Abstract
Background
The Family Planning (FP) Program is a national method of controlling population growth rates while improving maternal and child health. Indonesia, as one of the largest countries, has abysmally low contraceptive coverage. One of its main issues is unmet contraceptive needs. This study aims to determine the factors that influence women's unmet need of childbearing age (WCA) in Indonesia.
Methods
We performed an unpaired comparative analytic study with a cross-sectional method was conducted on secondary data obtained from 2012 to 2017 Indonesia Demographic and Health Survey (IDHS). The subjects in this study were all women of childbearing age (15–49 years). Subjects with incomplete data were excluded from the study. Unmet need was defined as WCA who did not use contraception but decline to have more children or wanted to delay their pregnancies. Chi-square analysis was performed on categorical data and Mann–Whitney U analysis on numerical data.
Result
A total of 45,607 WCA in the 2012 IDHS data and 29,627 WCA in the 2017 IDHS data were included in the study. In the 2012 IDHS data, factors influencing unmet needs were age (p = 0.023) and parity (p < 0.0001). In the 2017 IDHS data, factors influencing unmet needs were the residential area (p = 0.003), level of education (p = 0.008), level of spouse’s education (p < 0.0001), employment status (p = 0.03), possession of electricity (p = 0.001), and possession of television (p = 0.01).
Conclusion
Factors affecting unmet needs are age, parity, residential area, level of education, level of spouse’s education, employment status, possession of television, and possession of electricity. There were no recurring factors on 2012 and 2017 IDHS data.
Keywords: Family planning, Contraception, Unmet need
Plain language summary
The Family Planning (FP) Program is a national method of controlling population growth rates while improving maternal and child health. Indonesia, as one of the largest countries, has abysmally low contraceptive coverage. One of its main issues is unmet contraceptive needs. This study aims to determine the factors that influence women's unmet need of childbearing age (WCA) in Indonesia.
An unpaired comparative analytic study with a cross-sectional method was conducted on secondary data obtained from 2012 and 2017 Indonesia Demographic and Health Survey (IDHS). The subjects in this study were all women of childbearing age (15–49 years). Subjects with incomplete data were excluded from the study. Unmet need was defined as WCA who did not use contraception but decline to have more children or wanted to delay their pregnancies. Chi-square analysis was performed on categorical data and Mann–Whitney U analysis on numerical data. A total of 45,607 WCA in the 2012 IDHS data and 29,627 WCA in the 2017 IDHS data were included in the study. In the 2012 IDHS data, factors influencing unmet needs were age and parity. In the 2017 IDHS data, factors influencing unmet needs were the residential area, level of education, level of spouse’s education, employment status, possession of electricity, and possession of television.
In conclusion, factors affecting unmet needs are age, parity, residential area, level of education, level of spouse's education, employment status, possession of television, and possession of electricity. There were no recurring factors on 2012 and 2017 IDHS data.
Background
It is estimated that the global population would reach 7 billion people, And with increasing life expectancy every year, it is predicted to continue to grow and reach 8 billion in 2023 [1, 2].
Over the past 20 years, the usage of contraceptives in developing countries has decreased the number of maternal mortality simply by reducing unplanned pregnancies [3]. This action directly decreases the number of illegal abortions and high-risk pregnancies. Nevertheless, these successes are far from perfect. Previous research states that as many as 30% of maternal deaths can be further reduced by meeting unmet needs for contraception [3]. In addition, contraception may also increase perinatal outcomes by increasing the interval between pregnancies [3, 4].
Methods and forms of pregnancy planning differ widely, starting from traditional techniques, such as periodic abstinence, disrupted intercourse, and methods from myths and beliefs to modern techniques that have been studied for their efficacy. The intrauterine device (IUD), condoms, hormonal (pills), implants, and birth control injections are some well-known pregnancy planning approaches in the culture. There are also modern procedures, such as vasectomy and tubectomy, which are not commonly known or even feared by the public [5].
Based on data in 2017, traditional contraceptive methods were used by 4.6% of women of childbearing age (WCA) in Indonesia, while modern methods were used by 41.4% of WCA. The most widely used modern methods are injection (20.9%), pill (8.7%), IUD (3.5%), and implant (3.4%). Other methods, such as the lactational amenorrhea method (LAM) and male sterilization, were only used by 0.1% of all WCA [6, 7].
Unmet need is one of the persistent problems found in every country related to the provision of contraceptive services. Unmet need is defined as WCA who decline to have more children or delay pregnancies but do not use contraception [6, 8].
