Highlights
- •Literature suggests that highly empowered women can control their fertility behaviour, which can restrict unwanted pregnancy.
- •This is the first study that has directly assessed the role of women's autonomy in unintended pregnancy in Indian context.
- •We found a negative association between women's autonomy and unintended pregnancy after controlling for explanatory factors.
- •We found that husband's desire for more children can increase the risk of unintended pregnancy among women.
Abstract
Multiple studies from low- and middle-income countries have indicated that women's autonomy can play a key role in reducing unintended pregnancy. Such evidence is scarce in Indian context, where patriarchy prevails, and sociocultural norms limit women's participation in important decisions. Therefore, the primary aim of this study is to examine the association between women's autonomy and unintended pregnancy in India. Data collected as part of the National Family Health Survey-4 conducted in 2015-16 are used for the analysis. Information on a total of 205984 ever-married women was extracted from 699686 women aged 15–49 years. Bivariate logistic regression was used to examine the association of unintended pregnancy with women's autonomy, demographic, socio-economic, and social context variables. Multiple logistic regression was further employed to assess the effect of women's autonomy on unintended pregnancies after controlling various selected background characteristics. The study found that women's autonomy was a significant predictor of unintended pregnancy after adjusting different other covariates. For example, women with higher autonomy had 16% lower adjusted odds of having an unintended pregnancy than women with lower autonomy (OR adj = 0.84; 95% CI: 0.72–0.99). Moreover, other covariates such as age, children ever born, age at marriage, wealth status, and consonance over fertility exert a significant and robust influence over unintended pregnancy. The study recommends that policymakers design programmes to spread awareness about the importance of the decision-making power of women in household settings.
Keywords
1. Introduction
The definition of unintended pregnancies include pregnancies which are either unwanted or mistimed at the time of conception.
1
Pregnancies are considered 'unwanted' when women do not wish to have further births while a 'mistimed' pregnancy refers to births that occur earlier than the desired time. Unintended pregnancies, in general, can have serious consequences that impose a burden on women, children, and the entire family.2
Various maternal and child health issues are associated with unwanted pregnancies, including unsafe abortion, low birth weight, and under-utilization of prenatal care.3
The accurate assessment of pregnancy intentions is thus essential for estimating unmet needs of contraception, understanding fertility behaviours, and their impact on maternal and child health.4
Worldwide, some 44% of pregnancies were identified as unintended in 2010–14, approximately 56% of which ended in abortion.
5
The same study reported a global decline in the unintended pregnancy rate from 74 per thousand in 1990-94 to 62 per thousand in 2010–14. In India, of 48.1 million pregnancies reported in 2015, some 54% resulted in live births; 32% in induced abortions, and 14% in miscarriages.6
The study further reported that the unintended pregnancy rate was estimated to be 70.1 pregnancies per thousand among women aged 15–49 years.It is expected that highly empowered women can control their fertility behaviour, which in turn can restrict unintended pregnancy.
7
Women's autonomy is one of the indicators of women's empowerment and status. It is more closely associated with their power and agency. The present study have adopted women's autonomy which included decision-making autonomy, movement autonomy, and financial autonomy.8
, 9
, 10
Improving women's status also enhances their decision-making capacity at all levels in all spheres of life, especially in terms of their sexuality and reproduction.11
Substantial research has focused on the role that women's autonomy plays in some fertility-related outcomes, including contraceptive use,
12
, 13
, 14
length of birth intervals,- Wondie A.G.
The Association between Unmet Need for Contraception and Unintended Pregnancy Among Reproductive-Age Women in Ethiopia. vol. 5. 2021https://doi.org/10.1177/23992026211033436
15
low birth weight,16
child-rearing,17
child survival and development,18
,19
and pregnancy care.9
,20
However, the association between women's autonomy and unintended pregnancy is still largely unexplored.21
,22
Although some researchers have tried to explore this association in Philippines21
and Bangladesh23
and Cambodia,24
the subject remains highly relevant for future research in developing countries.- Rizvi F.
- Williams J.
- Hoban E.
Factors influencing unintended pregnancies amongst adolescent girls and young women in Cambodia.
Int J Environ Res Publ Health. 2019; 16 (2019;16(20):4006): 4006https://doi.org/10.3390/IJERPH16204006
To the best of our knowledge, no previous studies have directly assessed the role of women's autonomy with respect to unintended pregnancy in India. The present investigation therefore aims to fill this research gap by exploring the association of women's autonomy with unintended pregnancies among ever-married women across India.
2. Data and methods
2.1 Data
The present study is based on the fourth round of the National Family Health Survey (NFHS-4) conducted during 2015–16. NFHS-4 is a large-scale cross-sectional and a nationally representative survey which provides self-reported information on maternity history, fertility preference, socio-economic and demographic characteristics, contraceptive usage, and autonomy. NFHS-4 adopted a multistage stratified random sampling method for the collection of data. Details of the sample size, design, and sample weights can be obtained from the final report of NFHS-4 25. In the survey, a total of 699686 women aged 15–49 years were interviewed. A total of 190898 women had at least one live birth in the last five years, and 32428 women were pregnant at the time of the survey. Among the interviewed women, 16848 had at least one birth in the last five years as well as were pregnant at the time of the survey. As these women had reported on both their reproductive intentions for their last child and their current pregnancy, their responses on their reproductive intentions were recorded for the current pregnancy only.
