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. 2020 Dec 5;13(6):573–585. doi: 10.1093/inthealth/ihaa097

Individual and contextual factors associated with barriers to accessing healthcare among women in Papua New Guinea: insights from a nationwide demographic and health survey

Abdul-Aziz Seidu 1,2,✉, Ebenezer Agbaglo 3, Louis Kobina Dadzie 4, Bright Opoku Ahinkorah 5, Edward Kwabena Ameyaw 6, Justice Kanor Tetteh 7
PMCID: PMC8643437  PMID: 33278299

Abstract

Background

This study sought to assess the individual and contextual factors associated with barriers to accessing healthcare among women in Papua New Guinea.

Methods

The study was conducted among 14 653 women aged 15–49 y using data from the 2016–2018 Papua New Guinea Demographic and Health Survey. The outcome variable was barriers to accessing healthcare. Descriptive and multilevel logistic regression analyses were conducted. Statistical significance was declared at P < 0.05.

Results

Women aged 15–19 y were more likely to experience at least one barrier compared with those aged 40–49 y (adjusted OR [AOR]=1.48; 95% CI 1.18 to 1.86). Women with secondary/higher education (AOR=0.68; 95% CI 0.57 to 0.81), women in the richest wealth quintile (AOR=0.36; 95% CI 0.28 to 0.46) and those in the least disadvantaged socioeconomic status (AOR=0.46; 95% CI 0.33 to 0.64) had lower odds of having challenges with at least one barrier to healthcare. However, living in rural areas increased the odds of facing at least one barrier to healthcare (AOR=1.87; 95% CI 1.27 to 2.77).

Conclusions

This study has demonstrated that both individual and contextual factors are associated with barriers to healthcare accessibility among women in Papua New Guinea. To enhance the achievement of the Sustainable Development Goals 3.1, 3.7 and 3.8, it is critical to deem these factors necessary and reinforce prevailing policies to tackle barriers to accessing healthcare among women in Papua New Guinea.

Keywords: barriers, global health, healthcare, Papua New Guinea, public health, women's health

Introduction

Women play important roles in every society and their health is of immense importance to the international community.1,2 As such, international development agendas often place particular focus on their health. Currently, women's well-being is being given the utmost attention in the Sustainable Development Goals (SDGs). Specifically, SDGs 3.7 and 3.8 focus on ensuring universal accessibility to sexual and reproductive healthcare services, as well as the integration of such services into national strategies and programmes by 2030.3,4 Similarly, SDG 3.1 aims to ensure a reduction in the world maternal mortality ratio to as low as 70 per 100 000 live births by 2030. Despite these targets, globally, a large number of women still die each year. In 2017, maternal mortality alone claimed the lives of about 295 000 women.5 A significant number of women die of diseases such as ischaemic heart disease, stroke, obstructive pulmonary disease, Alzheimer's disease, diabetes, hypertension and lung cancer.6 However, the majority of these deaths could be prevented through utilisation of better and accessible healthcare delivery systems. In the Western Pacific, thousands of women face a variety of health problems at every stage of life. They also face gender discrimination, often backed by sociocultural norms that serve as obstacles to their health and socioeconomic status.7 In the Western Pacific, there are cross-country variations in terms of women's health, with Papua New Guinea (PNG) being among those countries with the poorest women's healthcare services.7

In PNG, healthcare is provided by institutions such as aid posts, health centres and hospitals.8 The hospitals (both provincial and district) provide emergency and outpatient care, as well as support to public health programmes. The services provided by rural health centres and urban clinics include management of chronic and acute conditions, basic surgical care, deliveries and paediatric care, as well as serving as intermediary referral points between lower-level district facilities and district hospitals. Health subcentres (which are based in rural areas) provide the same services as health centres, whereas aid posts deliver basic healthcare services such as mother and child care and community-based health promotion. In remote areas, health services are extended through outreach, patrols and village health volunteer networks.9 Healthcare professionals in PNG include doctors, health extension officers, nurses, community health workers and aid post orderlies. The doctor-to-population ratio is estimated at 1 : 19 399, with few doctors posted outside the capital city, Port Moresby.10 The mountainous nature of PNG’s topography, coupled with its dominant rural and remote settings as well as cultural and linguistic diversity, serve as impediments to accessing healthcare.8

In PNG, the state of women's healthcare has been deteriorating during recent years. From 2012 to 2016, the prevalence of women's antenatal healthcare visits decreased from 66% to 54%.11 Additionally, PNG has recorded the highest maternal mortality ratio in the Western Pacific, estimated at around 98 to 733 per 1000 live births,12 with most of the deaths resulting from obstetric haemorrhage, sepsis, embolism, eclampsia and unsafe abortion.13 In addition, a substantial number of women in PNG suffer from diseases such as diabetes, malaria and HIV.14 What worsens the situation is the lack of health practitioners and drugs, low education, poverty and poor water and sanitation.13 The health situation of women in PNG highlights the need to introduce health interventions and strengthen existing ones, so as to improve the health of women. The effectiveness of such interventions requires an understanding of the health situation of women in the country.

Previous studies of the health situation of women in PNG focused mainly on issues related to maternal mortality, such as maternal health indicators,13 the use of safe surgery and anaesthesia to reduce maternal mortality,12 the impact of maternal mortality on children15 and using maternal health telephone lines to save women.16 A few others have focused on women's health in general.17,18 What previous studies seem to have overlooked is the likelihood of women in PNG facing barriers in their attempts to access healthcare. This is critical, since women in PNG face gender inequality, as is evident, for example, in men dominating the political landscape while women's issues are rarely reflected in government policies and decision-making.17 One earlier attempt to investigate barriers to women’s access to healthcare in PNG was Hinston and Earnest's study,17 which used qualitative methods to explore barriers to healthcare access as infringements of the rights of women. The authors identified violence, a heavy workload, a lack of economic opportunities and limited use of health services as barriers to women's health in PNG. However, Hinston and Earnest's17 sample comprised only 70 women, which brings into question the generalisability of their findings to all women in PNG. In the current study, we used nationally representative data to quantitatively investigate the factors associated with barriers to women's access to healthcare in PNG, with the aim of producing findings that reflect the situation of women in PNG. Globally, the predictors of barriers to healthcare access among women are age, marital status, educational level, employment, religion, parity and health insurance subscription, all of which could be associated with barriers to women's healthcare access in PNG.18,19

Materials and Methods

Data source

The data used for this study form part of the 2016–2018 Papua New Guinea Demographic Health Survey (PDHS), which were collected from October 2016 to December 2018. The survey adopted a two-stage stratified sampling technique. The survey used the list of census units (CUs) from the 2011 PNG National Population and Housing Census as the sampling frame. Administratively, the country is divided into 22 provinces and each province is subdivided into urban and rural areas. Each province is also divided into districts and each district is divided into local level governments, which are in turn divided into wards. Each ward is composed of CUs. The average CU size is 50 households, with urban CUs having 70 households on average and rural CUs having 48. The sampling frame contains information on CU location, type of residence (urban or rural), the estimated number of residential households and population by gender. Before sampling, the provinces in the country were apportioned into urban and rural areas, which yielded 43 strata; however, the National Capital District only had urban areas. Samples of CUs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units at different levels, and by using a probability proportional-to-size selection at the first stage of sampling. Stage 1 involved the selection of 800 CUs. This was performed through probability proportional to CU size. The second stage saw the systematic selection of 24 households from each cluster through probability sampling and this yielded a total of 19 200 households. For this study, we focused on women of reproductive age (14 653); complete information on the variables used in the present study are available in the Papua New Guinea Demographic and Health Survey 2016–2018 (pp. 1–2).20 Details of the methodology, pretesting, training of field workers, sampling design and selection are available in the PDHS final report (https://dhsprogram.com/publications/publication-fr364-dhs-final-reports.cfm).

