When money, distance, and men decide: A cross-sectional analysis of multidimensional barriers to healthcare access among women in Liberia

When money, distance, and men decide: A cross-sectional analysis of multidimensional barriers to healthcare access among women in Liberia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article When money, distance, and men decide: A cross-sectional analysis of multidimensional barriers to healthcare access among women in Liberia Dorcas Nzilani Kanini, Caleb Nyakundi, Sharonmercy Okemwa, Solomon Kimutai Toweet, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7652528/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Ensuring comprehensive healthcare access is essential for promoting good health, preventing and managing diseases, and reducing adverse outcomes such as disability and mortality. In Liberia, however, research has documented persistently low levels of healthcare utilisation among women. This underutilisation is closely linked to pronounced gender-based power imbalances and limited female participation in household financial decision-making. In light of these challenges, this study sought to examine the barriers to healthcare access encountered by women in Liberia. Methods We performed a secondary analysis of data from 8,064 women who participated in the 2019–2020 Liberia Demographic and Health Survey (LDHS). The outcome variables were measured using four healthcare access concerns: ‘getting permission to go for treatment’, ‘getting money for treatment’, ‘distance to health facilities’, and ‘not wanting to go alone’. We used frequencies and percentages to describe sample characteristics and prevalence. Bivariate and multivariable logistic regression analyses were used to evaluate factors associated with barriers to accessing healthcare among women. Results The weighted prevalence of women with at least one barrier was 44.7% (95% CI: 41.2–48.1%), while that of women with all barriers was 7.0% (95% CI: 5.6–8.9%). Women with secondary education (aOR 0.56, 95% CI 0.39–0.80), higher education (aOR 0.18, 95% CI 0.04–0.73), working employment status (aOR 0.58, 95% CI 0.42–0.79), and who used the internet less than once a week (aOR 0.15, 95% CI 0.05–0.46) had reduced odds of encountering barriers to accessing healthcare. In contrast, women from the poorest wealth quintile (aOR 3.11, 95% CI 1.28–7.57) and the South Eastern B region (aOR 1.84, 95% CI 1.16–2.90) had increased odds of experiencing barriers to healthcare access. Conclusion Almost half of Liberian women encountered at least one barrier to accessing healthcare, which reveals a gap in health system inclusivity and responsiveness. The study findings highlight the need for interventions and policies to expand women’s financial and decision-making autonomy, as well as enhance community outreach tailored to women’s health needs. Women healthcare access barriers financial constraints distance permission and companionship. Figures Figure 1 Figure 2 Figure 3 Introduction Women’s health is fundamental to the growth of any society since it translates to economic growth, increased productivity, reduced poverty, improved child health, and other benefits [ 1 ]. Healthcare access refers to the availability, accessibility, cost, and acceptability of health services [ 2 ]. Access to healthcare is a basic human right that significantly impacts the overall quality of life, encompassing physical, social, mental, and emotional well-being [ 1 , 3 ]. Access to comprehensive and premium care is key to ensuring good health, preventing and managing diseases, and decreasing adverse effects like disability and death [ 4 ]. Sustainable Development Goals (SDGs) 3.1, 3.7, and 3.8 focus on lowering maternal mortality worldwide, enhancing reproductive healthcare access, and achieving universal health coverage, respectively [ 5 ]. Additionally, SDG 5 aims to boost women’s empowerment and gender equality by ensuring universal sexual and reproductive healthcare access for all by 2030 [ 6 ]. Approximately 400 million people around the world face significant challenges in accessing medical care, and an estimated eight million die each year from treatable diseases [ 1 ]. Among these preventable tragedies, maternal mortality remains a pressing concern. In 2020, around 287,000 women died from pregnancy-related causes globally [ 7 ]. Most of the leading causes of maternal mortality could be prevented and managed with access to premium care [ 8 ]. Despite the global efforts to strengthen health systems through policy reforms and increased investment in service delivery, women, particularly in low-income countries, continue to encounter substantial barriers to care [ 9 ]. These barriers are not just systemic but also deeply rooted in individual and contextual factors that shape women’s ability to seek and receive medical attention [ 10 ]. Surveys conducted in Ghana, Benin, Ethiopia, and Sierra Leone revealed that 51%, 60.4%, 69.9%, and 71.9% of women, respectively, experienced at least one obstacle to accessing healthcare services [ 1 , 4 , 10 , 11 ]. Factors associated with barriers to healthcare access include sociodemographic characteristics like age [ 1 , 4 , 10 – 14 ], women’s education and occupation [ 1 , 4 , 10 – 14 ], husband’s education and occupation [ 1 , 13 ], religion [ 1 , 10 , 11 , 13 , 14 ], wealth status [ 1 , 4 , 10 – 14 ], and ethnicity [ 4 , 11 ]. Moreover, health insurance subscription [ 4 , 10 – 14 ], exposure to mass media [ 10 – 13 ], sex of head of household [ 11 – 14 ], reproductive characteristics [ 4 , 13 ], parity [ 1 , 10 – 12 ], marital status [ 1 , 4 , 10 – 14 ], residence [ 1 , 4 , 10 , 12 , 13 ], region [ 1 , 10 , 13 ], community literacy level [ 1 ], and community socioeconomic level [ 1 , 10 ] are associated with healthcare access. Liberia has made significant efforts to ensure that health services are equitable and accessible to everyone [ 15 ]. The implementation of national health policies and other policies aimed at enhancing the availability and affordability of services linked to women’s health at all facilities has played a vital role in improving their health [ 15 – 17 ]. The Ministry of Health also implemented the National Community Health program to increase accessibility and delivery of health services to every community [ 18 ]. Despite substantial improvements, Liberia still faces major health issues [ 19 ]. In 2020, although Liberia's maternal mortality rates had reduced to 742 per 100,000 live births, it was still reported among the highest in Sub-Saharan Africa [ 19 , 20 ]. As such, much effort is required to attain the set national maternal mortality target of less than 520 women per 100,000 live births by 2026[ 21 ] as well as the third SDGs’ targets by 2030 [ 5 ]. In 2020, 80% of Liberian women had facility-based deliveries, while 87% had four or more antenatal care visits [ 22 ]. These proportions remain below the 90% global coverage target by 2025 [ 23 ]. Besides, malaria is endemic in Liberia and continues to rank as one of the major public health issues, mainly affecting pregnant women [ 24 ]. In 2019–2020, the proportion of overweight or obese women had increased to 37% which is a significant risk factor for most chronic diseases [ 24 ]. Existing studies in Liberia have documented low levels of healthcare utilisation among women and identified strong associations between healthcare use and women’s empowerment, particularly in relation to gender power dynamics and limited financial decision-making autonomy [ 25 ]. However, the current evidence base remains limited, with most research narrowly focused on maternal healthcare utilisation, leaving broader access barriers underexplored [ 26 ]. To address this gap, the present study uses data from the nationally representative 2019–2020 Liberia Demographic Health Survey (LDHS) to examine the determinants of healthcare access among women. The findings aim to inform targeted interventions that can reduce access barriers and improve healthcare utilisation in this population. Methods Study Setting Liberia is a low-income nation