Correspondence to Mr Richard Gyan Aboagye; [email protected]
Strengths and limitations of this study
Our study used a nationally representative data to examine inequalities in modern contraceptives use among women of reproductive age in Papua New Guinea.
The use of secondary data limited the analysis to only the variables found in the dataset; therefore, interpretations and inferences made from the study are based on the variables used.
The survey data used a cross-sectional design, restricting the study’s ability to assess causality.
The variables included in this study were based on self-report, which raises the possibility of recall and social desirability biases.
Introduction
Contraception is one of the most effective development programmes in the last 50 years, with unique advantages including economic growth, improvement in mother and child health, educational advancements and women’s empowerment.1 2 Modern contraception has played an important role in lowering the global total fertility rate and is therefore considered one of the significant discoveries in public health. Contraceptives are primarily used in preventing pregnancy but possess other benefits. Increased use of modern contraceptives has significantly lowered maternal mortality in low-and middle-income countries (LMICs).3 By lowering the yearly number of unwanted births, facilitating access to modern contraceptives for women with unmet family planning needs has the potential to improve mother and child health and reduce mortality.3–5 Aside the role contraception plays in fertility control, modern contraceptive techniques have an impact on the overall health of women, children and households. Annually, contraception saves an estimated 2.7 million infant mortalities and 60 million healthy life years globally.6 It prevents 40% of all maternal mortalities and almost 10% of all paediatric deaths and has the potential to alleviate poverty and hunger, particularly in LMICs.6 7
Papua New Guinea (PNG) is an Oceanian nation in the southwestern Pacific Ocean that includes the eastern half of New Guinea and the western half of the Indonesian provinces of Papua and West Papua. The country’s population was reported to be 7.3 million in 20118 and is projected to be between 8.8 million and 9.6 million in mid-2020.9 Like most countries, PNG faces reproductive health challenges, some of which could be curtailed with improved contraception usage. First, PNG has one of the highest maternal mortality ratios in the world, with an estimated 594 per 100 000 live births in 2013.10 11 PNG has an estimated maternal mortality rate of 733 deaths per 100 000 births in 2006, which has reduced to 171 per 100 000 deaths per birth currently.12 13 This rate is still high, necessitating urgent public health intervention through policies and maternal health improvement.
Second, PNG faces the issue of an increased birth rate and unintended pregnancies. Unwanted pregnancies expose women to obstetric hazards, unsafe abortions, insufficient birth spacing and high-risk pregnancies.14 15 It is reported that the preferred number of children for all women, regardless of age, location of residence or education, is consistently lower than the number of children born, and this difference worsened with time.16 In 2012, almost half (49.4%) of pregnant women reported that their pregnancy was unintended.17 In 2021, more than half (55%) of women in PNG reported that their pregnancy was unintended, with a small number of them reporting that they had ever used a family planning method.10 These estimates are more than the worldwide estimate of 40%.14 The numerous reports of increased abortions18–21 further heighten the burden of unintended pregnancies in PNG. If all women seeking to avoid pregnancy had access to adequate contraception, an estimated 54 million unwanted pregnancies, 21 million unplanned births, 16 million unsafe abortions, 1.1 million infant deaths and 118 000 maternal fatalities may have been avoided.22 In addition, the ability to space births and reach the desired family size allows women to attain their educational and career goals.15
Consequently, there are numerous calls in PNG advocating for access and usage of modern contraceptives, including the country’s National Family Planning Policy, in line with the World Health Organization’s recommendation.10 23 However, there exists the challenge of the unmet need for contraception. In 2015, an estimated 317 000 women of reproductive age had an unmet need for contraception, which is expected to rise to 337 000 by 2030.10 Report from the 2016–2018 PNG Demographic and Health Survey (PNG DHS) suggests that 32.2% of women in PNG continue to have an unmet need for contraception, which is associated with several sociodemographic and socioeconomic factors.24 It is estimated that resolving unmet contraceptive needs might avert nearly half (47.4%) of all maternal deaths in PNG.25
Contraceptive usage patterns vary between and within countries,26 27 thus, the need to examine country-specific trends to inform local policymakers on optimal policies, resource allocations and interventions to address the unmet needs of the most vulnerable groups.5 Expanding access to effective contraception in PNG is an objective of the United Nations Sustainable Development Goals and forms part of PNG’s National Health Plan for reducing the maternal death rate by 2030.10 28 The unmet need for contraception in PNG is linked to a lack of information about contraceptive use, contraceptive side effects, health concerns, behavioural requirements and opposition from spouses, and religious or cultural constraints.24 29 Unmet need for contraception may also be associated with an inequality in contraceptive utilisation, which has been demonstrated in some countries such as Ghana, Nigeria, Malawi, Latin America, the Caribbean and Columbia.5 30–34 These reports reveal the existence of disparities in the uptake of contraceptives, with the poor, less educated or adolescents mostly being left out.35 36 However, this issue is not well understood in the case of PNG. In this study, we examined the magnitude of wealth-based inequalities in contraceptive utilisation and examined the factors that influence this. The use of modern contraceptives may be more impactful in addressing reproductive issues in PNG if these barriers are clarified and eliminated or mitigated by relevant institutions through policy directions.
