Socioeconomic determinants of access to preventive healthcare services challenges and policy implications: A systematic review

INTRODUCTION

Role of Preventive Healthcare in Health Systems

Preventive healthcare services are one of the essential components of modern health care systems. They help to reduce the disease burden with proper identification of diseases, provision of appropriate intervention, and healthy behaviors [1]. Such services involve various measures such as screenings, vaccinations, health education, and lifestyle counseling. Some benefits that come along with such services include improved health status of the population and reduced health care expenditures [2]. The benefits have been well established; however, preventive care remains inaccessible, as it depends on multiple determinants like socioeconomic, geographic, and systemic determinants [3][4]. Such inequity prevents optimal effectiveness in the utilization of preventive approaches and escalates health inequities at both intra and inter population levels.

Socioeconomic Determinants and Access to Healthcare

Socioeconomic factors such as income, education, and employment determine access to preventive care services. People from a lower-income background are confined by financial issues, poor health literacy, and access to fewer health facilities, which prevent them from making use of preventive services [5]. In addition, geographic disparities face challenges, especially in the rural and remote settings where infrastructure and access to providers are inadequate. In low- and middle-income regions, the social determinants include gender, norms, and stigma, influencing healthcare-seeking behavior [6–8]. Together, they make up this complex set of barriers and hinder equal health care services.

Global Scenario on Preventive Healthcare Disparities

Globally, the distribution of preventive healthcare is characterized by striking inequalities that disproportionately affect low- and middle-income countries. These have been exacerbated by fragmented systems, resource constraints, and inadequate policies. High-income countries, with otherwise strong health systems, are also experiencing lack of access, primarily from socioeconomic stratification and work-related disparities [9–11]. All these measures, such as universal health coverage, community-based programs, and policy reforms oriented toward integrating preventive service delivery into primary healthcare, have varying degrees of effectiveness and much that is unexplained about the impact and constraint of these efforts in various contexts [12][13].

Need for Equity-Focused Policies and Interventions

Equity in preventive care access is a foundational means of achieving universal health coverage and, by extension, sustainable development goals. The multifaceted barriers require a holistic approach to bring together financial protection, infrastructure building, and culturally sensitive interventions [14]. All health systems must focus on catering to the needs of populations that are underserved: socioeconomic and geographic disadvantages are not acceptable reasons for depriving people of necessary service [7][12]. Systematic assessment and monitoring of equity-oriented policy interventions are also critical components to identify gaps and strengthen healthcare delivery [11]. This review, therefore, aims to systematically examine the influence of socioeconomic determinants on access to preventive healthcare services across diverse income settings, with the primary intention being that the analysis of barriers, facilitators, and equity considerations would shed light on critical insights into global disparities and offer evidence-based recommendations for policy and practice.

The aims of this systematic review is to assess barriers, facilitators, and policy implications for healthcare equity from the global data.

METHODS

Study Design and Hypotheses

This systematic review was based on the PRISMA reporting guidelines [15] and was designed to evaluate the effects of socioeconomic determinants on access to preventive healthcare services worldwide. It was hypothesized that lower SES, geographic barriers, and cultural factors significantly impede access to preventive healthcare. On the other hand, policies such as UHC and community-based interventions were hypothesized to reduce disparities. Both cross-sectional and cohort studies were considered to give a proper review of the current trend as well as the temporal association.

Eligibility criteria

Inclusion Criteria

Peer-reviewed, cross-sectional, and cohort studies published in English were considered for review. Cross-sectional studies were chosen because they capture the snapshot of the disparities in access to healthcare, while cohort studies gave insights into the temporal and causal associations. Studies were included if they examined the association between socioeconomic determinants such as income, education, or employment and access to preventive healthcare services. Only those studies with clearly defined outcomes concerning utilization of preventive care were chosen. The rationale for focusing on cross-sectional and cohort studies was to ensure a robust quantitative analysis of trends and associations.

Exclusion Criteria

Excluded studies included interventional studies, qualitative studies, conference abstracts, and studies that had no adequate data on socioeconomic determinants or health care access.

Information sources

A systematic search was conducted across seven databases: PubMed, Scopus, Web of Science, Embase, Cochrane Library, CINAHL, and ProQuest. The analysis of literature sources covered 10 years (from 2014 to 2024).

Search strategy

Boolean operators and MeSH keywords were used to create specific comprehensive search strategies for each of the databases. The selected terms were “socioeconomic determinants”, “health care access”, “preventive health care”, “disparities”, and “inequities”. Each database search string was modified considering the indexing system of the database to keep the sensitivity and specificity at optimal levels. Specific filters for cross-sectional and cohort studies were applied when available; only studies published in the English language were considered.

Selection process

The PECOS framework was constructed to systematically define the scope and inclusion criteria of the review:

- Population (P): All age groups from diverse socioeconomic backgrounds across high-, middle-, and low-income countries;

- Exposure (E): Socioeconomic determinants such as income, education, and employment;

- Comparison (C): Different socioeconomic strata or geographic locations;

- Outcome (O): Preventive health care service delivery, such as screening, vaccination, and health education;

- Study Design (S): Cross-sectional and cohort studies.

Data Extraction Process and Items Extracted

Data extraction was conducted using a structured and standardized template developed to ensure comprehensive and consistent collection of relevant information across studies. The protocol followed focused on extracting data on the following: study characteristics, demographics of the population, exposure variables, outcomes, and methodological details. The protocol followed in the data extraction process included the year of publication, country, income classification, study design, cross-sectional or cohort, sample size, and follow-up duration (where applicable). Population demographics captured mean or median ages, gender distribution, socioeconomic variables, and geographic location.

For exposure variables, all the socioeconomic determinants information was extracted in detail, like income levels, education status, employment types, and geographic disparities. The outcome variables included measures of access to preventive healthcare services, such as utilization rates of screenings, vaccinations, and health education programs. Barriers and facilitators to access, as reported by the studies, were documented to provide context to the findings. In addition, any equity considerations, such as pro-rich or pro-poor trends, were systematically recorded. Details about the methodology, including statistical analyses that had been conducted (e.g., regression models, concentration indices), and any controls or adjustments for confounding variables were noted. Where applicable, information on policy implications or recommendations was retrieved from the studies. Independent data extraction by two reviewers was conducted to avoid mistakes and bias, and differences in opinion were resolved by discussion or consultation with a third reviewer.

Study risk of bias assessment and Reporting bias assessment

For the evaluation of risk of bias in cohort studies, the ROBINS-I tool [16] was utilized while focusing on confounding, selection bias, and outcome measurement domains. For cross-sectional studies, the AXIS tool [17] was applied to assess study quality with regard to sample selection, data integrity, and objectives. Both tools were used by two independent reviewers, and disagreement between the two was discussed.

RESULTS

Study selection

A total of 427 records were identified through database searches with no additional records from other sources. The removal of 48 duplicates left 379 to be screened. From the screening, 37 were excluded based on title and abstract. Then, full-text retrieval was sought for 342 reports, but only 34 were inaccessible because of access issues. In total, 308 reports underwent eligibility assessment. It was established that 287 were excluded, reasons for which were non-relevance to PECOS criteria (n = 80), failure to align with the main research question under investigation (n = 60), and other methodological issues (n = 147). A total of 21 studies [18–38] was included in the review.

Results of individual studies

Baseline variables assessed (Table 1)

The studies were spread across different geographic locations and populations, with large variability in sample sizes, study designs, and population demographics. Cohort studies, such as the Swedish study [18], had sample sizes of more than 126,000 epilepsy cases and controls, while much smaller cross-sectional studies, such as the one conducted in the USA [27], included only 108 participants. These differences point to different scopes of studies. Age distributions were also quite heterogeneous, with some studies targeting older populations, such as China [21], where participants aged 65+ were targeted, while others included broader age groups, such as South Korea [23], which covered workers aged 15+. Gender representation was also diverse, with studies from Germany [24] achieving near gender parity, contrasting with male-dominated samples, such as in Morocco [29], where 74.7% were male. The follow-up periods of the study further showed heterogeneity in methodologies. Register-based cohort studies like in Sweden [18] were 15 years, which allowed for longitudinal analysis, while most cross-sectional studies did not have follow-up periods, which limited the temporal inferences. These differences indicate methodological considerations that influence the depth and applicability of findings.