The selection of the final sub-sample was based on the response of women about their reproductive intentions. A total of 173952 of 190898 women who had a birth in the last five years but were not pregnant at the time of survey reported reproductive intentions related to their latest birth. By comparison, all 32428 women pregnant at the time of the survey provided information on their reproductive intention(s). The final analytical sample size for the current study comprised 205984 ever married women.
2.2 Dependent variable
In NFHS-4, female respondents were asked two different questions about their reproductive intentions for their pregnancy (if the woman was pregnant at the time of the survey) and their latest birth in the last five years. For pregnant women, the first question was: "When you got pregnant, did you want to get pregnant at that time?" while the second question was: "Did you want to have the baby later on, or did you not want any (more) children?" Women who had at least one child but were not pregnant at the time of the survey were asked about their pregnancy intentions in an essentially similar manner. The first question asked, "When you got pregnant with (NAME), did you want to get pregnant at that time?" The second question was, "Did you want to have a baby later on, or did you not want any (more) children?" These questions are a powerful indicator of the degree to which couples can successfully control childbearing.
25
In this study, the pregnancy intentions were assessed as a binary variable where women wanted a children ‘then’ considered as intended and coded as ‘0’ and ‘later on or did not want any (more) children’ were considered as unintended and coded as ‘1’.2.3 Explanatory variables
Female autonomy was used as the primary exposure variable in our study. By adopting the methodology proposed by Jatrana & Pasupuleti,
26
we have created an autonomy index for women based on the information collected on: (i) household decisions, (ii) movement and (iii) finance. Movement autonomy comprises three questions on whether women are allowed to go to market, health facilities, and places outside their own village/community, with three possible responses: "alone," "with someone else," and "not at all." These responses were scored: 2 for 'alone,' 1 for 'with someone else,' and 0 for 'not at all.' Autonomy based on household decisions was derived from the questions of whether women made decisions related to their health care, major household purchases, how to use their husband's earnings, and visits to family or relatives. There were five possible responses to these questions: 'respondent alone,' 'respondent and husband jointly,' 'husband alone,' 'someone else,' and 'others.' These responses were scored as 2 for 'respondent alone,' 1 for 'respondent and husband jointly,' and otherwise 0. Finally, financial autonomy was assessed from the question of whether the respondent had the money for her own use with two possible responses 'yes' or 'no', with a score of 2 if the response was 'yes,' and otherwise 0. Confirmatory factor analysis derives a single construct behind the various dimensions of women's autonomy,27
and the index was categorized into terciles (low = 0, medium = 1, and high = 2).Various control variables, such as demographic, economic, and social context, were also included in the analysis. The demographic variables include age in years (15–24 = 0, 25–34 = 1, and 35–49 = 3); place of residence (urban = 0, rural = 1); total children ever born (no children = 0, 1 to 2 = 1, and 3 or more = 3); and age at first marriage (less than 18 years = 0, equal to or more than 18 years = 1). The economic variables include woman's education (no education = 0, primary = 1, secondary = 2, and higher = 3); husband's education (no education = 0, primary = 1, secondary = 2, and higher = 3); wealth index (poorest = 0, poorer = 1, middle = 2, richer = 3, and richest = 4); and employment (Not working = 0, agricultural = 1, and non-agricultural working = 2). Social context variables include religion (Hindu = 0, Muslim = 1, Others = 2); caste (Others = 0, Scheduled Castes/Scheduled Tribes (SC/ST) = 1, Other Backward Class (OBC) = 2); ever-used modern contraceptive method (no = 0, yes = 1); consonance of fertility preferences (both want the same = 0, husband wants more = 1, husband wants fewer = 2, and don't know/missing = 3); and whether women are the main decision-makers on contraception use (no = 0, yes = 1).
2.4 Statistical analysis
Bivariate analysis was used to examine the strength of an association between unintended pregnancy and selected demographic, economic, and social context covariates. Multiple logistic regression analyses were used to investigate the relationship of unintended pregnancy with women's autonomy after adjusting for selected covariates. While cross-tabulating, the chi-square test was employed to examine the association between dependent and selected independent variables. Since our dependent variable, unintended pregnancy, is a dichotomous variable, binary logistic regression was an appropriate statistical tool in the analysis. The value of the dependent variable was 'one' if the pregnancy was unintended and 'zero' if the pregnancy was intended. Bivariate logistic regression provides the unadjusted odds ratio, i.e., the effect of each independent variable on the dependent variable. Moreover, multiple logistic regression was used to study the influence of selected independent variables on unintended pregnancy. We have set the minimal statistical significance level at p < 0.05. The statistical analyses were conducted using Stata 15.1SE for Windows.
2.5 Ethical consideration
The NFHS-4 received ethical clearance from IIPS's Ethical Review Board. The survey agencies that conducted the field survey for the data collection have obtained prior informed consent from the respondents.