in West Africa, with an estimated population of 5.5 million as of 2023 [27]. In 2024, the economy was reported to have expanded by 4.8% fueled by mining and construction services, and agricultural output [28]. The healthcare system still faces significant challenges due to the effects of the 1989–2003 civil war and the 2014–2016 Ebola epidemic, which disrupted the delivery of essential health services [29]. Liberia’s health system is organised into three levels of care: (1) The primary level, which consists of small health facilities; (2) Secondary level, which comprises larger health centres and county hospitals; (3) Tertiary level, which includes the national teaching and referral hospitals[30]. The country has a total of 935 health facilities; however, their distribution is uneven, with some areas having only one facility serving over 5,000 people [31]. Data Source and Sampling We performed a secondary analysis of the 2019–2020 LDHS data. The LDHS was a cross-sectional survey involving 9,745 households sampled from the 2008 National Population and Housing Census. The survey employed a two-stage stratified sample design whereby, in the first stage, 325 clusters consisting of enumeration areas were selected, followed by a systematic sampling of 30 households from each cluster [32]. A total of 8,065 women aged 15–49 were successfully interviewed (96% response rate) [22]. Details on the sampling procedure are contained elsewhere [32]. Variables Outcome Variable We constructed two outcome variables, measured using four healthcare access problems: ‘getting permission to go for treatment’, ‘getting money for treatment’, ‘distance to health facilities’, and ‘not wanting to go alone’ [10, 11]. The first one, “all barriers,” was defined as a woman having all four problems with healthcare access. Those who reported having a “big problem” in all four questions were coded “1” and those with “no big problem” were coded “0”. The second outcome variable, “at least one barrier,” was defined as a woman having at least one of the four problems. Those who reported having a “big problem” in any of the four questions were coded “1” and those with “no big problem” were coded “0”. We examined the factors associated with having all the barriers in our final analysis. Explanatory Variables Following an extensive literature review [1, 4, 10–14], we developed a conceptual framework with both individual and contextual level variables. The individual-level variables included age, marital status, education level, employment status, religion, parity, health insurance, radio use, television use, newspaper use, and internet use [1, 4, 10–14]. Sex of head of household, wealth status, residence, and region were the contextual-level variables [1, 4, 10–14] (Fig. 1). The variables were defined following operational definitions used in previous studies (Table 1). Statistical Analysis We used frequencies and percentages to describe sample characteristics. The prevalence of barriers to accessing healthcare by predictors was summarised using weighted proportions and confidence intervals. We performed weighted analyses using the “svy” command in Stata and applied the sample weights to account for the clustered sampling design. Bivariate and multivariable logistic regression analyses were used to assess the factors associated with barriers to accessing healthcare. We used the variance inflation factor (VIF) to assess multicollinearity among the predictor variables before including them in the multivariable model. Bivariate and multivariable logistic regression analyses results were reported using both crude and adjusted odds ratios with 95% CI, and statistical significance was set at p-value < 0.05. We performed the Hosmer-Lemeshow test to evaluate the goodness of fit for the multivariable logistic regression model. All statistical analyses were performed using STATA version 17.0 (Stata Corporation, College Station, Texas), while R version 4.5.1 was used for data visualisation. The Strengthening Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used in this study [33] (Supplementary Table 1). Results Sample Characteristics This study involved 8,064 women; most of whom were aged 20-34 years (45.4%), had no education (37.0%), were working (64.0%), and were from the poorest wealth quintile (26.0%). The majority of the women had no health insurance cover (96.3%), did not read newspapers weekly (90.4%), did not use the internet in the past month (86.7%), resided in rural areas (58.6%), and were from the South Central region (28.5%) (Table 2). Prevalence of Healthcare Access Barriers among Women in Liberia Approximately 44.7% (95% CI: 41.2 - 48.1%) of women reported having at least one barrier, while 7.0% (95% CI: 5.6 - 8.9%) had all four barriers to accessing healthcare. Specifically, 36.2%, 28.3%, 18.9%, and 14.4% reported having a problem getting money needed for treatment, distance to the nearest health facility, companionship, and getting permission, respectively (Figure 2) The county prevalence of women with at least one barrier to healthcare access was highest in Gbarpolu, Grand Cape Mount, Bomi, Maryland, and Grand Kru counties, while Grand Kru and Maryland counties had the highest prevalence of all barriers. The North Western region, which comprises Gbarpolu, Grand Cape Mount, and Bomi counties, had the highest prevalence of women encountering at least one barrier. Besides, the proportion of women with all barriers was highest in the South Eastern B region, which includes Grand Kru and Maryland counties (Figure 3). The prevalence of all barriers to healthcare access was highest among women aged 15-19 years (8.0%), those without education (10.3%), those not working (7.8%), and those from the poorest wealth quintile (13.5%). Healthcare access barriers were also highly prevalent among women without health insurance coverage (7.3%), those who had not used the internet in the past month (8.7%), those residing in rural areas (10.7%), and those living in the South Eastern B region (14.0%) (Table 3). Women aged 35-49 years (50.0%), those without education (51.7%), working women (45.1%), and those belonging to the poorest wealth status (62.4%) had an increased prevalence of at least one barrier to healthcare access. Moreover, women with no health insurance coverage (45.0%), those who did not use the internet in the past month (48.4%), those residing in rural areas (58.9%), and those living in the North Western region (66.9%) had a high prevalence of healthcare access barriers (Table 3). Factors associated with experiencing all barriers to accessing healthcare among women In the bivariate logistic regression analysis, being in a marital union, having secondary and higher education, having four or more children, wealth index, and health insurance were associated with barriers to accessing healthcare among women. Moreover, using the radio and television at least once a week, reading newspapers, using the internet, rural residence, the South Eastern B, and North Central regions were associated with barriers to accessing healthcare among women. In the multivariable logistic regression analysis, women with secondary and higher education had 44% (aOR 0.56, 95% CI 0.39, 0.80, P=0.001) and 82% (aOR 0.18, 95% CI 0.04, 0.73, P=0.016) lower odds of experiencing barriers to healthcare access compared to those with no education, respectively. Working women and those who used the internet at least once a week had 42% (aOR 0.58, 95% CI 0.42, 0.79, P=0.001) and 56% (aOR 0.44, 95% CI 0.20, 0.98, P=0.044) reduced odds of experiencing barriers to accessing healthcare compared to those not working and those who did not use the internet in the past month, respectively. Compared to the richest wealth quintile, women from the poorest had 3.1 times (aOR 3.11, 95% CI 1.28, 7.57, P=0.012) higher odds of experiencing barriers to accessing healthcare. The South Eastern B region women had 1.8-fold (aOR 1.84, 95% CI 1.16, 2.90, P=0.009) higher odds of encountering barriers to healthcare access than those from the South-Central region (Table 4). Model Diagnostics The