Dataset Information

Characteristics of seasonal influenza A and B in Latin America: Influenza surveillance data from ten countries.

ABSTRACT:

Introduction

The increased availability of influenza surveillance data in recent years justifies an actual and more complete overview of influenza epidemiology in Latin America. We compared the influenza surveillance systems and assessed the epidemiology of influenza A and B, including the spatio-temporal patterns of influenza epidemics, in ten countries and sub-national regions in Latin America.

Methods

We aggregated the data by year and country and characteristics of eighty-two years were analysed. We calculated the median proportion of laboratory-confirmed influenza cases caused by each virus strain, and compared the timing and amplitude of the primary and secondary peaks between countries.

Results

37,087 influenza cases were reported during 2004-2012. Influenza A and B accounted for a median of 79% and, respectively, 21% of cases in a year. The percentage of influenza A cases that were subtyped was 82.5%; for influenza B, 15.6% of cases were characterized. Influenza A and B were dominant in seventy-five (91%) and seven (9%) years, respectively. In half (51%) of the influenza A years, influenza A(H3N2) was dominant, followed by influenza A(H1N1)pdm2009 (41%) and pre-pandemic A(H1N1) (8%). The primary peak of influenza activity was in June-September in temperate climate countries, with little or no secondary peak. Tropical climate countries had smaller primary peaks taking place in different months and frequently detectable secondary peaks.

Conclusions

We found that good influenza surveillance data exists in Latin America, although improvements can still be made (e.g. a better characterization of influenza B specimens); that influenza B plays a considerable role in the seasonal influenza burden; and that there is substantial heterogeneity of spatio-temporal patterns of influenza epidemics. To improve the effectiveness of influenza control measures in Latin America, tropical climate countries may need to develop innovative prevention strategies specifically tailored to the spatio-temporal patterns of influenza in this region.

SUBMITTER: Caini S

PROVIDER: S-EPMC5367818 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characteristics of seasonal influenza A and B in Latin America: Influenza surveillance data from ten countries.

Caini Saverio S Alonso Wladimir J WJ Balmaseda Angel A Bruno Alfredo A Bustos Patricia P Castillo Leticia L de Lozano Celina C de Mora Doménica D Fasce Rodrigo A RA Ferreira de Almeida Walquiria Aparecida WA Kusznierz Gabriela F GF Lara Jenny J Matute Maria Luisa ML Moreno Brechla B Pessanha Henriques Claudio Maierovitch CM Rudi Juan Manuel JM El-Guerche Séblain Clotilde C Schellevis François F Paget John J

PloS one 20170327 3

<h4>Introduction</h4>The increased availability of influenza surveillance data in recent years justifies an actual and more complete overview of influenza epidemiology in Latin America. We compared the influenza surveillance systems and assessed the epidemiology of influenza A and B, including the spatio-temporal patterns of influenza epidemics, in ten countries and sub-national regions in Latin America.<h4>Methods</h4>We aggregated the data by year and country and characteristics of eighty-two ...[more]

PMID: 28346498

Similar Datasets

Project description:BackgroundDespite widespread utilization of influenza vaccines, effectiveness (VE) has not been routinely measured in Latin America.MethodsWe used a case test-negative control design to estimate trivalent inactivated influenza VE against laboratory-confirmed influenza among hospitalized children aged 6months-5years and adults aged ≥60years which are age-groups targeted for vaccination. We sought persons with severe acute respiratory infections (SARI), hospitalized at 71 sentinel hospitals in Argentina, Brazil, Chile, Colombia, Costa Rica, El Salvador, Honduras, Panama, and Paraguay during January-December 2013. Cases had an influenza virus infection confirmed by real-time reverse transcription PCR (rRT-PCR); controls had a negative rRT-PCR result for influenza viruses. We used a two-stage random effects model to estimate pooled VE per target age-group, adjusting for the month of illness onset, age and preexisting medical conditions.ResultsWe identified 2620 SARI patients across sites: 246 influenza cases and 720 influenza-negative controls aged ≤5years and 448 cases and 1206 controls aged ≥60years. The most commonly identified subtype among participants (48%) was the influenza A(H1N1)pdm09 virus followed by influenza A(H3N2) (34%) and influenza B (18%) viruses. Among children, the adjusted VE of full vaccination (one dose for previously vaccinated or two if vaccine naïve) against any influenza virus SARI was 47% (95% confidence interval [CI]: 14-71%); VE was 58% (95% CI: 16-79%) against influenza A(H1N1)pdm09, and 65% (95% CI: -9; 89%) against influenza A(H3N2) viruses associated SARI. Crude VE of full vaccination against influenza B viruses associated SARI among children was 3% (95% CI: -150; 63). Among adults aged ≥60years, adjusted VE against any influenza SARI was 48% (95% CI: 34-60%); VE was 54% (95% CI: 37-69%) against influenza A(H1N1)pdm09, 43% (95% CI: 18-61%) against influenza A(H3N2) and 34% (95% CI: -4; 58%) against B viruses associated SARI.ConclusionInfluenza vaccine provided moderate protection against severe influenza illness among fully vaccinated young children and older adults, supporting current vaccination strategies.

Project description:BackgroundNovel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates.ObjectiveThe aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America.MethodsA machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information.ResultsOur results show that ARGO-like models' predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available.ConclusionsWe demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates.

Project description:In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system.The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI.We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system.Nearly half of the participants (47.1%, 89/189) sent one or more reports. We received 468 reports, 83.5% (391/468) via text message and 16.4% (77/468) via app. Nine-tenths of the reports (93.6%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1% (41/124) of reported cases. Medical attention was sought in 27.4% (34/124) of reported cases. The cost of identifying an ILI case was US $6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017).Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people's fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions.

Dataset Information

Characteristics of seasonal influenza A and B in Latin America: Influenza surveillance data from ten countries.

Introduction

Methods

Results

Conclusions

Publications

Characteristics of seasonal influenza A and B in Latin America: Influenza surveillance data from ten countries.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets