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Pulmonary tuberculosis (PTB) remains a critical global health challenge, with a disproportionate burden on elderly populations. In 2021, 18% of global tuberculosis cases were among individuals aged 65 years and older (1). In China, elderly TB cases accounted for 25.9% of all TB diagnoses (2) and 61.6% of TB-related deaths in 2020 (3). The aging population in China is a pressing concern, with 13.5% of the population over 65 years old in 2020 (4). Despite national TB-control efforts that have succeeded in reducing overall TB cases, patient delay in TB diagnosis remains a persistent challenge. Patient delay is defined as more than 2 weeks from symptom onset to the first medical consultation (5). It is caused by patients failing to seek timely medical attention. Consequently, long delays in TB diagnosis not only increase community infectivity but can also lead to a more advanced disease state, potentially increasing complications and the risk of death (6). This study aims to investigate patient delay among the elderly in China from 2015 to 2023 to provide insights to enhance TB detection and improve management strategies.
Data were extracted from the Tuberculosis Information Management System (TBIMS) within the Chinese Center for Disease Control and Prevention Information System for the study, which focused on PTB patients aged 65 years and older. Patients in the Xinjiang Production and Construction Corps were merged into Xinjiang. A total of 31 provincial-level administrative divisions (PLADs) were divided into eastern, central, and western regions based on the National Bureau of Statistics regional division criteria. The analysis included cases with accurate symptom onset dates and initial medical consultations. Illogical delay results (e.g., negative numbers) were excluded from the final analysis. Case-finding pathways were categorized as: 1) active, for those detected through active screening and health checkup; and 2) passive, for those detected through direct visits to designated TB institutes, referral, tracing, and recommendation by non-designated TB institutes. In this study, the median (M) and interquartile range (IQR) were used to summarize patient delay. The Kruskal–Wallis independent-samples median test was used to compare the medians of patient delay among different groups. The time trend of patient delay was tested using the Mann–Kendall method. All statistical analyses were performed using R software (version 4.0.3; R Core Team, Vienna, Austria). A two-sidedPvalue of less than 0.05 was considered statistically significant.
A total of 1,559,849 elderly patients with PTB were included in the final analysis, representing 99.54% of reported elderly PTB cases between January 1, 2015, and December 31, 2023. The patients’ sociodemographic, clinical, and management characteristics are presented inTable 1.
Group Number of patients DaysMedian (IQR) P Number of patientdelay,n(%) P Total 1,559,849 22 (8, 49) – 967,676 (62) – Sex <0.0001 <0.0001 Male 1,096,171 22 (7, 48) 676,131 (62) Female 463,678 23 (8, 53) 290,840 (63) Age (years) <0.0001 <0.0001 65–69 568,477 24 (8, 52) 358,960 (63) 70–74 436,480 23 (8, 50) 272,677 (62) 75–79 364,894 21 (7, 47) 223,407 (61) 80–84 133,625 20 (7, 44) 80,013 (60) 85 and over 56,373 19 (7, 41) 32,619 (58) Ethnicity <0.0001 <0.0001 Han 1,326,513 22 (7, 48) 814,751 (61) Minorities 233,336 27 (9, 56) 152,925 (66) Occupation <0.0001 <0.0001 Farmers, herders, and fishermen 1,177,120 23 (8, 50) 741,473 (63) Retirees 200,970 19 (6, 44) 116,130 (58) Houseworkers or unemployees 147,384 22 (7, 48) 89,668 (61) Others 34,375 21 (7, 48) 20,405 (59) Region <0.0001 <0.0001 Eastern 445,470 21 (7, 46) 270,992 (61) Central 567,791 20 (7, 43) 339,011 (60) Western 546,588 27 (9, 60) 357,673 (65) Primary care institute <0.0001 <0.0001 Designated institute 1,187,319 22 (7, 48) 726,404 (61) Non-designated institute 372,530 25 (9, 53) 241,272 (65) Case finding <0.0001 <0.0001 Active screening 2,653 10 (1, 31) 1,113 (42) Health checkup 44,033 25 (8, 41) 27,798 (63) Passive case finding 1,513,163 22 (8, 50) 938,765 (62) Treatment history <0.0001 0.0004 New 1,413,988 22 (8, 49) 878,454 (62) Retreated 145,861 22 (7, 52) 89,222 (61) Bacteriological results <0.0001 <0.0001 Positive 786,362 22 (7, 52) 480,666 (61) Negative 704,452 23 (8, 48) 445,483 (63) Tuberculous pleurisy only 57,800 20 (8, 40) 35,329 (61) Unknown 11,235 18 (4, 47) 6,198 (55) Comorbidities <0.0001 <0.0001 Yes 209,661 23 (8, 57) 131,578 (63) No 714,583 23 (8, 53) 449,400 (63) Unknown 635,605 21 (7, 44) 386,698 (61) Note: Patient delay means more than two weeks from symptom onset to the first medical consultation.