Variables

Outcome variable

The outcome variable was barrier to healthcare accessibility. In the PDHS, each woman was interviewed to answer four questions on barriers to accessing healthcare based on obtaining money, distance to health facility, receiving permission for treatment and not wanting to go alone. If a woman faced at least one of the problems (money, distance, companionship or permission) then she was considered to have a barrier to healthcare access and coded as ‘1’. Conversely, if she did not report any barriers she was considered to have no barrier to healthcare access and was coded as ‘0’.18,19

Independent variables

Individual and contextual factors were considered as independent variables in this study. These variables were selected based on their statistical significance in previous studies.18,19 The individual level factors included age, marital status, educational level, employment, religion, parity, health insurance subscription and exposure to mass media (radio, newspaper and television). The contextual variables were gender of the head of the household, household wealth status, residence, regional and community level socioeconomic status (Table 1). Community level socioeconomic status was generated by aggregating the individual level data into clusters, except for place of residence and geographical region, which were taken as they were. It was operationalised with a principal component comprising the proportion of respondents with no formal education, unemployed, rural resident and living below the poverty level (asset index <20% poorest quintile). A standardised score with mean 0 and SD 1 was generated from this index, with higher scores being indicative of a lower socioeconomic position. We divided the resultant scores into tertiles to allow for non-linear effects and provided results that were more readily interpretable in the policy arena.21

Table 1.

Background characteristics and barriers to healthcare access among women in Papua New Guinea (n=14 653)

Receiving Money needed Distance to Not wanting to At least
permission to go for treatment health facility go alone one barrier
Variable N % Big problem Big problem Big problem Big problem Yes
Age, y p<0.001 p=0.323 p=0.176 p<0.001 p<0.001
 15–19 2894 19.8 993 (34.3) 1807 (62.4) 1452 (50.2) 1513 (52.3) 679 (23.5)
 20–24 2621 17.9 761 (29.0) 1592 (60.7) 1307 (49.9) 1099 (41.9) 727 (27.7)
 25–29 2379 16.2 706 (29.7) 1456 (61.2) 1226 (51.5) 940 (39.5) 685 (28.8)
 30–34 2117 14.5 604 (28.5) 1259 (59.5) 1106 (52.2) 816 (38.6) 644 (30.4)
 35–39 1964 13.4 556 (28.3) 1219 (62.1) 1050 (53.5) 759 (38.7) 576 (29.3
 40–44 1488 10.2 406 (27.3) 888 (59.7) 751 (50.5) 517 (34.7) 463 (31.1)
 45–49 1190 8.1 349 (29.3) 718 (60.3) 622 (52.3) 469 (39.4) 356 (29.9)
Marital status p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Never married 4049 27.6 1321 (32.6) 2477 (61.2) 1977 (48.8) 2005 (49.5) 1048 (25.9)
 Married/living together 9547 65.2 2799 (29.3) 5797 (60.7) 5028 (52.7) 3752 (39.3) 2760 (28.9)
 Widowed/divorced 379 2.6 90 (23.8) 258 (68.1) 208 (54.9) 150 (39.6) 97 (25.6)
 Married but not together 678 4.6 165 (24.3) 407 (60.0) 301 (44.4) 206 (30.4) 225 (33.2)
Educational level p<0.000 p<0.000 p<0.000 p<0.000 p<0.000
 No education 2735 18.7 1042 (38.1) 2102 (76.9) 1936 (70.8) 1532 (56.0) 464 (17.0)
 Primary 7616 52.0 2451 (32.2) 4960 (65.1) 4244 (55.7) 3369 (44.2) 1825 (24.0)
 Secondary or higher 4302 29.4 882 (20.5) 1877 (43.6) 1334 (31.0) 1212 (28.2) 1841 (42.8)
Occupation p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Unemployed 9730 66.4 2861 (29.4) 6176 (63.5) 5248 (53.9) 4283 (44.0) 2514 (25.8)
 Managerial 728 5.0 127 (17.5) 201 (27.6) 190 (26.1) 157 (21.6) 414 (56.9)
 Clerical/agric. 4085 27.9 1360 (33.3) 2502 (61.3) 2034 (49.8) 1638 (40.1) 1164 (28.5)
Manual 110 0.8 27 (24.6) 60 (54.6) 42 (38.2) 35 (31.8) 38 (34.6)
Religion p<0.001 p<0.001 p<0.001 p<0.001 p=0.007
 Orthodox 4549 31.0 1541 (33.9) 2810 (61.8) 2449 (53.8) 2012 (44.2) 1225 (26.9)
 Protestant 7040 48.0 1861 (26.4) 4208 (59.8) 3477 (49.4) 2792 (39.7) 2070 (29.4)
 Other 3064 20.9 973 (31.8) 1921 (62.7) 1588 (51.8) 1309 (42.7) 835 (27.3)
Number of children p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 None 4734 32.3 1509 (31.9) 2864 (60.5) 2289 (48.4) 2258 (47.7) 1261 (26.6)
 1–4 7348 50.2 2062 (28.1) 4387 (59.7) 3717 (50.6) 2749 (37.4) 2223 (30.3)
≥5 2571 17.6 804 (31.3) 1688 (65.7) 1508 (58.7) 1106 (43.0) 646 (25.1)
Covered by health insurance p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 No 14 080 96.1 4269 (30.3) 8761 (62.2) 7386 (52.5) 5970 (42.4) 3827 (27.2)
 Yes 573 3.9 106 (18.5) 178 (31.1) 128 (22.3) 143 (25.0) 303 (52.9)
Frequency of reading newspaper or magazine p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Not at all 8554 58.4 2938 (34.4) 6001 (70.2) 5361 (62.7) 4156 (48.6) 1798 (21.0)
 Less than once a week 3220 22.0 908 (28.2) 1699 (52.8) 1290 (40.1) 1135 (35.3) 1081 (33.6)
 At least once a week 2879 19.7 529 (18.4) 1239 (43.0) 863 (30.0) 822 (28.6) 1251 (43.5)
Frequency of listening to radio p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Not at all 8886 60.6 2915 (32.8) 6009 (67.6) 5391 (60.7) 4209 (47.4) 2042 (23.0)
 Less than once a week 2746 18.7 866 (31.5) 1538 (56.0) 1151 (41.9) 1021 (37.2) 854 (31.1)
 At least once a week 3021 20.6 594 (19.7) 1392 (46.1) 972 (32.2) 883 (29.2) 1234 (40.9)
Frequency of watching television p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Not at all 10 618 72.5 3461 (32.6) 7117 (67.0) 6353 (59.8) 4955 (46.7) 2441 (23.0)
 Less than once a week 1531 10.5 444 (29.0) 791 (51.7) 546 (35.7) 485 (31.7) 552 (36.1)
 At least once a week 2504 17.1 470 (18.8) 1031 (41.2) 615 (24.6) 673 (26.9) 1137 (45.4)
Gender of head of household p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Male 11 915 81.3 3640 (30.6) 7355 (61.7) 6266 (52.6) 5072 (42.6) 3237 (27.2)
 Female 2738 18.7 735 (26.8) 1584 (57.9) 1248 (45.6) 1041 (38.0) 893 (32.6)
Wealth p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Poorest 2048 14.0 907 (44.3) 1713 (83.6) 1639 (80.0) 1319 (64.4) 214 (10.5)
 Poorer 2221 15.2 822 (37.0) 1677 (75.5) 1567 (70.6) 1187 (53.4) 379 (17.1)
 Middle 2565 17.5 779 (30.4) 1761 (68.7) 1609 (62.7) 1201 (46.8) 541 (21.1)
 Richer 3589 24.5 1039 (29.0) 2135 (59.5) 1739 (48.5) 1358 (37.8) 1006 (28.0)
 Richest 4230 28.9 828 (19.6) 1653 (39.1) 960 (22.7) 1048 (24.8) 1990 (47.0)
Residence p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Urban 3894 26.6 741 (19.0) 1662 (42.7) 831 (21.3) 971 (24.9) 1787 (45.9)
 Rural 10,759 73.4 3634 (33.8) 7277 (67.6) 6683 (62.1) 5142 (47.8) 2343 (21.8)
Region p<0.000 p<0.000 p<0.000 p<0.000 p<0.000
 Southern 4221 28.8 1064 (25.2) 2352 (55.7) 2031 (48.1) 1742 (41.3) 1334 (31.6)
 Highlands 3916 26.7 1116 (28.5) 2609 (66.6) 2039 (52.1) 1621 (41.4) 1047 (26.7)
 Momase 2982 20.4 1078 (36.2) 1962 (65.8) 1778 (59.6) 1396 (46.8) 700 (23.5)
 Islands 3534 24.1 1117 (31.6) 2016 (57.1) 1666 (47.1) 1354 (38.3) 1049 (29.7)
Community level socioeconomic status p<0.001 p<0.001 p<0.001 p<0.001 p<0.001
 Tertile 1 (least disadvantaged) 4907 33.49 1269 (25.9) 2320 (47.3) 1465 (29.9) 1427 (29.1) 1961 (40.0)
 Tertile 2 (middle disadvantaged) 4884 33.33 1255 (25.7) 2882 (59.0) 2513 (51.5) 1893 (38.8) 1408 (28.8)
 Tertile 3 (most disadvantaged) 4862 33.18 1851 (38.1) 3737 (76.9) 3536 (72.7) 2793 (57.5) 761 (15.7)
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Statistical analyses

