Temporal Trends in Food Insecurity (Hunger) among School-Going Adolescents from 31 Countries from Africa, Asia, and the Americas
by
, , , , , , and
Lee Smith
1
,
Guillermo F. López Sánchez
2,*
,
Mark A. Tully
3
,
Louis Jacob
4,5,
Karel Kostev
6,
Hans Oh
7,
Laurie Butler
1,
Yvonne Barnett
1,
Jae Il Shin
8,*
and
Ai Koyanagi
4,9 1
Centre for Health Performance and Wellbeing, Anglia Ruskin University, Cambridge CB1 1PT, UK
2
Division of Preventive Medicine and Public Health, Department of Public Health Sciences, School of Medicine, University of Murcia, 30120 Murcia, Spain
3
School of Medicine, Ulster University, Londonderry BT48 7JL, UK
4
Research and Development Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, ISCIII, Dr. Antoni Pujadas, Sant Boi de Llobregat, 08830 Barcelona, Spain
5
Department of Physical and Rehabilitation Medicine, Lariboisière-Fernand Widal Hospital, AP-HP, University Paris Cité, 75010 Paris, France
6
University Clinic of Marburg, 35037 Marburg, Germany
7
Suzanne Dworak Peck School of Social Work, University of Southern California, Los Angeles, CA 90007, USA
8
Department of Pediatrics, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
9
ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain
*
Authors to whom correspondence should be addressed.
Nutrients 2023, 15(14), 3226; https://doi.org/10.3390/nu15143226
Submission received: 21 June 2023
/
Revised: 9 July 2023
/
Accepted: 18 July 2023
/
Published: 20 July 2023
(This article belongs to the Special Issue Healthy Lifestyle Interventions to Combat Noncommunicable Disease)
Abstract
:(1) Background: Temporal trends of food insecurity among adolescents are largely unknown. Therefore, we aimed to examine this trend among school-going adolescents aged 12–15 years from 31 countries in Africa, Asia, and the Americas. (2) Methods: Data from the Global School-based Student Health Survey 2003–2017 were analyzed in 193,388 students [mean (SD) age: 13.7 (1.0) years; 49.0% boys]. The prevalence and 95%CI of moderate (rarely/sometimes hungry), severe (most of the time/always hungry), and any (moderate or severe) food insecurity (past 30-day) was calculated for each survey. Crude linear trends in food insecurity were assessed by linear regression models. (3) Results: The mean prevalence of any food insecurity was 52.2% (moderate 46.5%; severe 5.7%). Significant increasing and decreasing trends of any food insecurity were found in seven countries each. A sizeable decrease and increase were observed in Benin (71.2% in 2009 to 49.2% in 2016) and Mauritius (25.0% in 2011 to 43.6% in 2017), respectively. Severe food insecurity increased in countries such as Vanuatu (4.9% in 2011 to 8.4% in 2016) and Mauritius (3.5% in 2011 to 8.2% in 2017). The rate of decrease was modest in most countries with a significant decreasing trend, while many countries with stable trends showed consistently high prevalence of food insecurity. (4) Conclusion: Global action is urgently required to address food insecurity among adolescents, as our data show that achieving the United Nations Sustainable Development Goal 2 to end hunger and all forms of malnutrition by 2030 would be difficult without strong global commitment.
1. Introduction
Food insecurity may be defined as “limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire food in socially acceptable ways” [1]. Hunger is a related concept and is an uncomfortable or painful physical sensation that can be due to severe food insecurity. Undernourishment (an indicator of hunger) occurs in 10.8% of the global population, with rates varying from 5.5% in South America to 30.8% in Eastern Africa. Furthermore, approximately 828 million people were affected by hunger in 2021—46 million people more from 2020, and 150 million more from 2019 [2]. Importantly, the phenomenon of food insecurity is not unique to low- and middle-income countries (LMICs); it is also prevalent in high-income countries (HICs) such as the USA [3]. This highlights the challenge and difficulty in achieving the Sustainable Development Goal (SDG) 2 set by the United Nations to end hunger and all forms of malnutrition by 2030 [4].
Food insecurity, particularly among adolescents, is a public health concern, as it is associated with a plethora of detrimental outcomes [1,5]. For example, adolescents who experience food insecurity are at higher risk of mental health and psychosocial problems (e.g., social behavioral problems and worse academic performance) [1,6,7], overweight and obesity [8,9], and risky health behaviors (e.g., risky sexual activity [as a means to acquire food] and substance use) [10,11]. Moreover, hunger in youth increases the risk of chronic disease in adulthood, including, for example, diabetes and osteoporosis [12,13].
Improving our understanding of food insecurity is more important now than ever, as the COVID-19 pandemic has exacerbated food insecurity globally, interrupting many programs that promote well-being among adolescents (e.g., schools and school meals programs). The COVID-19 pandemic and other global challenges such as social unrest and environmental disasters compound existing vulnerabilities, such that the greatest challenges accrue to adolescents who have access to the fewest resources to promote their healthy development within their households, communities, and countries [14]. It is thus essential to understand the prevalence and temporal trends of food insecurity among adolescents to combat food insecurity in this population and to understand where we stand in the pursuit to achieve SDG 2. However, there is only one study on the temporal trends of food insecurity among adolescents, which reported the US trends in measures of food insecurity. Specifically, this study found that between 2007 and 2008, both the fraction of children/adolescents in food-insecure households and the rate of food insecurity among children/adolescents rose by one-third across those two years, and the rate of very low food security among children/adolescents increased by two-thirds, from 0.9 percent in 2007 to 1.5 percent in 2008, and after 2008, levels of these parameters remained high and stable [15]. Importantly, in general, there is limited literature on adolescent food insecurity outside the United States, raising uncertainty about the extent to which current thinking about food insecurity is specific to the social construction of adolescence in the United States [14]. More data are needed to understand the temporal trends in food insecurity among the global adolescent population. This is especially so for LMICs, where the prevalence of food insecurity has been reported to be high [16]. Moreover, conducting multi-country studies using standard questionnaires across countries on this topic is important, as it can allow for comparison between countries and provide insights on the reasons why some countries fare better or worse than others.
Given this background, the aim of the present study was to examine the temporal trends of food insecurity in a sample of 193,388 students aged 12–15 years from 31 countries in Africa, Asia, and the Americas (predominantly LMICs), where temporal trends of food insecurity are largely unknown.
2. Methods
Data from the Global School-based Student Health Survey (GSHS) were analyzed. Details on this survey can be found at https://www.who.int/teams/noncommunicable-diseases/surveillance/data and http://www.cdc.gov/gshs (accessed on 20 June 2023). Briefly, the GSHS was jointly developed by the WHO and the US Centers for Disease Control and Prevention (CDC), and other UN allies. The main aim of this survey was to examine and quantify risk and protective factors of major non-communicable diseases. The survey used a standardized two-stage probability sampling design for the selection process within each participating country. For the first stage, schools were selected with probability proportional to size sampling. The second stage involved the random selection of classrooms, which included students aged 13–15 years within each selected school. All students in the selected classrooms were eligible to participate in the survey regardless of age. Thus, the sample could have included adolescents who were not 13–15 years old. Data collection was performed during one regular class period. The questionnaire was translated into the local language in each country and consisted of multiple-choice response options. Students recorded their response on computer scannable sheets. All GSHS surveys were approved, in each country, by both a national government administration (most often the Ministry of Health or Education) and an institutional review board or ethics committee. Student privacy was protected through anonymous and voluntary participation, and informed consent was obtained as appropriate from the students, parents, and/or school officials. Data were weighted for non-response and probability selection.
From all publicly available data, we selected all nationally representative datasets that included the variables pertaining to our analysis and for which data on at least two waves were available from the same country. A total of 31 countries were included in the current study. The characteristics of each country, including the survey year, country income level, response rate, and sample size, are provided in Table A1 of Appendix A. The surveys included in the current study were conducted between 2003 and 2017, and were mainly from LMICs.
2.1. Food Insecurity (Hunger)
Food insecurity (hunger) was assessed by the question, “During the past 30 days, how often did you go hungry because there was not enough food in your home?” Answer options were categorized as no food insecurity (never), moderate food insecurity (rarely/sometimes), and severe food insecurity (most of the time/always) [17]. We named these categories as such since moderate food insecurity is often considered to be an indication that quality/quantity of food consumed has been compromised, while severe food insecurity refers to reduced food intake and disrupted eating patterns [18]. Any food insecurity referred to both moderate and severe food insecurity.
2.2. Statistical Analysis
Statistical analyses were performed with Stata 14.2 (Stata Corp LP, College Station, TX, USA). The analysis only included those aged 12–15 years, as most students were within this age group, while information on the exact age outside of this age range was not available. The prevalence and 95% CI of any food insecurity, moderate food insecurity, and severe food insecurity were calculated for each survey. Crude linear trends in any food insecurity, moderate food insecurity, and severe food insecurity were assessed by linear regression models across surveys within the same country to estimate regression coefficients (beta) and 95% CI for every one-year change. p for trends were estimated using the survey year as a continuous variable. Sampling weights and the clustered sampling design of the surveys were taken into account in all analyses.
3. Results
Data on 193,388 students aged 12–15 years [mean (SD) age 13.7 (1.0) years; 49.0% boys] were analyzed. Across all surveys, the mean prevalence of any food insecurity was 52.2%, while that of moderate and severe food insecurity were 46.5% and 5.7%, respectively. At the individual survey level, the prevalence of any food insecurity ranged from 18.7% (Uruguay in 2006) to 81.2% (Samoa in 2011). High prevalence of severe food insecurity was observed in Samoa in 2011 (36.0%), Benin in 2009 (19.0%), Seychelles in 2007 (16.6%), and Yemen in 2008 (15.4%).
The trends in prevalence of any food insecurity are shown in Table 1 and Figure 1. Of the 31 countries included in the study, significant increasing and decreasing trends of any food insecurity were found in seven countries each. Specifically, significant increasing trends were found in: Mauritius between 2011 (25.0%) and 2017 (43.6%) (beta = 3.09; 95%CI = 2.16, 4.03), Suriname between 2009 (33.0%) and 2016 (42.1%) (beta = 1.30; 95%CI = 0.37, 2.23), Trinidad & Tobago between 2007 (39.2%) and 2017 (49.1%) (beta = 0.97; 95%CI = 0.39, 1.55), Uruguay between 2006 (18.7%) and 2021 (22.4%) (beta = 0.61; 95%CI = 0.15, 1.08), Kuwait between 2011 (37.8%) and 2015 (49.6%) (beta = 2.95; 95%CI = 1.76, 4.15), United Arab Emirates between 2005 (41.1%) and 2016 (48.0%) (beta = 0.76; 95%CI = 0.34, 1.18), and Vanuatu between 2011 (49.7%) and 2016 (62.0%) (beta = 2.47; 95%CI = 0.73, 4.21). The beta can be interpreted as the average point change in prevalence (%) per year. On the other hand, significant decreasing trends were found in: Benin between 2009 (71.2%) and 2016 (49.2%) (beta = −3.14; 95%CI = −4.61, −1.66), Seychelles between 2007 (58.6%) and 2015 (44.2%) (beta = −1.80; 95%CI = −2.21, −1.39), Jamaica between 2010 (59.6%) and 2017 (47.0%) (beta = −1.79; 95%CI = −2.79, −0.80), Lebanon between 2005 (39.0%) and 2017 (30.6%) (beta = −0.70; 95%CI = −1.00, −0.39), Morocco between 2006 (44.7%) and 2016 (29.5%) (beta = −1.35; 95%CI = −1.74, −0.95), Indonesia between 2007 (64.4%) and 2015 (53.9%) (beta = −1.31, 95%CI = −1.95, −0.67), and Tonga between 2010 (74.1%) and 2017 (66.6%) (beta = −1.06; 95%CI = −1.61, −0.51). No significant increasing or decreasing trends were found in the remaining 17 countries.
The trends in the prevalence of moderate and severe food insecurity are shown in Table 2, and the average percentage point change in prevalence of any, moderate, and severe food insecurity is visually displayed in Figure 2. While the directions of the trend were the same for any, moderate, and severe food insecurity in most countries, in countries such as Lebanon, Morocco, Indonesia, and Tonga, a significant decreasing trend was only observed for moderate food insecurity and not for severe food insecurity. Furthermore, in Samoa, even though there were no significant trends based on any food insecurity, there was a significant increase in moderate food insecurity but a significant decrease in severe food insecurity. Finally, the significant increasing trend of any food insecurity in Kuwait was largely explained by increase in moderate food insecurity but not severe food insecurity.
4. Discussion
4.1. Main Findings
In the present study, including large representative samples of school-going adolescents aged 12–15 years from 31 countries in Africa, Asia, and the Americas, significant increasing and decreasing trends of any food insecurity were observed in seven countries each. Mauritius observed the greatest increasing trend [2011 (25.0%) and 2017 (43.6%)], whereas Benin experienced the greatest decreasing trend [2009 (71.2%) and 2016 (49.2%)]. The rate of decrease was modest in the majority of countries with a significant declining trend. No significant changes in trends were observed in 17 countries, but it is important to note that levels of food insecurity in the majority of these countries were high across multiple years. When considering moderate and severe food insecurity, the direction of the trend was the same for the majority of countries, with some nuances observed in seven countries. To the best of the authors’ knowledge, this is the first global study to examine trends in food insecurity among adolescents across multiple continents, while it is the first to include data from LMICs.
4.2. Interpretation of the Findings
The fact that decreasing trends in food insecurity were observed in seven countries is encouraging. Such decreasing trends may have been achieved from country-wide initiatives and/or policies to combat food insecurity. For example, in Benin, in 2015, the World Food Programme was implemented to support and enhance existing initiatives. Benin considers nutrition at the center of development and utilizes nutrition-specific or related interventions through a multi-sectorial approach via its Strategic Plan for Food and Nutrition Development, with an emphasis on the implementation of community-level nutrition activities. In addition, the Plan for Development of the Education Sector highlights the importance of school meals to ameliorate retention rates. The government’s policy on school meals involves a multi-sector approach connecting local food production, nutrition, and education, and the national school meals policy’s long-term goal is to guarantee school meals for all Beninese schoolchildren [19]. However, an equal number of countries (n = 7) observed a significant increasing trend in food insecurity. A rise in an already high level of food insecurity is of upmost concern and may be explained by socio-economic hardship and lack of governmental support to address such issues. For example, in Mauritius, prices for staple food items have been increasing for the past 10 years, and less than 25% of its food products are produced locally. Moreover, in some areas of Mauritius, there is severely limited access to food products. For instance, the villages of Tamarin, Rivière Noire, and Petite Rivière Noire have access to only one vegetable seller. For fruits and vegetables, inhabitants of the region rely mainly on two supermarkets, which commercially target only middle- to upper-income customers. In this context, low-income households face challenges to have a nutritionally diversified and balanced diet, and importantly, this situation is common in other parts of the country [20].
Finally, our study also highlights the importance of assessing food insecurity in terms of severity. For instance, in countries such as Lebanon, Morocco, Indonesia, and Tonga, although there was a significant decreasing trend for any food insecurity, there was no significant decreasing trend for severe food insecurity. Furthermore, in Samoa, even though there was no significant trend for any food insecurity, there was a significant increasing trend for severe food insecurity. While the reasons for these findings are unclear, this may be related to the degree of inequality or the difficulty to reach the poorest or the most vulnerable segment within a country. For example, since 2005, Lebanon has been characterized by extreme inequality in both income and wealth. The richest one percent of the population receives, on average, 25 percent of the national income, while the poorest half receives less than 10 percent [21]. Given that the consequences of severe food insecurity are likely to be particularly severe, these data highlight the importance of considering severity of food insecurity when analyzing trends, as focusing solely on any food insecurity may mask important trends.
4.3. Policy Implications
Our findings underscore the importance of adopting a multi-dimensional poverty lens, as people can experience major deprivations irrespective of household income. Food insecurity remains a key indicator of well-being and may be developmentally impactful on children, adolescents, and young people, who are not financially independent. According to the World Food Programme, 135 million people suffer from acute hunger, owing to man-made conflicts, climate change, and economic downturns, with a greater impact now expected due to the COVID-19 pandemic [22]. Our data also suggest that there is a long way to achieve the United Nations SDG 2, which has as an aim “Zero Hunger by 2030”. While there are general strategies to reduce food insecurity that apply to the entire population (e.g., increasing agricultural productivity, investment in rural infrastructure [22]), there are some strategies which are aimed at reducing food insecurity specifically among school-going adolescents. For example, a common response to food insecurity is means-tested or universal social programming implemented in school settings, and may include initiatives such as free school meals, breakfast clubs, and school-operated food banks. However, it is important to highlight that such initiatives require either strong political or community buy-in [23].
4.4. Strengths and Limitations
The large representative sample of school-going adolescents from 31 countries, and the use of standard methodology across surveys are clear strengths of the present study. However, findings must be interpreted in light of the study’s limitations. First, food insecurity was self-reported, potentially introducing some level of bias (e.g., recall bias, social desirability bias) into the findings. Second, our study results are only generalizable to school-going adolescents, as only students were included in the study. However, it is worth noting that school attendance rates are known to be generally high in the countries included in our study. Finally, surveys were undertaken in different years depending on the country, and more data points were available in some countries than others. Thus, the beta coefficients estimated in our study are not totally comparable across countries, and they should always be interpreted in conjunction with the year in which the surveys were conducted.
5. Conclusions
In the present study including large representative samples of school-going adolescents aged 12–15 years from 31 countries in Africa, Asia, and the Americas, we observed a generally high level of food insecurity, with significant increasing and decreasing trends being observed in seven countries each. Global action is required to address food insecurity among adolescents, as our data reinforce the notion that the world is unlikely to be on track to achieve the SDG 2, which has as a goal to end hunger by 2030. It thus may be prudent to implement means-tested or universal social programming in school settings, and this may include initiatives such as free school meals, breakfast clubs, and school-operated food banks.
Author Contributions
Writing—original draft preparation, L.S., G.F.L.S., M.A.T., L.J., K.K., H.O., L.B., Y.B., J.I.S. and A.K.; writing—review and editing, L.S., G.F.L.S., M.A.T., L.J., K.K., H.O., L.B., Y.B., J.I.S. and A.K. All authors have read and agreed to the published version of the manuscript.
Funding
Guillermo F. López Sánchez is funded by the European Union—Next Generation EU.
Institutional Review Board Statement
All GSHS surveys were approved, in each country, by both a national government administration (most often the Ministry of Health or Education) and an institutional review board or ethics committee.
Informed Consent Statement
Student privacy was protected through anonymous and voluntary participation, and informed consent was obtained as appropriate from the students, parents and/or school officials.
Data Availability Statement
The data presented in this study are available on request from the corresponding author.
Acknowledgments
This paper uses data from the Global School-Based Student Health Survey (GSHS). GSHS is supported by the World Health Organization and the US Centers for Disease Control and Prevention.
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Table A1.