mean VIF was 1.53, indicating no multicollinearity among the independent variables [34] (Supplementary Table 2). The Hosmer-Lemeshow test produced a p-value of 0.793, which suggested that the model was a good fit [35]. Discussion To the best of our knowledge, this study is the first to examine associations between individual and contextual level factors, and barriers to accessing healthcare among women in Liberia. Women aged 15–19 years, those without education, those who were not working, those who belonged to the poorest wealth status, those without health insurance cover, those who had not used the internet in the past month, those who resided in rural areas, and those from the South Eastern B region had a high prevalence of healthcare access barriers. The proportion of women with challenges in getting money needed for treatment, distance to the nearest health facility, companionship, and getting permission was 36.2%, 28.3%, 18.9% and 14.4%, respectively. Education level, employment status, wealth status, internet use, and the South Eastern B region were associated with barriers to accessing healthcare among women. The proportion of women with all barriers was 7.0% while those with at least one barrier was 44.7%. Similar studies conducted in Ghana, Benin, Ethiopia, and Sierra Leone revealed slightly higher prevalence of women with at least one barrier in accessing healthcare 51%, 60.4%, 69.9%, and 71.9%, respectively [ 1 , 4 , 10 , 11 ]. Sociocultural and economic disparities among the countries influencing how people seek healthcare could be the reason for the difference in prevalence [ 36 ]. However, the barriers still affect almost half of the Liberian women, and the maternal mortality rate is among the highest in Sub-Saharan Africa, indicating a significant impact of the barriers [ 37 ]. The most common challenge in accessing healthcare among Liberian women was getting the finances needed for treatment, which had a prevalence of 36.2%. Compared to the reported prevalence in 2013 (47%)[ 38 ] and 2007 (54%), this study unearthed a lower prevalence [ 39 ]. Interventions and investment in national policies promoting the delivery of comprehensive and quality health services for all people could explain this significant improvement [ 15 ]. The Ministry of Health and Social Welfare implemented several policies and plans to ensure equitable access to comprehensive and quality healthcare services [ 15 ]. Several studies have also reported similar results, with cost-related barriers being the primary issue [ 10 , 12 , 13 ]. Similar to previous studies conducted in Liberia [ 25 ], Senegal [ 40 ], Ethiopia [ 41 ], and East African countries [ 42 ], women with secondary and higher education had reduced odds of experiencing barriers to accessing healthcare compared to those with no education. Attributable to that pattern could be the increased awareness and improved healthcare-seeking behaviour among women with higher education [ 41 ]. Education significantly impacts women’s health decision-making and empowers them to challenge societal norms that may deter them from seeking healthcare services [ 11 ]. Moreover, education substantially influences employment opportunities, income, and overall national economic growth, which may also make healthcare services more accessible [ 1 ]. Consistent with previous studies in Sub-Saharan Africa [ 10 , 43 ], working women had lower odds of encountering barriers to accessing healthcare compared to the unemployed, possibly due to increased financial independence and ability to afford medical services among those working. This study found a negative association between wealth index and the barriers to healthcare access among women. Women from the poorest wealth quintile had higher odds of experiencing barriers to accessing healthcare than those from the richest. Previous studies conducted in Liberia [ 25 ], Sierra Leone [ 11 ], The Gambia [ 13 ], Ethiopia [ 41 ], and Sub-Saharan Africa [ 44 ] had corresponding findings. The negative association could be explained by the increased ability of women from the richest wealth quintile to afford healthcare services compared to those from the poorest [ 13 ]. Compared to women who did not use the internet in the past month, internet users had lower odds of experiencing barriers to healthcare access, consistent with previous studies conducted in Sierra Leone[ 11 ]. Internet use could increase health literacy and bridge geographical barriers through online health platforms[ 11 , 45 ]. We also found that women from the South Eastern B region had increased odds of experiencing barriers to healthcare access compared to those from the South Central region. The South Eastern B region comprises counties like Maryland and Grand Kru, which had a high proportion of women experiencing all barriers to healthcare access. Challenges such as poor infrastructure and having minimal health facilities that offer comprehensive basic health services in the South Eastern B region could result in difficulties in healthcare access [46, 47]. Strengths and Limitations Our study used the 2019–2020 nationally representative LDHS data, making the findings generalisable to all women in Liberia. This study provides up-to-date evidence on barriers to accessing healthcare among women in Liberia using the latest LDHS dataset, which will influence interventions and policy making. Additionally, using two outcome variables provides a comprehensive understanding of the barriers to healthcare access among women. Despite these, the study has some limitations. The cross-sectional design employed by this study limits causal inference. Moreover, since the data was collected through self-report, there may be chances of recall and response biases. Policy implications and recommendations This study highlights significant challenges in achieving healthcare access, which threaten the attainment of SDGs 3 and 5. The findings call for strengthening the healthcare infrastructure by increasing the healthcare facilities, especially in rural and underdeveloped regions, including South Eastern B, to reduce regional disparities in healthcare access. While improving facility coverage, it is necessary to guarantee universal health coverage and ensure the facilities are well-equipped. Moreover, our findings point out the need for women empowerment interventions through education and the provision of employment opportunities. Since Liberia is implementing a national health insurance scheme, it is important to consider terms that favour the poor and vulnerable to increase their ability to afford and access health services. Incorporating such terms will lessen the financial burden when accessing healthcare by reducing out-of-pocket costs. Policymakers should develop and implement policies that address women’s barriers, such as expanding women’s financial and decision-making autonomy, and enhancing community outreach tailored to women’s health needs. There is a need for health education and promotion through media, educational campaigns, and other platforms to raise health awareness among women and influence community involvement in the development and execution of programs targeting their health. These health education programs should also foster male involvement in women’s health needs. Conclusion The study showed that almost half of Liberian women encountered at least one barrier to accessing healthcare. Having secondary and higher education, being employed, and using the internet were associated with reduced odds of encountering hurdles in healthcare access, while belonging to the poorest wealth quintile and residing in the South Eastern B region were associated with increased odds. These findings highlight the need to strengthen existing healthcare systems in Liberia to ensure healthcare accessibility for all, including women. Targeted interventions, such as policies that promote women's education, employment, and independence in money and decision-making, should be prioritised within national health strategies. Additionally, there is a need for affordable health insurance coverage to lessen the financial strain associated with accessing healthcare. Eliminating the barriers to accessing health among women can help Liberia achieve SDG 3 and SDG 5 targets. Abbreviations aOR: Adjusted Odds Ratio cOR: Crude Odds Ratio CI: Confidence Interval CHWs: Community Health Workers DHS: Demographic Health Survey IRB: Institutional Review Board LDHS: Liberia Demographic Health Survey SDG: Sustainable Development Goal VIF: Variance Inflation Factor Declarations Acknowledgements We appreciate the MEASURE DHS for giving us access to their data. Authors’ contributions JOO, CA, CN, SO, and ST conceptualised and supervised the study. DK and CN analysed the data, with inputs from JOO, CA, SO, and ST. DK wrote the first draft of the manuscript, which was subsequently revised by GM, AA, CA, JOO, CN, SO, and ST. All authors critically read, reviewed, and approved the final manuscript for publication. Funding This study was not funded. Availability of data and materials Data used in this study can be accessed from the DHS website at https://dhsprogram.com/data/dataset/Liberia_Standard-DHS_2019.cfm. Ethical approval and consent to participate This study used secondary data from the LDHS 2019-2020 surveys, which were approved by the Institutional Review Board (IRB) of the ICF International and conducted in accordance with the Declaration of Helsinki. Additionally, the LDHS was implemented following approval by the Liberian National Ethics Committee. The IRB of the ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the protection of human subjects (45 CFR 46), while the host country's IRB ensures the survey complies with the laws and norms of the nation. Informed consent was obtained from every participant, as indicated in the 2019-2020 LDHS report, and the data was anonymised during analysis [24]. Since this study utilised secondary data from the LDHS, which is publicly available, no ethical approval was required. The data used for this study was accessed from the DHS after permission was granted. 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ResearchGate. 2018. https://doi.org/10.56801/seejph.vi.125. Additional Declarations No competing interests reported. Supplementary Files WhenMoneyDistanceandMenDecideSupplementaryfiles.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Nov, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor invited by journal 25 Sep, 2025 Editor assigned by journal 24 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 18 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7652528","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522040442,"identity":"4472dedd-ab61-46c8-82b6-16d382db3798","order_by":0,"name":"Dorcas Nzilani 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10:39:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of barriers to accessing healthcare\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7652528/v1/a7cbb85b25eb2b86eb96acee.png"},{"id":92709934,"identity":"c6b19783-f54f-46c6-88f0-20f39df772fe","added_by":"auto","created_at":"2025-10-03 10:47:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92535,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrevalence of healthcare access barriers in Liberia by 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Healthcare access refers to the availability, accessibility, cost, and acceptability of health services [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Access to healthcare is a basic human right that significantly impacts the overall quality of life, encompassing physical, social, mental, and emotional well-being [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Access to comprehensive and premium care is key to ensuring good health, preventing and managing diseases, and decreasing adverse effects like disability and death [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sustainable Development Goals (SDGs) 3.1, 3.7, and 3.8 focus on lowering maternal mortality worldwide, enhancing reproductive healthcare access, and achieving universal health coverage, respectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, SDG 5 aims to boost women\u0026rsquo;s empowerment and gender equality by ensuring universal sexual and reproductive healthcare access for all by 2030 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eApproximately 400\u0026nbsp;million people around the world face significant challenges in accessing medical care, and an estimated eight million die each year from treatable diseases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among these preventable tragedies, maternal mortality remains a pressing concern. In 2020, around 287,000 women died from pregnancy-related causes globally [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMost of the leading causes of maternal mortality could be prevented and managed with access to premium care [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite the global efforts to strengthen health systems through policy reforms and increased investment in service delivery, women, particularly in low-income countries, continue to encounter substantial barriers to care [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These barriers are not just systemic but also deeply rooted in individual and contextual factors that shape women\u0026rsquo;s ability to seek and receive medical attention [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSurveys conducted in Ghana, Benin, Ethiopia, and Sierra Leone revealed that 51%, 60.4%, 69.9%, and 71.9% of women, respectively, experienced at least one obstacle to accessing healthcare services [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Factors associated with barriers to healthcare access include sociodemographic characteristics like age [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], women\u0026rsquo;s education and occupation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], husband\u0026rsquo;s education and occupation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], religion [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], wealth status [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and ethnicity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, health insurance subscription [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], exposure to mass media [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], sex of head of household [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], reproductive characteristics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], parity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], marital status [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], residence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], community literacy level [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and community socioeconomic level [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] are associated with healthcare access.