3. Results
Table 1 provides descriptive statistics of the sample and bivariate analysis of pregnancy intentions of the respondents with their demographic, economic, and social context covariates. Some 17% of respondents had higher autonomy, nearly half of the respondents belong to the age group 25–34 years. Some 6% of the respondents were pregnant for the first time, while 33% had three or more children. One-third of women had married before the legal age of 18 years. Respondent's education level was comparatively lower than their husbands. More than half of the respondents have used contraception in their lifetime. In terms of partner fertility preference, almost 84% of couples wanted to have the same number of children. Only 8% of respondents were the main decision-maker regarding contraception use. The results further depict significant bivariate associations between pregnancy intention and various background characteristics. The highest proportion of unintended pregnancies (10.4%) were found in the women with low autonomy. The prevalence of unintended pregnancy was higher among the women who were in the age group 35–49 years, in the poorest wealth quintile, lived in a rural area, had more than three children, married before the age of 18 years, with no education, and had used the contraceptive method in a lifetime.
Table 1Descriptive statistics and percent distribution of reproductive intentions of ever-married women by selected background characteristics, India, NFHS-4 (2015-16).
| Characteristics | N | Intended (%) | Unintended (%) | Total (%) |
|---|---|---|---|---|
| Pregnancy IntentionW | ||||
| Intended | 185728 (90.2) | - | - | |
| Unintended | 20256 (9.8) | - | - | |
| Women Autonomy | p-value = 0.002 | |||
| Low | 16365 (47.4) | 89.6 | 10.4 | 100 |
| Medium | 12158 (35.2) | 90.9 | 9.1 | 100 |
| High | 5990 (17.4) | 91.0 | 9.0 | 100 |
| Demographic factors | ||||
| Age-groups | p-value = 0.000 | |||
| 15–24 years | 71788 (34.9) | 91.8 | 8.2 | 100 |
| 25–34 | 112274 (54.5) | 90.3 | 9.7 | 100 |
| 35–49 | 21922 (10.6) | 82.7 | 17.3 | 100 |
| Residence | p-value = 0.000 | |||
| Urban | 52070 (25.3) | 91.1 | 8.9 | 100 |
| Rural | 153914 (74.7) | 89.8 | 10.3 | 100 |
| Total children ever born | p-value = 0.000 | |||
| No children (but pregnant at time of survey) | 12580 (6.1) | 94.9 | 5.1 | 100 |
| 1-2′ | 126332 (61.3) | 92.9 | 7.1 | 100 |
| ≥3 | 67072 (32.6) | 83.3 | 16.7 | 100 |
| Age at first marriage | p-value = 0.000 | |||
| Less than 18 years | 70008 (34.5) | 88.6 | 11.4 | 100 |
| 18 or more years | 132975 (65.5) | 91.1 | 8.9 | 100 |
| Economic factors | ||||
| Woman's education | p-value = 0.000 | |||
| No education | 57537 (27.9) | 87.1 | 12.9 | 100 |
| Primary | 28326 (13.8) | 89.2 | 10.8 | 100 |
| Secondary | 97321 (47.3) | 91.4 | 8.6 | 100 |
| Higher | 22800 (11.1) | 92.9 | 7.1 | 100 |
| Husband's education | p-value = 0.000 | |||
| No education | 5873 (16.4) | 88.1 | 11.9 | 100 |
| Primary | 4890 (13.6) | 88.9 | 11.1 | 100 |
| Secondary | 19765 (55.1) | 90.4 | 9.6 | 100 |
| Higher | 5317 (14.8) | 92.9 | 7.1 | 100 |
| Wealth Index | p-value = 0.000 | |||
| Poorest | 49389 (24) | 87.2 | 12.8 | 100 |
| Poorer | 46955 (22.8) | 89.0 | 11.0 | 100 |
| Middle | 41582 (20.2) | 90.8 | 9.2 | 100 |
| Richer | 36151 (17.6) | 91.9 | 8.2 | 100 |
| Richest | 31907 (15.5) | 93.0 | 7.0 | 100 |
| Occupation | p-value = 0.114 | |||
| Not working | 27630 (76.9) | 90.4 | 9.6 | 100 |
| Agricultural | 4544 (12.6) | 89.8 | 10.2 | 100 |
| Non-agriculture | 3778 (10.5) | 89.6 | 10.4 | 100 |
| Social-context factors | ||||
| Ever used contraceptive method | p-value = 0.000 | |||
| No | 93201 (45.3) | 90.8 | 9.3 | 100 |
| Yes | 112783 (54.8) | 89.7 | 10.3 | 100 |
| Consonance of fertility preferences | p-value = 0.000 | |||
| Both want the same | 39681 (83.4) | 90.5 | 9.5 | 100 |
| Husband wants more | 4560 (9.6) | 84.1 | 15.9 | 100 |
| Husband wants fewer | 2079 (4.4) | 87.9 | 12.1 | 100 |
| Don't know/missing | 1274 (2.7) | 85.5 | 14.5 | 100 |
| Women main decision-maker about contraception | p-value = 0.000 | |||
| No | 71046 (92.3) | 90.6 | 9.4 | 100 |
| Yes | 5933 (7.7) | 87.5 | 12.5 | 100 |
| Caste | p-value = 0.066 | |||
| Others | 38933 (19.7) | 90.0 | 10.0 | 100 |
| SC/ST | 78518 (39.8) | 90.1 | 9.9 | 100 |
| OBC | 80007 (40.5) | 90.4 | 9.6 | 100 |
| Religion | p-value = 0.000 | |||
| Hindu | 149391 (72.5) | 90.6 | 9.5 | 100 |
| Muslim | 31525 (15.3) | 87.9 | 12.1 | 100 |
| Others 1 | 25068 (12.2) | 91.5 | 8.5 | 100 |
Note. W- Weighted estimate, 1- Others includes Christian, Sikh, Buddhist/Neo-Buddhist, Jain, Jewish, Parsi/Zoroastrian, and No religion.
