Forecasting the Value for Money of Mobile Maternal Health Information Messages on Improving Utilization of Maternal and Child Health Services in Gauteng, South Africa: Cost-Effectiveness Analysis
Bibliographic record
Abstract
BACKGROUND: Limited evidence exists on the value for money of mHealth information programs in low resource settings. OBJECTIVE: This study sought to model the incremental cost-effectiveness of gradually scaling up text messaging services to pregnant women throughout Gauteng province, South Africa from 2012 to 2017. METHODS: Data collection occurred as part of a retrospective study in 6 health centers in Gauteng province. Stage-based short message service (SMS) text messages on maternal health were sent to pregnant women twice per week during pregnancy and continued until the infant's first birthday. Program costs, incremental costs to users, and the health system costs for these women were measured along with changes in the utilization of antenatal care visits and childhood immunizations and compared with those from a control group of pregnant women who received no SMS text messages. Incremental changes in utilization were entered into the Lives Saved Tool and used to forecast lives saved and disability adjusted life years (DALYs) averted by scaling up program activities over 5 years to reach 60% of pregnant women across Gauteng province. Uncertainty was characterized using one-way and probabilistic uncertainty analyses. RESULTS: Five-year program costs were estimated to be US $1.2 million, 17% of which were incurred by costs on program development and 31% on SMS text message delivery costs. Costs to users were US $1.66 to attend clinic-based services, nearly 90% of which was attributed to wages lost. Costs to the health system included provider time costs to register users (US $0.08) and to provide antenatal care (US $4.36) and postnatal care (US $3.08) services. Incremental costs per DALY averted from a societal perspective ranged from US $1985 in the first year of implementation to US $200 in the 5th year. At a willingness-to-pay threshold of US $2000, the project had a 40% probability of being cost-effective in year 1 versus 100% in all years thereafter. CONCLUSIONS: Study findings suggest that delivering SMS text messages on maternal health information to pregnant and postpartum women may be a cost-effective strategy for bolstering antenatal care and childhood immunizations, even at very small margins of coverage increases. Primary data obtained prospectively as part of more rigorous study designs are needed to validate modeled results.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".