Socioeconomic Factors and Access to Healthcare (Table 2)

Income and education were the frequent factors determining access to healthcare. Brazil [20] and Mongolia [22] highlighted horizontal inequities where income inequalities really dominated usage rates. In studies from China [21] and Kenya [30], there was generally a split between urban-rural areas, which has proven to be a multiplicand of geography and poverty.

Barriers to Access to Health Care

Financial costs were another main hindrance among the studies. South Asia [33] and West Africa [34] have reported cost as a primary barrier of access to health service. Likewise, insecurity in employment also linked up to unmet healthcare need among South Koreans [23]. Geographical accessibility and the availability of a healthcare service were significant obstacles in such a distant location like Tibet [38].

Access Facilitators

Government policies and health care delivery systems were instrumental in reducing the barriers to access. Universal health coverage systems, as observed in Europe [25] and Australia [28], had a strong effect on the reduction of unmet needs for health care. Other interventions included community-based health interventions in Mali [34] and public health insurance schemes in China [38].

Outcome Measures

Most studies [24][26][28][35] often measured outcomes such as health care use or unmet needs. Examples include measurements of unmet surgical needs in slum populations in India [26] and health-related quality of life in Vietnam [35]. More subtle insights into policy effectiveness were obtained from measuring health inequalities by employing advanced statistical indices in Australia [28] and Germany [24].

Methods of Evaluation

Logistic regression models were commonly applied, such as in South Korea [23] and Kenya [30], to analyze factors determining unmet health-care needs. Concentration indices, applied in Mongolia [22] and Nepal [33], assessed horizontal inequity. The use of multilevel regression techniques helped break down individual and contextual factors determining health care access in some European studies [25]. These methodological differences underpin the different analytical intensity and purpose of the studies included.

Policy Implications

The results highlight the need for targeted health policies that focus on inequities. For instance, infrastructural development of public healthcare systems in low-income regions is recommended by African [19] and Asian [30] studies to reduce inequalities. Similarly, integrating financial services into healthcare delivery is envisioned to reduce socioeconomic barriers, as explored in North America [27]. The European [25] and Australian [28] studies have shown that universality is effective in reducing inequalities, and other regions can draw scalable models from them.

Barriers to Access (Table 3)

Barriers to access healthcare were multi-dimensional, and differed considerably by income category and health system characteristics. Low-income settings were characterized by pronounced financial and geographic barriers, and were observed in settings with fragmented systems and low availability of providers [22][31]. Rural populations faced compound challenges because of geographic inaccessibility and infrastructure deficits [38]. High-income settings also identified inequities, often in relation to SES, type of employment, and insurance status, where precarious employment was a common feature that resulted in unmet health needs despite universal healthcare arrangements [23]. Gender and cultural norms also worsened inequities in some settings, especially where traditional roles and beliefs restricted women's utilization of health services [29][34].

Health System Characteristics and Healthcare Provider Availability

Generally well-integrated, but with much variability in accessibility based on SES and regional disparities, most universal health care systems in developed economies are represented [18, 24]. Middle-income countries evidenced weaknesses in public-private integration and regional segmentation, allowing inefficiencies in resource utilization and poor services delivery [20][36]. In Low income settings, decentralized and/or fragmented systems were seen in shortages of skilled medical staff, especially for secondary and surgical services [31, 34]. Community-based health centers and traditional medicine practitioners were widespread in rural settings, but the quality of service was often low and services were not standardized [35].

Cultural and Behavioral Factors

Behavioral factors, including health literacy and stigma, were common in both low- and high-income countries. In lower-middle-income countries, stigma about disability [32] and epilepsy [18] resulted in delayed or avoided use of health services. Health literacy gaps were particularly pronounced in urban slums and among rural populations, further limiting access to available healthcare resources [19][29]. In high-income countries, self-perceived health needs and insurance complexity influenced healthcare-seeking behavior, particularly among the elderly [24, 28].