The data were analysed with STATA version 14.2 for MacOS (Statacorp college station, TX, USA). Three basic steps were followed to analyse the data. The first step was the use of descriptive statistics to describe the sample (univariate analysis) and also the tabulation of all the independent variables against each type of barrier to healthcare access. The second step was a bivariate analysis to select potential variables for the regression analysis. Variables that were statistically significant in the bivariate analysis at p<0.05 were retained. Afterwards, a three-level multilevel binary logistic regression analysis was performed to assess the individual and contextual (household and community level) factors associated with barriers to healthcare access. In this study, women were nested within clusters (primary sampling units [PSUs]) and clusters were nested within the regions. We measured the extent of the proportion of the variance explained by clustering at the PSU and regional level using intraclass correlation coefficients in STATA.22 Clusters were considered as random effects to account for the unexplained variability at the regional level. We fitted four models. First, we fitted the empty model, model 0, that had no predictors (random intercept). This was followed by model 1, which contained only the individual level variables, model 2 with only contextual level variables and model 3 with both individual and contextual level variables. For all models, we present the adjusted ORs (AORs) and associated 95% CIs. For model comparison, we used the Akaike information criteria test.23 Using the variance inflation factor (VIF), the multicollinearity test showed that there was no evidence of collinearity among the independent variables (mean VIF=1.7, maximum VIF=2.4 and minimum VIF=1.01). The dataset was declared survey data due to the complex sampling approach employed. Sample weight was applied in all the analyses to correct for oversampling and undersampling of the respondents. We relied on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement in conducting this study and writing the manuscript.

Results

Background characteristics and barriers to healthcare access among women in PNG

Table 1 presents results of the background characteristics of the study participants in relation to barriers to healthcare accessibility. Women who participated in the study were women of reproductive age; 19.8% were aged 15–19 y, 65% were married, 52% had primary level education, 66.4% were unemployed, 48.0% were Protestants, 50.2% had 1–4 children and 96.1% were not covered by health insurance. In terms of exposure to mass media, 58.4%, 60.6% and 72.5% were not exposed to newspapers/magazines, radio or television, respectively. The majority (81.3%) were in male-headed households, 28.9% were in the richest wealth quintile, 73.4% were in rural areas, 28.8% were in the southern region and 33.5% were of low socioeconomic status. χ2 analysis showed that all the independent variables had statistically significant associations with securing permission to go to the hospital, not wanting to go alone and at least one barrier to healthcare accessibility at p<0.05. Conversely, age was not significant in terms of money needed for treatment and distance to the health facility, but the other variables showed significant associations (Table 1).

Individual and contextual factors associated with barriers to healthcare in PNG

Table 2 presents results of the multilevel logistic regression of individual and contextual factors associated with barriers to healthcare concerning securing permission to go to hospital, getting the money needed for treatment, distance to the health facility, not wanting to go alone and at least one barrier. The results showed that women aged 15–19 y were more likely to experience difficulty with wanting to go alone to the health facility (AOR=1.51; 95% CI 1.22 to 1.87) and experienced at least one barrier compared with those aged 40–49 y (AOR=1.48; 95% CI 1.18 to 1.86).

Table 2.

