Association between women's autonomy and unintended pregnancy in India

Ram, Rajan; Kumar, Manish; Kumari, Nutan

doi:10.1016/j.cegh.2022.101060

Cited by 9 publications

(6 citation statements)

References 39 publications

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“…To determine the levels of women’s autonomy, an index was constructed with the sum of the final scores for each of the women’s characteristics, where higher scores indicate greater autonomy. This index of women’s autonomy was categorized into tertiles to delineate its three levels: high, moderate, and low [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

Women’s Autonomy and Intimate Partner Violence in Peru: Analysis of a National Health Survey

Barón-Lozada

Basualdo-Meléndez

Vargas-Fernández

et al. 2022

IJERPH

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To assess the association between women’s autonomy and intimate partner violence (IPV) against women of childbearing age. Secondary analysis of the 2019 Demographic and Family Health Survey (ENDES / acronym in Spanish) was carried out. The study population was women aged 15–49 years who are currently married or living with a partner. A Poisson family generalized linear regression model was estimated to calculate adjusted prevalence ratios (aPR) for the association between women’s autonomy and IPV with their respective 95% confidence intervals (CI). Data from 18,621 women were analyzed. The highest proportion of women had low autonomy (low: 42%; moderate: 39.2%; high: 18.8%). A prevalence of IPV of 40.1% was found (psychological/verbal: 38.8%; physical: 8.8%; sexual: 2.3%). The adjusted model found that women with a low level of autonomy (aPR: 1.15, 95%CI: 1.01–1.31) had a higher prevalence of IPV compared to women with high autonomy. This association was also found for the specific case of psychological/verbal violence (aPR: 1.15, 95%CI: 1.01–1.31). No association was found between women’s level of autonomy and physical or sexual violence by a partner. Four out of 10 women of childbearing age have experienced IPV in the last 12 months. In general, women with lower levels of autonomy are more likely to present IPV compared to women with high autonomy.

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Section: Methodsmentioning

confidence: 99%

Women’s Autonomy and Intimate Partner Violence in Peru: Analysis of a National Health Survey

Barón-Lozada

Basualdo-Meléndez

Vargas-Fernández

et al. 2022

IJERPH

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“…The machine learning classifier identified that the wealth index was a highly important feature for predicting unintended pregnancies. This finding is supported by research conducted in India [ 30 ], Bangladesh [ 31 ], Iran [ 32 ], Nepal [ 33 ], Nigeria [ 34 ], Kenya [ 35 ], and across different parts of Ethiopia [ 36 – 38 ], revealed that women who have high wealth status are more empowered to take charge of their sexual and reproductive health matters than women who have poorer wealth status. The relationship between income status, occupation, and unintended pregnancies may be explained by the connection between formal employment and social networks, and earning potential [ 39 ].…”

Section: Discussionmentioning

confidence: 52%

Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016

Mamo,

Gebremariam,

Adem

et al. 2024

BMC Women's Health

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Background An unintended pregnancy is a pregnancy that is either unwanted or mistimed, such as when it occurs earlier than desired. It is one of the most important issues the public health system is currently facing, and it comes at a significant cost to society both economically and socially. The burden of an undesired pregnancy still weighs heavily on Ethiopia. The purpose of this study was to assess the effectiveness of machine learning algorithms in predicting unintended pregnancy in Ethiopia and to identify the key predictors. Method Machine learning techniques were used in the study to analyze secondary data from the 2016 Ethiopian Demographic and Health Survey. To predict and identify significant determinants of unintended pregnancy using Python software, six machine-learning algorithms were applied to a total sample of 7193 women. The top unplanned pregnancy predictors were chosen using the feature importance technique. The effectiveness of such models was evaluated using sensitivity, specificity, accuracy, and area under the curve. Result The ExtraTrees classifier was chosen as the top machine learning model after various performance evaluations. The region, the ideal number of children, religion, wealth index, age at first sex, husband education, refusal sex, total births, age at first birth, and mother’s educational status are identified as contributing factors in that predict unintended pregnancy. Conclusion The ExtraTrees machine learning model has a better predictive performance for identifying predictors of unintended pregnancies among the chosen algorithms and could improve with better policy decision-making in this area. Using these important features to help direct appropriate policy can significantly increase the chances of mother survival.

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“…Furthermore, demographic factors, including age, urban residency, and region of residence, have been associated with higher levels of women's autonomy (see, e.g., Grady, 2016, 2018;Thankian, 2020). So far, the literature on autonomy and marital status has always focused on whether a woman is 'currently married' (see Hogan et al, 1999;Jejeebhoy, 2002;Acharya et al, 2010;Mondal et al, 2020) or 'ever married' (also see Niraula and Morgan, 1996;Al Riyami et al, 2004;Ram et al, 2022) and how household dynamics may influence their level of autonomy. Fewer studies have examined the relationship between autonomy and household structure (i.e., monogamy vs polygamy) (see, e.g., Dhillon et al, 2023), and almost no study has investigated this relationship in Mauritania, a country in the North-Western part of Africa with unique cultural and religious traditions that are critical to understanding women's autonomy within this context.…”

Section: Introductionmentioning

confidence: 99%

Exploring the link between household structure and women’s household decision-making autonomy in Mauritania

Anfaara,

Amoak,

Kye

et al. 2024

J. Biosoc. Sci.

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Governments in sub-Saharan African countries aim to increase married women’s household decision-making autonomy as it remains a critical determinant of desirable health behaviours such as healthcare utilisation, antenatal care visits, and safer sex negotiation. However, very few studies explore how household structure (i.e., monogamous or polygamous) is associated with married women’s household decision-making autonomy. Our paper seeks to address this gap. Using the 2019–20 Mauritania Demographic and Health Survey, a nationally representative dataset, and applying logistic regression analysis, we explore how married women’s household structure is associated with their household decision-making autonomy. We find that 9% of married women are in polygamous marriages, while 63% and 65% are involved in decision-making about their health and large household purchases, respectively. Additionally, 76% and 56% are involved in decision-making about visiting family or relatives and household expenditures. After accounting for socio-economic and demographic factors, we find that compared to women from monogamous households, those from polygamous households are less likely to participate in decision-making about their health (OR=0.65, p < 0.001), making large household purchases (OR=0.65, p < 0.001), visiting family or relatives (OR=0.72, p < 0.001), and household expenditure (OR=0.58, p < 0.001). Based on our findings, we recommend the urgent need to review and re-evaluate policies and approaches seeking to promote gender equality and women’s autonomy in Mauritania. Specifically, it may be critical for intervention programmes to work around reducing power imbalances in polygamous household structures that continue to impact married women’s household decision-making autonomy adversely. Such interventions should centre married women’s socio-economic status as a central component of their empowerment strategies in Mauritania.

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Association between women's autonomy and unintended pregnancy in India

Cited by 9 publications

References 39 publications

Women’s Autonomy and Intimate Partner Violence in Peru: Analysis of a National Health Survey

Women’s Autonomy and Intimate Partner Violence in Peru: Analysis of a National Health Survey

Machine learning to predict unintended pregnancy among reproductive-age women in Ethiopia: evidence from EDHS 2016

Exploring the link between household structure and women’s household decision-making autonomy in Mauritania

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