Facilitators to Access

Targeted policy interventions and health system reforms were facilitators. UHC and public insurance systems played a crucial role in reducing inequities, especially in high-income countries with established structures [18][25]. Community health workers and free essential medicines were instrumental in increasing access in lower-income settings, especially among rural and nomadic populations [34][38]. Incorporation of financial services into clinics for low-income populations was promising in reducing SES-based barriers in high-income countries [27].

Equity Considerations

Equity in healthcare utilization remained a major concern with continued disparities across SES, gender, and geographic divisions. Pro-rich and pro-urban utilization trends were observed in those countries with fragmented systems and a lack of public healthcare coverage [22][33]. In high-income countries, SES-sensitive policies reduced some disparities but were not enough to remove inequities [23][28]. Gender-sensitive reforms and focused interventions for marginalized groups brought positive effects in regions with strong disparities along gender and regional lines [29][36].

Long-term effects and policy implications (Table 4)

By income classification, long-run impacts of health policies were heterogeneous, whereas high-income regions with UHC frameworks in place showed better health outcomes together with reduced unmet needs even as SES-related disparities lingered, indicating a need to better fine-tune reforms [25][28]. Middle-income countries demonstrated evidence for targeted reforms, such as Brazil's Family Health Strategy, in reducing inequities and improving access in more vulnerable populations [20, 37]. Low-income areas experienced important implementation challenges that related primarily to workforce shortages and limitations in infrastructure, but community-based interventions did show promise in overcoming geographic and cultural barriers [34, 35].

Comparison across various regions revealed that decentralization and community engagement best suited low-income settings, whereas high-income countries relied on integrated systems with SES-sensitive policies. Middle-income transitional models tried to balance the problems of equity and efficiency through innovative supplementary insurance and localized interventions approaches [36][38].

Statistics

Various statistical methodologies were used in evaluating access and equity in health care. Logistic regression models and odds ratios dominated high-income and middle-income researches in the identification of predictors for unmet needs [23, 24]. Concentration indices and inequity measures, such as the Relative Index of Inequality (RII), were used to quantify disparities in both utilization and access across SES strata [22, 28]. Descriptive statistics and chi-squared tests were the most common analysis tools used in low-income settings to assess community-based interventions and regional inequities [3438].

Bias assessment observations

For the cross-sectional studies (Figure 2), Bakibinga et al. [19] demonstrated moderate selection, performance, and reporting biases with low overall bias. Serious or moderate biases were detected by Boccolini et al. [20] and Dorjdagva et al. [22] in individual domains but remained low overall bias. Ha et al. [23] and Hoebel et al. [24] had serious concerns with respect to multiple domains with the respective moderate and low overall bias. Jindal et al. [26], Njagi et al. [30], and Tran et al. [35] showed moderate bias in several domains, thereby having moderate overall risk. Other studies like Liu et al. [27] and Petroze et al. [31] have serious bias in the detection and attrition domains, leading to high overall bias. Sangare et al. [34], Vahedi et al. [36], and Wendt et al. [38] had moderate to serious detection and attrition biases, which gave rise to moderate to serious overall risks.

For the cohort studies (Figure 3), Andersson et al. [18] showed low bias in all domains with a low overall risk. Chen et al. [21] showed serious detection bias but retained a low overall bias because of minimal concerns in other domains. Hoven et al. [25] showed moderate selection bias but low bias in other domains, culminating in moderate overall bias.

DISCUSSION

Impact of Healthcare Policies on Addressing Health Disparities

Healthcare policies tend to play a critical role in defining strategies and outcomes when addressing health disparities. Policies at all levels-national, regional, and institutional-impacted access to and quality of health care services. Such factors as funding mechanisms, insurance frameworks, reimbursement policies, and models of healthcare delivery determine the manner in which resources are equitably distributed and utilized. Thus, a complete understanding of the dynamics of these aspects should be sought by health managers in order to design and implement interventions that have the appropriate impact [39].