\u003c/p\u003e\u003cp\u003eLiberia has made significant efforts to ensure that health services are equitable and accessible to everyone [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The implementation of national health policies and other policies aimed at enhancing the availability and affordability of services linked to women\u0026rsquo;s health at all facilities has played a vital role in improving their health [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The Ministry of Health also implemented the National Community Health program to increase accessibility and delivery of health services to every community [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite substantial improvements, Liberia still faces major health issues [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In 2020, although Liberia's maternal mortality rates had reduced to 742 per 100,000 live births, it was still reported among the highest in Sub-Saharan Africa [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. As such, much effort is required to attain the set national maternal mortality target of less than 520 women per 100,000 live births by 2026[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] as well as the third SDGs\u0026rsquo; targets by 2030 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In 2020, 80% of Liberian women had facility-based deliveries, while 87% had four or more antenatal care visits [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These proportions remain below the 90% global coverage target by 2025 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Besides, malaria is endemic in Liberia and continues to rank as one of the major public health issues, mainly affecting pregnant women [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In 2019\u0026ndash;2020, the proportion of overweight or obese women had increased to 37% which is a significant risk factor for most chronic diseases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExisting studies in Liberia have documented low levels of healthcare utilisation among women and identified strong associations between healthcare use and women\u0026rsquo;s empowerment, particularly in relation to gender power dynamics and limited financial decision-making autonomy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, the current evidence base remains limited, with most research narrowly focused on maternal healthcare utilisation, leaving broader access barriers underexplored [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To address this gap, the present study uses data from the nationally representative 2019\u0026ndash;2020 Liberia Demographic Health Survey (LDHS) to examine the determinants of healthcare access among women. The findings aim to inform targeted interventions that can reduce access barriers and improve healthcare utilisation in this population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy Setting\u003c/h2\u003e\n \u003cp\u003eLiberia is a low-income nation in West Africa, with an estimated population of 5.5\u0026nbsp;million as of 2023 [27]. In 2024, the economy was reported to have expanded by 4.8% fueled by mining and construction services, and agricultural output [28]. The healthcare system still faces significant challenges due to the effects of the 1989\u0026ndash;2003 civil war and the 2014\u0026ndash;2016 Ebola epidemic, which disrupted the delivery of essential health services [29]. Liberia\u0026rsquo;s health system is organised into three levels of care: (1) The primary level, which consists of small health facilities; (2) Secondary level, which comprises larger health centres and county hospitals; (3) Tertiary level, which includes the national teaching and referral hospitals[30]. The country has a total of 935 health facilities; however, their distribution is uneven, with some areas having only one facility serving over 5,000 people [31].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eData Source and Sampling\u003c/h3\u003e\n\u003cp\u003eWe performed a secondary analysis of the 2019\u0026ndash;2020 LDHS data. The LDHS was a cross-sectional survey involving 9,745 households sampled from the 2008 National Population and Housing Census. The survey employed a two-stage stratified sample design whereby, in the first stage, 325 clusters consisting of enumeration areas were selected, followed by a systematic sampling of 30 households from each cluster [32]. A total of 8,065 women aged 15\u0026ndash;49 were successfully interviewed (96% response rate) [22]. Details on the sampling procedure are contained elsewhere [32].\u003c/p\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eOutcome Variable\u003c/h2\u003e\n \u003cp\u003eWe constructed two outcome variables, measured using four healthcare access problems: \u0026lsquo;getting permission to go for treatment\u0026rsquo;, \u0026lsquo;getting money for treatment\u0026rsquo;, \u0026lsquo;distance to health facilities\u0026rsquo;, and \u0026lsquo;not wanting to go alone\u0026rsquo; [10, 11]. The first one, \u0026ldquo;all barriers,\u0026rdquo; was defined as a woman having all four problems with healthcare access. Those who reported having a \u0026ldquo;big problem\u0026rdquo; in all four questions were coded \u0026ldquo;1\u0026rdquo; and those with \u0026ldquo;no big problem\u0026rdquo; were coded \u0026ldquo;0\u0026rdquo;. The second outcome variable, \u0026ldquo;at least one barrier,\u0026rdquo; was defined as a woman having at least one of the four problems. Those who reported having a \u0026ldquo;big problem\u0026rdquo; in any of the four questions were coded \u0026ldquo;1\u0026rdquo; and those with \u0026ldquo;no big problem\u0026rdquo; were coded \u0026ldquo;0\u0026rdquo;. We examined the factors associated with having all the barriers in our final analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eExplanatory Variables\u003c/h3\u003e\n\u003cp\u003eFollowing an extensive literature review [1, 4, 10\u0026ndash;14], we developed a conceptual framework with both individual and contextual level variables. The individual-level variables included age, marital status, education level, employment status, religion, parity, health insurance, radio use, television use, newspaper use, and internet use [1, 4, 10\u0026ndash;14]. Sex of head of household, wealth status, residence, and region were the contextual-level variables [1, 4, 10\u0026ndash;14] (Fig. 1).\u003c/p\u003e\n\u003cp\u003eThe variables were defined following operational definitions used in previous studies (Table 1).\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eWe used frequencies and percentages to describe sample characteristics. The prevalence of barriers to accessing healthcare by predictors was summarised using weighted proportions and confidence intervals. We performed weighted analyses using the \u0026ldquo;svy\u0026rdquo; command in Stata and applied the sample weights to account for the clustered sampling design. Bivariate and multivariable logistic regression analyses were used to assess the factors associated with barriers to accessing healthcare. We used the variance inflation factor (VIF) to assess multicollinearity among the predictor variables before including them in the multivariable model. Bivariate and multivariable logistic regression analyses results were reported using both crude and adjusted odds ratios with 95% CI, and statistical significance was set at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We performed the Hosmer-Lemeshow test to evaluate the goodness of fit for the multivariable logistic regression model. All statistical analyses were performed using STATA version 17.0 (Stata Corporation, College Station, Texas), while R version 4.5.1 was used for data visualisation. The Strengthening Reporting of Observational Studies in Epidemiology (STROBE) guidelines were used in this study [33] (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSample Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved 8,064 women; most of whom were aged 20-34 years (45.4%), had no education (37.0%), were working (64.0%), and were from the poorest wealth quintile (26.0%). The majority of the women had no health insurance cover (96.3%), did not read newspapers weekly (90.4%), did not use the internet in the past month (86.7%), resided in rural areas (58.6%), and were from the South Central region (28.5%) (Table 2).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of Healthcare Access Barriers among Women in Liberia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 44.7% (95% CI: 41.2 - 48.1%) of women reported having at least one barrier, while 7.0% (95% CI: 5.6 - 8.9%) had all four barriers to accessing healthcare. Specifically, 36.2%, 28.3%, 18.9%, and 14.4% reported having a problem getting money needed for treatment, distance to the nearest health facility, companionship, and getting permission, respectively (Figure 2)\u003c/p\u003e\n\u003cp\u003eThe county prevalence of women with at least one barrier to healthcare access was highest in Gbarpolu, Grand Cape Mount, Bomi, Maryland, and Grand Kru counties, while Grand Kru and Maryland counties had the highest prevalence of all barriers. The North Western region, which comprises Gbarpolu, Grand Cape Mount, and Bomi counties, had the highest prevalence of women encountering at least one barrier. Besides, the proportion of women with all barriers was highest in the South Eastern B region, which includes Grand Kru and Maryland counties (Figure 3).\u003c/p\u003e\n\u003cp\u003eThe prevalence of all barriers to healthcare access was highest among women aged 15-19 years (8.0%), those without education (10.3%), those not working (7.8%), and those from the poorest wealth quintile (13.5%). Healthcare access barriers were also highly prevalent among women without health insurance coverage (7.3%), those who had not used the internet in the past month (8.7%), those residing in rural areas (10.7%), and those living in the South Eastern B region (14.0%) (Table 3).\u003c/p\u003e\n\u003cp\u003eWomen aged 35-49 years (50.0%), those without education (51.7%), working women (45.1%), and those belonging to the poorest wealth status (62.4%) had an increased prevalence of at least one barrier to healthcare access. Moreover, women with no health insurance coverage (45.0%), those who did not use the internet in the past month (48.4%), those residing in rural areas (58.9%), and those living in the North Western region (66.9%) had a high prevalence of healthcare access barriers (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFactors associated with experiencing all barriers to accessing healthcare among women\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the bivariate logistic regression analysis, being in a marital union, having secondary and higher education, having four or more children, wealth index, and health insurance were associated with barriers to accessing healthcare among women. Moreover, using the radio and television at least once a week, reading newspapers, using the internet, rural residence, the South Eastern B, and North Central regions were associated with barriers to accessing healthcare among women. In the multivariable logistic regression analysis, women with secondary and higher education had 44% (aOR 0.56, 95% CI 0.39, 0.80, P=0.001) and 82% (aOR 0.18, 95% CI 0.04, 0.73, P=0.016) lower odds of experiencing barriers to healthcare access compared to those with no education, respectively. Working women and those who used the internet at least once a week had 42% (aOR 0.58, 95% CI 0.42, 0.79, P=0.001) and 56% (aOR 0.44, 95% CI 0.20, 0.98, P=0.044) reduced odds of experiencing barriers to accessing healthcare compared to those not working and those who did not use the internet in the past month, respectively. Compared to the richest wealth quintile, women from the poorest had 3.1 times (aOR 3.11, 95% CI 1.28, 7.57, P=0.012) higher odds of experiencing barriers to accessing healthcare. The South Eastern B region women had 1.8-fold (aOR 1.84, 95% CI 1.16, 2.90, P=0.009) higher odds of encountering barriers to healthcare access than those from the South-Central region (Table 4).\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Diagnostics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean VIF was 1.53, indicating no multicollinearity among the independent variables [34] (Supplementary Table 2). The Hosmer-Lemeshow test produced a p-value of 0.793, which suggested that the model was a good fit [35].\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to examine associations between individual and contextual level factors, and barriers to accessing healthcare among women in Liberia. Women aged 15\u0026ndash;19 years, those without education, those who were not working, those who belonged to the poorest wealth status, those without health insurance cover, those who had not used the internet in the past month, those who resided in rural areas, and those from the South Eastern B region had a high prevalence of healthcare access barriers. The proportion of women with challenges in getting money needed for treatment, distance to the nearest health facility, companionship, and getting permission was 36.2%, 28.3%, 18.9% and 14.4%, respectively. Education level, employment status, wealth status, internet use, and the South Eastern B region were associated with barriers to accessing healthcare among women.\u003c/p\u003e\u003cp\u003eThe proportion of women with all barriers was 7.0% while those with at least one barrier was 44.7%. Similar studies conducted in Ghana, Benin, Ethiopia, and Sierra Leone revealed slightly higher prevalence of women with at least one barrier in accessing healthcare 51%, 60.4%, 69.9%, and 71.9%, respectively [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Sociocultural and economic disparities among the countries influencing how people seek healthcare could be the reason for the difference in prevalence [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, the barriers still affect almost half of the Liberian women, and the maternal mortality rate is among the highest in Sub-Saharan Africa, indicating a significant impact of the barriers [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The most common challenge in accessing healthcare among Liberian women was getting the finances needed for treatment, which had a prevalence of 36.2%. Compared to the reported prevalence in 2013 (47%)[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and 2007 (54%), this study unearthed a lower prevalence [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Interventions and investment in national policies promoting the delivery of comprehensive and quality health services for all people could explain this significant improvement [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The Ministry of Health and Social Welfare implemented several policies and plans to ensure equitable access to comprehensive and quality healthcare services [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Several studies have also reported similar results, with cost-related barriers being the primary issue [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilar to previous studies conducted in Liberia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Senegal [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], Ethiopia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and East African countries [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], women with secondary and higher education had reduced odds of experiencing barriers to accessing healthcare compared to those with no education. Attributable to that pattern could be the increased awareness and improved healthcare-seeking behaviour among women with higher education [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Education significantly impacts women\u0026rsquo;s health decision-making and empowers them to challenge societal norms that may deter them from seeking healthcare services [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, education substantially influences employment opportunities, income, and overall national economic growth, which may also make healthcare services more accessible [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Consistent with previous studies in Sub-Saharan Africa [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e43\u003c/span\u003e], working women had lower odds of encountering barriers to accessing healthcare compared to the unemployed, possibly due to increased financial independence and ability to afford medical services among those working.