Table 2 summarises the unadjusted and adjusted odds ratios of unintended pregnancy according to different demographic, economic, and social context covariates. We utilized five models in the analysis, Model-I presents the unadjusted odds ratio for all included variables, and Model-II includes women's autonomy and demographic factors; Model-III includes women's autonomy and economic factors; Model-IV includes women's autonomy and social context variables; and Model-V includes all variables except two covariates (consonance with respect to fertility, and leading decision-maker of contraception use). We excluded these two variables from the Model-V because of comparatively lesser number of observations. In Model-I, without controlling all other variables, women with higher autonomy have 15% lesser odds of unintended pregnancy than women with low autonomy (UOR = 0.85; 95% CI: 0.72–0.99). Various demographic, economic and social context variables were significantly associated with the unintended pregnancy in the unadjusted model. By comparing the consonance over fertility preference, if their husbands wanted more children, women reported being 1.8 times more likely to have unwanted pregnancies than couples who wanted the same number of children (UOR = 1.80; 95% CI: 1.59–2.04). In Model-II, when demographic covariates have controlled, the results obtained were similar to Model-I, except for the place of residence and age at first marriage, both of which lost their significant relationship with an unwanted pregnancy. In Model-III (with control for economic variables) and Model-IV (with control for social context variables), women's autonomy was again negatively associated with an unintended pregnancy, albeit with low significance. After adjusting for various covariates in Model-III and Model-IV, variables such as place of residence, age at marriage, husband's education, occupation, contraceptive use, caste, and religion became insignificant.
Table 2Bivariate and Multiple logistic regression analysis of unintended pregnancy and selected background characteristics, India, NFHS-4 (2015-16).
| Characteristics | Model- Ia | Model- IIb | Model- IIIc | Model- IVd | Model-Ve |
|---|---|---|---|---|---|
| UOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Women Autonomy | |||||
| Low® | 1 | 1 | 1 | 1 | 1 |
| Medium | 0.87*(0.77,0.97) | 0.86**(0.77,0.96) | 0.89*(0.79,0.99) | 0.87(0.7,1.08) | 0.86**(0.77,0.97) |
| High | 0.85*(0.72,0.99) | 0.85*(0.72,0.99) | 0.91(0.81,1.06) | 0.70*(0.52,0.92) | 0.84*(0.72,0.99) |
| Demographic factors | |||||
| Age-groups | |||||
| 15–24 years® | 1 | 1 | 1 | ||
| 25–34 | 1.20***(1.15,1.26) | 0.85*(0.74,0.97) | 0.86*(0.75,0.98) | ||
| 35–49 | 2.33***(2.18,2.48) | 1.17(0.97,1.4) | 1.18(0.98,1.43) | ||
| Residence | |||||
| Urban® | 1 | 1 | |||
| Rural | 1.17***(1.11,1.24) | 1.01(0.89,1.15) | 0.91(0.78,1.06) | ||
| Total children ever born | |||||
| No children® | 1 | 1 | 1 | ||
| 1-2′ | 1.42***(1.26,1.59) | 1.45**(1.11,1.88) | 1.46**(1.11,1.91) | ||
| ≥3 | 3.68***(3.29,4.12) | 4.06***(3.07,5.36) | 3.95***(2.96,5.28) | ||
| Age at first marriage | |||||
| <18 years ® | 1 | 1 | 1 | ||
| ≥18 | 0.77***(0.73,0.8) | 1.00(0.89,1.11) | 1.02(0.91,1.14) | ||
| Economic factors | |||||
| Woman's education | |||||
| No education® | 1 | 1 | 1 | ||
| Primary | 0.82***(0.77,0.87) | 0.91(0.76,1.08) | 1.02(0.86,1.22) | ||
| Secondary | 0.63***(0.61,0.66) | 0.77***(0.68,0.89) | 1.06(0.92,1.23) | ||
| Higher | 0.51***(0.47,0.56) | 0.80*(0.61,1.05) | 1.30(0.99,1.71) | ||
| Husband's education | |||||
| No education® | 1 | 1 | 1 | ||
| Primary | 0.93(0.79,1.09) | 1.04(0.88,1.23) | 1.08(0.91,1.28) | ||
| Secondary | 0.79***(0.7,0.89) | 1.04(0.89,1.20) | 1.15(0.99,1.33) | ||
| Higher | 0.57***(0.47,0.68) | 0.91(0.71,1.16) | 1.06(0.82,1.38) | ||
| Wealth Index | |||||
| Poorest® | 1 | 1 | 1 | ||
| Poorer | 0.84***(0.79,0.88) | 0.95(0.83,1.10) | 0.96(0.83,1.11) | ||
| Middle | 0.69***(0.65,0.74) | 0.77***(0.67,0.91) | 0.79**(0.67,0.93) | ||
| Richer | 0.60***(0.56,0.65) | 0.59***(0.50,0.71) | 0.59***(0.49,0.72) | ||
| Richest | 0.51***(0.47,0.56) | 0.62***(0.50,0.76) | 0.54***(0.43,0.69) | ||
| Occupation | |||||
| Not working® | 1 | 1 | 1 | ||
| Agricultural | 1.08(0.94,1.24) | 0.88(0.76,1.02) | 0.87(0.75,1.01) | ||
| Non-agriculture | 1.08(0.93,1.27) | 1.15(0.97,1.36) | 1.07(0.9,1.27) | ||
| Social-context factors | |||||
| Ever used contraceptive method | |||||
| No® | 1 | 1 | 1 | ||
| Yes | 1.13***(1.08,1.17) | 1.49(0.17,13.12) | 1.09(0.98,1.22) | ||
| Consonance of fertility preferences | |||||
| Both want the same® | 1 | 1 | |||