The level of unmet need varies from country to country, with a higher percentage in developing countries such as Uganda, Haiti, and Ghana [9]. Based on 2014 data, it was found that the amount of unmet need in Indonesia ranged from 10 to 11%, more or less the same as other Asian countries [9].
Previous studies have shown that several interventions may be utilized to increase contraceptive use rates. However, unmet need is one of the most prevalent problems to be addressed. Currently, there were only a few studies regarding unmet contraceptive needs in Indonesia. This study aims to determine the factors influencing unmet needs in Indonesia.
Methods
An analytic observational study with a cross-sectional method was done using re-analysis of 2012 and 2017 Indonesia Demographic and Health Survey (IDHS) raw data. The study population was WCA, whose data was recorded on IDHS. Patients with incomplete records were excluded from this study. 45,607 subjects were recorded on 2012 IDHS, while 29,267 subjects were recorded on 2017 IDHS.
Risk factors analyzed in this study were age, parity, history of sexually transmitted disease, residential area, level of education, level of spouse’s education, employment status, socioeconomic status, possession of electricity, radio, television and cellphone, smoking status, and the willingness of discussing puberty with daughter. Unmet need is defined as WCA who did not use any form of modern contraception but decided to delay or prevent birth.
Baseline characteristics were then analyzed and compared. Bivariate analysis between subjects’ characteristics and contraceptive knowledge was done. Multivariable analysis was done to determine factors associated with contraceptive knowledge and unmet need. Ethical clearance was issued from the health research and ethical committee in Faculty of Medicine, University of Indonesia.
Results
Using the raw data of Indonesian Demographic and Health Survey (IDHS), 45,607 respondents from 2012 IDHS data and 29,267 respondents from 2017 IDHS data were analyzed. Table 1 (2012 IDHS) and Table 2 (2017 IDHS) investigated the relationship between characteristics of subjects and unmet needs.
Table 1.
Relationship between characteristics and Unmet need in 2012 IDHS
| Characteristics | Unmet need | p | OR (CI 95%) | |
|---|---|---|---|---|
| Yes | No | |||
| Age | 35 (15–49) | 31 (15–49) | < 0.001 | |
| Parity | 2 (0–13) | 1 (0–14) | 0.001 | |
| Sexually transmitted disease history | ||||
| Yes | 1 (2.6%) | 37 (97.4%) | 0.270 | 0.42 (0.06–3.09) |
| No | 2.717 (6.0%) | 42.559 (94.0%) | ||
| Residential area | ||||
| City | 1.402 (5.9%) | 22.404 (94.1%) | 0.322 | 0.96 (0.89–1.04) |
| Rural | 1.332 (6.1%) | 20.469 (93.9%) | ||
| Education | ||||
| Uneducated | 110 (7.3%) | 1.390 (92.7%) | < 0.001 | |
| Primary | 1100 (7.3%) | 14.025 (92.7%) | ||
| Junior high | 1194 (5.1%) | 22.236 (94.9%) | ||
| Senior high | 331 (6.0%) | 5.222 (94.0%) | ||
| Education (years) | 6 (0–6) | 5 (0–6) | < 0.001 | |
| Spouse’s education | ||||
| Uneducated | 69 (7.1%) | 909 (92.9%) | < 0.001 | |
| Primary | 1.010 (7.4%) | 12.664 (92.6%) | ||
| Junior high | 1.287 (7.4%) | 16.045 (92.6%) | ||
| Senior high | 342 (9.7%) | 3.191 (90.3%) | ||
| Employment status | ||||
| Working | 1.558 (6.2%) | 23.701 (93.8%) | 0.084 | 1.07 (0.99–1.16) |
| Not working | 1.176 (5.8%) | 19.164 (94.2%) | ||
| Socioeconomic status | ||||
| First quintile | 621 (8.0%) | 7.146 (92.0%) | < 0.001 | |
| Second quintile | 513 (5.8%) | 8.272 (94.2%) | ||
| Third quintile | 483 (5.2%) | 8.760 (94.8%) | ||
| Fourth quintile | 535 (5.5%) | 9.208 (94.5%) | ||
| Fifth quintile | 583 (5.8%) | 9.488 (94.2%) | ||
| Possession of electricity | ||||
| Yes | 2.527 (5.8%) | 40.796 (94.2%) | < 0.001 | 0.51 (0.43–0.61) |
| No | 152 (10.8%) | 1.260 (89.2%) | ||
| Possession of radio | ||||
| Yes | 823 (5.2%) | 15.091 (94.8%) | < 0.001 | 0.79 (0.73–0.86) |
| No | 1.852 (6.4%) | 26.937 (93.6%) | ||
| Possession of television | ||||
| Yes | 2.275 (5.8%) | 37.289 (94.2%) | < 0.001 | 0.72 (0.64–0.80) |
| No | 406 (7.8%) | 4.776 (92.2%) | ||
| Smoking | ||||
| Yes | 102 (9.5%) | 969 (90.5%) | < 0.001 | 0.16 (0.14–0.20) |
| No | 2.629 (5.9%) | 41.897 (94.1%) | ||
| Puberty discussion | ||||
| Yes | 348 (8.3%) | 3.859 (91.7%) | 0.559 | 1.05 (0.90–1.22) |
| No | 370 (7.9%) | 4.294 (92.1%) | ||
Table 2.