However, a few policies might indirectly worsen disparities through the resultant imbalance in the distribution of resources. An example is funding mechanisms that may not take into account the needs of underserved populations or marginalized communities. Such mechanisms, therefore, will perpetuate existing disparities [40]. Balancing these imbalances calls upon healthcare managers to advocate for inclusive funding models that specifically target vulnerable communities. In that regard, cultural competence will be integrated into policy frameworks such that healthcare services are responsive to diverse population needs [41]. This includes policies which require cultural competency training, support for interpreter services and culturally tailored care, all of which enables healthcare managers to provide more responsive and inclusive care.

Demographic trends, such as aging populations, require policy changes responsive to their distinct health-care needs. Policies that can support targeted approaches for the elderly may be able to empower healthcare managers to place resources effectively in designing services meeting those particular needs [42]. Policies that focus on patient-centered care can also greatly decrease disparities. This kind of care approach focuses on involving the patient in decision-making and incorporates social determinants of health into care. This provides health managers with a structured approach to address inequities [43]. The advancement of health information technology, including electronic health records and telehealth, offers opportunities to enhance access, communication, and outcome monitoring [44]. Making policies to adopt these technologies is essential to support healthcare managers in minimizing disparities across diverse populations [45].

Thematic Assessments Obtained in this Review

Studies conducted in high-income areas, such as those of Andersson et al. [18], Hoebel et al. [24], and Hoven et al. [25], continue to find SES and work-related differences are major contributors to problems related to the accessibility of care, regardless of having a universal care system in place. These findings were similar in their focus on SES-sensitive inequities but differed in their emphasis; for example, Andersson et al. [18] linked low SES with specialized neurological care gaps, whereas Hoebel et al. [24] emphasized disparities in health literacy among elderly populations. Hoven et al. [25] added a broader European perspective, highlighting regional and employment-based inequities.

In middle-income countries like Brazil (Boccolini et al. [20]) and China (Chen et al. [21]), urban-rural divides and gaps in public-private healthcare integration were salient themes. They converged on financial barriers and systemic inefficiencies as challenges but diverged in scope. Boccolini et al. [20] focused on underutilization through the Family Health Strategy, whereas Chen et al. [21] looked at long-term care needs and aging-focused health reforms. Both studies showed the relevance of integrated health policies but presented different challenges for specific populations. Low-income contexts from Mali (Sangare et al. [34]) and Kenya (Njagi et al. [30]) reported high geographical and cost-related barriers, consistent with a lack of infrastructure and insufficient workforce. Sangare et al. [34] pointed to cultural barriers as including gender roles, but Njagi et al. [30] referenced regional inequalities that are enhanced through cost-related barriers. Results were similar in indicating multiple layers of poverty, though they differed in focusing more on cultural or systemic aspects.

Community-based interventions studies, such as Petroze et al. [31] in Rwanda and Tran et al. [35] in Vietnam, emphasize the same kind of success into healthcare access among the rural and deprived populations. Both the studies emphasized the importance of the local health center and community involvement, though Petroze et al. [31] emphasized surgical care gaps, whereas Tran et al. [35] stated to integrate traditional and modern medicines. Methodologically, studies such as Dorjdagva et al. [22] and Saito et al. [33] of Asia applied more advanced measurements like concentration indices to the measurement of inequity. These statistical approaches made it possible to have relatively comparable insights into pro-rich trends in utilization, with analyses of similar rigor. However, the context of inequities—financial costs in Mongolia [22] versus provider shortages in Nepal [33]—emphasized contextual variability.

Misinformation and Health Care Decisions

Our review findings indicated that structural barriers include socioeconomic disparities, and Reyna et al. [47] pointed out cognitive processing and misinformation in healthcare decision-making. Reyna et al. [47] determined how misinformation shapes perceptions of health care, and "gist" processing influences decisions more strongly than factual knowledge. This dimension was absent in our review; however, it emphasizes that public health strategies must address misinformation in order to improve the uptake of preventive care services.

