Factors Influencing Delays in Seeking Medical Care Among elderly Patients with Pulmonary Tuberculosis in Ningbo: A Study Conducted from 2015 to 2023

Factors Influencing Delays in Seeking Medical Care Among elderly Patients with Pulmonary Tuberculosis in Ningbo: A Study Conducted from 2015 to 2023 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF comment Factors Influencing Delays in Seeking Medical Care Among elderly Patients with Pulmonary Tuberculosis in Ningbo: A Study Conducted from 2015 to 2023 Haiyan Tian, Jingjing Qi, Tong Chen, Xujun Qian, Weitao Yao, Guoxin Sang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7909914/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction:To investigate the factors causing delays in medical treatment for elderly patients with pulmonary tuberculosis over various time periods. Methodology:Data were collected from 7,821 elderly individuals aged 60 and above diagnosed with pulmonary tuberculosis between 2015 and 2023. Univariate and multivariate regression analyses were performed to examine the characteristics and influencing factors associated with delays in seeking medical care. Results:The median delay time for elderly patients with pulmonary tuberculosis in Ningbo was 17 days, with a delay rate of 55.06%. Comparative analysis revealed that farmers and individuals from less economically developed areas faced higher risks of delay (OR = 1.097, 95% CI: 1.002–1.202; OR = 1.231, 95% CI: 1.120–1.354). Notably, the risk of delayed medical treatment decreased from 2020 to 2023 (OR = 0.793, 95% CI: 0.725–0.869) and among males (OR = 0.813, 95% CI: 0.735–0.901), compared to the period from 2015 to 2019 and females.Among various sources, health examination patients had a lower risk than referral patients (OR = 0.486, 95% CI:0.329–0.719), while those from other sources showed higher risk compared to referred patients (OR = 3.362, 95% CI:1.118–10.113 ). Additionally, there was a significant age difference between patients with and without delays (Z =-2.725 , P = 0.006 ). Conclusions:Elderly patients with pulmonary tuberculosis often face delays in treatment. Contributing factors include being female, farmer, living in less developed areas, receiving referrals, and the years 2015–2019. Pulmonary tuberculosis Patient delay Elderly people Influence factor 1. Background Tuberculosis(TB), caused by Mycobacterium tuberculosis , is a respiratory infectious disease that significantly impacts public health[ 1 ]. The elderly population in China exhibits a heightened prevalence of pulmonary tuberculosis(PTB) due to the presence of multiple underlying health conditions, compromised immune systems, and an increased susceptibility to tuberculosis bacilli [ 2 , 3 ]. Furthermore, elderly individuals diagnosed with pulmonary tuberculosis often manifest subtle clinical symptoms, coupled with a relatively low awareness of their health status[ 4 ]. Some patients may misinterpret the symptoms of pulmonary tuberculosis as a common cold, consequently underestimating the severity of the condition. Consequently, delayed medical consultation among elderly patients with pulmonary tuberculosis is a significant concern. The ramifications of this delay are twofold. Firstly, it not only exacerbates the patient's condition, missing the optimal choice for early diagnosis and treatment, but also heightens the complexity of subsequent medical interventions. Secondly, the delay contributes to the wider dissemination of tuberculosis within society, fostering an increased prevalence of the disease[ 5 – 8 ], and also escalating the burden on the local healthcare system [ 9 – 11 ]. Notably, China’s vast territory results in significant disparities in economic development, healthcare resource allocation, tuberculosis (TB) prevention and control policies, and population health behaviors across different regions, leading to distinct regional heterogeneity in the factors influencing delays in medical treatment among elderly patients with pulmonary tuberculosis (PTB)[ 12 – 14 ]. Most existing domestic studies have focused on northern provinces, underdeveloped western regions, or first-tier cities [ 9 , 15 – 16 ], while targeted research on economically developed eastern coastal regions remains relatively scarce. As an economic hub on the southern wing of the Yangtze River Delta, Ningbo features both urban and rural attributes. Its proportion of elderly population has been rising year by year, and there is a large population of elderly rural farmers and elderly migrant workers in the city, which leaves it facing unique challenges in PTB prevention and control: boasting a developed economy yet a complex population structure, and abundant healthcare resources yet uneven distribution [ 17 – 18 ]. From an international perspective, the challenges confronted by Ningbo are shared by many economically developed regions worldwide with aging populations and large-scale migrant populations[ 19 – 20 ]. Given that the COVID-19 outbreak since 2020 has substantially changed public health awareness, respiratory symptom consultation behavior, and the implementation of tuberculosis prevention and control strategies, the study period from 2015 to 2023 was divided into two phases: 2015–2019 (pre-pandemic period) and 2020–2023 (pandemic and post-pandemic period). This grouping allows a comparative analysis of temporal trends in medical seeking delay among elderly pulmonary tuberculosis patients and helps explore the underlying driving factors. This study intends to analyze the characteristics of delayed healthcare-seeking among elderly PTB patients in Ningbo in recent years. Taking elderly PTB patients registered in Ningbo from 2015 to 2023 as the research subjects, it will systematically examine the occurrence of delayed healthcare-seeking behaviors and their associated influencing factors. The study aims to provide a scientific basis for formulating targeted prevention and control measures for elderly PTB patients in Ningbo, enrich the global evidence base for TB prevention and control against the backdrop of population aging, and also offer reference insights for TB prevention and control efforts in other regions facing similar situations. 2. Methods 2.1 Subjects The data utilized in this study were sourced from the Tuberculosis Management Information System within the Chinese Disease Prevention and Control Information System. The study focused on all tuberculosis patients aged ≥ 60 years in Ningbo City from 2015 to 2023. A comprehensive retrieval and registration process within the management information system yielded a total of 8006 cases. Subsequently, after excluding 185 cases with incomplete or unclear information and those diagnosed with extrapulmonary tuberculosis, the dataset was refined to include 7821 valid patient records for the purpose of this study. 2.2 Data collection The medical records of elderly patients diagnosed with pulmonary tuberculosis encompassed various parameters, including gender, ethnicity, age, occupational classification, current address, household registration, patient source, diagnosis, treatment classification, date of first symptom appearance, and date of first visit. Gender was dichotomized into female and male categories, while ethnicity was classified as Han and other ethnic groups. Occupational classification was stratified into two groups: farmers and non-farmers. The administrative counties in Ningbo were categorized based on GDP ranking, resulting in economically developed areas (Haishu District, Cixi City, Yinzhou District, Yuyao City, Beilun District) and economically underdeveloped areas (Fenghua District, Xiangshan County, Ninghai County, Zhenhai District, Jiangbei District). The registered address type was delineated as local or non-local. Patient sources were categorized into referred, recommendation and follow-up, direct treatment, health examination, and others. Diagnostic results were classified into three categories derived from microbiological examination outcomes: pathogen positive, pathogen negative, and pathogen untested. Temporal divisions were established as two stages: 2015–2019 and 2020–2023. Treatment classifications included initial treatment and retreatment. The onset date of symptoms refers to the specific date when the patient first presents typical symptoms of pulmonary tuberculosis (such as cough and sputum for ≥ 2 weeks, hemoptysis, low-grade fever, night sweats, etc.), and is based on the patient's self description and clinical verification.The age of patients was treated as a continuous variable in the analysis. 2.3 Relevant definitions Visit days were specifically defined as the duration in days between the onset of symptoms, and the initial visit to a healthcare institution. Consistent with the criteria outlined in the Technical Specifications for Tuberculosis Prevention and Control in China (2020 Edition)[ 21 ], the term "delay in seeking medical treatment" was characterized by an interval exceeding 14 days between the manifestation of symptoms and the first visit to a medical institution.And this cut-off has been uniformly adopted and nationally standardized by the National Health Commission and the Chinese Center for Disease Control and Prevention as the official boundary for patient delay in tuberculosis routine surveillance, epidemiological investigations, and professional performance assessment across China. All enrolled pulmonary tuberculosis cases were classified into three subgroups. Etiological positive was defined as cases with positive Mycobacterium tuberculosis culture, PCR nucleic acid test, or both. Etiological negative referred to patients with typical clinical and radiological manifestations consistent with tuberculosis, who were diagnosed by comprehensive clinical assessment with negative etiological results. Etiology not checked included clinically suspected PTB patients who failed to complete etiological tests due to personal refusal, poor specimen quality or hospital referral; diagnosis was determined based on clinical symptoms, imaging features and epidemiological exposure history[ 21 ]. 2.4 Statistical Analysis The delay data of elderly patients with pulmonary tuberculosis were analyzed using the SPSS 27.0 statistical software package. Single-factor analysis employed the chi-square test to examine the differences between influencing factors and the delay in seeking medical care across various parameters, including gender, ethnicity, occupation, region, household registration, patient source, diagnosis, year, and treatment classification. In instances where age exhibited a non-normal distribution, the rank sum test was applied to assess the statistical significance of age in relation to the delay. For multi-factor analysis, an unconditional Logistic stepwise regression model was utilized. Initially, different demographic variables were designated, and a gradual screening process determined their inclusion in the model. Statistical significance was considered at P < 0.05. 3. Results 3.1 General characteristics of the study population This study encompassed a total of 7821 elderly patients diagnosed with pulmonary tuberculosis. The age range of the patients spanned from 60 to 102 years, with an average age of 70.96 years old. In terms of gender distribution, 73.06% were male, while 26.94% were female. Occupational classification revealed that 52.22% of the patients were engaged in farming, whereas 47.78% identified as non-farmers. Geographically, 64.83% of the patients resided in economically developed areas, while 35.17% lived in less economically developed areas. Regarding the sources of patients, 61.53% were referred, 8.73% were recommended and tracked, 28.07% received direct treatment, 1.42% sought healthcare through health examinations, and 0.26% had other sources. In terms of temporal distribution, 54.07% of patients sought medical attention from 2015 to 2019, while 45.93% visited healthcare facilities from 2020 to 2023, as illustrated in Table 1 . 3.2 Single-Factor Analysis of Delayed Consultation From 2015 to 2023, the observed rate of medical care delay among elderly patients diagnosed with pulmonary tuberculosis in Ningbo was 55.06%. The quantile (Q1, Q3) for the number of days of medical care delay in this population were (7, 36) days. The quantile of male medical delay days (Q1, Q3) is (6, 35) days, farmers' medical delay days (Q1, Q3) is (7, 35) days, as presented in Table 2 . The results of the rank sum test revealed a statistically significant difference in the delay rate among patients of different ages ( Z =-2.725, P = 0.006). Additionally, the chi-square test results indicated significant variations in the delay rate across different gender, occupational categories, geographical areas, census register, patient sources, and years. Specifically, in terms of gender, the delay rate among males (53.73%, 2644/3070) was lower than that among females (58.66%, 871/1236). With respect to occupation, farmers exhibited a higher delay rate (56.59%, 2311/4084) compared to non-farmers (53.39%, 1995/3737). Geographically, the delay rate in economically developed regions (53.16%, 2695/5070) was lower than that in less economically developed regions (58.56%, 1611/2751). Regarding household registration status, the delay rate for individuals with local household registration (53.92%, 2939/2512) was lower than that for those with non-local household registration (57.68%, 1367/1003). The delay rate for elderly pulmonary tuberculosis patients in 2020–2023 (51.67%, 1856/3592) was lower than that in 2015–2019 (57.93%, 2450/4229). Concerning patient sources, the delay rate was notably lowest among health check-up patients (37.84%, 42/111), which was lower than that observed in referral, recommendation and tracking, direct treatment, and other patient sources, as outlined in Table 3 . 3.3 Multi-Factor Logistic Regression Analysis of Delayed Consultation In the multifactorial analysis, the delay in seeking medical care for elderly patients diagnosed with pulmonary tuberculosis served as the dependent variable. Factors exhibiting statistical significance ( P < 0.05) in univariate analysis were incorporated into a binary unconditional logistic regression model. The collinearity test was conducted on the independent variables, and the results showed that the tolerance of all variables was greater than 0.1, and all VIFs were less than 10, indicating that there was no serious collinearity problem among the variables. A stepwise regression model was constructed, with the inclusion criterion for independent variables set at an alpha level of 0.05 and the exclusion criterion set at an alpha level of 0.10. The results showed that six independent variables, including age, gender, occupational classification, area, patient source, and year, were included in the model. The results of the Omnibus Tests of Model Coefficients showed that χ² =102.574, P < 0.05, indicating that the model is meaningful. The Hosmer Lemeshow test results showed that χ² =6.613, P = 0.578, indicating a good fitting effect of the model. Specifically, The risk of delay in seeking medical care was lower for males than for females, lower for non-farmers than for farmers, and lower for patients in economically developed areas than for those in economically underdeveloped areas. Furthermore, the risk of delayed care among elderly tuberculosis patients registered in 2020–2023 was lower than those registered in 2015–2019. Remarkably, the risk of delay in transfer treatment was higher than that in health examination. Age emerged as a protective factor against care delay in elderly patients with pulmonary tuberculosis, with the risk of delay decreasing with age, as delineated in Table 4 . Table 1 Demographic and clinical characteristics of theregistered tuberculosis patients aged ≥ 60 years in Ningbo, 2015–2023[n(%)] Factor Group Sample size Proportion (%) Gender Female 2107 26.94 Male 5714 73.06 Nationality Han nationality 7798 99.71 Other nationalities 23 0.29 Occupational classification Non-farmer 3737 47.78 Farmer 4084 52.22 Area Economically developed area 5070 64.83 Economically underdeveloped areas 2751 35.17 Census register Local registered residence 5451 69.70 Non-local registered residence 2370 30.30 Patient Source Referral 4812 65.53 Recommendation and follow-up 683 8.73 Direct treatment 2195 28.07 Health examination 111 1.42 Others 20 0.26 Year 2015–2019 4229 54.07 2020–2023 3592 45.93 Diagnosis Etiological positive 5041 64.45 Etiological negative 1959 25.08 Etiology not checked 821 10.50 Therapeutic Category Initial treatment 6862 87.74 Retreatment 959 12.26 Table 2 Distribution of delayed medical treatment time for tuberculosis patients with different characteristics Factor Group Sample size Median value Interquartile range Gender Male 5714 16.00 (6.00,35.00) Female 2107 19.00 (7.00,41.00) Occupational classification Non-farmer 3737 16.00 (6.00,37.00) Farmer 4084 18.00 (7.00,35.00) Area Economically underdeveloped areas 5070 19.00 (7.00,42.00) Economically developed areas 2751 16.00 (6.00,33.00) Patient Source Referral 4812 17.00 (7.00,34.00) Recommendation and follow-up 683 16.00 (5.00,38.00) Direct treatment 2195 18.00 (7.00,44.00) Health examination 111 8.00 (1.00,30.00) Others 20 58.50 (22.25,159.50) Year 2015–2019 4229 19.00 (7.00,38.00) 2020–2023 3592 15.00 (6.00,34.00) Table 3 Single factor analysis of health-care seeking delay among elderly PTB patients from 2015 to 2023 in Ningbo[n(%)] Factor Patient delay Patient delay rate% Chi-square value P value Yes No Gender 15.144 0.000 Female 1236 871 58.66 Male 3070 2644 53.73 Nationality 1.962 0.161 Han nationality 4290 3508 55.14 Other nationalities 16 7 69.57 Occupational classification 8.084 0.004 Non-farmer 1995 1742 53.39 Farmer 2311 1773 56.59 Area 21.053 0.000 Economically developed area 2695 2375 53.16 Economically underdeveloped areas 1611 1140 58.56 Census register 9.451 0.002 Local registered residence 2939 2512 53.92 Non-local registered residence 1367 1003 57.68 Patient source 22.336 0.000 Referral 2637 2175 54.8 Recommendation and follow-up 363 320 53.15 Direct treatment 1248 947 56.86 Health examination 42 69 37.84 Others 16 4 80 Diagnosis 3.337 0.189 Etiological positive 2738 2303 54.31 Etiological negative 1110 849 56.66 Etiology not checked 458 363 55.79 Year 30.789 0.000 2015–2019 2450 1779 57.93 2020–2023 1856 1736 51.67 Therapeutic Category 0.389 0.533 Initial treatment 3787 3075 55.19 Retreatment 519 440 54.12 Table 4 Multi-factor logistic regression analysis of health-care seeking delay among elderly PTB patients from 2015 to 2023 in Ningbo Variables Classification β value Standard error Chi-square value P value OR value 95% CI Age -0.009 0.003 10.132 0.001 0.991 0.985–0.996 Gender Female Male -0.207 0.052 15.772 0.000 0.813 0.735–0.901 Occupational classification Non-farmer 1.000 Farmer 0.093 0.046 4.009 0.045 1.097 1.002–1.202 Area Economically developed area 1.000 Economically undeveloped area 0.208 0.048 18.427 0.000 1.231 1.120–1.354 Patient source Referral 21.410 1.000 Recommendation and follow-up -0.07 0.082 0.721 0.396 0.932 0.793–1.096 Direct treatment 0.07 0.052 1.783 0.182 1.072 0.968–1.188 Health examination -0.721 0.199 13.087 0.000 0.486 0.329–0.719 Others 1.213 0.562 4.656 0.031 3.362 1.118–10.113 Year 2015–2019 1.000 2020–2023 -0.231 0.046 25.126 0.000 0.793 0.725–0.869 4. Discussion The results of this study reveal that the median delay in seeking medical attention for elderly pulmonary tuberculosis patients in Ningbo City from 2015 to 2023 is 17 days, with a delay rate of 55.06%, which was lower than that in Lishui City(71.10%)[ 22 ], Yantai(70%)[ 23 ] and Qinghai Province (61.44%)[ 15 ], and that reported by Xiaogang Liao in Meta-analysis of elderly pulmonary tuberculosis patients in China (55.1%)[ 24 ]. However, it was higher than the delay rate in Huizhou City for the elderly (36.87%)[ 25 ] and Beijing(47.71%)[ 16 ]. In conclusion, these inter-regional variations in delay rates can be comprehensively attributed to multiple disparities. Firstly, regional economic development level differs substantially, which shapes local medical resource allocation, infrastructure investment, and service accessibility for the elderly. Secondly, population aging degree and elderly health literacy vary across regions, leading to inconsistent awareness of tuberculosis symptoms and willingness to seek early medical care. Thirdly, the implementation intensity of tuberculosis health education, active screening programs, and free diagnosis and treatment policies is not uniform nationwide. Additionally, geographical location, transportation convenience, and the layout of designated tuberculosis hospitals also create disparities in the timeliness of medical consultation, collectively resulting in the divergence of medical seeking delay rates among different areas. The multivariate analysis results indicate that gender, age, area of residence, source of patient, and year were influencing factors for delayed medical attention. This study showed that elderly pulmonary tuberculosis female patients had a higher risk of delayed medical attention compared to male elderly patients. Consistent with other domestic research results [ 26 – 28 ], but inconsistent with other countries such as Japan [ 29 ]. In China, compared with elderly men, elderly women have lower education status and lower awareness rate of core information of tuberculosis[ 30 ]. The higher risk for females may be related to their own weak health awareness of tuberculosis, inadequate personal economic capacity, insufficient willingness for early medical attention, and lower levels of education[ 31 ]. By this way, they are less exposed to national free diagnosis and treatment policies and basic health knowledge about tuberculosis in daily life. Therefore, improving the core knowledge about tuberculosis, especially among the elderly population, can enhance their understanding and awareness, encouraging early medical attention and treatment. From the above analysis, it can be seen that education level and health awareness may have an impact on tuberculosis patients' delay in seeing a doctor, but this study did not analyze it. The study demonstrates that age is a factor influencing delayed medical attention for elderly pulmonary tuberculosis patients, with the risk decreasing as the patient's age increases. This difference may be attributed to the fact that older individuals tend to utilize healthcare services more. Zhu found that individuals over 80 had a relatively higher utilization of healthcare services, and those aged 90 and above have a higher level of medical security, resulting in increased healthcare needs and utilization[ 32 ]. This study indicates that the economic status of the region where patients are located can affect their delay in seeking medical treatment, with a relatively lower risk of delay in economically developed areas. Due to the differences in investment in infrastructure and public services among regions with different levels of economic development, this directly relates to the allocation and development of local medical resources, and subsequently influences the overall level of medical services[ 33 ]. Regarding patient source, elderly pulmonary tuberculosis patients referred for transfer treatment have a higher risk of delayed medical attention compared to those identified through health examinations, which is consistent with the research by Lijuan Fu[ 34 ]. It may be due to higher health awareness among health examination participants, leading to a higher willingness to seek medical attention. Health check participants are more likely to be discovered before or at the early stages of clinical symptoms, resulting in a lower delay rate. In addition, the analysis results of this study found that there was no significant difference in latency between pathogen positive and negative patients, which is inconsistent with clinical experience and other research findings [ 21 , 23 ]. This is a noteworthy issue. The physiological and clinical characteristics of elderly tuberculosis patients aged 60 and above, who may be the subject of this study, are significantly different from those of middle-aged and young patients, becoming a core factor in weakening the correlation between symptoms. Elderly patients often experience a decline in their body's response ability, and even in advanced cases with bacterial positive and high bacterial load, symptoms often become atypical. At the same time, the medical behavior of the elderly population is not solely dominated by the severity of tuberculosis symptoms, but is influenced by multiple factors such as health checkups, diagnosis and treatment of underlying diseases, and family intervention. This feature may dissolve the impact of symptom differences between bacterial positive/negative patients on delayed medical treatment. It may also be due to the heterogeneity of pathogen detection results, deviation from the linear correlation between bacterial load and disease staging, as well as sample size and inter group distribution characteristics, which may reduce the effectiveness of statistical testing. This study reveals that the risk of delayed medical treatment for elderly patients with pulmonary tuberculosis significantly decreased during 2020–2023 compared to 2015–2019. This positive change may be driven by multiple factors. Firstly, the COVID-19 pandemic might have unexpectedly raised public health awareness. During the pandemic, the public's vigilance towards respiratory symptoms such as coughing and fever generally increased, prompting the elderly and their families to seek medical services more promptly when experiencing symptoms similar to those of pulmonary tuberculosis (such as coughing, hemoptysis, and weight loss), thereby reducing the delay time for patients. Secondly, at the national level, policy guidance was strengthened. In 2020, the National Health Commission of China issued a notice to further enhance tuberculosis prevention and control efforts[ 35 ], emphasizing the need to intensify work and increase the detection rate of patients. Against this backdrop, the People's Government of Ningbo City continuously strengthened the prevention and control of tuberculosis and other respiratory infectious diseases, establishing a comprehensive prevention and control system where designated hospitals are responsible for treatment, community health service institutions for follow-up, and disease prevention and control institutions for guidance and management. This effectively improved the level of diagnosis and treatment and the capacity for community management. Particularly, the popularization of chest imaging examinations in elderly health check-ups enabled some patients to be detected before the onset of symptoms, with a zero delay time, thereby effectively lowering the overall delay level; the proportion of patients discovered through health check-ups also significantly increased. At the same time, the in-depth development of community health education and family doctor contract services, through forms such as community lectures and home visits, promoted the dissemination of tuberculosis prevention and control knowledge, enhancing the health literacy of the elderly and their awareness of core information about tuberculosis, and strengthening their willingness to seek medical treatment proactively. In conclusion, the reduction in the risk of delayed medical treatment for elderly pulmonary tuberculosis patients during 2020–2023 is the result of the combined effects of increased health awareness, the popularization of active screening, and the deepening of community health education. This study has several limitations that need to be acknowledged. First, this is a retrospective observational study based on data from a single region of Ningbo City. Potential selection bias may exist, and the research findings cannot be easily generalized to other regions with different economic levels and tuberculosis prevention and control conditions. Second, this study adopted a 14-day cut-off value to define medical seeking delay, but did not further quantitatively analyze the continuous impact of specific delayed days on the risk of tuberculosis transmission. Third, as a retrospective study, relevant information on disease severity and treatment outcomes was not collected, and thus these potentially influential variables were not included in the analysis. Fourth, potential confounding factors such as educational level, health literacy, family economic status, distance to medical facilities, and underlying chronic diseases were not incorporated into the multivariate regression model, which may affect the robustness of the results. Fifth, the absence of data on medical system delay is another limitation of this study. Integrating both patient-side delay and medical system delay could better reflect the entire medical seeking pathway and identify barriers to timely diagnosis and treatment. In the future, our research team will incorporate indicators such as disease severity, treatment outcomes, health literacy, family economic status, and distance to medical care, as well as patient-side delay and medical system delay, to conduct more in-depth research and further verify the relevant conclusions. 