Background: Influenza is a significant contributor to acute respiratory infections (ARI), and children < 5 years are at increased risk of severe influenza disease. In Kenya the influenza vaccine is not included in the Kenya Expanded Programme on Immunization (KEPI). To inform roll-out of a national influenza vaccination program, we implemented an influenza vaccine demonstration project in Nakuru and Mombasa counties in Kenya from 2019 to 2021 and set out to establish factors driving influenza vaccine acceptance and hesitancy among caregivers of children aged 6–23 months. Methods: Using semi-structured questionnaires, we conducted eight focus group discussions among community members and twelve key informant interviews among healthcare workers to elicit both lay and expert opinions. Thematic analysis of the interviews was conducted using the World Health Organization's “3 Cs” model of vaccine hesitancy to determine reasons for acceptance or hesitancy of the influenza vaccine. Results: The influenza vaccine was well received among community members and healthcare workers though concerns were raised. Vaccine hesitancy was fuelled by misconceptions about reasons for introducing the vaccine (confidence), perceptions that influenza was not a serious disease (complacency) and administrative fees required at some facilities (convenience). Despite the use of various advocacy, communication and social mobilisation strategies targeted at educating the community on the influenza disease and importance of vaccination, there remained a perception of inadequate reach of the sensitization among some community members. Contextual factors such as the COVID-19 pandemic affected uptake, and parents expressed concern over the growing number of vaccines recommended for children. Conclusion: Despite lingering concerns, caregivers had their children vaccinated indicating that vaccine hesitancy exists, even among those who accepted the vaccine for their children. Efforts targeted at increasing confidence in and reducing misconceptions towards vaccines through effective communication strategies, are likely to lead to increased vaccine uptake. © 2024

BACKGROUND: Effective public health surveillance systems are crucial for early detection and response to outbreaks. In 2016, Kenya transitioned its surveillance system from a standalone web-based surveillance system to the more sustainable and integrated District Health Information System 2 (DHIS2). As part of Global Health Security Agenda (GHSA) initiatives in Kenya, training on use of the new system was conducted among surveillance officers. We evaluated the surveillance indicators during the transition period in order to assess the impact of this training on surveillance metrics and identify challenges affecting reporting rates. METHODS: From February to May 2017, we analysed surveillance data for 13 intervention and 13 comparison counties. An intervention county was defined as one that had received refresher training on DHIS2 while a comparison county was one that had not received training. We evaluated the impact of the training by analysing completeness and timeliness of reporting 15 weeks before and 12 weeks after the training. A chi-square test of independence was used to compare the reporting rates between the two groups. A structured questionnaire was administered to the training participants to assess the challenges affecting surveillance reporting. RESULTS: The average completeness of reporting for the intervention counties increased from 45 to 62%, i.e. by 17 percentage points (95% CI 16.14-17.86) compared to an increase from 49 to 52% for the comparison group, i.e. by 3 percentage points (95% CI 2.23-3.77). The timeliness of reporting increased from 30 to 51%, i.e. by 21 percentage points (95% CI 20.16-21.84) for the intervention group, compared to an increase from 31 to 38% for the comparison group, i.e.by 7 percentage points (95% CI 6.27-7.73). Major challenges for the low reporting rates included lack of budget support from government, lack of airtime for reporting, health workers strike, health facilities not sending surveillance data, use of wrong denominator to calculate reporting rates and surveillance officers having other competing tasks. CONCLUSIONS: Training plays an important role in improving public health surveillance reporting. However, to improve surveillance reporting rates to the desired national targets, other challenges affecting reporting must be identified and addressed accordingly.

BACKGROUND: Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya. METHODS: RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs. RESULTS: 27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32-0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25-3.33). CONCLUSIONS: This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.