Introduction Tuberculosis (TB) is a major public health problem and one of the ten main causes of death worldwide. Portugal stands out in the western European context for having one of the highest TB rates, being one of the seven countries that has an intermedium rate of TB notifications (17.8 per 100,000 habitants, in 2016). Several authors have demonstrated that the delay in TB diagnosis is a major obstacle to epidemiologic control. Demographic, socioeconomic and clinical factors seem to influence the delay in the diagnosis and initiation of treatment. Although there are several studies on TB diagnosis and treatment delay, the majority was conducted in low-income countries, where the TB epidemic and the socioeconomic and healthcare systems are of a different nature. Studies from high-income countries are rare, and the majority focus on clinical and demographic factors. In Portugal, specifically, more studies are needed to understand the factors that influence TB treatment delay to make TB control programs more effective. Given the previous gaps, we aimed to determine the patient and healthcare system delay in TB patients, and to identify associated factors at individual and contextual level in Portugal. Methods We conducted a cross-sectional study. All TB cases notified in Portugal between 2010 and 2014 were analysed using data from the national surveillance system. Patient, healthcare system and treatment delay were computed, log-transformed, and used as outcomes. Patient delay denotes the time interval between the onset of symptoms and the first contact with healthcare system services; healthcare system delay the time interval from the first contact with healthcare services to the beginning of treatment; treatment delay is the sum the two. Between 2010 and 2014, 12,334 TB cases were notified, 5496 were excluded due to treatment delay missing data: 35% due to missing in patient delay, 24% in healthcare system delay and 41% due to both. Univariable and multivariable linear models were fitted to identify sociodemographic, contextual and clinical predictors. Results Among the 6838 patients included in this study, median patient delay, healthcare system and treatment delay were 33 (IQR = 51), 17 (IQR = 40) and 68 (IQR = 72) days, respectively. On the multivariable analysis, we observed: higher patient delay occurred in foreign patients (exponentiated beta: 1.165, 95% CI: 1.081–1.255) and in those addicted to alcohol (1.168, 1.076–1.266); higher healthcare system delay was observed in older patients (1.011, 1.009–1.013), patients with extra-pulmonary TB (2.061, 1.879–2.259), lung cancer (2.387, 1.654–3.449), sarcoidosis (3.245, 1.342–7.852) and COPD (1.297, 1.061–1.586), and in residents living further from healthcare services (1.040, 1.018–1.062). Higher total treatment delay was observed in older patients (1.004, 1.003–1.005), patients with extra-pulmonary TB (1.343, 1.275–1.413) and lung cancer (1.343, 1.101–1.639). Excluded participants had greater delays (patient delay or healthcare system delay) (P = 0.01), were younger (P = 0.01), lived in more disadvantaged areas (P = 0.008), at a further distance from healthcare services (P = 0.006), were more likely to have HIV (P Conclusion Although the median delay in diagnosis and in the initiation of treatment in Portugal matches what has been reported in other developed countries, there is still room for improvement, especially in certain sociodemographic and clinical groups. It is fundamental to increase the awareness towards TB not only among the general population, so that they become more aware of the TB signs and symptoms, but also among health professionals to increase their clinical suspicion of TB. The results from this study may contribute to a new analysis on the TB transmission dynamics, and to reformulate prevention and control strategies.
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Oct 2, 2018
Mutsawashe Mutendi + 1
Open Access