Background
Influenza is a major cause of morbidity and mortality in Africa. However, a lack of epidemiological data remains for this pathology, and the performances of the influenza-like illness (ILI) case definitions used for sentinel surveillance have never been evaluated in Senegal. This study aimed to i) assess the performance of three different ILI case definitions, adopted by the WHO, USA-CDC (CDC) and European-CDC (ECDC) and ii) identify clinical factors associated with a positive diagnosis for Influenza in order to develop an algorithm fitted for the Senegalese context.
Methods
All 657 patients with a febrile pathological episode (FPE) between January 2013 and December 2016 were followed in a cohort study in two rural villages in Senegal, accounting for 1653 FPE observations with nasopharyngeal sampling and influenza virus screening by rRT-PCR. For each FPE, general characteristics and clinical signs presented by patients were collected. Sensitivity, Specificity, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) for the three ILI case definitions were assessed using PCR result as the reference test. Associations between clinical signs and influenza infection were analyzed using logistic regression with generalized estimating equations. Sore throat, arthralgia or myalgia were missing for children under 5 years.
Results
WHO, CDC and ECDC case definitions had similar sensitivity (81.0%; 95%CI: 77.0–85.0) and NPV (91.0%; 95%CI: 89.0–93.1) while the WHO and CDC ILI case definitions had the highest specificity (52.0%; 95%CI: 49.1–54.5) and PPV (32.0%; 95%CI: 30.0–35.0). These performances varied by age groups. In children < 5 years, the significant predictors of influenza virus infection were cough and nasal discharge. In patients from 5 years, cough, nasal discharge, sore throat and asthenia grade 3 best predicted influenza infection. The addition of “nasal discharge” as a symptom to the WHO case definition decreased sensitivity but increased specificity, particularly in the pediatric population.
Conclusion
In summary, all three definitions studies (WHO, ECDC & CDC) have similar performance, even by age group. The revised WHO ILI definition could be chosen for surveillance purposes for its simplicity. Symptomatic predictors of influenza virus infection vary according the age group.
Knowing the burden of influenza is helpful for policy decisions. Here we estimated the contribution of influenza-like illness (ILI) visits associated with laboratory-confirmed influenza among all clinic visits in a Senegal sentinel network. ILI data from ten sentinel sites were collected from January 2013 to December 2015. ILI was defined as an axillary measured fever of more than 37.5 °C with a cough or a sore throat. Collected nasopharyngeal swabs were tested for influenza viruses by rRT-PCR. Influenza-associated ILI was defined as ILI with laboratory-confirmed influenza. For the influenza disease burden estimation, we used all-case outpatient visits during the study period who sought care at selected sites. Of 4030 ILI outpatients tested, 1022 were influenza positive. The estimated proportional contribution of influenza-associated ILI was, per 100 outpatients, 1.2 (95% CI 1.1-1.3), 0.32 (95% CI 0.28-0.35), 1.11 (95% CI 1.05-1.16) during 2013, 2014, 2015, respectively. The age-specific outpatient visits proportions of influenza-associated ILI were higher among children under 5 years (0.68%, 95% CI: 0.62-0.70). The predominant virus during years 2013 and 2015 was influenza B while A/H3N2 subtype was predominant during 2014. Influenza viruses cause a substantial burden of outpatient visits particularly among children under 5 of age in Senegal and highlight the need of vaccination in risk groups.
The World Health Organization (WHO) African Region set a goal for regional measles elimination by 2020; however, regional measles incidence was 125/1,000,000 in 2012. To support elimination efforts, the WHO and U.S. Centers for Disease Control and Prevention developed a tool to assess performance of measles control activities and identify high-risk areas at the subnational level. The tool uses routinely collected data to generate district-level risk scores across four categories: population immunity, surveillance quality, program performance, and threat assessment. To pilot test this tool, we used retrospective data from 2006 to 2008 to identify high-risk districts in Senegal; results were compared with measles case-based surveillance data from 2009 when Senegal experienced a large measles outbreak. Seventeen (25%) of 69 districts in Senegal were classified as high or very high risk. The tool highlighted how each of the four categories contributed to the total risk scores for high or very high risk districts. Measles case-based surveillance reported 986 cases during 2009, including 368 laboratory-confirmed, 540 epidemiologically linked, and 78 clinically compatible cases. The seven districts with the highest numbers of laboratory-confirmed or epidemiologically linked cases were within the capital region of Dakar. All except one of these seven districts were estimated to be high or very high risk, suggesting that districts identified as high risk by the tool have the potential for measles outbreaks. Prospective use of this tool is recommended to help immunization and surveillance program managers identify high-risk areas in which to strengthen specific programmatic weaknesses and mitigate risk for potential measles outbreaks.
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