\n \u003cli\u003ePetersen, R., \u0026amp; Moos, M. K. (1997). Defining and measuring unintended pregnancy: issues and concerns. \u003cem\u003eWomen\u0026apos;s health issues\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(4), 234-240.\u003c/li\u003e\n \u003cli\u003eSarder, A., Islam, S. M. S., Maniruzzaman, Talukder, A., \u0026amp; Ahammed, B. (2021). Prevalence of unintended pregnancy and its associated factors: evidence from six south Asian countries. \u003cem\u003ePLoS One, 16(2), e0245923.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eSarvestani, K. A., Ahmadi, A., Enayat, H., \u0026amp; Movahed, M. (2017). Level and factors related to unintended pregnancy with a brief review of new population policies in Iran. \u003cem\u003eIranian journal of public health\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(7), 973.\u003c/li\u003e\n \u003cli\u003eSato, R., Rohr, J., Huber-Krum, S., Esmer, Y., Ok\u0026ccedil;uoğlu, B. A., Karadon, D., ... \u0026amp; Canning, D. (2021). Effect of distance to health facilities and access to contraceptive services among urban Turkish women. \u003cem\u003eThe European Journal of Contraception \u0026amp; Reproductive Health Care\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(5), 374-382.\u003c/li\u003e\n \u003cli\u003eShaheen, A. A., Diaaeldin, M., Chaaya, M., \u0026amp; El Roueiheb, Z. (2007). Unintended pregnancy in Egypt: evidence from the national study on women giving birth in 1999.\u003c/li\u003e\n \u003cli\u003eSingh, A., Chalasani, S., Koenig, M. A., \u0026amp; Mahapatra, B. (2012). The consequences of unintended births for maternal and child health in India.\u0026nbsp;\u003cem\u003ePopulation Studies, 66(3), 223\u0026ndash;39\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSully, E. A., Biddlecom, A., Darroch, J. E., Riley, T., Ashford, L. S., Lince-Deroche, N., ... \u0026amp; Murro, R. (2020). Adding it up: investing in sexual and reproductive health 2019.\u003c/li\u003e\n \u003cli\u003eUNFPA. (2023). National Strategy for Population and Development 2023\u0026ndash;2030 \u0026ndash; Egypt. Retrieved from: https://egypt.unfpa.org\u003c/li\u003e\n \u003cli\u003eUnited Nations Population Fund (UNFPA 2018). Addressing unintended pregnancy in the Arab region. United Nations Population Fund Arab States Regional Office.\u003c/li\u003e\n \u003cli\u003eUnited Nations Population Fund (UNFPA 2022). Seeing the Unseen: The case for action in the neglected crisis of unintended pregnancy. www.unfpa.org/swp2022\u003c/li\u003e\n \u003cli\u003eWagstaff, A. (2000). Socioeconomic inequalities in child mortality: comparisons across nine developing countries. \u003cem\u003eBulletin of the World Health Organization\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e, 19-29.\u003c/li\u003e\n \u003cli\u003eWagstaff, A. (2005). The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality. \u003cem\u003eHealth economics\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4), 429-432.\u003c/li\u003e\n \u003cli\u003eWagstaff, A., Van Doorslaer, E., \u0026amp; Watanabe, N. (2003). On decomposing the causes of health sector inequalities with an application to malnutrition inequalities in Vietnam. \u003cem\u003eJournal of Econometrics\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(1), 207-223.\u003c/li\u003e\n \u003cli\u003eWagstaff, A., Paci, P., \u0026amp; Van Doorslaer, E. (1991). On the measurement of inequalities in health. \u003cem\u003eSocial science \u0026amp; medicine\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(5), 545-557.\u003c/li\u003e\n \u003cli\u003eYoussef, R. M., Moubarak, I. I., Gaffar, Y. A., \u0026amp; Atta, H. Y. (2002). Correlates of unintended pregnancy in Beheira governorate, Egypt. \u003cem\u003eEastern Mediterranean Health Journal\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4-5), 521-536.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Decomposition, Socioeconomic factors, Inequality, unintended pregnancies, Egypt","lastPublishedDoi":"10.21203/rs.3.rs-7453953/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7453953/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study aims to examine and quantify disparities in unintended pregnancies in Egypt utilizing data from the 2021 Egypt Family Health Survey (EFHS). The inequality in unintended pregnancy is assessed by using the concentration curve, the Wagstaff normalized concentration index (WCI). This study uses decomposition analysis to identify the factors contributing to unintended pregnancy inequality. The concentration curve confirms that the primary driver is socioeconomic disparity in unintended pregnancies, with mothers from disadvantaged economic backgrounds bearing a disproportionate burden. The decomposition analysis also reveals that household economic status and maternal education are the primary determinants of unintended pregnancy inequalities. Furthermore, the findings indicate that a substantial number of disparities in unintended pregnancies were caused by the usage of contraceptives, residency, number of children, age of the current mother, and father's educational attainment.\u003c/p\u003e","manuscriptTitle":"Socioeconomic Inequality in Unintended Pregnancy in Egypt: A Decomposition Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:29:32","doi":"10.21203/rs.3.rs-7453953/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"68c27663-a469-460b-8ea0-a2befff314d0","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T08:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 16:29:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7453953","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7453953","identity":"rs-7453953","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}