Methods
Data source and study sample
The PNG DHS, conducted from 2016 to 2018, was used for this study.24 This cross-sectional survey used a two-stage stratified sampling procedure to sample census units from each stratum. The first stage involved the selection of units using a probability proportional to the census units, while the second stage involved the selection of households from the clusters through probability sampling. We focused on sexually active women. Women who had not had sex and those who responded ‘don’t know’ to the question on whether or not they had ever had sex were excluded. A sample of 11 618 women aged 15 to 49 were included in the analyses (figure 1). Information on contraception usage was extracted from the individual recode file. We relied on the Strengthening the Reporting of Observational Studies in Epidemiology guidelines in writing this paper (online supplemental table S1).
Study variables
Outcome variable
The outcome variable was the utilisation of modern contraceptives. To assess this variable, women were asked to indicate the specific method of contraception they used. In the Demographic and Health Survey (DHS), there were 13 responses to this question. We grouped the response options into traditional and modern contraception. The traditional methods included periodic abstinence, withdrawal and other traditional methods/no method while the modern methods involved pills, intrauterine devices, injections, male condoms, female sterilisation, male sterilisation, implants/Norplant, female condoms and other modern methods. Those who were not using any method were included in the traditional/no method category, since our focus was on modern contraceptives against all other methods of contraception.
Explanatory variables
The explanatory variables comprised age, educational level, age at first sex, current marital status, total children ever born, number of living children, wealth, place of residence, region, religion, occupation, frequency of reading newspapers, frequency of listening to radio and frequency of watching television. We recoded age at first sex as less than 18, 18–24 and 25+ years. Current marital status was recoded as never married, married or living together, widowed or divorced, and married but not together. The total number of children ever born and the number of living children were defined as no child, 1–2, 3–4 and 5+. Religion was treated as either Christian, non-Christian or no religion. For occupation, groups included not working, professional/technical/managerial/clerical, sales, agricultural and services/manual. Missing responses for occupation were dropped.
Wealth quintile
To evaluate wealth quintile, the DHS employed a principal component analysis (PCA) methodology to quantify household assets continuously, including vehicles, bicycles, televisions, building materials, sanitation facilities and water supplies. Then, this continuous measure was divided into five groups and converted into a quintiles (poorest, poorer, middle, richer, richest).
Statistical analyses
The prevalence of contraception usage by method and their relationship with the independent variables were assessed using the χ2 test and presented as bivariate results in a table. We estimated the crude odds ratios (cORs) and adjusted odds ratios (aOR) and their 95% confidence intervals (CIs) from a binary logistic regression analysis. Statistical significance was set at p<0.05. To explore the inequalities, the household wealth quintile was used. Also, we created dummy variables for the various independent variables to calculate and obtain the concentration index value and the curve. Afterwards, a concentration index decomposition analysis was fitted to estimate the contributions of individual factors to wealth-related inequality in the utilisation of modern contraception in PNG. This is a statistical method for analysing socioeconomic inequality in the distribution of a certain variable (in this case, wealth index) among people or groups within a community. It aids in understanding how certain factors contribute to disparities in that variable across the population. When a factor is shown to have a positive impact, it means that the disparity already in place is being exacerbated by this factor. On the other hand, a negative contribution means that the factor is reducing inequality.37
We estimated the slope index of inequality (SII) and the relative index of inequality (RII) in modern contraceptive utilisation to further provide summary evidence of inequality concerning wealth status. The SII measures the difference between the expected value of an outcome in both endpoints of the distribution of an explanatory (equity) variable. It is a predictor of absolute inequalities. It is calculated as the slope of the weighted linear regression and is the absolute equivalent of the RII. It represents the average gap between the equity variable’s bottom-ranking individuals and those at the top of the hierarchy in terms of modern contraception usage. It ranges from −1 to +1, where 0 denotes a lack of inequality. Positive SII values reflect unequal access for individuals in the poorest and poorer half since contemporary contraception use is concentrated among the better half (the richest wealth index group). The relative variation in the equity variable’s subcategories was considered using the RII. It is a measurement based on a weighted linear regression that links the usage of contemporary contraceptives with a person’s location within the distribution of an equity variable. When compared with the prevalence rate at the top of the hierarchy of the same factors, this can be understood as the prevalence rate of using a modern contraceptive technique in the bottom categories of the equity variables.38–40 All analyses were carried out with Stata statistical software (V.17.0), accounting for sampling weights and multistage survey design.
