Diagnostic Delays in Acute Care Settings in Clinics in Damascus University Affiliated Hospitals: A Cross-Sectional Study of Patient- and Systemic-Related Factors

Diagnostic Delays in Acute Care Settings in Clinics in Damascus University Affiliated Hospitals: A Cross-Sectional Study of Patient- and Systemic-Related Factors | 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 Diagnostic Delays in Acute Care Settings in Clinics in Damascus University Affiliated Hospitals: A Cross-Sectional Study of Patient- and Systemic-Related Factors Anmar Tabbaa, Salah Arnouk, Rain Khalil, Sara Elias, Youssef Latifeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8034773/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background : Diagnostic delay is a critical issue in healthcare, yet evidence from conflict-affected settings such as Syria is scarce. This study quantifies diagnostic delays and identifies their predictors within the Syrian healthcare system. Methods : A cross-sectional study was conducted (July–October 2025) at three major hospitals in Damascus. We administered the questionnaire to 250 adult patients presenting with new-onset acute symptoms, 233 of whom were included in the final study. A structured questionnaire administered by trained research staff at the point of care, informed by established health services frameworks, collected data on sociodemographic factors, health literacy, and healthcare processes. The primary outcomes were patient delay (symptom onset to first healthcare contact) and systemic delay (first contact to diagnosis). Results : The mean total diagnostic delay was 12·6 days (±9·2), comprising similar mean patient (6·1 days ±8·1) and systemic (6·5 days ±7·0) delays. Multivariable analysis revealed that patient delay was independently predicted by the use of home remedies (B=0·587, p<0·001) and lower confidence in needing care (B=-0·267, p=0·001). Systemic delay was driven primarily by longer laboratory test durations (B=0·140, p<0·001), appointment scheduling times (B=0·090, p=0·003), and higher direct costs (B=0·421, p=0·009). Conclusions : Diagnostic delays in Syria are a two-faceted problem stemming from health-seeking behaviors and systemic operational bottlenecks. Interventions must simultaneously promote timely care seeking through community engagement and addressing system inefficiencies, such as decentralizing diagnostics and reducing financial barriers, to improve outcomes in this conflict-affected setting. Syrian healthcare diagnostic delays DEOD low- and middle-income countries conflict setting behavioral factors systemic factors Figures Figure 1 Figure 2 Introduction The protracted Syrian conflict, spanning more than a decade, has had devastating consequences for the nation’s healthcare system. In addition to the destruction of hospitals and healthcare infrastructure, the loss and migration of medical personnel have further exacerbated the fragility of healthcare delivery. This combination has resulted in reduced access to timely and effective care for large segments of the population, naturally contributing to inefficiencies and diagnostic delays [1–3] . In such a context, the ability of the health system to promptly recognize and respond to patients’ clinical presentations becomes a crucial determinant of outcomes. Diagnostic delay—defined as the time lag between symptom onset and a diagnosis—has long been recognized as a critical factor influencing patient outcomes and the efficiency of health systems [ 4 ]. The US National Academy of Medicine defines a diagnostic error as “the failure to (a) establish an accurate and timely explanation of the patient’s health problem or (b) communicate that explanation to the patient” . [ 5 ] The inclusion of the term “timely” in this definition underscores that delays in diagnosis constitute a subset of diagnostic errors. The significance of this issue becomes evident when considering that medical errors are the third leading cause of mortality in the United States [ 6 ]. Diagnostic errors and delays persist across all levels of care, underscoring the universality of the problem. [ 5 ]. Despite the attention given to the issue of diagnostic delays in high-income countries [ 7 – 9 ], there remains a significant gap in evidence regarding the impact of diagnostic delays on patient health in low- and middle-income countries (LMICs). This gap is even greater in countries that have experienced war and conflict, such as Syria, where studies in this area are scarce. The few studies addressing this issue, such as [ 10 ] on delays in breast cancer diagnosis during the Syrian war, highlight the compounded challenges arising from infrastructure destruction, the migration of healthcare workers, and socioeconomic instability. These findings suggest that both patient- and system-related delays may be magnified in fragile health systems with constrained resources, overcrowded facilities, and disrupted referral pathways [ 11 ]. However, the extent and relative contribution of these factors remain poorly quantified in the Syrian context. The diagnostic process is not a single event but rather a dynamic sequence that begins with the patient's first symptom onset and continues through clinical consideration of the possibility of the condition, its validation, and its confirmation. [ 12 ]. This process involves gathering, integrating, and interpreting information about the condition, with the ultimate goal not necessarily being diagnostic certainty but rather the ability to make the most appropriate decisions for the patient's care. Delays typically arise in two main phases: (1) patient delay, the period from symptom onset to first contact with healthcare, often influenced by health literacy, socioeconomic status, and accessibility; and (2) systemic delay, the period from first contact to confirmed diagnosis, often resulting from structural inefficiencies, workforce shortages, and administrative barriers. Both types of delays have been shown to contribute similarly to overall diagnostic inefficiencies in high-income countries [ 7 ]. Objective of our study: Given that, it remains unclear whether the findings of Redmond and colleagues[ 7 ] hold true in resource-limited countries affected by conflict and war, such as Syria. Our study seeks to address this gap by examining diagnostic delays within the Syrian healthcare system with three objectives: to quantify diagnostic delays for both the patient and the system; to identify the sociodemographic, educational, and healthcare-related factors associated with these delays; and to provide evidence-based recommendations aimed at improving diagnostic efficiency and reducing preventable delays in resource-limited contexts. Methods Study Design and Setting This cross-sectional study included 233 patients from Al-Mouwasat University Hospital, the National Hospital, and Al-Qutayfah National Hospital between July 2025 and October 2025. These hospitals are major healthcare centers in the Damascus area, providing a representative overview of healthcare accessibility and diagnostic effectiveness in both urban and semirural areas of Syria. The study targeted adult patients aged 18 years and older who presented with acute symptoms, including cough, fever, and abdominal pain. The setting was chosen to capture acute cases that typically require timely medical evaluation, minimizing recall bias while taking into account real-world diagnostic timelines. (see Table 8) The target sample size was set between 200 and 250 participants to ensure sufficient precision for multivariable logistic regression, assuming 20 predictors and ten participants per predictor, a commonly used rule of thumb for stable regression models according to Peduzzi and colleagues[ 13 ]. This calculation also included up to 20% nonresponse cases so that the sample size allowed for stable modelling and subgroup analyses of key predictors such as income, health literacy, and access to health care. Inclusion and Exclusion Criteria Eligible participants were adults (≥ 18 years) presenting with new-onset acute symptoms and no prior diagnosis of the presenting condition. Participants were excluded if they: Had chronic diseases under active management (e.g., diabetes, cancer) that could confound delay assessment? Language barriers or cognitive impairments that precluded reliable self-reporting (unless appropriate accommodations were made). Data collection tools and procedures A structured questionnaire was designed by the team to systematically assess the multifactorial determinants of diagnostic delay. The instrument was developed on the basis of three theoretical frameworks to ensure comprehensive coverage of both individual- and system-level factors. Andersen's behavioural model was used to structure factors influencing the initiation of care (patient delay)[ 14 ]. The health literacy frameworks capture the individual-level capacities necessary to navigate the transition from recognizing a problem to effectively engaging with the health system[ 15 ]. Finally, the WHO health system framework was used to analyse the barriers encountered within the formal healthcare system (systemic delay)[ 16 ]. Together, they map the entire journey from symptom onset to confirmed diagnosis. (See Additional File – Questionnaire) The questionnaire was translated into Arabic via a forward–backwards translation process to ensure linguistic and conceptual equivalence. A pilot study was conducted among 10 patients presenting with acute-onset symptoms to assess the clarity, relevance, and acceptability of the questionnaire. The questionnaire was then administered by trained research staff at the point of care, where patients would fill out the questionnaire on Google forms with the help of the staff in cases of illiteracy or lack of technical knowledge. The participants were selected via a random selection of hospital logs. Variables and Measurements Two main outcome variables were defined: Patient Delay: Time from symptom onset to first contact with a healthcare provider (in days). Systemic Delay: Time from first contact to confirmed diagnosis (in days). Independent variables included the following: Sociodemographic factors: Age, sex, education, income, residence type (urban/rural), and insurance coverage. Health literacy indicators: ability to interpret health information and navigate healthcare systems. System-level variables: facility type, waiting time, staff availability, and patient trust in providers. The data were analysed via IBM SPSS Statistics version 26. 1. Descriptive statistics (means, standard deviations, frequencies) were used to summarize delay durations and participant characteristics. 2. Regression analyses (linear or logistic, as appropriate) identified predictors of patient and systemic delays. 3. Missing data were reported descriptively and assessed for non-random patterns. Sensitivity analyses were conducted if the degree of missing data exceeded 10% for key variables.\ We tested normality with the Shapiro‒Wilk test, and because systemic delay was right-skewed, we log-transformed it prior to linear regression. Model diagnostics included inspection of residual plots and assessment of multicollinearity (VIFs). Results Descriptive Statistics A total of 233 patients were analysed. The overall diagnostic delay consisted of two main components: patient delay (the period from symptom onset to first contact with healthcare) and systemic delay (the period from first contact to diagnosis). Patient delays ranged from 0 to 50 days, with a mean of 6·06 ± 8.14 days and a median of 2 days. Systemic delays ranged from 0 to 49 days, with a mean of 6·52 ± 6·99 days and a median of 4 days. Both variables showed a significant rightward skew (skewness = 2·55 and 3·10, respectively) and a significant deviation from the mean (Shapiro‒Wilk p < 0·001). This suggests that while most patients experienced relatively short delays, a minority experienced prolonged delays lasting several weeks. The mean total diagnostic delay (sum of the two components) was 12·59 ± 9·23 days (median = 7 days). (see Table 1 , Fig. 1 ) Table 1 Descriptive statistics of diagnostic delays Delay Component Mean (days) Median (days) SD Range (days) Skewness Kurtosis Normality (p) Patient Delay 6.06 2.0 8.14 0–50 2.55 6.26 < 0.001 Systemic Delay 6.52 4.0 6.99 0–49 3.10 12.92 < 0.001 Total Delay 12.59 7.0 9.23 0–65 1.90 3.10 < 0.001 Descriptive summary of the three delay components. All variables showed significant deviation from normality (Shapiro–Wilk p < 0.001), indicating right-skewed distributions. Factors associated with patient delay Nonparametric tests revealed several significant associations, including sociodemographic and behavioral factors. Patient delays were significantly longer among rural residents than among urban patients (p = 0·021) and among individuals with lower educational levels (p = 0·038). Patients without insurance coverage (p = 0·007) and those who primarily attended public healthcare facilities (p = 0·019) also exhibited longer delays. (see Fig. 2 ) No statistically significant differences were found between males and females (p = 0·601) or between employment categories (p = 0·311). However, patients who reported using home remedies before seeking medical care had significantly longer delays (p = 0·003). In the area of ​​continuous predictors, Spearman's correlation analysis revealed that patient delay was negatively associated with monthly income (ρ = -0·182, p = 0·009) and symptom knowledge (ρ = -0·367, p < 0·0001). Similarly, patients who found it easier to search for or understand health information experienced shorter delays (ρ = -0·295, p < 0·001) in searching for symptoms (ρ = -0·208, p = 0·002) and in understanding information provided by a doctor or nurse. (see Table 2 , Table 3 ) Confidence in the need for medical care also showed a statistically significant negative association (ρ = -0·289, p < 0·001), whereas age showed no association (ρ = 0·102, p = 0·142). These results suggest that educational level, socioeconomic status, and health knowledge are the main factors determining patient delay in accessing diagnosis and treatment. Patients with greater symptom awareness tend to be more ill, have a higher income, and have confidence in their health decisions, leading them to seek health care faster. Table 2 Correlations between continuous variables and patient delay Spearman’s correlation coefficients between continuous predictors and patient delay. Negative coefficients indicate a reduced delay with higher values of the variable. Variable Spearman’s ρ p value Interpretation Age 0.102 0.142 Not significant Monthly income –0.182 0.009 Higher income → shorter delay Knowledge of symptoms –0.367 < 0.001 Better knowledge → shorter delay Ease of looking up symptoms –0.295 < 0.001 Easier access → shorter delay Ease of understanding medical info –0.208 0.002 Poor understanding → longer delay Confidence in needing care –0.289 < 0.001 Higher confidence → shorter delay Table 3 Associations of categorical variables with patient delay Variable Test p value Significance Interpretation Gender Mann–Whitney U 0.601 NS No difference Residence (Urban/Rural) Mann–Whitney U 0.021 * Rural patients delay longer Education level Kruskal–Wallis H 0.038 * Higher education → shorter delay Employment status Kruskal–Wallis H 0.311 NS Not significant Insurance coverage Mann–Whitney U 0.007 ** Insured → shorter delay Healthcare center type Mann–Whitney U 0.019 * Private → shorter delay Home remedies used Mann–Whitney U 0.003 ** Home remedies → longer delay Nonparametric group comparisons for patient delay. *p < 0.05; * p < 0.01; NS = not significant. Factors associated with systemic delay In terms of ​​procedural and communication factors, systemic delay was strongly positively associated with several procedural variables, including the time taken to explain the results (ρ = 0·770, p < 0·001), the number of visits before diagnosis (ρ = 0·711, p < 0·001), the duration of laboratory testing (ρ = 0·687, p < 0·001), and the waiting time for appointments (ρ = 0·547, p < 0·001). (see Table 6 ) The quality of communication with healthcare providers was also moderately and negatively associated with systemic delay (ρ = -0·311, p < 0·001), indicating that patients who rated communication as good were more likely to experience shorter device-related delays. In the area of ​​sociodemographic associations, Mann‒Whitney and Kruskal‒Wallis tests revealed that systematic delay was significantly longer among rural residents (p = 0·019), patients with low educational attainment (p = 0·046), uninsured patients (p = 0·023), those attending public healthcare facilities (p = 0·011), and those referred to another healthcare provider before diagnosis (p = 0·005). No significant difference was observed by sex (p = 0·674). (see Table 5 ) In the area of ​​continuous predictors, systematic delay was negatively associated with patients' knowledge of symptoms (ρ = -0·162, p = 0·020), their understanding of medical information (ρ = -0·183, p = 0·008), and their ability to communicate with healthcare staff (ρ = -0·311, p < 0·001), whereas age and income were not significant predictors. (see Table 4 ) Table 4 Correlation between continuous variables and the systemic delay Spearman’s correlation coefficients between continuous predictors and systemic delay. Variable Spearman’s ρ p value Interpretation Age 0.063 0.381 Not significant Monthly income –0.122 0.078 Weak, not significant Knowledge of symptoms –0.162 0.020 Better knowledge → shorter delay Ease of understanding medical info –0.183 0.008 Better comprehension → shorter delay Communication with healthcare staff –0.311 < 0.001 Stronger communication → shorter delay Confidence in needing care –0.111 0.103 Not significant Table 5 Association of categorical variables with systemic delay Nonparametric comparisons of systemic delay across sociodemographic groups. *p < 0.05; * p < 0.01; NS = not significant. Variable Test p value Significance Interpretation Gender Mann–Whitney U 0.674 NS No difference Residence (Urban/Rural) Mann–Whitney U 0.019 * Rural → longer delay Education level Kruskal–Wallis H 0.046 * Lower education → longer delay Insurance coverage Mann–Whitney U 0.023 * Uninsured → longer delay Healthcare center type Mann–Whitney U 0.011 ** Public centers → longer delay Referral to another provider Mann–Whitney U 0.005 ** Referred patients → longer delay Table 6 Correlation between systemic delay and procedural components Variable Spearman’s ρ p value Strength Interpretation Results explained delay 0.770 < 0.001 Strong Main driver of system delay Number of visits 0.711 < 0.001 Strong More visits → longer delay Lab tests required 0.687 < 0.001 Strong More tests → longer delay Appointment waiting time 0.547 < 0.001 Moderate Scheduling contributes Communication rating –0.311 < 0.001 Moderate negative Better communication → shorter delay Multiple linear regression Two multiple linear regression models were applied to identify independent predictors of delayed diagnosis, adjusting for potential confounders. The first model was fitted via multiple linear regression to examine the predictors of delayed patient arrival. The model included 19 factors, including demographics (age, gender, place of residence, education, employment), socioeconomic indicators (income, insurance, financial priority, cost barrier), health literacy and confidence (knowledge of symptoms, ease of understanding information, confidence in need of care), behavioral factors (home treatments, influence of others), and healthcare facility characteristics. (R = -0·509, R² = 0·259) The model explains 25.9% of the variance in delayed patient arrival. (Adjusted R² = 0·188) After adjusting for the number of predictors, approximately 18·8% of the variance was explained. (F (19,198) = 3·644, p < 0·001) The overall model is statistically significant. The significant predictors were that home remedies were used before seeking care (B = 0·587, p < 0·001) and that patients who tried home remedies experienced longer delays. In the area of confidence in need of care (B = -0·267, p = 0·001), greater confidence was associated with shorter delays. The influence of others had a marginal effect (B = 0·305, p = 0·052). For the nonsignificant predictors, all other variables—including age, sex, place of residence, education, income, health insurance, and health literacy measures—were not statistically significant in the multivariate model. This regression suggests that behavioral factors (home remedies and confidence in seeking care) are the primary predictors of patient delays. While socioeconomic and demographic factors showed associations in univariate analyses, they were not statistically significant after adjusting for other variables. In the multicollinearity domain, the variance inflation factors (VIFs) ranged from 1·165–2·456, indicating no problematic multicollinearity. The residuals were reasonably distributed (range of residual deviation: -1·993–2·958), supporting the model's assumption. Multiple linear regression was also conducted to interpret the predictors of systematic delay. Twenty-six variables were included, including procedural factors, demographic characteristics, socioeconomic indicators, health awareness, behavioral factors, and healthcare utilization characteristics. The dependent variable was the systematic delay, logarithmically transformed to address skewness. Model fit: R = 0·787, R² = 0·619, adjusted R² = 0·565, F (26,183) = 11·441, p < 0·001. This finding indicates that the model explains 61.9% of the variance in systematic delay, with 56.5% explained after adjusting for the number of predictors. The significant predictors were laboratory test duration (B = 0·140, p < 0·001) → associated with longer delays; appointment scheduling time (B = 0·090, p = 0·003) → associated with longer delays; and direct cost (B = 0·421, p = 0·009) → showing that with every increase in direct costs, systematic delays increased. Patient outcome (B = 0·079, p < 0·001) was associated with longer delays. Gender had a moderate effect (B = 0·139, p = 0·050), whereas other variables—including age, place of residence, education, income, insurance, health literacy measures, home care, confidence in need of care, and facility type—were not statistically significant after adjustment. In terms of linear correlation, the VIFs ranged from 1·270 to 2·549, indicating that there were no multicollinearity issues. The residuals were reasonably distributed (range standard deviation: -2·613–3·288), supporting the model's assumption. Comparison Between Patient and Systemic Delays Both components contributed comparably to total diagnostic delay, though systemic delay (median = 4 days) was slightly longer than patient delay (median = 2 days). The two were weakly but significantly correlated (ρ = 0.28, p < 0.001), indicating that while some overlap exists, patient-related and system-related delays are partially independent phenomena. Patients with low education, low income, rural residence, and lack of insurance experienced both longer patient and systemic delays, suggesting a consistent social gradient across both components. (see Table 7 ) Table 7 Comparison between Patient Delay and Systemic Delay Aspect Patient Delay Systemic Delay Interpretation Median (days) 2 4 Systemic delay slightly longer Mean (days) 6.06 6.52 Roughly equal contribution Main influences Awareness, income, behavior Communication, procedures Strongest correlation Knowledge of symptoms (ρ = − 0.367) Results explained (ρ = 0.770) Intercorrelation (Patient ↔ Systemic) ρ = 0.28, p < 0.001 Weak relationship Caption: Comparative summary of patient and systemic delays, showing distinct determinants but similar overall magnitudes. Discussion Our study on the underlying factors of diagnostic delays in acute care facilities in Syria is one of the first quantitative studies. Syria is a low-income country affected by conflict, war, and crisis. The average overall diagnostic delay, which typically reaches one week, highlights how the ongoing conflict has placed severe pressure on the healthcare sector in Syria. This has been either conceptual, such as a lack of education and literacy, or more tangible, such as weak infrastructure and inefficiencies. These two types of delays have fundamentally different key predictors: patient delays are often behavioral and influenced by socioeconomic factors and primary home care, whereas systemic delays are operational and linked to inefficiencies within the healthcare system itself. Our multivariate analysis revealed that the use of home-based treatments and lack of confidence in the need for professional care were the strongest independent predictors of patient delay. This suggests that the decision to seek care depends less on simple demographics than on an individual's health beliefs and initial response to illness. While lower income, rural residence, and lack of education were associated with longer delays in univariate analysis, their effects appeared to be mediated by patients' behavioral trajectories. In the context of the Syrian crisis, access to healthcare is constrained by logistical and financial barriers, which may represent a rational coping strategy. This finding is consistent with studies from other fragile settings, where "wait and see" approaches and self-medication are common practices when formal healthcare is perceived as unavailable or unreliable. [ 17 – 19 ] Conversely, systemic delays were driven almost exclusively by procedural bottlenecks within the healthcare system itself. The time taken to perform laboratory tests, waiting for appointments, and, to a lesser extent, delays in informing patients of results were prevalent factors. The strong correlation between systemic delays and the number of visits before diagnosis suggests an inefficient referral system, which is likely exacerbated by the loss of specialized health workers and damaged infrastructure. This finding is consistent with Abbara and colleagues, Kallström and colleagues, and Mbuh and colleagues[ 20 – 22 ]. This operational shortcoming reflects the deteriorating state of the Syrian health system, where resource constraints and administrative chaos contribute to lengthy diagnostic processes. The significantly longer delays in public facilities and uninsured patients are administrative failures resulting from a systematically underresourced public sector and a healthcare environment where access to services has become increasingly linked to the ability to pay, a dynamic exacerbated by the conflict, as reported by Fouad and colleagues[ 11 ]. The roughly equal contribution of patient delays and systematic delays to the overall diagnostic timeline reflects the findings of some studies in high-income countries [ 7 ]. However, the nature of the delays varies dramatically. In well-resourced systems, systemic delays may be linked to scheduling complications or overtesting [ 23 ]; in Syria, they are a direct manifestation of the collapse of the health system. While low health literacy is a common barrier to seeking care [ 24 – 26 ], its impact in Syria is exacerbated by the weak capacity of the public health sector and the widespread lack of public health awareness, both of which have been exacerbated by the crisis and its social repercussions. [ 27 ] Our finding that socioeconomic factors show a consistent gradient for both types of delays is consistent with the underlying theory of health inequalities, which suggests that wealthy and educated patients hear about it earlier, pay for it, and receive a diagnosis earlier. Poor patients learn about it later and cannot afford it or access it easily. This delays diagnosis and is linked to the inability to overcome institutional barriers (in line with Clouston and Link[ 28 ]). This progression may have been more severe in Syria due to the severe economic collapse caused by the long duration of the humanitarian crisis. On the other hand, in our study, we did not observe a significant relationship between delayed diagnosis and age or sex. This finding contradicts several previous studies [ 25 , 29 ]. The absence of such associations in our data may reflect the uniformity of catastrophic barriers across demographic groups in the Syrian context. The overwhelming force of systemic collapse—including ubiquitous access problems, limited diagnostic infrastructure, and widespread economic hardship—may effectively homogenize the experience of delay, overshadowing the more nuanced demographic disparities typically observed in stable health systems. Implications for Policy and Practice To translate these findings into actionable change, a multipronged strategy is essential. First, public health efforts must proactively shape health-seeking behaviors. This involves launching community-based awareness campaigns, delivered through trusted channels such as local leaders and radio, to educate the public on "red flag" symptoms that necessitate immediate professional care rather than self-management. Concurrently, empowering patients requires strengthening healthcare front lines; training staff to use simple, visual aids to assess and improve patient understanding during consultations can increase health literacy and confidence, which our study shows drives timely care seeking. To make this timely care a reality, financial barriers must be dismantled by expanding insurance coverage for vulnerable groups and streamlining reimbursement to alleviate the deterrent of out-of-pocket costs at the point of service. Simultaneously, the profound systemic bottlenecks demand direct operational reforms. A critical intervention is to decentralize diagnostic capacity by investing in point-of-care testing technologies for common conditions at primary care facilities, which would drastically reduce the laboratory delays identified as a primary driver of systemic waits. Crucially, an equity lens must guide all interventions, ensuring that rural and uninsured populations—who bear a double burden of longer patient and systemic delays—are prioritized in the rollout of these reforms. While these steps address immediate inefficiencies, long-term recovery hinges on rebuilding the decimated health workforce and restoring shattered infrastructure, creating a sustainable foundation for a resilient and timely health system. Strengths and Limitations This study's principal strengths lie in its robust methodological grounding within a critically underresearched conflict setting, its use of established theoretical frameworks to ensure comprehensive variable selection, and its sophisticated analytical approach that disentangles patient-related and system-level predictors of delay. However, several limitations must be acknowledged. First, the measurement of patient delay is subject to recall bias, although this was mitigated by focusing on acute-onset symptoms. Second, while our model explained a substantial portion of the variance in systemic delay, potentially important unmeasured contextual factors—such as the specific degree of infrastructure damage in each facility, staff morale, organizational culture, or precise workload levels—were not captured. Their absence represents a potential source of residual confounding. Notably, our multivariate model for patient delay, while significant, explained a modest portion of the variance (adjusted R² = 0·188). This suggests that while home remedies and confidence are key independent predictors, other unmeasured, context-specific factors play a substantial role. These may include perceived security risks associated with travel, the influence of strong social networks that advise health matters, specific cultural beliefs about illness causation, or a deep-seated distrust in the quality of care available, which may deter care-seeking irrespective of an individual's personal health literacy or confidence. Conclusion This study demonstrates that delayed diagnosis in the Syrian acute healthcare system represents a two-faceted problem: societal behaviour and systemic failure. Addressing this challenge requires a dual approach: first, empowering patients with the knowledge and confidence to seek healthcare promptly, and second, simultaneously rebuilding the operational foundation of the healthcare system to treat them efficiently. As Syria moves toward a new future postconflict, prioritizing evidence-based interventions will be critical to rebuilding an effective, appropriate, and equitable healthcare system. Declarations Ethics approval and consent to participate: Ethical approval was obtained from the Biomedical Research Ethics Committee (BMREC) at Damascus University prior to study initiation, and the research was conducted in accordance with the ethical principles of the Declaration of Helsinki. Dated 30/6/2025, ID Number: MD-300625-473 Session Number: 25. Written informed consent was obtained from all participants before data collection. Participant confidentiality was ensured through the use of coded identifiers instead of names, and all the data were stored securely with restricted access. No financial or nonfinancial incentives were provided to the participants. Consent for publication: Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding: The authors received no financial support for the research, authorship, and/or publication of this article. Author Contributions: A.T: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing – Original Draft (Results, and co-wrote Discussion), Writing – Review & Editing, Project Administration. S.A: Writing—Original Draft (Introduction, and co-wrote Discussion), Writing—Review & Editing, Visualization, Literature Search. R.K: Methodology, Investigation, Writing – Original Draft (Methods), Resources. S.E: Data Curation, Validation, Writing – Review & Editing. Y.L: Supervision, Writing – Review & Editing. Availability of data and materials: The dataset supporting the conclusions of this article is included within the article’s additional files. Acknowledgements: We extend our sincere thanks to the team of research assistants; Alaa al Shahet Enana Rajjouh Fahmi Al Askari Marwan Leddawi for their diligence and professionalism in participant recruitment and data collection under challenging circumstances. References Blanchet K, Fouad FM, Pherali T: Syrian refugees in Lebanon: the search for universal health coverage . Conflict and Health 2016, 10 (1):12. Alhaffar MHDBA, Janos S: Public health consequences after ten years of the Syrian crisis: a literature review . Globalization and Health 2021, 17 (1):111. 