Interoperability of Surveillance Data Collection Tools (District Health Information System 2 and District Vaccine Data Management Tools) in Enugu State, Nigeria, From 2015-2018
Bibliographic record
Abstract
Background Over the years, Nigeria has used District Vaccine Data Management Tool (DVDMT) for surveillance data collection including routine immunization. In 2012, Nigeria adopted District Health Information Software (DHIS2), a Java driving online real-time tool for data collection. In 2015, Enugu State commenced the use of DHIS2 alongside the traditional DVDMT as surveillance data capturing tools. Objective The objective was to carry out an evaluation of the two surveillance data tools to assess surveillance attributes, interoperability, effect in decision making, and preference of use. Methods We quantitatively and qualitatively assessed surveillance attributes of Enugu State’s DHIS2 and DVDMT from 2015 to 2018 using adapted CDC guidelines (2001). We administered semi-structured questionnaires to all 17 local immunization officers from the 17 local government areas (districts) to assess surveillance attributes. We carried out desk review at all levels, key informants done with 6 purposefully selected stakeholders, and focused group discussion carried out with 6 randomly selected heads of surveillance at local governments areas. We recorded proportions, interoperability, effect in decision making, and preference of use. Results Average completeness of data is 100% in both DHIS2 and DVDMT systems (target 90%). Eligibility is 100% in DHIS2 and 85% in DVDMT (target 80%). Timeliness of reporting is 100% and 80% in DHIS2 and DVDMT, respectively (target 80%). All stakeholders accepted both tools and agreed that they are simple and flexible. In addition to collection of all data recorded by DVDMT, DHIS2 captures vaccine utilization. Data collection and transmission of DVDMT and DHIS2 are carried out by the same surveillance personnel at health facility and local government area levels. Apart from vaccine utilization both tools can complement each other in case of missed data as they record the same thing. All key informants opined that it is double work managing the two tools and also agreed that DHIS2 is better than DVDMT in decision making because it has features for data visualization and real-time reporting. The focused group discussion agreed that both tools are good, although DVDMT is easier to work with as DHIS2 requires computer proficiency of current users alongside hardware management of the Java-enabled phones used in data capture and transmission. However, they also agreed that DHIS2 usage is less time consuming and opined they will prefer to use DHIS2 as the only data capturing tool in Enugu State if proper capacity building is done. Conclusions The DHIS2 and DVDMT surveillance data tools in Enugu State is meeting all its targets based on surveillance attributes, though DHIS2 provides better quality data. There is a good understanding and synergy in operation of the two systems in all levels and intermittently data from both tools can be compared. DHIS2 can enable prompt decision making than DVDMT as data can be assessed and visualized in real time. Surveillance officers prefer the use of DHIS2 as the only surveillance tool in Enugu State, although proficiency is a challenge. We recommended a gradual phase out of DVDMT for data capturing in Enugu State, while capacity building of users for DHIS2 should be addressed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".