Predisposing, enabling and need factors.

Factors under each component of Andersen’s model were drawn from previous studies [20, 25, 26], with the exclusion of contextual factors proposed by the model as they are beyond the scope of this research (e.g., social composition, community norms and values). Nine socio-demographic variables constituted the predisposing factors domain of Andersen’s model. These include sex, age, nationality, highest level of education, current marital status, number of children (if applicable), current place of residence (inside Riyadh or to specify if outside Riyadh), current employment status, and average household monthly income. Enabling factors are defined as financial and organizational elements that facilitate access to healthcare [13]. These include assessing the presence of social support, ease of scheduling appointments at WHC, having regular access to a vehicle for transportation, the proximity of the nearest PHC, time spent in waiting rooms, facing any language barriers, and satisfaction with quality of care. Finally, need factors which explain individuals’ perceived needs for healthcare services and healthcare providers’ (HCPs) evaluated needs [13]. Need factors included having a first-degree family history of CVD, presence of high blood pressure or diabetes or high blood cholesterol, and self-rated health status on a Likert 5-point-scale from excellent to poor.

Use of PCCS.

The Binary “Yes/No” questions about PCCS were based on the latest national and international guidelines. Both the Saudi Public Health Authority (PHA) Clinical Preventive Guidelines (Third Edition, 2023) [27] and the United States Preventive Services Task Force (USPSTF) recommendations [28–34] focus on the prevention of cardiovascular diseases in three categories (screenings, counseling, and preventive medications) mainly in adults. Nine preventive cardiovascular services were assessed: i) Screening for hypertension, prediabetes (PreDM) and type 2 diabetes mellitus (T2DM), Abdominal Aortic Aneurysm (AAA), and waist-circumference for obesity; ii) counseling for weight-loss, healthy diet and physical activity, and smoking-cessation; and iii) use of statins for the primary prevention of CVD. Only interventions categorized as grades ‘A’ or ‘B’ were included as they signify the highest level of evidence and are universally applicable to all intended populations, unlike grade ‘C’. These guidelines were developed to guide clinicians in incorporating preventive services into their practice. Details about each recommendation are provided in Table 1. The inclusion criteria for each recommendation were formulated to encompass the largest group possible. For instance, the interval for blood-pressure screening is set at five years rather than annually. We assumed this extended screening frequency would enhance our group classification and reduce potential recall bias.

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Table 1. Operational Definitions of Preventive Cardiovascular Clinical Services.

https://doi.org/10.1371/journal.pone.0341074.t001

Screening for hypertension.

Analysis of predisposing factors revealed a significant increase in screening with age (p ≤ 0.001). Married individuals and individuals who have children saw significant increase in utilization (p < 0.020 and p < 0.001, respectively). While all enabling factors showed no statistical significance, all need factors were statistically significant (p ≤ 0.001). Individuals were more likely to receive blood-pressure screening if they had a family history of CVD, had one or more CVD risk factor, or perceived their health as poor (See Table 4).

Screening for prediabetes and type 2 diabetes.

Predisposing factors such as age and female sex have significantly influenced utilization. For age, rates progressively increased from 63% (18–34 years) to 97% (≥65 years). Married respondents were more likely to be screened compared to unmarried ones (p < 0.032). Parenthood and employment differed significantly between subgroup; employed individuals were more likely to utilize services (p < 0.033). Similar to hypertension screening, analysis of enabling factors revealed no statistically significant factors, while all three need factors were statistically significant (p ≤ 0.001) (See Table 4).

Screening for waist circumference for obesity.

Waist-circumferences screening had the greatest number of significant predisposing factors. Sex showed a significant difference, with male individuals exhibiting lower utilization rates compared to female individuals (p < 0.001). Older age, higher educational attainment, parenthood, and active employment status were all significant factors. Respondents with higher household monthly income had lower screening rates (13%) than those with lower income (22%) (p = 0.043). Examination of enabling factors identified language barriers as the only statistically significant factor; respondents facing barriers were more likely to be screened than those with no language barriers (p = 0.014). Finally, the analysis of need factors showed significant associations among individuals with CVD risk factors or those who perceived their health as poor (p < 0.001 and p = 0.001, respectively) (See Table 4).

Use of statins for the primary prevention of CVD.

Analysis of predisposing factors highlighted significant variation in the utilization based on age (p = 0.001) and parenthood status (p = 0.006). Statins were offered more frequently in older age groups, with utilization rates reaching 74% (35–44 years) and 95% (≥65 years). Enabling and need factors showed no statistically significant differences (Table 4).