Relationship between characteristics and Unmet need in 2017 IDHS
| Characteristics | Unmet need | p | OR (CI 95%) | |
|---|---|---|---|---|
| Yes | No | |||
| Age | 36 (15–49) | 32 (15–49) | < 0.001 | |
| Parity | 2 (0–13) | 1 (0–13) | 0.001 | |
| Sexually transmitted disease history | ||||
| Yes | 6 (6.5%) | 86 (93.5%) | 0.097 | 1.17 (0.51–2.69) |
| No | 2.771 (5.6%) | 46.590 (94.4%) | ||
| Residential area | ||||
| City | 1.434 (5.6%) | 24.109 (94.4%) | 0.858 | 0.99 (0.92–1.07) |
| Rural | 1.361 (5.7%) | 22.723 (94.3%) | ||
| Education | ||||
| Uneducated | 53 (6.4%) | 770 (93.6%) | < 0.001 | |
| Primary | 888 (6.5%) | 12.675 (93.5%) | ||
| Junior high | 1.501 (5.5%) | 25.998 (94.0%) | ||
| Senior high | 351 (4.5%) | 7.390 (95.5%) | ||
| Education (years) | 6 (0–6) | 5 (0–12) | < 0.001 | |
| Spouse’s education | ||||
| Uneducated | 59 (10.5%) | 504 (89.5%) | < 0.001 | |
| Primary | 850 (7.1%) | 11.083 (92.9%) | ||
| Junior high | 1.511 (8.0%) | 17.296 (92.0%) | ||
| Senior high | 330 (7.7%) | 3.934 (92.3%) | ||
| Employment status | ||||
| Working | 1.492 (5.6%) | 24.955 (94.4%) | 0.932 | 1.00 (0.93–1.08) |
| Not working | 1.302 (5.6%) | 21.850 (94.4%) | ||
| Socioeconomic status | ||||
| First quintile | 523 (6.2%) | 7.941 (93.8%) | 0.097 | |
| Second quintile | 549 (5.8%) | 8958 (94.2%) | ||
| Third quintile | 550 (5.5%) | 9.539 (94.5%) | ||
| Fourth quintile | 563 (5.3%) | 10.020 (94.7%) | ||
| Fifth quintile | 609 (5.5%) | 10.375 (94.5%) | ||
| Possession of electricity | ||||
| Yes | 2.621 (5.6%) | 44.488 (94.4%) | 0.013 | 0.75 (0.60–0.94) |
| No | 85 (7.3%) | 1.084 (92.7%) | ||
| Possession of radio | ||||
| Yes | 563 (5.0%) | 10.665 (95.0%) | 0.002 | 0.86 (0.78–0.95) |
| No | 2.142 (5.8%) | 34.885 (94.2%) | ||
| Possession of television | ||||
| Yes | 2.461 (5.5%) | 42.089 (94.5%) | 0.010 | 0.83 (0.73–0.96) |
| No | 244 (6.5%) | 3.483 (93.5%) | ||
| Access to internet | ||||
| Yes | 1.095 (4.6%) | 22.813 (95.4%) | < 0.001 | 0.68 (0.62–0.73) |
| No | 1.657 (6.6%) | 23.337 (93.4%) | ||
| Possession of cellphone | ||||
| Yes | 2.121 (5.5%) | 36.769 (94.5%) | 0.002 | 0.86 (0.79–0.95) |
| No | 665 (6.2%) | 10.014 (93.8%) | ||
Subsequently, a multivariable analysis was done between characteristics and unmet needs. The result could be seen in Table 3 (2012 IDHS) and Table 4 (2017 IDHS).
Table 3.