| Husband wants more | 1.80***(1.59,2.04) | 1.77***(1.35,2.32) | |||
| Husband wants fewer | 1.31*(1.02,1.67) | 1.36(0.9,2.05) | |||
| Don't know/missing | 1.61***(1.25,2.07) | 1.88(0.92,3.85) | |||
| Women decision-maker about contraception | |||||
| No® | 1 | 1 | |||
| Yes | 1.38***(1.28,1.50) | 0.89(0.63,1.27) | |||
| Caste | |||||
| Others® | 1 | 1 | 1 | ||
| SC/ST | 0.99(0.93,1.06) | 1.12(0.85,1.48) | 0.72***(0.61,0.85) | ||
| OBC | 0.95(0.9,1.01) | 0.96(0.75,1.24) | 0.72***(0.62,0.84) | ||
| Religion | |||||
| Hindu® | 1 | 1 | 1 | ||
| Muslim | 1.32***(1.25,1.4) | 1.14(0.87,1.5) | 1.05(0.9,1.22) | ||
| Others | 0.89(0.8,1.0) | 0.74(0.49,1.12) | 0.94(0.73,1.2) | ||
Note. *P < 0.05 **P < 0.01 ***P < 0.001; ®: Reference; UOR-Unadjusted odds ratio; OR- Odds ratio; CI- Confidence Interval; OBC- Other Backward Class, SC/ST- Schedule Caste/Schedule Tribe; aBivariate logistic regression analysis provides unadjusted odds ratio; bAdjusted for women's autonomy and demographic variables; cAdjusted for women's autonomy and economic variables; dAdjusted for women's autonomy and social-context variables; eAdjusted for women's autonomy, demographic, economic and social-context variables except for two variables (consonance over fertility, and main decision-maker of contraception use).
After controlling for all covariates, in Model-V, women with higher autonomy had significantly lower odds of unintended pregnancy than women with lower autonomy (AOR = 0.84; 95% CI: 0.72–0.99). As expected, in Model-V, the women in the richest group had lower odds of reporting unintended pregnancy compared to the poorest women (AOR = 0.54; 95% CI: 0.43–0.69). However, we were unable to find any significant association between unintended pregnancy and various covariates, such as residence, age at first marriage, women, and their husbands' education, occupation, and religion. Surprisingly, in the final model, we found that women in the 'OBC' category had lower odds of reporting unintended pregnancy as compared to women in the 'Other' category (AOR = 0.54; 95% CI: 0.43–0.69).
4. Discussion
The present study aimed to investigate the association of women's autonomy with their reproductive intentions by unitizing national representative data in India. This study found a negative association between women's autonomy and unintended pregnancy after adjusting for various demographic, economic, and social context-related factors. Furthermore, the study suggests that women's household, financial, and sexual decision-making empowers them to influence their reproductive intentions. In recent time, women's autonomy has appeared as a potential determinant of reproductive health, specifically in the developing world.
28
The understanding of female autonomy and its relationship with reproductive intentions was important in India, where patriarchy prevails, and sociocultural norms limit women's participation in important decisions.- Rossouw L.
- Ross H.
Understanding period poverty: socio-economic inequalities in menstrual hygiene management in eight low- and middle-income countries.
Int J Environ Res Publ Health. 2021; 18 (2021, Vol 18, Page 2571): 2571https://doi.org/10.3390/IJERPH18052571
29
While investigating the influence of female autonomy on unintended pregnancy, multiple studies have revealed inconsistent results.
21
, 22
, 23
Consistent with our findings, study in many settings have shown that women with greater autonomy have lower odds of reporting unintended births or pregnancies.21
,23
,30
On the other hand, a study from Nepal reported no signification association between women's autonomy and unintended pregnancy.- Merga J.
- Wirtu D.
- Bekuma T.T.
- Regasa M.T.
Unintended pregnancy and the factors among currently pregnant married youths in Western Oromia, Ethiopia: a mixed method.
PLoS One. 2021; 16e0259262https://doi.org/10.1371/JOURNAL.PONE.0259262
22
These contrast results may occur due to differences in the definition of female autonomy.22
The findings from this study recommend designing interventions that help gain in the autonomy among Indian women, which may reduce unwanted pregnancies and significantly improve maternal and child health outcomes.We found that various demographic, economic, and social context covariates included in our analysis can influence the reproductive intentions of women. Thus, it is crucial to understand the underlying association between these correlates and unintended pregnancy. The present study found the risk of unintended pregnancy increases with increased women's age and greater number of children. Consistent findings were reported in different Asian countries.