Multilevel logistic regression of individual and contextual factors associated with barriers to healthcare among women in Papua New Guinea (fixed effects results)

Getting permission to go to hospital Getting money needed for treatment Distance to health facility Not wanting to go alone At least one barriers
Variable AOR (95% CI) AOR (95% CI) AOR (95% CI) AOR (95% CI) AOR (95% CI)
Age, y
 15–19 1.23 (0.98 to 1.52) 1.51*** (1.22 to 1.87) 1.48*** (1.18 to 1.86)
 20–24 0.99 (0.81 to 1.21) 1.17 (0.97 to 1.42) 1.39** (1.13 to 1.69)
 25–29 1.04 (0.86 to 1.26) 1.06 (0.88 to 1.28) 1.22* (1.01 to 1.48)
 30–34 0.96 (0.79 to 1.15) 1.00 (0.84 to 1.20) 1.09 (0.90 to 1.32)
 35–39 0.88 (0.73 to 1.06) 0.93 (0.78 to 1.12) 1.06 (0.88 to 1.28)
 40–44 0.90 (0.74 to 1.10) 0.83 (0.68 to 1.00) 1.04 (0.86 to 1.27)
 45–49 Ref. Ref. Ref.
Marital status
 Never married 1.50* (1.09 to 2.07) 0.86 (0.64 to 1.15) 1.00 (0.74 to 1.36) 1.42* (1.06 to 1.91) 0.90 (0.65 to 1.23)
 Married/living together 1.37* (1.03 to 1.81) 0.67** (0.52 to 0.87) 0.88 (0.67 to 1.16) 0.96 (0.74 to 1.23) 0.76 (0.57 to 1.00)
 Married but not together 1.19 (0.85 to 1.67) 0.85 (0.63 to 1.16) 0.88 (0.63 to 1.22) 0.79 (0.58 to 1.08) 0.83 (0.59 to 1.15)
 Widowed/divorced Ref. Ref. Ref. Ref. Ref.
Educational level
 Primary 0.93 (0.82 to 1.05) 0.85* (0.75 to 0.97) 0.86* (0.75 to 0.99) 0.75*** (0.66 to 0.85) 0.89 (0.77 to 1.04)
 Secondary or higher 0.75*** (0.64 to 0.89) 0.67*** (0.56 to 0.77) 0.64*** (0.54 to 0.76) 0.65*** (0.55 to 0.76) 0.68*** (0.57 to 0.81)
 No education Ref. Ref. Ref. Ref. Ref.
Occupation
 Unemployed 0.89 (0.54 to 1.48) 1.00 (0.65 to 1.54) 1.00 (0.62 to 1.62) 1.00 (0.63 to 1.59) 1.14 (0.72 to 1.81)
 Managerial 0.94 (0.54 to 1.62) 0.49** (0.30 to 0.78) 0.94 (0.56 to 1.57) 0.90 (0.54 to 1.49) 0.69 (0.42 to 1.13)
 Clerical/agric. 1.09 (0.66 to 1.81) 0.98 (0.63 to 1.51) 0.94 (0.58 to 1.52) 1.01 (0.63 to 1.61) 1.03 (0.65 to 1.64)
Manual Ref. Ref. Ref. Ref. Ref.
Religion
 Orthodox 0.97 (0.85 to 1.11) 1.01 (0.88 to 1.14) 1.05 (0.91 to 1.22) 1.03 (0.90 to 1.17) 0.98 (0.85 to 1.13)
 Protestant 0.83** (0.74 to 0.94) 0.94 (0.84 to 1.05) 0.97 (0.85 to 1.10) 0.93 (0.83 to 1.05) 0.92 (0.81 to 1.04)
 Other Ref. Ref. Ref. Ref. Ref.
Number of children
0 1.12 (0.93 to 1.36) 0.93 (0.79 to 1.09) 0.92 (0.77 to 1.10) 1.07 (0.89 to 1.28) 0.92 (0.76 to 1.12)
 1–4 1.05 (0.92 to 1.20) 0.97 (0.86 to 1.08) 0.98 (0.87 to 1.11) 0.92 (0.81 to 1.04) 0.89 (0.78 to 1.02)
≥5 Ref. Ref. Ref. Ref. Ref.
Covered by health insurance
 No 0.88 (0.68 to 1.13) 1.30* (1.05 to 1.61) 0.94 (0.73 to 1.20) 0.85 (0.67 to 1.07) 1.06 (0.87 to 1.30)
 Yes Ref. Ref. Ref. Ref. Ref.
Frequency of reading newspaper or magazine
 Not at all 1.30*** (1.12 to 1.51) 1.22** (1.07 to 1.38) 1.23** (1.06 to 1.42) 1.10 (0.96 to 1.27) 1.19* (1.03 to 1.36)
 Less than once a week 1.22* (1.05 to 1.43) 1.08 (0.95 to 1.23) 1.07 (0.92 to 1.25) 1.16* (1.00 to 1.34) 1.12 (0.98 to 1.29)
 At least once a week Ref. Ref. Ref. Ref. Ref.
Frequency of listening to radio
 Not at all 0.95 (0.83 to 1.09) 0.90 (0.79 to 1.01) 0.96 (0.84 to 1.10) 0.89 (0.79 to 1.02) 0.93 (0.81 to 1.06)
 At least once a week 0.82* (0.70 to 0.97) 0.85* (0.74 to 0.97) 0.89 (0.76 to 1.04) 0.81** (0.70 to 0.94) 0.86 (0.75 to 1.00)
 Less than once a week Ref. Ref. Ref. Ref. Ref.
Frequency of watching television
 Not at all 1.00 (0.85 to 1.18) 1.12 (0.97 to 1.29) 1.06 (0.90 to 1.24) 1.14 (0.97 to 1.33) 1.20* (1.03 to 1.40)
 At least once a week 0.95 (0.78 to 1.15) 0.95 (0.81 to 1.12) 0.93 (0.77 to 1.12) 1.11 (0.92 to 1.32) 1.02 (0.86 to 1.21)
 Less than once a week Ref. Ref. Ref. Ref. Ref.
Gender of head of household
 Male 1.01 (0.90 to 1.14) 1.05 (0.95 to 1.17) 1.09 (0.97 to 1.23) 1.09 (0.98 to 1.22) 1.11 (0.99 to 1.24)
 Female Ref. Ref. Ref. Ref. Ref.
Wealth
 Richest 0.58*** (0.46 to 0.72) 0.31*** (0.25 to 0.39) 0.42*** (0.33 to 0.53) 0.50*** (0.40 to 0.62) 0.36*** (0.28 to 0.46)
 Richer 0.69*** (0.57 to 0.82) 0.49*** (0.41 to 0.59) 0.54*** (0.44 to 0.65) 0.58*** (0.48 to 0.69) 0.50*** (0.40 to 0.62)
 Middle 0.75*** (0.64 to 0.88) 0.66*** (0.55 to 0.78) 0.66*** (0.55 to 0.80) 0.75*** (0.63 to 0.88) 0.64*** (0.52 to 0.79)
 Poorer 0.91 (0.78 to 1.05) 0.77** (0.64 to 0.92) 0.76** (0.63 to 0.91) 0.83* (0.71 to 0.97) 0.71** (0.58 to 0.88)
 Poorest Ref. Ref. Ref. Ref. Ref.
Residence
 Rural 1.55* (1.04 to 2.30) 1.39 (0.98 to 1.98) 4.10*** (2.72 to 6.18) 1.75** (1.15 to 2.65) 1.87** (1.27 to 2.77)
 Urban Ref. Ref. Ref. Ref. Ref.
Region
 Southern 0.69 (0.42 to 1.14) 0.79 (0.50 to 1.24) 0.89 (0.56 to 1.42) 1.12 (0.66 to 1.89) 0.79 (0.48 to 1.30)
 Highlands 0.63 (0.39 to 1.04) 0.90 (0.58 to 1.41) 0.37*** (0.23 to 0.59) 0.64 (0.38 to 1.07) 0.617 (0.38 to 1.01)
 Islands 0.96 (0.57 to 1.61) 1.00 (0.62 to 1.60) 0.94 (0.57 to 1.54) 1.12 (0.65 to 1.93) 0.97 (0.58 to 1.64)
 Momase Ref. Ref. Ref. Ref. Ref.
Community level socioeconomic status
 Tertile 1 (least disadvantaged) 0.91 (0.66 to 1.27) 0.56*** (0.42 to 0.74) 0.22*** (0.15 to 0.33) 0.40*** (0.28 to 0.56) 0.46*** (0.33 to 0.64)
 Tertile 2 (middle disadvantaged) 0.71** (0.55 to 0.90) 0.55*** (0.44 to 0.68) 0.29*** (0.21 to 0.39) 0.41*** (0.32 to 0.53) 0.45*** (0.34 to 0.58)
 Tertile 3 (most disadvantaged) Ref. Ref. Ref. Ref. Ref.
N 14 653 14 653 14 653 14 653 14 653
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AIC, Akaike's information criterion; ICC, intraclass correlation; PSU; primary sampling units; Ref., Reference.

