Despite the severity of food insecurity in Afghanistan, little is known about the factors contributing to household food insecurity (HFI) under the Taliban regime. Therefore, this paper investigated the social determinants of severe HFI in rural areas of Afghanistan. We used the fifth-round survey of 6019 rural households from 25 provinces, collected between July and August 2022 by the Food and Agriculture Organization. We used binary logistic regression to examine the association between household characteristics and HFI. The majority of household heads were male (97.8%) with no education (62.8%). The findings showed that female-headed households had significantly higher odds of severe HFI. Household heads with any level of formal education had significantly reduced odds of severe HFI, while the odds of severe HFI was not different among those with religious/informal household-head education compared to those with no education. Likewise, engagement in any type of agricultural activity decreased the odds of severe HFI. Additionally, household income per member was negatively, while household size was positively associated with severe HFI. In summary, interventions to alleviate HFI among rural households should prioritize income-generating opportunities and skills targeting households with female heads, low levels of household-head education, larger size, no agricultural activities, and low income.

A study was undertaken to explore the differences in nutrition knowledge, attitudes, beliefs and practices and their correlations among adults in both urban and rural communities in the Free State ...

Objective: Pair‐feeding is a study design element where one group's food intake is provided to another group to assess whether a treatment effect is independent of food intake. Investigators often assume equivalent food intake across experimental conditions and exclude it from the statistical analysis. However, the impact of this practice on type I error (T1Er) rates has not been quantified. Methods: We conducted a Monte Carlo simulation in which animals were assigned baseline weights and food intakes, then randomized to non‐pair‐fed or pair‐fed groups. Daily food intake for both groups was initially drawn from the baseline food intake distribution. For pair‐fed animals, food intake was truncated if it exceeded the previous day's intake of the matched non‐pair‐fed animal (individual pair‐feeding) or the group's average (group pair‐feeding). Weight changes were calculated as a function of food intake, and final weight change was analyzed with and without adjusting for mean food intake. Results: Both individual and group pair‐feeding inflated T1Er rates ranging from 0.12 to 0.71 in unadjusted models. However, adjustment for food intake reduced error rates to around 0.05. Conclusions: Under some circumstances, pair‐feeding designs can inflate T1Er rates. Investigators can mitigate this inflation by adjusting the analyses for food intake.

Despite extensive research on life-extending interventions, rigorous statistical techniques to determine their impact on compression of morbidity (CoM) are rarely used. We present a case example of an analytical method for examining the effect of life-extending dietary interventions on CoM by comparing the rates of decline in vitality and survival toward the end of life. Using data from previous experimental studies in mice, we calculated the average rate of vitality decline by fitting exponential decay models to individual vitality trajectories and compared this rate with the rate of survival decline estimated from Cox proportional hazards model. The results showed that, surprisingly, the life-extending interventions failed to compress morbidity. Instead, the models suggested a potential expansion of morbidity with the interventions, particularly for chronic caloric restriction, as evidenced by a greater difference between the rates of vitality decline and survival decline in the intervention group than in the control group. The results further showed age-dependent effects, with interventions likely to demonstrate CoM at very old ages. These findings challenge the assumption that lifespan extension necessarily compresses morbidity, highlighting the need to consider lifespan, healthspan, and CoM as endpoints when evaluating anti-aging interventions. We do not claim that life-extending interventions categorically fail to achieve CoM; rather, we demonstrate how a rigorous statistical approach can be applied to test the CoM hypothesis. Our framework offers a valuable tool for future studies, and further refining this method will be crucial to determine under which circumstances lifespan extension leads to CoM. The potential to choose from multiple analyses of CoM calls for leadership in the geroscience community both to standardize the nomenclature so that different CoM analysis approaches can be communicated clearly and to establish a “default” CoM analysis for everyone to use in addition to their analyses of choice, thus facilitating comparisons and meta-analyses across studies.