The Cost of Maternal Complications and Its Associated Factors Among Mothers Attending Public Hospitals in Harari Region and Dire Dawa City Administration, Eastern Ethiopia: An Institution-Based Cross-Sectional Study
Bibliographic record
Abstract
Background: Pregnant women face high costs for health-care services despite being advertised as free. These costs include non-medical expenses, lost productivity, difficulties caring for family members, and long-term financial impact from complications. Limited research has been done on the cost burden of maternal services and complications, despite numerous studies on maternal health service provision. This is notable considering the government's claim of providing free maternal health-care services. Methods: A cross-sectional study was conducted in July (1-30) 2022 among 425 randomly selected mothers in Harari and Dire Dawa City, Eastern Ethiopia. Data were collected through structured questionnaires and medical record reviews. The collected data was entered into Epi-Data version 3.02 and analyzed using STATA version 14.0 after data cleaning. Descriptive statistics and linear regression analysis were used to examine the data, ensuring assumptions of linearity, independence, homoscedasticity, and normality were met. The correlation coefficient was used to assess the strength of the association. Results: The median cost of maternal complications was around 4250 ETB (81.3 USD; IQR = 2900-5833.3), factors that predicted cost were monthly family income of ≥3001 birr (β=1.13; 95% CI: 1.00, 1.26), distance from hospital (β=0.73; 95% CI = 0.64-0.83), being admitted for less than 4 days (β=0.60; 95% CI = 0.53-0.69), accompanied by relatives besides their husbands (β=1.93; 95% CI = 1.52-2.46), caesarian sections delivery (β=1.17; 95% CI = 1.04-1.31), and giving birth to a normal baby (β=0.86; 95% CI = 0.77-0.97). Conclusion: Maternal complications incur significant costs, with factors such as family income, travel time, hospital stay, caregiver presence, mode of delivery, and neonatal outcome predicting these costs. The Ethiopian health system should address the additional expenses faced by mothers with complications and their caregivers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".