Exponentiated coefficients; 95% CIs in brackets.

*p<0.05, **p< 0.01, ***p< 0.001.

Model 0 is the null model, a baseline model without any determinant variable.

Model 1 = individual level variables.

Model 2 = community level variables.

Model 3 is the final model adjusted for individual and household/community level variables.

Women who had never married were more likely to have difficulty in securing permission to go to hospital (AOR=1.50; 95% CI 1.09 to 2.07) and not wanting to go alone (AOR=1.42; 95% CI 1.06 to 1.91) compared with women who were widowed or divorced. Compared with women with no formal education, those with secondary/higher education were less likely to experience difficulties in securing permission to go to hospital (AOR=0.75; 95% CI 0.064 to 0.89), have difficulties in getting the money needed for treatment (AOR=0.67; 95% CI 0.56 to 0.77), have difficulties with the distance to the health facility [AOR=0.64; 95% CI 0.54 to 0.76), have difficulties in going alone to the health facility (AOR=0.65; 95% CI 0.55 to 0.76) and facing at least one barrier (AOR=0.68; 95% CI 0.57 to 0.81).

Occupation only had a significant association with difficulties in getting the money needed for treatment, with women in managerial positions less likely to face challenges in this regard compared with those in manual occupations (AOR=0.49; 95% CI 0.30 to 0.78). The likelihood of experiencing difficulties in securing permission to go to hospital was lower among women who professed orthodox faith (AOR=0.84; 95% CI 0.74 to 0.94). Women who were not covered by health insurance were more likely to have challenges in getting the money needed for treatment (AOR=1.30; 95% CI 1.05 to 1.61).

Exposure to newspaper and radio were associated with facing challenges in securing permission to go to hospital, getting the money needed for treatment, the distance to the health facility and not wanting to go alone. Women in the richest wealth quintile had lower odds of facing challenges in securing permission to go to hospital (AOR=0.58; 95% CI 0.46 to 0.72), getting the money needed for treatment (AOR=0.31; 95% CI 0.25 to 0.39), distance to the health facility (AOR=0.42; 95% CI 0.33 to 0.53), not wanting to go alone (AOR=0.50; 95% CI 0.40 to 0.62) and facing at least one barrier to healthcare (AOR=0.36; 95% CI 0.28 to 0.46) compared with those in the poorest wealth quintile. Living in a rural area increased the odds of facing challenges in securing permission to go to hospital (AOR=1.55; 95% CI 1.04 to 2.30), distance to health facility [AOR=4.10; 95% CI 2.72 to 6.18), not wanting to go alone (AOR=1.75; 95% CI 1.15 to 2.65) and facing at least one barrier to healthcare (AOR=1.87; 95% CI 1.27 to 2.77).

The odds of experiencing problems with distance to the health facility were low among women who lived in The Highlands region (AOR=0.37; 95% CI 0.23 to 0.59) compared with those who lived in the Momase region. Women of the least disadvantaged socioeconomic status had lower odds of having barriers to healthcare concerning securing permission to go to hospital, getting the money needed for treatment, distance to the health facility, not wanting to go alone and at least one barrier. Further details of all the models for each of the barriers to healthcare as well as at least one barrier to healthcare are presented in the Supplementary Tables S1-S3.

Table 3 presents results of the random effects of the association between the explanatory variables and barriers to healthcare access. In relation to securing the permission needed for treatment, the results indicate that 8.4% and 32.6% of variance was attributable to the variance between PSUs and regions, respectively (model 0). From model 0, individual level variables alone (model 1) contributed 6.8% of the variation of PSUs and 29.9% of regions. The contextual variables (model 2) alone contributed 4.6% of the variation of PSUs and 27.9% of regions. All covariates (model 3) explained 4.4% and 27.1% variation levels for PSUs and regions, respectively. This indicated that most of the variation in securing the permission needed for treatment could be attributed to individual level factors.

Table 3.

Multilevel logistic regression of individual and contextual factors associated with barriers to healthcare among women in Papua New Guinea (random effects results)

Barriers Model 0 Model 1 Model 2 Model 3
Getting permission to go to hospital
N 14 653 14 653 14 653
Variance PSU 0.41 0.32 0.21 0.23
Variance region 1.18 1.09 1.06 1.03
AIC 15 685.71 15 559.55 15 618.38 15 526.05
ICC PSU 0.08 0.07 0.05 0.04
ICC region 0.33 0.30 0.28 0.27
Distance to health facility
N 14 653 14 653 14 653
Variance PSU 1.96 1.46 0.10 0.11
Variance region 2.63 2.31 1.90 1.86
AIC 15 128.07 14 994.63 14 840.61 14 787.14
ICC PSU 0.25 0.21 0.02 0.03
ICC region 0.58 0.53 0.38 0.37
Getting money needed for treatment
N 14 653 14 653 14 653
Variance PSU 1.96 1.46 0.10 0.11
Variance region 2.63 2.31 1.90 1.86
AIC 15 128.07 14 994.63 14 840.61 14 787.14
ICC PSU 0.25 0.21 0.02 0.03
ICC region 0.58 0.53 0.38 0.37
Not wanting to go alone
N 14 653 14 653 14 653
Variance PSU 0.64 0.53 0.21 0.21
Variance region 1.51 1.44 1.18 1.22
AIC 16 907.78 16 535.31 16 748.45 16 411.60
ICC PSU 0.12 0.10 0.04 0.05
ICC region 0.40 0.37 0.30 0.30
At least one barrier
N 14 653 14 653 14 653
Variance PSU 0.81 0.53 0.19 0.20
Variance region 1.40 1.19 1.01 1.00
AIC 15 075.49 14 786.58 14 809.11 14 631.64
ICC PSU 0.15 0.11 0.04 0.04
ICC region 0.40 0.34 0.27 0.27
Open in a new tab

AIC, Akaike's information criterion; ICC, intraclass correlation; PSU, primary sampling units.