Economic Crises and Health Inequities

Our review identified financial resource constraints as an important obstacle for access to healthcare, particularly for low- and middle-income settings. Broadbent et al. [48] extended this by examining the wider ripple effects of economic crises; for example, cost-of-living crisis, on population health, pointing out how policy intervention, such as welfare provision, can help mitigate vulnerable populations. While our review looked specifically at direct financial barriers from the healthcare system, Broadbent et al. [48] took a broader macroeconomic view, highlighting the role of integrated policies in tackling health inequities.

Equity in Public Health Practice

Both our review and Liburd et al. [49] highlighted the role of health equity in public health practice. Liburd et al. [48] noted the need for multifaceted frameworks and culturally competent interventions in achieving equity, similar to the specific recommendations in our review about tailoring interventions to underserved groups. In contrast, Liburd et al. [49] emphasized the conceptual approach to integrate equity into public health functions and added more to the idea of the integration of equity in public health functions.

Population-Specific Challenges

Our review discussed barriers and facilitators to health care access among different population groups, whereas Mytton et al. [50] have emphasized the significance of adolescence as a critical period in the life cycle. They underscored long-term consequences of health behaviors and rising risks during adolescence, including vaping and online harms. Although our results were more general, addressing population-level inequities, Mytton et al. [50] provided some insight into how to address vulnerabilities in a particular population, emphasizing the importance of age-sensitive policy interventions.

Healthcare Quality and Safety

Vikan et al. [51] assessed the linkage of patient safety culture score to the rate of adverse events as part of highlighting the safety considerations in service delivery. Though our literature review is centered around inequities and equity in the delivery of health care, Vikan et al. [51] was keen on quality aspects in healthcare services. The general consensus, as observed by the review, suggested the requirement for consistent approaches and more elaborate analysis of assessments toward healthcare in the entire sector.

Implementation of Universal Health Coverage

Our review and Preker et al. [52] both highlighted the potential of UHC to reduce inequities. Preker et al. [51] specifically looked at the implementation of UHC in middle- and upper-middle-income countries, focusing on challenges related to political sustainability and resource constraints. While our review looked at access barriers and facilitators, Preker et al. [52] provided a detailed analysis of institutional factors that affect UHC sustainability, thus providing a complementary perspective on how to achieve equitable healthcare systems.

Limitations

This study was limited by the heterogeneity in study designs, populations, and methodologies that limited the possibility of comparisons between findings. The inclusion of cross-sectional studies prevented inferring causality. Differences in socioeconomic and healthcare system characteristics across regions introduced contextual biases, and the generalizability of results was compromised. In addition, the lack of standardized metrics for evaluating healthcare access and equity led to inconsistencies in reported outcomes. Numerous studies suffered from missing longitudinal follow-up, which made it not possible to evaluate properly long-term impacts of policies. Many studies were based on self-reported information, which increased recall and reporting bias, and some regions were underrepresented in rural and marginalized populations, limiting the generalizability of results.

Future Implications and Recommendations

Access to care could be made fairer through financial and geographical barriers reduction, especially in low- and middle-income regions. Reducing disparities will require integrated healthcare systems with a balance of urban and rural focuses. Community-based interventions should be a part of Universal Health Coverage initiatives, and there must be a strengthening of health infrastructure in underserved regions. Health literacy improvement and stigma reduction should be especially emphasized among vulnerable populations. Consistent measures to evaluate access to and equity in healthcare need to be developed to maintain standards in research and policy review. Additionally, longitudinal studies should be encouraged to better understand the long-term impact of health policies.

CONCLUSION

The obtained findings collectively demonstrate that healthcare access is universally influenced by SES, financial, and geographic barriers, albeit with regional variations. High-income studies provided insights into nuanced inequities within robust systems, while middle- and low-income studies highlighted structural and resource-related challenges. Despite differences in focus, the findings consistently underscored the need for equity-sensitive, context-specific health policies to mitigate disparities globally. Such comparisons highlighted large thematic consistencies while demonstrating the vast regional challenges that shape healthcare access.