Patient and public involvement
None.
Results
Distribution of contraceptive use across the sociodemographic characteristics of the respondents
The results in table 1 show that 27.5% of the women used modern contraceptive methods. Apart from religion, all the sociodemographic characteristics of women showed statistically significant associations with use of modern contraceptives at p<0.05. Specifically, the differences in the use of modern contraceptives were based on age, women’s level of education, age at first sex, marital status, total children ever born, number of living children, wealth index, place of residence, region, occupation, and exposure to newspaper/magazine, radio and television.
Table 1Sociodemographic characteristics of women accross contraceptive utilisation in Papua New Guinea
| Variables | Frequency (%) | Traditional/no method | Modern | P value |
| % (CI) | % (CI) | |||
| Current contraceptive method | ||||
| 8423 (72.5) | ||||
| 3195 (27.5) | ||||
| Age | <0.001 | |||
| 720 (6.2) | 87.2 (82.6 to 90.7) | 12.8 (9.3 to 17.4) | ||
| 2121 (18.3) | 77.5 (74.2 to 80.4) | 22.5 (19.6 to 25.8) | ||
| 2302 (19.8) | 70.8 (67.7 to 73.8) | 29.2 (26.2 to 32.3) | ||
| 2031 (17.5) | 68.5 (65.2 to 71.6) | 31.5 (28.4 to 34.8) | ||
| 1915 (16.5) | 67.6 (63.9 to 71.2) | 32.4 (28.8 to 36.1) | ||
| 1389 (12.0) | 69.0 (64.9 to 72.8) | 31.0 (27.2 to 35.1) | ||
| 1139 (9.8) | 77.0 (73.4 to 80.3) | 23.0 (19.7 to 26.6) | ||
| Women’s level of education | <0.000 | |||
| 3002 (25.8) | 81.4 (78.7 to 83.7) | 18.6 (16.3 to 21.3) | ||
| 5622 (48.4) | 70.3 (68.4 to 72.1) | 29.7 (27.9 to 31.6) | ||
| 2491 (21.4) | 67.1 (64.0 to 70.1) | 32.9 (29.9 to 36.0) | ||
| 502 (4.3) | 71.1 (62.5 to 78.3) | 28.9 (21.7 to 37.5) | ||
| Age at first sex | 0.001 | |||
| 4579 (39.4) | 72.4 (69.5 to 75.1) | 27.6 (24.9 to 30.5) | ||
| 6191 (53.3) | 71.4 (69.5 to 73.2) | 28.6 (26.8 to 30.5) | ||
| 848 (7.3) | 81.4 (77.8 to 84.4) | 18.6 (15.6 to 22.2) | ||
| Current marital status | <0.001 | |||
| 958 (8.2) | 87.9 (82.5 to 91.8) | 12.1 (8.2 to 17.5) | ||
| 9519 (81.9) | 69.7 (67.7 to 71.6) | 30.3 (28.4 to 32.3) | ||
| 387 (3.3) | 84.1 (77.7 to 88.9) | 15.9 (11.1 to 22.3) | ||
| 753 (6.5) | 83.0 (76.6 to 87.9) | 17.0 (12.1 to 23.4) | ||
| Total children ever born | <0.001 | |||
| 1791 (15.4) | 94.4 (91.2 to 96.5) | 5.6 (3.5 to 8.8) | ||
| 3960 (34.1) | 75.0 (72.0 to 77.8) | 25.0 (22.2 to 28.0) | ||
| 3302 (28.4) | 63.9 (61.2 to 66.6) | 36.1 (33.4 to 38.8) | ||
| 2564 (22.1) | 64.3 (61.1 to 67.5) | 35.7 (32.5 to 38.9) | ||
| Number of living children | <0.001 | |||
| 1906 (16.4) | 93.9 (90.9 to 96.0) | 6.1 (4.0 to 9.1) | ||
| 4110 (35.4) | 74.8 (72.0 to 77.3) | 25.2 (22.7 to 28.0) | ||
| 3357 (28.9) | 63.2 (60.4 to 65.9) | 36.8 (34.1 to 39.6) | ||
| 2245 (19.3) | 64.1 (60.7 to 67.4) | 35.9 (32.6 to 39.3) | ||
| Wealth index | <0.001 | |||
| 2197 (18.9) | 82.7 (80.0 to 85.2) | 17.3 (14.8 to 20.0) | ||
