| Literature DB >> 35983359 |
Ling Chen1, Xiaodan Wang1, Xudong Jia2, Yuanbo Lan1, Haibo Yi2, Xiaomin Wang2, Peng Xu2.
Abstract
Background: As one of the top three high tuberculosis (TB) burden countries, China is a country where the overall TB incidence continues to decline. However, due to its large population and area, the increased TB burden exists in regional areas.Entities:
Keywords: diagnosis; epidemic; rifampicin-resistant TB; tendency; tuberculosis (TB)
Mesh:
Substances:
Year: 2022 PMID: 35983359 PMCID: PMC9381004 DOI: 10.3389/fpubh.2022.941183
Source DB: PubMed Journal: Front Public Health ISSN: 2296-2565
Figure 1The annual distributions of TB cases (A), M.tb confirmed TB cases (B) and RR-TB cases (C). TB, tuberculosis; RR-TB, rifampicin-resistant TB; AFB, acid-fast bacilli.
Figure 2The Venn diagram of M.tb positive TB cases. This Venn diagram was created by jvenn web application (7). The oval-shaped areas and numbers indicated the M.tb positive TB cases that were detected by different methods, which green indicated acid-fast bacilli (AFB), blue indicated Löwenstein-Jensen (L-J) solid media culture, pink indicated GeneXpert MTB/RIF (Xpert) and yellow indicated Loopamp MTBC Detection Kit (LAMP).
Figure 3The proportions of RR-TB in different areas of China. The proportions of RR-TB at the beginning (left) and end (right) of each study were marked on the map of China by the color of the circle. A warmer color indicated a higher RR-TB proportion, while a cooler color indicated the opposite. The size of cycles indicated the size of the study population. The studied periods and the provinces were labeled next to the circles. The light blue oval area indicated the southeastern coast of China, where studies showed decreased or stable RR-TB proportions. The data of Anhui was obtained from Meng (8), Beijing from Zhang et al. (9), Guangdong from Han et al. (10), Guizhou from this study (*), Henan from Wang et al. (11), Shaanxi from Lei et al. (12), Shandong from Song et al. (13), Shanghai from Wang et al. (14), Sichuan from Zhou et al. (15), Tianjin from Bai et al. (16), Zhejiang (2011 and 2015) from Li et al. (17) and Zhejiang (2016–2018) from Zheng et al. (18).
Univariate and multivariate logistic regression analysis of characteristics associated with rifampicin resistance.
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| Male | 612 | 85.2% | 106 | 14.8% | 718 | 1.00 | 1.00 | ||
| Female | 358 | 85.4% | 61 | 14.6% | 419 | 0.98 (0.70–1.38) | 0.925 | 1.04 (0.72–1.49) | 0.829 |
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| ≤ 20 | 108 | 86.4% | 17 | 13.6% | 125 | 1.00 | 1.00 | ||
| 21–40 | 237 | 78.5% | 65 | 21.5% | 302 | 1.74 (0.98–3.11) | 0.061 | 1.49 (0.82–2.72) | 0.192 |
| 41–60 | 284 | 83.0% | 58 | 17.0% | 342 | 1.30 (0.72–2.33) | 0.382 | 0.93 (0.50–1.72) | 0.816 |
| ≥61 | 341 | 92.7% | 27 | 7.3% | 368 | 0.50 (0.26–0.96) |
| 0.43 (0.22–0.83) |
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| New case | 786 | 90.3% | 84 | 9.7% | 870 | 1.00 | 1.00 | ||
| Previously treated case | 184 | 68.9% | 83 | 31.1% | 267 | 4.22 (2.99–5.95) |
| 4.24 (2.97–6.05) |
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| 970 | 85.3% | 167 | 14.7% | 1,137 | ||||
aP-values in bold denote statistical significance at the P < 0.05 level. RIF, rifampicin; CI, confidence interval; OR, odds ratio.