A comparison of passive surveillance and active cluster-based surveillance for dengue fever in southern coastal Ecuador

• Main Authors: Melissa Vitale (Author), Christina D. Lupone (Author), Aileen Kenneson-Adams (Author), Robinson Jaramillo Ochoa (Author), Tania Ordoñez (Author), Efráin Beltran-Ayala (Author), Timothy P. Endy (Author), Paula F. Rosenbaum (Author), Anna M. Stewart-Ibarra (Author)
• Format: Book
• Published: BMC, 2020-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12889-020-09168-5; 1471-2458
• Keywords: Dengue; Active surveillance; Passive surveillance; Latin America; Arboviral; Public health intervention; Public aspects of medicine; RA1-1270
• Source: BMC Public Health, Vol 20, Iss 1, Pp 1-10 (2020)

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.