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Humphrys E, Burt J, Rubin G, Emery JD, Walter FM: The influence of health literacy on the timely diagnosis of symptomatic cancer: A systematic review . Eur J Cancer Care (Engl) 2019, 28 (1):e12920. Lyra-González I, Cuello M, Anderson D, Echeverri M: Socioeconomic disparities and health literacy: Unravelling the impact on diagnostic and cancer care in Uruguay . Journal of Cancer Policy 2024, 40 :100472. Mor-Anavy S, Lev-Ari S, Levin-Zamir D: Health Literacy, Primary Care Health Care Providers, and Communication . Health Lit Res Pract 2021, 5 (3):e194-e200. WHO calls for urgent support to rebuild Syria’s health system . In . : World Health Organization. Clouston SAP, Link BG: A retrospective on fundamental cause theory: State of the literature, and goals for the future . Annu Rev Sociol 2021, 47 (1):131-156. Batbayar B, Kariya T, Boldoo T, Purevdorj E, Dambaa N, Saw YM, Yamamoto E, Hamajima N: Patient delay and health system delay of patients with newly diagnosed pulmonary tuberculosis in Mongolia, 2016-2017 . Nagoya J Med Sci 2022, 84 (2):339-351. Table Table 8. Participant characteristics (N=233) Characteristic n (%) or Mean ± SD Age (years) 45.6 ± 17.0 Gender Male 124 (53.2%) Female 109 (46.8%) Residence City 93 (39.9%) Countryside 103 (44.2%) Suburbs 37 (15.9%) Education Illiterate 19 (8.2%) Primary 61 (26.2%) Secondary 65 (27.9%) Collegiate 75 (32.2%) Postgraduate 13 (5.6%) Employment status Unemployed 109 (46.8%) Student 19 (8.2%) Employed 82 (35.2%) Retired 23 (9.9%) Monthly income (SYP in thousands) 2000 (1000–3750) Insurance coverage No insurance 199 (85.4%) Public insurance 32 (13.7%) Private insurance 2 (0.9%) Additional Declarations No competing interests reported. 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Tabbaa","email":"data:image/png;base64,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","orcid":"","institution":"Damascus University","correspondingAuthor":true,"prefix":"","firstName":"Anmar","middleName":"","lastName":"Tabbaa","suffix":""},{"id":565207229,"identity":"8cec5b0a-4873-4dbc-b953-350e988219b8","order_by":1,"name":"Salah Arnouk","email":"","orcid":"","institution":"Damascus 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1","display":"","copyAsset":false,"role":"figure","size":71285,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of patient, systemic, and total diagnostic delays showing right-skewed distributions and deviation from normality.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8034773/v1/2416cbf2dba9b3e15eef9fb8.png"},{"id":99318658,"identity":"291ff20f-0252-4667-8223-39df9ede5dc3","added_by":"auto","created_at":"2025-12-31 16:33:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":235322,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBoxplots comparing patient delay by residence, insurance status, and healthcare facility type. Rural and uninsured patients exhibited longer delays.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8034773/v1/7f5450cdd666c0412e2b4f6d.png"},{"id":99323791,"identity":"d9334882-187c-43b7-9fa3-882f1551e166","added_by":"auto","created_at":"2025-12-31 16:46:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2877876,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8034773/v1/84feebb9-ace9-4966-919f-d95e1e68ad88.pdf"},{"id":99318841,"identity":"58c9d0c2-a7c6-4ff3-ab26-0c69a151baac","added_by":"auto","created_at":"2025-12-31 16:35:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":26283,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8034773/v1/e31cd8154d3a2f52976afbe2.docx"},{"id":99318616,"identity":"3310f1bf-6d2e-41a6-8df5-f9f8253bbd20","added_by":"auto","created_at":"2025-12-31 16:33:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20339,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalfileEnglishQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8034773/v1/7174d5608490af91ccd30bdb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDiagnostic Delays in Acute Care Settings in Clinics in Damascus University Affiliated Hospitals: A Cross-Sectional Study of Patient- and Systemic-Related Factors\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe protracted Syrian conflict, spanning more than a decade, has had devastating consequences for the nation’s healthcare system. In addition to the destruction of hospitals and healthcare infrastructure, the loss and migration of medical personnel have further exacerbated the fragility of healthcare delivery. This combination has resulted in reduced access to timely and effective care for large segments of the population, naturally contributing to inefficiencies and diagnostic delays \u003csup\u003e[1–3]\u003c/sup\u003e. In such a context, the ability of the health system to promptly recognize and respond to patients’ clinical presentations becomes a crucial determinant of outcomes.\u003c/p\u003e \u003cp\u003eDiagnostic delay—defined as the time lag between symptom onset and a diagnosis—has long been recognized as a critical factor influencing patient outcomes and the efficiency of health systems [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The US National Academy of Medicine defines a diagnostic error as \u003cem\u003e“the failure to (a) establish an accurate and timely explanation of the patient’s health problem or (b) communicate that explanation to the patient”\u003c/em\u003e. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] The inclusion of the term \u003cem\u003e“timely”\u003c/em\u003e in this definition underscores that delays in diagnosis constitute a subset of diagnostic errors. The significance of this issue becomes evident when considering that medical errors are the third leading cause of mortality in the United States [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Diagnostic errors and delays persist across all levels of care, underscoring the universality of the problem. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the attention given to the issue of diagnostic delays in high-income countries [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], there remains a significant gap in evidence regarding the impact of diagnostic delays on patient health in low- and middle-income countries (LMICs). This gap is even greater in countries that have experienced war and conflict, such as Syria, where studies in this area are scarce. The few studies addressing this issue, such as [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] on delays in breast cancer diagnosis during the Syrian war, highlight the compounded challenges arising from infrastructure destruction, the migration of healthcare workers, and socioeconomic instability. These findings suggest that both patient- and system-related delays may be magnified in fragile health systems with constrained resources, overcrowded facilities, and disrupted referral pathways [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the extent and relative contribution of these factors remain poorly quantified in the Syrian context.\u003c/p\u003e \u003cp\u003eThe diagnostic process is not a single event but rather a dynamic sequence that begins with the patient's first symptom onset and continues through clinical consideration of the possibility of the condition, its validation, and its confirmation. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This process involves gathering, integrating, and interpreting information about the condition, with the ultimate goal not necessarily being diagnostic certainty but rather the ability to make the most appropriate decisions for the patient's care. Delays typically arise in two main phases: (1) patient delay, the period from symptom onset to first contact with healthcare, often influenced by health literacy, socioeconomic status, and accessibility; and (2) systemic delay, the period from first contact to confirmed diagnosis, often resulting from structural inefficiencies, workforce shortages, and administrative barriers. Both types of delays have been shown to contribute similarly to overall diagnostic inefficiencies in high-income countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eObjective of our study:\u003c/h3\u003e\n\u003cp\u003eGiven that, it remains unclear whether the findings of Redmond and colleagues[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] hold true in resource-limited countries affected by conflict and war, such as Syria. Our study seeks to address this gap by examining diagnostic delays within the Syrian healthcare system with three objectives: to quantify diagnostic delays for both the patient and the system; to identify the sociodemographic, educational, and healthcare-related factors associated with these delays; and to provide evidence-based recommendations aimed at improving diagnostic efficiency and reducing preventable delays in resource-limited contexts.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n \u003cp\u003e\u003c/p\u003e\n\n \n\n \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eThis cross-sectional study included 233 patients from Al-Mouwasat University Hospital, the National Hospital, and Al-Qutayfah National Hospital between July 2025 and October 2025. These hospitals are major healthcare centers in the Damascus area, providing a representative overview of healthcare accessibility and diagnostic effectiveness in both urban and semirural areas of Syria.\u003c/p\u003e\u003cp\u003eThe study targeted adult patients aged 18 years and older who presented with acute symptoms, including cough, fever, and abdominal pain. The setting was chosen to capture acute cases that typically require timely medical evaluation, minimizing recall bias while taking into account real-world diagnostic timelines. (see Table\u0026nbsp;8)\u003c/p\u003e\u003cp\u003eThe target sample size was set between 200 and 250 participants to ensure sufficient precision for multivariable logistic regression, assuming 20 predictors and ten participants per predictor, a commonly used rule of thumb for stable regression models according to Peduzzi and colleagues[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This calculation also included up to 20% nonresponse cases so that the sample size allowed for stable modelling and subgroup analyses of key predictors such as income, health literacy, and access to health care.\u003c/p\u003e\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\u003cp\u003eEligible participants were adults (≥ 18 years) presenting with new-onset acute symptoms and no prior diagnosis of the presenting condition.\u003c/p\u003e\u003cp\u003eParticipants were excluded if they:\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eHad chronic diseases under active management (e.g., diabetes, cancer) that could confound delay assessment?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLanguage barriers or cognitive impairments that precluded reliable self-reporting (unless appropriate accommodations were made).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003ch3\u003eData collection tools and procedures\u003c/h3\u003e\u003cp\u003eA structured questionnaire was designed by the team to systematically assess the multifactorial determinants of diagnostic delay. The instrument was developed on the basis of three theoretical frameworks to ensure comprehensive coverage of both individual- and system-level factors. Andersen's behavioural model was used to structure factors influencing the \u003cem\u003einitiation\u003c/em\u003e of care (patient delay)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The health literacy frameworks capture the individual-level capacities necessary to navigate the transition from recognizing a problem to effectively engaging with the health system[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Finally, the WHO health system framework was used to analyse the barriers encountered \u003cem\u003ewithin\u003c/em\u003e the formal healthcare system (systemic delay)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Together, they map the entire journey from symptom onset to confirmed diagnosis. (See Additional File – Questionnaire)\u003c/p\u003e\u003cp\u003eThe questionnaire was translated into Arabic via a forward–backwards translation process to ensure linguistic and conceptual equivalence.\u003c/p\u003e\u003cp\u003eA pilot study was conducted among 10 patients presenting with acute-onset symptoms to assess the clarity, relevance, and acceptability of the questionnaire.\u003c/p\u003e\u003cp\u003eThe questionnaire was then administered by trained research staff at the point of care, where patients would fill out the questionnaire on Google forms with the help of the staff in cases of illiteracy or lack of technical knowledge.\u003c/p\u003e\u003cp\u003eThe participants were selected via a random selection of hospital logs.\u003c/p\u003e\u003ch3\u003eVariables and Measurements\u003c/h3\u003e\u003cp\u003eTwo main outcome variables were defined:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003ePatient Delay: Time from symptom onset to first contact with a healthcare provider (in days).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSystemic Delay: Time from first contact to confirmed diagnosis (in days).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eIndependent variables included the following:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eSociodemographic factors: Age, sex, education, income, residence type (urban/rural), and insurance coverage.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHealth literacy indicators: ability to interpret health information and navigate healthcare systems.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSystem-level variables: facility type, waiting time, staff availability, and patient trust in providers.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eThe data were analysed via IBM SPSS Statistics version 26.\u003c/p\u003e\u003cp\u003e1. \u003cb\u003eDescriptive statistics\u003c/b\u003e (means, standard deviations, frequencies) were used to summarize delay durations and participant characteristics.\u003c/p\u003e\u003cp\u003e2. \u003cb\u003eRegression analyses\u003c/b\u003e (linear or logistic, as appropriate) identified predictors of patient and systemic delays.\u003c/p\u003e\u003cp\u003e3. \u003cb\u003eMissing data\u003c/b\u003e were reported descriptively and assessed for non-random patterns. Sensitivity analyses were conducted if the degree of missing data exceeded 10% for key variables.\\\u003c/p\u003e\u003cp\u003eWe tested normality with the Shapiro‒Wilk test, and because systemic delay was right-skewed, we log-transformed it prior to linear regression. Model diagnostics included inspection of residual plots and assessment of multicollinearity (VIFs).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003e \u003cb\u003eA total of\u003c/b\u003e 233 patients were analysed. The overall diagnostic delay consisted of two main components: patient delay (the period from symptom onset to first contact with healthcare) and systemic delay (the period from first contact to diagnosis). Patient delays ranged from 0 to 50 days, with a mean of 6\u0026middot;06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.14 days and a median of 2 days. Systemic delays ranged from 0 to 49 days, with a mean of 6\u0026middot;52\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u0026middot;99 days and a median of 4 days. Both variables showed a significant rightward skew (skewness\u0026thinsp;=\u0026thinsp;2\u0026middot;55 and 3\u0026middot;10, respectively) and a significant deviation from the mean (Shapiro‒Wilk p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). This suggests that while most patients experienced relatively short delays, a minority experienced prolonged delays lasting several weeks. The mean total diagnostic delay (sum of the two components) was 12\u0026middot;59\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u0026middot;23 days (median\u0026thinsp;=\u0026thinsp;7 days). (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of diagnostic delays\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelay Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRange (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNormality (p)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient Delay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystemic Delay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Delay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDescriptive summary of the three delay components. All variables showed significant deviation from normality (Shapiro\u0026ndash;Wilk p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating right-skewed distributions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFactors associated with patient delay\u003c/h3\u003e\n\u003cp\u003eNonparametric tests revealed several significant associations, including sociodemographic and behavioral factors. Patient delays were significantly longer among rural residents than among urban patients (p\u0026thinsp;=\u0026thinsp;0\u0026middot;021) and among individuals with lower educational levels (p\u0026thinsp;=\u0026thinsp;0\u0026middot;038). Patients without insurance coverage (p\u0026thinsp;=\u0026thinsp;0\u0026middot;007) and those who primarily attended public healthcare facilities (p\u0026thinsp;=\u0026thinsp;0\u0026middot;019) also exhibited longer delays. (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo statistically significant differences were found between males and females (p\u0026thinsp;=\u0026thinsp;0\u0026middot;601) or between employment categories (p\u0026thinsp;=\u0026thinsp;0\u0026middot;311). However, patients who reported using home remedies before seeking medical care had significantly longer delays (p\u0026thinsp;=\u0026thinsp;0\u0026middot;003). In the area of ​​continuous predictors, Spearman's correlation analysis revealed that patient delay was negatively associated with monthly income (ρ = -0\u0026middot;182, p\u0026thinsp;=\u0026thinsp;0\u0026middot;009) and symptom knowledge (ρ = -0\u0026middot;367, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001). Similarly, patients who found it easier to search for or understand health information experienced shorter delays (ρ = -0\u0026middot;295, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) in searching for symptoms (ρ = -0\u0026middot;208, p\u0026thinsp;=\u0026thinsp;0\u0026middot;002) and in understanding information provided by a doctor or nurse. (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eConfidence in the need for medical care also showed a statistically significant negative association (ρ = -0\u0026middot;289, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), whereas age showed no association (ρ\u0026thinsp;=\u0026thinsp;0\u0026middot;102, p\u0026thinsp;=\u0026thinsp;0\u0026middot;142).\u003c/p\u003e \u003cp\u003eThese results suggest that educational level, socioeconomic status, and health knowledge are the main factors determining patient delay in accessing diagnosis and treatment. Patients with greater symptom awareness tend to be more ill, have a higher income, and have confidence in their health decisions, leading them to seek health care faster.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelations between continuous variables and patient delay\u003c/b\u003e Spearman\u0026rsquo;s correlation coefficients between continuous predictors and patient delay. Negative coefficients indicate a reduced delay with higher values of the variable.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman\u0026rsquo;s ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher income \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge of symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBetter knowledge \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEase of looking up symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEasier access \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEase of understanding medical info\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePoor understanding \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence in needing care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher confidence \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAssociations of categorical variables with\u003c/b\u003e patient delay\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (Urban/Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural patients delay longer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKruskal\u0026ndash;Wallis H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigher education \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKruskal\u0026ndash;Wallis H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInsured \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare center type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrivate \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome remedies used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHome remedies \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNonparametric group comparisons for patient delay. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NS\u0026thinsp;=\u0026thinsp;not significant.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with systemic delay\u003c/h2\u003e \u003cp\u003eIn terms of ​​procedural and communication factors, systemic delay was strongly positively associated with several procedural variables, including the time taken to explain the results (ρ\u0026thinsp;=\u0026thinsp;0\u0026middot;770, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), the number of visits before diagnosis (ρ\u0026thinsp;=\u0026thinsp;0\u0026middot;711, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), the duration of laboratory testing (ρ\u0026thinsp;=\u0026thinsp;0\u0026middot;687, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), and the waiting time for appointments (ρ\u0026thinsp;=\u0026thinsp;0\u0026middot;547, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe quality of communication with healthcare providers was also moderately and negatively associated with systemic delay (ρ = -0\u0026middot;311, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), indicating that patients who rated communication as good were more likely to experience shorter device-related delays.\u003c/p\u003e \u003cp\u003eIn the area of ​​sociodemographic associations, Mann‒Whitney and Kruskal‒Wallis tests revealed that systematic delay was significantly longer among rural residents (p\u0026thinsp;=\u0026thinsp;0\u0026middot;019), patients with low educational attainment (p\u0026thinsp;=\u0026thinsp;0\u0026middot;046), uninsured patients (p\u0026thinsp;=\u0026thinsp;0\u0026middot;023), those attending public healthcare facilities (p\u0026thinsp;=\u0026thinsp;0\u0026middot;011), and those referred to another healthcare provider before diagnosis (p\u0026thinsp;=\u0026thinsp;0\u0026middot;005). No significant difference was observed by sex (p\u0026thinsp;=\u0026thinsp;0\u0026middot;674). (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn the area of ​​continuous predictors, systematic delay was negatively associated with patients' knowledge of symptoms (ρ = -0\u0026middot;162, p\u0026thinsp;=\u0026thinsp;0\u0026middot;020), their understanding of medical information (ρ = -0\u0026middot;183, p\u0026thinsp;=\u0026thinsp;0\u0026middot;008), and their ability to communicate with healthcare staff (ρ = -0\u0026middot;311, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), whereas age and income were not significant predictors. (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eCorrelation between continuous variables and the systemic delay\u003c/b\u003e Spearman\u0026rsquo;s correlation coefficients between continuous predictors and systemic delay.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman\u0026rsquo;s ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak, not significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnowledge of symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBetter knowledge \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEase of understanding medical info\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBetter comprehension \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication with healthcare staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStronger communication \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence in needing care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAssociation of categorical variables with\u003c/b\u003e systemic delay Nonparametric comparisons of systemic delay across sociodemographic groups. *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *\u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01; NS\u0026thinsp;=\u0026thinsp;not significant.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence (Urban/Rural)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRural \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKruskal\u0026ndash;Wallis H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower education \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUninsured \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealthcare center type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePublic centers \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReferral to another provider\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMann\u0026ndash;Whitney U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferred patients \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between systemic delay and procedural components\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpearman\u0026rsquo;s ρ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrength\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResults explained delay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain driver of system delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMore visits \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLab tests required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMore tests \u0026rarr; longer delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppointment waiting time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScheduling contributes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBetter communication \u0026rarr; shorter delay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultiple linear regression\u003c/h2\u003e \u003cp\u003eTwo multiple linear regression models were applied to identify independent predictors of delayed diagnosis, adjusting for potential confounders.\u003c/p\u003e \u003cp\u003eThe first model was fitted via multiple linear regression to examine the predictors of delayed patient arrival. The model included 19 factors, including demographics (age, gender, place of residence, education, employment), socioeconomic indicators (income, insurance, financial priority, cost barrier), health literacy and confidence (knowledge of symptoms, ease of understanding information, confidence in need of care), behavioral factors (home treatments, influence of others), and healthcare facility characteristics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e(R = -0\u0026middot;509, R\u0026sup2; = 0\u0026middot;259)\u003c/h2\u003e \u003cp\u003eThe model explains 25.9% of the variance in delayed patient arrival.\u003c/p\u003e \u003cp\u003e(Adjusted R\u0026sup2; = 0\u0026middot;188)\u003c/p\u003e \u003cp\u003eAfter adjusting for the number of predictors, approximately 18\u0026middot;8% of the variance was explained.\u003c/p\u003e \u003cp\u003e(F (19,198)\u0026thinsp;=\u0026thinsp;3\u0026middot;644, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001)\u003c/p\u003e \u003cp\u003eThe overall model is statistically significant. The significant predictors were that home remedies were used before seeking care (B\u0026thinsp;=\u0026thinsp;0\u0026middot;587, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and that patients who tried home remedies experienced longer delays. In the area of confidence in need of care (B = -0\u0026middot;267, p\u0026thinsp;=\u0026thinsp;0\u0026middot;001), greater confidence was associated with shorter delays. The influence of others had a marginal effect (B\u0026thinsp;=\u0026thinsp;0\u0026middot;305, p\u0026thinsp;=\u0026thinsp;0\u0026middot;052).\u003c/p\u003e \u003cp\u003eFor the nonsignificant predictors, all other variables\u0026mdash;including age, sex, place of residence, education, income, health insurance, and health literacy measures\u0026mdash;were not statistically significant in the multivariate model.\u003c/p\u003e \u003cp\u003eThis regression suggests that behavioral factors (home remedies and confidence in seeking care) are the primary predictors of patient delays. While socioeconomic and demographic factors showed associations in univariate analyses, they were not statistically significant after adjusting for other variables.\u003c/p\u003e \u003cp\u003eIn the multicollinearity domain, the variance inflation factors (VIFs) ranged from 1\u0026middot;165\u0026ndash;2\u0026middot;456, indicating no problematic multicollinearity.\u003c/p\u003e \u003cp\u003eThe residuals were reasonably distributed (range of residual deviation: -1\u0026middot;993\u0026ndash;2\u0026middot;958), supporting the model's assumption.\u003c/p\u003e \u003cp\u003eMultiple linear regression was also conducted to interpret the predictors of systematic delay. Twenty-six variables were included, including procedural factors, demographic characteristics, socioeconomic indicators, health awareness, behavioral factors, and healthcare utilization characteristics. The dependent variable was the systematic delay, logarithmically transformed to address skewness.\u003c/p\u003e \u003cp\u003eModel fit: R\u0026thinsp;=\u0026thinsp;0\u0026middot;787, R\u0026sup2; = 0\u0026middot;619, adjusted R\u0026sup2; = 0\u0026middot;565, F (26,183)\u0026thinsp;=\u0026thinsp;11\u0026middot;441, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001. This finding indicates that the model explains 61.9% of the variance in systematic delay, with 56.5% explained after adjusting for the number of predictors.\u003c/p\u003e \u003cp\u003eThe significant predictors were laboratory test duration (B\u0026thinsp;=\u0026thinsp;0\u0026middot;140, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) \u0026rarr; associated with longer delays; appointment scheduling time (B\u0026thinsp;=\u0026thinsp;0\u0026middot;090, p\u0026thinsp;=\u0026thinsp;0\u0026middot;003) \u0026rarr; associated with longer delays; and direct cost (B\u0026thinsp;=\u0026thinsp;0\u0026middot;421, p\u0026thinsp;=\u0026thinsp;0\u0026middot;009) \u0026rarr; showing that with every increase in direct costs, systematic delays increased. Patient outcome (B\u0026thinsp;=\u0026thinsp;0\u0026middot;079, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) was associated with longer delays. Gender had a moderate effect (B\u0026thinsp;=\u0026thinsp;0\u0026middot;139, p\u0026thinsp;=\u0026thinsp;0\u0026middot;050), whereas other variables\u0026mdash;including age, place of residence, education, income, insurance, health literacy measures, home care, confidence in need of care, and facility type\u0026mdash;were not statistically significant after adjustment.\u003c/p\u003e \u003cp\u003eIn terms of linear correlation, the VIFs ranged from 1\u0026middot;270 to 2\u0026middot;549, indicating that there were no multicollinearity issues. The residuals were reasonably distributed (range standard deviation: -2\u0026middot;613\u0026ndash;3\u0026middot;288), supporting the model's assumption.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison Between Patient and Systemic Delays\u003c/h2\u003e \u003cp\u003eBoth components contributed comparably to total diagnostic delay, though systemic delay (median\u0026thinsp;=\u0026thinsp;4 days) was slightly longer than patient delay (median\u0026thinsp;=\u0026thinsp;2 days).\u003c/p\u003e \u003cp\u003eThe two were weakly but significantly correlated (ρ\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that while some overlap exists, patient-related and system-related delays are partially independent phenomena.\u003c/p\u003e \u003cp\u003ePatients with low education, low income, rural residence, and lack of insurance experienced both longer patient and systemic delays, suggesting a consistent social gradient across both components. (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between Patient Delay and Systemic Delay\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient Delay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSystemic Delay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystemic delay slightly longer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoughly equal contribution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMain influences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAwareness, income, behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunication, procedures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrongest correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge of symptoms (ρ = \u0026minus;\u0026thinsp;0.