"–" indicates that noP-value was calculated for the totals.
Abbreviation: PTB=pulmonary tuberculosis.Table 1.Characteristics and timeliness of elderly pulmonary tuberculosis (PTB) patients registered in China, 2015–2023.
From 2015 to 2023, approximately 62% of elderly tuberculosis patients in China experienced delays in seeking care, with a median delay of 22 days (IQR 8–49). The 65–69 age group showed the longest median delays at 24 days (IQR 8–52), with shorter delays in older age groups. Farmers, herders, and fishermen faced the longest delays (23 days, IQR 8–50), while retirees had shorter delays (19 days, IQR 6–44). Geographically, patients in the western region experienced significantly longer delays (27 days, IQR 9–60) compared to those in the eastern (21 days, IQR 7–46) and central regions (20 days, IQR 7–43). Patients visiting non-designated medical institutions had longer delays (25 days, IQR 9–53) compared to those visiting designated institutions (22 days, IQR 7–48). Actively screened patients experienced fewer delays (10 days, IQR 1–31) than those detected through other means. However, over 60% of patients identified through ACF still experienced delays (Table 1).
The patient delay rate demonstrated an overall decreasing trend from 2015 to 2023, declining from 66% in 2015 to 60% in 2023 (Mann-Kendall trendPvalue=0.0049;Figure 1), despite a slight increase in 2022 and 2023. Specifically, the median (IQR) delay was 25 (9,54) days in 2015, followed by 25 (9,53) days, 24 (8,51) days, 24 (8,52) days, 23 (7,49) days, 21 (7,49) days, 20 (7,45) days, 20 (7,48) days, and 21 (7,45) days in subsequent years.
Figure 1.Registered patients with patient delay and proportions in China, 2015–2023.
Note: The horizontal coordinate represents the year of the patient's registration in the system (2015–2023). The left vertical coordinate signifies the count of patients experiencing delays, while the right vertical axis represents the patient delay rate. The bars symbolize the number of patients facing delays each year, and the polyline illustrates the annual fluctuation in the patient delay rate.The geographical distribution of elderly tuberculosis patients in China revealed significant interprovincial variations in the number of registered patients, patients experiencing delays, the proportion of patient delays, and delay durations over the 9 years. Chongqing (77%), Jilin (72%), and Gansu (72%) had the highest proportions of delays, while Beijing (41%) and Shanghai (44%) had the lowest. Chongqing (37 days, IQR 15–92) had the longest patient delay duration, while Beijing (8 days, IQR 0–33) and Shanghai (12 days, IQR 4–25) had the shortest (Table 2).
PLADs Number of patients Number of patients with delay Proportion of patients with delay (%) Days of delay
Median (IQR)Beijing 9,960 4,129 41 8 (0–33) Tianjin 7,053 3,489 49 14 (3–36) Hebei 59,412 35,164 59 19 (7–39) Inner Mongolia 26,819 18,572 69 30 (11–64) Shanxi 24,855 17,725 71 30 (12–62) Liaoning 40,849 27,228 67 27 (10–48) Jilin 17,995 13,035 72 30 (13–59) Heilongjiang 41,370 27,873 67 27 (10–54) Shanghai 12,717 5,581 44 12 (4–25) Jiangsu 67,391 37,994 56 18 (6–35) Zhejiang 60,518 34,108 56 18 (6–40) Fujian 28,125 18,113 64 25 (9–61) Shandong 61,409 42,817 70 30 (11–59) Anhui 86,949 55,150 63 21 (9–38) Jiangxi 71,190 40,088 56 17 (7–39) Henan 104,624 51,111 49 14 (2–32) Hubei 81,283 49,596 61 21 (8–45) Hunan 139,525 84,433 61 21 (7–50) Guangdong 88,019 55,825 63 25 (8–61) Hainan 10,017 6,544 65 30 (9–61) Guangxi 85,875 58,000 68 29 (10–66) Chongqing 38,970 29,921 77 37 (15–92) Sichuan 84,219 53,377 63 23 (8–54) Guizhou 70,323 46,924 67 29 (10–69) Yunnan 39,763 25,166 63 26 (9–61) Xizang 3,523 2,371 67 26 (11–47) Shaanxi 37,098 19,683 53 16 (4–41) Gansu 25,281 18,260 72 30 (13–62) Qinghai 7,051 3,739 53 16 (6–37) Ningxia 7,704 4,851 63 24 (8–58) Xinjiang 119,962 76,809 64 25 (8–45) Note: Patient delay means from symptom onset to first seeking medical care.
