1Ph.D. in Obstetrics and Gynaecology, 2MSc. in statistics, 3MD, Gynaecologist.
National Research Center for Reproductive Health, Taleghani St., Tehran, Iran.
Fax: 0098 21 8740499 E-mail: jahan@iums.ac.ir
Summary
A survey of pre-natal clinical attendance in Tehran (10 university hospitals) has indicated that 33.9% of pregnancies were unwanted. Using a logistic regression model, it was found that history of unwanted pregnancy, educational and occupational status of couples and adequecy of children, economical problems, and worries about child future were related to unwanted pregnancy. History of unwanted pregnancy, unfamiliarity with contraceptive use were to be the main factors of unwanted pregnancy.
Introduction
Unintended pregnancy is experienced by (1, 2, 3). Of the 6.4 million pregnancies occurring in the united states, more than half (56%) were unintended (4). Reduced options for fertility control over the past decade have been blamed for increase in the rate of unwanted pregnancy (5). In addition, other factors such as being an adolescent (6, 7), unqualified family planning services (3), low socio-economical status (4), misuse and discontinuation of contraceptive methods (2,8) and many other factors have been explained to affect the rate of unwanted pregnancies. But a question arises that from many factors that may influence the rate of unwanted pregnancy rate, which ones are capable of predicting it’s risk. In this article, we seek to find factors that may predict an unwanted pregnancy. On the best of our knowledge, there has been no such study in Iran, yet.
Subjects and Method
From May 1997 to March 1998, the National Research Center for Reproductive Health, Iran launched this study. The study subjects were women who were seeking prenatal care from 10 university hospitals, scattered throughout Tehran.
The study adopted the method of hospital-based descriptive epidemiology. In order to enhance the reliability of the study, sample was selected from multiple hospitals.
The study sample-size was calculated based on a proportion-stratification sampling method. According to the sample size attending at each pre-natal care unit a specific sample size was considered for each hospital, resulting in a total sample size of 3028
(for 10 hospitals). It was assumed that the prevalence of unwanted pregnancy was proportional to the number of clinical attendance.
The main method of investigation was face-to-face interviews to ascertain the variables of demographic characteristics (such as age, parity, marital status, educational level, etc.), previous contraceptive practices, and contraceptive failures as well as previous unwanted pregnancy. In standard demographic usage, unwanted pregnancies include both of those that are unwanted by mother and or by father. The sequential questions begins with a question about whether a baby was wanted by mother, and or by father. If a woman indicated that either she or her husband was unhappy, resentful, or upset about the pregnancy or did not want the pregnancy, the pregnancy was categorized as unwanted. If they indicated that they wanted, accepted, or planned the pregnancy, the pregnancy was categorized as accepted. Even if women reported that they or their husband had contradictory feelings about the pregnancy were included in a group of unwanted pregnancy.
A woman who responded that a baby was unwanted was asked if she could identify the stated reasons in the questionnaire such as economical problems, educational- occupational status,... . Thus a respondent had to state that her pregnancy was wanted in response to the first question to be asked the follow-up question about reasoning of the unwanted pregnancy.
Other questions assessed factors that may influence unwanted pregnancy, including contraceptive usage, familiarity with different contraceptive methods, their availability, acceptability and easiness to achieve them on the subject’s opinion. This study provided us with a series of valuable information about factors that influence the risk of unwanted pregnancy based on which we were able to predict unwanted pregnancy using a model building techniques.
Data analysis
Mean value and the standard deviation of each continuos variables were calculated using SPSS software. Prediction and estimation of effective factors influencing unwanted pregnancy with odds ratio related to each variable was done using multiple logistic regression model.
Equation Number 1: [F(x) = β0 + β1 x1+ ... + βp x p]
where F(x) = logit transformation as F(x) = Ln p/ (1-p), and “p” is the prevalence of unwanted pregnancy. Backward stepwise modeling was used considering a significant level of α = 0.15 for entering a variable and a α = 0.2 for removing a variable (9). Sensitivity of this model was 88.35% and it’s specificity was 96.09%.
Results
Demographic results:
Among 3028 pre-natal care seekers, the mean age was 25.43 (± 5.55 SD) years. Women were predominantly younger than 30 years (2509, 83%). About 14% of the subjects (415) were under 19 years of age (adolescent). The mean age at first pregnancy was 20.75 (±3.96 SD), with the mean age of menarche being 13.41 (± 1.8 SD). Forty six percent of subjects (1389) were primiparous, 53% (1578) had 4 times or less delivery. History of abortion was reported in 526 (17%) subjects, 13.9% of which has aborted only once. The number of subjects with 4 children or less were about 1523 (50%) with the sex ratio of 0.1 (1004 boys over 971 girls). Only 0.7 percent of subjects reported having a temporary marriage, 99.2% were permanently married, 0.2% were widowed , divorced or separated. Occupational state of women and their husbands were as follows:
Table 1. Occupational status of women and their husbands (n=3028).
Occupational status |
Women |
Husbands |
Working |
236 |
2903 |
Housewife |
2751 |
92 |
Out of job |
16 |
13 |
Student |
24 |
15 |
The educational status of women and their husbands were also asked. Most of subjects had passed secondary school (36.5% women and 39.0% men) (Figure 1).
Figure 2 shows that the frequency of children is declined suggesting that 50% of our sample group had 4 children and less (mean = 0.91, SD = 1.19).
Figure 2. Frequency of children of women attending in the study (n=3028).
Economical status of subjects was assessed by asking about their accommodation; 52.7% of which were tenant, 23.3% owners and the remaining 24% were either paying mortgage or were living on a property without payment (living with their parents,...). Accommodation facilities were found to be uniformly distributed among citizens as follows: Water 99%, electricity 99.5%, bath 87.1%, gas 63.4%, television 92.9%, and 44% of subjects had telephone. The majority of interviewed subjects had one (33.9%) or two rooms (47%).
The distribution of men’s and women’s income were quite skewed. Seventy-nine percent of the men had an average income of 50000 Tomans (each US dollar approximately equals to 300 Tomans). The mode of male’s income was 30000 Tomans. Nevertheless 78.6% of subjects reported that their income were less than expenses, while 20.4% said the reverse, suggesting that only 20% could save money.
2. Predictors for unwanted pregnancy
Table 2 shows the multivariable odds associated with the women’s likelihood of having an unwanted pregnancy. The odds ratio in the first part of the table consists of continuos variables such as the number of deliveries, abortions, daughters or the amount of income, while the second part of the table shows the odd ratios for categorical variables. Using this table, a model can be built to predict the odds of unwanted pregnancy for the subject.
Table 2. Multivariable odds (indicating the women’s likelihood of having an unwanted pregnancy), standard deviation and the level of significance.
Variables |
β |
Standard Deviation |
Signification level |
Intercept |
-2.566 |
0.798 |
0.0013 |
Continious Variables |
|
|
|
Number of Abortions |
-0.346 |
0.2253 |
0.1245 |
Number of Daughters |
0.429 |
0.1644 |
0.0092 |
Husband’s Income |
1.29×10-5 |
4.032×10-6 |
0.0014 |
Categorical Variables |
|
|
|
Familiarity with Pills |
0.808 |
0.3010 |
0.0073 |
Not Using Contraception |
-1.827 |
0.745 |
0.0143 |
Pill Usage |
1.649 |
0.951 |
0.0828 |
Condom Usage |
3.085 |
1.2890 |
0.0167 |
Natural Method |
2.470 |
0.8088 |
0.0023 |
Rhythmic Method |
3.816 |
1.309 |
0.0036 |
Awareness of Ovulation Time |
0.517 |
0.2547 |
0.0423 |
Husband’s Cooperation: |
|
|
|
Awareness of Contraception Accessibility Through Physicians |
-1.002 |
0.4288 |
0.0194 |
Thinking about Abortion |
4.075 |
0.4000 |
0.000 |
Estimation of odds ratio for continuous variables
For each variable, the specific β can be substituted in the equation number one. Number of deliveries, daughters and the amount of husband’s income were found to have a positive β, indicating that the risk of unwanted pregnancy will be increased as these increase. For example, as it has been shown in Table 2, the β for the number of deliveries equals to 0.228, meaning that if subject “A” has “C1” children and subject “B” has “C2” Children, and if C2>C1, thereby subject “B” is e 0.288(C2-C1) times more likely (in comparison to subject A) to be at risk of unwanted pregnancy. Number of abortions, however, is negatively correlated with the risk of unwanted pregnancy, suggesting that the higher the number of abortions, the lower the risk of unwanted pregnancy. In other words, if subject “A” with (A1) number of abortions and subject “B” with (A2) number of abortions, and if A2>A1, therefore subject “A” is e-0.346(a-b) times more likely to be at risk of unwanted pregnancy as compared with subject “B”.