| 2181 (18.8) | 76.4 (73.5 to 79.1) | 23.6 (20.9 to 26.5) | ||
| 2242 (19.3) | 72.6 (69.8 to 75.2) | 27.4 (24.8 to 30.2) | ||
| 2372 (20.4) | 66.1 (63.0 to 69.0) | 33.9 (31.0 to 37.0) | ||
| 2626 (22.6) | 66.4 (61.0 to 71.4) | 33.6 (28.6 to 39.0) | ||
| Place of residence | <0.001 | |||
| 1477 (12.7) | 64.1 (61.2 to 67.0) | 35.9 (33.0 to 38.8) | ||
| 10 141 (87.3) | 73.7 (71.8 to 75.5) | 26.3 (24.5 to 28.2) | ||
| Region | <0.001 | |||
| 2233 (19.2) | 62.4 (59.7 to 65.0) | 37.6 (35.0 to 40.3) | ||
| 4659 (40.1) | 78.0 (75.0 to 80.8) | 22.0 (19.2 to 25.0) | ||
| 3078 (26.5) | 71.5 (67.2 to 75.4) | 28.5 (24.6 to 32.8) | ||
| 1647 (14.2) | 72.5 (69.0 to 75.8) | 27.5 (24.2 to 31.0) | ||
| Religion | 0.407 | |||
| 11 473 (98.8) | 72.4 (70.7 to 74.1) | 27.6 (25.9 to 29.3) | ||
| 73 (0.6) | 71.1 (56.7 to 82.2) | 28.9 (17.8 to 43.3) | ||
| 72 (0.6) | 82.7 (62.8 to 93.1) | 17.3 (6.9 to 37.2) | ||
| Respondent’s occupation | <0.001 | |||
| 7452 (64.1) | 74.3 (72.2 to 76.3) | 25.7 (23.7 to 27.8) | ||
| 850 (7.3) | 65.8 (59.6 to 71.5) | 34.2 (28.5 to 40.4) | ||
| 493 (4.2) | 68.9 (62.8 to 74.3) | 31.1 (25.7 to 37.2) | ||
| 1870 (16.1) | 73.0 (69.4 to 76.2) | 27.0 (23.8 to 30.6) | ||
| 953 (8.2) | 65.4 (61.2 to 69.3) | 34.6 (30.7 to 38.8) | ||
| Frequency of reading newspapers/magazine | <0.001 | |||
| 7578 (65.2) | 75.9 (73.9 to 77.7) | 24.1 (22.3 to 26.1) | ||
| 2197 (18.9) | 66.1 (62.9 to 69.2) | 33.9 (30.8 to 37.1) | ||
| 1842 (15.9) | 66.3 (61.9 to 70.5) | 33.7 (29.5 to 38.1) | ||
| Frequency of listening to radio | <0.001 | |||
| 7553 (65.0) | 76.1 (74.2 to 77.8) | 23.9 (22.2 to 25.8) | ||
| 2088 (18.0) | 66.3 (62.0 to 70.3) | 33.7 (29.7 to 38.0) | ||
| 1976 (17.0) | 65.4 (62.5 to 68.2) | 34.6 (31.8 to 37.5) | ||
| Frequency of watching television | <0.001 | |||
| 8932 (76.9) | 74.5 (72.7 to 76.1) | 25.5 (23.9 to 27.3) | ||
| 1047 (9.0) | 67.0 (63.0 to 70.7) | 33.0 (29.3 to 37.0) | ||
| 1638 (14.1) | 65.4 (60.0 to 70.3) | 34.6 (29.7 to 40.0) |
Factors associated with modern contraceptive use in PNG
The results of the adjusted model, in the third column of online supplemental table S2, indicate that women aged 45–49 (aOR=0.49, CI 0.30 to 0.80) and those whose age at first sex was 25 years and above (aOR=0.70, CI 0.53 to 0.92) were significantly less likely to use modern contraceptives compared with those aged 15–19 and those whose first sex occurred at age less than 18 respectively. Women whose level of education was primary (aOR=1.44, CI 1.21 to 1.71) or secondary (aOR=1.53, CI 1.20 to 1.96) were more likely to use modern contraceptives compared with those with no education. The likelihood of modern contraceptive use increased with parity, with the highest odds among those who had five or more children at the time of the survey (aOR=6.66, CI 2.47 to 17.95). Women of all wealth indices were more likely to use modern contraceptives compared with those of the poorest wealth quintile, with the highest odds among those of the richer wealth index (aOR=2.10, CI 1.69 to 2.62). The odds of modern contraceptive use were lower in all regions of PNG compared with the Southern region. Women who were exposed to listening to radio were more likely to use modern contraceptives compared with those who were not exposed (aOR=1.30, CI 1.02 to 1.66).
Slope and relative index of inequality in modern contraceptive utilisation
The SII (0.210, CI 0.182 to 0.239) indicates that the women in the richest household wealth index use more modern contraceptives. The RII depicts a relative difference of 2.044 in using modern contraception between women in the richest household wealth index and those in the poorest wealth index in PNG (table 2). In other words, it shows the extent to which utilisation of modern contraception varies with wealth status.
Table 2Slope and relative index of inequality in modern contraceptives utilisation
| Coefficient | SE | z | P>|z| | 95% CI | |
| SII | 0.210 | 0 | 14.51 | 0.000 | 0.182 to 0.239 |
| RII | 2.044 | 0.104 | 19.7 | 0.000 | 1.840 to 2.247 |
RII, Relative index of inequality; SII, Slope index of inequality.
Modern contraception use by wealth index
Figure 2 shows that women of the richer wealth index have the highest mean contraceptive utilisation. The concentration curve shows the inequality in modern contraception use among the sample by wealth index. The curve shows that modern contraception use is highly concentrated among the richest wealth index as the concentration curve lies below the equality line. In other words, the figure depicts that women of the richest wealth index had a higher propensity to use modern contraception relative to those of the poorest wealth index (figure 3).
Enlarge this image.Figure 3. Contraception curve showing inequality in modern contraception use by wealth index.
Decomposition of concentration index analysis for modern contraception use
In table 3, we present the results of the decomposition analysis by illustrating how the sociodemographic characteristics of women contribute to inequality in modern contraceptive use. The results are presented in terms of absolute (ie, concentration index) and percentage contribution (ie, adjusted percentage contribution of inequality). The disadvantaged concentration of modern contraception use was found among those aged 35–39, those who had their first sex at 25 years and above, who were widowed/divorced, with five or more children, who were poorer, those were in rural areas and the Highlands region and those with no religion. Conversely, favourable concentration in modern contraceptive use was noted among those aged 20–24, those who had their first sex at 18–24 years, with one to two children, with a higher level of education, the richest, those in the Islands region and those who were exposed to media.
Table 3Contribution of sociodemographic characteristics based on the decomposition of concentration index analysis for modern contraception use
| Variables | Elasticity | Concentration index | Absolute contribution | Contribution (%) | |
| Age | −0.2 | ||||
| Ref | |||||
| 0.002 | 0.076 | 0 | 0.112 | ||
| 0.005 | −0.028 | 0 | −0.108 | ||
| −0.01 | −0.012 | 0 | 0.096 | ||
| −0.015 | −0.048 | 0.001 | 0.591 | ||
| −0.029 | 0.058 | −0.002 | −1.377 | ||
| −0.049 | −0.012 | 0.001 | 0.478 | ||
| Women’s level of education | 16.9 | ||||
| Ref | |||||
| 0.122 | −0.038 | −0.005 | −3.721 | ||
| 0.064 | 0.377 | 0.024 | 19.435 | ||
| 0.002 | 0.665 | 0.001 | 1.153 | ||
| Age at first sex | 0.3 | ||||
| Ref | |||||
| 0.004 | 0.048 | 0 | 0.171 | ||
| −0.018 | −0.006 | 0 | 0.094 | ||
| Marital status | −1.2 | ||||
| Ref | |||||
| 0.096 | −0.015 | −0.001 | −1.167 | ||
| −0.013 | −0.115 | 0.002 | 1.217 | ||
| −0.021 | 0.073 | −0.001 | −1.211 | ||
| Total children ever born | −16.8 | ||||
| Ref | |||||
| 0.348 | 0.041 | 0.014 | 11.624 | ||
| 0.342 | −0.01 | −0.003 | −2.763 | ||
| 0.293 | −0.108 | −0.032 | −25.65 | ||
| Number of living children | −8.1 | ||||
| Ref | |||||
| 0.09 | 0.037 | 0.003 | 2.712 | ||
| 0.161 | −0.015 | −0.002 | −1.931 | ||
| 0.111 | −0.099 | −0.011 | −8.875 | ||
| Wealth index | 83.4 | ||||
| Ref | |||||
| 0.04 | −0.434 | −0.017 | −13.976 | ||
| 0.061 | −0.053 | −0.003 | −2.655 | ||
| 0.106 | 0.344 | 0.036 | 29.525 | ||
| 0.113 | 0.774 | 0.087 | 70.517 | ||
| Residence | −0.7 | ||||
| Ref | |||||
| 0.01 | −0.089 | −0.001 | −0.701 | ||
| Region | 4 | ||||
| Ref | |||||
| −0.131 | −0.164 | 0.021 | 17.361 | ||
| −0.061 | −0.007 | 0 | 0.349 | ||
| −0.062 | 0.275 | −0.017 | −13.753 | ||
| Religion | 0.3 | ||||
| Ref | |||||
| 0.001 | −0.085 | 0 | −0.078 | ||
| −0.002 | −0.283 | 0 | 0.387 | ||
| Occupation | 6.4 | ||||
| Ref | |||||
| 0.007 | 0.581 | 0.004 | 3.498 | ||
| 0.005 | 0.195 | 0.001 | 0.792 | ||
| 0.004 | −0.203 | −0.001 | −0.652 | ||
| 0.013 | 0.265 | 0.003 | 2.755 | ||
| Frequency of reading newspapers and magazines | 6.9 | ||||
| Ref | |||||
| 0.025 | 0.233 | 0.006 | 4.736 | ||
| 0.005 | 0.482 | 0.003 | 2.12 | ||
| Frequency of listening to radio | 15 | ||||
| Ref | |||||
| 0.024 | 0.26 | 0.006 | 5.051 | ||
| 0.031 | 0.39 | 0.012 | 9.916 | ||
| Frequency of watching television | 4.1 | ||||
| Ref | |||||
| 0.002 | 0.327 | 0.001 | 0.569 | ||
| 0.008 | 0.561 | 0.004 | 3.58 | ||
| Calculated CII | 0.136 | ||||
| Actual CII | 0.124 | ||||
| Residual | −0.013 |
CII, Concentration indices of inequality; Ref, Reference.
Discussion
This study examined the wealth-related inequalities in modern contraceptive use in PNG. PNG suffers from the challenge of increased maternal mortality, increased birth rates, unintended pregnancies and other reproductive issues10 16 17 and could potentially benefit from improved access to modern contraceptives. The present study is expected to help recount the efforts made and identify areas for improvement and future policy direction concerning sexual and reproductive health interventions. Overall, the findings of this study showed that a large majority of women in PNG who use contraceptives continue to dwell on traditional methods of contraception (72.5%), with only a few (27.5%) using modern methods. In exploring the reasons that might contribute to this phenomenon, we identified socioeconomic inequality in modern contraceptive utilisation, which seems to favour richer quintile women. We also saw that women aged 20–24 years, those who had their first sex at 18–24 years, had one to two children, with a higher level of education, those in the Islands region and those who were exposed to media were advantaged in the utilisation of modern contraceptives. Unfortunately, women aged 35–39 years, those who had their first sex at 25 years and above, were widowed/divorced, with five or more children, were poorer, those in rural areas and the Highlands region and those with no religion were disadvantaged in modern contraceptive use.