21
,23
,31
This may be because older women could already have acheived their desired number of children and therefore consider any additional pregnancy as unwanted. Although in contrast, negative associations between age and unintended pregnancy have been reported elsewhere.- Sarder A.
- Islam S.M.S.
- Maniruzzaman
- Talukder A.
- Ahammed B.
Prevalence of unintended pregnancy and its associated factors: evidence from six south Asian countries.
PLoS One. 2021; 16e0245923https://doi.org/10.1371/JOURNAL.PONE.0245923
32
,33
Furthermore, we found a significant relationship between place of residence and unintended pregnancy in bivariate analysis but failed to establish such association in the multivariate analysis.- Nyarko S.H.
Unintended pregnancy among pregnant women in Ghana: prevalence and predictors.
J Pregnancy. 2019; 2019https://doi.org/10.1155/2019/2920491
The bivariate analysis suggests that women with a marital age of 18 years or more had lower chances of unintended pregnancy. A study based on two Indian states, Andhra Pradesh and Madhya Pradesh, reported similar results,
34
and a possible reason could be that early marriage initiates earlier sexual intercourse as well as extending the time-period of pregnancy risk, and lack of knowledge about contraception thus increasing the chance of unintended pregnancy.- Santhya K.G.
- Jejeebhoy S.J.
- Ghosh S.
Early Marriage and Sexual and Reproductive Health Risks: Experiences of Young Women and Men in Andhra Pradesh and Madhya Pradesh.
2008
http://www.popcouncil.org/asia/india.html
Date accessed: August 4, 2020
Women's socioeconomic status may influence their fertility preferences. This study found that education status of women was negatively associated with unintended pregnancy. The association between educational attainment and unintended pregnancy has been evident in past studies.
35
, 36
, 37
Education can help in empowering women to make decisions related to their sexual and reproductive health. Furthermore, we found that rich women were less likely to have unintended pregnancies than their poor counterparts. These findings are consistent with other studies.22
,35
,36
Richer women are expected to be well educated and decision-makers about their fertility behaviour as compared to poor women. The wealth index is an indicator of the economy, occupation and educational status of a household, and their members.35
,38
Still, surprisingly, we did not find any association between women's occupation and unintended pregnancy. A study reported that not working women recorded a high prevalence of unintended pregnancies compared with women working in various occupational categories and explain that working women are more aware of their pregnancies due to their work demands, limited maternity periods, and other disincentives.32
The consonance of fertility preference between couples could potentially indicate unintended pregnancy. We found that husband's desire for more children can increase the risk of unintended pregnancy, and these results are supported by a study in southern Ethiopia.
39
Further, women who had used any contraceptive method during her lifetime were more likely to have an unintended pregnancy, mirroring similar results in Ethiopia,- Hamdela B.
- mariam A G.
- Tilahun T.
Unwanted pregnancy and associated factors among pregnant married women in Hosanna town, Southern Ethiopia.
PLoS One. 2012; 7https://doi.org/10.1371/journal.pone.0039074
40
and Malawi.41
It could be hypothesized that the more excellent knowledge a woman has of family planning methods, the more aware she would be of the related benefits. However, a lack of knowledge and misunderstandings on the impact of modern contraceptive methods on health may lead to reluctance in their use, thus increasing the chances of unintended pregnancy.42
A study on Indian women found that nearly 40% of women had discontinued the use of contraception due to method failure,34
with the recommendation that misunderstandings of various modern contraceptive methods among both young men and women should be addressed.- Santhya K.G.
- Jejeebhoy S.J.
- Ghosh S.
Early Marriage and Sexual and Reproductive Health Risks: Experiences of Young Women and Men in Andhra Pradesh and Madhya Pradesh.
2008
http://www.popcouncil.org/asia/india.html
Date accessed: August 4, 2020
According to religion, in bivariate analysis, we found that Muslim women were more likely to have an unintended pregnancy. However, after adjusting for various other covariates, we failed to find any such association in multivariate analysis. In India, according to the NFHS-4 report, the prevalence of contraception use among currently married Muslim women is lower than Hindu women (45% vs 54%). Moreover, the total fertility rate is also higher among Muslim women. Fertility behaviour patterns among Muslim women are a highly debated topic in India. All four rounds of the NFHS have shown that Muslim women mostly prefer the temporary method of contraception over sterilization.
25
Possible contraception failure and a lack of knowledge of its correct use may contribute to their high rate of unintended pregnancy. Some insignificant factors such as occupation, age at marriage, and husband's education were also included in the regression model because, as per the literature available, those factors were important to control for examining the precise association between women's autonomy and unintended pregnancy.5. Limitations and strengths of the study
Several limitations of the study are worth mentioning. NFHS records responses either for current pregnancy or the pregnancies resulted in live births. The unavailability of information about reproductive intentions for pregnancies that resulted in miscarriages, stillbirths, or induced abortions may underestimate the level of unintended pregnancy. The recall bias may influence information regarding the intentions about the pregnancy that occurred many years ago. Moreover, their intentions about unwanted pregnancies may also have altered after the child is born and has become a beloved family member. The data were collected during pre-pandemic and COVID-19 may change the perception of individuals which has not been covered in this paper.