Weight-loss counseling to prevent obesity-related morbidity and mortality.

Predisposing factors analysis for weight-loss counseling identified significant differences in utilization based on sex (female individuals, p = 0.009), age (older adults, p = 0.039), education (no formal education, p = 0.002), and employment status (unemployed, p = 0.001).

Enabling factors analysis showed that respondents who found booking appointments easy were more likely to be offered counseling than those who found it difficult (p = 0.020). Among need factors, the presence of CVD risk factors significantly increased counseling utilization (p = 0.002). Additionally, individuals with poor perceived health were significantly more likely to receive counseling compared to those reporting excellent health (p = 0.003) (See Table 5).

Counseling for healthy diet and physical activity for cardiovascular prevention in adults with cardiovascular risk factors.

When analyzing healthy diet and physical activity separately, significant disparities were observed for two predisposing factors. Female individuals were significantly more likely to receive both healthy-diet (p = 0.008) and physical activity counseling (p = 0.004) compared to male individuals, and respondents earning <15000/month showed higher counseling utilization for both services. Notably, for enabling factors, owning a vehicle significantly impacted physical activity counseling (p = 0.006). Finally, the presence of one or more CVD risk factors significantly influenced utilization for both services (p < 0.001), with all recipients having at least one CVD risk factor (See Table 5).

Tobacco smoking-cessation counseling.

Analysis revealed a statistically significant difference by sex in the provision of counseling (p < 0.001), with 52% male respondents receiving counseling compared to 13% of female respondents. Among enabling factors, vehicle ownership emerged as significant; vehicle owners were more likely to participate in counseling than non-owners (p = 0.011). The presence of one or more CVD risk factors and poor perceived health status were significant need factors (p = 0.031 and p = 0.037, respectively) (See Table 5).

Screening for abdominal aortic aneurysm.

Due to small cell counts, Fisher’s exact test was performed to examine the association between risk group and receiving AAA screening. The results indicated that no single predisposing, enabling, or need factor had a significant association with screening utilization. Additionally, some variables, such as nationality, parenthood, and education were not analyzed due to the lack of variation in the sample.

Model-1: Screening for hypertension.

The model was statistically significant (x² (5) = 68.7, p < 0.001) explained up to 40% of the variance (Nagelkerke R square) and correctly classified 92.7% of cases. However, the Hosmer-Lemeshow test indicated poor fit, x² (6) =64.49, p < 0.001). The model revealed that respondents aged ≥35 years (AOR = 22.1, p < 0.001), who were socially supported (AOR = 19, p = 0.010), traveled more than one hour to the nearest PHC (AOR = 3.07, p = 0.040), had language barriers (AOR = 8.2, p = 0.054), and had a positive family history of CVD (AOR = 4.8, p = 0.006) were more likely to utilize hypertension screening services.

Model-2: Screening for prediabetes and type 2 diabetes.

This model was also statistically significant (x² (3) = 66.6, p < 0.001) explaining up to 38% of the variance in service use and correctly classifying 86.6% of cases, with a good fit to the data (x² (3) =5.8, p = 0.119). This model shows that respondents who have children (AOR = 4.8, p < 0.001), waited <30 minutes in the waiting room (AOR = 4.4 p = 0.027), and had a CVD risk factor (AOR = 8.7, p < 0.001), were more likely to use PreDM and T2DM screenings.

Model-3: Screening for waist circumference for obesity.

Waist-circumference screening model was statistically significant (x² (5) = 62.2, p < 0.001). The model explained up to 26% of the variance in screening utilization and correctly classified 84% of cases. Goodness-of-fit test suggested a good fit (x² (8) =1.6, p = 0.990). Female respondents (AOR = 9.2, p < 0.001), who had a high school education or less (AOR = 2.8, p = 0.015), own a vehicle (AOR = 2.8, p = 0.003), traveled for more than one hour to the nearest PHC (AOR = 2.2, p = 0.014), and had at least one CVD risk factor (AOR = 5.3, p < 0.001) were more likely to utilize waist-circumference screening.

Model-4: Use of statins for the primary prevention of CVD.

This model correctly classified 86.6% of cases and was statistically significant (x² (3) = 15.318, p = 0.002). However, it explained up to 13% of the variance using Nagelkerke R square, with a good fit to the data (x² (4) =2.43, p = 0.656). The model shows that female respondents (AOR = 3.5, p = 0.011) who have children (AOR = 6.1, p = 0.003), and own a vehicle (AOR = 2.6, p = 0.036), were using more statin prescription services.

Model-5: Weight-loss counseling to prevent obesity-related morbidity and mortality.