The introduction of modern contraceptives in PNG dates back to 1948 by the Australian Air Force Medical Team, and it was subsequently integrated into the country’s national health policy in 1975.41 Over the years, contraceptive utilisation in PNG has generally varied with time. For instance, contraceptive utilisation has varied from 22.4% (in 1996) to 29.5% (in 2006), to 32.1% (in 2016).16 Similarly, modern contraceptive utilisation has varied from 14.3% (in 1996) to 19.8% (in 2006), to 29.4% (in 2016).16 The present study, based on the 2016–2018 PNG DHS, reports that only 27.5% of women are using modern contraceptives. The number is relatively lower than the 74.4% prevalence, also based on the 2016–2018 PDHS data,12 which considered only married and cohabiting women. This low modern contraceptive use is in line with those recently reported in other LMICs such as Rwanda,42 Ghana43 and Chad.44 Promotion of the utilisation of modern contraceptives could help prevent sexually transmitted diseases, unwanted pregnancies, unintended births, abortion, miscarriages and maternal death.45 46 To understand the factors influencing this phenomenon, we hypothesised that socioeconomic inequalities between various social classes may affect the use of modern contraceptives in PNG as they do in other sub-Saharan African countries.47–49
In this study, socioeconomic disparity was observed in modern contraceptive use among women in PNG. Specifically, we observed that the richest women have a highly concentrated usage of modern contraceptives. This shows that, among a given population in PNG, the richest quintile of women favourably use modern contraceptives much more than less wealthy women. This result is consistent with the positive concentration index among women who are richer and those richest, and it shows a pro-rich inequality in the use of modern contraception. In line with this finding, an earlier study in PNG found that the richest women have the lowest likelihood of having an unmet contraceptive need compared with poor women.50 The pro-rich disparity observed among women in PNG has similarly been reported in several LMICs such as Mozambique,51 Benin,47 Ghana and Nigeria.52 This highlights the existence of health inequalities disfavouring the less affluent population in LMICs. Richer women are reportedly less likely to experience the unfavourable attitudes of healthcare providers that are frequently observed in private healthcare facilities and are better able to overcome obstacles related to informal payments in cash or kind.32 53–55 The cost of medical services is generally high in PNG for the average person.56 57 To bridge the gap in contraceptive utilisation, it would be important for the government to invest in reproductive health services to make these services affordable to women of all socioeconomic groups.
Through decomposition analysis, we observed the contributions of individual variables to the overall socioeconomic inequality of modern contraceptive utilisation in PNG. This study found a disadvantaged concentration of modern contraception use based on age. Particularly, women aged 35–39 were found to be disadvantaged, while those who were 20–24 were favoured. Contrary to what we observed in PNG, large cohort studies have reported that in LMICs younger women lag behind adult women in contraceptive use.36 58 Age-related disparity may be linked to individuals’ financial capacities, knowledge of contraception and decision-making ability. These reasons may account for the differences and call for a look at the growing gap between adolescents and older women through the implementation of age-appropriate strategies that could financially empower women.
Our study reports demographic disparity which showed that women in rural areas and the Highlands region are disadvantaged in the use of modern contraceptives. In line with this observation, earlier reports show that women in the Highland regions are more likely to discontinue contraceptive usage, which has been associated with their poor knowledge of contraception and reduced partner involvement.59 60 Our study corroborates with the results of a study conducted in Benin where rural women were similarly disadvantaged in the use of modern contraceptives.47 Previous studies have also shown that place of residence has a positive relationship with health service access and maternal health service utilisation.61 62 Rural women’s limited access to health services and sociocultural factors relating to a lack of male engagement and support are probable contributing factors to this disparity. Equitable distribution of reproductive health resources at the social and economic levels in PNG may help close the wealth-related disparity that has been observed.
We observed that widowed/divorced women are disadvantaged in the use of contraceptives, which may be attributed to a lack of partner involvement. Studies in PNG and other LMICs have shown that partner involvement is very important in the uptake of reproductive health services.60 63 Male partners could support women financially and influence their decision to take up contraceptives.
In this study, we found a disparity in contraceptive use related to women’s age at sexual debut, with women who had their first sex at 25 years and above being disadvantaged. The age at which a person engages in their first sexual activity is crucial since it denotes the beginning of the exposure period to consequences related to sexual and reproductive health, such as unwanted pregnancy and sexually transmitted diseases. For this reason, it is important to close this gap in contraceptive use.