Estimation of odds ratio for categorical variables
These estimations were also possible using a model building method (Table 2). Those who have already experienced unwanted pregnancy were 3.8 times more at risk of unintentional pregnancies. Unfamiliarity with contraceptive pills has put subjects at more risk (with the rate of 2.2 times). Also, if no contraceptive methods were used, the probability of occurring unwanted pregnancy were 6 times more than those who didn’t use it..
Similarly, usage of pills, condoms, natural and rhythmic contraceptive methods lead to 5, 21, 11 and 45 times more risk of unwanted pregnancy, respectively. The level of husband’s cooperation for using contraceptive methods was assessed.(Table 3). This table shows the risk level of unwanted pregnancy when comparison between different groups were made.
Table 3. The risk level of unwanted pregnancy when husband’s cooperation was compared between different groups.
Comparison between groups* |
Risk level/times |
a, b |
0.695 |
a, c |
5 |
a, d |
1.7 |
* a = complete cooperation, b= absolute incorporation, c = carelessness and d= unknown
For instance, the first row shows that those subjects that their husbands completely cooperated with their wives were 0.695 times more likely to be at risk of unwanted pregnancy in comparison with those that their husband didn’t cooperate at all. As it is shown by table 3, carelessness of husbands toward contraceptive use will greatly put women at risk of unwanted pregnancy.
Amazingly, study finding suggest that those who said to be aware of the most probable part of the cycle for getting pregnant, were 1.6 times more likely to be at risk of unintended pregnancy. Those subjects who were unaware of the possibility of providing contraceptives from a physician were 2.7 times more likely to get pregnant unintentionally. Those who thought about abortion during their current pregnancy were 58 times more at risk. Descriptive analysis shows that 19.5% of subjects admitted to think about abortion, but only 7.2% have actually acted to terminate their pregnancy. The most frequent reason for not acting upon their thoughts was stated to be religious affliction (52.6%). Other reasons were found to be as follows: 12.2% fear of abortion, 3.9% fear of low, 18.7% husband’s disagreement, 8.4% other’s disagreement, 8.3% couldn’t afford it, 89.8% stated inaccessibility and 18.4% mentioned some other.
Factors that didn’t have any predictability
Following items were found to have no predictability on the risk of unwanted pregnancy: Age, age of first pregnancy, age of menarche, educational and occupational status of subjects and their husbands and also their marital status. The β for husband’s income was very low, meaning that it’s effect is quite slight. In addition, the distribution of the data was quite skewed and since it was only an estimation reported by wives, the result can be doubtful.
Discussion
Using a model-building method, this study aims at predicting the risk of unwanted pregnancy. Our data analysis has suggested that many factors can determine the risk of unintended pregnancy (Table 2). Among those were included the familiarity with contraceptive pills. Data suggested that unfamiliarity of respondent with contraceptive usage, puts them at more risk. Unintended pregnancies are a recognized occurrence among oral contraceptive pill (OCP) users which may be due to both user and method failure (2, 10). However, OCP use also influence the occurrence of unintended pregnancies through an additional, poorly recognized knowledge of its usage.
A study in China which has developed a program for prevention of unwanted pregnancy suggested that the most frequent cause of unplanned pregnancy was contraceptive failure. Among the contraceptive failures, in this study, the proportion of condom mishaps was the highest, next was intrauterine device failure, then rhythm miscalculation (11). Our study suggest that pill users and those subjects who used condoms, rhythm and natural contraception were highly at risk of unwanted pregnancy. Yimin and his colleague discuss that the most failure results from noncompliance. Noncompliance can result from two main reasons: 1) subjects don’t know concrete use instructions; and 2) in spite of knowing them, they do not take them seriously. In Iran, family planning services are distributed by health workers, but the contraceptives are also available on market. Thus, many users of condoms and pills rely on possibly poorly informed, non-medical sources for advice and information or rely on the package instructions alone. The instructions for condom and pills usage are usually very simple and don’t include concrete information for correct usage. Particularly they don’t have any information on pill’s side-effects and how they could be adjusted to overcome bleeding and spotting. Therefore, many users considering themselves experienced and knowledgeable may use pills in inappropriate dosage; which either results in complications and or pregnancy.
This study also suggests that only 10% of our subjects were aware of the fact that medical doctors could provide them with contraceptive devices. Thus, it is likely that insufficient information is one of the reasons behind unintended pregnancy. Therefore, it is necessary to disseminate relevant concrete knowledge about how to use condoms and how to take pills correctly.
Our study also showed that those subjects who claimed that they are aware of the most proper timing for conception were highly at risk. This will further support the fact that our subjects were fairly knowledgeable and therefore the users of rhythm or natural contraception methods were also at higher risk. Thus, it is necessary to strengthen the dissemination of concrete knowledge regarding the safe time for coitus, if subjects are to use natural contraception. It is also important to notify contraceptive seekers that the failure rate for these methods are high and there is a definite need to identify the time of ovulation.
It is suggested that the most effective methods of contraception i.e., the pill, the implant and injectables be obtained from a doctor or other health care provider, so contraceptive use is closely tied to use of the health care system. It is necessary to understand where and how women obtain contraception, why some women provide contraception from free markets instead of using free-of-charge family planning services. Where do they get their information about conception and contraception and why some cannot or don’t obtain a method when they need one.
Women who are pregnant unintentionally, naturally think about abortion and possible ways to do it. Whether they act upon their thought or not is difficult to find. They are frequently unwilling to admit to a survey interviewer that they have intended to terminate a pregnancy. This of course is because of social opprobrium or religious prescription. In Iran, abortion is illegal and that will also inflict the reliability of our information. However, thinking about abortion was found to greatly increase the risk of unwanted pregnancy (54 times). This will highlight the demand for contraception and to target those most in need of services
In conclusion, many factors may influence the risk of unwanted pregnancy. The level of their influence can be predicted using a model building model. This study suggests that the following items can highly predict the risk of unwanted pregnancy: Number of deliveries, abortions, daughters, husband’s income, history of unwanted pregnancy, familiarity with contraceptive methods such as pills, usage of condom, pills, natural and rhythm methods awareness of ovulation time and physician’s ability to prescribe contraception, husband’s cooperation, and finally thinking about abortion.
Acknowledgments
The authors are grateful to MS. Rostami M., Ale-Ebrahim A., Sahrai R., Majidi N., Atef V., Hejazi A., Sajadpoor A.
References
xxxiiJ.D. Forrest, J.J. Frost, “The family planning attitudes and experiences of low-income women” Fam Plann Perspect, 28:246-255& 277, 1996.
xxxiiiM.J. Rosenberg, M.S. Waugh, S. Long. “Unintended pregnancies and use, misuse and discontinuation of oral contraceptives” J Reprod Med, 40:355-360, 1995.
xxxivS. Peckham, “Preventing unintended teenage pregnancies” Public Health, 107(2):125-33, 1993.
xxxvJ.D. Forrest, “Epidemiology of unintended pregnancy and contraceptive use” Am J Obstet Gynecol, 170(5 pt 2): 1485-9, 1994.
xxxviM.N. Bustan, A.L. Coker, “Maternal attitude toward pregnancy and the risk of neonatal death” Am J Public Health, 84(3):411-4, 1994.
xxxviiC.F. Westoff, “Unintended pregnancy in America and abroad” Fam Plann Perspect 20(6):254-61, 1998.
xxxviiiP.J. Sulak, A.F. Haney, “Unwanted pregnancies: understanding contraceptive use and benefits in adolescents and older women” Am J Obstet Gynecol, 168(6 pt 2):2042-8, 1993.
xxxixJ. Bontis , D. Vavilis, T. Theodoridis, A. Sidiropoulou “Copper intrauterine contraceptive device and pregnancy rate”. Contracept, 10:205-211, 1994.
xlD.W. Hosmer, S. Lemeshow, “Applied logistic regression”. A Willey- Interscience Publication, New York, Chichester, Brisbane, Totonto, Singapore, 1989.
xliS. Singh, W. Deirdre “Estimated levels of induced abortion in six Latin American countries”. Int Fam Plann Perspect, 20;1:4-13, 1994.
xliiC. Yimin, Z. Wei, L. Zhimin, Z. Yang, W. Aiying “Contraceptive practices of women requesting termination of pregnancy. A study from China”. Contracept, 55:15-17, 1997.
