Food Insecurity and Chronic Diseases: The Editorial
Bibliographic record
Abstract
ood Insecurity (FI) defines as the limited or uncertain availability of nutritionally foods adequately or the inability to achieve them in ways which is socially acceptable (1).FI is considered as an international public health issue particularly in developing countries (2). In 1995 the U.S. Department of Agriculture (USDA) reported the prevalence of FI in USA as 11.9% which increased to 14.6% in 2008 (3). Its prevalence was reported 5.7% in Finland, 10% in Canada (4), 50% in Malaysia (5) and 70% in Bolivia (6). The FI populations are at risk of low intake of fruits and vegetables, lower quality diets and so lower micronutrients intake and iron deficiency anemia (7). Limited available budget in FI subjects, leads to purchase cheaper and high dense calorie foods; which might contribute to obesity, and an increased susceptibility to chronic illnesses including type 2 diabetes, cancer, depression and other medical condition such as acne, dyspepsia and so on (8). The very first step to reduce the burden of FI is considering understanding ofits effects on health. By now, anumerous studies have aimed to examine the impacts of FI on health outcomes in different age/ gender groups such as children, adults and elder lies. Over the past few years, the paradox of FI and obesity/ overweight, diabetes, cancer and depression has been focused in literatures. Recently the FI have been considered in many other disorders. Literatures show a positive association between FI and acne (9), dyslipidemia (10) and dyspepsia (34% FI with hunger in patients versus 10% in counterparts) (9). There are a set of evidences in Iran (mostly published by the author of this editorial), which show food security is significantly associated with many problems and diseases including low birth weight, short stature in children (as FI increase, the height of children decrease), pre-eclampsia in pregnancy, the type of delivery, obesity/ overweight in women, lower intelligence quotient in children, osteoporosis (33.8% in food secure versus 66.2% in FI group), diabetes mellitus, rheumatoid arthritis and premenstrual syndrome (FI without hunger in teenagers with PMS was approximately 2 times more than those without PMS) [Published in Farsi Language]. However, other studies concerning association of FI with other diseases / problems are still needed. On the other hand, finding the prevalence of FI in different Iran provinces to draw a clear picture of FI looks necessary. Therefore, investigators are expected to work on prevalence and diseases associated to FI in their future studies. Financial disclosure The author declare no financial interest. References 1. Nord M, Jemison K, Bickel GW. Measuring food security in the United States prevalence of food insecurity and hunger, by state, 1996–1998. Washington, D.C: U.S. Dept. of Agriculture, Economic Research Service 1999.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".