Finally, we found that having five or more children increases the observed wealth gap in the use of modern contraceptives among PNG women. As the number of children increases, women tend to discontinue using contraceptives.59 64 This decision may be explained by the desire for even more children or perhaps a lack of financial resources to assess reproductive health services.
Strengths and limitations
One strength of this study lies in the use of a nationally representative dataset to examine wealth-related inequalities in modern contraceptive use among women of reproductive age in PNG. Data from the DHS have many pros due to use of standardised questionnaires, extensive interviewer training and a high response rate. However, issues related to sexual and reproductive health are seen as private and therefore social desirability bias cannot be excluded from a self-reported study like this. Furthermore, the DHS employed a cross-sectional design in collecting the study’s data, which limits the ability to draw causal inferences.
Conclusion
Our study has shown that the utilisation of modern contraceptives among women in PNG is low. Women from wealthy homes had the highest propensity to use modern contraceptives in comparison with those from poorer homes. A considerable amount of disparity existed in the uptake of contraceptives, with women aged 35–39, those who had their first sex at 25 years and above, are widowed/divorced, with five or more children, are poorer, live in rural areas and the Highlands region and with no religion being the most disadvantaged. Given the slow progress in the use of modern contraceptives, efforts aimed at contraceptive uptake, such as the PNG Family Planning 2020 global partnership, need to be intensified. It is important to intensify targeted interventions and educational campaigns to considerably improve knowledge of family planning services, their access and utilisation. The Ministry of Health and other stakeholders must design and carry out initiatives aimed at enhancing the availability of and use of modern contraceptives among women from less affluent backgrounds. Interventions aimed at promoting the utilisation of modern contraceptives should also prioritise the needs of those in the identified disadvantaged groups. These strategies may include increasing mobile outreach, employing community health workers, lowering fees or providing voucher programmes. The contribution of age to this disparity may be curbed by addressing age-related social barriers through community sensitisation programmes. Transportation and referral systems need to be employed to address place-based disparities.
We are grateful to MEASURE DHS for making the DHS dataset free and accessible to use for the study. We also acknowledge Abdul-Aziz Seidu for his inputs during the drafting of the manuscript and his critical
Data availability statement
Data are available in a public, open access repository. The data set is freely accessible at https://dhsprogram.com/data/dataset/Papua-New-Guinea_Standard-DHS_2017.cfm?flag=1.
Ethics statements
Patient consent for publication
Not required.
Ethics approval
Ethical clearance was not required for the study since the data set used is freely available in the public domain. However, we sought permission from the MEASURE DHS, and the approval was given before using the data for this study. We complied and strictly adhered to all the ethical guidelines concerning the use of secondary data sets in the publication. Detailed information about the DHS data usage and ethical standards is available at https://dhsprogram.com/methodology/Protecting-the-Privacy-of-DHS-Survey-Respondents.cfm.
Contributors LKD and BOA conceived the study. RGA, EKA and EB wrote the Methods section and performed the data analysis. LKD, MAE, EB, EKA, RGA and BOA were responsible for the initial draft of the manuscript. BOA served as the guarantor for this study. All the authors reviewed and approved the final version of the manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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Details
; Essuman, Mainprice Akuoko 2
; Budu, Eugene 3
; Ameyaw, Edward Kwabena 4
; Richard Gyan Aboagye 5
; Bright, Opoku Ahinkorah 6
1 Department of Population and Health, University of Cape Coast, Cape Coast, Ghana; Cape Coast Teaching Hospital, Cape Coast, Ghana
2 Department of Medical Laboratory Science, School of Allied Health Sciences, College of Health and Allied Sciences, University of Cape Coast, Cape Coast, Ghana
3 Korle Bu Teaching Hospital, Accra, Ghana
4 Institute of Policy Studies and School of Graduate Studies, Lingnan University, Tuen Mun, New Territories, Hong Kong SAR; L & E Research Consult Ltd, Wa, Upper West Region, Ghana
5 School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia; Department of Family and Community Health, Fred N. Binka School of Public Health, University of Health and Allied Sciences, Hohoe, Ghana
6 REMS Consult Limited, Sekondi Takoradi, Ghana; School of Clinical Medicine, University of New South Wales Sydney, Sydney, New South Wales, Australia; School of Public Health, Faculty of Health, University of Technology Sydney, Sydney, New South Wales, Australia