User Training Evaluation of Surveillance Outbreak Response Management & Analysis System (SORMAS) among Disease Surveillance Notification Officers (DSNOs) in Nigeria 2018 (Preprint)
Authors: Tom-Aba Daniel , Silenou Bernard Chawo , Arinze Chinedu Chukwujekwu , Oyiri Ferdinand , Adeoye Olawunmi , Doerbecker Juliane , Iheakweazu Chikwe , Krause Gerard 
Published: August 2019

BACKGROUND

Electronic health (eHealth) systems increase the efficiency of disease surveillance by reducing delays in the availability of data, usability, improve processing of data and detect outbreaks. Mobile health (mHealth) technology plays a strong role in containing any disease outbreak and eHealth interventions are being used in many of the countries in sub-Saharan Africa to track global progress towards health related outcomes and to help guide clinical decision making and management. The Center for Disease Control and Prevention (CDC) guideline recommends that in evaluating surveillance systems, effectiveness and efficiency of surveillance systems are to be improved by continuous monitoring and evaluation and this cannot be obtained without effective training of health workers.

OBJECTIVE

The basis of this study is evaluate the knowledge gained before and after Surveillance Outbreak Response Management and Analysis System (SORMAS) training by measuring the following attributes: usefulness, acceptability, data quality and work load time of SORMAS when used by public health officers for their daily tasks.

METHODS

Our study is a pre/post observational study design which accesses two types of evaluation (pre-evaluation and post-evaluation questionnaires) administered during the very first SORMAS training of the district level officers. We asked the participants to select correct responses out of a 9-multiple choice option what they thought were the functionalities of SORMAS before and after the training. We provided 6/9 correct responses (67%) and 3 incorrect responses (33%). Users were scored based on the correct responses and a proportion score assigned to each user for the pre-training score and the post training score. The outcome of the measurement which was the post training score (percentage) was used to generate a pass/fail score within a 75% dichotomized threshold per user.

RESULTS

We rejected the null hypothesis that there is no difference between the scores obtained before and after the training by the SORMAS users. The mean score of those who passed was 83% after the training compared to the mean score of 68% before the training. For contact tracing experience, effect was 0.681 (p-value=0.03, OR=1.98, 95%CI [0.069, 1.293]). For participants who stated that they would need same time per case record, effect was 1.771 (p-value=0.001, OR=5.88, 95%CI [0.425, 3.118]). For participants who stated that data quality will improve, the effect was 2.963 (p-value=<0.001, OR=19.34, 95%CI [1.301, 4.624]). For participants who stated that they would recommend SORMAS to their colleagues, the effect was 0.332 (p-value=0,692, OR=1.39, 95%CI [-1.314, 1.979]).

CONCLUSIONS

Contact tracing experience, data quality, workload and acceptability predictor variables were observed to have a direct effect on the outcome (pass score). The model generated fitted the data and we are 82% accurate that there was indeed knowledge gain comparing before and after the training