Undernourishment trends and determinants: an ecological study of 76 countries

Elucidating the Macroeconomic Determinants of Undernourishment in South Asian Countries: Building the Framework for Action

Undernourishment is a big challenge for humanity across the world. Considering the significance of reducing undernourishment, the current study focuses on exploring the macroeconomic determinants of undernourishment in the South Asian panel. The study employed econometric models that are more robust to underpin cross-sectional dependency and heterogeneity in a panel data set. The overall findings reveal that an increase in food production increases undernourishment and infer that food availability at the national level is insufficient to reduce undernourishment unless poor people also had economic and physical access to food. In the case of economic growth and governance, the results are negatively significant in some countries. The results infer that GDP and quality of governance are nuanced in declining the rate of undernourishment in some countries, while in other countries where the results are found insignificant, the government should seek other interventions to curtail the prevalence of undernourishment. Unexpectedly, an increase in food prices lessens the undernourishment in developing countries that reflect that food prices might transform the dietary patterns of poor people from nutrient-rich foods to nutrient-poor staples, thus lead to undernourishment reduction but trigger overweight and obesity alongside. In conclusion, the results depict that policymakers should devise strategies keeping in view fundamental aspects of the country to reduce undernourishment.

The construction of the air quality health index (AQHI) and a validity comparison based on three different methods

The most common currently used air quality risk communication tool, the Air Quality Index (AQI), has been criticized. As a result, Canada proposed the Air Quality Health Index (AQHI) to communicate the health risks of multiple pollutants. However, the AQHI is calculated by directly summing the excess risks from single-pollutant models, which may overestimate the effects of the pollutants. To solve this problem, we introduced two methods for estimating the joint effects of multiple pollutants: the cumulative risk index (CRI) and supervised principal component analysis (SPCA). Based on three methods, i.e., the standard, CRI and SPCA methods, we constructed three types of AQHIs and compared their validity to select the best communication tool. Our results showed that compared with the AQI, all three AQHIs had a linear relationship with mortality. In addition, the CRI-AQHI had the best goodness of fit and captured the overall health risk of pollution mixtures most robustly among various cause-specific mortalities when identifying health risks. Our study indicated that the CRI-AQHI may have the potential to be a better alternative to the standard AQHI in communicating air pollution-related health risks to the public.