Survey characteristics.
| Region | Country | Country Income | Year | Response Rate (%) | N a |
|---|---|---|---|---|---|
| AFR | Benin | L | 2009 | 90 | 1170 |
| L | 2016 | 78 | 717 | ||
| Mauritius | UM | 2011 | 82 | 2074 | |
| UM | 2017 | 84 | 1955 | ||
| Seychelles | UM | 2007 | 82 | 1154 | |
| H | 2015 | 82 | 2061 | ||
| Swaziland | LM | 2003 | 96 | 6866 | |
| LM | 2013 | 97 | 1318 | ||
| AMR | Anguilla | NA | 2009 | 84 | 701 |
| NA | 2016 | 88 | 564 | ||
| Argentina | UM | 2007 | 77 | 1537 | |
| UM | 2012 | 71 | 21,528 | ||
| Guatemala | LM | 2009 | 81 | 4495 | |
| LM | 2015 | 82 | 3611 | ||
| Guyana | LM | 2004 | 80 | 1070 | |
| LM | 2010 | 76 | 1973 | ||
| Jamaica | UM | 2010 | 72 | 1204 | |
| UM | 2017 | 60 | 1061 | ||
| Suriname | UM | 2009 | 89 | 1046 | |
| UM | 2016 | 83 | 1453 | ||
| Trinidad & Tobago | H | 2007 | 78 | 2450 | |
| H | 2011 | 90 | 2363 | ||
| H | 2017 | 89 | 2763 | ||
| Uruguay | UM | 2006 | 71 | 2882 | |
| H | 2012 | 77 | 2869 | ||
| EMR | Egypt | LM | 2006 | 87 | 4981 |
| LM | 2011 | 85 | 2364 | ||
| Jordan | LM | 2004 | 95 | 1848 | |
| LM | 2007 | 99.8 | 1648 | ||
| Kuwait | H | 2011 | 85 | 2298 | |
| H | 2015 | 78 | 2034 | ||
| Lebanon | UM | 2005 | 88 | 4524 | |
| UM | 2011 | 87 | 1982 | ||
| UM | 2017 | 82 | 3347 | ||
| Morocco | LM | 2006 | 84 | 1986 | |
| LM | 2010 | 92 | 2405 | ||
| LM | 2016 | 91 | 3975 | ||
| Oman | UM | 2005 | 97 | 2426 | |
| H | 2010 | 89 | 1000 | ||
| H | 2015 | 92 | 1669 | ||
| United Arab Emirates | H | 2005 | 89 | 12,819 | |
| H | 2010 | 91 | 2302 | ||
| H | 2016 | 80 | 3471 | ||
| Yemen | L | 2008 | 82 | 905 | |
| LM | 2014 | 75 | 1553 | ||
| SEAR | Indonesia | LM | 2007 | 93 | 3022 |
| LM | 2015 | 94 | 8806 | ||
| Maldives | LM | 2009 | 80 | 1981 | |
| UM | 2014 | 60 | 1781 | ||
| Myanmar | L | 2007 | 95 | 2227 | |
| LM | 2016 | 86 | 2237 | ||
| Sri Lanka | LM | 2008 | 89 | 2504 | |
| LM | 2016 | 89 | 2254 | ||
| Thailand | LM | 2008 | 93 | 2675 | |
| UM | 2015 | 89 | 4132 | ||
| WPR | Cook Islands | NA | 2011 | 84 | 849 |
| NA | 2015 | 65 | 366 | ||
| Fiji | LM | 2010 | 90 | 1495 | |
| UM | 2016 | 79 | 1537 | ||
| Philippines | LM | 2003 | 84 | 4198 | |
| LM | 2007 | 81 | 3484 | ||
| LM | 2011 | 82 | 3845 | ||
| LM | 2015 | 79 | 6162 | ||
| Samoa | LM | 2011 | 79 | 2200 | |
| LM | 2017 | 59 | 1058 | ||
| Tonga | LM | 2010 | 80 | 1946 | |
| UM | 2017 | 90 | 2067 | ||
| Vanuatu | LM | 2011 | 72 | 852 | |
| LM | 2016 | 57 | 1288 |
a Based on sample aged 12–15 years. Abbreviation: AFR African region; AMR region of the Americas; EMR Eastern Mediterranean region; SEAR South-East Asia region; WPR Western Pacific region; H high income; L low income; LM lower middle income; UM upper middle income. Data on country income were not available from Cook Islands and Anguilla.
References
- Bickel, G.; Nord, M.; Price, C.; Hamilton, W.; Cook, J. Guide to Measuring Household Food Security; United States Department of Agriculture: Washington, DC, USA, 2000. [Google Scholar]
- World Health Organization. UN Report: Global Hunger Numbers Rose to as Many as 828 Million in 2021. Available online: https://www.who.int/news/item/06-07-2022-un-report--global-hunger-numbers-rose-to-as-many-as-828-million-in-2021 (accessed on 20 June 2023).
- World Health Organization. The State of Food Security and Nutrition in the World 2019: Safeguarding against Economic Slowdowns and Downturns; Food & Agriculture Organization of the United Nations: Roma, Italy, 2019; Volume 2019. [Google Scholar]
- United Nations. The 17 Goals. Available online: https://sdgs.un.org/goals (accessed on 20 June 2023).
