Improving disease surveillance data analysis, interpretation, and use at the district level in Tanzania

Abstract

An effective disease surveillance system is critical for early detection and response to disease epidemics. This study aimed to assess the capacity to manage and utilize disease surveillance data and implement an intervention to improve data analysis and use at the district level in Tanzania. Mapping, in-depth interview and desk review were employed for data collection in Ilala and Kinondoni districts in Tanzania. Interviews were conducted with members of the council health management teams (CHMT) to assess attitudes, motivation and practices related to surveillance data analysis and use. Based on identified gaps, an intervention package was developed on basic data analysis, interpretation and use. The effectiveness of the intervention package was assessed using pre-and post-intervention tests. Individual interviews involved 21 CHMT members (females = 10; males = 11) with an overall median age of 44.5 years (IQR = 37, 53). Over half of the participants regarded their data analytical capacities and skills as excellent. Analytical capacity was higher in Kinondoni (61%) than Ilala (52%). Agreement on the availability of the opportunities to enhance capacity and skills was reported by 68% and 91% of the participants from Ilala and Kinondoni, respectively. Reported challenges in disease surveillance included data incompleteness and difficulties in storage and accessibility. Training related to enhancement of data management was reported to be infrequently done. In terms of data interpretation and use, despite reporting of incidence of viral haemorrhagic fevers for five years, no actions were taken to either investigate or mitigate, indicating poor use of surveillance data in monitoring disease occurrence. The overall percentage increase on surveillance knowledge between pre-and post-training was 37.6% for Ilala and 20.4% for Kinondoni indicating a positive impact on of the training. Most of CHMT members had limited skills and practices on data analysis, interpretation and use. The training in data analysis and interpretation significantly improved skills of the participants.

An in-depth review of the UNICEF NutriDash platform, lessons learnt and future perspectives: a mixed-methods study

“… 25 26 One suggestion to help team members realise the value and importance of their work was to increase engagement through training opportunities. 27 28 However, it has been argued that training alone is insufficient to build this awareness, and it must be coupled with efforts to hold regular meetings, data reviews and mentoring opportunities in the use of data in decision-making to successfully engage workers so that the value of data and their input is demonstrated to them. 29 For NutriDash, the data generated can only be as good as the national nutrition data generated at the country level and challenges remain that may prohibit collection and reporting of essential nutrition data via NutriDash in certain countries or contexts.…”

“…Research has suggested that increased awareness of the importance of timeliness could help with reducing any reporting delays from those responsible for inputting data which in turn, would increase data quality 25 26. One suggestion to help team members realise the value and importance of their work was to increase engagement through training opportunities 27 28. However, it has been argued that training alone is insufficient to build this awareness, and it must be coupled with efforts to hold regular meetings, data reviews and mentoring opportunities in the use of data in decision-making to successfully engage workers so that the value of data and their input is demonstrated to them 29.…”

Big Data Analytics for Integrated Infectious Disease Surveillance in sub-Saharan Africa

“…Several studies have investigated and assessed the status of digital health technologies application for integrated disease surveillance and response in SSA (Karimuribo et al 2012;Mremi et al 2022;Mwabukusi et al 2014). There is evidence of improved early detection and timely response in some cases, whereas other studies found weak and ineffective surveillance systems to control potential infectious disease outbreaks.…”

“…However, what the study reveals is that as data aggregation increases over time, it is critical to scale up and use advanced techniques such as big data analytics to predict threats and prescribe appropriate responses. Mremi et al (2022) examined the attitudes and practices of members of Tanzania's Council Health Management Teams (CHMT) regarding their capacity to use disease surveillance data in the implementation of a data analysis intervention.…”

“…However, with the increased adoption and use of integrated digital health technologies across SSA's healthcare systems, as well as the presence of the IDSR strategy, there is a promise of coordinated health data management for infectious disease surveillance and response (Akinnagbe et al 2018;Ayeni et al 2015;Mremi et al 2022). The use of digital health technologies enables the collection and integration of large volumes of health data from various sources over time, which can then be integrated and subjected to big data analytics.…”