Factors associated with unintended pregnancies in India among married women over the past one and half decade (2005–2021): a multivariable decomposition analysis

Abstract

Background

Along with other low- and middle-income countries unintended pregnancies are a matter of grave concern for India as well as world. Preventing unintended pregnancy can significantly reduce fertility as well as population health.

Methods

Our study used data from three recent rounds of national family health survey (NFHS) which were conducted in 2005-06 (NFHS-3), 2015-16 (NFHS-4) and 2019-21 (NFHS-5). In union, currently married and pregnant women who have given birth to at least one child in last five years were taken into consideration for study. Dependent variable was unintended pregnancy (current pregnancy) which included mistimed as well as unwanted pregnancy. Univariate, bivariate analysis with point-to-point change was done to know aboutdependent variable. To know about important covariate of change in unintended pregnancy logistic regression has been used followed by multivariable decomposition analysis.

Results

Over all three - survey rounds considered in our study; prevalence of unintended pregnancy declined from 31.76% (NFHS-3) to 15.87% (NFHS-5). Highest percentage decline of 23.02% from NFHS-3 (39.01%) to NFHS-5 (15.99%) in unintended pregnancy was in the women of Muslim religion. Women of rural area have 19% lower chance of unintended pregnancy with adjusted odds ratio 0.81. Odds of having current unintended pregnancy were about 8 times in women whose last birth was unwanted. Women with incorrect knowledge of ovulatory cycle have 20% higher chance of having unintended pregnancy. After analysis it was found that out of total change in unintended pregnancies was proximately 23% due to compositional change and about 77% change was due to behavioural change.

Conclusions

Over the time prevalence of unintended pregnancies declining which can be helpful for better health to both child and women. Important factors leading to a decline in unintended pregnancy were young age groups, high education level, unwanted last birth, no and negative fertility gap, no intention to contraceptive use and incorrect knowledge of the ovulatory cycle. Most of decline in unintended pregnancies was due to behavioural change of women considered in our study.

Background

Unintended pregnancies are those pregnancies which are either not wanted any time in future or pregnancy which occurred earlier than expected. These both are common phenomena of human reproductive behaviour [1]. Bongaarts (2001) stated that reducing unintended pregnancy can help in controlling fertility and population growth in the long term. Although the declining percentage of unintended pregnancy provides a little comfort but population expansion over the past 30 years (1990–2019) has also led to an approximate 13% increase in the total number of women who become pregnant unintentionally.

With 64 unintended pregnancies for every 1,000 women, approximately 6% of women worldwide become pregnant unintentionally each year [3]. Especially in low- and middle-income countries unintended pregnancies have resulted in many unsafe abortion and causing maternal death [4, 5]. Unintended pregnancy could be the result of many factors such as not being a user of contraception, incorrect use of contraceptive, education, women autonomy etc [6]. Scenarios of unintended pregnancy have declined in developed countries in comparison to low-income countries [7].

International conference on population and development (ICPD) held in Cairo states that “it’s a fundamental human reproductive right of all the couples to decide how many children to have, spacing between consecutive birth, information and education related to that” [ICPD 1994: Principal 8]. In 1995 committee on unintended pregnancy stated that end results of unintended pregnancy are dangerous putting a lot of pressure not only on women but also on men as well as on children [8, 9]. Although the total fertility rate is a good measure for measuring long term and short-term population goals in a region but they are not helpful in providing individual women reproductive right to decide when she wants to get pregnant. Desire to become or not to become pregnant appears to be a reliable indicator in this context. In many developing countries, births that women have but do not want to have, makes up a large fraction of total birth in the region [10,11,12,13].

Study on unintended pregnancies is important from a public health point of view since these pregnancies are negatively associated with various demographic indicators and have a definite role in hampering economic development of the country [14]. Numerous studies have proved negative relationship of unintended pregnancies with various dimension of human well-being which includes health, social and mental condition of women and children [1, 8, 15, 16]. Recent studies had indicated unintended pregnancy as one of the major health problems and an important reproductive health issue which includes accidental pregnancies. Unintended pregnancy sometimes led to high-risk pregnancies causing high rates of negative consequence for mother, husband and newly born child [17]. In some cases, children that are a result of unintended birth may face illness, negligence, malnutrition and even death. In many studies it had been mentioned that most of the unintended births generally resulted in some or other kind of abortion which is risky for mothers’ life [11]. Family planning (avoiding unintended pregnancy) have been regarded as one of the most effective ways of achieving gender equality and equity by providing enough information to women in the reproductive years (15–49 years) about their bodies and reproductive choices by having access to family planning programme and contraceptive use [18].

In India, the prevalence of unintended pregnancy was not widely reported. Even in each round of the national family health survey (NFHS) conducted previously (up-to NFHS-4) considerable amount of unintended pregnancy were taking place [4, 6, 19]. Many studies have been done to investigate trends and determinants of unintended pregnancy in India using the recent round of NFHS. A study which tries to assess one step further is to differentiate whether change was due to composition or behaviour is needed for better policy implementation. Here in the present study, we have used data from three rounds (2005-06, 2015-16 and 2019-21) of NFHS to assess prevalence and possible independent variables which led to change in prevalence of unintended pregnancies.

In this study a dependent variable is unintended pregnancy among union, married, pregnant women who have given at least one birth in the last five years. Those pregnancies which were either not wanted at the time women got pregnant but wanted in future (mistimed) or not wanted in future at all (unwanted) were taken as ‘unintended pregnancy.’ Aim of our study is to [1] To assess socio-demographic, fertility related, knowledge and intention of family planning related variables affecting unintended pregnancies in recent round (NFHS-5) and point to point change in unintended pregnancies throughout survey periods NFHS-3 (2005-06), NFHS-4 (2015-16) and NFHS-5 (2019-21) and [2] After getting idea about significant variables in recent round our aim is also to check whether change in unintended pregnancy is due to change in composition of the population or due to change in behaviour of the women and the comparison is done between NFHS-5 and NFHS-3. Objective of our study was to examine the factors contributing to unintended pregnancies, specifically in the context of current pregnancies. Moreover, we aimed to explore the dependency of current pregnancies on the status of previous births (one of the dependent variable in our study variable) — whether wanted or unwanted. That is why it was necessary to have women with at least one birth (leading to exclusion of women with no birth).

Also, the focus on women with recent reproductive experiences (births within the last five years) allows for a more accurate analysis of factors influencing unintended pregnancies within a shorter recall period, minimizing recall bias. For this reason, we excluded women who had not experienced any births in the last five years, as their inclusion would not align with the study’s focus. This approach ensures greater relevance to the study objectives and the dynamics of unintended pregnancies.

Material and methods

Data source and population

The present study is based on three rounds of nationally representative NFHS data which were conducted in years 2005-06, 2015-16 and 2019-21. The survey in 2005-06 used Census of India 2001 as sampling frame and remaining two surveys conducted in 2015-16 and 2019-21 used Census of India 2011 as sampling frame. NFHS uses stratified two stage sampling plan in which probability is proportional to size for selection of sampling unit. NFHS is a sample survey which provides sampling information on different demographic characteristics which includes information regarding contraceptive use, fertility, women’s health, child health, vaccination, etc. Any other information regarding sampling, questions and methodology can be obtained from the final report of national family health surveys, India [20,21,22].

Variables

The entire variables included in our study are shown below in Conceptual framework (Figure 1). Based on current pregnancy, dependent variable in our study was unintended pregnancy which includes current pregnancy which was not at all wanted or wanted later. In this study we classified independent variables in three broad categories: socio-demographic variable, fertility related variables and variables on knowledge and intention of family planning. Socio-demographic variable includes residence, age-group, religion, caste, women education level, wealth index and type of state. In fertility related variables: ‘last birth status, son-preference, fertility gap and parity of women’ were taken into consideration. Variables on knowledge and intention of family planning were: knowledge of ovulatory cycle, family planning awareness and contraceptive intention.

Fig. 1

Conceptual framework

Data management and analysis

Necessary recoding of the variables was done to make appropriate for this study. Women from Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha, Rajasthan, Uttarakhand, and Uttar Pradesh were combined to define a type of state empowered action group (EAG) and all others belong to non-EAG states [23].We had categorised a woman having son-preference if she wished to have a greater number of boys than girls in her entire life span. If the total number of living children were fewer or higher than those women wished (ideal family size) to have in her reproductive period, such women were categorised as having a fertility gap [24].Here if the total number of living children were fewer than ideal family size it is termed as positive gap and if total number of living children is higher than ideal family size it is termed as negative gap. Also, if the number of living children were equal to ideal family size it is termed as no gap.

Women who had the correct idea (in the middle of the cycle of their periods) about their ovulatory cycle were classified as having correct knowledge of ovulatory cycle and all others were classified as incorrect knowledge of ovulatory cycle. “We categorized family planning awareness based on women’s exposure to family planning messages through radio, television, and newspapers/magazines. Women who reported exposure to all three mediums (in last few months) were classified as having complete awareness of family planning. Those exposed to messages through one or two mediums (in last few months) were categorized as having partial awareness, while women reporting no exposure (in last few months) were classified as having no awareness. All those women who were non-user of contraceptives but intended to use later were classified as ‘yes’ in variable intention to contraceptive use and rest were classified as ‘no.’ For all the analysis purposes Stata software 16 was used.

Inclusion of respondents in our study has been done according to Fig. 2. For the analysis the eligible women were 3,086 in NFHS-3, 16,147 in NFHS-4 and 12,438 in NFHS-5 (all samples were unweighted). Since sampling selection of NFHS was multistage stratified probability proportional to size, in order to avoid any bias, weighted analysis have been used using variable v005 [25]. Final weighted samples considered for our analysis purpose were 3,592, 15,355 and 12,120 for NFHS-3, NFHS-4 and NFHS-5 respectively. Univariate analysis, bivariate analysis and binomial logistic regression were used [26]. Three separate binomial logistic regressions were used model-I based on socio-demographic variable, model-II based on fertility characteristics and final model-III used socio-demographic variables, fertility related characteristics along with variable on knowledge and intention of family planning. The rationale for using these three models was to systematically assess the contributions of different groups of variables. By analysing socio-demographic factors in isolation (Model I) and fertility-related characteristics separately (Model II), we could evaluate their independent associations with unintended pregnancies. The third model (Model III) integrated all these variables along with knowledge and intention of family planning to capture a broader and more nuanced understanding of the factors influencing unintended pregnancies. In order to have an idea about whether changes in unintended pregnancy in NFHS-5 (based on logistic regression) were due to endowment effect or coefficient effect taking NFHS-3 as base we had used multivariable decomposition analysis [27].

Fig. 2

Respondent selection in each survey round

Results

Univariate profile of the women participants

Table 1 shows profile of the women included in our study. Rural respondents continuously outnumber urban respondents in all NFHS rounds, increasing somewhat from 77.63% in NFHS-3 to 79.01% in NFHS-5, while urban respondents fell to 20.99% in NFHS-5. While the percentage of respondents with no education decreased from 55.44 to 23.29%, secondary education increased from 28.67% in NFHS-3 to 51.28% in NFHS-5, indicating a considerable improvement in education. Fertility statistics indicate that while son preference decreased from 31.78 to 20.87%, desired births increased from 83.43% in NFHS-3 to 94.63% in NFHS-5. Despite a steep decline in contraceptive intention from 90.19% in NFHS-3 to 18.50% in NFHS-5, family planning awareness increased, with full awareness increasing to 9.03% in NFHS-5. In NFHS-5, ovulatory cycle knowledge rose to 24%, while most people are still affected by inaccurate understanding.

Table 1 Socio-economic and demographic profile of the sample

Prevalence of unintended pregnancy

Out of all predictors last birth status of women can be one possible predictor for unintended pregnancy. Table 2 shows univariate statistics related to status of last birth to women. Out of all recent last birth, around 16.57% births were unintended in NFHS-3, which have reduced to 5.37% in NFHS-5. Prevalence of current unintended pregnancy has gone down from 31.76% in NFHS-3 to 15.87% as shown in Table 3.

Table 2 Status of unwanted births in last birth among women of study

Table 3 Prevalence of unintended pregnancy out of last unwanted births among women of the study

In NFHS-3 out of all women whose last birth was unwanted around 72% women said their current pregnancy was also unintended while in NFHS-4 and NFHS-5 out of all those whose last birth was unwanted around 60% reported their current pregnancy was unintended.

Prevalence and point to point change in unintended pregnancy

Table 4 presents the change and percent distribution of the factors which are associated with unintended pregnancies and its change over the period. Prevalence of unintended pregnancy had decreased from 31.76% in NFHS-3 to 15.87% in NFHS-5. The significant change in unintended pregnancy is observed between NFHS-3 and NFHS-4 and only slight change is observed between NFHS-4 and NFHS-5. Unintended pregnancy was higher in urban areas in NFHS-3, which has been changed in next round of survey as unintended pregnancy was higher in rural areas in NFHS-4 and NFHS-5.Unintended pregnancies were higher in women with age group 35 + years and above in NFHS-3 (41.41%), NFHS-4 (28.40%) but in NFHS-5 they were higher in women with age group 15–19 years (19.87%). It is clear from the data that in NFHS-3 and NFHS-4 as the age increases the percentage of unintended pregnancies also increases but this trend is not observed in NFHS-5. Decrease in unintended pregnancies was highest in women of the age groups 30–34 years (21.93%) and 35 + years (21.60%) when we looked at the change from NFHS-3 to NFHS-5. Looking at the religion variable although unintended pregnancies were always higher in the women who belong to Muslim religion but over the survey periods highest decrease (23.02%)in unintended pregnancies were observed in the women who belongs to Muslim religion from NFHS-3 to NFHS-5 and in NFHS-5 the percent of unintended pregnancies of Hindu and Muslim women are almost same. Women with no and primary education have a higher percentage of unintended pregnancies in all three rounds of surveys in comparison to secondary and higher education groups.

Table 4 Level and trends in unintended pregnancies: A percent distribution across NFHS surveys

Over the survey rounds highest change (-20.91) in unintended pregnancies was noted in women with poorer wealth group. Prevalence of unintended pregnancies is higher in EAG states than non-EAG states in all rounds of NFHS survey under consideration. Unintended pregnancies are always higher in the women who have more children than her desired family size in all rounds of survey than the women who have achieved the desired family size and the women who have not yet achieved the desired family size. Positive relationship is observed between order of parity and chance of having unintended pregnancies. Higher the parity of women were higher chances of having unintended pregnancies in all the survey rounds. Point to point change in unintended pregnancies, when NFHS-3 was compared with NFHS-5 was found highest (21.27%) for women with parity 3/3+. Prevalence of unintended pregnancies in women who did not have any knowledge of their ovulatory cycle was 32.00% in NFHS-3, 18.03% in NFHS-4 and 16.75% in NFHS-5. Highest decline (16.36%)in unintended pregnancies was for women with certain knowledge of family planning awareness. Women who had intention to use contraceptives in future saw a decline of 20.53%in unintended pregnancies from NFHS-3 to NFHS-5. A steep drop has been observed in the unintended pregnancies from NFHS-3 to NFHS − 4 (20.29%) if the women have intention to use any family planning method in future.

Results of binary logistic regression

Table 5 presents the logistic regression results by taking unintended pregnancy as depended variable for the data of NFHS-5 only. Regression results in Table were analysed using three different sets of independent variables. In Model-I we have considered only socio-demographic variables as independent variables. In Model-II fertility related characteristics have been considered as independent variables. In Model-III we have considered both socio-demographic variables and fertility related characteristics along with the variable knowledge and intention regarding family planning of women as independent variables. Adjusted odds ratio, p-value and 95% confidence interval were also presented in the Table.

Table 5 Determinants of unintended pregnancies: regression results from NFHS-5 (2019-21)

From the Table considering Model – I it is observed that women of rural areas had 19% lower chances [OR 0.81, C.I: 0.7, 0.93] of having unintended pregnancy when urban women were taken as the reference category and the difference is statistically significant. When women of age-group 15–19 years were taken as base for comparison, although women in all other age-group had lower chance of having unintended pregnancy but results were significant only for the women belonging to age-group 25–29 years and they had 31% [OR 0.69, CI: 0.53, 0.89] lower chance of unintended pregnancy. When women with no education was taken as a base for comparison, women with primary education have 40% higher chances [OR 1.40, CI: 1.17, 1.62] of having unintended pregnancy. Looking at wealth index variable when women from richest group were considered as base for comparison, women of all other income group have higher chances of having unintended pregnancy with 49% higher chances in poorest group [OR 1.49, C.I: 1.02, 1.57].Women belonging to EAG state have 57% higher chances [OR 1.40, CI: 1.40, 1.77] of having unintended pregnancy in comparison of women from non-EAG state.

Looking at regression results after applying Model-II, which was based on fertility related variables, it is observed that women whose last birth was unwanted have around 8 times [OR 7.94, CI: 6.70, 9.40] high chance of unintended pregnancy as compared to women whose last birth was wanted. Women with no fertility gap and also having a negative fertility gap have a higher chance of unintended fertility than the females who have a positive fertility gap, i.e., the women have not yet achieved desired family size and the result is highly significant. If we consider women with parity 2 as reference for comparison, women with parity 3 have a significantly and high chance of unintended pregnancy. Women having parity 1 and 4 and above also have a higher chance of unintended pregnancy than the women who have parity 2 but the difference is not statistically significant.

Results of Model-III were based on socio-demographic variables, fertility related variables and variables on knowledge and intention of family planning of the women. Once you look on the results of the age variable it is different than those obtained from Model-I. On considering women of age group 15–19 as reference for comparison, odds of having unintended pregnancy were 29% low [OR 0.71, C.I: 0.54, 0.92] in women of 20–24 years, 50%low [OR 0.50, C.I: 0.38, 0.65] in women of 25–29 years. There was a significant decrease of having unintended pregnancy in women of 30–34 years and in women of age 35 years and above as well. Women with primary education [OR 1.49, C.I: 1.25, 1.77] and secondary education [OR 1.23, C.I: 1.02, 1.43 ] have higher reporting of unintended pregnancies than the women who are illiterate. Wealth index was not a significant variable for our study when studied in Model-III. Independent variables: type of state (of Model-I), status of last birth (of Model-II), fertility gap(of Model-II) and parity of women (of Model-II) have similar behaviour to Model-I and Model-II. Women with incorrect knowledge of ovulatory cycle had 20% more chances [OR 1.20, C.I: 1.05, 1.37] of unintended pregnancy in comparison to women with correct knowledge of their ovulatory cycle. In women with no awareness of family planning chances of unintended pregnancy were 28% higher [OR 1.28, C.I: 1.04, 1.59] when women with complete family planning awareness were taken as base for comparison. Those women who did not have any intention to use contraceptives in future have 1.34 time’s higher chance [OR 1.34, C.I: 1.15, 1.55] of having unintended pregnancy in comparison to those women who had intention to use contraceptives.

Results of decomposition analysis

Result of decomposition analysis are presented in Table 6. Only those variables which were significant in logistic regression were taken into consideration for decomposition analysis. Out of total decline in unintended pregnancy from NFHS-3 to NFHS-5 about 23% was due to change in compositional characteristics (endowment effect) and about 77% was due to change in behavioural characteristics (coefficient effect). In compositional change: residence, age-group, education level, type of state, last birth status, fertility gap, parity of women, knowledge of ovulatory cycle and contraceptive intention had a significant contribution in the change. In coefficient change few subgroup of variables: age group, education level and contraceptive intention have significant contribution in the change.

Table 6 Decomposition analysis overview: comparing 2005-06 and 2019-21

Around 5% decline in unintended pregnancy was because of a lower proportion of women in urban areas than rural areas from NFHS-3 to NFHS-5. Decrease in composition of women in age group 25–29, 30–34 years lead to decrease in unintended pregnancy was 9.74% and 1.69% and both are significant than the women in the age group 15–19 years. Increase in women composition with secondary education has led to an increase of 4.62% and an increase in the number of women in EAG states over the period (2005-06 to 2019-21) have contributed to an increase of about 5% in unintended pregnancies. Reductions in number of women having unwanted last birth status has led to decline of around 30.90% in unintended pregnancy. Decline in the women’s composition results in a decline of 4.30% and 4.77% in unintended pregnancy for women with no gap and women with negative gap respectively. Decrease in compositional change of women with parity 4 and above results in a decrease of 6.52 in unintended pregnancy. Because of an increase in women not having any contraceptive use intention in future 29.16% increase in unintended pregnancy was noted over the period under consideration of the survey.

Controlling all the compositional change factors, decline in unintended pregnancy due to change in behaviour of women in the age groups 20–24, 25–29, 30–34 and 35 + years was 9.46%, 13.98.%, 4.21% and 2.35% respectively (Table 6). A significant decline (Around 9%) in unintended pregnancy was because of behavioural changes among women with secondary education and this was found to be significant (p < 0.05). The behavioural changes of women considering the independent variable not having any intention to use contraceptive resulted in an increase of 6.01% in the unintended pregnancies.

Discussion

Our study suggests a significant reduction in prevalence of unintended pregnancies over the time considered. The study shows chances of unintended pregnancies were significantly low in rural areas in both the models, which could be because of in many rural settings, there may be a strong traditional and cultural acceptance around larger family size and children are often seen as economic assets, helping in agricultural work or contributing to household income. Therefore, even if a pregnancy is not actively planned, it might still be welcomed as part of the family’s long-term economic strategy, reducing the perception of it being unintended which is consistent with the other studies as well [19]. Likelihood of unintended pregnancy in higher age group was low which is justified by some other studies as well [6, 28]. This result make sense because younger women have more frequent sexual intercourse, lower knowledge of contraceptive methods and high rate. On the similar lines women who were highly educated have better knowledge about their roles, rights and responsibility, involvement in contraceptive choices and family planning as compared to women with no education [29]. EAG states like Uttar Pradesh, Bihar, Madhya Pradesh and others have been focus states from population polices and family planning point of view because of significant proportion of younger women are getting married at early age and are sexually active at an early age who and therefore chances of prevalence of unintended pregnancies are high, this result is justified by some other studies in India [30,31,32]. Women not have any desire to have more children (with no or negative fertility gap) have significant lower chances of having unintended pregnancy. Possible reasons behind this could be woman with no desire for more children have greater psychological commitment to avoid pregnancy, leading to increased vigilance in managing her fertility. This can result in fewer cases of contraceptive misuse or risk-taking behaviours that might otherwise lead to unintended pregnancies similar results have been obtained in studies around the world [33, 34]. Parity for women was significantly associated with prevalence of unintended pregnancies, women with high parity have high chance of having unintended pregnancies. Women with high parity often do not have knowledge of long-acting contraceptives (LARCs) and may rely on less effective traditional methods. Economic vulnerability, limited healthcare access, no proper contraceptive use counselling after childbirth among these women increases the risk of unintended pregnancies. Finding of our study are consistent with the available literature [35,36,37].

If a women have correct knowledge of timing of their ovulatory cycle, they can plan their family even if they have unmet need or they are not using any contraceptive methods. Women who understand their ovulatory cycle can use natural family planning methods, like abstinence or withdrawal, during their fertile window and these results were consistent with the available literature we have found that women’s correct knowledge of ovulatory cycle was a significant predictor of unintended pregnancies [38]. Women with complete awareness are more likely to use contraception consistently and correctly, reducing the likelihood of failure and unintended pregnancies. Our study has found significant low chance of unintended pregnancies in those women who have complete awareness regarding family planning and this finding is comparable to other studies [39, 40]. Our study has also found, women having no intention to use contraceptive have high chances of unintended pregnancies, as consistent with other study [41]. Majority of the change in unintended pregnancies was because of behavioural change which indicates positive effect, good reach of various governmental policies and schemes for the well-being of child and mothers. When effective policies are implemented, they lead to significant behaviour changes, resulting in lower rates of unintended pregnancies [42,43,44].

Conclusion

From logistic regression point of view, our study highlights key factors influencing unintended pregnancies in India, with educational attainment, Empowered Action Group (EAG) states, last birth status, fertility gap, knowledge of the ovulatory cycle, and contraceptive use intentions emerging as the most critical variables. Interestingly, traditional variables like caste and religion do not have much differences within group. Wealth was an important variable in model-I but when adjusted with other variables in model-III was not a significant variable. Decomposition analysis revealed several important compositional factors contributing to the decline in unintended pregnancies. These include reductions in unwanted last births, no and negative fertility gaps, and a decrease in the proportion of high-parity women. Behavioural changes were also pivotal, particularly women aged 25–29 years and those with secondary education, who played a significant role in reducing unintended pregnancies. However, the study also identified an alarming trend: an increase in the proportion of women lacking intentions to use contraceptives, which contributed to a rise in unintended pregnancies. This underscores persistent gaps in reproductive health awareness and access. This finding is one of the most crucial finding of our study resulted from decomposition analysis.

Strength and limitations

We have considered data from three rounds of the National Family Health Survey (NFHS), which covered a 15-year period from 2005 to 06 to 2019–21 allowing for a thorough evaluation of changes over time by offering a strong temporal analysis of trends in unintended pregnancy in India. The study identifies crucial socio-demographic factors that influence unintended pregnancies, offering specific insights that could inform targeted interventions. Variables like knowledge of ovulatory cycles, intention to use contraceptive, status of last birth were strength of our study. To name few limitations of the study, since the data on unintended pregnancies and contraceptive use are self-reported, there may be issues with recall bias or social desirability bias, which could affect the accuracy of the reported rates. We have considered women with at-least one birth in our analysis, excluding women whose first birth might be unintended considering our objective to deep dive into dependence of current pregnancy on recent/previous birth. Since the Wealth Index is a relative measure of economic status within each population, these minor differences do not compromise its comparability, these limitations should be kept in mind when doing any sort of comparison in near future.