43
Besides, the cross-sectional design of the study limits to infer cause-effect designations. Despite these potential limitations, the study has several significant strengths. In particular, the results are based on a large, nationally representative sample, with extensive information collected from women and households. Also, in our study, rather than using different proxies for women's autonomy, we have derived a single index to capture a range of different dimensions of female autonomy.- Roberts L.R.
- Solomon S.
- Renati S.J.
- Montgomery S.
Exploring mental health during the initial COVID-19 lockdown in Mumbai: serendipity for some women.
Int J Environ Res Publ Health. 2021; 18 (2021, Vol 18, Page 12542)12542https://doi.org/10.3390/IJERPH182312542
6. Conclusion
In this study, we found that improvement in women's autonomy can significantly reduce unintended pregnancy in India. Women with higher autonomy can regulate their fertility and can appropriately limit their family size. Moreover, various maternal factors such as increasing age, being a resident of rural areas, low education status, having more than three children, less than 18 years of marital age, poor wealth index, husband's dominance in decisions related to fertility, all can increase the risk of unintended pregnancy. Intriguingly, one of the major findings suggests that if couples lack consonance over fertility, women generally face higher chances of unintended pregnancy. Thus, a holistic approach is needed to make couples understand the importance of having a minimum number of children through education and communication. This study recommends that policymakers should design programs and services which spread awareness about the importance of acknowledging the participation of women in decision-making related to themselves and their household. Moreover, unintended pregnancy is higher among couples in which husbands want more children; therefore, awareness about the importance of fewer children among couples is recommended.
Funding
This research did not receive any specific grant from any funding agencies in the public, commercial, or not-for-profit sectors.
Availability of data and material
The data set used in the present study is available in public domain which can be downloaded on request from the website: https://dhsprogram.com/data/available-datasets.cfm.
Ethics approval
This study is based on secondary data which is available in public domain. Therefore, ethical approval is not required for conducting this study.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Author's contributions
Mr. Rajan, Mr. Manish, and Ms. Nutan have conceptualized and designed the study. Mr. Manish analysed the data. Mr. Rajan, Mr. Manish, and Ms. Nutan wrote and revised the manuscript. All authors have read and approved the manuscript.
Declaration of competing interest
The authors declare that there is no conflict of interest.
Acknowledgements
The authors wish to thank Prof. Alan H. Bittles for his support in polishing the language of the manuscript.
References
- Intended and unintended births in the United States: 1982-2010.Natl Health Stat Rep. 2012; 55: 1-28
- The effects of unintended pregnancy on infant, child, and parental health: a review of the literature.Stud Fam Plann. 2008; 39: 18-38https://doi.org/10.1111/j.1728-4465.2008.00148.x
- Adverse outcomes of planned and unplanned pregnancies among users of natural family planning: a prospective study.Am J Publ Health. 1997; 87: 338-343https://doi.org/10.2105/AJPH.87.3.338
- The intendedness of pregnancy: a concept in transition.Matern Child Health J. 2000; 4: 155-162https://doi.org/10.1023/A:1009534612388
- Global, regional, and subregional trends in unintended pregnancy and its outcomes from 1990 to 2014: estimates from a Bayesian hierarchical model.Lancet Global Health. 2018; 6: e380-e389https://doi.org/10.1016/S2214-109X(18)30029-9
- The incidence of abortion and unintended pregnancy in India, 2015.Lancet Global Health. 2018; 6: e111-e120https://doi.org/10.1016/S2214-109X(17)30453-9
- Determinants of unintended pregnancy among women in Ecuador.Int Fam Plann Perspect. 1999; 25: 27-33https://doi.org/10.2307/2991899
- Husbands' and wives' reports of women's decision-making power in Western Guatemala and their effects on preventive health behaviors.Soc Sci Med. 2006; 62: 2313-2326https://doi.org/10.1016/j.socscimed.2005.10.006
- Dimensions of women's autonomy and the influence on maternal health care utilization in a North Indian city.Demography. 2001; 38: 67-78https://doi.org/10.1353/dem.2001.0001
- Measurement of women's autonomy according to women and their husbands: results from five Asian countries.Soc Sci Res. 2006; 35: 1-28https://doi.org/10.1016/j.ssresearch.2004.06.001
- Report of the International Conference on Population and Development. United Nations publication, Cairo1995
- Women's autonomy, education and contraception use in Pakistan: a national study.Reprod Health. 2005; 2: 1-8https://doi.org/10.1186/1742-4755-2-8
- Women's autonomy, education and employment in Oman and their influence on contraceptive use.Reprod Health Matters. 2004; 12: 144-154https://doi.org/10.1016/S0968-8080(04)23113-5
- (239920262110334)The Association between Unmet Need for Contraception and Unintended Pregnancy Among Reproductive-Age Women in Ethiopia. vol. 5. 2021https://doi.org/10.1177/23992026211033436
