Analysis
The data was transcribed from the laboratory registers into Microsoft excel 2016 and exported as CSV file for analysis purpose. Data analysis was carried out using an open software R, (R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.) and use of two packages, mStats package, (MyoMinnOo (2020). mStats: Epidemiological Data Analysis. R package version 3.4.0.) and dplyr (Hadley Wickham, Romain François, Lionel Henry and KirillMüller (2021). dplyr: A Grammar of Data Manipulation. Rpackage version 1.0.7. https://CRAN.R-project.org/package=dplyr)
Analysis included all explanatory variables which included the (1) age, (2) sex, (3) month of the tests advised and (4) the site of the health centers. While the review of registers for more than just the two years have been considered, it was not feasible as two of the health centers did not have the laboratory registers before 2019.
Information on main characteristics of tests advised was summarized based on the explanatory variable of age, sex, months/seasons, health centre and the test results. Primary outcome was test results defined as positive across different age groups, gender, health centres and season.We identified predictors of positive results using a multi-variable logistic regression model. Multiple models with all the predictor variables were tested and the final model was selected based on the lowest AIC score.