5. Conclusion Factors such as being a female, farmer, residing in less economically developed areas, and referral may be risk factors for delayed medical attention among elderly pulmonary tuberculosis patients in Ningbo. In contrast, increasing age is associated with a reduced probability of such delay. Based on the observed associations, targeted strategies are recommended to address potential delays in medical care for this population in Ningbo. First, under the current management, the prevention and control of pulmonary tuberculosis in the elderly in Ningbo should focus on the economically underdeveloped areas in the future, especially in rural areas where farmers account for a large proportion. Then health personnel should publicize the core information of pulmonary tuberculosis to local people and strengthen the health awareness of prevention of pulmonary tuberculosis in the elderly. Active screening in healthcare service institutions, striving for the "three early" goals, should be implemented. Additionally, training medical personnel in basic medical knowledge related to tuberculosis at grassroots healthcare institutions can improve local diagnostic capabilities and reduce the occurrence of delayed medical attention for elderly pulmonary tuberculosis patients.The findings of this study provide observational evidence for understanding the characteristics of delayed medical care-seeking among elderly patients with pulmonary tuberculosis in Ningbo, and offer practical references for local tuberculosis prevention and control strategies. In the future, further research, such as prospective studies may help clarify the potential mechanisms underlying the observed associations and provide more robust evidence. Declarations Author Contributions Tianfeng He was responsible for data collection and put forward the initial ideas.Haiyan Tian performed preliminary statistical analyses and drafted the initial manuscript,Jingjing Qi conducted data analysis and explained the results of the article,Tong Chen and Xujun Qian interpreted the results,Weitao Yao and Heng Fan revised the manuscript,Guoxin Sang was responsible for the data quality control,Siwei Tong revised manuscript critically for important content. All authors read and approved the final manuscript. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by Medical Technology Program Foundation of Zhejiang (CN) (grant number 2021KY334); the Project of Zhejiang Public Welfare Fund (CN) (grant number LGF19H260010), (grant number LGF22H260003); Ningbo Natural Science Fund (grant number 2022J173), (grant number 2023Z174)), (grant number 2023S038); and Ningbo Top Medical and Health Research Program(grant number 2023020713). The funding body/bodies did not provide any assistance in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The data involved in this study do not contain any personal privacy data or other information that could potentially compromise individual privacy. The work was approved by Institutional Review Board of Ningbo Municipal CDC [Jan 8, 2021; Reference No. IRB2021010]. We confirm that all methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Acknowledgments We are grateful to all individuals who contributed to this work. References World Health Organization. Global tuberculosis report 2023. Geneva: World Health Organization; 2023. Dong ZX, Zhang SC. Analysis of influencing factors for the cure of smear-positive pulmonary tuberculosis in elderly patients. Prev Med. 2018;30:1142–4. Wang W, et al. The burden and predictors of latent tuberculosis infection among elder adults in high epidemic rural area of tuberculosis in Zhejiang, China. Front Cell Infect Microbiol. 2022;12:990197. Bilchut AH, Mekonnen AG, Assen TA. Knowledge of symptoms and delays in diagnosis of extrapulmonary tuberculosis patients in North Shewa zone, Ethiopia. Volume 17. PLoS ONE; 2022. p. e0270002. 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Hu JF et al. (2024)Status and influencing factors of care delay among pulmonary tuberculosis patients in Qingpu district of Shanghai from 2011 to 2022. China tropical medicine; 24: 333–9. Reina, Yoshikawa, et al. Delay in health-care-seeking treatment among tuberculosis patients in Japan: what are the implications for control in the era of universal health coverage? Western Pac Surveillance Response. 2020;11:37–47. Luo D, et al. Analyzing spatial delays of tuberculosis from surveillance and awareness surveys in Eastern China. Sci Rep. 2024;14:19799. Wang CP, et al. Influencing factors of delayed care seeking in patients with pulmonary tuberculosis in Inner Mongolia Autonomous Region from 2016 to 2020. Dis Surveillance. 2020;38:152–6. Zhu XJ, et al. Analysis of health service needs and utilization and related factors among the elderly living at home in a community in Shanghai. Shanghai Prev Med. 2015;27:145–8. Xiao YQ. Spatial and temporal differentiation and influencing factors of urban medical resources in China (Dissertation). Hangzhou, Zhejiang, China: Zhejiang University, 2022. Fu LJ, et al. Analysis of delayed care seeking and influencing factors among pulmonary tuberculosis patients in Huzhou, 2008–2018. Chin J Disease Control. 2021;25:235–9. National Health Commission of China. (2020) Notice on further strengthening the prevention and control of pulmonary tuberculosis [EB/OL]. https://www.nhc.gov.cn/wjw/c100175/202004/82f884926155487584eff65c2a004f1a.shtml Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7909914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"comment","associatedPublications":[],"authors":[{"id":639612026,"identity":"4b5ee9ef-0543-4622-950a-da7b8f5bb0b5","order_by":0,"name":"Haiyan Tian","email":"","orcid":"","institution":"Ningbo City Haishu District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Tian","suffix":""},{"id":639612027,"identity":"99a1e3cb-f612-4962-a9bd-80e1e31ed3b3","order_by":1,"name":"Jingjing Qi","email":"","orcid":"","institution":"Gansu University Of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Qi","suffix":""},{"id":639612028,"identity":"60ef5a2a-56a3-46e7-9178-21b612586925","order_by":2,"name":"Tong Chen","email":"","orcid":"","institution":"Ningbo Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Chen","suffix":""},{"id":639612029,"identity":"43d91db1-c928-4420-8590-ecac69a7d6a6","order_by":3,"name":"Xujun Qian","email":"","orcid":"","institution":"The First Affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Xujun","middleName":"","lastName":"Qian","suffix":""},{"id":639612030,"identity":"82240857-4b65-4ec0-b053-a0e0770e483f","order_by":4,"name":"Weitao Yao","email":"","orcid":"","institution":"Ningbo Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Weitao","middleName":"","lastName":"Yao","suffix":""},{"id":639612031,"identity":"b805d3bf-bed2-4e15-b399-dfcef0978c47","order_by":5,"name":"Guoxin Sang","email":"","orcid":"","institution":"Ningbo Municipal Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Guoxin","middleName":"","lastName":"Sang","suffix":""},{"id":639612032,"identity":"5d6d8928-a8f3-4eaf-8e0c-a07b68220aaa","order_by":6,"name":"Heng Fan","email":"","orcid":"","institution":"The First Affiliated Hospital of Ningbo University","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Fan","suffix":""},{"id":639612033,"identity":"345a40b0-8ace-489b-bcd1-1ad717490a4c","order_by":7,"name":"Siwei Tong","email":"","orcid":"","institution":"Ningbo City Haishu District Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Siwei","middleName":"","lastName":"Tong","suffix":""},{"id":639612034,"identity":"737f0cda-39c6-4b16-91ad-2ac69f070248","order_by":8,"name":"Tianfeng He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYBACAyD+wNjAwNjA3tj48ANxWpgZZ4C18BxuNpYgTYtEepsADzFazNn7DzYX7rCT7Z/5sI1BgsFOTreBgBbLnsOMzTPPJBvPuJ3Y9qCAIdnY7AAhh91IZn/M23YgcYN0YruBBMOBxG0Etdx/zNgM1iJ5sE2ChygtN5ihWiQYidVyJtmwmRfklzOJwEA2IMYvxw8+bOYFhVj78YcPP1TYyRHUgm4CacpHwSgYBaNgFOAAAKy2R0w6yFskAAAAAElFTkSuQmCC","orcid":"","institution":"Ningbo Municipal Center for Disease Control and Prevention","correspondingAuthor":true,"prefix":"","firstName":"Tianfeng","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2025-10-21 03:08:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7909914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7909914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109297773,"identity":"cfdca988-2305-44f9-b787-df09518fac87","added_by":"auto","created_at":"2026-05-15 09:05:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":346153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7909914/v1/1c84041e-2e95-40c9-8fd0-0d6c32012fb9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors Influencing Delays in Seeking Medical Care Among elderly Patients with Pulmonary Tuberculosis in Ningbo: A Study Conducted from 2015 to 2023","fulltext":[{"header":"1. Background","content":"\u003cp\u003eTuberculosis(TB), caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e, is a respiratory infectious disease that significantly impacts public health[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The elderly population in China exhibits a heightened prevalence of pulmonary tuberculosis(PTB) due to the presence of multiple underlying health conditions, compromised immune systems, and an increased susceptibility to tuberculosis bacilli [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, elderly individuals diagnosed with pulmonary tuberculosis often manifest subtle clinical symptoms, coupled with a relatively low awareness of their health status[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Some patients may misinterpret the symptoms of pulmonary tuberculosis as a common cold, consequently underestimating the severity of the condition. Consequently, delayed medical consultation among elderly patients with pulmonary tuberculosis is a significant concern. The ramifications of this delay are twofold. Firstly, it not only exacerbates the patient's condition, missing the optimal choice for early diagnosis and treatment, but also heightens the complexity of subsequent medical interventions. Secondly, the delay contributes to the wider dissemination of tuberculosis within society, fostering an increased prevalence of the disease[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and also escalating the burden on the local healthcare system [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, China\u0026rsquo;s vast territory results in significant disparities in economic development, healthcare resource allocation, tuberculosis (TB) prevention and control policies, and population health behaviors across different regions, leading to distinct regional heterogeneity in the factors influencing delays in medical treatment among elderly patients with pulmonary tuberculosis (PTB)[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Most existing domestic studies have focused on northern provinces, underdeveloped western regions, or first-tier cities [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], while targeted research on economically developed eastern coastal regions remains relatively scarce. As an economic hub on the southern wing of the Yangtze River Delta, Ningbo features both urban and rural attributes. Its proportion of elderly population has been rising year by year, and there is a large population of elderly rural farmers and elderly migrant workers in the city, which leaves it facing unique challenges in PTB prevention and control: boasting a developed economy yet a complex population structure, and abundant healthcare resources yet uneven distribution [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. From an international perspective, the challenges confronted by Ningbo are shared by many economically developed regions worldwide with aging populations and large-scale migrant populations[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given that the COVID-19 outbreak since 2020 has substantially changed public health awareness, respiratory symptom consultation behavior, and the implementation of tuberculosis prevention and control strategies, the study period from 2015 to 2023 was divided into two phases: 2015\u0026ndash;2019 (pre-pandemic period) and 2020\u0026ndash;2023 (pandemic and post-pandemic period). This grouping allows a comparative analysis of temporal trends in medical seeking delay among elderly pulmonary tuberculosis patients and helps explore the underlying driving factors.\u003c/p\u003e \u003cp\u003eThis study intends to analyze the characteristics of delayed healthcare-seeking among elderly PTB patients in Ningbo in recent years. Taking elderly PTB patients registered in Ningbo from 2015 to 2023 as the research subjects, it will systematically examine the occurrence of delayed healthcare-seeking behaviors and their associated influencing factors. The study aims to provide a scientific basis for formulating targeted prevention and control measures for elderly PTB patients in Ningbo, enrich the global evidence base for TB prevention and control against the backdrop of population aging, and also offer reference insights for TB prevention and control efforts in other regions facing similar situations.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Subjects\u003c/h2\u003e \u003cp\u003eThe data utilized in this study were sourced from the Tuberculosis Management Information System within the Chinese Disease Prevention and Control Information System. The study focused on all tuberculosis patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years in Ningbo City from 2015 to 2023. A comprehensive retrieval and registration process within the management information system yielded a total of 8006 cases. Subsequently, after excluding 185 cases with incomplete or unclear information and those diagnosed with extrapulmonary tuberculosis, the dataset was refined to include 7821 valid patient records for the purpose of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data collection\u003c/h2\u003e \u003cp\u003eThe medical records of elderly patients diagnosed with pulmonary tuberculosis encompassed various parameters, including gender, ethnicity, age, occupational classification, current address, household registration, patient source, diagnosis, treatment classification, date of first symptom appearance, and date of first visit. Gender was dichotomized into female and male categories, while ethnicity was classified as Han and other ethnic groups. Occupational classification was stratified into two groups: farmers and non-farmers. The administrative counties in Ningbo were categorized based on GDP ranking, resulting in economically developed areas (Haishu District, Cixi City, Yinzhou District, Yuyao City, Beilun District) and economically underdeveloped areas (Fenghua District, Xiangshan County, Ninghai County, Zhenhai District, Jiangbei District). The registered address type was delineated as local or non-local. Patient sources were categorized into referred, recommendation and follow-up, direct treatment, health examination, and others. Diagnostic results were classified into three categories derived from microbiological examination outcomes: pathogen positive, pathogen negative, and pathogen untested. Temporal divisions were established as two stages: 2015\u0026ndash;2019 and 2020\u0026ndash;2023. Treatment classifications included initial treatment and retreatment. The onset date of symptoms refers to the specific date when the patient first presents typical symptoms of pulmonary tuberculosis (such as cough and sputum for \u0026ge;\u0026thinsp;2 weeks, hemoptysis, low-grade fever, night sweats, etc.), and is based on the patient's self description and clinical verification.The age of patients was treated as a continuous variable in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Relevant definitions\u003c/h2\u003e \u003cp\u003eVisit days were specifically defined as the duration in days between the onset of symptoms, and the initial visit to a healthcare institution. Consistent with the criteria outlined in the Technical Specifications for Tuberculosis Prevention and Control in China (2020 Edition)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the term \"delay in seeking medical treatment\" was characterized by an interval exceeding 14 days between the manifestation of symptoms and the first visit to a medical institution.And this cut-off has been uniformly adopted and nationally standardized by the National Health Commission and the Chinese Center for Disease Control and Prevention as the official boundary for patient delay in tuberculosis routine surveillance, epidemiological investigations, and professional performance assessment across China.\u003c/p\u003e \u003cp\u003eAll enrolled pulmonary tuberculosis cases were classified into three subgroups. Etiological positive was defined as cases with positive Mycobacterium tuberculosis culture, PCR nucleic acid test, or both. Etiological negative referred to patients with typical clinical and radiological manifestations consistent with tuberculosis, who were diagnosed by comprehensive clinical assessment with negative etiological results. Etiology not checked included clinically suspected PTB patients who failed to complete etiological tests due to personal refusal, poor specimen quality or hospital referral; diagnosis was determined based on clinical symptoms, imaging features and epidemiological exposure history[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe delay data of elderly patients with pulmonary tuberculosis were analyzed using the SPSS 27.0 statistical software package. Single-factor analysis employed the chi-square test to examine the differences between influencing factors and the delay in seeking medical care across various parameters, including gender, ethnicity, occupation, region, household registration, patient source, diagnosis, year, and treatment classification. In instances where age exhibited a non-normal distribution, the rank sum test was applied to assess the statistical significance of age in relation to the delay.\u003c/p\u003e \u003cp\u003eFor multi-factor analysis, an unconditional Logistic stepwise regression model was utilized. Initially, different demographic variables were designated, and a gradual screening process determined their inclusion in the model. Statistical significance was considered at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.1\u003c/b\u003e General characteristics of the study population\u003c/h2\u003e \u003cp\u003eThis study encompassed a total of 7821 elderly patients diagnosed with pulmonary tuberculosis. The age range of the patients spanned from 60 to 102 years, with an average age of 70.96 years old. In terms of gender distribution, 73.06% were male, while 26.94% were female. Occupational classification revealed that 52.22% of the patients were engaged in farming, whereas 47.78% identified as non-farmers. Geographically, 64.83% of the patients resided in economically developed areas, while 35.17% lived in less economically developed areas. Regarding the sources of patients, 61.53% were referred, 8.73% were recommended and tracked, 28.07% received direct treatment, 1.42% sought healthcare through health examinations, and 0.26% had other sources. In terms of temporal distribution, 54.07% of patients sought medical attention from 2015 to 2019, while 45.93% visited healthcare facilities from 2020 to 2023, as illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Single-Factor Analysis of Delayed Consultation\u003c/h2\u003e \u003cp\u003eFrom 2015 to 2023, the observed rate of medical care delay among elderly patients diagnosed with pulmonary tuberculosis in Ningbo was 55.06%. The quantile (Q1, Q3) for the number of days of medical care delay in this population were (7, 36) days. The quantile of male medical delay days (Q1, Q3) is (6, 35) days, farmers' medical delay days (Q1, Q3) is (7, 35) days, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results of the rank sum test revealed a statistically significant difference in the delay rate among patients of different ages (\u003cem\u003eZ\u003c/em\u003e=-2.725, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Additionally, the chi-square test results indicated significant variations in the delay rate across different gender, occupational categories, geographical areas, census register, patient sources, and years. Specifically, in terms of gender, the delay rate among males (53.73%, 2644/3070) was lower than that among females (58.66%, 871/1236). With respect to occupation, farmers exhibited a higher delay rate (56.59%, 2311/4084) compared to non-farmers (53.39%, 1995/3737). Geographically, the delay rate in economically developed regions (53.16%, 2695/5070) was lower than that in less economically developed regions (58.56%, 1611/2751). Regarding household registration status, the delay rate for individuals with local household registration (53.92%, 2939/2512) was lower than that for those with non-local household registration (57.68%, 1367/1003). The delay rate for elderly pulmonary tuberculosis patients in 2020\u0026ndash;2023 (51.67%, 1856/3592) was lower than that in 2015\u0026ndash;2019 (57.93%, 2450/4229). Concerning patient sources, the delay rate was notably lowest among health check-up patients (37.84%, 42/111), which was lower than that observed in referral, recommendation and tracking, direct treatment, and other patient sources, as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multi-Factor Logistic Regression Analysis of Delayed Consultation\u003c/h2\u003e \u003cp\u003eIn the multifactorial analysis, the delay in seeking medical care for elderly patients diagnosed with pulmonary tuberculosis served as the dependent variable. Factors exhibiting statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in univariate analysis were incorporated into a binary unconditional logistic regression model. The collinearity test was conducted on the independent variables, and the results showed that the tolerance of all variables was greater than 0.1, and all VIFs were less than 10, indicating that there was no serious collinearity problem among the variables. A stepwise regression model was constructed, with the inclusion criterion for independent variables set at an alpha level of 0.05 and the exclusion criterion set at an alpha level of 0.10. The results showed that six independent variables, including age, gender, occupational classification, area, patient source, and year, were included in the model. The results of the Omnibus Tests of Model Coefficients showed that \u003cem\u003eχ\u0026sup2;\u003c/em\u003e=102.574, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating that the model is meaningful. The Hosmer Lemeshow test results showed that \u003cem\u003eχ\u0026sup2;\u003c/em\u003e=6.613, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.578, indicating a good fitting effect of the model. Specifically, The risk of delay in seeking medical care was lower for males than for females, lower for non-farmers than for farmers, and lower for patients in economically developed areas than for those in economically underdeveloped areas. Furthermore, the risk of delayed care among elderly tuberculosis patients registered in 2020\u0026ndash;2023 was lower than those registered in 2015\u0026ndash;2019. Remarkably, the risk of delay in transfer treatment was higher than that in health examination. Age emerged as a protective factor against care delay in elderly patients with pulmonary tuberculosis, with the risk of delay decreasing with age, as delineated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\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\u003eDemographic and clinical characteristics of theregistered tuberculosis patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years in Ningbo, 2015\u0026ndash;2023[n(%)]\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProportion (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther nationalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupational classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-farmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically developed area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically underdeveloped areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCensus register\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal registered residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-local registered residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePatient Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecommendation and follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiological positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiological negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiology not checked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTherapeutic Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e87.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of delayed medical treatment time for tuberculosis patients with different characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterquartile range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(6.00,35.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.00,41.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupational classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-farmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(6.00,37.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.00,35.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically underdeveloped areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.00,42.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically developed areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(6.00,33.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003ePatient Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.00,34.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecommendation and follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(5.00,38.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.00,44.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.00,30.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(22.25,159.50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.00,38.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(6.00,34.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSingle factor analysis of health-care seeking delay among elderly PTB patients from 2015 to 2023 in Ningbo[n(%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePatient delay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c6\" namest=\"c5\" rowspan=\"2\"\u003e \u003cp\u003ePatient delay rate%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e15.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e58.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e55.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther nationalities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e69.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupational classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-farmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically developed area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically underdeveloped areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e58.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCensus register\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal registered residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-local registered residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e57.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecommendation and follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e53.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e37.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiological positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e54.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiological negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e56.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiology not checked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e55.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e57.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e51.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTherapeutic Category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e55.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e54.