With distance to health facility, the results indicated that 24.9% and 58.2% of variance was attributable to the variance between PSUs and regions, respectively (model 0). From model 0, individual level variables alone (model 1) were attributed with 20.7% of the variation of PSUs and 53.4% of regions. The contextual variables (model 2) alone contributed 1.9% of the variation of PSUs and 37.9% of regions. All covariates (model 3) explained 2.6% and 37.4% of variation levels for PSUs and regions, respectively. This indicated that most of the variation in distance to health facility could be attributed to individual level factors.

Regarding getting the money needed for treatment, the results indicate that 25% and 58% of its variance was attributable to the variation between PSUs and region, respectively (model 0). From model 0, individual level variables alone (model 1) contributed 21% of the variation of PSUs and 53% of regions. The contextual variables (model 2) alone contributed 2% of the variation of PSUs and 38% of regions. All covariates (model 3) explained 3% and 37% of variation at the levels of PSUs and regions, respectively. This meant that most of the variation in not wanting to go alone could be attributed to individual level factors.

Concerning not wanting to go alone, the results indicate that 11.8% and 39.5% of its variance was attributable to the variance between PSUs and regions, respectively (model 0). From model 0, individual level variables alone (model 1) contributed 10.0% of the variation of PSUs and 37.5% of regions. The contextual variables (model 2) alone were attributable for 4.5% variation of PSUs and 29.8% of regions. All covariates (model 3) explained 4.5% and 30.4% variation at the levels of PSUs and regions, respectively. This indicates that most of the variation in not wanting to go alone could be attributed to individual level factors.

Finally, with at least one barrier, the results indicate that 14.8% and 40.3% of its variance was attributable to the variance between PSUs and regions, respectively (model 0). From model 0, individual level variables alone (model 1) were attributable for 10.6% variation of PSUs and 34.3% of regions. The contextual variables (model 2) alone were attributable for 4.3% variation of PSUs and 26.8% of regions. All covariates (model 3) explained 4.4% and 26.7% variation at the levels of PSUs and regions, respectively. This indicates that most of the variations in facing at least one barrier could be attributed to individual level factors.

Discussion

The current study revealed that women aged 15–19 y are more likely to experience barriers to healthcare. Typically, women aged 15–19 y had a higher likelihood of experiencing difficulties in receiving permission to go to hospital and not wanting to go alone. This finding is consistent with previous studies in Nigeria24 and Malaysia.25 This observation could be attributed to the fact that younger women in PNG in the age bracket of 15–19 y are likely to be in school as students, unmarried, unemployed and dependent on their parents or guardians. This limits their ability to make decisions freely to access healthcare, unlike their older counterparts (aged 45–49 y), who are likely to be married, employed, independent, able to afford healthcare and also make free, independent informed decisions to access healthcare. This finding is, however, in disagreement with results from a study in Tanzania that found that ageing was associated with higher odds of facing barriers to accessing healthcare.18 The disparities in findings could be attributed to differences in the geographical settings of these two studies.

In the current study, educational level was found to be inversely associated with healthcare accessibility barriers. Women who had received at least secondary level education had lower odds of experiencing at least one barrier to healthcare compared with women who had no formal education. This inverse relationship is in agreement with results reported from studies in Ghana,26 Ethiopia,27,28 Tanzania18 and southern Mozambique.29 Education influences labour market outcomes such as employment and earnings as well as non-market outcomes such as healthcare accessibility.30 This implies that women's level of education in PNG has the potential to determine their type of employment and earnings, which can influence their ability to afford healthcare.

Marital status was a significant factor that influenced women's healthcare accessibility in PNG. Specifically, we noted that women who had never married were more likely to encounter difficulties in securing permission and not wanting to go to hospital alone compared with women who were widowed or divorced. Studies conducted in Malaysia,31 Montenegro,32 Ethiopia,27,33 Nigeria34 and Tanzania18 have reported similar findings. Women who have never married are likely to be younger and under the influence of their parents compared with those who are widowed or divorced. However, a study in Japan found no significant relationship between marital status and healthcare access.35 The differences in findings could be a consequence of disparities in socioeconomic conditions and sociocultural practices present in each study area.

We also observed that occupation was significant in determining the odds of encountering barriers to healthcare access. Women who were in a managerial profession had lower odds of experiencing barriers in getting the money to go to hospital compared with women engaged in manual occupations. Managerial professions require a higher level of education with correspondingly high remuneration. As such, women who are engaged in managerial professions are likely to be in a better position to afford the cost of healthcare. Such women may also have the ability to access information, which comes with increased health literacy.24 A similar finding was reported by Makmor et al.36 and Sun et al.37

Women who reported never reading newspapers or magazines or watching television each week had higher odds of experiencing barriers to healthcare accessibility compared with those who were exposed to newspapers, magazines and television. Reading newspapers and magazines and watching television at least once a week improve information and health literacy, which have been reported as important determinants of healthcare utilisation.38 Women who are exposed to mass media are better informed about their health and how to overcome barriers in accessing healthcare, which could explain the reason for this finding. Similar findings have been reported in Ethiopia,39 Malawi,40 Bangladesh41 and India,42 where exposure to mass media was found to significantly influence healthcare utilisation for diarrhoea.

There was an inverse relationship between household wealth index and the odds of experiencing barriers to healthcare access. Women from the richest households recorded lower odds of healthcare accessibility barriers compared with women from poorer households. We observed that the odds of healthcare accessibility barriers for women decreased as the household wealth increased. Women in communities with a poorer socioeconomic status also had higher odds of barriers to healthcare access compared with their counterparts in communities with good/rich socioeconomic status. This finding supports findings from studies in Afghanistan,43 Uganda44 and Tanzania.18 Women from richer households have an increased ability to afford the costs associated with healthcare access, which are a common barrier to healthcare accessibility in low- and middle-income countries, which could explain the situation in PNG.

From the results, place of residence was significant in predicting the likelihood of encountering barriers to healthcare access. Women living in rural areas had higher odds of barriers to healthcare access compared with women in urban areas. This finding confirms the findings of studies conducted in Minnesota45 and South Africa46 that reported that rural residents were more disadvantaged in accessing healthcare compared with urban residents. Women in urban PNG have the benefits of adequate health infrastructure with reduced patient-to-health facility distance compared with their counterparts in rural areas that are often characterised by inadequate health infrastructure and long distances to health facilities. Barriers to healthcare access reported among rural populations include financial barriers, distance to clinics, lack of available clinics or hospital facilities, provider–patient relationship problems, low satisfaction with and lack of confidence or trust in providers or the healthcare system, language barriers, concerns about confidentiality, concerns about stigma and resistance to medical interventions.47,48

The likelihood of experiencing difficulties in getting the money needed for treatment was high among women who were not covered by health insurance. Globally, health insurance has been identified as an edge-cutting cost-effective intervention for obviating out-of-pocket payment for health services.49,50 On this premise, our finding is plausible because women who subscribe to health insurance might only pay the extra amount not covered by insurance, or pay nothing for healthcare.51 However, those who have not subscribed may be required to pay in full for every health service. As such, encouraging women to subscribe to health insurance could reduce healthcare barriers for women in PNG.