- Fares, K.; Barada, D.; Hoteit, M.; Abou Haidar, M. Prevalence and Correlates of Food Insecurity among Lebanese University Students of Hadath Campus. Atena J. Public Health 2020, 2, 5. [Google Scholar]
- Baer, T.E.; Scherer, E.A.; Fleegler, E.W.; Hassan, A. Food Insecurity and the Burden of Health-Related Social Problems in an Urban Youth Population. J. Adolesc. Health 2015, 57, 601–607. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kotchick, B.A.; Whitsett, D.; Sherman, M.F. Food Insecurity and Adolescent Psychosocial Adjustment: Indirect Pathways through Caregiver Adjustment and Caregiver–Adolescent Relationship Quality. J. Youth Adolesc. 2021, 50, 89–102. [Google Scholar] [CrossRef] [PubMed]
- Pourmotabbed, A.; Moosavian, S.P.; Hadi, A.; Mohammadi, H.; Dadfarma, A.; Rezaei, S.; Babaei, A.; Moradi, S.; Mirzaei, K. The Relationship between Food Insecurity and Risk of Overweight or Obesity in under 18 Years Individuals: A Systematic Review and Meta-Analysis. Int. J. Prev. Med. 2020, 11, 158. [Google Scholar] [PubMed]
- Sánchez García, C.; Zauder, R.; López Sánchez, G.F. Analysis of Body Composition and Physical Fitness of Futsal Players at School Age According to Their Level of Physical Activity, Diet and Body Image. Atena J. Sport. Sci. 2019, 1, 4. [Google Scholar]
- Smith, L.; Ward, P.B.; Vancampfort, D.; López-Sánchez, G.F.; Yang, L.; Grabovac, I.; Jacob, L.; Pizzol, D.; Veronese, N.; Shin, J.I.; et al. Food Insecurity with Hunger and Sexual Behavior among Adolescents from 53 Countries. Int. J. Sex. Health 2021, 33, 88–98. [Google Scholar] [CrossRef]
- Oh, H.; Smith, L.; Jacob, L.; Du, J.; Shin, J.I.; Zhou, S.; Koyanagi, A. Food Insecurity and Substance Use among Young Adult College Students in the United States. J. Addict. Med. 2022, 17, 163–168. [Google Scholar] [CrossRef] [PubMed]
- Félix-Beltrán, L.; Seixas, B.V. Childhood Hunger Experiences and Chronic Health Conditions Later in Life among Brazilian Older Adults. Rev. Panam. Salud Pública 2021, 45, e39. [Google Scholar] [CrossRef] [PubMed]
- López Sánchez, G.F.; Vigueras Hernández, M.R.; Lucas Casas, P.; Zauder, R.; Jastrzębska, J.; Skalska, M.; RadzimiNski, L.; Jastrzebski, Z.; Pardhan, S. Impact of Physical Activity, BMI and Sociodemographic and Lifestyle Factors on the Risk of Diabetes in 9511 Ghanaian Adults. SPORT TK-Rev. EuroAmericana Cienc. Deporte 2022, 11, 15. [Google Scholar] [CrossRef]
- Fram, M.S.; Nguyen, H.T.; Frongillo, E.A. Food Insecurity among Adolescent Students from 95 Countries Is Associated with Diet, Behavior, and Health, and Associations Differ by Student Age and Sex. Curr. Dev. Nutr. 2022, 6, nzac024. [Google Scholar] [CrossRef] [PubMed]
- Gundersen, C.; Ziliak, J.P. Childhood Food Insecurity in the US: Trends, Causes, and Policy Options. Futur. Child. 2014, 1–19. Available online: https://www.jstor.org/stable/26304695 (accessed on 20 June 2023). [CrossRef]
- Smith, M.D.; Meade, B. Who Are the World’s Food Insecure? Identifying the Risk Factors of Food Insecurity Around the World. Amber Waves Econ. Food Farming Nat. Resour. Rural Am. 2019. Available online: https://ageconsearch.umn.edu/record/302721/ (accessed on 20 June 2023).
- McKinnon, B.; Gariépy, G.; Sentenac, M.; Elgar, F.J. Adolescent Suicidal Behaviours in 32 Low-and Middle-Income Countries. Bull. World Health Organ. 2016, 94, 340. [Google Scholar] [CrossRef] [PubMed]
- McIntyre, L.; Williams, J.V.A.; Lavorato, D.H.; Patten, S. Depression and Suicide Ideation in Late Adolescence and Early Adulthood Are an Outcome of Child Hunger. J. Affect. Disord. 2013, 150, 123–129. [Google Scholar] [CrossRef] [PubMed]
- World Food Programme Country Programme Benin 200721 (2015–2018). Available online: https://docs.wfp.org/api/documents/71faac146a864ad2855f2d6ad2ddd22b/download/ (accessed on 20 June 2023).
- Dookhee, A. New Threats to Food Security Ahead for Mauritius—What Are the Emerging Solutions? Available online: https://www.undp.org/mauritius-seychelles/blog/new-threats-food-security-ahead-mauritius-what-are-emerging-solutions (accessed on 20 June 2023).
- Assouad, L. Lebanon’s Political Economy: From Predatory to Self-Devouring; Carnegie Middle East Center: Beirut, Lebanon, 2021. [Google Scholar]
- United Nations. Goal 2: Zero Hunger. Available online: https://www.un.org/sustainabledevelopment/hunger/ (accessed on 20 June 2023).
- Baker, W. Food Banks in Schools and the ‘Cost of Living’ Crisis; British Educational Research Association: London, UK, 2022. [Google Scholar]
Figure 1.
Trends in prevalence of any food insecurity by country and region.
Figure 2.
Heat map of the average percentage point change in prevalence per year. Abbreviation: AFR African region; AMR region of the Americas; EMR Eastern Mediterranean region; SEAR South-East Asia region; WPR Western Pacific region. The estimates correspond to the beta coefficient based on linear regression including survey year as a continuous variable. Bold fonts signify statistical significance (p < 0.05). Warmer colors indicate higher values and colder colors indicate lower values. Red is the warmest color and purple is the coldest color.
Figure 2.
Heat map of the average percentage point change in prevalence per year. Abbreviation: AFR African region; AMR region of the Americas; EMR Eastern Mediterranean region; SEAR South-East Asia region; WPR Western Pacific region. The estimates correspond to the beta coefficient based on linear regression including survey year as a continuous variable. Bold fonts signify statistical significance (p < 0.05). Warmer colors indicate higher values and colder colors indicate lower values. Red is the warmest color and purple is the coldest color.
Table 1.
Trends in prevalence (%) of any food insecurity in 31 countries.
