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
Bibliographic Data
| ID | 15362041 |
|---|---|
| Authors | Bhaswar Chakma (University of Évora), Dulce Gomes (0000-0001-8085-1945, University of Évora, corresponding author), Patrícia A Filipe (University of Évora), Patrícia Soares (0000-0001-5033-9115, Universidade Nova de Lisboa), Bruno de Sousa (0000-0001-9918-8100, University of Coimbra), Carla Nunes (0000-0003-4562-1057, Universidade Nova de Lisboa) |
| Year | 2022 |
| Volume | 22 |
| Issue | 1 |
| Publication date | 2022-09-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | BMC Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Publisher | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-022-14216-3 |
| OpenAlex | W4297492732 |
| Language | EN |
| Citations received | 2 |
| References cited | 21 |
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
Biostatistics · Environmental health · Health services · Pathology · Population · Public health · Pulmonary tuberculosis · Tuberculosis · Chronic Disease Management Strategies · Demography · Global Health Care Issues · Medicine · Primary Care and Health Outcomes · Epidemiology · Pediatrics
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| Unique citing works | 2 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |