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Chakma, B., Gomes, D., Filipe, P. A., Soares, P., Sousa, B. De & Nunes, C. (2022). A temporal analysis on patient and health service delays in pulmonary tuberculosis in Portugal: Inter and intra‑regional differences and in(equalities) between gender and age. BMC Public Health . 22 (1)

B. Chakma et al.,  "A temporal analysis on patient and health service delays in pulmonary tuberculosis in Portugal: Inter and intra‑regional differences and in(equalities) between gender and age", in BMC Public Health , vol. 22, no. 1, 2022

@article{chakma2022_1733253560496,
	author = "Chakma, B. and Gomes, D. and Filipe, P. A. and Soares, P. and Sousa, B. De and Nunes, C.",
	title = "A temporal analysis on patient and health service delays in pulmonary tuberculosis in Portugal: Inter and intra‑regional differences and in(equalities) between gender and age",
	journal = "BMC Public Health ",
	year = "2022",
	volume = "22",
	number = "1",
	doi = "10.1186/s12889-022-14216-3",
	url = "https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-14216-3"
}

TY  - JOUR
TI  - A temporal analysis on patient and health service delays in pulmonary tuberculosis in Portugal: Inter and intra‑regional differences and in(equalities) between gender and age
T2  - BMC Public Health 
VL  - 22
IS  - 1
AU  - Chakma, B.
AU  - Gomes, D.
AU  - Filipe, P. A.
AU  - Soares, P.
AU  - Sousa, B. De
AU  - Nunes, C.
PY  - 2022
SN  - 1471-2458
DO  - 10.1186/s12889-022-14216-3
UR  - https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-022-14216-3
AB  - Background: Tuberculosis (TB) diagnosis and treatment delays increase the period of infectiousness, making TB control difficult and increasing the fatality rates. This study aimed to determine the evolution of health care service delay (time between the patient’s first contact with the health service and the diagnosis/start of treatment) and patient delay (time between onset symptoms date and the date of first contact with health services) for Pulmonary Tuberculosis (PTB) in Portugal between 2008 and 2017 across different regions, age groups and gender. Methods: An exploratory analysis was performed, trends of both delays were studied, and 36 months forecasts were generated. We used the permutation test to test differences between groups and the Seasonal and Trend decomposition using Loess (STL) method and Autoregressive Integrated Moving Average (ARIMA) models for forecasting for both Health and Patient delays. We used data from notified PTB cases in mainland Portugal between 2008 and 2017, provided by the national surveillance system. Results: Health delays remained relatively constant while patient delays increased. Females had significantly higher health delays in some regions. Individuals older than 64 had higher health delays than younger individuals, while patient delay for working-age individuals between 15 and 64 years old, presents higher patient delay. Conclusions: Forecasts presage that the upward trend of the delays is unlikely to fall in the coming years. It is important to understand the evolution of the delays and predict how these will evolve. Our understanding of the delays behaviours will contribute to better health policies and resources allocation. 
ER  -