Strengths and limitations of the study

This study used nationally representative data to assess the factors associated with barriers in accessing healthcare among women in PNG. There was a high response rate and the study's methodology followed best practice including using experienced data collectors and multistage sampling. The findings can, therefore, be generalised to all women of reproductive age in PNG. The study also employed advanced statistical models that accounted for individual, household and community level factors. Despite these, the study design was a cross-sectional one and, as a result, causal interpretation cannot be inferred. Finally, since this was a secondary data analysis, we could not account for the effects of the health system and health worker-related factors.

Conclusions

This study has demonstrated that both individual and contextual factors are associated with barriers to healthcare accessibility among women in PNG. It has further indicated that measures to offset barriers in healthcare need to prioritise female education, their wealth status, as well as those in rural settings. Further, to enhance the achievement of the SDGs 3.1, 3.7 and 3.8, it is critical to deem individual and contextual factors necessary and reinforce prevailing policies to tackle barriers to accessing healthcare among women in PNG.

Supplementary Material

ihaa097_Supplemental_File
Click here for additional data file. (59.2KB, docx)

Acknowledgements

We are grateful to the MEASURE DHS project for giving us free access to the original data.

Contributor Information

Abdul-Aziz Seidu, Department of Population and Health, University of Cape Coast, Cape Coast, Ghana; College of Public Health, Medical and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia.

Ebenezer Agbaglo, Department of English, University of Cape Coast, Cape Coast, Ghana.

Louis Kobina Dadzie, Department of Population and Health, University of Cape Coast, Cape Coast, Ghana.

Bright Opoku Ahinkorah, School of Public Health, Faculty of Health, University of Technology Sydney, Australia.

Edward Kwabena Ameyaw, School of Public Health, Faculty of Health, University of Technology Sydney, Australia.

Justice Kanor Tetteh, Department of Population and Health, University of Cape Coast, Cape Coast, Ghana.

Authors’ contributions

Conception and design of study: AS; analysis and/or interpretation of data: AS; drafting the manuscript: AS, EA, LKD, BOA, EKA and JKT; revising the manuscript critically for important intellectual content: AS, EA, LKD, BOA, EKA and JKT. All the authors read and approved the final manuscript.

Funding

This study did not receive any funding.

Competing interests

There are no competing interests identified in relation to this research. The authors declare that they have no conflicts of interest. There are no financial, copyright, trademark or patent implications arising from this research and no organisation has any vested interest in this research.

Ethical approval

The 2016–2018 Papua New Guinea Demographic and Health Survey Report indicated that ethical approval was granted by the ICF Institutional Review Board. Both written and oral informed consent was sought from all the participants, including the emancipated adults (i.e. those aged <16 y), during the data collection exercise. We requested the dataset on 10 March 2020 and were granted access. After downloading the data, we used ‘my lock box’ to keep it safe from third parties.

Data availability

The dataset can be accessed at https://dhsprogram.com/data/dataset/Papua-New-Guinea_Standard-DHS_2017.cfm?flag=0.

