Htet Kyaw Ko Ko, Chongsuvivatwong Virasakdi, Aung Si Thu
Department of Medical Research, Ministry of Health and Sports, Pyin Oo Lwin, Myanmar.
Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Hat Yai, 90110, Thailand.
Trop Med Health. 2021 Jan 7;49(1):3. doi: 10.1186/s41182-020-00292-x.
Globally, using tuberculosis signs and symptoms (TB-SS) as a screening tool has become less important due to its low sensitivity and specificity. We analyzed data from the Myanmar National Tuberculosis (TB) prevalence survey in 2010. The various TB screening models were developed to predict TB by using logistic regression analysis, and their performance on TB prediction was compared by the measures of overall performance, calibration and discrimination ability, and sensitivity and specificity to determine whether social pathology characteristics could be used as a TB screening tool.
Among 51,367 participants, 311 (0.6%) had bacteriologically confirmed TB, of which 37.2% were asymptomatic and 2% had a normal chest X-ray. Out of 32 various combinations of signs and symptoms, having any signs and symptoms gave the best sensitivity of 59.8% and specificity of 67.2%, but chest X-ray (CXR) alone gave the highest sensitivity (95.1%) and specificity (86.3%). The next best combination was cough only with a sensitivity of 24.4% and specificity of 85%. Other combinations had poor sensitivity (< 10%). Among various TB screening models, the overall performance R was higher in the combined models of social pathology and TB signs and symptoms as well as the social pathology model, compared to TB-SS models (> 10% versus < 3%), although all TB screening models were perfect to predict TB (Brier score = 0). The social pathology model shows a better calibration, more closer to 45° line of calibration plot with Hosmer-Lemeshow test p value = 0.787, than the combined models while it had a better discrimination ability in area under the curve, AUC = 80.4%, compared to TB-SS models with any signs and symptoms, AUC = 63.5% and with any cough, AUC = 57.1% (DeLong p value = 0.0001). Moreover, at the propensity score cutoff value ≥ 0.0053, the combined and social pathology models had sensitivity of ~ 80% and specificity of ~ 70%. The highest population attributable fraction to predict TB by social pathology characteristics was male gender (42.6%), age ≥ 55 years (31.0%), and underweight (30.4%).
Over one-third of bacteriologically confirmed TB was asymptomatic. The conventional TB-SS screening tool using any TB signs and symptoms had a lower sensitivity and specificity compared to CXR and social pathology screening tools. The social pathology characteristics as TB screening tool had good calibration and can improve the discrimination ability to predict TB than TB-SS screenings and should be encouraged.
在全球范围内，由于结核病体征和症状（TB-SS）的敏感性和特异性较低，将其用作筛查工具的重要性已降低。我们分析了2010年缅甸全国结核病患病率调查的数据。通过逻辑回归分析开发了各种结核病筛查模型来预测结核病，并通过总体性能、校准和鉴别能力以及敏感性和特异性等指标比较了它们在结核病预测方面的表现，以确定社会病理学特征是否可作为结核病筛查工具。
在51367名参与者中，311人（0.6%）经细菌学确诊为结核病，其中37.2%无症状，2%胸部X线检查正常。在32种体征和症状的不同组合中，出现任何体征和症状的敏感性最高，为59.8%，特异性为67.2%，但仅胸部X线检查（CXR）的敏感性最高（95.1%），特异性最高（86.3%）。次优组合是仅咳嗽，敏感性为24.4%，特异性为85%。其他组合的敏感性较差（<10%）。在各种结核病筛查模型中，与TB-SS模型（>10%对<3%）相比，社会病理学与结核病体征和症状的组合模型以及社会病理学模型的总体性能R更高，尽管所有结核病筛查模型在预测结核病方面都很完美（Brier评分=0）。社会病理学模型显示出更好的校准，与组合模型相比，更接近校准图的45°线，Hosmer-Lemeshow检验p值=0.787，同时与有任何体征和症状的TB-SS模型（曲线下面积，AUC=63.5%）和有任何咳嗽的TB-SS模型（AUC=57.1%）相比，其在曲线下面积方面具有更好的鉴别能力，AUC=80.4%（DeLong p值=0.0001）。此外，在倾向得分截止值≥0.0053时，组合模型和社会病理学模型的敏感性约为80%，特异性约为70%。通过社会病理学特征预测结核病的最高人群归因分数是男性（42.6%）、年龄≥55岁（31.0%）和体重过轻（30.4%）。
超过三分之一的细菌学确诊结核病患者无症状。与胸部X线检查和社会病理学筛查工具相比，使用任何结核病体征和症状的传统TB-SS筛查工具的敏感性和特异性较低。作为结核病筛查工具的社会病理学特征具有良好的校准，并且与TB-SS筛查相比，在预测结核病方面可以提高鉴别能力，应予以鼓励。