This significant model (x² (6) = 45.6, p < 0.001) correctly classified 51% of cases and was able to explain up to 50% of the variance and had a good fit to the data (x² (5) =3.7, p = 0.594). The significant variables in this model included the following: female individuals (AOR = 6.5, p = 0.005), who have no children (AOR = 5.3, p = 0.061), with the highest educational attainment being high school degree or less (AOR = 6.2, p = 0.031), who found it easy to book an appointment (AOR = 4.6, p = 0.013), and had both CVD risk factors (AOR = 11, p = 0.006), and a reported health status as poor (AOR = 14.3, p = 0.007) were more likely to receive weight-loss counseling services.

Model-6: Counseling for healthy diet for cardiovascular prevention.

Healthy-diet counseling model was statistically significant (x² (3) = 15.8, p = 0.001). The model explained up to 10% of the variance in counseling utilization and correctly classified 78% of cases. Goodness-of-fit test suggested a good fit (x² (4) =1.9, p = 0.748). The model revealed that female individuals (AOR = 2.05, p = 0.042), with income <15,000 SAR (AOR = 2.05, p = 0.034), and who reported receiving high-quality care (AOR = 3.17, p = 0.035) were more likely to receive healthy-diet counseling.

Model-7: Counseling for physical activity for cardiovascular prevention.

The physical activity counseling model showed good fit (x² (3) =1.5, p = 0.685), and was statistically significant (x² (3) = 20.7, p < 0.001). The model explained up to 15% of the variance in counseling utilization and correctly classified 84% of cases. The model shows that female individuals (AOR = 2.1, p = 0.089), who have a bachelor’s degree or more (AOR = 7.9, p = 0.047), and did not own a vehicle (AOR = 3.04, p = 0.027) were more likely to be offered this counseling service.

Model-8: Tobacco smoking-cessation counseling.

The model was significant (x² (2) = 32.2, p < 0.001), correctly classified 60% of cases, explained up to 23% of the variance, and showed good fit, (x² (2) =0.082, p = 0.960). Male respondents (AOR = 8.94, p < 0.001), and those reporting poor health (AOR = 3.4, p = 0.038) were more likely to receive smoking-cessation counseling.

Sex.

Sex emerged as the most common significant predisposing factor. Female participants demonstrated higher tendencies to receive PreDM and T2DM screening, weight-loss counseling, healthy-diet and physical activity counseling, and statin therapy. However, they were less likely to receive smoking-cessation counseling.

Despite a marginally higher proportion of female over male respondents, evidence suggests that female individuals tend to utilize healthcare services more frequently [37, 38]. Female individuals generally exhibit higher awareness of health and are more engaged in preventive behaviors [39]. However, despite their proactive health habits, female individuals tend to experience worse outcomes and higher mortality rates following cardiovascular events compared to male individuals, who have a higher incidence of CVD [40, 41].

In the context of the Kingdom of Saudi Arabia, several sociocultural dynamics may still influence healthcare utilization, such as reliance on male guardians and mobility constraints [42]. Our findings indicate a strong positive association between sex and the utilization of many PCCS. However, female individuals were less likely to receive smoking-cessation counseling. Despite the relatively low prevalence of smoking among women (4.2% as of 2019) [43], societal stigma and HCPs’ reluctance to screen for smoking in female individuals exacerbate the challenge. These challenges often stem from stigmatization of female smokers and hesitancy to disclose smoking, further exacerbated by tobacco marketing strategies that normalize smoking across different communities [44].

Parenthood.

Having children significantly influenced the utilization of PCCS in several models, including PreDM and T2DM screening, weight-loss counseling, and statin use. The effect of parenthood can be attributed to a combination of behavioral and social factors.

First, the incidence of obesity has been reported to increase by ~4% with each additional child [45], suggesting a direct impact of parenthood on physical health conditions that might necessitate more healthcare utilization. Conversely, parental responsibility often increases personal health awareness, promoting greater engagement with healthcare services [46]. Moreover, parents might adopt a healthy lifestyle mainly to serve as role models for their children. One study emphasized how parental influence can significantly impact children’s health behaviors through role modeling [47]. Additionally, parents’ active involvement in community and social networks facilitates greater health consciousness, as the prevalence of chronic diseases within these networks often encourages the adoption of healthy behaviors. Altogether, these factors demonstrate how parenthood serves as an incentive for increased healthcare utilization.

Education and income.

