Text messaging app improves disease surveillance in rural South Sudan
Bibliographic record
Abstract
BACKGROUND: In South Sudan, remote health facilities face challenges in submitting weekly surveillance reports for epidemic-prone diseases due to long distances and difficult terrain health workers must cover to hand-deliver paper reports. Not only are patients unable to access care while health workers are away, identification of and timely response to an infectious disease outbreak is hampered. METHODS: Data journey mapping with stakeholders was conducted in three counties in Eastern Equatoria State to inform an appropriate mHealth solution. A short message service (SMS) application was selected because it did not require internet connection and only needed minimal equipment investments. The SMS app was designed using open source Android software due to low set-up and maintenance costs. Health facility staffs use personal phones to send an SMS in a predetermined format to the County Health Department (CHD) Android phone base. CHD staff review data; once verified, CHD exports the data to existing health information system software for onward submission to the State Ministry of Health (SMOH). To engender perceived value and incentive use, health workers must use personal airtime to send the SMS; they receive a bonus if they submit reports on time. For long-term sustainability of the system, CHDs have incorporated system maintenance costs into their monthly budgets. RESULTS: Eighty-nine health workers and 21 CHD staff were trained to use the SMS app. They found the innovation interesting and easy to use. All three counties increased on-time submissions upon introduction of the app. The predefined SMS template is important for data accuracy. Availability of a dedicated CHD staff and mobile network coverage in the most remote areas present ongoing challenges to timely report submissions in some counties. CHDs declare the SMS app has revolutionized weekly disease surveillance reporting in their counties. Eastern Equatoria SMOH has requested scale up of this app to all counties of the state. National Ministry of Health (MOH) has expressed strong interest in scaling up the initiative for monthly data reporting. CONCLUSIONS: The SMS app has improved timeliness and efficiency of weekly disease surveillance reporting. It overcomes transportation challenges of health reporting in remote areas and improves access to patient care since health workers do not need to leave post to deliver the report. Minimal start-up and operation costs make this an appropriate solution in resource-poor contexts with a high likelihood of long-term sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".