| Country | Year | % | [95%CI] | Beta a | [95%CI] | p for Trend a | Country | Year | % | [95%CI] | Beta a | [95%CI] | p for Trend a |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AFR | EMR (continued) | ||||||||||||
| Benin | 2009 | 71.2 | [66.4, 75.6] | −3.14 | [−4.61, −1.66] | <0.001 | Morocco | 2006 | 44.7 | [41.7, 47.8] | −1.35 | [−1.74, −0.95] | <0.001 |
| 2016 | 49.2 | [40.5, 58.1] | 2010 | 30.7 | [26.8, 34.9] | ||||||||
| Mauritius | 2011 | 25.0 | [21.6, 28.8] | 3.09 | [2.16, 4.03] | <0.001 | 2016 | 29.5 | [27.3, 31.8] | ||||
| 2017 | 43.6 | [39.7, 47.6] | Oman | 2005 | 40.2 | [37.3, 43.1] | −0.42 | [−0.86, 0.03] | 0.066 | ||||
| Seychelles | 2007 | 58.6 | [57.9, 59.3] | −1.80 | [−2.21, −1.39] | <0.001 | 2010 | 40.9 | [36.9, 45.1] | ||||
| 2015 | 44.2 | [41.0, 47.4] | 2015 | 35.6 | [32.3, 38.9] | ||||||||
| Swaziland | 2003 | 52.2 | [49.8, 54.7] | −0.51 | [−1.04, 0.01] | 0.055 | United Arab Emirates | 2005 | 41.1 | [39.7, 42.5] | 0.76 | [0.34, 1.18] | <0.001 |
| 2013 | 47.1 | [42.6, 51.7] | 2010 | 29.1 | [26.9, 31.4] | ||||||||
| AMR | 2016 | 48.0 | [43.9, 52.2] | ||||||||||
| Anguilla | 2009 | 40.9 | [40.9, 40.9] | −0.25 | [−0.89, 0.39] | 0.432 | Yemen | 2008 | 57.2 | [48.5, 65.5] | 0.18 | [−1.66, 2.02] | 0.845 |
| 2016 | 39.2 | [34.9, 43.6] | 2014 | 58.3 | [52.0, 64.3] | ||||||||
| Argentina | 2007 | 31.6 | [28.2, 35.3] | 0.67 | [−0.08, 1.42] | 0.081 | SEAR | ||||||
| 2012 | 35.0 | [33.8, 36.1] | Indonesia | 2007 | 64.4 | [59.9, 68.6] | −1.31 | [−1.95, −0.67] | <0.001 | ||||
| Guatemala | 2009 | 33.5 | [28.6, 38.7] | 0.50 | [−0.88, 1.89] | 0.475 | 2015 | 53.9 | [51.3, 56.4] | ||||
| 2015 | 36.5 | [30.3, 43.2] | Maldives | 2009 | 34.2 | [30.3, 38.3] | 0.95 | [−0.19, 2.09] | 0.101 | ||||
| Guyana | 2004 | 42.7 | [37.4, 48.2] | 0.43 | [−0.82, 1.69] | 0.488 | 2014 | 39.0 | [35.0, 43.0] | ||||
| 2010 | 45.3 | [40.6, 50.1] | Myanmar | 2007 | 35.3 | [30.7, 40.1] | −0.48 | [−1.10, 0.13] | 0.122 | ||||
| Jamaica | 2010 | 59.6 | [55.3, 63.7] | −1.79 | [−2.79, −0.80] | 0.001 | 2016 | 30.9 | [28.3, 33.6] | ||||
| 2017 | 47.0 | [41.8, 52.3] | Sri Lanka | 2008 | 32.9 | [30.1, 35.8] | −0.51 | [−1.11, 0.08] | 0.088 | ||||
| Suriname | 2009 | 33.0 | [30.1, 36.0] | 1.30 | [0.37, 2.23] | 0.008 | 2016 | 28.8 | [25.3, 32.5] | ||||
| 2016 | 42.1 | [36.7, 47.6] | Thailand | 2008 | 52.6 | [49.4, 55.7] | 0.15 | [−0.49, 0.78] | 0.641 | ||||
| Trinidad & Tobago | 2007 | 39.2 | [34.7, 43.9] | 0.97 | [0.39, 1.55] | 0.001 | 2015 | 53.6 | [50.7, 56.6] | ||||
| 2011 | 45.3 | [41.3, 49.3] | WPR | ||||||||||
| 2017 | 49.1 | [45.5, 52.6] | Cook Islands | 2011 | 67.4 | [67.4, 67.4] | 0.61 | [−0.45, 1.67] | 0.251 | ||||
| Uruguay | 2006 | 18.7 | [16.9, 20.7] | 0.61 | [0.15, 1.08] | 0.011 | 2015 | 69.8 | [65.6, 73.8] | ||||
| 2012 | 22.4 | [20.5, 24.4] | Fiji | 2010 | 65.4 | [59.3, 71.0] | −0.96 | [−2.21, 0.29] | 0.129 | ||||
| EMR | 2016 | 59.6 | [55.5, 63.7] | ||||||||||
| Egypt | 2006 | 41.8 | [35.8, 48.1] | 0.76 | [−1.46, 2.97] | 0.496 | Philippines | 2003 | 68.8 | [65.2, 72.1] | 0.04 | [−0.27, 0.35] | 0.799 |
| 2011 | 45.6 | [37.0, 54.6] | 2007 | 68.3 | [65.9, 70.7] | ||||||||
| Jordan | 2004 | 47.2 | [43.7, 50.8] | 1.02 | [−1.03, 3.06] | 0.317 | 2011 | 64.5 | [60.7, 68.1] | ||||
| 2007 | 50.3 | [45.6, 54.9] | 2015 | 69.4 | [67.1, 71.7] | ||||||||
| Kuwait | 2011 | 37.8 | [34.9, 40.8] | 2.95 | [1.76, 4.15] | <0.001 | Samoa | 2011 | 81.2 | [78.5, 83.6] | −0.66 | [−1.49, 0.18] | 0.119 |
| 2015 | 49.6 | [46.1, 53.2] | 2017 | 77.2 | [72.8, 81.1] | ||||||||
| Lebanon | 2005 | 39.0 | [36.9, 41.1] | −0.70 | [−1.00, −0.39] | <0.001 | Tonga | 2010 | 74.1 | [71.6, 76.4] | −1.06 | [−1.61, −0.51] | <0.001 |
| 2011 | 33.5 | [30.5, 36.6] | 2017 | 66.6 | [63.6, 69.5] | ||||||||
| 2017 | 30.6 | [27.7, 33.6] | Vanuatu | 2011 | 49.7 | [42.3, 57.1] | 2.47 | [0.73, 4.21] | 0.006 | ||||
| 2016 | 62.0 | [57.8, 66.0] | |||||||||||
Abbreviation: CI confidence interval; AFR African region; AMR region of the Americas; EMR Eastern Mediterranean region; SEAR South-East Asia region; WPR Western Pacific region. a The beta and p for trend are based on linear regression including survey year as a continuous variable. The beta can be interpreted as the average percentage point change in prevalence per year.
Table 2.
Trends in prevalence of moderate food insecurity and severe food insecurity in 31 countries.
Table 2.