Multivariable analysis of Unmet need 2012 IDHS
| Characteristics | p | OR | CI 95% |
|---|---|---|---|
| Age < 20 years | 0.023 | 1.30 | 1.04–1.62 |
| Parity: nulliparity | < 0.0001 | 8.96 | 7.17–11.20 |
Table 4.
Multivariable analysis of Unmet need 2017 IDHS
| Characteristics | p | OR | CI 95% |
|---|---|---|---|
| Residential area: city | 0.003 | 0.89 | 0.82–0.96 |
| Education: uneducated | 0.008 | 0.68 | 0.51–0.90 |
| Spouse’s education: uneducated | < 0.0001 | 14.58 | 11.76–18.09 |
| Employment status: working | 0.030 | 0.92 | 0.85–0.99 |
| Possession of electricity: no | 0.001 | 1.30 | 1.12–1.52 |
| Possession of television: no | 0.010 | 0.82 | 0.71–0.95 |
Discussion
In this study, it is clear that numerous factors were affecting unmet needs in WCA. The family planning program is a program that has succeeded in increasing contraceptive use by as much as 60% in couples worldwide [10]. It is estimated that there are 225 million women in the world whose contraceptive needs are still not being met each year. The situation is unfortunate, considering that contraception in an unmet need population can further prevent 36 million abortions, 70,000 maternal deaths, and 52 million unwanted pregnancies [10].
Age is one of the factors that determine the use of contraception. Previous research focusing on women aged 15–24 has shown that contraceptive knowledge and use among younger women tend to be lower, especially when combined with lower education and rural areas [11, 12]. Previous studies have also shown that this is related to more significant concern on younger women and would translate into lower contraceptive coverage in the younger age category [13].
Education, spouse’s education, and possession of various facilities (electricity, radio, television, cellphone, and internet) are linked to the availability of information flows that reach the WCA. Previous research in Bangladesh and Ghana has shown that education is a very influential factor in the use of contraception because women with higher levels of education tend to have a better understanding of the benefits and risks of using contraception [14, 15]. Better education would also lead to higher levels of contraceptive use [14, 16, 17].
Afterward, it was also known that factors associated with unmet needs are age, parity, residential area, level of education, level of spouse’s education, employment status, possession of television, and possession of electricity. The number of unmet needs is directly related to the number of unplanned and unwanted pregnancies. Previous research has shown a 16-fold chance of developing an unwanted pregnancy in women with unmet needs [17].
Age, parity, education, spouse's education, and access to information would influence the incidence of unmet needs in Indonesia. Previous research conducted in Indonesia in 2015 also showed similar results that age and parity would determine the incidence of unmet need in WCA [18]. Therefore, further education is needed, not only about family planning and contraceptive programs but also the ideal number of children for couples [19].
One of the considerations affecting the decision on contraceptive use is the characteristics of the spouse. As one of the countries with strong patriarchal values, WCA in Indonesia has difficulties ranging from accessing school and sexual education to not having the right to determine the number of children deemed appropriate [10]. In this report, women with lower spouse’s education are more likely to be identified as an unmet need. Previous research has shown that women in developing countries appear to be rejected by their spouses, who desire more offspring. They also have many obstacles and must struggle harder in order to have access to contraception [10, 20].
In conclusion, factors affecting unmet needs range from intrinsic characteristics such as age and parity to spouse’s characteristics such as education and socioeconomic status. There were no recurring risk factors. However, the risk factors multiplied in the later years. Comprehensive education and contraceptive provision would be beneficial to improve the rate of contraceptive use in Indonesia.
Conclusions
Factors affecting unmet needs are age, parity, residential area, level of education, level of spouse’s education, employment status, possession of television, and possession of electricity. No recurring factors were affecting unmet need on 2012 and 2017 IDHS data.
Acknowledgements
Not applicable.
Abbreviations
- FB
Family Planning
- WCA
Women of childbearing age
- IDHS
Indonesia demographic and health survey
- IUD
Intrauterine device
- LAM
Lactational amenorrhea method
Authors’ contributions
AKH conceptualized the idea and gathered the data; MM extracted and interpreted the results; FNH wrote the initial manuscript and managed the resources; AFA analyzed and contributed to writing the manuscript. All of the authors read and approved the final manuscript.
Funding
The authors fully financed the funding for this study.
Availability of data and materials
All data generated or analysed during this study are included in this published article.
Declarations
Ethics approval and consent to participate
The Ethical Committee for Medical Research of Faculty of Medicine, University of Indonesia, acknowledged this study.
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
All data generated or analysed during this study are included in this published article.