Melissa Vitale1,2,3, Christina D Lupone4,5, Aileen Kenneson-Adams1, Robinson Jaramillo Ochoa6, Tania Ordoñez6, Efráin Beltran-Ayala6,7, Timothy P Endy1,2,8, Paula F Rosenbaum1,2, Anna M Stewart-Ibarra1,2,9,10. 1. Institute for Global Health and Translational Science, SUNY Upstate Medical University, 505 Irving Avenue Suite 4200, Syracuse, NY, USA. 2. Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, USA. 3. College of Medicine, MD Program, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, USA. 4. Institute for Global Health and Translational Science, SUNY Upstate Medical University, 505 Irving Avenue Suite 4200, Syracuse, NY, USA. LuponeC@upstate.edu. 5. Department of Public Health and Preventive Medicine, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, USA. LuponeC@upstate.edu. 6. Ministry of Health, Machala, El Oro, Ecuador. 7. Department of Medicine, Universidad Técnica de Machala, Machala, El Oro, Ecuador. 8. Department of Microbiology and Immunology, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, USA. 9. Department of Medicine, SUNY Upstate Medical University, 750 East Adams Street, Syracuse, NY, USA. 10. Department of Montevideo, Inter-American Institute for Global Change Research, Montevideo, Uruguay.
Abstract
BACKGROUND: Dengue is a major emerging infectious disease, endemic throughout the tropics and subtropics, with approximately 2.5 billion people at risk globally. Active (AS) and passive surveillance (PS), when combined, can improve our understanding of dengue's complex disease dynamics to guide effective, targeted public health interventions. The objective of this study was to compare findings from the Ministry of Health (MoH) PS to a prospective AS arbovirus research study in Machala, Ecuador in 2014 and 2015. METHODS: Dengue cases in the PS system were compared to laboratory confirmed acute dengue illness cases that entered the AS study during the study period. Variables of interest included age class and sex. Outbreak detection curves by epidemiologic week, overall cumulative incidence and age-specific incidence proportions were calculated. Descriptive statistics were tabulated for all variables of interest. Chi-square tests were performed to compare demographic characteristics between the AS and PS data sets in 2014 and 2015. RESULTS: 177 and 245 cases were identified from 1/1/2014 to 12/31/2015 by PS and AS, respectively; nine cases appeared in both systems. AS identified a greater number of laboratory-confirmed cases in 2014, accounting for more than 60% of dengue cases in the study area. In 2015, the opposite trend was observed with PS identifying 60% of the dengue cases in the study area. Peak transmission time in laboratory confirmed dengue illness, as noted by AS and PS was similar in 2014, whereas earlier detection (7 weeks) was observed by AS in 2015. Younger patients were more frequently identified by PS, while older patients were identified more frequently by AS. The cumulative incidence proportion for laboratory confirmed dengue illness reported via PS to the MoH was 4.12 cases per 10,000 residents in 2014, and 2.21 cases per 10,000 residents in 2015. CONCLUSIONS: Each surveillance system captured distinct demographic subgroups within the Machala population, possibly due to differences in healthcare seeking behaviors, access to care, emerging threats of other viruses transmitted by the same mosquito vector and/or differences in clinical presentation. Integrating AS with pre-existing PS can aid in identifying additional cases in previously underdiagnosed subpopulations, improving our understanding of disease dynamics, and facilitating the implementation of timely public health interventions.
BACKGROUND: Dengue is a major emerging infectious disease, endemic throughout the tropics and subtropics, with approximately 2.5 billion people at risk globally. Active (AS) and passive surveillance (PS), when combined, can improve our understanding of dengue's complex disease dynamics to guide effective, targeted public health interventions. The objective of this study was to compare findings from the Ministry of Health (MoH) PS to a prospective AS arbovirus research study in Machala, Ecuador in 2014 and 2015. METHODS: Dengue cases in the PS system were compared to laboratory confirmed acute dengue illness cases that entered the AS study during the study period. Variables of interest included age class and sex. Outbreak detection curves by epidemiologic week, overall cumulative incidence and age-specific incidence proportions were calculated. Descriptive statistics were tabulated for all variables of interest. Chi-square tests were performed to compare demographic characteristics between the AS and PS data sets in 2014 and 2015. RESULTS: 177 and 245 cases were identified from 1/1/2014 to 12/31/2015 by PS and AS, respectively; nine cases appeared in both systems. AS identified a greater number of laboratory-confirmed cases in 2014, accounting for more than 60% of dengue cases in the study area. In 2015, the opposite trend was observed with PS identifying 60% of the dengue cases in the study area. Peak transmission time in laboratory confirmed dengue illness, as noted by AS and PS was similar in 2014, whereas earlier detection (7 weeks) was observed by AS in 2015. Younger patients were more frequently identified by PS, while older patients were identified more frequently by AS. The cumulative incidence proportion for laboratory confirmed dengue illness reported via PS to the MoH was 4.12 cases per 10,000 residents in 2014, and 2.21 cases per 10,000 residents in 2015. CONCLUSIONS: Each surveillance system captured distinct demographic subgroups within the Machala population, possibly due to differences in healthcare seeking behaviors, access to care, emerging threats of other viruses transmitted by the same mosquito vector and/or differences in clinical presentation. Integrating AS with pre-existing PS can aid in identifying additional cases in previously underdiagnosed subpopulations, improving our understanding of disease dynamics, and facilitating the implementation of timely public health interventions.
Entities:
Keywords:
Active surveillance; Arboviral; Dengue; Ecuador; Latin America; Passive surveillance; Public health intervention
Authors: Irina Chis Ster; Alejandro Rodriguez; Natalia Cristina Romero; Andrea Lopez; Martha Chico; Joel Montgomery; Philip Cooper Journal: BMJ Open Date: 2020-10-16 Impact factor: 2.692
Authors: Suyanne Freire de Macêdo; Kellyanne Abreu Silva; Renata Borges de Vasconcelos; Izautina Vasconcelos de Sousa; Lyvia Patrícia Soares Mesquita; Roberta Duarte Maia Barakat; Hélida Melo Conrado Fernandes; Ana Carolina Melo Queiroz; Gerarlene Ponte Guimarães Santos; Valter Cordeiro Barbosa Filho; Gabriel Carrasquilla; Andrea Caprara; José Wellington de Oliveira Lima Journal: Int J Environ Res Public Health Date: 2021-01-31 Impact factor: 3.390