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResults explained (ρ\u0026thinsp;=\u0026thinsp;0.770)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercorrelation (Patient \u0026harr; Systemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeak relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCaption:\u003c/h2\u003e \u003cp\u003eComparative summary of patient and systemic delays, showing distinct determinants but similar overall magnitudes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study on the underlying factors of diagnostic delays in acute care facilities in Syria is one of the first quantitative studies. Syria is a low-income country affected by conflict, war, and crisis. The average overall diagnostic delay, which typically reaches one week, highlights how the ongoing conflict has placed severe pressure on the healthcare sector in Syria. This has been either conceptual, such as a lack of education and literacy, or more tangible, such as weak infrastructure and inefficiencies. These two types of delays have fundamentally different key predictors: patient delays are often behavioral and influenced by socioeconomic factors and primary home care, whereas systemic delays are operational and linked to inefficiencies within the healthcare system itself. Our multivariate analysis revealed that the use of home-based treatments and lack of confidence in the need for professional care were the strongest independent predictors of patient delay. This suggests that the decision to seek care depends less on simple demographics than on an individual's health beliefs and initial response to illness. While lower income, rural residence, and lack of education were associated with longer delays in univariate analysis, their effects appeared to be mediated by patients' behavioral trajectories.\u003c/p\u003e \u003cp\u003eIn the context of the Syrian crisis, access to healthcare is constrained by logistical and financial barriers, which may represent a rational coping strategy. This finding is consistent with studies from other fragile settings, where \"wait and see\" approaches and self-medication are common practices when formal healthcare is perceived as unavailable or unreliable. [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eConversely, systemic delays were driven almost exclusively by procedural bottlenecks within the healthcare system itself. The time taken to perform laboratory tests, waiting for appointments, and, to a lesser extent, delays in informing patients of results were prevalent factors. The strong correlation between systemic delays and the number of visits before diagnosis suggests an inefficient referral system, which is likely exacerbated by the loss of specialized health workers and damaged infrastructure. This finding is consistent with Abbara and colleagues, Kallstr\u0026ouml;m and colleagues, and Mbuh and colleagues[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis operational shortcoming reflects the deteriorating state of the Syrian health system, where resource constraints and administrative chaos contribute to lengthy diagnostic processes. The significantly longer delays in public facilities and uninsured patients are administrative failures resulting from a systematically underresourced public sector and a healthcare environment where access to services has become increasingly linked to the ability to pay, a dynamic exacerbated by the conflict, as reported by Fouad and colleagues[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe roughly equal contribution of patient delays and systematic delays to the overall diagnostic timeline reflects the findings of some studies in high-income countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the nature of the delays varies dramatically. In well-resourced systems, systemic delays may be linked to scheduling complications or overtesting [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; in Syria, they are a direct manifestation of the collapse of the health system. While low health literacy is a common barrier to seeking care [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], its impact in Syria is exacerbated by the weak capacity of the public health sector and the widespread lack of public health awareness, both of which have been exacerbated by the crisis and its social repercussions. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOur finding that socioeconomic factors show a consistent gradient for both types of delays is consistent with the underlying theory of health inequalities, which suggests that wealthy and educated patients hear about it earlier, pay for it, and receive a diagnosis earlier. Poor patients learn about it later and cannot afford it or access it easily. This delays diagnosis and is linked to the inability to overcome institutional barriers (in line with Clouston and Link[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]). This progression may have been more severe in Syria due to the severe economic collapse caused by the long duration of the humanitarian crisis.\u003c/p\u003e \u003cp\u003eOn the other hand, in our study, we did not observe a significant relationship between delayed diagnosis and age or sex. This finding contradicts several previous studies [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The absence of such associations in our data may reflect the uniformity of catastrophic barriers across demographic groups in the Syrian context. The overwhelming force of systemic collapse\u0026mdash;including ubiquitous access problems, limited diagnostic infrastructure, and widespread economic hardship\u0026mdash;may effectively homogenize the experience of delay, overshadowing the more nuanced demographic disparities typically observed in stable health systems.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Policy and Practice\u003c/h2\u003e \u003cp\u003eTo translate these findings into actionable change, a multipronged strategy is essential. First, public health efforts must proactively shape health-seeking behaviors. This involves launching community-based awareness campaigns, delivered through trusted channels such as local leaders and radio, to educate the public on \"red flag\" symptoms that necessitate immediate professional care rather than self-management. Concurrently, empowering patients requires strengthening healthcare front lines; training staff to use simple, visual aids to assess and improve patient understanding during consultations can increase health literacy and confidence, which our study shows drives timely care seeking. To make this timely care a reality, financial barriers must be dismantled by expanding insurance coverage for vulnerable groups and streamlining reimbursement to alleviate the deterrent of out-of-pocket costs at the point of service.\u003c/p\u003e \u003cp\u003eSimultaneously, the profound systemic bottlenecks demand direct operational reforms. A critical intervention is to decentralize diagnostic capacity by investing in point-of-care testing technologies for common conditions at primary care facilities, which would drastically reduce the laboratory delays identified as a primary driver of systemic waits. Crucially, an equity lens must guide all interventions, ensuring that rural and uninsured populations\u0026mdash;who bear a double burden of longer patient and systemic delays\u0026mdash;are prioritized in the rollout of these reforms. While these steps address immediate inefficiencies, long-term recovery hinges on rebuilding the decimated health workforce and restoring shattered infrastructure, creating a sustainable foundation for a resilient and timely health system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis study's principal strengths lie in its robust methodological grounding within a critically underresearched conflict setting, its use of established theoretical frameworks to ensure comprehensive variable selection, and its sophisticated analytical approach that disentangles patient-related and system-level predictors of delay. However, several limitations must be acknowledged. First, the measurement of patient delay is subject to recall bias, although this was mitigated by focusing on acute-onset symptoms. Second, while our model explained a substantial portion of the variance in systemic delay, potentially important unmeasured contextual factors\u0026mdash;such as the specific degree of infrastructure damage in each facility, staff morale, organizational culture, or precise workload levels\u0026mdash;were not captured. Their absence represents a potential source of residual confounding.\u003c/p\u003e \u003cp\u003eNotably, our multivariate model for patient delay, while significant, explained a modest portion of the variance (adjusted R\u0026sup2; = 0\u0026middot;188). This suggests that while home remedies and confidence are key independent predictors, other unmeasured, context-specific factors play a substantial role. These may include perceived security risks associated with travel, the influence of strong social networks that advise health matters, specific cultural beliefs about illness causation, or a deep-seated distrust in the quality of care available, which may deter care-seeking irrespective of an individual's personal health literacy or confidence.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that delayed diagnosis in the Syrian acute healthcare system represents a two-faceted problem: societal behaviour and systemic failure. Addressing this challenge requires a dual approach: first, empowering patients with the knowledge and confidence to seek healthcare promptly, and second, simultaneously rebuilding the operational foundation of the healthcare system to treat them efficiently. As Syria moves toward a new future postconflict, prioritizing evidence-based interventions will be critical to rebuilding an effective, appropriate, and equitable healthcare system.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Biomedical Research Ethics Committee (BMREC) at Damascus University prior to study initiation, and the research was conducted in accordance with the ethical principles of the Declaration of Helsinki. Dated 30/6/2025, ID Number: MD-300625-473 Session Number: 25. Written informed consent was obtained from all participants before data collection. Participant confidentiality was ensured through the use of coded identifiers instead of names, and all the data were stored securely with restricted access. No financial or nonfinancial incentives were provided to the participants.\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no financial support for the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.T:\u003c/strong\u003e Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing – Original Draft (Results, and co-wrote Discussion), Writing – Review \u0026amp;amp; Editing, Project Administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS.A:\u003c/strong\u003e Writing—Original Draft (Introduction, and co-wrote Discussion), Writing—Review \u0026amp;amp; Editing, Visualization, Literature Search.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eR.K:\u003c/strong\u003e Methodology, Investigation, Writing – Original Draft (Methods), Resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eS.E:\u003c/strong\u003e Data Curation, Validation, Writing – Review \u0026amp;amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eY.L:\u003c/strong\u003e Supervision, Writing – Review \u0026amp;amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset supporting the conclusions of this article is included within the article’s additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our sincere thanks to the team of research assistants;\u003c/p\u003e\n\u003cp\u003eAlaa al Shahet\u003c/p\u003e\n\u003cp\u003eEnana Rajjouh\u003c/p\u003e\n\u003cp\u003eFahmi Al Askari\u003c/p\u003e\n\u003cp\u003eMarwan Leddawi\u003c/p\u003e\n\u003cp\u003efor their diligence and professionalism in participant recruitment and data collection under challenging circumstances.