Public health surveillance systems play a critical role in monitoring disease outbreaks and ensuring timely interventions. In Ghana, these systems are essential for managing various diseases effectively. However, their reliability and accuracy remain under scrutiny due to potential biases and inconsistencies. This research will employ a mixed-methods approach involving quantitative analysis of surveillance data alongside qualitative interviews with stakeholders. A logistic regression model (e. g. , p = racexp (eta) 1 + exp (eta) ) will be used to analyse the impact of system design on data accuracy, where p represents the probability of a positive outcome and eta reflects its associated log odds. The preliminary analysis reveals that over 70% of reported cases were consistent with national health records, indicating high reliability in routine surveillance activities. However, there is variability in reporting delays across different regions, which requires targeted interventions to improve timeliness. This study provides valuable insights into the current state of public health surveillance systems in Ghana and offers evidence-based recommendations for system enhancement. Stakeholders are advised to implement regular training sessions on data collection protocols and maintain consistent communication channels with healthcare facilities. Additionally, periodic audits should be conducted to ensure ongoing accuracy and reliability. public health surveillance, Ghana, quasi-experimental design, logistic regression
Kwesi et al. (Tue,) studied this question.
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