Glecaprevir/pibrentasvir (GLE/PIB), despite being a highly costly medication, is considered a cost-effective approach compared with sofosbuvir/velpatasvir (SOF/VEL) and sofosbuvir/daclatasvir (SOF/DCV) in the treatment of hepatitis C virus (HCV) infection. No study has evaluated the effect of GLE/PIB’s introduction into Iran’s drug list from a health policy perspective and estimated the budgetary impact change. Therefore, this study was conducted to analyse the fiscal effect of the introduction of GLE/PIB into Iran’s drug list.

Budget impact analysis. The assumptions and costs of including GLE/PIB in Iran’s drug list for the treatment of patients with hepatitis C were derived from a conducted cost-effectiveness analysis.

National level. In this study, the budgetary changes in Iran’s pharmaceutical market and health system, from the Ministry of Health’s perspective, have been estimated for a 5-year time horizon following the introduction of GLE/PIB in the country.

Based on the results obtained from the budget impact model, currently, 4112 patients are receiving SOF/DCV and SOF/VEL therapeutic regimens, which is expected to decrease to 1093 in 2029 owing to the affordability of medications and a 50% estimated market share for GLE/PIB. According to the results, with the introduction of GLE/PIB into the market and assuming a market share of 10% in the first year, growing to 50% by the fifth year, the healthcare system costs will increase by approximately $0.61, $1.77, $3.86, $7.45 and $13.51 million over the next 5 years, respectively. Additionally, based on the drug’s selling price, there will be a 468% increase in hepatitis C drug market costs after 5 years, resulting in an overall budget increase of approximately 0.13% for Iran’s pharmaceutical market. According to the sensitivity analysis, a 20% reduction in chronic hepatitis C (CHC) costs could decrease the projected increase in health sector costs from $13.51 million (an 18.84% increase) to $10.52 million (an 18.16% increase). Conversely, a 20% rise in CHC costs would raise those costs to $16.49 million (a 19.31% increase).

Considering the high price of the GLE/PIB compared with the available options in Iran, with the introduction of GLE/PIB into Iran’s drug list, insurance coverage and appropriate allocation of necessary resources, a reduction in the cost burden because of hepatitis C treatment is expected for individuals and households. Additionally, with a well-regulated market share of existing medications, the optimal treatment choice for patients will be feasible.

Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.

Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.

GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.

This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.