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Background: We sought to determine the prevalence and factors associated with the use of skilled assistance during delivery in Papua New Guinea. Methods: We analysed nationally representative data from 5210 women in Papua New Guinea using the 2016-2018 Demographic and Health survey. Both bivariate and multivariable analyses were performed. Statistical significance was set at p<0.05. Results: The prevalence of skilled assistance during delivery was 57.6%. The richest women (adjusted OR [AOR]=3.503, 95% CI 2.477 to 4.954), working women (AOR=1.221, 95% CI 1.037 to 1.439), women with primary (AOR=1.342, 95% CI 1.099 to 1.639), secondary or higher education (AOR=2.030, 95% CI 1.529 to 2.695), women whose partners had a secondary or higher level of education (AOR=1.712, 95% CI 1.343 to 2.181], women who indicated distance was not a big problem in terms of healthcare (AOR=1.424, 95% CI 1.181 to 1.718), women who had ≥4 antenatal care (ANC) visits (AOR=10.63, 95% CI 8.608 to 13.140), women from the Islands region (AOR=1.305, 95% CI 1.045 to 1.628), those who read newspapers or magazines (AOR=1.310, 95% CI 1.027 to 1.669) and women who watched television (AOR=1.477, 95% CI 1.054 to 2.069) less than once a week had higher odds of utilising skilled attendants during delivery. On the contrary, women in the Momase region (AOR=0.543, 95% CI 0.438 to 0.672), women in rural areas (AOR=0.409, 95% CI 0.306 to 0.546), as well as women with a parity of 3 (AOR=0.666, 95% CI 0.505 to 0.878) or ≥4 (AOR=0.645, 95% CI 0.490 to 0.850) had lower odds of utilising skilled attendance during delivery. Conclusion: There is relatively low use of skilled delivery services in Papua New Guinea. Wealth, employment status, educational level, parity and number of ANC visits, as well as access to healthcare and place of residence, influence the utilisation of skilled delivery services.
This study analysed data from the 2016–2018 Papua New Guinea Demographic and Health Survey (PNGDHS), which were collected from October 2016 to December 2018. Among the aims of the PNGDHS is to give current information on basic demographic and health pointers. The survey specifically gathered information on fertility, awareness and the use of family planning methods, breastfeeding practices, the nutritional status of children, maternal and child health, childhood immunisation, adult and childhood mortality, women's empowerment, domestic violence, malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections, as well as other health-related issues. Technical assistance for the survey was offered by inner city fund (ICF) through the Demographic and Health Survey Programme. Financial assistance was given by the Government of Papua New Guinea, the Australian Government Department of Foreign Affairs and Trade, the United Nations Population Fund (UNFPA) and UNICEF.17 The survey used the list of census units (CUs) from the 2011 Papua New Guinea National Population and Housing Census as the sampling frame. The survey adopted a two-stage stratified sampling technique. The provinces in the country of focus were further divided into 43 strata, paying attention to urban-rural differentials; however, the National Capital District did not have any rural strata. Each stratum provided samples of CUs, and this was done independently in two stages. The first stage involved the use of probability proportional-to-size sampling. The second stage of sampling involved the selection of 24 households from each of the clusters, using an equal probability systematic selection, with the resulting sample consisting of about 19 200 households. During the survey, the enumerators were able to cover 16 745 out of the 17 505; 16 021 of the occupied households were interviewed, with a response rate of 96%; 18 175 women of reproductive age were identified in the interviewed households for individual interviews, with 15 198 women completing the interviews at a response rate of 84%. The sample for the present study comprised 5210 women who had given birth to live babies within the 3 y prior to the survey. We realised that some women had given birth to more than one live birth during the selected period; in such cases, we only focused on the most recent birth. Details of the methodology, pretesting, training of field workers, the sampling design and selection are available in the PNGDHS final report, which is available at https://dhsprogram.com/publications/publication-fr364-dhs-final-reports.cfm. The dataset can be accessed at https://dhsprogram.com/data/dataset/Papua-New-Guinea_Standard-DHS_2017.cfm?flag=0. The binary response—whether or not a woman had given birth with the assistance of an SBA—was considered to be the outcome variable.17 From the PNGDHS, a skilled attendant delivery is a birth delivered with the assistance of doctors, midwives, nurses (including trained community health workers) or trained village health volunteers (p. 138).17 In this study, skilled delivery, supervised delivery and skilled provider at birth are used interchangeably. Seventeen explanatory variables were considered in this study, based on their availability in the dataset17 and conclusions drawn from them associated with skilled delivery in previous studies.9,10,13,18–21 The variables comprised maternal age, wealth, working status, education, partner's education, marital status, place of residence, region of residence, parity (birth order), getting money for treatment, distance to health facility, antenatal care (ANC) attendance, exposure to mass media (radio, television, newspapers) and gender of the head of the household. Some of these variables were recoded. ANC attendance was recoded into 0, 1, 2, 3 and ≥4 visits. Parity was categorised as 1, 2, 3 or ≥4 births. Education and partner's education were classified into three categories: no education, primary education and secondary education/higher education. Occupation was captured as working or not working and the decision-maker on healthcare was captured as either alone or not alone (Table 1). Background characteristics and uptake of skilled delivery services among women in Papua New Guinea Abbreviation: ANC, antenatal care. Source: 2016–2018 PNG DHS. Three key steps were followed to analyse the data. First, descriptive statistics, such as frequency with %, were executed to represent the background characteristics of study participants and the prevalence of skilled delivery services utilisation. Second, a bivariate analysis using χ2 was employed to select candidate variables for the regression analysis. Variables with p<0.05 were moved to the regression analysis stage. At the regression stage, crude and adjusted models were employed. The crude model was estimated to examine the effect of each independent variable on the outcome variable, while multivariable logistic regression was used to examine the effect of all the significant independent variables at the crude level on utilisation of skilled services during delivery. The output was reported as crude ORs (CORs) and adjusted ORs (AORs) with their corresponding 95% CIs. Using the variance inflation factor (VIF), the multicollinearity test showed that there was no evidence of collinearity among the independent variables (mean VIF=1.5, max. VIF=1.99, min. VIF=1.02). We applied sample weight to correct for oversampling and undersampling to ensure generalisation of the findings. The survey (svy) command was applied to take care of the complex sampling procedure involved in the demographic and health surveys. In other words, the svy command was used to declare the data survey data. We carried out the analyses with stata version 14.2 for MacOS (Stata Corporation, College Station, TX, USA).
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