- Do higher status and more autonomous women have longer birth intervals? Results from Cebu, Philippines.Soc Sci Med. 2005; 60: 2641-2655https://doi.org/10.1016/j.socscimed.2004.11.032
- Maternal autonomy and low birth weight in India.J Wom Health. 2011; 20: 1373-1382https://doi.org/10.1089/jwh.2010.2428
- Life course perspectives on women's anatomy and health outcomes.Am Anthropol. 1995; 97: 481-491
- Women's autonomy and child survival: a comparison of Muslims and non-Muslims in four Asian countries.Demography. 2003; 40: 419-436https://doi.org/10.2307/1515153
- Maternal autonomy is inversely related to child stunting in Andhra Pradesh, India.Matern Child Nutr. 2009; 5: 64-74https://doi.org/10.1111/j.1740-8709.2008.00161.x
- Women's autonomy and maternal health-seeking behavior in Ethiopia.Matern Child Health J. 2010; 14: 988-998https://doi.org/10.1007/s10995-009-0535-5
- Women's autonomy and unintended pregnancies in the Philippines.J Biosoc Sci. 2012; 44: 703-718https://doi.org/10.1017/S0021932012000120
- Correlates of unintended pregnancy among currently pregnant married women in Nepal.BMC Int Health Hum Right. 2009; 9: 1-10https://doi.org/10.1186/1472-698X-9-17
- Women's autonomy and unintended pregnancy among currently pregnant women in Bangladesh.Matern Child Health J. 2012; 16: 1206-1214https://doi.org/10.1007/s10995-011-0897-3
- Factors influencing unintended pregnancies amongst adolescent girls and young women in Cambodia.Int J Environ Res Publ Health. 2019; 16 (2019;16(20):4006): 4006https://doi.org/10.3390/IJERPH16204006
- National Family Health Survey (NFHS-4), 2015-16.India Mumbai IIPS, 2017: 1-192https://doi.org/10.1093/aje/kwm120 (Published online)
- Women's autonomy, education and birth intervals in India: visiting the less familiar.Asian Popul Stud. 2015; 11: 172-190https://doi.org/10.1080/17441730.2015.1050083
- Refining the measurement of women's autonomy: an international application of a multi-dimensional construct.Soc Forces. 2006; 84: 2077-2098https://doi.org/10.1353/sof.2006.0079
- Understanding period poverty: socio-economic inequalities in menstrual hygiene management in eight low- and middle-income countries.Int J Environ Res Publ Health. 2021; 18 (2021, Vol 18, Page 2571): 2571https://doi.org/10.3390/IJERPH18052571
Jejeebhoy SJ, Sathar ZA. Womens Autonomy in India and Pakistan - the Influence of Religion and Region.
- Unintended pregnancy and the factors among currently pregnant married youths in Western Oromia, Ethiopia: a mixed method.PLoS One. 2021; 16e0259262https://doi.org/10.1371/JOURNAL.PONE.0259262
- Prevalence of unintended pregnancy and its associated factors: evidence from six south Asian countries.PLoS One. 2021; 16e0245923https://doi.org/10.1371/JOURNAL.PONE.0245923
- Prevalence and determinants of unintended pregnancy in sub-Saharan Africa.PLoS One. 2019; 14: 1-16https://doi.org/10.1371/journal.pone.0220970
- Unintended pregnancy among pregnant women in Ghana: prevalence and predictors.J Pregnancy. 2019; 2019https://doi.org/10.1155/2019/2920491
- Early Marriage and Sexual and Reproductive Health Risks: Experiences of Young Women and Men in Andhra Pradesh and Madhya Pradesh.(India)2008http://www.popcouncil.org/asia/india.htmlDate accessed: August 4, 2020
- Prevalence of Unintended Pregnancy and its Associated Factors : Evidence from Six South Asian Countries.2021: 1-13https://doi.org/10.1371/journal.pone.0245923 (Published online)
- Determinants of unwanted pregnancies in India using matched case-control designs.BMC Pregnancy Childbirth. 2012; 12: 1https://doi.org/10.1186/1471-2393-12-84
- Level, trend and correlates of mistimed and unwanted pregnancies among currently pregnant ever married women in India.PLoS One. 2015; 10: 1-11https://doi.org/10.1371/journal.pone.0144400
- The DHS wealth index.
- Unwanted pregnancy and associated factors among pregnant married women in Hosanna town, Southern Ethiopia.PLoS One. 2012; 7https://doi.org/10.1371/journal.pone.0039074
- Prevalence and Determinants of Unintended Childbirth in Ethiopia.2014https://doi.org/10.1186/1471-2393-14-326
- Prevalence and determinants of unintended pregnancies in Malawi.Etude la Popul Africaine. 2014; 28: 551-563https://doi.org/10.11564/28-1-507
- Contraceptive Use and Unintended Pregnancy Among Young Women and Men in Accra.(Ghana)2018https://doi.org/10.1371/journal.pone.0201663 (Published online)
- Exploring mental health during the initial COVID-19 lockdown in Mumbai: serendipity for some women.Int J Environ Res Publ Health. 2021; 18 (2021, Vol 18, Page 12542)12542https://doi.org/10.3390/IJERPH182312542
Article Info
Publication History
Published online: May 09, 2022
Accepted:
May 1,
2022
Received in revised form:
April 25,
2022
Received:
January 20,
2022
Identification
Copyright
© 2022 The Authors. Published by Elsevier B.V. on behalf of INDIACLEN.
User License
Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0) | How you can reuse 
Elsevier's open access license policy
Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0)
Permitted
For non-commercial purposes:
- Read, print & download
- Redistribute or republish the final article
- Text & data mine
- Translate the article (private use only, not for distribution)
- Reuse portions or extracts from the article in other works
Not Permitted
- Sell or re-use for commercial purposes
- Distribute translations or adaptations of the article
Elsevier's open access license policy