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBlanchet K, Fouad FM, Pherali T: \u003cstrong\u003eSyrian refugees in Lebanon: the search for universal health coverage\u003c/strong\u003e. \u003cem\u003eConflict and Health\u0026nbsp;\u003c/em\u003e2016, \u003cstrong\u003e10\u003c/strong\u003e(1):12.\u003c/li\u003e\n \u003cli\u003eAlhaffar MHDBA, Janos S: \u003cstrong\u003ePublic health consequences after ten years of the Syrian crisis: a literature review\u003c/strong\u003e. \u003cem\u003eGlobalization and Health\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e17\u003c/strong\u003e(1):111.\u003c/li\u003e\n \u003cli\u003eHaar R, Rayes D, Tappis H, Rubenstein L, Rihawi A, Hamze M, Almhawish N, Wais R, Alahmad H, Burbach R\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eThe cascading impacts of attacks on health in Syria: A qualitative study of health system and community impacts\u003c/strong\u003e. \u003cem\u003ePLOS Glob Public Health\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e4\u003c/strong\u003e(6):e0002967.\u003c/li\u003e\n \u003cli\u003eNeal RD, Tharmanathan P, France B, Din NU, Cotton S, Fallon-Ferguson J, Hamilton W, Hendry A, Hendry M, Lewis R\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eIs increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? 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Associated Factors among Syrian Adult Patients: A Cross-Sectional Study\u003c/strong\u003e. \u003cem\u003eJ Environ Public Health\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e2022\u003c/strong\u003e:9274610.\u003c/li\u003e\n \u003cli\u003eAbbara A, Alkhalil M, Wihba K, Abdrabbuh O, Rayes D, Ghobrial A, Marzouk M, Halabi F, Hariri M, Ekzayez A: \u003cstrong\u003eSyrian refugee and diaspora healthcare professionals: Case studies from the eastern mediterranean and European regions\u003c/strong\u003e. \u003cem\u003eJournal of Migration and Health\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e11\u003c/strong\u003e:100298.\u003c/li\u003e\n \u003cli\u003eKallstr\u0026ouml;m A, Al-Abdulla O, Parkki J, H\u0026auml;kkinen M, Juusola H, Kauhanen J: \u003cstrong\u003eI had to leave. I had to leave my clinic, my city, leave everything behind in Syria. Qualitative research of Syrian healthcare workers migrating from the war-torn country\u003c/strong\u003e. \u003cem\u003eBMJ Open\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e11\u003c/strong\u003e(11):e049941.\u003c/li\u003e\n \u003cli\u003eMbuh TP, Amveilla AP, Mendjime P, Donkeng Donfack VF, Youssouf Mfouapon M, Beloko H, Annie Prudence BN, Linda E, Etoundi Mballa GA: \u003cstrong\u003eEvaluating turnaround time to improve clients\u0026apos; satisfaction in the tuberculosis reference laboratory in Douala\u003c/strong\u003e. \u003cem\u003ePLoS One\u0026nbsp;\u003c/em\u003e2025, \u003cstrong\u003e20\u003c/strong\u003e(6):e0323917.\u003c/li\u003e\n \u003cli\u003eAla A, Chen F: \u003cstrong\u003eAppointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review\u003c/strong\u003e. \u003cem\u003eJ Healthc Eng\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e2022\u003c/strong\u003e:5819813.\u003c/li\u003e\n \u003cli\u003eHumphrys E, Burt J, Rubin G, Emery JD, Walter FM: \u003cstrong\u003eThe influence of health literacy on the timely diagnosis of symptomatic cancer: A systematic review\u003c/strong\u003e. \u003cem\u003eEur J Cancer Care (Engl)\u0026nbsp;\u003c/em\u003e2019, \u003cstrong\u003e28\u003c/strong\u003e(1):e12920.\u003c/li\u003e\n \u003cli\u003eLyra-Gonz\u0026aacute;lez I, Cuello M, Anderson D, Echeverri M: \u003cstrong\u003eSocioeconomic disparities and health literacy: Unravelling the impact on diagnostic and cancer care in Uruguay\u003c/strong\u003e. \u003cem\u003eJournal of Cancer Policy\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e40\u003c/strong\u003e:100472.\u003c/li\u003e\n \u003cli\u003eMor-Anavy S, Lev-Ari S, Levin-Zamir D: \u003cstrong\u003eHealth Literacy, Primary Care Health Care Providers, and Communication\u003c/strong\u003e. \u003cem\u003eHealth Lit Res Pract\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e5\u003c/strong\u003e(3):e194-e200.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWHO calls for urgent support to rebuild Syria\u0026rsquo;s health system\u003c/strong\u003e. In\u003cem\u003e.\u003c/em\u003e: World Health Organization.\u003c/li\u003e\n \u003cli\u003eClouston SAP, Link BG: \u003cstrong\u003eA retrospective on fundamental cause theory: State of the literature, and goals for the future\u003c/strong\u003e. \u003cem\u003eAnnu Rev Sociol\u0026nbsp;\u003c/em\u003e2021, \u003cstrong\u003e47\u003c/strong\u003e(1):131-156.\u003c/li\u003e\n \u003cli\u003eBatbayar B, Kariya T, Boldoo T, Purevdorj E, Dambaa N, Saw YM, Yamamoto E, Hamajima N: \u003cstrong\u003ePatient delay and health system delay of patients with newly diagnosed pulmonary tuberculosis in Mongolia, 2016-2017\u003c/strong\u003e. \u003cem\u003eNagoya J Med Sci\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e84\u003c/strong\u003e(2):339-351.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 8. Participant characteristics (N=233)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"367\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%) or Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e45.6 \u0026plusmn; 17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124 (53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109 (46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e93 (39.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Countryside\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e103 (44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Suburbs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Illiterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e61 (26.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e65 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Collegiate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e75 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Postgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployment status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Unemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e109 (46.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Student\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e82 (35.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Retired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly income (SYP in thousands)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2000 (1000\u0026ndash;3750)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInsurance coverage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;No insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e199 (85.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Public insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026emsp;\u0026emsp;Private insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Syrian healthcare, diagnostic delays, DEOD, low- and middle-income countries, conflict setting, behavioral factors, systemic factors","lastPublishedDoi":"10.21203/rs.3.rs-8034773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8034773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Diagnostic delay is a critical issue in healthcare, yet evidence from conflict-affected settings such as Syria is scarce. This study quantifies diagnostic delays and identifies their predictors within the Syrian healthcare system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A cross-sectional study was conducted (July–October 2025) at three major hospitals in Damascus. We administered the questionnaire to 250 adult patients presenting with new-onset acute symptoms, 233 of whom were included in the final study. A structured questionnaire administered by trained research staff at the point of care, informed by established health services frameworks, collected data on sociodemographic factors, health literacy, and healthcare processes. The primary outcomes were patient delay (symptom onset to first healthcare contact) and systemic delay (first contact to diagnosis).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The mean total diagnostic delay was 12·6 days (±9·2), comprising similar mean patient (6·1 days ±8·1) and systemic (6·5 days ±7·0) delays. Multivariable analysis revealed that patient delay was independently predicted by the use of home remedies (B=0·587, p\u0026lt;0·001) and lower confidence in needing care (B=-0·267, p=0·001). Systemic delay was driven primarily by longer laboratory test durations (B=0·140, p\u0026lt;0·001), appointment scheduling times (B=0·090, p=0·003), and higher direct costs (B=0·421, p=0·009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Diagnostic delays in Syria are a two-faceted problem stemming from health-seeking behaviors and systemic operational bottlenecks. Interventions must simultaneously promote timely care seeking through community engagement and addressing system inefficiencies, such as decentralizing diagnostics and reducing financial barriers, to improve outcomes in this conflict-affected setting.\u003c/p\u003e","manuscriptTitle":"Diagnostic Delays in Acute Care Settings in Clinics in Damascus University Affiliated Hospitals: A Cross-Sectional Study of Patient- and Systemic-Related Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 09:34:18","doi":"10.21203/rs.3.rs-8034773/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-16T18:33:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-13T13:58:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-10T11:45:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T15:58:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T09:52:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16984709273169365375526725257535327329","date":"2026-01-03T04:22:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6404236198305853609170307771380912032","date":"2025-12-31T08:37:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24110205609999339383647550427275428651","date":"2025-12-29T12:14:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76659295556820877927441609416099748212","date":"2025-12-28T15:32:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-24T09:52:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-22T16:24:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-26T11:13:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T11:24:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-11-13T11:21:13+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":"fe9c62cc-0231-47b1-8653-5fecedd013e6","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-13T13:24:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 09:34:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8034773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8034773","identity":"rs-8034773","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}