Abbreviation: PTB=pulmonary tuberculosis; PLAD=provincial-level administrative division; IQR=interquartile range.Table 2.Patient delays among elderly PTB patients across 31 PLADs in China, 2015–2023.
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Elderly patients with PTB in China experience notable patient delays, with a slight decrease observed from 2015 to 2023. Patients who were farmers, herders, fishermen, and those who visited non-designated TB institutes experienced longer delays. Additionally, patients in western China experienced longer delays than those in the eastern and central regions. Patients identified through active case-finding (ACF) demonstrated shorter intervals before seeking medical care than those detected through passive case-finding (PCF). However, despite being identified through ACF, a significant proportion of patients still experience substantial delays before receiving necessary medical attention.
Although the proportion of patient delays among elderly patients has gradually decreased, these patients still experienced longer delays than the general population in China. From 2018 to 2022, patient delay for tuberculosis in China was 20 days (IQR 6–46) (7). However, delay among the elderly in China is relatively shorter, for instance, than the 29 days observed in Portugal for those aged 65 years and older (8). This shorter delay could be attributed to factors more prevalent in the elderly population, including mild symptoms, reduced mobility, and limited access to healthcare services.
Patients aged 65–69 years experience the longest and most frequent delays. Therefore, targeted interventions, including focused health education, should prioritize this age group to effectively address patient delays. Occupational disparities, particularly among agricultural workers who face additional barriers to healthcare access due to remote living conditions and lower incomes, significantly contribute to delays. Additionally, patients seeking care at designated TB institutes have shorter delays than those at non-designated facilities, indicating greater TB awareness and a preference for specialized care when symptoms arise, which reduces overall delay times.
Furthermore, regional disparities are apparent, with patients in the western region experiencing longer delays. This may be attributed to a less developed healthcare infrastructure compared with the eastern and central regions. The World Health Organization (WHO) acknowledges older people as a priority group for active case-finding (9). This study and a previous investigation conducted in Yunnan Province, China, demonstrate that ACF can significantly reduce the time between symptom onset and healthcare access among elderly patients with TB (10). Despite these positive outcomes, the implementation of ACF to address patient delays in this population remains insufficient. These findings emphasize the crucial role of ACF in enhancing early detection, reducing delays, and mitigating the transmission of PTB (11). Although ACF effectively reduces patient delays, resource limitations hinder its implementation, particularly in the western region. Improving patients’ awareness of TB symptoms and enhancing the diagnostic skills of primary healthcare workers are also crucial for reducing delays.
Regions like Chongqing, Jilin, and Gansu exhibited the highest proportions of patient delay, while Beijing and Shanghai, with their advanced medical infrastructure and higher health literacy, demonstrated shorter delays. In 2020, per capita health expenditures in Beijing and Shanghai exceeded 10,000 yuan, roughly double that in Chongqing, Jilin, and Gansu (12). These discrepancies in health investment likely influence health literacy, particularly among the elderly. This analysis highlights the impact of systemic healthcare initiatives and regional inequalities on patient delay patterns.
This study has several limitations. First, the reliance on surveillance data may impact the calculated average patient delay due to undetected cases. Second, the timing of symptom onset relies on patient self-reporting, which is subject to recall bias and may affect the precision of patient delay measurements. Third, our dataset lacked social and environmental factors, such as health service accessibility and income, which may have influenced our results. Future studies should investigate these potential risk factors in diverse settings for a more comprehensive understanding.
Delayed health seeking among elderly PTB patients in China remains a significant concern. Targeted strategies, such as active screening, are imperative, especially in regions with notable delays. Addressing this issue is crucial to mitigating the detrimental effects of patient delay and preventing TB transmission.
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