References

  • 1. Regan L. Addressing Unmet Needs in Global Women's Health. British Medical Association,2012. [Google Scholar]
  • 2. World Health Organisation . Addressing the challenge of women's health in Africa: a Summary of the Report of the Commission on Women's Health in the African Region, 2012. [Google Scholar]
  • 3. Badiuzzaman M, Murshed SM, Rieger M. Improving maternal health care in a post conflict setting: evidence from Chittagong Hill tracts of Bangladesh. J Dev Stud. 2018;36:1–17. [Google Scholar]
  • 4. World Health Organization . Primary Health Care on the Road to Universal Coverage: 2019 Global Monitoring Report. [Google Scholar]
  • 5. United Nations, Department of Economic and Social Affairs, Population Division . World mortality 2019: Highlights (ST/ESA/SER.A/432).
  • 6. The Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG) . Improving Women's Health in the Pacific. RANZCOG, 2017. [Google Scholar]
  • 7. National Department of Health (2017) . Sector performance annual review for 2016. Port Morseby, Government of Papua New Guinea. Available at: http://www.health.gov.pg/publications/SPAR_2016.pdf [accessed 20 May 2020]. [Google Scholar]
  • 8. Pilang CL, Gray MA, Oprescu FL.. The evolution of the community health worker program in Papua New Guinea. Rural Remote Health. 2017;17(4):133. [DOI] [PubMed] [Google Scholar]
  • 9. Grundy J, Dakulala P, Wai K, et al. Independent State of Papua New Guinea Health System Review, New Delhi: WHO Regional office for southeast Asia; 2019. [Google Scholar]
  • 10. Au N, Hollingsworth B, Spinks J.. Measuring the efficiency of health services in lower‐income countries: The case of Papua New Guinea. Dev Policy Rev. 2014;32(2):259–72. [Google Scholar]
  • 11. Dennis AT. Reducing maternal mortality in Papua New Guinea: contextualizing access to safe surgery and anesthesia. Anesth Analg. 2017;126(1):252–9. [DOI] [PubMed] [Google Scholar]
  • 12. Robbers G, Vogel JP, Mola G, et al. Maternal and newborn health indicators in Papua New Guinea - 2008–2018. Sex Reprod Health Matters. 2019;27(1):52–68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. WHO . World Health Statistics 2016. Monitoring Health for the Sustainable Development Goals. Geneva, Switzerland: World Health Organization Press; 2015. Available at: http://www.who.int/gho/publications/world_health_statistics/2016/en/ [accessed 20 May 2020]. [Google Scholar]
  • 14. Williams JC. Maternal deaths and their impact on children in Papua New Guinea. Aust N Z J Public Health. 2014;38(5):405–7. [DOI] [PubMed] [Google Scholar]
  • 15. Watson AHA, Sabumei G, Mola G, et al. Maternal health phone line: saving women in Papua New Guinea. J Pers Med. 2015;5:120–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Ganster-Breidler M. 2010. Gender-based violence and the impact on women's health and well-being in Papua New Guinea. Contemporary PNG Studies: DWU Res J. 2020;13:17–30. [Google Scholar]
  • 17. Hinton R, Earnest J.. The right to health: overcoming inequalities and barriers to women's health in Papua New Guinea. Women's Studies International Forum. 2010;33:180–7. [Google Scholar]
  • 18. Bintabara D, Nakamura K, Seino K. Improving access to healthcare for women in Tanzania by addressing socioeconomic determinants and health insurance: a population-based cross-sectional survey. BMJ Open. 2018;8(9):e023013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Nisingizwe MP, Tuyisenge G, Hategeka C, et al. Are perceived barriers to accessing health care associated with inadequate antenatal care visits among women of reproductive age in Rwanda? BMC Pregnancy Childbirth. 2020;20(1):88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. National Statistical Office (NSO) [Papua New Guinea] (PNG) and ICF. (2019) . Papua New Guinea Demographic and Health Survey 2016–2018. National Report. Port Moresby, PNG: National Statistical Office. [Google Scholar]
  • 21. Yaya S, Uthman OA, Ekholuenetale M, et al. Women empowerment as an enabling factor of contraceptive use in sub-Saharan Africa: a multilevel analysis of cross-sectional surveys of 32 countries. Reprod Health. 2018;15(1):214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Amegbor PM, Rosenberg MW, Kuuire VZ.. Does place matter? A multilevel analysis of victimization and satisfaction with personal safety of seniors in Canada. Health Place. 2018;53:17–25. [DOI] [PubMed] [Google Scholar]
  • 23. Merlo J, Wagner P, Ghith N, et al. An original stepwise multilevel logistic regression analysis of discriminatory accuracy: the case of neighbourhoods and health. PloS One. 2016;11(4):e0153778. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Azuh D, Fayomi O, Ajayi L. Socio-cultural factors of gender roles in women's healthcare utilization in Southwest Nigeria. Open J Soc Sci. 2015;3:105–17. [Google Scholar]
  • 25. Lau A, Spark S, Smith TM, et al. Socio-demographic and structural barriers to being tested for Chlamydia in general practice. Med J Australia. 2016;204(3):112. [DOI] [PubMed] [Google Scholar]
  • 26. Badu E, Gyamfi N, Opoku MP, et al. Enablers and barriers in accessing sexual and reproductive health services among visually impaired women in the Ashanti and Brong Ahafo regions of Ghana. Reprod Health Matters. 2018;26(54):51–60. [DOI] [PubMed] [Google Scholar]
  • 27. King R, Jackson R, Dietsch E, et al. Barriers and facilitators to accessing skilled birth attendants in Afar region. Ethiopia Midwifery 2015;31:540–6. [DOI] [PubMed] [Google Scholar]
  • 28. Okwaraji YB, Webb EL, Edmond KM. Barriers in physical access to maternal health services in rural Ethiopia. BMC Health Serv Res. 2015;15(1):493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Munguambe K, Boene H, Vidler M, et al. Barriers and facilitators to health care seeking behaviours in pregnancy in rural communities of southern Mozambique. Reprod Health. 2016;13:84–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Oreopoulos P, Salvanes K.. Priceless: the nonpecuniary benefits of schooling. J Econ Perspect. 2011;25(1):159–84. [Google Scholar]
  • 31. Wahab SN, Satar NM, Tumin M, et al.Socio-demographic factors and structural barrier in accessing public clinics among the urban poor in Malaysia. J Soc Sci Humanities. 2020;17(3):71–81. [Google Scholar]
  • 32. Bojovic O, Medenica M, Zivkovic D, et al. Factors associated with patient and health system delays in diagnosis and treatment of tuberculosis in Montenegro 2015–2016. PLoS ONE. 2018;13(3):e0193997. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Kea A, Tulloch O, Datiko D, et al. Exploring barriers to the use of formal maternal health services and priority areas for action in Sidama zone, southern Ethiopia. BMC Pregnancy Childbirth. 2018;18:1–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Ani F, Abiodun O, Sotunsa J, et al. Demographic factors related to male involvement in reproductive health care services in Nigeria. Eur J Contracept Reprod Health Care. 2016;21(1):57–67. [DOI] [PubMed] [Google Scholar]
  • 35. Murata C, Yamada T, Chen CC, et al. Barriers to health care among the elderly in Japan. Int J Environ Res Public Health. 2010;7:1330–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Makmor T, Khaled T, Ahmad FO, et al. Demographic and socioeconomic factors associated with access to public clinics. J Health Translat Med. 2018;21(1):28–33. [Google Scholar]
  • 37. Sun R, Wu L, Barnett S, et al. Socio-demographic predictors associated with capacity to engage in health care. Patient Exp J. 2019;6(2):35–41. [Google Scholar]
  • 38. Collins L, Glasser AM, Abudayyeh H, et al. E-cigarette marketing and communication: how e-cigarette companies market e-cigarettes and the public engages with e-cigarette information. Nicotine Tob Res. 2019;21(1):14–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Woldeamanuel BT. Trends and factors associated with healthcare utilization for childhood diarrhea and fever in Ethiopia: further analysis of the demographic and health surveys from 2000 to 2016. J Environ Public Health. 2020;1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Zamawe CO, Banda M, Dube AN. The impact of a community driven mass media campaign on the utilisation of maternal health care services in rural Malawi. BMC Pregnancy Childbirth. 2016;16(1):21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Uddin MF. Impact of Mass Media on Antenatal Care (ANC) Utilization in Bangladesh. Institute for Population and Social Research (IPSR), Mahidol University, 2009:149. [Google Scholar]
  • 42. Ghosh D. Effects of mother's exposure to electronic mass media on knowledge and use of prenatal care service: a comparative analysis of Indian States. In The Professional Geographer. Oxford, UK: Blackwell Publishing,2006. [Google Scholar]
  • 43. Steinhardt L, Waters H, Rao K, et al. The effect of wealth status on care seeking and health expenditures in Afghanistan. Health Policy Planning. 2009;24:238. [DOI] [PubMed] [Google Scholar]
  • 44. Kalule-Sabiti I, Amoateng AY, Ngake M. The effect of socio-demographic factors on the utilization of maternal health care services in Uganda. African Population Studies. 2015;28(1):515–25. [Google Scholar]
  • 45. Loftus J, Allen EM, Call KT, et al. Rural-urban differences in access to preventive health care among publicly insured Minnesotans. J Rural Health. 2018;34(Suppl 1):S48–S55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46. Silal SP, Penn-Kekana L, Harris B, et al. Exploring inequalities in access to and use of maternal health services in South Africa. BMC Health Serv Res. 2012;12(1):120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47. Douthit N, Kiv S, Dwolatzky T, et al. Exposing some important barriers to health care access in the rural USA. Public Health. 2015;129(6):611–20. [DOI] [PubMed] [Google Scholar]
  • 48. Baldwin LM, Hollow WB, Casey S, et al. Access to specialty health care for rural American Indians in two states. J Rural Health. 2008;24(3):269–78. [DOI] [PubMed] [Google Scholar]
  • 49. Sarquist CC, Soni S, Hwang H, et al. Rural HIV-infected women's access to medical care: Ongoing needs in California. AIDS Care. 2011;23(7):792–6. [DOI] [PubMed] [Google Scholar]
  • 50. Institute of Medicine (US) Committee on the Consequences of Uninsurance . Care Without Coverage: Too Little, Too Late. Washington (DC): National Academies Press (US); 2002. 3, Effects of Health Insurance on Health. Available at: https://www.ncbi.nlm.nih.gov/books/NBK220636/ [accessed 20 May 2020]. [PubMed] [Google Scholar]
  • 51. Spaan E, Mathijssen J, Tromp N, et al. The impact of health insurance in Africa and Asia: a systematic review. Bulletin of the World Health Organization. 2012;90:685–92. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ihaa097_Supplemental_File
Click here for additional data file. (59.2KB, docx)

Data Availability Statement

The dataset can be accessed at https://dhsprogram.com/data/dataset/Papua-New-Guinea_Standard-DHS_2017.cfm?flag=0.

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