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMulti-factor logistic regression analysis of health-care seeking delay among elderly PTB patients from 2015 to 2023 in Ningbo\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChi-square value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.985\u0026ndash;0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.735\u0026ndash;0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupational classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-farmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.002\u0026ndash;1.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically developed area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomically undeveloped area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.120\u0026ndash;1.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReferral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecommendation and follow-up\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.793\u0026ndash;1.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDirect treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.968\u0026ndash;1.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.329\u0026ndash;0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.118\u0026ndash;10.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2015\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2020\u0026ndash;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.725\u0026ndash;0.869\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":"4. Discussion","content":"\u003cp\u003eThe results of this study reveal that the median delay in seeking medical attention for elderly pulmonary tuberculosis patients in Ningbo City from 2015 to 2023 is 17 days, with a delay rate of 55.06%, which was lower than that in Lishui City(71.10%)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Yantai(70%)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and Qinghai Province (61.44%)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and that reported by Xiaogang Liao in Meta-analysis of elderly pulmonary tuberculosis patients in China (55.1%)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, it was higher than the delay rate in Huizhou City for the elderly (36.87%)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and Beijing(47.71%)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In conclusion, these inter-regional variations in delay rates can be comprehensively attributed to multiple disparities. Firstly, regional economic development level differs substantially, which shapes local medical resource allocation, infrastructure investment, and service accessibility for the elderly. Secondly, population aging degree and elderly health literacy vary across regions, leading to inconsistent awareness of tuberculosis symptoms and willingness to seek early medical care. Thirdly, the implementation intensity of tuberculosis health education, active screening programs, and free diagnosis and treatment policies is not uniform nationwide. Additionally, geographical location, transportation convenience, and the layout of designated tuberculosis hospitals also create disparities in the timeliness of medical consultation, collectively resulting in the divergence of medical seeking delay rates among different areas.\u003c/p\u003e \u003cp\u003eThe multivariate analysis results indicate that gender, age, area of residence, source of patient, and year were influencing factors for delayed medical attention. This study showed that elderly pulmonary tuberculosis female patients had a higher risk of delayed medical attention compared to male elderly patients. Consistent with other domestic research results [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], but inconsistent with other countries such as Japan [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In China, compared with elderly men, elderly women have lower education status and lower awareness rate of core information of tuberculosis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The higher risk for females may be related to their own weak health awareness of tuberculosis, inadequate personal economic capacity, insufficient willingness for early medical attention, and lower levels of education[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By this way, they are less exposed to national free diagnosis and treatment policies and basic health knowledge about tuberculosis in daily life. Therefore, improving the core knowledge about tuberculosis, especially among the elderly population, can enhance their understanding and awareness, encouraging early medical attention and treatment. From the above analysis, it can be seen that education level and health awareness may have an impact on tuberculosis patients' delay in seeing a doctor, but this study did not analyze it.\u003c/p\u003e \u003cp\u003eThe study demonstrates that age is a factor influencing delayed medical attention for elderly pulmonary tuberculosis patients, with the risk decreasing as the patient's age increases. This difference may be attributed to the fact that older individuals tend to utilize healthcare services more. Zhu found that individuals over 80 had a relatively higher utilization of healthcare services, and those aged 90 and above have a higher level of medical security, resulting in increased healthcare needs and utilization[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This study indicates that the economic status of the region where patients are located can affect their delay in seeking medical treatment, with a relatively lower risk of delay in economically developed areas. Due to the differences in investment in infrastructure and public services among regions with different levels of economic development, this directly relates to the allocation and development of local medical resources, and subsequently influences the overall level of medical services[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Regarding patient source, elderly pulmonary tuberculosis patients referred for transfer treatment have a higher risk of delayed medical attention compared to those identified through health examinations, which is consistent with the research by Lijuan Fu[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. It may be due to higher health awareness among health examination participants, leading to a higher willingness to seek medical attention. Health check participants are more likely to be discovered before or at the early stages of clinical symptoms, resulting in a lower delay rate.\u003c/p\u003e \u003cp\u003eIn addition, the analysis results of this study found that there was no significant difference in latency between pathogen positive and negative patients, which is inconsistent with clinical experience and other research findings [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This is a noteworthy issue. The physiological and clinical characteristics of elderly tuberculosis patients aged 60 and above, who may be the subject of this study, are significantly different from those of middle-aged and young patients, becoming a core factor in weakening the correlation between symptoms. Elderly patients often experience a decline in their body's response ability, and even in advanced cases with bacterial positive and high bacterial load, symptoms often become atypical. At the same time, the medical behavior of the elderly population is not solely dominated by the severity of tuberculosis symptoms, but is influenced by multiple factors such as health checkups, diagnosis and treatment of underlying diseases, and family intervention. This feature may dissolve the impact of symptom differences between bacterial positive/negative patients on delayed medical treatment. It may also be due to the heterogeneity of pathogen detection results, deviation from the linear correlation between bacterial load and disease staging, as well as sample size and inter group distribution characteristics, which may reduce the effectiveness of statistical testing.\u003c/p\u003e \u003cp\u003eThis study reveals that the risk of delayed medical treatment for elderly patients with pulmonary tuberculosis significantly decreased during 2020\u0026ndash;2023 compared to 2015\u0026ndash;2019. This positive change may be driven by multiple factors. Firstly, the COVID-19 pandemic might have unexpectedly raised public health awareness. During the pandemic, the public's vigilance towards respiratory symptoms such as coughing and fever generally increased, prompting the elderly and their families to seek medical services more promptly when experiencing symptoms similar to those of pulmonary tuberculosis (such as coughing, hemoptysis, and weight loss), thereby reducing the delay time for patients. Secondly, at the national level, policy guidance was strengthened. In 2020, the National Health Commission of China issued a notice to further enhance tuberculosis prevention and control efforts[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], emphasizing the need to intensify work and increase the detection rate of patients. Against this backdrop, the People's Government of Ningbo City continuously strengthened the prevention and control of tuberculosis and other respiratory infectious diseases, establishing a comprehensive prevention and control system where designated hospitals are responsible for treatment, community health service institutions for follow-up, and disease prevention and control institutions for guidance and management. This effectively improved the level of diagnosis and treatment and the capacity for community management. Particularly, the popularization of chest imaging examinations in elderly health check-ups enabled some patients to be detected before the onset of symptoms, with a zero delay time, thereby effectively lowering the overall delay level; the proportion of patients discovered through health check-ups also significantly increased. At the same time, the in-depth development of community health education and family doctor contract services, through forms such as community lectures and home visits, promoted the dissemination of tuberculosis prevention and control knowledge, enhancing the health literacy of the elderly and their awareness of core information about tuberculosis, and strengthening their willingness to seek medical treatment proactively. In conclusion, the reduction in the risk of delayed medical treatment for elderly pulmonary tuberculosis patients during 2020\u0026ndash;2023 is the result of the combined effects of increased health awareness, the popularization of active screening, and the deepening of community health education.\u003c/p\u003e \u003cp\u003eThis study has several limitations that need to be acknowledged. First, this is a retrospective observational study based on data from a single region of Ningbo City. Potential selection bias may exist, and the research findings cannot be easily generalized to other regions with different economic levels and tuberculosis prevention and control conditions. Second, this study adopted a 14-day cut-off value to define medical seeking delay, but did not further quantitatively analyze the continuous impact of specific delayed days on the risk of tuberculosis transmission. Third, as a retrospective study, relevant information on disease severity and treatment outcomes was not collected, and thus these potentially influential variables were not included in the analysis. Fourth, potential confounding factors such as educational level, health literacy, family economic status, distance to medical facilities, and underlying chronic diseases were not incorporated into the multivariate regression model, which may affect the robustness of the results. Fifth, the absence of data on medical system delay is another limitation of this study. Integrating both patient-side delay and medical system delay could better reflect the entire medical seeking pathway and identify barriers to timely diagnosis and treatment.\u003c/p\u003e \u003cp\u003eIn the future, our research team will incorporate indicators such as disease severity, treatment outcomes, health literacy, family economic status, and distance to medical care, as well as patient-side delay and medical system delay, to conduct more in-depth research and further verify the relevant conclusions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eFactors such as being a female, farmer, residing in less economically developed areas, and referral may be risk factors for delayed medical attention among elderly pulmonary tuberculosis patients in Ningbo. In contrast, increasing age is associated with a reduced probability of such delay. Based on the observed associations, targeted strategies are recommended to address potential delays in medical care for this population in Ningbo. First, under the current management, the prevention and control of pulmonary tuberculosis in the elderly in Ningbo should focus on the economically underdeveloped areas in the future, especially in rural areas where farmers account for a large proportion. Then health personnel should publicize the core information of pulmonary tuberculosis to local people and strengthen the health awareness of prevention of pulmonary tuberculosis in the elderly. Active screening in healthcare service institutions, striving for the \"three early\" goals, should be implemented. Additionally, training medical personnel in basic medical knowledge related to tuberculosis at grassroots healthcare institutions can improve local diagnostic capabilities and reduce the occurrence of delayed medical attention for elderly pulmonary tuberculosis patients.The findings of this study provide observational evidence for understanding the characteristics of delayed medical care-seeking among elderly patients with pulmonary tuberculosis in Ningbo, and offer practical references for local tuberculosis prevention and control strategies. In the future, further research, such as prospective studies may help clarify the potential mechanisms underlying the observed associations and provide more robust evidence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTianfeng He was responsible for data collection and put forward the initial ideas.Haiyan Tian performed preliminary statistical analyses and drafted the initial manuscript,Jingjing Qi conducted data analysis and explained the results of the article,Tong Chen and Xujun Qian interpreted the results,Weitao Yao and Heng Fan revised the manuscript,Guoxin Sang was \u0026nbsp;responsible for the data quality control,Siwei Tong revised manuscript critically for important content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Medical Technology Program Foundation of Zhejiang (CN) (grant number 2021KY334); the Project of Zhejiang Public Welfare Fund (CN) (grant number LGF19H260010), (grant number LGF22H260003); Ningbo Natural Science Fund (grant number 2022J173), (grant number 2023Z174)), (grant number 2023S038); and Ningbo Top Medical and Health Research Program(grant number 2023020713). The funding body/bodies did not provide any assistance in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the\u003cbr\u003e\u0026nbsp;corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data involved in this study do not contain any personal privacy data or other information that could potentially compromise individual privacy. The work was approved by Institutional Review Board of Ningbo Municipal CDC [Jan 8, 2021; Reference No. IRB2021010]. We confirm that all methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe are grateful to all individuals who contributed to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Global tuberculosis report 2023. Geneva: World Health Organization; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong ZX, Zhang SC. Analysis of influencing factors for the cure of smear-positive pulmonary tuberculosis in elderly patients. Prev Med. 2018;30:1142\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W, et al. 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(2020) Notice on further strengthening the prevention and control of pulmonary tuberculosis [EB/OL]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nhc.gov.cn/wjw/c100175/202004/82f884926155487584eff65c2a004f1a.shtml\u003c/span\u003e\u003cspan address=\"https://www.nhc.gov.cn/wjw/c100175/202004/82f884926155487584eff65c2a004f1a.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pulmonary tuberculosis, Patient delay, Elderly people, Influence factor","lastPublishedDoi":"10.21203/rs.3.rs-7909914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7909914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction:To investigate the factors causing delays in medical treatment for elderly patients with pulmonary tuberculosis over various time periods.\u003c/p\u003e \u003cp\u003eMethodology:Data were collected from 7,821 elderly individuals aged 60 and above diagnosed with pulmonary tuberculosis between 2015 and 2023. Univariate and multivariate regression analyses were performed to examine the characteristics and influencing factors associated with delays in seeking medical care.\u003c/p\u003e \u003cp\u003eResults:The median delay time for elderly patients with pulmonary tuberculosis in Ningbo was 17 days, with a delay rate of 55.06%. Comparative analysis revealed that farmers and individuals from less economically developed areas faced higher risks of delay (OR\u0026thinsp;=\u0026thinsp;1.097, 95% CI: 1.002\u0026ndash;1.202; OR\u0026thinsp;=\u0026thinsp;1.231, 95% CI: 1.120\u0026ndash;1.354). Notably, the risk of delayed medical treatment decreased from 2020 to 2023 (OR\u0026thinsp;=\u0026thinsp;0.793, 95% CI: 0.725\u0026ndash;0.869) and among males (OR\u0026thinsp;=\u0026thinsp;0.813, 95% CI: 0.735\u0026ndash;0.901), compared to the period from 2015 to 2019 and females.Among various sources, health examination patients had a lower risk than referral patients (OR\u0026thinsp;=\u0026thinsp;0.486, 95% CI:0.329\u0026ndash;0.719), while those from other sources showed higher risk compared to referred patients (OR\u0026thinsp;=\u0026thinsp;3.362, 95% CI:1.118\u0026ndash;10.113 ). Additionally, there was a significant age difference between patients with and without delays (Z =-2.725 ,\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006 ).\u003c/p\u003e \u003cp\u003eConclusions:Elderly patients with pulmonary tuberculosis often face delays in treatment. Contributing factors include being female, farmer, living in less developed areas, receiving referrals, and the years 2015\u0026ndash;2019.\u003c/p\u003e","manuscriptTitle":"Factors Influencing Delays in Seeking Medical Care Among elderly Patients with Pulmonary Tuberculosis in Ningbo: A Study Conducted from 2015 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 10:01:34","doi":"10.21203/rs.3.rs-7909914/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ef93d84d-edac-4cd7-b3f0-1c001ce4d96b","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"checksComplete","content":"","date":"2026-05-15T19:09:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2026-05-13T09:44:17+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T10:01:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 10:01:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7909914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7909914","identity":"rs-7909914","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}