Trends in prevalence of moderate food insecurity and severe food insecurity in 31 countries.
| Moderate Food Insecurity | Severe Food Insecurity | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Country | Year | % | [95%CI] | Beta | [95%CI] | p for Trend | % | [95%CI] | Beta | [95%CI] | p for Trend |
| AFR | |||||||||||
| Benin | 2009 | 52.2 | [48.3, 56.2] | −2.21 | [−3.35, −1.08] | <0.001 | 19.0 | [14.7, 24.2] | −0.92 | [−1.83, −0.02] | 0.044 |
| 2016 | 36.7 | [30.5, 43.5] | 12.5 | [9.2, 16.8] | |||||||
| Mauritius | 2011 | 21.5 | [18.5, 25.0] | 2.31 | [1.50, 3.11] | <0.001 | 3.5 | [2.7, 4.5] | 0.79 | [0.25, 1.33] | 0.006 |
| 2017 | 35.4 | [32.2, 38.7] | 8.2 | [5.7, 11.7] | |||||||
| Seychelles | 2007 | 41.9 | [41.2, 42.6] | −1.29 | [−1.64, −0.95] | <0.001 | 16.6 | [16.1, 17.2] | −0.51 | [−0.81, −0.20] | 0.001 |
| 2015 | 31.6 | [29.0, 34.3] | 12.6 | [10.4, 15.1] | |||||||
| Swaziland | 2003 | 42.8 | [40.8, 44.8] | −0.37 | [−0.82, 0.07] | 0.096 | 9.4 | [8.3, 10.6] | −0.14 | [−0.39, 0.11] | 0.282 |
| 2013 | 39.1 | [35.3, 43.0] | 8.0 | [6.1, 10.6] | |||||||
| AMR | |||||||||||
| Anguilla | 2009 | 33.9 | [33.9, 33.9] | 0.05 | [−0.63, 0.72] | 0.891 | 7.0 | [7.0, 7.0] | −0.30 | [−0.61, 0.01] | 0.058 |
| 2016 | 34.2 | [29.8, 38.9] | 4.9 | [3.2, 7.5] | |||||||
| Argentina | 2007 | 29.0 | [25.3, 33.0] | 0.49 | [−0.31, 1.29] | 0.227 | 2.6 | [1.6, 4.2] | 0.18 | [−0.10, 0.46] | 0.216 |
| 2012 | 31.5 | [30.5, 32.5] | 3.5 | [2.9, 4.2] | |||||||
| Guatemala | 2009 | 31.2 | [26.6, 36.3] | 0.43 | [−0.87, 1.74] | 0.511 | 2.3 | [1.7, 3.0] | 0.07 | [−0.13, 0.27] | 0.507 |
| 2015 | 33.8 | [28.1, 40.0] | 2.7 | [1.8, 3.9] | |||||||
| Guyana | 2004 | 34.3 | [31.8, 37.0] | 0.49 | [−0.32, 1.30] | 0.227 | 8.4 | [5.2, 13.1] | −0.06 | [−0.87, 0.75] | 0.882 |
| 2010 | 37.3 | [33.4, 41.3] | 8.0 | [5.7, 11.1] | |||||||
| Jamaica | 2010 | 46.4 | [40.3, 52.6] | −0.81 | [−1.95, 0.34] | 0.161 | 13.1 | [9.7, 17.5] | −0.99 | [−1.62, −0.35] | 0.003 |
| 2017 | 40.8 | [36.2, 45.5] | 6.2 | [4.6, 8.4] | |||||||
| Suriname | 2009 | 24.8 | [22.5, 27.3] | 1.10 | [0.30, 1.89] | 0.009 | 8.1 | [6.4, 10.2] | 0.20 | [−0.14, 0.54] | 0.234 |
| 2016 | 32.5 | [28.0, 37.4] | 9.5 | [8.4, 10.9] | |||||||
| Trinidad & Tobago | 2007 | 33.2 | [29.1, 37.4] | 0.75 | [0.20, 1.29] | 0.008 | 6.0 | [4.5, 8.1] | 0.22 | [0.01, 0.43] | 0.036 |
| 2011 | 39.0 | [35.3, 42.8] | 6.3 | [5.2, 7.7] | |||||||
| 2017 | 40.9 | [37.3, 44.5] | 8.2 | [7.1, 9.5] | |||||||
| Uruguay | 2006 | 17.2 | [15.5, 19.1] | 0.62 | [0.17, 1.06] | 0.007 | 1.5 | [1.1, 2.0] | 0.00 | [−0.11, 0.10] | 0.949 |
| 2012 | 20.9 | [19.1, 22.9] | 1.5 | [1.1, 1.9] | |||||||
| EMR | |||||||||||
| Egypt | 2006 | 36.8 | [31.0, 43.0] | 0.87 | [−1.35, 3.09] | 0.435 | 5.1 | [3.8, 6.8] | −0.11 | [−0.58, 0.35] | 0.622 |
| 2011 | 41.1 | [32.5, 50.4] | 4.5 | [3.1, 6.5] | |||||||
| Jordan | 2004 | 37.0 | [34.9, 39.2] | 0.03 | [−1.49, 1.56] | 0.964 | 10.2 | [8.5, 12.3] | 0.98 | [−0.02, 1.99] | 0.055 |
| 2007 | 37.1 | [33.4, 41.0] | 13.2 | [11.1, 15.5] | |||||||
| Kuwait | 2011 | 28.9 | [26.3, 31.6] | 3.58 | [2.60, 4.56] | <0.001 | 8.9 | [7.3, 10.9] | −0.62 | [−1.35, 0.10] | 0.089 |
| 2015 | 43.2 | [40.6, 45.9] | 6.4 | [4.6, 8.9] | |||||||
| Lebanon | 2005 | 36.0 | [34.0, 38.0] | −0.70 | [−0.98, −0.41] | <0.001 | 3.0 | [2.6, 3.6] | 0.00 | [−0.07, 0.07] | 0.951 |
| 2011 | 29.9 | [27.3, 32.6] | 3.6 | [2.5, 5.1] | |||||||
| 2017 | 27.5 | [24.9, 30.2] | 3.1 | [2.5, 3.8] | |||||||
| Morocco | 2006 | 35.7 | [33.3, 38.1] | −1.30 | [−1.71, −0.90] | <0.001 | 9.0 | [7.2, 11.4] | −0.04 | [−0.30, 0.22] | 0.741 |
| 2010 | 21.0 | [17.5, 24.9] | 9.7 | [8.5, 11.1] | |||||||
| 2016 | 20.7 | [18.0, 23.7] | 8.8 | [7.3, 10.5] | |||||||
| Oman | 2005 | 32.8 | [30.0, 35.6] | −0.16 | [−0.60, 0.27] | 0.453 | 7.4 | [6.2, 8.7] | −0.25 | [−0.43, −0.08] | 0.006 |
| 2010 | 32.0 | [28.7, 35.4] | 8.9 | [7.0, 11.3] | |||||||
| 2015 | 31.1 | [28.0, 34.5] | 4.4 | [3.4, 5.7] | |||||||
| United Arab Emirates | 2005 | 32.1 | [30.9, 33.4] | 0.85 | [0.47, 1.24] | <0.001 | 8.9 | [8.2, 9.7] | −0.10 | [−0.23, 0.04] | 0.163 |
| 2010 | 24.3 | [22.8, 25.8] | 4.8 | [3.6, 6.4] | |||||||
| 2016 | 40.5 | [36.7, 44.3] | 7.5 | [6.4, 8.9] | |||||||
