Diagnostic Interval and Social Determinants of Health in Lung Cancer: A Mixed-Methods Study in Chile

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Although Chile introduced an explicit guarantee (GES) of ≤60 days for diagnostic interval (DI: time between firsts consultation and diagnostic confirmation), evidence on how social determinants of health (SDH) shape this interval remains limited. Methods We conducted a convergent mixed-methods study with equal weighting (QUAL=QUAN). The quantitative strand was an analytical cross-sectional survey of adults with LC (n=80). The primary outcome was the DI, dichotomized at 60 days. Associations with SDH (gender, region, income quintile, education, health system) were assessed using bivariate tests and logistic regression. The qualitative strand comprised 37 semi-structured interviews (patients, caregivers, clinicians, civil society), thematically analyzed. Integration used a weaving narrative and joint displays to classify patterns as confirmation, expansion, or discordance. Results The reported DI was 30.5 days (mean 79.1; range up to 578). Quantitatively, income showed the clearest gradient: compared with the highest quintile, the second quintile had 5.79× higher odds of >60-day delay (p=0.049; 95%CI 1.011–33.21). Type of health system also suggested disadvantage in the public sector, with 57.1% versus 80.0% of private patients diagnosed within 60 days (OR for delay in public = 1.77; p=0.458). Differences by gender, region, and education were not statistically significant. Qualitative findings expanded these results by revealing the mechanisms underlying diagnostic delays: participants described fragmented pathways, centralization of specialists and technology in metropolitan areas, long-distance travel, test bottlenecks or malfunctions, frequent misdiagnoses (e.g., pneumonia), and health-literacy barriers in the public system and in regional areas. Taken together, the integrated analysis confirmed the quantitative trends for income and health system while expanding the understanding of geographic and educational influences, with no substantive discordances emerging across strands. Conclusions This is the first integration of social determinants of health and care trajectories in LC in Chile. Meeting a legal timeline between the first consultation to diagnostic confirmation interval < 60 days, does not ensure equity in diagnostic in Chile. Findings suggests the urgent need for stronger territory integration in oncology networks, decentralized diagnostic capacity, improved patient navigation, health-literacy education programs, and equity-stratified performance monitoring, including systematic SDH fields in cancer registries. These actions can shorten critical intervals, improve patient experience, and close access gaps in disadvantaged populations. lung cancer diagnostic interval social determinants of health mixed methods health equity Figures Figure 1 INTRODUCTION Lung cancer (LC) is the second most frequently diagnosed cancer worldwide and the leading cause of cancer-related deaths, with over 2.2 million new cases and 1.8 million deaths reported in 2020 ( 1 , 2 ). Its high lethality is largely related to late-stage diagnosis, which limits curative treatment options and negatively affects survival and quality of life. In Chile, LC shows one of the highest incidence and mortality rates in Latin America ( 3 ). In 2020, it caused 3,550 deaths, accounting for 12.4% of all cancer-related deaths in the country ( 4 ). National data reveal higher disease burden among men and individuals over 60 years of age, alongside regional disparities in mortality, suggesting a strong influence of social determinants of health (SDH) ( 5 , 6 ). Furthermore, in Chile LC is the leading cause of cancer-related deaths in men and the second in women ( 7 ). A parameter that has been recognized as critical in the scientific literature due to its impact on the prognosis and survival of lung cancer is the Diagnostic Interval (DI). This interval is defined as the period between the patient’s first consultation for symptoms related to the disease and the moment when the diagnosis is established ( 8 , 9 ). In lung cancer, median diagnostic intervals ranging from 21 to 54 days have been reported in high-income countries, although substantial variability is observed depending on the type of cancer and the healthcare context ( 8 , 9 ). Moreover, the diagnostic interval may be influenced by factors such as clinical presentation, accessibility to complementary tests, and the patient’s sociodemographic characteristics ( 9 , 10 ). Regarding sociodemographic characteristics, it has been identified that individuals living in areas of greater poverty, minority groups, and those with limited access to healthcare services are more likely to be diagnosed at advanced stages of lung cancer, which is associated with poorer prognosis and lower survival ( 11 , 12 ). Taken together, these findings underscore that the diagnostic interval is not only shaped by individual and contextual determinants but also by a wide range of barriers at different levels. Multiple studies have identified factors and barriers to timely diagnosis, including patient-related (symptom underestimation, low health literacy, economic and geographic barriers) ( 13 – 15 ), provider-related (diagnostic errors, multiple pre-referral consultations) ( 15 , 16 ), system-related (long waits, fragmented pathways, lack of coordination) ( 17 , 18 ), and disease-related (atypical early symptoms, comorbidities masking presentation) factors ( 18 , 19 ). These delays are associated with advanced-stage diagnoses, reduced treatment options, greater emotional distress, and increased health inequities, particularly in resource-constrained settings ( 16 , 19 , 20 ). These barriers are not exclusive to high-income or low-income countries; rather, they manifest differently depending on the structure and equity of the health system. In Chile, a high-income country in the South American region, the mixed public–private healthcare model is marked by fragmentation and unequal distribution of high-complexity services, leading to pro-rich inequities in access to timely care ( 21 ). The public insurance, known as FONASA (National Health Fund), covers approximately 78% of the population and is stratified by income level. The private system, ISAPRE (Health Insurance Institutions), insures about 17% of the population, primarily higher-income individuals. Recognizing the disease burden, lung cancer was incorporated into the explicit health guarantee program called “GES” in 2019, which established a maximum of 60 days from clinical suspicion to diagnostic confirmation for all people, regardless of health insurance ( 20 , 21 ). Hence, the 60-day threshold serves as a valuable analytical benchmark to assess potential diagnostic delays and explore how social determinants may influence the timeliness of cancer care. In Chile, women, individuals with lower educational attainment, those residing outside the capital, and users of the public healthcare system experience greater barriers to accessing healthcare, which prolongs the DI and contributes to detection at advanced stages of the disease ( 22 ). Moreover, the implementation of advanced technologies and targeted therapies, such as genomic profiling and EGFR inhibitors, is limited and inequitable, restricting access to innovative treatments for a considerable proportion of patients. ( 23 , 24 ). Additionally, while social participation in cancer is addressed in Chile’s National Cancer Plan ( 25 , 26 ) and the Cancer Law ( 27 ), among the 30 registered cancer patient organizations, only one mentions LC and none are exclusively dedicated to it ( 28 ). This underrepresentation in legal frameworks and clinical practice recommendations may contribute to delayed policy responses and limited resource allocation for this population ( 29 ). Against this backdrop, understanding the diagnostic interval in lung cancer requires acknowledging the interplay of patient-, system-, and society-level factors. The concept of therapeutic trajectories provides a comprehensive lens to study this process in depth, as it integrates both personal experiences and system-level dynamics across the continuum of care ( 30 – 33 ). Incorporating patient perspectives is essential to identify barriers, facilitators, and context-specific challenges that structured clinical pathways may overlook ( 34 , 35 ). Despite the existence of national policies such as the GES program and the National Cancer Plan, there is still limited evidence in Chile on how demographic, socioeconomic, and health system-related determinants influence the timeliness of lung cancer diagnosis. While international studies have extensively analyzed diagnostic intervals, research in Latin America—and particularly in Chile—remains scarce. Addressing this gap, the present study examines the association between selected social determinants of health and the diagnostic interval in lung cancer, integrating quantitative and qualitative evidence. By adopting this approach, the study aims to generate context-specific insights to inform equity-oriented cancer care policies and contribute to reducing diagnostic delays in Chile METHODS Type of study We employed a convergent mixed‑methods design to address the complexity of delays in LC diagnosis from complementary vantage points—quantitative estimation of associations and qualitative exploration of mechanisms and lived experiences. The convergent approach is appropriate when the research question benefits from simultaneous collection and separate analysis of qualitative and quantitative data, followed by integration to corroborate, expand, or nuance findings ( 36 – 40 ). This design enhances interpretive validity by allowing immediate comparison of strands on the same constructs (e.g., social determinants, diagnostic interval) while preserving each method’s strengths. The qualitative (interviews) and quantitative (survey) strands were conducted in parallel and analyzed independently. We assigned equal weighting (QUAL = QUAN) because both strands were theoretically and analytically indispensable to the research aim: the quantitative component quantified associations between social determinants and diagnostic timeliness, while the qualitative component elucidated barriers/facilitators and contextual processes shaping patient trajectories. Qualitative phase Assumptions The qualitative component was based on the epistemological assumption that multiple realities can coexist around a single phenomenon, emphasizing the subjective experiences and perspectives of participants ( 36 ). Given the complexity of the research topic, qualitative inquiry focused on a small number of cases to explore meanings in depth, using narrative data collection techniques ( 41 ). A case study design was adopted, with the case defined as 'The experience of adults with lung cancer diagnostic.' Sampling strategy: Participants in the qualitative phase were selected using purposive sampling to reflect diversity across relevant characteristics, guided by the Cochrane PROGRESS-Plus framework ( 36 ) ( 42 ), including place of residence, gender, educational level, and occupation. Sampling units included patients with lung cancer, significant others or unpaid caregivers, healthcare professionals, and civil society representatives. The initial sample aimed to recruit a minimum of 15 patients, 5 healthcare professionals, 10 significant others or caregivers, and 1 civil society leader. Inclusion and exclusion criteria for each participant group are summarized in Table 1 . Table 1 Inclusion and exclusion criteria for the qualitative phase participants. Type of Participant Inclusion Criteria Exclusion Criteria People with cancer 1. Lung cancer diagnosis confirmation. 2. Aged 18 years or older. 3. Currently receiving or previously received healthcare in the public or private system in Chile. 4. Have access to the internet or a telephone to complete the interview. Any physical or mental condition that limits the person's ability to decide to participate in the interview. Significant others or unpaid caregivers 1. Aged 18 years or older. 2. Currently or previously accompanied a person with LC during healthcare processes. 3. Have access to the internet to participate in an interview. Any physical or mental condition that limits the person's ability to decide to participate in the interview. Healthcare professionals 1. Aged 18 years or older. 2. Work in the public or private healthcare system in Chile. 3. Have a specialty related to LC. 4. Have access to the internet to participate in an interview. Any physical or mental condition that limits the person's ability to decide to participate in the interview. Civil society representatives 1. Aged 18 years or older. 2. Actively involved in civil society organizations related to LC. 3. Have access to the internet to participate in an interview. Any physical or mental condition that limits the person's ability to decide to participate in the interview. Sample size: Sampling remained flexible throughout the study and continued until data saturation was reached. Saturation was defined as the point at which no new themes or perspectives emerged from additional interviews. Final recruitment included 18 patients, 8 healthcare professionals, 10 caregivers or significant others, and 1 civil society representative, for a total of 37 participants. Recruitment: It was conducted in two sequential stages. In the first stage (October 2021–March 2022), we recruited patients with LC, healthcare professionals, and the civil society representative. This stage prioritized capturing direct experiences of the disease, professional perspectives, and advocacy viewpoints to inform early thematic saturation. In the second stage (March–October 2023), we recruited significant others or unpaid caregivers. This sequential approach allowed for the exploration of caregivers’ perspectives in the context of preliminary patient and professional narratives, enabling a richer understanding of the social and emotional dimensions of the therapeutic trajectory. Data collection: Online semi-structured individual interviews with all participant groups. Interviews explored participants’ perspectives, values, and lived experiences in relation to the research topic ( 41 ). A question guide was developed based on literature review and expert consultation ( 41 ). The interview guide was specifically designed for the objectives of this study based on literature review and expert consultation and has not been previously published. An English version is available as supplementary material. . Interviews with patients, healthcare professionals, and civil society leaders were conducted between October 2021 and March 2022. Interviews with caregivers and significant others were conducted between March and October 2023, all via online platforms including Zoom, Google Meet, WhatsApp Video, or phone, depending on participant preference. Interviews followed a flexible guide exploring seven thematic areas: (i) general experience of living with lung cancer, (ii) therapeutic trajectory within and outside the health system, (iii) barriers to healthcare access, (iv) facilitators of healthcare, (v) health needs, (vi) quality of care, and (vii) overall evaluation of the experience. The same themes were addressed across all participant groups, with tailored questions per role. Data analysis: A thematic analysis approach was used to analyze qualitative data, following the thematic categories defined in the interview guide ( 41 ). Thematic analysis is widely used in qualitative research to identify, analyze, and interpret patterns of meaning across datasets ( 43 ). All interviews were transcribed verbatim in Word documents to ensure data fidelity and support accurate analysis. Scientific rigor: Rigor in the qualitative phase was ensured through two strategies: (i) Participant triangulation: Findings were validated by comparing data from different participant groups (patients, healthcare professionals, caregivers, and civil society representatives), which reduces bias and enhances credibility ( 44 ), and (ii) Reflexivity: Researchers engaged in continuous self-reflection, documenting contextual observations and personal reactions throughout the interviews. Field notes were recorded concurrently to complement thematic analysis and ensure analytical transparency ( 41 , 45 ). Quantitative phase The quantitative phase of this mixed-methods study employed an analytical, observational, cross-sectional design. Study Population: The study targeted adults in Chile with a current or past diagnosis of LC. Inclusion criteria were: (i) being 18 years or older; (ii) having received or currently receiving care in either the public or private healthcare system in Chile; and (iii) having access to a phone or internet to complete the survey. Individuals with physical or mental conditions limiting their capacity to voluntarily participate or respond were excluded. Sample Size: In the absence of a national cancer registry and considering the exploratory nature of the study, the required sample size was calculated based on the study "Patient's and doctors' delays in the diagnosis of chest tumors" ( 46 ) using OpenEpi based on a chi-square test, with 95% confidence level and 80% power. The minimum sample size was estimated at 66 participants. The final sample included 80 participants, based on feasibility and recruitment outcomes. Sampling Strategy: A non-probabilistic, convenience sampling approach was adopted. This method was selected due to its practicality, lower cost, and feasibility in accessing participants within the study’s context ( 47 , 48 ). Moreover, convenience sampling is commonly employed in exploratory research where the primary objective is to identify patterns, generate hypotheses, and obtain initial insights rather than achieve statistical generalizability ( 36 ). Recruitment: Participants were recruited between August 2021 and October 2024. General recruitment strategies included dissemination through social media, printed and digital flyers with embedded QR codes, appearances in radio programs, livestreams hosted by patient organizations, and promotion through organizational websites. Given the absence of lung cancer-specific patient organizations, targeted recruitment was coordinated through oncologists who referred patients with their consent. These physicians obtained patient consent to share contact information with the research team, who then contacted potential participants directly. Data Collection: Participants completed the survey either independently or with assistance from a trained interviewer. The survey was designed specifically for this study and hosted on Alchemer, a secure encrypted platform. It covered dimensions related to social determinants of health, access to care, and therapeutic trajectories. Most questions were closed-ended, with mandatory fields indicated. The survey was pilot tested with representatives from academia, clinical practice, and patient organizations. Participants provided feedback on clarity, relevance, and usability. Two administration options were provided: (i) Self-administration via link access with instructions to complete in one session (30–45 minutes); and (ii) Assisted administration, where participants submitted contact details and were supported by a trained interviewer (YB). Study Variables: The dependent variable was the 'Interval from First Consultation to Diagnostic Confirmation' (DI), based on the GES timeframe of 60 days. Self-reported data on date of first consultation and diagnostic confirmation were used. Inconsistencies or missing values were excluded. The variable was operationalized as: 0 = ≤ 60 days (on time), 1 = > 60 days (delayed). Independent variables included gender, region of residence, household income quintile, education level, and type of health system. Data Analysis: Quantitative data were anonymized and stored securely on Alchemer. Analyses were performed using SPSS version 28.0. The initial stage involved data cleaning, variable recoding, and consistency checks. Missing data related to date intervals were excluded based on predefined criteria. Analysis included: (i) univariate descriptive analysis (means, medians, standard deviation, ranges, and proportions); (ii) bivariate analyses of associations between independent variables and ID, using Mann-Whitney U test and Kruskal-Wallis test; and (iii) logistic regression models to assess factors associated with diagnostic delay. Odds ratios (OR) and 95% confidence intervals (95% CI) were reported. Model adequacy was assessed with the Hosmer-Lemeshow test and Nagelkerke’s R². A significance level of 0.05 was applied. Risk and control of bias: Potential biases in the quantitative phase were mainly related to selection and information bias ( 49 ). Regarding selection bias, the use of non-probabilistic convenience sampling may have limited sample representativeness by relying on participants’ availability and accessibility ( 50 ). This approach may have excluded more vulnerable groups, such as individuals without internet access or those outside the networks used for recruitment. Information bias was also a potential concern, associated with several factors: (i) questionnaire clarity, as although the instrument was piloted, it was not formally validated ( 51 ); (ii) variability in administration, given that surveys were completed either independently online or with telephone support depending on participants’ needs ( 52 ); and (iii) recall bias, due to potential difficulties in accurately remembering details of DI ( 53 ). To mitigate these risks, several strategies were implemented, including partnerships with public and private organizations to diversify recruitment channels, offering multiple participation formats (computer-based and mobile-friendly surveys), piloting the questionnaire with input from academics and patient representatives, training interviewers to standardize data collection, and encouraging participants to use personal records to reduce recall errors. Integration Phase Data integration was carried out at the levels of interpretation and presentation of results. During the interpretation phase, a narrative approach was used to compare and combine qualitative and quantitative findings ( 39 , 40 ). This allowed for a comprehensive and contextualized description of the results from both components. Additionally, the weaving approach was employed to present qualitative and quantitative findings together, organized by the main analytical dimensions ( 39 , 40 ). This method facilitated a cohesive understanding of how both types of data related to each thematic domain. A final integrative analysis was conducted to assess the degree of coherence between the qualitative and quantitative results. This analysis explored whether the two strands of data confirmed, complemented, or contradicted each other ( 39 , 40 ). Three possible outcomes were considered: confirmation, expansion, and discordance. Confirmation occurred when both types of data reinforced and validated each other’s findings. Expansion referred to instances where data from one strand enriched or extended the understanding of the other. Discordance described findings that diverged or contradicted each other, highlighting complexity or inconsistency between data types ( 39 , 40 ). Ethical considerations This research adhered to international and national ethical standards, including the Declaration of Helsinki, the CIOMS guidelines, and Chilean National Law on Patients’ Rights and Duties. Several measures were implemented to protect the rights and well-being of participants. All participants received an online informed consent form, which included an informational sheet outlining the study’s objectives, procedures, potential risks, and benefits. Participants were encouraged to read the document carefully and to contact the research team with any questions. Participation was entirely voluntary. Survey responses were stored securely in the Alchemer platform during data collection and later downloaded to the principal investigator’s personal computer, where they were anonymized. Interview audio recordings were also stored on the investigator’s secured device. All qualitative and quantitative data were de-identified using coding strategies, and only aggregate findings were reported. The study received ethical approval from the Scientific Ethics Committee of Clínica Alemana – Universidad del Desarrollo. Approval reference number: 2023-60. RESULTS The results are presented in three sequential sections. First, a descriptive analysis of the study sample is provided, outlining key demographic, socioeconomic, and health system characteristics. Second, an overview of the DI of LC is reported, including measures of central tendency and distribution. Finally, integrated findings from the quantitative and qualitative strands are presented for this DI, organized by each of the selected social determinants of health: gender, region of residence, household income quintile, educational level, and type of health system. Figure 1 displays these integrated results in a joint image, providing a visual synthesis of the connections across strands. This structure enables a comprehensive understanding of how these determinants influence timely access to LC diagnosis. Description of the study sample The final sample of the quantitative phase consisted of 80 participants with diverse demographic, socioeconomic, and health system backgrounds. 60% were women, 60% residents of regions outside the Metropolitan Region, and 78.8% affiliated with the public health system. Nearly half had completed higher education (46.3%), and income distribution was relatively even across quintiles. Detailed information on sample characteristics is available in the supplementary material. The qualitative sample consisted of 37 participants, including 18 patients with LC, 8 healthcare professionals, 10 significant others or unpaid caregivers, and 1 civil society leader. Most patient participants were affiliated with the public healthcare system (FONASA), resided in the Metropolitan Region, and had incomplete formal education. Healthcare professionals represented a range of specialties—including oncology, thoracic surgery, and nursing—and were affiliated with both public and private systems across different regions. Caregivers were predominantly women, held higher education degrees, and were family members actively involved in the patient’s care. The full socio-demographic details of participants are available in supplementary material. Diagnostic Interval (first consultation to diagnostic confirmation) The average time of DI in LC was 79.08 days (SD: 118.81), with a median of 30.5 days. The maximum recorded duration was 578 days. More information on descriptive interval analysis is available at supplementary material. This interval comprised the identification of initial signs, symptoms, or incidental findings, the decision to seek medical care, and the stage of clinical suspicion. The qualitative analysis revealed three distinct patterns in the initiation of the therapeutic trajectory in lung cancer (Table 2 ): Trajectory initiated by respiratory signs or symptoms This was the most common pattern. The main symptoms reported were persistent cough, dyspnea, hemoptysis, fatigue, and pain while breathing. "In March or April, more or less, he had a persistent cough—he coughed, and coughed, and coughed—day and night, and he got tired…" (Patient5_Male_FONASA) Trajectory initiated by incidental findings Less frequent than the first group, this pattern involved the detection of lung cancer during consultations for other conditions, particularly cardiovascular diseases. “…it was incidental, because he had (says the granddaughter), uh, uh, an atrioventricular block and needed an emergency pacemaker. He was admitted to the local hospital for that, and when they checked the placement—through a scan or X-ray, I can’t recall—they saw small tumors in his lungs.” (Patient5_Male_FONASA) Trajectory initiated by non-respiratory signs or symptoms This was the least frequent pattern, where patients presented with musculoskeletal or gastrointestinal symptoms. These cases showed the most extended delays and required multiple consultations before the cancer was identified. "I started in February with irritable bowel symptoms. I saw eight, eight specialists. They all said it was IBS… I had all the abdominal tests, MRI and everything, all perfect… but the symptoms didn't go away. Then a doctor came to my home and said, 'This is not your colon. It is your spine—you need surgery due to spinal infiltration.' On June 25th, I was undergoing surgery. On the 26th, the doctor told me right away, 'This is a lung metastasis.' That same day, it was confirmed… At first, everyone was wrong… we spent a fortune on medication." (Patient3_Female_FONASA) Table 2 Characterization of the therapeutic trajectory of people with lung cancer: initiation, testing, and diagnosis Phase Main characteristics (based on qualitative findings) (i) Initiation (identification of initial signs, symptoms, or findings; decision to seek care) The therapeutic trajectory can begin either with symptoms/signs or incidental findings. The most commonly reported symptoms were cough, dyspnea, hemoptysis, fatigue, and chest pain. Incidental findings often occurred during evaluations for cardiovascular conditions. Symptoms were frequently misdiagnosed (e.g., as pneumonia). Patients often made multiple visits to the public health system without improvement. Those with financial resources accessed private services, where imaging tests were ordered. (ii) Testing and diagnostic confirmation (post-suspicion phase) This phase involved a series of tests and referrals to specialists after cancer was suspected. It was marked by emotional distress, including fear, anxiety, and uncertainty—especially when facing unfamiliar diagnostic procedures Access to specialists (pulmonologists, oncologists, thoracic surgeons) was crucial. According to national GES guidelines, diagnostic confirmation must occur within 60 days of suspicion. Diagnosis could be confirmed by various professionals using diverse procedures (e.g., biopsy, CT scan). Most patients diagnosed in advanced stages were within the public health system. Integrated Analysis of the first consultation to diagnostic confirmation interval A mixed-methods integration was conducted for this interval, examining each of the studied variables: demographic (gender, region of residence), socioeconomic (income quintile, educational level), and health system (public vs. private). The integrated results for each variable and interval are presented in the following sections, summarized in Table 3 and Fig. 1 . Integrated results by Gender The quantitative analysis showed that a majority of participants (both men and women), received a diagnostic confirmation of LC within 60 days following their first medical consultation (58% of men and 63.8% of women). This difference was not statistically significant, men exhibited 1.25 times higher odds of experiencing delays beyond the 60-day threshold compared to women (p = 0.664; 95% CI: 0.448–3.526). While quantitative results suggested no significant gender-based disparities, the qualitative data highlighted gender as a crucial social determinant influencing the therapeutic trajectory. Gendered social roles shaped patients’ experiences in distinct ways. Men were frequently accompanied by female relatives (typically spouses, daughters, or granddaughters) throughout their healthcare journey. This reflects the persistent social expectation that women act as primary caregivers, even in the context of others’ illness. Conversely, women diagnosed with LC often reported initiating and navigating the health system alone. Their narratives revealed a strong sense of duty toward fulfilling caregiving roles themselves, even while undergoing their own treatment. As their illness progressed and limited their capacity to perform these roles, women expressed feelings of frustration, guilt, and distress. “Look, my husband was diagnosed because I took him to the doctor, because he had a nasty cough.” (Patient13_Male_FONASA) “I did not tell my children. I went to the hospital alone—that is how it was.” (Patient2_Female_FONASA) Taken together, the integrated findings demonstrate a complementary expansion of results. While statistical analyses did not reveal significant gender-based differences in diagnostic timeliness, qualitative evidence underscored meaningful gendered patterns. These findings highlight the need to interpret gender-disaggregated data in the broader context of gender norms and caregiving dynamics, which may shape health-seeking behaviors, access to care, and emotional responses to the disease. Integrated results by Region of Residence Region of residence was analyzed by comparing participants living in the Metropolitan Region (Santiago and its surroundings) versus those residing in other areas of Chile. Quantitative analysis showed that a majority in both groups received a diagnostic confirmation within 60 days (62.5% in the Metropolitan Region vs. 60.85% in other regions). While not statistically significant, individuals residing outside the capital had 1.34 times higher odds of experiencing a diagnostic delay beyond 60 days (p = 0.563; 95% CI: 0.497–3.611). Despite the lack of significant quantitative differences, qualitative findings revealed critical geographic disparities that shaped the therapeutic trajectory for individuals living outside the Metropolitan Region. These disparities were most evident during the initiation, diagnostic testing, and confirmation phases, and were driven by several interconnected mechanisms: Limited healthcare infrastructure Many regional areas lack the facilities and equipment required for comprehensive oncologic care. High-complexity services, such as radiotherapy, are concentrated in urban centers, limiting timely access in peripheral areas. “...here in XXX region, healthcare is very precarious; we do not have anything. You always have to go elsewhere to look for treatment options, and the closest to us initially was other region where my sister lives... Everything is centralized in Santiago.” (Patient4_Male_FONASA) Shortage of oncology specialists The majority of pulmonologists, thoracic surgeons, and oncologists are based in the Metropolitan Region. This centralization results in lengthy referrals to tertiary hospitals, increasing wait times and treatment delays for patients from other regions. Challenges for rural populations navigating urban systems Participants from rural or remote areas described difficulties in navigating large healthcare institutions. Complex instructions, unfamiliar infrastructure, and the number of required appointments caused anxiety, confusion, and emotional distress. “When patients arrive at this hospital, I think the first thing they feel is intimidation, rather than reassurance. We try to calm them down during the consultation itself, but we have many people from the islands or very rural areas here. I give them the orders for the CT scan, the spirometry, the carbon monoxide diffusion test, referrals for the anesthesiologist and the cardiologist… and I am sending them to five different places in a hospital that’s already hard to navigate, even for us.” (Physician3, Public and Private System) Need for long-distance travel Patients were frequently advised to seek care in Santiago or regional capitals due to insufficient local capacity. For many, repeated travel placed a substantial financial and logistical burden on families. “...I saw an oncologist in Talca, but she was not able to confirm whether it was cancer. She suggested I go back to Santiago and not stay in the provinces to consult a thoracic surgeon. For my partner, it was cheaper to travel two hours to be with me than to stay in a hotel, so he travelled every other day... If I had not had the means—between my private insurance and complementary coverage—I do not know how far I would have gotten.” (Patient11_Female_ISAPRE) Limited availability of diagnostic equipment Regional centers often relied on a single imaging device (e.g., CT, MRI, PET). Equipment malfunction or limited scheduling resulted in delays, prompting many caregivers to seek services in the private sector at personal expense. “We had to look for another place quickly when the CT scanner broke down, to be able to get the diagnosis as soon as possible.” (Caregiver2_Male) “We do not have a PET scanner in Puerto Montt.” (Physician3, Public and Private System) “MRI appointments are really scarce—might take three weeks to a month. Moreover, now we are even having issues with simple tests like pulmonary function tests. Patients are requesting private appointments because the hospital is not doing spirometry... and there are no private slots available either. So things are getting delayed one way or another depending on the test.” (Nurse2, Public and Private System) Taken together, the integrated findings indicate an expansion of the quantitative results. Although no statistically significant differences in diagnostic delays were detected between regions, the qualitative evidence exposed meaningful structural and logistical barriers in non-metropolitan areas. These barriers, often mitigated only through personal resources or informal strategies, underscore geographic inequities in access to timely LC diagnosis in Chile. Integrated results by Income Quintile The integration of quantitative and qualitative data revealed significant differences in the timing and dynamics of the therapeutic trajectory for LC across income quintiles. Quantitatively, individuals belonging to the higher-income groups (fourth and fifth quintiles) were more likely to receive diagnostic confirmation within the 60-day threshold. In contrast, those in the second quintile had 5.79 times higher odds of receiving a diagnosis after 60 days compared to participants in the fifth quintile (p = 0.049; 95% CI: 1.011–33.21), highlighting a statistically significant delay for this group. Qualitative findings enriched the interpretation of this disparity by illuminating the mechanisms through which income level influenced healthcare access. Individuals in higher-income quintiles often expedited their diagnostic journey by paying out-of-pocket for private consultations and diagnostic procedures, even when initially served by the public system (FONASA). This hybrid approach—leveraging public services while supplementing with private options—enabled faster diagnosis but came at a substantial financial cost. “...but we wanted everything done quickly to know what he had and what the next steps were. If we had relied solely on the public system, who knows how long it would have taken? One, two, even three years—they take that long sometimes to call you. My grandfather had some savings and also used his pension fund withdrawal. All that money was used up... those time gaps were due to raising money, and also because there were no available appointments for tests.” (Patient4_Male_FONASA) In contrast, individuals with lower incomes reported substantial delays associated with financial constraints. Many could not afford private appointments or tests and were thus entirely dependent on the slower public system. This reliance led not only to prolonged wait times but also to emotional distress, particularly when diagnoses were delayed despite the persistence of symptoms. “Unfortunately, I have not had the money to see a private doctor to review my test results... I spoke to the nurse, I vented to her, I cried a lot.” (Patient2_Female_FONASA) To overcome financial barriers, patients and families often engaged in resource mobilization strategies, including support from relatives or withdrawal of retirement funds. These actions, while effective in some cases, introduced additional emotional and economic strain. “...my nephews, who have good jobs and all, gave me the money so I could get the test done.” (Patient9_Female_FONASA) The integrated analysis between income quintile and time to diagnosis reveals an expansion of findings. Quantitative results identified income as a significant determinant of diagnostic timeliness, while qualitative data uncovered the lived consequences of these disparities. Higher-income individuals were able to circumvent public system delays, whereas lower-income individuals remained vulnerable to systemic inefficiencies. These findings underscore the critical role of economic capital in navigating the Chilean health system and highlight how income inequality contributes to unequal opportunities for timely cancer diagnosis. Integrated results by Educational Level Educational attainment emerged as a relevant but nuanced factor in shaping the diagnostic pathway for individuals with lung cancer. Quantitative analysis showed that 57.1% of individuals with primary or secondary education and 66.6% of those with post-secondary education were diagnosed within 60 days of their first consultation. Although participants with lower educational levels had slightly greater odds of experiencing a delay beyond 60 days (OR = 1.08; p = 0.876; 95% CI: 0.385–3.07), this difference was not statistically significant. Despite the absence of strong statistical associations, qualitative findings provided important contextualization. Educational level was closely linked to health-seeking behaviors, system navigation skills, and patient-clinician communication. Participants with higher education levels generally reported earlier recognition of symptoms, faster initiation of care, and more effective interactions with healthcare professionals. These individuals were also more likely to question medical decisions, seek second opinions, and proactively manage referrals. “Access is probably much easier for patients in the private sector, usually due to socioeconomic status—they tend to have a higher level of education, which also favors earlier diagnoses. People with fewer resources and lower education may lack awareness of the importance of consulting a doctor.” (Nurse1, Public and Private System) Conversely, participants with lower education levels often encountered difficulties interpreting complex instructions, completing bureaucratic processes, or understanding the sequence of care, especially in tertiary centers. These barriers were exacerbated by a lack of familiarity with medical terminology and institutional procedures, occasionally leading to missed appointments, confusion, or delays in follow-up care. “When someone has a lower sociocultural level, they often struggle with this. Their cultural background makes it harder for them to understand instructions for complex care, and as a result, those patients fall behind—they miss appointments. And then health professionals wrongly assume, ‘Oh, this woman did not show up, she missed her appointment,’ and we jump to conclusions. We forget that we understand the system well, but for many, this is like Mandarin Chinese.” (Physician4, Public System) These narratives revealed clinician assumptions and biases about patient behavior, which may further disadvantage patients with limited health literacy. In some cases, health professionals attributed missed appointments to patient irresponsibility without accounting for contextual challenges, such as poor system comprehension or difficulty with digital platforms. In all, the integration of findings for educational level revealed a convergence between quantitative and qualitative data. While statistical analysis suggested only a modest trend, qualitative evidence pointed to education as a structural determinant that influences patients’ ability to navigate the healthcare system, advocate for themselves, and adhere to diagnostic processes. This convergence reinforces the need for health literacy-sensitive approaches and system-level interventions to mitigate the impact of educational inequalities on diagnostic timeliness. Integrated results by Health System Health system affiliation emerged as a key factor influencing the timeliness of diagnostic confirmation in lung cancer. Quantitative results indicated that a higher proportion of individuals receiving care in the private sector (ISAPRE) were diagnosed within 60 days of their first consultation (80%) compared to those in the public sector (FONASA, 57.1%). Although not statistically significant, individuals affiliated with the public system had 1.77 times greater odds of receiving a delayed diagnosis beyond 60 days (OR = 1.77; p = 0.458; 95% CI: 0.390–8.1). Qualitative findings enriched this analysis by identifying multiple mechanisms through which the public system contributed to diagnostic delays. These mechanisms reflected structural weaknesses and operational barriers that limited timely access to specialized oncology services: System fragmentation and referral complexity The public system operates across poorly coordinated levels of care (primary, secondary, tertiary). General practitioners in primary care often lack training in oncology pathways, resulting in inconsistent referrals and incomplete diagnostic workups. “They do not know where to send the patient, and once they do, they do not know which tests to include. So, a patient with suspected lung cancer gets referred somewhere the physician believes might accept them.” (Physician1, Public System) “The bigger issue is that healthcare professionals do not know who to refer the patient to—sometimes it is more than one specialist.” (Physician2, Public System) Patients navigating between systems—either by necessity or referral—often encounter challenges due to unfamiliarity with how the private system works. “Sometimes I get confused with so many different places.” (Patient13_Male_FONASA) Public–private disconnection Although GES regulations allow for cross-referral between systems, weak coordination leads to fragmented care, confusion, and increased burden for patients and caregivers. “Cancer patients do not have it easy—they face many obstacles, and they are sent from one place to another in a very disorganized system.” (Caregiver2_Male) Limited infrastructure and test availability Public facilities frequently lack sufficient diagnostic equipment or trained personnel, causing long waiting times for critical tests such as CT scans, spirometry, or MRIs. “We could not get an appointment for spirometry and another test—we had to wait a whole month.” (Patient4_Male_FONASA) “Getting a CT scan or MRI was difficult. We could not afford private care either.” (Patient7_Female_FONASA) Late-stage diagnoses and misdiagnoses Patients in the public system were more likely to receive a diagnosis at advanced stages (III or IV), with frequent misdiagnosis of LC as pneumonia in early consultations. “In the private sector, you find lung cancers at stage 1—small nodules removed quickly with high survival rates. In the public system, 70% arrive at stage 3b or 4, with a median survival of 4.4 months and a five-year survival rate below 30%.” (Physician1, Public System) “I lost five years…” (Patient14_Female_FONASA) “He kept getting diagnosed with pneumonia... but he had this cough that would not go away. They gave him cough syrup, that was all.” (Patient13_Male_FONASA) In contrast, participants in the private system described faster and more streamlined diagnostic processes. Physicians in this sector often ordered comprehensive tests at the first visit, facilitated rapid specialist referrals, and coordinated care through multidisciplinary teams. “In August 2012, I had a nasty cold. A doctor ordered a full panel of tests, including a chest CT... which revealed a nodule. She referred me to Santiago to begin more testing” (Patient11_Female_ISAPRE) “If I suspect a patient has lung cancer, I immediately call my colleague. We review the case, order a PET scan, consult the committee—everything is faster and more multidisciplinary here.” (Physician5, Private System) “I stayed with ISAPRE because it covered hospitalizations and medication, which FONASA does not. Private appointments are quicker and more efficient—so even if it is more expensive, you pay for quality and peace of mind.” (Patient16_Male_ISAPRE) Additionally, participants affiliated with FONASA but possessing financial means reported using the Free Choice Modality (FCM) to access private services with partial public funding. This hybrid strategy enabled faster diagnosis, though at a significant out-of-pocket cost. “We did everything privately because in the public system you just... die waiting. There is no other way to put it.” (Patient4_Male_FONASA) “I decided just to buy the voucher and take him to a private doctor. At the primary care of public centers, they make you wait forever and never give you test results.” (Patient13_Male_FONASA) Perception gap between clinicians and patients A further point of divergence emerged between healthcare providers and patients regarding their perception of delays. While clinicians often described the diagnostic process as efficient, patients highlighted the emotional, logistical, and financial burden of waiting, emphasizing the need for patient-centred metrics to assess system responsiveness. “In general, it is pretty quick, we usually have the patient fully evaluated, including staging and functional tests, within a month.” (Physician3, Public and Private System) In summary, the integrated findings demonstrated an expansion of the quantitative results: although both public and private patients were diagnosed within the GES timeframe in most cases, qualitative data revealed significant disparities in experience, pathway complexity, and systemic burden. The private system enabled more rapid and coordinated diagnostic confirmation, while the public sector was hindered by fragmentation, delays, and access limitations. These findings expose structural inequities in cancer care and underscore the importance of system-level reforms to ensure equitable diagnostic access, regardless of health system affiliation. Table 3 Integrated Results for the DI and SDH SDH Quantitative Findings Qualitative Findings Integrated Interpretation Gender • Majority diagnosed within 60 days: 58% men, 63.8% women. • Men had 1.25 times higher odds of delay > 60 days (p = 0.664; 95% CI: 0.448–3.526). • No statistically significant difference. • Gender norms shape therapeutic trajectories. • Men often accompanied by female relatives (wives, daughters, granddaughters). • Women often navigate illness alone; caregiving role persists during illness. • Emotional impact: frustration, guilt, distress when unable to fulfill caregiving roles. Quotes: - “Look, my husband was diagnosed because I took him to the doctor...” - “I did not tell my children. I went to the hospital alone—that is how it was.” Complementary expansion: • No significant difference in quantitative data, but qualitative evidence reveals strong gender-related patterns. • Highlights need to interpret gender-disaggregated data within the context of gender norms and caregiving dynamics. • Gender influences health-seeking behavior, access, and emotional experience. Region of residence • 62.5% of participants in the Metropolitan Region and 60.85% in other regions were diagnosed within 60 days. • OR for delay (> 60 days) in other regions vs. Metropolitan Region: 1.34 (p = 0.563; 95% CI: 0.497–3.611). No statistically significant difference. • Limited healthcare infrastructure in many regions; high-complexity services concentrated in Santiago. • Shortage of oncology specialists in non-metropolitan areas, leading to lengthy referrals. • Rural patients face difficulties navigating large urban hospitals. • Long-distance travel often required, adding financial and logistical burdens. • Limited availability of diagnostic equipment; breakdowns or scarcity cause delays. The quantitative results showed no statistically significant difference in diagnostic delays by region. However, qualitative evidence revealed substantial structural and logistical barriers in non-metropolitan areas, often mitigated through personal resources. These findings expand the quantitative results, highlighting geographic inequities in timely LC diagnosis in Chile. Income Quintile Higher-income groups (Q4–Q5) more likely to be diagnosed within 60 days. Q2 had 5.79× higher odds of > 60 days delay vs Q5 (p = 0.049; 95% CI: 1.011–33.21). High-income patients used out-of-pocket private care to expedite diagnosis, often combining public and private services. Low-income patients faced longer waits, emotional distress, and were dependent on slower public pathways. Expansion: Quantitative data identified income as a determinant; qualitative data explained mechanisms. Economic capital enabled bypassing delays; low-income reinforced vulnerability to systemic inefficiencies. Educational Level Diagnosis ≤ 60 days: 57.1% (primary/secondary) vs 66.6% (post-secondary). Lower education had slightly higher odds of > 60 days delay (OR = 1.08; p = 0.876). No statistically significant difference. Higher education linked to earlier symptom recognition, proactive care-seeking, and better system navigation. Lower education associated with difficulty understanding instructions, missed appointments, and health literacy barriers. Convergence: Quantitative trend towards delay with lower education; qualitative data contextualized through navigation skills, communication, and clinician bias. Highlights need for health literacy-sensitive interventions. Health System • 80% of private system patients diagnosed within ≤ 60 days vs. 57.1% in public system. • OR for delay in public system = 1.77 (p = 0.458; 95% CI: 0.390–8.1). No statistically significant difference. • Public system barriers: fragmentation, poor coordination, limited equipment, long waits, late-stage diagnoses, misdiagnosis as pneumonia. • Private system facilitators: rapid referrals, multidisciplinary care, faster imaging, better coordination. • Public patients with resources use Free Choice Modality (MLE) for private care. • Perception gap: clinicians see process as quick, patients perceive delays as burdensome. Quantitative results show faster diagnosis in private care. Qualitative analysis expands this, revealing systemic barriers in public care and structural inequities. Even within GES timelines, public patients face fragmented pathways, longer waits, and higher emotional/logistical burden. Reforms are needed to ensure equitable access regardless of health system affiliation. DISCUSSION This is the first integration of social determinants of health and care trajectories in LC in Chile. This convergent mixed-methods study analyzed the influence of SDH on the DI in LC, integrating quantitative and qualitative findings. While statistical analysis did not reveal significant differences by gender, region of residence, or educational level, the qualitative phase identified important barriers related to these SDH, such as gender norms shaping caregiving roles ( 54 ), geographic inequalities due to the centralization of specialists and diagnostic equipment ( 55 – 57 ), and health literacy gaps that hinder navigation of the healthcare system ( 58 ). Consistent with previous research, income and type of health system emerged as the most influential factors ( 55 – 57 ). People with higher incomes or those affiliated with the private system accessed specialist consultations and diagnostic tests more quickly, while those relying exclusively on the public system faced delays, fragmented referral processes, and diagnoses at more advanced stages. These findings align with evidence linking health system fragmentation and centralization to inequities in cancer outcomes ( 55 – 57 ). Although most diagnoses occurred within the 60-day maximum timeframe established by the GES guarantees ( 59 , 60 ), our findings show that meeting this deadline does not ensure smooth or equitable patient trajectory. In many cases, individuals—especially those with lower economic or social capital—had to bear high emotional, logistical, and financial costs to obtain timely care. This aligns with previous literature emphasizing that timely diagnosis is influenced not only by adherence to formal timeframes, but also by factors spanning patient-related, provider-related, system-related, and disease-related domains ( 13 – 19 , 22 ). Beyond Chile, evidence from Brazil shows marked disparities by race, education, and geography. Black Brazilians report discrimination within health services and stigma around smoking and limited awareness of screening benefits reduce uptake of preventive services ( 61 ). Uneven distribution of trained professionals and oncology services, together with constrained availability of chest computed tomography in some regions, contributes to wide variation in timely diagnosis and outcomes ( 61 , 62 ). At the regional level, social determinants such as income, education, and rural residence drive late diagnosis and poorer outcomes, while shortages of specialists and the urban concentration of resources perpetuate these gaps. Recent initiatives including telepathology networks, health education programs, and the strengthening of population-based cancer registries, aim to mitigate these inequities, but substantial challenges remain ( 63 ). The implications for health policy include strengthening integrated oncology health networks with a territorial and equity focus ( 64 ), reducing the centralization of human and technological resources, improving coordination between public and private systems, and implementing culturally appropriate interventions such as health literacy programs and patient navigation ( 58 , 65 – 67 ). It is also recommended that national cancer registries include socioeconomic, demographic, and support network variables ( 67 , 68 ) to better plan resources and strategies that address the real needs of the population. The findings of this study point to several actionable recommendations for different stakeholders. For the health system, strengthening integrated oncology networks with a territorial and equity focus is essential, alongside reducing centralization of diagnostic and treatment resources and improving coordination between public and private sectors. In this regard, concrete policy measures could include incorporating coverage within the GES for services provided by social workers or oncology nurses who support patients and caregivers in navigating care pathways and addressing access barriers; strengthening the role of primary health care in the early diagnostic stage; and developing mechanisms for better coordination between primary, secondary, and tertiary levels of care. The implementation of patient navigation programs, with trained navigators based in primary care who proactively detect and guide individuals with suspected lung cancer, could be a critical step toward reducing diagnostic delays. For patients, interventions should prioritize health literacy and navigation support to empower individuals in recognizing symptoms, seeking timely care, and understanding available guarantees. Caregivers play a crucial role in facilitating access, highlighting the need for psychosocial support programs and training to reduce the burden of care. Civil society organizations should be strengthened and supported to represent lung cancer more effectively in advocacy, ensuring that patient voices are incorporated into decision-making processes and policy reforms. Finally, academic and research institutions should continue to generate context-specific evidence that informs equitable cancer policies, while promoting participatory approaches that integrate the perspectives of patients and communities. Future research should examine the feasibility and effectiveness of these proposed interventions, particularly the integration of navigation roles in primary care and the expansion of GES guarantees, in order to guide policy implementation and evaluate their impact on reducing inequities in lung cancer trajectories. A notable strength of this research lies in its design. This is the first study in Chile to examine the DI in LC through the lens of therapeutic trajectories, employing a convergent mixed-methods approach that integrates the perspectives of patients, caregivers, healthcare professionals, and civil society leaders. The combination of quantitative and qualitative data enhanced the depth and contextualization of the findings, enabling not only the measurement of diagnostic delays but also a nuanced understanding of the mechanisms underlying them ( 69 ). This dual perspective generated actionable evidence that can inform health policy reforms and targeted interventions. However, several limitations should be acknowledged. In the qualitative phase, the health condition of some participants restricted their ability to engage fully in interviews, which led to allowing the presence of a significant other for support. Conducting interviews online with older adults posed challenges, which were mitigated by offering multiple participation modalities. In the quantitative phase and regarding potential biases, selection bias may have arisen from the use of non-probabilistic convenience sampling, which can limit representativeness by relying on participants’ availability and access( 50 ) potentially excluding more vulnerable groups, such as individuals without internet access or outside the networks used for recruitment ( 50 ). Information bias could also be present, related to survey clarity (although piloted, it was not formally validated) ( 51 ), variability in administration formats ( 52 ), and possible recall bias( 53 ) due to difficulties in accurately remembering details of therapeutic trajectories. To mitigate these biases, the study implemented several strategies, including partnerships with public and private organizations to diversify recruitment, offering multiple participation formats (e.g., computer-based and mobile-friendly surveys), piloting the survey with input from academics and patient representatives, training interviewers to standardize data collection, and encouraging participants to use personal records to reduce recall errors. In summary, this study provides novel evidence of inequities in timely LC diagnosis in Chile by documenting how demographic, socioeconomic, and health system-related determinants influence the diagnostic interval. Beyond highlighting structural barriers, it incorporates the lived experiences of patients, caregivers, healthcare professionals, and civil society leaders, offering a multidimensional understanding rarely captured in Latin American research. The findings underscore the importance of addressing not only systemic inefficiencies but also the social, economic, and cultural determinants that constrain patients’ access to timely care. By integrating multiple perspectives through a convergent mixed-methods design, this study provides robust, context-specific insights into the inequities shaping lung cancer diagnostic trajectories in Chile. Importantly, these findings transcend the national context, as they reflect structural barriers common to many Latin American countries, including health system fragmentation, centralization of oncology services, and persistent social and economic inequalities. Framing the results within this broader regional landscape highlights their potential to inform not only national reforms but also comparative analyses and collaborative strategies across Latin America. Such a regional perspective strengthens the contribution of this research to the global discourse on cancer care equity, offering evidence that can guide the design of more equitable and person-centered oncology services in settings characterized by similar health system challenges. Abbreviations LC Lung Cancer DI Diagnostic Interval NCP National Cancer Plan NCL National Cancer Law Declarations Ethics approval and consent to participate The study was approved by the Comité Ético Científico de la Facultad de Medicina Clínica Alemana Universidad del Desarrollo (number 2021-67). Informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Competing interests The authors declare that they have no competing interests. The entire study was conducted independently by the research team at UDD. Clinical trial number not applicable. Funding This research was funded by the Doctoral Program of Science and Innovation in Medicine and by the Center for Prevention and Control of Cancer in Chile (CECAN), FONDAP 152220002 ANID Chile. Author Contribution Conceptualization and study design: CC, BC, AO. Data collection and data analysis: CC, FV, YB. Manuscript writing: CC, BC. Review and editing: AO, FV, BN, YB. All authors approved the submitted version. Acknowledgments The authors express their gratitude to Doctoral program of Science and Innovation in Medicine, Universidad del Desarrollo, and Centre for Prevention and Control of Cancer in Chile (CECAN), FONDAP 152220002 ANID Chile. Data Availability Semi-structured interview guide is available. Full transcripts of interviews can be provided upon request (all anonymized). 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Available from: https://link.springer.com/article/ 10.1007/s11136-005-5263-8 Couper M, Leeuw ED. Nonresponse in cross-cultural and cross-national surveys. 2003. Althubaiti A. Information bias in health research: definition, pitfalls, and adjustment methods. J Multidiscip Healthc [Internet]. 2016 May 4 [cited 2024 Dec 14];9:211–7. Available from: https://pubmed.ncbi.nlm.nih.gov/27217764/ Donkin A, Goldblatt P, Allen J, Nathanson V, Marmot M. Global action on the social determinants of health. BMJ Glob Health [Internet]. 2018 Jan 1 [cited 2022 Apr 25];3(Suppl 1):e000603. Available from: https://gh.bmj.com/content/3/Suppl_1/e000603 Siqueira M, Coube M, Millett C, Rocha R, Hone T. The impacts of health systems financing fragmentation in low- and middle-income countries: a systematic review protocol. Syst Rev [Internet]. 2021 Dec 1 [cited 2024 Oct 2];10(1):1–8. Available from: https://systematicreviewsjournal.biomedcentral.com/articles/ 10.1186/s13643-021-01714-5 Ruano AL, Rodríguez D, Rossi PG, Maceira D. Understanding inequities in health and health systems in Latin America and the Caribbean: a thematic series. Int J Equity Health [Internet]. 2021 Dec 1 [cited 2024 Oct 2];20(1):1–4. Available from: https://equityhealthj.biomedcentral.com/articles/ 10.1186/s12939-021-01426-1 Brown S, Castelli M, Hunter DJ, Erskine J, Vedsted P, Foot C, et al. How might healthcare systems influence speed of cancer diagnosis: A narrative review. Soc Sci Med. 2014;116:56–63. Jones E, Naranjo A, Winestone LE, Umaretiya PJ, Aziz-Bose R, Ilcisin LAS et al. Feasibility and acceptability of social determinants of health data collection in the context of a Children’s Oncology Group trial. Journal of Clinical Oncology [Internet]. 2023 Jun 1 [cited 2024 Dec 9];41(16_suppl):10010–10010. Available from: https://ascopubs.org/doi/10.1200/JCO.2023.41.16_suppl.10010 Cáncer de pulmón en. personas de 15 años y más - Superintendencia de Salud, Gobierno de Chile [Internet]. [cited 2025 Jan 8]. Available from: https://www.superdesalud.gob.cl/orientacion-en-salud/cancer-de-pulmon-en-personas-de-15-anos-y-mas-2/ Cáncer de mama. - Superintendencia de Salud, Gobierno de Chile [Internet]. [cited 2025 Jan 8]. Available from: https://www.superdesalud.gob.cl/orientacion-en-salud/cancer-de-mama/ Campos MR, Rodrigues JM, Marques AP, Faria LV, Valerio TS, Silva MJS et al. da,. Smoking, mortality, access to diagnosis, and treatment of lung cancer in Brazil. Rev Saude Publica [Internet]. 2024 [cited 2025 Sep 7];58:18. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11090611/ Emmerick ICM, Rodrigues Campos M, Castanheira D, Muzy J, Marques A, Arueira Chaves L et al. Lung Cancer Screening in Brazil Comparing the 2013 and 2021 USPSTF Guidelines. JAMA Netw Open [Internet]. 2023 Dec 1 [cited 2025 Sep 7];6(12):e2346994–e2346994. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2812735 Guerron-Gomez G, Rojas-Fierro M, Parra-Medina R, Mosquera A, Suarez MG. A reflective analysis on the inequities in cancer diagnosis and treatment in Latin America: a call to action for public health. Int J Equity Health [Internet]. 2025 Dec 1 [cited 2025 Sep 7];24(1):1–7. Available from: https://equityhealthj.biomedcentral.com/articles/ 10.1186/s12939-025-02457-8 Biblioteca del Congreso Nacional. Acceso a tratamiento para el cáncer adulto y pediátrico en Chile [Internet]. [cited 2024 Dec 9]. Available from: https://obtienearchivo.bcn.cl/obtienearchivo?id=repositorio/10221/33292/1/BCN_Acceso_a_tratamiento_Cancer.pdf Freeman HP. The Origin, Evolution, and Principles of Patient Navigation. Cancer Epidemiology and Prevention Biomarkers [Internet]. 2012 Oct 1 [cited 2022 Jan 11];21(10):1614–7. Available from: https://cebp.aacrjournals.org/content/21/10/1614 Tucker-Seeley R, Abu-Khalaf M, Bona K, Shastri S, Johnson W, Phillips J et al. Social Determinants of Health and Cancer Care: An ASCO Policy Statement. JCO Oncol Pract [Internet]. 2024 May [cited 2024 Dec 9];20(5):621–30. Available from: https://ascopubs.org/doi/10.1200/OP.23.00810 Castrucci BC, Auerbach J, Meeting Individual Social Needs Falls Short Of Addressing Social Determinants Of Health. Health Affairs Forefront [Internet]. 2019 Jan 17 [cited 2024 Dec 9]; Available from: https://www.healthaffairs.org/do/ 10.1377/forefront.20190115.234942/full/ Burus T, Thompson JR, McAfee CR, Williams LB, Knight JR, Huang B et al. A framework and process for community-engaged, mixed-methods cancer needs assessments. Cancer Causes and Control [Internet]. 2024 Oct 1 [cited 2024 Dec 9];35(10):1319–32. Available from: https://link.springer.com/article/ 10.1007/s10552-024-01892-2 Patton MQ. Qualitative research and evaluation methods. 3a ed. Qualitative Inquiry. SAGE; 2002. 598 p. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers invited by journal 09 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Editor invited by journal 16 Sep, 2025 Submission checks completed at journal 15 Sep, 2025 First submitted to journal 15 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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15:48:43","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":195461,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7576484/v1/390d9b6cc4ac83d481695ec1.html"},{"id":94211857,"identity":"c11ecf5c-ac21-486f-8b88-b900d1a0d11e","added_by":"auto","created_at":"2025-10-23 15:48:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":745261,"visible":true,"origin":"","legend":"\u003cp\u003eJoint Display of Integrated Findings on Social Determinants of Timely Lung Cancer Diagnosis\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7576484/v1/4d6a7e5222306450acefb47b.png"},{"id":94213439,"identity":"19ae2350-4fb5-4f24-bc85-20d93a280a9c","added_by":"auto","created_at":"2025-10-23 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most frequently diagnosed cancer worldwide and the leading cause of cancer-related deaths, with over 2.2\u0026nbsp;million new cases and 1.8\u0026nbsp;million deaths reported in 2020 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Its high lethality is largely related to late-stage diagnosis, which limits curative treatment options and negatively affects survival and quality of life. In Chile, LC shows one of the highest incidence and mortality rates in Latin America (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In 2020, it caused 3,550 deaths, accounting for 12.4% of all cancer-related deaths in the country (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). National data reveal higher disease burden among men and individuals over 60 years of age, alongside regional disparities in mortality, suggesting a strong influence of social determinants of health (SDH) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Furthermore, in Chile LC is the leading cause of cancer-related deaths in men and the second in women (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA parameter that has been recognized as critical in the scientific literature due to its impact on the prognosis and survival of lung cancer is the Diagnostic Interval (DI). This interval is defined as the period between the patient\u0026rsquo;s first consultation for symptoms related to the disease and the moment when the diagnosis is established (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In lung cancer, median diagnostic intervals ranging from 21 to 54 days have been reported in high-income countries, although substantial variability is observed depending on the type of cancer and the healthcare context (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, the diagnostic interval may be influenced by factors such as clinical presentation, accessibility to complementary tests, and the patient\u0026rsquo;s sociodemographic characteristics (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Regarding sociodemographic characteristics, it has been identified that individuals living in areas of greater poverty, minority groups, and those with limited access to healthcare services are more likely to be diagnosed at advanced stages of lung cancer, which is associated with poorer prognosis and lower survival (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Taken together, these findings underscore that the diagnostic interval is not only shaped by individual and contextual determinants but also by a wide range of barriers at different levels. Multiple studies have identified factors and barriers to timely diagnosis, including patient-related (symptom underestimation, low health literacy, economic and geographic barriers) (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), provider-related (diagnostic errors, multiple pre-referral consultations) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), system-related (long waits, fragmented pathways, lack of coordination) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), and disease-related (atypical early symptoms, comorbidities masking presentation) factors (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). These delays are associated with advanced-stage diagnoses, reduced treatment options, greater emotional distress, and increased health inequities, particularly in resource-constrained settings (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese barriers are not exclusive to high-income or low-income countries; rather, they manifest differently depending on the structure and equity of the health system. In Chile, a high-income country in the South American region, the mixed public\u0026ndash;private healthcare model is marked by fragmentation and unequal distribution of high-complexity services, leading to pro-rich inequities in access to timely care (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). The public insurance, known as FONASA (National Health Fund), covers approximately 78% of the population and is stratified by income level. The private system, ISAPRE (Health Insurance Institutions), insures about 17% of the population, primarily higher-income individuals. Recognizing the disease burden, lung cancer was incorporated into the explicit health guarantee program called \u0026ldquo;GES\u0026rdquo; in 2019, which established a maximum of 60 days from clinical suspicion to diagnostic confirmation for all people, regardless of health insurance (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Hence, the 60-day threshold serves as a valuable analytical benchmark to assess potential diagnostic delays and explore how social determinants may influence the timeliness of cancer care.\u003c/p\u003e\u003cp\u003eIn Chile, women, individuals with lower educational attainment, those residing outside the capital, and users of the public healthcare system experience greater barriers to accessing healthcare, which prolongs the DI and contributes to detection at advanced stages of the disease (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Moreover, the implementation of advanced technologies and targeted therapies, such as genomic profiling and EGFR inhibitors, is limited and inequitable, restricting access to innovative treatments for a considerable proportion of patients. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Additionally, while social participation in cancer is addressed in Chile\u0026rsquo;s National Cancer Plan (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and the Cancer Law (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), among the 30 registered cancer patient organizations, only one mentions LC and none are exclusively dedicated to it (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This underrepresentation in legal frameworks and clinical practice recommendations may contribute to delayed policy responses and limited resource allocation for this population (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAgainst this backdrop, understanding the diagnostic interval in lung cancer requires acknowledging the interplay of patient-, system-, and society-level factors. The concept of therapeutic trajectories provides a comprehensive lens to study this process in depth, as it integrates both personal experiences and system-level dynamics across the continuum of care (\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Incorporating patient perspectives is essential to identify barriers, facilitators, and context-specific challenges that structured clinical pathways may overlook (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the existence of national policies such as the GES program and the National Cancer Plan, there is still limited evidence in Chile on how demographic, socioeconomic, and health system-related determinants influence the timeliness of lung cancer diagnosis. While international studies have extensively analyzed diagnostic intervals, research in Latin America\u0026mdash;and particularly in Chile\u0026mdash;remains scarce. Addressing this gap, the present study examines the association between selected social determinants of health and the diagnostic interval in lung cancer, integrating quantitative and qualitative evidence. By adopting this approach, the study aims to generate context-specific insights to inform equity-oriented cancer care policies and contribute to reducing diagnostic delays in Chile\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eType of study\u003c/h2\u003e\u003cp\u003eWe employed a convergent mixed‑methods design to address the complexity of delays in LC diagnosis from complementary vantage points\u0026mdash;quantitative estimation of associations and qualitative exploration of mechanisms and lived experiences. The convergent approach is appropriate when the research question benefits from simultaneous collection and separate analysis of qualitative and quantitative data, followed by integration to corroborate, expand, or nuance findings (\u003cspan additionalcitationids=\"CR37 CR38 CR39\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This design enhances interpretive validity by allowing immediate comparison of strands on the same constructs (e.g., social determinants, diagnostic interval) while preserving each method\u0026rsquo;s strengths.\u003c/p\u003e\u003cp\u003eThe qualitative (interviews) and quantitative (survey) strands were conducted in parallel and analyzed independently. We assigned equal weighting (QUAL\u0026thinsp;=\u0026thinsp;QUAN) because both strands were theoretically and analytically indispensable to the research aim: the quantitative component quantified associations between social determinants and diagnostic timeliness, while the qualitative component elucidated barriers/facilitators and contextual processes shaping patient trajectories.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQualitative phase\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eAssumptions\u003c/strong\u003e\u003cp\u003eThe qualitative component was based on the epistemological assumption that multiple realities can coexist around a single phenomenon, emphasizing the subjective experiences and perspectives of participants (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Given the complexity of the research topic, qualitative inquiry focused on a small number of cases to explore meanings in depth, using narrative data collection techniques (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). A case study design was adopted, with the case defined as 'The experience of adults with lung cancer diagnostic.'\u003c/p\u003e\u003c/p\u003e\u003cp\u003eSampling strategy: Participants in the qualitative phase were selected using purposive sampling to reflect diversity across relevant characteristics, guided by the Cochrane PROGRESS-Plus framework (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), including place of residence, gender, educational level, and occupation. Sampling units included patients with lung cancer, significant others or unpaid caregivers, healthcare professionals, and civil society representatives. The initial sample aimed to recruit a minimum of 15 patients, 5 healthcare professionals, 10 significant others or caregivers, and 1 civil society leader. Inclusion and exclusion criteria for each participant group are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" 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\u003eInclusion and exclusion criteria for the qualitative phase participants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Participant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInclusion Criteria\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExclusion Criteria\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeople with cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Lung cancer diagnosis confirmation.\u003c/p\u003e\u003cp\u003e2. Aged 18 years or older.\u003c/p\u003e\u003cp\u003e3. Currently receiving or previously received healthcare in the public or private system in Chile.\u003c/p\u003e\u003cp\u003e4. Have access to the internet or a telephone to complete the interview.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAny physical or mental condition that limits the person's ability to decide to participate in the interview.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSignificant others or unpaid caregivers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Aged 18 years or older.\u003c/p\u003e\u003cp\u003e2. Currently or previously accompanied a person with LC during healthcare processes.\u003c/p\u003e\u003cp\u003e3. Have access to the internet to participate in an interview.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAny physical or mental condition that limits the person's ability to decide to participate in the interview.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthcare professionals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Aged 18 years or older.\u003c/p\u003e\u003cp\u003e2. Work in the public or private healthcare system in Chile.\u003c/p\u003e\u003cp\u003e3. Have a specialty related to LC.\u003c/p\u003e\u003cp\u003e4. Have access to the internet to participate in an interview.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAny physical or mental condition that limits the person's ability to decide to participate in the interview.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCivil society representatives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1. Aged 18 years or older.\u003c/p\u003e\u003cp\u003e2. Actively involved in civil society organizations related to LC.\u003c/p\u003e\u003cp\u003e3. Have access to the internet to participate in an interview.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAny physical or mental condition that limits the person's ability to decide to participate in the interview.\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\u003eSample size: Sampling remained flexible throughout the study and continued until data saturation was reached. Saturation was defined as the point at which no new themes or perspectives emerged from additional interviews. Final recruitment included 18 patients, 8 healthcare professionals, 10 caregivers or significant others, and 1 civil society representative, for a total of 37 participants.\u003c/p\u003e\u003cp\u003eRecruitment: It was conducted in two sequential stages. In the first stage (October 2021\u0026ndash;March 2022), we recruited patients with LC, healthcare professionals, and the civil society representative. This stage prioritized capturing direct experiences of the disease, professional perspectives, and advocacy viewpoints to inform early thematic saturation. In the second stage (March\u0026ndash;October 2023), we recruited significant others or unpaid caregivers. This sequential approach allowed for the exploration of caregivers\u0026rsquo; perspectives in the context of preliminary patient and professional narratives, enabling a richer understanding of the social and emotional dimensions of the therapeutic trajectory.\u003c/p\u003e\u003cp\u003eData collection: Online semi-structured individual interviews with all participant groups. Interviews explored participants\u0026rsquo; perspectives, values, and lived experiences in relation to the research topic (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). A question guide was developed based on literature review and expert consultation (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The interview guide was specifically designed for the objectives of this study based on literature review and expert consultation and has not been previously published. An English version is available as supplementary material.\u003c/p\u003e\u003cp\u003e. Interviews with patients, healthcare professionals, and civil society leaders were conducted between October 2021 and March 2022. Interviews with caregivers and significant others were conducted between March and October 2023, all via online platforms including Zoom, Google Meet, WhatsApp Video, or phone, depending on participant preference. Interviews followed a flexible guide exploring seven thematic areas: (i) general experience of living with lung cancer, (ii) therapeutic trajectory within and outside the health system, (iii) barriers to healthcare access, (iv) facilitators of healthcare, (v) health needs, (vi) quality of care, and (vii) overall evaluation of the experience. The same themes were addressed across all participant groups, with tailored questions per role.\u003c/p\u003e\u003cp\u003eData analysis: A thematic analysis approach was used to analyze qualitative data, following the thematic categories defined in the interview guide (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Thematic analysis is widely used in qualitative research to identify, analyze, and interpret patterns of meaning across datasets (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). All interviews were transcribed verbatim in Word documents to ensure data fidelity and support accurate analysis.\u003c/p\u003e\u003cp\u003eScientific rigor: Rigor in the qualitative phase was ensured through two strategies: (i) Participant triangulation: Findings were validated by comparing data from different participant groups (patients, healthcare professionals, caregivers, and civil society representatives), which reduces bias and enhances credibility (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), and (ii) Reflexivity: Researchers engaged in continuous self-reflection, documenting contextual observations and personal reactions throughout the interviews. Field notes were recorded concurrently to complement thematic analysis and ensure analytical transparency (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eQuantitative phase\u003c/h3\u003e\n\u003cp\u003eThe quantitative phase of this mixed-methods study employed an analytical, observational, cross-sectional design.\u003c/p\u003e\u003cp\u003eStudy Population: The study targeted adults in Chile with a current or past diagnosis of LC. Inclusion criteria were: (i) being 18 years or older; (ii) having received or currently receiving care in either the public or private healthcare system in Chile; and (iii) having access to a phone or internet to complete the survey. Individuals with physical or mental conditions limiting their capacity to voluntarily participate or respond were excluded.\u003c/p\u003e\u003cp\u003eSample Size: In the absence of a national cancer registry and considering the exploratory nature of the study, the required sample size was calculated based on the study \"Patient's and doctors' delays in the diagnosis of chest tumors\" (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) using OpenEpi based on a chi-square test, with 95% confidence level and 80% power. The minimum sample size was estimated at 66 participants. The final sample included 80 participants, based on feasibility and recruitment outcomes.\u003c/p\u003e\u003cp\u003eSampling Strategy: A non-probabilistic, convenience sampling approach was adopted. This method was selected due to its practicality, lower cost, and feasibility in accessing participants within the study\u0026rsquo;s context (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Moreover, convenience sampling is commonly employed in exploratory research where the primary objective is to identify patterns, generate hypotheses, and obtain initial insights rather than achieve statistical generalizability (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecruitment: Participants were recruited between August 2021 and October 2024. General recruitment strategies included dissemination through social media, printed and digital flyers with embedded QR codes, appearances in radio programs, livestreams hosted by patient organizations, and promotion through organizational websites. Given the absence of lung cancer-specific patient organizations, targeted recruitment was coordinated through oncologists who referred patients with their consent. These physicians obtained patient consent to share contact information with the research team, who then contacted potential participants directly.\u003c/p\u003e\u003cp\u003eData Collection: Participants completed the survey either independently or with assistance from a trained interviewer. The survey was designed specifically for this study and hosted on Alchemer, a secure encrypted platform. It covered dimensions related to social determinants of health, access to care, and therapeutic trajectories. Most questions were closed-ended, with mandatory fields indicated. The survey was pilot tested with representatives from academia, clinical practice, and patient organizations. Participants provided feedback on clarity, relevance, and usability. Two administration options were provided: (i) Self-administration via link access with instructions to complete in one session (30\u0026ndash;45 minutes); and (ii) Assisted administration, where participants submitted contact details and were supported by a trained interviewer (YB).\u003c/p\u003e\u003cp\u003eStudy Variables: The dependent variable was the 'Interval from First Consultation to Diagnostic Confirmation' (DI), based on the GES timeframe of 60 days. Self-reported data on date of first consultation and diagnostic confirmation were used. Inconsistencies or missing values were excluded. The variable was operationalized as: 0\u0026thinsp;=\u0026thinsp;\u0026le;\u0026thinsp;60 days (on time), 1\u0026thinsp;=\u0026thinsp;\u0026gt;\u0026thinsp;60 days (delayed). Independent variables included gender, region of residence, household income quintile, education level, and type of health system.\u003c/p\u003e\u003cp\u003eData Analysis: Quantitative data were anonymized and stored securely on Alchemer. Analyses were performed using SPSS version 28.0. The initial stage involved data cleaning, variable recoding, and consistency checks. Missing data related to date intervals were excluded based on predefined criteria. Analysis included: (i) univariate descriptive analysis (means, medians, standard deviation, ranges, and proportions); (ii) bivariate analyses of associations between independent variables and ID, using Mann-Whitney U test and Kruskal-Wallis test; and (iii) logistic regression models to assess factors associated with diagnostic delay. Odds ratios (OR) and 95% confidence intervals (95% CI) were reported. Model adequacy was assessed with the Hosmer-Lemeshow test and Nagelkerke\u0026rsquo;s R\u0026sup2;. A significance level of 0.05 was applied.\u003c/p\u003e\u003cp\u003eRisk and control of bias:\u003c/p\u003e\u003cp\u003ePotential biases in the quantitative phase were mainly related to selection and information bias (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Regarding selection bias, the use of non-probabilistic convenience sampling may have limited sample representativeness by relying on participants\u0026rsquo; availability and accessibility (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). This approach may have excluded more vulnerable groups, such as individuals without internet access or those outside the networks used for recruitment. Information bias was also a potential concern, associated with several factors: (i) questionnaire clarity, as although the instrument was piloted, it was not formally validated (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e); (ii) variability in administration, given that surveys were completed either independently online or with telephone support depending on participants\u0026rsquo; needs (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e); and (iii) recall bias, due to potential difficulties in accurately remembering details of DI (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). To mitigate these risks, several strategies were implemented, including partnerships with public and private organizations to diversify recruitment channels, offering multiple participation formats (computer-based and mobile-friendly surveys), piloting the questionnaire with input from academics and patient representatives, training interviewers to standardize data collection, and encouraging participants to use personal records to reduce recall errors.\u003c/p\u003e\n\u003ch3\u003eIntegration Phase\u003c/h3\u003e\n\u003cp\u003eData integration was carried out at the levels of interpretation and presentation of results. During the interpretation phase, a narrative approach was used to compare and combine qualitative and quantitative findings (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This allowed for a comprehensive and contextualized description of the results from both components. Additionally, the weaving approach was employed to present qualitative and quantitative findings together, organized by the main analytical dimensions (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This method facilitated a cohesive understanding of how both types of data related to each thematic domain.\u003c/p\u003e\u003cp\u003eA final integrative analysis was conducted to assess the degree of coherence between the qualitative and quantitative results. This analysis explored whether the two strands of data confirmed, complemented, or contradicted each other (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Three possible outcomes were considered: confirmation, expansion, and discordance. Confirmation occurred when both types of data reinforced and validated each other\u0026rsquo;s findings. Expansion referred to instances where data from one strand enriched or extended the understanding of the other. Discordance described findings that diverged or contradicted each other, highlighting complexity or inconsistency between data types (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e This research adhered to international and national ethical standards, including the Declaration of Helsinki, the CIOMS guidelines, and Chilean National Law on Patients\u0026rsquo; Rights and Duties. Several measures were implemented to protect the rights and well-being of participants. All participants received an online informed consent form, which included an informational sheet outlining the study\u0026rsquo;s objectives, procedures, potential risks, and benefits. Participants were encouraged to read the document carefully and to contact the research team with any questions. Participation was entirely voluntary. Survey responses were stored securely in the Alchemer platform during data collection and later downloaded to the principal investigator\u0026rsquo;s personal computer, where they were anonymized. Interview audio recordings were also stored on the investigator\u0026rsquo;s secured device. All qualitative and quantitative data were de-identified using coding strategies, and only aggregate findings were reported. The study received ethical approval from the Scientific Ethics Committee of Cl\u0026iacute;nica Alemana \u0026ndash; Universidad del Desarrollo. Approval reference number: 2023-60.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe results are presented in three sequential sections. First, a descriptive analysis of the study sample is provided, outlining key demographic, socioeconomic, and health system characteristics. Second, an overview of the DI of LC is reported, including measures of central tendency and distribution. Finally, integrated findings from the quantitative and qualitative strands are presented for this DI, organized by each of the selected social determinants of health: gender, region of residence, household income quintile, educational level, and type of health system. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e displays these integrated results in a joint image, providing a visual synthesis of the connections across strands. This structure enables a comprehensive understanding of how these determinants influence timely access to LC diagnosis.\u003c/p\u003e\n\u003ch3\u003eDescription of the study sample\u003c/h3\u003e\n\u003cp\u003eThe final sample of the quantitative phase consisted of 80 participants with diverse demographic, socioeconomic, and health system backgrounds. 60% were women, 60% residents of regions outside the Metropolitan Region, and 78.8% affiliated with the public health system. Nearly half had completed higher education (46.3%), and income distribution was relatively even across quintiles. Detailed information on sample characteristics is available in the supplementary material.\u003c/p\u003e\u003cp\u003eThe qualitative sample consisted of 37 participants, including 18 patients with LC, 8 healthcare professionals, 10 significant others or unpaid caregivers, and 1 civil society leader. Most patient participants were affiliated with the public healthcare system (FONASA), resided in the Metropolitan Region, and had incomplete formal education. Healthcare professionals represented a range of specialties\u0026mdash;including oncology, thoracic surgery, and nursing\u0026mdash;and were affiliated with both public and private systems across different regions. Caregivers were predominantly women, held higher education degrees, and were family members actively involved in the patient\u0026rsquo;s care. The full socio-demographic details of participants are available in supplementary material.\u003c/p\u003e\n\u003ch3\u003eDiagnostic Interval (first consultation to diagnostic confirmation)\u003c/h3\u003e\n\u003cp\u003eThe average time of DI in LC was 79.08 days (SD: 118.81), with a median of 30.5 days. The maximum recorded duration was 578 days. More information on descriptive interval analysis is available at supplementary material. This interval comprised the identification of initial signs, symptoms, or incidental findings, the decision to seek medical care, and the stage of clinical suspicion. The qualitative analysis revealed three distinct patterns in the initiation of the therapeutic trajectory in lung cancer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrajectory initiated by respiratory signs or symptoms\u003c/strong\u003e\u003cp\u003eThis was the most common pattern. The main symptoms reported were persistent cough, dyspnea, hemoptysis, fatigue, and pain while breathing.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\"In March or April, more or less, he had a persistent cough\u0026mdash;he coughed, and coughed, and coughed\u0026mdash;day and night, and he got tired\u0026hellip;\" (Patient5_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrajectory initiated by incidental findings\u003c/strong\u003e\u003cp\u003eLess frequent than the first group, this pattern involved the detection of lung cancer during consultations for other conditions, particularly cardiovascular diseases.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;\u0026hellip;it was incidental, because he had (says the granddaughter), uh, uh, an atrioventricular block and needed an emergency pacemaker. He was admitted to the local hospital for that, and when they checked the placement\u0026mdash;through a scan or X-ray, I can\u0026rsquo;t recall\u0026mdash;they saw small tumors in his lungs.\u0026rdquo; (Patient5_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTrajectory initiated by non-respiratory signs or symptoms\u003c/strong\u003e\u003cp\u003eThis was the least frequent pattern, where patients presented with musculoskeletal or gastrointestinal symptoms. These cases showed the most extended delays and required multiple consultations before the cancer was identified.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\"I started in February with irritable bowel symptoms. I saw eight, eight specialists. They all said it was IBS\u0026hellip; I had all the abdominal tests, MRI and everything, all perfect\u0026hellip; but the symptoms didn't go away. Then a doctor came to my home and said, 'This is not your colon. It is your spine\u0026mdash;you need surgery due to spinal infiltration.' On June 25th, I was undergoing surgery. On the 26th, the doctor told me right away, 'This is a lung metastasis.' That same day, it was confirmed\u0026hellip; At first, everyone was wrong\u0026hellip; we spent a fortune on medication.\" (Patient3_Female_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\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\u003eCharacterization of the therapeutic trajectory of people with lung cancer: initiation, testing, and diagnosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMain characteristics (based on qualitative findings)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e(i) Initiation (identification of initial signs, symptoms, or findings; decision to seek care)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe therapeutic trajectory can begin either with symptoms/signs or incidental findings. The most commonly reported symptoms were cough, dyspnea, hemoptysis, fatigue, and chest pain.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncidental findings often occurred during evaluations for cardiovascular conditions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSymptoms were frequently misdiagnosed (e.g., as pneumonia).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatients often made multiple visits to the public health system without improvement. Those with financial resources accessed private services, where imaging tests were ordered.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e(ii) Testing and diagnostic confirmation (post-suspicion phase)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThis phase involved a series of tests and referrals to specialists after cancer was suspected.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIt was marked by emotional distress, including fear, anxiety, and uncertainty\u0026mdash;especially when facing unfamiliar diagnostic procedures\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccess to specialists (pulmonologists, oncologists, thoracic surgeons) was crucial.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAccording to national GES guidelines, diagnostic confirmation must occur within 60 days of suspicion.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiagnosis could be confirmed by various professionals using diverse procedures (e.g., biopsy, CT scan).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMost patients diagnosed in advanced stages were within the public health system.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated Analysis of the first consultation to diagnostic confirmation interval\u003c/h2\u003e\u003cp\u003eA mixed-methods integration was conducted for this interval, examining each of the studied variables: demographic (gender, region of residence), socioeconomic (income quintile, educational level), and health system (public vs. private). The integrated results for each variable and interval are presented in the following sections, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated results by Gender\u003c/h2\u003e\u003cp\u003e The quantitative analysis showed that a majority of participants (both men and women), received a diagnostic confirmation of LC within 60 days following their first medical consultation (58% of men and 63.8% of women). This difference was not statistically significant, men exhibited 1.25 times higher odds of experiencing delays beyond the 60-day threshold compared to women (p\u0026thinsp;=\u0026thinsp;0.664; 95% CI: 0.448\u0026ndash;3.526). While quantitative results suggested no significant gender-based disparities, the qualitative data highlighted gender as a crucial social determinant influencing the therapeutic trajectory. Gendered social roles shaped patients\u0026rsquo; experiences in distinct ways. Men were frequently accompanied by female relatives (typically spouses, daughters, or granddaughters) throughout their healthcare journey. This reflects the persistent social expectation that women act as primary caregivers, even in the context of others\u0026rsquo; illness. Conversely, women diagnosed with LC often reported initiating and navigating the health system alone. Their narratives revealed a strong sense of duty toward fulfilling caregiving roles themselves, even while undergoing their own treatment. As their illness progressed and limited their capacity to perform these roles, women expressed feelings of frustration, guilt, and distress.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Look, my husband was diagnosed because I took him to the doctor, because he had a nasty cough.\u0026rdquo; (Patient13_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I did not tell my children. I went to the hospital alone\u0026mdash;that is how it was.\u0026rdquo; (Patient2_Female_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTaken together, the integrated findings demonstrate a complementary expansion of results. While statistical analyses did not reveal significant gender-based differences in diagnostic timeliness, qualitative evidence underscored meaningful gendered patterns. These findings highlight the need to interpret gender-disaggregated data in the broader context of gender norms and caregiving dynamics, which may shape health-seeking behaviors, access to care, and emotional responses to the disease.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated results by Region of Residence\u003c/h2\u003e\u003cp\u003eRegion of residence was analyzed by comparing participants living in the Metropolitan Region (Santiago and its surroundings) versus those residing in other areas of Chile. Quantitative analysis showed that a majority in both groups received a diagnostic confirmation within 60 days (62.5% in the Metropolitan Region vs. 60.85% in other regions). While not statistically significant, individuals residing outside the capital had 1.34 times higher odds of experiencing a diagnostic delay beyond 60 days (p\u0026thinsp;=\u0026thinsp;0.563; 95% CI: 0.497\u0026ndash;3.611). Despite the lack of significant quantitative differences, qualitative findings revealed critical geographic disparities that shaped the therapeutic trajectory for individuals living outside the Metropolitan Region. These disparities were most evident during the initiation, diagnostic testing, and confirmation phases, and were driven by several interconnected mechanisms:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimited healthcare infrastructure\u003c/strong\u003e\u003cp\u003eMany regional areas lack the facilities and equipment required for comprehensive oncologic care. High-complexity services, such as radiotherapy, are concentrated in urban centers, limiting timely access in peripheral areas.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;...here in XXX region, healthcare is very precarious; we do not have anything. You always have to go elsewhere to look for treatment options, and the closest to us initially was other region where my sister lives... Everything is centralized in Santiago.\u0026rdquo; (Patient4_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eShortage of oncology specialists\u003c/strong\u003e\u003cp\u003eThe majority of pulmonologists, thoracic surgeons, and oncologists are based in the Metropolitan Region. This centralization results in lengthy referrals to tertiary hospitals, increasing wait times and treatment delays for patients from other regions.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eChallenges for rural populations navigating urban systems\u003c/strong\u003e\u003cp\u003eParticipants from rural or remote areas described difficulties in navigating large healthcare institutions. Complex instructions, unfamiliar infrastructure, and the number of required appointments caused anxiety, confusion, and emotional distress.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;When patients arrive at this hospital, I think the first thing they feel is intimidation, rather than reassurance. We try to calm them down during the consultation itself, but we have many people from the islands or very rural areas here. I give them the orders for the CT scan, the spirometry, the carbon monoxide diffusion test, referrals for the anesthesiologist and the cardiologist\u0026hellip; and I am sending them to five different places in a hospital that\u0026rsquo;s already hard to navigate, even for us.\u0026rdquo; (Physician3, Public and Private System)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNeed for long-distance travel\u003c/strong\u003e\u003cp\u003ePatients were frequently advised to seek care in Santiago or regional capitals due to insufficient local capacity. For many, repeated travel placed a substantial financial and logistical burden on families.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;...I saw an oncologist in Talca, but she was not able to confirm whether it was cancer. She suggested I go back to Santiago and not stay in the provinces to consult a thoracic surgeon. For my partner, it was cheaper to travel two hours to be with me than to stay in a hotel, so he travelled every other day... If I had not had the means\u0026mdash;between my private insurance and complementary coverage\u0026mdash;I do not know how far I would have gotten.\u0026rdquo; (Patient11_Female_ISAPRE)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimited availability of diagnostic equipment\u003c/strong\u003e\u003cp\u003eRegional centers often relied on a single imaging device (e.g., CT, MRI, PET). Equipment malfunction or limited scheduling resulted in delays, prompting many caregivers to seek services in the private sector at personal expense.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;We had to look for another place quickly when the CT scanner broke down, to be able to get the diagnosis as soon as possible.\u0026rdquo; (Caregiver2_Male)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;We do not have a PET scanner in Puerto Montt.\u0026rdquo; (Physician3, Public and Private System)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;MRI appointments are really scarce\u0026mdash;might take three weeks to a month. Moreover, now we are even having issues with simple tests like pulmonary function tests. Patients are requesting private appointments because the hospital is not doing spirometry... and there are no private slots available either. So things are getting delayed one way or another depending on the test.\u0026rdquo; (Nurse2, Public and Private System)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTaken together, the integrated findings indicate an expansion of the quantitative results. Although no statistically significant differences in diagnostic delays were detected between regions, the qualitative evidence exposed meaningful structural and logistical barriers in non-metropolitan areas. These barriers, often mitigated only through personal resources or informal strategies, underscore geographic inequities in access to timely LC diagnosis in Chile.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated results by Income Quintile\u003c/h2\u003e\u003cp\u003eThe integration of quantitative and qualitative data revealed significant differences in the timing and dynamics of the therapeutic trajectory for LC across income quintiles. Quantitatively, individuals belonging to the higher-income groups (fourth and fifth quintiles) were more likely to receive diagnostic confirmation within the 60-day threshold. In contrast, those in the second quintile had 5.79 times higher odds of receiving a diagnosis after 60 days compared to participants in the fifth quintile (p\u0026thinsp;=\u0026thinsp;0.049; 95% CI: 1.011\u0026ndash;33.21), highlighting a statistically significant delay for this group. Qualitative findings enriched the interpretation of this disparity by illuminating the mechanisms through which income level influenced healthcare access. Individuals in higher-income quintiles often expedited their diagnostic journey by paying out-of-pocket for private consultations and diagnostic procedures, even when initially served by the public system (FONASA). This hybrid approach\u0026mdash;leveraging public services while supplementing with private options\u0026mdash;enabled faster diagnosis but came at a substantial financial cost.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;...but we wanted everything done quickly to know what he had and what the next steps were. If we had relied solely on the public system, who knows how long it would have taken? One, two, even three years\u0026mdash;they take that long sometimes to call you. My grandfather had some savings and also used his pension fund withdrawal. All that money was used up... those time gaps were due to raising money, and also because there were no available appointments for tests.\u0026rdquo; (Patient4_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn contrast, individuals with lower incomes reported substantial delays associated with financial constraints. Many could not afford private appointments or tests and were thus entirely dependent on the slower public system. This reliance led not only to prolonged wait times but also to emotional distress, particularly when diagnoses were delayed despite the persistence of symptoms.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Unfortunately, I have not had the money to see a private doctor to review my test results... I spoke to the nurse, I vented to her, I cried a lot.\u0026rdquo; (Patient2_Female_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo overcome financial barriers, patients and families often engaged in resource mobilization strategies, including support from relatives or withdrawal of retirement funds. These actions, while effective in some cases, introduced additional emotional and economic strain.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;...my nephews, who have good jobs and all, gave me the money so I could get the test done.\u0026rdquo; (Patient9_Female_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe integrated analysis between income quintile and time to diagnosis reveals an expansion of findings. Quantitative results identified income as a significant determinant of diagnostic timeliness, while qualitative data uncovered the lived consequences of these disparities. Higher-income individuals were able to circumvent public system delays, whereas lower-income individuals remained vulnerable to systemic inefficiencies. These findings underscore the critical role of economic capital in navigating the Chilean health system and highlight how income inequality contributes to unequal opportunities for timely cancer diagnosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated results by Educational Level\u003c/h2\u003e\u003cp\u003eEducational attainment emerged as a relevant but nuanced factor in shaping the diagnostic pathway for individuals with lung cancer. Quantitative analysis showed that 57.1% of individuals with primary or secondary education and 66.6% of those with post-secondary education were diagnosed within 60 days of their first consultation. Although participants with lower educational levels had slightly greater odds of experiencing a delay beyond 60 days (OR\u0026thinsp;=\u0026thinsp;1.08; p\u0026thinsp;=\u0026thinsp;0.876; 95% CI: 0.385\u0026ndash;3.07), this difference was not statistically significant. Despite the absence of strong statistical associations, qualitative findings provided important contextualization. Educational level was closely linked to health-seeking behaviors, system navigation skills, and patient-clinician communication. Participants with higher education levels generally reported earlier recognition of symptoms, faster initiation of care, and more effective interactions with healthcare professionals. These individuals were also more likely to question medical decisions, seek second opinions, and proactively manage referrals.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Access is probably much easier for patients in the private sector, usually due to socioeconomic status\u0026mdash;they tend to have a higher level of education, which also favors earlier diagnoses. People with fewer resources and lower education may lack awareness of the importance of consulting a doctor.\u0026rdquo; (Nurse1, Public and Private System)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eConversely, participants with lower education levels often encountered difficulties interpreting complex instructions, completing bureaucratic processes, or understanding the sequence of care, especially in tertiary centers. These barriers were exacerbated by a lack of familiarity with medical terminology and institutional procedures, occasionally leading to missed appointments, confusion, or delays in follow-up care.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;When someone has a lower sociocultural level, they often struggle with this. Their cultural background makes it harder for them to understand instructions for complex care, and as a result, those patients fall behind\u0026mdash;they miss appointments. And then health professionals wrongly assume, \u0026lsquo;Oh, this woman did not show up, she missed her appointment,\u0026rsquo; and we jump to conclusions. We forget that we understand the system well, but for many, this is like Mandarin Chinese.\u0026rdquo; (Physician4, Public System)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese narratives revealed clinician assumptions and biases about patient behavior, which may further disadvantage patients with limited health literacy. In some cases, health professionals attributed missed appointments to patient irresponsibility without accounting for contextual challenges, such as poor system comprehension or difficulty with digital platforms. In all, the integration of findings for educational level revealed a convergence between quantitative and qualitative data. While statistical analysis suggested only a modest trend, qualitative evidence pointed to education as a structural determinant that influences patients\u0026rsquo; ability to navigate the healthcare system, advocate for themselves, and adhere to diagnostic processes. This convergence reinforces the need for health literacy-sensitive approaches and system-level interventions to mitigate the impact of educational inequalities on diagnostic timeliness.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated results by Health System\u003c/h2\u003e\u003cp\u003eHealth system affiliation emerged as a key factor influencing the timeliness of diagnostic confirmation in lung cancer. Quantitative results indicated that a higher proportion of individuals receiving care in the private sector (ISAPRE) were diagnosed within 60 days of their first consultation (80%) compared to those in the public sector (FONASA, 57.1%). Although not statistically significant, individuals affiliated with the public system had 1.77 times greater odds of receiving a delayed diagnosis beyond 60 days (OR\u0026thinsp;=\u0026thinsp;1.77; p\u0026thinsp;=\u0026thinsp;0.458; 95% CI: 0.390\u0026ndash;8.1). Qualitative findings enriched this analysis by identifying multiple mechanisms through which the public system contributed to diagnostic delays. These mechanisms reflected structural weaknesses and operational barriers that limited timely access to specialized oncology services:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSystem fragmentation and referral complexity\u003c/strong\u003e\u003cp\u003eThe public system operates across poorly coordinated levels of care (primary, secondary, tertiary). General practitioners in primary care often lack training in oncology pathways, resulting in inconsistent referrals and incomplete diagnostic workups.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;They do not know where to send the patient, and once they do, they do not know which tests to include. So, a patient with suspected lung cancer gets referred somewhere the physician believes might accept them.\u0026rdquo; (Physician1, Public System)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;The bigger issue is that healthcare professionals do not know who to refer the patient to\u0026mdash;sometimes it is more than one specialist.\u0026rdquo; (Physician2, Public System)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePatients navigating between systems\u0026mdash;either by necessity or referral\u0026mdash;often encounter challenges due to unfamiliarity with how the private system works.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Sometimes I get confused with so many different places.\u0026rdquo; (Patient13_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePublic\u0026ndash;private disconnection\u003c/strong\u003e\u003cp\u003eAlthough GES regulations allow for cross-referral between systems, weak coordination leads to fragmented care, confusion, and increased burden for patients and caregivers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Cancer patients do not have it easy\u0026mdash;they face many obstacles, and they are sent from one place to another in a very disorganized system.\u0026rdquo; (Caregiver2_Male)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimited infrastructure and test availability\u003c/strong\u003e\u003cp\u003ePublic facilities frequently lack sufficient diagnostic equipment or trained personnel, causing long waiting times for critical tests such as CT scans, spirometry, or MRIs.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;We could not get an appointment for spirometry and another test\u0026mdash;we had to wait a whole month.\u0026rdquo; (Patient4_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;Getting a CT scan or MRI was difficult. We could not afford private care either.\u0026rdquo; (Patient7_Female_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLate-stage diagnoses and misdiagnoses\u003c/strong\u003e\u003cp\u003ePatients in the public system were more likely to receive a diagnosis at advanced stages (III or IV), with frequent misdiagnosis of LC as pneumonia in early consultations.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;In the private sector, you find lung cancers at stage 1\u0026mdash;small nodules removed quickly with high survival rates. In the public system, 70% arrive at stage 3b or 4, with a median survival of 4.4 months and a five-year survival rate below 30%.\u0026rdquo; (Physician1, Public System)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I lost five years\u0026hellip;\u0026rdquo; (Patient14_Female_FONASA)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;He kept getting diagnosed with pneumonia... but he had this cough that would not go away. They gave him cough syrup, that was all.\u0026rdquo; (Patient13_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn contrast, participants in the private system described faster and more streamlined diagnostic processes. Physicians in this sector often ordered comprehensive tests at the first visit, facilitated rapid specialist referrals, and coordinated care through multidisciplinary teams.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;In August 2012, I had a nasty cold. A doctor ordered a full panel of tests, including a chest CT... which revealed a nodule. She referred me to Santiago to begin more testing\u0026rdquo; (Patient11_Female_ISAPRE)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;If I suspect a patient has lung cancer, I immediately call my colleague.\u003c/em\u003e We review the case, order a PET scan, consult the committee\u0026mdash;everything is faster and more multidisciplinary here.\u0026rdquo; \u003cem\u003e(Physician5, Private System)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I stayed with ISAPRE because it covered hospitalizations and medication, which FONASA does not. Private appointments are quicker and more efficient\u0026mdash;so even if it is more expensive, you pay for quality and peace of mind.\u0026rdquo; (Patient16_Male_ISAPRE)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAdditionally, participants affiliated with FONASA but possessing financial means reported using the Free Choice Modality (FCM) to access private services with partial public funding. This hybrid strategy enabled faster diagnosis, though at a significant out-of-pocket cost.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;We did everything privately because in the public system you just... die waiting. There is no other way to put it.\u0026rdquo; (Patient4_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I decided just to buy the voucher and take him to a private doctor. At the primary care of public centers, they make you wait forever and never give you test results.\u0026rdquo; (Patient13_Male_FONASA)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePerception gap between clinicians and patients\u003c/strong\u003e\u003cp\u003eA further point of divergence emerged between healthcare providers and patients regarding their perception of delays. While clinicians often described the diagnostic process as efficient, patients highlighted the emotional, logistical, and financial burden of waiting, emphasizing the need for patient-centred metrics to assess system responsiveness.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;In general, it is pretty quick, we usually have the patient fully evaluated, including staging and functional tests, within a month.\u0026rdquo; (Physician3, Public and Private System)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn summary, the integrated findings demonstrated an expansion of the quantitative results: although both public and private patients were diagnosed within the GES timeframe in most cases, qualitative data revealed significant disparities in experience, pathway complexity, and systemic burden. The private system enabled more rapid and coordinated diagnostic confirmation, while the public sector was hindered by fragmentation, delays, and access limitations. These findings expose structural inequities in cancer care and underscore the importance of system-level reforms to ensure equitable diagnostic access, regardless of health system affiliation.\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\u003eIntegrated Results for the DI and SDH\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\u003eSDH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQualitative Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegrated Interpretation\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\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull; Majority diagnosed within 60 days: 58% men, 63.8% women.\u003c/p\u003e\u003cp\u003e\u0026bull; Men had 1.25 times higher odds of delay\u0026thinsp;\u0026gt;\u0026thinsp;60 days (p\u0026thinsp;=\u0026thinsp;0.664; 95% CI: 0.448\u0026ndash;3.526).\u003c/p\u003e\u003cp\u003e\u0026bull; No statistically significant difference.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Gender norms shape therapeutic trajectories.\u003c/p\u003e\u003cp\u003e\u0026bull; Men often accompanied by female relatives (wives, daughters, granddaughters).\u003c/p\u003e\u003cp\u003e\u0026bull; Women often navigate illness alone; caregiving role persists during illness.\u003c/p\u003e\u003cp\u003e\u0026bull; Emotional impact: frustration, guilt, distress when unable to fulfill caregiving roles.\u003c/p\u003e\u003cp\u003eQuotes:\u003c/p\u003e\u003cp\u003e - \u0026ldquo;Look, my husband was diagnosed because I took him to the doctor...\u0026rdquo;\u003c/p\u003e\u003cp\u003e - \u0026ldquo;I did not tell my children. I went to the hospital alone\u0026mdash;that is how it was.\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComplementary expansion:\u003c/p\u003e\u003cp\u003e\u0026bull; No significant difference in quantitative data, but qualitative evidence reveals strong gender-related patterns.\u003c/p\u003e\u003cp\u003e\u0026bull; Highlights need to interpret gender-disaggregated data within the context of gender norms and caregiving dynamics.\u003c/p\u003e\u003cp\u003e\u0026bull; Gender influences health-seeking behavior, access, and emotional experience.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRegion of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull; 62.5% of participants in the Metropolitan Region and 60.85% in other regions were diagnosed within 60 days.\u003c/p\u003e\u003cp\u003e\u0026bull; OR for delay (\u0026gt;\u0026thinsp;60 days) in other regions vs. Metropolitan Region: 1.34 (p\u0026thinsp;=\u0026thinsp;0.563; 95% CI: 0.497\u0026ndash;3.611).\u003c/p\u003e\u003cp\u003eNo statistically significant difference.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Limited healthcare infrastructure in many regions; high-complexity services concentrated in Santiago.\u003c/p\u003e\u003cp\u003e\u0026bull; Shortage of oncology specialists in non-metropolitan areas, leading to lengthy referrals.\u003c/p\u003e\u003cp\u003e\u0026bull; Rural patients face difficulties navigating large urban hospitals.\u003c/p\u003e\u003cp\u003e\u0026bull; Long-distance travel often required, adding financial and logistical burdens.\u003c/p\u003e\u003cp\u003e\u0026bull; Limited availability of diagnostic equipment; breakdowns or scarcity cause delays.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe quantitative results showed no statistically significant difference in diagnostic delays by region. However, qualitative evidence revealed substantial structural and logistical barriers in non-metropolitan areas, often mitigated through personal resources. These findings expand the quantitative results, highlighting geographic inequities in timely LC diagnosis in Chile.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome Quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher-income groups (Q4\u0026ndash;Q5) more likely to be diagnosed within 60 days. Q2 had 5.79\u0026times; higher odds of \u0026gt;\u0026thinsp;60 days delay vs Q5 (p\u0026thinsp;=\u0026thinsp;0.049; 95% CI: 1.011\u0026ndash;33.21).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh-income patients used out-of-pocket private care to expedite diagnosis, often combining public and private services. Low-income patients faced longer waits, emotional distress, and were dependent on slower public pathways.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExpansion: Quantitative data identified income as a determinant; qualitative data explained mechanisms. Economic capital enabled bypassing delays; low-income reinforced vulnerability to systemic inefficiencies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiagnosis\u0026thinsp;\u0026le;\u0026thinsp;60 days: 57.1% (primary/secondary) vs 66.6% (post-secondary). Lower education had slightly higher odds of \u0026gt;\u0026thinsp;60 days delay (OR\u0026thinsp;=\u0026thinsp;1.08; p\u0026thinsp;=\u0026thinsp;0.876).\u003c/p\u003e\u003cp\u003eNo statistically significant difference.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigher education linked to earlier symptom recognition, proactive care-seeking, and better system navigation. Lower education associated with difficulty understanding instructions, missed appointments, and health literacy barriers.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConvergence: Quantitative trend towards delay with lower education; qualitative data contextualized through navigation skills, communication, and clinician bias. Highlights need for health literacy-sensitive interventions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth System\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026bull; 80% of private system patients diagnosed within \u0026le;\u0026thinsp;60 days vs. 57.1% in public system.\u003c/p\u003e\u003cp\u003e\u0026bull; OR for delay in public system\u0026thinsp;=\u0026thinsp;1.77 (p\u0026thinsp;=\u0026thinsp;0.458; 95% CI: 0.390\u0026ndash;8.1).\u003c/p\u003e\u003cp\u003eNo statistically significant difference.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026bull; Public system barriers: fragmentation, poor coordination, limited equipment, long waits, late-stage diagnoses, misdiagnosis as pneumonia.\u003c/p\u003e\u003cp\u003e\u0026bull; Private system facilitators: rapid referrals, multidisciplinary care, faster imaging, better coordination.\u003c/p\u003e\u003cp\u003e\u0026bull; Public patients with resources use Free Choice Modality (MLE) for private care.\u003c/p\u003e\u003cp\u003e\u0026bull; Perception gap: clinicians see process as quick, patients perceive delays as burdensome.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuantitative results show faster diagnosis in private care. Qualitative analysis expands this, revealing systemic barriers in public care and structural inequities. Even within GES timelines, public patients face fragmented pathways, longer waits, and higher emotional/logistical burden. Reforms are needed to ensure equitable access regardless of health system affiliation.\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"},{"header":"DISCUSSION","content":"\u003cp\u003eThis is the first integration of social determinants of health and care trajectories in LC in Chile. This convergent mixed-methods study analyzed the influence of SDH on the DI in LC, integrating quantitative and qualitative findings. While statistical analysis did not reveal significant differences by gender, region of residence, or educational level, the qualitative phase identified important barriers related to these SDH, such as gender norms shaping caregiving roles (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e), geographic inequalities due to the centralization of specialists and diagnostic equipment (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e), and health literacy gaps that hinder navigation of the healthcare system (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsistent with previous research, income and type of health system emerged as the most influential factors (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). People with higher incomes or those affiliated with the private system accessed specialist consultations and diagnostic tests more quickly, while those relying exclusively on the public system faced delays, fragmented referral processes, and diagnoses at more advanced stages. These findings align with evidence linking health system fragmentation and centralization to inequities in cancer outcomes (\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough most diagnoses occurred within the 60-day maximum timeframe established by the GES guarantees (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), our findings show that meeting this deadline does not ensure smooth or equitable patient trajectory. In many cases, individuals\u0026mdash;especially those with lower economic or social capital\u0026mdash;had to bear high emotional, logistical, and financial costs to obtain timely care. This aligns with previous literature emphasizing that timely diagnosis is influenced not only by adherence to formal timeframes, but also by factors spanning patient-related, provider-related, system-related, and disease-related domains (\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond Chile, evidence from Brazil shows marked disparities by race, education, and geography. Black Brazilians report discrimination within health services and stigma around smoking and limited awareness of screening benefits reduce uptake of preventive services (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Uneven distribution of trained professionals and oncology services, together with constrained availability of chest computed tomography in some regions, contributes to wide variation in timely diagnosis and outcomes (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). At the regional level, social determinants such as income, education, and rural residence drive late diagnosis and poorer outcomes, while shortages of specialists and the urban concentration of resources perpetuate these gaps. Recent initiatives including telepathology networks, health education programs, and the strengthening of population-based cancer registries, aim to mitigate these inequities, but substantial challenges remain (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe implications for health policy include strengthening integrated oncology health networks with a territorial and equity focus (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), reducing the centralization of human and technological resources, improving coordination between public and private systems, and implementing culturally appropriate interventions such as health literacy programs and patient navigation (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). It is also recommended that national cancer registries include socioeconomic, demographic, and support network variables (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) to better plan resources and strategies that address the real needs of the population.\u003c/p\u003e\u003cp\u003eThe findings of this study point to several actionable recommendations for different stakeholders. For the health system, strengthening integrated oncology networks with a territorial and equity focus is essential, alongside reducing centralization of diagnostic and treatment resources and improving coordination between public and private sectors. In this regard, concrete policy measures could include incorporating coverage within the GES for services provided by social workers or oncology nurses who support patients and caregivers in navigating care pathways and addressing access barriers; strengthening the role of primary health care in the early diagnostic stage; and developing mechanisms for better coordination between primary, secondary, and tertiary levels of care. The implementation of patient navigation programs, with trained navigators based in primary care who proactively detect and guide individuals with suspected lung cancer, could be a critical step toward reducing diagnostic delays. For patients, interventions should prioritize health literacy and navigation support to empower individuals in recognizing symptoms, seeking timely care, and understanding available guarantees. Caregivers play a crucial role in facilitating access, highlighting the need for psychosocial support programs and training to reduce the burden of care. Civil society organizations should be strengthened and supported to represent lung cancer more effectively in advocacy, ensuring that patient voices are incorporated into decision-making processes and policy reforms. Finally, academic and research institutions should continue to generate context-specific evidence that informs equitable cancer policies, while promoting participatory approaches that integrate the perspectives of patients and communities. Future research should examine the feasibility and effectiveness of these proposed interventions, particularly the integration of navigation roles in primary care and the expansion of GES guarantees, in order to guide policy implementation and evaluate their impact on reducing inequities in lung cancer trajectories.\u003c/p\u003e\u003cp\u003eA notable strength of this research lies in its design. This is the first study in Chile to examine the DI in LC through the lens of therapeutic trajectories, employing a convergent mixed-methods approach that integrates the perspectives of patients, caregivers, healthcare professionals, and civil society leaders. The combination of quantitative and qualitative data enhanced the depth and contextualization of the findings, enabling not only the measurement of diagnostic delays but also a nuanced understanding of the mechanisms underlying them (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). This dual perspective generated actionable evidence that can inform health policy reforms and targeted interventions. However, several limitations should be acknowledged. In the qualitative phase, the health condition of some participants restricted their ability to engage fully in interviews, which led to allowing the presence of a significant other for support. Conducting interviews online with older adults posed challenges, which were mitigated by offering multiple participation modalities. In the quantitative phase and regarding potential biases, selection bias may have arisen from the use of non-probabilistic convenience sampling, which can limit representativeness by relying on participants\u0026rsquo; availability and access(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) potentially excluding more vulnerable groups, such as individuals without internet access or outside the networks used for recruitment (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Information bias could also be present, related to survey clarity (although piloted, it was not formally validated) (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), variability in administration formats (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), and possible recall bias(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) due to difficulties in accurately remembering details of therapeutic trajectories. To mitigate these biases, the study implemented several strategies, including partnerships with public and private organizations to diversify recruitment, offering multiple participation formats (e.g., computer-based and mobile-friendly surveys), piloting the survey with input from academics and patient representatives, training interviewers to standardize data collection, and encouraging participants to use personal records to reduce recall errors.\u003c/p\u003e\u003cp\u003eIn summary, this study provides novel evidence of inequities in timely LC diagnosis in Chile by documenting how demographic, socioeconomic, and health system-related determinants influence the diagnostic interval. Beyond highlighting structural barriers, it incorporates the lived experiences of patients, caregivers, healthcare professionals, and civil society leaders, offering a multidimensional understanding rarely captured in Latin American research. The findings underscore the importance of addressing not only systemic inefficiencies but also the social, economic, and cultural determinants that constrain patients\u0026rsquo; access to timely care.\u003c/p\u003e\u003cp\u003eBy integrating multiple perspectives through a convergent mixed-methods design, this study provides robust, context-specific insights into the inequities shaping lung cancer diagnostic trajectories in Chile. Importantly, these findings transcend the national context, as they reflect structural barriers common to many Latin American countries, including health system fragmentation, centralization of oncology services, and persistent social and economic inequalities. Framing the results within this broader regional landscape highlights their potential to inform not only national reforms but also comparative analyses and collaborative strategies across Latin America. Such a regional perspective strengthens the contribution of this research to the global discourse on cancer care equity, offering evidence that can guide the design of more equitable and person-centered oncology services in settings characterized by similar health system challenges.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLung Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDiagnostic Interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Cancer Plan\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Cancer Law\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study was approved by the Comit\u0026eacute; \u0026Eacute;tico Cient\u0026iacute;fico de la Facultad de Medicina Cl\u0026iacute;nica Alemana Universidad del Desarrollo (number 2021-67). Informed consent was obtained from all participants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests. The entire study was conducted independently by the research team at UDD.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was funded by the Doctoral Program of Science and Innovation in Medicine and by the Center for Prevention and Control of Cancer in Chile (CECAN), FONDAP 152220002 ANID Chile.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization and study design: CC, BC, AO. Data collection and data analysis: CC, FV, YB. Manuscript writing: CC, BC. Review and editing: AO, FV, BN, YB. All authors approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors express their gratitude to Doctoral program of Science and Innovation in Medicine, Universidad del Desarrollo, and Centre for Prevention and Control of Cancer in Chile (CECAN), FONDAP 152220002 ANID Chile.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSemi-structured interview guide is available. Full transcripts of interviews can be provided upon request (all anonymized). The raw data supporting the findings of this study can be requested from the principal investigator, Carla Campa\u0026ntilde;a (mail cacampanacdd.cl).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKratzer TB, Bandi P, Freedman ND, Smith RA, Travis WD, Jemal A et al. Lung cancer statistics, 2023. Cancer [Internet]. 2024 Apr 15 [cited 2025 Jul 29];130(8):1330\u0026ndash;48. 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SAGE; 2002. 598 p.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"lung cancer, diagnostic interval, social determinants of health, mixed methods, health equity","lastPublishedDoi":"10.21203/rs.3.rs-7576484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7576484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLung cancer (LC) is the leading cause of cancer mortality worldwide and in Chile. Although Chile introduced an explicit guarantee (GES) of ≤60 days for diagnostic interval (DI: time between firsts consultation and diagnostic confirmation), evidence on how social determinants of health (SDH) shape this interval remains limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a convergent mixed-methods study with equal weighting (QUAL=QUAN). The quantitative strand was an analytical cross-sectional survey of adults with LC (n=80). The primary outcome was the DI, dichotomized at 60 days. Associations with SDH (gender, region, income quintile, education, health system) were assessed using bivariate tests and logistic regression. The qualitative strand comprised 37 semi-structured interviews (patients, caregivers, clinicians, civil society), thematically analyzed. Integration used a weaving narrative and joint displays to classify patterns as confirmation, expansion, or discordance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe reported DI was 30.5 days (mean 79.1; range up to 578). Quantitatively, income showed the clearest gradient: compared with the highest quintile, the second quintile had 5.79× higher odds of \u0026gt;60-day delay (p=0.049; 95%CI 1.011–33.21). Type of health system also suggested disadvantage in the public sector, with 57.1% versus 80.0% of private patients diagnosed within 60 days (OR for delay in public = 1.77; p=0.458). Differences by gender, region, and education were not statistically significant. Qualitative findings expanded these results by revealing the mechanisms underlying diagnostic delays: participants described fragmented pathways, centralization of specialists and technology in metropolitan areas, long-distance travel, test bottlenecks or malfunctions, frequent misdiagnoses (e.g., pneumonia), and health-literacy barriers in the public system and in regional areas. Taken together, the integrated analysis confirmed the quantitative trends for income and health system while expanding the understanding of geographic and educational influences, with no substantive discordances emerging across strands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is the first integration of social determinants of health and care trajectories in LC in Chile. Meeting a legal timeline between the first consultation to diagnostic confirmation interval \u0026lt; 60 days, does not ensure equity in diagnostic in Chile. Findings suggests the urgent need for stronger territory integration in oncology networks, decentralized diagnostic capacity, improved patient navigation, health-literacy education programs, and equity-stratified performance monitoring, including systematic SDH fields in cancer registries. These actions can shorten critical intervals, improve patient experience, and close access gaps in disadvantaged populations.\u003c/p\u003e","manuscriptTitle":"Diagnostic Interval and Social Determinants of Health in Lung Cancer: A Mixed-Methods Study in Chile","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 15:48:39","doi":"10.21203/rs.3.rs-7576484/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-30T18:52:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183072600186485750234067832818975969958","date":"2025-10-20T13:13:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-09T18:11:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-07T04:01:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-16T06:39:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-15T22:13:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-09-15T22:10:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ec435b0-febc-48ac-938c-a6827d913053","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-23T15:48:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 15:48:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7576484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7576484","identity":"rs-7576484","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}