Evaluation of the influenza sentinel surveillance system in Madagascar, 2009–2014

Abstract

ProblemEvaluation of influenza surveillance systems is poor, especially in Africa.ApproachIn 2007, the Institut Pasteur de Madagascar and the Malagasy Ministry of Public Health implemented a countrywide system for the prospective syndromic and virological surveillance of influenza-like illnesses. In assessing this system’s performance, we identified gaps and ways to promote the best use of resources. We investigated acceptability, data quality, flexibility, representativeness, simplicity, stability, timeliness and usefulness and developed qualitative and/or quantitative indicators for each of these attributes.Local settingUntil 2007, the influenza surveillance system in Madagascar was only operational in Antananarivo and the observations made could not be extrapolated to the entire country.Relevant changesBy 2014, the system covered 34 sentinel sites across the country. At 12 sites, nasopharyngeal and/or oropharyngeal samples were collected and tested for influenza virus. Between 2009 and 2014, 177 718 fever cases were detected, 25 809 (14.5%) of these fever cases were classified as cases of influenza-like illness. Of the 9192 samples from patients with influenza-like illness that were tested for influenza viruses, 3573 (38.9%) tested positive. Data quality for all evaluated indicators was categorized as above 90% and the system also appeared to be strong in terms of its acceptability, simplicity and stability. However, sample collection needed improvement.Lessons learntThe influenza surveillance system in Madagascar performed well and provided reliable and timely data for public health interventions. Given its flexibility and overall moderate cost, this system may become a useful platform for syndromic and laboratory-based surveillance in other low-resource settings.

An evaluation of the Zambia influenza sentinel surveillance system, 2011–2017

“…For consistency and comparability of findings we used the evaluation method and scoring system utilized for influenza surveillance evaluations conducted in other African countries [17][18][19][20][21]. Each of the above mentioned attribute was evaluated using pre-defined quantitative or qualitative indicators.…”

“…For each quantitative indicator we first obtained the proportion (expressed as percentage) of the outcome of interest over the total [18][19][20][21]. For instance, for the indicator on completeness of laboratory testing (one of the indicators used to evaluate the data quality and completeness attribute) we divided the number of samples with available influenza results by the total number of samples collected and received by the laboratory.…”

“…For instance, for the indicator on completeness of laboratory testing (one of the indicators used to evaluate the data quality and completeness attribute) we divided the number of samples with available influenza results by the total number of samples collected and received by the laboratory. Subsequently, similar to other influenza surveillance evaluations conducted in Africa, we used a scale from 1 to 3 to provide a score for each quantitative indicator as follows: < 60% (as obtained in the example above) scored 1 (weak performance); 60-79% scored 2 (moderate performance); ≥80% scored 3 (good performance) [18][19][20][21]. For indicators for which a proportion over a total could not be obtained (qualitative indicators) a score was assigned based on the same scale using expert consensus.…”

“…Thereafter, the scores assigned to each indicator were averaged for all indicators evaluated within each attribute to provide an overall score for each surveillance attribute assessed in this study. An overall score for the surveillance system was obtained by averaging the scores of all evaluated indicators as previously described [18][19][20][21]. All data generated by the surveillance system during the review period were included in the evaluation.…”

“…Such evaluations are useful to identify shortfalls, improve performance and provide evidence of data reliability for policy making and public health interventions. However, despite recent progress in describing the epidemiology and burden of influenza in sub-Saharan Africa [8][9][10][11][12][13][14][15][16], only a few countries have implemented a comprehensive evaluation of their surveillance systems [17][18][19][20][21].…”