\u003c/p\u003e\u003cp\u003eThis study found a negative association between wealth index and the barriers to healthcare access among women. Women from the poorest wealth quintile had higher odds of experiencing barriers to accessing healthcare than those from the richest. Previous studies conducted in Liberia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Sierra Leone [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], The Gambia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], Ethiopia [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and Sub-Saharan Africa [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e44\u003c/span\u003e] had corresponding findings. The negative association could be explained by the increased ability of women from the richest wealth quintile to afford healthcare services compared to those from the poorest [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Compared to women who did not use the internet in the past month, internet users had lower odds of experiencing barriers to healthcare access, consistent with previous studies conducted in Sierra Leone[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Internet use could increase health literacy and bridge geographical barriers through online health platforms[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We also found that women from the South Eastern B region had increased odds of experiencing barriers to healthcare access compared to those from the South Central region. The South Eastern B region comprises counties like Maryland and Grand Kru, which had a high proportion of women experiencing all barriers to healthcare access. Challenges such as poor infrastructure and having minimal health facilities that offer comprehensive basic health services in the South Eastern B region could result in difficulties in healthcare access [46, 47].\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eOur study used the 2019\u0026ndash;2020 nationally representative LDHS data, making the findings generalisable to all women in Liberia. This study provides up-to-date evidence on barriers to accessing healthcare among women in Liberia using the latest LDHS dataset, which will influence interventions and policy making. Additionally, using two outcome variables provides a comprehensive understanding of the barriers to healthcare access among women. Despite these, the study has some limitations. The cross-sectional design employed by this study limits causal inference. Moreover, since the data was collected through self-report, there may be chances of recall and response biases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003ePolicy implications and recommendations\u003c/h2\u003e\u003cp\u003eThis study highlights significant challenges in achieving healthcare access, which threaten the attainment of SDGs 3 and 5. The findings call for strengthening the healthcare infrastructure by increasing the healthcare facilities, especially in rural and underdeveloped regions, including South Eastern B, to reduce regional disparities in healthcare access. While improving facility coverage, it is necessary to guarantee universal health coverage and ensure the facilities are well-equipped. Moreover, our findings point out the need for women empowerment interventions through education and the provision of employment opportunities. Since Liberia is implementing a national health insurance scheme, it is important to consider terms that favour the poor and vulnerable to increase their ability to afford and access health services. Incorporating such terms will lessen the financial burden when accessing healthcare by reducing out-of-pocket costs. Policymakers should develop and implement policies that address women\u0026rsquo;s barriers, such as expanding women\u0026rsquo;s financial and decision-making autonomy, and enhancing community outreach tailored to women\u0026rsquo;s health needs. There is a need for health education and promotion through media, educational campaigns, and other platforms to raise health awareness among women and influence community involvement in the development and execution of programs targeting their health. These health education programs should also foster male involvement in women\u0026rsquo;s health needs.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study showed that almost half of Liberian women encountered at least one barrier to accessing healthcare. Having secondary and higher education, being employed, and using the internet were associated with reduced odds of encountering hurdles in healthcare access, while belonging to the poorest wealth quintile and residing in the South Eastern B region were associated with increased odds. These findings highlight the need to strengthen existing healthcare systems in Liberia to ensure healthcare accessibility for all, including women. Targeted interventions, such as policies that promote women's education, employment, and independence in money and decision-making, should be prioritised within national health strategies. Additionally, there is a need for affordable health insurance coverage to lessen the financial strain associated with accessing healthcare. Eliminating the barriers to accessing health among women can help Liberia achieve SDG 3 and SDG 5 targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eaOR: Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003ecOR: Crude Odds Ratio\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eCHWs: Community Health Workers\u003c/p\u003e\n\u003cp\u003eDHS: Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eIRB: Institutional Review Board\u003c/p\u003e\n\u003cp\u003eLDHS: Liberia Demographic Health Survey\u003c/p\u003e\n\u003cp\u003eSDG: Sustainable Development Goal\u003c/p\u003e\n\u003cp\u003eVIF: Variance Inflation Factor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the MEASURE DHS for giving us access to their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJOO, CA, CN, SO, and ST conceptualised and supervised the study. DK and CN analysed the data, with inputs from JOO, CA, SO, and ST. DK wrote the first draft of the manuscript, which was subsequently revised by GM, AA, CA, JOO, CN, SO, and ST. All authors critically read, reviewed, and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study can be accessed from the DHS website at https://dhsprogram.com/data/dataset/Liberia_Standard-DHS_2019.cfm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used secondary data from the LDHS 2019-2020 surveys, which were approved by the Institutional Review Board (IRB) of the ICF International and conducted in accordance with the Declaration of Helsinki. Additionally, the LDHS was implemented following approval by the Liberian National Ethics Committee. The IRB of the ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the protection of human subjects (45 CFR 46), while the host country\u0026apos;s IRB ensures the survey complies with the laws and norms of the nation. \u0026nbsp;Informed consent was obtained from every participant, as indicated in the 2019-2020 LDHS report, and the data was anonymised during analysis [24]. Since this study utilised secondary data from the LDHS, which is publicly available, no ethical approval was required. The data used for this study was accessed from the DHS after permission was granted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZegeye B, El-Khatib Z, Ameyaw EK, Seidu A-A, Ahinkorah BO, Keetile M, et al. Breaking Barriers to Healthcare Access: A Multilevel Analysis of Individual- and Community-Level Factors Affecting Women\u0026rsquo;s Access to Healthcare Services in Benin. 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Accessed 23 Jun 2025. \u003c/li\u003e\n\u003cli\u003eLiberia Institute of Statistics and Geo-Information Services. Liberia Demographic and Health Survey 2019-20. 2021. https://dhsprogram.com/pubs/pdf/FR362/FR362.pdf. Accessed 12 Jun 2025. \u003c/li\u003e\n\u003cli\u003eSipsma H, Callands T, Bradley E, Harris B, Johnson B, Hansen N. Healthcare Utilisation and Empowerment Among Women in Liberia. ResearchGate. 2013. https://doi.org/10.1136/jech-2013-202647. \u003c/li\u003e\n\u003cli\u003eYaya S, Uthman OA, Bishwajit G, Ekholuenetale M. Maternal health care service utilization in post-war Liberia: analysis of nationally representative cross-sectional household surveys. BMC Public Health. 2019;19:28. https://doi.org/10.1186/s12889-018-6365-x. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization Data. Liberia. WHO Data. https://data.who.int/countries/430. Accessed 17 Jun 2025. \u003c/li\u003e\n\u003cli\u003eWorld Bank Group. The World Bank in Liberia, Overview. 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Liberia - Demographic and Health Survey 2019-2020. 2021. https://microdata.worldbank.org/index.php/catalog/3896#study_desc1674579234511. Accessed 29 May 2025. \u003c/li\u003e\n\u003cli\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335:806\u0026ndash;8. https://doi.org/10.1136/bmj.39335.541782.AD. \u003c/li\u003e\n\u003cli\u003eKim JH. Multicollinearity and misleading statistical results. Korean J Anesthesiol. 2019;72:558\u0026ndash;69. https://doi.org/10.4097/kja.19087. \u003c/li\u003e\n\u003cli\u003eSurjanovic N, Loughin TM. Improving the Hosmer-Lemeshow goodness-of-fit test in large models with replicated Bernoulli trials. J Appl Stat. 51:1399\u0026ndash;411. https://doi.org/10.1080/02664763.2023.2272223. \u003c/li\u003e\n\u003cli\u003eHinata H, Lwin KS, Eguchi A, Ghaznavi C, Hashizume M, Nomura S. Factors associated with barriers to healthcare access among ever-married women of reproductive age in Bangladesh: Analysis from the 2017\u0026ndash;2018 Bangladesh Demographic and Health Survey. PLoS One. 2024;19:e0289324. https://doi.org/10.1371/journal.pone.0289324. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Maternal mortality: The urgency of a systemic and multisectoral approach in mitigating maternal deaths in Africa. 2023. https://files.aho.afro.who.int/afahobckpcontainer/production/files/iAHO_Maternal_Mortality_Regional_Factsheet.pdf. Accessed 26 Aug 2025. \u003c/li\u003e\n\u003cli\u003eLiberia Institute of Statistics and Geo-Information Services. Liberia Demographic and Health Survey 2013. 2014. https://dhsprogram.com/pubs/pdf/fr291/fr291.pdf. Accessed 11 Jun 2025. \u003c/li\u003e\n\u003cli\u003eLiberia Institute of Statistics and Geo-Information Services. Liberia Demographic and Health Survey 2007. 2008. https://dhsprogram.com/pubs/pdf/fr201/fr201.pdf. Accessed 11 Jun 2025. \u003c/li\u003e\n\u003cli\u003eAragie H, Negash HK, Getnet M, Tesfaye W, Gela YY, Asefa T, et al. Barriers to healthcare access: a multilevel analysis of individual- and community-level factors affecting female youths\u0026rsquo; access to healthcare services in Senegal. BMC Health Services Research. 2025;25:607. https://doi.org/10.1186/s12913-025-12761-2. \u003c/li\u003e\n\u003cli\u003eFentie E, Asmamaw D, Negash W, Belachew T, Baykeda T, Addis B, et al. Spatial distribution and determinants of barriers of health care access among female youths in Ethiopia, a mixed effect and spatial analysis | Scientific Reports. https://www.nature.com/articles/s41598-023-48473-y#Sec25. Accessed 24 Jun 2025. \u003c/li\u003e\n\u003cli\u003eMinyihun A, Tessema ZT. Determinants of Access to Health Care Among Women in East African Countries: A Multilevel Analysis of Recent Demographic and Health Surveys from 2008 to 2017\u0026lt;/p\u0026gt;. RMHP. 2020;13:1803\u0026ndash;13. https://doi.org/10.2147/RMHP.S263132. \u003c/li\u003e\n\u003cli\u003eAboagye RG, Okyere J, Seidu A-A, Ahinkorah BO, Budu E, Yaya S. Does women\u0026rsquo;s empowerment and socio-economic status predict adequacy of antenatal care in sub-Saharan Africa? Int Health. 2023;16:165\u0026ndash;73. https://doi.org/10.1093/inthealth/ihad016. \u003c/li\u003e\n\u003cli\u003eTessema ZT, Worku MG, Tesema GA, Alamneh TS, Teshale AB, Yeshaw Y, et al. Determinants of accessing healthcare in Sub-Saharan Africa: a mixed-effect analysis of recent Demographic and Health Surveys from 36 countries. BMJ Open. 2022;12:e054397. https://doi.org/10.1136/bmjopen-2021-054397. \u003c/li\u003e\n\u003cli\u003eMomanyi LK, Mogambi H. Digital media and maternal healthcare among young women in Kenya: Use, patterns, and perspectives. Digital Theory, Culture \u0026amp; Society. 2023;1:97\u0026ndash;115. https://doi.org/10.61126/dtcs.v1i2.19. \u003c/li\u003e\n\u003cli\u003eLawubah J, Umeokonkwo CD, Sesay HW, Obafemi BJ, Whesseh FT, Maximore L, et al. Factors contributing to increased Neonatal Mortality in Grand Kru County, Liberia, January 2017-December 2021. Journal of Interventional Epidemiology and Public Health. 2023;6. \u003c/li\u003e\n\u003cli\u003eKesselly R, Kwenah N, Gonyon E, Byepu S, Bawo L, Jacobs G, et al. The status of health services in the 15 counties of Liberia. ResearchGate. 2018. https://doi.org/10.56801/seejph.vi.125. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Women, healthcare access barriers, financial constraints, distance, permission, and companionship.","lastPublishedDoi":"10.21203/rs.3.rs-7652528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7652528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEnsuring comprehensive healthcare access is essential for promoting good health, preventing and managing diseases, and reducing adverse outcomes such as disability and mortality. In Liberia, however, research has documented persistently low levels of healthcare utilisation among women. This underutilisation is closely linked to pronounced gender-based power imbalances and limited female participation in household financial decision-making. In light of these challenges, this study sought to examine the barriers to healthcare access encountered by women in Liberia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed a secondary analysis of data from 8,064 women who participated in the 2019\u0026ndash;2020 Liberia Demographic and Health Survey (LDHS). The outcome variables were measured using four healthcare access concerns: \u0026lsquo;getting permission to go for treatment\u0026rsquo;, \u0026lsquo;getting money for treatment\u0026rsquo;, \u0026lsquo;distance to health facilities\u0026rsquo;, and \u0026lsquo;not wanting to go alone\u0026rsquo;. We used frequencies and percentages to describe sample characteristics and prevalence. Bivariate and multivariable logistic regression analyses were used to evaluate factors associated with barriers to accessing healthcare among women.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe weighted prevalence of women with at least one barrier was 44.7% (95% CI: 41.2\u0026ndash;48.1%), while that of women with all barriers was 7.0% (95% CI: 5.6\u0026ndash;8.9%). Women with secondary education (aOR 0.56, 95% CI 0.39\u0026ndash;0.80), higher education (aOR 0.18, 95% CI 0.04\u0026ndash;0.73), working employment status (aOR 0.58, 95% CI 0.42\u0026ndash;0.79), and who used the internet less than once a week (aOR 0.15, 95% CI 0.05\u0026ndash;0.46) had reduced odds of encountering barriers to accessing healthcare. In contrast, women from the poorest wealth quintile (aOR 3.11, 95% CI 1.28\u0026ndash;7.57) and the South Eastern B region (aOR 1.84, 95% CI 1.16\u0026ndash;2.90) had increased odds of experiencing barriers to healthcare access.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eAlmost half of Liberian women encountered at least one barrier to accessing healthcare, which reveals a gap in health system inclusivity and responsiveness. The study findings highlight the need for interventions and policies to expand women\u0026rsquo;s financial and decision-making autonomy, as well as enhance community outreach tailored to women\u0026rsquo;s health needs.\u003c/p\u003e","manuscriptTitle":"When money, distance, and men decide: A cross-sectional analysis of multidimensional barriers to healthcare access among women in Liberia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 10:31:08","doi":"10.21203/rs.3.rs-7652528/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-18T09:54:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100207672797915955519507943174936234796","date":"2025-10-26T11:37:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T09:52:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-25T04:46:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-24T07:58:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-24T07:57:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-09-18T19:25:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa592d1b-1b95-4b9c-8c0e-3c2367ba2ab0","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-21T10:14:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 10:31:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7652528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7652528","identity":"rs-7652528","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}