| Yemen | 2008 | 41.8 | [36.2, 47.6] | 0.90 | [−0.63, 2.42] | 0.240 | 15.4 | [10.3, 22.4] | −0.72 | [−1.82, 0.38] | 0.192 |
| 2014 | 47.2 | [40.6, 53.9] | 11.1 | [9.2, 13.3] | |||||||
| SEAR | |||||||||||
| Indonesia | 2007 | 58.7 | [55.0, 62.2] | −1.12 | [−1.69, −0.56] | <0.001 | 5.7 | [4.0, 8.0] | −0.19 | [−0.45, 0.07] | 0.157 |
| 2015 | 49.7 | [47.1, 52.2] | 4.2 | [3.6, 4.9] | |||||||
| Maldives | 2009 | 28.1 | [25.4, 31.1] | 0.96 | [0.03, 1.90] | 0.044 | 6.1 | [4.4, 8.3] | −0.01 | [−0.49, 0.47] | 0.966 |
| 2014 | 32.9 | [29.4, 36.7] | 6.0 | [4.8, 7.6] | |||||||
| Myanmar | 2007 | 32.4 | [28.2, 36.9] | −0.45 | [−1.00, 0.10] | 0.107 | 2.9 | [1.8, 4.6] | −0.03 | [−0.25, 0.18] | 0.745 |
| 2016 | 28.4 | [26.3, 30.5] | 2.5 | [1.5, 4.2] | |||||||
| Sri Lanka | 2008 | 26.2 | [23.2, 29.4] | −0.10 | [−0.71, 0.52] | 0.749 | 6.7 | [5.7, 7.9] | −0.42 | [−0.59, −0.25] | <0.001 |
| 2016 | 25.4 | [22.0, 29.2] | 3.4 | [2.7, 4.2] | |||||||
| Thailand | 2008 | 49.1 | [46.3, 52.0] | 0.03 | [−0.52, 0.59] | 0.900 | 3.5 | [2.7, 4.5] | 0.11 | [−0.08, 0.30] | 0.237 |
| 2015 | 49.4 | [46.9, 51.9] | 4.3 | [3.4, 5.3] | |||||||
| WPR | |||||||||||
| Cook Islands | 2011 | 59.0 | [59.0, 59.0] | 0.64 | [−0.79, 2.07] | 0.372 | 8.4 | [8.4, 8.4] | −0.03 | [−0.87, 0.81] | 0.943 |
| 2015 | 61.6 | [55.9, 66.9] | 8.3 | [5.5, 12.2] | |||||||
| Fiji | 2010 | 54.9 | [50.2, 59.5] | −1.13 | [−2.19, −0.08] | 0.036 | 10.5 | [8.6, 12.7] | 0.18 | [−0.32, 0.67] | 0.467 |
| 2016 | 48.1 | [44.2, 52.0] | 11.6 | [9.7, 13.7] | |||||||
| Philippines | 2003 | 61.5 | [58.5, 64.5] | 0.02 | [−0.24, 0.28] | 0.880 | 7.2 | [6.0, 8.6] | 0.02 | [−0.12, 0.16] | 0.775 |
| 2007 | 61.8 | [59.6, 64.0] | 6.5 | [5.7, 7.5] | |||||||
| 2011 | 57.8 | [54.1, 61.4] | 6.6 | [5.5, 8.1] | |||||||
| 2015 | 62.2 | [60.3, 64.0] | 7.2 | [6.1, 8.5] | |||||||
| Samoa | 2011 | 45.2 | [41.3, 49.1] | 3.02 | [1.99, 4.05] | <0.001 | 36.0 | [33.7, 38.3] | −3.68 | [−4.46, −2.90] | <0.001 |
| 2017 | 63.3 | [58.6, 67.8] | 13.9 | [10.4, 18.3] | |||||||
| Tonga | 2010 | 60.3 | [57.5, 63.1] | −0.78 | [−1.34, −0.22] | 0.007 | 13.7 | [12.0, 15.7] | −0.28 | [−0.66, 0.10] | 0.149 |
| 2017 | 54.9 | [52.1, 57.6] | 11.8 | [10.0, 13.8] | |||||||
| Vanuatu | 2011 | 44.7 | [38.2, 51.5] | 1.77 | [0.04, 3.49] | 0.045 | 4.9 | [3.3, 7.2] | 0.70 | [0.17, 1.23] | 0.011 |
| 2016 | 53.6 | [48.4, 58.6] | 8.4 | [6.8, 10.4] | |||||||
Abbreviation: CI confidence interval; AFR African region; AMR region of the Americas; EMR Eastern Mediterranean region; SEAR South-East Asia region; WPR Western Pacific region. The beta and p for trend are based on linear regression including survey year as a continuous variable. The beta can be interpreted as the average percentage point change in prevalence per year.
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Smith, L.; López Sánchez, G.F.; Tully, M.A.; Jacob, L.; Kostev, K.; Oh, H.; Butler, L.; Barnett, Y.; Shin, J.I.; Koyanagi, A. Temporal Trends in Food Insecurity (Hunger) among School-Going Adolescents from 31 Countries from Africa, Asia, and the Americas. Nutrients 2023, 15, 3226. https://doi.org/10.3390/nu15143226
AMA Style
Smith L, López Sánchez GF, Tully MA, Jacob L, Kostev K, Oh H, Butler L, Barnett Y, Shin JI, Koyanagi A. Temporal Trends in Food Insecurity (Hunger) among School-Going Adolescents from 31 Countries from Africa, Asia, and the Americas. Nutrients. 2023; 15(14):3226. https://doi.org/10.3390/nu15143226
Chicago/Turabian StyleSmith, Lee, Guillermo F. López Sánchez, Mark A. Tully, Louis Jacob, Karel Kostev, Hans Oh, Laurie Butler, Yvonne Barnett, Jae Il Shin, and Ai Koyanagi. 2023. "Temporal Trends in Food Insecurity (Hunger) among School-Going Adolescents from 31 Countries from Africa, Asia, and the Americas" Nutrients 15, no. 14: 3226. https://doi.org/10.3390/nu15143226
APA StyleSmith, L., López Sánchez, G. F., Tully, M. A., Jacob, L., Kostev, K., Oh, H., Butler, L., Barnett, Y., Shin, J. I., & Koyanagi, A. (2023). Temporal Trends in Food Insecurity (Hunger) among School-Going Adolescents from 31 Countries from Africa, Asia, and the Americas. Nutrients, 15(14), 3226. https://doi.org/10.3390/nu15143226
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