Education served as a significant variable in three models; individuals with lower education levels were more likely to receive waist-circumference screening and weight-loss counseling, while those with higher education levels were more often counseled on physical activity. The analysis revealed that individuals with lower educational attainment are more likely to engage in obesity-related services. This trend aligns with broader research findings that demonstrate a correlation between lower educational levels and higher obesity rates, necessitating greater service utilization. A study aggregating data from 1985 to 2015 identified a distinct association between lower educational levels and an elevated lifetime risk of CVD, with a hazard ratio of 1.58 (95% CI, 1.38–1.80) for those with less than high school education [48].

In the context of healthcare, educational attainment is intricately linked to socioeconomic factors such as income and employment status. Despite the nonsignificant impact of employment status across all models, lower income was found to be associated with healthy-diet counseling, highlighting the direct link between socioeconomic status (SES) and physical health. Research has found that individuals with lower SES have a higher incidence of CVD [49], and are more likely to utilize primary healthcare services, while those with higher SES tend to seek more specialized healthcare services [50, 51].

In Saudi Arabia, where healthcare services are provided free of charge for Saudi nationals, income has been an insignificant factor in healthcare utilization in multiple other studies [52, 53]. The complexities of SES, including the challenges it poses in implementing lifestyle changes [54], remain critical areas for policy intervention to enhance healthcare outcomes and utilization.

Age.

In our hypertension model, age ≥ 35 years emerged as a significant factor. Generally, blood pressure-screening, while a routine procedure at AFHS facilities, is particularly emphasized for older adults due to the increased prevalence of chronic conditions [13]. The prevalence of hypertension in Saudi Arabia exhibits a distinct age-related trend [55], which reflects broader global trends but is particularly pronounced locally due to demographic shifts and lifestyle factors [56].

In 2019, individuals aged 65 and above comprised 2.4% of Saudi Arabia’s total population; however, current projections estimate this demographic will reach 21% by 2050 [57]. In response to these major demographic changes, several national health initiatives have evolved to cater to an aging population. The Saudi Ministry of Health has implemented five comprehensive programs aimed at addressing chronic conditions that predominantly affect older adults [58]. Such initiatives are fundamental to the current Saudi Model of Care under the National Health Transformation Program [7], showcasing a strategic turn towards enhancing healthcare services to mitigate age-related conditions.

Social support.

Social support emerged as a significant factor in the hypertension screening model. Perceived social support, defined as the subjective evaluation of the availability of material and psychological support from one’s social environment —including family and friends [59]— has consistently been used as a predictor of self-reported disease outcomes, including hypertension [60]. Interestingly, individuals who perceive high levels of social support are more likely to engage in preventive health services; this includes those who have a spouse or children [61].

In Saudi Arabia, social support is a deeply ingrained cultural value. This support plays a vital role in health management as it extends beyond merely facilitating transportation to healthcare facilities. It includes aiding in navigating complex health systems, ensuring medication adherence, and providing reminders for appointments. Moreover, social support can influence health behaviors as encouragement and motivation from family and friends often lead to positive health behavior changes [62], all of which can affect healthcare utilization.

Easy appointment booking.

The ease of booking appointments at AFHS was identified as a significant enabling factor in the weight-loss counseling model, increasing the likelihood of accessing PCCS through improved patient facilitation, which is directly related to service utilization.

Globally, the shift towards electronic medical appointment scheduling reflects a broader trend aimed at enhancing patient-centeredness by simplifying access to care and reducing wait times, thereby improving overall healthcare outcomes and patient retention [63]. However, booking preferences among different service users can vary and may change over time [64], which occasionally presents as barriers such as the unavailability of suitable time slots. These limitations can deter new patients from utilizing PCCS, thereby negatively affecting the uptake of essential preventive services [65]. Another factor is related to the technological illiteracy among older adults, which may hinder their ability to book appointments by themselves, although telephone scheduling options remain available at AFHS.

Vehicle ownership.

Vehicle ownership emerged as a significant factor across three distinct analytical models. Specifically, Model-3 and Model-4 showed positive associations, with vehicle ownership correlating with increased likelihood of waist-circumference screening and statin use. Conversely, Model-6 revealed a negative correlation, indicating that vehicle ownership may decrease the likelihood of engaging in physical activity counseling.

Universally, the availability of personal transportation has been identified as a critical enabler for accessing healthcare services. Studies have consistently found that the lack of reliable transportation correlates with lower medication adherence and reduced attendance at medical appointments [66]. These findings are particularly relevant in Riyadh, where residents heavily rely on personal vehicles due to the expansive urban layout of the city and the limited public transportation options. This enabler is even more crucial for populations facing mobility restrictions, such as older adults who may have more disabilities, illnesses, and a greater need for frequent healthcare visits [67]. Notably, in 2020, female drivers in Saudi Arabia constituted one-third of total drivers according to 2018 projections [68]. Despite the royal resolution in 2018, which allowed women to drive, female non-drivers may still face barriers in accessing healthcare due to their dependency on available private or public transport.

Distance to PHC.

Individuals who traveled for more than one hour were more likely to utilize hypertension screening and waist-circumference services. Globally, our results align with findings from a 2013 study in China, where residents living far from hospitals were more likely to utilize health services, often with greater frequency [69]. This correlation confirms the global relevance of geographic barriers to healthcare access. However, our study’s respondents lived solely within Riyadh, which does not allow for a direct comparison between urban and rural healthcare utilization patterns. Nevertheless, studies in OECD member countries have demonstrated that individuals in remote rural areas often face significant healthcare challenges related to chronic diseases, further emphasizing the importance of addressing travel time as a critical enabling factor in access [70]. Fortunately, the AFHS has an extensive network of PHCs spread around Riyadh; however, this network does not extend into the rural areas surrounding the city. This insight is relevant for AFHS strategies, as geographical dispersion may influence engagement and frequency of access.

Language barriers.

Language barriers can negatively impact individuals’ satisfaction with care and communication with HCPs [71]. In the final hypertension-screening model, language barriers were associated with higher screening utilization. Moreover, individuals facing language barriers often experience poor health outcomes [72]. In the Kingdom, this can lead to misunderstanding, misdiagnosis, and medication errors [73].

This adverse impact on health outcomes may lead to increased utilization of healthcare services, which in turn adds to financial strain on the system [74]. On the other hand, patients experiencing language barriers frequently report lower satisfaction levels, which might deter them from seeking healthcare services [71], including PCCS. Addressing these barriers through training and implementing language-support services can enhance patient satisfaction, improve utilization, and lead to better health outcomes [75].

Waiting time.

Pre-consultation waiting time influenced the utilization of PreDM and T2DM screening services. Individuals waiting <30 minutes utilized blood glucose screening more frequently. Global research findings show that shorter waits increase satisfaction and can improve utilization [76]. Individuals may even prefer private over public healthcare centers for their shorter waiting times [77]. Effective waiting time management has been shown to improve not only patient satisfaction but also overall clinical outcomes and service efficiency [78]. This can be achieved by implementing more advanced appointment scheduling systems and optimizing staffing and patient-flow management techniques [79].

Family history of CVD.

Familial predisposition, specifically having a first-degree relative with CVD, was associated with a higher likelihood of hypertension screening. Notably, 90% of our respondents reported a positive family history of CVD. Although this factor was significant only in the hypertension screening model, familial predisposition is recognized as a risk factor for multiple chronic conditions. Family history plays a crucial role in CVD risk assessment and is incorporated into many risk calculators [80]. However, many individuals report that their familial CVD history was not addressed in consultations [81], which might affect their understanding of service need. Notably, a study found that awareness of family history of CVD or personal risk for CVD alone does not reliably predict changes in health-related behaviors [82].

CVD risk factors.

Individuals with CVD risk factors were more likely to use PreDM and T2DM screening, waist-circumference screening, and weight-loss counseling according to our models. This significant association might suggest an increased level of awareness among individuals about their elevated risk of CVD due to perceived vulnerability. Such awareness is likely to be amplified by HCPs who stress the importance of regular monitoring, thereby triggering more intensive healthcare interactions. HCPs might prioritize these patients for frequent reviews and screenings due to the potential severity of their conditions [83]. Given the high prevalence of CVD in the Saudi population, the AFHS provides its beneficiaries with a higher level of care through Chronic Illness Clinics. These clinics manage multimorbidity more closely, with a holistic team that includes diabetic educators, clinical pharmacists, and dietitians at WHC. This integrated care approach is essential not only for optimal disease management but also for prevention of secondary conditions, and may increase rates of screening and counseling.

Perceived health status.

Perceived health status is distinct from evaluated health status. Research has shown that individuals who perceive their health as poor are more likely to utilize healthcare services [84]. This was evident in our results, where perceived health status was a significant factor in weight-loss counseling and smoking-cessation counseling. Although this measure is subjective, it is considered a reliable predictor of mortality [85]. Several material, psychosocial, and behavioral factors play an integral role in self-reported health status [86]. Notably, those who perceive their health status as less than excellent were more likely to receive treatment than those with excellent self-rated health status [87]. Perceived health status could also affect patient satisfaction with healthcare services [88]. All these factors work concurrently to explain the utilization of PCCS in this community.