Vaccine safety surveillance in Kenya using GAIA standards: A feasibility assessment of existing national and subnational research and program systems.

BACKGROUND: Active surveillance systems for monitoring vaccine safety among pregnant women address some of the limitations of a current passive surveillance approach utilized in low- and middle-income countries (LMIC). However, few active surveillance systems in LMIC exist. Our study assessed the feasibility of utilizing three existing data collection systems in Kenya for active surveillance of maternal immunization and to assess the applicability of Global Alignment of Immunization Safety Assessment in pregnancy (GAIA) case definitions that were initially developed for clinical trials within these systems. METHODS: We assessed applicability of GAIA case definition for maternal Tetanus Toxoid exposure, stillbirth, low birth weight, small for gestational age, Neonatal Invasive Blood Stream Infection (NIBSI), prematurity and neonatal death in two routine web-based health information systems (Kenya EMR and DHIS-2), and a web-based population-based pregnancy research platform (ANCOV1) in Kenya. RESULTS: All three HIS were capable of reporting selected outcomes to varying degrees of GAIA certainty. The ANCOV platform was the most robust in collecting and collating clinical data for effective maternal pharmacovigilance. The utilization of facility- and district-aggregated data limits the usefulness of DHIS-2 in pharmacovigilance as currently operationalized. While the Kenya EMR contained individual level data and meets the key considerations for effective pharmacovigilance, it was used primarily for HIV care and treatment records in a small proportion of health facilities and would require additional resources to expand to all antenatal care facilities and to link maternal and infant records. DISCUSSION: Population-based research studies may offer a responsive short-term option for implementing maternal vaccine pharmacovigilance in LMICs. However, the foundation exists for long-term capacity building within the national health electronic data systems to provide this critical service as well as ensure participation of the country in international studies on maternal vaccine safety.

What are the most sensitive and specific sign and symptom combinations for influenza in patients hospitalized with acute respiratory illness? Results from western Kenya, January 2007-July 2010.

Influenza causes severe illness and deaths, and global surveillance systems use different clinical case definitions to identify patients for diagnostic testing. We used data collected during January 2007-July 2010 at hospital-based influenza surveillance sites in western Kenya to calculate sensitivity, specificity, positive predictive value, and negative predictive value for eight clinical sign/symptom combinations in hospitalized patients with acute respiratory illnesses, including severe acute respiratory illness (SARI) (persons aged 2-59 months: cough or difficulty breathing with an elevated respiratory rate or a danger sign; persons aged ≥5 years: temperature ≥38 °C, difficulty breathing, and cough or sore throat) and influenza-like illness (ILI) (all ages: temperature ≥38 °C and cough or sore throat). Overall, 4800 persons aged ≥2 months were tested for influenza; 416 (9%) had laboratory-confirmed influenza infections. The symptom combination of cough with fever (subjective or measured ≥38 °C) had high sensitivity [87·0%, 95% confidence interval (CI) 83·3-88·9], and ILI had high specificity (70·0%, 95% CI 68·6-71·3). The case definition combining cough and any fever is a simple, sensitive case definition for influenza in hospitalized persons of all age groups, whereas the ILI case definition is the most specific. The SARI case definition did not maximize sensitivity or specificity.