Comparing Sociodemographic, Health Status and Resources, Macroeconomic Status, and Environmental Factors on Infant Mortality Rates in Bahrain, Kuwait, and Oman: Longitudinal Time-Series Study
Bibliographic record
Abstract
Background: The United Nations considers children a crucial national asset and makes their welfare a top priority. However, infant mortality remains a persistent challenge, notably in Arab nations. Bahrain, Kuwait, and Oman, despite sharing similar income brackets and health care systems, differ in health policies, demographics, and maternal-child resource allocation. These countries also faced sharp fiscal deficits during the 2020 COVID-19 crisis. Compared to wealthier nearby nations like the United Arab Emirates, their lower gross domestic product further complicates efforts to reduce the Infant Mortality Rate (IMR) and sustain effective, equitable child health strategies. Objective: This study aimed to identify factors contributing to the IMR in Bahrain, Kuwait, and Oman by establishing an interpretative framework to examine the influence of sociodemographic, macroeconomic, health status and resource, and environmental factors. Methods: A longitudinal study collected annual time-series data (1990-2022) for Bahrain, Kuwait, and Oman from international open sources. To counterbalance the time-series effects on both IMR and explanatory factors, a generalized least squares model based on the Cochrane-Orcutt procedure with a first-order autoregressive model was used. Results: Generalized least squares shows that the total fertility rate has a strong effect on IMR among the 3 countries (Oman: β=1.138, P<.001; Kuwait: β=.429, P=.006; Bahrain: β=.610, P=.03). Health status and resources, such as female life expectancy at birth, had an inconsistent impact on the IMR, with a positive effect (β=.103, P=.002) for Oman and a negative effect (β=-4.0697, P<.001) for Kuwait. Macroeconomic factors, such as female unemployment, were significant in decreasing the IMR only for Kuwait (β=-.076, P=.008). Gross domestic product per capita is significant only for Bahrain (β=-.398, P<.001). Environmental factors included CO2 emissions, which negatively impacted Oman's IMR (β=-.077, P=.03), and N2O had a positive effect on Bahrain's IMR (β=.420, P=.04). Conclusions: This study indicated the substantial effects of sociodemographics, health status and resources, macroeconomics, and environment on the IMR in 3 Arab countries. Sociodemographic and health-related factors like female life expectancy, fertility regulation, and female unemployment level were identified as key determinants of infant mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".