J Almeida Santos1, P Soares2, A Leite3, R Duarte4, C Nunes5. 1. NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Avenida Padre Cruz, 1600-560 Lisboa, Portugal; National Health Institute Dr. Ricardo Jorge, Avenida Padre Cruz, 1600-560 Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Campo Mártires da Pátria, 1169-056 Lisboa, Portugal. Electronic address: jpa.santos@outlook.pt. 2. NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Avenida Padre Cruz, 1600-560 Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Campo Mártires da Pátria, 1169-056 Lisboa, Portugal. Electronic address: patseraos@gmail.com. 3. NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Avenida Padre Cruz, 1600-560 Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Campo Mártires da Pátria, 1169-056 Lisboa, Portugal. Electronic address: andreiaheitorleite@gmail.com. 4. Centro Hospitalar de Vila Nova de Gaia, Rua Conselheiro Veloso da Cruz, 4400-092 Vila Nova de Gaia, Portugal; Faculdade de Medicina da Universidade do Porto, Alameda Prof. Hernâni Monteiro, 4200-319 Porto, Portugal. Electronic address: raquelafduarte@gmail.com. 5. NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Avenida Padre Cruz, 1600-560 Lisboa, Portugal; Comprehensive Health Research Center, Universidade NOVA de Lisboa, Campo Mártires da Pátria, 1169-056 Lisboa, Portugal. Electronic address: cnunes@ensp.unl.pt.
Abstract
OBJECTIVES: To characterize patient, healthcare, and total delay in diagnosing pulmonary tuberculosis (PTB) in critical (higher PTB incidence) and non-critical (lower PTB incidence) areas and their determinants considering clinical and sociodemographic factors. STUDY DESIGN: Retrospective cohort study. METHODS: Data was retrieved from the Portuguese National Tuberculosis Surveillance System (SVIG-TB). Were included in the study all active PTB patients (n = 11,762) notified between 2008 and 2017. Spatial analysis was used to define critical and non-critical areas. Kaplan-Meier estimator, logrank test, and Cox regression were conducted, stratified by area. RESULTS: PTB cases in critical areas (n = 6594, 56.1%) presented longer patient median delay (41 vs 31days), shorter healthcare median delay (7 vs 10 days), and longer total median delay (63 vs 61days) t.han non-critical areas. Patient and total delay increased in both areas over time, while healthcare delay only increased in non-critical areas. Icn both areas, being from a high TB incidence country and alcohol abuse were associated with longer patient delays. Being female, older age, and oncologic diseases were associated with longer healthcare delays. Respiratory diseases were only associated with a longer healthcare delay in non-critical areas. Being female, older, and from a high TB incidence country were associated with a longer total delay in both areas. CONCLUSIONS: Patient delay was significantly longer in critical areas, and healthcare delay was significantly longer in non-critical areas. Several factors associated with longer delays have been identified, most of which are shared by critical and non-critical areas. Differences in patient and healthcare delay, for example, by sex, age, or country of birth, highlight the need for targeted public health interventions to help reduce these differences.
OBJECTIVES: To characterize patient, healthcare, and total delay in diagnosing pulmonary tuberculosis (PTB) in critical (higher PTB incidence) and non-critical (lower PTB incidence) areas and their determinants considering clinical and sociodemographic factors. STUDY DESIGN: Retrospective cohort study. METHODS: Data was retrieved from the Portuguese National Tuberculosis Surveillance System (SVIG-TB). Were included in the study all active PTB patients (n = 11,762) notified between 2008 and 2017. Spatial analysis was used to define critical and non-critical areas. Kaplan-Meier estimator, logrank test, and Cox regression were conducted, stratified by area. RESULTS: PTB cases in critical areas (n = 6594, 56.1%) presented longer patient median delay (41 vs 31days), shorter healthcare median delay (7 vs 10 days), and longer total median delay (63 vs 61days) t.han non-critical areas. Patient and total delay increased in both areas over time, while healthcare delay only increased in non-critical areas. Icn both areas, being from a high TB incidence country and alcohol abuse were associated with longer patient delays. Being female, older age, and oncologic diseases were associated with longer healthcare delays. Respiratory diseases were only associated with a longer healthcare delay in non-critical areas. Being female, older, and from a high TB incidence country were associated with a longer total delay in both areas. CONCLUSIONS: Patient delay was significantly longer in critical areas, and healthcare delay was significantly longer in non-critical areas. Several factors associated with longer delays have been identified, most of which are shared by critical and non-critical areas. Differences in patient and healthcare delay, for example, by sex, age, or country of birth, highlight the need for targeted public health interventions to help reduce these differences.
Authors: Bhaswar Chakma; Dulce Gomes; Patrícia A Filipe; Patrícia Soares; Bruno de Sousa; Carla Nunes Journal: BMC Public Health Date: 2022-09-28 Impact factor: 4.135